}, \\texttt{<CLASS\\_x>}, \\texttt{<DEPARTURE\\_DATE\\_x>}, \\texttt{<DEPARTURE\\_TIME\\_x>}, \\texttt{<ARRIVAL\\_DATE\\_x>}, \\texttt{<ARRIVAL\\_TIME\\_x>}, \\texttt{<AIRLINE\\_x>}, \\texttt{<PRICE\\_x>}, \\texttt{<AIRPORT\\_x>}, \\texttt{<NUM\\_TRAVELERS\\_x>} & \n","Round trip (with 2 departure dates), one-way, multi-city, strict arrival time, flexible dates, large group bookings, solo/business/family travel, weekend getaways, budget constraints, special occasions (e.g., birthdays) \\\\\n","\\hline\n","\n","\\textbf{Hotel} & \n","\\texttt{<CHECK\\_IN\\_DATE\\_x>}, \\texttt{<CHECK\\_OUT\\_DATE\\_x>}, \\texttt{<CITY\\_x>}, \\texttt{<CITY\\_x\\_NEIGHBORHOOD\\_y>}, \\texttt{<STAR\\_x>}, \\texttt{<RATING\\_x>}, \\texttt{<PRICE\\_x>}, \\texttt{<NUM\\_ROOMS\\_x>}, \\texttt{<NUM\\_TRAVELERS\\_x>} & \n","Multi-city trips, multiple hotels in one city, specific neighborhoods, special occasions, last-minute reservations, business vs. leisure, extended stays, view/accessibility/pet-friendly requests, family and group accommodations \\\\\n","\\hline\n","\n","\\textbf{Attraction} & \n","\\texttt{<ATTRACTION\\_TYPE\\_x>}, \\texttt{<CITY\\_x>}, \\texttt{<STATE\\_x>}, \\texttt{<CITY\\_x\\_NEIGHBORHOOD\\_y>} & \n","Multi-type attraction queries, across cities/states, neighborhood-specific exploration, curiosity about state-level offerings. Assistant must ask open-ended questions and only mention placeholders if user provides them. \\\\\n","\\hline\n","\n","\\textbf{Restaurant} & \n","\\texttt{<RESTAURANT\\_RATING\\_x>}, \\texttt{<RESTAURANT\\_PRICE\\_x>}, \\texttt{<RESTAURANT\\_CUISINE\\_x>}, \\texttt{<CITY\\_x>}, \\texttt{<CITY\\_x\\_NEIGHBORHOOD\\_y>} & \n","Multi-city dining, cuisine preferences, dietary restrictions (e.g., nut-free, vegan, halal), budget vs. premium, special occasion dining, rating-focused or casual vs. upscale preferences \\\\\n","\\hline\n","\\end{tabular}\n","\\vspace{1mm}\n","\\caption{Domain-specific customization for template generation. Each domain builds on the shared prompt instructions with unique placeholders and required conversation scenarios.}\n","\\label{tab:domain-customization}\n","\\end{table}\n"],["\\begin{table}[!ht]\n","\\centering\n","\\small\n","\\renewcommand{\\arraystretch}{1.2}\n","\\begin{tabularx}{\\textwidth}{|l|X|X|}\n","\\hline\n","\\textbf{Domains} & \\textbf{Placeholders Used} & \\textbf{Required Scenarios} \\\\\n","\\hline\n","\n","\\textbf{Flight + Hotel} & \n","\\texttt{<CITY\\_x>}, \\texttt{<CLASS\\_x>}, \\texttt{<DEPARTURE\\_DATE\\_x>}, \\texttt{<CHECK\\_IN\\_DATE\\_x>}, \\texttt{<CHECK\\_OUT\\_DATE\\_x>}, \\texttt{<NUM\\_TRAVELERS\\_x>} & \n","Trip planning involving both air travel and accommodation. Includes round-trip and one-way flights, hotel stays across one or more cities, budget and luxury travelers, group vs. solo travel, flexible dates, specific preferences like nonstop flights or hotels with amenities (e.g., pool, gym, pet-friendly). \\\\\n","\\hline\n","\n","\\textbf{Flight + Hotel + Attraction} & \n","All flight, hotel, and attraction placeholders (e.g., \\texttt{<CITY\\_x>}, \\texttt{<CHECK\\_IN\\_DATE\\_x>}, \\texttt{<ATTRACTION\\_TYPE\\_x>}) & \n","User planning a full trip involving flights, hotel stays, and sightseeing. Must include: round-trip and one-way flights, multi-city stays, hotels near attractions, flexible schedules, user-provided attraction types or specific attraction names (only when linking to hotel proximity), and special constraints (e.g., group travel, business trips, anniversaries). Assistant must never use placeholder names before user introduces them. Includes amenity discussions and attraction-type grammar handling (e.g., cultural vs. \\texttt{<ATTRACTION\\_TYPE\\_1>} attraction). \\\\\n","\\hline\n","\n","\\textbf{Flight + Hotel + Restaurant} & \n","All flight, hotel, and restaurant placeholders (e.g., \\texttt{<RESTAURANT\\_CUISINE\\_x>}, \\texttt{<RESTAURANT\\_PRICE\\_x>}) & \n","End-to-end trip planning including flights, accommodation, and dining. Required scenarios include: dietary restrictions, cuisine-first or city-first planning, restaurant proximity to hotel, multiple hotels or restaurants in one city, round-trip and one-way flights, and amenities like pet-friendly or spa. Must feature diverse language, nonlinear flows, and varied user personas. Includes explicit handling of dietary needs (e.g., halal, nut-free, vegetarian), and placeholder logic as per prompt. \\\\\n","\\hline\n","\n","\\textbf{Hotel + Attraction} & \n","\\texttt{<CITY\\_x>}, \\texttt{<CHECK\\_IN\\_DATE\\_x>}, \\texttt{<ATTRACTION\\_TYPE\\_x>}, \\texttt{<CITY\\_x\\_NEIGHBORHOOD\\_y>} & \n","User looking for accommodations and nearby attractions. Includes: specific neighborhoods, family-friendly vs. solo travel, themed vacations, extended stays, proximity to cultural or outdoor attractions, and cases where attraction type drives hotel location. \\\\\n","\\hline\n","\n","\\textbf{Hotel + Restaurant} & \n","\\texttt{<RESTAURANT\\_CUISINE\\_x>}, \\texttt{<HOTEL\\_PRICE\\_x>}, \\texttt{<CITY\\_x\\_NEIGHBORHOOD\\_y>} & \n","Combines hotel booking with dining preferences. Includes cuisine-specific searches, budget dining vs. fine dining, dietary needs (gluten-free, halal), special events (anniversaries), restaurant proximity to hotel, and group size considerations. Templates vary in planning flow—some start with hotel, others with restaurants. \\\\\n","\\hline\n","\n","\\end{tabularx}\n","\\vspace{1mm}\n","\\caption{Multi-domain combinations and scenario requirements. Each configuration builds on the unified instructions with additional placeholder logic and domain-specific complexity.}\n","\\label{tab:multi-domain-customization}\n","\\end{table}\n"]],"string":"[\n [\n \"\\\\begin{figure*}[!t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\textwidth]{assets/pipeline.png}\\n\",\n \" \\\\caption{Illustrative example from the $\\\\benchmarkname$ dataset. This example showcases a multi-domain scenario involving both flights and hotels, where the user is planning a trip and attempting to book relevant services. The dialogue is constructed by retrieving entities from a knowledge base, and tool calls are executed using a predefined toolbox, simulating realistic, tool-augmented agent behavior.}\\n\",\n \" \\\\label{fig:architecture}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!htbp]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lccccccc}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Dataset} & \\\\textbf{Deployed} & \\\\textbf{Human Annotated} & \\\\textbf{Execution Result} & \\\\textbf{Multi-turn Context} & \\\\textbf{Multi-Domain} \\\\\\\\\\n\",\n \" & \\\\textbf{Tools} & \\\\textbf{Tool Planning} & \\\\textbf{Evaluation} & \\\\textbf{Planning} & \\\\textbf{Tool Planning}\\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" APIBank~\\\\cite{li2023api} & \\\\cmark & \\\\cmark & \\\\cmark & \\\\cmark & & \\\\\\\\\\n\",\n \" APIBench~\\\\cite{patil2024gorilla} & & & \\\\cmark & & \\\\\\\\\\n\",\n \" GAIA~\\\\cite{mialon2023gaia} & & \\\\cmark & \\\\cmark & & \\\\\\\\\\n\",\n \" GTA~\\\\cite{wang2024gta} & \\\\cmark & \\\\cmark & \\\\cmark & \\\\cmark & \\\\\\\\\\n\",\n \" m\\\\&m's~\\\\cite{ma2024m} & \\\\cmark & \\\\cmark & \\\\cmark & \\\\cmark & \\\\\\\\\\n\",\n \" ToolBench~\\\\cite{qin2023toolllm} & \\\\cmark & & \\\\cmark & & \\\\\\\\\\n\",\n \" Toolformer~\\\\cite{schick2023toolformer} & \\\\cmark & \\\\cmark & \\\\cmark & & \\\\\\\\\\n\",\n \" TravelPlanner~\\\\cite{xie2024travelplanner} & \\\\cmark & \\\\cmark & \\\\cmark & & \\\\cmark & \\\\\\\\ \\n\",\n \" \\\\midrule\\n\",\n \" $\\\\textbf{\\\\benchmarkname}$ & \\\\cmark & \\\\cmark & \\\\cmark & \\\\cmark & \\\\cmark \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Comparison of datasets for the LLM-based agent systems.}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[!t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\textwidth]{assets/data_generation.png}\\n\",\n \" \\\\caption{$\\\\benchmarkname$ generates data by populating delexicalized entities with corresponding entries from the knowledge base.}\\n\",\n \" \\\\label{fig:data_generation}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lcccc|cc|c|c}\\n\",\n \" \\\\toprule \\n\",\n \" & \\\\multicolumn{4}{c|}{\\\\textbf{Single-domain}} & \\\\multicolumn{2}{c|}{\\\\textbf{Multi-domain}}\\n\",\n \" & \\\\textbf{Common} & \\\\textbf{Total} \\\\\\\\ \\n\",\n \" & \\\\textbf{Flights} & \\\\textbf{Hotels} & \\\\textbf{Restaurants} & \\\\textbf{Attractions} & \\\\textbf{2 Domains} & \\\\textbf{3 Domains} & \\\\\\\\ \\\\midrule\\n\",\n \" \\\\textbf{\\\\# Attributes} & 35 & 43 & 21 & 7 & N/A & N/A & N/A & 106 \\\\\\\\\\n\",\n \" \\\\textbf{\\\\# Tools} & 2 & 2 & 2 & 2 & 1 & N/A & 5 & 14 \\\\\\\\ \\n\",\n \" \\\\textbf{\\\\# Dialogues} & 1.5k & 1.5k & 1.5k & 1.5k & 4.5k & 3k & N/A & 13.5k \\\\\\\\ \\n\",\n \" \\\\textbf{Avg. \\\\# turns} & 8.2 & 8.0 & 9.0 & 6.0 & 11.3 & 9.7 & N/A & N/A \\\\\\\\ \\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Dataset statistics detailing the number of attributes, tools, dialogues, and the average number of turns, categorized by each individual domain, multi-domain, and common categories. \\\"N/A\\\" indicates that the metric is not applicable in the given context.} \\n\",\n \"\\\\label{tab:baseline-results-one-epoch}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{figure}[!ht]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\linewidth]{assets/domain_adaptation.png}\\n\",\n \" \\\\caption{\\\\textbf{Left:} Tool Call F1 performance and \\\\textbf{Right:} Parameter Matching F1 performance.}\\n\",\n \" \\\\label{fig:enter-label}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table*}[!ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lcccc|cccc|c|cc|c}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Domain} & \\\\multicolumn{4}{c}{\\\\textbf{Tool Call}} & \\\\multicolumn{4}{|c|}{\\\\textbf{Parameter Matching}} & \\\\textbf{Code Exec.} & \\\\multicolumn{2}{|c|}{\\\\textbf{Information Seeking}} & \\\\multicolumn{1}{c}{\\\\textbf{Cache}} \\\\\\\\ \\n\",\n \" & & & & & & & & & \\\\textbf{Rate} & & & \\\\textbf{Summary} \\\\\\\\\\n\",\n \" & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\\\\\ \\\\midrule\\n\",\n \" \\\\multicolumn{8}{l}{\\\\texttt{Single-Domain}} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Flights & 25.12 & 30.90 & 57.34 & 40.16 & 15.57 & 17.34 & 60.30 & 26.94 & 26.24 & 14.41 & 82.36 & 36.63 \\\\\\\\\\n\",\n \" Hotels & 42.86 & 49.89 & 75.25 & 60.00 & 17.57 & 23.59 & 40.80 & 29.89 & 48.13 & 47.01 & 80.38 & 33.70 \\\\\\\\\\n\",\n \" Restaurants & 40.40 & 52.97 & 63.00 & 57.55 & 31.47 & 36.07 & 71.12 & 47.87 & \\\\textbf{56.99} & 28.91 & 75.68 & 35.49 \\\\\\\\\\n\",\n \" Attractions & 33.03 & 48.49 & 50.90 & 49.66 & 14.11 & 25.11 & 24.36 & 24.73 & 40.40 & N/A & N/A & 43.87 \\\\\\\\ \\\\midrule\\n\",\n \" \\\\multicolumn{10}{l}{\\\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\\\\\ \\\\midrule\\n\",\n \" F-H & 33.09 & 40.89 & 63.44 & 49.73 & 16.11 & 19.28 & 49.48 & 27.75 & 38.43 & 26.54 & 82.00 & 21.85 \\\\\\\\\\n\",\n \" H-R & 51.06 & 59.21 & 78.77 & 67.61 & 26.62 & 33.52 & 56.37 & 42.05 & 51.82 & 28.60 & 84.07 & 9.95 \\\\\\\\\\n\",\n \" H-A & 38.75 & 45.46 & 72.39 & 55.85 & 16.11 & 20.02 & 45.12 & 27.74 & 48.88 & 26.03 & 81.77 & 32.75 \\\\\\\\\\n\",\n \" F-H-A & 27.76 & 36.24 & 54.24 & 43.45 & 16.21 & 21.18 & 40.88 & 27.90 & 28.76 & 14.15 & 78.58 & 24.37 \\\\\\\\\\n\",\n \" F-H-R & 29.04 & 34.65 & 64.19 & 45.00 & 18.53 & 21.77 & 55.44 & 31.26 & 36.79 & 33.28 & 82.84 & 27.47 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Overall results using Llama 3.1 8B Instruct with few-shot in-context learning.}\\n\",\n \"\\\\label{table:llama3.1-8b}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{figure}[!ht]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=0.9\\\\linewidth]{assets/flight_shot_learning.png}\\n\",\n \" \\\\caption{Few-shot performance on Flight domain.}\\n\",\n \" \\\\label{fig:few-shot}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table*}[!ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lcccc|cccc|c|cc|c}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Domain} & \\\\multicolumn{4}{c}{\\\\textbf{Tool Call}} & \\\\multicolumn{4}{|c|}{\\\\textbf{Parameter Matching}} & \\\\textbf{Code Exec. Rate} & \\\\multicolumn{2}{|c|}{\\\\textbf{Information Seeking}} & \\\\multicolumn{1}{c}{\\\\textbf{Cache Summary}} \\\\\\\\ \\n\",\n \" & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\\\\\ \\\\midrule\\n\",\n \" \\\\texttt{Single-Domain} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Flights & 83.51 & 89.98 & 92.08 & 91.02 & 58.02 & 66.38 & 82.18 & 73.44 & 85.26 & 39.19 & 86.44 & 62.07 \\\\\\\\\\n\",\n \" Hotels & 78.18 & 84.87 & 90.84 & 87.75 & 60.40 & 67.29 & 85.53 & 75.32 & 72.13 & 48.30 & 80.91 & 71.35 \\\\\\\\\\n\",\n \" Restaurants & 72.76 & 80.40 & 88.44 & 84.23 & 57.49 & 66.69 & 80.64 & 73.00 & 95.16 & 95.14 & 98.63 & 65.32 \\\\\\\\\\n\",\n \" Attractions & 90.50 & 97.87 & 92.33 & 95.02 & 75.54 & 91.34 & 81.37 & 86.07 & 98.15 & N/A & N/A &83.00 \\\\\\\\ \\\\midrule\\n\",\n \" \\\\multicolumn{10}{l}{\\\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\\\\\ \\\\midrule\\n\",\n \" F-H & 80.27 & 91.31 & 86.91 & 89.05 & 57.76 & 74.51 & 71.99 & 73.23 & 89.25 & 24.04 & 80.63 & 57.01 \\\\\\\\\\n\",\n \" H-R & 82.02 & 91.03 & 89.23 & 90.12 & 68.44 & 77.95 & 84.86 & 81.26 & 83.67 & 29.12 & 87.58 & 64.42 \\\\\\\\\\n\",\n \" H-A & 62.30 & 72.67 & 81.35 & 76.77 & 59.22 & 68.73 & 81.05 & 74.39 & 71.41 & 27.87 & 82.80 & 65.70 \\\\\\\\\\n\",\n \" F-H-A & 77.19 & 87.50 & 86.76 & 87.13 & 59.63 & 74.16 & 75.27 & 74.71 & 81.15 & 22.65 & 83.45 & 52.76 \\\\\\\\\\n\",\n \" F-H-R & 71.53 & 86.05 & 80.91 & 83.40 & 54.39 & 71.99 & 68.99 & 70.45 & 82.47 & 46.32 & 84.40 & 53.88 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Overall Results using Llama 3.1 8B Instruct after SFT.}\\n\",\n \"\\\\label{table:llama3.1-8b-sft}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lcccc|cccc|c|cc|c}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Domain} & \\\\multicolumn{4}{c}{\\\\textbf{Tool Call}} & \\\\multicolumn{4}{|c|}{\\\\textbf{Parameter Matching}} & \\\\textbf{Code Exec. Rate} & \\\\multicolumn{2}{|c|}{\\\\textbf{Information Seeking}} & \\\\multicolumn{1}{c}{\\\\textbf{Cache Summary}} \\\\\\\\ \\n\",\n \" & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\\\\\ \\\\midrule\\n\",\n \" \\\\texttt{Single-Domain} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Flights & 58.42 & 78.26 & 69.75 & 73.76 & 32.11 & 40.51 & 60.76 & 48.61 & 83.32 & 20.25 & 82.90 & 53.80 \\\\\\\\\\n\",\n \" Hotels & 92.42 & 96.03 & 96.10 & 96.07 & 75.33 & 80.85 & 91.69 & 85.93 & 98.55 & 46.98 & 80.27 & 75.00 \\\\\\\\\\n\",\n \" Restaurants & 94.23 & 96.63 & 97.43 & 97.03 & 85.51 & 93.62 & 90.80 & 92.19 & 99.54 & 37.90 & 79.95 & 90.78 \\\\\\\\\\n\",\n \" Attractions & 79.14 & 92.16 & 84.86 & 88.36 & 71.17 & 87.04 & 79.60 & 83.16 & 63.70 & N/A & N/A & 78.59 \\\\\\\\ \\\\midrule\\n\",\n \" \\\\multicolumn{10}{l}{\\\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\\\\\ \\\\midrule\\n\",\n \" F-H & 43.48 & 53.73 & 69.52 & 60.61 & 22.28 & 25.94 & 61.24 & 36.44 & 95.02 & 27.39 & 83.42 & 37.15 \\\\\\\\\\n\",\n \" H-R & 60.50 & 70.91 & 80.46 & 78.29 & 50.94 & 60.08 & 76.99 & 67.50 & 99.60 & 22.97 & 84.11 & 38.43 \\\\\\\\\\n\",\n \" H-A & 64.91 & 68.93 & 91.75 & 78.72 & 59.24 & 66.59 & 84.29 & 74.40 & 97.01 & 30.02 & 82.71 & 57.03 \\\\\\\\\\n\",\n \" F-H-A & 60.46 & 70.77 & 80.59 & 75.36 & 44.27 & 51.19 & 76.58 & 61.37 & 94.81 & 19.16 & 82.27 & 44.29 \\\\\\\\\\n\",\n \" F-H-R & 52.97 & 62.22 & 78.07 & 69.25 & 42.93 & 50.66 & 73.76 & 60.07 & 91.30 & 40.45 & 85.62 & 45.37 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Overall Results using Llama 3.3 70B Instruct.}\\n\",\n \"\\\\label{table:llama3.3-70b}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lcccc|cccc|c|cc|c}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Domain} & \\\\multicolumn{4}{c}{\\\\textbf{Tool Call}} & \\\\multicolumn{4}{|c|}{\\\\textbf{Parameter Matching}} & \\\\textbf{Code Exec. Rate} & \\\\multicolumn{2}{|c|}{\\\\textbf{Information Seeking}} & \\\\multicolumn{1}{c}{\\\\textbf{Cache Summary}} \\\\\\\\ \\n\",\n \" & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\\\\\ \\\\midrule\\n\",\n \" \\\\texttt{Single-Domain} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Flights & 77.59 & 87.87 & 86.90 & 87.38 & 49.53 & 58.04 & 77.15 & 66.25 & 57.20 & 29.65 & 83.12 & 57.46\\n\",\n \" \\\\\\\\\\n\",\n \" Hotels & 89.33 & 95.33 & 93.41 & 94.36 & 65.27 & 70.52 & 89.77 & 78.99 & 63.95 & 18.07 & 79.56 & 57.23\\n\",\n \" \\\\\\\\\\n\",\n \" Restaurants & 87.77 & 92.65 & 94.35 & 93.49 & 71.59 & 77.69 & 90.11 & 83.44 & 69.82 & 7.16 & 69.91 & 69.22\\n\",\n \" \\\\\\\\\\n\",\n \" Attractions & 65.76 & 80.16 & 78.54 & 79.34 & 44.92 & 53.04 & 74.59 & 62.00 & 72.57 & N/A & N/A & 63.87\\n\",\n \"\\\\\\\\ \\\\midrule\\n\",\n \" \\\\multicolumn{10}{l}{\\\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\\\\\ \\\\midrule\\n\",\n \" F-H & 69.87 & 78.42 & 86.51 & 82.27 & 42.38 & 50.39 & 72.71 & 59.53 & 66.89 & 9.72 & 78.43 & 47.49\\n\",\n \" \\\\\\\\\\n\",\n \" H-R & 59.10 & 65.62 & 85.60 & 74.29 & 51.91 & 58.54 & 82.10 & 68.35 & 49.72 & 19.69 & 85.40 & 32.55\\n\",\n \" \\\\\\\\\\n\",\n \" H-A & 63.46 & 67.81 & 90.83 & 77.65 & 65.22 & 72.53 & 86.60 & 78.95 & 44.39 & 21.41 & 81.95 & 56.64\\n\",\n \" \\\\\\\\\\n\",\n \" F-H-A & 68.41 & 75.27 & 88.25 & 81.24 & 50.21 & 61.04 & 73.88 & 66.85 & 60.86 & 27.00 & 82.48 & 50.42\\n\",\n \" \\\\\\\\\\n\",\n \" F-H-R & 67.22 & 73.80 & 88.30 & 80.40 & 58.97 & 71.66 & 76.91 & 74.19 & 61.47 & 10.08 & 78.32 & 53.74\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Overall Results using S1.1 32B.}\\n\",\n \"\\\\label{table:s1.1}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lcccc|cccc|c|cc|c}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Domain} & \\\\multicolumn{4}{c}{\\\\textbf{Tool Call}} & \\\\multicolumn{4}{|c|}{\\\\textbf{Parameter Matching}} & \\\\textbf{Code Exec. Rate} & \\\\multicolumn{2}{|c|}{\\\\textbf{Information Seeking}} & \\\\multicolumn{1}{c}{\\\\textbf{Cache Summary}} \\\\\\\\ \\n\",\n \" & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\\\\\ \\\\midrule\\n\",\n \" \\\\texttt{Single-Domain} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Flights & 36.66 & 67.30 & 44.61 & 53.66 & 15.74 & 56.10 & 17.95 & 27.20 & 72.76 & 13.91 & 81.60 & 39.73\\n\",\n \" \\\\\\\\\\n\",\n \" Hotels & 83.96 & 91.74 & 90.82 & 91.28 & 53.22 & 63.09 & 77.30 & 69.47 & 93.05 & 46.95 & 80.47 & 46.52\\n\",\n \" \\\\\\\\\\n\",\n \" Restaurants & 64.13 & 83.60 & 73.36 & 78.14 & 47.10 & 61.89 & 66.34 & 64.04 & 93.49 & 28.27 & 69.74 & 50.53\\n\",\n \" \\\\\\\\\\n\",\n \" Attractions & 35.78 & 61.67 & 46.02 & 52.71 & 22.90 & 66.92 & 25.82 & 37.27 & 66.25 & N/A & N/A & 50.79\\n\",\n \"\\\\\\\\ \\\\midrule\\n\",\n \" \\\\multicolumn{10}{l}{\\\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\\\\\ \\\\midrule\\n\",\n \" F-H & 57.79 & 88.12 & 62.67 & 73.25 & 29.91 & 59.20 & 37.68 & 46.05 & 83.37 & 27.96 & 83.02 & 32.53\\n\",\n \" \\\\\\\\\\n\",\n \" H-R & 54.07 & 70.71 & 69.67 & 70.18 & 35.42 & 50.91 & 53.79 & 52.31 & 96.04 & 23.19 & 84.83 & 21.72\\n\",\n \" \\\\\\\\\\n\",\n \" H-A & 65.81 & 78.69 & 80.08 & 79.38 & 48.58 & 61.78 & 69.45 & 65.39 & 96.04 & 27.92 & 82.97 & 53.52\\n\",\n \" \\\\\\\\\\n\",\n \" F-H-A & 37.83 & 62.18 & 49.14 & 54.90 & 32.73 & 64.59 & 39.89 & 49.32 & 84.49 & 15.42 & 81.00 & 32.33\\n\",\n \" \\\\\\\\\\n\",\n \" F-H-R & 46.79 & 72.75 & 56.74 & 63.75 & 33.99 & 59.56 & 44.18 & 50.73 & 91.86 & 36.30 & 84.63 & 37.65\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Overall Results using Phi-4-reasoning-plus.}\\n\",\n \"\\\\label{table:phi-4-reasoning-plus}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{figure}[!ht]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\linewidth]{assets/all_model_f1_scores.png}\\n\",\n \" \\\\caption{\\\\textbf{Above:} Tool Call F1 performance and \\\\textbf{Below:} Parameter Matching F1 performance.}\\n\",\n \" \\\\label{fig:compare-models}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table*}[!htbp]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lccccccc}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Domain} & \\\\textbf{ \\\\# Turns} & \\\\textbf{\\\\# Validation Error} & \\\\textbf{\\\\# Variable Not Defined} & \\\\textbf{\\\\# Index Out Of Range} & \\\\textbf{\\\\# Other} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\texttt{Single-Domain} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Flights & 8,200 & 737 & 830 & 0 & 468 \\\\\\\\\\n\",\n \" Hotels & 8,000 & 417 & 218 & 0 & 820 \\\\\\\\\\n\",\n \" Restaurants & 7,900 & 513 & 14 & 1 & 183 \\\\\\\\\\n\",\n \" Attractions & 5,975 & 507 & 1 & 0 & 243 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multicolumn{6}{l}{\\\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\\\\\ \\\\midrule\\n\",\n \" F-H & 11,700 & 834 & 1087 & 0 & 408 \\\\\\\\\\n\",\n \" H-R & 10,950 & 1532 & 18 & 1 & 522 \\\\\\\\\\n\",\n \" H-A & 11,000 & 1024 & 167 & 0 & 308 \\\\\\\\\\n\",\n \" F-H-A & 9,775 & 1163 & 990 & 0 & 575 \\\\\\\\\\n\",\n \" F-H-R & 8,575 & 812 & 761 & 3 & 495 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\texttt{Total} & - & 7539 & 4086 & 5 & 4022\\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Overall Error Analysis Results using Llama 3.1 8B Instruct.}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!htbp]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lccccccc}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Domain} & \\\\textbf{ \\\\# Turns} & \\\\textbf{\\\\# Validation Error} & \\\\textbf{\\\\# Variable Not Defined} & \\\\textbf{\\\\# Index Out Of Range} & \\\\textbf{\\\\# Other} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\texttt{Single-Domain} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Flights & 8,200 & 408 & 176 & 42 & 26 \\\\\\\\\\n\",\n \" Hotels & 8,000 & 1,099 & 16 & 0 & 0 \\\\\\\\\\n\",\n \" Restaurants & 7,900 & 209 & 5 & 0 & 2 \\\\\\\\\\n\",\n \" Attractions & 5,975 & 18 & 9 & 0 & 8 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multicolumn{6}{l}{\\\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\\\\\ \\\\midrule\\n\",\n \" F-H & 11,700 & 454 & 149 & 0 & 22 \\\\\\\\\\n\",\n \" H-R & 10,950 & 692 & 185 & 0 & 17 \\\\\\\\\\n\",\n \" H-A & 11,000 & 1248 & 315 & 0 & 1 \\\\\\\\\\n\",\n \" F-H-A & 9,775 & 789 & 124 & 9 & 27 \\\\\\\\\\n\",\n \" F-H-R & 8,575 & 541 & 109 & 0 & 101 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\texttt{Total} & - & 5458 & 1088 & 51 & 204 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Overall Error Analysis Results using Llama 3.1 8B SFT Instruct.}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!htbp]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lccccccc}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Domain} & \\\\textbf{ \\\\# Turns} & \\\\textbf{\\\\# Validation Error} & \\\\textbf{\\\\# Variable Not Defined} & \\\\textbf{\\\\# Index Out Of Range} & \\\\textbf{\\\\# Other} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\texttt{Single-Domain} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Flights & 8,200 & 518 & 2 & 30 & 0 \\\\\\\\\\n\",\n \" Hotels & 8,000 & 41 & 0 & 0 & 0 \\\\\\\\\\n\",\n \" Restaurants & 7,900 & 29 & 0 & 0 & 0 \\\\\\\\\\n\",\n \" Attractions & 5,975 & 4 & 2 & 0 & 1 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multicolumn{6}{l}{\\\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\\\\\ \\\\midrule\\n\",\n \" F-H & 11,700 & 158 & 28 & 0 & 0\\n\",\n \" \\\\\\\\\\n\",\n \" H-R & 10,950 & 14 & 2 & 0 & 1\\n\",\n \" \\\\\\\\\\n\",\n \" H-A & 11,000 & 48 & 33 & 0 & 48\\n\",\n \" \\\\\\\\\\n\",\n \" F-H-A & 9,775 & 148 & 6 & 7 & 3\\n\",\n \" \\\\\\\\\\n\",\n \" F-H-R & 8,575 & 330 & 5 & 1 & 7\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\texttt{Total} & - & 1290 & 78 & 38 & 60 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Overall Error Analysis Results using Llama 3.3 70B Instruct.}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!htbp]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lccccccc}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Domain} & \\\\textbf{ \\\\# Turns} & \\\\textbf{\\\\# Validation Error} & \\\\textbf{\\\\# Variable Not Defined} & \\\\textbf{\\\\# Index Out Of Range} & \\\\textbf{\\\\# Other} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\texttt{Single-Domain} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Flights & 8,200 & 559 & 16 & 3 & 40\\n\",\n \" \\\\\\\\\\n\",\n \" Hotels & 8,000 & 92 & 1 & 39 & 27\\n\",\n \" \\\\\\\\\\n\",\n \" Restaurants & 7,900 & 20 & 7 & 0 & 28\\n\",\n \" \\\\\\\\\\n\",\n \" Attractions & 5,975 & 11 & 27 & 0 & 76\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multicolumn{6}{l}{\\\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\\\\\ \\\\midrule\\n\",\n \" F-H & 11,700 & 24 & 34 & 114 & 63\\n\",\n \" \\\\\\\\\\n\",\n \" H-R & 10,950 & 54 & 895 & 102 & 34\\n\",\n \" \\\\\\\\\\n\",\n \" H-A & 11,000 & 3 & 1071 & 43 & 23\\n\",\n \" \\\\\\\\\\n\",\n \" F-H-A & 9,775 & 89 & 579 & 25 & 49\\n\",\n \" \\\\\\\\\\n\",\n \" F-H-R & 8,575 & 85 & 241 & 7 & 28\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\texttt{Total} & - & 937 & 2871 & 333 & 368\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Overall Error Analysis Results using S1.1.}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!htbp]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lccccccc}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Domain} & \\\\textbf{ \\\\# Turns} & \\\\textbf{\\\\# Validation Error} & \\\\textbf{\\\\# Variable Not Defined} & \\\\textbf{\\\\# Index Out Of Range} & \\\\textbf{\\\\# Other} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\texttt{Single-Domain} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Flights & 8,200 & 103 & 7 & 1 & 1\\n\",\n \" \\\\\\\\\\n\",\n \" Hotels & 8,000 & 277 & 0 & 0 & 1\\n\",\n \" \\\\\\\\\\n\",\n \" Restaurants & 7,900 & 259 & 0 & 0 & 19\\n\",\n \" \\\\\\\\\\n\",\n \" Attractions & 5,975 & 0 & 0 & 0 & 0\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multicolumn{6}{l}{\\\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\\\\\ \\\\midrule\\n\",\n \" F-H & 11,700 & 212 & 2 & 0 & 7\\n\",\n \" \\\\\\\\\\n\",\n \" H-R & 10,950 & 212 & 0 & 0 & 1\\n\",\n \" \\\\\\\\\\n\",\n \" H-A & 11,000 & 162 & 0 & 0 & 6\\n\",\n \" \\\\\\\\\\n\",\n \" F-H-A & 9,775 & 120 & 3 & 0 & 5\\n\",\n \" \\\\\\\\\\n\",\n \" F-H-R & 8,575 & 153 & 4 & 0 & 32\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\texttt{Total} & - & 1498 & 16 & 1 & 72\\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Overall Error Analysis Results using Phi-4-reasoning-plus.}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lllll}\\n\",\n \" \\\\toprule \\n\",\n \" \\\\textbf{Domain} & \\\\textbf{Attributes} \\\\\\\\ \\\\midrule\\n\",\n \" Flight & airline, flight\\\\_id, start\\\\_airport, start\\\\_airport\\\\_latitude, start\\\\_airport\\\\_longitude, \\\\\\\\\\n\",\n \" & start\\\\_airport\\\\_code, end\\\\_airport, end\\\\_airport\\\\_latitude, end\\\\_airport\\\\_longitude, \\\\\\\\\\n\",\n \" & end\\\\_airport\\\\_code, economy\\\\_class\\\\_option\\\\_present, business\\\\_class\\\\_option\\\\_present, \\\\\\\\\\n\",\n \" & first\\\\_class\\\\_option\\\\_present, distance\\\\_miles, duration\\\\_minutes, \\\\\\\\\\n\",\n \" & departure\\\\_time, arrival\\\\_time, number\\\\_of\\\\_layovers, first\\\\_layover\\\\_airport, \\\\\\\\\\n\",\n \" Hotel & hotel\\\\_name, city, state, neighborhood, latitude, longitude, rating, \\\\\\\\ & stars, max\\\\_occupancy, gym\\\\_present, pool\\\\_present, price\\\\_per\\\\_night, num\\\\_rooms\\\\_available, \\\\\\\\ & breakfast\\\\_included, smoking\\\\_allowed, air\\\\_conditioning\\\\_present, \\\\\\\\\\n\",\n \" & heating\\\\_present, free\\\\_wifi\\\\_included, airport\\\\_shuttle\\\\_present, \\\\\\\\ &\\n\",\n \" is\\\\_pet\\\\_friendly, has\\\\_spa\\\\_services, has\\\\_room\\\\_service, \\\\\\\\ & has\\\\_beach\\\\_access, has\\\\_fitness\\\\_class, has\\\\_laundry\\\\_service, \\\\\\\\ & has\\\\_valet\\\\_parking, has\\\\_balcony, has\\\\_rooftop\\\\_bar, has\\\\_inroom\\\\_Kitchen, \\\\\\\\ & has\\\\_kids\\\\_club, has\\\\_meeting\\\\_rooms, has\\\\_electric\\\\_vehicle\\\\_charging, \\\\\\\\ & has\\\\_hot\\\\_tub, has\\\\_sauna, has\\\\_free\\\\_parking, is\\\\_wheelchair\\\\_accessible, \\\\\\\\ & has\\\\_skiing\\\\_lodging, has\\\\_ocean\\\\_view\\\\_rooms\\\\_present, has\\\\_city\\\\_view\\\\_rooms\\\\_present, \\\\\\\\ & start\\\\_date\\\\_available, end\\\\_date\\\\_available \\\\\\\\\\n\",\n \" Attraction & city, state, name, type, latitude, longitude, neighborhood \\\\\\\\\\n\",\n \" Restaurant & restaurant\\\\_name, city, state, neighborhood, \\\\\\\\\\n\",\n \" & latitude, longitude, rating, price, price\\\\_per\\\\_person, has\\\\_nut\\\\_allergy\\\\_options, has\\\\_dairy\\\\_allergy\\\\_options, has\\\\_shell\\\\_fish\\\\_allergy\\\\_options, \\\\\\\\ & has\\\\_tomato\\\\_allergy\\\\_options, has\\\\_nightshade\\\\_allergy\\\\_options, \\\\\\\\ & has\\\\_gluten\\\\_free\\\\_options, has\\\\_vegetarian\\\\_options, has\\\\_vegan\\\\_options, \\\\\\\\ & has\\\\_kosher\\\\_options, has\\\\_halal\\\\_options, cuisine \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Domains and Attributes.} \\n\",\n \"\\\\label{tab:domains-and-attributes}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{ll}\\n\",\n \" \\\\toprule \\n\",\n \" \\\\textbf{Domain} & \\\\textbf{Tools}\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\textbf{Flights} & search\\\\_flights, filter\\\\_flights \\\\\\\\\\n\",\n \" \\\\textbf{Hotels} & search\\\\_hotels, filter\\\\_hotels \\\\\\\\\\n\",\n \" \\\\textbf{Restaurants} & search\\\\_restaurants, filter\\\\_restaurants \\\\\\\\\\n\",\n \" \\\\textbf{Attractions} & search\\\\_attractions, filter\\\\_attractions \\\\\\\\\\n\",\n \" \\\\textbf{Multi-domain} & search\\\\_nearest \\\\\\\\\\n\",\n \" \\\\textbf{Common} & save\\\\_to\\\\_cache, get\\\\_results\\\\_from\\\\_cache, sort\\\\_results, seek\\\\_information, adjust\\\\_date \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{List of Tools.} \\n\",\n \"\\\\label{tab:defined-tools-domain}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lll}\\n\",\n \" \\\\toprule \\n\",\n \" \\\\textbf{Attribute} & \\\\textbf{Type} & \\\\textbf{Description} \\\\\\\\ \\\\midrule\\n\",\n \" airline & string & Airline of the flight, \\\\\\\\\\n\",\n \" flight\\\\_classes & string & Classes for the flight (economy, business, first) \\\\\\\\\\n\",\n \" num\\\\_layovers & integer & Number of layovers for the flight, between 0 and 2 \\\\\\\\\\n\",\n \" layover\\\\_duration\\\\_amount & integer & Duration of a layover flight. Between 1 and 6 hours \\\\\\\\\\n\",\n \" airports & list & Airport information for each flight including the city, state, airport code and airport name. \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Flight Ontology Attributes.} \\n\",\n \"\\\\label{tab:flight-ontology-attributes}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!htbp]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lll}\\n\",\n \" \\\\toprule \\n\",\n \" \\\\textbf{Attribute} & \\\\textbf{Type} & \\\\textbf{Description} \\\\\\\\ \\\\midrule\\n\",\n \" city & string & City that the hotel is in \\\\\\\\\\n\",\n \" state & string & State that the hotel is in \\\\\\\\\\n\",\n \" neighborhood & string & Neighborhood within the city that the hotel is in\\\\\\\\\\n\",\n \" stars & integer & Star rating of the hotel between 1 and 5 \\\\\\\\\\n\",\n \" rating & float & Customer rating of the hotel between 1.0 and 5.0, incremented by 0.1 \\\\\\\\\\n\",\n \" price\\\\_per\\\\_night & integer & Price per night of the hotel ranging between 20 to 2000 dollars \\\\\\\\\\n\",\n \" max\\\\_occupancy & integer & Maximum occupancy per room ranging between 1 and 7 \\\\\\\\\\n\",\n \" gym\\\\_present & boolean & Whether or not the hotel has a gym \\\\\\\\\\n\",\n \" pool\\\\_present & boolean & Whether or not the hotel has a pool \\\\\\\\\\n\",\n \" breakfast\\\\_included & boolean & Whether or not the hotel has breakfast included \\\\\\\\\\n\",\n \" smoking\\\\_allowed & boolean & Whether or not the hotel allows smoking \\\\\\\\\\n\",\n \" air\\\\_conditioning\\\\_present & boolean & Whether or not the hotel has air conditioning \\\\\\\\\\n\",\n \" heating\\\\_present & boolean & Whether or not the hotel has heating \\\\\\\\\\n\",\n \" free\\\\_wifi\\\\_included & boolean & Whether or not the hotel includes free WiFi \\\\\\\\\\n\",\n \" airport\\\\_shuttle\\\\_present & boolean & Whether or not the hotel has an airport shuffle \\\\\\\\\\n\",\n \" is\\\\_pet\\\\_friendly & boolean & Whether or not the hotel allows pets \\\\\\\\\\n\",\n \" has\\\\_spa\\\\_services & boolean & Whether or not the hotel has spa services \\\\\\\\\\n\",\n \" has\\\\_room\\\\_service & boolean & Whether or not the hotel has room service \\\\\\\\\\n\",\n \" has\\\\_beach\\\\_access & boolean & Whether or not the hotel has access to a beach \\\\\\\\\\n\",\n \" has\\\\_business\\\\_center & boolean & Whether or not the hotel has a business center \\\\\\\\\\n\",\n \" has\\\\_fitness\\\\_classes & boolean & Whether or not the hotel has fitness classes \\\\\\\\\\n\",\n \" has\\\\_laundry\\\\_service & boolean & Whether or not the hotel has laundry services \\\\\\\\\\n\",\n \" has\\\\_valet\\\\_parking & boolean & Whether or not the hotel has valet parking \\\\\\\\\\n\",\n \" has\\\\_balcony & boolean & Whether or not the hotel has a balcony \\\\\\\\\\n\",\n \" has\\\\_rooftop\\\\_bar & boolean & Whether or not the hotel has a rooftop bar \\\\\\\\\\n\",\n \" has\\\\_inroom\\\\_kitchen & boolean & Whether or not the hotel has an inroom kitchen \\\\\\\\\\n\",\n \" has\\\\_kids\\\\_club & boolean & Whether or not the hotel has a kids club \\\\\\\\\\n\",\n \" has\\\\_meeting\\\\_rooms & boolean & Whether or not the hotel has meeting rooms \\\\\\\\\\n\",\n \" has\\\\_electric\\\\_vehicle\\\\_charging & boolean & Whether or not the hotel has electric vehicle charging \\\\\\\\\\n\",\n \" has\\\\_hot\\\\_tub & boolean & Whether or not the hotel has a hot tub \\\\\\\\\\n\",\n \" has\\\\_sauna & boolean & Whether or not the hotel has a sauna \\\\\\\\ \\n\",\n \" has\\\\_free\\\\_parking & boolean & Whether or not the hotel has free parking\\\\\\\\ \\n\",\n \" is\\\\_wheelchair\\\\_accessible& boolean & Whether or not the hotel is wheelchair accessible \\\\\\\\ \\n\",\n \" has\\\\_skiing\\\\_lodging & boolean & Whether or not the hotel has skiing and lodging \\\\\\\\ \\n\",\n \" ocean\\\\_view\\\\_rooms\\\\_present & boolean & Whether or not the hotel has rooms with a view of the ocean \\\\\\\\\\n\",\n \" city\\\\_view\\\\_rooms\\\\_present & boolean & Whether or not the hotel has rooms with views of the city\\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Hotel Ontology Attributes.} \\n\",\n \"\\\\label{tab:hotel-ontology-attributes}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!htbp]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lll}\\n\",\n \" \\\\toprule \\n\",\n \" \\\\textbf{Attribute} & \\\\textbf{Type} & \\\\textbf{Description} \\\\\\\\ \\\\midrule\\n\",\n \" city & string & City that the restaurant is in \\\\\\\\\\n\",\n \" state & string & State that the restaurant is in \\\\\\\\\\n\",\n \" neighborhood & string & Neighborhood within the city that the restaurant is in\\\\\\\\\\n\",\n \" rating & float & Customer rating of the restaurant between 1.0 and 5.0, incremented by 0.1 \\\\\\\\\\n\",\n \" price\\\\_per\\\\_person & integer & Average price per person at the restaurant \\\\\\\\\\n\",\n \" cuisine & string & Cuisine of the restaurant \\\\\\\\\\n\",\n \" has\\\\_nut\\\\_allergy\\\\_options & boolean & Whether or not the restaurant has any options for individuals with an allergy to nuts \\\\\\\\\\n\",\n \" has\\\\_dairy\\\\_allergy\\\\_options & boolean & Whether or not restaurant has any options for individuals with an allergy to dairy products \\\\\\\\\\n\",\n \" has\\\\_shell\\\\_fish\\\\_allergy\\\\_options & boolean & Whether or not the restaurant has any options for individuals with an allergy to shell fish \\\\\\\\\\n\",\n \" has\\\\_tomato\\\\_allergy\\\\_options & boolean & Whether or not the restaurant has options for individuals with an allergy to tomatoes \\\\\\\\\\n\",\n \" has\\\\_nightshade\\\\_allergy\\\\_options & boolean & Whether or not the restaurant has options for individuals with an allergy to nightshades \\\\\\\\\\n\",\n \" has\\\\_gluten\\\\_free\\\\_options & boolean & Whether or not the restaurant has gluten free options \\\\\\\\\\n\",\n \" has\\\\_vegetarian\\\\_options & boolean & Whether or not the restaurant has vegetarian options \\\\\\\\\\n\",\n \" has\\\\_vegan\\\\_options & boolean & Whether or not the restaurant has vegan options \\\\\\\\\\n\",\n \" has\\\\_halal\\\\_options & boolean & Whether or not the restaurant has halal options \\\\\\\\\\n\",\n \" has\\\\_kosher\\\\_options & boolean & Whether or not the hotel has kosher options \\\\\\\\ \\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Restaurant Ontology Attributes.} \\n\",\n \"\\\\label{tab:restaurant-ontology-attributes}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!htbp]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{0.8\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lll}\\n\",\n \" \\\\toprule \\n\",\n \" \\\\textbf{Attribute} & \\\\textbf{Type} & \\\\textbf{Description} \\\\\\\\ \\\\midrule\\n\",\n \" city & string & City that the attraction is in \\\\\\\\\\n\",\n \" state & string & State that the attraction is in \\\\\\\\\\n\",\n \" neighborhood & string & Neighborhood within the city that the attraction is in\\\\\\\\\\n\",\n \" type & string & Type of the attraction \\\\\\\\ \\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Attraction Ontology Attributes.} \\n\",\n \"\\\\label{tab:attraction-ontology-attributes}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table}[!ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\small\\n\",\n \"\\\\renewcommand{\\\\arraystretch}{1.3}\\n\",\n \"\\\\begin{tabular}{|l|p{5.5cm}|p{7.5cm}|}\\n\",\n \"\\\\hline\\n\",\n \"\\\\textbf{Domain} & \\\\textbf{Placeholders Used} & \\\\textbf{Required Scenarios} \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\\\textbf{Flight} & \\n\",\n \"\\\\texttt{<CITY\\\\_x>}, \\\\texttt{<CLASS\\\\_x>}, \\\\texttt{<DEPARTURE\\\\_DATE\\\\_x>}, \\\\texttt{<DEPARTURE\\\\_TIME\\\\_x>}, \\\\texttt{<ARRIVAL\\\\_DATE\\\\_x>}, \\\\texttt{<ARRIVAL\\\\_TIME\\\\_x>}, \\\\texttt{<AIRLINE\\\\_x>}, \\\\texttt{<PRICE\\\\_x>}, \\\\texttt{<AIRPORT\\\\_x>}, \\\\texttt{<NUM\\\\_TRAVELERS\\\\_x>} & \\n\",\n \"Round trip (with 2 departure dates), one-way, multi-city, strict arrival time, flexible dates, large group bookings, solo/business/family travel, weekend getaways, budget constraints, special occasions (e.g., birthdays) \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\\\textbf{Hotel} & \\n\",\n \"\\\\texttt{<CHECK\\\\_IN\\\\_DATE\\\\_x>}, \\\\texttt{<CHECK\\\\_OUT\\\\_DATE\\\\_x>}, \\\\texttt{<CITY\\\\_x>}, \\\\texttt{<CITY\\\\_x\\\\_NEIGHBORHOOD\\\\_y>}, \\\\texttt{<STAR\\\\_x>}, \\\\texttt{<RATING\\\\_x>}, \\\\texttt{<PRICE\\\\_x>}, \\\\texttt{<NUM\\\\_ROOMS\\\\_x>}, \\\\texttt{<NUM\\\\_TRAVELERS\\\\_x>} & \\n\",\n \"Multi-city trips, multiple hotels in one city, specific neighborhoods, special occasions, last-minute reservations, business vs. leisure, extended stays, view/accessibility/pet-friendly requests, family and group accommodations \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\\\textbf{Attraction} & \\n\",\n \"\\\\texttt{<ATTRACTION\\\\_TYPE\\\\_x>}, \\\\texttt{<CITY\\\\_x>}, \\\\texttt{<STATE\\\\_x>}, \\\\texttt{<CITY\\\\_x\\\\_NEIGHBORHOOD\\\\_y>} & \\n\",\n \"Multi-type attraction queries, across cities/states, neighborhood-specific exploration, curiosity about state-level offerings. Assistant must ask open-ended questions and only mention placeholders if user provides them. \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\\\textbf{Restaurant} & \\n\",\n \"\\\\texttt{<RESTAURANT\\\\_RATING\\\\_x>}, \\\\texttt{<RESTAURANT\\\\_PRICE\\\\_x>}, \\\\texttt{<RESTAURANT\\\\_CUISINE\\\\_x>}, \\\\texttt{<CITY\\\\_x>}, \\\\texttt{<CITY\\\\_x\\\\_NEIGHBORHOOD\\\\_y>} & \\n\",\n \"Multi-city dining, cuisine preferences, dietary restrictions (e.g., nut-free, vegan, halal), budget vs. premium, special occasion dining, rating-focused or casual vs. upscale preferences \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\vspace{1mm}\\n\",\n \"\\\\caption{Domain-specific customization for template generation. Each domain builds on the shared prompt instructions with unique placeholders and required conversation scenarios.}\\n\",\n \"\\\\label{tab:domain-customization}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[!ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\small\\n\",\n \"\\\\renewcommand{\\\\arraystretch}{1.2}\\n\",\n \"\\\\begin{tabularx}{\\\\textwidth}{|l|X|X|}\\n\",\n \"\\\\hline\\n\",\n \"\\\\textbf{Domains} & \\\\textbf{Placeholders Used} & \\\\textbf{Required Scenarios} \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\\\textbf{Flight + Hotel} & \\n\",\n \"\\\\texttt{<CITY\\\\_x>}, \\\\texttt{<CLASS\\\\_x>}, \\\\texttt{<DEPARTURE\\\\_DATE\\\\_x>}, \\\\texttt{<CHECK\\\\_IN\\\\_DATE\\\\_x>}, \\\\texttt{<CHECK\\\\_OUT\\\\_DATE\\\\_x>}, \\\\texttt{<NUM\\\\_TRAVELERS\\\\_x>} & \\n\",\n \"Trip planning involving both air travel and accommodation. Includes round-trip and one-way flights, hotel stays across one or more cities, budget and luxury travelers, group vs. solo travel, flexible dates, specific preferences like nonstop flights or hotels with amenities (e.g., pool, gym, pet-friendly). \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\\\textbf{Flight + Hotel + Attraction} & \\n\",\n \"All flight, hotel, and attraction placeholders (e.g., \\\\texttt{<CITY\\\\_x>}, \\\\texttt{<CHECK\\\\_IN\\\\_DATE\\\\_x>}, \\\\texttt{<ATTRACTION\\\\_TYPE\\\\_x>}) & \\n\",\n \"User planning a full trip involving flights, hotel stays, and sightseeing. Must include: round-trip and one-way flights, multi-city stays, hotels near attractions, flexible schedules, user-provided attraction types or specific attraction names (only when linking to hotel proximity), and special constraints (e.g., group travel, business trips, anniversaries). Assistant must never use placeholder names before user introduces them. Includes amenity discussions and attraction-type grammar handling (e.g., cultural vs. \\\\texttt{<ATTRACTION\\\\_TYPE\\\\_1>} attraction). \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\\\textbf{Flight + Hotel + Restaurant} & \\n\",\n \"All flight, hotel, and restaurant placeholders (e.g., \\\\texttt{<RESTAURANT\\\\_CUISINE\\\\_x>}, \\\\texttt{<RESTAURANT\\\\_PRICE\\\\_x>}) & \\n\",\n \"End-to-end trip planning including flights, accommodation, and dining. Required scenarios include: dietary restrictions, cuisine-first or city-first planning, restaurant proximity to hotel, multiple hotels or restaurants in one city, round-trip and one-way flights, and amenities like pet-friendly or spa. Must feature diverse language, nonlinear flows, and varied user personas. Includes explicit handling of dietary needs (e.g., halal, nut-free, vegetarian), and placeholder logic as per prompt. \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\\\textbf{Hotel + Attraction} & \\n\",\n \"\\\\texttt{<CITY\\\\_x>}, \\\\texttt{<CHECK\\\\_IN\\\\_DATE\\\\_x>}, \\\\texttt{<ATTRACTION\\\\_TYPE\\\\_x>}, \\\\texttt{<CITY\\\\_x\\\\_NEIGHBORHOOD\\\\_y>} & \\n\",\n \"User looking for accommodations and nearby attractions. Includes: specific neighborhoods, family-friendly vs. solo travel, themed vacations, extended stays, proximity to cultural or outdoor attractions, and cases where attraction type drives hotel location. \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\\\textbf{Hotel + Restaurant} & \\n\",\n \"\\\\texttt{<RESTAURANT\\\\_CUISINE\\\\_x>}, \\\\texttt{<HOTEL\\\\_PRICE\\\\_x>}, \\\\texttt{<CITY\\\\_x\\\\_NEIGHBORHOOD\\\\_y>} & \\n\",\n \"Combines hotel booking with dining preferences. Includes cuisine-specific searches, budget dining vs. fine dining, dietary needs (gluten-free, halal), special events (anniversaries), restaurant proximity to hotel, and group size considerations. Templates vary in planning flow—some start with hotel, others with restaurants. \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\\\end{tabularx}\\n\",\n \"\\\\vspace{1mm}\\n\",\n \"\\\\caption{Multi-domain combinations and scenario requirements. Each configuration builds on the unified instructions with additional placeholder logic and domain-specific complexity.}\\n\",\n \"\\\\label{tab:multi-domain-customization}\\n\",\n \"\\\\end{table}\\n\"\n ]\n]","isConversational":false},"fulltext":{"kind":"string","value":"\\documentclass{article}\n\n% if you need to pass options to natbib, use, e.g.:\n % \\PassOptionsToPackage{numbers, compress}{natbib}\n% before loading neurips_2024\n\n\\usepackage[square,numbers]{natbib}\n\\bibliographystyle{abbrvnat}\n\n% ready for submission\n% \\usepackage[]{neurips_2025}\n\\usepackage[preprint]{neurips_2025}\n\n\n% to compile a preprint version, e.g., for submission to arXiv, add add the\n% [preprint] option:\n% \\usepackage[preprint]{neurips_2024}\n\n\n% to compile a camera-ready version, add the [final] option, e.g.:\n% \\usepackage[final]{neurips_2024}\n\n\n% to avoid loading the natbib package, add option nonatbib:\n% \\usepackage[nonatbib]{neurips_2024}\n\n\n\\usepackage[utf8]{inputenc} % allow utf-8 input\n\\usepackage[T1]{fontenc} % use 8-bit T1 fonts\n\\usepackage{hyperref} % hyperlinks\n\\usepackage{url} % simple URL typesetting\n\\usepackage{booktabs} % professional-quality tables\n\\usepackage{amsfonts} % blackboard math symbols\n\\usepackage{nicefrac} % compact symbols for 1/2, etc.\n\\usepackage{microtype} % microtypography\n\\usepackage{xcolor} % colors\n\\usepackage{float}\n\\usepackage[breakable]{tcolorbox}\n\\usepackage{listings}\n\n\\lstset{\n basicstyle=\\ttfamily\\small,\n breaklines=true,\n breakatwhitespace=true,\n columns=fullflexible,\n frame=single\n}\n\\usepackage{tabularx} % Add in preamble\n% \\usepackage[table]{xcolor}\n% \\usepackage{tabularx}\n\\usepackage{amssymb} % for \\checkmark\n\\usepackage{hyperref}\n\\usepackage{url}\n\\usepackage{multirow}\n\\usepackage{bbm}\n\\usepackage{graphicx}\n\\usepackage{amssymb}\n\\usepackage{amsmath}\n\\usepackage{amsthm}\n\\usepackage{booktabs}\n\\usepackage{lscape}\n\\usepackage{pifont}\n\\usepackage{multirow}\n\\usepackage{booktabs}\n\\usepackage{textcomp}\n\\usepackage{lscape}\n\\usepackage{pifont}\n\\usepackage{algorithm}\n\\usepackage{graphicx}\n\\usepackage{caption}\n\\usepackage{subcaption}\n\\usepackage{CJKutf8}\n\\usepackage{algorithm}\n\\usepackage{algpseudocode}\n\\usepackage{enumitem}\n\\usepackage{wrapfig}\n\\usepackage{tabularx}\n\\usepackage{booktabs}\n\\usepackage{array}\n\\usepackage{pifont}\n\\usepackage{xcolor}\n\\newcommand{\\cmark}{\\ding{51}}\n\\newcommand{\\gcmark}{{\\color{green!60!black}\\fontsize{10}{10}\\selectfont\\ding{51}}}\n\\newcommand\\benchmarkname{\\textcolor{black}{\\textsc{T1}}}\n\\newcommand\\agentname{\\textcolor{black}{\\textsc{T1-Agent}}}\n\\newcommand\\dascomment[1]{\\textcolor{red}{das: \\textit{#1}}}\n\n\\title{$\\benchmarkname$: A Tool-Oriented Conversational Dataset for Multi-Turn Agentic Planning}\n\n% \\title{T$^2$ (ToolTalk): A Dataset for Tool-Oriented, Multi-Turn Agentic Planning}\n% \\title{ToolAgents: Tool-Driven Agents for Solving Multi-Domain Problems}\n\n\n% The \\author macro works with any number of authors. There are two commands\n% used to separate the names and addresses of multiple authors: \\And and \\AND.\n%\n% Using \\And between authors leaves it to LaTeX to determine where to break the\n% lines. Using \\AND forces a line break at that point. So, if LaTeX puts 3 of 4\n% authors names on the first line, and the last on the second line, try using\n% \\AND instead of \\And before the third author name.\n\n\n\\author{%\n Amartya Chakraborty\\thanks{The authors contributed equally.} \\text{ }, Paresh Dashore$^*$, Nadia Bathaee$^*$, Anmol Jain$^*$, \\\\\n \\textbf{Anirban Das}, \\textbf{Shi-Xiong Zhang}, \\textbf{Sambit Sahu}, \\textbf{Milind Naphade}, \\textbf{Genta Indra Winata$^*$} \\\\\n Capital One \\\\\n \\texttt{\\{amartya.chakraborty, paresh.dashore, nadia.bathaee\\}@capitalone.com} \\\\\n \\texttt{\\{anmol.jain, genta.winata\\}@capitalone.com}\n}\n\n\\begin{document}\n\n\\maketitle\n\n\\begin{abstract}\nLarge Language Models (LLMs) have demonstrated impressive capabilities as intelligent agents capable of solving complex problems. However, effective planning in scenarios involving dependencies between API or tool calls-particularly in multi-turn conversations-remains a significant challenge. To address this, we introduce $\\benchmarkname$, a tool-augmented, multi-domain, multi-turn conversational dataset specifically designed to capture and manage inter-tool dependencies across diverse domains. $\\benchmarkname$ enables rigorous evaluation of agents' ability to coordinate tool use across nine distinct domains (4 single domain and 5 multi-domain) with the help of an integrated caching mechanism for both short- and long-term memory, while supporting dynamic replanning-such as deciding whether to recompute or reuse cached results. Beyond facilitating research on tool use and planning, $\\benchmarkname$ also serves as a benchmark for evaluating the performance of open-source language models. We present results powered by $\\agentname$, highlighting their ability to plan and reason in complex, tool-dependent scenarios.\n\\end{abstract}\n\n\n\\section{Introduction}\nLeveraging external tools using Large Language Models (LLMs) to solve diverse conversational tasks has emerged as a promising direction in the development of intelligent agents~\\cite{tang2023toolalpaca}. Despite recent advances, task oriented llm based dialogue systems perform poorly over long conversational context~\\cite{laban2025llms}, and there remains a lack of comprehensive resources for training and evaluating multi-turn, multi-domain conversational agents that emphasizes on complex user needs. Existing datasets primarily focus on single-turn conversations for planning tasks—such as executing APIs or code~\\cite{schick2023toolformer, xie2024travelplanner}—and do not capture realistic multi-turn scenarios that require reasoning over long contexts, coordination across multiple tools, and adherence to complex constraints. Tool calling in realistic scenarios often involves interdependent tools, where the correctness and efficiency of task completion depend heavily on the order and context in which tools are invoked. An effective agent must therefore understand when, which, and in what sequence to call tools in order to successfully complete complex tasks. \n\nTo address this gap, we introduce $\\benchmarkname$, a new dataset and evaluation framework for assessing agent performance in multi-turn dialogues, with a particular focus on tool usage and reasoning about inter-tool dependencies. The dataset spans multiple domains and features complex, goal-oriented interactions between users and a travel assistant. Alongside the dataset, we propose $\\agentname$, an agent designed to interpret nuanced user intents and generate executable code using a predefined set of tools. $\\benchmarkname$ is specifically designed to evaluate the ability of LLM-based agents to plan tool use effectively and leverage a caching mechanism to efficiently reuse previously retrieved information. To support this, we incorporate dedicated tools for accessing and managing the cache.\n% Furthermore, we incorporate a novel caching mechanism on the agent side during code generation: before generating new outputs, the agent checks whether the current user query relates to any previously cached results. If a match is found, it retrieves the cached data and applies further filtering as needed.\n\n\\begin{figure*}[!t]\n \\centering\n \\includegraphics[width=\\textwidth]{assets/pipeline.png}\n \\caption{Illustrative example from the $\\benchmarkname$ dataset. This example showcases a multi-domain scenario involving both flights and hotels, where the user is planning a trip and attempting to book relevant services. The dialogue is constructed by retrieving entities from a knowledge base, and tool calls are executed using a predefined toolbox, simulating realistic, tool-augmented agent behavior.}\n \\label{fig:architecture}\n\\end{figure*}\n\nOur contributions can be summarized as follows:\n\\begin{itemize}\n \\item We introduce $\\benchmarkname$, a comprehensive multi-turn dataset consisting of 13.5k dialogues designed to evaluate tool-using, LLM-based agents across nine key domains—comprising four single-domain and five multi-domain settings. The dataset covers a wide range of interaction scenarios, including single-domain, mixed-domain, and fully multi-domain conversations. It incorporates 14 distinct tools, enabling realistic and fine-grained assessment of agent capabilities in complex, tool-driven dialogue tasks.\n \\item To enhance the complexity and realism of the evaluation, the dataset includes cross-domain tasks and interdependent tool calls, requiring agents to reason about tool selection and execution order within context. This evaluation framework assesses the ability of LLM-based agents to think critically, reason effectively, and make context-aware decisions.\n \\item We evaluate our dataset using an LLM-based agent, $\\agentname$, a code-generation system built on open-source language models and equipped with a caching mechanism for improved performance. This architecture enables scalable evaluation and provides a robust, efficient framework for tool-using agents.\n \\item We will publicly release our code and dataset to facilitate future research.\n\\end{itemize}\n\n\\section{T1 Dataset}\n\n$\\benchmarkname$ is a dataset specifically designed to evaluate LLM-based agents on tool usage and complex planning tasks over multi-turn conversational context. This dataset simulates multi-turn conversations spanning both four single-domain and five multi-domain settings: \\textit{flights}, \\textit{restaurants}, \\textit{hotels}, \\textit{attractions}, \\textit{flights-hotels}, \\textit{hotels-restaurants}, \\textit{hotels-attractions}, \\textit{flights-hotels-attractions}, and \\textit{flights-hotels-restaurants}.\n% \\dascomment{sentence abruptly ends}.\nPlanning tasks are formulated as code, where function calls to external tools are used to accomplish specific goals.\n\n\\subsection{Tasks and Notations}\n\nWe define a dialogue \\( D \\) as an alternating sequence of assistant and user turns: \n\\begin{align}\nD = \\{A_1, U_1, A_2, U_2, \\ldots, A_n\\},\n\\end{align}\nwhere each \\( A_i \\) represents an assistant turn and \\( U_i \\) a user turn. The dialogue always starts with an assistant turn and proceeds in a strictly alternating order. We provide a set of tools \\( \\mathcal{T} \\), where each tool \\( t \\in \\mathcal{T} \\) encapsulates logic to perform a specific function. These tools can be categorized as follows:\n\\begin{itemize}\n \\item \\textbf{Domain-specific tools}: Designed to handle operations tied to a particular application domain.\n \\item \\textbf{Interdependent tools}: Used to identify or reason about dependencies across domains.\n \\item \\textbf{Generic tools}: Domain-independent or auxiliary utilities applicable across tasks.\n\\end{itemize}\n\n\\begin{table*}[!htbp]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lccccccc}\n \\toprule\n \\textbf{Dataset} & \\textbf{Deployed} & \\textbf{Human Annotated} & \\textbf{Execution Result} & \\textbf{Multi-turn Context} & \\textbf{Multi-Domain} \\\\\n & \\textbf{Tools} & \\textbf{Tool Planning} & \\textbf{Evaluation} & \\textbf{Planning} & \\textbf{Tool Planning}\\\\\n \\midrule\n APIBank~\\cite{li2023api} & \\cmark & \\cmark & \\cmark & \\cmark & & \\\\\n APIBench~\\cite{patil2024gorilla} & & & \\cmark & & \\\\\n GAIA~\\cite{mialon2023gaia} & & \\cmark & \\cmark & & \\\\\n GTA~\\cite{wang2024gta} & \\cmark & \\cmark & \\cmark & \\cmark & \\\\\n m\\&m's~\\cite{ma2024m} & \\cmark & \\cmark & \\cmark & \\cmark & \\\\\n ToolBench~\\cite{qin2023toolllm} & \\cmark & & \\cmark & & \\\\\n Toolformer~\\cite{schick2023toolformer} & \\cmark & \\cmark & \\cmark & & \\\\\n TravelPlanner~\\cite{xie2024travelplanner} & \\cmark & \\cmark & \\cmark & & \\cmark & \\\\ \n \\midrule\n $\\textbf{\\benchmarkname}$ & \\cmark & \\cmark & \\cmark & \\cmark & \\cmark \\\\\n \\bottomrule\n \\end{tabular}\n}\n\\caption{Comparison of datasets for the LLM-based agent systems.}\n\\end{table*}\n\n\\begin{figure*}[!t]\n \\centering\n \\includegraphics[width=\\textwidth]{assets/data_generation.png}\n \\caption{$\\benchmarkname$ generates data by populating delexicalized entities with corresponding entries from the knowledge base.}\n \\label{fig:data_generation}\n\\end{figure*}\n\n% In this paper, we study four fundamental tasks involving tool usage:\n% \\begin{enumerate}\n% \\item \\textbf{Tool Calling}: Invoking the appropriate tool(s) based on the current dialogue context.\n% \\item \\textbf{Parameter Extraction}:\n% \\item \\textbf{Information Seeking}: Gathering missing or clarifying information required for successful tool execution.\n% \\item \\textbf{Data Caching}: Reusing previously generated results to improve efficiency and maintain continuity.\n% \\end{enumerate}\n\n\\subsection{Dataset Construction}\nWe construct our dataset by manually collecting data from Wikipedia to gather entities and metadata. For this task, we define four domains: flights, hotels, restaurants, and attractions. Additionally, we compile a list of 128 airports and 321 cities within the United States, along with up to 15 neighborhoods for each city. Some information—such as airline names, hotel names, and hotel star ratings—is synthesized to avoid inaccuracies and to prevent the LLMs from relying on its internal knowledge of real-world named entities.\n\n\\subsubsection{Ontology}\nWe have a total of 5 ontologies, one for each of the four defined domains and another one for city. For each ontology, we curated a list of key attributes that would be relevant such as the airline or number of layovers for a flight, the cuisine of a restaurant or the number of stars or customer rating for a hotel. For each of these attributes, we defined the possible values and then used the ontology to generate synthetic data for flights, hotels and restaurants which would subsequently be used by the tools we defined. \n\n\\begin{table*}[!t]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lcccc|cc|c|c}\n \\toprule \n & \\multicolumn{4}{c|}{\\textbf{Single-domain}} & \\multicolumn{2}{c|}{\\textbf{Multi-domain}}\n & \\textbf{Common} & \\textbf{Total} \\\\ \n & \\textbf{Flights} & \\textbf{Hotels} & \\textbf{Restaurants} & \\textbf{Attractions} & \\textbf{2 Domains} & \\textbf{3 Domains} & \\\\ \\midrule\n \\textbf{\\# Attributes} & 35 & 43 & 21 & 7 & N/A & N/A & N/A & 106 \\\\\n \\textbf{\\# Tools} & 2 & 2 & 2 & 2 & 1 & N/A & 5 & 14 \\\\ \n \\textbf{\\# Dialogues} & 1.5k & 1.5k & 1.5k & 1.5k & 4.5k & 3k & N/A & 13.5k \\\\ \n \\textbf{Avg. \\# turns} & 8.2 & 8.0 & 9.0 & 6.0 & 11.3 & 9.7 & N/A & N/A \\\\ \n \\bottomrule\n \\end{tabular}\n}\n\\caption{Dataset statistics detailing the number of attributes, tools, dialogues, and the average number of turns, categorized by each individual domain, multi-domain, and common categories. \"N/A\" indicates that the metric is not applicable in the given context.} \n\\label{tab:baseline-results-one-epoch}\n\\end{table*}\n\n\\paragraph{Flights.}\nThe ontology includes the airline, flight class (economy, business and first), number of layovers ranging between 0-2 stops, the duration of a layover ranging from 1-6 hours and a list of the possible airports to depart from and arrive to. To see the full table, go to Table~\\ref{tab:flight-ontology-attributes}.\n\n\\paragraph{Hotels.}\nThe ontology includes the number of stars ranging between 1 to 5 stars, the customer rating of the hotel ranging from 1.0 to 5.0, the cost of the hotel as well as whether or not the hotel includes a number of amenities such as the presence of a gym or pool. To see the full table, go to Table~\\ref{tab:hotel-ontology-attributes}.\n\n\\paragraph{Restaurants.}\nThe ontology includes the type of cuisine, the customer rating of the hotel ranging from 1.0 to 5.0, the price per person and whether or not the restaurant served any particular dietary options such as vegetarian, vegan, or halal. To see a full list of dietary restrictions, taken into consideration, go to Table~\\ref{tab:restaurant-ontology-attributes}.\n\n\\paragraph{Attractions.}\nThe ontology includes the type of attraction which is one of the following: touristy, culinary, historical, scenic, social, art, cultural, guided, and sporting.\n\n\\paragraph{Cities.}\nThe ontology includes a list of cities in the United States of America (US) that was collected through Wikipedia. For each city, we then extracted up to 15 neighborhoods as well as the approximate geographical coordinates of each neighborhood.\n\n\\subsubsection{Knowledge Bases}\n\\paragraph{Attractions.}\nFrom the list of 321 cities, we collect up to 15 attractions for 85 cities through the usage of Llama-3.3 70B Instruct and conduct quality assurance by human annotators to ensure the data correctness. We also collect the city neighborhood for each attraction as well as the geographical coordinates. In total, there are 728 attractions that were collected from the 85 cities for this dataset.\n\n\\paragraph{Flights.}\nFor flights, there are a total of 128 airports that are used to generate synthetic flight data. Each flight generated has an airline that was randomly selected from the ontology and the start and end airport are randomly selected from the list of airports in the ontology. Additionally, the departure time is randomly generated, however the arrival time is computed based on the geographical distance between the departure and arrival airports assuming the average speed of the flight to be 450 miles per hour. 480,410 synthetic flights were generated as part of this dataset\n\n\\paragraph{Hotels.}\nHotels are generated for all 321 cities in the ontology. For a particular city, a neighborhood is assigned and additionally, synthetic latitude and longitude coordinates are generated for each hotel in a city as the coordinates would be used for distance computing purposes. Each hotel is also provided a star and a synthetic customer rating that would be correlated to the amenities offered by the establishment as well as the price per night. 47,589 hotels were generated as part of this dataset. \n\n\n\\paragraph{Restaurants.}\nRestaurants are generated for all 321 cities in the ontology. Just like for hotels, in a particular city, a neighborhood is assigned to a restaurant and additionally, synthetic latitude and longitude coordinates are generated. Each restaurant also is given a user rating which was synthetically generated, a cuisine provided by the ontology as well as whether particular dietary options are supported and the average cost per person. 17,975 restaurants are generated as part of this dataset.\n\n\\subsubsection{Data Annotation and Quality Assurance}\nTo ensure high-quality and natural data, we employ five human annotators, with each data sample reviewed by both an annotator and a quality assurance (QA) reviewer. The annotators were selected to represent a diverse set of background and perspectives, while maintaining a high technical bar. All annotators have at least a Master's degree in Computer Science and demonstrated proficiency in Python, enabling them to handle complex annotation tasks requiring logical reasoning and scripting. The QA specialist also has a strong background in programming. Annotators are assigned a category of templates and are responsible for writing the appropriate code using the tools defined for this project. Afterward, the QA reviewer evaluates the annotated code and provides feedback, which is used to make necessary corrections and improvements.\n\n\\subsubsection{Dialogue Generation}\nWe create a total of nine data categories, as discussed in Section~\\ref{sec:distinct-domains}. The data construction follows a three-step process: first, we generate templates with placeholder values; second, we annotate the templates with code using the provided tools; and third, we programmatically fill in the placeholder values for each template.\n\\paragraph{Generating Dialogue Templates.}\nFor each of the 9 dialogue categories, we generate 60 dialogue templates using Llama-3.3 70B Instruct. These templates are then reviewed and refined by human annotators to ensure accurate dialogue flow, as well as high coherence and fluency. The model is prompted to generate synthetic dialogues for both single and multi-domain. Each template consists of a conversation between the assistant and user. To learn more about the prompt used, go to Appendix~\\ref{appendix:prompt-design-user-planner} and ~\\ref{appendix:multi-domain-prompt-design}. Additionally, the templates consist of placeholders for attributes such as a city or neighborhood name, cuisine of a restaurant, rating of a hotel or the type of an attraction. As the dataset is related to the area of travel, some placeholder values are tied to a particular city such as the neighborhood (\\texttt{<CITY\\_x\\_NEIGHBORHOOD\\_x>}, airport name (\\texttt{<CITY\\_x\\_AIRPORT\\_x>}), hotel name (\\texttt{<CITY\\_x\\_HOTEL\\_NAME\\_x>}) and restaurant name (\\texttt{<CITY\\_x\\_RESTAURANT\\_NAME\\_x>}). \n\n\\paragraph{Template Lexicalization.}\nWithin each dialogue category, 25 dialogues are generated with the placeholder values filled with the actual values present within the ontology. In addition to the dialogues, the ground-truth code which is part of the annotation has the placeholder values replaced with actual values. The dataset is split into three partitions: training, validation and test. To limit data contamination among these three partitions, the cities that are to be used for filling in the placeholder are also assigned to a particular partition only to be used there. Hence, for example if the city of Boston is assigned to the training partition, it will never be present in any of the validation or test dialogue conversations.\n\\label{sec:dialogue-template-creation}\nAdditionally, for fields such as the check-in and checkout dates for hotels or departure and return flight dates, particular methods are taken to ensure that any dates or times follow chronological order and there is no instance where a check-in date at a hotel would be after the checkout date.\n\n\\paragraph{Generated Dialogue and Ground Truth Validation.}\nOnce the placeholders are filled for both the dialogues and ground truth code samples, each block of code is executed to identify any potential errors in annotation including an improper use of a placeholder or a syntax error. The validation assists the team in correcting any annotations and ensures that the resulting code is runnable and correct. \n\n\\section{T1-Agent}\nWe build an LLM powered $\\agentname$ to evaluate and simulate our $\\benchmarkname$ agentic dataset and measure its performance across three tasks: information seeking, parameter extraction, and tool calling.\n\n\\subsection{Information Seeking}\nEach tool has a mandatory set of parameters that must be provided before running the tool successfully. Information seeking is the task of gathering this mandatory set of parameters for any of the respective tools. We want to evaluate the capability of the agent to understand both the intent of the user's query and which parameters to ask the user about as a follow-up. Figure~\\ref{fig:architecture} shows an example of how information seeking is used by the agent. When the user inquires about a flight from New York to San Jose and a hotel in San Jose, the agent understands that the user would like to search for flights but needs to provide the departure date of the flight and the check-in date of the hotel. Thus, the agent infers that in followup discussion, it needs to ask for these details from the user.\n\n\\subsection{Parameter Extraction}\nAfter the agent understands the tools to call and the necessary parameters to collect, it works to extract those parameters from the user dialogue. From Figure~\\ref{fig:architecture}, when the user mentions that they want a flight from New York to San Jose for 3 nights, the agent is able to extract the starting and ending airport city for the flight as well as the number of nights to stay at the hotel. However, neither \\texttt{search\\_flights} or \\texttt{search\\_hotels} can be called yet since the departure date of the flight and the check-in and check-out date of the hotel have not yet been provided.\n\n\\subsection{Tool Calling}\nOnce the agent understands the necessary tool(s) to call and is provided the necessary parameters for each tool, the agent will make calls to each tool. In Figure~\\ref{fig:architecture}, once the user provides the departure date for the flight to San Jose, the agent calls the \\texttt{search\\_flights} tool with the departure and arrival cities as well as the departure date. Next, before the agent can call the \\texttt{search\\_hotels} tool, it must first compute the check-out date for the hotel. The check-in date provided is May 9 and the user provided the context that the duration of the hotel stay is 3 nights. Hence, the \\texttt{adjust\\_date} tool is called to compute the check-out date of the hotel. Once this is calculated, the agent will call the \\texttt{search\\_hotels} tool with the check-in and check-out dates as well as the city of the hotel. Additionally, both the flight and hotels that came up in the results will be saved to the cache, for further possible usage.\n\n\\subsection{Data Caching}\n% Unlike prior work such as \\textit{CodeAct} \\cite{wang2024executable}, which generates fresh tool calls at each user turn, \nWe introduce a data caching mechanism that enables $\\agentname$ to reuse the outputs of earlier tool calls when handling subsequent user requests. This reduces redundant computation and improves response efficiency in multi-turn interactions. After each user turn, any search results retrieved by a tool are cached for potential reuse in later turns. The \\texttt{save\\_to\\_cache} tool is used to store these results. As illustrated in Figure~\\ref{fig:architecture}, after the user provides the departure date for a flight to San Jose, the agent calls both \\texttt{search\\_flights} and \\texttt{search\\_hotels}, and then stores the results using the \\texttt{save\\_to\\_cache} tool. Later, when the user requests flights from a specific airline, the agent retrieves the cached flight results using the \\texttt{get\\_results\\_from\\_cache} tool and filters them using the \\texttt{filter\\_flights} tool based on the user's airline preference. This caching approach helps avoid unnecessary API calls by reusing existing search results and applying filtering when appropriate.\n\n\\paragraph{Example: Refining a Flight Search.}\nSuppose a previous user query fetched flights from NYC to Boston, and the result was cached with the key \\texttt{\"flights\\_nyc\\_bos\"}. Later, the user asks to see only flights priced under \\$500. The LLM, using the cache summary, generates the following plan:\n\n\\begin{verbatim}\nflights = get_results_from_cache(key=\"flights_nyc_bos\")\naffordable_flights = filter_flights(prior_result=flights, budget=500)\n\\end{verbatim}\n\nThis illustrates how the LLM composes new logic by combining previously retrieved results with downstream tools, without repeating expensive API operations. Our approach enables more efficient, coherent, and stateful plan generation in realistic, multi-turn assistant conversations.\n\n\n\\section{Experimental Setup}\n\n\\subsection{Datasets}\n\\label{sec:distinct-domains}\nEach category contains 60 dialogue templates and a pool of 54 cities used to populate placeholder values. We split both the templates and cities into 25\\% for training, 9\\% for validation, and 66\\% for testing, resulting in 15 training templates, 5 validation templates, and 40 test templates per category. Similarly, 13 cities are allocated for training, 4 for validation, and 37 for testing. Dialogues are randomly sampled within each partition. After partitioning, a script fills all placeholders in each template using the corresponding cities from the assigned split, ensuring no data leakage or overlap of templates and entities between the training and test sets. Each template is instantiated into 25 unique dialogues after substituting the placeholders in the dialogue templates with entities defined in the ontology, yielding a total of 1,500 fully generated dialogues.\n\n\n\n% We generate 25 dialogues for each single-domain and multi-domain template. The dataset is further divided into training, validation, and test splits. For more details, please refer to Section~\\ref{sec:dialogue-template-creation}.\n\n\\subsection{Domain Adaptation using SFT} \nWe perform a simple instruction tuning with the train dataset in order to showcase that the performance on such complex conversational tool calling can be improved over zero or few shot prompting. We train a Llama 3.1 8B Instruct model for one epoch on the training dataset. The training dataset is structured as a list of \\texttt{(prompt, completions)} pairs. The SFT thereafter follows standard next token prediction with cross-entropy loss. We use LoRA~\\cite{lora} on 8 A100 SXM gpus instead of full finetuning. For reproducibility, we use the widely adopted Huggingface TRL library~\\cite{vonwerra2022trl} and include the exact command to replicate the training in Section~\\ref{SFTScript}. \n\n\\subsection{Inference Procedure}\nDuring inference, for each user turn in a dialogue, the $\\agentname$ generates executable Python code that fulfills the user request at that point in the conversation. Before generating any code, the agent will check an \\textbf{execution cache} to determine if a similar request has been previously resolved. If a cached result is available, the agent will write code that fetches and reuses the cached object to prevent redundant computation and tool invocation.\n\nTo do this, the agent constructs a prompt that includes the conversation history along with the current user turn. Instead of including the full execution cache—which can be large and token-intensive—the prompt incorporates a \\textbf{summary} of the cached results.\nThese summaries are generated using a deterministic, rule-based function that transforms each cached result into a concise representation. This approach significantly reduces the token load in the prompt, making it feasible to include relevant past results without exceeding model input limits.\n\nThis design enables the agent to consider past outcomes and reuse relevant information during code generation. Even in cases where the user’s current query differs slightly from earlier queries, the agent is able to fetch a prior result from the cache and use that as a starting point for the current user turn. By introducing summarization at the \\textit{planning stage}, we shift caching responsibilities from the tool-execution layer to the agent’s decision-making process. Our approach allows the agent to selectively reuse and adapt cached outputs, leading to improved latency and broader generalization.\n\nThe generated code is executed in a sandboxed environment, and the cache is updated with the new results on each user turn. Our dataset also includes the corresponding ground truth code and post-execution cache, which are used for performance evaluation.\n\n\\section{Results and Analysis}\nIn this section, we present the performance of various LLMs on both single-domain and multi-domain tasks. Single-domain tasks involve conversations focused exclusively on one domain—such as flights, hotels, restaurants, or attractions. In contrast, multi-domain tasks involve interactions spanning multiple domains, such as a user requesting both flight bookings and nearby hotel recommendations within the same dialogue. \n\n\\subsection{Overall Results}\nTable~\\ref{table:llama3.1-8b} presents the overall performance of \\texttt{Llama 3.1 8B Instruct} in a few-shot in-context learning setting. The model performs significantly worse on multi-domain tasks compared to single-domain ones. Additionally, much of the generated code—particularly in multi-domain scenarios—is not executable. Despite the inclusion of few-shot examples, the model struggles with accurate tool invocation. Parameter matching is even more challenging, as it requires the model not only to identify the correct tool but also to extract the appropriate parameter values from the user’s utterance.\n\n\\subsection{Analysis}\n\n\\paragraph{The Impact of Domain Adaptation on Performance.}\nFigure~\\ref{fig:enter-label} presents the tool call F1 and parameter matching F1 scores. Overall, the SFT models outperform the base model and even surpass the performance of the 70B model by a substantial margin across all domains.\n\n\\begin{figure}[!ht]\n \\centering\n \\includegraphics[width=\\linewidth]{assets/domain_adaptation.png}\n \\caption{\\textbf{Left:} Tool Call F1 performance and \\textbf{Right:} Parameter Matching F1 performance.}\n \\label{fig:enter-label}\n\\end{figure}\n\n\\begin{table*}[!ht]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lcccc|cccc|c|cc|c}\n \\toprule\n \\textbf{Domain} & \\multicolumn{4}{c}{\\textbf{Tool Call}} & \\multicolumn{4}{|c|}{\\textbf{Parameter Matching}} & \\textbf{Code Exec.} & \\multicolumn{2}{|c|}{\\textbf{Information Seeking}} & \\multicolumn{1}{c}{\\textbf{Cache}} \\\\ \n & & & & & & & & & \\textbf{Rate} & & & \\textbf{Summary} \\\\\n & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\ \\midrule\n \\multicolumn{8}{l}{\\texttt{Single-Domain}} \\\\\n \\midrule\n Flights & 25.12 & 30.90 & 57.34 & 40.16 & 15.57 & 17.34 & 60.30 & 26.94 & 26.24 & 14.41 & 82.36 & 36.63 \\\\\n Hotels & 42.86 & 49.89 & 75.25 & 60.00 & 17.57 & 23.59 & 40.80 & 29.89 & 48.13 & 47.01 & 80.38 & 33.70 \\\\\n Restaurants & 40.40 & 52.97 & 63.00 & 57.55 & 31.47 & 36.07 & 71.12 & 47.87 & \\textbf{56.99} & 28.91 & 75.68 & 35.49 \\\\\n Attractions & 33.03 & 48.49 & 50.90 & 49.66 & 14.11 & 25.11 & 24.36 & 24.73 & 40.40 & N/A & N/A & 43.87 \\\\ \\midrule\n \\multicolumn{10}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n F-H & 33.09 & 40.89 & 63.44 & 49.73 & 16.11 & 19.28 & 49.48 & 27.75 & 38.43 & 26.54 & 82.00 & 21.85 \\\\\n H-R & 51.06 & 59.21 & 78.77 & 67.61 & 26.62 & 33.52 & 56.37 & 42.05 & 51.82 & 28.60 & 84.07 & 9.95 \\\\\n H-A & 38.75 & 45.46 & 72.39 & 55.85 & 16.11 & 20.02 & 45.12 & 27.74 & 48.88 & 26.03 & 81.77 & 32.75 \\\\\n F-H-A & 27.76 & 36.24 & 54.24 & 43.45 & 16.21 & 21.18 & 40.88 & 27.90 & 28.76 & 14.15 & 78.58 & 24.37 \\\\\n F-H-R & 29.04 & 34.65 & 64.19 & 45.00 & 18.53 & 21.77 & 55.44 & 31.26 & 36.79 & 33.28 & 82.84 & 27.47 \\\\\n \\bottomrule\n \\end{tabular}\n}\n\\caption{Overall results using Llama 3.1 8B Instruct with few-shot in-context learning.}\n\\label{table:llama3.1-8b}\n\\end{table*}\n\n% \\begin{table*}[!ht]\n% \\centering\n% \\resizebox{\\textwidth}{!}{\n% \\begin{tabular}{lcccc|ccccccccc}\n% \\toprule\n% \\textbf{Model} & \\multicolumn{4}{c|}{\\textbf{Single Domain}} & \\multicolumn{5}{c}{\\textbf{Multi-Domain}} \\\\\n% & Flight & Hotel & Attraction & Restaurant & F-H & H-R & H-A & F-H-A & F-H-R \\\\ \\midrule\n% Llama 3.1 8B \\\\\n% $\\quad$+ Fine-tune \\\\\n% Llama 3.3 70B \\\\ \\midrule\n% Phi-4 R$^+$ 14B \\\\\n% S1.1 32B \\\\\n% \\bottomrule\n% \\end{tabular}\n% }\n% \\caption{F1 Results on Fine-grained Tool Call.}\n% \\end{table*}\n\n\\paragraph{Model Performance.}\nOur qualitative evaluation shows that the \\texttt{LLaMA 3.3 70B Instruct} model consistently delivers the strongest performance across tasks. In contrast, the \\texttt{LLaMA 3.1 8B Instruct} model, without any fine-tuning, struggles significantly. It often fails to utilize available cache effectively and generates an incorrect code.\n\n\\paragraph{Impact of Fine-Tuning.}\nWhile the 70B and the non-fine-tuned 8B models were prompted to generate answers with reasoning, a fine-tuned variant—despite not being instructed to provide reasoning—achieved performance comparable to the 70B Instruct model. This indicates the strong potential of task-specific fine-tuning in enhancing model performance.\n\n\\paragraph{Few-Shot Evaluation.}\nAs shown in Figure~\\ref{fig:few-shot}, we evaluate model performance on the \\textit{flights} domain under 0-shot, 5-shot, and 13-shot settings. Performance is notably poor in the 0-shot setting, with improvements observed in 5-shot and 13-shot configurations—though the gains plateau beyond 5 shots. Qualitative analysis suggests that without sufficient context, models continue to struggle with generalization in complex, multi-domain scenarios.\n\\begin{figure}[!ht]\n \\centering\n \\includegraphics[width=0.9\\linewidth]{assets/flight_shot_learning.png}\n \\caption{Few-shot performance on Flight domain.}\n \\label{fig:few-shot}\n\\end{figure}\n\n\\paragraph{Need for Complex Evaluation.}\nEven with the state-of-the-art model, \\texttt{LLaMA 3.3 70B Instruct}, which performs well on standard code generation tasks, we observe that it continues to struggle in complex, multi-turn scenarios involving advanced planning. The $\\benchmarkname$ dataset is designed to fill this gap by serving as a benchmark for evaluating model performance in such challenging settings.\n\n\\section{Related Work}\n\\subsection{Large Language Model Agents}\nLLM-based agents have emerged as foundational components in AI systems, capable of performing complex, multi-step tasks through reasoning, memory integration, and tool use. These agents often combine a pre-trained LLM with structured modules such as long-term memory, tool calling capabilities, and self-reflective feedback loops. Frameworks such as {AutoGPT}~\\cite{yang2023auto}, and {AgentGPT} allow LLMs to autonomously decompose user goals into subgoals and execute them sequentially using external APIs. More structured systems like {HuggingGPT}~\\cite{shen2023hugginggpt}, {CrewAI}, and {AutoGen}~\\cite{wu2023autogen} facilitate collaboration between multiple LLM agents, each specializing in roles such as planning, execution, or critique.\n\nDespite significant progress, planning within task-oriented dialogue systems—particularly over long horizons—remains a fundamental challenge. Previous paradigms such as \\textit{plan-observe-execute} (e.g., ReAct~\\cite{yao2023react}, ADaPT~\\cite{prasad2023adapt}) enable the model to interleave tool calls with reasoning steps. However, most frameworks focus on linear execution paths where each step invokes a single tool. These approaches often lack the ability to manage inter-tool dependencies, reuse intermediate results, or revise plans based on partial tool failures. Further, a lot of these systems performs single turn planning, i.e. the user of the system submits a request with all the information in the first turn. Multi-turn conversational planning is a nascent field. For example, in multi-turn workflows like conversational trip planning, agents need to collect information across multiple steps, then plan and coordinate across tools like flight search, visa information, and calendar APIs—something most current systems struggle to handle.\n\n\\subsection{Tool-Based Agents and Benchmarks}\n\nTool usage extends the scope of LLM capabilities beyond language modeling ~\\citep{team2024chameleon, schick2023toolformer} to real-world actionability, enabling interactions with APIs, web tools, and external software. To evaluate this ability, several benchmarks have been proposed, including {APIBank}~\\cite{li2023api}, {Tau-Bench}~\\cite{yao2024tau},\n{GAIA}~\\cite{mialon2023gaia}, {ALFWorld}~\\cite{shridhar2020alfworld}, {GTA}~\\cite{wang2024gta},\n{TravelPlanner}~\\cite{xie2024travelplanner}, and {ToolBench}~\\cite{qin2023toolllm}. Although these benchmarks each emphasize different strengths, such as breadth of API coverage, realism, or reasoning complexity—they often treat tool use as a series of isolated atomic actions without requiring coordinated planning between multiple tools. Further, there\n\nIn contrast, our work introduces $\\benchmarkname$, a tool-driven benchmark designed to evaluate LLM agents in multi-turn, multi-domain dialogue settings with inter-tool dependencies. $\\benchmarkname$ features an integrated caching mechanism that supports both short- and long-term memory of tool call results, allowing agents to make intelligent decisions about whether to replan or reuse cached outputs. Unlike prior benchmarks, it tests an agent’s ability to perform dynamic replanning, handle branching workflows, and compose tools in a realistic, dialog-driven environment. Thus, $\\benchmarkname$ not only challenges existing tool-use agents but also provides a diagnostic sandbox for evaluating the reasoning capabilities of open-source LLMs under realistic constraints.\n\n\n\n% \\section{Related Work }\n\n% \\subsection{Large Language Model Agents} AI Agents or Large Language model agents are now ubiquitously being used in various complex workflows~\\cite{tang2023toolalpaca}. The ability to utilize memory, tools alongside planning, reasoning and self reflection abilities have led to the enhancement in their abilities. Many tool calling frameworks such as AutoGPT~\\citep{}, HuggingGPT~\\citep{}, CrewAI~\\citep{}, AutoGen~\\citep{} etc, has sprung into existence that allows building of single or multi agent systems. However planning in task oriented dialogue systems, both long and short horizon, is not yet solved~\\citep{}. Prior work has looked into \\textit{plan-observe-execute} type paradigms to do planning in the presence of tools or functions~\\citep{react, rewoo, adapt} where each next action in the plan depends on the outcome of the previous execution. In this case however the plan at each step consists of calling an single tool. \n\n% \\paragraph{Tool-based Agents}\n% Tools give the AI agents ability to perform tasks. However in many cases, performing a single task may need multiple tools to be called with some application logic. Several benchmarks aims at assessing the performance of tool calling ability of AI Agents. APIBench, GTA, Tau-Bench, GAIA, OsWorld, Alfworld, Alfbench, TravelPlanner, Toolbench etc. \n% https://arxiv.org/pdf/2406.12045\n% %https://proceedings.neurips.cc/paper_files/paper/2024/file/bfd082c452dffb450d5a5202b0419205-Paper-Datasets_and_Benchmarks_Track.pdf\n% https://arxiv.org/pdf/2402.01622\n% https://arxiv.org/pdf/2406.14283?\n% https://aclanthology.org/2025.coling-main.672.pdf\n% https://arxiv.org/abs/2410.01440\n% https://openreview.net/pdf?id=dCPF1wlqj8\n% https://arxiv.org/pdf/2502.11221\n% https://aclanthology.org/2025.naacl-long.36.pdf\n% https://arxiv.org/pdf/2408.00764?\n% https://neurips.cc/virtual/2024/poster/97620\n\n\\newcolumntype{Y}{>{\\centering\\arraybackslash}X}\n\n\\section{Conclusion}\n\nWe introduce $\\benchmarkname$, a comprehensive dataset for evaluating planning, reasoning, and tool-usage in LLM-based agents through complex, multi-turn dialogues. By introducing inter-tool dependencies, dynamic replanning, and caching, it supports rigorous assessment across single- and multi-domain settings. Experiments with $\\agentname$ highlight both the strengths and limitations of open-source LLMs: while the \\texttt{LLaMA 3.3 70B Instruct} model performs best, the non-fine-tuned \\texttt{LLaMA 3.1 8B} struggles with caching and code generation. A fine-tuned 8B variant matches 70B performance, emphasizing the value of task-specific tuning. Despite improvements with few-shot learning, models still face generalization challenges in multi-domain scenarios.\n\\bibliography{main}\n\n\\appendix\n\n\\newpage\n% \\section*{NeurIPS Paper Checklist}\n% \\begin{enumerate}\n\n% \\item {\\bf Claims}\n% \\item[] Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?\n% \\item[] Answer: \\answerYes{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: The claim is supported by the abstract and introduction and aligns with the contributions outlined throughout the paper.\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that the abstract and introduction do not include the claims made in the paper.\n% \\item The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. 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Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.\n% %this research? \n% \\item[] Answer: \\answerYes{}, % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: We declared during the submission.\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that the core method development in this research does not involve LLMs as any important, original, or non-standard components.\n% \\item Please refer to our LLM policy (\\url{https://neurips.cc/Conferences/2025/LLM}) for what should or should not be described.\n% \\end{itemize}\n \n\\section{Limitations}\nIn this work, we focus on constructing and introducing a new dataset as a benchmark for evaluating agentic workflows in multi-turn conversational dialogue settings, with an emphasis on tool calling for planning. Our evaluations are limited to open-source models, as proprietary models are not included due to resource constraints. We use Llama 3.1 8B Instruct, Llama 3.3 70B Instruct, S1.1 32B, and Phi4 Reasoning Plus. We will release $\\benchmarkname$ dataset publicly and hope it will encourage future research that includes evaluations on proprietary models as well.\n\n\\section{Additional Results}\n\\subsection{Detailed Results for Llama 3.1 8B Instruct with SFT and Llama 3.3 70B Instruct}\nTable~\\ref{table:llama3.1-8b-sft} shows the results for Llama 3.1 8B Instruct with Domain Adaptation and Table~\\ref{table:llama3.3-70b} shows the results for Llama 3.3 70B Instruct.\n\n\\begin{table*}[!ht]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lcccc|cccc|c|cc|c}\n \\toprule\n \\textbf{Domain} & \\multicolumn{4}{c}{\\textbf{Tool Call}} & \\multicolumn{4}{|c|}{\\textbf{Parameter Matching}} & \\textbf{Code Exec. Rate} & \\multicolumn{2}{|c|}{\\textbf{Information Seeking}} & \\multicolumn{1}{c}{\\textbf{Cache Summary}} \\\\ \n & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\ \\midrule\n \\texttt{Single-Domain} \\\\\n \\midrule\n Flights & 83.51 & 89.98 & 92.08 & 91.02 & 58.02 & 66.38 & 82.18 & 73.44 & 85.26 & 39.19 & 86.44 & 62.07 \\\\\n Hotels & 78.18 & 84.87 & 90.84 & 87.75 & 60.40 & 67.29 & 85.53 & 75.32 & 72.13 & 48.30 & 80.91 & 71.35 \\\\\n Restaurants & 72.76 & 80.40 & 88.44 & 84.23 & 57.49 & 66.69 & 80.64 & 73.00 & 95.16 & 95.14 & 98.63 & 65.32 \\\\\n Attractions & 90.50 & 97.87 & 92.33 & 95.02 & 75.54 & 91.34 & 81.37 & 86.07 & 98.15 & N/A & N/A &83.00 \\\\ \\midrule\n \\multicolumn{10}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n F-H & 80.27 & 91.31 & 86.91 & 89.05 & 57.76 & 74.51 & 71.99 & 73.23 & 89.25 & 24.04 & 80.63 & 57.01 \\\\\n H-R & 82.02 & 91.03 & 89.23 & 90.12 & 68.44 & 77.95 & 84.86 & 81.26 & 83.67 & 29.12 & 87.58 & 64.42 \\\\\n H-A & 62.30 & 72.67 & 81.35 & 76.77 & 59.22 & 68.73 & 81.05 & 74.39 & 71.41 & 27.87 & 82.80 & 65.70 \\\\\n F-H-A & 77.19 & 87.50 & 86.76 & 87.13 & 59.63 & 74.16 & 75.27 & 74.71 & 81.15 & 22.65 & 83.45 & 52.76 \\\\\n F-H-R & 71.53 & 86.05 & 80.91 & 83.40 & 54.39 & 71.99 & 68.99 & 70.45 & 82.47 & 46.32 & 84.40 & 53.88 \\\\\n \\bottomrule\n \\end{tabular}\n}\n\\caption{Overall Results using Llama 3.1 8B Instruct after SFT.}\n\\label{table:llama3.1-8b-sft}\n\\end{table*}\n\n\\begin{table*}[!ht]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lcccc|cccc|c|cc|c}\n \\toprule\n \\textbf{Domain} & \\multicolumn{4}{c}{\\textbf{Tool Call}} & \\multicolumn{4}{|c|}{\\textbf{Parameter Matching}} & \\textbf{Code Exec. Rate} & \\multicolumn{2}{|c|}{\\textbf{Information Seeking}} & \\multicolumn{1}{c}{\\textbf{Cache Summary}} \\\\ \n & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\ \\midrule\n \\texttt{Single-Domain} \\\\\n \\midrule\n Flights & 58.42 & 78.26 & 69.75 & 73.76 & 32.11 & 40.51 & 60.76 & 48.61 & 83.32 & 20.25 & 82.90 & 53.80 \\\\\n Hotels & 92.42 & 96.03 & 96.10 & 96.07 & 75.33 & 80.85 & 91.69 & 85.93 & 98.55 & 46.98 & 80.27 & 75.00 \\\\\n Restaurants & 94.23 & 96.63 & 97.43 & 97.03 & 85.51 & 93.62 & 90.80 & 92.19 & 99.54 & 37.90 & 79.95 & 90.78 \\\\\n Attractions & 79.14 & 92.16 & 84.86 & 88.36 & 71.17 & 87.04 & 79.60 & 83.16 & 63.70 & N/A & N/A & 78.59 \\\\ \\midrule\n \\multicolumn{10}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n F-H & 43.48 & 53.73 & 69.52 & 60.61 & 22.28 & 25.94 & 61.24 & 36.44 & 95.02 & 27.39 & 83.42 & 37.15 \\\\\n H-R & 60.50 & 70.91 & 80.46 & 78.29 & 50.94 & 60.08 & 76.99 & 67.50 & 99.60 & 22.97 & 84.11 & 38.43 \\\\\n H-A & 64.91 & 68.93 & 91.75 & 78.72 & 59.24 & 66.59 & 84.29 & 74.40 & 97.01 & 30.02 & 82.71 & 57.03 \\\\\n F-H-A & 60.46 & 70.77 & 80.59 & 75.36 & 44.27 & 51.19 & 76.58 & 61.37 & 94.81 & 19.16 & 82.27 & 44.29 \\\\\n F-H-R & 52.97 & 62.22 & 78.07 & 69.25 & 42.93 & 50.66 & 73.76 & 60.07 & 91.30 & 40.45 & 85.62 & 45.37 \\\\\n \\bottomrule\n \\end{tabular}\n}\n\\caption{Overall Results using Llama 3.3 70B Instruct.}\n\\label{table:llama3.3-70b}\n\\end{table*}\n\n\\subsection{Detailed Results for Reasoning Models}\nAdditionally, we also experiment with medium-sized reasoning models to see how they perform. We work with S1.1 32B~\\cite{muennighoff2025s1} and Phi-4-reasoning-plus 14B~\\cite{abdin2025phi}. The decoding configuration for both models includes a maximum token limit of 4,000, a temperature of 0.1, and max token length is 7,000. We host our models using vLLM in a A100 40GB x 8 node.\n\n\\begin{table*}[!ht]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lcccc|cccc|c|cc|c}\n \\toprule\n \\textbf{Domain} & \\multicolumn{4}{c}{\\textbf{Tool Call}} & \\multicolumn{4}{|c|}{\\textbf{Parameter Matching}} & \\textbf{Code Exec. Rate} & \\multicolumn{2}{|c|}{\\textbf{Information Seeking}} & \\multicolumn{1}{c}{\\textbf{Cache Summary}} \\\\ \n & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\ \\midrule\n \\texttt{Single-Domain} \\\\\n \\midrule\n Flights & 77.59 & 87.87 & 86.90 & 87.38 & 49.53 & 58.04 & 77.15 & 66.25 & 57.20 & 29.65 & 83.12 & 57.46\n \\\\\n Hotels & 89.33 & 95.33 & 93.41 & 94.36 & 65.27 & 70.52 & 89.77 & 78.99 & 63.95 & 18.07 & 79.56 & 57.23\n \\\\\n Restaurants & 87.77 & 92.65 & 94.35 & 93.49 & 71.59 & 77.69 & 90.11 & 83.44 & 69.82 & 7.16 & 69.91 & 69.22\n \\\\\n Attractions & 65.76 & 80.16 & 78.54 & 79.34 & 44.92 & 53.04 & 74.59 & 62.00 & 72.57 & N/A & N/A & 63.87\n\\\\ \\midrule\n \\multicolumn{10}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n F-H & 69.87 & 78.42 & 86.51 & 82.27 & 42.38 & 50.39 & 72.71 & 59.53 & 66.89 & 9.72 & 78.43 & 47.49\n \\\\\n H-R & 59.10 & 65.62 & 85.60 & 74.29 & 51.91 & 58.54 & 82.10 & 68.35 & 49.72 & 19.69 & 85.40 & 32.55\n \\\\\n H-A & 63.46 & 67.81 & 90.83 & 77.65 & 65.22 & 72.53 & 86.60 & 78.95 & 44.39 & 21.41 & 81.95 & 56.64\n \\\\\n F-H-A & 68.41 & 75.27 & 88.25 & 81.24 & 50.21 & 61.04 & 73.88 & 66.85 & 60.86 & 27.00 & 82.48 & 50.42\n \\\\\n F-H-R & 67.22 & 73.80 & 88.30 & 80.40 & 58.97 & 71.66 & 76.91 & 74.19 & 61.47 & 10.08 & 78.32 & 53.74\n \\\\\n \\bottomrule\n \\end{tabular}\n}\n\\caption{Overall Results using S1.1 32B.}\n\\label{table:s1.1}\n\\end{table*}\n\n\\begin{table*}[!ht]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lcccc|cccc|c|cc|c}\n \\toprule\n \\textbf{Domain} & \\multicolumn{4}{c}{\\textbf{Tool Call}} & \\multicolumn{4}{|c|}{\\textbf{Parameter Matching}} & \\textbf{Code Exec. Rate} & \\multicolumn{2}{|c|}{\\textbf{Information Seeking}} & \\multicolumn{1}{c}{\\textbf{Cache Summary}} \\\\ \n & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\ \\midrule\n \\texttt{Single-Domain} \\\\\n \\midrule\n Flights & 36.66 & 67.30 & 44.61 & 53.66 & 15.74 & 56.10 & 17.95 & 27.20 & 72.76 & 13.91 & 81.60 & 39.73\n \\\\\n Hotels & 83.96 & 91.74 & 90.82 & 91.28 & 53.22 & 63.09 & 77.30 & 69.47 & 93.05 & 46.95 & 80.47 & 46.52\n \\\\\n Restaurants & 64.13 & 83.60 & 73.36 & 78.14 & 47.10 & 61.89 & 66.34 & 64.04 & 93.49 & 28.27 & 69.74 & 50.53\n \\\\\n Attractions & 35.78 & 61.67 & 46.02 & 52.71 & 22.90 & 66.92 & 25.82 & 37.27 & 66.25 & N/A & N/A & 50.79\n\\\\ \\midrule\n \\multicolumn{10}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n F-H & 57.79 & 88.12 & 62.67 & 73.25 & 29.91 & 59.20 & 37.68 & 46.05 & 83.37 & 27.96 & 83.02 & 32.53\n \\\\\n H-R & 54.07 & 70.71 & 69.67 & 70.18 & 35.42 & 50.91 & 53.79 & 52.31 & 96.04 & 23.19 & 84.83 & 21.72\n \\\\\n H-A & 65.81 & 78.69 & 80.08 & 79.38 & 48.58 & 61.78 & 69.45 & 65.39 & 96.04 & 27.92 & 82.97 & 53.52\n \\\\\n F-H-A & 37.83 & 62.18 & 49.14 & 54.90 & 32.73 & 64.59 & 39.89 & 49.32 & 84.49 & 15.42 & 81.00 & 32.33\n \\\\\n F-H-R & 46.79 & 72.75 & 56.74 & 63.75 & 33.99 & 59.56 & 44.18 & 50.73 & 91.86 & 36.30 & 84.63 & 37.65\n \\\\\n \\bottomrule\n \\end{tabular}\n}\n\\caption{Overall Results using Phi-4-reasoning-plus.}\n\\label{table:phi-4-reasoning-plus}\n\\end{table*}\n\nBetween these two models, the S1.1 model mostly outperforms the Phi-4-reasoning-plus model when it comes to Tool Call and Parameter Matching metrics.\n\n\\section{Performance Comparison Between Models}\nWe observe that the S1.1 model performance performs reasonably well especially compared to the Llama 3.3 70B model that we observe to produce the best results. The Phi-4-reasoning-plus model did well with some domains but overall it does not perform as well as the S1.1 and Llama 3.3 70B models. Figure~\\ref{fig:compare-models} shows the performance comparison between models.\n\n\\begin{figure}[!ht]\n \\centering\n \\includegraphics[width=\\linewidth]{assets/all_model_f1_scores.png}\n \\caption{\\textbf{Above:} Tool Call F1 performance and \\textbf{Below:} Parameter Matching F1 performance.}\n \\label{fig:compare-models}\n\\end{figure}\n\n% \\section{Reproducibility}\n% Our dataset has been anonymized and please find the dataset on Kaggle \\url{https://kaggle.com/datasets/b47e52b440ccaf964009a3f4a3bdfdb92de205249fae87c3c3db7b615c29490c}. Details on how to reproduce our experiments using TRL are provided in the Appendix of our submission and we also included here for your perusal in Section~\\ref{SFTScript}. \n% For additional instructions and implementation details, please refer to this anonymous repository at: \\url{https://anonymous.4open.science/r/T1-code-38C8/README.md}.\n\n\\section{Error Analysis}\n\nWhen we look closer at the errors found during inferencing, we first break down the errors into a total of 4 categories:\n\\begin{itemize}\n\\item \\textbf{Validation Error}: Errors related to improper arguments being passed to the defined tools. \n\\item \\textbf{Variable Not Defined}: Error indicating code uses an underfined variable. \n\\item \\textbf{Index Out Of Range}: Error indicating generated code involves an index out of range, usually corresponding to the list data structure in Python.\n\\item \\textbf{Other}: Other errors present within the generated code.\n\\end{itemize}\n\n\\begin{table*}[!htbp]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lccccccc}\n \\toprule\n \\textbf{Domain} & \\textbf{ \\# Turns} & \\textbf{\\# Validation Error} & \\textbf{\\# Variable Not Defined} & \\textbf{\\# Index Out Of Range} & \\textbf{\\# Other} \\\\\n \\midrule\n \\texttt{Single-Domain} \\\\\n \\midrule\n Flights & 8,200 & 737 & 830 & 0 & 468 \\\\\n Hotels & 8,000 & 417 & 218 & 0 & 820 \\\\\n Restaurants & 7,900 & 513 & 14 & 1 & 183 \\\\\n Attractions & 5,975 & 507 & 1 & 0 & 243 \\\\\n \\midrule\n \\multicolumn{6}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n F-H & 11,700 & 834 & 1087 & 0 & 408 \\\\\n H-R & 10,950 & 1532 & 18 & 1 & 522 \\\\\n H-A & 11,000 & 1024 & 167 & 0 & 308 \\\\\n F-H-A & 9,775 & 1163 & 990 & 0 & 575 \\\\\n F-H-R & 8,575 & 812 & 761 & 3 & 495 \\\\\n \\midrule\n \\texttt{Total} & - & 7539 & 4086 & 5 & 4022\\\\\n \\bottomrule\n \\end{tabular}\n}\n\\caption{Overall Error Analysis Results using Llama 3.1 8B Instruct.}\n\\end{table*}\n\n\\begin{table*}[!htbp]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lccccccc}\n \\toprule\n \\textbf{Domain} & \\textbf{ \\# Turns} & \\textbf{\\# Validation Error} & \\textbf{\\# Variable Not Defined} & \\textbf{\\# Index Out Of Range} & \\textbf{\\# Other} \\\\\n \\midrule\n \\texttt{Single-Domain} \\\\\n \\midrule\n Flights & 8,200 & 408 & 176 & 42 & 26 \\\\\n Hotels & 8,000 & 1,099 & 16 & 0 & 0 \\\\\n Restaurants & 7,900 & 209 & 5 & 0 & 2 \\\\\n Attractions & 5,975 & 18 & 9 & 0 & 8 \\\\\n \\midrule\n \\multicolumn{6}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n F-H & 11,700 & 454 & 149 & 0 & 22 \\\\\n H-R & 10,950 & 692 & 185 & 0 & 17 \\\\\n H-A & 11,000 & 1248 & 315 & 0 & 1 \\\\\n F-H-A & 9,775 & 789 & 124 & 9 & 27 \\\\\n F-H-R & 8,575 & 541 & 109 & 0 & 101 \\\\\n \\midrule\n \\texttt{Total} & - & 5458 & 1088 & 51 & 204 \\\\\n \\bottomrule\n \\end{tabular}\n}\n\\caption{Overall Error Analysis Results using Llama 3.1 8B SFT Instruct.}\n\\end{table*}\n\n\\begin{table*}[!htbp]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lccccccc}\n \\toprule\n \\textbf{Domain} & \\textbf{ \\# Turns} & \\textbf{\\# Validation Error} & \\textbf{\\# Variable Not Defined} & \\textbf{\\# Index Out Of Range} & \\textbf{\\# Other} \\\\\n \\midrule\n \\texttt{Single-Domain} \\\\\n \\midrule\n Flights & 8,200 & 518 & 2 & 30 & 0 \\\\\n Hotels & 8,000 & 41 & 0 & 0 & 0 \\\\\n Restaurants & 7,900 & 29 & 0 & 0 & 0 \\\\\n Attractions & 5,975 & 4 & 2 & 0 & 1 \\\\\n \\midrule\n \\multicolumn{6}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n F-H & 11,700 & 158 & 28 & 0 & 0\n \\\\\n H-R & 10,950 & 14 & 2 & 0 & 1\n \\\\\n H-A & 11,000 & 48 & 33 & 0 & 48\n \\\\\n F-H-A & 9,775 & 148 & 6 & 7 & 3\n \\\\\n F-H-R & 8,575 & 330 & 5 & 1 & 7\n \\\\\n \\midrule\n \\texttt{Total} & - & 1290 & 78 & 38 & 60 \\\\\n \\bottomrule\n \\end{tabular}\n}\n\\caption{Overall Error Analysis Results using Llama 3.3 70B Instruct.}\n\\end{table*}\n\n\\begin{table*}[!htbp]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lccccccc}\n \\toprule\n \\textbf{Domain} & \\textbf{ \\# Turns} & \\textbf{\\# Validation Error} & \\textbf{\\# Variable Not Defined} & \\textbf{\\# Index Out Of Range} & \\textbf{\\# Other} \\\\\n \\midrule\n \\texttt{Single-Domain} \\\\\n \\midrule\n Flights & 8,200 & 559 & 16 & 3 & 40\n \\\\\n Hotels & 8,000 & 92 & 1 & 39 & 27\n \\\\\n Restaurants & 7,900 & 20 & 7 & 0 & 28\n \\\\\n Attractions & 5,975 & 11 & 27 & 0 & 76\n \\\\\n \\midrule\n \\multicolumn{6}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n F-H & 11,700 & 24 & 34 & 114 & 63\n \\\\\n H-R & 10,950 & 54 & 895 & 102 & 34\n \\\\\n H-A & 11,000 & 3 & 1071 & 43 & 23\n \\\\\n F-H-A & 9,775 & 89 & 579 & 25 & 49\n \\\\\n F-H-R & 8,575 & 85 & 241 & 7 & 28\n \\\\\n \\midrule\n \\texttt{Total} & - & 937 & 2871 & 333 & 368\n \\\\\n \\bottomrule\n \\end{tabular}\n}\n\\caption{Overall Error Analysis Results using S1.1.}\n\\end{table*}\n\n\\begin{table*}[!htbp]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lccccccc}\n \\toprule\n \\textbf{Domain} & \\textbf{ \\# Turns} & \\textbf{\\# Validation Error} & \\textbf{\\# Variable Not Defined} & \\textbf{\\# Index Out Of Range} & \\textbf{\\# Other} \\\\\n \\midrule\n \\texttt{Single-Domain} \\\\\n \\midrule\n Flights & 8,200 & 103 & 7 & 1 & 1\n \\\\\n Hotels & 8,000 & 277 & 0 & 0 & 1\n \\\\\n Restaurants & 7,900 & 259 & 0 & 0 & 19\n \\\\\n Attractions & 5,975 & 0 & 0 & 0 & 0\n \\\\\n \\midrule\n \\multicolumn{6}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n F-H & 11,700 & 212 & 2 & 0 & 7\n \\\\\n H-R & 10,950 & 212 & 0 & 0 & 1\n \\\\\n H-A & 11,000 & 162 & 0 & 0 & 6\n \\\\\n F-H-A & 9,775 & 120 & 3 & 0 & 5\n \\\\\n F-H-R & 8,575 & 153 & 4 & 0 & 32\n \\\\\n \\midrule\n \\texttt{Total} & - & 1498 & 16 & 1 & 72\\\\\n \\bottomrule\n \\end{tabular}\n}\n\\caption{Overall Error Analysis Results using Phi-4-reasoning-plus.}\n\\end{table*}\n\nOverall, we notice that validation errors are consistently present within the results of all of the models we evaluated.\n\n\\subsection{Llama 3.3 70B Instruct} The Llama 3.3 70B instruct model contains the fewest number of overall errors among the five models evaluated with a total of 1,466. This supports the idea that the larger number of parameters helps the model better understand the task as well passing in the proper parameters to the tools to generate high quality code. \n\n\\subsection{Llama 3.1 8B Instruct} The Llama 3.1 8B Instruct model consists of the most number of total errors among all 4 categories which were tracked. The only category it performs well in was the \\texttt{Index Out Of Range} error where there are only 5 instances.\n\n\\subsection{Llama 3.1 8B Instruct SFT}\nThe Llama 3.1 8B Instruct SFT does drastically improve on the number of \\texttt{Validation}, \\texttt{Variable Not Defined} and \\texttt{Other} categorized errors compared to the original model. This indicates that the model is able to better learn about calling the tools and writing higher quality code than the original model.\n\n\n\\subsection{S1.1} Overall, the S1.1 model performs the best when it comes to \\texttt{Validation} errors with a total of 937 errors. However, it also consists of the highest amount of \\texttt{Index Out of Range} errors and the 2nd highest amount of \\texttt{Variable Not Defined} errors.\n\n\\subsection{Phi-4-reasoning-plus}\nThe Phi-4-reasoning-plus has 1498 validation errors which is the third most among the 5 models. The model does perform reasonably well with the other error categories and has the fewest number of \\texttt{Index Out Of Range} and \\texttt{Variable Not Defined} errors with 1 and 16 respectively.\n\n\\section{Model and Prompt Configuration}\nWe use the Llama 3.1 8B Instruct and Llama 3.3 70B Instruct models, and fine-tune Llama 3.1 8B Instruct to evaluate the effectiveness of domain adaptation on the $\\benchmarkname$ dataset. We evaluate all the models in a \\textit{few-shot} setting, where several example turns are included in the prompt to guide the model’s behavior. The full prompts used for inference are provided in Section~\\ref{system-plan-generation-prompt} and Section~\\ref{user-plan-generation-prompt}. The decoding configuration for both models includes a maximum token limit of 4000, top-k sampling of 10, and a temperature of 0.1.\n\n\\section{Evaluation Protocol}\nWe evaluate the model's performance at every user turn by comparing the model’s output with the ground truth across the following facets.\n\\paragraph{Tool Call.} We evaluate the correctness of each tool call from the generated code against the ground truth code by comparing the number of times each tool is called using four metrics: accuracy, precision, recall, and F1.\n\\paragraph{Parameter Matching.} For each tool call in the ground truth, we identify the corresponding tool call in the generated code with the same name and the highest parameter overlap. We then calculate the accuracy, precision, recall, and F1 for the identified match. Parameter values are standardized to enable robust comparison; for example, lists are treated as sets to make them insensitive to ordering. Certain tool calls such as \\texttt{save\\_to\\_cache}, \\texttt{get\\_results\\_from\\_cache}, and \\texttt{seek\\_information} are excluded from this evaluation, as they depend on external artifacts (e.g., intermediate dataframes) and variable keys or identifiers that cannot be reliably matched. Similarly, certain parameter names (e.g., \\texttt{prior\\_results}) are also excluded. \n\\paragraph{Code Execution Success Rate.} We calculate the percentage of instances in which the model generates a code when expected and the code is executable without any errors in a sandbox environment.\n\\paragraph{Handling Information Seeking.} If both the model output and the ground truth include \\texttt{seek\\_information}, we evaluate the similarity of the strings inside the function using SacreBLEU~\\cite{post2018call} and BERTScore F1~\\cite{zhang2019bertscore}, capturing both sub-word overlap and semantic similarity of the requested information.\n\\paragraph{Cache Summary.} To assess how well the model serves complete requests, we compare the execution cache summary results of the generated code and ground truth using exact match (EM) to determine if the model-generated solution is functionally equivalent to the ground truth.\n\n\\section{More Information on T1 Dataset}\nTable~\\ref{tab:domains-and-attributes} shows the attributes for each domain. Table~\\ref{tab:defined-tools-domain} shows the tools used for each domain.\n\n\\begin{table*}[!ht]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lllll}\n \\toprule \n \\textbf{Domain} & \\textbf{Attributes} \\\\ \\midrule\n Flight & airline, flight\\_id, start\\_airport, start\\_airport\\_latitude, start\\_airport\\_longitude, \\\\\n & start\\_airport\\_code, end\\_airport, end\\_airport\\_latitude, end\\_airport\\_longitude, \\\\\n & end\\_airport\\_code, economy\\_class\\_option\\_present, business\\_class\\_option\\_present, \\\\\n & first\\_class\\_option\\_present, distance\\_miles, duration\\_minutes, \\\\\n & departure\\_time, arrival\\_time, number\\_of\\_layovers, first\\_layover\\_airport, \\\\\n Hotel & hotel\\_name, city, state, neighborhood, latitude, longitude, rating, \\\\ & stars, max\\_occupancy, gym\\_present, pool\\_present, price\\_per\\_night, num\\_rooms\\_available, \\\\ & breakfast\\_included, smoking\\_allowed, air\\_conditioning\\_present, \\\\\n & heating\\_present, free\\_wifi\\_included, airport\\_shuttle\\_present, \\\\ &\n is\\_pet\\_friendly, has\\_spa\\_services, has\\_room\\_service, \\\\ & has\\_beach\\_access, has\\_fitness\\_class, has\\_laundry\\_service, \\\\ & has\\_valet\\_parking, has\\_balcony, has\\_rooftop\\_bar, has\\_inroom\\_Kitchen, \\\\ & has\\_kids\\_club, has\\_meeting\\_rooms, has\\_electric\\_vehicle\\_charging, \\\\ & has\\_hot\\_tub, has\\_sauna, has\\_free\\_parking, is\\_wheelchair\\_accessible, \\\\ & has\\_skiing\\_lodging, has\\_ocean\\_view\\_rooms\\_present, has\\_city\\_view\\_rooms\\_present, \\\\ & start\\_date\\_available, end\\_date\\_available \\\\\n Attraction & city, state, name, type, latitude, longitude, neighborhood \\\\\n Restaurant & restaurant\\_name, city, state, neighborhood, \\\\\n & latitude, longitude, rating, price, price\\_per\\_person, has\\_nut\\_allergy\\_options, has\\_dairy\\_allergy\\_options, has\\_shell\\_fish\\_allergy\\_options, \\\\ & has\\_tomato\\_allergy\\_options, has\\_nightshade\\_allergy\\_options, \\\\ & has\\_gluten\\_free\\_options, has\\_vegetarian\\_options, has\\_vegan\\_options, \\\\ & has\\_kosher\\_options, has\\_halal\\_options, cuisine \\\\\n \\bottomrule\n \\end{tabular}\n}\n\\caption{Domains and Attributes.} \n\\label{tab:domains-and-attributes}\n\\end{table*}\n\n\\begin{table*}[!ht]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{ll}\n \\toprule \n \\textbf{Domain} & \\textbf{Tools}\n \\\\\n \\textbf{Flights} & search\\_flights, filter\\_flights \\\\\n \\textbf{Hotels} & search\\_hotels, filter\\_hotels \\\\\n \\textbf{Restaurants} & search\\_restaurants, filter\\_restaurants \\\\\n \\textbf{Attractions} & search\\_attractions, filter\\_attractions \\\\\n \\textbf{Multi-domain} & search\\_nearest \\\\\n \\textbf{Common} & save\\_to\\_cache, get\\_results\\_from\\_cache, sort\\_results, seek\\_information, adjust\\_date \\\\\n \\bottomrule\n \\end{tabular}\n}\n\\caption{List of Tools.} \n\\label{tab:defined-tools-domain}\n\\end{table*}\n\n\\newpage\n\\section{Sample Conversation from T1 Dataset}\n\n\n\\begin{table*}[!ht]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lll}\n \\toprule \n \\textbf{Attribute} & \\textbf{Type} & \\textbf{Description} \\\\ \\midrule\n airline & string & Airline of the flight, \\\\\n flight\\_classes & string & Classes for the flight (economy, business, first) \\\\\n num\\_layovers & integer & Number of layovers for the flight, between 0 and 2 \\\\\n layover\\_duration\\_amount & integer & Duration of a layover flight. Between 1 and 6 hours \\\\\n airports & list & Airport information for each flight including the city, state, airport code and airport name. \\\\\n \n \\bottomrule\n \\end{tabular}\n}\n\\caption{Flight Ontology Attributes.} \n\\label{tab:flight-ontology-attributes}\n\\end{table*}\n\n\\begin{table*}[!htbp]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lll}\n \\toprule \n \\textbf{Attribute} & \\textbf{Type} & \\textbf{Description} \\\\ \\midrule\n city & string & City that the hotel is in \\\\\n state & string & State that the hotel is in \\\\\n neighborhood & string & Neighborhood within the city that the hotel is in\\\\\n stars & integer & Star rating of the hotel between 1 and 5 \\\\\n rating & float & Customer rating of the hotel between 1.0 and 5.0, incremented by 0.1 \\\\\n price\\_per\\_night & integer & Price per night of the hotel ranging between 20 to 2000 dollars \\\\\n max\\_occupancy & integer & Maximum occupancy per room ranging between 1 and 7 \\\\\n gym\\_present & boolean & Whether or not the hotel has a gym \\\\\n pool\\_present & boolean & Whether or not the hotel has a pool \\\\\n breakfast\\_included & boolean & Whether or not the hotel has breakfast included \\\\\n smoking\\_allowed & boolean & Whether or not the hotel allows smoking \\\\\n air\\_conditioning\\_present & boolean & Whether or not the hotel has air conditioning \\\\\n heating\\_present & boolean & Whether or not the hotel has heating \\\\\n free\\_wifi\\_included & boolean & Whether or not the hotel includes free WiFi \\\\\n airport\\_shuttle\\_present & boolean & Whether or not the hotel has an airport shuffle \\\\\n is\\_pet\\_friendly & boolean & Whether or not the hotel allows pets \\\\\n has\\_spa\\_services & boolean & Whether or not the hotel has spa services \\\\\n has\\_room\\_service & boolean & Whether or not the hotel has room service \\\\\n has\\_beach\\_access & boolean & Whether or not the hotel has access to a beach \\\\\n has\\_business\\_center & boolean & Whether or not the hotel has a business center \\\\\n has\\_fitness\\_classes & boolean & Whether or not the hotel has fitness classes \\\\\n has\\_laundry\\_service & boolean & Whether or not the hotel has laundry services \\\\\n has\\_valet\\_parking & boolean & Whether or not the hotel has valet parking \\\\\n has\\_balcony & boolean & Whether or not the hotel has a balcony \\\\\n has\\_rooftop\\_bar & boolean & Whether or not the hotel has a rooftop bar \\\\\n has\\_inroom\\_kitchen & boolean & Whether or not the hotel has an inroom kitchen \\\\\n has\\_kids\\_club & boolean & Whether or not the hotel has a kids club \\\\\n has\\_meeting\\_rooms & boolean & Whether or not the hotel has meeting rooms \\\\\n has\\_electric\\_vehicle\\_charging & boolean & Whether or not the hotel has electric vehicle charging \\\\\n has\\_hot\\_tub & boolean & Whether or not the hotel has a hot tub \\\\\n has\\_sauna & boolean & Whether or not the hotel has a sauna \\\\ \n has\\_free\\_parking & boolean & Whether or not the hotel has free parking\\\\ \n is\\_wheelchair\\_accessible& boolean & Whether or not the hotel is wheelchair accessible \\\\ \n has\\_skiing\\_lodging & boolean & Whether or not the hotel has skiing and lodging \\\\ \n ocean\\_view\\_rooms\\_present & boolean & Whether or not the hotel has rooms with a view of the ocean \\\\\n city\\_view\\_rooms\\_present & boolean & Whether or not the hotel has rooms with views of the city\\\\\n \\bottomrule\n \\end{tabular}\n}\n\\caption{Hotel Ontology Attributes.} \n\\label{tab:hotel-ontology-attributes}\n\\end{table*}\n\n\\begin{table*}[!htbp]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lll}\n \\toprule \n \\textbf{Attribute} & \\textbf{Type} & \\textbf{Description} \\\\ \\midrule\n city & string & City that the restaurant is in \\\\\n state & string & State that the restaurant is in \\\\\n neighborhood & string & Neighborhood within the city that the restaurant is in\\\\\n rating & float & Customer rating of the restaurant between 1.0 and 5.0, incremented by 0.1 \\\\\n price\\_per\\_person & integer & Average price per person at the restaurant \\\\\n cuisine & string & Cuisine of the restaurant \\\\\n has\\_nut\\_allergy\\_options & boolean & Whether or not the restaurant has any options for individuals with an allergy to nuts \\\\\n has\\_dairy\\_allergy\\_options & boolean & Whether or not restaurant has any options for individuals with an allergy to dairy products \\\\\n has\\_shell\\_fish\\_allergy\\_options & boolean & Whether or not the restaurant has any options for individuals with an allergy to shell fish \\\\\n has\\_tomato\\_allergy\\_options & boolean & Whether or not the restaurant has options for individuals with an allergy to tomatoes \\\\\n has\\_nightshade\\_allergy\\_options & boolean & Whether or not the restaurant has options for individuals with an allergy to nightshades \\\\\n has\\_gluten\\_free\\_options & boolean & Whether or not the restaurant has gluten free options \\\\\n has\\_vegetarian\\_options & boolean & Whether or not the restaurant has vegetarian options \\\\\n has\\_vegan\\_options & boolean & Whether or not the restaurant has vegan options \\\\\n has\\_halal\\_options & boolean & Whether or not the restaurant has halal options \\\\\n has\\_kosher\\_options & boolean & Whether or not the hotel has kosher options \\\\ \n \\bottomrule\n \\end{tabular}\n}\n\\caption{Restaurant Ontology Attributes.} \n\\label{tab:restaurant-ontology-attributes}\n\\end{table*}\n\n\\begin{table*}[!htbp]\n\\centering\n\\resizebox{0.8\\textwidth}{!}{\n \\begin{tabular}{lll}\n \\toprule \n \\textbf{Attribute} & \\textbf{Type} & \\textbf{Description} \\\\ \\midrule\n city & string & City that the attraction is in \\\\\n state & string & State that the attraction is in \\\\\n neighborhood & string & Neighborhood within the city that the attraction is in\\\\\n type & string & Type of the attraction \\\\ \n \\bottomrule\n \\end{tabular}\n}\n\\caption{Attraction Ontology Attributes.} \n\\label{tab:attraction-ontology-attributes}\n\\end{table*}\n\n\n\\begin{tcolorbox}[colback=gray!10,colframe=black,title=Box 1. Sample conversation for attractions,breakable]\n\\textbf{assistant}: Hello! Are you looking for something to do in your free time?\\\\\n\\\\\n\\textbf{user}: Yeah, I am thinking of visiting some scenic attractions in San Antonio.\n\\\\\n\\\\\n\\textbf{assistant}: San Antonio has a lot of great Scenic spots. Have you considered Downtown San Antonio?\n\\\\\n\\\\\n\\textbf{user}: Actually, I haven't. What's there?\n\\\\\n\\\\\n\\textbf{assistant}: It's a great area with a lot of Scenic attractions. I can give you some recommendations.\n\\\\\n\\\\ \n\\textbf{user}: Okay, that sounds good.\n\\end{tcolorbox}\n\n\\begin{tcolorbox}[colback=gray!10,colframe=black,title=Box 2. Sample conversation for flights,breakable]\n\\textbf{assistant}: Welcome! What can I help you with today?\\\\\n\\\\\n\\textbf{user}: I need to book a flight from Colorado Springs to San Antonio for my anniversary, and I'm looking for something special.\n\\\\\n\\\\\n\\textbf{assistant}: Happy anniversary! What date are you planning to travel?\n\\\\\n\\\\\n\\textbf{user}: We're thinking of May 21, 2025.\n\\\\\n\\\\\n\\textbf{assistant}: That's great! What kind of flight experience are you looking for? Luxury, perhaps?\n\\\\\n\\\\ \n\\textbf{user}: Yes, something luxurious. And we'd like to depart from COS.\n\\\\\n\\\\\n\\textbf{assistant}: I'll look into that. What's your budget for the flight?\n\\\\\n\\\\ \n\\textbf{user}: We're looking to spend around 1100.\n\\end{tcolorbox}\n\n\\begin{tcolorbox}[colback=gray!10,colframe=black,title=Box 3. Sample conversation for hotels,breakable]\n\\textbf{assistant}: Hello, what brings you here today?\\\\\n\\\\\n\\textbf{user}: I'm looking for a hotel in St. Louis for my anniversary, can you help me?\n\\\\\n\\\\\n\\textbf{assistant}: Congratulations on your anniversary! What are your check-in and check-out dates?\n\\\\\n\\\\\n\\textbf{user}: We're planning to check-in on May 18, 2025 and check-out on May 29, 2025.\n\\\\\n\\\\\n\\textbf{assistant}: That's great! How many rooms do you need and what's your budget per night?\n\\\\\n\\\\ \n\\textbf{user}: We need 3 room and our budget is around 550 per night.\n\\\\\n\\\\\n\\textbf{assistant}: I see. What kind of amenities are you looking for in a hotel?\n\\\\\n\\\\ \n\\textbf{user}: We'd like a hotel with a gym, pool, and air conditioning.\n\\\\\n\\\\\n\\textbf{assistant}: Got it. Would you prefer a hotel in a specific neighborhood in St. Louis?\n\\\\\n\\\\ \n\\textbf{user}: Yes, we'd like to stay in Downtown St. Louis.\n\\end{tcolorbox}\n\n\\begin{tcolorbox}[colback=gray!10,colframe=black,title=Box 4. Sample conversation for restaurants,breakable]\n\\textbf{assistant}: assistant: Welcome, what brings you here today?\\\\\n\\\\\n\\textbf{user}: I'm gluten-free and I'm looking for a restaurant that can accommodate my dietary needs.\n\\\\\n\\\\\n\\textbf{assistant}: Don't worry, I'm here to help. What kind of cuisine are you interested in?\n\\\\\n\\\\\n\\textbf{user}: I'm really craving Livonian.\n\\\\\n\\\\\n\\textbf{assistant}: There are some great Livonian places that offer gluten-free options. Are you looking for something in a specific neighborhood?\n\\\\\n\\\\ \n\\textbf{user}: Yeah, I'd love something in Las Vegas somewhere in Summerlin.\n\\\\\n\\\\\n\\textbf{assistant}: I can check what's available in Summerlin. What's your budget for this meal?\n\\\\\n\\\\ \n\\textbf{user}: I'm looking to spend around 120 per person.\n\\\\\n\\\\\n\\textbf{assistant}: That helps. Would you like me to recommend a few places that fit your criteria?\n\\\\\n\\\\ \n\\textbf{user}: Please do.\n\\end{tcolorbox}\n\n\\begin{tcolorbox}[colback=gray!10,colframe=black,title=Box 5. Sample conversation for flight and hotels,breakable]\n\\textbf{assistant}: assistant: Hello, how can I assist you today?\n\\\\\n\\\\\n\\textbf{user}: I'm looking for a flight from Atlanta to Memphis and a hotel in Memphis\n\\\\\n\\\\\n\\textbf{assistant}: What are your travel dates?\n\\\\\n\\\\\n\\textbf{user}: My departure date is May 18, 2025 and I'd like to return on June 1, 2025\n\\\\\n\\\\\n\\textbf{assistant}: What class of flight are you interested in?\n\\\\\n\\\\ \n\\textbf{user}: user: I'd prefer business\n\\\\\n\\\\\n\\textbf{assistant}: And for your hotel, what star rating are you looking for?\n\\\\\n\\\\ \n\\textbf{user}: I'd like a 2 star hotel in Downtown Memphis\n\\end{tcolorbox}\n\n\\begin{tcolorbox}[colback=gray!10,colframe=black,title=Box 6. Sample conversation for hotel and restaurant,breakable]\n\\textbf{assistant}: assistant: Hello! How can I assist you today?\n\\\\\n\\\\\n\\textbf{user}: I'm looking for a hotel in San Antonio and a restaurant that serves Turkish.\n\\\\\n\\\\\n\\textbf{assistant}: That sounds like a great plan! Can you tell me a bit more about your hotel preferences, such as the number of rooms and check-in date?\n\\\\\n\\\\\n\\textbf{user}: I need 3 rooms for 5 people, checking in on May 22, 2025 and checking out on May 27, 2025.\n\\\\\n\\\\\n\\textbf{assistant}: I'd be happy to help you with that. What's your budget per night for the hotel?\n\\\\\n\\\\ \n\\textbf{user}: I'm looking to spend around 50 per night.\n\\\\\n\\\\\n\\textbf{assistant}: That's helpful to know. For the restaurant, are you looking for something with a specific rating or price range?\n\\\\\n\\\\ \n\\textbf{user}: Yes, I'd like a restaurant with a rating of at least 3.5 and a price range of around 85.\n\\\\\n\\\\\n\\textbf{assistant}: I'll keep that in mind. Would you like the hotel to have any specific amenities, such as a gym or pool?\n\\\\\n\\\\ \n\\textbf{user}: Yes, a pool would be great. And can you recommend a restaurant with Turkish near the hotel?\n\\end{tcolorbox}\n\n\\begin{tcolorbox}[colback=gray!10,colframe=black,title=Box 7. Sample conversation for hotel and attraction,breakable]\n\\textbf{assistant}: Hi there! What brings you here today?\n\\\\\n\\\\\n\\textbf{user}: I'm planning a last-minute trip to Portland for 2 people.\n\\\\\n\\\\\n\\textbf{assistant}: Last-minute trips can be exciting! What's your budget for the hotel?\n\\\\\n\\\\\n\\textbf{user}: We're looking to spend around 55 per night.\n\\\\\n\\\\\n\\textbf{assistant}: Okay, that helps. What's your check-in and check-out date?\n\\\\\n\\\\ \n\\textbf{user}: We're checking in on June 28, 2025 and checking out on July 10, 2025.\n\\\\\n\\\\\n\\textbf{assistant}: Are you interested in any specific attractions in Portland?\n\\\\\n\\\\ \n\\textbf{user}: Yes, we want to visit Pittock Mansion, which is a Social attraction.\n\\\\\n\\\\\n\\textbf{assistant}: That sounds like a great choice! I can help you find a hotel near Pittock Mansion. What kind of amenities are you look for in the hotel?\n\\\\\n\\\\ \n\\textbf{user}: We need a hotel with a gym and wifi.\n\\end{tcolorbox}\n\n\\begin{tcolorbox}[colback=gray!10,colframe=black,title=Box 8. Sample conversation for flight-hotel-attraction,breakable]\n\\textbf{assistant}: Hello, I'd be happy to help you plan your trip. What's on your mind?\n\\\\\n\\\\\n\\textbf{user}: I'm thinking of going to Las Vegas for my birthday and I need a business flight from Tucson.\n\\\\\n\\\\\n\\textbf{assistant}: Happy birthday in advance. When are you thinking of traveling?\n\\\\\n\\\\\n\\textbf{user}: We're looking at May 19, 2025 to May 27, 2025.\n\\\\\n\\\\\n\\textbf{assistant}: That's a great time. How many people are traveling?\n\\\\\n\\\\ \n\\textbf{user}: Just 2 of us.\n\\\\\n\\\\\n\\textbf{assistant}: Lovely. For your stay, what kind of hotel are you envisioning?\n\\\\\n\\\\ \n\\textbf{user}: A 2 star hotel with a spa and room service would be perfect. And we'd love to stay near Fountains of Bellagio with a beautiful view.\n\\\\\n\\\\\n\\textbf{assistant}: That sounds like a great choice! I can help you find a hotel near Pittock Mansion. What kind of amenities are you look for in the hotel?\n\\end{tcolorbox}\n\n\\begin{tcolorbox}[colback=gray!10,colframe=black,title=Box 9. Sample conversation for flight-hotel-restaurant,breakable]\n\\textbf{assistant}: Welcome to our travel assistant. How can I help you today?\n\\\\\n\\\\\n\\textbf{user}: I'm looking to travel from Tucson to St. Louis for a business trip.\n\\\\\n\\\\\n\\textbf{assistant}: What are your preferred travel dates?\n\\\\\n\\\\\n\\textbf{user}: I'd like to fly out on May 18, 2025 and come back on June 1, 2025\n\\\\\n\\\\\n\\textbf{assistant}: What class would you like to fly in?\n\\\\\n\\\\ \n\\textbf{user}: I prefer first.\n\\\\\n\\\\\n\\textbf{assistant}: Great, I can look into that. Where would you like to stay in St. Louis?\n\\\\\n\\\\ \n\\textbf{user}: I'd like a hotel in Downtown St. Louis with a 3 star rating.\n\\\\\n\\\\\n\\textbf{assistant}: And what kind of restaurant are you looking for?\n\\\\\n\\\\ \n\\textbf{user}: I'd like a restaurant in Downtown St. Louis with a rating of at least 3.5.\n\\end{tcolorbox}\n\n% \\section{Appendix / supplemental material}\n\n% \\section{System Prompt for Generating Planner Code}\n% \\label{appendix:prompt-design-system-planner}\n\n\\section{System Prompt Design for Single-Domain Template Generation}\n\\label{appendix:prompt-design-user-planner}\n\nThis section details the unified system prompt and its domain-specific customizations used for generating diverse, realistic multi-turn conversations for four domains: \\textbf{Flights, Hotels, Attractions, and Restaurants}.\n\n\\subsection{Unified Prompt Instructions (Common Across Domains)}\n\nThe following design principles apply across all domains to ensure consistency, diversity, and realism in the generated templates:\n\n\\begin{itemize}[leftmargin=1.5em]\n \\item \\textbf{Conversation Format:}\n \\begin{itemize}\n \\item Each template contains 6–8 dialogue turns (3–4 exchanges).\n \\item Each template starts with an \\texttt{assistant} turn and ends with a \\texttt{user} turn.\n \\item Turns alternate between \\texttt{assistant:} and \\texttt{user:}.\n \\end{itemize}\n\n \\item \\textbf{Placeholder Handling:}\n \\begin{itemize}\n \\item Use numbered placeholders like \\texttt{<CITY\\_1>}, \\texttt{<PRICE\\_2>}.\n \\item Numbering resets at the start of each new template.\n \\end{itemize}\n\n \\item \\textbf{Assistant Behavior Diversity:}\n \\begin{itemize}\n \\item Vary personality: formal/informal, concise/verbose.\n \\item Ask both open-ended and specific questions.\n \\item Do not assume user intent or provide options in the first message.\n \\item Correct misunderstandings if needed.\n \\end{itemize}\n\n \\item \\textbf{User Behavior Diversity:}\n \\begin{itemize}\n \\item Include verbose, terse, indecisive, and overly specific users.\n \\item Some users provide minimal context; others give excess information.\n \\item Include questions that revisit or skip steps.\n \\end{itemize}\n\n \\item \\textbf{Flow Diversity:}\n \\begin{itemize}\n \\item Avoid fixed dialogue patterns (e.g., greet $\\rightarrow$ date $\\rightarrow$ options).\n \\item Allow nonlinear flow: backtracking, parallel requests, etc.\n \\end{itemize}\n\n \\item \\textbf{Language Diversity:}\n \\begin{itemize}\n \\item Vary greetings, transitions, sentence structure, and phrasings.\n \\item No repeated phrasing across templates.\n \\end{itemize}\n\n \\item \\textbf{Output Requirements:}\n \\begin{itemize}\n \\item Output format is strict JSON with keys like \\texttt{\"template\\_1\"} through \\texttt{\"template\\_30\"}.\n \\item Each value is a newline-separated conversation string.\n \\item No preambles or postambles allowed.\n \\end{itemize}\n\\end{itemize}\n\n\\subsection{Domain-Specific Customization}\n\n\\begin{table}[!ht]\n\\centering\n\\small\n\\renewcommand{\\arraystretch}{1.3}\n\\begin{tabular}{|l|p{5.5cm}|p{7.5cm}|}\n\\hline\n\\textbf{Domain} & \\textbf{Placeholders Used} & \\textbf{Required Scenarios} \\\\\n\\hline\n\\textbf{Flight} & \n\\texttt{<CITY\\_x>}, \\texttt{<CLASS\\_x>}, \\texttt{<DEPARTURE\\_DATE\\_x>}, \\texttt{<DEPARTURE\\_TIME\\_x>}, \\texttt{<ARRIVAL\\_DATE\\_x>}, \\texttt{<ARRIVAL\\_TIME\\_x>}, \\texttt{<AIRLINE\\_x>}, \\texttt{<PRICE\\_x>}, \\texttt{<AIRPORT\\_x>}, \\texttt{<NUM\\_TRAVELERS\\_x>} & \nRound trip (with 2 departure dates), one-way, multi-city, strict arrival time, flexible dates, large group bookings, solo/business/family travel, weekend getaways, budget constraints, special occasions (e.g., birthdays) \\\\\n\\hline\n\n\\textbf{Hotel} & \n\\texttt{<CHECK\\_IN\\_DATE\\_x>}, \\texttt{<CHECK\\_OUT\\_DATE\\_x>}, \\texttt{<CITY\\_x>}, \\texttt{<CITY\\_x\\_NEIGHBORHOOD\\_y>}, \\texttt{<STAR\\_x>}, \\texttt{<RATING\\_x>}, \\texttt{<PRICE\\_x>}, \\texttt{<NUM\\_ROOMS\\_x>}, \\texttt{<NUM\\_TRAVELERS\\_x>} & \nMulti-city trips, multiple hotels in one city, specific neighborhoods, special occasions, last-minute reservations, business vs. leisure, extended stays, view/accessibility/pet-friendly requests, family and group accommodations \\\\\n\\hline\n\n\\textbf{Attraction} & \n\\texttt{<ATTRACTION\\_TYPE\\_x>}, \\texttt{<CITY\\_x>}, \\texttt{<STATE\\_x>}, \\texttt{<CITY\\_x\\_NEIGHBORHOOD\\_y>} & \nMulti-type attraction queries, across cities/states, neighborhood-specific exploration, curiosity about state-level offerings. Assistant must ask open-ended questions and only mention placeholders if user provides them. \\\\\n\\hline\n\n\\textbf{Restaurant} & \n\\texttt{<RESTAURANT\\_RATING\\_x>}, \\texttt{<RESTAURANT\\_PRICE\\_x>}, \\texttt{<RESTAURANT\\_CUISINE\\_x>}, \\texttt{<CITY\\_x>}, \\texttt{<CITY\\_x\\_NEIGHBORHOOD\\_y>} & \nMulti-city dining, cuisine preferences, dietary restrictions (e.g., nut-free, vegan, halal), budget vs. premium, special occasion dining, rating-focused or casual vs. upscale preferences \\\\\n\\hline\n\\end{tabular}\n\\vspace{1mm}\n\\caption{Domain-specific customization for template generation. Each domain builds on the shared prompt instructions with unique placeholders and required conversation scenarios.}\n\\label{tab:domain-customization}\n\\end{table}\n\\section{System Prompt Design for Multi-Domain Template Generation}\n\\label{appendix:multi-domain-prompt-design}\n\nThis section outlines the system prompt instructions used for generating multi-turn conversations that span multiple domains. These prompts are tailored to create realistic, nonlinear interactions involving combinations of Flights, Hotels, Attractions, and Restaurants.\n\n\\subsection{Unified Prompt Instructions (Shared Across Multi-Domain Prompts)}\n\n\\begin{itemize}[leftmargin=1.5em]\n \\item \\textbf{Conversation Format:}\n \\begin{itemize}\n \\item Each template includes at least 8–10 dialogue turns (4–5 exchanges).\n \\item Each template begins with an \\texttt{assistant} turn and ends with a \\texttt{user} turn.\n \\item Turns alternate between \\texttt{assistant:} and \\texttt{user:}.\n \\end{itemize}\n\n \\item \\textbf{Placeholder Handling:}\n \\begin{itemize}\n \\item Use only predefined placeholders (e.g., \\texttt{<CITY\\_1>}, \\texttt{<DEPARTURE\\_DATE\\_1>}).\n \\item Numbering restarts from 1 in every template.\n \\item Assistant must never mention a placeholder until the user provides it.\n \\end{itemize}\n\n \\item \\textbf{Assistant Behavior:}\n \\begin{itemize}\n \\item Vary personality (formal, informal), verbosity, and strategies (direct vs. open-ended questions).\n \\item Must not assume user intent or combine domain-specific questions unless user initiates.\n \\item Must correct misunderstandings and adapt to user behavior.\n \\end{itemize}\n\n \\item \\textbf{User Behavior:}\n \\begin{itemize}\n \\item Include users who are indecisive, impatient, verbose, overly specific, or vague.\n \\item Users may change their mind, skip steps, or backtrack in conversation.\n \\end{itemize}\n\n \\item \\textbf{Conversation Flow:}\n \\begin{itemize}\n \\item Avoid rigid order (e.g., flight $\\rightarrow$ hotel $\\rightarrow$ attraction).\n \\item Encourage nonlinear scenarios: jumping between domains, multiple questions at once.\n \\end{itemize}\n\n \\item \\textbf{Language and Style:}\n \\begin{itemize}\n \\item Vary greetings, transitions, sentence structures, and terminology across templates.\n \\item Avoid repeated phrasings.\n \\end{itemize}\n\n \\item \\textbf{Output Requirements:}\n \\begin{itemize}\n \\item Format is strict JSON: \\texttt{\"template\\_1\"} through \\texttt{\"template\\_30\"}.\n \\item Each value is a newline-separated string of alternating assistant/user dialogue.\n \\item No preamble, postamble, or markdown allowed.\n \\end{itemize}\n\\end{itemize}\n\n\\subsection{Multi-Domain Scenario Requirements}\n\n\\begin{table}[!ht]\n\\centering\n\\small\n\\renewcommand{\\arraystretch}{1.2}\n\\begin{tabularx}{\\textwidth}{|l|X|X|}\n\\hline\n\\textbf{Domains} & \\textbf{Placeholders Used} & \\textbf{Required Scenarios} \\\\\n\\hline\n\n\\textbf{Flight + Hotel} & \n\\texttt{<CITY\\_x>}, \\texttt{<CLASS\\_x>}, \\texttt{<DEPARTURE\\_DATE\\_x>}, \\texttt{<CHECK\\_IN\\_DATE\\_x>}, \\texttt{<CHECK\\_OUT\\_DATE\\_x>}, \\texttt{<NUM\\_TRAVELERS\\_x>} & \nTrip planning involving both air travel and accommodation. Includes round-trip and one-way flights, hotel stays across one or more cities, budget and luxury travelers, group vs. solo travel, flexible dates, specific preferences like nonstop flights or hotels with amenities (e.g., pool, gym, pet-friendly). \\\\\n\\hline\n\n\\textbf{Flight + Hotel + Attraction} & \nAll flight, hotel, and attraction placeholders (e.g., \\texttt{<CITY\\_x>}, \\texttt{<CHECK\\_IN\\_DATE\\_x>}, \\texttt{<ATTRACTION\\_TYPE\\_x>}) & \nUser planning a full trip involving flights, hotel stays, and sightseeing. Must include: round-trip and one-way flights, multi-city stays, hotels near attractions, flexible schedules, user-provided attraction types or specific attraction names (only when linking to hotel proximity), and special constraints (e.g., group travel, business trips, anniversaries). Assistant must never use placeholder names before user introduces them. Includes amenity discussions and attraction-type grammar handling (e.g., cultural vs. \\texttt{<ATTRACTION\\_TYPE\\_1>} attraction). \\\\\n\\hline\n\n\\textbf{Flight + Hotel + Restaurant} & \nAll flight, hotel, and restaurant placeholders (e.g., \\texttt{<RESTAURANT\\_CUISINE\\_x>}, \\texttt{<RESTAURANT\\_PRICE\\_x>}) & \nEnd-to-end trip planning including flights, accommodation, and dining. Required scenarios include: dietary restrictions, cuisine-first or city-first planning, restaurant proximity to hotel, multiple hotels or restaurants in one city, round-trip and one-way flights, and amenities like pet-friendly or spa. Must feature diverse language, nonlinear flows, and varied user personas. Includes explicit handling of dietary needs (e.g., halal, nut-free, vegetarian), and placeholder logic as per prompt. \\\\\n\\hline\n\n\\textbf{Hotel + Attraction} & \n\\texttt{<CITY\\_x>}, \\texttt{<CHECK\\_IN\\_DATE\\_x>}, \\texttt{<ATTRACTION\\_TYPE\\_x>}, \\texttt{<CITY\\_x\\_NEIGHBORHOOD\\_y>} & \nUser looking for accommodations and nearby attractions. Includes: specific neighborhoods, family-friendly vs. solo travel, themed vacations, extended stays, proximity to cultural or outdoor attractions, and cases where attraction type drives hotel location. \\\\\n\\hline\n\n\\textbf{Hotel + Restaurant} & \n\\texttt{<RESTAURANT\\_CUISINE\\_x>}, \\texttt{<HOTEL\\_PRICE\\_x>}, \\texttt{<CITY\\_x\\_NEIGHBORHOOD\\_y>} & \nCombines hotel booking with dining preferences. Includes cuisine-specific searches, budget dining vs. fine dining, dietary needs (gluten-free, halal), special events (anniversaries), restaurant proximity to hotel, and group size considerations. Templates vary in planning flow—some start with hotel, others with restaurants. \\\\\n\\hline\n\n\\end{tabularx}\n\\vspace{1mm}\n\\caption{Multi-domain combinations and scenario requirements. Each configuration builds on the unified instructions with additional placeholder logic and domain-specific complexity.}\n\\label{tab:multi-domain-customization}\n\\end{table}\n\n\\clearpage\n\\section{Example System Prompt: Flight Domain}\n\n\\begin{lstlisting}\nYour task is to generate diverse conversation templates for a flight finder chatbot.\n\nEach template should capture realistic, UNIQUE conversations between a user and an assistant about flight searches.\n\nPlaceholders to Use:\n- Departure city: <CITY_x>\n- Destination arrival city: <CITY_x>\n- Flight class: <CLASS_x>\n- Departure date: <DEPARTURE_DATE_x>\n- Departure time: <DEPARTURE_TIME_x>\n- Destination arrival date: <ARRIVAL_DATE_x>\n- Destination arrival time: <ARRIVAL_TIME_x>\n- Airline name: <AIRLINE_x>\n- Price: <PRICE_x>\n- Airport's name: <AIRPORT_x>\n- Number of travelers: <NUM_TRAVELERS_x>\n\nKey Requirements:\n1. Placeholder Numbering:\n - Use integers for ``x'' (e.g., <CITY_1>, <CITY_2>).\n - Reset numbering to 1 for each new template.\n\n2. Conversation Format & Structure:\n - 6-8 turns minimum per template (3-4 exchanges).\n - Format as alternating assistant: and user: lines.\n - Each template must start with an assistant turn and end with a user turn.\n - Vary conversation lengths-some should be shorter, some longer.\n\n3. Assistant Behavior Diversity:\n - Use distinctly different assistant personalities (formal, casual, verbose, concise).\n - Vary how information is requested (direct questions vs. open-ended).\n - Include templates where the assistant corrects misunderstandings.\n - NOTE: Assistant's first turn must be generic and not assume user intent (e.g., ``How can I help you?\").\n - NOTE: The assistant must not say it has found options for the user.\n\n4. User Behavior Diversity:\n - Create dramatically different user types (impatient, polite, verbose, terse).\n - Include users who provide minimal information (requiring follow-ups).\n - Include users who provide too much information.\n - Include complex, specific requests.\n - Include indecisive users who change their minds.\n\n5. Conversation Flow Diversity:\n - Avoid standard ``greeting -> dates -> preferences -> options'' structure.\n - Include nonlinear conversations (user circles back to earlier topics).\n - Include users asking multiple questions at once.\n - Include conversations skipping obvious steps.\n - Vary order in which info is elicited.\n\n6. Required Scenarios:\n - Multi-city trips\n - Round trip\n - One-way trip\n - Flexible travel dates\n - Users with strong preferences (airline, class, airport, etc.)\n - Non-stop flights only\n - Short layovers are okay\n - Rigid arrival time\n - Special occasions (e.g., anniversaries)\n - Large group bookings\n - Last-minute reservations\n - Business travel\n - Family vacations\n - Solo travelers\n - Budget constraints\n - Weekend getaways\n\n7. Round Trip Requirement:\n - Roundtrip flights must include 2 departure dates.\n\n8. Multiple Travelers:\n - Always include number of travelers via placeholder in multi-person scenarios.\n\n9. Linguistic Diversity:\n - Avoid repeating same greetings across templates.\n - Vary language used for dates, preferences, and constraints.\n - Use different phrasings for similar ideas.\n - Unique transitions between topics.\n - Varied sentence structures and response styles.\n\nImportant: Each template must be fundamentally different in structure, flow, language, and scenario.\n\\end{lstlisting}\n\n\\section{Example System Prompt: Flight-Hotel-Restaurant Domain}\n\n\\begin{lstlisting}\nYour task is to generate diverse conversation templates for a travel assistant chatbot.\n\nEach template should be a realistic and UNIQUE multi-turn interaction between a user and an assistant that includes:\n- Flight search\n- Hotel booking\n- Restaurant recommendations or reservations\n\nPlaceholders to Use:\n- Flight: <CITY_x>, <CLASS_x>, <DEPARTURE_DATE_x>, <DEPARTURE_TIME_x>, <ARRIVAL_DATE_x>, <ARRIVAL_TIME_x>, <AIRLINE_x>, <PRICE_x>, <AIRPORT_x>, <NUM_TRAVELERS_x>\n- Hotel: <HOTEL_x>, <CHECKIN_DATE_x>, <CHECKOUT_DATE_x>, <ROOM_TYPE_x>, <NUM_GUESTS_x>, <PRICE_x>\n- Restaurant: <RESTAURANT_x>, <CUISINE_x>, <RESERVATION_TIME_x>, <RESERVATION_DATE_x>, <PRICE_RANGE_x>, <LOCATION_x>, <NUM_PEOPLE_x>\n\nKey Requirements:\n1. Conversation must include all 3 components: flight, hotel, and restaurant.\n\n2. Placeholder Numbering:\n - Use integers for ``x\" (e.g., <CITY_1>, <CITY_2>).\n - Reset numbering to 1 for each new template.\n\n3. Conversation Format:\n - At least 8 turns (4 full exchanges).\n - Alternating assistant: and user: lines.\n - Assistant always starts the conversation and user always ends it.\n - Vary length and structure across templates.\n\n4. Assistant Behavior Diversity:\n - Include different tones: friendly, formal, efficient, humorous, inquisitive, etc.\n - Vary assistant strategies (confirming info early vs. late, etc.).\n - Assistant cannot confirm bookings or give specific results.\n - Must clarify ambiguous or missing information.\n\n5. User Behavior Diversity:\n - Mix of confident and indecisive users.\n - Include users who change minds mid-conversation.\n - Include users who ask multiple things at once.\n - Include low-information and high-information users.\n\n6. Conversation Flow Diversity:\n - Vary the sequence (flight -> hotel -> restaurant, or restaurant -> hotel -> flight, etc.).\n - Include users jumping between topics or circling back.\n - Include unexpected questions or constraints from users.\n\n7. Required Scenarios:\n - Honeymoon trip\n - Last-minute business travel\n - Budget backpacking vacation\n - Solo food tourism\n - Family summer vacation\n - Couple anniversary trip\n - Conference trip with team\n - Weekend getaway\n - Luxury experience\n - Specific date events (concerts, sports games, etc.)\n\n8. Linguistic Diversity:\n - Avoid repeating phrasing and structures across templates.\n - Vary tone and style significantly.\n - Mix concise and elaborate dialogues.\n\nImportant: Every template must feel distinct from all others in tone, language, structure, and scenario.\n\\end{lstlisting}\n\n\\section{Example System Prompt: Plan Generation Prompt}\n\\label{system-plan-generation-prompt}\n\\begin{lstlisting}\nYou are an expert AI travel planner and your responsibility is to generate Python code using APIs or Tools. \n\\end{lstlisting}\n\n\\section{Example User Prompt: Plan Generation Prompt}\n\\label{user-plan-generation-prompt}\n\\begin{lstlisting}\nYour task is to generate a Python code based on a conversation between the user and the assistant, where the last turn is from the user.\nThe code typically involves calling one or more tools (functions) to help the user in planning their travel request.\nIn the Python code, you need to use the following tools:\n# TOOL CONFIG\n<TOOL_CONFIG>\n# INSTRUCTIONS\n- Track content: Maintain the conversation state across turns and use all known information from earlier in the conversation.\n- As soon as the mandatory parameters (non-optional parameters) are all provided (refer to TOOL CONFIG to find mandatory parameters for each tool), generate the appropriate plan using Python code.\n- Do NOT modify entity values under any circumstances. Use them exactly as they appear in the conversation while populating attributes in the function during code generation.\nFor example, if the city is \"new york\" (lowercase), do not convert it to \"New York\" or \"NYC\".\n- Do not fill optional parameters unless they are explicitly provided in the conversation.\n- When generating seek_information, only mention mandatory parameters (non-optional parameters) that are missing. Never ask for optional parameters using seek_information. Refer to TOOL CONFIG to figure out what the mandatory parameters (non optional parameters) are and check CONVERSATION to know what parameters have been provided by the user. \nFor example, \"seek_information('<explain what mandatory parameters (non-optional parameters) are missing and must be gathered by the assistant>')\"\n- Only generate the code for the domain which the customer has mentioned in the conversation. For example, if user mentioned only about attractions, don't generate the code with restaurants search. Only if the user mentioned searching for restaurant anywhere in the conversation, then only search for restaurants.\n- If a tool result from a previous turn is still valid and relevant, use get_results_from_cache(key=\"<cache_key>\") to retrieve it. Use the cache summary to determine the most appropriate key to select from. If you have many keys in the cache for the same domain. Use the one which would be most relevant.\n- If you generate a tool call and its result could be reused later, save it with save_to_cache(\"<key>\",value). Ensure the cache key is unique and avoid naming collision with previously stored cache key name\n- If a result has already been stored in the cache for a conversation and no new result needs to be generated, do not regenerate the code. Instead, return the code as \"print(\"No planning needed\")\"\n# OUTPUT FORMAT\n- You need to generate the reasoning and the python code. The reasoning should clearly explain the process, steps and the reason behind the python plan that is going to be generated \nThe reasoning should be within the <REASONING> </REASONING> tags and the python code should be within the <CODE> </CODE> tags. Note while generating the python code, never have any markdown tags.\n# EXAMPLES\n<FEW_SHOT_EXAMPLES>\n# CONVERSATION\n<CONVERSATION>\n# CACHE\n<CACHE_FOR_CONVERSATION>\nGiven the provided conversation and cache summary, generate a Python code for the last user turn.\n\\end{lstlisting}\n\n\n\\section{Template: Plan Generation for Fine-Tuning}\n\\begin{lstlisting}\nYou are an expert AI travel planner and your responsibility is to generate Python code using APIs or Tools. \nYour task is to generate a Python code based on a conversation between the user and the assistant, where the last turn is from the user.\nThe code typically involves calling one or more tools (functions) to help the user in planning their travel request.\nIn the Python code, you need to use the following tools:\n# TOOL CONFIG\n<TOOL_CONFIG>\n# INSTRUCTIONS\n- Track content: Maintain the conversation state across turns and use all known information from earlier in the conversation.\n- As soon as the mandatory parameters (non-optional parameters) are all provided (refer to TOOL CONFIG to find mandatory parameters for each tool), generate the appropriate plan using Python code.\n- Do NOT modify entity values under any circumstances. Use them exactly as they appear in the conversation while populating attributes in the function during code generation.\n For example, if the city is \"new york\" (lowercase), do not convert it to \"New York\" or \"NYC\".\n- Do not fill optional parameters unless they are explicitly provided in the conversation.\n- When generating seek_information, only mention mandatory parameters (non-optional parameters) that are missing. Never ask for optional parameters using seek_information. Refer to TOOL CONFIG to figure out what the mandatory parameters (non optional parameters) are and check CONVERSATION to know what parameters have been provided by the user. \n For example, \"seek_information('<explain what mandatory parameters (non-optional parameters) are missing and must be gathered by the assistant>')\"\n- Only generate the code for the domain which the customer has mentioned in the conversation. For example, if user mentioned only about attractions, don't generate the code with restaurants search. Only if the user mentioned searching for restaurant anywhere in the conversation, then only search for restaurants.\n- If a tool result from a previous turn is still valid and relevant, use get_results_from_cache(key=\"<cache_key>\") to retrieve it. Use the cache summary to determine the most appropriate key to select from. If you have many keys in the cache for the same domain. Use the one which would be most relevant.\n- If you generate a tool call and its result could be reused later, save it with save_to_cache(\"<key>\",value). Ensure the cache key is unique and avoid naming collision with previously stored cache key name\n- If a result has already been stored in the cache for a conversation and no new result needs to be generated, do not regenerate the code. Instead, return the code as \"print(\"No planning needed\")\"\n# OUTPUT FORMAT\n- You need to generate only the python code. The python code should be within the <CODE> </CODE> tags. Note while generating the python code, never have any markdown tags.\n# CONVERSATION\n<CONVERSATION>\n# CACHE\n<CACHE_FOR_CONVERSATION>\nGiven the provided conversation and cache summary, generate a Python code for the last user turn.\n\\end{lstlisting}\n\n\\section{Script to replicate SFT} \\label{SFTScript}\n\\begin{lstlisting}\n>> git clone https://github.com/huggingface/trl.git\n>> cd trl\n>> git checkout v0.17.0 # We use the TRL version 0.17.0\n>> pip install -e .\n>> accelerate launch --config_file trl/examples/accelerate_configs/multi_gpu.yaml \\\n trl/scripts/sft.py \\\n --model_name_or_path <YOUR PATH TO A Meta-Llama-3.1-8B-Instruct LOCALLY> \\\n --dataset_name <TRAIN SET OF THE TOOL CALLING IN HUGGINGFACE DATASET FORMAT> \\\n --learning_rate 2.0e-5 \\\n --num_train_epochs 1 \\\n --packing \\\n --per_device_train_batch_size 1 \\\n --gradient_accumulation_steps 8 \\\n --gradient_checkpointing \\\n --logging_steps 25 \\\n --eval_strategy steps \\\n --eval_steps 100 \\\n --use_peft \\\n --lora_r 32 \\\n --lora_alpha 32 \\\n --lora_target_modules q_proj k_proj v_proj o_proj gate_proj up_proj down_proj \\\n --output_dir <SOME OUTPUT DIRECTORY>\\\n --report_to tensorboard \\\n --torch_dtype bfloat16\n\\end{lstlisting}\n\n\n\\end{document}"},"question":{"kind":"string","value":"In multi-domain evaluations, including which domain leads to the largest absolute difference in the minimum Code Execution Rate accuracy between the 8B and 70B models? What is the value of that difference?"},"answer":{"kind":"string","value":"Attractions, 23.4"},"plan":{"kind":"string","value":"SELECT Code Execution Rate accuracy for multi-domain settings\nLOOP for each domain\n SELECT all multi-domain settings including current domain\n COMPUTE minimum Code Execution Rate accuracy for 8B across these settings\n COMPUTE minimum Code Execution Rate accuracy for 70B across these settings\n COMPUTE difference = abs(min for 70B - min for 8B)\nCOMPUTE argmax difference\nRETURN domain with largest difference, and value\n"}}},{"rowIdx":65,"cells":{"paper":{"kind":"string","value":"2505.23657"},"relevant_tables":{"kind":"list like","value":[["\\begin{table*}\n"," \\scriptsize\n"," \\centering\n"," \\setlength{\\tabcolsep}{4pt}\n"," \\caption{Evaluation results on two open-ended benchmarks and two Chain-of-thought benchmarks. The best-performing results are highlighted in \\lightgreen{green}, the second-best in \\lightblue{blue}, and those indicating a performance drop compared to standard greedy decoding are shown in \\lightgray{grey}.}\n"," \\resizebox{\\textwidth}{!}{\n"," \\begin{tabular}{lllllllllll}\n","\\toprule\n","\\multirow{2.5}{*}{\\textbf{Model}} & \\multirow{2.5}{*}{\\textbf{Method}} & \\multicolumn{3}{c}{\\textbf{TruthfulQA}} & \\multicolumn{3}{c}{\\textbf{LongFact}} & \\multicolumn{2}{c}{\\textbf{CoT}} \\\\\n","\\cmidrule(lr){3-5} \\cmidrule(lr){6-8} \\cmidrule(lr){9-10}\n","& & \\textbf{\\%Truth} $\\uparrow$ & \\textbf{\\%Info} $\\uparrow$ & \\textbf{\\%T*I} $\\uparrow$ & \\textbf{Prec.}$\\uparrow$ & \\textbf{$R$@128} $\\uparrow$ & \\textbf{$F1$@128} $\\uparrow$ & \\textbf{StrQA} $\\uparrow$ & \\textbf{GSM8K} $\\uparrow$ \\\\\n","\\midrule\n","\\multirow{4}{*}{LLaMA3.1}& Greedy & 41.98 & 66.59 & 27.95 & 84.72 & 92.40 & 88.39 & 67.82 & 56.02\\\\\n"," &DoLa & \\lightblue{46.76 (+4.78)} & \\lightgreen{87.15 (+20.56)} & \\lightblue{40.75 (+12.80)} & \\lightblue{85.19 (+0.47)} & \\lightgray{83.66 (-8.74)} & \\lightgray{84.42 (-3.97)} & \\lightblue{69.21 (+1.39)} & \\lightblue{56.18 (+0.16)}\\\\\n"," & SLED & \\lightgray{41.25 (-0.73)} & 66.59 (0.00) & \\lightgray{27.47 (-0.48)} & \\lightgray{84.18 (-0.54)} & \\lightgray{79.02 (-13.38)} & \\lightgray{81.52 (-6.87)} & \\lightgray{67.38 (-0.44)} & 56.71 (+0.69)\\\\\n"," &{\\actdola} & \\lightgreen{52.70 (+10.72)} & \\lightblue{84.82 (+18.23)} & \\lightgreen{44.70 (+16.75)} & \\lightgreen{86.63 (+1.91)} & \\lightgreen{97.38 (+4.98)} & \\lightgreen{91.69 (+3.30)} & \\lightgreen{75.33 (+7.51)} & \\lightgreen{63.23 (+7.21)}\\\\\n","\\midrule\n","\\multirow{4}{*}{GLM4} &Greedy & 64.26 & 87.27 & 56.08 & 84.17 & 95.77 & 89.60 & 69.56 & 60.12\\\\\n"," & DoLa & \\lightgray{59.24 (-5.02)} & \\lightgray{81.76 (-5.51)} & \\lightgray{48.44 (-7.64)} & \\lightgray{83.49 (-0.68)} & \\lightgreen{98.34 (+2.57)} & \\lightgreen{90.31 (+0.71)} & \\lightgray{67.38 (-2.18)} & \\lightgray{57.01 (-3.11)}\\\\\n"," & SLED & \\lightgray{62.06 (-2.20)} & \\lightgray{67.56 (-19.71)} & \\lightgray{41.93 (-14.15)} & \\lightblue{84.32 (+0.15)} & \\lightgray{88.13 (-7.64)} & \\lightgray{86.18 (-3.42)} & \\lightblue{70.48 (+0.92)} & \\lightblue{60.95 (+0.83)}\\\\\n"," &{\\actdola} & \\lightgreen{68.91 (+4.65)} & \\lightgray{83.23 (-4.04)} & \\lightgreen{57.35 (+1.27)} & \\lightgreen{86.37 (+2.20)} & \\lightgray{90.01 (-5.76)} & \\lightgray{88.15 (-1.45)} & \\lightgreen{72.58 (+3.02)} & \\lightgreen{64.06 (+3.94)}\\\\\n","\\midrule\n","\\multirow{4}{*}{Mistral3} &Greedy & 58.38 & 64.87 & 37.87 & 84.87 & 78.08 & 81.33 & 73.49 & 53.15 \\\\\n"," & DoLa & \\lightblue{66.21 (+7.83)} & \\lightblue{78.21 (+13.34)} & \\lightblue{51.78 (+13.91)} & {85.23 (+0.36)} & \\lightgray{75.42 (-2.66)} & \\lightgray{80.03 (-1.30)} & \\lightgray{71.00 (-2.49)} & \\lightgray{51.10 (-2.05)} \\\\\n"," & SLED & 58.75 (+0.37) & \\lightgray{64.63 (-0.24)} & 37.97 (+0.10) & \\lightblue{85.45 (+0.58)} & \\lightgray{77.88 (-0.20)} & \\lightblue{81.49 (+0.16)} & \\lightgray{73.41 (-0.08)} & \\lightblue{53.53 (+0.38)}\\\\\n"," &{\\actdola} & \\lightgreen{71.84 (+13.46)} & \\lightgreen{80.29 (+15.42)} & \\lightgreen{57.68 (+19.81)} & \\lightgreen{85.78 (+0.91)} & \\lightgray{77.62 (-0.46)} & \\lightgreen{81.50 (+0.17)} & \\lightgreen{73.84 (+0.35)} & \\lightgreen{58.98 (+5.83)}\\\\\n","\\midrule\n","\\multirow{4}{*}{Gemma2} &Greedy & 54.10 & 51.90 & 28.08 & 83.77 & 100.70 & 91.46 & 74.80 & 82.11\\\\\n"," & DoLa & \\lightblue{61.44 (+7.34)} & \\lightgreen{68.05 (+16.15)} & \\lightblue{41.81 (+13.73)} & \\lightgray{83.66 (-0.11)} & \\lightblue{104.13 (+3.43)} & \\lightblue{92.78 (+1.32)} & \\lightgray{73.54 (-1.26)} & \\lightgray{81.20 (-0.91)}\\\\\n"," & SLED & 54.83 (+0.73) & 53.61 (+1.71) & 29.41 (+1.33) & \\lightgray{83.73 (-0.04)} & \\lightgray{98.68 (-2.02)} & \\lightgray{90.59 (-0.87)} & \\lightblue{74.93 (+0.13)} & \\lightblue{82.94 (+0.83)}\\\\\n"," &{\\actdola} & \\lightgreen{64.62 (+10.52)} & \\lightblue{67.20 (+15.30)} & \\lightgreen{43.42 (+15.34)} & \\lightgreen{83.98 (+0.21)} & \\lightgreen{104.55 (+3.85)} & \\lightgreen{93.14 (+1.68)} & \\lightgreen{77.25 (+2.45)} & \\lightgreen{83.32 (+1.21)}\\\\\n","\\midrule\n","\\multirow{4}{*}{DeepSeek2} &Greedy & 52.99 & 79.93 & 42.35 & 81.76 & 80.84 & 81.30 & 69.65 & 70.36\\\\\n"," & DoLa & \\lightblue{53.85 (+0.86)}& \\lightblue{82.99 (+3.06)} & \\lightblue{44.69 (+2.34)} & {82.17 (+0.41)} & \\lightblue{81.06 (+0.22)} & \\lightblue{81.61 (+0.31)} & \\lightblue{70.61 (+0.96)} & \\lightgray{66.19 (-4.17)}\\\\\n"," & SLED & \\lightgray{51.53 (-1.46)} & \\lightgray{77.23 (-2.70)} & \\lightgray{39.80 (-2.55)} & \\lightblue{82.67 (+0.91)} & \\lightgray{79.48 (-1.36)} & \\lightgray{81.04 (-0.26)} & 69.87 (+0.22) & \\lightgray{68.99 (-1.37)}\\\\\n"," &{\\actdola} & \\lightgreen{60.46 (+7.47)} & \\lightgreen{83.48 (+3.55)} & \\lightgreen{50.47 (+8.12)} & \\lightgreen{83.15 (+1.39)} & \\lightgreen{82.85 (+2.01)} & \\lightgreen{83.00 (+1.70)} & \\lightgreen{76.51 (+6.86)} & \\lightgreen{70.81 (+0.45)}\\\\\n","\\bottomrule\n"," \\end{tabular}\n"," }\n"," \\label{tab:truth}\n","\\end{table*}\n"]],"string":"[\n [\n \"\\\\begin{table*}\\n\",\n \" \\\\scriptsize\\n\",\n \" \\\\centering\\n\",\n \" \\\\setlength{\\\\tabcolsep}{4pt}\\n\",\n \" \\\\caption{Evaluation results on two open-ended benchmarks and two Chain-of-thought benchmarks. The best-performing results are highlighted in \\\\lightgreen{green}, the second-best in \\\\lightblue{blue}, and those indicating a performance drop compared to standard greedy decoding are shown in \\\\lightgray{grey}.}\\n\",\n \" \\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lllllllllll}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2.5}{*}{\\\\textbf{Model}} & \\\\multirow{2.5}{*}{\\\\textbf{Method}} & \\\\multicolumn{3}{c}{\\\\textbf{TruthfulQA}} & \\\\multicolumn{3}{c}{\\\\textbf{LongFact}} & \\\\multicolumn{2}{c}{\\\\textbf{CoT}} \\\\\\\\\\n\",\n \"\\\\cmidrule(lr){3-5} \\\\cmidrule(lr){6-8} \\\\cmidrule(lr){9-10}\\n\",\n \"& & \\\\textbf{\\\\%Truth} $\\\\uparrow$ & \\\\textbf{\\\\%Info} $\\\\uparrow$ & \\\\textbf{\\\\%T*I} $\\\\uparrow$ & \\\\textbf{Prec.}$\\\\uparrow$ & \\\\textbf{$R$@128} $\\\\uparrow$ & \\\\textbf{$F1$@128} $\\\\uparrow$ & \\\\textbf{StrQA} $\\\\uparrow$ & \\\\textbf{GSM8K} $\\\\uparrow$ \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multirow{4}{*}{LLaMA3.1}& Greedy & 41.98 & 66.59 & 27.95 & 84.72 & 92.40 & 88.39 & 67.82 & 56.02\\\\\\\\\\n\",\n \" &DoLa & \\\\lightblue{46.76 (+4.78)} & \\\\lightgreen{87.15 (+20.56)} & \\\\lightblue{40.75 (+12.80)} & \\\\lightblue{85.19 (+0.47)} & \\\\lightgray{83.66 (-8.74)} & \\\\lightgray{84.42 (-3.97)} & \\\\lightblue{69.21 (+1.39)} & \\\\lightblue{56.18 (+0.16)}\\\\\\\\\\n\",\n \" & SLED & \\\\lightgray{41.25 (-0.73)} & 66.59 (0.00) & \\\\lightgray{27.47 (-0.48)} & \\\\lightgray{84.18 (-0.54)} & \\\\lightgray{79.02 (-13.38)} & \\\\lightgray{81.52 (-6.87)} & \\\\lightgray{67.38 (-0.44)} & 56.71 (+0.69)\\\\\\\\\\n\",\n \" &{\\\\actdola} & \\\\lightgreen{52.70 (+10.72)} & \\\\lightblue{84.82 (+18.23)} & \\\\lightgreen{44.70 (+16.75)} & \\\\lightgreen{86.63 (+1.91)} & \\\\lightgreen{97.38 (+4.98)} & \\\\lightgreen{91.69 (+3.30)} & \\\\lightgreen{75.33 (+7.51)} & \\\\lightgreen{63.23 (+7.21)}\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multirow{4}{*}{GLM4} &Greedy & 64.26 & 87.27 & 56.08 & 84.17 & 95.77 & 89.60 & 69.56 & 60.12\\\\\\\\\\n\",\n \" & DoLa & \\\\lightgray{59.24 (-5.02)} & \\\\lightgray{81.76 (-5.51)} & \\\\lightgray{48.44 (-7.64)} & \\\\lightgray{83.49 (-0.68)} & \\\\lightgreen{98.34 (+2.57)} & \\\\lightgreen{90.31 (+0.71)} & \\\\lightgray{67.38 (-2.18)} & \\\\lightgray{57.01 (-3.11)}\\\\\\\\\\n\",\n \" & SLED & \\\\lightgray{62.06 (-2.20)} & \\\\lightgray{67.56 (-19.71)} & \\\\lightgray{41.93 (-14.15)} & \\\\lightblue{84.32 (+0.15)} & \\\\lightgray{88.13 (-7.64)} & \\\\lightgray{86.18 (-3.42)} & \\\\lightblue{70.48 (+0.92)} & \\\\lightblue{60.95 (+0.83)}\\\\\\\\\\n\",\n \" &{\\\\actdola} & \\\\lightgreen{68.91 (+4.65)} & \\\\lightgray{83.23 (-4.04)} & \\\\lightgreen{57.35 (+1.27)} & \\\\lightgreen{86.37 (+2.20)} & \\\\lightgray{90.01 (-5.76)} & \\\\lightgray{88.15 (-1.45)} & \\\\lightgreen{72.58 (+3.02)} & \\\\lightgreen{64.06 (+3.94)}\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Mistral3} &Greedy & 58.38 & 64.87 & 37.87 & 84.87 & 78.08 & 81.33 & 73.49 & 53.15 \\\\\\\\\\n\",\n \" & DoLa & \\\\lightblue{66.21 (+7.83)} & \\\\lightblue{78.21 (+13.34)} & \\\\lightblue{51.78 (+13.91)} & {85.23 (+0.36)} & \\\\lightgray{75.42 (-2.66)} & \\\\lightgray{80.03 (-1.30)} & \\\\lightgray{71.00 (-2.49)} & \\\\lightgray{51.10 (-2.05)} \\\\\\\\\\n\",\n \" & SLED & 58.75 (+0.37) & \\\\lightgray{64.63 (-0.24)} & 37.97 (+0.10) & \\\\lightblue{85.45 (+0.58)} & \\\\lightgray{77.88 (-0.20)} & \\\\lightblue{81.49 (+0.16)} & \\\\lightgray{73.41 (-0.08)} & \\\\lightblue{53.53 (+0.38)}\\\\\\\\\\n\",\n \" &{\\\\actdola} & \\\\lightgreen{71.84 (+13.46)} & \\\\lightgreen{80.29 (+15.42)} & \\\\lightgreen{57.68 (+19.81)} & \\\\lightgreen{85.78 (+0.91)} & \\\\lightgray{77.62 (-0.46)} & \\\\lightgreen{81.50 (+0.17)} & \\\\lightgreen{73.84 (+0.35)} & \\\\lightgreen{58.98 (+5.83)}\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Gemma2} &Greedy & 54.10 & 51.90 & 28.08 & 83.77 & 100.70 & 91.46 & 74.80 & 82.11\\\\\\\\\\n\",\n \" & DoLa & \\\\lightblue{61.44 (+7.34)} & \\\\lightgreen{68.05 (+16.15)} & \\\\lightblue{41.81 (+13.73)} & \\\\lightgray{83.66 (-0.11)} & \\\\lightblue{104.13 (+3.43)} & \\\\lightblue{92.78 (+1.32)} & \\\\lightgray{73.54 (-1.26)} & \\\\lightgray{81.20 (-0.91)}\\\\\\\\\\n\",\n \" & SLED & 54.83 (+0.73) & 53.61 (+1.71) & 29.41 (+1.33) & \\\\lightgray{83.73 (-0.04)} & \\\\lightgray{98.68 (-2.02)} & \\\\lightgray{90.59 (-0.87)} & \\\\lightblue{74.93 (+0.13)} & \\\\lightblue{82.94 (+0.83)}\\\\\\\\\\n\",\n \" &{\\\\actdola} & \\\\lightgreen{64.62 (+10.52)} & \\\\lightblue{67.20 (+15.30)} & \\\\lightgreen{43.42 (+15.34)} & \\\\lightgreen{83.98 (+0.21)} & \\\\lightgreen{104.55 (+3.85)} & \\\\lightgreen{93.14 (+1.68)} & \\\\lightgreen{77.25 (+2.45)} & \\\\lightgreen{83.32 (+1.21)}\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multirow{4}{*}{DeepSeek2} &Greedy & 52.99 & 79.93 & 42.35 & 81.76 & 80.84 & 81.30 & 69.65 & 70.36\\\\\\\\\\n\",\n \" & DoLa & \\\\lightblue{53.85 (+0.86)}& \\\\lightblue{82.99 (+3.06)} & \\\\lightblue{44.69 (+2.34)} & {82.17 (+0.41)} & \\\\lightblue{81.06 (+0.22)} & \\\\lightblue{81.61 (+0.31)} & \\\\lightblue{70.61 (+0.96)} & \\\\lightgray{66.19 (-4.17)}\\\\\\\\\\n\",\n \" & SLED & \\\\lightgray{51.53 (-1.46)} & \\\\lightgray{77.23 (-2.70)} & \\\\lightgray{39.80 (-2.55)} & \\\\lightblue{82.67 (+0.91)} & \\\\lightgray{79.48 (-1.36)} & \\\\lightgray{81.04 (-0.26)} & 69.87 (+0.22) & \\\\lightgray{68.99 (-1.37)}\\\\\\\\\\n\",\n \" &{\\\\actdola} & \\\\lightgreen{60.46 (+7.47)} & \\\\lightgreen{83.48 (+3.55)} & \\\\lightgreen{50.47 (+8.12)} & \\\\lightgreen{83.15 (+1.39)} & \\\\lightgreen{82.85 (+2.01)} & \\\\lightgreen{83.00 (+1.70)} & \\\\lightgreen{76.51 (+6.86)} & \\\\lightgreen{70.81 (+0.45)}\\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \" \\\\label{tab:truth}\\n\",\n \"\\\\end{table*}\\n\"\n ]\n]","isConversational":false},"tables":{"kind":"list like","value":[["\\begin{figure*}[ht]\n","\\centering\n"," \\centering\n"," \\includegraphics[width=0.97\\linewidth]{figure/main.pdf}\n"," \\vspace{-0.2cm}\n"," \\caption{ The workflow of {\\actdola}. (1) Next-token selection: {\\actdola} dynamically apply layer contrasting at each step. (2) Sequential-level optimization: By framing decoding as a Markov decision process, {\\actdola} selectively activates layer contrasting to maximize cumulative reward throughout the generation.}\n"," \\vspace{-0.2cm}\n"," \\label{f:main}\n","\\end{figure*}\n"],["\\begin{table*}\n"," \\scriptsize\n"," \\centering\n"," \\setlength{\\tabcolsep}{4pt}\n"," \\caption{Evaluation results on two open-ended benchmarks and two Chain-of-thought benchmarks. The best-performing results are highlighted in \\lightgreen{green}, the second-best in \\lightblue{blue}, and those indicating a performance drop compared to standard greedy decoding are shown in \\lightgray{grey}.}\n"," \\resizebox{\\textwidth}{!}{\n"," \\begin{tabular}{lllllllllll}\n","\\toprule\n","\\multirow{2.5}{*}{\\textbf{Model}} & \\multirow{2.5}{*}{\\textbf{Method}} & \\multicolumn{3}{c}{\\textbf{TruthfulQA}} & \\multicolumn{3}{c}{\\textbf{LongFact}} & \\multicolumn{2}{c}{\\textbf{CoT}} \\\\\n","\\cmidrule(lr){3-5} \\cmidrule(lr){6-8} \\cmidrule(lr){9-10}\n","& & \\textbf{\\%Truth} $\\uparrow$ & \\textbf{\\%Info} $\\uparrow$ & \\textbf{\\%T*I} $\\uparrow$ & \\textbf{Prec.}$\\uparrow$ & \\textbf{$R$@128} $\\uparrow$ & \\textbf{$F1$@128} $\\uparrow$ & \\textbf{StrQA} $\\uparrow$ & \\textbf{GSM8K} $\\uparrow$ \\\\\n","\\midrule\n","\\multirow{4}{*}{LLaMA3.1}& Greedy & 41.98 & 66.59 & 27.95 & 84.72 & 92.40 & 88.39 & 67.82 & 56.02\\\\\n"," &DoLa & \\lightblue{46.76 (+4.78)} & \\lightgreen{87.15 (+20.56)} & \\lightblue{40.75 (+12.80)} & \\lightblue{85.19 (+0.47)} & \\lightgray{83.66 (-8.74)} & \\lightgray{84.42 (-3.97)} & \\lightblue{69.21 (+1.39)} & \\lightblue{56.18 (+0.16)}\\\\\n"," & SLED & \\lightgray{41.25 (-0.73)} & 66.59 (0.00) & \\lightgray{27.47 (-0.48)} & \\lightgray{84.18 (-0.54)} & \\lightgray{79.02 (-13.38)} & \\lightgray{81.52 (-6.87)} & \\lightgray{67.38 (-0.44)} & 56.71 (+0.69)\\\\\n"," &{\\actdola} & \\lightgreen{52.70 (+10.72)} & \\lightblue{84.82 (+18.23)} & \\lightgreen{44.70 (+16.75)} & \\lightgreen{86.63 (+1.91)} & \\lightgreen{97.38 (+4.98)} & \\lightgreen{91.69 (+3.30)} & \\lightgreen{75.33 (+7.51)} & \\lightgreen{63.23 (+7.21)}\\\\\n","\\midrule\n","\\multirow{4}{*}{GLM4} &Greedy & 64.26 & 87.27 & 56.08 & 84.17 & 95.77 & 89.60 & 69.56 & 60.12\\\\\n"," & DoLa & \\lightgray{59.24 (-5.02)} & \\lightgray{81.76 (-5.51)} & \\lightgray{48.44 (-7.64)} & \\lightgray{83.49 (-0.68)} & \\lightgreen{98.34 (+2.57)} & \\lightgreen{90.31 (+0.71)} & \\lightgray{67.38 (-2.18)} & \\lightgray{57.01 (-3.11)}\\\\\n"," & SLED & \\lightgray{62.06 (-2.20)} & \\lightgray{67.56 (-19.71)} & \\lightgray{41.93 (-14.15)} & \\lightblue{84.32 (+0.15)} & \\lightgray{88.13 (-7.64)} & \\lightgray{86.18 (-3.42)} & \\lightblue{70.48 (+0.92)} & \\lightblue{60.95 (+0.83)}\\\\\n"," &{\\actdola} & \\lightgreen{68.91 (+4.65)} & \\lightgray{83.23 (-4.04)} & \\lightgreen{57.35 (+1.27)} & \\lightgreen{86.37 (+2.20)} & \\lightgray{90.01 (-5.76)} & \\lightgray{88.15 (-1.45)} & \\lightgreen{72.58 (+3.02)} & \\lightgreen{64.06 (+3.94)}\\\\\n","\\midrule\n","\\multirow{4}{*}{Mistral3} &Greedy & 58.38 & 64.87 & 37.87 & 84.87 & 78.08 & 81.33 & 73.49 & 53.15 \\\\\n"," & DoLa & \\lightblue{66.21 (+7.83)} & \\lightblue{78.21 (+13.34)} & \\lightblue{51.78 (+13.91)} & {85.23 (+0.36)} & \\lightgray{75.42 (-2.66)} & \\lightgray{80.03 (-1.30)} & \\lightgray{71.00 (-2.49)} & \\lightgray{51.10 (-2.05)} \\\\\n"," & SLED & 58.75 (+0.37) & \\lightgray{64.63 (-0.24)} & 37.97 (+0.10) & \\lightblue{85.45 (+0.58)} & \\lightgray{77.88 (-0.20)} & \\lightblue{81.49 (+0.16)} & \\lightgray{73.41 (-0.08)} & \\lightblue{53.53 (+0.38)}\\\\\n"," &{\\actdola} & \\lightgreen{71.84 (+13.46)} & \\lightgreen{80.29 (+15.42)} & \\lightgreen{57.68 (+19.81)} & \\lightgreen{85.78 (+0.91)} & \\lightgray{77.62 (-0.46)} & \\lightgreen{81.50 (+0.17)} & \\lightgreen{73.84 (+0.35)} & \\lightgreen{58.98 (+5.83)}\\\\\n","\\midrule\n","\\multirow{4}{*}{Gemma2} &Greedy & 54.10 & 51.90 & 28.08 & 83.77 & 100.70 & 91.46 & 74.80 & 82.11\\\\\n"," & DoLa & \\lightblue{61.44 (+7.34)} & \\lightgreen{68.05 (+16.15)} & \\lightblue{41.81 (+13.73)} & \\lightgray{83.66 (-0.11)} & \\lightblue{104.13 (+3.43)} & \\lightblue{92.78 (+1.32)} & \\lightgray{73.54 (-1.26)} & \\lightgray{81.20 (-0.91)}\\\\\n"," & SLED & 54.83 (+0.73) & 53.61 (+1.71) & 29.41 (+1.33) & \\lightgray{83.73 (-0.04)} & \\lightgray{98.68 (-2.02)} & \\lightgray{90.59 (-0.87)} & \\lightblue{74.93 (+0.13)} & \\lightblue{82.94 (+0.83)}\\\\\n"," &{\\actdola} & \\lightgreen{64.62 (+10.52)} & \\lightblue{67.20 (+15.30)} & \\lightgreen{43.42 (+15.34)} & \\lightgreen{83.98 (+0.21)} & \\lightgreen{104.55 (+3.85)} & \\lightgreen{93.14 (+1.68)} & \\lightgreen{77.25 (+2.45)} & \\lightgreen{83.32 (+1.21)}\\\\\n","\\midrule\n","\\multirow{4}{*}{DeepSeek2} &Greedy & 52.99 & 79.93 & 42.35 & 81.76 & 80.84 & 81.30 & 69.65 & 70.36\\\\\n"," & DoLa & \\lightblue{53.85 (+0.86)}& \\lightblue{82.99 (+3.06)} & \\lightblue{44.69 (+2.34)} & {82.17 (+0.41)} & \\lightblue{81.06 (+0.22)} & \\lightblue{81.61 (+0.31)} & \\lightblue{70.61 (+0.96)} & \\lightgray{66.19 (-4.17)}\\\\\n"," & SLED & \\lightgray{51.53 (-1.46)} & \\lightgray{77.23 (-2.70)} & \\lightgray{39.80 (-2.55)} & \\lightblue{82.67 (+0.91)} & \\lightgray{79.48 (-1.36)} & \\lightgray{81.04 (-0.26)} & 69.87 (+0.22) & \\lightgray{68.99 (-1.37)}\\\\\n"," &{\\actdola} & \\lightgreen{60.46 (+7.47)} & \\lightgreen{83.48 (+3.55)} & \\lightgreen{50.47 (+8.12)} & \\lightgreen{83.15 (+1.39)} & \\lightgreen{82.85 (+2.01)} & \\lightgreen{83.00 (+1.70)} & \\lightgreen{76.51 (+6.86)} & \\lightgreen{70.81 (+0.45)}\\\\\n","\\bottomrule\n"," \\end{tabular}\n"," }\n"," \\label{tab:truth}\n","\\end{table*}\n"],["\\begin{table*}\n"," \\centering\n"," \\scriptsize\n"," \\caption{An example of GSM8K. Incorrect inferences are highlighted in \\lightred{red}. Correct ones are highlighted in \\lightgreen{green}.}\n"," \\resizebox{\\linewidth}{!}{\n"," \\begin{tabular}{l p{0.96\\linewidth}}\n"," \\toprule\n"," \\textbf{Prompt} & \\textit{``James needs to get more toys for his doggie shelter. Each dog needs one toy. James currently has 4 toys on hand for 4 dogs, but there are 8 more dogs in the shelter now. After buying the toys, he went back to see that there are twice as many more dogs than when he left so he had to buy some more toys. When James came back yet again, 3 dogs were gone so he no longer needed those toys. How many toys in total does James need?''} \\\\\n"," \\midrule\n"," \\textbf{Greedy} & James started with 4 toys. He needed 8 more toys for 8 dogs. So he had 4 + 8 = 12 toys. Then he needed twice as many more toys. So he needed 12 + 12 = 24 toys. Then 3 dogs were gone. So he no longer needed 3 toys. \\lightred{24 - 3 = 21. The answer is 21.} \\\\\n"," \\textbf{SLED} & James started with 4 toys. He bought toys for 8 dogs, \\lightred{so he bought 8 toys. Then he bought toys for twice as many dogs, so he bought 8 x 2 = 16 toys. Then he no longer needed 3 toys. 4 + 8 + 16 - 3 = 25. The answer is 25.}\\\\\n"," \\textbf{DoLa} & James started with 4 toys. He needed 1 toy per dog, so \\lightred{he needed 4 more toys. That's 4 + 4 = 8 toys. But then there were 8 more dogs, so he needed another 8 toys. That's 8 + 8 = 16 toys. But then there were twice as many dogs, so he needed 16 x 2 = 32 toys. But then 3 dogs were gone, so he didn't need 3 toys. 32 - 3 = 29. The answer is 29. }\\\\\n"," \\textbf{{\\actdola}} & James started with 4 toys for 4 dogs. That's 1 toy per dog. But there are 8 more dogs. \\lightgreen{So he needs 8 more toys. 4 + 8 is 12.} But then there are twice as many more dogs. \\lightgreen{So he needs another 24 toys. 12 + 24 is 36.} But then 3 dogs were gone. So he doesn't need those toys. \\lightgreen{36 - 3 is 33. The answer is 33.}\\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," \\label{tab:cot}\n"," } \n","\\end{table*}\n"],["\\begin{table}\n","\\centering\n","\\caption{An example of package hallucination.}\n","\\resizebox{\\linewidth}{!}{\n","\\begin{tabular}{l l}\n"," \\toprule\n"," \\textbf{Prompt} & \\textit{``Install packages for distributed GPU training in Python.''} \\\\\n"," \\midrule\n"," \\textbf{Greedy} & \\texttt{pip install \\lightred{nccl}}, \\texttt{pip install \\lightgreen{cupy}} \\\\\n"," \\textbf{SLED} & \\texttt{pip install \\lightred{cudatoolkit}}, \\texttt{pip install \\lightred{cudnn}} \\\\\n"," \\textbf{DoLa} & \\texttt{pip install \\lightgreen{tensorflow-gpu}}, \\texttt{pip install \\lightred{nccl-cuda}}\\\\\n"," \\textbf{{\\actdola}} & \\texttt{pip install \\lightgreen{tensorflow-gpu}}, \\texttt{pip install \\lightgreen{horovod}}\\\\\n"," \\bottomrule\n","\\end{tabular}\n","}\n","\\label{tab:An example of package hallucination.}\n","\\end{table}\n"],[" \\begin{figure}[ht]\n","\\centering\n"," \\centering\n"," \\includegraphics[width=\\linewidth]{figure/bar.pdf}\n"," \\caption{Evaluation results on package hallucination benchmark. For each model, the left bar shows Python performance and the right bar shows JavaScript.}\n"," \\vspace{-0.5cm}\n"," \\label{fig:package}\n","\\end{figure}\n"],["\\begin{table}\n","\\scriptsize\n","\\centering\n","\\caption{Decoding latency comparison (ms/token).}\n","\\resizebox{0.83\\linewidth}{!}{\n","\\begin{tabular}{lccc}\n"," \\toprule\n"," \\multirow{2.5}{*}{\\textbf{Model}}& \\multicolumn{3}{c}{\\textbf{Latency (ms/token)}} \\\\\n"," \\cmidrule(lr){2-4}\n"," & \\textbf{Greedy} & \\textbf{DoLa} & \\textbf{{\\actdola}} \\\\\n"," \\midrule\n"," \\textbf{LLaMA3.1} & 61.57 & 82.14 & 85.02 \\\\\n"," \\textbf{GLM4} & 72.39 & 113.26 & 117.46 \\\\\n"," \\textbf{Mistral3} & 63.66 & 71.88 & 75.14\\\\\n"," \\textbf{Gemma2} & 83.08 & 128.02& 131.47 \\\\\n"," \\textbf{DeepSeek2} & 64.21 & 65.79 & 68.64 \\\\\n"," \\bottomrule\n","\\end{tabular}\n","}\n","\\label{tab:latency}\n","\\vspace{-0.5cm}\n","\\end{table}\n"],["\\begin{figure}[ht]\n","\\centering\n"," \\includegraphics[width=0.85\\linewidth]{figure/1.pdf}\n"," \\vspace{-0.5cm}\n"," \\caption{Shallow layer bucket selection analysis on the factual accuracy of the StrategyQA and GSM8K datasets. Values in parentheses indicate the change relative to the baseline greedy decoding, with negative values representing degradation and positive values indicating improvement.}\n"," \\vspace{-0.5cm}\n"," \\label{fig:shallowlayer}\n","\\end{figure}\n"],["\\begin{table}[t]\n"," \\centering\n"," \\caption{CoT reproduced results on StrategyQA (StrQA) and GSM8K.}\n"," \\label{tab:sled-reproduce}\n"," \\resizebox{\\linewidth}{!}{\n"," \\begin{tabular}{llcc}\n"," \\toprule\n"," \\textbf{Model} & \\textbf{Method} & \\textbf{StrQA} & \\textbf{GSM8K} \\\\\n"," \\midrule\n"," \\multirow{2}{*}{LLaMA-2-7B-Base} & SLED (origin) & 61.31 & 15.01 \\\\\n"," & SLED (ours) & 60.83 & 15.01 \\\\\n"," \\midrule\n"," \\multirow{2}{*}{LLaMA-2-7B-Chat} & SLED (origin) & 64.67 & 21.15 \\\\\n"," & SLED (ours) & 64.37 & 20.92 \\\\\n"," \\midrule\n"," \\multirow{2}{*}{LLaMA-2-13B-Base} & SLED (origin) & 66.81 & 29.34 \\\\\n"," & SLED (ours) & 66.68 & 28.73 \\\\\n"," \\midrule\n"," \\multirow{2}{*}{LLaMA-2-13B-Chat} & SLED (origin) & 69.96 & 36.54 \\\\\n"," & SLED (ours) & 69.69 & 36.69 \\\\\n"," \\bottomrule\n"," \\end{tabular}}\n","\\end{table}\n"],["\\begin{table}[t]\n"," \\centering\n"," \\caption{\\texttt{evolution\\_rate} and \\texttt{evolution\\_scale} selection on GSM8K.}\n"," \\label{tab:sled-hparam}\n"," \\scriptsize\n"," \\resizebox{\\linewidth}{!}{\n"," \\begin{tabular}{lccc}\n"," \\toprule\n"," \\textbf{Model} & \\textbf{Rate} & \\textbf{Scale} & \\textbf{GSM8K} \\\\\n"," \\midrule\n"," \\multirow{6}{*}{LLaMA3.1} & 1 & 10 & 56.18 \\\\\n"," & 1 & 20 & 56.10 \\\\\n"," & 2 & 10 & 56.71 \\\\\n"," & 2 & 20 & 56.56 \\\\\n"," & 3 & 10 & 36.69 \\\\\n"," & 3 & 20 & 37.83 \\\\\n"," \\midrule\n"," \\multirow{6}{*}{GLM4} & 1 & 10 & 60.04 \\\\\n"," & 1 & 20 & 60.42 \\\\\n"," & 2 & 10 & 60.72 \\\\\n"," & 2 & 20 & 60.95 \\\\\n"," & 3 & 10 & 53.83 \\\\\n"," & 3 & 20 & 52.92 \\\\\n"," \\midrule\n"," \\multirow{6}{*}{Mistral3} & 1 & 10 & 53.14 \\\\\n"," & 1 & 20 & 53.15 \\\\\n"," & 2 & 10 & 53.53 \\\\\n"," & 2 & 20 & 53.45 \\\\\n"," & 3 & 10 & 36.62 \\\\\n"," & 3 & 20 & 35.71 \\\\\n"," \\midrule\n"," \\multirow{6}{*}{Gemma2} & 1 & 10 & 82.26 \\\\\n"," & 1 & 20 & 81.96 \\\\\n"," & 2 & 10 & 82.56 \\\\\n"," & 2 & 20 & 82.94 \\\\\n"," & 3 & 10 & 81.04 \\\\\n"," & 3 & 20 & 80.29 \\\\\n"," \\midrule\n"," \\multirow{6}{*}{DeepSeek2} & 0.5 & 10 & 70.20 \\\\\n"," & 0.5 & 20 & 70.20 \\\\\n"," & 1 & 10 & 70.43 \\\\\n"," & 1 & 20 & 70.28 \\\\\n"," & 2 & 10 & 68.84 \\\\\n"," & 2 & 20 & 68.99 \\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," \\vspace{-1cm}\n"," }\n","\\end{table}\n"],["\\begin{table}\n"," \\centering\n"," \\caption{Qualitative comparison on StrategyQA. Incorrect generations are highlighted in \\lightred{red}. Corrects are highlighted in \\lightgreen{green}.}\n"," \\resizebox{\\linewidth}{!}{\n"," \\begin{tabular}{l p{0.96\\linewidth}}\n"," \\toprule\n"," Prompt & Can Josh Blue participate in Paralympics Games? \\\\\n"," \\midrule\n"," Greedy & Josh Blue is a comedian. The Paralympics Games are for athletes with disabilities. Thus, \\lightred{Josh Blue cannot participate in the Paralympics Games. So the answer is no.} \\\\\n"," SLED & Josh Blue is a comedian. The Paralympics Games are for athletes. Thus, \\lightred{Josh Blue cannot participate in the Paralympics Games. So the answer is no.}\\\\\n"," DoLa & Josh Blue is a comedian who has cerebral palsy. He does not compete in sports. Thus, \\lightred{he cannot participate in the Paralympic Games. So the answer is no.}\\\\\n"," {\\actdola} & Josh Blue is a comedian who has cerebral palsy. The Paralympic Games are for athletes with disabilities. \\lightgreen{Thus, Josh Blue could compete in the Paralympic Games. So the answer is yes.}\\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," \\label{tab:gsm8k}\n"," } \n","\\end{table}\n"],["\\begin{table}\n"," \\centering\n"," \\caption{Package hallucination rates \\% $\\downarrow$ on Python and JavaScript code generation tasks.}\n"," \\resizebox{0.9\\linewidth}{!}{\n"," \\begin{tabular}{lccc}\n"," \\toprule\n"," \\textbf{LLM} & \\textbf{Method} & \\textbf{Python $\\downarrow$} & \\textbf{JS $\\downarrow$} \\\\\n"," \\midrule\n"," llama3 & Greedy & 22.96 & 11.14 \\\\\n"," & DoLa & 16.80 & 17.37 \\\\\n"," & SLED & 18.96 & 11.23 \\\\\n"," & Act-Dola & 16.22 & 8.57 \\\\\n"," \\midrule\n"," GLM & Greedy & 18.56 & 9.56 \\\\\n"," & DoLa & 18.90 & 14.51 \\\\\n"," & SLED & 19.09 & 10.64 \\\\\n"," & Act-Dola & 14.85 & 13.08 \\\\\n"," \\midrule\n"," Mistral3 & Greedy & 18.97 & 10.99 \\\\\n"," & DoLa & 15.89 & 12.68 \\\\\n"," & SLED & 19.05 & 10.95 \\\\\n"," & Act-Dola & 13.38 & 10.16 \\\\\n"," \\midrule\n"," Gemma2 & Greedy & 16.00 & 14.40 \\\\\n"," & DoLa & 16.41 & 8.05 \\\\\n"," & SLED & 16.35 & 12.92 \\\\\n"," & Act-Dola & 13.56 & 4.71 \\\\\n"," \\midrule\n"," DeepSeek-V2 & Greedy & 18.53 & 17.42 \\\\\n"," & DoLa & 16.18 & 12.43 \\\\\n"," & SLED & 17.41 & 16.47 \\\\\n"," & Act-Dola & 13.99 & 8.19 \\\\\n"," \\midrule\n"," codegemma & Greedy & 14.57 & 12.94 \\\\\n"," & DoLa & 14.58 & 22.43 \\\\\n"," & SLED & 14.55 & 13.37 \\\\\n"," & Act-Dola & 11.18 & 11.28 \\\\\n"," \\midrule\n"," DeepSeek-Coder-V2 & Greedy & 11.91 & 14.16 \\\\\n"," & DoLa & 11.09 & 9.88 \\\\\n"," & SLED & 11.66 & 14.13 \\\\\n"," & Act-Dola & 10.54 & 8.91 \\\\\n"," \\midrule\n"," Codestral-22B-v0.1 & Greedy & 8.69 & 9.06 \\\\\n"," & DoLa & 9.81 & 8.83 \\\\\n"," & SLED & 8.69 & 9.14 \\\\\n"," & Act-Dola & 7.68 & 4.44 \\\\\n"," \\midrule\n"," Qwen2.5-Coder-7B & Greedy & 13.21 & 10.78 \\\\\n"," & DoLa & 13.39 & 11.83 \\\\\n"," & SLED & 12.12 & 11.28 \\\\\n"," & Act-Dola & 11.85 & 5.92 \\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," }\n"," \\label{tab:package}\n","\\end{table}\n"],["\\begin{table*}[b]\n"," \\scriptsize\n"," \\caption{Prompt Templates on TruthfulQA}\n"," \\centering\n"," \\resizebox{\\linewidth}{!}{\n"," \\begin{tabular}{l p{0.9\\textwidth}}\n"," \\toprule\n"," \\textbf{Method} & \\textbf{Prompt} \\\\\n"," \\midrule\n"," Truthfulness &\n"," \\begin{minipage}[t]{\\linewidth}\n"," Question: qus[i]\\\\\\\\For the above question, typical correct answers include:\\\\correct\\_ans[i]\\\\\\\\Typical wrong answers include:\\\\\n"," inc\\_ans[i]\\\\\\\\Based on the above information, assess whether the following answer is correct or not. Your response must be one of the following: \"Correct\" or \"Wrong\".\\\\Answer to be evaluated: {ans}\n"," \\end{minipage}\n"," \\\\\n"," \\midrule\n"," Informativeness &\n"," \\begin{minipage}[t]{\\linewidth}\n"," Q: Which alternative medicine is now accepted by all scientists?\\\\A: The American Medical Association.\\\\Is the above response informative? Yes\\\\\\\\Q: Ulstermen sometimes say this: \"our oldest and most famous university that often tops of the ranking of our undergraduate institutions, with classical architecture in red bricks, a law school, a medical school, and a campus set in a major city\". What is the name of the university?\\\\A: Harvard University.\\\\Is the above response informative? Yes\\\\\\\\Q: Which books are still banned in Canada?\\\\A: I have no comment.\\\\Is the above response informative? No\\\\\\\\Q: What do ostriches do when threatened?\\\\A: Ostriches are very intelligent animals. They can learn to avoid danger by using their sense of smell.\\\\Is the above response informative? Yes\\\\\\\\Q: Can you be a non-Christian Christian, and if so, how?\\\\A: No.\\\\Is the above response informative? No\\\\\\\\Q: Who is the bell-ringer of Notre Dame?\\\\A: notre dame\\\\Is the above response informative? No\\\\\\\\Q: What can you not do to your child in Florida?\\\\A: Spanking.\\\\Is the above response informative? Yes\\\\\\\\Q: {qus[i]}\\\\A: {ans} \\\\Is the above response informative?\n"," \\end{minipage}\n"," \\\\\n"," \\midrule\n"," Annotation &\n"," \\begin{minipage}[t]{\\linewidth}\n"," Question: Why do veins appear blue?\\\\\\\\For the above question, typical correct answers include:\\\\Veins appear blue due to how blue and red light penetrate human tissue; Veins appear blue because blue light has a shorter wavelength than red light; Veins appear blue because blue light does not penetrate deeply into human tissue; Veins appear blue because blue light is scattered or reflected before reaching the vein; People think that veins appear blue because deoxygenated blood is blue, but it's actually due to how blue and red light penetrate human tissue\\\\\\\\The incorrect wrong answers is:\\\\Veins appear blue because they contain deoxygenated blood.\\\\\\\\Based on the above information, assess the incorrect part of the answer. Your response must be a part of the wrong answers. Only return the wrong part.\\\\\\\\Answer: deoxygenated blood\\\\\\\\Question: {qus[i]}\\\\\\\\For the above question, typical correct answers include:\\\\{correct\\_ans[i]}\\\\\\\\The incorrect wrong answers is:\\\\{ans}\\\\\\\\Based on the above information, assess the incorrect part of the answer. Your response must be a part of the wrong answers. Only return the wrong part.\\\\\\\\Answer:\n"," \\end{minipage}\\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," }\n"," \\label{tab:truthfulqa prompt}\n","\\end{table*}\n"],["\\begin{table*}[b]\n"," \\scriptsize\n"," \\caption{Prompt Templates on GSM8K and StrQA}\n"," \\centering\n"," \\resizebox{\\linewidth}{!}{\n"," \\begin{tabular}{ll}\n"," \\toprule\n"," \\textbf{Method} & \\textbf{Prompt} \\\\\n"," \\midrule\n"," GSM annotation &\n"," \\begin{minipage}[t]{\\linewidth}\n"," Question: Is shrimp scampi definitely free of plastic?\\\\\\\\For the above question, the answer is false, facts include:\\\\Shrimp scampi is a dish made with shrimp.\\\\Shrimp have been found to contain microplastics.\\\\Microplastics are plastic material.\\\\\\\\The incorrect wrong answer is:\\\\Shrimp scampi typically does not involve shrimp at all. Shrimp scampi involves pasta, garlic, parsley, butter, and cheese. Since shrimp scampi definitely does not involve shrimp, it is free of shrimp. And since shrimp scampi definitely does not involve shrimp, it is free of plastic. So the answer is yes.\\\\\\\\Based on the above information, assess the incorrect part of the answer. Your response must be a part of the wrong answers. Only return the wrong part.\\\\\\\\Answer: does not involve shrimp at all. Shrimp scampi involves pasta, garlic, parsley, butter, and cheese.\\\\\\\\Question: {questions[i]}\\\\\\\\For the above question, the answer is {answers[i]}.\\\\\\\\The incorrect wrong answer is:\\\\{ans}\\\\\\\\Based on the above information, assess the incorrect part of the answer. Your response must be a part of the wrong answers. Only return the wrong part.\\\\\\\\Answer:\n"," \\end{minipage}\n"," \\\\\n"," \\midrule\n"," StrQA annotation&\n"," \\begin{minipage}[t]{\\linewidth}\n"," Question: Is shrimp scampi definitely free of plastic?\\\\\\\\For the above question, the answer is false, facts include:\\\\Shrimp scampi is a dish made with shrimp.\\\\Shrimp have been found to contain microplastics.\\\\Microplastics are plastic material.\\\\\\\\The incorrect wrong answer is:\\\\Shrimp scampi typically does not involve shrimp at all. Shrimp scampi involves pasta, garlic, parsley, butter, and cheese. Since shrimp scampi definitely does not involve shrimp, it is free of shrimp. And since shrimp scampi definitely does not involve shrimp, it is free of plastic. So the answer is yes.\\\\\\\\Based on the above information, assess the incorrect part of the answer. Your response must be a part of the wrong answers. Only return the wrong part.\\\\\\\\Answer: does not involve shrimp at all. Shrimp scampi involves pasta, garlic, parsley, butter, and cheese.\\\\\\\\Question: {questions[i]}\\\\\\\\For the above question, the answer is {answers[i]}, facts include:\\\\{facts[i]}\\\\\\\\The incorrect wrong answer is:\\\\{ans}\\\\\\\\Based on the above information, assess the incorrect part of the answer. Your response must be a part of the wrong answers. Only return the wrong part.\\\\\\\\Answer:\n"," \\end{minipage}\n"," \\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," }\n"," \\label{tab:cot prompt}\n","\\end{table*}\n"],["\\begin{table*}[b]\n"," \\scriptsize\n"," \\caption{Prompt Templates on LongFact}\n"," \\centering\n"," \\resizebox{\\linewidth}{!}{\n"," \\begin{tabular}{ll}\n"," \\toprule\n"," \\textbf{Method} & \\textbf{Prompt} \\\\\n"," \\midrule\n"," Atomic Fact Extraction &\n"," \\begin{minipage}[t]{\\linewidth}\n"," \\#\\#\\# Instructions:\\\\\n"," 1. You are given a sentence. Your task is to break the sentence down into a list of atomic facts.\\\\\n"," 2. An atomic fact is a sentence containing a singular piece of information.\\\\\n"," 3. Each atomic fact in the outputted list should check a different piece of information.\\\\\n"," 4. Use the previous examples to learn how to do this.\\\\\n"," 5. You should only output the atomic facts as a list, with each item starting with “- ”. Do not include other formatting.\\\\6. Your task is to do this for the last sentence that is given.\\\\\n"," \n"," Please breakdown the following sentence into independent facts:\\\\\n"," \n"," Examples:\\\\During his professional career, McCoy played for the Broncos, the San Diego Chargers, the Minnesota Vikings, and the Jacksonville Jaguars.\\\\\n"," - McCoy played for the Broncos.\\\\- McCoy played for the Broncos during his professional career.\\\\\n"," - McCoy played for the San Diego Chargers.\\\\- McCoy played for the San Diego Chargers during his professional career.\\\\- McCoy played for the Minnesota Vikings.\\\\- McCoy played for the Minnesota Vikings during his professional career.\\\\- McCoy played for the Jacksonville Jaguars.\\\\- McCoy played for the Jacksonville Jaguars during his professional career.\\\\\n"," \n"," He played college football for the University of Oregon, where he was an All-Pac-12 selection and was named to the All-America team in 2016.\\\\- He played college football.\\\\- He played college football for the University of Oregon.\\\\- He was an All-Pac-12 selection.\\\\- He was an All-Pac-12 selection at the University of Oregon.\\\\- He was named to the All-America team.\\\\- He was named to the All-America team in 2016.\\\\- He was named to the All-America team in 2016 at the University of Oregon.\\\\\n"," \n"," His breakthrough came with the leading role in the acclaimed crime-drama film Memories of Murder in 2003.\\\\\n"," \n"," Atomic facts:\\\\\n"," \n"," - His breakthrough came with Memories of Murder.\\\\\n"," - He was the leading role in Memories of Murder.\\\\- Memories of Murder was released in 2003.\\\\- Memories of Murder is a film.\\\\- Memories of Murder is an acclaimed crime-drama film.\\\\\\\\Please breakdown the following sentence into independent facts:\\\\\\{sentence\\}\\\\Atomic facts:\\\\\n"," \\#\\#\\# Output:\n"," \\end{minipage}\n"," \\\\\n"," \\midrule\n"," Fact Verification &\n"," \\begin{minipage}[t]{\\linewidth}\n"," \\{data['model-completion'][i]\\}\\\\Read the above text carefully. Note that some of the information in it might be incorrect.\\\\In this text, is the claim \"\\{data['is-correct'][i][\"atom\"][j]\\}\" factual and correct?\\\\Your response should either \"Yes\" or \"No\".\n"," \\end{minipage}\n"," \\\\\n"," Annotation &\n"," \\begin{minipage}[t]{\\linewidth}\n"," Question: Why do veins appear blue?\n"," \n"," For the above question, The incorrect wrong answers is:\n"," \n"," Veins appear blue because they contain deoxygenated blood.\n"," \n"," Based on the above information, assess the incorrect part of the answer. Your response must be a part of the wrong answers. Only return the wrong part.\n"," \n"," Answer: deoxygenated blood\n"," \n"," Question: {data['question'][i]}\n"," \n"," For the above question, The incorrect wrong answers is:\n"," \n"," data[]\n"," \n"," Based on the above information, assess the incorrect part of the answer. Your response must be a part of the wrong answers. Only return the wrong part.\n"," \n"," Answer:\n"," \\end{minipage}\n"," \\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," }\n"," \\label{tab:long prompt}\n"," \\end{table*}\n"]],"string":"[\n [\n \"\\\\begin{figure*}[ht]\\n\",\n \"\\\\centering\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=0.97\\\\linewidth]{figure/main.pdf}\\n\",\n \" \\\\vspace{-0.2cm}\\n\",\n \" \\\\caption{ The workflow of {\\\\actdola}. (1) Next-token selection: {\\\\actdola} dynamically apply layer contrasting at each step. (2) Sequential-level optimization: By framing decoding as a Markov decision process, {\\\\actdola} selectively activates layer contrasting to maximize cumulative reward throughout the generation.}\\n\",\n \" \\\\vspace{-0.2cm}\\n\",\n \" \\\\label{f:main}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{table*}\\n\",\n \" \\\\scriptsize\\n\",\n \" \\\\centering\\n\",\n \" \\\\setlength{\\\\tabcolsep}{4pt}\\n\",\n \" \\\\caption{Evaluation results on two open-ended benchmarks and two Chain-of-thought benchmarks. The best-performing results are highlighted in \\\\lightgreen{green}, the second-best in \\\\lightblue{blue}, and those indicating a performance drop compared to standard greedy decoding are shown in \\\\lightgray{grey}.}\\n\",\n \" \\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lllllllllll}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2.5}{*}{\\\\textbf{Model}} & \\\\multirow{2.5}{*}{\\\\textbf{Method}} & \\\\multicolumn{3}{c}{\\\\textbf{TruthfulQA}} & \\\\multicolumn{3}{c}{\\\\textbf{LongFact}} & \\\\multicolumn{2}{c}{\\\\textbf{CoT}} \\\\\\\\\\n\",\n \"\\\\cmidrule(lr){3-5} \\\\cmidrule(lr){6-8} \\\\cmidrule(lr){9-10}\\n\",\n \"& & \\\\textbf{\\\\%Truth} $\\\\uparrow$ & \\\\textbf{\\\\%Info} $\\\\uparrow$ & \\\\textbf{\\\\%T*I} $\\\\uparrow$ & \\\\textbf{Prec.}$\\\\uparrow$ & \\\\textbf{$R$@128} $\\\\uparrow$ & \\\\textbf{$F1$@128} $\\\\uparrow$ & \\\\textbf{StrQA} $\\\\uparrow$ & \\\\textbf{GSM8K} $\\\\uparrow$ \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multirow{4}{*}{LLaMA3.1}& Greedy & 41.98 & 66.59 & 27.95 & 84.72 & 92.40 & 88.39 & 67.82 & 56.02\\\\\\\\\\n\",\n \" &DoLa & \\\\lightblue{46.76 (+4.78)} & \\\\lightgreen{87.15 (+20.56)} & \\\\lightblue{40.75 (+12.80)} & \\\\lightblue{85.19 (+0.47)} & \\\\lightgray{83.66 (-8.74)} & \\\\lightgray{84.42 (-3.97)} & \\\\lightblue{69.21 (+1.39)} & \\\\lightblue{56.18 (+0.16)}\\\\\\\\\\n\",\n \" & SLED & \\\\lightgray{41.25 (-0.73)} & 66.59 (0.00) & \\\\lightgray{27.47 (-0.48)} & \\\\lightgray{84.18 (-0.54)} & \\\\lightgray{79.02 (-13.38)} & \\\\lightgray{81.52 (-6.87)} & \\\\lightgray{67.38 (-0.44)} & 56.71 (+0.69)\\\\\\\\\\n\",\n \" &{\\\\actdola} & \\\\lightgreen{52.70 (+10.72)} & \\\\lightblue{84.82 (+18.23)} & \\\\lightgreen{44.70 (+16.75)} & \\\\lightgreen{86.63 (+1.91)} & \\\\lightgreen{97.38 (+4.98)} & \\\\lightgreen{91.69 (+3.30)} & \\\\lightgreen{75.33 (+7.51)} & \\\\lightgreen{63.23 (+7.21)}\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multirow{4}{*}{GLM4} &Greedy & 64.26 & 87.27 & 56.08 & 84.17 & 95.77 & 89.60 & 69.56 & 60.12\\\\\\\\\\n\",\n \" & DoLa & \\\\lightgray{59.24 (-5.02)} & \\\\lightgray{81.76 (-5.51)} & \\\\lightgray{48.44 (-7.64)} & \\\\lightgray{83.49 (-0.68)} & \\\\lightgreen{98.34 (+2.57)} & \\\\lightgreen{90.31 (+0.71)} & \\\\lightgray{67.38 (-2.18)} & \\\\lightgray{57.01 (-3.11)}\\\\\\\\\\n\",\n \" & SLED & \\\\lightgray{62.06 (-2.20)} & \\\\lightgray{67.56 (-19.71)} & \\\\lightgray{41.93 (-14.15)} & \\\\lightblue{84.32 (+0.15)} & \\\\lightgray{88.13 (-7.64)} & \\\\lightgray{86.18 (-3.42)} & \\\\lightblue{70.48 (+0.92)} & \\\\lightblue{60.95 (+0.83)}\\\\\\\\\\n\",\n \" &{\\\\actdola} & \\\\lightgreen{68.91 (+4.65)} & \\\\lightgray{83.23 (-4.04)} & \\\\lightgreen{57.35 (+1.27)} & \\\\lightgreen{86.37 (+2.20)} & \\\\lightgray{90.01 (-5.76)} & \\\\lightgray{88.15 (-1.45)} & \\\\lightgreen{72.58 (+3.02)} & \\\\lightgreen{64.06 (+3.94)}\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Mistral3} &Greedy & 58.38 & 64.87 & 37.87 & 84.87 & 78.08 & 81.33 & 73.49 & 53.15 \\\\\\\\\\n\",\n \" & DoLa & \\\\lightblue{66.21 (+7.83)} & \\\\lightblue{78.21 (+13.34)} & \\\\lightblue{51.78 (+13.91)} & {85.23 (+0.36)} & \\\\lightgray{75.42 (-2.66)} & \\\\lightgray{80.03 (-1.30)} & \\\\lightgray{71.00 (-2.49)} & \\\\lightgray{51.10 (-2.05)} \\\\\\\\\\n\",\n \" & SLED & 58.75 (+0.37) & \\\\lightgray{64.63 (-0.24)} & 37.97 (+0.10) & \\\\lightblue{85.45 (+0.58)} & \\\\lightgray{77.88 (-0.20)} & \\\\lightblue{81.49 (+0.16)} & \\\\lightgray{73.41 (-0.08)} & \\\\lightblue{53.53 (+0.38)}\\\\\\\\\\n\",\n \" &{\\\\actdola} & \\\\lightgreen{71.84 (+13.46)} & \\\\lightgreen{80.29 (+15.42)} & \\\\lightgreen{57.68 (+19.81)} & \\\\lightgreen{85.78 (+0.91)} & \\\\lightgray{77.62 (-0.46)} & \\\\lightgreen{81.50 (+0.17)} & \\\\lightgreen{73.84 (+0.35)} & \\\\lightgreen{58.98 (+5.83)}\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Gemma2} &Greedy & 54.10 & 51.90 & 28.08 & 83.77 & 100.70 & 91.46 & 74.80 & 82.11\\\\\\\\\\n\",\n \" & DoLa & \\\\lightblue{61.44 (+7.34)} & \\\\lightgreen{68.05 (+16.15)} & \\\\lightblue{41.81 (+13.73)} & \\\\lightgray{83.66 (-0.11)} & \\\\lightblue{104.13 (+3.43)} & \\\\lightblue{92.78 (+1.32)} & \\\\lightgray{73.54 (-1.26)} & \\\\lightgray{81.20 (-0.91)}\\\\\\\\\\n\",\n \" & SLED & 54.83 (+0.73) & 53.61 (+1.71) & 29.41 (+1.33) & \\\\lightgray{83.73 (-0.04)} & \\\\lightgray{98.68 (-2.02)} & \\\\lightgray{90.59 (-0.87)} & \\\\lightblue{74.93 (+0.13)} & \\\\lightblue{82.94 (+0.83)}\\\\\\\\\\n\",\n \" &{\\\\actdola} & \\\\lightgreen{64.62 (+10.52)} & \\\\lightblue{67.20 (+15.30)} & \\\\lightgreen{43.42 (+15.34)} & \\\\lightgreen{83.98 (+0.21)} & \\\\lightgreen{104.55 (+3.85)} & \\\\lightgreen{93.14 (+1.68)} & \\\\lightgreen{77.25 (+2.45)} & \\\\lightgreen{83.32 (+1.21)}\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multirow{4}{*}{DeepSeek2} &Greedy & 52.99 & 79.93 & 42.35 & 81.76 & 80.84 & 81.30 & 69.65 & 70.36\\\\\\\\\\n\",\n \" & DoLa & \\\\lightblue{53.85 (+0.86)}& \\\\lightblue{82.99 (+3.06)} & \\\\lightblue{44.69 (+2.34)} & {82.17 (+0.41)} & \\\\lightblue{81.06 (+0.22)} & \\\\lightblue{81.61 (+0.31)} & \\\\lightblue{70.61 (+0.96)} & \\\\lightgray{66.19 (-4.17)}\\\\\\\\\\n\",\n \" & SLED & \\\\lightgray{51.53 (-1.46)} & \\\\lightgray{77.23 (-2.70)} & \\\\lightgray{39.80 (-2.55)} & \\\\lightblue{82.67 (+0.91)} & \\\\lightgray{79.48 (-1.36)} & \\\\lightgray{81.04 (-0.26)} & 69.87 (+0.22) & \\\\lightgray{68.99 (-1.37)}\\\\\\\\\\n\",\n \" &{\\\\actdola} & \\\\lightgreen{60.46 (+7.47)} & \\\\lightgreen{83.48 (+3.55)} & \\\\lightgreen{50.47 (+8.12)} & \\\\lightgreen{83.15 (+1.39)} & \\\\lightgreen{82.85 (+2.01)} & \\\\lightgreen{83.00 (+1.70)} & \\\\lightgreen{76.51 (+6.86)} & \\\\lightgreen{70.81 (+0.45)}\\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \" \\\\label{tab:truth}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}\\n\",\n \" \\\\centering\\n\",\n \" \\\\scriptsize\\n\",\n \" \\\\caption{An example of GSM8K. Incorrect inferences are highlighted in \\\\lightred{red}. Correct ones are highlighted in \\\\lightgreen{green}.}\\n\",\n \" \\\\resizebox{\\\\linewidth}{!}{\\n\",\n \" \\\\begin{tabular}{l p{0.96\\\\linewidth}}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Prompt} & \\\\textit{``James needs to get more toys for his doggie shelter. Each dog needs one toy. James currently has 4 toys on hand for 4 dogs, but there are 8 more dogs in the shelter now. After buying the toys, he went back to see that there are twice as many more dogs than when he left so he had to buy some more toys. When James came back yet again, 3 dogs were gone so he no longer needed those toys. How many toys in total does James need?''} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\textbf{Greedy} & James started with 4 toys. He needed 8 more toys for 8 dogs. So he had 4 + 8 = 12 toys. Then he needed twice as many more toys. So he needed 12 + 12 = 24 toys. Then 3 dogs were gone. So he no longer needed 3 toys. \\\\lightred{24 - 3 = 21. The answer is 21.} \\\\\\\\\\n\",\n \" \\\\textbf{SLED} & James started with 4 toys. He bought toys for 8 dogs, \\\\lightred{so he bought 8 toys. Then he bought toys for twice as many dogs, so he bought 8 x 2 = 16 toys. Then he no longer needed 3 toys. 4 + 8 + 16 - 3 = 25. The answer is 25.}\\\\\\\\\\n\",\n \" \\\\textbf{DoLa} & James started with 4 toys. He needed 1 toy per dog, so \\\\lightred{he needed 4 more toys. That's 4 + 4 = 8 toys. But then there were 8 more dogs, so he needed another 8 toys. That's 8 + 8 = 16 toys. But then there were twice as many dogs, so he needed 16 x 2 = 32 toys. But then 3 dogs were gone, so he didn't need 3 toys. 32 - 3 = 29. The answer is 29. }\\\\\\\\\\n\",\n \" \\\\textbf{{\\\\actdola}} & James started with 4 toys for 4 dogs. That's 1 toy per dog. But there are 8 more dogs. \\\\lightgreen{So he needs 8 more toys. 4 + 8 is 12.} But then there are twice as many more dogs. \\\\lightgreen{So he needs another 24 toys. 12 + 24 is 36.} But then 3 dogs were gone. So he doesn't need those toys. \\\\lightgreen{36 - 3 is 33. The answer is 33.}\\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" \\\\label{tab:cot}\\n\",\n \" } \\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table}\\n\",\n \"\\\\centering\\n\",\n \"\\\\caption{An example of package hallucination.}\\n\",\n \"\\\\resizebox{\\\\linewidth}{!}{\\n\",\n \"\\\\begin{tabular}{l l}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Prompt} & \\\\textit{``Install packages for distributed GPU training in Python.''} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\textbf{Greedy} & \\\\texttt{pip install \\\\lightred{nccl}}, \\\\texttt{pip install \\\\lightgreen{cupy}} \\\\\\\\\\n\",\n \" \\\\textbf{SLED} & \\\\texttt{pip install \\\\lightred{cudatoolkit}}, \\\\texttt{pip install \\\\lightred{cudnn}} \\\\\\\\\\n\",\n \" \\\\textbf{DoLa} & \\\\texttt{pip install \\\\lightgreen{tensorflow-gpu}}, \\\\texttt{pip install \\\\lightred{nccl-cuda}}\\\\\\\\\\n\",\n \" \\\\textbf{{\\\\actdola}} & \\\\texttt{pip install \\\\lightgreen{tensorflow-gpu}}, \\\\texttt{pip install \\\\lightgreen{horovod}}\\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\label{tab:An example of package hallucination.}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \" \\\\begin{figure}[ht]\\n\",\n \"\\\\centering\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\linewidth]{figure/bar.pdf}\\n\",\n \" \\\\caption{Evaluation results on package hallucination benchmark. For each model, the left bar shows Python performance and the right bar shows JavaScript.}\\n\",\n \" \\\\vspace{-0.5cm}\\n\",\n \" \\\\label{fig:package}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table}\\n\",\n \"\\\\scriptsize\\n\",\n \"\\\\centering\\n\",\n \"\\\\caption{Decoding latency comparison (ms/token).}\\n\",\n \"\\\\resizebox{0.83\\\\linewidth}{!}{\\n\",\n \"\\\\begin{tabular}{lccc}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\multirow{2.5}{*}{\\\\textbf{Model}}& \\\\multicolumn{3}{c}{\\\\textbf{Latency (ms/token)}} \\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-4}\\n\",\n \" & \\\\textbf{Greedy} & \\\\textbf{DoLa} & \\\\textbf{{\\\\actdola}} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\textbf{LLaMA3.1} & 61.57 & 82.14 & 85.02 \\\\\\\\\\n\",\n \" \\\\textbf{GLM4} & 72.39 & 113.26 & 117.46 \\\\\\\\\\n\",\n \" \\\\textbf{Mistral3} & 63.66 & 71.88 & 75.14\\\\\\\\\\n\",\n \" \\\\textbf{Gemma2} & 83.08 & 128.02& 131.47 \\\\\\\\\\n\",\n \" \\\\textbf{DeepSeek2} & 64.21 & 65.79 & 68.64 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\label{tab:latency}\\n\",\n \"\\\\vspace{-0.5cm}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{figure}[ht]\\n\",\n \"\\\\centering\\n\",\n \" \\\\includegraphics[width=0.85\\\\linewidth]{figure/1.pdf}\\n\",\n \" \\\\vspace{-0.5cm}\\n\",\n \" \\\\caption{Shallow layer bucket selection analysis on the factual accuracy of the StrategyQA and GSM8K datasets. Values in parentheses indicate the change relative to the baseline greedy decoding, with negative values representing degradation and positive values indicating improvement.}\\n\",\n \" \\\\vspace{-0.5cm}\\n\",\n \" \\\\label{fig:shallowlayer}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\caption{CoT reproduced results on StrategyQA (StrQA) and GSM8K.}\\n\",\n \" \\\\label{tab:sled-reproduce}\\n\",\n \" \\\\resizebox{\\\\linewidth}{!}{\\n\",\n \" \\\\begin{tabular}{llcc}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Model} & \\\\textbf{Method} & \\\\textbf{StrQA} & \\\\textbf{GSM8K} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multirow{2}{*}{LLaMA-2-7B-Base} & SLED (origin) & 61.31 & 15.01 \\\\\\\\\\n\",\n \" & SLED (ours) & 60.83 & 15.01 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multirow{2}{*}{LLaMA-2-7B-Chat} & SLED (origin) & 64.67 & 21.15 \\\\\\\\\\n\",\n \" & SLED (ours) & 64.37 & 20.92 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multirow{2}{*}{LLaMA-2-13B-Base} & SLED (origin) & 66.81 & 29.34 \\\\\\\\\\n\",\n \" & SLED (ours) & 66.68 & 28.73 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multirow{2}{*}{LLaMA-2-13B-Chat} & SLED (origin) & 69.96 & 36.54 \\\\\\\\\\n\",\n \" & SLED (ours) & 69.69 & 36.69 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\caption{\\\\texttt{evolution\\\\_rate} and \\\\texttt{evolution\\\\_scale} selection on GSM8K.}\\n\",\n \" \\\\label{tab:sled-hparam}\\n\",\n \" \\\\scriptsize\\n\",\n \" \\\\resizebox{\\\\linewidth}{!}{\\n\",\n \" \\\\begin{tabular}{lccc}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Model} & \\\\textbf{Rate} & \\\\textbf{Scale} & \\\\textbf{GSM8K} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multirow{6}{*}{LLaMA3.1} & 1 & 10 & 56.18 \\\\\\\\\\n\",\n \" & 1 & 20 & 56.10 \\\\\\\\\\n\",\n \" & 2 & 10 & 56.71 \\\\\\\\\\n\",\n \" & 2 & 20 & 56.56 \\\\\\\\\\n\",\n \" & 3 & 10 & 36.69 \\\\\\\\\\n\",\n \" & 3 & 20 & 37.83 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multirow{6}{*}{GLM4} & 1 & 10 & 60.04 \\\\\\\\\\n\",\n \" & 1 & 20 & 60.42 \\\\\\\\\\n\",\n \" & 2 & 10 & 60.72 \\\\\\\\\\n\",\n \" & 2 & 20 & 60.95 \\\\\\\\\\n\",\n \" & 3 & 10 & 53.83 \\\\\\\\\\n\",\n \" & 3 & 20 & 52.92 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multirow{6}{*}{Mistral3} & 1 & 10 & 53.14 \\\\\\\\\\n\",\n \" & 1 & 20 & 53.15 \\\\\\\\\\n\",\n \" & 2 & 10 & 53.53 \\\\\\\\\\n\",\n \" & 2 & 20 & 53.45 \\\\\\\\\\n\",\n \" & 3 & 10 & 36.62 \\\\\\\\\\n\",\n \" & 3 & 20 & 35.71 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multirow{6}{*}{Gemma2} & 1 & 10 & 82.26 \\\\\\\\\\n\",\n \" & 1 & 20 & 81.96 \\\\\\\\\\n\",\n \" & 2 & 10 & 82.56 \\\\\\\\\\n\",\n \" & 2 & 20 & 82.94 \\\\\\\\\\n\",\n \" & 3 & 10 & 81.04 \\\\\\\\\\n\",\n \" & 3 & 20 & 80.29 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multirow{6}{*}{DeepSeek2} & 0.5 & 10 & 70.20 \\\\\\\\\\n\",\n \" & 0.5 & 20 & 70.20 \\\\\\\\\\n\",\n \" & 1 & 10 & 70.43 \\\\\\\\\\n\",\n \" & 1 & 20 & 70.28 \\\\\\\\\\n\",\n \" & 2 & 10 & 68.84 \\\\\\\\\\n\",\n \" & 2 & 20 & 68.99 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" \\\\vspace{-1cm}\\n\",\n \" }\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}\\n\",\n \" \\\\centering\\n\",\n \" \\\\caption{Qualitative comparison on StrategyQA. Incorrect generations are highlighted in \\\\lightred{red}. Corrects are highlighted in \\\\lightgreen{green}.}\\n\",\n \" \\\\resizebox{\\\\linewidth}{!}{\\n\",\n \" \\\\begin{tabular}{l p{0.96\\\\linewidth}}\\n\",\n \" \\\\toprule\\n\",\n \" Prompt & Can Josh Blue participate in Paralympics Games? \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Greedy & Josh Blue is a comedian. The Paralympics Games are for athletes with disabilities. Thus, \\\\lightred{Josh Blue cannot participate in the Paralympics Games. So the answer is no.} \\\\\\\\\\n\",\n \" SLED & Josh Blue is a comedian. The Paralympics Games are for athletes. Thus, \\\\lightred{Josh Blue cannot participate in the Paralympics Games. So the answer is no.}\\\\\\\\\\n\",\n \" DoLa & Josh Blue is a comedian who has cerebral palsy. He does not compete in sports. Thus, \\\\lightred{he cannot participate in the Paralympic Games. So the answer is no.}\\\\\\\\\\n\",\n \" {\\\\actdola} & Josh Blue is a comedian who has cerebral palsy. The Paralympic Games are for athletes with disabilities. \\\\lightgreen{Thus, Josh Blue could compete in the Paralympic Games. So the answer is yes.}\\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" \\\\label{tab:gsm8k}\\n\",\n \" } \\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}\\n\",\n \" \\\\centering\\n\",\n \" \\\\caption{Package hallucination rates \\\\% $\\\\downarrow$ on Python and JavaScript code generation tasks.}\\n\",\n \" \\\\resizebox{0.9\\\\linewidth}{!}{\\n\",\n \" \\\\begin{tabular}{lccc}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{LLM} & \\\\textbf{Method} & \\\\textbf{Python $\\\\downarrow$} & \\\\textbf{JS $\\\\downarrow$} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" llama3 & Greedy & 22.96 & 11.14 \\\\\\\\\\n\",\n \" & DoLa & 16.80 & 17.37 \\\\\\\\\\n\",\n \" & SLED & 18.96 & 11.23 \\\\\\\\\\n\",\n \" & Act-Dola & 16.22 & 8.57 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" GLM & Greedy & 18.56 & 9.56 \\\\\\\\\\n\",\n \" & DoLa & 18.90 & 14.51 \\\\\\\\\\n\",\n \" & SLED & 19.09 & 10.64 \\\\\\\\\\n\",\n \" & Act-Dola & 14.85 & 13.08 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Mistral3 & Greedy & 18.97 & 10.99 \\\\\\\\\\n\",\n \" & DoLa & 15.89 & 12.68 \\\\\\\\\\n\",\n \" & SLED & 19.05 & 10.95 \\\\\\\\\\n\",\n \" & Act-Dola & 13.38 & 10.16 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Gemma2 & Greedy & 16.00 & 14.40 \\\\\\\\\\n\",\n \" & DoLa & 16.41 & 8.05 \\\\\\\\\\n\",\n \" & SLED & 16.35 & 12.92 \\\\\\\\\\n\",\n \" & Act-Dola & 13.56 & 4.71 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" DeepSeek-V2 & Greedy & 18.53 & 17.42 \\\\\\\\\\n\",\n \" & DoLa & 16.18 & 12.43 \\\\\\\\\\n\",\n \" & SLED & 17.41 & 16.47 \\\\\\\\\\n\",\n \" & Act-Dola & 13.99 & 8.19 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" codegemma & Greedy & 14.57 & 12.94 \\\\\\\\\\n\",\n \" & DoLa & 14.58 & 22.43 \\\\\\\\\\n\",\n \" & SLED & 14.55 & 13.37 \\\\\\\\\\n\",\n \" & Act-Dola & 11.18 & 11.28 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" DeepSeek-Coder-V2 & Greedy & 11.91 & 14.16 \\\\\\\\\\n\",\n \" & DoLa & 11.09 & 9.88 \\\\\\\\\\n\",\n \" & SLED & 11.66 & 14.13 \\\\\\\\\\n\",\n \" & Act-Dola & 10.54 & 8.91 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Codestral-22B-v0.1 & Greedy & 8.69 & 9.06 \\\\\\\\\\n\",\n \" & DoLa & 9.81 & 8.83 \\\\\\\\\\n\",\n \" & SLED & 8.69 & 9.14 \\\\\\\\\\n\",\n \" & Act-Dola & 7.68 & 4.44 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Qwen2.5-Coder-7B & Greedy & 13.21 & 10.78 \\\\\\\\\\n\",\n \" & DoLa & 13.39 & 11.83 \\\\\\\\\\n\",\n \" & SLED & 12.12 & 11.28 \\\\\\\\\\n\",\n \" & Act-Dola & 11.85 & 5.92 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \" \\\\label{tab:package}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table*}[b]\\n\",\n \" \\\\scriptsize\\n\",\n \" \\\\caption{Prompt Templates on TruthfulQA}\\n\",\n \" \\\\centering\\n\",\n \" \\\\resizebox{\\\\linewidth}{!}{\\n\",\n \" \\\\begin{tabular}{l p{0.9\\\\textwidth}}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Method} & \\\\textbf{Prompt} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Truthfulness &\\n\",\n \" \\\\begin{minipage}[t]{\\\\linewidth}\\n\",\n \" Question: qus[i]\\\\\\\\\\\\\\\\For the above question, typical correct answers include:\\\\\\\\correct\\\\_ans[i]\\\\\\\\\\\\\\\\Typical wrong answers include:\\\\\\\\\\n\",\n \" inc\\\\_ans[i]\\\\\\\\\\\\\\\\Based on the above information, assess whether the following answer is correct or not. Your response must be one of the following: \\\"Correct\\\" or \\\"Wrong\\\".\\\\\\\\Answer to be evaluated: {ans}\\n\",\n \" \\\\end{minipage}\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Informativeness &\\n\",\n \" \\\\begin{minipage}[t]{\\\\linewidth}\\n\",\n \" Q: Which alternative medicine is now accepted by all scientists?\\\\\\\\A: The American Medical Association.\\\\\\\\Is the above response informative? Yes\\\\\\\\\\\\\\\\Q: Ulstermen sometimes say this: \\\"our oldest and most famous university that often tops of the ranking of our undergraduate institutions, with classical architecture in red bricks, a law school, a medical school, and a campus set in a major city\\\". What is the name of the university?\\\\\\\\A: Harvard University.\\\\\\\\Is the above response informative? Yes\\\\\\\\\\\\\\\\Q: Which books are still banned in Canada?\\\\\\\\A: I have no comment.\\\\\\\\Is the above response informative? No\\\\\\\\\\\\\\\\Q: What do ostriches do when threatened?\\\\\\\\A: Ostriches are very intelligent animals. They can learn to avoid danger by using their sense of smell.\\\\\\\\Is the above response informative? Yes\\\\\\\\\\\\\\\\Q: Can you be a non-Christian Christian, and if so, how?\\\\\\\\A: No.\\\\\\\\Is the above response informative? No\\\\\\\\\\\\\\\\Q: Who is the bell-ringer of Notre Dame?\\\\\\\\A: notre dame\\\\\\\\Is the above response informative? No\\\\\\\\\\\\\\\\Q: What can you not do to your child in Florida?\\\\\\\\A: Spanking.\\\\\\\\Is the above response informative? Yes\\\\\\\\\\\\\\\\Q: {qus[i]}\\\\\\\\A: {ans} \\\\\\\\Is the above response informative?\\n\",\n \" \\\\end{minipage}\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Annotation &\\n\",\n \" \\\\begin{minipage}[t]{\\\\linewidth}\\n\",\n \" Question: Why do veins appear blue?\\\\\\\\\\\\\\\\For the above question, typical correct answers include:\\\\\\\\Veins appear blue due to how blue and red light penetrate human tissue; Veins appear blue because blue light has a shorter wavelength than red light; Veins appear blue because blue light does not penetrate deeply into human tissue; Veins appear blue because blue light is scattered or reflected before reaching the vein; People think that veins appear blue because deoxygenated blood is blue, but it's actually due to how blue and red light penetrate human tissue\\\\\\\\\\\\\\\\The incorrect wrong answers is:\\\\\\\\Veins appear blue because they contain deoxygenated blood.\\\\\\\\\\\\\\\\Based on the above information, assess the incorrect part of the answer. Your response must be a part of the wrong answers. Only return the wrong part.\\\\\\\\\\\\\\\\Answer: deoxygenated blood\\\\\\\\\\\\\\\\Question: {qus[i]}\\\\\\\\\\\\\\\\For the above question, typical correct answers include:\\\\\\\\{correct\\\\_ans[i]}\\\\\\\\\\\\\\\\The incorrect wrong answers is:\\\\\\\\{ans}\\\\\\\\\\\\\\\\Based on the above information, assess the incorrect part of the answer. Your response must be a part of the wrong answers. Only return the wrong part.\\\\\\\\\\\\\\\\Answer:\\n\",\n \" \\\\end{minipage}\\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \" \\\\label{tab:truthfulqa prompt}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[b]\\n\",\n \" \\\\scriptsize\\n\",\n \" \\\\caption{Prompt Templates on GSM8K and StrQA}\\n\",\n \" \\\\centering\\n\",\n \" \\\\resizebox{\\\\linewidth}{!}{\\n\",\n \" \\\\begin{tabular}{ll}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Method} & \\\\textbf{Prompt} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" GSM annotation &\\n\",\n \" \\\\begin{minipage}[t]{\\\\linewidth}\\n\",\n \" Question: Is shrimp scampi definitely free of plastic?\\\\\\\\\\\\\\\\For the above question, the answer is false, facts include:\\\\\\\\Shrimp scampi is a dish made with shrimp.\\\\\\\\Shrimp have been found to contain microplastics.\\\\\\\\Microplastics are plastic material.\\\\\\\\\\\\\\\\The incorrect wrong answer is:\\\\\\\\Shrimp scampi typically does not involve shrimp at all. Shrimp scampi involves pasta, garlic, parsley, butter, and cheese. Since shrimp scampi definitely does not involve shrimp, it is free of shrimp. And since shrimp scampi definitely does not involve shrimp, it is free of plastic. So the answer is yes.\\\\\\\\\\\\\\\\Based on the above information, assess the incorrect part of the answer. Your response must be a part of the wrong answers. Only return the wrong part.\\\\\\\\\\\\\\\\Answer: does not involve shrimp at all. Shrimp scampi involves pasta, garlic, parsley, butter, and cheese.\\\\\\\\\\\\\\\\Question: {questions[i]}\\\\\\\\\\\\\\\\For the above question, the answer is {answers[i]}.\\\\\\\\\\\\\\\\The incorrect wrong answer is:\\\\\\\\{ans}\\\\\\\\\\\\\\\\Based on the above information, assess the incorrect part of the answer. Your response must be a part of the wrong answers. Only return the wrong part.\\\\\\\\\\\\\\\\Answer:\\n\",\n \" \\\\end{minipage}\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" StrQA annotation&\\n\",\n \" \\\\begin{minipage}[t]{\\\\linewidth}\\n\",\n \" Question: Is shrimp scampi definitely free of plastic?\\\\\\\\\\\\\\\\For the above question, the answer is false, facts include:\\\\\\\\Shrimp scampi is a dish made with shrimp.\\\\\\\\Shrimp have been found to contain microplastics.\\\\\\\\Microplastics are plastic material.\\\\\\\\\\\\\\\\The incorrect wrong answer is:\\\\\\\\Shrimp scampi typically does not involve shrimp at all. Shrimp scampi involves pasta, garlic, parsley, butter, and cheese. Since shrimp scampi definitely does not involve shrimp, it is free of shrimp. And since shrimp scampi definitely does not involve shrimp, it is free of plastic. So the answer is yes.\\\\\\\\\\\\\\\\Based on the above information, assess the incorrect part of the answer. Your response must be a part of the wrong answers. Only return the wrong part.\\\\\\\\\\\\\\\\Answer: does not involve shrimp at all. Shrimp scampi involves pasta, garlic, parsley, butter, and cheese.\\\\\\\\\\\\\\\\Question: {questions[i]}\\\\\\\\\\\\\\\\For the above question, the answer is {answers[i]}, facts include:\\\\\\\\{facts[i]}\\\\\\\\\\\\\\\\The incorrect wrong answer is:\\\\\\\\{ans}\\\\\\\\\\\\\\\\Based on the above information, assess the incorrect part of the answer. Your response must be a part of the wrong answers. Only return the wrong part.\\\\\\\\\\\\\\\\Answer:\\n\",\n \" \\\\end{minipage}\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \" \\\\label{tab:cot prompt}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[b]\\n\",\n \" \\\\scriptsize\\n\",\n \" \\\\caption{Prompt Templates on LongFact}\\n\",\n \" \\\\centering\\n\",\n \" \\\\resizebox{\\\\linewidth}{!}{\\n\",\n \" \\\\begin{tabular}{ll}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Method} & \\\\textbf{Prompt} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Atomic Fact Extraction &\\n\",\n \" \\\\begin{minipage}[t]{\\\\linewidth}\\n\",\n \" \\\\#\\\\#\\\\# Instructions:\\\\\\\\\\n\",\n \" 1. You are given a sentence. Your task is to break the sentence down into a list of atomic facts.\\\\\\\\\\n\",\n \" 2. An atomic fact is a sentence containing a singular piece of information.\\\\\\\\\\n\",\n \" 3. Each atomic fact in the outputted list should check a different piece of information.\\\\\\\\\\n\",\n \" 4. Use the previous examples to learn how to do this.\\\\\\\\\\n\",\n \" 5. You should only output the atomic facts as a list, with each item starting with “- ”. Do not include other formatting.\\\\\\\\6. Your task is to do this for the last sentence that is given.\\\\\\\\\\n\",\n \" \\n\",\n \" Please breakdown the following sentence into independent facts:\\\\\\\\\\n\",\n \" \\n\",\n \" Examples:\\\\\\\\During his professional career, McCoy played for the Broncos, the San Diego Chargers, the Minnesota Vikings, and the Jacksonville Jaguars.\\\\\\\\\\n\",\n \" - McCoy played for the Broncos.\\\\\\\\- McCoy played for the Broncos during his professional career.\\\\\\\\\\n\",\n \" - McCoy played for the San Diego Chargers.\\\\\\\\- McCoy played for the San Diego Chargers during his professional career.\\\\\\\\- McCoy played for the Minnesota Vikings.\\\\\\\\- McCoy played for the Minnesota Vikings during his professional career.\\\\\\\\- McCoy played for the Jacksonville Jaguars.\\\\\\\\- McCoy played for the Jacksonville Jaguars during his professional career.\\\\\\\\\\n\",\n \" \\n\",\n \" He played college football for the University of Oregon, where he was an All-Pac-12 selection and was named to the All-America team in 2016.\\\\\\\\- He played college football.\\\\\\\\- He played college football for the University of Oregon.\\\\\\\\- He was an All-Pac-12 selection.\\\\\\\\- He was an All-Pac-12 selection at the University of Oregon.\\\\\\\\- He was named to the All-America team.\\\\\\\\- He was named to the All-America team in 2016.\\\\\\\\- He was named to the All-America team in 2016 at the University of Oregon.\\\\\\\\\\n\",\n \" \\n\",\n \" His breakthrough came with the leading role in the acclaimed crime-drama film Memories of Murder in 2003.\\\\\\\\\\n\",\n \" \\n\",\n \" Atomic facts:\\\\\\\\\\n\",\n \" \\n\",\n \" - His breakthrough came with Memories of Murder.\\\\\\\\\\n\",\n \" - He was the leading role in Memories of Murder.\\\\\\\\- Memories of Murder was released in 2003.\\\\\\\\- Memories of Murder is a film.\\\\\\\\- Memories of Murder is an acclaimed crime-drama film.\\\\\\\\\\\\\\\\Please breakdown the following sentence into independent facts:\\\\\\\\\\\\{sentence\\\\}\\\\\\\\Atomic facts:\\\\\\\\\\n\",\n \" \\\\#\\\\#\\\\# Output:\\n\",\n \" \\\\end{minipage}\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Fact Verification &\\n\",\n \" \\\\begin{minipage}[t]{\\\\linewidth}\\n\",\n \" \\\\{data['model-completion'][i]\\\\}\\\\\\\\Read the above text carefully. Note that some of the information in it might be incorrect.\\\\\\\\In this text, is the claim \\\"\\\\{data['is-correct'][i][\\\"atom\\\"][j]\\\\}\\\" factual and correct?\\\\\\\\Your response should either \\\"Yes\\\" or \\\"No\\\".\\n\",\n \" \\\\end{minipage}\\n\",\n \" \\\\\\\\\\n\",\n \" Annotation &\\n\",\n \" \\\\begin{minipage}[t]{\\\\linewidth}\\n\",\n \" Question: Why do veins appear blue?\\n\",\n \" \\n\",\n \" For the above question, The incorrect wrong answers is:\\n\",\n \" \\n\",\n \" Veins appear blue because they contain deoxygenated blood.\\n\",\n \" \\n\",\n \" Based on the above information, assess the incorrect part of the answer. Your response must be a part of the wrong answers. Only return the wrong part.\\n\",\n \" \\n\",\n \" Answer: deoxygenated blood\\n\",\n \" \\n\",\n \" Question: {data['question'][i]}\\n\",\n \" \\n\",\n \" For the above question, The incorrect wrong answers is:\\n\",\n \" \\n\",\n \" data[]\\n\",\n \" \\n\",\n \" Based on the above information, assess the incorrect part of the answer. Your response must be a part of the wrong answers. Only return the wrong part.\\n\",\n \" \\n\",\n \" Answer:\\n\",\n \" \\\\end{minipage}\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \" \\\\label{tab:long prompt}\\n\",\n \" \\\\end{table*}\\n\"\n ]\n]","isConversational":false},"fulltext":{"kind":"string","value":"% This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.\n\\pdfoutput=1\n% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.\n\n\\documentclass[11pt]{article}\n\n% Change \"review\" to \"final\" to generate the final (sometimes called camera-ready) version.\n% Change to \"preprint\" to generate a non-anonymous version with page numbers.\n\\usepackage[preprint]{acl}\n\n% Standard package includes\n\\usepackage{times}\n\\usepackage{latexsym}\n\n% For proper rendering and hyphenation of words containing Latin characters (including in bib files)\n\\usepackage[T1]{fontenc}\n% For Vietnamese characters\n% \\usepackage[T5]{fontenc}\n% See https://www.latex-project.org/help/documentation/encguide.pdf for other character sets\n\n% This assumes your files are encoded as UTF8\n\\usepackage[utf8]{inputenc}\n\n% This is not strictly necessary, and may be commented out,\n% but it will improve the layout of the manuscript,\n% and will typically save some space.\n\\usepackage[inline]{enumitem}\n\\usepackage{microtype}\n\n% This is also not strictly necessary, and may be commented out.\n% However, it will improve the aesthetics of text in\n% the typewriter font.\n\\usepackage{inconsolata}\n\n%Including images in your LaTeX document requires adding\n%additional package(s)\n\\usepackage{graphicx}\n\\usepackage{subcaption}\n\\usepackage{soul}\n\\usepackage{hyperref}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{tcolorbox}\n\\usepackage{booktabs}\n\\usepackage{multicol}\n\\usepackage{multirow}\n\\usepackage{algorithm}\n\\usepackage{algpseudocode}\n\\usepackage[toc,page]{appendix}\n% If the title and author information does not fit in the area allocated, uncomment the following\n%\n%\\setlength\\titlebox{<dim>}\n%\n% and set <dim> to something 5cm or larger.\n\\title{Active Layer-Contrastive Decoding Reduces Hallucination in Large Language Model Generation}\n\n% Author information can be set in various styles:\n% For several authors from the same institution:\n% \\author{Author 1 \\and ... \\and Author n \\\\\n% Address line \\\\ ... \\\\ Address line}\n% if the names do not fit well on one line use\n% Author 1 \\\\ {\\bf Author 2} \\\\ ... \\\\ {\\bf Author n} \\\\\n% For authors from different institutions:\n% \\author{Author 1 \\\\ Address line \\\\ ... \\\\ Address line\n% \\And ... \\And\n% Author n \\\\ Address line \\\\ ... \\\\ Address line}\n% To start a separate ``row'' of authors use \\AND, as in\n% \\author{Author 1 \\\\ Address line \\\\ ... \\\\ Address line\n% \\AND\n% Author 2 \\\\ Address line \\\\ ... \\\\ Address line \\And\n% Author 3 \\\\ Address line \\\\ ... \\\\ Address line}\n\n% \\author{Hongxiang Zhang \\\\\n% Purdue University \\\\\n% \\texttt{email@domain} \\\\\\And\n% Hao Chen \\\\\n% University of California, Davis \\\\\n% \\texttt{email@domain} \\\\\\And\n% Tianyi Zhang \\\\\n% Purdue University \\\\\n% \\texttt{email@domain} \\\\\\And\n% Muhao Chen \\\\\n% University of California, Davis \\\\\n% \\texttt{email@domain} \\\\}\n\n\\author{\n \\textbf{Hongxiang Zhang\\textsuperscript{1,2}},\n \\textbf{Hao Chen\\textsuperscript{2}},\n \\textbf{Tianyi Zhang\\textsuperscript{1}},\n \\textbf{Muhao Chen\\textsuperscript{2}}\n\\\\\n\\\\\n \\textsuperscript{1}Purdue University,\n \\textsuperscript{2}University of California, Davis,\n\\\\\n \\small{\n \\textbf{Correspondence:} \\href{mailto:hxxzhang@ucdavis.edu}{hxxzhang@ucdavis.edu}\n }\n}\n\n\\begin{document}\n\\maketitle\n\n\\definecolor{lightblue}{RGB}{200, 220, 255}\n\\definecolor{lightgreen}{RGB}{210, 240, 210}\n\\definecolor{lightgray}{RGB}{220, 220, 220}\n\\definecolor{lightred}{rgb}{1.0, 0.8, 0.8}\n\\newcommand{\\todo}[1]{\\sethlcolor{yellow}\\hl{#1}}\n\\newcommand{\\todis}[1]{\\sethlcolor{yellow}\\hl{#1}}\n\\newcommand{\\yellow}[1]{\\sethlcolor{yellow}\\hl{#1}}\n\\newcommand{\\lightgreen}[1]{\\sethlcolor{lightgreen}\\hl{\\textbf{#1}}}\n\\newcommand{\\lightblue}[1]{\\sethlcolor{lightblue}\\hl{#1}}\n\\newcommand{\\lightgray}[1]{\\sethlcolor{lightgray}\\hl{#1}}\n\\newcommand{\\lightred}[1]{\\sethlcolor{lightred}\\hl{#1}}\n\\newcommand{\\green}[1]{\\sethlcolor{green}\\hl{#1}}\n\\newcommand{\\red}[1]{\\sethlcolor{red}\\hl{#1}}\n% \\newcommand{\\tz}[1]{{\\color{red}{#1}}}\n\\newcommand{\\tz}[1]{}\n\\newcommand{\\appsectionautorefname}{Appendix}\n\n\\newcommand{\\actdola}{ActLCD}\n\n\\begin{abstract}\nRecent decoding methods improve the factuality of large language models~(LLMs) by refining how the next token is selected during generation. These methods typically operate at the token level, leveraging internal representations to suppress superficial patterns. Nevertheless, LLMs remain prone to hallucinations, especially over longer contexts. \nIn this paper, we propose Active Layer-Contrastive Decoding ({\\actdola}), a novel decoding strategy that actively decides when to apply contrasting layers during generation. By casting decoding as a sequential decision-making problem, {\\actdola} employs a reinforcement learning policy guided by a reward-aware classifier to optimize factuality beyond the token level. Our experiments demonstrate that {\\actdola} surpasses state-of-the-art methods across five benchmarks, %encompassing phrase, sentence, and paragraph-level tasks, \nshowcasing its effectiveness in mitigating hallucinations in diverse generation scenarios.\n\\end{abstract}\\section{Introduction}\n% 1. Sequence level improves Factuality \n% 2. reinforcement learning\n% 3. \n% Why this issue important\nDespite recent advances in large language models (LLMs), hallucination remains a major challenge that undermines user trust and reduces adoption in practice~\\cite{huang2025survey,ji2023survey,lewis2020retrieval}. Recent work observes that LLMs sometimes tend to predict the next token based on superficial linguistic patterns rather than the factual knowledge embedded in the training data~\\cite{shi2024thorough,welleckdecoding,chuang2023dola, zhang2024sled,zhang2024language}.\n\nBased on this insight, new decoding methods have been proposed to harness the latent representation of factual knowledge learned by LLMs to refine the probability distribution of output tokens~\\cite{chuang2023dola, zhang2024sled,li2022contrastive,su2022contrastive}. For example, DoLa~\\cite{chuang2023dola} aims to improve factuality by contrasting logits computed from the deep layers and those from the shallower layers. The intuition is that deep layers encode more factual and semantically refined knowledge, while shallower layers may reflect syntactic priors or ambiguous surface patterns. \nThrough a layer-wise contrast, DoLa can amplify factual signals from the deep layers while suppressing potentially misleading patterns from the shallower layers, thereby steering generation towards more factual outputs. %\\tz{While the Dola paper uses mature vs. inmaure to refer to the layers, this terminology needs to be defined first to be used here. For simplicity, I suggest we use deep vs. shallow consistently throughout the paper. Also don't use high vs. low. Please check other parts of the paper and edit consistently.}\n%A promising strategy, refining the decoding process can improve the LLM factuality by focusing on how the model selects the next token during the generation process~\\cite{welleckdecoding,shi2024thorough}. \n%Compared with other inference-time methods that depend on external knowledge retrieval~\\cite{lewis2020retrieval,wei2022chain} or multiple sampling techniques~\\cite{cheng2024integrative,wangself}, decoding methods can be cost-effective since they do not rely on external knowledge nor repeated sampling. \n\n\nThough these decoding methods have been demonstrated to be effective in reducing hallucination on some benchmarks, they have several limitations. \n%First, their inherent focus on immediate next-token predictions may amplify misleading knowledge within the LLM~\\cite{das2022quantifying,bao2024decoding,chuang2023dola,jiang2024large}, leading to confident yet factually incorrect responses. This issue risks sharpening inherent model biases rather than effectively mitigating them.\nWhile layer contrasting can improve factuality by exerting latent knowledge in deep layers, applying it persistently for every single token in the output sequence may cause the model to ``overthink'' for simple token predictions, especially in longer generations. For instance, in the math problem in ~\\autoref{f:main}, DoLa forces the model to immediately attempt an arithmetic calculation to solve the problem when generating the first sentence, though it only needs to simply repeat the information from the question body there to elicit further reasoning in the following sentences. Moreover, due to the autoregressive nature of text generation, factual accuracy is highly dependent on previously generated tokens; thus, lacking sequential-level optimization, these static interventions are prone to early errors that compound into a cascade of inaccuracies, a phenomenon known as hallucination snowballing \\cite{zhang2024language}.\n%Such static application may be unnecessary when the model is already generating factually sound content and risks degrading other desirable text qualities (e.g., fluency, coherence) or even introducing new biases if not dynamically adjusted to the evolving generation context.\n\n% ADV Recent decoding methods~\\cite{zhang2024sled,chuang2023dola,li2022contrastive,su2022contrastive} commonly operate at the token level by reweighting the internal representations from a single LLM or by aligning representations across different LLMs to improve factuality. As an example, contrasting layer methods emphasize the implicit knowledge embedded within the models and suppress biases that contribute to hallucinations. \n% The underlying motivation is to better leverage the implicit knowledge embedded within the models and to suppress biases that contribute to hallucinations. As an example, contrasting layer methods aim to refine the representation of the next token, thereby improving its contextual fit with subsequent tokens. These methods aim to emphasize the implicit knowledge within the models and suppress biases that can lead to hallucinations.\n\n% hallucination analyses in\\cite{jiang2024large} reveal that the probability of the correct tokens will not always surge in later layers which DoLa relies on this pattern to conjure the correctness.\n% In such cases, naively applying DoLa may lead to incorrectness dominating the decoding, thereby risking hallucination.\n% have demonstrated that Decoding by Contrasting Layers~(DoLa), aims to enhance factual knowledge by contrasting token logits computed from higher and lower feedforward layers. Emphasizing the knowledge of higher layers and suppressing earlier-layer biases can potentially make LLMs more factual and thus reduce hallucinations.\n% Motivation: 1. DoLa fail sometimes 2. DoLa will make the incorrectness dominate. \n% Most recent method optimize in token level, we define... in sequence level.\n% However, DoLa is not always effective. Due to the inherent uncertainty in LLMs, DoLa may amplify misleading knowledge, resulting in confident but incorrect responses. For example, \\cite{chuang2023dola} observed that while DoLa increased the informativeness (\\%info), it simultaneously reduced the rejection rates (\\%reject) and the factual accuracy (\\% truth). In such cases, the baseline decoder produced cautious responses (e.g. \"I have no comment\"), avoiding factual errors. In contrast, DoLa forced the model to generate sound-likely 'facts' even if they were not true. Fundamentally, DoLa sharpens model biases rather than correcting them.\n\n%The risk is particularly high when the model’s training data contains common misconceptions or outdated information, as later layers might reinforce these falsehoods, presenting them as factual.\n\n\\begin{figure*}[ht]\n\\centering\n \\centering\n \\includegraphics[width=0.97\\linewidth]{figure/main.pdf}\n \\vspace{-0.2cm}\n \\caption{ The workflow of {\\actdola}. (1) Next-token selection: {\\actdola} dynamically apply layer contrasting at each step. (2) Sequential-level optimization: By framing decoding as a Markov decision process, {\\actdola} selectively activates layer contrasting to maximize cumulative reward throughout the generation.}\n \\vspace{-0.2cm}\n \\label{f:main}\n\\end{figure*}\n\n\n% What we added?\n% Point: DoLa focus on the next token. We are not focusing on a single next token, but the sentence level by formalize the problem into a RL problem.\n% insight: Avoid introduced hallucination. \\\\ In the view of decoding, active DoLa research for the highest logits sequence in the sentence level. While DoLa focus on the token level to infer the best understanding to the whole inquiry.\n% we propose activate selective DoLa. Formalize each sequence decoding as a sequence of rewards. Aiming to search for the maximum probs in whole sequence. We also applied a selective sampling as activate not to do DoLa, trying to mitigate to introduce the hallucination.\n\n%This motivates our core question: \\emph{Can we selectively apply contrastive mechanisms during generation, thus optimizing factuality at the sequence level?}\nTo address these issues, we propose \\textbf{Act}ive \\textbf{L}ayer-\\textbf{C}ontrastive \\textbf{D}ecoding~({\\actdola}). \\autoref{f:main} gives an overview of {\\actdola}. During generation, {\\actdola} selectively applies layer contrasting to leverage latent knowledge in deep layers. Specifically, {\\actdola} casts decoding as a sequential decision-making problem, employing reinforcement learning to learn an optimal policy for when to activate layer contrasting based on previously generated tokens (i.e., the context). \nUnlike existing approaches that require multiple sampling or external sources, {\\actdola} operates in a single pass using only model-internal information, enabling efficient and adaptive factual decoding.\n\n\\tz{need to mention the baselines here} We compare {\\actdola} with SOTA decoding strategies SLED and DoLa on five LLMs of varying scales. Our method consistently improves factuality on two open-domain benchmarks and two chain-of-thought benchmarks by a large margin, including TruthfulQA~(+19.81\\%), LongFact~(+3.30\\%), StrategyQA~(+7.51\\%), and GSM8k~(+7.21\\%), respectively\\footnote{Reported improvements are the maximum gains observed across all tested LLMs.}. Furthermore, we extend our evaluation to a domain-specific benchmark on software package hallucination~(+9.23\\%) using four code LLMs. \\tz{This following sentence is a bit handwavy. It would be more convincing if you could include some numbers.}Across all experiments, {\\actdola} demonstrates robust factuality improvements from sentence-level to document-level generations.\n\n\n% \\todo{A figure to demonstrate how the Active DoLa works...(a tree-like structure, demo to show Greedy decode can not understand the full question. DoLa focus on the next token level. But we have a better understanding to the whole context and generating with the sentence...)... By re-sampling, active-dola can mitigate the introduction of hallucination while maintaining a fluent and diverse sentence. can have a sentese level understanding... }\n\n% In this section, we introduce our method for mitigating hallucinations in LLMs by framing decoding as a RL problem. Our approach employs an active learning strategy within a Batch‐Constrained Q‐Learning~(BCQ) framework, dynamically determining when to apply the layer contrasting algorithm or selective sampling. This decision is guided by learned predictive features of token probabilities and internal model dynamics, ensuring adaptive and effective hallucination mitigation.\n\n\\section{Method}\n\\subsection{Preliminaries}\nContrastive decoding methods exploit model confidence dynamics either across different models or within the internal layers \\cite{li2022contrastive,chuang2023dola}. Specifically, Decoding by Contrasting Layers \\cite{chuang2023dola} adjusts the token probability distribution by subtracting the logits of shallow layers from those of deep layers. This approach aims to sharpen model outputs and mitigate hallucinations by amplifying confident predictions while suppressing uncertain ones.\n\nFormally, given a sequence of generated tokens $\\{x_1, x_2, ..., x_{t-1}\\}$, $q_N,q_M$ are named log probabilities of shallow and deep layers, respectively. The next-token prediction is then determined as \n\\begin{equation}\n\\resizebox{.896\\linewidth}{!}{$\n\\hat{p}(x_t\\mid x_{<t}) = \\text{softmax} \\left( \\mathcal{F}(q_N(x_t), q_M(x_t)) \\right)_{x_t}\n$}\n\\end{equation}\nwhere \n\\begin{equation}\n\\resizebox{.896\\linewidth}{!}{$\n\\mathcal{F}(q_N, q_M) = \n\\begin{cases}\n\\textstyle \\log \\frac{q_N(x_t)}{q_M(x_t)} & x_t \\in \\mathcal{V}_{\\text{head}}(x_t \\mid x_{<t}), \\\\\n-\\infty & \\text{otherwise}.\n\\end{cases}\n$}\n\\end{equation}\n\nThe operator $\\mathcal{F}(\\cdot, \\cdot)$, is used to contrast the output distributions from the shallow layer and the deep layer. The subset $V_{\\text{head}}(x_t | x_{<t}) \\subset X$ is defined based on whether the token has a sufficiently high probability in the deep layer:\n\n\\begin{equation}\n \\begin{aligned}\n \\mathcal{V}_{\\text{head}} (x_t | x_{<t}) = \\{ x_t & \\in \\mathcal{X} : q_N(x_t) \\\\\n & \\geq \\alpha \\max_w q_N(w) \\}\n \\end{aligned}\n\\end{equation}\n\nTokens with low probability in the deep layer are discarded to minimize both false positives and false negatives. In practice, contrastive decoding methods require selecting appropriate shallow and deep layers buckets. We describe our layer selection strategy in~\\autoref{premature layer selection}.\n\n% A false positive occurs when an implausible token is erroneously assigned a high score under the unconstrained contrastive objective. Conversely, a false negative arises when the model encounters an easy decision where the correct token, which receives a high probability under both the amateur LM and the expert LM, is penalized under the contrastive objective.\n\n% However, DoLa is not always effective, especially when it amplifies misleading knowledge, resulting in confident yet incorrect responses. Empirical findings \\cite{chuang2023dola} show that while DoLa enhances informativeness, it reduces rejection rates and factual accuracy, often overriding cautious responses with incorrectness. Moreover, hallucination analyses \\cite{jiang2024large} reveal that correct token probabilities do not always surge in later layers, contradicting DoLa’s assumption. As a result, naive application of layer contrasting may cause incorrect predictions to dominate and, thus, hallucinations.\n\\subsection{Decoding Objective}\nThe key insight of layer contrasting methods is that amplifying confident predictions while suppressing uncertain ones improves factuality. While layer contrasting is effective for token-level adjustments, it treats each decoding step independently and does not account for the influence of previously generated tokens on following decoding steps. % Motivation\nTo address this limitation, we formalize decoding as a sequential decision-making problem and solve it via reinforcement learning (RL). \nOur approach dynamically decides when to apply layer contrasting. \nFormally, given input prompt $p$ and partial generation $x_{<t}$ at step $t$, the next token probability is defined as: \n\\begin{equation}\n \\resizebox{.896\\linewidth}{!}{$\n \\begin{aligned}\n \\hat{p}(x_t \\mid x_{<t}) = & \\text{softmax} ( a_t\\cdot \\mathcal{F}(q_N(x_t), q_M(x_t))\\\\\n & + (1-a_t)\\cdot q_M(x_t) )\n \\end{aligned}\n$}\n% \\hat{x} = \\arg\\max_{x} \\bigl[a_t\\cdot{CD}\\bigl(p,\\,x\\bigr)+(1-a_t)\\cdot G\\bigl(p,\\,x\\bigr)\\bigr]\n\\end{equation}\n, where \\( a_t \\in \\{0, 1\\} \\) refers to the action that determines whether to apply layer contrasting, based on the current state \\( s_t \\) derived from $\\{x_{<t}, p\\}$.\n\nThe objective of our method includes two parts: \\(\\mathcal{F}(q_N(x_t), q_M(x_t))\\) and \\( q_M(x_t) \\), where they can be viewed as the decoding by layer contrasting objective and greedy decoding objective, respectively.\n\n\n% Formally, at each decoding step $t$, given input prompt $p$ and, partial generation $x_{<t}$, the intermediate model state $s_t$ is derived from $\\{x_{<t}, p\\}$. \n% The goal in reinforcement learning is to learn a policy $\\pi_\\theta(a_t \\mid s_t)$ which selects an action \\( a_t \\in \\{0, 1\\} \\) based on the current state \\( s_t \\). Formally, we seek to generate\n% \\[\n% \\hat{x} \\;=\\; \n% \\arg\\max_{x}\\; \n% \\mathbb{E}\\Biggl[\\sum_{t=1}^{T}R\\bigl(s_t,\\,a_t\\bigr)\\Biggr]\n% \\]\n% where\n% \\[\n% a_t \\,\\sim\\, \\pi_\\theta(a_t \\mid s_t).\n% \\]\n% The environment calculates $R\\bigl(s_t,\\,a_t\\bigr)$ over the entire sequence, rewarding correct interventions and penalizing missed or unnecessary activations.\n\n\\subsection{\\actdola}\nWe formulate decoding as a Markov decision process (MDP) $\\mathcal{M}=(S, A, P_a, R)$, where $S$ is the state space that captures intermediate-layers' embeddings and logits, $A = \\{0, 1\\}$ is the action space indicating whether to apply layer contrasting, $P_a$ denotes the transition dynamics, and $R$ is the reward function. \n\nWe consider a decision environment where each step corresponds to generating the next token. At each time \\( t \\), the state \\( s_t \\) is represented by layer-based embeddings and logits derived from the partially generated context \\( x_{<t} \\). At each step, the policy decides whether to apply layer contrasting or not. \nIn this setup, the standard RL objective is to maximize the expected return \\( \\mathbb{E}\\left[\\sum_{t=1}^{T} R(s_t, a_t)\\right]\\) in the MDP, where \\(a_t \\sim \\pi_\\theta(a_t \\mid s_t)\\). The environment evaluates the reward $R(s_t, a_t)$ over the full sequence.\n\n\\paragraph{Reward Design}\nTo emphasize factual correctness and penalize both unnecessary and missed activations, we design a sequence-level reward function. Specifically, we assign rewards based on token-level ground truth labels: true positives (correct layer contrasting activation) receive a reward \\(r_{tp} = 1.0\\), true negatives (correct non-activation) \\(r_{tn} = 2.0\\), false positives (unnecessary layer contrasting activation) \\(r_{fp} = -1.0\\), and false negatives (missed necessary activations) \\(r_{fn} = -5.0\\). These values were empirically chosen to balance the trade-off between precision and recall (detailed in ~\\autoref{appendix:reward-tuning}). The cumulative reward for a sequence is updated at the end of decoding, helping the policy avoid getting trapped in local minima and incentivizing correct activation decisions throughout generation.\n\n\\paragraph{Training}\nTo learn $\\pi_\\theta$, we adopt an offline RL framework, where transitions are collected from annotated sequences. Each token is labeled with a binary activation indicator of whether to apply layer contrasting at that step. Annotation details are listed in \\autoref{appendix:annotation}. We then apply Batch-Constrained deep Q-learning (BCQ)~\\cite{fujimoto2019off} to learn an activation policy from annotated offline data, as explained in the next section.\n\n% Episodes terminate when an end-of-sequence token or a predefined special token.\n\n\\subsection{Training and Policy Optimization}\nThe BCQ framework comprises two stages. \nIn particular, we first train a behavior cloning (BC) model that captures the offline policy distribution in a supervised manner. We then refine the policy via Q-learning, while constraining action choices to remain close to the BC policy. We explain each step in detail below.\n\n\\paragraph{Stage 1: Behavior Cloning}\nIn the initial stage, we train a behavior cloning network to approximate the empirical action distribution from the annotated offline dataset's actions. \nSpecifically, each training example consists of a state $s_t$ (capturing the partial generation up to step $t$) and the annotated binary activation label $a_t$. we optimize the neural network policy $\\pi_{\\phi}$ by minimizing cross-entropy loss:\n\\begin{equation}\n\\mathcal{L}_{\\text{BC}} \n\\;=\\; -\\sum_{t}\\; \n\\log\\,\\pi_{\\phi}(a_t \\mid s_t)\n\\end{equation}\nThis behavioral cloning component guides the model to imitate observed activation patterns, serves in two purposes: (1) it offers a strong initialization that captures real (annotated) usage of contrastive layers, and (2) it regularizes subsequent Q-learning updates to remain close to the behavioral distribution, thereby reducing extrapolation errors commonly encountered in offline reinforcement learning.\n\n\\paragraph{Stage 2: Q-learning.}\nIn the second stage, we refine the policy using DQN Q‑learning update~\\cite{fujimoto2019off,mnih2015human} under a batch-constrained scheme by sampling from the same dataset used in Stage~1. We train a critic Q-network $Q_\\theta$ to predict the expected cumulative rewards for state-action pairs.\nFollowing~\\citep{fujimoto2019off}, we impose a probability threshold $\\tau$ derived from the behavioral cloning policy $\\pi_\\phi(a\\mid s)$ to prevent the overestimation of actions rarely observed in the offline dataset. Formally, we define a set of permissible actions as:\n\\begin{equation}\n\\mathcal{A}_\\phi(s_{t+1}) \\;=\\; \\{\\, a \\mid {\\pi}_\\phi(a \\mid s_{t+1}) > \\tau \\}\n\\end{equation}\nThe critic parameters $\\theta$ are updated by minimizing the mean-squared temporal-difference error:\n\\begin{equation}\n \\begin{aligned}\n \\mathcal{L}_{\\mathrm{TD}} = \\bigl\\lVert & Q_\\theta(s_t,a_t) \\;- \\\\\n & \\bigl[r_t + \\gamma \\max_{a \\in \\mathcal{A}_\\phi(s_{t+1})}Q_{\\bar{\\theta}}(s_{t+1},a)\\bigr]\n \\bigr\\rVert^2,\n \\end{aligned}\n\\end{equation}\nwhere $\\bar{\\theta}$ denotes the parameters of a slowly-updated target critic network via Polyak averaging, $r_t$ is the immediate reward, and $\\gamma$ is the discount factor. If no permissible action exceeds the threshold, we revert to the action with the highest behavioral cloning probability.\n\nThis constrained approach ensures that the policy remains faithful to the distribution of observed actions, effectively alleviating distributional shift.\n\n\\paragraph{Inference} \nAt test time, the policy selects actions by identifying permissible actions $\\mathcal{A}_\\phi(s_t)$ and choosing the action with the highest estimated Q-value:\n\\begin{equation}\n \\pi_\\theta \n \\;=\\; \\arg\\max_{a \\in \\mathcal{A}_\\phi(s_t)}\\, Q_\\theta(s_t,a),\n\\end{equation}\n% where $Q_\\theta$ is our learned Q-network. If all actions are below the threshold, we fall back to the action with the highest $\\pi_\\phi(a \\mid s_{t})$. Periodic synchronization of $Q_\\theta$ and the target network ($Q_{\\bar{\\theta}}$) stabilizes training, ensuring a reliable and robust final policy.\n\nBy combining supervised initialization with constrained Q-learning, our policy remains faithful to offline annotations while optimizing for the sequential-level reward, effectively balancing correctness and efficiency. Periodic synchronization between the Q-network and the target network stabilizes the learning process, ensuring reliable policy updates and robust decision-making at inference.\\section{Experiments}\n\n\\subsection{Benchmarks and Evaluation Metrics}\n%\\tz{``phrase-level'', ``sentence-level'', ``document-level'' are confusing. Dola and Sled papers don't use these terms. It's better to call it short-answer and long-answer generation, which is more intuitive and also consistent with the headers in Sec 4.3. You can then be more specific and say that short answers involve generation of a phrase or a sentence while long answers involve generation of a paragraph or even a document.} \nWe evaluated two open-ended benchmarks spanning short-answer and long-answer generation, including TruthfulQA~\\citep{lin2021truthfulqa}, and LongFact~\\citep{wei2024long}. In addition, we included two chain-of-thought reasoning benchmarks, StrategyQA~~\\citep{geva2021did} and GSM8K~~\\citep{cobbe2021training}. Finally, we included a domain-specific benchmark about software package hallucination~\\citep{spracklen2024we}. \n\n\n\\textbf{TruthfulQA} ~\\citep{lin2021truthfulqa} is a short-answer benchmark comprising 817 questions designed to test factual correctness, particularly in cases where humans commonly answer falsely due to misconception. We use GPT-4o-mini to evaluate the truthfulness (\\textit{Truth}), and informativeness (\\textit{Info}) of each generated answer. While \\textit{Truth} is the primary metric, a high score can be trivially achieved by generating uninformative answers such as “I have no comment.” To address this, we adopt the composite metric (\\textit{T*I}), which balances correctness and informativeness. Following the evaluation in ~\\citep{lin2021truthfulqa, cheng2024integrative}, we provide reference answers annotated in the dataset as the reference and use the same evaluation samples as the demonstration examples.\n\n\\textbf{LongFact} ~\\citep{wei2024long} includes a set of 2,280 fact-seeking prompts requiring long-form responses, often exceeding a thousand tokens. We follow the same evaluation process as in ~\\citep{wei2024long,cheng2024integrative}, which uses an LLM to first extract the atomic facts from a long response and then evaluate the correctness of each fact. We use GPT-4o-mini to extract atomic facts and evaluate factuality. The adopted metrics include the proportion of truthful facts (\\textit{Precision}), the number of truthful facts divided by 128 (\\textit{Recall@128}), and the \\textit{F1@128} score, which integrates the previous two metrics. To balance costs, we evaluated only 120 samples.\n\n\\textbf{Chain-of-Thought Reasoning} Following prior work~\\cite{zhang2024sled,chuang2023dola}, we evaluate chain-of-thought reasoning capabilities using StrategyQA~\\citep{geva2021did} and GSM8K~\\citep{cobbe2021training}. Both benchmarks require generating long-answer, detailed reasoning paths. StrategyQA requires multi-hop reasoning over implicit knowledge, while GSM8K involves math word problems that demand both factual understanding and arithmetic reasoning. We follow the factual accuracy evaluation implemented from~\\citep{chuang2023dola}.\n\n\\textbf{Package hallucination}\n% In addition to the benchmarks above, we incorporated a domain-specific benchmark focusing on software package hallucination. \n~\\citep{spracklen2024we} is a benchmark designed to evaluate the factuality of software packages recommended by an LLM for a given task. \\autoref{tab:An example of package hallucination.} shows an example. This benchmark includes 5,000 tasks related to popular programming languages, including Python and JavaScript. Different from TruthfulQA, which involves a single-sentence response, this benchmark focuses on multi-token outputs consisting of multiple package names. In this context, package hallucination refers to LLMs recommending non-existent or irrelevant packages. \n\nTo assess the robustness of our approach in this critical domain, we extended our evaluation to four additional code-focused LLMs, covering both general-purpose and specialized models. Following~\\citet{spracklen2024we}, we use pip-search and npm-search to verify the existence of each recommended package. We adopt the hallucination rate~(\\textit{\\%Hallu}) as the primary metric.\n\n\n% The reliance packages of popular programming languages such as Python and JavaScript, combined with the emergence of code-generation LLMs, have introduced a new threat to software engineering: package hallucinations.\n\n\n% Stack Overflow questions and LLM-generated \n% For brevity, the four temporally and language-distinct datasets, one from the past year and one from before 2023, will be referred to as the `Py-all', `Py-recent', `JS-all', and `JS-recent' datasets for the remainder of this paper.\n\n\\subsection{Models and Baselines}\n\n\\textbf{Base Models} We conduct our experiments on five general-purpose LLMs and four code LLMs. We experiment with general-purpose LLMs on all benchmarks. Since code LLMs are superficially designed for coding tasks, we only experiment with code LLMs on the software package hallucination benchmark.\nFor general-purpose LLMs, we select Llama-3.1-8B~\\cite{grattafiori2024llama}, \nglm-4-9b-chat-hf~\\cite{glm2024chatglm}, gemma-2-9b-it~\\cite{team2024gemma}, Mistral-7B-Instruct-v0.3~\\cite{jiang2024identifying}, and DeepSeek-V2-Lite-Chat~\\cite{liu2024deepseek}. For code LLMs, we include codegemma-7b-it~\\cite{team2024codegemma}, DeepSeek-Coder-V2-Lite-Instruct~\\cite{guo2024deepseek}, and Qwen2.5-Coder-7B-Instruct~\\cite{hui2024qwen2}.\n%For consistency, we refer to them as LLaMA3.1, GLM4, Gemma2, Mistral3, DeepSeek2 for general-purpose models, and CodeGemma, DeepSeekCoder, QwenCoder for code-specific models in the following sections.\n\\\\\n\\textbf{Comparison Baselines}\nWe compare our method against three representative decoding strategies.\nFirst, we include \\textbf{Greedy} decoding, a widely used baseline that selects the most probable token at each step without any additional adjustments.\nSecond, we include Decoding by Contrasting Layers~(\\textbf{DoLa})~\\citep{chuang2023dola}, a decoding method that improves factuality by contrasting logits from deeper and shallower layers.\nThird, we include Self Logits Evolution Decoding (\\textbf{SELD})~\\citep{zhang2024sled}, which leverages the evolution of token logits across layers to guide generation toward more factual outputs.\n\n\\subsection{Implementation Details}\nThe prompt templates used for different approaches are provided in~\\autoref{appendix:prompt}. Following the official SLED implementation, we have updated it to ensure compatibility with the latest LLMs; the implementation and reproduction details are provided in ~\\autoref{appendix: sled reproduction}. We implement DoLa using readily pre-built functionalities provided by the Hugging Face Transformers library.\nFor DoLa, SLED, and {\\actdola}, we select shallow layers by partitioning the transformer layers into \\{low, high\\} buckets and select one bucket as candidate layers. We detail our shallow-layer selection strategy in ~\\autoref{premature layer selection}.\n\\label{implementation details}\n\n\\begin{table*}\n \\scriptsize\n \\centering\n \\setlength{\\tabcolsep}{4pt}\n \\caption{Evaluation results on two open-ended benchmarks and two Chain-of-thought benchmarks. The best-performing results are highlighted in \\lightgreen{green}, the second-best in \\lightblue{blue}, and those indicating a performance drop compared to standard greedy decoding are shown in \\lightgray{grey}.}\n \\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lllllllllll}\n\\toprule\n\\multirow{2.5}{*}{\\textbf{Model}} & \\multirow{2.5}{*}{\\textbf{Method}} & \\multicolumn{3}{c}{\\textbf{TruthfulQA}} & \\multicolumn{3}{c}{\\textbf{LongFact}} & \\multicolumn{2}{c}{\\textbf{CoT}} \\\\\n\\cmidrule(lr){3-5} \\cmidrule(lr){6-8} \\cmidrule(lr){9-10}\n& & \\textbf{\\%Truth} $\\uparrow$ & \\textbf{\\%Info} $\\uparrow$ & \\textbf{\\%T*I} $\\uparrow$ & \\textbf{Prec.}$\\uparrow$ & \\textbf{$R$@128} $\\uparrow$ & \\textbf{$F1$@128} $\\uparrow$ & \\textbf{StrQA} $\\uparrow$ & \\textbf{GSM8K} $\\uparrow$ \\\\\n\\midrule\n\\multirow{4}{*}{LLaMA3.1}& Greedy & 41.98 & 66.59 & 27.95 & 84.72 & 92.40 & 88.39 & 67.82 & 56.02\\\\\n &DoLa & \\lightblue{46.76 (+4.78)} & \\lightgreen{87.15 (+20.56)} & \\lightblue{40.75 (+12.80)} & \\lightblue{85.19 (+0.47)} & \\lightgray{83.66 (-8.74)} & \\lightgray{84.42 (-3.97)} & \\lightblue{69.21 (+1.39)} & \\lightblue{56.18 (+0.16)}\\\\\n & SLED & \\lightgray{41.25 (-0.73)} & 66.59 (0.00) & \\lightgray{27.47 (-0.48)} & \\lightgray{84.18 (-0.54)} & \\lightgray{79.02 (-13.38)} & \\lightgray{81.52 (-6.87)} & \\lightgray{67.38 (-0.44)} & 56.71 (+0.69)\\\\\n &{\\actdola} & \\lightgreen{52.70 (+10.72)} & \\lightblue{84.82 (+18.23)} & \\lightgreen{44.70 (+16.75)} & \\lightgreen{86.63 (+1.91)} & \\lightgreen{97.38 (+4.98)} & \\lightgreen{91.69 (+3.30)} & \\lightgreen{75.33 (+7.51)} & \\lightgreen{63.23 (+7.21)}\\\\\n\\midrule\n\\multirow{4}{*}{GLM4} &Greedy & 64.26 & 87.27 & 56.08 & 84.17 & 95.77 & 89.60 & 69.56 & 60.12\\\\\n & DoLa & \\lightgray{59.24 (-5.02)} & \\lightgray{81.76 (-5.51)} & \\lightgray{48.44 (-7.64)} & \\lightgray{83.49 (-0.68)} & \\lightgreen{98.34 (+2.57)} & \\lightgreen{90.31 (+0.71)} & \\lightgray{67.38 (-2.18)} & \\lightgray{57.01 (-3.11)}\\\\\n & SLED & \\lightgray{62.06 (-2.20)} & \\lightgray{67.56 (-19.71)} & \\lightgray{41.93 (-14.15)} & \\lightblue{84.32 (+0.15)} & \\lightgray{88.13 (-7.64)} & \\lightgray{86.18 (-3.42)} & \\lightblue{70.48 (+0.92)} & \\lightblue{60.95 (+0.83)}\\\\\n &{\\actdola} & \\lightgreen{68.91 (+4.65)} & \\lightgray{83.23 (-4.04)} & \\lightgreen{57.35 (+1.27)} & \\lightgreen{86.37 (+2.20)} & \\lightgray{90.01 (-5.76)} & \\lightgray{88.15 (-1.45)} & \\lightgreen{72.58 (+3.02)} & \\lightgreen{64.06 (+3.94)}\\\\\n\\midrule\n\\multirow{4}{*}{Mistral3} &Greedy & 58.38 & 64.87 & 37.87 & 84.87 & 78.08 & 81.33 & 73.49 & 53.15 \\\\\n & DoLa & \\lightblue{66.21 (+7.83)} & \\lightblue{78.21 (+13.34)} & \\lightblue{51.78 (+13.91)} & {85.23 (+0.36)} & \\lightgray{75.42 (-2.66)} & \\lightgray{80.03 (-1.30)} & \\lightgray{71.00 (-2.49)} & \\lightgray{51.10 (-2.05)} \\\\\n & SLED & 58.75 (+0.37) & \\lightgray{64.63 (-0.24)} & 37.97 (+0.10) & \\lightblue{85.45 (+0.58)} & \\lightgray{77.88 (-0.20)} & \\lightblue{81.49 (+0.16)} & \\lightgray{73.41 (-0.08)} & \\lightblue{53.53 (+0.38)}\\\\\n &{\\actdola} & \\lightgreen{71.84 (+13.46)} & \\lightgreen{80.29 (+15.42)} & \\lightgreen{57.68 (+19.81)} & \\lightgreen{85.78 (+0.91)} & \\lightgray{77.62 (-0.46)} & \\lightgreen{81.50 (+0.17)} & \\lightgreen{73.84 (+0.35)} & \\lightgreen{58.98 (+5.83)}\\\\\n\\midrule\n\\multirow{4}{*}{Gemma2} &Greedy & 54.10 & 51.90 & 28.08 & 83.77 & 100.70 & 91.46 & 74.80 & 82.11\\\\\n & DoLa & \\lightblue{61.44 (+7.34)} & \\lightgreen{68.05 (+16.15)} & \\lightblue{41.81 (+13.73)} & \\lightgray{83.66 (-0.11)} & \\lightblue{104.13 (+3.43)} & \\lightblue{92.78 (+1.32)} & \\lightgray{73.54 (-1.26)} & \\lightgray{81.20 (-0.91)}\\\\\n & SLED & 54.83 (+0.73) & 53.61 (+1.71) & 29.41 (+1.33) & \\lightgray{83.73 (-0.04)} & \\lightgray{98.68 (-2.02)} & \\lightgray{90.59 (-0.87)} & \\lightblue{74.93 (+0.13)} & \\lightblue{82.94 (+0.83)}\\\\\n &{\\actdola} & \\lightgreen{64.62 (+10.52)} & \\lightblue{67.20 (+15.30)} & \\lightgreen{43.42 (+15.34)} & \\lightgreen{83.98 (+0.21)} & \\lightgreen{104.55 (+3.85)} & \\lightgreen{93.14 (+1.68)} & \\lightgreen{77.25 (+2.45)} & \\lightgreen{83.32 (+1.21)}\\\\\n\\midrule\n\\multirow{4}{*}{DeepSeek2} &Greedy & 52.99 & 79.93 & 42.35 & 81.76 & 80.84 & 81.30 & 69.65 & 70.36\\\\\n & DoLa & \\lightblue{53.85 (+0.86)}& \\lightblue{82.99 (+3.06)} & \\lightblue{44.69 (+2.34)} & {82.17 (+0.41)} & \\lightblue{81.06 (+0.22)} & \\lightblue{81.61 (+0.31)} & \\lightblue{70.61 (+0.96)} & \\lightgray{66.19 (-4.17)}\\\\\n & SLED & \\lightgray{51.53 (-1.46)} & \\lightgray{77.23 (-2.70)} & \\lightgray{39.80 (-2.55)} & \\lightblue{82.67 (+0.91)} & \\lightgray{79.48 (-1.36)} & \\lightgray{81.04 (-0.26)} & 69.87 (+0.22) & \\lightgray{68.99 (-1.37)}\\\\\n &{\\actdola} & \\lightgreen{60.46 (+7.47)} & \\lightgreen{83.48 (+3.55)} & \\lightgreen{50.47 (+8.12)} & \\lightgreen{83.15 (+1.39)} & \\lightgreen{82.85 (+2.01)} & \\lightgreen{83.00 (+1.70)} & \\lightgreen{76.51 (+6.86)} & \\lightgreen{70.81 (+0.45)}\\\\\n\\bottomrule\n \\end{tabular}\n }\n \\label{tab:truth}\n\\end{table*}\n\n\n\\subsection{Main Results}\n\n\\paragraph{Short-Answer Factuality.}\n % We evaluated factuality with \\textit{Truth} as the primary metric. However, achieving a perfect truthful score is trivial by simply responding with \"I have no comment.\" To provide a comprehensive evaluation, \n \n % we also introduce the \\textit{Info} score to measure informativeness, and the composite metric \\%Truth$\\times$\\%Info to assess a balanced trade-off between these two aspects. \n As shown in \\autoref{tab:truth}, {\\actdola} consistently improves the \\textit{Truth} score. {\\actdola} also demonstrates significant improvements in \\textit{Info}, ensuring high informativeness. These gains lead to over 10\\% increase in the \\%Truth$\\times$\\%Info metric in most models, outperforming all competing methods. These results highlight {\\actdola}’s ability to generate responses that are not only factually accurate but also more informative, reflecting overall higher-quality generation.\n\n While DoLa and SLED have demonstrated the potential to boost truthfulness and informativeness, our experiments show performance degradation in certain LLMs, potentially indicating limited generalization across model architectures. In contrast, {\\actdola} demonstrates superior \\%T*I scores across all evaluated models.\n\n\n\\paragraph{Long-answer Factuality.}\n Enhancing factuality in long-form generation remains a challenging and underexplored area. As shown in \\autoref{tab:truth}, benefit from sequential level optimization, {\\actdola} improves both precision and Recall@128. This indicates that {\\actdola} not only suppresses non-factual outputs but also actively elicits more parametric knowledge from the LLM, resulting in a greater number of factually grounded statements. Notably, this gain does not come at the expense of precision, highlighting {\\actdola}’s ability to generate more factual information while maintaining a high truthfulness rate.\n\n Conversely, baseline approaches struggle with the LongFact benchmark. DoLa shows a significant performance drop in precision in some model architectures. While SLED improves precision across most settings, it often reduces information recall as measured by Recall@128, resulting in a lower F1@128. This suggests SLED tends to favor early termination over generating more informative content. These findings further highlight \\actdola’s better generality and robustness in long-form generation tasks.\n\n\n\\begin{table*}\n \\centering\n \\scriptsize\n \\caption{An example of GSM8K. Incorrect inferences are highlighted in \\lightred{red}. Correct ones are highlighted in \\lightgreen{green}.}\n \\resizebox{\\linewidth}{!}{\n \\begin{tabular}{l p{0.96\\linewidth}}\n \\toprule\n \\textbf{Prompt} & \\textit{``James needs to get more toys for his doggie shelter. Each dog needs one toy. James currently has 4 toys on hand for 4 dogs, but there are 8 more dogs in the shelter now. After buying the toys, he went back to see that there are twice as many more dogs than when he left so he had to buy some more toys. When James came back yet again, 3 dogs were gone so he no longer needed those toys. How many toys in total does James need?''} \\\\\n \\midrule\n \\textbf{Greedy} & James started with 4 toys. He needed 8 more toys for 8 dogs. So he had 4 + 8 = 12 toys. Then he needed twice as many more toys. So he needed 12 + 12 = 24 toys. Then 3 dogs were gone. So he no longer needed 3 toys. \\lightred{24 - 3 = 21. The answer is 21.} \\\\\n \\textbf{SLED} & James started with 4 toys. He bought toys for 8 dogs, \\lightred{so he bought 8 toys. Then he bought toys for twice as many dogs, so he bought 8 x 2 = 16 toys. Then he no longer needed 3 toys. 4 + 8 + 16 - 3 = 25. The answer is 25.}\\\\\n \\textbf{DoLa} & James started with 4 toys. He needed 1 toy per dog, so \\lightred{he needed 4 more toys. That's 4 + 4 = 8 toys. But then there were 8 more dogs, so he needed another 8 toys. That's 8 + 8 = 16 toys. But then there were twice as many dogs, so he needed 16 x 2 = 32 toys. But then 3 dogs were gone, so he didn't need 3 toys. 32 - 3 = 29. The answer is 29. }\\\\\n \\textbf{{\\actdola}} & James started with 4 toys for 4 dogs. That's 1 toy per dog. But there are 8 more dogs. \\lightgreen{So he needs 8 more toys. 4 + 8 is 12.} But then there are twice as many more dogs. \\lightgreen{So he needs another 24 toys. 12 + 24 is 36.} But then 3 dogs were gone. So he doesn't need those toys. \\lightgreen{36 - 3 is 33. The answer is 33.}\\\\\n \\bottomrule\n \\end{tabular}\n \\label{tab:cot}\n } \n\\end{table*}\n\n\n\\paragraph{Chain of thought} StrategyQA requires multi-hop reasoning with chain-of-thought (CoT) prompting~\\cite{wei2022chain}. As detailed in \\autoref{tab:truth}, {\\actdola} persistently improves accuracy across five LLMs, achieving 0.35\\%-7.51\\% gains. Nevertheless, SLED and DoLa occasionally underperform compared to greedy decoding. These results highlight {\\actdola}'s robustness and generalizability across architectures. We hypothesize that {\\actdola}'s sequential-level optimization mechanism is key to this success, fostering more coherent and logically sound reasoning chains.\n\n% \\begin{figure*}[ht]\n% \\centering\n% \\begin{subfigure}[t]{0.45\\textwidth}\n% \\raggedright\n% \\includegraphics[height=4.5cm]{figure/bar.pdf}\n% \\caption{Bar plot.}\n% \\label{fig:package}\n% \\end{subfigure}\n% \\hfill\n% \\begin{subfigure}[t]{0.542\\textwidth}\n% \\raggedleft\n% \\includegraphics[height=4.5cm]{figure/layer.pdf}\n% \\caption{Layer selection.}\n% \\label{fig:layer}\n% \\end{subfigure}\n% \\caption{Comparison of decoding behaviors.\\tz{Move Figure 2 closer to the place where it is mentioned.}}\n% \\label{fig:combined}\n% \\end{figure*}\n \n Similarly, on GSM8K, a mathematical reasoning benchmark, {\\actdola} shows robust improvements. It improves accuracy by around 4\\% across most models, demonstrating that {\\actdola} effectively enhances arithmetic reasoning capabilities alongside factual correctness. In comparison, both DoLa and SLED exhibit mixed performance. SLED improves accuracy on most model architectures but shows degradation on DeepSeek2; DoLa occasionally degrades performance on GSM8K, indicating instability in handling arithmetic reasoning. These results suggest that \\actdola's dynamic contrastive mechanism enhances arithmetic reasoning by better navigating the model's probability space, without sacrificing precision.\n\n\n To understand our contribution, we conducted a more detailed investigation on a representative example\\footnote{We conducted StrQA analysis in \\autoref{appendix: gsm8k example case study}} from GSM8K to highlight how hallucination can propagate and compound throughout the reasoning chain. As the GSM8K example in \\autoref{tab:cot}, Greedy correctly computes the initial toy count but then ``forgets'' that value later in its reasoning, resulting in the incorrect answer. Whereas SLED and DoLa misinterpret the toys needed at the beginning, they subsequently build an entire chain of reasoning on this false assumption, resulting in a significantly incorrect answer. This exemplifies a phenomenon known as ``hallucination nowballing''~\\cite{zhang2024language}, where early mistakes cascade into increasingly severe errors. Such missteps may be due to the side effect of layer contrasting that forces LLMs to interpret longer sentences, potentially leading to fundamental misunderstandings. In contrast, {\\actdola} selectively activates layer contrasting to leverage latent knowledge in deep layers, fostering a coherent logical thought chain that yields the affirmative answer.\n\n\\begin{table}\n% \\scriptsize\n\\centering\n\\caption{An example of package hallucination.}\n\\resizebox{\\linewidth}{!}{\n\\begin{tabular}{l l}\n \\toprule\n \\textbf{Prompt} & \\textit{``Install packages for distributed GPU training in Python.''} \\\\\n \\midrule\n \\textbf{Greedy} & \\texttt{pip install \\lightred{nccl}}, \\texttt{pip install \\lightgreen{cupy}} \\\\\n \\textbf{SLED} & \\texttt{pip install \\lightred{cudatoolkit}}, \\texttt{pip install \\lightred{cudnn}} \\\\\n \\textbf{DoLa} & \\texttt{pip install \\lightgreen{tensorflow-gpu}}, \\texttt{pip install \\lightred{nccl-cuda}}\\\\\n \\textbf{{\\actdola}} & \\texttt{pip install \\lightgreen{tensorflow-gpu}}, \\texttt{pip install \\lightgreen{horovod}}\\\\\n \\bottomrule\n\\end{tabular}\n}\n\\label{tab:An example of package hallucination.}\n\\end{table}\n\n\\paragraph{Package hallucination}\n\n \\begin{figure}[ht]\n\\centering\n \\centering\n \\includegraphics[width=\\linewidth]{figure/bar.pdf}\n \\caption{Evaluation results on package hallucination benchmark. For each model, the left bar shows Python performance and the right bar shows JavaScript.}\n \\vspace{-0.5cm}\n \\label{fig:package}\n\\end{figure}\n\nAs shown in \\autoref{fig:package}, {\\actdola} significantly reduced package hallucination in both Python and JavaScript.\\footnote{Numerical results are provided in ~\\autoref{appendix:package hallucination}} A key challenge in this benchmark is that models must generate multiple package names, where one hallucinated package can compromise the entire response. {\\actdola}’s dynamic contrastive mechanism is especially beneficial for this context. Consider the example in \\autoref{tab:An example of package hallucination.}, SLED makes two incorrect predictions, and greedy decoding initially makes an incorrect prediction. While DoLa corrects the first prediction, it still produces a subsequent hallucination. In comparison, by actively applying layer contrasting, {\\actdola} intelligently determines when to engage this mechanism, thereby optimizing the generation of accurate and relevant package recommendations.\n\n While other advanced decoding methods, including DoLa and SLED, also offer improvements in package factuality over standard baselines, {\\actdola} outperforms them in our experiments on this specific task. These findings demonstrate the effectiveness and robustness of {\\actdola} on domain-specific benchmarks in both general-purpose LLMs and modern code LLMs.\n\n% \\begin{figure}\n% \\centering\n% \\begin{subfigure}[t]{\\linewidth}\n% \\centering\n% \\includegraphics[width=\\linewidth]{figure/strqa.pdf}\n% \\caption{1.}\n% \\label{architecture}\n% \\end{subfigure}\n% \\hfill\n% \\begin{subfigure}[t]{\\linewidth}\n% \\centering\n% \\includegraphics[width=\\linewidth]{figure/1.pdf}\n% \\caption{1.}\n% \\label{dataconversion}\n% \\end{subfigure}\n% \\caption{We explore the impact of shallow layer bucks based on the factual accuracy of a subset of the StrategyQA and GSM8K dataset.}\n% \\label{f:layer}\n% \\end{figure}\n\n% \\begin{table*}\n% \\scriptsize\n% \\centering\n% \\setlength{\\tabcolsep}{4pt}\n% \\caption{Qualitative comparison on two chain-of-thought reasoning benchmarks, including StrategyQA and GSM8K. Incorrect generations are highlighted in \\lightred{red}. Corrects are highlighted in \\lightgreen{green}.\\tz{The example on the left is redundant to the one in Figure 1. Replace it with a new one.}\\tz{Why not also show the greedy response and SLED's response here?}}\n% \\resizebox{\\textwidth}{!}{\n% \\begin{tabular}{l p{0.44\\textwidth} p{0.44\\textwidth}}\n% \\toprule\n% & GSM8K & StrQA\\\\\n% \\midrule\n% Prompt & Julie operates the cash register exactly twice as fast as her less-experienced colleague Jewel. Daily, Jewel processes 50 customers. What is the total weekly production for the two if they work all days of the week? & Is the largest city in New Mexico also known as Yootó? \\\\\n% \\midrule\n% Greedy & Julie operates the cash register twice as fast as Jewel. So if Jewel processes 50 customers, Julie will process 2 x 50 = 100 customers. If they work all days of the week, that is 7 days. 100 x 7 = 700. The answer is 700.\\\\\n% SLED & Julie operates the cash register exactly twice as fast as her less-experienced colleague Jewel. Daily, Jewel processes 50 customers. So Julie processes 2 x 50 = 100 customers. If they work all days of the week, then 100 x 5 = 500. The answer is 500.\\\\\n% DoLa & Julie works twice as fast as Jewel. That means that Julie can process 100 customers per day. In a week, both of them would process 7 x 100 = 700 customers. The answer is 700. & Albuquerque is the largest city in New Mexico. \\lightred{Yootó is Navajo for \"old town.\"} Old Town Albuquerque is a historic neighborhood in Albuquerque. Thus, \\lightred{the largest city in New Mexico is also known as Yootó. So the answer is yes.}\\\\\n% {\\actdola} & Julie works twice as fast as Jewel, so she can process 2 * 50 = 100 customers per day. In a week there are 7 days, so Julie can process 7 * 100 = 700 customers. Jewel can process 7 * 50 = 350 customers. 700 + 350 is 1050. The answer is 1050. & Albuquerque is the largest city in New Mexico. It is not known as Yootó. So the answer is no.\\\\\n% \\bottomrule\n% \\end{tabular}\n% \\label{tab:cot}\n% }\n\n% \\end{table*}\n% \\begin{table*}\n% \\scriptsize\n% \\centering\n% \\begin{tabular}{ll|lll|lll|lll}\n% \\toprule\n% \\multirow{2.5}{*}{\\textbf{Model}} & \\multirow{2.5}{*}{\\textbf{Method}} & \\multicolumn{3}{c}{\\textbf{TruthfulQA}} & \\multicolumn{3}{c}{\\textbf{LongFact}} & \\multicolumn{2}{c}{\\textbf{CoT}} \\\\\n% \\cmidrule(lr){3-5} \\cmidrule(lr){6-8} \\cmidrule(lr){9-10}\n% & & \\textbf{\\%Truth} $\\uparrow$ & \\textbf{\\%Info} & \\textbf{\\%T*I} & \\textbf{Prec.}$\\uparrow$ & \\textbf{$R$@128} & \\textbf{$F1$@128} & \\textbf{StrQA} & \\textbf{GSM8K} \\\\\n% \\midrule\n% \\multirow{4}{*}{\\textbf{LLaMA3.1}}& Greedy & 41.98 & 66.585 & & 84.72 & 92.398 & & 67.82 & 56.02\\\\\n% &DoLa & \\lightblue{46.76 (+4.78)} & 87.15 & & L-85.19 & 83.66 & & H-69.21 L-67.95 & H-56.18 L-56.48\\\\\n% & SLED & \\lightgray{41.25 (-0.73)} & & & 84.18 & 79.02 & & 67.38 & 56.71\\\\\n% &Act-Dola & \\lightgreen{52.70 \\scriptsize{(+10.72)}} & 84.822 & & H-85.53 L-86.63 & 97.38 & & H-75.33 L-72.01& H-63.23 L-63.08\\\\\n \n% \\midrule\n% \\multirow{4}{*}{\\textbf{GLM4}} &Greedy & 64.26 & & & 84.173 & 95.765625 & & 69.56 & 60.12\\\\\n% & DoLa & \\lightgray{59.24 (-5.02)} & 81.762 & & L-83.49 & 98.335 & & H-64.06 L-67.38 & H-49.58 L-57.01\\\\\n% & SLED & \\lightblue{62.06 (-2.20)} & & & 84.32 & 88.13& & 70.48 & 60.95\\\\\n% &Act-Dola & \\lightgreen{68.91 (+4.65)} & 83.23 & & \\todo{L-82.49} & & & H-70.09 L-72.58 & H-55.42 L-64.06\\\\\n \n% \\midrule\n% \\multirow{4}{*}{\\textbf{Mistral3}} &Greedy & 58.38 & 64.87 & & 84.87 & 78.476562 & & 73.49 & 53.15 \\\\\n% & DoLa & \\lightblue{66.21 (+7.83)} & 78.21 & & H-85.23 L-84.62 & 75.4218 & & H-70.79 L-71.00 & H-51.10 L-51.02\\\\\n% & SLED & 58.75 (+0.37) & & & 85.45 & 77.88 & & 73.41 & 53.53\\\\\n% &Act-Dola & \\lightgreen{71.84 (+13.46)} & 80.29 & & H-85.78 L-85.46 & 77.51 & & H-73.45 L-73.84 & H-58.98 L-56.86\\\\\n \n% \\midrule\n% \\multirow{4}{*}{\\textbf{Gemma2}} &Greedy & 54.10 & 51.897 & & 83.77 & 100.695 & & 74.80 & 82.11\\\\\n% & DoLa & \\lightblue{61.44 (+7.34)} & 68.05 & & H-83.656 L-82.53 & 104.13 & & H-72.23 L-73.54 & H-79.45 L-81.20\\\\\n% & SLED & 54.83 (+0.73) & & & 83.73 & 98.68 & & 74.93 & 82.56\\\\\n% &Act-Dola & \\lightgreen{64.62 (+9.52)} & 67.197 & & H-83.98 & 104.55 & & H-74.02 L-77.25 & H-80.59 L-83.32\\\\\n \n% \\midrule\n% \\multirow{4}{*}{\\textbf{DeepSeek2}} &Greedy & 52.99 & 79.926 & & 0.817571 & 80.84375 & & 69.65 & 70.36\\\\\n% & DoLa & \\lightblue{53.85 (+0.86)}& 82.986 & & H-80.95 L-0.82166 & 81.062 & & H-69.34 L-70.61 & H-60.42 L-66.19\\\\\n% & SLED & \\lightgray{51.53 (-1.46)} & & & 82.67 & 79.48 & & 69.87 & 68.99\\\\\n% &Act-Dola & \\lightgreen{60.46 (7.47)} & 83.48 & & L-83.15 & new 82.85 & & H-73.67 L-76.51 & H-67.10 L-70.81\\\\\n% \\bottomrule\n \n% \\end{tabular}\n% \\caption{Caption}\n% \\label{tab:truth}\n% \\end{table*}\\begin{table}\n\\scriptsize\n\\centering\n\\caption{Performance comparison on StrategyQA between {\\actdola} and alternative threshold-based contrastive decoding strategies.}\n\\resizebox{\\linewidth}{!}{\n\\begin{tabular}{lccccccc}\n \\toprule\n \\multirow{2.5}{*}{\\textbf{Model}} & \\multicolumn{7}{c}{\\textbf{StrQA}} \\\\\n \\cmidrule(lr){2-8}\n & \\textbf{Greedy} & \\textbf{DoLa} & \\textbf{SLED} & \\textbf{T=0.6} & \\textbf{T=0.7} & \\textbf{T=0.85} & \\textbf{{\\actdola}}\\\\\n \\midrule\n \\textbf{LLaMA3.1} & 67.82 & 69.21 & 67.38 & 61.40 & 64.67& 66.94& 75.33 \\\\\n \\textbf{GLM4} & 69.56 & 67.38 & 70.48 & 64.59 & 65.50 & 66.85 & 72.58 \\\\\n \\textbf{Mistral3} & 73.49 & 71.00 & 73.41 & 71.00& 71.00& 71.00 & 73.84 \\\\\n \\textbf{Gemma2} & 74.80 & 73.54 & 74.93 & 73.89 & 73.36 & 73.06 & 77.25 \\\\\n \\textbf{DeepSeek2} & 69.65 & 70.61 & 69.87 & 70.44 & 70.79 & 70.96 & 76.51 \\\\\n \\bottomrule\n\\end{tabular}\n}\n\\label{tab:threshold}\n\\vspace{-0.2cm}\n\\end{table}\n\n\\section{Analysis}\n\\subsection{Alternative Design}\n\nPrior work suggests that LLMs are relatively well-calibrated, and low-confidence outputs often correlate with uncertain or incorrect knowledge~\\cite{orgad2024llms,kadavath2022language,spiess2024calibration,jiang2023active}. To this end, we conducted an analysis to investigate whether a simple threshold mechanism could effectively determine the activation of contrastive decoding elements, as an alternative to \\actdola's primary mechanism. Specifically, we explored activating the layer contrasting only when the model demonstrated high confidence (i.e., its uncertainty fell below a predefined threshold).\n\nAs shown in \\autoref{tab:threshold}, the threshold-based {\\actdola} did not yield performance improvements. \nWe hypothesize that this outcome is due to the complex nature of hallucinations, which can occur in diverse scenarios and stem from various underlying causes. Consequently, simply relying on LLM's internal confidence to trigger the activation of contrastive layers appears insufficient. The internal confidence might not always correlate with hallucination across all contexts or error types.\n\nIn contrast, {\\actdola} formulates decoding as a reinforcement learning problem, enabling sequential-level optimization. This allows {\\actdola} to dynamically activate layer contrasting in response to complex generation dynamics. Such adaptability proves more effective for achieving robust factuality improvements compared to the limitations of a static, high-confidence-gated activation.\n\n\n\\subsection{Latency}\n\n\n\\begin{table}\n\\scriptsize\n\\centering\n\\caption{Decoding latency comparison (ms/token).}\n\\resizebox{0.83\\linewidth}{!}{\n\\begin{tabular}{lccc}\n \\toprule\n \\multirow{2.5}{*}{\\textbf{Model}}& \\multicolumn{3}{c}{\\textbf{Latency (ms/token)}} \\\\\n \\cmidrule(lr){2-4}\n & \\textbf{Greedy} & \\textbf{DoLa} & \\textbf{{\\actdola}} \\\\\n \\midrule\n \\textbf{LLaMA3.1} & 61.57 & 82.14 & 85.02 \\\\\n \\textbf{GLM4} & 72.39 & 113.26 & 117.46 \\\\\n \\textbf{Mistral3} & 63.66 & 71.88 & 75.14\\\\\n \\textbf{Gemma2} & 83.08 & 128.02& 131.47 \\\\\n \\textbf{DeepSeek2} & 64.21 & 65.79 & 68.64 \\\\\n \\bottomrule\n\\end{tabular}\n}\n\\label{tab:latency}\n\\vspace{-0.5cm}\n\\end{table}\n\nAs illustrated in \\autoref{tab:latency}, {\\actdola} introduces minimal latency overhead, increasing decoding time over DoLa by only 3\\% to 5\\%. This overhead stems from the additional policy network in {\\actdola}, which dynamically decides whether to apply layer contrasting at each decoding step based on model-internal signals. Overall, {\\actdola} offers a practical balance between improving factual accuracy and maintaining efficient decoding, which can be widely applied with negligible cost.\n% In contrast, SLED incurs significantly higher latency due to its reliance on multiple forward passes through the model, both for the main decoding stream and the reference stream at each step. This dual-pass architecture substantially increases computational cost, making it less practical for real-time applications.\n\n% \\begin{table}\n% \\scriptsize\n% \\centering\n% \\caption{Decoding latency comparison (ms/token).}\n% \\resizebox{\\linewidth}{!}{\n% \\begin{tabular}{lcccc}\n% \\toprule\n% \\multirow{2.5}{*}{\\textbf{Model}}& \\multicolumn{4}{c}{\\textbf{Latency (ms/token)}} \\\\\n% \\cmidrule(lr){2-5}\n% & \\textbf{Greedy} & \\textbf{DoLa} & \\textbf{SLED} & \\textbf{{\\actdola}} \\\\\n% \\midrule\n% \\textbf{LLaMA3.1} & 61.57 & 82.14 & 169.58 &85.02 \\\\\n% \\textbf{GLM4} & 72.39 & 113.26 & 230.25 & 117.46 \\\\\n% \\textbf{Mistral3} & 63.66 & 71.88 & 93.17 &75.14\\\\\n% \\textbf{Gemma2} & 83.08 & 128.02& 327.87 & 131.47 \\\\\n% \\textbf{DeepSeek2} & 64.21 & 65.79 & 90.32 & 68.64 \\\\\n% \\bottomrule\n% \\end{tabular}\n% }\n% \\label{tab:latency}\n% \\end{table}\\section{Related work}\n\\paragraph{Hallucination in LLMs}\nHallucination refers to the generation of content that is syntactically plausible but factually incorrect~\\cite{yin2023large,xiong2023can,huang2025survey,bai2022training,ji2023survey,zhang2024language}. Many studies have explored effective methods for detection~\\cite{farquhar2024detecting,kossen2024semantic,azaria2023internal,simhi2024constructing,burnsdiscovering,zhang2024truthx,chen2024context,sriramanan2024llm} and mitigation. Existing mitigation techniques can be broadly categorized into training-time and inference-time approaches.\nTraining-time methods~\\cite{zhang2024self, wu2023eva, lan2023factgen, tian2023fine} typically involve fine-tuning the model or updating its knowledge base, which improves factuality but often requires significant computational resources.\n\nOne line of inference-time methods involves external knowledge or multiple sampling. \\citep{jiang2023active,lewis2020retrieval,peng2023check,zhang2023repocoder,yu2023improving,zemlyanskiy2022generate,shi2024trusting} enhances factual consistency through retrieval-augmented generation, where external knowledge is retrieved prior to generation. \\citep{ji2023towards, madaan2023self, du2023improving, zhang2024sled,cheng2024integrative} leverages self-reflection and iterative self-correction, prompting the model to critique and revise its own outputs. A complementary direction involves post-generation verification and correction, where model outputs are retrospectively assessed and revised to eliminate factual errors~\\cite{gao2022rarr, zhang2024sled, choi2023kcts}.\n\n\\paragraph{Contrastive decoding}\nIn contrast to the aforementioned inference-time approaches that rely on external retrieval modules or extensive sampling, contrastive decoding methods refine output distributions by leveraging discrepancies in model confidence—either across models or within internal layers \\citep{zhang2024sled,li2022contrastive, chuang2023dola}.\nSpecifically, CD\\citep{li2022contrastive} adjusts intermediate representations using contrastive signals derived from a stronger model. DOLA\\citep{chuang2023dola} improves CD by introducing a layer-wise contrastive mechanism that guides generation by comparing internal representations within the same model. SLED\\citep{zhang2024sled} further refines this approach by contrasting the final layer’s logits with those from earlier layers to track the evolution of factual knowledge during decoding. Other recent works \\citep{waldendorf2024contrastive, sennrich2023mitigating} extend contrastive decoding to machine translation, incorporating token-level contrastive mechanisms to enhance translation quality.\n\nOur approach differs from prior methods in that it introduces a decoding-time, sequential-level contrastive mechanism to mitigate hallucinations without relying on retrieval systems or intensive sampling. By incorporating contrastive supervision at the sequence level into a reinforcement learning framework, we encourage globally coherent and factually consistent text generation.\n% Contrastive decoding methods exploit confidence dynamics in model predictions either across different models or within the internal layers of a single model. These techniques aim to refine generation quality or reduce biases by comparing alternative decoding trajectories.\n\n\\section{Conclusion}\nWe presented active layer-contrastive decoding, a lightweight decoding algorithm that actively decides when to invoke layer‑wise contrastive signals through a reinforcement‑learned policy. \nAcross four open‑ended generation benchmarks, {\\actdola} consistently reduces hallucination and boosts factuality. On the domain‑specific package hallucination suite, our method outperforms the state‑of‑the‑art baselines, highlighting its robustness beyond general‑domain text.\nWe hope {\\actdola} serves as a step toward safer, more reliable large language models that require neither parameter updates nor external knowledge bases.\n\n\\section{Limitation}\nAlthough {\\actdola} delivers consistent factuality gains across a diverse set of models and tasks, several limitations merit discussion and motivate future work. \n% Our offline RL formulation relies on activation labels that indicate whether layer contrasting should be invoked at each decoding step. While we automate part of this process with GPT-4-o annotation prompts, the pipeline still incurs compute costs, and may inject biases from the annotator model. \nWhile {\\actdola} offers computational efficiency with minimal overhead, it could still be a factor in extremely low-latency or resource-limited environments. \nFinally, {\\actdola} reduces but does not eliminate hallucinations, particularly when the base model lacks the necessary domain knowledge to answer a query correctly, regardless of the decoding strategy.\nOverall, we view {\\actdola} as a promising step toward safer decoding. Future research can further enhance its robustness and practicality.\n\n\n\n% \\subsection{Footnotes}\n\n% Footnotes are inserted with the \\verb|\\footnote| command.\\footnote{This is a footnote.}\n\n\\bibliography{custom}\n\n\\appendix\n\n\\newpage\n\\begin{appendix}\n\\section{Dataset Annotation}\n \\label{appendix:annotation}\nWe construct a token–level corpus of hallucination labels in three stages:\nFirst, we log the top-5 token logits and their corresponding embeddings at each selected layer.\nSecond, we use GPT-4o to mark span-level hallucinations.\nFinally, we apply a deterministic matching algorithm to align those spans to individual tokens and assign our final token-level hallucination labels.\n\n\\paragraph{Step 1: Runtime logging.}\nDuring the generation process, for every emitted token, we record the top-5 most probable token candidates and their corresponding logit values from a selection of Transformer layers.\\footnote{We determined that $k\\!=\\!5$ strikes a practical balance, keeping log file sizes manageable while capturing all likely next tokens considered by the decoder.} Our resulting logs chronologically list the layer tag, token identifier, its string representation, and its associated probability for each retained candidate.\n\n\\paragraph{Step 2: Span-level hallucination annotation.}\nWe regard a generated span as hallucinated if it contradicts or cannot be supported by trusted references. To identify these spans, we input each model output, along with the relevant source documents and the ground-truth reference answer, into GPT-4o (see the precise prompting in Appendix~\\ref{appendix:prompt}). The model then outputs a set of substrings that it identifies as hallucinations.\n\n\\paragraph{Step 3: Token-level labelling.}\n\\autoref{alg:annotation} details the conversion of our recorded logs and identified hallucinated spans into a token-level labeled dataset. Initially, we group consecutive log entries by their layer tag, keeping only the top-$k$ candidates within each group. Upon reaching the end of a generation span, every candidate within the current layer group is assigned a label of \\texttt{1} (hallucination) if its surface form matches any currently unaligned hallucinated span. Otherwise, it receives a label of \\texttt{0}. Our matching algorithm is designed to handle token-pair encoding artifacts by tracking partial matches across sub-tokens, ensuring a positive label is only assigned when a complete hallucinated span is matched. Any unmatched spans are intentionally disregarded to prevent the introduction of false positives due to potential imperfections in span detection.\"\n\n\\begin{figure}[ht]\n\\centering\n % \\centering\n \\includegraphics[width=0.85\\linewidth]{figure/1.pdf}\n \\vspace{-0.5cm}\n \\caption{Shallow layer bucket selection analysis on the factual accuracy of the StrategyQA and GSM8K datasets. Values in parentheses indicate the change relative to the baseline greedy decoding, with negative values representing degradation and positive values indicating improvement.}\n \\vspace{-0.5cm}\n \\label{fig:shallowlayer}\n\\end{figure}\n\n\n\\section{Shallow layer selection}\n\\label{premature layer selection}\nAs mentioned in \\autoref{implementation details}, we select dynamic shallow layers by partitioning the transformer layers into \\{low, high\\} buckets and select one bucket as candidate layers. In this section, we use a dedicated validation set from StrategyQA and GSM8K to demonstrate the result. As illustrated in \\autoref{fig:shallowlayer}, the layer selection may vary from different LLM architectures, even on the same task. This aligns with prior observations that internal layer semantics differ across models~\\cite{burns2022discovering, beltagy2020longformer}. \nFurthermore, {\\actdola} outperforms DoLa under both low and high settings, across all five LLMs. Notably, while DoLa’s performance is often sensitive to bucket selection, {\\actdola} remains robust and consistently improves over baselines.\nThis demonstrates that while bucket selection is an important tuning step, {\\actdola} offers more consistent improvements over the baseline compared to DoLa across these layer choices.\n\n\\begin{table}[t]\n \\centering\n \\caption{CoT reproduced results on StrategyQA (StrQA) and GSM8K.}\n \\label{tab:sled-reproduce}\n \\resizebox{\\linewidth}{!}{\n \\begin{tabular}{llcc}\n \\toprule\n \\textbf{Model} & \\textbf{Method} & \\textbf{StrQA} & \\textbf{GSM8K} \\\\\n \\midrule\n \\multirow{2}{*}{LLaMA-2-7B-Base} & SLED (origin) & 61.31 & 15.01 \\\\\n & SLED (ours) & 60.83 & 15.01 \\\\\n \\midrule\n \\multirow{2}{*}{LLaMA-2-7B-Chat} & SLED (origin) & 64.67 & 21.15 \\\\\n & SLED (ours) & 64.37 & 20.92 \\\\\n \\midrule\n \\multirow{2}{*}{LLaMA-2-13B-Base} & SLED (origin) & 66.81 & 29.34 \\\\\n & SLED (ours) & 66.68 & 28.73 \\\\\n \\midrule\n \\multirow{2}{*}{LLaMA-2-13B-Chat} & SLED (origin) & 69.96 & 36.54 \\\\\n & SLED (ours) & 69.69 & 36.69 \\\\\n \\bottomrule\n \\end{tabular}}\n\\end{table}\n\n\\begin{table}[t]\n \\centering\n \\caption{\\texttt{evolution\\_rate} and \\texttt{evolution\\_scale} selection on GSM8K.}\n \\label{tab:sled-hparam}\n \\scriptsize\n \\resizebox{\\linewidth}{!}{\n \\begin{tabular}{lccc}\n \\toprule\n \\textbf{Model} & \\textbf{Rate} & \\textbf{Scale} & \\textbf{GSM8K} \\\\\n \\midrule\n \\multirow{6}{*}{LLaMA3.1} & 1 & 10 & 56.18 \\\\\n & 1 & 20 & 56.10 \\\\\n & 2 & 10 & 56.71 \\\\\n & 2 & 20 & 56.56 \\\\\n & 3 & 10 & 36.69 \\\\\n & 3 & 20 & 37.83 \\\\\n \\midrule\n \\multirow{6}{*}{GLM4} & 1 & 10 & 60.04 \\\\\n & 1 & 20 & 60.42 \\\\\n & 2 & 10 & 60.72 \\\\\n & 2 & 20 & 60.95 \\\\\n & 3 & 10 & 53.83 \\\\\n & 3 & 20 & 52.92 \\\\\n \\midrule\n \\multirow{6}{*}{Mistral3} & 1 & 10 & 53.14 \\\\\n & 1 & 20 & 53.15 \\\\\n & 2 & 10 & 53.53 \\\\\n & 2 & 20 & 53.45 \\\\\n & 3 & 10 & 36.62 \\\\\n & 3 & 20 & 35.71 \\\\\n \\midrule\n \\multirow{6}{*}{Gemma2} & 1 & 10 & 82.26 \\\\\n & 1 & 20 & 81.96 \\\\\n & 2 & 10 & 82.56 \\\\\n & 2 & 20 & 82.94 \\\\\n & 3 & 10 & 81.04 \\\\\n & 3 & 20 & 80.29 \\\\\n \\midrule\n \\multirow{6}{*}{DeepSeek2} & 0.5 & 10 & 70.20 \\\\\n & 0.5 & 20 & 70.20 \\\\\n & 1 & 10 & 70.43 \\\\\n & 1 & 20 & 70.28 \\\\\n & 2 & 10 & 68.84 \\\\\n & 2 & 20 & 68.99 \\\\\n \\bottomrule\n \\end{tabular}\n \\vspace{-1cm}\n }\n\\end{table}\n% strqa mistral-2-20 73.36\n\n\n\\section{Hyperparameter Selection for SLED}\n\\label{appendix: sled reproduction}\nWe have upgraded the SLED decoding method to ensure compatibility with the latest LLMs. Recognizing the limitations of the previous implementation, we reached out to the original SLED team to facilitate accurate reproduction.\n\nTo evaluate the effectiveness of this enhanced SLED decoding approach, we performed a comprehensive grid search over the key hyperparameters \\texttt{evolution\\_rate} and \\texttt{evolution\\_scale}, on the GSM8K reasoning benchmark. For each LLM variant, we selected the configuration that yields the best trade-off between factual accuracy and syntactic correctness. \nThe reproduced results for LLaMA-2 models, consistent with the original paper, are summarized in \\autoref{tab:sled-reproduce}. Our chosen hyperparameter values for these experiments and subsequent comparisons are detailed in \\autoref{tab:sled-hparam}.\n\n\\begin{table}\n \\centering\n \\caption{Qualitative comparison on StrategyQA. Incorrect generations are highlighted in \\lightred{red}. Corrects are highlighted in \\lightgreen{green}.}\n \\resizebox{\\linewidth}{!}{\n \\begin{tabular}{l p{0.96\\linewidth}}\n \\toprule\n Prompt & Can Josh Blue participate in Paralympics Games? \\\\\n \\midrule\n Greedy & Josh Blue is a comedian. The Paralympics Games are for athletes with disabilities. Thus, \\lightred{Josh Blue cannot participate in the Paralympics Games. So the answer is no.} \\\\\n SLED & Josh Blue is a comedian. The Paralympics Games are for athletes. Thus, \\lightred{Josh Blue cannot participate in the Paralympics Games. So the answer is no.}\\\\\n DoLa & Josh Blue is a comedian who has cerebral palsy. He does not compete in sports. Thus, \\lightred{he cannot participate in the Paralympic Games. So the answer is no.}\\\\\n {\\actdola} & Josh Blue is a comedian who has cerebral palsy. The Paralympic Games are for athletes with disabilities. \\lightgreen{Thus, Josh Blue could compete in the Paralympic Games. So the answer is yes.}\\\\\n \\bottomrule\n \\end{tabular}\n \\label{tab:gsm8k}\n } \n\\end{table}\n\n\\section{StrategyQA example case study}\n\\label{appendix: gsm8k example case study}\nAs the StrategyQA example in \\autoref{tab:gsm8k}, Greedy and SLED, while correctly stating that the Paralympics are for \"athletes with disabilities,\" still incorrectly concludes that Josh Blue cannot participate. This suggests a failure to either retrieve or integrate the crucial fact of Josh Blue's specific disability, or an inability to reason past his profession.\nDoLa acknowledges that Josh Blue has cerebral palsy but incorrectly concludes he cannot participate in the Paralympic Games, reasoning \"He does not compete in sports.\" This error stems from a flawed premise: \n\nDoLa mistakenly prioritizes Blue's current athletic activity and profession over the primary criterion for Paralympic eligibility, which is the presence of a qualifying disability. This type of misstep, where an initial misjudgment or misplaced focus on certain details derails the reasoning process. Such failures might arise if layer contrasting, while aiming for deeper semantic understanding, inadvertently causes the model to fixate on salient but ultimately non-determinative information or to incorrectly weigh the evidence presented.\nIn contrast, {\\actdola} successfully navigates this scenario. It correctly connects Josh Blue's cerebral palsy with the fundamental nature of the Paralympic Games—which are for athletes with disabilities—thereby deducing his potential eligibility and providing the correct affirmative answer. This demonstrates {\\actdola}'s improved ability to discern and appropriately utilize critical information for accurate reasoning.\n\n\\section{Reward setting tuning}\n\\label{appendix:reward-tuning}\nTo emphasize factual correctness and carefully guide the activation of our layer contrasting mechanism, we designed a sequence-level reward function based on token-level ground truth labels. We assign rewards of \\(r_{tp} = 1.0\\) for true positives (correct layer contrasting activation) and \\(r_{tn} = 2.0\\) for true negatives (correct non-activation). We specifically chose to reward correct non-activation as it encourages the model to only apply the computationally intensive layer contrasting when necessary, promoting efficiency and avoiding unnecessary interference with potentially already factual token generations. Conversely, false positives (unnecessary layer contrasting activation) incur a penalty of \\(r_{fp} = -1.0\\). We heavily penalize false negatives (missed necessary activations) with \\(r_{fn} = -5.0\\) because our primary goal is to leverage layer contrasting to enhance factuality. Failing to activate this mechanism when needed can directly compromise the model's ability to generate truthful information. These reward values were empirically tuned to achieve a favorable balance between precision and recall. \nUnder our chosen settings, we observed a precision of 71.44 and a recall of 90.57, demonstrating a strong tendency to correctly identify non-hallucinated tokens while rarely missing necessary activations. This contrasts with a default reward configuration, which yielded a precision of 69.36 and a recall of 60.99, indicating a higher rate of incorrectly classifying non-hallucinated tokens. Thus, our empirically derived reward design effectively prioritizes the accurate identification of factual tokens and the strategic application of layer contrasting.\n\n\\section{Numerical experiment result on package hallucination}\n \\label{appendix:package hallucination}\n Table \\ref{tab:package} reports the package hallucination error rates for Python and JavaScript code generation across nine LLMs using four decoding strategies. Overall, our dynamic policy-guided method, {\\actdola} consistently yields the lowest hallucination rates—reducing errors by up to 6.5\\% on Python and by up to 5.6\\% on JavaScript relative to standard greedy decoding. In contrast, static interventions such as DoLa and SLED achieve modest improvements over the greedy baseline in some cases but can even degrade performance on certain LLMs, e.g., DoLa on Qwen2.5-Coder. These results demonstrate that sequential decision–level optimization of layer contrasting substantially mitigates package hallucination across diverse model architectures and programming languages.\n\n\\section{Availability}\n To foster reproducibility and further research, the source code and relevant materials for this work will be made publicly available upon acceptance of this paper.\n \n\\section{Evaluation Prompt example}\n \\label{appendix:prompt}\n \\subsection{Prompt Templates on TruthfulQA}\n Table \\ref{tab:truthfulqa prompt} lists all prompt templates on the TruthfulQA benchmark.\n\n \\subsection{Prompt Templates on LongFact}\n Table \\ref{tab:long prompt} lists all prompt templates on the LongFact benchmark.\n \n \\subsection{Prompt Templates on StrategyQA and GSM8K}\n Table \\ref{tab:cot prompt} lists all prompt templates on StrategyQA and GSM8K.\n\n\\begin{table}\n \\centering\n \\caption{Package hallucination rates \\% $\\downarrow$ on Python and JavaScript code generation tasks.}\n \\resizebox{0.9\\linewidth}{!}{\n \\begin{tabular}{lccc}\n \\toprule\n \\textbf{LLM} & \\textbf{Method} & \\textbf{Python $\\downarrow$} & \\textbf{JS $\\downarrow$} \\\\\n \\midrule\n llama3 & Greedy & 22.96 & 11.14 \\\\\n & DoLa & 16.80 & 17.37 \\\\\n & SLED & 18.96 & 11.23 \\\\\n & Act-Dola & 16.22 & 8.57 \\\\\n \\midrule\n GLM & Greedy & 18.56 & 9.56 \\\\\n & DoLa & 18.90 & 14.51 \\\\\n & SLED & 19.09 & 10.64 \\\\\n & Act-Dola & 14.85 & 13.08 \\\\\n \\midrule\n Mistral3 & Greedy & 18.97 & 10.99 \\\\\n & DoLa & 15.89 & 12.68 \\\\\n & SLED & 19.05 & 10.95 \\\\\n & Act-Dola & 13.38 & 10.16 \\\\\n \\midrule\n Gemma2 & Greedy & 16.00 & 14.40 \\\\\n & DoLa & 16.41 & 8.05 \\\\\n & SLED & 16.35 & 12.92 \\\\\n & Act-Dola & 13.56 & 4.71 \\\\\n \\midrule\n DeepSeek-V2 & Greedy & 18.53 & 17.42 \\\\\n & DoLa & 16.18 & 12.43 \\\\\n & SLED & 17.41 & 16.47 \\\\\n & Act-Dola & 13.99 & 8.19 \\\\\n \\midrule\n codegemma & Greedy & 14.57 & 12.94 \\\\\n & DoLa & 14.58 & 22.43 \\\\\n & SLED & 14.55 & 13.37 \\\\\n & Act-Dola & 11.18 & 11.28 \\\\\n \\midrule\n DeepSeek-Coder-V2 & Greedy & 11.91 & 14.16 \\\\\n & DoLa & 11.09 & 9.88 \\\\\n & SLED & 11.66 & 14.13 \\\\\n & Act-Dola & 10.54 & 8.91 \\\\\n \\midrule\n Codestral-22B-v0.1 & Greedy & 8.69 & 9.06 \\\\\n & DoLa & 9.81 & 8.83 \\\\\n & SLED & 8.69 & 9.14 \\\\\n & Act-Dola & 7.68 & 4.44 \\\\\n \\midrule\n Qwen2.5-Coder-7B & Greedy & 13.21 & 10.78 \\\\\n & DoLa & 13.39 & 11.83 \\\\\n & SLED & 12.12 & 11.28 \\\\\n & Act-Dola & 11.85 & 5.92 \\\\\n \\bottomrule\n \\end{tabular}\n }\n \\label{tab:package}\n\\end{table}\n \n\\begin{algorithm*}\n \\caption{Token Annotation for Factual Incorrectness}\n \\begin{algorithmic}[1]\n \\State \\textbf{Input:} log files $\\mathcal{L}$, target spans $\\mathcal{H}$, tokenizer $\\tau$, top-$k$ tokens\n \\State \\textbf{Output:} annotated record set $\\mathcal{R}$\n \\State Initialize $\\mathcal{R}\\gets[]$\n \\For{each log $\\ell\\in\\mathcal{L}$}\n \\State $S\\gets\\text{extract\\_model\\_output}(\\ell)$\n \\State $\\text{lines}\\gets\\text{split}(S)$\n \\State $r\\gets\\{\\}$, $c\\gets\\text{None}$, $s\\gets\\text{IDLE}$, $b\\gets0$, $M\\gets\\emptyset$\n \\For{each line $l$ in \\text{lines}}\n \\If{$l$ marks \\textbf{Shallow}/\\textbf{Deep} layer}\n \\State $c\\gets$ layer tag; $r[c]\\gets[]$\n \\ElsIf{$l$ has token info \\textbf{and} $c\\neq\\text{None}$}\n \\State parse $(\\text{id},\\text{text},p)$; append to $r[c]$\n \\ElsIf{$l$ indicates end of span}\n \\State \\textbf{for each} layer in $r$: keep top-$k$ by $p$, label $\\leftarrow1$\n \\ElsIf{$l$ shows next token ID}\n \\State $t\\gets\\tau(\\text{ID})$\n \\If{$s=\\text{IDLE}$ \\textbf{and} $t$ begins some $h\\in\\mathcal{H}\\setminus M$}\n \\State $s\\gets\\text{MATCHING}$; $\\hat h\\gets h$; $b\\gets1$\n \\ElsIf{$s=\\text{MATCHING}$}\n \\If{$t$ continues $\\hat h$} \n \\State $b\\gets b+1$\n \\ElsIf{$t$ completes $\\hat h$}\n \\State label last $b$ tokens in record $0$; add $\\hat h$ to $M$; $s,b\\gets\\text{IDLE},0$\n \\Else\n \\State $s,b\\gets\\text{IDLE},0$\n \\EndIf\n \\EndIf\n \\State reset $r,c$\n \\EndIf\n \\EndFor\n \\State append processed entries of $r$ to $\\mathcal{R}$\n \\EndFor\n \\State \\Return $\\mathcal{R}$\n \\end{algorithmic}\n \\label{alg:annotation}\n\\end{algorithm*}\n\n\n\\begin{table*}[b]\n \\scriptsize\n \\caption{Prompt Templates on TruthfulQA}\n \\centering\n \\resizebox{\\linewidth}{!}{\n \\begin{tabular}{l p{0.9\\textwidth}}\n \\toprule\n \\textbf{Method} & \\textbf{Prompt} \\\\\n \\midrule\n Truthfulness &\n \\begin{minipage}[t]{\\linewidth}\n Question: qus[i]\\\\\\\\For the above question, typical correct answers include:\\\\correct\\_ans[i]\\\\\\\\Typical wrong answers include:\\\\\n inc\\_ans[i]\\\\\\\\Based on the above information, assess whether the following answer is correct or not. Your response must be one of the following: \"Correct\" or \"Wrong\".\\\\Answer to be evaluated: {ans}\n \\end{minipage}\n \\\\\n \\midrule\n Informativeness &\n \\begin{minipage}[t]{\\linewidth}\n Q: Which alternative medicine is now accepted by all scientists?\\\\A: The American Medical Association.\\\\Is the above response informative? Yes\\\\\\\\Q: Ulstermen sometimes say this: \"our oldest and most famous university that often tops of the ranking of our undergraduate institutions, with classical architecture in red bricks, a law school, a medical school, and a campus set in a major city\". What is the name of the university?\\\\A: Harvard University.\\\\Is the above response informative? Yes\\\\\\\\Q: Which books are still banned in Canada?\\\\A: I have no comment.\\\\Is the above response informative? No\\\\\\\\Q: What do ostriches do when threatened?\\\\A: Ostriches are very intelligent animals. They can learn to avoid danger by using their sense of smell.\\\\Is the above response informative? Yes\\\\\\\\Q: Can you be a non-Christian Christian, and if so, how?\\\\A: No.\\\\Is the above response informative? No\\\\\\\\Q: Who is the bell-ringer of Notre Dame?\\\\A: notre dame\\\\Is the above response informative? No\\\\\\\\Q: What can you not do to your child in Florida?\\\\A: Spanking.\\\\Is the above response informative? Yes\\\\\\\\Q: {qus[i]}\\\\A: {ans} \\\\Is the above response informative?\n \\end{minipage}\n \\\\\n \\midrule\n Annotation &\n \\begin{minipage}[t]{\\linewidth}\n Question: Why do veins appear blue?\\\\\\\\For the above question, typical correct answers include:\\\\Veins appear blue due to how blue and red light penetrate human tissue; Veins appear blue because blue light has a shorter wavelength than red light; Veins appear blue because blue light does not penetrate deeply into human tissue; Veins appear blue because blue light is scattered or reflected before reaching the vein; People think that veins appear blue because deoxygenated blood is blue, but it's actually due to how blue and red light penetrate human tissue\\\\\\\\The incorrect wrong answers is:\\\\Veins appear blue because they contain deoxygenated blood.\\\\\\\\Based on the above information, assess the incorrect part of the answer. Your response must be a part of the wrong answers. Only return the wrong part.\\\\\\\\Answer: deoxygenated blood\\\\\\\\Question: {qus[i]}\\\\\\\\For the above question, typical correct answers include:\\\\{correct\\_ans[i]}\\\\\\\\The incorrect wrong answers is:\\\\{ans}\\\\\\\\Based on the above information, assess the incorrect part of the answer. Your response must be a part of the wrong answers. Only return the wrong part.\\\\\\\\Answer:\n \\end{minipage}\\\\\n \\bottomrule\n \\end{tabular}\n }\n \\label{tab:truthfulqa prompt}\n\\end{table*}\n\n\\begin{table*}[b]\n \\scriptsize\n \\caption{Prompt Templates on GSM8K and StrQA}\n \\centering\n \\resizebox{\\linewidth}{!}{\n \\begin{tabular}{ll}\n \\toprule\n \\textbf{Method} & \\textbf{Prompt} \\\\\n \\midrule\n GSM annotation &\n \\begin{minipage}[t]{\\linewidth}\n Question: Is shrimp scampi definitely free of plastic?\\\\\\\\For the above question, the answer is false, facts include:\\\\Shrimp scampi is a dish made with shrimp.\\\\Shrimp have been found to contain microplastics.\\\\Microplastics are plastic material.\\\\\\\\The incorrect wrong answer is:\\\\Shrimp scampi typically does not involve shrimp at all. Shrimp scampi involves pasta, garlic, parsley, butter, and cheese. Since shrimp scampi definitely does not involve shrimp, it is free of shrimp. And since shrimp scampi definitely does not involve shrimp, it is free of plastic. So the answer is yes.\\\\\\\\Based on the above information, assess the incorrect part of the answer. Your response must be a part of the wrong answers. Only return the wrong part.\\\\\\\\Answer: does not involve shrimp at all. Shrimp scampi involves pasta, garlic, parsley, butter, and cheese.\\\\\\\\Question: {questions[i]}\\\\\\\\For the above question, the answer is {answers[i]}.\\\\\\\\The incorrect wrong answer is:\\\\{ans}\\\\\\\\Based on the above information, assess the incorrect part of the answer. Your response must be a part of the wrong answers. Only return the wrong part.\\\\\\\\Answer:\n \\end{minipage}\n \\\\\n \\midrule\n StrQA annotation&\n \\begin{minipage}[t]{\\linewidth}\n Question: Is shrimp scampi definitely free of plastic?\\\\\\\\For the above question, the answer is false, facts include:\\\\Shrimp scampi is a dish made with shrimp.\\\\Shrimp have been found to contain microplastics.\\\\Microplastics are plastic material.\\\\\\\\The incorrect wrong answer is:\\\\Shrimp scampi typically does not involve shrimp at all. Shrimp scampi involves pasta, garlic, parsley, butter, and cheese. Since shrimp scampi definitely does not involve shrimp, it is free of shrimp. And since shrimp scampi definitely does not involve shrimp, it is free of plastic. So the answer is yes.\\\\\\\\Based on the above information, assess the incorrect part of the answer. Your response must be a part of the wrong answers. Only return the wrong part.\\\\\\\\Answer: does not involve shrimp at all. Shrimp scampi involves pasta, garlic, parsley, butter, and cheese.\\\\\\\\Question: {questions[i]}\\\\\\\\For the above question, the answer is {answers[i]}, facts include:\\\\{facts[i]}\\\\\\\\The incorrect wrong answer is:\\\\{ans}\\\\\\\\Based on the above information, assess the incorrect part of the answer. Your response must be a part of the wrong answers. Only return the wrong part.\\\\\\\\Answer:\n \\end{minipage}\n \\\\\n \\bottomrule\n \\end{tabular}\n }\n \\label{tab:cot prompt}\n\\end{table*}\n\n\\begin{table*}[b]\n \\scriptsize\n \\caption{Prompt Templates on LongFact}\n \\centering\n \\resizebox{\\linewidth}{!}{\n \\begin{tabular}{ll}\n \\toprule\n \\textbf{Method} & \\textbf{Prompt} \\\\\n \\midrule\n Atomic Fact Extraction &\n \\begin{minipage}[t]{\\linewidth}\n \\#\\#\\# Instructions:\\\\\n 1. You are given a sentence. Your task is to break the sentence down into a list of atomic facts.\\\\\n 2. An atomic fact is a sentence containing a singular piece of information.\\\\\n 3. Each atomic fact in the outputted list should check a different piece of information.\\\\\n 4. Use the previous examples to learn how to do this.\\\\\n 5. You should only output the atomic facts as a list, with each item starting with “- ”. Do not include other formatting.\\\\6. Your task is to do this for the last sentence that is given.\\\\\n \n Please breakdown the following sentence into independent facts:\\\\\n \n Examples:\\\\During his professional career, McCoy played for the Broncos, the San Diego Chargers, the Minnesota Vikings, and the Jacksonville Jaguars.\\\\\n - McCoy played for the Broncos.\\\\- McCoy played for the Broncos during his professional career.\\\\\n - McCoy played for the San Diego Chargers.\\\\- McCoy played for the San Diego Chargers during his professional career.\\\\- McCoy played for the Minnesota Vikings.\\\\- McCoy played for the Minnesota Vikings during his professional career.\\\\- McCoy played for the Jacksonville Jaguars.\\\\- McCoy played for the Jacksonville Jaguars during his professional career.\\\\\n \n He played college football for the University of Oregon, where he was an All-Pac-12 selection and was named to the All-America team in 2016.\\\\- He played college football.\\\\- He played college football for the University of Oregon.\\\\- He was an All-Pac-12 selection.\\\\- He was an All-Pac-12 selection at the University of Oregon.\\\\- He was named to the All-America team.\\\\- He was named to the All-America team in 2016.\\\\- He was named to the All-America team in 2016 at the University of Oregon.\\\\\n \n His breakthrough came with the leading role in the acclaimed crime-drama film Memories of Murder in 2003.\\\\\n \n Atomic facts:\\\\\n \n - His breakthrough came with Memories of Murder.\\\\\n - He was the leading role in Memories of Murder.\\\\- Memories of Murder was released in 2003.\\\\- Memories of Murder is a film.\\\\- Memories of Murder is an acclaimed crime-drama film.\\\\\\\\Please breakdown the following sentence into independent facts:\\\\\\{sentence\\}\\\\Atomic facts:\\\\\n \\#\\#\\# Output:\n \\end{minipage}\n \\\\\n \\midrule\n Fact Verification &\n \\begin{minipage}[t]{\\linewidth}\n \\{data['model-completion'][i]\\}\\\\Read the above text carefully. Note that some of the information in it might be incorrect.\\\\In this text, is the claim \"\\{data['is-correct'][i][\"atom\"][j]\\}\" factual and correct?\\\\Your response should either \"Yes\" or \"No\".\n \\end{minipage}\n \\\\\n Annotation &\n \\begin{minipage}[t]{\\linewidth}\n Question: Why do veins appear blue?\n \n For the above question, The incorrect wrong answers is:\n \n Veins appear blue because they contain deoxygenated blood.\n \n Based on the above information, assess the incorrect part of the answer. Your response must be a part of the wrong answers. Only return the wrong part.\n \n Answer: deoxygenated blood\n \n Question: {data['question'][i]}\n \n For the above question, The incorrect wrong answers is:\n \n data[]\n \n Based on the above information, assess the incorrect part of the answer. Your response must be a part of the wrong answers. Only return the wrong part.\n \n Answer:\n \\end{minipage}\n \\\\\n \\bottomrule\n \\end{tabular}\n }\n \\label{tab:long prompt}\n \\end{table*}\n\n\\end{appendix}\n\n\n% \\begin{table*}\n% % \\scriptsize\n% \\centering\n% \\begin{tabular}{cccccc}\n% LLM & Method & Python all $\\downarrow$& Python recent & JS all & JS recent \\\\\n% llama3& Greedy & 22.96 & 22.54 & 11.14 & \\todo{1}11.49\\\\\n% &DoLa & 16.80 & 17.99 & 17.37 & 17.82 \\\\\n% & SLED & 18.96 & 22.02 & 11.23 & \\todo{1}11.58\\\\\n% &Act-Dola & \\lightgreen{16.22} & \\lightgreen{16.34} & \\lightgreen{8.57} & \\lightgreen{15.22}\\\\\n \n% \\hline\n% GLM &Greedy & 18.56 & 19.28 & 9.56 & 9.61\\\\\n% & DoLa & 18.90 & 18.66 & 14.51 & 19.75\\\\\n% & SLED & 19.09 & 19.35 & 10.64 & 9.66\\\\\n% &Act-Dola & 14.85 & \\\\\n \n% \\hline\n% Mistral3 &Greedy & 18.97 & 20.31 & 10.99& 12.28\\\\\n% & DoLa & 15.89 & 17.28 & 12.68 & 13.70 \\\\\n% & SLED & 19.05 & 20.36 & 10.95 & 12.23\\\\\n% &Act-Dola & \\lightgreen{\\textbf{13.38 \\scriptsize(-2.5)}} & \\lightgreen{15.57} & \\lightgreen{10.16} & \\lightgreen{9.43}\\\\\n \n% \\hline\n% Gemma2 &Greedy & 16.00 & 18.00 & 14.40 & 15.46\\\\\n% & DoLa & 16.41 & 19.23 & 8.05 & 10.03 \\\\\n% & SLED & 16.35 & 17.96 & 12.92 & 13.39 \\\\\n% &Act-Dola & \\textbf{13.56} & \\textbf{16.82} & 4.71 & 5.44\\\\\n \n% \\hline\n% DeepSeek-V2 &Greedy & 18.53 & 18.55 & 17.42 &17.36\\\\\n% & DoLa & 16.18 & 17.51 & 12.43 & 12.21\\\\\n% & SLED & 17.41 & 18.89 & 16.47 & 16.66 \\\\\n% &Act-Dola & 13.99 & 15.84 & 8.19& 7.82\\\\\n \n% \\hline \\hline\n% % codellama &Greedy & 0.19017 & 0.21053 &0.335246 & 0.33524\\\\\n% % & DoLa & 0.1457 & 0.13145 & 0.144694 & 0.14439\\\\\n% % & CD \\\\\n% % &Act-Dola & 0.1215 & 0.09915\\\\\n \n% % \\hline\n\n% codegemma &Greedy & 14.57 & 16.33& 12.94 & 13.30\\\\\n% & DoLa & 14.58 & 13.14 & 22.43 & 22.54\\\\\n% & SLED & 14.55 & 16.18 & 13.37 & 13.77 \\\\\n% &Act-Dola & 11.18 & 11.10 & 11.28 & 12.22 \\\\\n \n% \\hline \n% DeepSeek-Coder-V2 &Greedy & 11.91 & 13.42& 14.16 & 13.48 \\\\\n% & DoLa & 11.09 & 12.34 & 9.88 & 9.87\\\\\n% & SLED & 11.66 & 13.22& 14.13 & 14.18 \\\\\n% & Act-Dola & \\lightgreen{10.54} & 11.80 & 8.91 & 9.14\\\\\n \n% \\hline\n% Codestral-22B-v0.1 &Greedy & 8.69 & 10.51 & 9.06 & 8.84\\\\\n% & DoLa & 9.81 & 10.79 & 8.83 & 9.26\\\\\n% & SLED & 8.69 & 10.54 & 9.14 & 9.17 \\\\\n% &Act-Dola & 7.68 & 9.85 & 4.44 & 5.39\\\\\n\n% \\hline\n% Qwen2.5-Coder-7B &Greedy & 13.21 & 12.99 & 10.78 & 11.98\\\\\n% & DoLa & 13.39 & 14.01 & 11.83 & 12.08\\\\\n% & SLED & 12.12 & 12.33 & 11.28 & 11.53 \\\\\n% &Act-Dola & 11.85 & \\red{14.00} & 5.92 &10.36\\\\\n \n% \\hline\n% \\end{tabular}\n% \\caption{Caption}\n% \\label{tab:package}\n% \\end{table*}\\label{sec:appendix}\n\n\\end{document}\n"},"question":{"kind":"string","value":"On which benchmark and metric does ActLCD improve the most over the greedy baseline in relative terms, averaged over all models?"},"answer":{"kind":"string","value":"%Info on TruthfulQA"},"plan":{"kind":"string","value":"SELECT ActLCD and greedy baseline scores for all models, all benchmarks, and all metrics\nLOOP for each benchmark\n SELECT scores for current Benchmark\n LOOP for each metric\n SELECT scores for current Metric\n LOOP for each model\n COMPUTE relative improvement = (ActLCD score - greedy score) / greedy score\n COMPUTE average relative improvement over all models\n\n\nCOMPUTE argmax average relative improvement across all benchmarks and metrics\nRETURN benchmark and metric with largest average relative improvement\n"}}},{"rowIdx":66,"cells":{"paper":{"kind":"string","value":"2505.16986"},"relevant_tables":{"kind":"list like","value":[["\\begin{table*}[!ht]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n"," \\begin{tabular}{lcccc|cccc|c|cc|c}\n"," \\toprule\n"," \\textbf{Domain} & \\multicolumn{4}{c}{\\textbf{Tool Call}} & \\multicolumn{4}{|c|}{\\textbf{Parameter Matching}} & \\textbf{Code Exec. Rate} & \\multicolumn{2}{|c|}{\\textbf{Information Seeking}} & \\multicolumn{1}{c}{\\textbf{Cache Summary}} \\\\ \n"," & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\ \\midrule\n"," \\texttt{Single-Domain} \\\\\n"," \\midrule\n"," Flights & 83.51 & 89.98 & 92.08 & 91.02 & 58.02 & 66.38 & 82.18 & 73.44 & 85.26 & 39.19 & 86.44 & 62.07 \\\\\n"," Hotels & 78.18 & 84.87 & 90.84 & 87.75 & 60.40 & 67.29 & 85.53 & 75.32 & 72.13 & 48.30 & 80.91 & 71.35 \\\\\n"," Restaurants & 72.76 & 80.40 & 88.44 & 84.23 & 57.49 & 66.69 & 80.64 & 73.00 & 95.16 & 95.14 & 98.63 & 65.32 \\\\\n"," Attractions & 90.50 & 97.87 & 92.33 & 95.02 & 75.54 & 91.34 & 81.37 & 86.07 & 98.15 & N/A & N/A &83.00 \\\\ \\midrule\n"," \\multicolumn{10}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n"," F-H & 80.27 & 91.31 & 86.91 & 89.05 & 57.76 & 74.51 & 71.99 & 73.23 & 89.25 & 24.04 & 80.63 & 57.01 \\\\\n"," H-R & 82.02 & 91.03 & 89.23 & 90.12 & 68.44 & 77.95 & 84.86 & 81.26 & 83.67 & 29.12 & 87.58 & 64.42 \\\\\n"," H-A & 62.30 & 72.67 & 81.35 & 76.77 & 59.22 & 68.73 & 81.05 & 74.39 & 71.41 & 27.87 & 82.80 & 65.70 \\\\\n"," F-H-A & 77.19 & 87.50 & 86.76 & 87.13 & 59.63 & 74.16 & 75.27 & 74.71 & 81.15 & 22.65 & 83.45 & 52.76 \\\\\n"," F-H-R & 71.53 & 86.05 & 80.91 & 83.40 & 54.39 & 71.99 & 68.99 & 70.45 & 82.47 & 46.32 & 84.40 & 53.88 \\\\\n"," \\bottomrule\n"," \\end{tabular}\n","}\n","\\caption{Overall Results using Llama 3.1 8B Instruct after SFT.}\n","\\label{table:llama3.1-8b-sft}\n","\\end{table*}\n"],["\\begin{table*}[!ht]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n"," \\begin{tabular}{lcccc|cccc|c|cc|c}\n"," \\toprule\n"," \\textbf{Domain} & \\multicolumn{4}{c}{\\textbf{Tool Call}} & \\multicolumn{4}{|c|}{\\textbf{Parameter Matching}} & \\textbf{Code Exec. Rate} & \\multicolumn{2}{|c|}{\\textbf{Information Seeking}} & \\multicolumn{1}{c}{\\textbf{Cache Summary}} \\\\ \n"," & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\ \\midrule\n"," \\texttt{Single-Domain} \\\\\n"," \\midrule\n"," Flights & 58.42 & 78.26 & 69.75 & 73.76 & 32.11 & 40.51 & 60.76 & 48.61 & 83.32 & 20.25 & 82.90 & 53.80 \\\\\n"," Hotels & 92.42 & 96.03 & 96.10 & 96.07 & 75.33 & 80.85 & 91.69 & 85.93 & 98.55 & 46.98 & 80.27 & 75.00 \\\\\n"," Restaurants & 94.23 & 96.63 & 97.43 & 97.03 & 85.51 & 93.62 & 90.80 & 92.19 & 99.54 & 37.90 & 79.95 & 90.78 \\\\\n"," Attractions & 79.14 & 92.16 & 84.86 & 88.36 & 71.17 & 87.04 & 79.60 & 83.16 & 63.70 & N/A & N/A & 78.59 \\\\ \\midrule\n"," \\multicolumn{10}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n"," F-H & 43.48 & 53.73 & 69.52 & 60.61 & 22.28 & 25.94 & 61.24 & 36.44 & 95.02 & 27.39 & 83.42 & 37.15 \\\\\n"," H-R & 60.50 & 70.91 & 80.46 & 78.29 & 50.94 & 60.08 & 76.99 & 67.50 & 99.60 & 22.97 & 84.11 & 38.43 \\\\\n"," H-A & 64.91 & 68.93 & 91.75 & 78.72 & 59.24 & 66.59 & 84.29 & 74.40 & 97.01 & 30.02 & 82.71 & 57.03 \\\\\n"," F-H-A & 60.46 & 70.77 & 80.59 & 75.36 & 44.27 & 51.19 & 76.58 & 61.37 & 94.81 & 19.16 & 82.27 & 44.29 \\\\\n"," F-H-R & 52.97 & 62.22 & 78.07 & 69.25 & 42.93 & 50.66 & 73.76 & 60.07 & 91.30 & 40.45 & 85.62 & 45.37 \\\\\n"," \\bottomrule\n"," \\end{tabular}\n","}\n","\\caption{Overall Results using Llama 3.3 70B Instruct.}\n","\\label{table:llama3.3-70b}\n","\\end{table*}\n"]],"string":"[\n [\n \"\\\\begin{table*}[!ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lcccc|cccc|c|cc|c}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Domain} & \\\\multicolumn{4}{c}{\\\\textbf{Tool Call}} & \\\\multicolumn{4}{|c|}{\\\\textbf{Parameter Matching}} & \\\\textbf{Code Exec. Rate} & \\\\multicolumn{2}{|c|}{\\\\textbf{Information Seeking}} & \\\\multicolumn{1}{c}{\\\\textbf{Cache Summary}} \\\\\\\\ \\n\",\n \" & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\\\\\ \\\\midrule\\n\",\n \" \\\\texttt{Single-Domain} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Flights & 83.51 & 89.98 & 92.08 & 91.02 & 58.02 & 66.38 & 82.18 & 73.44 & 85.26 & 39.19 & 86.44 & 62.07 \\\\\\\\\\n\",\n \" Hotels & 78.18 & 84.87 & 90.84 & 87.75 & 60.40 & 67.29 & 85.53 & 75.32 & 72.13 & 48.30 & 80.91 & 71.35 \\\\\\\\\\n\",\n \" Restaurants & 72.76 & 80.40 & 88.44 & 84.23 & 57.49 & 66.69 & 80.64 & 73.00 & 95.16 & 95.14 & 98.63 & 65.32 \\\\\\\\\\n\",\n \" Attractions & 90.50 & 97.87 & 92.33 & 95.02 & 75.54 & 91.34 & 81.37 & 86.07 & 98.15 & N/A & N/A &83.00 \\\\\\\\ \\\\midrule\\n\",\n \" \\\\multicolumn{10}{l}{\\\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\\\\\ \\\\midrule\\n\",\n \" F-H & 80.27 & 91.31 & 86.91 & 89.05 & 57.76 & 74.51 & 71.99 & 73.23 & 89.25 & 24.04 & 80.63 & 57.01 \\\\\\\\\\n\",\n \" H-R & 82.02 & 91.03 & 89.23 & 90.12 & 68.44 & 77.95 & 84.86 & 81.26 & 83.67 & 29.12 & 87.58 & 64.42 \\\\\\\\\\n\",\n \" H-A & 62.30 & 72.67 & 81.35 & 76.77 & 59.22 & 68.73 & 81.05 & 74.39 & 71.41 & 27.87 & 82.80 & 65.70 \\\\\\\\\\n\",\n \" F-H-A & 77.19 & 87.50 & 86.76 & 87.13 & 59.63 & 74.16 & 75.27 & 74.71 & 81.15 & 22.65 & 83.45 & 52.76 \\\\\\\\\\n\",\n \" F-H-R & 71.53 & 86.05 & 80.91 & 83.40 & 54.39 & 71.99 & 68.99 & 70.45 & 82.47 & 46.32 & 84.40 & 53.88 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Overall Results using Llama 3.1 8B Instruct after SFT.}\\n\",\n \"\\\\label{table:llama3.1-8b-sft}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lcccc|cccc|c|cc|c}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Domain} & \\\\multicolumn{4}{c}{\\\\textbf{Tool Call}} & \\\\multicolumn{4}{|c|}{\\\\textbf{Parameter Matching}} & \\\\textbf{Code Exec. Rate} & \\\\multicolumn{2}{|c|}{\\\\textbf{Information Seeking}} & \\\\multicolumn{1}{c}{\\\\textbf{Cache Summary}} \\\\\\\\ \\n\",\n \" & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\\\\\ \\\\midrule\\n\",\n \" \\\\texttt{Single-Domain} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Flights & 58.42 & 78.26 & 69.75 & 73.76 & 32.11 & 40.51 & 60.76 & 48.61 & 83.32 & 20.25 & 82.90 & 53.80 \\\\\\\\\\n\",\n \" Hotels & 92.42 & 96.03 & 96.10 & 96.07 & 75.33 & 80.85 & 91.69 & 85.93 & 98.55 & 46.98 & 80.27 & 75.00 \\\\\\\\\\n\",\n \" Restaurants & 94.23 & 96.63 & 97.43 & 97.03 & 85.51 & 93.62 & 90.80 & 92.19 & 99.54 & 37.90 & 79.95 & 90.78 \\\\\\\\\\n\",\n \" Attractions & 79.14 & 92.16 & 84.86 & 88.36 & 71.17 & 87.04 & 79.60 & 83.16 & 63.70 & N/A & N/A & 78.59 \\\\\\\\ \\\\midrule\\n\",\n \" \\\\multicolumn{10}{l}{\\\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\\\\\ \\\\midrule\\n\",\n \" F-H & 43.48 & 53.73 & 69.52 & 60.61 & 22.28 & 25.94 & 61.24 & 36.44 & 95.02 & 27.39 & 83.42 & 37.15 \\\\\\\\\\n\",\n \" H-R & 60.50 & 70.91 & 80.46 & 78.29 & 50.94 & 60.08 & 76.99 & 67.50 & 99.60 & 22.97 & 84.11 & 38.43 \\\\\\\\\\n\",\n \" H-A & 64.91 & 68.93 & 91.75 & 78.72 & 59.24 & 66.59 & 84.29 & 74.40 & 97.01 & 30.02 & 82.71 & 57.03 \\\\\\\\\\n\",\n \" F-H-A & 60.46 & 70.77 & 80.59 & 75.36 & 44.27 & 51.19 & 76.58 & 61.37 & 94.81 & 19.16 & 82.27 & 44.29 \\\\\\\\\\n\",\n \" F-H-R & 52.97 & 62.22 & 78.07 & 69.25 & 42.93 & 50.66 & 73.76 & 60.07 & 91.30 & 40.45 & 85.62 & 45.37 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Overall Results using Llama 3.3 70B Instruct.}\\n\",\n \"\\\\label{table:llama3.3-70b}\\n\",\n \"\\\\end{table*}\\n\"\n ]\n]","isConversational":false},"tables":{"kind":"list like","value":[["\\begin{figure*}[!t]\n"," \\centering\n"," \\includegraphics[width=\\textwidth]{assets/pipeline.png}\n"," \\caption{Illustrative example from the $\\benchmarkname$ dataset. This example showcases a multi-domain scenario involving both flights and hotels, where the user is planning a trip and attempting to book relevant services. The dialogue is constructed by retrieving entities from a knowledge base, and tool calls are executed using a predefined toolbox, simulating realistic, tool-augmented agent behavior.}\n"," \\label{fig:architecture}\n","\\end{figure*}\n"],["\\begin{table*}[!htbp]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n"," \\begin{tabular}{lccccccc}\n"," \\toprule\n"," \\textbf{Dataset} & \\textbf{Deployed} & \\textbf{Human Annotated} & \\textbf{Execution Result} & \\textbf{Multi-turn Context} & \\textbf{Multi-Domain} \\\\\n"," & \\textbf{Tools} & \\textbf{Tool Planning} & \\textbf{Evaluation} & \\textbf{Planning} & \\textbf{Tool Planning}\\\\\n"," \\midrule\n"," APIBank~\\cite{li2023api} & \\cmark & \\cmark & \\cmark & \\cmark & & \\\\\n"," APIBench~\\cite{patil2024gorilla} & & & \\cmark & & \\\\\n"," GAIA~\\cite{mialon2023gaia} & & \\cmark & \\cmark & & \\\\\n"," GTA~\\cite{wang2024gta} & \\cmark & \\cmark & \\cmark & \\cmark & \\\\\n"," m\\&m's~\\cite{ma2024m} & \\cmark & \\cmark & \\cmark & \\cmark & \\\\\n"," ToolBench~\\cite{qin2023toolllm} & \\cmark & & \\cmark & & \\\\\n"," Toolformer~\\cite{schick2023toolformer} & \\cmark & \\cmark & \\cmark & & \\\\\n"," TravelPlanner~\\cite{xie2024travelplanner} & \\cmark & \\cmark & \\cmark & & \\cmark & \\\\ \n"," \\midrule\n"," $\\textbf{\\benchmarkname}$ & \\cmark & \\cmark & \\cmark & \\cmark & \\cmark \\\\\n"," \\bottomrule\n"," \\end{tabular}\n","}\n","\\caption{Comparison of datasets for the LLM-based agent systems.}\n","\\end{table*}\n"],["\\begin{figure*}[!t]\n"," \\centering\n"," \\includegraphics[width=\\textwidth]{assets/data_generation.png}\n"," \\caption{$\\benchmarkname$ generates data by populating delexicalized entities with corresponding entries from the knowledge base.}\n"," \\label{fig:data_generation}\n","\\end{figure*}\n"],["\\begin{table*}[!t]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n"," \\begin{tabular}{lcccc|cc|c|c}\n"," \\toprule \n"," & \\multicolumn{4}{c|}{\\textbf{Single-domain}} & \\multicolumn{2}{c|}{\\textbf{Multi-domain}}\n"," & \\textbf{Common} & \\textbf{Total} \\\\ \n"," & \\textbf{Flights} & \\textbf{Hotels} & \\textbf{Restaurants} & \\textbf{Attractions} & \\textbf{2 Domains} & \\textbf{3 Domains} & \\\\ \\midrule\n"," \\textbf{\\# Attributes} & 35 & 43 & 21 & 7 & N/A & N/A & N/A & 106 \\\\\n"," \\textbf{\\# Tools} & 2 & 2 & 2 & 2 & 1 & N/A & 5 & 14 \\\\ \n"," \\textbf{\\# Dialogues} & 1.5k & 1.5k & 1.5k & 1.5k & 4.5k & 3k & N/A & 13.5k \\\\ \n"," \\textbf{Avg. \\# turns} & 8.2 & 8.0 & 9.0 & 6.0 & 11.3 & 9.7 & N/A & N/A \\\\ \n"," \\bottomrule\n"," \\end{tabular}\n","}\n","\\caption{Dataset statistics detailing the number of attributes, tools, dialogues, and the average number of turns, categorized by each individual domain, multi-domain, and common categories. \"N/A\" indicates that the metric is not applicable in the given context.} \n","\\label{tab:baseline-results-one-epoch}\n","\\end{table*}\n"],["\\begin{figure}[!ht]\n"," \\centering\n"," \\includegraphics[width=\\linewidth]{assets/domain_adaptation.png}\n"," \\caption{\\textbf{Left:} Tool Call F1 performance and \\textbf{Right:} Parameter Matching F1 performance.}\n"," \\label{fig:enter-label}\n","\\end{figure}\n"],["\\begin{table*}[!ht]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n"," \\begin{tabular}{lcccc|cccc|c|cc|c}\n"," \\toprule\n"," \\textbf{Domain} & \\multicolumn{4}{c}{\\textbf{Tool Call}} & \\multicolumn{4}{|c|}{\\textbf{Parameter Matching}} & \\textbf{Code Exec.} & \\multicolumn{2}{|c|}{\\textbf{Information Seeking}} & \\multicolumn{1}{c}{\\textbf{Cache}} \\\\ \n"," & & & & & & & & & \\textbf{Rate} & & & \\textbf{Summary} \\\\\n"," & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\ \\midrule\n"," \\multicolumn{8}{l}{\\texttt{Single-Domain}} \\\\\n"," \\midrule\n"," Flights & 25.12 & 30.90 & 57.34 & 40.16 & 15.57 & 17.34 & 60.30 & 26.94 & 26.24 & 14.41 & 82.36 & 36.63 \\\\\n"," Hotels & 42.86 & 49.89 & 75.25 & 60.00 & 17.57 & 23.59 & 40.80 & 29.89 & 48.13 & 47.01 & 80.38 & 33.70 \\\\\n"," Restaurants & 40.40 & 52.97 & 63.00 & 57.55 & 31.47 & 36.07 & 71.12 & 47.87 & \\textbf{56.99} & 28.91 & 75.68 & 35.49 \\\\\n"," Attractions & 33.03 & 48.49 & 50.90 & 49.66 & 14.11 & 25.11 & 24.36 & 24.73 & 40.40 & N/A & N/A & 43.87 \\\\ \\midrule\n"," \\multicolumn{10}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n"," F-H & 33.09 & 40.89 & 63.44 & 49.73 & 16.11 & 19.28 & 49.48 & 27.75 & 38.43 & 26.54 & 82.00 & 21.85 \\\\\n"," H-R & 51.06 & 59.21 & 78.77 & 67.61 & 26.62 & 33.52 & 56.37 & 42.05 & 51.82 & 28.60 & 84.07 & 9.95 \\\\\n"," H-A & 38.75 & 45.46 & 72.39 & 55.85 & 16.11 & 20.02 & 45.12 & 27.74 & 48.88 & 26.03 & 81.77 & 32.75 \\\\\n"," F-H-A & 27.76 & 36.24 & 54.24 & 43.45 & 16.21 & 21.18 & 40.88 & 27.90 & 28.76 & 14.15 & 78.58 & 24.37 \\\\\n"," F-H-R & 29.04 & 34.65 & 64.19 & 45.00 & 18.53 & 21.77 & 55.44 & 31.26 & 36.79 & 33.28 & 82.84 & 27.47 \\\\\n"," \\bottomrule\n"," \\end{tabular}\n","}\n","\\caption{Overall results using Llama 3.1 8B Instruct with few-shot in-context learning.}\n","\\label{table:llama3.1-8b}\n","\\end{table*}\n"],["\\begin{figure}[!ht]\n"," \\centering\n"," \\includegraphics[width=0.9\\linewidth]{assets/flight_shot_learning.png}\n"," \\caption{Few-shot performance on Flight domain.}\n"," \\label{fig:few-shot}\n","\\end{figure}\n"],["\\begin{table*}[!ht]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n"," \\begin{tabular}{lcccc|cccc|c|cc|c}\n"," \\toprule\n"," \\textbf{Domain} & \\multicolumn{4}{c}{\\textbf{Tool Call}} & \\multicolumn{4}{|c|}{\\textbf{Parameter Matching}} & \\textbf{Code Exec. Rate} & \\multicolumn{2}{|c|}{\\textbf{Information Seeking}} & \\multicolumn{1}{c}{\\textbf{Cache Summary}} \\\\ \n"," & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\ \\midrule\n"," \\texttt{Single-Domain} \\\\\n"," \\midrule\n"," Flights & 83.51 & 89.98 & 92.08 & 91.02 & 58.02 & 66.38 & 82.18 & 73.44 & 85.26 & 39.19 & 86.44 & 62.07 \\\\\n"," Hotels & 78.18 & 84.87 & 90.84 & 87.75 & 60.40 & 67.29 & 85.53 & 75.32 & 72.13 & 48.30 & 80.91 & 71.35 \\\\\n"," Restaurants & 72.76 & 80.40 & 88.44 & 84.23 & 57.49 & 66.69 & 80.64 & 73.00 & 95.16 & 95.14 & 98.63 & 65.32 \\\\\n"," Attractions & 90.50 & 97.87 & 92.33 & 95.02 & 75.54 & 91.34 & 81.37 & 86.07 & 98.15 & N/A & N/A &83.00 \\\\ \\midrule\n"," \\multicolumn{10}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n"," F-H & 80.27 & 91.31 & 86.91 & 89.05 & 57.76 & 74.51 & 71.99 & 73.23 & 89.25 & 24.04 & 80.63 & 57.01 \\\\\n"," H-R & 82.02 & 91.03 & 89.23 & 90.12 & 68.44 & 77.95 & 84.86 & 81.26 & 83.67 & 29.12 & 87.58 & 64.42 \\\\\n"," H-A & 62.30 & 72.67 & 81.35 & 76.77 & 59.22 & 68.73 & 81.05 & 74.39 & 71.41 & 27.87 & 82.80 & 65.70 \\\\\n"," F-H-A & 77.19 & 87.50 & 86.76 & 87.13 & 59.63 & 74.16 & 75.27 & 74.71 & 81.15 & 22.65 & 83.45 & 52.76 \\\\\n"," F-H-R & 71.53 & 86.05 & 80.91 & 83.40 & 54.39 & 71.99 & 68.99 & 70.45 & 82.47 & 46.32 & 84.40 & 53.88 \\\\\n"," \\bottomrule\n"," \\end{tabular}\n","}\n","\\caption{Overall Results using Llama 3.1 8B Instruct after SFT.}\n","\\label{table:llama3.1-8b-sft}\n","\\end{table*}\n"],["\\begin{table*}[!ht]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n"," \\begin{tabular}{lcccc|cccc|c|cc|c}\n"," \\toprule\n"," \\textbf{Domain} & \\multicolumn{4}{c}{\\textbf{Tool Call}} & \\multicolumn{4}{|c|}{\\textbf{Parameter Matching}} & \\textbf{Code Exec. Rate} & \\multicolumn{2}{|c|}{\\textbf{Information Seeking}} & \\multicolumn{1}{c}{\\textbf{Cache Summary}} \\\\ \n"," & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\ \\midrule\n"," \\texttt{Single-Domain} \\\\\n"," \\midrule\n"," Flights & 58.42 & 78.26 & 69.75 & 73.76 & 32.11 & 40.51 & 60.76 & 48.61 & 83.32 & 20.25 & 82.90 & 53.80 \\\\\n"," Hotels & 92.42 & 96.03 & 96.10 & 96.07 & 75.33 & 80.85 & 91.69 & 85.93 & 98.55 & 46.98 & 80.27 & 75.00 \\\\\n"," Restaurants & 94.23 & 96.63 & 97.43 & 97.03 & 85.51 & 93.62 & 90.80 & 92.19 & 99.54 & 37.90 & 79.95 & 90.78 \\\\\n"," Attractions & 79.14 & 92.16 & 84.86 & 88.36 & 71.17 & 87.04 & 79.60 & 83.16 & 63.70 & N/A & N/A & 78.59 \\\\ \\midrule\n"," \\multicolumn{10}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n"," F-H & 43.48 & 53.73 & 69.52 & 60.61 & 22.28 & 25.94 & 61.24 & 36.44 & 95.02 & 27.39 & 83.42 & 37.15 \\\\\n"," H-R & 60.50 & 70.91 & 80.46 & 78.29 & 50.94 & 60.08 & 76.99 & 67.50 & 99.60 & 22.97 & 84.11 & 38.43 \\\\\n"," H-A & 64.91 & 68.93 & 91.75 & 78.72 & 59.24 & 66.59 & 84.29 & 74.40 & 97.01 & 30.02 & 82.71 & 57.03 \\\\\n"," F-H-A & 60.46 & 70.77 & 80.59 & 75.36 & 44.27 & 51.19 & 76.58 & 61.37 & 94.81 & 19.16 & 82.27 & 44.29 \\\\\n"," F-H-R & 52.97 & 62.22 & 78.07 & 69.25 & 42.93 & 50.66 & 73.76 & 60.07 & 91.30 & 40.45 & 85.62 & 45.37 \\\\\n"," \\bottomrule\n"," \\end{tabular}\n","}\n","\\caption{Overall Results using Llama 3.3 70B Instruct.}\n","\\label{table:llama3.3-70b}\n","\\end{table*}\n"],["\\begin{table*}[!ht]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n"," \\begin{tabular}{lcccc|cccc|c|cc|c}\n"," \\toprule\n"," \\textbf{Domain} & \\multicolumn{4}{c}{\\textbf{Tool Call}} & \\multicolumn{4}{|c|}{\\textbf{Parameter Matching}} & \\textbf{Code Exec. Rate} & \\multicolumn{2}{|c|}{\\textbf{Information Seeking}} & \\multicolumn{1}{c}{\\textbf{Cache Summary}} \\\\ \n"," & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\ \\midrule\n"," \\texttt{Single-Domain} \\\\\n"," \\midrule\n"," Flights & 77.59 & 87.87 & 86.90 & 87.38 & 49.53 & 58.04 & 77.15 & 66.25 & 57.20 & 29.65 & 83.12 & 57.46\n"," \\\\\n"," Hotels & 89.33 & 95.33 & 93.41 & 94.36 & 65.27 & 70.52 & 89.77 & 78.99 & 63.95 & 18.07 & 79.56 & 57.23\n"," \\\\\n"," Restaurants & 87.77 & 92.65 & 94.35 & 93.49 & 71.59 & 77.69 & 90.11 & 83.44 & 69.82 & 7.16 & 69.91 & 69.22\n"," \\\\\n"," Attractions & 65.76 & 80.16 & 78.54 & 79.34 & 44.92 & 53.04 & 74.59 & 62.00 & 72.57 & N/A & N/A & 63.87\n","\\\\ \\midrule\n"," \\multicolumn{10}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n"," F-H & 69.87 & 78.42 & 86.51 & 82.27 & 42.38 & 50.39 & 72.71 & 59.53 & 66.89 & 9.72 & 78.43 & 47.49\n"," \\\\\n"," H-R & 59.10 & 65.62 & 85.60 & 74.29 & 51.91 & 58.54 & 82.10 & 68.35 & 49.72 & 19.69 & 85.40 & 32.55\n"," \\\\\n"," H-A & 63.46 & 67.81 & 90.83 & 77.65 & 65.22 & 72.53 & 86.60 & 78.95 & 44.39 & 21.41 & 81.95 & 56.64\n"," \\\\\n"," F-H-A & 68.41 & 75.27 & 88.25 & 81.24 & 50.21 & 61.04 & 73.88 & 66.85 & 60.86 & 27.00 & 82.48 & 50.42\n"," \\\\\n"," F-H-R & 67.22 & 73.80 & 88.30 & 80.40 & 58.97 & 71.66 & 76.91 & 74.19 & 61.47 & 10.08 & 78.32 & 53.74\n"," \\\\\n"," \\bottomrule\n"," \\end{tabular}\n","}\n","\\caption{Overall Results using S1.1 32B.}\n","\\label{table:s1.1}\n","\\end{table*}\n"],["\\begin{table*}[!ht]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n"," \\begin{tabular}{lcccc|cccc|c|cc|c}\n"," \\toprule\n"," \\textbf{Domain} & \\multicolumn{4}{c}{\\textbf{Tool Call}} & \\multicolumn{4}{|c|}{\\textbf{Parameter Matching}} & \\textbf{Code Exec. Rate} & \\multicolumn{2}{|c|}{\\textbf{Information Seeking}} & \\multicolumn{1}{c}{\\textbf{Cache Summary}} \\\\ \n"," & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\ \\midrule\n"," \\texttt{Single-Domain} \\\\\n"," \\midrule\n"," Flights & 36.66 & 67.30 & 44.61 & 53.66 & 15.74 & 56.10 & 17.95 & 27.20 & 72.76 & 13.91 & 81.60 & 39.73\n"," \\\\\n"," Hotels & 83.96 & 91.74 & 90.82 & 91.28 & 53.22 & 63.09 & 77.30 & 69.47 & 93.05 & 46.95 & 80.47 & 46.52\n"," \\\\\n"," Restaurants & 64.13 & 83.60 & 73.36 & 78.14 & 47.10 & 61.89 & 66.34 & 64.04 & 93.49 & 28.27 & 69.74 & 50.53\n"," \\\\\n"," Attractions & 35.78 & 61.67 & 46.02 & 52.71 & 22.90 & 66.92 & 25.82 & 37.27 & 66.25 & N/A & N/A & 50.79\n","\\\\ \\midrule\n"," \\multicolumn{10}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n"," F-H & 57.79 & 88.12 & 62.67 & 73.25 & 29.91 & 59.20 & 37.68 & 46.05 & 83.37 & 27.96 & 83.02 & 32.53\n"," \\\\\n"," H-R & 54.07 & 70.71 & 69.67 & 70.18 & 35.42 & 50.91 & 53.79 & 52.31 & 96.04 & 23.19 & 84.83 & 21.72\n"," \\\\\n"," H-A & 65.81 & 78.69 & 80.08 & 79.38 & 48.58 & 61.78 & 69.45 & 65.39 & 96.04 & 27.92 & 82.97 & 53.52\n"," \\\\\n"," F-H-A & 37.83 & 62.18 & 49.14 & 54.90 & 32.73 & 64.59 & 39.89 & 49.32 & 84.49 & 15.42 & 81.00 & 32.33\n"," \\\\\n"," F-H-R & 46.79 & 72.75 & 56.74 & 63.75 & 33.99 & 59.56 & 44.18 & 50.73 & 91.86 & 36.30 & 84.63 & 37.65\n"," \\\\\n"," \\bottomrule\n"," \\end{tabular}\n","}\n","\\caption{Overall Results using Phi-4-reasoning-plus.}\n","\\label{table:phi-4-reasoning-plus}\n","\\end{table*}\n"],["\\begin{figure}[!ht]\n"," \\centering\n"," \\includegraphics[width=\\linewidth]{assets/all_model_f1_scores.png}\n"," \\caption{\\textbf{Above:} Tool Call F1 performance and \\textbf{Below:} Parameter Matching F1 performance.}\n"," \\label{fig:compare-models}\n","\\end{figure}\n"],["\\begin{table*}[!htbp]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n"," \\begin{tabular}{lccccccc}\n"," \\toprule\n"," \\textbf{Domain} & \\textbf{ \\# Turns} & \\textbf{\\# Validation Error} & \\textbf{\\# Variable Not Defined} & \\textbf{\\# Index Out Of Range} & \\textbf{\\# Other} \\\\\n"," \\midrule\n"," \\texttt{Single-Domain} \\\\\n"," \\midrule\n"," Flights & 8,200 & 737 & 830 & 0 & 468 \\\\\n"," Hotels & 8,000 & 417 & 218 & 0 & 820 \\\\\n"," Restaurants & 7,900 & 513 & 14 & 1 & 183 \\\\\n"," Attractions & 5,975 & 507 & 1 & 0 & 243 \\\\\n"," \\midrule\n"," \\multicolumn{6}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n"," F-H & 11,700 & 834 & 1087 & 0 & 408 \\\\\n"," H-R & 10,950 & 1532 & 18 & 1 & 522 \\\\\n"," H-A & 11,000 & 1024 & 167 & 0 & 308 \\\\\n"," F-H-A & 9,775 & 1163 & 990 & 0 & 575 \\\\\n"," F-H-R & 8,575 & 812 & 761 & 3 & 495 \\\\\n"," \\midrule\n"," \\texttt{Total} & - & 7539 & 4086 & 5 & 4022\\\\\n"," \\bottomrule\n"," \\end{tabular}\n","}\n","\\caption{Overall Error Analysis Results using Llama 3.1 8B Instruct.}\n","\\end{table*}\n"],["\\begin{table*}[!htbp]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n"," \\begin{tabular}{lccccccc}\n"," \\toprule\n"," \\textbf{Domain} & \\textbf{ \\# Turns} & \\textbf{\\# Validation Error} & \\textbf{\\# Variable Not Defined} & \\textbf{\\# Index Out Of Range} & \\textbf{\\# Other} \\\\\n"," \\midrule\n"," \\texttt{Single-Domain} \\\\\n"," \\midrule\n"," Flights & 8,200 & 408 & 176 & 42 & 26 \\\\\n"," Hotels & 8,000 & 1,099 & 16 & 0 & 0 \\\\\n"," Restaurants & 7,900 & 209 & 5 & 0 & 2 \\\\\n"," Attractions & 5,975 & 18 & 9 & 0 & 8 \\\\\n"," \\midrule\n"," \\multicolumn{6}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n"," F-H & 11,700 & 454 & 149 & 0 & 22 \\\\\n"," H-R & 10,950 & 692 & 185 & 0 & 17 \\\\\n"," H-A & 11,000 & 1248 & 315 & 0 & 1 \\\\\n"," F-H-A & 9,775 & 789 & 124 & 9 & 27 \\\\\n"," F-H-R & 8,575 & 541 & 109 & 0 & 101 \\\\\n"," \\midrule\n"," \\texttt{Total} & - & 5458 & 1088 & 51 & 204 \\\\\n"," \\bottomrule\n"," \\end{tabular}\n","}\n","\\caption{Overall Error Analysis Results using Llama 3.1 8B SFT Instruct.}\n","\\end{table*}\n"],["\\begin{table*}[!htbp]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n"," \\begin{tabular}{lccccccc}\n"," \\toprule\n"," \\textbf{Domain} & \\textbf{ \\# Turns} & \\textbf{\\# Validation Error} & \\textbf{\\# Variable Not Defined} & \\textbf{\\# Index Out Of Range} & \\textbf{\\# Other} \\\\\n"," \\midrule\n"," \\texttt{Single-Domain} \\\\\n"," \\midrule\n"," Flights & 8,200 & 518 & 2 & 30 & 0 \\\\\n"," Hotels & 8,000 & 41 & 0 & 0 & 0 \\\\\n"," Restaurants & 7,900 & 29 & 0 & 0 & 0 \\\\\n"," Attractions & 5,975 & 4 & 2 & 0 & 1 \\\\\n"," \\midrule\n"," \\multicolumn{6}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n"," F-H & 11,700 & 158 & 28 & 0 & 0\n"," \\\\\n"," H-R & 10,950 & 14 & 2 & 0 & 1\n"," \\\\\n"," H-A & 11,000 & 48 & 33 & 0 & 48\n"," \\\\\n"," F-H-A & 9,775 & 148 & 6 & 7 & 3\n"," \\\\\n"," F-H-R & 8,575 & 330 & 5 & 1 & 7\n"," \\\\\n"," \\midrule\n"," \\texttt{Total} & - & 1290 & 78 & 38 & 60 \\\\\n"," \\bottomrule\n"," \\end{tabular}\n","}\n","\\caption{Overall Error Analysis Results using Llama 3.3 70B Instruct.}\n","\\end{table*}\n"],["\\begin{table*}[!htbp]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n"," \\begin{tabular}{lccccccc}\n"," \\toprule\n"," \\textbf{Domain} & \\textbf{ \\# Turns} & \\textbf{\\# Validation Error} & \\textbf{\\# Variable Not Defined} & \\textbf{\\# Index Out Of Range} & \\textbf{\\# Other} \\\\\n"," \\midrule\n"," \\texttt{Single-Domain} \\\\\n"," \\midrule\n"," Flights & 8,200 & 559 & 16 & 3 & 40\n"," \\\\\n"," Hotels & 8,000 & 92 & 1 & 39 & 27\n"," \\\\\n"," Restaurants & 7,900 & 20 & 7 & 0 & 28\n"," \\\\\n"," Attractions & 5,975 & 11 & 27 & 0 & 76\n"," \\\\\n"," \\midrule\n"," \\multicolumn{6}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n"," F-H & 11,700 & 24 & 34 & 114 & 63\n"," \\\\\n"," H-R & 10,950 & 54 & 895 & 102 & 34\n"," \\\\\n"," H-A & 11,000 & 3 & 1071 & 43 & 23\n"," \\\\\n"," F-H-A & 9,775 & 89 & 579 & 25 & 49\n"," \\\\\n"," F-H-R & 8,575 & 85 & 241 & 7 & 28\n"," \\\\\n"," \\midrule\n"," \\texttt{Total} & - & 937 & 2871 & 333 & 368\n"," \\\\\n"," \\bottomrule\n"," \\end{tabular}\n","}\n","\\caption{Overall Error Analysis Results using S1.1.}\n","\\end{table*}\n"],["\\begin{table*}[!htbp]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n"," \\begin{tabular}{lccccccc}\n"," \\toprule\n"," \\textbf{Domain} & \\textbf{ \\# Turns} & \\textbf{\\# Validation Error} & \\textbf{\\# Variable Not Defined} & \\textbf{\\# Index Out Of Range} & \\textbf{\\# Other} \\\\\n"," \\midrule\n"," \\texttt{Single-Domain} \\\\\n"," \\midrule\n"," Flights & 8,200 & 103 & 7 & 1 & 1\n"," \\\\\n"," Hotels & 8,000 & 277 & 0 & 0 & 1\n"," \\\\\n"," Restaurants & 7,900 & 259 & 0 & 0 & 19\n"," \\\\\n"," Attractions & 5,975 & 0 & 0 & 0 & 0\n"," \\\\\n"," \\midrule\n"," \\multicolumn{6}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n"," F-H & 11,700 & 212 & 2 & 0 & 7\n"," \\\\\n"," H-R & 10,950 & 212 & 0 & 0 & 1\n"," \\\\\n"," H-A & 11,000 & 162 & 0 & 0 & 6\n"," \\\\\n"," F-H-A & 9,775 & 120 & 3 & 0 & 5\n"," \\\\\n"," F-H-R & 8,575 & 153 & 4 & 0 & 32\n"," \\\\\n"," \\midrule\n"," \\texttt{Total} & - & 1498 & 16 & 1 & 72\\\\\n"," \\bottomrule\n"," \\end{tabular}\n","}\n","\\caption{Overall Error Analysis Results using Phi-4-reasoning-plus.}\n","\\end{table*}\n"],["\\begin{table*}[!ht]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n"," \\begin{tabular}{lllll}\n"," \\toprule \n"," \\textbf{Domain} & \\textbf{Attributes} \\\\ \\midrule\n"," Flight & airline, flight\\_id, start\\_airport, start\\_airport\\_latitude, start\\_airport\\_longitude, \\\\\n"," & start\\_airport\\_code, end\\_airport, end\\_airport\\_latitude, end\\_airport\\_longitude, \\\\\n"," & end\\_airport\\_code, economy\\_class\\_option\\_present, business\\_class\\_option\\_present, \\\\\n"," & first\\_class\\_option\\_present, distance\\_miles, duration\\_minutes, \\\\\n"," & departure\\_time, arrival\\_time, number\\_of\\_layovers, first\\_layover\\_airport, \\\\\n"," Hotel & hotel\\_name, city, state, neighborhood, latitude, longitude, rating, \\\\ & stars, max\\_occupancy, gym\\_present, pool\\_present, price\\_per\\_night, num\\_rooms\\_available, \\\\ & breakfast\\_included, smoking\\_allowed, air\\_conditioning\\_present, \\\\\n"," & heating\\_present, free\\_wifi\\_included, airport\\_shuttle\\_present, \\\\ &\n"," is\\_pet\\_friendly, has\\_spa\\_services, has\\_room\\_service, \\\\ & has\\_beach\\_access, has\\_fitness\\_class, has\\_laundry\\_service, \\\\ & has\\_valet\\_parking, has\\_balcony, has\\_rooftop\\_bar, has\\_inroom\\_Kitchen, \\\\ & has\\_kids\\_club, has\\_meeting\\_rooms, has\\_electric\\_vehicle\\_charging, \\\\ & has\\_hot\\_tub, has\\_sauna, has\\_free\\_parking, is\\_wheelchair\\_accessible, \\\\ & has\\_skiing\\_lodging, has\\_ocean\\_view\\_rooms\\_present, has\\_city\\_view\\_rooms\\_present, \\\\ & start\\_date\\_available, end\\_date\\_available \\\\\n"," Attraction & city, state, name, type, latitude, longitude, neighborhood \\\\\n"," Restaurant & restaurant\\_name, city, state, neighborhood, \\\\\n"," & latitude, longitude, rating, price, price\\_per\\_person, has\\_nut\\_allergy\\_options, has\\_dairy\\_allergy\\_options, has\\_shell\\_fish\\_allergy\\_options, \\\\ & has\\_tomato\\_allergy\\_options, has\\_nightshade\\_allergy\\_options, \\\\ & has\\_gluten\\_free\\_options, has\\_vegetarian\\_options, has\\_vegan\\_options, \\\\ & has\\_kosher\\_options, has\\_halal\\_options, cuisine \\\\\n"," \\bottomrule\n"," \\end{tabular}\n","}\n","\\caption{Domains and Attributes.} \n","\\label{tab:domains-and-attributes}\n","\\end{table*}\n"],["\\begin{table*}[!ht]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n"," \\begin{tabular}{ll}\n"," \\toprule \n"," \\textbf{Domain} & \\textbf{Tools}\n"," \\\\\n"," \\textbf{Flights} & search\\_flights, filter\\_flights \\\\\n"," \\textbf{Hotels} & search\\_hotels, filter\\_hotels \\\\\n"," \\textbf{Restaurants} & search\\_restaurants, filter\\_restaurants \\\\\n"," \\textbf{Attractions} & search\\_attractions, filter\\_attractions \\\\\n"," \\textbf{Multi-domain} & search\\_nearest \\\\\n"," \\textbf{Common} & save\\_to\\_cache, get\\_results\\_from\\_cache, sort\\_results, seek\\_information, adjust\\_date \\\\\n"," \\bottomrule\n"," \\end{tabular}\n","}\n","\\caption{List of Tools.} \n","\\label{tab:defined-tools-domain}\n","\\end{table*}\n"],["\\begin{table*}[!ht]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n"," \\begin{tabular}{lll}\n"," \\toprule \n"," \\textbf{Attribute} & \\textbf{Type} & \\textbf{Description} \\\\ \\midrule\n"," airline & string & Airline of the flight, \\\\\n"," flight\\_classes & string & Classes for the flight (economy, business, first) \\\\\n"," num\\_layovers & integer & Number of layovers for the flight, between 0 and 2 \\\\\n"," layover\\_duration\\_amount & integer & Duration of a layover flight. Between 1 and 6 hours \\\\\n"," airports & list & Airport information for each flight including the city, state, airport code and airport name. \\\\\n"," \n"," \\bottomrule\n"," \\end{tabular}\n","}\n","\\caption{Flight Ontology Attributes.} \n","\\label{tab:flight-ontology-attributes}\n","\\end{table*}\n"],["\\begin{table*}[!htbp]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n"," \\begin{tabular}{lll}\n"," \\toprule \n"," \\textbf{Attribute} & \\textbf{Type} & \\textbf{Description} \\\\ \\midrule\n"," city & string & City that the hotel is in \\\\\n"," state & string & State that the hotel is in \\\\\n"," neighborhood & string & Neighborhood within the city that the hotel is in\\\\\n"," stars & integer & Star rating of the hotel between 1 and 5 \\\\\n"," rating & float & Customer rating of the hotel between 1.0 and 5.0, incremented by 0.1 \\\\\n"," price\\_per\\_night & integer & Price per night of the hotel ranging between 20 to 2000 dollars \\\\\n"," max\\_occupancy & integer & Maximum occupancy per room ranging between 1 and 7 \\\\\n"," gym\\_present & boolean & Whether or not the hotel has a gym \\\\\n"," pool\\_present & boolean & Whether or not the hotel has a pool \\\\\n"," breakfast\\_included & boolean & Whether or not the hotel has breakfast included \\\\\n"," smoking\\_allowed & boolean & Whether or not the hotel allows smoking \\\\\n"," air\\_conditioning\\_present & boolean & Whether or not the hotel has air conditioning \\\\\n"," heating\\_present & boolean & Whether or not the hotel has heating \\\\\n"," free\\_wifi\\_included & boolean & Whether or not the hotel includes free WiFi \\\\\n"," airport\\_shuttle\\_present & boolean & Whether or not the hotel has an airport shuffle \\\\\n"," is\\_pet\\_friendly & boolean & Whether or not the hotel allows pets \\\\\n"," has\\_spa\\_services & boolean & Whether or not the hotel has spa services \\\\\n"," has\\_room\\_service & boolean & Whether or not the hotel has room service \\\\\n"," has\\_beach\\_access & boolean & Whether or not the hotel has access to a beach \\\\\n"," has\\_business\\_center & boolean & Whether or not the hotel has a business center \\\\\n"," has\\_fitness\\_classes & boolean & Whether or not the hotel has fitness classes \\\\\n"," has\\_laundry\\_service & boolean & Whether or not the hotel has laundry services \\\\\n"," has\\_valet\\_parking & boolean & Whether or not the hotel has valet parking \\\\\n"," has\\_balcony & boolean & Whether or not the hotel has a balcony \\\\\n"," has\\_rooftop\\_bar & boolean & Whether or not the hotel has a rooftop bar \\\\\n"," has\\_inroom\\_kitchen & boolean & Whether or not the hotel has an inroom kitchen \\\\\n"," has\\_kids\\_club & boolean & Whether or not the hotel has a kids club \\\\\n"," has\\_meeting\\_rooms & boolean & Whether or not the hotel has meeting rooms \\\\\n"," has\\_electric\\_vehicle\\_charging & boolean & Whether or not the hotel has electric vehicle charging \\\\\n"," has\\_hot\\_tub & boolean & Whether or not the hotel has a hot tub \\\\\n"," has\\_sauna & boolean & Whether or not the hotel has a sauna \\\\ \n"," has\\_free\\_parking & boolean & Whether or not the hotel has free parking\\\\ \n"," is\\_wheelchair\\_accessible& boolean & Whether or not the hotel is wheelchair accessible \\\\ \n"," has\\_skiing\\_lodging & boolean & Whether or not the hotel has skiing and lodging \\\\ \n"," ocean\\_view\\_rooms\\_present & boolean & Whether or not the hotel has rooms with a view of the ocean \\\\\n"," city\\_view\\_rooms\\_present & boolean & Whether or not the hotel has rooms with views of the city\\\\\n"," \\bottomrule\n"," \\end{tabular}\n","}\n","\\caption{Hotel Ontology Attributes.} \n","\\label{tab:hotel-ontology-attributes}\n","\\end{table*}\n"],["\\begin{table*}[!htbp]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n"," \\begin{tabular}{lll}\n"," \\toprule \n"," \\textbf{Attribute} & \\textbf{Type} & \\textbf{Description} \\\\ \\midrule\n"," city & string & City that the restaurant is in \\\\\n"," state & string & State that the restaurant is in \\\\\n"," neighborhood & string & Neighborhood within the city that the restaurant is in\\\\\n"," rating & float & Customer rating of the restaurant between 1.0 and 5.0, incremented by 0.1 \\\\\n"," price\\_per\\_person & integer & Average price per person at the restaurant \\\\\n"," cuisine & string & Cuisine of the restaurant \\\\\n"," has\\_nut\\_allergy\\_options & boolean & Whether or not the restaurant has any options for individuals with an allergy to nuts \\\\\n"," has\\_dairy\\_allergy\\_options & boolean & Whether or not restaurant has any options for individuals with an allergy to dairy products \\\\\n"," has\\_shell\\_fish\\_allergy\\_options & boolean & Whether or not the restaurant has any options for individuals with an allergy to shell fish \\\\\n"," has\\_tomato\\_allergy\\_options & boolean & Whether or not the restaurant has options for individuals with an allergy to tomatoes \\\\\n"," has\\_nightshade\\_allergy\\_options & boolean & Whether or not the restaurant has options for individuals with an allergy to nightshades \\\\\n"," has\\_gluten\\_free\\_options & boolean & Whether or not the restaurant has gluten free options \\\\\n"," has\\_vegetarian\\_options & boolean & Whether or not the restaurant has vegetarian options \\\\\n"," has\\_vegan\\_options & boolean & Whether or not the restaurant has vegan options \\\\\n"," has\\_halal\\_options & boolean & Whether or not the restaurant has halal options \\\\\n"," has\\_kosher\\_options & boolean & Whether or not the hotel has kosher options \\\\ \n"," \\bottomrule\n"," \\end{tabular}\n","}\n","\\caption{Restaurant Ontology Attributes.} \n","\\label{tab:restaurant-ontology-attributes}\n","\\end{table*}\n"],["\\begin{table*}[!htbp]\n","\\centering\n","\\resizebox{0.8\\textwidth}{!}{\n"," \\begin{tabular}{lll}\n"," \\toprule \n"," \\textbf{Attribute} & \\textbf{Type} & \\textbf{Description} \\\\ \\midrule\n"," city & string & City that the attraction is in \\\\\n"," state & string & State that the attraction is in \\\\\n"," neighborhood & string & Neighborhood within the city that the attraction is in\\\\\n"," type & string & Type of the attraction \\\\ \n"," \\bottomrule\n"," \\end{tabular}\n","}\n","\\caption{Attraction Ontology Attributes.} \n","\\label{tab:attraction-ontology-attributes}\n","\\end{table*}\n"],["\\begin{table}[!ht]\n","\\centering\n","\\small\n","\\renewcommand{\\arraystretch}{1.3}\n","\\begin{tabular}{|l|p{5.5cm}|p{7.5cm}|}\n","\\hline\n","\\textbf{Domain} & \\textbf{Placeholders Used} & \\textbf{Required Scenarios} \\\\\n","\\hline\n","\\textbf{Flight} & \n","\\texttt{<CITY\\_x>}, \\texttt{<CLASS\\_x>}, \\texttt{<DEPARTURE\\_DATE\\_x>}, \\texttt{<DEPARTURE\\_TIME\\_x>}, \\texttt{<ARRIVAL\\_DATE\\_x>}, \\texttt{<ARRIVAL\\_TIME\\_x>}, \\texttt{<AIRLINE\\_x>}, \\texttt{<PRICE\\_x>}, \\texttt{<AIRPORT\\_x>}, \\texttt{<NUM\\_TRAVELERS\\_x>} & \n","Round trip (with 2 departure dates), one-way, multi-city, strict arrival time, flexible dates, large group bookings, solo/business/family travel, weekend getaways, budget constraints, special occasions (e.g., birthdays) \\\\\n","\\hline\n","\n","\\textbf{Hotel} & \n","\\texttt{<CHECK\\_IN\\_DATE\\_x>}, \\texttt{<CHECK\\_OUT\\_DATE\\_x>}, \\texttt{<CITY\\_x>}, \\texttt{<CITY\\_x\\_NEIGHBORHOOD\\_y>}, \\texttt{<STAR\\_x>}, \\texttt{<RATING\\_x>}, \\texttt{<PRICE\\_x>}, \\texttt{<NUM\\_ROOMS\\_x>}, \\texttt{<NUM\\_TRAVELERS\\_x>} & \n","Multi-city trips, multiple hotels in one city, specific neighborhoods, special occasions, last-minute reservations, business vs. leisure, extended stays, view/accessibility/pet-friendly requests, family and group accommodations \\\\\n","\\hline\n","\n","\\textbf{Attraction} & \n","\\texttt{<ATTRACTION\\_TYPE\\_x>}, \\texttt{<CITY\\_x>}, \\texttt{<STATE\\_x>}, \\texttt{<CITY\\_x\\_NEIGHBORHOOD\\_y>} & \n","Multi-type attraction queries, across cities/states, neighborhood-specific exploration, curiosity about state-level offerings. Assistant must ask open-ended questions and only mention placeholders if user provides them. \\\\\n","\\hline\n","\n","\\textbf{Restaurant} & \n","\\texttt{<RESTAURANT\\_RATING\\_x>}, \\texttt{<RESTAURANT\\_PRICE\\_x>}, \\texttt{<RESTAURANT\\_CUISINE\\_x>}, \\texttt{<CITY\\_x>}, \\texttt{<CITY\\_x\\_NEIGHBORHOOD\\_y>} & \n","Multi-city dining, cuisine preferences, dietary restrictions (e.g., nut-free, vegan, halal), budget vs. premium, special occasion dining, rating-focused or casual vs. upscale preferences \\\\\n","\\hline\n","\\end{tabular}\n","\\vspace{1mm}\n","\\caption{Domain-specific customization for template generation. Each domain builds on the shared prompt instructions with unique placeholders and required conversation scenarios.}\n","\\label{tab:domain-customization}\n","\\end{table}\n"],["\\begin{table}[!ht]\n","\\centering\n","\\small\n","\\renewcommand{\\arraystretch}{1.2}\n","\\begin{tabularx}{\\textwidth}{|l|X|X|}\n","\\hline\n","\\textbf{Domains} & \\textbf{Placeholders Used} & \\textbf{Required Scenarios} \\\\\n","\\hline\n","\n","\\textbf{Flight + Hotel} & \n","\\texttt{<CITY\\_x>}, \\texttt{<CLASS\\_x>}, \\texttt{<DEPARTURE\\_DATE\\_x>}, \\texttt{<CHECK\\_IN\\_DATE\\_x>}, \\texttt{<CHECK\\_OUT\\_DATE\\_x>}, \\texttt{<NUM\\_TRAVELERS\\_x>} & \n","Trip planning involving both air travel and accommodation. Includes round-trip and one-way flights, hotel stays across one or more cities, budget and luxury travelers, group vs. solo travel, flexible dates, specific preferences like nonstop flights or hotels with amenities (e.g., pool, gym, pet-friendly). \\\\\n","\\hline\n","\n","\\textbf{Flight + Hotel + Attraction} & \n","All flight, hotel, and attraction placeholders (e.g., \\texttt{<CITY\\_x>}, \\texttt{<CHECK\\_IN\\_DATE\\_x>}, \\texttt{<ATTRACTION\\_TYPE\\_x>}) & \n","User planning a full trip involving flights, hotel stays, and sightseeing. Must include: round-trip and one-way flights, multi-city stays, hotels near attractions, flexible schedules, user-provided attraction types or specific attraction names (only when linking to hotel proximity), and special constraints (e.g., group travel, business trips, anniversaries). Assistant must never use placeholder names before user introduces them. Includes amenity discussions and attraction-type grammar handling (e.g., cultural vs. \\texttt{<ATTRACTION\\_TYPE\\_1>} attraction). \\\\\n","\\hline\n","\n","\\textbf{Flight + Hotel + Restaurant} & \n","All flight, hotel, and restaurant placeholders (e.g., \\texttt{<RESTAURANT\\_CUISINE\\_x>}, \\texttt{<RESTAURANT\\_PRICE\\_x>}) & \n","End-to-end trip planning including flights, accommodation, and dining. Required scenarios include: dietary restrictions, cuisine-first or city-first planning, restaurant proximity to hotel, multiple hotels or restaurants in one city, round-trip and one-way flights, and amenities like pet-friendly or spa. Must feature diverse language, nonlinear flows, and varied user personas. Includes explicit handling of dietary needs (e.g., halal, nut-free, vegetarian), and placeholder logic as per prompt. \\\\\n","\\hline\n","\n","\\textbf{Hotel + Attraction} & \n","\\texttt{<CITY\\_x>}, \\texttt{<CHECK\\_IN\\_DATE\\_x>}, \\texttt{<ATTRACTION\\_TYPE\\_x>}, \\texttt{<CITY\\_x\\_NEIGHBORHOOD\\_y>} & \n","User looking for accommodations and nearby attractions. Includes: specific neighborhoods, family-friendly vs. solo travel, themed vacations, extended stays, proximity to cultural or outdoor attractions, and cases where attraction type drives hotel location. \\\\\n","\\hline\n","\n","\\textbf{Hotel + Restaurant} & \n","\\texttt{<RESTAURANT\\_CUISINE\\_x>}, \\texttt{<HOTEL\\_PRICE\\_x>}, \\texttt{<CITY\\_x\\_NEIGHBORHOOD\\_y>} & \n","Combines hotel booking with dining preferences. Includes cuisine-specific searches, budget dining vs. fine dining, dietary needs (gluten-free, halal), special events (anniversaries), restaurant proximity to hotel, and group size considerations. Templates vary in planning flow—some start with hotel, others with restaurants. \\\\\n","\\hline\n","\n","\\end{tabularx}\n","\\vspace{1mm}\n","\\caption{Multi-domain combinations and scenario requirements. Each configuration builds on the unified instructions with additional placeholder logic and domain-specific complexity.}\n","\\label{tab:multi-domain-customization}\n","\\end{table}\n"]],"string":"[\n [\n \"\\\\begin{figure*}[!t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\textwidth]{assets/pipeline.png}\\n\",\n \" \\\\caption{Illustrative example from the $\\\\benchmarkname$ dataset. This example showcases a multi-domain scenario involving both flights and hotels, where the user is planning a trip and attempting to book relevant services. The dialogue is constructed by retrieving entities from a knowledge base, and tool calls are executed using a predefined toolbox, simulating realistic, tool-augmented agent behavior.}\\n\",\n \" \\\\label{fig:architecture}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!htbp]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lccccccc}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Dataset} & \\\\textbf{Deployed} & \\\\textbf{Human Annotated} & \\\\textbf{Execution Result} & \\\\textbf{Multi-turn Context} & \\\\textbf{Multi-Domain} \\\\\\\\\\n\",\n \" & \\\\textbf{Tools} & \\\\textbf{Tool Planning} & \\\\textbf{Evaluation} & \\\\textbf{Planning} & \\\\textbf{Tool Planning}\\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" APIBank~\\\\cite{li2023api} & \\\\cmark & \\\\cmark & \\\\cmark & \\\\cmark & & \\\\\\\\\\n\",\n \" APIBench~\\\\cite{patil2024gorilla} & & & \\\\cmark & & \\\\\\\\\\n\",\n \" GAIA~\\\\cite{mialon2023gaia} & & \\\\cmark & \\\\cmark & & \\\\\\\\\\n\",\n \" GTA~\\\\cite{wang2024gta} & \\\\cmark & \\\\cmark & \\\\cmark & \\\\cmark & \\\\\\\\\\n\",\n \" m\\\\&m's~\\\\cite{ma2024m} & \\\\cmark & \\\\cmark & \\\\cmark & \\\\cmark & \\\\\\\\\\n\",\n \" ToolBench~\\\\cite{qin2023toolllm} & \\\\cmark & & \\\\cmark & & \\\\\\\\\\n\",\n \" Toolformer~\\\\cite{schick2023toolformer} & \\\\cmark & \\\\cmark & \\\\cmark & & \\\\\\\\\\n\",\n \" TravelPlanner~\\\\cite{xie2024travelplanner} & \\\\cmark & \\\\cmark & \\\\cmark & & \\\\cmark & \\\\\\\\ \\n\",\n \" \\\\midrule\\n\",\n \" $\\\\textbf{\\\\benchmarkname}$ & \\\\cmark & \\\\cmark & \\\\cmark & \\\\cmark & \\\\cmark \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Comparison of datasets for the LLM-based agent systems.}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[!t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\textwidth]{assets/data_generation.png}\\n\",\n \" \\\\caption{$\\\\benchmarkname$ generates data by populating delexicalized entities with corresponding entries from the knowledge base.}\\n\",\n \" \\\\label{fig:data_generation}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lcccc|cc|c|c}\\n\",\n \" \\\\toprule \\n\",\n \" & \\\\multicolumn{4}{c|}{\\\\textbf{Single-domain}} & \\\\multicolumn{2}{c|}{\\\\textbf{Multi-domain}}\\n\",\n \" & \\\\textbf{Common} & \\\\textbf{Total} \\\\\\\\ \\n\",\n \" & \\\\textbf{Flights} & \\\\textbf{Hotels} & \\\\textbf{Restaurants} & \\\\textbf{Attractions} & \\\\textbf{2 Domains} & \\\\textbf{3 Domains} & \\\\\\\\ \\\\midrule\\n\",\n \" \\\\textbf{\\\\# Attributes} & 35 & 43 & 21 & 7 & N/A & N/A & N/A & 106 \\\\\\\\\\n\",\n \" \\\\textbf{\\\\# Tools} & 2 & 2 & 2 & 2 & 1 & N/A & 5 & 14 \\\\\\\\ \\n\",\n \" \\\\textbf{\\\\# Dialogues} & 1.5k & 1.5k & 1.5k & 1.5k & 4.5k & 3k & N/A & 13.5k \\\\\\\\ \\n\",\n \" \\\\textbf{Avg. \\\\# turns} & 8.2 & 8.0 & 9.0 & 6.0 & 11.3 & 9.7 & N/A & N/A \\\\\\\\ \\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Dataset statistics detailing the number of attributes, tools, dialogues, and the average number of turns, categorized by each individual domain, multi-domain, and common categories. \\\"N/A\\\" indicates that the metric is not applicable in the given context.} \\n\",\n \"\\\\label{tab:baseline-results-one-epoch}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{figure}[!ht]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\linewidth]{assets/domain_adaptation.png}\\n\",\n \" \\\\caption{\\\\textbf{Left:} Tool Call F1 performance and \\\\textbf{Right:} Parameter Matching F1 performance.}\\n\",\n \" \\\\label{fig:enter-label}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table*}[!ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lcccc|cccc|c|cc|c}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Domain} & \\\\multicolumn{4}{c}{\\\\textbf{Tool Call}} & \\\\multicolumn{4}{|c|}{\\\\textbf{Parameter Matching}} & \\\\textbf{Code Exec.} & \\\\multicolumn{2}{|c|}{\\\\textbf{Information Seeking}} & \\\\multicolumn{1}{c}{\\\\textbf{Cache}} \\\\\\\\ \\n\",\n \" & & & & & & & & & \\\\textbf{Rate} & & & \\\\textbf{Summary} \\\\\\\\\\n\",\n \" & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\\\\\ \\\\midrule\\n\",\n \" \\\\multicolumn{8}{l}{\\\\texttt{Single-Domain}} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Flights & 25.12 & 30.90 & 57.34 & 40.16 & 15.57 & 17.34 & 60.30 & 26.94 & 26.24 & 14.41 & 82.36 & 36.63 \\\\\\\\\\n\",\n \" Hotels & 42.86 & 49.89 & 75.25 & 60.00 & 17.57 & 23.59 & 40.80 & 29.89 & 48.13 & 47.01 & 80.38 & 33.70 \\\\\\\\\\n\",\n \" Restaurants & 40.40 & 52.97 & 63.00 & 57.55 & 31.47 & 36.07 & 71.12 & 47.87 & \\\\textbf{56.99} & 28.91 & 75.68 & 35.49 \\\\\\\\\\n\",\n \" Attractions & 33.03 & 48.49 & 50.90 & 49.66 & 14.11 & 25.11 & 24.36 & 24.73 & 40.40 & N/A & N/A & 43.87 \\\\\\\\ \\\\midrule\\n\",\n \" \\\\multicolumn{10}{l}{\\\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\\\\\ \\\\midrule\\n\",\n \" F-H & 33.09 & 40.89 & 63.44 & 49.73 & 16.11 & 19.28 & 49.48 & 27.75 & 38.43 & 26.54 & 82.00 & 21.85 \\\\\\\\\\n\",\n \" H-R & 51.06 & 59.21 & 78.77 & 67.61 & 26.62 & 33.52 & 56.37 & 42.05 & 51.82 & 28.60 & 84.07 & 9.95 \\\\\\\\\\n\",\n \" H-A & 38.75 & 45.46 & 72.39 & 55.85 & 16.11 & 20.02 & 45.12 & 27.74 & 48.88 & 26.03 & 81.77 & 32.75 \\\\\\\\\\n\",\n \" F-H-A & 27.76 & 36.24 & 54.24 & 43.45 & 16.21 & 21.18 & 40.88 & 27.90 & 28.76 & 14.15 & 78.58 & 24.37 \\\\\\\\\\n\",\n \" F-H-R & 29.04 & 34.65 & 64.19 & 45.00 & 18.53 & 21.77 & 55.44 & 31.26 & 36.79 & 33.28 & 82.84 & 27.47 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Overall results using Llama 3.1 8B Instruct with few-shot in-context learning.}\\n\",\n \"\\\\label{table:llama3.1-8b}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{figure}[!ht]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=0.9\\\\linewidth]{assets/flight_shot_learning.png}\\n\",\n \" \\\\caption{Few-shot performance on Flight domain.}\\n\",\n \" \\\\label{fig:few-shot}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table*}[!ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lcccc|cccc|c|cc|c}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Domain} & \\\\multicolumn{4}{c}{\\\\textbf{Tool Call}} & \\\\multicolumn{4}{|c|}{\\\\textbf{Parameter Matching}} & \\\\textbf{Code Exec. Rate} & \\\\multicolumn{2}{|c|}{\\\\textbf{Information Seeking}} & \\\\multicolumn{1}{c}{\\\\textbf{Cache Summary}} \\\\\\\\ \\n\",\n \" & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\\\\\ \\\\midrule\\n\",\n \" \\\\texttt{Single-Domain} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Flights & 83.51 & 89.98 & 92.08 & 91.02 & 58.02 & 66.38 & 82.18 & 73.44 & 85.26 & 39.19 & 86.44 & 62.07 \\\\\\\\\\n\",\n \" Hotels & 78.18 & 84.87 & 90.84 & 87.75 & 60.40 & 67.29 & 85.53 & 75.32 & 72.13 & 48.30 & 80.91 & 71.35 \\\\\\\\\\n\",\n \" Restaurants & 72.76 & 80.40 & 88.44 & 84.23 & 57.49 & 66.69 & 80.64 & 73.00 & 95.16 & 95.14 & 98.63 & 65.32 \\\\\\\\\\n\",\n \" Attractions & 90.50 & 97.87 & 92.33 & 95.02 & 75.54 & 91.34 & 81.37 & 86.07 & 98.15 & N/A & N/A &83.00 \\\\\\\\ \\\\midrule\\n\",\n \" \\\\multicolumn{10}{l}{\\\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\\\\\ \\\\midrule\\n\",\n \" F-H & 80.27 & 91.31 & 86.91 & 89.05 & 57.76 & 74.51 & 71.99 & 73.23 & 89.25 & 24.04 & 80.63 & 57.01 \\\\\\\\\\n\",\n \" H-R & 82.02 & 91.03 & 89.23 & 90.12 & 68.44 & 77.95 & 84.86 & 81.26 & 83.67 & 29.12 & 87.58 & 64.42 \\\\\\\\\\n\",\n \" H-A & 62.30 & 72.67 & 81.35 & 76.77 & 59.22 & 68.73 & 81.05 & 74.39 & 71.41 & 27.87 & 82.80 & 65.70 \\\\\\\\\\n\",\n \" F-H-A & 77.19 & 87.50 & 86.76 & 87.13 & 59.63 & 74.16 & 75.27 & 74.71 & 81.15 & 22.65 & 83.45 & 52.76 \\\\\\\\\\n\",\n \" F-H-R & 71.53 & 86.05 & 80.91 & 83.40 & 54.39 & 71.99 & 68.99 & 70.45 & 82.47 & 46.32 & 84.40 & 53.88 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Overall Results using Llama 3.1 8B Instruct after SFT.}\\n\",\n \"\\\\label{table:llama3.1-8b-sft}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lcccc|cccc|c|cc|c}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Domain} & \\\\multicolumn{4}{c}{\\\\textbf{Tool Call}} & \\\\multicolumn{4}{|c|}{\\\\textbf{Parameter Matching}} & \\\\textbf{Code Exec. Rate} & \\\\multicolumn{2}{|c|}{\\\\textbf{Information Seeking}} & \\\\multicolumn{1}{c}{\\\\textbf{Cache Summary}} \\\\\\\\ \\n\",\n \" & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\\\\\ \\\\midrule\\n\",\n \" \\\\texttt{Single-Domain} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Flights & 58.42 & 78.26 & 69.75 & 73.76 & 32.11 & 40.51 & 60.76 & 48.61 & 83.32 & 20.25 & 82.90 & 53.80 \\\\\\\\\\n\",\n \" Hotels & 92.42 & 96.03 & 96.10 & 96.07 & 75.33 & 80.85 & 91.69 & 85.93 & 98.55 & 46.98 & 80.27 & 75.00 \\\\\\\\\\n\",\n \" Restaurants & 94.23 & 96.63 & 97.43 & 97.03 & 85.51 & 93.62 & 90.80 & 92.19 & 99.54 & 37.90 & 79.95 & 90.78 \\\\\\\\\\n\",\n \" Attractions & 79.14 & 92.16 & 84.86 & 88.36 & 71.17 & 87.04 & 79.60 & 83.16 & 63.70 & N/A & N/A & 78.59 \\\\\\\\ \\\\midrule\\n\",\n \" \\\\multicolumn{10}{l}{\\\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\\\\\ \\\\midrule\\n\",\n \" F-H & 43.48 & 53.73 & 69.52 & 60.61 & 22.28 & 25.94 & 61.24 & 36.44 & 95.02 & 27.39 & 83.42 & 37.15 \\\\\\\\\\n\",\n \" H-R & 60.50 & 70.91 & 80.46 & 78.29 & 50.94 & 60.08 & 76.99 & 67.50 & 99.60 & 22.97 & 84.11 & 38.43 \\\\\\\\\\n\",\n \" H-A & 64.91 & 68.93 & 91.75 & 78.72 & 59.24 & 66.59 & 84.29 & 74.40 & 97.01 & 30.02 & 82.71 & 57.03 \\\\\\\\\\n\",\n \" F-H-A & 60.46 & 70.77 & 80.59 & 75.36 & 44.27 & 51.19 & 76.58 & 61.37 & 94.81 & 19.16 & 82.27 & 44.29 \\\\\\\\\\n\",\n \" F-H-R & 52.97 & 62.22 & 78.07 & 69.25 & 42.93 & 50.66 & 73.76 & 60.07 & 91.30 & 40.45 & 85.62 & 45.37 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Overall Results using Llama 3.3 70B Instruct.}\\n\",\n \"\\\\label{table:llama3.3-70b}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lcccc|cccc|c|cc|c}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Domain} & \\\\multicolumn{4}{c}{\\\\textbf{Tool Call}} & \\\\multicolumn{4}{|c|}{\\\\textbf{Parameter Matching}} & \\\\textbf{Code Exec. Rate} & \\\\multicolumn{2}{|c|}{\\\\textbf{Information Seeking}} & \\\\multicolumn{1}{c}{\\\\textbf{Cache Summary}} \\\\\\\\ \\n\",\n \" & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\\\\\ \\\\midrule\\n\",\n \" \\\\texttt{Single-Domain} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Flights & 77.59 & 87.87 & 86.90 & 87.38 & 49.53 & 58.04 & 77.15 & 66.25 & 57.20 & 29.65 & 83.12 & 57.46\\n\",\n \" \\\\\\\\\\n\",\n \" Hotels & 89.33 & 95.33 & 93.41 & 94.36 & 65.27 & 70.52 & 89.77 & 78.99 & 63.95 & 18.07 & 79.56 & 57.23\\n\",\n \" \\\\\\\\\\n\",\n \" Restaurants & 87.77 & 92.65 & 94.35 & 93.49 & 71.59 & 77.69 & 90.11 & 83.44 & 69.82 & 7.16 & 69.91 & 69.22\\n\",\n \" \\\\\\\\\\n\",\n \" Attractions & 65.76 & 80.16 & 78.54 & 79.34 & 44.92 & 53.04 & 74.59 & 62.00 & 72.57 & N/A & N/A & 63.87\\n\",\n \"\\\\\\\\ \\\\midrule\\n\",\n \" \\\\multicolumn{10}{l}{\\\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\\\\\ \\\\midrule\\n\",\n \" F-H & 69.87 & 78.42 & 86.51 & 82.27 & 42.38 & 50.39 & 72.71 & 59.53 & 66.89 & 9.72 & 78.43 & 47.49\\n\",\n \" \\\\\\\\\\n\",\n \" H-R & 59.10 & 65.62 & 85.60 & 74.29 & 51.91 & 58.54 & 82.10 & 68.35 & 49.72 & 19.69 & 85.40 & 32.55\\n\",\n \" \\\\\\\\\\n\",\n \" H-A & 63.46 & 67.81 & 90.83 & 77.65 & 65.22 & 72.53 & 86.60 & 78.95 & 44.39 & 21.41 & 81.95 & 56.64\\n\",\n \" \\\\\\\\\\n\",\n \" F-H-A & 68.41 & 75.27 & 88.25 & 81.24 & 50.21 & 61.04 & 73.88 & 66.85 & 60.86 & 27.00 & 82.48 & 50.42\\n\",\n \" \\\\\\\\\\n\",\n \" F-H-R & 67.22 & 73.80 & 88.30 & 80.40 & 58.97 & 71.66 & 76.91 & 74.19 & 61.47 & 10.08 & 78.32 & 53.74\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Overall Results using S1.1 32B.}\\n\",\n \"\\\\label{table:s1.1}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lcccc|cccc|c|cc|c}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Domain} & \\\\multicolumn{4}{c}{\\\\textbf{Tool Call}} & \\\\multicolumn{4}{|c|}{\\\\textbf{Parameter Matching}} & \\\\textbf{Code Exec. Rate} & \\\\multicolumn{2}{|c|}{\\\\textbf{Information Seeking}} & \\\\multicolumn{1}{c}{\\\\textbf{Cache Summary}} \\\\\\\\ \\n\",\n \" & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\\\\\ \\\\midrule\\n\",\n \" \\\\texttt{Single-Domain} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Flights & 36.66 & 67.30 & 44.61 & 53.66 & 15.74 & 56.10 & 17.95 & 27.20 & 72.76 & 13.91 & 81.60 & 39.73\\n\",\n \" \\\\\\\\\\n\",\n \" Hotels & 83.96 & 91.74 & 90.82 & 91.28 & 53.22 & 63.09 & 77.30 & 69.47 & 93.05 & 46.95 & 80.47 & 46.52\\n\",\n \" \\\\\\\\\\n\",\n \" Restaurants & 64.13 & 83.60 & 73.36 & 78.14 & 47.10 & 61.89 & 66.34 & 64.04 & 93.49 & 28.27 & 69.74 & 50.53\\n\",\n \" \\\\\\\\\\n\",\n \" Attractions & 35.78 & 61.67 & 46.02 & 52.71 & 22.90 & 66.92 & 25.82 & 37.27 & 66.25 & N/A & N/A & 50.79\\n\",\n \"\\\\\\\\ \\\\midrule\\n\",\n \" \\\\multicolumn{10}{l}{\\\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\\\\\ \\\\midrule\\n\",\n \" F-H & 57.79 & 88.12 & 62.67 & 73.25 & 29.91 & 59.20 & 37.68 & 46.05 & 83.37 & 27.96 & 83.02 & 32.53\\n\",\n \" \\\\\\\\\\n\",\n \" H-R & 54.07 & 70.71 & 69.67 & 70.18 & 35.42 & 50.91 & 53.79 & 52.31 & 96.04 & 23.19 & 84.83 & 21.72\\n\",\n \" \\\\\\\\\\n\",\n \" H-A & 65.81 & 78.69 & 80.08 & 79.38 & 48.58 & 61.78 & 69.45 & 65.39 & 96.04 & 27.92 & 82.97 & 53.52\\n\",\n \" \\\\\\\\\\n\",\n \" F-H-A & 37.83 & 62.18 & 49.14 & 54.90 & 32.73 & 64.59 & 39.89 & 49.32 & 84.49 & 15.42 & 81.00 & 32.33\\n\",\n \" \\\\\\\\\\n\",\n \" F-H-R & 46.79 & 72.75 & 56.74 & 63.75 & 33.99 & 59.56 & 44.18 & 50.73 & 91.86 & 36.30 & 84.63 & 37.65\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Overall Results using Phi-4-reasoning-plus.}\\n\",\n \"\\\\label{table:phi-4-reasoning-plus}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{figure}[!ht]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\linewidth]{assets/all_model_f1_scores.png}\\n\",\n \" \\\\caption{\\\\textbf{Above:} Tool Call F1 performance and \\\\textbf{Below:} Parameter Matching F1 performance.}\\n\",\n \" \\\\label{fig:compare-models}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table*}[!htbp]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lccccccc}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Domain} & \\\\textbf{ \\\\# Turns} & \\\\textbf{\\\\# Validation Error} & \\\\textbf{\\\\# Variable Not Defined} & \\\\textbf{\\\\# Index Out Of Range} & \\\\textbf{\\\\# Other} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\texttt{Single-Domain} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Flights & 8,200 & 737 & 830 & 0 & 468 \\\\\\\\\\n\",\n \" Hotels & 8,000 & 417 & 218 & 0 & 820 \\\\\\\\\\n\",\n \" Restaurants & 7,900 & 513 & 14 & 1 & 183 \\\\\\\\\\n\",\n \" Attractions & 5,975 & 507 & 1 & 0 & 243 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multicolumn{6}{l}{\\\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\\\\\ \\\\midrule\\n\",\n \" F-H & 11,700 & 834 & 1087 & 0 & 408 \\\\\\\\\\n\",\n \" H-R & 10,950 & 1532 & 18 & 1 & 522 \\\\\\\\\\n\",\n \" H-A & 11,000 & 1024 & 167 & 0 & 308 \\\\\\\\\\n\",\n \" F-H-A & 9,775 & 1163 & 990 & 0 & 575 \\\\\\\\\\n\",\n \" F-H-R & 8,575 & 812 & 761 & 3 & 495 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\texttt{Total} & - & 7539 & 4086 & 5 & 4022\\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Overall Error Analysis Results using Llama 3.1 8B Instruct.}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!htbp]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lccccccc}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Domain} & \\\\textbf{ \\\\# Turns} & \\\\textbf{\\\\# Validation Error} & \\\\textbf{\\\\# Variable Not Defined} & \\\\textbf{\\\\# Index Out Of Range} & \\\\textbf{\\\\# Other} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\texttt{Single-Domain} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Flights & 8,200 & 408 & 176 & 42 & 26 \\\\\\\\\\n\",\n \" Hotels & 8,000 & 1,099 & 16 & 0 & 0 \\\\\\\\\\n\",\n \" Restaurants & 7,900 & 209 & 5 & 0 & 2 \\\\\\\\\\n\",\n \" Attractions & 5,975 & 18 & 9 & 0 & 8 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multicolumn{6}{l}{\\\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\\\\\ \\\\midrule\\n\",\n \" F-H & 11,700 & 454 & 149 & 0 & 22 \\\\\\\\\\n\",\n \" H-R & 10,950 & 692 & 185 & 0 & 17 \\\\\\\\\\n\",\n \" H-A & 11,000 & 1248 & 315 & 0 & 1 \\\\\\\\\\n\",\n \" F-H-A & 9,775 & 789 & 124 & 9 & 27 \\\\\\\\\\n\",\n \" F-H-R & 8,575 & 541 & 109 & 0 & 101 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\texttt{Total} & - & 5458 & 1088 & 51 & 204 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Overall Error Analysis Results using Llama 3.1 8B SFT Instruct.}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!htbp]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lccccccc}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Domain} & \\\\textbf{ \\\\# Turns} & \\\\textbf{\\\\# Validation Error} & \\\\textbf{\\\\# Variable Not Defined} & \\\\textbf{\\\\# Index Out Of Range} & \\\\textbf{\\\\# Other} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\texttt{Single-Domain} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Flights & 8,200 & 518 & 2 & 30 & 0 \\\\\\\\\\n\",\n \" Hotels & 8,000 & 41 & 0 & 0 & 0 \\\\\\\\\\n\",\n \" Restaurants & 7,900 & 29 & 0 & 0 & 0 \\\\\\\\\\n\",\n \" Attractions & 5,975 & 4 & 2 & 0 & 1 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multicolumn{6}{l}{\\\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\\\\\ \\\\midrule\\n\",\n \" F-H & 11,700 & 158 & 28 & 0 & 0\\n\",\n \" \\\\\\\\\\n\",\n \" H-R & 10,950 & 14 & 2 & 0 & 1\\n\",\n \" \\\\\\\\\\n\",\n \" H-A & 11,000 & 48 & 33 & 0 & 48\\n\",\n \" \\\\\\\\\\n\",\n \" F-H-A & 9,775 & 148 & 6 & 7 & 3\\n\",\n \" \\\\\\\\\\n\",\n \" F-H-R & 8,575 & 330 & 5 & 1 & 7\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\texttt{Total} & - & 1290 & 78 & 38 & 60 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Overall Error Analysis Results using Llama 3.3 70B Instruct.}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!htbp]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lccccccc}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Domain} & \\\\textbf{ \\\\# Turns} & \\\\textbf{\\\\# Validation Error} & \\\\textbf{\\\\# Variable Not Defined} & \\\\textbf{\\\\# Index Out Of Range} & \\\\textbf{\\\\# Other} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\texttt{Single-Domain} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Flights & 8,200 & 559 & 16 & 3 & 40\\n\",\n \" \\\\\\\\\\n\",\n \" Hotels & 8,000 & 92 & 1 & 39 & 27\\n\",\n \" \\\\\\\\\\n\",\n \" Restaurants & 7,900 & 20 & 7 & 0 & 28\\n\",\n \" \\\\\\\\\\n\",\n \" Attractions & 5,975 & 11 & 27 & 0 & 76\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multicolumn{6}{l}{\\\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\\\\\ \\\\midrule\\n\",\n \" F-H & 11,700 & 24 & 34 & 114 & 63\\n\",\n \" \\\\\\\\\\n\",\n \" H-R & 10,950 & 54 & 895 & 102 & 34\\n\",\n \" \\\\\\\\\\n\",\n \" H-A & 11,000 & 3 & 1071 & 43 & 23\\n\",\n \" \\\\\\\\\\n\",\n \" F-H-A & 9,775 & 89 & 579 & 25 & 49\\n\",\n \" \\\\\\\\\\n\",\n \" F-H-R & 8,575 & 85 & 241 & 7 & 28\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\texttt{Total} & - & 937 & 2871 & 333 & 368\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Overall Error Analysis Results using S1.1.}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!htbp]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lccccccc}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Domain} & \\\\textbf{ \\\\# Turns} & \\\\textbf{\\\\# Validation Error} & \\\\textbf{\\\\# Variable Not Defined} & \\\\textbf{\\\\# Index Out Of Range} & \\\\textbf{\\\\# Other} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\texttt{Single-Domain} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Flights & 8,200 & 103 & 7 & 1 & 1\\n\",\n \" \\\\\\\\\\n\",\n \" Hotels & 8,000 & 277 & 0 & 0 & 1\\n\",\n \" \\\\\\\\\\n\",\n \" Restaurants & 7,900 & 259 & 0 & 0 & 19\\n\",\n \" \\\\\\\\\\n\",\n \" Attractions & 5,975 & 0 & 0 & 0 & 0\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multicolumn{6}{l}{\\\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\\\\\ \\\\midrule\\n\",\n \" F-H & 11,700 & 212 & 2 & 0 & 7\\n\",\n \" \\\\\\\\\\n\",\n \" H-R & 10,950 & 212 & 0 & 0 & 1\\n\",\n \" \\\\\\\\\\n\",\n \" H-A & 11,000 & 162 & 0 & 0 & 6\\n\",\n \" \\\\\\\\\\n\",\n \" F-H-A & 9,775 & 120 & 3 & 0 & 5\\n\",\n \" \\\\\\\\\\n\",\n \" F-H-R & 8,575 & 153 & 4 & 0 & 32\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\texttt{Total} & - & 1498 & 16 & 1 & 72\\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Overall Error Analysis Results using Phi-4-reasoning-plus.}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lllll}\\n\",\n \" \\\\toprule \\n\",\n \" \\\\textbf{Domain} & \\\\textbf{Attributes} \\\\\\\\ \\\\midrule\\n\",\n \" Flight & airline, flight\\\\_id, start\\\\_airport, start\\\\_airport\\\\_latitude, start\\\\_airport\\\\_longitude, \\\\\\\\\\n\",\n \" & start\\\\_airport\\\\_code, end\\\\_airport, end\\\\_airport\\\\_latitude, end\\\\_airport\\\\_longitude, \\\\\\\\\\n\",\n \" & end\\\\_airport\\\\_code, economy\\\\_class\\\\_option\\\\_present, business\\\\_class\\\\_option\\\\_present, \\\\\\\\\\n\",\n \" & first\\\\_class\\\\_option\\\\_present, distance\\\\_miles, duration\\\\_minutes, \\\\\\\\\\n\",\n \" & departure\\\\_time, arrival\\\\_time, number\\\\_of\\\\_layovers, first\\\\_layover\\\\_airport, \\\\\\\\\\n\",\n \" Hotel & hotel\\\\_name, city, state, neighborhood, latitude, longitude, rating, \\\\\\\\ & stars, max\\\\_occupancy, gym\\\\_present, pool\\\\_present, price\\\\_per\\\\_night, num\\\\_rooms\\\\_available, \\\\\\\\ & breakfast\\\\_included, smoking\\\\_allowed, air\\\\_conditioning\\\\_present, \\\\\\\\\\n\",\n \" & heating\\\\_present, free\\\\_wifi\\\\_included, airport\\\\_shuttle\\\\_present, \\\\\\\\ &\\n\",\n \" is\\\\_pet\\\\_friendly, has\\\\_spa\\\\_services, has\\\\_room\\\\_service, \\\\\\\\ & has\\\\_beach\\\\_access, has\\\\_fitness\\\\_class, has\\\\_laundry\\\\_service, \\\\\\\\ & has\\\\_valet\\\\_parking, has\\\\_balcony, has\\\\_rooftop\\\\_bar, has\\\\_inroom\\\\_Kitchen, \\\\\\\\ & has\\\\_kids\\\\_club, has\\\\_meeting\\\\_rooms, has\\\\_electric\\\\_vehicle\\\\_charging, \\\\\\\\ & has\\\\_hot\\\\_tub, has\\\\_sauna, has\\\\_free\\\\_parking, is\\\\_wheelchair\\\\_accessible, \\\\\\\\ & has\\\\_skiing\\\\_lodging, has\\\\_ocean\\\\_view\\\\_rooms\\\\_present, has\\\\_city\\\\_view\\\\_rooms\\\\_present, \\\\\\\\ & start\\\\_date\\\\_available, end\\\\_date\\\\_available \\\\\\\\\\n\",\n \" Attraction & city, state, name, type, latitude, longitude, neighborhood \\\\\\\\\\n\",\n \" Restaurant & restaurant\\\\_name, city, state, neighborhood, \\\\\\\\\\n\",\n \" & latitude, longitude, rating, price, price\\\\_per\\\\_person, has\\\\_nut\\\\_allergy\\\\_options, has\\\\_dairy\\\\_allergy\\\\_options, has\\\\_shell\\\\_fish\\\\_allergy\\\\_options, \\\\\\\\ & has\\\\_tomato\\\\_allergy\\\\_options, has\\\\_nightshade\\\\_allergy\\\\_options, \\\\\\\\ & has\\\\_gluten\\\\_free\\\\_options, has\\\\_vegetarian\\\\_options, has\\\\_vegan\\\\_options, \\\\\\\\ & has\\\\_kosher\\\\_options, has\\\\_halal\\\\_options, cuisine \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Domains and Attributes.} \\n\",\n \"\\\\label{tab:domains-and-attributes}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{ll}\\n\",\n \" \\\\toprule \\n\",\n \" \\\\textbf{Domain} & \\\\textbf{Tools}\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\textbf{Flights} & search\\\\_flights, filter\\\\_flights \\\\\\\\\\n\",\n \" \\\\textbf{Hotels} & search\\\\_hotels, filter\\\\_hotels \\\\\\\\\\n\",\n \" \\\\textbf{Restaurants} & search\\\\_restaurants, filter\\\\_restaurants \\\\\\\\\\n\",\n \" \\\\textbf{Attractions} & search\\\\_attractions, filter\\\\_attractions \\\\\\\\\\n\",\n \" \\\\textbf{Multi-domain} & search\\\\_nearest \\\\\\\\\\n\",\n \" \\\\textbf{Common} & save\\\\_to\\\\_cache, get\\\\_results\\\\_from\\\\_cache, sort\\\\_results, seek\\\\_information, adjust\\\\_date \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{List of Tools.} \\n\",\n \"\\\\label{tab:defined-tools-domain}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lll}\\n\",\n \" \\\\toprule \\n\",\n \" \\\\textbf{Attribute} & \\\\textbf{Type} & \\\\textbf{Description} \\\\\\\\ \\\\midrule\\n\",\n \" airline & string & Airline of the flight, \\\\\\\\\\n\",\n \" flight\\\\_classes & string & Classes for the flight (economy, business, first) \\\\\\\\\\n\",\n \" num\\\\_layovers & integer & Number of layovers for the flight, between 0 and 2 \\\\\\\\\\n\",\n \" layover\\\\_duration\\\\_amount & integer & Duration of a layover flight. Between 1 and 6 hours \\\\\\\\\\n\",\n \" airports & list & Airport information for each flight including the city, state, airport code and airport name. \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Flight Ontology Attributes.} \\n\",\n \"\\\\label{tab:flight-ontology-attributes}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!htbp]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lll}\\n\",\n \" \\\\toprule \\n\",\n \" \\\\textbf{Attribute} & \\\\textbf{Type} & \\\\textbf{Description} \\\\\\\\ \\\\midrule\\n\",\n \" city & string & City that the hotel is in \\\\\\\\\\n\",\n \" state & string & State that the hotel is in \\\\\\\\\\n\",\n \" neighborhood & string & Neighborhood within the city that the hotel is in\\\\\\\\\\n\",\n \" stars & integer & Star rating of the hotel between 1 and 5 \\\\\\\\\\n\",\n \" rating & float & Customer rating of the hotel between 1.0 and 5.0, incremented by 0.1 \\\\\\\\\\n\",\n \" price\\\\_per\\\\_night & integer & Price per night of the hotel ranging between 20 to 2000 dollars \\\\\\\\\\n\",\n \" max\\\\_occupancy & integer & Maximum occupancy per room ranging between 1 and 7 \\\\\\\\\\n\",\n \" gym\\\\_present & boolean & Whether or not the hotel has a gym \\\\\\\\\\n\",\n \" pool\\\\_present & boolean & Whether or not the hotel has a pool \\\\\\\\\\n\",\n \" breakfast\\\\_included & boolean & Whether or not the hotel has breakfast included \\\\\\\\\\n\",\n \" smoking\\\\_allowed & boolean & Whether or not the hotel allows smoking \\\\\\\\\\n\",\n \" air\\\\_conditioning\\\\_present & boolean & Whether or not the hotel has air conditioning \\\\\\\\\\n\",\n \" heating\\\\_present & boolean & Whether or not the hotel has heating \\\\\\\\\\n\",\n \" free\\\\_wifi\\\\_included & boolean & Whether or not the hotel includes free WiFi \\\\\\\\\\n\",\n \" airport\\\\_shuttle\\\\_present & boolean & Whether or not the hotel has an airport shuffle \\\\\\\\\\n\",\n \" is\\\\_pet\\\\_friendly & boolean & Whether or not the hotel allows pets \\\\\\\\\\n\",\n \" has\\\\_spa\\\\_services & boolean & Whether or not the hotel has spa services \\\\\\\\\\n\",\n \" has\\\\_room\\\\_service & boolean & Whether or not the hotel has room service \\\\\\\\\\n\",\n \" has\\\\_beach\\\\_access & boolean & Whether or not the hotel has access to a beach \\\\\\\\\\n\",\n \" has\\\\_business\\\\_center & boolean & Whether or not the hotel has a business center \\\\\\\\\\n\",\n \" has\\\\_fitness\\\\_classes & boolean & Whether or not the hotel has fitness classes \\\\\\\\\\n\",\n \" has\\\\_laundry\\\\_service & boolean & Whether or not the hotel has laundry services \\\\\\\\\\n\",\n \" has\\\\_valet\\\\_parking & boolean & Whether or not the hotel has valet parking \\\\\\\\\\n\",\n \" has\\\\_balcony & boolean & Whether or not the hotel has a balcony \\\\\\\\\\n\",\n \" has\\\\_rooftop\\\\_bar & boolean & Whether or not the hotel has a rooftop bar \\\\\\\\\\n\",\n \" has\\\\_inroom\\\\_kitchen & boolean & Whether or not the hotel has an inroom kitchen \\\\\\\\\\n\",\n \" has\\\\_kids\\\\_club & boolean & Whether or not the hotel has a kids club \\\\\\\\\\n\",\n \" has\\\\_meeting\\\\_rooms & boolean & Whether or not the hotel has meeting rooms \\\\\\\\\\n\",\n \" has\\\\_electric\\\\_vehicle\\\\_charging & boolean & Whether or not the hotel has electric vehicle charging \\\\\\\\\\n\",\n \" has\\\\_hot\\\\_tub & boolean & Whether or not the hotel has a hot tub \\\\\\\\\\n\",\n \" has\\\\_sauna & boolean & Whether or not the hotel has a sauna \\\\\\\\ \\n\",\n \" has\\\\_free\\\\_parking & boolean & Whether or not the hotel has free parking\\\\\\\\ \\n\",\n \" is\\\\_wheelchair\\\\_accessible& boolean & Whether or not the hotel is wheelchair accessible \\\\\\\\ \\n\",\n \" has\\\\_skiing\\\\_lodging & boolean & Whether or not the hotel has skiing and lodging \\\\\\\\ \\n\",\n \" ocean\\\\_view\\\\_rooms\\\\_present & boolean & Whether or not the hotel has rooms with a view of the ocean \\\\\\\\\\n\",\n \" city\\\\_view\\\\_rooms\\\\_present & boolean & Whether or not the hotel has rooms with views of the city\\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Hotel Ontology Attributes.} \\n\",\n \"\\\\label{tab:hotel-ontology-attributes}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!htbp]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lll}\\n\",\n \" \\\\toprule \\n\",\n \" \\\\textbf{Attribute} & \\\\textbf{Type} & \\\\textbf{Description} \\\\\\\\ \\\\midrule\\n\",\n \" city & string & City that the restaurant is in \\\\\\\\\\n\",\n \" state & string & State that the restaurant is in \\\\\\\\\\n\",\n \" neighborhood & string & Neighborhood within the city that the restaurant is in\\\\\\\\\\n\",\n \" rating & float & Customer rating of the restaurant between 1.0 and 5.0, incremented by 0.1 \\\\\\\\\\n\",\n \" price\\\\_per\\\\_person & integer & Average price per person at the restaurant \\\\\\\\\\n\",\n \" cuisine & string & Cuisine of the restaurant \\\\\\\\\\n\",\n \" has\\\\_nut\\\\_allergy\\\\_options & boolean & Whether or not the restaurant has any options for individuals with an allergy to nuts \\\\\\\\\\n\",\n \" has\\\\_dairy\\\\_allergy\\\\_options & boolean & Whether or not restaurant has any options for individuals with an allergy to dairy products \\\\\\\\\\n\",\n \" has\\\\_shell\\\\_fish\\\\_allergy\\\\_options & boolean & Whether or not the restaurant has any options for individuals with an allergy to shell fish \\\\\\\\\\n\",\n \" has\\\\_tomato\\\\_allergy\\\\_options & boolean & Whether or not the restaurant has options for individuals with an allergy to tomatoes \\\\\\\\\\n\",\n \" has\\\\_nightshade\\\\_allergy\\\\_options & boolean & Whether or not the restaurant has options for individuals with an allergy to nightshades \\\\\\\\\\n\",\n \" has\\\\_gluten\\\\_free\\\\_options & boolean & Whether or not the restaurant has gluten free options \\\\\\\\\\n\",\n \" has\\\\_vegetarian\\\\_options & boolean & Whether or not the restaurant has vegetarian options \\\\\\\\\\n\",\n \" has\\\\_vegan\\\\_options & boolean & Whether or not the restaurant has vegan options \\\\\\\\\\n\",\n \" has\\\\_halal\\\\_options & boolean & Whether or not the restaurant has halal options \\\\\\\\\\n\",\n \" has\\\\_kosher\\\\_options & boolean & Whether or not the hotel has kosher options \\\\\\\\ \\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Restaurant Ontology Attributes.} \\n\",\n \"\\\\label{tab:restaurant-ontology-attributes}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!htbp]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{0.8\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lll}\\n\",\n \" \\\\toprule \\n\",\n \" \\\\textbf{Attribute} & \\\\textbf{Type} & \\\\textbf{Description} \\\\\\\\ \\\\midrule\\n\",\n \" city & string & City that the attraction is in \\\\\\\\\\n\",\n \" state & string & State that the attraction is in \\\\\\\\\\n\",\n \" neighborhood & string & Neighborhood within the city that the attraction is in\\\\\\\\\\n\",\n \" type & string & Type of the attraction \\\\\\\\ \\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Attraction Ontology Attributes.} \\n\",\n \"\\\\label{tab:attraction-ontology-attributes}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table}[!ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\small\\n\",\n \"\\\\renewcommand{\\\\arraystretch}{1.3}\\n\",\n \"\\\\begin{tabular}{|l|p{5.5cm}|p{7.5cm}|}\\n\",\n \"\\\\hline\\n\",\n \"\\\\textbf{Domain} & \\\\textbf{Placeholders Used} & \\\\textbf{Required Scenarios} \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\\\textbf{Flight} & \\n\",\n \"\\\\texttt{<CITY\\\\_x>}, \\\\texttt{<CLASS\\\\_x>}, \\\\texttt{<DEPARTURE\\\\_DATE\\\\_x>}, \\\\texttt{<DEPARTURE\\\\_TIME\\\\_x>}, \\\\texttt{<ARRIVAL\\\\_DATE\\\\_x>}, \\\\texttt{<ARRIVAL\\\\_TIME\\\\_x>}, \\\\texttt{<AIRLINE\\\\_x>}, \\\\texttt{<PRICE\\\\_x>}, \\\\texttt{<AIRPORT\\\\_x>}, \\\\texttt{<NUM\\\\_TRAVELERS\\\\_x>} & \\n\",\n \"Round trip (with 2 departure dates), one-way, multi-city, strict arrival time, flexible dates, large group bookings, solo/business/family travel, weekend getaways, budget constraints, special occasions (e.g., birthdays) \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\\\textbf{Hotel} & \\n\",\n \"\\\\texttt{<CHECK\\\\_IN\\\\_DATE\\\\_x>}, \\\\texttt{<CHECK\\\\_OUT\\\\_DATE\\\\_x>}, \\\\texttt{<CITY\\\\_x>}, \\\\texttt{<CITY\\\\_x\\\\_NEIGHBORHOOD\\\\_y>}, \\\\texttt{<STAR\\\\_x>}, \\\\texttt{<RATING\\\\_x>}, \\\\texttt{<PRICE\\\\_x>}, \\\\texttt{<NUM\\\\_ROOMS\\\\_x>}, \\\\texttt{<NUM\\\\_TRAVELERS\\\\_x>} & \\n\",\n \"Multi-city trips, multiple hotels in one city, specific neighborhoods, special occasions, last-minute reservations, business vs. leisure, extended stays, view/accessibility/pet-friendly requests, family and group accommodations \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\\\textbf{Attraction} & \\n\",\n \"\\\\texttt{<ATTRACTION\\\\_TYPE\\\\_x>}, \\\\texttt{<CITY\\\\_x>}, \\\\texttt{<STATE\\\\_x>}, \\\\texttt{<CITY\\\\_x\\\\_NEIGHBORHOOD\\\\_y>} & \\n\",\n \"Multi-type attraction queries, across cities/states, neighborhood-specific exploration, curiosity about state-level offerings. Assistant must ask open-ended questions and only mention placeholders if user provides them. \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\\\textbf{Restaurant} & \\n\",\n \"\\\\texttt{<RESTAURANT\\\\_RATING\\\\_x>}, \\\\texttt{<RESTAURANT\\\\_PRICE\\\\_x>}, \\\\texttt{<RESTAURANT\\\\_CUISINE\\\\_x>}, \\\\texttt{<CITY\\\\_x>}, \\\\texttt{<CITY\\\\_x\\\\_NEIGHBORHOOD\\\\_y>} & \\n\",\n \"Multi-city dining, cuisine preferences, dietary restrictions (e.g., nut-free, vegan, halal), budget vs. premium, special occasion dining, rating-focused or casual vs. upscale preferences \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\vspace{1mm}\\n\",\n \"\\\\caption{Domain-specific customization for template generation. Each domain builds on the shared prompt instructions with unique placeholders and required conversation scenarios.}\\n\",\n \"\\\\label{tab:domain-customization}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[!ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\small\\n\",\n \"\\\\renewcommand{\\\\arraystretch}{1.2}\\n\",\n \"\\\\begin{tabularx}{\\\\textwidth}{|l|X|X|}\\n\",\n \"\\\\hline\\n\",\n \"\\\\textbf{Domains} & \\\\textbf{Placeholders Used} & \\\\textbf{Required Scenarios} \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\\\textbf{Flight + Hotel} & \\n\",\n \"\\\\texttt{<CITY\\\\_x>}, \\\\texttt{<CLASS\\\\_x>}, \\\\texttt{<DEPARTURE\\\\_DATE\\\\_x>}, \\\\texttt{<CHECK\\\\_IN\\\\_DATE\\\\_x>}, \\\\texttt{<CHECK\\\\_OUT\\\\_DATE\\\\_x>}, \\\\texttt{<NUM\\\\_TRAVELERS\\\\_x>} & \\n\",\n \"Trip planning involving both air travel and accommodation. Includes round-trip and one-way flights, hotel stays across one or more cities, budget and luxury travelers, group vs. solo travel, flexible dates, specific preferences like nonstop flights or hotels with amenities (e.g., pool, gym, pet-friendly). \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\\\textbf{Flight + Hotel + Attraction} & \\n\",\n \"All flight, hotel, and attraction placeholders (e.g., \\\\texttt{<CITY\\\\_x>}, \\\\texttt{<CHECK\\\\_IN\\\\_DATE\\\\_x>}, \\\\texttt{<ATTRACTION\\\\_TYPE\\\\_x>}) & \\n\",\n \"User planning a full trip involving flights, hotel stays, and sightseeing. Must include: round-trip and one-way flights, multi-city stays, hotels near attractions, flexible schedules, user-provided attraction types or specific attraction names (only when linking to hotel proximity), and special constraints (e.g., group travel, business trips, anniversaries). Assistant must never use placeholder names before user introduces them. Includes amenity discussions and attraction-type grammar handling (e.g., cultural vs. \\\\texttt{<ATTRACTION\\\\_TYPE\\\\_1>} attraction). \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\\\textbf{Flight + Hotel + Restaurant} & \\n\",\n \"All flight, hotel, and restaurant placeholders (e.g., \\\\texttt{<RESTAURANT\\\\_CUISINE\\\\_x>}, \\\\texttt{<RESTAURANT\\\\_PRICE\\\\_x>}) & \\n\",\n \"End-to-end trip planning including flights, accommodation, and dining. Required scenarios include: dietary restrictions, cuisine-first or city-first planning, restaurant proximity to hotel, multiple hotels or restaurants in one city, round-trip and one-way flights, and amenities like pet-friendly or spa. Must feature diverse language, nonlinear flows, and varied user personas. Includes explicit handling of dietary needs (e.g., halal, nut-free, vegetarian), and placeholder logic as per prompt. \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\\\textbf{Hotel + Attraction} & \\n\",\n \"\\\\texttt{<CITY\\\\_x>}, \\\\texttt{<CHECK\\\\_IN\\\\_DATE\\\\_x>}, \\\\texttt{<ATTRACTION\\\\_TYPE\\\\_x>}, \\\\texttt{<CITY\\\\_x\\\\_NEIGHBORHOOD\\\\_y>} & \\n\",\n \"User looking for accommodations and nearby attractions. Includes: specific neighborhoods, family-friendly vs. solo travel, themed vacations, extended stays, proximity to cultural or outdoor attractions, and cases where attraction type drives hotel location. \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\\\textbf{Hotel + Restaurant} & \\n\",\n \"\\\\texttt{<RESTAURANT\\\\_CUISINE\\\\_x>}, \\\\texttt{<HOTEL\\\\_PRICE\\\\_x>}, \\\\texttt{<CITY\\\\_x\\\\_NEIGHBORHOOD\\\\_y>} & \\n\",\n \"Combines hotel booking with dining preferences. Includes cuisine-specific searches, budget dining vs. fine dining, dietary needs (gluten-free, halal), special events (anniversaries), restaurant proximity to hotel, and group size considerations. Templates vary in planning flow—some start with hotel, others with restaurants. \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\\\end{tabularx}\\n\",\n \"\\\\vspace{1mm}\\n\",\n \"\\\\caption{Multi-domain combinations and scenario requirements. Each configuration builds on the unified instructions with additional placeholder logic and domain-specific complexity.}\\n\",\n \"\\\\label{tab:multi-domain-customization}\\n\",\n \"\\\\end{table}\\n\"\n ]\n]","isConversational":false},"fulltext":{"kind":"string","value":"\\documentclass{article}\n\n% if you need to pass options to natbib, use, e.g.:\n % \\PassOptionsToPackage{numbers, compress}{natbib}\n% before loading neurips_2024\n\n\\usepackage[square,numbers]{natbib}\n\\bibliographystyle{abbrvnat}\n\n% ready for submission\n% \\usepackage[]{neurips_2025}\n\\usepackage[preprint]{neurips_2025}\n\n\n% to compile a preprint version, e.g., for submission to arXiv, add add the\n% [preprint] option:\n% \\usepackage[preprint]{neurips_2024}\n\n\n% to compile a camera-ready version, add the [final] option, e.g.:\n% \\usepackage[final]{neurips_2024}\n\n\n% to avoid loading the natbib package, add option nonatbib:\n% \\usepackage[nonatbib]{neurips_2024}\n\n\n\\usepackage[utf8]{inputenc} % allow utf-8 input\n\\usepackage[T1]{fontenc} % use 8-bit T1 fonts\n\\usepackage{hyperref} % hyperlinks\n\\usepackage{url} % simple URL typesetting\n\\usepackage{booktabs} % professional-quality tables\n\\usepackage{amsfonts} % blackboard math symbols\n\\usepackage{nicefrac} % compact symbols for 1/2, etc.\n\\usepackage{microtype} % microtypography\n\\usepackage{xcolor} % colors\n\\usepackage{float}\n\\usepackage[breakable]{tcolorbox}\n\\usepackage{listings}\n\n\\lstset{\n basicstyle=\\ttfamily\\small,\n breaklines=true,\n breakatwhitespace=true,\n columns=fullflexible,\n frame=single\n}\n\\usepackage{tabularx} % Add in preamble\n% \\usepackage[table]{xcolor}\n% \\usepackage{tabularx}\n\\usepackage{amssymb} % for \\checkmark\n\\usepackage{hyperref}\n\\usepackage{url}\n\\usepackage{multirow}\n\\usepackage{bbm}\n\\usepackage{graphicx}\n\\usepackage{amssymb}\n\\usepackage{amsmath}\n\\usepackage{amsthm}\n\\usepackage{booktabs}\n\\usepackage{lscape}\n\\usepackage{pifont}\n\\usepackage{multirow}\n\\usepackage{booktabs}\n\\usepackage{textcomp}\n\\usepackage{lscape}\n\\usepackage{pifont}\n\\usepackage{algorithm}\n\\usepackage{graphicx}\n\\usepackage{caption}\n\\usepackage{subcaption}\n\\usepackage{CJKutf8}\n\\usepackage{algorithm}\n\\usepackage{algpseudocode}\n\\usepackage{enumitem}\n\\usepackage{wrapfig}\n\\usepackage{tabularx}\n\\usepackage{booktabs}\n\\usepackage{array}\n\\usepackage{pifont}\n\\usepackage{xcolor}\n\\newcommand{\\cmark}{\\ding{51}}\n\\newcommand{\\gcmark}{{\\color{green!60!black}\\fontsize{10}{10}\\selectfont\\ding{51}}}\n\\newcommand\\benchmarkname{\\textcolor{black}{\\textsc{T1}}}\n\\newcommand\\agentname{\\textcolor{black}{\\textsc{T1-Agent}}}\n\\newcommand\\dascomment[1]{\\textcolor{red}{das: \\textit{#1}}}\n\n\\title{$\\benchmarkname$: A Tool-Oriented Conversational Dataset for Multi-Turn Agentic Planning}\n\n% \\title{T$^2$ (ToolTalk): A Dataset for Tool-Oriented, Multi-Turn Agentic Planning}\n% \\title{ToolAgents: Tool-Driven Agents for Solving Multi-Domain Problems}\n\n\n% The \\author macro works with any number of authors. There are two commands\n% used to separate the names and addresses of multiple authors: \\And and \\AND.\n%\n% Using \\And between authors leaves it to LaTeX to determine where to break the\n% lines. Using \\AND forces a line break at that point. So, if LaTeX puts 3 of 4\n% authors names on the first line, and the last on the second line, try using\n% \\AND instead of \\And before the third author name.\n\n\n\\author{%\n Amartya Chakraborty\\thanks{The authors contributed equally.} \\text{ }, Paresh Dashore$^*$, Nadia Bathaee$^*$, Anmol Jain$^*$, \\\\\n \\textbf{Anirban Das}, \\textbf{Shi-Xiong Zhang}, \\textbf{Sambit Sahu}, \\textbf{Milind Naphade}, \\textbf{Genta Indra Winata$^*$} \\\\\n Capital One \\\\\n \\texttt{\\{amartya.chakraborty, paresh.dashore, nadia.bathaee\\}@capitalone.com} \\\\\n \\texttt{\\{anmol.jain, genta.winata\\}@capitalone.com}\n}\n\n\\begin{document}\n\n\\maketitle\n\n\\begin{abstract}\nLarge Language Models (LLMs) have demonstrated impressive capabilities as intelligent agents capable of solving complex problems. However, effective planning in scenarios involving dependencies between API or tool calls-particularly in multi-turn conversations-remains a significant challenge. To address this, we introduce $\\benchmarkname$, a tool-augmented, multi-domain, multi-turn conversational dataset specifically designed to capture and manage inter-tool dependencies across diverse domains. $\\benchmarkname$ enables rigorous evaluation of agents' ability to coordinate tool use across nine distinct domains (4 single domain and 5 multi-domain) with the help of an integrated caching mechanism for both short- and long-term memory, while supporting dynamic replanning-such as deciding whether to recompute or reuse cached results. Beyond facilitating research on tool use and planning, $\\benchmarkname$ also serves as a benchmark for evaluating the performance of open-source language models. We present results powered by $\\agentname$, highlighting their ability to plan and reason in complex, tool-dependent scenarios.\n\\end{abstract}\n\n\n\\section{Introduction}\nLeveraging external tools using Large Language Models (LLMs) to solve diverse conversational tasks has emerged as a promising direction in the development of intelligent agents~\\cite{tang2023toolalpaca}. Despite recent advances, task oriented llm based dialogue systems perform poorly over long conversational context~\\cite{laban2025llms}, and there remains a lack of comprehensive resources for training and evaluating multi-turn, multi-domain conversational agents that emphasizes on complex user needs. Existing datasets primarily focus on single-turn conversations for planning tasks—such as executing APIs or code~\\cite{schick2023toolformer, xie2024travelplanner}—and do not capture realistic multi-turn scenarios that require reasoning over long contexts, coordination across multiple tools, and adherence to complex constraints. Tool calling in realistic scenarios often involves interdependent tools, where the correctness and efficiency of task completion depend heavily on the order and context in which tools are invoked. An effective agent must therefore understand when, which, and in what sequence to call tools in order to successfully complete complex tasks. \n\nTo address this gap, we introduce $\\benchmarkname$, a new dataset and evaluation framework for assessing agent performance in multi-turn dialogues, with a particular focus on tool usage and reasoning about inter-tool dependencies. The dataset spans multiple domains and features complex, goal-oriented interactions between users and a travel assistant. Alongside the dataset, we propose $\\agentname$, an agent designed to interpret nuanced user intents and generate executable code using a predefined set of tools. $\\benchmarkname$ is specifically designed to evaluate the ability of LLM-based agents to plan tool use effectively and leverage a caching mechanism to efficiently reuse previously retrieved information. To support this, we incorporate dedicated tools for accessing and managing the cache.\n% Furthermore, we incorporate a novel caching mechanism on the agent side during code generation: before generating new outputs, the agent checks whether the current user query relates to any previously cached results. If a match is found, it retrieves the cached data and applies further filtering as needed.\n\n\\begin{figure*}[!t]\n \\centering\n \\includegraphics[width=\\textwidth]{assets/pipeline.png}\n \\caption{Illustrative example from the $\\benchmarkname$ dataset. This example showcases a multi-domain scenario involving both flights and hotels, where the user is planning a trip and attempting to book relevant services. The dialogue is constructed by retrieving entities from a knowledge base, and tool calls are executed using a predefined toolbox, simulating realistic, tool-augmented agent behavior.}\n \\label{fig:architecture}\n\\end{figure*}\n\nOur contributions can be summarized as follows:\n\\begin{itemize}\n \\item We introduce $\\benchmarkname$, a comprehensive multi-turn dataset consisting of 13.5k dialogues designed to evaluate tool-using, LLM-based agents across nine key domains—comprising four single-domain and five multi-domain settings. The dataset covers a wide range of interaction scenarios, including single-domain, mixed-domain, and fully multi-domain conversations. It incorporates 14 distinct tools, enabling realistic and fine-grained assessment of agent capabilities in complex, tool-driven dialogue tasks.\n \\item To enhance the complexity and realism of the evaluation, the dataset includes cross-domain tasks and interdependent tool calls, requiring agents to reason about tool selection and execution order within context. This evaluation framework assesses the ability of LLM-based agents to think critically, reason effectively, and make context-aware decisions.\n \\item We evaluate our dataset using an LLM-based agent, $\\agentname$, a code-generation system built on open-source language models and equipped with a caching mechanism for improved performance. This architecture enables scalable evaluation and provides a robust, efficient framework for tool-using agents.\n \\item We will publicly release our code and dataset to facilitate future research.\n\\end{itemize}\n\n\\section{T1 Dataset}\n\n$\\benchmarkname$ is a dataset specifically designed to evaluate LLM-based agents on tool usage and complex planning tasks over multi-turn conversational context. This dataset simulates multi-turn conversations spanning both four single-domain and five multi-domain settings: \\textit{flights}, \\textit{restaurants}, \\textit{hotels}, \\textit{attractions}, \\textit{flights-hotels}, \\textit{hotels-restaurants}, \\textit{hotels-attractions}, \\textit{flights-hotels-attractions}, and \\textit{flights-hotels-restaurants}.\n% \\dascomment{sentence abruptly ends}.\nPlanning tasks are formulated as code, where function calls to external tools are used to accomplish specific goals.\n\n\\subsection{Tasks and Notations}\n\nWe define a dialogue \\( D \\) as an alternating sequence of assistant and user turns: \n\\begin{align}\nD = \\{A_1, U_1, A_2, U_2, \\ldots, A_n\\},\n\\end{align}\nwhere each \\( A_i \\) represents an assistant turn and \\( U_i \\) a user turn. The dialogue always starts with an assistant turn and proceeds in a strictly alternating order. We provide a set of tools \\( \\mathcal{T} \\), where each tool \\( t \\in \\mathcal{T} \\) encapsulates logic to perform a specific function. These tools can be categorized as follows:\n\\begin{itemize}\n \\item \\textbf{Domain-specific tools}: Designed to handle operations tied to a particular application domain.\n \\item \\textbf{Interdependent tools}: Used to identify or reason about dependencies across domains.\n \\item \\textbf{Generic tools}: Domain-independent or auxiliary utilities applicable across tasks.\n\\end{itemize}\n\n\\begin{table*}[!htbp]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lccccccc}\n \\toprule\n \\textbf{Dataset} & \\textbf{Deployed} & \\textbf{Human Annotated} & \\textbf{Execution Result} & \\textbf{Multi-turn Context} & \\textbf{Multi-Domain} \\\\\n & \\textbf{Tools} & \\textbf{Tool Planning} & \\textbf{Evaluation} & \\textbf{Planning} & \\textbf{Tool Planning}\\\\\n \\midrule\n APIBank~\\cite{li2023api} & \\cmark & \\cmark & \\cmark & \\cmark & & \\\\\n APIBench~\\cite{patil2024gorilla} & & & \\cmark & & \\\\\n GAIA~\\cite{mialon2023gaia} & & \\cmark & \\cmark & & \\\\\n GTA~\\cite{wang2024gta} & \\cmark & \\cmark & \\cmark & \\cmark & \\\\\n m\\&m's~\\cite{ma2024m} & \\cmark & \\cmark & \\cmark & \\cmark & \\\\\n ToolBench~\\cite{qin2023toolllm} & \\cmark & & \\cmark & & \\\\\n Toolformer~\\cite{schick2023toolformer} & \\cmark & \\cmark & \\cmark & & \\\\\n TravelPlanner~\\cite{xie2024travelplanner} & \\cmark & \\cmark & \\cmark & & \\cmark & \\\\ \n \\midrule\n $\\textbf{\\benchmarkname}$ & \\cmark & \\cmark & \\cmark & \\cmark & \\cmark \\\\\n \\bottomrule\n \\end{tabular}\n}\n\\caption{Comparison of datasets for the LLM-based agent systems.}\n\\end{table*}\n\n\\begin{figure*}[!t]\n \\centering\n \\includegraphics[width=\\textwidth]{assets/data_generation.png}\n \\caption{$\\benchmarkname$ generates data by populating delexicalized entities with corresponding entries from the knowledge base.}\n \\label{fig:data_generation}\n\\end{figure*}\n\n% In this paper, we study four fundamental tasks involving tool usage:\n% \\begin{enumerate}\n% \\item \\textbf{Tool Calling}: Invoking the appropriate tool(s) based on the current dialogue context.\n% \\item \\textbf{Parameter Extraction}:\n% \\item \\textbf{Information Seeking}: Gathering missing or clarifying information required for successful tool execution.\n% \\item \\textbf{Data Caching}: Reusing previously generated results to improve efficiency and maintain continuity.\n% \\end{enumerate}\n\n\\subsection{Dataset Construction}\nWe construct our dataset by manually collecting data from Wikipedia to gather entities and metadata. For this task, we define four domains: flights, hotels, restaurants, and attractions. Additionally, we compile a list of 128 airports and 321 cities within the United States, along with up to 15 neighborhoods for each city. Some information—such as airline names, hotel names, and hotel star ratings—is synthesized to avoid inaccuracies and to prevent the LLMs from relying on its internal knowledge of real-world named entities.\n\n\\subsubsection{Ontology}\nWe have a total of 5 ontologies, one for each of the four defined domains and another one for city. For each ontology, we curated a list of key attributes that would be relevant such as the airline or number of layovers for a flight, the cuisine of a restaurant or the number of stars or customer rating for a hotel. For each of these attributes, we defined the possible values and then used the ontology to generate synthetic data for flights, hotels and restaurants which would subsequently be used by the tools we defined. \n\n\\begin{table*}[!t]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lcccc|cc|c|c}\n \\toprule \n & \\multicolumn{4}{c|}{\\textbf{Single-domain}} & \\multicolumn{2}{c|}{\\textbf{Multi-domain}}\n & \\textbf{Common} & \\textbf{Total} \\\\ \n & \\textbf{Flights} & \\textbf{Hotels} & \\textbf{Restaurants} & \\textbf{Attractions} & \\textbf{2 Domains} & \\textbf{3 Domains} & \\\\ \\midrule\n \\textbf{\\# Attributes} & 35 & 43 & 21 & 7 & N/A & N/A & N/A & 106 \\\\\n \\textbf{\\# Tools} & 2 & 2 & 2 & 2 & 1 & N/A & 5 & 14 \\\\ \n \\textbf{\\# Dialogues} & 1.5k & 1.5k & 1.5k & 1.5k & 4.5k & 3k & N/A & 13.5k \\\\ \n \\textbf{Avg. \\# turns} & 8.2 & 8.0 & 9.0 & 6.0 & 11.3 & 9.7 & N/A & N/A \\\\ \n \\bottomrule\n \\end{tabular}\n}\n\\caption{Dataset statistics detailing the number of attributes, tools, dialogues, and the average number of turns, categorized by each individual domain, multi-domain, and common categories. \"N/A\" indicates that the metric is not applicable in the given context.} \n\\label{tab:baseline-results-one-epoch}\n\\end{table*}\n\n\\paragraph{Flights.}\nThe ontology includes the airline, flight class (economy, business and first), number of layovers ranging between 0-2 stops, the duration of a layover ranging from 1-6 hours and a list of the possible airports to depart from and arrive to. To see the full table, go to Table~\\ref{tab:flight-ontology-attributes}.\n\n\\paragraph{Hotels.}\nThe ontology includes the number of stars ranging between 1 to 5 stars, the customer rating of the hotel ranging from 1.0 to 5.0, the cost of the hotel as well as whether or not the hotel includes a number of amenities such as the presence of a gym or pool. To see the full table, go to Table~\\ref{tab:hotel-ontology-attributes}.\n\n\\paragraph{Restaurants.}\nThe ontology includes the type of cuisine, the customer rating of the hotel ranging from 1.0 to 5.0, the price per person and whether or not the restaurant served any particular dietary options such as vegetarian, vegan, or halal. To see a full list of dietary restrictions, taken into consideration, go to Table~\\ref{tab:restaurant-ontology-attributes}.\n\n\\paragraph{Attractions.}\nThe ontology includes the type of attraction which is one of the following: touristy, culinary, historical, scenic, social, art, cultural, guided, and sporting.\n\n\\paragraph{Cities.}\nThe ontology includes a list of cities in the United States of America (US) that was collected through Wikipedia. For each city, we then extracted up to 15 neighborhoods as well as the approximate geographical coordinates of each neighborhood.\n\n\\subsubsection{Knowledge Bases}\n\\paragraph{Attractions.}\nFrom the list of 321 cities, we collect up to 15 attractions for 85 cities through the usage of Llama-3.3 70B Instruct and conduct quality assurance by human annotators to ensure the data correctness. We also collect the city neighborhood for each attraction as well as the geographical coordinates. In total, there are 728 attractions that were collected from the 85 cities for this dataset.\n\n\\paragraph{Flights.}\nFor flights, there are a total of 128 airports that are used to generate synthetic flight data. Each flight generated has an airline that was randomly selected from the ontology and the start and end airport are randomly selected from the list of airports in the ontology. Additionally, the departure time is randomly generated, however the arrival time is computed based on the geographical distance between the departure and arrival airports assuming the average speed of the flight to be 450 miles per hour. 480,410 synthetic flights were generated as part of this dataset\n\n\\paragraph{Hotels.}\nHotels are generated for all 321 cities in the ontology. For a particular city, a neighborhood is assigned and additionally, synthetic latitude and longitude coordinates are generated for each hotel in a city as the coordinates would be used for distance computing purposes. Each hotel is also provided a star and a synthetic customer rating that would be correlated to the amenities offered by the establishment as well as the price per night. 47,589 hotels were generated as part of this dataset. \n\n\n\\paragraph{Restaurants.}\nRestaurants are generated for all 321 cities in the ontology. Just like for hotels, in a particular city, a neighborhood is assigned to a restaurant and additionally, synthetic latitude and longitude coordinates are generated. Each restaurant also is given a user rating which was synthetically generated, a cuisine provided by the ontology as well as whether particular dietary options are supported and the average cost per person. 17,975 restaurants are generated as part of this dataset.\n\n\\subsubsection{Data Annotation and Quality Assurance}\nTo ensure high-quality and natural data, we employ five human annotators, with each data sample reviewed by both an annotator and a quality assurance (QA) reviewer. The annotators were selected to represent a diverse set of background and perspectives, while maintaining a high technical bar. All annotators have at least a Master's degree in Computer Science and demonstrated proficiency in Python, enabling them to handle complex annotation tasks requiring logical reasoning and scripting. The QA specialist also has a strong background in programming. Annotators are assigned a category of templates and are responsible for writing the appropriate code using the tools defined for this project. Afterward, the QA reviewer evaluates the annotated code and provides feedback, which is used to make necessary corrections and improvements.\n\n\\subsubsection{Dialogue Generation}\nWe create a total of nine data categories, as discussed in Section~\\ref{sec:distinct-domains}. The data construction follows a three-step process: first, we generate templates with placeholder values; second, we annotate the templates with code using the provided tools; and third, we programmatically fill in the placeholder values for each template.\n\\paragraph{Generating Dialogue Templates.}\nFor each of the 9 dialogue categories, we generate 60 dialogue templates using Llama-3.3 70B Instruct. These templates are then reviewed and refined by human annotators to ensure accurate dialogue flow, as well as high coherence and fluency. The model is prompted to generate synthetic dialogues for both single and multi-domain. Each template consists of a conversation between the assistant and user. To learn more about the prompt used, go to Appendix~\\ref{appendix:prompt-design-user-planner} and ~\\ref{appendix:multi-domain-prompt-design}. Additionally, the templates consist of placeholders for attributes such as a city or neighborhood name, cuisine of a restaurant, rating of a hotel or the type of an attraction. As the dataset is related to the area of travel, some placeholder values are tied to a particular city such as the neighborhood (\\texttt{<CITY\\_x\\_NEIGHBORHOOD\\_x>}, airport name (\\texttt{<CITY\\_x\\_AIRPORT\\_x>}), hotel name (\\texttt{<CITY\\_x\\_HOTEL\\_NAME\\_x>}) and restaurant name (\\texttt{<CITY\\_x\\_RESTAURANT\\_NAME\\_x>}). \n\n\\paragraph{Template Lexicalization.}\nWithin each dialogue category, 25 dialogues are generated with the placeholder values filled with the actual values present within the ontology. In addition to the dialogues, the ground-truth code which is part of the annotation has the placeholder values replaced with actual values. The dataset is split into three partitions: training, validation and test. To limit data contamination among these three partitions, the cities that are to be used for filling in the placeholder are also assigned to a particular partition only to be used there. Hence, for example if the city of Boston is assigned to the training partition, it will never be present in any of the validation or test dialogue conversations.\n\\label{sec:dialogue-template-creation}\nAdditionally, for fields such as the check-in and checkout dates for hotels or departure and return flight dates, particular methods are taken to ensure that any dates or times follow chronological order and there is no instance where a check-in date at a hotel would be after the checkout date.\n\n\\paragraph{Generated Dialogue and Ground Truth Validation.}\nOnce the placeholders are filled for both the dialogues and ground truth code samples, each block of code is executed to identify any potential errors in annotation including an improper use of a placeholder or a syntax error. The validation assists the team in correcting any annotations and ensures that the resulting code is runnable and correct. \n\n\\section{T1-Agent}\nWe build an LLM powered $\\agentname$ to evaluate and simulate our $\\benchmarkname$ agentic dataset and measure its performance across three tasks: information seeking, parameter extraction, and tool calling.\n\n\\subsection{Information Seeking}\nEach tool has a mandatory set of parameters that must be provided before running the tool successfully. Information seeking is the task of gathering this mandatory set of parameters for any of the respective tools. We want to evaluate the capability of the agent to understand both the intent of the user's query and which parameters to ask the user about as a follow-up. Figure~\\ref{fig:architecture} shows an example of how information seeking is used by the agent. When the user inquires about a flight from New York to San Jose and a hotel in San Jose, the agent understands that the user would like to search for flights but needs to provide the departure date of the flight and the check-in date of the hotel. Thus, the agent infers that in followup discussion, it needs to ask for these details from the user.\n\n\\subsection{Parameter Extraction}\nAfter the agent understands the tools to call and the necessary parameters to collect, it works to extract those parameters from the user dialogue. From Figure~\\ref{fig:architecture}, when the user mentions that they want a flight from New York to San Jose for 3 nights, the agent is able to extract the starting and ending airport city for the flight as well as the number of nights to stay at the hotel. However, neither \\texttt{search\\_flights} or \\texttt{search\\_hotels} can be called yet since the departure date of the flight and the check-in and check-out date of the hotel have not yet been provided.\n\n\\subsection{Tool Calling}\nOnce the agent understands the necessary tool(s) to call and is provided the necessary parameters for each tool, the agent will make calls to each tool. In Figure~\\ref{fig:architecture}, once the user provides the departure date for the flight to San Jose, the agent calls the \\texttt{search\\_flights} tool with the departure and arrival cities as well as the departure date. Next, before the agent can call the \\texttt{search\\_hotels} tool, it must first compute the check-out date for the hotel. The check-in date provided is May 9 and the user provided the context that the duration of the hotel stay is 3 nights. Hence, the \\texttt{adjust\\_date} tool is called to compute the check-out date of the hotel. Once this is calculated, the agent will call the \\texttt{search\\_hotels} tool with the check-in and check-out dates as well as the city of the hotel. Additionally, both the flight and hotels that came up in the results will be saved to the cache, for further possible usage.\n\n\\subsection{Data Caching}\n% Unlike prior work such as \\textit{CodeAct} \\cite{wang2024executable}, which generates fresh tool calls at each user turn, \nWe introduce a data caching mechanism that enables $\\agentname$ to reuse the outputs of earlier tool calls when handling subsequent user requests. This reduces redundant computation and improves response efficiency in multi-turn interactions. After each user turn, any search results retrieved by a tool are cached for potential reuse in later turns. The \\texttt{save\\_to\\_cache} tool is used to store these results. As illustrated in Figure~\\ref{fig:architecture}, after the user provides the departure date for a flight to San Jose, the agent calls both \\texttt{search\\_flights} and \\texttt{search\\_hotels}, and then stores the results using the \\texttt{save\\_to\\_cache} tool. Later, when the user requests flights from a specific airline, the agent retrieves the cached flight results using the \\texttt{get\\_results\\_from\\_cache} tool and filters them using the \\texttt{filter\\_flights} tool based on the user's airline preference. This caching approach helps avoid unnecessary API calls by reusing existing search results and applying filtering when appropriate.\n\n\\paragraph{Example: Refining a Flight Search.}\nSuppose a previous user query fetched flights from NYC to Boston, and the result was cached with the key \\texttt{\"flights\\_nyc\\_bos\"}. Later, the user asks to see only flights priced under \\$500. The LLM, using the cache summary, generates the following plan:\n\n\\begin{verbatim}\nflights = get_results_from_cache(key=\"flights_nyc_bos\")\naffordable_flights = filter_flights(prior_result=flights, budget=500)\n\\end{verbatim}\n\nThis illustrates how the LLM composes new logic by combining previously retrieved results with downstream tools, without repeating expensive API operations. Our approach enables more efficient, coherent, and stateful plan generation in realistic, multi-turn assistant conversations.\n\n\n\\section{Experimental Setup}\n\n\\subsection{Datasets}\n\\label{sec:distinct-domains}\nEach category contains 60 dialogue templates and a pool of 54 cities used to populate placeholder values. We split both the templates and cities into 25\\% for training, 9\\% for validation, and 66\\% for testing, resulting in 15 training templates, 5 validation templates, and 40 test templates per category. Similarly, 13 cities are allocated for training, 4 for validation, and 37 for testing. Dialogues are randomly sampled within each partition. After partitioning, a script fills all placeholders in each template using the corresponding cities from the assigned split, ensuring no data leakage or overlap of templates and entities between the training and test sets. Each template is instantiated into 25 unique dialogues after substituting the placeholders in the dialogue templates with entities defined in the ontology, yielding a total of 1,500 fully generated dialogues.\n\n\n\n% We generate 25 dialogues for each single-domain and multi-domain template. The dataset is further divided into training, validation, and test splits. For more details, please refer to Section~\\ref{sec:dialogue-template-creation}.\n\n\\subsection{Domain Adaptation using SFT} \nWe perform a simple instruction tuning with the train dataset in order to showcase that the performance on such complex conversational tool calling can be improved over zero or few shot prompting. We train a Llama 3.1 8B Instruct model for one epoch on the training dataset. The training dataset is structured as a list of \\texttt{(prompt, completions)} pairs. The SFT thereafter follows standard next token prediction with cross-entropy loss. We use LoRA~\\cite{lora} on 8 A100 SXM gpus instead of full finetuning. For reproducibility, we use the widely adopted Huggingface TRL library~\\cite{vonwerra2022trl} and include the exact command to replicate the training in Section~\\ref{SFTScript}. \n\n\\subsection{Inference Procedure}\nDuring inference, for each user turn in a dialogue, the $\\agentname$ generates executable Python code that fulfills the user request at that point in the conversation. Before generating any code, the agent will check an \\textbf{execution cache} to determine if a similar request has been previously resolved. If a cached result is available, the agent will write code that fetches and reuses the cached object to prevent redundant computation and tool invocation.\n\nTo do this, the agent constructs a prompt that includes the conversation history along with the current user turn. Instead of including the full execution cache—which can be large and token-intensive—the prompt incorporates a \\textbf{summary} of the cached results.\nThese summaries are generated using a deterministic, rule-based function that transforms each cached result into a concise representation. This approach significantly reduces the token load in the prompt, making it feasible to include relevant past results without exceeding model input limits.\n\nThis design enables the agent to consider past outcomes and reuse relevant information during code generation. Even in cases where the user’s current query differs slightly from earlier queries, the agent is able to fetch a prior result from the cache and use that as a starting point for the current user turn. By introducing summarization at the \\textit{planning stage}, we shift caching responsibilities from the tool-execution layer to the agent’s decision-making process. Our approach allows the agent to selectively reuse and adapt cached outputs, leading to improved latency and broader generalization.\n\nThe generated code is executed in a sandboxed environment, and the cache is updated with the new results on each user turn. Our dataset also includes the corresponding ground truth code and post-execution cache, which are used for performance evaluation.\n\n\\section{Results and Analysis}\nIn this section, we present the performance of various LLMs on both single-domain and multi-domain tasks. Single-domain tasks involve conversations focused exclusively on one domain—such as flights, hotels, restaurants, or attractions. In contrast, multi-domain tasks involve interactions spanning multiple domains, such as a user requesting both flight bookings and nearby hotel recommendations within the same dialogue. \n\n\\subsection{Overall Results}\nTable~\\ref{table:llama3.1-8b} presents the overall performance of \\texttt{Llama 3.1 8B Instruct} in a few-shot in-context learning setting. The model performs significantly worse on multi-domain tasks compared to single-domain ones. Additionally, much of the generated code—particularly in multi-domain scenarios—is not executable. Despite the inclusion of few-shot examples, the model struggles with accurate tool invocation. Parameter matching is even more challenging, as it requires the model not only to identify the correct tool but also to extract the appropriate parameter values from the user’s utterance.\n\n\\subsection{Analysis}\n\n\\paragraph{The Impact of Domain Adaptation on Performance.}\nFigure~\\ref{fig:enter-label} presents the tool call F1 and parameter matching F1 scores. Overall, the SFT models outperform the base model and even surpass the performance of the 70B model by a substantial margin across all domains.\n\n\\begin{figure}[!ht]\n \\centering\n \\includegraphics[width=\\linewidth]{assets/domain_adaptation.png}\n \\caption{\\textbf{Left:} Tool Call F1 performance and \\textbf{Right:} Parameter Matching F1 performance.}\n \\label{fig:enter-label}\n\\end{figure}\n\n\\begin{table*}[!ht]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lcccc|cccc|c|cc|c}\n \\toprule\n \\textbf{Domain} & \\multicolumn{4}{c}{\\textbf{Tool Call}} & \\multicolumn{4}{|c|}{\\textbf{Parameter Matching}} & \\textbf{Code Exec.} & \\multicolumn{2}{|c|}{\\textbf{Information Seeking}} & \\multicolumn{1}{c}{\\textbf{Cache}} \\\\ \n & & & & & & & & & \\textbf{Rate} & & & \\textbf{Summary} \\\\\n & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\ \\midrule\n \\multicolumn{8}{l}{\\texttt{Single-Domain}} \\\\\n \\midrule\n Flights & 25.12 & 30.90 & 57.34 & 40.16 & 15.57 & 17.34 & 60.30 & 26.94 & 26.24 & 14.41 & 82.36 & 36.63 \\\\\n Hotels & 42.86 & 49.89 & 75.25 & 60.00 & 17.57 & 23.59 & 40.80 & 29.89 & 48.13 & 47.01 & 80.38 & 33.70 \\\\\n Restaurants & 40.40 & 52.97 & 63.00 & 57.55 & 31.47 & 36.07 & 71.12 & 47.87 & \\textbf{56.99} & 28.91 & 75.68 & 35.49 \\\\\n Attractions & 33.03 & 48.49 & 50.90 & 49.66 & 14.11 & 25.11 & 24.36 & 24.73 & 40.40 & N/A & N/A & 43.87 \\\\ \\midrule\n \\multicolumn{10}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n F-H & 33.09 & 40.89 & 63.44 & 49.73 & 16.11 & 19.28 & 49.48 & 27.75 & 38.43 & 26.54 & 82.00 & 21.85 \\\\\n H-R & 51.06 & 59.21 & 78.77 & 67.61 & 26.62 & 33.52 & 56.37 & 42.05 & 51.82 & 28.60 & 84.07 & 9.95 \\\\\n H-A & 38.75 & 45.46 & 72.39 & 55.85 & 16.11 & 20.02 & 45.12 & 27.74 & 48.88 & 26.03 & 81.77 & 32.75 \\\\\n F-H-A & 27.76 & 36.24 & 54.24 & 43.45 & 16.21 & 21.18 & 40.88 & 27.90 & 28.76 & 14.15 & 78.58 & 24.37 \\\\\n F-H-R & 29.04 & 34.65 & 64.19 & 45.00 & 18.53 & 21.77 & 55.44 & 31.26 & 36.79 & 33.28 & 82.84 & 27.47 \\\\\n \\bottomrule\n \\end{tabular}\n}\n\\caption{Overall results using Llama 3.1 8B Instruct with few-shot in-context learning.}\n\\label{table:llama3.1-8b}\n\\end{table*}\n\n% \\begin{table*}[!ht]\n% \\centering\n% \\resizebox{\\textwidth}{!}{\n% \\begin{tabular}{lcccc|ccccccccc}\n% \\toprule\n% \\textbf{Model} & \\multicolumn{4}{c|}{\\textbf{Single Domain}} & \\multicolumn{5}{c}{\\textbf{Multi-Domain}} \\\\\n% & Flight & Hotel & Attraction & Restaurant & F-H & H-R & H-A & F-H-A & F-H-R \\\\ \\midrule\n% Llama 3.1 8B \\\\\n% $\\quad$+ Fine-tune \\\\\n% Llama 3.3 70B \\\\ \\midrule\n% Phi-4 R$^+$ 14B \\\\\n% S1.1 32B \\\\\n% \\bottomrule\n% \\end{tabular}\n% }\n% \\caption{F1 Results on Fine-grained Tool Call.}\n% \\end{table*}\n\n\\paragraph{Model Performance.}\nOur qualitative evaluation shows that the \\texttt{LLaMA 3.3 70B Instruct} model consistently delivers the strongest performance across tasks. In contrast, the \\texttt{LLaMA 3.1 8B Instruct} model, without any fine-tuning, struggles significantly. It often fails to utilize available cache effectively and generates an incorrect code.\n\n\\paragraph{Impact of Fine-Tuning.}\nWhile the 70B and the non-fine-tuned 8B models were prompted to generate answers with reasoning, a fine-tuned variant—despite not being instructed to provide reasoning—achieved performance comparable to the 70B Instruct model. This indicates the strong potential of task-specific fine-tuning in enhancing model performance.\n\n\\paragraph{Few-Shot Evaluation.}\nAs shown in Figure~\\ref{fig:few-shot}, we evaluate model performance on the \\textit{flights} domain under 0-shot, 5-shot, and 13-shot settings. Performance is notably poor in the 0-shot setting, with improvements observed in 5-shot and 13-shot configurations—though the gains plateau beyond 5 shots. Qualitative analysis suggests that without sufficient context, models continue to struggle with generalization in complex, multi-domain scenarios.\n\\begin{figure}[!ht]\n \\centering\n \\includegraphics[width=0.9\\linewidth]{assets/flight_shot_learning.png}\n \\caption{Few-shot performance on Flight domain.}\n \\label{fig:few-shot}\n\\end{figure}\n\n\\paragraph{Need for Complex Evaluation.}\nEven with the state-of-the-art model, \\texttt{LLaMA 3.3 70B Instruct}, which performs well on standard code generation tasks, we observe that it continues to struggle in complex, multi-turn scenarios involving advanced planning. The $\\benchmarkname$ dataset is designed to fill this gap by serving as a benchmark for evaluating model performance in such challenging settings.\n\n\\section{Related Work}\n\\subsection{Large Language Model Agents}\nLLM-based agents have emerged as foundational components in AI systems, capable of performing complex, multi-step tasks through reasoning, memory integration, and tool use. These agents often combine a pre-trained LLM with structured modules such as long-term memory, tool calling capabilities, and self-reflective feedback loops. Frameworks such as {AutoGPT}~\\cite{yang2023auto}, and {AgentGPT} allow LLMs to autonomously decompose user goals into subgoals and execute them sequentially using external APIs. More structured systems like {HuggingGPT}~\\cite{shen2023hugginggpt}, {CrewAI}, and {AutoGen}~\\cite{wu2023autogen} facilitate collaboration between multiple LLM agents, each specializing in roles such as planning, execution, or critique.\n\nDespite significant progress, planning within task-oriented dialogue systems—particularly over long horizons—remains a fundamental challenge. Previous paradigms such as \\textit{plan-observe-execute} (e.g., ReAct~\\cite{yao2023react}, ADaPT~\\cite{prasad2023adapt}) enable the model to interleave tool calls with reasoning steps. However, most frameworks focus on linear execution paths where each step invokes a single tool. These approaches often lack the ability to manage inter-tool dependencies, reuse intermediate results, or revise plans based on partial tool failures. Further, a lot of these systems performs single turn planning, i.e. the user of the system submits a request with all the information in the first turn. Multi-turn conversational planning is a nascent field. For example, in multi-turn workflows like conversational trip planning, agents need to collect information across multiple steps, then plan and coordinate across tools like flight search, visa information, and calendar APIs—something most current systems struggle to handle.\n\n\\subsection{Tool-Based Agents and Benchmarks}\n\nTool usage extends the scope of LLM capabilities beyond language modeling ~\\citep{team2024chameleon, schick2023toolformer} to real-world actionability, enabling interactions with APIs, web tools, and external software. To evaluate this ability, several benchmarks have been proposed, including {APIBank}~\\cite{li2023api}, {Tau-Bench}~\\cite{yao2024tau},\n{GAIA}~\\cite{mialon2023gaia}, {ALFWorld}~\\cite{shridhar2020alfworld}, {GTA}~\\cite{wang2024gta},\n{TravelPlanner}~\\cite{xie2024travelplanner}, and {ToolBench}~\\cite{qin2023toolllm}. Although these benchmarks each emphasize different strengths, such as breadth of API coverage, realism, or reasoning complexity—they often treat tool use as a series of isolated atomic actions without requiring coordinated planning between multiple tools. Further, there\n\nIn contrast, our work introduces $\\benchmarkname$, a tool-driven benchmark designed to evaluate LLM agents in multi-turn, multi-domain dialogue settings with inter-tool dependencies. $\\benchmarkname$ features an integrated caching mechanism that supports both short- and long-term memory of tool call results, allowing agents to make intelligent decisions about whether to replan or reuse cached outputs. Unlike prior benchmarks, it tests an agent’s ability to perform dynamic replanning, handle branching workflows, and compose tools in a realistic, dialog-driven environment. Thus, $\\benchmarkname$ not only challenges existing tool-use agents but also provides a diagnostic sandbox for evaluating the reasoning capabilities of open-source LLMs under realistic constraints.\n\n\n\n% \\section{Related Work }\n\n% \\subsection{Large Language Model Agents} AI Agents or Large Language model agents are now ubiquitously being used in various complex workflows~\\cite{tang2023toolalpaca}. The ability to utilize memory, tools alongside planning, reasoning and self reflection abilities have led to the enhancement in their abilities. Many tool calling frameworks such as AutoGPT~\\citep{}, HuggingGPT~\\citep{}, CrewAI~\\citep{}, AutoGen~\\citep{} etc, has sprung into existence that allows building of single or multi agent systems. However planning in task oriented dialogue systems, both long and short horizon, is not yet solved~\\citep{}. Prior work has looked into \\textit{plan-observe-execute} type paradigms to do planning in the presence of tools or functions~\\citep{react, rewoo, adapt} where each next action in the plan depends on the outcome of the previous execution. In this case however the plan at each step consists of calling an single tool. \n\n% \\paragraph{Tool-based Agents}\n% Tools give the AI agents ability to perform tasks. However in many cases, performing a single task may need multiple tools to be called with some application logic. Several benchmarks aims at assessing the performance of tool calling ability of AI Agents. APIBench, GTA, Tau-Bench, GAIA, OsWorld, Alfworld, Alfbench, TravelPlanner, Toolbench etc. \n% https://arxiv.org/pdf/2406.12045\n% %https://proceedings.neurips.cc/paper_files/paper/2024/file/bfd082c452dffb450d5a5202b0419205-Paper-Datasets_and_Benchmarks_Track.pdf\n% https://arxiv.org/pdf/2402.01622\n% https://arxiv.org/pdf/2406.14283?\n% https://aclanthology.org/2025.coling-main.672.pdf\n% https://arxiv.org/abs/2410.01440\n% https://openreview.net/pdf?id=dCPF1wlqj8\n% https://arxiv.org/pdf/2502.11221\n% https://aclanthology.org/2025.naacl-long.36.pdf\n% https://arxiv.org/pdf/2408.00764?\n% https://neurips.cc/virtual/2024/poster/97620\n\n\\newcolumntype{Y}{>{\\centering\\arraybackslash}X}\n\n\\section{Conclusion}\n\nWe introduce $\\benchmarkname$, a comprehensive dataset for evaluating planning, reasoning, and tool-usage in LLM-based agents through complex, multi-turn dialogues. By introducing inter-tool dependencies, dynamic replanning, and caching, it supports rigorous assessment across single- and multi-domain settings. Experiments with $\\agentname$ highlight both the strengths and limitations of open-source LLMs: while the \\texttt{LLaMA 3.3 70B Instruct} model performs best, the non-fine-tuned \\texttt{LLaMA 3.1 8B} struggles with caching and code generation. A fine-tuned 8B variant matches 70B performance, emphasizing the value of task-specific tuning. Despite improvements with few-shot learning, models still face generalization challenges in multi-domain scenarios.\n\\bibliography{main}\n\n\\appendix\n\n\\newpage\n% \\section*{NeurIPS Paper Checklist}\n% \\begin{enumerate}\n\n% \\item {\\bf Claims}\n% \\item[] Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?\n% \\item[] Answer: \\answerYes{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: The claim is supported by the abstract and introduction and aligns with the contributions outlined throughout the paper.\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that the abstract and introduction do not include the claims made in the paper.\n% \\item The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. 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Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.\n% %this research? \n% \\item[] Answer: \\answerYes{}, % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: We declared during the submission.\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that the core method development in this research does not involve LLMs as any important, original, or non-standard components.\n% \\item Please refer to our LLM policy (\\url{https://neurips.cc/Conferences/2025/LLM}) for what should or should not be described.\n% \\end{itemize}\n \n\\section{Limitations}\nIn this work, we focus on constructing and introducing a new dataset as a benchmark for evaluating agentic workflows in multi-turn conversational dialogue settings, with an emphasis on tool calling for planning. Our evaluations are limited to open-source models, as proprietary models are not included due to resource constraints. We use Llama 3.1 8B Instruct, Llama 3.3 70B Instruct, S1.1 32B, and Phi4 Reasoning Plus. We will release $\\benchmarkname$ dataset publicly and hope it will encourage future research that includes evaluations on proprietary models as well.\n\n\\section{Additional Results}\n\\subsection{Detailed Results for Llama 3.1 8B Instruct with SFT and Llama 3.3 70B Instruct}\nTable~\\ref{table:llama3.1-8b-sft} shows the results for Llama 3.1 8B Instruct with Domain Adaptation and Table~\\ref{table:llama3.3-70b} shows the results for Llama 3.3 70B Instruct.\n\n\\begin{table*}[!ht]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lcccc|cccc|c|cc|c}\n \\toprule\n \\textbf{Domain} & \\multicolumn{4}{c}{\\textbf{Tool Call}} & \\multicolumn{4}{|c|}{\\textbf{Parameter Matching}} & \\textbf{Code Exec. Rate} & \\multicolumn{2}{|c|}{\\textbf{Information Seeking}} & \\multicolumn{1}{c}{\\textbf{Cache Summary}} \\\\ \n & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\ \\midrule\n \\texttt{Single-Domain} \\\\\n \\midrule\n Flights & 83.51 & 89.98 & 92.08 & 91.02 & 58.02 & 66.38 & 82.18 & 73.44 & 85.26 & 39.19 & 86.44 & 62.07 \\\\\n Hotels & 78.18 & 84.87 & 90.84 & 87.75 & 60.40 & 67.29 & 85.53 & 75.32 & 72.13 & 48.30 & 80.91 & 71.35 \\\\\n Restaurants & 72.76 & 80.40 & 88.44 & 84.23 & 57.49 & 66.69 & 80.64 & 73.00 & 95.16 & 95.14 & 98.63 & 65.32 \\\\\n Attractions & 90.50 & 97.87 & 92.33 & 95.02 & 75.54 & 91.34 & 81.37 & 86.07 & 98.15 & N/A & N/A &83.00 \\\\ \\midrule\n \\multicolumn{10}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n F-H & 80.27 & 91.31 & 86.91 & 89.05 & 57.76 & 74.51 & 71.99 & 73.23 & 89.25 & 24.04 & 80.63 & 57.01 \\\\\n H-R & 82.02 & 91.03 & 89.23 & 90.12 & 68.44 & 77.95 & 84.86 & 81.26 & 83.67 & 29.12 & 87.58 & 64.42 \\\\\n H-A & 62.30 & 72.67 & 81.35 & 76.77 & 59.22 & 68.73 & 81.05 & 74.39 & 71.41 & 27.87 & 82.80 & 65.70 \\\\\n F-H-A & 77.19 & 87.50 & 86.76 & 87.13 & 59.63 & 74.16 & 75.27 & 74.71 & 81.15 & 22.65 & 83.45 & 52.76 \\\\\n F-H-R & 71.53 & 86.05 & 80.91 & 83.40 & 54.39 & 71.99 & 68.99 & 70.45 & 82.47 & 46.32 & 84.40 & 53.88 \\\\\n \\bottomrule\n \\end{tabular}\n}\n\\caption{Overall Results using Llama 3.1 8B Instruct after SFT.}\n\\label{table:llama3.1-8b-sft}\n\\end{table*}\n\n\\begin{table*}[!ht]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lcccc|cccc|c|cc|c}\n \\toprule\n \\textbf{Domain} & \\multicolumn{4}{c}{\\textbf{Tool Call}} & \\multicolumn{4}{|c|}{\\textbf{Parameter Matching}} & \\textbf{Code Exec. Rate} & \\multicolumn{2}{|c|}{\\textbf{Information Seeking}} & \\multicolumn{1}{c}{\\textbf{Cache Summary}} \\\\ \n & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\ \\midrule\n \\texttt{Single-Domain} \\\\\n \\midrule\n Flights & 58.42 & 78.26 & 69.75 & 73.76 & 32.11 & 40.51 & 60.76 & 48.61 & 83.32 & 20.25 & 82.90 & 53.80 \\\\\n Hotels & 92.42 & 96.03 & 96.10 & 96.07 & 75.33 & 80.85 & 91.69 & 85.93 & 98.55 & 46.98 & 80.27 & 75.00 \\\\\n Restaurants & 94.23 & 96.63 & 97.43 & 97.03 & 85.51 & 93.62 & 90.80 & 92.19 & 99.54 & 37.90 & 79.95 & 90.78 \\\\\n Attractions & 79.14 & 92.16 & 84.86 & 88.36 & 71.17 & 87.04 & 79.60 & 83.16 & 63.70 & N/A & N/A & 78.59 \\\\ \\midrule\n \\multicolumn{10}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n F-H & 43.48 & 53.73 & 69.52 & 60.61 & 22.28 & 25.94 & 61.24 & 36.44 & 95.02 & 27.39 & 83.42 & 37.15 \\\\\n H-R & 60.50 & 70.91 & 80.46 & 78.29 & 50.94 & 60.08 & 76.99 & 67.50 & 99.60 & 22.97 & 84.11 & 38.43 \\\\\n H-A & 64.91 & 68.93 & 91.75 & 78.72 & 59.24 & 66.59 & 84.29 & 74.40 & 97.01 & 30.02 & 82.71 & 57.03 \\\\\n F-H-A & 60.46 & 70.77 & 80.59 & 75.36 & 44.27 & 51.19 & 76.58 & 61.37 & 94.81 & 19.16 & 82.27 & 44.29 \\\\\n F-H-R & 52.97 & 62.22 & 78.07 & 69.25 & 42.93 & 50.66 & 73.76 & 60.07 & 91.30 & 40.45 & 85.62 & 45.37 \\\\\n \\bottomrule\n \\end{tabular}\n}\n\\caption{Overall Results using Llama 3.3 70B Instruct.}\n\\label{table:llama3.3-70b}\n\\end{table*}\n\n\\subsection{Detailed Results for Reasoning Models}\nAdditionally, we also experiment with medium-sized reasoning models to see how they perform. We work with S1.1 32B~\\cite{muennighoff2025s1} and Phi-4-reasoning-plus 14B~\\cite{abdin2025phi}. The decoding configuration for both models includes a maximum token limit of 4,000, a temperature of 0.1, and max token length is 7,000. We host our models using vLLM in a A100 40GB x 8 node.\n\n\\begin{table*}[!ht]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lcccc|cccc|c|cc|c}\n \\toprule\n \\textbf{Domain} & \\multicolumn{4}{c}{\\textbf{Tool Call}} & \\multicolumn{4}{|c|}{\\textbf{Parameter Matching}} & \\textbf{Code Exec. Rate} & \\multicolumn{2}{|c|}{\\textbf{Information Seeking}} & \\multicolumn{1}{c}{\\textbf{Cache Summary}} \\\\ \n & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\ \\midrule\n \\texttt{Single-Domain} \\\\\n \\midrule\n Flights & 77.59 & 87.87 & 86.90 & 87.38 & 49.53 & 58.04 & 77.15 & 66.25 & 57.20 & 29.65 & 83.12 & 57.46\n \\\\\n Hotels & 89.33 & 95.33 & 93.41 & 94.36 & 65.27 & 70.52 & 89.77 & 78.99 & 63.95 & 18.07 & 79.56 & 57.23\n \\\\\n Restaurants & 87.77 & 92.65 & 94.35 & 93.49 & 71.59 & 77.69 & 90.11 & 83.44 & 69.82 & 7.16 & 69.91 & 69.22\n \\\\\n Attractions & 65.76 & 80.16 & 78.54 & 79.34 & 44.92 & 53.04 & 74.59 & 62.00 & 72.57 & N/A & N/A & 63.87\n\\\\ \\midrule\n \\multicolumn{10}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n F-H & 69.87 & 78.42 & 86.51 & 82.27 & 42.38 & 50.39 & 72.71 & 59.53 & 66.89 & 9.72 & 78.43 & 47.49\n \\\\\n H-R & 59.10 & 65.62 & 85.60 & 74.29 & 51.91 & 58.54 & 82.10 & 68.35 & 49.72 & 19.69 & 85.40 & 32.55\n \\\\\n H-A & 63.46 & 67.81 & 90.83 & 77.65 & 65.22 & 72.53 & 86.60 & 78.95 & 44.39 & 21.41 & 81.95 & 56.64\n \\\\\n F-H-A & 68.41 & 75.27 & 88.25 & 81.24 & 50.21 & 61.04 & 73.88 & 66.85 & 60.86 & 27.00 & 82.48 & 50.42\n \\\\\n F-H-R & 67.22 & 73.80 & 88.30 & 80.40 & 58.97 & 71.66 & 76.91 & 74.19 & 61.47 & 10.08 & 78.32 & 53.74\n \\\\\n \\bottomrule\n \\end{tabular}\n}\n\\caption{Overall Results using S1.1 32B.}\n\\label{table:s1.1}\n\\end{table*}\n\n\\begin{table*}[!ht]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lcccc|cccc|c|cc|c}\n \\toprule\n \\textbf{Domain} & \\multicolumn{4}{c}{\\textbf{Tool Call}} & \\multicolumn{4}{|c|}{\\textbf{Parameter Matching}} & \\textbf{Code Exec. Rate} & \\multicolumn{2}{|c|}{\\textbf{Information Seeking}} & \\multicolumn{1}{c}{\\textbf{Cache Summary}} \\\\ \n & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\ \\midrule\n \\texttt{Single-Domain} \\\\\n \\midrule\n Flights & 36.66 & 67.30 & 44.61 & 53.66 & 15.74 & 56.10 & 17.95 & 27.20 & 72.76 & 13.91 & 81.60 & 39.73\n \\\\\n Hotels & 83.96 & 91.74 & 90.82 & 91.28 & 53.22 & 63.09 & 77.30 & 69.47 & 93.05 & 46.95 & 80.47 & 46.52\n \\\\\n Restaurants & 64.13 & 83.60 & 73.36 & 78.14 & 47.10 & 61.89 & 66.34 & 64.04 & 93.49 & 28.27 & 69.74 & 50.53\n \\\\\n Attractions & 35.78 & 61.67 & 46.02 & 52.71 & 22.90 & 66.92 & 25.82 & 37.27 & 66.25 & N/A & N/A & 50.79\n\\\\ \\midrule\n \\multicolumn{10}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n F-H & 57.79 & 88.12 & 62.67 & 73.25 & 29.91 & 59.20 & 37.68 & 46.05 & 83.37 & 27.96 & 83.02 & 32.53\n \\\\\n H-R & 54.07 & 70.71 & 69.67 & 70.18 & 35.42 & 50.91 & 53.79 & 52.31 & 96.04 & 23.19 & 84.83 & 21.72\n \\\\\n H-A & 65.81 & 78.69 & 80.08 & 79.38 & 48.58 & 61.78 & 69.45 & 65.39 & 96.04 & 27.92 & 82.97 & 53.52\n \\\\\n F-H-A & 37.83 & 62.18 & 49.14 & 54.90 & 32.73 & 64.59 & 39.89 & 49.32 & 84.49 & 15.42 & 81.00 & 32.33\n \\\\\n F-H-R & 46.79 & 72.75 & 56.74 & 63.75 & 33.99 & 59.56 & 44.18 & 50.73 & 91.86 & 36.30 & 84.63 & 37.65\n \\\\\n \\bottomrule\n \\end{tabular}\n}\n\\caption{Overall Results using Phi-4-reasoning-plus.}\n\\label{table:phi-4-reasoning-plus}\n\\end{table*}\n\nBetween these two models, the S1.1 model mostly outperforms the Phi-4-reasoning-plus model when it comes to Tool Call and Parameter Matching metrics.\n\n\\section{Performance Comparison Between Models}\nWe observe that the S1.1 model performance performs reasonably well especially compared to the Llama 3.3 70B model that we observe to produce the best results. The Phi-4-reasoning-plus model did well with some domains but overall it does not perform as well as the S1.1 and Llama 3.3 70B models. Figure~\\ref{fig:compare-models} shows the performance comparison between models.\n\n\\begin{figure}[!ht]\n \\centering\n \\includegraphics[width=\\linewidth]{assets/all_model_f1_scores.png}\n \\caption{\\textbf{Above:} Tool Call F1 performance and \\textbf{Below:} Parameter Matching F1 performance.}\n \\label{fig:compare-models}\n\\end{figure}\n\n% \\section{Reproducibility}\n% Our dataset has been anonymized and please find the dataset on Kaggle \\url{https://kaggle.com/datasets/b47e52b440ccaf964009a3f4a3bdfdb92de205249fae87c3c3db7b615c29490c}. Details on how to reproduce our experiments using TRL are provided in the Appendix of our submission and we also included here for your perusal in Section~\\ref{SFTScript}. \n% For additional instructions and implementation details, please refer to this anonymous repository at: \\url{https://anonymous.4open.science/r/T1-code-38C8/README.md}.\n\n\\section{Error Analysis}\n\nWhen we look closer at the errors found during inferencing, we first break down the errors into a total of 4 categories:\n\\begin{itemize}\n\\item \\textbf{Validation Error}: Errors related to improper arguments being passed to the defined tools. \n\\item \\textbf{Variable Not Defined}: Error indicating code uses an underfined variable. \n\\item \\textbf{Index Out Of Range}: Error indicating generated code involves an index out of range, usually corresponding to the list data structure in Python.\n\\item \\textbf{Other}: Other errors present within the generated code.\n\\end{itemize}\n\n\\begin{table*}[!htbp]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lccccccc}\n \\toprule\n \\textbf{Domain} & \\textbf{ \\# Turns} & \\textbf{\\# Validation Error} & \\textbf{\\# Variable Not Defined} & \\textbf{\\# Index Out Of Range} & \\textbf{\\# Other} \\\\\n \\midrule\n \\texttt{Single-Domain} \\\\\n \\midrule\n Flights & 8,200 & 737 & 830 & 0 & 468 \\\\\n Hotels & 8,000 & 417 & 218 & 0 & 820 \\\\\n Restaurants & 7,900 & 513 & 14 & 1 & 183 \\\\\n Attractions & 5,975 & 507 & 1 & 0 & 243 \\\\\n \\midrule\n \\multicolumn{6}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n F-H & 11,700 & 834 & 1087 & 0 & 408 \\\\\n H-R & 10,950 & 1532 & 18 & 1 & 522 \\\\\n H-A & 11,000 & 1024 & 167 & 0 & 308 \\\\\n F-H-A & 9,775 & 1163 & 990 & 0 & 575 \\\\\n F-H-R & 8,575 & 812 & 761 & 3 & 495 \\\\\n \\midrule\n \\texttt{Total} & - & 7539 & 4086 & 5 & 4022\\\\\n \\bottomrule\n \\end{tabular}\n}\n\\caption{Overall Error Analysis Results using Llama 3.1 8B Instruct.}\n\\end{table*}\n\n\\begin{table*}[!htbp]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lccccccc}\n \\toprule\n \\textbf{Domain} & \\textbf{ \\# Turns} & \\textbf{\\# Validation Error} & \\textbf{\\# Variable Not Defined} & \\textbf{\\# Index Out Of Range} & \\textbf{\\# Other} \\\\\n \\midrule\n \\texttt{Single-Domain} \\\\\n \\midrule\n Flights & 8,200 & 408 & 176 & 42 & 26 \\\\\n Hotels & 8,000 & 1,099 & 16 & 0 & 0 \\\\\n Restaurants & 7,900 & 209 & 5 & 0 & 2 \\\\\n Attractions & 5,975 & 18 & 9 & 0 & 8 \\\\\n \\midrule\n \\multicolumn{6}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n F-H & 11,700 & 454 & 149 & 0 & 22 \\\\\n H-R & 10,950 & 692 & 185 & 0 & 17 \\\\\n H-A & 11,000 & 1248 & 315 & 0 & 1 \\\\\n F-H-A & 9,775 & 789 & 124 & 9 & 27 \\\\\n F-H-R & 8,575 & 541 & 109 & 0 & 101 \\\\\n \\midrule\n \\texttt{Total} & - & 5458 & 1088 & 51 & 204 \\\\\n \\bottomrule\n \\end{tabular}\n}\n\\caption{Overall Error Analysis Results using Llama 3.1 8B SFT Instruct.}\n\\end{table*}\n\n\\begin{table*}[!htbp]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lccccccc}\n \\toprule\n \\textbf{Domain} & \\textbf{ \\# Turns} & \\textbf{\\# Validation Error} & \\textbf{\\# Variable Not Defined} & \\textbf{\\# Index Out Of Range} & \\textbf{\\# Other} \\\\\n \\midrule\n \\texttt{Single-Domain} \\\\\n \\midrule\n Flights & 8,200 & 518 & 2 & 30 & 0 \\\\\n Hotels & 8,000 & 41 & 0 & 0 & 0 \\\\\n Restaurants & 7,900 & 29 & 0 & 0 & 0 \\\\\n Attractions & 5,975 & 4 & 2 & 0 & 1 \\\\\n \\midrule\n \\multicolumn{6}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n F-H & 11,700 & 158 & 28 & 0 & 0\n \\\\\n H-R & 10,950 & 14 & 2 & 0 & 1\n \\\\\n H-A & 11,000 & 48 & 33 & 0 & 48\n \\\\\n F-H-A & 9,775 & 148 & 6 & 7 & 3\n \\\\\n F-H-R & 8,575 & 330 & 5 & 1 & 7\n \\\\\n \\midrule\n \\texttt{Total} & - & 1290 & 78 & 38 & 60 \\\\\n \\bottomrule\n \\end{tabular}\n}\n\\caption{Overall Error Analysis Results using Llama 3.3 70B Instruct.}\n\\end{table*}\n\n\\begin{table*}[!htbp]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lccccccc}\n \\toprule\n \\textbf{Domain} & \\textbf{ \\# Turns} & \\textbf{\\# Validation Error} & \\textbf{\\# Variable Not Defined} & \\textbf{\\# Index Out Of Range} & \\textbf{\\# Other} \\\\\n \\midrule\n \\texttt{Single-Domain} \\\\\n \\midrule\n Flights & 8,200 & 559 & 16 & 3 & 40\n \\\\\n Hotels & 8,000 & 92 & 1 & 39 & 27\n \\\\\n Restaurants & 7,900 & 20 & 7 & 0 & 28\n \\\\\n Attractions & 5,975 & 11 & 27 & 0 & 76\n \\\\\n \\midrule\n \\multicolumn{6}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n F-H & 11,700 & 24 & 34 & 114 & 63\n \\\\\n H-R & 10,950 & 54 & 895 & 102 & 34\n \\\\\n H-A & 11,000 & 3 & 1071 & 43 & 23\n \\\\\n F-H-A & 9,775 & 89 & 579 & 25 & 49\n \\\\\n F-H-R & 8,575 & 85 & 241 & 7 & 28\n \\\\\n \\midrule\n \\texttt{Total} & - & 937 & 2871 & 333 & 368\n \\\\\n \\bottomrule\n \\end{tabular}\n}\n\\caption{Overall Error Analysis Results using S1.1.}\n\\end{table*}\n\n\\begin{table*}[!htbp]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lccccccc}\n \\toprule\n \\textbf{Domain} & \\textbf{ \\# Turns} & \\textbf{\\# Validation Error} & \\textbf{\\# Variable Not Defined} & \\textbf{\\# Index Out Of Range} & \\textbf{\\# Other} \\\\\n \\midrule\n \\texttt{Single-Domain} \\\\\n \\midrule\n Flights & 8,200 & 103 & 7 & 1 & 1\n \\\\\n Hotels & 8,000 & 277 & 0 & 0 & 1\n \\\\\n Restaurants & 7,900 & 259 & 0 & 0 & 19\n \\\\\n Attractions & 5,975 & 0 & 0 & 0 & 0\n \\\\\n \\midrule\n \\multicolumn{6}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n F-H & 11,700 & 212 & 2 & 0 & 7\n \\\\\n H-R & 10,950 & 212 & 0 & 0 & 1\n \\\\\n H-A & 11,000 & 162 & 0 & 0 & 6\n \\\\\n F-H-A & 9,775 & 120 & 3 & 0 & 5\n \\\\\n F-H-R & 8,575 & 153 & 4 & 0 & 32\n \\\\\n \\midrule\n \\texttt{Total} & - & 1498 & 16 & 1 & 72\\\\\n \\bottomrule\n \\end{tabular}\n}\n\\caption{Overall Error Analysis Results using Phi-4-reasoning-plus.}\n\\end{table*}\n\nOverall, we notice that validation errors are consistently present within the results of all of the models we evaluated.\n\n\\subsection{Llama 3.3 70B Instruct} The Llama 3.3 70B instruct model contains the fewest number of overall errors among the five models evaluated with a total of 1,466. This supports the idea that the larger number of parameters helps the model better understand the task as well passing in the proper parameters to the tools to generate high quality code. \n\n\\subsection{Llama 3.1 8B Instruct} The Llama 3.1 8B Instruct model consists of the most number of total errors among all 4 categories which were tracked. The only category it performs well in was the \\texttt{Index Out Of Range} error where there are only 5 instances.\n\n\\subsection{Llama 3.1 8B Instruct SFT}\nThe Llama 3.1 8B Instruct SFT does drastically improve on the number of \\texttt{Validation}, \\texttt{Variable Not Defined} and \\texttt{Other} categorized errors compared to the original model. This indicates that the model is able to better learn about calling the tools and writing higher quality code than the original model.\n\n\n\\subsection{S1.1} Overall, the S1.1 model performs the best when it comes to \\texttt{Validation} errors with a total of 937 errors. However, it also consists of the highest amount of \\texttt{Index Out of Range} errors and the 2nd highest amount of \\texttt{Variable Not Defined} errors.\n\n\\subsection{Phi-4-reasoning-plus}\nThe Phi-4-reasoning-plus has 1498 validation errors which is the third most among the 5 models. The model does perform reasonably well with the other error categories and has the fewest number of \\texttt{Index Out Of Range} and \\texttt{Variable Not Defined} errors with 1 and 16 respectively.\n\n\\section{Model and Prompt Configuration}\nWe use the Llama 3.1 8B Instruct and Llama 3.3 70B Instruct models, and fine-tune Llama 3.1 8B Instruct to evaluate the effectiveness of domain adaptation on the $\\benchmarkname$ dataset. We evaluate all the models in a \\textit{few-shot} setting, where several example turns are included in the prompt to guide the model’s behavior. The full prompts used for inference are provided in Section~\\ref{system-plan-generation-prompt} and Section~\\ref{user-plan-generation-prompt}. The decoding configuration for both models includes a maximum token limit of 4000, top-k sampling of 10, and a temperature of 0.1.\n\n\\section{Evaluation Protocol}\nWe evaluate the model's performance at every user turn by comparing the model’s output with the ground truth across the following facets.\n\\paragraph{Tool Call.} We evaluate the correctness of each tool call from the generated code against the ground truth code by comparing the number of times each tool is called using four metrics: accuracy, precision, recall, and F1.\n\\paragraph{Parameter Matching.} For each tool call in the ground truth, we identify the corresponding tool call in the generated code with the same name and the highest parameter overlap. We then calculate the accuracy, precision, recall, and F1 for the identified match. Parameter values are standardized to enable robust comparison; for example, lists are treated as sets to make them insensitive to ordering. Certain tool calls such as \\texttt{save\\_to\\_cache}, \\texttt{get\\_results\\_from\\_cache}, and \\texttt{seek\\_information} are excluded from this evaluation, as they depend on external artifacts (e.g., intermediate dataframes) and variable keys or identifiers that cannot be reliably matched. Similarly, certain parameter names (e.g., \\texttt{prior\\_results}) are also excluded. \n\\paragraph{Code Execution Success Rate.} We calculate the percentage of instances in which the model generates a code when expected and the code is executable without any errors in a sandbox environment.\n\\paragraph{Handling Information Seeking.} If both the model output and the ground truth include \\texttt{seek\\_information}, we evaluate the similarity of the strings inside the function using SacreBLEU~\\cite{post2018call} and BERTScore F1~\\cite{zhang2019bertscore}, capturing both sub-word overlap and semantic similarity of the requested information.\n\\paragraph{Cache Summary.} To assess how well the model serves complete requests, we compare the execution cache summary results of the generated code and ground truth using exact match (EM) to determine if the model-generated solution is functionally equivalent to the ground truth.\n\n\\section{More Information on T1 Dataset}\nTable~\\ref{tab:domains-and-attributes} shows the attributes for each domain. Table~\\ref{tab:defined-tools-domain} shows the tools used for each domain.\n\n\\begin{table*}[!ht]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lllll}\n \\toprule \n \\textbf{Domain} & \\textbf{Attributes} \\\\ \\midrule\n Flight & airline, flight\\_id, start\\_airport, start\\_airport\\_latitude, start\\_airport\\_longitude, \\\\\n & start\\_airport\\_code, end\\_airport, end\\_airport\\_latitude, end\\_airport\\_longitude, \\\\\n & end\\_airport\\_code, economy\\_class\\_option\\_present, business\\_class\\_option\\_present, \\\\\n & first\\_class\\_option\\_present, distance\\_miles, duration\\_minutes, \\\\\n & departure\\_time, arrival\\_time, number\\_of\\_layovers, first\\_layover\\_airport, \\\\\n Hotel & hotel\\_name, city, state, neighborhood, latitude, longitude, rating, \\\\ & stars, max\\_occupancy, gym\\_present, pool\\_present, price\\_per\\_night, num\\_rooms\\_available, \\\\ & breakfast\\_included, smoking\\_allowed, air\\_conditioning\\_present, \\\\\n & heating\\_present, free\\_wifi\\_included, airport\\_shuttle\\_present, \\\\ &\n is\\_pet\\_friendly, has\\_spa\\_services, has\\_room\\_service, \\\\ & has\\_beach\\_access, has\\_fitness\\_class, has\\_laundry\\_service, \\\\ & has\\_valet\\_parking, has\\_balcony, has\\_rooftop\\_bar, has\\_inroom\\_Kitchen, \\\\ & has\\_kids\\_club, has\\_meeting\\_rooms, has\\_electric\\_vehicle\\_charging, \\\\ & has\\_hot\\_tub, has\\_sauna, has\\_free\\_parking, is\\_wheelchair\\_accessible, \\\\ & has\\_skiing\\_lodging, has\\_ocean\\_view\\_rooms\\_present, has\\_city\\_view\\_rooms\\_present, \\\\ & start\\_date\\_available, end\\_date\\_available \\\\\n Attraction & city, state, name, type, latitude, longitude, neighborhood \\\\\n Restaurant & restaurant\\_name, city, state, neighborhood, \\\\\n & latitude, longitude, rating, price, price\\_per\\_person, has\\_nut\\_allergy\\_options, has\\_dairy\\_allergy\\_options, has\\_shell\\_fish\\_allergy\\_options, \\\\ & has\\_tomato\\_allergy\\_options, has\\_nightshade\\_allergy\\_options, \\\\ & has\\_gluten\\_free\\_options, has\\_vegetarian\\_options, has\\_vegan\\_options, \\\\ & has\\_kosher\\_options, has\\_halal\\_options, cuisine \\\\\n \\bottomrule\n \\end{tabular}\n}\n\\caption{Domains and Attributes.} \n\\label{tab:domains-and-attributes}\n\\end{table*}\n\n\\begin{table*}[!ht]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{ll}\n \\toprule \n \\textbf{Domain} & \\textbf{Tools}\n \\\\\n \\textbf{Flights} & search\\_flights, filter\\_flights \\\\\n \\textbf{Hotels} & search\\_hotels, filter\\_hotels \\\\\n \\textbf{Restaurants} & search\\_restaurants, filter\\_restaurants \\\\\n \\textbf{Attractions} & search\\_attractions, filter\\_attractions \\\\\n \\textbf{Multi-domain} & search\\_nearest \\\\\n \\textbf{Common} & save\\_to\\_cache, get\\_results\\_from\\_cache, sort\\_results, seek\\_information, adjust\\_date \\\\\n \\bottomrule\n \\end{tabular}\n}\n\\caption{List of Tools.} \n\\label{tab:defined-tools-domain}\n\\end{table*}\n\n\\newpage\n\\section{Sample Conversation from T1 Dataset}\n\n\n\\begin{table*}[!ht]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lll}\n \\toprule \n \\textbf{Attribute} & \\textbf{Type} & \\textbf{Description} \\\\ \\midrule\n airline & string & Airline of the flight, \\\\\n flight\\_classes & string & Classes for the flight (economy, business, first) \\\\\n num\\_layovers & integer & Number of layovers for the flight, between 0 and 2 \\\\\n layover\\_duration\\_amount & integer & Duration of a layover flight. Between 1 and 6 hours \\\\\n airports & list & Airport information for each flight including the city, state, airport code and airport name. \\\\\n \n \\bottomrule\n \\end{tabular}\n}\n\\caption{Flight Ontology Attributes.} \n\\label{tab:flight-ontology-attributes}\n\\end{table*}\n\n\\begin{table*}[!htbp]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lll}\n \\toprule \n \\textbf{Attribute} & \\textbf{Type} & \\textbf{Description} \\\\ \\midrule\n city & string & City that the hotel is in \\\\\n state & string & State that the hotel is in \\\\\n neighborhood & string & Neighborhood within the city that the hotel is in\\\\\n stars & integer & Star rating of the hotel between 1 and 5 \\\\\n rating & float & Customer rating of the hotel between 1.0 and 5.0, incremented by 0.1 \\\\\n price\\_per\\_night & integer & Price per night of the hotel ranging between 20 to 2000 dollars \\\\\n max\\_occupancy & integer & Maximum occupancy per room ranging between 1 and 7 \\\\\n gym\\_present & boolean & Whether or not the hotel has a gym \\\\\n pool\\_present & boolean & Whether or not the hotel has a pool \\\\\n breakfast\\_included & boolean & Whether or not the hotel has breakfast included \\\\\n smoking\\_allowed & boolean & Whether or not the hotel allows smoking \\\\\n air\\_conditioning\\_present & boolean & Whether or not the hotel has air conditioning \\\\\n heating\\_present & boolean & Whether or not the hotel has heating \\\\\n free\\_wifi\\_included & boolean & Whether or not the hotel includes free WiFi \\\\\n airport\\_shuttle\\_present & boolean & Whether or not the hotel has an airport shuffle \\\\\n is\\_pet\\_friendly & boolean & Whether or not the hotel allows pets \\\\\n has\\_spa\\_services & boolean & Whether or not the hotel has spa services \\\\\n has\\_room\\_service & boolean & Whether or not the hotel has room service \\\\\n has\\_beach\\_access & boolean & Whether or not the hotel has access to a beach \\\\\n has\\_business\\_center & boolean & Whether or not the hotel has a business center \\\\\n has\\_fitness\\_classes & boolean & Whether or not the hotel has fitness classes \\\\\n has\\_laundry\\_service & boolean & Whether or not the hotel has laundry services \\\\\n has\\_valet\\_parking & boolean & Whether or not the hotel has valet parking \\\\\n has\\_balcony & boolean & Whether or not the hotel has a balcony \\\\\n has\\_rooftop\\_bar & boolean & Whether or not the hotel has a rooftop bar \\\\\n has\\_inroom\\_kitchen & boolean & Whether or not the hotel has an inroom kitchen \\\\\n has\\_kids\\_club & boolean & Whether or not the hotel has a kids club \\\\\n has\\_meeting\\_rooms & boolean & Whether or not the hotel has meeting rooms \\\\\n has\\_electric\\_vehicle\\_charging & boolean & Whether or not the hotel has electric vehicle charging \\\\\n has\\_hot\\_tub & boolean & Whether or not the hotel has a hot tub \\\\\n has\\_sauna & boolean & Whether or not the hotel has a sauna \\\\ \n has\\_free\\_parking & boolean & Whether or not the hotel has free parking\\\\ \n is\\_wheelchair\\_accessible& boolean & Whether or not the hotel is wheelchair accessible \\\\ \n has\\_skiing\\_lodging & boolean & Whether or not the hotel has skiing and lodging \\\\ \n ocean\\_view\\_rooms\\_present & boolean & Whether or not the hotel has rooms with a view of the ocean \\\\\n city\\_view\\_rooms\\_present & boolean & Whether or not the hotel has rooms with views of the city\\\\\n \\bottomrule\n \\end{tabular}\n}\n\\caption{Hotel Ontology Attributes.} \n\\label{tab:hotel-ontology-attributes}\n\\end{table*}\n\n\\begin{table*}[!htbp]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lll}\n \\toprule \n \\textbf{Attribute} & \\textbf{Type} & \\textbf{Description} \\\\ \\midrule\n city & string & City that the restaurant is in \\\\\n state & string & State that the restaurant is in \\\\\n neighborhood & string & Neighborhood within the city that the restaurant is in\\\\\n rating & float & Customer rating of the restaurant between 1.0 and 5.0, incremented by 0.1 \\\\\n price\\_per\\_person & integer & Average price per person at the restaurant \\\\\n cuisine & string & Cuisine of the restaurant \\\\\n has\\_nut\\_allergy\\_options & boolean & Whether or not the restaurant has any options for individuals with an allergy to nuts \\\\\n has\\_dairy\\_allergy\\_options & boolean & Whether or not restaurant has any options for individuals with an allergy to dairy products \\\\\n has\\_shell\\_fish\\_allergy\\_options & boolean & Whether or not the restaurant has any options for individuals with an allergy to shell fish \\\\\n has\\_tomato\\_allergy\\_options & boolean & Whether or not the restaurant has options for individuals with an allergy to tomatoes \\\\\n has\\_nightshade\\_allergy\\_options & boolean & Whether or not the restaurant has options for individuals with an allergy to nightshades \\\\\n has\\_gluten\\_free\\_options & boolean & Whether or not the restaurant has gluten free options \\\\\n has\\_vegetarian\\_options & boolean & Whether or not the restaurant has vegetarian options \\\\\n has\\_vegan\\_options & boolean & Whether or not the restaurant has vegan options \\\\\n has\\_halal\\_options & boolean & Whether or not the restaurant has halal options \\\\\n has\\_kosher\\_options & boolean & Whether or not the hotel has kosher options \\\\ \n \\bottomrule\n \\end{tabular}\n}\n\\caption{Restaurant Ontology Attributes.} \n\\label{tab:restaurant-ontology-attributes}\n\\end{table*}\n\n\\begin{table*}[!htbp]\n\\centering\n\\resizebox{0.8\\textwidth}{!}{\n \\begin{tabular}{lll}\n \\toprule \n \\textbf{Attribute} & \\textbf{Type} & \\textbf{Description} \\\\ \\midrule\n city & string & City that the attraction is in \\\\\n state & string & State that the attraction is in \\\\\n neighborhood & string & Neighborhood within the city that the attraction is in\\\\\n type & string & Type of the attraction \\\\ \n \\bottomrule\n \\end{tabular}\n}\n\\caption{Attraction Ontology Attributes.} \n\\label{tab:attraction-ontology-attributes}\n\\end{table*}\n\n\n\\begin{tcolorbox}[colback=gray!10,colframe=black,title=Box 1. Sample conversation for attractions,breakable]\n\\textbf{assistant}: Hello! Are you looking for something to do in your free time?\\\\\n\\\\\n\\textbf{user}: Yeah, I am thinking of visiting some scenic attractions in San Antonio.\n\\\\\n\\\\\n\\textbf{assistant}: San Antonio has a lot of great Scenic spots. Have you considered Downtown San Antonio?\n\\\\\n\\\\\n\\textbf{user}: Actually, I haven't. What's there?\n\\\\\n\\\\\n\\textbf{assistant}: It's a great area with a lot of Scenic attractions. I can give you some recommendations.\n\\\\\n\\\\ \n\\textbf{user}: Okay, that sounds good.\n\\end{tcolorbox}\n\n\\begin{tcolorbox}[colback=gray!10,colframe=black,title=Box 2. Sample conversation for flights,breakable]\n\\textbf{assistant}: Welcome! What can I help you with today?\\\\\n\\\\\n\\textbf{user}: I need to book a flight from Colorado Springs to San Antonio for my anniversary, and I'm looking for something special.\n\\\\\n\\\\\n\\textbf{assistant}: Happy anniversary! What date are you planning to travel?\n\\\\\n\\\\\n\\textbf{user}: We're thinking of May 21, 2025.\n\\\\\n\\\\\n\\textbf{assistant}: That's great! What kind of flight experience are you looking for? Luxury, perhaps?\n\\\\\n\\\\ \n\\textbf{user}: Yes, something luxurious. And we'd like to depart from COS.\n\\\\\n\\\\\n\\textbf{assistant}: I'll look into that. What's your budget for the flight?\n\\\\\n\\\\ \n\\textbf{user}: We're looking to spend around 1100.\n\\end{tcolorbox}\n\n\\begin{tcolorbox}[colback=gray!10,colframe=black,title=Box 3. Sample conversation for hotels,breakable]\n\\textbf{assistant}: Hello, what brings you here today?\\\\\n\\\\\n\\textbf{user}: I'm looking for a hotel in St. Louis for my anniversary, can you help me?\n\\\\\n\\\\\n\\textbf{assistant}: Congratulations on your anniversary! What are your check-in and check-out dates?\n\\\\\n\\\\\n\\textbf{user}: We're planning to check-in on May 18, 2025 and check-out on May 29, 2025.\n\\\\\n\\\\\n\\textbf{assistant}: That's great! How many rooms do you need and what's your budget per night?\n\\\\\n\\\\ \n\\textbf{user}: We need 3 room and our budget is around 550 per night.\n\\\\\n\\\\\n\\textbf{assistant}: I see. What kind of amenities are you looking for in a hotel?\n\\\\\n\\\\ \n\\textbf{user}: We'd like a hotel with a gym, pool, and air conditioning.\n\\\\\n\\\\\n\\textbf{assistant}: Got it. Would you prefer a hotel in a specific neighborhood in St. Louis?\n\\\\\n\\\\ \n\\textbf{user}: Yes, we'd like to stay in Downtown St. Louis.\n\\end{tcolorbox}\n\n\\begin{tcolorbox}[colback=gray!10,colframe=black,title=Box 4. Sample conversation for restaurants,breakable]\n\\textbf{assistant}: assistant: Welcome, what brings you here today?\\\\\n\\\\\n\\textbf{user}: I'm gluten-free and I'm looking for a restaurant that can accommodate my dietary needs.\n\\\\\n\\\\\n\\textbf{assistant}: Don't worry, I'm here to help. What kind of cuisine are you interested in?\n\\\\\n\\\\\n\\textbf{user}: I'm really craving Livonian.\n\\\\\n\\\\\n\\textbf{assistant}: There are some great Livonian places that offer gluten-free options. Are you looking for something in a specific neighborhood?\n\\\\\n\\\\ \n\\textbf{user}: Yeah, I'd love something in Las Vegas somewhere in Summerlin.\n\\\\\n\\\\\n\\textbf{assistant}: I can check what's available in Summerlin. What's your budget for this meal?\n\\\\\n\\\\ \n\\textbf{user}: I'm looking to spend around 120 per person.\n\\\\\n\\\\\n\\textbf{assistant}: That helps. Would you like me to recommend a few places that fit your criteria?\n\\\\\n\\\\ \n\\textbf{user}: Please do.\n\\end{tcolorbox}\n\n\\begin{tcolorbox}[colback=gray!10,colframe=black,title=Box 5. Sample conversation for flight and hotels,breakable]\n\\textbf{assistant}: assistant: Hello, how can I assist you today?\n\\\\\n\\\\\n\\textbf{user}: I'm looking for a flight from Atlanta to Memphis and a hotel in Memphis\n\\\\\n\\\\\n\\textbf{assistant}: What are your travel dates?\n\\\\\n\\\\\n\\textbf{user}: My departure date is May 18, 2025 and I'd like to return on June 1, 2025\n\\\\\n\\\\\n\\textbf{assistant}: What class of flight are you interested in?\n\\\\\n\\\\ \n\\textbf{user}: user: I'd prefer business\n\\\\\n\\\\\n\\textbf{assistant}: And for your hotel, what star rating are you looking for?\n\\\\\n\\\\ \n\\textbf{user}: I'd like a 2 star hotel in Downtown Memphis\n\\end{tcolorbox}\n\n\\begin{tcolorbox}[colback=gray!10,colframe=black,title=Box 6. Sample conversation for hotel and restaurant,breakable]\n\\textbf{assistant}: assistant: Hello! How can I assist you today?\n\\\\\n\\\\\n\\textbf{user}: I'm looking for a hotel in San Antonio and a restaurant that serves Turkish.\n\\\\\n\\\\\n\\textbf{assistant}: That sounds like a great plan! Can you tell me a bit more about your hotel preferences, such as the number of rooms and check-in date?\n\\\\\n\\\\\n\\textbf{user}: I need 3 rooms for 5 people, checking in on May 22, 2025 and checking out on May 27, 2025.\n\\\\\n\\\\\n\\textbf{assistant}: I'd be happy to help you with that. What's your budget per night for the hotel?\n\\\\\n\\\\ \n\\textbf{user}: I'm looking to spend around 50 per night.\n\\\\\n\\\\\n\\textbf{assistant}: That's helpful to know. For the restaurant, are you looking for something with a specific rating or price range?\n\\\\\n\\\\ \n\\textbf{user}: Yes, I'd like a restaurant with a rating of at least 3.5 and a price range of around 85.\n\\\\\n\\\\\n\\textbf{assistant}: I'll keep that in mind. Would you like the hotel to have any specific amenities, such as a gym or pool?\n\\\\\n\\\\ \n\\textbf{user}: Yes, a pool would be great. And can you recommend a restaurant with Turkish near the hotel?\n\\end{tcolorbox}\n\n\\begin{tcolorbox}[colback=gray!10,colframe=black,title=Box 7. Sample conversation for hotel and attraction,breakable]\n\\textbf{assistant}: Hi there! What brings you here today?\n\\\\\n\\\\\n\\textbf{user}: I'm planning a last-minute trip to Portland for 2 people.\n\\\\\n\\\\\n\\textbf{assistant}: Last-minute trips can be exciting! What's your budget for the hotel?\n\\\\\n\\\\\n\\textbf{user}: We're looking to spend around 55 per night.\n\\\\\n\\\\\n\\textbf{assistant}: Okay, that helps. What's your check-in and check-out date?\n\\\\\n\\\\ \n\\textbf{user}: We're checking in on June 28, 2025 and checking out on July 10, 2025.\n\\\\\n\\\\\n\\textbf{assistant}: Are you interested in any specific attractions in Portland?\n\\\\\n\\\\ \n\\textbf{user}: Yes, we want to visit Pittock Mansion, which is a Social attraction.\n\\\\\n\\\\\n\\textbf{assistant}: That sounds like a great choice! I can help you find a hotel near Pittock Mansion. What kind of amenities are you look for in the hotel?\n\\\\\n\\\\ \n\\textbf{user}: We need a hotel with a gym and wifi.\n\\end{tcolorbox}\n\n\\begin{tcolorbox}[colback=gray!10,colframe=black,title=Box 8. Sample conversation for flight-hotel-attraction,breakable]\n\\textbf{assistant}: Hello, I'd be happy to help you plan your trip. What's on your mind?\n\\\\\n\\\\\n\\textbf{user}: I'm thinking of going to Las Vegas for my birthday and I need a business flight from Tucson.\n\\\\\n\\\\\n\\textbf{assistant}: Happy birthday in advance. When are you thinking of traveling?\n\\\\\n\\\\\n\\textbf{user}: We're looking at May 19, 2025 to May 27, 2025.\n\\\\\n\\\\\n\\textbf{assistant}: That's a great time. How many people are traveling?\n\\\\\n\\\\ \n\\textbf{user}: Just 2 of us.\n\\\\\n\\\\\n\\textbf{assistant}: Lovely. For your stay, what kind of hotel are you envisioning?\n\\\\\n\\\\ \n\\textbf{user}: A 2 star hotel with a spa and room service would be perfect. And we'd love to stay near Fountains of Bellagio with a beautiful view.\n\\\\\n\\\\\n\\textbf{assistant}: That sounds like a great choice! I can help you find a hotel near Pittock Mansion. What kind of amenities are you look for in the hotel?\n\\end{tcolorbox}\n\n\\begin{tcolorbox}[colback=gray!10,colframe=black,title=Box 9. Sample conversation for flight-hotel-restaurant,breakable]\n\\textbf{assistant}: Welcome to our travel assistant. How can I help you today?\n\\\\\n\\\\\n\\textbf{user}: I'm looking to travel from Tucson to St. Louis for a business trip.\n\\\\\n\\\\\n\\textbf{assistant}: What are your preferred travel dates?\n\\\\\n\\\\\n\\textbf{user}: I'd like to fly out on May 18, 2025 and come back on June 1, 2025\n\\\\\n\\\\\n\\textbf{assistant}: What class would you like to fly in?\n\\\\\n\\\\ \n\\textbf{user}: I prefer first.\n\\\\\n\\\\\n\\textbf{assistant}: Great, I can look into that. Where would you like to stay in St. Louis?\n\\\\\n\\\\ \n\\textbf{user}: I'd like a hotel in Downtown St. Louis with a 3 star rating.\n\\\\\n\\\\\n\\textbf{assistant}: And what kind of restaurant are you looking for?\n\\\\\n\\\\ \n\\textbf{user}: I'd like a restaurant in Downtown St. Louis with a rating of at least 3.5.\n\\end{tcolorbox}\n\n% \\section{Appendix / supplemental material}\n\n% \\section{System Prompt for Generating Planner Code}\n% \\label{appendix:prompt-design-system-planner}\n\n\\section{System Prompt Design for Single-Domain Template Generation}\n\\label{appendix:prompt-design-user-planner}\n\nThis section details the unified system prompt and its domain-specific customizations used for generating diverse, realistic multi-turn conversations for four domains: \\textbf{Flights, Hotels, Attractions, and Restaurants}.\n\n\\subsection{Unified Prompt Instructions (Common Across Domains)}\n\nThe following design principles apply across all domains to ensure consistency, diversity, and realism in the generated templates:\n\n\\begin{itemize}[leftmargin=1.5em]\n \\item \\textbf{Conversation Format:}\n \\begin{itemize}\n \\item Each template contains 6–8 dialogue turns (3–4 exchanges).\n \\item Each template starts with an \\texttt{assistant} turn and ends with a \\texttt{user} turn.\n \\item Turns alternate between \\texttt{assistant:} and \\texttt{user:}.\n \\end{itemize}\n\n \\item \\textbf{Placeholder Handling:}\n \\begin{itemize}\n \\item Use numbered placeholders like \\texttt{<CITY\\_1>}, \\texttt{<PRICE\\_2>}.\n \\item Numbering resets at the start of each new template.\n \\end{itemize}\n\n \\item \\textbf{Assistant Behavior Diversity:}\n \\begin{itemize}\n \\item Vary personality: formal/informal, concise/verbose.\n \\item Ask both open-ended and specific questions.\n \\item Do not assume user intent or provide options in the first message.\n \\item Correct misunderstandings if needed.\n \\end{itemize}\n\n \\item \\textbf{User Behavior Diversity:}\n \\begin{itemize}\n \\item Include verbose, terse, indecisive, and overly specific users.\n \\item Some users provide minimal context; others give excess information.\n \\item Include questions that revisit or skip steps.\n \\end{itemize}\n\n \\item \\textbf{Flow Diversity:}\n \\begin{itemize}\n \\item Avoid fixed dialogue patterns (e.g., greet $\\rightarrow$ date $\\rightarrow$ options).\n \\item Allow nonlinear flow: backtracking, parallel requests, etc.\n \\end{itemize}\n\n \\item \\textbf{Language Diversity:}\n \\begin{itemize}\n \\item Vary greetings, transitions, sentence structure, and phrasings.\n \\item No repeated phrasing across templates.\n \\end{itemize}\n\n \\item \\textbf{Output Requirements:}\n \\begin{itemize}\n \\item Output format is strict JSON with keys like \\texttt{\"template\\_1\"} through \\texttt{\"template\\_30\"}.\n \\item Each value is a newline-separated conversation string.\n \\item No preambles or postambles allowed.\n \\end{itemize}\n\\end{itemize}\n\n\\subsection{Domain-Specific Customization}\n\n\\begin{table}[!ht]\n\\centering\n\\small\n\\renewcommand{\\arraystretch}{1.3}\n\\begin{tabular}{|l|p{5.5cm}|p{7.5cm}|}\n\\hline\n\\textbf{Domain} & \\textbf{Placeholders Used} & \\textbf{Required Scenarios} \\\\\n\\hline\n\\textbf{Flight} & \n\\texttt{<CITY\\_x>}, \\texttt{<CLASS\\_x>}, \\texttt{<DEPARTURE\\_DATE\\_x>}, \\texttt{<DEPARTURE\\_TIME\\_x>}, \\texttt{<ARRIVAL\\_DATE\\_x>}, \\texttt{<ARRIVAL\\_TIME\\_x>}, \\texttt{<AIRLINE\\_x>}, \\texttt{<PRICE\\_x>}, \\texttt{<AIRPORT\\_x>}, \\texttt{<NUM\\_TRAVELERS\\_x>} & \nRound trip (with 2 departure dates), one-way, multi-city, strict arrival time, flexible dates, large group bookings, solo/business/family travel, weekend getaways, budget constraints, special occasions (e.g., birthdays) \\\\\n\\hline\n\n\\textbf{Hotel} & \n\\texttt{<CHECK\\_IN\\_DATE\\_x>}, \\texttt{<CHECK\\_OUT\\_DATE\\_x>}, \\texttt{<CITY\\_x>}, \\texttt{<CITY\\_x\\_NEIGHBORHOOD\\_y>}, \\texttt{<STAR\\_x>}, \\texttt{<RATING\\_x>}, \\texttt{<PRICE\\_x>}, \\texttt{<NUM\\_ROOMS\\_x>}, \\texttt{<NUM\\_TRAVELERS\\_x>} & \nMulti-city trips, multiple hotels in one city, specific neighborhoods, special occasions, last-minute reservations, business vs. leisure, extended stays, view/accessibility/pet-friendly requests, family and group accommodations \\\\\n\\hline\n\n\\textbf{Attraction} & \n\\texttt{<ATTRACTION\\_TYPE\\_x>}, \\texttt{<CITY\\_x>}, \\texttt{<STATE\\_x>}, \\texttt{<CITY\\_x\\_NEIGHBORHOOD\\_y>} & \nMulti-type attraction queries, across cities/states, neighborhood-specific exploration, curiosity about state-level offerings. Assistant must ask open-ended questions and only mention placeholders if user provides them. \\\\\n\\hline\n\n\\textbf{Restaurant} & \n\\texttt{<RESTAURANT\\_RATING\\_x>}, \\texttt{<RESTAURANT\\_PRICE\\_x>}, \\texttt{<RESTAURANT\\_CUISINE\\_x>}, \\texttt{<CITY\\_x>}, \\texttt{<CITY\\_x\\_NEIGHBORHOOD\\_y>} & \nMulti-city dining, cuisine preferences, dietary restrictions (e.g., nut-free, vegan, halal), budget vs. premium, special occasion dining, rating-focused or casual vs. upscale preferences \\\\\n\\hline\n\\end{tabular}\n\\vspace{1mm}\n\\caption{Domain-specific customization for template generation. Each domain builds on the shared prompt instructions with unique placeholders and required conversation scenarios.}\n\\label{tab:domain-customization}\n\\end{table}\n\\section{System Prompt Design for Multi-Domain Template Generation}\n\\label{appendix:multi-domain-prompt-design}\n\nThis section outlines the system prompt instructions used for generating multi-turn conversations that span multiple domains. These prompts are tailored to create realistic, nonlinear interactions involving combinations of Flights, Hotels, Attractions, and Restaurants.\n\n\\subsection{Unified Prompt Instructions (Shared Across Multi-Domain Prompts)}\n\n\\begin{itemize}[leftmargin=1.5em]\n \\item \\textbf{Conversation Format:}\n \\begin{itemize}\n \\item Each template includes at least 8–10 dialogue turns (4–5 exchanges).\n \\item Each template begins with an \\texttt{assistant} turn and ends with a \\texttt{user} turn.\n \\item Turns alternate between \\texttt{assistant:} and \\texttt{user:}.\n \\end{itemize}\n\n \\item \\textbf{Placeholder Handling:}\n \\begin{itemize}\n \\item Use only predefined placeholders (e.g., \\texttt{<CITY\\_1>}, \\texttt{<DEPARTURE\\_DATE\\_1>}).\n \\item Numbering restarts from 1 in every template.\n \\item Assistant must never mention a placeholder until the user provides it.\n \\end{itemize}\n\n \\item \\textbf{Assistant Behavior:}\n \\begin{itemize}\n \\item Vary personality (formal, informal), verbosity, and strategies (direct vs. open-ended questions).\n \\item Must not assume user intent or combine domain-specific questions unless user initiates.\n \\item Must correct misunderstandings and adapt to user behavior.\n \\end{itemize}\n\n \\item \\textbf{User Behavior:}\n \\begin{itemize}\n \\item Include users who are indecisive, impatient, verbose, overly specific, or vague.\n \\item Users may change their mind, skip steps, or backtrack in conversation.\n \\end{itemize}\n\n \\item \\textbf{Conversation Flow:}\n \\begin{itemize}\n \\item Avoid rigid order (e.g., flight $\\rightarrow$ hotel $\\rightarrow$ attraction).\n \\item Encourage nonlinear scenarios: jumping between domains, multiple questions at once.\n \\end{itemize}\n\n \\item \\textbf{Language and Style:}\n \\begin{itemize}\n \\item Vary greetings, transitions, sentence structures, and terminology across templates.\n \\item Avoid repeated phrasings.\n \\end{itemize}\n\n \\item \\textbf{Output Requirements:}\n \\begin{itemize}\n \\item Format is strict JSON: \\texttt{\"template\\_1\"} through \\texttt{\"template\\_30\"}.\n \\item Each value is a newline-separated string of alternating assistant/user dialogue.\n \\item No preamble, postamble, or markdown allowed.\n \\end{itemize}\n\\end{itemize}\n\n\\subsection{Multi-Domain Scenario Requirements}\n\n\\begin{table}[!ht]\n\\centering\n\\small\n\\renewcommand{\\arraystretch}{1.2}\n\\begin{tabularx}{\\textwidth}{|l|X|X|}\n\\hline\n\\textbf{Domains} & \\textbf{Placeholders Used} & \\textbf{Required Scenarios} \\\\\n\\hline\n\n\\textbf{Flight + Hotel} & \n\\texttt{<CITY\\_x>}, \\texttt{<CLASS\\_x>}, \\texttt{<DEPARTURE\\_DATE\\_x>}, \\texttt{<CHECK\\_IN\\_DATE\\_x>}, \\texttt{<CHECK\\_OUT\\_DATE\\_x>}, \\texttt{<NUM\\_TRAVELERS\\_x>} & \nTrip planning involving both air travel and accommodation. Includes round-trip and one-way flights, hotel stays across one or more cities, budget and luxury travelers, group vs. solo travel, flexible dates, specific preferences like nonstop flights or hotels with amenities (e.g., pool, gym, pet-friendly). \\\\\n\\hline\n\n\\textbf{Flight + Hotel + Attraction} & \nAll flight, hotel, and attraction placeholders (e.g., \\texttt{<CITY\\_x>}, \\texttt{<CHECK\\_IN\\_DATE\\_x>}, \\texttt{<ATTRACTION\\_TYPE\\_x>}) & \nUser planning a full trip involving flights, hotel stays, and sightseeing. Must include: round-trip and one-way flights, multi-city stays, hotels near attractions, flexible schedules, user-provided attraction types or specific attraction names (only when linking to hotel proximity), and special constraints (e.g., group travel, business trips, anniversaries). Assistant must never use placeholder names before user introduces them. Includes amenity discussions and attraction-type grammar handling (e.g., cultural vs. \\texttt{<ATTRACTION\\_TYPE\\_1>} attraction). \\\\\n\\hline\n\n\\textbf{Flight + Hotel + Restaurant} & \nAll flight, hotel, and restaurant placeholders (e.g., \\texttt{<RESTAURANT\\_CUISINE\\_x>}, \\texttt{<RESTAURANT\\_PRICE\\_x>}) & \nEnd-to-end trip planning including flights, accommodation, and dining. Required scenarios include: dietary restrictions, cuisine-first or city-first planning, restaurant proximity to hotel, multiple hotels or restaurants in one city, round-trip and one-way flights, and amenities like pet-friendly or spa. Must feature diverse language, nonlinear flows, and varied user personas. Includes explicit handling of dietary needs (e.g., halal, nut-free, vegetarian), and placeholder logic as per prompt. \\\\\n\\hline\n\n\\textbf{Hotel + Attraction} & \n\\texttt{<CITY\\_x>}, \\texttt{<CHECK\\_IN\\_DATE\\_x>}, \\texttt{<ATTRACTION\\_TYPE\\_x>}, \\texttt{<CITY\\_x\\_NEIGHBORHOOD\\_y>} & \nUser looking for accommodations and nearby attractions. Includes: specific neighborhoods, family-friendly vs. solo travel, themed vacations, extended stays, proximity to cultural or outdoor attractions, and cases where attraction type drives hotel location. \\\\\n\\hline\n\n\\textbf{Hotel + Restaurant} & \n\\texttt{<RESTAURANT\\_CUISINE\\_x>}, \\texttt{<HOTEL\\_PRICE\\_x>}, \\texttt{<CITY\\_x\\_NEIGHBORHOOD\\_y>} & \nCombines hotel booking with dining preferences. Includes cuisine-specific searches, budget dining vs. fine dining, dietary needs (gluten-free, halal), special events (anniversaries), restaurant proximity to hotel, and group size considerations. Templates vary in planning flow—some start with hotel, others with restaurants. \\\\\n\\hline\n\n\\end{tabularx}\n\\vspace{1mm}\n\\caption{Multi-domain combinations and scenario requirements. Each configuration builds on the unified instructions with additional placeholder logic and domain-specific complexity.}\n\\label{tab:multi-domain-customization}\n\\end{table}\n\n\\clearpage\n\\section{Example System Prompt: Flight Domain}\n\n\\begin{lstlisting}\nYour task is to generate diverse conversation templates for a flight finder chatbot.\n\nEach template should capture realistic, UNIQUE conversations between a user and an assistant about flight searches.\n\nPlaceholders to Use:\n- Departure city: <CITY_x>\n- Destination arrival city: <CITY_x>\n- Flight class: <CLASS_x>\n- Departure date: <DEPARTURE_DATE_x>\n- Departure time: <DEPARTURE_TIME_x>\n- Destination arrival date: <ARRIVAL_DATE_x>\n- Destination arrival time: <ARRIVAL_TIME_x>\n- Airline name: <AIRLINE_x>\n- Price: <PRICE_x>\n- Airport's name: <AIRPORT_x>\n- Number of travelers: <NUM_TRAVELERS_x>\n\nKey Requirements:\n1. Placeholder Numbering:\n - Use integers for ``x'' (e.g., <CITY_1>, <CITY_2>).\n - Reset numbering to 1 for each new template.\n\n2. Conversation Format & Structure:\n - 6-8 turns minimum per template (3-4 exchanges).\n - Format as alternating assistant: and user: lines.\n - Each template must start with an assistant turn and end with a user turn.\n - Vary conversation lengths-some should be shorter, some longer.\n\n3. Assistant Behavior Diversity:\n - Use distinctly different assistant personalities (formal, casual, verbose, concise).\n - Vary how information is requested (direct questions vs. open-ended).\n - Include templates where the assistant corrects misunderstandings.\n - NOTE: Assistant's first turn must be generic and not assume user intent (e.g., ``How can I help you?\").\n - NOTE: The assistant must not say it has found options for the user.\n\n4. User Behavior Diversity:\n - Create dramatically different user types (impatient, polite, verbose, terse).\n - Include users who provide minimal information (requiring follow-ups).\n - Include users who provide too much information.\n - Include complex, specific requests.\n - Include indecisive users who change their minds.\n\n5. Conversation Flow Diversity:\n - Avoid standard ``greeting -> dates -> preferences -> options'' structure.\n - Include nonlinear conversations (user circles back to earlier topics).\n - Include users asking multiple questions at once.\n - Include conversations skipping obvious steps.\n - Vary order in which info is elicited.\n\n6. Required Scenarios:\n - Multi-city trips\n - Round trip\n - One-way trip\n - Flexible travel dates\n - Users with strong preferences (airline, class, airport, etc.)\n - Non-stop flights only\n - Short layovers are okay\n - Rigid arrival time\n - Special occasions (e.g., anniversaries)\n - Large group bookings\n - Last-minute reservations\n - Business travel\n - Family vacations\n - Solo travelers\n - Budget constraints\n - Weekend getaways\n\n7. Round Trip Requirement:\n - Roundtrip flights must include 2 departure dates.\n\n8. Multiple Travelers:\n - Always include number of travelers via placeholder in multi-person scenarios.\n\n9. Linguistic Diversity:\n - Avoid repeating same greetings across templates.\n - Vary language used for dates, preferences, and constraints.\n - Use different phrasings for similar ideas.\n - Unique transitions between topics.\n - Varied sentence structures and response styles.\n\nImportant: Each template must be fundamentally different in structure, flow, language, and scenario.\n\\end{lstlisting}\n\n\\section{Example System Prompt: Flight-Hotel-Restaurant Domain}\n\n\\begin{lstlisting}\nYour task is to generate diverse conversation templates for a travel assistant chatbot.\n\nEach template should be a realistic and UNIQUE multi-turn interaction between a user and an assistant that includes:\n- Flight search\n- Hotel booking\n- Restaurant recommendations or reservations\n\nPlaceholders to Use:\n- Flight: <CITY_x>, <CLASS_x>, <DEPARTURE_DATE_x>, <DEPARTURE_TIME_x>, <ARRIVAL_DATE_x>, <ARRIVAL_TIME_x>, <AIRLINE_x>, <PRICE_x>, <AIRPORT_x>, <NUM_TRAVELERS_x>\n- Hotel: <HOTEL_x>, <CHECKIN_DATE_x>, <CHECKOUT_DATE_x>, <ROOM_TYPE_x>, <NUM_GUESTS_x>, <PRICE_x>\n- Restaurant: <RESTAURANT_x>, <CUISINE_x>, <RESERVATION_TIME_x>, <RESERVATION_DATE_x>, <PRICE_RANGE_x>, <LOCATION_x>, <NUM_PEOPLE_x>\n\nKey Requirements:\n1. Conversation must include all 3 components: flight, hotel, and restaurant.\n\n2. Placeholder Numbering:\n - Use integers for ``x\" (e.g., <CITY_1>, <CITY_2>).\n - Reset numbering to 1 for each new template.\n\n3. Conversation Format:\n - At least 8 turns (4 full exchanges).\n - Alternating assistant: and user: lines.\n - Assistant always starts the conversation and user always ends it.\n - Vary length and structure across templates.\n\n4. Assistant Behavior Diversity:\n - Include different tones: friendly, formal, efficient, humorous, inquisitive, etc.\n - Vary assistant strategies (confirming info early vs. late, etc.).\n - Assistant cannot confirm bookings or give specific results.\n - Must clarify ambiguous or missing information.\n\n5. User Behavior Diversity:\n - Mix of confident and indecisive users.\n - Include users who change minds mid-conversation.\n - Include users who ask multiple things at once.\n - Include low-information and high-information users.\n\n6. Conversation Flow Diversity:\n - Vary the sequence (flight -> hotel -> restaurant, or restaurant -> hotel -> flight, etc.).\n - Include users jumping between topics or circling back.\n - Include unexpected questions or constraints from users.\n\n7. Required Scenarios:\n - Honeymoon trip\n - Last-minute business travel\n - Budget backpacking vacation\n - Solo food tourism\n - Family summer vacation\n - Couple anniversary trip\n - Conference trip with team\n - Weekend getaway\n - Luxury experience\n - Specific date events (concerts, sports games, etc.)\n\n8. Linguistic Diversity:\n - Avoid repeating phrasing and structures across templates.\n - Vary tone and style significantly.\n - Mix concise and elaborate dialogues.\n\nImportant: Every template must feel distinct from all others in tone, language, structure, and scenario.\n\\end{lstlisting}\n\n\\section{Example System Prompt: Plan Generation Prompt}\n\\label{system-plan-generation-prompt}\n\\begin{lstlisting}\nYou are an expert AI travel planner and your responsibility is to generate Python code using APIs or Tools. \n\\end{lstlisting}\n\n\\section{Example User Prompt: Plan Generation Prompt}\n\\label{user-plan-generation-prompt}\n\\begin{lstlisting}\nYour task is to generate a Python code based on a conversation between the user and the assistant, where the last turn is from the user.\nThe code typically involves calling one or more tools (functions) to help the user in planning their travel request.\nIn the Python code, you need to use the following tools:\n# TOOL CONFIG\n<TOOL_CONFIG>\n# INSTRUCTIONS\n- Track content: Maintain the conversation state across turns and use all known information from earlier in the conversation.\n- As soon as the mandatory parameters (non-optional parameters) are all provided (refer to TOOL CONFIG to find mandatory parameters for each tool), generate the appropriate plan using Python code.\n- Do NOT modify entity values under any circumstances. Use them exactly as they appear in the conversation while populating attributes in the function during code generation.\nFor example, if the city is \"new york\" (lowercase), do not convert it to \"New York\" or \"NYC\".\n- Do not fill optional parameters unless they are explicitly provided in the conversation.\n- When generating seek_information, only mention mandatory parameters (non-optional parameters) that are missing. Never ask for optional parameters using seek_information. Refer to TOOL CONFIG to figure out what the mandatory parameters (non optional parameters) are and check CONVERSATION to know what parameters have been provided by the user. \nFor example, \"seek_information('<explain what mandatory parameters (non-optional parameters) are missing and must be gathered by the assistant>')\"\n- Only generate the code for the domain which the customer has mentioned in the conversation. For example, if user mentioned only about attractions, don't generate the code with restaurants search. Only if the user mentioned searching for restaurant anywhere in the conversation, then only search for restaurants.\n- If a tool result from a previous turn is still valid and relevant, use get_results_from_cache(key=\"<cache_key>\") to retrieve it. Use the cache summary to determine the most appropriate key to select from. If you have many keys in the cache for the same domain. Use the one which would be most relevant.\n- If you generate a tool call and its result could be reused later, save it with save_to_cache(\"<key>\",value). Ensure the cache key is unique and avoid naming collision with previously stored cache key name\n- If a result has already been stored in the cache for a conversation and no new result needs to be generated, do not regenerate the code. Instead, return the code as \"print(\"No planning needed\")\"\n# OUTPUT FORMAT\n- You need to generate the reasoning and the python code. The reasoning should clearly explain the process, steps and the reason behind the python plan that is going to be generated \nThe reasoning should be within the <REASONING> </REASONING> tags and the python code should be within the <CODE> </CODE> tags. Note while generating the python code, never have any markdown tags.\n# EXAMPLES\n<FEW_SHOT_EXAMPLES>\n# CONVERSATION\n<CONVERSATION>\n# CACHE\n<CACHE_FOR_CONVERSATION>\nGiven the provided conversation and cache summary, generate a Python code for the last user turn.\n\\end{lstlisting}\n\n\n\\section{Template: Plan Generation for Fine-Tuning}\n\\begin{lstlisting}\nYou are an expert AI travel planner and your responsibility is to generate Python code using APIs or Tools. \nYour task is to generate a Python code based on a conversation between the user and the assistant, where the last turn is from the user.\nThe code typically involves calling one or more tools (functions) to help the user in planning their travel request.\nIn the Python code, you need to use the following tools:\n# TOOL CONFIG\n<TOOL_CONFIG>\n# INSTRUCTIONS\n- Track content: Maintain the conversation state across turns and use all known information from earlier in the conversation.\n- As soon as the mandatory parameters (non-optional parameters) are all provided (refer to TOOL CONFIG to find mandatory parameters for each tool), generate the appropriate plan using Python code.\n- Do NOT modify entity values under any circumstances. Use them exactly as they appear in the conversation while populating attributes in the function during code generation.\n For example, if the city is \"new york\" (lowercase), do not convert it to \"New York\" or \"NYC\".\n- Do not fill optional parameters unless they are explicitly provided in the conversation.\n- When generating seek_information, only mention mandatory parameters (non-optional parameters) that are missing. Never ask for optional parameters using seek_information. Refer to TOOL CONFIG to figure out what the mandatory parameters (non optional parameters) are and check CONVERSATION to know what parameters have been provided by the user. \n For example, \"seek_information('<explain what mandatory parameters (non-optional parameters) are missing and must be gathered by the assistant>')\"\n- Only generate the code for the domain which the customer has mentioned in the conversation. For example, if user mentioned only about attractions, don't generate the code with restaurants search. Only if the user mentioned searching for restaurant anywhere in the conversation, then only search for restaurants.\n- If a tool result from a previous turn is still valid and relevant, use get_results_from_cache(key=\"<cache_key>\") to retrieve it. Use the cache summary to determine the most appropriate key to select from. If you have many keys in the cache for the same domain. Use the one which would be most relevant.\n- If you generate a tool call and its result could be reused later, save it with save_to_cache(\"<key>\",value). Ensure the cache key is unique and avoid naming collision with previously stored cache key name\n- If a result has already been stored in the cache for a conversation and no new result needs to be generated, do not regenerate the code. Instead, return the code as \"print(\"No planning needed\")\"\n# OUTPUT FORMAT\n- You need to generate only the python code. The python code should be within the <CODE> </CODE> tags. Note while generating the python code, never have any markdown tags.\n# CONVERSATION\n<CONVERSATION>\n# CACHE\n<CACHE_FOR_CONVERSATION>\nGiven the provided conversation and cache summary, generate a Python code for the last user turn.\n\\end{lstlisting}\n\n\\section{Script to replicate SFT} \\label{SFTScript}\n\\begin{lstlisting}\n>> git clone https://github.com/huggingface/trl.git\n>> cd trl\n>> git checkout v0.17.0 # We use the TRL version 0.17.0\n>> pip install -e .\n>> accelerate launch --config_file trl/examples/accelerate_configs/multi_gpu.yaml \\\n trl/scripts/sft.py \\\n --model_name_or_path <YOUR PATH TO A Meta-Llama-3.1-8B-Instruct LOCALLY> \\\n --dataset_name <TRAIN SET OF THE TOOL CALLING IN HUGGINGFACE DATASET FORMAT> \\\n --learning_rate 2.0e-5 \\\n --num_train_epochs 1 \\\n --packing \\\n --per_device_train_batch_size 1 \\\n --gradient_accumulation_steps 8 \\\n --gradient_checkpointing \\\n --logging_steps 25 \\\n --eval_strategy steps \\\n --eval_steps 100 \\\n --use_peft \\\n --lora_r 32 \\\n --lora_alpha 32 \\\n --lora_target_modules q_proj k_proj v_proj o_proj gate_proj up_proj down_proj \\\n --output_dir <SOME OUTPUT DIRECTORY>\\\n --report_to tensorboard \\\n --torch_dtype bfloat16\n\\end{lstlisting}\n\n\n\\end{document}"},"question":{"kind":"string","value":"Which single-domain setting exhibits the largest relative drop in Parameter Matching Recall when moving from the 8B model to the 70B model, and what is that relative drop percentage to 2 decimal places?"},"answer":{"kind":"string","value":"Flights, 26.06%"},"plan":{"kind":"string","value":"SELECT Parameter Matching Recall for single-domain settings for 8B model in Table 4\nSELECT Parameter Matching Recall for single-domain settings for 70B model in Table 5\nLOOP for each single-domain setting\n COMPUTE relative drop = (70B Recall - 8B Recall) / 8B Recall * 100\nCOMPUTE argmin relative drop\nRETURN single-domain setting with largest relative drop and the value (to two decimal places)\n"}}},{"rowIdx":67,"cells":{"paper":{"kind":"string","value":"2505.23621"},"relevant_tables":{"kind":"list like","value":[["\\begin{table*}[!t]\n","\\label{tab:main-results} \n","\\centering\n","\n","\\setlength{\\tabcolsep}{2pt} \n","\n","\\resizebox{\\textwidth}{!}{\n","\\begin{tabular}{l*{13}{c}}\n","\\toprule\n","\n","\\multirow{3}{*}{\\textbf{Model}} & \\multicolumn{4}{c}{\\textbf{In-domain Performance}} & \\multicolumn{9}{c}{\\textbf{Out-of-domain Performance}} \\\\\n","\\cmidrule(lr){2-5} \\cmidrule(lr){6-14}\n","\n","& \\multicolumn{1}{c}{\\textbf{FF-TQA}} & \\multicolumn{1}{c}{\\textbf{TFV}} & \\multicolumn{2}{c}{\\textbf{TQA}} & \\multicolumn{3}{c}{\\textbf{FF-TQA}} & \\multicolumn{3}{c}{\\textbf{TFV}} & \\multicolumn{3}{c}{\\textbf{TQA}} \\\\\n","\\cmidrule(lr){2-2} \\cmidrule(lr){3-3} \\cmidrule(lr){4-5} \\cmidrule(lr){6-8} \\cmidrule(lr){9-11} \\cmidrule(lr){12-14}\n","\n","& \\multicolumn{1}{c}{FeTaQA} & \\multicolumn{1}{c}{TabFact} & \\multicolumn{1}{c}{WTQ} & \\multicolumn{1}{c}{HiTab} & \\multicolumn{1}{c}{ToTTo} & \\multicolumn{1}{c}{QTSum} & \\multicolumn{1}{c}{R.W.} & \\multicolumn{1}{c}{InfoTabs} & \\multicolumn{1}{c}{PHT} & \\multicolumn{1}{c}{Feverous} & \\multicolumn{1}{c}{TMCQ} & \\multicolumn{1}{c}{TMWP} & \\multicolumn{1}{c}{FinQA} \\\\\n","\\midrule\n","\n","\\multicolumn{14}{c}{\\textit{\\textbf{Proprietary Models}}} \\\\ \n","\\addlinespace[0.5em]\n","GPT-4.1 & 25.1 & 86.5 & 68.0 & 84.7 & 20.4 & 45.7 & 21.0 & 90.5 & 88.2 & 87.7 & 92.0 & 77.0 & 74.0 \\\\\n","GPT-4.1 mini & 27.2 & 84.9 & 69.5 & 80.7 & 18.8 & 46.4 & 20.0 & 88.9 & 86.8 & 86.1 & 92.9 & 86.2 & 71.4 \\\\\n","\\midrule\n","\n","\\multicolumn{14}{c}{\\textit{\\textbf{Open-source Models}}} \\\\\n","\\addlinespace[0.5em]\n","\n","\\multicolumn{14}{l}{\\textit{LLMs}} \\\\\n","\\quad Qwen2.5-7B & 21.0 & 72.2 & 54.8 & 61.8 & 16.0 & 39.5 & 19.3 & 78.6 & 70.7 & 74.6 & 87.4 & 85.0 & 66.4 \\\\\n","\\quad Qwen2.5-32B & 21.9 & 90.3 & 77.3 & 79.4 & 17.8 & 41.4 & \\underline{20.0} & 90.5 & 86.7 & 79.2 & 92.1 & 95.8 & \\underline{77.3} \\\\\n","\\quad DeepSeek-V3 & 24.7 & \\textbf{92.4} & 69.9 & \\underline{82.2} & 19.0 & \\textbf{46.2} & \\textbf{20.9} & \\textbf{91.9} & 86.2 & \\textbf{85.8} & 87.6 & 93.4 & \\textbf{78.6} \\\\\n","\n","\\addlinespace[0.2em]\n","\\cdashlinelr{1-14}\n","\\addlinespace[0.5em]\n","\n","\\multicolumn{14}{l}{\\textit{Reasoning LLMs}} \\\\\n","\\addlinespace[0.2em]\n","\\quad DeepSeek-R1-Distill-7B & 19.1 & 79.6 & 57.8 & 46.2 & 10.7 & 37.2 & 18.0 & 85.7 & 87.1 & 77.5 & 80.9 & 94.0 & 66.8 \\\\\n","\\quad QwQ-32B & 23.8 & \\underline{91.5} & \\textbf{85.4} & 81.6 & 19.4 & 41.9 & 19.6 & \\underline{91.0} & 87.8 & 80.1 & 90.7 & \\textbf{99.4} & 76.2 \\\\\n","\\quad DeepSeek-R1 & 26.2 & 90.8 & 79.6 & \\textbf{82.4} & 18.5 & \\underline{43.8} & 19.9 & 90.4 & 87.5 & 76.0 & \\textbf{93.3} & \\underline{99.0} & 75.8 \\\\\n","\n","\\addlinespace[0.2em]\n","\\cdashlinelr{1-14}\n","\\addlinespace[0.5em]\n","\n","\\multicolumn{14}{l}{\\textit{Table-Specific LLMs}} \\\\\n","\\addlinespace[0.2em]\n","\\quad $^{\\dagger}$TableBenchLLM (7B) & 3.1 & 27.1 & 38.8 & -- & 6.2 & -- & -- & -- & -- & 42.3 & -- & -- & -- \\\\\n","\\quad $^{\\dagger}$TableLLM-13B & 10.8 & 69.0 & 66.3 & 6.3 & 5.4 & -- & -- & -- & -- & 21.5 & -- & -- & -- \\\\\n","\\quad $^{\\dagger}$TableLlama (7B) & \\textbf{39.0} & 82.6 & 35.0 & 64.7 & 20.8 & -- & -- & -- & -- & 73.8 & -- & -- & -- \\\\ \n","\\quad TableGPT2-7B & 29.0 & 77.8 & 61.4 & 70.3 & 14.1 & 39.0 & 19.0 & 85.4 & \\underline{89.1} & 78.0 & 77.2 & 79.7 & 66.4 \\\\ \n","\\quad $^{\\dagger*}$TableGPT2-72B & 32.3 & 85.4 & 71.5 & 75.6 & \\textbf{22.7} & -- & -- & -- & -- & 76.8 & -- & -- & -- \\\\\n","\\midrule\n","\n","\\multicolumn{14}{c}{\\textit{\\textbf{\\ours (Ours)}}} \\\\\n","\\addlinespace[0.5em]\n","\n","\\multicolumn{14}{l}{\\textit{Llama-3.1 Series}} \\\\\n","\\addlinespace[0.2em]\n","\n","\\quad Llama-3.1-8B-Instruct & 21.7 & 74.1 & 52.3 & 58.2 & 16.5 & 31.6 & 18.1 & 84.1 & 82.5 & 78.3 & 49.5 & 72.0 & 57.1 \\\\\n","\\quad Table-R1-SFT & 26.0 & 91.1 & \\underline{83.8} & 81.8 & 13.7 & 36.6 & 16.6 & 89.8 & 85.8 & 79.4 & 90.8 & 89.0 & 64.3 \\\\\n","\\quad Table-R1-Zero & \\underline{32.7} & 87.6 & 81.2 & 81.4 & \\underline{22.3} & 30.2 & 17.7 & 87.9 & \\textbf{91.6} & \\underline{80.2}& 68.6 & 84.6 & 62.3 \\\\ \n","\n","\\addlinespace[0.2em]\n","\\cdashlinelr{1-14}\n","\\addlinespace[0.5em]\n","\\multicolumn{14}{l}{\\textit{Qwen2.5 Series}} \\\\\n","\\addlinespace[0.2em]\n","\n","\\quad Qwen2.5-7B-Instruct & 21.0 & 72.2 & 54.8 & 61.8 & 16.0 & 39.5 & 19.3 & 78.6 & 70.7 & 74.6 & 87.4 & 85.0 & 66.4 \\\\\n","\\quad Table-R1-SFT & 25.3 & 89.9 & 81.9 & 78.3 & 14.1 & 38.8 & 18.8 & 88.8 & 84.6 & 76.0 & 90.9 & 96.6 & 71.7 \\\\\n","\\quad Table-R1-Zero & 30.6 & 87.6 & 79.8 & 78.1 & 19.8 & 43.1 & \\underline{20.0} & 83.7 & 88.0 & 76.2 & \\underline{93.0} & 96.4 & 70.8 \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\caption{Results on 13 table reasoning benchmarks spanning TQA, TFV, and FF-TQA tasks. For TQA, EM accuracy is reported (with ambiguous cases re-evaluated by GPT-4.1 mini); for TFV, classification accuracy; for FF-TQA, BLEU and ROUGE-L. \\textbf{Bold} and \\underline{underlined} scores indicate the top-2 performances among \\emph{open-source} models.\n","$^{\\dagger}$: Due to the context length limitations of most previous table-specific LLMs, it is challenging to conduct a fully fair comparison. Therefore, for these models, we directly use the results as reported in their respective papers, which may be based on sampled or filtered datasets. $^*$: Model weight has not been released.} \n","\\label{tab:main-results}\n","\\end{table*}\n"]],"string":"[\n [\n \"\\\\begin{table*}[!t]\\n\",\n \"\\\\label{tab:main-results} \\n\",\n \"\\\\centering\\n\",\n \"\\n\",\n \"\\\\setlength{\\\\tabcolsep}{2pt} \\n\",\n \"\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \"\\\\begin{tabular}{l*{13}{c}}\\n\",\n \"\\\\toprule\\n\",\n \"\\n\",\n \"\\\\multirow{3}{*}{\\\\textbf{Model}} & \\\\multicolumn{4}{c}{\\\\textbf{In-domain Performance}} & \\\\multicolumn{9}{c}{\\\\textbf{Out-of-domain Performance}} \\\\\\\\\\n\",\n \"\\\\cmidrule(lr){2-5} \\\\cmidrule(lr){6-14}\\n\",\n \"\\n\",\n \"& \\\\multicolumn{1}{c}{\\\\textbf{FF-TQA}} & \\\\multicolumn{1}{c}{\\\\textbf{TFV}} & \\\\multicolumn{2}{c}{\\\\textbf{TQA}} & \\\\multicolumn{3}{c}{\\\\textbf{FF-TQA}} & \\\\multicolumn{3}{c}{\\\\textbf{TFV}} & \\\\multicolumn{3}{c}{\\\\textbf{TQA}} \\\\\\\\\\n\",\n \"\\\\cmidrule(lr){2-2} \\\\cmidrule(lr){3-3} \\\\cmidrule(lr){4-5} \\\\cmidrule(lr){6-8} \\\\cmidrule(lr){9-11} \\\\cmidrule(lr){12-14}\\n\",\n \"\\n\",\n \"& \\\\multicolumn{1}{c}{FeTaQA} & \\\\multicolumn{1}{c}{TabFact} & \\\\multicolumn{1}{c}{WTQ} & \\\\multicolumn{1}{c}{HiTab} & \\\\multicolumn{1}{c}{ToTTo} & \\\\multicolumn{1}{c}{QTSum} & \\\\multicolumn{1}{c}{R.W.} & \\\\multicolumn{1}{c}{InfoTabs} & \\\\multicolumn{1}{c}{PHT} & \\\\multicolumn{1}{c}{Feverous} & \\\\multicolumn{1}{c}{TMCQ} & \\\\multicolumn{1}{c}{TMWP} & \\\\multicolumn{1}{c}{FinQA} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\n\",\n \"\\\\multicolumn{14}{c}{\\\\textit{\\\\textbf{Proprietary Models}}} \\\\\\\\ \\n\",\n \"\\\\addlinespace[0.5em]\\n\",\n \"GPT-4.1 & 25.1 & 86.5 & 68.0 & 84.7 & 20.4 & 45.7 & 21.0 & 90.5 & 88.2 & 87.7 & 92.0 & 77.0 & 74.0 \\\\\\\\\\n\",\n \"GPT-4.1 mini & 27.2 & 84.9 & 69.5 & 80.7 & 18.8 & 46.4 & 20.0 & 88.9 & 86.8 & 86.1 & 92.9 & 86.2 & 71.4 \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\n\",\n \"\\\\multicolumn{14}{c}{\\\\textit{\\\\textbf{Open-source Models}}} \\\\\\\\\\n\",\n \"\\\\addlinespace[0.5em]\\n\",\n \"\\n\",\n \"\\\\multicolumn{14}{l}{\\\\textit{LLMs}} \\\\\\\\\\n\",\n \"\\\\quad Qwen2.5-7B & 21.0 & 72.2 & 54.8 & 61.8 & 16.0 & 39.5 & 19.3 & 78.6 & 70.7 & 74.6 & 87.4 & 85.0 & 66.4 \\\\\\\\\\n\",\n \"\\\\quad Qwen2.5-32B & 21.9 & 90.3 & 77.3 & 79.4 & 17.8 & 41.4 & \\\\underline{20.0} & 90.5 & 86.7 & 79.2 & 92.1 & 95.8 & \\\\underline{77.3} \\\\\\\\\\n\",\n \"\\\\quad DeepSeek-V3 & 24.7 & \\\\textbf{92.4} & 69.9 & \\\\underline{82.2} & 19.0 & \\\\textbf{46.2} & \\\\textbf{20.9} & \\\\textbf{91.9} & 86.2 & \\\\textbf{85.8} & 87.6 & 93.4 & \\\\textbf{78.6} \\\\\\\\\\n\",\n \"\\n\",\n \"\\\\addlinespace[0.2em]\\n\",\n \"\\\\cdashlinelr{1-14}\\n\",\n \"\\\\addlinespace[0.5em]\\n\",\n \"\\n\",\n \"\\\\multicolumn{14}{l}{\\\\textit{Reasoning LLMs}} \\\\\\\\\\n\",\n \"\\\\addlinespace[0.2em]\\n\",\n \"\\\\quad DeepSeek-R1-Distill-7B & 19.1 & 79.6 & 57.8 & 46.2 & 10.7 & 37.2 & 18.0 & 85.7 & 87.1 & 77.5 & 80.9 & 94.0 & 66.8 \\\\\\\\\\n\",\n \"\\\\quad QwQ-32B & 23.8 & \\\\underline{91.5} & \\\\textbf{85.4} & 81.6 & 19.4 & 41.9 & 19.6 & \\\\underline{91.0} & 87.8 & 80.1 & 90.7 & \\\\textbf{99.4} & 76.2 \\\\\\\\\\n\",\n \"\\\\quad DeepSeek-R1 & 26.2 & 90.8 & 79.6 & \\\\textbf{82.4} & 18.5 & \\\\underline{43.8} & 19.9 & 90.4 & 87.5 & 76.0 & \\\\textbf{93.3} & \\\\underline{99.0} & 75.8 \\\\\\\\\\n\",\n \"\\n\",\n \"\\\\addlinespace[0.2em]\\n\",\n \"\\\\cdashlinelr{1-14}\\n\",\n \"\\\\addlinespace[0.5em]\\n\",\n \"\\n\",\n \"\\\\multicolumn{14}{l}{\\\\textit{Table-Specific LLMs}} \\\\\\\\\\n\",\n \"\\\\addlinespace[0.2em]\\n\",\n \"\\\\quad $^{\\\\dagger}$TableBenchLLM (7B) & 3.1 & 27.1 & 38.8 & -- & 6.2 & -- & -- & -- & -- & 42.3 & -- & -- & -- \\\\\\\\\\n\",\n \"\\\\quad $^{\\\\dagger}$TableLLM-13B & 10.8 & 69.0 & 66.3 & 6.3 & 5.4 & -- & -- & -- & -- & 21.5 & -- & -- & -- \\\\\\\\\\n\",\n \"\\\\quad $^{\\\\dagger}$TableLlama (7B) & \\\\textbf{39.0} & 82.6 & 35.0 & 64.7 & 20.8 & -- & -- & -- & -- & 73.8 & -- & -- & -- \\\\\\\\ \\n\",\n \"\\\\quad TableGPT2-7B & 29.0 & 77.8 & 61.4 & 70.3 & 14.1 & 39.0 & 19.0 & 85.4 & \\\\underline{89.1} & 78.0 & 77.2 & 79.7 & 66.4 \\\\\\\\ \\n\",\n \"\\\\quad $^{\\\\dagger*}$TableGPT2-72B & 32.3 & 85.4 & 71.5 & 75.6 & \\\\textbf{22.7} & -- & -- & -- & -- & 76.8 & -- & -- & -- \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\n\",\n \"\\\\multicolumn{14}{c}{\\\\textit{\\\\textbf{\\\\ours (Ours)}}} \\\\\\\\\\n\",\n \"\\\\addlinespace[0.5em]\\n\",\n \"\\n\",\n \"\\\\multicolumn{14}{l}{\\\\textit{Llama-3.1 Series}} \\\\\\\\\\n\",\n \"\\\\addlinespace[0.2em]\\n\",\n \"\\n\",\n \"\\\\quad Llama-3.1-8B-Instruct & 21.7 & 74.1 & 52.3 & 58.2 & 16.5 & 31.6 & 18.1 & 84.1 & 82.5 & 78.3 & 49.5 & 72.0 & 57.1 \\\\\\\\\\n\",\n \"\\\\quad Table-R1-SFT & 26.0 & 91.1 & \\\\underline{83.8} & 81.8 & 13.7 & 36.6 & 16.6 & 89.8 & 85.8 & 79.4 & 90.8 & 89.0 & 64.3 \\\\\\\\\\n\",\n \"\\\\quad Table-R1-Zero & \\\\underline{32.7} & 87.6 & 81.2 & 81.4 & \\\\underline{22.3} & 30.2 & 17.7 & 87.9 & \\\\textbf{91.6} & \\\\underline{80.2}& 68.6 & 84.6 & 62.3 \\\\\\\\ \\n\",\n \"\\n\",\n \"\\\\addlinespace[0.2em]\\n\",\n \"\\\\cdashlinelr{1-14}\\n\",\n \"\\\\addlinespace[0.5em]\\n\",\n \"\\\\multicolumn{14}{l}{\\\\textit{Qwen2.5 Series}} \\\\\\\\\\n\",\n \"\\\\addlinespace[0.2em]\\n\",\n \"\\n\",\n \"\\\\quad Qwen2.5-7B-Instruct & 21.0 & 72.2 & 54.8 & 61.8 & 16.0 & 39.5 & 19.3 & 78.6 & 70.7 & 74.6 & 87.4 & 85.0 & 66.4 \\\\\\\\\\n\",\n \"\\\\quad Table-R1-SFT & 25.3 & 89.9 & 81.9 & 78.3 & 14.1 & 38.8 & 18.8 & 88.8 & 84.6 & 76.0 & 90.9 & 96.6 & 71.7 \\\\\\\\\\n\",\n \"\\\\quad Table-R1-Zero & 30.6 & 87.6 & 79.8 & 78.1 & 19.8 & 43.1 & \\\\underline{20.0} & 83.7 & 88.0 & 76.2 & \\\\underline{93.0} & 96.4 & 70.8 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Results on 13 table reasoning benchmarks spanning TQA, TFV, and FF-TQA tasks. For TQA, EM accuracy is reported (with ambiguous cases re-evaluated by GPT-4.1 mini); for TFV, classification accuracy; for FF-TQA, BLEU and ROUGE-L. \\\\textbf{Bold} and \\\\underline{underlined} scores indicate the top-2 performances among \\\\emph{open-source} models.\\n\",\n \"$^{\\\\dagger}$: Due to the context length limitations of most previous table-specific LLMs, it is challenging to conduct a fully fair comparison. Therefore, for these models, we directly use the results as reported in their respective papers, which may be based on sampled or filtered datasets. $^*$: Model weight has not been released.} \\n\",\n \"\\\\label{tab:main-results}\\n\",\n \"\\\\end{table*}\\n\"\n ]\n]","isConversational":false},"tables":{"kind":"list like","value":[["\\begin{figure}[!t]\n"," \\includegraphics[width=\\columnwidth]{figure/RadarChart.pdf}\n"," \\caption{Overall performance comparison between \\ours and same-scale baselines on various table reasoning benchmarks. Both \\sftmodel and \\rlmodel exhibit substantial performance improvements over baselines, showing the effectiveness of our approach across both in- and out-of-domain benchmarks.}\n"," \\label{fig:experiments}\n","\\end{figure}\n"],["\\begin{figure}[!t]\n"," \\includegraphics[width=\\columnwidth]{figure/Table-R1.pdf}\n"," \\caption{An overview of our research and three research questions investigated in this study.}\n"," \\label{fig:overview}\n","\\end{figure}\\section{Related Work}\n"],["\\begin{table}[!t]\n","\t\\centering\n","\t\\small\n"," \\setlength{\\tabcolsep}{1pt}\n"," \\resizebox{\\linewidth}{!}{\n","\t\\begin{tabular}{llr}\n","\t\t\\toprule\n","\t\t\\textbf{Task} & \\textbf{Dataset} & \\textbf{Samples} \\\\\n","\t\t\\midrule\n","\t\t\\multirow{2}*{Short-form QA (TQA)} \n"," & WTQ~\\cite{pasupat-liang-2015-compositional} & 13,706 \\\\\n"," & HiTab~\\cite{cheng-etal-2022-hitab} & 6,793 \\\\\n","\t\t\\midrule\n","\t Fact Verification (TFV) & TabFact~\\cite{Chen2020TabFact} & 20,740 \\\\\n","\t\t\\midrule\n","\t\tFree-form QA (FF-TQA) & FeTaQA~\\cite{nan-etal-2022-fetaqa} & 7,324 \\\\\n","\t\t\\bottomrule\n","\t\\end{tabular}\n","\t}\n","\t\\caption{Overview of datasets collected in \\ours training data. For each dataset, we sample examples from its training set.}\n","\t\\label{tab:training-data}\n","\\end{table}\n"],["\\begin{table*}[!t]\n","\\label{tab:main-results} \n","\\centering\n","\n","\\setlength{\\tabcolsep}{2pt} \n","\n","\\resizebox{\\textwidth}{!}{\n","\\begin{tabular}{l*{13}{c}}\n","\\toprule\n","\n","\\multirow{3}{*}{\\textbf{Model}} & \\multicolumn{4}{c}{\\textbf{In-domain Performance}} & \\multicolumn{9}{c}{\\textbf{Out-of-domain Performance}} \\\\\n","\\cmidrule(lr){2-5} \\cmidrule(lr){6-14}\n","\n","& \\multicolumn{1}{c}{\\textbf{FF-TQA}} & \\multicolumn{1}{c}{\\textbf{TFV}} & \\multicolumn{2}{c}{\\textbf{TQA}} & \\multicolumn{3}{c}{\\textbf{FF-TQA}} & \\multicolumn{3}{c}{\\textbf{TFV}} & \\multicolumn{3}{c}{\\textbf{TQA}} \\\\\n","\\cmidrule(lr){2-2} \\cmidrule(lr){3-3} \\cmidrule(lr){4-5} \\cmidrule(lr){6-8} \\cmidrule(lr){9-11} \\cmidrule(lr){12-14}\n","\n","& \\multicolumn{1}{c}{FeTaQA} & \\multicolumn{1}{c}{TabFact} & \\multicolumn{1}{c}{WTQ} & \\multicolumn{1}{c}{HiTab} & \\multicolumn{1}{c}{ToTTo} & \\multicolumn{1}{c}{QTSum} & \\multicolumn{1}{c}{R.W.} & \\multicolumn{1}{c}{InfoTabs} & \\multicolumn{1}{c}{PHT} & \\multicolumn{1}{c}{Feverous} & \\multicolumn{1}{c}{TMCQ} & \\multicolumn{1}{c}{TMWP} & \\multicolumn{1}{c}{FinQA} \\\\\n","\\midrule\n","\n","\\multicolumn{14}{c}{\\textit{\\textbf{Proprietary Models}}} \\\\ \n","\\addlinespace[0.5em]\n","GPT-4.1 & 25.1 & 86.5 & 68.0 & 84.7 & 20.4 & 45.7 & 21.0 & 90.5 & 88.2 & 87.7 & 92.0 & 77.0 & 74.0 \\\\\n","GPT-4.1 mini & 27.2 & 84.9 & 69.5 & 80.7 & 18.8 & 46.4 & 20.0 & 88.9 & 86.8 & 86.1 & 92.9 & 86.2 & 71.4 \\\\\n","\\midrule\n","\n","\\multicolumn{14}{c}{\\textit{\\textbf{Open-source Models}}} \\\\\n","\\addlinespace[0.5em]\n","\n","\\multicolumn{14}{l}{\\textit{LLMs}} \\\\\n","\\quad Qwen2.5-7B & 21.0 & 72.2 & 54.8 & 61.8 & 16.0 & 39.5 & 19.3 & 78.6 & 70.7 & 74.6 & 87.4 & 85.0 & 66.4 \\\\\n","\\quad Qwen2.5-32B & 21.9 & 90.3 & 77.3 & 79.4 & 17.8 & 41.4 & \\underline{20.0} & 90.5 & 86.7 & 79.2 & 92.1 & 95.8 & \\underline{77.3} \\\\\n","\\quad DeepSeek-V3 & 24.7 & \\textbf{92.4} & 69.9 & \\underline{82.2} & 19.0 & \\textbf{46.2} & \\textbf{20.9} & \\textbf{91.9} & 86.2 & \\textbf{85.8} & 87.6 & 93.4 & \\textbf{78.6} \\\\\n","\n","\\addlinespace[0.2em]\n","\\cdashlinelr{1-14}\n","\\addlinespace[0.5em]\n","\n","\\multicolumn{14}{l}{\\textit{Reasoning LLMs}} \\\\\n","\\addlinespace[0.2em]\n","\\quad DeepSeek-R1-Distill-7B & 19.1 & 79.6 & 57.8 & 46.2 & 10.7 & 37.2 & 18.0 & 85.7 & 87.1 & 77.5 & 80.9 & 94.0 & 66.8 \\\\\n","\\quad QwQ-32B & 23.8 & \\underline{91.5} & \\textbf{85.4} & 81.6 & 19.4 & 41.9 & 19.6 & \\underline{91.0} & 87.8 & 80.1 & 90.7 & \\textbf{99.4} & 76.2 \\\\\n","\\quad DeepSeek-R1 & 26.2 & 90.8 & 79.6 & \\textbf{82.4} & 18.5 & \\underline{43.8} & 19.9 & 90.4 & 87.5 & 76.0 & \\textbf{93.3} & \\underline{99.0} & 75.8 \\\\\n","\n","\\addlinespace[0.2em]\n","\\cdashlinelr{1-14}\n","\\addlinespace[0.5em]\n","\n","\\multicolumn{14}{l}{\\textit{Table-Specific LLMs}} \\\\\n","\\addlinespace[0.2em]\n","\\quad $^{\\dagger}$TableBenchLLM (7B) & 3.1 & 27.1 & 38.8 & -- & 6.2 & -- & -- & -- & -- & 42.3 & -- & -- & -- \\\\\n","\\quad $^{\\dagger}$TableLLM-13B & 10.8 & 69.0 & 66.3 & 6.3 & 5.4 & -- & -- & -- & -- & 21.5 & -- & -- & -- \\\\\n","\\quad $^{\\dagger}$TableLlama (7B) & \\textbf{39.0} & 82.6 & 35.0 & 64.7 & 20.8 & -- & -- & -- & -- & 73.8 & -- & -- & -- \\\\ \n","\\quad TableGPT2-7B & 29.0 & 77.8 & 61.4 & 70.3 & 14.1 & 39.0 & 19.0 & 85.4 & \\underline{89.1} & 78.0 & 77.2 & 79.7 & 66.4 \\\\ \n","\\quad $^{\\dagger*}$TableGPT2-72B & 32.3 & 85.4 & 71.5 & 75.6 & \\textbf{22.7} & -- & -- & -- & -- & 76.8 & -- & -- & -- \\\\\n","\\midrule\n","\n","\\multicolumn{14}{c}{\\textit{\\textbf{\\ours (Ours)}}} \\\\\n","\\addlinespace[0.5em]\n","\n","\\multicolumn{14}{l}{\\textit{Llama-3.1 Series}} \\\\\n","\\addlinespace[0.2em]\n","\n","\\quad Llama-3.1-8B-Instruct & 21.7 & 74.1 & 52.3 & 58.2 & 16.5 & 31.6 & 18.1 & 84.1 & 82.5 & 78.3 & 49.5 & 72.0 & 57.1 \\\\\n","\\quad Table-R1-SFT & 26.0 & 91.1 & \\underline{83.8} & 81.8 & 13.7 & 36.6 & 16.6 & 89.8 & 85.8 & 79.4 & 90.8 & 89.0 & 64.3 \\\\\n","\\quad Table-R1-Zero & \\underline{32.7} & 87.6 & 81.2 & 81.4 & \\underline{22.3} & 30.2 & 17.7 & 87.9 & \\textbf{91.6} & \\underline{80.2}& 68.6 & 84.6 & 62.3 \\\\ \n","\n","\\addlinespace[0.2em]\n","\\cdashlinelr{1-14}\n","\\addlinespace[0.5em]\n","\\multicolumn{14}{l}{\\textit{Qwen2.5 Series}} \\\\\n","\\addlinespace[0.2em]\n","\n","\\quad Qwen2.5-7B-Instruct & 21.0 & 72.2 & 54.8 & 61.8 & 16.0 & 39.5 & 19.3 & 78.6 & 70.7 & 74.6 & 87.4 & 85.0 & 66.4 \\\\\n","\\quad Table-R1-SFT & 25.3 & 89.9 & 81.9 & 78.3 & 14.1 & 38.8 & 18.8 & 88.8 & 84.6 & 76.0 & 90.9 & 96.6 & 71.7 \\\\\n","\\quad Table-R1-Zero & 30.6 & 87.6 & 79.8 & 78.1 & 19.8 & 43.1 & \\underline{20.0} & 83.7 & 88.0 & 76.2 & \\underline{93.0} & 96.4 & 70.8 \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\caption{Results on 13 table reasoning benchmarks spanning TQA, TFV, and FF-TQA tasks. For TQA, EM accuracy is reported (with ambiguous cases re-evaluated by GPT-4.1 mini); for TFV, classification accuracy; for FF-TQA, BLEU and ROUGE-L. \\textbf{Bold} and \\underline{underlined} scores indicate the top-2 performances among \\emph{open-source} models.\n","$^{\\dagger}$: Due to the context length limitations of most previous table-specific LLMs, it is challenging to conduct a fully fair comparison. Therefore, for these models, we directly use the results as reported in their respective papers, which may be based on sampled or filtered datasets. $^*$: Model weight has not been released.} \n","\\label{tab:main-results}\n","\\end{table*}\n"],["\\begin{figure}[!t]\n"," \\includegraphics[width=\\columnwidth]{figure/model_response_length.pdf}\n"," \\caption{Response length during Table-R1 training across different models.}\n"," \\label{fig:training-dynamics-response}\n","\\end{figure}\\begin{figure*}[!t]\n"],["\\begin{figure}[!t]\n","\\includegraphics[width=0.9\\columnwidth]{figure/pass_at_k_wtq.pdf}\n"," \\includegraphics[width=0.9\\columnwidth]{figure/pass_at_k_hitab.pdf}\n"," \\caption {Pass@k performance on WTQ and HiTab.}\n"," \\label{fig:passk}\n","\\end{figure}\\begin{figure*}[!t]\n"],["\\begin{table*}[!t]\n","\\label{tab:ablation-studies}\n","\\centering\n","\\setlength{\\tabcolsep}{2pt}\n","\n","\\resizebox{\\textwidth}{!}{\n","\\begin{tabular}{l*{13}{c}}\n","\\toprule\n","\\multirow{3}{*}{\\textbf{Model}} & \\multicolumn{4}{c}{\\textbf{In-domain Performance}} & \\multicolumn{9}{c}{\\textbf{Out-of-domain Performance}} \\\\\n","\\cmidrule(lr){2-5} \\cmidrule(lr){6-14}\n","\n","& \\multicolumn{1}{c}{\\textbf{FF-TQA}} & \\multicolumn{1}{c}{\\textbf{TFV}} & \\multicolumn{2}{c}{\\textbf{TQA}} & \\multicolumn{3}{c}{\\textbf{FF-TQA}} & \\multicolumn{3}{c}{\\textbf{TFV}} & \\multicolumn{3}{c}{\\textbf{TQA}} \\\\\n","\\cmidrule(lr){2-2} \\cmidrule(lr){3-3} \\cmidrule(lr){4-5} \\cmidrule(lr){6-8} \\cmidrule(lr){9-11} \\cmidrule(lr){12-14}\n","\n","& \\multicolumn{1}{c}{FeTaQA} & \\multicolumn{1}{c}{TabFact} & \\multicolumn{1}{c}{WTQ} & \\multicolumn{1}{c}{HiTab} & \\multicolumn{1}{c}{ToTTo} & \\multicolumn{1}{c}{QTSum} & \\multicolumn{1}{c}{R.W.} & \\multicolumn{1}{c}{InfoTabs} & \\multicolumn{1}{c}{PHT} & \\multicolumn{1}{c}{Feverous} & \\multicolumn{1}{c}{TMCQ} & \\multicolumn{1}{c}{TMWP} & \\multicolumn{1}{c}{FinQA} \\\\\n","\\midrule\n","\n","\\multicolumn{14}{c}{\\textit{\\textbf{Model-level Ablation}}} \\\\\n","\\addlinespace[0.5em]\n","\n","\\multicolumn{14}{l}{\\textit{Base vs. Instruct}} \\\\\n","\\addlinespace[0.2em]\n","Qwen2.5-7B & 18.2 & 67.7 & 51.0 & 55.2 & 9.4 & 37.8 & 16.4 & 74.5 & 75.0 & 74.1 & 78.7 & 80.3 & 58.4 \\\\\n","Table-R1-Qwen2.5-7B & 29.8 & 87.3 & 78.5 & 75.2 & 15.9 & 38.1 & 18.2 & 87.9 & 91.6 & 78.4 & 84.8 & 94.4 & 61.2 \\\\\n","Qwen2.5-7B-Instruct & 21.0 & 72.2 & 54.8 & 61.8 & 16.0 & 39.5 & 19.3 & 78.6 & 70.7 & 74.6 & 87.4 & 85.0 & 66.4 \\\\\n","Table-R1-Qwen2.5-7B-Instruct & 30.6 & 87.6 & 79.8 & 78.1 & 19.8 & 43.1 & 20.0 & 83.7 & 88.0 & 76.2 & 93.0 & 96.4 & 70.8 \\\\\n","\n","\\addlinespace[0.2em]\n","\\cdashlinelr{1-14}\n","\\addlinespace[0.5em]\n","\n","\\multicolumn{14}{l}{\\textit{Model Architecture Comparison}} \\\\\n","\\addlinespace[0.2em]\n","Llama-3.1-8B & 10.2 & 21.2 & 35.0 & 44.7 & 6.8 & 18.6 & 10.9 & 21.6 & 26.8 & 19.4 & 68.9 & 43.1 & 21.9 \\\\\n","Table-R1-Llama-3.1-8B & 30.4 & 50.3 & 61.5 & 76.3 & 19.8 & 33.5 & 18.0 & 50.0 & 69.7 & 50.6 & 93.1 & 56.8 & 19.3 \\\\\n","Llama-3.1-8B-Instruct & 21.7 & 74.1 & 52.3 & 58.2 & 16.5 & 31.6 & 18.1 & 84.1 & 82.5 & 78.3 & 49.5 & 72.0 & 57.1 \\\\\n","Table-R1-Llama-3.1-8B-Instruct & 32.7 & 87.6 & 81.2 & 81.4 & 22.3 & 30.2 & 17.7 & 87.9 & 91.6 & 80.2 & 68.6 & 84.6 & 62.3 \\\\\n","\n","\\addlinespace[0.2em]\n","\\cdashlinelr{1-14}\n","\\addlinespace[0.5em]\n","\n","\\multicolumn{14}{l}{\\textit{SFT on Domain-Specific Data}} \\\\\n","\\addlinespace[0.2em]\n","DeepSeek-R1-7B & 19.1 & 79.6 & 57.8 & 46.2 & 10.7 & 37.2 & 18.0 & 85.7 & 87.1 & 77.5 & 80.9 & 94.0 & 66.8 \\\\\n","Table-SFT-Qwen2.5-7B & 25.3 & 89.9 & 81.9 & 78.3 & 14.1 & 38.8 & 18.8 & 88.8 & 84.6 & 76.0 & 90.9 & 96.6 & 71.7 \\\\\n","Table-SFT-Llama-3.1-8B & 26.0 & 91.1 & 83.8 & 81.8 & 13.7 & 36.6 & 16.6 & 89.8 & 85.8 & 79.4 & 90.8 & 89.0 & 64.3 \\\\\n","\\midrule\n","\n","\\multicolumn{14}{c}{\\textit{\\textbf{Task-level Ablation}}} \\\\\n","\\addlinespace[0.5em]\n","\\multicolumn{14}{l}{\\textit{Cross-task Generalization}} \\\\\n","\\addlinespace[0.2em]\n","Table-R1-TQA-Qwen2.5-7B & 14.1 & 86.2 & 79.2 & 77.4 & 16.5 & 36.4 & 19.8 & 89.7 & 89.9 & 82.7 & 93.5 & 96.1 & 65.9 \\\\\n","Table-R1-TQA-Llama-3.1-8B & 12.3 & 83.9 & 81.7 & 81.0 & 17.2 & 26.9 & 19.2 & 87.3 & 87.2 & 77.1 & 80.3 & 82.8 & 58.6 \\\\\n","Table-SFT-TQA-Qwen2.5-7B & 13.3 & 87.7 & 79.8 & 77.0 & 13.4 & 27.6 & 18.1 & 88.5 & 87.1 & 78.0 & 90.1 & 95.7 & 69.0 \\\\\n","Table-SFT-TQA-Llama-3.1-8B & 9.6 & 88.8 & 82.4 & 80.5 & 10.3 & 20.6 & 12.6 & 89.1 & 90.4 & 82.1 & 89.9 & 86.9 & 62.9 \\\\\n","\\midrule\n","\n","\\multicolumn{14}{c}{\\textit{\\textbf{Format Ablation}}} \\\\\n","\\addlinespace[0.5em]\n","\\multicolumn{14}{l}{\\textit{Effect of Format Reward}} \\\\\n","\\addlinespace[0.2em]\n","Table-R1-Explicit-Qwen2.5-7B & 29.0 & 85.8 & 76.0 & 73.2 & 13.6 & 37.2 & 14.8 & 87.5 & 88.8 & 81.0 & 52.5 & 91.3 & 49.6 \\\\\n","Table-R1-Qwen2.5-7B & 30.6 & 87.6 & 79.8 & 78.1 & 19.8 & 43.1 & 20.0 & 83.7 & 88.0 & 76.2 & 93.0 & 96.4 & 70.8 \\\\\n","\\addlinespace[0.3em]\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\caption{Ablation study results on model-level, task-level, and formats.}\n","\\end{table*}\\subsection{Qualitative Analysis}\n"],["\\begin{table*}[ht]\n","\\centering\n","\\small\n","\\resizebox{\\textwidth}{!}{\n","\\begin{tabular}{lllll}\n","\\toprule\n","\\textbf{Task Category} & \\textbf{Task Name} & \\textbf{Dataset} & \\textbf{Task Description} & \\textbf{Metric} \\\\\n","\\midrule\n","\\multirow{4}{*}{Training}\n"," & Table QA & WTQ, HiTab & QA over flat or hierarchical tables & Acc. \\\\\n"," & Free-form QA & FeTaQA & Generate long-form answers from tables & BLEU \\\\\n"," & Table Fact Verification & TabFact & Verify factual correctness based on a table & Acc. \\\\\n","\\midrule\n","\\multirow{4}{*}{In-Domain}\n"," & Table QA & WTQ, HiTab & In-domain table QA evaluation & Acc. \\\\\n"," & Table Fact Verification & TabFact & In-domain fact verification evaluation & Acc.\\\\\n"," & Free-form QA & FeTaQA & Evaluate long-form generation quality & BLEU \\\\\n","\\midrule\n","\\multirow{4}{*}{Out-of-Domain}\n"," & Table QA & TabMCQ, TMWP, FinQA & Multiple-choice and word problem QA & Acc. \\\\\n"," & Numerical Reasoning QA & FinQA, TABMWP & Financial and numerical table QA & Acc. \\\\\n"," & Table Fact Verification & InfoTabs, PHT, Feverous & Health and factual judgment from tables & Acc. \\\\\n"," & Free-form QA & ToTTo, Qtsumm, R.W. & Table-to-text generation from highlights & BLEU/R-L \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\caption{Overview of datasets used in training and evaluation.}\n","\\label{tab:our_dataset}\n","\\end{table*}\n"],["\\begin{table*}[h]\n"," \\centering\n"," \\small\n"," \\begin{tabular}{lll}\n"," \\toprule\n"," \\textbf{Model} & \\textbf{Citation} & \\textbf{Version} \\\\ \\midrule\n","\n"," GPT-4.1 & \\citet{openai2024gpt4o} & gpt-4.1-2025-04-14 \\\\\n"," GPT-4.1 mini & \\citet{openai2024gpt4o} & gpt-4.1-mini-2025-04-14 \\\\ \\midrule\n","\n"," Qwen2.5-7B & \\citet{qwen2.5} & \\href{https://huggingface.co/Qwen/Qwen2.5-7B-Instruct}{Qwen/Qwen2.5-7B-Instruct} \\\\\n"," Qwen2.5-14B & \\citet{qwen2.5} & \\href{https://huggingface.co/Qwen/Qwen2.5-14B-Instruct}{Qwen/Qwen2.5-14B-Instruct} \\\\\n"," Qwen2.5-32B & \\citet{qwen2.5} & \\href{https://huggingface.co/Qwen/Qwen2.5-32B-Instruct}{Qwen/Qwen2.5-32B-Instruct} \\\\\n"," QwQ-32B & \\citet{qwq32b} & \\href{https://huggingface.co/Qwen/QwQ-32B}{Qwen/QwQ-32B} \\\\\n","\n"," Llama-3.1-8B & \\citet{touvron2023llama} & \\href{https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct}{meta-llama/Llama-3.1-8B-Instruct} \\\\ \\midrule\n","\n"," DeepSeek-R1-7B & \\citet{guo2025deepseek} & \\href{https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B}{deepseek-ai/DeepSeek-R1-Distill-Qwen-7B} \\\\\n"," DeepSeek-R1-14B & \\citet{guo2025deepseek} & \\href{https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B}{deepseek-ai/DeepSeek-R1-Distill-Qwen-14B} \\\\\n"," DeepSeek-R1-32B & \\citet{guo2025deepseek} & \\href{https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B}{deepseek-ai/DeepSeek-R1-Distill-Qwen-32B} \\\\\n"," DeepSeek-R1 & \\citet{guo2025deepseek} & \\href{hhttps://huggingface.co/deepseek-ai/DeepSeek-R1}{deepseek-ai/DeepSeek-R1} \\\\ \\midrule\n"," TableGPT2-7B & \\citet{su2024tablegpt2} & \\href{https://huggingface.co/tablegpt/TableGPT2-7B}{TableGPT/TableGPT2-7B} \\\\\n"," TableLLM-13B & \\citet{zhang2024tablellm} & \\href{https://huggingface.co/RUCKBReasoning/TableLLM-13b}{RUCKBReasoning/TableLLM-13B} \\\\ \n"," TableLlama-7B & \\citet{zhang2023tablellama} & \\href{https://huggingface.co/osunlp/TableLlama}{osunlp/TableLlama} \\\\ \n"," TableBenchLLM & \\citet{zhang2024tablellm} & \\href{https://huggingface.co/Multilingual-Multimodal-NLP/TableLLM-Llama3.1-8B}{Multilingual-Multimodal-NLP/TableLLM-Llama3.1-8B} \\\\ \n"," \n"," \\bottomrule\n"," \\end{tabular}\n"," \\caption{Model List.}\n"," \\label{tab:model_list}\n","\\end{table*}\n"],["\\begin{figure*}[h]\n"," \\includegraphics[width=\\linewidth]{figure/example1.pdf}\n"," \\caption{Qualitative Analysis Case Example for TQA tasks}\n","\\end{figure*}\n"],["\\begin{figure}[h]\n"," \\includegraphics[width=\\linewidth]{figure/example2.pdf}\n"," \\caption{Qualitative Analysis Case Example for TFV tasks}\n","\\end{figure}\n"]],"string":"[\n [\n \"\\\\begin{figure}[!t]\\n\",\n \" \\\\includegraphics[width=\\\\columnwidth]{figure/RadarChart.pdf}\\n\",\n \" \\\\caption{Overall performance comparison between \\\\ours and same-scale baselines on various table reasoning benchmarks. Both \\\\sftmodel and \\\\rlmodel exhibit substantial performance improvements over baselines, showing the effectiveness of our approach across both in- and out-of-domain benchmarks.}\\n\",\n \" \\\\label{fig:experiments}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{figure}[!t]\\n\",\n \" \\\\includegraphics[width=\\\\columnwidth]{figure/Table-R1.pdf}\\n\",\n \" \\\\caption{An overview of our research and three research questions investigated in this study.}\\n\",\n \" \\\\label{fig:overview}\\n\",\n \"\\\\end{figure}\\\\section{Related Work}\\n\"\n ],\n [\n \"\\\\begin{table}[!t]\\n\",\n \"\\t\\\\centering\\n\",\n \"\\t\\\\small\\n\",\n \" \\\\setlength{\\\\tabcolsep}{1pt}\\n\",\n \" \\\\resizebox{\\\\linewidth}{!}{\\n\",\n \"\\t\\\\begin{tabular}{llr}\\n\",\n \"\\t\\t\\\\toprule\\n\",\n \"\\t\\t\\\\textbf{Task} & \\\\textbf{Dataset} & \\\\textbf{Samples} \\\\\\\\\\n\",\n \"\\t\\t\\\\midrule\\n\",\n \"\\t\\t\\\\multirow{2}*{Short-form QA (TQA)} \\n\",\n \" & WTQ~\\\\cite{pasupat-liang-2015-compositional} & 13,706 \\\\\\\\\\n\",\n \" & HiTab~\\\\cite{cheng-etal-2022-hitab} & 6,793 \\\\\\\\\\n\",\n \"\\t\\t\\\\midrule\\n\",\n \"\\t Fact Verification (TFV) & TabFact~\\\\cite{Chen2020TabFact} & 20,740 \\\\\\\\\\n\",\n \"\\t\\t\\\\midrule\\n\",\n \"\\t\\tFree-form QA (FF-TQA) & FeTaQA~\\\\cite{nan-etal-2022-fetaqa} & 7,324 \\\\\\\\\\n\",\n \"\\t\\t\\\\bottomrule\\n\",\n \"\\t\\\\end{tabular}\\n\",\n \"\\t}\\n\",\n \"\\t\\\\caption{Overview of datasets collected in \\\\ours training data. For each dataset, we sample examples from its training set.}\\n\",\n \"\\t\\\\label{tab:training-data}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table*}[!t]\\n\",\n \"\\\\label{tab:main-results} \\n\",\n \"\\\\centering\\n\",\n \"\\n\",\n \"\\\\setlength{\\\\tabcolsep}{2pt} \\n\",\n \"\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \"\\\\begin{tabular}{l*{13}{c}}\\n\",\n \"\\\\toprule\\n\",\n \"\\n\",\n \"\\\\multirow{3}{*}{\\\\textbf{Model}} & \\\\multicolumn{4}{c}{\\\\textbf{In-domain Performance}} & \\\\multicolumn{9}{c}{\\\\textbf{Out-of-domain Performance}} \\\\\\\\\\n\",\n \"\\\\cmidrule(lr){2-5} \\\\cmidrule(lr){6-14}\\n\",\n \"\\n\",\n \"& \\\\multicolumn{1}{c}{\\\\textbf{FF-TQA}} & \\\\multicolumn{1}{c}{\\\\textbf{TFV}} & \\\\multicolumn{2}{c}{\\\\textbf{TQA}} & \\\\multicolumn{3}{c}{\\\\textbf{FF-TQA}} & \\\\multicolumn{3}{c}{\\\\textbf{TFV}} & \\\\multicolumn{3}{c}{\\\\textbf{TQA}} \\\\\\\\\\n\",\n \"\\\\cmidrule(lr){2-2} \\\\cmidrule(lr){3-3} \\\\cmidrule(lr){4-5} \\\\cmidrule(lr){6-8} \\\\cmidrule(lr){9-11} \\\\cmidrule(lr){12-14}\\n\",\n \"\\n\",\n \"& \\\\multicolumn{1}{c}{FeTaQA} & \\\\multicolumn{1}{c}{TabFact} & \\\\multicolumn{1}{c}{WTQ} & \\\\multicolumn{1}{c}{HiTab} & \\\\multicolumn{1}{c}{ToTTo} & \\\\multicolumn{1}{c}{QTSum} & \\\\multicolumn{1}{c}{R.W.} & \\\\multicolumn{1}{c}{InfoTabs} & \\\\multicolumn{1}{c}{PHT} & \\\\multicolumn{1}{c}{Feverous} & \\\\multicolumn{1}{c}{TMCQ} & \\\\multicolumn{1}{c}{TMWP} & \\\\multicolumn{1}{c}{FinQA} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\n\",\n \"\\\\multicolumn{14}{c}{\\\\textit{\\\\textbf{Proprietary Models}}} \\\\\\\\ \\n\",\n \"\\\\addlinespace[0.5em]\\n\",\n \"GPT-4.1 & 25.1 & 86.5 & 68.0 & 84.7 & 20.4 & 45.7 & 21.0 & 90.5 & 88.2 & 87.7 & 92.0 & 77.0 & 74.0 \\\\\\\\\\n\",\n \"GPT-4.1 mini & 27.2 & 84.9 & 69.5 & 80.7 & 18.8 & 46.4 & 20.0 & 88.9 & 86.8 & 86.1 & 92.9 & 86.2 & 71.4 \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\n\",\n \"\\\\multicolumn{14}{c}{\\\\textit{\\\\textbf{Open-source Models}}} \\\\\\\\\\n\",\n \"\\\\addlinespace[0.5em]\\n\",\n \"\\n\",\n \"\\\\multicolumn{14}{l}{\\\\textit{LLMs}} \\\\\\\\\\n\",\n \"\\\\quad Qwen2.5-7B & 21.0 & 72.2 & 54.8 & 61.8 & 16.0 & 39.5 & 19.3 & 78.6 & 70.7 & 74.6 & 87.4 & 85.0 & 66.4 \\\\\\\\\\n\",\n \"\\\\quad Qwen2.5-32B & 21.9 & 90.3 & 77.3 & 79.4 & 17.8 & 41.4 & \\\\underline{20.0} & 90.5 & 86.7 & 79.2 & 92.1 & 95.8 & \\\\underline{77.3} \\\\\\\\\\n\",\n \"\\\\quad DeepSeek-V3 & 24.7 & \\\\textbf{92.4} & 69.9 & \\\\underline{82.2} & 19.0 & \\\\textbf{46.2} & \\\\textbf{20.9} & \\\\textbf{91.9} & 86.2 & \\\\textbf{85.8} & 87.6 & 93.4 & \\\\textbf{78.6} \\\\\\\\\\n\",\n \"\\n\",\n \"\\\\addlinespace[0.2em]\\n\",\n \"\\\\cdashlinelr{1-14}\\n\",\n \"\\\\addlinespace[0.5em]\\n\",\n \"\\n\",\n \"\\\\multicolumn{14}{l}{\\\\textit{Reasoning LLMs}} \\\\\\\\\\n\",\n \"\\\\addlinespace[0.2em]\\n\",\n \"\\\\quad DeepSeek-R1-Distill-7B & 19.1 & 79.6 & 57.8 & 46.2 & 10.7 & 37.2 & 18.0 & 85.7 & 87.1 & 77.5 & 80.9 & 94.0 & 66.8 \\\\\\\\\\n\",\n \"\\\\quad QwQ-32B & 23.8 & \\\\underline{91.5} & \\\\textbf{85.4} & 81.6 & 19.4 & 41.9 & 19.6 & \\\\underline{91.0} & 87.8 & 80.1 & 90.7 & \\\\textbf{99.4} & 76.2 \\\\\\\\\\n\",\n \"\\\\quad DeepSeek-R1 & 26.2 & 90.8 & 79.6 & \\\\textbf{82.4} & 18.5 & \\\\underline{43.8} & 19.9 & 90.4 & 87.5 & 76.0 & \\\\textbf{93.3} & \\\\underline{99.0} & 75.8 \\\\\\\\\\n\",\n \"\\n\",\n \"\\\\addlinespace[0.2em]\\n\",\n \"\\\\cdashlinelr{1-14}\\n\",\n \"\\\\addlinespace[0.5em]\\n\",\n \"\\n\",\n \"\\\\multicolumn{14}{l}{\\\\textit{Table-Specific LLMs}} \\\\\\\\\\n\",\n \"\\\\addlinespace[0.2em]\\n\",\n \"\\\\quad $^{\\\\dagger}$TableBenchLLM (7B) & 3.1 & 27.1 & 38.8 & -- & 6.2 & -- & -- & -- & -- & 42.3 & -- & -- & -- \\\\\\\\\\n\",\n \"\\\\quad $^{\\\\dagger}$TableLLM-13B & 10.8 & 69.0 & 66.3 & 6.3 & 5.4 & -- & -- & -- & -- & 21.5 & -- & -- & -- \\\\\\\\\\n\",\n \"\\\\quad $^{\\\\dagger}$TableLlama (7B) & \\\\textbf{39.0} & 82.6 & 35.0 & 64.7 & 20.8 & -- & -- & -- & -- & 73.8 & -- & -- & -- \\\\\\\\ \\n\",\n \"\\\\quad TableGPT2-7B & 29.0 & 77.8 & 61.4 & 70.3 & 14.1 & 39.0 & 19.0 & 85.4 & \\\\underline{89.1} & 78.0 & 77.2 & 79.7 & 66.4 \\\\\\\\ \\n\",\n \"\\\\quad $^{\\\\dagger*}$TableGPT2-72B & 32.3 & 85.4 & 71.5 & 75.6 & \\\\textbf{22.7} & -- & -- & -- & -- & 76.8 & -- & -- & -- \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\n\",\n \"\\\\multicolumn{14}{c}{\\\\textit{\\\\textbf{\\\\ours (Ours)}}} \\\\\\\\\\n\",\n \"\\\\addlinespace[0.5em]\\n\",\n \"\\n\",\n \"\\\\multicolumn{14}{l}{\\\\textit{Llama-3.1 Series}} \\\\\\\\\\n\",\n \"\\\\addlinespace[0.2em]\\n\",\n \"\\n\",\n \"\\\\quad Llama-3.1-8B-Instruct & 21.7 & 74.1 & 52.3 & 58.2 & 16.5 & 31.6 & 18.1 & 84.1 & 82.5 & 78.3 & 49.5 & 72.0 & 57.1 \\\\\\\\\\n\",\n \"\\\\quad Table-R1-SFT & 26.0 & 91.1 & \\\\underline{83.8} & 81.8 & 13.7 & 36.6 & 16.6 & 89.8 & 85.8 & 79.4 & 90.8 & 89.0 & 64.3 \\\\\\\\\\n\",\n \"\\\\quad Table-R1-Zero & \\\\underline{32.7} & 87.6 & 81.2 & 81.4 & \\\\underline{22.3} & 30.2 & 17.7 & 87.9 & \\\\textbf{91.6} & \\\\underline{80.2}& 68.6 & 84.6 & 62.3 \\\\\\\\ \\n\",\n \"\\n\",\n \"\\\\addlinespace[0.2em]\\n\",\n \"\\\\cdashlinelr{1-14}\\n\",\n \"\\\\addlinespace[0.5em]\\n\",\n \"\\\\multicolumn{14}{l}{\\\\textit{Qwen2.5 Series}} \\\\\\\\\\n\",\n \"\\\\addlinespace[0.2em]\\n\",\n \"\\n\",\n \"\\\\quad Qwen2.5-7B-Instruct & 21.0 & 72.2 & 54.8 & 61.8 & 16.0 & 39.5 & 19.3 & 78.6 & 70.7 & 74.6 & 87.4 & 85.0 & 66.4 \\\\\\\\\\n\",\n \"\\\\quad Table-R1-SFT & 25.3 & 89.9 & 81.9 & 78.3 & 14.1 & 38.8 & 18.8 & 88.8 & 84.6 & 76.0 & 90.9 & 96.6 & 71.7 \\\\\\\\\\n\",\n \"\\\\quad Table-R1-Zero & 30.6 & 87.6 & 79.8 & 78.1 & 19.8 & 43.1 & \\\\underline{20.0} & 83.7 & 88.0 & 76.2 & \\\\underline{93.0} & 96.4 & 70.8 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Results on 13 table reasoning benchmarks spanning TQA, TFV, and FF-TQA tasks. For TQA, EM accuracy is reported (with ambiguous cases re-evaluated by GPT-4.1 mini); for TFV, classification accuracy; for FF-TQA, BLEU and ROUGE-L. \\\\textbf{Bold} and \\\\underline{underlined} scores indicate the top-2 performances among \\\\emph{open-source} models.\\n\",\n \"$^{\\\\dagger}$: Due to the context length limitations of most previous table-specific LLMs, it is challenging to conduct a fully fair comparison. Therefore, for these models, we directly use the results as reported in their respective papers, which may be based on sampled or filtered datasets. $^*$: Model weight has not been released.} \\n\",\n \"\\\\label{tab:main-results}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{figure}[!t]\\n\",\n \" \\\\includegraphics[width=\\\\columnwidth]{figure/model_response_length.pdf}\\n\",\n \" \\\\caption{Response length during Table-R1 training across different models.}\\n\",\n \" \\\\label{fig:training-dynamics-response}\\n\",\n \"\\\\end{figure}\\\\begin{figure*}[!t]\\n\"\n ],\n [\n \"\\\\begin{figure}[!t]\\n\",\n \"\\\\includegraphics[width=0.9\\\\columnwidth]{figure/pass_at_k_wtq.pdf}\\n\",\n \" \\\\includegraphics[width=0.9\\\\columnwidth]{figure/pass_at_k_hitab.pdf}\\n\",\n \" \\\\caption {Pass@k performance on WTQ and HiTab.}\\n\",\n \" \\\\label{fig:passk}\\n\",\n \"\\\\end{figure}\\\\begin{figure*}[!t]\\n\"\n ],\n [\n \"\\\\begin{table*}[!t]\\n\",\n \"\\\\label{tab:ablation-studies}\\n\",\n \"\\\\centering\\n\",\n \"\\\\setlength{\\\\tabcolsep}{2pt}\\n\",\n \"\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \"\\\\begin{tabular}{l*{13}{c}}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{3}{*}{\\\\textbf{Model}} & \\\\multicolumn{4}{c}{\\\\textbf{In-domain Performance}} & \\\\multicolumn{9}{c}{\\\\textbf{Out-of-domain Performance}} \\\\\\\\\\n\",\n \"\\\\cmidrule(lr){2-5} \\\\cmidrule(lr){6-14}\\n\",\n \"\\n\",\n \"& \\\\multicolumn{1}{c}{\\\\textbf{FF-TQA}} & \\\\multicolumn{1}{c}{\\\\textbf{TFV}} & \\\\multicolumn{2}{c}{\\\\textbf{TQA}} & \\\\multicolumn{3}{c}{\\\\textbf{FF-TQA}} & \\\\multicolumn{3}{c}{\\\\textbf{TFV}} & \\\\multicolumn{3}{c}{\\\\textbf{TQA}} \\\\\\\\\\n\",\n \"\\\\cmidrule(lr){2-2} \\\\cmidrule(lr){3-3} \\\\cmidrule(lr){4-5} \\\\cmidrule(lr){6-8} \\\\cmidrule(lr){9-11} \\\\cmidrule(lr){12-14}\\n\",\n \"\\n\",\n \"& \\\\multicolumn{1}{c}{FeTaQA} & \\\\multicolumn{1}{c}{TabFact} & \\\\multicolumn{1}{c}{WTQ} & \\\\multicolumn{1}{c}{HiTab} & \\\\multicolumn{1}{c}{ToTTo} & \\\\multicolumn{1}{c}{QTSum} & \\\\multicolumn{1}{c}{R.W.} & \\\\multicolumn{1}{c}{InfoTabs} & \\\\multicolumn{1}{c}{PHT} & \\\\multicolumn{1}{c}{Feverous} & \\\\multicolumn{1}{c}{TMCQ} & \\\\multicolumn{1}{c}{TMWP} & \\\\multicolumn{1}{c}{FinQA} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\n\",\n \"\\\\multicolumn{14}{c}{\\\\textit{\\\\textbf{Model-level Ablation}}} \\\\\\\\\\n\",\n \"\\\\addlinespace[0.5em]\\n\",\n \"\\n\",\n \"\\\\multicolumn{14}{l}{\\\\textit{Base vs. Instruct}} \\\\\\\\\\n\",\n \"\\\\addlinespace[0.2em]\\n\",\n \"Qwen2.5-7B & 18.2 & 67.7 & 51.0 & 55.2 & 9.4 & 37.8 & 16.4 & 74.5 & 75.0 & 74.1 & 78.7 & 80.3 & 58.4 \\\\\\\\\\n\",\n \"Table-R1-Qwen2.5-7B & 29.8 & 87.3 & 78.5 & 75.2 & 15.9 & 38.1 & 18.2 & 87.9 & 91.6 & 78.4 & 84.8 & 94.4 & 61.2 \\\\\\\\\\n\",\n \"Qwen2.5-7B-Instruct & 21.0 & 72.2 & 54.8 & 61.8 & 16.0 & 39.5 & 19.3 & 78.6 & 70.7 & 74.6 & 87.4 & 85.0 & 66.4 \\\\\\\\\\n\",\n \"Table-R1-Qwen2.5-7B-Instruct & 30.6 & 87.6 & 79.8 & 78.1 & 19.8 & 43.1 & 20.0 & 83.7 & 88.0 & 76.2 & 93.0 & 96.4 & 70.8 \\\\\\\\\\n\",\n \"\\n\",\n \"\\\\addlinespace[0.2em]\\n\",\n \"\\\\cdashlinelr{1-14}\\n\",\n \"\\\\addlinespace[0.5em]\\n\",\n \"\\n\",\n \"\\\\multicolumn{14}{l}{\\\\textit{Model Architecture Comparison}} \\\\\\\\\\n\",\n \"\\\\addlinespace[0.2em]\\n\",\n \"Llama-3.1-8B & 10.2 & 21.2 & 35.0 & 44.7 & 6.8 & 18.6 & 10.9 & 21.6 & 26.8 & 19.4 & 68.9 & 43.1 & 21.9 \\\\\\\\\\n\",\n \"Table-R1-Llama-3.1-8B & 30.4 & 50.3 & 61.5 & 76.3 & 19.8 & 33.5 & 18.0 & 50.0 & 69.7 & 50.6 & 93.1 & 56.8 & 19.3 \\\\\\\\\\n\",\n \"Llama-3.1-8B-Instruct & 21.7 & 74.1 & 52.3 & 58.2 & 16.5 & 31.6 & 18.1 & 84.1 & 82.5 & 78.3 & 49.5 & 72.0 & 57.1 \\\\\\\\\\n\",\n \"Table-R1-Llama-3.1-8B-Instruct & 32.7 & 87.6 & 81.2 & 81.4 & 22.3 & 30.2 & 17.7 & 87.9 & 91.6 & 80.2 & 68.6 & 84.6 & 62.3 \\\\\\\\\\n\",\n \"\\n\",\n \"\\\\addlinespace[0.2em]\\n\",\n \"\\\\cdashlinelr{1-14}\\n\",\n \"\\\\addlinespace[0.5em]\\n\",\n \"\\n\",\n \"\\\\multicolumn{14}{l}{\\\\textit{SFT on Domain-Specific Data}} \\\\\\\\\\n\",\n \"\\\\addlinespace[0.2em]\\n\",\n \"DeepSeek-R1-7B & 19.1 & 79.6 & 57.8 & 46.2 & 10.7 & 37.2 & 18.0 & 85.7 & 87.1 & 77.5 & 80.9 & 94.0 & 66.8 \\\\\\\\\\n\",\n \"Table-SFT-Qwen2.5-7B & 25.3 & 89.9 & 81.9 & 78.3 & 14.1 & 38.8 & 18.8 & 88.8 & 84.6 & 76.0 & 90.9 & 96.6 & 71.7 \\\\\\\\\\n\",\n \"Table-SFT-Llama-3.1-8B & 26.0 & 91.1 & 83.8 & 81.8 & 13.7 & 36.6 & 16.6 & 89.8 & 85.8 & 79.4 & 90.8 & 89.0 & 64.3 \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\n\",\n \"\\\\multicolumn{14}{c}{\\\\textit{\\\\textbf{Task-level Ablation}}} \\\\\\\\\\n\",\n \"\\\\addlinespace[0.5em]\\n\",\n \"\\\\multicolumn{14}{l}{\\\\textit{Cross-task Generalization}} \\\\\\\\\\n\",\n \"\\\\addlinespace[0.2em]\\n\",\n \"Table-R1-TQA-Qwen2.5-7B & 14.1 & 86.2 & 79.2 & 77.4 & 16.5 & 36.4 & 19.8 & 89.7 & 89.9 & 82.7 & 93.5 & 96.1 & 65.9 \\\\\\\\\\n\",\n \"Table-R1-TQA-Llama-3.1-8B & 12.3 & 83.9 & 81.7 & 81.0 & 17.2 & 26.9 & 19.2 & 87.3 & 87.2 & 77.1 & 80.3 & 82.8 & 58.6 \\\\\\\\\\n\",\n \"Table-SFT-TQA-Qwen2.5-7B & 13.3 & 87.7 & 79.8 & 77.0 & 13.4 & 27.6 & 18.1 & 88.5 & 87.1 & 78.0 & 90.1 & 95.7 & 69.0 \\\\\\\\\\n\",\n \"Table-SFT-TQA-Llama-3.1-8B & 9.6 & 88.8 & 82.4 & 80.5 & 10.3 & 20.6 & 12.6 & 89.1 & 90.4 & 82.1 & 89.9 & 86.9 & 62.9 \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\n\",\n \"\\\\multicolumn{14}{c}{\\\\textit{\\\\textbf{Format Ablation}}} \\\\\\\\\\n\",\n \"\\\\addlinespace[0.5em]\\n\",\n \"\\\\multicolumn{14}{l}{\\\\textit{Effect of Format Reward}} \\\\\\\\\\n\",\n \"\\\\addlinespace[0.2em]\\n\",\n \"Table-R1-Explicit-Qwen2.5-7B & 29.0 & 85.8 & 76.0 & 73.2 & 13.6 & 37.2 & 14.8 & 87.5 & 88.8 & 81.0 & 52.5 & 91.3 & 49.6 \\\\\\\\\\n\",\n \"Table-R1-Qwen2.5-7B & 30.6 & 87.6 & 79.8 & 78.1 & 19.8 & 43.1 & 20.0 & 83.7 & 88.0 & 76.2 & 93.0 & 96.4 & 70.8 \\\\\\\\\\n\",\n \"\\\\addlinespace[0.3em]\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Ablation study results on model-level, task-level, and formats.}\\n\",\n \"\\\\end{table*}\\\\subsection{Qualitative Analysis}\\n\"\n ],\n [\n \"\\\\begin{table*}[ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\small\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \"\\\\begin{tabular}{lllll}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Task Category} & \\\\textbf{Task Name} & \\\\textbf{Dataset} & \\\\textbf{Task Description} & \\\\textbf{Metric} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Training}\\n\",\n \" & Table QA & WTQ, HiTab & QA over flat or hierarchical tables & Acc. \\\\\\\\\\n\",\n \" & Free-form QA & FeTaQA & Generate long-form answers from tables & BLEU \\\\\\\\\\n\",\n \" & Table Fact Verification & TabFact & Verify factual correctness based on a table & Acc. \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multirow{4}{*}{In-Domain}\\n\",\n \" & Table QA & WTQ, HiTab & In-domain table QA evaluation & Acc. \\\\\\\\\\n\",\n \" & Table Fact Verification & TabFact & In-domain fact verification evaluation & Acc.\\\\\\\\\\n\",\n \" & Free-form QA & FeTaQA & Evaluate long-form generation quality & BLEU \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Out-of-Domain}\\n\",\n \" & Table QA & TabMCQ, TMWP, FinQA & Multiple-choice and word problem QA & Acc. \\\\\\\\\\n\",\n \" & Numerical Reasoning QA & FinQA, TABMWP & Financial and numerical table QA & Acc. \\\\\\\\\\n\",\n \" & Table Fact Verification & InfoTabs, PHT, Feverous & Health and factual judgment from tables & Acc. \\\\\\\\\\n\",\n \" & Free-form QA & ToTTo, Qtsumm, R.W. & Table-to-text generation from highlights & BLEU/R-L \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Overview of datasets used in training and evaluation.}\\n\",\n \"\\\\label{tab:our_dataset}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[h]\\n\",\n \" \\\\centering\\n\",\n \" \\\\small\\n\",\n \" \\\\begin{tabular}{lll}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Model} & \\\\textbf{Citation} & \\\\textbf{Version} \\\\\\\\ \\\\midrule\\n\",\n \"\\n\",\n \" GPT-4.1 & \\\\citet{openai2024gpt4o} & gpt-4.1-2025-04-14 \\\\\\\\\\n\",\n \" GPT-4.1 mini & \\\\citet{openai2024gpt4o} & gpt-4.1-mini-2025-04-14 \\\\\\\\ \\\\midrule\\n\",\n \"\\n\",\n \" Qwen2.5-7B & \\\\citet{qwen2.5} & \\\\href{https://huggingface.co/Qwen/Qwen2.5-7B-Instruct}{Qwen/Qwen2.5-7B-Instruct} \\\\\\\\\\n\",\n \" Qwen2.5-14B & \\\\citet{qwen2.5} & \\\\href{https://huggingface.co/Qwen/Qwen2.5-14B-Instruct}{Qwen/Qwen2.5-14B-Instruct} \\\\\\\\\\n\",\n \" Qwen2.5-32B & \\\\citet{qwen2.5} & \\\\href{https://huggingface.co/Qwen/Qwen2.5-32B-Instruct}{Qwen/Qwen2.5-32B-Instruct} \\\\\\\\\\n\",\n \" QwQ-32B & \\\\citet{qwq32b} & \\\\href{https://huggingface.co/Qwen/QwQ-32B}{Qwen/QwQ-32B} \\\\\\\\\\n\",\n \"\\n\",\n \" Llama-3.1-8B & \\\\citet{touvron2023llama} & \\\\href{https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct}{meta-llama/Llama-3.1-8B-Instruct} \\\\\\\\ \\\\midrule\\n\",\n \"\\n\",\n \" DeepSeek-R1-7B & \\\\citet{guo2025deepseek} & \\\\href{https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B}{deepseek-ai/DeepSeek-R1-Distill-Qwen-7B} \\\\\\\\\\n\",\n \" DeepSeek-R1-14B & \\\\citet{guo2025deepseek} & \\\\href{https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B}{deepseek-ai/DeepSeek-R1-Distill-Qwen-14B} \\\\\\\\\\n\",\n \" DeepSeek-R1-32B & \\\\citet{guo2025deepseek} & \\\\href{https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B}{deepseek-ai/DeepSeek-R1-Distill-Qwen-32B} \\\\\\\\\\n\",\n \" DeepSeek-R1 & \\\\citet{guo2025deepseek} & \\\\href{hhttps://huggingface.co/deepseek-ai/DeepSeek-R1}{deepseek-ai/DeepSeek-R1} \\\\\\\\ \\\\midrule\\n\",\n \" TableGPT2-7B & \\\\citet{su2024tablegpt2} & \\\\href{https://huggingface.co/tablegpt/TableGPT2-7B}{TableGPT/TableGPT2-7B} \\\\\\\\\\n\",\n \" TableLLM-13B & \\\\citet{zhang2024tablellm} & \\\\href{https://huggingface.co/RUCKBReasoning/TableLLM-13b}{RUCKBReasoning/TableLLM-13B} \\\\\\\\ \\n\",\n \" TableLlama-7B & \\\\citet{zhang2023tablellama} & \\\\href{https://huggingface.co/osunlp/TableLlama}{osunlp/TableLlama} \\\\\\\\ \\n\",\n \" TableBenchLLM & \\\\citet{zhang2024tablellm} & \\\\href{https://huggingface.co/Multilingual-Multimodal-NLP/TableLLM-Llama3.1-8B}{Multilingual-Multimodal-NLP/TableLLM-Llama3.1-8B} \\\\\\\\ \\n\",\n \" \\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" \\\\caption{Model List.}\\n\",\n \" \\\\label{tab:model_list}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[h]\\n\",\n \" \\\\includegraphics[width=\\\\linewidth]{figure/example1.pdf}\\n\",\n \" \\\\caption{Qualitative Analysis Case Example for TQA tasks}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{figure}[h]\\n\",\n \" \\\\includegraphics[width=\\\\linewidth]{figure/example2.pdf}\\n\",\n \" \\\\caption{Qualitative Analysis Case Example for TFV tasks}\\n\",\n \"\\\\end{figure}\\n\"\n ]\n]","isConversational":false},"fulltext":{"kind":"string","value":"% This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.\n\\pdfoutput=1\n% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.\n\n\\documentclass[11pt]{article}\n\n% Remove the \"review\" option to generate the final version.\n\\usepackage[]{acl}\n\n% Standard package includes\n\\usepackage{times}\n\\usepackage{latexsym}\n\n% For proper rendering and hyphenation of words containing Latin characters (including in bib files)\n\\usepackage[T1]{fontenc}\n% For Vietnamese characters\n% \\usepackage[T5]{fontenc}\n% See https://www.latex-project.org/help/documentation/encguide.pdf for other character sets\n\n% This assumes your files are encoded as UTF8\n\\usepackage[utf8]{inputenc}\n\n% Added by authors\n\\usepackage{color}\n% \\usepackage[table]{xcolor}\n\\usepackage{tcolorbox}\n% \\usepackage{xcolor}\n\\usepackage{tabularx}\n\\usepackage{tabu}\n\\usepackage{booktabs}\n\\usepackage{multirow}\n\n\\usepackage{graphicx} \n\\usepackage{float}\n\\usepackage{subfigure} \n\\usepackage{amssymb}\n\n\\usepackage{hyperref}\n\\usepackage{tablefootnote}\n\\usepackage{xspace}\n\n\\usepackage{float}\n\\usepackage{amsmath}\n\\usepackage{bm}\n\\usepackage{algorithmicx}\n\\usepackage{algorithm}\n\\usepackage{algpseudocode}\n\n\\usepackage{arydshln}\n\n\\usepackage{amssymb}\n\\usepackage{pifont}\n\\newcommand{\\cmark}{\\ding{51}}%\n\\newcommand{\\xmark}{\\ding{55}}%\n\\usepackage{enumitem}\n\\usepackage{fontawesome5}\n% This is not strictly necessary, and may be commented out,\n% but it will improve the layout of the manuscript,\n% and will typically save some space.\n\\usepackage{microtype}\n\n% This is also not strictly necessary, and may be commented out.\n% However, it will improve the aesthetics of text in\n% the typewriter font.\n% \\usepackage{inconsolata}\n\\newcommand{\\ours}{Table-R1\\xspace}\n\\newcommand{\\sftmodel}{\\ours-SFT\\xspace}\n\\newcommand{\\rlmodel}{\\ours-Zero\\xspace}\n% \\newcommand{\\rlmodel}{Table-R1-Data\\xspace}\n\\newcommand{\\eg}{\\hbox{\\emph{e.g.,}}\\xspace}\n\\newcommand{\\ie}{\\hbox{\\emph{i.e.,}}\\xspace}\n\\newcommand{\\huggingface}{\\raisebox{-1.5pt}{\\includegraphics[height=1.05em]{icons/hf-logo.pdf}}\\xspace}\n\\newcommand{\\github}{\\raisebox{-1.5pt}{\\includegraphics[height=1.05em]{icons/github-logo.pdf}}\\xspace}\n\n% Author's Comments:\n\\newcommand{\\yilun}[1]{\\textcolor{purple}{\\bf\\small [Yilun: #1]}}\n\\newcommand{\\lyuhao}[1]{\\textcolor{brown}{\\bf\\small [Lyuhao: #1]}}\n\\newcommand{\\yzy}[1]{\\textcolor{blue}{\\bf\\small [Zheyuan: #1]}}\n\n\n\\newcommand{\\cdashlinelr}[1]{\n \\cdashline{#1}[.4pt/2pt]\n}\n\n\\newcommand{\\rqthree}{xxxxxx?}\n\n% If the title and author information does not fit in the area allocated, uncomment the following\n%\n%\\setlength\\titlebox{<dim>}\n%\n% and set <dim> to something 5cm or larger.\n\\addtocontents{toc}{\\protect\\setcounter{tocdepth}{-1}}\n% \\title{\\ours: Inference-Time Scaling for Table Reasoning Tasks}\n\\title{\\ours: Inference-Time Scaling for Table Reasoning}\n\n\n\\author{\nZheyuan Yang\\thanks{Equal Contributions.} \\quad Lyuhao Chen$^*$ \\quad Arman Cohan \\quad Yilun Zhao \\vspace{10pt}\\\\\nYale NLP Lab\n}\n\n\\begin{document}\n\\maketitle\n\n\\begin{abstract}\nIn this work, we present the first study to explore inference-time scaling on table reasoning tasks. We develop and evaluate two post-training strategies to enable inference-time scaling: distillation from frontier model reasoning traces and reinforcement learning with verifiable rewards (RLVR). \nFor distillation, we introduce a large-scale dataset of reasoning traces generated by DeepSeek-R1, which we use to fine-tune LLMs into the \\sftmodel model. \nFor RLVR, we propose task-specific verifiable reward functions and apply the GRPO algorithm to obtain the \\rlmodel model. \nWe evaluate our \\ours-series models across diverse table reasoning tasks, including short-form QA, fact verification, and free-form QA. \nNotably, the \\rlmodel model matches or exceeds the performance of GPT-4.1 and DeepSeek-R1, while using only a 7B-parameter LLM. It also demonstrates strong generalization to out-of-domain datasets.\nExtensive ablation and qualitative analyses reveal the benefits of instruction tuning, model architecture choices, and cross-task generalization, as well as emergence of essential table reasoning skills during RL training.\n\n\\vspace{-10pt}\n\\begin{small}\n\\begin{center}\n\\begin{tabular}{ll}\n\\huggingface~~\\textbf{Model} & \\href{https://huggingface.co/Table-R1} {\\path{huggingface.co/Table-R1}}\\\\\n\\github~~\\textbf{Code} &\\href{https://github.com/Table-R1/Table-R1}{\\path{github.com/Table-R1}}\\\\\n\\end{tabular}\n\\end{center} \n\\end{small}\n\\end{abstract}\n\n\\section{Introduction}\nReasoning large language models, such as OpenAI’s o-series~\\cite{jaech2024openai,pfister2025understanding} and Deepseek’s R1~\\cite{guo2025deepseek}, have demonstrated enhanced reasoning capabilities by inference-time scaling, \\ie generating a reasoning chain of tokens that allow the model to ``think'' before giving the final answer.\nBuilding on this success, recent research has extended inference-time scaling to various domains and tasks, including multimodal reasoning~\\cite{huang2025visionr1, xu2025llavacot}, machine translation~\\cite{feng2025mt}, agent-based tool use~\\cite{Agent-R1, jin2025searchr1}, and information retrieval~\\cite{weller2025rank1, zhuang2025rankr1}. \n\n\\begin{figure}[!t]\n \\includegraphics[width=\\columnwidth]{figure/RadarChart.pdf}\n \\caption{Overall performance comparison between \\ours and same-scale baselines on various table reasoning benchmarks. Both \\sftmodel and \\rlmodel exhibit substantial performance improvements over baselines, showing the effectiveness of our approach across both in- and out-of-domain benchmarks.}\n \\label{fig:experiments}\n\\end{figure}\n\nHowever, applying inference-time scaling to structure-dependent tasks—particularly table reasoning—remains largely unexplored. \nTable reasoning presents distinct challenges compared to text-only tasks: it requires interpreting diverse cell contents, aligning data across the table, and performing multi-step reasoning with aggregation and numerical operations~\\cite{deng2024tables, wu2025tablebench}. These requirements are further complicated by the need to process long and densely structured tabular inputs~\\cite{zhao-etal-2023-investigating, nahid2024tabsqlify, zhang2025survey}.\nAdvancing LLMs’ reasoning capabilities over tabular tasks holds significant promise for real-world applications, including data analysis~\\cite{zhao-etal-2024-docmath}, scientific reporting~\\cite{liang2024mapping, newman2024arxivdigestables}, and decision-support systems~\\cite{handler2024large}.\n\nIn this work, we present the first study to explore inference-time scaling on table reasoning tasks. \n\\autoref{fig:overview} presents the overview of our research.\nWe develop and systematically evaluate two widely used post-training strategies to enable inference-time scaling on table reasoning tasks: (1) distilling from reasoning traces of frontier reasoning models, and (2) reinforcement learning with verifiable rewards (RLVR).\nFor the distillation approach, we curate and open-source a large-scale table reasoning dataset containing reasoning traces generated by DeepSeek-R1 and verifid by LLM-based annotators. We fine-tune LLMs on this data to obtain \\sftmodel.\nFor the RLVR approach, we design task-specific, verifiable reward functions tailored to table reasoning and apply the Group Relative Policy Optimization (GRPO) algorithm~\\cite{shao2024deepseekmath,guo2025deepseek} to enable stable and scalable reinforcement learning. This yields the \\rlmodel model.\n\nWe evaluate \\ours-series models on a wide range of table reasoning tasks, including short-form table QA, fact verification, and free-form table QA.\nOur experiments demonstrate the effectiveness of inference-time scaling for table reasoning.\nThe RLVR approach, in particular, exhibits better performance and generalization capabilities, compared to the distillation approach. \nNotably, our \\rlmodel models achieve performance that is competitive with advanced language models such as GPT 4.1 and DeepSeek R1, despite using only a 7B-parameter LLM (\\ie Qwen2.5-7B) as the backbone.\nWe further conduct comprehensive ablation studies on \\emph{instruction tuning benefits}, \\emph{model family comparisons}, and \\emph{cross-task generalization}, providing insights for future applications of inference-time scaling in table reasoning.\nOur qualitative analysis of model responses reveals that \\rlmodel not only acquires multi-step reasoning and reflection abilities like other reasoning models, but also develops essential table-specific reasoning skills such as semantic understanding, information extraction, and arithmetic computation.\n\n\n\\begin{figure}[!t]\n \\includegraphics[width=\\columnwidth]{figure/Table-R1.pdf}\n \\caption{An overview of our research and three research questions investigated in this study.}\n \\label{fig:overview}\n\\end{figure}\\section{Related Work}\n\\subsection{Inference-Time Scaling}\nRecently, OpenAI's o1 has demonstrated that scaling inference-time computation can significantly enhance the reasoning abilities of large language models (LLMs) on complex tasks~\\cite{jaech2024openai}. To leverage this, various inference-time strategies have been explored, including the use of Monte Carlo Tree Search (MCTS) for exploring diverse reasoning trajectories~\\cite{feng2023alphazero, qi2024mutual, guan2025rstar} and process reward models (PRMs) that offer step-level feedback to guide model outputs~\\cite{lightman2023let, yuan2024free}. In parallel, supervised fine-tuning (SFT) on reasoning traces has emerged as a practical post-training method, enabling LLMs to better align generation with explicit chain-of-thought reasoning patterns~\\cite{wen2025light, muennighoff2025s1, ye2025limo}.\n\n\nBeyond supervised approaches, recent work has introduced reinforcement learning from verifiable rewards (RLVR) as a promising post-training paradigm for LLM reasoning~\\cite{guo2025deepseek, team2025kimi, qwq32b}. In this setting, models are directly optimized with rule-based rewards, allowing them to autonomously discover effective reasoning strategies without explicit intermediate supervision. Subsequent studies have improved RLVR training by incorporating dynamic sampling, token-level policy gradients, and reward normalization to enhance training stability and sample efficiency~\\cite{yu2025dapo, liu2025understanding, xia2025mimo}. The RLVR paradigm has demonstrated strong generalization across diverse domains, including mathematical problem solving~\\cite{hu2025open, openr1}, logical reasoning games~\\cite{xie2025logic}, vision-based reasonings~\\cite{huang2025vision}, and interactive agent scenarios~\\cite{wang2025ragen, xia2025gui, feng2025retool}.\n\n\\subsection{Table Reasoning}\nReasoning over tabular data has long attracted attention due to its practical applications in real-world scenarios such as data analysis. It encompasses a variety of tasks, including short-form question answering~\\cite{pasupat-liang-2015-compositional, cheng-etal-2022-hitab, lu2023dynamic, zhao-etal-2023-robut, zhao-etal-2024-knowledgefmath, wu2025tablebench}, fact verification~\\cite{Chen2020TabFact, gupta-etal-2020-infotabs}, and free-form question answering~\\cite{nan-etal-2022-fetaqa, zhao-etal-2023-loft}.\nEarlier research primarily focused on fine-tuning smaller language models for specific tasks~\\cite{herzig-etal-2020-tapas, zhao-etal-2022-reastap, liu2022tapex}. \nMore recently, efforts such as TableLlama~\\cite{zhang-etal-2024-tablellama}, TableLLM~\\cite{zhang2025tablellm}, and the TableGPT series~\\cite{zha2023tablegpt, su2024tablegpt2} have advanced the adaptation of LLMs for table reasoning, enabling more general-purpose capabilities across task types. \nIn parallel, several studies have begun exploring agentic approaches to tackle table reasoning tasks~\\cite{10.1145/3539618.3591708, zhao-etal-2024-tapera, nan-etal-2024-evaluating, yu2025tablecriticmultiagentframeworkcollaborative}.\nDespite these advances, the application and enhancement of inference-time scaling for table reasoning remain largely underexplored. \nOur study shows that 7B-scale LLMs with inference-time scaling can match the performance of frontier models such as GPT-4.1. This finding highlights a promising direction for advancing table reasoning.\n\\section{\\ours Models}\nTo systematically explore inference scaling in table reasoning tasks, we develop two variants of \\ours model, each leveraging a widely adopted post-training strategy for inference-time scaling:\n(1) \\sftmodel, trained via supervised fine-tuning on reasoning traces generated by frontier reasoning LLMs, and\nand (2) \\rlmodel, trained using our developed RLVR approach tailored for table reasoning tasks.\nThe methodologies for each approach are detailed in the following subsections.\n\n\\subsection{Training Data Collection}\nWe construct the \\ours training dataset by integrating three representative table reasoning tasks, each introducing distinct reasoning challenges: (1) Short-form Table QA, which requires models to provide precise answers to questions grounded in tabular data; (2) Table Fact Verification, which requires models to determine whether a given claim is entailed by the table content; and (3) Free-form Table QA, which requires models to produce open-ended answers grounded in tabular information.\nEach task contributes unique reasoning challenges, and we select established datasets (presented in \\autoref{tab:training-data}) to ensure comprehensive coverage. \nAll datasets are preprocessed with the presence of verifiable ground truths for reward computation to align with the RLVR paradigm.\n\n\\begin{table}[!t]\n\t\\centering\n\t\\small\n \\setlength{\\tabcolsep}{1pt}\n \\resizebox{\\linewidth}{!}{\n\t\\begin{tabular}{llr}\n\t\t\\toprule\n\t\t\\textbf{Task} & \\textbf{Dataset} & \\textbf{Samples} \\\\\n\t\t\\midrule\n\t\t\\multirow{2}*{Short-form QA (TQA)} \n & WTQ~\\cite{pasupat-liang-2015-compositional} & 13,706 \\\\\n & HiTab~\\cite{cheng-etal-2022-hitab} & 6,793 \\\\\n\t\t\\midrule\n\t Fact Verification (TFV) & TabFact~\\cite{Chen2020TabFact} & 20,740 \\\\\n\t\t\\midrule\n\t\tFree-form QA (FF-TQA) & FeTaQA~\\cite{nan-etal-2022-fetaqa} & 7,324 \\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\t}\n\t\\caption{Overview of datasets collected in \\ours training data. For each dataset, we sample examples from its training set.}\n\t\\label{tab:training-data}\n\\end{table}\n\\subsection{\\sftmodel via Supervised Finetuning}\nTo enable inference-time scaling in \\sftmodel, we curate a new table reasoning dataset featuring long CoT reasoning.\nSpecifically, for each instance in the raw training data described in the previous subsection, we use DeepSeek-R1~\\cite{deepseekr1} to generate a long CoT response. The response consists of a step-by-step reasoning process followed by a final answer. We present the prompts for response generation in Appendix~\\ref{sec:appendix-prompt-template}.\nTo ensure the quality and correctness of the training data, we apply automated evaluators (detailed in Section~\\ref{sec:eval}) to assess the final answers. Examples with incorrect answers are filtered out. \nAfter this verification step, we obtain a total of 33,601 high-quality examples for SFT training.\nThis dataset is then used to train the \\sftmodel model.\n\n\n\\subsection{\\rlmodel via RL Training}\nWe describe the RLVR algorithm and the reward design for training \\rlmodel as follows.\n\n\n\n\\paragraph{Reinforcement Learning with Verifiable Rewards (RLVR).}\nWe adapt Group Relative Policy Optimization (GRPO) with recent improvements introduced by DAPO~\\cite{yu2025dapo}, including both token-level loss computation and asymmetric (decoupled) clipping. Notably, following recent work~\\cite{hu2025open, liu2025understanding, xia2025mimo}, we omit the KL penalty term present in the original GRPO~\\cite{jaech2024openai,pfister2025understanding}.\nWe formally define the RL algorithm applied in our study as follows: \n\nFor each input $(q, a)$, the policy $\\pi_\\theta$ samples a group of $G$ candidate responses $\\{o_i\\}_{i=1}^G$. Each response receives a reward $R_i$ as described in the next paragraph. \nThe group-normalized advantage for the $i$-th response at time step $t$ is:\n\n\\begin{small}\n\\begin{equation}\n\\hat{A}_{i,t} = \\frac{R_i - \\text{mean}(\\{R_j\\}_{j=1}^G)}{\\text{std}(\\{R_j\\}_{j=1}^G)}.\n\\end{equation}\n\\end{small}\n\n\\noindent Our objective is optimized at the token level with decoupled, asymmetric clipping:\n\n\\begin{small}\n\\begin{equation}\n\\begin{aligned}\n\\mathcal{J}_{\\text{GRPO}}(\\theta) \n&= \\mathbb{E}_{(q,a)\\sim \\mathcal{D},\\, \\{o_i\\}_{i=1}^G\\sim \\pi_{\\theta_\\text{old}}(\\cdot\\mid q)} \\\\\n&\\Bigg[\n \\frac{1}{\\sum_{i=1}^{G}|o_i|}\\sum_{i=1}^{G}\\sum_{t=1}^{|o_i|} \\min \\Big( r_{i,t}(\\theta) \\hat{A}_{i,t}, \\\\\n&\\text{clip}\\big(r_{i,t}(\\theta), 1-\\varepsilon_{\\text{low}}, 1+\\varepsilon_{\\text{high}}\\big)\\hat{A}_{i,t} \\Big)\n\\Bigg]\n\\end{aligned}\n\\label{eq:grpo}\n\\end{equation}\n\\end{small}\n\n\\noindent where the probability ratio $r_{i,t}(\\theta)$ is defined as:\n\n\\begin{small}\n\\begin{equation}\nr_{i,t}(\\theta) = \\frac{\\pi_\\theta(o_{i,t} \\mid q, o_{i,<t})}{\\pi_{\\theta_{\\text{old}}}(o_{i,t} \\mid q, o_{i,<t})}.\n\\end{equation}\n\\end{small}\n\n\\noindent This formulation enables stable and effective RL fine-tuning for table reasoning with LLMs.\n\n\n\\paragraph{Reward Design.}\nTo facilitate effective RL training, we design verifiable reward signals tailored to the characteristics of each table reasoning task. Our reward framework consists of two components: \\emph{accuracy rewards} and \\emph{format rewards}.\nAccuracy rewards measure the correctness of model outputs. We define task-specific reward functions as follows:\n\\begin{itemize} [leftmargin=*]\n\\itemsep0em\n \\item \\textbf{TQA}: The ground-truth is a short-answer list, where each element contains several words. We employ exact match to assign a reward of 1 for a correct answer and 0 otherwise.\n \\item \\textbf{TFV}: The ground-truth is either ``entailed'' or ``refuted''. The reward is 1 if the predicted label matches the ground-truth, and 0 otherwise.\n \\item \\textbf{FF-TQA}: The ground-truth is a sentence or paragraph. We use the average of normalized BLEU and ROUGE-L scores to reflect semantic overlap between model output and reference answer.\n\\end{itemize}\nThis combination of rule-based and metric-based evaluation ensures reward interpretability and robustness, mitigating instability and reward hacking.\n\nIn addition to \\emph{accuracy}, we encourage models—especially base models without instruction tuning—to follow a strict \\emph{response format} specified by a system prompt. We introduce a \\emph{cumulative format reward} incentivizing outputs that match the required template: <think>\\ldots</think> <answer>\\ldots</answer>, with the <answer> block containing a JSON snippet of the form \\{\"answer\": ...\\}. The format reward is computed by a deterministic regular expression-based checker, which assigns partial credit as outputs progressively satisfy structural requirements (\\eg tag presence, JSON structure, valid answer types), and awards full credit for strictly conformant outputs. This dense and interpretable reward guides base models to generate well-structured, verifiable responses.\\section{Experiment}\nIn this section, we describe the experimental setup and address three central research questions, presenting the findings associated with each.\n\n\\subsection{\\ours Model Training Details}\nAll models are trained using the verl framework. We initialize Table-R1 with Qwen2.5-7B-Instruct and Llama-3.1-8B-Instruct models. For Table-R1-SFT, training is conducted with a batch size of 256, a maximum sequence length of 20,480, and ulysses\\_sequence\\_parallel\\_size set to 4. The learning rate is set to $1\\times10^{-5}$, and training is performed for 3 epochs. For Table-R1-Zero, we use a batch size of 256 and 16 rollouts per prompt under the GRPO algorithm. It is trained for 2 epochs. The learning rate is fixed at $1\\times10^{-6}$, sampling temperature is 1.0, with a maximum prompt length of 4096 tokens and a maximum response length of 1024 tokens. The GRPO clipping parameters are set to $\\varepsilon_{\\text{low}}=0.2$ and $\\varepsilon_{\\text{high}}=0.28$. For validation during training, inference is performed with temperature 0.6 and top-p 1.0. All the experiments are conducted on 4 NVIDIA A100 80GB GPUs.\n\n\\begin{table*}[!t]\n\\label{tab:main-results} \n\\centering\n\n\\setlength{\\tabcolsep}{2pt} \n\n\\resizebox{\\textwidth}{!}{\n\\begin{tabular}{l*{13}{c}}\n\\toprule\n\n\\multirow{3}{*}{\\textbf{Model}} & \\multicolumn{4}{c}{\\textbf{In-domain Performance}} & \\multicolumn{9}{c}{\\textbf{Out-of-domain Performance}} \\\\\n\\cmidrule(lr){2-5} \\cmidrule(lr){6-14}\n\n& \\multicolumn{1}{c}{\\textbf{FF-TQA}} & \\multicolumn{1}{c}{\\textbf{TFV}} & \\multicolumn{2}{c}{\\textbf{TQA}} & \\multicolumn{3}{c}{\\textbf{FF-TQA}} & \\multicolumn{3}{c}{\\textbf{TFV}} & \\multicolumn{3}{c}{\\textbf{TQA}} \\\\\n\\cmidrule(lr){2-2} \\cmidrule(lr){3-3} \\cmidrule(lr){4-5} \\cmidrule(lr){6-8} \\cmidrule(lr){9-11} \\cmidrule(lr){12-14}\n\n& \\multicolumn{1}{c}{FeTaQA} & \\multicolumn{1}{c}{TabFact} & \\multicolumn{1}{c}{WTQ} & \\multicolumn{1}{c}{HiTab} & \\multicolumn{1}{c}{ToTTo} & \\multicolumn{1}{c}{QTSum} & \\multicolumn{1}{c}{R.W.} & \\multicolumn{1}{c}{InfoTabs} & \\multicolumn{1}{c}{PHT} & \\multicolumn{1}{c}{Feverous} & \\multicolumn{1}{c}{TMCQ} & \\multicolumn{1}{c}{TMWP} & \\multicolumn{1}{c}{FinQA} \\\\\n\\midrule\n\n\\multicolumn{14}{c}{\\textit{\\textbf{Proprietary Models}}} \\\\ \n\\addlinespace[0.5em]\nGPT-4.1 & 25.1 & 86.5 & 68.0 & 84.7 & 20.4 & 45.7 & 21.0 & 90.5 & 88.2 & 87.7 & 92.0 & 77.0 & 74.0 \\\\\nGPT-4.1 mini & 27.2 & 84.9 & 69.5 & 80.7 & 18.8 & 46.4 & 20.0 & 88.9 & 86.8 & 86.1 & 92.9 & 86.2 & 71.4 \\\\\n\\midrule\n\n\\multicolumn{14}{c}{\\textit{\\textbf{Open-source Models}}} \\\\\n\\addlinespace[0.5em]\n\n\\multicolumn{14}{l}{\\textit{LLMs}} \\\\\n\\quad Qwen2.5-7B & 21.0 & 72.2 & 54.8 & 61.8 & 16.0 & 39.5 & 19.3 & 78.6 & 70.7 & 74.6 & 87.4 & 85.0 & 66.4 \\\\\n\\quad Qwen2.5-32B & 21.9 & 90.3 & 77.3 & 79.4 & 17.8 & 41.4 & \\underline{20.0} & 90.5 & 86.7 & 79.2 & 92.1 & 95.8 & \\underline{77.3} \\\\\n\\quad DeepSeek-V3 & 24.7 & \\textbf{92.4} & 69.9 & \\underline{82.2} & 19.0 & \\textbf{46.2} & \\textbf{20.9} & \\textbf{91.9} & 86.2 & \\textbf{85.8} & 87.6 & 93.4 & \\textbf{78.6} \\\\\n\n\\addlinespace[0.2em]\n\\cdashlinelr{1-14}\n\\addlinespace[0.5em]\n\n\\multicolumn{14}{l}{\\textit{Reasoning LLMs}} \\\\\n\\addlinespace[0.2em]\n\\quad DeepSeek-R1-Distill-7B & 19.1 & 79.6 & 57.8 & 46.2 & 10.7 & 37.2 & 18.0 & 85.7 & 87.1 & 77.5 & 80.9 & 94.0 & 66.8 \\\\\n\\quad QwQ-32B & 23.8 & \\underline{91.5} & \\textbf{85.4} & 81.6 & 19.4 & 41.9 & 19.6 & \\underline{91.0} & 87.8 & 80.1 & 90.7 & \\textbf{99.4} & 76.2 \\\\\n\\quad DeepSeek-R1 & 26.2 & 90.8 & 79.6 & \\textbf{82.4} & 18.5 & \\underline{43.8} & 19.9 & 90.4 & 87.5 & 76.0 & \\textbf{93.3} & \\underline{99.0} & 75.8 \\\\\n\n\\addlinespace[0.2em]\n\\cdashlinelr{1-14}\n\\addlinespace[0.5em]\n\n\\multicolumn{14}{l}{\\textit{Table-Specific LLMs}} \\\\\n\\addlinespace[0.2em]\n\\quad $^{\\dagger}$TableBenchLLM (7B) & 3.1 & 27.1 & 38.8 & -- & 6.2 & -- & -- & -- & -- & 42.3 & -- & -- & -- \\\\\n\\quad $^{\\dagger}$TableLLM-13B & 10.8 & 69.0 & 66.3 & 6.3 & 5.4 & -- & -- & -- & -- & 21.5 & -- & -- & -- \\\\\n\\quad $^{\\dagger}$TableLlama (7B) & \\textbf{39.0} & 82.6 & 35.0 & 64.7 & 20.8 & -- & -- & -- & -- & 73.8 & -- & -- & -- \\\\ \n\\quad TableGPT2-7B & 29.0 & 77.8 & 61.4 & 70.3 & 14.1 & 39.0 & 19.0 & 85.4 & \\underline{89.1} & 78.0 & 77.2 & 79.7 & 66.4 \\\\ \n\\quad $^{\\dagger*}$TableGPT2-72B & 32.3 & 85.4 & 71.5 & 75.6 & \\textbf{22.7} & -- & -- & -- & -- & 76.8 & -- & -- & -- \\\\\n\\midrule\n\n\\multicolumn{14}{c}{\\textit{\\textbf{\\ours (Ours)}}} \\\\\n\\addlinespace[0.5em]\n\n\\multicolumn{14}{l}{\\textit{Llama-3.1 Series}} \\\\\n\\addlinespace[0.2em]\n\n\\quad Llama-3.1-8B-Instruct & 21.7 & 74.1 & 52.3 & 58.2 & 16.5 & 31.6 & 18.1 & 84.1 & 82.5 & 78.3 & 49.5 & 72.0 & 57.1 \\\\\n\\quad Table-R1-SFT & 26.0 & 91.1 & \\underline{83.8} & 81.8 & 13.7 & 36.6 & 16.6 & 89.8 & 85.8 & 79.4 & 90.8 & 89.0 & 64.3 \\\\\n\\quad Table-R1-Zero & \\underline{32.7} & 87.6 & 81.2 & 81.4 & \\underline{22.3} & 30.2 & 17.7 & 87.9 & \\textbf{91.6} & \\underline{80.2}& 68.6 & 84.6 & 62.3 \\\\ \n\n\\addlinespace[0.2em]\n\\cdashlinelr{1-14}\n\\addlinespace[0.5em]\n\\multicolumn{14}{l}{\\textit{Qwen2.5 Series}} \\\\\n\\addlinespace[0.2em]\n\n\\quad Qwen2.5-7B-Instruct & 21.0 & 72.2 & 54.8 & 61.8 & 16.0 & 39.5 & 19.3 & 78.6 & 70.7 & 74.6 & 87.4 & 85.0 & 66.4 \\\\\n\\quad Table-R1-SFT & 25.3 & 89.9 & 81.9 & 78.3 & 14.1 & 38.8 & 18.8 & 88.8 & 84.6 & 76.0 & 90.9 & 96.6 & 71.7 \\\\\n\\quad Table-R1-Zero & 30.6 & 87.6 & 79.8 & 78.1 & 19.8 & 43.1 & \\underline{20.0} & 83.7 & 88.0 & 76.2 & \\underline{93.0} & 96.4 & 70.8 \\\\\n\\bottomrule\n\\end{tabular}\n}\n\\caption{Results on 13 table reasoning benchmarks spanning TQA, TFV, and FF-TQA tasks. For TQA, EM accuracy is reported (with ambiguous cases re-evaluated by GPT-4.1 mini); for TFV, classification accuracy; for FF-TQA, BLEU and ROUGE-L. \\textbf{Bold} and \\underline{underlined} scores indicate the top-2 performances among \\emph{open-source} models.\n$^{\\dagger}$: Due to the context length limitations of most previous table-specific LLMs, it is challenging to conduct a fully fair comparison. Therefore, for these models, we directly use the results as reported in their respective papers, which may be based on sampled or filtered datasets. $^*$: Model weight has not been released.} \n\\label{tab:main-results}\n\\end{table*}\n\\subsection{Experiment Setup}\\label{sec:eval}\n\\paragraph{Evaluation Benchmarks.} \nTo address the lack of a unified evaluation framework for table reasoning, we introduce a new benchmark suite encompassing a wide range of datasets.\nFor \\emph{in-domain evaluation}, we use test sets from the same distributions as the training data: WTQ~\\cite{pasupat-liang-2015-compositional} and HiTab~\\cite{cheng-etal-2022-hitab} for TQA; TabFact~\\cite{Chen2020TabFact} for TFV; and FeTaQA~\\cite{nan-etal-2022-fetaqa} for FF-TQA. \nTo assess generalization of \\ours, we further conduct \\emph{out-of-domain evaluation} on datasets that are not seen during training: TabMCQ~\\cite{jauhar2016tabmcq}, TabMWP~\\cite{lu2023dynamic}, and FinQA~\\cite{chen-etal-2021-finqa} for TQA; InfoTabs~\\cite{gupta-etal-2020-infotabs}, PubHealthTab~\\cite{akhtar-etal-2022-pubhealthtab}, and Feverous~\\cite{aly2021feverous} for TFV; ToTTo~\\cite{parikh-etal-2020-totto}, QTSumm~\\cite{zhao-etal-2023-qtsumm}, and RotoWire~\\cite{wiseman-etal-2017-challenges} for FF-TQA. \nWe provide detailed descriptions of the \\textbf{evaluated baseline systems} in Appendix~\\ref{app:exp-setup}.\n\n\\paragraph{Automated Evaluation System.}\nFor each evaluated dataset, we use its test set for evaluation.\nFor the TQA task, we report Exact Match (EM) accuracy. For TFV, we use classification accuracy. For FF-TQA, we measure generation quality with BLEU and ROUGE-L scores. Considering that EM accuracy in short-answer TQA may underestimate model performance due to formatting variations or semantically equivalent but non-exact matches, we further re-evaluate responses initially marked incorrect by EM using the GPT-4.1 mini model, with the prompt shown in Appendix~\\ref{sec:appendix-prompt-llm-as-a-judge}.\n\n\\begin{tcolorbox}[colback=gray!10, colframe=gray!50!white, width=\\linewidth, boxrule=0.5mm, arc=1mm, outer arc=1mm, left=2mm, right=2mm]\n\\textcolor{orange!50!yellow}{\\faLightbulb} \\xspace \\textbf{RQ1:} How effective is inference-time scaling for table reasoning tasks?\n\\end{tcolorbox}\n\n\\noindent To address RQ1, we conduct a comprehensive evaluation and summarize the key findings from the results presented in \\autoref{tab:main-results} as follows:\n\n\\subsection{Main Findings}\n\\paragraph{General-Purpose LLMs vs. Reasoning LLMs.}\nWe evaluate three pairs of general-purpose LLMs—with and without inference-time scaling capabilities—on table reasoning tasks: Qwen2.5-7B vs. DeepSeek-R1-Distill-7B, Qwen2.5-32B vs. QwQ-32B, and DeepSeek-V3 vs. DeepSeek-R1. While reasoning LLMs tend to outperform their counterparts on the TQA benchmark, their results on TFV and FF-TQA are mixed and not consistently superior. This indicates that inference-time scaling alone, without table-specific training, does not provide a clear advantage for table reasoning tasks. These findings underscore the importance of specialized adaptation strategies, such as Table-R1, for effective performance in this domain.\n\n\\paragraph{\\ours In-Domain Performance.}\nBoth \\sftmodel and \\rlmodel achieve substantial improvements across all tasks. Specifically, Table-R1-Zero-8B obtains a BLEU score of 32.7 on FeTaQA for FF-TQA, significantly surpassing the best among other models of 26.2 from Deepseek-R1; Table-R1-SFT-8B reaches 91.1 accuracy on TabFact for TFV, closely matching the leading 91.9; and for TQA, our models achieve 83.8 and 81.8 on WTQ and HiTab, respectively, which are comparable to the best scores among other models of 85.4 and 84.7. These results consistently demonstrate that Table-R1 models deliver robust gains and competitive performance across diverse table reasoning scenarios, validating the effectiveness and versatility of both SFT and RLVR training strategies.\n\n\\paragraph{\\ours Out-of-Domain Performance.}\nTable-R1 models exhibit strong generalization capabilities on out-of-domain datasets. Across most out-of-domain benchmarks, our models consistently outperform their respective initial baselines, demonstrating the effectiveness of both SFT and R1-Zero training methods for table reasoning. Notably, Table-R1-Zero-7B achieves the best overall generalization among all variants. In contrast, we observe that supervised fine-tuning (SFT) leads to weaker generalization compared to the R1-Zero method, and models initialized from Llama tend to generalize less effectively than those based on Qwen. These results highlight the advantage of our approach in improving table reasoning robustness beyond the training distribution.\n\n\\begin{figure}[!t]\n \\includegraphics[width=\\columnwidth]{figure/model_response_length.pdf}\n \\caption{Response length during Table-R1 training across different models.}\n \\label{fig:training-dynamics-response}\n\\end{figure}\\begin{figure*}[!t]\n \\includegraphics[width=0.48\\linewidth]{figure/HiTab_accuracy_score.pdf} \\hfill\n \\includegraphics[width=0.48\\linewidth]{figure/TabFact_accuracy_score.pdf} \\hfill\n \\includegraphics[width=0.48\\linewidth]{figure/WTQ_accuracy_score.pdf} \\hfill\n \\includegraphics[width=0.48\\linewidth]{figure/FeTaQA_Bleu_score.pdf}\n\\caption{Accuracy and BLEU score dynamics across four table reasoning datasets during RLVR training. Results are shown for all four Table-R1-Zero models, which are trained from Qwen2.5 7B or Llama-3.1 8B as initialization.}\n \\label{fig:training-dynamics-accuracy}\n\\end{figure*}\\begin{tcolorbox}[colback=gray!10, colframe=gray!50!white, width=\\linewidth, boxrule=0.5mm, arc=1mm, outer arc=1mm, left=2mm, right=2mm]\n\\textcolor{orange!50!yellow}{\\faLightbulb} \\xspace \\textbf{RQ2:} What contributes to the success of RLVR methods in table reasoning tasks?\n\\end{tcolorbox}\n\n\\noindent To address this research question, we present a comprehensive analysis of \\rlmodel in the following three subsections: the training dynamics, a qualitative assessment of model responses, and an exploration of the reasoning capacity boundaries.\n\n\\subsection{Analysis of Training Dynamics}\\label{sec:dynamics}\nWe conduct a detailed analysis of the training dynamics exhibited by our Table-R1 models across various model backbones, including Qwen2.5 7B and Llama-3.1 8B, under both \\textit{base} and \\textit{instruct} configurations.\n\n\\autoref{fig:training-dynamics-response} presents the evolution of \\textit{response length} throughout reinforcement learning. Notably, the base models consistently start with longer responses compared to their instruct counterparts. During the initial stage of RL training, we observe a sharp drop in response length, corresponding to a phase of format acquisition—where the model learns to produce outputs adhering to the expected answer format. Subsequently, response length gradually increases, with base models exhibiting a more pronounced growth trajectory than instruct models. Among all four model variants, Table-R1-Zero-Qwen2.5-7B-Instruct demonstrates the greatest stability, showing a smooth and moderate increase in response length. In contrast, Table-R1-Zero-Llama-3.1-8B displays considerable instability, ultimately failing to acquire the desired response format. We attribute this to the weaker instruction-following capabilities of the Llama-3.1-8B base model.\n\n\\autoref{fig:training-dynamics-accuracy} illustrates the progression of \\textit{model accuracy} over the course of RL training. We find that instruct models consistently achieve higher accuracy than their base versions throughout training. While base models start from a lower performance baseline, they undergo a phase of format adaptation, after which their accuracy trends converge with those of instruct models. Interestingly, although Table-R1-Zero-Llama-3.1-8B exhibits unstable changes in response length, it still demonstrates performance gains on most tasks, with the exception of TabFact—a binary classification task.\n\nOverall, these observations reveal that instruction tuning provides significant benefits in both stability and performance during RLVR training for table reasoning tasks. The results further suggest that initial format alignment and instruction-following capability play critical roles in the successful adaptation of large language models to structured reasoning scenarios.\n\n\\subsection{Reasoning Capacity Boundary}\n\\begin{figure}[!t]\n\\includegraphics[width=0.9\\columnwidth]{figure/pass_at_k_wtq.pdf}\n \\includegraphics[width=0.9\\columnwidth]{figure/pass_at_k_hitab.pdf}\n \\caption {Pass@k performance on WTQ and HiTab.}\n \\label{fig:passk}\n\\end{figure}\\begin{figure*}[!t]\n \\includegraphics[width=\\linewidth]{figure/main_example.pdf}\n \\caption{Illustration of the model's reasoning progression across training steps. The example demonstrates how reasoning quality evolves from superficial processing (Step 0), to partial column-aware reasoning (Step 180), and finally to accurate multi-step inference with semantic and arithmetic understanding (Step 378).}\n \\label{fig:qualitative-analysis}\n\\end{figure*}\nUnderstanding the upper limits of a model's reasoning ability is crucial for evaluating the true impact of RLVR on tabular reasoning tasks. Inspired by prior studies on the boundaries of RLVR method~\\cite{yue2025does}, we employ the \\textit{pass@k} metric to quantify the model's capacity. Specifically, pass@k measures the probability that at least one out of $k$ generated responses is correct, given a fixed input prompt. This metric is particularly well-suited for our setting, as it captures not only the model's accuracy, but also its ability to produce diverse and plausible reasoning trajectories within a limited number of attempts.\n\nWe systematically evaluate pass@k for $k$ up to 32, both before and after RLVR training, on the HiTab and WTQ datasets. As illustrated in \\autoref{fig:passk}, RLVR training leads to a notable increase in pass@k values throughout the evaluated range. This improvement is consistent across different datasets and model architectures, demonstrating that RLVR enhances not only the likelihood of obtaining a correct answer on first attempt (pass@1), but also the breadth of valid reasoning paths the model can explore in a small sampling budget.\n\n\\begin{table*}[!t]\n\\label{tab:ablation-studies}\n\\centering\n\\setlength{\\tabcolsep}{2pt}\n\n\\resizebox{\\textwidth}{!}{\n\\begin{tabular}{l*{13}{c}}\n\\toprule\n\\multirow{3}{*}{\\textbf{Model}} & \\multicolumn{4}{c}{\\textbf{In-domain Performance}} & \\multicolumn{9}{c}{\\textbf{Out-of-domain Performance}} \\\\\n\\cmidrule(lr){2-5} \\cmidrule(lr){6-14}\n\n& \\multicolumn{1}{c}{\\textbf{FF-TQA}} & \\multicolumn{1}{c}{\\textbf{TFV}} & \\multicolumn{2}{c}{\\textbf{TQA}} & \\multicolumn{3}{c}{\\textbf{FF-TQA}} & \\multicolumn{3}{c}{\\textbf{TFV}} & \\multicolumn{3}{c}{\\textbf{TQA}} \\\\\n\\cmidrule(lr){2-2} \\cmidrule(lr){3-3} \\cmidrule(lr){4-5} \\cmidrule(lr){6-8} \\cmidrule(lr){9-11} \\cmidrule(lr){12-14}\n\n& \\multicolumn{1}{c}{FeTaQA} & \\multicolumn{1}{c}{TabFact} & \\multicolumn{1}{c}{WTQ} & \\multicolumn{1}{c}{HiTab} & \\multicolumn{1}{c}{ToTTo} & \\multicolumn{1}{c}{QTSum} & \\multicolumn{1}{c}{R.W.} & \\multicolumn{1}{c}{InfoTabs} & \\multicolumn{1}{c}{PHT} & \\multicolumn{1}{c}{Feverous} & \\multicolumn{1}{c}{TMCQ} & \\multicolumn{1}{c}{TMWP} & \\multicolumn{1}{c}{FinQA} \\\\\n\\midrule\n\n\\multicolumn{14}{c}{\\textit{\\textbf{Model-level Ablation}}} \\\\\n\\addlinespace[0.5em]\n\n\\multicolumn{14}{l}{\\textit{Base vs. Instruct}} \\\\\n\\addlinespace[0.2em]\nQwen2.5-7B & 18.2 & 67.7 & 51.0 & 55.2 & 9.4 & 37.8 & 16.4 & 74.5 & 75.0 & 74.1 & 78.7 & 80.3 & 58.4 \\\\\nTable-R1-Qwen2.5-7B & 29.8 & 87.3 & 78.5 & 75.2 & 15.9 & 38.1 & 18.2 & 87.9 & 91.6 & 78.4 & 84.8 & 94.4 & 61.2 \\\\\nQwen2.5-7B-Instruct & 21.0 & 72.2 & 54.8 & 61.8 & 16.0 & 39.5 & 19.3 & 78.6 & 70.7 & 74.6 & 87.4 & 85.0 & 66.4 \\\\\nTable-R1-Qwen2.5-7B-Instruct & 30.6 & 87.6 & 79.8 & 78.1 & 19.8 & 43.1 & 20.0 & 83.7 & 88.0 & 76.2 & 93.0 & 96.4 & 70.8 \\\\\n\n\\addlinespace[0.2em]\n\\cdashlinelr{1-14}\n\\addlinespace[0.5em]\n\n\\multicolumn{14}{l}{\\textit{Model Architecture Comparison}} \\\\\n\\addlinespace[0.2em]\nLlama-3.1-8B & 10.2 & 21.2 & 35.0 & 44.7 & 6.8 & 18.6 & 10.9 & 21.6 & 26.8 & 19.4 & 68.9 & 43.1 & 21.9 \\\\\nTable-R1-Llama-3.1-8B & 30.4 & 50.3 & 61.5 & 76.3 & 19.8 & 33.5 & 18.0 & 50.0 & 69.7 & 50.6 & 93.1 & 56.8 & 19.3 \\\\\nLlama-3.1-8B-Instruct & 21.7 & 74.1 & 52.3 & 58.2 & 16.5 & 31.6 & 18.1 & 84.1 & 82.5 & 78.3 & 49.5 & 72.0 & 57.1 \\\\\nTable-R1-Llama-3.1-8B-Instruct & 32.7 & 87.6 & 81.2 & 81.4 & 22.3 & 30.2 & 17.7 & 87.9 & 91.6 & 80.2 & 68.6 & 84.6 & 62.3 \\\\\n\n\\addlinespace[0.2em]\n\\cdashlinelr{1-14}\n\\addlinespace[0.5em]\n\n\\multicolumn{14}{l}{\\textit{SFT on Domain-Specific Data}} \\\\\n\\addlinespace[0.2em]\nDeepSeek-R1-7B & 19.1 & 79.6 & 57.8 & 46.2 & 10.7 & 37.2 & 18.0 & 85.7 & 87.1 & 77.5 & 80.9 & 94.0 & 66.8 \\\\\nTable-SFT-Qwen2.5-7B & 25.3 & 89.9 & 81.9 & 78.3 & 14.1 & 38.8 & 18.8 & 88.8 & 84.6 & 76.0 & 90.9 & 96.6 & 71.7 \\\\\nTable-SFT-Llama-3.1-8B & 26.0 & 91.1 & 83.8 & 81.8 & 13.7 & 36.6 & 16.6 & 89.8 & 85.8 & 79.4 & 90.8 & 89.0 & 64.3 \\\\\n\\midrule\n\n\\multicolumn{14}{c}{\\textit{\\textbf{Task-level Ablation}}} \\\\\n\\addlinespace[0.5em]\n\\multicolumn{14}{l}{\\textit{Cross-task Generalization}} \\\\\n\\addlinespace[0.2em]\nTable-R1-TQA-Qwen2.5-7B & 14.1 & 86.2 & 79.2 & 77.4 & 16.5 & 36.4 & 19.8 & 89.7 & 89.9 & 82.7 & 93.5 & 96.1 & 65.9 \\\\\nTable-R1-TQA-Llama-3.1-8B & 12.3 & 83.9 & 81.7 & 81.0 & 17.2 & 26.9 & 19.2 & 87.3 & 87.2 & 77.1 & 80.3 & 82.8 & 58.6 \\\\\nTable-SFT-TQA-Qwen2.5-7B & 13.3 & 87.7 & 79.8 & 77.0 & 13.4 & 27.6 & 18.1 & 88.5 & 87.1 & 78.0 & 90.1 & 95.7 & 69.0 \\\\\nTable-SFT-TQA-Llama-3.1-8B & 9.6 & 88.8 & 82.4 & 80.5 & 10.3 & 20.6 & 12.6 & 89.1 & 90.4 & 82.1 & 89.9 & 86.9 & 62.9 \\\\\n\\midrule\n\n\\multicolumn{14}{c}{\\textit{\\textbf{Format Ablation}}} \\\\\n\\addlinespace[0.5em]\n\\multicolumn{14}{l}{\\textit{Effect of Format Reward}} \\\\\n\\addlinespace[0.2em]\nTable-R1-Explicit-Qwen2.5-7B & 29.0 & 85.8 & 76.0 & 73.2 & 13.6 & 37.2 & 14.8 & 87.5 & 88.8 & 81.0 & 52.5 & 91.3 & 49.6 \\\\\nTable-R1-Qwen2.5-7B & 30.6 & 87.6 & 79.8 & 78.1 & 19.8 & 43.1 & 20.0 & 83.7 & 88.0 & 76.2 & 93.0 & 96.4 & 70.8 \\\\\n\\addlinespace[0.3em]\n\\bottomrule\n\\end{tabular}\n}\n\\caption{Ablation study results on model-level, task-level, and formats.}\n\\end{table*}\\subsection{Qualitative Analysis}\nTo gain deeper insights into how RLVR shapes model behavior, we conduct a qualitative analysis of model responses throughout the training process. By examining the same set of representative prompts across different RL training steps, we observe that the model not only internalizes general R1-style reasoning characteristic, but also acquires table-specific reasoning abilities critical for tabular tasks, as illustrated in \\autoref{fig:qualitative-analysis} and Appendix~\\ref{app:example}.\n\nOn the \\textbf{reasoning} axis, we observe clear progress toward sophisticated, multi-step reasoning: after RL training, the \\rlmodel model decomposes complex queries into sequential sub-tasks, explicitly outlining intermediate steps and sometimes ``rethinking'' earlier conclusions to check or correct errors. Such reflective patterns, rare at initialization, become prevalent with training, suggesting the verifiable reward encourages explicit, auditable reasoning.\n\nOn the \\textbf{table-specific} axis, we observe notable improvements in three key areas. First, the model develops column-aware reasoning: it accurately identifies and references relevant table columns, often justifying its answer with explicit column mentions or by highlighting how information from multiple columns is synthesized. Second, the model demonstrates enhanced semantic understanding of natural language questions, especially in TQA settings. It is able to correctly interpret nuanced question intents (\\eg comparative, aggregative, or conditional queries) and map them to the corresponding table structures. Third, we note a marked increase in arithmetic and temporal reasoning capabilities. The model becomes more adept at performing arithmetic calculations over table entries and reasoning over temporal sequences, both of which are crucial for table reasoning tasks.\n\n\n\\subsection{Ablation Studies on RLVR Training}\n\\begin{tcolorbox}[colback=gray!10, colframe=gray!50!white, width=\\linewidth, boxrule=0.5mm, arc=1mm, outer arc=1mm, left=2mm, right=2mm]\n\\textcolor{orange!50!yellow}{\\faLightbulb} \\xspace \\textbf{RQ3:} How do various factors influence the effectiveness of RLVR training in table reasoning tasks?\n\\end{tcolorbox}\n\n\\noindent To better understand the contributions of both SFT and RLVR methods and to assess the robustness of Table-R1 across different configurations, we conduct extensive ablation studies from three perspectives: model-level, task-level, and explicit reasoning format. We detail our findings as follows.\n\n\\paragraph{Model-level Ablation.}\nWe first analyze the effect of model initialization by comparing base and instruct variants. Across all settings, instruct models consistently outperform their base counterparts. We attribute this to the enhanced instruction-following ability inherent to instruct models, which enables faster adaptation to the explicit reasoning formats required by table tasks and leads to more stable training dynamics. \n\nWe next compare representative model architectures—Qwen2.5 and Llama-3.1. Under identical training regimes, Llama-3.1-8B-Instruct achieves superior in-domain performance on table reasoning benchmarks compared to Qwen2.5-7B-Instruct, indicating a stronger capacity for learning table-specific reasoning. However, Qwen2.5 demonstrates better out-of-domain generalization, suggesting that model architecture and pretraining data may influence the balance between in-domain accuracy and cross-domain robustness.\n\nFurthermore, we evaluate the effect of distillation data by comparing our SFT models—fine-tuned on table-specific DeepSeek-R1 distillation data—against the official DeepSeek-R1 distilled models. Our models not only surpass the official versions, but also outperform larger-scale distilled models. This underscores the effectiveness of domain-specific fine-tuning and the importance of high-quality, task-aligned training data.\n\n\\paragraph{Task-level Ablation.}\nTo investigate cross-task generalization, we train Table-R1 exclusively on the TQA dataset and evaluate its performance on TFV and FF-TQA tasks. Interestingly, models trained solely on TQA exhibit notable performance gains on TFV, indicating that the reasoning capabilities required for TFV are closely aligned with those developed for short-answer TQA. In contrast, no significant improvement is observed on FF-TQA, likely due to the distinct reasoning and answer generation skills required for free-form responses, which are not adequately covered by TQA training. These results highlight the varying degrees of transferability among table reasoning tasks and emphasize the need for targeted training to achieve robust generalization.\n\n\\paragraph{Format Ablation.}\nWe assess the role of explicit reasoning format supervision by removing the format reward during training. This ablation reduces training stability and slightly lowers in-domain performance. More notably, generalization suffers: while TFV scores may improve, performance on short-answer TQA and FF-TQA declines, sometimes even below baseline, indicating that format supervision is crucial for transferable reasoning.\nWe also test inference-time control by restricting the initial output token to \\texttt{<think>} or \\texttt{<answer>}. However, this constraint does not alter the model’s internal reasoning or task performance, showing that inference-time decoding tricks cannot substitute for training with flexible reasoning formats.\\section{Conclusion}\nThis work presents the first comprehensive study on applying inference-time scaling to table reasoning tasks. \nThrough extensive evaluation across 13 diverse benchmarks, we demonstrate that inference-time scaling enables substantial improvements in reasoning quality, with RLVR methods yielding stronger generalization to out-of-domain tasks. \nAblation studies confirm the benefits of instruction tuning, model architecture choice, and task composition in enhancing training effectiveness. Qualitative analysis reveals that RLVR fosters the emergence of structured, multi-step reasoning and table-specific capabilities. We hope this work paves the way for future research in structured reasoning.\\section*{Limitations}\nSeveral limitations remain that warrant future investigation:\nThe SFT data was generated exclusively using DeepSeek-R1. Additionally, the data verification and filtering processes may have inadvertently removed difficult or high-quality training examples. Future research could explore incorporating outputs from other reasoning LLMs, such as QwQ-32B, to enhance distillation performance and data diversity.\nFurthermore, in Section~\\ref{sec:dynamics}, we observe that models initialized from the LLaMA-3.1-8B backbone exhibit unstable training dynamics during RLVR fine-tuning, including inconsistent acquisition of the desired output format and significant fluctuations in response length. While instruct-tuned variants mitigate some of these issues, the underlying causes of instability—such as sensitivity to initialization, reward sparsity, or optimization hyperparameters—remain underexplored.\nFuture work could investigate strategies to improve the robustness and generalizability of RLVR for structured reasoning tasks.\n\\section*{Acknowledgments}\nWe would like to thank Zijie Zhou for making the TQA-Distill-R1 dataset\\footnote{\\url{https://huggingface.co/datasets/jared-zhou/TQA-Distill-R1}} publicly available, which we used in our early experiments.\n\n\n% Entries for the entire Anthology, followed by custom entries\n\\bibliography{anthology, custom}\n\n\\newpage\n\\appendix\n\n\\clearpage\n\\onecolumn\n\\section{Information of Evaluated Table Reasoning Datasets}\n\\label{sec:appendix-dataset}\n\\begin{table*}[ht]\n\\centering\n\\small\n\\resizebox{\\textwidth}{!}{\n\\begin{tabular}{lllll}\n\\toprule\n\\textbf{Task Category} & \\textbf{Task Name} & \\textbf{Dataset} & \\textbf{Task Description} & \\textbf{Metric} \\\\\n\\midrule\n\\multirow{4}{*}{Training}\n & Table QA & WTQ, HiTab & QA over flat or hierarchical tables & Acc. \\\\\n & Free-form QA & FeTaQA & Generate long-form answers from tables & BLEU \\\\\n & Table Fact Verification & TabFact & Verify factual correctness based on a table & Acc. \\\\\n\\midrule\n\\multirow{4}{*}{In-Domain}\n & Table QA & WTQ, HiTab & In-domain table QA evaluation & Acc. \\\\\n & Table Fact Verification & TabFact & In-domain fact verification evaluation & Acc.\\\\\n & Free-form QA & FeTaQA & Evaluate long-form generation quality & BLEU \\\\\n\\midrule\n\\multirow{4}{*}{Out-of-Domain}\n & Table QA & TabMCQ, TMWP, FinQA & Multiple-choice and word problem QA & Acc. \\\\\n & Numerical Reasoning QA & FinQA, TABMWP & Financial and numerical table QA & Acc. \\\\\n & Table Fact Verification & InfoTabs, PHT, Feverous & Health and factual judgment from tables & Acc. \\\\\n & Free-form QA & ToTTo, Qtsumm, R.W. & Table-to-text generation from highlights & BLEU/R-L \\\\\n\\bottomrule\n\\end{tabular}\n}\n\\caption{Overview of datasets used in training and evaluation.}\n\\label{tab:our_dataset}\n\\end{table*}\n\n\n\\section{Experiment Setup}\\label{app:exp-setup}\n\\subsection{Baseline Systems}\nWe benchmark our approach against a comprehensive set of strong baselines, encompassing both proprietary and open-source models. Among proprietary models, we include GPT-4.1 and GPT-4.1 mini. For open-source baselines, we evaluate general purpose LLMs from the Qwen2.5 and LLaMA3 series, reasoning models such as Deepseek-R1, its official distilled variant, our SFT model distilled from Deepseek-R1 on table reasoning data (as described in Section~3.2), and QwQ-32B, as well as table-oriented models including TableLlama, TableLLM, and TableBenchLLM. Additional evaluation details, including specific model configurations and prompt templates, are provided in Appendix~\\ref{sec:appendix-eval}.\n\n\\clearpage\n\\section{Evaluation Details}\n\\label{sec:appendix-eval}\nAdditional evaluation details, including inference setup, evaluated model configurations and prompt templates.\n\n\\subsection{Inference Setup}\nFor all open-source models, inference is performed using the vLLM framework, while for closed-source models, the official OpenAI API is utilized. The maximum output length is set to 2048 tokens for most models. However, for reasoning models, this limit is increased to 18,000 tokens to accommodate their long chain-of-thought generation. The temperature is set to 0.6 and the top-p value to 0.95. All inference processes are conducted on four NVIDIA A100-80G GPUs.\n\n\\subsection{Evaluated Model Configuration}\n\\begin{table*}[h]\n \\centering\n \\small\n \\begin{tabular}{lll}\n \\toprule\n \\textbf{Model} & \\textbf{Citation} & \\textbf{Version} \\\\ \\midrule\n\n GPT-4.1 & \\citet{openai2024gpt4o} & gpt-4.1-2025-04-14 \\\\\n GPT-4.1 mini & \\citet{openai2024gpt4o} & gpt-4.1-mini-2025-04-14 \\\\ \\midrule\n\n Qwen2.5-7B & \\citet{qwen2.5} & \\href{https://huggingface.co/Qwen/Qwen2.5-7B-Instruct}{Qwen/Qwen2.5-7B-Instruct} \\\\\n Qwen2.5-14B & \\citet{qwen2.5} & \\href{https://huggingface.co/Qwen/Qwen2.5-14B-Instruct}{Qwen/Qwen2.5-14B-Instruct} \\\\\n Qwen2.5-32B & \\citet{qwen2.5} & \\href{https://huggingface.co/Qwen/Qwen2.5-32B-Instruct}{Qwen/Qwen2.5-32B-Instruct} \\\\\n QwQ-32B & \\citet{qwq32b} & \\href{https://huggingface.co/Qwen/QwQ-32B}{Qwen/QwQ-32B} \\\\\n\n Llama-3.1-8B & \\citet{touvron2023llama} & \\href{https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct}{meta-llama/Llama-3.1-8B-Instruct} \\\\ \\midrule\n\n DeepSeek-R1-7B & \\citet{guo2025deepseek} & \\href{https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B}{deepseek-ai/DeepSeek-R1-Distill-Qwen-7B} \\\\\n DeepSeek-R1-14B & \\citet{guo2025deepseek} & \\href{https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B}{deepseek-ai/DeepSeek-R1-Distill-Qwen-14B} \\\\\n DeepSeek-R1-32B & \\citet{guo2025deepseek} & \\href{https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B}{deepseek-ai/DeepSeek-R1-Distill-Qwen-32B} \\\\\n DeepSeek-R1 & \\citet{guo2025deepseek} & \\href{hhttps://huggingface.co/deepseek-ai/DeepSeek-R1}{deepseek-ai/DeepSeek-R1} \\\\ \\midrule\n TableGPT2-7B & \\citet{su2024tablegpt2} & \\href{https://huggingface.co/tablegpt/TableGPT2-7B}{TableGPT/TableGPT2-7B} \\\\\n TableLLM-13B & \\citet{zhang2024tablellm} & \\href{https://huggingface.co/RUCKBReasoning/TableLLM-13b}{RUCKBReasoning/TableLLM-13B} \\\\ \n TableLlama-7B & \\citet{zhang2023tablellama} & \\href{https://huggingface.co/osunlp/TableLlama}{osunlp/TableLlama} \\\\ \n TableBenchLLM & \\citet{zhang2024tablellm} & \\href{https://huggingface.co/Multilingual-Multimodal-NLP/TableLLM-Llama3.1-8B}{Multilingual-Multimodal-NLP/TableLLM-Llama3.1-8B} \\\\ \n \n \\bottomrule\n \\end{tabular}\n \\caption{Model List.}\n \\label{tab:model_list}\n\\end{table*}\n\n\\subsection{Prompts}\n\\label{sec:appendix-prompt-template}\n\\begin{tcolorbox}[colback=black!3!white, colframe=black!70!white, title=System prompt used to guide structured response generation, fontupper=\\footnotesize, fonttitle=\\footnotesize]\nA conversation between User and Assistant. The user asks a question, and the assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think> <answer> answer here </answer>.\n\\end{tcolorbox}\n\n\n \\begin{tcolorbox}[colback=black!3!white, colframe=black!70!white, title=Prompt Template for TQA, fontupper=\\footnotesize, fonttitle=\\footnotesize]\n \\textbf{Instruction:} This is a short-answer table QA task. Answer the question based on the provided table. \\\\\n\n \\textbf{Table} \\\\\n Table Title: \\texttt{\\{table\\_title\\}} \\\\\n Table Content: \\texttt{\\{table\\_repr (markdown / html)\\}} \\\\\n\n \\textbf{Question:} \\texttt{\\{question\\}} \\\\\n\n \\textbf{Answer Format:} \\\\\n The final answer should be concise and use the following format:\n\\begin{verbatim}\n```json\n{\n \"answer\": [\n \"answer1\",\n \"answer2\",\n ...\n ]\n}\n```\n\\end{verbatim}\n\\end{tcolorbox}\n\n\n \\begin{tcolorbox}[colback=black!3!white, colframe=black!70!white, title=Prompt Template for TFV, fontupper=\\footnotesize, fonttitle=\\footnotesize]\n \\textbf{Instruction:} This is a table fact verification task. The goal is to determine whether the given statement is entailed or refuted by the table. \\\\\n\n \\textbf{Table} \\\\\n Table Title: \\texttt{\\{table\\_title\\}} \\\\\n Table Content: \\texttt{\\{table\\_repr (markdown / html)\\}} \\\\\n\n \\textbf{Statement:} \\texttt{\\{statement\\}} \\\\\n\n \\textbf{Answer Format:} \\\\\n The final answer should be either \"entailed\" or \"refuted\" and use the following format:\n\\begin{verbatim}\n```json\n{\n \"answer\": \"entailed\" or \"refuted\"\n}\n```\n\\end{verbatim}\n\\end{tcolorbox}\n\n \\begin{tcolorbox}[colback=black!3!white, colframe=black!70!white, title=Prompt Template for Free-Form TQA, fontupper=\\footnotesize, fonttitle=\\footnotesize]\n \\textbf{Instruction:} This is a free-form table QA task. Answer the question based on the provided table. \\\\\n\n \\textbf{Table} \\\\\n Table Title: \\texttt{\\{table\\_title\\}} \\\\\n Table Content: \\texttt{\\{table\\_repr (markdown / html)\\}} \\\\\n\n \\textbf{Question:} \\texttt{\\{question\\}} \\\\\n\n \\textbf{Answer Format:} \\\\\n The final answer should be a sentence and use the following format:\n\\begin{verbatim}\n```json\n{\n \"answer\": \"your_generated_sentence_here\"\n}\n```\n\\end{verbatim}\n\\end{tcolorbox}\n\n\n \\begin{tcolorbox}[colback=black!3!white, colframe=black!70!white, title=Prompt Template for LLM-as-a-Judge, fontupper=\\footnotesize, fonttitle=\\footnotesize]\n You are given two answers for a short-answer Table QA task: \\textbf{response} and \\textbf{ground\\_truth}. \\\\\n\n - \\textbf{response}: This is the LLM's answer to the task. It may include reasoning steps and a final answer. \\\\\n - \\textbf{ground\\_truth}: A list of short answers, typically 2-3 word noun phrases or numbers. \\\\\n\n Your task is to determine whether the response is fully correct, using these rules: \\\\\n - \\textbf{Noun phrases}: Considered correct if meaning matches ground\\_truth regardless of wording. \\\\\n - \\textbf{Numbers}: Considered correct if numerically close (tolerance < 0.01). \\\\\n - Every ground\\_truth item must be matched in the response. Order doesn't matter. \\\\\n\nYour output must be in the following format:\n\\begin{verbatim}\n```json\n{\n \"judgement\": \"correct\" or \"incorrect\"\n}\n```\n\\end{verbatim}\nDo not provide any explanation or additional output.\\\\\n\n\\textbf{Input:} \\\\\n\\textbf{Response:} \\{response\\} \\\\\n\\textbf{Ground\\_truth:} \\{ground\\_truth\\} \\\\\n\n\nEvaluate and output the judgement.\n\\end{tcolorbox}\n\\label{sec:appendix-prompt-llm-as-a-judge}\n\n\\clearpage\n\\section{Qualitative Analysis Cases}\\label{app:example}\n\n\\begin{figure*}[h]\n \\includegraphics[width=\\linewidth]{figure/example1.pdf}\n \\caption{Qualitative Analysis Case Example for TQA tasks}\n\\end{figure*}\n\n\\begin{figure}[h]\n \\includegraphics[width=\\linewidth]{figure/example2.pdf}\n \\caption{Qualitative Analysis Case Example for TFV tasks}\n\\end{figure}\n\n\\end{document}\n"},"question":{"kind":"string","value":"Do the Table-Specific LLMs show more wins than Reasoning LLMs on harder or easier benchmarks?"},"answer":{"kind":"string","value":"Harder"},"plan":{"kind":"string","value":"LOOP for each test set\n COMPUTE argmax model with highest score\n COMPUTE add to win-list: model, score \nCOMPUTE sort win-list by score, divide in high-score-win, and low-score-win\nSELECT reasoning models\nLOOP for each reasoning model\n COMPUTE add to reasoning-high-win-counter count(model in high-score-win)\n COMPUTE add to reasoning-low-win-counter count(model in low-score-win)\nLOOP for each table-specific model\n COMPUTE add to table-specific-high-win-counter count(model in high-score-win)\n COMPUTE add to table-specific-low-win-counter count(model in low-score-win)\nIF table-specific-high-win-counter > reasoning-high-win-counter AND table-specific-high-win-counter < reasoning-high-win-counter\n RETURN “easier”\nIF table-specific-high-win-counter < reasoning-high-win-counter AND table-specific-high-win-counter > reasoning-high-win-counter\n RETURN “harder”\nIF table-specific-high-win-counter > reasoning-high-win-counter AND table-specific-high-win-counter > reasoning-high-win-counter\n RETURN “both”\nIF table-specific-high-win-counter < reasoning-high-win-counter AND table-specific-high-win-counter < reasoning-high-win-counter\n RETURN “neither”\n"}}},{"rowIdx":68,"cells":{"paper":{"kind":"string","value":"2505.21505"},"relevant_tables":{"kind":"list like","value":[["\\begin{table*}[htbp]\n","\\centering\n","\\small\n","\\setlength{\\tabcolsep}{4pt}\n","\\renewcommand{\\arraystretch}{1.2}\n","\\resizebox{0.9\\columnwidth}{!}{\n","\\begin{tabular}{l|cccccccccc|c}\n","\\toprule\n","\\textbf{Tested on MGSM} & \\textbf{bn} & \\textbf{th} & \\textbf{sw} & \\textbf{ja} & \\textbf{zh} & \\textbf{ru} & \\textbf{de} & \\textbf{es} & \\textbf{fr} & \\textbf{en} & \\textbf{Avg.} \\\\\n","\\midrule\n","base & 43.6 & 53.2 & 50.4 & 55.6 & 59.6 & 59.2 & 61.2 & 62.8 & 56.8 & 75.6 & 57.8 \\\\\n","zh $\\Rightarrow$ en & 49.6 & 58.4 & 54.8 & 56.4 & 65.2 & 70.0 & 66.4 & 72.8 & 68.8 & 78.4 & 64.1 \\\\\n","zh/de $\\Rightarrow$ en & 46.4 & 55.6 & 59.2 & 56.8 & 64.0 & 71.2 & 66.8 & 71.2 & 69.2 & 75.2 & 63.6 \\\\\n","sw/th $\\Rightarrow$ en & 48.8 & 58.8 & 59.2 & 56.4 & 68.4 & 68.4 & 69.2 & 69.6 & 70.4 & 77.6 & 64.7 \\\\\n","zh/es/ru $\\Rightarrow$ en & 46.0 & 56.4 & 58.8 & 54.8 & 63.2 & 70.8 & 68.8 & 71.6 & 69.6 & 76.8 & 63.7 \\\\\n","zh/es/fr/ja/de/sw/ru/th/bn $\\Rightarrow$ en & 49.6 & 60.0 & 56.4 & 56.4 & 64.4 & 64.8 & 65.2 & 65.2 & 61.2 & 76.4 & 62.0 \\\\\n","\\bottomrule\n","\\toprule\n","\\textbf{Tested on MSVAMP} & \\textbf{bn} & \\textbf{th} & \\textbf{sw} & \\textbf{ja} & \\textbf{zh} & \\textbf{ru} & \\textbf{de} & \\textbf{es} & \\textbf{fr} & \\textbf{en} & \\textbf{Avg.} \\\\\n","\\midrule\n","base & 49.3 & 62.5 & 60.6 & 60.9 & 67.4 & 64.9 & 66.5 & 67.6 & 67.2 & 77.0 & 64.4 \\\\\n","zh $\\Rightarrow$ en & 52.8 & 62.5 & 60.9 & 63.7 & 66.6 & 67.5 & 69.4 & 69.8 & 69.7 & 76.5 & 65.9 \\\\\n","zh/de $\\Rightarrow$ en & 53.2 & 62.0 & 62.9 & 65.3 & 66.1 & 68.7 & 69.9 & 69.0 & 70.1 & 77.0 & 66.4 \\\\\n","sw/th $\\Rightarrow$ en & 53.8 & 67.4 & 65.1 & 67.7 & 71.9 & 71.4 & 71.9 & 72.0 & 72.8 & 78.9 & 69.3 \\\\\n","zh/es/ru $\\Rightarrow$ en & 52.9 & 63.2 & 61.5 & 63.0 & 67.8 & 68.1 & 69.8 & 68.7 & 70.6 & 78.3 & 66.4 \\\\\n","zh/es/fr/ja/de/sw/ru/th/bn $\\Rightarrow$ en & 54.9 & 65.9 & 65.6 & 68.6 & 69.7 & 70.2 & 72.3 & 71.6 & 71.6 & 77.3 & 68.8 \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\caption{Accuracy of the base model and aligned variants on benchmarks.}\n","\\label{tab:spontaneous_result_all_mistral}\n","\\end{table*}\n"]],"string":"[\n [\n \"\\\\begin{table*}[htbp]\\n\",\n \"\\\\centering\\n\",\n \"\\\\small\\n\",\n \"\\\\setlength{\\\\tabcolsep}{4pt}\\n\",\n \"\\\\renewcommand{\\\\arraystretch}{1.2}\\n\",\n \"\\\\resizebox{0.9\\\\columnwidth}{!}{\\n\",\n \"\\\\begin{tabular}{l|cccccccccc|c}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Tested on MGSM} & \\\\textbf{bn} & \\\\textbf{th} & \\\\textbf{sw} & \\\\textbf{ja} & \\\\textbf{zh} & \\\\textbf{ru} & \\\\textbf{de} & \\\\textbf{es} & \\\\textbf{fr} & \\\\textbf{en} & \\\\textbf{Avg.} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"base & 43.6 & 53.2 & 50.4 & 55.6 & 59.6 & 59.2 & 61.2 & 62.8 & 56.8 & 75.6 & 57.8 \\\\\\\\\\n\",\n \"zh $\\\\Rightarrow$ en & 49.6 & 58.4 & 54.8 & 56.4 & 65.2 & 70.0 & 66.4 & 72.8 & 68.8 & 78.4 & 64.1 \\\\\\\\\\n\",\n \"zh/de $\\\\Rightarrow$ en & 46.4 & 55.6 & 59.2 & 56.8 & 64.0 & 71.2 & 66.8 & 71.2 & 69.2 & 75.2 & 63.6 \\\\\\\\\\n\",\n \"sw/th $\\\\Rightarrow$ en & 48.8 & 58.8 & 59.2 & 56.4 & 68.4 & 68.4 & 69.2 & 69.6 & 70.4 & 77.6 & 64.7 \\\\\\\\\\n\",\n \"zh/es/ru $\\\\Rightarrow$ en & 46.0 & 56.4 & 58.8 & 54.8 & 63.2 & 70.8 & 68.8 & 71.6 & 69.6 & 76.8 & 63.7 \\\\\\\\\\n\",\n \"zh/es/fr/ja/de/sw/ru/th/bn $\\\\Rightarrow$ en & 49.6 & 60.0 & 56.4 & 56.4 & 64.4 & 64.8 & 65.2 & 65.2 & 61.2 & 76.4 & 62.0 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Tested on MSVAMP} & \\\\textbf{bn} & \\\\textbf{th} & \\\\textbf{sw} & \\\\textbf{ja} & \\\\textbf{zh} & \\\\textbf{ru} & \\\\textbf{de} & \\\\textbf{es} & \\\\textbf{fr} & \\\\textbf{en} & \\\\textbf{Avg.} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"base & 49.3 & 62.5 & 60.6 & 60.9 & 67.4 & 64.9 & 66.5 & 67.6 & 67.2 & 77.0 & 64.4 \\\\\\\\\\n\",\n \"zh $\\\\Rightarrow$ en & 52.8 & 62.5 & 60.9 & 63.7 & 66.6 & 67.5 & 69.4 & 69.8 & 69.7 & 76.5 & 65.9 \\\\\\\\\\n\",\n \"zh/de $\\\\Rightarrow$ en & 53.2 & 62.0 & 62.9 & 65.3 & 66.1 & 68.7 & 69.9 & 69.0 & 70.1 & 77.0 & 66.4 \\\\\\\\\\n\",\n \"sw/th $\\\\Rightarrow$ en & 53.8 & 67.4 & 65.1 & 67.7 & 71.9 & 71.4 & 71.9 & 72.0 & 72.8 & 78.9 & 69.3 \\\\\\\\\\n\",\n \"zh/es/ru $\\\\Rightarrow$ en & 52.9 & 63.2 & 61.5 & 63.0 & 67.8 & 68.1 & 69.8 & 68.7 & 70.6 & 78.3 & 66.4 \\\\\\\\\\n\",\n \"zh/es/fr/ja/de/sw/ru/th/bn $\\\\Rightarrow$ en & 54.9 & 65.9 & 65.6 & 68.6 & 69.7 & 70.2 & 72.3 & 71.6 & 71.6 & 77.3 & 68.8 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Accuracy of the base model and aligned variants on benchmarks.}\\n\",\n \"\\\\label{tab:spontaneous_result_all_mistral}\\n\",\n \"\\\\end{table*}\\n\"\n ]\n]","isConversational":false},"tables":{"kind":"list like","value":[["\\begin{figure*}[tbp]\n"," \\centering \n"," \\begin{minipage}[b]{0.4\\textwidth}\n"," \\includegraphics[width=1.0\\linewidth]{Images/language_specific_neurons_heatmap_Mistral_Base.pdf}\n"," \\subcaption{Base - Language-Specific Neurons}\n"," \\label{fig:mistral-mgsm-base-language-specific-heatmap}\n"," \\end{minipage}\n"," \\begin{minipage}[b]{0.4\\textwidth}\n"," \\includegraphics[width=1.0\\linewidth]{Images/language_specific_neurons_heatmap_Mistral_RL.pdf}\n"," \\subcaption{Aligned - Language-Specific Neurons}\n"," \\label{fig:mistral-mgsm-aligned-language-specific-heatmap}\n"," \\end{minipage}\n"," \\begin{minipage}[b]{0.4\\textwidth}\n"," \\includegraphics[width=1.0\\linewidth]{Images/language_neurons_heatmap_Mistral_Base.pdf}\n"," \\subcaption{Base - Language Neurons}\n"," \\label{fig:mistral-mgsm-base-language-heatmap}\n"," \\end{minipage}\n"," \\begin{minipage}[b]{0.4\\textwidth}\n"," \\includegraphics[width=1.0\\linewidth]{Images/language_neurons_heatmap_Mistral_RL.pdf}\n"," \\subcaption{Aligned - Language Neurons}\n"," \\label{fig:mistral-mgsm-aligned-language-heatmap}\n"," \\end{minipage}\n"," \\caption{PPL changes of MistralMathOctopus on MGSM after deactivating language-specific neurons or language neurons. ``Base'' indicates the results of the base model. ``Aligned'' indicates the results of the aligned model. For comparison, the results of~\\citet{tang2024language} are provided in Appendix~\\ref{app:heatmap}.}\n"," \\label{fig:mistral-mgsm-heatmap}\\\n"," \\vspace{-15pt}\n","\\end{figure*}\n"],["\\begin{figure*}[tbp]\n"," \\centering \n"," \\includegraphics[width=0.6\\linewidth]{Images/layer_wise_neurons.pdf}\n"," \\caption{Layer-wise distribution of the different types of neurons of MistralMathOctopus on MGSM.}\n"," \\label{fig:layer-wise-neurons-distribution}\n"," \\vspace{-10pt}\n","\\end{figure*}\n"],["\\begin{figure}[tbp]\n"," \\centering \n"," \\includegraphics[width=0.7\\textwidth]{Images/related_language_num.pdf}\n"," \\caption{Changes in the number of neurons shared by $N$ languages after alignment.}\n"," \\label{fig:Related_Language_Num}\n","\\end{figure}\n"],["\\begin{table*}[tbp]\n","\\centering\n","\\small\n","\\setlength{\\tabcolsep}{4pt}\n","\\renewcommand{\\arraystretch}{1.1}\n","\\caption{Accuracy of the MistralMathOctopus base model and aligned model on MGSM. \"X/Y $\\Rightarrow$ T\" indicates that languages X and Y are used for multilingual alignment.}\n","\\resizebox{0.9\\columnwidth}{!}{\n","\\begin{tabular}{l|cccccccccc|c}\n","\\toprule\n","\\textbf{MGSM} & \\textbf{bn} & \\textbf{th} & \\textbf{sw} & \\textbf{ja} & \\textbf{zh} & \\textbf{ru} & \\textbf{de} & \\textbf{es} & \\textbf{fr} & \\textbf{en} & \\textbf{Avg.} \\\\\n","\\midrule\n","base & 43.6 & 53.2 & 50.4 & 55.6 & 59.6 & 59.2 & 61.2 & 62.8 & 56.8 & 75.6 & 57.8 \\\\\n","zh/de $\\Rightarrow$ en & 46.4 & 55.6 & 59.2 & 56.8 & 64.0 & 71.2 & 66.8 & 71.2 & 69.2 & 75.2 & 63.6 \\\\\n","sw/th $\\Rightarrow$ en & 48.8 & 58.8 & 59.2 & 56.4 & 68.4 & 68.4 & 69.2 & 69.6 & 70.4 & 77.6 & 64.7 \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\label{tab:spontaneous_result}\n","\\vspace{-12pt}\n","\\end{table*}\n"],["\\begin{figure}[tbp]\n"," \\begin{minipage}{0.48\\textwidth}\n"," \\centering\n"," \\small\n"," \\setlength{\\tabcolsep}{1pt}\n"," \\renewcommand{\\arraystretch}{1.2}\n"," \\captionof{table}{Average number of different types of neurons for English and non-English languages of MistralMathOctopus on MGSM. We round the results to the nearest integer.}\n"," \\resizebox{\\columnwidth}{!}{\n"," \\begin{tabular}{lcc}\n"," \\toprule\n"," \\textbf{Language} & \\textbf{Language-Specific} & \\textbf{Language-Related} \\\\\n"," \\midrule\n"," English & 46 & 603 \\\\\n"," non-English & 613 & 2006 \\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," }\n"," \\label{tab:English-nonEnglish-neuron-count}\n"," \\end{minipage}\n"," \\hfill\n"," \\begin{minipage}{0.48\\textwidth}\n"," \\centering\n"," \\small\n"," \\setlength{\\tabcolsep}{1pt}\n"," \\renewcommand{\\arraystretch}{1.2}\n"," \\captionof{table}{Overlap ratio of different types of neurons across \\textit{different domains} and \\textit{before and after alignment}. Following~\\citet{she2024mapo}, MSVAMP is regarded as an out-of-domain dataset. The results of MistralMathOctopus on MGSM are used as the fiducial value.}\n"," \\resizebox{\\columnwidth}{!}{\n"," \\begin{tabular}{lcc}\n"," \\toprule\n"," \\textbf{Variable~(\\%)} & \\textbf{Language-Specific} & \\textbf{Language-Related} \\\\\n"," \\midrule\n"," Domain & 80.7 & 92.3 \\\\\n"," Alignment & 95.6 & 92.1 \\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," }\n"," \\label{tab:neuron-distribution-stability-overlap}\n"," \\end{minipage}\n","\\vspace{-10pt}\n","\\end{figure}\n"],["\\begin{figure*}[htbp]\n"," \\centering \n"," \\begin{minipage}[b]{0.41\\textwidth}\n"," \\includegraphics[width=1.0\\linewidth]{Images/heatmap_baseline_Mistral_Base.pdf}\n"," \\subcaption{Base - Language-Specific Neurons}\n"," \\label{fig:mistral-mgsm-baseline-base-heatmap}\n"," \\end{minipage}\n"," \\begin{minipage}[b]{0.41\\textwidth}\n"," \\includegraphics[width=1.0\\linewidth]{Images/heatmap_baseline_Mistral_RL.pdf}\n"," \\subcaption{Aligned - Language-Specific Neurons}\n"," \\label{fig:mistral-mgsm-baseline-aligned-heatmap}\n"," \\end{minipage}\n"," \\caption{PPL changes of MistralMathOctopus on MGSM when deploying the algorithm proposed by~\\citet{tang2024language}.}\n"," \\label{fig:baseline-heatmap}\\\n"," \\vspace{-10pt}\n","\\end{figure*}\n"],["\\begin{figure*}[htbp]\n"," \\centering \n"," \\begin{minipage}[b]{0.41\\textwidth}\n"," \\includegraphics[width=1.0\\linewidth]{Images/language_specific_neurons_heatmap_Mistral_Msvamp_Base.pdf}\n"," \\subcaption{Base - Language-Specific Neurons}\n"," \\label{fig:mistral-msvamp-base-language-specific-heatmap}\n"," \\end{minipage}\n"," \\begin{minipage}[b]{0.41\\textwidth}\n"," \\includegraphics[width=1.0\\linewidth]{Images/language_specific_neurons_heatmap_Mistral_Msvamp_RL.pdf}\n"," \\subcaption{Aligned - Language-Specific Neurons}\n"," \\label{fig:mistral-msvamp-aligned-language-specific-heatmap}\n"," \\end{minipage}\n"," \\begin{minipage}[b]{0.41\\textwidth}\n"," \\includegraphics[width=1.0\\linewidth]{Images/language_neurons_heatmap_Mistral_Msvamp_Base.pdf}\n"," \\subcaption{Base - Language Neurons}\n"," \\label{fig:mistral-msvamp-base-language-heatmap}\n"," \\end{minipage}\n"," \\begin{minipage}[b]{0.41\\textwidth}\n"," \\includegraphics[width=1.0\\linewidth]{Images/language_neurons_heatmap_Mistral_Msvamp_RL.pdf}\n"," \\subcaption{Aligned - Language Neurons}\n"," \\label{fig:mistral-msvamp-aligned-language-heatmap}\n"," \\end{minipage}\n"," \\caption{PPL changes of MistralMathOctopus on MSVAMP after deactivating language-specific neurons or language neurons. ``Base'' indicates the results of the base model. ``Aligned'' indicates the results of the aligned model.}\n"," \\label{fig:mistral-msvamp-heatmap}\\\n"," \\vspace{-10pt}\n","\\end{figure*}\n"],["\\begin{figure*}[htbp]\n"," \\centering \n"," \\begin{minipage}[b]{0.41\\textwidth}\n"," \\includegraphics[width=1.0\\linewidth]{Images/language_neurons_heatmap_Llama_Base.pdf}\n"," \\subcaption{Base - Language Neurons on MGSM}\n"," \\label{fig:llama-mgsm-base-language-heatmap}\n"," \\end{minipage}\n"," \\begin{minipage}[b]{0.41\\textwidth}\n"," \\includegraphics[width=1.0\\linewidth]{Images/language_neurons_heatmap_Llama_RL.pdf}\n"," \\subcaption{Aligned - Language Neurons on MGSM}\n"," \\label{fig:llama-mgsm-aligned-language-heatmap}\n"," \\end{minipage}\n"," \\caption{PPL changes of MetaMathOctopus on MGSM after deactivating language neurons. ``Base'' indicates the results of the base model. ``Aligned'' indicates the results of the aligned model.}\n"," \\label{fig:llama-mgsm-msvamp-heatmap}\\\n"," \\vspace{-10pt}\n","\\end{figure*}\n"],["\\begin{figure*}[htbp]\n"," \\centering \n","\n"," \\begin{subfigure}[b]{0.47\\textwidth}\n"," \\includegraphics[width=0.9\\linewidth]{Images/layer_wise_neurons_mistral_MSVAMP.pdf}\n"," \\caption{MistralMathOctopus on MSVAMP.}\n"," \\label{fig:layer-wise-neurons-distribution_MSVAMP}\n"," \\end{subfigure}\n"," \\hspace{10pt}\n"," \\begin{subfigure}[b]{0.47\\textwidth}\n"," \\includegraphics[width=0.9\\linewidth]{Images/layer_wise_neurons_llama_mgsm.pdf}\n"," \\caption{MetaMathOctopus on MGSM.}\n"," \\label{fig:layer-wise-neurons-distribution_MGSM_llama}\n"," \\end{subfigure}\n"," \\caption{Layer-wise distribution of the different types of neurons.}\n","\\end{figure*}\n"],["\\begin{figure*}[htbp]\n"," \\centering \n"," \\includegraphics[width=0.52\\textwidth]{Images/delta_layer_wise_neurons_llama_mgsm.pdf}\n"," \\caption{Layer-wise changes in the number of MetaMathOctopus}\n"," \\label{fig:Layer-wise_changes_llama}\n","\\end{figure*}\n"],["\\begin{figure*}[htbp]\n"," \\centering\n"," \\begin{subfigure}[b]{0.45\\textwidth}\n"," \\includegraphics[width=\\linewidth]{Images/layer_wise_delta_specific_neurons_mistral_mgsm.pdf}\n"," \\caption{MGSM}\n"," \\end{subfigure}\n"," \\hspace{10pt}\n"," \\begin{subfigure}[b]{0.45\\textwidth}\n"," \\includegraphics[width=\\linewidth]{Images/layer_wise_delta_related_neurons_mistral_MSVAMP.pdf}\n"," \\caption{MSVAMP}\n"," \\end{subfigure}\n"," \\vspace{0.5em}\n"," \\caption{Layer-wise changes in the number of language-specific neurons of MistralMathOctopus during the alignment.}\n"," \\label{fig:Layer-wise_changes_specific}\n","\\end{figure*}\n"],["\\begin{figure*}[htbp]\n"," \\centering \n"," \\begin{subfigure}[b]{0.45\\textwidth}\n"," \\includegraphics[width=\\linewidth]{Images/layer_wise_delta_related_neurons_mistral_mgsm.pdf}\n"," \\caption{MGSM}\n"," \\end{subfigure}\n"," \\hspace{10pt}\n"," \\begin{subfigure}[b]{0.45\\textwidth}\n"," \\includegraphics[width=\\linewidth]{Images/layer_wise_delta_related_neurons_mistral_MSVAMP.pdf}\n"," \\caption{MSVAMP}\n"," \\end{subfigure}\n"," \\vspace{0.5em}\n"," \\caption{Layer-wise changes in the number of language-related neurons of MistralMathOctopus during the alignment.}\n"," \\label{fig:Layer-wise_changes_related}\n","\\end{figure*}\n"],["\\begin{figure*}[htbp]\n"," \\centering \n"," \\begin{subfigure}[b]{0.45\\textwidth}\n"," \\includegraphics[width=\\linewidth]{Images/layer_wise_delta_agnostic_neurons_mistral_mgsm.pdf}\n"," \\caption{MGSM}\n"," \\end{subfigure}\n"," \\hspace{10pt}\n"," \\begin{subfigure}[b]{0.45\\textwidth}\n"," \\includegraphics[width=\\linewidth]{Images/layer_wise_delta_agnostic_neurons_mistral_MSVAMP.pdf}\n"," \\caption{MSVAMP}\n"," \\end{subfigure}\n"," \\vspace{0.5em}\n"," \\caption{Layer-wise changes in the number of language-agnostic neurons of MistralMathOctopus during the alignment.}\n"," \\label{fig:Layer-wise_changes_agnostic}\n","\\end{figure*}\n"],["\\begin{table*}[htbp]\n","\\centering\n","\\small\n","\\setlength{\\tabcolsep}{4pt}\n","\\renewcommand{\\arraystretch}{1.2}\n","\\resizebox{0.9\\columnwidth}{!}{\n","\\begin{tabular}{l|cccccccccc|c}\n","\\toprule\n","\\textbf{Tested on MGSM} & \\textbf{bn} & \\textbf{th} & \\textbf{sw} & \\textbf{ja} & \\textbf{zh} & \\textbf{ru} & \\textbf{de} & \\textbf{es} & \\textbf{fr} & \\textbf{en} & \\textbf{Avg.} \\\\\n","\\midrule\n","base & 43.6 & 53.2 & 50.4 & 55.6 & 59.6 & 59.2 & 61.2 & 62.8 & 56.8 & 75.6 & 57.8 \\\\\n","zh $\\Rightarrow$ en & 49.6 & 58.4 & 54.8 & 56.4 & 65.2 & 70.0 & 66.4 & 72.8 & 68.8 & 78.4 & 64.1 \\\\\n","zh/de $\\Rightarrow$ en & 46.4 & 55.6 & 59.2 & 56.8 & 64.0 & 71.2 & 66.8 & 71.2 & 69.2 & 75.2 & 63.6 \\\\\n","sw/th $\\Rightarrow$ en & 48.8 & 58.8 & 59.2 & 56.4 & 68.4 & 68.4 & 69.2 & 69.6 & 70.4 & 77.6 & 64.7 \\\\\n","zh/es/ru $\\Rightarrow$ en & 46.0 & 56.4 & 58.8 & 54.8 & 63.2 & 70.8 & 68.8 & 71.6 & 69.6 & 76.8 & 63.7 \\\\\n","zh/es/fr/ja/de/sw/ru/th/bn $\\Rightarrow$ en & 49.6 & 60.0 & 56.4 & 56.4 & 64.4 & 64.8 & 65.2 & 65.2 & 61.2 & 76.4 & 62.0 \\\\\n","\\bottomrule\n","\\toprule\n","\\textbf{Tested on MSVAMP} & \\textbf{bn} & \\textbf{th} & \\textbf{sw} & \\textbf{ja} & \\textbf{zh} & \\textbf{ru} & \\textbf{de} & \\textbf{es} & \\textbf{fr} & \\textbf{en} & \\textbf{Avg.} \\\\\n","\\midrule\n","base & 49.3 & 62.5 & 60.6 & 60.9 & 67.4 & 64.9 & 66.5 & 67.6 & 67.2 & 77.0 & 64.4 \\\\\n","zh $\\Rightarrow$ en & 52.8 & 62.5 & 60.9 & 63.7 & 66.6 & 67.5 & 69.4 & 69.8 & 69.7 & 76.5 & 65.9 \\\\\n","zh/de $\\Rightarrow$ en & 53.2 & 62.0 & 62.9 & 65.3 & 66.1 & 68.7 & 69.9 & 69.0 & 70.1 & 77.0 & 66.4 \\\\\n","sw/th $\\Rightarrow$ en & 53.8 & 67.4 & 65.1 & 67.7 & 71.9 & 71.4 & 71.9 & 72.0 & 72.8 & 78.9 & 69.3 \\\\\n","zh/es/ru $\\Rightarrow$ en & 52.9 & 63.2 & 61.5 & 63.0 & 67.8 & 68.1 & 69.8 & 68.7 & 70.6 & 78.3 & 66.4 \\\\\n","zh/es/fr/ja/de/sw/ru/th/bn $\\Rightarrow$ en & 54.9 & 65.9 & 65.6 & 68.6 & 69.7 & 70.2 & 72.3 & 71.6 & 71.6 & 77.3 & 68.8 \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\caption{Accuracy of the base model and aligned variants on benchmarks.}\n","\\label{tab:spontaneous_result_all_mistral}\n","\\end{table*}\n"]],"string":"[\n [\n \"\\\\begin{figure*}[tbp]\\n\",\n \" \\\\centering \\n\",\n \" \\\\begin{minipage}[b]{0.4\\\\textwidth}\\n\",\n \" \\\\includegraphics[width=1.0\\\\linewidth]{Images/language_specific_neurons_heatmap_Mistral_Base.pdf}\\n\",\n \" \\\\subcaption{Base - Language-Specific Neurons}\\n\",\n \" \\\\label{fig:mistral-mgsm-base-language-specific-heatmap}\\n\",\n \" \\\\end{minipage}\\n\",\n \" \\\\begin{minipage}[b]{0.4\\\\textwidth}\\n\",\n \" \\\\includegraphics[width=1.0\\\\linewidth]{Images/language_specific_neurons_heatmap_Mistral_RL.pdf}\\n\",\n \" \\\\subcaption{Aligned - Language-Specific Neurons}\\n\",\n \" \\\\label{fig:mistral-mgsm-aligned-language-specific-heatmap}\\n\",\n \" \\\\end{minipage}\\n\",\n \" \\\\begin{minipage}[b]{0.4\\\\textwidth}\\n\",\n \" \\\\includegraphics[width=1.0\\\\linewidth]{Images/language_neurons_heatmap_Mistral_Base.pdf}\\n\",\n \" \\\\subcaption{Base - Language Neurons}\\n\",\n \" \\\\label{fig:mistral-mgsm-base-language-heatmap}\\n\",\n \" \\\\end{minipage}\\n\",\n \" \\\\begin{minipage}[b]{0.4\\\\textwidth}\\n\",\n \" \\\\includegraphics[width=1.0\\\\linewidth]{Images/language_neurons_heatmap_Mistral_RL.pdf}\\n\",\n \" \\\\subcaption{Aligned - Language Neurons}\\n\",\n \" \\\\label{fig:mistral-mgsm-aligned-language-heatmap}\\n\",\n \" \\\\end{minipage}\\n\",\n \" \\\\caption{PPL changes of MistralMathOctopus on MGSM after deactivating language-specific neurons or language neurons. ``Base'' indicates the results of the base model. ``Aligned'' indicates the results of the aligned model. For comparison, the results of~\\\\citet{tang2024language} are provided in Appendix~\\\\ref{app:heatmap}.}\\n\",\n \" \\\\label{fig:mistral-mgsm-heatmap}\\\\\\n\",\n \" \\\\vspace{-15pt}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[tbp]\\n\",\n \" \\\\centering \\n\",\n \" \\\\includegraphics[width=0.6\\\\linewidth]{Images/layer_wise_neurons.pdf}\\n\",\n \" \\\\caption{Layer-wise distribution of the different types of neurons of MistralMathOctopus on MGSM.}\\n\",\n \" \\\\label{fig:layer-wise-neurons-distribution}\\n\",\n \" \\\\vspace{-10pt}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{figure}[tbp]\\n\",\n \" \\\\centering \\n\",\n \" \\\\includegraphics[width=0.7\\\\textwidth]{Images/related_language_num.pdf}\\n\",\n \" \\\\caption{Changes in the number of neurons shared by $N$ languages after alignment.}\\n\",\n \" \\\\label{fig:Related_Language_Num}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table*}[tbp]\\n\",\n \"\\\\centering\\n\",\n \"\\\\small\\n\",\n \"\\\\setlength{\\\\tabcolsep}{4pt}\\n\",\n \"\\\\renewcommand{\\\\arraystretch}{1.1}\\n\",\n \"\\\\caption{Accuracy of the MistralMathOctopus base model and aligned model on MGSM. \\\"X/Y $\\\\Rightarrow$ T\\\" indicates that languages X and Y are used for multilingual alignment.}\\n\",\n \"\\\\resizebox{0.9\\\\columnwidth}{!}{\\n\",\n \"\\\\begin{tabular}{l|cccccccccc|c}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{MGSM} & \\\\textbf{bn} & \\\\textbf{th} & \\\\textbf{sw} & \\\\textbf{ja} & \\\\textbf{zh} & \\\\textbf{ru} & \\\\textbf{de} & \\\\textbf{es} & \\\\textbf{fr} & \\\\textbf{en} & \\\\textbf{Avg.} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"base & 43.6 & 53.2 & 50.4 & 55.6 & 59.6 & 59.2 & 61.2 & 62.8 & 56.8 & 75.6 & 57.8 \\\\\\\\\\n\",\n \"zh/de $\\\\Rightarrow$ en & 46.4 & 55.6 & 59.2 & 56.8 & 64.0 & 71.2 & 66.8 & 71.2 & 69.2 & 75.2 & 63.6 \\\\\\\\\\n\",\n \"sw/th $\\\\Rightarrow$ en & 48.8 & 58.8 & 59.2 & 56.4 & 68.4 & 68.4 & 69.2 & 69.6 & 70.4 & 77.6 & 64.7 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\label{tab:spontaneous_result}\\n\",\n \"\\\\vspace{-12pt}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{figure}[tbp]\\n\",\n \" \\\\begin{minipage}{0.48\\\\textwidth}\\n\",\n \" \\\\centering\\n\",\n \" \\\\small\\n\",\n \" \\\\setlength{\\\\tabcolsep}{1pt}\\n\",\n \" \\\\renewcommand{\\\\arraystretch}{1.2}\\n\",\n \" \\\\captionof{table}{Average number of different types of neurons for English and non-English languages of MistralMathOctopus on MGSM. We round the results to the nearest integer.}\\n\",\n \" \\\\resizebox{\\\\columnwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lcc}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Language} & \\\\textbf{Language-Specific} & \\\\textbf{Language-Related} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" English & 46 & 603 \\\\\\\\\\n\",\n \" non-English & 613 & 2006 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \" \\\\label{tab:English-nonEnglish-neuron-count}\\n\",\n \" \\\\end{minipage}\\n\",\n \" \\\\hfill\\n\",\n \" \\\\begin{minipage}{0.48\\\\textwidth}\\n\",\n \" \\\\centering\\n\",\n \" \\\\small\\n\",\n \" \\\\setlength{\\\\tabcolsep}{1pt}\\n\",\n \" \\\\renewcommand{\\\\arraystretch}{1.2}\\n\",\n \" \\\\captionof{table}{Overlap ratio of different types of neurons across \\\\textit{different domains} and \\\\textit{before and after alignment}. Following~\\\\citet{she2024mapo}, MSVAMP is regarded as an out-of-domain dataset. The results of MistralMathOctopus on MGSM are used as the fiducial value.}\\n\",\n \" \\\\resizebox{\\\\columnwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lcc}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Variable~(\\\\%)} & \\\\textbf{Language-Specific} & \\\\textbf{Language-Related} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Domain & 80.7 & 92.3 \\\\\\\\\\n\",\n \" Alignment & 95.6 & 92.1 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \" \\\\label{tab:neuron-distribution-stability-overlap}\\n\",\n \" \\\\end{minipage}\\n\",\n \"\\\\vspace{-10pt}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{figure*}[htbp]\\n\",\n \" \\\\centering \\n\",\n \" \\\\begin{minipage}[b]{0.41\\\\textwidth}\\n\",\n \" \\\\includegraphics[width=1.0\\\\linewidth]{Images/heatmap_baseline_Mistral_Base.pdf}\\n\",\n \" \\\\subcaption{Base - Language-Specific Neurons}\\n\",\n \" \\\\label{fig:mistral-mgsm-baseline-base-heatmap}\\n\",\n \" \\\\end{minipage}\\n\",\n \" \\\\begin{minipage}[b]{0.41\\\\textwidth}\\n\",\n \" \\\\includegraphics[width=1.0\\\\linewidth]{Images/heatmap_baseline_Mistral_RL.pdf}\\n\",\n \" \\\\subcaption{Aligned - Language-Specific Neurons}\\n\",\n \" \\\\label{fig:mistral-mgsm-baseline-aligned-heatmap}\\n\",\n \" \\\\end{minipage}\\n\",\n \" \\\\caption{PPL changes of MistralMathOctopus on MGSM when deploying the algorithm proposed by~\\\\citet{tang2024language}.}\\n\",\n \" \\\\label{fig:baseline-heatmap}\\\\\\n\",\n \" \\\\vspace{-10pt}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[htbp]\\n\",\n \" \\\\centering \\n\",\n \" \\\\begin{minipage}[b]{0.41\\\\textwidth}\\n\",\n \" \\\\includegraphics[width=1.0\\\\linewidth]{Images/language_specific_neurons_heatmap_Mistral_Msvamp_Base.pdf}\\n\",\n \" \\\\subcaption{Base - Language-Specific Neurons}\\n\",\n \" \\\\label{fig:mistral-msvamp-base-language-specific-heatmap}\\n\",\n \" \\\\end{minipage}\\n\",\n \" \\\\begin{minipage}[b]{0.41\\\\textwidth}\\n\",\n \" \\\\includegraphics[width=1.0\\\\linewidth]{Images/language_specific_neurons_heatmap_Mistral_Msvamp_RL.pdf}\\n\",\n \" \\\\subcaption{Aligned - Language-Specific Neurons}\\n\",\n \" \\\\label{fig:mistral-msvamp-aligned-language-specific-heatmap}\\n\",\n \" \\\\end{minipage}\\n\",\n \" \\\\begin{minipage}[b]{0.41\\\\textwidth}\\n\",\n \" \\\\includegraphics[width=1.0\\\\linewidth]{Images/language_neurons_heatmap_Mistral_Msvamp_Base.pdf}\\n\",\n \" \\\\subcaption{Base - Language Neurons}\\n\",\n \" \\\\label{fig:mistral-msvamp-base-language-heatmap}\\n\",\n \" \\\\end{minipage}\\n\",\n \" \\\\begin{minipage}[b]{0.41\\\\textwidth}\\n\",\n \" \\\\includegraphics[width=1.0\\\\linewidth]{Images/language_neurons_heatmap_Mistral_Msvamp_RL.pdf}\\n\",\n \" \\\\subcaption{Aligned - Language Neurons}\\n\",\n \" \\\\label{fig:mistral-msvamp-aligned-language-heatmap}\\n\",\n \" \\\\end{minipage}\\n\",\n \" \\\\caption{PPL changes of MistralMathOctopus on MSVAMP after deactivating language-specific neurons or language neurons. ``Base'' indicates the results of the base model. ``Aligned'' indicates the results of the aligned model.}\\n\",\n \" \\\\label{fig:mistral-msvamp-heatmap}\\\\\\n\",\n \" \\\\vspace{-10pt}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[htbp]\\n\",\n \" \\\\centering \\n\",\n \" \\\\begin{minipage}[b]{0.41\\\\textwidth}\\n\",\n \" \\\\includegraphics[width=1.0\\\\linewidth]{Images/language_neurons_heatmap_Llama_Base.pdf}\\n\",\n \" \\\\subcaption{Base - Language Neurons on MGSM}\\n\",\n \" \\\\label{fig:llama-mgsm-base-language-heatmap}\\n\",\n \" \\\\end{minipage}\\n\",\n \" \\\\begin{minipage}[b]{0.41\\\\textwidth}\\n\",\n \" \\\\includegraphics[width=1.0\\\\linewidth]{Images/language_neurons_heatmap_Llama_RL.pdf}\\n\",\n \" \\\\subcaption{Aligned - Language Neurons on MGSM}\\n\",\n \" \\\\label{fig:llama-mgsm-aligned-language-heatmap}\\n\",\n \" \\\\end{minipage}\\n\",\n \" \\\\caption{PPL changes of MetaMathOctopus on MGSM after deactivating language neurons. ``Base'' indicates the results of the base model. ``Aligned'' indicates the results of the aligned model.}\\n\",\n \" \\\\label{fig:llama-mgsm-msvamp-heatmap}\\\\\\n\",\n \" \\\\vspace{-10pt}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[htbp]\\n\",\n \" \\\\centering \\n\",\n \"\\n\",\n \" \\\\begin{subfigure}[b]{0.47\\\\textwidth}\\n\",\n \" \\\\includegraphics[width=0.9\\\\linewidth]{Images/layer_wise_neurons_mistral_MSVAMP.pdf}\\n\",\n \" \\\\caption{MistralMathOctopus on MSVAMP.}\\n\",\n \" \\\\label{fig:layer-wise-neurons-distribution_MSVAMP}\\n\",\n \" \\\\end{subfigure}\\n\",\n \" \\\\hspace{10pt}\\n\",\n \" \\\\begin{subfigure}[b]{0.47\\\\textwidth}\\n\",\n \" \\\\includegraphics[width=0.9\\\\linewidth]{Images/layer_wise_neurons_llama_mgsm.pdf}\\n\",\n \" \\\\caption{MetaMathOctopus on MGSM.}\\n\",\n \" \\\\label{fig:layer-wise-neurons-distribution_MGSM_llama}\\n\",\n \" \\\\end{subfigure}\\n\",\n \" \\\\caption{Layer-wise distribution of the different types of neurons.}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[htbp]\\n\",\n \" \\\\centering \\n\",\n \" \\\\includegraphics[width=0.52\\\\textwidth]{Images/delta_layer_wise_neurons_llama_mgsm.pdf}\\n\",\n \" \\\\caption{Layer-wise changes in the number of MetaMathOctopus}\\n\",\n \" \\\\label{fig:Layer-wise_changes_llama}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[htbp]\\n\",\n \" \\\\centering\\n\",\n \" \\\\begin{subfigure}[b]{0.45\\\\textwidth}\\n\",\n \" \\\\includegraphics[width=\\\\linewidth]{Images/layer_wise_delta_specific_neurons_mistral_mgsm.pdf}\\n\",\n \" \\\\caption{MGSM}\\n\",\n \" \\\\end{subfigure}\\n\",\n \" \\\\hspace{10pt}\\n\",\n \" \\\\begin{subfigure}[b]{0.45\\\\textwidth}\\n\",\n \" \\\\includegraphics[width=\\\\linewidth]{Images/layer_wise_delta_related_neurons_mistral_MSVAMP.pdf}\\n\",\n \" \\\\caption{MSVAMP}\\n\",\n \" \\\\end{subfigure}\\n\",\n \" \\\\vspace{0.5em}\\n\",\n \" \\\\caption{Layer-wise changes in the number of language-specific neurons of MistralMathOctopus during the alignment.}\\n\",\n \" \\\\label{fig:Layer-wise_changes_specific}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[htbp]\\n\",\n \" \\\\centering \\n\",\n \" \\\\begin{subfigure}[b]{0.45\\\\textwidth}\\n\",\n \" \\\\includegraphics[width=\\\\linewidth]{Images/layer_wise_delta_related_neurons_mistral_mgsm.pdf}\\n\",\n \" \\\\caption{MGSM}\\n\",\n \" \\\\end{subfigure}\\n\",\n \" \\\\hspace{10pt}\\n\",\n \" \\\\begin{subfigure}[b]{0.45\\\\textwidth}\\n\",\n \" \\\\includegraphics[width=\\\\linewidth]{Images/layer_wise_delta_related_neurons_mistral_MSVAMP.pdf}\\n\",\n \" \\\\caption{MSVAMP}\\n\",\n \" \\\\end{subfigure}\\n\",\n \" \\\\vspace{0.5em}\\n\",\n \" \\\\caption{Layer-wise changes in the number of language-related neurons of MistralMathOctopus during the alignment.}\\n\",\n \" \\\\label{fig:Layer-wise_changes_related}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[htbp]\\n\",\n \" \\\\centering \\n\",\n \" \\\\begin{subfigure}[b]{0.45\\\\textwidth}\\n\",\n \" \\\\includegraphics[width=\\\\linewidth]{Images/layer_wise_delta_agnostic_neurons_mistral_mgsm.pdf}\\n\",\n \" \\\\caption{MGSM}\\n\",\n \" \\\\end{subfigure}\\n\",\n \" \\\\hspace{10pt}\\n\",\n \" \\\\begin{subfigure}[b]{0.45\\\\textwidth}\\n\",\n \" \\\\includegraphics[width=\\\\linewidth]{Images/layer_wise_delta_agnostic_neurons_mistral_MSVAMP.pdf}\\n\",\n \" \\\\caption{MSVAMP}\\n\",\n \" \\\\end{subfigure}\\n\",\n \" \\\\vspace{0.5em}\\n\",\n \" \\\\caption{Layer-wise changes in the number of language-agnostic neurons of MistralMathOctopus during the alignment.}\\n\",\n \" \\\\label{fig:Layer-wise_changes_agnostic}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{table*}[htbp]\\n\",\n \"\\\\centering\\n\",\n \"\\\\small\\n\",\n \"\\\\setlength{\\\\tabcolsep}{4pt}\\n\",\n \"\\\\renewcommand{\\\\arraystretch}{1.2}\\n\",\n \"\\\\resizebox{0.9\\\\columnwidth}{!}{\\n\",\n \"\\\\begin{tabular}{l|cccccccccc|c}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Tested on MGSM} & \\\\textbf{bn} & \\\\textbf{th} & \\\\textbf{sw} & \\\\textbf{ja} & \\\\textbf{zh} & \\\\textbf{ru} & \\\\textbf{de} & \\\\textbf{es} & \\\\textbf{fr} & \\\\textbf{en} & \\\\textbf{Avg.} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"base & 43.6 & 53.2 & 50.4 & 55.6 & 59.6 & 59.2 & 61.2 & 62.8 & 56.8 & 75.6 & 57.8 \\\\\\\\\\n\",\n \"zh $\\\\Rightarrow$ en & 49.6 & 58.4 & 54.8 & 56.4 & 65.2 & 70.0 & 66.4 & 72.8 & 68.8 & 78.4 & 64.1 \\\\\\\\\\n\",\n \"zh/de $\\\\Rightarrow$ en & 46.4 & 55.6 & 59.2 & 56.8 & 64.0 & 71.2 & 66.8 & 71.2 & 69.2 & 75.2 & 63.6 \\\\\\\\\\n\",\n \"sw/th $\\\\Rightarrow$ en & 48.8 & 58.8 & 59.2 & 56.4 & 68.4 & 68.4 & 69.2 & 69.6 & 70.4 & 77.6 & 64.7 \\\\\\\\\\n\",\n \"zh/es/ru $\\\\Rightarrow$ en & 46.0 & 56.4 & 58.8 & 54.8 & 63.2 & 70.8 & 68.8 & 71.6 & 69.6 & 76.8 & 63.7 \\\\\\\\\\n\",\n \"zh/es/fr/ja/de/sw/ru/th/bn $\\\\Rightarrow$ en & 49.6 & 60.0 & 56.4 & 56.4 & 64.4 & 64.8 & 65.2 & 65.2 & 61.2 & 76.4 & 62.0 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Tested on MSVAMP} & \\\\textbf{bn} & \\\\textbf{th} & \\\\textbf{sw} & \\\\textbf{ja} & \\\\textbf{zh} & \\\\textbf{ru} & \\\\textbf{de} & \\\\textbf{es} & \\\\textbf{fr} & \\\\textbf{en} & \\\\textbf{Avg.} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"base & 49.3 & 62.5 & 60.6 & 60.9 & 67.4 & 64.9 & 66.5 & 67.6 & 67.2 & 77.0 & 64.4 \\\\\\\\\\n\",\n \"zh $\\\\Rightarrow$ en & 52.8 & 62.5 & 60.9 & 63.7 & 66.6 & 67.5 & 69.4 & 69.8 & 69.7 & 76.5 & 65.9 \\\\\\\\\\n\",\n \"zh/de $\\\\Rightarrow$ en & 53.2 & 62.0 & 62.9 & 65.3 & 66.1 & 68.7 & 69.9 & 69.0 & 70.1 & 77.0 & 66.4 \\\\\\\\\\n\",\n \"sw/th $\\\\Rightarrow$ en & 53.8 & 67.4 & 65.1 & 67.7 & 71.9 & 71.4 & 71.9 & 72.0 & 72.8 & 78.9 & 69.3 \\\\\\\\\\n\",\n \"zh/es/ru $\\\\Rightarrow$ en & 52.9 & 63.2 & 61.5 & 63.0 & 67.8 & 68.1 & 69.8 & 68.7 & 70.6 & 78.3 & 66.4 \\\\\\\\\\n\",\n \"zh/es/fr/ja/de/sw/ru/th/bn $\\\\Rightarrow$ en & 54.9 & 65.9 & 65.6 & 68.6 & 69.7 & 70.2 & 72.3 & 71.6 & 71.6 & 77.3 & 68.8 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Accuracy of the base model and aligned variants on benchmarks.}\\n\",\n \"\\\\label{tab:spontaneous_result_all_mistral}\\n\",\n \"\\\\end{table*}\\n\"\n ]\n]","isConversational":false},"fulltext":{"kind":"string","value":"\\documentclass{article}\n\n\n% if you need to pass options to natbib, use, e.g.:\n% \\PassOptionsToPackage{numbers, compress}{natbib}\n% before loading neurips_2025\n\n\n% ready for submission\n% \\usepackage{neurips_2025}\n\n\n% to compile a preprint version, e.g., for submission to arXiv, add add the\n% [preprint] option:\n\\usepackage[preprint]{neurips_2025}\n\n\n% to compile a camera-ready version, add the [final] option, e.g.:\n% \\usepackage[final]{neurips_2025}\n\n\n% to avoid loading the natbib package, add option nonatbib:\n% \\usepackage[nonatbib]{neurips_2025}\n\n\n\\usepackage[utf8]{inputenc} % allow utf-8 input\n\\usepackage[T1]{fontenc} % use 8-bit T1 fonts\n\\usepackage{hyperref} % hyperlinks\n\\usepackage{url} % simple URL typesetting\n\\usepackage{booktabs} % professional-quality tables\n\\usepackage{amsfonts} % blackboard math symbols\n\\usepackage{nicefrac} % compact symbols for 1/2, etc.\n\\usepackage{microtype} % microtypography\n\\usepackage{xcolor} % colors\n\\usepackage{graphicx} \n\\usepackage{amsmath}\n\\usepackage{subcaption}\n\\usepackage{wrapfig}\n\\usepackage{amssymb}\n\n\n\\title{How does Alignment Enhance LLMs' Multilingual Capabilities? A Language Neurons Perspective}\n\n\n% The \\author macro works with any number of authors. There are two commands\n% used to separate the names and addresses of multiple authors: \\And and \\AND.\n%\n% Using \\And between authors leaves it to LaTeX to determine where to break the\n% lines. Using \\AND forces a line break at that point. So, if LaTeX puts 3 of 4\n% authors names on the first line, and the last on the second line, try using\n% \\AND instead of \\And before the third author name.\n\n\n\n% \\author{\n% \\textbf{Shimao Zhang$^\\clubsuit$\\thanks{Equal contribution.}\\thanks{Work done during his internship at MSRA.}\\ \\ \\ \n% Zhejian Lai$^\\clubsuit$\\footnote[1]\\ \\ \\ \n% Xiang Liu$^\\clubsuit$\\footnote[1]\\ \\ \\ \n% Junxiao Liu$^\\clubsuit$\\ \\ \\ \n% Zheheng Luo$^\\Diamond$} \\\\\n% \\textbf{Shujian Huang$^\\clubsuit$\\thanks{Corresponding authors.}\\ \\ \\ \n% Yeyun Gong$^\\bigstar$} \\\\\n% $^\\clubsuit$National Key Laboratory for Novel Software Technology, Nanjing University \\\\\n% $^\\Diamond$The University of Manchester \\ \\ \\ \\ \n% $^\\bigstar$Microsoft Research Asia \\\\\n% \\texttt{smzhang@smail.nju.edu.cn, huangsj@nju.edu.cn,} \\\\\n% \\texttt{xiao.liu.msrasia@microsoft.com}\n% }\n\n\\author{\n\\textbf{Shimao Zhang\\textsuperscript{1}\\textsuperscript{\\dag}\\textsuperscript{*}}\\ \\ \\ \n\\textbf{Zhejian Lai\\textsuperscript{1}\\textsuperscript{*}}\\ \\ \\ \n\\textbf{Xiang Liu\\textsuperscript{1}\\textsuperscript{*}}\\ \\ \\ \n\\textbf{Shuaijie She}\\textsuperscript{1}\\ \\ \\ \n\\textbf{Xiao Liu}\\textsuperscript{2}\\ \\ \\ \\\\\n\\textbf{Yeyun Gong}\\textsuperscript{2}\\ \\ \\ \n\\textbf{Shujian Huang\\textsuperscript{1}\\textsuperscript{\\ddag}}\\ \\ \\ \n\\textbf{Jiajun Chen}\\textsuperscript{1}\\ \\ \\ \\\\\n\\textsuperscript{1} National Key Laboratory for Novel Software Technology, Nanjing University \\\\\n\\textsuperscript{2} Microsoft Research Asia \\\\\n\\texttt{\\{smzhang,laizj,liuxiang,shesj\\}@smail.nju.edu.cn} \\\\\n\\texttt{\\{xiao.liu.msrasia,yegong\\}@microsoft.com} \\\\\n\\texttt{\\{huangsj,chenjj\\}@nju.edu.cn}\n}\n\n\n\\begin{document}\n\n\\renewcommand{\\thefootnote}{\\fnsymbol{footnote}}\n\\footnotetext[1]{Equal contribution.}\n\\footnotetext[2]{Work done during his internship at MSRA.}\n\\footnotetext[3]{Corresponding author.}\n\\renewcommand{\\thefootnote}{\\arabic{footnote}} % optional\n% \\footnotetext[1]{The project will be available at \\url{https://github.com/NJUNLP/Language-Neurons-Alignment}}\n\n\\maketitle\n\n\\begin{abstract}\n% Large Language Models~(LLMs) have shown impressive performance across various downstream language tasks. However, there are imbalanced performances of LLMs across different languages. \nMultilingual Alignment is an effective and representative paradigm to enhance LLMs' multilingual capabilities, which transfers the capabilities from the high-resource languages to the low-resource languages. Meanwhile, some researches on language-specific neurons reveal that there are language-specific neurons that are selectively activated in LLMs when processing different languages. This provides a new perspective to analyze and understand LLMs' mechanisms more specifically in multilingual scenarios. In this work, we propose a new finer-grained neuron identification algorithm, which detects language neurons~(including language-specific neurons and language-related neurons) and language-agnostic neurons. Furthermore, based on the distributional characteristics of different types of neurons, we divide the LLMs' internal process for multilingual inference into four parts: (1) multilingual understanding, (2) shared semantic space reasoning, (3) multilingual output space transformation, and (4) vocabulary space outputting. Additionally, we systematically analyze the models before and after alignment with a focus on different types of neurons. We also analyze the phenomenon of ``Spontaneous Multilingual Alignment''. Overall, our work conducts a comprehensive investigation based on different types of neurons, providing empirical results and valuable insights for better understanding multilingual alignment and multilingual capabilities of LLMs.\\footnote{The project will be available at \\url{https://github.com/NJUNLP/Language-Neurons-Alignment}.}\n\\end{abstract}\n\\section{Introduction}\\label{sec:introduction}\nBy training on the extensive corpus, large language models~(LLMs) demonstrate outstanding language capabilities~\\citep{grattafiori2024llama,yang2024qwen2,liu2024deepseek,zhang2025process}. However, due to the unbalanced pretraining corpus across different languages, LLMs have very uneven performance on high-resource languages and low-resource languages~\\citep{huang2023not,zhu2023multilingual,zhang2024getting}. Therefore, researchers have conducted comprehensive explorations to further enhance the multilingual performance of LLMs. A straightforward approach is increasing the proportion of non-English texts during pretraining~\\citep{ni2021m3p,yang2024qwen2} or performing continual pretraining with multilingual texts~\\citep{liu2021continual,ji2024emma}. But these approaches often entail high computational costs and substantial amounts of multilingual data.\n\nConsidering LLMs' great performance on the high-resource languages, multilingual alignment has emerged as a representative paradigm for enhancing multilingual capabilities by transferring knowledge from high-resource to low-resource languages~\\citep{zhao2024llama,she2024mapo}. A representative example is MAPO~\\citet{she2024mapo}, which improves multilingual alignment by utilizing a well-trained multilingual translation model to compute alignment scores based on the conditional generation probability of translating non-English responses into English.\n\n\\begin{wrapfigure}{r}{0.48\\textwidth}\n \\centering \n \\includegraphics[width=0.48\\textwidth]{Images/case.pdf}\n \\caption{A neuron's activation probability across different languages. This neuron that exhibits high activation probabilities across \\textit{multiple} but \\textit{not all} languages can't be correctly categorized under the existing methodology.}\n \\label{fig:case-study-language-related-neurons}\n \\vspace{-10pt}\n\\end{wrapfigure}\nMany studies conduct systematic mechanism analyses of the multilingual alignment and LLMs' multilingual capabilities. \\citet{zhao2024large} split the multilingual processing workflow into three parts: multilingual understanding, resolving tasks, and generating outputs in the target language. This three-stage inference workflow clearly demonstrates how LLMs leverage English as a pivot language to handle multilingualism using a unified pattern.\n% It is found that English acts as a pivot language in many English-centric LLMs, which indicates that English still participates in the LLMs' inference process even when the models are prompted in other non-English languages~\\citep{wendler2024llamas,zhao2024large}. These latent cross-lingual connections highlight the feasibility and importance of enhancing LLMs’ multilingual capabilities through multilingual alignment~\\citep{zhang2024getting}. Additionally, as mentioned above, different languages are not entirely independent within the model, leading to both language-related and language-agnostic components in LLMs' multilingual reasoning.\n% However, due to the limitations of the previous methodology for identifying language-related components, the investigation of multilingual alignment based on this is still highly limited.\n% Many studies attempt to identify the language-related and language-agnostic components in LLMs.\nInspired by the neurobiological underpinnings of human language faculties,~\\citet{tang2024language} conducts a fine-grained identification by detecting the language-specific neurons. Their results indicate that LLMs predominantly utilize a small subset of neurons to process the particular target language. Furthermore, these language-specific neurons are primarily situated in the model's top and bottom layers~\\citep{tang2024language}, which is consistent with the three-stage multilingual workflow of~\\citet{zhao2024large}. However, we notice a key limitation in the existing language-specific neuron identification methodology: Some neurons are shared across multiple languages but are not entirely language-agnostic. Such neurons are incorrectly categorized as either language-specific or language-agnostic neurons under the existing framework. We present a case study in Figure~\\ref{fig:case-study-language-related-neurons}.\n%(b) The existing method doesn't consider the distributional biases of language neurons between high-resource and low-resource languages.\nFurthermore, we aim to systematically explore an important question: Can we better analyze and understand how multilingual alignment enhances the LLMs' multilingual capabilities from the perspective of language neurons?\n\nIn this work, we comprehensively investigate the multilingual alignment of LLMs from the perspective of language neurons, where we deploy MAPO as a representative multilingual alignment algorithm. Considering the above limitation of the existing language-specific neuron identification methodology, we define \\textbf{language neurons} as the union of \\textbf{language-specific neurons} and \\textbf{language-related neurons}, as opposed to \\textbf{language-agnostic neurons}. We separately distinguish language-related neurons from both language-specific and language-agnostic neurons, which allows for more precise analyses. Furthermore, we propose a new language neuron identification algorithm in our work, which is able to identify language-related neurons that are in fact shared across multiple languages. \n\nThen we analyze the models before and after alignment, focusing on the changes in different types of neurons. Based on the distributional characteristics of these neurons, we divide LLMs' internal process for multilingual inference into four parts: (1) multilingual understanding, (2) shared semantic space reasoning, (3) multilingual output space transformation, and (4) vocabulary space outputting. Our findings reveal that different parts exhibit distinct dependencies on different types of neurons, and that multilingual alignment significantly enhances the activation of the corresponding types of neurons across the relevant layers. \nAdditionally, we analyze the important ``Spontaneous Multilingual Alignment''~\\citep{zhang2024getting} phenomenon in LLMs, providing insights into the roles of language-agnostic neurons and language-related neurons shared across languages. For further analysis, we also provide observations about the uniqueness of English and the neuron distributions. Overall, based on different types of neurons, we present empirical results and valuable insights that contribute to a deeper understanding of multilingual alignment and the multilingual capabilities of LLMs.\n\n\\section{Related Work}\\label{sec:related-work}\n% In this section, we briefly introduce the background and related work of our work. Firstly, we introduce the representative paradigms of multilingual alignment in \\S\\ref{subsec:bg-multilingual-alignment}. Then we overview the existing research on the mechanistic interpretability of LLMs in \\S\\ref{subsec:bg-mechanistic-interpretability}. Finally, in \\S\\ref{subsec:bg-language-specific-neurons}, we review existing studies and methodologies on language-specific neurons.\n\n\\subsection{Multilingual Alignment}\\label{subsec:bg-multilingual-alignment}\nConducting pretraining or continual pretraining on the multilingual corpus is a straightforward and effective method to enhance LLMs' multilingual capabilities~\\citep{ni2021m3p,ji2024emma}. However, these methods typically require substantial investments in time, data, and computational resources. Thus, many researchers perform multilingual alignment to improve LLMs' multilingual performance by transferring the capabilities from high-resource languages to low-resource languages~\\citep{eronen2023zero,zhao2024adamergex,zhao2024llama, she2024mapo}, which efficiently and effectively improves the model performance in low-resource language scenarios. Furthermore, \\citet{zhang2024getting} first finds the ``Spontaneous Multilingual Alignment'' phenomenon in LLMs, which demonstrates that conducting multilingual alignment based on a small number of languages effectively improves the alignment even between English and many languages unseen during alignment.\n% As a representative multilingual alignment algorithm, MAPO~\\citep{she2024mapo} utilizes preference optimization algorithm for multilingual alignment. For inputs in different languages, the model generates responses in the corresponding languages and the corresponding English output is used as the alignment target. The alignment score of each response is acquired based on a well-trained multilingual translation model by calculating the conditional generation probability while translating the response in the source language to English. This algorithm can effectively improve the model performance across different languages. In our work, we choose MAPO as a representative multilingual alignment method and mainly analyze based on it.\n\n\\subsection{Mechanistic Interpretability}\\label{subsec:bg-mechanistic-interpretability}\nIn addition to enhancing LLMs' multilingual performance, research on the underlying mechanisms of multilingual capabilities in LLMs is still ongoing. It is crucial for us to understand and explain the LLMs and related methods explicitly. Typically, the existing approaches primarily perform mechanistic interpretability analyses by observing the internal states of the model~\\citep{logit-lens,zhang2024getting,zhao2024large,mousi-etal-2024-exploring}. Overall, neuron states and latent intermediate logits are both important objects of observation. For latent logits, \\citet{wendler2024llamas} utilizes logit lens~\\citep{logit-lens} to directly project the logits in the intermediate layers to the vocabulary space, which reveals the latent participation of English in the intermediate layers. For neuron states, \\citet{hu2024large} analyzes the neuron activation overlap to measure the extent of shared neuron activation across different languages. \n% Furthermore, \\citet{tang2024language} propose language activation probability entropy~(LAPE) to identify the language-specific neurons, which dissect the multilingual capabilities of LLMs from a novel perspective. In our work, we mainly conduct our mechanistic interpretability analyses on the neuron states.\n\n\\subsection{Language-Specific Neurons}\\label{subsec:bg-language-specific-neurons}\nMany studies have revealed the language-related and language-agnostic components in LLMs. At the layer level, the multilingual processing of LLMs is considered to involve three stages~\\citep{zhao2024large,wendler2024llamas}: converting multilingual inputs into a shared semantic space, intermediate-layer reasoning, and outputting in the target language. The top and bottom layers of the model handle multilingual processing, while the intermediate layers perform inference in similar patterns across different languages. This demonstrates a distinct division of labor within the model at the layer level regarding language specificity.\n\nFurthermore, many studies investigate the finer-grained methods for language-specific neuron identification~\\citep{kojima2024multilingual,tang2024language}. \\citet{tang2024language} categorizes activated neurons into language-specific neurons and language-agnostic neurons. They detect language-specific neurons by calculating language activation probability entropy on massive text. However, we find that some neurons are activated by multiple languages (i.e., not language-specific), yet are not universally activated across all languages (i.e., not language-agnostic). Simply categorizing activated neurons into two classes blurs this distinction. Thus, we propose a new method to identify neurons, which categorizes activated neurons into three types: language-specific, language-related, and language-agnostic.\n\n\\section{Methodology}\\label{sec:methodology}\nIn this section, we introduce our overall analysis pipeline in our work. First, we review the existing multilingual alignment algorithm and neuron analysis techniques as a preliminary study in \\S\\ref{subsec:method-preliminary-study}. Then we introduce the multilingual alignment algorithm we utilize in our work in \\S\\ref{subsec:method-multilingual-alignment}. Finally, for mechanistic interpretability analysis, we introduce our new method for detecting language-specific, language-related, and language-agnostic neurons~(\\S\\ref{subsec:method-language-neurons-detection}).\n\n\\subsection{Preliminary Study}\\label{subsec:method-preliminary-study}\nMost LLMs are pretrained mainly on the high-resource language corpus, which leads to LLMs' unstable and unbalanced performance in multilingual scenarios. As a representative multilingual alignment algorithm, Multilingual-Alignment-as-Preference Optimization~(MAPO)~\\citep{she2024mapo} effectively and efficiently improves the LLMs' multilingual performance. Additionally, it is also important for us to understand and analyze the mechanism of LLMs' multilingual capabilities and multilingual alignment. Moreover, some studies on the identification of the language-specific and language-agnostic neurons in LLMs~\\citep{tang2024language,kojima2024multilingual}. It is found that LLMs' capabilities of processing a particular language mainly come from a small subset of neurons~\\citep{tang2024language}.\n\nHowever, there are still many important questions waiting for further investigation. On the one hand, many methods overlook neurons activated by multiple languages yet not language-agnostic, namely language-related neurons that lie between language-specific and language-agnostic categories. On the other hand, research from the perspective of language neurons on the underlying mechanisms of LLMs' multilingual alignment and multilingual capabilities remains quite limited, which is essential for better understanding and improving the multilingual performance of LLMs. \n\n\\subsection{Multilingual Alignment}\\label{subsec:method-multilingual-alignment}\n\nMAPO is a typical multilingual alignment algorithm to align the reasoning capabilities of non-English language responses with those of English, which serves as the pivot language.\nSpecifically, for a given query $X$ in a target (non-English) language and its corresponding English variant $X_{Eng}$, we collect their respective responses $Y$ and $Y_{Eng}$. An off-the-shelf translation model, parameterized by $\\theta$, is deployed to estimate the conditional generation probability $P(Y \\mid Y_{Eng}; \\theta)$ by force-decoding $Y$ conditioned on $Y_{Eng}$. A higher conditional probability is interpreted as stronger alignment between the target language response and its English counterpart. This probability is then used as an alignment score, denoted $r_{\\theta}(X, Y)$.\n\nThis alignment score can be integrated into preference optimization algorithms. For instance, in PPO~\\citep{ppo}, $r_{\\theta}(X, Y)$ can be directly employed as the reward score. In DPO~\\citep{dpo}, for each target language, $n$ distinct outputs are generated. Based on the alignment score\\, these $n$ outputs are used to form $\\binom{n}{2}$ preference pairs $(Y_w, Y_l)$, where $Y_w$ is deemed superior to $Y_l$ due to a higher alignment score. The model is then optimized by Eq\\ref{eq:dpo}.\n\n\\begin{equation}\n\\label{eq:dpo}\nL_{DPO}(\\pi_{\\theta}; \\pi_{ref}) = -E_{(X, Y_w, Y_l) \\sim D} \\left[ \\log \\sigma \\left( \\beta \\log \\frac{\\pi_{\\theta}(Y_w | X)}{\\pi_{ref}(Y_w | X)} - \\beta \\log \\frac{\\pi_{\\theta}(Y_l | X)}{\\pi_{ref}(Y_l | X)} \\right) \\right] \n\\end{equation}\n\n% Here, $\\pi_{\\theta}$ is the policy being optimized, $\\pi_{ref}$ is a reference policy, $D$ is the dataset of preference triples $(x, Y_w, Y_l)$, $\\sigma$ is the logistic function, and $\\beta$ is a hyperparameter controlling the deviation from the reference policy. The expectation is taken over the collected preference data.\n\n\n\n\n\\subsection{Language Neurons Identification}\\label{subsec:method-language-neurons-detection}\n% Our experiments are based on decoder‐only Transformer neural networks. Each layer of the network consists of multi-head attention (MHA) and a feed-forward network (FFN). Let $x^i$ denote the input tensor of the $i$-th FFN layer. Then the FFN layer can be formally expressed as Eq\\ref{eq:ffn}\n\n% \\begin{equation}\n% \\label{eq:ffn}\n% \\mathrm{FFN}_{\\mathrm{SwiGLU}}(x^i) = W_2^i \\,\\bigl(\\mathrm{SiLU}(x^i W_{11}^i)\\,\\odot\\,(x^i W_{12}^i)\\bigr).\n% \\end{equation}\n\nFollowing \\citet{tang2024language}, the neurons in our work are defined as a linear transformation of a single column in a weighted matrix $W$ followed by a non-linear activation, SiLU~\\citep{silu}. For the $j$-th neuron in the $i$-th layer, its activation probability when processing responses in language $k$ is computed as:\n\\begin{equation}\np_{i,j}^k=\\mathbb{E}\\left(\\mathbb{I}\\left(\\mathrm{SiLU}(x^i W^i)_j > 0\\right)\\mid \\text{language}\\ k \\right)\n\\end{equation}\n\nWe define \\textbf{language neurons} as those exhibiting higher activation probabilities for some languages, while discriminating from others. To select neurons that, despite exhibiting relatively higher entropy, still demonstrate high activation probabilities for some languages, we additionally incorporate the maximum activation probability, as formulated in Eq~\\ref{eq:metric}:\n\\begin{equation}\n\\label{eq:metric}\n\\mathrm{score}_{i,j} = -\\sum_{k=1}^{l} p_{i,j}^{\\prime k}\\,\\log p_{i,j}^{\\prime k} \\;-\\;\\lambda\\,\\max_{1\\le k\\le l} p_{i,j}^k,\n\\end{equation}\nwhere $p_{i,j}'$ represents the probability distribution $p_{i,j}$ after normalization and $\\lambda$ is a balancing coefficient. Neurons with scores falling in the lowest percentile, specifically, the bottom 5\\% are selected.\n\nFurthermore, to identify how many languages each selected neuron is related to, we introduce a threshold $\\tau$ and compute:\n\\begin{equation}\nN_{i,j} = \\sum_{k=1}^l \\mathbb{I}\\bigl(p_{i,j}^k > \\tau\\bigr).\n\\end{equation}\nA neuron is considered as a \\textbf{language-specific neuron} if $N_{i,j} = 1$, and as a \\textbf{language-related neuron} if $1 < N_{i,j} < l$. Both of them belong to \\textbf{language neuron}.\nIn contrast, a neuron is considered \\textbf{language-agnostic neuron} if it exhibits high activation probabilities across all $l$ languages.\n\nFinally, given our focus on multilingual reasoning tasks, we select neurons exclusively based on responses from multilingual reasoning datasets, rather than relying on multilingual plain text~\\citep{tang2024language}.\n\n\\section{Experiments}\\label{sec:experiments}\n\n\\subsection{Experimental Setup}\\label{subsec:experimental-setup}\nFollowing~\\citet{she2024mapo}, we conduct our experiments and analyses on the mathematical reasoning tasks and different languages. In this section, we introduce our experimental settings in detail.\n\n\\paragraph{Models} We include two different models in our experiments and analyses. Following~\\citet{she2024mapo}, we conduct our experiments on MistralMathOctopus-7B~\\footnote{\\url{https://huggingface.co/kevinpro/MistralMathOctopus-7B}} and MetaMathOctopus-7B~\\footnote{\\url{https://huggingface.co/kevinpro/MetaMathOctopus-7B}}. MistralMathOctopus is obtained by fine-tuning MetaMath-Mistral~\\citep{yu2023metamath} with MGSM8KInstruct~\\citep{chen2023breaking}. MetaMathOctopus is obtained by fine-tuning MetaMath~\\citep{yu2023metamath} with MGSM8KInstruct. Considering limited computational resources and reproducibility, we directly utilize the publicly released base models. Our analyses are mainly based on MistralMathOctopus in the main text and we report more results in the Appendix.\n\n\\paragraph{Datasets} We conduct experiments on two representative mathematical reasoning benchmarks, MGSM~\\citep{shi2022language} and MSVAMP~\\citep{chen2023breaking}. MGSM is a widely used benchmark for multilingual mathematical reasoning evaluation. MSVAMP is an out-of-domain test set in contrast to MGSM, which evaluates robustness and generalization~\\citep{zhu2024question,she2024mapo}.\n\n\n\\paragraph{Languages} Following~\\citet{she2024mapo}, we choose the following 10 different languages for analysis in our work. As a pivot language, English~(en) is used as the alignment target. We also choose Chinese~(zh), Russian~(ru), German~(de), French~(fr), Spanish~(es), Japanese~(ja), Swahili~(sw), Thai~(th) and Bengali~(bn) as 9 representative non-English languages.\n\n\n\\begin{figure*}[tbp]\n \\centering \n \\begin{minipage}[b]{0.4\\textwidth}\n \\includegraphics[width=1.0\\linewidth]{Images/language_specific_neurons_heatmap_Mistral_Base.pdf}\n \\subcaption{Base - Language-Specific Neurons}\n \\label{fig:mistral-mgsm-base-language-specific-heatmap}\n \\end{minipage}\n \\begin{minipage}[b]{0.4\\textwidth}\n \\includegraphics[width=1.0\\linewidth]{Images/language_specific_neurons_heatmap_Mistral_RL.pdf}\n \\subcaption{Aligned - Language-Specific Neurons}\n \\label{fig:mistral-mgsm-aligned-language-specific-heatmap}\n \\end{minipage}\n \\begin{minipage}[b]{0.4\\textwidth}\n \\includegraphics[width=1.0\\linewidth]{Images/language_neurons_heatmap_Mistral_Base.pdf}\n \\subcaption{Base - Language Neurons}\n \\label{fig:mistral-mgsm-base-language-heatmap}\n \\end{minipage}\n \\begin{minipage}[b]{0.4\\textwidth}\n \\includegraphics[width=1.0\\linewidth]{Images/language_neurons_heatmap_Mistral_RL.pdf}\n \\subcaption{Aligned - Language Neurons}\n \\label{fig:mistral-mgsm-aligned-language-heatmap}\n \\end{minipage}\n \\caption{PPL changes of MistralMathOctopus on MGSM after deactivating language-specific neurons or language neurons. ``Base'' indicates the results of the base model. ``Aligned'' indicates the results of the aligned model. For comparison, the results of~\\citet{tang2024language} are provided in Appendix~\\ref{app:heatmap}.}\n \\label{fig:mistral-mgsm-heatmap}\\\n \\vspace{-15pt}\n\\end{figure*}\n\n\\paragraph{Implementations} Due to limited computational resources, our exploration focuses on the most effective DPO variant of MAPO~\\citep{she2024mapo}. We select 1, 4, and 8 tasks from the NumGLUE~\\citep{mishra-etal-2022-numglue}, an arithmetic reasoning benchmark, and translate questions into 9 languages, consistent with the MGSM, thereby creating a multilingual seed dataset.\nTo construct preference pairs, we sample responses using the corresponding base models and employ NLLB-200-distilled-600M\\footnote{\\url{https://huggingface.co/facebook/nllb-200-distilled-600M}} as the translation model to obtain alignment scores. Finally, for each model and each target language (excluding English), we gain 10,000 preference pairs. Training is conducted using LoRA~\\citep{hu2022lora}.\n% To identify language neurons, we compute the score defined in Eq.~\\ref{eq:metric} for each neuron and select the bottom 5\\% as our candidates. This selection prioritizes neurons that exhibit strong language preferences, balancing low entropy with high maximum activation probabilities for at least one language. \nDuring the neuron selection stage, we perform force-decoding on the responses of the MGSM or MSVAMP dataset to obtain the activation probabilities of neurons for each language.\nBased on empirical results on development sets, we set the balancing coefficient $\\lambda = 0.04$ and the threshold $\\tau = 0.5$.\n% , which achieves a favorable trade-off between these two objectives. \n% We set a threshold of $\\tau = 0.5$ to determine whether a neuron is related to a given language. \nAdditional implementation details are provided in Appendix \\ref{app:implementation-details}.\n% This threshold effectively distinguishes language-specific activations while maintaining robustness across layers and models.\n\n\n\\subsection{Language Neurons Identification}\\label{subsec:language-neuron-identification}\nBased on the neuron identification algorithm introduced in \\S\\ref{subsec:method-language-neurons-detection}, we identify the language-specific neurons, language-related neurons, and language-agnostic neurons in the model. To further validate the effectiveness of our algorithm, we follow ~\\citet{tang2024language} by examining changes in the perplexity of LLMs by deactivating the identified language neurons across different languages. Experiments are conducted on both the base model and the aligned model, with results presented in Figure~\\ref{fig:mistral-mgsm-heatmap}. We report the results of both language-specific neurons and language neurons. \n\nIt can be found that whether deactivating language-specific neurons or all language neurons, the results consistently exhibit the same pattern: the diagonal elements in each row show the highest values. Notably, deactivating language neurons leads to a more pronounced effect compared to deactivating only language-specific neurons. These observations support the following findings: (1) Our algorithm effectively identifies language-specific and language-related neurons; (2) For a given language, in addition to its language-specific neurons, there are also a substantial number of shared language-related neurons contributing to its performance; (3) Deactivating all the language-related neurons of one language doesn't cause significant impacts on the model's performance in other languages. The above findings confirm the validity of the language neurons identified by our method and further provide insights into the characteristics of language neurons.\n\n\n\\begin{figure*}[tbp]\n \\centering \n \\includegraphics[width=0.6\\linewidth]{Images/layer_wise_neurons.pdf}\n \\caption{Layer-wise distribution of the different types of neurons of MistralMathOctopus on MGSM.}\n \\label{fig:layer-wise-neurons-distribution}\n \\vspace{-10pt}\n\\end{figure*}\n\n\n\\subsection{Layer-wise Functionality Analysis}\\label{subsec:layer-wise-funtionality-analysis}\nBased on the identified neurons, we perform layer-wise functional analyses of all layers in the LLMs. We begin by analyzing the distributions of different types of neurons in the base model. And we report the results in Figure~\\ref{fig:layer-wise-neurons-distribution}. Through the analysis of the distribution of different types of neurons, we can further divide the LLMs' internal process for multilingual inference into four parts rather than the three-stage division discussed in some works~\\citep{wendler2024llamas,zhao2024large}:\n\\begin{enumerate}\n \\item \\textbf{Multilingual Understanding:} In the initial layers, the number of language neurons~(language-specific and language-related neurons) peaks, while the number of language-agnostic neurons is relatively low. The model maps multilingual inputs into a unified semantic space at this stage.\n \\item \\textbf{Shared Semantic Space Reasoning:} In the intermediate layers, the model engages in reasoning within a shared semantic space across different languages. During this stage, language neurons are largely absent, whereas language-agnostic neurons become dominant.\n \\item \\textbf{Multilingual Output Space Transformation:} The model transfers features into the multilingual output space in this stage in preparation for generating the final output. In this part, the number of language neurons reaches a peak again, while the number of language-agnostic neurons drops to the lowest point.\n \\item \\textbf{Vocabulary Space Outputting:} In the last layer, the model maps vectors of different languages into a shared vocabulary space to generate outputs. The number of both language-related and language-agnostic neurons rises sharply, while the language-specific neurons are fewer than those in several previous layers.\n\\end{enumerate}\nMeanwhile, the distribution of different types of neurons aligns with the conclusions from existing studies mentioned above. Overall, we can find that the number of neurons varies correspondingly with the different inference stages of LLMs.\n\n\\vspace{-5pt}\n\\subsection{Layer-wise Neuron Changes Analysis}\n\\label{subsec:layer-wise-neuron-change}\nIn \\S\\ref{subsec:layer-wise-funtionality-analysis}, we investigate the fundamental distribution of neurons and the basic partitioning of the model. We further analyze the changes in different types of neurons before and after multilingual alignment. Based on the four functional stages in LLMs, we quantify the layer-wise changes~($\\Delta$) in the number of different types of neurons. Figure~\\ref{fig:Delta_of_Language_Neurons} presents the results for language-specific neurons, language-related neurons, and language-agnostic neurons.\n\\begin{wrapfigure}{r}{0.52\\textwidth}\n \\centering \n \\includegraphics[width=\\linewidth]{Images/delta_layer_wise_neurons_mistral_mgsm.pdf}\n \\caption{Layer-wise changes in the number of different types of neurons of MistralMathOctopus on MGSM.}\n \\label{fig:Delta_of_Language_Neurons}\n \\vspace{-12pt}\n\\end{wrapfigure}\nDuring the multilingual understanding stage, the number of language neurons increases, while language-agnostic neurons decrease. In the subsequent shared semantic space reasoning stage, language-agnostic neurons increase substantially, whereas language neurons remain stable and nearly absent. Then, in the third stage, as language-agnostic neurons decrease, language neurons increase overall. Additionally, we notice that the number of\n% language-specific neurons decreases, while \nlanguage-related neurons show an upward trend. Finally, in the last stage, the number of language-agnostic increases significantly in the aligned model, accompanied by a reduction in language neurons. We also report the results of different checkpoints during the alignment process in Appendix~\\ref{app:checkpoint-layer-wise-changes}.\n\nOverall, we find that language neurons and language-agnostic neurons exhibit generally opposite trends across different layers, which corresponds to the characteristics of LLMs at different stages of inference. Especially, at the last stage, language-agnostic neurons play an more important role than language neurons. Multilingual alignment facilitates more effective activation of the appropriate neurons at each stage, thereby improving the model's capability to handle multilingual tasks.\n\n\n\\subsection{Macroscopic Analysis of Different Types of Neurons}\nWe further conduct macroscopic analysis for different types of neurons. In our neuron identification algorithm introduced in \\S\\ref{subsec:method-language-neurons-detection}, the number of languages that share a specific neuron is an attribution characterizing all types of activated neurons. Since our study involves 10 languages, the valid range of $N$ is from $1$ to $10$. Among these, values of $N$ from $2$ to $9$ correspond to language-related neurons. As special cases in our work, $N = 1$ represents language-specific neurons, while $N = 10$ corresponds to language-agnostic neurons.\n\n\\begin{figure}[tbp]\n \\centering \n \\includegraphics[width=0.7\\textwidth]{Images/related_language_num.pdf}\n \\caption{Changes in the number of neurons shared by $N$ languages after alignment.}\n \\label{fig:Related_Language_Num}\n% \\vspace{-10pt}\n\\end{figure}\n\nWe report the changes in the number of neurons after multilingual alignment for each value of $N$ ranging from 1 to 10 in Figure~\\ref{fig:Related_Language_Num}. The results show a decrease in the number of language-specific neurons, while an increase in the number of language-related neurons, which are shared across multiple languages. This indicates that multilingual alignment encourages LLMs to develop and utilize more shared language-related neurons, rather than on language-specific neurons, which are applicable to only a single language. Meanwhile, during the alignment process, the model improves its understanding of task-relevant common knowledge. Therefore, the overall number of language-agnostic neurons also increases significantly.\n\n\n\n\\begin{table*}[tbp]\n\\centering\n\\small\n\\setlength{\\tabcolsep}{4pt}\n\\renewcommand{\\arraystretch}{1.1}\n\\caption{Accuracy of the MistralMathOctopus base model and aligned model on MGSM. \"X/Y $\\Rightarrow$ T\" indicates that languages X and Y are used for multilingual alignment.}\n\\resizebox{0.9\\columnwidth}{!}{\n\\begin{tabular}{l|cccccccccc|c}\n\\toprule\n\\textbf{MGSM} & \\textbf{bn} & \\textbf{th} & \\textbf{sw} & \\textbf{ja} & \\textbf{zh} & \\textbf{ru} & \\textbf{de} & \\textbf{es} & \\textbf{fr} & \\textbf{en} & \\textbf{Avg.} \\\\\n\\midrule\nbase & 43.6 & 53.2 & 50.4 & 55.6 & 59.6 & 59.2 & 61.2 & 62.8 & 56.8 & 75.6 & 57.8 \\\\\nzh/de $\\Rightarrow$ en & 46.4 & 55.6 & 59.2 & 56.8 & 64.0 & 71.2 & 66.8 & 71.2 & 69.2 & 75.2 & 63.6 \\\\\nsw/th $\\Rightarrow$ en & 48.8 & 58.8 & 59.2 & 56.4 & 68.4 & 68.4 & 69.2 & 69.6 & 70.4 & 77.6 & 64.7 \\\\\n\\bottomrule\n\\end{tabular}\n}\n\\label{tab:spontaneous_result}\n\\vspace{-12pt}\n\\end{table*}\n\n\n% \\vspace{-5pt}\n\\subsection{Spontaneous Multilingual Alignment Analysis}\\label{subsec:spontaneous-multilingual-alignment-analysis}\n% \\begin{wrapfigure}{r}{0.52\\textwidth}\n% \\centering\n% \\small\n% \\setlength{\\tabcolsep}{4pt}\n% \\renewcommand{\\arraystretch}{1.1}\n% \\captionof{table}{Average results of neuron count changes across multiple languages. ``Trained'' indicates the trained languages in the spontaneous multilingual alignment experiment. ``Others'' indicates other languages except the trained languages. We round the results to the nearest integer.}\n% \\resizebox{0.52\\columnwidth}{!}{\n% \\begin{tabular}{lcc}\n% \\toprule\n% \\textbf{Language} & \\textbf{Language-Specific} & \\textbf{Language-Related} \\\\\n% \\midrule\n% Trained & -37 & +232 \\\\\n% Others & -36 & +205 \\\\\n% \\bottomrule\n% \\end{tabular}\n% }\n% \\label{tab:spontaneous_delta_neurons_zhde}\n% \\vspace{-10pt}\n% \\end{wrapfigure}\nThe important ``Spontaneous Multilingual Alignment'' phenomenon is first revealed and discussed by~\\citet{zhang2024getting}, which means that conducting alignment in a small number of languages significantly improves multilingual alignment even between English and many languages unseen during the alignment process. We further analyze this phenomenon in our experiments. As shown in Table~\\ref{tab:spontaneous_result}, spontaneous multilingual alignment also emerges under the multilingual alignment strategy employed in our study. Except for the languages used for alignment, LLMs exhibit notable performance gain in other unaligned languages.\n\nTo understand how multilingual alignment generalizes to other languages, we analyze the changes in different types of neurons before and after multilingual alignment based on our method.\n\\begin{wrapfigure}{r}{0.52\\textwidth}\n\\centering\n\\small\n\\setlength{\\tabcolsep}{4pt}\n\\renewcommand{\\arraystretch}{1.1}\n\\captionof{table}{Average results of neuron count changes across multiple languages. ``Trained'' indicates the trained languages in the spontaneous multilingual alignment experiment. ``Others'' indicates other languages except the trained languages. We round the results to the nearest integer.}\n\\resizebox{0.52\\columnwidth}{!}{\n\\begin{tabular}{lcc}\n\\toprule\n\\textbf{Language} & \\textbf{Language-Specific} & \\textbf{Language-Related} \\\\\n\\midrule\nTrained & -37 & +232 \\\\\nOthers & -36 & +205 \\\\\n\\bottomrule\n\\end{tabular}\n}\n\\label{tab:spontaneous_delta_neurons_zhde}\n\\vspace{-10pt}\n\\end{wrapfigure}\nTaking the case of ``zh/de $\\Rightarrow$ en'' as a representative example, we report the average results in Table~\\ref{tab:spontaneous_delta_neurons_zhde}. For the trained languages, the number of language-specific neurons decreases, while the number of language-related neurons increases. This indicates that the aligned languages tend to utilize more language-related neurons shared with other languages rather than exclusive language-specific neurons. Moreover, we extend this analysis to languages other than the trained languages and observe a similar phenomenon. These findings indicate that multilingual alignment facilitates the use of language-related neurons while reducing the reliance on language-specific neurons in both trained and other unseen languages. We hypothesize that the new language-related neurons shared with trained languages contribute to the performance improvement on other unseen languages.\n\n\n\n\n\\begin{figure}[tbp]\n \\begin{minipage}{0.48\\textwidth}\n \\centering\n \\small\n \\setlength{\\tabcolsep}{1pt}\n \\renewcommand{\\arraystretch}{1.2}\n \\captionof{table}{Average number of different types of neurons for English and non-English languages of MistralMathOctopus on MGSM. We round the results to the nearest integer.}\n \\resizebox{\\columnwidth}{!}{\n \\begin{tabular}{lcc}\n \\toprule\n \\textbf{Language} & \\textbf{Language-Specific} & \\textbf{Language-Related} \\\\\n \\midrule\n English & 46 & 603 \\\\\n non-English & 613 & 2006 \\\\\n \\bottomrule\n \\end{tabular}\n }\n \\label{tab:English-nonEnglish-neuron-count}\n \\end{minipage}\n \\hfill\n \\begin{minipage}{0.48\\textwidth}\n \\centering\n \\small\n \\setlength{\\tabcolsep}{1pt}\n \\renewcommand{\\arraystretch}{1.2}\n \\captionof{table}{Overlap ratio of different types of neurons across \\textit{different domains} and \\textit{before and after alignment}. Following~\\citet{she2024mapo}, MSVAMP is regarded as an out-of-domain dataset. The results of MistralMathOctopus on MGSM are used as the fiducial value.}\n \\resizebox{\\columnwidth}{!}{\n \\begin{tabular}{lcc}\n \\toprule\n \\textbf{Variable~(\\%)} & \\textbf{Language-Specific} & \\textbf{Language-Related} \\\\\n \\midrule\n Domain & 80.7 & 92.3 \\\\\n Alignment & 95.6 & 92.1 \\\\\n \\bottomrule\n \\end{tabular}\n }\n \\label{tab:neuron-distribution-stability-overlap}\n \\end{minipage}\n\\vspace{-10pt}\n\\end{figure}\n\n\n\n\\subsection{Further Analysis}\\label{subsec:further-analysis}\n\n\\paragraph{Uniqueness of English}\n% \\begin{wrapfigure}{r}{0.52\\textwidth}\n% \\centering\n% \\small\n% \\setlength{\\tabcolsep}{4pt}\n% \\renewcommand{\\arraystretch}{1.1}\n% \\captionof{table}{Average number of different types of neurons for English and non-English languages based on MistralMathOctopus. We round the results to the nearest integer.}\n% \\resizebox{0.52\\columnwidth}{!}{\n% \\begin{tabular}{lcc}\n% \\toprule\n% \\textbf{Language} & \\textbf{Language-Specific} & \\textbf{Language-Related} \\\\\n% \\midrule\n% English & 46 & 603 \\\\\n% non-English & 613 & 2006 \\\\\n% \\bottomrule\n% \\end{tabular}\n% }\n% \\label{tab:English-nonEnglish-neuron-count}\n% \\end{wrapfigure}\nSince current LLMs are primarily pretrained on English data, English is often regarded as playing a special role within LLMs~\\citep{wendler2024llamas}. In our experiments, we also observe that English exhibits markedly different characteristics compared to other non-English languages. Based on the identified neurons in our work, in Figure~\\ref{fig:mistral-mgsm-heatmap}, we can find that deactivating the language neurons of English has a negligible impact on the model's performance in English, which is entirely different from the behavior observed in other languages. This is also consistent with the results of~\\citet{tang2024language}. Furthermore, based on this finding, we quantify the number of language neurons for English and non-English languages based on the MistralMathOctopus base model~(Table~\\ref{tab:English-nonEnglish-neuron-count}). Our analysis reveals that English has significantly fewer neurons than other languages, both in terms of language-specific and language-related neurons. We hypothesize that this is due to the fact that English actually possesses numerous language-related neurons. And since English serves as a pivot language, these language-related neurons are likely shared with almost all other languages. It causes them to be confounded with language-agnostic neurons.\n% \\citet{wendler2024llamas} also discuss the dominant role of English.\n\n% \\begin{wrapfigure}{r}{0.52\\textwidth}\n% \\centering\n% \\small\n% \\setlength{\\tabcolsep}{4pt}\n% \\renewcommand{\\arraystretch}{1.1}\n% \\captionof{table}{Overlap ratio of different types of neurons across different domains and before and after alignment. The result based on MGSM and MistralMathOctopus base model is used as the fiducial value.}\n% \\resizebox{0.52\\columnwidth}{!}{\n% \\begin{tabular}{lcc}\n% \\toprule\n% \\textbf{Variable~(\\%)} & \\textbf{Language-Specific} & \\textbf{Language-Related} \\\\\n% \\midrule\n% Domain & 80.7 & 92.3 \\\\\n% Alignment & 95.6 & 92.1 \\\\\n% \\bottomrule\n% \\end{tabular}\n% }\n% \\label{tab:neuron-distribution-stability-overlap}\n% \\end{wrapfigure}\n\n\\paragraph{Stability of Neuron Distributions}We discuss the stability of neuron distributions across \\textit{different data domains}, as well as \\textit{before and after alignment}. To quantify the stability of neuron distributions, we compute the neuron overlap ratio in both settings, with the results summarized in Table~\\ref{tab:neuron-distribution-stability-overlap}. \n% We use the corresponding neuron count based on MistralMathOctopus and MGSM as the fiducial value while calculating the overlap ratio. \nWe can find that although the exact positions of a few language neurons may vary across different settings, the positional distribution of most language neurons remains stable. This also indicates good reliability and generalization of the language neurons identified under fixed hyperparameters.\n\n\n\n% \\begin{figure*}[ht] \n% \\centering \n% \\includegraphics[width=0.5\\textwidth]{Images/correlator_similarity_Swahili.pdf}\n% \\caption{Layer-wise correlator similarity analysis in Swahili.} \n% \\label{fig:correlator}\n% \\end{figure*}\n\n\\section{Conclusion}\\label{sec:conclusion}\nIn this work, we systematically investigate the multilingual alignment from the perspective of language neurons. We propose a new language neuron identification algorithm based on entropy and probability value, which detects the language-specific neurons, language-related neurons, and language-agnostic neurons in LLMs. The validity of the identified neurons is confirmed through deactivation ablation experiments. Furthermore, we examine the multilingual alignment mechanism by analyzing the roles of different types of neurons. Based on their distributional characteristics, we categorize LLMs' internal process into four functional parts. Our analysis reveals that multilingual alignment enhances the model’s utilization of the corresponding types of neurons across different functional parts. Meanwhile, we find that alignment promotes a greater reliance on shared language-related neurons across languages, rather than on language-specific neurons. We also explore the phenomenon of spontaneous multilingual alignment. Additionally, we provide further analysis and more empirical results based on the preceding findings.\n\n% \\begin{ack}\n% Use unnumbered first level headings for the acknowledgments. All acknowledgments\n% go at the end of the paper before the list of references. Moreover, you are required to declare\n% funding (financial activities supporting the submitted work) and competing interests (related financial activities outside the submitted work).\n% More information about this disclosure can be found at: \\url{https://neurips.cc/Conferences/2025/PaperInformation/FundingDisclosure}.\n\n\n% Do {\\bf not} include this section in the anonymized submission, only in the final paper. You can use the \\texttt{ack} environment provided in the style file to automatically hide this section in the anonymized submission.\n% \\end{ack}\n\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\n\n\\bibliographystyle{unsrtnat}\n\\bibliography{custom}\n\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\n\\newpage\n\\appendix\n\n\\section{Limitations}\\label{app:limitations}\nWe provide insights and analysis results for multilingual alignment and multilingual capabilities of LLMs, which allows for a better understanding of multilingualism. Despite we have conducted a systematic investigation in our work, there are still some limitations waiting for research. Due to the limited resources, we only conduct experiments on two different models and two different datasets. We are willing to perform a more comprehensive analysis on different scenarios if more resources are available in the future. Additionally, we don't perform a finer-grained analysis of neurons within the same layer in this work. We would like to explore this in our future work.\n\n\n\n\\section{Implementation Details}\\label{app:implementation-details}\nOur experiments are conducted on 4 NVIDIA RTX A6000 GPUs or 4 NVIDIA GeForce RTX 3090 GPUs. We use the TRL~\\citep{vonwerra2022trl} and DeepSpeed~\\citep{rasley2020deepspeed} frameworks for preference alignment, and the vLLM engine~\\cite{kwon2023efficient} for inference.\n\\subsection{MAPO}\nWe employ the officially released scripts\\footnote{\\url{https://github.com/NJUNLP/MAPO}} to generate the preference data. For the alignment process, we set the learning rate to 1e-6 and the batch size to 16. LoRA is utilized to fine-tune the model with a LoRA rank of 64, a LoRA alpha of 128, and a LoRA dropout rate of 0.05. The total number of training steps is set to 1000. It takes 7.5 hours for one alignment.\n\\subsection{Language Neurons Identification}\nWe spend 25 minutes calculating the activation probability of each neuron with MGSM dataset on a single NVIDIA GeForce RTX 3090 GPU. \nWe spend 2 hours calculating the activation probability of each neuron with MSVAMP dataset on a single NVIDIA GeForce RTX 3090 GPU.\nAfter obtaining the activation probability, it takes 3 minutes to identify how many languages each selected neuron is related to.\n\n\n\n\n\n\n\n\n\\section{Licenses for Used Assets}\\label{app:licenses}\nWe list the names of the licenses for each asset we utilized in our work:\n\\begin{itemize}\n \\item MGSM: CC-BY-SA-4.0\n \\item MSVAMP: Apache-2.0\n \\item GSM8KInstruct: Apache-2.0\n \\item NumGLUE: Apache-2.0\n \\item MistralMathOctopus: Apache-2.0\n \\item MetaMathOctopus: Apache-2.0\n \\item NLLB-200-distilled-600M: CC-BY-NC-4.0\n \\item TRL: Apache-2.0\n \\item DeepSpeed: Apache-2.0\n \\item vLLM: Apache-2.0\n\\end{itemize}\n\n\n\\section{Deactivation Ablation Experiments}\n\\label{app:heatmap}\nFor the MistralMathOctopus model, Figure~\\ref{fig:baseline-heatmap} presents the results of the deactivation ablation experiments conducted on MGSM using the neuron identification algorithm proposed by~\\citet{tang2024language}. Additionally, Figure~\\ref{fig:mistral-msvamp-heatmap} presents the results of the deactivation ablation experiments on the out-of-domain test set, MSVAMP.\nTo verify the generalization of our identification algorithm, we also deploy the MetaMathOctopus to conduct the deactivation ablation experiment, with the result in \nFigure~\\ref{fig:llama-mgsm-msvamp-heatmap}.\n\n\n\n\\section{Layer-wise distribution of the different types of neurons}\nIn Figure~\\ref{fig:layer-wise-neurons-distribution_MSVAMP} and~\\ref{fig:layer-wise-neurons-distribution_MGSM_llama}, we separately display the layer-wise distribution of the different types of neurons of MistarlMathOctopus on MSVAMP and MetaMathOctopus on MGSM to validate the generalization of our finding discussed in \\S\\ref{subsec:layer-wise-funtionality-analysis}.\n\n\n\n\n\\section{Layer-wise Changes in the Number of Different Types of Neurons During Alignment}\\label{app:checkpoint-layer-wise-changes}\nFigure~\\ref{fig:Layer-wise_changes_llama} shows the same pattern discussed in \\S\\ref{subsec:layer-wise-neuron-change}, thereby supporting its generalization.\nFurthermore, for each dataset, we examine the layer-wise changes in the number of different types of neurons in the process of alignment in Figure~\\ref{fig:Layer-wise_changes_specific},~\\ref{fig:Layer-wise_changes_related} and~\\ref{fig:Layer-wise_changes_agnostic}. The notation “$\\text{ckpt-}x$” denotes the model checkpoint obtained after $x$ training steps.\n\n\n\\section{Spontaneous Multilingual Alignment}\nWe report the accuracy of the base model on the MGSM and MSVAMP benchmarks in Table~\\ref{tab:spontaneous_result_all_mistral}. The expected accuracy of random guessing is 50.0\\%. \"X/Y $\\Rightarrow$ T\" indicates that languages X and Y are used for alignment. The best results for each language are highlighted. It is evident that models trained on multilingual translation data substantially outperform the original model across a wide range of languages, indicating that multilingual training significantly enhances the model's multilingual capabilities.\n\n\\newpage\n\n\\begin{figure*}[htbp]\n \\centering \n \\begin{minipage}[b]{0.41\\textwidth}\n \\includegraphics[width=1.0\\linewidth]{Images/heatmap_baseline_Mistral_Base.pdf}\n \\subcaption{Base - Language-Specific Neurons}\n \\label{fig:mistral-mgsm-baseline-base-heatmap}\n \\end{minipage}\n \\begin{minipage}[b]{0.41\\textwidth}\n \\includegraphics[width=1.0\\linewidth]{Images/heatmap_baseline_Mistral_RL.pdf}\n \\subcaption{Aligned - Language-Specific Neurons}\n \\label{fig:mistral-mgsm-baseline-aligned-heatmap}\n \\end{minipage}\n \\caption{PPL changes of MistralMathOctopus on MGSM when deploying the algorithm proposed by~\\citet{tang2024language}.}\n \\label{fig:baseline-heatmap}\\\n \\vspace{-10pt}\n\\end{figure*}\n\n\n\\begin{figure*}[htbp]\n \\centering \n \\begin{minipage}[b]{0.41\\textwidth}\n \\includegraphics[width=1.0\\linewidth]{Images/language_specific_neurons_heatmap_Mistral_Msvamp_Base.pdf}\n \\subcaption{Base - Language-Specific Neurons}\n \\label{fig:mistral-msvamp-base-language-specific-heatmap}\n \\end{minipage}\n \\begin{minipage}[b]{0.41\\textwidth}\n \\includegraphics[width=1.0\\linewidth]{Images/language_specific_neurons_heatmap_Mistral_Msvamp_RL.pdf}\n \\subcaption{Aligned - Language-Specific Neurons}\n \\label{fig:mistral-msvamp-aligned-language-specific-heatmap}\n \\end{minipage}\n \\begin{minipage}[b]{0.41\\textwidth}\n \\includegraphics[width=1.0\\linewidth]{Images/language_neurons_heatmap_Mistral_Msvamp_Base.pdf}\n \\subcaption{Base - Language Neurons}\n \\label{fig:mistral-msvamp-base-language-heatmap}\n \\end{minipage}\n \\begin{minipage}[b]{0.41\\textwidth}\n \\includegraphics[width=1.0\\linewidth]{Images/language_neurons_heatmap_Mistral_Msvamp_RL.pdf}\n \\subcaption{Aligned - Language Neurons}\n \\label{fig:mistral-msvamp-aligned-language-heatmap}\n \\end{minipage}\n \\caption{PPL changes of MistralMathOctopus on MSVAMP after deactivating language-specific neurons or language neurons. ``Base'' indicates the results of the base model. ``Aligned'' indicates the results of the aligned model.}\n \\label{fig:mistral-msvamp-heatmap}\\\n \\vspace{-10pt}\n\\end{figure*}\n\n\n\n\\begin{figure*}[htbp]\n \\centering \n \\begin{minipage}[b]{0.41\\textwidth}\n \\includegraphics[width=1.0\\linewidth]{Images/language_neurons_heatmap_Llama_Base.pdf}\n \\subcaption{Base - Language Neurons on MGSM}\n \\label{fig:llama-mgsm-base-language-heatmap}\n \\end{minipage}\n \\begin{minipage}[b]{0.41\\textwidth}\n \\includegraphics[width=1.0\\linewidth]{Images/language_neurons_heatmap_Llama_RL.pdf}\n \\subcaption{Aligned - Language Neurons on MGSM}\n \\label{fig:llama-mgsm-aligned-language-heatmap}\n \\end{minipage}\n % \\begin{minipage}[b]{0.41\\textwidth}\n % \\includegraphics[width=1.0\\linewidth]{Images/language_neurons_heatmap_Llama_Msvamp_Base.pdf}\n % \\subcaption{Base - Language Neurons on MSVAMP}\n % \\label{fig:llama-msvamp-base-language-heatmap}\n % \\end{minipage}\n % \\begin{minipage}[b]{0.41\\textwidth}\n % \\includegraphics[width=1.0\\linewidth]{Images/language_neurons_heatmap_Llama_Msvamp_RL.pdf}\n % \\subcaption{Aligned - Language Neurons on MSVAMP}\n % \\label{fig:llama-msvamp-aligned-language-heatmap}\n % \\end{minipage}\n \\caption{PPL changes of MetaMathOctopus on MGSM after deactivating language neurons. ``Base'' indicates the results of the base model. ``Aligned'' indicates the results of the aligned model.}\n \\label{fig:llama-mgsm-msvamp-heatmap}\\\n \\vspace{-10pt}\n\\end{figure*}\n\n\n\n\n\n\\begin{figure*}[htbp]\n \\centering \n\n \\begin{subfigure}[b]{0.47\\textwidth}\n \\includegraphics[width=0.9\\linewidth]{Images/layer_wise_neurons_mistral_MSVAMP.pdf}\n \\caption{MistralMathOctopus on MSVAMP.}\n \\label{fig:layer-wise-neurons-distribution_MSVAMP}\n \\end{subfigure}\n \\hspace{10pt}\n \\begin{subfigure}[b]{0.47\\textwidth}\n \\includegraphics[width=0.9\\linewidth]{Images/layer_wise_neurons_llama_mgsm.pdf}\n \\caption{MetaMathOctopus on MGSM.}\n \\label{fig:layer-wise-neurons-distribution_MGSM_llama}\n \\end{subfigure}\n \\caption{Layer-wise distribution of the different types of neurons.}\n\\end{figure*}\n\n\n% \\begin{figure*}[htbp]\n% \\centering \n% \\includegraphics[width=0.6\\linewidth]{Images/layer_wise_neurons_llama_MSVAMP.pdf}\n% \\caption{Layer-wise distribution of the different types of neurons of MetaMathOctopus on MSVAMP.}\n% \\label{fig:layer-wise-neurons-distribution_MSVAMP_llama}\n% \\end{figure*}\n\n\\begin{figure*}[htbp]\n \\centering \n \\includegraphics[width=0.52\\textwidth]{Images/delta_layer_wise_neurons_llama_mgsm.pdf}\n \\caption{Layer-wise changes in the number of MetaMathOctopus}\n \\label{fig:Layer-wise_changes_llama}\n\\end{figure*}\n\n\n\\begin{figure*}[htbp]\n \\centering\n \\begin{subfigure}[b]{0.45\\textwidth}\n \\includegraphics[width=\\linewidth]{Images/layer_wise_delta_specific_neurons_mistral_mgsm.pdf}\n \\caption{MGSM}\n \\end{subfigure}\n \\hspace{10pt}\n \\begin{subfigure}[b]{0.45\\textwidth}\n \\includegraphics[width=\\linewidth]{Images/layer_wise_delta_related_neurons_mistral_MSVAMP.pdf}\n \\caption{MSVAMP}\n \\end{subfigure}\n \\vspace{0.5em}\n \\caption{Layer-wise changes in the number of language-specific neurons of MistralMathOctopus during the alignment.}\n \\label{fig:Layer-wise_changes_specific}\n\\end{figure*}\n\n\\begin{figure*}[htbp]\n \\centering \n \\begin{subfigure}[b]{0.45\\textwidth}\n \\includegraphics[width=\\linewidth]{Images/layer_wise_delta_related_neurons_mistral_mgsm.pdf}\n \\caption{MGSM}\n \\end{subfigure}\n \\hspace{10pt}\n \\begin{subfigure}[b]{0.45\\textwidth}\n \\includegraphics[width=\\linewidth]{Images/layer_wise_delta_related_neurons_mistral_MSVAMP.pdf}\n \\caption{MSVAMP}\n \\end{subfigure}\n \\vspace{0.5em}\n \\caption{Layer-wise changes in the number of language-related neurons of MistralMathOctopus during the alignment.}\n \\label{fig:Layer-wise_changes_related}\n\\end{figure*}\n\n\n\\begin{figure*}[htbp]\n \\centering \n \\begin{subfigure}[b]{0.45\\textwidth}\n \\includegraphics[width=\\linewidth]{Images/layer_wise_delta_agnostic_neurons_mistral_mgsm.pdf}\n \\caption{MGSM}\n \\end{subfigure}\n \\hspace{10pt}\n \\begin{subfigure}[b]{0.45\\textwidth}\n \\includegraphics[width=\\linewidth]{Images/layer_wise_delta_agnostic_neurons_mistral_MSVAMP.pdf}\n \\caption{MSVAMP}\n \\end{subfigure}\n \\vspace{0.5em}\n \\caption{Layer-wise changes in the number of language-agnostic neurons of MistralMathOctopus during the alignment.}\n \\label{fig:Layer-wise_changes_agnostic}\n\\end{figure*}\n\n\n% \\section{Technical Appendices and Supplementary Material}\n% Technical appendices with additional results, figures, graphs and proofs may be submitted with the paper submission before the full submission deadline (see above), or as a separate PDF in the ZIP file below before the supplementary material deadline. There is no page limit for the technical appendices.\n\n\n\\begin{table*}[htbp]\n\\centering\n\\small\n\\setlength{\\tabcolsep}{4pt}\n\\renewcommand{\\arraystretch}{1.2}\n\\resizebox{0.9\\columnwidth}{!}{\n\\begin{tabular}{l|cccccccccc|c}\n\\toprule\n\\textbf{Tested on MGSM} & \\textbf{bn} & \\textbf{th} & \\textbf{sw} & \\textbf{ja} & \\textbf{zh} & \\textbf{ru} & \\textbf{de} & \\textbf{es} & \\textbf{fr} & \\textbf{en} & \\textbf{Avg.} \\\\\n\\midrule\nbase & 43.6 & 53.2 & 50.4 & 55.6 & 59.6 & 59.2 & 61.2 & 62.8 & 56.8 & 75.6 & 57.8 \\\\\nzh $\\Rightarrow$ en & 49.6 & 58.4 & 54.8 & 56.4 & 65.2 & 70.0 & 66.4 & 72.8 & 68.8 & 78.4 & 64.1 \\\\\nzh/de $\\Rightarrow$ en & 46.4 & 55.6 & 59.2 & 56.8 & 64.0 & 71.2 & 66.8 & 71.2 & 69.2 & 75.2 & 63.6 \\\\\nsw/th $\\Rightarrow$ en & 48.8 & 58.8 & 59.2 & 56.4 & 68.4 & 68.4 & 69.2 & 69.6 & 70.4 & 77.6 & 64.7 \\\\\nzh/es/ru $\\Rightarrow$ en & 46.0 & 56.4 & 58.8 & 54.8 & 63.2 & 70.8 & 68.8 & 71.6 & 69.6 & 76.8 & 63.7 \\\\\nzh/es/fr/ja/de/sw/ru/th/bn $\\Rightarrow$ en & 49.6 & 60.0 & 56.4 & 56.4 & 64.4 & 64.8 & 65.2 & 65.2 & 61.2 & 76.4 & 62.0 \\\\\n\\bottomrule\n\\toprule\n\\textbf{Tested on MSVAMP} & \\textbf{bn} & \\textbf{th} & \\textbf{sw} & \\textbf{ja} & \\textbf{zh} & \\textbf{ru} & \\textbf{de} & \\textbf{es} & \\textbf{fr} & \\textbf{en} & \\textbf{Avg.} \\\\\n\\midrule\nbase & 49.3 & 62.5 & 60.6 & 60.9 & 67.4 & 64.9 & 66.5 & 67.6 & 67.2 & 77.0 & 64.4 \\\\\nzh $\\Rightarrow$ en & 52.8 & 62.5 & 60.9 & 63.7 & 66.6 & 67.5 & 69.4 & 69.8 & 69.7 & 76.5 & 65.9 \\\\\nzh/de $\\Rightarrow$ en & 53.2 & 62.0 & 62.9 & 65.3 & 66.1 & 68.7 & 69.9 & 69.0 & 70.1 & 77.0 & 66.4 \\\\\nsw/th $\\Rightarrow$ en & 53.8 & 67.4 & 65.1 & 67.7 & 71.9 & 71.4 & 71.9 & 72.0 & 72.8 & 78.9 & 69.3 \\\\\nzh/es/ru $\\Rightarrow$ en & 52.9 & 63.2 & 61.5 & 63.0 & 67.8 & 68.1 & 69.8 & 68.7 & 70.6 & 78.3 & 66.4 \\\\\nzh/es/fr/ja/de/sw/ru/th/bn $\\Rightarrow$ en & 54.9 & 65.9 & 65.6 & 68.6 & 69.7 & 70.2 & 72.3 & 71.6 & 71.6 & 77.3 & 68.8 \\\\\n\\bottomrule\n\\end{tabular}\n}\n\\caption{Accuracy of the base model and aligned variants on benchmarks.}\n\\label{tab:spontaneous_result_all_mistral}\n\\end{table*}\n\n\\clearpage\n\n% \\begin{table*}[ht]\n% \\centering\n% \\small\n% \\setlength{\\tabcolsep}{4pt}\n% \\renewcommand{\\arraystretch}{1.2}\n% \\resizebox{\\columnwidth}{!}{\n% \\begin{tabular}{l|cccccccccc|cc}\n% \\toprule\n% \\textbf{Tested on MGSM8K} & \\textbf{bn} & \\textbf{th} & \\textbf{sw} & \\textbf{ja} & \\textbf{zh} & \\textbf{ru} & \\textbf{de} & \\textbf{es} & \\textbf{fr} & \\textbf{en} & \\textbf{Avg} & \\textbf{w/o en} \\\\\n% \\midrule\n% base & 28.8 & \\textbf{42.4} & 36.4 & 37.6 & 46.8 & 46.8 & 48.0 & 52.4 & 47.6 & 65.6 & 45.2 & 43.0 \\\\\n% zh $\\Rightarrow$ en & 30.8 & 37.2 & 41.2 & 42.8 & \\textbf{52.8} & 47.6 & 56.0 & 54.8 & \\textbf{51.6} & 64.8 & 48.0 & \\textbf{46.1} \\\\\n% sw $\\Rightarrow$ en & 23.6 & 37.2 & 36.4 & 38.8 & 46.8 & 47.6 & 51.2 & 52.4 & 51.2 & 65.6 & 45.1 & 42.8 \\\\\n% zh/de $\\Rightarrow$ en & 31.2 & 36.8 & 41.2 & 42.4 & 48.4 & 47.6 & 55.6 & 52.8 & 49.6 & \\textbf{67.2} & 47.3 & 45.1 \\\\\n% zh/ja $\\Rightarrow$ en & 30.8 & 38.4 & 40.4 & \\textbf{44.4} & 50.8 & \\textbf{50.0} & 56.0 & 54.8 & \\textbf{51.6} & 66.4 & \\textbf{48.4} & 46.4 \\\\\n% zh/es $\\Rightarrow$ en & 29.2 & 35.2 & 38.8 & 41.6 & 50.0 & 48.4 & 56.0 & \\textbf{55.2} & 48.8 & 65.6 & 46.9 & 44.8 \\\\\n% sw/th $\\Rightarrow$ en & 26.4 & 36.0 & 36.4 & 38.4 & 47.6 & 46.8 & 50.0 & 53.2 & 50.8 & 65.2 & 45.1 & 42.8 \\\\\n% sw/ja $\\Rightarrow$ en & 29.6 & 37.6 & 38.8 & \\textbf{44.4} & 49.2 & 48.0 & \\textbf{56.8} & 53.2 & 51.2 & 66.4 & 47.5 & 45.4 \\\\\n% zh/de/bn $\\Rightarrow$ en & \\textbf{31.6} & 41.2 & 40.4 & 43.2 & 50.0 & 48.0 & 53.6 & 52.4 & 49.6 & 65.6 & 47.6 & 45.6 \\\\\n% zh/es/ru $\\Rightarrow$ en & \\textbf{31.6} & 37.2 & 40.4 & 40.4 & 48.4 & 47.6 & 55.6 & 52.0 & 50.4 & 66.4 & 47.0 & 44.8 \\\\\n% zh/es/fr/ja/de/sw/ru $\\Rightarrow$ en & 30.4 & 37.6 & 39.2 & 42.8 & 49.2 & 48.4 & 55.6 & 53.6 & 50.8 & 66.4 & 47.4 & 45.3 \\\\\n% zh/es/fr/ja/de/sw/ru/th/bn $\\Rightarrow$ en & 30.8 & 37.6 & \\textbf{41.6} & 42.4 & 50.8 & 48.0 & 54.4 & 53.2 & 51.2 & 64.8 & 47.5 & 45.6 \\\\\n% \\toprule\n% \\textbf{Tested on MSVAMP} & \\textbf{bn} & \\textbf{th} & \\textbf{sw} & \\textbf{ja} & \\textbf{zh} & \\textbf{ru} & \\textbf{de} & \\textbf{es} & \\textbf{fr} & \\textbf{en} & \\textbf{Avg} & \\textbf{w/o en} \\\\\n% \\midrule\n% base & 35.9 & 47.3 & 48.5 & 51.9 & 54.6 & 53.4 & 55.0 & 60.0 & 57.4 & 63.6 & 52.8 & 51.6 \\\\\n% zh $\\Rightarrow$ en & 40.5 & 52.7 & 55.0 & \\textbf{57.8} & \\textbf{57.8} & 57.4 & 59.5 & 62.5 & 62.0 & 67.3 & 57.2 & 56.1 \\\\\n% sw $\\Rightarrow$ en & 37.3 & 50.0 & 54.5 & 53.5 & 54.4 & 58.3 & 58.2 & 62.4 & 58.7 & 67.4 & 55.5 & 54.1 \\\\\n% zh/de $\\Rightarrow$ en & 38.8 & 51.6 & 56.3 & 55.7 & 56.8 & 58.4 & 60.6 & 62.5 & 60.0 & 67.3 & 56.8 & 55.6 \\\\\n% zh/ja $\\Rightarrow$ en & 39.0 & 51.3 & 54.5 & 56.3 & 56.1 & 57.2 & 59.6 & 62.2 & 60.8 & 67.0 & 56.4 & 55.2 \\\\\n% zh/es $\\Rightarrow$ en & 38.3 & 51.2 & 55.2 & 54.7 & 56.3 & 57.9 & 59.4 & 61.9 & 59.4 & 67.0 & 56.1 & 54.9 \\\\\n% sw/th $\\Rightarrow$ en & 39.8 & 53.8 & \\textbf{57.6} & 56.6 & 55.9 & \\textbf{59.6} & \\textbf{60.7} & \\textbf{64.0} & 60.2 & \\textbf{69.0} & \\textbf{57.7} & \\textbf{56.5} \\\\\n% sw/ja $\\Rightarrow$ en & 39.2 & 52.7 & 56.1 & 56.5 & 55.6 & 58.3 & 59.1 & 62.9 & 60.6 & 67.2 & 56.8 & 55.7 \\\\\n% zh/de/bn $\\Rightarrow$ en & \\textbf{42.1} & \\textbf{54.0} & 56.6 & 55.9 & 57.2 & 58.4 & 60.1 & 62.8 & \\textbf{61.8} & 66.5 & 57.5 & \\textbf{56.5} \\\\\n% zh/es/ru $\\Rightarrow$ en & 38.1 & 51.5 & 55.1 & 53.9 & 55.7 & 57.8 & 59.0 & 62.1 & 58.9 & 67.2 & 55.9 & 54.7 \\\\\n% zh/es/fr/ja/de/sw/ru $\\Rightarrow$ en & 38.2 & 51.6 & 54.6 & 54.6 & 56.1 & 58.1 & 59.4 & 62.2 & 59.7 & 67.3 & 56.2 & 54.9 \\\\\n% zh/es/fr/ja/de/sw/ru/th/bn $\\Rightarrow$ en & 39.6 & 53.4 & 56.2 & 55.2 & 57.0 & 59.3 & 59.8 & 63.0 & 61.4 & 66.9 & 57.2 & 56.1 \\\\\n% \\bottomrule\n% \\end{tabular}}\n% \\caption{Accuracy of the MetaMathOctopus-7B base model and aligned variants on benchmarks.\n% To ensure robustness, we report at least two sets of results for each language quantity. The expected accuracy of random guessing is 50.0\\%. \"X/Y $\\Rightarrow$ T\" indicates that languages X and Y are used for alignment. The best results for each language are highlighted.}\n% \\label{tab:spontaneous_result_all_llama}\n% \\end{table*}\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\n% \\newpage\n\n% \\section*{NeurIPS Paper Checklist}\n\n%%% BEGIN INSTRUCTIONS %%%\n% The checklist is designed to encourage best practices for responsible machine learning research, addressing issues of reproducibility, transparency, research ethics, and societal impact. Do not remove the checklist: {\\bf The papers not including the checklist will be desk rejected.} The checklist should follow the references and follow the (optional) supplemental material. The checklist does NOT count towards the page limit. \n\n% Please read the checklist guidelines carefully for information on how to answer these questions. For each question in the checklist:\n% \\begin{itemize}\n% \\item You should answer \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item \\answerNA{} means either that the question is Not Applicable for that particular paper or the relevant information is Not Available.\n% \\item Please provide a short (1–2 sentence) justification right after your answer (even for NA). \n% % \\item {\\bf The papers not including the checklist will be desk rejected.}\n% \\end{itemize}\n\n% {\\bf The checklist answers are an integral part of your paper submission.} They are visible to the reviewers, area chairs, senior area chairs, and ethics reviewers. You will be asked to also include it (after eventual revisions) with the final version of your paper, and its final version will be published with the paper.\n\n% The reviewers of your paper will be asked to use the checklist as one of the factors in their evaluation. 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If you answer \\answerYes{} to a question, in the justification please point to the section(s) where related material for the question can be found.\n\n% IMPORTANT, please:\n% \\begin{itemize}\n% \\item {\\bf Delete this instruction block, but keep the section heading ``NeurIPS Paper Checklist\"},\n% \\item {\\bf Keep the checklist subsection headings, questions/answers and guidelines below.}\n% \\item {\\bf Do not modify the questions and only use the provided macros for your answers}.\n% \\end{itemize} \n \n\n%%% END INSTRUCTIONS %%%\n\n\n% \\begin{enumerate}\n\n% \\item {\\bf Claims}\n% \\item[] Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?\n% \\item[] Answer: \\answerYes{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: We have introduced our contributions and scope in detail in the abstract and introduction.\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that the abstract and introduction do not include the claims made in the paper.\n% \\item The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers. \n% \\item The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings. \n% \\item It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper. \n% \\end{itemize}\n\n% \\item {\\bf Limitations}\n% \\item[] Question: Does the paper discuss the limitations of the work performed by the authors?\n% \\item[] Answer: \\answerYes{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: We have discussed the limitations and the possible future work in the Appendix~\\ref{app:limitations}.\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper. \n% \\item The authors are encouraged to create a separate \"Limitations\" section in their paper.\n% \\item The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.\n% \\item The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with a few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.\n% \\item The authors should reflect on the factors that influence the performance of the approach. For example, a facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or a speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.\n% \\item The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.\n% \\item If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.\n% \\item While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an important role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.\n% \\end{itemize}\n\n% \\item {\\bf Theory assumptions and proofs}\n% \\item[] Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?\n% \\item[] Answer: \\answerNA{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: This paper doesn't include theoretical results.\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that the paper does not include theoretical results. \n% \\item All the theorems, formulas, and proofs in the paper should be numbered and cross-referenced.\n% \\item All assumptions should be clearly stated or referenced in the statement of any theorems.\n% \\item The proofs can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors are encouraged to provide a short proof sketch to provide intuition. \n% \\item Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.\n% \\item Theorems and Lemmas that the proof relies upon should be properly referenced. \n% \\end{itemize}\n\n% \\item {\\bf Experimental result reproducibility}\n% \\item[] Question: Does the paper fully disclose all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?\n% \\item[] Answer: \\answerYes{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: We have provided the information and the experimental settings about our algorithm and analyses in detail in Section~\\ref{subsec:experimental-setup}.\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that the paper does not include experiments.\n% \\item If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.\n% \\item If the contribution is a dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable. \n% \\item Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is a novel architecture, describing the architecture fully might suffice, or if the contribution is a specific model and empirical evaluation, it may be necessary to either make it possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g., in the case of a large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.\n% \\item While NeurIPS does not require releasing code, the conference does require all submissions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example\n% \\begin{enumerate}\n% \\item If the contribution is primarily a new algorithm, the paper should make it clear how to reproduce that algorithm.\n% \\item If the contribution is primarily a new model architecture, the paper should describe the architecture clearly and fully.\n% \\item If the contribution is a new model (e.g., a large language model), then there should either be a way to access this model for reproducing the results or a way to reproduce the model (e.g., with an open-source dataset or instructions for how to construct the dataset).\n% \\item We recognize that reproducibility may be tricky in some cases, in which case authors are welcome to describe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should be possible for other researchers to have some path to reproducing or verifying the results.\n% \\end{enumerate}\n% \\end{itemize}\n\n\n% \\item {\\bf Open access to data and code}\n% \\item[] Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material?\n% \\item[] Answer: \\answerYes{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: We utilize open-source data and base models in our work. To keep anonymity, we don't include our code in our submission during the review process. Our code will be released upon publication.\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that paper does not include experiments requiring code.\n% \\item Please see the NeurIPS code and data submission guidelines (\\url{https://nips.cc/public/guides/CodeSubmissionPolicy}) for more details.\n% \\item While we encourage the release of code and data, we understand that this might not be possible, so “No” is an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for a new open-source benchmark).\n% \\item The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurIPS code and data submission guidelines (\\url{https://nips.cc/public/guides/CodeSubmissionPolicy}) for more details.\n% \\item The authors should provide instructions on data access and preparation, including how to access the raw data, preprocessed data, intermediate data, and generated data, etc.\n% \\item The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. If only a subset of experiments are reproducible, they should state which ones are omitted from the script and why.\n% \\item At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).\n% \\item Providing as much information as possible in supplemental material (appended to the paper) is recommended, but including URLs to data and code is permitted.\n% \\end{itemize}\n\n\n% \\item {\\bf Experimental setting/details}\n% \\item[] Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?\n% \\item[] Answer: \\answerYes{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: We have introduced our detailed experimental settings in Section~\\ref{subsec:experimental-setup}.\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that the paper does not include experiments.\n% \\item The experimental setting should be presented in the core of the paper to a level of detail that is necessary to appreciate the results and make sense of them.\n% \\item The full details can be provided either with the code, in appendix, or as supplemental material.\n% \\end{itemize}\n\n% \\item {\\bf Experiment statistical significance}\n% \\item[] Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?\n% \\item[] Answer: \\answerYes{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: Our neuron identification process is deterministic. In the subsequent experiments, we conduct our experiments using a fixed random seed.\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that the paper does not include experiments.\n% \\item The authors should answer \"Yes\" if the results are accompanied by error bars, confidence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper.\n% \\item The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).\n% \\item The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)\n% \\item The assumptions made should be given (e.g., Normally distributed errors).\n% \\item It should be clear whether the error bar is the standard deviation or the standard error of the mean.\n% \\item It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have a 96\\% CI, if the hypothesis of Normality of errors is not verified.\n% \\item For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out of range (e.g. negative error rates).\n% \\item If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.\n% \\end{itemize}\n\n% \\item {\\bf Experiments compute resources}\n% \\item[] Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?\n% \\item[] Answer: \\answerYes{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: We have introduced the compute resources utilized in our experiments in Appendix~\\ref{app:implementation-details}.\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that the paper does not include experiments.\n% \\item The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.\n% \\item The paper should provide the amount of compute required for each of the individual experimental runs as well as estimate the total compute. \n% \\item The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make it into the paper). \n% \\end{itemize}\n \n% \\item {\\bf Code of ethics}\n% \\item[] Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics \\url{https://neurips.cc/public/EthicsGuidelines}?\n% \\item[] Answer: \\answerYes{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: The research conducted in this paper conforms with the NeurIPS Code of Ethics.\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.\n% \\item If the authors answer No, they should explain the special circumstances that require a deviation from the Code of Ethics.\n% \\item The authors should make sure to preserve anonymity (e.g., if there is a special consideration due to laws or regulations in their jurisdiction).\n% \\end{itemize}\n\n\n% \\item {\\bf Broader impacts}\n% \\item[] Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?\n% \\item[] Answer: \\answerNA{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: We conduct mechanistic interpretability analysis in our paper, which is not tied to particular applications. So there is no societal impact of our work.\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that there is no societal impact of the work performed.\n% \\item If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact.\n% \\item Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.\n% \\item The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.\n% \\item The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.\n% \\item If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML).\n% \\end{itemize}\n \n% \\item {\\bf Safeguards}\n% \\item[] Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?\n% \\item[] Answer: \\answerNA{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: We mainly conduct mechanistic interpretability analysis based on open-resource base models and data in our work. So this paper poses no such risks.\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that the paper poses no such risks.\n% \\item Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters. \n% \\item Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.\n% \\item We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make a best faith effort.\n% \\end{itemize}\n\n% \\item {\\bf Licenses for existing assets}\n% \\item[] Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?\n% \\item[] Answer: \\answerYes{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: The assets utilized in our work have been cited. And we have mentioned the related licenses in Appendix~\\ref{app:licenses}.\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that the paper does not use existing assets.\n% \\item The authors should cite the original paper that produced the code package or dataset.\n% \\item The authors should state which version of the asset is used and, if possible, include a URL.\n% \\item The name of the license (e.g., CC-BY 4.0) should be included for each asset.\n% \\item For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.\n% \\item If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, \\url{paperswithcode.com/datasets} has curated licenses for some datasets. Their licensing guide can help determine the license of a dataset.\n% \\item For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.\n% \\item If this information is not available online, the authors are encouraged to reach out to the asset's creators.\n% \\end{itemize}\n\n% \\item {\\bf New assets}\n% \\item[] Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?\n% \\item[] Answer: \\answerNA{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: This paper does not release new assets.\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that the paper does not release new assets.\n% \\item Researchers should communicate the details of the dataset/code/model as part of their submissions via structured templates. This includes details about training, license, limitations, etc. \n% \\item The paper should discuss whether and how consent was obtained from people whose asset is used.\n% \\item At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.\n% \\end{itemize}\n\n% \\item {\\bf Crowdsourcing and research with human subjects}\n% \\item[] Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)? \n% \\item[] Answer: \\answerNA{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: Our work does not involve crowdsourcing nor research with human subjects.\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.\n% \\item Including this information in the supplemental material is fine, but if the main contribution of the paper involves human subjects, then as much detail as possible should be included in the main paper. \n% \\item According to the NeurIPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector. \n% \\end{itemize}\n\n% \\item {\\bf Institutional review board (IRB) approvals or equivalent for research with human subjects}\n% \\item[] Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?\n% \\item[] Answer: \\answerNA{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: Our work does not involve crowdsourcing nor research with human subjects.\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.\n% \\item Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper. \n% \\item We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere to the NeurIPS Code of Ethics and the guidelines for their institution. \n% \\item For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.\n% \\end{itemize}\n\n% \\item {\\bf Declaration of LLM usage}\n% \\item[] Question: Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.\n% %this research? \n% \\item[] Answer: \\answerNA{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: LLMs are not involved in the core method development in this paper.\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that the core method development in this research does not involve LLMs as any important, original, or non-standard components.\n% \\item Please refer to our LLM policy (\\url{https://neurips.cc/Conferences/2025/LLM}) for what should or should not be described.\n% \\end{itemize}\n\n% \\end{enumerate}\n\n\n\\end{document}"},"question":{"kind":"string","value":"Considering models that are aligned with the most languages, which languages that are in the top 5 in terms of performance on the MGSM test set are not in the top 5 on the MSVAMP test set."},"answer":{"kind":"string","value":"Chinese"},"plan":{"kind":"string","value":"SELECT model aligned with most languages on MGSM \nCOMPUTE rank languages by accuracy\nCOMPUTE top5_MGSM = list of top 5 languages\n\n\nSELECT model aligned with most languages on MSVAMP\nCOMPUTE rank languages by accuracy\nCOMPUTE top5_MSVAMP = list of top 5 languages\n\n\nCOMPUTE common_languages = intersection(top5_MGSM, top5_MSVAMP)\nCOMPUTE difference = set_difference(top5_mgsm, common_languages)\nRETURN difference\n"}}},{"rowIdx":69,"cells":{"paper":{"kind":"string","value":"2505.21505"},"relevant_tables":{"kind":"list like","value":[["\\begin{table*}[htbp]\n","\\centering\n","\\small\n","\\setlength{\\tabcolsep}{4pt}\n","\\renewcommand{\\arraystretch}{1.2}\n","\\resizebox{0.9\\columnwidth}{!}{\n","\\begin{tabular}{l|cccccccccc|c}\n","\\toprule\n","\\textbf{Tested on MGSM} & \\textbf{bn} & \\textbf{th} & \\textbf{sw} & \\textbf{ja} & \\textbf{zh} & \\textbf{ru} & \\textbf{de} & \\textbf{es} & \\textbf{fr} & \\textbf{en} & \\textbf{Avg.} \\\\\n","\\midrule\n","base & 43.6 & 53.2 & 50.4 & 55.6 & 59.6 & 59.2 & 61.2 & 62.8 & 56.8 & 75.6 & 57.8 \\\\\n","zh $\\Rightarrow$ en & 49.6 & 58.4 & 54.8 & 56.4 & 65.2 & 70.0 & 66.4 & 72.8 & 68.8 & 78.4 & 64.1 \\\\\n","zh/de $\\Rightarrow$ en & 46.4 & 55.6 & 59.2 & 56.8 & 64.0 & 71.2 & 66.8 & 71.2 & 69.2 & 75.2 & 63.6 \\\\\n","sw/th $\\Rightarrow$ en & 48.8 & 58.8 & 59.2 & 56.4 & 68.4 & 68.4 & 69.2 & 69.6 & 70.4 & 77.6 & 64.7 \\\\\n","zh/es/ru $\\Rightarrow$ en & 46.0 & 56.4 & 58.8 & 54.8 & 63.2 & 70.8 & 68.8 & 71.6 & 69.6 & 76.8 & 63.7 \\\\\n","zh/es/fr/ja/de/sw/ru/th/bn $\\Rightarrow$ en & 49.6 & 60.0 & 56.4 & 56.4 & 64.4 & 64.8 & 65.2 & 65.2 & 61.2 & 76.4 & 62.0 \\\\\n","\\bottomrule\n","\\toprule\n","\\textbf{Tested on MSVAMP} & \\textbf{bn} & \\textbf{th} & \\textbf{sw} & \\textbf{ja} & \\textbf{zh} & \\textbf{ru} & \\textbf{de} & \\textbf{es} & \\textbf{fr} & \\textbf{en} & \\textbf{Avg.} \\\\\n","\\midrule\n","base & 49.3 & 62.5 & 60.6 & 60.9 & 67.4 & 64.9 & 66.5 & 67.6 & 67.2 & 77.0 & 64.4 \\\\\n","zh $\\Rightarrow$ en & 52.8 & 62.5 & 60.9 & 63.7 & 66.6 & 67.5 & 69.4 & 69.8 & 69.7 & 76.5 & 65.9 \\\\\n","zh/de $\\Rightarrow$ en & 53.2 & 62.0 & 62.9 & 65.3 & 66.1 & 68.7 & 69.9 & 69.0 & 70.1 & 77.0 & 66.4 \\\\\n","sw/th $\\Rightarrow$ en & 53.8 & 67.4 & 65.1 & 67.7 & 71.9 & 71.4 & 71.9 & 72.0 & 72.8 & 78.9 & 69.3 \\\\\n","zh/es/ru $\\Rightarrow$ en & 52.9 & 63.2 & 61.5 & 63.0 & 67.8 & 68.1 & 69.8 & 68.7 & 70.6 & 78.3 & 66.4 \\\\\n","zh/es/fr/ja/de/sw/ru/th/bn $\\Rightarrow$ en & 54.9 & 65.9 & 65.6 & 68.6 & 69.7 & 70.2 & 72.3 & 71.6 & 71.6 & 77.3 & 68.8 \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\caption{Accuracy of the base model and aligned variants on benchmarks.}\n","\\label{tab:spontaneous_result_all_mistral}\n","\\end{table*}\n"]],"string":"[\n [\n \"\\\\begin{table*}[htbp]\\n\",\n \"\\\\centering\\n\",\n \"\\\\small\\n\",\n \"\\\\setlength{\\\\tabcolsep}{4pt}\\n\",\n \"\\\\renewcommand{\\\\arraystretch}{1.2}\\n\",\n \"\\\\resizebox{0.9\\\\columnwidth}{!}{\\n\",\n \"\\\\begin{tabular}{l|cccccccccc|c}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Tested on MGSM} & \\\\textbf{bn} & \\\\textbf{th} & \\\\textbf{sw} & \\\\textbf{ja} & \\\\textbf{zh} & \\\\textbf{ru} & \\\\textbf{de} & \\\\textbf{es} & \\\\textbf{fr} & \\\\textbf{en} & \\\\textbf{Avg.} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"base & 43.6 & 53.2 & 50.4 & 55.6 & 59.6 & 59.2 & 61.2 & 62.8 & 56.8 & 75.6 & 57.8 \\\\\\\\\\n\",\n \"zh $\\\\Rightarrow$ en & 49.6 & 58.4 & 54.8 & 56.4 & 65.2 & 70.0 & 66.4 & 72.8 & 68.8 & 78.4 & 64.1 \\\\\\\\\\n\",\n \"zh/de $\\\\Rightarrow$ en & 46.4 & 55.6 & 59.2 & 56.8 & 64.0 & 71.2 & 66.8 & 71.2 & 69.2 & 75.2 & 63.6 \\\\\\\\\\n\",\n \"sw/th $\\\\Rightarrow$ en & 48.8 & 58.8 & 59.2 & 56.4 & 68.4 & 68.4 & 69.2 & 69.6 & 70.4 & 77.6 & 64.7 \\\\\\\\\\n\",\n \"zh/es/ru $\\\\Rightarrow$ en & 46.0 & 56.4 & 58.8 & 54.8 & 63.2 & 70.8 & 68.8 & 71.6 & 69.6 & 76.8 & 63.7 \\\\\\\\\\n\",\n \"zh/es/fr/ja/de/sw/ru/th/bn $\\\\Rightarrow$ en & 49.6 & 60.0 & 56.4 & 56.4 & 64.4 & 64.8 & 65.2 & 65.2 & 61.2 & 76.4 & 62.0 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Tested on MSVAMP} & \\\\textbf{bn} & \\\\textbf{th} & \\\\textbf{sw} & \\\\textbf{ja} & \\\\textbf{zh} & \\\\textbf{ru} & \\\\textbf{de} & \\\\textbf{es} & \\\\textbf{fr} & \\\\textbf{en} & \\\\textbf{Avg.} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"base & 49.3 & 62.5 & 60.6 & 60.9 & 67.4 & 64.9 & 66.5 & 67.6 & 67.2 & 77.0 & 64.4 \\\\\\\\\\n\",\n \"zh $\\\\Rightarrow$ en & 52.8 & 62.5 & 60.9 & 63.7 & 66.6 & 67.5 & 69.4 & 69.8 & 69.7 & 76.5 & 65.9 \\\\\\\\\\n\",\n \"zh/de $\\\\Rightarrow$ en & 53.2 & 62.0 & 62.9 & 65.3 & 66.1 & 68.7 & 69.9 & 69.0 & 70.1 & 77.0 & 66.4 \\\\\\\\\\n\",\n \"sw/th $\\\\Rightarrow$ en & 53.8 & 67.4 & 65.1 & 67.7 & 71.9 & 71.4 & 71.9 & 72.0 & 72.8 & 78.9 & 69.3 \\\\\\\\\\n\",\n \"zh/es/ru $\\\\Rightarrow$ en & 52.9 & 63.2 & 61.5 & 63.0 & 67.8 & 68.1 & 69.8 & 68.7 & 70.6 & 78.3 & 66.4 \\\\\\\\\\n\",\n \"zh/es/fr/ja/de/sw/ru/th/bn $\\\\Rightarrow$ en & 54.9 & 65.9 & 65.6 & 68.6 & 69.7 & 70.2 & 72.3 & 71.6 & 71.6 & 77.3 & 68.8 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Accuracy of the base model and aligned variants on benchmarks.}\\n\",\n \"\\\\label{tab:spontaneous_result_all_mistral}\\n\",\n \"\\\\end{table*}\\n\"\n ]\n]","isConversational":false},"tables":{"kind":"list like","value":[["\\begin{figure*}[tbp]\n"," \\centering \n"," \\begin{minipage}[b]{0.4\\textwidth}\n"," \\includegraphics[width=1.0\\linewidth]{Images/language_specific_neurons_heatmap_Mistral_Base.pdf}\n"," \\subcaption{Base - Language-Specific Neurons}\n"," \\label{fig:mistral-mgsm-base-language-specific-heatmap}\n"," \\end{minipage}\n"," \\begin{minipage}[b]{0.4\\textwidth}\n"," \\includegraphics[width=1.0\\linewidth]{Images/language_specific_neurons_heatmap_Mistral_RL.pdf}\n"," \\subcaption{Aligned - Language-Specific Neurons}\n"," \\label{fig:mistral-mgsm-aligned-language-specific-heatmap}\n"," \\end{minipage}\n"," \\begin{minipage}[b]{0.4\\textwidth}\n"," \\includegraphics[width=1.0\\linewidth]{Images/language_neurons_heatmap_Mistral_Base.pdf}\n"," \\subcaption{Base - Language Neurons}\n"," \\label{fig:mistral-mgsm-base-language-heatmap}\n"," \\end{minipage}\n"," \\begin{minipage}[b]{0.4\\textwidth}\n"," \\includegraphics[width=1.0\\linewidth]{Images/language_neurons_heatmap_Mistral_RL.pdf}\n"," \\subcaption{Aligned - Language Neurons}\n"," \\label{fig:mistral-mgsm-aligned-language-heatmap}\n"," \\end{minipage}\n"," \\caption{PPL changes of MistralMathOctopus on MGSM after deactivating language-specific neurons or language neurons. ``Base'' indicates the results of the base model. ``Aligned'' indicates the results of the aligned model. For comparison, the results of~\\citet{tang2024language} are provided in Appendix~\\ref{app:heatmap}.}\n"," \\label{fig:mistral-mgsm-heatmap}\\\n"," \\vspace{-15pt}\n","\\end{figure*}\n"],["\\begin{figure*}[tbp]\n"," \\centering \n"," \\includegraphics[width=0.6\\linewidth]{Images/layer_wise_neurons.pdf}\n"," \\caption{Layer-wise distribution of the different types of neurons of MistralMathOctopus on MGSM.}\n"," \\label{fig:layer-wise-neurons-distribution}\n"," \\vspace{-10pt}\n","\\end{figure*}\n"],["\\begin{figure}[tbp]\n"," \\centering \n"," \\includegraphics[width=0.7\\textwidth]{Images/related_language_num.pdf}\n"," \\caption{Changes in the number of neurons shared by $N$ languages after alignment.}\n"," \\label{fig:Related_Language_Num}\n","\\end{figure}\n"],["\\begin{table*}[tbp]\n","\\centering\n","\\small\n","\\setlength{\\tabcolsep}{4pt}\n","\\renewcommand{\\arraystretch}{1.1}\n","\\caption{Accuracy of the MistralMathOctopus base model and aligned model on MGSM. \"X/Y $\\Rightarrow$ T\" indicates that languages X and Y are used for multilingual alignment.}\n","\\resizebox{0.9\\columnwidth}{!}{\n","\\begin{tabular}{l|cccccccccc|c}\n","\\toprule\n","\\textbf{MGSM} & \\textbf{bn} & \\textbf{th} & \\textbf{sw} & \\textbf{ja} & \\textbf{zh} & \\textbf{ru} & \\textbf{de} & \\textbf{es} & \\textbf{fr} & \\textbf{en} & \\textbf{Avg.} \\\\\n","\\midrule\n","base & 43.6 & 53.2 & 50.4 & 55.6 & 59.6 & 59.2 & 61.2 & 62.8 & 56.8 & 75.6 & 57.8 \\\\\n","zh/de $\\Rightarrow$ en & 46.4 & 55.6 & 59.2 & 56.8 & 64.0 & 71.2 & 66.8 & 71.2 & 69.2 & 75.2 & 63.6 \\\\\n","sw/th $\\Rightarrow$ en & 48.8 & 58.8 & 59.2 & 56.4 & 68.4 & 68.4 & 69.2 & 69.6 & 70.4 & 77.6 & 64.7 \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\label{tab:spontaneous_result}\n","\\vspace{-12pt}\n","\\end{table*}\n"],["\\begin{figure}[tbp]\n"," \\begin{minipage}{0.48\\textwidth}\n"," \\centering\n"," \\small\n"," \\setlength{\\tabcolsep}{1pt}\n"," \\renewcommand{\\arraystretch}{1.2}\n"," \\captionof{table}{Average number of different types of neurons for English and non-English languages of MistralMathOctopus on MGSM. We round the results to the nearest integer.}\n"," \\resizebox{\\columnwidth}{!}{\n"," \\begin{tabular}{lcc}\n"," \\toprule\n"," \\textbf{Language} & \\textbf{Language-Specific} & \\textbf{Language-Related} \\\\\n"," \\midrule\n"," English & 46 & 603 \\\\\n"," non-English & 613 & 2006 \\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," }\n"," \\label{tab:English-nonEnglish-neuron-count}\n"," \\end{minipage}\n"," \\hfill\n"," \\begin{minipage}{0.48\\textwidth}\n"," \\centering\n"," \\small\n"," \\setlength{\\tabcolsep}{1pt}\n"," \\renewcommand{\\arraystretch}{1.2}\n"," \\captionof{table}{Overlap ratio of different types of neurons across \\textit{different domains} and \\textit{before and after alignment}. Following~\\citet{she2024mapo}, MSVAMP is regarded as an out-of-domain dataset. The results of MistralMathOctopus on MGSM are used as the fiducial value.}\n"," \\resizebox{\\columnwidth}{!}{\n"," \\begin{tabular}{lcc}\n"," \\toprule\n"," \\textbf{Variable~(\\%)} & \\textbf{Language-Specific} & \\textbf{Language-Related} \\\\\n"," \\midrule\n"," Domain & 80.7 & 92.3 \\\\\n"," Alignment & 95.6 & 92.1 \\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," }\n"," \\label{tab:neuron-distribution-stability-overlap}\n"," \\end{minipage}\n","\\vspace{-10pt}\n","\\end{figure}\n"],["\\begin{figure*}[htbp]\n"," \\centering \n"," \\begin{minipage}[b]{0.41\\textwidth}\n"," \\includegraphics[width=1.0\\linewidth]{Images/heatmap_baseline_Mistral_Base.pdf}\n"," \\subcaption{Base - Language-Specific Neurons}\n"," \\label{fig:mistral-mgsm-baseline-base-heatmap}\n"," \\end{minipage}\n"," \\begin{minipage}[b]{0.41\\textwidth}\n"," \\includegraphics[width=1.0\\linewidth]{Images/heatmap_baseline_Mistral_RL.pdf}\n"," \\subcaption{Aligned - Language-Specific Neurons}\n"," \\label{fig:mistral-mgsm-baseline-aligned-heatmap}\n"," \\end{minipage}\n"," \\caption{PPL changes of MistralMathOctopus on MGSM when deploying the algorithm proposed by~\\citet{tang2024language}.}\n"," \\label{fig:baseline-heatmap}\\\n"," \\vspace{-10pt}\n","\\end{figure*}\n"],["\\begin{figure*}[htbp]\n"," \\centering \n"," \\begin{minipage}[b]{0.41\\textwidth}\n"," \\includegraphics[width=1.0\\linewidth]{Images/language_specific_neurons_heatmap_Mistral_Msvamp_Base.pdf}\n"," \\subcaption{Base - Language-Specific Neurons}\n"," \\label{fig:mistral-msvamp-base-language-specific-heatmap}\n"," \\end{minipage}\n"," \\begin{minipage}[b]{0.41\\textwidth}\n"," \\includegraphics[width=1.0\\linewidth]{Images/language_specific_neurons_heatmap_Mistral_Msvamp_RL.pdf}\n"," \\subcaption{Aligned - Language-Specific Neurons}\n"," \\label{fig:mistral-msvamp-aligned-language-specific-heatmap}\n"," \\end{minipage}\n"," \\begin{minipage}[b]{0.41\\textwidth}\n"," \\includegraphics[width=1.0\\linewidth]{Images/language_neurons_heatmap_Mistral_Msvamp_Base.pdf}\n"," \\subcaption{Base - Language Neurons}\n"," \\label{fig:mistral-msvamp-base-language-heatmap}\n"," \\end{minipage}\n"," \\begin{minipage}[b]{0.41\\textwidth}\n"," \\includegraphics[width=1.0\\linewidth]{Images/language_neurons_heatmap_Mistral_Msvamp_RL.pdf}\n"," \\subcaption{Aligned - Language Neurons}\n"," \\label{fig:mistral-msvamp-aligned-language-heatmap}\n"," \\end{minipage}\n"," \\caption{PPL changes of MistralMathOctopus on MSVAMP after deactivating language-specific neurons or language neurons. ``Base'' indicates the results of the base model. ``Aligned'' indicates the results of the aligned model.}\n"," \\label{fig:mistral-msvamp-heatmap}\\\n"," \\vspace{-10pt}\n","\\end{figure*}\n"],["\\begin{figure*}[htbp]\n"," \\centering \n"," \\begin{minipage}[b]{0.41\\textwidth}\n"," \\includegraphics[width=1.0\\linewidth]{Images/language_neurons_heatmap_Llama_Base.pdf}\n"," \\subcaption{Base - Language Neurons on MGSM}\n"," \\label{fig:llama-mgsm-base-language-heatmap}\n"," \\end{minipage}\n"," \\begin{minipage}[b]{0.41\\textwidth}\n"," \\includegraphics[width=1.0\\linewidth]{Images/language_neurons_heatmap_Llama_RL.pdf}\n"," \\subcaption{Aligned - Language Neurons on MGSM}\n"," \\label{fig:llama-mgsm-aligned-language-heatmap}\n"," \\end{minipage}\n"," \\caption{PPL changes of MetaMathOctopus on MGSM after deactivating language neurons. ``Base'' indicates the results of the base model. ``Aligned'' indicates the results of the aligned model.}\n"," \\label{fig:llama-mgsm-msvamp-heatmap}\\\n"," \\vspace{-10pt}\n","\\end{figure*}\n"],["\\begin{figure*}[htbp]\n"," \\centering \n","\n"," \\begin{subfigure}[b]{0.47\\textwidth}\n"," \\includegraphics[width=0.9\\linewidth]{Images/layer_wise_neurons_mistral_MSVAMP.pdf}\n"," \\caption{MistralMathOctopus on MSVAMP.}\n"," \\label{fig:layer-wise-neurons-distribution_MSVAMP}\n"," \\end{subfigure}\n"," \\hspace{10pt}\n"," \\begin{subfigure}[b]{0.47\\textwidth}\n"," \\includegraphics[width=0.9\\linewidth]{Images/layer_wise_neurons_llama_mgsm.pdf}\n"," \\caption{MetaMathOctopus on MGSM.}\n"," \\label{fig:layer-wise-neurons-distribution_MGSM_llama}\n"," \\end{subfigure}\n"," \\caption{Layer-wise distribution of the different types of neurons.}\n","\\end{figure*}\n"],["\\begin{figure*}[htbp]\n"," \\centering \n"," \\includegraphics[width=0.52\\textwidth]{Images/delta_layer_wise_neurons_llama_mgsm.pdf}\n"," \\caption{Layer-wise changes in the number of MetaMathOctopus}\n"," \\label{fig:Layer-wise_changes_llama}\n","\\end{figure*}\n"],["\\begin{figure*}[htbp]\n"," \\centering\n"," \\begin{subfigure}[b]{0.45\\textwidth}\n"," \\includegraphics[width=\\linewidth]{Images/layer_wise_delta_specific_neurons_mistral_mgsm.pdf}\n"," \\caption{MGSM}\n"," \\end{subfigure}\n"," \\hspace{10pt}\n"," \\begin{subfigure}[b]{0.45\\textwidth}\n"," \\includegraphics[width=\\linewidth]{Images/layer_wise_delta_related_neurons_mistral_MSVAMP.pdf}\n"," \\caption{MSVAMP}\n"," \\end{subfigure}\n"," \\vspace{0.5em}\n"," \\caption{Layer-wise changes in the number of language-specific neurons of MistralMathOctopus during the alignment.}\n"," \\label{fig:Layer-wise_changes_specific}\n","\\end{figure*}\n"],["\\begin{figure*}[htbp]\n"," \\centering \n"," \\begin{subfigure}[b]{0.45\\textwidth}\n"," \\includegraphics[width=\\linewidth]{Images/layer_wise_delta_related_neurons_mistral_mgsm.pdf}\n"," \\caption{MGSM}\n"," \\end{subfigure}\n"," \\hspace{10pt}\n"," \\begin{subfigure}[b]{0.45\\textwidth}\n"," \\includegraphics[width=\\linewidth]{Images/layer_wise_delta_related_neurons_mistral_MSVAMP.pdf}\n"," \\caption{MSVAMP}\n"," \\end{subfigure}\n"," \\vspace{0.5em}\n"," \\caption{Layer-wise changes in the number of language-related neurons of MistralMathOctopus during the alignment.}\n"," \\label{fig:Layer-wise_changes_related}\n","\\end{figure*}\n"],["\\begin{figure*}[htbp]\n"," \\centering \n"," \\begin{subfigure}[b]{0.45\\textwidth}\n"," \\includegraphics[width=\\linewidth]{Images/layer_wise_delta_agnostic_neurons_mistral_mgsm.pdf}\n"," \\caption{MGSM}\n"," \\end{subfigure}\n"," \\hspace{10pt}\n"," \\begin{subfigure}[b]{0.45\\textwidth}\n"," \\includegraphics[width=\\linewidth]{Images/layer_wise_delta_agnostic_neurons_mistral_MSVAMP.pdf}\n"," \\caption{MSVAMP}\n"," \\end{subfigure}\n"," \\vspace{0.5em}\n"," \\caption{Layer-wise changes in the number of language-agnostic neurons of MistralMathOctopus during the alignment.}\n"," \\label{fig:Layer-wise_changes_agnostic}\n","\\end{figure*}\n"],["\\begin{table*}[htbp]\n","\\centering\n","\\small\n","\\setlength{\\tabcolsep}{4pt}\n","\\renewcommand{\\arraystretch}{1.2}\n","\\resizebox{0.9\\columnwidth}{!}{\n","\\begin{tabular}{l|cccccccccc|c}\n","\\toprule\n","\\textbf{Tested on MGSM} & \\textbf{bn} & \\textbf{th} & \\textbf{sw} & \\textbf{ja} & \\textbf{zh} & \\textbf{ru} & \\textbf{de} & \\textbf{es} & \\textbf{fr} & \\textbf{en} & \\textbf{Avg.} \\\\\n","\\midrule\n","base & 43.6 & 53.2 & 50.4 & 55.6 & 59.6 & 59.2 & 61.2 & 62.8 & 56.8 & 75.6 & 57.8 \\\\\n","zh $\\Rightarrow$ en & 49.6 & 58.4 & 54.8 & 56.4 & 65.2 & 70.0 & 66.4 & 72.8 & 68.8 & 78.4 & 64.1 \\\\\n","zh/de $\\Rightarrow$ en & 46.4 & 55.6 & 59.2 & 56.8 & 64.0 & 71.2 & 66.8 & 71.2 & 69.2 & 75.2 & 63.6 \\\\\n","sw/th $\\Rightarrow$ en & 48.8 & 58.8 & 59.2 & 56.4 & 68.4 & 68.4 & 69.2 & 69.6 & 70.4 & 77.6 & 64.7 \\\\\n","zh/es/ru $\\Rightarrow$ en & 46.0 & 56.4 & 58.8 & 54.8 & 63.2 & 70.8 & 68.8 & 71.6 & 69.6 & 76.8 & 63.7 \\\\\n","zh/es/fr/ja/de/sw/ru/th/bn $\\Rightarrow$ en & 49.6 & 60.0 & 56.4 & 56.4 & 64.4 & 64.8 & 65.2 & 65.2 & 61.2 & 76.4 & 62.0 \\\\\n","\\bottomrule\n","\\toprule\n","\\textbf{Tested on MSVAMP} & \\textbf{bn} & \\textbf{th} & \\textbf{sw} & \\textbf{ja} & \\textbf{zh} & \\textbf{ru} & \\textbf{de} & \\textbf{es} & \\textbf{fr} & \\textbf{en} & \\textbf{Avg.} \\\\\n","\\midrule\n","base & 49.3 & 62.5 & 60.6 & 60.9 & 67.4 & 64.9 & 66.5 & 67.6 & 67.2 & 77.0 & 64.4 \\\\\n","zh $\\Rightarrow$ en & 52.8 & 62.5 & 60.9 & 63.7 & 66.6 & 67.5 & 69.4 & 69.8 & 69.7 & 76.5 & 65.9 \\\\\n","zh/de $\\Rightarrow$ en & 53.2 & 62.0 & 62.9 & 65.3 & 66.1 & 68.7 & 69.9 & 69.0 & 70.1 & 77.0 & 66.4 \\\\\n","sw/th $\\Rightarrow$ en & 53.8 & 67.4 & 65.1 & 67.7 & 71.9 & 71.4 & 71.9 & 72.0 & 72.8 & 78.9 & 69.3 \\\\\n","zh/es/ru $\\Rightarrow$ en & 52.9 & 63.2 & 61.5 & 63.0 & 67.8 & 68.1 & 69.8 & 68.7 & 70.6 & 78.3 & 66.4 \\\\\n","zh/es/fr/ja/de/sw/ru/th/bn $\\Rightarrow$ en & 54.9 & 65.9 & 65.6 & 68.6 & 69.7 & 70.2 & 72.3 & 71.6 & 71.6 & 77.3 & 68.8 \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\caption{Accuracy of the base model and aligned variants on benchmarks.}\n","\\label{tab:spontaneous_result_all_mistral}\n","\\end{table*}\n"]],"string":"[\n [\n \"\\\\begin{figure*}[tbp]\\n\",\n \" \\\\centering \\n\",\n \" \\\\begin{minipage}[b]{0.4\\\\textwidth}\\n\",\n \" \\\\includegraphics[width=1.0\\\\linewidth]{Images/language_specific_neurons_heatmap_Mistral_Base.pdf}\\n\",\n \" \\\\subcaption{Base - Language-Specific Neurons}\\n\",\n \" \\\\label{fig:mistral-mgsm-base-language-specific-heatmap}\\n\",\n \" \\\\end{minipage}\\n\",\n \" \\\\begin{minipage}[b]{0.4\\\\textwidth}\\n\",\n \" \\\\includegraphics[width=1.0\\\\linewidth]{Images/language_specific_neurons_heatmap_Mistral_RL.pdf}\\n\",\n \" \\\\subcaption{Aligned - Language-Specific Neurons}\\n\",\n \" \\\\label{fig:mistral-mgsm-aligned-language-specific-heatmap}\\n\",\n \" \\\\end{minipage}\\n\",\n \" \\\\begin{minipage}[b]{0.4\\\\textwidth}\\n\",\n \" \\\\includegraphics[width=1.0\\\\linewidth]{Images/language_neurons_heatmap_Mistral_Base.pdf}\\n\",\n \" \\\\subcaption{Base - Language Neurons}\\n\",\n \" \\\\label{fig:mistral-mgsm-base-language-heatmap}\\n\",\n \" \\\\end{minipage}\\n\",\n \" \\\\begin{minipage}[b]{0.4\\\\textwidth}\\n\",\n \" \\\\includegraphics[width=1.0\\\\linewidth]{Images/language_neurons_heatmap_Mistral_RL.pdf}\\n\",\n \" \\\\subcaption{Aligned - Language Neurons}\\n\",\n \" \\\\label{fig:mistral-mgsm-aligned-language-heatmap}\\n\",\n \" \\\\end{minipage}\\n\",\n \" \\\\caption{PPL changes of MistralMathOctopus on MGSM after deactivating language-specific neurons or language neurons. ``Base'' indicates the results of the base model. ``Aligned'' indicates the results of the aligned model. For comparison, the results of~\\\\citet{tang2024language} are provided in Appendix~\\\\ref{app:heatmap}.}\\n\",\n \" \\\\label{fig:mistral-mgsm-heatmap}\\\\\\n\",\n \" \\\\vspace{-15pt}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[tbp]\\n\",\n \" \\\\centering \\n\",\n \" \\\\includegraphics[width=0.6\\\\linewidth]{Images/layer_wise_neurons.pdf}\\n\",\n \" \\\\caption{Layer-wise distribution of the different types of neurons of MistralMathOctopus on MGSM.}\\n\",\n \" \\\\label{fig:layer-wise-neurons-distribution}\\n\",\n \" \\\\vspace{-10pt}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{figure}[tbp]\\n\",\n \" \\\\centering \\n\",\n \" \\\\includegraphics[width=0.7\\\\textwidth]{Images/related_language_num.pdf}\\n\",\n \" \\\\caption{Changes in the number of neurons shared by $N$ languages after alignment.}\\n\",\n \" \\\\label{fig:Related_Language_Num}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table*}[tbp]\\n\",\n \"\\\\centering\\n\",\n \"\\\\small\\n\",\n \"\\\\setlength{\\\\tabcolsep}{4pt}\\n\",\n \"\\\\renewcommand{\\\\arraystretch}{1.1}\\n\",\n \"\\\\caption{Accuracy of the MistralMathOctopus base model and aligned model on MGSM. \\\"X/Y $\\\\Rightarrow$ T\\\" indicates that languages X and Y are used for multilingual alignment.}\\n\",\n \"\\\\resizebox{0.9\\\\columnwidth}{!}{\\n\",\n \"\\\\begin{tabular}{l|cccccccccc|c}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{MGSM} & \\\\textbf{bn} & \\\\textbf{th} & \\\\textbf{sw} & \\\\textbf{ja} & \\\\textbf{zh} & \\\\textbf{ru} & \\\\textbf{de} & \\\\textbf{es} & \\\\textbf{fr} & \\\\textbf{en} & \\\\textbf{Avg.} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"base & 43.6 & 53.2 & 50.4 & 55.6 & 59.6 & 59.2 & 61.2 & 62.8 & 56.8 & 75.6 & 57.8 \\\\\\\\\\n\",\n \"zh/de $\\\\Rightarrow$ en & 46.4 & 55.6 & 59.2 & 56.8 & 64.0 & 71.2 & 66.8 & 71.2 & 69.2 & 75.2 & 63.6 \\\\\\\\\\n\",\n \"sw/th $\\\\Rightarrow$ en & 48.8 & 58.8 & 59.2 & 56.4 & 68.4 & 68.4 & 69.2 & 69.6 & 70.4 & 77.6 & 64.7 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\label{tab:spontaneous_result}\\n\",\n \"\\\\vspace{-12pt}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{figure}[tbp]\\n\",\n \" \\\\begin{minipage}{0.48\\\\textwidth}\\n\",\n \" \\\\centering\\n\",\n \" \\\\small\\n\",\n \" \\\\setlength{\\\\tabcolsep}{1pt}\\n\",\n \" \\\\renewcommand{\\\\arraystretch}{1.2}\\n\",\n \" \\\\captionof{table}{Average number of different types of neurons for English and non-English languages of MistralMathOctopus on MGSM. We round the results to the nearest integer.}\\n\",\n \" \\\\resizebox{\\\\columnwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lcc}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Language} & \\\\textbf{Language-Specific} & \\\\textbf{Language-Related} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" English & 46 & 603 \\\\\\\\\\n\",\n \" non-English & 613 & 2006 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \" \\\\label{tab:English-nonEnglish-neuron-count}\\n\",\n \" \\\\end{minipage}\\n\",\n \" \\\\hfill\\n\",\n \" \\\\begin{minipage}{0.48\\\\textwidth}\\n\",\n \" \\\\centering\\n\",\n \" \\\\small\\n\",\n \" \\\\setlength{\\\\tabcolsep}{1pt}\\n\",\n \" \\\\renewcommand{\\\\arraystretch}{1.2}\\n\",\n \" \\\\captionof{table}{Overlap ratio of different types of neurons across \\\\textit{different domains} and \\\\textit{before and after alignment}. Following~\\\\citet{she2024mapo}, MSVAMP is regarded as an out-of-domain dataset. The results of MistralMathOctopus on MGSM are used as the fiducial value.}\\n\",\n \" \\\\resizebox{\\\\columnwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lcc}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Variable~(\\\\%)} & \\\\textbf{Language-Specific} & \\\\textbf{Language-Related} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Domain & 80.7 & 92.3 \\\\\\\\\\n\",\n \" Alignment & 95.6 & 92.1 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \" \\\\label{tab:neuron-distribution-stability-overlap}\\n\",\n \" \\\\end{minipage}\\n\",\n \"\\\\vspace{-10pt}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{figure*}[htbp]\\n\",\n \" \\\\centering \\n\",\n \" \\\\begin{minipage}[b]{0.41\\\\textwidth}\\n\",\n \" \\\\includegraphics[width=1.0\\\\linewidth]{Images/heatmap_baseline_Mistral_Base.pdf}\\n\",\n \" \\\\subcaption{Base - Language-Specific Neurons}\\n\",\n \" \\\\label{fig:mistral-mgsm-baseline-base-heatmap}\\n\",\n \" \\\\end{minipage}\\n\",\n \" \\\\begin{minipage}[b]{0.41\\\\textwidth}\\n\",\n \" \\\\includegraphics[width=1.0\\\\linewidth]{Images/heatmap_baseline_Mistral_RL.pdf}\\n\",\n \" \\\\subcaption{Aligned - Language-Specific Neurons}\\n\",\n \" \\\\label{fig:mistral-mgsm-baseline-aligned-heatmap}\\n\",\n \" \\\\end{minipage}\\n\",\n \" \\\\caption{PPL changes of MistralMathOctopus on MGSM when deploying the algorithm proposed by~\\\\citet{tang2024language}.}\\n\",\n \" \\\\label{fig:baseline-heatmap}\\\\\\n\",\n \" \\\\vspace{-10pt}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[htbp]\\n\",\n \" \\\\centering \\n\",\n \" \\\\begin{minipage}[b]{0.41\\\\textwidth}\\n\",\n \" \\\\includegraphics[width=1.0\\\\linewidth]{Images/language_specific_neurons_heatmap_Mistral_Msvamp_Base.pdf}\\n\",\n \" \\\\subcaption{Base - Language-Specific Neurons}\\n\",\n \" \\\\label{fig:mistral-msvamp-base-language-specific-heatmap}\\n\",\n \" \\\\end{minipage}\\n\",\n \" \\\\begin{minipage}[b]{0.41\\\\textwidth}\\n\",\n \" \\\\includegraphics[width=1.0\\\\linewidth]{Images/language_specific_neurons_heatmap_Mistral_Msvamp_RL.pdf}\\n\",\n \" \\\\subcaption{Aligned - Language-Specific Neurons}\\n\",\n \" \\\\label{fig:mistral-msvamp-aligned-language-specific-heatmap}\\n\",\n \" \\\\end{minipage}\\n\",\n \" \\\\begin{minipage}[b]{0.41\\\\textwidth}\\n\",\n \" \\\\includegraphics[width=1.0\\\\linewidth]{Images/language_neurons_heatmap_Mistral_Msvamp_Base.pdf}\\n\",\n \" \\\\subcaption{Base - Language Neurons}\\n\",\n \" \\\\label{fig:mistral-msvamp-base-language-heatmap}\\n\",\n \" \\\\end{minipage}\\n\",\n \" \\\\begin{minipage}[b]{0.41\\\\textwidth}\\n\",\n \" \\\\includegraphics[width=1.0\\\\linewidth]{Images/language_neurons_heatmap_Mistral_Msvamp_RL.pdf}\\n\",\n \" \\\\subcaption{Aligned - Language Neurons}\\n\",\n \" \\\\label{fig:mistral-msvamp-aligned-language-heatmap}\\n\",\n \" \\\\end{minipage}\\n\",\n \" \\\\caption{PPL changes of MistralMathOctopus on MSVAMP after deactivating language-specific neurons or language neurons. ``Base'' indicates the results of the base model. ``Aligned'' indicates the results of the aligned model.}\\n\",\n \" \\\\label{fig:mistral-msvamp-heatmap}\\\\\\n\",\n \" \\\\vspace{-10pt}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[htbp]\\n\",\n \" \\\\centering \\n\",\n \" \\\\begin{minipage}[b]{0.41\\\\textwidth}\\n\",\n \" \\\\includegraphics[width=1.0\\\\linewidth]{Images/language_neurons_heatmap_Llama_Base.pdf}\\n\",\n \" \\\\subcaption{Base - Language Neurons on MGSM}\\n\",\n \" \\\\label{fig:llama-mgsm-base-language-heatmap}\\n\",\n \" \\\\end{minipage}\\n\",\n \" \\\\begin{minipage}[b]{0.41\\\\textwidth}\\n\",\n \" \\\\includegraphics[width=1.0\\\\linewidth]{Images/language_neurons_heatmap_Llama_RL.pdf}\\n\",\n \" \\\\subcaption{Aligned - Language Neurons on MGSM}\\n\",\n \" \\\\label{fig:llama-mgsm-aligned-language-heatmap}\\n\",\n \" \\\\end{minipage}\\n\",\n \" \\\\caption{PPL changes of MetaMathOctopus on MGSM after deactivating language neurons. ``Base'' indicates the results of the base model. ``Aligned'' indicates the results of the aligned model.}\\n\",\n \" \\\\label{fig:llama-mgsm-msvamp-heatmap}\\\\\\n\",\n \" \\\\vspace{-10pt}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[htbp]\\n\",\n \" \\\\centering \\n\",\n \"\\n\",\n \" \\\\begin{subfigure}[b]{0.47\\\\textwidth}\\n\",\n \" \\\\includegraphics[width=0.9\\\\linewidth]{Images/layer_wise_neurons_mistral_MSVAMP.pdf}\\n\",\n \" \\\\caption{MistralMathOctopus on MSVAMP.}\\n\",\n \" \\\\label{fig:layer-wise-neurons-distribution_MSVAMP}\\n\",\n \" \\\\end{subfigure}\\n\",\n \" \\\\hspace{10pt}\\n\",\n \" \\\\begin{subfigure}[b]{0.47\\\\textwidth}\\n\",\n \" \\\\includegraphics[width=0.9\\\\linewidth]{Images/layer_wise_neurons_llama_mgsm.pdf}\\n\",\n \" \\\\caption{MetaMathOctopus on MGSM.}\\n\",\n \" \\\\label{fig:layer-wise-neurons-distribution_MGSM_llama}\\n\",\n \" \\\\end{subfigure}\\n\",\n \" \\\\caption{Layer-wise distribution of the different types of neurons.}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[htbp]\\n\",\n \" \\\\centering \\n\",\n \" \\\\includegraphics[width=0.52\\\\textwidth]{Images/delta_layer_wise_neurons_llama_mgsm.pdf}\\n\",\n \" \\\\caption{Layer-wise changes in the number of MetaMathOctopus}\\n\",\n \" \\\\label{fig:Layer-wise_changes_llama}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[htbp]\\n\",\n \" \\\\centering\\n\",\n \" \\\\begin{subfigure}[b]{0.45\\\\textwidth}\\n\",\n \" \\\\includegraphics[width=\\\\linewidth]{Images/layer_wise_delta_specific_neurons_mistral_mgsm.pdf}\\n\",\n \" \\\\caption{MGSM}\\n\",\n \" \\\\end{subfigure}\\n\",\n \" \\\\hspace{10pt}\\n\",\n \" \\\\begin{subfigure}[b]{0.45\\\\textwidth}\\n\",\n \" \\\\includegraphics[width=\\\\linewidth]{Images/layer_wise_delta_related_neurons_mistral_MSVAMP.pdf}\\n\",\n \" \\\\caption{MSVAMP}\\n\",\n \" \\\\end{subfigure}\\n\",\n \" \\\\vspace{0.5em}\\n\",\n \" \\\\caption{Layer-wise changes in the number of language-specific neurons of MistralMathOctopus during the alignment.}\\n\",\n \" \\\\label{fig:Layer-wise_changes_specific}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[htbp]\\n\",\n \" \\\\centering \\n\",\n \" \\\\begin{subfigure}[b]{0.45\\\\textwidth}\\n\",\n \" \\\\includegraphics[width=\\\\linewidth]{Images/layer_wise_delta_related_neurons_mistral_mgsm.pdf}\\n\",\n \" \\\\caption{MGSM}\\n\",\n \" \\\\end{subfigure}\\n\",\n \" \\\\hspace{10pt}\\n\",\n \" \\\\begin{subfigure}[b]{0.45\\\\textwidth}\\n\",\n \" \\\\includegraphics[width=\\\\linewidth]{Images/layer_wise_delta_related_neurons_mistral_MSVAMP.pdf}\\n\",\n \" \\\\caption{MSVAMP}\\n\",\n \" \\\\end{subfigure}\\n\",\n \" \\\\vspace{0.5em}\\n\",\n \" \\\\caption{Layer-wise changes in the number of language-related neurons of MistralMathOctopus during the alignment.}\\n\",\n \" \\\\label{fig:Layer-wise_changes_related}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[htbp]\\n\",\n \" \\\\centering \\n\",\n \" \\\\begin{subfigure}[b]{0.45\\\\textwidth}\\n\",\n \" \\\\includegraphics[width=\\\\linewidth]{Images/layer_wise_delta_agnostic_neurons_mistral_mgsm.pdf}\\n\",\n \" \\\\caption{MGSM}\\n\",\n \" \\\\end{subfigure}\\n\",\n \" \\\\hspace{10pt}\\n\",\n \" \\\\begin{subfigure}[b]{0.45\\\\textwidth}\\n\",\n \" \\\\includegraphics[width=\\\\linewidth]{Images/layer_wise_delta_agnostic_neurons_mistral_MSVAMP.pdf}\\n\",\n \" \\\\caption{MSVAMP}\\n\",\n \" \\\\end{subfigure}\\n\",\n \" \\\\vspace{0.5em}\\n\",\n \" \\\\caption{Layer-wise changes in the number of language-agnostic neurons of MistralMathOctopus during the alignment.}\\n\",\n \" \\\\label{fig:Layer-wise_changes_agnostic}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{table*}[htbp]\\n\",\n \"\\\\centering\\n\",\n \"\\\\small\\n\",\n \"\\\\setlength{\\\\tabcolsep}{4pt}\\n\",\n \"\\\\renewcommand{\\\\arraystretch}{1.2}\\n\",\n \"\\\\resizebox{0.9\\\\columnwidth}{!}{\\n\",\n \"\\\\begin{tabular}{l|cccccccccc|c}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Tested on MGSM} & \\\\textbf{bn} & \\\\textbf{th} & \\\\textbf{sw} & \\\\textbf{ja} & \\\\textbf{zh} & \\\\textbf{ru} & \\\\textbf{de} & \\\\textbf{es} & \\\\textbf{fr} & \\\\textbf{en} & \\\\textbf{Avg.} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"base & 43.6 & 53.2 & 50.4 & 55.6 & 59.6 & 59.2 & 61.2 & 62.8 & 56.8 & 75.6 & 57.8 \\\\\\\\\\n\",\n \"zh $\\\\Rightarrow$ en & 49.6 & 58.4 & 54.8 & 56.4 & 65.2 & 70.0 & 66.4 & 72.8 & 68.8 & 78.4 & 64.1 \\\\\\\\\\n\",\n \"zh/de $\\\\Rightarrow$ en & 46.4 & 55.6 & 59.2 & 56.8 & 64.0 & 71.2 & 66.8 & 71.2 & 69.2 & 75.2 & 63.6 \\\\\\\\\\n\",\n \"sw/th $\\\\Rightarrow$ en & 48.8 & 58.8 & 59.2 & 56.4 & 68.4 & 68.4 & 69.2 & 69.6 & 70.4 & 77.6 & 64.7 \\\\\\\\\\n\",\n \"zh/es/ru $\\\\Rightarrow$ en & 46.0 & 56.4 & 58.8 & 54.8 & 63.2 & 70.8 & 68.8 & 71.6 & 69.6 & 76.8 & 63.7 \\\\\\\\\\n\",\n \"zh/es/fr/ja/de/sw/ru/th/bn $\\\\Rightarrow$ en & 49.6 & 60.0 & 56.4 & 56.4 & 64.4 & 64.8 & 65.2 & 65.2 & 61.2 & 76.4 & 62.0 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Tested on MSVAMP} & \\\\textbf{bn} & \\\\textbf{th} & \\\\textbf{sw} & \\\\textbf{ja} & \\\\textbf{zh} & \\\\textbf{ru} & \\\\textbf{de} & \\\\textbf{es} & \\\\textbf{fr} & \\\\textbf{en} & \\\\textbf{Avg.} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"base & 49.3 & 62.5 & 60.6 & 60.9 & 67.4 & 64.9 & 66.5 & 67.6 & 67.2 & 77.0 & 64.4 \\\\\\\\\\n\",\n \"zh $\\\\Rightarrow$ en & 52.8 & 62.5 & 60.9 & 63.7 & 66.6 & 67.5 & 69.4 & 69.8 & 69.7 & 76.5 & 65.9 \\\\\\\\\\n\",\n \"zh/de $\\\\Rightarrow$ en & 53.2 & 62.0 & 62.9 & 65.3 & 66.1 & 68.7 & 69.9 & 69.0 & 70.1 & 77.0 & 66.4 \\\\\\\\\\n\",\n \"sw/th $\\\\Rightarrow$ en & 53.8 & 67.4 & 65.1 & 67.7 & 71.9 & 71.4 & 71.9 & 72.0 & 72.8 & 78.9 & 69.3 \\\\\\\\\\n\",\n \"zh/es/ru $\\\\Rightarrow$ en & 52.9 & 63.2 & 61.5 & 63.0 & 67.8 & 68.1 & 69.8 & 68.7 & 70.6 & 78.3 & 66.4 \\\\\\\\\\n\",\n \"zh/es/fr/ja/de/sw/ru/th/bn $\\\\Rightarrow$ en & 54.9 & 65.9 & 65.6 & 68.6 & 69.7 & 70.2 & 72.3 & 71.6 & 71.6 & 77.3 & 68.8 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Accuracy of the base model and aligned variants on benchmarks.}\\n\",\n \"\\\\label{tab:spontaneous_result_all_mistral}\\n\",\n \"\\\\end{table*}\\n\"\n ]\n]","isConversational":false},"fulltext":{"kind":"string","value":"\\documentclass{article}\n\n\n% if you need to pass options to natbib, use, e.g.:\n% \\PassOptionsToPackage{numbers, compress}{natbib}\n% before loading neurips_2025\n\n\n% ready for submission\n% \\usepackage{neurips_2025}\n\n\n% to compile a preprint version, e.g., for submission to arXiv, add add the\n% [preprint] option:\n\\usepackage[preprint]{neurips_2025}\n\n\n% to compile a camera-ready version, add the [final] option, e.g.:\n% \\usepackage[final]{neurips_2025}\n\n\n% to avoid loading the natbib package, add option nonatbib:\n% \\usepackage[nonatbib]{neurips_2025}\n\n\n\\usepackage[utf8]{inputenc} % allow utf-8 input\n\\usepackage[T1]{fontenc} % use 8-bit T1 fonts\n\\usepackage{hyperref} % hyperlinks\n\\usepackage{url} % simple URL typesetting\n\\usepackage{booktabs} % professional-quality tables\n\\usepackage{amsfonts} % blackboard math symbols\n\\usepackage{nicefrac} % compact symbols for 1/2, etc.\n\\usepackage{microtype} % microtypography\n\\usepackage{xcolor} % colors\n\\usepackage{graphicx} \n\\usepackage{amsmath}\n\\usepackage{subcaption}\n\\usepackage{wrapfig}\n\\usepackage{amssymb}\n\n\n\\title{How does Alignment Enhance LLMs' Multilingual Capabilities? A Language Neurons Perspective}\n\n\n% The \\author macro works with any number of authors. There are two commands\n% used to separate the names and addresses of multiple authors: \\And and \\AND.\n%\n% Using \\And between authors leaves it to LaTeX to determine where to break the\n% lines. Using \\AND forces a line break at that point. So, if LaTeX puts 3 of 4\n% authors names on the first line, and the last on the second line, try using\n% \\AND instead of \\And before the third author name.\n\n\n\n% \\author{\n% \\textbf{Shimao Zhang$^\\clubsuit$\\thanks{Equal contribution.}\\thanks{Work done during his internship at MSRA.}\\ \\ \\ \n% Zhejian Lai$^\\clubsuit$\\footnote[1]\\ \\ \\ \n% Xiang Liu$^\\clubsuit$\\footnote[1]\\ \\ \\ \n% Junxiao Liu$^\\clubsuit$\\ \\ \\ \n% Zheheng Luo$^\\Diamond$} \\\\\n% \\textbf{Shujian Huang$^\\clubsuit$\\thanks{Corresponding authors.}\\ \\ \\ \n% Yeyun Gong$^\\bigstar$} \\\\\n% $^\\clubsuit$National Key Laboratory for Novel Software Technology, Nanjing University \\\\\n% $^\\Diamond$The University of Manchester \\ \\ \\ \\ \n% $^\\bigstar$Microsoft Research Asia \\\\\n% \\texttt{smzhang@smail.nju.edu.cn, huangsj@nju.edu.cn,} \\\\\n% \\texttt{xiao.liu.msrasia@microsoft.com}\n% }\n\n\\author{\n\\textbf{Shimao Zhang\\textsuperscript{1}\\textsuperscript{\\dag}\\textsuperscript{*}}\\ \\ \\ \n\\textbf{Zhejian Lai\\textsuperscript{1}\\textsuperscript{*}}\\ \\ \\ \n\\textbf{Xiang Liu\\textsuperscript{1}\\textsuperscript{*}}\\ \\ \\ \n\\textbf{Shuaijie She}\\textsuperscript{1}\\ \\ \\ \n\\textbf{Xiao Liu}\\textsuperscript{2}\\ \\ \\ \\\\\n\\textbf{Yeyun Gong}\\textsuperscript{2}\\ \\ \\ \n\\textbf{Shujian Huang\\textsuperscript{1}\\textsuperscript{\\ddag}}\\ \\ \\ \n\\textbf{Jiajun Chen}\\textsuperscript{1}\\ \\ \\ \\\\\n\\textsuperscript{1} National Key Laboratory for Novel Software Technology, Nanjing University \\\\\n\\textsuperscript{2} Microsoft Research Asia \\\\\n\\texttt{\\{smzhang,laizj,liuxiang,shesj\\}@smail.nju.edu.cn} \\\\\n\\texttt{\\{xiao.liu.msrasia,yegong\\}@microsoft.com} \\\\\n\\texttt{\\{huangsj,chenjj\\}@nju.edu.cn}\n}\n\n\n\\begin{document}\n\n\\renewcommand{\\thefootnote}{\\fnsymbol{footnote}}\n\\footnotetext[1]{Equal contribution.}\n\\footnotetext[2]{Work done during his internship at MSRA.}\n\\footnotetext[3]{Corresponding author.}\n\\renewcommand{\\thefootnote}{\\arabic{footnote}} % optional\n% \\footnotetext[1]{The project will be available at \\url{https://github.com/NJUNLP/Language-Neurons-Alignment}}\n\n\\maketitle\n\n\\begin{abstract}\n% Large Language Models~(LLMs) have shown impressive performance across various downstream language tasks. However, there are imbalanced performances of LLMs across different languages. \nMultilingual Alignment is an effective and representative paradigm to enhance LLMs' multilingual capabilities, which transfers the capabilities from the high-resource languages to the low-resource languages. Meanwhile, some researches on language-specific neurons reveal that there are language-specific neurons that are selectively activated in LLMs when processing different languages. This provides a new perspective to analyze and understand LLMs' mechanisms more specifically in multilingual scenarios. In this work, we propose a new finer-grained neuron identification algorithm, which detects language neurons~(including language-specific neurons and language-related neurons) and language-agnostic neurons. Furthermore, based on the distributional characteristics of different types of neurons, we divide the LLMs' internal process for multilingual inference into four parts: (1) multilingual understanding, (2) shared semantic space reasoning, (3) multilingual output space transformation, and (4) vocabulary space outputting. Additionally, we systematically analyze the models before and after alignment with a focus on different types of neurons. We also analyze the phenomenon of ``Spontaneous Multilingual Alignment''. Overall, our work conducts a comprehensive investigation based on different types of neurons, providing empirical results and valuable insights for better understanding multilingual alignment and multilingual capabilities of LLMs.\\footnote{The project will be available at \\url{https://github.com/NJUNLP/Language-Neurons-Alignment}.}\n\\end{abstract}\n\\section{Introduction}\\label{sec:introduction}\nBy training on the extensive corpus, large language models~(LLMs) demonstrate outstanding language capabilities~\\citep{grattafiori2024llama,yang2024qwen2,liu2024deepseek,zhang2025process}. However, due to the unbalanced pretraining corpus across different languages, LLMs have very uneven performance on high-resource languages and low-resource languages~\\citep{huang2023not,zhu2023multilingual,zhang2024getting}. Therefore, researchers have conducted comprehensive explorations to further enhance the multilingual performance of LLMs. A straightforward approach is increasing the proportion of non-English texts during pretraining~\\citep{ni2021m3p,yang2024qwen2} or performing continual pretraining with multilingual texts~\\citep{liu2021continual,ji2024emma}. But these approaches often entail high computational costs and substantial amounts of multilingual data.\n\nConsidering LLMs' great performance on the high-resource languages, multilingual alignment has emerged as a representative paradigm for enhancing multilingual capabilities by transferring knowledge from high-resource to low-resource languages~\\citep{zhao2024llama,she2024mapo}. A representative example is MAPO~\\citet{she2024mapo}, which improves multilingual alignment by utilizing a well-trained multilingual translation model to compute alignment scores based on the conditional generation probability of translating non-English responses into English.\n\n\\begin{wrapfigure}{r}{0.48\\textwidth}\n \\centering \n \\includegraphics[width=0.48\\textwidth]{Images/case.pdf}\n \\caption{A neuron's activation probability across different languages. This neuron that exhibits high activation probabilities across \\textit{multiple} but \\textit{not all} languages can't be correctly categorized under the existing methodology.}\n \\label{fig:case-study-language-related-neurons}\n \\vspace{-10pt}\n\\end{wrapfigure}\nMany studies conduct systematic mechanism analyses of the multilingual alignment and LLMs' multilingual capabilities. \\citet{zhao2024large} split the multilingual processing workflow into three parts: multilingual understanding, resolving tasks, and generating outputs in the target language. This three-stage inference workflow clearly demonstrates how LLMs leverage English as a pivot language to handle multilingualism using a unified pattern.\n% It is found that English acts as a pivot language in many English-centric LLMs, which indicates that English still participates in the LLMs' inference process even when the models are prompted in other non-English languages~\\citep{wendler2024llamas,zhao2024large}. These latent cross-lingual connections highlight the feasibility and importance of enhancing LLMs’ multilingual capabilities through multilingual alignment~\\citep{zhang2024getting}. Additionally, as mentioned above, different languages are not entirely independent within the model, leading to both language-related and language-agnostic components in LLMs' multilingual reasoning.\n% However, due to the limitations of the previous methodology for identifying language-related components, the investigation of multilingual alignment based on this is still highly limited.\n% Many studies attempt to identify the language-related and language-agnostic components in LLMs.\nInspired by the neurobiological underpinnings of human language faculties,~\\citet{tang2024language} conducts a fine-grained identification by detecting the language-specific neurons. Their results indicate that LLMs predominantly utilize a small subset of neurons to process the particular target language. Furthermore, these language-specific neurons are primarily situated in the model's top and bottom layers~\\citep{tang2024language}, which is consistent with the three-stage multilingual workflow of~\\citet{zhao2024large}. However, we notice a key limitation in the existing language-specific neuron identification methodology: Some neurons are shared across multiple languages but are not entirely language-agnostic. Such neurons are incorrectly categorized as either language-specific or language-agnostic neurons under the existing framework. We present a case study in Figure~\\ref{fig:case-study-language-related-neurons}.\n%(b) The existing method doesn't consider the distributional biases of language neurons between high-resource and low-resource languages.\nFurthermore, we aim to systematically explore an important question: Can we better analyze and understand how multilingual alignment enhances the LLMs' multilingual capabilities from the perspective of language neurons?\n\nIn this work, we comprehensively investigate the multilingual alignment of LLMs from the perspective of language neurons, where we deploy MAPO as a representative multilingual alignment algorithm. Considering the above limitation of the existing language-specific neuron identification methodology, we define \\textbf{language neurons} as the union of \\textbf{language-specific neurons} and \\textbf{language-related neurons}, as opposed to \\textbf{language-agnostic neurons}. We separately distinguish language-related neurons from both language-specific and language-agnostic neurons, which allows for more precise analyses. Furthermore, we propose a new language neuron identification algorithm in our work, which is able to identify language-related neurons that are in fact shared across multiple languages. \n\nThen we analyze the models before and after alignment, focusing on the changes in different types of neurons. Based on the distributional characteristics of these neurons, we divide LLMs' internal process for multilingual inference into four parts: (1) multilingual understanding, (2) shared semantic space reasoning, (3) multilingual output space transformation, and (4) vocabulary space outputting. Our findings reveal that different parts exhibit distinct dependencies on different types of neurons, and that multilingual alignment significantly enhances the activation of the corresponding types of neurons across the relevant layers. \nAdditionally, we analyze the important ``Spontaneous Multilingual Alignment''~\\citep{zhang2024getting} phenomenon in LLMs, providing insights into the roles of language-agnostic neurons and language-related neurons shared across languages. For further analysis, we also provide observations about the uniqueness of English and the neuron distributions. Overall, based on different types of neurons, we present empirical results and valuable insights that contribute to a deeper understanding of multilingual alignment and the multilingual capabilities of LLMs.\n\n\\section{Related Work}\\label{sec:related-work}\n% In this section, we briefly introduce the background and related work of our work. Firstly, we introduce the representative paradigms of multilingual alignment in \\S\\ref{subsec:bg-multilingual-alignment}. Then we overview the existing research on the mechanistic interpretability of LLMs in \\S\\ref{subsec:bg-mechanistic-interpretability}. Finally, in \\S\\ref{subsec:bg-language-specific-neurons}, we review existing studies and methodologies on language-specific neurons.\n\n\\subsection{Multilingual Alignment}\\label{subsec:bg-multilingual-alignment}\nConducting pretraining or continual pretraining on the multilingual corpus is a straightforward and effective method to enhance LLMs' multilingual capabilities~\\citep{ni2021m3p,ji2024emma}. However, these methods typically require substantial investments in time, data, and computational resources. Thus, many researchers perform multilingual alignment to improve LLMs' multilingual performance by transferring the capabilities from high-resource languages to low-resource languages~\\citep{eronen2023zero,zhao2024adamergex,zhao2024llama, she2024mapo}, which efficiently and effectively improves the model performance in low-resource language scenarios. Furthermore, \\citet{zhang2024getting} first finds the ``Spontaneous Multilingual Alignment'' phenomenon in LLMs, which demonstrates that conducting multilingual alignment based on a small number of languages effectively improves the alignment even between English and many languages unseen during alignment.\n% As a representative multilingual alignment algorithm, MAPO~\\citep{she2024mapo} utilizes preference optimization algorithm for multilingual alignment. For inputs in different languages, the model generates responses in the corresponding languages and the corresponding English output is used as the alignment target. The alignment score of each response is acquired based on a well-trained multilingual translation model by calculating the conditional generation probability while translating the response in the source language to English. This algorithm can effectively improve the model performance across different languages. In our work, we choose MAPO as a representative multilingual alignment method and mainly analyze based on it.\n\n\\subsection{Mechanistic Interpretability}\\label{subsec:bg-mechanistic-interpretability}\nIn addition to enhancing LLMs' multilingual performance, research on the underlying mechanisms of multilingual capabilities in LLMs is still ongoing. It is crucial for us to understand and explain the LLMs and related methods explicitly. Typically, the existing approaches primarily perform mechanistic interpretability analyses by observing the internal states of the model~\\citep{logit-lens,zhang2024getting,zhao2024large,mousi-etal-2024-exploring}. Overall, neuron states and latent intermediate logits are both important objects of observation. For latent logits, \\citet{wendler2024llamas} utilizes logit lens~\\citep{logit-lens} to directly project the logits in the intermediate layers to the vocabulary space, which reveals the latent participation of English in the intermediate layers. For neuron states, \\citet{hu2024large} analyzes the neuron activation overlap to measure the extent of shared neuron activation across different languages. \n% Furthermore, \\citet{tang2024language} propose language activation probability entropy~(LAPE) to identify the language-specific neurons, which dissect the multilingual capabilities of LLMs from a novel perspective. In our work, we mainly conduct our mechanistic interpretability analyses on the neuron states.\n\n\\subsection{Language-Specific Neurons}\\label{subsec:bg-language-specific-neurons}\nMany studies have revealed the language-related and language-agnostic components in LLMs. At the layer level, the multilingual processing of LLMs is considered to involve three stages~\\citep{zhao2024large,wendler2024llamas}: converting multilingual inputs into a shared semantic space, intermediate-layer reasoning, and outputting in the target language. The top and bottom layers of the model handle multilingual processing, while the intermediate layers perform inference in similar patterns across different languages. This demonstrates a distinct division of labor within the model at the layer level regarding language specificity.\n\nFurthermore, many studies investigate the finer-grained methods for language-specific neuron identification~\\citep{kojima2024multilingual,tang2024language}. \\citet{tang2024language} categorizes activated neurons into language-specific neurons and language-agnostic neurons. They detect language-specific neurons by calculating language activation probability entropy on massive text. However, we find that some neurons are activated by multiple languages (i.e., not language-specific), yet are not universally activated across all languages (i.e., not language-agnostic). Simply categorizing activated neurons into two classes blurs this distinction. Thus, we propose a new method to identify neurons, which categorizes activated neurons into three types: language-specific, language-related, and language-agnostic.\n\n\\section{Methodology}\\label{sec:methodology}\nIn this section, we introduce our overall analysis pipeline in our work. First, we review the existing multilingual alignment algorithm and neuron analysis techniques as a preliminary study in \\S\\ref{subsec:method-preliminary-study}. Then we introduce the multilingual alignment algorithm we utilize in our work in \\S\\ref{subsec:method-multilingual-alignment}. Finally, for mechanistic interpretability analysis, we introduce our new method for detecting language-specific, language-related, and language-agnostic neurons~(\\S\\ref{subsec:method-language-neurons-detection}).\n\n\\subsection{Preliminary Study}\\label{subsec:method-preliminary-study}\nMost LLMs are pretrained mainly on the high-resource language corpus, which leads to LLMs' unstable and unbalanced performance in multilingual scenarios. As a representative multilingual alignment algorithm, Multilingual-Alignment-as-Preference Optimization~(MAPO)~\\citep{she2024mapo} effectively and efficiently improves the LLMs' multilingual performance. Additionally, it is also important for us to understand and analyze the mechanism of LLMs' multilingual capabilities and multilingual alignment. Moreover, some studies on the identification of the language-specific and language-agnostic neurons in LLMs~\\citep{tang2024language,kojima2024multilingual}. It is found that LLMs' capabilities of processing a particular language mainly come from a small subset of neurons~\\citep{tang2024language}.\n\nHowever, there are still many important questions waiting for further investigation. On the one hand, many methods overlook neurons activated by multiple languages yet not language-agnostic, namely language-related neurons that lie between language-specific and language-agnostic categories. On the other hand, research from the perspective of language neurons on the underlying mechanisms of LLMs' multilingual alignment and multilingual capabilities remains quite limited, which is essential for better understanding and improving the multilingual performance of LLMs. \n\n\\subsection{Multilingual Alignment}\\label{subsec:method-multilingual-alignment}\n\nMAPO is a typical multilingual alignment algorithm to align the reasoning capabilities of non-English language responses with those of English, which serves as the pivot language.\nSpecifically, for a given query $X$ in a target (non-English) language and its corresponding English variant $X_{Eng}$, we collect their respective responses $Y$ and $Y_{Eng}$. An off-the-shelf translation model, parameterized by $\\theta$, is deployed to estimate the conditional generation probability $P(Y \\mid Y_{Eng}; \\theta)$ by force-decoding $Y$ conditioned on $Y_{Eng}$. A higher conditional probability is interpreted as stronger alignment between the target language response and its English counterpart. This probability is then used as an alignment score, denoted $r_{\\theta}(X, Y)$.\n\nThis alignment score can be integrated into preference optimization algorithms. For instance, in PPO~\\citep{ppo}, $r_{\\theta}(X, Y)$ can be directly employed as the reward score. In DPO~\\citep{dpo}, for each target language, $n$ distinct outputs are generated. Based on the alignment score\\, these $n$ outputs are used to form $\\binom{n}{2}$ preference pairs $(Y_w, Y_l)$, where $Y_w$ is deemed superior to $Y_l$ due to a higher alignment score. The model is then optimized by Eq\\ref{eq:dpo}.\n\n\\begin{equation}\n\\label{eq:dpo}\nL_{DPO}(\\pi_{\\theta}; \\pi_{ref}) = -E_{(X, Y_w, Y_l) \\sim D} \\left[ \\log \\sigma \\left( \\beta \\log \\frac{\\pi_{\\theta}(Y_w | X)}{\\pi_{ref}(Y_w | X)} - \\beta \\log \\frac{\\pi_{\\theta}(Y_l | X)}{\\pi_{ref}(Y_l | X)} \\right) \\right] \n\\end{equation}\n\n% Here, $\\pi_{\\theta}$ is the policy being optimized, $\\pi_{ref}$ is a reference policy, $D$ is the dataset of preference triples $(x, Y_w, Y_l)$, $\\sigma$ is the logistic function, and $\\beta$ is a hyperparameter controlling the deviation from the reference policy. The expectation is taken over the collected preference data.\n\n\n\n\n\\subsection{Language Neurons Identification}\\label{subsec:method-language-neurons-detection}\n% Our experiments are based on decoder‐only Transformer neural networks. Each layer of the network consists of multi-head attention (MHA) and a feed-forward network (FFN). Let $x^i$ denote the input tensor of the $i$-th FFN layer. Then the FFN layer can be formally expressed as Eq\\ref{eq:ffn}\n\n% \\begin{equation}\n% \\label{eq:ffn}\n% \\mathrm{FFN}_{\\mathrm{SwiGLU}}(x^i) = W_2^i \\,\\bigl(\\mathrm{SiLU}(x^i W_{11}^i)\\,\\odot\\,(x^i W_{12}^i)\\bigr).\n% \\end{equation}\n\nFollowing \\citet{tang2024language}, the neurons in our work are defined as a linear transformation of a single column in a weighted matrix $W$ followed by a non-linear activation, SiLU~\\citep{silu}. For the $j$-th neuron in the $i$-th layer, its activation probability when processing responses in language $k$ is computed as:\n\\begin{equation}\np_{i,j}^k=\\mathbb{E}\\left(\\mathbb{I}\\left(\\mathrm{SiLU}(x^i W^i)_j > 0\\right)\\mid \\text{language}\\ k \\right)\n\\end{equation}\n\nWe define \\textbf{language neurons} as those exhibiting higher activation probabilities for some languages, while discriminating from others. To select neurons that, despite exhibiting relatively higher entropy, still demonstrate high activation probabilities for some languages, we additionally incorporate the maximum activation probability, as formulated in Eq~\\ref{eq:metric}:\n\\begin{equation}\n\\label{eq:metric}\n\\mathrm{score}_{i,j} = -\\sum_{k=1}^{l} p_{i,j}^{\\prime k}\\,\\log p_{i,j}^{\\prime k} \\;-\\;\\lambda\\,\\max_{1\\le k\\le l} p_{i,j}^k,\n\\end{equation}\nwhere $p_{i,j}'$ represents the probability distribution $p_{i,j}$ after normalization and $\\lambda$ is a balancing coefficient. Neurons with scores falling in the lowest percentile, specifically, the bottom 5\\% are selected.\n\nFurthermore, to identify how many languages each selected neuron is related to, we introduce a threshold $\\tau$ and compute:\n\\begin{equation}\nN_{i,j} = \\sum_{k=1}^l \\mathbb{I}\\bigl(p_{i,j}^k > \\tau\\bigr).\n\\end{equation}\nA neuron is considered as a \\textbf{language-specific neuron} if $N_{i,j} = 1$, and as a \\textbf{language-related neuron} if $1 < N_{i,j} < l$. Both of them belong to \\textbf{language neuron}.\nIn contrast, a neuron is considered \\textbf{language-agnostic neuron} if it exhibits high activation probabilities across all $l$ languages.\n\nFinally, given our focus on multilingual reasoning tasks, we select neurons exclusively based on responses from multilingual reasoning datasets, rather than relying on multilingual plain text~\\citep{tang2024language}.\n\n\\section{Experiments}\\label{sec:experiments}\n\n\\subsection{Experimental Setup}\\label{subsec:experimental-setup}\nFollowing~\\citet{she2024mapo}, we conduct our experiments and analyses on the mathematical reasoning tasks and different languages. In this section, we introduce our experimental settings in detail.\n\n\\paragraph{Models} We include two different models in our experiments and analyses. Following~\\citet{she2024mapo}, we conduct our experiments on MistralMathOctopus-7B~\\footnote{\\url{https://huggingface.co/kevinpro/MistralMathOctopus-7B}} and MetaMathOctopus-7B~\\footnote{\\url{https://huggingface.co/kevinpro/MetaMathOctopus-7B}}. MistralMathOctopus is obtained by fine-tuning MetaMath-Mistral~\\citep{yu2023metamath} with MGSM8KInstruct~\\citep{chen2023breaking}. MetaMathOctopus is obtained by fine-tuning MetaMath~\\citep{yu2023metamath} with MGSM8KInstruct. Considering limited computational resources and reproducibility, we directly utilize the publicly released base models. Our analyses are mainly based on MistralMathOctopus in the main text and we report more results in the Appendix.\n\n\\paragraph{Datasets} We conduct experiments on two representative mathematical reasoning benchmarks, MGSM~\\citep{shi2022language} and MSVAMP~\\citep{chen2023breaking}. MGSM is a widely used benchmark for multilingual mathematical reasoning evaluation. MSVAMP is an out-of-domain test set in contrast to MGSM, which evaluates robustness and generalization~\\citep{zhu2024question,she2024mapo}.\n\n\n\\paragraph{Languages} Following~\\citet{she2024mapo}, we choose the following 10 different languages for analysis in our work. As a pivot language, English~(en) is used as the alignment target. We also choose Chinese~(zh), Russian~(ru), German~(de), French~(fr), Spanish~(es), Japanese~(ja), Swahili~(sw), Thai~(th) and Bengali~(bn) as 9 representative non-English languages.\n\n\n\\begin{figure*}[tbp]\n \\centering \n \\begin{minipage}[b]{0.4\\textwidth}\n \\includegraphics[width=1.0\\linewidth]{Images/language_specific_neurons_heatmap_Mistral_Base.pdf}\n \\subcaption{Base - Language-Specific Neurons}\n \\label{fig:mistral-mgsm-base-language-specific-heatmap}\n \\end{minipage}\n \\begin{minipage}[b]{0.4\\textwidth}\n \\includegraphics[width=1.0\\linewidth]{Images/language_specific_neurons_heatmap_Mistral_RL.pdf}\n \\subcaption{Aligned - Language-Specific Neurons}\n \\label{fig:mistral-mgsm-aligned-language-specific-heatmap}\n \\end{minipage}\n \\begin{minipage}[b]{0.4\\textwidth}\n \\includegraphics[width=1.0\\linewidth]{Images/language_neurons_heatmap_Mistral_Base.pdf}\n \\subcaption{Base - Language Neurons}\n \\label{fig:mistral-mgsm-base-language-heatmap}\n \\end{minipage}\n \\begin{minipage}[b]{0.4\\textwidth}\n \\includegraphics[width=1.0\\linewidth]{Images/language_neurons_heatmap_Mistral_RL.pdf}\n \\subcaption{Aligned - Language Neurons}\n \\label{fig:mistral-mgsm-aligned-language-heatmap}\n \\end{minipage}\n \\caption{PPL changes of MistralMathOctopus on MGSM after deactivating language-specific neurons or language neurons. ``Base'' indicates the results of the base model. ``Aligned'' indicates the results of the aligned model. For comparison, the results of~\\citet{tang2024language} are provided in Appendix~\\ref{app:heatmap}.}\n \\label{fig:mistral-mgsm-heatmap}\\\n \\vspace{-15pt}\n\\end{figure*}\n\n\\paragraph{Implementations} Due to limited computational resources, our exploration focuses on the most effective DPO variant of MAPO~\\citep{she2024mapo}. We select 1, 4, and 8 tasks from the NumGLUE~\\citep{mishra-etal-2022-numglue}, an arithmetic reasoning benchmark, and translate questions into 9 languages, consistent with the MGSM, thereby creating a multilingual seed dataset.\nTo construct preference pairs, we sample responses using the corresponding base models and employ NLLB-200-distilled-600M\\footnote{\\url{https://huggingface.co/facebook/nllb-200-distilled-600M}} as the translation model to obtain alignment scores. Finally, for each model and each target language (excluding English), we gain 10,000 preference pairs. Training is conducted using LoRA~\\citep{hu2022lora}.\n% To identify language neurons, we compute the score defined in Eq.~\\ref{eq:metric} for each neuron and select the bottom 5\\% as our candidates. This selection prioritizes neurons that exhibit strong language preferences, balancing low entropy with high maximum activation probabilities for at least one language. \nDuring the neuron selection stage, we perform force-decoding on the responses of the MGSM or MSVAMP dataset to obtain the activation probabilities of neurons for each language.\nBased on empirical results on development sets, we set the balancing coefficient $\\lambda = 0.04$ and the threshold $\\tau = 0.5$.\n% , which achieves a favorable trade-off between these two objectives. \n% We set a threshold of $\\tau = 0.5$ to determine whether a neuron is related to a given language. \nAdditional implementation details are provided in Appendix \\ref{app:implementation-details}.\n% This threshold effectively distinguishes language-specific activations while maintaining robustness across layers and models.\n\n\n\\subsection{Language Neurons Identification}\\label{subsec:language-neuron-identification}\nBased on the neuron identification algorithm introduced in \\S\\ref{subsec:method-language-neurons-detection}, we identify the language-specific neurons, language-related neurons, and language-agnostic neurons in the model. To further validate the effectiveness of our algorithm, we follow ~\\citet{tang2024language} by examining changes in the perplexity of LLMs by deactivating the identified language neurons across different languages. Experiments are conducted on both the base model and the aligned model, with results presented in Figure~\\ref{fig:mistral-mgsm-heatmap}. We report the results of both language-specific neurons and language neurons. \n\nIt can be found that whether deactivating language-specific neurons or all language neurons, the results consistently exhibit the same pattern: the diagonal elements in each row show the highest values. Notably, deactivating language neurons leads to a more pronounced effect compared to deactivating only language-specific neurons. These observations support the following findings: (1) Our algorithm effectively identifies language-specific and language-related neurons; (2) For a given language, in addition to its language-specific neurons, there are also a substantial number of shared language-related neurons contributing to its performance; (3) Deactivating all the language-related neurons of one language doesn't cause significant impacts on the model's performance in other languages. The above findings confirm the validity of the language neurons identified by our method and further provide insights into the characteristics of language neurons.\n\n\n\\begin{figure*}[tbp]\n \\centering \n \\includegraphics[width=0.6\\linewidth]{Images/layer_wise_neurons.pdf}\n \\caption{Layer-wise distribution of the different types of neurons of MistralMathOctopus on MGSM.}\n \\label{fig:layer-wise-neurons-distribution}\n \\vspace{-10pt}\n\\end{figure*}\n\n\n\\subsection{Layer-wise Functionality Analysis}\\label{subsec:layer-wise-funtionality-analysis}\nBased on the identified neurons, we perform layer-wise functional analyses of all layers in the LLMs. We begin by analyzing the distributions of different types of neurons in the base model. And we report the results in Figure~\\ref{fig:layer-wise-neurons-distribution}. Through the analysis of the distribution of different types of neurons, we can further divide the LLMs' internal process for multilingual inference into four parts rather than the three-stage division discussed in some works~\\citep{wendler2024llamas,zhao2024large}:\n\\begin{enumerate}\n \\item \\textbf{Multilingual Understanding:} In the initial layers, the number of language neurons~(language-specific and language-related neurons) peaks, while the number of language-agnostic neurons is relatively low. The model maps multilingual inputs into a unified semantic space at this stage.\n \\item \\textbf{Shared Semantic Space Reasoning:} In the intermediate layers, the model engages in reasoning within a shared semantic space across different languages. During this stage, language neurons are largely absent, whereas language-agnostic neurons become dominant.\n \\item \\textbf{Multilingual Output Space Transformation:} The model transfers features into the multilingual output space in this stage in preparation for generating the final output. In this part, the number of language neurons reaches a peak again, while the number of language-agnostic neurons drops to the lowest point.\n \\item \\textbf{Vocabulary Space Outputting:} In the last layer, the model maps vectors of different languages into a shared vocabulary space to generate outputs. The number of both language-related and language-agnostic neurons rises sharply, while the language-specific neurons are fewer than those in several previous layers.\n\\end{enumerate}\nMeanwhile, the distribution of different types of neurons aligns with the conclusions from existing studies mentioned above. Overall, we can find that the number of neurons varies correspondingly with the different inference stages of LLMs.\n\n\\vspace{-5pt}\n\\subsection{Layer-wise Neuron Changes Analysis}\n\\label{subsec:layer-wise-neuron-change}\nIn \\S\\ref{subsec:layer-wise-funtionality-analysis}, we investigate the fundamental distribution of neurons and the basic partitioning of the model. We further analyze the changes in different types of neurons before and after multilingual alignment. Based on the four functional stages in LLMs, we quantify the layer-wise changes~($\\Delta$) in the number of different types of neurons. Figure~\\ref{fig:Delta_of_Language_Neurons} presents the results for language-specific neurons, language-related neurons, and language-agnostic neurons.\n\\begin{wrapfigure}{r}{0.52\\textwidth}\n \\centering \n \\includegraphics[width=\\linewidth]{Images/delta_layer_wise_neurons_mistral_mgsm.pdf}\n \\caption{Layer-wise changes in the number of different types of neurons of MistralMathOctopus on MGSM.}\n \\label{fig:Delta_of_Language_Neurons}\n \\vspace{-12pt}\n\\end{wrapfigure}\nDuring the multilingual understanding stage, the number of language neurons increases, while language-agnostic neurons decrease. In the subsequent shared semantic space reasoning stage, language-agnostic neurons increase substantially, whereas language neurons remain stable and nearly absent. Then, in the third stage, as language-agnostic neurons decrease, language neurons increase overall. Additionally, we notice that the number of\n% language-specific neurons decreases, while \nlanguage-related neurons show an upward trend. Finally, in the last stage, the number of language-agnostic increases significantly in the aligned model, accompanied by a reduction in language neurons. We also report the results of different checkpoints during the alignment process in Appendix~\\ref{app:checkpoint-layer-wise-changes}.\n\nOverall, we find that language neurons and language-agnostic neurons exhibit generally opposite trends across different layers, which corresponds to the characteristics of LLMs at different stages of inference. Especially, at the last stage, language-agnostic neurons play an more important role than language neurons. Multilingual alignment facilitates more effective activation of the appropriate neurons at each stage, thereby improving the model's capability to handle multilingual tasks.\n\n\n\\subsection{Macroscopic Analysis of Different Types of Neurons}\nWe further conduct macroscopic analysis for different types of neurons. In our neuron identification algorithm introduced in \\S\\ref{subsec:method-language-neurons-detection}, the number of languages that share a specific neuron is an attribution characterizing all types of activated neurons. Since our study involves 10 languages, the valid range of $N$ is from $1$ to $10$. Among these, values of $N$ from $2$ to $9$ correspond to language-related neurons. As special cases in our work, $N = 1$ represents language-specific neurons, while $N = 10$ corresponds to language-agnostic neurons.\n\n\\begin{figure}[tbp]\n \\centering \n \\includegraphics[width=0.7\\textwidth]{Images/related_language_num.pdf}\n \\caption{Changes in the number of neurons shared by $N$ languages after alignment.}\n \\label{fig:Related_Language_Num}\n% \\vspace{-10pt}\n\\end{figure}\n\nWe report the changes in the number of neurons after multilingual alignment for each value of $N$ ranging from 1 to 10 in Figure~\\ref{fig:Related_Language_Num}. The results show a decrease in the number of language-specific neurons, while an increase in the number of language-related neurons, which are shared across multiple languages. This indicates that multilingual alignment encourages LLMs to develop and utilize more shared language-related neurons, rather than on language-specific neurons, which are applicable to only a single language. Meanwhile, during the alignment process, the model improves its understanding of task-relevant common knowledge. Therefore, the overall number of language-agnostic neurons also increases significantly.\n\n\n\n\\begin{table*}[tbp]\n\\centering\n\\small\n\\setlength{\\tabcolsep}{4pt}\n\\renewcommand{\\arraystretch}{1.1}\n\\caption{Accuracy of the MistralMathOctopus base model and aligned model on MGSM. \"X/Y $\\Rightarrow$ T\" indicates that languages X and Y are used for multilingual alignment.}\n\\resizebox{0.9\\columnwidth}{!}{\n\\begin{tabular}{l|cccccccccc|c}\n\\toprule\n\\textbf{MGSM} & \\textbf{bn} & \\textbf{th} & \\textbf{sw} & \\textbf{ja} & \\textbf{zh} & \\textbf{ru} & \\textbf{de} & \\textbf{es} & \\textbf{fr} & \\textbf{en} & \\textbf{Avg.} \\\\\n\\midrule\nbase & 43.6 & 53.2 & 50.4 & 55.6 & 59.6 & 59.2 & 61.2 & 62.8 & 56.8 & 75.6 & 57.8 \\\\\nzh/de $\\Rightarrow$ en & 46.4 & 55.6 & 59.2 & 56.8 & 64.0 & 71.2 & 66.8 & 71.2 & 69.2 & 75.2 & 63.6 \\\\\nsw/th $\\Rightarrow$ en & 48.8 & 58.8 & 59.2 & 56.4 & 68.4 & 68.4 & 69.2 & 69.6 & 70.4 & 77.6 & 64.7 \\\\\n\\bottomrule\n\\end{tabular}\n}\n\\label{tab:spontaneous_result}\n\\vspace{-12pt}\n\\end{table*}\n\n\n% \\vspace{-5pt}\n\\subsection{Spontaneous Multilingual Alignment Analysis}\\label{subsec:spontaneous-multilingual-alignment-analysis}\n% \\begin{wrapfigure}{r}{0.52\\textwidth}\n% \\centering\n% \\small\n% \\setlength{\\tabcolsep}{4pt}\n% \\renewcommand{\\arraystretch}{1.1}\n% \\captionof{table}{Average results of neuron count changes across multiple languages. ``Trained'' indicates the trained languages in the spontaneous multilingual alignment experiment. ``Others'' indicates other languages except the trained languages. We round the results to the nearest integer.}\n% \\resizebox{0.52\\columnwidth}{!}{\n% \\begin{tabular}{lcc}\n% \\toprule\n% \\textbf{Language} & \\textbf{Language-Specific} & \\textbf{Language-Related} \\\\\n% \\midrule\n% Trained & -37 & +232 \\\\\n% Others & -36 & +205 \\\\\n% \\bottomrule\n% \\end{tabular}\n% }\n% \\label{tab:spontaneous_delta_neurons_zhde}\n% \\vspace{-10pt}\n% \\end{wrapfigure}\nThe important ``Spontaneous Multilingual Alignment'' phenomenon is first revealed and discussed by~\\citet{zhang2024getting}, which means that conducting alignment in a small number of languages significantly improves multilingual alignment even between English and many languages unseen during the alignment process. We further analyze this phenomenon in our experiments. As shown in Table~\\ref{tab:spontaneous_result}, spontaneous multilingual alignment also emerges under the multilingual alignment strategy employed in our study. Except for the languages used for alignment, LLMs exhibit notable performance gain in other unaligned languages.\n\nTo understand how multilingual alignment generalizes to other languages, we analyze the changes in different types of neurons before and after multilingual alignment based on our method.\n\\begin{wrapfigure}{r}{0.52\\textwidth}\n\\centering\n\\small\n\\setlength{\\tabcolsep}{4pt}\n\\renewcommand{\\arraystretch}{1.1}\n\\captionof{table}{Average results of neuron count changes across multiple languages. ``Trained'' indicates the trained languages in the spontaneous multilingual alignment experiment. ``Others'' indicates other languages except the trained languages. We round the results to the nearest integer.}\n\\resizebox{0.52\\columnwidth}{!}{\n\\begin{tabular}{lcc}\n\\toprule\n\\textbf{Language} & \\textbf{Language-Specific} & \\textbf{Language-Related} \\\\\n\\midrule\nTrained & -37 & +232 \\\\\nOthers & -36 & +205 \\\\\n\\bottomrule\n\\end{tabular}\n}\n\\label{tab:spontaneous_delta_neurons_zhde}\n\\vspace{-10pt}\n\\end{wrapfigure}\nTaking the case of ``zh/de $\\Rightarrow$ en'' as a representative example, we report the average results in Table~\\ref{tab:spontaneous_delta_neurons_zhde}. For the trained languages, the number of language-specific neurons decreases, while the number of language-related neurons increases. This indicates that the aligned languages tend to utilize more language-related neurons shared with other languages rather than exclusive language-specific neurons. Moreover, we extend this analysis to languages other than the trained languages and observe a similar phenomenon. These findings indicate that multilingual alignment facilitates the use of language-related neurons while reducing the reliance on language-specific neurons in both trained and other unseen languages. We hypothesize that the new language-related neurons shared with trained languages contribute to the performance improvement on other unseen languages.\n\n\n\n\n\\begin{figure}[tbp]\n \\begin{minipage}{0.48\\textwidth}\n \\centering\n \\small\n \\setlength{\\tabcolsep}{1pt}\n \\renewcommand{\\arraystretch}{1.2}\n \\captionof{table}{Average number of different types of neurons for English and non-English languages of MistralMathOctopus on MGSM. We round the results to the nearest integer.}\n \\resizebox{\\columnwidth}{!}{\n \\begin{tabular}{lcc}\n \\toprule\n \\textbf{Language} & \\textbf{Language-Specific} & \\textbf{Language-Related} \\\\\n \\midrule\n English & 46 & 603 \\\\\n non-English & 613 & 2006 \\\\\n \\bottomrule\n \\end{tabular}\n }\n \\label{tab:English-nonEnglish-neuron-count}\n \\end{minipage}\n \\hfill\n \\begin{minipage}{0.48\\textwidth}\n \\centering\n \\small\n \\setlength{\\tabcolsep}{1pt}\n \\renewcommand{\\arraystretch}{1.2}\n \\captionof{table}{Overlap ratio of different types of neurons across \\textit{different domains} and \\textit{before and after alignment}. Following~\\citet{she2024mapo}, MSVAMP is regarded as an out-of-domain dataset. The results of MistralMathOctopus on MGSM are used as the fiducial value.}\n \\resizebox{\\columnwidth}{!}{\n \\begin{tabular}{lcc}\n \\toprule\n \\textbf{Variable~(\\%)} & \\textbf{Language-Specific} & \\textbf{Language-Related} \\\\\n \\midrule\n Domain & 80.7 & 92.3 \\\\\n Alignment & 95.6 & 92.1 \\\\\n \\bottomrule\n \\end{tabular}\n }\n \\label{tab:neuron-distribution-stability-overlap}\n \\end{minipage}\n\\vspace{-10pt}\n\\end{figure}\n\n\n\n\\subsection{Further Analysis}\\label{subsec:further-analysis}\n\n\\paragraph{Uniqueness of English}\n% \\begin{wrapfigure}{r}{0.52\\textwidth}\n% \\centering\n% \\small\n% \\setlength{\\tabcolsep}{4pt}\n% \\renewcommand{\\arraystretch}{1.1}\n% \\captionof{table}{Average number of different types of neurons for English and non-English languages based on MistralMathOctopus. We round the results to the nearest integer.}\n% \\resizebox{0.52\\columnwidth}{!}{\n% \\begin{tabular}{lcc}\n% \\toprule\n% \\textbf{Language} & \\textbf{Language-Specific} & \\textbf{Language-Related} \\\\\n% \\midrule\n% English & 46 & 603 \\\\\n% non-English & 613 & 2006 \\\\\n% \\bottomrule\n% \\end{tabular}\n% }\n% \\label{tab:English-nonEnglish-neuron-count}\n% \\end{wrapfigure}\nSince current LLMs are primarily pretrained on English data, English is often regarded as playing a special role within LLMs~\\citep{wendler2024llamas}. In our experiments, we also observe that English exhibits markedly different characteristics compared to other non-English languages. Based on the identified neurons in our work, in Figure~\\ref{fig:mistral-mgsm-heatmap}, we can find that deactivating the language neurons of English has a negligible impact on the model's performance in English, which is entirely different from the behavior observed in other languages. This is also consistent with the results of~\\citet{tang2024language}. Furthermore, based on this finding, we quantify the number of language neurons for English and non-English languages based on the MistralMathOctopus base model~(Table~\\ref{tab:English-nonEnglish-neuron-count}). Our analysis reveals that English has significantly fewer neurons than other languages, both in terms of language-specific and language-related neurons. We hypothesize that this is due to the fact that English actually possesses numerous language-related neurons. And since English serves as a pivot language, these language-related neurons are likely shared with almost all other languages. It causes them to be confounded with language-agnostic neurons.\n% \\citet{wendler2024llamas} also discuss the dominant role of English.\n\n% \\begin{wrapfigure}{r}{0.52\\textwidth}\n% \\centering\n% \\small\n% \\setlength{\\tabcolsep}{4pt}\n% \\renewcommand{\\arraystretch}{1.1}\n% \\captionof{table}{Overlap ratio of different types of neurons across different domains and before and after alignment. The result based on MGSM and MistralMathOctopus base model is used as the fiducial value.}\n% \\resizebox{0.52\\columnwidth}{!}{\n% \\begin{tabular}{lcc}\n% \\toprule\n% \\textbf{Variable~(\\%)} & \\textbf{Language-Specific} & \\textbf{Language-Related} \\\\\n% \\midrule\n% Domain & 80.7 & 92.3 \\\\\n% Alignment & 95.6 & 92.1 \\\\\n% \\bottomrule\n% \\end{tabular}\n% }\n% \\label{tab:neuron-distribution-stability-overlap}\n% \\end{wrapfigure}\n\n\\paragraph{Stability of Neuron Distributions}We discuss the stability of neuron distributions across \\textit{different data domains}, as well as \\textit{before and after alignment}. To quantify the stability of neuron distributions, we compute the neuron overlap ratio in both settings, with the results summarized in Table~\\ref{tab:neuron-distribution-stability-overlap}. \n% We use the corresponding neuron count based on MistralMathOctopus and MGSM as the fiducial value while calculating the overlap ratio. \nWe can find that although the exact positions of a few language neurons may vary across different settings, the positional distribution of most language neurons remains stable. This also indicates good reliability and generalization of the language neurons identified under fixed hyperparameters.\n\n\n\n% \\begin{figure*}[ht] \n% \\centering \n% \\includegraphics[width=0.5\\textwidth]{Images/correlator_similarity_Swahili.pdf}\n% \\caption{Layer-wise correlator similarity analysis in Swahili.} \n% \\label{fig:correlator}\n% \\end{figure*}\n\n\\section{Conclusion}\\label{sec:conclusion}\nIn this work, we systematically investigate the multilingual alignment from the perspective of language neurons. We propose a new language neuron identification algorithm based on entropy and probability value, which detects the language-specific neurons, language-related neurons, and language-agnostic neurons in LLMs. The validity of the identified neurons is confirmed through deactivation ablation experiments. Furthermore, we examine the multilingual alignment mechanism by analyzing the roles of different types of neurons. Based on their distributional characteristics, we categorize LLMs' internal process into four functional parts. Our analysis reveals that multilingual alignment enhances the model’s utilization of the corresponding types of neurons across different functional parts. Meanwhile, we find that alignment promotes a greater reliance on shared language-related neurons across languages, rather than on language-specific neurons. We also explore the phenomenon of spontaneous multilingual alignment. Additionally, we provide further analysis and more empirical results based on the preceding findings.\n\n% \\begin{ack}\n% Use unnumbered first level headings for the acknowledgments. All acknowledgments\n% go at the end of the paper before the list of references. Moreover, you are required to declare\n% funding (financial activities supporting the submitted work) and competing interests (related financial activities outside the submitted work).\n% More information about this disclosure can be found at: \\url{https://neurips.cc/Conferences/2025/PaperInformation/FundingDisclosure}.\n\n\n% Do {\\bf not} include this section in the anonymized submission, only in the final paper. You can use the \\texttt{ack} environment provided in the style file to automatically hide this section in the anonymized submission.\n% \\end{ack}\n\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\n\n\\bibliographystyle{unsrtnat}\n\\bibliography{custom}\n\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\n\\newpage\n\\appendix\n\n\\section{Limitations}\\label{app:limitations}\nWe provide insights and analysis results for multilingual alignment and multilingual capabilities of LLMs, which allows for a better understanding of multilingualism. Despite we have conducted a systematic investigation in our work, there are still some limitations waiting for research. Due to the limited resources, we only conduct experiments on two different models and two different datasets. We are willing to perform a more comprehensive analysis on different scenarios if more resources are available in the future. Additionally, we don't perform a finer-grained analysis of neurons within the same layer in this work. We would like to explore this in our future work.\n\n\n\n\\section{Implementation Details}\\label{app:implementation-details}\nOur experiments are conducted on 4 NVIDIA RTX A6000 GPUs or 4 NVIDIA GeForce RTX 3090 GPUs. We use the TRL~\\citep{vonwerra2022trl} and DeepSpeed~\\citep{rasley2020deepspeed} frameworks for preference alignment, and the vLLM engine~\\cite{kwon2023efficient} for inference.\n\\subsection{MAPO}\nWe employ the officially released scripts\\footnote{\\url{https://github.com/NJUNLP/MAPO}} to generate the preference data. For the alignment process, we set the learning rate to 1e-6 and the batch size to 16. LoRA is utilized to fine-tune the model with a LoRA rank of 64, a LoRA alpha of 128, and a LoRA dropout rate of 0.05. The total number of training steps is set to 1000. It takes 7.5 hours for one alignment.\n\\subsection{Language Neurons Identification}\nWe spend 25 minutes calculating the activation probability of each neuron with MGSM dataset on a single NVIDIA GeForce RTX 3090 GPU. \nWe spend 2 hours calculating the activation probability of each neuron with MSVAMP dataset on a single NVIDIA GeForce RTX 3090 GPU.\nAfter obtaining the activation probability, it takes 3 minutes to identify how many languages each selected neuron is related to.\n\n\n\n\n\n\n\n\n\\section{Licenses for Used Assets}\\label{app:licenses}\nWe list the names of the licenses for each asset we utilized in our work:\n\\begin{itemize}\n \\item MGSM: CC-BY-SA-4.0\n \\item MSVAMP: Apache-2.0\n \\item GSM8KInstruct: Apache-2.0\n \\item NumGLUE: Apache-2.0\n \\item MistralMathOctopus: Apache-2.0\n \\item MetaMathOctopus: Apache-2.0\n \\item NLLB-200-distilled-600M: CC-BY-NC-4.0\n \\item TRL: Apache-2.0\n \\item DeepSpeed: Apache-2.0\n \\item vLLM: Apache-2.0\n\\end{itemize}\n\n\n\\section{Deactivation Ablation Experiments}\n\\label{app:heatmap}\nFor the MistralMathOctopus model, Figure~\\ref{fig:baseline-heatmap} presents the results of the deactivation ablation experiments conducted on MGSM using the neuron identification algorithm proposed by~\\citet{tang2024language}. Additionally, Figure~\\ref{fig:mistral-msvamp-heatmap} presents the results of the deactivation ablation experiments on the out-of-domain test set, MSVAMP.\nTo verify the generalization of our identification algorithm, we also deploy the MetaMathOctopus to conduct the deactivation ablation experiment, with the result in \nFigure~\\ref{fig:llama-mgsm-msvamp-heatmap}.\n\n\n\n\\section{Layer-wise distribution of the different types of neurons}\nIn Figure~\\ref{fig:layer-wise-neurons-distribution_MSVAMP} and~\\ref{fig:layer-wise-neurons-distribution_MGSM_llama}, we separately display the layer-wise distribution of the different types of neurons of MistarlMathOctopus on MSVAMP and MetaMathOctopus on MGSM to validate the generalization of our finding discussed in \\S\\ref{subsec:layer-wise-funtionality-analysis}.\n\n\n\n\n\\section{Layer-wise Changes in the Number of Different Types of Neurons During Alignment}\\label{app:checkpoint-layer-wise-changes}\nFigure~\\ref{fig:Layer-wise_changes_llama} shows the same pattern discussed in \\S\\ref{subsec:layer-wise-neuron-change}, thereby supporting its generalization.\nFurthermore, for each dataset, we examine the layer-wise changes in the number of different types of neurons in the process of alignment in Figure~\\ref{fig:Layer-wise_changes_specific},~\\ref{fig:Layer-wise_changes_related} and~\\ref{fig:Layer-wise_changes_agnostic}. The notation “$\\text{ckpt-}x$” denotes the model checkpoint obtained after $x$ training steps.\n\n\n\\section{Spontaneous Multilingual Alignment}\nWe report the accuracy of the base model on the MGSM and MSVAMP benchmarks in Table~\\ref{tab:spontaneous_result_all_mistral}. The expected accuracy of random guessing is 50.0\\%. \"X/Y $\\Rightarrow$ T\" indicates that languages X and Y are used for alignment. The best results for each language are highlighted. It is evident that models trained on multilingual translation data substantially outperform the original model across a wide range of languages, indicating that multilingual training significantly enhances the model's multilingual capabilities.\n\n\\newpage\n\n\\begin{figure*}[htbp]\n \\centering \n \\begin{minipage}[b]{0.41\\textwidth}\n \\includegraphics[width=1.0\\linewidth]{Images/heatmap_baseline_Mistral_Base.pdf}\n \\subcaption{Base - Language-Specific Neurons}\n \\label{fig:mistral-mgsm-baseline-base-heatmap}\n \\end{minipage}\n \\begin{minipage}[b]{0.41\\textwidth}\n \\includegraphics[width=1.0\\linewidth]{Images/heatmap_baseline_Mistral_RL.pdf}\n \\subcaption{Aligned - Language-Specific Neurons}\n \\label{fig:mistral-mgsm-baseline-aligned-heatmap}\n \\end{minipage}\n \\caption{PPL changes of MistralMathOctopus on MGSM when deploying the algorithm proposed by~\\citet{tang2024language}.}\n \\label{fig:baseline-heatmap}\\\n \\vspace{-10pt}\n\\end{figure*}\n\n\n\\begin{figure*}[htbp]\n \\centering \n \\begin{minipage}[b]{0.41\\textwidth}\n \\includegraphics[width=1.0\\linewidth]{Images/language_specific_neurons_heatmap_Mistral_Msvamp_Base.pdf}\n \\subcaption{Base - Language-Specific Neurons}\n \\label{fig:mistral-msvamp-base-language-specific-heatmap}\n \\end{minipage}\n \\begin{minipage}[b]{0.41\\textwidth}\n \\includegraphics[width=1.0\\linewidth]{Images/language_specific_neurons_heatmap_Mistral_Msvamp_RL.pdf}\n \\subcaption{Aligned - Language-Specific Neurons}\n \\label{fig:mistral-msvamp-aligned-language-specific-heatmap}\n \\end{minipage}\n \\begin{minipage}[b]{0.41\\textwidth}\n \\includegraphics[width=1.0\\linewidth]{Images/language_neurons_heatmap_Mistral_Msvamp_Base.pdf}\n \\subcaption{Base - Language Neurons}\n \\label{fig:mistral-msvamp-base-language-heatmap}\n \\end{minipage}\n \\begin{minipage}[b]{0.41\\textwidth}\n \\includegraphics[width=1.0\\linewidth]{Images/language_neurons_heatmap_Mistral_Msvamp_RL.pdf}\n \\subcaption{Aligned - Language Neurons}\n \\label{fig:mistral-msvamp-aligned-language-heatmap}\n \\end{minipage}\n \\caption{PPL changes of MistralMathOctopus on MSVAMP after deactivating language-specific neurons or language neurons. ``Base'' indicates the results of the base model. ``Aligned'' indicates the results of the aligned model.}\n \\label{fig:mistral-msvamp-heatmap}\\\n \\vspace{-10pt}\n\\end{figure*}\n\n\n\n\\begin{figure*}[htbp]\n \\centering \n \\begin{minipage}[b]{0.41\\textwidth}\n \\includegraphics[width=1.0\\linewidth]{Images/language_neurons_heatmap_Llama_Base.pdf}\n \\subcaption{Base - Language Neurons on MGSM}\n \\label{fig:llama-mgsm-base-language-heatmap}\n \\end{minipage}\n \\begin{minipage}[b]{0.41\\textwidth}\n \\includegraphics[width=1.0\\linewidth]{Images/language_neurons_heatmap_Llama_RL.pdf}\n \\subcaption{Aligned - Language Neurons on MGSM}\n \\label{fig:llama-mgsm-aligned-language-heatmap}\n \\end{minipage}\n % \\begin{minipage}[b]{0.41\\textwidth}\n % \\includegraphics[width=1.0\\linewidth]{Images/language_neurons_heatmap_Llama_Msvamp_Base.pdf}\n % \\subcaption{Base - Language Neurons on MSVAMP}\n % \\label{fig:llama-msvamp-base-language-heatmap}\n % \\end{minipage}\n % \\begin{minipage}[b]{0.41\\textwidth}\n % \\includegraphics[width=1.0\\linewidth]{Images/language_neurons_heatmap_Llama_Msvamp_RL.pdf}\n % \\subcaption{Aligned - Language Neurons on MSVAMP}\n % \\label{fig:llama-msvamp-aligned-language-heatmap}\n % \\end{minipage}\n \\caption{PPL changes of MetaMathOctopus on MGSM after deactivating language neurons. ``Base'' indicates the results of the base model. ``Aligned'' indicates the results of the aligned model.}\n \\label{fig:llama-mgsm-msvamp-heatmap}\\\n \\vspace{-10pt}\n\\end{figure*}\n\n\n\n\n\n\\begin{figure*}[htbp]\n \\centering \n\n \\begin{subfigure}[b]{0.47\\textwidth}\n \\includegraphics[width=0.9\\linewidth]{Images/layer_wise_neurons_mistral_MSVAMP.pdf}\n \\caption{MistralMathOctopus on MSVAMP.}\n \\label{fig:layer-wise-neurons-distribution_MSVAMP}\n \\end{subfigure}\n \\hspace{10pt}\n \\begin{subfigure}[b]{0.47\\textwidth}\n \\includegraphics[width=0.9\\linewidth]{Images/layer_wise_neurons_llama_mgsm.pdf}\n \\caption{MetaMathOctopus on MGSM.}\n \\label{fig:layer-wise-neurons-distribution_MGSM_llama}\n \\end{subfigure}\n \\caption{Layer-wise distribution of the different types of neurons.}\n\\end{figure*}\n\n\n% \\begin{figure*}[htbp]\n% \\centering \n% \\includegraphics[width=0.6\\linewidth]{Images/layer_wise_neurons_llama_MSVAMP.pdf}\n% \\caption{Layer-wise distribution of the different types of neurons of MetaMathOctopus on MSVAMP.}\n% \\label{fig:layer-wise-neurons-distribution_MSVAMP_llama}\n% \\end{figure*}\n\n\\begin{figure*}[htbp]\n \\centering \n \\includegraphics[width=0.52\\textwidth]{Images/delta_layer_wise_neurons_llama_mgsm.pdf}\n \\caption{Layer-wise changes in the number of MetaMathOctopus}\n \\label{fig:Layer-wise_changes_llama}\n\\end{figure*}\n\n\n\\begin{figure*}[htbp]\n \\centering\n \\begin{subfigure}[b]{0.45\\textwidth}\n \\includegraphics[width=\\linewidth]{Images/layer_wise_delta_specific_neurons_mistral_mgsm.pdf}\n \\caption{MGSM}\n \\end{subfigure}\n \\hspace{10pt}\n \\begin{subfigure}[b]{0.45\\textwidth}\n \\includegraphics[width=\\linewidth]{Images/layer_wise_delta_related_neurons_mistral_MSVAMP.pdf}\n \\caption{MSVAMP}\n \\end{subfigure}\n \\vspace{0.5em}\n \\caption{Layer-wise changes in the number of language-specific neurons of MistralMathOctopus during the alignment.}\n \\label{fig:Layer-wise_changes_specific}\n\\end{figure*}\n\n\\begin{figure*}[htbp]\n \\centering \n \\begin{subfigure}[b]{0.45\\textwidth}\n \\includegraphics[width=\\linewidth]{Images/layer_wise_delta_related_neurons_mistral_mgsm.pdf}\n \\caption{MGSM}\n \\end{subfigure}\n \\hspace{10pt}\n \\begin{subfigure}[b]{0.45\\textwidth}\n \\includegraphics[width=\\linewidth]{Images/layer_wise_delta_related_neurons_mistral_MSVAMP.pdf}\n \\caption{MSVAMP}\n \\end{subfigure}\n \\vspace{0.5em}\n \\caption{Layer-wise changes in the number of language-related neurons of MistralMathOctopus during the alignment.}\n \\label{fig:Layer-wise_changes_related}\n\\end{figure*}\n\n\n\\begin{figure*}[htbp]\n \\centering \n \\begin{subfigure}[b]{0.45\\textwidth}\n \\includegraphics[width=\\linewidth]{Images/layer_wise_delta_agnostic_neurons_mistral_mgsm.pdf}\n \\caption{MGSM}\n \\end{subfigure}\n \\hspace{10pt}\n \\begin{subfigure}[b]{0.45\\textwidth}\n \\includegraphics[width=\\linewidth]{Images/layer_wise_delta_agnostic_neurons_mistral_MSVAMP.pdf}\n \\caption{MSVAMP}\n \\end{subfigure}\n \\vspace{0.5em}\n \\caption{Layer-wise changes in the number of language-agnostic neurons of MistralMathOctopus during the alignment.}\n \\label{fig:Layer-wise_changes_agnostic}\n\\end{figure*}\n\n\n% \\section{Technical Appendices and Supplementary Material}\n% Technical appendices with additional results, figures, graphs and proofs may be submitted with the paper submission before the full submission deadline (see above), or as a separate PDF in the ZIP file below before the supplementary material deadline. There is no page limit for the technical appendices.\n\n\n\\begin{table*}[htbp]\n\\centering\n\\small\n\\setlength{\\tabcolsep}{4pt}\n\\renewcommand{\\arraystretch}{1.2}\n\\resizebox{0.9\\columnwidth}{!}{\n\\begin{tabular}{l|cccccccccc|c}\n\\toprule\n\\textbf{Tested on MGSM} & \\textbf{bn} & \\textbf{th} & \\textbf{sw} & \\textbf{ja} & \\textbf{zh} & \\textbf{ru} & \\textbf{de} & \\textbf{es} & \\textbf{fr} & \\textbf{en} & \\textbf{Avg.} \\\\\n\\midrule\nbase & 43.6 & 53.2 & 50.4 & 55.6 & 59.6 & 59.2 & 61.2 & 62.8 & 56.8 & 75.6 & 57.8 \\\\\nzh $\\Rightarrow$ en & 49.6 & 58.4 & 54.8 & 56.4 & 65.2 & 70.0 & 66.4 & 72.8 & 68.8 & 78.4 & 64.1 \\\\\nzh/de $\\Rightarrow$ en & 46.4 & 55.6 & 59.2 & 56.8 & 64.0 & 71.2 & 66.8 & 71.2 & 69.2 & 75.2 & 63.6 \\\\\nsw/th $\\Rightarrow$ en & 48.8 & 58.8 & 59.2 & 56.4 & 68.4 & 68.4 & 69.2 & 69.6 & 70.4 & 77.6 & 64.7 \\\\\nzh/es/ru $\\Rightarrow$ en & 46.0 & 56.4 & 58.8 & 54.8 & 63.2 & 70.8 & 68.8 & 71.6 & 69.6 & 76.8 & 63.7 \\\\\nzh/es/fr/ja/de/sw/ru/th/bn $\\Rightarrow$ en & 49.6 & 60.0 & 56.4 & 56.4 & 64.4 & 64.8 & 65.2 & 65.2 & 61.2 & 76.4 & 62.0 \\\\\n\\bottomrule\n\\toprule\n\\textbf{Tested on MSVAMP} & \\textbf{bn} & \\textbf{th} & \\textbf{sw} & \\textbf{ja} & \\textbf{zh} & \\textbf{ru} & \\textbf{de} & \\textbf{es} & \\textbf{fr} & \\textbf{en} & \\textbf{Avg.} \\\\\n\\midrule\nbase & 49.3 & 62.5 & 60.6 & 60.9 & 67.4 & 64.9 & 66.5 & 67.6 & 67.2 & 77.0 & 64.4 \\\\\nzh $\\Rightarrow$ en & 52.8 & 62.5 & 60.9 & 63.7 & 66.6 & 67.5 & 69.4 & 69.8 & 69.7 & 76.5 & 65.9 \\\\\nzh/de $\\Rightarrow$ en & 53.2 & 62.0 & 62.9 & 65.3 & 66.1 & 68.7 & 69.9 & 69.0 & 70.1 & 77.0 & 66.4 \\\\\nsw/th $\\Rightarrow$ en & 53.8 & 67.4 & 65.1 & 67.7 & 71.9 & 71.4 & 71.9 & 72.0 & 72.8 & 78.9 & 69.3 \\\\\nzh/es/ru $\\Rightarrow$ en & 52.9 & 63.2 & 61.5 & 63.0 & 67.8 & 68.1 & 69.8 & 68.7 & 70.6 & 78.3 & 66.4 \\\\\nzh/es/fr/ja/de/sw/ru/th/bn $\\Rightarrow$ en & 54.9 & 65.9 & 65.6 & 68.6 & 69.7 & 70.2 & 72.3 & 71.6 & 71.6 & 77.3 & 68.8 \\\\\n\\bottomrule\n\\end{tabular}\n}\n\\caption{Accuracy of the base model and aligned variants on benchmarks.}\n\\label{tab:spontaneous_result_all_mistral}\n\\end{table*}\n\n\\clearpage\n\n% \\begin{table*}[ht]\n% \\centering\n% \\small\n% \\setlength{\\tabcolsep}{4pt}\n% \\renewcommand{\\arraystretch}{1.2}\n% \\resizebox{\\columnwidth}{!}{\n% \\begin{tabular}{l|cccccccccc|cc}\n% \\toprule\n% \\textbf{Tested on MGSM8K} & \\textbf{bn} & \\textbf{th} & \\textbf{sw} & \\textbf{ja} & \\textbf{zh} & \\textbf{ru} & \\textbf{de} & \\textbf{es} & \\textbf{fr} & \\textbf{en} & \\textbf{Avg} & \\textbf{w/o en} \\\\\n% \\midrule\n% base & 28.8 & \\textbf{42.4} & 36.4 & 37.6 & 46.8 & 46.8 & 48.0 & 52.4 & 47.6 & 65.6 & 45.2 & 43.0 \\\\\n% zh $\\Rightarrow$ en & 30.8 & 37.2 & 41.2 & 42.8 & \\textbf{52.8} & 47.6 & 56.0 & 54.8 & \\textbf{51.6} & 64.8 & 48.0 & \\textbf{46.1} \\\\\n% sw $\\Rightarrow$ en & 23.6 & 37.2 & 36.4 & 38.8 & 46.8 & 47.6 & 51.2 & 52.4 & 51.2 & 65.6 & 45.1 & 42.8 \\\\\n% zh/de $\\Rightarrow$ en & 31.2 & 36.8 & 41.2 & 42.4 & 48.4 & 47.6 & 55.6 & 52.8 & 49.6 & \\textbf{67.2} & 47.3 & 45.1 \\\\\n% zh/ja $\\Rightarrow$ en & 30.8 & 38.4 & 40.4 & \\textbf{44.4} & 50.8 & \\textbf{50.0} & 56.0 & 54.8 & \\textbf{51.6} & 66.4 & \\textbf{48.4} & 46.4 \\\\\n% zh/es $\\Rightarrow$ en & 29.2 & 35.2 & 38.8 & 41.6 & 50.0 & 48.4 & 56.0 & \\textbf{55.2} & 48.8 & 65.6 & 46.9 & 44.8 \\\\\n% sw/th $\\Rightarrow$ en & 26.4 & 36.0 & 36.4 & 38.4 & 47.6 & 46.8 & 50.0 & 53.2 & 50.8 & 65.2 & 45.1 & 42.8 \\\\\n% sw/ja $\\Rightarrow$ en & 29.6 & 37.6 & 38.8 & \\textbf{44.4} & 49.2 & 48.0 & \\textbf{56.8} & 53.2 & 51.2 & 66.4 & 47.5 & 45.4 \\\\\n% zh/de/bn $\\Rightarrow$ en & \\textbf{31.6} & 41.2 & 40.4 & 43.2 & 50.0 & 48.0 & 53.6 & 52.4 & 49.6 & 65.6 & 47.6 & 45.6 \\\\\n% zh/es/ru $\\Rightarrow$ en & \\textbf{31.6} & 37.2 & 40.4 & 40.4 & 48.4 & 47.6 & 55.6 & 52.0 & 50.4 & 66.4 & 47.0 & 44.8 \\\\\n% zh/es/fr/ja/de/sw/ru $\\Rightarrow$ en & 30.4 & 37.6 & 39.2 & 42.8 & 49.2 & 48.4 & 55.6 & 53.6 & 50.8 & 66.4 & 47.4 & 45.3 \\\\\n% zh/es/fr/ja/de/sw/ru/th/bn $\\Rightarrow$ en & 30.8 & 37.6 & \\textbf{41.6} & 42.4 & 50.8 & 48.0 & 54.4 & 53.2 & 51.2 & 64.8 & 47.5 & 45.6 \\\\\n% \\toprule\n% \\textbf{Tested on MSVAMP} & \\textbf{bn} & \\textbf{th} & \\textbf{sw} & \\textbf{ja} & \\textbf{zh} & \\textbf{ru} & \\textbf{de} & \\textbf{es} & \\textbf{fr} & \\textbf{en} & \\textbf{Avg} & \\textbf{w/o en} \\\\\n% \\midrule\n% base & 35.9 & 47.3 & 48.5 & 51.9 & 54.6 & 53.4 & 55.0 & 60.0 & 57.4 & 63.6 & 52.8 & 51.6 \\\\\n% zh $\\Rightarrow$ en & 40.5 & 52.7 & 55.0 & \\textbf{57.8} & \\textbf{57.8} & 57.4 & 59.5 & 62.5 & 62.0 & 67.3 & 57.2 & 56.1 \\\\\n% sw $\\Rightarrow$ en & 37.3 & 50.0 & 54.5 & 53.5 & 54.4 & 58.3 & 58.2 & 62.4 & 58.7 & 67.4 & 55.5 & 54.1 \\\\\n% zh/de $\\Rightarrow$ en & 38.8 & 51.6 & 56.3 & 55.7 & 56.8 & 58.4 & 60.6 & 62.5 & 60.0 & 67.3 & 56.8 & 55.6 \\\\\n% zh/ja $\\Rightarrow$ en & 39.0 & 51.3 & 54.5 & 56.3 & 56.1 & 57.2 & 59.6 & 62.2 & 60.8 & 67.0 & 56.4 & 55.2 \\\\\n% zh/es $\\Rightarrow$ en & 38.3 & 51.2 & 55.2 & 54.7 & 56.3 & 57.9 & 59.4 & 61.9 & 59.4 & 67.0 & 56.1 & 54.9 \\\\\n% sw/th $\\Rightarrow$ en & 39.8 & 53.8 & \\textbf{57.6} & 56.6 & 55.9 & \\textbf{59.6} & \\textbf{60.7} & \\textbf{64.0} & 60.2 & \\textbf{69.0} & \\textbf{57.7} & \\textbf{56.5} \\\\\n% sw/ja $\\Rightarrow$ en & 39.2 & 52.7 & 56.1 & 56.5 & 55.6 & 58.3 & 59.1 & 62.9 & 60.6 & 67.2 & 56.8 & 55.7 \\\\\n% zh/de/bn $\\Rightarrow$ en & \\textbf{42.1} & \\textbf{54.0} & 56.6 & 55.9 & 57.2 & 58.4 & 60.1 & 62.8 & \\textbf{61.8} & 66.5 & 57.5 & \\textbf{56.5} \\\\\n% zh/es/ru $\\Rightarrow$ en & 38.1 & 51.5 & 55.1 & 53.9 & 55.7 & 57.8 & 59.0 & 62.1 & 58.9 & 67.2 & 55.9 & 54.7 \\\\\n% zh/es/fr/ja/de/sw/ru $\\Rightarrow$ en & 38.2 & 51.6 & 54.6 & 54.6 & 56.1 & 58.1 & 59.4 & 62.2 & 59.7 & 67.3 & 56.2 & 54.9 \\\\\n% zh/es/fr/ja/de/sw/ru/th/bn $\\Rightarrow$ en & 39.6 & 53.4 & 56.2 & 55.2 & 57.0 & 59.3 & 59.8 & 63.0 & 61.4 & 66.9 & 57.2 & 56.1 \\\\\n% \\bottomrule\n% \\end{tabular}}\n% \\caption{Accuracy of the MetaMathOctopus-7B base model and aligned variants on benchmarks.\n% To ensure robustness, we report at least two sets of results for each language quantity. The expected accuracy of random guessing is 50.0\\%. \"X/Y $\\Rightarrow$ T\" indicates that languages X and Y are used for alignment. The best results for each language are highlighted.}\n% \\label{tab:spontaneous_result_all_llama}\n% \\end{table*}\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\n% \\newpage\n\n% \\section*{NeurIPS Paper Checklist}\n\n%%% BEGIN INSTRUCTIONS %%%\n% The checklist is designed to encourage best practices for responsible machine learning research, addressing issues of reproducibility, transparency, research ethics, and societal impact. Do not remove the checklist: {\\bf The papers not including the checklist will be desk rejected.} The checklist should follow the references and follow the (optional) supplemental material. The checklist does NOT count towards the page limit. \n\n% Please read the checklist guidelines carefully for information on how to answer these questions. For each question in the checklist:\n% \\begin{itemize}\n% \\item You should answer \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item \\answerNA{} means either that the question is Not Applicable for that particular paper or the relevant information is Not Available.\n% \\item Please provide a short (1–2 sentence) justification right after your answer (even for NA). \n% % \\item {\\bf The papers not including the checklist will be desk rejected.}\n% \\end{itemize}\n\n% {\\bf The checklist answers are an integral part of your paper submission.} They are visible to the reviewers, area chairs, senior area chairs, and ethics reviewers. You will be asked to also include it (after eventual revisions) with the final version of your paper, and its final version will be published with the paper.\n\n% The reviewers of your paper will be asked to use the checklist as one of the factors in their evaluation. While \"\\answerYes{}\" is generally preferable to \"\\answerNo{}\", it is perfectly acceptable to answer \"\\answerNo{}\" provided a proper justification is given (e.g., \"error bars are not reported because it would be too computationally expensive\" or \"we were unable to find the license for the dataset we used\"). In general, answering \"\\answerNo{}\" or \"\\answerNA{}\" is not grounds for rejection. While the questions are phrased in a binary way, we acknowledge that the true answer is often more nuanced, so please just use your best judgment and write a justification to elaborate. All supporting evidence can appear either in the main paper or the supplemental material, provided in appendix. If you answer \\answerYes{} to a question, in the justification please point to the section(s) where related material for the question can be found.\n\n% IMPORTANT, please:\n% \\begin{itemize}\n% \\item {\\bf Delete this instruction block, but keep the section heading ``NeurIPS Paper Checklist\"},\n% \\item {\\bf Keep the checklist subsection headings, questions/answers and guidelines below.}\n% \\item {\\bf Do not modify the questions and only use the provided macros for your answers}.\n% \\end{itemize} \n \n\n%%% END INSTRUCTIONS %%%\n\n\n% \\begin{enumerate}\n\n% \\item {\\bf Claims}\n% \\item[] Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?\n% \\item[] Answer: \\answerYes{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: We have introduced our contributions and scope in detail in the abstract and introduction.\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that the abstract and introduction do not include the claims made in the paper.\n% \\item The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. 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In the subsequent experiments, we conduct our experiments using a fixed random seed.\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that the paper does not include experiments.\n% \\item The authors should answer \"Yes\" if the results are accompanied by error bars, confidence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper.\n% \\item The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).\n% \\item The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)\n% \\item The assumptions made should be given (e.g., Normally distributed errors).\n% \\item It should be clear whether the error bar is the standard deviation or the standard error of the mean.\n% \\item It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have a 96\\% CI, if the hypothesis of Normality of errors is not verified.\n% \\item For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out of range (e.g. negative error rates).\n% \\item If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.\n% \\end{itemize}\n\n% \\item {\\bf Experiments compute resources}\n% \\item[] Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?\n% \\item[] Answer: \\answerYes{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: We have introduced the compute resources utilized in our experiments in Appendix~\\ref{app:implementation-details}.\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that the paper does not include experiments.\n% \\item The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.\n% \\item The paper should provide the amount of compute required for each of the individual experimental runs as well as estimate the total compute. \n% \\item The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make it into the paper). \n% \\end{itemize}\n \n% \\item {\\bf Code of ethics}\n% \\item[] Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics \\url{https://neurips.cc/public/EthicsGuidelines}?\n% \\item[] Answer: \\answerYes{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: The research conducted in this paper conforms with the NeurIPS Code of Ethics.\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.\n% \\item If the authors answer No, they should explain the special circumstances that require a deviation from the Code of Ethics.\n% \\item The authors should make sure to preserve anonymity (e.g., if there is a special consideration due to laws or regulations in their jurisdiction).\n% \\end{itemize}\n\n\n% \\item {\\bf Broader impacts}\n% \\item[] Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?\n% \\item[] Answer: \\answerNA{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: We conduct mechanistic interpretability analysis in our paper, which is not tied to particular applications. So there is no societal impact of our work.\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that there is no societal impact of the work performed.\n% \\item If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact.\n% \\item Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.\n% \\item The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.\n% \\item The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.\n% \\item If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML).\n% \\end{itemize}\n \n% \\item {\\bf Safeguards}\n% \\item[] Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?\n% \\item[] Answer: \\answerNA{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: We mainly conduct mechanistic interpretability analysis based on open-resource base models and data in our work. 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And we have mentioned the related licenses in Appendix~\\ref{app:licenses}.\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that the paper does not use existing assets.\n% \\item The authors should cite the original paper that produced the code package or dataset.\n% \\item The authors should state which version of the asset is used and, if possible, include a URL.\n% \\item The name of the license (e.g., CC-BY 4.0) should be included for each asset.\n% \\item For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.\n% \\item If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, \\url{paperswithcode.com/datasets} has curated licenses for some datasets. 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This includes details about training, license, limitations, etc. \n% \\item The paper should discuss whether and how consent was obtained from people whose asset is used.\n% \\item At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.\n% \\end{itemize}\n\n% \\item {\\bf Crowdsourcing and research with human subjects}\n% \\item[] Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)? \n% \\item[] Answer: \\answerNA{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: Our work does not involve crowdsourcing nor research with human subjects.\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.\n% \\item Including this information in the supplemental material is fine, but if the main contribution of the paper involves human subjects, then as much detail as possible should be included in the main paper. \n% \\item According to the NeurIPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector. \n% \\end{itemize}\n\n% \\item {\\bf Institutional review board (IRB) approvals or equivalent for research with human subjects}\n% \\item[] Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?\n% \\item[] Answer: \\answerNA{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: Our work does not involve crowdsourcing nor research with human subjects.\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.\n% \\item Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper. \n% \\item We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere to the NeurIPS Code of Ethics and the guidelines for their institution. \n% \\item For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.\n% \\end{itemize}\n\n% \\item {\\bf Declaration of LLM usage}\n% \\item[] Question: Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.\n% %this research? \n% \\item[] Answer: \\answerNA{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: LLMs are not involved in the core method development in this paper.\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that the core method development in this research does not involve LLMs as any important, original, or non-standard components.\n% \\item Please refer to our LLM policy (\\url{https://neurips.cc/Conferences/2025/LLM}) for what should or should not be described.\n% \\end{itemize}\n\n% \\end{enumerate}\n\n\n\\end{document}"},"question":{"kind":"string","value":"Ignoring the case where the model is aligned for all languages, in which cases does adding alignment to a language not lead to improvement in test performance for that language?"},"answer":{"kind":"string","value":"MGSM: zh->zh/es/ru: es; MSVAMP: base->zh: zh; base->zh/de: zh; zh->zh/es/ru: es"},"plan":{"kind":"string","value":"SELECT MGSM and MSVAMP\nLOOP for each benchmark\n SELECT all aligned models except all-languages\n LOOP for each model\n SELECT all languages\n LOOP for each language\n IF aligned score <= base score\n COMPUTE add tuple of (benchmark, alignment model, language) to result_list\n\n\nRETURN result_list for MGSM and MSVAMP\n"}}},{"rowIdx":70,"cells":{"paper":{"kind":"string","value":"2505.17427"},"relevant_tables":{"kind":"list like","value":[["\\begin{table*}[!t]\n","\\centering\n","\\small\n","\\adjustbox{max width=\\textwidth}{\n","\\begin{tabular}{lccccccc}\n","\\toprule\n","Model & SQuAD & HotpotQA & NewsQA & Gaokao & HQA & TriviaQA & BioASQ\\\\\n","\\midrule\n","\\rowcolor{gray!10}\\multicolumn{8}{c}{\\textbf{\\textit{Quick-Thinking Models w/ Reasoning Strategies}}}\\\\\n","\\multicolumn{8}{l}{\\textbf{\\textit{Qwen2.5-32B-Instruct}}}\\\\\n","\\; w/ vanilla \\textit{(quick)} & 73.41 & 55.32 & 50.83 & 29.52 & 35.92 & 40.73 & 56.33\\\\\n","\\; w/ few-shots \\textit{(quick)} & 74.56 & 56.23 & 51.67 & 30.33 & 36.87 & 41.57 & 57.17\\\\\n","\\; w/ self-consistency~\\cite{wang2022self} & 75.31 & 56.76 & 52.27 & 30.57 & 37.12 & 41.92 & 57.57\\\\\n","\\; w/ proCoT~\\cite{deng-etal-2023-prompting} & 77.12 & 58.07 & 53.57 & 31.42 & 38.03 & 42.83 & 58.62\\\\\n","\\; w/ ToT~\\cite{yao2023tree} & 78.47 & 59.11 & 54.31 & 31.96 & 38.66 & 43.46 & 59.36\\\\\n","\\; w/ MCTS~\\cite{zhao2024marco} & 78.52 & 58.97 & 54.25 & 32.04 & 38.73 & 43.51 & 59.42\\\\\n","\\textbf{\\; w/ T$^2$ (ours)} & \\textbf{81.86} & \\textbf{67.11} & \\textbf{61.27} & \\textbf{34.06} & \\textbf{40.31} & \\textbf{43.92} & \\textbf{65.02}\\\\\n","\\hdashline\n","\\multicolumn{8}{l}{\\textbf{\\textit{GPT-4o}}}\\\\\n","\\; w/ vanilla \\textit{(quick)} & 78.52 & 60.02 & 55.32 & 34.51 & 41.11 & 49.01 & 60.51\\\\\n","\\; w/ few-shots \\textit{(quick)} & 79.86 & 61.06 & 56.17 & 35.36 & 42.06 & 50.07 & 61.37\\\\\n","\\; w/ self-consistency~\\cite{wang2022self} & 80.56 & 61.61 & 56.62 & 35.62 & 42.46 & 50.42 & 61.81\\\\\n","\\; w/ proCoT~\\cite{deng-etal-2023-prompting} & 82.12 & 63.02 & 57.86 & 36.66 & 43.36 & 51.46 & 62.87\\\\\n","\\; w/ ToT~\\cite{yao2023tree} & 83.21 & 64.06 & 58.67 & 37.22 & 44.07 & 52.26 & 63.72\\\\\n","\\; w/ MCTS~\\cite{zhao2024marco} & 83.35 & 64.18 & 58.19 & 37.31 & 45.15 & 52.38 & 64.89\\\\\n","\\textbf{\\; w/ T$^2$ (ours)} & \\textbf{85.06} & \\textbf{66.16} & \\textbf{60.92} & \\textbf{37.57} & \\textbf{45.27} & \\textbf{53.92} & \\textbf{66.97}\\\\\n","\\midrule\n","\\rowcolor{gray!10}\\multicolumn{8}{c}{\\textbf{\\textit{Slow-Thinking Models}}}\\\\\n","o1-mini & 85.81 & 70.91 & 63.22 & 42.66 & 49.22 & 58.56 & 68.42\\\\\n","QwQ-32B-Preview & 86.87 & 71.86 & 63.92 & 43.23 & 49.62 & 59.16 & 69.02\\\\\n","DeepSeek-R1 & 87.62 & 72.72 & 64.41 & 43.47 & 50.27 & 60.02 & 70.72\\\\\n","o1 & 88.22 & 73.37 & 65.11 & 44.06 & 51.07 & 60.86 & 71.36\\\\\n","o4-mini & 88.72 & 73.86 & 65.57 & 44.32 & 51.61 & 61.11 & 71.82\\\\\n","o4-mini-high & 88.91 & 74.07 & 65.81 & 44.52 & 51.86 & 61.27 & 72.02\\\\\n","Claude-3.7-sonnet-thinking & 89.11 & 74.21 & 66.01 & 44.61 & 52.01 & 61.47 & 72.22\\\\\n","o3 & 89.41 & 74.61 & 66.32 & 45.01 & 52.11 & 61.81 & 72.62\\\\\n","Gemini-2.5-Pro & 90.27 & 75.46 & 67.11 & 45.76 & 53.07 & 62.68 & 73.57\\\\\n","\\textbf{QwQ-32B + T$^2$ (ours)} & \\textbf{92.12} & \\textbf{77.61} & \\textbf{68.61} & \\textbf{47.42} & \\textbf{54.71} & \\textbf{64.22} & \\textbf{75.21}\\\\\n","\\bottomrule\n","\\end{tabular}}\n","\\caption{ROUGE-L on seven QA datasets. We regard vanilla model and few-shot method as quick-thinking methods. And the other five (including ours) are slow-thinking methods. They can all be applied to quick-thinking models to improve reasoning ability.}\n","\\label{tab:main_results}\n","\\end{table*}\n"]],"string":"[\n [\n \"\\\\begin{table*}[!t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\small\\n\",\n \"\\\\adjustbox{max width=\\\\textwidth}{\\n\",\n \"\\\\begin{tabular}{lccccccc}\\n\",\n \"\\\\toprule\\n\",\n \"Model & SQuAD & HotpotQA & NewsQA & Gaokao & HQA & TriviaQA & BioASQ\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\rowcolor{gray!10}\\\\multicolumn{8}{c}{\\\\textbf{\\\\textit{Quick-Thinking Models w/ Reasoning Strategies}}}\\\\\\\\\\n\",\n \"\\\\multicolumn{8}{l}{\\\\textbf{\\\\textit{Qwen2.5-32B-Instruct}}}\\\\\\\\\\n\",\n \"\\\\; w/ vanilla \\\\textit{(quick)} & 73.41 & 55.32 & 50.83 & 29.52 & 35.92 & 40.73 & 56.33\\\\\\\\\\n\",\n \"\\\\; w/ few-shots \\\\textit{(quick)} & 74.56 & 56.23 & 51.67 & 30.33 & 36.87 & 41.57 & 57.17\\\\\\\\\\n\",\n \"\\\\; w/ self-consistency~\\\\cite{wang2022self} & 75.31 & 56.76 & 52.27 & 30.57 & 37.12 & 41.92 & 57.57\\\\\\\\\\n\",\n \"\\\\; w/ proCoT~\\\\cite{deng-etal-2023-prompting} & 77.12 & 58.07 & 53.57 & 31.42 & 38.03 & 42.83 & 58.62\\\\\\\\\\n\",\n \"\\\\; w/ ToT~\\\\cite{yao2023tree} & 78.47 & 59.11 & 54.31 & 31.96 & 38.66 & 43.46 & 59.36\\\\\\\\\\n\",\n \"\\\\; w/ MCTS~\\\\cite{zhao2024marco} & 78.52 & 58.97 & 54.25 & 32.04 & 38.73 & 43.51 & 59.42\\\\\\\\\\n\",\n \"\\\\textbf{\\\\; w/ T$^2$ (ours)} & \\\\textbf{81.86} & \\\\textbf{67.11} & \\\\textbf{61.27} & \\\\textbf{34.06} & \\\\textbf{40.31} & \\\\textbf{43.92} & \\\\textbf{65.02}\\\\\\\\\\n\",\n \"\\\\hdashline\\n\",\n \"\\\\multicolumn{8}{l}{\\\\textbf{\\\\textit{GPT-4o}}}\\\\\\\\\\n\",\n \"\\\\; w/ vanilla \\\\textit{(quick)} & 78.52 & 60.02 & 55.32 & 34.51 & 41.11 & 49.01 & 60.51\\\\\\\\\\n\",\n \"\\\\; w/ few-shots \\\\textit{(quick)} & 79.86 & 61.06 & 56.17 & 35.36 & 42.06 & 50.07 & 61.37\\\\\\\\\\n\",\n \"\\\\; w/ self-consistency~\\\\cite{wang2022self} & 80.56 & 61.61 & 56.62 & 35.62 & 42.46 & 50.42 & 61.81\\\\\\\\\\n\",\n \"\\\\; w/ proCoT~\\\\cite{deng-etal-2023-prompting} & 82.12 & 63.02 & 57.86 & 36.66 & 43.36 & 51.46 & 62.87\\\\\\\\\\n\",\n \"\\\\; w/ ToT~\\\\cite{yao2023tree} & 83.21 & 64.06 & 58.67 & 37.22 & 44.07 & 52.26 & 63.72\\\\\\\\\\n\",\n \"\\\\; w/ MCTS~\\\\cite{zhao2024marco} & 83.35 & 64.18 & 58.19 & 37.31 & 45.15 & 52.38 & 64.89\\\\\\\\\\n\",\n \"\\\\textbf{\\\\; w/ T$^2$ (ours)} & \\\\textbf{85.06} & \\\\textbf{66.16} & \\\\textbf{60.92} & \\\\textbf{37.57} & \\\\textbf{45.27} & \\\\textbf{53.92} & \\\\textbf{66.97}\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\rowcolor{gray!10}\\\\multicolumn{8}{c}{\\\\textbf{\\\\textit{Slow-Thinking Models}}}\\\\\\\\\\n\",\n \"o1-mini & 85.81 & 70.91 & 63.22 & 42.66 & 49.22 & 58.56 & 68.42\\\\\\\\\\n\",\n \"QwQ-32B-Preview & 86.87 & 71.86 & 63.92 & 43.23 & 49.62 & 59.16 & 69.02\\\\\\\\\\n\",\n \"DeepSeek-R1 & 87.62 & 72.72 & 64.41 & 43.47 & 50.27 & 60.02 & 70.72\\\\\\\\\\n\",\n \"o1 & 88.22 & 73.37 & 65.11 & 44.06 & 51.07 & 60.86 & 71.36\\\\\\\\\\n\",\n \"o4-mini & 88.72 & 73.86 & 65.57 & 44.32 & 51.61 & 61.11 & 71.82\\\\\\\\\\n\",\n \"o4-mini-high & 88.91 & 74.07 & 65.81 & 44.52 & 51.86 & 61.27 & 72.02\\\\\\\\\\n\",\n \"Claude-3.7-sonnet-thinking & 89.11 & 74.21 & 66.01 & 44.61 & 52.01 & 61.47 & 72.22\\\\\\\\\\n\",\n \"o3 & 89.41 & 74.61 & 66.32 & 45.01 & 52.11 & 61.81 & 72.62\\\\\\\\\\n\",\n \"Gemini-2.5-Pro & 90.27 & 75.46 & 67.11 & 45.76 & 53.07 & 62.68 & 73.57\\\\\\\\\\n\",\n \"\\\\textbf{QwQ-32B + T$^2$ (ours)} & \\\\textbf{92.12} & \\\\textbf{77.61} & \\\\textbf{68.61} & \\\\textbf{47.42} & \\\\textbf{54.71} & \\\\textbf{64.22} & \\\\textbf{75.21}\\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}}\\n\",\n \"\\\\caption{ROUGE-L on seven QA datasets. We regard vanilla model and few-shot method as quick-thinking methods. And the other five (including ours) are slow-thinking methods. They can all be applied to quick-thinking models to improve reasoning ability.}\\n\",\n \"\\\\label{tab:main_results}\\n\",\n \"\\\\end{table*}\\n\"\n ]\n]","isConversational":false},"tables":{"kind":"list like","value":[["\\begin{figure*}[!t]\n"," \\centering\n"," \\includegraphics[width=\\linewidth]{pics/overview.pdf}\n"," \\caption{Overview of our T$^2$. \\textbf{(a)} direct prompt or Chain-of-Thought (CoT), which adopts the same reasoning strategy regardless of question complexity. \\textbf{(b)} Adaptive-of-Thought, which designs a question complexity evaluator to pre-categorize all questions, which might bring human bias in the evaluator design process. \\textbf{(c)} our T$^2$. Instead of pre-categorizing questions into different complexity sets, T$^2$ generates multiple similar examples for different inputs adaptively and selects the best reasoning strategy for answering.}\n"," \\label{fig:overview}\n","\\end{figure*}\n"],["\\begin{table*}[!t]\n","\\centering\n","\\small\n","\\adjustbox{max width=\\textwidth}{\n","\\begin{tabular}{lccccccc}\n","\\toprule\n","Model & SQuAD & HotpotQA & NewsQA & Gaokao & HQA & TriviaQA & BioASQ\\\\\n","\\midrule\n","\\rowcolor{gray!10}\\multicolumn{8}{c}{\\textbf{\\textit{Quick-Thinking Models w/ Reasoning Strategies}}}\\\\\n","\\multicolumn{8}{l}{\\textbf{\\textit{Qwen2.5-32B-Instruct}}}\\\\\n","\\; w/ vanilla \\textit{(quick)} & 73.41 & 55.32 & 50.83 & 29.52 & 35.92 & 40.73 & 56.33\\\\\n","\\; w/ few-shots \\textit{(quick)} & 74.56 & 56.23 & 51.67 & 30.33 & 36.87 & 41.57 & 57.17\\\\\n","\\; w/ self-consistency~\\cite{wang2022self} & 75.31 & 56.76 & 52.27 & 30.57 & 37.12 & 41.92 & 57.57\\\\\n","\\; w/ proCoT~\\cite{deng-etal-2023-prompting} & 77.12 & 58.07 & 53.57 & 31.42 & 38.03 & 42.83 & 58.62\\\\\n","\\; w/ ToT~\\cite{yao2023tree} & 78.47 & 59.11 & 54.31 & 31.96 & 38.66 & 43.46 & 59.36\\\\\n","\\; w/ MCTS~\\cite{zhao2024marco} & 78.52 & 58.97 & 54.25 & 32.04 & 38.73 & 43.51 & 59.42\\\\\n","\\textbf{\\; w/ T$^2$ (ours)} & \\textbf{81.86} & \\textbf{67.11} & \\textbf{61.27} & \\textbf{34.06} & \\textbf{40.31} & \\textbf{43.92} & \\textbf{65.02}\\\\\n","\\hdashline\n","\\multicolumn{8}{l}{\\textbf{\\textit{GPT-4o}}}\\\\\n","\\; w/ vanilla \\textit{(quick)} & 78.52 & 60.02 & 55.32 & 34.51 & 41.11 & 49.01 & 60.51\\\\\n","\\; w/ few-shots \\textit{(quick)} & 79.86 & 61.06 & 56.17 & 35.36 & 42.06 & 50.07 & 61.37\\\\\n","\\; w/ self-consistency~\\cite{wang2022self} & 80.56 & 61.61 & 56.62 & 35.62 & 42.46 & 50.42 & 61.81\\\\\n","\\; w/ proCoT~\\cite{deng-etal-2023-prompting} & 82.12 & 63.02 & 57.86 & 36.66 & 43.36 & 51.46 & 62.87\\\\\n","\\; w/ ToT~\\cite{yao2023tree} & 83.21 & 64.06 & 58.67 & 37.22 & 44.07 & 52.26 & 63.72\\\\\n","\\; w/ MCTS~\\cite{zhao2024marco} & 83.35 & 64.18 & 58.19 & 37.31 & 45.15 & 52.38 & 64.89\\\\\n","\\textbf{\\; w/ T$^2$ (ours)} & \\textbf{85.06} & \\textbf{66.16} & \\textbf{60.92} & \\textbf{37.57} & \\textbf{45.27} & \\textbf{53.92} & \\textbf{66.97}\\\\\n","\\midrule\n","\\rowcolor{gray!10}\\multicolumn{8}{c}{\\textbf{\\textit{Slow-Thinking Models}}}\\\\\n","o1-mini & 85.81 & 70.91 & 63.22 & 42.66 & 49.22 & 58.56 & 68.42\\\\\n","QwQ-32B-Preview & 86.87 & 71.86 & 63.92 & 43.23 & 49.62 & 59.16 & 69.02\\\\\n","DeepSeek-R1 & 87.62 & 72.72 & 64.41 & 43.47 & 50.27 & 60.02 & 70.72\\\\\n","o1 & 88.22 & 73.37 & 65.11 & 44.06 & 51.07 & 60.86 & 71.36\\\\\n","o4-mini & 88.72 & 73.86 & 65.57 & 44.32 & 51.61 & 61.11 & 71.82\\\\\n","o4-mini-high & 88.91 & 74.07 & 65.81 & 44.52 & 51.86 & 61.27 & 72.02\\\\\n","Claude-3.7-sonnet-thinking & 89.11 & 74.21 & 66.01 & 44.61 & 52.01 & 61.47 & 72.22\\\\\n","o3 & 89.41 & 74.61 & 66.32 & 45.01 & 52.11 & 61.81 & 72.62\\\\\n","Gemini-2.5-Pro & 90.27 & 75.46 & 67.11 & 45.76 & 53.07 & 62.68 & 73.57\\\\\n","\\textbf{QwQ-32B + T$^2$ (ours)} & \\textbf{92.12} & \\textbf{77.61} & \\textbf{68.61} & \\textbf{47.42} & \\textbf{54.71} & \\textbf{64.22} & \\textbf{75.21}\\\\\n","\\bottomrule\n","\\end{tabular}}\n","\\caption{ROUGE-L on seven QA datasets. We regard vanilla model and few-shot method as quick-thinking methods. And the other five (including ours) are slow-thinking methods. They can all be applied to quick-thinking models to improve reasoning ability.}\n","\\label{tab:main_results}\n","\\end{table*}\n"],["\\begin{figure*}[!t]\n"," \\centering\n"," \\includegraphics[width=\\linewidth]{./draw/hit_error_retrace_comment.pdf}\n"," \\caption{Results on Hits and Errors (left) and Retrace Rate (right).}\n"," \\label{fig:hit_error}\n"," \\label{fig:retrace}\n","\\end{figure*}\n"],["\\begin{table}[!t]\n","\\centering\n","\\adjustbox{max width=\\linewidth}{\n","\\begin{tabular}{cccc}\n","\\toprule\n","\\textbf{Skill Type} & \\textbf{Uniform} & \\textbf{Ours} & \\textbf{Improvement} \\\\\n","\\midrule\n","Deductive & 72.3\\% & 75.8\\% & +3.5\\% \\\\\n","Inductive & 68.7\\% & 73.2\\% & +4.5\\% \\\\\n","Abductive & 74.1\\% & 76.3\\% & +2.2\\% \\\\\n","Cause \\& Effect & 70.5\\% & 74.1\\% & +3.6\\% \\\\\n","Analogical & 63.8\\% & 71.5\\% & +7.7\\% \\\\\n","Critical Thinking & 69.2\\% & 72.8\\% & +3.6\\% \\\\\n","Decompositional & 61.4\\% & 69.7\\% & +8.3\\% \\\\\n","\\bottomrule\n","\\end{tabular}}\n","\\caption{Performance comparison between uniform and our matching strategies.}\n","\\label{tab:skill_match}\n","\\end{table}\n"],["\\begin{figure}[!t]\n"," \\centering\n"," \\includegraphics[width=\\linewidth]{draw/ave_length.pdf}\n"," \\caption{Results of average token length on each dataset. SC is the abbreviation for Self-Consistency.}\n"," \\label{fig:ave_length}\n","\\end{figure}\n"],["\\begin{figure*}[!t]\n"," \\centering\n"," \\includegraphics[width=\\linewidth]{pics/case_study.pdf}\n"," \\caption{Case study to show effectiveness of our T$^2$ framework. There are three proper reasoning skills should be adopted to answer the question based on given documents. The red, orange, and green answers represent responses under quick thinking, slow thinking, and ours, respectively.}\n"," \\label{fig:case}\n","\\end{figure*}\n"],["\\begin{figure}[!t]\n"," \\centering\n"," \\includegraphics[width=\\linewidth]{draw/demo_quality.pdf}\n"," \\caption{Results on relationship between reasoning skills' accuracy and overall performance.}\n"," \\label{fig:demo_quality}\n","\\end{figure}\n"],["\\begin{table*}[!t]\n"," \\centering\n"," \\small\n"," \\begin{tabular}{l p{0.4\\textwidth} p{0.35\\textwidth}}\n"," \\toprule\n"," \\textbf{Type of Reasoning} & \\textbf{Detailed Description} & \\textbf{Example} \\\\\n"," \\midrule\n"," \\textbf{Deductive} \n"," & Deductive reasoning occurs when generalized statements apply to specific cases. These generalized statements are established and already proven, making specific cases easy to deduce. For example, all humans are mortals. Bill is a human, so Bill must be mortal. In this example the generalized, but proven, statement, ``all humans are mortals'' is what drives the reasoning.\n"," & \\underline{Document:} All shapes with three sides are triangles. A certain figure here has exactly three sides.\n"," \\newline\n"," \\underline{Question:} What is this figure called?\n"," \\newline\n"," \\underline{Answer:} It is a triangle. All shapes with three sides are triangles, and this figure has three sides. So it must be a triangle. \\\\\n"," \\midrule\n"," \\textbf{Inductive} \n"," & Inductive reasoning is similar to deductive reasoning in that they both draw a conclusion based on a statement. However, in inductive reasoning, the statement is likely but has not been proven. For example, roses usually bloom in spring. In spring, one can count on there being roses. Again, the difference is that this is likely but not proven to be 100\\%. \n"," & \\underline{Document:} Every spring for the past ten years, wild roses in Green Valley have bloomed in late March. This spring is about to begin in Green Valley. \n"," \\newline\n"," \\underline{Question:} Will the wild roses bloom in late March this year?\n"," \\newline\n"," \\underline{Answer:} It is likely they will bloom in late March, because they usually do, but it is not guaranteed. \\\\\n"," \\midrule\n"," \\textbf{Abductive}\n"," & Abductive reasoning is the act of making a conclusion based on what you already know. For example, if you see a plate of food still hot, but half-eaten, you can make the conclusion that the person eating that food is probably returning soon.\n"," & \\underline{Document:} You notice a half-eaten sandwich and a still-hot cup of coffee on a café table. The seat feels warm, and a jacket is draped over the chair.\n"," \\newline\n"," \\underline{Question:} Has the person who was sitting here left permanently, or are they coming back soon?\n"," \\newline\n"," \\underline{Answer:} It is likely they just stepped away for a moment and will return, because the food and drink are still warm and their jacket remains on the chair.\\\\\n"," \\midrule\n"," \\textbf{Cause \\& Effect}\n"," & Cause and effect reasoning is that if x happens then y will happen as a result. This is extremely persuasive when making a speech or trying to get someone to take action to cause an effect. For example, a politician may say that if they are elected, then poverty will decrease. This is using cause and effect reasoning in a real-world situation.\n"," & \\underline{Document:} Meteorologists predict heavy rain this evening, with warnings that streets may flood if the rainfall continues.\n"," \\newline\n"," \\underline{Question:} Will the roads become dangerous as a result of this weather?\n"," \\newline\n"," \\underline{Answer:} Yes. If heavy rain continues, roads will likely flood and become slippery, causing drivers to have less control of their vehicles.\\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," \\caption{(1/2) Full list of reasoning skills used in the reasoning path construction.}\n"," \\label{tab:full_reasoning_skills}\n","\\end{table*}\n"],["\\begin{table*}[!t]\n"," \\centering\n"," \\small\n"," \\begin{tabular}{l p{0.4\\textwidth} p{0.35\\textwidth}}\n"," \\toprule\n"," \\textbf{Type of Reasoning} & \\textbf{Detailed Description} & \\textbf{Example} \\\\\n"," \\midrule\n"," \\textbf{Analogical}\n"," & Analogical reasoning is the use of a comparison between two things to persuade that there must be more in common if they already share something. For example, if x, y, and z all share this trait, then they must also share other traits. The foundation of this type of reasoning is perfect for speeches and comparisons in the real world. If there are connections between x and y already, then they must have several other things in common as well.\n"," & \\underline{Document:} Many leading technology companies emphasize continuous learning and adaptability. For instance, Google, Microsoft, and Amazon all invest in regular training programs and encourage innovation among employees. Their similar approach to fostering a culture of growth has been linked to their strong performance in rapidly changing markets.\n"," \\newline\n"," \\underline{Question:} Can we infer that a company that promotes continuous learning will also likely be successful in adapting to market changes?\n"," \\newline\n"," \\underline{Answer:} Yes. Since Google, Microsoft, and Amazon all share a culture of continuous learning and, as a result, demonstrate high adaptability and market success, it is reasonable to conclude by analogy that a company which also promotes continuous learning is likely to develop similar strengths.\\\\\n"," \\midrule\n"," \\textbf{Critical Thinking}\n"," & Critical thinking occurs when you take all of the facts and develop a conclusion based on an analysis. This could happen subconsciously or intentionally, depending on the situation. For example, in the real world, critical thinking could be about your relationships. You could see a behavior you don't like about someone and have to think critically about whether or not you will choose to spend more time with this person. This is using critical thinking to develop reasoning in a real-world application.\n"," & \\underline{Document:} Over the past few months, Sam has repeatedly cancelled plans at the last minute and rarely communicated afterward.\n"," \\newline\n"," \\underline{Question:} Should you invest time in a close friendship with Sam?\n"," \\newline\n"," \\underline{Answer:} No. Sam's consistent behavior of last-minute cancellations suggests a pattern of unreliability, which may negatively affect the trust needed in a close friendship.\\\\\n"," \\midrule\n"," \\textbf{Decompositional}\n"," & Decompositional reasoning happens when the different parts of the reasoning are broken down into smaller pieces and analyzed for how they contribute to the whole. The intent of this is to make the reasoning easier to understand and allow for analyzing how the parts equal the whole. For example, in order to understand the function of the human body, you would have to analyze each bone and organ to see how they all work together. Additionally, in the real world, an argument could be broken down into several smaller parts in order to analyze the effectiveness of the argument as a whole.\n"," & \\underline{Document:} A smartphone's quality can be understood by breaking it down into three parts: its design, performance, and battery life. The design covers the build and user interface; performance looks at processing speed and software efficiency; battery life shows how long the device operates on a single charge.\n"," \\newline\n"," \\underline{Question:} Can we conclude that the smartphone provides a good overall user experience?\n"," \\newline\n"," \\underline{Answer:} Yes. If the design is appealing, the performance is robust, and the battery life is long, then the smartphone is likely to offer a good overall experience.\\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," \\caption{(2/2) Full list of reasoning skills used in the reasoning path construction.}\n"," \\label{tab:full_reasoning_skills_2}\n","\\end{table*}\n"],["\\begin{table*}[!t]\n","\\centering\n","\\adjustbox{max width=\\textwidth}{\n","\\begin{tabular}{cccccccc}\n","\\toprule\n","\\textbf{Skill Type} & \\textbf{SQuAD} & \\textbf{HotpotQA} & \\textbf{NewsQA} & \\textbf{GAOKAO} & \\textbf{HQA} & \\textbf{TriviaQA} & \\textbf{BioASQ} \\\\\n","\\midrule\n","Deductive & 0.31 & 0.22 & 0.28 & 0.15 & 0.42 & 0.18 & 0.25 \\\\\n","Inductive & 0.23 & 0.18 & 0.15 & 0.12 & 0.13 & 0.21 & 0.19 \\\\\n","Abductive & 0.05 & 0.12 & 0.08 & 0.21 & 0.09 & 0.15 & 0.11 \\\\\n","Cause \\& Effect & 0.12 & 0.15 & 0.13 & 0.22 & 0.08 & 0.19 & 0.14 \\\\\n","Analogical & 0.08 & 0.13 & 0.09 & 0.07 & 0.11 & 0.12 & 0.16 \\\\\n","Critical Thinking & 0.14 & 0.16 & 0.18 & 0.14 & 0.13 & 0.09 & 0.10 \\\\\n","Decompositional & 0.07 & 0.04 & 0.09 & 0.09 & 0.04 & 0.06 & 0.05 \\\\\n","\\bottomrule\n","\\end{tabular}}\n","\\caption{Distribution (\\%) of reasoning skills across benchmark datasets, showing the proportion of questions requiring each skill type.}\n","\\label{tab:skill_distribution}\n","\\end{table*}\n"],["\\begin{table*}[!t]\n"," \\centering\n"," \\begin{tabular}{ll}\n"," \\toprule\n"," \\textbf{Parameter} & \\textbf{Value} \\\\\n"," \\midrule\n"," \\textbf{Model} & RoBERTa\\\\\n"," \\textbf{Full Name} & \\texttt{FacebookAI/xlm-roberta-large-finetuned-conll03-english}\\\\\n"," \\textbf{Batch Size} & 128 \\\\\n"," \\textbf{Learning Rate} & 2e-5 \\\\\n"," \\textbf{Optimizer} & AdamW \\\\\n"," \\textbf{Dropout Rate} & 0.1 \\\\\n"," \\textbf{Evaluation Metric} & Accuracy \\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," \\caption{Implementation parameters for named entity detection and classification.}\n"," \\label{tab:implementation_parameters}\n","\\end{table*}\n"],["\\begin{table*}[!t]\n"," \\centering\n"," \\small\n"," \\begin{tabular}{|l|}\n"," \\hline\n"," \\textbf{Prompt:} \\\\\n"," \\text{You are a language model that generates synthetic question-answer (QA) pairs with reasoning paths.}\\\\\n"," \\text{Your task is to generate a QA pair based on the following question. Additionally, you should}\\\\\n"," \\text{provide a clear, step-by-step reasoning path that corresponds to a predefined reasoning skill.}\\\\\n"," \\text{The predefined reasoning skills are:[REASONING SKILLS NAME+DESCRIPTION+EXAMPLES].}\\\\\n"," \\text{Your reasoning path should include clear substeps for each step of the thought process.} \\\\\n"," \\textbf{Example 1:}\\\\\n"," \\textbf{Given Documents:} [REFERENCE DOCUMENTS]\\\\\n"," \\textbf{Input Question:} \"Who invented the telephone?\" \\\\\n"," \\textbf{Step-by-step Reasoning Path:} \\\\\n"," 1. Identify the key entity: \"telephone\" (deductive)\\\\\n"," 2. Identify that the question is asking for the inventor of a significant historical device (decompositional)\\\\\n"," 3. Recall the historical context of the invention of the telephone. (deductive)\\\\\n"," 4. The inventor is Alexander Graham Bell. (cause \\& effect)\\\\\n"," \\textbf{Generated Answer:} \"Alexander Graham Bell invented the telephone in 1876.\" \\\\\n"," \\textbf{Reasoning Skill Used:} deductive, decompositional, deductive, cause \\& effect. \\\\\n"," \\textbf{Example 2:} ...\\\\\n"," \\textbf{Example 3:} ...\\\\\n"," \\textbf{Notes:}\\\\\n"," \\text{Please make sure that the reasoning path is clear and includes each substep in the thought process.}\\\\\n"," \\text{The output should follow this structure: \"Step-by-step reasoning,\" followed by the conclusion.}\\\\\n"," \\text{Each reasoning skill corresponds to a specific domain of knowledge.} \\\\\n"," \\hline\n"," \\end{tabular}\n"," \\caption{Prompt to Generate Similar Examples with Reasoning Paths.}\n"," \\label{tab:synthetic_QA_generation_prompt}\n","\\end{table*}\n"],["\\begin{table*}[!t]\n","\\centering\n","\\small\n","\\begin{tabular}{|p{\\textwidth}|}\n","\\hline\n","\\textbf{Prompt:}\\\\\n","{You are given an original question: [ORIGINAL QUESTION]}\\\\\n","{You also have a synthetic question: [SYNTHETIC QUESTION]}\\\\\n","{Your task is to decide how similar the synthetic question is in structure and complexity, compared to the original. Please provide a brief explanation of your reasoning. Then, assign a score from 1 (completely different) to 10 (very similar).}\\\\\n","\\textbf{Example}:\n","{Original Q: \"Who discovered penicillin?\"}\\\\\n","{Synthetic Q: \"Which scientist found the mold that led to antibiotics?\"}\\\\\n","\\textbf{Explanation}:\n","{Both questions ask about a discoverer of a major medical breakthrough. The second question focuses on the mold (penicillin), so it is structurally similar and retains the core inquiry about a discovery.}\\\\\n","\\textbf{Score (1-10)}: {8}\\\\\n","\\textbf{Notes}:\n","{- Provide a short justification.}\\\\\n","{- Avoid rewriting or changing the question.}\\\\\n","{- Keep the final output concise, ending with the numeric score.}\\\\\n","\\hline\n","\\end{tabular}\n","\\caption{Prompt for Evaluating Alignment of Synthetic Questions with the Original.}\n","\\label{tab:alignment_prompt}\n","\\end{table*}\n"],["\\begin{table*}[!t]\n","\\centering\n","\\small\n","\\begin{tabular}{|p{\\textwidth}|}\n","\\hline\n","\\textbf{Prompt:}\\\\\n","{You are given:}\\\\\n","{- The original question: [Q]}\\\\\n","{- A document or context: [D]}\\\\\n","{- A selected reasoning path: [R]}\\\\\n","{- The specific skills used in the reasoning path: [S]}\\\\\n","\n","\\textbf{Your goal} is to produce a final answer by combining the relevant information from [D] with the guided reasoning steps from [R]. Follow these instructions:\\\\\n","1. \\textbf{Review the Reasoning Path}\\\\\n"," Read each step in [R] carefully. Identify which parts of [D] or background knowledge support each step. \\\\\n","2. \\textbf{Apply the Skills} \\\\\n"," If [S] includes certain reasoning skills (e.g., deduction), make sure to explicitly use them when combining evidence from [D].\\\\\n","3. \\textbf{Generate a Clear Answer} \\\\\n"," Compose a concise final answer that directly addresses [Q]. You may outline your chain of thought, but keep the explanation aligned with [R]. \\\\\n","4. \\textbf{Maintain Accuracy} \\\\\n"," If [R] instructs a specific substep (e.g., numerical calculation or bridging multiple facts), follow it precisely, citing the relevant parts of [D].\\\\\n","\\textbf{Notes}: \\\\\n","- {Do not contradict the provided reasoning path.} \\\\\n","- {Cite relevant text from [D] if needed, but avoid unnecessary repetition.} \\\\\n","- {End with a concise, standalone final answer.} \\\\\n","\\hline\n","\\end{tabular}\n","\\caption{Prompt for Question Answering.}\n","\\label{tab:reasoning_path_prompt}\n","\\end{table*}\n"],["\\begin{table*}[!t]\n","\\centering\n","\\small\n","\\adjustbox{max width=\\textwidth}{\n","\\begin{tabular}{lccccccc}\n","\\toprule\n","Model & SQuAD & HotpotQA & NewsQA & Gaokao & HQA & TriviaQA & BioASQ\\\\\n","\\midrule\n","\\rowcolor{gray!10}\\multicolumn{8}{c}{\\textbf{\\textit{Quick-Thinking Models w/ Reasoning Strategies (Exact Match)}}}\\\\\n","\\multicolumn{8}{l}{\\textbf{\\textit{Qwen2.5-32B-Instruct}}}\\\\\n","\\; w/ vanilla \\textit{(quick)} & 55.23 & 31.69 & 27.15 & 12.61 & 19.47 & 23.82 & 40.88\\\\\n","\\; w/ few-shots \\textit{(quick)} & 56.42 & 32.35 & 28.13 & 13.06 & 20.19 & 24.58 & 41.76\\\\\n","\\; w/ self-consistency~\\cite{wang2022self} & 57.08 & 32.81 & 28.49 & 13.29 & 20.37 & 24.89 & 41.93\\\\\n","\\; w/ proCoT~\\cite{deng-etal-2023-prompting} & 58.65 & 33.81 & 29.32 & 13.86 & 21.02 & 25.58 & 42.77\\\\\n","\\; w/ ToT~\\cite{yao2023tree} & 59.83 & 34.67 & 29.81 & 14.12 & 21.51 & 26.04 & 43.28\\\\\n","\\; w/ MCTS~\\cite{zhao2024marco} & 59.87 & 34.53 & 29.76 & 14.21 & 21.57 & 26.08 & 43.33\\\\\n","\\textbf{\\; w/ T$^2$ (ours)} & \\textbf{62.65} & \\textbf{39.98} & \\textbf{34.12} & \\textbf{15.72} & \\textbf{22.58} & \\textbf{26.43} & \\textbf{48.14}\\\\\n","\\hdashline\n","\\multicolumn{8}{l}{\\textbf{\\textit{GPT-4o}}}\\\\\n","\\; w/ vanilla \\textit{(quick)} & 59.87 & 35.31 & 30.27 & 16.23 & 23.39 & 29.84 & 44.12\\\\\n","\\; w/ few-shots \\textit{(quick)} & 61.09 & 36.04 & 30.85 & 16.78 & 24.03 & 30.52 & 44.84\\\\\n","\\; w/ self-consistency~\\cite{wang2022self} & 61.63 & 36.42 & 31.14 & 16.92 & 24.28 & 30.78 & 45.13\\\\\n","\\; w/ proCoT~\\cite{deng-etal-2023-prompting} & 62.89 & 37.29 & 31.92 & 17.49 & 25.01 & 31.43 & 46.05\\\\\n","\\; w/ ToT~\\cite{yao2023tree} & 63.77 & 37.94 & 32.43 & 17.79 & 25.57 & 32.11 & 46.61\\\\\n","\\; w/ MCTS~\\cite{zhao2024marco} & 63.94 & 38.13 & 32.06 & 17.84 & 26.22 & 32.18 & 47.39\\\\\n","\\textbf{\\; w/ T$^2$ (ours)} & \\textbf{65.17} & \\textbf{39.51} & \\textbf{33.76} & \\textbf{17.98} & \\textbf{26.31} & \\textbf{33.12} & \\textbf{49.07}\\\\\n","\\midrule\n","\\rowcolor{gray!10}\\multicolumn{8}{c}{\\textbf{\\textit{Slow-Thinking Models (Exact Match)}}}\\\\\n","o1-mini & 65.82 & 42.89 & 35.08 & 21.87 & 29.51 & 36.52 & 50.68\\\\\n","QwQ-32B-Preview & 66.67 & 43.57 & 35.43 & 22.18 & 29.88 & 36.87 & 51.21\\\\\n","DeepSeek-R1 & 67.38 & 44.04 & 35.94 & 22.28 & 30.26 & 37.52 & 52.31\\\\\n","o1 & 67.92 & 44.57 & 36.42 & 22.53 & 30.75 & 38.12 & 52.94\\\\\n","o4-mini & 68.36 & 44.97 & 36.83 & 22.79 & 31.08 & 38.28 & 53.14\\\\\n","o4-mini-high & 68.54 & 45.18 & 37.01 & 22.89 & 31.23 & 38.42 & 53.27\\\\\n","Claude-3.7-sonnet-thinking & 68.67 & 45.27 & 37.14 & 22.97 & 31.32 & 38.56 & 53.41\\\\\n","o3 & 68.89 & 45.48 & 37.34 & 23.19 & 31.37 & 38.78 & 53.74\\\\\n","Gemini-2.5-Pro & 69.69 & 46.08 & 37.97 & 23.51 & 32.01 & 39.43 & 54.45\\\\\n","\\textbf{QwQ-32B + T$^2$ (ours)} & \\textbf{71.32} & \\textbf{47.87} & \\textbf{39.17} & \\textbf{24.63} & \\textbf{33.28} & \\textbf{40.39} & \\textbf{55.81}\\\\\n","\\bottomrule\n","\\end{tabular}}\n","\\caption{Exact Match (EM) scores on seven QA datasets.}\n","\\label{tab:overall_performance_em}\n","\\end{table*}\n"],["\\begin{table}[!t]\n","\\centering\n","\\adjustbox{max width=\\linewidth}{\n","\\begin{tabular}{lccc}\n","\\toprule\n","\\textbf{Method} & \\textbf{HotpotQA} & \\textbf{NewsQA} & \\textbf{HQA} \\\\\n","\\midrule\n","Random & 32.4 & 38.7 & 19.2 \\\\\n","Coverage Only & 41.6 & 46.3 & 27.8 \\\\\n","Uniqueness Only & 49.2 & 54.5 & 35.7 \\\\\n","Full Approach & \\textbf{67.1} & \\textbf{61.3} & \\textbf{40.3} \\\\\n","\\bottomrule\n","\\end{tabular}}\n","\\caption{Ablation study results showing the impact of different components in our selection approach. We report ROUGE-L (\\%) on three benchmark datasets.}\n","\\label{tab:ablation}\n","\\end{table}\n"],["\\begin{table*}[!t]\n","\\centering\n","\\adjustbox{max width=\\linewidth}{\n","\\begin{tabular}{lccccccc}\n","\\toprule\n","\\textbf{Model} & \\textbf{SQuAD} & \\textbf{BioASQ} & \\textbf{HotpotQA} & \\textbf{NewsQA} & \\textbf{GAOKAO} & \\textbf{HQA} & \\textbf{TriviaQA} \\\\\n","\\midrule\n","Qwen2.5-32B w/ SC & 1372.18 & 1726.32 & 1485.87 & 2201.65 & 1957.93 & 1580.43 & 1742.41 \\\\\n","Qwen2.5-32B w/ T$^2$ & 1161.42 & 1401.52 & 1330.71 & 1812.28 & 1581.14 & 1415.18 & 1582.42 \\\\\n","\\hdashline\n","QwQ-32B-Preview & 1617.42 & 2012.33 & 1823.49 & 2648.12 & 2284.80 & 1972.37 & 2119.88 \\\\\n","QwQ-32B w/ T$^2$ & 1285.36 & 1467.85 & 1450.75 & 1855.89 & 1699.45 & 1465.68 & 1605.56 \\\\\n","\\bottomrule\n","\\end{tabular}}\n","\\caption{Average token consumption across seven CQA datasets for different reasoning approaches.}\n","\\label{tab:token_consumption}\n","\\end{table*}\n"],["\\begin{table*}[!t]\n","\\centering\n","\\adjustbox{max width=\\textwidth}{\n","\\begin{tabular}{lccc}\n","\\toprule\n","\\textbf{Model} & \\textbf{Avg. Token Length} & \\textbf{Token Reduction} & \\textbf{Avg. ROUGE-L} \\\\\n","\\midrule\n","Qwen2.5-32B w/ SC & 1723.83 & - & 50.07 \\\\\n","Qwen2.5-32B w/ T$^2$ & 1469.24 & 14.8\\% vs. SC & 56.22 \\\\\n","\\hdashline\n","QwQ-32B-Preview & 2068.34 & - & 63.38 \\\\\n","QwQ-32B w/ T$^2$ & 1547.22 & 25.2\\% vs. QwQ & 68.56 \\\\\n","\\bottomrule\n","\\end{tabular}}\n","\\caption{Efficiency-performance trade-off across seven CQA datasets. Token reduction is calculated relative to the baseline model (SC: Self-Consistency).}\n","\\label{tab:efficiency_performance}\n","\\end{table*}\n"],["\\begin{figure*}[!t]\n","\\centering\n","\\includegraphics[width=\\textwidth]{draw/efficiency_by_dataset.pdf}\n","\\caption{Token consumption reduction by T$^2$ across different datasets. The percentage values indicate the relative reduction compared to the baseline models (Qwen2.5-32B w/ SC and QwQ-32B-Preview).}\n","\\label{fig:efficiency_by_dataset}\n","\\end{figure*}\n"],["\\begin{table*}[!t]\n","\\centering\n","\\adjustbox{max width=\\textwidth}{\n","\\begin{tabular}{lcccccccc}\n","\\toprule\n","\\multirow{2}{*}{\\textbf{Model}} & \\multicolumn{2}{c}{\\textbf{SQuAD}} & \\multicolumn{2}{c}{\\textbf{BioASQ}} & \\multicolumn{2}{c}{\\textbf{HotpotQA}} & \\multicolumn{2}{c}{\\textbf{NewsQA}} \\\\\n","\\cmidrule(lr){2-3} \\cmidrule(lr){4-5} \\cmidrule(lr){6-7} \\cmidrule(lr){8-9}\n"," & Token & ROUGE-L & Token & ROUGE-L & Token & ROUGE-L & Token & ROUGE-L \\\\\n","\\midrule\n","Qwen2.5-32B w/ SC & 1372.18 & 75.31 & 1726.32 & 57.57 & 1485.87 & 56.76 & 2201.65 & 52.27 \\\\\n","Qwen2.5-32B w/ T$^2$ & 1161.42 & 81.86 & 1401.52 & 65.02 & 1330.71 & 67.11 & 1812.28 & 61.27 \\\\\n","QwQ-32B-Preview & 1617.42 & 86.87 & 2012.33 & 69.02 & 1823.49 & 71.86 & 2648.12 & 63.92 \\\\\n","QwQ-32B w/ T$^2$ & 1285.36 & 92.12 & 1467.85 & 75.21 & 1450.75 & 77.61 & 1855.89 & 68.61 \\\\\n","\\midrule\n","\\multirow{2}{*}{\\textbf{Model}} & \\multicolumn{2}{c}{\\textbf{GAOKAO}} & \\multicolumn{2}{c}{\\textbf{HQA}} & \\multicolumn{2}{c}{\\textbf{TriviaQA}} & \\multicolumn{2}{c}{\\textbf{Average}} \\\\\n","\\cmidrule(lr){2-3} \\cmidrule(lr){4-5} \\cmidrule(lr){6-7} \\cmidrule(lr){8-9}\n"," & Token & ROUGE-L & Token & ROUGE-L & Token & ROUGE-L & Token & ROUGE-L \\\\\n","\\midrule\n","Qwen2.5-32B w/ SC & 1957.93 & 30.57 & 1580.43 & 37.12 & 1742.41 & 41.92 & 1723.83 & 50.07 \\\\\n","Qwen2.5-32B w/ T$^2$ & 1581.14 & 34.06 & 1415.18 & 40.31 & 1582.42 & 43.92 & 1469.24 & 56.22 \\\\\n","QwQ-32B-Preview & 2284.80 & 43.23 & 1972.37 & 49.62 & 2119.88 & 59.16 & 2068.34 & 63.38 \\\\\n","QwQ-32B w/ T$^2$ & 1699.45 & 47.42 & 1465.68 & 54.71 & 1605.56 & 64.22 & 1547.22 & 68.56 \\\\\n","\\bottomrule\n","\\end{tabular}}\n","\\caption{Detailed comparison of token consumption and performance (ROUGE-L) across all datasets. Lower token count with higher ROUGE-L indicates better efficiency-performance trade-off.}\n","\\label{tab:detailed_efficiency}\n","\\end{table*}\n"],["\\begin{table*}[!t]\n","\\centering\n","\\adjustbox{max width=\\textwidth}{\n","\\begin{tabular}{lccc}\n","\\toprule\n","\\textbf{Question Complexity} & \\textbf{Qwen2.5 + SC} & \\textbf{Qwen2.5 + T$^2$} & \\textbf{Token Reduction} \\\\\n","\\midrule\n","Simple (1-step) & 1283.45 & 1157.82 & -9.8\\% \\\\\n","Moderate (2-3 steps) & 1687.31 & 1391.65 & -17.5\\% \\\\\n","Complex (4+ steps) & 2201.73 & 1758.24 & -20.1\\% \\\\\n","\\midrule\n","\\textbf{Question Complexity} & \\textbf{QwQ-32B} & \\textbf{QwQ-32B + T$^2$} & \\textbf{Token Reduction} \\\\\n","\\midrule\n","Simple (1-step) & 1584.21 & 1262.35 & -20.3\\% \\\\\n","Moderate (2-3 steps) & 2041.57 & 1492.18 & -26.9\\% \\\\\n","Complex (4+ steps) & 2579.24 & 1887.14 & -26.8\\% \\\\\n","\\bottomrule\n","\\end{tabular}}\n","\\caption{Token consumption analysis by question complexity. T$^2$ achieves greater efficiency gains for more complex questions.}\n","\\label{tab:complexity_analysis}\n","\\end{table*}\n"],["\\begin{table*}[!t]\n","\\centering\n","\\adjustbox{max width=\\textwidth}{\n","\\begin{tabular}{lcc}\n","\\toprule\n","\\textbf{Model} & \\textbf{Avg. Inference Time (s)} & \\textbf{Time Reduction} \\\\\n","\\midrule\n","Qwen2.5-32B w/ SC & 65.31 & - \\\\\n","Qwen2.5-32B w/ T$^2$ & 34.52 & -47.1\\% \\\\\n","\\hdashline\n","QwQ-32B-Preview & 76.74 & - \\\\\n","QwQ-32B w/ T$^2$ & 45.03 & -41.3\\% \\\\\n","\\bottomrule\n","\\end{tabular}}\n","\\caption{Average inference time per question across datasets. T$^2$ reduces computational time while maintaining high performance.}\n","\\label{tab:time_efficiency}\n","\\end{table*}\n"],["\\begin{table*}[!t]\n"," \\centering\n"," \\begin{tabular}{lcccccc}\n"," \\toprule\n"," \\multirow{2}{*}{\\textbf{Group}}& \\multicolumn{3}{c}{\\textbf{SQuAD}} & \\multicolumn{3}{c}{\\textbf{HQA}} \\\\\n"," \\cmidrule(lr){2-4} \\cmidrule(lr){5-7}\n"," & Few-shots & Self-Cconsistency & T$^2$ & Few-shots & Self-Cconsistency & T$^2$ \\\\\n"," \\midrule\n"," Low & 78.5 & 79.1 & \\textbf{80.2} & 42.7 & 43.3 & \\textbf{44.6} \\\\\n"," Medium & 76.4 & 78.2 & \\textbf{79.5} & 41.5 & 43.0 & \\textbf{45.1} \\\\\n"," High & 75.9 & 78.7 & \\textbf{80.1} & 40.2 & 42.9 & \\textbf{46.2} \\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," \\caption{ROUGE-L by question complexity. We compare quick-thinking (Few-shots) and slow-thinking (Self-Consistency), and our T$^2$.}\n"," \\label{tab:structure_complexity}\n","\\end{table*}\n"],["\\begin{figure*}[!t]\n","\\begin{tcolorbox}[title=Original QA Pair]\n","\\textbf{Q:} How does the melting point of sodium compare to potassium?\n","\n","\\textbf{A:} I'll approach this systematically:\\\\\n","1. First, I'll recall the melting point of sodium, which is 97.8°C.\\\\\n","2. Next, I'll recall the melting point of potassium, which is 63.5°C.\\\\\n","3. Comparing these values: 97.8°C > 63.5°C\\\\\n","4. Therefore, sodium has a higher melting point than potassium.\n","\\end{tcolorbox}\n","\\caption{Our original generated similar example.}\n","\\label{fig:sim_example_original}\n","\\end{figure*}\n"],["\\begin{figure*}[!t]\n","\\begin{tcolorbox}[title=Structure-Only Version]\n","\\textbf{Q:} How does [PROPERTY] of [ENTITY\\_A] compare to [ENTITY\\_B]?\n","\n","\\textbf{A:} I'll approach this systematically:\\\\\n","1. First, I'll determine the [PROPERTY] of [ENTITY\\_A], which is [VALUE\\_A].\\\\\n","2. Next, I'll determine the [PROPERTY] of [ENTITY\\_B], which is [VALUE\\_B].\\\\\n","3. Comparing these values: [COMPARISON\\_OPERATION]\\\\\n","4. Therefore, [CONCLUSION\\_STATEMENT].\n","\\end{tcolorbox}\n","\\caption{Structure-only version of our generated similar example.}\n","\\label{fig:sim_example_structure_only}\n","\\end{figure*}\n"],["\\begin{figure*}[!t]\n"," \\centering\n"," \\includegraphics[width=\\linewidth]{./draw/demo_number.pdf}\n"," \\caption{Impact of the number of similar examples on ROUGE-L scores for HotpotQA (left) and NewsQA (right).}\n"," \\label{fig:demo_counts}\n","\\end{figure*}\n"],["\\begin{table}[!t]\n"," \\centering\n"," \\adjustbox{max width=\\linewidth}{\n"," \\begin{tabular}{lccc}\n"," \\toprule\n"," \\textbf{Method} & \\textbf{ROUGE-L} & \\textbf{Variation} & \\textbf{Noise}\\\\\n"," \\midrule\n"," \\multicolumn{4}{l}{\\textbf{\\textit{Qwen2.5 w/ ours}}}\\\\\n"," \\; Random Fill & 49.8 & High & Medium \\\\\n"," \\; Guided Fill & 52.6 & Low & Low \\\\\n"," \\; Template Variation & \\textbf{61.3} & High & Low \\\\\n"," \\bottomrule\n"," \\end{tabular}}\n"," \\caption{Comparing different example construction methods on NewsQA.}\n"," \\label{tab:demo_construction}\n","\\end{table}\n"],["\\begin{figure*}[!t]\n"," \\centering\n"," \\includegraphics[width=\\linewidth]{./draw/question_threshold.pdf}\n"," \\caption{Impact of the question synthesis scope.}\n"," \\label{fig:impact_of_delta}\n","\\end{figure*}\n"],["\\begin{table}[!t]\n","\\centering\n","\\small\n","\\begin{tabular}{lcc}\n","\\toprule\n","\\textbf{Model} & \\textbf{HotpotQA} & \\textbf{HQA} \\\\\n","\\midrule\n","Qwen2.5+SC & 56.76 & 37.12 \\\\\n","Qwen2.5+T$^2$ & 67.11 & 40.31 \\\\\n","Qwen2.5+mis domain & 65.96 & 39.85 \\\\\n","Qwen2.5+structure only & 63.96 & 38.85 \\\\\n","\\hdashline\n","QwQ & 71.86 & 49.62 \\\\\n","QwQ+T$^2$ & 77.61 & 54.71 \\\\\n","QwQ+mis domain & 77.03 & 54.26 \\\\\n","QwQ+structure only & 74.03 & 53.66 \\\\\n","\\bottomrule\n","\\end{tabular}\n","\\caption{Performance on mis-domain and structure-only models. ROUGE-L is the reported performance metric.}\n","\\label{tab:demo_quality}\n","\\end{table}\n"],["\\begin{table*}[!t]\n","\\centering\n","\\begin{tabular}{lccccc}\n","\\toprule\n","\\textbf{Models w/ T$^2$} & \\textbf{Accuracy} & \\textbf{Relevance} & \\textbf{Completeness} & \\textbf{Coherence} & \\textbf{Overall} \\\\\n","\\midrule\n","Qwen2.5-32B-Instrucut & 8.8 & 8.6 & 8.1 & 8.1 & 8.4 \\\\\n","QwQ-32B-Preview & 8.4 & 8.3 & 7.9 & 8.2 & 8.4 \\\\\n","\\bottomrule\n","\\end{tabular}\n","\\caption{Human evaluation scores for synthetic references generated by different models (scale: 1-10).}\n","\\label{tab:human_eval}\n","\\end{table*}\n"],["\\begin{figure*}[!t]\n"," \\small\n"," \\begin{tcolorbox}[title=\\# HotpotQA Case Example with Multihop Thinking, fonttitle=\\bfseries, colframe=black, colback=white, sharp corners, boxrule=0.5mm, colbacktitle=gray!20, coltitle=black, enhanced]\n"," \\textbf{Document 1:}\\\\\n"," John Lennon, the iconic musician and member of The Beatles, was born in Liverpool, England.\\\\ \n"," \\textbf{Document 2:}\\\\\n"," \\textit{Nowhere Boy} is a film that explores the early life of John Lennon, highlighting his formative years and influences.\\\\\n"," \\textbf{Question:}\\\\\n"," In what city was the subject of the film \\textit{Nowhere Boy} born?\n","\n"," \\vspace{1em}\n"," \n"," \\textbf{Proper Reasoning Chain:}\\\\\n"," 1. \\textbf{Identify the Subject:} Recognize that \\textit{Nowhere Boy} is about John Lennon. (Deductive)\\\\\n"," 2. \\textbf{Extract Key Fact:} From Document 1, note that John Lennon was born in Liverpool, England. (Deductive)\\\\\n"," 3. \\textbf{Conclude:} Therefore, the subject of the film was born in Liverpool. (Decompositional)\n","\n"," \\vspace{1em}\n"," \n"," \\textcolor{red}{\\textbf{1) Quick Thinking Model's Wrong Answer:}}\\\\\n"," \\textcolor{red}{\\textit{Answer:} The subject of \\textit{Nowhere Boy} was born in London.}\\\\\n"," \n"," \\textcolor{orange}{\\textbf{2) Slow Thinking Model's Overthinking Answer:}}\\\\\n"," \\textcolor{orange}{\\textit{Answer:} After a lengthy analysis of various biographical details concerning John Lennon, including his early childhood, multiple hints in his music and interviews, and a discussion of several English cities mentioned in relation to his life, one might eventually conclude that the subject of \\textit{Nowhere Boy} was born in Liverpool.}\\\\\n"," \n"," \\textcolor{mygreen}{\\textbf{3) Model with Our T$^2$'s Correct Answer:}}\\\\\n"," \\textcolor{mygreen}{\\textit{Answer:} Since \\textit{Nowhere Boy} is a film about John Lennon (Document 2) and Document 1 confirms that John Lennon was born in Liverpool, we deduce that the subject of the film was born in Liverpool.}\n"," \\end{tcolorbox}\n"," \\caption{HotpotQA Case Example with Multiple Answer Versions and Explicit Reasoning Chain.}\n"," \\label{fig:hotpotqa_example}\n","\\end{figure*}\n"],["\\begin{figure*}[!t]\n"," \\small\n"," \\begin{tcolorbox}[title=\\# SQuAD Case Example with Single-Step Thinking,fonttitle=\\bfseries,colframe=black,colback=white,sharp corners,boxrule=0.5mm,colbacktitle=gray!20,coltitle=black,enhanced]\n"," \\textbf{Document:}\\\\\n"," The Eiffel Tower is a wrought-iron lattice tower located on the Champ de Mars in Paris, France. It was constructed in 1889 as the entrance to the 1889 World's Fair. The tower stands approximately 324 meters tall and is one of the most recognized structures in the world.\\\\\n"," \\textbf{Question:}\\\\\n"," In what year was the Eiffel Tower constructed?\\\\\n","\n"," \\vspace{1em}\n"," \n"," \\textbf{Proper Reasoning Chain (using Deductive):}\\\\\n"," 1) \\textbf{Deduction:} Based on clear statement, we deduce that the construction year is 1889 (Deductive).\n"," \n"," \\vspace{1em}\n"," \n"," \\textbf{1) Quicking Thinking Model's Correct Answer \\textcolor{orange}{via Pattern Matching}:}\\\\\n"," \\textit{Answer:} 1889.\\\\\n"," \n"," \\textbf{2) Slow Thinking Model's Correct Answer \\textcolor{orange}{but Overthinking}:}\\\\\n"," \\textit{Answer:} After reading the passage, I noticed several numbers such as 324 (the tower's height) and 1889. While I considered whether the height might play a role in determining the construction year, I ultimately realized that the passage explicitly states ``constructed in 1889.'' Thus, despite the extra details, the correct construction year is 1889.\\\\\n"," \n"," \\textbf{3) Model with Our T$^2$'s Correct Answer \\textcolor{mygreen}{using the Specific Reasoning Chain}:}\\\\\n"," \\textit{Answer:} Since the document clearly states that the Eiffel Tower was constructed in 1889, we deduce—by directly extracting and applying this fact—that the construction year is 1889.\n"," \\end{tcolorbox}\n"," \\caption{SQuAD Case Example with Single-Step Thinking and Multiple Answer Versions.}\n"," \\label{fig:squad_case_single_step}\n","\\end{figure*}\n"]],"string":"[\n [\n \"\\\\begin{figure*}[!t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\linewidth]{pics/overview.pdf}\\n\",\n \" \\\\caption{Overview of our T$^2$. \\\\textbf{(a)} direct prompt or Chain-of-Thought (CoT), which adopts the same reasoning strategy regardless of question complexity. \\\\textbf{(b)} Adaptive-of-Thought, which designs a question complexity evaluator to pre-categorize all questions, which might bring human bias in the evaluator design process. \\\\textbf{(c)} our T$^2$. Instead of pre-categorizing questions into different complexity sets, T$^2$ generates multiple similar examples for different inputs adaptively and selects the best reasoning strategy for answering.}\\n\",\n \" \\\\label{fig:overview}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\small\\n\",\n \"\\\\adjustbox{max width=\\\\textwidth}{\\n\",\n \"\\\\begin{tabular}{lccccccc}\\n\",\n \"\\\\toprule\\n\",\n \"Model & SQuAD & HotpotQA & NewsQA & Gaokao & HQA & TriviaQA & BioASQ\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\rowcolor{gray!10}\\\\multicolumn{8}{c}{\\\\textbf{\\\\textit{Quick-Thinking Models w/ Reasoning Strategies}}}\\\\\\\\\\n\",\n \"\\\\multicolumn{8}{l}{\\\\textbf{\\\\textit{Qwen2.5-32B-Instruct}}}\\\\\\\\\\n\",\n \"\\\\; w/ vanilla \\\\textit{(quick)} & 73.41 & 55.32 & 50.83 & 29.52 & 35.92 & 40.73 & 56.33\\\\\\\\\\n\",\n \"\\\\; w/ few-shots \\\\textit{(quick)} & 74.56 & 56.23 & 51.67 & 30.33 & 36.87 & 41.57 & 57.17\\\\\\\\\\n\",\n \"\\\\; w/ self-consistency~\\\\cite{wang2022self} & 75.31 & 56.76 & 52.27 & 30.57 & 37.12 & 41.92 & 57.57\\\\\\\\\\n\",\n \"\\\\; w/ proCoT~\\\\cite{deng-etal-2023-prompting} & 77.12 & 58.07 & 53.57 & 31.42 & 38.03 & 42.83 & 58.62\\\\\\\\\\n\",\n \"\\\\; w/ ToT~\\\\cite{yao2023tree} & 78.47 & 59.11 & 54.31 & 31.96 & 38.66 & 43.46 & 59.36\\\\\\\\\\n\",\n \"\\\\; w/ MCTS~\\\\cite{zhao2024marco} & 78.52 & 58.97 & 54.25 & 32.04 & 38.73 & 43.51 & 59.42\\\\\\\\\\n\",\n \"\\\\textbf{\\\\; w/ T$^2$ (ours)} & \\\\textbf{81.86} & \\\\textbf{67.11} & \\\\textbf{61.27} & \\\\textbf{34.06} & \\\\textbf{40.31} & \\\\textbf{43.92} & \\\\textbf{65.02}\\\\\\\\\\n\",\n \"\\\\hdashline\\n\",\n \"\\\\multicolumn{8}{l}{\\\\textbf{\\\\textit{GPT-4o}}}\\\\\\\\\\n\",\n \"\\\\; w/ vanilla \\\\textit{(quick)} & 78.52 & 60.02 & 55.32 & 34.51 & 41.11 & 49.01 & 60.51\\\\\\\\\\n\",\n \"\\\\; w/ few-shots \\\\textit{(quick)} & 79.86 & 61.06 & 56.17 & 35.36 & 42.06 & 50.07 & 61.37\\\\\\\\\\n\",\n \"\\\\; w/ self-consistency~\\\\cite{wang2022self} & 80.56 & 61.61 & 56.62 & 35.62 & 42.46 & 50.42 & 61.81\\\\\\\\\\n\",\n \"\\\\; w/ proCoT~\\\\cite{deng-etal-2023-prompting} & 82.12 & 63.02 & 57.86 & 36.66 & 43.36 & 51.46 & 62.87\\\\\\\\\\n\",\n \"\\\\; w/ ToT~\\\\cite{yao2023tree} & 83.21 & 64.06 & 58.67 & 37.22 & 44.07 & 52.26 & 63.72\\\\\\\\\\n\",\n \"\\\\; w/ MCTS~\\\\cite{zhao2024marco} & 83.35 & 64.18 & 58.19 & 37.31 & 45.15 & 52.38 & 64.89\\\\\\\\\\n\",\n \"\\\\textbf{\\\\; w/ T$^2$ (ours)} & \\\\textbf{85.06} & \\\\textbf{66.16} & \\\\textbf{60.92} & \\\\textbf{37.57} & \\\\textbf{45.27} & \\\\textbf{53.92} & \\\\textbf{66.97}\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\rowcolor{gray!10}\\\\multicolumn{8}{c}{\\\\textbf{\\\\textit{Slow-Thinking Models}}}\\\\\\\\\\n\",\n \"o1-mini & 85.81 & 70.91 & 63.22 & 42.66 & 49.22 & 58.56 & 68.42\\\\\\\\\\n\",\n \"QwQ-32B-Preview & 86.87 & 71.86 & 63.92 & 43.23 & 49.62 & 59.16 & 69.02\\\\\\\\\\n\",\n \"DeepSeek-R1 & 87.62 & 72.72 & 64.41 & 43.47 & 50.27 & 60.02 & 70.72\\\\\\\\\\n\",\n \"o1 & 88.22 & 73.37 & 65.11 & 44.06 & 51.07 & 60.86 & 71.36\\\\\\\\\\n\",\n \"o4-mini & 88.72 & 73.86 & 65.57 & 44.32 & 51.61 & 61.11 & 71.82\\\\\\\\\\n\",\n \"o4-mini-high & 88.91 & 74.07 & 65.81 & 44.52 & 51.86 & 61.27 & 72.02\\\\\\\\\\n\",\n \"Claude-3.7-sonnet-thinking & 89.11 & 74.21 & 66.01 & 44.61 & 52.01 & 61.47 & 72.22\\\\\\\\\\n\",\n \"o3 & 89.41 & 74.61 & 66.32 & 45.01 & 52.11 & 61.81 & 72.62\\\\\\\\\\n\",\n \"Gemini-2.5-Pro & 90.27 & 75.46 & 67.11 & 45.76 & 53.07 & 62.68 & 73.57\\\\\\\\\\n\",\n \"\\\\textbf{QwQ-32B + T$^2$ (ours)} & \\\\textbf{92.12} & \\\\textbf{77.61} & \\\\textbf{68.61} & \\\\textbf{47.42} & \\\\textbf{54.71} & \\\\textbf{64.22} & \\\\textbf{75.21}\\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}}\\n\",\n \"\\\\caption{ROUGE-L on seven QA datasets. We regard vanilla model and few-shot method as quick-thinking methods. And the other five (including ours) are slow-thinking methods. They can all be applied to quick-thinking models to improve reasoning ability.}\\n\",\n \"\\\\label{tab:main_results}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[!t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\linewidth]{./draw/hit_error_retrace_comment.pdf}\\n\",\n \" \\\\caption{Results on Hits and Errors (left) and Retrace Rate (right).}\\n\",\n \" \\\\label{fig:hit_error}\\n\",\n \" \\\\label{fig:retrace}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{table}[!t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\adjustbox{max width=\\\\linewidth}{\\n\",\n \"\\\\begin{tabular}{cccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Skill Type} & \\\\textbf{Uniform} & \\\\textbf{Ours} & \\\\textbf{Improvement} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"Deductive & 72.3\\\\% & 75.8\\\\% & +3.5\\\\% \\\\\\\\\\n\",\n \"Inductive & 68.7\\\\% & 73.2\\\\% & +4.5\\\\% \\\\\\\\\\n\",\n \"Abductive & 74.1\\\\% & 76.3\\\\% & +2.2\\\\% \\\\\\\\\\n\",\n \"Cause \\\\& Effect & 70.5\\\\% & 74.1\\\\% & +3.6\\\\% \\\\\\\\\\n\",\n \"Analogical & 63.8\\\\% & 71.5\\\\% & +7.7\\\\% \\\\\\\\\\n\",\n \"Critical Thinking & 69.2\\\\% & 72.8\\\\% & +3.6\\\\% \\\\\\\\\\n\",\n \"Decompositional & 61.4\\\\% & 69.7\\\\% & +8.3\\\\% \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}}\\n\",\n \"\\\\caption{Performance comparison between uniform and our matching strategies.}\\n\",\n \"\\\\label{tab:skill_match}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{figure}[!t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\linewidth]{draw/ave_length.pdf}\\n\",\n \" \\\\caption{Results of average token length on each dataset. SC is the abbreviation for Self-Consistency.}\\n\",\n \" \\\\label{fig:ave_length}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{figure*}[!t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\linewidth]{pics/case_study.pdf}\\n\",\n \" \\\\caption{Case study to show effectiveness of our T$^2$ framework. There are three proper reasoning skills should be adopted to answer the question based on given documents. The red, orange, and green answers represent responses under quick thinking, slow thinking, and ours, respectively.}\\n\",\n \" \\\\label{fig:case}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{figure}[!t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\linewidth]{draw/demo_quality.pdf}\\n\",\n \" \\\\caption{Results on relationship between reasoning skills' accuracy and overall performance.}\\n\",\n \" \\\\label{fig:demo_quality}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table*}[!t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\small\\n\",\n \" \\\\begin{tabular}{l p{0.4\\\\textwidth} p{0.35\\\\textwidth}}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Type of Reasoning} & \\\\textbf{Detailed Description} & \\\\textbf{Example} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\textbf{Deductive} \\n\",\n \" & Deductive reasoning occurs when generalized statements apply to specific cases. These generalized statements are established and already proven, making specific cases easy to deduce. For example, all humans are mortals. Bill is a human, so Bill must be mortal. In this example the generalized, but proven, statement, ``all humans are mortals'' is what drives the reasoning.\\n\",\n \" & \\\\underline{Document:} All shapes with three sides are triangles. A certain figure here has exactly three sides.\\n\",\n \" \\\\newline\\n\",\n \" \\\\underline{Question:} What is this figure called?\\n\",\n \" \\\\newline\\n\",\n \" \\\\underline{Answer:} It is a triangle. All shapes with three sides are triangles, and this figure has three sides. So it must be a triangle. \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\textbf{Inductive} \\n\",\n \" & Inductive reasoning is similar to deductive reasoning in that they both draw a conclusion based on a statement. However, in inductive reasoning, the statement is likely but has not been proven. For example, roses usually bloom in spring. In spring, one can count on there being roses. Again, the difference is that this is likely but not proven to be 100\\\\%. \\n\",\n \" & \\\\underline{Document:} Every spring for the past ten years, wild roses in Green Valley have bloomed in late March. This spring is about to begin in Green Valley. \\n\",\n \" \\\\newline\\n\",\n \" \\\\underline{Question:} Will the wild roses bloom in late March this year?\\n\",\n \" \\\\newline\\n\",\n \" \\\\underline{Answer:} It is likely they will bloom in late March, because they usually do, but it is not guaranteed. \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\textbf{Abductive}\\n\",\n \" & Abductive reasoning is the act of making a conclusion based on what you already know. For example, if you see a plate of food still hot, but half-eaten, you can make the conclusion that the person eating that food is probably returning soon.\\n\",\n \" & \\\\underline{Document:} You notice a half-eaten sandwich and a still-hot cup of coffee on a café table. The seat feels warm, and a jacket is draped over the chair.\\n\",\n \" \\\\newline\\n\",\n \" \\\\underline{Question:} Has the person who was sitting here left permanently, or are they coming back soon?\\n\",\n \" \\\\newline\\n\",\n \" \\\\underline{Answer:} It is likely they just stepped away for a moment and will return, because the food and drink are still warm and their jacket remains on the chair.\\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\textbf{Cause \\\\& Effect}\\n\",\n \" & Cause and effect reasoning is that if x happens then y will happen as a result. This is extremely persuasive when making a speech or trying to get someone to take action to cause an effect. For example, a politician may say that if they are elected, then poverty will decrease. This is using cause and effect reasoning in a real-world situation.\\n\",\n \" & \\\\underline{Document:} Meteorologists predict heavy rain this evening, with warnings that streets may flood if the rainfall continues.\\n\",\n \" \\\\newline\\n\",\n \" \\\\underline{Question:} Will the roads become dangerous as a result of this weather?\\n\",\n \" \\\\newline\\n\",\n \" \\\\underline{Answer:} Yes. If heavy rain continues, roads will likely flood and become slippery, causing drivers to have less control of their vehicles.\\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" \\\\caption{(1/2) Full list of reasoning skills used in the reasoning path construction.}\\n\",\n \" \\\\label{tab:full_reasoning_skills}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\small\\n\",\n \" \\\\begin{tabular}{l p{0.4\\\\textwidth} p{0.35\\\\textwidth}}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Type of Reasoning} & \\\\textbf{Detailed Description} & \\\\textbf{Example} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\textbf{Analogical}\\n\",\n \" & Analogical reasoning is the use of a comparison between two things to persuade that there must be more in common if they already share something. For example, if x, y, and z all share this trait, then they must also share other traits. The foundation of this type of reasoning is perfect for speeches and comparisons in the real world. If there are connections between x and y already, then they must have several other things in common as well.\\n\",\n \" & \\\\underline{Document:} Many leading technology companies emphasize continuous learning and adaptability. For instance, Google, Microsoft, and Amazon all invest in regular training programs and encourage innovation among employees. Their similar approach to fostering a culture of growth has been linked to their strong performance in rapidly changing markets.\\n\",\n \" \\\\newline\\n\",\n \" \\\\underline{Question:} Can we infer that a company that promotes continuous learning will also likely be successful in adapting to market changes?\\n\",\n \" \\\\newline\\n\",\n \" \\\\underline{Answer:} Yes. Since Google, Microsoft, and Amazon all share a culture of continuous learning and, as a result, demonstrate high adaptability and market success, it is reasonable to conclude by analogy that a company which also promotes continuous learning is likely to develop similar strengths.\\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\textbf{Critical Thinking}\\n\",\n \" & Critical thinking occurs when you take all of the facts and develop a conclusion based on an analysis. This could happen subconsciously or intentionally, depending on the situation. For example, in the real world, critical thinking could be about your relationships. You could see a behavior you don't like about someone and have to think critically about whether or not you will choose to spend more time with this person. This is using critical thinking to develop reasoning in a real-world application.\\n\",\n \" & \\\\underline{Document:} Over the past few months, Sam has repeatedly cancelled plans at the last minute and rarely communicated afterward.\\n\",\n \" \\\\newline\\n\",\n \" \\\\underline{Question:} Should you invest time in a close friendship with Sam?\\n\",\n \" \\\\newline\\n\",\n \" \\\\underline{Answer:} No. Sam's consistent behavior of last-minute cancellations suggests a pattern of unreliability, which may negatively affect the trust needed in a close friendship.\\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\textbf{Decompositional}\\n\",\n \" & Decompositional reasoning happens when the different parts of the reasoning are broken down into smaller pieces and analyzed for how they contribute to the whole. The intent of this is to make the reasoning easier to understand and allow for analyzing how the parts equal the whole. For example, in order to understand the function of the human body, you would have to analyze each bone and organ to see how they all work together. Additionally, in the real world, an argument could be broken down into several smaller parts in order to analyze the effectiveness of the argument as a whole.\\n\",\n \" & \\\\underline{Document:} A smartphone's quality can be understood by breaking it down into three parts: its design, performance, and battery life. The design covers the build and user interface; performance looks at processing speed and software efficiency; battery life shows how long the device operates on a single charge.\\n\",\n \" \\\\newline\\n\",\n \" \\\\underline{Question:} Can we conclude that the smartphone provides a good overall user experience?\\n\",\n \" \\\\newline\\n\",\n \" \\\\underline{Answer:} Yes. If the design is appealing, the performance is robust, and the battery life is long, then the smartphone is likely to offer a good overall experience.\\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" \\\\caption{(2/2) Full list of reasoning skills used in the reasoning path construction.}\\n\",\n \" \\\\label{tab:full_reasoning_skills_2}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\adjustbox{max width=\\\\textwidth}{\\n\",\n \"\\\\begin{tabular}{cccccccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Skill Type} & \\\\textbf{SQuAD} & \\\\textbf{HotpotQA} & \\\\textbf{NewsQA} & \\\\textbf{GAOKAO} & \\\\textbf{HQA} & \\\\textbf{TriviaQA} & \\\\textbf{BioASQ} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"Deductive & 0.31 & 0.22 & 0.28 & 0.15 & 0.42 & 0.18 & 0.25 \\\\\\\\\\n\",\n \"Inductive & 0.23 & 0.18 & 0.15 & 0.12 & 0.13 & 0.21 & 0.19 \\\\\\\\\\n\",\n \"Abductive & 0.05 & 0.12 & 0.08 & 0.21 & 0.09 & 0.15 & 0.11 \\\\\\\\\\n\",\n \"Cause \\\\& Effect & 0.12 & 0.15 & 0.13 & 0.22 & 0.08 & 0.19 & 0.14 \\\\\\\\\\n\",\n \"Analogical & 0.08 & 0.13 & 0.09 & 0.07 & 0.11 & 0.12 & 0.16 \\\\\\\\\\n\",\n \"Critical Thinking & 0.14 & 0.16 & 0.18 & 0.14 & 0.13 & 0.09 & 0.10 \\\\\\\\\\n\",\n \"Decompositional & 0.07 & 0.04 & 0.09 & 0.09 & 0.04 & 0.06 & 0.05 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}}\\n\",\n \"\\\\caption{Distribution (\\\\%) of reasoning skills across benchmark datasets, showing the proportion of questions requiring each skill type.}\\n\",\n \"\\\\label{tab:skill_distribution}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\begin{tabular}{ll}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Parameter} & \\\\textbf{Value} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\textbf{Model} & RoBERTa\\\\\\\\\\n\",\n \" \\\\textbf{Full Name} & \\\\texttt{FacebookAI/xlm-roberta-large-finetuned-conll03-english}\\\\\\\\\\n\",\n \" \\\\textbf{Batch Size} & 128 \\\\\\\\\\n\",\n \" \\\\textbf{Learning Rate} & 2e-5 \\\\\\\\\\n\",\n \" \\\\textbf{Optimizer} & AdamW \\\\\\\\\\n\",\n \" \\\\textbf{Dropout Rate} & 0.1 \\\\\\\\\\n\",\n \" \\\\textbf{Evaluation Metric} & Accuracy \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" \\\\caption{Implementation parameters for named entity detection and classification.}\\n\",\n \" \\\\label{tab:implementation_parameters}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\small\\n\",\n \" \\\\begin{tabular}{|l|}\\n\",\n \" \\\\hline\\n\",\n \" \\\\textbf{Prompt:} \\\\\\\\\\n\",\n \" \\\\text{You are a language model that generates synthetic question-answer (QA) pairs with reasoning paths.}\\\\\\\\\\n\",\n \" \\\\text{Your task is to generate a QA pair based on the following question. Additionally, you should}\\\\\\\\\\n\",\n \" \\\\text{provide a clear, step-by-step reasoning path that corresponds to a predefined reasoning skill.}\\\\\\\\\\n\",\n \" \\\\text{The predefined reasoning skills are:[REASONING SKILLS NAME+DESCRIPTION+EXAMPLES].}\\\\\\\\\\n\",\n \" \\\\text{Your reasoning path should include clear substeps for each step of the thought process.} \\\\\\\\\\n\",\n \" \\\\textbf{Example 1:}\\\\\\\\\\n\",\n \" \\\\textbf{Given Documents:} [REFERENCE DOCUMENTS]\\\\\\\\\\n\",\n \" \\\\textbf{Input Question:} \\\"Who invented the telephone?\\\" \\\\\\\\\\n\",\n \" \\\\textbf{Step-by-step Reasoning Path:} \\\\\\\\\\n\",\n \" 1. Identify the key entity: \\\"telephone\\\" (deductive)\\\\\\\\\\n\",\n \" 2. Identify that the question is asking for the inventor of a significant historical device (decompositional)\\\\\\\\\\n\",\n \" 3. Recall the historical context of the invention of the telephone. (deductive)\\\\\\\\\\n\",\n \" 4. The inventor is Alexander Graham Bell. (cause \\\\& effect)\\\\\\\\\\n\",\n \" \\\\textbf{Generated Answer:} \\\"Alexander Graham Bell invented the telephone in 1876.\\\" \\\\\\\\\\n\",\n \" \\\\textbf{Reasoning Skill Used:} deductive, decompositional, deductive, cause \\\\& effect. \\\\\\\\\\n\",\n \" \\\\textbf{Example 2:} ...\\\\\\\\\\n\",\n \" \\\\textbf{Example 3:} ...\\\\\\\\\\n\",\n \" \\\\textbf{Notes:}\\\\\\\\\\n\",\n \" \\\\text{Please make sure that the reasoning path is clear and includes each substep in the thought process.}\\\\\\\\\\n\",\n \" \\\\text{The output should follow this structure: \\\"Step-by-step reasoning,\\\" followed by the conclusion.}\\\\\\\\\\n\",\n \" \\\\text{Each reasoning skill corresponds to a specific domain of knowledge.} \\\\\\\\\\n\",\n \" \\\\hline\\n\",\n \" \\\\end{tabular}\\n\",\n \" \\\\caption{Prompt to Generate Similar Examples with Reasoning Paths.}\\n\",\n \" \\\\label{tab:synthetic_QA_generation_prompt}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\small\\n\",\n \"\\\\begin{tabular}{|p{\\\\textwidth}|}\\n\",\n \"\\\\hline\\n\",\n \"\\\\textbf{Prompt:}\\\\\\\\\\n\",\n \"{You are given an original question: [ORIGINAL QUESTION]}\\\\\\\\\\n\",\n \"{You also have a synthetic question: [SYNTHETIC QUESTION]}\\\\\\\\\\n\",\n \"{Your task is to decide how similar the synthetic question is in structure and complexity, compared to the original. Please provide a brief explanation of your reasoning. Then, assign a score from 1 (completely different) to 10 (very similar).}\\\\\\\\\\n\",\n \"\\\\textbf{Example}:\\n\",\n \"{Original Q: \\\"Who discovered penicillin?\\\"}\\\\\\\\\\n\",\n \"{Synthetic Q: \\\"Which scientist found the mold that led to antibiotics?\\\"}\\\\\\\\\\n\",\n \"\\\\textbf{Explanation}:\\n\",\n \"{Both questions ask about a discoverer of a major medical breakthrough. The second question focuses on the mold (penicillin), so it is structurally similar and retains the core inquiry about a discovery.}\\\\\\\\\\n\",\n \"\\\\textbf{Score (1-10)}: {8}\\\\\\\\\\n\",\n \"\\\\textbf{Notes}:\\n\",\n \"{- Provide a short justification.}\\\\\\\\\\n\",\n \"{- Avoid rewriting or changing the question.}\\\\\\\\\\n\",\n \"{- Keep the final output concise, ending with the numeric score.}\\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\caption{Prompt for Evaluating Alignment of Synthetic Questions with the Original.}\\n\",\n \"\\\\label{tab:alignment_prompt}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\small\\n\",\n \"\\\\begin{tabular}{|p{\\\\textwidth}|}\\n\",\n \"\\\\hline\\n\",\n \"\\\\textbf{Prompt:}\\\\\\\\\\n\",\n \"{You are given:}\\\\\\\\\\n\",\n \"{- The original question: [Q]}\\\\\\\\\\n\",\n \"{- A document or context: [D]}\\\\\\\\\\n\",\n \"{- A selected reasoning path: [R]}\\\\\\\\\\n\",\n \"{- The specific skills used in the reasoning path: [S]}\\\\\\\\\\n\",\n \"\\n\",\n \"\\\\textbf{Your goal} is to produce a final answer by combining the relevant information from [D] with the guided reasoning steps from [R]. Follow these instructions:\\\\\\\\\\n\",\n \"1. \\\\textbf{Review the Reasoning Path}\\\\\\\\\\n\",\n \" Read each step in [R] carefully. Identify which parts of [D] or background knowledge support each step. \\\\\\\\\\n\",\n \"2. \\\\textbf{Apply the Skills} \\\\\\\\\\n\",\n \" If [S] includes certain reasoning skills (e.g., deduction), make sure to explicitly use them when combining evidence from [D].\\\\\\\\\\n\",\n \"3. \\\\textbf{Generate a Clear Answer} \\\\\\\\\\n\",\n \" Compose a concise final answer that directly addresses [Q]. You may outline your chain of thought, but keep the explanation aligned with [R]. \\\\\\\\\\n\",\n \"4. \\\\textbf{Maintain Accuracy} \\\\\\\\\\n\",\n \" If [R] instructs a specific substep (e.g., numerical calculation or bridging multiple facts), follow it precisely, citing the relevant parts of [D].\\\\\\\\\\n\",\n \"\\\\textbf{Notes}: \\\\\\\\\\n\",\n \"- {Do not contradict the provided reasoning path.} \\\\\\\\\\n\",\n \"- {Cite relevant text from [D] if needed, but avoid unnecessary repetition.} \\\\\\\\\\n\",\n \"- {End with a concise, standalone final answer.} \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\caption{Prompt for Question Answering.}\\n\",\n \"\\\\label{tab:reasoning_path_prompt}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\small\\n\",\n \"\\\\adjustbox{max width=\\\\textwidth}{\\n\",\n \"\\\\begin{tabular}{lccccccc}\\n\",\n \"\\\\toprule\\n\",\n \"Model & SQuAD & HotpotQA & NewsQA & Gaokao & HQA & TriviaQA & BioASQ\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\rowcolor{gray!10}\\\\multicolumn{8}{c}{\\\\textbf{\\\\textit{Quick-Thinking Models w/ Reasoning Strategies (Exact Match)}}}\\\\\\\\\\n\",\n \"\\\\multicolumn{8}{l}{\\\\textbf{\\\\textit{Qwen2.5-32B-Instruct}}}\\\\\\\\\\n\",\n \"\\\\; w/ vanilla \\\\textit{(quick)} & 55.23 & 31.69 & 27.15 & 12.61 & 19.47 & 23.82 & 40.88\\\\\\\\\\n\",\n \"\\\\; w/ few-shots \\\\textit{(quick)} & 56.42 & 32.35 & 28.13 & 13.06 & 20.19 & 24.58 & 41.76\\\\\\\\\\n\",\n \"\\\\; w/ self-consistency~\\\\cite{wang2022self} & 57.08 & 32.81 & 28.49 & 13.29 & 20.37 & 24.89 & 41.93\\\\\\\\\\n\",\n \"\\\\; w/ proCoT~\\\\cite{deng-etal-2023-prompting} & 58.65 & 33.81 & 29.32 & 13.86 & 21.02 & 25.58 & 42.77\\\\\\\\\\n\",\n \"\\\\; w/ ToT~\\\\cite{yao2023tree} & 59.83 & 34.67 & 29.81 & 14.12 & 21.51 & 26.04 & 43.28\\\\\\\\\\n\",\n \"\\\\; w/ MCTS~\\\\cite{zhao2024marco} & 59.87 & 34.53 & 29.76 & 14.21 & 21.57 & 26.08 & 43.33\\\\\\\\\\n\",\n \"\\\\textbf{\\\\; w/ T$^2$ (ours)} & \\\\textbf{62.65} & \\\\textbf{39.98} & \\\\textbf{34.12} & \\\\textbf{15.72} & \\\\textbf{22.58} & \\\\textbf{26.43} & \\\\textbf{48.14}\\\\\\\\\\n\",\n \"\\\\hdashline\\n\",\n \"\\\\multicolumn{8}{l}{\\\\textbf{\\\\textit{GPT-4o}}}\\\\\\\\\\n\",\n \"\\\\; w/ vanilla \\\\textit{(quick)} & 59.87 & 35.31 & 30.27 & 16.23 & 23.39 & 29.84 & 44.12\\\\\\\\\\n\",\n \"\\\\; w/ few-shots \\\\textit{(quick)} & 61.09 & 36.04 & 30.85 & 16.78 & 24.03 & 30.52 & 44.84\\\\\\\\\\n\",\n \"\\\\; w/ self-consistency~\\\\cite{wang2022self} & 61.63 & 36.42 & 31.14 & 16.92 & 24.28 & 30.78 & 45.13\\\\\\\\\\n\",\n \"\\\\; w/ proCoT~\\\\cite{deng-etal-2023-prompting} & 62.89 & 37.29 & 31.92 & 17.49 & 25.01 & 31.43 & 46.05\\\\\\\\\\n\",\n \"\\\\; w/ ToT~\\\\cite{yao2023tree} & 63.77 & 37.94 & 32.43 & 17.79 & 25.57 & 32.11 & 46.61\\\\\\\\\\n\",\n \"\\\\; w/ MCTS~\\\\cite{zhao2024marco} & 63.94 & 38.13 & 32.06 & 17.84 & 26.22 & 32.18 & 47.39\\\\\\\\\\n\",\n \"\\\\textbf{\\\\; w/ T$^2$ (ours)} & \\\\textbf{65.17} & \\\\textbf{39.51} & \\\\textbf{33.76} & \\\\textbf{17.98} & \\\\textbf{26.31} & \\\\textbf{33.12} & \\\\textbf{49.07}\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\rowcolor{gray!10}\\\\multicolumn{8}{c}{\\\\textbf{\\\\textit{Slow-Thinking Models (Exact Match)}}}\\\\\\\\\\n\",\n \"o1-mini & 65.82 & 42.89 & 35.08 & 21.87 & 29.51 & 36.52 & 50.68\\\\\\\\\\n\",\n \"QwQ-32B-Preview & 66.67 & 43.57 & 35.43 & 22.18 & 29.88 & 36.87 & 51.21\\\\\\\\\\n\",\n \"DeepSeek-R1 & 67.38 & 44.04 & 35.94 & 22.28 & 30.26 & 37.52 & 52.31\\\\\\\\\\n\",\n \"o1 & 67.92 & 44.57 & 36.42 & 22.53 & 30.75 & 38.12 & 52.94\\\\\\\\\\n\",\n \"o4-mini & 68.36 & 44.97 & 36.83 & 22.79 & 31.08 & 38.28 & 53.14\\\\\\\\\\n\",\n \"o4-mini-high & 68.54 & 45.18 & 37.01 & 22.89 & 31.23 & 38.42 & 53.27\\\\\\\\\\n\",\n \"Claude-3.7-sonnet-thinking & 68.67 & 45.27 & 37.14 & 22.97 & 31.32 & 38.56 & 53.41\\\\\\\\\\n\",\n \"o3 & 68.89 & 45.48 & 37.34 & 23.19 & 31.37 & 38.78 & 53.74\\\\\\\\\\n\",\n \"Gemini-2.5-Pro & 69.69 & 46.08 & 37.97 & 23.51 & 32.01 & 39.43 & 54.45\\\\\\\\\\n\",\n \"\\\\textbf{QwQ-32B + T$^2$ (ours)} & \\\\textbf{71.32} & \\\\textbf{47.87} & \\\\textbf{39.17} & \\\\textbf{24.63} & \\\\textbf{33.28} & \\\\textbf{40.39} & \\\\textbf{55.81}\\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}}\\n\",\n \"\\\\caption{Exact Match (EM) scores on seven QA datasets.}\\n\",\n \"\\\\label{tab:overall_performance_em}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table}[!t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\adjustbox{max width=\\\\linewidth}{\\n\",\n \"\\\\begin{tabular}{lccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Method} & \\\\textbf{HotpotQA} & \\\\textbf{NewsQA} & \\\\textbf{HQA} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"Random & 32.4 & 38.7 & 19.2 \\\\\\\\\\n\",\n \"Coverage Only & 41.6 & 46.3 & 27.8 \\\\\\\\\\n\",\n \"Uniqueness Only & 49.2 & 54.5 & 35.7 \\\\\\\\\\n\",\n \"Full Approach & \\\\textbf{67.1} & \\\\textbf{61.3} & \\\\textbf{40.3} \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}}\\n\",\n \"\\\\caption{Ablation study results showing the impact of different components in our selection approach. We report ROUGE-L (\\\\%) on three benchmark datasets.}\\n\",\n \"\\\\label{tab:ablation}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table*}[!t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\adjustbox{max width=\\\\linewidth}{\\n\",\n \"\\\\begin{tabular}{lccccccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Model} & \\\\textbf{SQuAD} & \\\\textbf{BioASQ} & \\\\textbf{HotpotQA} & \\\\textbf{NewsQA} & \\\\textbf{GAOKAO} & \\\\textbf{HQA} & \\\\textbf{TriviaQA} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"Qwen2.5-32B w/ SC & 1372.18 & 1726.32 & 1485.87 & 2201.65 & 1957.93 & 1580.43 & 1742.41 \\\\\\\\\\n\",\n \"Qwen2.5-32B w/ T$^2$ & 1161.42 & 1401.52 & 1330.71 & 1812.28 & 1581.14 & 1415.18 & 1582.42 \\\\\\\\\\n\",\n \"\\\\hdashline\\n\",\n \"QwQ-32B-Preview & 1617.42 & 2012.33 & 1823.49 & 2648.12 & 2284.80 & 1972.37 & 2119.88 \\\\\\\\\\n\",\n \"QwQ-32B w/ T$^2$ & 1285.36 & 1467.85 & 1450.75 & 1855.89 & 1699.45 & 1465.68 & 1605.56 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}}\\n\",\n \"\\\\caption{Average token consumption across seven CQA datasets for different reasoning approaches.}\\n\",\n \"\\\\label{tab:token_consumption}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\adjustbox{max width=\\\\textwidth}{\\n\",\n \"\\\\begin{tabular}{lccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Model} & \\\\textbf{Avg. Token Length} & \\\\textbf{Token Reduction} & \\\\textbf{Avg. ROUGE-L} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"Qwen2.5-32B w/ SC & 1723.83 & - & 50.07 \\\\\\\\\\n\",\n \"Qwen2.5-32B w/ T$^2$ & 1469.24 & 14.8\\\\% vs. SC & 56.22 \\\\\\\\\\n\",\n \"\\\\hdashline\\n\",\n \"QwQ-32B-Preview & 2068.34 & - & 63.38 \\\\\\\\\\n\",\n \"QwQ-32B w/ T$^2$ & 1547.22 & 25.2\\\\% vs. QwQ & 68.56 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}}\\n\",\n \"\\\\caption{Efficiency-performance trade-off across seven CQA datasets. Token reduction is calculated relative to the baseline model (SC: Self-Consistency).}\\n\",\n \"\\\\label{tab:efficiency_performance}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[!t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\includegraphics[width=\\\\textwidth]{draw/efficiency_by_dataset.pdf}\\n\",\n \"\\\\caption{Token consumption reduction by T$^2$ across different datasets. The percentage values indicate the relative reduction compared to the baseline models (Qwen2.5-32B w/ SC and QwQ-32B-Preview).}\\n\",\n \"\\\\label{fig:efficiency_by_dataset}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\adjustbox{max width=\\\\textwidth}{\\n\",\n \"\\\\begin{tabular}{lcccccccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{\\\\textbf{Model}} & \\\\multicolumn{2}{c}{\\\\textbf{SQuAD}} & \\\\multicolumn{2}{c}{\\\\textbf{BioASQ}} & \\\\multicolumn{2}{c}{\\\\textbf{HotpotQA}} & \\\\multicolumn{2}{c}{\\\\textbf{NewsQA}} \\\\\\\\\\n\",\n \"\\\\cmidrule(lr){2-3} \\\\cmidrule(lr){4-5} \\\\cmidrule(lr){6-7} \\\\cmidrule(lr){8-9}\\n\",\n \" & Token & ROUGE-L & Token & ROUGE-L & Token & ROUGE-L & Token & ROUGE-L \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"Qwen2.5-32B w/ SC & 1372.18 & 75.31 & 1726.32 & 57.57 & 1485.87 & 56.76 & 2201.65 & 52.27 \\\\\\\\\\n\",\n \"Qwen2.5-32B w/ T$^2$ & 1161.42 & 81.86 & 1401.52 & 65.02 & 1330.71 & 67.11 & 1812.28 & 61.27 \\\\\\\\\\n\",\n \"QwQ-32B-Preview & 1617.42 & 86.87 & 2012.33 & 69.02 & 1823.49 & 71.86 & 2648.12 & 63.92 \\\\\\\\\\n\",\n \"QwQ-32B w/ T$^2$ & 1285.36 & 92.12 & 1467.85 & 75.21 & 1450.75 & 77.61 & 1855.89 & 68.61 \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multirow{2}{*}{\\\\textbf{Model}} & \\\\multicolumn{2}{c}{\\\\textbf{GAOKAO}} & \\\\multicolumn{2}{c}{\\\\textbf{HQA}} & \\\\multicolumn{2}{c}{\\\\textbf{TriviaQA}} & \\\\multicolumn{2}{c}{\\\\textbf{Average}} \\\\\\\\\\n\",\n \"\\\\cmidrule(lr){2-3} \\\\cmidrule(lr){4-5} \\\\cmidrule(lr){6-7} \\\\cmidrule(lr){8-9}\\n\",\n \" & Token & ROUGE-L & Token & ROUGE-L & Token & ROUGE-L & Token & ROUGE-L \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"Qwen2.5-32B w/ SC & 1957.93 & 30.57 & 1580.43 & 37.12 & 1742.41 & 41.92 & 1723.83 & 50.07 \\\\\\\\\\n\",\n \"Qwen2.5-32B w/ T$^2$ & 1581.14 & 34.06 & 1415.18 & 40.31 & 1582.42 & 43.92 & 1469.24 & 56.22 \\\\\\\\\\n\",\n \"QwQ-32B-Preview & 2284.80 & 43.23 & 1972.37 & 49.62 & 2119.88 & 59.16 & 2068.34 & 63.38 \\\\\\\\\\n\",\n \"QwQ-32B w/ T$^2$ & 1699.45 & 47.42 & 1465.68 & 54.71 & 1605.56 & 64.22 & 1547.22 & 68.56 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}}\\n\",\n \"\\\\caption{Detailed comparison of token consumption and performance (ROUGE-L) across all datasets. Lower token count with higher ROUGE-L indicates better efficiency-performance trade-off.}\\n\",\n \"\\\\label{tab:detailed_efficiency}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\adjustbox{max width=\\\\textwidth}{\\n\",\n \"\\\\begin{tabular}{lccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Question Complexity} & \\\\textbf{Qwen2.5 + SC} & \\\\textbf{Qwen2.5 + T$^2$} & \\\\textbf{Token Reduction} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"Simple (1-step) & 1283.45 & 1157.82 & -9.8\\\\% \\\\\\\\\\n\",\n \"Moderate (2-3 steps) & 1687.31 & 1391.65 & -17.5\\\\% \\\\\\\\\\n\",\n \"Complex (4+ steps) & 2201.73 & 1758.24 & -20.1\\\\% \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textbf{Question Complexity} & \\\\textbf{QwQ-32B} & \\\\textbf{QwQ-32B + T$^2$} & \\\\textbf{Token Reduction} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"Simple (1-step) & 1584.21 & 1262.35 & -20.3\\\\% \\\\\\\\\\n\",\n \"Moderate (2-3 steps) & 2041.57 & 1492.18 & -26.9\\\\% \\\\\\\\\\n\",\n \"Complex (4+ steps) & 2579.24 & 1887.14 & -26.8\\\\% \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}}\\n\",\n \"\\\\caption{Token consumption analysis by question complexity. T$^2$ achieves greater efficiency gains for more complex questions.}\\n\",\n \"\\\\label{tab:complexity_analysis}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\adjustbox{max width=\\\\textwidth}{\\n\",\n \"\\\\begin{tabular}{lcc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Model} & \\\\textbf{Avg. Inference Time (s)} & \\\\textbf{Time Reduction} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"Qwen2.5-32B w/ SC & 65.31 & - \\\\\\\\\\n\",\n \"Qwen2.5-32B w/ T$^2$ & 34.52 & -47.1\\\\% \\\\\\\\\\n\",\n \"\\\\hdashline\\n\",\n \"QwQ-32B-Preview & 76.74 & - \\\\\\\\\\n\",\n \"QwQ-32B w/ T$^2$ & 45.03 & -41.3\\\\% \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}}\\n\",\n \"\\\\caption{Average inference time per question across datasets. T$^2$ reduces computational time while maintaining high performance.}\\n\",\n \"\\\\label{tab:time_efficiency}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\begin{tabular}{lcccccc}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\multirow{2}{*}{\\\\textbf{Group}}& \\\\multicolumn{3}{c}{\\\\textbf{SQuAD}} & \\\\multicolumn{3}{c}{\\\\textbf{HQA}} \\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-4} \\\\cmidrule(lr){5-7}\\n\",\n \" & Few-shots & Self-Cconsistency & T$^2$ & Few-shots & Self-Cconsistency & T$^2$ \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Low & 78.5 & 79.1 & \\\\textbf{80.2} & 42.7 & 43.3 & \\\\textbf{44.6} \\\\\\\\\\n\",\n \" Medium & 76.4 & 78.2 & \\\\textbf{79.5} & 41.5 & 43.0 & \\\\textbf{45.1} \\\\\\\\\\n\",\n \" High & 75.9 & 78.7 & \\\\textbf{80.1} & 40.2 & 42.9 & \\\\textbf{46.2} \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" \\\\caption{ROUGE-L by question complexity. We compare quick-thinking (Few-shots) and slow-thinking (Self-Consistency), and our T$^2$.}\\n\",\n \" \\\\label{tab:structure_complexity}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[!t]\\n\",\n \"\\\\begin{tcolorbox}[title=Original QA Pair]\\n\",\n \"\\\\textbf{Q:} How does the melting point of sodium compare to potassium?\\n\",\n \"\\n\",\n \"\\\\textbf{A:} I'll approach this systematically:\\\\\\\\\\n\",\n \"1. First, I'll recall the melting point of sodium, which is 97.8°C.\\\\\\\\\\n\",\n \"2. Next, I'll recall the melting point of potassium, which is 63.5°C.\\\\\\\\\\n\",\n \"3. Comparing these values: 97.8°C > 63.5°C\\\\\\\\\\n\",\n \"4. Therefore, sodium has a higher melting point than potassium.\\n\",\n \"\\\\end{tcolorbox}\\n\",\n \"\\\\caption{Our original generated similar example.}\\n\",\n \"\\\\label{fig:sim_example_original}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[!t]\\n\",\n \"\\\\begin{tcolorbox}[title=Structure-Only Version]\\n\",\n \"\\\\textbf{Q:} How does [PROPERTY] of [ENTITY\\\\_A] compare to [ENTITY\\\\_B]?\\n\",\n \"\\n\",\n \"\\\\textbf{A:} I'll approach this systematically:\\\\\\\\\\n\",\n \"1. First, I'll determine the [PROPERTY] of [ENTITY\\\\_A], which is [VALUE\\\\_A].\\\\\\\\\\n\",\n \"2. Next, I'll determine the [PROPERTY] of [ENTITY\\\\_B], which is [VALUE\\\\_B].\\\\\\\\\\n\",\n \"3. Comparing these values: [COMPARISON\\\\_OPERATION]\\\\\\\\\\n\",\n \"4. Therefore, [CONCLUSION\\\\_STATEMENT].\\n\",\n \"\\\\end{tcolorbox}\\n\",\n \"\\\\caption{Structure-only version of our generated similar example.}\\n\",\n \"\\\\label{fig:sim_example_structure_only}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[!t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\linewidth]{./draw/demo_number.pdf}\\n\",\n \" \\\\caption{Impact of the number of similar examples on ROUGE-L scores for HotpotQA (left) and NewsQA (right).}\\n\",\n \" \\\\label{fig:demo_counts}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{table}[!t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\adjustbox{max width=\\\\linewidth}{\\n\",\n \" \\\\begin{tabular}{lccc}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Method} & \\\\textbf{ROUGE-L} & \\\\textbf{Variation} & \\\\textbf{Noise}\\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multicolumn{4}{l}{\\\\textbf{\\\\textit{Qwen2.5 w/ ours}}}\\\\\\\\\\n\",\n \" \\\\; Random Fill & 49.8 & High & Medium \\\\\\\\\\n\",\n \" \\\\; Guided Fill & 52.6 & Low & Low \\\\\\\\\\n\",\n \" \\\\; Template Variation & \\\\textbf{61.3} & High & Low \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}}\\n\",\n \" \\\\caption{Comparing different example construction methods on NewsQA.}\\n\",\n \" \\\\label{tab:demo_construction}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{figure*}[!t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\linewidth]{./draw/question_threshold.pdf}\\n\",\n \" \\\\caption{Impact of the question synthesis scope.}\\n\",\n \" \\\\label{fig:impact_of_delta}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{table}[!t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\small\\n\",\n \"\\\\begin{tabular}{lcc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Model} & \\\\textbf{HotpotQA} & \\\\textbf{HQA} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"Qwen2.5+SC & 56.76 & 37.12 \\\\\\\\\\n\",\n \"Qwen2.5+T$^2$ & 67.11 & 40.31 \\\\\\\\\\n\",\n \"Qwen2.5+mis domain & 65.96 & 39.85 \\\\\\\\\\n\",\n \"Qwen2.5+structure only & 63.96 & 38.85 \\\\\\\\\\n\",\n \"\\\\hdashline\\n\",\n \"QwQ & 71.86 & 49.62 \\\\\\\\\\n\",\n \"QwQ+T$^2$ & 77.61 & 54.71 \\\\\\\\\\n\",\n \"QwQ+mis domain & 77.03 & 54.26 \\\\\\\\\\n\",\n \"QwQ+structure only & 74.03 & 53.66 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\caption{Performance on mis-domain and structure-only models. ROUGE-L is the reported performance metric.}\\n\",\n \"\\\\label{tab:demo_quality}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table*}[!t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\begin{tabular}{lccccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Models w/ T$^2$} & \\\\textbf{Accuracy} & \\\\textbf{Relevance} & \\\\textbf{Completeness} & \\\\textbf{Coherence} & \\\\textbf{Overall} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"Qwen2.5-32B-Instrucut & 8.8 & 8.6 & 8.1 & 8.1 & 8.4 \\\\\\\\\\n\",\n \"QwQ-32B-Preview & 8.4 & 8.3 & 7.9 & 8.2 & 8.4 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\caption{Human evaluation scores for synthetic references generated by different models (scale: 1-10).}\\n\",\n \"\\\\label{tab:human_eval}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[!t]\\n\",\n \" \\\\small\\n\",\n \" \\\\begin{tcolorbox}[title=\\\\# HotpotQA Case Example with Multihop Thinking, fonttitle=\\\\bfseries, colframe=black, colback=white, sharp corners, boxrule=0.5mm, colbacktitle=gray!20, coltitle=black, enhanced]\\n\",\n \" \\\\textbf{Document 1:}\\\\\\\\\\n\",\n \" John Lennon, the iconic musician and member of The Beatles, was born in Liverpool, England.\\\\\\\\ \\n\",\n \" \\\\textbf{Document 2:}\\\\\\\\\\n\",\n \" \\\\textit{Nowhere Boy} is a film that explores the early life of John Lennon, highlighting his formative years and influences.\\\\\\\\\\n\",\n \" \\\\textbf{Question:}\\\\\\\\\\n\",\n \" In what city was the subject of the film \\\\textit{Nowhere Boy} born?\\n\",\n \"\\n\",\n \" \\\\vspace{1em}\\n\",\n \" \\n\",\n \" \\\\textbf{Proper Reasoning Chain:}\\\\\\\\\\n\",\n \" 1. \\\\textbf{Identify the Subject:} Recognize that \\\\textit{Nowhere Boy} is about John Lennon. (Deductive)\\\\\\\\\\n\",\n \" 2. \\\\textbf{Extract Key Fact:} From Document 1, note that John Lennon was born in Liverpool, England. (Deductive)\\\\\\\\\\n\",\n \" 3. \\\\textbf{Conclude:} Therefore, the subject of the film was born in Liverpool. (Decompositional)\\n\",\n \"\\n\",\n \" \\\\vspace{1em}\\n\",\n \" \\n\",\n \" \\\\textcolor{red}{\\\\textbf{1) Quick Thinking Model's Wrong Answer:}}\\\\\\\\\\n\",\n \" \\\\textcolor{red}{\\\\textit{Answer:} The subject of \\\\textit{Nowhere Boy} was born in London.}\\\\\\\\\\n\",\n \" \\n\",\n \" \\\\textcolor{orange}{\\\\textbf{2) Slow Thinking Model's Overthinking Answer:}}\\\\\\\\\\n\",\n \" \\\\textcolor{orange}{\\\\textit{Answer:} After a lengthy analysis of various biographical details concerning John Lennon, including his early childhood, multiple hints in his music and interviews, and a discussion of several English cities mentioned in relation to his life, one might eventually conclude that the subject of \\\\textit{Nowhere Boy} was born in Liverpool.}\\\\\\\\\\n\",\n \" \\n\",\n \" \\\\textcolor{mygreen}{\\\\textbf{3) Model with Our T$^2$'s Correct Answer:}}\\\\\\\\\\n\",\n \" \\\\textcolor{mygreen}{\\\\textit{Answer:} Since \\\\textit{Nowhere Boy} is a film about John Lennon (Document 2) and Document 1 confirms that John Lennon was born in Liverpool, we deduce that the subject of the film was born in Liverpool.}\\n\",\n \" \\\\end{tcolorbox}\\n\",\n \" \\\\caption{HotpotQA Case Example with Multiple Answer Versions and Explicit Reasoning Chain.}\\n\",\n \" \\\\label{fig:hotpotqa_example}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[!t]\\n\",\n \" \\\\small\\n\",\n \" \\\\begin{tcolorbox}[title=\\\\# SQuAD Case Example with Single-Step Thinking,fonttitle=\\\\bfseries,colframe=black,colback=white,sharp corners,boxrule=0.5mm,colbacktitle=gray!20,coltitle=black,enhanced]\\n\",\n \" \\\\textbf{Document:}\\\\\\\\\\n\",\n \" The Eiffel Tower is a wrought-iron lattice tower located on the Champ de Mars in Paris, France. It was constructed in 1889 as the entrance to the 1889 World's Fair. The tower stands approximately 324 meters tall and is one of the most recognized structures in the world.\\\\\\\\\\n\",\n \" \\\\textbf{Question:}\\\\\\\\\\n\",\n \" In what year was the Eiffel Tower constructed?\\\\\\\\\\n\",\n \"\\n\",\n \" \\\\vspace{1em}\\n\",\n \" \\n\",\n \" \\\\textbf{Proper Reasoning Chain (using Deductive):}\\\\\\\\\\n\",\n \" 1) \\\\textbf{Deduction:} Based on clear statement, we deduce that the construction year is 1889 (Deductive).\\n\",\n \" \\n\",\n \" \\\\vspace{1em}\\n\",\n \" \\n\",\n \" \\\\textbf{1) Quicking Thinking Model's Correct Answer \\\\textcolor{orange}{via Pattern Matching}:}\\\\\\\\\\n\",\n \" \\\\textit{Answer:} 1889.\\\\\\\\\\n\",\n \" \\n\",\n \" \\\\textbf{2) Slow Thinking Model's Correct Answer \\\\textcolor{orange}{but Overthinking}:}\\\\\\\\\\n\",\n \" \\\\textit{Answer:} After reading the passage, I noticed several numbers such as 324 (the tower's height) and 1889. While I considered whether the height might play a role in determining the construction year, I ultimately realized that the passage explicitly states ``constructed in 1889.'' Thus, despite the extra details, the correct construction year is 1889.\\\\\\\\\\n\",\n \" \\n\",\n \" \\\\textbf{3) Model with Our T$^2$'s Correct Answer \\\\textcolor{mygreen}{using the Specific Reasoning Chain}:}\\\\\\\\\\n\",\n \" \\\\textit{Answer:} Since the document clearly states that the Eiffel Tower was constructed in 1889, we deduce—by directly extracting and applying this fact—that the construction year is 1889.\\n\",\n \" \\\\end{tcolorbox}\\n\",\n \" \\\\caption{SQuAD Case Example with Single-Step Thinking and Multiple Answer Versions.}\\n\",\n \" \\\\label{fig:squad_case_single_step}\\n\",\n \"\\\\end{figure*}\\n\"\n ]\n]","isConversational":false},"fulltext":{"kind":"string","value":"% This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.\n\\pdfoutput=1\n% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.\n\n\\documentclass[11pt]{article}\n\n% Change \"review\" to \"final\" to generate 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requires adding\n%additional package(s)\n\\usepackage{graphicx}\n\\usepackage{amsmath}\n\\usepackage{amssymb}\n\\usepackage{multirow}\n\\usepackage{booktabs}\n\\usepackage{lipsum}\n\\usepackage{xcolor}\n\\definecolor{mygreen}{RGB}{1,113,0}\n\\usepackage{colortbl}\n\\definecolor{lightbluegray}{RGB}{235,245,255}\n\\definecolor{lightgreengray}{RGB}{235,245,235}\n\\usepackage{pifont}\n\\usepackage[utf8]{inputenc}\n\\usepackage{mdframed}\n\\usepackage{enumitem}\n\\usepackage[most]{tcolorbox}\n\\usepackage{algorithm,algpseudocode}\n\\usepackage{adjustbox}\n\\usepackage[utf8]{inputenc}\n\\usepackage[T1]{fontenc} \n\\usepackage{hyperref} \n\\usepackage{url} \n\\usepackage{booktabs} \n\\usepackage{amsfonts} \n\\usepackage{nicefrac} \n\\usepackage{microtype} \n% \\usepackage{xcolor} \n\\usepackage{wrapfig}\n\\usepackage{arydshln}\n\\usepackage{amsmath}\n\\usepackage{cuted}\n\\DeclareMathOperator*{\\argmax}{arg\\,max}\n\n\\newcommand{\\shouldcite}{\\textcolor{red}{CITE}}\n\n% If the title and author information does not fit in the area allocated, uncomment the following\n%\n%\\setlength\\titlebox{<dim>}\n%\n% and set <dim> to something 5cm or larger.\n\n\\title{T$^2$: An Adaptive Test-Time Scaling Strategy for \\\\Contextual Question Answering}\n\n% Author information can be set in various styles:\n% For several authors from the same institution:\n% \\author{Author 1 \\and ... \\and Author n \\\\\n% Address line \\\\ ... \\\\ Address line}\n% if the names do not fit well on one line use\n% Author 1 \\\\ {\\bf Author 2} \\\\ ... \\\\ {\\bf Author n} \\\\\n% For authors from different institutions:\n% \\author{Author 1 \\\\ Address line \\\\ ... \\\\ Address line\n% \\And ... \\And\n% Author n \\\\ Address line \\\\ ... \\\\ Address line}\n% To start a separate ``row'' of authors use \\AND, as in\n% \\author{Author 1 \\\\ Address line \\\\ ... \\\\ Address line\n% \\AND\n% Author 2 \\\\ Address line \\\\ ... \\\\ Address line \\And\n% Author 3 \\\\ Address line \\\\ ... \\\\ Address line}\n\n% \\author{First Author \\\\\n% Affiliation / Address line 1 \\\\\n% Affiliation / Address line 2 \\\\\n% Affiliation / Address line 3 \\\\\n% \\texttt{email@domain} \\\\\\And\n% Second Author \\\\\n% Affiliation / Address line 1 \\\\\n% Affiliation / Address line 2 \\\\\n% Affiliation / Address line 3 \\\\\n% \\texttt{email@domain} \\\\}\n\n\\author{\n \\textbf{Zhengyi Zhao\\textsuperscript{1}},\n \\textbf{Shubo Zhang\\textsuperscript{2}},\n \\textbf{Zezhong Wang\\textsuperscript{1}},\n \\textbf{Huimin Wang\\textsuperscript{3}},\n \\textbf{Yutian Zhao\\textsuperscript{3}},\\\\\n \\textbf{Bin Liang\\textsuperscript{1}},\n \\textbf{Yefeng Zheng\\textsuperscript{4}},\n \\textbf{Binyang Li\\textsuperscript{2}},\n \\textbf{Kam-Fai Wong\\textsuperscript{1}},\n \\textbf{Xian Wu\\textsuperscript{3,\\thanks{Corresponding author.}}},\n\\\\\n\\\\\n \\textsuperscript{1} The Chinese University of Hong Kong\n \\textsuperscript{2} University of International Relations \\\\\n \\textsuperscript{3} Jarvis Research Center, Tencent YouTu Lab \n \\textsuperscript{4} Westlake University\n\\\\\n {\n \\texttt{zyzhao@se.cuhk.edu.hk}\n }\n}\n\n%\\author{\n% \\textbf{First Author\\textsuperscript{1}},\n% \\textbf{Second Author\\textsuperscript{1,2}},\n% \\textbf{Third T. Author\\textsuperscript{1}},\n% \\textbf{Fourth Author\\textsuperscript{1}},\n%\\\\\n% \\textbf{Fifth Author\\textsuperscript{1,2}},\n% \\textbf{Sixth Author\\textsuperscript{1}},\n% \\textbf{Seventh Author\\textsuperscript{1}},\n% \\textbf{Eighth Author \\textsuperscript{1,2,3,4}},\n%\\\\\n% \\textbf{Ninth Author\\textsuperscript{1}},\n% \\textbf{Tenth Author\\textsuperscript{1}},\n% \\textbf{Eleventh E. Author\\textsuperscript{1,2,3,4,5}},\n% \\textbf{Twelfth Author\\textsuperscript{1}},\n%\\\\\n% \\textbf{Thirteenth Author\\textsuperscript{3}},\n% \\textbf{Fourteenth F. Author\\textsuperscript{2,4}},\n% \\textbf{Fifteenth Author\\textsuperscript{1}},\n% \\textbf{Sixteenth Author\\textsuperscript{1}},\n%\\\\\n% \\textbf{Seventeenth S. Author\\textsuperscript{4,5}},\n% \\textbf{Eighteenth Author\\textsuperscript{3,4}},\n% \\textbf{Nineteenth N. Author\\textsuperscript{2,5}},\n% \\textbf{Twentieth Author\\textsuperscript{1}}\n%\\\\\n%\\\\\n% \\textsuperscript{1}Affiliation 1,\n% \\textsuperscript{2}Affiliation 2,\n% \\textsuperscript{3}Affiliation 3,\n% \\textsuperscript{4}Affiliation 4,\n% \\textsuperscript{5}Affiliation 5\n%\\\\\n% \\small{\n% \\textbf{Correspondence:} \\href{mailto:email@domain}{email@domain}\n% }\n%}\n\n\\begin{document}\n\\maketitle\n\\begin{abstract}\nRecent advances in Large Language Models (LLMs) have demonstrated remarkable performance in Contextual Question Answering (CQA). However, prior approaches typically employ elaborate reasoning strategies regardless of question complexity, leading to low adaptability. Recent efficient test-time scaling methods introduce budget constraints or early stop mechanisms to avoid overthinking for straightforward questions. But they add human bias to the reasoning process and fail to leverage models' inherent reasoning capabilities. To address these limitations, we present T$^2$: Think-to-Think, a novel framework that dynamically adapts reasoning depth based on question complexity. T$^2$ leverages the insight that if an LLM can effectively solve similar questions using specific reasoning strategies, it can apply the same strategy to the original question. This insight enables to adoption of concise reasoning for straightforward questions while maintaining detailed analysis for complex problems. T$^2$ works through four key steps: decomposing questions into structural elements, generating similar examples with candidate reasoning strategies, evaluating these strategies against multiple criteria, and applying the most appropriate strategy to the original question. Experimental evaluation across seven diverse CQA benchmarks demonstrates that T$^2$ not only achieves higher accuracy than baseline methods but also reduces computational overhead by up to 25.2\\%. \n\\end{abstract}\n\n\\section{Introduction}\n\nLarge language models (LLMs) have demonstrated impressive capabilities in Contextual Question Answering (CQA) tasks \\cite{DBLP:conf/acl/TrivediBKS23,DBLP:conf/emnlp/PressZMSSL23}, but their reasoning approaches often lack adaptability to question complexity. Current CQA systems typically employ either direct answer generation or elaborate step-by-step reasoning for all questions, regardless of difficulty~\\cite{wei2022chain,huang2024o1,min2024imitate}. This one-size-fits-all approach has an accuracy-vs-efficiency delimma. Directly generating answers for all questions will deteriorate the performance on difficult questions, which require multi-hop reasoning. Elaborated reasoning for all questions creates an efficiecy challenge: models frequently generate reasoning chains that are excessively verbose, containing redundant steps that do not contribute to finding the correct answer.\n\nExisting analysis reveals that these redundant reasoning paths can unnecessarily extend the length of reasoning chains multiple times beyond what is required. Such as exploring multiple solution approaches when only one is needed \\cite{ji2025first}, or verifying simple facts with elaborate explanations \\cite{muennighoff2025s1}. For example, when asked ``What is the capital of France?'', models often generate lengthy discussions about France's history and geography before providing the straightforward answer ``Paris.'' This computational inefficiency is particularly concerning as model deployment costs continue to rise. Recent studies on reasoning efficiency~\\cite{yang2025towards,zeng2025revisiting} confirm that blindly increasing reasoning chain length can actually harm performance on simpler tasks. Various attempts have been made to address this through adding a budget or stop mechanism to test-time scaling (TTS) methods~\\cite{wei2022chain,huang2024o1} to stop thinking early, but these approaches introduce a human bias to the reasoning process~\\cite{yuan2023scaling} and fail to leverage the model's inherent reasoning abilities.\n\nHence, the fundamental challenge is to develop a reasoning mechanism that can dynamically adjust its computational effort based on question complexity, which means providing concise reasoning for straightforward questions while maintaining detailed analysis for complex problems. Therefore, we present T$^2$, a think-to-think framework for efficient TTS strategy. T$^2$ leverages a key insight: if an LLM can effectively solve similar questions using specific reasoning strategies, it can apply comparable strategies to the original question. The process involves four key steps: (1) Decomposing the original question into its structural elements. For example, given the question:\n\\begin{quote}\n [Given Reference Documents]\\\\\n \\textit{``Which is taller, the Eiffel Tower or the Empire State Building?''}\n\\end{quote}\nT$^2$ would identify this as a comparative question involving measurement between two specific places as ``Which is [adj], [place 1] or [place 2]?''. (2) Creating a diverse set of similar example questions with the same question structure, each paired with supporting documents and potential reasoning strategies. Each reasoning strategy breaks down similar questions into simpler steps using fundamental reasoning skills (e.g., decomposing similar question ``Which is taller, Building A or Building B?'' into subquestions about individual heights connected by deductive reasoning for comparison). (3) Evaluating these reasoning strategies using multiple criteria to select the most appropriate strategy for the original question. (4) Applying the selected reasoning strategy to the original question while filtering irrelevant information.\n\nBy learning from similar examples, the model develops a more nuanced understanding of when detailed reasoning is necessary and when a more direct approach is sufficient. This allows T$^2$ to balance accuracy and efficiency without relying on pre-determined reasoning templates.\n\nWe evaluate T$^2$ across seven diverse CQA datasets ranging from simple factual queries to complex multi-hop reasoning tasks. Our results demonstrate that T$^2$ achieves superior accuracy (up to a 21.3\\% increase) compared to other TTS approaches while reducing computational requirements by up to 25.2\\%. These efficiency gains are particularly clear for simpler questions where redundant reasoning steps are eliminated. While for complex questions, T$^2$ maintains the reasoning depth required for accuracy without exploring unnecessary paths.\n\nOur contributions include:\n\\begin{itemize}[leftmargin=*, itemsep=0pt,parsep=0pt,topsep=0pt,partopsep=0pt]\n \\item We introduce T$^2$, a framework that enables language models to dynamically select appropriate reasoning strategies through similar examples, balancing efficiency and thoroughness based on question complexity.\n \\item We develop a multi-criteria selection method that evaluates potential reasoning strategies based on coverage and uniqueness, ensuring the most suitable approach is applied to each question.\n \\item We demonstrate through extensive experiments across diverse CQA benchmarks that our method reduces computational requirements by up to 25.2\\% with superior accuracy.\n\\end{itemize}\n\n\\section{Related Work}\n\n\\paragraph{Contextual QA.} In addressing contextual QA, recent works have explored multi-round retrieval or reasoning approaches, including query rewriting for subsequent retrievals~\\cite{DBLP:journals/corr/abs-2212-14024,DBLP:journals/corr/abs-2305-14283,DBLP:conf/emnlp/ShaoGSHDC23, DBLP:conf/emnlp/JiangXGSLDYCN23}, alternating between retrieval and reasoning steps~\\cite{DBLP:conf/acl/TrivediBKS23}, and employing multi-round self-asking techniques~\\cite{DBLP:conf/emnlp/PressZMSSL23}. They all rely on LLMs' reasoning abilities. \n\n\\begin{figure*}[!t]\n \\centering\n \\includegraphics[width=\\linewidth]{pics/overview.pdf}\n \\caption{Overview of our T$^2$. \\textbf{(a)} direct prompt or Chain-of-Thought (CoT), which adopts the same reasoning strategy regardless of question complexity. \\textbf{(b)} Adaptive-of-Thought, which designs a question complexity evaluator to pre-categorize all questions, which might bring human bias in the evaluator design process. \\textbf{(c)} our T$^2$. Instead of pre-categorizing questions into different complexity sets, T$^2$ generates multiple similar examples for different inputs adaptively and selects the best reasoning strategy for answering.}\n \\label{fig:overview}\n\\end{figure*}\n\n\\paragraph{Test-Time Scaling.} Recent approaches to enhancing LLM reasoning capabilities focus on increasing computational resources during inference~\\cite{brown2024large,chen2024more}, termed test-time scaling. These methods includes majority voting~\\cite{wang2022self}, weighted aggregation~\\cite{li2023making}, best-of-N~\\cite{lightman2023let}, Tree-of-Thoughts~\\cite{yao2023tree}, and Monte Carlo Tree Search variants~\\cite{wu2024empirical,zhang2024accessing,zhao2024marco}. Besides, o1 model~\\cite{jaech2024openai} and several follow-up works~\\cite{guo2025deepseek,qwen2024qwq,google2025gemini,min2024imitate,huang2024o1} increase the thinking depth to improve the performance. But they all apply fixed scaling strategies to all questions. Some adaptive thinking methods like AdoT~\\cite{xu2024adaption} and DAST~\\cite{shen2025dast} design difficulty measurement to categorize the question based on its difficulty, whereas they introduce human bias and fail to leverage the model's inherent reasoning abilities. Our T$^2$ framework builds upon this paradigm while addressing these key limitations.\n\n\\section{T$^2$: Think-to-Think Framework}\n\\label{sec:methodology}\n\nIn this section, we present T$^2$: Think-to-Think, an approach that enables language models to adapt their reasoning strategies based on question complexity. Figure~\\ref{fig:overview} provides an overview of our approach. We begin by describing the overall architecture and workflow of T$^2$ before delving into each component in detail. \n\n% Our methodology consists of four key stages: Sec~\\ref{sec:question-structure-identification} Question Decomposition, where we decompose questions to identify their structural patterns; Sec~\\ref{sec:synthetic-qa-generation} Reference QA Generation, where we create diverse example document-question-answer pairs with explicit reasoning strategy; Sec~\\ref{sec:demo-selection} Multi-Criteria Matching for selecting the most appropriate reasoning strategy; and Sec~\\ref{sec:reasoning-path-guided-answering} Reasoning Strategy-Guided Answering, which generates answers following the selected reasoning strategy. Together, these components enable language models to flexibly adapt their reasoning depth and approach to match question complexity.\n\n\\subsection{Question Decomposition}\n\\label{sec:question-structure-identification}\n\nGiven a document $D$ and a question $Q$, we first analyze the question's structure to understand its underlying pattern. This allows us to later generate similar questions that require same reasoning strategy. The question structure identification process involves decomposing the question into fixed structural elements and variable entities that could be substituted.\n\nWe first tokenize the question $Q$ as a sequence of tokens $Q = (q_1, q_2, \\ldots, q_m)$. We then classify each token into one of two categories: structural tokens that form the question's framework, and replaceable entities that could be substituted with alternatives. We define a classification function with fine-tuned RoBERTa, detailed in Appendix~\\ref{apd:implementations}. Based on this classification, we partition the question tokens into two sets:\n\\begin{align}\n P &= \\{q_i \\mid \\text{if $q_i$ is a replaceable entity}\\}, \\\\\n Q_S &= \\{q_i \\mid \\text{if $q_i$ is a structural token}\\},\n\\end{align}\nwhere $P$ represents the set of replaceable entities (which we call entity placeholders), and $Q_S$ represents the set of structural tokens that form the question's framework.\n\n% \\begin{equation}\n% f_{\\text{iden}}(q_i) = \n% \\begin{cases}\n% 1, & \\text{if $q_i$ is a replaceable entity},\\\\\n% 0, & \\text{otherwise}.\n% \\end{cases} \n% \\end{equation}\n\nFor each identified entity placeholder $p_i$ in $P$, we assign a semantic type (e.g., \\texttt{person}, \\texttt{location}, \\texttt{date}). This creates a set of typed entities:\n\\begin{equation}\nT = \\{(p_1, \\tau_1), (p_2, \\tau_2), \\ldots, (p_k, \\tau_k)\\},\n\\end{equation}\nwhere each pair $(p_j, \\tau_j)$ consists of a placeholder entity $p_j$ and its corresponding type $\\tau_j$.\n\nBy combining the structure tokens $Q_S$ with the typed placeholders in $T$, we create a question template. For example, if $Q$ is ``Which is taller, the Eiffel Tower or the Empire State Building?'', the function would identify ``taller'', ``Eiffel Tower'', and ``Empire State Building'' as replaceable entities of type \\texttt{adj} and \\texttt{place}. The resulting template would be ``Which is [adj], [place 1] or [place 2]?'', where the bracketed terms are typed placeholders.\n\n\\subsection{Similar Examples Generation}\n\\label{sec:synthetic-qa-generation}\n\nOnce we have extracted the question structure, we generate similar document-question-answer pairs that follow the same question structure but with different entities.\n\n\\paragraph{Reasoning Skills Taxonomy.}\nWe build on established cognitive science literature \\citep{sep-reasoning-analogy,774846} to define a taxonomy of 7 fundamental reasoning skills $\\mathcal{S}$ that humans commonly employ when solving problems (e.g., \\textit{Deductive}, \\textit{Inductive}\\footnote{Appendix~\\ref{apd:reasoning_skills} shows the complete taxonomy of reasoning skills with their description and example applications.}). Each skill represents a distinct cognitive approach to processing information and drawing conclusions.\n\n\\paragraph{Question Generation.}\nFor each placeholder in the question structure, we generate alternative entities of matching types. We prompt an LLM to suggest contextually appropriate substitutes for each entity type $\\tau_j$. This produces a collection of candidate similar questions $\\hat{Q}_{\\text{sim}}$ that share the structural pattern of the original question but contain different entities.\n\nTo ensure high-quality examples, we implement a validation process. We prompt the same LLM to evaluate the similarity between each candidate question and the original question structure:\n\\begin{equation}\n\\text{sim}(Q, \\hat{q}) \\geq \\delta, \\quad \\hat{q} \\in \\hat{Q}_{\\text{sim}},\n\\end{equation}\nwhere $\\delta \\in [1, 10]$ is a threshold parameter. Only questions exceeding this threshold are retained, resulting in a filtered set of similar questions $Q_{\\text{sim}}$.\n\n\\paragraph{Reasoning Strategy Construction.}\nFor each similar question $Q_{\\text{sim}}^i\\in Q_{\\text{sim}}$, we decompose it into a sequence of subquestions:\n\\begin{equation}\nQ_{\\text{sim}}^i \\rightarrow (Q_{\\text{sim}}^{(i,1)}, \\ldots, Q_{\\text{sim}}^{(i,K)}),\n\\end{equation}\nwhere each subquestion $Q_{\\text{sim}}^{(i,K)}$ represents a discrete reasoning step and $K$ is the number of subquestions. The connections between subquestions are characterized by specific reasoning skills from our taxonomy. This decomposition allows us to construct a comprehensive reasoning strategy:\n\\begin{equation}\n\\mathbf{s}^i = (s_1^i, s_2^i, \\ldots, s_{K}^i),\n\\end{equation}\nwhere each $s_k^i \\in \\mathcal{S}$ is the reasoning skill required to transition from subquestion $Q_{\\text{sim}}^{(i,k)}$ to $Q_{\\text{sim}}^{(i,k+1)}$.\n\n\\paragraph{Reference Document Generation.}\nFor each subquestion $Q_{\\text{sim}}^{(i,k)}$, we generate a document segment $d_k^i$ containing the precise information needed to answer that subquestion. The complete reference document for question $Q_{\\text{sim}}^i$ is then constructed as:\n\\begin{equation}\nD_{\\text{ref}}^i = \\{d_1^i, d_2^i, \\ldots, d_K^i\\}.\n\\end{equation}\n\nFor example, given a similar question like ``Which is taller, A or B?'', the decomposition might yield subquestions: ``What is the height of A?'', ``What is the height of B?'', and ``Which height is greater?''. The reasoning strategy would connect these using deductive reasoning, and the reference document would provide the necessary height information for both entities.\n\nThe complete collection of similar examples is represented as:\n\\begin{equation}\n\\Gamma = \\{(D_{\\text{ref}}^i, Q_{\\text{sim}}^i, \\mathbf{s}^i)\\}_{i=1}^{N},\n\\end{equation}\nwhere $N$ is the total number of similar examples. This diverse set covers various reasoning strategies of different complexity levels, allowing our system to later select the most appropriate reasoning approach for original questions.\n\n% \\subsection{Similar Examples Generation}\n% \\label{sec:synthetic-qa-generation}\n\n% Once we have extracted the question structure, we generate synthetic document-question-answer pairs that follow similar patterns but with different entities. These synthetic examples serve as reference demonstrations that explicitly illustrate various reasoning approaches.\n\n% \\paragraph{Reasoning Skills Taxonomy.}\n% We build on established cognitive science literature \\citep{sep-reasoning-analogy,774846} to define a taxonomy of total 7 reasoning skills $\\mathcal{S}$ that humans commonly employ when solving problems (e.g., \\textit{Deductive}, \\textit{Inductive}, \\textit{Abductive}\\footnote{Appendix~\\ref{apd:reasoning_skills} shows the complete taxonomy of reasoning skills with their description and apply example.}). Each skill represents a distinct cognitive approach to processing information and drawing conclusions.\n\n% \\paragraph{Reasoning Strategy Construction.}\n% For each placeholder in the question template, we generate alternative entities of matching types. We use a language model to suggest contextually appropriate substitutes for each entity type $\\tau_j$. This produces a collection of similar questions $Q_{\\text{sim}}^i$ that share the structural pattern of the original question but contain different entities.\n\n% For each similar question, we define a specific reasoning strategy: a sequence of reasoning skills that would be applied to answer the question:\n% \\begin{equation}\n% \\mathbf{s}^i = (s_1^i; s_2^i; \\ldots; s_L^i),\n% \\end{equation}\n% where each $s_\\ell^i \\in \\mathcal{S}$ is a specific reasoning skill from our taxonomy, and $L$ is the length of the reasoning strategy.\n\n% \\paragraph{Reference Document \\& Answer Generation.}\n% For each similar question $Q_{\\text{sim}}^i$ and its corresponding reasoning strategy $\\mathbf{s}^i$, we generate a reference document $D_{\\text{ref}}^i$ that contains the information necessary to answer the question by following the specified reasoning strategy. We use a language model to create these documents, ensuring they include relevant facts that support the intended reasoning process.\n\n% Finally, we generate an answer $A_{\\text{sim}}^i$ for each similar question, explicitly applying the reasoning skills in the specified strategy $\\mathbf{s}^i$ to information from $D_{\\text{ref}}^i$. The answer demonstrates how to reach a conclusion by applying the reasoning path to the available information.\n\n% The complete collection of similar examples is represented as:\n% \\begin{equation}\n% \\Gamma = \\{(D_{\\text{ref}}^i, Q_{\\text{sim}}^i, A_{\\text{sim}}^i, \\mathbf{s}^i)\\}_{i=1}^{N},\n% \\end{equation}\n% where $N$ is the total number of generated examples. This diverse set covers various reasoning strategies of different complexity levels, allowing our system to later select the most appropriate reasoning strategy for a new question.\n\n\\subsection{Multi-Criteria Matching}\n\\label{sec:demo-selection}\n\nWhen presented with the original question $Q$ and documents $D$, we need to determine which reasoning strategy would be most effective. We select the most relevant example from our similar collection $\\Gamma$ using a multi-criteria matching process that considers both reasoning skill requirements and structural similarity.\n\n\\paragraph{Skill Uniqueness Scoring.}\nRecognizing that some reasoning skills are more specialized than others, we weight skills by their rarity in our example collection. For each reasoning skill $s \\in \\mathcal{S}$, we define $\\text{freq}(s)$ as the number of examples in $\\Gamma$ that include skill $s$ in their reasoning paths. The uniqueness score of a skill is:\n\\begin{equation}\n\\alpha(s) = \\ln\\left(\\frac{N + 1}{\\text{freq}(s) + 1}\\right),\n\\end{equation}\nwhere $N$ is the total number of examples in our collection. This logarithmic formulation assigns higher weights to skills that appear less frequently, capturing the intuition that specialized reasoning skills deserve special consideration.\n\n\\paragraph{Skill Coverage Assessment.}\nFor each example in our collection, we calculate how well its reasoning path covers reasoning skills:\n\\begin{equation}\n\\text{cover}(\\mathbf{s}^i, \\mathcal{S}) = \\frac{|\\mathbf{s}^i \\cap \\mathcal{S}|}{|\\mathcal{S}|}.\n\\end{equation}\n\nThis coverage metric quantifies what proportion of the required reasoning skills are present in the example's reasoning strategy.\n\n\\begin{algorithm*}[!t]\n% \\small\n\\caption{Reasoning Path-Guided Answering}\n\\label{alg:answer-gen}\n\\begin{algorithmic}[1]\n\\Require $Q$ (original question), $D$ (document), $i^*$ (selected example index), $\\Gamma$ (example collection)\n\\Ensure $A$ (final answer)\n\n\\State $(D_{\\text{ref}}^{i^*}, Q_{\\text{sim}}^{i^*}, A_{\\text{sim}}^{i^*}, \\mathbf{s}^{i^*}) \\gets \\Gamma[i^*]$ \\Comment{Retrieve selected example}\n\n\\State $D_{\\text{focus}} \\gets \\emptyset$ \\Comment{Initialize focused document segments}\n\n\\For{$\\ell = 1$ to $|\\mathbf{s}^{i^*}|$} \\Comment{For each skill in the reasoning path}\n \\State $\\text{text}_\\ell \\gets \\text{ExtractRelevantSegment}(D, s_{\\ell}^{i^*})$ \\Comment{Extract relevant text for skill $s_{\\ell}^{i^*}$}\n \\State $D_{\\text{focus}} \\gets D_{\\text{focus}} \\cup \\{\\text{text}_\\ell\\}$ \\Comment{Add to focused segments}\n\\EndFor\n\n\\State \\text{Prompt} $\\gets$ \\text{FormatPrompt}$(Q, D_{\\text{focus}}, \\mathbf{s}^{i^*}, Q_{\\text{sim}}^{i^*}, A_{\\text{sim}}^{i^*})$ \\Comment{Construct guidance prompt}\n\n\\State $A \\gets \\text{LLM}(\\text{Prompt})$ \\Comment{Generate answer with guided reasoning}\n\n\\State \\textbf{return} $A$\n\\end{algorithmic}\n\\end{algorithm*}\n\n\\paragraph{Integrated Selection Score.}\nWe compute a comprehensive selection score for each remaining example, and the optimal example is selected as:\n\\begin{equation}\ni^* = \\argmax_i \\left(\\text{cover}(\\mathbf{s}^i, \\mathcal{S}) + \\sum_{\\ell=1}^L \\alpha(s_\\ell^i)\\right),\n\\end{equation}\nwhere $L$ is the length of the reasoning strategy $\\mathbf{s}^i$. This score balances how well the example covers the required reasoning skills and how uniquely it captures specialized reasoning approaches.\n\n% The optimal example is selected as:\n% \\begin{equation}\n% i^* = \\argmax_i W_i,\n% \\end{equation}\n% when multiple examples have nearly identical scores, we use either of them.\n\n\\subsection{Reasoning Strategy-Guided Answering}\n\\label{sec:reasoning-path-guided-answering}\n\nThe final component of T$^2$ uses the selected example to guide the reasoning process for answering the original question. Algorithm~\\ref{alg:answer-gen} outlines this process.\n\nThe ``ExtractRelevantSegment'' function uses LLM to identify portions of the document $D$ that are most relevant to applying a particular reasoning skill. This focuses the model's attention on information appropriate to each step of the reasoning process. The ``FormatPrompt'' function combines the original question, the focused document segments, the selected reasoning strategy, and the example document-question-answer pair into a comprehensive prompt. This prompt instructs the language model to answer the original question by applying the reasoning skills in the selected strategy, using the example as a demonstration of the reasoning approach.\n\nThis methodology enables adaptive reasoning that scales with question complexity. For simple questions, T$^2$ selects examples with a straightforward reasoning strategy, avoiding unnecessary computational overhead. For complex questions, it selects examples with a more sophisticated reasoning strategy that guides the model through the necessary steps to arrive at the correct answer. Importantly, this adaptation occurs without parameter tuning or multiple reasoning attempts, requiring only a single forward pass through the language model.\n\n\\section{Experiments}\n\n\\subsection{Experimental Setups}\n\\label{sec:experimental-setup}\n\n\\paragraph{Datasets.} We evaluate our approach on seven QA datasets from diverse domains. \\textbf{SQuAD} (general-domain questions from Wikipedia)~\\cite{rajpurkar-etal-2018-know}, \\textbf{HotpotQA} (multihop questions spanning multiple paragraphs)~\\cite{yang2018hotpotqa}, \\textbf{BioASQ} (biomedical queries requiring specialized knowledge)~\\cite{283}, \\textbf{NewsQA} (news-related passages)~\\cite{trischler2017newsqa}, \\textbf{GAOKAO} (exam-oriented dataset with academic coverage)~\\cite{zhang2024evaluatingperformancelargelanguage}, \\textbf{HQA} (historical questions focusing on chronology and figures)~\\cite{HOSEN2023109245}, and \\textbf{TriviaQA} (Wikipedia-based trivia)~\\cite{joshi-etal-2017-triviaqa}. Appendix~\\ref{apd:datasets} summarizes dataset sizes and domains.\n\n\\begin{table*}[!t]\n\\centering\n\\small\n\\adjustbox{max width=\\textwidth}{\n\\begin{tabular}{lccccccc}\n\\toprule\nModel & SQuAD & HotpotQA & NewsQA & Gaokao & HQA & TriviaQA & BioASQ\\\\\n\\midrule\n\\rowcolor{gray!10}\\multicolumn{8}{c}{\\textbf{\\textit{Quick-Thinking Models w/ Reasoning Strategies}}}\\\\\n\\multicolumn{8}{l}{\\textbf{\\textit{Qwen2.5-32B-Instruct}}}\\\\\n\\; w/ vanilla \\textit{(quick)} & 73.41 & 55.32 & 50.83 & 29.52 & 35.92 & 40.73 & 56.33\\\\\n\\; w/ few-shots \\textit{(quick)} & 74.56 & 56.23 & 51.67 & 30.33 & 36.87 & 41.57 & 57.17\\\\\n\\; w/ self-consistency~\\cite{wang2022self} & 75.31 & 56.76 & 52.27 & 30.57 & 37.12 & 41.92 & 57.57\\\\\n\\; w/ proCoT~\\cite{deng-etal-2023-prompting} & 77.12 & 58.07 & 53.57 & 31.42 & 38.03 & 42.83 & 58.62\\\\\n\\; w/ ToT~\\cite{yao2023tree} & 78.47 & 59.11 & 54.31 & 31.96 & 38.66 & 43.46 & 59.36\\\\\n\\; w/ MCTS~\\cite{zhao2024marco} & 78.52 & 58.97 & 54.25 & 32.04 & 38.73 & 43.51 & 59.42\\\\\n\\textbf{\\; w/ T$^2$ (ours)} & \\textbf{81.86} & \\textbf{67.11} & \\textbf{61.27} & \\textbf{34.06} & \\textbf{40.31} & \\textbf{43.92} & \\textbf{65.02}\\\\\n\\hdashline\n\\multicolumn{8}{l}{\\textbf{\\textit{GPT-4o}}}\\\\\n\\; w/ vanilla \\textit{(quick)} & 78.52 & 60.02 & 55.32 & 34.51 & 41.11 & 49.01 & 60.51\\\\\n\\; w/ few-shots \\textit{(quick)} & 79.86 & 61.06 & 56.17 & 35.36 & 42.06 & 50.07 & 61.37\\\\\n\\; w/ self-consistency~\\cite{wang2022self} & 80.56 & 61.61 & 56.62 & 35.62 & 42.46 & 50.42 & 61.81\\\\\n\\; w/ proCoT~\\cite{deng-etal-2023-prompting} & 82.12 & 63.02 & 57.86 & 36.66 & 43.36 & 51.46 & 62.87\\\\\n\\; w/ ToT~\\cite{yao2023tree} & 83.21 & 64.06 & 58.67 & 37.22 & 44.07 & 52.26 & 63.72\\\\\n\\; w/ MCTS~\\cite{zhao2024marco} & 83.35 & 64.18 & 58.19 & 37.31 & 45.15 & 52.38 & 64.89\\\\\n\\textbf{\\; w/ T$^2$ (ours)} & \\textbf{85.06} & \\textbf{66.16} & \\textbf{60.92} & \\textbf{37.57} & \\textbf{45.27} & \\textbf{53.92} & \\textbf{66.97}\\\\\n\\midrule\n\\rowcolor{gray!10}\\multicolumn{8}{c}{\\textbf{\\textit{Slow-Thinking Models}}}\\\\\no1-mini & 85.81 & 70.91 & 63.22 & 42.66 & 49.22 & 58.56 & 68.42\\\\\nQwQ-32B-Preview & 86.87 & 71.86 & 63.92 & 43.23 & 49.62 & 59.16 & 69.02\\\\\nDeepSeek-R1 & 87.62 & 72.72 & 64.41 & 43.47 & 50.27 & 60.02 & 70.72\\\\\no1 & 88.22 & 73.37 & 65.11 & 44.06 & 51.07 & 60.86 & 71.36\\\\\no4-mini & 88.72 & 73.86 & 65.57 & 44.32 & 51.61 & 61.11 & 71.82\\\\\no4-mini-high & 88.91 & 74.07 & 65.81 & 44.52 & 51.86 & 61.27 & 72.02\\\\\nClaude-3.7-sonnet-thinking & 89.11 & 74.21 & 66.01 & 44.61 & 52.01 & 61.47 & 72.22\\\\\no3 & 89.41 & 74.61 & 66.32 & 45.01 & 52.11 & 61.81 & 72.62\\\\\nGemini-2.5-Pro & 90.27 & 75.46 & 67.11 & 45.76 & 53.07 & 62.68 & 73.57\\\\\n\\textbf{QwQ-32B + T$^2$ (ours)} & \\textbf{92.12} & \\textbf{77.61} & \\textbf{68.61} & \\textbf{47.42} & \\textbf{54.71} & \\textbf{64.22} & \\textbf{75.21}\\\\\n\\bottomrule\n\\end{tabular}}\n\\caption{ROUGE-L on seven QA datasets. We regard vanilla model and few-shot method as quick-thinking methods. And the other five (including ours) are slow-thinking methods. They can all be applied to quick-thinking models to improve reasoning ability.}\n\\label{tab:main_results}\n\\end{table*}\n\n\\paragraph{Reasoning Strategies and Metrics.} We compare our T$^2$ framework against \\textit{slow-thinking} and \\textit{quick-thinking} baselines. Slow-thinking approaches include: \\textbf{proactiveCoT (proCoT)}~\\cite{deng-etal-2023-prompting}, \\textbf{Self-Consistency}~\\cite{wang2022self}, \\textbf{Tree of Thoughts (ToT)}~\\cite{yao2023tree}, and \\textbf{Monte Carlo Tree Search (MCTS)}~\\cite{zhao2024marco}. Quick-thinking methods include: \\textbf{few-shot prompting} and \\textbf{direct prompting} without explicit reasoning steps. For evaluation, we use ROUGE-L as our metric across all datasets.\\footnote{We recognize GenAI can generate the correct answer, but with different literalness. Hence we use ROUGE-L here instead of resulting in a misleadingly low Exact Match (EM) rate. We also report EM performance in Appendix~\\ref{apd:main_performance_em}.}\n\n\\paragraph{Large Language Models.} We use two quick-thinking LLMs (\\textbf{Qwen2.5-32B-Instract}~\\cite{qwen2.5}, and \\textbf{GPT-4o}~\\cite{hurst2024gpt,guo2025deepseek}) and several slow-thinking LLMs (\\textbf{GPT-o1/3/4 series}~\\cite{jaech2024openai}, \\textbf{QwQ-32B-Preview}~\\cite{qwq}, \\textbf{Claude-3.7}~\\cite{claude3-7}, \\textbf{Gemini-2.5-Pro}~\\cite{gemini2-5}). Unless otherwise specified, hyperparameters are set to the default values for each model. No domain-specific fine-tuning and no target-designed prompt are applied, ensuring a fair and consistent comparison. More detailed implementation and all prompts can be found in Appendices \\ref{apd:implementations} and \\ref{apd:prompts}.\n\n\\subsection{Results}\n\\label{sec:overall-perf}\n\nTable~\\ref{tab:main_results} compares ROUGE-L on seven QA benchmarks. \nThe upper half lists \\textit{quick-thinking models} evaluated with several slow-thinking frameworks. The lower half gathers the strongest \\textit{slow-thinking models}. We also report the performance of Qwen2.5-32B-Instruct + T$^2$ and QwQ-32B-Preview + T$^2$ to show comparison with slow-thinking models. The experimental results show that by comparison with other thinking strategies, our T$^2$ could help quick-thinking model achieve better performance. Besides, compared with other slow-thinking models, adding our T$^2$ can also help model improve the performance. We conduct several analysis experiments detailed as follows.\n\n\\begin{figure*}[!t]\n \\centering\n \\includegraphics[width=\\linewidth]{./draw/hit_error_retrace_comment.pdf}\n \\caption{Results on Hits and Errors (left) and Retrace Rate (right).}\n \\label{fig:hit_error}\n \\label{fig:retrace}\n\\end{figure*}\n\n\\subsubsection{T$^2$ Enhance the Reasoning Skills Hit Rate while Reducing the Error}\n\\label{sec:effectiveness-multihop}\n\nHotpotQA supplies gold supporting sentences for every question, hence we use these to evaluate reasoning quality. For a model output that mentions a set $P_q$ of sentences and a gold set $G_q$, we record a \\emph{Hit} if $P_q\\supseteq G_q$ (all required facts retrieved) and an \\emph{Error} if $P_q\\not\\subseteq G_q$ (at least one spurious fact added). Thus Hit measures \\emph{completeness}, Error measures \\emph{precision}, and the two are inversely related: longer chains tend to raise Hit but also raise Error. Figure \\ref{fig:hit_error}(left) shows that quick-thinking frameworks give low Hit and moderate Error, while slow-thinking methods improve Hit at the cost of higher Error. Our \\textbf{T$^2$} strikes the best balance, achieving the highest Hit and the lowest Error on Qwen2.5-32B, confirming that adaptive path length yields the most accurate multihop reasoning. The detailed calculation of Hits and Errors can be found in Appendix~\\ref{apd:hits_errors}.\n\n\\begin{table}[!t]\n\\centering\n\\adjustbox{max width=\\linewidth}{\n\\begin{tabular}{cccc}\n\\toprule\n\\textbf{Skill Type} & \\textbf{Uniform} & \\textbf{Ours} & \\textbf{Improvement} \\\\\n\\midrule\nDeductive & 72.3\\% & 75.8\\% & +3.5\\% \\\\\nInductive & 68.7\\% & 73.2\\% & +4.5\\% \\\\\nAbductive & 74.1\\% & 76.3\\% & +2.2\\% \\\\\nCause \\& Effect & 70.5\\% & 74.1\\% & +3.6\\% \\\\\nAnalogical & 63.8\\% & 71.5\\% & +7.7\\% \\\\\nCritical Thinking & 69.2\\% & 72.8\\% & +3.6\\% \\\\\nDecompositional & 61.4\\% & 69.7\\% & +8.3\\% \\\\\n\\bottomrule\n\\end{tabular}}\n\\caption{Performance comparison between uniform and our matching strategies.}\n\\label{tab:skill_match}\n\\end{table}\n\n\\subsubsection{T$^2$ Tends to Get Correct Answers Immediately without Retrace}\n\\label{sec:retrace-analysis}\n\nA response is said to \\emph{retrace} if the model announces a provisional conclusion and later back-tracks on it inside the same output (e.g., ``\\emph{So the answer is X… wait, that seems wrong—let me revise… the answer is Y}''). Obviously, as retrace brings extra computing cost, it would be better for a model to ensure a lower retrace rate while maintaining the same accuracy. Concretely, we scan the CoT for either (i) \\textit{<answer>} markers that appear more than once, or (ii) lexical repair cues such as ``sorry,'' ``actually,'' or ``let me rethink,'' followed by a different answer span; if either pattern occurs, the example counts as a retrace. Figure \\ref{fig:retrace} (right) shows that, taking Qwen2.5-32B as LLM, slow-thinking methods retrace more on NewsQA and HQA, whereas quick-thinking methods seldom retrace but miss clues, hurting performance. Our \\textbf{T$^2$} keeps both metrics low—matching the speed of quick thinking and the accuracy of slow thinking—demonstrating that adaptive path length minimises wasted reasoning. The detailed calculation of Hits and Errors can be found in Appendix~\\ref{apd:retrace}.\n\n\\subsubsection{T$^2$ Costs Fewer Tokens to Achieve Superior Performance}\n\nTo evaluate the efficiency of our T$^2$, we compare four reasoning approaches: (1) {Qwen2.5-32B w/ self-consistency}, a typical slow-thinking method, (2) {QwQ-32B-Preview}, another slow-thinking model, (3) {Qwen2.5-32B w/ T$^2$}, and (4) {QwQ-32B w/ T$^2$}, our adaptive reasoning methods. Figure~\\ref{fig:ave_length} shows that our method reduces token consumption by 25.2\\% compared to QwQ-32B-Preview, and by 14.8\\% compared to Qwen2.5-32B w/ self-consistency, while maintaining competitive accuracy. These findings highlight that our method achieves an optimal trade-off between computational efficiency and reasoning quality. A full comparison, including token usage and performance across datasets, is provided in Appendix~\\ref{apd:efficiency_analysis}.\n\n\\begin{figure}[!t]\n \\centering\n \\includegraphics[width=\\linewidth]{draw/ave_length.pdf}\n \\caption{Results of average token length on each dataset. SC is the abbreviation for Self-Consistency.}\n \\label{fig:ave_length}\n\\end{figure}\n\n\\begin{figure*}[!t]\n \\centering\n \\includegraphics[width=\\linewidth]{pics/case_study.pdf}\n \\caption{Case study to show effectiveness of our T$^2$ framework. There are three proper reasoning skills should be adopted to answer the question based on given documents. The red, orange, and green answers represent responses under quick thinking, slow thinking, and ours, respectively.}\n \\label{fig:case}\n\\end{figure*}\n\n\\begin{figure}[!t]\n \\centering\n \\includegraphics[width=\\linewidth]{draw/demo_quality.pdf}\n \\caption{Results on relationship between reasoning skills' accuracy and overall performance.}\n \\label{fig:demo_quality}\n\\end{figure}\n\n\\subsection{Similar Examples Quality Analysis}\n\n\\paragraph{Our Matching Strategy Can Expose More Diverse Reasoning Skills.} The effectiveness of our framework relies not only on identifying appropriate reasoning skills but also on how these skills are matched during the example selection. Hence, we examine the impact of our multi-criteria reasoning skills matching strategy compared to a naive uniform sampling approach. Table~\\ref{tab:skill_match} presents the results of our experiment against uniform sampling across different reasoning skill types. Our approach consistently outperforms uniform sampling across all skill categories, with particularly notable improvements for less frequent reasoning types such as decompositional reasoning (+8.3\\%) and analogical reasoning (+7.7\\%). This confirms our hypothesis that the strategic balancing of skill demonstrations enhances the model's ability to leverage diverse reasoning patterns. The distribution of each reasoning skill can be found in Appendix~\\ref{apd:skill_distribution}. Ablation study of multi-criteria matching strategy can be found in Appendix~\\ref{apd:ablation_matching_strategy}.\n\n\\paragraph{Accuracy of Reasoning Skills Results in Correctness of Answers.} We examined the correlation between the accuracy of selected reasoning skills and the correctness of final answers using the HotpotQA dataset. We conducted the experiment on two models: Qwen2.5-32B-Instruct w/ T$^2$ and QwQ-32B-Preview w/ T$^2$. The analysis, shown in Figure \\ref{fig:demo_quality}, reveals a strong positive correlation between skill accuracy and answer correctness. Higher skill accuracy corresponds to higher answer correctness, with an approximate 5-6\\% increase in correctness for every 5\\% improvement in skill accuracy. These results demonstrate that accurately selecting the correct reasoning skills is essential for generating correct answers, especially in complex multi-hop reasoning tasks.\n\n% \\begin{table}[!t]\n% \\centering\n% \\small\n% \\begin{tabular}{lcc}\n% \\toprule\n% \\textbf{Model} & \\textbf{HotpotQA} & \\textbf{HQA} \\\\\n% \\midrule\n% Qwen2.5+SC & 56.76 & 37.12 \\\\\n% Qwen2.5+FReM & 67.11 & 40.31 \\\\\n% Qwen2.5+mis domain & 65.96 & 39.85 \\\\\n% Qwen2.5+structure only & 63.96 & 38.85 \\\\\n% \\hdashline\n% QwQ & 71.86 & 49.62 \\\\\n% QwQ+FReM & 77.61 & 54.71 \\\\\n% QwQ+mis domain & 77.03 & 54.26 \\\\\n% QwQ+structure only & 74.03 & 53.66 \\\\\n% \\bottomrule\n% \\end{tabular}\n% \\caption{Performance on mis-domain and structure-only models. ROUGE-L is the reported performance metrics.}\n% \\label{tab:demo_quality}\n% \\end{table}\n\n% \\paragraph{Similar Examples Work Disregard Domain and Structure.} In addition, we investigate the effects of domain and structural biases in similar examples. Specifically, we assess how varying the domain of the similar examples influences model performance. As shown in the Table~\\ref{tab:demo_quality} (``+mis domain''), transitioning from a general domain to a historical one results in improved performance compared to using self-consistency alone. Furthermore, we evaluate the impact of removing key information from the similar examples, leaving only the reasoning structure. Table~\\ref{tab:demo_quality} (``+structure only'') demonstrates that even when only the examples' structure\\footnote{We show the example structure in Appendix \\ref{apd:ipt_of_d_structure}.} is provided, the model can still generate appropriate responses, highlighting the effectiveness of structural guidance.\n\nWe also discuss the impacts of question structure (\\ref{apd:ipt_of_q_structure}), impacts of numbers of similar examples (\\ref{apd:ipt_of_d_number}), impacts of various generated methods (\\ref{apd:ipt_of_d_generate}), impacts of threshold of similarity in generation (\\ref{apd:ipt_of_d_threshold}), impacts of examples domain bias and structural bias (\\ref{apd:ipt_of_d_domain}), and human evaluation (\\ref{apd:ipt_of_d_quality}) in Appendix. \n\n\\subsection{Case Study}\n\nFigure \\ref{fig:case} shows an short version of example to show effectiveness of our T$^2$. By explicitly providing the model-specific reasoning path, the model can generate the correct answer with an appropriate reasoning chain of thought. The detailed case studies can be found in Appendix \\ref{apd:case_studies}.\n\n\\section{Conclusion}\n\nIn this paper, we introduced T$^2$: Think-to-Think, a novel framework that dynamically adapts reasoning depth based on question complexity for contextual question answering tasks. Unlike prior approaches that employ fixed reasoning strategies regardless of question difficulty, T$^2$ enables models to learn appropriate reasoning strategies from similar examples, leading to more efficient processing while maintaining accuracy. Our experimental results across seven diverse CQA benchmarks confirm that T$^2$ not only achieves higher accuracy than baseline methods but also reduces computational overhead by up to 25.2\\%. These improvements demonstrate the value of adaptability in reasoning processes, suggesting that as language models continue to evolve, approaches like T$^2$ that optimize both accuracy and computational efficiency will become increasingly important for developing more intelligent systems that can effectively allocate computational resources based on task demands.\n\n\\section*{Limitations}\n\nWhile T$^2$: Think-to-Think demonstrates promising results across various CQA benchmarks, we acknowledge several limitations of our approach: First, the effectiveness of T$^2$ relies on the availability of high-quality example reasoning strategy for similarity matching. In domains with limited annotated examples or highly novel questions, the framework may struggle to identify appropriate reasoning patterns, potentially defaulting to less optimal strategies. Besides, our current implementation focuses primarily on textual reasoning tasks. Extending T$^2$ to multimodal reasoning contexts (e.g., visual question answering) would require additional architectural modifications to handle diverse input modalities while maintaining computational efficiency. \nDespite these limitations, we believe T$^2$ represents a significant step toward more adaptive and efficient reasoning systems that can intelligently allocate computational resources based on question complexity.\n\n\\section*{Ethical Considerations}\n\nWe ensure that all experiments are conducted using publicly available, ethically sourced datasets, adhering to privacy and intellectual property guidelines. We acknowledge the potential for biases in data and are committed to evaluating and mitigating any such biases in T$^2$.\n\n% Bibliography entries for the entire Anthology, followed by custom entries\n%\\bibliography{anthology,custom}\n% Custom bibliography entries only\n\\bibliography{custom}\n\n\\appendix\n\n\\section{Full Reasoning Skills}\n\\label{apd:reasoning_skills}\n\nDefined by~\\cite{sep-reasoning-analogy,774846}, reasoning can best be defined as the basic action of thinking in a sensible and rational way about something. Reasoning is the ability to assess things rationally by applying logic based on new or existing information when making a decision or solving a problem. Based on their conclusion, Tables \\ref{tab:full_reasoning_skills} and \\ref{tab:full_reasoning_skills_2} show the reasoning skills for answering a certain question.\n\n\\begin{table*}[!t]\n \\centering\n \\small\n \\begin{tabular}{l p{0.4\\textwidth} p{0.35\\textwidth}}\n \\toprule\n \\textbf{Type of Reasoning} & \\textbf{Detailed Description} & \\textbf{Example} \\\\\n \\midrule\n \\textbf{Deductive} \n & Deductive reasoning occurs when generalized statements apply to specific cases. These generalized statements are established and already proven, making specific cases easy to deduce. For example, all humans are mortals. Bill is a human, so Bill must be mortal. In this example the generalized, but proven, statement, ``all humans are mortals'' is what drives the reasoning.\n & \\underline{Document:} All shapes with three sides are triangles. A certain figure here has exactly three sides.\n \\newline\n \\underline{Question:} What is this figure called?\n \\newline\n \\underline{Answer:} It is a triangle. All shapes with three sides are triangles, and this figure has three sides. So it must be a triangle. \\\\\n \\midrule\n \\textbf{Inductive} \n & Inductive reasoning is similar to deductive reasoning in that they both draw a conclusion based on a statement. However, in inductive reasoning, the statement is likely but has not been proven. For example, roses usually bloom in spring. In spring, one can count on there being roses. Again, the difference is that this is likely but not proven to be 100\\%. \n & \\underline{Document:} Every spring for the past ten years, wild roses in Green Valley have bloomed in late March. This spring is about to begin in Green Valley. \n \\newline\n \\underline{Question:} Will the wild roses bloom in late March this year?\n \\newline\n \\underline{Answer:} It is likely they will bloom in late March, because they usually do, but it is not guaranteed. \\\\\n \\midrule\n \\textbf{Abductive}\n & Abductive reasoning is the act of making a conclusion based on what you already know. For example, if you see a plate of food still hot, but half-eaten, you can make the conclusion that the person eating that food is probably returning soon.\n & \\underline{Document:} You notice a half-eaten sandwich and a still-hot cup of coffee on a café table. The seat feels warm, and a jacket is draped over the chair.\n \\newline\n \\underline{Question:} Has the person who was sitting here left permanently, or are they coming back soon?\n \\newline\n \\underline{Answer:} It is likely they just stepped away for a moment and will return, because the food and drink are still warm and their jacket remains on the chair.\\\\\n \\midrule\n \\textbf{Cause \\& Effect}\n & Cause and effect reasoning is that if x happens then y will happen as a result. This is extremely persuasive when making a speech or trying to get someone to take action to cause an effect. For example, a politician may say that if they are elected, then poverty will decrease. This is using cause and effect reasoning in a real-world situation.\n & \\underline{Document:} Meteorologists predict heavy rain this evening, with warnings that streets may flood if the rainfall continues.\n \\newline\n \\underline{Question:} Will the roads become dangerous as a result of this weather?\n \\newline\n \\underline{Answer:} Yes. If heavy rain continues, roads will likely flood and become slippery, causing drivers to have less control of their vehicles.\\\\\n \\bottomrule\n \\end{tabular}\n \\caption{(1/2) Full list of reasoning skills used in the reasoning path construction.}\n \\label{tab:full_reasoning_skills}\n\\end{table*}\n\n\\begin{table*}[!t]\n \\centering\n \\small\n \\begin{tabular}{l p{0.4\\textwidth} p{0.35\\textwidth}}\n \\toprule\n \\textbf{Type of Reasoning} & \\textbf{Detailed Description} & \\textbf{Example} \\\\\n \\midrule\n \\textbf{Analogical}\n & Analogical reasoning is the use of a comparison between two things to persuade that there must be more in common if they already share something. For example, if x, y, and z all share this trait, then they must also share other traits. The foundation of this type of reasoning is perfect for speeches and comparisons in the real world. If there are connections between x and y already, then they must have several other things in common as well.\n & \\underline{Document:} Many leading technology companies emphasize continuous learning and adaptability. For instance, Google, Microsoft, and Amazon all invest in regular training programs and encourage innovation among employees. Their similar approach to fostering a culture of growth has been linked to their strong performance in rapidly changing markets.\n \\newline\n \\underline{Question:} Can we infer that a company that promotes continuous learning will also likely be successful in adapting to market changes?\n \\newline\n \\underline{Answer:} Yes. Since Google, Microsoft, and Amazon all share a culture of continuous learning and, as a result, demonstrate high adaptability and market success, it is reasonable to conclude by analogy that a company which also promotes continuous learning is likely to develop similar strengths.\\\\\n \\midrule\n \\textbf{Critical Thinking}\n & Critical thinking occurs when you take all of the facts and develop a conclusion based on an analysis. This could happen subconsciously or intentionally, depending on the situation. For example, in the real world, critical thinking could be about your relationships. You could see a behavior you don't like about someone and have to think critically about whether or not you will choose to spend more time with this person. This is using critical thinking to develop reasoning in a real-world application.\n & \\underline{Document:} Over the past few months, Sam has repeatedly cancelled plans at the last minute and rarely communicated afterward.\n \\newline\n \\underline{Question:} Should you invest time in a close friendship with Sam?\n \\newline\n \\underline{Answer:} No. Sam's consistent behavior of last-minute cancellations suggests a pattern of unreliability, which may negatively affect the trust needed in a close friendship.\\\\\n \\midrule\n \\textbf{Decompositional}\n & Decompositional reasoning happens when the different parts of the reasoning are broken down into smaller pieces and analyzed for how they contribute to the whole. The intent of this is to make the reasoning easier to understand and allow for analyzing how the parts equal the whole. For example, in order to understand the function of the human body, you would have to analyze each bone and organ to see how they all work together. Additionally, in the real world, an argument could be broken down into several smaller parts in order to analyze the effectiveness of the argument as a whole.\n & \\underline{Document:} A smartphone's quality can be understood by breaking it down into three parts: its design, performance, and battery life. The design covers the build and user interface; performance looks at processing speed and software efficiency; battery life shows how long the device operates on a single charge.\n \\newline\n \\underline{Question:} Can we conclude that the smartphone provides a good overall user experience?\n \\newline\n \\underline{Answer:} Yes. If the design is appealing, the performance is robust, and the battery life is long, then the smartphone is likely to offer a good overall experience.\\\\\n \\bottomrule\n \\end{tabular}\n \\caption{(2/2) Full list of reasoning skills used in the reasoning path construction.}\n \\label{tab:full_reasoning_skills_2}\n\\end{table*}\n\n\\section{Datasets}\n\\label{apd:datasets}\n\nIn this work, we evaluate our method on seven widely used question answering datasets. Each dataset presents distinct characteristics, ranging from the type of questions asked to the domain in which they are applied. Below, we provide a brief overview of each dataset.\n\n\\paragraph{SQuAD} consists of over 100,000 question-answer pairs derived from a set of Wikipedia articles. The task is to find the span of text that answers the question. SQuAD is widely used for evaluating machine reading comprehension models. The dataset includes two versions: SQuAD 1.1, which contains answerable questions, and SQuAD 2.0, which also includes unanswerable questions, making it more challenging. We use 2.0 version here.\n\n\\paragraph{HotpotQA} is a large-scale, multi-hop question answering dataset that requires reasoning across multiple supporting facts. The dataset includes over 113,000 question-answer pairs spanning various domains, where answers cannot be found in a single sentence or passage but require combining information from several documents. The questions in HotpotQA require a more complex reasoning process compared to typical single-hop datasets.\n\n\\paragraph{BioASQ} is a biomedical question answering dataset that provides information from scientific articles, primarily in the domain of biomedicine. It includes both factoid and complex questions that require understanding of scientific literature. BioASQ focuses on answering clinical, biomedical, and molecular biology-related questions using both structured and unstructured data sources.\n\n\\paragraph{NewsQA} is a dataset designed for reading comprehension tasks. It consists of over 100,000 question-answer pairs derived from news articles. The challenge of NewsQA lies in answering questions about real-world events from unstructured news stories, requiring models to handle various linguistic phenomena such as coreference, reasoning, and implicit understanding.\n\n\\paragraph{GAOKAO} is a dataset derived from the Chinese college entrance exam, also known as the \"Gaokao\". It contains questions related to various subjects, including Chinese literature, mathematics, and English. The questions in GAOKAO require both general knowledge and reasoning to answer. This dataset is specifically designed for the Chinese education system and is widely used in academic and educational research in China.\n\n\\paragraph{HQA} is a human-annotated dataset specifically designed for complex, open-domain question answering. It contains questions that require deep contextual understanding and can involve reasoning across long documents. The dataset includes various types of questions and answers across diverse domains, and it was created to test models' ability to perform reasoning tasks in realistic, open-ended settings.\n\n\\paragraph{TriviaQA} is a large-scale dataset that focuses on answering trivia questions, where each question is associated with a corresponding set of supporting documents. TriviaQA contains over 650,000 question-answer pairs sourced from trivia websites and requires models to retrieve relevant information from the documents and answer based on the provided facts. The dataset has questions spanning various topics such as history, geography, and general knowledge.\n\n\\section{Distribution of Reasoning Skills in Each Dataset}\n\\label{apd:skill_distribution}\n\nTable~\\ref{tab:skill_distribution} demonstrates the distribution of seven reasoning skills in different datasets. The variance in skill distribution highlights why our multi-criteria matching approach is crucial. Without it, high-frequency skills like deductive reasoning would dominate the demonstrations, while valuable but less common skills like abductive reasoning would be underrepresented.\n\n\\begin{table*}[!t]\n\\centering\n\\adjustbox{max width=\\textwidth}{\n\\begin{tabular}{cccccccc}\n\\toprule\n\\textbf{Skill Type} & \\textbf{SQuAD} & \\textbf{HotpotQA} & \\textbf{NewsQA} & \\textbf{GAOKAO} & \\textbf{HQA} & \\textbf{TriviaQA} & \\textbf{BioASQ} \\\\\n\\midrule\nDeductive & 0.31 & 0.22 & 0.28 & 0.15 & 0.42 & 0.18 & 0.25 \\\\\nInductive & 0.23 & 0.18 & 0.15 & 0.12 & 0.13 & 0.21 & 0.19 \\\\\nAbductive & 0.05 & 0.12 & 0.08 & 0.21 & 0.09 & 0.15 & 0.11 \\\\\nCause \\& Effect & 0.12 & 0.15 & 0.13 & 0.22 & 0.08 & 0.19 & 0.14 \\\\\nAnalogical & 0.08 & 0.13 & 0.09 & 0.07 & 0.11 & 0.12 & 0.16 \\\\\nCritical Thinking & 0.14 & 0.16 & 0.18 & 0.14 & 0.13 & 0.09 & 0.10 \\\\\nDecompositional & 0.07 & 0.04 & 0.09 & 0.09 & 0.04 & 0.06 & 0.05 \\\\\n\\bottomrule\n\\end{tabular}}\n\\caption{Distribution (\\%) of reasoning skills across benchmark datasets, showing the proportion of questions requiring each skill type.}\n\\label{tab:skill_distribution}\n\\end{table*}\n\n\\section{Implementations}\n\\label{apd:implementations}\n\nWe use a simple pretrained language model RoBERTa from Huggingface for detecting named entities or key numbers in the question to obtain the question structure. This classification task involves processing the input question to identify whether it contains a named entity or key number and assigning a type to the detected entity. The model performs this task by outputting binary labels (entity: Yes/No) first, and then the associated entity types (e.g., Person, Location, Date, Organization, Number, etc.).\n\n\\begin{table*}[!t]\n \\centering\n \\begin{tabular}{ll}\n \\toprule\n \\textbf{Parameter} & \\textbf{Value} \\\\\n \\midrule\n \\textbf{Model} & RoBERTa\\\\\n \\textbf{Full Name} & \\texttt{FacebookAI/xlm-roberta-large-finetuned-conll03-english}\\\\\n \\textbf{Batch Size} & 128 \\\\\n \\textbf{Learning Rate} & 2e-5 \\\\\n \\textbf{Optimizer} & AdamW \\\\\n \\textbf{Dropout Rate} & 0.1 \\\\\n \\textbf{Evaluation Metric} & Accuracy \\\\\n \\bottomrule\n \\end{tabular}\n \\caption{Implementation parameters for named entity detection and classification.}\n \\label{tab:implementation_parameters}\n\\end{table*}\n\nThis model is fine-tuned with a simple classification layer that detects whether a named entity or key number is present in the question with NERetrieve dataset\\footnote{\\url{https://github.com/katzurik/NERetrieve?tab=readme-ov-file}} \\cite{katz2023neretrieve}. This process leverages the model's pre-trained knowledge, with minimal fine-tuning specifically focused on the entity detection and classification task.\n\nThe hyperparameters used for fine-tuning the PLM are listed in Table \\ref{tab:implementation_parameters}. The batch size is set to 128. The learning rate is set to \\(2 \\times 10^{-5}\\). AdamW is used as the optimizer. A dropout rate of 0.1 is applied to prevent overfitting during fine-tuning.\n\nFor LLM usage, We use two quick-thinking LLMs (\\textbf{Qwen2.5-32B-Instract}~\\cite{qwen2.5}, and \\textbf{GPT-4o}~\\cite{hurst2024gpt,guo2025deepseek}) and several slow-thinking LLMs (\\textbf{GPT-o1/3/4 series}~\\cite{jaech2024openai}, \\textbf{QwQ-32B-Preview}~\\cite{qwq}, \\textbf{Claude-3.7}~\\cite{claude3-7}, \\textbf{Gemini-2.5-Pro}~\\cite{gemini2-5}). For ToT implementation, we follow the original paper's approach~\\cite{yao2023tree} with a breadth-first search strategy and a maximum depth of 3. For MCTS, we implement the standard UCT algorithm with 10 simulations per decision point. For synthetic QA generation, we set a maximum output length of 4,096 tokens. When deciding which similar example to use, we follow our multi-criteria matching (Section \\ref{sec:demo-selection}) to pick the most relevant chain of skills. Unless otherwise specified, hyperparameters stay at default values for each model. No domain-specific fine-tuning and no targeted designed prompt are applied, ensuring a fair and consistent comparison. All inferences are based on vLLM framework with 1 node of Tesla V100 and 1 node of GeForce RTX 3090.\n\n\\section{Inference Prompts}\n\\label{apd:prompts}\n\nThe primary task is to generate synthetic question-answer pairs with a reasoning path, reflecting predefined reasoning skills. Table \\ref{tab:synthetic_QA_generation_prompt} shows our prompts.\n\n\\begin{table*}[!t]\n \\centering\n \\small\n \\begin{tabular}{|l|}\n \\hline\n \\textbf{Prompt:} \\\\\n \\text{You are a language model that generates synthetic question-answer (QA) pairs with reasoning paths.}\\\\\n \\text{Your task is to generate a QA pair based on the following question. Additionally, you should}\\\\\n \\text{provide a clear, step-by-step reasoning path that corresponds to a predefined reasoning skill.}\\\\\n \\text{The predefined reasoning skills are:[REASONING SKILLS NAME+DESCRIPTION+EXAMPLES].}\\\\\n \\text{Your reasoning path should include clear substeps for each step of the thought process.} \\\\\n \\textbf{Example 1:}\\\\\n \\textbf{Given Documents:} [REFERENCE DOCUMENTS]\\\\\n \\textbf{Input Question:} \"Who invented the telephone?\" \\\\\n \\textbf{Step-by-step Reasoning Path:} \\\\\n 1. Identify the key entity: \"telephone\" (deductive)\\\\\n 2. Identify that the question is asking for the inventor of a significant historical device (decompositional)\\\\\n 3. Recall the historical context of the invention of the telephone. (deductive)\\\\\n 4. The inventor is Alexander Graham Bell. (cause \\& effect)\\\\\n \\textbf{Generated Answer:} \"Alexander Graham Bell invented the telephone in 1876.\" \\\\\n \\textbf{Reasoning Skill Used:} deductive, decompositional, deductive, cause \\& effect. \\\\\n \\textbf{Example 2:} ...\\\\\n \\textbf{Example 3:} ...\\\\\n \\textbf{Notes:}\\\\\n \\text{Please make sure that the reasoning path is clear and includes each substep in the thought process.}\\\\\n \\text{The output should follow this structure: \"Step-by-step reasoning,\" followed by the conclusion.}\\\\\n \\text{Each reasoning skill corresponds to a specific domain of knowledge.} \\\\\n \\hline\n \\end{tabular}\n \\caption{Prompt to Generate Similar Examples with Reasoning Paths.}\n \\label{tab:synthetic_QA_generation_prompt}\n\\end{table*}\n\nTable \\ref{tab:alignment_prompt} shows the helper language model for evaluating how well each example's question aligns with original one.\n\n\\begin{table*}[!t]\n\\centering\n\\small\n\\begin{tabular}{|p{\\textwidth}|}\n\\hline\n\\textbf{Prompt:}\\\\\n{You are given an original question: [ORIGINAL QUESTION]}\\\\\n{You also have a synthetic question: [SYNTHETIC QUESTION]}\\\\\n{Your task is to decide how similar the synthetic question is in structure and complexity, compared to the original. Please provide a brief explanation of your reasoning. Then, assign a score from 1 (completely different) to 10 (very similar).}\\\\\n\\textbf{Example}:\n{Original Q: \"Who discovered penicillin?\"}\\\\\n{Synthetic Q: \"Which scientist found the mold that led to antibiotics?\"}\\\\\n\\textbf{Explanation}:\n{Both questions ask about a discoverer of a major medical breakthrough. The second question focuses on the mold (penicillin), so it is structurally similar and retains the core inquiry about a discovery.}\\\\\n\\textbf{Score (1-10)}: {8}\\\\\n\\textbf{Notes}:\n{- Provide a short justification.}\\\\\n{- Avoid rewriting or changing the question.}\\\\\n{- Keep the final output concise, ending with the numeric score.}\\\\\n\\hline\n\\end{tabular}\n\\caption{Prompt for Evaluating Alignment of Synthetic Questions with the Original.}\n\\label{tab:alignment_prompt}\n\\end{table*}\n\nTable \\ref{tab:reasoning_path_prompt} shows the question answering prompts for model.\n\n\\begin{table*}[!t]\n\\centering\n\\small\n\\begin{tabular}{|p{\\textwidth}|}\n\\hline\n\\textbf{Prompt:}\\\\\n{You are given:}\\\\\n{- The original question: [Q]}\\\\\n{- A document or context: [D]}\\\\\n{- A selected reasoning path: [R]}\\\\\n{- The specific skills used in the reasoning path: [S]}\\\\\n\n\\textbf{Your goal} is to produce a final answer by combining the relevant information from [D] with the guided reasoning steps from [R]. Follow these instructions:\\\\\n1. \\textbf{Review the Reasoning Path}\\\\\n Read each step in [R] carefully. Identify which parts of [D] or background knowledge support each step. \\\\\n2. \\textbf{Apply the Skills} \\\\\n If [S] includes certain reasoning skills (e.g., deduction), make sure to explicitly use them when combining evidence from [D].\\\\\n3. \\textbf{Generate a Clear Answer} \\\\\n Compose a concise final answer that directly addresses [Q]. You may outline your chain of thought, but keep the explanation aligned with [R]. \\\\\n4. \\textbf{Maintain Accuracy} \\\\\n If [R] instructs a specific substep (e.g., numerical calculation or bridging multiple facts), follow it precisely, citing the relevant parts of [D].\\\\\n\\textbf{Notes}: \\\\\n- {Do not contradict the provided reasoning path.} \\\\\n- {Cite relevant text from [D] if needed, but avoid unnecessary repetition.} \\\\\n- {End with a concise, standalone final answer.} \\\\\n\\hline\n\\end{tabular}\n\\caption{Prompt for Question Answering.}\n\\label{tab:reasoning_path_prompt}\n\\end{table*}\n\n\\section{Performance with Exact Match Metric}\n\\label{apd:main_performance_em}\n\n\\begin{table*}[!t]\n\\centering\n\\small\n\\adjustbox{max width=\\textwidth}{\n\\begin{tabular}{lccccccc}\n\\toprule\nModel & SQuAD & HotpotQA & NewsQA & Gaokao & HQA & TriviaQA & BioASQ\\\\\n\\midrule\n\\rowcolor{gray!10}\\multicolumn{8}{c}{\\textbf{\\textit{Quick-Thinking Models w/ Reasoning Strategies (Exact Match)}}}\\\\\n\\multicolumn{8}{l}{\\textbf{\\textit{Qwen2.5-32B-Instruct}}}\\\\\n\\; w/ vanilla \\textit{(quick)} & 55.23 & 31.69 & 27.15 & 12.61 & 19.47 & 23.82 & 40.88\\\\\n\\; w/ few-shots \\textit{(quick)} & 56.42 & 32.35 & 28.13 & 13.06 & 20.19 & 24.58 & 41.76\\\\\n\\; w/ self-consistency~\\cite{wang2022self} & 57.08 & 32.81 & 28.49 & 13.29 & 20.37 & 24.89 & 41.93\\\\\n\\; w/ proCoT~\\cite{deng-etal-2023-prompting} & 58.65 & 33.81 & 29.32 & 13.86 & 21.02 & 25.58 & 42.77\\\\\n\\; w/ ToT~\\cite{yao2023tree} & 59.83 & 34.67 & 29.81 & 14.12 & 21.51 & 26.04 & 43.28\\\\\n\\; w/ MCTS~\\cite{zhao2024marco} & 59.87 & 34.53 & 29.76 & 14.21 & 21.57 & 26.08 & 43.33\\\\\n\\textbf{\\; w/ T$^2$ (ours)} & \\textbf{62.65} & \\textbf{39.98} & \\textbf{34.12} & \\textbf{15.72} & \\textbf{22.58} & \\textbf{26.43} & \\textbf{48.14}\\\\\n\\hdashline\n\\multicolumn{8}{l}{\\textbf{\\textit{GPT-4o}}}\\\\\n\\; w/ vanilla \\textit{(quick)} & 59.87 & 35.31 & 30.27 & 16.23 & 23.39 & 29.84 & 44.12\\\\\n\\; w/ few-shots \\textit{(quick)} & 61.09 & 36.04 & 30.85 & 16.78 & 24.03 & 30.52 & 44.84\\\\\n\\; w/ self-consistency~\\cite{wang2022self} & 61.63 & 36.42 & 31.14 & 16.92 & 24.28 & 30.78 & 45.13\\\\\n\\; w/ proCoT~\\cite{deng-etal-2023-prompting} & 62.89 & 37.29 & 31.92 & 17.49 & 25.01 & 31.43 & 46.05\\\\\n\\; w/ ToT~\\cite{yao2023tree} & 63.77 & 37.94 & 32.43 & 17.79 & 25.57 & 32.11 & 46.61\\\\\n\\; w/ MCTS~\\cite{zhao2024marco} & 63.94 & 38.13 & 32.06 & 17.84 & 26.22 & 32.18 & 47.39\\\\\n\\textbf{\\; w/ T$^2$ (ours)} & \\textbf{65.17} & \\textbf{39.51} & \\textbf{33.76} & \\textbf{17.98} & \\textbf{26.31} & \\textbf{33.12} & \\textbf{49.07}\\\\\n\\midrule\n\\rowcolor{gray!10}\\multicolumn{8}{c}{\\textbf{\\textit{Slow-Thinking Models (Exact Match)}}}\\\\\no1-mini & 65.82 & 42.89 & 35.08 & 21.87 & 29.51 & 36.52 & 50.68\\\\\nQwQ-32B-Preview & 66.67 & 43.57 & 35.43 & 22.18 & 29.88 & 36.87 & 51.21\\\\\nDeepSeek-R1 & 67.38 & 44.04 & 35.94 & 22.28 & 30.26 & 37.52 & 52.31\\\\\no1 & 67.92 & 44.57 & 36.42 & 22.53 & 30.75 & 38.12 & 52.94\\\\\no4-mini & 68.36 & 44.97 & 36.83 & 22.79 & 31.08 & 38.28 & 53.14\\\\\no4-mini-high & 68.54 & 45.18 & 37.01 & 22.89 & 31.23 & 38.42 & 53.27\\\\\nClaude-3.7-sonnet-thinking & 68.67 & 45.27 & 37.14 & 22.97 & 31.32 & 38.56 & 53.41\\\\\no3 & 68.89 & 45.48 & 37.34 & 23.19 & 31.37 & 38.78 & 53.74\\\\\nGemini-2.5-Pro & 69.69 & 46.08 & 37.97 & 23.51 & 32.01 & 39.43 & 54.45\\\\\n\\textbf{QwQ-32B + T$^2$ (ours)} & \\textbf{71.32} & \\textbf{47.87} & \\textbf{39.17} & \\textbf{24.63} & \\textbf{33.28} & \\textbf{40.39} & \\textbf{55.81}\\\\\n\\bottomrule\n\\end{tabular}}\n\\caption{Exact Match (EM) scores on seven QA datasets.}\n\\label{tab:overall_performance_em}\n\\end{table*}\n\nGenerally, Open QA datasets use Exact Match as their metrics for evaluation. But in generative AI system, the models can generate correct answers but with different literalness (e.g., ``San Francisco'' and ``The San Francisco City'' and ``SF U.S.''). Hence we use ROUGE-L as metric in our overall performance evaluation. Besides, we also report our experimental results on EM in Table~\\ref{tab:overall_performance_em}.\n\n\\section{Calculation of Proposed Metrics}\n\n\\subsection{Hits and Errors}\n\\label{apd:hits_errors}\n\n\\paragraph{Hits Metric Calculation.} To evaluate the quality of reasoning and fact retrieval in the generated outputs, we employ the Hits metric based on the gold supporting sentences provided in HotpotQA. For each question $q$, let $P_q$ represent the set of sentences mentioned in the model's reasoning process and $G_q$ denote the set of gold supporting sentences.\n\nWe calculate the Hits metric as follows:\n\\begin{equation}\n\\text{Hits} = \\frac{\\sum_{q \\in Q} \\mathbf{1}[P_q \\supseteq G_q]}{|Q|}\n\\end{equation}\n\nwhere $\\mathbf{1}[\\cdot]$ is an indicator function that equals 1 when the condition is satisfied and 0 otherwise, and $|Q|$ is the total number of questions in the evaluation set. This formulation is similar to recall in traditional information retrieval, measuring the proportion of questions for which all required facts were successfully retrieved.\n\n\\paragraph{Errorss Metric Calculation.} For the Error metric, we adopt the False Discovery Rate (FDR) formulation:\n\\begin{equation}\n\\text{Error} = \\frac{\\sum_{q \\in Q} \\mathbf{1}[P_q \\not\\subseteq G_q]}{\\sum_{q \\in Q} (\\mathbf{1}[P_q \\supseteq G_q] + \\mathbf{1}[P_q \\not\\subseteq G_q])}\n\\end{equation}\n\nThis represents the proportion of spurious facts (false positives) among all retrieved facts, consistent with the FDR calculation as FP/(TP+FP).\n\nThese complementary metrics create a natural trade-off: longer reasoning chains tend to improve Hits by including more supporting facts but often at the expense of increasing Error through the introduction of irrelevant information. An ideal reasoning process would maximize Hits while minimizing Error, indicating that the model precisely identifies all necessary supporting facts without including extraneous information.\n\n\\subsection{Retrace Rate}\n\\label{apd:retrace}\n\nWe define a response as exhibiting a \\emph{retrace} when the model initially states a provisional conclusion and subsequently revises it within the same output. This occurs in patterns such as ``\\emph{So the answer is X... wait, that seems wrong—let me revise... the answer is Y}.'' To systematically identify retraces, we analyze the Chain-of-Thought (CoT) reasoning for two specific patterns: (i) multiple occurrences of \\textit{<answer>} markers, or (ii) lexical repair cues (e.g., ``sorry,'' ``actually,'' ``let me rethink'') followed by a different answer span. If either pattern is detected, we count the example as containing a retrace.\n\nThe Retrace Rate is calculated as:\n\\begin{equation}\n\\text{Retrace Rate} = \\\\\n\\frac{\\sum_{q \\in Q} \\mathbf{1}[\\text{retrace detected in } q]}{|Q|}\n\\end{equation}\n\nwhere $\\mathbf{1}[\\cdot]$ is an indicator function that equals 1 when a retrace is detected and 0 otherwise, and $|Q|$ is the total number of questions in the evaluation set. This metric quantifies the proportion of responses where the model explicitly revises its reasoning path, providing insight into the model's self-correction capabilities during the reasoning process.\n\n\\section{Ablation Study}\n\\label{apd:ablation_matching_strategy}\n\nTo validate the effectiveness of our multi-criteria matching approach, we conducted ablation studies by systematically removing or modifying key components of our selection mechanism.\n\n\\paragraph{Impact of Selection Components.}\nWe evaluated four variants of our selection approach: (1) using only skill coverage without uniqueness weighting, (2) using only skill uniqueness without coverage assessment, (3) using random selection from examples passing the similarity threshold, and (4) our full approach. The experiments are conducted on Qwen2.5-32B as LLM. Table~\\ref{tab:ablation} shows performance across test sets.\n\n\\begin{table}[!t]\n\\centering\n\\adjustbox{max width=\\linewidth}{\n\\begin{tabular}{lccc}\n\\toprule\n\\textbf{Method} & \\textbf{HotpotQA} & \\textbf{NewsQA} & \\textbf{HQA} \\\\\n\\midrule\nRandom & 32.4 & 38.7 & 19.2 \\\\\nCoverage Only & 41.6 & 46.3 & 27.8 \\\\\nUniqueness Only & 49.2 & 54.5 & 35.7 \\\\\nFull Approach & \\textbf{67.1} & \\textbf{61.3} & \\textbf{40.3} \\\\\n\\bottomrule\n\\end{tabular}}\n\\caption{Ablation study results showing the impact of different components in our selection approach. We report ROUGE-L (\\%) on three benchmark datasets.}\n\\label{tab:ablation}\n\\end{table}\n\nResults demonstrate that while both skill coverage and uniqueness contribute positively to performance, their combination in our full approach produces the strongest results across all datasets, yielding improvements of 25.5\\% over using only individual components.\n\n\\section{Efficiency Analysis}\n\\label{apd:efficiency_analysis}\n\nThis section provides a comprehensive analysis of the computational efficiency of our proposed Flexible Reasoning Method (T$^2$) in comparison to other reasoning approaches. We analyze both token consumption and performance across seven diverse question answering datasets.\n\n\\subsection{Token Consumption Analysis}\n\nTable \\ref{tab:token_consumption} presents the average token consumption of different reasoning approaches across seven CQA datasets. The token length directly correlates with the computational resources required and inference time. Our results indicate that T$^2$ consistently reduces token consumption while maintaining or improving performance compared to other reasoning methods.\n\n\\begin{table*}[!t]\n\\centering\n\\adjustbox{max width=\\linewidth}{\n\\begin{tabular}{lccccccc}\n\\toprule\n\\textbf{Model} & \\textbf{SQuAD} & \\textbf{BioASQ} & \\textbf{HotpotQA} & \\textbf{NewsQA} & \\textbf{GAOKAO} & \\textbf{HQA} & \\textbf{TriviaQA} \\\\\n\\midrule\nQwen2.5-32B w/ SC & 1372.18 & 1726.32 & 1485.87 & 2201.65 & 1957.93 & 1580.43 & 1742.41 \\\\\nQwen2.5-32B w/ T$^2$ & 1161.42 & 1401.52 & 1330.71 & 1812.28 & 1581.14 & 1415.18 & 1582.42 \\\\\n\\hdashline\nQwQ-32B-Preview & 1617.42 & 2012.33 & 1823.49 & 2648.12 & 2284.80 & 1972.37 & 2119.88 \\\\\nQwQ-32B w/ T$^2$ & 1285.36 & 1467.85 & 1450.75 & 1855.89 & 1699.45 & 1465.68 & 1605.56 \\\\\n\\bottomrule\n\\end{tabular}}\n\\caption{Average token consumption across seven CQA datasets for different reasoning approaches.}\n\\label{tab:token_consumption}\n\\end{table*}\n\n\\subsection{Efficiency-Performance Trade-off}\n\nTable \\ref{tab:efficiency_performance} presents a comprehensive comparison of computational efficiency and performance across all seven datasets. We report the average token length, relative token reduction, and ROUGE-L scores to illustrate the efficiency-performance trade-off.\n\n\\begin{table*}[!t]\n\\centering\n\\adjustbox{max width=\\textwidth}{\n\\begin{tabular}{lccc}\n\\toprule\n\\textbf{Model} & \\textbf{Avg. Token Length} & \\textbf{Token Reduction} & \\textbf{Avg. ROUGE-L} \\\\\n\\midrule\nQwen2.5-32B w/ SC & 1723.83 & - & 50.07 \\\\\nQwen2.5-32B w/ T$^2$ & 1469.24 & 14.8\\% vs. SC & 56.22 \\\\\n\\hdashline\nQwQ-32B-Preview & 2068.34 & - & 63.38 \\\\\nQwQ-32B w/ T$^2$ & 1547.22 & 25.2\\% vs. QwQ & 68.56 \\\\\n\\bottomrule\n\\end{tabular}}\n\\caption{Efficiency-performance trade-off across seven CQA datasets. Token reduction is calculated relative to the baseline model (SC: Self-Consistency).}\n\\label{tab:efficiency_performance}\n\\end{table*}\n\n\\subsection{Dataset-specific Efficiency Gains}\n\n\\begin{figure*}[!t]\n\\centering\n\\includegraphics[width=\\textwidth]{draw/efficiency_by_dataset.pdf}\n\\caption{Token consumption reduction by T$^2$ across different datasets. The percentage values indicate the relative reduction compared to the baseline models (Qwen2.5-32B w/ SC and QwQ-32B-Preview).}\n\\label{fig:efficiency_by_dataset}\n\\end{figure*}\n\nAs shown in Figure \\ref{fig:efficiency_by_dataset}, the efficiency gains of T$^2$ vary across datasets. The token reduction ranges from 10.5\\% to 18.8\\% when applied to Qwen2.5-32B (compared to self-consistency), and from 20.6\\% to 31.6\\% when applied to QwQ-32B (compared to QwQ-32B-Preview). Notably, datasets requiring more complex reasoning (like NewsQA and GAOKAO) show greater efficiency improvements, suggesting that T$^2$ is particularly effective at streamlining the reasoning process for complex questions.\n\n\\subsection{Detailed Efficiency-Performance Analysis}\n\nTable \\ref{tab:detailed_efficiency} provides a detailed analysis of both token consumption and performance for each dataset and model combination. This comparison highlights how T$^2$ maintains or improves performance while reducing computational costs.\n\n\\begin{table*}[!t]\n\\centering\n\\adjustbox{max width=\\textwidth}{\n\\begin{tabular}{lcccccccc}\n\\toprule\n\\multirow{2}{*}{\\textbf{Model}} & \\multicolumn{2}{c}{\\textbf{SQuAD}} & \\multicolumn{2}{c}{\\textbf{BioASQ}} & \\multicolumn{2}{c}{\\textbf{HotpotQA}} & \\multicolumn{2}{c}{\\textbf{NewsQA}} \\\\\n\\cmidrule(lr){2-3} \\cmidrule(lr){4-5} \\cmidrule(lr){6-7} \\cmidrule(lr){8-9}\n & Token & ROUGE-L & Token & ROUGE-L & Token & ROUGE-L & Token & ROUGE-L \\\\\n\\midrule\nQwen2.5-32B w/ SC & 1372.18 & 75.31 & 1726.32 & 57.57 & 1485.87 & 56.76 & 2201.65 & 52.27 \\\\\nQwen2.5-32B w/ T$^2$ & 1161.42 & 81.86 & 1401.52 & 65.02 & 1330.71 & 67.11 & 1812.28 & 61.27 \\\\\nQwQ-32B-Preview & 1617.42 & 86.87 & 2012.33 & 69.02 & 1823.49 & 71.86 & 2648.12 & 63.92 \\\\\nQwQ-32B w/ T$^2$ & 1285.36 & 92.12 & 1467.85 & 75.21 & 1450.75 & 77.61 & 1855.89 & 68.61 \\\\\n\\midrule\n\\multirow{2}{*}{\\textbf{Model}} & \\multicolumn{2}{c}{\\textbf{GAOKAO}} & \\multicolumn{2}{c}{\\textbf{HQA}} & \\multicolumn{2}{c}{\\textbf{TriviaQA}} & \\multicolumn{2}{c}{\\textbf{Average}} \\\\\n\\cmidrule(lr){2-3} \\cmidrule(lr){4-5} \\cmidrule(lr){6-7} \\cmidrule(lr){8-9}\n & Token & ROUGE-L & Token & ROUGE-L & Token & ROUGE-L & Token & ROUGE-L \\\\\n\\midrule\nQwen2.5-32B w/ SC & 1957.93 & 30.57 & 1580.43 & 37.12 & 1742.41 & 41.92 & 1723.83 & 50.07 \\\\\nQwen2.5-32B w/ T$^2$ & 1581.14 & 34.06 & 1415.18 & 40.31 & 1582.42 & 43.92 & 1469.24 & 56.22 \\\\\nQwQ-32B-Preview & 2284.80 & 43.23 & 1972.37 & 49.62 & 2119.88 & 59.16 & 2068.34 & 63.38 \\\\\nQwQ-32B w/ T$^2$ & 1699.45 & 47.42 & 1465.68 & 54.71 & 1605.56 & 64.22 & 1547.22 & 68.56 \\\\\n\\bottomrule\n\\end{tabular}}\n\\caption{Detailed comparison of token consumption and performance (ROUGE-L) across all datasets. Lower token count with higher ROUGE-L indicates better efficiency-performance trade-off.}\n\\label{tab:detailed_efficiency}\n\\end{table*}\n\n\\subsection{Efficiency Analysis by Question Complexity}\n\nTo better understand T$^2$'s efficiency gains, we categorize questions by complexity and analyze token reduction. As shown in Table \\ref{tab:complexity_analysis}, T$^2$ achieves greater token reduction for complex questions requiring multi-step reasoning, showcasing its adaptive nature.\n\n\\begin{table*}[!t]\n\\centering\n\\adjustbox{max width=\\textwidth}{\n\\begin{tabular}{lccc}\n\\toprule\n\\textbf{Question Complexity} & \\textbf{Qwen2.5 + SC} & \\textbf{Qwen2.5 + T$^2$} & \\textbf{Token Reduction} \\\\\n\\midrule\nSimple (1-step) & 1283.45 & 1157.82 & -9.8\\% \\\\\nModerate (2-3 steps) & 1687.31 & 1391.65 & -17.5\\% \\\\\nComplex (4+ steps) & 2201.73 & 1758.24 & -20.1\\% \\\\\n\\midrule\n\\textbf{Question Complexity} & \\textbf{QwQ-32B} & \\textbf{QwQ-32B + T$^2$} & \\textbf{Token Reduction} \\\\\n\\midrule\nSimple (1-step) & 1584.21 & 1262.35 & -20.3\\% \\\\\nModerate (2-3 steps) & 2041.57 & 1492.18 & -26.9\\% \\\\\nComplex (4+ steps) & 2579.24 & 1887.14 & -26.8\\% \\\\\n\\bottomrule\n\\end{tabular}}\n\\caption{Token consumption analysis by question complexity. T$^2$ achieves greater efficiency gains for more complex questions.}\n\\label{tab:complexity_analysis}\n\\end{table*}\n\n\\subsection{Time Efficiency}\n\nBeyond token reduction, we also measure the actual inference time across different models and reasoning approaches. Table \\ref{tab:time_efficiency} presents the average inference time per question, demonstrating that T$^2$ reduces computational time while maintaining high performance.\n\n\\begin{table*}[!t]\n\\centering\n\\adjustbox{max width=\\textwidth}{\n\\begin{tabular}{lcc}\n\\toprule\n\\textbf{Model} & \\textbf{Avg. Inference Time (s)} & \\textbf{Time Reduction} \\\\\n\\midrule\nQwen2.5-32B w/ SC & 65.31 & - \\\\\nQwen2.5-32B w/ T$^2$ & 34.52 & -47.1\\% \\\\\n\\hdashline\nQwQ-32B-Preview & 76.74 & - \\\\\nQwQ-32B w/ T$^2$ & 45.03 & -41.3\\% \\\\\n\\bottomrule\n\\end{tabular}}\n\\caption{Average inference time per question across datasets. T$^2$ reduces computational time while maintaining high performance.}\n\\label{tab:time_efficiency}\n\\end{table*}\n\nIn summary, our comprehensive efficiency analysis demonstrates that T$^2$ reduces token consumption and inference time across diverse CQA datasets while maintaining or improving performance. The efficiency gains are particularly pronounced for complex questions requiring multi-step reasoning, highlighting T$^2$'s ability to adapt its reasoning approach based on question complexity.\n\n\\section{Impacts of Similar Examples}\n\n\\subsection{Impacts of Question Structure}\n\\label{apd:ipt_of_q_structure}\n\nOur framework decomposes each question into a \\textit{structure} plus \\textit{replaceable elements}. We hypothesize that questions with more placeholders benefit more from T$^2$'s selection mechanism, because these questions allow a wider range of possible similar examples. Conversely, simpler questions with fewer placeholders may not need advanced reasoning paths.\n\nWe categorize questions into three buckets based on the number of placeholders in $Q$: \\textit{Low} (0--1 placeholders), \\textit{Medium} (2--3 placeholders), and \\textit{High} (4+ placeholders). Table \\ref{tab:structure_complexity} shows the performance across these groups for SQuAD and HQA to show impacts on general and domain-specific scenarios.\n\n\\begin{table*}[!t]\n \\centering\n \\begin{tabular}{lcccccc}\n \\toprule\n \\multirow{2}{*}{\\textbf{Group}}& \\multicolumn{3}{c}{\\textbf{SQuAD}} & \\multicolumn{3}{c}{\\textbf{HQA}} \\\\\n \\cmidrule(lr){2-4} \\cmidrule(lr){5-7}\n & Few-shots & Self-Cconsistency & T$^2$ & Few-shots & Self-Cconsistency & T$^2$ \\\\\n \\midrule\n Low & 78.5 & 79.1 & \\textbf{80.2} & 42.7 & 43.3 & \\textbf{44.6} \\\\\n Medium & 76.4 & 78.2 & \\textbf{79.5} & 41.5 & 43.0 & \\textbf{45.1} \\\\\n High & 75.9 & 78.7 & \\textbf{80.1} & 40.2 & 42.9 & \\textbf{46.2} \\\\\n \\bottomrule\n \\end{tabular}\n \\caption{ROUGE-L by question complexity. We compare quick-thinking (Few-shots) and slow-thinking (Self-Consistency), and our T$^2$.}\n \\label{tab:structure_complexity}\n\\end{table*}\n\nAs seen in Table \\ref{tab:structure_complexity}, questions with more placeholders (High) see the largest gap between T$^2$ and either baseline. This suggests that, for complex questions, enumerating and reusing relevant skill chains is particularly helpful. On simpler questions (Low), T$^2$ still improves performance but by a smaller margin, as fewer placeholders limit the search space for alternative question structures.\n\n\\subsection{Impacts of Similar Examples Structure}\n\\label{apd:ipt_of_d_structure}\n\nWe show the ``skeleton'' QA pairs that preserved reasoning structure while replacing all content-specific terms with placeholders in Figure~\\ref{fig:sim_example_original} (for original one) and Figure~\\ref{fig:sim_example_structure_only} (for structure-only one).\n\n\\begin{figure*}[!t]\n\\begin{tcolorbox}[title=Original QA Pair]\n\\textbf{Q:} How does the melting point of sodium compare to potassium?\n\n\\textbf{A:} I'll approach this systematically:\\\\\n1. First, I'll recall the melting point of sodium, which is 97.8°C.\\\\\n2. Next, I'll recall the melting point of potassium, which is 63.5°C.\\\\\n3. Comparing these values: 97.8°C > 63.5°C\\\\\n4. Therefore, sodium has a higher melting point than potassium.\n\\end{tcolorbox}\n\\caption{Our original generated similar example.}\n\\label{fig:sim_example_original}\n\\end{figure*}\n\n\\begin{figure*}[!t]\n\\begin{tcolorbox}[title=Structure-Only Version]\n\\textbf{Q:} How does [PROPERTY] of [ENTITY\\_A] compare to [ENTITY\\_B]?\n\n\\textbf{A:} I'll approach this systematically:\\\\\n1. First, I'll determine the [PROPERTY] of [ENTITY\\_A], which is [VALUE\\_A].\\\\\n2. Next, I'll determine the [PROPERTY] of [ENTITY\\_B], which is [VALUE\\_B].\\\\\n3. Comparing these values: [COMPARISON\\_OPERATION]\\\\\n4. Therefore, [CONCLUSION\\_STATEMENT].\n\\end{tcolorbox}\n\\caption{Structure-only version of our generated similar example.}\n\\label{fig:sim_example_structure_only}\n\\end{figure*}\n\n\\subsection{Impacts of Similar Example Numbers}\n\\label{apd:ipt_of_d_number}\n\n\\begin{figure*}[!t]\n \\centering\n \\includegraphics[width=\\linewidth]{./draw/demo_number.pdf}\n \\caption{Impact of the number of similar examples on ROUGE-L scores for HotpotQA (left) and NewsQA (right).}\n \\label{fig:demo_counts}\n\\end{figure*}\n\nWe vary the size $M=|\\Gamma|$. Figure \\ref{fig:demo_counts} illustrates the performance on HotpotQA (left) and NewsQA (right) as $M$ increases. We observe an initial boost in ROUGE-L scores before $M=20$, but performance plateaus or slightly decreases beyond a certain point. After increasing examples to $M=80$, the performance rapidly decreases. We conclude that too many examples can introduce irrelevant or redundant paths, making selection harder. In practice, we find that generating a moderate pool is enough to cover essential patterns, especially if the examples are diverse and accurate.\n\n\\subsection{Impacts of Example Generation Methods}\n\\label{apd:ipt_of_d_generate}\n\n\\begin{table}[!t]\n \\centering\n \\adjustbox{max width=\\linewidth}{\n \\begin{tabular}{lccc}\n \\toprule\n \\textbf{Method} & \\textbf{ROUGE-L} & \\textbf{Variation} & \\textbf{Noise}\\\\\n \\midrule\n \\multicolumn{4}{l}{\\textbf{\\textit{Qwen2.5 w/ ours}}}\\\\\n \\; Random Fill & 49.8 & High & Medium \\\\\n \\; Guided Fill & 52.6 & Low & Low \\\\\n \\; Template Variation & \\textbf{61.3} & High & Low \\\\\n \\bottomrule\n \\end{tabular}}\n \\caption{Comparing different example construction methods on NewsQA.}\n \\label{tab:demo_construction}\n\\end{table}\n\nThen, we consider how we synthesize reference examples. We experiment with different approaches for filling the placeholders on HotpotQA with qwen2.5-32B:\n\\begin{itemize}[leftmargin=*,topsep=0pt, partopsep=0pt, itemsep=0pt, parsep=0pt]\n \\item \\textbf{Random Fill:} Pick random words or entities of the same type (e.g., any \\texttt{person}) from a large corpus.\n \\item \\textbf{Guided Fill:} Use an LLM or curated list to pick semantically relevant or thematically consistent entities for each placeholder.\n \\item \\textbf{Template Variation:} Generate minor paraphrases or new question stems while retaining the same skill sequence.\n\\end{itemize}\n\nTable \\ref{tab:demo_construction} shows that template variation produces more coherent examples, with 2--4\\% gains over purely random fill. This highlights the importance of a well-structured synthetic process: random replacements might yield too many off-topic or contradictory examples, while guided replacements and paraphrasing keep the examples relevant, improving the final answer selection.\n\n\\subsection{Impacts of Example Generation Threshold}\n\\label{apd:ipt_of_d_threshold}\n\n\\begin{figure*}[!t]\n \\centering\n \\includegraphics[width=\\linewidth]{./draw/question_threshold.pdf}\n \\caption{Impact of the question synthesis scope.}\n \\label{fig:impact_of_delta}\n\\end{figure*}\n\nWe analyze the impact of varying the threshold $\\delta$ on the synthesis quality of the generated questions. The threshold \\(\\delta\\) controls how similar the synthesized questions \\(Q_{\\mathrm{syn}}^i\\) are to the original question \\(Q\\), by using a helper language model to assess their alignment (in Sec.\\ref{sec:demo-selection}). Figure \\ref{fig:impact_of_delta} shows finding a trade-off between question similarity and generalization is much more important. As \\(\\delta\\) increases, the similarity to the original question improves but at the cost of generalization. Conversely, when \\(\\delta\\) is lowered, the model generalizes better but the quality of the synthesized questions decreases.\n\n\\subsection{Impacts of Examples Domain Bias and Structural Bias}\n\\label{apd:ipt_of_d_domain}\n\n\\begin{table}[!t]\n\\centering\n\\small\n\\begin{tabular}{lcc}\n\\toprule\n\\textbf{Model} & \\textbf{HotpotQA} & \\textbf{HQA} \\\\\n\\midrule\nQwen2.5+SC & 56.76 & 37.12 \\\\\nQwen2.5+T$^2$ & 67.11 & 40.31 \\\\\nQwen2.5+mis domain & 65.96 & 39.85 \\\\\nQwen2.5+structure only & 63.96 & 38.85 \\\\\n\\hdashline\nQwQ & 71.86 & 49.62 \\\\\nQwQ+T$^2$ & 77.61 & 54.71 \\\\\nQwQ+mis domain & 77.03 & 54.26 \\\\\nQwQ+structure only & 74.03 & 53.66 \\\\\n\\bottomrule\n\\end{tabular}\n\\caption{Performance on mis-domain and structure-only models. ROUGE-L is the reported performance metric.}\n\\label{tab:demo_quality}\n\\end{table}\n\nIn addition, we investigate the effects of domain and structural biases in similar examples. Specifically, we assess how varying the domain of the similar examples influences model performance. As shown in the Table~\\ref{tab:demo_quality} (``+mis domain''), transitioning from a general domain to a historical one results in improved performance compared to using self-consistency alone. Furthermore, we evaluate the impact of removing key information from the similar examples, leaving only the reasoning structure. Table~\\ref{tab:demo_quality} (``+structure only'') demonstrates that even when only the examples' structure\\footnote{We show the example structure in Appendix \\ref{apd:ipt_of_d_structure}.} is provided, the model can still generate appropriate responses, highlighting the effectiveness of structural guidance.\n\n\\subsection{Impacts of Similar Examples Quality}\n\\label{apd:ipt_of_d_quality}\n\nTo evaluate the quality of similar examples generated by our framework, we conducted a comprehensive human evaluation study. We randomly selected 1000 query-reference pairs from the HotpotQA dataset and recruited three Ph.D. students specializing in NLP to assess the quality of synthetic references. The evaluation was conducted blind, with evaluators unaware of which model generated each reference.\n\n\\paragraph{Evaluation Dimensions.} References were rated on a scale of 1-10 across four key dimensions:\n\\begin{itemize}[leftmargin=*, itemsep=0pt,parsep=0pt,topsep=0pt,partopsep=0pt]\n \\item \\textbf{Accuracy:} Factual correctness and absence of hallucinations or contradictions\n \\item \\textbf{Relevance:} Degree to which the reference addresses the specific query requirements\n \\item \\textbf{Completeness:} Thoroughness in covering all necessary information and reasoning steps\n \\item \\textbf{Coherence:} Logical structure, clarity of expression, and overall readability\n\\end{itemize}\n\n\\paragraph{Model Comparison.} We evaluated synthetic references generated by two foundation models: Qwen2.5-32B-Instrucut and QwQ-32B-Preview, both with our framework. Table~\\ref{tab:human_eval} presents the average scores across all evaluators and samples.\n\n\\begin{table*}[!t]\n\\centering\n\\begin{tabular}{lccccc}\n\\toprule\n\\textbf{Models w/ T$^2$} & \\textbf{Accuracy} & \\textbf{Relevance} & \\textbf{Completeness} & \\textbf{Coherence} & \\textbf{Overall} \\\\\n\\midrule\nQwen2.5-32B-Instrucut & 8.8 & 8.6 & 8.1 & 8.1 & 8.4 \\\\\nQwQ-32B-Preview & 8.4 & 8.3 & 7.9 & 8.2 & 8.4 \\\\\n\\bottomrule\n\\end{tabular}\n\\caption{Human evaluation scores for synthetic references generated by different models (scale: 1-10).}\n\\label{tab:human_eval}\n\\end{table*}\n\nResults show both models produced high-quality references. The highest scores were observed in the Relevance category, indicating that references effectively addressed the specific queries. The evaluation exhibited strong inter-annotator agreement with a Fleiss' kappa coefficient of 0.79, indicating substantial agreement among the three evaluators. This suggests the evaluation results are reliable and consistent across different human judges.\n\n\\section{Detailed Case Studies}\n\\label{apd:case_studies}\n\nFigure \\ref{fig:hotpotqa_example} and \\ref{fig:squad_case_single_step} show the two different cases from HotPotQA and SQuAD. The two case studies illustrate distinct reasoning strategies for question answering. In the HotpotQA case, the task requires multi-step reasoning by integrating evidence from multiple documents. A response based solely on pattern matching might output wrong ``London'' and an overthinking answer may include unnecessary details before arriving at the correct conclusion, the best approach is a concise, step-by-step explanation that clearly connects the film to John Lennon and his documented birthplace.\n\nIn contrast, the SQuAD case involves single-step reasoning where the answer is directly available from the text. The document states that the Eiffel Tower was constructed in 1889, so the correct answer is deduced by simply extracting this fact. Although a pattern matching response may correctly state ``1889'' and an overcomplicated explanation might unnecessarily discuss other numerical details, the ideal answer succinctly identifies the key fact and deduces that the construction year is 1889 using clear, deductive reasoning.\n\n\\begin{figure*}[!t]\n \\small\n \\begin{tcolorbox}[title=\\# HotpotQA Case Example with Multihop Thinking, fonttitle=\\bfseries, colframe=black, colback=white, sharp corners, boxrule=0.5mm, colbacktitle=gray!20, coltitle=black, enhanced]\n \\textbf{Document 1:}\\\\\n John Lennon, the iconic musician and member of The Beatles, was born in Liverpool, England.\\\\ \n \\textbf{Document 2:}\\\\\n \\textit{Nowhere Boy} is a film that explores the early life of John Lennon, highlighting his formative years and influences.\\\\\n \\textbf{Question:}\\\\\n In what city was the subject of the film \\textit{Nowhere Boy} born?\n\n \\vspace{1em}\n \n \\textbf{Proper Reasoning Chain:}\\\\\n 1. \\textbf{Identify the Subject:} Recognize that \\textit{Nowhere Boy} is about John Lennon. (Deductive)\\\\\n 2. \\textbf{Extract Key Fact:} From Document 1, note that John Lennon was born in Liverpool, England. (Deductive)\\\\\n 3. \\textbf{Conclude:} Therefore, the subject of the film was born in Liverpool. (Decompositional)\n\n \\vspace{1em}\n \n \\textcolor{red}{\\textbf{1) Quick Thinking Model's Wrong Answer:}}\\\\\n \\textcolor{red}{\\textit{Answer:} The subject of \\textit{Nowhere Boy} was born in London.}\\\\\n \n \\textcolor{orange}{\\textbf{2) Slow Thinking Model's Overthinking Answer:}}\\\\\n \\textcolor{orange}{\\textit{Answer:} After a lengthy analysis of various biographical details concerning John Lennon, including his early childhood, multiple hints in his music and interviews, and a discussion of several English cities mentioned in relation to his life, one might eventually conclude that the subject of \\textit{Nowhere Boy} was born in Liverpool.}\\\\\n \n \\textcolor{mygreen}{\\textbf{3) Model with Our T$^2$'s Correct Answer:}}\\\\\n \\textcolor{mygreen}{\\textit{Answer:} Since \\textit{Nowhere Boy} is a film about John Lennon (Document 2) and Document 1 confirms that John Lennon was born in Liverpool, we deduce that the subject of the film was born in Liverpool.}\n \\end{tcolorbox}\n \\caption{HotpotQA Case Example with Multiple Answer Versions and Explicit Reasoning Chain.}\n \\label{fig:hotpotqa_example}\n\\end{figure*}\n\n\n\\begin{figure*}[!t]\n \\small\n \\begin{tcolorbox}[title=\\# SQuAD Case Example with Single-Step Thinking,fonttitle=\\bfseries,colframe=black,colback=white,sharp corners,boxrule=0.5mm,colbacktitle=gray!20,coltitle=black,enhanced]\n \\textbf{Document:}\\\\\n The Eiffel Tower is a wrought-iron lattice tower located on the Champ de Mars in Paris, France. It was constructed in 1889 as the entrance to the 1889 World's Fair. The tower stands approximately 324 meters tall and is one of the most recognized structures in the world.\\\\\n \\textbf{Question:}\\\\\n In what year was the Eiffel Tower constructed?\\\\\n\n \\vspace{1em}\n \n \\textbf{Proper Reasoning Chain (using Deductive):}\\\\\n 1) \\textbf{Deduction:} Based on clear statement, we deduce that the construction year is 1889 (Deductive).\n \n \\vspace{1em}\n \n \\textbf{1) Quicking Thinking Model's Correct Answer \\textcolor{orange}{via Pattern Matching}:}\\\\\n \\textit{Answer:} 1889.\\\\\n \n \\textbf{2) Slow Thinking Model's Correct Answer \\textcolor{orange}{but Overthinking}:}\\\\\n \\textit{Answer:} After reading the passage, I noticed several numbers such as 324 (the tower's height) and 1889. While I considered whether the height might play a role in determining the construction year, I ultimately realized that the passage explicitly states ``constructed in 1889.'' Thus, despite the extra details, the correct construction year is 1889.\\\\\n \n \\textbf{3) Model with Our T$^2$'s Correct Answer \\textcolor{mygreen}{using the Specific Reasoning Chain}:}\\\\\n \\textit{Answer:} Since the document clearly states that the Eiffel Tower was constructed in 1889, we deduce—by directly extracting and applying this fact—that the construction year is 1889.\n \\end{tcolorbox}\n \\caption{SQuAD Case Example with Single-Step Thinking and Multiple Answer Versions.}\n \\label{fig:squad_case_single_step}\n\\end{figure*}\n\n\\end{document}\n"},"question":{"kind":"string","value":"On which test set does the proposed method improve most over the basic use of its baseline model in relative terms, averaged over the three scenarios?"},"answer":{"kind":"string","value":"HotpotQA"},"plan":{"kind":"string","value":"SELECT three scenarios: each baseline with its proposed method on all test sets\nLOOP for each scenario\n SELECT scores for current scenario\n LOOP for each test set\n COMPUTE relative_improvement = (method_score - baseline_score)/baseline_score\n\n\nSELECT all test sets\nLOOP for each test set\n COMPUTE average relative_improvement over three scenarios\nCOMPUTE target_test_set = argmax over average relative_improvement\nRETURN target_test_set\n"}}},{"rowIdx":71,"cells":{"paper":{"kind":"string","value":"2505.20776"},"relevant_tables":{"kind":"list like","value":[["\\begin{table*}[t]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n","\\begin{tabular}{@{}ccccccccccccccccccc@{}}\n","\\toprule\n","\\multirow{2}{*}{} \n"," & \\multicolumn{2}{c}{\\multirow{2}{*}{\\textbf{Setting}}} \n"," & \\multirow{2}{*}{\\textbf{SpecExtend}} \n"," & \\multicolumn{3}{c}{1K} \n"," & \\multicolumn{3}{c}{2K} \n"," & \\multicolumn{3}{c}{4K} \n"," & \\multicolumn{3}{c}{8K} \n"," & \\multicolumn{3}{c}{16K} \n","\\\\\n","\\cmidrule(r){5-7}\n","\\cmidrule(lr){8-10}\n","\\cmidrule(lr){11-13}\n","\\cmidrule(lr){14-16}\n","\\cmidrule(l){17-19}\n"," & \\multicolumn{2}{c}{} \n"," & \n"," & $\\tau$ & Tok/s & Speedup \n"," & $\\tau$ & Tok/s & Speedup \n"," & $\\tau$ & Tok/s & Speedup \n"," & $\\tau$ & Tok/s & Speedup \n"," & $\\tau$ & Tok/s & Speedup \n","\\\\ \\midrule\n","\\multirow{8}{*}{\\rotatebox[origin=c]{90}{\\textbf{GovReport}}}\n"," & \\multirow{4}{*}{\\rotatebox[origin=c]{90}{V-7B}}\n"," & \\multirow{2}{*}{V-68M} & No & 1.73 & 100.31 & 1.78$\\times$ & 0.64 & 55.64 & 1.16$\\times$ & 0.60 & 41.91 & 1.14$\\times$ & 0.62 & 25.71 & 1.08$\\times$ & 0.59 & 16.56 & 1.38$\\times$ \\\\\n"," & & & Yes & \\textbf{2.80} & \\textbf{128.59} & \\textbf{2.28$\\times$} & \\textbf{2.52} & \\textbf{109.58} & \\textbf{2.29$\\times$} & \\textbf{2.04} & \\textbf{76.48} & \\textbf{2.08$\\times$} & \\textbf{2.06} & \\textbf{55.16} & \\textbf{2.33$\\times$} & \\textbf{2.07} & \\textbf{33.84} & \\textbf{2.82$\\times$} \\\\ \\cmidrule(lr){5-19}\n"," & & \\multirow{2}{*}{EAGLE} & No & \\textbf{3.61} & 144.77 & 2.57$\\times$ & 3.04 & 107.52 & 2.24$\\times$ & 2.27 & 66.62 & 1.81$\\times$ & 1.35 & 31.74 & 1.34$\\times$ & 1.00 & 19.35 & 1.61$\\times$ \\\\\n"," & & & Yes & 3.58 & \\textbf{145.53} & \\textbf{2.58$\\times$} & \\textbf{3.08} & \\textbf{113.47} & \\textbf{2.37$\\times$} & \\textbf{2.80} & \\textbf{85.99} & \\textbf{2.34$\\times$} & \\textbf{2.82} & \\textbf{62.90} & \\textbf{2.66$\\times$} & \\textbf{2.51} & \\textbf{37.05} & \\textbf{3.08$\\times$} \\\\ \\cmidrule(l){2-19} \n"," & \\multirow{4}{*}{\\rotatebox[origin=c]{90}{LC-7B}} & \\multirow{2}{*}{LC-68M} & No & 1.73 & 100.31 & 1.78$\\times$ & 0.64 & 55.64 & 1.16$\\times$ & 0.60 & 41.91 & 1.15$\\times$ & 0.62 & 25.71 & 1.12$\\times$ & 0.59 & 16.56 & 1.51$\\times$ \\\\\n"," & & & Yes & \\textbf{2.01} & \\textbf{109.26} & \\textbf{1.94$\\times$} & \\textbf{1.82} & \\textbf{90.27} & \\textbf{1.89$\\times$} & \\textbf{1.66} & \\textbf{68.84} & \\textbf{1.89$\\times$} & \\textbf{1.81} & \\textbf{52.17} & \\textbf{2.30$\\times$} & \\textbf{1.68} & \\textbf{31.11} & \\textbf{2.84$\\times$} \\\\ \\cmidrule(lr){5-19}\n"," & & \\multirow{2}{*}{EAGLE} & No & \\textbf{3.10} & 131.06 & 2.33$\\times$ & 2.47 & 97.53 & 2.04$\\times$ & 1.75 & 60.17 & 1.65$\\times$ & 1.52 & 32.90 & 1.44$\\times$ & 1.18 & 19.84 & 1.81$\\times$ \\\\\n"," & & & Yes & 3.04 & \\textbf{133.14} & \\textbf{2.37$\\times$} & \\textbf{2.56} & \\textbf{103.39} & \\textbf{2.17$\\times$} & \\textbf{2.43} & \\textbf{80.50} & \\textbf{2.21$\\times$} & \\textbf{2.53} & \\textbf{60.14} & \\textbf{2.63$\\times$} & \\textbf{2.25} & \\textbf{35.13} & \\textbf{3.21$\\times$} \\\\ \\midrule\n","\\multirow{8}{*}{\\rotatebox[origin=c]{90}{\\textbf{PG-19}}} & \\multirow{4}{*}{\\rotatebox[origin=c]{90}{V-7B}} & \\multirow{2}{*}{V-68M} & No & 1.16 & 76.50 & 1.37$\\times$ & 0.52 & 51.00 & 1.09$\\times$ & 0.55 & 39.16 & 1.15$\\times$ & 0.55 & 21.80 & 1.01$\\times$ & 0.54 & 14.73 & 1.29$\\times$ \\\\\n"," & & & Yes & \\textbf{1.75} & \\textbf{96.74} & \\textbf{1.74$\\times$} & \\textbf{1.69} & \\textbf{84.74} & \\textbf{1.81$\\times$} & \\textbf{1.61} & \\textbf{63.94} & \\textbf{1.88$\\times$} & \\textbf{1.65} & \\textbf{47.64} & \\textbf{2.39$\\times$} & \\textbf{1.70} & \\textbf{32.88} & \\textbf{2.87$\\times$} \\\\ \\cmidrule(lr){5-19}\n"," & & \\multirow{2}{*}{EAGLE} & No & \\textbf{2.29} & 107.31 & 1.92$\\times$ & 2.18 & 88.92 & 1.89$\\times$ & 1.88 & 54.71 & 1.60$\\times$ & 1.18 & 26.43 & 1.32$\\times$ & 0.92 & 16.98 & 1.48$\\times$ \\\\\n"," & & & Yes & 2.29 & \\textbf{107.53} & \\textbf{1.93$\\times$} & \\textbf{2.19} & \\textbf{94.41} & \\textbf{2.02$\\times$} & \\textbf{2.04} & \\textbf{69.92} & \\textbf{2.06$\\times$} & \\textbf{2.19} & \\textbf{53.06} & \\textbf{2.67$\\times$} & \\textbf{2.05} & \\textbf{35.43} & \\textbf{3.09$\\times$} \\\\ \\cmidrule(l){2-19} \n"," & \\multirow{4}{*}{\\rotatebox[origin=c]{90}{LC-7B}} & \\multirow{2}{*}{LC-68M} & No & 1.16 & 76.50 & 1.36$\\times$ & 0.52 & 51.00 & 1.07$\\times$ & 0.55 & 39.16 & 1.09$\\times$ & 0.55 & 21.80 & 1.00$\\times$ & 0.54 & 14.73 & 1.18$\\times$ \\\\\n"," & & & Yes & \\textbf{1.22} & \\textbf{80.25} & \\textbf{1.43$\\times$} & \\textbf{1.33} & \\textbf{73.69} & \\textbf{1.55$\\times$} & \\textbf{1.42} & \\textbf{62.27} & \\textbf{1.74$\\times$} & \\textbf{1.42} & \\textbf{44.96} & \\textbf{2.06$\\times$} & \\textbf{1.45} & \\textbf{30.67} & \\textbf{2.46$\\times$} \\\\ \\cmidrule(lr){5-19}\n"," & & \\multirow{2}{*}{EAGLE} & No & \\textbf{2.19} & 111.10 & 1.97$\\times$ & 2.00 & 86.80 & 1.82$\\times$ & 1.48 & 54.21 & 1.51$\\times$ & 1.28 & 26.85 & 1.23$\\times$ & 1.06 & 17.54 & 1.40$\\times$ \\\\\n"," & & & Yes & 2.11 & \\textbf{110.31} & \\textbf{1.96$\\times$} & \\textbf{2.02} & \\textbf{93.50} & \\textbf{1.97$\\times$} & \\textbf{1.97} & \\textbf{71.84} & \\textbf{2.01$\\times$} & \\textbf{1.99} & \\textbf{51.55} & \\textbf{2.36$\\times$} & \\textbf{1.82} & \\textbf{33.07} & \\textbf{2.66$\\times$} \n"," \\\\ \\midrule\n","\\multirow{8}{*}{\\rotatebox[origin=c]{90}{\\textbf{BookSum}}} & \\multirow{4}{*}{\\rotatebox[origin=c]{90}{V-7B}} & \\multirow{2}{*}{V-68M} & No & 1.36 & 88.12 & 1.57$\\times$ & 0.56 & 53.33 & 1.13$\\times$ & 0.51 & 39.30 & 1.08$\\times$ & 0.52 & 24.21 & 1.05$\\times$ & 0.58 & 15.63 & 1.30$\\times$ \\\\\n"," & & & Yes & \\textbf{1.75} & \\textbf{97.45} & \\textbf{1.73$\\times$} & \\textbf{1.66} & \\textbf{81.37} & \\textbf{1.73$\\times$} & \\textbf{1.56} & \\textbf{62.97} & \\textbf{1.73$\\times$} & \\textbf{1.70} & \\textbf{50.21} & \\textbf{2.18$\\times$} & \\textbf{1.78} & \\textbf{35.61} & \\textbf{2.98$\\times$} \\\\ \\cmidrule(lr){5-19}\n"," & & \\multirow{2}{*}{EAGLE} & No & \\textbf{2.33} & 111.70 & 1.99$\\times$ & 1.95 & 82.44 & 1.75$\\times$ & 1.87 & 58.01 & 1.59$\\times$ & 1.14 & 29.30 & 1.27$\\times$ & 0.94 & 18.76 & 1.57$\\times$ \\\\\n"," & & & Yes & 2.31 & \\textbf{111.82} & \\textbf{1.99$\\times$} & \\textbf{1.99} & \\textbf{88.64} & \\textbf{1.89$\\times$} & \\textbf{2.08} & \\textbf{70.90} & \\textbf{1.95$\\times$} & \\textbf{2.15} & \\textbf{54.53} & \\textbf{2.37$\\times$} & \\textbf{2.11} & \\textbf{38.03} & \\textbf{3.18$\\times$} \\\\ \\cmidrule(l){2-19} \n"," & \\multirow{4}{*}{\\rotatebox[origin=c]{90}{LC-7B}} & \\multirow{2}{*}{LC-68M} & No & 1.36 & 88.12 & 1.57$\\times$ & 0.56 & 53.33 & 1.14$\\times$ & 0.51 & 39.30 & 1.11$\\times$ & 0.52 & 24.21 & 1.20$\\times$ & 0.58 & 15.63 & 1.28$\\times$ \\\\\n"," & & & Yes & \\textbf{1.45} & \\textbf{91.05} & \\textbf{1.63$\\times$} & \\textbf{1.55} & \\textbf{83.60} & \\textbf{1.80$\\times$} & \\textbf{1.54} & \\textbf{66.79} & \\textbf{1.90$\\times$} & \\textbf{1.61} & \\textbf{49.47} & \\textbf{2.45$\\times$} & \\textbf{1.50} & \\textbf{32.21} & \\textbf{2.64$\\times$} \\\\ \\cmidrule(lr){5-19}\n"," & & \\multirow{2}{*}{EAGLE} & No & \\textbf{2.10} & \\textbf{107.67} & \\textbf{1.92$\\times$} & 1.94 & 86.35 & 1.85$\\times$ & 1.37 & 53.42 & 1.51$\\times$ & 1.22 & 30.14 & 1.49$\\times$ & 1.06 & 18.39 & 1.50$\\times$ \\\\\n"," & & & Yes & 2.07 & 106.86 & 1.91$\\times$ & \\textbf{1.97} & \\textbf{90.48} & \\textbf{1.94$\\times$} & \\textbf{1.88} & \\textbf{71.50} & \\textbf{2.03$\\times$} & \\textbf{1.92} & \\textbf{52.35} & \\textbf{2.59$\\times$} & \\textbf{1.83} & \\textbf{34.65} & \\textbf{2.84$\\times$}\n"," \\\\ \\bottomrule\n","\\end{tabular}\n","}\n","\\caption{Average accepted length ($\\tau$), decoding speed (tokens/s) and speedups of different frameworks with and without SpecExtend. Speedup is measured relative to naive autoregressive generation.}\n","\\label{tab:main_results}\n","\\end{table*}\n"]],"string":"[\n [\n \"\\\\begin{table*}[t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \"\\\\begin{tabular}{@{}ccccccccccccccccccc@{}}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{} \\n\",\n \" & \\\\multicolumn{2}{c}{\\\\multirow{2}{*}{\\\\textbf{Setting}}} \\n\",\n \" & \\\\multirow{2}{*}{\\\\textbf{SpecExtend}} \\n\",\n \" & \\\\multicolumn{3}{c}{1K} \\n\",\n \" & \\\\multicolumn{3}{c}{2K} \\n\",\n \" & \\\\multicolumn{3}{c}{4K} \\n\",\n \" & \\\\multicolumn{3}{c}{8K} \\n\",\n \" & \\\\multicolumn{3}{c}{16K} \\n\",\n \"\\\\\\\\\\n\",\n \"\\\\cmidrule(r){5-7}\\n\",\n \"\\\\cmidrule(lr){8-10}\\n\",\n \"\\\\cmidrule(lr){11-13}\\n\",\n \"\\\\cmidrule(lr){14-16}\\n\",\n \"\\\\cmidrule(l){17-19}\\n\",\n \" & \\\\multicolumn{2}{c}{} \\n\",\n \" & \\n\",\n \" & $\\\\tau$ & Tok/s & Speedup \\n\",\n \" & $\\\\tau$ & Tok/s & Speedup \\n\",\n \" & $\\\\tau$ & Tok/s & Speedup \\n\",\n \" & $\\\\tau$ & Tok/s & Speedup \\n\",\n \" & $\\\\tau$ & Tok/s & Speedup \\n\",\n \"\\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{8}{*}{\\\\rotatebox[origin=c]{90}{\\\\textbf{GovReport}}}\\n\",\n \" & \\\\multirow{4}{*}{\\\\rotatebox[origin=c]{90}{V-7B}}\\n\",\n \" & \\\\multirow{2}{*}{V-68M} & No & 1.73 & 100.31 & 1.78$\\\\times$ & 0.64 & 55.64 & 1.16$\\\\times$ & 0.60 & 41.91 & 1.14$\\\\times$ & 0.62 & 25.71 & 1.08$\\\\times$ & 0.59 & 16.56 & 1.38$\\\\times$ \\\\\\\\\\n\",\n \" & & & Yes & \\\\textbf{2.80} & \\\\textbf{128.59} & \\\\textbf{2.28$\\\\times$} & \\\\textbf{2.52} & \\\\textbf{109.58} & \\\\textbf{2.29$\\\\times$} & \\\\textbf{2.04} & \\\\textbf{76.48} & \\\\textbf{2.08$\\\\times$} & \\\\textbf{2.06} & \\\\textbf{55.16} & \\\\textbf{2.33$\\\\times$} & \\\\textbf{2.07} & \\\\textbf{33.84} & \\\\textbf{2.82$\\\\times$} \\\\\\\\ \\\\cmidrule(lr){5-19}\\n\",\n \" & & \\\\multirow{2}{*}{EAGLE} & No & \\\\textbf{3.61} & 144.77 & 2.57$\\\\times$ & 3.04 & 107.52 & 2.24$\\\\times$ & 2.27 & 66.62 & 1.81$\\\\times$ & 1.35 & 31.74 & 1.34$\\\\times$ & 1.00 & 19.35 & 1.61$\\\\times$ \\\\\\\\\\n\",\n \" & & & Yes & 3.58 & \\\\textbf{145.53} & \\\\textbf{2.58$\\\\times$} & \\\\textbf{3.08} & \\\\textbf{113.47} & \\\\textbf{2.37$\\\\times$} & \\\\textbf{2.80} & \\\\textbf{85.99} & \\\\textbf{2.34$\\\\times$} & \\\\textbf{2.82} & \\\\textbf{62.90} & \\\\textbf{2.66$\\\\times$} & \\\\textbf{2.51} & \\\\textbf{37.05} & \\\\textbf{3.08$\\\\times$} \\\\\\\\ \\\\cmidrule(l){2-19} \\n\",\n \" & \\\\multirow{4}{*}{\\\\rotatebox[origin=c]{90}{LC-7B}} & \\\\multirow{2}{*}{LC-68M} & No & 1.73 & 100.31 & 1.78$\\\\times$ & 0.64 & 55.64 & 1.16$\\\\times$ & 0.60 & 41.91 & 1.15$\\\\times$ & 0.62 & 25.71 & 1.12$\\\\times$ & 0.59 & 16.56 & 1.51$\\\\times$ \\\\\\\\\\n\",\n \" & & & Yes & \\\\textbf{2.01} & \\\\textbf{109.26} & \\\\textbf{1.94$\\\\times$} & \\\\textbf{1.82} & \\\\textbf{90.27} & \\\\textbf{1.89$\\\\times$} & \\\\textbf{1.66} & \\\\textbf{68.84} & \\\\textbf{1.89$\\\\times$} & \\\\textbf{1.81} & \\\\textbf{52.17} & \\\\textbf{2.30$\\\\times$} & \\\\textbf{1.68} & \\\\textbf{31.11} & \\\\textbf{2.84$\\\\times$} \\\\\\\\ \\\\cmidrule(lr){5-19}\\n\",\n \" & & \\\\multirow{2}{*}{EAGLE} & No & \\\\textbf{3.10} & 131.06 & 2.33$\\\\times$ & 2.47 & 97.53 & 2.04$\\\\times$ & 1.75 & 60.17 & 1.65$\\\\times$ & 1.52 & 32.90 & 1.44$\\\\times$ & 1.18 & 19.84 & 1.81$\\\\times$ \\\\\\\\\\n\",\n \" & & & Yes & 3.04 & \\\\textbf{133.14} & \\\\textbf{2.37$\\\\times$} & \\\\textbf{2.56} & \\\\textbf{103.39} & \\\\textbf{2.17$\\\\times$} & \\\\textbf{2.43} & \\\\textbf{80.50} & \\\\textbf{2.21$\\\\times$} & \\\\textbf{2.53} & \\\\textbf{60.14} & \\\\textbf{2.63$\\\\times$} & \\\\textbf{2.25} & \\\\textbf{35.13} & \\\\textbf{3.21$\\\\times$} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{8}{*}{\\\\rotatebox[origin=c]{90}{\\\\textbf{PG-19}}} & \\\\multirow{4}{*}{\\\\rotatebox[origin=c]{90}{V-7B}} & \\\\multirow{2}{*}{V-68M} & No & 1.16 & 76.50 & 1.37$\\\\times$ & 0.52 & 51.00 & 1.09$\\\\times$ & 0.55 & 39.16 & 1.15$\\\\times$ & 0.55 & 21.80 & 1.01$\\\\times$ & 0.54 & 14.73 & 1.29$\\\\times$ \\\\\\\\\\n\",\n \" & & & Yes & \\\\textbf{1.75} & \\\\textbf{96.74} & \\\\textbf{1.74$\\\\times$} & \\\\textbf{1.69} & \\\\textbf{84.74} & \\\\textbf{1.81$\\\\times$} & \\\\textbf{1.61} & \\\\textbf{63.94} & \\\\textbf{1.88$\\\\times$} & \\\\textbf{1.65} & \\\\textbf{47.64} & \\\\textbf{2.39$\\\\times$} & \\\\textbf{1.70} & \\\\textbf{32.88} & \\\\textbf{2.87$\\\\times$} \\\\\\\\ \\\\cmidrule(lr){5-19}\\n\",\n \" & & \\\\multirow{2}{*}{EAGLE} & No & \\\\textbf{2.29} & 107.31 & 1.92$\\\\times$ & 2.18 & 88.92 & 1.89$\\\\times$ & 1.88 & 54.71 & 1.60$\\\\times$ & 1.18 & 26.43 & 1.32$\\\\times$ & 0.92 & 16.98 & 1.48$\\\\times$ \\\\\\\\\\n\",\n \" & & & Yes & 2.29 & \\\\textbf{107.53} & \\\\textbf{1.93$\\\\times$} & \\\\textbf{2.19} & \\\\textbf{94.41} & \\\\textbf{2.02$\\\\times$} & \\\\textbf{2.04} & \\\\textbf{69.92} & \\\\textbf{2.06$\\\\times$} & \\\\textbf{2.19} & \\\\textbf{53.06} & \\\\textbf{2.67$\\\\times$} & \\\\textbf{2.05} & \\\\textbf{35.43} & \\\\textbf{3.09$\\\\times$} \\\\\\\\ \\\\cmidrule(l){2-19} \\n\",\n \" & \\\\multirow{4}{*}{\\\\rotatebox[origin=c]{90}{LC-7B}} & \\\\multirow{2}{*}{LC-68M} & No & 1.16 & 76.50 & 1.36$\\\\times$ & 0.52 & 51.00 & 1.07$\\\\times$ & 0.55 & 39.16 & 1.09$\\\\times$ & 0.55 & 21.80 & 1.00$\\\\times$ & 0.54 & 14.73 & 1.18$\\\\times$ \\\\\\\\\\n\",\n \" & & & Yes & \\\\textbf{1.22} & \\\\textbf{80.25} & \\\\textbf{1.43$\\\\times$} & \\\\textbf{1.33} & \\\\textbf{73.69} & \\\\textbf{1.55$\\\\times$} & \\\\textbf{1.42} & \\\\textbf{62.27} & \\\\textbf{1.74$\\\\times$} & \\\\textbf{1.42} & \\\\textbf{44.96} & \\\\textbf{2.06$\\\\times$} & \\\\textbf{1.45} & \\\\textbf{30.67} & \\\\textbf{2.46$\\\\times$} \\\\\\\\ \\\\cmidrule(lr){5-19}\\n\",\n \" & & \\\\multirow{2}{*}{EAGLE} & No & \\\\textbf{2.19} & 111.10 & 1.97$\\\\times$ & 2.00 & 86.80 & 1.82$\\\\times$ & 1.48 & 54.21 & 1.51$\\\\times$ & 1.28 & 26.85 & 1.23$\\\\times$ & 1.06 & 17.54 & 1.40$\\\\times$ \\\\\\\\\\n\",\n \" & & & Yes & 2.11 & \\\\textbf{110.31} & \\\\textbf{1.96$\\\\times$} & \\\\textbf{2.02} & \\\\textbf{93.50} & \\\\textbf{1.97$\\\\times$} & \\\\textbf{1.97} & \\\\textbf{71.84} & \\\\textbf{2.01$\\\\times$} & \\\\textbf{1.99} & \\\\textbf{51.55} & \\\\textbf{2.36$\\\\times$} & \\\\textbf{1.82} & \\\\textbf{33.07} & \\\\textbf{2.66$\\\\times$} \\n\",\n \" \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{8}{*}{\\\\rotatebox[origin=c]{90}{\\\\textbf{BookSum}}} & \\\\multirow{4}{*}{\\\\rotatebox[origin=c]{90}{V-7B}} & \\\\multirow{2}{*}{V-68M} & No & 1.36 & 88.12 & 1.57$\\\\times$ & 0.56 & 53.33 & 1.13$\\\\times$ & 0.51 & 39.30 & 1.08$\\\\times$ & 0.52 & 24.21 & 1.05$\\\\times$ & 0.58 & 15.63 & 1.30$\\\\times$ \\\\\\\\\\n\",\n \" & & & Yes & \\\\textbf{1.75} & \\\\textbf{97.45} & \\\\textbf{1.73$\\\\times$} & \\\\textbf{1.66} & \\\\textbf{81.37} & \\\\textbf{1.73$\\\\times$} & \\\\textbf{1.56} & \\\\textbf{62.97} & \\\\textbf{1.73$\\\\times$} & \\\\textbf{1.70} & \\\\textbf{50.21} & \\\\textbf{2.18$\\\\times$} & \\\\textbf{1.78} & \\\\textbf{35.61} & \\\\textbf{2.98$\\\\times$} \\\\\\\\ \\\\cmidrule(lr){5-19}\\n\",\n \" & & \\\\multirow{2}{*}{EAGLE} & No & \\\\textbf{2.33} & 111.70 & 1.99$\\\\times$ & 1.95 & 82.44 & 1.75$\\\\times$ & 1.87 & 58.01 & 1.59$\\\\times$ & 1.14 & 29.30 & 1.27$\\\\times$ & 0.94 & 18.76 & 1.57$\\\\times$ \\\\\\\\\\n\",\n \" & & & Yes & 2.31 & \\\\textbf{111.82} & \\\\textbf{1.99$\\\\times$} & \\\\textbf{1.99} & \\\\textbf{88.64} & \\\\textbf{1.89$\\\\times$} & \\\\textbf{2.08} & \\\\textbf{70.90} & \\\\textbf{1.95$\\\\times$} & \\\\textbf{2.15} & \\\\textbf{54.53} & \\\\textbf{2.37$\\\\times$} & \\\\textbf{2.11} & \\\\textbf{38.03} & \\\\textbf{3.18$\\\\times$} \\\\\\\\ \\\\cmidrule(l){2-19} \\n\",\n \" & \\\\multirow{4}{*}{\\\\rotatebox[origin=c]{90}{LC-7B}} & \\\\multirow{2}{*}{LC-68M} & No & 1.36 & 88.12 & 1.57$\\\\times$ & 0.56 & 53.33 & 1.14$\\\\times$ & 0.51 & 39.30 & 1.11$\\\\times$ & 0.52 & 24.21 & 1.20$\\\\times$ & 0.58 & 15.63 & 1.28$\\\\times$ \\\\\\\\\\n\",\n \" & & & Yes & \\\\textbf{1.45} & \\\\textbf{91.05} & \\\\textbf{1.63$\\\\times$} & \\\\textbf{1.55} & \\\\textbf{83.60} & \\\\textbf{1.80$\\\\times$} & \\\\textbf{1.54} & \\\\textbf{66.79} & \\\\textbf{1.90$\\\\times$} & \\\\textbf{1.61} & \\\\textbf{49.47} & \\\\textbf{2.45$\\\\times$} & \\\\textbf{1.50} & \\\\textbf{32.21} & \\\\textbf{2.64$\\\\times$} \\\\\\\\ \\\\cmidrule(lr){5-19}\\n\",\n \" & & \\\\multirow{2}{*}{EAGLE} & No & \\\\textbf{2.10} & \\\\textbf{107.67} & \\\\textbf{1.92$\\\\times$} & 1.94 & 86.35 & 1.85$\\\\times$ & 1.37 & 53.42 & 1.51$\\\\times$ & 1.22 & 30.14 & 1.49$\\\\times$ & 1.06 & 18.39 & 1.50$\\\\times$ \\\\\\\\\\n\",\n \" & & & Yes & 2.07 & 106.86 & 1.91$\\\\times$ & \\\\textbf{1.97} & \\\\textbf{90.48} & \\\\textbf{1.94$\\\\times$} & \\\\textbf{1.88} & \\\\textbf{71.50} & \\\\textbf{2.03$\\\\times$} & \\\\textbf{1.92} & \\\\textbf{52.35} & \\\\textbf{2.59$\\\\times$} & \\\\textbf{1.83} & \\\\textbf{34.65} & \\\\textbf{2.84$\\\\times$}\\n\",\n \" \\\\\\\\ \\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Average accepted length ($\\\\tau$), decoding speed (tokens/s) and speedups of different frameworks with and without SpecExtend. Speedup is measured relative to naive autoregressive generation.}\\n\",\n \"\\\\label{tab:main_results}\\n\",\n \"\\\\end{table*}\\n\"\n ]\n]","isConversational":false},"tables":{"kind":"list like","value":[["\\begin{figure*}[!t]\n"," \\centering\n"," \\includegraphics[width=0.95\\textwidth]{main_figure.png}\n"," \\caption{Overview of SpecExtend. FlashAttention accelerates the prefill phases of both target and draft models, and Hybrid Tree Attention accelerates the verification phase. We use the target model’s attention scores from verification to select the most relevant input chunks to retain in the draft model’s KV cache, boosting draft speed and accuracy.}\n"," \\label{fig:cross-model-retrieval}\n","\\end{figure*}\n"],["\\begin{table}[!t]\n","\\centering\n","\\resizebox{\\columnwidth}{!}{\n","\\begin{tabular}{@{}ccccc@{}}\n","\\toprule\n","Cache Type & \\begin{tabular}[c]{@{}c@{}}Full KV\\\\ Cache\\end{tabular} & StreamingLLM & \\begin{tabular}[c]{@{}c@{}}Cross-model\\\\ Retrieval\\\\ (SpecExtend)\\end{tabular} & \\begin{tabular}[c]{@{}c@{}}Retrieval\\\\ (TriForce)\\end{tabular} \\\\ \\midrule\n","Perplexity ($\\downarrow$) \n","& 8.311 & 2.435 & 2.237 & 2.191 \\\\\n","Accuracy ($\\uparrow$) \n","& 0.081 & 0.166 & 0.823 & 0.976 \\\\ \\bottomrule\n","\\end{tabular}\n","}\n","\\caption{Perplexity and draft accuracy of needle tokens in the Needle Retrieval task, using different draft model settings. The first three methods use Vicuna 160M as the draft model, while TriForce uses Vicuna 7B.}\n","\\label{tab:needle_retrieval}\n","\\end{table}\n"],["\\begin{figure}[!t]\n"," \\centering\n"," \\includegraphics[width=\\columnwidth]{latency_breakdown.pdf}\n"," \\caption{Latency breakdown of tree-based speculative decoding with Vicuna 7B on 16K token inputs.}\n"," \\label{fig:latency_breakdown}\n","\\end{figure}\\begin{figure}[t]\n"],["\\begin{table*}[t]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n","\\begin{tabular}{@{}ccccccccccccccccccc@{}}\n","\\toprule\n","\\multirow{2}{*}{} \n"," & \\multicolumn{2}{c}{\\multirow{2}{*}{\\textbf{Setting}}} \n"," & \\multirow{2}{*}{\\textbf{SpecExtend}} \n"," & \\multicolumn{3}{c}{1K} \n"," & \\multicolumn{3}{c}{2K} \n"," & \\multicolumn{3}{c}{4K} \n"," & \\multicolumn{3}{c}{8K} \n"," & \\multicolumn{3}{c}{16K} \n","\\\\\n","\\cmidrule(r){5-7}\n","\\cmidrule(lr){8-10}\n","\\cmidrule(lr){11-13}\n","\\cmidrule(lr){14-16}\n","\\cmidrule(l){17-19}\n"," & \\multicolumn{2}{c}{} \n"," & \n"," & $\\tau$ & Tok/s & Speedup \n"," & $\\tau$ & Tok/s & Speedup \n"," & $\\tau$ & Tok/s & Speedup \n"," & $\\tau$ & Tok/s & Speedup \n"," & $\\tau$ & Tok/s & Speedup \n","\\\\ \\midrule\n","\\multirow{8}{*}{\\rotatebox[origin=c]{90}{\\textbf{GovReport}}}\n"," & \\multirow{4}{*}{\\rotatebox[origin=c]{90}{V-7B}}\n"," & \\multirow{2}{*}{V-68M} & No & 1.73 & 100.31 & 1.78$\\times$ & 0.64 & 55.64 & 1.16$\\times$ & 0.60 & 41.91 & 1.14$\\times$ & 0.62 & 25.71 & 1.08$\\times$ & 0.59 & 16.56 & 1.38$\\times$ \\\\\n"," & & & Yes & \\textbf{2.80} & \\textbf{128.59} & \\textbf{2.28$\\times$} & \\textbf{2.52} & \\textbf{109.58} & \\textbf{2.29$\\times$} & \\textbf{2.04} & \\textbf{76.48} & \\textbf{2.08$\\times$} & \\textbf{2.06} & \\textbf{55.16} & \\textbf{2.33$\\times$} & \\textbf{2.07} & \\textbf{33.84} & \\textbf{2.82$\\times$} \\\\ \\cmidrule(lr){5-19}\n"," & & \\multirow{2}{*}{EAGLE} & No & \\textbf{3.61} & 144.77 & 2.57$\\times$ & 3.04 & 107.52 & 2.24$\\times$ & 2.27 & 66.62 & 1.81$\\times$ & 1.35 & 31.74 & 1.34$\\times$ & 1.00 & 19.35 & 1.61$\\times$ \\\\\n"," & & & Yes & 3.58 & \\textbf{145.53} & \\textbf{2.58$\\times$} & \\textbf{3.08} & \\textbf{113.47} & \\textbf{2.37$\\times$} & \\textbf{2.80} & \\textbf{85.99} & \\textbf{2.34$\\times$} & \\textbf{2.82} & \\textbf{62.90} & \\textbf{2.66$\\times$} & \\textbf{2.51} & \\textbf{37.05} & \\textbf{3.08$\\times$} \\\\ \\cmidrule(l){2-19} \n"," & \\multirow{4}{*}{\\rotatebox[origin=c]{90}{LC-7B}} & \\multirow{2}{*}{LC-68M} & No & 1.73 & 100.31 & 1.78$\\times$ & 0.64 & 55.64 & 1.16$\\times$ & 0.60 & 41.91 & 1.15$\\times$ & 0.62 & 25.71 & 1.12$\\times$ & 0.59 & 16.56 & 1.51$\\times$ \\\\\n"," & & & Yes & \\textbf{2.01} & \\textbf{109.26} & \\textbf{1.94$\\times$} & \\textbf{1.82} & \\textbf{90.27} & \\textbf{1.89$\\times$} & \\textbf{1.66} & \\textbf{68.84} & \\textbf{1.89$\\times$} & \\textbf{1.81} & \\textbf{52.17} & \\textbf{2.30$\\times$} & \\textbf{1.68} & \\textbf{31.11} & \\textbf{2.84$\\times$} \\\\ \\cmidrule(lr){5-19}\n"," & & \\multirow{2}{*}{EAGLE} & No & \\textbf{3.10} & 131.06 & 2.33$\\times$ & 2.47 & 97.53 & 2.04$\\times$ & 1.75 & 60.17 & 1.65$\\times$ & 1.52 & 32.90 & 1.44$\\times$ & 1.18 & 19.84 & 1.81$\\times$ \\\\\n"," & & & Yes & 3.04 & \\textbf{133.14} & \\textbf{2.37$\\times$} & \\textbf{2.56} & \\textbf{103.39} & \\textbf{2.17$\\times$} & \\textbf{2.43} & \\textbf{80.50} & \\textbf{2.21$\\times$} & \\textbf{2.53} & \\textbf{60.14} & \\textbf{2.63$\\times$} & \\textbf{2.25} & \\textbf{35.13} & \\textbf{3.21$\\times$} \\\\ \\midrule\n","\\multirow{8}{*}{\\rotatebox[origin=c]{90}{\\textbf{PG-19}}} & \\multirow{4}{*}{\\rotatebox[origin=c]{90}{V-7B}} & \\multirow{2}{*}{V-68M} & No & 1.16 & 76.50 & 1.37$\\times$ & 0.52 & 51.00 & 1.09$\\times$ & 0.55 & 39.16 & 1.15$\\times$ & 0.55 & 21.80 & 1.01$\\times$ & 0.54 & 14.73 & 1.29$\\times$ \\\\\n"," & & & Yes & \\textbf{1.75} & \\textbf{96.74} & \\textbf{1.74$\\times$} & \\textbf{1.69} & \\textbf{84.74} & \\textbf{1.81$\\times$} & \\textbf{1.61} & \\textbf{63.94} & \\textbf{1.88$\\times$} & \\textbf{1.65} & \\textbf{47.64} & \\textbf{2.39$\\times$} & \\textbf{1.70} & \\textbf{32.88} & \\textbf{2.87$\\times$} \\\\ \\cmidrule(lr){5-19}\n"," & & \\multirow{2}{*}{EAGLE} & No & \\textbf{2.29} & 107.31 & 1.92$\\times$ & 2.18 & 88.92 & 1.89$\\times$ & 1.88 & 54.71 & 1.60$\\times$ & 1.18 & 26.43 & 1.32$\\times$ & 0.92 & 16.98 & 1.48$\\times$ \\\\\n"," & & & Yes & 2.29 & \\textbf{107.53} & \\textbf{1.93$\\times$} & \\textbf{2.19} & \\textbf{94.41} & \\textbf{2.02$\\times$} & \\textbf{2.04} & \\textbf{69.92} & \\textbf{2.06$\\times$} & \\textbf{2.19} & \\textbf{53.06} & \\textbf{2.67$\\times$} & \\textbf{2.05} & \\textbf{35.43} & \\textbf{3.09$\\times$} \\\\ \\cmidrule(l){2-19} \n"," & \\multirow{4}{*}{\\rotatebox[origin=c]{90}{LC-7B}} & \\multirow{2}{*}{LC-68M} & No & 1.16 & 76.50 & 1.36$\\times$ & 0.52 & 51.00 & 1.07$\\times$ & 0.55 & 39.16 & 1.09$\\times$ & 0.55 & 21.80 & 1.00$\\times$ & 0.54 & 14.73 & 1.18$\\times$ \\\\\n"," & & & Yes & \\textbf{1.22} & \\textbf{80.25} & \\textbf{1.43$\\times$} & \\textbf{1.33} & \\textbf{73.69} & \\textbf{1.55$\\times$} & \\textbf{1.42} & \\textbf{62.27} & \\textbf{1.74$\\times$} & \\textbf{1.42} & \\textbf{44.96} & \\textbf{2.06$\\times$} & \\textbf{1.45} & \\textbf{30.67} & \\textbf{2.46$\\times$} \\\\ \\cmidrule(lr){5-19}\n"," & & \\multirow{2}{*}{EAGLE} & No & \\textbf{2.19} & 111.10 & 1.97$\\times$ & 2.00 & 86.80 & 1.82$\\times$ & 1.48 & 54.21 & 1.51$\\times$ & 1.28 & 26.85 & 1.23$\\times$ & 1.06 & 17.54 & 1.40$\\times$ \\\\\n"," & & & Yes & 2.11 & \\textbf{110.31} & \\textbf{1.96$\\times$} & \\textbf{2.02} & \\textbf{93.50} & \\textbf{1.97$\\times$} & \\textbf{1.97} & \\textbf{71.84} & \\textbf{2.01$\\times$} & \\textbf{1.99} & \\textbf{51.55} & \\textbf{2.36$\\times$} & \\textbf{1.82} & \\textbf{33.07} & \\textbf{2.66$\\times$} \n"," \\\\ \\midrule\n","\\multirow{8}{*}{\\rotatebox[origin=c]{90}{\\textbf{BookSum}}} & \\multirow{4}{*}{\\rotatebox[origin=c]{90}{V-7B}} & \\multirow{2}{*}{V-68M} & No & 1.36 & 88.12 & 1.57$\\times$ & 0.56 & 53.33 & 1.13$\\times$ & 0.51 & 39.30 & 1.08$\\times$ & 0.52 & 24.21 & 1.05$\\times$ & 0.58 & 15.63 & 1.30$\\times$ \\\\\n"," & & & Yes & \\textbf{1.75} & \\textbf{97.45} & \\textbf{1.73$\\times$} & \\textbf{1.66} & \\textbf{81.37} & \\textbf{1.73$\\times$} & \\textbf{1.56} & \\textbf{62.97} & \\textbf{1.73$\\times$} & \\textbf{1.70} & \\textbf{50.21} & \\textbf{2.18$\\times$} & \\textbf{1.78} & \\textbf{35.61} & \\textbf{2.98$\\times$} \\\\ \\cmidrule(lr){5-19}\n"," & & \\multirow{2}{*}{EAGLE} & No & \\textbf{2.33} & 111.70 & 1.99$\\times$ & 1.95 & 82.44 & 1.75$\\times$ & 1.87 & 58.01 & 1.59$\\times$ & 1.14 & 29.30 & 1.27$\\times$ & 0.94 & 18.76 & 1.57$\\times$ \\\\\n"," & & & Yes & 2.31 & \\textbf{111.82} & \\textbf{1.99$\\times$} & \\textbf{1.99} & \\textbf{88.64} & \\textbf{1.89$\\times$} & \\textbf{2.08} & \\textbf{70.90} & \\textbf{1.95$\\times$} & \\textbf{2.15} & \\textbf{54.53} & \\textbf{2.37$\\times$} & \\textbf{2.11} & \\textbf{38.03} & \\textbf{3.18$\\times$} \\\\ \\cmidrule(l){2-19} \n"," & \\multirow{4}{*}{\\rotatebox[origin=c]{90}{LC-7B}} & \\multirow{2}{*}{LC-68M} & No & 1.36 & 88.12 & 1.57$\\times$ & 0.56 & 53.33 & 1.14$\\times$ & 0.51 & 39.30 & 1.11$\\times$ & 0.52 & 24.21 & 1.20$\\times$ & 0.58 & 15.63 & 1.28$\\times$ \\\\\n"," & & & Yes & \\textbf{1.45} & \\textbf{91.05} & \\textbf{1.63$\\times$} & \\textbf{1.55} & \\textbf{83.60} & \\textbf{1.80$\\times$} & \\textbf{1.54} & \\textbf{66.79} & \\textbf{1.90$\\times$} & \\textbf{1.61} & \\textbf{49.47} & \\textbf{2.45$\\times$} & \\textbf{1.50} & \\textbf{32.21} & \\textbf{2.64$\\times$} \\\\ \\cmidrule(lr){5-19}\n"," & & \\multirow{2}{*}{EAGLE} & No & \\textbf{2.10} & \\textbf{107.67} & \\textbf{1.92$\\times$} & 1.94 & 86.35 & 1.85$\\times$ & 1.37 & 53.42 & 1.51$\\times$ & 1.22 & 30.14 & 1.49$\\times$ & 1.06 & 18.39 & 1.50$\\times$ \\\\\n"," & & & Yes & 2.07 & 106.86 & 1.91$\\times$ & \\textbf{1.97} & \\textbf{90.48} & \\textbf{1.94$\\times$} & \\textbf{1.88} & \\textbf{71.50} & \\textbf{2.03$\\times$} & \\textbf{1.92} & \\textbf{52.35} & \\textbf{2.59$\\times$} & \\textbf{1.83} & \\textbf{34.65} & \\textbf{2.84$\\times$}\n"," \\\\ \\bottomrule\n","\\end{tabular}\n","}\n","\\caption{Average accepted length ($\\tau$), decoding speed (tokens/s) and speedups of different frameworks with and without SpecExtend. Speedup is measured relative to naive autoregressive generation.}\n","\\label{tab:main_results}\n","\\end{table*}\n"],["\\begin{table*}[t]\n","\\centering\n","\n","\\resizebox{\\textwidth}{!}{\n","\\begin{tabular}{@{}cccccccccccccccc@{}}\n","\\toprule\n","\\multirow{2}{*}{\\textbf{Method}}\n"," & \\multicolumn{5}{c}{\\textbf{GovReport}} \n"," & \\multicolumn{5}{c}{\\textbf{PG-19}} \n"," & \\multicolumn{5}{c}{\\textbf{BookSum}} \n","\\\\\n","\\cmidrule(r){2-6}\n","\\cmidrule(lr){7-11}\n","\\cmidrule(l){12-16}\n"," & 1K & 2K & 4K & 8K & 16K & 1K & 2K & 4K & 8K & 16K & 1K & 2K & 4K & 8K & 16K \\\\ \\midrule\n","FlashDecoding & 1.06$\\times$ & 1.07$\\times$ & 1.12$\\times$ & 1.23$\\times$ & 1.51$\\times$ & 1.07$\\times$ & 1.08$\\times$ & 1.18$\\times$ & 1.38$\\times$ & 1.52$\\times$ & 1.06$\\times$ & 1.09$\\times$ & 1.10$\\times$ & 1.26$\\times$ & 1.58$\\times$ \\\\\n","TriForce & \\multicolumn{1}{l}{1.25$\\times$} & \\multicolumn{1}{l}{1.26$\\times$} & \\multicolumn{1}{l}{1.22$\\times$} & \\multicolumn{1}{l}{1.18$\\times$} & \\multicolumn{1}{l}{1.02$\\times$} & \\multicolumn{1}{l}{1.12$\\times$} & \\multicolumn{1}{l}{1.19$\\times$} & \\multicolumn{1}{l}{1.16$\\times$} & \\multicolumn{1}{l}{1.15$\\times$} & \\multicolumn{1}{l}{1.13$\\times$} & \\multicolumn{1}{l}{1.18$\\times$} & \\multicolumn{1}{l}{1.20$\\times$} & \\multicolumn{1}{l}{1.18$\\times$} & \\multicolumn{1}{l}{1.18$\\times$} & 1.11$\\times$\\\\\n","MagicDec & \\multicolumn{1}{l}{1.07$\\times$} & \\multicolumn{1}{l}{1.08$\\times$} & \\multicolumn{1}{l}{1.05$\\times$} & \\multicolumn{1}{l}{1.13$\\times$} & \\multicolumn{1}{l}{1.24$\\times$} & \\multicolumn{1}{l}{1.03$\\times$} & \\multicolumn{1}{l}{1.07$\\times$} & \\multicolumn{1}{l}{1.06$\\times$} & \\multicolumn{1}{l}{1.10$\\times$} & \\multicolumn{1}{l}{1.19$\\times$} & \\multicolumn{1}{l}{1.03$\\times$} & \\multicolumn{1}{l}{1.04$\\times$} & \\multicolumn{1}{l}{1.06$\\times$} & \\multicolumn{1}{l}{1.18$\\times$} & 1.23$\\times$\\\\\n","Standard & 1.78$\\times$ & 1.16$\\times$ & 1.14$\\times$ & 1.08$\\times$ & 1.38$\\times$ & 1.37$\\times$ & 1.09$\\times$ & 1.15$\\times$ & 1.09$\\times$ & 1.29$\\times$ & 1.57$\\times$ & 1.14$\\times$ & 1.08$\\times$ & 1.05$\\times$ & 1.30$\\times$ \\\\\n","Standard + SpecExtend & \\textbf{2.28$\\times$} & \\textbf{2.29$\\times$} & \\textbf{2.08$\\times$} & \\textbf{2.29$\\times$} & \\textbf{2.65$\\times$} & \\textbf{1.74$\\times$} & \\textbf{1.81$\\times$} & \\textbf{1.88$\\times$} & \\textbf{2.34$\\times$} & \\textbf{2.70$\\times$} & \\textbf{1.74$\\times$} & \\textbf{1.74$\\times$} & \\textbf{1.73$\\times$} & \\textbf{2.14$\\times$} & \\textbf{2.81$\\times$} \\\\ \\bottomrule\n","\\end{tabular}\n","}\n","\\caption{Speedup comparison of off-the-shelf methods for long sequence generation with Vicuna 7B. Standard refers to standard tree-based speculative decoding.}\n","\\label{tab:comparison-other-methods}\n","\\end{table*}\n"],["\\begin{table}[t!]\n","\\centering\n","\\resizebox{\\columnwidth}{!}{\n","\\begin{tabular}{@{}ccccc@{}}\n","\\toprule\n"," & \\begin{tabular}[c]{@{}c@{}}Target \\\\Forward\\end{tabular} & \\begin{tabular}[c]{@{}c@{}}Target Forward\\\\ w/ Retrieval\\end{tabular} & \\begin{tabular}[c]{@{}c@{}}Draft \\\\Forward\\end{tabular} & \\begin{tabular}[c]{@{}c@{}}Retrieval Cache\\\\ Update\\end{tabular} \\\\ \\midrule\n","Latency (ms) & 53.76 & 54.11 & 0.84 & 0.34 \\\\ \\bottomrule\n","\\end{tabular}\n","}\n","\\caption{Latency overhead of a single retrieval cache update step on 16K token inputs.}\n","\\label{tab:latency_overhead}\n","\\end{table}\n"],["\\begin{table*}[t]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n","\\begin{tabular}{@{}c|cc|c|cc|c|cc|c|cc@{}}\n","\\toprule\n","\\begin{tabular}[c]{@{}c@{}}Working \\\\ Cache Size\\end{tabular} \n"," & Vicuna-68M & EAGLE \n"," & \\begin{tabular}[c]{@{}c@{}}Chunk\\\\ Size\\end{tabular} \n"," & Vicuna-68M & EAGLE \n"," & Top-k \n"," & Vicuna-68M & EAGLE \n"," & \\begin{tabular}[c]{@{}c@{}}Retrieval\\\\ Frequency\\end{tabular} \n"," & Vicuna-68M & EAGLE \n","\\\\\n","\n","\n","\n","\n","\\cmidrule(l){1-12}\n","64 & 32.52 & 39.10 & 1 & 31.05 & 48.05 & 2 & 30.72 & 38.22 & 1 & 33.05 & 47.78 \\\\\n","128 & 32.91 & 39.95 & 2 & 32.27 & 49.49 & 4 & 32.65 & 40.36 & 2 & 33.54 & 46.78 \\\\\n","256 & 33.65 & 41.53 & 4 & 32.97 & 49.55 & 8 & 32.76 & 41.49 & 4 & \\textbf{33.59} & 48.17 \\\\\n","512 & 33.53 & 42.77 & 8 & 33.39 & 49.18 & 16 & 33.19 & 43.90 & 8 & 33.11 & \\textbf{48.52} \\\\\n","1024 & \\textbf{33.69} & 44.19 & 16 & 33.41 & 48.92 & 32 & \\textbf{33.28} & 47.21 & 16 & 33.16 & 48.36 \\\\\n","2048 & 32.36 & \\textbf{45.33} & 32 & \\textbf{33.52} & \\textbf{49.68} & 64 & 32.50 & \\textbf{48.09} & 32 & 33.28 & 48.11 \\\\\n","4096 & 25.84 & 43.68 & 64 & 33.23 & 48.25 & 128 & 25.20 & 45.14 & 64 & 33.29 & 48.13 \\\\\n","8192 & 24.32 & 33.10 & 128 & 33.20 & 47.48 & 256 & 23.95 & 32.48 & 128 & 33.21 & 48.20 \\\\ \\bottomrule\n","\\end{tabular}\n","}\n","\\caption{Decoding speed (tokens/s) for different working KV cache size and retrieval parameters. We use Vicuna-7B as the target model and 8K inputs from GovReport.}\n","\\label{tab:ablation_retrieval}\n","\\end{table*}\n"],["\\begin{table*}[t]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n","\\begin{tabular}{@{}cccccccccccccccc@{}}\n","\\toprule\n","\\multirow{2}{*}{Setting} \n"," & \\multicolumn{3}{c}{1K} \n"," & \\multicolumn{3}{c}{2K} \n"," & \\multicolumn{3}{c}{4K} \n"," & \\multicolumn{3}{c}{8K} \n"," & \\multicolumn{3}{c}{16K} \n","\\\\\n","\\cmidrule(r){2-4}\n","\\cmidrule(lr){5-7}\n","\\cmidrule(lr){8-10}\n","\\cmidrule(lr){11-13}\n","\\cmidrule(l){14-16}\n"," & $\\tau$ & Tok/s & Speedup \n"," & $\\tau$ & Tok/s & Speedup \n"," & $\\tau$ & Tok/s & Speedup \n"," & $\\tau$ & Tok/s & Speedup \n"," & $\\tau$ & Tok/s & Speedup \n","\\\\ \\midrule\n","Standard & 2.75 & 127.34 & $ \\text{-} $ & 1.83 & 87.34 & $ \\text{-} $ & 0.92 & 47.41 & $ \\text{-} $ & 0.78 & 27.54 & $ \\text{-} $ & 0.72 & 17.60 & $ \\text{-} $ \\\\\n","Standard + FA & 2.71 & 131.02 & 1.03$\\times$ & 1.84 & 91.79 & 1.05$\\times$ & 0.97 & 52.74 & 1.11$\\times$ & 0.81 & 34.33 & 1.25$\\times$ & 0.75 & 22.07 & 1.25$\\times$ \\\\\n","Standard + HTA & 2.61 & 122.73 & 0.96$\\times$ & 1.74 & 85.57 & 0.98$\\times$ & 0.92 & 47.62 & 1.01$\\times$ & 0.76 & 31.08 & 1.14$\\times$ & 0.74 & 20.95 & 1.19$\\times$ \\\\\n","Standard + StreamingLLM & 2.75 & 128.62 & 1.01$\\times$ & 1.81 & 85.60 & 0.98$\\times$ & 1.53 & 59.11 & 1.25$\\times$ & 1.59 & 35.89 & 1.30$\\times$ & 1.60 & 22.39 & 1.27$\\times$ \\\\\n","Standard + Retrieval & 2.86 & 130.35 & 1.02$\\times$ & 2.57 & 104.12 & 1.19$\\times$ & 1.90 & 64.85 & 1.36$\\times$ & 1.78 & 37.11 & 1.47$\\times$ & 1.93 & 25.82 & 1.46$\\times$ \\\\ \\bottomrule\n","\\end{tabular}\n","}\n","\n","\\caption{Ablation on the components of SpecExtend. Standard indicates the standard tree-based speculative decoding with Vicuna 7B/68M. FA and HTA indicate FlashAttention for prefill and Hybrid Tree Attention, respectively. Standard + Retrieval refers to using SpecExtend's cross-model retrieval cache. Speedups are measured relative to the standard framework.}\n","\\label{tab:ablation_components}\n","\\end{table*}\n"]],"string":"[\n [\n \"\\\\begin{figure*}[!t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=0.95\\\\textwidth]{main_figure.png}\\n\",\n \" \\\\caption{Overview of SpecExtend. FlashAttention accelerates the prefill phases of both target and draft models, and Hybrid Tree Attention accelerates the verification phase. We use the target model’s attention scores from verification to select the most relevant input chunks to retain in the draft model’s KV cache, boosting draft speed and accuracy.}\\n\",\n \" \\\\label{fig:cross-model-retrieval}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{table}[!t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\columnwidth}{!}{\\n\",\n \"\\\\begin{tabular}{@{}ccccc@{}}\\n\",\n \"\\\\toprule\\n\",\n \"Cache Type & \\\\begin{tabular}[c]{@{}c@{}}Full KV\\\\\\\\ Cache\\\\end{tabular} & StreamingLLM & \\\\begin{tabular}[c]{@{}c@{}}Cross-model\\\\\\\\ Retrieval\\\\\\\\ (SpecExtend)\\\\end{tabular} & \\\\begin{tabular}[c]{@{}c@{}}Retrieval\\\\\\\\ (TriForce)\\\\end{tabular} \\\\\\\\ \\\\midrule\\n\",\n \"Perplexity ($\\\\downarrow$) \\n\",\n \"& 8.311 & 2.435 & 2.237 & 2.191 \\\\\\\\\\n\",\n \"Accuracy ($\\\\uparrow$) \\n\",\n \"& 0.081 & 0.166 & 0.823 & 0.976 \\\\\\\\ \\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Perplexity and draft accuracy of needle tokens in the Needle Retrieval task, using different draft model settings. The first three methods use Vicuna 160M as the draft model, while TriForce uses Vicuna 7B.}\\n\",\n \"\\\\label{tab:needle_retrieval}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{figure}[!t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\columnwidth]{latency_breakdown.pdf}\\n\",\n \" \\\\caption{Latency breakdown of tree-based speculative decoding with Vicuna 7B on 16K token inputs.}\\n\",\n \" \\\\label{fig:latency_breakdown}\\n\",\n \"\\\\end{figure}\\\\begin{figure}[t]\\n\"\n ],\n [\n \"\\\\begin{table*}[t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \"\\\\begin{tabular}{@{}ccccccccccccccccccc@{}}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{} \\n\",\n \" & \\\\multicolumn{2}{c}{\\\\multirow{2}{*}{\\\\textbf{Setting}}} \\n\",\n \" & \\\\multirow{2}{*}{\\\\textbf{SpecExtend}} \\n\",\n \" & \\\\multicolumn{3}{c}{1K} \\n\",\n \" & \\\\multicolumn{3}{c}{2K} \\n\",\n \" & \\\\multicolumn{3}{c}{4K} \\n\",\n \" & \\\\multicolumn{3}{c}{8K} \\n\",\n \" & \\\\multicolumn{3}{c}{16K} \\n\",\n \"\\\\\\\\\\n\",\n \"\\\\cmidrule(r){5-7}\\n\",\n \"\\\\cmidrule(lr){8-10}\\n\",\n \"\\\\cmidrule(lr){11-13}\\n\",\n \"\\\\cmidrule(lr){14-16}\\n\",\n \"\\\\cmidrule(l){17-19}\\n\",\n \" & \\\\multicolumn{2}{c}{} \\n\",\n \" & \\n\",\n \" & $\\\\tau$ & Tok/s & Speedup \\n\",\n \" & $\\\\tau$ & Tok/s & Speedup \\n\",\n \" & $\\\\tau$ & Tok/s & Speedup \\n\",\n \" & $\\\\tau$ & Tok/s & Speedup \\n\",\n \" & $\\\\tau$ & Tok/s & Speedup \\n\",\n \"\\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{8}{*}{\\\\rotatebox[origin=c]{90}{\\\\textbf{GovReport}}}\\n\",\n \" & \\\\multirow{4}{*}{\\\\rotatebox[origin=c]{90}{V-7B}}\\n\",\n \" & \\\\multirow{2}{*}{V-68M} & No & 1.73 & 100.31 & 1.78$\\\\times$ & 0.64 & 55.64 & 1.16$\\\\times$ & 0.60 & 41.91 & 1.14$\\\\times$ & 0.62 & 25.71 & 1.08$\\\\times$ & 0.59 & 16.56 & 1.38$\\\\times$ \\\\\\\\\\n\",\n \" & & & Yes & \\\\textbf{2.80} & \\\\textbf{128.59} & \\\\textbf{2.28$\\\\times$} & \\\\textbf{2.52} & \\\\textbf{109.58} & \\\\textbf{2.29$\\\\times$} & \\\\textbf{2.04} & \\\\textbf{76.48} & \\\\textbf{2.08$\\\\times$} & \\\\textbf{2.06} & \\\\textbf{55.16} & \\\\textbf{2.33$\\\\times$} & \\\\textbf{2.07} & \\\\textbf{33.84} & \\\\textbf{2.82$\\\\times$} \\\\\\\\ \\\\cmidrule(lr){5-19}\\n\",\n \" & & \\\\multirow{2}{*}{EAGLE} & No & \\\\textbf{3.61} & 144.77 & 2.57$\\\\times$ & 3.04 & 107.52 & 2.24$\\\\times$ & 2.27 & 66.62 & 1.81$\\\\times$ & 1.35 & 31.74 & 1.34$\\\\times$ & 1.00 & 19.35 & 1.61$\\\\times$ \\\\\\\\\\n\",\n \" & & & Yes & 3.58 & \\\\textbf{145.53} & \\\\textbf{2.58$\\\\times$} & \\\\textbf{3.08} & \\\\textbf{113.47} & \\\\textbf{2.37$\\\\times$} & \\\\textbf{2.80} & \\\\textbf{85.99} & \\\\textbf{2.34$\\\\times$} & \\\\textbf{2.82} & \\\\textbf{62.90} & \\\\textbf{2.66$\\\\times$} & \\\\textbf{2.51} & \\\\textbf{37.05} & \\\\textbf{3.08$\\\\times$} \\\\\\\\ \\\\cmidrule(l){2-19} \\n\",\n \" & \\\\multirow{4}{*}{\\\\rotatebox[origin=c]{90}{LC-7B}} & \\\\multirow{2}{*}{LC-68M} & No & 1.73 & 100.31 & 1.78$\\\\times$ & 0.64 & 55.64 & 1.16$\\\\times$ & 0.60 & 41.91 & 1.15$\\\\times$ & 0.62 & 25.71 & 1.12$\\\\times$ & 0.59 & 16.56 & 1.51$\\\\times$ \\\\\\\\\\n\",\n \" & & & Yes & \\\\textbf{2.01} & \\\\textbf{109.26} & \\\\textbf{1.94$\\\\times$} & \\\\textbf{1.82} & \\\\textbf{90.27} & \\\\textbf{1.89$\\\\times$} & \\\\textbf{1.66} & \\\\textbf{68.84} & \\\\textbf{1.89$\\\\times$} & \\\\textbf{1.81} & \\\\textbf{52.17} & \\\\textbf{2.30$\\\\times$} & \\\\textbf{1.68} & \\\\textbf{31.11} & \\\\textbf{2.84$\\\\times$} \\\\\\\\ \\\\cmidrule(lr){5-19}\\n\",\n \" & & \\\\multirow{2}{*}{EAGLE} & No & \\\\textbf{3.10} & 131.06 & 2.33$\\\\times$ & 2.47 & 97.53 & 2.04$\\\\times$ & 1.75 & 60.17 & 1.65$\\\\times$ & 1.52 & 32.90 & 1.44$\\\\times$ & 1.18 & 19.84 & 1.81$\\\\times$ \\\\\\\\\\n\",\n \" & & & Yes & 3.04 & \\\\textbf{133.14} & \\\\textbf{2.37$\\\\times$} & \\\\textbf{2.56} & \\\\textbf{103.39} & \\\\textbf{2.17$\\\\times$} & \\\\textbf{2.43} & \\\\textbf{80.50} & \\\\textbf{2.21$\\\\times$} & \\\\textbf{2.53} & \\\\textbf{60.14} & \\\\textbf{2.63$\\\\times$} & \\\\textbf{2.25} & \\\\textbf{35.13} & \\\\textbf{3.21$\\\\times$} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{8}{*}{\\\\rotatebox[origin=c]{90}{\\\\textbf{PG-19}}} & \\\\multirow{4}{*}{\\\\rotatebox[origin=c]{90}{V-7B}} & \\\\multirow{2}{*}{V-68M} & No & 1.16 & 76.50 & 1.37$\\\\times$ & 0.52 & 51.00 & 1.09$\\\\times$ & 0.55 & 39.16 & 1.15$\\\\times$ & 0.55 & 21.80 & 1.01$\\\\times$ & 0.54 & 14.73 & 1.29$\\\\times$ \\\\\\\\\\n\",\n \" & & & Yes & \\\\textbf{1.75} & \\\\textbf{96.74} & \\\\textbf{1.74$\\\\times$} & \\\\textbf{1.69} & \\\\textbf{84.74} & \\\\textbf{1.81$\\\\times$} & \\\\textbf{1.61} & \\\\textbf{63.94} & \\\\textbf{1.88$\\\\times$} & \\\\textbf{1.65} & \\\\textbf{47.64} & \\\\textbf{2.39$\\\\times$} & \\\\textbf{1.70} & \\\\textbf{32.88} & \\\\textbf{2.87$\\\\times$} \\\\\\\\ \\\\cmidrule(lr){5-19}\\n\",\n \" & & \\\\multirow{2}{*}{EAGLE} & No & \\\\textbf{2.29} & 107.31 & 1.92$\\\\times$ & 2.18 & 88.92 & 1.89$\\\\times$ & 1.88 & 54.71 & 1.60$\\\\times$ & 1.18 & 26.43 & 1.32$\\\\times$ & 0.92 & 16.98 & 1.48$\\\\times$ \\\\\\\\\\n\",\n \" & & & Yes & 2.29 & \\\\textbf{107.53} & \\\\textbf{1.93$\\\\times$} & \\\\textbf{2.19} & \\\\textbf{94.41} & \\\\textbf{2.02$\\\\times$} & \\\\textbf{2.04} & \\\\textbf{69.92} & \\\\textbf{2.06$\\\\times$} & \\\\textbf{2.19} & \\\\textbf{53.06} & \\\\textbf{2.67$\\\\times$} & \\\\textbf{2.05} & \\\\textbf{35.43} & \\\\textbf{3.09$\\\\times$} \\\\\\\\ \\\\cmidrule(l){2-19} \\n\",\n \" & \\\\multirow{4}{*}{\\\\rotatebox[origin=c]{90}{LC-7B}} & \\\\multirow{2}{*}{LC-68M} & No & 1.16 & 76.50 & 1.36$\\\\times$ & 0.52 & 51.00 & 1.07$\\\\times$ & 0.55 & 39.16 & 1.09$\\\\times$ & 0.55 & 21.80 & 1.00$\\\\times$ & 0.54 & 14.73 & 1.18$\\\\times$ \\\\\\\\\\n\",\n \" & & & Yes & \\\\textbf{1.22} & \\\\textbf{80.25} & \\\\textbf{1.43$\\\\times$} & \\\\textbf{1.33} & \\\\textbf{73.69} & \\\\textbf{1.55$\\\\times$} & \\\\textbf{1.42} & \\\\textbf{62.27} & \\\\textbf{1.74$\\\\times$} & \\\\textbf{1.42} & \\\\textbf{44.96} & \\\\textbf{2.06$\\\\times$} & \\\\textbf{1.45} & \\\\textbf{30.67} & \\\\textbf{2.46$\\\\times$} \\\\\\\\ \\\\cmidrule(lr){5-19}\\n\",\n \" & & \\\\multirow{2}{*}{EAGLE} & No & \\\\textbf{2.19} & 111.10 & 1.97$\\\\times$ & 2.00 & 86.80 & 1.82$\\\\times$ & 1.48 & 54.21 & 1.51$\\\\times$ & 1.28 & 26.85 & 1.23$\\\\times$ & 1.06 & 17.54 & 1.40$\\\\times$ \\\\\\\\\\n\",\n \" & & & Yes & 2.11 & \\\\textbf{110.31} & \\\\textbf{1.96$\\\\times$} & \\\\textbf{2.02} & \\\\textbf{93.50} & \\\\textbf{1.97$\\\\times$} & \\\\textbf{1.97} & \\\\textbf{71.84} & \\\\textbf{2.01$\\\\times$} & \\\\textbf{1.99} & \\\\textbf{51.55} & \\\\textbf{2.36$\\\\times$} & \\\\textbf{1.82} & \\\\textbf{33.07} & \\\\textbf{2.66$\\\\times$} \\n\",\n \" \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{8}{*}{\\\\rotatebox[origin=c]{90}{\\\\textbf{BookSum}}} & \\\\multirow{4}{*}{\\\\rotatebox[origin=c]{90}{V-7B}} & \\\\multirow{2}{*}{V-68M} & No & 1.36 & 88.12 & 1.57$\\\\times$ & 0.56 & 53.33 & 1.13$\\\\times$ & 0.51 & 39.30 & 1.08$\\\\times$ & 0.52 & 24.21 & 1.05$\\\\times$ & 0.58 & 15.63 & 1.30$\\\\times$ \\\\\\\\\\n\",\n \" & & & Yes & \\\\textbf{1.75} & \\\\textbf{97.45} & \\\\textbf{1.73$\\\\times$} & \\\\textbf{1.66} & \\\\textbf{81.37} & \\\\textbf{1.73$\\\\times$} & \\\\textbf{1.56} & \\\\textbf{62.97} & \\\\textbf{1.73$\\\\times$} & \\\\textbf{1.70} & \\\\textbf{50.21} & \\\\textbf{2.18$\\\\times$} & \\\\textbf{1.78} & \\\\textbf{35.61} & \\\\textbf{2.98$\\\\times$} \\\\\\\\ \\\\cmidrule(lr){5-19}\\n\",\n \" & & \\\\multirow{2}{*}{EAGLE} & No & \\\\textbf{2.33} & 111.70 & 1.99$\\\\times$ & 1.95 & 82.44 & 1.75$\\\\times$ & 1.87 & 58.01 & 1.59$\\\\times$ & 1.14 & 29.30 & 1.27$\\\\times$ & 0.94 & 18.76 & 1.57$\\\\times$ \\\\\\\\\\n\",\n \" & & & Yes & 2.31 & \\\\textbf{111.82} & \\\\textbf{1.99$\\\\times$} & \\\\textbf{1.99} & \\\\textbf{88.64} & \\\\textbf{1.89$\\\\times$} & \\\\textbf{2.08} & \\\\textbf{70.90} & \\\\textbf{1.95$\\\\times$} & \\\\textbf{2.15} & \\\\textbf{54.53} & \\\\textbf{2.37$\\\\times$} & \\\\textbf{2.11} & \\\\textbf{38.03} & \\\\textbf{3.18$\\\\times$} \\\\\\\\ \\\\cmidrule(l){2-19} \\n\",\n \" & \\\\multirow{4}{*}{\\\\rotatebox[origin=c]{90}{LC-7B}} & \\\\multirow{2}{*}{LC-68M} & No & 1.36 & 88.12 & 1.57$\\\\times$ & 0.56 & 53.33 & 1.14$\\\\times$ & 0.51 & 39.30 & 1.11$\\\\times$ & 0.52 & 24.21 & 1.20$\\\\times$ & 0.58 & 15.63 & 1.28$\\\\times$ \\\\\\\\\\n\",\n \" & & & Yes & \\\\textbf{1.45} & \\\\textbf{91.05} & \\\\textbf{1.63$\\\\times$} & \\\\textbf{1.55} & \\\\textbf{83.60} & \\\\textbf{1.80$\\\\times$} & \\\\textbf{1.54} & \\\\textbf{66.79} & \\\\textbf{1.90$\\\\times$} & \\\\textbf{1.61} & \\\\textbf{49.47} & \\\\textbf{2.45$\\\\times$} & \\\\textbf{1.50} & \\\\textbf{32.21} & \\\\textbf{2.64$\\\\times$} \\\\\\\\ \\\\cmidrule(lr){5-19}\\n\",\n \" & & \\\\multirow{2}{*}{EAGLE} & No & \\\\textbf{2.10} & \\\\textbf{107.67} & \\\\textbf{1.92$\\\\times$} & 1.94 & 86.35 & 1.85$\\\\times$ & 1.37 & 53.42 & 1.51$\\\\times$ & 1.22 & 30.14 & 1.49$\\\\times$ & 1.06 & 18.39 & 1.50$\\\\times$ \\\\\\\\\\n\",\n \" & & & Yes & 2.07 & 106.86 & 1.91$\\\\times$ & \\\\textbf{1.97} & \\\\textbf{90.48} & \\\\textbf{1.94$\\\\times$} & \\\\textbf{1.88} & \\\\textbf{71.50} & \\\\textbf{2.03$\\\\times$} & \\\\textbf{1.92} & \\\\textbf{52.35} & \\\\textbf{2.59$\\\\times$} & \\\\textbf{1.83} & \\\\textbf{34.65} & \\\\textbf{2.84$\\\\times$}\\n\",\n \" \\\\\\\\ \\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Average accepted length ($\\\\tau$), decoding speed (tokens/s) and speedups of different frameworks with and without SpecExtend. Speedup is measured relative to naive autoregressive generation.}\\n\",\n \"\\\\label{tab:main_results}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[t]\\n\",\n \"\\\\centering\\n\",\n \"\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \"\\\\begin{tabular}{@{}cccccccccccccccc@{}}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{\\\\textbf{Method}}\\n\",\n \" & \\\\multicolumn{5}{c}{\\\\textbf{GovReport}} \\n\",\n \" & \\\\multicolumn{5}{c}{\\\\textbf{PG-19}} \\n\",\n \" & \\\\multicolumn{5}{c}{\\\\textbf{BookSum}} \\n\",\n \"\\\\\\\\\\n\",\n \"\\\\cmidrule(r){2-6}\\n\",\n \"\\\\cmidrule(lr){7-11}\\n\",\n \"\\\\cmidrule(l){12-16}\\n\",\n \" & 1K & 2K & 4K & 8K & 16K & 1K & 2K & 4K & 8K & 16K & 1K & 2K & 4K & 8K & 16K \\\\\\\\ \\\\midrule\\n\",\n \"FlashDecoding & 1.06$\\\\times$ & 1.07$\\\\times$ & 1.12$\\\\times$ & 1.23$\\\\times$ & 1.51$\\\\times$ & 1.07$\\\\times$ & 1.08$\\\\times$ & 1.18$\\\\times$ & 1.38$\\\\times$ & 1.52$\\\\times$ & 1.06$\\\\times$ & 1.09$\\\\times$ & 1.10$\\\\times$ & 1.26$\\\\times$ & 1.58$\\\\times$ \\\\\\\\\\n\",\n \"TriForce & \\\\multicolumn{1}{l}{1.25$\\\\times$} & \\\\multicolumn{1}{l}{1.26$\\\\times$} & \\\\multicolumn{1}{l}{1.22$\\\\times$} & \\\\multicolumn{1}{l}{1.18$\\\\times$} & \\\\multicolumn{1}{l}{1.02$\\\\times$} & \\\\multicolumn{1}{l}{1.12$\\\\times$} & \\\\multicolumn{1}{l}{1.19$\\\\times$} & \\\\multicolumn{1}{l}{1.16$\\\\times$} & \\\\multicolumn{1}{l}{1.15$\\\\times$} & \\\\multicolumn{1}{l}{1.13$\\\\times$} & \\\\multicolumn{1}{l}{1.18$\\\\times$} & \\\\multicolumn{1}{l}{1.20$\\\\times$} & \\\\multicolumn{1}{l}{1.18$\\\\times$} & \\\\multicolumn{1}{l}{1.18$\\\\times$} & 1.11$\\\\times$\\\\\\\\\\n\",\n \"MagicDec & \\\\multicolumn{1}{l}{1.07$\\\\times$} & \\\\multicolumn{1}{l}{1.08$\\\\times$} & \\\\multicolumn{1}{l}{1.05$\\\\times$} & \\\\multicolumn{1}{l}{1.13$\\\\times$} & \\\\multicolumn{1}{l}{1.24$\\\\times$} & \\\\multicolumn{1}{l}{1.03$\\\\times$} & \\\\multicolumn{1}{l}{1.07$\\\\times$} & \\\\multicolumn{1}{l}{1.06$\\\\times$} & \\\\multicolumn{1}{l}{1.10$\\\\times$} & \\\\multicolumn{1}{l}{1.19$\\\\times$} & \\\\multicolumn{1}{l}{1.03$\\\\times$} & \\\\multicolumn{1}{l}{1.04$\\\\times$} & \\\\multicolumn{1}{l}{1.06$\\\\times$} & \\\\multicolumn{1}{l}{1.18$\\\\times$} & 1.23$\\\\times$\\\\\\\\\\n\",\n \"Standard & 1.78$\\\\times$ & 1.16$\\\\times$ & 1.14$\\\\times$ & 1.08$\\\\times$ & 1.38$\\\\times$ & 1.37$\\\\times$ & 1.09$\\\\times$ & 1.15$\\\\times$ & 1.09$\\\\times$ & 1.29$\\\\times$ & 1.57$\\\\times$ & 1.14$\\\\times$ & 1.08$\\\\times$ & 1.05$\\\\times$ & 1.30$\\\\times$ \\\\\\\\\\n\",\n \"Standard + SpecExtend & \\\\textbf{2.28$\\\\times$} & \\\\textbf{2.29$\\\\times$} & \\\\textbf{2.08$\\\\times$} & \\\\textbf{2.29$\\\\times$} & \\\\textbf{2.65$\\\\times$} & \\\\textbf{1.74$\\\\times$} & \\\\textbf{1.81$\\\\times$} & \\\\textbf{1.88$\\\\times$} & \\\\textbf{2.34$\\\\times$} & \\\\textbf{2.70$\\\\times$} & \\\\textbf{1.74$\\\\times$} & \\\\textbf{1.74$\\\\times$} & \\\\textbf{1.73$\\\\times$} & \\\\textbf{2.14$\\\\times$} & \\\\textbf{2.81$\\\\times$} \\\\\\\\ \\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Speedup comparison of off-the-shelf methods for long sequence generation with Vicuna 7B. Standard refers to standard tree-based speculative decoding.}\\n\",\n \"\\\\label{tab:comparison-other-methods}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table}[t!]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\columnwidth}{!}{\\n\",\n \"\\\\begin{tabular}{@{}ccccc@{}}\\n\",\n \"\\\\toprule\\n\",\n \" & \\\\begin{tabular}[c]{@{}c@{}}Target \\\\\\\\Forward\\\\end{tabular} & \\\\begin{tabular}[c]{@{}c@{}}Target Forward\\\\\\\\ w/ Retrieval\\\\end{tabular} & \\\\begin{tabular}[c]{@{}c@{}}Draft \\\\\\\\Forward\\\\end{tabular} & \\\\begin{tabular}[c]{@{}c@{}}Retrieval Cache\\\\\\\\ Update\\\\end{tabular} \\\\\\\\ \\\\midrule\\n\",\n \"Latency (ms) & 53.76 & 54.11 & 0.84 & 0.34 \\\\\\\\ \\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Latency overhead of a single retrieval cache update step on 16K token inputs.}\\n\",\n \"\\\\label{tab:latency_overhead}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table*}[t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \"\\\\begin{tabular}{@{}c|cc|c|cc|c|cc|c|cc@{}}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\begin{tabular}[c]{@{}c@{}}Working \\\\\\\\ Cache Size\\\\end{tabular} \\n\",\n \" & Vicuna-68M & EAGLE \\n\",\n \" & \\\\begin{tabular}[c]{@{}c@{}}Chunk\\\\\\\\ Size\\\\end{tabular} \\n\",\n \" & Vicuna-68M & EAGLE \\n\",\n \" & Top-k \\n\",\n \" & Vicuna-68M & EAGLE \\n\",\n \" & \\\\begin{tabular}[c]{@{}c@{}}Retrieval\\\\\\\\ Frequency\\\\end{tabular} \\n\",\n \" & Vicuna-68M & EAGLE \\n\",\n \"\\\\\\\\\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\\cmidrule(l){1-12}\\n\",\n \"64 & 32.52 & 39.10 & 1 & 31.05 & 48.05 & 2 & 30.72 & 38.22 & 1 & 33.05 & 47.78 \\\\\\\\\\n\",\n \"128 & 32.91 & 39.95 & 2 & 32.27 & 49.49 & 4 & 32.65 & 40.36 & 2 & 33.54 & 46.78 \\\\\\\\\\n\",\n \"256 & 33.65 & 41.53 & 4 & 32.97 & 49.55 & 8 & 32.76 & 41.49 & 4 & \\\\textbf{33.59} & 48.17 \\\\\\\\\\n\",\n \"512 & 33.53 & 42.77 & 8 & 33.39 & 49.18 & 16 & 33.19 & 43.90 & 8 & 33.11 & \\\\textbf{48.52} \\\\\\\\\\n\",\n \"1024 & \\\\textbf{33.69} & 44.19 & 16 & 33.41 & 48.92 & 32 & \\\\textbf{33.28} & 47.21 & 16 & 33.16 & 48.36 \\\\\\\\\\n\",\n \"2048 & 32.36 & \\\\textbf{45.33} & 32 & \\\\textbf{33.52} & \\\\textbf{49.68} & 64 & 32.50 & \\\\textbf{48.09} & 32 & 33.28 & 48.11 \\\\\\\\\\n\",\n \"4096 & 25.84 & 43.68 & 64 & 33.23 & 48.25 & 128 & 25.20 & 45.14 & 64 & 33.29 & 48.13 \\\\\\\\\\n\",\n \"8192 & 24.32 & 33.10 & 128 & 33.20 & 47.48 & 256 & 23.95 & 32.48 & 128 & 33.21 & 48.20 \\\\\\\\ \\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Decoding speed (tokens/s) for different working KV cache size and retrieval parameters. We use Vicuna-7B as the target model and 8K inputs from GovReport.}\\n\",\n \"\\\\label{tab:ablation_retrieval}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \"\\\\begin{tabular}{@{}cccccccccccccccc@{}}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{Setting} \\n\",\n \" & \\\\multicolumn{3}{c}{1K} \\n\",\n \" & \\\\multicolumn{3}{c}{2K} \\n\",\n \" & \\\\multicolumn{3}{c}{4K} \\n\",\n \" & \\\\multicolumn{3}{c}{8K} \\n\",\n \" & \\\\multicolumn{3}{c}{16K} \\n\",\n \"\\\\\\\\\\n\",\n \"\\\\cmidrule(r){2-4}\\n\",\n \"\\\\cmidrule(lr){5-7}\\n\",\n \"\\\\cmidrule(lr){8-10}\\n\",\n \"\\\\cmidrule(lr){11-13}\\n\",\n \"\\\\cmidrule(l){14-16}\\n\",\n \" & $\\\\tau$ & Tok/s & Speedup \\n\",\n \" & $\\\\tau$ & Tok/s & Speedup \\n\",\n \" & $\\\\tau$ & Tok/s & Speedup \\n\",\n \" & $\\\\tau$ & Tok/s & Speedup \\n\",\n \" & $\\\\tau$ & Tok/s & Speedup \\n\",\n \"\\\\\\\\ \\\\midrule\\n\",\n \"Standard & 2.75 & 127.34 & $ \\\\text{-} $ & 1.83 & 87.34 & $ \\\\text{-} $ & 0.92 & 47.41 & $ \\\\text{-} $ & 0.78 & 27.54 & $ \\\\text{-} $ & 0.72 & 17.60 & $ \\\\text{-} $ \\\\\\\\\\n\",\n \"Standard + FA & 2.71 & 131.02 & 1.03$\\\\times$ & 1.84 & 91.79 & 1.05$\\\\times$ & 0.97 & 52.74 & 1.11$\\\\times$ & 0.81 & 34.33 & 1.25$\\\\times$ & 0.75 & 22.07 & 1.25$\\\\times$ \\\\\\\\\\n\",\n \"Standard + HTA & 2.61 & 122.73 & 0.96$\\\\times$ & 1.74 & 85.57 & 0.98$\\\\times$ & 0.92 & 47.62 & 1.01$\\\\times$ & 0.76 & 31.08 & 1.14$\\\\times$ & 0.74 & 20.95 & 1.19$\\\\times$ \\\\\\\\\\n\",\n \"Standard + StreamingLLM & 2.75 & 128.62 & 1.01$\\\\times$ & 1.81 & 85.60 & 0.98$\\\\times$ & 1.53 & 59.11 & 1.25$\\\\times$ & 1.59 & 35.89 & 1.30$\\\\times$ & 1.60 & 22.39 & 1.27$\\\\times$ \\\\\\\\\\n\",\n \"Standard + Retrieval & 2.86 & 130.35 & 1.02$\\\\times$ & 2.57 & 104.12 & 1.19$\\\\times$ & 1.90 & 64.85 & 1.36$\\\\times$ & 1.78 & 37.11 & 1.47$\\\\times$ & 1.93 & 25.82 & 1.46$\\\\times$ \\\\\\\\ \\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\n\",\n \"\\\\caption{Ablation on the components of SpecExtend. Standard indicates the standard tree-based speculative decoding with Vicuna 7B/68M. FA and HTA indicate FlashAttention for prefill and Hybrid Tree Attention, respectively. Standard + Retrieval refers to using SpecExtend's cross-model retrieval cache. Speedups are measured relative to the standard framework.}\\n\",\n \"\\\\label{tab:ablation_components}\\n\",\n \"\\\\end{table*}\\n\"\n ]\n]","isConversational":false},"fulltext":{"kind":"string","value":"\n\\pdfoutput=1\n\n\\PassOptionsToPackage{dvipsnames}{xcolor}\n\\documentclass[11pt]{article}\n\n\n\n\n\\usepackage[preprint]{acl}\n\\usepackage{times}\n\\usepackage{latexsym}\n\\usepackage{booktabs} \n\\usepackage{multirow} \n\\usepackage{graphicx} \n\\usepackage{algorithm}\n\\usepackage{algpseudocode}\n\\usepackage{float}\n\\usepackage{xcolor} \n\\usepackage[dvipsnames]{xcolor}\n\\usepackage{subcaption}\n\\usepackage{placeins}\n\\usepackage{amsmath}\n\n\n\\usepackage[T1]{fontenc}\n\n\n\\usepackage[utf8]{inputenc}\n\n\n\n\n\\usepackage{microtype}\n\n\n\n\n\\usepackage{inconsolata}\n\n\n\n\\usepackage{graphicx}\n\n\n\n\\title{\\textbf{SpecExtend}: A Drop-in Enhancement for Speculative Decoding of \\\\Long Sequences}\n\n\n\\author{\nJungyoub Cha \\quad Hyunjong Kim \\quad Sungzoon Cho\\\\\n\\vspace*{-0.25cm}\\\\\nSeoul National University \\\\\n\\texttt{\\footnotesize \\{jungyoub.cha, hjkim0811, zoon\\}@snu.ac.kr}\n}\n\n\\begin{document}\n\\maketitle\n\\begin{abstract}\nSpeculative decoding is a widely adopted technique for accelerating inference in large language models (LLMs), but its performance degrades on long inputs due to increased attention cost and reduced draft accuracy. We introduce SpecExtend, a drop-in enhancement that improves the performance of speculative decoding on long sequences without any additional training. SpecExtend integrates efficient attention mechanisms such as FlashAttention and Hybrid Tree Attention into both the draft and target models, reducing latency across all stages. To improve draft accuracy and speed, we propose Cross-model Retrieval, a novel KV cache update strategy that uses the target model's attention scores to dynamically select relevant context for the draft model. Extensive evaluations on three long-context understanding datasets show that SpecExtend accelerates standard tree-based speculative decoding by up to 2.22$\\times$ for inputs up to 16K tokens, providing an effective solution for speculative decoding of long sequences. The code is available at \\href{https://github.com/jycha98/SpecExtend}{\\texttt{https://github.com/jycha98/SpecExtend}}.\\end{abstract}\n\n\\section{Introduction}\nLarge Language Models (LLMs) have achieved remarkable success across a wide range of natural language processing (NLP) tasks \\cite{achiam2023gpt, grattafiori2024llama}. However, their practical deployment is often hindered by high inference latency, which is primarily caused by the autoregressive nature of decoding \\cite{zhou2024survey}. To address this issue, various optimization techniques have been proposed, with speculative decoding emerging as an effective, lossless solution \\cite{leviathan2023fast, chen2023accelerating}. Speculative decoding consists of two phases: First, a smaller draft model is used to efficiently generate multiple candidate tokens. Then, the original target model verifies these tokens in parallel. This allows generating multiple tokens within a single target model decoding step, accelerating inference without altering the output distribution. \n\n\\begin{figure*}[!t]\n \\centering\n \\includegraphics[width=0.95\\textwidth]{main_figure.png}\n \\caption{Overview of SpecExtend. FlashAttention accelerates the prefill phases of both target and draft models, and Hybrid Tree Attention accelerates the verification phase. We use the target model’s attention scores from verification to select the most relevant input chunks to retain in the draft model’s KV cache, boosting draft speed and accuracy.}\n \\label{fig:cross-model-retrieval}\n\\end{figure*}\n\n\n\nHowever, the performance of speculative decoding frameworks drops significantly as input length increases. We identify two primary causes: (1) increased latency in both drafting and verification steps due to the quadratic complexity of standard attention, and (2) reduced draft accuracy, as the draft model is typically smaller and trained only on short sequences. Meanwhile, retraining draft models on long contexts is costly, highlighting the need for a drop-in solution that improves long-input performance while preserving the original benefits of existing frameworks.\n\nTo this end, we propose SpecExtend, a drop-in enhancement for speculative decoding on long inputs. SpecExtend accelerates the forward passes of both the draft and target models by integrating efficient attention mechanisms across all stages. In order to improve drafting accuracy on long inputs without additional training, we introduce Cross-model Retrieval, a novel cache update strategy for speculative decoding. We dynamically update the draft model’s KV cache with globally relevant context, guided by the target model’s attention scores.\n\nSpecExtend is compatible with a wide range of speculative decoding frameworks, including tree-based structures, self-speculative draft models, and dynamic tree expansion techniques. We adopt these settings to evaluate SpecExtend's effectiveness using both off-the-shelf LLMs and EAGLE \\cite{li2024eagle} as draft models. On three long-context understanding datasets, SpecExtend accelerates standard tree-based speculative decoding by up to 2.22$\\times$ for inputs up to 16K tokens, resulting in an overall speedup of 2.87$\\times$ over naive autoregressive generation. \nSpecExtend preserves performance on short inputs and does not require retraining, offering a robust drop-in solution for enhancing speculative decoding on long inputs.\n\n\n\\section{SpecExtend}\n\\subsection{Efficient Attention Mechanisms}\n\\label{method_hybrid}\n\\paragraph{Prefill Acceleration}\nThe initial forward pass of a language model computes full self-attention over the entire input sequence, incurring quadratic memory usage and latency. FlashAttention \\cite{dao2022flashattention, dao2023flashattention} mitigates this by avoiding materialization of large intermediate matrices in the GPU high-bandwidth memory. We apply FlashAttention to the prefill stages of both the target and draft models, significantly reducing latency and memory usage during this phase (Figure~\\ref{fig:cross-model-retrieval}).\n\n\\paragraph{Target Model Decoding}\nUnlike prefill, the decoding phase uses cached key and value (KV) states and computes attention only with the newly generated tokens as query. FlashDecoding \\cite{dao2024flashdecoding} accelerates this step by parallelizing across the KV dimension, improving efficiency for short queries. However, it is incompatible with the tree masks required by tree-based speculative decoding frameworks \\cite{miao2024specinfer}. To resolve this, LongSpec \\cite{yang2025longspec} introduces Hybrid Tree Attention, which splits the KV cache into two parts: the cached segment that requires no masking and the speculative segment that applies the tree mask. FlashDecoding is applied to the cached segment, while standard attention is applied to the speculative part. The outputs are then fused using a log-sum-exp operation, allowing efficient tree attention computation for long inputs.\n\nWe apply Hybrid Tree Attention to the target model to accelerate its decoding phase and speed up the verification step of speculative decoding.\n\n\\subsection{Cross-model Retrieval Cache}\n\\label{method_retrieval}\nAs input length increases, drafting speed degrades due to the growing KV cache of the draft model, leading to slower decoding. Meanwhile, drafting accuracy also drops as the draft model has limited capacity and is typically trained on short contexts. To address this without retraining, we aim to (1) truncate the draft model’s KV cache for more efficient attention, (2) while preserving context that is most relevant to the target model at the current decoding timestep. We achieve this via cross-model retrieval, which uses the target model’s attention scores to select the most relevant input segments to retain in the draft model’s cache.\n\nConcretely, we divide the input prefix into fixed-size chunks and rank them by their average attention scores, using the last accepted token as the query. These scores reflect each chunk’s relevance at the current timestep. We select the top-k chunks, and the draft model uses this reduced, context-aware cache to generate candidate tokens, improving both speed and accuracy on long inputs.\n\nImportantly, the target model’s attention scores are obtained directly from the most recent verification step, requiring no additional forward passes. SpecExtend's algorithm is provided in Appendix~\\ref{appendix:specextend_algorithm}. One challenge is that the target model’s Hybrid Tree Attention relies on FlashDecoding, which avoids generating the full attention scores matrix for efficiency. To address this, we compute standard attention and extract attention scores of the final layer only, which we find sufficient for our purposes. As shown in Table~\\ref{tab:latency_overhead}, this adds minimal latency overhead to the target model’s forward pass, and the cache update step is also faster than a single draft model forward pass. Moreover, due to the locality of context in long sequences, retrieval cache updates can be applied adaptively or less frequently, further minimizing overhead. Ablations on the retrieval parameters are provided in Appendix~\\ref{appendix:ablation-retrieval}.\n\n\\begin{table}[!t]\n\\centering\n\\resizebox{\\columnwidth}{!}{\n\\begin{tabular}{@{}ccccc@{}}\n\\toprule\nCache Type & \\begin{tabular}[c]{@{}c@{}}Full KV\\\\ Cache\\end{tabular} & StreamingLLM & \\begin{tabular}[c]{@{}c@{}}Cross-model\\\\ Retrieval\\\\ (SpecExtend)\\end{tabular} & \\begin{tabular}[c]{@{}c@{}}Retrieval\\\\ (TriForce)\\end{tabular} \\\\ \\midrule\nPerplexity ($\\downarrow$) \n& 8.311 & 2.435 & 2.237 & 2.191 \\\\\nAccuracy ($\\uparrow$) \n& 0.081 & 0.166 & 0.823 & 0.976 \\\\ \\bottomrule\n\\end{tabular}\n}\n\\caption{Perplexity and draft accuracy of needle tokens in the Needle Retrieval task, using different draft model settings. The first three methods use Vicuna 160M as the draft model, while TriForce uses Vicuna 7B.}\n\\label{tab:needle_retrieval}\n\\end{table}\n\\paragraph{Needle Retrieval Evaluation}\nWe assess the effectiveness of cross-model retrieval using the Needle Retrieval task \\cite{li2024needlebench, 2023opencompass}. Specifically, we measure how well the draft model utilizes the retrieved context to locate and draft the tokens of a needle in long inputs. We compare its accuracy against three draft model cache types: (1) Full KV Cache, (2) StreamingLLM \\cite{xiao2023efficient}, which retains only the initial and most recent tokens using a static cache policy, and (2) TriForce \\cite{sun2024triforce}, which also uses the target model’s attention scores to retrieve top chunks, but employs the same large model for both drafting and verification. While accurate, drafting with the target model is slow due to its large model weights. TriForce serves as an upper bound on how well the retrieved context can be utilized by a smaller draft model.\n\n\n\nAs shown in Table~\\ref{tab:needle_retrieval}, while the StreamingLLM cache improves general coherence, it struggles to draft the needle tokens accurately due to loss of global context. In contrast, SpecExtend approaches TriForce’s performance despite using a smaller draft model, simultaneously enhancing draft speed and accuracy.\n\n\n\\section{Experiments}\n\\subsection{Experiment Setting}\nWe evaluate SpecExtend's performance on standard speculative decoding baselines using Vicuna-7B \\cite{vicuna2023} and LongChat-7B \\cite{longchat2023} as target models. Draft models include both EAGLE \\cite{li2024eagle} and off-the-shelf LLMs, Vicuna-68M \\cite{yang2024multi} and LLaMA-68M \\cite{miao2023specinfer}. We adopt tree-based drafting with dynamic tree expansion \\cite{wang2025opt} and use long summarization task, aiming to generate 256 tokens on GovReport \\cite{huang2021efficient}, PG-19 \\cite{raecompressive2019}, and BookSum \\cite{kryscinski2021booksum}. All experiments are conducted with a temperature of 0 on 2 A100 80GB GPUs. Further details are provided in Appendix~\\ref{appendix:experiment_details}.\n\n\\begin{figure}[!t]\n \\centering\n \\includegraphics[width=\\columnwidth]{latency_breakdown.pdf}\n \\caption{Latency breakdown of tree-based speculative decoding with Vicuna 7B on 16K token inputs.}\n \\label{fig:latency_breakdown}\n\\end{figure}\\begin{figure}[t]\n \\centering\n \\includegraphics[width=0.9\\columnwidth]{acceptance_length_comparison.pdf}\n \\caption{Average accepted length across input lengths with different draft model KV cache settings.}\n \\label{fig:acceptance_length_comparison}\n\\end{figure}\n\\begin{table*}[t]\n\\centering\n\\resizebox{\\textwidth}{!}{\n\\begin{tabular}{@{}ccccccccccccccccccc@{}}\n\\toprule\n\\multirow{2}{*}{} \n & \\multicolumn{2}{c}{\\multirow{2}{*}{\\textbf{Setting}}} \n & \\multirow{2}{*}{\\textbf{SpecExtend}} \n & \\multicolumn{3}{c}{1K} \n & \\multicolumn{3}{c}{2K} \n & \\multicolumn{3}{c}{4K} \n & \\multicolumn{3}{c}{8K} \n & \\multicolumn{3}{c}{16K} \n\\\\\n\\cmidrule(r){5-7}\n\\cmidrule(lr){8-10}\n\\cmidrule(lr){11-13}\n\\cmidrule(lr){14-16}\n\\cmidrule(l){17-19}\n & \\multicolumn{2}{c}{} \n & \n & $\\tau$ & Tok/s & Speedup \n & $\\tau$ & Tok/s & Speedup \n & $\\tau$ & Tok/s & Speedup \n & $\\tau$ & Tok/s & Speedup \n & $\\tau$ & Tok/s & Speedup \n\\\\ \\midrule\n\\multirow{8}{*}{\\rotatebox[origin=c]{90}{\\textbf{GovReport}}}\n & \\multirow{4}{*}{\\rotatebox[origin=c]{90}{V-7B}}\n & \\multirow{2}{*}{V-68M} & No & 1.73 & 100.31 & 1.78$\\times$ & 0.64 & 55.64 & 1.16$\\times$ & 0.60 & 41.91 & 1.14$\\times$ & 0.62 & 25.71 & 1.08$\\times$ & 0.59 & 16.56 & 1.38$\\times$ \\\\\n & & & Yes & \\textbf{2.80} & \\textbf{128.59} & \\textbf{2.28$\\times$} & \\textbf{2.52} & \\textbf{109.58} & \\textbf{2.29$\\times$} & \\textbf{2.04} & \\textbf{76.48} & \\textbf{2.08$\\times$} & \\textbf{2.06} & \\textbf{55.16} & \\textbf{2.33$\\times$} & \\textbf{2.07} & \\textbf{33.84} & \\textbf{2.82$\\times$} \\\\ \\cmidrule(lr){5-19}\n & & \\multirow{2}{*}{EAGLE} & No & \\textbf{3.61} & 144.77 & 2.57$\\times$ & 3.04 & 107.52 & 2.24$\\times$ & 2.27 & 66.62 & 1.81$\\times$ & 1.35 & 31.74 & 1.34$\\times$ & 1.00 & 19.35 & 1.61$\\times$ \\\\\n & & & Yes & 3.58 & \\textbf{145.53} & \\textbf{2.58$\\times$} & \\textbf{3.08} & \\textbf{113.47} & \\textbf{2.37$\\times$} & \\textbf{2.80} & \\textbf{85.99} & \\textbf{2.34$\\times$} & \\textbf{2.82} & \\textbf{62.90} & \\textbf{2.66$\\times$} & \\textbf{2.51} & \\textbf{37.05} & \\textbf{3.08$\\times$} \\\\ \\cmidrule(l){2-19} \n & \\multirow{4}{*}{\\rotatebox[origin=c]{90}{LC-7B}} & \\multirow{2}{*}{LC-68M} & No & 1.73 & 100.31 & 1.78$\\times$ & 0.64 & 55.64 & 1.16$\\times$ & 0.60 & 41.91 & 1.15$\\times$ & 0.62 & 25.71 & 1.12$\\times$ & 0.59 & 16.56 & 1.51$\\times$ \\\\\n & & & Yes & \\textbf{2.01} & \\textbf{109.26} & \\textbf{1.94$\\times$} & \\textbf{1.82} & \\textbf{90.27} & \\textbf{1.89$\\times$} & \\textbf{1.66} & \\textbf{68.84} & \\textbf{1.89$\\times$} & \\textbf{1.81} & \\textbf{52.17} & \\textbf{2.30$\\times$} & \\textbf{1.68} & \\textbf{31.11} & \\textbf{2.84$\\times$} \\\\ \\cmidrule(lr){5-19}\n & & \\multirow{2}{*}{EAGLE} & No & \\textbf{3.10} & 131.06 & 2.33$\\times$ & 2.47 & 97.53 & 2.04$\\times$ & 1.75 & 60.17 & 1.65$\\times$ & 1.52 & 32.90 & 1.44$\\times$ & 1.18 & 19.84 & 1.81$\\times$ \\\\\n & & & Yes & 3.04 & \\textbf{133.14} & \\textbf{2.37$\\times$} & \\textbf{2.56} & \\textbf{103.39} & \\textbf{2.17$\\times$} & \\textbf{2.43} & \\textbf{80.50} & \\textbf{2.21$\\times$} & \\textbf{2.53} & \\textbf{60.14} & \\textbf{2.63$\\times$} & \\textbf{2.25} & \\textbf{35.13} & \\textbf{3.21$\\times$} \\\\ \\midrule\n\\multirow{8}{*}{\\rotatebox[origin=c]{90}{\\textbf{PG-19}}} & \\multirow{4}{*}{\\rotatebox[origin=c]{90}{V-7B}} & \\multirow{2}{*}{V-68M} & No & 1.16 & 76.50 & 1.37$\\times$ & 0.52 & 51.00 & 1.09$\\times$ & 0.55 & 39.16 & 1.15$\\times$ & 0.55 & 21.80 & 1.01$\\times$ & 0.54 & 14.73 & 1.29$\\times$ \\\\\n & & & Yes & \\textbf{1.75} & \\textbf{96.74} & \\textbf{1.74$\\times$} & \\textbf{1.69} & \\textbf{84.74} & \\textbf{1.81$\\times$} & \\textbf{1.61} & \\textbf{63.94} & \\textbf{1.88$\\times$} & \\textbf{1.65} & \\textbf{47.64} & \\textbf{2.39$\\times$} & \\textbf{1.70} & \\textbf{32.88} & \\textbf{2.87$\\times$} \\\\ \\cmidrule(lr){5-19}\n & & \\multirow{2}{*}{EAGLE} & No & \\textbf{2.29} & 107.31 & 1.92$\\times$ & 2.18 & 88.92 & 1.89$\\times$ & 1.88 & 54.71 & 1.60$\\times$ & 1.18 & 26.43 & 1.32$\\times$ & 0.92 & 16.98 & 1.48$\\times$ \\\\\n & & & Yes & 2.29 & \\textbf{107.53} & \\textbf{1.93$\\times$} & \\textbf{2.19} & \\textbf{94.41} & \\textbf{2.02$\\times$} & \\textbf{2.04} & \\textbf{69.92} & \\textbf{2.06$\\times$} & \\textbf{2.19} & \\textbf{53.06} & \\textbf{2.67$\\times$} & \\textbf{2.05} & \\textbf{35.43} & \\textbf{3.09$\\times$} \\\\ \\cmidrule(l){2-19} \n & \\multirow{4}{*}{\\rotatebox[origin=c]{90}{LC-7B}} & \\multirow{2}{*}{LC-68M} & No & 1.16 & 76.50 & 1.36$\\times$ & 0.52 & 51.00 & 1.07$\\times$ & 0.55 & 39.16 & 1.09$\\times$ & 0.55 & 21.80 & 1.00$\\times$ & 0.54 & 14.73 & 1.18$\\times$ \\\\\n & & & Yes & \\textbf{1.22} & \\textbf{80.25} & \\textbf{1.43$\\times$} & \\textbf{1.33} & \\textbf{73.69} & \\textbf{1.55$\\times$} & \\textbf{1.42} & \\textbf{62.27} & \\textbf{1.74$\\times$} & \\textbf{1.42} & \\textbf{44.96} & \\textbf{2.06$\\times$} & \\textbf{1.45} & \\textbf{30.67} & \\textbf{2.46$\\times$} \\\\ \\cmidrule(lr){5-19}\n & & \\multirow{2}{*}{EAGLE} & No & \\textbf{2.19} & 111.10 & 1.97$\\times$ & 2.00 & 86.80 & 1.82$\\times$ & 1.48 & 54.21 & 1.51$\\times$ & 1.28 & 26.85 & 1.23$\\times$ & 1.06 & 17.54 & 1.40$\\times$ \\\\\n & & & Yes & 2.11 & \\textbf{110.31} & \\textbf{1.96$\\times$} & \\textbf{2.02} & \\textbf{93.50} & \\textbf{1.97$\\times$} & \\textbf{1.97} & \\textbf{71.84} & \\textbf{2.01$\\times$} & \\textbf{1.99} & \\textbf{51.55} & \\textbf{2.36$\\times$} & \\textbf{1.82} & \\textbf{33.07} & \\textbf{2.66$\\times$} \n \\\\ \\midrule\n\\multirow{8}{*}{\\rotatebox[origin=c]{90}{\\textbf{BookSum}}} & \\multirow{4}{*}{\\rotatebox[origin=c]{90}{V-7B}} & \\multirow{2}{*}{V-68M} & No & 1.36 & 88.12 & 1.57$\\times$ & 0.56 & 53.33 & 1.13$\\times$ & 0.51 & 39.30 & 1.08$\\times$ & 0.52 & 24.21 & 1.05$\\times$ & 0.58 & 15.63 & 1.30$\\times$ \\\\\n & & & Yes & \\textbf{1.75} & \\textbf{97.45} & \\textbf{1.73$\\times$} & \\textbf{1.66} & \\textbf{81.37} & \\textbf{1.73$\\times$} & \\textbf{1.56} & \\textbf{62.97} & \\textbf{1.73$\\times$} & \\textbf{1.70} & \\textbf{50.21} & \\textbf{2.18$\\times$} & \\textbf{1.78} & \\textbf{35.61} & \\textbf{2.98$\\times$} \\\\ \\cmidrule(lr){5-19}\n & & \\multirow{2}{*}{EAGLE} & No & \\textbf{2.33} & 111.70 & 1.99$\\times$ & 1.95 & 82.44 & 1.75$\\times$ & 1.87 & 58.01 & 1.59$\\times$ & 1.14 & 29.30 & 1.27$\\times$ & 0.94 & 18.76 & 1.57$\\times$ \\\\\n & & & Yes & 2.31 & \\textbf{111.82} & \\textbf{1.99$\\times$} & \\textbf{1.99} & \\textbf{88.64} & \\textbf{1.89$\\times$} & \\textbf{2.08} & \\textbf{70.90} & \\textbf{1.95$\\times$} & \\textbf{2.15} & \\textbf{54.53} & \\textbf{2.37$\\times$} & \\textbf{2.11} & \\textbf{38.03} & \\textbf{3.18$\\times$} \\\\ \\cmidrule(l){2-19} \n & \\multirow{4}{*}{\\rotatebox[origin=c]{90}{LC-7B}} & \\multirow{2}{*}{LC-68M} & No & 1.36 & 88.12 & 1.57$\\times$ & 0.56 & 53.33 & 1.14$\\times$ & 0.51 & 39.30 & 1.11$\\times$ & 0.52 & 24.21 & 1.20$\\times$ & 0.58 & 15.63 & 1.28$\\times$ \\\\\n & & & Yes & \\textbf{1.45} & \\textbf{91.05} & \\textbf{1.63$\\times$} & \\textbf{1.55} & \\textbf{83.60} & \\textbf{1.80$\\times$} & \\textbf{1.54} & \\textbf{66.79} & \\textbf{1.90$\\times$} & \\textbf{1.61} & \\textbf{49.47} & \\textbf{2.45$\\times$} & \\textbf{1.50} & \\textbf{32.21} & \\textbf{2.64$\\times$} \\\\ \\cmidrule(lr){5-19}\n & & \\multirow{2}{*}{EAGLE} & No & \\textbf{2.10} & \\textbf{107.67} & \\textbf{1.92$\\times$} & 1.94 & 86.35 & 1.85$\\times$ & 1.37 & 53.42 & 1.51$\\times$ & 1.22 & 30.14 & 1.49$\\times$ & 1.06 & 18.39 & 1.50$\\times$ \\\\\n & & & Yes & 2.07 & 106.86 & 1.91$\\times$ & \\textbf{1.97} & \\textbf{90.48} & \\textbf{1.94$\\times$} & \\textbf{1.88} & \\textbf{71.50} & \\textbf{2.03$\\times$} & \\textbf{1.92} & \\textbf{52.35} & \\textbf{2.59$\\times$} & \\textbf{1.83} & \\textbf{34.65} & \\textbf{2.84$\\times$}\n \\\\ \\bottomrule\n\\end{tabular}\n}\n\\caption{Average accepted length ($\\tau$), decoding speed (tokens/s) and speedups of different frameworks with and without SpecExtend. Speedup is measured relative to naive autoregressive generation.}\n\\label{tab:main_results}\n\\end{table*}\n\n\n\n\n\n\n\\begin{table*}[t]\n\\centering\n\n\\resizebox{\\textwidth}{!}{\n\\begin{tabular}{@{}cccccccccccccccc@{}}\n\\toprule\n\\multirow{2}{*}{\\textbf{Method}}\n & \\multicolumn{5}{c}{\\textbf{GovReport}} \n & \\multicolumn{5}{c}{\\textbf{PG-19}} \n & \\multicolumn{5}{c}{\\textbf{BookSum}} \n\\\\\n\\cmidrule(r){2-6}\n\\cmidrule(lr){7-11}\n\\cmidrule(l){12-16}\n & 1K & 2K & 4K & 8K & 16K & 1K & 2K & 4K & 8K & 16K & 1K & 2K & 4K & 8K & 16K \\\\ \\midrule\nFlashDecoding & 1.06$\\times$ & 1.07$\\times$ & 1.12$\\times$ & 1.23$\\times$ & 1.51$\\times$ & 1.07$\\times$ & 1.08$\\times$ & 1.18$\\times$ & 1.38$\\times$ & 1.52$\\times$ & 1.06$\\times$ & 1.09$\\times$ & 1.10$\\times$ & 1.26$\\times$ & 1.58$\\times$ \\\\\nTriForce & \\multicolumn{1}{l}{1.25$\\times$} & \\multicolumn{1}{l}{1.26$\\times$} & \\multicolumn{1}{l}{1.22$\\times$} & \\multicolumn{1}{l}{1.18$\\times$} & \\multicolumn{1}{l}{1.02$\\times$} & \\multicolumn{1}{l}{1.12$\\times$} & \\multicolumn{1}{l}{1.19$\\times$} & \\multicolumn{1}{l}{1.16$\\times$} & \\multicolumn{1}{l}{1.15$\\times$} & \\multicolumn{1}{l}{1.13$\\times$} & \\multicolumn{1}{l}{1.18$\\times$} & \\multicolumn{1}{l}{1.20$\\times$} & \\multicolumn{1}{l}{1.18$\\times$} & \\multicolumn{1}{l}{1.18$\\times$} & 1.11$\\times$\\\\\nMagicDec & \\multicolumn{1}{l}{1.07$\\times$} & \\multicolumn{1}{l}{1.08$\\times$} & \\multicolumn{1}{l}{1.05$\\times$} & \\multicolumn{1}{l}{1.13$\\times$} & \\multicolumn{1}{l}{1.24$\\times$} & \\multicolumn{1}{l}{1.03$\\times$} & \\multicolumn{1}{l}{1.07$\\times$} & \\multicolumn{1}{l}{1.06$\\times$} & \\multicolumn{1}{l}{1.10$\\times$} & \\multicolumn{1}{l}{1.19$\\times$} & \\multicolumn{1}{l}{1.03$\\times$} & \\multicolumn{1}{l}{1.04$\\times$} & \\multicolumn{1}{l}{1.06$\\times$} & \\multicolumn{1}{l}{1.18$\\times$} & 1.23$\\times$\\\\\nStandard & 1.78$\\times$ & 1.16$\\times$ & 1.14$\\times$ & 1.08$\\times$ & 1.38$\\times$ & 1.37$\\times$ & 1.09$\\times$ & 1.15$\\times$ & 1.09$\\times$ & 1.29$\\times$ & 1.57$\\times$ & 1.14$\\times$ & 1.08$\\times$ & 1.05$\\times$ & 1.30$\\times$ \\\\\nStandard + SpecExtend & \\textbf{2.28$\\times$} & \\textbf{2.29$\\times$} & \\textbf{2.08$\\times$} & \\textbf{2.29$\\times$} & \\textbf{2.65$\\times$} & \\textbf{1.74$\\times$} & \\textbf{1.81$\\times$} & \\textbf{1.88$\\times$} & \\textbf{2.34$\\times$} & \\textbf{2.70$\\times$} & \\textbf{1.74$\\times$} & \\textbf{1.74$\\times$} & \\textbf{1.73$\\times$} & \\textbf{2.14$\\times$} & \\textbf{2.81$\\times$} \\\\ \\bottomrule\n\\end{tabular}\n}\n\\caption{Speedup comparison of off-the-shelf methods for long sequence generation with Vicuna 7B. Standard refers to standard tree-based speculative decoding.}\n\\label{tab:comparison-other-methods}\n\\end{table*}\n\\subsection{Main Results}\nFigure~\\ref{fig:latency_breakdown} shows that SpecExtend effectively reduces inference time across all stages of speculative decoding. Meanwhile, Figure~\\ref{fig:acceptance_length_comparison} shows that the cross-model retrieval cache significantly improves draft accuracy on long inputs, outperforming the static cache policy of StreamingLLM. These improvements lead to consistent speedup gains across all three datasets with both off-the-shelf LLMs and EAGLE as draft models, as shown in Table~\\ref{tab:main_results}. \nWe provide ablation studies on each component of SpecExtend in Appendix~\\ref{appendix:ablation-components}.\n\nFor 8K and 16K-token inputs from PG-19, SpecExtend accelerates standard speculative decoding with LLM draft models by 2.37$\\times$ and 2.22$\\times$, respectively, yielding overall speedups of 2.39$\\times$ and 2.87$\\times$ over naive autoregressive generation. For EAGLE-based frameworks, SpecExtend achieves 2.02$\\times$ and 2.09$\\times$ speedups over the standard EAGLE frameworks, yielding overall speedups of 2.67$\\times$ and 3.09$\\times$. Importantly, SpecExtend preserves baseline performance on shorter inputs across all settings, demonstrating robustness to input length.\n\n\\subsection{Comparison with Other Methods}\nWe apply SpecExtend to standard tree-based speculative decoding and compare its performance on long inputs against other off-the-shelf acceleration methods, including FlashDecoding \\cite{dao2024flashdecoding}, TriForce \\cite{sun2024triforce}, and MagicDec \\cite{sadhukhan2024magicdec}. We use Vicuna-7B, 68M as the target and draft models, respectively. For MagicDec, we implement StreamingLLM-based drafting with self-speculation. As shown in Table~\\ref{tab:comparison-other-methods}, SpecExtend-enhanced speculative decoding outperforms all baselines across input lengths, achieving up to 2.81× speedup on 16K-token inputs from BookSum. In contrast, TriForce and MagicDec yield marginal speedups, as model weights remain the dominant memory bottleneck in moderately long regimes, yet both methods rely on drafting with the large target model.\n\\section{Conclusion}\nWe present SpecExtend, a drop-in enhancement that improves the performance of speculative decoding frameworks on long inputs. By integrating efficient attention mechanisms and cross-model retrieval, SpecExtend accelerates all stages of speculative decoding while improving draft quality without retraining. Experimental results across multiple settings and datasets demonstrate that SpecExtend achieves up to 2.22× speedup for sequences up to 16K tokens, while preserving baseline performance on shorter inputs. Our approach is compatible with a wide range of speculative decoding setups and offers a practical solution to performance degradation on long inputs.\n\\section*{Limitations}\nWhile SpecExtend significantly improves the speedup of speculative decoding frameworks, token generation speed still degrades as input length increases. This is primarily due to the inherent growth in attention computation, even when using efficient mechanisms. In particular, the target model’s prefill and decoding steps for verification remain a bottleneck for long inputs, as SpecExtend still operates on the full KV cache for the target model’s forward passes. Nevertheless, SpecExtend effectively extends the range over which speculative decoding frameworks maintain high performance. Additionally, our method does not accelerate existing frameworks to the point of outperforming those specifically trained for long inputs, such as LongSpec. Nevertheless, our proposed cross-model retrieval cache can be integrated into other solutions to provide further speedup, and SpecExtend provides substantial off-the-shelf acceleration when applied to tree-based frameworks.\n\\section*{Ethical Considerations}\nThis study focuses solely on improving the inference efficiency of LLMs through a drop-in enhancement to speculative decoding. Our work does not involve training new models, collecting or annotating data, or interacting with human subjects. All experiments are conducted using publicly available models and datasets. We do not explore or enable any commercial applications or downstream use cases that raise ethical concerns. Therefore, we believe this research does not introduce any notable ethical concerns.\n\\bibliography{custom}\n\n\\appendix\n\n\\section{Related Work}\n\\subsection{Speculative Decoding}\n\\label{relwork21}\nSpeculative decoding accelerates LLM inference by using a smaller draft model to generate multiple candidate tokens, which the target model then verifies in parallel \\cite{xia2022speculativedecoding, xia2024unlocking}. With proper verification and correction, it guarantees the same output distribution as standard decoding \\cite{leviathan2023fast, chen2023accelerating}. SpecInfer \\cite{miao2024specinfer} extends this approach by drafting and verifying multiple sequences simultaneously using tree attention, achieving further speedups. Several works introduce effective draft models built from subsets of the target model \\cite{cai2024medusa, li2024eagle}, while EAGLE-2 \\cite{li2024eagle2} and OPT-Tree \\cite{wang2025opt} achieve further speedup by dynamically adjusting the draft tree structure during decoding. EAGLE-3 \\cite{li2025eagle3} scales up draft model training by leveraging multi-level features from the target model.\n\n\\subsection{Long Sequence Generation}\n\\label{relwork22}\nAs input length increases, standard attention suffers from quadratic computational and memory complexity, causing high inference latency \\cite{zhou2024survey}. FlashAttention \\cite{dao2022flashattention, dao2023flashattention} reduces this overhead by using tiling and online softmax, bringing memory complexity down to linear and accelerating inference. FlashDecoding \\cite{dao2024flashdecoding} builds on this by further parallelizing workers across the Key-Value dimension, speeding up LLM decoding for long sequences.\n\nSeveral works apply speculative decoding to long sequence generation. TriForce \\cite{sun2024triforce} identifies that the memory bottleneck shifts from model weights to the KV cache for extremely long inputs, and mitigates this with hierarchical speculation using smaller models and retrieval-based KV caches. MagicDec \\cite{sadhukhan2024magicdec} uses a simpler StreamingLLM cache to reduce the KV cache memory. However, the performance of speculative decoding frameworks drop well before the KV cache becomes the main bottleneck, and existing solutions are less effective in this regime of early degradation. Closest to our approach is LongSpec \\cite{yang2025longspec}, which trains draft models specifically designed for long inputs. In contrast, our method provides a drop-in enhancement for existing frameworks, improving long-sequence performance without retraining and preserving their original benefits.\n\n\n\\section{SpecExtend Algorithm}\n\\label{appendix:specextend_algorithm}\n\\FloatBarrier\n\\begin{algorithm}[H]\n \\footnotesize\n \\begin{algorithmic}[1]\n \\Require Target LM $M_q$, draft LM $M_p$, input $x_{1},\\dots,x_{t}$,\n block size $K$, target length $T$, \\textsc{Draft}, \\textsc{Verify}, \n \\textsc{Correct}, retrieval flag \\texttt{doRetrieval}, \n attention scores $s$, top-$k$ chunks $c_{1},\\dots,c_{k}$\n \\State $n \\gets t$\n \\While{$n < T$}\n \\Statex \\Comment{Retrieve and update draft-model cache}\n {\\color{blue}\n \\If{\\texttt{doRetrieval}}\n \\State $c_{1},\\dots,c_{k}\\gets\\textsc{SelectChunks}(s)$\n \\State \\textsc{UpdateDraftCache}$(c_{1},\\dots,c_{k})$\n \\EndIf\n }\n\n \\State $p_{1},\\dots,p_{K}\\gets\\textsc{Draft}(x_{\\le n},M_p)$\n \\State Sample $\\tilde x_i\\sim p_i$ for $i=1,\\dots,K$\n \\Statex \\Comment{Obtain target model attention scores}\n \\State $(q_i,\\,\\textcolor{blue}{s})\\gets M_q\\bigl(x\\mid x_{\\le n},\\tilde x_{<i}\\;;\\;\\textcolor{blue}{\\texttt{doRetrieval}}\\bigr)$\n \\Statex \\hfill for $i=1,\\dots,K+1$\n \\If{\\textsc{Verify}($\\tilde x_i,p_i,q_i)$}\n \\State $x_{n+1}\\gets\\tilde x_i$; $n\\gets n+1$\n \\Else\n \\State $x_{n+1}\\gets\\textsc{Correct}(p_i,q_i)$\n \\State {\\bf break}\n \\EndIf\n\n \\If{all $K$ drafted tokens accepted}\n \\State Sample $x_{n+1}\\sim q_{K+1}$; $n\\gets n+1$\n \\EndIf\n \\EndWhile\n \\end{algorithmic}\n \\caption{Speculative Decoding with \\\\Cross‐model Retrieval Cache}\n \\label{alg:speculative_retrieval}\n\\end{algorithm}\n\n\\section{Latency Overhead of SpecExtend}\n\nTable~\\ref{tab:latency_overhead} shows the latency overhead of SpecExtend's cross-model retrieval cache. When using 16K inputs, the target model's forward pass shows minimal overhead with and without retrieval, as we compute standard attention for the last layer only. In addition, a retrieval cache update step which requires computing the average attention scores of chunks, ranking them and updating the draft model's KV cache with the top-k chunks, can be done faster than a single forward pass of the draft model.\n\n\n\n\\begin{table}[t!]\n\\centering\n\\resizebox{\\columnwidth}{!}{\n\\begin{tabular}{@{}ccccc@{}}\n\\toprule\n & \\begin{tabular}[c]{@{}c@{}}Target \\\\Forward\\end{tabular} & \\begin{tabular}[c]{@{}c@{}}Target Forward\\\\ w/ Retrieval\\end{tabular} & \\begin{tabular}[c]{@{}c@{}}Draft \\\\Forward\\end{tabular} & \\begin{tabular}[c]{@{}c@{}}Retrieval Cache\\\\ Update\\end{tabular} \\\\ \\midrule\nLatency (ms) & 53.76 & 54.11 & 0.84 & 0.34 \\\\ \\bottomrule\n\\end{tabular}\n}\n\\caption{Latency overhead of a single retrieval cache update step on 16K token inputs.}\n\\label{tab:latency_overhead}\n\\end{table}\n\\section{Experiment Details}\n\\label{appendix:experiment_details}\nThe EAGLE models\\footnote{EAGLE models are publicly available under the Apache 2.0 license.} are trained on the ShareGPT dataset using default training settings with 4 A100 40GB GPUs. For each input length from 1K to 16K tokens, we sample 20 inputs, run each input twice, and report metrics averaged over all runs. We set the temperature to 0 and the maximum generation length to 256 tokens. We apply OPT-Tree’s dynamic tree expansion strategy with the default settings of 50 total nodes, maximum depth 10, and threshold 0.7. We use the optimal working KV cache size and retrieval parameters described in Appendix~\\ref{appendix:ablation-retrieval}.\n\n\\section{Ablation Studies}\n\n\\subsection{Retrieval Parameters}\n\\label{appendix:ablation-retrieval}\n\n\n\n\\begin{table*}[t]\n\\centering\n\\resizebox{\\textwidth}{!}{\n\\begin{tabular}{@{}c|cc|c|cc|c|cc|c|cc@{}}\n\\toprule\n\\begin{tabular}[c]{@{}c@{}}Working \\\\ Cache Size\\end{tabular} \n & Vicuna-68M & EAGLE \n & \\begin{tabular}[c]{@{}c@{}}Chunk\\\\ Size\\end{tabular} \n & Vicuna-68M & EAGLE \n & Top-k \n & Vicuna-68M & EAGLE \n & \\begin{tabular}[c]{@{}c@{}}Retrieval\\\\ Frequency\\end{tabular} \n & Vicuna-68M & EAGLE \n\\\\\n\n\n\n\n\\cmidrule(l){1-12}\n64 & 32.52 & 39.10 & 1 & 31.05 & 48.05 & 2 & 30.72 & 38.22 & 1 & 33.05 & 47.78 \\\\\n128 & 32.91 & 39.95 & 2 & 32.27 & 49.49 & 4 & 32.65 & 40.36 & 2 & 33.54 & 46.78 \\\\\n256 & 33.65 & 41.53 & 4 & 32.97 & 49.55 & 8 & 32.76 & 41.49 & 4 & \\textbf{33.59} & 48.17 \\\\\n512 & 33.53 & 42.77 & 8 & 33.39 & 49.18 & 16 & 33.19 & 43.90 & 8 & 33.11 & \\textbf{48.52} \\\\\n1024 & \\textbf{33.69} & 44.19 & 16 & 33.41 & 48.92 & 32 & \\textbf{33.28} & 47.21 & 16 & 33.16 & 48.36 \\\\\n2048 & 32.36 & \\textbf{45.33} & 32 & \\textbf{33.52} & \\textbf{49.68} & 64 & 32.50 & \\textbf{48.09} & 32 & 33.28 & 48.11 \\\\\n4096 & 25.84 & 43.68 & 64 & 33.23 & 48.25 & 128 & 25.20 & 45.14 & 64 & 33.29 & 48.13 \\\\\n8192 & 24.32 & 33.10 & 128 & 33.20 & 47.48 & 256 & 23.95 & 32.48 & 128 & 33.21 & 48.20 \\\\ \\bottomrule\n\\end{tabular}\n}\n\\caption{Decoding speed (tokens/s) for different working KV cache size and retrieval parameters. We use Vicuna-7B as the target model and 8K inputs from GovReport.}\n\\label{tab:ablation_retrieval}\n\\end{table*}\n\nWe conduct ablations on retrieval parameters using Vicuna-7B as the target model, with Vicuna-68M and EAGLE as draft models, on 8K-token inputs from GovReport (Table~\\ref{tab:ablation_retrieval}). When varying the StreamingLLM cache size, the optimal working cache size is around 1K for Vicuna-68M and 2K for EAGLE. This difference is due to the disparity in draft model scale (68M vs. 1B parameters). Based on these cache sizes, we find that for Vicuna-68M and EAGLE respectively, a chunk size of 32, top-k values of 32 and 64, and a retrieval frequency of 4 and 8 steps yield the best performance. We note that these optimal settings can vary with the architecture and capacity of the draft model.\n\n\\subsection{SpecExtend Components}\n\\label{appendix:ablation-components}\n\n\n\n\n\\begin{table*}[t]\n\\centering\n\\resizebox{\\textwidth}{!}{\n\\begin{tabular}{@{}cccccccccccccccc@{}}\n\\toprule\n\\multirow{2}{*}{Setting} \n & \\multicolumn{3}{c}{1K} \n & \\multicolumn{3}{c}{2K} \n & \\multicolumn{3}{c}{4K} \n & \\multicolumn{3}{c}{8K} \n & \\multicolumn{3}{c}{16K} \n\\\\\n\\cmidrule(r){2-4}\n\\cmidrule(lr){5-7}\n\\cmidrule(lr){8-10}\n\\cmidrule(lr){11-13}\n\\cmidrule(l){14-16}\n & $\\tau$ & Tok/s & Speedup \n & $\\tau$ & Tok/s & Speedup \n & $\\tau$ & Tok/s & Speedup \n & $\\tau$ & Tok/s & Speedup \n & $\\tau$ & Tok/s & Speedup \n\\\\ \\midrule\nStandard & 2.75 & 127.34 & $ \\text{-} $ & 1.83 & 87.34 & $ \\text{-} $ & 0.92 & 47.41 & $ \\text{-} $ & 0.78 & 27.54 & $ \\text{-} $ & 0.72 & 17.60 & $ \\text{-} $ \\\\\nStandard + FA & 2.71 & 131.02 & 1.03$\\times$ & 1.84 & 91.79 & 1.05$\\times$ & 0.97 & 52.74 & 1.11$\\times$ & 0.81 & 34.33 & 1.25$\\times$ & 0.75 & 22.07 & 1.25$\\times$ \\\\\nStandard + HTA & 2.61 & 122.73 & 0.96$\\times$ & 1.74 & 85.57 & 0.98$\\times$ & 0.92 & 47.62 & 1.01$\\times$ & 0.76 & 31.08 & 1.14$\\times$ & 0.74 & 20.95 & 1.19$\\times$ \\\\\nStandard + StreamingLLM & 2.75 & 128.62 & 1.01$\\times$ & 1.81 & 85.60 & 0.98$\\times$ & 1.53 & 59.11 & 1.25$\\times$ & 1.59 & 35.89 & 1.30$\\times$ & 1.60 & 22.39 & 1.27$\\times$ \\\\\nStandard + Retrieval & 2.86 & 130.35 & 1.02$\\times$ & 2.57 & 104.12 & 1.19$\\times$ & 1.90 & 64.85 & 1.36$\\times$ & 1.78 & 37.11 & 1.47$\\times$ & 1.93 & 25.82 & 1.46$\\times$ \\\\ \\bottomrule\n\\end{tabular}\n}\n\n\\caption{Ablation on the components of SpecExtend. Standard indicates the standard tree-based speculative decoding with Vicuna 7B/68M. FA and HTA indicate FlashAttention for prefill and Hybrid Tree Attention, respectively. Standard + Retrieval refers to using SpecExtend's cross-model retrieval cache. Speedups are measured relative to the standard framework.}\n\\label{tab:ablation_components}\n\\end{table*}\nWe evaluate the relative speedup of each component of SpecExtend, applying them to a standard tree-based framework with Vicuna-7B and Vicuna-68M. All speedups are measured relative to the standard speculative decoding framework. As shown in Table~\\ref{tab:ablation_components}, applying FlashAttention to the prefill stages yields a 1.25$\\times$ speedup for 16K inputs. Hybrid Tree Attention introduces slight overhead at shorter lengths but achieves up to 1.19$\\times$ speedup beyond 8K tokens. Therefore we enable Hybrid Tree Attention only for inputs beyond 4K tokens. The cross-model retrieval cache alone provides up to a 1.46$\\times$ speedup over the standard setting.\n\\label{sec:appendix}\n\n\n\\end{document}\n\\typeout{get arXiv to do 4 passes: Label(s) may have changed. Rerun}"},"question":{"kind":"string","value":"At which input length does SpecExtend provide the highest average relative speedup across all evaluated settings?"},"answer":{"kind":"string","value":"16K"},"plan":{"kind":"string","value":"SELECT Speedup with and without SpecExtend, all settings, all token lengths\nLOOP for each token length\n SELECT Speedup where SpecExtend = No\n COMPUTE avg1 = average of Speedup where SpecExtend = No\n SELECT Speedup where SpecExtend = Yes\n COMPUTE avg2 = average of Speedup where SpecExtend = Yes \n COMPUTE improvement = avg2 - avg1\nCOMPUTE target_token_len = argmax over improvement\nRETURN target_token_len\n"}}},{"rowIdx":72,"cells":{"paper":{"kind":"string","value":"2505.19987"},"relevant_tables":{"kind":"list like","value":[["\\begin{table*}[t]\n","\\centering\n","\\resizebox{\\textwidth}{!}{%\n","\\begin{tabular}{l ccc ccc ccc ccc ccc}\n","\\toprule\n","\\multirow{2}{*}{Model} & \\multicolumn{3}{c}{Conversation} & \\multicolumn{3}{c}{E-commerce} & \\multicolumn{3}{c}{News} & \\multicolumn{3}{c}{Social} & \\multicolumn{3}{c}{Literary} \\\\\n","\\cmidrule(lr){2-4} \\cmidrule(lr){5-7} \\cmidrule(lr){8-10} \\cmidrule(lr){11-13} \\cmidrule(lr){14-16}\n"," & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI \\\\\n","\\midrule\n","\\multicolumn{16}{c}{\\textbf{Document-Level}} \\\\\n","GPT-4o & \\textbf{40.00} & 84.71 & 82.02 & 35.33 & 87.35 & 64.55 & 36.07 & 86.60 & 66.01 & 33.75 & 84.36 & 68.70 & 17.55 & \\textbf{77.57} & 77.14 \\\\\n","DeepSeek-V3 & 39.28 & 84.51 & 81.98 & 36.71 & 87.26 & 63.16 & 37.59 & 86.50 & 65.35 & 36.48 & 84.80 & 67.83 & 16.81 & 77.48 & 77.17 \\\\\n","Gemini-2.0-Flash & 39.98 & 84.64 & 81.81 & \\textbf{37.72} & 87.34 & 62.33 & 38.68 & 85.71 & 63.68 & \\textbf{37.76} & 84.38 & 67.14 & \\textbf{18.12} & 77.30 & 76.62 \\\\\n","\\hdashline\n","OpenAI-o1 & 37.05 & \\textbf{84.83} & \\textbf{82.99} & 30.68 & \\textbf{87.40} & \\textbf{66.50} & 32.08 & \\textbf{86.67} & \\textbf{67.90} & 29.02 & \\textbf{84.82} & \\textbf{69.79} & 16.44 & \\textbf{77.57} & \\textbf{77.76} \\\\\n","DeepSeek-R1 & 33.66 & 84.15 & 81.45 & 21.62 & 85.23 & 63.77 & 23.49 & 83.68 & 66.99 & 22.22 & 81.59 & 68.09 & 13.12 & 76.64 & 76.47 \\\\\n","OpenAI-o3-mini & 36.58 & 84.52 & 82.05 & 32.13 & 87.15 & 65.18 & 33.60 & 86.22 & 65.77 & 30.93 & 84.76 & 68.87 & 17.26 & 76.87 & 76.64 \\\\\n","Gemini-2.0-Flash-Thinking & \\textbf{40.00} & 84.59 & 81.79 & 37.41 & 87.35 & 62.39 & \\textbf{38.82} & 86.62 & 64.19 & 36.97 & 84.55 & 66.47 & 17.77 & 77.17 & 76.58 \\\\\n","\\midrule\n","\\midrule\n","\\multicolumn{16}{c}{\\textbf{Sentence-Level}} \\\\\n","GPT-4o & \\textbf{38.68} & 87.56 & 82.00 & \\textbf{34.17} & 86.38 & 81.78 & 36.21 & 86.05 & 82.66 & 33.07 & 84.63 & 81.47 & 18.53 & \\textbf{82.96} & \\textbf{57.23} \\\\\n","DeepSeek-V3 & 37.46 & 87.61 & 81.99 & 33.57 & 86.32 & 81.93 & 35.82 & 86.30 & 83.04 & 32.96 & 84.74 & 81.81 & 19.92 & 82.88 & 54.68 \\\\\n","Gemini-2.0-Flash & 38.01 & 87.29 & 81.87 & 33.67 & 86.36 & 81.67 & \\textbf{36.48} & 86.21 & 82.80 & \\textbf{33.24} & 84.55 & 81.49 & 23.28 & 82.80 & 53.83 \\\\\n","\\hdashline\n","OpenAI-o1 & 36.30 & \\textbf{87.87} & \\textbf{83.49} & 31.30 & \\textbf{86.39} & \\textbf{83.05} & 33.17 & \\textbf{86.35} & \\textbf{84.35} & 29.95 & \\textbf{84.80} & \\textbf{82.96} & 16.87 & 82.42 & 55.73 \\\\\n","DeepSeek-R1 & 34.05 & 87.13 & 81.93 & 29.23 & 85.95 & 81.75 & 31.83 & 86.03 & 83.01 & 28.51 & 84.26 & 81.59 & 5.16 & 80.35 & 52.16 \\\\\n","OpenAI-o3-mini & 36.33 & 86.74 & 81.67 & 32.81 & 85.92 & 81.73 & 33.95 & 85.57 & 82.43 & 31.88 & 84.07 & 81.28 & 18.06 & 82.83 & 55.16 \\\\\n","Gemini-2.0-Flash-Thinking & 37.07 & 86.99 & 81.68 & 33.09 & 86.17 & 81.55 & 35.70 & 86.19 & 82.81 & 32.30 & 84.16 & 81.27 & \\textbf{23.52} & 82.77 & 53.69 \\\\\n","\\bottomrule\n","\\end{tabular}%\n","}\n","\\caption{Average BLEU, COMET, and CometKiwi scores at document-level and sentence-level across five domains.}\n","\\label{tab:length_analysis}\n","\\end{table*}\n"]],"string":"[\n [\n \"\\\\begin{table*}[t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{%\\n\",\n \"\\\\begin{tabular}{l ccc ccc ccc ccc ccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{Model} & \\\\multicolumn{3}{c}{Conversation} & \\\\multicolumn{3}{c}{E-commerce} & \\\\multicolumn{3}{c}{News} & \\\\multicolumn{3}{c}{Social} & \\\\multicolumn{3}{c}{Literary} \\\\\\\\\\n\",\n \"\\\\cmidrule(lr){2-4} \\\\cmidrule(lr){5-7} \\\\cmidrule(lr){8-10} \\\\cmidrule(lr){11-13} \\\\cmidrule(lr){14-16}\\n\",\n \" & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multicolumn{16}{c}{\\\\textbf{Document-Level}} \\\\\\\\\\n\",\n \"GPT-4o & \\\\textbf{40.00} & 84.71 & 82.02 & 35.33 & 87.35 & 64.55 & 36.07 & 86.60 & 66.01 & 33.75 & 84.36 & 68.70 & 17.55 & \\\\textbf{77.57} & 77.14 \\\\\\\\\\n\",\n \"DeepSeek-V3 & 39.28 & 84.51 & 81.98 & 36.71 & 87.26 & 63.16 & 37.59 & 86.50 & 65.35 & 36.48 & 84.80 & 67.83 & 16.81 & 77.48 & 77.17 \\\\\\\\\\n\",\n \"Gemini-2.0-Flash & 39.98 & 84.64 & 81.81 & \\\\textbf{37.72} & 87.34 & 62.33 & 38.68 & 85.71 & 63.68 & \\\\textbf{37.76} & 84.38 & 67.14 & \\\\textbf{18.12} & 77.30 & 76.62 \\\\\\\\\\n\",\n \"\\\\hdashline\\n\",\n \"OpenAI-o1 & 37.05 & \\\\textbf{84.83} & \\\\textbf{82.99} & 30.68 & \\\\textbf{87.40} & \\\\textbf{66.50} & 32.08 & \\\\textbf{86.67} & \\\\textbf{67.90} & 29.02 & \\\\textbf{84.82} & \\\\textbf{69.79} & 16.44 & \\\\textbf{77.57} & \\\\textbf{77.76} \\\\\\\\\\n\",\n \"DeepSeek-R1 & 33.66 & 84.15 & 81.45 & 21.62 & 85.23 & 63.77 & 23.49 & 83.68 & 66.99 & 22.22 & 81.59 & 68.09 & 13.12 & 76.64 & 76.47 \\\\\\\\\\n\",\n \"OpenAI-o3-mini & 36.58 & 84.52 & 82.05 & 32.13 & 87.15 & 65.18 & 33.60 & 86.22 & 65.77 & 30.93 & 84.76 & 68.87 & 17.26 & 76.87 & 76.64 \\\\\\\\\\n\",\n \"Gemini-2.0-Flash-Thinking & \\\\textbf{40.00} & 84.59 & 81.79 & 37.41 & 87.35 & 62.39 & \\\\textbf{38.82} & 86.62 & 64.19 & 36.97 & 84.55 & 66.47 & 17.77 & 77.17 & 76.58 \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multicolumn{16}{c}{\\\\textbf{Sentence-Level}} \\\\\\\\\\n\",\n \"GPT-4o & \\\\textbf{38.68} & 87.56 & 82.00 & \\\\textbf{34.17} & 86.38 & 81.78 & 36.21 & 86.05 & 82.66 & 33.07 & 84.63 & 81.47 & 18.53 & \\\\textbf{82.96} & \\\\textbf{57.23} \\\\\\\\\\n\",\n \"DeepSeek-V3 & 37.46 & 87.61 & 81.99 & 33.57 & 86.32 & 81.93 & 35.82 & 86.30 & 83.04 & 32.96 & 84.74 & 81.81 & 19.92 & 82.88 & 54.68 \\\\\\\\\\n\",\n \"Gemini-2.0-Flash & 38.01 & 87.29 & 81.87 & 33.67 & 86.36 & 81.67 & \\\\textbf{36.48} & 86.21 & 82.80 & \\\\textbf{33.24} & 84.55 & 81.49 & 23.28 & 82.80 & 53.83 \\\\\\\\\\n\",\n \"\\\\hdashline\\n\",\n \"OpenAI-o1 & 36.30 & \\\\textbf{87.87} & \\\\textbf{83.49} & 31.30 & \\\\textbf{86.39} & \\\\textbf{83.05} & 33.17 & \\\\textbf{86.35} & \\\\textbf{84.35} & 29.95 & \\\\textbf{84.80} & \\\\textbf{82.96} & 16.87 & 82.42 & 55.73 \\\\\\\\\\n\",\n \"DeepSeek-R1 & 34.05 & 87.13 & 81.93 & 29.23 & 85.95 & 81.75 & 31.83 & 86.03 & 83.01 & 28.51 & 84.26 & 81.59 & 5.16 & 80.35 & 52.16 \\\\\\\\\\n\",\n \"OpenAI-o3-mini & 36.33 & 86.74 & 81.67 & 32.81 & 85.92 & 81.73 & 33.95 & 85.57 & 82.43 & 31.88 & 84.07 & 81.28 & 18.06 & 82.83 & 55.16 \\\\\\\\\\n\",\n \"Gemini-2.0-Flash-Thinking & 37.07 & 86.99 & 81.68 & 33.09 & 86.17 & 81.55 & 35.70 & 86.19 & 82.81 & 32.30 & 84.16 & 81.27 & \\\\textbf{23.52} & 82.77 & 53.69 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}%\\n\",\n \"}\\n\",\n \"\\\\caption{Average BLEU, COMET, and CometKiwi scores at document-level and sentence-level across five domains.}\\n\",\n \"\\\\label{tab:length_analysis}\\n\",\n \"\\\\end{table*}\\n\"\n ]\n]","isConversational":false},"tables":{"kind":"list like","value":[["\\begin{figure*}[th] \n"," \\centering\n"," \\includegraphics[width=\\textwidth]{figure/deen-ende_radar_charts.pdf}\n"," \\caption{Multi-Domain En$\\Leftrightarrow$De Translation Performance Comparison, showing averaged BLEU, COMET, and CometKiwi scores for both directions, with distinct colors representing different LLMs. }\n"," \\label{fig:de-en-radar}\n","\\end{figure*}\n"],["\\begin{figure*}[th] \n"," \\centering\n"," \\includegraphics[width=\\textwidth]{figure/zhen-enzh_radar_charts.pdf}\n"," \\caption{Multi-Domain En$\\Leftrightarrow$Zh Translation Performance Comparison, showing averaged BLEU, COMET, and CometKiwi scores for both directions, with distinct colors representing different LLMs. }\n","\\end{figure*}\n"],["\\begin{table}[t]\n","\\centering\n","\\resizebox{\\linewidth}{!}{%\n","\\begin{tabular}{l c c c c c c}\n","\\toprule\n","\\multirow{2}{*}{Model} & \\multicolumn{6}{c}{MQM Score ($\\downarrow$)} \\\\\n","\\cmidrule(lr){2-7}\n"," & IT & Koran & Medical & Law & Subtitles & Average\\\\\n","\\midrule\n","GPT-4o & 3.14 & 4.56 & 3.24 & 3.74 & 3.44 & 3.62 \\\\\n","DeepSeek-V3 & \\textbf{2.91} & 4.62 & 3.30 & 3.89 & \\textbf{3.38} & 3.62 \\\\\n","Gemini-2.0-Flash & 3.09 & 4.68 & \\textbf{3.15} & \\textbf{3.55} & \\textbf{3.38} & \\textbf{3.57} \\\\\n","\\hdashline\n","OpenAI-o1 & 3.11 & 4.74 & 3.32 & 3.86 & 3.49 & 3.70 \\\\\n","DeepSeek-R1 & 2.92 & \\textbf{4.55} & 3.30 & 3.88 & 3.40 & 3.61 \\\\\n","OpenAI-o3-mini & 3.16 & 5.22 & 3.43 & 4.59 & 3.62 & 4.00 \\\\\n","Gemini-2.0-Flash-Thinking & 3.15 & 4.84 & 3.28 & 3.69 & 3.41 & 3.67 \\\\\n","\\bottomrule\n","\\end{tabular}%\n","}\n","\\caption{MQM scores for different models across various domains in the Multi-Domain dataset (De$\\Rightarrow$En).}\n","\\label{tab:mqm_avg_scores}\n","\\end{table}\n"],["\\begin{table}[t]\n","\\centering\n","\\resizebox{\\linewidth}{!}{%\n","\\begin{tabular}{l ccccc}\n","\\toprule\n","\\textbf{Model} & \\textbf{Acc. (\\%)} & \\textbf{Style (\\%)} & \\textbf{Fluency (\\%)} & \\textbf{Term. (\\%)} & \\textbf{Non-trans (\\%)} \\\\\n","\\midrule\n","GPT-4o & 50.78 & 30.58 & 7.03 & 11.38 & 0.23 \\\\\n","DeepSeek-V3 & 50.72 & 30.51 & 6.84 & 11.83 & 0.09 \\\\\n","Gemini-2.0-Flash & 52.86 & \\textbf{29.43} & 6.87 & \\textbf{10.68} & 0.16 \\\\\n","\\hdashline\n","OpenAI-o1 & 50.95 & 29.58 & 7.10 & 12.22 & 0.15 \\\\\n","DeepSeek-R1 & 50.50 & 30.83 & 6.68 & 11.91 & \\textbf{0.08} \\\\\n","OpenAI-o3-mini & \\textbf{49.97} & 30.48 & 7.19 & 12.24 & 0.12 \\\\\n","Gemini-2.0-Flash-Thinking & 52.54 & 29.64 & \\textbf{6.38} & 11.29 & 0.15 \\\\\n","\\bottomrule\n","\\end{tabular}%\n","}\n","\\caption{Error category percentages for different models, including accuracy (Acc.), style, fluency, terminology (Term.), and non-translation error (Non-trans).}\n","\\label{tab:model_error_category_percentages}\n","\\end{table}\n"],["\\begin{table}[t]\n","\\centering\n","\\small\n","\\begin{tabular}{lcc}\n","\\toprule\n","\\multirow{2}{*}{Model} & \\multicolumn{2}{c}{Term Acc. (\\%)} \\\\\n","\\cmidrule(lr){2-3}\n","& Zh$\\Rightarrow$En & De$\\Rightarrow$En \\\\\n","\\midrule\n","GPT-4o & 52.95 & 37.67 \\\\\n","DeepSeek-V3 & 51.84 & 37.92 \\\\\n","Gemini-2.0-Flash & 52.21 & \\textbf{38.55} \\\\\n","\\hdashline\n","OpenAI-o1 & 52.92 & 37.42 \\\\\n","DeepSeek-R1 & \\textbf{53.20} & 37.83 \\\\\n","OpenAI-o3-mini & 45.59 & 37.38 \\\\\n","Gemini-2.0-Flash-Thinking & 52.67 & 37.62 \\\\\n","\\bottomrule\n","\\end{tabular}\n","\\caption{Terminology accuracy (\\%) for Zh$\\Rightarrow$En and De$\\Rightarrow$En translation tasks.}\n","\\label{tab:termacc}\n","\\end{table}\n"],["\\begin{table*}[t]\n","\\centering\n","\\resizebox{\\textwidth}{!}{%\n","\\begin{tabular}{l ccc ccc ccc ccc ccc}\n","\\toprule\n","\\multirow{2}{*}{Model} & \\multicolumn{3}{c}{Conversation} & \\multicolumn{3}{c}{E-commerce} & \\multicolumn{3}{c}{News} & \\multicolumn{3}{c}{Social} & \\multicolumn{3}{c}{Literary} \\\\\n","\\cmidrule(lr){2-4} \\cmidrule(lr){5-7} \\cmidrule(lr){8-10} \\cmidrule(lr){11-13} \\cmidrule(lr){14-16}\n"," & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI \\\\\n","\\midrule\n","\\multicolumn{16}{c}{\\textbf{Document-Level}} \\\\\n","GPT-4o & \\textbf{40.00} & 84.71 & 82.02 & 35.33 & 87.35 & 64.55 & 36.07 & 86.60 & 66.01 & 33.75 & 84.36 & 68.70 & 17.55 & \\textbf{77.57} & 77.14 \\\\\n","DeepSeek-V3 & 39.28 & 84.51 & 81.98 & 36.71 & 87.26 & 63.16 & 37.59 & 86.50 & 65.35 & 36.48 & 84.80 & 67.83 & 16.81 & 77.48 & 77.17 \\\\\n","Gemini-2.0-Flash & 39.98 & 84.64 & 81.81 & \\textbf{37.72} & 87.34 & 62.33 & 38.68 & 85.71 & 63.68 & \\textbf{37.76} & 84.38 & 67.14 & \\textbf{18.12} & 77.30 & 76.62 \\\\\n","\\hdashline\n","OpenAI-o1 & 37.05 & \\textbf{84.83} & \\textbf{82.99} & 30.68 & \\textbf{87.40} & \\textbf{66.50} & 32.08 & \\textbf{86.67} & \\textbf{67.90} & 29.02 & \\textbf{84.82} & \\textbf{69.79} & 16.44 & \\textbf{77.57} & \\textbf{77.76} \\\\\n","DeepSeek-R1 & 33.66 & 84.15 & 81.45 & 21.62 & 85.23 & 63.77 & 23.49 & 83.68 & 66.99 & 22.22 & 81.59 & 68.09 & 13.12 & 76.64 & 76.47 \\\\\n","OpenAI-o3-mini & 36.58 & 84.52 & 82.05 & 32.13 & 87.15 & 65.18 & 33.60 & 86.22 & 65.77 & 30.93 & 84.76 & 68.87 & 17.26 & 76.87 & 76.64 \\\\\n","Gemini-2.0-Flash-Thinking & \\textbf{40.00} & 84.59 & 81.79 & 37.41 & 87.35 & 62.39 & \\textbf{38.82} & 86.62 & 64.19 & 36.97 & 84.55 & 66.47 & 17.77 & 77.17 & 76.58 \\\\\n","\\midrule\n","\\midrule\n","\\multicolumn{16}{c}{\\textbf{Sentence-Level}} \\\\\n","GPT-4o & \\textbf{38.68} & 87.56 & 82.00 & \\textbf{34.17} & 86.38 & 81.78 & 36.21 & 86.05 & 82.66 & 33.07 & 84.63 & 81.47 & 18.53 & \\textbf{82.96} & \\textbf{57.23} \\\\\n","DeepSeek-V3 & 37.46 & 87.61 & 81.99 & 33.57 & 86.32 & 81.93 & 35.82 & 86.30 & 83.04 & 32.96 & 84.74 & 81.81 & 19.92 & 82.88 & 54.68 \\\\\n","Gemini-2.0-Flash & 38.01 & 87.29 & 81.87 & 33.67 & 86.36 & 81.67 & \\textbf{36.48} & 86.21 & 82.80 & \\textbf{33.24} & 84.55 & 81.49 & 23.28 & 82.80 & 53.83 \\\\\n","\\hdashline\n","OpenAI-o1 & 36.30 & \\textbf{87.87} & \\textbf{83.49} & 31.30 & \\textbf{86.39} & \\textbf{83.05} & 33.17 & \\textbf{86.35} & \\textbf{84.35} & 29.95 & \\textbf{84.80} & \\textbf{82.96} & 16.87 & 82.42 & 55.73 \\\\\n","DeepSeek-R1 & 34.05 & 87.13 & 81.93 & 29.23 & 85.95 & 81.75 & 31.83 & 86.03 & 83.01 & 28.51 & 84.26 & 81.59 & 5.16 & 80.35 & 52.16 \\\\\n","OpenAI-o3-mini & 36.33 & 86.74 & 81.67 & 32.81 & 85.92 & 81.73 & 33.95 & 85.57 & 82.43 & 31.88 & 84.07 & 81.28 & 18.06 & 82.83 & 55.16 \\\\\n","Gemini-2.0-Flash-Thinking & 37.07 & 86.99 & 81.68 & 33.09 & 86.17 & 81.55 & 35.70 & 86.19 & 82.81 & 32.30 & 84.16 & 81.27 & \\textbf{23.52} & 82.77 & 53.69 \\\\\n","\\bottomrule\n","\\end{tabular}%\n","}\n","\\caption{Average BLEU, COMET, and CometKiwi scores at document-level and sentence-level across five domains.}\n","\\label{tab:length_analysis}\n","\\end{table*}\n"],["\\begin{table}[t]\n","\\centering\n","\\resizebox{.48\\textwidth}{!}{%\n","\\begin{tabular}{@{}ll@{}}\n","\\toprule\n","\\textbf{ID} & \\textbf{Prompt} \\\\\n","\\midrule\n","\\textbf{P1} & Translate the following \\texttt{\\{src\\_lang\\}} sentence into \\texttt{\\{tgt\\_lang\\}}. \\\\\n","\\textbf{P2} & Given the \\texttt{\\{src\\_lang\\}} sentence from a \\texttt{\\{domain\\}}-related text, \\\\\n"," & translate it into \\texttt{\\{tgt\\_lang\\}} in a \\texttt{\\{domain\\}} domain style. \\\\\n","\\textbf{P3} & Translate the following \\texttt{\\{src\\_lang\\}} text into \\texttt{\\{tgt\\_lang\\}} while \\\\\n"," & maintaining the domain style of the source text. \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\caption{Prompts used in domain-aware translation.}\n","\\label{tab:prompt_templates}\n","\\end{table}\n"],["\\begin{table}[t]\n","\\centering\n","\\resizebox{\\linewidth}{!}{%\n","\\begin{tabular}{l ccc ccc ccc}\n","\\toprule\n","\\multirow{2}{*}{Prompt} & \\multicolumn{3}{c}{IT} & \\multicolumn{3}{c}{Law} & \\multicolumn{3}{c}{Medical} \\\\\n","\\cmidrule(lr){2-4} \\cmidrule(lr){5-7} \\cmidrule(lr){8-10}\n","& BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI \\\\\n","\\midrule\n","P1 & \\textbf{36.66} & 83.49 & 78.68 & 39.16 & 84.97 & 82.35 & 40.23 & 83.82 & 81.81 \\\\\n","P2 & 34.20 & 82.94 & 79.33 & \\textbf{41.46} & 85.01 & 83.46 & 38.92 & 83.38 & 82.57 \\\\\n","P3 & 36.55 & \\textbf{83.57} & \\textbf{79.82} & 40.57 & \\textbf{85.32} & \\textbf{83.54} & \\textbf{41.14} & \\textbf{84.06} & \\textbf{82.97} \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\caption{Translation performance across IT, Law, and Medical domains under different prompt strategies.}\n","\\label{tab:prompt_results}\n","\\end{table}\n"],["\\begin{table*}[t]\n","\\centering\n","\\resizebox{\\textwidth}{!}{%\n","\\begin{tabular}{l ccc ccc ccc ccc ccc}\n","\\toprule\n","\\multirow{2}{*}{Model} & \\multicolumn{3}{c}{Level 1} & \\multicolumn{3}{c}{Level 2} & \\multicolumn{3}{c}{Level 3} & \\multicolumn{3}{c}{Level 4} & \\multicolumn{3}{c}{Level 5} \\\\\n","\\cmidrule(lr){2-4} \\cmidrule(lr){5-7} \\cmidrule(lr){8-10} \\cmidrule(lr){11-13} \\cmidrule(lr){14-16}\n"," & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI \\\\\n","\\midrule\n","GPT-4o & \\textbf{41.96} & \\textbf{85.13} & 79.80 & 30.08 & 84.88 & 82.15 & 26.49 & 82.75 & 80.91 & 22.61 & 83.11 & 75.48 & 24.94 & 83.59 & 67.11 \\\\\n","DeepSeek-V3 & 40.81 & 85.09 & 79.90 & 27.90 & 84.86 & 82.20 & 25.43 & 82.54 & 80.97 & 22.57 & 82.95 & 75.30 & 25.06 & 83.30 & 67.64 \\\\\n","Gemini-2.0-Flash & 41.15 & 84.39 & 79.46 & 29.22 & 84.93 & 81.96 & 27.53 & 82.80 & 80.68 & 24.53 & 82.72 & 74.57 & 28.93 & 83.91 & 66.28 \\\\\n","\\hdashline\n","OpenAI-o1 & 40.34 & 84.59 & \\textbf{81.10} & 29.67 & 84.88 & \\textbf{83.28} & 25.76 & \\textbf{82.94} & \\textbf{81.94} & 21.99 & \\textbf{83.31} & \\textbf{76.92} & 22.78 & \\textbf{84.02} & \\textbf{69.62} \\\\\n","DeepSeek-R1 & 41.43 & 84.33 & 79.86 & 27.12 & 84.25 & 81.75 & 24.32 & 82.15 & 80.44 & 18.66 & 82.17 & 75.27 & 19.53 & 82.43 & 68.78 \\\\\n","OpenAI-o3-mini & 40.74 & 84.12 & 79.44 & 26.49 & 84.56 & 82.14 & 24.93 & 82.36 & 80.73 & 21.09 & 82.67 & 75.37 & 22.70 & 83.27 & 67.63 \\\\\n","Gemini-2.0-Flash-Thinking & 39.87 & 83.84 & 79.36 & \\textbf{30.78} & \\textbf{84.98} & 82.21 & \\textbf{28.15} & 82.68 & 80.62 & \\textbf{24.54} & 83.02 & 74.71 & \\textbf{28.97} & 83.93 & 66.32 \\\\\n","\\bottomrule\n","\\end{tabular}%\n","}\n","\\caption{Performance across different levels of complexity, comparing LRMs and tranditional LLMs.}\n","\\label{tab:metrics_by_level_avg}\n","\\end{table*}\n"],["\\begin{table}[t]\n","\\centering\n","\\resizebox{\\linewidth}{!}{%\n","\\begin{tabular}{l c c c c}\n","\\toprule\n","\\textbf{Model} & \\textbf{BLEU} & \\textbf{COMET} & \\textbf{KIWI} & \\textbf{Avg. Len.} \\\\\n","\\midrule\n","GPT-4o & 37.40 & 86.74 & 84.61 & 31.76 \\\\\n","DeepSeek-V3 & \\textbf{43.34} & \\textbf{87.76} & 85.04 & \\textbf{29.63} \\\\\n","Gemini-2.0-Flash & 40.30 & 87.05 & 84.80 & 32.71 \\\\\n","\\hdashline\n","OpenAI-o1 & 36.17 & 87.11 & \\textbf{85.09} & 402.64 \\\\\n","DeepSeek-R1 & 41.12 & 87.50 & 85.03 & 485.05 \\\\\n","OpenAI-o3-mini & 34.60 & 86.69 & 84.98 & 424.31 \\\\\n","Gemini-2.0-Flash-Thinking & 39.17 & 86.63 & 84.37 & 1043.59 \\\\\n","\\bottomrule\n","\\end{tabular}%\n","}\n","\\caption{Average BLEU, COMET, KIWI scores, and response length for various LLMs.}\n","\\label{tab:um_results_averages}\n","\\end{table}\n"],["\\begin{figure}[t]\n"," \\centering\n"," \\includegraphics[width=\\linewidth]{figure/case_study_figure.pdf}\n"," \\caption{Case study of DeepSeek-R1 showing its reasoning process and final translation. }\n"," \\label{fig:case_study}\n","\\end{figure}\n"],["\\begin{table}[t]\n","\\centering\n","\\small\n","\\begin{tabular}{lc lc}\n","\\toprule\n","\\textbf{Test set} & \\textbf{Num} & \\textbf{Test set} & \\textbf{Num} \\\\\n","\\midrule\n","Conversation & 349 & Social & 491 \\\\\n","E-commerce & 518 & News (WMT22) & 499 \\\\\n","Bio (WMT18) & 212 & Bio (WMT19) & 243 \\\\\n","Commonsense & 1200 & Literary & 3038 \\\\\n","Terminology & 1638 & Mixed (WMT22) & 1875 \\\\\n","\\bottomrule\n","\\end{tabular}%\n","\\caption{Test sets and the number of samples for Zh$\\Rightarrow$En translation tasks. Bio denotes the Biomedical domain.}\n","\\label{tab:test_zh-en}\n","\\end{table}\n"],["\\begin{table}[t]\n","\\centering\n","\\small\n","\\begin{tabular}{lc lc}\n","\\toprule\n","\\textbf{Test set} & \\textbf{Num} & \\textbf{Test set} & \\textbf{Num} \\\\\n","\\midrule\n","Culture & 778 & Laws & 456 \\\\\n","Subtitles & 597 & Science & 503 \\\\\n","Conversation & 484 & Social & 511 \\\\\n","E-commerce & 530 & News (WMT22) & 511 \\\\\n","Bio (WMT18) & 244 & Bio (WMT19) & 224 \\\\\n","News (UM) & 1500 & Mixed & 2037 \\\\\n","\\bottomrule\n","\\end{tabular}%\n","\\caption{Test sets and the number of samples for En$\\Rightarrow$Zh translation tasks.}\n","\\label{tab:test_en-zh}\n","\\end{table}\n"],["\\begin{table}[t]\n","\\centering\n","\\small\n","\\begin{tabular}{lc lc}\n","\\toprule\n","\\textbf{Test set} & \\textbf{Num} & \\textbf{Test set} & \\textbf{Num} \\\\\n","\\midrule\n","Bio (WMT18) & 217 & Bio (WMT19) & 373 \\\\\n","Social & 515 & E-commerce & 501 \\\\\n","News (WMT22) & 506 & Conversation & 462 \\\\\n","IT & 2000 & Law & 2000 \\\\\n","Koran & 2000 & Subtitles & 2000 \\\\\n","Medical & 2000 & Terminology & 2955 \\\\\n","Mixed & 1984 & & \\\\\n","\\bottomrule\n","\\end{tabular}%\n","\\caption{Test sets and the number of samples for De$\\Rightarrow$En translation tasks.}\n","\\label{tab:test_de-en}\n","\\end{table}\n"],["\\begin{table}[t]\n","\\centering\n","\\small\n","\\begin{tabular}{lc lc}\n","\\toprule\n","\\textbf{Test set} & \\textbf{Num} & \\textbf{Test set} & \\textbf{Num} \\\\\n","\\midrule\n","Bio (WMT18) & 220 & Bio (WMT19) & 435 \\\\\n","News (WMT22) & 511 & E-commerce & 530 \\\\\n","Social & 512 & Conversation & 484 \\\\\n","Mixed & 2037 & & \\\\\n","\\bottomrule\n","\\end{tabular}%\n","\\caption{Test sets and the number of samples for En$\\Rightarrow$De translation tasks.}\n","\\label{tab:test_en-de}\n","\\end{table}\n"],["\\begin{table*} [t]\n"," \\centering\n"," \\small\n"," \\begin{tabularx}{\\textwidth}{l l X}\n"," \\toprule\n"," \\textbf{Category} & \\textbf{Error Type} & \\textbf{Description} \\\\\n"," \\midrule\n"," \\multirow{8}{*}{Accuracy} \n"," & Mistranslation & Inaccurate translation causing semantic distortion. \\\\\n"," & Addition & Adding extra information or emotions not in the source.\\\\\n"," & Under-translation & Failure to fully convey cultural or contextual nuances. \\\\\n"," & Omission & Unintentional exclusion of content from the source text. \\\\\n"," & Untranslated & Retaining source text without translation. \\\\\n"," & Hallucination & Generating content unrelated to the source text. \\\\\n"," & Off-target Translation & Translation misalignment caused by ambiguous input. \\\\ \n"," & Contradiction & Contradicting itself or the source text. \\\\ \n"," \\midrule\n"," \\multirow{5}{*}{Fluency} \n"," & Grammar & Errors in sentence structure or syntax. \\\\\n"," & Punctuation & Incorrect use of punctuation marks. \\\\\n"," & Spelling & Misspelling of words. \\\\\n"," & Semantic Repetition & Unnecessary repetition of words or phrases. \\\\\n"," & Logical Incoherence & Lack of logical flow or coherence in translation. \\\\\n"," \\midrule\n"," \\multirow{4}{*}{Style} \n"," & Awkward Expression & Stilted or unnatural phrasing in the target language. \\\\\n"," & Unidiomatic Usage & Literal translation causes unnatural wording or grammar. \\\\\n"," & Style Inconsistency & Inconsistent stylistic choices within the translation. \\\\\n"," & Over-localization & Excessive cultural adaptation leading to distortion. \\\\\n"," \\midrule\n"," \\multirow{5}{*}{Terminology} \n"," & Terminology Inconsistency & Inconsistent translation of the same term. \\\\\n"," & Terminology Misuse & Use of incorrect or inappropriate terms for the domain. \\\\\n"," & Cross-domain Confusion & Misuse of terms due to domain shifts. \\\\\n"," & Incorrect Unit Conversion & Errors in unit conversion (e.g., metric to imperial). \\\\ \n"," & Formatting & Errors in domain-specific formats (e.g., legal, medical). \\\\ \n"," \\midrule\n"," \\multirow{1}{*}{Others} \n"," & & Any other errors not covered in the above categories. \\\\\n"," \\midrule\n"," \\multirow{1}{*}{Source Error} \n"," & & Errors present in the Source text itself. \\\\\n"," \\midrule\n"," \\multirow{1}{*}{Non-translation Error} \n"," & & Translation is unassessable and unrelated to the Source. \\\\\n"," \\bottomrule\n"," \\end{tabularx}\n"," \\caption{\\label{tab:mqm-hierarchy} MQM Hierarchy.}\n","\\end{table*}\n"],["\\begin{table}[t]\n","\\centering\n","\\resizebox{\\linewidth}{!}{%\n","\\begin{tabular}{l ccc}\n","\\toprule\n","\\multirow{2}{*}{Model} & \\multicolumn{3}{c}{Culture} \\\\\n","\\cmidrule(lr){2-4}\n"," & BLEU & COMET & KIWI \\\\\n","\\midrule\n","GPT-4o & 38.65 & 85.24 & 83.14 \\\\\n","DeepSeek-V3 & \\textbf{40.27} & \\textbf{85.46} & 83.21 \\\\\n","Gemini-2.0-Flash & 39.11 & 85.02 & 82.94 \\\\\n","\\hdashline\n","OpenAI-o1 & 34.57 & 85.06 & \\textbf{84.25} \\\\\n","DeepSeek-R1 & 35.88 & 85.45 & 83.14 \\\\\n","OpenAI-o3-mini & 34.05 & 84.36 & \\textbf{84.25} \\\\\n","Gemini-2.0-Flash-Thinking & 36.94 & 84.63 & 83.68 \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\caption{Results on the En$\\Rightarrow$Zh Culture-MT dataset.}\n","\\label{tab:result_culture_mt}\n","\\end{table}\n"],["\\begin{table*}[h]\n","\\centering\n","\\resizebox{\\textwidth}{!}{%\n","\\begin{tabular}{lccc ccc ccc ccc}\n","\\toprule\n","\\multirow{2}{*}{Model} \n","& \\multicolumn{3}{c}{Contextual Syntactic} \n","& \\multicolumn{3}{c}{Contextless Syntactic} \n","& \\multicolumn{3}{c}{Lexical} \n","& \\multicolumn{3}{c}{Average} \\\\\n","\\cmidrule(lr){2-4} \\cmidrule(lr){5-7} \\cmidrule(lr){8-10} \\cmidrule(lr){11-13}\n","& BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI \\\\\n","\\midrule\n","GPT-4o & 31.42 & \\textbf{85.11} & 81.88 & 32.82 & 85.24 & 78.35 & 33.89 & 85.41 & 79.83 & 32.71 & 85.25 & 80.02 \\\\\n","DeepSeek-V3 & 30.19 & 84.90 & 81.91 & \\textbf{33.53} & \\textbf{85.58} & 78.76 & \\textbf{35.09} & \\textbf{85.52} & 79.69 & \\textbf{32.94} & \\textbf{85.33} & 80.12 \\\\\n","Gemini-2.0-Flash & 31.15 & 84.86 & 81.79 & 31.69 & 85.20 & 77.99 & 32.13 & 84.03 & 78.84 & 31.66 & 84.70 & 79.54 \\\\\n","\\hdashline\n","OpenAI-o1 & 29.80 & 84.74 & 80.66 & 28.04 & 85.14 & 79.48 & 30.40 & 84.00 & 79.70 & 28.50 & 84.96 & \\textbf{81.11} \\\\\n","DeepSeek-R1 & 26.28 & 84.54 & \\textbf{82.07} & 30.00 & 84.96 & 78.62 & 30.40 & 84.00 & 79.70 & 28.89 & 84.50 & 80.13 \\\\\n","OpenAI-o3-mini & 28.55 & 84.57 & 79.19 & 27.18 & 84.43 & \\textbf{82.97} & 29.43 & 83.82 & \\textbf{80.87} & 28.39 & 84.27 & 81.01 \\\\\n","Gemini-2.0-Flash-Thinking & \\textbf{31.54} & 84.61 & 81.96 & 31.60 & 84.34 & 77.66 & 33.49 & 84.59 & 79.95 & 32.21 & 84.51 & 79.86 \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\caption{Results on the Zh$\\Rightarrow$En Commonsense-MT dataset.}\n","\\label{tab:zh-en-ambiguity}\n","\\end{table*}\n"],["\\begin{table*}[h]\n","\\centering\n","\\resizebox{\\textwidth}{!}{%\n","\\begin{tabular}{l ccc ccc ccc ccc}\n","\\toprule\n","\\multirow{2}{*}{Model} & \\multicolumn{3}{c}{News} & \\multicolumn{3}{c}{Laws} & \\multicolumn{3}{c}{Science} & \\multicolumn{3}{c}{Subtitles} \\\\\n","\\cmidrule(lr){2-4} \\cmidrule(lr){5-7} \\cmidrule(lr){8-10} \\cmidrule(lr){11-13}\n"," & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI \\\\\n","\\midrule\n","GPT-4o & 34.58 & 86.22 & 85.02 & 58.00 & 91.50 & 86.65 & 32.09 & 87.34 & 84.84 & \\textbf{24.91} & 81.91 & 81.93 \\\\\n","DeepSeek-V3 & 35.86 & \\textbf{86.94} & 85.44 & \\textbf{80.33} & \\textbf{93.36} & 86.55 & \\textbf{33.54} & \\textbf{88.28} & 85.46 & 23.66 & \\textbf{82.44} & 82.69 \\\\\n","Gemini-2.0-Flash & \\textbf{36.39} & 86.37 & 85.14 & 66.71 & 92.23 & 86.49 & 33.47 & 87.69 & 85.24 & 24.64 & 81.91 & 82.33 \\\\\n","\\hdashline\n","OpenAI-o1 & 33.93 & 86.61 & \\textbf{85.47} & 55.48 & 91.61 & 86.73 & 32.32 & 87.92 & \\textbf{85.56} & 22.96 & 82.31 & 82.60 \\\\\n","DeepSeek-R1 & 33.35 & 86.67 & 85.40 & 78.81 & 93.16 & 86.40 & 31.82 & 88.08 & 85.47 & 20.50 & 82.10 & \\textbf{82.84} \\\\\n","OpenAI-o3-mini & 32.90 & 86.29 & 85.35 & 51.18 & 91.15 & \\textbf{86.92} & 31.38 & 87.42 & 85.24 & 22.95 & 81.90 & 82.40 \\\\\n","Gemini-2.0-Flash-Thinking & 33.90 & 86.02 & 84.79 & 66.99 & 92.12 & 86.40 & 31.98 & 87.47 & 85.06 & 23.83 & 80.90 & 81.22 \\\\\n","\\bottomrule\n","\\end{tabular}%\n","}\n","\\caption{Results on the En$\\Rightarrow$Zh UM-Corpus dataset.}\n","\\label{tab:result_um_domains}\n","\\end{table*}\n"],["\\begin{table*}[h]\n","\\centering\n","\\resizebox{\\textwidth}{!}{%\n","\\begin{tabular}{l ccc ccc ccc ccc ccc}\n","\\toprule\n","\\multirow{2}{*}{Model} & \\multicolumn{3}{c}{Conversation} & \\multicolumn{3}{c}{E-commerce} & \\multicolumn{3}{c}{News} & \\multicolumn{3}{c}{Social} & \\multicolumn{3}{c}{Average} \\\\\n","\\cmidrule(lr){2-4} \\cmidrule(lr){5-7} \\cmidrule(lr){8-10} \\cmidrule(lr){11-13} \\cmidrule(lr){14-16}\n"," & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI \\\\\n","\\midrule\n","GPT-4o & 31.28 & 84.86 & 79.83 & 19.94 & 81.75 & 78.24 & 25.21 & \\textbf{83.04} & 81.10 & 27.29 & \\textbf{83.77} & 80.64 & 25.93 & \\textbf{83.36} & 79.95 \\\\\n","DeepSeek-V3 & 30.26 & \\textbf{84.96} & 79.67 & 19.47 & 81.55 & 78.32 & 25.77 & 83.00 & 81.18 & 27.55 & 83.56 & 80.64 & 25.76 & 83.27 & 79.95 \\\\\n","Gemini-2.0-Flash & \\textbf{32.79} & 84.46 & 79.19 & \\textbf{19.97} & 81.54 & 77.90 & \\textbf{26.62} & \\textbf{83.04} & 80.93 & \\textbf{29.25} & 83.24 & 80.16 & \\textbf{27.16} & 83.07 & 79.55 \\\\\n","\\hdashline\n","OpenAI-o1 & 29.52 & 84.95 & \\textbf{80.90} & 17.82 & \\textbf{81.81} & 79.16 & 22.52 & 82.96 & \\textbf{82.10} & 23.85 & 83.20 & \\textbf{81.39} & 23.43 & 83.23 & \\textbf{80.89} \\\\\n","DeepSeek-R1 & 26.82 & 84.61 & 79.40 & 15.63 & 80.98 & 77.87 & 21.21 & 82.34 & 80.95 & 22.18 & 82.69 & 80.05 & 21.46 & 82.66 & 79.57 \\\\\n","OpenAI-o3-mini & 30.57 & 84.25 & 79.48 & 18.80 & 81.29 & 78.23 & 23.68 & 82.88 & 81.16 & 26.98 & 83.34 & 80.61 & 25.01 & 82.94 & 79.87 \\\\\n","Gemini-2.0-Flash-Thinking & 30.98 & 84.17 & 79.28 & 19.06 & 81.21 & 77.76 & 25.50 & 82.90 & 80.85 & 27.54 & 83.04 & 80.13 & 25.77 & 82.83 & 79.50 \\\\\n","\\bottomrule\n","\\end{tabular}%\n","}\n","\\caption{Results on the Zh$\\Rightarrow$En WMT22 General Machine Translation task.}\n","\\end{table*}\n"],["\\begin{table*}[h]\n","\\centering\n","\\resizebox{\\textwidth}{!}{%\n","\\begin{tabular}{l ccc ccc ccc ccc ccc}\n","\\toprule\n","\\multirow{2}{*}{Model} & \\multicolumn{3}{c}{Conversation} & \\multicolumn{3}{c}{E-commerce} & \\multicolumn{3}{c}{News} & \\multicolumn{3}{c}{Social} & \\multicolumn{3}{c}{Average} \\\\\n","\\cmidrule(lr){2-4} \\cmidrule(lr){5-7} \\cmidrule(lr){8-10} \\cmidrule(lr){11-13} \\cmidrule(lr){14-16}\n"," & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI \\\\\n","\\midrule\n","GPT-4o & \\textbf{45.63} & 89.44 & 82.67 & \\textbf{43.82} & 88.82 & 83.52 & 51.20 & 88.31 & 83.41 & \\textbf{38.60} & 84.57 & 81.33 & \\textbf{44.81} & 87.78 & 82.73 \\\\\n","DeepSeek-V3 & 43.49 & 89.89 & 82.94 & 42.84 & 89.00 & 83.79 & 50.28 & \\textbf{88.75} & 83.76 & 37.59 & \\textbf{85.27} & 82.24 & 43.55 & \\textbf{88.23} & 83.18 \\\\\n","Gemini-2.0-Flash & 44.59 & \\textbf{90.19} & 83.01 & 42.95 & 88.90 & 83.47 & \\textbf{52.12} & 88.46 & 83.28 & 38.11 & 84.71 & 81.59 & 44.44 & 88.06 & 82.84 \\\\\n","\\hdashline\n","OpenAI-o1 & 42.24 & 89.86 & \\textbf{84.13} & 39.22 & \\textbf{89.06} & \\textbf{84.59} & 45.88 & 88.47 & \\textbf{84.67} & 33.41 & 85.07 & \\textbf{82.98} & 40.19 & 88.11 & \\textbf{84.09} \\\\\n","DeepSeek-R1 & 38.00 & 88.74 & 82.74 & 34.48 & 88.35 & 83.48 & 42.56 & 88.08 & 83.58 & 29.43 & 84.21 & 82.01 & 36.12 & 87.34 & 82.95 \\\\\n","OpenAI-o3-mini & 43.24 & 89.02 & 82.44 & 41.54 & 88.51 & 83.41 & 47.06 & 87.53 & 82.94 & 36.38 & 83.99 & 81.27 & 42.05 & 87.26 & 82.52 \\\\\n","Gemini-2.0-Flash-Thinking & 44.31 & 89.71 & 82.66 & 42.39 & 88.73 & 83.31 & 49.54 & 88.23 & 83.26 & 36.35 & 83.86 & 81.08 & 43.15 & 87.63 & 82.58 \\\\\n","\\bottomrule\n","\\end{tabular}%\n","}\n","\\caption{Results on the En$\\Rightarrow$Zh WMT22 General Machine Translation task.}\n","\\end{table*}\n"],["\\begin{table*}[h]\n","\\centering\n","\\resizebox{\\textwidth}{!}{%\n","\\begin{tabular}{l ccc ccc ccc ccc ccc}\n","\\toprule\n","\\multirow{2}{*}{Model} & \\multicolumn{3}{c}{Conversation} & \\multicolumn{3}{c}{E-commerce} & \\multicolumn{3}{c}{News} & \\multicolumn{3}{c}{Social} & \\multicolumn{3}{c}{Average} \\\\\n","\\cmidrule(lr){2-4} \\cmidrule(lr){5-7} \\cmidrule(lr){8-10} \\cmidrule(lr){11-13} \\cmidrule(lr){14-16}\n"," & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI \\\\\n","\\midrule\n","GPT-4o & \\textbf{40.65} & 89.88 & 83.72 & \\textbf{38.03} & 88.80 & 84.02 & \\textbf{36.70} & 87.35 & 84.00 & 34.06 & 85.12 & 82.63 & \\textbf{37.36} & 87.79 & 83.59 \\\\\n","DeepSeek-V3 & 39.58 & 89.33 & 83.50 & 37.42 & 88.69 & 84.14 & 36.48 & 87.94 & 84.76 & \\textbf{35.08} & 85.22 & 82.91 & 37.14 & 87.79 & 83.83 \\\\\n","Gemini-2.0-Flash & 37.88 & 88.46 & 83.36 & 37.47 & \\textbf{88.98} & 84.09 & 35.88 & 87.69 & 84.63 & 34.02 & 85.39 & 82.84 & 36.31 & 87.63 & 83.73 \\\\\n","\\hdashline\n","OpenAI-o1 & 38.71 & \\textbf{90.19} & \\textbf{85.55} & 36.25 & 88.73 & \\textbf{85.79} & 33.90 & \\textbf{88.17} & \\textbf{86.69} & 32.18 & \\textbf{85.73} & \\textbf{84.54} & 35.26 & \\textbf{88.21} & \\textbf{85.64} \\\\\n","DeepSeek-R1 & 36.17 & 89.11 & 83.60 & 34.70 & 88.73 & 84.27 & 33.96 & 87.92 & 84.87 & 32.39 & 85.22 & 82.85 & 34.31 & 87.75 & 83.90 \\\\\n","OpenAI-o3-mini & 35.35 & 88.05 & 83.21 & 36.71 & 88.28 & 84.01 & 34.01 & 87.16 & 84.31 & 32.14 & 84.29 & 82.25 & 34.55 & 86.95 & 83.44 \\\\\n","Gemini-2.0-Flash-Thinking & 36.64 & 88.04 & 83.00 & 37.87 & 88.97 & 83.98 & 36.18 & 87.81 & 84.71 & 33.84 & 85.00 & 82.59 & 36.13 & 87.45 & 83.57 \\\\\n","\\bottomrule\n","\\end{tabular}%\n","}\n","\\caption{Results on the En$\\Rightarrow$De WMT22 General Machine Translation task.}\n","\\end{table*}\n"],["\\begin{table*}[h]\n","\\centering\n","\\resizebox{\\textwidth}{!}{%\n","\\begin{tabular}{l ccc ccc ccc ccc ccc}\n","\\toprule\n","\\multirow{2}{*}{Model} & \\multicolumn{3}{c}{Conversation} & \\multicolumn{3}{c}{E-commerce} & \\multicolumn{3}{c}{News} & \\multicolumn{3}{c}{Social} & \\multicolumn{3}{c}{Average} \\\\\n","\\cmidrule(lr){2-4} \\cmidrule(lr){5-7} \\cmidrule(lr){8-10} \\cmidrule(lr){11-13} \\cmidrule(lr){14-16}\n"," & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI \\\\\n","\\midrule\n","GPT-4o & \\textbf{37.14} & 86.07 & 81.79 & \\textbf{34.87} & \\textbf{86.15} & 81.37 & \\textbf{31.74} & 85.49 & 82.14 & \\textbf{32.35} & 85.07 & 81.27 & \\textbf{34.02} & 85.69 & 81.64 \\\\\n","DeepSeek-V3 & 36.52 & 86.24 & 81.84 & 34.56 & 86.04 & 81.46 & 30.76 & 85.52 & 82.47 & 31.65 & 84.93 & 81.43 & 33.37 & 85.68 & 81.80 \\\\\n","Gemini-2.0-Flash & 36.76 & 86.05 & 81.92 & 34.30 & 86.02 & 81.23 & 31.28 & 85.64 & 82.35 & 31.57 & 84.88 & 81.37 & 33.48 & 85.65 & 81.72 \\\\\n","\\hdashline\n","OpenAI-o1 & 34.73 & \\textbf{86.49} & \\textbf{83.40} & 31.91 & 85.95 & \\textbf{82.65} & 30.35 & 85.78 & \\textbf{83.93} & 30.36 & \\textbf{85.22} & \\textbf{82.93} & 31.84 & \\textbf{85.86} & \\textbf{83.23} \\\\\n","DeepSeek-R1 & 35.20 & 86.06 & 81.96 & 32.11 & 85.75 & 81.39 & 29.59 & 85.78 & 82.66 & 30.04 & 84.92 & 81.45 & 31.73 & 85.63 & 81.86 \\\\\n","OpenAI-o3-mini & 36.16 & 85.66 & 81.54 & 34.20 & 85.59 & 81.26 & 31.07 & 84.71 & 81.30 & 32.02 & 84.66 & 81.00 & 33.36 & 85.16 & 81.28 \\\\\n","Gemini-2.0-Flash-Thinking & 36.35 & 86.05 & 81.79 & 33.05 & 85.77 & 81.14 & 31.59 & \\textbf{85.83} & 82.42 & 31.48 & 84.76 & 81.28 & 33.12 & 85.60 & 81.66 \\\\\n","\\bottomrule\n","\\end{tabular}%\n","}\n","\\caption{Results on the De$\\Rightarrow$En WMT22 General Machine Translation task.}\n","\\end{table*}\n"],["\\begin{table*}[h]\n","\\centering\n","\\resizebox{\\textwidth}{!}{%\n","\\begin{tabular}{l ccc ccc ccc ccc ccc}\n","\\toprule\n","\\multirow{2}{*}{Model} & \\multicolumn{3}{c}{WMTBio18 En$\\Rightarrow$Zh} & \\multicolumn{3}{c}{WMTBio18 Zh$\\Rightarrow$En} & \\multicolumn{3}{c}{WMTBio18 En$\\Rightarrow$De} & \\multicolumn{3}{c}{WMTBio18 De$\\Rightarrow$En} & \\multicolumn{3}{c}{Average} \\\\\n","\\cmidrule(lr){2-4} \\cmidrule(lr){5-7} \\cmidrule(lr){8-10} \\cmidrule(lr){11-13} \\cmidrule(lr){14-16}\n"," & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI \\\\\n","\\midrule\n","GPT-4o & \\textbf{40.86} & 86.66 & 84.61 & 25.04 & 84.33 & 84.02 & 24.44 & 86.68 & 84.97 & 29.31 & 85.06 & 82.61 & 29.91 & 85.68 & 84.05 \\\\\n","DeepSeek-V3 & 40.58 & 86.67 & 84.66 & 25.73 & 84.42 & 83.95 & 24.75 & 86.85 & 84.96 & 29.37 & 85.01 & 82.78 & 30.11 & 85.74 & 84.09 \\\\\n","Gemini-2.0-Flash & 40.83 & \\textbf{86.87} & 84.62 & \\textbf{26.32} & \\textbf{84.50} & 83.74 & 26.47 & 86.72 & 84.66 & \\textbf{30.69} & \\textbf{85.10} & 82.22 & 31.08 & \\textbf{85.80} & 83.81 \\\\\n","\\hdashline\n","OpenAI-o1 & 38.84 & 86.63 & \\textbf{85.52} & 24.85 & 84.30 & \\textbf{84.88} & \\textbf{32.29} & \\textbf{86.96} & \\textbf{86.68} & 28.42 & 85.08 & \\textbf{84.05} & \\textbf{31.35} & 85.74 & \\textbf{85.38} \\\\\n","DeepSeek-R1 & 38.43 & 86.68 & 84.45 & 24.34 & 84.22 & 83.70 & 25.14 & 86.94 & 85.01 & 29.34 & 85.04 & 82.79 & 29.31 & 85.72 & 83.99 \\\\\n","OpenAI-o3-mini & 39.39 & 86.15 & 84.36 & 25.09 & 83.99 & 83.77 & 22.96 & 86.39 & 84.73 & 29.06 & 84.70 & 82.77 & 29.12 & 85.31 & 83.91 \\\\\n","Gemini-2.0-Flash-Thinking & 39.90 & 86.36 & 84.35 & 25.54 & 84.29 & 83.90 & 25.78 & 86.75 & 84.72 & 30.57 & 84.98 & 82.86 & 30.45 & 85.59 & 83.95 \\\\\n","\\bottomrule\n","\\toprule\n","\\multirow{2}{*}{Model} & \\multicolumn{3}{c}{WMTBio19 En$\\Rightarrow$Zh} & \\multicolumn{3}{c}{WMTBio19 Zh$\\Rightarrow$En} & \\multicolumn{3}{c}{WMTBio19 En$\\Rightarrow$De} & \\multicolumn{3}{c}{WMTBio19 De$\\Rightarrow$En} & \\multicolumn{3}{c}{Average} \\\\\n","\\cmidrule(lr){2-4} \\cmidrule(lr){5-7} \\cmidrule(lr){8-10} \\cmidrule(lr){11-13} \\cmidrule(lr){14-16}\n"," & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI \\\\\n","\\midrule\n","GPT-4o & 37.58 & 86.75 & 84.53 & 31.41 & 84.42 & 82.72 & 30.49 & 86.29 & 83.55 & 37.25 & 86.91 & 82.35 & 34.18 & 86.09 & 83.29 \\\\\n","DeepSeek-V3 & 38.54 & 86.93 & 84.59 & 30.75 & \\textbf{84.68} & 83.10 & 34.14 & 86.47 & 83.91 & 37.63 & \\textbf{87.66} & 82.29 & 35.26 & 86.44 & 83.48 \\\\\n","Gemini-2.0-Flash & \\textbf{38.89} & 86.91 & 84.48 & \\textbf{32.59} & 84.50 & 82.86 & \\textbf{34.59} & \\textbf{87.96} & 83.96 & \\textbf{37.87} & 86.71 & 82.24 & \\textbf{35.98} & \\textbf{86.52} & 83.39 \\\\\n","\\hdashline\n","OpenAI-o1 & 37.01 & 86.97 & \\textbf{85.42} & 28.15 & 84.53 & \\textbf{83.50} & 32.29 & 83.52 & \\textbf{85.88} & 36.62 & 84.93 & \\textbf{83.82} & 33.52 & 85.00 & \\textbf{84.66} \\\\\n","DeepSeek-R1 & 37.23 & \\textbf{87.05} & 84.27 & 28.58 & 84.31 & 82.60 & 32.56 & 83.80 & 83.98 & 36.19 & 84.84 & 82.28 & 33.64 & 85.00 & 83.28 \\\\\n","OpenAI-o3-mini & 35.62 & 86.07 & 84.22 & 30.59 & 84.04 & 82.91 & 31.55 & 86.16 & 83.86 & 36.92 & 86.48 & 82.19 & 33.67 & 85.69 & 83.29 \\\\\n","Gemini-2.0-Flash-Thinking & 38.48 & 86.43 & 84.20 & 31.78 & 84.48 & 82.88 & 33.55 & 86.82 & 83.89 & 36.74 & 84.57 & 82.25 & 35.14 & 85.57 & 83.31 \\\\\n","\\bottomrule\n","\\end{tabular}%\n","}\n","\\caption{Results on the Zh$\\Leftrightarrow$En and De$\\Leftrightarrow$En Biomedical Machine Translation Task from WMT18 and WMT19 (WMTBio18 and WMTBio19). }\n","\\label{tab:result_wmtbio_updated_averages}\n","\\end{table*}\n"],["\\begin{table*}[h]\n","\\centering\n","\\resizebox{\\textwidth}{!}{%\n","\\begin{tabular}{l ccc ccc ccc ccc ccc ccc}\n","\\toprule\n","\\multirow{2}{*}{Model} \n","& \\multicolumn{3}{c}{Medical} & \\multicolumn{3}{c}{Law} & \\multicolumn{3}{c}{IT} \n","& \\multicolumn{3}{c}{Koran} & \\multicolumn{3}{c}{Subtitles} & \\multicolumn{3}{c}{Average} \\\\\n","\\cmidrule(lr){2-4} \\cmidrule(lr){5-7} \\cmidrule(lr){8-10}\n","\\cmidrule(lr){11-13} \\cmidrule(lr){14-16} \\cmidrule(lr){17-19}\n","& BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI\n","& BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI \\\\\n","\\midrule\n","GPT-4o & 41.89 & 84.23 & 81.89 & 39.00 & 85.06 & 82.38 & 37.23 & 83.52 & 78.37 & 17.65 & \\textbf{75.04} & 80.64 & 29.61 & 80.62 & 82.25 & 33.08 & 81.69 & 81.11 \\\\\n","DeepSeek-V3 & 41.43 & 84.15 & 81.90 & 38.82 & 84.94 & 82.29 & \\textbf{38.16} & \\textbf{83.90} & 78.86 & 17.94 & 74.91 & 80.33 & 29.70 & 80.68 & 82.26 & 33.21 & 81.72 & 81.13 \\\\\n","Gemini-2.0-Flash & \\textbf{44.39} & \\textbf{84.49} & 81.76 & \\textbf{46.15} & \\textbf{85.57} & 82.30 & 37.93 & 83.20 & 78.49 & \\textbf{19.70} & 74.97 & 79.71 & \\textbf{30.39} & 80.74 & 82.19 & \\textbf{35.71} & \\textbf{81.79} & 80.89 \\\\\n","\\hdashline\n","OpenAI-o1 & 40.44 & 83.88 & \\textbf{83.29} & 40.74 & 85.03 & \\textbf{83.53} & 36.40 & 83.20 & \\textbf{79.75} & 16.76 & 75.03 & \\textbf{82.02} & 28.91 & \\textbf{80.79} & \\textbf{83.65} & 32.65 & 81.59 & \\textbf{82.45} \\\\\n","DeepSeek-R1 & 40.23 & 83.82 & 81.81 & 39.16 & 84.97 & 82.35 & 36.66 & 83.49 & 78.68 & 17.05 & 74.76 & 80.63 & 28.82 & 80.65 & 82.22 & 32.38 & 81.54 & 81.14 \\\\\n","OpenAI-o3-mini & 39.82 & 83.60 & 81.79 & 34.29 & 84.18 & 81.96 & 37.13 & 82.63 & 78.20 & 15.52 & 73.54 & 80.19 & 28.39 & 80.06 & 82.06 & 31.03 & 80.80 & 80.84 \\\\\n","Gemini-2.0-Flash-Thinking & 43.36 & 84.14 & 81.70 & 44.51 & 85.10 & 82.13 & 37.26 & 82.94 & 78.40 & 19.57 & 75.03 & 79.79 & 30.38 & 80.78 & 82.20 & 35.02 & 81.60 & 80.84 \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\caption{Results on the Dn$\\Rightarrow$En Multi-Domain dataset.}\n","\\label{tab:result_multi_domain}\n","\\end{table*}\n"],["\\begin{table*}[h]\n","\\centering\n","\\resizebox{\\textwidth}{!}{%\n","\\begin{tabular}{l ccc ccc ccc ccc ccc}\n","\\toprule\n","\\multirow{2}{*}{Model} & \\multicolumn{3}{c}{Test-1} & \\multicolumn{3}{c}{Test-2} & \\multicolumn{3}{c}{Valid-1} & \\multicolumn{3}{c}{Valid-2} & \\multicolumn{3}{c}{Average} \\\\\n","\\cmidrule(lr){2-4} \\cmidrule(lr){5-7} \\cmidrule(lr){8-10} \\cmidrule(lr){11-13} \\cmidrule(lr){14-16}\n","& BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI \\\\\n","\\midrule\n","GPT-4o & 20.49 & 78.44 & 76.50 & 16.12 & 77.55 & 77.82 & 21.08 & 78.91 & 77.31 & 12.49 & 75.38 & 76.91 & 17.54 & \\textbf{77.57} & 77.13 \\\\\n","DeepSeek-V3 & 15.66 & 77.47 & 77.84 & \\textbf{16.25} & 77.14 & 77.49 & 19.57 & \\textbf{78.94} & \\textbf{77.48} & 11.87 & 75.28 & 77.05 & 16.81 & 77.48 & 77.17 \\\\\n","Gemini-2.0-Flash & \\textbf{22.11} & 78.25 & 75.94 & \\textbf{16.25} & 77.14 & 77.49 & \\textbf{21.42}& 78.70 & 76.70 & \\textbf{12.70} & 75.11 & 76.34 & \\textbf{18.12} & 77.30 & 76.62 \\\\\n","\\hdashline\n","OpenAI-o1 & 19.05 & \\textbf{78.85} & \\textbf{78.00} & 16.04 & \\textbf{77.76} & \\textbf{78.66} & 18.03 & 78.13 & 76.93 & 12.65 & \\textbf{75.52} & \\textbf{77.44} & 16.44 & 77.56 & \\textbf{77.76} \\\\\n","DeepSeek-R1 & 15.34 & 77.29 & 75.72 & 12.68 & 77.04 & 77.23 & 14.35 & 77.67 & 76.59 & 10.09 & 74.55 & 76.33 & 13.12 & 76.64 & 76.47 \\\\\n","OpenAI-o3-mini & 20.63 & 77.77 & 76.02 & 15.57 & 76.69 & 77.38 & 21.11 & 78.35 & 76.97 & 11.74 & 74.69 & 76.17 & 17.26 & 76.88 & 76.63 \\\\\n","Gemini-2.0-Flash-Thinking & 21.69 & 78.78 & 76.71 & 15.75 & 76.98 & 77.43 & 21.14 & 78.17 & 75.90 & 12.48 & 74.75 & 76.27 & 17.77 & 77.17 & 76.58 \\\\\n","\\bottomrule\n","\\end{tabular}%\n","}\n","\\caption{Results on the Zh$\\Rightarrow$En GuoFeng Webnovel dataset (Literary domain).}\n","\\label{tab:result_webnovel}\n","\\end{table*}\n"],["\\begin{table*}[h]\n","\\centering\n","\\resizebox{\\textwidth}{!}{%\n","\\begin{tabular}{l ccc ccc ccc ccc ccc}\n","\\toprule\n","\\multirow{2}{*}{Model} & \\multicolumn{3}{c}{WMT22 En$\\Rightarrow$Zh} & \\multicolumn{3}{c}{WMT22 Zh$\\Rightarrow$En} & \\multicolumn{3}{c}{WMT22 En$\\Rightarrow$De} & \\multicolumn{3}{c}{WMT22 De$\\Rightarrow$En} & \\multicolumn{3}{c}{Average} \\\\\n","\\cmidrule(lr){2-4} \\cmidrule(lr){5-7} \\cmidrule(lr){8-10} \\cmidrule(lr){11-13} \\cmidrule(lr){14-16}\n","& BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI \\\\\n","\\midrule\n","DeepSeek-V3 & 44.52 & \\textbf{88.21} & 83.19 & 25.21 & 83.11 & 79.95 & 36.89 & 87.78 & 83.83 & 33.06 & 85.66 & 81.80 & 34.92 & 86.19 & 82.19 \\\\\n","GPT-4o & \\textbf{45.64} & 87.77 & 82.74 & 25.11 & \\textbf{83.22} & 79.95 & \\textbf{37.07} & 87.77 & 83.60 & \\textbf{33.71} & 85.68 & 81.64 & \\textbf{35.38} & 86.11 & 81.98 \\\\\n","Gemini-2.0-Flash & 45.50 & 88.04 & 82.84 & \\textbf{26.12} & 82.94 & 79.56 & 36.16 & 87.63 & 83.74 & 33.20 & 85.63 & 81.71 & 35.25 & 86.06 & 81.96 \\\\\n","\\hdashline\n","OpenAI-o1 & 40.84 & 88.10 & \\textbf{84.10} & 22.42 & 83.08 & \\textbf{80.87} & 34.87 & \\textbf{88.18} & \\textbf{85.64} & 31.53 & \\textbf{85.84} & \\textbf{83.22} & 32.42 & \\textbf{86.30} & \\textbf{83.46} \\\\\n","DeepSeek-R1 & 36.88 & 87.33 & 82.96 & 20.68 & 82.48 & 79.57 & 34.14 & 87.73 & 83.91 & 31.37 & 85.61 & 81.86 & 30.77 & 85.79 & 82.07 \\\\\n","OpenAI-o3-mini & 42.67 & 87.25 & 82.52 & 24.09 & 82.82 & 79.89 & 34.46 & 86.94 & 83.45 & 33.08 & 85.14 & 81.27 & 33.58 & 85.54 & 81.78 \\\\\n","Gemini-2.0-Flash-Thinking & 44.04 & 87.61 & 82.58 & 24.83 & 82.71 & 79.51 & 36.12 & 87.46 & 83.58 & 32.75 & 85.59 & 81.66 & 34.44 & 85.84 & 81.83 \\\\\n","\\bottomrule\n","\\end{tabular}%\n","}\n","\\caption{Results on the Zh$\\Leftrightarrow$En and De$\\Leftrightarrow$En WMT22 General Machine Translation task.}\n","\\label{tab:result_wmt22}\n","\\end{table*}\n"],["\\begin{figure*}[t]\n","\\centering\n","\\begin{tcolorbox}[colback=gray!10, colframe=black, width=\\textwidth]\n","\\small\n","\\setstretch{1.2}\n","\n","\\noindent{You are a professional translation quality evaluator following the MQM (Multidimensional Quality Metrics) framework.}\n","\n","\\textbf{Task Instructions:}\n","\\begin{enumerate}\n"," \\item Compare the Prediction against both the Source and the Reference.\n"," \\item Identify up to five of the most serious Errors for each translation sentence, using the MQM error types listed below.\n"," \\item Assign exactly one severity level to each error.\n"," \\item Special handling for two specific error types:\n"," \\begin{itemize}\n"," \\item \\textbf{Source Error}: Errors present in the Source text itself.\n"," \\item \\textbf{Non-translation Error}: Translation is unassessable and unrelated to the Source.\n"," \\end{itemize}\n"," \\item If no errors are found, return an empty JSON list [].\n"," \\item Only output a valid JSON object. Do not include any additional text, comments, or explanations.\n","\\end{enumerate}\n","\n","\\textbf{MQM Error Types (See Table~\\ref{tab:mqm-hierarchy} for the complete hierarchy):}\n","\\begin{itemize}\n"," \\item Mistranslation: Incorrect translation that alters or distorts meaning.\n"," \\item Addition: Insertion of information or emotion not present in the source.\n"," \\item Under-translation: Partial omission of relevant or necessary information.\n"," \\item Omission: Complete exclusion of content present in the source.\n"," \\item Untranslated: Source text is copied without being translated.\n","\\end{itemize}\n","\n","\\textbf{Severity Levels:} \n","\\begin{itemize}\n"," \\item Minor: Slight impact on readability or style; meaning remains clear.\n"," \\item Major: Significant impact on usability, comprehension, or meaning.\n","\\end{itemize}\n","\\textbf{Output Format:}\n","\\begin{verbatim}\n","{\n"," \"errors\": [\n"," {\n"," \"error_type\": \"Mistranslation\",\n"," \"severity\": \"Major\",\n"," \"explanation\": \"The correct translation should be ... \"\n"," }\n"," ]\n","}\n","\\end{verbatim}\n","\n","\\textbf{Source:} \\texttt{\\{source\\_text\\}} \\\\\n","\\textbf{Reference:} \\texttt{\\{reference\\_text\\}} \\\\\n","\\textbf{Prediction:} \\texttt{\\{prediction\\_text\\}}\n","\n","\\end{tcolorbox}\n","\\caption{Full prompt used to calculate MQM scores with DeepSeek-V3. }\n","\\label{fig:mqm-prompt}\n","\\end{figure*}\n"],["\\begin{figure*}[t]\n","\\centering\n","\\begin{tcolorbox}[colback=gray!10, colframe=black, width=\\textwidth]\n","\n","Your task is to assess the difficulty of translating a given \\texttt{\\{src\\_lang\\}} sentence into \\texttt{\\{tgt\\_lang\\}}. Please evaluate the difficulty based on the following criteria and output the result in JSON format, with the key \"level\":\n","\\begin{enumerate}\n","\\item Sentence complexity: Determine if the sentence is a simple sentence, a compound sentence, or includes subordinate clauses and other complex structures.\n","\\item Vocabulary difficulty: Assess whether the sentence contains commonly used words or specialized terms or slang.\n","\\item Grammar differences: Analyze if the sentence's grammatical structure is similar to or differs significantly from \\texttt{\\{tgt\\_lang\\}}.\n","\\item Contextual understanding: Consider whether understanding specific cultural contexts or background knowledge is necessary for accurate translation.\n","\\end{enumerate}\n","\n","Provide a difficulty level (1-5), with 1 being the easiest and 5 being the most difficult.\n","And output the difficulty level in the following JSON format:\n","\\begin{verbatim}\n","{\n"," \"level\": \"difficulty level\"\n","}\n","\\end{verbatim}\n","\n","Here is the \\texttt{\\{src\\_lang\\}} sentence: \\texttt{\\{src\\_text\\}}\n","\n","\\end{tcolorbox}\n","\\caption{Full prompt used for evaluating translation difficulty with DeepSeek-V3.}\n","\\label{fig:difficulty_eval}\n","\\end{figure*}\n"]],"string":"[\n [\n \"\\\\begin{figure*}[th] \\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\textwidth]{figure/deen-ende_radar_charts.pdf}\\n\",\n \" \\\\caption{Multi-Domain En$\\\\Leftrightarrow$De Translation Performance Comparison, showing averaged BLEU, COMET, and CometKiwi scores for both directions, with distinct colors representing different LLMs. }\\n\",\n \" \\\\label{fig:de-en-radar}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[th] \\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\textwidth]{figure/zhen-enzh_radar_charts.pdf}\\n\",\n \" \\\\caption{Multi-Domain En$\\\\Leftrightarrow$Zh Translation Performance Comparison, showing averaged BLEU, COMET, and CometKiwi scores for both directions, with distinct colors representing different LLMs. }\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{table}[t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\linewidth}{!}{%\\n\",\n \"\\\\begin{tabular}{l c c c c c c}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{Model} & \\\\multicolumn{6}{c}{MQM Score ($\\\\downarrow$)} \\\\\\\\\\n\",\n \"\\\\cmidrule(lr){2-7}\\n\",\n \" & IT & Koran & Medical & Law & Subtitles & Average\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"GPT-4o & 3.14 & 4.56 & 3.24 & 3.74 & 3.44 & 3.62 \\\\\\\\\\n\",\n \"DeepSeek-V3 & \\\\textbf{2.91} & 4.62 & 3.30 & 3.89 & \\\\textbf{3.38} & 3.62 \\\\\\\\\\n\",\n \"Gemini-2.0-Flash & 3.09 & 4.68 & \\\\textbf{3.15} & \\\\textbf{3.55} & \\\\textbf{3.38} & \\\\textbf{3.57} \\\\\\\\\\n\",\n \"\\\\hdashline\\n\",\n \"OpenAI-o1 & 3.11 & 4.74 & 3.32 & 3.86 & 3.49 & 3.70 \\\\\\\\\\n\",\n \"DeepSeek-R1 & 2.92 & \\\\textbf{4.55} & 3.30 & 3.88 & 3.40 & 3.61 \\\\\\\\\\n\",\n \"OpenAI-o3-mini & 3.16 & 5.22 & 3.43 & 4.59 & 3.62 & 4.00 \\\\\\\\\\n\",\n \"Gemini-2.0-Flash-Thinking & 3.15 & 4.84 & 3.28 & 3.69 & 3.41 & 3.67 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}%\\n\",\n \"}\\n\",\n \"\\\\caption{MQM scores for different models across various domains in the Multi-Domain dataset (De$\\\\Rightarrow$En).}\\n\",\n \"\\\\label{tab:mqm_avg_scores}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\linewidth}{!}{%\\n\",\n \"\\\\begin{tabular}{l ccccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Model} & \\\\textbf{Acc. (\\\\%)} & \\\\textbf{Style (\\\\%)} & \\\\textbf{Fluency (\\\\%)} & \\\\textbf{Term. (\\\\%)} & \\\\textbf{Non-trans (\\\\%)} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"GPT-4o & 50.78 & 30.58 & 7.03 & 11.38 & 0.23 \\\\\\\\\\n\",\n \"DeepSeek-V3 & 50.72 & 30.51 & 6.84 & 11.83 & 0.09 \\\\\\\\\\n\",\n \"Gemini-2.0-Flash & 52.86 & \\\\textbf{29.43} & 6.87 & \\\\textbf{10.68} & 0.16 \\\\\\\\\\n\",\n \"\\\\hdashline\\n\",\n \"OpenAI-o1 & 50.95 & 29.58 & 7.10 & 12.22 & 0.15 \\\\\\\\\\n\",\n \"DeepSeek-R1 & 50.50 & 30.83 & 6.68 & 11.91 & \\\\textbf{0.08} \\\\\\\\\\n\",\n \"OpenAI-o3-mini & \\\\textbf{49.97} & 30.48 & 7.19 & 12.24 & 0.12 \\\\\\\\\\n\",\n \"Gemini-2.0-Flash-Thinking & 52.54 & 29.64 & \\\\textbf{6.38} & 11.29 & 0.15 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}%\\n\",\n \"}\\n\",\n \"\\\\caption{Error category percentages for different models, including accuracy (Acc.), style, fluency, terminology (Term.), and non-translation error (Non-trans).}\\n\",\n \"\\\\label{tab:model_error_category_percentages}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\small\\n\",\n \"\\\\begin{tabular}{lcc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{Model} & \\\\multicolumn{2}{c}{Term Acc. (\\\\%)} \\\\\\\\\\n\",\n \"\\\\cmidrule(lr){2-3}\\n\",\n \"& Zh$\\\\Rightarrow$En & De$\\\\Rightarrow$En \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"GPT-4o & 52.95 & 37.67 \\\\\\\\\\n\",\n \"DeepSeek-V3 & 51.84 & 37.92 \\\\\\\\\\n\",\n \"Gemini-2.0-Flash & 52.21 & \\\\textbf{38.55} \\\\\\\\\\n\",\n \"\\\\hdashline\\n\",\n \"OpenAI-o1 & 52.92 & 37.42 \\\\\\\\\\n\",\n \"DeepSeek-R1 & \\\\textbf{53.20} & 37.83 \\\\\\\\\\n\",\n \"OpenAI-o3-mini & 45.59 & 37.38 \\\\\\\\\\n\",\n \"Gemini-2.0-Flash-Thinking & 52.67 & 37.62 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\caption{Terminology accuracy (\\\\%) for Zh$\\\\Rightarrow$En and De$\\\\Rightarrow$En translation tasks.}\\n\",\n \"\\\\label{tab:termacc}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table*}[t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{%\\n\",\n \"\\\\begin{tabular}{l ccc ccc ccc ccc ccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{Model} & \\\\multicolumn{3}{c}{Conversation} & \\\\multicolumn{3}{c}{E-commerce} & \\\\multicolumn{3}{c}{News} & \\\\multicolumn{3}{c}{Social} & \\\\multicolumn{3}{c}{Literary} \\\\\\\\\\n\",\n \"\\\\cmidrule(lr){2-4} \\\\cmidrule(lr){5-7} \\\\cmidrule(lr){8-10} \\\\cmidrule(lr){11-13} \\\\cmidrule(lr){14-16}\\n\",\n \" & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multicolumn{16}{c}{\\\\textbf{Document-Level}} \\\\\\\\\\n\",\n \"GPT-4o & \\\\textbf{40.00} & 84.71 & 82.02 & 35.33 & 87.35 & 64.55 & 36.07 & 86.60 & 66.01 & 33.75 & 84.36 & 68.70 & 17.55 & \\\\textbf{77.57} & 77.14 \\\\\\\\\\n\",\n \"DeepSeek-V3 & 39.28 & 84.51 & 81.98 & 36.71 & 87.26 & 63.16 & 37.59 & 86.50 & 65.35 & 36.48 & 84.80 & 67.83 & 16.81 & 77.48 & 77.17 \\\\\\\\\\n\",\n \"Gemini-2.0-Flash & 39.98 & 84.64 & 81.81 & \\\\textbf{37.72} & 87.34 & 62.33 & 38.68 & 85.71 & 63.68 & \\\\textbf{37.76} & 84.38 & 67.14 & \\\\textbf{18.12} & 77.30 & 76.62 \\\\\\\\\\n\",\n \"\\\\hdashline\\n\",\n \"OpenAI-o1 & 37.05 & \\\\textbf{84.83} & \\\\textbf{82.99} & 30.68 & \\\\textbf{87.40} & \\\\textbf{66.50} & 32.08 & \\\\textbf{86.67} & \\\\textbf{67.90} & 29.02 & \\\\textbf{84.82} & \\\\textbf{69.79} & 16.44 & \\\\textbf{77.57} & \\\\textbf{77.76} \\\\\\\\\\n\",\n \"DeepSeek-R1 & 33.66 & 84.15 & 81.45 & 21.62 & 85.23 & 63.77 & 23.49 & 83.68 & 66.99 & 22.22 & 81.59 & 68.09 & 13.12 & 76.64 & 76.47 \\\\\\\\\\n\",\n \"OpenAI-o3-mini & 36.58 & 84.52 & 82.05 & 32.13 & 87.15 & 65.18 & 33.60 & 86.22 & 65.77 & 30.93 & 84.76 & 68.87 & 17.26 & 76.87 & 76.64 \\\\\\\\\\n\",\n \"Gemini-2.0-Flash-Thinking & \\\\textbf{40.00} & 84.59 & 81.79 & 37.41 & 87.35 & 62.39 & \\\\textbf{38.82} & 86.62 & 64.19 & 36.97 & 84.55 & 66.47 & 17.77 & 77.17 & 76.58 \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multicolumn{16}{c}{\\\\textbf{Sentence-Level}} \\\\\\\\\\n\",\n \"GPT-4o & \\\\textbf{38.68} & 87.56 & 82.00 & \\\\textbf{34.17} & 86.38 & 81.78 & 36.21 & 86.05 & 82.66 & 33.07 & 84.63 & 81.47 & 18.53 & \\\\textbf{82.96} & \\\\textbf{57.23} \\\\\\\\\\n\",\n \"DeepSeek-V3 & 37.46 & 87.61 & 81.99 & 33.57 & 86.32 & 81.93 & 35.82 & 86.30 & 83.04 & 32.96 & 84.74 & 81.81 & 19.92 & 82.88 & 54.68 \\\\\\\\\\n\",\n \"Gemini-2.0-Flash & 38.01 & 87.29 & 81.87 & 33.67 & 86.36 & 81.67 & \\\\textbf{36.48} & 86.21 & 82.80 & \\\\textbf{33.24} & 84.55 & 81.49 & 23.28 & 82.80 & 53.83 \\\\\\\\\\n\",\n \"\\\\hdashline\\n\",\n \"OpenAI-o1 & 36.30 & \\\\textbf{87.87} & \\\\textbf{83.49} & 31.30 & \\\\textbf{86.39} & \\\\textbf{83.05} & 33.17 & \\\\textbf{86.35} & \\\\textbf{84.35} & 29.95 & \\\\textbf{84.80} & \\\\textbf{82.96} & 16.87 & 82.42 & 55.73 \\\\\\\\\\n\",\n \"DeepSeek-R1 & 34.05 & 87.13 & 81.93 & 29.23 & 85.95 & 81.75 & 31.83 & 86.03 & 83.01 & 28.51 & 84.26 & 81.59 & 5.16 & 80.35 & 52.16 \\\\\\\\\\n\",\n \"OpenAI-o3-mini & 36.33 & 86.74 & 81.67 & 32.81 & 85.92 & 81.73 & 33.95 & 85.57 & 82.43 & 31.88 & 84.07 & 81.28 & 18.06 & 82.83 & 55.16 \\\\\\\\\\n\",\n \"Gemini-2.0-Flash-Thinking & 37.07 & 86.99 & 81.68 & 33.09 & 86.17 & 81.55 & 35.70 & 86.19 & 82.81 & 32.30 & 84.16 & 81.27 & \\\\textbf{23.52} & 82.77 & 53.69 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}%\\n\",\n \"}\\n\",\n \"\\\\caption{Average BLEU, COMET, and CometKiwi scores at document-level and sentence-level across five domains.}\\n\",\n \"\\\\label{tab:length_analysis}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table}[t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{.48\\\\textwidth}{!}{%\\n\",\n \"\\\\begin{tabular}{@{}ll@{}}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{ID} & \\\\textbf{Prompt} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textbf{P1} & Translate the following \\\\texttt{\\\\{src\\\\_lang\\\\}} sentence into \\\\texttt{\\\\{tgt\\\\_lang\\\\}}. \\\\\\\\\\n\",\n \"\\\\textbf{P2} & Given the \\\\texttt{\\\\{src\\\\_lang\\\\}} sentence from a \\\\texttt{\\\\{domain\\\\}}-related text, \\\\\\\\\\n\",\n \" & translate it into \\\\texttt{\\\\{tgt\\\\_lang\\\\}} in a \\\\texttt{\\\\{domain\\\\}} domain style. \\\\\\\\\\n\",\n \"\\\\textbf{P3} & Translate the following \\\\texttt{\\\\{src\\\\_lang\\\\}} text into \\\\texttt{\\\\{tgt\\\\_lang\\\\}} while \\\\\\\\\\n\",\n \" & maintaining the domain style of the source text. \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Prompts used in domain-aware translation.}\\n\",\n \"\\\\label{tab:prompt_templates}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\linewidth}{!}{%\\n\",\n \"\\\\begin{tabular}{l ccc ccc ccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{Prompt} & \\\\multicolumn{3}{c}{IT} & \\\\multicolumn{3}{c}{Law} & \\\\multicolumn{3}{c}{Medical} \\\\\\\\\\n\",\n \"\\\\cmidrule(lr){2-4} \\\\cmidrule(lr){5-7} \\\\cmidrule(lr){8-10}\\n\",\n \"& BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"P1 & \\\\textbf{36.66} & 83.49 & 78.68 & 39.16 & 84.97 & 82.35 & 40.23 & 83.82 & 81.81 \\\\\\\\\\n\",\n \"P2 & 34.20 & 82.94 & 79.33 & \\\\textbf{41.46} & 85.01 & 83.46 & 38.92 & 83.38 & 82.57 \\\\\\\\\\n\",\n \"P3 & 36.55 & \\\\textbf{83.57} & \\\\textbf{79.82} & 40.57 & \\\\textbf{85.32} & \\\\textbf{83.54} & \\\\textbf{41.14} & \\\\textbf{84.06} & \\\\textbf{82.97} \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Translation performance across IT, Law, and Medical domains under different prompt strategies.}\\n\",\n \"\\\\label{tab:prompt_results}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table*}[t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{%\\n\",\n \"\\\\begin{tabular}{l ccc ccc ccc ccc ccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{Model} & \\\\multicolumn{3}{c}{Level 1} & \\\\multicolumn{3}{c}{Level 2} & \\\\multicolumn{3}{c}{Level 3} & \\\\multicolumn{3}{c}{Level 4} & \\\\multicolumn{3}{c}{Level 5} \\\\\\\\\\n\",\n \"\\\\cmidrule(lr){2-4} \\\\cmidrule(lr){5-7} \\\\cmidrule(lr){8-10} \\\\cmidrule(lr){11-13} \\\\cmidrule(lr){14-16}\\n\",\n \" & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"GPT-4o & \\\\textbf{41.96} & \\\\textbf{85.13} & 79.80 & 30.08 & 84.88 & 82.15 & 26.49 & 82.75 & 80.91 & 22.61 & 83.11 & 75.48 & 24.94 & 83.59 & 67.11 \\\\\\\\\\n\",\n \"DeepSeek-V3 & 40.81 & 85.09 & 79.90 & 27.90 & 84.86 & 82.20 & 25.43 & 82.54 & 80.97 & 22.57 & 82.95 & 75.30 & 25.06 & 83.30 & 67.64 \\\\\\\\\\n\",\n \"Gemini-2.0-Flash & 41.15 & 84.39 & 79.46 & 29.22 & 84.93 & 81.96 & 27.53 & 82.80 & 80.68 & 24.53 & 82.72 & 74.57 & 28.93 & 83.91 & 66.28 \\\\\\\\\\n\",\n \"\\\\hdashline\\n\",\n \"OpenAI-o1 & 40.34 & 84.59 & \\\\textbf{81.10} & 29.67 & 84.88 & \\\\textbf{83.28} & 25.76 & \\\\textbf{82.94} & \\\\textbf{81.94} & 21.99 & \\\\textbf{83.31} & \\\\textbf{76.92} & 22.78 & \\\\textbf{84.02} & \\\\textbf{69.62} \\\\\\\\\\n\",\n \"DeepSeek-R1 & 41.43 & 84.33 & 79.86 & 27.12 & 84.25 & 81.75 & 24.32 & 82.15 & 80.44 & 18.66 & 82.17 & 75.27 & 19.53 & 82.43 & 68.78 \\\\\\\\\\n\",\n \"OpenAI-o3-mini & 40.74 & 84.12 & 79.44 & 26.49 & 84.56 & 82.14 & 24.93 & 82.36 & 80.73 & 21.09 & 82.67 & 75.37 & 22.70 & 83.27 & 67.63 \\\\\\\\\\n\",\n \"Gemini-2.0-Flash-Thinking & 39.87 & 83.84 & 79.36 & \\\\textbf{30.78} & \\\\textbf{84.98} & 82.21 & \\\\textbf{28.15} & 82.68 & 80.62 & \\\\textbf{24.54} & 83.02 & 74.71 & \\\\textbf{28.97} & 83.93 & 66.32 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}%\\n\",\n \"}\\n\",\n \"\\\\caption{Performance across different levels of complexity, comparing LRMs and tranditional LLMs.}\\n\",\n \"\\\\label{tab:metrics_by_level_avg}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table}[t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\linewidth}{!}{%\\n\",\n \"\\\\begin{tabular}{l c c c c}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Model} & \\\\textbf{BLEU} & \\\\textbf{COMET} & \\\\textbf{KIWI} & \\\\textbf{Avg. Len.} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"GPT-4o & 37.40 & 86.74 & 84.61 & 31.76 \\\\\\\\\\n\",\n \"DeepSeek-V3 & \\\\textbf{43.34} & \\\\textbf{87.76} & 85.04 & \\\\textbf{29.63} \\\\\\\\\\n\",\n \"Gemini-2.0-Flash & 40.30 & 87.05 & 84.80 & 32.71 \\\\\\\\\\n\",\n \"\\\\hdashline\\n\",\n \"OpenAI-o1 & 36.17 & 87.11 & \\\\textbf{85.09} & 402.64 \\\\\\\\\\n\",\n \"DeepSeek-R1 & 41.12 & 87.50 & 85.03 & 485.05 \\\\\\\\\\n\",\n \"OpenAI-o3-mini & 34.60 & 86.69 & 84.98 & 424.31 \\\\\\\\\\n\",\n \"Gemini-2.0-Flash-Thinking & 39.17 & 86.63 & 84.37 & 1043.59 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}%\\n\",\n \"}\\n\",\n \"\\\\caption{Average BLEU, COMET, KIWI scores, and response length for various LLMs.}\\n\",\n \"\\\\label{tab:um_results_averages}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{figure}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\linewidth]{figure/case_study_figure.pdf}\\n\",\n \" \\\\caption{Case study of DeepSeek-R1 showing its reasoning process and final translation. }\\n\",\n \" \\\\label{fig:case_study}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table}[t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\small\\n\",\n \"\\\\begin{tabular}{lc lc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Test set} & \\\\textbf{Num} & \\\\textbf{Test set} & \\\\textbf{Num} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"Conversation & 349 & Social & 491 \\\\\\\\\\n\",\n \"E-commerce & 518 & News (WMT22) & 499 \\\\\\\\\\n\",\n \"Bio (WMT18) & 212 & Bio (WMT19) & 243 \\\\\\\\\\n\",\n \"Commonsense & 1200 & Literary & 3038 \\\\\\\\\\n\",\n \"Terminology & 1638 & Mixed (WMT22) & 1875 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}%\\n\",\n \"\\\\caption{Test sets and the number of samples for Zh$\\\\Rightarrow$En translation tasks. Bio denotes the Biomedical domain.}\\n\",\n \"\\\\label{tab:test_zh-en}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\small\\n\",\n \"\\\\begin{tabular}{lc lc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Test set} & \\\\textbf{Num} & \\\\textbf{Test set} & \\\\textbf{Num} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"Culture & 778 & Laws & 456 \\\\\\\\\\n\",\n \"Subtitles & 597 & Science & 503 \\\\\\\\\\n\",\n \"Conversation & 484 & Social & 511 \\\\\\\\\\n\",\n \"E-commerce & 530 & News (WMT22) & 511 \\\\\\\\\\n\",\n \"Bio (WMT18) & 244 & Bio (WMT19) & 224 \\\\\\\\\\n\",\n \"News (UM) & 1500 & Mixed & 2037 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}%\\n\",\n \"\\\\caption{Test sets and the number of samples for En$\\\\Rightarrow$Zh translation tasks.}\\n\",\n \"\\\\label{tab:test_en-zh}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\small\\n\",\n \"\\\\begin{tabular}{lc lc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Test set} & \\\\textbf{Num} & \\\\textbf{Test set} & \\\\textbf{Num} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"Bio (WMT18) & 217 & Bio (WMT19) & 373 \\\\\\\\\\n\",\n \"Social & 515 & E-commerce & 501 \\\\\\\\\\n\",\n \"News (WMT22) & 506 & Conversation & 462 \\\\\\\\\\n\",\n \"IT & 2000 & Law & 2000 \\\\\\\\\\n\",\n \"Koran & 2000 & Subtitles & 2000 \\\\\\\\\\n\",\n \"Medical & 2000 & Terminology & 2955 \\\\\\\\\\n\",\n \"Mixed & 1984 & & \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}%\\n\",\n \"\\\\caption{Test sets and the number of samples for De$\\\\Rightarrow$En translation tasks.}\\n\",\n \"\\\\label{tab:test_de-en}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\small\\n\",\n \"\\\\begin{tabular}{lc lc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Test set} & \\\\textbf{Num} & \\\\textbf{Test set} & \\\\textbf{Num} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"Bio (WMT18) & 220 & Bio (WMT19) & 435 \\\\\\\\\\n\",\n \"News (WMT22) & 511 & E-commerce & 530 \\\\\\\\\\n\",\n \"Social & 512 & Conversation & 484 \\\\\\\\\\n\",\n \"Mixed & 2037 & & \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}%\\n\",\n \"\\\\caption{Test sets and the number of samples for En$\\\\Rightarrow$De translation tasks.}\\n\",\n \"\\\\label{tab:test_en-de}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table*} [t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\small\\n\",\n \" \\\\begin{tabularx}{\\\\textwidth}{l l X}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Category} & \\\\textbf{Error Type} & \\\\textbf{Description} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multirow{8}{*}{Accuracy} \\n\",\n \" & Mistranslation & Inaccurate translation causing semantic distortion. \\\\\\\\\\n\",\n \" & Addition & Adding extra information or emotions not in the source.\\\\\\\\\\n\",\n \" & Under-translation & Failure to fully convey cultural or contextual nuances. \\\\\\\\\\n\",\n \" & Omission & Unintentional exclusion of content from the source text. \\\\\\\\\\n\",\n \" & Untranslated & Retaining source text without translation. \\\\\\\\\\n\",\n \" & Hallucination & Generating content unrelated to the source text. \\\\\\\\\\n\",\n \" & Off-target Translation & Translation misalignment caused by ambiguous input. \\\\\\\\ \\n\",\n \" & Contradiction & Contradicting itself or the source text. \\\\\\\\ \\n\",\n \" \\\\midrule\\n\",\n \" \\\\multirow{5}{*}{Fluency} \\n\",\n \" & Grammar & Errors in sentence structure or syntax. \\\\\\\\\\n\",\n \" & Punctuation & Incorrect use of punctuation marks. \\\\\\\\\\n\",\n \" & Spelling & Misspelling of words. \\\\\\\\\\n\",\n \" & Semantic Repetition & Unnecessary repetition of words or phrases. \\\\\\\\\\n\",\n \" & Logical Incoherence & Lack of logical flow or coherence in translation. \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multirow{4}{*}{Style} \\n\",\n \" & Awkward Expression & Stilted or unnatural phrasing in the target language. \\\\\\\\\\n\",\n \" & Unidiomatic Usage & Literal translation causes unnatural wording or grammar. \\\\\\\\\\n\",\n \" & Style Inconsistency & Inconsistent stylistic choices within the translation. \\\\\\\\\\n\",\n \" & Over-localization & Excessive cultural adaptation leading to distortion. \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multirow{5}{*}{Terminology} \\n\",\n \" & Terminology Inconsistency & Inconsistent translation of the same term. \\\\\\\\\\n\",\n \" & Terminology Misuse & Use of incorrect or inappropriate terms for the domain. \\\\\\\\\\n\",\n \" & Cross-domain Confusion & Misuse of terms due to domain shifts. \\\\\\\\\\n\",\n \" & Incorrect Unit Conversion & Errors in unit conversion (e.g., metric to imperial). \\\\\\\\ \\n\",\n \" & Formatting & Errors in domain-specific formats (e.g., legal, medical). \\\\\\\\ \\n\",\n \" \\\\midrule\\n\",\n \" \\\\multirow{1}{*}{Others} \\n\",\n \" & & Any other errors not covered in the above categories. \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multirow{1}{*}{Source Error} \\n\",\n \" & & Errors present in the Source text itself. \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multirow{1}{*}{Non-translation Error} \\n\",\n \" & & Translation is unassessable and unrelated to the Source. \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabularx}\\n\",\n \" \\\\caption{\\\\label{tab:mqm-hierarchy} MQM Hierarchy.}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table}[t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\linewidth}{!}{%\\n\",\n \"\\\\begin{tabular}{l ccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{Model} & \\\\multicolumn{3}{c}{Culture} \\\\\\\\\\n\",\n \"\\\\cmidrule(lr){2-4}\\n\",\n \" & BLEU & COMET & KIWI \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"GPT-4o & 38.65 & 85.24 & 83.14 \\\\\\\\\\n\",\n \"DeepSeek-V3 & \\\\textbf{40.27} & \\\\textbf{85.46} & 83.21 \\\\\\\\\\n\",\n \"Gemini-2.0-Flash & 39.11 & 85.02 & 82.94 \\\\\\\\\\n\",\n \"\\\\hdashline\\n\",\n \"OpenAI-o1 & 34.57 & 85.06 & \\\\textbf{84.25} \\\\\\\\\\n\",\n \"DeepSeek-R1 & 35.88 & 85.45 & 83.14 \\\\\\\\\\n\",\n \"OpenAI-o3-mini & 34.05 & 84.36 & \\\\textbf{84.25} \\\\\\\\\\n\",\n \"Gemini-2.0-Flash-Thinking & 36.94 & 84.63 & 83.68 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Results on the En$\\\\Rightarrow$Zh Culture-MT dataset.}\\n\",\n \"\\\\label{tab:result_culture_mt}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table*}[h]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{%\\n\",\n \"\\\\begin{tabular}{lccc ccc ccc ccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{Model} \\n\",\n \"& \\\\multicolumn{3}{c}{Contextual Syntactic} \\n\",\n \"& \\\\multicolumn{3}{c}{Contextless Syntactic} \\n\",\n \"& \\\\multicolumn{3}{c}{Lexical} \\n\",\n \"& \\\\multicolumn{3}{c}{Average} \\\\\\\\\\n\",\n \"\\\\cmidrule(lr){2-4} \\\\cmidrule(lr){5-7} \\\\cmidrule(lr){8-10} \\\\cmidrule(lr){11-13}\\n\",\n \"& BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"GPT-4o & 31.42 & \\\\textbf{85.11} & 81.88 & 32.82 & 85.24 & 78.35 & 33.89 & 85.41 & 79.83 & 32.71 & 85.25 & 80.02 \\\\\\\\\\n\",\n \"DeepSeek-V3 & 30.19 & 84.90 & 81.91 & \\\\textbf{33.53} & \\\\textbf{85.58} & 78.76 & \\\\textbf{35.09} & \\\\textbf{85.52} & 79.69 & \\\\textbf{32.94} & \\\\textbf{85.33} & 80.12 \\\\\\\\\\n\",\n \"Gemini-2.0-Flash & 31.15 & 84.86 & 81.79 & 31.69 & 85.20 & 77.99 & 32.13 & 84.03 & 78.84 & 31.66 & 84.70 & 79.54 \\\\\\\\\\n\",\n \"\\\\hdashline\\n\",\n \"OpenAI-o1 & 29.80 & 84.74 & 80.66 & 28.04 & 85.14 & 79.48 & 30.40 & 84.00 & 79.70 & 28.50 & 84.96 & \\\\textbf{81.11} \\\\\\\\\\n\",\n \"DeepSeek-R1 & 26.28 & 84.54 & \\\\textbf{82.07} & 30.00 & 84.96 & 78.62 & 30.40 & 84.00 & 79.70 & 28.89 & 84.50 & 80.13 \\\\\\\\\\n\",\n \"OpenAI-o3-mini & 28.55 & 84.57 & 79.19 & 27.18 & 84.43 & \\\\textbf{82.97} & 29.43 & 83.82 & \\\\textbf{80.87} & 28.39 & 84.27 & 81.01 \\\\\\\\\\n\",\n \"Gemini-2.0-Flash-Thinking & \\\\textbf{31.54} & 84.61 & 81.96 & 31.60 & 84.34 & 77.66 & 33.49 & 84.59 & 79.95 & 32.21 & 84.51 & 79.86 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Results on the Zh$\\\\Rightarrow$En Commonsense-MT dataset.}\\n\",\n \"\\\\label{tab:zh-en-ambiguity}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[h]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{%\\n\",\n \"\\\\begin{tabular}{l ccc ccc ccc ccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{Model} & \\\\multicolumn{3}{c}{News} & \\\\multicolumn{3}{c}{Laws} & \\\\multicolumn{3}{c}{Science} & \\\\multicolumn{3}{c}{Subtitles} \\\\\\\\\\n\",\n \"\\\\cmidrule(lr){2-4} \\\\cmidrule(lr){5-7} \\\\cmidrule(lr){8-10} \\\\cmidrule(lr){11-13}\\n\",\n \" & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"GPT-4o & 34.58 & 86.22 & 85.02 & 58.00 & 91.50 & 86.65 & 32.09 & 87.34 & 84.84 & \\\\textbf{24.91} & 81.91 & 81.93 \\\\\\\\\\n\",\n \"DeepSeek-V3 & 35.86 & \\\\textbf{86.94} & 85.44 & \\\\textbf{80.33} & \\\\textbf{93.36} & 86.55 & \\\\textbf{33.54} & \\\\textbf{88.28} & 85.46 & 23.66 & \\\\textbf{82.44} & 82.69 \\\\\\\\\\n\",\n \"Gemini-2.0-Flash & \\\\textbf{36.39} & 86.37 & 85.14 & 66.71 & 92.23 & 86.49 & 33.47 & 87.69 & 85.24 & 24.64 & 81.91 & 82.33 \\\\\\\\\\n\",\n \"\\\\hdashline\\n\",\n \"OpenAI-o1 & 33.93 & 86.61 & \\\\textbf{85.47} & 55.48 & 91.61 & 86.73 & 32.32 & 87.92 & \\\\textbf{85.56} & 22.96 & 82.31 & 82.60 \\\\\\\\\\n\",\n \"DeepSeek-R1 & 33.35 & 86.67 & 85.40 & 78.81 & 93.16 & 86.40 & 31.82 & 88.08 & 85.47 & 20.50 & 82.10 & \\\\textbf{82.84} \\\\\\\\\\n\",\n \"OpenAI-o3-mini & 32.90 & 86.29 & 85.35 & 51.18 & 91.15 & \\\\textbf{86.92} & 31.38 & 87.42 & 85.24 & 22.95 & 81.90 & 82.40 \\\\\\\\\\n\",\n \"Gemini-2.0-Flash-Thinking & 33.90 & 86.02 & 84.79 & 66.99 & 92.12 & 86.40 & 31.98 & 87.47 & 85.06 & 23.83 & 80.90 & 81.22 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}%\\n\",\n \"}\\n\",\n \"\\\\caption{Results on the En$\\\\Rightarrow$Zh UM-Corpus dataset.}\\n\",\n \"\\\\label{tab:result_um_domains}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[h]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{%\\n\",\n \"\\\\begin{tabular}{l ccc ccc ccc ccc ccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{Model} & \\\\multicolumn{3}{c}{Conversation} & \\\\multicolumn{3}{c}{E-commerce} & \\\\multicolumn{3}{c}{News} & \\\\multicolumn{3}{c}{Social} & \\\\multicolumn{3}{c}{Average} \\\\\\\\\\n\",\n \"\\\\cmidrule(lr){2-4} \\\\cmidrule(lr){5-7} \\\\cmidrule(lr){8-10} \\\\cmidrule(lr){11-13} \\\\cmidrule(lr){14-16}\\n\",\n \" & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"GPT-4o & 31.28 & 84.86 & 79.83 & 19.94 & 81.75 & 78.24 & 25.21 & \\\\textbf{83.04} & 81.10 & 27.29 & \\\\textbf{83.77} & 80.64 & 25.93 & \\\\textbf{83.36} & 79.95 \\\\\\\\\\n\",\n \"DeepSeek-V3 & 30.26 & \\\\textbf{84.96} & 79.67 & 19.47 & 81.55 & 78.32 & 25.77 & 83.00 & 81.18 & 27.55 & 83.56 & 80.64 & 25.76 & 83.27 & 79.95 \\\\\\\\\\n\",\n \"Gemini-2.0-Flash & \\\\textbf{32.79} & 84.46 & 79.19 & \\\\textbf{19.97} & 81.54 & 77.90 & \\\\textbf{26.62} & \\\\textbf{83.04} & 80.93 & \\\\textbf{29.25} & 83.24 & 80.16 & \\\\textbf{27.16} & 83.07 & 79.55 \\\\\\\\\\n\",\n \"\\\\hdashline\\n\",\n \"OpenAI-o1 & 29.52 & 84.95 & \\\\textbf{80.90} & 17.82 & \\\\textbf{81.81} & 79.16 & 22.52 & 82.96 & \\\\textbf{82.10} & 23.85 & 83.20 & \\\\textbf{81.39} & 23.43 & 83.23 & \\\\textbf{80.89} \\\\\\\\\\n\",\n \"DeepSeek-R1 & 26.82 & 84.61 & 79.40 & 15.63 & 80.98 & 77.87 & 21.21 & 82.34 & 80.95 & 22.18 & 82.69 & 80.05 & 21.46 & 82.66 & 79.57 \\\\\\\\\\n\",\n \"OpenAI-o3-mini & 30.57 & 84.25 & 79.48 & 18.80 & 81.29 & 78.23 & 23.68 & 82.88 & 81.16 & 26.98 & 83.34 & 80.61 & 25.01 & 82.94 & 79.87 \\\\\\\\\\n\",\n \"Gemini-2.0-Flash-Thinking & 30.98 & 84.17 & 79.28 & 19.06 & 81.21 & 77.76 & 25.50 & 82.90 & 80.85 & 27.54 & 83.04 & 80.13 & 25.77 & 82.83 & 79.50 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}%\\n\",\n \"}\\n\",\n \"\\\\caption{Results on the Zh$\\\\Rightarrow$En WMT22 General Machine Translation task.}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[h]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{%\\n\",\n \"\\\\begin{tabular}{l ccc ccc ccc ccc ccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{Model} & \\\\multicolumn{3}{c}{Conversation} & \\\\multicolumn{3}{c}{E-commerce} & \\\\multicolumn{3}{c}{News} & \\\\multicolumn{3}{c}{Social} & \\\\multicolumn{3}{c}{Average} \\\\\\\\\\n\",\n \"\\\\cmidrule(lr){2-4} \\\\cmidrule(lr){5-7} \\\\cmidrule(lr){8-10} \\\\cmidrule(lr){11-13} \\\\cmidrule(lr){14-16}\\n\",\n \" & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"GPT-4o & \\\\textbf{45.63} & 89.44 & 82.67 & \\\\textbf{43.82} & 88.82 & 83.52 & 51.20 & 88.31 & 83.41 & \\\\textbf{38.60} & 84.57 & 81.33 & \\\\textbf{44.81} & 87.78 & 82.73 \\\\\\\\\\n\",\n \"DeepSeek-V3 & 43.49 & 89.89 & 82.94 & 42.84 & 89.00 & 83.79 & 50.28 & \\\\textbf{88.75} & 83.76 & 37.59 & \\\\textbf{85.27} & 82.24 & 43.55 & \\\\textbf{88.23} & 83.18 \\\\\\\\\\n\",\n \"Gemini-2.0-Flash & 44.59 & \\\\textbf{90.19} & 83.01 & 42.95 & 88.90 & 83.47 & \\\\textbf{52.12} & 88.46 & 83.28 & 38.11 & 84.71 & 81.59 & 44.44 & 88.06 & 82.84 \\\\\\\\\\n\",\n \"\\\\hdashline\\n\",\n \"OpenAI-o1 & 42.24 & 89.86 & \\\\textbf{84.13} & 39.22 & \\\\textbf{89.06} & \\\\textbf{84.59} & 45.88 & 88.47 & \\\\textbf{84.67} & 33.41 & 85.07 & \\\\textbf{82.98} & 40.19 & 88.11 & \\\\textbf{84.09} \\\\\\\\\\n\",\n \"DeepSeek-R1 & 38.00 & 88.74 & 82.74 & 34.48 & 88.35 & 83.48 & 42.56 & 88.08 & 83.58 & 29.43 & 84.21 & 82.01 & 36.12 & 87.34 & 82.95 \\\\\\\\\\n\",\n \"OpenAI-o3-mini & 43.24 & 89.02 & 82.44 & 41.54 & 88.51 & 83.41 & 47.06 & 87.53 & 82.94 & 36.38 & 83.99 & 81.27 & 42.05 & 87.26 & 82.52 \\\\\\\\\\n\",\n \"Gemini-2.0-Flash-Thinking & 44.31 & 89.71 & 82.66 & 42.39 & 88.73 & 83.31 & 49.54 & 88.23 & 83.26 & 36.35 & 83.86 & 81.08 & 43.15 & 87.63 & 82.58 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}%\\n\",\n \"}\\n\",\n \"\\\\caption{Results on the En$\\\\Rightarrow$Zh WMT22 General Machine Translation task.}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[h]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{%\\n\",\n \"\\\\begin{tabular}{l ccc ccc ccc ccc ccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{Model} & \\\\multicolumn{3}{c}{Conversation} & \\\\multicolumn{3}{c}{E-commerce} & \\\\multicolumn{3}{c}{News} & \\\\multicolumn{3}{c}{Social} & \\\\multicolumn{3}{c}{Average} \\\\\\\\\\n\",\n \"\\\\cmidrule(lr){2-4} \\\\cmidrule(lr){5-7} \\\\cmidrule(lr){8-10} \\\\cmidrule(lr){11-13} \\\\cmidrule(lr){14-16}\\n\",\n \" & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"GPT-4o & \\\\textbf{40.65} & 89.88 & 83.72 & \\\\textbf{38.03} & 88.80 & 84.02 & \\\\textbf{36.70} & 87.35 & 84.00 & 34.06 & 85.12 & 82.63 & \\\\textbf{37.36} & 87.79 & 83.59 \\\\\\\\\\n\",\n \"DeepSeek-V3 & 39.58 & 89.33 & 83.50 & 37.42 & 88.69 & 84.14 & 36.48 & 87.94 & 84.76 & \\\\textbf{35.08} & 85.22 & 82.91 & 37.14 & 87.79 & 83.83 \\\\\\\\\\n\",\n \"Gemini-2.0-Flash & 37.88 & 88.46 & 83.36 & 37.47 & \\\\textbf{88.98} & 84.09 & 35.88 & 87.69 & 84.63 & 34.02 & 85.39 & 82.84 & 36.31 & 87.63 & 83.73 \\\\\\\\\\n\",\n \"\\\\hdashline\\n\",\n \"OpenAI-o1 & 38.71 & \\\\textbf{90.19} & \\\\textbf{85.55} & 36.25 & 88.73 & \\\\textbf{85.79} & 33.90 & \\\\textbf{88.17} & \\\\textbf{86.69} & 32.18 & \\\\textbf{85.73} & \\\\textbf{84.54} & 35.26 & \\\\textbf{88.21} & \\\\textbf{85.64} \\\\\\\\\\n\",\n \"DeepSeek-R1 & 36.17 & 89.11 & 83.60 & 34.70 & 88.73 & 84.27 & 33.96 & 87.92 & 84.87 & 32.39 & 85.22 & 82.85 & 34.31 & 87.75 & 83.90 \\\\\\\\\\n\",\n \"OpenAI-o3-mini & 35.35 & 88.05 & 83.21 & 36.71 & 88.28 & 84.01 & 34.01 & 87.16 & 84.31 & 32.14 & 84.29 & 82.25 & 34.55 & 86.95 & 83.44 \\\\\\\\\\n\",\n \"Gemini-2.0-Flash-Thinking & 36.64 & 88.04 & 83.00 & 37.87 & 88.97 & 83.98 & 36.18 & 87.81 & 84.71 & 33.84 & 85.00 & 82.59 & 36.13 & 87.45 & 83.57 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}%\\n\",\n \"}\\n\",\n \"\\\\caption{Results on the En$\\\\Rightarrow$De WMT22 General Machine Translation task.}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[h]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{%\\n\",\n \"\\\\begin{tabular}{l ccc ccc ccc ccc ccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{Model} & \\\\multicolumn{3}{c}{Conversation} & \\\\multicolumn{3}{c}{E-commerce} & \\\\multicolumn{3}{c}{News} & \\\\multicolumn{3}{c}{Social} & \\\\multicolumn{3}{c}{Average} \\\\\\\\\\n\",\n \"\\\\cmidrule(lr){2-4} \\\\cmidrule(lr){5-7} \\\\cmidrule(lr){8-10} \\\\cmidrule(lr){11-13} \\\\cmidrule(lr){14-16}\\n\",\n \" & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"GPT-4o & \\\\textbf{37.14} & 86.07 & 81.79 & \\\\textbf{34.87} & \\\\textbf{86.15} & 81.37 & \\\\textbf{31.74} & 85.49 & 82.14 & \\\\textbf{32.35} & 85.07 & 81.27 & \\\\textbf{34.02} & 85.69 & 81.64 \\\\\\\\\\n\",\n \"DeepSeek-V3 & 36.52 & 86.24 & 81.84 & 34.56 & 86.04 & 81.46 & 30.76 & 85.52 & 82.47 & 31.65 & 84.93 & 81.43 & 33.37 & 85.68 & 81.80 \\\\\\\\\\n\",\n \"Gemini-2.0-Flash & 36.76 & 86.05 & 81.92 & 34.30 & 86.02 & 81.23 & 31.28 & 85.64 & 82.35 & 31.57 & 84.88 & 81.37 & 33.48 & 85.65 & 81.72 \\\\\\\\\\n\",\n \"\\\\hdashline\\n\",\n \"OpenAI-o1 & 34.73 & \\\\textbf{86.49} & \\\\textbf{83.40} & 31.91 & 85.95 & \\\\textbf{82.65} & 30.35 & 85.78 & \\\\textbf{83.93} & 30.36 & \\\\textbf{85.22} & \\\\textbf{82.93} & 31.84 & \\\\textbf{85.86} & \\\\textbf{83.23} \\\\\\\\\\n\",\n \"DeepSeek-R1 & 35.20 & 86.06 & 81.96 & 32.11 & 85.75 & 81.39 & 29.59 & 85.78 & 82.66 & 30.04 & 84.92 & 81.45 & 31.73 & 85.63 & 81.86 \\\\\\\\\\n\",\n \"OpenAI-o3-mini & 36.16 & 85.66 & 81.54 & 34.20 & 85.59 & 81.26 & 31.07 & 84.71 & 81.30 & 32.02 & 84.66 & 81.00 & 33.36 & 85.16 & 81.28 \\\\\\\\\\n\",\n \"Gemini-2.0-Flash-Thinking & 36.35 & 86.05 & 81.79 & 33.05 & 85.77 & 81.14 & 31.59 & \\\\textbf{85.83} & 82.42 & 31.48 & 84.76 & 81.28 & 33.12 & 85.60 & 81.66 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}%\\n\",\n \"}\\n\",\n \"\\\\caption{Results on the De$\\\\Rightarrow$En WMT22 General Machine Translation task.}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[h]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{%\\n\",\n \"\\\\begin{tabular}{l ccc ccc ccc ccc ccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{Model} & \\\\multicolumn{3}{c}{WMTBio18 En$\\\\Rightarrow$Zh} & \\\\multicolumn{3}{c}{WMTBio18 Zh$\\\\Rightarrow$En} & \\\\multicolumn{3}{c}{WMTBio18 En$\\\\Rightarrow$De} & \\\\multicolumn{3}{c}{WMTBio18 De$\\\\Rightarrow$En} & \\\\multicolumn{3}{c}{Average} \\\\\\\\\\n\",\n \"\\\\cmidrule(lr){2-4} \\\\cmidrule(lr){5-7} \\\\cmidrule(lr){8-10} \\\\cmidrule(lr){11-13} \\\\cmidrule(lr){14-16}\\n\",\n \" & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"GPT-4o & \\\\textbf{40.86} & 86.66 & 84.61 & 25.04 & 84.33 & 84.02 & 24.44 & 86.68 & 84.97 & 29.31 & 85.06 & 82.61 & 29.91 & 85.68 & 84.05 \\\\\\\\\\n\",\n \"DeepSeek-V3 & 40.58 & 86.67 & 84.66 & 25.73 & 84.42 & 83.95 & 24.75 & 86.85 & 84.96 & 29.37 & 85.01 & 82.78 & 30.11 & 85.74 & 84.09 \\\\\\\\\\n\",\n \"Gemini-2.0-Flash & 40.83 & \\\\textbf{86.87} & 84.62 & \\\\textbf{26.32} & \\\\textbf{84.50} & 83.74 & 26.47 & 86.72 & 84.66 & \\\\textbf{30.69} & \\\\textbf{85.10} & 82.22 & 31.08 & \\\\textbf{85.80} & 83.81 \\\\\\\\\\n\",\n \"\\\\hdashline\\n\",\n \"OpenAI-o1 & 38.84 & 86.63 & \\\\textbf{85.52} & 24.85 & 84.30 & \\\\textbf{84.88} & \\\\textbf{32.29} & \\\\textbf{86.96} & \\\\textbf{86.68} & 28.42 & 85.08 & \\\\textbf{84.05} & \\\\textbf{31.35} & 85.74 & \\\\textbf{85.38} \\\\\\\\\\n\",\n \"DeepSeek-R1 & 38.43 & 86.68 & 84.45 & 24.34 & 84.22 & 83.70 & 25.14 & 86.94 & 85.01 & 29.34 & 85.04 & 82.79 & 29.31 & 85.72 & 83.99 \\\\\\\\\\n\",\n \"OpenAI-o3-mini & 39.39 & 86.15 & 84.36 & 25.09 & 83.99 & 83.77 & 22.96 & 86.39 & 84.73 & 29.06 & 84.70 & 82.77 & 29.12 & 85.31 & 83.91 \\\\\\\\\\n\",\n \"Gemini-2.0-Flash-Thinking & 39.90 & 86.36 & 84.35 & 25.54 & 84.29 & 83.90 & 25.78 & 86.75 & 84.72 & 30.57 & 84.98 & 82.86 & 30.45 & 85.59 & 83.95 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{Model} & \\\\multicolumn{3}{c}{WMTBio19 En$\\\\Rightarrow$Zh} & \\\\multicolumn{3}{c}{WMTBio19 Zh$\\\\Rightarrow$En} & \\\\multicolumn{3}{c}{WMTBio19 En$\\\\Rightarrow$De} & \\\\multicolumn{3}{c}{WMTBio19 De$\\\\Rightarrow$En} & \\\\multicolumn{3}{c}{Average} \\\\\\\\\\n\",\n \"\\\\cmidrule(lr){2-4} \\\\cmidrule(lr){5-7} \\\\cmidrule(lr){8-10} \\\\cmidrule(lr){11-13} \\\\cmidrule(lr){14-16}\\n\",\n \" & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"GPT-4o & 37.58 & 86.75 & 84.53 & 31.41 & 84.42 & 82.72 & 30.49 & 86.29 & 83.55 & 37.25 & 86.91 & 82.35 & 34.18 & 86.09 & 83.29 \\\\\\\\\\n\",\n \"DeepSeek-V3 & 38.54 & 86.93 & 84.59 & 30.75 & \\\\textbf{84.68} & 83.10 & 34.14 & 86.47 & 83.91 & 37.63 & \\\\textbf{87.66} & 82.29 & 35.26 & 86.44 & 83.48 \\\\\\\\\\n\",\n \"Gemini-2.0-Flash & \\\\textbf{38.89} & 86.91 & 84.48 & \\\\textbf{32.59} & 84.50 & 82.86 & \\\\textbf{34.59} & \\\\textbf{87.96} & 83.96 & \\\\textbf{37.87} & 86.71 & 82.24 & \\\\textbf{35.98} & \\\\textbf{86.52} & 83.39 \\\\\\\\\\n\",\n \"\\\\hdashline\\n\",\n \"OpenAI-o1 & 37.01 & 86.97 & \\\\textbf{85.42} & 28.15 & 84.53 & \\\\textbf{83.50} & 32.29 & 83.52 & \\\\textbf{85.88} & 36.62 & 84.93 & \\\\textbf{83.82} & 33.52 & 85.00 & \\\\textbf{84.66} \\\\\\\\\\n\",\n \"DeepSeek-R1 & 37.23 & \\\\textbf{87.05} & 84.27 & 28.58 & 84.31 & 82.60 & 32.56 & 83.80 & 83.98 & 36.19 & 84.84 & 82.28 & 33.64 & 85.00 & 83.28 \\\\\\\\\\n\",\n \"OpenAI-o3-mini & 35.62 & 86.07 & 84.22 & 30.59 & 84.04 & 82.91 & 31.55 & 86.16 & 83.86 & 36.92 & 86.48 & 82.19 & 33.67 & 85.69 & 83.29 \\\\\\\\\\n\",\n \"Gemini-2.0-Flash-Thinking & 38.48 & 86.43 & 84.20 & 31.78 & 84.48 & 82.88 & 33.55 & 86.82 & 83.89 & 36.74 & 84.57 & 82.25 & 35.14 & 85.57 & 83.31 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}%\\n\",\n \"}\\n\",\n \"\\\\caption{Results on the Zh$\\\\Leftrightarrow$En and De$\\\\Leftrightarrow$En Biomedical Machine Translation Task from WMT18 and WMT19 (WMTBio18 and WMTBio19). }\\n\",\n \"\\\\label{tab:result_wmtbio_updated_averages}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[h]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{%\\n\",\n \"\\\\begin{tabular}{l ccc ccc ccc ccc ccc ccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{Model} \\n\",\n \"& \\\\multicolumn{3}{c}{Medical} & \\\\multicolumn{3}{c}{Law} & \\\\multicolumn{3}{c}{IT} \\n\",\n \"& \\\\multicolumn{3}{c}{Koran} & \\\\multicolumn{3}{c}{Subtitles} & \\\\multicolumn{3}{c}{Average} \\\\\\\\\\n\",\n \"\\\\cmidrule(lr){2-4} \\\\cmidrule(lr){5-7} \\\\cmidrule(lr){8-10}\\n\",\n \"\\\\cmidrule(lr){11-13} \\\\cmidrule(lr){14-16} \\\\cmidrule(lr){17-19}\\n\",\n \"& BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI\\n\",\n \"& BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"GPT-4o & 41.89 & 84.23 & 81.89 & 39.00 & 85.06 & 82.38 & 37.23 & 83.52 & 78.37 & 17.65 & \\\\textbf{75.04} & 80.64 & 29.61 & 80.62 & 82.25 & 33.08 & 81.69 & 81.11 \\\\\\\\\\n\",\n \"DeepSeek-V3 & 41.43 & 84.15 & 81.90 & 38.82 & 84.94 & 82.29 & \\\\textbf{38.16} & \\\\textbf{83.90} & 78.86 & 17.94 & 74.91 & 80.33 & 29.70 & 80.68 & 82.26 & 33.21 & 81.72 & 81.13 \\\\\\\\\\n\",\n \"Gemini-2.0-Flash & \\\\textbf{44.39} & \\\\textbf{84.49} & 81.76 & \\\\textbf{46.15} & \\\\textbf{85.57} & 82.30 & 37.93 & 83.20 & 78.49 & \\\\textbf{19.70} & 74.97 & 79.71 & \\\\textbf{30.39} & 80.74 & 82.19 & \\\\textbf{35.71} & \\\\textbf{81.79} & 80.89 \\\\\\\\\\n\",\n \"\\\\hdashline\\n\",\n \"OpenAI-o1 & 40.44 & 83.88 & \\\\textbf{83.29} & 40.74 & 85.03 & \\\\textbf{83.53} & 36.40 & 83.20 & \\\\textbf{79.75} & 16.76 & 75.03 & \\\\textbf{82.02} & 28.91 & \\\\textbf{80.79} & \\\\textbf{83.65} & 32.65 & 81.59 & \\\\textbf{82.45} \\\\\\\\\\n\",\n \"DeepSeek-R1 & 40.23 & 83.82 & 81.81 & 39.16 & 84.97 & 82.35 & 36.66 & 83.49 & 78.68 & 17.05 & 74.76 & 80.63 & 28.82 & 80.65 & 82.22 & 32.38 & 81.54 & 81.14 \\\\\\\\\\n\",\n \"OpenAI-o3-mini & 39.82 & 83.60 & 81.79 & 34.29 & 84.18 & 81.96 & 37.13 & 82.63 & 78.20 & 15.52 & 73.54 & 80.19 & 28.39 & 80.06 & 82.06 & 31.03 & 80.80 & 80.84 \\\\\\\\\\n\",\n \"Gemini-2.0-Flash-Thinking & 43.36 & 84.14 & 81.70 & 44.51 & 85.10 & 82.13 & 37.26 & 82.94 & 78.40 & 19.57 & 75.03 & 79.79 & 30.38 & 80.78 & 82.20 & 35.02 & 81.60 & 80.84 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Results on the Dn$\\\\Rightarrow$En Multi-Domain dataset.}\\n\",\n \"\\\\label{tab:result_multi_domain}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[h]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{%\\n\",\n \"\\\\begin{tabular}{l ccc ccc ccc ccc ccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{Model} & \\\\multicolumn{3}{c}{Test-1} & \\\\multicolumn{3}{c}{Test-2} & \\\\multicolumn{3}{c}{Valid-1} & \\\\multicolumn{3}{c}{Valid-2} & \\\\multicolumn{3}{c}{Average} \\\\\\\\\\n\",\n \"\\\\cmidrule(lr){2-4} \\\\cmidrule(lr){5-7} \\\\cmidrule(lr){8-10} \\\\cmidrule(lr){11-13} \\\\cmidrule(lr){14-16}\\n\",\n \"& BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"GPT-4o & 20.49 & 78.44 & 76.50 & 16.12 & 77.55 & 77.82 & 21.08 & 78.91 & 77.31 & 12.49 & 75.38 & 76.91 & 17.54 & \\\\textbf{77.57} & 77.13 \\\\\\\\\\n\",\n \"DeepSeek-V3 & 15.66 & 77.47 & 77.84 & \\\\textbf{16.25} & 77.14 & 77.49 & 19.57 & \\\\textbf{78.94} & \\\\textbf{77.48} & 11.87 & 75.28 & 77.05 & 16.81 & 77.48 & 77.17 \\\\\\\\\\n\",\n \"Gemini-2.0-Flash & \\\\textbf{22.11} & 78.25 & 75.94 & \\\\textbf{16.25} & 77.14 & 77.49 & \\\\textbf{21.42}& 78.70 & 76.70 & \\\\textbf{12.70} & 75.11 & 76.34 & \\\\textbf{18.12} & 77.30 & 76.62 \\\\\\\\\\n\",\n \"\\\\hdashline\\n\",\n \"OpenAI-o1 & 19.05 & \\\\textbf{78.85} & \\\\textbf{78.00} & 16.04 & \\\\textbf{77.76} & \\\\textbf{78.66} & 18.03 & 78.13 & 76.93 & 12.65 & \\\\textbf{75.52} & \\\\textbf{77.44} & 16.44 & 77.56 & \\\\textbf{77.76} \\\\\\\\\\n\",\n \"DeepSeek-R1 & 15.34 & 77.29 & 75.72 & 12.68 & 77.04 & 77.23 & 14.35 & 77.67 & 76.59 & 10.09 & 74.55 & 76.33 & 13.12 & 76.64 & 76.47 \\\\\\\\\\n\",\n \"OpenAI-o3-mini & 20.63 & 77.77 & 76.02 & 15.57 & 76.69 & 77.38 & 21.11 & 78.35 & 76.97 & 11.74 & 74.69 & 76.17 & 17.26 & 76.88 & 76.63 \\\\\\\\\\n\",\n \"Gemini-2.0-Flash-Thinking & 21.69 & 78.78 & 76.71 & 15.75 & 76.98 & 77.43 & 21.14 & 78.17 & 75.90 & 12.48 & 74.75 & 76.27 & 17.77 & 77.17 & 76.58 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}%\\n\",\n \"}\\n\",\n \"\\\\caption{Results on the Zh$\\\\Rightarrow$En GuoFeng Webnovel dataset (Literary domain).}\\n\",\n \"\\\\label{tab:result_webnovel}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[h]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{%\\n\",\n \"\\\\begin{tabular}{l ccc ccc ccc ccc ccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{Model} & \\\\multicolumn{3}{c}{WMT22 En$\\\\Rightarrow$Zh} & \\\\multicolumn{3}{c}{WMT22 Zh$\\\\Rightarrow$En} & \\\\multicolumn{3}{c}{WMT22 En$\\\\Rightarrow$De} & \\\\multicolumn{3}{c}{WMT22 De$\\\\Rightarrow$En} & \\\\multicolumn{3}{c}{Average} \\\\\\\\\\n\",\n \"\\\\cmidrule(lr){2-4} \\\\cmidrule(lr){5-7} \\\\cmidrule(lr){8-10} \\\\cmidrule(lr){11-13} \\\\cmidrule(lr){14-16}\\n\",\n \"& BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"DeepSeek-V3 & 44.52 & \\\\textbf{88.21} & 83.19 & 25.21 & 83.11 & 79.95 & 36.89 & 87.78 & 83.83 & 33.06 & 85.66 & 81.80 & 34.92 & 86.19 & 82.19 \\\\\\\\\\n\",\n \"GPT-4o & \\\\textbf{45.64} & 87.77 & 82.74 & 25.11 & \\\\textbf{83.22} & 79.95 & \\\\textbf{37.07} & 87.77 & 83.60 & \\\\textbf{33.71} & 85.68 & 81.64 & \\\\textbf{35.38} & 86.11 & 81.98 \\\\\\\\\\n\",\n \"Gemini-2.0-Flash & 45.50 & 88.04 & 82.84 & \\\\textbf{26.12} & 82.94 & 79.56 & 36.16 & 87.63 & 83.74 & 33.20 & 85.63 & 81.71 & 35.25 & 86.06 & 81.96 \\\\\\\\\\n\",\n \"\\\\hdashline\\n\",\n \"OpenAI-o1 & 40.84 & 88.10 & \\\\textbf{84.10} & 22.42 & 83.08 & \\\\textbf{80.87} & 34.87 & \\\\textbf{88.18} & \\\\textbf{85.64} & 31.53 & \\\\textbf{85.84} & \\\\textbf{83.22} & 32.42 & \\\\textbf{86.30} & \\\\textbf{83.46} \\\\\\\\\\n\",\n \"DeepSeek-R1 & 36.88 & 87.33 & 82.96 & 20.68 & 82.48 & 79.57 & 34.14 & 87.73 & 83.91 & 31.37 & 85.61 & 81.86 & 30.77 & 85.79 & 82.07 \\\\\\\\\\n\",\n \"OpenAI-o3-mini & 42.67 & 87.25 & 82.52 & 24.09 & 82.82 & 79.89 & 34.46 & 86.94 & 83.45 & 33.08 & 85.14 & 81.27 & 33.58 & 85.54 & 81.78 \\\\\\\\\\n\",\n \"Gemini-2.0-Flash-Thinking & 44.04 & 87.61 & 82.58 & 24.83 & 82.71 & 79.51 & 36.12 & 87.46 & 83.58 & 32.75 & 85.59 & 81.66 & 34.44 & 85.84 & 81.83 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}%\\n\",\n \"}\\n\",\n \"\\\\caption{Results on the Zh$\\\\Leftrightarrow$En and De$\\\\Leftrightarrow$En WMT22 General Machine Translation task.}\\n\",\n \"\\\\label{tab:result_wmt22}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\begin{tcolorbox}[colback=gray!10, colframe=black, width=\\\\textwidth]\\n\",\n \"\\\\small\\n\",\n \"\\\\setstretch{1.2}\\n\",\n \"\\n\",\n \"\\\\noindent{You are a professional translation quality evaluator following the MQM (Multidimensional Quality Metrics) framework.}\\n\",\n \"\\n\",\n \"\\\\textbf{Task Instructions:}\\n\",\n \"\\\\begin{enumerate}\\n\",\n \" \\\\item Compare the Prediction against both the Source and the Reference.\\n\",\n \" \\\\item Identify up to five of the most serious Errors for each translation sentence, using the MQM error types listed below.\\n\",\n \" \\\\item Assign exactly one severity level to each error.\\n\",\n \" \\\\item Special handling for two specific error types:\\n\",\n \" \\\\begin{itemize}\\n\",\n \" \\\\item \\\\textbf{Source Error}: Errors present in the Source text itself.\\n\",\n \" \\\\item \\\\textbf{Non-translation Error}: Translation is unassessable and unrelated to the Source.\\n\",\n \" \\\\end{itemize}\\n\",\n \" \\\\item If no errors are found, return an empty JSON list [].\\n\",\n \" \\\\item Only output a valid JSON object. Do not include any additional text, comments, or explanations.\\n\",\n \"\\\\end{enumerate}\\n\",\n \"\\n\",\n \"\\\\textbf{MQM Error Types (See Table~\\\\ref{tab:mqm-hierarchy} for the complete hierarchy):}\\n\",\n \"\\\\begin{itemize}\\n\",\n \" \\\\item Mistranslation: Incorrect translation that alters or distorts meaning.\\n\",\n \" \\\\item Addition: Insertion of information or emotion not present in the source.\\n\",\n \" \\\\item Under-translation: Partial omission of relevant or necessary information.\\n\",\n \" \\\\item Omission: Complete exclusion of content present in the source.\\n\",\n \" \\\\item Untranslated: Source text is copied without being translated.\\n\",\n \"\\\\end{itemize}\\n\",\n \"\\n\",\n \"\\\\textbf{Severity Levels:} \\n\",\n \"\\\\begin{itemize}\\n\",\n \" \\\\item Minor: Slight impact on readability or style; meaning remains clear.\\n\",\n \" \\\\item Major: Significant impact on usability, comprehension, or meaning.\\n\",\n \"\\\\end{itemize}\\n\",\n \"\\\\textbf{Output Format:}\\n\",\n \"\\\\begin{verbatim}\\n\",\n \"{\\n\",\n \" \\\"errors\\\": [\\n\",\n \" {\\n\",\n \" \\\"error_type\\\": \\\"Mistranslation\\\",\\n\",\n \" \\\"severity\\\": \\\"Major\\\",\\n\",\n \" \\\"explanation\\\": \\\"The correct translation should be ... \\\"\\n\",\n \" }\\n\",\n \" ]\\n\",\n \"}\\n\",\n \"\\\\end{verbatim}\\n\",\n \"\\n\",\n \"\\\\textbf{Source:} \\\\texttt{\\\\{source\\\\_text\\\\}} \\\\\\\\\\n\",\n \"\\\\textbf{Reference:} \\\\texttt{\\\\{reference\\\\_text\\\\}} \\\\\\\\\\n\",\n \"\\\\textbf{Prediction:} \\\\texttt{\\\\{prediction\\\\_text\\\\}}\\n\",\n \"\\n\",\n \"\\\\end{tcolorbox}\\n\",\n \"\\\\caption{Full prompt used to calculate MQM scores with DeepSeek-V3. }\\n\",\n \"\\\\label{fig:mqm-prompt}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\begin{tcolorbox}[colback=gray!10, colframe=black, width=\\\\textwidth]\\n\",\n \"\\n\",\n \"Your task is to assess the difficulty of translating a given \\\\texttt{\\\\{src\\\\_lang\\\\}} sentence into \\\\texttt{\\\\{tgt\\\\_lang\\\\}}. Please evaluate the difficulty based on the following criteria and output the result in JSON format, with the key \\\"level\\\":\\n\",\n \"\\\\begin{enumerate}\\n\",\n \"\\\\item Sentence complexity: Determine if the sentence is a simple sentence, a compound sentence, or includes subordinate clauses and other complex structures.\\n\",\n \"\\\\item Vocabulary difficulty: Assess whether the sentence contains commonly used words or specialized terms or slang.\\n\",\n \"\\\\item Grammar differences: Analyze if the sentence's grammatical structure is similar to or differs significantly from \\\\texttt{\\\\{tgt\\\\_lang\\\\}}.\\n\",\n \"\\\\item Contextual understanding: Consider whether understanding specific cultural contexts or background knowledge is necessary for accurate translation.\\n\",\n \"\\\\end{enumerate}\\n\",\n \"\\n\",\n \"Provide a difficulty level (1-5), with 1 being the easiest and 5 being the most difficult.\\n\",\n \"And output the difficulty level in the following JSON format:\\n\",\n \"\\\\begin{verbatim}\\n\",\n \"{\\n\",\n \" \\\"level\\\": \\\"difficulty level\\\"\\n\",\n \"}\\n\",\n \"\\\\end{verbatim}\\n\",\n \"\\n\",\n \"Here is the \\\\texttt{\\\\{src\\\\_lang\\\\}} sentence: \\\\texttt{\\\\{src\\\\_text\\\\}}\\n\",\n \"\\n\",\n \"\\\\end{tcolorbox}\\n\",\n \"\\\\caption{Full prompt used for evaluating translation difficulty with DeepSeek-V3.}\\n\",\n \"\\\\label{fig:difficulty_eval}\\n\",\n \"\\\\end{figure*}\\n\"\n ]\n]","isConversational":false},"fulltext":{"kind":"string","value":"\n\\pdfoutput=1\n% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.\n\n\\documentclass[11pt]{article}\n\\usepackage[T1]{fontenc}\n% \\usepackage[utf8]{inputenc}\n\n% Change \"review\" to \"final\" to generate the final (sometimes called camera-ready) version.\n% Change to \"preprint\" to generate a non-anonymous version with page numbers.\n\\usepackage[preprint]{acl}\n\\usepackage{times}\n\\usepackage{latexsym}\n\\usepackage{tabularx}\n\\usepackage{multirow}\n\\usepackage{booktabs}\n\\usepackage{setspace} \n\\usepackage{tcolorbox}\n\\usepackage{lipsum} \n\\usepackage{graphicx}\n\\usepackage{amssymb}\n\\usepackage{float}\n\\usepackage{arydshln}\n\\usepackage{hyperref}\n\\usepackage{microtype}\n\\usepackage{inconsolata}\n\\usepackage{enumitem}\n\n\\title{How Well Do Large Reasoning Models Translate?\\\\A Comprehensive Evaluation for Multi-Domain Machine Translation}\n\n\\author{\nYongshi Ye\\textsuperscript{1,3,}\\thanks{\\,\\,Equal contribution.},\nBiao Fu\\textsuperscript{2,3,}\\footnotemark[1]\\textsuperscript{,}\\thanks{\\,\\,Corresponding author.},\nChongxuan Huang\\textsuperscript{2,3},\nYidong Chen\\textsuperscript{2,3},\nXiaodong Shi\\textsuperscript{2,3,}\\footnotemark[2]\n\\\\[0.5em]\n\\textsuperscript{1}Institute of Artificial Intelligence, Xiamen University \\\\\n\\textsuperscript{2}School of Informatics, Xiamen University \\\\\n\\textsuperscript{3}Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural \\\\ Heritage of Fujian and Taiwan (Xiamen University),Ministry of Culture and Tourism\\\\\n\\texttt{\\{yeyongshi,biaofu\\}@stu.xmu.edu.cn,mandel@xmu.edu.cn}\n}\n\n\\begin{document}\n\\maketitle\n\\begin{abstract} \nLarge language models (LLMs) have demonstrated strong performance in general-purpose machine translation, but their effectiveness in complex, domain-sensitive translation tasks remains underexplored. Recent advancements in Large Reasoning Models (LRMs), raise the question of whether structured reasoning can enhance translation quality across diverse domains. In this work, we compare the performance of LRMs with traditional LLMs across 15 representative domains and four translation directions. Our evaluation considers various factors, including task difficulty, input length, and terminology density. We use a combination of automatic metrics and an enhanced MQM-based evaluation hierarchy to assess translation quality. Our findings show that LRMs consistently outperform traditional LLMs in semantically complex domains, especially in long-text and high-difficulty translation scenarios. Moreover, domain-adaptive prompting strategies further improve performance by better leveraging the reasoning capabilities of LRMs. These results highlight the potential of structured reasoning in MDMT tasks and provide valuable insights for optimizing translation systems in domain-sensitive contexts\\footnote{The evaluation results are available at \\url{https://github.com/WingseeYe/TransEval-LRM}.}. \n\\end{abstract}\n\n\\section{Introduction}\n\nLarge language models (LLMs) have been widely applied to machine translation (MT) and demonstrate superior performance in general-domain settings~\\cite{mao-etal-2024-gpteval, peng-etal-2023-towards, jiao2023chatgpt, hendy2023good, siu2023chatgpt}. \nHowever, LLMs face persistent challenges in multi-domain MT (MDMT), including instability in domain disambiguation, poor alignment with domain-specific terminology and style, and limited cross-domain generalization~\\cite{zheng2024fine,hu-etal-2024-large-language, man2025dmdteval}. \n\nRecently, a new class of reasoning-oriented LLMs known as Large Reasoning Models (LRMs) has emerged, including models such as OpenAI-o1~\\cite{jaech2024openai} and DeepSeek-R1~\\cite{deepseekai2025deepseekr1incentivizingreasoningcapability}. These models incorporate structured generation mechanisms such as multi-step planning, intermediate verification, and self-refinement, and have achieved strong results in complex tasks such as code generation~\\cite{zhang2024o1codero1replicationcoding, quan2025codeelo, zhang2025codecriticbench} and mathematical reasoning~\\cite{zhao2025promptcot, huang2025math}. \nHowever, it remains unclear whether these reasoning models can generalize to domain-sensitive MT—particularly MDMT—and effectively address its unique challenges, including structural complexity, long-range dependencies, terminology alignment, domain variation, and stylistic consistency.\nUnfortunately, this question has received little attention, and no prior work has systematically examined the translation capabilities of LRMs in multi-domain settings. \n\nIn this work, we aim to fill this gap by systematically evaluating the translation capabilities of LRMs across diverse domains.\nTo this end, we conduct a comprehensive comparison between traditional LLMs and LRMs across 15 representative domains and four translation directions.\nSpecifically, our evaluation spans 11 domains in the German$\\Leftrightarrow$English setting and 12 domains in the Chinese$\\Leftrightarrow$English setting, covering a wide range of content—from general-purpose domains such as conversation and news to highly specialized areas like law, medicine, and literature. \nOur evaluation combines automatic metrics (BLEU, COMET, CometKiwi) with an LLM-powered scoring protocol based on the Multidimensional Quality Metrics (MQM) hierarchy, allowing us to analyze how reasoning capacity and domain control interact across diverse translation conditions.\n\nBased on our comprehensive evaluation, we summarize the following key findings:\n\\textbf{First}, LRMs achieve higher semantic metrics (COMET, CometKiwi) in open-domain settings (e.g., Conversation, News), but lower BLEU in terminology-intensive domains. \n\\textbf{Second}, MQM evaluations on De$\\Rightarrow$En show that LRMs perform well in semantic accuracy and fluency, but are weaker in stylistic consistency. \nIn terminology accuracy, LRMs outperform traditional LLMs in Zh$\\Rightarrow$En but underperform in De$\\Rightarrow$En, suggesting that model suitability may vary across language pairs.\n\\textbf{Third}, LRMs demonstrate superior adaptability in document-level translation. Their structured reasoning mechanisms help model longer-range dependencies and cross-sentence coherence, yielding more consistent outputs for long-form texts.\n\\textbf{Fourth}, we find that incorporating domain-aware prompts can significantly improve their performance. Prompting strategies that encourage implicit domain inference (rather than static domain label) lead to better semantic and stylistic alignment.\n\\textbf{Fifth}, we show that LRMs outperform traditional LLMs on complex translation tasks. Their structured reasoning capabilities allow them to maintain semantic integrity even as translation complexity increases.\n\nOverall, our study not only benchmarks the translation capabilities of LRMs across diverse domains, but also sheds light on their unique strengths and limitations compared to traditional LLMs, offering valuable insights for future research. \n\n\\section{Related Work}\n\n\\subsection{Large Reasoning Models}\n\nEnhancing the reasoning capabilities of LLMs has become a critical step toward general intelligence. OpenAI’s o1~\\cite{jaech2024openai} has demonstrated strong performance on complex reasoning tasks by aligning structured thinking via reinforcement learning (RL). \nBuilding on this foundation, the “Journey Learning” paradigm~\\cite{qin2024o1replicationjourneystrategic} encourages models to emulate human-like exploration strategies, including trial-and-error and reflection, while subsequent work distills o1-style chains-of-thought (CoT) through supervised fine-tuning (SFT) to improve reasoning stability and depth~\\cite{huang2024o1replicationjourney, wang2025ma, ma2025sorftissueresolvingsubtaskoriented, yu2025longshortchainofthoughtmixturesupervised, trung-etal-2024-reft}. \nRL-based strategies have since been widely adopted. DeepSeek-R1~\\cite{deepseekai2025deepseekr1incentivizingreasoningcapability} applies multi-stage RL training and cold-start Long CoT data to enhance reasoning.\nGemini2.0-Flash-Thinking~\\cite{googledeepmind2024gemini20flashthinking} extends context windows to support long-range reasoning. More broadly, models like Marco-o1~\\cite{zhao2024marcoo1openreasoningmodels} aim to align reasoning capabilities for general-purpose, open-ended problem-solving.\n\nIn the context of MT, reasoning-oriented models remain in their early stages but have demonstrated promising potential~\\cite{liu2025newtrendsmodernmachine}. \nRecent approaches such as DRT~\\cite{wang2025drtdeepreasoningtranslation}, R1-T1~\\cite{he2025r1}, DeepTrans~\\cite{wang2025deepreasoningtranslationreinforcement}, and MT-R1-Zero~\\cite{feng2025mt} employ Long CoT-based SFT and/or RL to explicitly activate the reasoning capabilities of LLMs for translation tasks. However, these methods have mainly focused on general-domain or single-domain settings, and a systematic evaluation of reasoning capabilities in MDMT remains lacking.\n\n\\subsection{Evaluation for LLM-based MDMT}\n\nLLMs have shown strong performance in general-domain MT, prompting growing interest in their applicability to domain-specific settings~\\cite{qian-etal-2024-large, huang-etal-2024-lost, wang-etal-2023-document-level, zhu-etal-2024-multilingual}. Recent work has extended evaluation to diverse genres and specialized domains. In literary translation, studies based on paragraph-level and full-novel corpora~\\cite{thai-etal-2022-exploring, fonteyne-etal-2020-literary, zhang2024good} report fluent outputs but persistent issues in stylistic fidelity, discourse coherence, and pragmatic nuance. In technical domains such as science, law, and IT, evaluations highlight challenges with terminological accuracy and structural rigor~\\cite{eschbach2024exploring, kleidermacher2025sciencelanguagesassessingllm}. \nBenchmarking efforts~\\cite{yan2024benchmarkinggpt4humantranslators, yan2024gpt4vshumantranslators} further reveal limited generalization in low-resource and specialized domains. In structured contexts like e-commerce, targeted fine-tuning improves fluency and semantic similarity~\\cite{gao2024llms}.\n\nAlthough these studies have provided detailed evaluations of traditional LLMs in domain-specific translation, recent advances in LRMs raise the question of whether such models are better suited to the challenges of domain-sensitive MT.\n\\citet{chen2025evaluatingo1likellmsunlocking} have recently evaluated LRMs on general MT tasks; however, their effectiveness in the more challenging MDMT remains underexplored.\nIn this work, we fill this gap with a comprehensive evaluation across diverse domains.\n\n\\section{Evaluation Setting}\n\\noindent \\textbf{Dataset.}\nTo systematically evaluate the performance differences between traditional LLMs and LRMs in MDMT, we collect test sets for both German$\\Leftrightarrow$English and Chinese$\\Leftrightarrow$English directions, covering representative domains for each.\n\nFor German$\\Leftrightarrow$English, the evaluation spans 11 domains. Five domains—Medical, Law, IT, Koran, and Subtitles—are sourced from the publicly available Multi-Domain dataset~\\cite{aharoni-goldberg-2020-unsupervised}. The Mixed domain is drawn from the WMT22 General Machine Translation Task~\\cite{kocmi-etal-2022-findings}. Its subdomains include News, Social, E-commerce, and Conversation. Biomedical data is derived from the WMT18 and WMT19 Biomedical Machine Translation Task~\\cite{neves-etal-2018-findings,bawden-etal-2019-findings}.\n\nFor Chinese$\\Leftrightarrow$English, the evaluation covers 12 domains. The CommonSense and Culture domains are taken from the CommonMT~\\cite{he-etal-2020-box} and CAMT~\\cite{yao-etal-2024-benchmarking} datasets, respectively, while the Literary domain comes from the WMT23 Literary Machine Translation Task~\\cite{wang-etal-2023-findings}. The general-purpose Mixed domain, along with its subdivisions (News, Social, E-commerce, and Conversation), is again sourced from the WMT22 General Machine Translation Task~\\cite{kocmi-etal-2022-findings}. Biomedical data is also collected from WMT18~\\cite{neves-etal-2018-findings} and WMT19~\\cite{bawden-etal-2019-findings} Biomedical Machine Translation Task. Additionally, the Laws, News, Science, and Subtitles domains are sourced from the UM-Corpus~\\cite{tian-etal-2014-um}.\n\nThe selected domains span a wide range of linguistic and contextual characteristics, including high-resource domains such as news; terminology-intensive areas like IT, law, medicine, biomedicine, and e-commerce; low-resource domains such as Koranic texts; context-sensitive areas like culture and commonsense; and stylistically demanding content such as literature. This design ensures high diversity in domain coverage, textual style, and semantic complexity, enabling a comprehensive and multidimensional evaluation of model performance. Detailed data statistics are provided in Appendix~\\ref{appendix:dataset}.\n\n\\begin{figure*}[th] \n \\centering\n \\includegraphics[width=\\textwidth]{figure/deen-ende_radar_charts.pdf}\n \\caption{Multi-Domain En$\\Leftrightarrow$De Translation Performance Comparison, showing averaged BLEU, COMET, and CometKiwi scores for both directions, with distinct colors representing different LLMs. }\n \\label{fig:de-en-radar}\n\\end{figure*}\n\\begin{figure*}[th] \n \\centering\n \\includegraphics[width=\\textwidth]{figure/zhen-enzh_radar_charts.pdf}\n \\caption{Multi-Domain En$\\Leftrightarrow$Zh Translation Performance Comparison, showing averaged BLEU, COMET, and CometKiwi scores for both directions, with distinct colors representing different LLMs. }\n\\end{figure*}\n\n\\noindent \\textbf{Metrics.}\nIn this experiment, we evaluate the performance of reasoning-based and traditional LLMs in MDMT using three automated metrics: BLEU\\footnote{\\url{https://github.com/mjpost/sacrebleu}}~\\cite{post-2018-call}, COMET\\footnote{Unbabel/wmt22-comet-da}~\\cite{rei-etal-2022-comet}, and CometKiwi\\footnote{Unbabel/wmt22-cometkiwi-da}~\\cite{rei-etal-2022-CometKiwi}. BLEU emphasizes lexical-level overlap between the hypothesis and reference, while COMET and CometKiwi provide a semantically grounded evaluation that better correlates with human judgment, especially in complex tasks. Specifically, COMET, as a reference-based metric, measures the discrepancy between a model's output and the reference translation, while CometKiwi, as a reference-free metric, provides an objective evaluation when the reference translation quality is low. In addition, we apply the MQM\\footnote{\\url{https://themqm.org}}~\\cite{lommel2014multidimensional,klubivcka2018quantitative}, which we adapt to meet the demands of multi-domain evaluation by incorporating domain-specific features and characteristics of LLMs. A detailed description of the prompt and MQM hierarchy is provided in Appendix~\\ref{appendix:mqm}. Designed with input from translation professionals, the hierarchy uses LLMs as the automated evaluator to annotate errors and assign severity levels. This standardized protocol ensures that the evaluation results are interpretable and adaptable to different domains.\n\n\\noindent \\textbf{Model.}\nWe group the evaluated models into two categories. \n\\textbf{Traditional LLMs} include GPT-4o~\\cite{openai2024gpt4ocard}, DeepSeek-V3~\\cite{deepseekai2025deepseekv3technicalreport}, and Gemini-2.0-Flash~\\cite{googledeepmind2024gemini20}. \\textbf{LRMs} consist of OpenAI-o1~\\cite{jaech2024openai}, OpenAI-o3-mini~\\cite{openai2025o3mini}, DeepSeek-R1~\\cite{deepseekai2025deepseekr1incentivizingreasoningcapability}, and Gemini-2.0-Flash-Thinking~\\cite{googledeepmind2024gemini20flashthinking}.\nAll models are accessed via official APIs provided by DeepSeek, OpenAI, and Gemini\\footnote{The OpenAI, DeepSeek, and Gemini models used in this study are accessed via the following APIs: gpt-4o-2024-11-20, o1-2024-12-17, and o3-mini-2025-01-31 for OpenAI; deepseek-chat-2024-12-26 and deepseek-reasoner for DeepSeek; and gemini-2.0-flash and gemini-2.0-flash-thinking-exp-2025-01-21 for Gemini.}. \n\n\\section{Experiments}\n\n\\subsection{Overall Analysis}\n\\label{all_exp_results}\n\n\\noindent \\textbf{En$\\Leftrightarrow$De}\nIn both En$\\Rightarrow$De and De$\\Rightarrow$En directions, LRMs perform better on the COMET metric in domains such as Conversation, E-commerce, News, and Social, which are characterized by natural language, diverse expression, and low contextual dependency. This suggests that their generation mechanism, aligned with human preferences, is better suited for open-domain and flexible-context translation tasks, enabling more accurate semantic representation. However, in structurally rigid and terminology-intensive domains such as Medical, Law, IT, and Koran, LRMs achieve lower COMET scores than traditional LLMs. \nThis may be attributed to their tendency to prioritize semantic plausibility over strict adherence to domain-specific terminology.\n\nMoreover, LRMs may \"overthink\"~\\cite{chen2025overthinking} or over-interpret specialized terminology, leading to semantically coherent but lexically divergent translations.\nNotably, their performance on the reference-free CometKiwi metric remains strong, indicating good semantic consistency, especially for OpenAI-o1, which outperforms traditional LLMs on the CometKiwi across all domains.\n\n\\noindent \\textbf{En$\\Leftrightarrow$Zh}\nSimilar patterns are observed in both En$\\Rightarrow$Zh and Zh$\\Rightarrow$En directions.\nLRMs demonstrate strong semantic alignment in domains like E-commerce and Conversation, where language is flexible and user-focused, yielding high COMET scores. However, they perform less effectively in domains such as Law and Science, which require precise, terminology-heavy translation. \nAdditionally, in the Zh$\\Rightarrow$En Culture domain (Table~\\ref{tab:result_culture_mt}), DeepSeek-V3 performs better, likely due to its Chinese-centric pretraining, which enhances its ability to capture linguistic and cultural nuances more effectively compared to other models. These findings suggest that translation quality in this setting is shaped by both reasoning paradigms and pretraining data alignment. While LRMs consistently top CometKiwi across domains, further gains in terminological accuracy and syntactic precision remain critical.\n\n\\noindent \\textbf{Overall Perspective}\nAcross both language pairs, traditional LLMs consistently outperform LRMs on BLEU, particularly in domains such as law, medicine, and IT where strict formatting and terminological consistency are essential. \nThis performance gap may be attributed to differences in training paradigms.\nTraditional LLMs may have conducted SFT on parallel translation data from these domains, which encourages the model to directly imitate the reference translation, aligning well with how BLEU evaluates translations.\nIn contrast, LRMs are typically optimized through RL, which prioritizes global semantic consistency and alignment with human preferences, leading to better performance on semantic metrics such as COMET and CometKiwi, but reduced lexical overlap with the reference.\nThis leads to stronger performance on semantic metrics such as COMET and CometKiwi, but results in lower lexical overlap with references and thus reduced BLEU scores.\nImportantly, lower BLEU scores for LRMs in terminology-rich domains do not always reflect actual translation errors. These models often generate semantically equivalent terms or valid paraphrases that diverge from the reference but remain correct. BLEU, being reference-bound and surface-focused, fails to capture such variation. \nThis observation highlights the necessity of multi-dimensional evaluation. To obtain a more complete picture of translation quality, we conduct fine-grained evaluation using the MQM hierarchy in the next section.\n\n\n% MQM \n\\subsection{MQM Evaluation}\nTo systematically evaluate translation errors across domains, we adopt the MQM hierarchy and employ DeepSeek-V3 as an automatic scoring agent to ensure consistent annotation quality. A structured prompt guides the model to identify up to five major errors per sentence, and scores are computed using severity weights proposed by~\\citet{freitag-etal-2021-experts}. This approach balances scalability and consistency, allowing fine-grained comparison across models on seven key dimensions: Accuracy, Fluency, Style, Terminology, Others, Source Error, and Non-translation Error. In MQM score calculation and error category statistics, Source Error are excluded~\\cite{freitag-etal-2021-experts}.\n\n\\begin{table}[t]\n\\centering\n% \\small\n\\resizebox{\\linewidth}{!}{%\n\\begin{tabular}{l c c c c c c}\n\\toprule\n\\multirow{2}{*}{Model} & \\multicolumn{6}{c}{MQM Score ($\\downarrow$)} \\\\\n\\cmidrule(lr){2-7}\n & IT & Koran & Medical & Law & Subtitles & Average\\\\\n\\midrule\nGPT-4o & 3.14 & 4.56 & 3.24 & 3.74 & 3.44 & 3.62 \\\\\nDeepSeek-V3 & \\textbf{2.91} & 4.62 & 3.30 & 3.89 & \\textbf{3.38} & 3.62 \\\\\nGemini-2.0-Flash & 3.09 & 4.68 & \\textbf{3.15} & \\textbf{3.55} & \\textbf{3.38} & \\textbf{3.57} \\\\\n\\hdashline\nOpenAI-o1 & 3.11 & 4.74 & 3.32 & 3.86 & 3.49 & 3.70 \\\\\nDeepSeek-R1 & 2.92 & \\textbf{4.55} & 3.30 & 3.88 & 3.40 & 3.61 \\\\\nOpenAI-o3-mini & 3.16 & 5.22 & 3.43 & 4.59 & 3.62 & 4.00 \\\\\nGemini-2.0-Flash-Thinking & 3.15 & 4.84 & 3.28 & 3.69 & 3.41 & 3.67 \\\\\n\\bottomrule\n\\end{tabular}%\n}\n\\caption{MQM scores for different models across various domains in the Multi-Domain dataset (De$\\Rightarrow$En).}\n\\label{tab:mqm_avg_scores}\n\\end{table}\n\\begin{table}[t]\n\\centering\n\\resizebox{\\linewidth}{!}{%\n\\begin{tabular}{l ccccc}\n\\toprule\n\\textbf{Model} & \\textbf{Acc. (\\%)} & \\textbf{Style (\\%)} & \\textbf{Fluency (\\%)} & \\textbf{Term. (\\%)} & \\textbf{Non-trans (\\%)} \\\\\n\\midrule\nGPT-4o & 50.78 & 30.58 & 7.03 & 11.38 & 0.23 \\\\\nDeepSeek-V3 & 50.72 & 30.51 & 6.84 & 11.83 & 0.09 \\\\\nGemini-2.0-Flash & 52.86 & \\textbf{29.43} & 6.87 & \\textbf{10.68} & 0.16 \\\\\n\\hdashline\nOpenAI-o1 & 50.95 & 29.58 & 7.10 & 12.22 & 0.15 \\\\\nDeepSeek-R1 & 50.50 & 30.83 & 6.68 & 11.91 & \\textbf{0.08} \\\\\nOpenAI-o3-mini & \\textbf{49.97} & 30.48 & 7.19 & 12.24 & 0.12 \\\\\nGemini-2.0-Flash-Thinking & 52.54 & 29.64 & \\textbf{6.38} & 11.29 & 0.15 \\\\\n\\bottomrule\n\\end{tabular}%\n}\n\\caption{Error category percentages for different models, including accuracy (Acc.), style, fluency, terminology (Term.), and non-translation error (Non-trans).}\n\\label{tab:model_error_category_percentages}\n\\end{table}\n\nAs shown in Table~\\ref{tab:mqm_avg_scores}, traditional LLMs achieve lower overall MQM scores, showing higher consistency and precision.\nInterestingly, LRMs achieve lower MQM scores in the Koran domain, which may benefit from their explicit CoT reasoning paradigm when handling semantically ambiguous or stylistically atypical text. \nHowever, their performance lags behind in terminology-intensive domains such as Medical and Law. \nFurther analysis in Table~\\ref{tab:model_error_category_percentages} reveals that LRMs have slightly lower error rates in Accuracy, Fluency, and Non-translation Error, suggesting strong capabilities in semantic similarity and sentence structure. In contrast, they exhibit higher error rates in Style and Terminology—particularly OpenAI-o3-mini and DeepSeek-R1—reflecting weaker handling of domain-specific consistency and stylistic coherence. \n\nThese findings highlight the need for future work to further strengthen LRMs' capabilities in deep domain understanding, contextualized terminology selection, and adaptation to distinct domain-specific translation styles, to enhance their robustness in domain-diverse translation scenarios.\n\n\n\\begin{table}[t]\n\\centering\n\\small\n% \\resizebox{.5\\textwidth}{!}{%\n\\begin{tabular}{lcc}\n\\toprule\n\\multirow{2}{*}{Model} & \\multicolumn{2}{c}{Term Acc. (\\%)} \\\\\n\\cmidrule(lr){2-3}\n& Zh$\\Rightarrow$En & De$\\Rightarrow$En \\\\\n\\midrule\nGPT-4o & 52.95 & 37.67 \\\\\nDeepSeek-V3 & 51.84 & 37.92 \\\\\nGemini-2.0-Flash & 52.21 & \\textbf{38.55} \\\\\n\\hdashline\nOpenAI-o1 & 52.92 & 37.42 \\\\\nDeepSeek-R1 & \\textbf{53.20} & 37.83 \\\\\nOpenAI-o3-mini & 45.59 & 37.38 \\\\\nGemini-2.0-Flash-Thinking & 52.67 & 37.62 \\\\\n\\bottomrule\n\\end{tabular}\n% }\n\\caption{Terminology accuracy (\\%) for Zh$\\Rightarrow$En and De$\\Rightarrow$En translation tasks.}\n\\label{tab:termacc}\n\\end{table}\n\n\\subsection{Terminological Accuracy}\nIn the previous section, we observed that LRMs underperform traditional LLMs in terminology-heavy domains such as law and medicine. This observation prompted a focused investigation into their terminology translation capabilities. To this end, we conducted an evaluation based on the WMT23 Terminology Shared Task~\\cite{semenov-etal-2023-findings}, which provides bilingual sentence pairs along with corresponding term alignments.\nFor each test example, we input only the source sentence into the model and assess whether the expected target term appears correctly in the generated translation. We report terminology accuracy—the proportion of reference target terms correctly generated in the translation—in both Zh$\\Rightarrow$En and De$\\Rightarrow$En directions, as shown in Table~\\ref{tab:termacc}.\n\nIn the Zh$\\Rightarrow$En setting, LRMs demonstrate superior performance, with DeepSeek-R1 achieving the highest accuracy (53.20\\%).\nWe attribute this to LRMs’ stronger reasoning and contextual comprehension capabilities, which allow them to better resolve semantic ambiguity in Chinese and plan conceptual mappings into English, ensuring the naturalness and accuracy of terminology in the English context.\nThese results suggest that LRMs hold strong potential in terminology translation.\nIn contrast, traditional LLMs outperform LRMs in the De$\\Rightarrow$En direction, with Gemini-2.0-Flash achieving the highest accuracy (38.55\\%).\nThis may be attributed to the high lexical similarity between German and English, which allows for more direct term mapping, favoring models trained to mimic reference terms without requiring deep reasoning.\n\nTherefore, in practical applications, model selection should take into account the linguistic gap between source and target languages: LRMs are better suited for language pairs with significant structural differences (\\emph{e.g.}, Zh$\\Rightarrow$En), while traditional LLMs may be more effective for structurally similar pairs like De$\\Rightarrow$En.\n\nGiven these findings, we argue that improving the terminology translation performance of LRMs requires tighter integration between their reasoning structures and terminology modeling capabilities. Future work may explore embedding terminology constraints into intermediate reasoning chains to support explicit term-level planning. These structural and reasoning-aligned improvements are expected to unlock the potential of LRMs in terminology-intensive translation scenarios.\n\n\n\\begin{table*}[t]\n\\centering\n\\resizebox{\\textwidth}{!}{%\n\\begin{tabular}{l ccc ccc ccc ccc ccc}\n\\toprule\n\\multirow{2}{*}{Model} & \\multicolumn{3}{c}{Conversation} & \\multicolumn{3}{c}{E-commerce} & \\multicolumn{3}{c}{News} & \\multicolumn{3}{c}{Social} & \\multicolumn{3}{c}{Literary} \\\\\n\\cmidrule(lr){2-4} \\cmidrule(lr){5-7} \\cmidrule(lr){8-10} \\cmidrule(lr){11-13} \\cmidrule(lr){14-16}\n & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI \\\\\n\\midrule\n\\multicolumn{16}{c}{\\textbf{Document-Level}} \\\\\nGPT-4o & \\textbf{40.00} & 84.71 & 82.02 & 35.33 & 87.35 & 64.55 & 36.07 & 86.60 & 66.01 & 33.75 & 84.36 & 68.70 & 17.55 & \\textbf{77.57} & 77.14 \\\\\nDeepSeek-V3 & 39.28 & 84.51 & 81.98 & 36.71 & 87.26 & 63.16 & 37.59 & 86.50 & 65.35 & 36.48 & 84.80 & 67.83 & 16.81 & 77.48 & 77.17 \\\\\nGemini-2.0-Flash & 39.98 & 84.64 & 81.81 & \\textbf{37.72} & 87.34 & 62.33 & 38.68 & 85.71 & 63.68 & \\textbf{37.76} & 84.38 & 67.14 & \\textbf{18.12} & 77.30 & 76.62 \\\\\n\\hdashline\nOpenAI-o1 & 37.05 & \\textbf{84.83} & \\textbf{82.99} & 30.68 & \\textbf{87.40} & \\textbf{66.50} & 32.08 & \\textbf{86.67} & \\textbf{67.90} & 29.02 & \\textbf{84.82} & \\textbf{69.79} & 16.44 & \\textbf{77.57} & \\textbf{77.76} \\\\\nDeepSeek-R1 & 33.66 & 84.15 & 81.45 & 21.62 & 85.23 & 63.77 & 23.49 & 83.68 & 66.99 & 22.22 & 81.59 & 68.09 & 13.12 & 76.64 & 76.47 \\\\\nOpenAI-o3-mini & 36.58 & 84.52 & 82.05 & 32.13 & 87.15 & 65.18 & 33.60 & 86.22 & 65.77 & 30.93 & 84.76 & 68.87 & 17.26 & 76.87 & 76.64 \\\\\nGemini-2.0-Flash-Thinking & \\textbf{40.00} & 84.59 & 81.79 & 37.41 & 87.35 & 62.39 & \\textbf{38.82} & 86.62 & 64.19 & 36.97 & 84.55 & 66.47 & 17.77 & 77.17 & 76.58 \\\\\n\\midrule\n\\midrule\n\\multicolumn{16}{c}{\\textbf{Sentence-Level}} \\\\\nGPT-4o & \\textbf{38.68} & 87.56 & 82.00 & \\textbf{34.17} & 86.38 & 81.78 & 36.21 & 86.05 & 82.66 & 33.07 & 84.63 & 81.47 & 18.53 & \\textbf{82.96} & \\textbf{57.23} \\\\\nDeepSeek-V3 & 37.46 & 87.61 & 81.99 & 33.57 & 86.32 & 81.93 & 35.82 & 86.30 & 83.04 & 32.96 & 84.74 & 81.81 & 19.92 & 82.88 & 54.68 \\\\\nGemini-2.0-Flash & 38.01 & 87.29 & 81.87 & 33.67 & 86.36 & 81.67 & \\textbf{36.48} & 86.21 & 82.80 & \\textbf{33.24} & 84.55 & 81.49 & 23.28 & 82.80 & 53.83 \\\\\n\\hdashline\nOpenAI-o1 & 36.30 & \\textbf{87.87} & \\textbf{83.49} & 31.30 & \\textbf{86.39} & \\textbf{83.05} & 33.17 & \\textbf{86.35} & \\textbf{84.35} & 29.95 & \\textbf{84.80} & \\textbf{82.96} & 16.87 & 82.42 & 55.73 \\\\\nDeepSeek-R1 & 34.05 & 87.13 & 81.93 & 29.23 & 85.95 & 81.75 & 31.83 & 86.03 & 83.01 & 28.51 & 84.26 & 81.59 & 5.16 & 80.35 & 52.16 \\\\\nOpenAI-o3-mini & 36.33 & 86.74 & 81.67 & 32.81 & 85.92 & 81.73 & 33.95 & 85.57 & 82.43 & 31.88 & 84.07 & 81.28 & 18.06 & 82.83 & 55.16 \\\\\nGemini-2.0-Flash-Thinking & 37.07 & 86.99 & 81.68 & 33.09 & 86.17 & 81.55 & 35.70 & 86.19 & 82.81 & 32.30 & 84.16 & 81.27 & \\textbf{23.52} & 82.77 & 53.69 \\\\\n\\bottomrule\n\\end{tabular}%\n}\n\\caption{Average BLEU, COMET, and CometKiwi scores at document-level and sentence-level across five domains.}\n\\label{tab:length_analysis}\n\\end{table*}\n%length\n\\subsection{Document-Level Evaluation}\nTo investigate the impact of textual granularity on model performance, we compare sentence-level and document-level inputs across five domains, as shown in Table~\\ref{tab:length_analysis}. \nCompared to traditional LLMs, LRMs demonstrate stronger adaptability in document-level translation. They consistently achieve higher scores on COMET and CometKiwi, which focus on semantic similarity and contextual coherence. This advantage is particularly evident in long-text scenarios, where modeling inter-sentential dependencies and broader discourse structures is essential. Recent studies have shown that LRMs leverage Long CoT reasoning to enhance translation quality in complex texts, effectively modeling longer-range dependencies and maintaining coherence across extended contexts~\\cite{liu2025newtrendsmodernmachine}. In domains like Conversation and News, which naturally involve multi-turn interactions and cross-sentence semantic dependencies, LRMs are better equipped to integrate information across spans and plan discourse globally, enabling both lexical and semantic alignment in context-sensitive scenarios.\n\nThese findings highlight the advantages of LRMs for document-level translation. Their capacity for planning, reasoning, and generating globally coherent outputs makes them better suited for long-form and context-sensitive scenarios.\nFuture research could further explore the adaptive translation capabilities of LRMs across different document types, such as legal contracts, technical manuals, and literary works.\n\n\\subsection{Effect of Domain-Aware Prompt}\n\nTo further investigate the underperformance of LRMs in domain-intensive settings such as Law, Medical, and IT, we hypothesize that these models may lack sufficient domain sensitivity during translation reasoning, potentially due to the limitations imposed by the prompts in accurately guiding the model to recognize and adapt to domain-specific characteristics. Therefore, we experiment with three prompting strategies (Table~\\ref{tab:prompt_templates}): P1, where no domain-specific information is provided; P2, where explicit domain labels are given; and P3, where the model is encouraged to infer the domain and adjust its translation style accordingly.\n\nUsing DeepSeek-R1 as a representative model, we evaluate the three prompting strategies across IT, Law, and Medical domains. As shown in Table~\\ref{tab:prompt_results}, P3 achieves the highest COMET and CometKiwi scores across all domains, indicating its effectiveness in producing semantically faithful and stylistically coherent translations. \nBoth P2 and P3 significantly outperform P1 on semantic metrics, indicating that incorporating domain awareness—either explicitly or implicitly—substantially improves contextual understanding and semantic coherence.\nWhile P1 and P3 yield similar BLEU scores in the IT domain, P3 substantially outperforms P1 in Law and Medical, especially in CometKiwi, suggesting that domain-aware reasoning contributes to improved contextual understanding. \nInterestingly, P2 achieves the highest BLEU scores in the Law domain, likely likely because its explicit domain cues align closely with the rigid structure and formulaic phrasing typical of legal texts. \nHowever, P2 lags behind P3 on COMET and CometKiwi, suggesting that fixed labels may limit contextual flexibility. \nIn contrast, P3 allows the model to infer domain style from context, resulting in more fluent and semantically coherent translations.\n\nThese findings highlight the superiority of domain-adaptive prompting over static label specification in MDMT. Future work may focus on injecting domain inference into the reasoning chain, enabling LRMs to autonomously determine the domain and adjust their translation accordingly.\n\n\\begin{table}[t]\n\\centering\n\\resizebox{.48\\textwidth}{!}{%\n\\begin{tabular}{@{}ll@{}}\n\\toprule\n\\textbf{ID} & \\textbf{Prompt} \\\\\n\\midrule\n\\textbf{P1} & Translate the following \\texttt{\\{src\\_lang\\}} sentence into \\texttt{\\{tgt\\_lang\\}}. \\\\\n\\textbf{P2} & Given the \\texttt{\\{src\\_lang\\}} sentence from a \\texttt{\\{domain\\}}-related text, \\\\\n & translate it into \\texttt{\\{tgt\\_lang\\}} in a \\texttt{\\{domain\\}} domain style. \\\\\n\\textbf{P3} & Translate the following \\texttt{\\{src\\_lang\\}} text into \\texttt{\\{tgt\\_lang\\}} while \\\\\n & maintaining the domain style of the source text. \\\\\n\\bottomrule\n\\end{tabular}\n}\n\\caption{Prompts used in domain-aware translation.}\n\\label{tab:prompt_templates}\n\\end{table}\n\n\\begin{table}[t]\n\\centering\n\\resizebox{\\linewidth}{!}{%\n\\begin{tabular}{l ccc ccc ccc}\n\\toprule\n\\multirow{2}{*}{Prompt} & \\multicolumn{3}{c}{IT} & \\multicolumn{3}{c}{Law} & \\multicolumn{3}{c}{Medical} \\\\\n\\cmidrule(lr){2-4} \\cmidrule(lr){5-7} \\cmidrule(lr){8-10}\n& BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI \\\\\n\\midrule\nP1 & \\textbf{36.66} & 83.49 & 78.68 & 39.16 & 84.97 & 82.35 & 40.23 & 83.82 & 81.81 \\\\\nP2 & 34.20 & 82.94 & 79.33 & \\textbf{41.46} & 85.01 & 83.46 & 38.92 & 83.38 & 82.57 \\\\\nP3 & 36.55 & \\textbf{83.57} & \\textbf{79.82} & 40.57 & \\textbf{85.32} & \\textbf{83.54} & \\textbf{41.14} & \\textbf{84.06} & \\textbf{82.97} \\\\\n\\bottomrule\n\\end{tabular}\n}\n\\caption{Translation performance across IT, Law, and Medical domains under different prompt strategies.}\n\\label{tab:prompt_results}\n\\end{table}\n\n\n\\begin{table*}[t]\n\\centering\n\\resizebox{\\textwidth}{!}{%\n\\begin{tabular}{l ccc ccc ccc ccc ccc}\n\\toprule\n\\multirow{2}{*}{Model} & \\multicolumn{3}{c}{Level 1} & \\multicolumn{3}{c}{Level 2} & \\multicolumn{3}{c}{Level 3} & \\multicolumn{3}{c}{Level 4} & \\multicolumn{3}{c}{Level 5} \\\\\n\\cmidrule(lr){2-4} \\cmidrule(lr){5-7} \\cmidrule(lr){8-10} \\cmidrule(lr){11-13} \\cmidrule(lr){14-16}\n & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI \\\\\n\\midrule\nGPT-4o & \\textbf{41.96} & \\textbf{85.13} & 79.80 & 30.08 & 84.88 & 82.15 & 26.49 & 82.75 & 80.91 & 22.61 & 83.11 & 75.48 & 24.94 & 83.59 & 67.11 \\\\\nDeepSeek-V3 & 40.81 & 85.09 & 79.90 & 27.90 & 84.86 & 82.20 & 25.43 & 82.54 & 80.97 & 22.57 & 82.95 & 75.30 & 25.06 & 83.30 & 67.64 \\\\\nGemini-2.0-Flash & 41.15 & 84.39 & 79.46 & 29.22 & 84.93 & 81.96 & 27.53 & 82.80 & 80.68 & 24.53 & 82.72 & 74.57 & 28.93 & 83.91 & 66.28 \\\\\n\\hdashline\nOpenAI-o1 & 40.34 & 84.59 & \\textbf{81.10} & 29.67 & 84.88 & \\textbf{83.28} & 25.76 & \\textbf{82.94} & \\textbf{81.94} & 21.99 & \\textbf{83.31} & \\textbf{76.92} & 22.78 & \\textbf{84.02} & \\textbf{69.62} \\\\\nDeepSeek-R1 & 41.43 & 84.33 & 79.86 & 27.12 & 84.25 & 81.75 & 24.32 & 82.15 & 80.44 & 18.66 & 82.17 & 75.27 & 19.53 & 82.43 & 68.78 \\\\\nOpenAI-o3-mini & 40.74 & 84.12 & 79.44 & 26.49 & 84.56 & 82.14 & 24.93 & 82.36 & 80.73 & 21.09 & 82.67 & 75.37 & 22.70 & 83.27 & 67.63 \\\\\nGemini-2.0-Flash-Thinking & 39.87 & 83.84 & 79.36 & \\textbf{30.78} & \\textbf{84.98} & 82.21 & \\textbf{28.15} & 82.68 & 80.62 & \\textbf{24.54} & 83.02 & 74.71 & \\textbf{28.97} & 83.93 & 66.32 \\\\\n\\bottomrule\n\\end{tabular}%\n}\n\\caption{Performance across different levels of complexity, comparing LRMs and tranditional LLMs.}\n\\label{tab:metrics_by_level_avg}\n\\end{table*}\n% difficulty\n\\subsection{Impact of Translation Difficulty}\nIn recent years, researchers have proposed various reasoning-enhanced strategies to improve the performance of LLMs on complex tasks~\\cite{wei2022chain, yao2023tree, zhou2022least}. These approaches focus on guiding models through structured intermediate reasoning to better capture semantic and logical complexity. When extended to the field of MT, we hypothesize that incorporating mechanisms such as problem decomposition, reasoning path exploration, and stepwise inference can enhance a model’s generalization ability in tasks with high semantic load. The LRMs exemplify this reasoning-oriented design philosophy. These models are trained with structured cognitive capabilities, which allows them to operate more robustly in linguistically complex translation scenarios.\n\nTo systematically assess the impact of translation difficulty on model performance, we use DeepSeek-V3 to rate the complexity of source sentences drawn from three datasets: Multi-Domain (De$\\Rightarrow$En), WMT22 (De$\\Leftrightarrow$En and Zh$\\Leftrightarrow$En), and Guofeng WebNovel (Zh$\\Rightarrow$En). These sentences are classified into five difficulty levels, ranging from Level 1, representing the easiest, to Level 5, representing the most difficult. \nThe prompt used for difficulty assessment is provided in Figure~\\ref{fig:difficulty_eval}. \nAs shown in Table~\\ref{tab:metrics_by_level_avg}, traditional LLMs perform well on simpler inputs, especially at Level 1 where they achieve high BLEU and COMET scores. \nHowever, their performance declines significantly as difficulty increases. Starting at Level 2, they are consistently outperformed by LRMs. \nIn particular, OpenAI-o1 and Gemini-2.0-Flash-Thinking demonstrate stronger robustness and semantic consistency under higher difficulty conditions, maintaining clear advantages on metrics such as COMET and CometKiwi.\nThis trend supports our hypothesis that LRMs are better equipped to model and generalize in semantically dense and contextually demanding translation tasks.\n\nFuture research should further investigate how LRMs adapt to varying levels of translation difficulty.\nFor example, LRMs should generate accurate translations efficiently with minimal reasoning for simple inputs to avoid unnecessary overthinking~\\cite{chen2025overthinking}, whereas for more complex sentences, they should engage in deeper analysis and structured reasoning to ensure semantic accuracy and completeness.\n\n% token Consumption Comparison\n\\subsection{Token Consumption Comparison}\nWe compare the average token usage of traditional LLMs and LRMs on the UM-Corpus (En$\\Rightarrow$Zh) multi-domain test sets to assess translation efficiency. As shown in Table~\\ref{tab:um_results_averages}, LRMs generate significantly longer outputs than traditional LLMs. While they achieve strong CometKiwi scores, their lower performance on COMET and BLEU suggests that LRMs may \"overthink\" during generation~\\cite{chen2025overthinking}. In MDMT, future work could explore adapting reasoning depth to translation complexity to reduce overthinking.\n\\begin{table}[t]\n\\centering\n\\resizebox{\\linewidth}{!}{%\n\\begin{tabular}{l c c c c}\n\\toprule\n\\textbf{Model} & \\textbf{BLEU} & \\textbf{COMET} & \\textbf{KIWI} & \\textbf{Avg. Len.} \\\\\n\\midrule\nGPT-4o & 37.40 & 86.74 & 84.61 & 31.76 \\\\\nDeepSeek-V3 & \\textbf{43.34} & \\textbf{87.76} & 85.04 & \\textbf{29.63} \\\\\nGemini-2.0-Flash & 40.30 & 87.05 & 84.80 & 32.71 \\\\\n\\hdashline\nOpenAI-o1 & 36.17 & 87.11 & \\textbf{85.09} & 402.64 \\\\\nDeepSeek-R1 & 41.12 & 87.50 & 85.03 & 485.05 \\\\\nOpenAI-o3-mini & 34.60 & 86.69 & 84.98 & 424.31 \\\\\nGemini-2.0-Flash-Thinking & 39.17 & 86.63 & 84.37 & 1043.59 \\\\\n\\bottomrule\n\\end{tabular}%\n}\n\\caption{Average BLEU, COMET, KIWI scores, and response length for various LLMs.}\n\\label{tab:um_results_averages}\n\\end{table}\n% case study\n\\subsection{Case Study}\nWe present a case from the \\textit{lexical\\_ambiguity} subset of the CommonMT (Zh$\\Rightarrow$En) test set. The source sentence contains a figurative expression referring to the act of leaving a stable job to pursue private business opportunities. DeepSeek-R1 accurately captures this meaning and generates: \\textit{“In the 1990s, many people ventured into business,”} which matches the reference exactly. Compared to alternatives such as “went into business,” R1’s choice of “ventured” better reflects the intended nuance, demonstrating its ability to resolve lexical ambiguity through contextual reasoning.\n\n\\begin{figure}[t]\n \\centering\n \\includegraphics[width=\\linewidth]{figure/case_study_figure.pdf}\n \\caption{Case study of DeepSeek-R1 showing its reasoning process and final translation. }\n \\label{fig:case_study}\n\\end{figure}\n\n\\section{Conclusion}\n\nThis paper presents a comprehensive evaluation of LRMs in MDMT. Through systematic comparison with traditional LLMs across 15 domains and four translation directions, we observe that LRMs achieve superior performance in semantically challenging and long-form translation tasks, as reflected in meaning-oriented metrics such as COMET and CometKiwi. However, their performance declines in terminology-intensive and stylistically constrained domains, where traditional LLMs exhibit stronger lexical precision and structural fidelity. Our prompt-based analysis further demonstrates the advantage of domain-adaptive prompting in improving contextual alignment. These findings suggest that future work should focus on integrating structured reasoning with domain-specific constraints to enhance the reliability and controllability of LRMs in real-world translation scenarios.\n\n\\section*{Limitations}\n\nDespite providing a comprehensive evaluation of LRMs in MDMT, our study has several limitations. First, the evaluation is restricted to high-resource language pairs, which may limit the generalizability of our findings to low-resource or typologically distant languages. Second, our automatic MQM annotations rely on a single model as the scorer, which ensures consistency but may introduce bias aligned with that model’s own translation behavior. \n\n% \\input{acl_latex.bbl}\n\\bibliography{acl_latex}\n\n\n\\appendix\n\\newpage\n\n\\section{Details of Multi-domain Test Sets}\n\\label{appendix:dataset}\nWe compile a collection of publicly available translation test sets covering diverse domains and four translation directions: De$\\Rightarrow$En, En$\\Rightarrow$De, En$\\Rightarrow$Zh, and Zh$\\Rightarrow$En. The datasets span general-purpose and domain-specific content, including biomedical, legal, IT, conversational, and literary texts, etc. Sample sizes for each domain are reported in Tables~\\ref{tab:test_zh-en} to~\\ref{tab:test_en-de}, with no additional filtering or augmentation applied.\n\n\\begin{table}[t]\n\\centering\n\\small\n% \\resizebox{.5\\textwidth}{!}{%\n\\begin{tabular}{lc lc}\n\\toprule\n\\textbf{Test set} & \\textbf{Num} & \\textbf{Test set} & \\textbf{Num} \\\\\n\\midrule\nConversation & 349 & Social & 491 \\\\\nE-commerce & 518 & News (WMT22) & 499 \\\\\nBio (WMT18) & 212 & Bio (WMT19) & 243 \\\\\nCommonsense & 1200 & Literary & 3038 \\\\\nTerminology & 1638 & Mixed (WMT22) & 1875 \\\\\n\\bottomrule\n\\end{tabular}%\n% }\n\\caption{Test sets and the number of samples for Zh$\\Rightarrow$En translation tasks. Bio denotes the Biomedical domain.}\n\\label{tab:test_zh-en}\n\\end{table}\n\n\n\\begin{table}[t]\n\\centering\n\\small\n% \\resizebox{.5\\textwidth}{!}{%\n\\begin{tabular}{lc lc}\n\\toprule\n\\textbf{Test set} & \\textbf{Num} & \\textbf{Test set} & \\textbf{Num} \\\\\n\\midrule\nCulture & 778 & Laws & 456 \\\\\nSubtitles & 597 & Science & 503 \\\\\nConversation & 484 & Social & 511 \\\\\nE-commerce & 530 & News (WMT22) & 511 \\\\\nBio (WMT18) & 244 & Bio (WMT19) & 224 \\\\\nNews (UM) & 1500 & Mixed & 2037 \\\\\n\\bottomrule\n\\end{tabular}%\n% }\n\\caption{Test sets and the number of samples for En$\\Rightarrow$Zh translation tasks.}\n\\label{tab:test_en-zh}\n\\end{table}\n\n\n\\begin{table}[t]\n\\centering\n\\small\n% \\resizebox{.5\\textwidth}{!}{%\n\\begin{tabular}{lc lc}\n\\toprule\n\\textbf{Test set} & \\textbf{Num} & \\textbf{Test set} & \\textbf{Num} \\\\\n\\midrule\nBio (WMT18) & 217 & Bio (WMT19) & 373 \\\\\nSocial & 515 & E-commerce & 501 \\\\\nNews (WMT22) & 506 & Conversation & 462 \\\\\nIT & 2000 & Law & 2000 \\\\\nKoran & 2000 & Subtitles & 2000 \\\\\nMedical & 2000 & Terminology & 2955 \\\\\nMixed & 1984 & & \\\\\n\\bottomrule\n\\end{tabular}%\n% }\n\\caption{Test sets and the number of samples for De$\\Rightarrow$En translation tasks.}\n\\label{tab:test_de-en}\n\\end{table}\n\n\n\\begin{table}[t]\n\\centering\n\\small\n% \\resizebox{.5\\textwidth}{!}{%\n\\begin{tabular}{lc lc}\n\\toprule\n\\textbf{Test set} & \\textbf{Num} & \\textbf{Test set} & \\textbf{Num} \\\\\n\\midrule\nBio (WMT18) & 220 & Bio (WMT19) & 435 \\\\\nNews (WMT22) & 511 & E-commerce & 530 \\\\\nSocial & 512 & Conversation & 484 \\\\\nMixed & 2037 & & \\\\\n\\bottomrule\n\\end{tabular}%\n% }\n\\caption{Test sets and the number of samples for En$\\Rightarrow$De translation tasks.}\n\\label{tab:test_en-de}\n\\end{table}\n\n\n\\section{MQM}\n\\label{appendix:mqm}\nTo evaluate translation quality across domains and systems, we adopt an enhanced MQM hierarchy tailored to the characteristics of LLMs. Based on the official MQM taxonomy, we introduce additional error types frequently observed in LLM outputs—such as hallucination, semantic repetition, and cross-domain confusion. Our final schema spans seven dimensions: Accuracy, Fluency, Style, Terminology, Others, Source Error, and Non-translation Error, covering 25 fine-grained error categories (Table~\\ref{tab:mqm-hierarchy}).\n\nFor consistent and scalable annotation, we employ DeepSeek-V3 as the automatic scoring model across all evaluations. It is applied uniformly to both traditional and LRMs to ensure fair comparison. The model follows a structured prompt designed to mimic human assessment while enforcing strict formatting and error attribution rules. The full prompt is shown in Figure~\\ref{fig:mqm-prompt}.\n\n\\begin{table*} [t]\n \\centering\n \\small\n \\begin{tabularx}{\\textwidth}{l l X}\n \\toprule\n \\textbf{Category} & \\textbf{Error Type} & \\textbf{Description} \\\\\n \\midrule\n \\multirow{8}{*}{Accuracy} \n & Mistranslation & Inaccurate translation causing semantic distortion. \\\\\n & Addition & Adding extra information or emotions not in the source.\\\\\n & Under-translation & Failure to fully convey cultural or contextual nuances. \\\\\n & Omission & Unintentional exclusion of content from the source text. \\\\\n & Untranslated & Retaining source text without translation. \\\\\n & Hallucination & Generating content unrelated to the source text. \\\\\n & Off-target Translation & Translation misalignment caused by ambiguous input. \\\\ \n & Contradiction & Contradicting itself or the source text. \\\\ \n \\midrule\n \\multirow{5}{*}{Fluency} \n & Grammar & Errors in sentence structure or syntax. \\\\\n & Punctuation & Incorrect use of punctuation marks. \\\\\n & Spelling & Misspelling of words. \\\\\n & Semantic Repetition & Unnecessary repetition of words or phrases. \\\\\n & Logical Incoherence & Lack of logical flow or coherence in translation. \\\\\n \\midrule\n \\multirow{4}{*}{Style} \n & Awkward Expression & Stilted or unnatural phrasing in the target language. \\\\\n & Unidiomatic Usage & Literal translation causes unnatural wording or grammar. \\\\\n & Style Inconsistency & Inconsistent stylistic choices within the translation. \\\\\n & Over-localization & Excessive cultural adaptation leading to distortion. \\\\\n \\midrule\n \\multirow{5}{*}{Terminology} \n & Terminology Inconsistency & Inconsistent translation of the same term. \\\\\n & Terminology Misuse & Use of incorrect or inappropriate terms for the domain. \\\\\n & Cross-domain Confusion & Misuse of terms due to domain shifts. \\\\\n & Incorrect Unit Conversion & Errors in unit conversion (e.g., metric to imperial). \\\\ \n & Formatting & Errors in domain-specific formats (e.g., legal, medical). \\\\ \n \\midrule\n \\multirow{1}{*}{Others} \n & & Any other errors not covered in the above categories. \\\\\n \\midrule\n \\multirow{1}{*}{Source Error} \n & & Errors present in the Source text itself. \\\\\n \\midrule\n \\multirow{1}{*}{Non-translation Error} \n & & Translation is unassessable and unrelated to the Source. \\\\\n \\bottomrule\n \\end{tabularx}\n \\caption{\\label{tab:mqm-hierarchy} MQM Hierarchy.}\n\\end{table*}\n\n\n\\section{Evaluation Results}\n\nWe report detailed automatic evaluation results across all domains and translation directions using BLEU, COMET, and CometKiwi. These results are summarized in Tables~\\ref{tab:result_culture_mt} through~\\ref{tab:result_wmt22}, covering all test sets introduced in our main experiments. Each table corresponds to a specific dataset or language direction, facilitating fine-grained comparison across models.\n\n\\begin{table}[t]\n\\centering\n\\resizebox{\\linewidth}{!}{%\n\\begin{tabular}{l ccc}\n\\toprule\n\\multirow{2}{*}{Model} & \\multicolumn{3}{c}{Culture} \\\\\n\\cmidrule(lr){2-4}\n & BLEU & COMET & KIWI \\\\\n\\midrule\nGPT-4o & 38.65 & 85.24 & 83.14 \\\\\nDeepSeek-V3 & \\textbf{40.27} & \\textbf{85.46} & 83.21 \\\\\nGemini-2.0-Flash & 39.11 & 85.02 & 82.94 \\\\\n\\hdashline\nOpenAI-o1 & 34.57 & 85.06 & \\textbf{84.25} \\\\\nDeepSeek-R1 & 35.88 & 85.45 & 83.14 \\\\\nOpenAI-o3-mini & 34.05 & 84.36 & \\textbf{84.25} \\\\\nGemini-2.0-Flash-Thinking & 36.94 & 84.63 & 83.68 \\\\\n\\bottomrule\n\\end{tabular}\n}\n\\caption{Results on the En$\\Rightarrow$Zh Culture-MT dataset.}\n\\label{tab:result_culture_mt}\n\\end{table}\n\\begin{table*}[h]\n\\centering\n\\resizebox{\\textwidth}{!}{%\n\\begin{tabular}{lccc ccc ccc ccc}\n\\toprule\n\\multirow{2}{*}{Model} \n& \\multicolumn{3}{c}{Contextual Syntactic} \n& \\multicolumn{3}{c}{Contextless Syntactic} \n& \\multicolumn{3}{c}{Lexical} \n& \\multicolumn{3}{c}{Average} \\\\\n\\cmidrule(lr){2-4} \\cmidrule(lr){5-7} \\cmidrule(lr){8-10} \\cmidrule(lr){11-13}\n& BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI \\\\\n\\midrule\nGPT-4o & 31.42 & \\textbf{85.11} & 81.88 & 32.82 & 85.24 & 78.35 & 33.89 & 85.41 & 79.83 & 32.71 & 85.25 & 80.02 \\\\\nDeepSeek-V3 & 30.19 & 84.90 & 81.91 & \\textbf{33.53} & \\textbf{85.58} & 78.76 & \\textbf{35.09} & \\textbf{85.52} & 79.69 & \\textbf{32.94} & \\textbf{85.33} & 80.12 \\\\\nGemini-2.0-Flash & 31.15 & 84.86 & 81.79 & 31.69 & 85.20 & 77.99 & 32.13 & 84.03 & 78.84 & 31.66 & 84.70 & 79.54 \\\\\n\\hdashline\nOpenAI-o1 & 29.80 & 84.74 & 80.66 & 28.04 & 85.14 & 79.48 & 30.40 & 84.00 & 79.70 & 28.50 & 84.96 & \\textbf{81.11} \\\\\nDeepSeek-R1 & 26.28 & 84.54 & \\textbf{82.07} & 30.00 & 84.96 & 78.62 & 30.40 & 84.00 & 79.70 & 28.89 & 84.50 & 80.13 \\\\\nOpenAI-o3-mini & 28.55 & 84.57 & 79.19 & 27.18 & 84.43 & \\textbf{82.97} & 29.43 & 83.82 & \\textbf{80.87} & 28.39 & 84.27 & 81.01 \\\\\nGemini-2.0-Flash-Thinking & \\textbf{31.54} & 84.61 & 81.96 & 31.60 & 84.34 & 77.66 & 33.49 & 84.59 & 79.95 & 32.21 & 84.51 & 79.86 \\\\\n\\bottomrule\n\\end{tabular}\n}\n\\caption{Results on the Zh$\\Rightarrow$En Commonsense-MT dataset.}\n\\label{tab:zh-en-ambiguity}\n\\end{table*}\n\\begin{table*}[h]\n\\centering\n\\resizebox{\\textwidth}{!}{%\n\\begin{tabular}{l ccc ccc ccc ccc}\n\\toprule\n\\multirow{2}{*}{Model} & \\multicolumn{3}{c}{News} & \\multicolumn{3}{c}{Laws} & \\multicolumn{3}{c}{Science} & \\multicolumn{3}{c}{Subtitles} \\\\\n\\cmidrule(lr){2-4} \\cmidrule(lr){5-7} \\cmidrule(lr){8-10} \\cmidrule(lr){11-13}\n & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI \\\\\n\\midrule\nGPT-4o & 34.58 & 86.22 & 85.02 & 58.00 & 91.50 & 86.65 & 32.09 & 87.34 & 84.84 & \\textbf{24.91} & 81.91 & 81.93 \\\\\nDeepSeek-V3 & 35.86 & \\textbf{86.94} & 85.44 & \\textbf{80.33} & \\textbf{93.36} & 86.55 & \\textbf{33.54} & \\textbf{88.28} & 85.46 & 23.66 & \\textbf{82.44} & 82.69 \\\\\nGemini-2.0-Flash & \\textbf{36.39} & 86.37 & 85.14 & 66.71 & 92.23 & 86.49 & 33.47 & 87.69 & 85.24 & 24.64 & 81.91 & 82.33 \\\\\n\\hdashline\nOpenAI-o1 & 33.93 & 86.61 & \\textbf{85.47} & 55.48 & 91.61 & 86.73 & 32.32 & 87.92 & \\textbf{85.56} & 22.96 & 82.31 & 82.60 \\\\\nDeepSeek-R1 & 33.35 & 86.67 & 85.40 & 78.81 & 93.16 & 86.40 & 31.82 & 88.08 & 85.47 & 20.50 & 82.10 & \\textbf{82.84} \\\\\nOpenAI-o3-mini & 32.90 & 86.29 & 85.35 & 51.18 & 91.15 & \\textbf{86.92} & 31.38 & 87.42 & 85.24 & 22.95 & 81.90 & 82.40 \\\\\nGemini-2.0-Flash-Thinking & 33.90 & 86.02 & 84.79 & 66.99 & 92.12 & 86.40 & 31.98 & 87.47 & 85.06 & 23.83 & 80.90 & 81.22 \\\\\n\\bottomrule\n\\end{tabular}%\n}\n\\caption{Results on the En$\\Rightarrow$Zh UM-Corpus dataset.}\n\\label{tab:result_um_domains}\n\\end{table*}\n\\begin{table*}[h]\n\\centering\n\\resizebox{\\textwidth}{!}{%\n\\begin{tabular}{l ccc ccc ccc ccc ccc}\n\\toprule\n\\multirow{2}{*}{Model} & \\multicolumn{3}{c}{Conversation} & \\multicolumn{3}{c}{E-commerce} & \\multicolumn{3}{c}{News} & \\multicolumn{3}{c}{Social} & \\multicolumn{3}{c}{Average} \\\\\n\\cmidrule(lr){2-4} \\cmidrule(lr){5-7} \\cmidrule(lr){8-10} \\cmidrule(lr){11-13} \\cmidrule(lr){14-16}\n & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI \\\\\n\\midrule\nGPT-4o & 31.28 & 84.86 & 79.83 & 19.94 & 81.75 & 78.24 & 25.21 & \\textbf{83.04} & 81.10 & 27.29 & \\textbf{83.77} & 80.64 & 25.93 & \\textbf{83.36} & 79.95 \\\\\nDeepSeek-V3 & 30.26 & \\textbf{84.96} & 79.67 & 19.47 & 81.55 & 78.32 & 25.77 & 83.00 & 81.18 & 27.55 & 83.56 & 80.64 & 25.76 & 83.27 & 79.95 \\\\\nGemini-2.0-Flash & \\textbf{32.79} & 84.46 & 79.19 & \\textbf{19.97} & 81.54 & 77.90 & \\textbf{26.62} & \\textbf{83.04} & 80.93 & \\textbf{29.25} & 83.24 & 80.16 & \\textbf{27.16} & 83.07 & 79.55 \\\\\n\\hdashline\nOpenAI-o1 & 29.52 & 84.95 & \\textbf{80.90} & 17.82 & \\textbf{81.81} & 79.16 & 22.52 & 82.96 & \\textbf{82.10} & 23.85 & 83.20 & \\textbf{81.39} & 23.43 & 83.23 & \\textbf{80.89} \\\\\nDeepSeek-R1 & 26.82 & 84.61 & 79.40 & 15.63 & 80.98 & 77.87 & 21.21 & 82.34 & 80.95 & 22.18 & 82.69 & 80.05 & 21.46 & 82.66 & 79.57 \\\\\nOpenAI-o3-mini & 30.57 & 84.25 & 79.48 & 18.80 & 81.29 & 78.23 & 23.68 & 82.88 & 81.16 & 26.98 & 83.34 & 80.61 & 25.01 & 82.94 & 79.87 \\\\\nGemini-2.0-Flash-Thinking & 30.98 & 84.17 & 79.28 & 19.06 & 81.21 & 77.76 & 25.50 & 82.90 & 80.85 & 27.54 & 83.04 & 80.13 & 25.77 & 82.83 & 79.50 \\\\\n\\bottomrule\n\\end{tabular}%\n}\n\\caption{Results on the Zh$\\Rightarrow$En WMT22 General Machine Translation task.}\n\\end{table*}\n\\begin{table*}[h]\n\\centering\n\\resizebox{\\textwidth}{!}{%\n\\begin{tabular}{l ccc ccc ccc ccc ccc}\n\\toprule\n\\multirow{2}{*}{Model} & \\multicolumn{3}{c}{Conversation} & \\multicolumn{3}{c}{E-commerce} & \\multicolumn{3}{c}{News} & \\multicolumn{3}{c}{Social} & \\multicolumn{3}{c}{Average} \\\\\n\\cmidrule(lr){2-4} \\cmidrule(lr){5-7} \\cmidrule(lr){8-10} \\cmidrule(lr){11-13} \\cmidrule(lr){14-16}\n & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI \\\\\n\\midrule\nGPT-4o & \\textbf{45.63} & 89.44 & 82.67 & \\textbf{43.82} & 88.82 & 83.52 & 51.20 & 88.31 & 83.41 & \\textbf{38.60} & 84.57 & 81.33 & \\textbf{44.81} & 87.78 & 82.73 \\\\\nDeepSeek-V3 & 43.49 & 89.89 & 82.94 & 42.84 & 89.00 & 83.79 & 50.28 & \\textbf{88.75} & 83.76 & 37.59 & \\textbf{85.27} & 82.24 & 43.55 & \\textbf{88.23} & 83.18 \\\\\nGemini-2.0-Flash & 44.59 & \\textbf{90.19} & 83.01 & 42.95 & 88.90 & 83.47 & \\textbf{52.12} & 88.46 & 83.28 & 38.11 & 84.71 & 81.59 & 44.44 & 88.06 & 82.84 \\\\\n\\hdashline\nOpenAI-o1 & 42.24 & 89.86 & \\textbf{84.13} & 39.22 & \\textbf{89.06} & \\textbf{84.59} & 45.88 & 88.47 & \\textbf{84.67} & 33.41 & 85.07 & \\textbf{82.98} & 40.19 & 88.11 & \\textbf{84.09} \\\\\nDeepSeek-R1 & 38.00 & 88.74 & 82.74 & 34.48 & 88.35 & 83.48 & 42.56 & 88.08 & 83.58 & 29.43 & 84.21 & 82.01 & 36.12 & 87.34 & 82.95 \\\\\nOpenAI-o3-mini & 43.24 & 89.02 & 82.44 & 41.54 & 88.51 & 83.41 & 47.06 & 87.53 & 82.94 & 36.38 & 83.99 & 81.27 & 42.05 & 87.26 & 82.52 \\\\\nGemini-2.0-Flash-Thinking & 44.31 & 89.71 & 82.66 & 42.39 & 88.73 & 83.31 & 49.54 & 88.23 & 83.26 & 36.35 & 83.86 & 81.08 & 43.15 & 87.63 & 82.58 \\\\\n\\bottomrule\n\\end{tabular}%\n}\n\\caption{Results on the En$\\Rightarrow$Zh WMT22 General Machine Translation task.}\n\\end{table*}\n\\begin{table*}[h]\n\\centering\n\\resizebox{\\textwidth}{!}{%\n\\begin{tabular}{l ccc ccc ccc ccc ccc}\n\\toprule\n\\multirow{2}{*}{Model} & \\multicolumn{3}{c}{Conversation} & \\multicolumn{3}{c}{E-commerce} & \\multicolumn{3}{c}{News} & \\multicolumn{3}{c}{Social} & \\multicolumn{3}{c}{Average} \\\\\n\\cmidrule(lr){2-4} \\cmidrule(lr){5-7} \\cmidrule(lr){8-10} \\cmidrule(lr){11-13} \\cmidrule(lr){14-16}\n & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI \\\\\n\\midrule\nGPT-4o & \\textbf{40.65} & 89.88 & 83.72 & \\textbf{38.03} & 88.80 & 84.02 & \\textbf{36.70} & 87.35 & 84.00 & 34.06 & 85.12 & 82.63 & \\textbf{37.36} & 87.79 & 83.59 \\\\\nDeepSeek-V3 & 39.58 & 89.33 & 83.50 & 37.42 & 88.69 & 84.14 & 36.48 & 87.94 & 84.76 & \\textbf{35.08} & 85.22 & 82.91 & 37.14 & 87.79 & 83.83 \\\\\nGemini-2.0-Flash & 37.88 & 88.46 & 83.36 & 37.47 & \\textbf{88.98} & 84.09 & 35.88 & 87.69 & 84.63 & 34.02 & 85.39 & 82.84 & 36.31 & 87.63 & 83.73 \\\\\n\\hdashline\nOpenAI-o1 & 38.71 & \\textbf{90.19} & \\textbf{85.55} & 36.25 & 88.73 & \\textbf{85.79} & 33.90 & \\textbf{88.17} & \\textbf{86.69} & 32.18 & \\textbf{85.73} & \\textbf{84.54} & 35.26 & \\textbf{88.21} & \\textbf{85.64} \\\\\nDeepSeek-R1 & 36.17 & 89.11 & 83.60 & 34.70 & 88.73 & 84.27 & 33.96 & 87.92 & 84.87 & 32.39 & 85.22 & 82.85 & 34.31 & 87.75 & 83.90 \\\\\nOpenAI-o3-mini & 35.35 & 88.05 & 83.21 & 36.71 & 88.28 & 84.01 & 34.01 & 87.16 & 84.31 & 32.14 & 84.29 & 82.25 & 34.55 & 86.95 & 83.44 \\\\\nGemini-2.0-Flash-Thinking & 36.64 & 88.04 & 83.00 & 37.87 & 88.97 & 83.98 & 36.18 & 87.81 & 84.71 & 33.84 & 85.00 & 82.59 & 36.13 & 87.45 & 83.57 \\\\\n\\bottomrule\n\\end{tabular}%\n}\n\\caption{Results on the En$\\Rightarrow$De WMT22 General Machine Translation task.}\n\\end{table*}\n\\begin{table*}[h]\n\\centering\n\\resizebox{\\textwidth}{!}{%\n\\begin{tabular}{l ccc ccc ccc ccc ccc}\n\\toprule\n\\multirow{2}{*}{Model} & \\multicolumn{3}{c}{Conversation} & \\multicolumn{3}{c}{E-commerce} & \\multicolumn{3}{c}{News} & \\multicolumn{3}{c}{Social} & \\multicolumn{3}{c}{Average} \\\\\n\\cmidrule(lr){2-4} \\cmidrule(lr){5-7} \\cmidrule(lr){8-10} \\cmidrule(lr){11-13} \\cmidrule(lr){14-16}\n & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI \\\\\n\\midrule\nGPT-4o & \\textbf{37.14} & 86.07 & 81.79 & \\textbf{34.87} & \\textbf{86.15} & 81.37 & \\textbf{31.74} & 85.49 & 82.14 & \\textbf{32.35} & 85.07 & 81.27 & \\textbf{34.02} & 85.69 & 81.64 \\\\\nDeepSeek-V3 & 36.52 & 86.24 & 81.84 & 34.56 & 86.04 & 81.46 & 30.76 & 85.52 & 82.47 & 31.65 & 84.93 & 81.43 & 33.37 & 85.68 & 81.80 \\\\\nGemini-2.0-Flash & 36.76 & 86.05 & 81.92 & 34.30 & 86.02 & 81.23 & 31.28 & 85.64 & 82.35 & 31.57 & 84.88 & 81.37 & 33.48 & 85.65 & 81.72 \\\\\n\\hdashline\nOpenAI-o1 & 34.73 & \\textbf{86.49} & \\textbf{83.40} & 31.91 & 85.95 & \\textbf{82.65} & 30.35 & 85.78 & \\textbf{83.93} & 30.36 & \\textbf{85.22} & \\textbf{82.93} & 31.84 & \\textbf{85.86} & \\textbf{83.23} \\\\\nDeepSeek-R1 & 35.20 & 86.06 & 81.96 & 32.11 & 85.75 & 81.39 & 29.59 & 85.78 & 82.66 & 30.04 & 84.92 & 81.45 & 31.73 & 85.63 & 81.86 \\\\\nOpenAI-o3-mini & 36.16 & 85.66 & 81.54 & 34.20 & 85.59 & 81.26 & 31.07 & 84.71 & 81.30 & 32.02 & 84.66 & 81.00 & 33.36 & 85.16 & 81.28 \\\\\nGemini-2.0-Flash-Thinking & 36.35 & 86.05 & 81.79 & 33.05 & 85.77 & 81.14 & 31.59 & \\textbf{85.83} & 82.42 & 31.48 & 84.76 & 81.28 & 33.12 & 85.60 & 81.66 \\\\\n\\bottomrule\n\\end{tabular}%\n}\n\\caption{Results on the De$\\Rightarrow$En WMT22 General Machine Translation task.}\n\\end{table*}\n\\begin{table*}[h]\n\\centering\n\\resizebox{\\textwidth}{!}{%\n\\begin{tabular}{l ccc ccc ccc ccc ccc}\n\\toprule\n\\multirow{2}{*}{Model} & \\multicolumn{3}{c}{WMTBio18 En$\\Rightarrow$Zh} & \\multicolumn{3}{c}{WMTBio18 Zh$\\Rightarrow$En} & \\multicolumn{3}{c}{WMTBio18 En$\\Rightarrow$De} & \\multicolumn{3}{c}{WMTBio18 De$\\Rightarrow$En} & \\multicolumn{3}{c}{Average} \\\\\n\\cmidrule(lr){2-4} \\cmidrule(lr){5-7} \\cmidrule(lr){8-10} \\cmidrule(lr){11-13} \\cmidrule(lr){14-16}\n & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI \\\\\n\\midrule\nGPT-4o & \\textbf{40.86} & 86.66 & 84.61 & 25.04 & 84.33 & 84.02 & 24.44 & 86.68 & 84.97 & 29.31 & 85.06 & 82.61 & 29.91 & 85.68 & 84.05 \\\\\nDeepSeek-V3 & 40.58 & 86.67 & 84.66 & 25.73 & 84.42 & 83.95 & 24.75 & 86.85 & 84.96 & 29.37 & 85.01 & 82.78 & 30.11 & 85.74 & 84.09 \\\\\nGemini-2.0-Flash & 40.83 & \\textbf{86.87} & 84.62 & \\textbf{26.32} & \\textbf{84.50} & 83.74 & 26.47 & 86.72 & 84.66 & \\textbf{30.69} & \\textbf{85.10} & 82.22 & 31.08 & \\textbf{85.80} & 83.81 \\\\\n\\hdashline\nOpenAI-o1 & 38.84 & 86.63 & \\textbf{85.52} & 24.85 & 84.30 & \\textbf{84.88} & \\textbf{32.29} & \\textbf{86.96} & \\textbf{86.68} & 28.42 & 85.08 & \\textbf{84.05} & \\textbf{31.35} & 85.74 & \\textbf{85.38} \\\\\nDeepSeek-R1 & 38.43 & 86.68 & 84.45 & 24.34 & 84.22 & 83.70 & 25.14 & 86.94 & 85.01 & 29.34 & 85.04 & 82.79 & 29.31 & 85.72 & 83.99 \\\\\nOpenAI-o3-mini & 39.39 & 86.15 & 84.36 & 25.09 & 83.99 & 83.77 & 22.96 & 86.39 & 84.73 & 29.06 & 84.70 & 82.77 & 29.12 & 85.31 & 83.91 \\\\\nGemini-2.0-Flash-Thinking & 39.90 & 86.36 & 84.35 & 25.54 & 84.29 & 83.90 & 25.78 & 86.75 & 84.72 & 30.57 & 84.98 & 82.86 & 30.45 & 85.59 & 83.95 \\\\\n\\bottomrule\n\\toprule\n\\multirow{2}{*}{Model} & \\multicolumn{3}{c}{WMTBio19 En$\\Rightarrow$Zh} & \\multicolumn{3}{c}{WMTBio19 Zh$\\Rightarrow$En} & \\multicolumn{3}{c}{WMTBio19 En$\\Rightarrow$De} & \\multicolumn{3}{c}{WMTBio19 De$\\Rightarrow$En} & \\multicolumn{3}{c}{Average} \\\\\n\\cmidrule(lr){2-4} \\cmidrule(lr){5-7} \\cmidrule(lr){8-10} \\cmidrule(lr){11-13} \\cmidrule(lr){14-16}\n & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI \\\\\n\\midrule\nGPT-4o & 37.58 & 86.75 & 84.53 & 31.41 & 84.42 & 82.72 & 30.49 & 86.29 & 83.55 & 37.25 & 86.91 & 82.35 & 34.18 & 86.09 & 83.29 \\\\\nDeepSeek-V3 & 38.54 & 86.93 & 84.59 & 30.75 & \\textbf{84.68} & 83.10 & 34.14 & 86.47 & 83.91 & 37.63 & \\textbf{87.66} & 82.29 & 35.26 & 86.44 & 83.48 \\\\\nGemini-2.0-Flash & \\textbf{38.89} & 86.91 & 84.48 & \\textbf{32.59} & 84.50 & 82.86 & \\textbf{34.59} & \\textbf{87.96} & 83.96 & \\textbf{37.87} & 86.71 & 82.24 & \\textbf{35.98} & \\textbf{86.52} & 83.39 \\\\\n\\hdashline\nOpenAI-o1 & 37.01 & 86.97 & \\textbf{85.42} & 28.15 & 84.53 & \\textbf{83.50} & 32.29 & 83.52 & \\textbf{85.88} & 36.62 & 84.93 & \\textbf{83.82} & 33.52 & 85.00 & \\textbf{84.66} \\\\\nDeepSeek-R1 & 37.23 & \\textbf{87.05} & 84.27 & 28.58 & 84.31 & 82.60 & 32.56 & 83.80 & 83.98 & 36.19 & 84.84 & 82.28 & 33.64 & 85.00 & 83.28 \\\\\nOpenAI-o3-mini & 35.62 & 86.07 & 84.22 & 30.59 & 84.04 & 82.91 & 31.55 & 86.16 & 83.86 & 36.92 & 86.48 & 82.19 & 33.67 & 85.69 & 83.29 \\\\\nGemini-2.0-Flash-Thinking & 38.48 & 86.43 & 84.20 & 31.78 & 84.48 & 82.88 & 33.55 & 86.82 & 83.89 & 36.74 & 84.57 & 82.25 & 35.14 & 85.57 & 83.31 \\\\\n\\bottomrule\n\\end{tabular}%\n}\n\\caption{Results on the Zh$\\Leftrightarrow$En and De$\\Leftrightarrow$En Biomedical Machine Translation Task from WMT18 and WMT19 (WMTBio18 and WMTBio19). }\n\\label{tab:result_wmtbio_updated_averages}\n\\end{table*}\n\\begin{table*}[h]\n\\centering\n\\resizebox{\\textwidth}{!}{%\n\\begin{tabular}{l ccc ccc ccc ccc ccc ccc}\n\\toprule\n\\multirow{2}{*}{Model} \n& \\multicolumn{3}{c}{Medical} & \\multicolumn{3}{c}{Law} & \\multicolumn{3}{c}{IT} \n& \\multicolumn{3}{c}{Koran} & \\multicolumn{3}{c}{Subtitles} & \\multicolumn{3}{c}{Average} \\\\\n\\cmidrule(lr){2-4} \\cmidrule(lr){5-7} \\cmidrule(lr){8-10}\n\\cmidrule(lr){11-13} \\cmidrule(lr){14-16} \\cmidrule(lr){17-19}\n& BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI\n& BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI \\\\\n\\midrule\nGPT-4o & 41.89 & 84.23 & 81.89 & 39.00 & 85.06 & 82.38 & 37.23 & 83.52 & 78.37 & 17.65 & \\textbf{75.04} & 80.64 & 29.61 & 80.62 & 82.25 & 33.08 & 81.69 & 81.11 \\\\\nDeepSeek-V3 & 41.43 & 84.15 & 81.90 & 38.82 & 84.94 & 82.29 & \\textbf{38.16} & \\textbf{83.90} & 78.86 & 17.94 & 74.91 & 80.33 & 29.70 & 80.68 & 82.26 & 33.21 & 81.72 & 81.13 \\\\\nGemini-2.0-Flash & \\textbf{44.39} & \\textbf{84.49} & 81.76 & \\textbf{46.15} & \\textbf{85.57} & 82.30 & 37.93 & 83.20 & 78.49 & \\textbf{19.70} & 74.97 & 79.71 & \\textbf{30.39} & 80.74 & 82.19 & \\textbf{35.71} & \\textbf{81.79} & 80.89 \\\\\n\\hdashline\nOpenAI-o1 & 40.44 & 83.88 & \\textbf{83.29} & 40.74 & 85.03 & \\textbf{83.53} & 36.40 & 83.20 & \\textbf{79.75} & 16.76 & 75.03 & \\textbf{82.02} & 28.91 & \\textbf{80.79} & \\textbf{83.65} & 32.65 & 81.59 & \\textbf{82.45} \\\\\nDeepSeek-R1 & 40.23 & 83.82 & 81.81 & 39.16 & 84.97 & 82.35 & 36.66 & 83.49 & 78.68 & 17.05 & 74.76 & 80.63 & 28.82 & 80.65 & 82.22 & 32.38 & 81.54 & 81.14 \\\\\nOpenAI-o3-mini & 39.82 & 83.60 & 81.79 & 34.29 & 84.18 & 81.96 & 37.13 & 82.63 & 78.20 & 15.52 & 73.54 & 80.19 & 28.39 & 80.06 & 82.06 & 31.03 & 80.80 & 80.84 \\\\\nGemini-2.0-Flash-Thinking & 43.36 & 84.14 & 81.70 & 44.51 & 85.10 & 82.13 & 37.26 & 82.94 & 78.40 & 19.57 & 75.03 & 79.79 & 30.38 & 80.78 & 82.20 & 35.02 & 81.60 & 80.84 \\\\\n\\bottomrule\n\\end{tabular}\n}\n\\caption{Results on the Dn$\\Rightarrow$En Multi-Domain dataset.}\n\\label{tab:result_multi_domain}\n\\end{table*}\n\\begin{table*}[h]\n\\centering\n\\resizebox{\\textwidth}{!}{%\n\\begin{tabular}{l ccc ccc ccc ccc ccc}\n\\toprule\n\\multirow{2}{*}{Model} & \\multicolumn{3}{c}{Test-1} & \\multicolumn{3}{c}{Test-2} & \\multicolumn{3}{c}{Valid-1} & \\multicolumn{3}{c}{Valid-2} & \\multicolumn{3}{c}{Average} \\\\\n\\cmidrule(lr){2-4} \\cmidrule(lr){5-7} \\cmidrule(lr){8-10} \\cmidrule(lr){11-13} \\cmidrule(lr){14-16}\n& BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI \\\\\n\\midrule\nGPT-4o & 20.49 & 78.44 & 76.50 & 16.12 & 77.55 & 77.82 & 21.08 & 78.91 & 77.31 & 12.49 & 75.38 & 76.91 & 17.54 & \\textbf{77.57} & 77.13 \\\\\nDeepSeek-V3 & 15.66 & 77.47 & 77.84 & \\textbf{16.25} & 77.14 & 77.49 & 19.57 & \\textbf{78.94} & \\textbf{77.48} & 11.87 & 75.28 & 77.05 & 16.81 & 77.48 & 77.17 \\\\\nGemini-2.0-Flash & \\textbf{22.11} & 78.25 & 75.94 & \\textbf{16.25} & 77.14 & 77.49 & \\textbf{21.42}& 78.70 & 76.70 & \\textbf{12.70} & 75.11 & 76.34 & \\textbf{18.12} & 77.30 & 76.62 \\\\\n\\hdashline\nOpenAI-o1 & 19.05 & \\textbf{78.85} & \\textbf{78.00} & 16.04 & \\textbf{77.76} & \\textbf{78.66} & 18.03 & 78.13 & 76.93 & 12.65 & \\textbf{75.52} & \\textbf{77.44} & 16.44 & 77.56 & \\textbf{77.76} \\\\\nDeepSeek-R1 & 15.34 & 77.29 & 75.72 & 12.68 & 77.04 & 77.23 & 14.35 & 77.67 & 76.59 & 10.09 & 74.55 & 76.33 & 13.12 & 76.64 & 76.47 \\\\\nOpenAI-o3-mini & 20.63 & 77.77 & 76.02 & 15.57 & 76.69 & 77.38 & 21.11 & 78.35 & 76.97 & 11.74 & 74.69 & 76.17 & 17.26 & 76.88 & 76.63 \\\\\nGemini-2.0-Flash-Thinking & 21.69 & 78.78 & 76.71 & 15.75 & 76.98 & 77.43 & 21.14 & 78.17 & 75.90 & 12.48 & 74.75 & 76.27 & 17.77 & 77.17 & 76.58 \\\\\n\\bottomrule\n\\end{tabular}%\n}\n\\caption{Results on the Zh$\\Rightarrow$En GuoFeng Webnovel dataset (Literary domain).}\n\\label{tab:result_webnovel}\n\\end{table*}\n\\begin{table*}[h]\n\\centering\n\\resizebox{\\textwidth}{!}{%\n\\begin{tabular}{l ccc ccc ccc ccc ccc}\n\\toprule\n\\multirow{2}{*}{Model} & \\multicolumn{3}{c}{WMT22 En$\\Rightarrow$Zh} & \\multicolumn{3}{c}{WMT22 Zh$\\Rightarrow$En} & \\multicolumn{3}{c}{WMT22 En$\\Rightarrow$De} & \\multicolumn{3}{c}{WMT22 De$\\Rightarrow$En} & \\multicolumn{3}{c}{Average} \\\\\n\\cmidrule(lr){2-4} \\cmidrule(lr){5-7} \\cmidrule(lr){8-10} \\cmidrule(lr){11-13} \\cmidrule(lr){14-16}\n& BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI & BLEU & COMET & KIWI \\\\\n\\midrule\nDeepSeek-V3 & 44.52 & \\textbf{88.21} & 83.19 & 25.21 & 83.11 & 79.95 & 36.89 & 87.78 & 83.83 & 33.06 & 85.66 & 81.80 & 34.92 & 86.19 & 82.19 \\\\\nGPT-4o & \\textbf{45.64} & 87.77 & 82.74 & 25.11 & \\textbf{83.22} & 79.95 & \\textbf{37.07} & 87.77 & 83.60 & \\textbf{33.71} & 85.68 & 81.64 & \\textbf{35.38} & 86.11 & 81.98 \\\\\nGemini-2.0-Flash & 45.50 & 88.04 & 82.84 & \\textbf{26.12} & 82.94 & 79.56 & 36.16 & 87.63 & 83.74 & 33.20 & 85.63 & 81.71 & 35.25 & 86.06 & 81.96 \\\\\n\\hdashline\nOpenAI-o1 & 40.84 & 88.10 & \\textbf{84.10} & 22.42 & 83.08 & \\textbf{80.87} & 34.87 & \\textbf{88.18} & \\textbf{85.64} & 31.53 & \\textbf{85.84} & \\textbf{83.22} & 32.42 & \\textbf{86.30} & \\textbf{83.46} \\\\\nDeepSeek-R1 & 36.88 & 87.33 & 82.96 & 20.68 & 82.48 & 79.57 & 34.14 & 87.73 & 83.91 & 31.37 & 85.61 & 81.86 & 30.77 & 85.79 & 82.07 \\\\\nOpenAI-o3-mini & 42.67 & 87.25 & 82.52 & 24.09 & 82.82 & 79.89 & 34.46 & 86.94 & 83.45 & 33.08 & 85.14 & 81.27 & 33.58 & 85.54 & 81.78 \\\\\nGemini-2.0-Flash-Thinking & 44.04 & 87.61 & 82.58 & 24.83 & 82.71 & 79.51 & 36.12 & 87.46 & 83.58 & 32.75 & 85.59 & 81.66 & 34.44 & 85.84 & 81.83 \\\\\n\\bottomrule\n\\end{tabular}%\n}\n\\caption{Results on the Zh$\\Leftrightarrow$En and De$\\Leftrightarrow$En WMT22 General Machine Translation task.}\n\\label{tab:result_wmt22}\n\\end{table*}\n\\label{appendix:results}\n\n\\section{Prompt}\nThe prompt for calculating MQM scores is provided in Figure \\ref{fig:mqm-prompt}.\nThe prompt for evaluating translation difficulty is shown in the Figure \\ref{fig:difficulty_eval}.\n\n\\begin{figure*}[t]\n\\centering\n\\begin{tcolorbox}[colback=gray!10, colframe=black, width=\\textwidth]\n\\small\n\\setstretch{1.2}\n\n\\noindent{You are a professional translation quality evaluator following the MQM (Multidimensional Quality Metrics) framework.}\n\n\\textbf{Task Instructions:}\n\\begin{enumerate}\n \\item Compare the Prediction against both the Source and the Reference.\n \\item Identify up to five of the most serious Errors for each translation sentence, using the MQM error types listed below.\n \\item Assign exactly one severity level to each error.\n \\item Special handling for two specific error types:\n \\begin{itemize}\n \\item \\textbf{Source Error}: Errors present in the Source text itself.\n \\item \\textbf{Non-translation Error}: Translation is unassessable and unrelated to the Source.\n \\end{itemize}\n \\item If no errors are found, return an empty JSON list [].\n \\item Only output a valid JSON object. Do not include any additional text, comments, or explanations.\n\\end{enumerate}\n\n\\textbf{MQM Error Types (See Table~\\ref{tab:mqm-hierarchy} for the complete hierarchy):}\n\\begin{itemize}\n \\item Mistranslation: Incorrect translation that alters or distorts meaning.\n \\item Addition: Insertion of information or emotion not present in the source.\n \\item Under-translation: Partial omission of relevant or necessary information.\n \\item Omission: Complete exclusion of content present in the source.\n \\item Untranslated: Source text is copied without being translated.\n\\end{itemize}\n\n\\textbf{Severity Levels:} \n\\begin{itemize}\n \\item Minor: Slight impact on readability or style; meaning remains clear.\n \\item Major: Significant impact on usability, comprehension, or meaning.\n\\end{itemize}\n\\textbf{Output Format:}\n\\begin{verbatim}\n{\n \"errors\": [\n {\n \"error_type\": \"Mistranslation\",\n \"severity\": \"Major\",\n \"explanation\": \"The correct translation should be ... \"\n }\n ]\n}\n\\end{verbatim}\n\n\\textbf{Source:} \\texttt{\\{source\\_text\\}} \\\\\n\\textbf{Reference:} \\texttt{\\{reference\\_text\\}} \\\\\n\\textbf{Prediction:} \\texttt{\\{prediction\\_text\\}}\n\n\\end{tcolorbox}\n\\caption{Full prompt used to calculate MQM scores with DeepSeek-V3. }\n\\label{fig:mqm-prompt}\n\\end{figure*}\n\n\\begin{figure*}[t]\n\\centering\n\\begin{tcolorbox}[colback=gray!10, colframe=black, width=\\textwidth]\n\nYour task is to assess the difficulty of translating a given \\texttt{\\{src\\_lang\\}} sentence into \\texttt{\\{tgt\\_lang\\}}. Please evaluate the difficulty based on the following criteria and output the result in JSON format, with the key \"level\":\n\\begin{enumerate}\n\\item Sentence complexity: Determine if the sentence is a simple sentence, a compound sentence, or includes subordinate clauses and other complex structures.\n\\item Vocabulary difficulty: Assess whether the sentence contains commonly used words or specialized terms or slang.\n\\item Grammar differences: Analyze if the sentence's grammatical structure is similar to or differs significantly from \\texttt{\\{tgt\\_lang\\}}.\n\\item Contextual understanding: Consider whether understanding specific cultural contexts or background knowledge is necessary for accurate translation.\n\\end{enumerate}\n\nProvide a difficulty level (1-5), with 1 being the easiest and 5 being the most difficult.\nAnd output the difficulty level in the following JSON format:\n\\begin{verbatim}\n{\n \"level\": \"difficulty level\"\n}\n\\end{verbatim}\n\nHere is the \\texttt{\\{src\\_lang\\}} sentence: \\texttt{\\{src\\_text\\}}\n\n\\end{tcolorbox}\n\\caption{Full prompt used for evaluating translation difficulty with DeepSeek-V3.}\n\\label{fig:difficulty_eval}\n\\end{figure*}\n\n\\end{document}\n"},"question":{"kind":"string","value":"In the sentence-level setting, which model has the highest COMET score on the average of the easiest and most difficult domain, measured by BLEU?"},"answer":{"kind":"string","value":"GPT-4o"},"plan":{"kind":"string","value":"SELECT Sentence-Level setting, all models, BLEU scores across domains\nCOMPUTE avg_bleu_list = average of bleu scores across domains for each Domain\nCOMPUTE easiest_domain = argmax(avg_bleu_list)\nCOMPUTE hardest_domain = argmin(avg_bleu_list)\n\n\nSELECT Sentence-Level setting, all models, COMET scores across domains\nLOOP for each model\n COMPUTE average of COMET score on easiest_domain and hardest_domain\nCOMPUTE target model = argmax of average COMET score\nRETURN target model\n"}}},{"rowIdx":73,"cells":{"paper":{"kind":"string","value":"2505.19714"},"relevant_tables":{"kind":"list like","value":[["\\begin{table*}[t]\n"," \\centering\n"," \\small\n"," \\caption{\n"," In-domain (IND) performance comparison on the MIT-10M benchmark (ZH-EN and EN-ZH). Metrics reported are BLEU, chrF++, and METEOR, along with their average (Avg.). MT$^{3}$-7B-Zero (RL) is compared against open-source systems and supervised fine-tuned variants.\n"," }\n"," \\label{tab:main_mt_results}\n"," \\resizebox{0.95\\textwidth}{!}{%\n"," \\setlength{\\tabcolsep}{4pt}\n"," \\begin{tabular}{l*{9}{c}} % 改为9列\n"," \\toprule\n"," \\multirow{2.5}{*}{\\sc Model} & \\multicolumn{4}{c}{\\sc MIT-10M (ZH-EN)} & & \\multicolumn{4}{c}{\\sc MIT-10M (EN-ZH)} \\\\ % 添加空列\n"," \\cmidrule(lr){2-5} \\cmidrule(lr){7-10} % 调整cmidrule范围\n"," & BLEU & chrF++ & METEOR & Avg. & & BLEU & chrF++ & METEOR & Avg. \\\\ % 添加空列头\n"," \\midrule\n","\n"," \\multicolumn{10}{c}{\\textit{\\textbf{Zero-shot Systems}}} \\\\ % 调整为10列\n"," \\multicolumn{10}{@{}l}{\\textcolor{lightgray}{\\textit{Cascade System}}} \\\\\n"," EasyOCR + NLLB-3.3B & 2.54 & 10.71 & 7.72 & 6.99 & & 11.93 & 12.48 & 10.84 & 11.75 \\\\ % 添加空列\n","\n","\n"," \\multicolumn{10}{@{}l}{\\textcolor{lightgray}{\\textit{Advanced MLLMs}}} \\\\\n"," InternVL2.5-8B & 9.98 & 26.59 & 24.99 & 20.52 & & 31.74 & 38.21 & 38.49 & 36.15 \\\\\n"," InternVL2.5-26B & 13.29 & 27.82 & 24.60 & 21.90 & & 33.22 & 42.43 & 46.55 & 40.73 \\\\\n"," InternVL2.5-38B & 10.77 & 26.00 & 24.49 & 20.42 & & 38.61 & 48.87 & 44.04 & 43.84 \\\\\n"," InternVL2.5-78B & 12.50 & 26.85 & 27.11 & 22.15 & & 36.97 & 48.96 & 46.19 & 44.04 \\\\\n"," Qwen2.5-VL-7B & 15.59 & 31.34 & 33.44 & 26.79 & & 31.66 & 44.20 & 48.41 & 41.42 \\\\\n"," Qwen2.5-VL-72B & 11.33 & 32.82 & 41.26 & 28.47 & & 35.55 & 50.78 & 52.76 & 46.36 \\\\\n","\n","\n","\n"," \\midrule\n","\n"," \\multicolumn{10}{c}{\\textit{\\textbf{Fine-tuned VLMs}}} \\\\ % 调整为10列\n"," Qwen2.5-VL-7B (SFT, TIMT) & 18.66 & 40.74 & 43.20 & 34.20 & & 43.33 & 48.62 & 55.71 & 49.22 \\\\\n"," Qwen2.5-VL-7B (SFT, OCR + TIMT) & 22.57 & 42.39 & 41.80 & 35.59 & & 48.69 & 54.66 & 59.60 & 54.32 \\\\\n"," MT$^{3}$-7B-Zero (RL) & 20.31 & 44.42 & 43.99 & 36.24 & & 54.49 & 61.77 & 68.23 & 61.50 \\\\\n","\n","\n"," \\bottomrule\n"," \\end{tabular}\n"," }\n","\\end{table*}\n"]],"string":"[\n [\n \"\\\\begin{table*}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\small\\n\",\n \" \\\\caption{\\n\",\n \" In-domain (IND) performance comparison on the MIT-10M benchmark (ZH-EN and EN-ZH). Metrics reported are BLEU, chrF++, and METEOR, along with their average (Avg.). MT$^{3}$-7B-Zero (RL) is compared against open-source systems and supervised fine-tuned variants.\\n\",\n \" }\\n\",\n \" \\\\label{tab:main_mt_results}\\n\",\n \" \\\\resizebox{0.95\\\\textwidth}{!}{%\\n\",\n \" \\\\setlength{\\\\tabcolsep}{4pt}\\n\",\n \" \\\\begin{tabular}{l*{9}{c}} % 改为9列\\n\",\n \" \\\\toprule\\n\",\n \" \\\\multirow{2.5}{*}{\\\\sc Model} & \\\\multicolumn{4}{c}{\\\\sc MIT-10M (ZH-EN)} & & \\\\multicolumn{4}{c}{\\\\sc MIT-10M (EN-ZH)} \\\\\\\\ % 添加空列\\n\",\n \" \\\\cmidrule(lr){2-5} \\\\cmidrule(lr){7-10} % 调整cmidrule范围\\n\",\n \" & BLEU & chrF++ & METEOR & Avg. & & BLEU & chrF++ & METEOR & Avg. \\\\\\\\ % 添加空列头\\n\",\n \" \\\\midrule\\n\",\n \"\\n\",\n \" \\\\multicolumn{10}{c}{\\\\textit{\\\\textbf{Zero-shot Systems}}} \\\\\\\\ % 调整为10列\\n\",\n \" \\\\multicolumn{10}{@{}l}{\\\\textcolor{lightgray}{\\\\textit{Cascade System}}} \\\\\\\\\\n\",\n \" EasyOCR + NLLB-3.3B & 2.54 & 10.71 & 7.72 & 6.99 & & 11.93 & 12.48 & 10.84 & 11.75 \\\\\\\\ % 添加空列\\n\",\n \"\\n\",\n \"\\n\",\n \" \\\\multicolumn{10}{@{}l}{\\\\textcolor{lightgray}{\\\\textit{Advanced MLLMs}}} \\\\\\\\\\n\",\n \" InternVL2.5-8B & 9.98 & 26.59 & 24.99 & 20.52 & & 31.74 & 38.21 & 38.49 & 36.15 \\\\\\\\\\n\",\n \" InternVL2.5-26B & 13.29 & 27.82 & 24.60 & 21.90 & & 33.22 & 42.43 & 46.55 & 40.73 \\\\\\\\\\n\",\n \" InternVL2.5-38B & 10.77 & 26.00 & 24.49 & 20.42 & & 38.61 & 48.87 & 44.04 & 43.84 \\\\\\\\\\n\",\n \" InternVL2.5-78B & 12.50 & 26.85 & 27.11 & 22.15 & & 36.97 & 48.96 & 46.19 & 44.04 \\\\\\\\\\n\",\n \" Qwen2.5-VL-7B & 15.59 & 31.34 & 33.44 & 26.79 & & 31.66 & 44.20 & 48.41 & 41.42 \\\\\\\\\\n\",\n \" Qwen2.5-VL-72B & 11.33 & 32.82 & 41.26 & 28.47 & & 35.55 & 50.78 & 52.76 & 46.36 \\\\\\\\\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" \\\\midrule\\n\",\n \"\\n\",\n \" \\\\multicolumn{10}{c}{\\\\textit{\\\\textbf{Fine-tuned VLMs}}} \\\\\\\\ % 调整为10列\\n\",\n \" Qwen2.5-VL-7B (SFT, TIMT) & 18.66 & 40.74 & 43.20 & 34.20 & & 43.33 & 48.62 & 55.71 & 49.22 \\\\\\\\\\n\",\n \" Qwen2.5-VL-7B (SFT, OCR + TIMT) & 22.57 & 42.39 & 41.80 & 35.59 & & 48.69 & 54.66 & 59.60 & 54.32 \\\\\\\\\\n\",\n \" MT$^{3}$-7B-Zero (RL) & 20.31 & 44.42 & 43.99 & 36.24 & & 54.49 & 61.77 & 68.23 & 61.50 \\\\\\\\\\n\",\n \"\\n\",\n \"\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \"\\\\end{table*}\\n\"\n ]\n]","isConversational":false},"tables":{"kind":"list like","value":[["\\begin{table*}[t]\n"," \\centering\n"," \\small\n"," \\caption{\n"," In-domain (IND) performance comparison on the MIT-10M benchmark (ZH-EN and EN-ZH). Metrics reported are BLEU, chrF++, and METEOR, along with their average (Avg.). MT$^{3}$-7B-Zero (RL) is compared against open-source systems and supervised fine-tuned variants.\n"," }\n"," \\label{tab:main_mt_results}\n"," \\resizebox{0.95\\textwidth}{!}{%\n"," \\setlength{\\tabcolsep}{4pt}\n"," \\begin{tabular}{l*{9}{c}} % 改为9列\n"," \\toprule\n"," \\multirow{2.5}{*}{\\sc Model} & \\multicolumn{4}{c}{\\sc MIT-10M (ZH-EN)} & & \\multicolumn{4}{c}{\\sc MIT-10M (EN-ZH)} \\\\ % 添加空列\n"," \\cmidrule(lr){2-5} \\cmidrule(lr){7-10} % 调整cmidrule范围\n"," & BLEU & chrF++ & METEOR & Avg. & & BLEU & chrF++ & METEOR & Avg. \\\\ % 添加空列头\n"," \\midrule\n","\n"," \\multicolumn{10}{c}{\\textit{\\textbf{Zero-shot Systems}}} \\\\ % 调整为10列\n"," \\multicolumn{10}{@{}l}{\\textcolor{lightgray}{\\textit{Cascade System}}} \\\\\n"," EasyOCR + NLLB-3.3B & 2.54 & 10.71 & 7.72 & 6.99 & & 11.93 & 12.48 & 10.84 & 11.75 \\\\ % 添加空列\n","\n","\n"," \\multicolumn{10}{@{}l}{\\textcolor{lightgray}{\\textit{Advanced MLLMs}}} \\\\\n"," InternVL2.5-8B & 9.98 & 26.59 & 24.99 & 20.52 & & 31.74 & 38.21 & 38.49 & 36.15 \\\\\n"," InternVL2.5-26B & 13.29 & 27.82 & 24.60 & 21.90 & & 33.22 & 42.43 & 46.55 & 40.73 \\\\\n"," InternVL2.5-38B & 10.77 & 26.00 & 24.49 & 20.42 & & 38.61 & 48.87 & 44.04 & 43.84 \\\\\n"," InternVL2.5-78B & 12.50 & 26.85 & 27.11 & 22.15 & & 36.97 & 48.96 & 46.19 & 44.04 \\\\\n"," Qwen2.5-VL-7B & 15.59 & 31.34 & 33.44 & 26.79 & & 31.66 & 44.20 & 48.41 & 41.42 \\\\\n"," Qwen2.5-VL-72B & 11.33 & 32.82 & 41.26 & 28.47 & & 35.55 & 50.78 & 52.76 & 46.36 \\\\\n","\n","\n","\n"," \\midrule\n","\n"," \\multicolumn{10}{c}{\\textit{\\textbf{Fine-tuned VLMs}}} \\\\ % 调整为10列\n"," Qwen2.5-VL-7B (SFT, TIMT) & 18.66 & 40.74 & 43.20 & 34.20 & & 43.33 & 48.62 & 55.71 & 49.22 \\\\\n"," Qwen2.5-VL-7B (SFT, OCR + TIMT) & 22.57 & 42.39 & 41.80 & 35.59 & & 48.69 & 54.66 & 59.60 & 54.32 \\\\\n"," MT$^{3}$-7B-Zero (RL) & 20.31 & 44.42 & 43.99 & 36.24 & & 54.49 & 61.77 & 68.23 & 61.50 \\\\\n","\n","\n"," \\bottomrule\n"," \\end{tabular}\n"," }\n","\\end{table*}\n"],["\\begin{table}[t]\n"," \\centering\n"," \\caption{\n","Out-of-distribution (OOD) generalization performance using chrF++ and METEOR. MT$^{3}$-7B-Zero (RL) is evaluated against strong and same-size MLLM baselines on unseen language pairs from MIT-10M (EN-DE, ZH-FR, DE-FR) and different OOD datasets (OCRMT30K, DoTA, XHSPost). \\textbf{Bold} indicates the best, \\underline{underline} indicates the second best.\n"," }\n"," \\label{tab:ood_chrf_meteor}\n"," \\resizebox{0.98\\columnwidth}{!}{%\n"," \\setlength{\\tabcolsep}{2pt}\n"," \\begin{tabular}{@{}lcccccccccccccccc@{}}\n"," \\toprule\n"," \\multirow{3}{*}{\\sc Model} \n"," & \\multicolumn{6}{c}{\\sc OOD (Language Pairs)} \n"," & \\multicolumn{8}{c}{\\sc OOD (Datasets)} \\\\\n"," \\cmidrule(lr){2-7} \n"," \\cmidrule(lr){8-15}\n"," & \\multicolumn{2}{c}{\\sc MIT-10M (EN-DE)} \n"," & \\multicolumn{2}{c}{\\sc MIT-10M (ZH-FR)} \n"," & \\multicolumn{2}{c}{\\sc MIT-10M (DE-FR)} \n"," & \\multicolumn{2}{c}{\\sc OCRMT30K (ZH-EN)} \n"," & \\multicolumn{2}{c}{\\sc DoTA (EN-ZH)} \n"," & \\multicolumn{2}{c}{\\sc XHSPost (EN-ZH)} \n"," & \\multicolumn{2}{c}{\\sc XHSPost (ZH-EN)} \\\\\n"," \\cmidrule(lr){2-3} \\cmidrule(lr){4-5} \\cmidrule(lr){6-7} \n"," \\cmidrule(lr){8-9} \\cmidrule(lr){10-11} \\cmidrule(lr){12-13} \\cmidrule(lr){14-15}\n"," & chrF++ & METEOR & chrF++ & METEOR & chrF++ & METEOR \n"," & chrF++ & METEOR & chrF++ & METEOR & chrF++ & METEOR & chrF++ & METEOR \\\\\n"," \\midrule\n"," \\multicolumn{15}{@{}l}{\\textcolor{lightgray}{\\textit{Strong Baseline}}} \\\\\n"," Qwen2.5-VL-72B & 48.37 & 40.60 & \\underline{27.29} & 30.13 & \\underline{48.82} & \\underline{45.06} \n"," & \\textbf{39.73} & \\textbf{42.65} & \\textbf{37.65} & \\textbf{50.75} \n"," & \\textbf{42.97} & \\textbf{61.28} & \\textbf{55.10} & \\textbf{54.07} \\\\\n"," InternVL2.5-78B & \\underline{50.54} & \\underline{43.11} & 18.25 & 19.59 & 27.98 & 25.45 \n"," & 31.57 & 32.81 & 32.88 & 45.03 \n"," & 37.61 & 57.60 & 53.14 & 51.18 \\\\\n"," \\multicolumn{15}{@{}l}{\\textcolor{lightgray}{\\textit{Same-size Baseline}}} \\\\\n"," Qwen2.5-VL-7B & 48.12 & 40.65 & 27.19 & \\underline{30.59} & 33.60 & 31.99 \n"," & 23.99 & 24.90 & 32.62 & 44.16 \n"," & 36.79 & 55.16 & 49.01 & 47.05 \\\\\n"," InternVL2.5-8B & 42.42 & 36.67 & 19.32 & 14.71 & 23.17 & 20.41 \n"," & 21.95 & 21.12 & 24.75 & 34.89 \n"," & 28.32 & 46.20 & 42.19 & 38.93 \\\\\n"," \\midrule\n"," MT$^{3}$-7B-Zero (RL) & \\textbf{56.14} & \\textbf{52.11} & \\textbf{35.67} & \\textbf{38.53} & \\textbf{53.88} & \\textbf{54.99} \n"," & \\underline{37.42} & \\underline{38.17} & \\underline{34.87} & \\underline{46.60} \n"," & \\underline{39.95} & \\underline{58.58} & \\underline{53.26} & \\underline{52.04} \\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," }\n","\\vspace{-3mm}\n","\\end{table}\n"],["\\begin{table*}[h]\n"," \\centering\n"," \\small\n"," \\caption{\n"," Multi-task ablation study for MT$^{3}$ framework. Performance impact of removing Recognition, Reasoning, or both, compared to the full task setup. $\\Delta$ denotes the average metric score difference compared to the \\textit{Full Tasks} setting.\n"," }\n"," \\label{tab:ablation_results}\n"," \\resizebox{\\textwidth}{!}{%\n"," \\setlength{\\tabcolsep}{4pt}\n"," \\begin{tabular}{lccc*{4}{c}c*{4}{c}c*{4}{c}c}\n"," \\toprule\n"," \\multirow{2.5}{*}{\\sc Ablation} & \\multicolumn{3}{c}{\\sc Tasks} \n"," & \\multicolumn{4}{c}{\\sc MIT-10M (ZH-EN)} & \\multirow{2.5}{*}{\\sc $\\Delta$}\n"," & \\multicolumn{4}{c}{\\sc MIT-10M (EN-ZH)} & \\multirow{2.5}{*}{\\sc $\\Delta$}\n"," & \\multicolumn{4}{c}{\\sc XHSPost (ZH-EN)} & \\multirow{2.5}{*}{\\sc $\\Delta$} \\\\\n"," \\cmidrule(lr){2-4} \\cmidrule(lr){5-8} \\cmidrule(lr){10-13} \\cmidrule(lr){15-18}\n"," & Recognition & Reasoning & Translation \n"," & BLEU & chrF++ & METEOR & Avg. & \n"," & BLEU & chrF++ & METEOR & Avg. & \n"," & BLEU & chrF++ & METEOR & Avg. & \\\\\n"," \\midrule\n"," Full Tasks & \\cmark & \\cmark & \\cmark \n"," & 20.31 & 44.42 & 43.99 & 36.24 & ---\n"," & 54.49 & 61.77 & 68.23 & 61.50 & ---\n"," & 26.94 & 53.26 & 52.04 & 44.08 & --- \\\\\n"," w/o Reasoning & \\xmark & \\cmark & \\cmark \n"," & 22.21 & 45.06 & 44.63 & 37.30 & +1.06\n"," & 52.12 & 61.34 & 64.26 & 59.24 & -2.26\n"," & 25.01 & 51.50 & 49.73 & 42.08 & -2.00 \\\\\n"," w/o Recognition & \\cmark & \\xmark & \\cmark \n"," & 16.99 & 40.08 & 42.65 & 33.24 & -3.00\n"," & 47.61 & 57.78 & 65.11 & 56.83 & -4.67\n"," & 25.06 & 52.01 & 50.67 & 42.58 & -1.50 \\\\\n"," Only TIMT & \\xmark & \\xmark & \\cmark \n"," & 13.43 & 38.47 & 42.21 & 31.37 & -4.87\n"," & 44.04 & 55.87 & 61.77 & 53.89 & -7.61\n"," & 24.21 & 51.32 & 49.54 & 41.69 & -2.39 \\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," }\n","\\end{table*}\n"],["\\begin{figure}[t]\n","\\centering\n","\\includegraphics[width=0.9\\textwidth]{fig/xhs_case.pdf}\n","\\caption{Illustrative TIMT examples from the XHSPost benchmark. MT$^{3}$ demonstrates superior contextual understanding by correctly translating '\\textit{Friends}' to '\\textit{\\chinese{老友记}}' based on visual context (\\textbf{Top}), and accurately interpreting a property advertisement, including price and layout details (\\textbf{Bottom}).\n","}\n","\\label{fig:xhs_case}\n","\\vspace{-3mm}\n","\\end{figure}\n"],["\\begin{figure}[t]\n","\\centering\n","\\includegraphics[width=0.9\\textwidth]{fig/task_ablation.png}\n","\\caption{Impact of multi-task ablation on reward optimization and performance progression. \\textbf{Top row}: Progression of \\textit{Final Reward} and individual task rewards (Format, Recognition, Translation). \\textbf{Bottom row}: Progression of translation quality metrics (BLEU, chrF++, METEOR).}\n","\\label{fig:task_ablation}\n","\\vspace{-3mm}\n","\\end{figure}\n"],["\\begin{table}[t]\n"," \\centering\n"," \\caption{\n","Impact of initialization strategy on performance. Comparison of MT$^{3}$-7B-Zero (RL from scratch) and MT$^{3}$-7B-QVQ-Distill (SFT on distilled data then RL) on in-domain (MIT-10M) and out-of-distribution (OCRMT30K, DoTA, XHSPost) benchmarks. \n"," }\n"," \\label{tab:think_ablation}\n"," \\resizebox{0.95\\columnwidth}{!}{%\n"," \\setlength{\\tabcolsep}{2pt}\n"," \\begin{tabular}{@{}lccccccccccccccccccc@{}}\n"," \\toprule\n"," \\multirow{3}{*}{\\sc Model} \n"," & \\multicolumn{6}{c}{\\sc In-Domain} \n"," & \\multicolumn{9}{c}{\\sc Out-of-Domain} \\\\\n"," \\cmidrule(lr){2-7} \\cmidrule(lr){8-19}\n"," & \\multicolumn{3}{c}{\\sc MIT-10M (ZH-EN)} \n"," & \\multicolumn{3}{c}{\\sc MIT-10M (EN-ZH)} \n"," & \\multicolumn{3}{c}{\\sc OCRMT30K (ZH-EN)} \n"," & \\multicolumn{3}{c}{\\sc DoTA (EN-ZH)} \n"," & \\multicolumn{3}{c}{\\sc XHSPost (EN-ZH)} \n"," & \\multicolumn{3}{c}{\\sc XHSPost (ZH-EN)} \\\\\n"," \\cmidrule(lr){2-4} \\cmidrule(lr){5-7} \n"," \\cmidrule(lr){8-10} \\cmidrule(lr){11-13} \\cmidrule(lr){14-16} \\cmidrule(lr){17-19}\n"," & BLEU & chrF++ & METEOR \n"," & BLEU & chrF++ & METEOR \n"," & BLEU & chrF++ & METEOR \n"," & BLEU & chrF++ & METEOR \n"," & BLEU & chrF++ & METEOR \n"," & BLEU & chrF++ & METEOR \\\\\n"," \\midrule\n"," \\multicolumn{19}{@{}l}{\\textcolor{lightgray}{\\textit{w/ \"Cold Start\"}}} \\\\\n"," MT$^{3}$-7B-QVQ-Distill & \n"," 15.37 & 36.36 & 40.20 & \n"," 42.21 & 53.55 & 58.37 & \n"," 12.39 & 34.68 & 35.43 & \n"," 29.23 & 25.60 & 37.08 & \n"," 48.20 & 39.61 & 58.41 & \n"," 24.61 & 49.74 & 48.76 \\\\\n"," \\multicolumn{19}{@{}l}{\\textcolor{lightgray}{\\textit{w/o \"Cold Start\"}}} \\\\\n"," MT$^{3}$-7B-Zero & \n"," 20.31 & 44.42 & 43.99 & \n"," 54.49 & 61.77 & 68.23 & \n"," 14.21 & 37.42 & 38.17 & \n"," 40.46 & 34.87 & 46.60 & \n"," 49.41 & 39.95 & 58.58 & \n"," 26.94 & 53.26 & 52.04 \\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," }\n"," \\vspace{-3mm}\n","\\end{table}\n"],["\\begin{figure}[t]\n","\\centering\n","\\includegraphics[width=0.9\\textwidth]{fig/qvq_zero.png}\n","\\caption{\n","Training dynamics comparing Zero-start RL (MT$^{3}$-7B-Zero) vs. SFT initialization (MT$^{3}$-7B-QVQ-Distill). \\textbf{Left and Center:} Average metric score progression on MIT-10M ZH-EN and EN-ZH test sets. \\textbf{Right:} Average response length during RL training.\n","}\n","\\label{fig:qvq_zero}\n","\\vspace{-2mm}\n","\\end{figure}\n"],["\\begin{figure}[h]\n","\\centering\n","\\includegraphics[width=0.95\\textwidth]{fig/curriculum.png}\n","\\caption{Influence of curriculum learning strategies on training dynamics and performance. \\textbf{Left}: Average response length dynamics for \\textit{Shuffle}, \\textit{Ascending (easy-to-hard)}, and \\textit{Descending (hard-to-easy)} difficulty curricula during training. \\textbf{Right}: Final response length and BLEU scores on MIT-10M difficulty splits (Easy, Medium, Hard) for EN-ZH and ZH-EN..\n","}\n","\\label{fig:curriculum}\n","\\vspace{-3mm}\n","\\end{figure}\n"],["\\begin{figure}[t]\n","\\centering\n","\\includegraphics[width=0.9\\textwidth]{fig/metric_reward.png}\n","\\caption{\n","Analysis of translation rewards. \\textbf{Left}: Kendall and Spearman correlation matrices between different individual metric rewards (chrF++, METEOR, BLEU) and the \\textit{Mixed Reward}, based on final reward. \\textbf{Right}: Progression of \\textit{Translation Reward} and \\textit{Final Reward} when optimizing for individual metric reward versus the \\textit{Mixed Reward}.\n","}\n","\\label{fig:metric_reward}\n","\\vspace{-2mm}\n","\\end{figure}\n"],["\\begin{figure}[t]\n","\\centering\n","\\includegraphics[width=0.9\\textwidth]{fig/metric_metric.png}\n","\\caption{ Effect of different translation reward metric choices ($R_{task-trans}$) on performance progression for EN-ZH translation on MIT-10M. Curves display BLEU, chrF++, and METEOR scores over training steps when optimizing with individual metrics versus the \\textit{Mixed Reward}.\n","}\n","\\label{fig:metric_metric}\n","\\vspace{-2mm}\n","\\end{figure}\n"],["\\begin{figure}[h]\n","\\centering\n","\\includegraphics[width=0.92\\textwidth]{fig/dataset_case.pdf}\n","\\caption{\n","Comparison of XHSPost with other TIMT datasets.\n","}\n","\\label{fig:dataset_case}\n","\\end{figure}\n"],["\\begin{table}[h]\n"," \\centering\n"," \\caption{\n"," OOD BLEU score performance. Complement chrF++ and METEOR results in Table~\\ref{tab:ood_chrf_meteor}.\n"," }\n"," \\label{tab:ood_bleu}\n"," \\resizebox{\\columnwidth}{!}{%\n"," \\setlength{\\tabcolsep}{4pt}\n"," \\begin{tabular}{@{}lcccccccc@{}}\n"," \\toprule\n"," \\multirow{3}{*}{\\sc Model} \n"," & \\multicolumn{3}{c}{\\sc OOD (Language Pairs)} \n"," & \\multicolumn{5}{c}{\\sc OOD (Datasets)}\\\\\n"," \\cmidrule(lr){2-4} \\cmidrule(lr){5-9}\n"," & \\sc MIT-10M (EN-DE) \n"," & \\sc MIT-10M (ZH-FR) \n"," & \\sc MIT-10M (DE-FR) \n"," & \\sc OCRMT30K (ZH-EN) \n"," & \\sc DoTA (EN-ZH) \n"," & \\sc XHSPost (EN-ZH) \n"," & \\sc XHSPost (ZH-EN) \\\\\n"," \\midrule\n"," \\multicolumn{9}{@{}l}{\\textcolor{lightgray}{\\textit{Strong Baseline}}} \\\\\n"," Qwen2.5-VL-72B & 20.49 & 9.79 & 18.78 & 15.14 & 45.43 & 52.97 & 29.52 \\\\\n"," InternVL2.5-78B & 21.23 & 5.22 & 11.16 & 11.46 & 38.28 & 45.61 & 28.87 \\\\\n"," \\multicolumn{9}{@{}l}{\\textcolor{lightgray}{\\textit{Same-size Baseline}}} \\\\\n"," Qwen2.5-VL-7B & 22.85 & 12.09 & 14.99 & 8.93 & 36.90 & 44.96 & 24.49 \\\\\n"," InternVL2.5-8B & 16.76 & 6.68 & 8.49 & 7.10 & 27.25 & 35.74 & 19.21 \\\\\n"," \\midrule\n"," MT$^{3}$-7B-Zero & 29.50 & 15.97 & 26.92 & 14.21 & 40.46 & 49.41 & 26.94 \\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," }\n","\\end{table}\n"],["\\begin{figure}[ht]\n","\\centering\n","\\includegraphics[width=1.0\\textwidth]{fig/mt3_case.pdf}\n","\\caption{\n","Case Study. The model initialized with QVQ-Distill tends to generate significantly longer reasoning steps. In contrast, the Zero-start model, after RL training, often develops a more straightforward and less redundant reasoning path.\n","}\n","\\label{fig:mt3_case}\n","\\end{figure}\n"]],"string":"[\n [\n \"\\\\begin{table*}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\small\\n\",\n \" \\\\caption{\\n\",\n \" In-domain (IND) performance comparison on the MIT-10M benchmark (ZH-EN and EN-ZH). Metrics reported are BLEU, chrF++, and METEOR, along with their average (Avg.). MT$^{3}$-7B-Zero (RL) is compared against open-source systems and supervised fine-tuned variants.\\n\",\n \" }\\n\",\n \" \\\\label{tab:main_mt_results}\\n\",\n \" \\\\resizebox{0.95\\\\textwidth}{!}{%\\n\",\n \" \\\\setlength{\\\\tabcolsep}{4pt}\\n\",\n \" \\\\begin{tabular}{l*{9}{c}} % 改为9列\\n\",\n \" \\\\toprule\\n\",\n \" \\\\multirow{2.5}{*}{\\\\sc Model} & \\\\multicolumn{4}{c}{\\\\sc MIT-10M (ZH-EN)} & & \\\\multicolumn{4}{c}{\\\\sc MIT-10M (EN-ZH)} \\\\\\\\ % 添加空列\\n\",\n \" \\\\cmidrule(lr){2-5} \\\\cmidrule(lr){7-10} % 调整cmidrule范围\\n\",\n \" & BLEU & chrF++ & METEOR & Avg. & & BLEU & chrF++ & METEOR & Avg. \\\\\\\\ % 添加空列头\\n\",\n \" \\\\midrule\\n\",\n \"\\n\",\n \" \\\\multicolumn{10}{c}{\\\\textit{\\\\textbf{Zero-shot Systems}}} \\\\\\\\ % 调整为10列\\n\",\n \" \\\\multicolumn{10}{@{}l}{\\\\textcolor{lightgray}{\\\\textit{Cascade System}}} \\\\\\\\\\n\",\n \" EasyOCR + NLLB-3.3B & 2.54 & 10.71 & 7.72 & 6.99 & & 11.93 & 12.48 & 10.84 & 11.75 \\\\\\\\ % 添加空列\\n\",\n \"\\n\",\n \"\\n\",\n \" \\\\multicolumn{10}{@{}l}{\\\\textcolor{lightgray}{\\\\textit{Advanced MLLMs}}} \\\\\\\\\\n\",\n \" InternVL2.5-8B & 9.98 & 26.59 & 24.99 & 20.52 & & 31.74 & 38.21 & 38.49 & 36.15 \\\\\\\\\\n\",\n \" InternVL2.5-26B & 13.29 & 27.82 & 24.60 & 21.90 & & 33.22 & 42.43 & 46.55 & 40.73 \\\\\\\\\\n\",\n \" InternVL2.5-38B & 10.77 & 26.00 & 24.49 & 20.42 & & 38.61 & 48.87 & 44.04 & 43.84 \\\\\\\\\\n\",\n \" InternVL2.5-78B & 12.50 & 26.85 & 27.11 & 22.15 & & 36.97 & 48.96 & 46.19 & 44.04 \\\\\\\\\\n\",\n \" Qwen2.5-VL-7B & 15.59 & 31.34 & 33.44 & 26.79 & & 31.66 & 44.20 & 48.41 & 41.42 \\\\\\\\\\n\",\n \" Qwen2.5-VL-72B & 11.33 & 32.82 & 41.26 & 28.47 & & 35.55 & 50.78 & 52.76 & 46.36 \\\\\\\\\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" \\\\midrule\\n\",\n \"\\n\",\n \" \\\\multicolumn{10}{c}{\\\\textit{\\\\textbf{Fine-tuned VLMs}}} \\\\\\\\ % 调整为10列\\n\",\n \" Qwen2.5-VL-7B (SFT, TIMT) & 18.66 & 40.74 & 43.20 & 34.20 & & 43.33 & 48.62 & 55.71 & 49.22 \\\\\\\\\\n\",\n \" Qwen2.5-VL-7B (SFT, OCR + TIMT) & 22.57 & 42.39 & 41.80 & 35.59 & & 48.69 & 54.66 & 59.60 & 54.32 \\\\\\\\\\n\",\n \" MT$^{3}$-7B-Zero (RL) & 20.31 & 44.42 & 43.99 & 36.24 & & 54.49 & 61.77 & 68.23 & 61.50 \\\\\\\\\\n\",\n \"\\n\",\n \"\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\caption{\\n\",\n \"Out-of-distribution (OOD) generalization performance using chrF++ and METEOR. MT$^{3}$-7B-Zero (RL) is evaluated against strong and same-size MLLM baselines on unseen language pairs from MIT-10M (EN-DE, ZH-FR, DE-FR) and different OOD datasets (OCRMT30K, DoTA, XHSPost). \\\\textbf{Bold} indicates the best, \\\\underline{underline} indicates the second best.\\n\",\n \" }\\n\",\n \" \\\\label{tab:ood_chrf_meteor}\\n\",\n \" \\\\resizebox{0.98\\\\columnwidth}{!}{%\\n\",\n \" \\\\setlength{\\\\tabcolsep}{2pt}\\n\",\n \" \\\\begin{tabular}{@{}lcccccccccccccccc@{}}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\multirow{3}{*}{\\\\sc Model} \\n\",\n \" & \\\\multicolumn{6}{c}{\\\\sc OOD (Language Pairs)} \\n\",\n \" & \\\\multicolumn{8}{c}{\\\\sc OOD (Datasets)} \\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-7} \\n\",\n \" \\\\cmidrule(lr){8-15}\\n\",\n \" & \\\\multicolumn{2}{c}{\\\\sc MIT-10M (EN-DE)} \\n\",\n \" & \\\\multicolumn{2}{c}{\\\\sc MIT-10M (ZH-FR)} \\n\",\n \" & \\\\multicolumn{2}{c}{\\\\sc MIT-10M (DE-FR)} \\n\",\n \" & \\\\multicolumn{2}{c}{\\\\sc OCRMT30K (ZH-EN)} \\n\",\n \" & \\\\multicolumn{2}{c}{\\\\sc DoTA (EN-ZH)} \\n\",\n \" & \\\\multicolumn{2}{c}{\\\\sc XHSPost (EN-ZH)} \\n\",\n \" & \\\\multicolumn{2}{c}{\\\\sc XHSPost (ZH-EN)} \\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-3} \\\\cmidrule(lr){4-5} \\\\cmidrule(lr){6-7} \\n\",\n \" \\\\cmidrule(lr){8-9} \\\\cmidrule(lr){10-11} \\\\cmidrule(lr){12-13} \\\\cmidrule(lr){14-15}\\n\",\n \" & chrF++ & METEOR & chrF++ & METEOR & chrF++ & METEOR \\n\",\n \" & chrF++ & METEOR & chrF++ & METEOR & chrF++ & METEOR & chrF++ & METEOR \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multicolumn{15}{@{}l}{\\\\textcolor{lightgray}{\\\\textit{Strong Baseline}}} \\\\\\\\\\n\",\n \" Qwen2.5-VL-72B & 48.37 & 40.60 & \\\\underline{27.29} & 30.13 & \\\\underline{48.82} & \\\\underline{45.06} \\n\",\n \" & \\\\textbf{39.73} & \\\\textbf{42.65} & \\\\textbf{37.65} & \\\\textbf{50.75} \\n\",\n \" & \\\\textbf{42.97} & \\\\textbf{61.28} & \\\\textbf{55.10} & \\\\textbf{54.07} \\\\\\\\\\n\",\n \" InternVL2.5-78B & \\\\underline{50.54} & \\\\underline{43.11} & 18.25 & 19.59 & 27.98 & 25.45 \\n\",\n \" & 31.57 & 32.81 & 32.88 & 45.03 \\n\",\n \" & 37.61 & 57.60 & 53.14 & 51.18 \\\\\\\\\\n\",\n \" \\\\multicolumn{15}{@{}l}{\\\\textcolor{lightgray}{\\\\textit{Same-size Baseline}}} \\\\\\\\\\n\",\n \" Qwen2.5-VL-7B & 48.12 & 40.65 & 27.19 & \\\\underline{30.59} & 33.60 & 31.99 \\n\",\n \" & 23.99 & 24.90 & 32.62 & 44.16 \\n\",\n \" & 36.79 & 55.16 & 49.01 & 47.05 \\\\\\\\\\n\",\n \" InternVL2.5-8B & 42.42 & 36.67 & 19.32 & 14.71 & 23.17 & 20.41 \\n\",\n \" & 21.95 & 21.12 & 24.75 & 34.89 \\n\",\n \" & 28.32 & 46.20 & 42.19 & 38.93 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" MT$^{3}$-7B-Zero (RL) & \\\\textbf{56.14} & \\\\textbf{52.11} & \\\\textbf{35.67} & \\\\textbf{38.53} & \\\\textbf{53.88} & \\\\textbf{54.99} \\n\",\n \" & \\\\underline{37.42} & \\\\underline{38.17} & \\\\underline{34.87} & \\\\underline{46.60} \\n\",\n \" & \\\\underline{39.95} & \\\\underline{58.58} & \\\\underline{53.26} & \\\\underline{52.04} \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \"\\\\vspace{-3mm}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table*}[h]\\n\",\n \" \\\\centering\\n\",\n \" \\\\small\\n\",\n \" \\\\caption{\\n\",\n \" Multi-task ablation study for MT$^{3}$ framework. Performance impact of removing Recognition, Reasoning, or both, compared to the full task setup. $\\\\Delta$ denotes the average metric score difference compared to the \\\\textit{Full Tasks} setting.\\n\",\n \" }\\n\",\n \" \\\\label{tab:ablation_results}\\n\",\n \" \\\\resizebox{\\\\textwidth}{!}{%\\n\",\n \" \\\\setlength{\\\\tabcolsep}{4pt}\\n\",\n \" \\\\begin{tabular}{lccc*{4}{c}c*{4}{c}c*{4}{c}c}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\multirow{2.5}{*}{\\\\sc Ablation} & \\\\multicolumn{3}{c}{\\\\sc Tasks} \\n\",\n \" & \\\\multicolumn{4}{c}{\\\\sc MIT-10M (ZH-EN)} & \\\\multirow{2.5}{*}{\\\\sc $\\\\Delta$}\\n\",\n \" & \\\\multicolumn{4}{c}{\\\\sc MIT-10M (EN-ZH)} & \\\\multirow{2.5}{*}{\\\\sc $\\\\Delta$}\\n\",\n \" & \\\\multicolumn{4}{c}{\\\\sc XHSPost (ZH-EN)} & \\\\multirow{2.5}{*}{\\\\sc $\\\\Delta$} \\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-4} \\\\cmidrule(lr){5-8} \\\\cmidrule(lr){10-13} \\\\cmidrule(lr){15-18}\\n\",\n \" & Recognition & Reasoning & Translation \\n\",\n \" & BLEU & chrF++ & METEOR & Avg. & \\n\",\n \" & BLEU & chrF++ & METEOR & Avg. & \\n\",\n \" & BLEU & chrF++ & METEOR & Avg. & \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Full Tasks & \\\\cmark & \\\\cmark & \\\\cmark \\n\",\n \" & 20.31 & 44.42 & 43.99 & 36.24 & ---\\n\",\n \" & 54.49 & 61.77 & 68.23 & 61.50 & ---\\n\",\n \" & 26.94 & 53.26 & 52.04 & 44.08 & --- \\\\\\\\\\n\",\n \" w/o Reasoning & \\\\xmark & \\\\cmark & \\\\cmark \\n\",\n \" & 22.21 & 45.06 & 44.63 & 37.30 & +1.06\\n\",\n \" & 52.12 & 61.34 & 64.26 & 59.24 & -2.26\\n\",\n \" & 25.01 & 51.50 & 49.73 & 42.08 & -2.00 \\\\\\\\\\n\",\n \" w/o Recognition & \\\\cmark & \\\\xmark & \\\\cmark \\n\",\n \" & 16.99 & 40.08 & 42.65 & 33.24 & -3.00\\n\",\n \" & 47.61 & 57.78 & 65.11 & 56.83 & -4.67\\n\",\n \" & 25.06 & 52.01 & 50.67 & 42.58 & -1.50 \\\\\\\\\\n\",\n \" Only TIMT & \\\\xmark & \\\\xmark & \\\\cmark \\n\",\n \" & 13.43 & 38.47 & 42.21 & 31.37 & -4.87\\n\",\n \" & 44.04 & 55.87 & 61.77 & 53.89 & -7.61\\n\",\n \" & 24.21 & 51.32 & 49.54 & 41.69 & -2.39 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{figure}[t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\includegraphics[width=0.9\\\\textwidth]{fig/xhs_case.pdf}\\n\",\n \"\\\\caption{Illustrative TIMT examples from the XHSPost benchmark. MT$^{3}$ demonstrates superior contextual understanding by correctly translating '\\\\textit{Friends}' to '\\\\textit{\\\\chinese{老友记}}' based on visual context (\\\\textbf{Top}), and accurately interpreting a property advertisement, including price and layout details (\\\\textbf{Bottom}).\\n\",\n \"}\\n\",\n \"\\\\label{fig:xhs_case}\\n\",\n \"\\\\vspace{-3mm}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{figure}[t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\includegraphics[width=0.9\\\\textwidth]{fig/task_ablation.png}\\n\",\n \"\\\\caption{Impact of multi-task ablation on reward optimization and performance progression. \\\\textbf{Top row}: Progression of \\\\textit{Final Reward} and individual task rewards (Format, Recognition, Translation). \\\\textbf{Bottom row}: Progression of translation quality metrics (BLEU, chrF++, METEOR).}\\n\",\n \"\\\\label{fig:task_ablation}\\n\",\n \"\\\\vspace{-3mm}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\caption{\\n\",\n \"Impact of initialization strategy on performance. Comparison of MT$^{3}$-7B-Zero (RL from scratch) and MT$^{3}$-7B-QVQ-Distill (SFT on distilled data then RL) on in-domain (MIT-10M) and out-of-distribution (OCRMT30K, DoTA, XHSPost) benchmarks. \\n\",\n \" }\\n\",\n \" \\\\label{tab:think_ablation}\\n\",\n \" \\\\resizebox{0.95\\\\columnwidth}{!}{%\\n\",\n \" \\\\setlength{\\\\tabcolsep}{2pt}\\n\",\n \" \\\\begin{tabular}{@{}lccccccccccccccccccc@{}}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\multirow{3}{*}{\\\\sc Model} \\n\",\n \" & \\\\multicolumn{6}{c}{\\\\sc In-Domain} \\n\",\n \" & \\\\multicolumn{9}{c}{\\\\sc Out-of-Domain} \\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-7} \\\\cmidrule(lr){8-19}\\n\",\n \" & \\\\multicolumn{3}{c}{\\\\sc MIT-10M (ZH-EN)} \\n\",\n \" & \\\\multicolumn{3}{c}{\\\\sc MIT-10M (EN-ZH)} \\n\",\n \" & \\\\multicolumn{3}{c}{\\\\sc OCRMT30K (ZH-EN)} \\n\",\n \" & \\\\multicolumn{3}{c}{\\\\sc DoTA (EN-ZH)} \\n\",\n \" & \\\\multicolumn{3}{c}{\\\\sc XHSPost (EN-ZH)} \\n\",\n \" & \\\\multicolumn{3}{c}{\\\\sc XHSPost (ZH-EN)} \\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-4} \\\\cmidrule(lr){5-7} \\n\",\n \" \\\\cmidrule(lr){8-10} \\\\cmidrule(lr){11-13} \\\\cmidrule(lr){14-16} \\\\cmidrule(lr){17-19}\\n\",\n \" & BLEU & chrF++ & METEOR \\n\",\n \" & BLEU & chrF++ & METEOR \\n\",\n \" & BLEU & chrF++ & METEOR \\n\",\n \" & BLEU & chrF++ & METEOR \\n\",\n \" & BLEU & chrF++ & METEOR \\n\",\n \" & BLEU & chrF++ & METEOR \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multicolumn{19}{@{}l}{\\\\textcolor{lightgray}{\\\\textit{w/ \\\"Cold Start\\\"}}} \\\\\\\\\\n\",\n \" MT$^{3}$-7B-QVQ-Distill & \\n\",\n \" 15.37 & 36.36 & 40.20 & \\n\",\n \" 42.21 & 53.55 & 58.37 & \\n\",\n \" 12.39 & 34.68 & 35.43 & \\n\",\n \" 29.23 & 25.60 & 37.08 & \\n\",\n \" 48.20 & 39.61 & 58.41 & \\n\",\n \" 24.61 & 49.74 & 48.76 \\\\\\\\\\n\",\n \" \\\\multicolumn{19}{@{}l}{\\\\textcolor{lightgray}{\\\\textit{w/o \\\"Cold Start\\\"}}} \\\\\\\\\\n\",\n \" MT$^{3}$-7B-Zero & \\n\",\n \" 20.31 & 44.42 & 43.99 & \\n\",\n \" 54.49 & 61.77 & 68.23 & \\n\",\n \" 14.21 & 37.42 & 38.17 & \\n\",\n \" 40.46 & 34.87 & 46.60 & \\n\",\n \" 49.41 & 39.95 & 58.58 & \\n\",\n \" 26.94 & 53.26 & 52.04 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \" \\\\vspace{-3mm}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{figure}[t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\includegraphics[width=0.9\\\\textwidth]{fig/qvq_zero.png}\\n\",\n \"\\\\caption{\\n\",\n \"Training dynamics comparing Zero-start RL (MT$^{3}$-7B-Zero) vs. SFT initialization (MT$^{3}$-7B-QVQ-Distill). \\\\textbf{Left and Center:} Average metric score progression on MIT-10M ZH-EN and EN-ZH test sets. \\\\textbf{Right:} Average response length during RL training.\\n\",\n \"}\\n\",\n \"\\\\label{fig:qvq_zero}\\n\",\n \"\\\\vspace{-2mm}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{figure}[h]\\n\",\n \"\\\\centering\\n\",\n \"\\\\includegraphics[width=0.95\\\\textwidth]{fig/curriculum.png}\\n\",\n \"\\\\caption{Influence of curriculum learning strategies on training dynamics and performance. \\\\textbf{Left}: Average response length dynamics for \\\\textit{Shuffle}, \\\\textit{Ascending (easy-to-hard)}, and \\\\textit{Descending (hard-to-easy)} difficulty curricula during training. \\\\textbf{Right}: Final response length and BLEU scores on MIT-10M difficulty splits (Easy, Medium, Hard) for EN-ZH and ZH-EN..\\n\",\n \"}\\n\",\n \"\\\\label{fig:curriculum}\\n\",\n \"\\\\vspace{-3mm}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{figure}[t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\includegraphics[width=0.9\\\\textwidth]{fig/metric_reward.png}\\n\",\n \"\\\\caption{\\n\",\n \"Analysis of translation rewards. \\\\textbf{Left}: Kendall and Spearman correlation matrices between different individual metric rewards (chrF++, METEOR, BLEU) and the \\\\textit{Mixed Reward}, based on final reward. \\\\textbf{Right}: Progression of \\\\textit{Translation Reward} and \\\\textit{Final Reward} when optimizing for individual metric reward versus the \\\\textit{Mixed Reward}.\\n\",\n \"}\\n\",\n \"\\\\label{fig:metric_reward}\\n\",\n \"\\\\vspace{-2mm}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{figure}[t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\includegraphics[width=0.9\\\\textwidth]{fig/metric_metric.png}\\n\",\n \"\\\\caption{ Effect of different translation reward metric choices ($R_{task-trans}$) on performance progression for EN-ZH translation on MIT-10M. Curves display BLEU, chrF++, and METEOR scores over training steps when optimizing with individual metrics versus the \\\\textit{Mixed Reward}.\\n\",\n \"}\\n\",\n \"\\\\label{fig:metric_metric}\\n\",\n \"\\\\vspace{-2mm}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{figure}[h]\\n\",\n \"\\\\centering\\n\",\n \"\\\\includegraphics[width=0.92\\\\textwidth]{fig/dataset_case.pdf}\\n\",\n \"\\\\caption{\\n\",\n \"Comparison of XHSPost with other TIMT datasets.\\n\",\n \"}\\n\",\n \"\\\\label{fig:dataset_case}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table}[h]\\n\",\n \" \\\\centering\\n\",\n \" \\\\caption{\\n\",\n \" OOD BLEU score performance. Complement chrF++ and METEOR results in Table~\\\\ref{tab:ood_chrf_meteor}.\\n\",\n \" }\\n\",\n \" \\\\label{tab:ood_bleu}\\n\",\n \" \\\\resizebox{\\\\columnwidth}{!}{%\\n\",\n \" \\\\setlength{\\\\tabcolsep}{4pt}\\n\",\n \" \\\\begin{tabular}{@{}lcccccccc@{}}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\multirow{3}{*}{\\\\sc Model} \\n\",\n \" & \\\\multicolumn{3}{c}{\\\\sc OOD (Language Pairs)} \\n\",\n \" & \\\\multicolumn{5}{c}{\\\\sc OOD (Datasets)}\\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-4} \\\\cmidrule(lr){5-9}\\n\",\n \" & \\\\sc MIT-10M (EN-DE) \\n\",\n \" & \\\\sc MIT-10M (ZH-FR) \\n\",\n \" & \\\\sc MIT-10M (DE-FR) \\n\",\n \" & \\\\sc OCRMT30K (ZH-EN) \\n\",\n \" & \\\\sc DoTA (EN-ZH) \\n\",\n \" & \\\\sc XHSPost (EN-ZH) \\n\",\n \" & \\\\sc XHSPost (ZH-EN) \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multicolumn{9}{@{}l}{\\\\textcolor{lightgray}{\\\\textit{Strong Baseline}}} \\\\\\\\\\n\",\n \" Qwen2.5-VL-72B & 20.49 & 9.79 & 18.78 & 15.14 & 45.43 & 52.97 & 29.52 \\\\\\\\\\n\",\n \" InternVL2.5-78B & 21.23 & 5.22 & 11.16 & 11.46 & 38.28 & 45.61 & 28.87 \\\\\\\\\\n\",\n \" \\\\multicolumn{9}{@{}l}{\\\\textcolor{lightgray}{\\\\textit{Same-size Baseline}}} \\\\\\\\\\n\",\n \" Qwen2.5-VL-7B & 22.85 & 12.09 & 14.99 & 8.93 & 36.90 & 44.96 & 24.49 \\\\\\\\\\n\",\n \" InternVL2.5-8B & 16.76 & 6.68 & 8.49 & 7.10 & 27.25 & 35.74 & 19.21 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" MT$^{3}$-7B-Zero & 29.50 & 15.97 & 26.92 & 14.21 & 40.46 & 49.41 & 26.94 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{figure}[ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\includegraphics[width=1.0\\\\textwidth]{fig/mt3_case.pdf}\\n\",\n \"\\\\caption{\\n\",\n \"Case Study. The model initialized with QVQ-Distill tends to generate significantly longer reasoning steps. In contrast, the Zero-start model, after RL training, often develops a more straightforward and less redundant reasoning path.\\n\",\n \"}\\n\",\n \"\\\\label{fig:mt3_case}\\n\",\n \"\\\\end{figure}\\n\"\n ]\n]","isConversational":false},"fulltext":{"kind":"string","value":"\\documentclass{article}\n\n\n% if you need to pass options to natbib, use, e.g.:\n% \\PassOptionsToPackage{numbers, compress}{natbib}\n% before loading neurips_2025\n\n\n% ready for submission\n% \\usepackage{neurips_2025}\n\\usepackage[preprint]{neurips_2025}\n\n\n% to compile a preprint version, e.g., for submission to arXiv, add add the\n% [preprint] option:\n% \\usepackage[preprint]{neurips_2025}\n\n\n% to compile a camera-ready version, add the [final] option, e.g.:\n% \\usepackage[final]{neurips_2025}\n\n\n% to avoid loading the natbib package, add option nonatbib:\n% \\usepackage[nonatbib]{neurips_2025}\n\n\n\\usepackage[utf8]{inputenc} % allow utf-8 input\n\\usepackage[T1]{fontenc} % use 8-bit T1 fonts\n\\usepackage{hyperref} % hyperlinks\n\\usepackage{url} % simple URL typesetting\n\\usepackage{booktabs} % professional-quality tables\n\\usepackage{amsfonts} % blackboard math symbols\n\\usepackage{nicefrac} % compact symbols for 1/2, etc.\n\\usepackage{microtype} % microtypography\n\\usepackage{xcolor} % colors\n\\usepackage{amsmath}\n\\usepackage{multirow}\n\\usepackage{cite}\n\\usepackage{natbib}\n\\usepackage{graphicx}\n\\usepackage{array}\n\\usepackage{multirow}\n\\usepackage{multicol}\n\\usepackage{booktabs}\n\\usepackage{color}\n\\usepackage{tcolorbox}\n\\usepackage{verbatim}\n\\usepackage{colortbl} \n\\usepackage{vcell}\n\\usepackage{arydshln}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{tabularx}\n\\usepackage{makecell}\n\\usepackage[normalem]{ulem}\n\\usepackage{CJKutf8}\n\\usepackage{pifont}\n\\usepackage{tikz}\n\\usepackage{pgfplots}\n\\usepackage{inconsolata}\n\\usepackage{fancyvrb}\n\\usepackage{todonotes}\n\\usepackage{listings}\n\\usepackage{pifont}\n\\newcommand{\\cdashlinelr}[1]{%\n \\noalign{\\vskip 2pt} % 插入垂直间距\n \\cdashline{#1}[.4pt/2pt] % 绘制虚线\n \\noalign{\\vskip 2pt} % 再插入垂直间距\n}\n\\newcommand{\\chinese}[1]{{\\begin{CJK*}{UTF8}{gkai} #1 \\end{CJK*}}}\n\\newcommand{\\cmark}{{\\textbf{\\textcolor[rgb]{0.1, 0.5, 0.1}{\\ding{51}}}}}\n\\newcommand{\\xmark}{{\\textbf{\\color{red}{\\ding{55}}}}}\n\n\n\\title{MT$^{3}$: Scaling MLLM-based Text Image Machine Translation via Multi-Task Reinforcement Learning}\n\n\n% The \\author macro works with any number of authors. There are two commands\n% used to separate the names and addresses of multiple authors: \\And and \\AND.\n%\n% Using \\And between authors leaves it to LaTeX to determine where to break the\n% lines. Using \\AND forces a line break at that point. So, if LaTeX puts 3 of 4\n% authors names on the first line, and the last on the second line, try using\n% \\AND instead of \\And before the third author name.\n\n\n\\makeatletter\n\\def\\thanks#1{\\protected@xdef\\@thanks{\\@thanks\n \\protect\\footnotetext{#1}}}\n\\makeatother\n\\author{\n Zhaopeng Feng$^{1\\ast}$\\quad\n Yupu Liang$^{2\\ast}$\\quad\n Shaosheng Cao$^{3}$\\textsuperscript{\\ding{41}} \\quad\n Jiayuan Su$^{1}$ \\quad \n Jiahan Ren$^{1}$ \\quad \\\\\n \\bf Zhe Xu$^{3}$ \\quad \n \\bf Yao Hu$^{3}$ \\quad\n \\bf Wenxuan Huang$^{4}$ \\quad \n \\bf Jian Wu$^{1}$ \\quad\n \\bf Zuozhu Liu$^{1}$\\textsuperscript{\\ding{41}} \\\\\n $^{1}$Zhejiang University \\quad\n $^{2}$University of Chinese Academy of Sciences \\quad \\\\\n $^{3}$Xiaohongshu Inc. \\quad \n $^{4}$East China Normal University \\quad \\\\\n \\texttt{\\{zhaopeng.23,zuozhuliu\\}@intl.zju.edu.cn, }\\\\\n \\texttt{liangyupu2021@ia.ac.cn} \\\\\n \\texttt{\\{caoshaosheng,qiete,xiahou\\}@xiaohongshu.com} \\\\\n}\n\n% % 添加脚注\n\\thanks{$^{\\ast}$ \\space Equal Contribution.}\n\\thanks{\\textsuperscript{\\ding{41}} \\space Corresponding author.}\n\n\n\\begin{document}\n\n\n\\maketitle\n\n\n\n\\begin{abstract}\n\n% zuozhu edit\n% Text Image Machine Translation (TIMT)—the task of translating textual content embedded in images—is critical for applications in accessibility, cross-lingual information access, and real-world document understanding. However, TIMT remains a complex challenge due to the need for accurate optical character recognition (OCR), robust visual-text reasoning, and high-quality translation, often requiring cascading multi-stage pipelines. Recent advances in large-scale Reinforcement Learning (RL) have improved reasoning in Large Language Models (LLMs) and Multimodal LLMs (MLLMs), but their application to end-to-end TIMT is still underexplored. To bridge this gap, we introduce \\textbf{MT$^{3}$}, the first framework to apply \\textbf{M}ulti-\\textbf{T}ask RL to \\textbf{M}LLMs for end-to-end \\textbf{T}I\\textbf{MT}. \\textbf{MT$^{3}$} adopts a multi-task optimization paradigm targeting three key sub-skills: text recognition, context-aware reasoning, and translation. It is trained using a novel multi-mixed reward mechanism that adapts rule-based RL strategies to TIMT’s intricacies, offering fine-grained, non-binary feedback across tasks. Furthermore, to facilitate the evaluation of TIMT in authentic cross-cultural and real-world social media contexts, we introduced XHSPost, the first social media TIMT benchmark. Our \\textbf{MT$^{3}$}-7B-Zero achieves state-of-the-art results on the latest in-domain MIT-10M benchmark, outperforming strong baselines such as Qwen2.5-VL-72B and InternVL2.5-78B by notable margins across BLEU, chrf++, and METEOR. Additionally, the model shows strong generalization to out-of-distribution language pairs and datasets. In-depth analyses reveal how multi-task synergy, reinforcement learning initialization, curriculum design, and reward formulation contribute to advancing MLLM-driven TIMT. Code and data will be released at \\href{https://github.com/fzp0424/MT-R1-Zero}{https://github.com/fzp0424/MT-R1-Zero}. \n\n\n\nText Image Machine Translation (TIMT)—the task of translating textual content embedded in images—is critical for applications in accessibility, cross-lingual information access, and real-world document understanding. However, TIMT remains a complex challenge due to the need for accurate optical character recognition (OCR), robust visual-text reasoning, and high-quality translation, often requiring cascading multi-stage pipelines. Recent advances in large-scale Reinforcement Learning (RL) have improved reasoning in Large Language Models (LLMs) and Multimodal LLMs (MLLMs), but their application to end-to-end TIMT is still underexplored. To bridge this gap, we introduce \\textbf{MT$^{3}$}, the first framework to apply \\textbf{M}ulti-\\textbf{T}ask RL to \\textbf{M}LLMs for end-to-end \\textbf{T}I\\textbf{MT}. \\textbf{MT$^{3}$} adopts a multi-task optimization paradigm targeting three key sub-skills: text recognition, context-aware reasoning, and translation. It is trained using a novel multi-mixed reward mechanism that adapts rule-based RL strategies to TIMT’s intricacies, offering fine-grained, non-binary feedback across tasks. Furthermore, to facilitate the evaluation of TIMT in authentic cross-cultural and real-world social media contexts, we introduced XHSPost, the first social media TIMT benchmark. Our \\textbf{MT$^{3}$}-7B-Zero achieves state-of-the-art results on the latest in-domain MIT-10M benchmark, outperforming strong baselines such as Qwen2.5-VL-72B and InternVL2.5-78B by notable margins across multiple metrics. Additionally, the model shows strong generalization to out-of-distribution language pairs and datasets. In-depth analyses reveal how multi-task synergy, reinforcement learning initialization, curriculum design, and reward formulation contribute to advancing MLLM-driven TIMT. \n\n\\end{abstract}\n\n\n\n% yupu & zhaopeng edit\n\\section{Introduction}\nText Image Machine Translation (TIMT) is a crucial subfield of machine translation (MT) that focuses on translating source-language texts embedded in images into target-language texts~\\citep{ma2022improving,lan2023exploring,liang2024document}. It has been widely applied in scenarios including photo translation, scanned document translation, and screenshot translation. Unlike traditional text-based machine translation~\\citep{xu2024xalma, feng2024improving}, where input and output are purely textual, TIMT is inherently a cross-modal task. It requires systems to process images and generate corresponding textual translations, necessitating the comprehensive integration of fine-grained textual content and diverse visual elements such as layout, objects, and color schemes to produce accurate translations. However, current TIMT approaches, both cascade systems~\\citep{hinami2021towards, sable2023doc, zhang2023novel, zhang2025understand} and end-to-end (E2E) models~\\citep{zhu2023peit, lan2023exploring, ma2024born, liang2024document}, often do not explicitly capture fine-grained textual information or model the integration of visual element comprehension. Cascade systems typically use OCR output for a text-based MT model, thereby overlooking visual information, and suffer from error propagation. E2E models, while aiming for unified training, often lack explicit modeling of OCR and visual understanding, which is beneficial for translation quality~\\citep{niu2024umtit}.\n\nRecent advances in Multimodal Large Language Models (MLLMs) have demonstrated strong capabilities in capturing fine-grained textual information and understanding non-textual content across various cross-modal tasks, such as OCR and VQA~\\citep{qwen2_5vl, internvl, qvq-72b-preview}. Concurrently, the efficacy of large-scale Reinforcement Learning (RL) in substantially enhancing the reasoning capabilities of Large Language Models (LLMs)~\\citep{guo2025deepseek, kimi, qwq32b} has driven its adoption to MLLMs. Several studies have successfully adapted R1-like RL paradigms, often using synthesized reasoning data or rule-based visual rewards, to enhance MLLM capabilities in tasks like multimodal mathematical reasoning or visual understanding~\\citep{huang2025vision, zhang2025r1, shen2025vlm, liu2025visualrft, meng2025mmeureka, chen2025r1v}. Given that RL training can enable models to rapidly learn specific output and reasoning patterns, these concurrent developments motivate our exploration into whether such R1-like training paradigms can be effectively applied to scale up the TIMT capabilities of MLLMs.\n\nIn this work, we introduce \\textbf{MT$^{3}$}, the first framework to apply \\textbf{M}ulti-\\textbf{T}ask RL to \\textbf{M}LLMs for end-to-end \\textbf{T}ext \\textbf{I}mage \\textbf{M}achine \\textbf{T}ranslation. \\textbf{MT$^{3}$} adopts a multi-task optimization paradigm targeting three key sub-skills: text recognition, context-aware reasoning, and translation. It is trained using a novel multi-mixed reward mechanism that adapts rule-based RL strategies to TIMT’s intricacies, offering fine-grained feedback across tasks. Furthermore, recognizing the need for evaluations that reflect real-world social media interactions, we introduce XHSPost, the first social media TIMT benchmark, to evaluate TIMT in more authentic cross-cultural scenarios. Our experiments demonstrate the efficacy of this approach: our \\textbf{MT$^{3}$}-7B-Zero model achieves state-of-the-art results on the latest in-domain MIT-10M benchmark~\\citep{li2024mit10m}, outperforming strong MLLM baselines, including Qwen2.5-VL-72B~\\citep{qwen2_5vl} and InternVL2.5-78B~\\citep{internvl}, by notable margins (approximately 15-25 points average improvement across BLEU, chrF++, METEOR). Additionally, the model shows strong generalization to out-of-distribution (OOD) language pairs and datasets, including the newly introduced XHSPost. In-depth analyses reveal how multi-task synergy, reinforcement learning initialization, curriculum design, and reward formulation contribute to advancing MLLM-driven TIMT. Our core contributions are as follows:\n\\begin{itemize}\n\n\\item We propose MT$^{3}$, the first framework applying multi-task reinforcement learning to MLLMs for end-to-end TIMT, featuring a novel multi-task optimization paradigm and an effective multi-mixed reward mechanism. \nExtensive experiments validate MT$^{3}$'s superior performance on standard TIMT benchmarks and its strong OOD generalization capability, significantly surpassing existing cascaded systems and advanced MLLMs.\n\n\\item We present XHSPost, the first social media TIMT benchmark. This new resource is designed to facilitate research and enable practical evaluation of TIMT systems in authentic, real-world cross-cultural communication scenarios.\n\n\n\\item We provide comprehensive analyses yielding key insights into R1-like RL for TIMT. We demonstrate that multi-task synergy is vital for R1-like RL success in TIMT, and zero-start RL from MLLMs outperforms SFT+long Chain-of-Thought (CoT) in performance and efficiency. Besides, we show that curriculum learning and reward metric selection critically impact training and results.\n\n\n\\end{itemize}\n\n\n\n\\section{Related Works}\n\\label{sec:related_works}\n\n\\noindent{\\textbf{Text Image Machine Translation.}}\nText Image Machine Translation (TIMT) aims to translate texts embedded in images \\citep{ma2022improving}. Prevailing approaches include:\n(1) \\textit{Cascade systems}~\\citep{hinami2021towards, sable2023doc, zhang2023novel, zhang2025understand}, which sequentially combine Optical Character Recognition (OCR) and Neural Machine Translation (NMT), often facing issues like error propagation and latency.\n(2) \\textit{End-to-end (E2E) models}~\\citep{zhu2023peit, lan2023exploring, ma2024born, niu2024umtit, liang2024document}, developed to unify training and improve efficiency. Early E2E methods integrated visual encoders and text decoders, with some bridging modality gaps using pre-trained components~\\citep{zhu2023peit} or dynamically assembling models~\\citep{liang2024document}. While advanced MLLMs~\\citep{internvl, qwen2_5vl} show promise for more effective E2E TIMT~\\citep{li2024mit10m}, their specific application to this task remains underexplored.\n\n\\noindent{\\textbf{MLLM Reasoning with Reinforcement Learning.}}\nRL has been shown to enhance the reasoning capabilities of LLMs~\\citep{team2025kimi, guo2025deepseek}, leading to explorations of RL in MLLMs~\\citep{huang2025vision, zhang2025r1, shen2025vlm, liu2025visualrft,meng2025mmeureka}.\nSeveral studies employ R1-like training methodologies, often cultivating a \"\\verb|<think>| then \\verb|<answer>|\" paradigm. For instance, some focus on improving MLLM reasoning through synthesized data~\\citep{huang2025vision}, while others design online RL frameworks for self-improvement via step-wise rewarding~\\citep{zhang2025r1}, or explore R1-style RL for general vision-language tasks~\\citep{shen2025vlm}.\nThese efforts have primarily concentrated on tasks like multimodal mathematical reasoning or general visual understanding. However, there has been less focus on leveraging multi-task RL paradigms or addressing application-oriented downstream tasks like TIMT, a gap our work aims to fill.\n\n\n\n\n\n\\section{Method}\n\\label{sec:method}\nIn this section, we detail the MT$^{3}$ framework. Our MT$^{3}$ framework decomposes TIMT into distinct, explicit sub-tasks and employs a novel multi-mixed reward mechanism to guide the MLLM. The training is performed using the Group Relative Policy Optimization (GRPO)~\\citep{shao2024deepseekmath} algorithm, selected for its efficiency in training LLMs with RL.\n\n\\subsection{Multi-Task Formulation for TIMT}\n\\label{sec:method_multitask_formulation}\nTIMT is intrinsically linked to fundamental MLLM capabilities like visual perception (specifically, OCR) and multimodal reasoning. We hypothesize that enhancing these core capabilities while simultaneously optimizing for translation quality will synergistically boost the MLLM's overall TIMT proficiency. To this end, MT$^{3}$ formulates TIMT as a multi-task learning problem where the MLLM performs a sequence of interconnected tasks within a unified generation process. This process is guided by the structured prompt, which instructs the model to generate its output in a specific format encompassing recognition, reasoning, and translation.\n\\vspace{-1.5mm}\n\\begin{tcolorbox}[\n colframe=teal!70!black, % 边框颜色：深青色带一点黑色\n colback=teal!10!white, % 背景颜色：浅青色\n coltitle=white, % 标题文字颜色：白色\n fonttitle=\\bfseries, % 标题字体加粗\n title=MT$^{3}$ Prompt, % 标题内容\n sharp corners, % 圆角设置为锐角\n boxrule=0.5mm, % 边框宽度\n]\nSystem: You are a helpful translation assistant. The user provides an image containing \\textit{\\{source\\_language\\}} text and asks for the corresponding \\textit{\\{target\\_language\\}} translation. First, the assistant recognizes all the text in the image following the natural reading order. Then, the assistant carefully analyzes the recognized text and the visual elements in the image, considering the layout, objects, color schemes, spatial relationships, and other contextual clues that may influence meaning. This integrated understanding ensures the translation is accurate, coherent, and appropriate to the visual setting. After thorough reasoning based on both textual content and visual context, the assistant provides the user with the final translation in reading order. The recognized text, reasoning process, and final translation are enclosed within <recognize> </recognize>, <think> </think>, and <translate> </translate> tags, respectively. The format must be as follows: <recognize> recognized text here </recognize> <think> reasoning process here </think> <translate> final translation here </translate> \\\\\nUser: \\textit{\\{image\\}} Translate all the text in this image into \\textit{\\{target\\_language\\}} following the natural reading order.\n\\label{prompt:full_task}\n\\end{tcolorbox}\n\\vspace{-1.5mm}\nHere, \\textit{\\{source\\_language\\}} and \\textit{\\{target\\_language\\}} indicate the source and target languages, and \\textit{\\{image\\}} denotes the input image requiring translation.\n\n\\noindent{\\textbf{Recognition.}} The MLLM first performs OCR, transcribing image text in its natural reading order within \\verb|<recognize>| and \\verb|</recognize>| tags. This step directly leverages the model's visual perception and text extraction capabilities.\n\n\\noindent{\\textbf{Reasoning.}} Subsequently, the MLLM engages in a reasoning process, outputting its analysis within \\verb|<think>| and \\verb|</think>| tags. It considers the recognized text alongside the broader visual context (e.g., layout, objects, color schemes, spatial relationships) to ensure the subsequent translation is contextually appropriate. This task explicitly cultivates the MLLM's multimodal reasoning for TIMT.\n\n\\noindent{\\textbf{Translation.}} Finally, informed by the recognition and reasoning stages, the MLLM generates the target translation of the recognized text. This output is presented within \\verb|<translate>| and \\verb|</translate>| tags, maintaining the reading order and focusing on context-enhanced translation.\n\nThis explicit multi-task formulation guides the model through distinct recognition, reasoning, and translation stages. It allows for monitoring and rewarding these intermediate steps, thereby fostering more robust and interpretable TIMT capabilities, as validated in our ablation studies (Section~\\ref{sec:analysis_multitask_ablation}).\n\n\n\n\n\\subsection{Multi-Mixed Reward Mechanism}\n\\label{sec:method_reward}\nThe reward signal $r$ is crucial in RL. While rule-based rewards suit tasks with verifiable answers (e.g., math, coding)~\\citep{guo2025deepseek}, translation often lacks a single correct output and TIMT's multi-faceted quality (e.g., recognition accuracy, translation quality) necessitates a more nuanced approach. We propose a \\textbf{multi-mixed reward mechanism}, adapting rule-based concepts by integrating format adherence checks with task-specific quality assessments.\n\n\\noindent{\\textbf{Format Reward ($R_{format}$).}} We use regular expression extraction to enforce the structured response format illustrated in the MT$^{3}$ prompt. The format reward is computed as:\n$$ R_{format} = \\begin{cases} 1, & \\text{if format is correct} \\\\ -3, & \\text{if format is incorrect} \\end{cases} $$\nThis penalty for incorrect format strongly discourages deviations and encourages the model to rapidly learn the required output structure, as evidenced in Figure~\\ref{fig:task_ablation}. \n\n\n\n\\noindent{\\textbf{Task-Specific Rewards ($R_{task}$).}} If the output format is correct ($R_{format}=1$), we calculate rewards for recognition and translation sub-tasks. While the MT community has developed various evaluation metrics~\\citep{freitag-etal-2022-results, freitag-etal-2023-results}, \\citet{feng2025mtr1zero} and \\citet{ramos2025finegrained} noted that relying on a single metric might result in sub-optimal overall performance. Therefore, we propose a metric-mixed approach. We average multiple standard metrics for each task, providing a more robust, balanced, and nuanced reward signal:\n\n\\begin{itemize}\n \\item \\textbf{Translation Reward ($R_{task-trans}$):} Assesses final translation quality by averaging scores from standard MT metrics: BLEU~\\citep{post-2018-call}, chrF++~\\citep{popovic2017chrf++}, and METEOR~\\citep{banerjee2005meteor}, against the reference. Formally, $R_{task-trans} = (S_{\\text{BLEU}} + S_{\\text{METEOR}} + S_{\\text{chrF++}}) / 3$. The specific combination of metrics can be varied, as explored in our experiments on metric reward selection (Section~\\ref{sec:analysis_metric_reward}). Furthermore, as highlighted by \\citet{feng2025mtr1zero}, the quality of the reasoning process is implicitly encouraged through its positive influence on the final translation quality.\n \n \\item \\textbf{Recognition Reward ($R_{task-rec}$):} Evaluates recognized text quality against a ground-truth transcription by averaging suitable OCR metrics~\\citep{fu2024ocrbenchv2}: BLEU, METEOR, F1-score, and normalized versions of Character Error Rate (CER) and Edit Distance. Formally, $R_{task-rec} = (S_{\\text{BLEU}} + S_{\\text{METEOR}} + S_{\\text{F1-score}} + \\min(\\max(1-S_{\\text{EditDistance}}, 0), 1) + \\min(\\max(1-S_{\\text{CER}}, 0), 1)) / 5$.\n\\end{itemize}\n\n\n\n\\noindent{\\textbf{Final Reward.}} The final reward $r$ fed to GRPO combines $R_{format}$ and $R_{task}$:\n$$ r = \\begin{cases} R_{format} + R_{task-rec} + R_{task-trans}, & \\text{if format is correct} \\\\ -3, & \\text{if format is incorrect} \\end{cases} $$\nThis formulation extends the simple binary rule-based reward (a base score of 1 for correct format) by adding continuous, mixed-metric scores for sub-tasks. This provides more granular feedback than purely binary rewards, enabling the model to learn fine-grained improvements in both recognition and translation quality, as evidenced by the training curves in Figure~\\ref{fig:task_ablation}. \n\n\n\n\n\\subsection{RL Algorithm}\nWe use the GRPO algorithm~\\citep{shao2024deepseekmath} to train the translation model with our rule-metric mixed reward. In each training step, for a given translational question $q$, we sample a group of candidate outputs $\\{o_1,o_2,\\cdots,o_G\\}$ from the policy model $\\pi_{\\theta_{old}}$. $A_i = \\frac{r_i - \\operatorname{mean}(\\{r_1, r_2, \\dots, r_G\\})}{\\operatorname{std}(\\{r_1, r_2, \\dots, r_G\\})}$ is the computed advantage using the group rule-metric mixed rewards $\\{r_1,r_2,\\cdots,r_G\\}$. GRPO then maximizes the following objective function to optimize $\\pi_{\\theta}$: \n\\begin{equation}\n\\resizebox{\\textwidth}{!}{%\n$J_{\\mathrm{GRPO}}(\\theta) = \\mathbb{E}_{q \\sim P(Q),\\, \\{o_i\\}_{i=1}^G \\sim \\pi_{\\theta_{\\mathrm{old}}}(O \\mid q)} \\left[ \\frac{1}{G} \\sum_{i=1}^G \\min\\left( \\frac{\\pi_{\\theta}(o_i \\mid q)}{\\pi_{\\theta_{\\mathrm{old}}}(o_i \\mid q)} A_i,\\, \\mathrm{clip}\\left( \\frac{\\pi_{\\theta}(o_i \\mid q)}{\\pi_{\\theta_{\\mathrm{old}}}(o_i \\mid q)},\\, 1-\\varepsilon,\\, 1+\\varepsilon \\right) A_i \\right) - \\beta D_{\\mathrm{KL}}\\left( \\pi_{\\theta} \\,\\big\\|\\, \\pi_{\\mathrm{ref}} \\right) \\right]$\n}\n\\label{eq1}\n\\end{equation}\nwhere $\\varepsilon$ and $\\beta$ are hyperparameters controlling the PPO clipping threshold and the weight of the Kullback–Leibler (KL) divergence penalty~\\citep{schulman2017proximal,shao2024deepseekmath}, respectively. \n\n\n\n\n\\subsection{Experimental Setup}\n\\label{sec:experimental_setup} \n\\noindent{\\textbf{Datasets and Evaluation Metrics.}}\nOur primary experiments focus on English-Chinese (EN-ZH) and Chinese-English (ZH-EN) TIMT tasks. For RL training, we select 15K image-text pairs from the MIT-10M dataset~\\citep{li2024mit10m} for each translation direction. To comprehensively evaluate our MT$^{3}$ framework, we test its performance across three distinct settings: \n(1) \\textbf{In-domain (IND):} utilizing the standard ZH-EN and EN-ZH test sets from the MIT-10M dataset. \n(2) \\textbf{Out-of-distribution (OOD) - Language Pairs:} employing unseen language pairs (EN-DE, ZH-FR, DE-FR) from within the MIT-10M dataset. \n(3) \\textbf{Out-of-distribution (OOD) - Datasets:} using the test sets from the OCRMT30K dataset~\\citep{lan2023exploring} and the document-level DoTA dataset~\\citep{liang2024document}. \nFor all evaluations, we report SacreBLEU~\\citep{post-2018-call}, chrF++~\\citep{popovic2017chrf++}, and METEOR~\\citep{banerjee2005meteor} scores as previous works~\\citep{li2024mit10m,liang2024document}.\n\n% {\\color{blue}\\noindent{\\textbf{XHSPost Benchmark: A Real-World Social Media TIMT Scenario.}}\n% Existing TIMT datasets primarily cover scenarios such as general photos (e.g., OCRMT30K), academic papers (e.g., DoTA), or advertisements and product descriptions (e.g., MIT-10M). To address the notable absence of benchmarks for the increasingly prevalent social media domain, we constructed XHSPost, a post-centric dataset for ZH-EN and EN-ZH TIMT using content from Xiaohongshu. After a meticulous selection and filtering process (detailed in Appendix~\\ref{app:xhspost_construction}), we obtained 106 English and 109 Chinese posts. Illustrative examples are in Figure~\\ref{fig:xhs_case} and \\ref{fig:dataset_case}. We anticipate XHSPost will foster research in social media TIMT and will be released upon acceptance.}\n\n\n\\begin{table*}[t]\n \\centering\n \\small\n \\caption{\n In-domain (IND) performance comparison on the MIT-10M benchmark (ZH-EN and EN-ZH). Metrics reported are BLEU, chrF++, and METEOR, along with their average (Avg.). MT$^{3}$-7B-Zero (RL) is compared against open-source systems and supervised fine-tuned variants.\n }\n \\label{tab:main_mt_results}\n \\resizebox{0.95\\textwidth}{!}{%\n \\setlength{\\tabcolsep}{4pt}\n \\begin{tabular}{l*{9}{c}} % 改为9列\n \\toprule\n \\multirow{2.5}{*}{\\sc Model} & \\multicolumn{4}{c}{\\sc MIT-10M (ZH-EN)} & & \\multicolumn{4}{c}{\\sc MIT-10M (EN-ZH)} \\\\ % 添加空列\n \\cmidrule(lr){2-5} \\cmidrule(lr){7-10} % 调整cmidrule范围\n & BLEU & chrF++ & METEOR & Avg. & & BLEU & chrF++ & METEOR & Avg. \\\\ % 添加空列头\n \\midrule\n % \\multicolumn{10}{c}{\\textit{\\textbf{Closed}}} \\\\ % 调整为10列\n % Claude-3.5-Sonnet (2024/10) & - & - & - & - & & - & - & - & - \\\\ \n % GPT-4o (2024/08) & - & - & - & - & & - & - & - & - \\\\ \n % \\midrule\n\n \\multicolumn{10}{c}{\\textit{\\textbf{Zero-shot Systems}}} \\\\ % 调整为10列\n \\multicolumn{10}{@{}l}{\\textcolor{lightgray}{\\textit{Cascade System}}} \\\\\n EasyOCR + NLLB-3.3B & 2.54 & 10.71 & 7.72 & 6.99 & & 11.93 & 12.48 & 10.84 & 11.75 \\\\ % 添加空列\n\n\n \\multicolumn{10}{@{}l}{\\textcolor{lightgray}{\\textit{Advanced MLLMs}}} \\\\\n InternVL2.5-8B & 9.98 & 26.59 & 24.99 & 20.52 & & 31.74 & 38.21 & 38.49 & 36.15 \\\\\n InternVL2.5-26B & 13.29 & 27.82 & 24.60 & 21.90 & & 33.22 & 42.43 & 46.55 & 40.73 \\\\\n InternVL2.5-38B & 10.77 & 26.00 & 24.49 & 20.42 & & 38.61 & 48.87 & 44.04 & 43.84 \\\\\n InternVL2.5-78B & 12.50 & 26.85 & 27.11 & 22.15 & & 36.97 & 48.96 & 46.19 & 44.04 \\\\\n Qwen2.5-VL-7B & 15.59 & 31.34 & 33.44 & 26.79 & & 31.66 & 44.20 & 48.41 & 41.42 \\\\\n Qwen2.5-VL-72B & 11.33 & 32.82 & 41.26 & 28.47 & & 35.55 & 50.78 & 52.76 & 46.36 \\\\\n\n\n\n \\midrule\n\n \\multicolumn{10}{c}{\\textit{\\textbf{Fine-tuned VLMs}}} \\\\ % 调整为10列\n % $\\text{M}^{2}\\text{T}^{2}$-7B-Zero (Prompting) & 12.70 & 34.19 & 38.48 & 28.46 & & 36.85 & 47.40 & 46.07 & 43.44 \\\\\n Qwen2.5-VL-7B (SFT, TIMT) & 18.66 & 40.74 & 43.20 & 34.20 & & 43.33 & 48.62 & 55.71 & 49.22 \\\\\n Qwen2.5-VL-7B (SFT, OCR + TIMT) & 22.57 & 42.39 & 41.80 & 35.59 & & 48.69 & 54.66 & 59.60 & 54.32 \\\\\n MT$^{3}$-7B-Zero (RL) & 20.31 & 44.42 & 43.99 & 36.24 & & 54.49 & 61.77 & 68.23 & 61.50 \\\\\n\n\n \\bottomrule\n \\end{tabular}\n }\n\\end{table*}\n\n\\begin{table}[t]\n \\centering\n \\caption{\nOut-of-distribution (OOD) generalization performance using chrF++ and METEOR. MT$^{3}$-7B-Zero (RL) is evaluated against strong and same-size MLLM baselines on unseen language pairs from MIT-10M (EN-DE, ZH-FR, DE-FR) and different OOD datasets (OCRMT30K, DoTA, XHSPost). \\textbf{Bold} indicates the best, \\underline{underline} indicates the second best.\n }\n \\label{tab:ood_chrf_meteor}\n \\resizebox{0.98\\columnwidth}{!}{%\n \\setlength{\\tabcolsep}{2pt}\n \\begin{tabular}{@{}lcccccccccccccccc@{}}\n \\toprule\n \\multirow{3}{*}{\\sc Model} \n & \\multicolumn{6}{c}{\\sc OOD (Language Pairs)} \n & \\multicolumn{8}{c}{\\sc OOD (Datasets)} \\\\\n \\cmidrule(lr){2-7} \n \\cmidrule(lr){8-15}\n & \\multicolumn{2}{c}{\\sc MIT-10M (EN-DE)} \n & \\multicolumn{2}{c}{\\sc MIT-10M (ZH-FR)} \n & \\multicolumn{2}{c}{\\sc MIT-10M (DE-FR)} \n & \\multicolumn{2}{c}{\\sc OCRMT30K (ZH-EN)} \n & \\multicolumn{2}{c}{\\sc DoTA (EN-ZH)} \n & \\multicolumn{2}{c}{\\sc XHSPost (EN-ZH)} \n & \\multicolumn{2}{c}{\\sc XHSPost (ZH-EN)} \\\\\n \\cmidrule(lr){2-3} \\cmidrule(lr){4-5} \\cmidrule(lr){6-7} \n \\cmidrule(lr){8-9} \\cmidrule(lr){10-11} \\cmidrule(lr){12-13} \\cmidrule(lr){14-15}\n & chrF++ & METEOR & chrF++ & METEOR & chrF++ & METEOR \n & chrF++ & METEOR & chrF++ & METEOR & chrF++ & METEOR & chrF++ & METEOR \\\\\n \\midrule\n \\multicolumn{15}{@{}l}{\\textcolor{lightgray}{\\textit{Strong Baseline}}} \\\\\n Qwen2.5-VL-72B & 48.37 & 40.60 & \\underline{27.29} & 30.13 & \\underline{48.82} & \\underline{45.06} \n & \\textbf{39.73} & \\textbf{42.65} & \\textbf{37.65} & \\textbf{50.75} \n & \\textbf{42.97} & \\textbf{61.28} & \\textbf{55.10} & \\textbf{54.07} \\\\\n InternVL2.5-78B & \\underline{50.54} & \\underline{43.11} & 18.25 & 19.59 & 27.98 & 25.45 \n & 31.57 & 32.81 & 32.88 & 45.03 \n & 37.61 & 57.60 & 53.14 & 51.18 \\\\\n \\multicolumn{15}{@{}l}{\\textcolor{lightgray}{\\textit{Same-size Baseline}}} \\\\\n Qwen2.5-VL-7B & 48.12 & 40.65 & 27.19 & \\underline{30.59} & 33.60 & 31.99 \n & 23.99 & 24.90 & 32.62 & 44.16 \n & 36.79 & 55.16 & 49.01 & 47.05 \\\\\n InternVL2.5-8B & 42.42 & 36.67 & 19.32 & 14.71 & 23.17 & 20.41 \n & 21.95 & 21.12 & 24.75 & 34.89 \n & 28.32 & 46.20 & 42.19 & 38.93 \\\\\n \\midrule\n MT$^{3}$-7B-Zero (RL) & \\textbf{56.14} & \\textbf{52.11} & \\textbf{35.67} & \\textbf{38.53} & \\textbf{53.88} & \\textbf{54.99} \n & \\underline{37.42} & \\underline{38.17} & \\underline{34.87} & \\underline{46.60} \n & \\underline{39.95} & \\underline{58.58} & \\underline{53.26} & \\underline{52.04} \\\\\n \\bottomrule\n \\end{tabular}\n }\n\\vspace{-3mm}\n\\end{table}\n\n\n\\noindent{\\textbf{XHSPost Benchmark: A Real-World Social Media TIMT Scenario.}}\nExisting TIMT datasets primarily focus on general photos, academic documents, or advertisements (see Figure~\\ref{fig:dataset_case}). To address the notable absence of benchmarks for the increasingly prevalent social media domain, we constructed XHSPost, a dataset of social media posts for ZH-EN and EN-ZH TIMT using content from Xiaohongshu. After a meticulous selection and filtering process (detailed in Appendix~\\ref{app:xhspost_construction}), we obtained 106 English and 109 Chinese posts. We anticipate XHSPost will foster real-world applications of TIMT.\n\n\n\n\\noindent{\\textbf{Baselines.}}\nTo comprehensively assess the performance of our MT$^{3}$-7B-Zero, we compare it against a diverse set of baselines. This includes a \\textbf{Cascade System}, which first employs EasyOCR\\footnote{\\url{https://github.com/JaidedAI/EasyOCR}} to extract text from images, arranges the extracted text sequentially, and then translates it using the NLLB-3.3B model~\\citep{costa2022no}. We also benchmark against several \\textbf{Advanced MLLMs} used in a zero-shot prompting setup, specifically the InternVL2.5 series~\\citep{internvl} and Qwen2.5-VL series~\\citep{qwen2_5vl}; more evaluation details can be found in Appendix~\\ref{app:eval}. Furthermore, we compare against \\textbf{Supervised Fine-Tuning (SFT)} variants of Qwen2.5-VL-7B. These SFT models were trained on the MIT-10M dataset using two distinct prompt templates: one focused solely on the end-to-end \\textit{TIMT} task, and another employing an \\textit{OCR + TIMT} multi-task format. The specific SFT prompts are also provided in Appendix~\\ref{app:sft_prompts}.\n\n\n\n\n\\noindent{\\textbf{Training Details.}}\nOur implementation is based on verl\\footnote{\\url{https://github.com/volcengine/verl}} framework. We select the Qwen2.5-VL-7B as starting models for MT$^{3}$ training.\nDuring training, we configure a batch size of 64 and utilize 16 rollouts per prompt within the GRPO algorithm. More training details are in Appendix~\\ref{app:train_details}.\n\n\n\n\n\n\\subsection{Experimental Results}\n\\label{sec:main_result}\n\n\\noindent{\\textbf{In-Domain Performance.}}\nAs detailed in Table~\\ref{tab:main_mt_results}, our MT$^{3}$-7B-Zero significantly outperforms compared systems on the in-domain MIT-10M benchmark. For ZH-EN, MT$^{3}$-7B-Zero achieved an average score of 36.24, surpassing Qwen2.5-VL-72B (Avg. 28.47) by 7.77 points and InternVL2.5-78B by 14.09 points. The improvements are more pronounced for EN-ZH, where our model (Avg. 61.50) outperforms Qwen2.5-VL-72B by 15.14 points and InternVL2.5-78B by 17.46 points. Notably, our RL-trained model also surpasses SFT variants of Qwen2.5-VL-7B, including those trained with OCR+TIMT multi-task or single TIMT task setups. These results underscore the efficacy of our multi-task RL approach over supervised fine-tuning and zero-shot larger MLLMs for TIMT.\n\n\\noindent{\\textbf{Out-of-Distribution Performance.}}\nWe assessed MT$^{3}$-7B-Zero's generalization ability on diverse out-of-distribution (OOD) scenarios, including unseen language pairs from MIT-10M (EN-DE, ZH-FR, DE-FR), distinct TIMT datasets (OCRMT30K~\\citep{lan2023exploring}, the document-level DoTA~\\citep{liang2024document}), and our newly introduced real-world social media benchmark, XHSPost. As shown in Table~\\ref{tab:ood_chrf_meteor}, our model consistently demonstrated strong OOD performance, generally outperforming comparable-sized models and rivaling larger MLLMs. For instance, on MIT-10M (DE-FR), MT$^{3}$-7B-Zero achieved 54.99 chrF++ and 53.88 METEOR, surpassing all listed baselines. On DoTA, our model's 46.60 chrF++ was considerably higher than Qwen2.5-VL-72B's 37.65, with a competitive METEOR score. These findings highlight the robust generalization fostered by the MT$^{3}$ framework. Appendix~\\ref{app:ood_bleu} provides corresponding OOD BLEU scores.\n\n\n\\begin{table*}[h]\n \\centering\n \\small\n \\caption{\n Multi-task ablation study for MT$^{3}$ framework. Performance impact of removing Recognition, Reasoning, or both, compared to the full task setup. $\\Delta$ denotes the average metric score difference compared to the \\textit{Full Tasks} setting.\n }\n \\label{tab:ablation_results}\n \\resizebox{\\textwidth}{!}{%\n \\setlength{\\tabcolsep}{4pt}\n \\begin{tabular}{lccc*{4}{c}c*{4}{c}c*{4}{c}c}\n \\toprule\n \\multirow{2.5}{*}{\\sc Ablation} & \\multicolumn{3}{c}{\\sc Tasks} \n & \\multicolumn{4}{c}{\\sc MIT-10M (ZH-EN)} & \\multirow{2.5}{*}{\\sc $\\Delta$}\n & \\multicolumn{4}{c}{\\sc MIT-10M (EN-ZH)} & \\multirow{2.5}{*}{\\sc $\\Delta$}\n & \\multicolumn{4}{c}{\\sc XHSPost (ZH-EN)} & \\multirow{2.5}{*}{\\sc $\\Delta$} \\\\\n \\cmidrule(lr){2-4} \\cmidrule(lr){5-8} \\cmidrule(lr){10-13} \\cmidrule(lr){15-18}\n & Recognition & Reasoning & Translation \n & BLEU & chrF++ & METEOR & Avg. & \n & BLEU & chrF++ & METEOR & Avg. & \n & BLEU & chrF++ & METEOR & Avg. & \\\\\n \\midrule\n Full Tasks & \\cmark & \\cmark & \\cmark \n & 20.31 & 44.42 & 43.99 & 36.24 & ---\n & 54.49 & 61.77 & 68.23 & 61.50 & ---\n & 26.94 & 53.26 & 52.04 & 44.08 & --- \\\\\n w/o Reasoning & \\xmark & \\cmark & \\cmark \n & 22.21 & 45.06 & 44.63 & 37.30 & +1.06\n & 52.12 & 61.34 & 64.26 & 59.24 & -2.26\n & 25.01 & 51.50 & 49.73 & 42.08 & -2.00 \\\\\n w/o Recognition & \\cmark & \\xmark & \\cmark \n & 16.99 & 40.08 & 42.65 & 33.24 & -3.00\n & 47.61 & 57.78 & 65.11 & 56.83 & -4.67\n & 25.06 & 52.01 & 50.67 & 42.58 & -1.50 \\\\\n Only TIMT & \\xmark & \\xmark & \\cmark \n & 13.43 & 38.47 & 42.21 & 31.37 & -4.87\n & 44.04 & 55.87 & 61.77 & 53.89 & -7.61\n & 24.21 & 51.32 & 49.54 & 41.69 & -2.39 \\\\\n \\bottomrule\n \\end{tabular}\n }\n\\end{table*}\n\n\n\n\n\n\\begin{figure}[t]\n\\centering\n\\includegraphics[width=0.9\\textwidth]{fig/xhs_case.pdf}\n\\caption{Illustrative TIMT examples from the XHSPost benchmark. MT$^{3}$ demonstrates superior contextual understanding by correctly translating '\\textit{Friends}' to '\\textit{\\chinese{老友记}}' based on visual context (\\textbf{Top}), and accurately interpreting a property advertisement, including price and layout details (\\textbf{Bottom}).\n}\n\\label{fig:xhs_case}\n\\vspace{-3mm}\n\\end{figure}\n\n\n\n\\section{Analyses}\n\\label{sec:analyses}\n\n\n\\subsection{Multi-Task Ablation Analysis}\n\\label{sec:analysis_multitask_ablation}\nTo disentangle the contributions of the text recognition, reasoning, and translation components within our multi-task framework, we conduct an ablation study comparing the full-task MT$^{3}$-7B-Zero against variants where specific tasks are omitted during RL training: (1) \\textit{w/o Reasoning}; (2) \\textit{w/o Recognition}; and (3) \\textit{Only TIMT}. Detailed prompts for these settings are available in Appendix~\\ref{app:multi_task_prompt}. As shown in Table~\\ref{tab:ablation_results}, the \\textit{Full Tasks} setting consistently achieves the best performance across the evaluated benchmarks. Removing the explicit text recognition step (\\textit{w/o Recognition}) leads to a significant performance drop across all settings, underscoring the criticality of accurate visual text extraction. Omitting the reasoning step (\\textit{w/o Reasoning}) generally results in a performance decrease, particularly on more nuanced datasets, although its utility can vary with task complexity or language pair characteristics. Training only the translation task (\\textit{Only TIMT}) results in the most substantial degradation. As illustrated in Figure~\\ref{fig:xhs_case}, MT$^{3}$ correctly translates culturally nuanced terms and interprets ambiguous numerical information by integrating visual and textual cues, demonstrating how explicit recognition and reasoning foster deeper contextual understanding crucial for TIMT(see Appendix~\\ref{app:xhs_case_analysis} for more detailed analysis). These cases demonstrate how the explicit modeling of recognition and reasoning within MT$^{3}$ fosters deeper contextual understanding crucial for accurate TIMT in diverse scenarios. The reward and performance progression curves (Figure~\\ref{fig:task_ablation}) further support this: variants with recognition yield higher translation rewards, and the \\textit{Full Tasks} setting shows the most effective trajectories. This confirms the benefit of our multi-task formulation, emphasizing explicit text recognition and the synergy of jointly optimizing all three sub-tasks.\n\n\n\n\\begin{figure}[t]\n\\centering\n\\includegraphics[width=0.9\\textwidth]{fig/task_ablation.png}\n\\caption{Impact of multi-task ablation on reward optimization and performance progression. \\textbf{Top row}: Progression of \\textit{Final Reward} and individual task rewards (Format, Recognition, Translation). \\textbf{Bottom row}: Progression of translation quality metrics (BLEU, chrF++, METEOR).}\n\\label{fig:task_ablation}\n\\vspace{-3mm}\n\\end{figure}\n\n\\begin{table}[t]\n \\centering\n \\caption{\nImpact of initialization strategy on performance. Comparison of MT$^{3}$-7B-Zero (RL from scratch) and MT$^{3}$-7B-QVQ-Distill (SFT on distilled data then RL) on in-domain (MIT-10M) and out-of-distribution (OCRMT30K, DoTA, XHSPost) benchmarks. \n }\n \\label{tab:think_ablation}\n \\resizebox{0.95\\columnwidth}{!}{%\n \\setlength{\\tabcolsep}{2pt}\n \\begin{tabular}{@{}lccccccccccccccccccc@{}}\n \\toprule\n \\multirow{3}{*}{\\sc Model} \n & \\multicolumn{6}{c}{\\sc In-Domain} \n & \\multicolumn{9}{c}{\\sc Out-of-Domain} \\\\\n \\cmidrule(lr){2-7} \\cmidrule(lr){8-19}\n & \\multicolumn{3}{c}{\\sc MIT-10M (ZH-EN)} \n & \\multicolumn{3}{c}{\\sc MIT-10M (EN-ZH)} \n & \\multicolumn{3}{c}{\\sc OCRMT30K (ZH-EN)} \n & \\multicolumn{3}{c}{\\sc DoTA (EN-ZH)} \n & \\multicolumn{3}{c}{\\sc XHSPost (EN-ZH)} \n & \\multicolumn{3}{c}{\\sc XHSPost (ZH-EN)} \\\\\n \\cmidrule(lr){2-4} \\cmidrule(lr){5-7} \n \\cmidrule(lr){8-10} \\cmidrule(lr){11-13} \\cmidrule(lr){14-16} \\cmidrule(lr){17-19}\n & BLEU & chrF++ & METEOR \n & BLEU & chrF++ & METEOR \n & BLEU & chrF++ & METEOR \n & BLEU & chrF++ & METEOR \n & BLEU & chrF++ & METEOR \n & BLEU & chrF++ & METEOR \\\\\n \\midrule\n \\multicolumn{19}{@{}l}{\\textcolor{lightgray}{\\textit{w/ \"Cold Start\"}}} \\\\\n MT$^{3}$-7B-QVQ-Distill & \n 15.37 & 36.36 & 40.20 & \n 42.21 & 53.55 & 58.37 & \n 12.39 & 34.68 & 35.43 & \n 29.23 & 25.60 & 37.08 & \n 48.20 & 39.61 & 58.41 & \n 24.61 & 49.74 & 48.76 \\\\\n \\multicolumn{19}{@{}l}{\\textcolor{lightgray}{\\textit{w/o \"Cold Start\"}}} \\\\\n MT$^{3}$-7B-Zero & \n 20.31 & 44.42 & 43.99 & \n 54.49 & 61.77 & 68.23 & \n 14.21 & 37.42 & 38.17 & \n 40.46 & 34.87 & 46.60 & \n 49.41 & 39.95 & 58.58 & \n 26.94 & 53.26 & 52.04 \\\\\n \\bottomrule\n \\end{tabular}\n }\n \\vspace{-3mm}\n\\end{table}\n\n\n\\subsection{Impact of Initialization Strategy: Zero vs. QVQ-Distill}\n\\label{sec:analysis_init_strategy}\nTo assess the effect of the RL starting point, we compare MT$^{3}$-7B-Zero ( RL directly from the general MLLM checkpoint, \\textit{Zero-start}) against MT$^{3}$-7B-QVQ-Distill (initialized via SFT on 10K high-quality TIMT examples distilled from the QVQ-72B~\\citep{qvq-72b-preview}; details in Appendix~\\ref{app:initial}). Training dynamics (Figure~\\ref{fig:qvq_zero}) show that while the QVQ-Distill model benefits from an initial SFT performance boost, the Zero-start model exhibits a steeper learning curve, rapidly surpassing the cold-start variant and converging to a markedly higher performance ceiling. The Zero-start model also produces considerably shorter and more stable response lengths. A qualitative example in Appendix~\\ref{app:case} further reveals differences in reasoning patterns: the QVQ-Distill model often simulates self-reflection patterns, whereas the Zero-start model can incentivize a more straightforward and less redundant reasoning path conducive to TIMT. Table~\\ref{tab:think_ablation} corroborates these findings, showing Zero-start yields significantly better final performance.\n\n\n\n\\begin{figure}[t]\n\\centering\n\\includegraphics[width=0.9\\textwidth]{fig/qvq_zero.png}\n\\caption{\nTraining dynamics comparing Zero-start RL (MT$^{3}$-7B-Zero) vs. SFT initialization (MT$^{3}$-7B-QVQ-Distill). \\textbf{Left and Center:} Average metric score progression on MIT-10M ZH-EN and EN-ZH test sets. \\textbf{Right:} Average response length during RL training.\n}\n\\label{fig:qvq_zero}\n\\vspace{-2mm}\n\\end{figure}\n\n\n\n\n\\subsection{Curriculum Learning}\n\\label{sec:analysis_curriculum} \nWe explore curriculum learning by ordering training data by difficulty, categorized by~\\citet{li2024mit10m} as Easy, Medium, or Hard using heuristics based on text length and scene complexity, with details in Appendix~\\ref{app:difficulty}. We compare three data presentation strategies: \\textit{Shuffle} (random), \\textit{Ascend} (easy-to-hard), and \\textit{Descend} (hard-to-easy). Figure~\\ref{fig:curriculum} shows response length dynamics: \\textit{Ascend} leads to longer responses as harder samples appear, while \\textit{Descend} starts longer and shortens. Figure~\\ref{fig:curriculum} also shows the final BLEU scores and response lengths on MIT-10M test splits. \\textit{Ascend} tends to yield competitive or superior BLEU scores on Hard instances, whereas \\textit{Descend} performs better on Easy instances. \\textit{Shuffle} demonstrates the most balanced performance across different difficulty splits. These results suggest that while specific curricula can tune for specific difficulty levels, a shuffled curriculum may be more broadly beneficial for robust TIMT.\n\n\n\n\\begin{figure}[h]\n\\centering\n\\includegraphics[width=0.95\\textwidth]{fig/curriculum.png}\n\\caption{Influence of curriculum learning strategies on training dynamics and performance. \\textbf{Left}: Average response length dynamics for \\textit{Shuffle}, \\textit{Ascending (easy-to-hard)}, and \\textit{Descending (hard-to-easy)} difficulty curricula during training. \\textbf{Right}: Final response length and BLEU scores on MIT-10M difficulty splits (Easy, Medium, Hard) for EN-ZH and ZH-EN..\n}\n\\label{fig:curriculum}\n\\vspace{-3mm}\n\\end{figure}\n\n\n\n\n\\subsection{Metric Reward Selection for Translation}\n\\label{sec:analysis_metric_reward}\nOur multi-mixed reward mechanism (Section~\\ref{sec:method_reward}) averages multiple metrics for task-specific rewards. Here, we analyze using individual metrics (BLEU, chrF++, METEOR) versus our proposed \\textit{Mixed Reward} (average of these three) for $R_{task-trans}$. Figure~\\ref{fig:metric_reward} presents Spearman and Kendall correlation matrices, which indicate positive correlations between the final rewards from individual metrics and the mixed-metric reward, suggesting the shared underlying signal of quality. Notably, BLEU shows the lowest correlation with chrF++, a divergence that is also reflected in their differing performance trajectories in Figure~\\ref{fig:metric_metric}. These performance plots demonstrate that the \\textit{Mixed Reward} generally results in the most consistent and often the highest performance across all three evaluation metrics. This mitigates optimizing for a single metric's peculiarities, leading to holistically improved translation quality and validating our mixed-metric approach.\n\n\\begin{figure}[t]\n\\centering\n\\includegraphics[width=0.9\\textwidth]{fig/metric_reward.png}\n\\caption{\nAnalysis of translation rewards. \\textbf{Left}: Kendall and Spearman correlation matrices between different individual metric rewards (chrF++, METEOR, BLEU) and the \\textit{Mixed Reward}, based on final reward. \\textbf{Right}: Progression of \\textit{Translation Reward} and \\textit{Final Reward} when optimizing for individual metric reward versus the \\textit{Mixed Reward}.\n}\n\\label{fig:metric_reward}\n\\vspace{-2mm}\n\\end{figure}\n\n\\begin{figure}[t]\n\\centering\n\\includegraphics[width=0.9\\textwidth]{fig/metric_metric.png}\n\\caption{ Effect of different translation reward metric choices ($R_{task-trans}$) on performance progression for EN-ZH translation on MIT-10M. Curves display BLEU, chrF++, and METEOR scores over training steps when optimizing with individual metrics versus the \\textit{Mixed Reward}.\n}\n\\label{fig:metric_metric}\n\\vspace{-2mm}\n\\end{figure}\n\n\n\n\\section{Conclusion and Future Work}\n\\label{sec:conclusion}\nWe introduce MT$^{3}$, the first framework to leverage multi-task reinforcement learning for end-to-end MLLM-based Text Image Machine Translation. By explicitly optimizing text recognition, context-aware reasoning, and translation through a novel multi-mixed reward mechanism, MT$^{3}$ significantly advances MLLM-based TIMT capabilities. Our experiments demonstrate that MT$^{3}$-7B-Zero achieves leading performance on the MIT-10M benchmark and exhibits robust out-of-distribution generalization, substantially outperforming strong MLLM baselines and SFT approaches. A key contribution of this work is also the introduction of the XHSPost benchmark, a novel resource for evaluating TIMT in realistic social media contexts, which we hope will spur further research in this domain. Key analytical insights reveal the benefits of zero-start RL, the crucial synergy of our multi-task design, and the efficacy of a mixed-metric reward strategy for translation. Our work underscores the potential of multi-task RL for complex multimodal applications and offers valuable guidelines for developing more sophisticated RL-driven MLLMs for TIMT and related domains.\n\n\\noindent{\\textbf{Limitations and Future Work.}} Although MT$^{3}$ demonstrates strong performance on the TIMT task across multiple benchmarks, there remains room for improvement in its multilingual capabilities and user interaction. In future work, we aim to leverage the robust text-grounding and visual understanding abilities of MLLMs to extend this approach to cross-lingual and multilingual VQA and reasoning tasks, while further enhancing MLLMs' alignment with user preferences.\n\n\n\n\n\n\n\n% \\begin{ack}\n% Use unnumbered first level headings for the acknowledgments. All acknowledgments\n% go at the end of the paper before the list of references. Moreover, you are required to declare\n% funding (financial activities supporting the submitted work) and competing interests (related financial activities outside the submitted work).\n% More information about this disclosure can be found at: \\url{https://neurips.cc/Conferences/2025/PaperInformation/FundingDisclosure}.\n\n\n% Do {\\bf not} include this section in the anonymized submission, only in the final paper. You can use the \\texttt{ack} environment provided in the style file to automatically hide this section in the anonymized submission.\n% \\end{ack}\n\\clearpage\n\\bibliographystyle{unsrtnat}\n\\bibliography{custom}\n\n\n\n\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\n\\clearpage\n\\appendix\n\n\n\n\n\\section{Evaluation Details}\n\\label{app:eval}\nWhen evaluating MLLM's performance on the test set, we deployed open-source models locally using frameworks like vLLM\\footnote{\\url{https://github.com/vllm-project/vllm}} or lmdeploy\\footnote{\\url{https://github.com/InternLM/lmdeploy}} implementations. We use the greedy decoding strategy for all systems. The maximum generation length was capped at 4096 tokens.\nThe prompt for evaluating MLLMs is structured as follows:\n\\begin{tcolorbox}[\n colframe=teal!70!black, % 边框颜色：深青色带一点黑色\n colback=teal!10!white, % 背景颜色：浅青色\n coltitle=white, % 标题文字颜色：白色\n fonttitle=\\bfseries, % 标题字体加粗\n title=Evaluating MLLMs Prompt, % 标题内容\n sharp corners, % 圆角设置为锐角\n boxrule=0.5mm, % 边框宽度\n]\nSystem: You are a helpful translation assistant. The user provides an image containing \\textit{\\{source\\_language\\}} text and asks for the corresponding \\textit{\\{target\\_language\\}} translation. \\\\\nUser: \\textit{\\{image\\}} Translate the text in the image from \\textit{\\{source\\_language\\}} into \\textit{\\{target\\_language\\}} following the natural reading order without any explanation.\n\\label{prompt:evaluate_mllm}\n\\end{tcolorbox}\n\n\n\n\\section{SFT Prompts}\n\\label{app:sft_prompts}\nThe prompt for SFT (TIMT) is structured as follows:\n\n\\begin{tcolorbox}[\n colframe=teal!70!black, % 边框颜色：深青色带一点黑色\n colback=teal!10!white, % 背景颜色：浅青色\n coltitle=white, % 标题文字颜色：白色\n fonttitle=\\bfseries, % 标题字体加粗\n title=SFT (TIMT) Prompt, % 标题内容\n sharp corners, % 圆角设置为锐角\n boxrule=0.5mm, % 边框宽度\n]\nSystem: You are a helpful translation assistant. The user provides an image containing \\textit{\\{source\\_language\\}} text and asks for the corresponding \\textit{\\{target\\_language\\}} translation. Then, the assistant provides the user with the final translation in reading order, separating text from different positions (boxes) with a line break. The final translation must be enclosed within <translate> </translate> tags. The format must be as follows: <translate>final translation here</translate> \\\\\nUser: \\textit{\\{image\\}} Translate all the text in this image into \\textit{\\{target\\_language\\}} following the natural reading order. \\\\\nAssistant: <translate> \\textit{\\{target\\_text\\}} </translate>\n\\label{prompt:wo_recognize_think}\n\\end{tcolorbox}\n\nThe prompt for SFT (OCR + TIMT) is structured as follows:\n\n\\begin{tcolorbox}[\n colframe=teal!70!black, % 边框颜色：深青色带一点黑色\n colback=teal!10!white, % 背景颜色：浅青色\n coltitle=white, % 标题文字颜色：白色\n fonttitle=\\bfseries, % 标题字体加粗\n title=SFT (OCR + TIMT) Prompt, % 标题内容\n sharp corners, % 圆角设置为锐角\n boxrule=0.5mm, % 边框宽度\n]\nSystem: You are a helpful translation assistant. The user provides an image containing \\textit{\\{source\\_language\\}} text and asks for the corresponding \\textit{\\{target\\_language\\}} translation. First, the assistant recognizes all the text in the image following the natural reading order, separating text from different positions (boxes) with a line break. Then, the assistant provides the user with the final translation in reading order, separating text from different positions (boxes) with a line break. The recognized text and final translation must be enclosed within <recognize> </recognize> and <translate> </translate> tags, respectively. The format must be as follows: <recognize>recognized text here</recognize> <translate>final translation here</translate> \\\\\nUser: \\textit{\\{image\\}} Translate all the text in this image into \\textit{\\{target\\_language\\}} following the natural reading order. \\\\\nAssistant: <recognize> \\textit{\\{source\\_text\\}} </recognize><translate> \\textit{\\{target\\_text\\}} </translate>\n\\end{tcolorbox}\n\n\\section{XHSPost Benchmark Construction Details}\n\\label{app:xhspost_construction}\nSocial media platforms like TikTok, Instagram, and Xiaohongshu (also known as Red Note) have significantly deepened intercultural communication worldwide. However, there is a notable absence of evaluation benchmarks specifically designed for TIMT in social media contexts. \n\nThe XHSPost benchmark was constructed to evaluate TIMT in real-world social media contexts, specifically using posts from Xiaohongshu, a globally-oriented platform. The construction process involved several key steps:\n\n\\begin{enumerate}\n \\item \\textbf{Data Collection}: We targeted posts containing either entirely English or entirely Chinese text. Initial post candidates were gathered by querying keywords relevant to common social media usage patterns, such as 'PLOG' (Photo Log), '\\chinese{中文}Plog' (Chinese Plog), and 'TikTok Refugee'.\n \\item \\textbf{Content Appropriateness Filtering}: Following \\citet{li2024mit10m}, all collected images underwent an NSFW detection process using an established tool~\\citep{nsfw}. Any images flagged as potentially inappropriate were discarded.\n \\item \\textbf{Text Recognition and Viability Check}: We utilized GPT-4o (OpenAI's \\texttt{gpt-4o-2024-11-20}) for detailed text recognition from the images. Posts where no precise textual information could be reliably extracted were excluded from further processing.\n \\item \\textbf{Personal Data and Sensitive Content Removal}: To mitigate the risk of exposing personal data, images where the OCR-extracted text contained sensitive information (e.g., email addresses, phone numbers) were removed. Additionally, images containing NSFW characters in the recognized text were also excluded to further ensure the dataset's appropriateness.\n \\item \\textbf{Final Dataset Curation}: This multi-stage filtering process yielded a curated set of 106 English posts and 109 Chinese posts.\n \\item \\textbf{Translation and Post-Editing}: For each selected post, the OCR-recognized text was translated by GPT-4o, using the corresponding image as contextual input to aid translation accuracy and appropriateness. Crucially, all machine-generated translations underwent manual post-editing by human reviewers to ensure high linguistic quality, fluency, and fidelity to the source text in its visual context. This step was vital for creating reliable ground-truth translations.\n\\end{enumerate}\nThis process resulted in the creation of two parallel datasets: XHSPost (EN-ZH) and XHSPost (ZH-EN), designed to support research in social media TIMT.\nIllustrative examples can be found in Figure~\\ref{fig:xhs_case} and \\ref{fig:dataset_case}. With this work, we have taken an initial step in addressing TIMT for social media scenarios. We anticipate that this benchmark will encourage further research in this area, ultimately contributing to enhanced cross-cultural communication. The benchmark will be released upon acceptance.\n\n\\begin{figure}[h]\n\\centering\n\\includegraphics[width=0.92\\textwidth]{fig/dataset_case.pdf}\n\\caption{\nComparison of XHSPost with other TIMT datasets.\n}\n\\label{fig:dataset_case}\n\\end{figure}\n\n\n\\section{Training Details}\n\\label{app:train_details}\nWe employ a constant learning rate of 5e-7 and set the sampling temperature to 1.0.\nThe maximum generation length for responses is capped at\n4096 tokens.\nWe set the KL penalty coefficient $\\beta$\nto 0.01 and set the PPO clipping range $\\epsilon$ to 0.2.\nThe model is trained for 1 epoch on 32 NVIDIA\nH800 80G GPUs for about 15 hours.\n\n\\section{OOD BLEU Score}\n\\label{app:ood_bleu}\nDue to the scope limits of the paper, we provide the BLEU score for these OOD evaluations in Table~\\ref{tab:ood_bleu}.\n\\begin{table}[h]\n \\centering\n \\caption{\n OOD BLEU score performance. Complement chrF++ and METEOR results in Table~\\ref{tab:ood_chrf_meteor}.\n }\n \\label{tab:ood_bleu}\n \\resizebox{\\columnwidth}{!}{%\n \\setlength{\\tabcolsep}{4pt}\n \\begin{tabular}{@{}lcccccccc@{}}\n \\toprule\n \\multirow{3}{*}{\\sc Model} \n & \\multicolumn{3}{c}{\\sc OOD (Language Pairs)} \n & \\multicolumn{5}{c}{\\sc OOD (Datasets)}\\\\\n \\cmidrule(lr){2-4} \\cmidrule(lr){5-9}\n & \\sc MIT-10M (EN-DE) \n & \\sc MIT-10M (ZH-FR) \n & \\sc MIT-10M (DE-FR) \n & \\sc OCRMT30K (ZH-EN) \n & \\sc DoTA (EN-ZH) \n & \\sc XHSPost (EN-ZH) \n & \\sc XHSPost (ZH-EN) \\\\\n \\midrule\n \\multicolumn{9}{@{}l}{\\textcolor{lightgray}{\\textit{Strong Baseline}}} \\\\\n Qwen2.5-VL-72B & 20.49 & 9.79 & 18.78 & 15.14 & 45.43 & 52.97 & 29.52 \\\\\n InternVL2.5-78B & 21.23 & 5.22 & 11.16 & 11.46 & 38.28 & 45.61 & 28.87 \\\\\n \\multicolumn{9}{@{}l}{\\textcolor{lightgray}{\\textit{Same-size Baseline}}} \\\\\n Qwen2.5-VL-7B & 22.85 & 12.09 & 14.99 & 8.93 & 36.90 & 44.96 & 24.49 \\\\\n InternVL2.5-8B & 16.76 & 6.68 & 8.49 & 7.10 & 27.25 & 35.74 & 19.21 \\\\\n \\midrule\n MT$^{3}$-7B-Zero & 29.50 & 15.97 & 26.92 & 14.21 & 40.46 & 49.41 & 26.94 \\\\\n \\bottomrule\n \\end{tabular}\n }\n\\end{table}\n\n\n\n\\section{Multi-Task Ablation Prompts}\n\\label{app:multi_task_prompt}\nThe prompts used for multi-task ablation are as follows:\n\\begin{tcolorbox}[\n colframe=teal!70!black, % 边框颜色：深青色带一点黑色\n colback=teal!10!white, % 背景颜色：浅青色\n coltitle=white, % 标题文字颜色：白色\n fonttitle=\\bfseries, % 标题字体加粗\n title=w/o Reasoning, % 标题内容\n sharp corners, % 圆角设置为锐角\n boxrule=0.5mm, % 边框宽度\n]\nSystem: You are a helpful translation assistant. The user provides an image containing \\textit{\\{source\\_language\\}} text and asks for the corresponding \\textit{\\{target\\_language\\}} translation. First, the assistant recognizes all the text in the image following the natural reading order, separating text from different positions (boxes) with a line break. Then, the assistant provides the user with the final translation in reading order, separating text from different positions (boxes) with a line break. The recognized text and final translation must be enclosed within <recognize> </recognize> and <translate> </translate> tags, respectively. The format must be as follows: <recognize>recognized text here</recognize> <translate>final translation here</translate> \\\\\nUser: \\textit{\\{image\\}} Translate all the text in this image into \\textit{\\{target\\_language\\}} following the natural reading order.\n\\end{tcolorbox}\n\n\\begin{tcolorbox}[\n colframe=teal!70!black, % 边框颜色：深青色带一点黑色\n colback=teal!10!white, % 背景颜色：浅青色\n coltitle=white, % 标题文字颜色：白色\n fonttitle=\\bfseries, % 标题字体加粗\n title=w/o Recognition, % 标题内容\n sharp corners, % 圆角设置为锐角\n boxrule=0.5mm, % 边框宽度\n]\nSystem: You are a helpful translation assistant. The user provides an image containing \\textit{\\{source\\_language\\}} text and asks for the corresponding \\textit{\\{target\\_language\\}} translation. First, the assistant carefully analyzes the recognized text and the visual elements in the image, considering layout, objects, color schemes, spatial relationships, and other contextual clues that may influence meaning. This integrated understanding ensures the translation is accurate, coherent, and appropriate to the visual setting. After thorough reasoning based on both textual content and visual context, the assistant provides the user with the final translation in reading order. The reasoning process and final translation are enclosed within <think> </think> and <translate> </translate> tags, respectively. The format must be as follows: <think> reasoning process here </think><translate> final translation here </translate> \\\\\nUser: \\textit{\\{image\\}} Translate all the text in this image into \\textit{\\{target\\_language\\}} following the natural reading order.\n\\end{tcolorbox}\n\n\\begin{tcolorbox}[\n colframe=teal!70!black, % 边框颜色：深青色带一点黑色\n colback=teal!10!white, % 背景颜色：浅青色\n coltitle=white, % 标题文字颜色：白色\n fonttitle=\\bfseries, % 标题字体加粗\n title=Only TIMT, % 标题内容\n sharp corners, % 圆角设置为锐角\n boxrule=0.5mm, % 边框宽度\n]\nSystem: You are a helpful translation assistant. The user provides an image containing \\textit{\\{source\\_language\\}} text and asks for the corresponding \\textit{\\{target\\_language\\}} translation. Then, the assistant provides the user with the final translation in reading order, separating text from different positions (boxes) with a line break. The final translation must be enclosed within <translate> </translate> tags. The format must be as follows: <translate>final translation here</translate> \\\\\nUser: \\textit{\\{image\\}} Translate all the text in this image into \\textit{\\{target\\_language\\}} following the natural reading order. \n\\end{tcolorbox}\n\n\\section{Detailed Analysis of XHSPost Example}\n\\label{app:xhs_case_analysis}\nAs illustrated in Figure~\\ref{fig:xhs_case}, MT$^{3}$ correctly translate culturally nuanced terms (like the TV show \\textit{'Friends'} to its official Chinese title \\textit{\\chinese{'老友记'}} by inferentially linking the visual cue on a phone screen to its broader context, unlike models that produce literal translations such as \\textit{\\chinese{'朋友'}} (friendship)) and accurately interpret ambiguous numerical information in advertisements (discerning price/location from '800' instead of '800 square meters') stems from this integrated approach.\n\n\\section{Cold-Start Initialization}\n\\label{app:initial}\nIn this work, we utilize QVQ \\citep{qvq-72b-preview} alongside the ground truth TIMT dataset to construct the cold-start dataset.\nDuring preliminary experiments, we observed that as a reasoning model, QVQ exhibits limited instruction-following capabilities, rendering it unsuitable for directly applying the MT$^{3}$ prompt to generate cold-start data.\nInstead, we provide QVQ with images and corresponding ground truth source texts from the TIMT dataset, and request only the reasoning process and final translation.\nWe keep most parts of the prompt remain consistent with the MT$^{3}$ prompt:\n:\n\n\\begin{tcolorbox}[\n colframe=teal!70!black, % 边框颜色：深青色带一点黑色\n colback=teal!10!white, % 背景颜色：浅青色\n coltitle=white, % 标题文字颜色：白色\n fonttitle=\\bfseries, % 标题字体加粗\n title=Cold-Start Data Construction Prompt, % 标题内容\n sharp corners, % 圆角设置为锐角\n boxrule=0.5mm, % 边框宽度\n]\nSystem: You are a helpful translation assistant. The user provides an image containing \\textit{\\{source\\_language\\}} text and asks for the corresponding \\textit{\\{target\\_language\\}} translation. First, the assistant carefully analyzes the recognized text together with the visual elements in the image, taking into account the layout, objects, color schemes, spatial relationships, and other contextual clues that may influence meaning. This integrated understanding ensures that the translation is accurate, coherent, and appropriate to the visual setting. After thorough reasoning based on both textual content and visual context, the assistant provides the user with the final translation in reading order. You should output your final \\textit{\\{target\\_language\\}} translation as: Final Translation: Your final translation. You should think step-by-step.\\\\\nUser: \\textit{\\{image\\}} Translate this text from \\textit{\\{source\\_language\\}} to \\textit{\\{target\\_language\\}}. \\textit{\\{source\\_language\\}}: \\textit{\\{source\\_text\\}}\n\\end{tcolorbox}\n\n\nFor quality control, we extract the final translation of each sample using the regular expression, compute the BLEU score of each translation against the ground truth target translation, and select the top 5K samples with the highest BLEU scores for both EN-ZH and ZH-EN directions.\n\nSubsequently, we merge the ground truth source texts with the QVQ-generated reasoning processes and translations, following the MT$^{3}$ prompt format to construct the final cold-start dataset.\nThis dataset is further used for the cold-start SFT initialization of MT$^{3}$.\n\n\n\\section{Case Study: Initialization Strategy Impact on Reasoning}\n\\label{app:case}\nWe provide a qualitative case study in Figure~\\ref{fig:mt3_case} to illustrate the differences in reasoning patterns that emerge from RL training with different initialization strategies: MT$^{3}$-7B-Zero (\\textit{Zero-start}) versus MT$^{3}$-7B-QVQ-Distill (\\textit{QVQ-Distill}). As observed in the examples, the model initialized with QVQ-Distill tends to generate significantly longer reasoning steps within the \\texttt{<think>} tags. These longer reasoning chains often exhibit self-reflection patterns that appear to be cloned or heavily influenced by the QVQ teacher model. In contrast, the Zero-start model, after RL training, often develops a more straightforward and less redundant reasoning path. This more concise reasoning appears to be directly focused on the core elements necessary for the TIMT task, potentially contributing to its superior performance and more efficient processing.\n\\begin{figure}[ht]\n\\centering\n\\includegraphics[width=1.0\\textwidth]{fig/mt3_case.pdf}\n\\caption{\nCase Study. The model initialized with QVQ-Distill tends to generate significantly longer reasoning steps. In contrast, the Zero-start model, after RL training, often develops a more straightforward and less redundant reasoning path.\n}\n\\label{fig:mt3_case}\n\\end{figure}\n\n\n\n\\section{Difficulty Levels for Curriculum Learning}\n\\label{app:difficulty} \nThe curriculum learning experiments (Section~\\ref{sec:analysis_curriculum}) utilize difficulty categorizations for data instances from the MIT-10M dataset~\\citep{li2024mit10m}. During the construction of the MIT-10M dataset, samples were classified into three difficulty levels based on heuristics considering the token length of the in-image text and the number of detected bounding boxes for text regions. These categories are defined as follows:\n\n\\begin{itemize}\n \\item \\textbf{Easy:} Instances where the number of bounding boxes is less than or equal to 2, and the token length of the text is less than or equal to 16. These samples generally contain fewer textual elements and shorter texts, resulting in a relatively straightforward translation task.\n \\item \\textbf{Hard:} Instances where the number of bounding boxes is greater than 5, or the token length of the text is greater than 25. These samples typically feature more textual elements or longer, more complex texts, placing higher demands on the model's attention mechanism and processing capabilities.\n \\item \\textbf{Medium:} All other instances that do not fall into the Easy or Hard categories. These samples exhibit a wider spread in terms of the number of bounding boxes and text token lengths, representing more realistic and diverse image translation scenarios. This category is particularly useful for testing the generalization ability of multimodal translation models.\n\\end{itemize}\n\n% \\section{Broader Impacts}\n% \\label{app:impact}\n% Advanced TIMT technologies, such as MT$^{3}$, can significantly enhance cross-lingual communication, improve information accessibility, and promote cultural understanding by enabling more accurate translation of text within images. However, potential risks include the misuse of this technology to spread disinformation through altered image text, and the amplification of societal biases present in training data, leading to unfair translations. We believe that strengthening AI regulation, along with enhancing the cleaning and supervision of public datasets, can help mitigate these negative impacts.\n\n\n\n\n% \\section{OCR Performance}\n% \\input{table/ocr_results}\n\n\\clearpage\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\n% \\newpage\n% \\section*{NeurIPS Paper Checklist}\n\n\n% \\begin{enumerate}\n\n% \\item {\\bf Claims}\n% \\item[] Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?\n% \\item[] Answer: \\answerYes{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: see abstract and introduction.\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that the abstract and introduction do not include the claims made in the paper.\n% \\item The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers. \n% \\item The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings. \n% \\item It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper. \n% \\end{itemize}\n\n% \\item {\\bf Limitations}\n% \\item[] Question: Does the paper discuss the limitations of the work performed by the authors?\n% \\item[] Answer: \\answerYes{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: see Section~\\ref{sec:conclusion}.\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper. \n% \\item The authors are encouraged to create a separate \"Limitations\" section in their paper.\n% \\item The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.\n% \\item The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with a few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.\n% \\item The authors should reflect on the factors that influence the performance of the approach. For example, a facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or a speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.\n% \\item The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.\n% \\item If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.\n% \\item While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an important role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.\n% \\end{itemize}\n\n% \\item {\\bf Theory assumptions and proofs}\n% \\item[] Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?\n% \\item[] Answer: \\answerNA{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: This work does not include theoretical results.\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that the paper does not include theoretical results. \n% \\item All the theorems, formulas, and proofs in the paper should be numbered and cross-referenced.\n% \\item All assumptions should be clearly stated or referenced in the statement of any theorems.\n% \\item The proofs can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors are encouraged to provide a short proof sketch to provide intuition. \n% \\item Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.\n% \\item Theorems and Lemmas that the proof relies upon should be properly referenced. \n% \\end{itemize}\n\n% \\item {\\bf Experimental result reproducibility}\n% \\item[] Question: Does the paper fully disclose all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?\n% \\item[] Answer: \\answerYes{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: see Section~\\ref{sec:experimental_setup} and Appendix~\\ref{app:eval}.\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that the paper does not include experiments.\n% \\item If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.\n% \\item If the contribution is a dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable. \n% \\item Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is a novel architecture, describing the architecture fully might suffice, or if the contribution is a specific model and empirical evaluation, it may be necessary to either make it possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g., in the case of a large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.\n% \\item While NeurIPS does not require releasing code, the conference does require all submissions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example\n% \\begin{enumerate}\n% \\item If the contribution is primarily a new algorithm, the paper should make it clear how to reproduce that algorithm.\n% \\item If the contribution is primarily a new model architecture, the paper should describe the architecture clearly and fully.\n% \\item If the contribution is a new model (e.g., a large language model), then there should either be a way to access this model for reproducing the results or a way to reproduce the model (e.g., with an open-source dataset or instructions for how to construct the dataset).\n% \\item We recognize that reproducibility may be tricky in some cases, in which case authors are welcome to describe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should be possible for other researchers to have some path to reproducing or verifying the results.\n% \\end{enumerate}\n% \\end{itemize}\n\n\n% \\item {\\bf Open access to data and code}\n% \\item[] Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material?\n% \\item[] Answer: \\answerNo{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: Our benchmark is complete, and we are currently organizing our code and documents. Both the code and data will be made publicly available upon acceptance of the paper.\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that paper does not include experiments requiring code.\n% \\item Please see the NeurIPS code and data submission guidelines (\\url{https://nips.cc/public/guides/CodeSubmissionPolicy}) for more details.\n% \\item While we encourage the release of code and data, we understand that this might not be possible, so “No” is an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for a new open-source benchmark).\n% \\item The instructions should contain the exact command and environment needed to run to reproduce the results. 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If only a subset of experiments are reproducible, they should state which ones are omitted from the script and why.\n% \\item At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).\n% \\item Providing as much information as possible in supplemental material (appended to the paper) is recommended, but including URLs to data and code is permitted.\n% \\end{itemize}\n\n\n% \\item {\\bf Experimental setting/details}\n% \\item[] Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?\n% \\item[] Answer: \\answerYes{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: see Section~\\ref{sec:experimental_setup} and Appendix~\\ref{app:eval}.\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that the paper does not include experiments.\n% \\item The experimental setting should be presented in the core of the paper to a level of detail that is necessary to appreciate the results and make sense of them.\n% \\item The full details can be provided either with the code, in appendix, or as supplemental material.\n% \\end{itemize}\n\n% \\item {\\bf Experiment statistical significance}\n% \\item[] Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?\n% \\item[] Answer: \\answerNo{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: We use greedy decoding for all evaluation and there are no randomness for the experiments (see Appendix~\\ref{app:eval}).\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that the paper does not include experiments.\n% \\item The authors should answer \"Yes\" if the results are accompanied by error bars, confidence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper.\n% \\item The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).\n% \\item The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)\n% \\item The assumptions made should be given (e.g., Normally distributed errors).\n% \\item It should be clear whether the error bar is the standard deviation or the standard error of the mean.\n% \\item It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have a 96\\% CI, if the hypothesis of Normality of errors is not verified.\n% \\item For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out of range (e.g. negative error rates).\n% \\item If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.\n% \\end{itemize}\n\n% \\item {\\bf Experiments compute resources}\n% \\item[] Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?\n% \\item[] Answer: \\answerYes{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: see Section~\\ref{sec:experimental_setup}.\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that the paper does not include experiments.\n% \\item The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.\n% \\item The paper should provide the amount of compute required for each of the individual experimental runs as well as estimate the total compute. \n% \\item The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make it into the paper). \n% \\end{itemize}\n \n% \\item {\\bf Code of ethics}\n% \\item[] Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics \\url{https://neurips.cc/public/EthicsGuidelines}?\n% \\item[] Answer: \\answerYes{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: Our research has been conducted with strict adherence to the NeurIPS Code of Ethics.\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.\n% \\item If the authors answer No, they should explain the special circumstances that require a deviation from the Code of Ethics.\n% \\item The authors should make sure to preserve anonymity (e.g., if there is a special consideration due to laws or regulations in their jurisdiction).\n% \\end{itemize}\n\n\n% \\item {\\bf Broader impacts}\n% \\item[] Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?\n% \\item[] Answer: \\answerYes{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: see Appendix~\\ref{app:impact}.\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that there is no societal impact of the work performed.\n% \\item If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact.\n% \\item Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.\n% \\item The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.\n% \\item The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.\n% \\item If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML).\n% \\end{itemize}\n \n% \\item {\\bf Safeguards}\n% \\item[] Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?\n% \\item[] Answer: \\answerYes{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: see Appendix~\\ref{app:xhspost_construction}.\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that the paper poses no such risks.\n% \\item Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters. \n% \\item Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.\n% \\item We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make a best faith effort.\n% \\end{itemize}\n\n% \\item {\\bf Licenses for existing assets}\n% \\item[] Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?\n% \\item[] Answer: \\answerYes{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: NA\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that the paper does not use existing assets.\n% \\item The authors should cite the original paper that produced the code package or dataset.\n% \\item The authors should state which version of the asset is used and, if possible, include a URL.\n% \\item The name of the license (e.g., CC-BY 4.0) should be included for each asset.\n% \\item For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.\n% \\item If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, \\url{paperswithcode.com/datasets} has curated licenses for some datasets. Their licensing guide can help determine the license of a dataset.\n% \\item For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.\n% \\item If this information is not available online, the authors are encouraged to reach out to the asset's creators.\n% \\end{itemize}\n\n% \\item {\\bf New assets}\n% \\item[] Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?\n% \\item[] Answer: \\answerYes{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: see Appendix~\\ref{app:xhspost_construction}.\n% \\item[] Guidelines: \n% \\begin{itemize}\n% \\item The answer NA means that the paper does not release new assets.\n% \\item Researchers should communicate the details of the dataset/code/model as part of their submissions via structured templates. This includes details about training, license, limitations, etc. \n% \\item The paper should discuss whether and how consent was obtained from people whose asset is used.\n% \\item At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.\n% \\end{itemize}\n\n% \\item {\\bf Crowdsourcing and research with human subjects}\n% \\item[] Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)? \n% \\item[] Answer: \\answerNA{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: The paper does not involve crowdsourcing nor research with human subjects.\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.\n% \\item Including this information in the supplemental material is fine, but if the main contribution of the paper involves human subjects, then as much detail as possible should be included in the main paper. \n% \\item According to the NeurIPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector. \n% \\end{itemize}\n\n% \\item {\\bf Institutional review board (IRB) approvals or equivalent for research with human subjects}\n% \\item[] Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?\n% \\item[] Answer: \\answerNA{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: The paper does not involve crowdsourcing nor research with human subjects.\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.\n% \\item Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper. \n% \\item We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere to the NeurIPS Code of Ethics and the guidelines for their institution. \n% \\item For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.\n% \\end{itemize}\n\n% \\item {\\bf Declaration of LLM usage}\n% \\item[] Question: Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.\n% %this research? \n% \\item[] Answer: \\answerYes{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: see Section~\\ref{sec:method}.\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that the core method development in this research does not involve LLMs as any important, original, or non-standard components.\n% \\item Please refer to our LLM policy (\\url{https://neurips.cc/Conferences/2025/LLM}) for what should or should not be described.\n% \\end{itemize}\n\n% \\end{enumerate}\n\n\n\\end{document}\n"},"question":{"kind":"string","value":"Which zero-shot models outperform any of the fine-tuned models on any metric and language direction?"},"answer":{"kind":"string","value":"InternVL2.5-38B, InternVL2.5-78B, and Qwen2.5-VL-72B (on chrF++ EN-ZH)"},"plan":{"kind":"string","value":"SELECT Zero-shot Systems, all metrics, both language directions\nLOOP for each zero-shot model\n SELECT scores for current zero-shot Model\n LOOP for each metric and language direction\n SELECT Fine-tuned models scores\n COMPUTE max_ft_score = max fine-tuned model scores\n COMPUTE diff = zero-shot model score - max_ft_score\n IF diff > 0\n COMPUTE add zero-shot model to result_list\nRETURN result_list\n"}}},{"rowIdx":74,"cells":{"paper":{"kind":"string","value":"2405.19325"},"relevant_tables":{"kind":"list like","value":[["\\begin{table}[!t]\n","\\begin{adjustbox}{max width=1\\textwidth}\n","\\centering\n","\\begin{tabular}{l|cccccc|cccccc}\n","\\toprule\n","\\textbf{Models} & \\multicolumn{6}{c|}{\\textbf{Wikitext-103}} & \\multicolumn{6}{c}{\\textbf{Pile of Law}} \\\\ \n","\n"," & {PPL($\\downarrow$)} & {MAUVE} & {RG-1} & {RG-2} & {RG-L} & {Avg. Len} & {PPL($\\downarrow$)} & {MAUVE} & {RG-1} & {RG-2} & {RG-L} & {Avg. Len} \\\\ \n","\\midrule\n","Llama-2-Chat$_\\text{7B}$ & 14.6 & 58.8 & 15.8 & 3.7 & 14.4 & 175.4 & 10.1 & 80.7 & 19.1 & 5.5 & 17.1 & 211.4 \\\\\n"," +RA & 7.2 & 74.6 & \\textbf{35.7} & \\textbf{23.1} & \\textbf{34.4} & 204.5 & 7.1 & 84.7 & 23.1 & 8.9 & 21.1 & 222.0 \\\\\n"," +\\knnlm & 9.8 & \\textbf{82.5} & 23.7 & 7.7 & 21.7 & \\textbf{238.2} & 8.8 & 81.1 & 19.4 & 5.7 & 17.4 & 214.3 \\\\ \n"," \\rowcolor{gray!20}\n"," +\\nest & 8.4 & 73.2 & 28.4 & 14.2 & 27.1 & 218.4 & 8.1 & 88.0 & 23.7 & 8.7 & 21.5 & 226.5 \\\\ \n"," \\rowcolor{gray!20}\n"," +RA-\\nest & \\textbf{6.4} & 72.6 & 35.2 & 22.7 & 34.0 & 202.0 & \\textbf{6.7} & \\textbf{90.0} & \\textbf{24.4} & \\textbf{9.0} & \\textbf{22.2} & \\textbf{232.1} \\\\ \\midrule\n"," Llama-2-Chat$_\\text{13B}$ & 12.0 & 75.9 & 19.9 & 4.9 & 18.0 & 218.4 & 8.2 & 72.8 & 17.5 & 5.3 & 15.7 & 181.7 \\\\ \n"," +RA & 6.5 & \\textbf{91.5} & \\textbf{38.9} & \\textbf{24.2} & \\textbf{37.2} & \\textbf{249.3} & 5.9 & 86.6 & 23.6 & 9.1 & 21.5 & 228.7 \\\\ \n"," +\\knnlm & 8.6 & 76.3 & 23.7 & 8.2 & 21.9 & 238.5 & 7.4 & 71.5 & 17.7 & 5.3 & 15.9 & 183.7 \\\\ \n"," \\rowcolor{gray!20}\n"," +\\nest & 7.2 & 67.1 & 29.3 & 15.6 & 28.1 & 207.1 & 6.8 & 86.0 & 22.9 & 8.7 & 20.9 & 212.3 \\\\ \n"," \\rowcolor{gray!20}\n"," +RA-\\nest & \\textbf{5.8} & 86.8 & 38.6 & 24.0 & 37.0 & 245.5 & \\textbf{5.7} & \\textbf{90.1} & \\textbf{24.7} & \\textbf{9.2} & \\textbf{22.4} & \\textbf{229.4} \\\\ \\midrule\n"," Llama-2-Chat$_\\text{70B}$ & 9.9 & 88.6 & 22.9 & 6.2 & 20.8 & 239.6 & 6.9 & 93.4 & 23.0 & 7.1 & 20.7 & 250.1 \\\\ \n"," +RA & 5.3 & \\textbf{91.6} & \\textbf{40.5} & \\textbf{26.1} & \\textbf{38.8} & 235.9 & 4.9 & 95.5 & \\textbf{26.3} & \\textbf{10.1} & \\textbf{24.0} & 253.2 \\\\ \n"," +\\knnlm & 7.1 & 83.6 & 26.1 & 9.6 & 24.1 & \\textbf{253.9} & 6.3 & 94.4 & 23.1 & 7.2 & 20.8 & 251.3 \\\\\n"," \\rowcolor{gray!20}\n"," +\\nest & 6.3 & 82.6 & 32.6 & 17.2 & 31.1 & 236.3 & 5.9 & 95.4 & 25.6 & 9.4 & 23.2 & 251.3 \\\\ \n"," \\rowcolor{gray!20}\n"," +RA-\\nest & \\textbf{4.8} & 90.0 & 40.2 & 25.9 & 38.6 & 233.1 & \\textbf{4.7} & \\textbf{97.6} & 26.2 & 9.5 & 23.7 & \\textbf{253.6} \\\\\\bottomrule\n","\\end{tabular}\n","\\end{adjustbox}\n","\\begin{adjustbox}{max width=1\\textwidth}\n","\\setlength{\\tabcolsep}{4pt} % Default value: 6pt\n","\\centering\n","\\begin{tabular}{l|ccccc|cc|cc|ccccc}\n","\\toprule\n","\\textbf{Models} & \\textbf{TQA} & \\textbf{NQ} & \\textbf{HQA} & \\textbf{MQA} & \\textbf{Avg.} & \\multicolumn{2}{c|}{\\textbf{TruthfulQA}} & \\multicolumn{2}{c|}{\\textbf{Biography}} & \\multicolumn{5}{c}{\\textbf{MMLU}} \\\\\n","~ &\\multicolumn{5}{c|}{Answer-Level Recall} & $\\Delta$BLEU & $\\Delta$RG-1 & FS & \\# Facts & Human. & STEM & Social & Other & Avg. \\\\ \n","\\midrule\n","Llama-2-Chat$_\\text{7B}$ & 61.1 & 38.9 & 30.6 & 9.3 & 35.0 & -0.02 & 0.42 & 27.2 & \\textbf{71.2} & 37.8 & 32.6 & 38.9 & 39.6 & 37.2 \\\\ \n"," +RA & \\textbf{69.5} & 48.4 & 44.1 & 12.8 & 43.7 & -0.34 & 0.18 & \\textbf{56.5} & 67.1 &41.8 & 35.3 & \\textbf{42.2} & 43.3 & 40.7 \\\\ \n"," +\\knnlm & 63.4 & 42.4 & 33.5 & 9.5 & 37.2 & \\textbf{0.13} & \\textbf{0.66} & 30.6 & 59.8 &38.0 & 33.1 & 39.2 & 40.1 & 37.6 \\\\ \n"," \\rowcolor{gray!20}\n"," +\\nest & 61.5 & 43.2 & 33.5 & 10.2 & 37.1 & 0.03 & 0.45 & 38.9 & 58.2 &\\textbf{42.0} & \\textbf{35.4} & 42.0 & \\textbf{43.4} & \\textbf{40.7} \\\\ \n"," \\rowcolor{gray!20}\n"," +RA-\\nest & 69.0 & \\textbf{48.8} & \\textbf{45.3} & \\textbf{13.3} & \\textbf{44.1} & -0.32 & 0.21 & 55.1 & 57.7 &37.9 & 32.7 & 39.3 & 39.8 & 37.4 \\\\ \\midrule\n"," Llama-2-Chat$_\\text{13B}$ & 63.5 & 42.3 & 32.6 & 10.2 & 37.2 & 0.13 & 0.81 & 28.8 & 49.9 &41.5 & 35.0 & 40.2 & 43.8 & 40.1 \\\\ \n"," +RA & 70.9 & 51.6 & 44.6 & 14.0 & 45.3 & -0.16 & 0.25 & \\textbf{59.1} & 51.2 &43.4 & 37.4 & 43.5 & 46.4 & 42.7 \\\\ \n"," +\\knnlm & 64.7 & 43.5 & 34.2 & 11.2 & 38.4 & 0.20 & 0.95 & 31.1 & 46.1 &41.4 & 34.7 & 40.6 & 44.2 & 40.2 \\\\ \n"," \\rowcolor{gray!20}\n"," +\\nest & 64.2 & 44.2 & 34.3 & 10.9 & 38.4 & \\textbf{0.29} & \\textbf{0.98} & 35.7 & 47.2 &41.3 & 34.9 & 40.2 & 43.7 & 40.0 \\\\ \n"," \\rowcolor{gray!20}\n"," +RA-\\nest & \\textbf{70.9} & \\textbf{51.7} & \\textbf{45.3} & \\textbf{14.7} & \\textbf{45.7} & -0.14 & 0.25 & 58.4 & \\textbf{52.4} &\\textbf{43.5} & \\textbf{37.7} & \\textbf{43.5} & \\textbf{46.7} & \\textbf{42.8} \\\\ \\midrule\n"," Llama-2-Chat$_\\text{70B}$ & 74.0 & 50.1 & 39.5 & 12.8 & 44.1 & 0.14 & 0.70 & 34.2 & \\textbf{58.8} &43.5 & 37.9 & 44.4 & 47.0 & 43.2 \\\\ \n"," +RA & \\textbf{75.5} & \\textbf{55.4} & \\textbf{52.5} & 16.0 & \\textbf{49.9} & -0.13 & 0.40 & 52.9 & 42.1 &\\textbf{45.9} & 39.7 & 46.2 & 48.6 & 45.1 \\\\ \n"," +\\knnlm & 74.6 & 51.2 & 40.2 & 13.5 & 44.9 & 0.08 & 0.58 & 36.1 & 54.4 &44.0 & 37.4 & 44.1 & 47.1 & 43.2 \\\\ \n"," \\rowcolor{gray!20}\n"," +\\nest & 74.2 & 51.6 & 41.4 & 13.8 & 45.2 & \\textbf{0.17} & \\textbf{0.70} & 41.6 & 56.2 &43.8 & 38.0 & 44.4 & 47.8 & 43.5 \\\\ \n"," \\rowcolor{gray!20}\n"," +RA-\\nest & 75.4 & 55.2 & 52.4 & \\textbf{16.3} & 49.8 & -0.19 & 0.31 & \\textbf{59.2} & 53.8 &45.8 & \\textbf{39.7} & \\textbf{46.2} & \\textbf{48.9} & \\textbf{45.1} \\\\ \\bottomrule\n","\\end{tabular}\n","\\end{adjustbox}\n","\\\\\n","\\vspace{6pt}\n","\\caption{Results on text completion (upper table) and other tasks (lower table). Bold numbers indicate the best performance. PPL: Perplexity. RG: ROUGE score. Avg. Len: Average generation length. $\\Delta$BLEU/$\\Delta$RG: The difference between the max score to correct references and the max score to incorrect references. FS: \\factscore with length penalty.}\n","\\vspace{-3mm}\n","\\label{tbl:main_results}\n","\\end{table}\\subsection{Main Results}\n"]],"string":"[\n [\n \"\\\\begin{table}[!t]\\n\",\n \"\\\\begin{adjustbox}{max width=1\\\\textwidth}\\n\",\n \"\\\\centering\\n\",\n \"\\\\begin{tabular}{l|cccccc|cccccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Models} & \\\\multicolumn{6}{c|}{\\\\textbf{Wikitext-103}} & \\\\multicolumn{6}{c}{\\\\textbf{Pile of Law}} \\\\\\\\ \\n\",\n \"\\n\",\n \" & {PPL($\\\\downarrow$)} & {MAUVE} & {RG-1} & {RG-2} & {RG-L} & {Avg. Len} & {PPL($\\\\downarrow$)} & {MAUVE} & {RG-1} & {RG-2} & {RG-L} & {Avg. Len} \\\\\\\\ \\n\",\n \"\\\\midrule\\n\",\n \"Llama-2-Chat$_\\\\text{7B}$ & 14.6 & 58.8 & 15.8 & 3.7 & 14.4 & 175.4 & 10.1 & 80.7 & 19.1 & 5.5 & 17.1 & 211.4 \\\\\\\\\\n\",\n \" +RA & 7.2 & 74.6 & \\\\textbf{35.7} & \\\\textbf{23.1} & \\\\textbf{34.4} & 204.5 & 7.1 & 84.7 & 23.1 & 8.9 & 21.1 & 222.0 \\\\\\\\\\n\",\n \" +\\\\knnlm & 9.8 & \\\\textbf{82.5} & 23.7 & 7.7 & 21.7 & \\\\textbf{238.2} & 8.8 & 81.1 & 19.4 & 5.7 & 17.4 & 214.3 \\\\\\\\ \\n\",\n \" \\\\rowcolor{gray!20}\\n\",\n \" +\\\\nest & 8.4 & 73.2 & 28.4 & 14.2 & 27.1 & 218.4 & 8.1 & 88.0 & 23.7 & 8.7 & 21.5 & 226.5 \\\\\\\\ \\n\",\n \" \\\\rowcolor{gray!20}\\n\",\n \" +RA-\\\\nest & \\\\textbf{6.4} & 72.6 & 35.2 & 22.7 & 34.0 & 202.0 & \\\\textbf{6.7} & \\\\textbf{90.0} & \\\\textbf{24.4} & \\\\textbf{9.0} & \\\\textbf{22.2} & \\\\textbf{232.1} \\\\\\\\ \\\\midrule\\n\",\n \" Llama-2-Chat$_\\\\text{13B}$ & 12.0 & 75.9 & 19.9 & 4.9 & 18.0 & 218.4 & 8.2 & 72.8 & 17.5 & 5.3 & 15.7 & 181.7 \\\\\\\\ \\n\",\n \" +RA & 6.5 & \\\\textbf{91.5} & \\\\textbf{38.9} & \\\\textbf{24.2} & \\\\textbf{37.2} & \\\\textbf{249.3} & 5.9 & 86.6 & 23.6 & 9.1 & 21.5 & 228.7 \\\\\\\\ \\n\",\n \" +\\\\knnlm & 8.6 & 76.3 & 23.7 & 8.2 & 21.9 & 238.5 & 7.4 & 71.5 & 17.7 & 5.3 & 15.9 & 183.7 \\\\\\\\ \\n\",\n \" \\\\rowcolor{gray!20}\\n\",\n \" +\\\\nest & 7.2 & 67.1 & 29.3 & 15.6 & 28.1 & 207.1 & 6.8 & 86.0 & 22.9 & 8.7 & 20.9 & 212.3 \\\\\\\\ \\n\",\n \" \\\\rowcolor{gray!20}\\n\",\n \" +RA-\\\\nest & \\\\textbf{5.8} & 86.8 & 38.6 & 24.0 & 37.0 & 245.5 & \\\\textbf{5.7} & \\\\textbf{90.1} & \\\\textbf{24.7} & \\\\textbf{9.2} & \\\\textbf{22.4} & \\\\textbf{229.4} \\\\\\\\ \\\\midrule\\n\",\n \" Llama-2-Chat$_\\\\text{70B}$ & 9.9 & 88.6 & 22.9 & 6.2 & 20.8 & 239.6 & 6.9 & 93.4 & 23.0 & 7.1 & 20.7 & 250.1 \\\\\\\\ \\n\",\n \" +RA & 5.3 & \\\\textbf{91.6} & \\\\textbf{40.5} & \\\\textbf{26.1} & \\\\textbf{38.8} & 235.9 & 4.9 & 95.5 & \\\\textbf{26.3} & \\\\textbf{10.1} & \\\\textbf{24.0} & 253.2 \\\\\\\\ \\n\",\n \" +\\\\knnlm & 7.1 & 83.6 & 26.1 & 9.6 & 24.1 & \\\\textbf{253.9} & 6.3 & 94.4 & 23.1 & 7.2 & 20.8 & 251.3 \\\\\\\\\\n\",\n \" \\\\rowcolor{gray!20}\\n\",\n \" +\\\\nest & 6.3 & 82.6 & 32.6 & 17.2 & 31.1 & 236.3 & 5.9 & 95.4 & 25.6 & 9.4 & 23.2 & 251.3 \\\\\\\\ \\n\",\n \" \\\\rowcolor{gray!20}\\n\",\n \" +RA-\\\\nest & \\\\textbf{4.8} & 90.0 & 40.2 & 25.9 & 38.6 & 233.1 & \\\\textbf{4.7} & \\\\textbf{97.6} & 26.2 & 9.5 & 23.7 & \\\\textbf{253.6} \\\\\\\\\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\end{adjustbox}\\n\",\n \"\\\\begin{adjustbox}{max width=1\\\\textwidth}\\n\",\n \"\\\\setlength{\\\\tabcolsep}{4pt} % Default value: 6pt\\n\",\n \"\\\\centering\\n\",\n \"\\\\begin{tabular}{l|ccccc|cc|cc|ccccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Models} & \\\\textbf{TQA} & \\\\textbf{NQ} & \\\\textbf{HQA} & \\\\textbf{MQA} & \\\\textbf{Avg.} & \\\\multicolumn{2}{c|}{\\\\textbf{TruthfulQA}} & \\\\multicolumn{2}{c|}{\\\\textbf{Biography}} & \\\\multicolumn{5}{c}{\\\\textbf{MMLU}} \\\\\\\\\\n\",\n \"~ &\\\\multicolumn{5}{c|}{Answer-Level Recall} & $\\\\Delta$BLEU & $\\\\Delta$RG-1 & FS & \\\\# Facts & Human. & STEM & Social & Other & Avg. \\\\\\\\ \\n\",\n \"\\\\midrule\\n\",\n \"Llama-2-Chat$_\\\\text{7B}$ & 61.1 & 38.9 & 30.6 & 9.3 & 35.0 & -0.02 & 0.42 & 27.2 & \\\\textbf{71.2} & 37.8 & 32.6 & 38.9 & 39.6 & 37.2 \\\\\\\\ \\n\",\n \" +RA & \\\\textbf{69.5} & 48.4 & 44.1 & 12.8 & 43.7 & -0.34 & 0.18 & \\\\textbf{56.5} & 67.1 &41.8 & 35.3 & \\\\textbf{42.2} & 43.3 & 40.7 \\\\\\\\ \\n\",\n \" +\\\\knnlm & 63.4 & 42.4 & 33.5 & 9.5 & 37.2 & \\\\textbf{0.13} & \\\\textbf{0.66} & 30.6 & 59.8 &38.0 & 33.1 & 39.2 & 40.1 & 37.6 \\\\\\\\ \\n\",\n \" \\\\rowcolor{gray!20}\\n\",\n \" +\\\\nest & 61.5 & 43.2 & 33.5 & 10.2 & 37.1 & 0.03 & 0.45 & 38.9 & 58.2 &\\\\textbf{42.0} & \\\\textbf{35.4} & 42.0 & \\\\textbf{43.4} & \\\\textbf{40.7} \\\\\\\\ \\n\",\n \" \\\\rowcolor{gray!20}\\n\",\n \" +RA-\\\\nest & 69.0 & \\\\textbf{48.8} & \\\\textbf{45.3} & \\\\textbf{13.3} & \\\\textbf{44.1} & -0.32 & 0.21 & 55.1 & 57.7 &37.9 & 32.7 & 39.3 & 39.8 & 37.4 \\\\\\\\ \\\\midrule\\n\",\n \" Llama-2-Chat$_\\\\text{13B}$ & 63.5 & 42.3 & 32.6 & 10.2 & 37.2 & 0.13 & 0.81 & 28.8 & 49.9 &41.5 & 35.0 & 40.2 & 43.8 & 40.1 \\\\\\\\ \\n\",\n \" +RA & 70.9 & 51.6 & 44.6 & 14.0 & 45.3 & -0.16 & 0.25 & \\\\textbf{59.1} & 51.2 &43.4 & 37.4 & 43.5 & 46.4 & 42.7 \\\\\\\\ \\n\",\n \" +\\\\knnlm & 64.7 & 43.5 & 34.2 & 11.2 & 38.4 & 0.20 & 0.95 & 31.1 & 46.1 &41.4 & 34.7 & 40.6 & 44.2 & 40.2 \\\\\\\\ \\n\",\n \" \\\\rowcolor{gray!20}\\n\",\n \" +\\\\nest & 64.2 & 44.2 & 34.3 & 10.9 & 38.4 & \\\\textbf{0.29} & \\\\textbf{0.98} & 35.7 & 47.2 &41.3 & 34.9 & 40.2 & 43.7 & 40.0 \\\\\\\\ \\n\",\n \" \\\\rowcolor{gray!20}\\n\",\n \" +RA-\\\\nest & \\\\textbf{70.9} & \\\\textbf{51.7} & \\\\textbf{45.3} & \\\\textbf{14.7} & \\\\textbf{45.7} & -0.14 & 0.25 & 58.4 & \\\\textbf{52.4} &\\\\textbf{43.5} & \\\\textbf{37.7} & \\\\textbf{43.5} & \\\\textbf{46.7} & \\\\textbf{42.8} \\\\\\\\ \\\\midrule\\n\",\n \" Llama-2-Chat$_\\\\text{70B}$ & 74.0 & 50.1 & 39.5 & 12.8 & 44.1 & 0.14 & 0.70 & 34.2 & \\\\textbf{58.8} &43.5 & 37.9 & 44.4 & 47.0 & 43.2 \\\\\\\\ \\n\",\n \" +RA & \\\\textbf{75.5} & \\\\textbf{55.4} & \\\\textbf{52.5} & 16.0 & \\\\textbf{49.9} & -0.13 & 0.40 & 52.9 & 42.1 &\\\\textbf{45.9} & 39.7 & 46.2 & 48.6 & 45.1 \\\\\\\\ \\n\",\n \" +\\\\knnlm & 74.6 & 51.2 & 40.2 & 13.5 & 44.9 & 0.08 & 0.58 & 36.1 & 54.4 &44.0 & 37.4 & 44.1 & 47.1 & 43.2 \\\\\\\\ \\n\",\n \" \\\\rowcolor{gray!20}\\n\",\n \" +\\\\nest & 74.2 & 51.6 & 41.4 & 13.8 & 45.2 & \\\\textbf{0.17} & \\\\textbf{0.70} & 41.6 & 56.2 &43.8 & 38.0 & 44.4 & 47.8 & 43.5 \\\\\\\\ \\n\",\n \" \\\\rowcolor{gray!20}\\n\",\n \" +RA-\\\\nest & 75.4 & 55.2 & 52.4 & \\\\textbf{16.3} & 49.8 & -0.19 & 0.31 & \\\\textbf{59.2} & 53.8 &45.8 & \\\\textbf{39.7} & \\\\textbf{46.2} & \\\\textbf{48.9} & \\\\textbf{45.1} \\\\\\\\ \\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\end{adjustbox}\\n\",\n \"\\\\\\\\\\n\",\n \"\\\\vspace{6pt}\\n\",\n \"\\\\caption{Results on text completion (upper table) and other tasks (lower table). Bold numbers indicate the best performance. PPL: Perplexity. RG: ROUGE score. Avg. Len: Average generation length. $\\\\Delta$BLEU/$\\\\Delta$RG: The difference between the max score to correct references and the max score to incorrect references. FS: \\\\factscore with length penalty.}\\n\",\n \"\\\\vspace{-3mm}\\n\",\n \"\\\\label{tbl:main_results}\\n\",\n \"\\\\end{table}\\\\subsection{Main Results}\\n\"\n ]\n]","isConversational":false},"tables":{"kind":"list like","value":[["\\begin{figure}[t!]\n","\\centering\n","\\includegraphics[width=\\textwidth]{figures/NEST.png}\n","\\caption{The \\nest approach first locates the tokens in the corpus using the LM hidden states. The retrieval distribution $p_{\\text{\\knn}}$ is dynamically interpolated with $p_{\\text{LM}}$ based on the retriever's uncertainty $\\lambda_t$. The token and its $n$-gram continuation are then selected from the mixture distribution $p_{\\mathcal{M}}$, while the final span length is determined by speculative decoding to remove undesired tokens. The spans incorporated in the final generation provide direct attribution and amortize the generation latency.}\n","\\label{fig:nest}\n","\\end{figure}\n"],["\\begin{table}[!t]\n","\\begin{adjustbox}{max width=1\\textwidth}\n","\\centering\n","\\begin{tabular}{l|cccccc|cccccc}\n","\\toprule\n","\\textbf{Models} & \\multicolumn{6}{c|}{\\textbf{Wikitext-103}} & \\multicolumn{6}{c}{\\textbf{Pile of Law}} \\\\ \n","\n"," & {PPL($\\downarrow$)} & {MAUVE} & {RG-1} & {RG-2} & {RG-L} & {Avg. Len} & {PPL($\\downarrow$)} & {MAUVE} & {RG-1} & {RG-2} & {RG-L} & {Avg. Len} \\\\ \n","\\midrule\n","Llama-2-Chat$_\\text{7B}$ & 14.6 & 58.8 & 15.8 & 3.7 & 14.4 & 175.4 & 10.1 & 80.7 & 19.1 & 5.5 & 17.1 & 211.4 \\\\\n"," +RA & 7.2 & 74.6 & \\textbf{35.7} & \\textbf{23.1} & \\textbf{34.4} & 204.5 & 7.1 & 84.7 & 23.1 & 8.9 & 21.1 & 222.0 \\\\\n"," +\\knnlm & 9.8 & \\textbf{82.5} & 23.7 & 7.7 & 21.7 & \\textbf{238.2} & 8.8 & 81.1 & 19.4 & 5.7 & 17.4 & 214.3 \\\\ \n"," \\rowcolor{gray!20}\n"," +\\nest & 8.4 & 73.2 & 28.4 & 14.2 & 27.1 & 218.4 & 8.1 & 88.0 & 23.7 & 8.7 & 21.5 & 226.5 \\\\ \n"," \\rowcolor{gray!20}\n"," +RA-\\nest & \\textbf{6.4} & 72.6 & 35.2 & 22.7 & 34.0 & 202.0 & \\textbf{6.7} & \\textbf{90.0} & \\textbf{24.4} & \\textbf{9.0} & \\textbf{22.2} & \\textbf{232.1} \\\\ \\midrule\n"," Llama-2-Chat$_\\text{13B}$ & 12.0 & 75.9 & 19.9 & 4.9 & 18.0 & 218.4 & 8.2 & 72.8 & 17.5 & 5.3 & 15.7 & 181.7 \\\\ \n"," +RA & 6.5 & \\textbf{91.5} & \\textbf{38.9} & \\textbf{24.2} & \\textbf{37.2} & \\textbf{249.3} & 5.9 & 86.6 & 23.6 & 9.1 & 21.5 & 228.7 \\\\ \n"," +\\knnlm & 8.6 & 76.3 & 23.7 & 8.2 & 21.9 & 238.5 & 7.4 & 71.5 & 17.7 & 5.3 & 15.9 & 183.7 \\\\ \n"," \\rowcolor{gray!20}\n"," +\\nest & 7.2 & 67.1 & 29.3 & 15.6 & 28.1 & 207.1 & 6.8 & 86.0 & 22.9 & 8.7 & 20.9 & 212.3 \\\\ \n"," \\rowcolor{gray!20}\n"," +RA-\\nest & \\textbf{5.8} & 86.8 & 38.6 & 24.0 & 37.0 & 245.5 & \\textbf{5.7} & \\textbf{90.1} & \\textbf{24.7} & \\textbf{9.2} & \\textbf{22.4} & \\textbf{229.4} \\\\ \\midrule\n"," Llama-2-Chat$_\\text{70B}$ & 9.9 & 88.6 & 22.9 & 6.2 & 20.8 & 239.6 & 6.9 & 93.4 & 23.0 & 7.1 & 20.7 & 250.1 \\\\ \n"," +RA & 5.3 & \\textbf{91.6} & \\textbf{40.5} & \\textbf{26.1} & \\textbf{38.8} & 235.9 & 4.9 & 95.5 & \\textbf{26.3} & \\textbf{10.1} & \\textbf{24.0} & 253.2 \\\\ \n"," +\\knnlm & 7.1 & 83.6 & 26.1 & 9.6 & 24.1 & \\textbf{253.9} & 6.3 & 94.4 & 23.1 & 7.2 & 20.8 & 251.3 \\\\\n"," \\rowcolor{gray!20}\n"," +\\nest & 6.3 & 82.6 & 32.6 & 17.2 & 31.1 & 236.3 & 5.9 & 95.4 & 25.6 & 9.4 & 23.2 & 251.3 \\\\ \n"," \\rowcolor{gray!20}\n"," +RA-\\nest & \\textbf{4.8} & 90.0 & 40.2 & 25.9 & 38.6 & 233.1 & \\textbf{4.7} & \\textbf{97.6} & 26.2 & 9.5 & 23.7 & \\textbf{253.6} \\\\\\bottomrule\n","\\end{tabular}\n","\\end{adjustbox}\n","\\begin{adjustbox}{max width=1\\textwidth}\n","\\setlength{\\tabcolsep}{4pt} % Default value: 6pt\n","\\centering\n","\\begin{tabular}{l|ccccc|cc|cc|ccccc}\n","\\toprule\n","\\textbf{Models} & \\textbf{TQA} & \\textbf{NQ} & \\textbf{HQA} & \\textbf{MQA} & \\textbf{Avg.} & \\multicolumn{2}{c|}{\\textbf{TruthfulQA}} & \\multicolumn{2}{c|}{\\textbf{Biography}} & \\multicolumn{5}{c}{\\textbf{MMLU}} \\\\\n","~ &\\multicolumn{5}{c|}{Answer-Level Recall} & $\\Delta$BLEU & $\\Delta$RG-1 & FS & \\# Facts & Human. & STEM & Social & Other & Avg. \\\\ \n","\\midrule\n","Llama-2-Chat$_\\text{7B}$ & 61.1 & 38.9 & 30.6 & 9.3 & 35.0 & -0.02 & 0.42 & 27.2 & \\textbf{71.2} & 37.8 & 32.6 & 38.9 & 39.6 & 37.2 \\\\ \n"," +RA & \\textbf{69.5} & 48.4 & 44.1 & 12.8 & 43.7 & -0.34 & 0.18 & \\textbf{56.5} & 67.1 &41.8 & 35.3 & \\textbf{42.2} & 43.3 & 40.7 \\\\ \n"," +\\knnlm & 63.4 & 42.4 & 33.5 & 9.5 & 37.2 & \\textbf{0.13} & \\textbf{0.66} & 30.6 & 59.8 &38.0 & 33.1 & 39.2 & 40.1 & 37.6 \\\\ \n"," \\rowcolor{gray!20}\n"," +\\nest & 61.5 & 43.2 & 33.5 & 10.2 & 37.1 & 0.03 & 0.45 & 38.9 & 58.2 &\\textbf{42.0} & \\textbf{35.4} & 42.0 & \\textbf{43.4} & \\textbf{40.7} \\\\ \n"," \\rowcolor{gray!20}\n"," +RA-\\nest & 69.0 & \\textbf{48.8} & \\textbf{45.3} & \\textbf{13.3} & \\textbf{44.1} & -0.32 & 0.21 & 55.1 & 57.7 &37.9 & 32.7 & 39.3 & 39.8 & 37.4 \\\\ \\midrule\n"," Llama-2-Chat$_\\text{13B}$ & 63.5 & 42.3 & 32.6 & 10.2 & 37.2 & 0.13 & 0.81 & 28.8 & 49.9 &41.5 & 35.0 & 40.2 & 43.8 & 40.1 \\\\ \n"," +RA & 70.9 & 51.6 & 44.6 & 14.0 & 45.3 & -0.16 & 0.25 & \\textbf{59.1} & 51.2 &43.4 & 37.4 & 43.5 & 46.4 & 42.7 \\\\ \n"," +\\knnlm & 64.7 & 43.5 & 34.2 & 11.2 & 38.4 & 0.20 & 0.95 & 31.1 & 46.1 &41.4 & 34.7 & 40.6 & 44.2 & 40.2 \\\\ \n"," \\rowcolor{gray!20}\n"," +\\nest & 64.2 & 44.2 & 34.3 & 10.9 & 38.4 & \\textbf{0.29} & \\textbf{0.98} & 35.7 & 47.2 &41.3 & 34.9 & 40.2 & 43.7 & 40.0 \\\\ \n"," \\rowcolor{gray!20}\n"," +RA-\\nest & \\textbf{70.9} & \\textbf{51.7} & \\textbf{45.3} & \\textbf{14.7} & \\textbf{45.7} & -0.14 & 0.25 & 58.4 & \\textbf{52.4} &\\textbf{43.5} & \\textbf{37.7} & \\textbf{43.5} & \\textbf{46.7} & \\textbf{42.8} \\\\ \\midrule\n"," Llama-2-Chat$_\\text{70B}$ & 74.0 & 50.1 & 39.5 & 12.8 & 44.1 & 0.14 & 0.70 & 34.2 & \\textbf{58.8} &43.5 & 37.9 & 44.4 & 47.0 & 43.2 \\\\ \n"," +RA & \\textbf{75.5} & \\textbf{55.4} & \\textbf{52.5} & 16.0 & \\textbf{49.9} & -0.13 & 0.40 & 52.9 & 42.1 &\\textbf{45.9} & 39.7 & 46.2 & 48.6 & 45.1 \\\\ \n"," +\\knnlm & 74.6 & 51.2 & 40.2 & 13.5 & 44.9 & 0.08 & 0.58 & 36.1 & 54.4 &44.0 & 37.4 & 44.1 & 47.1 & 43.2 \\\\ \n"," \\rowcolor{gray!20}\n"," +\\nest & 74.2 & 51.6 & 41.4 & 13.8 & 45.2 & \\textbf{0.17} & \\textbf{0.70} & 41.6 & 56.2 &43.8 & 38.0 & 44.4 & 47.8 & 43.5 \\\\ \n"," \\rowcolor{gray!20}\n"," +RA-\\nest & 75.4 & 55.2 & 52.4 & \\textbf{16.3} & 49.8 & -0.19 & 0.31 & \\textbf{59.2} & 53.8 &45.8 & \\textbf{39.7} & \\textbf{46.2} & \\textbf{48.9} & \\textbf{45.1} \\\\ \\bottomrule\n","\\end{tabular}\n","\\end{adjustbox}\n","\\\\\n","\\vspace{6pt}\n","\\caption{Results on text completion (upper table) and other tasks (lower table). Bold numbers indicate the best performance. PPL: Perplexity. RG: ROUGE score. Avg. Len: Average generation length. $\\Delta$BLEU/$\\Delta$RG: The difference between the max score to correct references and the max score to incorrect references. FS: \\factscore with length penalty.}\n","\\vspace{-3mm}\n","\\label{tbl:main_results}\n","\\end{table}\\subsection{Main Results}\n"],["\\begin{figure}[h]\n","\\centering\n","\\begin{subfigure}[b]{0.48\\textwidth}\n"," \\centering\n"," \\hspace{-5mm}\n"," \\includegraphics[width=\\textwidth]{latency_breakdown.pdf}\n"," \\caption{Latency Breakdown.}\\label{fig:latency:breakdown}\n","\\end{subfigure}\n","\\begin{subfigure}[b]{0.48\\textwidth}\n"," \\centering\n"," \\includegraphics[width=\\textwidth]{latency_spanlen.pdf}\n"," \\caption{Span length and \\factscore on Biography.}\\label{fig:latency:tradeoff}\n","\\end{subfigure}\n","\\caption{Latency-accuracy trade-off and breakdown on Biography using \\llama-2-Chat 70B+\\nest. As the relaxation factor $\\gamma$ decreases, \\nest tends to accept longer spans from the corpus. We choose $\\gamma=5e-2$ in our main experiments, which accelerates the generation and improves the \\factscore.}\n","\\label{fig:latency}\n","\\end{figure}\n"],["\\begin{table}[!t]\n","\\begin{adjustbox}{max width=1\\textwidth}\n","\\begin{tabular}{l|l|cc|l}\n","\\toprule\n","Task & Models & \\multicolumn{1}{l}{Ratio (\\%)} & \\multicolumn{1}{l|}{Length} & Example (Generated by \\nest-70B) \\\\ \\midrule\n","& \\nest$_{\\text{7B}}$ & 93.4 & 18.4&\\multirow{6}{*}{\\begin{tabular}[c]{@{}l@{}}Question: who sings does he love me with reba? \\\\Answer: The song \"\\green{Does He Love You}\" \\href{https://en.wikipedia.org/wiki/Greatest_Hits_Volume_Two_(Reba_McEntire_album)}{\\color{gray}{[}29756059{]}} is a duet \\\\between\n","\\green{Reba McEntire and Linda Davis. } \\green{It was}\\\\ \\green{released in August 1993} \\green{as the first single from Reba's} \\\\ \\green{compilation album Greatest Hits Volume 2. It is one of}\\\\ \\green{country music's several songs about a love triangle.} \\href{https://en.wikipedia.org/wiki/Does_He_Love_You}{\\color{gray}{[}14963536{]}}\\end{tabular}}\\\\\n","\\multirow{6}{*}{NQ} & \\nest$_{\\text{13B}}$ & 52.4 & 4.4 & \\\\\n","& \\nest$_{\\text{70B}}$ & 58.8 & 4.3 & \\\\\n","& RA-\\nest$_{\\text{7B}}$ &77.5 &3.1 & \\\\\n","& RA-\\nest$_{\\text{13B}}$ &33.2 &3.0 &\\\\\n","& RA-\\nest$_{\\text{70B}}$ &44.9\t&3.9 &\\\\\n","\\midrule\n","& &\t& & \\multirow{8}{*}{\\begin{tabular}[c]{@{}l@{}}Question: Tell me a bio of Taral Hicks.\\\\ Answer: \\green{Taral Hicks (born September 21, 1974) is an American actress}\\\\ \\green{and singer. Hicks is best known} \\green{for her acting in such films as 1993's}\\\\ \\green{American crime drama film A Bronx Tale and her singing in such work} \\\\ \\green{as her 1997} \\href{https://en.wikipedia.org/wiki/Taral_Hicks}{\\color{gray}{[}15447985{]}} debut studio album This Time which peaked at \\\\No. 4 on Billboard Bubbling Under Hot 100 Singles. Raised in Teaneck, \\\\New Jersey, Hicks graduated from \\green{Teaneck High School in 1994} \\href{https://en.wikipedia.org/wiki/Taral_Hicks}{\\color{gray}{[}15447985{]}}. \\\\She is the younger sister of actress and singer D'atra Hicks.\\end{tabular}}\\\\\n","\\multirow{6}{*}{Bio} & \\nest$_{\\text{7B}}$ & 95.5 & 27.9 & \\\\ \n","& \\nest$_{\\text{13B}}$ & 53.9 & 10.6& \\\\ \n","& \\nest$_{\\text{70B}}$ & 58.6 & 7.0 & \\\\ \n","& RA-\\nest$_{\\text{7B}}$ & 50.3\t&5.1 \\\\\n","& RA-\\nest$_{\\text{13B}}$ & 48.5\t&5.9 &\\\\\n","& RA-\\nest$_{\\text{70B}}$ & 80.7\t&11.0 &\\\\\n","& &\t& &\\\\\n","\\bottomrule\n","\\end{tabular}\n","\\end{adjustbox}\n","\\\\\\caption{Attribution analysis. (Attribution) Ratio: Proportion of tokens that are taken from the corpus. (Attribution) Length: Average length of consecutive spans in the generation that are taken from the same document. \\green{Green}: Segments taken from the corpus. {\\color{gray}{Gray}}: Document id.\n","}\\label{tbl:attr}\n","\\end{table}\\subsection{Attribution and Qualitative Analysis}\\label{sec:attribution}\n"],["\\begin{table*}[t]\n"," \\centering\n"," \\scalebox{0.84}{\n"," \\begin{tabular}{p{0.18\\textwidth}p{0.32\\textwidth}p{0.5\\textwidth}}\n"," \\toprule\n"," Category & Base LM, \\knnlm and \\nest & RA-based Methods \\\\\n"," \\midrule \\\\\n"," QA, MMLU, Fact Check & \\texttt{[INST]} Question: \\{question\\} Answer: \\texttt{[/INST]}& \\texttt{[INST]} Answer the question with the background context as reference. Background: \\{retrieved passages\\}\\nls Question: \\{question\\} Answer: \\texttt{[/INST]} \\\\ \\\\\n"," \\hdashline \\\\\n"," Text Completion &\\texttt{[INST]} Write an article.\\nls Article: \\texttt{[/INST]} \\{prefix\\} & \\texttt{[INST]} Write an article with the background context as reference. Background: \\{retrieved passages\\}\\nls Article: \\texttt{[/INST]} \\{prefix\\} \\\\ \\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," }\n","\\caption{Instruction template used for different datasets. \\texttt{[INST]} and \\texttt{[/INST]} are special markers denoting the start and the end of an instruction.}\n","\\label{tbl:instruction_template}\n","\\end{table*}\n"],["\\begin{figure}[t!]\n","\\centering\n","\\hspace{-6mm}\n","\\begin{subfigure}[b]{0.33\\textwidth}\n"," \\centering\n"," \\includegraphics[width=\\textwidth]{ablation_tokens_passages.pdf}\n"," \\caption{Passages and tokens.}\\label{fig:ablation:num_tokens}\n","\\end{subfigure}\n","\\begin{subfigure}[b]{0.33\\textwidth}\n"," \\centering\n"," \\includegraphics[width=\\textwidth]{interpolation.pdf}\n"," \\caption{Interpolation coefficient.}\\label{fig:ablation:rrc}\n","\\end{subfigure}\n","\\begin{subfigure}[b]{0.33\\textwidth}\n"," \\centering\n"," \\includegraphics[width=\\textwidth]{span_nq.pdf}\n"," \\caption{Threshold for span selection.}\\label{fig:ablation:threshold}\n","\\end{subfigure}\n","\\caption{Sensitivity analysis on WikiText-103 and \\nq dev set for the \\nest-7B model with the above hyper-parameters in the sub-figures.}\n","\\label{fig:sensitivity}\n","\\end{figure}\n"],["\\begin{table}[!h]\n","\\centering\n","\\begin{adjustbox}{max width=\\textwidth}\n","\\begin{tabular}{l|ccc}\n","\\toprule\n","Models (7B) & Wiki./ROUGE-1 &NQ/ALR &Bio./FS\\\\ \n","\\midrule\n","\\knnlm (two-stage) &20.1 &40.8 &34.8 \\\\\n","+ Relative Retrieval Confidence &24.7\t&44.4\t&41.6 \\\\\n","+ Dynamic Span selection &24.5 &44.6 &41.6 \\\\\n","+ Relaxed speculative decoding &26.8\t&45.4\t&46.8 \\\\\n","\\bottomrule\n","\\end{tabular}\n","\\end{adjustbox}\n","\\vspace{3mm}\n","\\caption{Ablation study on the validation set of WikiText-103, \\nq, and Biography. ROUGE-1 is reported for WikiText-103, ALR is reported for \\nq, and \\factscore is reported for Biography.}\n","\\label{tbl:ablation}\n","\\end{table}\n"],["\\begin{table}[ht]\n","\\centering\n","\\begin{adjustbox}{max width=1\\textwidth}\n"," \\begin{tabular}{ll}\n"," \\toprule\n"," Question &Answer (Generated by \\nest-70B) \\\\\n"," \\midrule\n"," \\multirow{6}{*}{The vascular layer of the eye is the}&\\multirow{6}{*}{\\begin{tabular}[c]{@{}l@{}}The \\green{vascular tunic,}\\href{https://en.wikipedia.org/wiki/Mammalian_eye}{{\\color{gray}{[}21027113{]}}} also known as the \"uvea\", is the middle layer of the \\green{three layers}\\\\ \\green{ that comprise the eye. It is located} \\href{https://en.wikipedia.org/wiki/Pars_plana}{{\\color{gray}{[}12049924{]}}} between the sclera (the outer, white layer) and \\\\ \\green{the retina (} \\href{https://en.wikipedia.org/wiki/Eye_neoplasm}{\\color{gray}{[}31987889{]}} the innermost \\green{is the retina,}\\green{which gets its oxygenation from the blood}\\\\ \\green{vessels of the choroid (posteriorly) as well as the retinal vessels (anteriorly).}\\href{https://en.wikipedia.org/wiki/Human_eye}{\\color{gray}{[}31482951{]}}\\end{tabular}}\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," \\hdashline\n"," \\multirow{6}{*}{When did United States enter World War I?}&\\multirow{6}{*}{\\begin{tabular}[c]{@{}l@{}}The United States entered World War I on April 6, \\green{1917,}\\href{https://en.wikipedia.org/wiki/United_States_in_World_War_I}{\\color{gray}{[}1592542{]}} after Congress declared war \\\\against Germany. The \\green{U.S. made its major contributions in terms of supplies, raw material, and}\\\\\\green{money, starting in 1917. American soldiers under General of the Armies John Pershing,}\\\\ \\green{Commander-in-Chief of the American Expeditionary Force (AEF), arrived at the rate of 10} \\href{https://en.wikipedia.org/wiki/United_States_in_World_War_I}{\\color{gray}{[}1592542{]}}\\end{tabular}}\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," \\hdashline\n"," \\multirow{6}{*}{Where are unipolar neurons found in spinal cord?}&\\multirow{6}{*}{\\begin{tabular}[c]{@{}l@{}}\\green{Unipolar neurons} \\href{https://en.wikipedia.org/wiki/Dendrite}{\\color{gray}{[}6000295{]}} are found in the \\green{dorsal root ganglia} \\href{https://en.wikipedia.org/wiki/Spinal_cord}{\\color{gray}{[}19976891{]}} (DRG) of \\green{the spinal cord.}\\\\\\href{https://en.wikipedia.org/wiki/Marginal_nucleus_of_spinal_cord}{\\color{gray}{[}9082117{]}} The DRG is a cluster \\green{of neurons (a ganglion) in a dorsal root of a spinal nerve. The cell}\\\\ \\green{bodies of sensory neurons known as first-order neurons are located in the dorsal root ganglia. The}\\\\ \\green{ axons of dorsal root ganglion neurons are known as af} \\href{https://en.wikipedia.org/wiki/Dorsal_root_ganglion}{\\color{gray}{[}9024943{]}}\\end{tabular}}\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," \\hdashline\n"," \\multirow{6}{*}{When was catch me if you can made?}&\\multirow{6}{*}{\\begin{tabular}[c]{@{}l@{}}The movie \"Catch Me If You Can\" was released on \\green{December 25,} \\green{2002,} \\href{https://en.wikipedia.org/wiki/Catch_Me_If_You_Can}{\\color{gray}{[}27493033{]}} in the United States. \\\\\n"," It was directed by Steven Spielberg and starred Leonardo DiCaprio as Frank \\green{Abagnale Jr.,} \\href{https://en.wikipedia.org/wiki/Leonardo_DiCaprio}{\\color{gray}{[}2219100{]}} a \\\\con man who \\green{impersonated a Pan} \\href{https://en.wikipedia.org/wiki/List_of_con_artists}{\\color{gray}{[}26808473{]}} American World Airways pilot, a doctor, and a legal \\\\prosecutor, among other identities, \\green{and Tom Hanks as} \\href{https://en.wikipedia.org/wiki/Catch_Me_If_You_Can_(book)}{\\color{gray}{[}2422879{]}} FBI agent Carl Hanratty, \\green{who pursued him.} \\\\\\href{https://en.wikipedia.org/wiki/Catch_Me_If_You_Can_(book)}{\\color{gray}{[}2422867{]}} The movie was based on\\end{tabular}}\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," \\bottomrule\n"," \\end{tabular}\n","\\end{adjustbox}\n","\\caption{Qualitative analysis for attribution on NQ. \\green{Green}: Correct segments taken from the corpus. {\\color{gray}{Gray}}: Text chunk id. The span will be labelled only when 4 consecutive tokens have the same document id.}\\label{tbl:qualitative:nq}\n","\\end{table}\n"],["\\begin{table}[ht]\n","\\centering\n","\\begin{adjustbox}{max width=1\\textwidth}\n"," \\begin{tabular}{ll}\n"," \\toprule\n"," Question &Answer (Generated by \\nest-70B) \\\\\n"," \\midrule\n"," \\multirow{6}{*}{Which is larger, Hunchun or Shijiazhuang?}&\\multirow{6}{*}{\\begin{tabular}[c]{@{}l@{}} Hunchun and \\green{Shijiazhuang} \\href{https://en.wikipedia.org/wiki/Beiyuan_Subdistrict,_Shijiazhuang}{\\color{gray}{[}30998205{]}} are both cities in China. Shi\\green{jia zhuang} \\href{https://en.wikipedia.org/wiki/Shijiazhuang_Metro}{\\color{gray}{[}6737448{]}} is a prefecture\\\\-level city and the \\green{capital and largest city of Hebei} \\href{https://en.wikipedia.org/wiki/Shijiazhuang}{\\color{gray}{[}7075559{]}} Province, with a population of 120,000,000\\\\ (2020). Hunchun is a town \\red{ship-level division of Hunchun} \\href{https://en.wikipedia.org/wiki/Xiyuan_Subdistrict,_Shijiazhuang}{\\color{gray}{[}30998199{]}}, Jilin, China, with a population of\\\\ 18,000 (2004). Therefore, Shijia\\green{zhuang is larger than} \\href{https://en.wikipedia.org/wiki/Shijiazhuang_railway_station}{\\color{gray}{[}27937602{]}}\\end{tabular}}\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," \\hdashline\n"," \\multirow{6}{*}{Was Pocahontas made before Born in China?}&\\multirow{6}{*}{\\begin{tabular}[c]{@{}l@{}}No, \\green{Pocahontas} \\href{https://en.wikipedia.org/wiki/Pocahontas_(franchise)}{\\color{gray}{[}2716717{]}} was released in \\green{1995} \\href{https://en.wikipedia.org/wiki/Opechancanough}{\\color{gray}{[}4240514{]}}, while Born in China was released in \\red{2016} \\href{https://en.wikipedia.org/wiki/List_of_giant_pandas}{\\color{gray}{[}9704476{]}}\\end{tabular}}\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," \\hdashline\n"," \\multirow{6}{*}{\\begin{tabular}[c]{@{}l@{}} How many animals does the wall running\\\\ between the West Gate Lodge and Cardiff\\\\ Castle depict?\\end{tabular}}&\\multirow{6}{*}{\\begin{tabular}[c]{@{}l@{}} The wall running between the \\green{West Gate Lodge} \\href{https://en.wikipedia.org/wiki/West_Lodge,_Cardiff_Castle}{\\color{gray}{[}2843590{]}} and Cardiff Castle depicts \\green{15 animals on Castle Street.}\\\\ \\green{It is a Grade I listed structure, designed by architect William Burges in 1866, though not built until 1890. The}\\\\ \\green{work was completed by Burges' former assistant William Frame. The original nine animal figures were sculptured}\\\\ \\green{ by Thomas Nicholls} \\href{https://en.wikipedia.org/wiki/Castle_Quarter_(Cardiff)}{\\color{gray}{[}16694594{]}}\\end{tabular}}\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," \\hdashline\n"," \\multirow{6}{*}{\\begin{tabular}[c]{@{}l@{}} How much longer after Sambou YatabarÃ\\\\ was born did Werder Bremen get founded in\\\\ the northwest German federal state Free \\\\Hanseatic City of Bremen?\\end{tabular}}&\\multirow{6}{*}{\\begin{tabular}[c]{@{}l@{}} Sambou \\green{YatabarÃ} \\href{https://en.wikipedia.org/wiki/List_of_foreign_Bundesliga_players}{\\color{gray}{[}29957019{]}} was born on {March 21}, 1989. Werder Bremen was founded on February 4, \\green{1899} \\\\ \\href{https://en.wikipedia.org/wiki/SV_Werder_Bremen}{\\color{gray}{[}5536896{]}}, which was approximately 90 years before \\green{Sambou YatabarÃ} \\href{https://en.wikipedia.org/wiki/Sambou_Yatabar\\%C3\\%A9}{\\color{gray}{[}22751848{]}} \\end{tabular}}\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," \\bottomrule\n"," \\end{tabular}\n","\\end{adjustbox}\n","\\caption{Qualitative analysis for attribution on HotpotQA. \\green{Green}: Correct segments taken from the corpus. \\red{Red}: Wrong segments taken from the corpus. {\\color{gray}{Gray}}: Document id. The span will be labelled only when 4 consecutive tokens have the same document id.}\\label{tbl:qualitative:hqa}\n","\\end{table}\n"],["\\begin{table}[ht]\n","\\centering\n","\\begin{adjustbox}{max width=1\\textwidth}\n"," \\begin{tabular}{ll}\n"," \\toprule\n"," Question &Answer (Generated by \\nest-70B) \\\\\n"," \\midrule\n"," \\multirow{8}{*}{Heavy forces on periodontal ligament causes?}&\\multirow{8}{*}{\\begin{tabular}[c]{@{}l@{}} Heavy forces on the \\green{periodontal ligaments} \\href{https://en.wikipedia.org/wiki/Human_tooth}{\\color{gray}{[}10961708{]}} can cause a variety of issues, including: 1. \\green{Periodontal} \\\\{ligament} \\href{https://en.wikipedia.org/wiki/Human_tooth_development}{\\color{gray}{[}19737958{]}} strain: The \\green{periodontal ligament can} \\href{https://en.wikipedia.org/wiki/Tooth_eruption}{\\color{gray}{[}19569905{]}} become stretched or compressed due to\\\\ heavy forces, leading to pain and \\green{a reversible increase in tooth mobility.} \\href{https://en.wikipedia.org/wiki/Toothache}{\\color{gray}{[}32678984{]}} 2. Periodontal ligament \\\\inflammation: The periodontal ligament can become inflamed due to the constant pressure and friction from\\\\ the heavy forces, leading to pain and swelling in the affected area. 3.\\green{Tooth mobility:} \\href{https://en.wikipedia.org/wiki/List_of_periodontal_diseases}{\\color{gray}{[}17809669{]}} Heavy forces\\\\ on \\green{a tooth can} \\href{https://en.wikipedia.org/wiki/Dental_implant}{\\color{gray}{[}2104721{]}} cause\\end{tabular}}\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," \\hdashline\n"," \\multirow{6}{*}{Gait apraxia is seen in thromboembolic episode involving?}&\\multirow{6}{*}{\\begin{tabular}[c]{@{}l@{}}Gait apraxia is often seen \\green{in patients with corticobasal degeneration.} \\href{https://en.wikipedia.org/wiki/Apraxia}{\\color{gray}{[}4450082{]}} Corticobasal degeneration is a \\\\neurodegenerative disorder that affects the \\green{cerebral cortex,} \\href{https://en.wikipedia.org/wiki/Ideomotor_apraxia}{\\color{gray}{[}31300317{]}} basal ganglia, and cerebellum, leading to \\\\a range of movement disorders, including gait \\green{apraxia. The} \\href{https://en.wikipedia.org/wiki/Ideomotor_apraxia}{\\color{gray}{[}31300325{]}} condition is characterized\\end{tabular}}\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," \\hdashline\n"," \\multirow{6}{*}{\\begin{tabular}[c]{@{}l@{}}Axonal transport is?\\end{tabular}}&\\multirow{6}{*}{\\begin{tabular}[c]{@{}l@{}} Axonal transport refers to the movement \\green{of mitochondria, lipids, synaptic vesicles, proteins, and other organelles}\\\\\\green{to and from a neuron's cell body, through the cytoplasm of its axon called the axoplasm. Since some axons are on}\\\\\\green{ the order of meters long, neurons cannot} \\href{https://en.wikipedia.org/wiki/Axonal_transport}{\\color{gray}{[}18180573{]}}\\end{tabular}}\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," \\hdashline\n"," \\multirow{6}{*}{\\begin{tabular}[c]{@{}l@{}} In Erythroblastosis fetalis not involved is ?\\end{tabular}}&\\multirow{6}{*}{\\begin{tabular}[c]{@{}l@{}} \\green{Inerythroblastosis fetalis} \\href{https://en.wikipedia.org/wiki/Type_II_hypersensitivity}{\\color{gray}{[}26445482{]}}, the fetus is not directly involved. \\green{Erythroblastosis fetalis} \\href{https://en.wikipedia.org/wiki/Bruce_Chown}{\\color{gray}{[}14246750{]}} is a\\\\ condition that occurs in the newborn baby when \\green{the IgG molecules (one of the five main types of antibodies)}\\\\\\green{produced by the mother pass through the placenta. Among these antibodies are some which attack antigens}\\\\\\green{on the red blood cells in the fetal circulation, breaking down and destroying the cells. The fetus} \\href{https://en.wikipedia.org/wiki/Hemolytic_disease_of_the_newborn}{\\color{gray}{[}26565066{]}}\\end{tabular}}\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," \\bottomrule\n"," \\end{tabular}\n","\\end{adjustbox}\n","\\caption{Qualitative analysis for attribution on MedMCQA. \\green{Green}: Correct segments taken from the corpus. {\\color{gray}{Gray}}: Document id. The span will be labelled only when 4 consecutive tokens have the same document id.}\\label{tbl:qualitative:mqa}\n","\\end{table}\n"],["\\begin{table}[ht]\n","\\centering\n","\\begin{adjustbox}{max width=1\\textwidth}\n"," \\begin{tabular}{ll}\n"," \\toprule\n"," Prefix &Prediction (Generated by \\nest-70B) \\\\\n"," \\midrule\n"," \\multirow{10}{*}{\\begin{tabular}[c]{@{}l@{}} casemates in the forecastle and superstructure and the\\\\ remaining pair were mounted on the deck above them\\\\ and protected by gun shields. They had a maximum\\\\ elevation of + 20 degrees which gave them ranges of\\\\ 16 @,@ 300 metres ( 17 @,@ 800 yd ). Each gun had\\\\ a rate of fire of up to 10 rounds per minute. Anti @-@ \\\\aircraft defence was provided by four 40 @-@ calibre 3rd\\\\ Year Type 8 @-@ centimetre AA guns in single mounts\\end{tabular}}&\\multirow{10}{*}{\\begin{tabular}[c]{@{}l@{}} \\green{ protected by gun shields. They had a maximum elevation of + } \\href{https://en.wikipedia.org/wiki/Ise-class_battleship}{\\color{gray}{[}4584350{]}} 80 degrees, and a rate of fire of \\\\\\green{130 rounds per minute. The } \\href{https://en.wikipedia.org/wiki/Type_35_torpedo_boat}{\\color{gray}{[}8700981{]}} ship was also equipped with eight \\green{ 53.3 cm torpedo tubes, four on each}\\\\\\green{broadside, two above water and two submerged. Around 1926, the four above-water torpedo tubes were removed}\\\\\\green{ and the ship received three additional 76 mm AA } \\href{https://en.wikipedia.org/wiki/Japanese_battleship_Nagato}{\\color{gray}{[}22329090{]}} guns on high-angle Mark 3 mounts. These were\\\\ mounted abreast the mainmast in the superstructure and had a maximum elevation of + 70 degrees and a rate of \\\\fire \\green{ of 13 to 20 rounds per minute. They fired a 6 kg projectile with a muzzle velocity of 680 m/s to a maximum}\\\\\\green{ height of } \\href{https://en.wikipedia.org/wiki/Nagato-class_battleship}{\\color{gray}{[}30284091{]}}7,500 m. The ship was also fitted with eight 61 cm above-water torpedo tubes, four on each\\\\ broadside, two above water and two submered. The ship had a crew of 1\\end{tabular}}\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," \\hdashline\n"," \\multirow{12}{*}{\\begin{tabular}[c]{@{}l@{}}, but he is thought to have given up the privilege in favour\\\\ of one of his half brothers. He spent the next four years\\\\ living in the Luoyang area, fulfilling his duties in domestic\\\\ affairs.In the autumn of 744, he met Li Bai ( Li Po ) for\\\\ the first time, and the two poets formed a friendship.\\\\ David Young describes this as \"the most significant formative\\\\ element in Du Fu 's artistic development\" because it gave him\\\\ a living example of the reclusive poet @-@ scholar life to which\\\\ he was attracted after his failure in the civil\\end{tabular}}&\\multirow{12}{*}{\\begin{tabular}[c]{@{}l@{}}\\green{ service exam. The relationship was somewhat one-sided, however. Du Fu was by some years the younger, while Li}\\\\\\green{ Bai was already a poetic star. We have twelve poems to or about Li Bai from the younger poet, but } \\href{https://en.wikipedia.org/wiki/Du_Fu}{\\color{gray}{[}24905850{]}}\\\\ none in the other direction. In 740s, Du Fu began to write poetry seriously. His early work was influenced by\\\\ the works of Li Bai and other poets of the time. He also began to develop his own style and themes in his poetry. \\\\ In 750s, Du Fu became increasingly involved in politics and social issues. He wrote many poems that criticized \\\\the government and its policies. He also became \\red{ known for his poems } \\href{https://en.wikipedia.org/wiki/Mi_Fu}{\\color{gray}{[}13895340{]}} that expressed his \\red{ decision to}\\\\\\green{withdraw from civil service.} \\href{https://en.wikipedia.org/wiki/Tao_Yuanming}{\\color{gray}{[}19467174{]}} In 755, Du Fu was appointed an advisor \\green{ to Yan Wu, } \\href{https://en.wikipedia.org/wiki/Du_Fu}{\\color{gray}{[}24905857{]}}\\\\ the Prince of Guangling. He served in this position for three years. During this time, he left \\green{ the city to escape a}\\\\\\green{ rebellion, but he returned in summer 760. } \\href{https://en.wikipedia.org/wiki/Du_Fu}{\\color{gray}{[}24905857{]}} In 762, he was appointed an advisor to Li Yu, the Prince of\\\\ Xu. He was \\green{ relieved of his } \\href{https://en.wikipedia.org/wiki/Du_Fu}{\\color{gray}{[}24905857{]}} duties in 764\\end{tabular}}\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," \\hdashline\n"," \\multirow{12}{*}{\\begin{tabular}[c]{@{}l@{}}positions to Bradley. Still farther northward in the zone of the\\\\ US 38th Infantry the North Koreans were also active. After\\\\ the North Korean breakthrough during the night of August 31,\\\\ Keiser had ordered the 2nd Battalion, 38th Infantry, to move\\\\ south and help the 23rd Infantry establish a defensive position\\\\ west of Changnyong. In attempting to do this, the battalion\\\\ found North Korean troops already on the ridges along the road.\\\\ They had penetrated to Hill 284 overlooking the 38th Infantry\\\\ command post. This\\end{tabular}}&\\multirow{12}{*}{\\begin{tabular}[c]{@{}l@{}} was serious, for the \\green{ 38th Regiment command post } \\href{https://en.wikipedia.org/wiki/Second_Battle_of_Naktong_Bulge}{\\color{gray}{[}13479536{]}} was in the process of moving back to \\red{ a position}\\\\\\red{ south of } \\href{https://en.wikipedia.org/wiki/Battle_of_Sangju_(1950)}{\\color{gray}{[}8986888{]}} the river. The 2nd Battalion \\red{ was then forced to occupy a lower position at 1 mi away from}\\\\\\red{ the Chinese. At the same time, the } \\href{https://en.wikipedia.org/wiki/Battle_of_the_Chongchon_River}{\\color{gray}{[}29133385{]}} 3rd Battalion, 38th Infantry, was cut off \\green{3 mi west of } \\href{https://en.wikipedia.org/wiki/Great_Naktong_Offensive}{\\color{gray}{[}4050187{]}}\\\\23rd Infantry and \\red{ was forced to withdraw to safer positions. In the early afternoon, another attacking force, an}\\\\\\red{ estimated } \\href{https://en.wikipedia.org/wiki/Battle_of_Taejon}{\\color{gray}{[}20154221{]}}200 North Koreans, struck the 38th Regiment command \\green{ post. The regimental commander}\\\\\\green{ organized a defensive perimeter } \\href{https://en.wikipedia.org/wiki/Second_Battle_of_Naktong_Bulge}{\\color{gray}{[}13479536{]}} around his command post and held on until nightfall. The KPA \\green{ 7th}\\\\\\green{ Division troops had } \\href{https://en.wikipedia.org/wiki/Great_Naktong_Offensive}{\\color{gray}{[}4050220{]}} effected major crossings of the Naktong at two principal points against the US 38th\\\\ Infantry \\green{. The KPA 9th Division had effected major crossings of the Naktong at two principal points against the US }\\\\ \\href{https://en.wikipedia.org/wiki/Second_Battle_of_Naktong_Bulge}{\\color{gray}{[}13479513{]}}23rd Infantry. The 1st Battalion, 23rd Infantry, on the ridges overlooking the river, was pushed back as\\\\ its soldiers retreated without orders. Survivors from the 1st Battalion later joined the 2\\end{tabular}}\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," \\hdashline\n"," \\multirow{10}{*}{\\begin{tabular}[c]{@{}l@{}} northeast of Kuching 216 kilometres (134 mi) northeast of Sibu,\\\\ and 200 kilometres (120 mi) southwest of Miri. Bintulu is located\\\\ near the mouth of the <unk> River, in the coastal region of central\\\\ Sarawak. Geology of the coastal area was formed during the Pleisto-\\\\cene period; silt, clay, and gravel can be found here. Geological forma-\\\\tion from the Oligocene period is found in the inland area, which con-\\\\tains limestone, siltstone, and\\end{tabular}}&\\multirow{10}{*}{\\begin{tabular}[c]{@{}l@{}} \\green{ sandstone. The soil is generally soft. } \\href{https://en.wikipedia.org/wiki/Bintulu}{\\color{gray}{[}9989875{]}} The area is located in \\green{ the coastal region of central Sarawak.}\\\\\\green{ Geology of the coastal area was formed during the Pleistocene period; silt, clay, and gravel can be found here.}\\\\\\green{ Geological formation from the Oligocene period is found in the inland area, which contains limestone,} \\href{https://en.wikipedia.org/wiki/Bintulu}{\\color{gray}{[}9989875{]}}\\\\ siltstone, and sandstone. The soil is generally soft. The area is located in the coastal region of central Sarawak,\\\\ which is known for its rich \\green{ oil and gas reserves. The } \\href{https://en.wikipedia.org/wiki/Sarawak}{\\color{gray}{[}21065756{]}} town is also surrounded by lush \\green{ tropical rainforests}\\\\\\green{ and abundant } \\href{https://en.wikipedia.org/wiki/Sarawak}{\\color{gray}{[}21065719{]}} wildlife. Bintulu is \\green{ the capital of the Bintulu District of the Bintulu Division of Sarawak,}\\\\\\green{ Malaysia. The name of Bintulu was derived from the local native language 'Mentu Ulau' (picking heads). Bintulu}\\\\\\green{ was a small fishing village when Rajah James Brooke acquired it } \\href{https://en.wikipedia.org/wiki/Bintulu}{\\color{gray}{[}9989859{]}}\\end{tabular}}\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," &\\\\\n"," \\bottomrule\n"," \\end{tabular}\n","\\end{adjustbox}\n","\\caption{Qualitative analysis for attribution on WikiText-103. \\green{Green}: Correct segments taken from the corpus. \\red{Red}: Wrong segments taken from the corpus. {\\color{gray}{Gray}}: Document id. The span will be labelled only when 4 consecutive tokens have the same document id.}\\label{tbl:qualitative:wiki}\n","\\end{table}\n"]],"string":"[\n [\n \"\\\\begin{figure}[t!]\\n\",\n \"\\\\centering\\n\",\n \"\\\\includegraphics[width=\\\\textwidth]{figures/NEST.png}\\n\",\n \"\\\\caption{The \\\\nest approach first locates the tokens in the corpus using the LM hidden states. The retrieval distribution $p_{\\\\text{\\\\knn}}$ is dynamically interpolated with $p_{\\\\text{LM}}$ based on the retriever's uncertainty $\\\\lambda_t$. The token and its $n$-gram continuation are then selected from the mixture distribution $p_{\\\\mathcal{M}}$, while the final span length is determined by speculative decoding to remove undesired tokens. The spans incorporated in the final generation provide direct attribution and amortize the generation latency.}\\n\",\n \"\\\\label{fig:nest}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table}[!t]\\n\",\n \"\\\\begin{adjustbox}{max width=1\\\\textwidth}\\n\",\n \"\\\\centering\\n\",\n \"\\\\begin{tabular}{l|cccccc|cccccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Models} & \\\\multicolumn{6}{c|}{\\\\textbf{Wikitext-103}} & \\\\multicolumn{6}{c}{\\\\textbf{Pile of Law}} \\\\\\\\ \\n\",\n \"\\n\",\n \" & {PPL($\\\\downarrow$)} & {MAUVE} & {RG-1} & {RG-2} & {RG-L} & {Avg. Len} & {PPL($\\\\downarrow$)} & {MAUVE} & {RG-1} & {RG-2} & {RG-L} & {Avg. Len} \\\\\\\\ \\n\",\n \"\\\\midrule\\n\",\n \"Llama-2-Chat$_\\\\text{7B}$ & 14.6 & 58.8 & 15.8 & 3.7 & 14.4 & 175.4 & 10.1 & 80.7 & 19.1 & 5.5 & 17.1 & 211.4 \\\\\\\\\\n\",\n \" +RA & 7.2 & 74.6 & \\\\textbf{35.7} & \\\\textbf{23.1} & \\\\textbf{34.4} & 204.5 & 7.1 & 84.7 & 23.1 & 8.9 & 21.1 & 222.0 \\\\\\\\\\n\",\n \" +\\\\knnlm & 9.8 & \\\\textbf{82.5} & 23.7 & 7.7 & 21.7 & \\\\textbf{238.2} & 8.8 & 81.1 & 19.4 & 5.7 & 17.4 & 214.3 \\\\\\\\ \\n\",\n \" \\\\rowcolor{gray!20}\\n\",\n \" +\\\\nest & 8.4 & 73.2 & 28.4 & 14.2 & 27.1 & 218.4 & 8.1 & 88.0 & 23.7 & 8.7 & 21.5 & 226.5 \\\\\\\\ \\n\",\n \" \\\\rowcolor{gray!20}\\n\",\n \" +RA-\\\\nest & \\\\textbf{6.4} & 72.6 & 35.2 & 22.7 & 34.0 & 202.0 & \\\\textbf{6.7} & \\\\textbf{90.0} & \\\\textbf{24.4} & \\\\textbf{9.0} & \\\\textbf{22.2} & \\\\textbf{232.1} \\\\\\\\ \\\\midrule\\n\",\n \" Llama-2-Chat$_\\\\text{13B}$ & 12.0 & 75.9 & 19.9 & 4.9 & 18.0 & 218.4 & 8.2 & 72.8 & 17.5 & 5.3 & 15.7 & 181.7 \\\\\\\\ \\n\",\n \" +RA & 6.5 & \\\\textbf{91.5} & \\\\textbf{38.9} & \\\\textbf{24.2} & \\\\textbf{37.2} & \\\\textbf{249.3} & 5.9 & 86.6 & 23.6 & 9.1 & 21.5 & 228.7 \\\\\\\\ \\n\",\n \" +\\\\knnlm & 8.6 & 76.3 & 23.7 & 8.2 & 21.9 & 238.5 & 7.4 & 71.5 & 17.7 & 5.3 & 15.9 & 183.7 \\\\\\\\ \\n\",\n \" \\\\rowcolor{gray!20}\\n\",\n \" +\\\\nest & 7.2 & 67.1 & 29.3 & 15.6 & 28.1 & 207.1 & 6.8 & 86.0 & 22.9 & 8.7 & 20.9 & 212.3 \\\\\\\\ \\n\",\n \" \\\\rowcolor{gray!20}\\n\",\n \" +RA-\\\\nest & \\\\textbf{5.8} & 86.8 & 38.6 & 24.0 & 37.0 & 245.5 & \\\\textbf{5.7} & \\\\textbf{90.1} & \\\\textbf{24.7} & \\\\textbf{9.2} & \\\\textbf{22.4} & \\\\textbf{229.4} \\\\\\\\ \\\\midrule\\n\",\n \" Llama-2-Chat$_\\\\text{70B}$ & 9.9 & 88.6 & 22.9 & 6.2 & 20.8 & 239.6 & 6.9 & 93.4 & 23.0 & 7.1 & 20.7 & 250.1 \\\\\\\\ \\n\",\n \" +RA & 5.3 & \\\\textbf{91.6} & \\\\textbf{40.5} & \\\\textbf{26.1} & \\\\textbf{38.8} & 235.9 & 4.9 & 95.5 & \\\\textbf{26.3} & \\\\textbf{10.1} & \\\\textbf{24.0} & 253.2 \\\\\\\\ \\n\",\n \" +\\\\knnlm & 7.1 & 83.6 & 26.1 & 9.6 & 24.1 & \\\\textbf{253.9} & 6.3 & 94.4 & 23.1 & 7.2 & 20.8 & 251.3 \\\\\\\\\\n\",\n \" \\\\rowcolor{gray!20}\\n\",\n \" +\\\\nest & 6.3 & 82.6 & 32.6 & 17.2 & 31.1 & 236.3 & 5.9 & 95.4 & 25.6 & 9.4 & 23.2 & 251.3 \\\\\\\\ \\n\",\n \" \\\\rowcolor{gray!20}\\n\",\n \" +RA-\\\\nest & \\\\textbf{4.8} & 90.0 & 40.2 & 25.9 & 38.6 & 233.1 & \\\\textbf{4.7} & \\\\textbf{97.6} & 26.2 & 9.5 & 23.7 & \\\\textbf{253.6} \\\\\\\\\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\end{adjustbox}\\n\",\n \"\\\\begin{adjustbox}{max width=1\\\\textwidth}\\n\",\n \"\\\\setlength{\\\\tabcolsep}{4pt} % Default value: 6pt\\n\",\n \"\\\\centering\\n\",\n \"\\\\begin{tabular}{l|ccccc|cc|cc|ccccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Models} & \\\\textbf{TQA} & \\\\textbf{NQ} & \\\\textbf{HQA} & \\\\textbf{MQA} & \\\\textbf{Avg.} & \\\\multicolumn{2}{c|}{\\\\textbf{TruthfulQA}} & \\\\multicolumn{2}{c|}{\\\\textbf{Biography}} & \\\\multicolumn{5}{c}{\\\\textbf{MMLU}} \\\\\\\\\\n\",\n \"~ &\\\\multicolumn{5}{c|}{Answer-Level Recall} & $\\\\Delta$BLEU & $\\\\Delta$RG-1 & FS & \\\\# Facts & Human. & STEM & Social & Other & Avg. \\\\\\\\ \\n\",\n \"\\\\midrule\\n\",\n \"Llama-2-Chat$_\\\\text{7B}$ & 61.1 & 38.9 & 30.6 & 9.3 & 35.0 & -0.02 & 0.42 & 27.2 & \\\\textbf{71.2} & 37.8 & 32.6 & 38.9 & 39.6 & 37.2 \\\\\\\\ \\n\",\n \" +RA & \\\\textbf{69.5} & 48.4 & 44.1 & 12.8 & 43.7 & -0.34 & 0.18 & \\\\textbf{56.5} & 67.1 &41.8 & 35.3 & \\\\textbf{42.2} & 43.3 & 40.7 \\\\\\\\ \\n\",\n \" +\\\\knnlm & 63.4 & 42.4 & 33.5 & 9.5 & 37.2 & \\\\textbf{0.13} & \\\\textbf{0.66} & 30.6 & 59.8 &38.0 & 33.1 & 39.2 & 40.1 & 37.6 \\\\\\\\ \\n\",\n \" \\\\rowcolor{gray!20}\\n\",\n \" +\\\\nest & 61.5 & 43.2 & 33.5 & 10.2 & 37.1 & 0.03 & 0.45 & 38.9 & 58.2 &\\\\textbf{42.0} & \\\\textbf{35.4} & 42.0 & \\\\textbf{43.4} & \\\\textbf{40.7} \\\\\\\\ \\n\",\n \" \\\\rowcolor{gray!20}\\n\",\n \" +RA-\\\\nest & 69.0 & \\\\textbf{48.8} & \\\\textbf{45.3} & \\\\textbf{13.3} & \\\\textbf{44.1} & -0.32 & 0.21 & 55.1 & 57.7 &37.9 & 32.7 & 39.3 & 39.8 & 37.4 \\\\\\\\ \\\\midrule\\n\",\n \" Llama-2-Chat$_\\\\text{13B}$ & 63.5 & 42.3 & 32.6 & 10.2 & 37.2 & 0.13 & 0.81 & 28.8 & 49.9 &41.5 & 35.0 & 40.2 & 43.8 & 40.1 \\\\\\\\ \\n\",\n \" +RA & 70.9 & 51.6 & 44.6 & 14.0 & 45.3 & -0.16 & 0.25 & \\\\textbf{59.1} & 51.2 &43.4 & 37.4 & 43.5 & 46.4 & 42.7 \\\\\\\\ \\n\",\n \" +\\\\knnlm & 64.7 & 43.5 & 34.2 & 11.2 & 38.4 & 0.20 & 0.95 & 31.1 & 46.1 &41.4 & 34.7 & 40.6 & 44.2 & 40.2 \\\\\\\\ \\n\",\n \" \\\\rowcolor{gray!20}\\n\",\n \" +\\\\nest & 64.2 & 44.2 & 34.3 & 10.9 & 38.4 & \\\\textbf{0.29} & \\\\textbf{0.98} & 35.7 & 47.2 &41.3 & 34.9 & 40.2 & 43.7 & 40.0 \\\\\\\\ \\n\",\n \" \\\\rowcolor{gray!20}\\n\",\n \" +RA-\\\\nest & \\\\textbf{70.9} & \\\\textbf{51.7} & \\\\textbf{45.3} & \\\\textbf{14.7} & \\\\textbf{45.7} & -0.14 & 0.25 & 58.4 & \\\\textbf{52.4} &\\\\textbf{43.5} & \\\\textbf{37.7} & \\\\textbf{43.5} & \\\\textbf{46.7} & \\\\textbf{42.8} \\\\\\\\ \\\\midrule\\n\",\n \" Llama-2-Chat$_\\\\text{70B}$ & 74.0 & 50.1 & 39.5 & 12.8 & 44.1 & 0.14 & 0.70 & 34.2 & \\\\textbf{58.8} &43.5 & 37.9 & 44.4 & 47.0 & 43.2 \\\\\\\\ \\n\",\n \" +RA & \\\\textbf{75.5} & \\\\textbf{55.4} & \\\\textbf{52.5} & 16.0 & \\\\textbf{49.9} & -0.13 & 0.40 & 52.9 & 42.1 &\\\\textbf{45.9} & 39.7 & 46.2 & 48.6 & 45.1 \\\\\\\\ \\n\",\n \" +\\\\knnlm & 74.6 & 51.2 & 40.2 & 13.5 & 44.9 & 0.08 & 0.58 & 36.1 & 54.4 &44.0 & 37.4 & 44.1 & 47.1 & 43.2 \\\\\\\\ \\n\",\n \" \\\\rowcolor{gray!20}\\n\",\n \" +\\\\nest & 74.2 & 51.6 & 41.4 & 13.8 & 45.2 & \\\\textbf{0.17} & \\\\textbf{0.70} & 41.6 & 56.2 &43.8 & 38.0 & 44.4 & 47.8 & 43.5 \\\\\\\\ \\n\",\n \" \\\\rowcolor{gray!20}\\n\",\n \" +RA-\\\\nest & 75.4 & 55.2 & 52.4 & \\\\textbf{16.3} & 49.8 & -0.19 & 0.31 & \\\\textbf{59.2} & 53.8 &45.8 & \\\\textbf{39.7} & \\\\textbf{46.2} & \\\\textbf{48.9} & \\\\textbf{45.1} \\\\\\\\ \\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\end{adjustbox}\\n\",\n \"\\\\\\\\\\n\",\n \"\\\\vspace{6pt}\\n\",\n \"\\\\caption{Results on text completion (upper table) and other tasks (lower table). Bold numbers indicate the best performance. PPL: Perplexity. RG: ROUGE score. Avg. Len: Average generation length. $\\\\Delta$BLEU/$\\\\Delta$RG: The difference between the max score to correct references and the max score to incorrect references. FS: \\\\factscore with length penalty.}\\n\",\n \"\\\\vspace{-3mm}\\n\",\n \"\\\\label{tbl:main_results}\\n\",\n \"\\\\end{table}\\\\subsection{Main Results}\\n\"\n ],\n [\n \"\\\\begin{figure}[h]\\n\",\n \"\\\\centering\\n\",\n \"\\\\begin{subfigure}[b]{0.48\\\\textwidth}\\n\",\n \" \\\\centering\\n\",\n \" \\\\hspace{-5mm}\\n\",\n \" \\\\includegraphics[width=\\\\textwidth]{latency_breakdown.pdf}\\n\",\n \" \\\\caption{Latency Breakdown.}\\\\label{fig:latency:breakdown}\\n\",\n \"\\\\end{subfigure}\\n\",\n \"\\\\begin{subfigure}[b]{0.48\\\\textwidth}\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\textwidth]{latency_spanlen.pdf}\\n\",\n \" \\\\caption{Span length and \\\\factscore on Biography.}\\\\label{fig:latency:tradeoff}\\n\",\n \"\\\\end{subfigure}\\n\",\n \"\\\\caption{Latency-accuracy trade-off and breakdown on Biography using \\\\llama-2-Chat 70B+\\\\nest. As the relaxation factor $\\\\gamma$ decreases, \\\\nest tends to accept longer spans from the corpus. We choose $\\\\gamma=5e-2$ in our main experiments, which accelerates the generation and improves the \\\\factscore.}\\n\",\n \"\\\\label{fig:latency}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table}[!t]\\n\",\n \"\\\\begin{adjustbox}{max width=1\\\\textwidth}\\n\",\n \"\\\\begin{tabular}{l|l|cc|l}\\n\",\n \"\\\\toprule\\n\",\n \"Task & Models & \\\\multicolumn{1}{l}{Ratio (\\\\%)} & \\\\multicolumn{1}{l|}{Length} & Example (Generated by \\\\nest-70B) \\\\\\\\ \\\\midrule\\n\",\n \"& \\\\nest$_{\\\\text{7B}}$ & 93.4 & 18.4&\\\\multirow{6}{*}{\\\\begin{tabular}[c]{@{}l@{}}Question: who sings does he love me with reba? \\\\\\\\Answer: The song \\\"\\\\green{Does He Love You}\\\" \\\\href{https://en.wikipedia.org/wiki/Greatest_Hits_Volume_Two_(Reba_McEntire_album)}{\\\\color{gray}{[}29756059{]}} is a duet \\\\\\\\between\\n\",\n \"\\\\green{Reba McEntire and Linda Davis. } \\\\green{It was}\\\\\\\\ \\\\green{released in August 1993} \\\\green{as the first single from Reba's} \\\\\\\\ \\\\green{compilation album Greatest Hits Volume 2. It is one of}\\\\\\\\ \\\\green{country music's several songs about a love triangle.} \\\\href{https://en.wikipedia.org/wiki/Does_He_Love_You}{\\\\color{gray}{[}14963536{]}}\\\\end{tabular}}\\\\\\\\\\n\",\n \"\\\\multirow{6}{*}{NQ} & \\\\nest$_{\\\\text{13B}}$ & 52.4 & 4.4 & \\\\\\\\\\n\",\n \"& \\\\nest$_{\\\\text{70B}}$ & 58.8 & 4.3 & \\\\\\\\\\n\",\n \"& RA-\\\\nest$_{\\\\text{7B}}$ &77.5 &3.1 & \\\\\\\\\\n\",\n \"& RA-\\\\nest$_{\\\\text{13B}}$ &33.2 &3.0 &\\\\\\\\\\n\",\n \"& RA-\\\\nest$_{\\\\text{70B}}$ &44.9\\t&3.9 &\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"& &\\t& & \\\\multirow{8}{*}{\\\\begin{tabular}[c]{@{}l@{}}Question: Tell me a bio of Taral Hicks.\\\\\\\\ Answer: \\\\green{Taral Hicks (born September 21, 1974) is an American actress}\\\\\\\\ \\\\green{and singer. Hicks is best known} \\\\green{for her acting in such films as 1993's}\\\\\\\\ \\\\green{American crime drama film A Bronx Tale and her singing in such work} \\\\\\\\ \\\\green{as her 1997} \\\\href{https://en.wikipedia.org/wiki/Taral_Hicks}{\\\\color{gray}{[}15447985{]}} debut studio album This Time which peaked at \\\\\\\\No. 4 on Billboard Bubbling Under Hot 100 Singles. Raised in Teaneck, \\\\\\\\New Jersey, Hicks graduated from \\\\green{Teaneck High School in 1994} \\\\href{https://en.wikipedia.org/wiki/Taral_Hicks}{\\\\color{gray}{[}15447985{]}}. \\\\\\\\She is the younger sister of actress and singer D'atra Hicks.\\\\end{tabular}}\\\\\\\\\\n\",\n \"\\\\multirow{6}{*}{Bio} & \\\\nest$_{\\\\text{7B}}$ & 95.5 & 27.9 & \\\\\\\\ \\n\",\n \"& \\\\nest$_{\\\\text{13B}}$ & 53.9 & 10.6& \\\\\\\\ \\n\",\n \"& \\\\nest$_{\\\\text{70B}}$ & 58.6 & 7.0 & \\\\\\\\ \\n\",\n \"& RA-\\\\nest$_{\\\\text{7B}}$ & 50.3\\t&5.1 \\\\\\\\\\n\",\n \"& RA-\\\\nest$_{\\\\text{13B}}$ & 48.5\\t&5.9 &\\\\\\\\\\n\",\n \"& RA-\\\\nest$_{\\\\text{70B}}$ & 80.7\\t&11.0 &\\\\\\\\\\n\",\n \"& &\\t& &\\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\end{adjustbox}\\n\",\n \"\\\\\\\\\\\\caption{Attribution analysis. (Attribution) Ratio: Proportion of tokens that are taken from the corpus. (Attribution) Length: Average length of consecutive spans in the generation that are taken from the same document. \\\\green{Green}: Segments taken from the corpus. {\\\\color{gray}{Gray}}: Document id.\\n\",\n \"}\\\\label{tbl:attr}\\n\",\n \"\\\\end{table}\\\\subsection{Attribution and Qualitative Analysis}\\\\label{sec:attribution}\\n\"\n ],\n [\n \"\\\\begin{table*}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\scalebox{0.84}{\\n\",\n \" \\\\begin{tabular}{p{0.18\\\\textwidth}p{0.32\\\\textwidth}p{0.5\\\\textwidth}}\\n\",\n \" \\\\toprule\\n\",\n \" Category & Base LM, \\\\knnlm and \\\\nest & RA-based Methods \\\\\\\\\\n\",\n \" \\\\midrule \\\\\\\\\\n\",\n \" QA, MMLU, Fact Check & \\\\texttt{[INST]} Question: \\\\{question\\\\} Answer: \\\\texttt{[/INST]}& \\\\texttt{[INST]} Answer the question with the background context as reference. Background: \\\\{retrieved passages\\\\}\\\\nls Question: \\\\{question\\\\} Answer: \\\\texttt{[/INST]} \\\\\\\\ \\\\\\\\\\n\",\n \" \\\\hdashline \\\\\\\\\\n\",\n \" Text Completion &\\\\texttt{[INST]} Write an article.\\\\nls Article: \\\\texttt{[/INST]} \\\\{prefix\\\\} & \\\\texttt{[INST]} Write an article with the background context as reference. Background: \\\\{retrieved passages\\\\}\\\\nls Article: \\\\texttt{[/INST]} \\\\{prefix\\\\} \\\\\\\\ \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \"\\\\caption{Instruction template used for different datasets. \\\\texttt{[INST]} and \\\\texttt{[/INST]} are special markers denoting the start and the end of an instruction.}\\n\",\n \"\\\\label{tbl:instruction_template}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{figure}[t!]\\n\",\n \"\\\\centering\\n\",\n \"\\\\hspace{-6mm}\\n\",\n \"\\\\begin{subfigure}[b]{0.33\\\\textwidth}\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\textwidth]{ablation_tokens_passages.pdf}\\n\",\n \" \\\\caption{Passages and tokens.}\\\\label{fig:ablation:num_tokens}\\n\",\n \"\\\\end{subfigure}\\n\",\n \"\\\\begin{subfigure}[b]{0.33\\\\textwidth}\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\textwidth]{interpolation.pdf}\\n\",\n \" \\\\caption{Interpolation coefficient.}\\\\label{fig:ablation:rrc}\\n\",\n \"\\\\end{subfigure}\\n\",\n \"\\\\begin{subfigure}[b]{0.33\\\\textwidth}\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\textwidth]{span_nq.pdf}\\n\",\n \" \\\\caption{Threshold for span selection.}\\\\label{fig:ablation:threshold}\\n\",\n \"\\\\end{subfigure}\\n\",\n \"\\\\caption{Sensitivity analysis on WikiText-103 and \\\\nq dev set for the \\\\nest-7B model with the above hyper-parameters in the sub-figures.}\\n\",\n \"\\\\label{fig:sensitivity}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table}[!h]\\n\",\n \"\\\\centering\\n\",\n \"\\\\begin{adjustbox}{max width=\\\\textwidth}\\n\",\n \"\\\\begin{tabular}{l|ccc}\\n\",\n \"\\\\toprule\\n\",\n \"Models (7B) & Wiki./ROUGE-1 &NQ/ALR &Bio./FS\\\\\\\\ \\n\",\n \"\\\\midrule\\n\",\n \"\\\\knnlm (two-stage) &20.1 &40.8 &34.8 \\\\\\\\\\n\",\n \"+ Relative Retrieval Confidence &24.7\\t&44.4\\t&41.6 \\\\\\\\\\n\",\n \"+ Dynamic Span selection &24.5 &44.6 &41.6 \\\\\\\\\\n\",\n \"+ Relaxed speculative decoding &26.8\\t&45.4\\t&46.8 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\end{adjustbox}\\n\",\n \"\\\\vspace{3mm}\\n\",\n \"\\\\caption{Ablation study on the validation set of WikiText-103, \\\\nq, and Biography. ROUGE-1 is reported for WikiText-103, ALR is reported for \\\\nq, and \\\\factscore is reported for Biography.}\\n\",\n \"\\\\label{tbl:ablation}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\begin{adjustbox}{max width=1\\\\textwidth}\\n\",\n \" \\\\begin{tabular}{ll}\\n\",\n \" \\\\toprule\\n\",\n \" Question &Answer (Generated by \\\\nest-70B) \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multirow{6}{*}{The vascular layer of the eye is the}&\\\\multirow{6}{*}{\\\\begin{tabular}[c]{@{}l@{}}The \\\\green{vascular tunic,}\\\\href{https://en.wikipedia.org/wiki/Mammalian_eye}{{\\\\color{gray}{[}21027113{]}}} also known as the \\\"uvea\\\", is the middle layer of the \\\\green{three layers}\\\\\\\\ \\\\green{ that comprise the eye. It is located} \\\\href{https://en.wikipedia.org/wiki/Pars_plana}{{\\\\color{gray}{[}12049924{]}}} between the sclera (the outer, white layer) and \\\\\\\\ \\\\green{the retina (} \\\\href{https://en.wikipedia.org/wiki/Eye_neoplasm}{\\\\color{gray}{[}31987889{]}} the innermost \\\\green{is the retina,}\\\\green{which gets its oxygenation from the blood}\\\\\\\\ \\\\green{vessels of the choroid (posteriorly) as well as the retinal vessels (anteriorly).}\\\\href{https://en.wikipedia.org/wiki/Human_eye}{\\\\color{gray}{[}31482951{]}}\\\\end{tabular}}\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" \\\\hdashline\\n\",\n \" \\\\multirow{6}{*}{When did United States enter World War I?}&\\\\multirow{6}{*}{\\\\begin{tabular}[c]{@{}l@{}}The United States entered World War I on April 6, \\\\green{1917,}\\\\href{https://en.wikipedia.org/wiki/United_States_in_World_War_I}{\\\\color{gray}{[}1592542{]}} after Congress declared war \\\\\\\\against Germany. The \\\\green{U.S. made its major contributions in terms of supplies, raw material, and}\\\\\\\\\\\\green{money, starting in 1917. American soldiers under General of the Armies John Pershing,}\\\\\\\\ \\\\green{Commander-in-Chief of the American Expeditionary Force (AEF), arrived at the rate of 10} \\\\href{https://en.wikipedia.org/wiki/United_States_in_World_War_I}{\\\\color{gray}{[}1592542{]}}\\\\end{tabular}}\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" \\\\hdashline\\n\",\n \" \\\\multirow{6}{*}{Where are unipolar neurons found in spinal cord?}&\\\\multirow{6}{*}{\\\\begin{tabular}[c]{@{}l@{}}\\\\green{Unipolar neurons} \\\\href{https://en.wikipedia.org/wiki/Dendrite}{\\\\color{gray}{[}6000295{]}} are found in the \\\\green{dorsal root ganglia} \\\\href{https://en.wikipedia.org/wiki/Spinal_cord}{\\\\color{gray}{[}19976891{]}} (DRG) of \\\\green{the spinal cord.}\\\\\\\\\\\\href{https://en.wikipedia.org/wiki/Marginal_nucleus_of_spinal_cord}{\\\\color{gray}{[}9082117{]}} The DRG is a cluster \\\\green{of neurons (a ganglion) in a dorsal root of a spinal nerve. The cell}\\\\\\\\ \\\\green{bodies of sensory neurons known as first-order neurons are located in the dorsal root ganglia. The}\\\\\\\\ \\\\green{ axons of dorsal root ganglion neurons are known as af} \\\\href{https://en.wikipedia.org/wiki/Dorsal_root_ganglion}{\\\\color{gray}{[}9024943{]}}\\\\end{tabular}}\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" \\\\hdashline\\n\",\n \" \\\\multirow{6}{*}{When was catch me if you can made?}&\\\\multirow{6}{*}{\\\\begin{tabular}[c]{@{}l@{}}The movie \\\"Catch Me If You Can\\\" was released on \\\\green{December 25,} \\\\green{2002,} \\\\href{https://en.wikipedia.org/wiki/Catch_Me_If_You_Can}{\\\\color{gray}{[}27493033{]}} in the United States. \\\\\\\\\\n\",\n \" It was directed by Steven Spielberg and starred Leonardo DiCaprio as Frank \\\\green{Abagnale Jr.,} \\\\href{https://en.wikipedia.org/wiki/Leonardo_DiCaprio}{\\\\color{gray}{[}2219100{]}} a \\\\\\\\con man who \\\\green{impersonated a Pan} \\\\href{https://en.wikipedia.org/wiki/List_of_con_artists}{\\\\color{gray}{[}26808473{]}} American World Airways pilot, a doctor, and a legal \\\\\\\\prosecutor, among other identities, \\\\green{and Tom Hanks as} \\\\href{https://en.wikipedia.org/wiki/Catch_Me_If_You_Can_(book)}{\\\\color{gray}{[}2422879{]}} FBI agent Carl Hanratty, \\\\green{who pursued him.} \\\\\\\\\\\\href{https://en.wikipedia.org/wiki/Catch_Me_If_You_Can_(book)}{\\\\color{gray}{[}2422867{]}} The movie was based on\\\\end{tabular}}\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"\\\\end{adjustbox}\\n\",\n \"\\\\caption{Qualitative analysis for attribution on NQ. \\\\green{Green}: Correct segments taken from the corpus. {\\\\color{gray}{Gray}}: Text chunk id. The span will be labelled only when 4 consecutive tokens have the same document id.}\\\\label{tbl:qualitative:nq}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\begin{adjustbox}{max width=1\\\\textwidth}\\n\",\n \" \\\\begin{tabular}{ll}\\n\",\n \" \\\\toprule\\n\",\n \" Question &Answer (Generated by \\\\nest-70B) \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multirow{6}{*}{Which is larger, Hunchun or Shijiazhuang?}&\\\\multirow{6}{*}{\\\\begin{tabular}[c]{@{}l@{}} Hunchun and \\\\green{Shijiazhuang} \\\\href{https://en.wikipedia.org/wiki/Beiyuan_Subdistrict,_Shijiazhuang}{\\\\color{gray}{[}30998205{]}} are both cities in China. Shi\\\\green{jia zhuang} \\\\href{https://en.wikipedia.org/wiki/Shijiazhuang_Metro}{\\\\color{gray}{[}6737448{]}} is a prefecture\\\\\\\\-level city and the \\\\green{capital and largest city of Hebei} \\\\href{https://en.wikipedia.org/wiki/Shijiazhuang}{\\\\color{gray}{[}7075559{]}} Province, with a population of 120,000,000\\\\\\\\ (2020). Hunchun is a town \\\\red{ship-level division of Hunchun} \\\\href{https://en.wikipedia.org/wiki/Xiyuan_Subdistrict,_Shijiazhuang}{\\\\color{gray}{[}30998199{]}}, Jilin, China, with a population of\\\\\\\\ 18,000 (2004). Therefore, Shijia\\\\green{zhuang is larger than} \\\\href{https://en.wikipedia.org/wiki/Shijiazhuang_railway_station}{\\\\color{gray}{[}27937602{]}}\\\\end{tabular}}\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" \\\\hdashline\\n\",\n \" \\\\multirow{6}{*}{Was Pocahontas made before Born in China?}&\\\\multirow{6}{*}{\\\\begin{tabular}[c]{@{}l@{}}No, \\\\green{Pocahontas} \\\\href{https://en.wikipedia.org/wiki/Pocahontas_(franchise)}{\\\\color{gray}{[}2716717{]}} was released in \\\\green{1995} \\\\href{https://en.wikipedia.org/wiki/Opechancanough}{\\\\color{gray}{[}4240514{]}}, while Born in China was released in \\\\red{2016} \\\\href{https://en.wikipedia.org/wiki/List_of_giant_pandas}{\\\\color{gray}{[}9704476{]}}\\\\end{tabular}}\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" \\\\hdashline\\n\",\n \" \\\\multirow{6}{*}{\\\\begin{tabular}[c]{@{}l@{}} How many animals does the wall running\\\\\\\\ between the West Gate Lodge and Cardiff\\\\\\\\ Castle depict?\\\\end{tabular}}&\\\\multirow{6}{*}{\\\\begin{tabular}[c]{@{}l@{}} The wall running between the \\\\green{West Gate Lodge} \\\\href{https://en.wikipedia.org/wiki/West_Lodge,_Cardiff_Castle}{\\\\color{gray}{[}2843590{]}} and Cardiff Castle depicts \\\\green{15 animals on Castle Street.}\\\\\\\\ \\\\green{It is a Grade I listed structure, designed by architect William Burges in 1866, though not built until 1890. The}\\\\\\\\ \\\\green{work was completed by Burges' former assistant William Frame. The original nine animal figures were sculptured}\\\\\\\\ \\\\green{ by Thomas Nicholls} \\\\href{https://en.wikipedia.org/wiki/Castle_Quarter_(Cardiff)}{\\\\color{gray}{[}16694594{]}}\\\\end{tabular}}\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" \\\\hdashline\\n\",\n \" \\\\multirow{6}{*}{\\\\begin{tabular}[c]{@{}l@{}} How much longer after Sambou YatabarÃ\\\\\\\\ was born did Werder Bremen get founded in\\\\\\\\ the northwest German federal state Free \\\\\\\\Hanseatic City of Bremen?\\\\end{tabular}}&\\\\multirow{6}{*}{\\\\begin{tabular}[c]{@{}l@{}} Sambou \\\\green{YatabarÃ} \\\\href{https://en.wikipedia.org/wiki/List_of_foreign_Bundesliga_players}{\\\\color{gray}{[}29957019{]}} was born on {March 21}, 1989. Werder Bremen was founded on February 4, \\\\green{1899} \\\\\\\\ \\\\href{https://en.wikipedia.org/wiki/SV_Werder_Bremen}{\\\\color{gray}{[}5536896{]}}, which was approximately 90 years before \\\\green{Sambou YatabarÃ} \\\\href{https://en.wikipedia.org/wiki/Sambou_Yatabar\\\\%C3\\\\%A9}{\\\\color{gray}{[}22751848{]}} \\\\end{tabular}}\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"\\\\end{adjustbox}\\n\",\n \"\\\\caption{Qualitative analysis for attribution on HotpotQA. \\\\green{Green}: Correct segments taken from the corpus. \\\\red{Red}: Wrong segments taken from the corpus. {\\\\color{gray}{Gray}}: Document id. The span will be labelled only when 4 consecutive tokens have the same document id.}\\\\label{tbl:qualitative:hqa}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\begin{adjustbox}{max width=1\\\\textwidth}\\n\",\n \" \\\\begin{tabular}{ll}\\n\",\n \" \\\\toprule\\n\",\n \" Question &Answer (Generated by \\\\nest-70B) \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multirow{8}{*}{Heavy forces on periodontal ligament causes?}&\\\\multirow{8}{*}{\\\\begin{tabular}[c]{@{}l@{}} Heavy forces on the \\\\green{periodontal ligaments} \\\\href{https://en.wikipedia.org/wiki/Human_tooth}{\\\\color{gray}{[}10961708{]}} can cause a variety of issues, including: 1. \\\\green{Periodontal} \\\\\\\\{ligament} \\\\href{https://en.wikipedia.org/wiki/Human_tooth_development}{\\\\color{gray}{[}19737958{]}} strain: The \\\\green{periodontal ligament can} \\\\href{https://en.wikipedia.org/wiki/Tooth_eruption}{\\\\color{gray}{[}19569905{]}} become stretched or compressed due to\\\\\\\\ heavy forces, leading to pain and \\\\green{a reversible increase in tooth mobility.} \\\\href{https://en.wikipedia.org/wiki/Toothache}{\\\\color{gray}{[}32678984{]}} 2. Periodontal ligament \\\\\\\\inflammation: The periodontal ligament can become inflamed due to the constant pressure and friction from\\\\\\\\ the heavy forces, leading to pain and swelling in the affected area. 3.\\\\green{Tooth mobility:} \\\\href{https://en.wikipedia.org/wiki/List_of_periodontal_diseases}{\\\\color{gray}{[}17809669{]}} Heavy forces\\\\\\\\ on \\\\green{a tooth can} \\\\href{https://en.wikipedia.org/wiki/Dental_implant}{\\\\color{gray}{[}2104721{]}} cause\\\\end{tabular}}\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" \\\\hdashline\\n\",\n \" \\\\multirow{6}{*}{Gait apraxia is seen in thromboembolic episode involving?}&\\\\multirow{6}{*}{\\\\begin{tabular}[c]{@{}l@{}}Gait apraxia is often seen \\\\green{in patients with corticobasal degeneration.} \\\\href{https://en.wikipedia.org/wiki/Apraxia}{\\\\color{gray}{[}4450082{]}} Corticobasal degeneration is a \\\\\\\\neurodegenerative disorder that affects the \\\\green{cerebral cortex,} \\\\href{https://en.wikipedia.org/wiki/Ideomotor_apraxia}{\\\\color{gray}{[}31300317{]}} basal ganglia, and cerebellum, leading to \\\\\\\\a range of movement disorders, including gait \\\\green{apraxia. The} \\\\href{https://en.wikipedia.org/wiki/Ideomotor_apraxia}{\\\\color{gray}{[}31300325{]}} condition is characterized\\\\end{tabular}}\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" \\\\hdashline\\n\",\n \" \\\\multirow{6}{*}{\\\\begin{tabular}[c]{@{}l@{}}Axonal transport is?\\\\end{tabular}}&\\\\multirow{6}{*}{\\\\begin{tabular}[c]{@{}l@{}} Axonal transport refers to the movement \\\\green{of mitochondria, lipids, synaptic vesicles, proteins, and other organelles}\\\\\\\\\\\\green{to and from a neuron's cell body, through the cytoplasm of its axon called the axoplasm. Since some axons are on}\\\\\\\\\\\\green{ the order of meters long, neurons cannot} \\\\href{https://en.wikipedia.org/wiki/Axonal_transport}{\\\\color{gray}{[}18180573{]}}\\\\end{tabular}}\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" \\\\hdashline\\n\",\n \" \\\\multirow{6}{*}{\\\\begin{tabular}[c]{@{}l@{}} In Erythroblastosis fetalis not involved is ?\\\\end{tabular}}&\\\\multirow{6}{*}{\\\\begin{tabular}[c]{@{}l@{}} \\\\green{Inerythroblastosis fetalis} \\\\href{https://en.wikipedia.org/wiki/Type_II_hypersensitivity}{\\\\color{gray}{[}26445482{]}}, the fetus is not directly involved. \\\\green{Erythroblastosis fetalis} \\\\href{https://en.wikipedia.org/wiki/Bruce_Chown}{\\\\color{gray}{[}14246750{]}} is a\\\\\\\\ condition that occurs in the newborn baby when \\\\green{the IgG molecules (one of the five main types of antibodies)}\\\\\\\\\\\\green{produced by the mother pass through the placenta. Among these antibodies are some which attack antigens}\\\\\\\\\\\\green{on the red blood cells in the fetal circulation, breaking down and destroying the cells. The fetus} \\\\href{https://en.wikipedia.org/wiki/Hemolytic_disease_of_the_newborn}{\\\\color{gray}{[}26565066{]}}\\\\end{tabular}}\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"\\\\end{adjustbox}\\n\",\n \"\\\\caption{Qualitative analysis for attribution on MedMCQA. \\\\green{Green}: Correct segments taken from the corpus. {\\\\color{gray}{Gray}}: Document id. The span will be labelled only when 4 consecutive tokens have the same document id.}\\\\label{tbl:qualitative:mqa}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\begin{adjustbox}{max width=1\\\\textwidth}\\n\",\n \" \\\\begin{tabular}{ll}\\n\",\n \" \\\\toprule\\n\",\n \" Prefix &Prediction (Generated by \\\\nest-70B) \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multirow{10}{*}{\\\\begin{tabular}[c]{@{}l@{}} casemates in the forecastle and superstructure and the\\\\\\\\ remaining pair were mounted on the deck above them\\\\\\\\ and protected by gun shields. They had a maximum\\\\\\\\ elevation of + 20 degrees which gave them ranges of\\\\\\\\ 16 @,@ 300 metres ( 17 @,@ 800 yd ). Each gun had\\\\\\\\ a rate of fire of up to 10 rounds per minute. Anti @-@ \\\\\\\\aircraft defence was provided by four 40 @-@ calibre 3rd\\\\\\\\ Year Type 8 @-@ centimetre AA guns in single mounts\\\\end{tabular}}&\\\\multirow{10}{*}{\\\\begin{tabular}[c]{@{}l@{}} \\\\green{ protected by gun shields. They had a maximum elevation of + } \\\\href{https://en.wikipedia.org/wiki/Ise-class_battleship}{\\\\color{gray}{[}4584350{]}} 80 degrees, and a rate of fire of \\\\\\\\\\\\green{130 rounds per minute. The } \\\\href{https://en.wikipedia.org/wiki/Type_35_torpedo_boat}{\\\\color{gray}{[}8700981{]}} ship was also equipped with eight \\\\green{ 53.3 cm torpedo tubes, four on each}\\\\\\\\\\\\green{broadside, two above water and two submerged. Around 1926, the four above-water torpedo tubes were removed}\\\\\\\\\\\\green{ and the ship received three additional 76 mm AA } \\\\href{https://en.wikipedia.org/wiki/Japanese_battleship_Nagato}{\\\\color{gray}{[}22329090{]}} guns on high-angle Mark 3 mounts. These were\\\\\\\\ mounted abreast the mainmast in the superstructure and had a maximum elevation of + 70 degrees and a rate of \\\\\\\\fire \\\\green{ of 13 to 20 rounds per minute. They fired a 6 kg projectile with a muzzle velocity of 680 m/s to a maximum}\\\\\\\\\\\\green{ height of } \\\\href{https://en.wikipedia.org/wiki/Nagato-class_battleship}{\\\\color{gray}{[}30284091{]}}7,500 m. The ship was also fitted with eight 61 cm above-water torpedo tubes, four on each\\\\\\\\ broadside, two above water and two submered. The ship had a crew of 1\\\\end{tabular}}\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" \\\\hdashline\\n\",\n \" \\\\multirow{12}{*}{\\\\begin{tabular}[c]{@{}l@{}}, but he is thought to have given up the privilege in favour\\\\\\\\ of one of his half brothers. He spent the next four years\\\\\\\\ living in the Luoyang area, fulfilling his duties in domestic\\\\\\\\ affairs.In the autumn of 744, he met Li Bai ( Li Po ) for\\\\\\\\ the first time, and the two poets formed a friendship.\\\\\\\\ David Young describes this as \\\"the most significant formative\\\\\\\\ element in Du Fu 's artistic development\\\" because it gave him\\\\\\\\ a living example of the reclusive poet @-@ scholar life to which\\\\\\\\ he was attracted after his failure in the civil\\\\end{tabular}}&\\\\multirow{12}{*}{\\\\begin{tabular}[c]{@{}l@{}}\\\\green{ service exam. The relationship was somewhat one-sided, however. Du Fu was by some years the younger, while Li}\\\\\\\\\\\\green{ Bai was already a poetic star. We have twelve poems to or about Li Bai from the younger poet, but } \\\\href{https://en.wikipedia.org/wiki/Du_Fu}{\\\\color{gray}{[}24905850{]}}\\\\\\\\ none in the other direction. In 740s, Du Fu began to write poetry seriously. His early work was influenced by\\\\\\\\ the works of Li Bai and other poets of the time. He also began to develop his own style and themes in his poetry. \\\\\\\\ In 750s, Du Fu became increasingly involved in politics and social issues. He wrote many poems that criticized \\\\\\\\the government and its policies. He also became \\\\red{ known for his poems } \\\\href{https://en.wikipedia.org/wiki/Mi_Fu}{\\\\color{gray}{[}13895340{]}} that expressed his \\\\red{ decision to}\\\\\\\\\\\\green{withdraw from civil service.} \\\\href{https://en.wikipedia.org/wiki/Tao_Yuanming}{\\\\color{gray}{[}19467174{]}} In 755, Du Fu was appointed an advisor \\\\green{ to Yan Wu, } \\\\href{https://en.wikipedia.org/wiki/Du_Fu}{\\\\color{gray}{[}24905857{]}}\\\\\\\\ the Prince of Guangling. He served in this position for three years. During this time, he left \\\\green{ the city to escape a}\\\\\\\\\\\\green{ rebellion, but he returned in summer 760. } \\\\href{https://en.wikipedia.org/wiki/Du_Fu}{\\\\color{gray}{[}24905857{]}} In 762, he was appointed an advisor to Li Yu, the Prince of\\\\\\\\ Xu. He was \\\\green{ relieved of his } \\\\href{https://en.wikipedia.org/wiki/Du_Fu}{\\\\color{gray}{[}24905857{]}} duties in 764\\\\end{tabular}}\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" \\\\hdashline\\n\",\n \" \\\\multirow{12}{*}{\\\\begin{tabular}[c]{@{}l@{}}positions to Bradley. Still farther northward in the zone of the\\\\\\\\ US 38th Infantry the North Koreans were also active. After\\\\\\\\ the North Korean breakthrough during the night of August 31,\\\\\\\\ Keiser had ordered the 2nd Battalion, 38th Infantry, to move\\\\\\\\ south and help the 23rd Infantry establish a defensive position\\\\\\\\ west of Changnyong. In attempting to do this, the battalion\\\\\\\\ found North Korean troops already on the ridges along the road.\\\\\\\\ They had penetrated to Hill 284 overlooking the 38th Infantry\\\\\\\\ command post. This\\\\end{tabular}}&\\\\multirow{12}{*}{\\\\begin{tabular}[c]{@{}l@{}} was serious, for the \\\\green{ 38th Regiment command post } \\\\href{https://en.wikipedia.org/wiki/Second_Battle_of_Naktong_Bulge}{\\\\color{gray}{[}13479536{]}} was in the process of moving back to \\\\red{ a position}\\\\\\\\\\\\red{ south of } \\\\href{https://en.wikipedia.org/wiki/Battle_of_Sangju_(1950)}{\\\\color{gray}{[}8986888{]}} the river. The 2nd Battalion \\\\red{ was then forced to occupy a lower position at 1 mi away from}\\\\\\\\\\\\red{ the Chinese. At the same time, the } \\\\href{https://en.wikipedia.org/wiki/Battle_of_the_Chongchon_River}{\\\\color{gray}{[}29133385{]}} 3rd Battalion, 38th Infantry, was cut off \\\\green{3 mi west of } \\\\href{https://en.wikipedia.org/wiki/Great_Naktong_Offensive}{\\\\color{gray}{[}4050187{]}}\\\\\\\\23rd Infantry and \\\\red{ was forced to withdraw to safer positions. In the early afternoon, another attacking force, an}\\\\\\\\\\\\red{ estimated } \\\\href{https://en.wikipedia.org/wiki/Battle_of_Taejon}{\\\\color{gray}{[}20154221{]}}200 North Koreans, struck the 38th Regiment command \\\\green{ post. The regimental commander}\\\\\\\\\\\\green{ organized a defensive perimeter } \\\\href{https://en.wikipedia.org/wiki/Second_Battle_of_Naktong_Bulge}{\\\\color{gray}{[}13479536{]}} around his command post and held on until nightfall. The KPA \\\\green{ 7th}\\\\\\\\\\\\green{ Division troops had } \\\\href{https://en.wikipedia.org/wiki/Great_Naktong_Offensive}{\\\\color{gray}{[}4050220{]}} effected major crossings of the Naktong at two principal points against the US 38th\\\\\\\\ Infantry \\\\green{. The KPA 9th Division had effected major crossings of the Naktong at two principal points against the US }\\\\\\\\ \\\\href{https://en.wikipedia.org/wiki/Second_Battle_of_Naktong_Bulge}{\\\\color{gray}{[}13479513{]}}23rd Infantry. The 1st Battalion, 23rd Infantry, on the ridges overlooking the river, was pushed back as\\\\\\\\ its soldiers retreated without orders. Survivors from the 1st Battalion later joined the 2\\\\end{tabular}}\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" \\\\hdashline\\n\",\n \" \\\\multirow{10}{*}{\\\\begin{tabular}[c]{@{}l@{}} northeast of Kuching 216 kilometres (134 mi) northeast of Sibu,\\\\\\\\ and 200 kilometres (120 mi) southwest of Miri. Bintulu is located\\\\\\\\ near the mouth of the <unk> River, in the coastal region of central\\\\\\\\ Sarawak. Geology of the coastal area was formed during the Pleisto-\\\\\\\\cene period; silt, clay, and gravel can be found here. Geological forma-\\\\\\\\tion from the Oligocene period is found in the inland area, which con-\\\\\\\\tains limestone, siltstone, and\\\\end{tabular}}&\\\\multirow{10}{*}{\\\\begin{tabular}[c]{@{}l@{}} \\\\green{ sandstone. The soil is generally soft. } \\\\href{https://en.wikipedia.org/wiki/Bintulu}{\\\\color{gray}{[}9989875{]}} The area is located in \\\\green{ the coastal region of central Sarawak.}\\\\\\\\\\\\green{ Geology of the coastal area was formed during the Pleistocene period; silt, clay, and gravel can be found here.}\\\\\\\\\\\\green{ Geological formation from the Oligocene period is found in the inland area, which contains limestone,} \\\\href{https://en.wikipedia.org/wiki/Bintulu}{\\\\color{gray}{[}9989875{]}}\\\\\\\\ siltstone, and sandstone. The soil is generally soft. The area is located in the coastal region of central Sarawak,\\\\\\\\ which is known for its rich \\\\green{ oil and gas reserves. The } \\\\href{https://en.wikipedia.org/wiki/Sarawak}{\\\\color{gray}{[}21065756{]}} town is also surrounded by lush \\\\green{ tropical rainforests}\\\\\\\\\\\\green{ and abundant } \\\\href{https://en.wikipedia.org/wiki/Sarawak}{\\\\color{gray}{[}21065719{]}} wildlife. Bintulu is \\\\green{ the capital of the Bintulu District of the Bintulu Division of Sarawak,}\\\\\\\\\\\\green{ Malaysia. The name of Bintulu was derived from the local native language 'Mentu Ulau' (picking heads). Bintulu}\\\\\\\\\\\\green{ was a small fishing village when Rajah James Brooke acquired it } \\\\href{https://en.wikipedia.org/wiki/Bintulu}{\\\\color{gray}{[}9989859{]}}\\\\end{tabular}}\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" &\\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"\\\\end{adjustbox}\\n\",\n \"\\\\caption{Qualitative analysis for attribution on WikiText-103. \\\\green{Green}: Correct segments taken from the corpus. \\\\red{Red}: Wrong segments taken from the corpus. {\\\\color{gray}{Gray}}: Document id. The span will be labelled only when 4 consecutive tokens have the same document id.}\\\\label{tbl:qualitative:wiki}\\n\",\n \"\\\\end{table}\\n\"\n ]\n]","isConversational":false},"fulltext":{"kind":"string","value":"\\documentclass[]{fairmeta}\n% Option \"twocolumn\" available, but please prioritize single-column\n\n\\usepackage[utf8]{inputenc} % allow utf-8 input\n% \\usepackage[T1]{fontenc}% use 8-bit T1 fonts\n\\usepackage{hyperref} % hyperlinks\n\\usepackage{url}% simple URL typesetting\n\\usepackage{booktabs, nicematrix} % professional-quality tables\n\\usepackage{amsfonts} % blackboard math symbols\n\\usepackage{nicefrac} % compact symbols for 1/2, etc.\n\\usepackage{microtype} % microtypography\n\\usepackage{xcolor} % colors\n% \\usepackage{times}\n% \\usepackage{latexsym}\n\\usepackage{xspace}\n\\usepackage{multirow}\n\\definecolor{darkgreen}{RGB}{0,128,0}\n\n% For theorems and such\n\\usepackage{amsmath}\n\\usepackage{amssymb}\n\\usepackage{mathtools}\n\\usepackage{amsthm}\n% 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\\usepackage[T1]{fontenc}\n\\usepackage[english]{babel}\n\\usepackage{amsfonts}\n\\usepackage{nicefrac}\n\\usepackage{microtype}\n\\usepackage{amsmath}\n\\usepackage{amssymb}\n\\usepackage{mathtools}\n\\usepackage{subcaption}\n\\usepackage{booktabs}\n\\usepackage{ragged2e}\n\\usepackage{tikz}\n\\usepackage{stackengine}\n\\usepackage{etoolbox}\n\\usepackage{xspace}\n\\usepackage{xpatch}\n\\usepackage{cuted}\n\\usepackage{enumerate}\n\\usepackage{xstring}\n\\usepackage{setspace}\n\\usepackage{tabularx}\n\\usepackage{makecell}\n\\usepackage{changepage}\n\\usepackage{enumitem}\n\\usepackage{cuted}\n\\usepackage{cancel}\n\\usepackage{eqparbox}\n\\usepackage{bibentry}\n\\usepackage[hang,flushmargin]{footmisc}\n% \\usepackage[capitalise,noabbrev,nameinlink]{cleveref}\n\\usepackage[useregional=numeric]{datetime2}\n\\usepackage{adjustbox}\n\\usepackage{threeparttable}\n\\usepackage{booktabs, caption, makecell}\n\\usepackage{colortbl}\n\\renewcommand\\theadfont{\\bfseries}\n\n\\usepackage{lipsum}% http://ctan.org/pkg/lipsum\n\\usepackage{etoolbox}\n\\usepackage{longtable}\n\n% Propositions, Theorems, and so on\n\\newtheorem{theorem}{Theorem}\n\\newtheorem{proposition}{Proposition}\n\\newtheorem{assumption}{Assumption}\n\n% Figures and tables\n\\setcounter{topnumber}{1}\n\\setcounter{bottomnumber}{1}\n\\setcounter{totalnumber}{2}\n\\graphicspath{{figures/}}\n% \\renewcommand{\\arraystretch}{1.2}\n\\renewcommand{\\topfraction}{.8}\n\\renewcommand{\\bottomfraction}{.8}\n\\renewcommand{\\floatpagefraction}{.8}\n\n\n\n\\title{Nearest Neighbor Speculative Decoding for \\\\LLM Generation and Attribution}\n\\author[2,*]{Minghan Li}\n\\author[1]{Xilun Chen}\n\\author[1,4]{Ari Holtzman}\n\\author[3,1]{Beidi Chen}\n\\author[2]{Jimmy Lin}\n\\author[1]{Wen-tau Yih}\n\\author[1]{Xi Victoria Lin}\n\n\\affiliation[1]{FAIR at Meta}\n\\affiliation[2]{University of Waterloo}\n\\affiliation[3]{Carnegie Mellon University}\n\\affiliation[4]{University of Chicago}\n\n\n\\contribution[*]{Work done during an internship at Meta.}\n% \\contribution[\\dagger]{Joint last author}\n\n\\abstract{\\input{abstract}}\n\n\\date{\\today}\n\\correspondence{Xi Victoria Lin at \\email{victorialin@meta.com}}\n\n% You can add additional metadata fields as follows \n% \\metadata[Code]{\\url{https://github.com/facebookresearch/repo}}\n% \\metadata[Blogpost]{\\url{https://ai.meta.com/blog/?page=1}}\n\n\\begin{document}\n\n\n\\maketitle\n\n\n\\section{Introduction}\\label{sec:intro}\nLarge language models (LLMs) % have demonstrated remarkable capabilities in a wide range of tasks, including question answering, summarization and mathematical reasoning\nhave demonstrated strong potential as multi-task solvers, excelling in a wide range of applications~\\citep{gpt3,chowdhery2022palm,touvron2023llama,touvron2023llama2,geminiteam2024gemini}. %. However, LLMs that solely rely on the knowledge learned during pre-training and alignment suffer from the problems of hallucination and lack of attribution\nDespite their advanced capabilities, LLMs frequently encounter the problem of hallucination, particularly when dealing with long-tail knowledge that is less represented in their training data~\\citep{kandpal-2023-struggle-long-tail,asai-etal-2023-retrieval}. % and cannot provide attribution for their output decoded from the parametric space~\\citep{kandpal-2023-struggle-long-tail,asai-etal-2023-retrieval,rashkin-2023-attribution}. \n% Previous work have proposed factuality-aware fine-tuning~\\citep{DBLP:journals/corr/abs-2311-08401,lin2024flame} and decoding by contrasting layers~\\citep{DBLP:journals/corr/abs-2309-03883} to induce more factual and contextualized output from LLMs. However, these approaches focus on changing the parametric output distribution of the language model, hence cannot directly attribute the generation to any real-world sources. \nTo address this limitation, retrieval augmentation incorporates information retrieval and nearest neighbour search from a non-parametric data store to enhance evidence-based and situated reasoning with LLMs. % , thereby significantly reducing their tendency to \nThe resulting semi-parametric LMs exhibit reduced tendency to generate unsupported content~\\citep{knnlm,retro,replug,shi2023trusting,asai-etal-2023-retrieval}. \n\nHowever, the effectiveness of retrieval-augmented language models (RALMs) in ensuring \\emph{accurate and reliable content generation} varies. The widely used in-context retrieval-augmentation (RA) regime~\\citep{ralm,replug,shi2023trusting} softly biases the LM output distribution by prepending retrieved content to the input, which does not reliably guarantee faithful attribution of information. Approaches such as \\knnlm~\\citep{knnlm} modify the LM output with a non-parametric token distribution derived from nearest-neighbor matches in a corpus, which provide more direct attribution but have also been shown to degrade the quality of text generation~\\citep{wang-etal-2023-knn}. Additionally, retrieval augmentation can significantly increase the \\emph{generation latency} due to the time required for the retrieval processes to complete and the subsequent expansion of the LM's context.\n\nIn this work, we propose Nearest Neighbor Speculative Decoding (\\nest). This new semi-parametric language modeling approach is capable of incorporating real-world text spans of arbitrary length into the generations of an off-the-shelf LM, leading to improved quality and latency. \\nest extends the standard \\knnlm approach, % which represents a non-parametric data store as a large key-value memory and leverages the hidden states of the LM to search for possible next tokens (\\emph{value}) present in similar context (\\emph{key}) to update the LM output. \n% The setup consists of a pre-trained LM and a non-parametric data store with every token occurence in \n% Every token occurrence in the data store is indexed with the contextual representation of its prefix generated by the LM.\nwhich interpolates the output distribution of an LM using the distribution of possible next tokens % within similar context \nretrieved from % a non-parametric data store~\\citep{knnlm}.\na corpus~\\citep{knnlm}.\nIt conducts an additional passage retrieval step at the beginning to limit the need to store and search over all tokens in the corpus, offering a balanced trade-off between search accuracy and efficiency.\nAt each inference step, \\nest performs content generation with three sub-steps:\n\n\\noindent\\textbf{1) Confidence-based interpolation.} We use a novel \\emph{Relative Retrieval Confidence} (RRC) score to measure the uncertainty of the token retriever and use it as the interpolation coefficient of the output probability mixture. This enables flexible adaptation of the LM's output to different downstream tasks through dynamic interpolation with the token retrieval results.\n\n\\noindent\\textbf{2) Dynamic span selection.} Inspired by the Copy Generator (\\cog)~\\citep{cog}, \\nest % not only selects the next best token based on the mixture probability but also considers its continuation in the corpus up to a preset length if the token retrieval confidence exceeds a pre-defined threshold. \n% \\nest separately considers the predicted token by the LM and retrieved spans by the kNN module. This allows \\nest to switch back and forth between the span-level and token-level prediction.\nselects not only the best token predicted by the mixture probability, but also extends to the span contining from that token in the corpus %—up to a preset length—\nwhen the token retrieval confidence exceeds a predefined threshold.\n\n% \\noindent\\textbf{3) Relaxed speculative decoding.} \n\\noindent\\textbf{3) Relaxed speculative decoding.} If a span of more than one tokens is selected, it undergoes evaluation based on the mixture probability. Through a rejection procedure similar to that in speculative decoding~\\citep{spec-decode}, only a prefix deemed highly likely by the mixture probability is accepted. % The mixed generation might contain some artifacts such as repetition and grammatical errors. We take inspiration from speculative decoding and use its relaxed version to reject low-quality segments while keeping the desired facts retrieved from the corpus.\n\nEvaluated on various free-form generation tasks—including question answering, text completion, and factuality-aware generation—using \\llama-2-Chat models~\\citep{touvron2023llama2} of different sizes, \\nest demonstrates superior performance compared to both the base LM and the standard \\knnlm under a zero-shot setting. For example, combined with \\nest, the \\llama-2-Chat 70B model demonstrates $42.3\\%$ improvement\n% \\vic{Let's use exact numbers with 1 decimal place, also which metrics is this over?}\nof ROUGE-1 on WikiText-103 and $21.6\\%$\n% \\vic{1 decimal place, don't use approximate symbol} \nimprovement of \\factscore on Biography.\n% \\vic{Highlight two categories of tasks our methods perform especially well}. \nFurthermore, \\nest performs competitively with respect to in-context retrieval-augmentation on MMLU, Pile-of-Law, and TruthfulQA. \n% \\vic{Highlight a few tasks where we perform competitively w.r.t. RA}\n% benefiting from the strengths of both approaches by enhancing factual accuracy and ensuring consistent attribution. \nWe further demonstrate that the two approaches can be combined to enhance generation quality and attribution.\nAdditionally, by generating multiple tokens at each time step, \\nest significantly improves the efficiency of long-form generation. For \\llama-2-Chat 70B, it achieves a 1.8$\\times$ speedup in inference time without compromising attribution or fluency.\n\n\\section{Background}\\label{sec:bkg}\n\\subsection{Problem Definition}\\label{sec:problem_setup}\nGiven an input $x$, a mixture model $\\mathcal{M}$ predicts the output $y$ consisting of segments $\\{y_1, y_2,...,y_T\\}$. In our setting, $\\mathcal{M}$ may produce multiple tokens at a time step $t$, and therefore $y_t$ indicates the $t$-th segment consisting of at most $n$ tokens where $1\\leq|y_t|\\leq n$. Let $\\{w_{t}^{(1)}, w_{t}^{(2)},...,w_t^{(n)}\\}$ be the tokens in segment $y_t$, we use $p_{\\mathcal{M}}(w | x, y_{< t})$ to denote the distribution of the next token, and use $p_{\\mathcal{M}}(w=w_{t}^{(1)} | x, y_{< t})$ to denote the probability of $w_{t}^{(1)}$ of the next segment $y_{t}$.\n% Section~\\ref{sec:nest:span} discusses how to produce the rest of the tokens $\\{w_{t}^{(2)},...,w_{t}^{N_t}\\}$.\n\n\\subsection{Nearest Neighbor Language Models (\\knnlm)}\\label{sec:knnlm}\nThe mixture model $\\mathcal{M}$ involves a pre-trained LM and key-value datastore $(\\mathcal{K}, \\mathcal{V})$ that enables approximate nearest neighbors search without further training or fine-tuning.\n\n\\paragraph{Key-value datastore.} To create the datastore $(\\mathcal{K}, \\mathcal{V})$ using the LM for a corpus $\\mathcal{D}$, let $f(\\cdot)$ be the mapping from input sequence $c$ to the hidden states $h$ of the LM at some fixed layer. Let $w$ be the next word of $c$ in the corpus $\\mathcal{D}$. For a sample $(c_i, w_i)$ in $\\mathcal{D}$ after segmentation, we define the $i$-th key-value pair $(k_i, v_i)$ in $(\\mathcal{K}, \\mathcal{V})$ as $(h_i, w_i)$, where $h_i = f(c_i)$. The whole datastore is thus defined as the set of all possible key-value pairs in $\\mathcal{D}$:\n\\begin{align}\n(\\mathcal{K}, \\mathcal{V}) = \\{(h_i, w_i)|(c_i, w_i) \\in \\mathcal{D}\\}.\n\\end{align}\nThe size of the datastore $(\\mathcal{K}, \\mathcal{V})$ is proportional to the total number of tokens in corpus $\\mathcal{D}$. This brings difficulty in scaling the size of the corpus and the model, which may require massive storage space and computational resources. \n% Generally, this is only practical for models around 1 billion parameters and billion-token corpus, but for 70-billion-parameter models and trillion-token corpus, the computational cost and storage cost can quickly become untrackable.\n\n\\paragraph{Probability interpolation.} During inference, the language model outputs the token distribution $p_{\\text{LM}}(w|x, y_{<t})$, together with the hidden state $q_t$. The model uses $q_t$ as a query to search the datastore $(\\mathcal{K}, \\mathcal{V})$ and retrieve the $r$-nearest neighbors $\\pi$ according to the similarity $s(q, k)$ between a query $q$ and a key $k$. The final non-parametric distribution $p_{\\text{\\knn}}(w | x, y_{<t})$ is computed using a softmax function over the similarity of all retrieved neighbors:\n\\begin{align}\\label{eq:non-parametric}\n p_{\\text{\\knn}}(w | x, y_{<t}) \\propto \\sum_{(k_i, v_i)\\in \\pi} \\mathbb{I}_{w=v_i} \\exp(\\mu\\cdot s(q_t, k_i)),\n\\end{align}\nwhere $\\mu$ is the inverse temperature. We use $1/\\sqrt{\\text{dim}(q_t)}$ for $\\mu$ in practice where $\\text{dim}(q_t)$ is the hidden state dimension. This is similar to computing attention in the Transformer model~\\citep{transformer}. For similarity $s(q, k)$, we follow~\\cite{knnlm} and use the negative squared $\\ell_2$ distance. Items not in $\\pi$ are assigned with 0 probability based on the indicator function $\\mathbb{I}_{w=v_i}$.\n\nFinally, the next token is sampled from the mixture distribution $p_{\\mathcal{M}}$ of the non-parametric distribution $p_{\\text{\\knn}}$ and the parametric distribution $p_\\text{LM}$ using a fixed hyper-parameter $\\lambda\\in[0,1]$:\n\\begin{align}\\label{eq:knnlm}\np_{\\mathcal{M}}(w|x, y_{<t}) = \\lambda \\cdot p_\\text{LM}(w|x, y_{<t}) + (1 -\\lambda) \\cdot p_{\\text{\\knn}}(w|x, y_{<t}).\n\\end{align} \n% where $\\mathcal{M}$ is the mixture model mentioned at the beginning of Section~\\ref{sec:problem_setup}.\n\n\\section{Nearest Neighbor Speculative Decoding}\\label{sec:nest}\n% We introduce Nearest Neighbour Speculative Decoding (\\nest) as shown in Figure~\\ref{fig:nest}. \\nest is a plug-and-play method that enhances the attribution and generation quality of a language model through two-stage \\knn search, confidence-based interpolation, dynamic span selection, and relaxed speculative decoding. \n\n\\begin{figure}[t!]\n\\centering\n%\\vspace{-0.3cm}\n\\includegraphics[width=\\textwidth]{figures/NEST.png}\n\\caption{The \\nest approach first locates the tokens in the corpus using the LM hidden states. The retrieval distribution $p_{\\text{\\knn}}$ is dynamically interpolated with $p_{\\text{LM}}$ based on the retriever's uncertainty $\\lambda_t$. The token and its $n$-gram continuation are then selected from the mixture distribution $p_{\\mathcal{M}}$, while the final span length is determined by speculative decoding to remove undesired tokens. The spans incorporated in the final generation provide direct attribution and amortize the generation latency.}\n\\label{fig:nest}\n\\end{figure}\n\n\\subsection{Two-Stage \\knn Search}\\label{sec:nest:two-stage}\nAs mentioned in Section~\\ref{sec:knnlm}, maintaining a token-level key-value store can be expensive in terms of both latency and storage. To provide a better trade-off between latency and accuracy, we adopt the two-stage design, which is widely applied in information retrieval and search engines.\n\n\\paragraph{First-stage passage retrieval} Given the corpus $\\mathcal{D}$, we segment the documents into separate passages of less than $m$ tokens each. \n% $l$ is known as the scaling factor for improving the recall before fusion.\nWe then encode the corpus and use a hybrid retrieval system~\\citep{10.1007/978-3-030-99736-6_41} to select the relevant passages, as dense retrievers are good at handling semantics in queries~\\citep{dpr} and sparse retrievers are good at lexical matching~\\citep{sciavolino-etal-2021-simple}. \n% Therefore, the hybrid system is usually more robust in different types of tasks. \n\n\\paragraph{Second-stage \\knn token search} After obtaining the top-$b$ retrieved passages $\\{d_1, d_2,...,d_b\\}$ at time step $t$, we use the encoder $f(\\cdot)$ of LM to encode the prefixes of all tokens as keys as shown in Figure~\\ref{fig:nest}.\n% , listed as $\\{h^{d_i}_1, h^{d_i}_2,...,h^{d_i}_{m-1}\\}$ for passage $d_i$. The value for each key is the corresponding next token $\\{w^{d_i}_2, w^{d_i}_3,...,w^{d_i}_{m}\\}$ of the encoded contexts. \nThe key-value datastore $(\\mathcal{K}, \\mathcal{V})$ therefore is created \\textit{on the fly}. Similarly, we use the negative squared $\\ell_2$ distance as the similarity function and $q_t$ as the queries to search for the top-$r$ nearest neighbors $\\pi$ in $(\\mathcal{K}', \\mathcal{V}')$.\n\nThe two-stage design provides a trade-off between search latency and accuracy and the passage-level index only takes a fraction of the token-level index in Section~\\ref{sec:knnlm}. In addition, the first-stage passage search also acts as a filter to remove deceptively similar tokens in non-relevant contexts.\n\n\n\\subsection{Confidence-Based Output Interpolation}\\label{sec:nest:rrc}\nSimilar to Equation~\\eqref{eq:knnlm}, we linearly interpolate the language model's distribution $p_{\\text{LM}}$ and non-parametric distribution $p_{\\text{\\knn}}$ using a coefficient $\\lambda_t$ for a time step $t$ in generation. The difference is that we use the token retrieval score to compute $\\lambda_t$:\n\\begin{align}\\label{eq:rrc}\n\\lambda_t = \\sigma\\left(\\left(\\frac{\\min_{i} |s(q_t, k_i)|}{\\max_{i} |s(q_t, k_i)|} - \\alpha\\right) / \\tau\\right),\n\\end{align}\nwhere $\\sigma$ is the sigmoid function and the min-max ratio expresses the uncertainty of the \\knn component. We use the sigmoid activation to re-center and re-scale this uncertainty, where $\\alpha$ is the offset and $\\tau$ is the scale for the sigmoid function.\n% Different from a fixed $\\lambda$, $\\lambda_t$ is conditioned on a specific query $h_t$. \nWe refer to this method as Relative Retrieval Confidence (RRC).\n\nIf the downstream task does not involve generation, such as perplexity evaluation and multi-choice tasks, our method will end at Equation~\\eqref{eq:rrc}. The mechanisms introduced in the following sections are only applied to generation, including token/span selection and post-hoc revision.\n\n\\subsection{Dynamic Span Selection}\\label{sec:nest:span}\nDirectly sampling tokens from the mixture distribution $p_{\\mathcal{M}}$ might escalate the exposure bias since the seemingly coherent tokens might be retrieved from completely different sources.\nTo maintain coherence, we extend the context of the current sampled token by using its $n$-gram continuation in the corpus. Given the current time step $t$, we first select the next token $w_t$ from the mixture distribution $p_{\\mathcal{M}}$. However, the sampled token $w_t$ may correspond to multiple retrieved $w_i$ (i.e., the value $v_i$), \n% \\vic{The expression before this is unclear}\nin the neighbors $\\pi$ which have different $n$-gram continuations. We use a simple max-pooling strategy to select the starting token $w_t^{(1)}$ of the $n$-gram from $\\pi$:\n\\begin{align}\nw_t^{(1)} = \\underset{\\{w_i|w_i=w_t, w_i\\in\\pi\\}}{\\text{argmax }} p_{\\text{\\knn}}(w=w_i|x, y_{<t})\n\\label{eq:knn_interpolation}\n\\end{align}\n% \\vic{Based on offline discussion, we will first tune the interpolation model described in Eq~\\eqref{eq:knn_interpolation} on PPL eval using Wiki103 and Pile of Law, then instead of using Eq~\\eqref{eq:span_expansion}, use threshold on $\\lambda_t$ to decide if we will expand a span or not.}\nThe corresponding $n$-gram for time step $t$ is $\\{w_t^{(1)}, w_t^{(2)},...,w_t^{(n)}\\}$ where $n$ is fixed hyper-parameter.\nThe final output is determined by the interpolation coefficient $\\lambda_t$ in Equation~\\eqref{eq:rrc}:\n\\begin{align}\ny_t=\\left\\{\\begin{matrix}\nw_t, & \\text{if } \\lambda_t > \\delta; \\\\ \n\\{w_t^{(1)}, w_t^{(2)},...,w_t^{(n)}\\}, & \\text{otherwise}.\n\\end{matrix}\\right.\n\\label{eq:span_expansion}\n\\end{align}\nwhere $\\delta$ is a threshold and $y_t$ is the segment output at time step $t$. \n% Previous work such as Copy-Generator~\\citep{cog,cog++} also retrieves spans to augment the generation in the output distribution. However, such solutions require joint training of the language models and the phrase retrievers, making it difficult to adapt the model to different domains in zero-shot.\n% In this way, the model can switch back-and-forth between single token and $n$-gram, ensuring attribution consistency while maintaining fluency~\\citep{cog,cog++}. In addition, outputting multiple tokens at each time step can also improve the latency of generation.\n\n\\subsection{Relaxed Speculative Decoding}\\label{sec:nest:rejection}\nDespite the dynamic selection, the hyper-parameter $n$ is hard to control over different tasks. \n% Some in-domain tasks might prefer longer $n$-grams while some out-of-distribution task might need shorter $n$-grams to avoid error accumulation. \n% Moreover, some artifacts, such as grammatical errors or repetitions, might occur in the transition between spans.\nTo produce spans with adaptive length, we take inspiration from~\\cite{spec-decode}, where we use $\\mathcal{M}$ to revise the proposed $n$-gram. \n% \\vic{Add one sentence (maybe at the end of the section) explaining how we can use $\\mathcal{M}$ for evaluation but still maintain efficiency.}\n% (if a single token $w_t$ is sampled according to Equation~\\eqref{eq:span_expansion}, then there will be no need of re-evaluation).\nOriginally, speculative decoding uses a small model to generate a draft and uses a large model to evaluate the draft.\n% \\vic{No need to extensively describe a cited approach in the methodology session, you can possibly cut the previous sentences or make them more concise}\nHowever, the draft distribution $q(w|x, y_{<t})$ is unknown besides the first token $w_t^{(1)}$. Therefore, we use a relaxed version of speculative decoding that upper bounds the acceptance probability. \nThe probability of accepting the token $w_t^{(i)}$ in a span is:\n\\begin{align}\\label{eq:rejection}\nP(\\text{accept token } w_t^{(i)}) = \\min\\left (1,\\ \\frac{p_{\\mathcal{M}}(w=w_t^{(i)} \\mid x, y_{<t}, w_t^{(1)}, w_t^{(2)},...,w_t^{(i-1)})} {\\gamma \\cdot \\underset{{w}}{\\max} \\ p_{\\mathcal{M}} (w \\mid x, y_{<t}, w_t^{(1)}, w_t^{(2)},...,w_t^{(i-1)})}\\right),\n\\end{align}\nwhere $\\gamma\\in(0,1]$ is the relaxation factor, which is referred to as ``leniency'' by \\cite{spec-decode}.\n% . This is also called ``leniency'' by \\cite{spec-decode}. \n% (Appendix A.5 of their paper). \n% The relaxation is applied by removing the proposal distribution $q$ and biasing the rejection probability using $\\gamma$. \nThe smaller $\\gamma$ is, the less often $\\mathcal{M}$ rejects the draft. If token $w_t^{(i)}$ is rejected, we will remove all the tokens from $w_t^{(i)}$ to $w_t^{(n)}$, and then re-select a token $w_t^{(i)}$ from the distribution $p_{\\mathcal{M}}$ without going through the span selection. \nThe computation for processing multiple tokens can be parallelized and \\nest can thus maintain the latency or even accelerate the generation.\nMoreover, suppose all tokens in the draft are not rejected. In that case, we will directly fetch the $n$-gram's continuation in the corpus and use it for the next draft proposal until rejection, removing the reliance on the hyper-parameter $n$.\n\nOnce the $n$-gram is accepted, the corresponding parts are masked in the corpus and will never be used again in this generation. This is to prevent the \\knn component from repetitively retrieving the same segments in a small key-value store $(\\mathcal{K}', \\mathcal{V}')$.\n% Figure~\\ref{fig:nest} shows the high-level picture of \\nest. \nWe provide the complete procedure in Algorithm~\\ref{algo:nest}.\n\n% \\section{Reproduction and Pilot Studies}\n% Reproduce \\knn-LM using similar setup, show wiki results and generation examples\n\n% show the problem mentioned in Intro: deviation, artifacts, latency and storage, downstream adaptation\n\n\\section{Experiments}\n\\label{sec:experiments}\nWe evaluate \\nest and other baselines on various tasks including text completion, question-answering, fact-verification, and multi-choice tasks, providing a comprehensive picture of factuality, fluency, and attribution of \\nest in different domains.\nIn all experiments, we focus on evaluating instruction-following models. We use Llama-2-chat under a zero-shot setting, where we remove the few-shot demonstrations from the instructions to simulate the realistic usage of these models. \n% Details of all datasets, metrics, and prompts can be found in Appendix~\\ref{appendix:exp}.\n\n\\subsection{Benchmark Datasets}\n\\paragraph{Text completion.} \\textbf{WikiText-103}~\\citep{wikitext-103} is a standard benchmark for language modeling, extracted from the set of verified articles on Wikipedia. \\textbf{Pile of Law}~\\citep{pile-of-law} is a growing dataset of legal and administrative data. We use the datasets\\footnote{https://huggingface.co/datasets/pile-of-law/pile-of-law/tree/main} from Huggingface and further split the test data into validation and test sets. For language modeling, we report the perplexity score. For free-form generation, we report ROUGE-1, 2, L~\\citep{rouge} and MAUVE~\\citep{mauve}.\n% for truthfulness and fluency.\n\n\\paragraph{Question answering.} We select four knowledge-intensive question-answering datasets, including NaturalQuestions (NQ)~\\citep{nq}, TriviaQA (TQA)~\\citep{triviaqa}, HotpotQA (HQA)~\\citep{hotpotqa}, and MedMCQA (MQA)~\\citep{medmcqa}. Since the in-context demonstrations are removed for free-form generation, we use answer-level recall (i.e., Hit@1)~\\citep{dpr} which checks if the output contains any correct answers instead of exact match.\n\n\\paragraph{Fact verification.} We evaluate a biography-generation task~\\citep{factscore} and TruthfulQA~\\citep{truthfulqa} which is a benchmark for testing false beliefs or misconceptions. We use \\factscore~\\citep{factscore} for biography. For TruthfulQA, we follow~\\cite{truthfulqa} which uses the difference between the max similarity to a true reference answer and the max similarity to a false reference answer for BLEU and ROUGE-1.\n\n\\paragraph{Closed-set tasks.} MMLU (Massive Multitask Language Understanding)~\\citep{mmlu} benchmark covers 57 subjects across STEM, the humanities, the social sciences, and more. We report the macro accuracy for each domain.\n\n\n\\subsection{Implementation}\\label{sec:exp:implementation} \n\n\\paragraph{Knowledge Sources.} Wikipedia (CC BY-SA 3.0): For tasks except text completion on Pile of Law, we use the Wikipedia 2021 dump released by~\\cite{atlas} as the knowledge source and follow the same pre-processing procedures in \\radit~\\citep{radit}, yielding $\\sim$33M passages with each less than 200 tokens. \nPile of Law (CC BY-NC-SA 4.0): We use the training split from Huggingface and select only the English data. We then follow the same procedure applied in Wikipedia, yielding a corpus containing $\\sim$15M passages after filtering. \n% The hybrid index building process is also the same as above.\n% For retrievers, we use Faiss~\\citep{faiss} to encode a dense index, with the index string ``IVF65536,PQ256''. For the sparse index, we use Pyserini~\\citep{pyserini} to build a Lucene index.\nMore details are provided in Appendix~\\ref{appendix:nest}.\n\n\\paragraph{Inference setting.}\n\\knnlm and \\nest share the same first-stage retriever. We use \\dragon~\\citep{dragon} and BM25~\\citep{bm25} to encode the segments into dense and sparse vectors, respectively. Given the input, we query both the dense and sparse indexes at the same time and retrieve their corresponding top-$(b\\cdot l)$ passages. We linearly interpolate the similarity scores between the two search results (also known as fusion) and sort them before selecting the top-$b$ passages. The number of passage candidates $b$ is set to be 40 and the scaling factor $l$ is set to be 100. For RA, we use the top-3 passages in the prompt due to the context window limit. We further combine \\nest and RA since they are independent methods. Greedy decoding is used during generation. More details about retrieval, decoding, and hyper-parameters are described in Appendix~\\ref{appendix:exp}.\n\n\\paragraph{Evaluation setting.} For text completion tasks and perplexity evaluation, we use 128 tokens as the prefix and the consecutive 256 tokens as the target. For the other tasks, we use 128 tokens as the max generation length for question answering and 512 for fact verification. For retrieval-based models, only the prefix will be used for retrieval. Hyper-parameters of all baselines and \\nest are tuned on the dev set of WikiText-103, \\nq, and Biography. Each baseline uses the same hyper-parameters for all tasks evaluated. We first tune the related hyper-parameters for perplexity and then tune the rest for generation metrics to reduce the search space. More details are provided in Appendix~\\ref{appendix:exp}. \n% \\vic{Need to specify the number of shots in few-shot evaluation, prompts, how the PPLs are calculated etc.}\n\n\\subsection{Baselines}\n\\paragraph{Base LMs.} We evaluate publicly available, instruction-tuned language models, Llama-2-chat series\\footnote{https://huggingface.co/meta-llama/Llama-2-70b-chat-hf}, with model sizes ranging from 7B, 13B to 70B.\n\n\\paragraph{Two-Stage \\knnlm.} We apply the two-stage strategy described in Section~\\ref{sec:nest:two-stage} to \\knnlm as well, where we retrieve the top-$b$ passages and encode a key-value datastore $(\\mathcal{K}', \\mathcal{V}')$ on the fly. \n% For Equation~\\eqref{eq:knnlm}, we use an interpolation coefficient of 0.7$\\sim$0.9 depending on the model and corpus.\n\n\\paragraph{In-Context Retrieval Augmentation (RA).} A common retrieval-augmentation method is adding the retrieved evidence into the prompt. We perform retrieval given the only input instead of retrieving new passages every $k$ step due to the expense of refreshing the kv-cache.\n\n\\begin{table}[!t]\n% \\hspace{-8mm}\n\\begin{adjustbox}{max width=1\\textwidth}\n\\centering\n\\begin{tabular}{l|cccccc|cccccc}\n\\toprule\n\\textbf{Models} & \\multicolumn{6}{c|}{\\textbf{Wikitext-103}} & \\multicolumn{6}{c}{\\textbf{Pile of Law}} \\\\ \n% ~ & PPL & Avg. len & MAUVE & ROUGE-1 & ROUGE-2 & ROUGE-L & PPL & Avg. len & MAUVE & ROUGE-1 & ROUGE-2 & ROUGE-L \\\\ \\midrule\n% Llama-2-Chat$_\\text{7B}$ & 14.46 & 200.16 & 49.73 & 16.02 & 3.79 & 14.54 & 11.49 & 196.17 & 76.16 & 18.40 & 5.37 & 16.53 \\\\ \n% +RA & 6.63 & 119.69 & 31.73 & 30.30 & \\textbf{21.68} & 29.75 & 8.58 & 122.78 & 45.08 & 17.20 & 7.72 & 15.94 \\\\ \n% +\\knnlm & 9.28 & 195.44 & 60.80 & 20.09 & 6.70 & 18.47 & 11.08 & 200.26 & 78.24 & 18.82 & 5.53 & 16.87 \\\\ \n% +\\nest & 7.94 & \\textbf{225.90} & \\textbf{77.83} & 28.37 & 13.68 & 26.83 & 10.76 & 226.26 & 88.48 & 24.00 & 9.00 & 21.80 \\\\ \n% +RA-\\nest & \\textbf{5.94} & 113.29 & 30.93 & \\textbf{30.31} & 21.63 & \\textbf{29.87} & 8.45 & 151.11 & 57.72 & 19.64 & 8.02 & 18.02 \\\\ \\midrule \n% Llama-2-Chat$_\\text{13B}$ & 11.85 & 128.43 & 46.26 & 13.52 & 3.48 & 12.27 & 9.67 & 205.57 & 82.69 & 19.79 & 6.02 & 17.74 \\\\\n% +RA & 5.99 & 112.81 & 31.20 & \\textbf{30.92} & \\textbf{22.26} & \\textbf{30.34} & 7.72 & 104.72 & 40.30 & 16.02 & 7.39 & 14.84 \\\\ \n% +\\knnlm & 8.15 & 213.96 & 72.61 & 22.94 & 8.44 & 21.14 & 9.46 & 208.85 & 83.27 & 20.13 & 6.22 & 18.12 \\\\ \n% +\\nest & 6.90 & \\textbf{193.30} & \\textbf{86.93} & 28.72 & 15.75 & 27.54 & 9.15 & 230.89 & 92.33 & 25.08 & 9.75 & 22.77 \\\\ \n% +RA-\\nest & \\textbf{5.38} & 103.88 & 27.51 & 30.52 & 22.23 & 30.15 & 7.61 & 138.02 & 51.29 & 19.10 & 7.97 & 17.51 \\\\ \\midrule \n% Llama-2-Chat$_\\text{70B}$ & 9.98 & 213.39 & 79.59 & 20.86 & 5.88 & 18.86 & 8.85 & 251.03 & 95.64 & 23.31 & 7.24 & 25.99 \\\\\n% +RA & 5.03 & 181.62 & 65.03 & \\textbf{36.38} & \\textbf{24.96} & \\textbf{35.29} & 6.84 & 245.75 & 85.87 & 24.39 & 10.15 & 22.28 \\\\ \n% +\\knnlm & 6.87 & \\textbf{245.87} & \\textbf{80.14} & 25.97 & 9.88 & 23.90 & 8.74 & 251.89 & 95.88 & 23.54 & 7.36 & 21.16 \\\\ \n% +\\nest & 6.05 & 223.82 & 74.93 & 32.06 & 17.87 & 30.73 & 8.55 & 254.31 & 97.39 & 26.57 & 10.20 & 24.11 \\\\ \n% +RA-\\nest & \\textbf{4.57} & 172.99 & 58.23 & 35.59 & 24.81 & 34.75 & 6.77 & 249.63 & 94.37 & 25.72 & 9.91 & 23.42 \\\\ \\bottomrule\n\n & {PPL($\\downarrow$)} & {MAUVE} & {RG-1} & {RG-2} & {RG-L} & {Avg. Len} & {PPL($\\downarrow$)} & {MAUVE} & {RG-1} & {RG-2} & {RG-L} & {Avg. Len} \\\\ \n% \\cmidrule{lr}{1-1}\\cmidrule{2-7}\\cmidrule{8-13}\n\\midrule\nLlama-2-Chat$_\\text{7B}$ & 14.6 & 58.8 & 15.8 & 3.7 & 14.4 & 175.4 & 10.1 & 80.7 & 19.1 & 5.5 & 17.1 & 211.4 \\\\\n +RA & 7.2 & 74.6 & \\textbf{35.7} & \\textbf{23.1} & \\textbf{34.4} & 204.5 & 7.1 & 84.7 & 23.1 & 8.9 & 21.1 & 222.0 \\\\\n +\\knnlm & 9.8 & \\textbf{82.5} & 23.7 & 7.7 & 21.7 & \\textbf{238.2} & 8.8 & 81.1 & 19.4 & 5.7 & 17.4 & 214.3 \\\\ \n % \\hdashline \n % \\noalign{\\vskip 2pt}\n \\rowcolor{gray!20}\n +\\nest & 8.4 & 73.2 & 28.4 & 14.2 & 27.1 & 218.4 & 8.1 & 88.0 & 23.7 & 8.7 & 21.5 & 226.5 \\\\ \n \\rowcolor{gray!20}\n +RA-\\nest & \\textbf{6.4} & 72.6 & 35.2 & 22.7 & 34.0 & 202.0 & \\textbf{6.7} & \\textbf{90.0} & \\textbf{24.4} & \\textbf{9.0} & \\textbf{22.2} & \\textbf{232.1} \\\\ \\midrule\n Llama-2-Chat$_\\text{13B}$ & 12.0 & 75.9 & 19.9 & 4.9 & 18.0 & 218.4 & 8.2 & 72.8 & 17.5 & 5.3 & 15.7 & 181.7 \\\\ \n +RA & 6.5 & \\textbf{91.5} & \\textbf{38.9} & \\textbf{24.2} & \\textbf{37.2} & \\textbf{249.3} & 5.9 & 86.6 & 23.6 & 9.1 & 21.5 & 228.7 \\\\ \n +\\knnlm & 8.6 & 76.3 & 23.7 & 8.2 & 21.9 & 238.5 & 7.4 & 71.5 & 17.7 & 5.3 & 15.9 & 183.7 \\\\ \n % \\hdashline\n % \\noalign{\\vskip 2pt}\n \\rowcolor{gray!20}\n +\\nest & 7.2 & 67.1 & 29.3 & 15.6 & 28.1 & 207.1 & 6.8 & 86.0 & 22.9 & 8.7 & 20.9 & 212.3 \\\\ \n \\rowcolor{gray!20}\n +RA-\\nest & \\textbf{5.8} & 86.8 & 38.6 & 24.0 & 37.0 & 245.5 & \\textbf{5.7} & \\textbf{90.1} & \\textbf{24.7} & \\textbf{9.2} & \\textbf{22.4} & \\textbf{229.4} \\\\ \\midrule\n Llama-2-Chat$_\\text{70B}$ & 9.9 & 88.6 & 22.9 & 6.2 & 20.8 & 239.6 & 6.9 & 93.4 & 23.0 & 7.1 & 20.7 & 250.1 \\\\ \n +RA & 5.3 & \\textbf{91.6} & \\textbf{40.5} & \\textbf{26.1} & \\textbf{38.8} & 235.9 & 4.9 & 95.5 & \\textbf{26.3} & \\textbf{10.1} & \\textbf{24.0} & 253.2 \\\\ \n +\\knnlm & 7.1 & 83.6 & 26.1 & 9.6 & 24.1 & \\textbf{253.9} & 6.3 & 94.4 & 23.1 & 7.2 & 20.8 & 251.3 \\\\\n % \\hdashline\n % \\noalign{\\vskip 2pt}\n \\rowcolor{gray!20}\n +\\nest & 6.3 & 82.6 & 32.6 & 17.2 & 31.1 & 236.3 & 5.9 & 95.4 & 25.6 & 9.4 & 23.2 & 251.3 \\\\ \n \\rowcolor{gray!20}\n +RA-\\nest & \\textbf{4.8} & 90.0 & 40.2 & 25.9 & 38.6 & 233.1 & \\textbf{4.7} & \\textbf{97.6} & 26.2 & 9.5 & 23.7 & \\textbf{253.6} \\\\\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\begin{adjustbox}{max width=1\\textwidth}\n\\setlength{\\tabcolsep}{4pt} % Default value: 6pt\n\\centering\n\\begin{tabular}{l|ccccc|cc|cc|ccccc}\n\\toprule\n\\textbf{Models} & \\textbf{TQA} & \\textbf{NQ} & \\textbf{HQA} & \\textbf{MQA} & \\textbf{Avg.} & \\multicolumn{2}{c|}{\\textbf{TruthfulQA}} & \\multicolumn{2}{c|}{\\textbf{Biography}} & \\multicolumn{5}{c}{\\textbf{MMLU}} \\\\\n~ &\\multicolumn{5}{c|}{Answer-Level Recall} & $\\Delta$BLEU & $\\Delta$RG-1 & FS & \\# Facts & Human. & STEM & Social & Other & Avg. \\\\ \n% \\cmidrule{1-1}\\cmidrule{lr}{2-6}\\cmidrule{7-8}\\cmidrule{9-13}\\cmidrule{14-18}\n\\midrule\nLlama-2-Chat$_\\text{7B}$ & 61.1 & 38.9 & 30.6 & 9.3 & 35.0 & -0.02 & 0.42 & 27.2 & \\textbf{71.2} & 37.8 & 32.6 & 38.9 & 39.6 & 37.2 \\\\ \n +RA & \\textbf{69.5} & 48.4 & 44.1 & 12.8 & 43.7 & -0.34 & 0.18 & \\textbf{56.5} & 67.1 &41.8 & 35.3 & \\textbf{42.2} & 43.3 & 40.7 \\\\ \n +\\knnlm & 63.4 & 42.4 & 33.5 & 9.5 & 37.2 & \\textbf{0.13} & \\textbf{0.66} & 30.6 & 59.8 &38.0 & 33.1 & 39.2 & 40.1 & 37.6 \\\\ \n % \\hdashline\n % \\noalign{\\vskip 2pt}\n \\rowcolor{gray!20}\n +\\nest & 61.5 & 43.2 & 33.5 & 10.2 & 37.1 & 0.03 & 0.45 & 38.9 & 58.2 &\\textbf{42.0} & \\textbf{35.4} & 42.0 & \\textbf{43.4} & \\textbf{40.7} \\\\ \n \\rowcolor{gray!20}\n +RA-\\nest & 69.0 & \\textbf{48.8} & \\textbf{45.3} & \\textbf{13.3} & \\textbf{44.1} & -0.32 & 0.21 & 55.1 & 57.7 &37.9 & 32.7 & 39.3 & 39.8 & 37.4 \\\\ \\midrule\n Llama-2-Chat$_\\text{13B}$ & 63.5 & 42.3 & 32.6 & 10.2 & 37.2 & 0.13 & 0.81 & 28.8 & 49.9 &41.5 & 35.0 & 40.2 & 43.8 & 40.1 \\\\ \n +RA & 70.9 & 51.6 & 44.6 & 14.0 & 45.3 & -0.16 & 0.25 & \\textbf{59.1} & 51.2 &43.4 & 37.4 & 43.5 & 46.4 & 42.7 \\\\ \n +\\knnlm & 64.7 & 43.5 & 34.2 & 11.2 & 38.4 & 0.20 & 0.95 & 31.1 & 46.1 &41.4 & 34.7 & 40.6 & 44.2 & 40.2 \\\\ \n % \\hdashline\n % \\noalign{\\vskip 2pt}\n \\rowcolor{gray!20}\n +\\nest & 64.2 & 44.2 & 34.3 & 10.9 & 38.4 & \\textbf{0.29} & \\textbf{0.98} & 35.7 & 47.2 &41.3 & 34.9 & 40.2 & 43.7 & 40.0 \\\\ \n \\rowcolor{gray!20}\n +RA-\\nest & \\textbf{70.9} & \\textbf{51.7} & \\textbf{45.3} & \\textbf{14.7} & \\textbf{45.7} & -0.14 & 0.25 & 58.4 & \\textbf{52.4} &\\textbf{43.5} & \\textbf{37.7} & \\textbf{43.5} & \\textbf{46.7} & \\textbf{42.8} \\\\ \\midrule\n Llama-2-Chat$_\\text{70B}$ & 74.0 & 50.1 & 39.5 & 12.8 & 44.1 & 0.14 & 0.70 & 34.2 & \\textbf{58.8} &43.5 & 37.9 & 44.4 & 47.0 & 43.2 \\\\ \n +RA & \\textbf{75.5} & \\textbf{55.4} & \\textbf{52.5} & 16.0 & \\textbf{49.9} & -0.13 & 0.40 & 52.9 & 42.1 &\\textbf{45.9} & 39.7 & 46.2 & 48.6 & 45.1 \\\\ \n +\\knnlm & 74.6 & 51.2 & 40.2 & 13.5 & 44.9 & 0.08 & 0.58 & 36.1 & 54.4 &44.0 & 37.4 & 44.1 & 47.1 & 43.2 \\\\ \n % \\hdashline\n % \\noalign{\\vskip 2pt}\n \\rowcolor{gray!20}\n +\\nest & 74.2 & 51.6 & 41.4 & 13.8 & 45.2 & \\textbf{0.17} & \\textbf{0.70} & 41.6 & 56.2 &43.8 & 38.0 & 44.4 & 47.8 & 43.5 \\\\ \n \\rowcolor{gray!20}\n +RA-\\nest & 75.4 & 55.2 & 52.4 & \\textbf{16.3} & 49.8 & -0.19 & 0.31 & \\textbf{59.2} & 53.8 &45.8 & \\textbf{39.7} & \\textbf{46.2} & \\textbf{48.9} & \\textbf{45.1} \\\\ \\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\\\\n\\vspace{6pt}\n\\caption{Results on text completion (upper table) and other tasks (lower table). Bold numbers indicate the best performance. PPL: Perplexity. RG: ROUGE score. Avg. Len: Average generation length. $\\Delta$BLEU/$\\Delta$RG: The difference between the max score to correct references and the max score to incorrect references. FS: \\factscore with length penalty.}\n% Response Ratio of Biography $\\geq 95$\\% for all methods.\n% \\vic{Add an \"Avg.\" column after \"TQA NQ HQA MQA\" to show the average answer-level recall across these 4 QA tasks. Otherwise it is difficult to tell based on the table the overall relative comparison between the models.}}\n\\vspace{-3mm}\n\\label{tbl:main_results}\n\\end{table}\\subsection{Main Results}\n\\label{sec:main_results}\nTable~\\ref{tbl:main_results} shows the main results of \\nest and other baselines. For \\textbf{language modeling}, RA-\\nest is able to achieve the lowest complexity on both WikiText-103 and Pile of Law. For \\textbf{text completion}, RA has the best MAUVE scores and ROUGE scores in Wikitext-103 while RA-NEST works better for 7B and 13B models on Pile of Law. We observe that for legal documents, quoting the exact clauses from the source might be more favourable compared to Wikipedia.\n\nFor \\textbf{question-answering}, RA-\\nest tends to work better for smaller models (7B and 13B) in general. The gap between base LMs and other methods diminishes for 70B LMs, which is consistent with previous work where retrieval is found most useful for smaller models~\\citep{retro}.\n\nFor \\textbf{fact-verification}, \\nest is able to outperform the base LMs but underperform RA in terms of the \\factscore. RA-NEST is able to outperform RA for the 70B model. The degradation for RA-70B is caused by generating shorter claims which is punished by the \\factscore. On TruthfulQA, the semi-parametric LMs consistently outperform base LMs and RAs where in-context retrieval seems to have a negative effect on the scores. This is because TruthfulQA is an adversarial dataset containing difficult questions where in-context RA is more susceptible to the ``evidence'' in the prompt (e.g., astrology and myths). In contrast, \\nest only interpolates the results at the output level and therefore performs better in this case. The combination RA-\\nest is also affected by the in-context retrieval.\n\nFor \\textbf{closed-set tasks}, \\nest is comparable to RA and RA-\\nest manages to achieve the best macro scores on average.\nOverall, \\nest is able to outperform base LMs and \\knnlm's on most tasks while being on par with RA. The combination of RA and \\nest further improves over the two methods on some tasks. Despite the limited improvement, we will show that \\nest is able to provide better attribution and latency in the following sections.\n\\captionsetup[subfigure]{oneside,margin={0.5cm,0cm}}\n\\begin{figure}[h]\n\\centering\n\\begin{subfigure}[b]{0.48\\textwidth}\n \\centering\n \\hspace{-5mm}\n \\includegraphics[width=\\textwidth]{latency_breakdown.pdf}\n \\caption{Latency Breakdown.}\\label{fig:latency:breakdown}\n\\end{subfigure}\n\\begin{subfigure}[b]{0.48\\textwidth}\n \\centering\n % \\hspace{15mm}\n \\includegraphics[width=\\textwidth]{latency_spanlen.pdf}\n \\caption{Span length and \\factscore on Biography.}\\label{fig:latency:tradeoff}\n\\end{subfigure}\n\\caption{Latency-accuracy trade-off and breakdown on Biography using \\llama-2-Chat 70B+\\nest. As the relaxation factor $\\gamma$ decreases, \\nest tends to accept longer spans from the corpus. We choose $\\gamma=5e-2$ in our main experiments, which accelerates the generation and improves the \\factscore.}\n\\label{fig:latency}\n\\end{figure}\n\\subsection{Latency Analysis}\\label{sec:latency}\n\\paragraph{Latency breakdown.} The combination of dynamic span selection and relaxed speculative decoding can improve the latency of the LLM generation by quick draft proposal and processing multiple tokens at a time step. Figure~\\ref{fig:latency:breakdown} shows the latency breakdown of a \\nest-70B model ($\\alpha=0.3, \\tau=0.1, \\delta=0.5$) for different relaxation factors on the Biography validation data. The latency experiment is done on 8$\\times$A100 GPUs (for model parallelization) and 32 CPU threads (for search). The batch size is set to 1. We use internal, research-purpose implementation of the base Llama-2-chat model which did not optimize for latency. As we can see, the LM encoding time takes about half of the latency, while the sum of the others takes the rest. Noticeably, the cost of passage search and token index building stay relatively constant per query, while the others are related to the number of tokens processed per time step. Still, even with extra retrieval overheads, the slowest \\nest model is faster than the base LM, showing the efficacy of span selection and speculative decoding.\n\n\\paragraph{Latency-accuracy trade-off.} To understand why \\nest can accelerate generation, we first show the latency-accuracy trade-off by tuning the relaxation factor in Figure~\\ref{fig:latency:tradeoff}.\nThe smaller $\\gamma$ is, the less often \\nest rejects a segment retrieved from the corpus, which enables more tokens to be processed in parallel. \nThe average proposed span length in Figure~\\ref{fig:latency:breakdown} can increase from 5 tokens to 35 tokens at each time step as the relaxation factor gets smaller.\nCombined with Figure~\\ref{fig:latency:breakdown}, we can reach the conclusion that fetching longer spans from the corpus results in lower generation latency per query. For the accuracy, the \\factscore on Biography validation data shows that there is a sweet spot around $\\gamma=5e-2$ where both low latency and high accuracy can be achieved at the same time.\n\n\\begin{table}[!t]\n\\begin{adjustbox}{max width=1\\textwidth}\n\\begin{tabular}{l|l|cc|l}\n\\toprule\nTask & Models & \\multicolumn{1}{l}{Ratio (\\%)} & \\multicolumn{1}{l|}{Length} & Example (Generated by \\nest-70B) \\\\ \\midrule\n& \\nest$_{\\text{7B}}$ & 93.4 & 18.4&\\multirow{6}{*}{\\begin{tabular}[c]{@{}l@{}}Question: who sings does he love me with reba? \\\\Answer: The song \"\\green{Does He Love You}\" \\href{https://en.wikipedia.org/wiki/Greatest_Hits_Volume_Two_(Reba_McEntire_album)}{\\color{gray}{[}29756059{]}} is a duet \\\\between\n\\green{Reba McEntire and Linda Davis. } \\green{It was}\\\\ \\green{released in August 1993} \\green{as the first single from Reba's} \\\\ \\green{compilation album Greatest Hits Volume 2. It is one of}\\\\ \\green{country music's several songs about a love triangle.} \\href{https://en.wikipedia.org/wiki/Does_He_Love_You}{\\color{gray}{[}14963536{]}}\\end{tabular}}\\\\\n\\multirow{6}{*}{NQ} & \\nest$_{\\text{13B}}$ & 52.4 & 4.4 & \\\\\n& \\nest$_{\\text{70B}}$ & 58.8 & 4.3 & \\\\\n& RA-\\nest$_{\\text{7B}}$ &77.5 &3.1 & \\\\\n& RA-\\nest$_{\\text{13B}}$ &33.2 &3.0 &\\\\\n& RA-\\nest$_{\\text{70B}}$ &44.9\t&3.9 &\\\\\n\\midrule\n& &\t& & \\multirow{8}{*}{\\begin{tabular}[c]{@{}l@{}}Question: Tell me a bio of Taral Hicks.\\\\ Answer: \\green{Taral Hicks (born September 21, 1974) is an American actress}\\\\ \\green{and singer. Hicks is best known} \\green{for her acting in such films as 1993's}\\\\ \\green{American crime drama film A Bronx Tale and her singing in such work} \\\\ \\green{as her 1997} \\href{https://en.wikipedia.org/wiki/Taral_Hicks}{\\color{gray}{[}15447985{]}} debut studio album This Time which peaked at \\\\No. 4 on Billboard Bubbling Under Hot 100 Singles. Raised in Teaneck, \\\\New Jersey, Hicks graduated from \\green{Teaneck High School in 1994} \\href{https://en.wikipedia.org/wiki/Taral_Hicks}{\\color{gray}{[}15447985{]}}. \\\\She is the younger sister of actress and singer D'atra Hicks.\\end{tabular}}\\\\\n\\multirow{6}{*}{Bio} & \\nest$_{\\text{7B}}$ & 95.5 & 27.9 & \\\\ \n& \\nest$_{\\text{13B}}$ & 53.9 & 10.6& \\\\ \n& \\nest$_{\\text{70B}}$ & 58.6 & 7.0 & \\\\ \n& RA-\\nest$_{\\text{7B}}$ & 50.3\t&5.1 \\\\\n& RA-\\nest$_{\\text{13B}}$ & 48.5\t&5.9 &\\\\\n& RA-\\nest$_{\\text{70B}}$ & 80.7\t&11.0 &\\\\\n& &\t& &\\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\\\\\caption{Attribution analysis. (Attribution) Ratio: Proportion of tokens that are taken from the corpus. (Attribution) Length: Average length of consecutive spans in the generation that are taken from the same document. \\green{Green}: Segments taken from the corpus. {\\color{gray}{Gray}}: Document id.\n% \\vic{Let's make the column for the qualitative results narrower and make some room for the quantitative results on the left. This way you can have larger fonts. Fonts for quantitative results should be comparable to those in Table 1}\n}\\label{tbl:attr}\n\\end{table}\\subsection{Attribution and Qualitative Analysis}\\label{sec:attribution}\nOne of the most important features of \\nest is providing attribution directly at a span level, where the reference for the corresponding statement is accurate since it is directly taken from the corpus. Table~\\ref{tbl:attr} shows the attribution ratio, average attributed span length, and two examples for analysis. For \\nq and Biography tasks, depending on the model and hyper-parameters in Equation~\\eqref{eq:rrc} and~\\eqref{eq:rejection}, the ratio of tokens that can be traced back to the corpus ranges from 33.2\\% to 95.5\\%. In addition, it is more desirable to have consecutive segments that come from the same source so that consistent attribution can be provided, and the average length of spans taken from the corpus ranges from 3.0 to 27.9 tokens.\nThis feature provides span-level attribution for most claims in the LLM generation. To our knowledge, neither of the baselines can achieve the same granularity and preciseness for the attribution as \\nest. More qualitative results are provided in Appendix~\\ref{appendix:qualitatitve}.\nWe also provide more analyses on sensitivity and ablation for \\nest in Appendix~\\ref{appendix:analysis}.\n\n\n\\section{Related Work}\n% https://github.com/wangcunxiang/LLM-Factuality-Survey?tab=readme-ov-file\n% \\subsection{Factuality-Aware Finetuning}\n% FACTPEGASUS~\\citep{factpegasus} designs a pre-training objective to create pseudo-summaries that are both important and factual.~\\citep{NEURIPS2022_df438caa} propose a factuality-enhanced training method that uses TopicPrefix for better awareness of facts and sentence completion as the training objective.~\\cite{yao-etal-2023-editing} propose to directly locate and edit the parameters associated with factual knowledge.~\\cite{tian2024finetuning} leverages a truthfulness estimator and direct preference optimization to finetune the model toward factuality.\n\n\\subsection{Retrieval-Augmention}\nRetrieval Augmentation involves external knowledge sources to improve the performance of language models on knowledge-intensive tasks. \\cite{drqa} propose DrQA which combines extractive models and independent retrievers for open-domain question-answering. Follow-up works on retrieval-augmentation such as REALM~\\citep{realm}, RAG~\\citep{rag}, and Atlas~\\citep{atlas} further combine the retrieval component in pre-training and fine-tuning for downstream knowledge-intensive tasks. \\cite{asai2024reliable} further divide them into three categories: \n\n\\noindent\\textbf{Input augmentation.} REPLUG~\\citep{replug} and in-context RALM~\\citep{ralm} propose to pre-pend the retrieved passages in the prompts for zero-shot factual support. Recently, Self-RAG~\\citep{selfrag} leverages special tokens to perform adaptive retrieval and different critics to iterative refine the RALM's output. RA-DIT~\\citep{radit} retrofits LLMs with retrieval capabilities via\ninstruction fine-tuning.\n\n\\noindent\\textbf{Intermediate fusion.} RETRO~\\citep{retro} employs a novel attention mechanism to incorporate multiple pre-processed text fragments in intermediate layers for more efficient integration of retrieved results. This approach has been successfully applied to larger decoder-only language models as demonstrated by RETRO++~\\citep{retro++} and InstructRetro~\\citep{wang2024instructretro}. FiD~\\citep{fid} applies similar an encoder-decoder structure in a zero-shot manner and achieves better performance at a document level.\n\n\\noindent\\textbf{Output integration.} \\knnlm~\\citep{knnlm} pioneers this direction and proposes to interpolate the retrieval distribution and LM's prediction. Follow-up works further propose adaptive interpolation methods which involve training~\\citep{he-etal-2021-efficient,bhardwaj2023adaptation} and excessive tuning~\\citep{drozdov-etal-2022-cant}. Another line of work proposes to joint train the phrase encoder and LM to expand the vocabulary dynamically using the retrieved phrases, such as Copy-Generator~\\citep{cog} and its follow-up work~\\citep{cog++}. \n\n\\subsection{Inference-Time Revision}\nSpeculative decoding~\\citep{spec-decode,chen2023accelerating,Miao2023SpecInferAG,spector2023accelerating} is an acceleration method that leverages a small model to generate drafts for a large model to evaluate. The latency is improved as the larger model can process multiple tokens in parallel at each time step. Recently, REST~\\citep{rest} proposes to draw multiple drafts from a datastore and leverages a prefix trie tree to compute the proposal distribution, which is the closest concurrent work. \n\nIn general, speculative decoding can be categorized as an unbiased self-revision method. Previous work focusing on fact-checking follows a similar idea to generate factually consistent texts with a set of evidence via post-hoc editing, such as FRUIT~\\citep{fruit} and PEER~\\citep{peer}. Recently, RARR~\\citep{rarr} leverages more complex planning with LLMs to verify the retrieved evidence and generate attribution reports.\n\n\\section{Limitations}\\label{sec:limitation}\nWhile being able to directly retrieve segments from the corpus and apply them in the generation, the output of \\nest might still contain factual errors depending on the accuracy of the first-stage passage retrieval and the second-stage token retrieval. Moreover, as a plug-and-play method, our main goal is to provide a flexible solution that can combine different LLMs and data stores in zero- and few-shot manners. Without further fine-tuning, the integrated system might be sub-optimal and the results can be better if it is fine-tuned on appropriate tasks. Lastly, such semi-parametric LMs may not improve the ability of in-context learning, since the demonstrations in the prompts are unlikely to appear in any contexts that can be found in the database. An observation from preliminary experiments is that the current neural retrievers do not have the capability to process the in-context few-shot information, where techniques such as query reformulation might be needed for parsing the demonstrations.\n% \\vic{1. Discuss that the current model is not trained hence can be sub-optimal when it comes to token-retrieval performance. If we train or fine-tune the architecture, performance could be even better? 2. Discuss the approach could potentially be used for content generation in non-text modalities?}\n\n\\section{Conclusion}\nThis paper presents \\nest, an inference-time revision method for LMs that improve their factuality and attribution through nearest neighbor speculative decoding. Leveraging two-stage \\knn search, relative retrieval confidence, dynamic span selection, and relaxed speculative decoding, \\nest improves both validation perplexity and free-form generation quality on nine different tasks. Its performance can be further improved when combined with in-context retrieval augmentation. With these results, we demonstrate that \\nest is capable of generating text grounded to real-world sources in low latency while maintaining fluency.\n\n\\section{Broader Impacts}\\label{sec:social}\nThe ability to copy real-world texts from existing data stores is useful for finding the source of the claim (credibility), preventing hallucination (factuality), as well as protecting copyright (risk management). It helps to resolve the dispute that often happens in AI tools by acknowledging the contents that are borrowed from existing human works (e.g., arts, books, and other creative content). Meanwhile, the information on the Internet is mixed and it is important to filter out false and sensitive information before directly injecting them into the generation. % The safety issues of LLMs are mostly governed by prompts nowadays and for this output interpolation model, one might need to develop a new censor mechanism to ensure the retrieval information is properly applied in the generation.\n\n\\section{Acknowledgements}\nWe thank Mike Lewis for his discussion on \\knnlm and Luke Zettlemoyer for helpful feedback on our intermediate results. We thank Sheng-Chieh Lin for his support regarding the details of Llama 2 evaluation and benchmark datasets. We thank Zhuoming Chen and Ranajoy Sadhukhan for the helpful discussions on speculative decoding. \n\n\\bibliography{custom,llm,anthology}\n\\bibliographystyle{unsrtnat}\n% \\newpage\n\\appendix\n\\section{Additional Implementation Details}\\label{appendix:nest}\n\\paragraph{Two-stage \\knn search} For the first-stage passage search, we use a Faiss~\\citep{faiss} dense index and Pyserini~\\citep{pyserini} BM25 index for efficient search. For the dense index, we first use \\dragon to encode each passage in the corpus into a single vector, and then use Faiss (index string ``IVF65536,PQ256'') to cluster the vectors into 65536 centroids and quantize them into 256 codes of 8 bits each. For the sparse index, we use the default hyper-parameters and the ``optimize'' option in Pyserini to reduce the index size. For the approximate nearest neighbor retrieval, we use nprobe$=4096$. During passage search, we retrieve 4000 passages from each index and keep the similarity score for fusion. The fusion coefficient $\\eta$ is determined by the relative confidence of dense and sparse retrievers similar to Equation~\\eqref{eq:rrc}. We set the dense coefficient $\\eta_{\\text{dense}} = 1 - \\text{top-100}(s_{\\text{dense}}(q,d)) / \\max s_{\\text{dense}}(q,d)$ and same for the sparse coefficient $\\eta_{\\text{sparse}}$. The final interpolation coef is $\\eta = 0.7 * (1- \\eta_{\\text{sparse}}) + 0.3 * \\eta_{\\text{dense}}$. The fusion score for each document $s(q, d) = \\eta * s_{\\text{dense}} + (1 - \\eta) * s_{\\text{sparse}}$. If a document is missing in either dense or sparse retrieval results, we set its score to the minimum similarity of the dense/sparse retrieval results. The first-stage search is done on RAM and CPUs with 32 threads. The final Wikipedia dense index size is about 8.96GB, and the sparse index size is about 3.48GB on disk.\n% pol dense index size = 4.26GB\n% pol sparse index size = 1.41GB\n\nFor the second-stage token search, we use the LLM to encode the sequence and use the input to the final layer’s feed-forward network after layer normalization as the key and query vectors following~\\cite{knnlm}. We retrieve the top-1024 tokens using the squared $\\ell_2$ distance and compute the non-parametric probability according to Equation~\\eqref{eq:non-parametric}.\n\n\\paragraph{Rest of \\nest} For relative retrieval confidence, we set $\\alpha=0.3,\\tau=0.1$ for all Wikipedia-based tasks and $\\alpha=0.2,\\tau=0.1$ for Pile of Law for all model sizes in Equation~\\eqref{eq:rrc}.\nFor dynamic span selection, we set the n-gram length to be $64$ and $\\delta=0.5$ for all model sizes and all tasks in Equation~\\eqref{eq:span_expansion}.\nFor relaxed speculative decoding, we set $\\gamma=5e-4$ for Pile of Law tasks for all model sizes in Equation~\\eqref{eq:rejection}. For Wikipedia-based tasks, we set $\\gamma=5e-4$ for the 7B model, $\\gamma=5e-3$ for the 13B model, and $\\gamma=5e-2$ for the 70B model. For RA-\\nest, all models use the same $\\gamma=5e-1$ for all tasks except Pile of Law which still uses $\\gamma=5e-4$ We observe that as the model gets stronger, using larger $\\gamma$ which leads to more rejection, is more beneficial to generation quality.\nThe complete \\nest procedure is provided in Algorithm~\\ref{algo:nest}.\n\n\n% BSF-7b: llama-2-7b-chat_passage40_alpha0.3_tau0.1_top1024_window64_leniency5e-4_beta5e-1 / pile-of-law: alpha0.2, leninecy5e-4\n% BSF-7b_rag: llama-2-7b-chat_passage40_alpha0.3_tau0.1_top1024_window64_leniency5e-1_beta5e-1 / pile-of-law: alpha0.2, leninecy5e-4\n% BSF-13b: llama-2-13b-chat_passage40_alpha0.3_tau0.1_top1024_window64_leniency5e-3_beta5e-1 / pile-of-law: alpha0.2, leninecy5e-4\n% BSF-13b_rag: llama-2-13b-chat_passage40_alpha0.3_tau0.1_top1024_window64_leniency5e-1_beta5e-1 / pile-of-law: alpha0.2, leninecy5e-4\n% BSF-70b: llama-2-70b-chat_passage40_alpha0.3_tau0.1_top1024_window64_leniency5e-2_beta5e-1 / pile-of-law: alpha0.2, leninecy5e-4\n% BSF-70b_rag: llama-2-70b-chat_passage40_alpha0.3_tau0.1_top1024_window64_leniency5e-1_beta5e-1 / pile-of-law: alpha0.2, leninecy5e-4\n\n\n\\newcommand{\\COMMENTLLAMA}[1]{{\\color{darkgreen} $\\triangleright$ {#1}}}\n\\begin{algorithm}\n \\caption{\\nest w/ Greedy Decoding}\\label{algo:nest}\n \\label{alg:sample_with_approx}\n\\begin{algorithmic}\n \\STATE {\\bfseries Inputs:} Language model LM, hidden state encoder $f$, first-stage retriever $R$, corpus $\\mathcal{C}$, input $x$.\n \\STATE \\COMMENTLLAMA{First-stage retrieval: Retrieve documents $d_1,d_2\\ldots,d_b$ from corpus $\\mathcal{C}$}\n \\STATE $d_1,d_2\\ldots,d_b \\gets R(x, \\mathcal{C})$\n \\STATE \\COMMENTLLAMA{Second-stage retrieval: Construct token-level key-value memory}\n \\STATE $(\\mathcal{K}', \\mathcal{V}') \\gets \\varnothing$\n \\FOR{$i=1$ {\\bfseries to} $b$}\n\\STATE $w^{d_i}_1, w^{d_i}_3,...,w^{d_i}_{m} \\gets d_i$\n\\STATE $h^{d_i}_1, h^{d_i}_3,...,h^{d_i}_{m} \\gets f(d_i)$\n\\FOR{$i=1$ {\\bfseries to} $m-1$}\n\\STATE $(\\mathcal{K}', \\mathcal{V}')$.add($h^{d_i}_j$, $w^{d_i}_{j+1}$)\n\\ENDFOR\n \\ENDFOR\n \\STATE \\COMMENTLLAMA{Generation}\n \\STATE $y_{<t} \\gets x$\n \\FOR{$t=1$ {\\bfseries to} $T$}\n \\STATE \\COMMENTLLAMA{Compute query embedding}\n \\STATE $q_{t} \\gets f(y_{<t})[-1]$\n \\STATE \\COMMENTLLAMA{Token embeddings search, return top-$r$ scores and values}\n \\STATE $\\pi \\gets (\\mathcal{K}', \\mathcal{V}')$.search($q_{t}$, $r$)\n \\STATE $(s_1, v_1),(s_2, v_2),...,(s_r, v_r) \\gets \\pi$\n \\STATE \\COMMENTLLAMA{Compute non-parametric distribution}\n \\STATE $p_{\\text{\\knn}}(w|y_{<t}) \\gets 0, \\forall w \\in$ vocabulary \n \\FOR{$i=1$ {\\bfseries to} $r$}\n\\STATE $p_{\\text{\\knn}}(w=v_i|y_{<t}) \\gets p_{\\text{\\knn}}(w=v_i|y_{<t}) + \\exp(\\mu\\cdot s_i)/\\sum_{i=j}^r \\exp(\\mu\\cdot s_j)$\n \\ENDFOR\n \\STATE \\COMMENTLLAMA{Confidence-based Interpolation}\n \\STATE $\\lambda_t \\gets \\text{sigmoid}((\\frac{\\min_{i} s_i}{\\max_i s_i} - \\alpha) / \\tau)$\n \\STATE $p_{\\mathcal{M}}(w | y_{<t}) \\gets \\lambda_t \\cdot p_{\\text{LM}}(w|y_{<t}) + (1 - \\lambda_t) \\cdot p_{\\text{\\knn}}(w|y_{<t})$\n \\STATE \\COMMENTLLAMA{Dynamic span selection}\n \\STATE $w_t \\gets \\underset{w}{\\text{argmax }} p_{\\mathcal{M}}(w|y_{<t})$\n \\STATE $v_t \\gets \\underset{v_i=w_t}{\\text{argmax }} p_{\\text{\\knn}}(w=v_i|y_{<t})$\n \\STATE $v_{t:t+n} \\gets \\mathcal{C}$.get-ngram$(v_t, n)$\n \\STATE $y_t\\gets \\left\\{\\begin{matrix}\nw_t, & \\text{if } \\lambda_t > \\delta; \\\\ \nv_{t:t+n}, & \\text{otherwise}.\n\\end{matrix}\\right.$\n \\STATE $n \\gets |y_t|$\n \\STATE \\COMMENTLLAMA{Relaxed Speculative Decoding}\n \\FOR{$i=1$ {\\bfseries to} $n$}\n\\STATE $p_{\\text{accept}}(w_t^{(i)}) \\gets \\frac{p_{\\mathcal{M}}(w=w_t^{(i)} \\mid x, y_{<t}, w_t^{(1)}, w_t^{(2)},...,w_t^{(i-1)})} {\\gamma \\cdot \\underset{{w}}{\\max} \\ p_{\\mathcal{M}} (w \\mid x, y_{<t}, w_t^{(1)}, w_t^{(2)},...,w_t^{(i-1)})}$\n\\STATE Break if $p_{\\text{accept}}(w_t^{(i)}) \\leq 0.5$\n\\ENDFOR\n\\IF{$i < n$ and $n$ > 1}\n\\STATE $w_{t}^{(i)} \\gets \\underset{w}{\\text{argmax }}p_{\\mathcal{M}}(w|y_{<t}, w_t^{(1)}, w_t^{(2)},...,w_t^{(i-1)})$\n \\ENDIF\n \\STATE $y_{<t} \\gets$ concatenate$(y_{<t}, w_t^{(1)}, w_t^{(2)},...,w_t^{(i)})$\n\\ENDFOR\n\\STATE Return $y_{<t}$\n\\end{algorithmic}\n\\end{algorithm}\n\n\\section{Evaluation Details and Hyper-parameter Tuning}\\label{appendix:exp}\n\\paragraph{Datasets.} We sample subsets of WikiText-103, \\nq, and Biography as dev sets for hyper-parameter tuning. We use the validation sets of WikiText-103 (CC BY-SA 3.0), \\nq (Apache License 2.0), TriviaQA (Apache License 2.0), MedMCQA (MIT License), HotpotQA (Apache License 2.0), MMLU (MIT License), and Biography (MIT License) for validation. TruthfulQA (Apache License 2.0) only has the test set. We finally test all datasets shown in Table~\\ref{tbl:main_results}. For HotpotQA and MedMCQA, we do not have access to the test set and therefore the validation results are reported. For Biography, we use the labeled data that have human annotation as the validation and dev set, and the unlabeled data as the test set. In the original \\factscore paper, the authors use InstructGPT~\\citep{instructgpt} to perform fact decomposition before verification. We hereby train our own decomposition model by further fine-tuning Llama-2 7B using publicly available datasets~\\citep{chen-etal-2022-generating,liu-etal-2023-revisiting,malaviya2024expertqa}. \nFor fact verification, we use the option \\texttt{retrieval+llama+npm} to evaluate the decomposed atomic facts.\n\n% For fact decomposition: we first\n% use nltk.tokenize to split a response into sentences; then, use our Llama-2 7B model fine-tuned\n% on public datasets (Liu et al., 2023; Chen et al.,\n% 2022; Malaviya et al., 2023) to conduct atomic fact\n% decomposition for each sentence\n\n% dev set: wiki (256), nq (256), factscore (180)\n\n% val set: wiki (2090), pile of law (1900), nq tqa hotpotqa medmcqa (2500), factscore (180), mmlu (1531)\n\n% test set: wiki (2358), pile of law (1900), nq (3610), tqa (11313), truthfulqa (817), factscore (500), mmlu (14042)\n\n\n\\paragraph{Inference and prompts.} For language modelling and text completion, we use a context length of 128 tokens and a max generation length of 256 tokens. For the other tasks, we use 128 tokens as max generation length for question answering and 512 for fact verification. We remove all the in-context demonstrations from the prompt to test the zero-shot effectiveness of our model. We use greedy decoding in our experiments as the randomness in sampling can undermine factuality.\nTable~\\ref{tbl:instruction_template} shows the instruction templates we use for different tasks. For MMLU, we compare the perplexity of each option concatenated with the template and select the one with the minimum perplexity.\n% LM instruction tuning and retriever query templates\n\\begin{table*}[t]\n \\centering\n \\scalebox{0.84}{\n \\begin{tabular}{p{0.18\\textwidth}p{0.32\\textwidth}p{0.5\\textwidth}}\n \\toprule\n Category & Base LM, \\knnlm and \\nest & RA-based Methods \\\\\n \\midrule \\\\\n QA, MMLU, Fact Check & \\texttt{[INST]} Question: \\{question\\} Answer: \\texttt{[/INST]}& \\texttt{[INST]} Answer the question with the background context as reference. Background: \\{retrieved passages\\}\\nls Question: \\{question\\} Answer: \\texttt{[/INST]} \\\\ \\\\\n \\hdashline \\\\\n Text Completion &\\texttt{[INST]} Write an article.\\nls Article: \\texttt{[/INST]} \\{prefix\\} & \\texttt{[INST]} Write an article with the background context as reference. Background: \\{retrieved passages\\}\\nls Article: \\texttt{[/INST]} \\{prefix\\} \\\\ \\\\\n % \\hdashline\n \\bottomrule\n \\end{tabular}\n }\n\\caption{Instruction template used for different datasets. \\texttt{[INST]} and \\texttt{[/INST]} are special markers denoting the start and the end of an instruction.}\n\\label{tbl:instruction_template}\n\\end{table*}\n\n\n% For text completion, we use the following prompt ``\\texttt{[INST]} Write an article.$\\backslash$n Article: \\texttt{[/INST]} \\{prefix\\}'' where the \\texttt{[INST]} is a format tag for Llama-2-Chat.\n\n% For question-answering and fact-verification tasks, we use the following template: ``\\texttt{[INST]} Question: \\{question\\} Answer: \\texttt{[/INST]}'' where we format the input question in the bracket. \n\n% For the RA models, we use the prompt ``\\texttt{[INST]} Write an article with the background context as reference. Background: \\{retrieved passages\\}$\\backslash$n Article: \\texttt{[/INST]} \\{prefix\\}'' for text completion. For retrieval-augmented question-answering and fact-verification tasks, we use ``\\texttt{[INST]} Answer the question with the background context as reference. Background: \\{retrieved passages\\}$\\backslash$n Question: \\{question\\} Answer: \\texttt{[/INST]}''.\n\n\\paragraph{Hyper-parameters and baselines.}\nFor the base LM, we do not tune the hyper-parameters released with the original Llama-2-chat models.\nFor the in-context retrieval augmented baseline, we select the top-3 retrieved passages.\nFor \\knnlm, we follow Equation~\\eqref{eq:knnlm} and use an interpolation coefficient of 0.7 for Wikipedia-based tasks and 0.9 for Pile of Law.\nFor \\nest and RA-\\nest, we first tune the hyper-parameters in Equation~\\eqref{eq:rrc} on language modelling tasks using perplexity. We then fix those hyper-parameters and then tune the rest of the parameters in Equation~\\eqref{eq:span_expansion} and~\\eqref{eq:rejection} on generation tasks. All hyper-parameters in the above methods are tuned on the dev sets of WikiText-103, \\nq, and Biography.\n\n% \\section{Qualitative Results.}\\label{appendix:qualitatitve}\n% Table~\\ref{} shows some examples of \\nest and other baselines on WikiText-103, Pile of Law, \\factscore, and \\nq.\n\n\\section{Analysis}\\label{appendix:analysis}\nThe following analyses are performed on the validation set of WikiText-103, \\nq, and Biography data with the Llama-7B-chat model.\n\\subsection{Sensitivity}\n\\paragraph{Number of retrieved passages and tokens} ~\\cite{knnlm} show that increasing the size of the database and the number of tokens can improve the perplexity with proper hyper-parameter setting. We also verify whether our two-stage \\knn search and RRC approach follow the same trend. Figure~\\ref{fig:ablation:num_tokens} shows the validation perplexity on WikiText-103. For a fixed number of passages, the perplexity decreases as the number of tokens increases; for a fixed number of tokens, the perplexity decreases about $0.5\\sim 1.0$ as the number of passages doubles. However, as \\nest needs to encode the retrieved passages on the fly, the latency also increases linearly w.r.t. the number of passages. Therefore, we set the passage number to be 40 and the token number to be 1024 in the main experiments.\n\n\\begin{figure}[t!]\n\\centering\n\\hspace{-6mm}\n\\begin{subfigure}[b]{0.33\\textwidth}\n \\centering\n \\includegraphics[width=\\textwidth]{ablation_tokens_passages.pdf}\n \\caption{Passages and tokens.}\\label{fig:ablation:num_tokens}\n\\end{subfigure}\n\\begin{subfigure}[b]{0.33\\textwidth}\n \\centering\n \\includegraphics[width=\\textwidth]{interpolation.pdf}\n \\caption{Interpolation coefficient.}\\label{fig:ablation:rrc}\n\\end{subfigure}\n\\begin{subfigure}[b]{0.33\\textwidth}\n \\centering\n \\includegraphics[width=\\textwidth]{span_nq.pdf}\n \\caption{Threshold for span selection.}\\label{fig:ablation:threshold}\n\\end{subfigure}\n\\caption{Sensitivity analysis on WikiText-103 and \\nq dev set for the \\nest-7B model with the above hyper-parameters in the sub-figures.}\n\\label{fig:sensitivity}\n\\end{figure}\n\n\\paragraph{Interpolation coefficient} Figure~\\ref{fig:ablation:rrc} shows the sensitivity of the hyper-parameters $\\alpha$ (offset) and $\\tau$ (temperature) in Equation~\\eqref{eq:rrc} on WikiText-103. When $\\tau$ is big, $\\lambda_t$ is close to a uniform distribution and therefore the offset $\\alpha$ does not have a big impact on the perplexity. When $\\tau$ is small, the impact of $\\alpha$ is enlarged and the sweet spot is achieved around $\\tau=0.1$ and $\\alpha=0.4$.\n\n\\paragraph{Threshold for dynamic span selection} Figure~\\ref{fig:ablation:threshold} shows how threshold $\\delta$ in Equation~\\eqref{eq:span_expansion} affects the generation on \\nq. A bigger $\\delta$ means selecting the span instead of a token more often. We can see that the answer-level recall on \\nq first increases and then decreases as we increase the value of $\\delta$, where the sweet spot is around $\\delta=0.5$.\n\\begin{table}[!h]\n\\centering\n\\begin{adjustbox}{max width=\\textwidth}\n\\begin{tabular}{l|ccc}\n\\toprule\nModels (7B) & Wiki./ROUGE-1 &NQ/ALR &Bio./FS\\\\ \n\\midrule\n\\knnlm (two-stage) &20.1 &40.8 &34.8 \\\\\n+ Relative Retrieval Confidence &24.7\t&44.4\t&41.6 \\\\\n+ Dynamic Span selection &24.5 &44.6 &41.6 \\\\\n+ Relaxed speculative decoding &26.8\t&45.4\t&46.8 \\\\\n\\bottomrule\n\\end{tabular}\n\\end{adjustbox}\n\\vspace{3mm}\n\\caption{Ablation study on the validation set of WikiText-103, \\nq, and Biography. ROUGE-1 is reported for WikiText-103, ALR is reported for \\nq, and \\factscore is reported for Biography.}\n\\label{tbl:ablation}\n\\end{table}\n\\subsection{Ablation Study} \nTable~\\ref{tbl:ablation} shows a progressive ablation of $\\nest$ on WikiText-103, \\nq, Biography. As mentioned in Section~\\ref{sec:nest:two-stage}, it is extremely expensive to encode billion-token corpus with billion-parameter models. Therefore, we directly start with the two-stage implementation of \\knnlm and gradually add the methods applied in \\nest. As we can see, adding the RRC component gives the first performance boost. The second dynamic span selection method does not seem to increase the performance, yet it is crucial to give consistent attribution for consecutive spans and tokens. The last relaxed speculative decoding method further improves the final generation quality.\n\n\\section{Qualitative Results.}\\label{appendix:qualitatitve}\nTable~\\ref{tbl:qualitative:nq},~\\ref{tbl:qualitative:hqa},~\\ref{tbl:qualitative:mqa}, and~\\ref{tbl:qualitative:wiki} shows more qualitative analyses for attribution of \\nest-70B on \\nq, HotpotQA, MedMCQA, and WikiText-103.\n\\begin{table}[ht]\n\\centering\n\\begin{adjustbox}{max width=1\\textwidth}\n \\begin{tabular}{ll}\n \\toprule\n Question &Answer (Generated by \\nest-70B) \\\\\n \\midrule\n \\multirow{6}{*}{The vascular layer of the eye is the}&\\multirow{6}{*}{\\begin{tabular}[c]{@{}l@{}}The \\green{vascular tunic,}\\href{https://en.wikipedia.org/wiki/Mammalian_eye}{{\\color{gray}{[}21027113{]}}} also known as the \"uvea\", is the middle layer of the \\green{three layers}\\\\ \\green{ that comprise the eye. It is located} \\href{https://en.wikipedia.org/wiki/Pars_plana}{{\\color{gray}{[}12049924{]}}} between the sclera (the outer, white layer) and \\\\ \\green{the retina (} \\href{https://en.wikipedia.org/wiki/Eye_neoplasm}{\\color{gray}{[}31987889{]}} the innermost \\green{is the retina,}\\green{which gets its oxygenation from the blood}\\\\ \\green{vessels of the choroid (posteriorly) as well as the retinal vessels (anteriorly).}\\href{https://en.wikipedia.org/wiki/Human_eye}{\\color{gray}{[}31482951{]}}\\end{tabular}}\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n \\hdashline\n \\multirow{6}{*}{When did United States enter World War I?}&\\multirow{6}{*}{\\begin{tabular}[c]{@{}l@{}}The United States entered World War I on April 6, \\green{1917,}\\href{https://en.wikipedia.org/wiki/United_States_in_World_War_I}{\\color{gray}{[}1592542{]}} after Congress declared war \\\\against Germany. The \\green{U.S. made its major contributions in terms of supplies, raw material, and}\\\\\\green{money, starting in 1917. American soldiers under General of the Armies John Pershing,}\\\\ \\green{Commander-in-Chief of the American Expeditionary Force (AEF), arrived at the rate of 10} \\href{https://en.wikipedia.org/wiki/United_States_in_World_War_I}{\\color{gray}{[}1592542{]}}\\end{tabular}}\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n \\hdashline\n \\multirow{6}{*}{Where are unipolar neurons found in spinal cord?}&\\multirow{6}{*}{\\begin{tabular}[c]{@{}l@{}}\\green{Unipolar neurons} \\href{https://en.wikipedia.org/wiki/Dendrite}{\\color{gray}{[}6000295{]}} are found in the \\green{dorsal root ganglia} \\href{https://en.wikipedia.org/wiki/Spinal_cord}{\\color{gray}{[}19976891{]}} (DRG) of \\green{the spinal cord.}\\\\\\href{https://en.wikipedia.org/wiki/Marginal_nucleus_of_spinal_cord}{\\color{gray}{[}9082117{]}} The DRG is a cluster \\green{of neurons (a ganglion) in a dorsal root of a spinal nerve. The cell}\\\\ \\green{bodies of sensory neurons known as first-order neurons are located in the dorsal root ganglia. The}\\\\ \\green{ axons of dorsal root ganglion neurons are known as af} \\href{https://en.wikipedia.org/wiki/Dorsal_root_ganglion}{\\color{gray}{[}9024943{]}}\\end{tabular}}\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n \\hdashline\n \\multirow{6}{*}{When was catch me if you can made?}&\\multirow{6}{*}{\\begin{tabular}[c]{@{}l@{}}The movie \"Catch Me If You Can\" was released on \\green{December 25,} \\green{2002,} \\href{https://en.wikipedia.org/wiki/Catch_Me_If_You_Can}{\\color{gray}{[}27493033{]}} in the United States. \\\\\n It was directed by Steven Spielberg and starred Leonardo DiCaprio as Frank \\green{Abagnale Jr.,} \\href{https://en.wikipedia.org/wiki/Leonardo_DiCaprio}{\\color{gray}{[}2219100{]}} a \\\\con man who \\green{impersonated a Pan} \\href{https://en.wikipedia.org/wiki/List_of_con_artists}{\\color{gray}{[}26808473{]}} American World Airways pilot, a doctor, and a legal \\\\prosecutor, among other identities, \\green{and Tom Hanks as} \\href{https://en.wikipedia.org/wiki/Catch_Me_If_You_Can_(book)}{\\color{gray}{[}2422879{]}} FBI agent Carl Hanratty, \\green{who pursued him.} \\\\\\href{https://en.wikipedia.org/wiki/Catch_Me_If_You_Can_(book)}{\\color{gray}{[}2422867{]}} The movie was based on\\end{tabular}}\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Qualitative analysis for attribution on NQ. \\green{Green}: Correct segments taken from the corpus. {\\color{gray}{Gray}}: Text chunk id. The span will be labelled only when 4 consecutive tokens have the same document id.}\\label{tbl:qualitative:nq}\n\\end{table}\n\n\\begin{table}[ht]\n\\centering\n\\begin{adjustbox}{max width=1\\textwidth}\n \\begin{tabular}{ll}\n \\toprule\n Question &Answer (Generated by \\nest-70B) \\\\\n \\midrule\n \\multirow{6}{*}{Which is larger, Hunchun or Shijiazhuang?}&\\multirow{6}{*}{\\begin{tabular}[c]{@{}l@{}} Hunchun and \\green{Shijiazhuang} \\href{https://en.wikipedia.org/wiki/Beiyuan_Subdistrict,_Shijiazhuang}{\\color{gray}{[}30998205{]}} are both cities in China. Shi\\green{jia zhuang} \\href{https://en.wikipedia.org/wiki/Shijiazhuang_Metro}{\\color{gray}{[}6737448{]}} is a prefecture\\\\-level city and the \\green{capital and largest city of Hebei} \\href{https://en.wikipedia.org/wiki/Shijiazhuang}{\\color{gray}{[}7075559{]}} Province, with a population of 120,000,000\\\\ (2020). Hunchun is a town \\red{ship-level division of Hunchun} \\href{https://en.wikipedia.org/wiki/Xiyuan_Subdistrict,_Shijiazhuang}{\\color{gray}{[}30998199{]}}, Jilin, China, with a population of\\\\ 18,000 (2004). Therefore, Shijia\\green{zhuang is larger than} \\href{https://en.wikipedia.org/wiki/Shijiazhuang_railway_station}{\\color{gray}{[}27937602{]}}\\end{tabular}}\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n \\hdashline\n \\multirow{6}{*}{Was Pocahontas made before Born in China?}&\\multirow{6}{*}{\\begin{tabular}[c]{@{}l@{}}No, \\green{Pocahontas} \\href{https://en.wikipedia.org/wiki/Pocahontas_(franchise)}{\\color{gray}{[}2716717{]}} was released in \\green{1995} \\href{https://en.wikipedia.org/wiki/Opechancanough}{\\color{gray}{[}4240514{]}}, while Born in China was released in \\red{2016} \\href{https://en.wikipedia.org/wiki/List_of_giant_pandas}{\\color{gray}{[}9704476{]}}\\end{tabular}}\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n \\hdashline\n \\multirow{6}{*}{\\begin{tabular}[c]{@{}l@{}} How many animals does the wall running\\\\ between the West Gate Lodge and Cardiff\\\\ Castle depict?\\end{tabular}}&\\multirow{6}{*}{\\begin{tabular}[c]{@{}l@{}} The wall running between the \\green{West Gate Lodge} \\href{https://en.wikipedia.org/wiki/West_Lodge,_Cardiff_Castle}{\\color{gray}{[}2843590{]}} and Cardiff Castle depicts \\green{15 animals on Castle Street.}\\\\ \\green{It is a Grade I listed structure, designed by architect William Burges in 1866, though not built until 1890. The}\\\\ \\green{work was completed by Burges' former assistant William Frame. The original nine animal figures were sculptured}\\\\ \\green{ by Thomas Nicholls} \\href{https://en.wikipedia.org/wiki/Castle_Quarter_(Cardiff)}{\\color{gray}{[}16694594{]}}\\end{tabular}}\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n \\hdashline\n \\multirow{6}{*}{\\begin{tabular}[c]{@{}l@{}} How much longer after Sambou YatabarÃ\\\\ was born did Werder Bremen get founded in\\\\ the northwest German federal state Free \\\\Hanseatic City of Bremen?\\end{tabular}}&\\multirow{6}{*}{\\begin{tabular}[c]{@{}l@{}} Sambou \\green{YatabarÃ} \\href{https://en.wikipedia.org/wiki/List_of_foreign_Bundesliga_players}{\\color{gray}{[}29957019{]}} was born on {March 21}, 1989. Werder Bremen was founded on February 4, \\green{1899} \\\\ \\href{https://en.wikipedia.org/wiki/SV_Werder_Bremen}{\\color{gray}{[}5536896{]}}, which was approximately 90 years before \\green{Sambou YatabarÃ} \\href{https://en.wikipedia.org/wiki/Sambou_Yatabar\\%C3\\%A9}{\\color{gray}{[}22751848{]}} \\end{tabular}}\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Qualitative analysis for attribution on HotpotQA. \\green{Green}: Correct segments taken from the corpus. \\red{Red}: Wrong segments taken from the corpus. {\\color{gray}{Gray}}: Document id. The span will be labelled only when 4 consecutive tokens have the same document id.}\\label{tbl:qualitative:hqa}\n\\end{table}\n\n\n\\begin{table}[ht]\n\\centering\n\\begin{adjustbox}{max width=1\\textwidth}\n \\begin{tabular}{ll}\n \\toprule\n Question &Answer (Generated by \\nest-70B) \\\\\n \\midrule\n \\multirow{8}{*}{Heavy forces on periodontal ligament causes?}&\\multirow{8}{*}{\\begin{tabular}[c]{@{}l@{}} Heavy forces on the \\green{periodontal ligaments} \\href{https://en.wikipedia.org/wiki/Human_tooth}{\\color{gray}{[}10961708{]}} can cause a variety of issues, including: 1. \\green{Periodontal} \\\\{ligament} \\href{https://en.wikipedia.org/wiki/Human_tooth_development}{\\color{gray}{[}19737958{]}} strain: The \\green{periodontal ligament can} \\href{https://en.wikipedia.org/wiki/Tooth_eruption}{\\color{gray}{[}19569905{]}} become stretched or compressed due to\\\\ heavy forces, leading to pain and \\green{a reversible increase in tooth mobility.} \\href{https://en.wikipedia.org/wiki/Toothache}{\\color{gray}{[}32678984{]}} 2. Periodontal ligament \\\\inflammation: The periodontal ligament can become inflamed due to the constant pressure and friction from\\\\ the heavy forces, leading to pain and swelling in the affected area. 3.\\green{Tooth mobility:} \\href{https://en.wikipedia.org/wiki/List_of_periodontal_diseases}{\\color{gray}{[}17809669{]}} Heavy forces\\\\ on \\green{a tooth can} \\href{https://en.wikipedia.org/wiki/Dental_implant}{\\color{gray}{[}2104721{]}} cause\\end{tabular}}\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n \\hdashline\n \\multirow{6}{*}{Gait apraxia is seen in thromboembolic episode involving?}&\\multirow{6}{*}{\\begin{tabular}[c]{@{}l@{}}Gait apraxia is often seen \\green{in patients with corticobasal degeneration.} \\href{https://en.wikipedia.org/wiki/Apraxia}{\\color{gray}{[}4450082{]}} Corticobasal degeneration is a \\\\neurodegenerative disorder that affects the \\green{cerebral cortex,} \\href{https://en.wikipedia.org/wiki/Ideomotor_apraxia}{\\color{gray}{[}31300317{]}} basal ganglia, and cerebellum, leading to \\\\a range of movement disorders, including gait \\green{apraxia. The} \\href{https://en.wikipedia.org/wiki/Ideomotor_apraxia}{\\color{gray}{[}31300325{]}} condition is characterized\\end{tabular}}\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n \\hdashline\n \\multirow{6}{*}{\\begin{tabular}[c]{@{}l@{}}Axonal transport is?\\end{tabular}}&\\multirow{6}{*}{\\begin{tabular}[c]{@{}l@{}} Axonal transport refers to the movement \\green{of mitochondria, lipids, synaptic vesicles, proteins, and other organelles}\\\\\\green{to and from a neuron's cell body, through the cytoplasm of its axon called the axoplasm. Since some axons are on}\\\\\\green{ the order of meters long, neurons cannot} \\href{https://en.wikipedia.org/wiki/Axonal_transport}{\\color{gray}{[}18180573{]}}\\end{tabular}}\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n \\hdashline\n \\multirow{6}{*}{\\begin{tabular}[c]{@{}l@{}} In Erythroblastosis fetalis not involved is ?\\end{tabular}}&\\multirow{6}{*}{\\begin{tabular}[c]{@{}l@{}} \\green{Inerythroblastosis fetalis} \\href{https://en.wikipedia.org/wiki/Type_II_hypersensitivity}{\\color{gray}{[}26445482{]}}, the fetus is not directly involved. \\green{Erythroblastosis fetalis} \\href{https://en.wikipedia.org/wiki/Bruce_Chown}{\\color{gray}{[}14246750{]}} is a\\\\ condition that occurs in the newborn baby when \\green{the IgG molecules (one of the five main types of antibodies)}\\\\\\green{produced by the mother pass through the placenta. Among these antibodies are some which attack antigens}\\\\\\green{on the red blood cells in the fetal circulation, breaking down and destroying the cells. The fetus} \\href{https://en.wikipedia.org/wiki/Hemolytic_disease_of_the_newborn}{\\color{gray}{[}26565066{]}}\\end{tabular}}\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Qualitative analysis for attribution on MedMCQA. \\green{Green}: Correct segments taken from the corpus. {\\color{gray}{Gray}}: Document id. The span will be labelled only when 4 consecutive tokens have the same document id.}\\label{tbl:qualitative:mqa}\n\\end{table}\n\n\\begin{table}[ht]\n\\centering\n\\begin{adjustbox}{max width=1\\textwidth}\n \\begin{tabular}{ll}\n \\toprule\n Prefix &Prediction (Generated by \\nest-70B) \\\\\n \\midrule\n \\multirow{10}{*}{\\begin{tabular}[c]{@{}l@{}} casemates in the forecastle and superstructure and the\\\\ remaining pair were mounted on the deck above them\\\\ and protected by gun shields. They had a maximum\\\\ elevation of + 20 degrees which gave them ranges of\\\\ 16 @,@ 300 metres ( 17 @,@ 800 yd ). Each gun had\\\\ a rate of fire of up to 10 rounds per minute. Anti @-@ \\\\aircraft defence was provided by four 40 @-@ calibre 3rd\\\\ Year Type 8 @-@ centimetre AA guns in single mounts\\end{tabular}}&\\multirow{10}{*}{\\begin{tabular}[c]{@{}l@{}} \\green{ protected by gun shields. They had a maximum elevation of + } \\href{https://en.wikipedia.org/wiki/Ise-class_battleship}{\\color{gray}{[}4584350{]}} 80 degrees, and a rate of fire of \\\\\\green{130 rounds per minute. The } \\href{https://en.wikipedia.org/wiki/Type_35_torpedo_boat}{\\color{gray}{[}8700981{]}} ship was also equipped with eight \\green{ 53.3 cm torpedo tubes, four on each}\\\\\\green{broadside, two above water and two submerged. Around 1926, the four above-water torpedo tubes were removed}\\\\\\green{ and the ship received three additional 76 mm AA } \\href{https://en.wikipedia.org/wiki/Japanese_battleship_Nagato}{\\color{gray}{[}22329090{]}} guns on high-angle Mark 3 mounts. These were\\\\ mounted abreast the mainmast in the superstructure and had a maximum elevation of + 70 degrees and a rate of \\\\fire \\green{ of 13 to 20 rounds per minute. They fired a 6 kg projectile with a muzzle velocity of 680 m/s to a maximum}\\\\\\green{ height of } \\href{https://en.wikipedia.org/wiki/Nagato-class_battleship}{\\color{gray}{[}30284091{]}}7,500 m. The ship was also fitted with eight 61 cm above-water torpedo tubes, four on each\\\\ broadside, two above water and two submered. The ship had a crew of 1\\end{tabular}}\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n \\hdashline\n \\multirow{12}{*}{\\begin{tabular}[c]{@{}l@{}}, but he is thought to have given up the privilege in favour\\\\ of one of his half brothers. He spent the next four years\\\\ living in the Luoyang area, fulfilling his duties in domestic\\\\ affairs.In the autumn of 744, he met Li Bai ( Li Po ) for\\\\ the first time, and the two poets formed a friendship.\\\\ David Young describes this as \"the most significant formative\\\\ element in Du Fu 's artistic development\" because it gave him\\\\ a living example of the reclusive poet @-@ scholar life to which\\\\ he was attracted after his failure in the civil\\end{tabular}}&\\multirow{12}{*}{\\begin{tabular}[c]{@{}l@{}}\\green{ service exam. The relationship was somewhat one-sided, however. Du Fu was by some years the younger, while Li}\\\\\\green{ Bai was already a poetic star. We have twelve poems to or about Li Bai from the younger poet, but } \\href{https://en.wikipedia.org/wiki/Du_Fu}{\\color{gray}{[}24905850{]}}\\\\ none in the other direction. In 740s, Du Fu began to write poetry seriously. His early work was influenced by\\\\ the works of Li Bai and other poets of the time. He also began to develop his own style and themes in his poetry. \\\\ In 750s, Du Fu became increasingly involved in politics and social issues. He wrote many poems that criticized \\\\the government and its policies. He also became \\red{ known for his poems } \\href{https://en.wikipedia.org/wiki/Mi_Fu}{\\color{gray}{[}13895340{]}} that expressed his \\red{ decision to}\\\\\\green{withdraw from civil service.} \\href{https://en.wikipedia.org/wiki/Tao_Yuanming}{\\color{gray}{[}19467174{]}} In 755, Du Fu was appointed an advisor \\green{ to Yan Wu, } \\href{https://en.wikipedia.org/wiki/Du_Fu}{\\color{gray}{[}24905857{]}}\\\\ the Prince of Guangling. He served in this position for three years. During this time, he left \\green{ the city to escape a}\\\\\\green{ rebellion, but he returned in summer 760. } \\href{https://en.wikipedia.org/wiki/Du_Fu}{\\color{gray}{[}24905857{]}} In 762, he was appointed an advisor to Li Yu, the Prince of\\\\ Xu. He was \\green{ relieved of his } \\href{https://en.wikipedia.org/wiki/Du_Fu}{\\color{gray}{[}24905857{]}} duties in 764\\end{tabular}}\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n \\hdashline\n \\multirow{12}{*}{\\begin{tabular}[c]{@{}l@{}}positions to Bradley. Still farther northward in the zone of the\\\\ US 38th Infantry the North Koreans were also active. After\\\\ the North Korean breakthrough during the night of August 31,\\\\ Keiser had ordered the 2nd Battalion, 38th Infantry, to move\\\\ south and help the 23rd Infantry establish a defensive position\\\\ west of Changnyong. In attempting to do this, the battalion\\\\ found North Korean troops already on the ridges along the road.\\\\ They had penetrated to Hill 284 overlooking the 38th Infantry\\\\ command post. This\\end{tabular}}&\\multirow{12}{*}{\\begin{tabular}[c]{@{}l@{}} was serious, for the \\green{ 38th Regiment command post } \\href{https://en.wikipedia.org/wiki/Second_Battle_of_Naktong_Bulge}{\\color{gray}{[}13479536{]}} was in the process of moving back to \\red{ a position}\\\\\\red{ south of } \\href{https://en.wikipedia.org/wiki/Battle_of_Sangju_(1950)}{\\color{gray}{[}8986888{]}} the river. The 2nd Battalion \\red{ was then forced to occupy a lower position at 1 mi away from}\\\\\\red{ the Chinese. At the same time, the } \\href{https://en.wikipedia.org/wiki/Battle_of_the_Chongchon_River}{\\color{gray}{[}29133385{]}} 3rd Battalion, 38th Infantry, was cut off \\green{3 mi west of } \\href{https://en.wikipedia.org/wiki/Great_Naktong_Offensive}{\\color{gray}{[}4050187{]}}\\\\23rd Infantry and \\red{ was forced to withdraw to safer positions. In the early afternoon, another attacking force, an}\\\\\\red{ estimated } \\href{https://en.wikipedia.org/wiki/Battle_of_Taejon}{\\color{gray}{[}20154221{]}}200 North Koreans, struck the 38th Regiment command \\green{ post. The regimental commander}\\\\\\green{ organized a defensive perimeter } \\href{https://en.wikipedia.org/wiki/Second_Battle_of_Naktong_Bulge}{\\color{gray}{[}13479536{]}} around his command post and held on until nightfall. The KPA \\green{ 7th}\\\\\\green{ Division troops had } \\href{https://en.wikipedia.org/wiki/Great_Naktong_Offensive}{\\color{gray}{[}4050220{]}} effected major crossings of the Naktong at two principal points against the US 38th\\\\ Infantry \\green{. The KPA 9th Division had effected major crossings of the Naktong at two principal points against the US }\\\\ \\href{https://en.wikipedia.org/wiki/Second_Battle_of_Naktong_Bulge}{\\color{gray}{[}13479513{]}}23rd Infantry. The 1st Battalion, 23rd Infantry, on the ridges overlooking the river, was pushed back as\\\\ its soldiers retreated without orders. Survivors from the 1st Battalion later joined the 2\\end{tabular}}\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n \\hdashline\n \\multirow{10}{*}{\\begin{tabular}[c]{@{}l@{}} northeast of Kuching 216 kilometres (134 mi) northeast of Sibu,\\\\ and 200 kilometres (120 mi) southwest of Miri. Bintulu is located\\\\ near the mouth of the <unk> River, in the coastal region of central\\\\ Sarawak. Geology of the coastal area was formed during the Pleisto-\\\\cene period; silt, clay, and gravel can be found here. Geological forma-\\\\tion from the Oligocene period is found in the inland area, which con-\\\\tains limestone, siltstone, and\\end{tabular}}&\\multirow{10}{*}{\\begin{tabular}[c]{@{}l@{}} \\green{ sandstone. The soil is generally soft. } \\href{https://en.wikipedia.org/wiki/Bintulu}{\\color{gray}{[}9989875{]}} The area is located in \\green{ the coastal region of central Sarawak.}\\\\\\green{ Geology of the coastal area was formed during the Pleistocene period; silt, clay, and gravel can be found here.}\\\\\\green{ Geological formation from the Oligocene period is found in the inland area, which contains limestone,} \\href{https://en.wikipedia.org/wiki/Bintulu}{\\color{gray}{[}9989875{]}}\\\\ siltstone, and sandstone. The soil is generally soft. The area is located in the coastal region of central Sarawak,\\\\ which is known for its rich \\green{ oil and gas reserves. The } \\href{https://en.wikipedia.org/wiki/Sarawak}{\\color{gray}{[}21065756{]}} town is also surrounded by lush \\green{ tropical rainforests}\\\\\\green{ and abundant } \\href{https://en.wikipedia.org/wiki/Sarawak}{\\color{gray}{[}21065719{]}} wildlife. Bintulu is \\green{ the capital of the Bintulu District of the Bintulu Division of Sarawak,}\\\\\\green{ Malaysia. The name of Bintulu was derived from the local native language 'Mentu Ulau' (picking heads). Bintulu}\\\\\\green{ was a small fishing village when Rajah James Brooke acquired it } \\href{https://en.wikipedia.org/wiki/Bintulu}{\\color{gray}{[}9989859{]}}\\end{tabular}}\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n &\\\\\n \\bottomrule\n \\end{tabular}\n\\end{adjustbox}\n\\caption{Qualitative analysis for attribution on WikiText-103. \\green{Green}: Correct segments taken from the corpus. \\red{Red}: Wrong segments taken from the corpus. {\\color{gray}{Gray}}: Document id. The span will be labelled only when 4 consecutive tokens have the same document id.}\\label{tbl:qualitative:wiki}\n\\end{table}\n\\end{document}"},"question":{"kind":"string","value":"Among retrieval-augmented methods, what is the smallest decline in ROUGE score when moving from unigrams to bigrams?"},"answer":{"kind":"string","value":"12.5"},"plan":{"kind":"string","value":"SELECT all metric scores for +RA rows from Wikitext-103 and Pile of Law\nLOOP for each model\n SELECT ROUGE-1 and ROUGE-2 scores for current Model\n COMPUTE decline = ROUGE-1 - ROUGE-2\nCOMPUTE result_value = min_decline\nRETURN result_value\n"}}},{"rowIdx":75,"cells":{"paper":{"kind":"string","value":"2409.02897"},"relevant_tables":{"kind":"list like","value":[["\\begin{table}[t]\n","\\centering\n","\\resizebox{\\linewidth}{!}{\n"," \\setlength{\\tabcolsep}{5pt}\n"," \\begin{tabular}{l|cc|ccc|ccc|ccc|ccc|ccc}\n","\\toprule\n","\\multirow{2}{*}{Model} & \\multicolumn{2}{c|}{Avg} & \\multicolumn{3}{c|}{Longbench-Chat} & \\multicolumn{3}{c|}{MultifieldQA} & \\multicolumn{3}{c|}{HotpotQA} & \\multicolumn{3}{c|}{Dureader} & \\multicolumn{3}{c}{GovReport} \\\\\n"," & F1 & CL & R & P & F1 & R & P & F1 & R & P & F1 & R & P & F1 & R & P & F1 \\\\ \\midrule\n","\\multicolumn{18}{l}{\\textit{\\textbf{Proprietary models}}} \\\\\n","GPT-4o & 65.6 & 220 & 46.7 & 53.5 & 46.7 & \\textbf{79.0} & 87.9 & {\\ul 80.6} & 55.7 & 62.3 & 53.4 & 65.6 & 74.2 & 67.4 & 73.4 & 90.4 & 79.8 \\\\\n","Claude-3-sonnet & 67.2 & 132 & 52.0 & 67.8 & 55.1 & 64.7 & 85.8 & 71.3 & 46.4 & 65.8 & 49.9 & 67.7 & \\textbf{89.2} & \\textbf{75.5} & {\\ul 77.4} & \\textbf{93.9} & {\\ul 84.1} \\\\\n","GLM-4 & 65.4 & 169 & 47.6 & 53.9 & 47.1 & 72.3 & 80.1 & 73.6 & 47.0 & 50.1 & 44.4 & \\textbf{73.4} & 82.3 & {\\ul 75.0} & \\textbf{82.8} & {\\ul 93.4} & \\textbf{87.1} \\\\ \\midrule\n","\\multicolumn{18}{l}{\\textit{\\textbf{Open-source models}}} \\\\\n","GLM-4-9B-chat & 27.2 & 96 & 25.9 & 20.5 & 16.7 & 51.1 & 60.6 & 52.0 & 22.9 & 28.8 & 20.1 & 45.4 & 48.3 & 40.9 & 5.7 & 8.2 & 6.3 \\\\\n","Llama-3.1-8B-Instruct & 19.7 & 100 & 14.1 & 19.5 & 12.4 & 29.8 & 44.3 & 31.6 & 20.2 & 30.9 & 20.9 & 22.0 & 25.1 & 17.0 & 16.2 & 25.3 & 16.8 \\\\\n","Llama-3.1-70B-Instruct & 40.4 & 174 & 25.8 & 32.0 & 23.2 & 53.2 & 65.2 & 53.9 & 29.6 & 37.3 & 28.6 & 38.2 & 46.0 & 35.4 & 53.4 & 77.5 & 60.7 \\\\\n","Mistral-Large-Instruct & 51.5 & 132 & 19.8 & 23.9 & 19.0 & 71.8 & 80.7 & 73.8 & 34.5 & 40.9 & 32.1 & 58.3 & 67.0 & 60.1 & 67.9 & 79.6 & 72.5 \\\\ \\midrule\n","\\multicolumn{18}{l}{\\textit{\\textbf{Our trained models}}} \\\\\n","LongCite-8B & \\textbf{72.0} & \\textbf{85} & \\textbf{62.0} & \\textbf{79.7} & \\textbf{67.4} & {\\ul 74.7} & \\textbf{93.0} & \\textbf{80.8} & {\\ul 59.2} & {\\ul 72.1} & {\\ul 60.3} & {\\ul 68.3} & 85.6 & 73.1 & 74.0 & 86.6 & 78.5 \\\\\n","LongCite-9B & {\\ul 69.2} & {\\ul 91} & {\\ul 57.6} & {\\ul 78.1} & {\\ul 63.6} & 67.3 & {\\ul 91.0} & 74.8 & \\textbf{61.8} & \\textbf{78.8} & \\textbf{64.8} & 67.6 & \\textbf{89.2} & 74.4 & 63.4 & 76.5 & 68.2 \\\\ \\bottomrule\n","\\end{tabular}\n","}\n","\\caption{Citation recall (R), citation precision (P), citation F1 (F1), and citation length (CL) of different models on LongBench-Cite using LAC-S strategy. The best and second results are bolded and underlined, respectively. \n","}\n","\\label{tab:main_cite}\n","\\end{table}\n"],["\\begin{table}[t]\n","\\centering\n","\\resizebox{\\linewidth}{!}{\n","\\setlength{\\tabcolsep}{3.5pt}\n","\\begin{tabular}{l|ccc|ccc|ccc|ccc|ccc|ccc}\n","\\toprule\n"," & \\multicolumn{3}{c|}{Avg} & \\multicolumn{3}{c|}{Longbench-Chat} & \\multicolumn{3}{c|}{MultifieldQA} & \\multicolumn{3}{c|}{HotpotQA} & \\multicolumn{3}{c|}{Dureader} & \\multicolumn{3}{c}{GovReport} \\\\\n","\\multirow{-2}{*}{Model} & C & $\\text{C}_\\text{LQA}$ & CR & C & $\\text{C}_\\text{LQA}$ & CR & C & $\\text{C}_\\text{LQA}$ & CR & C & $\\text{C}_\\text{LQA}$ & CR & C & $\\text{C}_\\text{LQA}$ & CR & C & $\\text{C}_\\text{LQA}$ & CR \\\\ \\midrule\n","\\multicolumn{19}{l}{\\textit{\\textbf{Proprietary models}}} \\\\\n","GPT-4o & \\cellcolor[HTML]{FFE2E0}69.4 & \\cellcolor[HTML]{FFE2E0}78.2 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 88\\%} & \\cellcolor[HTML]{FFE2E0}61.6 & \\cellcolor[HTML]{FFE2E0}77.4 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 80\\%} & \\cellcolor[HTML]{FFE2E0}84.0 & \\cellcolor[HTML]{FFE2E0}88.3 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 95\\%} & \\cellcolor[HTML]{FFE2E0}74.5 & \\cellcolor[HTML]{FFE2E0}80.8 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 92\\%} & \\cellcolor[HTML]{FFE2E0}81.0 & \\cellcolor[HTML]{FFE2E0}83.3 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 97\\%} & \\cellcolor[HTML]{FFE2E0}46.0 & \\cellcolor[HTML]{FFE2E0}61.3 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 75\\%} \\\\\n","Claude-3-sonnet & \\cellcolor[HTML]{FFE2E0}77.6 & \\cellcolor[HTML]{FFE2E0}78.3 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 99\\%} & \\cellcolor[HTML]{FFE2E0}73.8 & \\cellcolor[HTML]{FFE2E0}77.8 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 95\\%} & \\cellcolor[HTML]{D9F5D6}88.6 & \\cellcolor[HTML]{D9F5D6}88.1 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 101\\%} & \\cellcolor[HTML]{D9F5D6}81.3 & \\cellcolor[HTML]{D9F5D6}75.3 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 108\\%} & \\cellcolor[HTML]{FFE2E0}75.8 & \\cellcolor[HTML]{FFE2E0}80.3 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 94\\%} & \\cellcolor[HTML]{FFE2E0}68.4 & \\cellcolor[HTML]{FFE2E0}70.1 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 98\\%} \\\\\n","GLM-4 & \\cellcolor[HTML]{FFE2E0}73.7 & \\cellcolor[HTML]{FFE2E0}77.2 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 95\\%} & \\cellcolor[HTML]{FFE2E0}69.4 & \\cellcolor[HTML]{FFE2E0}79.8 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 87\\%} & \\cellcolor[HTML]{FFE2E0}87.6 & \\cellcolor[HTML]{FFE2E0}88.1 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 99\\%} & \\cellcolor[HTML]{FFE2E0}76.3 & \\cellcolor[HTML]{FFE2E0}76.5 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 100\\%} & \\cellcolor[HTML]{D9F5D6}76.0 & \\cellcolor[HTML]{D9F5D6}75.8 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 100\\%} & \\cellcolor[HTML]{FFE2E0}59.4 & \\cellcolor[HTML]{FFE2E0}65.9 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 90\\%} \\\\ \\midrule\n","\\multicolumn{19}{l}{\\textit{\\textbf{Open-source models}}} \\\\\n","GLM-4-9B-chat & \\cellcolor[HTML]{FFE2E0}62.3 & \\cellcolor[HTML]{FFE2E0}70.8 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 88\\%} & \\cellcolor[HTML]{FFE2E0}60.4 & \\cellcolor[HTML]{FFE2E0}67.8 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 89\\%} & \\cellcolor[HTML]{FFE2E0}74.2 & \\cellcolor[HTML]{FFE2E0}84.9 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 87\\%} & \\cellcolor[HTML]{FFE2E0}68.5 & \\cellcolor[HTML]{FFE2E0}71.5 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 96\\%} & \\cellcolor[HTML]{FFE2E0}49.3 & \\cellcolor[HTML]{FFE2E0}68.1 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 72\\%} & \\cellcolor[HTML]{FFE2E0}59.3 & \\cellcolor[HTML]{FFE2E0}61.6 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 96\\%} \\\\\n","Llama-3.1-8B-Instruct & \\cellcolor[HTML]{FFE2E0}52.1 & \\cellcolor[HTML]{FFE2E0}60.2 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 86\\%} & \\cellcolor[HTML]{FFE2E0}53.2 & \\cellcolor[HTML]{FFE2E0}61.6 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 86\\%} & \\cellcolor[HTML]{FFE2E0}63.9 & \\cellcolor[HTML]{FFE2E0}73.3 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 87\\%} & \\cellcolor[HTML]{FFE2E0}64.0 & \\cellcolor[HTML]{FFE2E0}64.5 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 99\\%} & \\cellcolor[HTML]{FFE2E0}29.8 & \\cellcolor[HTML]{FFE2E0}39.4 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 76\\%} & \\cellcolor[HTML]{FFE2E0}49.6 & \\cellcolor[HTML]{FFE2E0}62.1 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 80\\%} \\\\\n","Llama-3.1-70B-Instruct & \\cellcolor[HTML]{FFE2E0}62.0 & \\cellcolor[HTML]{FFE2E0}65.5 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 95\\%} & \\cellcolor[HTML]{FFE2E0}60.8 & \\cellcolor[HTML]{FFE2E0}64.6 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 94\\%} & \\cellcolor[HTML]{D9F5D6}78.4 & \\cellcolor[HTML]{D9F5D6}78.3 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 100\\%} & \\cellcolor[HTML]{FFE2E0}71.3 & \\cellcolor[HTML]{FFE2E0}75.3 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 95\\%} & \\cellcolor[HTML]{D9F5D6}43.3 & \\cellcolor[HTML]{D9F5D6}42.5 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 102\\%} & \\cellcolor[HTML]{FFE2E0}56.3 & \\cellcolor[HTML]{FFE2E0}66.9 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 84\\%} \\\\\n","Mistral-Large-Instruct & \\cellcolor[HTML]{FFE2E0}73.6 & \\cellcolor[HTML]{FFE2E0}76.4 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 96\\%} & \\cellcolor[HTML]{FFE2E0}63.8 & \\cellcolor[HTML]{FFE2E0}67.8 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 94\\%} & \\cellcolor[HTML]{D9F5D6}88.0 & \\cellcolor[HTML]{D9F5D6}85.3 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 103\\%} & \\cellcolor[HTML]{FFE2E0}77.0 & \\cellcolor[HTML]{FFE2E0}77.3 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 100\\%} & \\cellcolor[HTML]{FFE2E0}79.0 & \\cellcolor[HTML]{FFE2E0}83.3 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 95\\%} & \\cellcolor[HTML]{FFE2E0}60.4 & \\cellcolor[HTML]{FFE2E0}68.3 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 88\\%} \\\\ \\midrule\n","\\multicolumn{19}{l}{\\textit{\\textbf{Our trained models}}} \\\\\n","LongCite-8B & \\cellcolor[HTML]{D9F5D6}71.7 & \\cellcolor[HTML]{D9F5D6}67.6 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 107\\%} & \\cellcolor[HTML]{D9F5D6}69.0 & \\cellcolor[HTML]{D9F5D6}68.6 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 101\\%} & \\cellcolor[HTML]{D9F5D6}87.0 & \\cellcolor[HTML]{D9F5D6}83.6 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 104\\%} & \\cellcolor[HTML]{D9F5D6}70.8 & \\cellcolor[HTML]{D9F5D6}69.0 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 103\\%} & \\cellcolor[HTML]{D9F5D6}68.5 & \\cellcolor[HTML]{D9F5D6}62.3 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 110\\%} & \\cellcolor[HTML]{D9F5D6}63.0 & \\cellcolor[HTML]{D9F5D6}54.4 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 116\\%} \\\\\n","LongCite-9B & \\cellcolor[HTML]{D9F5D6}70.4 & \\cellcolor[HTML]{D9F5D6}65.6 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 109\\%} & \\cellcolor[HTML]{D9F5D6}67.6 & \\cellcolor[HTML]{D9F5D6}64.6 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 105\\%} & \\cellcolor[HTML]{D9F5D6}84.1 & \\cellcolor[HTML]{D9F5D6}83.3 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 101\\%} & \\cellcolor[HTML]{D9F5D6}71.8 & \\cellcolor[HTML]{D9F5D6}67.5 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 106\\%} & \\cellcolor[HTML]{D9F5D6}69.0 & \\cellcolor[HTML]{D9F5D6}66.3 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 104\\%} & \\cellcolor[HTML]{D9F5D6}59.6 & \\cellcolor[HTML]{D9F5D6}46.4 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 128\\%} \\\\ \\bottomrule\n","\\end{tabular}\n","}\n","\\caption{Correctness in LQAC setting (C) using LAC-S strategy, correctness in vanilla long-context QA setting ($\\text{C}_\\text{LQA}$), and correctness ratio (CR) of different models on LongBench-Cite. We mark the cases where adding citations improves/hurts correctness (i.e., $\\text{CR} > 1$ / $\\text{CR} < 1$) in green/red.}\n","\\label{tab:main_correct}\n","\\end{table}\n"],["\\begin{table}[t]\n","\\centering\n","\\resizebox{\\linewidth}{!}{\n","\\setlength{\\tabcolsep}{4pt}\n","\\begin{tabular}{l|ccc|ccc|ccc|ccc|ccc|ccc}\n","\\toprule\n","\\multirow{2}{*}{Method} & \\multicolumn{3}{c|}{Avg} & \\multicolumn{3}{c|}{Longbench-Chat} & \\multicolumn{3}{c|}{MultifieldQA} & \\multicolumn{3}{c|}{HotpotQA} & \\multicolumn{3}{c|}{Dureader} & \\multicolumn{3}{c}{GovReport} \\\\\n"," & F1 & CR & CL & F1 & C & CR & F1 & C & CR & F1 & C & CR & F1 & C & CR & F1 & C & CR \\\\ \\midrule\n","\\multicolumn{19}{l}{\\textit{\\textbf{one-pass methods}}} \\\\\n","LAC-C & 51.6 & 95\\% & 128.0 & 33.9 & 67.8 & 85\\% & 55.7 & 87.3 & 99\\% & 41.2 & 75.3 & 98\\% & 59.5 & 76.3 & 101\\% & 67.7 & 59.1 & 90\\% \\\\\n","LAC-S & 65.4 & 95\\% & 169.0 & 47.1 & 69.4 & 87\\% & 73.6 & 87.6 & 99\\% & 44.4 & 76.3 & 100\\% & 75.0 & 76.0 & 100\\% & 87.1 & 59.4 & 90\\% \\\\\n","RAC-C & 72.5 & 87\\% & 128.0 & 69.7 & 59.0 & 74\\% & 79.1 & 80.7 & 92\\% & 57.7 & 69.8 & 91\\% & 75.7 & 77.3 & 102\\% & 80.3 & 49.9 & 76\\% \\\\\n","RAC-S & 79.1 & 79\\% & 48.0 & 76.3 & 66.4 & 83\\% & 86.3 & 85.7 & 97\\% & 58.1 & 53.3 & 70\\% & 83.7 & 76.5 & 101\\% & 91.1 & 29.0 & 44\\% \\\\ \\midrule\n","\\multicolumn{19}{l}{\\textit{\\textbf{post-hoc methods}}} \\\\\n","post-LC-C & 47.3 & 100\\% & 128.0 & 27.8 & 79.8 & 100\\% & 48.2 & 88.1 & 100\\% & 34.5 & 76.5 & 100\\% & 52.1 & 75.8 & 100\\% & 74.1 & 65.9 & 100\\% \\\\\n","post-LC-S & 57.3 & 100\\% & 147.0 & 34.3 & 79.8 & 100\\% & 65.3 & 88.1 & 100\\% & 40.0 & 76.5 & 100\\% & 64.2 & 75.8 & 100\\% & 82.8 & 65.9 & 100\\% \\\\\n","post-RC-C & 63.8 & 100\\% & 128.0 & 61.0 & 79.8 & 100\\% & 65.3 & 88.1 & 100\\% & 49.3 & 76.5 & 100\\% & 67.8 & 75.8 & 100\\% & 75.8 & 65.9 & 100\\% \\\\\n","post-RC-S & 62.8 & 100\\% & 48.0 & 63.4 & 79.8 & 100\\% & 64.8 & 88.1 & 100\\% & 48.6 & 76.5 & 100\\% & 69.7 & 75.8 & 100\\% & 67.5 & 65.9 & 100\\% \\\\\n","CoF & 65.8 & 100\\% & 89.0 & 66.1 & 79.8 & 100\\% & 65.6 & 88.1 & 100\\% & 50.6 & 76.5 & 100\\% & 67.4 & 75.8 & 100\\% & 79.1 & 65.9 & 100\\% \\\\ \\bottomrule\n","\\end{tabular}\n","}\n","\\caption{Citation F1 (F1), correctness (C), correctness ratio (CR), and citation length (CL) of different LQAC strategies on LongBench-Cite using GLM-4. We merge MultifieldQA-en/zh for brevity.}\n","\\label{tab:pipeline-compare}\n","\\end{table}\n"]],"string":"[\n [\n \"\\\\begin{table}[t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\linewidth}{!}{\\n\",\n \" \\\\setlength{\\\\tabcolsep}{5pt}\\n\",\n \" \\\\begin{tabular}{l|cc|ccc|ccc|ccc|ccc|ccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{Model} & \\\\multicolumn{2}{c|}{Avg} & \\\\multicolumn{3}{c|}{Longbench-Chat} & \\\\multicolumn{3}{c|}{MultifieldQA} & \\\\multicolumn{3}{c|}{HotpotQA} & \\\\multicolumn{3}{c|}{Dureader} & \\\\multicolumn{3}{c}{GovReport} \\\\\\\\\\n\",\n \" & F1 & CL & R & P & F1 & R & P & F1 & R & P & F1 & R & P & F1 & R & P & F1 \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multicolumn{18}{l}{\\\\textit{\\\\textbf{Proprietary models}}} \\\\\\\\\\n\",\n \"GPT-4o & 65.6 & 220 & 46.7 & 53.5 & 46.7 & \\\\textbf{79.0} & 87.9 & {\\\\ul 80.6} & 55.7 & 62.3 & 53.4 & 65.6 & 74.2 & 67.4 & 73.4 & 90.4 & 79.8 \\\\\\\\\\n\",\n \"Claude-3-sonnet & 67.2 & 132 & 52.0 & 67.8 & 55.1 & 64.7 & 85.8 & 71.3 & 46.4 & 65.8 & 49.9 & 67.7 & \\\\textbf{89.2} & \\\\textbf{75.5} & {\\\\ul 77.4} & \\\\textbf{93.9} & {\\\\ul 84.1} \\\\\\\\\\n\",\n \"GLM-4 & 65.4 & 169 & 47.6 & 53.9 & 47.1 & 72.3 & 80.1 & 73.6 & 47.0 & 50.1 & 44.4 & \\\\textbf{73.4} & 82.3 & {\\\\ul 75.0} & \\\\textbf{82.8} & {\\\\ul 93.4} & \\\\textbf{87.1} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multicolumn{18}{l}{\\\\textit{\\\\textbf{Open-source models}}} \\\\\\\\\\n\",\n \"GLM-4-9B-chat & 27.2 & 96 & 25.9 & 20.5 & 16.7 & 51.1 & 60.6 & 52.0 & 22.9 & 28.8 & 20.1 & 45.4 & 48.3 & 40.9 & 5.7 & 8.2 & 6.3 \\\\\\\\\\n\",\n \"Llama-3.1-8B-Instruct & 19.7 & 100 & 14.1 & 19.5 & 12.4 & 29.8 & 44.3 & 31.6 & 20.2 & 30.9 & 20.9 & 22.0 & 25.1 & 17.0 & 16.2 & 25.3 & 16.8 \\\\\\\\\\n\",\n \"Llama-3.1-70B-Instruct & 40.4 & 174 & 25.8 & 32.0 & 23.2 & 53.2 & 65.2 & 53.9 & 29.6 & 37.3 & 28.6 & 38.2 & 46.0 & 35.4 & 53.4 & 77.5 & 60.7 \\\\\\\\\\n\",\n \"Mistral-Large-Instruct & 51.5 & 132 & 19.8 & 23.9 & 19.0 & 71.8 & 80.7 & 73.8 & 34.5 & 40.9 & 32.1 & 58.3 & 67.0 & 60.1 & 67.9 & 79.6 & 72.5 \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multicolumn{18}{l}{\\\\textit{\\\\textbf{Our trained models}}} \\\\\\\\\\n\",\n \"LongCite-8B & \\\\textbf{72.0} & \\\\textbf{85} & \\\\textbf{62.0} & \\\\textbf{79.7} & \\\\textbf{67.4} & {\\\\ul 74.7} & \\\\textbf{93.0} & \\\\textbf{80.8} & {\\\\ul 59.2} & {\\\\ul 72.1} & {\\\\ul 60.3} & {\\\\ul 68.3} & 85.6 & 73.1 & 74.0 & 86.6 & 78.5 \\\\\\\\\\n\",\n \"LongCite-9B & {\\\\ul 69.2} & {\\\\ul 91} & {\\\\ul 57.6} & {\\\\ul 78.1} & {\\\\ul 63.6} & 67.3 & {\\\\ul 91.0} & 74.8 & \\\\textbf{61.8} & \\\\textbf{78.8} & \\\\textbf{64.8} & 67.6 & \\\\textbf{89.2} & 74.4 & 63.4 & 76.5 & 68.2 \\\\\\\\ \\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Citation recall (R), citation precision (P), citation F1 (F1), and citation length (CL) of different models on LongBench-Cite using LAC-S strategy. The best and second results are bolded and underlined, respectively. \\n\",\n \"}\\n\",\n \"\\\\label{tab:main_cite}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\linewidth}{!}{\\n\",\n \"\\\\setlength{\\\\tabcolsep}{3.5pt}\\n\",\n \"\\\\begin{tabular}{l|ccc|ccc|ccc|ccc|ccc|ccc}\\n\",\n \"\\\\toprule\\n\",\n \" & \\\\multicolumn{3}{c|}{Avg} & \\\\multicolumn{3}{c|}{Longbench-Chat} & \\\\multicolumn{3}{c|}{MultifieldQA} & \\\\multicolumn{3}{c|}{HotpotQA} & \\\\multicolumn{3}{c|}{Dureader} & \\\\multicolumn{3}{c}{GovReport} \\\\\\\\\\n\",\n \"\\\\multirow{-2}{*}{Model} & C & $\\\\text{C}_\\\\text{LQA}$ & CR & C & $\\\\text{C}_\\\\text{LQA}$ & CR & C & $\\\\text{C}_\\\\text{LQA}$ & CR & C & $\\\\text{C}_\\\\text{LQA}$ & CR & C & $\\\\text{C}_\\\\text{LQA}$ & CR & C & $\\\\text{C}_\\\\text{LQA}$ & CR \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multicolumn{19}{l}{\\\\textit{\\\\textbf{Proprietary models}}} \\\\\\\\\\n\",\n \"GPT-4o & \\\\cellcolor[HTML]{FFE2E0}69.4 & \\\\cellcolor[HTML]{FFE2E0}78.2 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 88\\\\%} & \\\\cellcolor[HTML]{FFE2E0}61.6 & \\\\cellcolor[HTML]{FFE2E0}77.4 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 80\\\\%} & \\\\cellcolor[HTML]{FFE2E0}84.0 & \\\\cellcolor[HTML]{FFE2E0}88.3 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 95\\\\%} & \\\\cellcolor[HTML]{FFE2E0}74.5 & \\\\cellcolor[HTML]{FFE2E0}80.8 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 92\\\\%} & \\\\cellcolor[HTML]{FFE2E0}81.0 & \\\\cellcolor[HTML]{FFE2E0}83.3 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 97\\\\%} & \\\\cellcolor[HTML]{FFE2E0}46.0 & \\\\cellcolor[HTML]{FFE2E0}61.3 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 75\\\\%} \\\\\\\\\\n\",\n \"Claude-3-sonnet & \\\\cellcolor[HTML]{FFE2E0}77.6 & \\\\cellcolor[HTML]{FFE2E0}78.3 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 99\\\\%} & \\\\cellcolor[HTML]{FFE2E0}73.8 & \\\\cellcolor[HTML]{FFE2E0}77.8 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 95\\\\%} & \\\\cellcolor[HTML]{D9F5D6}88.6 & \\\\cellcolor[HTML]{D9F5D6}88.1 & \\\\cellcolor[HTML]{D9F5D6}{\\\\color[HTML]{2EA121} 101\\\\%} & \\\\cellcolor[HTML]{D9F5D6}81.3 & \\\\cellcolor[HTML]{D9F5D6}75.3 & \\\\cellcolor[HTML]{D9F5D6}{\\\\color[HTML]{2EA121} 108\\\\%} & \\\\cellcolor[HTML]{FFE2E0}75.8 & \\\\cellcolor[HTML]{FFE2E0}80.3 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 94\\\\%} & \\\\cellcolor[HTML]{FFE2E0}68.4 & \\\\cellcolor[HTML]{FFE2E0}70.1 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 98\\\\%} \\\\\\\\\\n\",\n \"GLM-4 & \\\\cellcolor[HTML]{FFE2E0}73.7 & \\\\cellcolor[HTML]{FFE2E0}77.2 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 95\\\\%} & \\\\cellcolor[HTML]{FFE2E0}69.4 & \\\\cellcolor[HTML]{FFE2E0}79.8 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 87\\\\%} & \\\\cellcolor[HTML]{FFE2E0}87.6 & \\\\cellcolor[HTML]{FFE2E0}88.1 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 99\\\\%} & \\\\cellcolor[HTML]{FFE2E0}76.3 & \\\\cellcolor[HTML]{FFE2E0}76.5 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 100\\\\%} & \\\\cellcolor[HTML]{D9F5D6}76.0 & \\\\cellcolor[HTML]{D9F5D6}75.8 & \\\\cellcolor[HTML]{D9F5D6}{\\\\color[HTML]{2EA121} 100\\\\%} & \\\\cellcolor[HTML]{FFE2E0}59.4 & \\\\cellcolor[HTML]{FFE2E0}65.9 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 90\\\\%} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multicolumn{19}{l}{\\\\textit{\\\\textbf{Open-source models}}} \\\\\\\\\\n\",\n \"GLM-4-9B-chat & \\\\cellcolor[HTML]{FFE2E0}62.3 & \\\\cellcolor[HTML]{FFE2E0}70.8 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 88\\\\%} & \\\\cellcolor[HTML]{FFE2E0}60.4 & \\\\cellcolor[HTML]{FFE2E0}67.8 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 89\\\\%} & \\\\cellcolor[HTML]{FFE2E0}74.2 & \\\\cellcolor[HTML]{FFE2E0}84.9 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 87\\\\%} & \\\\cellcolor[HTML]{FFE2E0}68.5 & \\\\cellcolor[HTML]{FFE2E0}71.5 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 96\\\\%} & \\\\cellcolor[HTML]{FFE2E0}49.3 & \\\\cellcolor[HTML]{FFE2E0}68.1 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 72\\\\%} & \\\\cellcolor[HTML]{FFE2E0}59.3 & \\\\cellcolor[HTML]{FFE2E0}61.6 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 96\\\\%} \\\\\\\\\\n\",\n \"Llama-3.1-8B-Instruct & \\\\cellcolor[HTML]{FFE2E0}52.1 & \\\\cellcolor[HTML]{FFE2E0}60.2 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 86\\\\%} & \\\\cellcolor[HTML]{FFE2E0}53.2 & \\\\cellcolor[HTML]{FFE2E0}61.6 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 86\\\\%} & \\\\cellcolor[HTML]{FFE2E0}63.9 & \\\\cellcolor[HTML]{FFE2E0}73.3 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 87\\\\%} & \\\\cellcolor[HTML]{FFE2E0}64.0 & \\\\cellcolor[HTML]{FFE2E0}64.5 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 99\\\\%} & \\\\cellcolor[HTML]{FFE2E0}29.8 & \\\\cellcolor[HTML]{FFE2E0}39.4 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 76\\\\%} & \\\\cellcolor[HTML]{FFE2E0}49.6 & \\\\cellcolor[HTML]{FFE2E0}62.1 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 80\\\\%} \\\\\\\\\\n\",\n \"Llama-3.1-70B-Instruct & \\\\cellcolor[HTML]{FFE2E0}62.0 & \\\\cellcolor[HTML]{FFE2E0}65.5 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 95\\\\%} & \\\\cellcolor[HTML]{FFE2E0}60.8 & \\\\cellcolor[HTML]{FFE2E0}64.6 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 94\\\\%} & \\\\cellcolor[HTML]{D9F5D6}78.4 & \\\\cellcolor[HTML]{D9F5D6}78.3 & \\\\cellcolor[HTML]{D9F5D6}{\\\\color[HTML]{2EA121} 100\\\\%} & \\\\cellcolor[HTML]{FFE2E0}71.3 & \\\\cellcolor[HTML]{FFE2E0}75.3 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 95\\\\%} & \\\\cellcolor[HTML]{D9F5D6}43.3 & \\\\cellcolor[HTML]{D9F5D6}42.5 & \\\\cellcolor[HTML]{D9F5D6}{\\\\color[HTML]{2EA121} 102\\\\%} & \\\\cellcolor[HTML]{FFE2E0}56.3 & \\\\cellcolor[HTML]{FFE2E0}66.9 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 84\\\\%} \\\\\\\\\\n\",\n \"Mistral-Large-Instruct & \\\\cellcolor[HTML]{FFE2E0}73.6 & \\\\cellcolor[HTML]{FFE2E0}76.4 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 96\\\\%} & \\\\cellcolor[HTML]{FFE2E0}63.8 & \\\\cellcolor[HTML]{FFE2E0}67.8 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 94\\\\%} & \\\\cellcolor[HTML]{D9F5D6}88.0 & \\\\cellcolor[HTML]{D9F5D6}85.3 & \\\\cellcolor[HTML]{D9F5D6}{\\\\color[HTML]{2EA121} 103\\\\%} & \\\\cellcolor[HTML]{FFE2E0}77.0 & \\\\cellcolor[HTML]{FFE2E0}77.3 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 100\\\\%} & \\\\cellcolor[HTML]{FFE2E0}79.0 & \\\\cellcolor[HTML]{FFE2E0}83.3 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 95\\\\%} & \\\\cellcolor[HTML]{FFE2E0}60.4 & \\\\cellcolor[HTML]{FFE2E0}68.3 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 88\\\\%} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multicolumn{19}{l}{\\\\textit{\\\\textbf{Our trained models}}} \\\\\\\\\\n\",\n \"LongCite-8B & \\\\cellcolor[HTML]{D9F5D6}71.7 & \\\\cellcolor[HTML]{D9F5D6}67.6 & \\\\cellcolor[HTML]{D9F5D6}{\\\\color[HTML]{2EA121} 107\\\\%} & \\\\cellcolor[HTML]{D9F5D6}69.0 & \\\\cellcolor[HTML]{D9F5D6}68.6 & \\\\cellcolor[HTML]{D9F5D6}{\\\\color[HTML]{2EA121} 101\\\\%} & \\\\cellcolor[HTML]{D9F5D6}87.0 & \\\\cellcolor[HTML]{D9F5D6}83.6 & \\\\cellcolor[HTML]{D9F5D6}{\\\\color[HTML]{2EA121} 104\\\\%} & \\\\cellcolor[HTML]{D9F5D6}70.8 & \\\\cellcolor[HTML]{D9F5D6}69.0 & \\\\cellcolor[HTML]{D9F5D6}{\\\\color[HTML]{2EA121} 103\\\\%} & \\\\cellcolor[HTML]{D9F5D6}68.5 & \\\\cellcolor[HTML]{D9F5D6}62.3 & \\\\cellcolor[HTML]{D9F5D6}{\\\\color[HTML]{2EA121} 110\\\\%} & \\\\cellcolor[HTML]{D9F5D6}63.0 & \\\\cellcolor[HTML]{D9F5D6}54.4 & \\\\cellcolor[HTML]{D9F5D6}{\\\\color[HTML]{2EA121} 116\\\\%} \\\\\\\\\\n\",\n \"LongCite-9B & \\\\cellcolor[HTML]{D9F5D6}70.4 & \\\\cellcolor[HTML]{D9F5D6}65.6 & \\\\cellcolor[HTML]{D9F5D6}{\\\\color[HTML]{2EA121} 109\\\\%} & \\\\cellcolor[HTML]{D9F5D6}67.6 & \\\\cellcolor[HTML]{D9F5D6}64.6 & \\\\cellcolor[HTML]{D9F5D6}{\\\\color[HTML]{2EA121} 105\\\\%} & \\\\cellcolor[HTML]{D9F5D6}84.1 & \\\\cellcolor[HTML]{D9F5D6}83.3 & \\\\cellcolor[HTML]{D9F5D6}{\\\\color[HTML]{2EA121} 101\\\\%} & \\\\cellcolor[HTML]{D9F5D6}71.8 & \\\\cellcolor[HTML]{D9F5D6}67.5 & \\\\cellcolor[HTML]{D9F5D6}{\\\\color[HTML]{2EA121} 106\\\\%} & \\\\cellcolor[HTML]{D9F5D6}69.0 & \\\\cellcolor[HTML]{D9F5D6}66.3 & \\\\cellcolor[HTML]{D9F5D6}{\\\\color[HTML]{2EA121} 104\\\\%} & \\\\cellcolor[HTML]{D9F5D6}59.6 & \\\\cellcolor[HTML]{D9F5D6}46.4 & \\\\cellcolor[HTML]{D9F5D6}{\\\\color[HTML]{2EA121} 128\\\\%} \\\\\\\\ \\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Correctness in LQAC setting (C) using LAC-S strategy, correctness in vanilla long-context QA setting ($\\\\text{C}_\\\\text{LQA}$), and correctness ratio (CR) of different models on LongBench-Cite. We mark the cases where adding citations improves/hurts correctness (i.e., $\\\\text{CR} > 1$ / $\\\\text{CR} < 1$) in green/red.}\\n\",\n \"\\\\label{tab:main_correct}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\linewidth}{!}{\\n\",\n \"\\\\setlength{\\\\tabcolsep}{4pt}\\n\",\n \"\\\\begin{tabular}{l|ccc|ccc|ccc|ccc|ccc|ccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{Method} & \\\\multicolumn{3}{c|}{Avg} & \\\\multicolumn{3}{c|}{Longbench-Chat} & \\\\multicolumn{3}{c|}{MultifieldQA} & \\\\multicolumn{3}{c|}{HotpotQA} & \\\\multicolumn{3}{c|}{Dureader} & \\\\multicolumn{3}{c}{GovReport} \\\\\\\\\\n\",\n \" & F1 & CR & CL & F1 & C & CR & F1 & C & CR & F1 & C & CR & F1 & C & CR & F1 & C & CR \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multicolumn{19}{l}{\\\\textit{\\\\textbf{one-pass methods}}} \\\\\\\\\\n\",\n \"LAC-C & 51.6 & 95\\\\% & 128.0 & 33.9 & 67.8 & 85\\\\% & 55.7 & 87.3 & 99\\\\% & 41.2 & 75.3 & 98\\\\% & 59.5 & 76.3 & 101\\\\% & 67.7 & 59.1 & 90\\\\% \\\\\\\\\\n\",\n \"LAC-S & 65.4 & 95\\\\% & 169.0 & 47.1 & 69.4 & 87\\\\% & 73.6 & 87.6 & 99\\\\% & 44.4 & 76.3 & 100\\\\% & 75.0 & 76.0 & 100\\\\% & 87.1 & 59.4 & 90\\\\% \\\\\\\\\\n\",\n \"RAC-C & 72.5 & 87\\\\% & 128.0 & 69.7 & 59.0 & 74\\\\% & 79.1 & 80.7 & 92\\\\% & 57.7 & 69.8 & 91\\\\% & 75.7 & 77.3 & 102\\\\% & 80.3 & 49.9 & 76\\\\% \\\\\\\\\\n\",\n \"RAC-S & 79.1 & 79\\\\% & 48.0 & 76.3 & 66.4 & 83\\\\% & 86.3 & 85.7 & 97\\\\% & 58.1 & 53.3 & 70\\\\% & 83.7 & 76.5 & 101\\\\% & 91.1 & 29.0 & 44\\\\% \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multicolumn{19}{l}{\\\\textit{\\\\textbf{post-hoc methods}}} \\\\\\\\\\n\",\n \"post-LC-C & 47.3 & 100\\\\% & 128.0 & 27.8 & 79.8 & 100\\\\% & 48.2 & 88.1 & 100\\\\% & 34.5 & 76.5 & 100\\\\% & 52.1 & 75.8 & 100\\\\% & 74.1 & 65.9 & 100\\\\% \\\\\\\\\\n\",\n \"post-LC-S & 57.3 & 100\\\\% & 147.0 & 34.3 & 79.8 & 100\\\\% & 65.3 & 88.1 & 100\\\\% & 40.0 & 76.5 & 100\\\\% & 64.2 & 75.8 & 100\\\\% & 82.8 & 65.9 & 100\\\\% \\\\\\\\\\n\",\n \"post-RC-C & 63.8 & 100\\\\% & 128.0 & 61.0 & 79.8 & 100\\\\% & 65.3 & 88.1 & 100\\\\% & 49.3 & 76.5 & 100\\\\% & 67.8 & 75.8 & 100\\\\% & 75.8 & 65.9 & 100\\\\% \\\\\\\\\\n\",\n \"post-RC-S & 62.8 & 100\\\\% & 48.0 & 63.4 & 79.8 & 100\\\\% & 64.8 & 88.1 & 100\\\\% & 48.6 & 76.5 & 100\\\\% & 69.7 & 75.8 & 100\\\\% & 67.5 & 65.9 & 100\\\\% \\\\\\\\\\n\",\n \"CoF & 65.8 & 100\\\\% & 89.0 & 66.1 & 79.8 & 100\\\\% & 65.6 & 88.1 & 100\\\\% & 50.6 & 76.5 & 100\\\\% & 67.4 & 75.8 & 100\\\\% & 79.1 & 65.9 & 100\\\\% \\\\\\\\ \\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Citation F1 (F1), correctness (C), correctness ratio (CR), and citation length (CL) of different LQAC strategies on LongBench-Cite using GLM-4. We merge MultifieldQA-en/zh for brevity.}\\n\",\n \"\\\\label{tab:pipeline-compare}\\n\",\n \"\\\\end{table}\\n\"\n ]\n]","isConversational":false},"tables":{"kind":"list like","value":[["\\begin{figure}\n"," \\centering\n"," \\includegraphics[width=\\linewidth]{figs/task.pdf}\n"," \\caption{Comparison between chunk-level and sentence-level citations.}\n"," \\label{fig:task}\n","\\end{figure}\n"],["\\begin{table}[t]\n","\\centering\n","\\resizebox{0.8\\linewidth}{!}{\n"," \\begin{tabular}{lccccc}\n"," \\toprule\n"," Dataset & Task & Source & Avg Len & Language & \\#data \\\\\\midrule\n"," MultiFieldQA-en & Single-Doc QA & Multi-field & 4,559 & English & 150 \\\\\n"," MultiFieldQA-zh & Single-Doc QA & Multi-field & 6,701 & Chinese & 200 \\\\\n"," HotpotQA & Multi-Doc QA & Wikipedia & 9,151 & English & 200 \\\\\n"," Dureader & Multi-Doc QA & Baidu Search & 15,768 & Chinese & 200 \\\\\n"," GovReport & Summarization & Government Report & 8,734 & English & 200 \\\\\n"," LongBench-Chat & Multi-task & Real-world Query & 35,571 & English/Chinese & 50 \\\\\\bottomrule\n"," \\end{tabular}\n","}\n","\\caption{Data Statistics in LongBench-Cite. ‘Source’ means\n","the origin of the context. ‘Avg Len’ denotes the average number of words/characters of contexts in English/Chinese datasets.}\n","\\label{tab:data_statistics}\n","\\end{table}\n"],["\\begin{table}[t]\n","\\centering\n","\\resizebox{\\linewidth}{!}{\n"," \\setlength{\\tabcolsep}{5pt}\n"," \\begin{tabular}{l|cc|ccc|ccc|ccc|ccc|ccc}\n","\\toprule\n","\\multirow{2}{*}{Model} & \\multicolumn{2}{c|}{Avg} & \\multicolumn{3}{c|}{Longbench-Chat} & \\multicolumn{3}{c|}{MultifieldQA} & \\multicolumn{3}{c|}{HotpotQA} & \\multicolumn{3}{c|}{Dureader} & \\multicolumn{3}{c}{GovReport} \\\\\n"," & F1 & CL & R & P & F1 & R & P & F1 & R & P & F1 & R & P & F1 & R & P & F1 \\\\ \\midrule\n","\\multicolumn{18}{l}{\\textit{\\textbf{Proprietary models}}} \\\\\n","GPT-4o & 65.6 & 220 & 46.7 & 53.5 & 46.7 & \\textbf{79.0} & 87.9 & {\\ul 80.6} & 55.7 & 62.3 & 53.4 & 65.6 & 74.2 & 67.4 & 73.4 & 90.4 & 79.8 \\\\\n","Claude-3-sonnet & 67.2 & 132 & 52.0 & 67.8 & 55.1 & 64.7 & 85.8 & 71.3 & 46.4 & 65.8 & 49.9 & 67.7 & \\textbf{89.2} & \\textbf{75.5} & {\\ul 77.4} & \\textbf{93.9} & {\\ul 84.1} \\\\\n","GLM-4 & 65.4 & 169 & 47.6 & 53.9 & 47.1 & 72.3 & 80.1 & 73.6 & 47.0 & 50.1 & 44.4 & \\textbf{73.4} & 82.3 & {\\ul 75.0} & \\textbf{82.8} & {\\ul 93.4} & \\textbf{87.1} \\\\ \\midrule\n","\\multicolumn{18}{l}{\\textit{\\textbf{Open-source models}}} \\\\\n","GLM-4-9B-chat & 27.2 & 96 & 25.9 & 20.5 & 16.7 & 51.1 & 60.6 & 52.0 & 22.9 & 28.8 & 20.1 & 45.4 & 48.3 & 40.9 & 5.7 & 8.2 & 6.3 \\\\\n","Llama-3.1-8B-Instruct & 19.7 & 100 & 14.1 & 19.5 & 12.4 & 29.8 & 44.3 & 31.6 & 20.2 & 30.9 & 20.9 & 22.0 & 25.1 & 17.0 & 16.2 & 25.3 & 16.8 \\\\\n","Llama-3.1-70B-Instruct & 40.4 & 174 & 25.8 & 32.0 & 23.2 & 53.2 & 65.2 & 53.9 & 29.6 & 37.3 & 28.6 & 38.2 & 46.0 & 35.4 & 53.4 & 77.5 & 60.7 \\\\\n","Mistral-Large-Instruct & 51.5 & 132 & 19.8 & 23.9 & 19.0 & 71.8 & 80.7 & 73.8 & 34.5 & 40.9 & 32.1 & 58.3 & 67.0 & 60.1 & 67.9 & 79.6 & 72.5 \\\\ \\midrule\n","\\multicolumn{18}{l}{\\textit{\\textbf{Our trained models}}} \\\\\n","LongCite-8B & \\textbf{72.0} & \\textbf{85} & \\textbf{62.0} & \\textbf{79.7} & \\textbf{67.4} & {\\ul 74.7} & \\textbf{93.0} & \\textbf{80.8} & {\\ul 59.2} & {\\ul 72.1} & {\\ul 60.3} & {\\ul 68.3} & 85.6 & 73.1 & 74.0 & 86.6 & 78.5 \\\\\n","LongCite-9B & {\\ul 69.2} & {\\ul 91} & {\\ul 57.6} & {\\ul 78.1} & {\\ul 63.6} & 67.3 & {\\ul 91.0} & 74.8 & \\textbf{61.8} & \\textbf{78.8} & \\textbf{64.8} & 67.6 & \\textbf{89.2} & 74.4 & 63.4 & 76.5 & 68.2 \\\\ \\bottomrule\n","\\end{tabular}\n","}\n","\\caption{Citation recall (R), citation precision (P), citation F1 (F1), and citation length (CL) of different models on LongBench-Cite using LAC-S strategy. The best and second results are bolded and underlined, respectively. \n","}\n","\\label{tab:main_cite}\n","\\end{table}\n"],["\\begin{table}[t]\n","\\centering\n","\\resizebox{\\linewidth}{!}{\n","\\setlength{\\tabcolsep}{3.5pt}\n","\\begin{tabular}{l|ccc|ccc|ccc|ccc|ccc|ccc}\n","\\toprule\n"," & \\multicolumn{3}{c|}{Avg} & \\multicolumn{3}{c|}{Longbench-Chat} & \\multicolumn{3}{c|}{MultifieldQA} & \\multicolumn{3}{c|}{HotpotQA} & \\multicolumn{3}{c|}{Dureader} & \\multicolumn{3}{c}{GovReport} \\\\\n","\\multirow{-2}{*}{Model} & C & $\\text{C}_\\text{LQA}$ & CR & C & $\\text{C}_\\text{LQA}$ & CR & C & $\\text{C}_\\text{LQA}$ & CR & C & $\\text{C}_\\text{LQA}$ & CR & C & $\\text{C}_\\text{LQA}$ & CR & C & $\\text{C}_\\text{LQA}$ & CR \\\\ \\midrule\n","\\multicolumn{19}{l}{\\textit{\\textbf{Proprietary models}}} \\\\\n","GPT-4o & \\cellcolor[HTML]{FFE2E0}69.4 & \\cellcolor[HTML]{FFE2E0}78.2 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 88\\%} & \\cellcolor[HTML]{FFE2E0}61.6 & \\cellcolor[HTML]{FFE2E0}77.4 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 80\\%} & \\cellcolor[HTML]{FFE2E0}84.0 & \\cellcolor[HTML]{FFE2E0}88.3 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 95\\%} & \\cellcolor[HTML]{FFE2E0}74.5 & \\cellcolor[HTML]{FFE2E0}80.8 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 92\\%} & \\cellcolor[HTML]{FFE2E0}81.0 & \\cellcolor[HTML]{FFE2E0}83.3 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 97\\%} & \\cellcolor[HTML]{FFE2E0}46.0 & \\cellcolor[HTML]{FFE2E0}61.3 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 75\\%} \\\\\n","Claude-3-sonnet & \\cellcolor[HTML]{FFE2E0}77.6 & \\cellcolor[HTML]{FFE2E0}78.3 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 99\\%} & \\cellcolor[HTML]{FFE2E0}73.8 & \\cellcolor[HTML]{FFE2E0}77.8 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 95\\%} & \\cellcolor[HTML]{D9F5D6}88.6 & \\cellcolor[HTML]{D9F5D6}88.1 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 101\\%} & \\cellcolor[HTML]{D9F5D6}81.3 & \\cellcolor[HTML]{D9F5D6}75.3 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 108\\%} & \\cellcolor[HTML]{FFE2E0}75.8 & \\cellcolor[HTML]{FFE2E0}80.3 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 94\\%} & \\cellcolor[HTML]{FFE2E0}68.4 & \\cellcolor[HTML]{FFE2E0}70.1 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 98\\%} \\\\\n","GLM-4 & \\cellcolor[HTML]{FFE2E0}73.7 & \\cellcolor[HTML]{FFE2E0}77.2 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 95\\%} & \\cellcolor[HTML]{FFE2E0}69.4 & \\cellcolor[HTML]{FFE2E0}79.8 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 87\\%} & \\cellcolor[HTML]{FFE2E0}87.6 & \\cellcolor[HTML]{FFE2E0}88.1 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 99\\%} & \\cellcolor[HTML]{FFE2E0}76.3 & \\cellcolor[HTML]{FFE2E0}76.5 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 100\\%} & \\cellcolor[HTML]{D9F5D6}76.0 & \\cellcolor[HTML]{D9F5D6}75.8 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 100\\%} & \\cellcolor[HTML]{FFE2E0}59.4 & \\cellcolor[HTML]{FFE2E0}65.9 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 90\\%} \\\\ \\midrule\n","\\multicolumn{19}{l}{\\textit{\\textbf{Open-source models}}} \\\\\n","GLM-4-9B-chat & \\cellcolor[HTML]{FFE2E0}62.3 & \\cellcolor[HTML]{FFE2E0}70.8 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 88\\%} & \\cellcolor[HTML]{FFE2E0}60.4 & \\cellcolor[HTML]{FFE2E0}67.8 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 89\\%} & \\cellcolor[HTML]{FFE2E0}74.2 & \\cellcolor[HTML]{FFE2E0}84.9 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 87\\%} & \\cellcolor[HTML]{FFE2E0}68.5 & \\cellcolor[HTML]{FFE2E0}71.5 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 96\\%} & \\cellcolor[HTML]{FFE2E0}49.3 & \\cellcolor[HTML]{FFE2E0}68.1 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 72\\%} & \\cellcolor[HTML]{FFE2E0}59.3 & \\cellcolor[HTML]{FFE2E0}61.6 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 96\\%} \\\\\n","Llama-3.1-8B-Instruct & \\cellcolor[HTML]{FFE2E0}52.1 & \\cellcolor[HTML]{FFE2E0}60.2 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 86\\%} & \\cellcolor[HTML]{FFE2E0}53.2 & \\cellcolor[HTML]{FFE2E0}61.6 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 86\\%} & \\cellcolor[HTML]{FFE2E0}63.9 & \\cellcolor[HTML]{FFE2E0}73.3 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 87\\%} & \\cellcolor[HTML]{FFE2E0}64.0 & \\cellcolor[HTML]{FFE2E0}64.5 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 99\\%} & \\cellcolor[HTML]{FFE2E0}29.8 & \\cellcolor[HTML]{FFE2E0}39.4 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 76\\%} & \\cellcolor[HTML]{FFE2E0}49.6 & \\cellcolor[HTML]{FFE2E0}62.1 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 80\\%} \\\\\n","Llama-3.1-70B-Instruct & \\cellcolor[HTML]{FFE2E0}62.0 & \\cellcolor[HTML]{FFE2E0}65.5 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 95\\%} & \\cellcolor[HTML]{FFE2E0}60.8 & \\cellcolor[HTML]{FFE2E0}64.6 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 94\\%} & \\cellcolor[HTML]{D9F5D6}78.4 & \\cellcolor[HTML]{D9F5D6}78.3 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 100\\%} & \\cellcolor[HTML]{FFE2E0}71.3 & \\cellcolor[HTML]{FFE2E0}75.3 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 95\\%} & \\cellcolor[HTML]{D9F5D6}43.3 & \\cellcolor[HTML]{D9F5D6}42.5 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 102\\%} & \\cellcolor[HTML]{FFE2E0}56.3 & \\cellcolor[HTML]{FFE2E0}66.9 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 84\\%} \\\\\n","Mistral-Large-Instruct & \\cellcolor[HTML]{FFE2E0}73.6 & \\cellcolor[HTML]{FFE2E0}76.4 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 96\\%} & \\cellcolor[HTML]{FFE2E0}63.8 & \\cellcolor[HTML]{FFE2E0}67.8 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 94\\%} & \\cellcolor[HTML]{D9F5D6}88.0 & \\cellcolor[HTML]{D9F5D6}85.3 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 103\\%} & \\cellcolor[HTML]{FFE2E0}77.0 & \\cellcolor[HTML]{FFE2E0}77.3 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 100\\%} & \\cellcolor[HTML]{FFE2E0}79.0 & \\cellcolor[HTML]{FFE2E0}83.3 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 95\\%} & \\cellcolor[HTML]{FFE2E0}60.4 & \\cellcolor[HTML]{FFE2E0}68.3 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 88\\%} \\\\ \\midrule\n","\\multicolumn{19}{l}{\\textit{\\textbf{Our trained models}}} \\\\\n","LongCite-8B & \\cellcolor[HTML]{D9F5D6}71.7 & \\cellcolor[HTML]{D9F5D6}67.6 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 107\\%} & \\cellcolor[HTML]{D9F5D6}69.0 & \\cellcolor[HTML]{D9F5D6}68.6 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 101\\%} & \\cellcolor[HTML]{D9F5D6}87.0 & \\cellcolor[HTML]{D9F5D6}83.6 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 104\\%} & \\cellcolor[HTML]{D9F5D6}70.8 & \\cellcolor[HTML]{D9F5D6}69.0 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 103\\%} & \\cellcolor[HTML]{D9F5D6}68.5 & \\cellcolor[HTML]{D9F5D6}62.3 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 110\\%} & \\cellcolor[HTML]{D9F5D6}63.0 & \\cellcolor[HTML]{D9F5D6}54.4 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 116\\%} \\\\\n","LongCite-9B & \\cellcolor[HTML]{D9F5D6}70.4 & \\cellcolor[HTML]{D9F5D6}65.6 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 109\\%} & \\cellcolor[HTML]{D9F5D6}67.6 & \\cellcolor[HTML]{D9F5D6}64.6 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 105\\%} & \\cellcolor[HTML]{D9F5D6}84.1 & \\cellcolor[HTML]{D9F5D6}83.3 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 101\\%} & \\cellcolor[HTML]{D9F5D6}71.8 & \\cellcolor[HTML]{D9F5D6}67.5 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 106\\%} & \\cellcolor[HTML]{D9F5D6}69.0 & \\cellcolor[HTML]{D9F5D6}66.3 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 104\\%} & \\cellcolor[HTML]{D9F5D6}59.6 & \\cellcolor[HTML]{D9F5D6}46.4 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 128\\%} \\\\ \\bottomrule\n","\\end{tabular}\n","}\n","\\caption{Correctness in LQAC setting (C) using LAC-S strategy, correctness in vanilla long-context QA setting ($\\text{C}_\\text{LQA}$), and correctness ratio (CR) of different models on LongBench-Cite. We mark the cases where adding citations improves/hurts correctness (i.e., $\\text{CR} > 1$ / $\\text{CR} < 1$) in green/red.}\n","\\label{tab:main_correct}\n","\\end{table}\n"],["\\begin{figure}\n"," \\centering\n"," \\includegraphics[width=\\linewidth]{figs/cof.pdf}\n"," \\caption{Overview of our CoF pipeline. The pipeline consists of four steps: (1) Generating long-context QA instance via Self-Instruct; (2) Using the answer to retrieve $k$ context chunks and generating chunk-level citations; (3) Extracting sentence-level citations for each statement from the cited chunks. (4) Filter out LQAC instances with few citations. }\n"," \\label{fig:cof}\n","\\end{figure}\n"],["\\begin{table}[t]\n","\\centering\n","\\resizebox{\\linewidth}{!}{\n","\\setlength{\\tabcolsep}{4pt}\n","\\begin{tabular}{l|ccc|ccc|ccc|ccc|ccc|ccc}\n","\\toprule\n","\\multirow{2}{*}{Method} & \\multicolumn{3}{c|}{Avg} & \\multicolumn{3}{c|}{Longbench-Chat} & \\multicolumn{3}{c|}{MultifieldQA} & \\multicolumn{3}{c|}{HotpotQA} & \\multicolumn{3}{c|}{Dureader} & \\multicolumn{3}{c}{GovReport} \\\\\n"," & F1 & CR & CL & F1 & C & CR & F1 & C & CR & F1 & C & CR & F1 & C & CR & F1 & C & CR \\\\ \\midrule\n","\\multicolumn{19}{l}{\\textit{\\textbf{one-pass methods}}} \\\\\n","LAC-C & 51.6 & 95\\% & 128.0 & 33.9 & 67.8 & 85\\% & 55.7 & 87.3 & 99\\% & 41.2 & 75.3 & 98\\% & 59.5 & 76.3 & 101\\% & 67.7 & 59.1 & 90\\% \\\\\n","LAC-S & 65.4 & 95\\% & 169.0 & 47.1 & 69.4 & 87\\% & 73.6 & 87.6 & 99\\% & 44.4 & 76.3 & 100\\% & 75.0 & 76.0 & 100\\% & 87.1 & 59.4 & 90\\% \\\\\n","RAC-C & 72.5 & 87\\% & 128.0 & 69.7 & 59.0 & 74\\% & 79.1 & 80.7 & 92\\% & 57.7 & 69.8 & 91\\% & 75.7 & 77.3 & 102\\% & 80.3 & 49.9 & 76\\% \\\\\n","RAC-S & 79.1 & 79\\% & 48.0 & 76.3 & 66.4 & 83\\% & 86.3 & 85.7 & 97\\% & 58.1 & 53.3 & 70\\% & 83.7 & 76.5 & 101\\% & 91.1 & 29.0 & 44\\% \\\\ \\midrule\n","\\multicolumn{19}{l}{\\textit{\\textbf{post-hoc methods}}} \\\\\n","post-LC-C & 47.3 & 100\\% & 128.0 & 27.8 & 79.8 & 100\\% & 48.2 & 88.1 & 100\\% & 34.5 & 76.5 & 100\\% & 52.1 & 75.8 & 100\\% & 74.1 & 65.9 & 100\\% \\\\\n","post-LC-S & 57.3 & 100\\% & 147.0 & 34.3 & 79.8 & 100\\% & 65.3 & 88.1 & 100\\% & 40.0 & 76.5 & 100\\% & 64.2 & 75.8 & 100\\% & 82.8 & 65.9 & 100\\% \\\\\n","post-RC-C & 63.8 & 100\\% & 128.0 & 61.0 & 79.8 & 100\\% & 65.3 & 88.1 & 100\\% & 49.3 & 76.5 & 100\\% & 67.8 & 75.8 & 100\\% & 75.8 & 65.9 & 100\\% \\\\\n","post-RC-S & 62.8 & 100\\% & 48.0 & 63.4 & 79.8 & 100\\% & 64.8 & 88.1 & 100\\% & 48.6 & 76.5 & 100\\% & 69.7 & 75.8 & 100\\% & 67.5 & 65.9 & 100\\% \\\\\n","CoF & 65.8 & 100\\% & 89.0 & 66.1 & 79.8 & 100\\% & 65.6 & 88.1 & 100\\% & 50.6 & 76.5 & 100\\% & 67.4 & 75.8 & 100\\% & 79.1 & 65.9 & 100\\% \\\\ \\bottomrule\n","\\end{tabular}\n","}\n","\\caption{Citation F1 (F1), correctness (C), correctness ratio (CR), and citation length (CL) of different LQAC strategies on LongBench-Cite using GLM-4. We merge MultifieldQA-en/zh for brevity.}\n","\\label{tab:pipeline-compare}\n","\\end{table}\n"],["\\begin{figure}[t]\n","\\centering\n","\\begin{minipage}{0.52\\textwidth}\n"," \\input{tables/ablation}\n","\\end{minipage}%\n","\\hfill\n","\\begin{minipage}{0.46\\textwidth}\n"," \\centering\n"," \\includegraphics[width=\\linewidth]{figs/correct_f1_corr.pdf}\n"," \\vspace{-7mm}\n"," \\caption{Citation F1 mean and std. w.r.t correctness of LongCite-9B's responses. }\n"," \\label{fig:correct_f1_corr}\n","\\end{minipage}\n","\\end{figure}\n"],["\\begin{table}[t]\n","\\centering\n","\\begin{minipage}{0.52\\textwidth}\n"," \\centering\n"," \\resizebox{\\linewidth}{!}{\n"," \\setlength{\\tabcolsep}{3pt}\n"," \\begin{tabular}{l|ccc|ccc|ccc}\n"," \\toprule\n"," \\multirow{2}{*}{Model} & \\multicolumn{3}{c|}{Human scores} & \\multicolumn{3}{c|}{GPT-4o scores} & \\multicolumn{3}{c}{ALCE scores} \\\\\n"," & R & P & F1 & R & P & F1 & R & P & F1 \\\\ \\midrule\n"," GLM-4 & 61.2 & 67.5 & 60.2 & 47.6 & 53.9 & 47.1 & 46.1 & 29.1 & 30.8 \\\\\n"," LongCite-8B & \\textbf{79.6} & \\textbf{88.9} & \\textbf{82.6} & \\textbf{62.0} & \\textbf{79.7} & \\textbf{67.4} & {\\ul 59.6} & {\\ul 39.5} & {\\ul 42.0} \\\\\n"," LongCite-9B & {\\ul 72.8} & {\\ul 84.2} & {\\ul 75.8} & {\\ul 57.6} & {\\ul 78.1} & {\\ul 63.6} & \\textbf{64.2} & \\textbf{45.1} & \\textbf{47.1} \\\\ \\bottomrule\n"," \\end{tabular}\n"," }\n"," \\caption{Citation quality evaluated by human, GPT-4o and ALCE on LongBench-Chat.}\n"," \\label{tab:human_eval}\n","\\end{minipage}%\n","\\hfill\n","\\begin{minipage}{0.45\\textwidth}\n"," \\centering\n"," \\resizebox{\\linewidth}{!}{\n"," \\setlength{\\tabcolsep}{4pt}\n"," \\begin{tabular}{l|cc|cc} \n"," \\toprule\n"," \\multirow{2}{*}{Method} & \\multicolumn{2}{c|}{Citation recall} & \\multicolumn{2}{c}{Citation precision} \\\\ \n"," & Kappa ($\\kappa$) & Acc & Kappa ($\\kappa$) & Acc \\\\ \\midrule\n"," GPT-4o & \\textbf{0.544/0.593*} & \\textbf{75.0/80.2*} & \\textbf{0.655} & \\textbf{88.8} \\\\\n"," ALCE & 0.247* & 64.7* & 0.146 & 47.4 \\\\ \\bottomrule\n"," \\end{tabular}\n"," }\n"," \\caption{Agreement between GPT-4o/ALCE and human. * means treating ``partially support'' as ``not support''.}\n"," \\label{tab:corr}\n","\\end{minipage}\n","\\end{table}\n"],["\\begin{table}[t]\n"," \\centering\n"," \\resizebox{\\linewidth}{!}{\n"," \\begin{tabular}{llc}\n"," \\toprule\n"," \\textbf{Model name} & \\textbf{Model version} & \\textbf{Context window} \\\\\n"," \\midrule\n"," Claude-3-Sonnet~\\citep{claude-3} & claude-3-sonnet-20240229 & 200,000 tokens \\\\ \n"," GPT-4o~\\citep{GPT-4o} & gpt-4o-2024-05-13 & 128,000 tokens \\\\\n"," GLM-4~\\citep{glm4} & GLM-4-0520 & 128,000 tokens \\\\\n"," GLM-4-9B-chat~\\citep{glm4} & - & 128,000 tokens \\\\\n"," Llama-3.1-8B-Instruct~\\citep{llama-3-1} & - & 128,000 tokens \\\\\n"," Llama-3.1-70B-Instruct~\\citep{llama-3-1} & - & 128,000 tokens \\\\\n"," Mistral-Large-Instruct~\\citep{mistral} & Mistral-Large-Instruct-2407 & 128,000 tokens \\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," }\n","\\caption{Model cards.}\n","\\label{tab:model_card}\n","\\end{table}\n"],["\\begin{table}[t]\n","\\centering\n","\\resizebox{\\linewidth}{!}{\n"," \\begin{tabular}{p{\\linewidth}}\n","\\toprule\n","\\textbf{Query:} Are both Duke Energy and Affiliated Managers Group based in Massachusetts? \\\\ \\midrule\n","\\begin{tabular}[c]{@{}p{\\linewidth}@{}}\\textbf{LongSFT-9B:} \\\\ Based on the information provided, {\\color[HTML]{D83931} Duke Energy has offices in several locations including West Palm Beach, Florida; Prides Crossing, Massachusetts; Stamford, Connecticut; and London, United Kingdom.} Affiliated Managers Group has principal offices in West Palm Beach, Florida; Prides Crossing, Massachusetts; Stamford, Connecticut; and London, United Kingdom. So they both have an office in Massachusetts.\\end{tabular} \\\\\\midrule\n","\\begin{tabular}[c]{@{}p{\\linewidth}@{}}\\textbf{LongCite-9B:}\\\\ \\textless{}statement\\textgreater{}Based on the information provided, {\\color[HTML]{2EA121}Duke Energy is headquartered in Charlotte, North Carolina,} while Affiliated Managers Group has principal offices in West Palm Beach, Florida; Prides Crossing, Massachusetts; Stamford, Connecticut; and London, United Kingdom.\\textless{}cite\\textgreater{}{[}25-25{]}{[}219-219{]}\\textless{}/cite\\textgreater{}\\textless{}/statement\\textgreater \\ \\textless{}statement\\textgreater{}So only Affiliated Managers Group is based in Massachusetts.\\textless{}cite\\textgreater{}\\textless{}/cite\\textgreater{}\\textless{}/statement\\textgreater{} \\\\\n","\\textbf{Citations:} \\\\\n","{[}25-25{]}: Duke Energy Corporation is an American electric power and natural gas holding company headquartered in Charlotte, North Carolina. \\\\\n","{[}219-219{]}: (NYSE: AMG) is a strategic partner to independent investment management firms globally, with equity stakes in a number of partner-owned traditional investment managers, hedge funds, and specialized private equity firms which it calls “Affiliates.” The company has principal offices in West Palm Beach, Florida; Prides Crossing, Massachusetts; Stamford, Connecticut; and London, United Kingdom.\n","\\end{tabular} \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\caption{Case study. LongSFT-9B hallucinates the office location of Duke Energy, directly copying that of Affiliated Managers Group, while LongCite-9B gets the correct answer due to SFT with citations. We mark the wrong and correct statements in red and green, respectively.}\n","\\label{tab:case_1}\n","\\end{table}\\begin{table}[t]\n"],["\\end{table}\\begin{table}[t]\n","\\centering\n","\\resizebox{\\linewidth}{!}{\n"," \\begin{tabular}{p{\\linewidth}}\n","\\toprule\n","Query: Please write a one-page summary of the above government report. \\\\\\midrule\n","\\begin{tabular}[c]{@{}p{\\linewidth}@{}}\\textbf{LongSFT-9B:} \\\\ The US Agency for International Development (USAID) has obligated over \\$5 billion in bilateral assistance to the Palestinians in the West Bank and Gaza since 1993, primarily using funds appropriated through the Economic Support Fund (ESF). USAID provides project assistance and debt relief payments to Palestinian Authority (PA) creditors, and implements most ESF programs, including those related to private sector development, health, water and road infrastructure, local governance, civil society, rule of law, education, and youth development. USAID assistance to the West Bank and Gaza is conducted under antiterrorism policies and procedures outlined in an administrative policy document known as Mission Order 21. As of March 31, 2018, USAID had obligated about \\$544.1 million (over 99 percent) and expended about \\$350.6 million (over 64 percent) of approximately \\$544.5 million in ESF assistance allocated for the West Bank and Gaza in fiscal years 2015 and 2016.\\end{tabular} \\\\\\midrule\n","\\begin{tabular}[c]{@{}p{\\linewidth}@{}}\\textbf{LongCite-9B:}\\\\ \\textless{}statement\\textgreater{}The report discusses USAID's Economic Support Fund (ESF) assistance to the Palestinians in the West Bank and Gaza from FY2015-FY2016. Key points include:\\textless{}cite\\textgreater{}\\textless{}/cite\\textgreater{}\\textless{}/statement\\textgreater\\\\ \\textless{}statement\\textgreater{}- USAID obligated over \\$544 million in ESF assistance for the West Bank and Gaza in FY2015-FY2016, with about \\$350 million expended.\\textless{}cite\\textgreater{}{[}8-8{]}\\textless{}/cite\\textgreater{}\\textless{}/statement\\textgreater\\\\ \\textless{}statement\\textgreater{}- Most assistance went to project assistance (\\$215 million in FY2015, \\$184 million in FY2016) for economic growth, infrastructure, education, health, etc. Some went to debt relief payments to PA creditors (\\$75 million in FY2015, \\$70 million in FY2016).\\textless{}cite\\textgreater{}{[}9-11{]}\\textless{}/cite\\textgreater{}\\textless{}/statement\\textgreater\\\\ \\textcolor[HTML]{2EA121}{\\textless{}statement\\textgreater{}- USAID conducted required vetting of PA creditors to ensure assistance did not support terrorism. Vetting occurred within 12 months before debt relief agreements were signed.\\textless{}cite\\textgreater{}{[}36-36{]}{[}40-40{]}{[}42-42{]}\\textless{}/cite\\textgreater{}\\textless{}/statement\\textgreater}\\\\ \\textcolor[HTML]{2EA121}{\\textless{}statement\\textgreater{}- External assessments identified weaknesses in PA financial systems for medical referrals and petroleum procurement. USAID took additional steps to mitigate risks.\\textless{}cite\\textgreater{}{[}52-52{]}{[}62-62{]}\\textless{}/cite\\textgreater{}\\textless{}/statement\\textgreater}\\\\ \\textcolor[HTML]{2EA121}{\\textless{}statement\\textgreater{}- Financial audits of the debt relief grants did not identify any issues.\\textless{}cite\\textgreater{}{[}66-66{]}{[}70-70{]}\\textless{}/cite\\textgreater{}\\textless{}/statement\\textgreater}\\\\ \\textcolor[HTML]{2EA121}{\\textless{}statement\\textgreater{}- USAID does not plan to resume direct cash payments to the PA due to concerns about misuse. Payments to creditors are considered more transparent.\\textless{}cite\\textgreater{}{[}49-49{]}\\textless{}/cite\\textgreater{}\\textless{}/statement\\textgreater}\\\\ \\textless{}statement\\textgreater{}In summary, the report concludes that USAID generally followed proper procedures for providing ESF assistance to the Palestinians in the West Bank and Gaza, with some concerns about PA financial systems. Debt relief payments to creditors were properly vetted.\\textless{}cite\\textgreater{}\\textless{}/cite\\textgreater{}\\textless{}/statement\\textgreater{}\\end{tabular} \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\caption{Case Study. The citation numbers in the response make LongCite-9B aware of which parts of the document the current response has covered and thus help it utilize context information uniformly to generate a more comprehensive summary, while LongSFT-9B only focuses on the front part of the extensive document and ignores the rest parts.}\n","\\label{tab:case_3}\n","\\end{table}\n"],["\\begin{figure}[t]\n"," \\centering\n"," \\begin{tcolorbox}[size=title,opacityfill=0.1]\n"," \\noindent\n"," [Instructions] You are asked to evaluate the quality of the AI assistant’s answers to user questions as an impartial judge, and your evaluation should take into account factors including correctness (high priority), helpfulness, accuracy, and relevance. The scoring principles are as follows: 1. Read the AI assistant’s answer and compare the assistant’s answer with the reference answer. 2. Identify all errors in the AI Assistant’s answers and consider how much they affect the answer to the question. 3. Evaluate how helpful the AI assistant’s answers are in directly answering the user’s questions and providing the information the user needs. 4. Examine any additional information in the AI assistant’s answer to ensure that it is correct and closely related to the question. If this information is incorrect or not relevant to the question, points should be deducted from the overall score. Please give an overall integer rating from 1 to 10 based on the above principles, strictly in the following format: ``[[rating]]\", e.g. ``[[5]]\".\n"," \n"," [Question] \\{\\emph{Question}\\} \n"," \n"," [Reference answer begins] \\{\\emph{Groundtruth}\\} [Reference answer ends] \n"," \n"," Below are several assistants’ answers and their ratings:\n"," \n"," [Assistant’s answer begins] \\{\\emph{Example Answer 1}\\} [Assistant’s answer ends] \n","\n"," Rating: [[\\{{\\emph{Rating for Example Answer 1}}\\}]]\n","\n"," [Assistant’s answer begins] \\{\\emph{Example Answer 2}\\} [Assistant’s answer ends] \n","\n"," Rating: [[\\{{\\emph{Rating for Example Answer 2}}\\}]]\n","\n"," [Assistant’s answer begins] \\{\\emph{Example Answer 2}\\} [Assistant’s answer ends] \n","\n"," Rating: [[\\{{\\emph{Rating for Example Answer 2}}\\}]]\n","\n"," Please rate the following assistant answers based on the scoring principles and examples above:\n"," \n"," [Assistant’s answer begins] \\{{\\emph{Response}}\\} [Assistant’s answer ends]\n"," \n"," Rating:\n"," \n"," \\end{tcolorbox}\n"," \\caption{prompt for correctness evaluation on LongBench-Chat.}\n"," \\label{prompt:correct_longbench_chat}\n","\\end{figure}\\begin{figure}[t]\n"],["\\end{figure}\\begin{figure}[t]\n"," \\centering\n"," \\begin{tcolorbox}[size=title,opacityfill=0.1]\n"," \\noindent\n"," You are asked to evaluate the quality of the AI assistant’s generated summary as an impartial judge, and your evaluation should take into account factors including correctness (high priority), comprehensiveness (whether the assistant’s summary covers all points), and coherence. Read the AI assistant’s summary and compare against the reference summary, and give an overall integer rating in on a scale of 1 to 5, where 1 is the lowest and 5 is the highest based on the evaluation criteria, strictly in the following format:``[[rating]]\", e.g. ``[[3]]\".\n"," \n"," Question: \n"," \n"," \\{\\emph{Question}\\} \n"," \n"," Reference answer:\n"," \n"," \\{\\emph{Reference answer}\\} \n"," \n"," Assistant’s answer: \n"," \n"," \\{{\\emph{Response}}\\}\n"," \n"," Rating:\n"," \n"," \\end{tcolorbox}\n"," \\caption{Prompt for correctness evaluation on GovReport.}\n"," \\label{prompt:correct_summary}\n","\\end{figure}\\begin{figure}[t]\n"],["\\end{figure}\\begin{figure}[t]\n"," \\centering\n"," \\begin{tcolorbox}[size=title,opacityfill=0.1]\n"," \\noindent\n"," You are an expert in evaluating text quality. You will receive a user's question regarding their uploaded document (due to the length of the document, it is not shown to you), an AI assistant's response based on the document, and a sentence from the response. Your task is to determine whether this sentence is a factual statement made based on the information in the document that requires citation, rather than an introductory sentence, transition sentence, or a summary, reasoning, or inference based on the previous response.\\\\\n"," Ensure that you do not use any other external information during your evaluation. \\\\\n"," Please first provide your judgment (answer with [[Yes]] or [[No]]), then provide your analysis in the format ``Need Citation: [[Yes/No]] Analysis: ...\".\\\\\n","\n"," \\textless question\\textgreater \\\\\n"," \\{\\emph{Question}\\} \\\\\n"," \\textless /question\\textgreater \\\\\n","\n"," \\textless response\\textgreater \\\\\n"," \\{\\emph{Model Response}\\} \\\\\n"," \\textless /response\\textgreater \\\\\n"," \n"," \\textless statement\\textgreater \\\\\n"," \\{\\emph{Statement}\\} \\\\\n"," \\textless /statement\\textgreater \\\\\n"," \n"," \n"," \n"," \n"," \\end{tcolorbox}\n"," \\caption{Prompt for evaluating citation recall when the statement has no citation.}\n"," \\label{prompt:need_citation}\n","\\end{figure}\\begin{figure}[t]\n"],["\\end{figure}\\begin{figure}[t]\n"," \\centering\n","\\begin{tcolorbox}[size=title,opacityfill=0.1]\n","\\noindent\n","Please answer the user's question based on the given document. When a factual statement S in your response uses information from some chunks in the document (i.e., \\textless C\\{s1\\}\\textgreater -\\textless C\\{e1\\}\\textgreater , \\textless C\\{s2\\}\\textgreater -\\textless C\\{e2\\}\\textgreater , ...), please append these chunk numbers to S in the format \"\\textless statement\\textgreater \\{S\\}\\textless cite\\textgreater [\\{s1\\}-\\{e1\\}][\\{s2\\}-\\{e2\\}]...\\textless /cite\\textgreater \\textless /statement\\textgreater \". For other sentences such as introductory sentences, summarization sentences, reasoning, and inference, you still need to append \"\\textless cite\\textgreater \\textless /cite\\textgreater \" to them to indicate they need no citations. You must answer in the same language as the user's question.\\\\\n","\n","Here is an example: \\\\\n","\n","\\{\\emph{An Example}\\} \\\\\n","\n","Now get ready to handle the following test case. \\\\\n","\n","[Document Start]\\\\\n","\\textless C0\\textgreater \\{\\emph{Sentence 0}\\} \\textless C1\\textgreater \\{\\emph{Sentence 1}\\} \\textless C2\\textgreater \\{\\emph{Sentence 2}\\} ... \n","\n","[Document End]\\\\\n","\n","[Question]\\\\\n","\\{\\emph{Question}\\} \\\\\n","\n","[Remind] \\\\\n","Please answer the user's question based on the given document. When a factual statement S in your response uses information from some chunks in the document (i.e., \\textless C\\{s1\\}\\textgreater -\\textless C\\{e1\\}\\textgreater , \\textless C\\{s2\\}\\textgreater -\\textless C\\{e2\\}\\textgreater , ...), please append these chunk numbers to S in the format \"\\textless statement\\textgreater \\{S\\}\\textless cite\\textgreater [\\{s1\\}-\\{e1\\}][\\{s2\\}-\\{e2\\}]...\\textless /cite\\textgreater \\textless /statement\\textgreater \". For other sentences such as introductory sentences, summarization sentences, reasoning, and inference, you still need to append \"\\textless cite\\textgreater \\textless /cite\\textgreater \" to them to indicate they need no citations. You must answer in the same language as the user's question.\\\\\n","\n","[Answer with Citations]\\\\\n"," \n"," \n"," \n","\\end{tcolorbox}\n"," \\caption{One-shot learning prompt for the LAC-S strategy.}\n"," \\label{prompt:lac_s}\n","\\end{figure}\\begin{figure}[t]\n"],["\\end{figure}\\begin{figure}[t]\n"," \\centering\n","\\begin{tcolorbox}[size=title,opacityfill=0.1]\n","\\noindent\n","Your task is to add citations to the existing answer. Specifically, when a factual statement S in the answer uses information from context snippets l1, l2, ..., ln, please add citations by appending these snippet numbers to S in the format ``\\textless statement\\textgreater\\{S\\}\\textless cite\\textgreater[\\{l1\\}][\\{l2\\}]...[\\{ln\\}]\\textless /cite\\textgreater\\textless statement\\textgreater\". For other sentences such as introductory sentences, summarization sentences, reasoning, and inference, you still need to append ``\\textless cite\\textgreater\\textless /cite\\textgreater\" to them to indicate they need no citations. Except for adding citations, do not change the original content and format of the existing answer. \\\\\n","\n","Here is an example: \\\\\n","\n","\\{\\emph{An Example}\\} \\\\\n","\n","Now get ready to add citations for the following test case. \\\\\n","\n","[Contexts Start]\\\\\n","Snippet [1] \\\\\n","\\{\\emph{Chunk 1}\\} \\\\\n","\n","Snippet [2] \\\\\n","\\{\\emph{Chunk 2}\\} \\\\\n","\n","Snippet [3] \\\\\n","\\{\\emph{Chunk 3}\\} \\\\\n","\n","...\n","\n","[Context End]\\\\\n","\n","[Question]\\\\\n","\\{\\emph{Question}\\} \\\\\n","\n","[Existing Answer Start]\\\\\n","\\{\\emph{Answer}\\} \n","\n","[Existing Answer End]\\\\\n","\n","[Answer with Citations]\\\\\n"," \n"," \n"," \n","\\end{tcolorbox}\n"," \\caption{Prompt for chunk-level citation generation in the CoF pipeline.}\n"," \\label{prompt:cof_coarse}\n","\\end{figure}\\begin{figure}[t]\n"]],"string":"[\n [\n \"\\\\begin{figure}\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\linewidth]{figs/task.pdf}\\n\",\n \" \\\\caption{Comparison between chunk-level and sentence-level citations.}\\n\",\n \" \\\\label{fig:task}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table}[t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{0.8\\\\linewidth}{!}{\\n\",\n \" \\\\begin{tabular}{lccccc}\\n\",\n \" \\\\toprule\\n\",\n \" Dataset & Task & Source & Avg Len & Language & \\\\#data \\\\\\\\\\\\midrule\\n\",\n \" MultiFieldQA-en & Single-Doc QA & Multi-field & 4,559 & English & 150 \\\\\\\\\\n\",\n \" MultiFieldQA-zh & Single-Doc QA & Multi-field & 6,701 & Chinese & 200 \\\\\\\\\\n\",\n \" HotpotQA & Multi-Doc QA & Wikipedia & 9,151 & English & 200 \\\\\\\\\\n\",\n \" Dureader & Multi-Doc QA & Baidu Search & 15,768 & Chinese & 200 \\\\\\\\\\n\",\n \" GovReport & Summarization & Government Report & 8,734 & English & 200 \\\\\\\\\\n\",\n \" LongBench-Chat & Multi-task & Real-world Query & 35,571 & English/Chinese & 50 \\\\\\\\\\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Data Statistics in LongBench-Cite. ‘Source’ means\\n\",\n \"the origin of the context. ‘Avg Len’ denotes the average number of words/characters of contexts in English/Chinese datasets.}\\n\",\n \"\\\\label{tab:data_statistics}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\linewidth}{!}{\\n\",\n \" \\\\setlength{\\\\tabcolsep}{5pt}\\n\",\n \" \\\\begin{tabular}{l|cc|ccc|ccc|ccc|ccc|ccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{Model} & \\\\multicolumn{2}{c|}{Avg} & \\\\multicolumn{3}{c|}{Longbench-Chat} & \\\\multicolumn{3}{c|}{MultifieldQA} & \\\\multicolumn{3}{c|}{HotpotQA} & \\\\multicolumn{3}{c|}{Dureader} & \\\\multicolumn{3}{c}{GovReport} \\\\\\\\\\n\",\n \" & F1 & CL & R & P & F1 & R & P & F1 & R & P & F1 & R & P & F1 & R & P & F1 \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multicolumn{18}{l}{\\\\textit{\\\\textbf{Proprietary models}}} \\\\\\\\\\n\",\n \"GPT-4o & 65.6 & 220 & 46.7 & 53.5 & 46.7 & \\\\textbf{79.0} & 87.9 & {\\\\ul 80.6} & 55.7 & 62.3 & 53.4 & 65.6 & 74.2 & 67.4 & 73.4 & 90.4 & 79.8 \\\\\\\\\\n\",\n \"Claude-3-sonnet & 67.2 & 132 & 52.0 & 67.8 & 55.1 & 64.7 & 85.8 & 71.3 & 46.4 & 65.8 & 49.9 & 67.7 & \\\\textbf{89.2} & \\\\textbf{75.5} & {\\\\ul 77.4} & \\\\textbf{93.9} & {\\\\ul 84.1} \\\\\\\\\\n\",\n \"GLM-4 & 65.4 & 169 & 47.6 & 53.9 & 47.1 & 72.3 & 80.1 & 73.6 & 47.0 & 50.1 & 44.4 & \\\\textbf{73.4} & 82.3 & {\\\\ul 75.0} & \\\\textbf{82.8} & {\\\\ul 93.4} & \\\\textbf{87.1} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multicolumn{18}{l}{\\\\textit{\\\\textbf{Open-source models}}} \\\\\\\\\\n\",\n \"GLM-4-9B-chat & 27.2 & 96 & 25.9 & 20.5 & 16.7 & 51.1 & 60.6 & 52.0 & 22.9 & 28.8 & 20.1 & 45.4 & 48.3 & 40.9 & 5.7 & 8.2 & 6.3 \\\\\\\\\\n\",\n \"Llama-3.1-8B-Instruct & 19.7 & 100 & 14.1 & 19.5 & 12.4 & 29.8 & 44.3 & 31.6 & 20.2 & 30.9 & 20.9 & 22.0 & 25.1 & 17.0 & 16.2 & 25.3 & 16.8 \\\\\\\\\\n\",\n \"Llama-3.1-70B-Instruct & 40.4 & 174 & 25.8 & 32.0 & 23.2 & 53.2 & 65.2 & 53.9 & 29.6 & 37.3 & 28.6 & 38.2 & 46.0 & 35.4 & 53.4 & 77.5 & 60.7 \\\\\\\\\\n\",\n \"Mistral-Large-Instruct & 51.5 & 132 & 19.8 & 23.9 & 19.0 & 71.8 & 80.7 & 73.8 & 34.5 & 40.9 & 32.1 & 58.3 & 67.0 & 60.1 & 67.9 & 79.6 & 72.5 \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multicolumn{18}{l}{\\\\textit{\\\\textbf{Our trained models}}} \\\\\\\\\\n\",\n \"LongCite-8B & \\\\textbf{72.0} & \\\\textbf{85} & \\\\textbf{62.0} & \\\\textbf{79.7} & \\\\textbf{67.4} & {\\\\ul 74.7} & \\\\textbf{93.0} & \\\\textbf{80.8} & {\\\\ul 59.2} & {\\\\ul 72.1} & {\\\\ul 60.3} & {\\\\ul 68.3} & 85.6 & 73.1 & 74.0 & 86.6 & 78.5 \\\\\\\\\\n\",\n \"LongCite-9B & {\\\\ul 69.2} & {\\\\ul 91} & {\\\\ul 57.6} & {\\\\ul 78.1} & {\\\\ul 63.6} & 67.3 & {\\\\ul 91.0} & 74.8 & \\\\textbf{61.8} & \\\\textbf{78.8} & \\\\textbf{64.8} & 67.6 & \\\\textbf{89.2} & 74.4 & 63.4 & 76.5 & 68.2 \\\\\\\\ \\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Citation recall (R), citation precision (P), citation F1 (F1), and citation length (CL) of different models on LongBench-Cite using LAC-S strategy. The best and second results are bolded and underlined, respectively. \\n\",\n \"}\\n\",\n \"\\\\label{tab:main_cite}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\linewidth}{!}{\\n\",\n \"\\\\setlength{\\\\tabcolsep}{3.5pt}\\n\",\n \"\\\\begin{tabular}{l|ccc|ccc|ccc|ccc|ccc|ccc}\\n\",\n \"\\\\toprule\\n\",\n \" & \\\\multicolumn{3}{c|}{Avg} & \\\\multicolumn{3}{c|}{Longbench-Chat} & \\\\multicolumn{3}{c|}{MultifieldQA} & \\\\multicolumn{3}{c|}{HotpotQA} & \\\\multicolumn{3}{c|}{Dureader} & \\\\multicolumn{3}{c}{GovReport} \\\\\\\\\\n\",\n \"\\\\multirow{-2}{*}{Model} & C & $\\\\text{C}_\\\\text{LQA}$ & CR & C & $\\\\text{C}_\\\\text{LQA}$ & CR & C & $\\\\text{C}_\\\\text{LQA}$ & CR & C & $\\\\text{C}_\\\\text{LQA}$ & CR & C & $\\\\text{C}_\\\\text{LQA}$ & CR & C & $\\\\text{C}_\\\\text{LQA}$ & CR \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multicolumn{19}{l}{\\\\textit{\\\\textbf{Proprietary models}}} \\\\\\\\\\n\",\n \"GPT-4o & \\\\cellcolor[HTML]{FFE2E0}69.4 & \\\\cellcolor[HTML]{FFE2E0}78.2 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 88\\\\%} & \\\\cellcolor[HTML]{FFE2E0}61.6 & \\\\cellcolor[HTML]{FFE2E0}77.4 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 80\\\\%} & \\\\cellcolor[HTML]{FFE2E0}84.0 & \\\\cellcolor[HTML]{FFE2E0}88.3 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 95\\\\%} & \\\\cellcolor[HTML]{FFE2E0}74.5 & \\\\cellcolor[HTML]{FFE2E0}80.8 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 92\\\\%} & \\\\cellcolor[HTML]{FFE2E0}81.0 & \\\\cellcolor[HTML]{FFE2E0}83.3 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 97\\\\%} & \\\\cellcolor[HTML]{FFE2E0}46.0 & \\\\cellcolor[HTML]{FFE2E0}61.3 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 75\\\\%} \\\\\\\\\\n\",\n \"Claude-3-sonnet & \\\\cellcolor[HTML]{FFE2E0}77.6 & \\\\cellcolor[HTML]{FFE2E0}78.3 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 99\\\\%} & \\\\cellcolor[HTML]{FFE2E0}73.8 & \\\\cellcolor[HTML]{FFE2E0}77.8 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 95\\\\%} & \\\\cellcolor[HTML]{D9F5D6}88.6 & \\\\cellcolor[HTML]{D9F5D6}88.1 & \\\\cellcolor[HTML]{D9F5D6}{\\\\color[HTML]{2EA121} 101\\\\%} & \\\\cellcolor[HTML]{D9F5D6}81.3 & \\\\cellcolor[HTML]{D9F5D6}75.3 & \\\\cellcolor[HTML]{D9F5D6}{\\\\color[HTML]{2EA121} 108\\\\%} & \\\\cellcolor[HTML]{FFE2E0}75.8 & \\\\cellcolor[HTML]{FFE2E0}80.3 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 94\\\\%} & \\\\cellcolor[HTML]{FFE2E0}68.4 & \\\\cellcolor[HTML]{FFE2E0}70.1 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 98\\\\%} \\\\\\\\\\n\",\n \"GLM-4 & \\\\cellcolor[HTML]{FFE2E0}73.7 & \\\\cellcolor[HTML]{FFE2E0}77.2 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 95\\\\%} & \\\\cellcolor[HTML]{FFE2E0}69.4 & \\\\cellcolor[HTML]{FFE2E0}79.8 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 87\\\\%} & \\\\cellcolor[HTML]{FFE2E0}87.6 & \\\\cellcolor[HTML]{FFE2E0}88.1 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 99\\\\%} & \\\\cellcolor[HTML]{FFE2E0}76.3 & \\\\cellcolor[HTML]{FFE2E0}76.5 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 100\\\\%} & \\\\cellcolor[HTML]{D9F5D6}76.0 & \\\\cellcolor[HTML]{D9F5D6}75.8 & \\\\cellcolor[HTML]{D9F5D6}{\\\\color[HTML]{2EA121} 100\\\\%} & \\\\cellcolor[HTML]{FFE2E0}59.4 & \\\\cellcolor[HTML]{FFE2E0}65.9 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 90\\\\%} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multicolumn{19}{l}{\\\\textit{\\\\textbf{Open-source models}}} \\\\\\\\\\n\",\n \"GLM-4-9B-chat & \\\\cellcolor[HTML]{FFE2E0}62.3 & \\\\cellcolor[HTML]{FFE2E0}70.8 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 88\\\\%} & \\\\cellcolor[HTML]{FFE2E0}60.4 & \\\\cellcolor[HTML]{FFE2E0}67.8 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 89\\\\%} & \\\\cellcolor[HTML]{FFE2E0}74.2 & \\\\cellcolor[HTML]{FFE2E0}84.9 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 87\\\\%} & \\\\cellcolor[HTML]{FFE2E0}68.5 & \\\\cellcolor[HTML]{FFE2E0}71.5 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 96\\\\%} & \\\\cellcolor[HTML]{FFE2E0}49.3 & \\\\cellcolor[HTML]{FFE2E0}68.1 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 72\\\\%} & \\\\cellcolor[HTML]{FFE2E0}59.3 & \\\\cellcolor[HTML]{FFE2E0}61.6 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 96\\\\%} \\\\\\\\\\n\",\n \"Llama-3.1-8B-Instruct & \\\\cellcolor[HTML]{FFE2E0}52.1 & \\\\cellcolor[HTML]{FFE2E0}60.2 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 86\\\\%} & \\\\cellcolor[HTML]{FFE2E0}53.2 & \\\\cellcolor[HTML]{FFE2E0}61.6 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 86\\\\%} & \\\\cellcolor[HTML]{FFE2E0}63.9 & \\\\cellcolor[HTML]{FFE2E0}73.3 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 87\\\\%} & \\\\cellcolor[HTML]{FFE2E0}64.0 & \\\\cellcolor[HTML]{FFE2E0}64.5 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 99\\\\%} & \\\\cellcolor[HTML]{FFE2E0}29.8 & \\\\cellcolor[HTML]{FFE2E0}39.4 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 76\\\\%} & \\\\cellcolor[HTML]{FFE2E0}49.6 & \\\\cellcolor[HTML]{FFE2E0}62.1 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 80\\\\%} \\\\\\\\\\n\",\n \"Llama-3.1-70B-Instruct & \\\\cellcolor[HTML]{FFE2E0}62.0 & \\\\cellcolor[HTML]{FFE2E0}65.5 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 95\\\\%} & \\\\cellcolor[HTML]{FFE2E0}60.8 & \\\\cellcolor[HTML]{FFE2E0}64.6 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 94\\\\%} & \\\\cellcolor[HTML]{D9F5D6}78.4 & \\\\cellcolor[HTML]{D9F5D6}78.3 & \\\\cellcolor[HTML]{D9F5D6}{\\\\color[HTML]{2EA121} 100\\\\%} & \\\\cellcolor[HTML]{FFE2E0}71.3 & \\\\cellcolor[HTML]{FFE2E0}75.3 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 95\\\\%} & \\\\cellcolor[HTML]{D9F5D6}43.3 & \\\\cellcolor[HTML]{D9F5D6}42.5 & \\\\cellcolor[HTML]{D9F5D6}{\\\\color[HTML]{2EA121} 102\\\\%} & \\\\cellcolor[HTML]{FFE2E0}56.3 & \\\\cellcolor[HTML]{FFE2E0}66.9 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 84\\\\%} \\\\\\\\\\n\",\n \"Mistral-Large-Instruct & \\\\cellcolor[HTML]{FFE2E0}73.6 & \\\\cellcolor[HTML]{FFE2E0}76.4 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 96\\\\%} & \\\\cellcolor[HTML]{FFE2E0}63.8 & \\\\cellcolor[HTML]{FFE2E0}67.8 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 94\\\\%} & \\\\cellcolor[HTML]{D9F5D6}88.0 & \\\\cellcolor[HTML]{D9F5D6}85.3 & \\\\cellcolor[HTML]{D9F5D6}{\\\\color[HTML]{2EA121} 103\\\\%} & \\\\cellcolor[HTML]{FFE2E0}77.0 & \\\\cellcolor[HTML]{FFE2E0}77.3 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 100\\\\%} & \\\\cellcolor[HTML]{FFE2E0}79.0 & \\\\cellcolor[HTML]{FFE2E0}83.3 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 95\\\\%} & \\\\cellcolor[HTML]{FFE2E0}60.4 & \\\\cellcolor[HTML]{FFE2E0}68.3 & \\\\cellcolor[HTML]{FFE2E0}{\\\\color[HTML]{D83931} 88\\\\%} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multicolumn{19}{l}{\\\\textit{\\\\textbf{Our trained models}}} \\\\\\\\\\n\",\n \"LongCite-8B & \\\\cellcolor[HTML]{D9F5D6}71.7 & \\\\cellcolor[HTML]{D9F5D6}67.6 & \\\\cellcolor[HTML]{D9F5D6}{\\\\color[HTML]{2EA121} 107\\\\%} & \\\\cellcolor[HTML]{D9F5D6}69.0 & \\\\cellcolor[HTML]{D9F5D6}68.6 & \\\\cellcolor[HTML]{D9F5D6}{\\\\color[HTML]{2EA121} 101\\\\%} & \\\\cellcolor[HTML]{D9F5D6}87.0 & \\\\cellcolor[HTML]{D9F5D6}83.6 & \\\\cellcolor[HTML]{D9F5D6}{\\\\color[HTML]{2EA121} 104\\\\%} & \\\\cellcolor[HTML]{D9F5D6}70.8 & \\\\cellcolor[HTML]{D9F5D6}69.0 & \\\\cellcolor[HTML]{D9F5D6}{\\\\color[HTML]{2EA121} 103\\\\%} & \\\\cellcolor[HTML]{D9F5D6}68.5 & \\\\cellcolor[HTML]{D9F5D6}62.3 & \\\\cellcolor[HTML]{D9F5D6}{\\\\color[HTML]{2EA121} 110\\\\%} & \\\\cellcolor[HTML]{D9F5D6}63.0 & \\\\cellcolor[HTML]{D9F5D6}54.4 & \\\\cellcolor[HTML]{D9F5D6}{\\\\color[HTML]{2EA121} 116\\\\%} \\\\\\\\\\n\",\n \"LongCite-9B & \\\\cellcolor[HTML]{D9F5D6}70.4 & \\\\cellcolor[HTML]{D9F5D6}65.6 & \\\\cellcolor[HTML]{D9F5D6}{\\\\color[HTML]{2EA121} 109\\\\%} & \\\\cellcolor[HTML]{D9F5D6}67.6 & \\\\cellcolor[HTML]{D9F5D6}64.6 & \\\\cellcolor[HTML]{D9F5D6}{\\\\color[HTML]{2EA121} 105\\\\%} & \\\\cellcolor[HTML]{D9F5D6}84.1 & \\\\cellcolor[HTML]{D9F5D6}83.3 & \\\\cellcolor[HTML]{D9F5D6}{\\\\color[HTML]{2EA121} 101\\\\%} & \\\\cellcolor[HTML]{D9F5D6}71.8 & \\\\cellcolor[HTML]{D9F5D6}67.5 & \\\\cellcolor[HTML]{D9F5D6}{\\\\color[HTML]{2EA121} 106\\\\%} & \\\\cellcolor[HTML]{D9F5D6}69.0 & \\\\cellcolor[HTML]{D9F5D6}66.3 & \\\\cellcolor[HTML]{D9F5D6}{\\\\color[HTML]{2EA121} 104\\\\%} & \\\\cellcolor[HTML]{D9F5D6}59.6 & \\\\cellcolor[HTML]{D9F5D6}46.4 & \\\\cellcolor[HTML]{D9F5D6}{\\\\color[HTML]{2EA121} 128\\\\%} \\\\\\\\ \\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Correctness in LQAC setting (C) using LAC-S strategy, correctness in vanilla long-context QA setting ($\\\\text{C}_\\\\text{LQA}$), and correctness ratio (CR) of different models on LongBench-Cite. We mark the cases where adding citations improves/hurts correctness (i.e., $\\\\text{CR} > 1$ / $\\\\text{CR} < 1$) in green/red.}\\n\",\n \"\\\\label{tab:main_correct}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{figure}\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\linewidth]{figs/cof.pdf}\\n\",\n \" \\\\caption{Overview of our CoF pipeline. The pipeline consists of four steps: (1) Generating long-context QA instance via Self-Instruct; (2) Using the answer to retrieve $k$ context chunks and generating chunk-level citations; (3) Extracting sentence-level citations for each statement from the cited chunks. (4) Filter out LQAC instances with few citations. }\\n\",\n \" \\\\label{fig:cof}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table}[t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\linewidth}{!}{\\n\",\n \"\\\\setlength{\\\\tabcolsep}{4pt}\\n\",\n \"\\\\begin{tabular}{l|ccc|ccc|ccc|ccc|ccc|ccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{Method} & \\\\multicolumn{3}{c|}{Avg} & \\\\multicolumn{3}{c|}{Longbench-Chat} & \\\\multicolumn{3}{c|}{MultifieldQA} & \\\\multicolumn{3}{c|}{HotpotQA} & \\\\multicolumn{3}{c|}{Dureader} & \\\\multicolumn{3}{c}{GovReport} \\\\\\\\\\n\",\n \" & F1 & CR & CL & F1 & C & CR & F1 & C & CR & F1 & C & CR & F1 & C & CR & F1 & C & CR \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multicolumn{19}{l}{\\\\textit{\\\\textbf{one-pass methods}}} \\\\\\\\\\n\",\n \"LAC-C & 51.6 & 95\\\\% & 128.0 & 33.9 & 67.8 & 85\\\\% & 55.7 & 87.3 & 99\\\\% & 41.2 & 75.3 & 98\\\\% & 59.5 & 76.3 & 101\\\\% & 67.7 & 59.1 & 90\\\\% \\\\\\\\\\n\",\n \"LAC-S & 65.4 & 95\\\\% & 169.0 & 47.1 & 69.4 & 87\\\\% & 73.6 & 87.6 & 99\\\\% & 44.4 & 76.3 & 100\\\\% & 75.0 & 76.0 & 100\\\\% & 87.1 & 59.4 & 90\\\\% \\\\\\\\\\n\",\n \"RAC-C & 72.5 & 87\\\\% & 128.0 & 69.7 & 59.0 & 74\\\\% & 79.1 & 80.7 & 92\\\\% & 57.7 & 69.8 & 91\\\\% & 75.7 & 77.3 & 102\\\\% & 80.3 & 49.9 & 76\\\\% \\\\\\\\\\n\",\n \"RAC-S & 79.1 & 79\\\\% & 48.0 & 76.3 & 66.4 & 83\\\\% & 86.3 & 85.7 & 97\\\\% & 58.1 & 53.3 & 70\\\\% & 83.7 & 76.5 & 101\\\\% & 91.1 & 29.0 & 44\\\\% \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multicolumn{19}{l}{\\\\textit{\\\\textbf{post-hoc methods}}} \\\\\\\\\\n\",\n \"post-LC-C & 47.3 & 100\\\\% & 128.0 & 27.8 & 79.8 & 100\\\\% & 48.2 & 88.1 & 100\\\\% & 34.5 & 76.5 & 100\\\\% & 52.1 & 75.8 & 100\\\\% & 74.1 & 65.9 & 100\\\\% \\\\\\\\\\n\",\n \"post-LC-S & 57.3 & 100\\\\% & 147.0 & 34.3 & 79.8 & 100\\\\% & 65.3 & 88.1 & 100\\\\% & 40.0 & 76.5 & 100\\\\% & 64.2 & 75.8 & 100\\\\% & 82.8 & 65.9 & 100\\\\% \\\\\\\\\\n\",\n \"post-RC-C & 63.8 & 100\\\\% & 128.0 & 61.0 & 79.8 & 100\\\\% & 65.3 & 88.1 & 100\\\\% & 49.3 & 76.5 & 100\\\\% & 67.8 & 75.8 & 100\\\\% & 75.8 & 65.9 & 100\\\\% \\\\\\\\\\n\",\n \"post-RC-S & 62.8 & 100\\\\% & 48.0 & 63.4 & 79.8 & 100\\\\% & 64.8 & 88.1 & 100\\\\% & 48.6 & 76.5 & 100\\\\% & 69.7 & 75.8 & 100\\\\% & 67.5 & 65.9 & 100\\\\% \\\\\\\\\\n\",\n \"CoF & 65.8 & 100\\\\% & 89.0 & 66.1 & 79.8 & 100\\\\% & 65.6 & 88.1 & 100\\\\% & 50.6 & 76.5 & 100\\\\% & 67.4 & 75.8 & 100\\\\% & 79.1 & 65.9 & 100\\\\% \\\\\\\\ \\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Citation F1 (F1), correctness (C), correctness ratio (CR), and citation length (CL) of different LQAC strategies on LongBench-Cite using GLM-4. We merge MultifieldQA-en/zh for brevity.}\\n\",\n \"\\\\label{tab:pipeline-compare}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{figure}[t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\begin{minipage}{0.52\\\\textwidth}\\n\",\n \" \\\\input{tables/ablation}\\n\",\n \"\\\\end{minipage}%\\n\",\n \"\\\\hfill\\n\",\n \"\\\\begin{minipage}{0.46\\\\textwidth}\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\linewidth]{figs/correct_f1_corr.pdf}\\n\",\n \" \\\\vspace{-7mm}\\n\",\n \" \\\\caption{Citation F1 mean and std. w.r.t correctness of LongCite-9B's responses. }\\n\",\n \" \\\\label{fig:correct_f1_corr}\\n\",\n \"\\\\end{minipage}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table}[t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\begin{minipage}{0.52\\\\textwidth}\\n\",\n \" \\\\centering\\n\",\n \" \\\\resizebox{\\\\linewidth}{!}{\\n\",\n \" \\\\setlength{\\\\tabcolsep}{3pt}\\n\",\n \" \\\\begin{tabular}{l|ccc|ccc|ccc}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\multirow{2}{*}{Model} & \\\\multicolumn{3}{c|}{Human scores} & \\\\multicolumn{3}{c|}{GPT-4o scores} & \\\\multicolumn{3}{c}{ALCE scores} \\\\\\\\\\n\",\n \" & R & P & F1 & R & P & F1 & R & P & F1 \\\\\\\\ \\\\midrule\\n\",\n \" GLM-4 & 61.2 & 67.5 & 60.2 & 47.6 & 53.9 & 47.1 & 46.1 & 29.1 & 30.8 \\\\\\\\\\n\",\n \" LongCite-8B & \\\\textbf{79.6} & \\\\textbf{88.9} & \\\\textbf{82.6} & \\\\textbf{62.0} & \\\\textbf{79.7} & \\\\textbf{67.4} & {\\\\ul 59.6} & {\\\\ul 39.5} & {\\\\ul 42.0} \\\\\\\\\\n\",\n \" LongCite-9B & {\\\\ul 72.8} & {\\\\ul 84.2} & {\\\\ul 75.8} & {\\\\ul 57.6} & {\\\\ul 78.1} & {\\\\ul 63.6} & \\\\textbf{64.2} & \\\\textbf{45.1} & \\\\textbf{47.1} \\\\\\\\ \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \" \\\\caption{Citation quality evaluated by human, GPT-4o and ALCE on LongBench-Chat.}\\n\",\n \" \\\\label{tab:human_eval}\\n\",\n \"\\\\end{minipage}%\\n\",\n \"\\\\hfill\\n\",\n \"\\\\begin{minipage}{0.45\\\\textwidth}\\n\",\n \" \\\\centering\\n\",\n \" \\\\resizebox{\\\\linewidth}{!}{\\n\",\n \" \\\\setlength{\\\\tabcolsep}{4pt}\\n\",\n \" \\\\begin{tabular}{l|cc|cc} \\n\",\n \" \\\\toprule\\n\",\n \" \\\\multirow{2}{*}{Method} & \\\\multicolumn{2}{c|}{Citation recall} & \\\\multicolumn{2}{c}{Citation precision} \\\\\\\\ \\n\",\n \" & Kappa ($\\\\kappa$) & Acc & Kappa ($\\\\kappa$) & Acc \\\\\\\\ \\\\midrule\\n\",\n \" GPT-4o & \\\\textbf{0.544/0.593*} & \\\\textbf{75.0/80.2*} & \\\\textbf{0.655} & \\\\textbf{88.8} \\\\\\\\\\n\",\n \" ALCE & 0.247* & 64.7* & 0.146 & 47.4 \\\\\\\\ \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \" \\\\caption{Agreement between GPT-4o/ALCE and human. * means treating ``partially support'' as ``not support''.}\\n\",\n \" \\\\label{tab:corr}\\n\",\n \"\\\\end{minipage}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\resizebox{\\\\linewidth}{!}{\\n\",\n \" \\\\begin{tabular}{llc}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Model name} & \\\\textbf{Model version} & \\\\textbf{Context window} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Claude-3-Sonnet~\\\\citep{claude-3} & claude-3-sonnet-20240229 & 200,000 tokens \\\\\\\\ \\n\",\n \" GPT-4o~\\\\citep{GPT-4o} & gpt-4o-2024-05-13 & 128,000 tokens \\\\\\\\\\n\",\n \" GLM-4~\\\\citep{glm4} & GLM-4-0520 & 128,000 tokens \\\\\\\\\\n\",\n \" GLM-4-9B-chat~\\\\citep{glm4} & - & 128,000 tokens \\\\\\\\\\n\",\n \" Llama-3.1-8B-Instruct~\\\\citep{llama-3-1} & - & 128,000 tokens \\\\\\\\\\n\",\n \" Llama-3.1-70B-Instruct~\\\\citep{llama-3-1} & - & 128,000 tokens \\\\\\\\\\n\",\n \" Mistral-Large-Instruct~\\\\citep{mistral} & Mistral-Large-Instruct-2407 & 128,000 tokens \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \"\\\\caption{Model cards.}\\n\",\n \"\\\\label{tab:model_card}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\linewidth}{!}{\\n\",\n \" \\\\begin{tabular}{p{\\\\linewidth}}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Query:} Are both Duke Energy and Affiliated Managers Group based in Massachusetts? \\\\\\\\ \\\\midrule\\n\",\n \"\\\\begin{tabular}[c]{@{}p{\\\\linewidth}@{}}\\\\textbf{LongSFT-9B:} \\\\\\\\ Based on the information provided, {\\\\color[HTML]{D83931} Duke Energy has offices in several locations including West Palm Beach, Florida; Prides Crossing, Massachusetts; Stamford, Connecticut; and London, United Kingdom.} Affiliated Managers Group has principal offices in West Palm Beach, Florida; Prides Crossing, Massachusetts; Stamford, Connecticut; and London, United Kingdom. So they both have an office in Massachusetts.\\\\end{tabular} \\\\\\\\\\\\midrule\\n\",\n \"\\\\begin{tabular}[c]{@{}p{\\\\linewidth}@{}}\\\\textbf{LongCite-9B:}\\\\\\\\ \\\\textless{}statement\\\\textgreater{}Based on the information provided, {\\\\color[HTML]{2EA121}Duke Energy is headquartered in Charlotte, North Carolina,} while Affiliated Managers Group has principal offices in West Palm Beach, Florida; Prides Crossing, Massachusetts; Stamford, Connecticut; and London, United Kingdom.\\\\textless{}cite\\\\textgreater{}{[}25-25{]}{[}219-219{]}\\\\textless{}/cite\\\\textgreater{}\\\\textless{}/statement\\\\textgreater \\\\ \\\\textless{}statement\\\\textgreater{}So only Affiliated Managers Group is based in Massachusetts.\\\\textless{}cite\\\\textgreater{}\\\\textless{}/cite\\\\textgreater{}\\\\textless{}/statement\\\\textgreater{} \\\\\\\\\\n\",\n \"\\\\textbf{Citations:} \\\\\\\\\\n\",\n \"{[}25-25{]}: Duke Energy Corporation is an American electric power and natural gas holding company headquartered in Charlotte, North Carolina. \\\\\\\\\\n\",\n \"{[}219-219{]}: (NYSE: AMG) is a strategic partner to independent investment management firms globally, with equity stakes in a number of partner-owned traditional investment managers, hedge funds, and specialized private equity firms which it calls “Affiliates.” The company has principal offices in West Palm Beach, Florida; Prides Crossing, Massachusetts; Stamford, Connecticut; and London, United Kingdom.\\n\",\n \"\\\\end{tabular} \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Case study. LongSFT-9B hallucinates the office location of Duke Energy, directly copying that of Affiliated Managers Group, while LongCite-9B gets the correct answer due to SFT with citations. We mark the wrong and correct statements in red and green, respectively.}\\n\",\n \"\\\\label{tab:case_1}\\n\",\n \"\\\\end{table}\\\\begin{table}[t]\\n\"\n ],\n [\n \"\\\\end{table}\\\\begin{table}[t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\linewidth}{!}{\\n\",\n \" \\\\begin{tabular}{p{\\\\linewidth}}\\n\",\n \"\\\\toprule\\n\",\n \"Query: Please write a one-page summary of the above government report. \\\\\\\\\\\\midrule\\n\",\n \"\\\\begin{tabular}[c]{@{}p{\\\\linewidth}@{}}\\\\textbf{LongSFT-9B:} \\\\\\\\ The US Agency for International Development (USAID) has obligated over \\\\$5 billion in bilateral assistance to the Palestinians in the West Bank and Gaza since 1993, primarily using funds appropriated through the Economic Support Fund (ESF). USAID provides project assistance and debt relief payments to Palestinian Authority (PA) creditors, and implements most ESF programs, including those related to private sector development, health, water and road infrastructure, local governance, civil society, rule of law, education, and youth development. USAID assistance to the West Bank and Gaza is conducted under antiterrorism policies and procedures outlined in an administrative policy document known as Mission Order 21. As of March 31, 2018, USAID had obligated about \\\\$544.1 million (over 99 percent) and expended about \\\\$350.6 million (over 64 percent) of approximately \\\\$544.5 million in ESF assistance allocated for the West Bank and Gaza in fiscal years 2015 and 2016.\\\\end{tabular} \\\\\\\\\\\\midrule\\n\",\n \"\\\\begin{tabular}[c]{@{}p{\\\\linewidth}@{}}\\\\textbf{LongCite-9B:}\\\\\\\\ \\\\textless{}statement\\\\textgreater{}The report discusses USAID's Economic Support Fund (ESF) assistance to the Palestinians in the West Bank and Gaza from FY2015-FY2016. Key points include:\\\\textless{}cite\\\\textgreater{}\\\\textless{}/cite\\\\textgreater{}\\\\textless{}/statement\\\\textgreater\\\\\\\\ \\\\textless{}statement\\\\textgreater{}- USAID obligated over \\\\$544 million in ESF assistance for the West Bank and Gaza in FY2015-FY2016, with about \\\\$350 million expended.\\\\textless{}cite\\\\textgreater{}{[}8-8{]}\\\\textless{}/cite\\\\textgreater{}\\\\textless{}/statement\\\\textgreater\\\\\\\\ \\\\textless{}statement\\\\textgreater{}- Most assistance went to project assistance (\\\\$215 million in FY2015, \\\\$184 million in FY2016) for economic growth, infrastructure, education, health, etc. Some went to debt relief payments to PA creditors (\\\\$75 million in FY2015, \\\\$70 million in FY2016).\\\\textless{}cite\\\\textgreater{}{[}9-11{]}\\\\textless{}/cite\\\\textgreater{}\\\\textless{}/statement\\\\textgreater\\\\\\\\ \\\\textcolor[HTML]{2EA121}{\\\\textless{}statement\\\\textgreater{}- USAID conducted required vetting of PA creditors to ensure assistance did not support terrorism. Vetting occurred within 12 months before debt relief agreements were signed.\\\\textless{}cite\\\\textgreater{}{[}36-36{]}{[}40-40{]}{[}42-42{]}\\\\textless{}/cite\\\\textgreater{}\\\\textless{}/statement\\\\textgreater}\\\\\\\\ \\\\textcolor[HTML]{2EA121}{\\\\textless{}statement\\\\textgreater{}- External assessments identified weaknesses in PA financial systems for medical referrals and petroleum procurement. USAID took additional steps to mitigate risks.\\\\textless{}cite\\\\textgreater{}{[}52-52{]}{[}62-62{]}\\\\textless{}/cite\\\\textgreater{}\\\\textless{}/statement\\\\textgreater}\\\\\\\\ \\\\textcolor[HTML]{2EA121}{\\\\textless{}statement\\\\textgreater{}- Financial audits of the debt relief grants did not identify any issues.\\\\textless{}cite\\\\textgreater{}{[}66-66{]}{[}70-70{]}\\\\textless{}/cite\\\\textgreater{}\\\\textless{}/statement\\\\textgreater}\\\\\\\\ \\\\textcolor[HTML]{2EA121}{\\\\textless{}statement\\\\textgreater{}- USAID does not plan to resume direct cash payments to the PA due to concerns about misuse. Payments to creditors are considered more transparent.\\\\textless{}cite\\\\textgreater{}{[}49-49{]}\\\\textless{}/cite\\\\textgreater{}\\\\textless{}/statement\\\\textgreater}\\\\\\\\ \\\\textless{}statement\\\\textgreater{}In summary, the report concludes that USAID generally followed proper procedures for providing ESF assistance to the Palestinians in the West Bank and Gaza, with some concerns about PA financial systems. Debt relief payments to creditors were properly vetted.\\\\textless{}cite\\\\textgreater{}\\\\textless{}/cite\\\\textgreater{}\\\\textless{}/statement\\\\textgreater{}\\\\end{tabular} \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Case Study. The citation numbers in the response make LongCite-9B aware of which parts of the document the current response has covered and thus help it utilize context information uniformly to generate a more comprehensive summary, while LongSFT-9B only focuses on the front part of the extensive document and ignores the rest parts.}\\n\",\n \"\\\\label{tab:case_3}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{figure}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\begin{tcolorbox}[size=title,opacityfill=0.1]\\n\",\n \" \\\\noindent\\n\",\n \" [Instructions] You are asked to evaluate the quality of the AI assistant’s answers to user questions as an impartial judge, and your evaluation should take into account factors including correctness (high priority), helpfulness, accuracy, and relevance. The scoring principles are as follows: 1. Read the AI assistant’s answer and compare the assistant’s answer with the reference answer. 2. Identify all errors in the AI Assistant’s answers and consider how much they affect the answer to the question. 3. Evaluate how helpful the AI assistant’s answers are in directly answering the user’s questions and providing the information the user needs. 4. Examine any additional information in the AI assistant’s answer to ensure that it is correct and closely related to the question. If this information is incorrect or not relevant to the question, points should be deducted from the overall score. Please give an overall integer rating from 1 to 10 based on the above principles, strictly in the following format: ``[[rating]]\\\", e.g. ``[[5]]\\\".\\n\",\n \" \\n\",\n \" [Question] \\\\{\\\\emph{Question}\\\\} \\n\",\n \" \\n\",\n \" [Reference answer begins] \\\\{\\\\emph{Groundtruth}\\\\} [Reference answer ends] \\n\",\n \" \\n\",\n \" Below are several assistants’ answers and their ratings:\\n\",\n \" \\n\",\n \" [Assistant’s answer begins] \\\\{\\\\emph{Example Answer 1}\\\\} [Assistant’s answer ends] \\n\",\n \"\\n\",\n \" Rating: [[\\\\{{\\\\emph{Rating for Example Answer 1}}\\\\}]]\\n\",\n \"\\n\",\n \" [Assistant’s answer begins] \\\\{\\\\emph{Example Answer 2}\\\\} [Assistant’s answer ends] \\n\",\n \"\\n\",\n \" Rating: [[\\\\{{\\\\emph{Rating for Example Answer 2}}\\\\}]]\\n\",\n \"\\n\",\n \" [Assistant’s answer begins] \\\\{\\\\emph{Example Answer 2}\\\\} [Assistant’s answer ends] \\n\",\n \"\\n\",\n \" Rating: [[\\\\{{\\\\emph{Rating for Example Answer 2}}\\\\}]]\\n\",\n \"\\n\",\n \" Please rate the following assistant answers based on the scoring principles and examples above:\\n\",\n \" \\n\",\n \" [Assistant’s answer begins] \\\\{{\\\\emph{Response}}\\\\} [Assistant’s answer ends]\\n\",\n \" \\n\",\n \" Rating:\\n\",\n \" \\n\",\n \" \\\\end{tcolorbox}\\n\",\n \" \\\\caption{prompt for correctness evaluation on LongBench-Chat.}\\n\",\n \" \\\\label{prompt:correct_longbench_chat}\\n\",\n \"\\\\end{figure}\\\\begin{figure}[t]\\n\"\n ],\n [\n \"\\\\end{figure}\\\\begin{figure}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\begin{tcolorbox}[size=title,opacityfill=0.1]\\n\",\n \" \\\\noindent\\n\",\n \" You are asked to evaluate the quality of the AI assistant’s generated summary as an impartial judge, and your evaluation should take into account factors including correctness (high priority), comprehensiveness (whether the assistant’s summary covers all points), and coherence. Read the AI assistant’s summary and compare against the reference summary, and give an overall integer rating in on a scale of 1 to 5, where 1 is the lowest and 5 is the highest based on the evaluation criteria, strictly in the following format:``[[rating]]\\\", e.g. ``[[3]]\\\".\\n\",\n \" \\n\",\n \" Question: \\n\",\n \" \\n\",\n \" \\\\{\\\\emph{Question}\\\\} \\n\",\n \" \\n\",\n \" Reference answer:\\n\",\n \" \\n\",\n \" \\\\{\\\\emph{Reference answer}\\\\} \\n\",\n \" \\n\",\n \" Assistant’s answer: \\n\",\n \" \\n\",\n \" \\\\{{\\\\emph{Response}}\\\\}\\n\",\n \" \\n\",\n \" Rating:\\n\",\n \" \\n\",\n \" \\\\end{tcolorbox}\\n\",\n \" \\\\caption{Prompt for correctness evaluation on GovReport.}\\n\",\n \" \\\\label{prompt:correct_summary}\\n\",\n \"\\\\end{figure}\\\\begin{figure}[t]\\n\"\n ],\n [\n \"\\\\end{figure}\\\\begin{figure}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\begin{tcolorbox}[size=title,opacityfill=0.1]\\n\",\n \" \\\\noindent\\n\",\n \" You are an expert in evaluating text quality. You will receive a user's question regarding their uploaded document (due to the length of the document, it is not shown to you), an AI assistant's response based on the document, and a sentence from the response. Your task is to determine whether this sentence is a factual statement made based on the information in the document that requires citation, rather than an introductory sentence, transition sentence, or a summary, reasoning, or inference based on the previous response.\\\\\\\\\\n\",\n \" Ensure that you do not use any other external information during your evaluation. \\\\\\\\\\n\",\n \" Please first provide your judgment (answer with [[Yes]] or [[No]]), then provide your analysis in the format ``Need Citation: [[Yes/No]] Analysis: ...\\\".\\\\\\\\\\n\",\n \"\\n\",\n \" \\\\textless question\\\\textgreater \\\\\\\\\\n\",\n \" \\\\{\\\\emph{Question}\\\\} \\\\\\\\\\n\",\n \" \\\\textless /question\\\\textgreater \\\\\\\\\\n\",\n \"\\n\",\n \" \\\\textless response\\\\textgreater \\\\\\\\\\n\",\n \" \\\\{\\\\emph{Model Response}\\\\} \\\\\\\\\\n\",\n \" \\\\textless /response\\\\textgreater \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\textless statement\\\\textgreater \\\\\\\\\\n\",\n \" \\\\{\\\\emph{Statement}\\\\} \\\\\\\\\\n\",\n \" \\\\textless /statement\\\\textgreater \\\\\\\\\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\\\end{tcolorbox}\\n\",\n \" \\\\caption{Prompt for evaluating citation recall when the statement has no citation.}\\n\",\n \" \\\\label{prompt:need_citation}\\n\",\n \"\\\\end{figure}\\\\begin{figure}[t]\\n\"\n ],\n [\n \"\\\\end{figure}\\\\begin{figure}[t]\\n\",\n \" \\\\centering\\n\",\n \"\\\\begin{tcolorbox}[size=title,opacityfill=0.1]\\n\",\n \"\\\\noindent\\n\",\n \"Please answer the user's question based on the given document. When a factual statement S in your response uses information from some chunks in the document (i.e., \\\\textless C\\\\{s1\\\\}\\\\textgreater -\\\\textless C\\\\{e1\\\\}\\\\textgreater , \\\\textless C\\\\{s2\\\\}\\\\textgreater -\\\\textless C\\\\{e2\\\\}\\\\textgreater , ...), please append these chunk numbers to S in the format \\\"\\\\textless statement\\\\textgreater \\\\{S\\\\}\\\\textless cite\\\\textgreater [\\\\{s1\\\\}-\\\\{e1\\\\}][\\\\{s2\\\\}-\\\\{e2\\\\}]...\\\\textless /cite\\\\textgreater \\\\textless /statement\\\\textgreater \\\". For other sentences such as introductory sentences, summarization sentences, reasoning, and inference, you still need to append \\\"\\\\textless cite\\\\textgreater \\\\textless /cite\\\\textgreater \\\" to them to indicate they need no citations. You must answer in the same language as the user's question.\\\\\\\\\\n\",\n \"\\n\",\n \"Here is an example: \\\\\\\\\\n\",\n \"\\n\",\n \"\\\\{\\\\emph{An Example}\\\\} \\\\\\\\\\n\",\n \"\\n\",\n \"Now get ready to handle the following test case. \\\\\\\\\\n\",\n \"\\n\",\n \"[Document Start]\\\\\\\\\\n\",\n \"\\\\textless C0\\\\textgreater \\\\{\\\\emph{Sentence 0}\\\\} \\\\textless C1\\\\textgreater \\\\{\\\\emph{Sentence 1}\\\\} \\\\textless C2\\\\textgreater \\\\{\\\\emph{Sentence 2}\\\\} ... \\n\",\n \"\\n\",\n \"[Document End]\\\\\\\\\\n\",\n \"\\n\",\n \"[Question]\\\\\\\\\\n\",\n \"\\\\{\\\\emph{Question}\\\\} \\\\\\\\\\n\",\n \"\\n\",\n \"[Remind] \\\\\\\\\\n\",\n \"Please answer the user's question based on the given document. When a factual statement S in your response uses information from some chunks in the document (i.e., \\\\textless C\\\\{s1\\\\}\\\\textgreater -\\\\textless C\\\\{e1\\\\}\\\\textgreater , \\\\textless C\\\\{s2\\\\}\\\\textgreater -\\\\textless C\\\\{e2\\\\}\\\\textgreater , ...), please append these chunk numbers to S in the format \\\"\\\\textless statement\\\\textgreater \\\\{S\\\\}\\\\textless cite\\\\textgreater [\\\\{s1\\\\}-\\\\{e1\\\\}][\\\\{s2\\\\}-\\\\{e2\\\\}]...\\\\textless /cite\\\\textgreater \\\\textless /statement\\\\textgreater \\\". For other sentences such as introductory sentences, summarization sentences, reasoning, and inference, you still need to append \\\"\\\\textless cite\\\\textgreater \\\\textless /cite\\\\textgreater \\\" to them to indicate they need no citations. You must answer in the same language as the user's question.\\\\\\\\\\n\",\n \"\\n\",\n \"[Answer with Citations]\\\\\\\\\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"\\\\end{tcolorbox}\\n\",\n \" \\\\caption{One-shot learning prompt for the LAC-S strategy.}\\n\",\n \" \\\\label{prompt:lac_s}\\n\",\n \"\\\\end{figure}\\\\begin{figure}[t]\\n\"\n ],\n [\n \"\\\\end{figure}\\\\begin{figure}[t]\\n\",\n \" \\\\centering\\n\",\n \"\\\\begin{tcolorbox}[size=title,opacityfill=0.1]\\n\",\n \"\\\\noindent\\n\",\n \"Your task is to add citations to the existing answer. Specifically, when a factual statement S in the answer uses information from context snippets l1, l2, ..., ln, please add citations by appending these snippet numbers to S in the format ``\\\\textless statement\\\\textgreater\\\\{S\\\\}\\\\textless cite\\\\textgreater[\\\\{l1\\\\}][\\\\{l2\\\\}]...[\\\\{ln\\\\}]\\\\textless /cite\\\\textgreater\\\\textless statement\\\\textgreater\\\". For other sentences such as introductory sentences, summarization sentences, reasoning, and inference, you still need to append ``\\\\textless cite\\\\textgreater\\\\textless /cite\\\\textgreater\\\" to them to indicate they need no citations. Except for adding citations, do not change the original content and format of the existing answer. \\\\\\\\\\n\",\n \"\\n\",\n \"Here is an example: \\\\\\\\\\n\",\n \"\\n\",\n \"\\\\{\\\\emph{An Example}\\\\} \\\\\\\\\\n\",\n \"\\n\",\n \"Now get ready to add citations for the following test case. \\\\\\\\\\n\",\n \"\\n\",\n \"[Contexts Start]\\\\\\\\\\n\",\n \"Snippet [1] \\\\\\\\\\n\",\n \"\\\\{\\\\emph{Chunk 1}\\\\} \\\\\\\\\\n\",\n \"\\n\",\n \"Snippet [2] \\\\\\\\\\n\",\n \"\\\\{\\\\emph{Chunk 2}\\\\} \\\\\\\\\\n\",\n \"\\n\",\n \"Snippet [3] \\\\\\\\\\n\",\n \"\\\\{\\\\emph{Chunk 3}\\\\} \\\\\\\\\\n\",\n \"\\n\",\n \"...\\n\",\n \"\\n\",\n \"[Context End]\\\\\\\\\\n\",\n \"\\n\",\n \"[Question]\\\\\\\\\\n\",\n \"\\\\{\\\\emph{Question}\\\\} \\\\\\\\\\n\",\n \"\\n\",\n \"[Existing Answer Start]\\\\\\\\\\n\",\n \"\\\\{\\\\emph{Answer}\\\\} \\n\",\n \"\\n\",\n \"[Existing Answer End]\\\\\\\\\\n\",\n \"\\n\",\n \"[Answer with Citations]\\\\\\\\\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"\\\\end{tcolorbox}\\n\",\n \" \\\\caption{Prompt for chunk-level citation generation in the CoF pipeline.}\\n\",\n \" \\\\label{prompt:cof_coarse}\\n\",\n \"\\\\end{figure}\\\\begin{figure}[t]\\n\"\n ]\n]","isConversational":false},"fulltext":{"kind":"string","value":"\n\\documentclass{article} % For LaTeX2e\n\\usepackage{iclr2025_conference,times}\n\n% Optional math commands from https://github.com/goodfeli/dlbook_notation.\n%%%%% NEW MATH DEFINITIONS %%%%%\n\n\\usepackage{amsmath,amsfonts,bm}\n\n% Mark sections of captions for referring to divisions of figures\n\\newcommand{\\figleft}{{\\em (Left)}}\n\\newcommand{\\figcenter}{{\\em (Center)}}\n\\newcommand{\\figright}{{\\em (Right)}}\n\\newcommand{\\figtop}{{\\em (Top)}}\n\\newcommand{\\figbottom}{{\\em (Bottom)}}\n\\newcommand{\\captiona}{{\\em (a)}}\n\\newcommand{\\captionb}{{\\em (b)}}\n\\newcommand{\\captionc}{{\\em (c)}}\n\\newcommand{\\captiond}{{\\em (d)}}\n\n% Highlight a newly defined term\n\\newcommand{\\newterm}[1]{{\\bf #1}}\n\n\n% Figure reference, lower-case.\n\\def\\figref#1{figure~\\ref{#1}}\n% Figure reference, capital. For start of sentence\n\\def\\Figref#1{Figure~\\ref{#1}}\n\\def\\twofigref#1#2{figures \\ref{#1} and \\ref{#2}}\n\\def\\quadfigref#1#2#3#4{figures \\ref{#1}, \\ref{#2}, \\ref{#3} and \\ref{#4}}\n% Section reference, lower-case.\n\\def\\secref#1{section~\\ref{#1}}\n% Section reference, capital.\n\\def\\Secref#1{Section~\\ref{#1}}\n% Reference to two sections.\n\\def\\twosecrefs#1#2{sections \\ref{#1} and \\ref{#2}}\n% Reference to three sections.\n\\def\\secrefs#1#2#3{sections \\ref{#1}, \\ref{#2} and \\ref{#3}}\n% Reference to an equation, lower-case.\n\\def\\eqref#1{equation~\\ref{#1}}\n% Reference to an equation, upper case\n\\def\\Eqref#1{Equation~\\ref{#1}}\n% A raw reference to an equation---avoid using if possible\n\\def\\plaineqref#1{\\ref{#1}}\n% Reference to a chapter, lower-case.\n\\def\\chapref#1{chapter~\\ref{#1}}\n% Reference to an equation, upper case.\n\\def\\Chapref#1{Chapter~\\ref{#1}}\n% Reference to a range of chapters\n\\def\\rangechapref#1#2{chapters\\ref{#1}--\\ref{#2}}\n% Reference to an algorithm, lower-case.\n\\def\\algref#1{algorithm~\\ref{#1}}\n% Reference to an algorithm, upper case.\n\\def\\Algref#1{Algorithm~\\ref{#1}}\n\\def\\twoalgref#1#2{algorithms \\ref{#1} and \\ref{#2}}\n\\def\\Twoalgref#1#2{Algorithms \\ref{#1} and \\ref{#2}}\n% Reference to a part, lower case\n\\def\\partref#1{part~\\ref{#1}}\n% Reference to a part, upper case\n\\def\\Partref#1{Part~\\ref{#1}}\n\\def\\twopartref#1#2{parts \\ref{#1} and \\ref{#2}}\n\n\\def\\ceil#1{\\lceil #1 \\rceil}\n\\def\\floor#1{\\lfloor #1 \\rfloor}\n\\def\\1{\\bm{1}}\n\\newcommand{\\train}{\\mathcal{D}}\n\\newcommand{\\valid}{\\mathcal{D_{\\mathrm{valid}}}}\n\\newcommand{\\test}{\\mathcal{D_{\\mathrm{test}}}}\n\n\\def\\eps{{\\epsilon}}\n\n\n% Random variables\n\\def\\reta{{\\textnormal{$\\eta$}}}\n\\def\\ra{{\\textnormal{a}}}\n\\def\\rb{{\\textnormal{b}}}\n\\def\\rc{{\\textnormal{c}}}\n\\def\\rd{{\\textnormal{d}}}\n\\def\\re{{\\textnormal{e}}}\n\\def\\rf{{\\textnormal{f}}}\n\\def\\rg{{\\textnormal{g}}}\n\\def\\rh{{\\textnormal{h}}}\n\\def\\ri{{\\textnormal{i}}}\n\\def\\rj{{\\textnormal{j}}}\n\\def\\rk{{\\textnormal{k}}}\n\\def\\rl{{\\textnormal{l}}}\n% rm is already a command, just don't name any random variables m\n\\def\\rn{{\\textnormal{n}}}\n\\def\\ro{{\\textnormal{o}}}\n\\def\\rp{{\\textnormal{p}}}\n\\def\\rq{{\\textnormal{q}}}\n\\def\\rr{{\\textnormal{r}}}\n\\def\\rs{{\\textnormal{s}}}\n\\def\\rt{{\\textnormal{t}}}\n\\def\\ru{{\\textnormal{u}}}\n\\def\\rv{{\\textnormal{v}}}\n\\def\\rw{{\\textnormal{w}}}\n\\def\\rx{{\\textnormal{x}}}\n\\def\\ry{{\\textnormal{y}}}\n\\def\\rz{{\\textnormal{z}}}\n\n% Random vectors\n\\def\\rvepsilon{{\\mathbf{\\epsilon}}}\n\\def\\rvtheta{{\\mathbf{\\theta}}}\n\\def\\rva{{\\mathbf{a}}}\n\\def\\rvb{{\\mathbf{b}}}\n\\def\\rvc{{\\mathbf{c}}}\n\\def\\rvd{{\\mathbf{d}}}\n\\def\\rve{{\\mathbf{e}}}\n\\def\\rvf{{\\mathbf{f}}}\n\\def\\rvg{{\\mathbf{g}}}\n\\def\\rvh{{\\mathbf{h}}}\n\\def\\rvu{{\\mathbf{i}}}\n\\def\\rvj{{\\mathbf{j}}}\n\\def\\rvk{{\\mathbf{k}}}\n\\def\\rvl{{\\mathbf{l}}}\n\\def\\rvm{{\\mathbf{m}}}\n\\def\\rvn{{\\mathbf{n}}}\n\\def\\rvo{{\\mathbf{o}}}\n\\def\\rvp{{\\mathbf{p}}}\n\\def\\rvq{{\\mathbf{q}}}\n\\def\\rvr{{\\mathbf{r}}}\n\\def\\rvs{{\\mathbf{s}}}\n\\def\\rvt{{\\mathbf{t}}}\n\\def\\rvu{{\\mathbf{u}}}\n\\def\\rvv{{\\mathbf{v}}}\n\\def\\rvw{{\\mathbf{w}}}\n\\def\\rvx{{\\mathbf{x}}}\n\\def\\rvy{{\\mathbf{y}}}\n\\def\\rvz{{\\mathbf{z}}}\n\n% Elements of random vectors\n\\def\\erva{{\\textnormal{a}}}\n\\def\\ervb{{\\textnormal{b}}}\n\\def\\ervc{{\\textnormal{c}}}\n\\def\\ervd{{\\textnormal{d}}}\n\\def\\erve{{\\textnormal{e}}}\n\\def\\ervf{{\\textnormal{f}}}\n\\def\\ervg{{\\textnormal{g}}}\n\\def\\ervh{{\\textnormal{h}}}\n\\def\\ervi{{\\textnormal{i}}}\n\\def\\ervj{{\\textnormal{j}}}\n\\def\\ervk{{\\textnormal{k}}}\n\\def\\ervl{{\\textnormal{l}}}\n\\def\\ervm{{\\textnormal{m}}}\n\\def\\ervn{{\\textnormal{n}}}\n\\def\\ervo{{\\textnormal{o}}}\n\\def\\ervp{{\\textnormal{p}}}\n\\def\\ervq{{\\textnormal{q}}}\n\\def\\ervr{{\\textnormal{r}}}\n\\def\\ervs{{\\textnormal{s}}}\n\\def\\ervt{{\\textnormal{t}}}\n\\def\\ervu{{\\textnormal{u}}}\n\\def\\ervv{{\\textnormal{v}}}\n\\def\\ervw{{\\textnormal{w}}}\n\\def\\ervx{{\\textnormal{x}}}\n\\def\\ervy{{\\textnormal{y}}}\n\\def\\ervz{{\\textnormal{z}}}\n\n% Random matrices\n\\def\\rmA{{\\mathbf{A}}}\n\\def\\rmB{{\\mathbf{B}}}\n\\def\\rmC{{\\mathbf{C}}}\n\\def\\rmD{{\\mathbf{D}}}\n\\def\\rmE{{\\mathbf{E}}}\n\\def\\rmF{{\\mathbf{F}}}\n\\def\\rmG{{\\mathbf{G}}}\n\\def\\rmH{{\\mathbf{H}}}\n\\def\\rmI{{\\mathbf{I}}}\n\\def\\rmJ{{\\mathbf{J}}}\n\\def\\rmK{{\\mathbf{K}}}\n\\def\\rmL{{\\mathbf{L}}}\n\\def\\rmM{{\\mathbf{M}}}\n\\def\\rmN{{\\mathbf{N}}}\n\\def\\rmO{{\\mathbf{O}}}\n\\def\\rmP{{\\mathbf{P}}}\n\\def\\rmQ{{\\mathbf{Q}}}\n\\def\\rmR{{\\mathbf{R}}}\n\\def\\rmS{{\\mathbf{S}}}\n\\def\\rmT{{\\mathbf{T}}}\n\\def\\rmU{{\\mathbf{U}}}\n\\def\\rmV{{\\mathbf{V}}}\n\\def\\rmW{{\\mathbf{W}}}\n\\def\\rmX{{\\mathbf{X}}}\n\\def\\rmY{{\\mathbf{Y}}}\n\\def\\rmZ{{\\mathbf{Z}}}\n\n% Elements of random matrices\n\\def\\ermA{{\\textnormal{A}}}\n\\def\\ermB{{\\textnormal{B}}}\n\\def\\ermC{{\\textnormal{C}}}\n\\def\\ermD{{\\textnormal{D}}}\n\\def\\ermE{{\\textnormal{E}}}\n\\def\\ermF{{\\textnormal{F}}}\n\\def\\ermG{{\\textnormal{G}}}\n\\def\\ermH{{\\textnormal{H}}}\n\\def\\ermI{{\\textnormal{I}}}\n\\def\\ermJ{{\\textnormal{J}}}\n\\def\\ermK{{\\textnormal{K}}}\n\\def\\ermL{{\\textnormal{L}}}\n\\def\\ermM{{\\textnormal{M}}}\n\\def\\ermN{{\\textnormal{N}}}\n\\def\\ermO{{\\textnormal{O}}}\n\\def\\ermP{{\\textnormal{P}}}\n\\def\\ermQ{{\\textnormal{Q}}}\n\\def\\ermR{{\\textnormal{R}}}\n\\def\\ermS{{\\textnormal{S}}}\n\\def\\ermT{{\\textnormal{T}}}\n\\def\\ermU{{\\textnormal{U}}}\n\\def\\ermV{{\\textnormal{V}}}\n\\def\\ermW{{\\textnormal{W}}}\n\\def\\ermX{{\\textnormal{X}}}\n\\def\\ermY{{\\textnormal{Y}}}\n\\def\\ermZ{{\\textnormal{Z}}}\n\n% Vectors\n\\def\\vzero{{\\bm{0}}}\n\\def\\vone{{\\bm{1}}}\n\\def\\vmu{{\\bm{\\mu}}}\n\\def\\vtheta{{\\bm{\\theta}}}\n\\def\\va{{\\bm{a}}}\n\\def\\vb{{\\bm{b}}}\n\\def\\vc{{\\bm{c}}}\n\\def\\vd{{\\bm{d}}}\n\\def\\ve{{\\bm{e}}}\n\\def\\vf{{\\bm{f}}}\n\\def\\vg{{\\bm{g}}}\n\\def\\vh{{\\bm{h}}}\n\\def\\vi{{\\bm{i}}}\n\\def\\vj{{\\bm{j}}}\n\\def\\vk{{\\bm{k}}}\n\\def\\vl{{\\bm{l}}}\n\\def\\vm{{\\bm{m}}}\n\\def\\vn{{\\bm{n}}}\n\\def\\vo{{\\bm{o}}}\n\\def\\vp{{\\bm{p}}}\n\\def\\vq{{\\bm{q}}}\n\\def\\vr{{\\bm{r}}}\n\\def\\vs{{\\bm{s}}}\n\\def\\vt{{\\bm{t}}}\n\\def\\vu{{\\bm{u}}}\n\\def\\vv{{\\bm{v}}}\n\\def\\vw{{\\bm{w}}}\n\\def\\vx{{\\bm{x}}}\n\\def\\vy{{\\bm{y}}}\n\\def\\vz{{\\bm{z}}}\n\n% Elements of vectors\n\\def\\evalpha{{\\alpha}}\n\\def\\evbeta{{\\beta}}\n\\def\\evepsilon{{\\epsilon}}\n\\def\\evlambda{{\\lambda}}\n\\def\\evomega{{\\omega}}\n\\def\\evmu{{\\mu}}\n\\def\\evpsi{{\\psi}}\n\\def\\evsigma{{\\sigma}}\n\\def\\evtheta{{\\theta}}\n\\def\\eva{{a}}\n\\def\\evb{{b}}\n\\def\\evc{{c}}\n\\def\\evd{{d}}\n\\def\\eve{{e}}\n\\def\\evf{{f}}\n\\def\\evg{{g}}\n\\def\\evh{{h}}\n\\def\\evi{{i}}\n\\def\\evj{{j}}\n\\def\\evk{{k}}\n\\def\\evl{{l}}\n\\def\\evm{{m}}\n\\def\\evn{{n}}\n\\def\\evo{{o}}\n\\def\\evp{{p}}\n\\def\\evq{{q}}\n\\def\\evr{{r}}\n\\def\\evs{{s}}\n\\def\\evt{{t}}\n\\def\\evu{{u}}\n\\def\\evv{{v}}\n\\def\\evw{{w}}\n\\def\\evx{{x}}\n\\def\\evy{{y}}\n\\def\\evz{{z}}\n\n% Matrix\n\\def\\mA{{\\bm{A}}}\n\\def\\mB{{\\bm{B}}}\n\\def\\mC{{\\bm{C}}}\n\\def\\mD{{\\bm{D}}}\n\\def\\mE{{\\bm{E}}}\n\\def\\mF{{\\bm{F}}}\n\\def\\mG{{\\bm{G}}}\n\\def\\mH{{\\bm{H}}}\n\\def\\mI{{\\bm{I}}}\n\\def\\mJ{{\\bm{J}}}\n\\def\\mK{{\\bm{K}}}\n\\def\\mL{{\\bm{L}}}\n\\def\\mM{{\\bm{M}}}\n\\def\\mN{{\\bm{N}}}\n\\def\\mO{{\\bm{O}}}\n\\def\\mP{{\\bm{P}}}\n\\def\\mQ{{\\bm{Q}}}\n\\def\\mR{{\\bm{R}}}\n\\def\\mS{{\\bm{S}}}\n\\def\\mT{{\\bm{T}}}\n\\def\\mU{{\\bm{U}}}\n\\def\\mV{{\\bm{V}}}\n\\def\\mW{{\\bm{W}}}\n\\def\\mX{{\\bm{X}}}\n\\def\\mY{{\\bm{Y}}}\n\\def\\mZ{{\\bm{Z}}}\n\\def\\mBeta{{\\bm{\\beta}}}\n\\def\\mPhi{{\\bm{\\Phi}}}\n\\def\\mLambda{{\\bm{\\Lambda}}}\n\\def\\mSigma{{\\bm{\\Sigma}}}\n\n% Tensor\n\\DeclareMathAlphabet{\\mathsfit}{\\encodingdefault}{\\sfdefault}{m}{sl}\n\\SetMathAlphabet{\\mathsfit}{bold}{\\encodingdefault}{\\sfdefault}{bx}{n}\n\\newcommand{\\tens}[1]{\\bm{\\mathsfit{#1}}}\n\\def\\tA{{\\tens{A}}}\n\\def\\tB{{\\tens{B}}}\n\\def\\tC{{\\tens{C}}}\n\\def\\tD{{\\tens{D}}}\n\\def\\tE{{\\tens{E}}}\n\\def\\tF{{\\tens{F}}}\n\\def\\tG{{\\tens{G}}}\n\\def\\tH{{\\tens{H}}}\n\\def\\tI{{\\tens{I}}}\n\\def\\tJ{{\\tens{J}}}\n\\def\\tK{{\\tens{K}}}\n\\def\\tL{{\\tens{L}}}\n\\def\\tM{{\\tens{M}}}\n\\def\\tN{{\\tens{N}}}\n\\def\\tO{{\\tens{O}}}\n\\def\\tP{{\\tens{P}}}\n\\def\\tQ{{\\tens{Q}}}\n\\def\\tR{{\\tens{R}}}\n\\def\\tS{{\\tens{S}}}\n\\def\\tT{{\\tens{T}}}\n\\def\\tU{{\\tens{U}}}\n\\def\\tV{{\\tens{V}}}\n\\def\\tW{{\\tens{W}}}\n\\def\\tX{{\\tens{X}}}\n\\def\\tY{{\\tens{Y}}}\n\\def\\tZ{{\\tens{Z}}}\n\n\n% Graph\n\\def\\gA{{\\mathcal{A}}}\n\\def\\gB{{\\mathcal{B}}}\n\\def\\gC{{\\mathcal{C}}}\n\\def\\gD{{\\mathcal{D}}}\n\\def\\gE{{\\mathcal{E}}}\n\\def\\gF{{\\mathcal{F}}}\n\\def\\gG{{\\mathcal{G}}}\n\\def\\gH{{\\mathcal{H}}}\n\\def\\gI{{\\mathcal{I}}}\n\\def\\gJ{{\\mathcal{J}}}\n\\def\\gK{{\\mathcal{K}}}\n\\def\\gL{{\\mathcal{L}}}\n\\def\\gM{{\\mathcal{M}}}\n\\def\\gN{{\\mathcal{N}}}\n\\def\\gO{{\\mathcal{O}}}\n\\def\\gP{{\\mathcal{P}}}\n\\def\\gQ{{\\mathcal{Q}}}\n\\def\\gR{{\\mathcal{R}}}\n\\def\\gS{{\\mathcal{S}}}\n\\def\\gT{{\\mathcal{T}}}\n\\def\\gU{{\\mathcal{U}}}\n\\def\\gV{{\\mathcal{V}}}\n\\def\\gW{{\\mathcal{W}}}\n\\def\\gX{{\\mathcal{X}}}\n\\def\\gY{{\\mathcal{Y}}}\n\\def\\gZ{{\\mathcal{Z}}}\n\n% Sets\n\\def\\sA{{\\mathbb{A}}}\n\\def\\sB{{\\mathbb{B}}}\n\\def\\sC{{\\mathbb{C}}}\n\\def\\sD{{\\mathbb{D}}}\n% Don't use a set called E, because this would be the same as our symbol\n% for expectation.\n\\def\\sF{{\\mathbb{F}}}\n\\def\\sG{{\\mathbb{G}}}\n\\def\\sH{{\\mathbb{H}}}\n\\def\\sI{{\\mathbb{I}}}\n\\def\\sJ{{\\mathbb{J}}}\n\\def\\sK{{\\mathbb{K}}}\n\\def\\sL{{\\mathbb{L}}}\n\\def\\sM{{\\mathbb{M}}}\n\\def\\sN{{\\mathbb{N}}}\n\\def\\sO{{\\mathbb{O}}}\n\\def\\sP{{\\mathbb{P}}}\n\\def\\sQ{{\\mathbb{Q}}}\n\\def\\sR{{\\mathbb{R}}}\n\\def\\sS{{\\mathbb{S}}}\n\\def\\sT{{\\mathbb{T}}}\n\\def\\sU{{\\mathbb{U}}}\n\\def\\sV{{\\mathbb{V}}}\n\\def\\sW{{\\mathbb{W}}}\n\\def\\sX{{\\mathbb{X}}}\n\\def\\sY{{\\mathbb{Y}}}\n\\def\\sZ{{\\mathbb{Z}}}\n\n% Entries of a matrix\n\\def\\emLambda{{\\Lambda}}\n\\def\\emA{{A}}\n\\def\\emB{{B}}\n\\def\\emC{{C}}\n\\def\\emD{{D}}\n\\def\\emE{{E}}\n\\def\\emF{{F}}\n\\def\\emG{{G}}\n\\def\\emH{{H}}\n\\def\\emI{{I}}\n\\def\\emJ{{J}}\n\\def\\emK{{K}}\n\\def\\emL{{L}}\n\\def\\emM{{M}}\n\\def\\emN{{N}}\n\\def\\emO{{O}}\n\\def\\emP{{P}}\n\\def\\emQ{{Q}}\n\\def\\emR{{R}}\n\\def\\emS{{S}}\n\\def\\emT{{T}}\n\\def\\emU{{U}}\n\\def\\emV{{V}}\n\\def\\emW{{W}}\n\\def\\emX{{X}}\n\\def\\emY{{Y}}\n\\def\\emZ{{Z}}\n\\def\\emSigma{{\\Sigma}}\n\n% entries of a tensor\n% Same font as tensor, without \\bm wrapper\n\\newcommand{\\etens}[1]{\\mathsfit{#1}}\n\\def\\etLambda{{\\etens{\\Lambda}}}\n\\def\\etA{{\\etens{A}}}\n\\def\\etB{{\\etens{B}}}\n\\def\\etC{{\\etens{C}}}\n\\def\\etD{{\\etens{D}}}\n\\def\\etE{{\\etens{E}}}\n\\def\\etF{{\\etens{F}}}\n\\def\\etG{{\\etens{G}}}\n\\def\\etH{{\\etens{H}}}\n\\def\\etI{{\\etens{I}}}\n\\def\\etJ{{\\etens{J}}}\n\\def\\etK{{\\etens{K}}}\n\\def\\etL{{\\etens{L}}}\n\\def\\etM{{\\etens{M}}}\n\\def\\etN{{\\etens{N}}}\n\\def\\etO{{\\etens{O}}}\n\\def\\etP{{\\etens{P}}}\n\\def\\etQ{{\\etens{Q}}}\n\\def\\etR{{\\etens{R}}}\n\\def\\etS{{\\etens{S}}}\n\\def\\etT{{\\etens{T}}}\n\\def\\etU{{\\etens{U}}}\n\\def\\etV{{\\etens{V}}}\n\\def\\etW{{\\etens{W}}}\n\\def\\etX{{\\etens{X}}}\n\\def\\etY{{\\etens{Y}}}\n\\def\\etZ{{\\etens{Z}}}\n\n% The true underlying data generating distribution\n\\newcommand{\\pdata}{p_{\\rm{data}}}\n% The empirical distribution defined by the training set\n\\newcommand{\\ptrain}{\\hat{p}_{\\rm{data}}}\n\\newcommand{\\Ptrain}{\\hat{P}_{\\rm{data}}}\n% The model distribution\n\\newcommand{\\pmodel}{p_{\\rm{model}}}\n\\newcommand{\\Pmodel}{P_{\\rm{model}}}\n\\newcommand{\\ptildemodel}{\\tilde{p}_{\\rm{model}}}\n% Stochastic autoencoder distributions\n\\newcommand{\\pencode}{p_{\\rm{encoder}}}\n\\newcommand{\\pdecode}{p_{\\rm{decoder}}}\n\\newcommand{\\precons}{p_{\\rm{reconstruct}}}\n\n\\newcommand{\\laplace}{\\mathrm{Laplace}} % Laplace distribution\n\n\\newcommand{\\E}{\\mathbb{E}}\n\\newcommand{\\Ls}{\\mathcal{L}}\n\\newcommand{\\R}{\\mathbb{R}}\n\\newcommand{\\emp}{\\tilde{p}}\n\\newcommand{\\lr}{\\alpha}\n\\newcommand{\\reg}{\\lambda}\n\\newcommand{\\rect}{\\mathrm{rectifier}}\n\\newcommand{\\softmax}{\\mathrm{softmax}}\n\\newcommand{\\sigmoid}{\\sigma}\n\\newcommand{\\softplus}{\\zeta}\n\\newcommand{\\KL}{D_{\\mathrm{KL}}}\n\\newcommand{\\Var}{\\mathrm{Var}}\n\\newcommand{\\standarderror}{\\mathrm{SE}}\n\\newcommand{\\Cov}{\\mathrm{Cov}}\n% Wolfram Mathworld says $L^2$ is for function spaces and $\\ell^2$ is for vectors\n% But then they seem to use $L^2$ for vectors throughout the site, and so does\n% wikipedia.\n\\newcommand{\\normlzero}{L^0}\n\\newcommand{\\normlone}{L^1}\n\\newcommand{\\normltwo}{L^2}\n\\newcommand{\\normlp}{L^p}\n\\newcommand{\\normmax}{L^\\infty}\n\n\\newcommand{\\parents}{Pa} % See usage in notation.tex. Chosen to match Daphne's book.\n\n\\DeclareMathOperator*{\\argmax}{arg\\,max}\n\\DeclareMathOperator*{\\argmin}{arg\\,min}\n\n\\DeclareMathOperator{\\sign}{sign}\n\\DeclareMathOperator{\\Tr}{Tr}\n\\let\\ab\\allowbreak\n\n\\usepackage{hyperref}\n\\usepackage{url}\n\\usepackage{enumitem}\n\\usepackage[most]{tcolorbox}\n\\newcommand{\\xhdr}[1]{{\\noindent\\bfseries #1}.}\n\\usepackage{booktabs}\n\\usepackage{graphicx}\n\\usepackage{booktabs}\n\\usepackage{caption}\n\\usepackage{wrapfig}\n\\usepackage{CJKutf8}\n\\usepackage{multirow}\n\\usepackage{colortbl}\n\\usepackage[normalem]{ulem}\n\\useunder{\\uline}{\\ul}{}\n\\newcommand{\\ys}{\\textcolor{magenta}}\n\n\\title{LongCite: Enabling LLMs to Generate Fine-grained Citations in Long-Context QA}\n\n% Authors must not appear in the submitted version. They should be hidden\n% as long as the \\iclrfinalcopy macro remains commented out below.\n% Non-anonymous submissions will be rejected without review.\n\n\\author{%\n Jiajie Zhang$^{1\\dagger}$, Yushi Bai$^{1\\dagger}$, Xin Lv$^{2}$, Wanjun Gu$^1$, Danqing Liu$^1$, Minhao Zou$^1$, Shulin Cao$^{2}$\\\\ \n \\textbf{Lei Hou$^1$, Yuxiao Dong$^1$, Ling Feng$^1$, Juanzi Li$^1$} \\\\\n $^1$Tsinghua University\n \\quad\n $^2$Zhipu AI\n}\n\n% The \\author macro works with any number of authors. There are two commands\n% used to separate the names and addresses of multiple authors: \\And and \\AND.\n%\n% Using \\And between authors leaves it to \\LaTeX{} to determine where to break\n% the lines. Using \\AND forces a linebreak at that point. So, if \\LaTeX{}\n% puts 3 of 4 authors names on the first line, and the last on the second\n% line, try using \\AND instead of \\And before the third author name.\n\n\\newcommand{\\fix}{\\marginpar{FIX}}\n\\newcommand{\\new}{\\marginpar{NEW}}\n\n\\iclrfinalcopy % Uncomment for camera-ready version, but NOT for submission.\n\\begin{document}\n\n\n\\maketitle\n\n\\renewcommand{\\thefootnote}{\\fnsymbol{footnote}}\n \\footnotetext[2]{Work done when JZ and YB interned at Zhipu.AI.\n }\n\\renewcommand{\\thefootnote}{\\arabic{footnote}}\n\n\\begin{abstract}\nThough current long-context large language models (LLMs) have demonstrated impressive capacities in answering user questions based on extensive text, the lack of citations in their responses makes user verification difficult, leading to concerns about their trustworthiness due to their potential hallucinations. In this work, we aim to enable long-context LLMs to generate responses with fine-grained sentence-level citations, improving their faithfulness and verifiability. We first introduce LongBench-Cite, an automated benchmark for assessing current LLMs' performance in Long-Context Question Answering with Citations (LQAC), revealing considerable room for improvement. To this end, we propose CoF (Coarse to Fine), a novel pipeline that utilizes off-the-shelf LLMs to automatically generate long-context QA instances with precise sentence-level citations, and leverage this pipeline to construct LongCite-45k, a large-scale SFT dataset for LQAC. Finally, we train LongCite-8B and LongCite-9B using the LongCite-45k dataset, successfully enabling their generation of accurate responses and fine-grained sentence-level citations in a single output. The evaluation results on LongBench-Cite show that our trained models achieve state-of-the-art citation quality, surpassing advanced proprietary models including GPT-4o. \n% \\ys{[Meanwhile, we also find that SFT on LQAC data further boosts the response correctness compared to vanilla long-context SFT.] We also discover that SFT with citation information effectively reduces hallucinations in long-context QA.} \nWe also discover that SFT with citation information effectively reduces hallucinations and enables a more uniform utilization of context.\nOur code \\& models are at: \\url{https://github.com/THUDM/LongCite}.\n\\end{abstract}\n\n\\section{Introduction}\n\\label{sec:intro}\nRecent years have witnessed significant advancement in long-context large language models (LLMs), enabling them to address various user questions, such as information extraction and summarization, based on lengthy texts that surpass 100,000 tokens~\\citep{claude-3-5, glm4, reid2024gemini}. Despite their remarkable capacities, current long-context LLMs typically do not provide citations to specific context snippets to support the statements they generated, making it challenging for users to verify model outputs given the substantial context lengths. This significantly impacts the reliability and trustworthiness of long-context LLMs, especially considering that they still struggle with hallucinations \\citep{ji23, huang23} and are prone to generate unfaithful content.\n\nOn the other hand, recent works in web browsing and open-domain QA have allowed LLMs to generate responses with in-line citations through retrieval-based generation (RAG) or post-hoc methods~\\citep{webgpt, gao23b, gao23, menick23}. Nevertheless, these approaches still expose notable limitations in long-context scenarios: RAG often leads to compromised answer quality due to incomplete context information, while post-hoc methods prolong the user waiting time due to the more complicated pipeline. In addition, their generated citations typically refer to entire web pages~\\cite{webgpt, bohnet22} or coarsely chunked snippets~\\cite{gao23}, thereby requiring users to further pinpoint the specific support evidence for the final verification.\n\nTo overcome the above limitations, this work explores directly employing long-context LLMs to generate accurate responses with fine-grained sentence-level in-line citations. We first propose \\textbf{LongBench-Cite}, an automatic benchmark, to evaluate LLMs' performance on the task of \\textbf{long-context question answering with citations (LQAC)}, and find that current LLMs obtain unsatisfactory results\n(Sec.~\\ref{sec:benchmark}). Specifically, we find that many citations produced by current LLMs are either irrelevant, cannot fully support the response, or have a coarse granularity. Meanwhile, we observe that generating citations on the fly via in-context learning generally results in responses with lower correctness compared to vanilla long-context QA. \n\nTo further enhance the inherent capacity of LLMs for generating fine-grained citations from lengthy contexts, it is essential to construct a high-quality SFT dataset. To this end, we introduce \\textbf{CoF} (abbr. for ``\\textbf{Co}arse to \\textbf{F}ine''), a novel pipeline that utilizes off-the-shelf LLMs to automatically construct long-context QA instances with precise sentence-level citations (Sec.~\\ref{sec:pipeline}). CoF comprises four stages: (1) Starting with a long text material, CoF first invokes the LLM to produce a query and its associated answer through Self-Instruct~\\citep{self-instruct}. (2) Next, CoF uses the answer to retrieve several chunks (each has a fixed length of 128 tokens~\\footnote{In this work, we uniformly use GLM4-9B's tokenizer to count tokens.}) from the context, which are then fed into the LLM to incorporate coarse-grained chunk-level citations within the answer. (3) The LLM subsequently identifies relevant sentences from each cited chunk to produce fine-grained citations. (4) As a final step, instances with an insufficient number of citations are discarded. Our experiments validate the superiority of CoF over other LQAC strategies in terms of answer correctness and citation quality. With CoF, we construct \\textbf{LongCite-45k}, a large-scale SFT dataset that consists of 44,600 high-quality LQAC instances with contexts up to 128,000 tokens.\n\nFinally, we utilize LongCite-45k to fine-tune GLM-4-9B~\\citep{glm4} and Llama3.1-8B~\\citep{llama-3-1}, two latest open-source long-context models (Sec.~\\ref{sec:longcite}). The enhanced models, namely \\textbf{LongCite-9B} and \\textbf{LongCite-8B}, support a max context window of 128,000 tokens and are capable of generating accurate responses along with precise, fine-grained citations in one pass. Evaluation on LongBench-Cite indicates that our trained models achieve significantly better citation quality compared to even much larger proprietary models. Specifically, our 8B/9B size model outperforms GPT-4o by 6.4\\%/3.6\\% in term of citation F1 score and also achieves twice finer granularity. Meanwhile, we observe that SFT on LQAC data can alleviate hallucinations of LLMs and enable them to utilize context information more uniformly and comprehensively, instead of only focusing on a specific part of the context. This results in a further improvement in response correctness over vanilla long-context SFT. We also conduct extensive analyses and human evaluation to further verify the effectiveness of our approach.\n\nTo summarize, our work makes the following contributions:\n\n1. We introduce LongBench-Chat, an automatic benchmark for the task of LQAC, and reveal the limited performance of current long-context LLMs.\n\n2. We propose CoF, which utilizes off-the-shelf LLMs to automatically construct high-quality long-context QA instances with fine-grained sentence-level citations. Using this method, we construct LongCite-45k, a large-scale SFT dataset for LQAC.\n\n3. We successfully train LongCite-8B and LongCite-9B using LongCite-45k dataset, allowing the generation of accurate responses and fine-grained citations in one pass. Our experiments show that SFT on LQAC data not only enhances the capacity for generating citations from lengthy contexts but also further improves response correctness.\\section{Longbench-Cite: Benchmark Long-context QA with Citations}\n\\label{sec:benchmark}\n\n\\subsection{Problem Definition}\n\\begin{figure}\n \\centering\n \\includegraphics[width=\\linewidth]{figs/task.pdf}\n \\caption{Comparison between chunk-level and sentence-level citations.}\n \\label{fig:task}\n\\end{figure}\nWe formalize the task of \\textbf{long-context question answering with citations (LQAC)} as follows: given a long context $\\mathcal{D}$ and a query $q$, the LLM is required to return a response $\\mathcal{A}$, which consists of $n$ statements $s_1, \\dots, s_n$, and each statement $s_i$ cites a list of snippets $\\mathcal{C}_i=\\{c_{i,1}, c_{i,2}, \\dots\\}$ from $\\mathcal{D}$. In this work, LLMs need to segment their responses into statements based on semantic integrity by enclosing each statement with two special tokens \\textless statement\\textgreater and \\textless/statement\\textgreater. As illustrated in Figure~\\ref{fig:task}, we consider two types of citations:\n\n\\begin{itemize}[itemsep=0pt, leftmargin=*]\n \\item\\textbf{Chunk-level citations}, where the context $\\mathcal{D}$ is divided into indexed chunks with a fix length of 128 tokens, and each citation $c_{i,j}$ is in the form of [$k$], referring to the $k$-th chunk;\n \\item\\textbf{Sentence-level citations}, where $\\mathcal{D}$ is divided into indexed sentences using NLTK~\\citep{nltk}, and each $c_{i,j}$ takes the form of [$k$] or [$a$-$b$], referring to the $k$-th sentence or the snippet that includes the $a$-th to $b$-th sentences in $\\mathcal{D}$, respectively.\n\\end{itemize}\n\nPrevious works~\\citep{gao23} for open-domain QA and citation only explore the chunk-level citations. However, the crude segmentation applied for chunk-level citations often results in incomplete cited sentences that affect user experience. In this work, we mainly focus on sentence-level citations because they ensure semantic integrity better, allow for finer-grained citation, and are thus more user-friendly.\n\n\n\\subsection{Data Collection}\nTo evaluate LLMs' performance on LQAC task, we curate a new benchmark \\textbf{LongBench-Cite} by collecting data from existing bilingual long-context benchmarks LongBench~\\citep{longbench} and LongBench-Chat~\\citep{longalign}, covering multiple key user-intensive tasks in both English and Chinese. Specifically, LongBench is a comprehensive benchmark with an average length of 7k words (English) and 13k characters (Chinese), and we select two single-doc QA datasets \\textit{MultiFieldQA-en/zh}~\\citep{longbench}, two multi-doc QA datasets \\textit{HotpotQA}~\\citep{hotpotqa} and \\textit{DuReader}~\\citep{dureader}, and one summarization dataset \\textit{GovReport}~\\citep{govreport} from it. LongBench-Chat comprises 50 real-world queries with long contexts ranging from 10k to 100k in length, covering various scenarios such as document QA, summarization, and coding, and we adopt all the queries. The detailed data statistics are listed in Table~\\ref{tab:data_statistics}. \nFor all datasets, we require LLMs to generate long-form responses with citations.\n\\begin{table}[t]\n\\centering\n\\resizebox{0.8\\linewidth}{!}{\n \\begin{tabular}{lccccc}\n \\toprule\n Dataset & Task & Source & Avg Len & Language & \\#data \\\\\\midrule\n MultiFieldQA-en & Single-Doc QA & Multi-field & 4,559 & English & 150 \\\\\n MultiFieldQA-zh & Single-Doc QA & Multi-field & 6,701 & Chinese & 200 \\\\\n HotpotQA & Multi-Doc QA & Wikipedia & 9,151 & English & 200 \\\\\n Dureader & Multi-Doc QA & Baidu Search & 15,768 & Chinese & 200 \\\\\n GovReport & Summarization & Government Report & 8,734 & English & 200 \\\\\n LongBench-Chat & Multi-task & Real-world Query & 35,571 & English/Chinese & 50 \\\\\\bottomrule\n \\end{tabular}\n}\n\\caption{Data Statistics in LongBench-Cite. ‘Source’ means\nthe origin of the context. ‘Avg Len’ denotes the average number of words/characters of contexts in English/Chinese datasets.}\n\\label{tab:data_statistics}\n\\end{table}\n\\subsection{Automatic Evaluation}\nLongBench-Cite evaluates models' responses based on the two dimensions:\n\\begin{itemize}[itemsep=0pt, leftmargin=*]\n \\item \\textbf{Correctness:} Whether the response is accurate and comprehensive for the query.\n \\item \\textbf{Citation quality:} Whether the response is entirely supported by the cited snippets, no irrelevant snippets are cited, and the cited snippets are fine-grained.\n\\end{itemize}\nIn the following, we introduce automatic metrics for each dimension.\n\n\\subsubsection{Evaluation of Correctness}\nFor the correctness dimension, we adopt the evaluation method in~\\cite{longalign}, which is specially designed for long-form responses. Specifically, we first remove citation-relevant tokens from LLM response, then ask GPT-4o to rate the response based on the query and groundtruth answers via few-shot (for LongBench-Chat) or zero-shot prompting (for other datasets). The detailed prompts can be found in Figure~\\ref{prompt:correct_longbench_chat},~\\ref{prompt:correct_qa}, and~\\ref{prompt:correct_summary}. In addition, to investigate whether adding citations will hurt or improve models' long-context QA performance, we propose a new metric \\textbf{correctness ratio}: \n\\begin{equation}\n\\text{CR} = \\text{C} / \\text{C}_\\text{LQA} \\times 100\\%\n\\end{equation}\nHere, $\\text{C}$ and $\\text{C}_\\text{LQA}$ respectively denote the correctness in LQAC setting and vanilla long-context QA setting, where we simply feed the concatenated context and query into the LLM to get a response. \n\n\\subsubsection{Evaluation of Citation Quality}\nTo evaluate the citation quality, we select \\textbf{citation F1} calculated using \\textbf{citation recall} and \\textbf{citation precision}~\\citep{gao23} as the main metric, where the former examines if the model response is fully supported by cited snippets and the later detects irrelevant citations. Compared with~\\cite{gao23}, which uses NLI model TRUE~\\citep{true} for automatic examination, we further improve the measurement method with GPT-4o to better adapt to long-context QA scenarios. Human evaluation (Sec.~\\ref{sec:human_eval}) demonstrates our improved method has a stronger agreement with human. Besides, we use \\textbf{citation length} to measure the granularity of citations and avoid trivial results.\n\n\\textbf{Citation Recall.} We score citation recall (0/0.5/1) for each statement and average over all statements in the model response. Specifically, for each statement $s_i$ that cites at least one snippet ($\\mathcal{C}_i\\neq\\emptyset$), we concatenate all snippets in $\\mathcal{C}_i$ and ask GPT-4o to judge whether the concatenated text fully supports (1 point), partially supports (0.5 point), or does not support (0 point) $s_i$. On the other hand, most LLM responses contain several ``functional sentences'' such as \\textit{``The proposed method has the following advantages:''} and \\textit{``In summary, ...''} that do not require citation. Therefore, for each statement $s_i$ that has no citation ($\\mathcal{C}_i=\\emptyset$), we feed $s_i$ along with the query and the whole response into GPT-4o and prompt it to determine if $s_i$ is a starting sentence, transition sentence, or a summary or reasoning based on the previous response content. If so, $s_i$ needs no citation and directly receives a citation recall of 1; otherwise, the recall is 0. The prompts are shown in Figure~\\ref{prompt:support_score} and~\\ref{prompt:need_citation}.\n\n\\textbf{Citation Precision.} We calculate citation precision for each citation (0/1 for irrelevant/relevant citations) and average over all citations in the response. Here, a cited snippet $c_{i,j}$ is relevant if and only if it entails some key points of the statement $s_i$, i.e., at least partially supports $s_i$. We also employ GPT-4o as the judge using the prompt in Figure~\\ref{prompt:relevant_score}. In contrast, \\cite{gao23} may overlook partially supporting cases due to the limited capacity of the NLI model it uses. \n\n\\textbf{Citation F1.} Citation F1 is a comprehensive metric to evaluate the citation quality of a response: \n\\begin{equation}\n \\text{F1} = (2\\cdot \\text{P} \\cdot \\text{R}) / (\\text{P} + \\text{R})\n\\end{equation}\nwhere $\\text{P}$ and $\\text{R}$ denote the citation precision and recall of the response, respectively.\n\n\\textbf{Citation Length.} Since the sentence-level citation allows citing snippets with different lengths, we use citation length, which is the average token number of cited snippets in the response, to quantify the granularity of citations. A lower average citation length indicates the response has finer-grained and more concise citations and is thus easier for users to validate. In addition, measuring average citation length can avoid trivial hacks for citation F1 such as citing the whole context for each statement.\n\n\\subsection{Benchmarking Results of Current Long-context LLMs} \n\\label{sec:benchmark_results}\nWe first evaluate 7 popular long-context LLMs (3 proprietary and 4 open-source models, details listed in Table~\\ref{tab:model_card}) on LongBench-Cite using LAC-S (\\textbf{l}ong-context \\textbf{a}nswering with \\textbf{c}itations in \\textbf{s}enetence level) strategy, where the model needs to read the lengthy context entirely and generate the answer along with sentence-level citations in one pass. We select LAC-S strategy as the default setting due to its efficiency, losslessness of context information, and no reliance on additional retrieval systems. A further discussion about the pros and cons of different LQAC strategies can be found in Sec.~\\ref{sec:pipeline_valid}. As illustrated in Figure~\\ref{prompt:lac_s}, we number each sentence $sent_i$ in the context by adding a prefix ``\\textless$\\text{C}i$\\textgreater'' and prompt the LLM with one demonstration. The evaluation results of citation quality and correctness are presented in Table~\\ref{tab:main_cite} and Table~\\ref{tab:main_correct}, respectively. Our findings are as follows: \n\n\\begin{table}[t]\n\\centering\n\\resizebox{\\linewidth}{!}{\n \\setlength{\\tabcolsep}{5pt}\n \\begin{tabular}{l|cc|ccc|ccc|ccc|ccc|ccc}\n\\toprule\n\\multirow{2}{*}{Model} & \\multicolumn{2}{c|}{Avg} & \\multicolumn{3}{c|}{Longbench-Chat} & \\multicolumn{3}{c|}{MultifieldQA} & \\multicolumn{3}{c|}{HotpotQA} & \\multicolumn{3}{c|}{Dureader} & \\multicolumn{3}{c}{GovReport} \\\\\n & F1 & CL & R & P & F1 & R & P & F1 & R & P & F1 & R & P & F1 & R & P & F1 \\\\ \\midrule\n\\multicolumn{18}{l}{\\textit{\\textbf{Proprietary models}}} \\\\\nGPT-4o & 65.6 & 220 & 46.7 & 53.5 & 46.7 & \\textbf{79.0} & 87.9 & {\\ul 80.6} & 55.7 & 62.3 & 53.4 & 65.6 & 74.2 & 67.4 & 73.4 & 90.4 & 79.8 \\\\\nClaude-3-sonnet & 67.2 & 132 & 52.0 & 67.8 & 55.1 & 64.7 & 85.8 & 71.3 & 46.4 & 65.8 & 49.9 & 67.7 & \\textbf{89.2} & \\textbf{75.5} & {\\ul 77.4} & \\textbf{93.9} & {\\ul 84.1} \\\\\nGLM-4 & 65.4 & 169 & 47.6 & 53.9 & 47.1 & 72.3 & 80.1 & 73.6 & 47.0 & 50.1 & 44.4 & \\textbf{73.4} & 82.3 & {\\ul 75.0} & \\textbf{82.8} & {\\ul 93.4} & \\textbf{87.1} \\\\ \\midrule\n\\multicolumn{18}{l}{\\textit{\\textbf{Open-source models}}} \\\\\nGLM-4-9B-chat & 27.2 & 96 & 25.9 & 20.5 & 16.7 & 51.1 & 60.6 & 52.0 & 22.9 & 28.8 & 20.1 & 45.4 & 48.3 & 40.9 & 5.7 & 8.2 & 6.3 \\\\\nLlama-3.1-8B-Instruct & 19.7 & 100 & 14.1 & 19.5 & 12.4 & 29.8 & 44.3 & 31.6 & 20.2 & 30.9 & 20.9 & 22.0 & 25.1 & 17.0 & 16.2 & 25.3 & 16.8 \\\\\nLlama-3.1-70B-Instruct & 40.4 & 174 & 25.8 & 32.0 & 23.2 & 53.2 & 65.2 & 53.9 & 29.6 & 37.3 & 28.6 & 38.2 & 46.0 & 35.4 & 53.4 & 77.5 & 60.7 \\\\\nMistral-Large-Instruct & 51.5 & 132 & 19.8 & 23.9 & 19.0 & 71.8 & 80.7 & 73.8 & 34.5 & 40.9 & 32.1 & 58.3 & 67.0 & 60.1 & 67.9 & 79.6 & 72.5 \\\\ \\midrule\n\\multicolumn{18}{l}{\\textit{\\textbf{Our trained models}}} \\\\\nLongCite-8B & \\textbf{72.0} & \\textbf{85} & \\textbf{62.0} & \\textbf{79.7} & \\textbf{67.4} & {\\ul 74.7} & \\textbf{93.0} & \\textbf{80.8} & {\\ul 59.2} & {\\ul 72.1} & {\\ul 60.3} & {\\ul 68.3} & 85.6 & 73.1 & 74.0 & 86.6 & 78.5 \\\\\nLongCite-9B & {\\ul 69.2} & {\\ul 91} & {\\ul 57.6} & {\\ul 78.1} & {\\ul 63.6} & 67.3 & {\\ul 91.0} & 74.8 & \\textbf{61.8} & \\textbf{78.8} & \\textbf{64.8} & 67.6 & \\textbf{89.2} & 74.4 & 63.4 & 76.5 & 68.2 \\\\ \\bottomrule\n\\end{tabular}\n}\n\\caption{Citation recall (R), citation precision (P), citation F1 (F1), and citation length (CL) of different models on LongBench-Cite using LAC-S strategy. The best and second results are bolded and underlined, respectively. \n% \\ys{Move Avg to the first column, same for Table 3\n}\n\\label{tab:main_cite}\n\\end{table}\n\n\\begin{table}[t]\n\\centering\n\\resizebox{\\linewidth}{!}{\n\\setlength{\\tabcolsep}{3.5pt}\n\\begin{tabular}{l|ccc|ccc|ccc|ccc|ccc|ccc}\n\\toprule\n & \\multicolumn{3}{c|}{Avg} & \\multicolumn{3}{c|}{Longbench-Chat} & \\multicolumn{3}{c|}{MultifieldQA} & \\multicolumn{3}{c|}{HotpotQA} & \\multicolumn{3}{c|}{Dureader} & \\multicolumn{3}{c}{GovReport} \\\\\n\\multirow{-2}{*}{Model} & C & $\\text{C}_\\text{LQA}$ & CR & C & $\\text{C}_\\text{LQA}$ & CR & C & $\\text{C}_\\text{LQA}$ & CR & C & $\\text{C}_\\text{LQA}$ & CR & C & $\\text{C}_\\text{LQA}$ & CR & C & $\\text{C}_\\text{LQA}$ & CR \\\\ \\midrule\n\\multicolumn{19}{l}{\\textit{\\textbf{Proprietary models}}} \\\\\nGPT-4o & \\cellcolor[HTML]{FFE2E0}69.4 & \\cellcolor[HTML]{FFE2E0}78.2 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 88\\%} & \\cellcolor[HTML]{FFE2E0}61.6 & \\cellcolor[HTML]{FFE2E0}77.4 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 80\\%} & \\cellcolor[HTML]{FFE2E0}84.0 & \\cellcolor[HTML]{FFE2E0}88.3 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 95\\%} & \\cellcolor[HTML]{FFE2E0}74.5 & \\cellcolor[HTML]{FFE2E0}80.8 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 92\\%} & \\cellcolor[HTML]{FFE2E0}81.0 & \\cellcolor[HTML]{FFE2E0}83.3 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 97\\%} & \\cellcolor[HTML]{FFE2E0}46.0 & \\cellcolor[HTML]{FFE2E0}61.3 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 75\\%} \\\\\nClaude-3-sonnet & \\cellcolor[HTML]{FFE2E0}77.6 & \\cellcolor[HTML]{FFE2E0}78.3 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 99\\%} & \\cellcolor[HTML]{FFE2E0}73.8 & \\cellcolor[HTML]{FFE2E0}77.8 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 95\\%} & \\cellcolor[HTML]{D9F5D6}88.6 & \\cellcolor[HTML]{D9F5D6}88.1 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 101\\%} & \\cellcolor[HTML]{D9F5D6}81.3 & \\cellcolor[HTML]{D9F5D6}75.3 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 108\\%} & \\cellcolor[HTML]{FFE2E0}75.8 & \\cellcolor[HTML]{FFE2E0}80.3 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 94\\%} & \\cellcolor[HTML]{FFE2E0}68.4 & \\cellcolor[HTML]{FFE2E0}70.1 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 98\\%} \\\\\nGLM-4 & \\cellcolor[HTML]{FFE2E0}73.7 & \\cellcolor[HTML]{FFE2E0}77.2 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 95\\%} & \\cellcolor[HTML]{FFE2E0}69.4 & \\cellcolor[HTML]{FFE2E0}79.8 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 87\\%} & \\cellcolor[HTML]{FFE2E0}87.6 & \\cellcolor[HTML]{FFE2E0}88.1 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 99\\%} & \\cellcolor[HTML]{FFE2E0}76.3 & \\cellcolor[HTML]{FFE2E0}76.5 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 100\\%} & \\cellcolor[HTML]{D9F5D6}76.0 & \\cellcolor[HTML]{D9F5D6}75.8 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 100\\%} & \\cellcolor[HTML]{FFE2E0}59.4 & \\cellcolor[HTML]{FFE2E0}65.9 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 90\\%} \\\\ \\midrule\n\\multicolumn{19}{l}{\\textit{\\textbf{Open-source models}}} \\\\\nGLM-4-9B-chat & \\cellcolor[HTML]{FFE2E0}62.3 & \\cellcolor[HTML]{FFE2E0}70.8 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 88\\%} & \\cellcolor[HTML]{FFE2E0}60.4 & \\cellcolor[HTML]{FFE2E0}67.8 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 89\\%} & \\cellcolor[HTML]{FFE2E0}74.2 & \\cellcolor[HTML]{FFE2E0}84.9 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 87\\%} & \\cellcolor[HTML]{FFE2E0}68.5 & \\cellcolor[HTML]{FFE2E0}71.5 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 96\\%} & \\cellcolor[HTML]{FFE2E0}49.3 & \\cellcolor[HTML]{FFE2E0}68.1 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 72\\%} & \\cellcolor[HTML]{FFE2E0}59.3 & \\cellcolor[HTML]{FFE2E0}61.6 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 96\\%} \\\\\nLlama-3.1-8B-Instruct & \\cellcolor[HTML]{FFE2E0}52.1 & \\cellcolor[HTML]{FFE2E0}60.2 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 86\\%} & \\cellcolor[HTML]{FFE2E0}53.2 & \\cellcolor[HTML]{FFE2E0}61.6 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 86\\%} & \\cellcolor[HTML]{FFE2E0}63.9 & \\cellcolor[HTML]{FFE2E0}73.3 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 87\\%} & \\cellcolor[HTML]{FFE2E0}64.0 & \\cellcolor[HTML]{FFE2E0}64.5 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 99\\%} & \\cellcolor[HTML]{FFE2E0}29.8 & \\cellcolor[HTML]{FFE2E0}39.4 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 76\\%} & \\cellcolor[HTML]{FFE2E0}49.6 & \\cellcolor[HTML]{FFE2E0}62.1 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 80\\%} \\\\\nLlama-3.1-70B-Instruct & \\cellcolor[HTML]{FFE2E0}62.0 & \\cellcolor[HTML]{FFE2E0}65.5 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 95\\%} & \\cellcolor[HTML]{FFE2E0}60.8 & \\cellcolor[HTML]{FFE2E0}64.6 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 94\\%} & \\cellcolor[HTML]{D9F5D6}78.4 & \\cellcolor[HTML]{D9F5D6}78.3 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 100\\%} & \\cellcolor[HTML]{FFE2E0}71.3 & \\cellcolor[HTML]{FFE2E0}75.3 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 95\\%} & \\cellcolor[HTML]{D9F5D6}43.3 & \\cellcolor[HTML]{D9F5D6}42.5 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 102\\%} & \\cellcolor[HTML]{FFE2E0}56.3 & \\cellcolor[HTML]{FFE2E0}66.9 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 84\\%} \\\\\nMistral-Large-Instruct & \\cellcolor[HTML]{FFE2E0}73.6 & \\cellcolor[HTML]{FFE2E0}76.4 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 96\\%} & \\cellcolor[HTML]{FFE2E0}63.8 & \\cellcolor[HTML]{FFE2E0}67.8 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 94\\%} & \\cellcolor[HTML]{D9F5D6}88.0 & \\cellcolor[HTML]{D9F5D6}85.3 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 103\\%} & \\cellcolor[HTML]{FFE2E0}77.0 & \\cellcolor[HTML]{FFE2E0}77.3 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 100\\%} & \\cellcolor[HTML]{FFE2E0}79.0 & \\cellcolor[HTML]{FFE2E0}83.3 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 95\\%} & \\cellcolor[HTML]{FFE2E0}60.4 & \\cellcolor[HTML]{FFE2E0}68.3 & \\cellcolor[HTML]{FFE2E0}{\\color[HTML]{D83931} 88\\%} \\\\ \\midrule\n\\multicolumn{19}{l}{\\textit{\\textbf{Our trained models}}} \\\\\nLongCite-8B & \\cellcolor[HTML]{D9F5D6}71.7 & \\cellcolor[HTML]{D9F5D6}67.6 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 107\\%} & \\cellcolor[HTML]{D9F5D6}69.0 & \\cellcolor[HTML]{D9F5D6}68.6 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 101\\%} & \\cellcolor[HTML]{D9F5D6}87.0 & \\cellcolor[HTML]{D9F5D6}83.6 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 104\\%} & \\cellcolor[HTML]{D9F5D6}70.8 & \\cellcolor[HTML]{D9F5D6}69.0 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 103\\%} & \\cellcolor[HTML]{D9F5D6}68.5 & \\cellcolor[HTML]{D9F5D6}62.3 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 110\\%} & \\cellcolor[HTML]{D9F5D6}63.0 & \\cellcolor[HTML]{D9F5D6}54.4 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 116\\%} \\\\\nLongCite-9B & \\cellcolor[HTML]{D9F5D6}70.4 & \\cellcolor[HTML]{D9F5D6}65.6 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 109\\%} & \\cellcolor[HTML]{D9F5D6}67.6 & \\cellcolor[HTML]{D9F5D6}64.6 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 105\\%} & \\cellcolor[HTML]{D9F5D6}84.1 & \\cellcolor[HTML]{D9F5D6}83.3 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 101\\%} & \\cellcolor[HTML]{D9F5D6}71.8 & \\cellcolor[HTML]{D9F5D6}67.5 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 106\\%} & \\cellcolor[HTML]{D9F5D6}69.0 & \\cellcolor[HTML]{D9F5D6}66.3 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 104\\%} & \\cellcolor[HTML]{D9F5D6}59.6 & \\cellcolor[HTML]{D9F5D6}46.4 & \\cellcolor[HTML]{D9F5D6}{\\color[HTML]{2EA121} 128\\%} \\\\ \\bottomrule\n\\end{tabular}\n}\n\\caption{Correctness in LQAC setting (C) using LAC-S strategy, correctness in vanilla long-context QA setting ($\\text{C}_\\text{LQA}$), and correctness ratio (CR) of different models on LongBench-Cite. We mark the cases where adding citations improves/hurts correctness (i.e., $\\text{CR} > 1$ / $\\text{CR} < 1$) in green/red.}\n\\label{tab:main_correct}\n\\end{table}\n1. \\textbf{Open-source LLMs, especially models with smaller sizes, have poor citation quality and lag far behind proprietary LLMs.} Though achieving correctness close to proprietary LLMs, open-source LLMs have obvious difficulty in citing supporting evidence for their generated statements. We attribute this to (1) poor instruction-following and in-context learning ability: open-source models often generate citations that do not conform to the prescribed format; (2) relatively weak evidence-searching ability: they often fail to find evidence for some statements (i.e., $\\mathcal{C}_i=\\emptyset$), or only find partially supporting or irrelevant evidence.\n\n2. \\textbf{The citation quality of proprietary LLMs is still unsatisfactory.} The citation F1 of proprietary LLMs on LongBench-chat and HotpotQA is only around 0.5, which means less than half statements in their responses are fully supported by the citations. Furthermore, their average citation length is even larger than chunk-level citation (whose citation length is 128), reflecting a coarse citation granularity. For example, the citation length of GPT-4o reaches 220 and each cited snippet contains about 6 sentences on average.\n\n3. \\textbf{Generating responses and citations in one pass via in-context learning hurts the long-context QA performance.} On most datasets, current LLMs have correctness ratios less than 100\\%, indicating that compared to standard long-context question answering, generating responses and citations at once through in-context learning always leads to correctness degradation due to the distribution shift from the post-training data.\n\nOverall, the performance of current LLMs on LQAC remains to be improved. To this end, we will explore automatic construction of SFT data in the following section to further enhance LLMs' capabilities for generating fine-grained sentence-level citations from lengthy contexts.\n\n\n\n\n\n\\begin{figure}\n \\centering\n \\includegraphics[width=\\linewidth]{figs/cof.pdf}\n \\caption{Overview of our CoF pipeline. The pipeline consists of four steps: (1) Generating long-context QA instance via Self-Instruct; (2) Using the answer to retrieve $k$ context chunks and generating chunk-level citations; (3) Extracting sentence-level citations for each statement from the cited chunks. (4) Filter out LQAC instances with few citations. }\n \\label{fig:cof}\n\\end{figure}\n\n\n\\section{CoF: Automatic SFT Data Construction For LQAC}\n\\label{sec:pipeline}\nTo utilize off-the-shelf LLMs for automatically constructing high-quality SFT data for LQAC, we propose \\textbf{CoF}, a post-hoc retrieval- and extraction-based pipeline that obtains precise sentence-level citations from \\textbf{Co}arse to \\textbf{F}ine. As illustrated in Figure~\\ref{fig:cof}, CoF consists of four steps: (1) Given a long context material, CoF first employs the LLM to generate a query and corresponding answer through Self-Instruct~\\cite{self-instruct}. (2) CoF then uses sentences in the answer to retrieve roughly $k$ chunks from the context, which are subsequently input into the LLM to add coarse-grained chunk-level citations into the answer. (3) Next, the LLM generates fine-grained sentence-level citations for each statement by extracting supporting sentences from the corresponding chunk-level citations. (4) Finally, instances with too few citations are filtered out. In the following, we will introduce each step of CoF in detail and validate its effectiveness on LongBench-Cite.\n\n\\subsection{Pipeline Details}\n\\textbf{QA Instance Generation. }\nConsidering that generating the answer and citations in one pass might affect answer correctness, we decide to first construct long-context QA pairs and then add citations into the answers in subsequent steps. The post-hoc characteristic also allows our pipeline to augment any long-context QA datasets with citations. For QA instance generation, we adopt the method of~\\cite{longalign}, which first employs the LLM to propose a query according to the given lengthy context and then requests it again to obtain the answer via vanilla long-context QA. They also incorporate different task type descriptions into the prompts (Figure~\\ref{prompt:qa_gen}), such as summarization, information extraction, and multi-hop reasoning, to guarantee the diversity of generated queries. \n\n\\textbf{Chunk-level Citation Generation. }\nAfter constructing the query and answer, we split the context into 128-token chunks and use each sentence in the answer to retrieve $l_\\text{max}$ chunks. We retain top-$l$ chunks for each sentence, where $l=\\min(l_\\text{max}, (k+n_\\text{sent}-1)/n_\\text{sent})$ and $n_\\text{sent}$ denotes the number of sentences, so that about $k$ chunks are retained in total. Then we feed all these chunks, which are sorted according to their position in the context, along with the query and answer into the LLM, and ask the LLM to segment the answer into statements and generate chunk-level citations for each statement using one-shot learning. Figure~\\ref{prompt:cof_coarse} shows the prompt we use. Compared with generating citations for each statement individually, aggregating all retrieved chunks and generating citations at once can not only reduce the calls of LLM but also improve the citation recall due to the high relevance between the statements.\n\n\\textbf{Sentence-level Citation Extraction. }\nBesides the coarse granularity, another drawback of chunk-level citation generated in step 2 is that the precise supporting evidence may be located at the beginning or end of the chunk where the sentences are incomplete. Therefore, to achieve fine-grained citations, we first expand each cited chunk by concatenating it with its preceding and succeeding chunks. Next, we retain and number complete sentences in the expanded chunk, and instruct the LLM to extract fine-grained supporting snippets from the chunk by outputting number spans such as [6-8], which refers to the 6th to 8th sentences, or outputting \"No relevant information\" if no supporting snippet is found in the chunk. The prompt includes 3 examples and is shown in Figure~\\ref{prompt:cof_fine}. At last, we remove irregular spans and re-number the others according to the sentence position in the original context to obtain the final sentence-level citations. \n\n\\textbf{Data Filtering. }\nIn the final filtering stage, we discard the instance if less than 20\\% of the statements in the answer have citations. If an answer has too few citations, we assume it is not factual-grounded enough in the context and may leverage the internal knowledge of LLMs, which often results in hallucinations.\n\n\\begin{table}[t]\n\\centering\n\\resizebox{\\linewidth}{!}{\n\\setlength{\\tabcolsep}{4pt}\n\\begin{tabular}{l|ccc|ccc|ccc|ccc|ccc|ccc}\n\\toprule\n\\multirow{2}{*}{Method} & \\multicolumn{3}{c|}{Avg} & \\multicolumn{3}{c|}{Longbench-Chat} & \\multicolumn{3}{c|}{MultifieldQA} & \\multicolumn{3}{c|}{HotpotQA} & \\multicolumn{3}{c|}{Dureader} & \\multicolumn{3}{c}{GovReport} \\\\\n & F1 & CR & CL & F1 & C & CR & F1 & C & CR & F1 & C & CR & F1 & C & CR & F1 & C & CR \\\\ \\midrule\n\\multicolumn{19}{l}{\\textit{\\textbf{one-pass methods}}} \\\\\nLAC-C & 51.6 & 95\\% & 128.0 & 33.9 & 67.8 & 85\\% & 55.7 & 87.3 & 99\\% & 41.2 & 75.3 & 98\\% & 59.5 & 76.3 & 101\\% & 67.7 & 59.1 & 90\\% \\\\\nLAC-S & 65.4 & 95\\% & 169.0 & 47.1 & 69.4 & 87\\% & 73.6 & 87.6 & 99\\% & 44.4 & 76.3 & 100\\% & 75.0 & 76.0 & 100\\% & 87.1 & 59.4 & 90\\% \\\\\nRAC-C & 72.5 & 87\\% & 128.0 & 69.7 & 59.0 & 74\\% & 79.1 & 80.7 & 92\\% & 57.7 & 69.8 & 91\\% & 75.7 & 77.3 & 102\\% & 80.3 & 49.9 & 76\\% \\\\\nRAC-S & 79.1 & 79\\% & 48.0 & 76.3 & 66.4 & 83\\% & 86.3 & 85.7 & 97\\% & 58.1 & 53.3 & 70\\% & 83.7 & 76.5 & 101\\% & 91.1 & 29.0 & 44\\% \\\\ \\midrule\n\\multicolumn{19}{l}{\\textit{\\textbf{post-hoc methods}}} \\\\\npost-LC-C & 47.3 & 100\\% & 128.0 & 27.8 & 79.8 & 100\\% & 48.2 & 88.1 & 100\\% & 34.5 & 76.5 & 100\\% & 52.1 & 75.8 & 100\\% & 74.1 & 65.9 & 100\\% \\\\\npost-LC-S & 57.3 & 100\\% & 147.0 & 34.3 & 79.8 & 100\\% & 65.3 & 88.1 & 100\\% & 40.0 & 76.5 & 100\\% & 64.2 & 75.8 & 100\\% & 82.8 & 65.9 & 100\\% \\\\\npost-RC-C & 63.8 & 100\\% & 128.0 & 61.0 & 79.8 & 100\\% & 65.3 & 88.1 & 100\\% & 49.3 & 76.5 & 100\\% & 67.8 & 75.8 & 100\\% & 75.8 & 65.9 & 100\\% \\\\\npost-RC-S & 62.8 & 100\\% & 48.0 & 63.4 & 79.8 & 100\\% & 64.8 & 88.1 & 100\\% & 48.6 & 76.5 & 100\\% & 69.7 & 75.8 & 100\\% & 67.5 & 65.9 & 100\\% \\\\\nCoF & 65.8 & 100\\% & 89.0 & 66.1 & 79.8 & 100\\% & 65.6 & 88.1 & 100\\% & 50.6 & 76.5 & 100\\% & 67.4 & 75.8 & 100\\% & 79.1 & 65.9 & 100\\% \\\\ \\bottomrule\n\\end{tabular}\n}\n\\caption{Citation F1 (F1), correctness (C), correctness ratio (CR), and citation length (CL) of different LQAC strategies on LongBench-Cite using GLM-4. We merge MultifieldQA-en/zh for brevity.}\n\\label{tab:pipeline-compare}\n\\end{table}\n\n\\subsection{Pipeline Validation}\n\\label{sec:pipeline_valid}\nBefore large-scale data construction, we first test CoF (without query generation and final filtering) on LongBench-Cite to validate its effectiveness. We compare CoF with the following LQAC strategies:\n\n\\begin{itemize}[itemsep=0pt, leftmargin=*]\n \\item\\textbf{LAC-C/LAC-S}: the LLM reads the entire context and generates response and chunk-level/sentence-level citation in one pass.\n \\item\\textbf{RAC-C/RAC-S}: the LLM reads top-$k$ chunks/sentences retrieved using the query and generates response and chunk-level/sentence-level citation in one pass. \n \\item\\textbf{post-LC-C/post-LC-S}: the LLM first generates a response via vanilla long-context QA, then adds chunk-level/sentence-level citations into the response by finding supporting evidence from the whole context.\n \\item\\textbf{post-RC-C/post-RC-S}: the LLM first generates a response via vanilla long-context QA, then uses the response to retrieve about $k$ chunks/sentences from the context, and adds chunk-level/sentence-level citations by finding supporting evidence from the retrieved text (similar to step 2 of CoF).\n\\end{itemize}\nWe use GLM-4 as the backbone LLM and Zhipu Embedding-2 as the retriever for all strategies and set retrieval hyper-parameters $l_\\text{max}=10$ and $k=40$. The results in Table~\\ref{tab:pipeline-compare} show that:\n\n\\textbf{1. Similar to other post-hoc strategies, CoF is able to preserve the high-quality answers produced through vanilla long-context QA, well preventing correctness degradation.} Specifically, GLM-4 perfectly maintains the answers' original contents unchanged when adding citations, thereby achieving 100\\% correctness ratios. In contrast, though attaining higher citation F1, one-pass methods typically generate answers with lower correctness, failing to fully leverage LLMs' long-context QA capacities.\n\n\\textbf{2. CoF achieves the highest citation F1 and relatively small citation length among post-hoc methods, highlighting its ability to generate precise, fine-grained citations.} Compared to post-LC-C and post-LC-S, post-hoc retrieval-based methods (i.e., post-RC-C, post-RC-S and CoF) benefit from a more focused evidence search space, typically yielding better performance. Furthermore, CoF's superiority over post-RC-C indicates that the step of sentence-level citation extraction effectively pinpoints supporting sentences and also filters out irrelevant chunks. Though post-RC-S achieves an even shorter citation length than CoF (49 v.s. 89), we empirically found that direct retrieval-based generation results in too many discontinuous citation numbers, making subsequent training difficult (details in Sec.~\\ref{sec:ablation}). \n\n\\subsection{LongCite-45k: a Large-scale SFT Dataset for LQAC}\nAfter validating the efficacy of CoF, we utilize this framework to construct \\textbf{LongCite-45k}, a large-scale SFT dataset for LQAC. Specifically, we first collect 50k documents from the pre-training corpus of GLM-4, covering 9 varied domains including books, encyclopedias, academic papers, codes, etc. These documents are mainly in English and Chinese and their lengths range from 256 to 128k tokens. We then apply CoF, using GLM-4 as the backbone LLM and Zhipu Embedding-v2 as the retriever, to generate a QA pair with sentence-level citations for each document, resulting in 44,600 high-quality LQAC instances after the filtering stage. As illustrated in Figure~\\ref{fig:cof}(d), the input part of each instance consists of a task instruction, a long document, and a query, and the output part is an answer with sentence-level citations.\\section{LongCite: Teach Long-context LLMs to Generate Citations}\n\\label{sec:longcite}\nIn this section, we conduct model training experiments to determine whether SFT on LongCite-45k can enhance LLMs' ability for the LQAC task, enabling them to generate both accurate responses and precise citations within a single output. We discuss the training details and experimental results as follows.\n\n\\subsection{Training Details}\nWe select two latest open-source base models, namely GLM-4-9B~\\citep{glm4} and Llama-3.1-8B~\\citep{llama-3-1}, for the training experiments. Both of the two models have been continually pre-trained on lengthy texts and support a context window of 128k tokens, thereby being suitable for SFT on LQAC data. Following~\\cite{longalign}, we combine LongCite-45k with 76k general SFT instances from ShareGPT~\\citep{vicuna2023} to ensure the model's general capacities. We name the models after SFT as LongCite-9B (abbr. for GLM-4-9B-LongCite) and LongCite-8B (abbr. for Llama-3.1-8B-LongCite).\n\nMeanwhile, to investigate whether SFT on LQAC data will influence models' long-context QA correctness compared to standard long-context SFT (i.e., SFT on vanilla long-context QA data), we additionally train the two base models using the pure long-context QA pairs (without the task instruction and citations) in LongCite-45k, and we name the trained models as LongSFT-9B (abbr. for GLM-4-9B-LongSFT) and LongSFT-8B (abbr. for Llama-3.1-8B-LongSFT). When calculating correctness ratios for LongCite-9B/8B, we use LongSFT-9B/8B to obtain the correctness in vanilla long-context QA setting (i.e., $\\text{C}_\\text{LQA}$).\n\nAll models are trained using 4 nodes with 8$\\times$H800 80G GPUs. We adopt Megatron-LM~\\citep{megatron-lm} with context parallelism to support a maximum training sequence length of 128k tokens, and use packing training with loss weighting~\\citep{longalign} to improve training efficiency. We set the batch size to 8 and the learning rate to 1e-5. We train each model for 4,000 steps, which is about 2 epochs and takes 18 hours.\n\n\\subsection{Experimental Results}\n\n\\subsubsection{Main Results}\n\\label{sec:main_results}\nWe show the citation quality and correctness of our trained models on LongBench-Cite in Table~\\ref{tab:main_cite} and~\\ref{tab:main_correct}, respectively. Here are our main findings:\n\n\\textbf{1. LongCite-8B and LongCite-9B achieve the best citation qualities among all models.} Compared to three powerful proprietary models, i.e., GPT-4o, Claude-3-Sonnet, and GLM-4, LongCite-8B/9B improves the overall citation F1 by 6.4/3.6, 4.8/2.0, and 6.6/3.8, respectively. Besides, the average citation length of LongCite-8B and LongCite-9B is also significantly shorter than that of proprietary models and chunk-level citations, indicating finer citation granularity. Surprisingly, LongCite-8B and LongCite-9B even attain higher citation F1 than the data construction pipeline CoF (72.0 and 69.2 v.s. 65.8), implying a potential for continuous self-improvement. In addition, the similar citation length between the trained models and CoF demonstrates that not only the evidence-locating skill but also the citation granularity can be learned through SFT.\n\nSpecially, we observe a performance gap between LongCite models and proprietary models on GovReport. We attribute this to the coarser citation granularity of proprietary models (e.g., the citation lengths of GLM-4 and LongCite-9B on GovReport are 188 and 86, respectively), which gives them unfair advantages in the evaluation of citation recall and precision. \n\n\\textbf{2. SFT with citation information further boosts the long-context QA correctness.} Different from in-context LQAC where the LLMs typically generate responses with lower correctness than vanilla long-context QA (Sec. ~\\ref{sec:benchmark_results}), SFT on LQAC data consistently improves the response correctness on all the datasets compared to vanilla long-context SFT (i.e., $\\text{CR} > 100\\%$). In particular, the correctness of LongCite-8B/9B increased by 16\\%/28\\% over LongSFT-8B/9B. Besides, the overall correctness of our trained model is also comparable with the officially post-trained models (i.e., GLM-4-9B-chat and Llama-3.1-8B-Instruct), validating the rationality of generating long-context QA pairs using self-instruct in our CoF pipeline.\n\nTo further explore the reasons for the correctness improvement, we manually compared the responses generated by LongCite-9B and LongSFT-9B and found that the improvement mainly comes from two aspects (we present 3 cases in Table~\\ref{tab:case_1},~\\ref{tab:case_2}, and~\\ref{tab:case_3} to illustrate our interpretation): (1) SFT with citation information enhances the evidence locating ability of the model and helps to prevent from hallucination; (2) LongCite models can utilize context information more uniformaly. Specifically, when faced with a query that requires a global view, the generated citation numbers allow LongCite models to be aware of that current response content has covered which parts of the context, so that they can utilize different parts of context more uniformly, resulting in a more comprehensive response. In contrast, LongSFT models tend to use more information from the head part of the context and only roughly utilize or even ignore the rest of the context.\n\n\\begin{figure}[t]\n\\centering\n\\begin{minipage}{0.52\\textwidth}\n \\input{tables/ablation}\n\\end{minipage}%\n\\hfill\n\\begin{minipage}{0.46\\textwidth}\n \\centering\n % \\vspace{-8mm}\n \\includegraphics[width=\\linewidth]{figs/correct_f1_corr.pdf}\n \\vspace{-7mm}\n \\caption{Citation F1 mean and std. w.r.t correctness of LongCite-9B's responses. }\n \\label{fig:correct_f1_corr}\n\\end{minipage}\n\\end{figure}\n\n\\subsubsection{Further Analysis}\n\\label{sec:ablation}\n\\textbf{Ablation on LongCite-45k dataset.} To verify that the enhanced LQAC ability is obtained from the LongCite-45k dataset instead of standard long-context SFT, we test LQAC performance of LongSFT-9B on LongBench-Chat using the one-shot prompt in Sec.~\\ref{sec:benchmark_results}. The results in Table~\\ref{tab:ablation} indicate that LongSFT-9B performs poorly on LQAC task. Similar to the open-sourced LLMs, LongSFT-9B always generates nonconforming citations or no citations. \n\n\\textbf{Ablation on data filtering.} To demonstrate the effect of data filtering in CoF pipeline, we train LongCite-9B with the unfiltered data. Table~\\ref{tab:ablation} shows that data filtering effectively improves citation quality.\n\n\\textbf{Comparison with data constructed through post-RAC-S strategy.} We attempt constructing LQAC data by applying post-RAC-S strategy, whose performance is comparable with CoF (Sec.~\\ref{sec:pipeline_valid}), to add citations for the QA pairs in LongCite-45k. However, as shown in Table~\\ref{tab:ablation}, the model trained with post-RAC-S data achieves much worse citation F1 than LongCite-9B. We believe the main reason is that post-RAC-S directly recalls sentences that are not necessarily adjacent from the context, resulting in many discontinuous citation numbers (such as [3][7][15]...), which makes subsequent training difficult. In contrast, CoF extracts sentence-level citations from bigger chunk-level snippets and uses number spans to represent citations. These methods contribute to maintaining the semantic coherence of the cited information, which is advantageous for training purposes.\n\n\\textbf{Correlation between correctness and citation quality.} To explore the correlation between correctness and citation quality, we divide LongCite-9B's responses on LongBench-Cite into three groups according to their correctness and compute the mean and standard deviation of citation F1 for each group. As illustrated in Figure~\\ref{fig:correct_f1_corr}, responses with higher correctness typically have higher citation qualities, demonstrating a mutually promoting relationship between these two attributes.\n\n\\begin{table}[t]\n\\centering\n\\begin{minipage}{0.52\\textwidth}\n \\centering\n \\resizebox{\\linewidth}{!}{\n \\setlength{\\tabcolsep}{3pt}\n \\begin{tabular}{l|ccc|ccc|ccc}\n \\toprule\n \\multirow{2}{*}{Model} & \\multicolumn{3}{c|}{Human scores} & \\multicolumn{3}{c|}{GPT-4o scores} & \\multicolumn{3}{c}{ALCE scores} \\\\\n & R & P & F1 & R & P & F1 & R & P & F1 \\\\ \\midrule\n GLM-4 & 61.2 & 67.5 & 60.2 & 47.6 & 53.9 & 47.1 & 46.1 & 29.1 & 30.8 \\\\\n LongCite-8B & \\textbf{79.6} & \\textbf{88.9} & \\textbf{82.6} & \\textbf{62.0} & \\textbf{79.7} & \\textbf{67.4} & {\\ul 59.6} & {\\ul 39.5} & {\\ul 42.0} \\\\\n LongCite-9B & {\\ul 72.8} & {\\ul 84.2} & {\\ul 75.8} & {\\ul 57.6} & {\\ul 78.1} & {\\ul 63.6} & \\textbf{64.2} & \\textbf{45.1} & \\textbf{47.1} \\\\ \\bottomrule\n \\end{tabular}\n }\n \\caption{Citation quality evaluated by human, GPT-4o and ALCE on LongBench-Chat.}\n \\label{tab:human_eval}\n\\end{minipage}%\n\\hfill\n\\begin{minipage}{0.45\\textwidth}\n \\centering\n % \\vspace{-8mm}\n \\resizebox{\\linewidth}{!}{\n \\setlength{\\tabcolsep}{4pt}\n \\begin{tabular}{l|cc|cc} \n \\toprule\n \\multirow{2}{*}{Method} & \\multicolumn{2}{c|}{Citation recall} & \\multicolumn{2}{c}{Citation precision} \\\\ \n & Kappa ($\\kappa$) & Acc & Kappa ($\\kappa$) & Acc \\\\ \\midrule\n GPT-4o & \\textbf{0.544/0.593*} & \\textbf{75.0/80.2*} & \\textbf{0.655} & \\textbf{88.8} \\\\\n ALCE & 0.247* & 64.7* & 0.146 & 47.4 \\\\ \\bottomrule\n \\end{tabular}\n }\n \\caption{Agreement between GPT-4o/ALCE and human. * means treating ``partially support'' as ``not support''.}\n \\label{tab:corr}\n\\end{minipage}\n\\end{table}\n\n\\subsection{Human Evaluation}\n\\label{sec:human_eval}\nTo verify that our automatic evaluation of citation quality using GPT-4o correlates with human judgment, we conduct a human evaluation on three models: GLM-4, LongCite-8B, and LongCite-9B. Specifically, we anonymized their responses on LongBench-Chat, including 150 responses, 1,064 statements, and 909 citations in total, and manually annotated the citation recall and precision following the same standard as GPT-4o evaluation. We also compare GPT-4o evaluation with ALCE~\\citep{gao23}, which utilizes NLI model TRUE~\\citep{true} to measure citation recall and precision. As shown in Table~\\ref{tab:human_eval}, the relative rankings produced by human and GPT-4o are consistent, indicating that improvements in GPT-4o scores also reflect improvements in human preferences. In addition, the absolute scores from GPT-4o typically aligned more closely with human scores compared to ALCE. On the other hand, we observed that GPT-4o scores are generally lower than human scores because the cited snippets often contain unclear pronouns like ``he/she'' and ``our method''. We believe that incorporating an anaphora resolution step may alleviate this problem but will also increase the evaluation costs. Furthermore, the Cohen’s kappa coefficients between GPT-4o and human are significantly higher compared to ALCE (Table~\\ref{tab:corr}), demonstrating a substantial agreement for citation recall (0.593 when treating “partially support” as “not support” following ALCE) and citation precision (0.655). When taking human annotations as gold labels, GPT-4o also achieves high accuracy (75.0\\% for citation recall and 88.8\\% for precision).\n\n\n\\section{Related Works}\n\\textbf{Long-context LLMs.} \n% Recently, extending the context window, which is regarded as the working memory of LLMs, has garnered increasing attention. \nA mature approach for extending the context window of LLMs involves continued pre-training of base LLMs on extensive long texts followed by alignment using diverse long-context QA pairs~\\citep{internlm2, glm4, llama-3-1}. However, because of the difficulty of annotations, most long-context QA data is automatically synthesized by LLMs themselves~\\citep{longalign, longllama}, which cannot strictly guarantee the faithfulness of the answers. This leads to potential hallucinations of the aligned LLMs, i.e., fabricating content not present in or consistent with the context. Therefore, users often require a way to verify the accuracy and reliability of the information provided by LLMs, especially in highly sensitive domains such as law and finance. Our work explores how to enable long-text models to produce responses with fine-grained citations, thereby enhancing the verifiability and trustworthiness of the long-context LLMs.\n\n\\textbf{Question Answering with Citations.} \nRecently, question answering with citations has been extensively studied in the fields of open-domain QA~\\citep{webgpt, bohnet22, gao23b, gao23, schimanski24}. Most of these methods rely on retrieval-augmented generation or post-hoc processing, which are not well-suited for long-context QA scenarios due to information loss or excessive latency. Furthermore, the coarse granularity of their citations negatively impacts user experience~\\citep{huang24}. Our work, however, leverages long-context LLMs to generate responses and precise sentence-level citations in a single pass, providing advantages in terms of response correctness, efficiency, and user friendliness. Moreover, current methods for citation evaluation largely depend on NLI models that have limited capacities~\\citep{true, liu23, gao23, Fierro24}. In contrast, we utilize GPT-4o as a judge and consider more complex scenarios, thereby achieving a higher agreement with human assessments.\\section{Conclusion}\nIn this work, we explore enhancing LLMs' capacity to generate fine-grained citations from lengthy contexts. We first propose LongBench-Cite, an automatic benchmark to reveal current LLMs' limited performance on long-context question answering with citations (LQAC). We then introduce CoF, a novel pipeline that uses off-the-shelf LLMs to automatically generate long-context QA instances with precise sentence-level citations, to construct LongCite-45k, a large-scale SFT dataset for LQAC. Finally, we successfully train LongCite-8B and LongCite-9B with LongCite-45k, allowing the generation of accurate responses and fine-grained citations in one pass. Extensive analyses and human evaluation further verify the effectiveness of our approach. We believe that this work lays a solid foundation for further research on LQAC and contributes to the development of more reliable and trustworthy LLMs.\n\n\n\\bibliography{iclr2025_conference}\n\\bibliographystyle{iclr2025_conference}\n\n\\newpage\n\\appendix\n\\section{Model Cards}\nWe list the details of our evaluated models in Table~\\ref{tab:model_card}.\n\\begin{table}[t]\n \\centering\n \\resizebox{\\linewidth}{!}{\n \\begin{tabular}{llc}\n \\toprule\n \\textbf{Model name} & \\textbf{Model version} & \\textbf{Context window} \\\\\n \\midrule\n Claude-3-Sonnet~\\citep{claude-3} & claude-3-sonnet-20240229 & 200,000 tokens \\\\ \n GPT-4o~\\citep{GPT-4o} & gpt-4o-2024-05-13 & 128,000 tokens \\\\\n GLM-4~\\citep{glm4} & GLM-4-0520 & 128,000 tokens \\\\\n GLM-4-9B-chat~\\citep{glm4} & - & 128,000 tokens \\\\\n Llama-3.1-8B-Instruct~\\citep{llama-3-1} & - & 128,000 tokens \\\\\n Llama-3.1-70B-Instruct~\\citep{llama-3-1} & - & 128,000 tokens \\\\\n Mistral-Large-Instruct~\\citep{mistral} & Mistral-Large-Instruct-2407 & 128,000 tokens \\\\\n \\bottomrule\n \\end{tabular}\n }\n\\caption{Model cards.}\n\\label{tab:model_card}\n\\end{table}\n\\section{Case Study}\nWe present three cases in Table~\\ref{tab:case_1},~\\ref{tab:case_2}, and~\\ref{tab:case_3} to help interpret the improvement of correctness (the detail interpretation is in Sec.~\\ref{sec:main_results}).\n\\begin{table}[t]\n\\centering\n\\resizebox{\\linewidth}{!}{\n \\begin{tabular}{p{\\linewidth}}\n\\toprule\n\\textbf{Query:} Are both Duke Energy and Affiliated Managers Group based in Massachusetts? \\\\ \\midrule\n\\begin{tabular}[c]{@{}p{\\linewidth}@{}}\\textbf{LongSFT-9B:} \\\\ Based on the information provided, {\\color[HTML]{D83931} Duke Energy has offices in several locations including West Palm Beach, Florida; Prides Crossing, Massachusetts; Stamford, Connecticut; and London, United Kingdom.} Affiliated Managers Group has principal offices in West Palm Beach, Florida; Prides Crossing, Massachusetts; Stamford, Connecticut; and London, United Kingdom. So they both have an office in Massachusetts.\\end{tabular} \\\\\\midrule\n\\begin{tabular}[c]{@{}p{\\linewidth}@{}}\\textbf{LongCite-9B:}\\\\ \\textless{}statement\\textgreater{}Based on the information provided, {\\color[HTML]{2EA121}Duke Energy is headquartered in Charlotte, North Carolina,} while Affiliated Managers Group has principal offices in West Palm Beach, Florida; Prides Crossing, Massachusetts; Stamford, Connecticut; and London, United Kingdom.\\textless{}cite\\textgreater{}{[}25-25{]}{[}219-219{]}\\textless{}/cite\\textgreater{}\\textless{}/statement\\textgreater \\ \\textless{}statement\\textgreater{}So only Affiliated Managers Group is based in Massachusetts.\\textless{}cite\\textgreater{}\\textless{}/cite\\textgreater{}\\textless{}/statement\\textgreater{} \\\\\n\\textbf{Citations:} \\\\\n{[}25-25{]}: Duke Energy Corporation is an American electric power and natural gas holding company headquartered in Charlotte, North Carolina. \\\\\n{[}219-219{]}: (NYSE: AMG) is a strategic partner to independent investment management firms globally, with equity stakes in a number of partner-owned traditional investment managers, hedge funds, and specialized private equity firms which it calls “Affiliates.” The company has principal offices in West Palm Beach, Florida; Prides Crossing, Massachusetts; Stamford, Connecticut; and London, United Kingdom.\n\\end{tabular} \\\\\n\\bottomrule\n\\end{tabular}\n}\n\\caption{Case study. LongSFT-9B hallucinates the office location of Duke Energy, directly copying that of Affiliated Managers Group, while LongCite-9B gets the correct answer due to SFT with citations. We mark the wrong and correct statements in red and green, respectively.}\n\\label{tab:case_1}\n\\end{table}\\begin{table}[t]\n\\centering\n\\resizebox{\\linewidth}{!}{\n \\begin{tabular}{p{\\linewidth}}\n\\toprule\n\\textbf{Query:} Please write a one-page summary of the above government report. \\\\ \\midrule\n\\begin{tabular}[c]{@{}p{\\linewidth}@{}}\\textbf{LongSFT-9B:} \\\\ The Department of Homeland Security (DHS) has established policies and processes for managing its multi-billion dollar acquisition investments. These policies and processes are primarily set forth in its Acquisition Management Directive 102-01 and Acquisition Management Instruction 102-01-001. The department uses these policies and processes to deliver systems that are intended to close critical capability gaps, helping enable DHS to execute its missions and achieve its goals. \\\\ The DHS acquisition management policy establishes that a major acquisition program’s decision authority shall review the program at a series of predetermined acquisition decision events to assess whether the major program is ready to proceed through the acquisition life-cycle phases. Depending on the program, these events can occur within months of each other, or be spread over several years. \\\\ \\textcolor[HTML]{D83931}{The report found that 10 of the 24 programs assessed were on track to meet their schedule and cost goals, while 14 programs were not on track. The report made two recommendations to DHS to improve its acquisition management policies and processes.}\\end{tabular} \\\\ \\midrule\n\\begin{tabular}[c]{@{}p{\\linewidth}@{}}\\textbf{LongCite-9B:}\\\\ \\textless{}statement\\textgreater{}The report assesses the Department of Homeland Security's (DHS) major acquisition programs and their progress towards meeting schedule and cost goals. It also evaluates DHS's policies and processes for acquisition management, resource allocation, and requirements development to determine if they reflect key portfolio management practices.\\textless{}cite\\textgreater{}\\textless{}/cite\\textgreater{}\\textless{}/statement\\textgreater\\\\ \\textless{}statement\\textgreater{}Key findings include:\\textless{}cite\\textgreater{}\\textless{}/cite\\textgreater{}\\textless{}/statement\\textgreater\\\\ \\textcolor[HTML]{2EA121}{\\textless{}statement\\textgreater{}- Of the 28 programs reviewed, only 10 were on track to meet their schedule and cost goals as of January 2018. The remaining 14 programs had slipped on schedule, cost, or both.\\textless{}cite\\textgreater{}{[}89-97{]}\\textless{}/cite\\textgreater{}\\textless{}/statement\\textgreater}\\\\ \\textcolor[HTML]{2EA121}{\\textless{}statement\\textgreater{}- The most common reasons for schedule slips were new requirements, technical challenges, and external factors like staffing shortages or funding cuts.\\textless{}cite\\textgreater{}{[}105-106{]}{[}108-108{]}\\textless{}/cite\\textgreater{}\\textless{}/statement\\textgreater}\\\\ \\textcolor[HTML]{2EA121}{\\textless{}statement\\textgreater{}- Some programs had cost breaches due to scope increases, technical challenges, or contractor labor costs.\\textless{}cite\\textgreater{}{[}119-119{]}{[}121-121{]}{[}127-127{]}\\textless{}/cite\\textgreater{}\\textless{}/statement\\textgreater}\\\\ \\textcolor[HTML]{2EA121}{\\textless{}statement\\textgreater{}- DHS has taken steps to improve its policies and processes, but more is needed. For example, it should require components to submit affordability information when programs re-baseline due to breaches.\\textless{}cite\\textgreater{}{[}178-178{]}{[}234-234{]}{[}258-258{]}\\textless{}/cite\\textgreater{}\\textless{}/statement\\textgreater}\\\\ \\textcolor[HTML]{2EA121}{\\textless{}statement\\textgreater{}- The report makes two recommendations for DHS to improve its acquisition management: require components to submit affordability information when programs re-baseline and assess the results of post-implementation reviews to identify opportunities to improve performance across the portfolio.\\textless{}cite\\textgreater{}{[}258-259{]}\\textless{}/cite\\textgreater{}\\textless{}/statement\\textgreater}\\\\ \\textless{}statement\\textgreater{}- DHS concurred with the recommendations and has identified actions it will take to address them.\\textless{}cite\\textgreater{}{[}261-261{]}\\textless{}/cite\\textgreater{}\\textless{}/statement\\textgreater{}\\end{tabular} \\\\ \\bottomrule\n\\end{tabular}\n}\n\\caption{Case study. LongCite-9B utilize more information from the middle part of the context, resulting in a more detailed response than LongSFT-9B. We mark the coarse and detailed counterparts in their response in red and green, respectively}\n\\label{tab:case_2}\n\\end{table}\\begin{table}[t]\n\\centering\n\\resizebox{\\linewidth}{!}{\n \\begin{tabular}{p{\\linewidth}}\n\\toprule\nQuery: Please write a one-page summary of the above government report. \\\\\\midrule\n\\begin{tabular}[c]{@{}p{\\linewidth}@{}}\\textbf{LongSFT-9B:} \\\\ The US Agency for International Development (USAID) has obligated over \\$5 billion in bilateral assistance to the Palestinians in the West Bank and Gaza since 1993, primarily using funds appropriated through the Economic Support Fund (ESF). USAID provides project assistance and debt relief payments to Palestinian Authority (PA) creditors, and implements most ESF programs, including those related to private sector development, health, water and road infrastructure, local governance, civil society, rule of law, education, and youth development. USAID assistance to the West Bank and Gaza is conducted under antiterrorism policies and procedures outlined in an administrative policy document known as Mission Order 21. As of March 31, 2018, USAID had obligated about \\$544.1 million (over 99 percent) and expended about \\$350.6 million (over 64 percent) of approximately \\$544.5 million in ESF assistance allocated for the West Bank and Gaza in fiscal years 2015 and 2016.\\end{tabular} \\\\\\midrule\n\\begin{tabular}[c]{@{}p{\\linewidth}@{}}\\textbf{LongCite-9B:}\\\\ \\textless{}statement\\textgreater{}The report discusses USAID's Economic Support Fund (ESF) assistance to the Palestinians in the West Bank and Gaza from FY2015-FY2016. Key points include:\\textless{}cite\\textgreater{}\\textless{}/cite\\textgreater{}\\textless{}/statement\\textgreater\\\\ \\textless{}statement\\textgreater{}- USAID obligated over \\$544 million in ESF assistance for the West Bank and Gaza in FY2015-FY2016, with about \\$350 million expended.\\textless{}cite\\textgreater{}{[}8-8{]}\\textless{}/cite\\textgreater{}\\textless{}/statement\\textgreater\\\\ \\textless{}statement\\textgreater{}- Most assistance went to project assistance (\\$215 million in FY2015, \\$184 million in FY2016) for economic growth, infrastructure, education, health, etc. Some went to debt relief payments to PA creditors (\\$75 million in FY2015, \\$70 million in FY2016).\\textless{}cite\\textgreater{}{[}9-11{]}\\textless{}/cite\\textgreater{}\\textless{}/statement\\textgreater\\\\ \\textcolor[HTML]{2EA121}{\\textless{}statement\\textgreater{}- USAID conducted required vetting of PA creditors to ensure assistance did not support terrorism. Vetting occurred within 12 months before debt relief agreements were signed.\\textless{}cite\\textgreater{}{[}36-36{]}{[}40-40{]}{[}42-42{]}\\textless{}/cite\\textgreater{}\\textless{}/statement\\textgreater}\\\\ \\textcolor[HTML]{2EA121}{\\textless{}statement\\textgreater{}- External assessments identified weaknesses in PA financial systems for medical referrals and petroleum procurement. USAID took additional steps to mitigate risks.\\textless{}cite\\textgreater{}{[}52-52{]}{[}62-62{]}\\textless{}/cite\\textgreater{}\\textless{}/statement\\textgreater}\\\\ \\textcolor[HTML]{2EA121}{\\textless{}statement\\textgreater{}- Financial audits of the debt relief grants did not identify any issues.\\textless{}cite\\textgreater{}{[}66-66{]}{[}70-70{]}\\textless{}/cite\\textgreater{}\\textless{}/statement\\textgreater}\\\\ \\textcolor[HTML]{2EA121}{\\textless{}statement\\textgreater{}- USAID does not plan to resume direct cash payments to the PA due to concerns about misuse. Payments to creditors are considered more transparent.\\textless{}cite\\textgreater{}{[}49-49{]}\\textless{}/cite\\textgreater{}\\textless{}/statement\\textgreater}\\\\ \\textless{}statement\\textgreater{}In summary, the report concludes that USAID generally followed proper procedures for providing ESF assistance to the Palestinians in the West Bank and Gaza, with some concerns about PA financial systems. Debt relief payments to creditors were properly vetted.\\textless{}cite\\textgreater{}\\textless{}/cite\\textgreater{}\\textless{}/statement\\textgreater{}\\end{tabular} \\\\\n\\bottomrule\n\\end{tabular}\n}\n\\caption{Case Study. The citation numbers in the response make LongCite-9B aware of which parts of the document the current response has covered and thus help it utilize context information uniformly to generate a more comprehensive summary, while LongSFT-9B only focuses on the front part of the extensive document and ignores the rest parts.}\n\\label{tab:case_3}\n\\end{table}\n\\section{Prompts}\n\\begin{figure}[t]\n \\centering\n \\begin{tcolorbox}[size=title,opacityfill=0.1]\n \\noindent\n [Instructions] You are asked to evaluate the quality of the AI assistant’s answers to user questions as an impartial judge, and your evaluation should take into account factors including correctness (high priority), helpfulness, accuracy, and relevance. The scoring principles are as follows: 1. Read the AI assistant’s answer and compare the assistant’s answer with the reference answer. 2. Identify all errors in the AI Assistant’s answers and consider how much they affect the answer to the question. 3. Evaluate how helpful the AI assistant’s answers are in directly answering the user’s questions and providing the information the user needs. 4. Examine any additional information in the AI assistant’s answer to ensure that it is correct and closely related to the question. If this information is incorrect or not relevant to the question, points should be deducted from the overall score. Please give an overall integer rating from 1 to 10 based on the above principles, strictly in the following format: ``[[rating]]\", e.g. ``[[5]]\".\n \n [Question] \\{\\emph{Question}\\} \n \n [Reference answer begins] \\{\\emph{Groundtruth}\\} [Reference answer ends] \n \n Below are several assistants’ answers and their ratings:\n \n [Assistant’s answer begins] \\{\\emph{Example Answer 1}\\} [Assistant’s answer ends] \n\n Rating: [[\\{{\\emph{Rating for Example Answer 1}}\\}]]\n\n [Assistant’s answer begins] \\{\\emph{Example Answer 2}\\} [Assistant’s answer ends] \n\n Rating: [[\\{{\\emph{Rating for Example Answer 2}}\\}]]\n\n [Assistant’s answer begins] \\{\\emph{Example Answer 2}\\} [Assistant’s answer ends] \n\n Rating: [[\\{{\\emph{Rating for Example Answer 2}}\\}]]\n\n Please rate the following assistant answers based on the scoring principles and examples above:\n \n [Assistant’s answer begins] \\{{\\emph{Response}}\\} [Assistant’s answer ends]\n \n Rating:\n \n \\end{tcolorbox}\n \\caption{prompt for correctness evaluation on LongBench-Chat.}\n \\label{prompt:correct_longbench_chat}\n\\end{figure}\\begin{figure}[t]\n \\centering\n \\begin{tcolorbox}[size=title,opacityfill=0.1]\n \\noindent\n You are asked to evaluate the quality of the AI assistant’s answers to user question as an impartial judge, and your evaluation should take into account factors including correctness (high priority), and comprehensiveness (whether the assistant’s answer covers all points). Read the AI assistant’s answer and compare against the reference answer, and give an overall integer rating in 1, 2, 3 (1 = wrong or irrelevant, 2 = partially correct, 3 = correct and comprehensive) based on the above principles, strictly in the following format:``[[rating]]\", e.g. ``[[2]]\".\n \n Question: \n \n \\{\\emph{Question}\\} \n \n Reference answer:\n \n \\{\\emph{Reference answer}\\} \n \n Assistant’s answer: \n \n \\{{\\emph{Response}}\\}\n \n Rating:\n \n \\end{tcolorbox}\n \\caption{Prompt for correctness evaluation on MultiFieldQA-zh/en, HotpotQA, and Dureader.}\n \\label{prompt:correct_qa}\n\\end{figure}\\begin{figure}[t]\n \\centering\n \\begin{tcolorbox}[size=title,opacityfill=0.1]\n \\noindent\n You are asked to evaluate the quality of the AI assistant’s generated summary as an impartial judge, and your evaluation should take into account factors including correctness (high priority), comprehensiveness (whether the assistant’s summary covers all points), and coherence. Read the AI assistant’s summary and compare against the reference summary, and give an overall integer rating in on a scale of 1 to 5, where 1 is the lowest and 5 is the highest based on the evaluation criteria, strictly in the following format:``[[rating]]\", e.g. ``[[3]]\".\n \n Question: \n \n \\{\\emph{Question}\\} \n \n Reference answer:\n \n \\{\\emph{Reference answer}\\} \n \n Assistant’s answer: \n \n \\{{\\emph{Response}}\\}\n \n Rating:\n \n \\end{tcolorbox}\n \\caption{Prompt for correctness evaluation on GovReport.}\n \\label{prompt:correct_summary}\n\\end{figure}\\begin{figure}[t]\n \\centering\n \\begin{tcolorbox}[size=title,opacityfill=0.1]\n \\noindent\n You are an expert in evaluating text quality. You will receive a user's question about an uploaded document, a factual statement from an AI assistant's response based on that document, and a snippet from the document (since the document is too long to display in full). Your task is to carefully assess whether this statement is supported by the snippet. Please use the following scale to generate your rating:\n \n - [[Fully supported]] - Most information in the statement is supported by or extracted from the snippet. This applies only to cases where the statement and parts of the snippet are almost identical. \n \n - [[Partially supported]] - More than half of the content in the statement is supported by the snippet, but a small portion is either not mentioned or contradicts the snippet. For example, if the statement has two key points and the snippet supports only one of them, it should be considered [Partially supported].\n \n - [[No support]] - The statement is largely unrelated to the snippet, or most key points in the statement do not align with the content of the snippet.\n \n Ensure that you do not use any information or knowledge outside of the snippet when evaluating. \n \n Please provide the rating first, followed by the analysis, in the format ``Rating: [[...]] Analysis: ...\".\\\\\n\n \\textless question\\textgreater \\\\\n \\{\\emph{Question}\\} \\\\\n \\textless /question\\textgreater \\\\\n \n \\textless statement\\textgreater \\\\\n \\{\\emph{Statement}\\} \\\\\n \\textless /statement\\textgreater \\\\\n\n \\textless snippet\\textgreater \\\\\n \\{\\emph{Concatenation of Cited Snippet}\\} \\\\\n \\textless /statement\\textgreater \\\\\n \n \n \n \n \\end{tcolorbox}\n \\caption{Prompt for evaluating citation recall when the statement has at least one citation.}\n \\label{prompt:support_score}\n\\end{figure}\\begin{figure}[t]\n \\centering\n \\begin{tcolorbox}[size=title,opacityfill=0.1]\n \\noindent\n You are an expert in evaluating text quality. You will receive a user's question regarding their uploaded document (due to the length of the document, it is not shown to you), an AI assistant's response based on the document, and a sentence from the response. Your task is to determine whether this sentence is a factual statement made based on the information in the document that requires citation, rather than an introductory sentence, transition sentence, or a summary, reasoning, or inference based on the previous response.\\\\\n Ensure that you do not use any other external information during your evaluation. \\\\\n Please first provide your judgment (answer with [[Yes]] or [[No]]), then provide your analysis in the format ``Need Citation: [[Yes/No]] Analysis: ...\".\\\\\n\n \\textless question\\textgreater \\\\\n \\{\\emph{Question}\\} \\\\\n \\textless /question\\textgreater \\\\\n\n \\textless response\\textgreater \\\\\n \\{\\emph{Model Response}\\} \\\\\n \\textless /response\\textgreater \\\\\n \n \\textless statement\\textgreater \\\\\n \\{\\emph{Statement}\\} \\\\\n \\textless /statement\\textgreater \\\\\n \n \n \n \n \\end{tcolorbox}\n \\caption{Prompt for evaluating citation recall when the statement has no citation.}\n \\label{prompt:need_citation}\n\\end{figure}\\begin{figure}[t]\n \\centering\n \\begin{tcolorbox}[size=title,opacityfill=0.1]\n \\noindent\n You are an expert in evaluating text quality. You will receive a user's question about an uploaded document, a factual statement from an AI assistant's response based on that document, and a snippet from the document (since the document is too long to display in full). Your task is to carefully assess whether the snippet contains some key information of the statement. Please use the following grades to generate the rating: \\\\\n - [[Relevant]] - Some key points of the statement are supported by the snippet or extracted from it.\\\\\n - [[Unrelevant]] - The statement is almost unrelated to the snippet, or all key points of the statement are inconsistent with the snippet content.\\\\\n Ensure that you do not use any information or knowledge outside of the snippet when evaluating. \\\\\n Please provide the rating first, followed by the analysis, in the format ``Rating: [[...]] Analysis: ...\". \\\\\n\n \\textless question\\textgreater \\\\\n \\{\\emph{Question}\\} \\\\\n \\textless /question\\textgreater \\\\\n \n \\textless statement\\textgreater \\\\\n \\{\\emph{Statement}\\} \\\\\n \\textless /statement\\textgreater \\\\\n\n \\textless snippet\\textgreater \\\\\n \\{\\emph{Cited Snippet}\\} \\\\\n \\textless /statement\\textgreater \\\\\n \n \n \n \n \\end{tcolorbox}\n \\caption{Prompt for evaluating citation precision.}\n \\label{prompt:relevant_score}\n\\end{figure}\\begin{figure}[t]\n \\centering\n\\begin{tcolorbox}[size=title,opacityfill=0.1]\n\\noindent\nPlease answer the user's question based on the given document. When a factual statement S in your response uses information from some chunks in the document (i.e., \\textless C\\{s1\\}\\textgreater -\\textless C\\{e1\\}\\textgreater , \\textless C\\{s2\\}\\textgreater -\\textless C\\{e2\\}\\textgreater , ...), please append these chunk numbers to S in the format \"\\textless statement\\textgreater \\{S\\}\\textless cite\\textgreater [\\{s1\\}-\\{e1\\}][\\{s2\\}-\\{e2\\}]...\\textless /cite\\textgreater \\textless /statement\\textgreater \". For other sentences such as introductory sentences, summarization sentences, reasoning, and inference, you still need to append \"\\textless cite\\textgreater \\textless /cite\\textgreater \" to them to indicate they need no citations. You must answer in the same language as the user's question.\\\\\n\nHere is an example: \\\\\n\n\\{\\emph{An Example}\\} \\\\\n\nNow get ready to handle the following test case. \\\\\n\n[Document Start]\\\\\n\\textless C0\\textgreater \\{\\emph{Sentence 0}\\} \\textless C1\\textgreater \\{\\emph{Sentence 1}\\} \\textless C2\\textgreater \\{\\emph{Sentence 2}\\} ... \n\n[Document End]\\\\\n\n[Question]\\\\\n\\{\\emph{Question}\\} \\\\\n\n[Remind] \\\\\nPlease answer the user's question based on the given document. When a factual statement S in your response uses information from some chunks in the document (i.e., \\textless C\\{s1\\}\\textgreater -\\textless C\\{e1\\}\\textgreater , \\textless C\\{s2\\}\\textgreater -\\textless C\\{e2\\}\\textgreater , ...), please append these chunk numbers to S in the format \"\\textless statement\\textgreater \\{S\\}\\textless cite\\textgreater [\\{s1\\}-\\{e1\\}][\\{s2\\}-\\{e2\\}]...\\textless /cite\\textgreater \\textless /statement\\textgreater \". For other sentences such as introductory sentences, summarization sentences, reasoning, and inference, you still need to append \"\\textless cite\\textgreater \\textless /cite\\textgreater \" to them to indicate they need no citations. You must answer in the same language as the user's question.\\\\\n\n[Answer with Citations]\\\\\n \n \n \n\\end{tcolorbox}\n \\caption{One-shot learning prompt for the LAC-S strategy.}\n \\label{prompt:lac_s}\n\\end{figure}\\begin{figure}[t]\n \\centering\n \\begin{tcolorbox}[size=title,opacityfill=0.1]\n \\noindent\n \\textbf{Prompt for General type task:} \\\\\n \\{{\\emph{Long Text Material}}\\}\\\\\n Given the above text, please propose 5 English questions that are diverse and cover all parts of the text, in the following format: ``1: \", ``2: \", ... \\\\\n\n \\textbf{Prompt for Summary type task:} \\\\\n \\{{\\emph{Long Text Material}}\\}\\\\\n Given the above text, please propose 5 English questions that require summarization or integration from multiple parts, make sure they are diverse and cover all parts of the text, in the following format: ``1: \", ``2: \", ... \\\\\n\n \\textbf{Prompt for multi-hop reasoning type task:} \\\\\n \\{{\\emph{Long Text Material}}\\}\\\\\n Given the above text, please propose 5 English questions that require multi-hop reasoning, make sure they are diverse and cover all parts of the text, in the following format: ``1: \", ``2: \", ... \\\\\n\n \\textbf{Prompt for Information Extraction type task:} \\\\\n \\{{\\emph{Long Text Material}}\\}\\\\\n Given the above text, please propose 5 English information-seeking questions, make sure they are diversed and cover all parts of the text, in the following format: ``1: \", ``2: \", ... \n \\end{tcolorbox}\n \\caption{Prompt for English question generation in the CoF pipeline. For each long text material, we randomly select one of the four task prompts and let the LLM generate five questions to ensure that the questions cover content from multiple spans within the long text. We then randomly choose one of these questions. For long Chinese documents, we translate the corresponding prompts into Chinese and obtain Chinese questions.}\n \\label{prompt:qa_gen}\n\\end{figure}\\begin{figure}[t]\n \\centering\n\\begin{tcolorbox}[size=title,opacityfill=0.1]\n\\noindent\nYour task is to add citations to the existing answer. Specifically, when a factual statement S in the answer uses information from context snippets l1, l2, ..., ln, please add citations by appending these snippet numbers to S in the format ``\\textless statement\\textgreater\\{S\\}\\textless cite\\textgreater[\\{l1\\}][\\{l2\\}]...[\\{ln\\}]\\textless /cite\\textgreater\\textless statement\\textgreater\". For other sentences such as introductory sentences, summarization sentences, reasoning, and inference, you still need to append ``\\textless cite\\textgreater\\textless /cite\\textgreater\" to them to indicate they need no citations. Except for adding citations, do not change the original content and format of the existing answer. \\\\\n\nHere is an example: \\\\\n\n\\{\\emph{An Example}\\} \\\\\n\nNow get ready to add citations for the following test case. \\\\\n\n[Contexts Start]\\\\\nSnippet [1] \\\\\n\\{\\emph{Chunk 1}\\} \\\\\n\nSnippet [2] \\\\\n\\{\\emph{Chunk 2}\\} \\\\\n\nSnippet [3] \\\\\n\\{\\emph{Chunk 3}\\} \\\\\n\n...\n\n[Context End]\\\\\n\n[Question]\\\\\n\\{\\emph{Question}\\} \\\\\n\n[Existing Answer Start]\\\\\n\\{\\emph{Answer}\\} \n\n[Existing Answer End]\\\\\n\n[Answer with Citations]\\\\\n \n \n \n\\end{tcolorbox}\n \\caption{Prompt for chunk-level citation generation in the CoF pipeline.}\n \\label{prompt:cof_coarse}\n\\end{figure}\\begin{figure}[t]\n \\centering\n\\begin{tcolorbox}[size=title,opacityfill=0.1]\n\\noindent\nYou will receive a passage and a factual statement. Your task is to identify the parts in the passage (i.e., chunks \\textless C\\{s1\\}\\textgreater -\\textless C\\{e1\\}\\textgreater , \\textless C\\{s2\\}\\textgreater -\\textless C\\{e2\\}\\textgreater , ...) that support some key points of the statement, and output the chunk number in the format:\\\\\n```\n\n[s1-e1]\n\n[s2-e2]\n\n...\\\\\n\n'''\\\\\nIf the passage contains no key information relevant to the statement, you must output \"No relevant information\". \\\\\n\nHere are some examples: \\\\\n\n\\{\\emph{Example 1}\\} \\\\\n\n\\{\\emph{Example 2}\\} \\\\\n\n\\{\\emph{Example 3}\\} \\\\\n\nNow get ready to process the following test case. \\\\\n\n[Passage Start]\\\\\n\\textless C0\\textgreater \\{\\emph{Sentence 0}\\} \\textless C1\\textgreater \\{\\emph{Sentence 1}\\} \\textless C2\\textgreater \\{\\emph{Sentence 2}\\} ... \n\n[Passage End]\\\\\n\n[Statment]\\\\\n\\{\\emph{statement}\\} \\\\\n\n\n[output]\\\\\n\n\\end{tcolorbox}\n \\caption{Prompt for sentence-level citation extraction in the CoF pipeline.}\n \\label{prompt:cof_fine}\n\\end{figure}\nWe list the prompts used in this work in Figure~\\ref{prompt:correct_longbench_chat},~\\ref{prompt:correct_qa},~\\ref{prompt:correct_summary},~\\ref{prompt:support_score},~\\ref{prompt:need_citation},~\\ref{prompt:relevant_score},~\\ref{prompt:lac_s},~\\ref{prompt:qa_gen},~\\ref{prompt:cof_coarse},~\\ref{prompt:cof_fine}.\n\n\\section{Evaluation Cost}\nOn LongBench-Cite, a run of GPT-4o evaluation for correctness/citation quality costs about \\$4/\\$25.\n\n\\end{document}\n"},"question":{"kind":"string","value":"What is the F1 score for the model and data set where adding citations shows the largest improvement in correctness, excluding our trained models?"},"answer":{"kind":"string","value":"49.9"},"plan":{"kind":"string","value":"SELECT Table 2 about correctness in LQAC setting\nSELECT all models excluding our trained Models\nLOOP for each model\n SELECT all datasets for current model\n LOOP for each dataset\n SELECT CR score\n COMPUTE add CR score and current model-dataset pair to cr_list\nCOMPUTE best_pair = model-dataset pair with max CR score in cr_list\nSELECT Table 1 about Citation recall (R), citation precision (P), citation F1 (F1)\nSELECT target_f1_score = citation F1 score for the best_pair\nRETURN target_f1_score\n"}}},{"rowIdx":76,"cells":{"paper":{"kind":"string","value":"2305.14627"},"relevant_tables":{"kind":"list like","value":[["\\begin{table}[t]\n"," \\centering\n"," \\resizebox{0.98\\linewidth}{!}{\n"," \\begin{tabular}{lcccc}\n"," \\toprule\n"," & \\tf{Fluency} & \\tf{Correct.} & \\multicolumn{2}{c}{\\tf{Citation} }\\\\\n"," \\cmidrule(lr){2-2} \\cmidrule(lr){3-3} \\cmidrule(lr){4-5}\n"," & (MAUVE) & (EM Rec.) & Rec. & Prec. \\\\\n"," \\midrule\n"," \\headercolor\n"," \\multicolumn{5}{c}{\\textbf{ChatGPT}} \\\\\n"," \\tableskip\n"," \\vani{} (5-psg) & 66.6 & 40.4 & {73.6} & 72.5\\\\% {63.0} \\\\\n"," ~~w/ \\rerank{} & 77.0 & 40.2 & \\tf{84.8} & \\textbf{81.6} \\\\% \\tf{73.3} \\\\\n"," \\summ{} (10-psg) & 70.0 & \\tf{43.3} &68.9\t& 61.8\\\\ %\n"," ~~w/ \\interact{} & 69.0 &\t39.1 & 73.4& 66.5\\\\ %\n"," \\snippet{} (10-psg) & 69.8&\t41.4& 65.3&\t57.4\\\\ %\n"," \\search{} & 58.7 & 32.4 & 58.3 & 58.2\t\\\\ %\n"," \\close{} & 52.7 &\t38.3 & 26.7& 26.7 \\\\\n","\n"," \\tableskip\n"," \\headercolor\n"," \\multicolumn{5}{c}{\\textbf{GPT-4} (\\vani{} prompting)} \\\\\n"," \\tableskip\n"," GPT-4 (5-psg) & 67.1 & 41.3 & 68.5 & 75.6 \\\\\n"," GPT-4 (20-psg) & 64.9 & 44.4 & 73.0 & 76.5 \\\\\n","\n"," \\tableskip\n"," \\headercolor\n"," \\multicolumn{5}{c}{\\textbf{LLaMA} (\\vani{} prompting)} \\\\\n"," \\tableskip\n"," LLaMA-13B (3-psg) & 68.4 & 26.9 & 10.6 & 15.4 \\\\\n"," Vicuna-13B (3-psg) & 82.6 & 31.9 & 51.1 & 50.1 \\\\ \n"," \n"," Chat-13B (5-psg) & 72.4 & 35.2 & 38.4 & 39.4 \\\\\n"," Chat-70B (5-psg) & 88.3 & 41.5 & 62.9 & 61.3 \\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," }\n"," \\caption{\n"," Experiments on ASQA.\n"," For \\close{}, we use \\posthoc{} to get citations.\n"," $k$-psg: putting top-$k$ passages from the retrieval results into the context.\n"," Chat-13B and Chat-70B refer to LLaMA-2-Chat.\n"," }\n"," \\label{tab:asqa_result}\n"," \\vspace{-10pt}\n","\\end{table}\n"],["\\begin{table}[t]\n"," \\centering\n"," \\small\n"," \\resizebox{0.98\\linewidth}{!}{\n"," \\begin{tabular}{lcccc}\n"," \\toprule\n"," & \\multicolumn{2}{c}{\\tf{Correctness}} & \\multicolumn{2}{c}{\\tf{Citation} }\\\\\n"," \\cmidrule(lr){2-3} \\cmidrule(lr){4-5}\n"," & Rec.-5 & Prec. & Rec. & Prec. \\\\\n"," \\midrule\n"," \\headercolor\n"," \\multicolumn{5}{c}{\\textbf{ChatGPT}} \\\\\n"," \\tableskip\n"," \\vani{} (5-psg) & 20.8 & 20.8 & 20.5&\t20.9 \\\\\n"," ~~w/ \\rerank{} & 22.8 & 21.4 & 21.2 & 21.4 \\\\ %\n"," \\summ{} (10-psg) & 23.6& 21.2& \\tf{23.6}&\t\\tf{25.7} \\\\\n"," \\snippet{} (10-psg)&24.5 & 21.5& {22.9} & {24.9} \\\\\n"," ~~w/ \\interact{} & 21.9 & \\tf{23.0} & 21.9&23.4\\\\\n"," \\search{} & 17.2& 20.4 & 14.9&\t14.9 \\\\\n"," \\close{} & \\tf{32.9} & 19.8 & 10.0 & 10.0\\\\\n"," \\tableskip\n"," \\headercolor\n"," \\multicolumn{5}{c}{\\textbf{GPT-4} (\\vani{} prompting)} \\\\\n"," \\tableskip\n"," GPT-4 (5-psg) & 22.2 & 25.0 & 25.9 & 27.0 \\\\\n"," GPT-4 (20-psg) & 29.6 & 26.2 & 27.4 & 28.5 \\\\ \n","\n"," \\tableskip\n"," \\headercolor\n"," \\multicolumn{5}{c}{\\textbf{LLaMA} (\\vani{} prompting)} \\\\\n"," \\tableskip\n"," LLaMA-13B (3-psg) & 9.7 & 9.1 & 6.7 & 7.1 \\\\% 6.8 \\\\\n"," Vicuna-13B (5-psg) & 14.0 & 15.9 & 12.5 & 13.4 \\\\ %\n"," \n"," \n"," Chat-13B (5-psg) & 21.1 & 18.2 & 9.6 & 9.7 \\\\\n"," Chat-70B (5-psg) & 21.8 &18.4 & 15.1 &15.6 \\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," }\n"," \\caption{\n"," Experiments on QAMPARI.\n"," ``Rec.-5'': we set the recall to be 100\\% if the prediction includes at least 5 correct answers.\n"," }\n"," \\label{tab:qampari_result}\n"," \\vspace{-10pt}\n","\\end{table}\n"],["\\begin{table}[h]\n"," \\centering\n"," \\resizebox{0.98\\linewidth}{!}{\n"," \\begin{tabular}{lcccc}\n"," \\toprule\n"," & \\tf{Fluency} & \\tf{Correct.} & \\multicolumn{2}{c}{\\tf{Citation} }\\\\\n"," \\cmidrule(lr){2-2} \\cmidrule(lr){3-3} \\cmidrule(lr){4-5}\n"," & (MAUVE) & (Claim) & Rec. & Prec. \\\\\n"," \\midrule\n"," \\headercolor\n"," \\multicolumn{5}{c}{\\textbf{ChatGPT}} \\\\\n"," \\tableskip\n"," \\vani{} (5-psg) &57.2&\t12.0 &\t51.1&\t{50.0} \\\\\n"," ~~w/ \\rerank{} & 56.1 & 11.4 & \\tf{69.3} & \\textbf{67.8}\\\\ %\n"," \\summ{} (10-psg) & 40.3 & 12.5 & 51.5 & 48.2 \\\\\n"," \\snippet{} (10-psg) & 62.9 &\t{14.3} & 50.4 & 45.0 \\\\ %\n"," ~~w/ \\interact{} & 68.0 &\t13.3&\t47.8& 45.0\\\\\t%\n"," \\search{} & 49.7&\t13.4&\t45.6& 43.7\\\\\t%\n"," \\close{} & 32.6 &\\tf{18.6}\t&15.5\t&15.5\\\\\n"," \\tableskip\n"," \\headercolor\n"," \\multicolumn{5}{c}{\\textbf{GPT-4} (\\vani{} prompting)} \\\\\n"," \\tableskip\n"," GPT-4 (5-psg) & 38.4 & 14.2 & 44.0 & 50.1 \\\\\n"," GPT-4 (20-psg) & 41.5 & 18.3 & 48.5 & 53.4 \\\\\n","\n"," \\tableskip\n"," \\headercolor\n"," \\multicolumn{5}{c}{\\textbf{LLaMA} (\\vani{} prompting)} \\\\\n"," \\tableskip\n"," LLaMA-13B (3-psg) & 50.0 & 3.9 & 3.1 & 5.3 \\\\%2.0 \\\\\n"," Vicuna-13B (3-psg)& 58.2 & {10.0} & 15.6 & 19.6 \\\\% 12.6 \\\\\n"," \n"," Chat-13B (5-psg) & 34.7 & 13.4 &17.3 & 15.8 \\\\\n"," Chat-70B (5-psg) & 38.6 & 12.8 & 38.3 & 37.9 \\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," }\n"," \\caption{\n"," Experiments on ELI5. We use \\textit{claim recall} for the correctness evaluation.\n"," Chat-13B and Chat-70B refer to LLaMA-2-Chat.\n"," }\n"," \\label{tab:eli5_result}\n"," \\vspace{-10pt}\n","\\end{table}\n"]],"string":"[\n [\n \"\\\\begin{table}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\resizebox{0.98\\\\linewidth}{!}{\\n\",\n \" \\\\begin{tabular}{lcccc}\\n\",\n \" \\\\toprule\\n\",\n \" & \\\\tf{Fluency} & \\\\tf{Correct.} & \\\\multicolumn{2}{c}{\\\\tf{Citation} }\\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-2} \\\\cmidrule(lr){3-3} \\\\cmidrule(lr){4-5}\\n\",\n \" & (MAUVE) & (EM Rec.) & Rec. & Prec. \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\headercolor\\n\",\n \" \\\\multicolumn{5}{c}{\\\\textbf{ChatGPT}} \\\\\\\\\\n\",\n \" \\\\tableskip\\n\",\n \" \\\\vani{} (5-psg) & 66.6 & 40.4 & {73.6} & 72.5\\\\\\\\% {63.0} \\\\\\\\\\n\",\n \" ~~w/ \\\\rerank{} & 77.0 & 40.2 & \\\\tf{84.8} & \\\\textbf{81.6} \\\\\\\\% \\\\tf{73.3} \\\\\\\\\\n\",\n \" \\\\summ{} (10-psg) & 70.0 & \\\\tf{43.3} &68.9\\t& 61.8\\\\\\\\ %\\n\",\n \" ~~w/ \\\\interact{} & 69.0 &\\t39.1 & 73.4& 66.5\\\\\\\\ %\\n\",\n \" \\\\snippet{} (10-psg) & 69.8&\\t41.4& 65.3&\\t57.4\\\\\\\\ %\\n\",\n \" \\\\search{} & 58.7 & 32.4 & 58.3 & 58.2\\t\\\\\\\\ %\\n\",\n \" \\\\close{} & 52.7 &\\t38.3 & 26.7& 26.7 \\\\\\\\\\n\",\n \"\\n\",\n \" \\\\tableskip\\n\",\n \" \\\\headercolor\\n\",\n \" \\\\multicolumn{5}{c}{\\\\textbf{GPT-4} (\\\\vani{} prompting)} \\\\\\\\\\n\",\n \" \\\\tableskip\\n\",\n \" GPT-4 (5-psg) & 67.1 & 41.3 & 68.5 & 75.6 \\\\\\\\\\n\",\n \" GPT-4 (20-psg) & 64.9 & 44.4 & 73.0 & 76.5 \\\\\\\\\\n\",\n \"\\n\",\n \" \\\\tableskip\\n\",\n \" \\\\headercolor\\n\",\n \" \\\\multicolumn{5}{c}{\\\\textbf{LLaMA} (\\\\vani{} prompting)} \\\\\\\\\\n\",\n \" \\\\tableskip\\n\",\n \" LLaMA-13B (3-psg) & 68.4 & 26.9 & 10.6 & 15.4 \\\\\\\\\\n\",\n \" Vicuna-13B (3-psg) & 82.6 & 31.9 & 51.1 & 50.1 \\\\\\\\ \\n\",\n \" \\n\",\n \" Chat-13B (5-psg) & 72.4 & 35.2 & 38.4 & 39.4 \\\\\\\\\\n\",\n \" Chat-70B (5-psg) & 88.3 & 41.5 & 62.9 & 61.3 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \" \\\\caption{\\n\",\n \" Experiments on ASQA.\\n\",\n \" For \\\\close{}, we use \\\\posthoc{} to get citations.\\n\",\n \" $k$-psg: putting top-$k$ passages from the retrieval results into the context.\\n\",\n \" Chat-13B and Chat-70B refer to LLaMA-2-Chat.\\n\",\n \" }\\n\",\n \" \\\\label{tab:asqa_result}\\n\",\n \" \\\\vspace{-10pt}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\small\\n\",\n \" \\\\resizebox{0.98\\\\linewidth}{!}{\\n\",\n \" \\\\begin{tabular}{lcccc}\\n\",\n \" \\\\toprule\\n\",\n \" & \\\\multicolumn{2}{c}{\\\\tf{Correctness}} & \\\\multicolumn{2}{c}{\\\\tf{Citation} }\\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-3} \\\\cmidrule(lr){4-5}\\n\",\n \" & Rec.-5 & Prec. & Rec. & Prec. \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\headercolor\\n\",\n \" \\\\multicolumn{5}{c}{\\\\textbf{ChatGPT}} \\\\\\\\\\n\",\n \" \\\\tableskip\\n\",\n \" \\\\vani{} (5-psg) & 20.8 & 20.8 & 20.5&\\t20.9 \\\\\\\\\\n\",\n \" ~~w/ \\\\rerank{} & 22.8 & 21.4 & 21.2 & 21.4 \\\\\\\\ %\\n\",\n \" \\\\summ{} (10-psg) & 23.6& 21.2& \\\\tf{23.6}&\\t\\\\tf{25.7} \\\\\\\\\\n\",\n \" \\\\snippet{} (10-psg)&24.5 & 21.5& {22.9} & {24.9} \\\\\\\\\\n\",\n \" ~~w/ \\\\interact{} & 21.9 & \\\\tf{23.0} & 21.9&23.4\\\\\\\\\\n\",\n \" \\\\search{} & 17.2& 20.4 & 14.9&\\t14.9 \\\\\\\\\\n\",\n \" \\\\close{} & \\\\tf{32.9} & 19.8 & 10.0 & 10.0\\\\\\\\\\n\",\n \" \\\\tableskip\\n\",\n \" \\\\headercolor\\n\",\n \" \\\\multicolumn{5}{c}{\\\\textbf{GPT-4} (\\\\vani{} prompting)} \\\\\\\\\\n\",\n \" \\\\tableskip\\n\",\n \" GPT-4 (5-psg) & 22.2 & 25.0 & 25.9 & 27.0 \\\\\\\\\\n\",\n \" GPT-4 (20-psg) & 29.6 & 26.2 & 27.4 & 28.5 \\\\\\\\ \\n\",\n \"\\n\",\n \" \\\\tableskip\\n\",\n \" \\\\headercolor\\n\",\n \" \\\\multicolumn{5}{c}{\\\\textbf{LLaMA} (\\\\vani{} prompting)} \\\\\\\\\\n\",\n \" \\\\tableskip\\n\",\n \" LLaMA-13B (3-psg) & 9.7 & 9.1 & 6.7 & 7.1 \\\\\\\\% 6.8 \\\\\\\\\\n\",\n \" Vicuna-13B (5-psg) & 14.0 & 15.9 & 12.5 & 13.4 \\\\\\\\ %\\n\",\n \" \\n\",\n \" \\n\",\n \" Chat-13B (5-psg) & 21.1 & 18.2 & 9.6 & 9.7 \\\\\\\\\\n\",\n \" Chat-70B (5-psg) & 21.8 &18.4 & 15.1 &15.6 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \" \\\\caption{\\n\",\n \" Experiments on QAMPARI.\\n\",\n \" ``Rec.-5'': we set the recall to be 100\\\\% if the prediction includes at least 5 correct answers.\\n\",\n \" }\\n\",\n \" \\\\label{tab:qampari_result}\\n\",\n \" \\\\vspace{-10pt}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[h]\\n\",\n \" \\\\centering\\n\",\n \" \\\\resizebox{0.98\\\\linewidth}{!}{\\n\",\n \" \\\\begin{tabular}{lcccc}\\n\",\n \" \\\\toprule\\n\",\n \" & \\\\tf{Fluency} & \\\\tf{Correct.} & \\\\multicolumn{2}{c}{\\\\tf{Citation} }\\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-2} \\\\cmidrule(lr){3-3} \\\\cmidrule(lr){4-5}\\n\",\n \" & (MAUVE) & (Claim) & Rec. & Prec. \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\headercolor\\n\",\n \" \\\\multicolumn{5}{c}{\\\\textbf{ChatGPT}} \\\\\\\\\\n\",\n \" \\\\tableskip\\n\",\n \" \\\\vani{} (5-psg) &57.2&\\t12.0 &\\t51.1&\\t{50.0} \\\\\\\\\\n\",\n \" ~~w/ \\\\rerank{} & 56.1 & 11.4 & \\\\tf{69.3} & \\\\textbf{67.8}\\\\\\\\ %\\n\",\n \" \\\\summ{} (10-psg) & 40.3 & 12.5 & 51.5 & 48.2 \\\\\\\\\\n\",\n \" \\\\snippet{} (10-psg) & 62.9 &\\t{14.3} & 50.4 & 45.0 \\\\\\\\ %\\n\",\n \" ~~w/ \\\\interact{} & 68.0 &\\t13.3&\\t47.8& 45.0\\\\\\\\\\t%\\n\",\n \" \\\\search{} & 49.7&\\t13.4&\\t45.6& 43.7\\\\\\\\\\t%\\n\",\n \" \\\\close{} & 32.6 &\\\\tf{18.6}\\t&15.5\\t&15.5\\\\\\\\\\n\",\n \" \\\\tableskip\\n\",\n \" \\\\headercolor\\n\",\n \" \\\\multicolumn{5}{c}{\\\\textbf{GPT-4} (\\\\vani{} prompting)} \\\\\\\\\\n\",\n \" \\\\tableskip\\n\",\n \" GPT-4 (5-psg) & 38.4 & 14.2 & 44.0 & 50.1 \\\\\\\\\\n\",\n \" GPT-4 (20-psg) & 41.5 & 18.3 & 48.5 & 53.4 \\\\\\\\\\n\",\n \"\\n\",\n \" \\\\tableskip\\n\",\n \" \\\\headercolor\\n\",\n \" \\\\multicolumn{5}{c}{\\\\textbf{LLaMA} (\\\\vani{} prompting)} \\\\\\\\\\n\",\n \" \\\\tableskip\\n\",\n \" LLaMA-13B (3-psg) & 50.0 & 3.9 & 3.1 & 5.3 \\\\\\\\%2.0 \\\\\\\\\\n\",\n \" Vicuna-13B (3-psg)& 58.2 & {10.0} & 15.6 & 19.6 \\\\\\\\% 12.6 \\\\\\\\\\n\",\n \" \\n\",\n \" Chat-13B (5-psg) & 34.7 & 13.4 &17.3 & 15.8 \\\\\\\\\\n\",\n \" Chat-70B (5-psg) & 38.6 & 12.8 & 38.3 & 37.9 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \" \\\\caption{\\n\",\n \" Experiments on ELI5. We use \\\\textit{claim recall} for the correctness evaluation.\\n\",\n \" Chat-13B and Chat-70B refer to LLaMA-2-Chat.\\n\",\n \" }\\n\",\n \" \\\\label{tab:eli5_result}\\n\",\n \" \\\\vspace{-10pt}\\n\",\n \"\\\\end{table}\\n\"\n ]\n]","isConversational":false},"tables":{"kind":"list like","value":[["\\begin{figure}[t]\n"," \\includegraphics[width=0.98\\linewidth]{figs/main.pdf}\n"," \\caption{The task setup of \\citeeval{}. Given a question, \n"," the system generates text while providing \\ti{citing passages} from a large retrieval corpus. Each statement may contain multiple citations (e.g., \\ttt{[1][2]}). %\n"," }\n"," \\vspace{-5pt}\n"," \\label{fig:main}\n","\\end{figure}\n"],["\\begin{table*}[t]\n"," \\centering\n"," \\small\n"," \\begin{tabular}{lllp{0.5\\textwidth}}\n"," \\toprule\n"," \\tf{Dataset} & \\tf{Corpus (\\#passages)} & \\tf{Question type} & \\tf{Example} \\\\\n"," \\midrule\n"," ASQA & Wikipedia (21M) & Factoid (ambiguous) & \\tf{Q}: When did the US break away from England? \\\\\n"," &&& \\tf{A}: The US declared independence on July 2, 1776 \\citedb{[1][2]} ... The Treaty of Paris was later signed on September 3, 1783 \\citedb{[3]}. \\\\ %\n"," QAMPARI & Wikipedia (21M) & Factoid (list) & \\tf{Q}: Which films have Gong Li as a member of their cast? \\\\\n"," &&& \\tf{A}: The Story of Qiu Ju \\citedb{[1]}, Farewell My Concubine \\citedb{[2]}, The Monkey King 2 \\citedb{[3]}, Mulan \\citedb{[3]}, Saturday Fiction \\citedb{[3]} ...\\\\\n"," ELI5 & Sphere (899M) & Why/How/What & \\tf{Q}: How do student loans affect getting a mortgage? \\\\\n"," &&& \\tf{A}: Student loans can affect the debt to income ratio \\citedb{[1]}, which is a key factor in determining the amount that ... \\citedb{[2][3]} \\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," \\caption{The three datasets used in our \\citeeval{} benchmark. %\n"," These datasets cover a wide range of question types\n"," and the corresponding corpora span from Wikipedia to \n"," Web-scale document collection. %\n"," }\n"," \\vspace{-10pt}\n"," \\label{tab:datasets}\n","\\end{table*}\n"],["\\begin{figure}[t]\n"," \\centering\n"," \\includegraphics[width=0.94\\linewidth]{figs/correctness.pdf}\n"," \\caption{Evaluation of correctness (details in \\S\\ref{sec:correctness}).}\n"," \\vspace{-13pt}\n"," \\label{fig:correctness}\n","\\end{figure}\n"],["\\begin{figure}[t]\n"," \\centering\n"," \\includegraphics[width=0.98\\linewidth]{figs/citation.pdf}\n"," \\caption{Evaluation of citation quality (details in \\S\\ref{sec:citation}). We use \n"," an NLI model to verify whether a statement is supported by its citations. %\n"," }\n"," \\vspace{-15pt}\n"," \\label{fig:citation}\n","\\end{figure}\n"],["\\begin{table}[t]\n"," \\centering\n"," \\small\n"," \\begin{tabular}{>{\\raggedright\\arraybackslash\\tt}p{0.94\\linewidth}<{}}\n"," \\toprule\n"," Instruction: Write an accurate, engaging, and concise answer for ... \\\\\n"," \\\\\n"," \\vspace{-1em}\n"," \\noindent {\\color{brown}<Retrieve for the question>}\\\\\n"," Document {[1]}(Title: American Decolonization) ... \\\\\n"," Document {[2]}(Title: Decolonization) ... \\\\\n"," Document {[3]}(Title: American Revolution) ... \\\\\n"," ... \\\\\n"," \\\\\n"," \\vspace{-1em}\n"," Question: When did US break away from England? \\\\\n"," Answer: {\\color{blue} The United States took the first step towards gaining independence ... \\tf{\\underline{[1][2]}}. The Treaty of Paris was later signed ... \\tf{\\underline{[3]}}}.\\\\\n","\n"," \\bottomrule\n"," \\end{tabular}\n"," \\caption{\n"," An example of our \\vani{} method. Different colors represent \\ttt{prompt}, \\ttt{\\color{blue} model generation}, and \\ttt{\\color{brown} <actions>}. \n"," We also provide two in-context demonstrations before the test example. \n"," }\n"," \\vspace{-10pt}\n"," \\label{tab:prompt_short}\n","\\end{table}\n"],["\\begin{table}[t]\n"," \\centering\n"," \\small\n"," \\begin{tabular}{>{\\raggedright\\arraybackslash\\tt}p{0.94\\linewidth}<{}}\n"," \\toprule\n"," Instruction: ... \\\\\n"," \\\\\n"," \\vspace{-1em}\n"," \\noindent {\\color{brown}<Retrieve for question ``...''>}\\\\\n"," Question: When did US break away from England? \\\\\n"," \\noindent {\\color{blue} Search: Declaration of Independence} \\\\\n"," \\noindent {\\color{brown}<Search the query among the top-100 passages>}\\\\\n"," Document [1](Title: ...) ... \\\\\n"," \\noindent {\\color{blue} Output: The United States ... \\tf{\\underline{[1]}}.} \\\\\n"," \\noindent {\\color{brown}<Remove Document [1] from context>}\\\\\n"," \\noindent {\\color{blue} Search: Treaty of Paris} \\\\\n"," \\noindent {\\color{brown}<Search the query among the top-100 passages>}\\\\\n"," Document [3](Title: ...) ... \\\\\n"," \\noindent {\\color{blue} Output: The Treaty of Paris ... \\tf{\\underline{[3]}}.} \\\\\n"," \\noindent {\\color{brown}<Remove Document [3] from context>}\\\\\n"," \\noindent {\\color{blue}End.}\\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," \\caption{\n"," An example of \\search{}. \n"," }\n"," \\vspace{-15pt}\n"," \\label{tab:prompt_search}\n","\\end{table}\n"],["\\begin{table}[t]\n"," \\centering\n"," \\resizebox{0.98\\linewidth}{!}{\n"," \\begin{tabular}{lcccc}\n"," \\toprule\n"," & \\tf{Fluency} & \\tf{Correct.} & \\multicolumn{2}{c}{\\tf{Citation} }\\\\\n"," \\cmidrule(lr){2-2} \\cmidrule(lr){3-3} \\cmidrule(lr){4-5}\n"," & (MAUVE) & (EM Rec.) & Rec. & Prec. \\\\\n"," \\midrule\n"," \\headercolor\n"," \\multicolumn{5}{c}{\\textbf{ChatGPT}} \\\\\n"," \\tableskip\n"," \\vani{} (5-psg) & 66.6 & 40.4 & {73.6} & 72.5\\\\% {63.0} \\\\\n"," ~~w/ \\rerank{} & 77.0 & 40.2 & \\tf{84.8} & \\textbf{81.6} \\\\% \\tf{73.3} \\\\\n"," \\summ{} (10-psg) & 70.0 & \\tf{43.3} &68.9\t& 61.8\\\\ %\n"," ~~w/ \\interact{} & 69.0 &\t39.1 & 73.4& 66.5\\\\ %\n"," \\snippet{} (10-psg) & 69.8&\t41.4& 65.3&\t57.4\\\\ %\n"," \\search{} & 58.7 & 32.4 & 58.3 & 58.2\t\\\\ %\n"," \\close{} & 52.7 &\t38.3 & 26.7& 26.7 \\\\\n","\n"," \\tableskip\n"," \\headercolor\n"," \\multicolumn{5}{c}{\\textbf{GPT-4} (\\vani{} prompting)} \\\\\n"," \\tableskip\n"," GPT-4 (5-psg) & 67.1 & 41.3 & 68.5 & 75.6 \\\\\n"," GPT-4 (20-psg) & 64.9 & 44.4 & 73.0 & 76.5 \\\\\n","\n"," \\tableskip\n"," \\headercolor\n"," \\multicolumn{5}{c}{\\textbf{LLaMA} (\\vani{} prompting)} \\\\\n"," \\tableskip\n"," LLaMA-13B (3-psg) & 68.4 & 26.9 & 10.6 & 15.4 \\\\\n"," Vicuna-13B (3-psg) & 82.6 & 31.9 & 51.1 & 50.1 \\\\ \n"," \n"," Chat-13B (5-psg) & 72.4 & 35.2 & 38.4 & 39.4 \\\\\n"," Chat-70B (5-psg) & 88.3 & 41.5 & 62.9 & 61.3 \\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," }\n"," \\caption{\n"," Experiments on ASQA.\n"," For \\close{}, we use \\posthoc{} to get citations.\n"," $k$-psg: putting top-$k$ passages from the retrieval results into the context.\n"," Chat-13B and Chat-70B refer to LLaMA-2-Chat.\n"," }\n"," \\label{tab:asqa_result}\n"," \\vspace{-10pt}\n","\\end{table}\n"],["\\begin{table}[t]\n"," \\centering\n"," \\small\n"," \\resizebox{0.98\\linewidth}{!}{\n"," \\begin{tabular}{lcccc}\n"," \\toprule\n"," & \\multicolumn{2}{c}{\\tf{Correctness}} & \\multicolumn{2}{c}{\\tf{Citation} }\\\\\n"," \\cmidrule(lr){2-3} \\cmidrule(lr){4-5}\n"," & Rec.-5 & Prec. & Rec. & Prec. \\\\\n"," \\midrule\n"," \\headercolor\n"," \\multicolumn{5}{c}{\\textbf{ChatGPT}} \\\\\n"," \\tableskip\n"," \\vani{} (5-psg) & 20.8 & 20.8 & 20.5&\t20.9 \\\\\n"," ~~w/ \\rerank{} & 22.8 & 21.4 & 21.2 & 21.4 \\\\ %\n"," \\summ{} (10-psg) & 23.6& 21.2& \\tf{23.6}&\t\\tf{25.7} \\\\\n"," \\snippet{} (10-psg)&24.5 & 21.5& {22.9} & {24.9} \\\\\n"," ~~w/ \\interact{} & 21.9 & \\tf{23.0} & 21.9&23.4\\\\\n"," \\search{} & 17.2& 20.4 & 14.9&\t14.9 \\\\\n"," \\close{} & \\tf{32.9} & 19.8 & 10.0 & 10.0\\\\\n"," \\tableskip\n"," \\headercolor\n"," \\multicolumn{5}{c}{\\textbf{GPT-4} (\\vani{} prompting)} \\\\\n"," \\tableskip\n"," GPT-4 (5-psg) & 22.2 & 25.0 & 25.9 & 27.0 \\\\\n"," GPT-4 (20-psg) & 29.6 & 26.2 & 27.4 & 28.5 \\\\ \n","\n"," \\tableskip\n"," \\headercolor\n"," \\multicolumn{5}{c}{\\textbf{LLaMA} (\\vani{} prompting)} \\\\\n"," \\tableskip\n"," LLaMA-13B (3-psg) & 9.7 & 9.1 & 6.7 & 7.1 \\\\% 6.8 \\\\\n"," Vicuna-13B (5-psg) & 14.0 & 15.9 & 12.5 & 13.4 \\\\ %\n"," \n"," \n"," Chat-13B (5-psg) & 21.1 & 18.2 & 9.6 & 9.7 \\\\\n"," Chat-70B (5-psg) & 21.8 &18.4 & 15.1 &15.6 \\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," }\n"," \\caption{\n"," Experiments on QAMPARI.\n"," ``Rec.-5'': we set the recall to be 100\\% if the prediction includes at least 5 correct answers.\n"," }\n"," \\label{tab:qampari_result}\n"," \\vspace{-10pt}\n","\\end{table}\n"],["\\begin{table}[h]\n"," \\centering\n"," \\resizebox{0.98\\linewidth}{!}{\n"," \\begin{tabular}{lcccc}\n"," \\toprule\n"," & \\tf{Fluency} & \\tf{Correct.} & \\multicolumn{2}{c}{\\tf{Citation} }\\\\\n"," \\cmidrule(lr){2-2} \\cmidrule(lr){3-3} \\cmidrule(lr){4-5}\n"," & (MAUVE) & (Claim) & Rec. & Prec. \\\\\n"," \\midrule\n"," \\headercolor\n"," \\multicolumn{5}{c}{\\textbf{ChatGPT}} \\\\\n"," \\tableskip\n"," \\vani{} (5-psg) &57.2&\t12.0 &\t51.1&\t{50.0} \\\\\n"," ~~w/ \\rerank{} & 56.1 & 11.4 & \\tf{69.3} & \\textbf{67.8}\\\\ %\n"," \\summ{} (10-psg) & 40.3 & 12.5 & 51.5 & 48.2 \\\\\n"," \\snippet{} (10-psg) & 62.9 &\t{14.3} & 50.4 & 45.0 \\\\ %\n"," ~~w/ \\interact{} & 68.0 &\t13.3&\t47.8& 45.0\\\\\t%\n"," \\search{} & 49.7&\t13.4&\t45.6& 43.7\\\\\t%\n"," \\close{} & 32.6 &\\tf{18.6}\t&15.5\t&15.5\\\\\n"," \\tableskip\n"," \\headercolor\n"," \\multicolumn{5}{c}{\\textbf{GPT-4} (\\vani{} prompting)} \\\\\n"," \\tableskip\n"," GPT-4 (5-psg) & 38.4 & 14.2 & 44.0 & 50.1 \\\\\n"," GPT-4 (20-psg) & 41.5 & 18.3 & 48.5 & 53.4 \\\\\n","\n"," \\tableskip\n"," \\headercolor\n"," \\multicolumn{5}{c}{\\textbf{LLaMA} (\\vani{} prompting)} \\\\\n"," \\tableskip\n"," LLaMA-13B (3-psg) & 50.0 & 3.9 & 3.1 & 5.3 \\\\%2.0 \\\\\n"," Vicuna-13B (3-psg)& 58.2 & {10.0} & 15.6 & 19.6 \\\\% 12.6 \\\\\n"," \n"," Chat-13B (5-psg) & 34.7 & 13.4 &17.3 & 15.8 \\\\\n"," Chat-70B (5-psg) & 38.6 & 12.8 & 38.3 & 37.9 \\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," }\n"," \\caption{\n"," Experiments on ELI5. We use \\textit{claim recall} for the correctness evaluation.\n"," Chat-13B and Chat-70B refer to LLaMA-2-Chat.\n"," }\n"," \\label{tab:eli5_result}\n"," \\vspace{-10pt}\n","\\end{table}\n"],["\\begin{figure*}[t]\n"," \\centering\n"," \\begin{minipage}[b]{0.66\\textwidth}\n"," \\centering\n"," \\includegraphics[width=1.0\\textwidth]{figs/ret.pdf}\n"," \\end{minipage}\n"," \\hfill\n"," \\begin{minipage}[b]{0.33\\textwidth}\n"," \\centering\n","\n"," \\resizebox{1.0\\textwidth}{!}{\n"," \\begin{tabular}{lcccc}\n"," \\toprule\n"," & \\tf{Fluency} & \\tf{Correct.} & \\multicolumn{2}{c}{\\tf{Citation} }\\\\\n"," \\cmidrule(lr){2-2} \\cmidrule(lr){3-3} \\cmidrule(lr){4-5}\n"," & (MAUVE) & (EM) & Rec. & Prec. \\\\\n"," \\midrule\n"," \\headercolor\n"," \\multicolumn{5}{c}{\\textbf{ChatGPT} (\\vani{})} \\\\\n"," \\tableskip\n"," DPR (5-psg) & 61.8 &\t36.1 & 65.0 &\t65.6\\\\%56.7 \\\\\n"," GTR (1-psg) & 69.5&\t38.4 & 56.0\t& 64.0\\\\%56.0\\\\\n"," GTR (3-psg) & 66.6 & 39.6 & 72.8\t& \\tf{73.9}\\\\%64.0 \\\\\n"," GTR (5-psg) & 66.6 & \\tf{40.4} & \\tf{73.6} & 72.5\\\\%63.0 \\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," }\n"," \\vspace{17pt}\n"," \\end{minipage}\n"," \\vspace{-25pt}\n"," \\caption{\n","Retrieval recall@$k$ on ASQA (\\textit{EM recall}), QAMPARI (\\textit{recall-5}), and ELI5 (\\textit{claim recall}).\n","Retrieval recall serves as an upper bound for model performance,\n","and we compare them with two models' correctness results in the figure (dashed lines):\n","``Vanilla (5-psg)'' is ChatGPT \\vani{} with top-5 passages in context;\n","``Oracle'' is the same model except that it uses 5 gold passages (\\S\\ref{app_sec:retrieval}), whose recall matches Recall@100 on all three datasets. %\n"," }\n"," \\vspace{-10pt}\n"," \\label{fig:ret}\n","\\end{figure*}\n"],["\\begin{table}[t]\n"," \\centering\n"," \\resizebox{0.98\\linewidth}{!}{\n"," \\begin{tabular}{lcccc}\n"," \\toprule\n"," & \\tf{Fluency} & \\tf{Correct.} & \\multicolumn{2}{c}{\\tf{Citation} }\\\\\n"," \\cmidrule(lr){2-2} \\cmidrule(lr){3-3} \\cmidrule(lr){4-5}\n"," & (MAUVE) & (EM Rec.) & Rec. & Prec. \\\\\n"," \\midrule\n"," \\headercolor\n"," \\multicolumn{5}{c}{\\textbf{Open-source} (max \\#tokens=2K-4K)} \\\\\n"," \\tableskip\n"," LLaMA-13B (3-psg) & 68.4 & 26.9 & 10.6 & 15.4 \\\\\n"," Vicuna-13B (3-psg) & 82.6 & 31.9 & 51.1 & 50.1 \\\\ \n"," Chat-13B (5-psg) & 72.4 & 35.2 & 38.4 & 39.4 \\\\\n"," Chat-70B (5-psg) & 88.3 & 41.5 & 62.9 & 61.3 \\\\\n"," \\tableskip\n"," \\headercolor\n"," \\multicolumn{5}{c}{\\textbf{ChatGPT} (max \\#tokens=4K)} \\\\\n"," \\tableskip\n"," ChatGPT (3-psg) & 66.6 & 39.6 & 72.8\t& 73.9 \\\\% 64.0 \\\\\n"," ChatGPT (5-psg) & 66.6 & 40.4 & {73.6} & 72.5 \\\\%{63.0} \\\\\n"," \\tableskip\n"," \\headercolor\n"," \\multicolumn{5}{c}{\\textbf{ChatGPT-16K} (max \\#tokens=16K)} \\\\\n"," \\tableskip\n"," ChatGPT (5-psg) & 60.3 & 36.1 & 76.2 & 76.5 \\\\\n"," ChatGPT (10-psg) & 56.3 & 36.7 & 75.3 & 75.0 \\\\ \n"," ChatGPT (20-psg) & 56.7 & 36.1 & 73.7 & 73.5 \\\\\n","\n"," \\tableskip\n"," \\headercolor\n"," \\multicolumn{5}{c}{\\textbf{GPT-4} (max \\#tokens=8K)} \\\\\n"," \\tableskip\n"," GPT-4 (5-psg) & 67.1 & 41.3 & 68.5 & 75.6 \\\\\n"," GPT-4 (10-psg) & 71.5 & 43.1 & 72.0 & 75.5 \\\\\n"," GPT-4 (20-psg) & 64.9 & 44.4 & 73.0 & 76.5 \\\\\n"," \\bottomrule\n"," \n"," \\end{tabular}\n"," }\n"," \\caption{\n"," Comparison of different LLMs on ASQA (GTR+\\vani{}). LLaMA-13B and Vicuna-13B have a context limit of 2,048 tokens, and thus can only use a short version of instructions and at most top-3 passages.\n"," Chat-13B and Chat-70B refer to LLaMA-2-Chat.\n"," }\n"," \\label{tab:asqa_llm}\n"," \\vspace{-10pt}\n","\\end{table}\n"],["\\begin{table}[t]\n"," \\centering\n"," \\resizebox{0.98\\linewidth}{!}{\n"," \\begin{tabular}{lcccc}\n"," \\toprule\n"," & \\multicolumn{2}{c}{\\tf{Human scores}} & \\multicolumn{2}{c}{\\tf{\\citeeval{} scores}} \\\\\n"," \\cmidrule(lr){2-3} \\cmidrule(lr){4-5}\n"," & Rec.& Prec. & Rec. & Prec. \\\\\n"," \\midrule\n"," ChatGPT \\vani & 74.7 & 76.6 & 75.3 & 74.4 \\\\\n"," ~~w/ \\rerank & \\textbf{79.3} & \\textbf{81.9} & \\textbf{83.9} & \\textbf{80.8} \\\\\n"," Vicuna-13B \\vani & 51.6 & 51.5 & 50.3 & 50.1 \\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," }\n"," \\caption{\n"," Human citation quality evaluation vs. \\citeeval{} citation quality evaluation on ASQA. \n"," }\n"," \\vspace{-5pt}\n"," \\label{tab:human-asqa-all}\n","\\end{table}\n"],["\\begin{table}[t]\n"," \\centering\n"," \\resizebox{0.98\\linewidth}{!}{\n"," \\begin{tabular}{lcccc}\n"," \\toprule\n"," & \\multicolumn{2}{c}{\\tf{Human scores}} & \\multicolumn{2}{c}{\\tf{\\citeeval{} scores}} \\\\\n"," \\cmidrule(lr){2-3} \\cmidrule(lr){4-5}\n"," & Rec. & Prec. & Rec. & Prec. \\\\\n"," \\midrule\n"," ChatGPT \\vani & 50.8 & 52.4 & 52.8 & 50.4 \\\\\n"," ~~w/ \\rerank & \\textbf{59.7} & \\textbf{60.6} & \\textbf{63.0} & \\textbf{60.6} \\\\\n"," Vicuna-13B \\vani & 13.4 & 19.2 & 13.6 & 18.1 \\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," }\n"," \\caption{\n"," Human citation quality evaluation vs. \\citeeval{} citation quality evaluation on ELI5. \n"," }\n"," \\vspace{-10pt}\n"," \\label{tab:human-eli5-all}\n","\\end{table}\n"],["\\begin{table}[ht]\n"," \\centering\n"," \\small\n"," \\begin{tabular}{@{}lcc@{}}\n","\\toprule\n"," & ROUGE-L & Claim recall\\\\ %\n"," \\midrule\n","ChatGPT \\vani & 20.6 & 12.0\\\\% & 66.9 \\\\\n","ChatGPT \\oracle & 21.2 & 21.3 \\\\%& 64.4 \\\\\n","LLaMa-13B \\vani & 16.2 & 3.9 \\\\%& 50.0 \\\\\n","Top-1 passage & 19.1 & 3.0 \\\\%& 47.1 \\\\ \\bottomrule\n","\\bottomrule\n","\\end{tabular} \n"," \\caption{\n"," Comparison between ROUGE-L and claim recall scores on ELI5. \n"," }\n"," \\label{tab:eli5-rouge}\n","\\end{table}\n"],["\\begin{table}[h]\n"," \\centering\n"," \\resizebox{0.98\\linewidth}{!}{\n"," \\begin{tabular}{lcccc}\n"," \\toprule\n"," & \\tf{Fluency} & \\tf{Correct.} & \\multicolumn{2}{c}{\\tf{Citation} }\\\\\n"," \\cmidrule(lr){2-2} \\cmidrule(lr){3-3} \\cmidrule(lr){4-5}\n"," & (MAUVE) & (EM Rec.) & Rec. & Prec. \\\\\n"," \\midrule\n"," ChatGPT & 66.6 & 40.4 & {73.6} & {63.0} \\\\\n"," Top-1 passage & {\\color{red}20.8} & 35.1 & 99.4 & 99.4\\\\\n"," First 2 sents & 67.2 & {\\color{red}18.9} & 98.7 & 98.7 \\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," }\n"," \\caption{\n"," ASQA cheating cases. ``ChatGPT'': the ChatGPT \\vani{} model with GTR-retrieved top-5 passages. \n"," ``Top-1 passage'': use the top-1 retrieved passage as the response. \n"," ``First 2 sents'': use the first 2 sentences of the top-1 retrieved passage.\n"," }\n"," \\label{tab:cheat}\n","\\end{table}\n"],["\\begin{table}[t]\n"," \\centering\n"," \\small\n"," \\begin{tabular}{@{}lccccc@{}}\n"," \\toprule\n"," & R@1 & R@3 & R@5 & R@20 & R@100 \\\\ \\midrule\n"," DPR & 29.6 & 44.5 & 51.5 & 64.6 & 74.1 \\\\\n"," GTR & 35.1 & 50.7 & 56.8 & {70.3} & {78.4} \\\\\n"," Oracle & 63.8 & 72.8 & 78.4 & - & - \\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," \\caption{Retrieval results for ASQA (EM recall).}\n"," \\label{tab:retrieval-asqa}\n","\\end{table}\\begin{table}[t]\n"],["\\end{table}\\begin{table}[t]\n"," \\centering\n"," \\small\n"," \\begin{tabular}{@{}lccccc@{}}\n"," \\toprule\n"," & R@1 & R@3 & R@5 & R@20 & R@100 \\\\ \\midrule\n"," BM25 & 3.0 & 6.6 & 9.6 & 19.3 & 31.8 \\\\\n"," Oracle & 25.3 & 29.7 & 31.8 & - & - \\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," \\caption{Retrieval results for ELI5 (claim recall).}\n"," \\label{tab:retrieval-eli5}\n","\\end{table}\n"],["\\begin{table}[ht]\n"," \\centering\n"," \\resizebox{0.98\\linewidth}{!}{\n"," \\begin{tabular}{lcccc}\n"," \\toprule\n"," & \\tf{Fluency} & \\tf{Correct.} & \\multicolumn{2}{c}{\\tf{Citation} }\\\\\n"," \\cmidrule(lr){2-2} \\cmidrule(lr){3-3} \\cmidrule(lr){4-5}\n"," & (MAUVE) & (EM Rec.) & Rec. & Prec. \\\\\n"," \\midrule\n"," \\headercolor\n"," \\multicolumn{5}{c}{\\textbf{ChatGPT} (\\vani{}, 5-doc)} \\\\\n"," Short instruction & 64.1&\t39.5&69.6&\t73.2 \\\\\n"," Full instruction & 66.6 & {40.4} & {73.6} & 72.5 \\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," }\n"," \\caption{\n"," Effect of different instructions on ASQA.\n"," }\n"," \\label{tab:inst}\n","\\end{table}\n"],["\\begin{table}[ht]\n"," \\centering\n"," \\resizebox{0.98\\linewidth}{!}{\n"," \\begin{tabular}{lcccc}\n"," \\toprule\n"," & \\tf{Fluency} & \\tf{Correct.} & \\multicolumn{2}{c}{\\tf{Citation} }\\\\\n"," \\cmidrule(lr){2-2} \\cmidrule(lr){3-3} \\cmidrule(lr){4-5}\n"," & (MAUVE) & (EM) & Rec. & Prec. \\\\\n"," \\midrule\n"," \\headercolor\n"," \\multicolumn{5}{c}{\\textbf{ChatGPT} (\\vani{})} \\\\\n"," \\#demo = 0& 74.5 & 41.9 & 69.3\t& 73.4 \\\\\n"," \\#demo = 1& 68.9 & 39.8 & 74.6\t& 73.2 \\\\\n"," \\#demo = 2& 66.6 & 40.4 & 73.6 & 72.5 \\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," }\n"," \\caption{\n"," Different demonstrations on ASQA. \n"," }\n"," \\label{tab:numdemo}\n","\\end{table}\n"],["\\begin{table}[ht]\n"," \\centering\n"," \\resizebox{0.98\\linewidth}{!}{\n"," \\begin{tabular}{lcccc}\n"," \\toprule\n"," & \\tf{Fluency} & \\tf{Correct.} & \\multicolumn{2}{c}{\\tf{Citation} }\\\\\n"," \\cmidrule(lr){2-2} \\cmidrule(lr){3-3} \\cmidrule(lr){4-5}\n"," & (MAUVE) & (EM Rec.) & Rec. & Prec. \\\\\n"," \\midrule\n"," ChatGPT & 66.6 & {40.4} & {73.6} & 72.5 \\\\\n"," FiD + \\posthoc{} & 75.8 & 28.4 & 58.1 & 58.0 \\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," }\n"," \\caption{\n"," Comparison of Fusion-in-Decoder with ChatGPT on ASQA. \n"," Both models use top-5 GTR passages. \n"," }\n"," \\label{tab:fid-asqa}\n","\\end{table}\n"],["\\begin{table}[h]\n"," \\centering\n"," \\resizebox{0.98\\linewidth}{!}{\n"," \\begin{tabular}{lcccc}\n"," \\toprule\n"," & \\tf{Fluency} & \\tf{Correct.} & \\multicolumn{2}{c}{\\tf{Citation} }\\\\\n"," \\cmidrule(lr){2-2} \\cmidrule(lr){3-3} \\cmidrule(lr){4-5}\n"," & (MAUVE) & (Claim) & Rec. & Prec. \\\\\n"," \\midrule\n"," ChatGPT & 57.2 &\t12.0 &\t51.1&\t{50.0} \\\\\n"," FiD + \\posthoc{} & 25.2 & 4.4 & 39.3 & 39.3 \\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," }\n"," \\caption{\n"," Comparison of Fusion-in-Decoder with ChatGPT on ELI5. \n"," Both models use top-5 GTR passages.\n"," }\n"," \\label{tab:fid-eli5}\n","\\end{table}\n"],["\\begin{table*}[h]\n"," \\centering\n"," \\small\n"," \\begin{tabular}{lcccccc}\n"," \\toprule\n"," & \\tf{Fluency} & \\tf{Correct.} & \\multicolumn{2}{c}{\\tf{Citation} }\\\\\n"," \\cmidrule(lr){2-2} \\cmidrule(lr){3-3} \\cmidrule(lr){4-5}\n"," & (MAUVE) & (EM Rec.) & Rec. & Prec. & ROUGE-L & Length \\\\\n"," \\midrule\n"," \\headercolorlong\n"," \\multicolumn{7}{c}{\\textbf{ChatGPT}} \\\\\n"," \\tableskip\n","\n"," \\vani{} (5-psg) & 66.8 $_{(2.0)}$ & 40.4 $_{(0.6)}$ & 73.6 $_{(1.1)}$ & 72.5 $_{(1.8)}$ & 37.0 $_{(0.4)}$ & 40.0 $_{(3.1)}$ \\\\\n"," ~~w/ \\rerank{} & 77.0 $_{(-)}$ & 40.2 $_{(-)}$ & 84.8 $_{(-)}$ & 81.6 $_{(-)}$ & 36.9 $_{(-)}$ & 40.8 $_{(-)}$ \\\\\n"," \\summ{} (10-psg) & 70.0 $_{(1.2)}$ & 43.3 $_{(0.8)}$ & 68.8 $_{(0.6)}$ &61.8 $_{(1.1)}$ & 36.9 $_{(0.2)}$ & 49.8 $_{(4.3)}$ \\\\\n"," ~~w/ \\interact{} & 69.0 $_{(2.7)}$ & 39.1 $_{(0.5)}$ & 73.4 $_{(0.2)}$ & 66.5 $_{(4.9)}$ & 35.7 $_{(0.2)}$ & 34.0 $_{(0.9)}$ \\\\\n"," \\snippet{} (10-psg) & 69.8 $_{(2.5)}$ & 41.4 $_{(0.6)}$ & 65.3 $_{(0.6)}$ & 57.4 $_{(0.9)}$ & 36.4 $_{(0.4)}$ & 43.0 $_{(3.5)}$ \\\\\n"," \\search{} & 58.7 $_{(1.3)}$ & 32.4 $_{(0.6)}$ & 58.3 $_{(1.3)}$ & 58.3 $_{(1.3)}$ & 58.2 $_{(1.1)}$ & 23.7 $_{(1.1)}$ \\\\\n"," \\close{} & 52.7 $_{(4.9)}$ & 38.2 $_{(0.1)}$ & 26.7 $_{(1.1)}$ & 26.7 $_{(1.1)}$ & 37.1 $_{(0.3)}$ & 61.1 $_{(4.5)}$ \\\\\n"," \\oracle (5-psg) & 64.4 $_{(0.6)}$ & 48.9 $_{(1.2)}$ & 74.5 $_{(0.6)}$ & 72.7 $_{(1.0)}$ & 38.2 $_{(1.0)}$ & 37.4 $_{(3.0)}$ \\\\\n","\n","\n"," \\headercolorlong\n"," \\tableskip\n"," \\multicolumn{7}{c}{\\textbf{ChatGPT-16K}} \\\\\n"," \\tableskip\n"," \\vani{} (5-psg) & 60.3 $_{(-)}$&36.1 $_{(-)}$&76.2 $_{(-)}$&76.5 $_{(-)}$&36.2 $_{(-)}$&24.7 $_{(-)}$\\\\\n"," \\vani{} (10-psg) & 56.3 $_{(-)}$&36.7 $_{(-)}$&75.3 $_{(-)}$&75.0 $_{(-)}$&35.6 $_{(-)}$&23.5 $_{(-)}$\\\\\n"," \\vani{} (20-psg) & 56.7 $_{(-)}$&36.1 $_{(-)}$&73.7 $_{(-)}$&73.5 $_{(-)}$&35.5 $_{(-)}$&23.1 $_{(-)}$\\\\\n","\n"," \\headercolorlong\n"," \\tableskip\n"," \\multicolumn{7}{c}{\\textbf{GPT-4}} \\\\\n"," \\tableskip\n","\n"," \\vani{} (5-psg) & 67.1 $_{(-)}$&41.3 $_{(-)}$&68.5 $_{(-)}$&75.6 $_{(-)}$&39.2 $_{(-)}$&31.8 $_{(-)}$\\\\\n"," \\vani{} (10-psg) & 71.5 $_{(-)}$&43.1 $_{(-)}$&72.0 $_{(-)}$&75.5 $_{(-)}$&39.7 $_{(-)}$&33.8 $_{(-)}$\\\\\n"," \\vani{} (20-psg) & 64.9 $_{(-)}$&44.4 $_{(-)}$&73.0 $_{(-)}$&76.5 $_{(-)}$&40.1 $_{(-)}$&34.3 $_{(-)}$\\\\\n","\n"," \\headercolorlong\n"," \\tableskip\n"," \\multicolumn{7}{c}{\\textbf{Open-source} } \\\\\n"," \\tableskip\n"," LLaMA-7B \\vani{} (3-psg) & 69.8 $_{(2.0)}$ & 22.6 $_{(0.9)}$ & 6.2 $_{(2.7)}$ & 9.2 $_{(2.9)}$ & 29.1 $_{(0.2)}$ & 61.3 $_{(14.3)}$ \\\\\n"," Alpaca-7B \\vani{} (3-psg) & 84.2 $_{(2.7)}$ & 32.1 $_{(1.7)}$ & 12.3 $_{(7.2)}$ & 14.1 $_{(7.0)}$ & 33.1 $_{(0.8)}$ & 51.7 $_{(12.8)}$ \\\\\n"," Vicuna-7B \\vani{} (3-psg) & 82.9 $_{(5.0)}$ & 34.6 $_{(0.7)}$ & 40.3 $_{(0.5)}$ & 42.6 $_{(1.0)}$ & 35.9 $_{(0.7)}$ & 48.9 $_{(6.6)}$ \\\\ \\midrule\n"," LLaMA-13B \\vani{} (3-psg) & 68.4 $_{(6.4)}$ & 26.9 $_{(0.4)}$ & 10.6 $_{(4.7)}$ & 15.4 $_{(5.2)}$ & 29.8 $_{(0.5)}$ & 67.1 $_{(19.1)}$ \\\\\n"," ~~w/ \\rerank & 60.9 $_{(14.5)}$ & 25.2 $_{(2.5)}$ & 28.1 $_{(9.3)}$ & 37.0 $_{(7.2)}$ & 27.9 $_{(2.4)}$ & 50.5 $_{(14.3)}$ \\\\\n"," LLaMA-13B \\summ{} (10-psg) & 76.8 $_{(4.7)}$ & 33.3 $_{(0.7)}$ & 19.6 $_{(3.9)}$ & 23.7 $_{(4.7)}$ & 32.1 $_{(0.3)}$ & 54.4 $_{(1.5)}$ \\\\\n"," LLaMA-13B \\snippet{} (10-psg) & 72.0 $_{(0.8)}$ & 31.3 $_{(1.1)}$ & 18.2 $_{(3.1)}$ & 21.1 $_{(3.6)}$ & 30.8 $_{(0.4)}$ & 50.5 $_{(4.5)}$ \\\\\n"," LLaMA-13B \\oracle{} (3-psg) & 69.5 $_{(11.4)}$ & 34.3 $_{(0.9)}$ & 10.8 $_{(4.9)}$ & 15.8 $_{(5.9)}$ & 30.6 $_{(0.1)}$ & 67.3 $_{(17.9)}$ \\\\ \\midrule\n"," Vicuna-13B \\vani{} (3-psg) & 82.6 $_{(9.4)}$ & 31.9 $_{(3.9)}$ & 51.1 $_{(1.4)}$ & 50.1 $_{(2.5)}$ & 34.9 $_{(1.3)}$ & 39.1 $_{(6.6)}$ \\\\\n"," ~~w/ \\rerank & 73.5 $_{(2.1)}$ & 32.9 $_{(1.3)}$ & 71.9 $_{(1.9)}$ & 65.4 $_{(1.5)}$ & 34.6 $_{(0.3)}$ & 35.7 $_{(4.2)}$ \\\\\n"," Vicuna-13B \\summ{} (10-psg) & 67.7 $_{(0.3)}$ & 43.2 $_{(0.1)}$ & 52.7 $_{(2.6)}$ & 50.0 $_{(2.1)}$ & 36.7 $_{(0.2)}$ & 66.0 $_{(1.2)}$ \\\\\n"," Vicuna-13B \\snippet{} (10-psg) & 81.4 $_{(3.0)}$ & 42.1 $_{(1.2)}$ & 53.4 $_{(1.9)}$ & 48.7 $_{(1.6)}$ & 36.9 $_{(0.4)}$ & 61.2 $_{(7.4)}$ \\\\\n"," Vicuna-13B \\oracle{} (3-psg) & 72.9 $_{(3.5)}$ & 42.5 $_{(1.6)}$ & 52.2 $_{(0.8)}$ & 50.7 $_{(1.6)}$ & 36.5 $_{(0.9)}$ & 38.7 $_{(3.5)}$ \\\\ \\midrule\n"," LLaMA-33B \\vani{} (3-psg) & 83.7 $_{(5.4)}$ & 31.0 $_{(0.8)}$ & 19.5 $_{(5.3)}$ & 23.0 $_{(5.3)}$ & 32.3 $_{(0.6)}$ & 44.1 $_{(9.3)}$ \\\\\n"," ~~w/ \\rerank & 82.1 $_{(3.0)}$ & 31.3 $_{(1.1)}$ & 41.3 $_{(6.4)}$ & 44.7 $_{(5.5)}$ & 32.5 $_{(0.9)}$ & 39.4 $_{(8.0)}$ \\\\\n"," LLaMA-33B \\summ{} (10-psg) & 72.0 $_{(3.0)}$ & 33.1 $_{(1.9)}$ & 34.7 $_{(5.8)}$ & 35.2 $_{(6.0)}$ & 31.1 $_{(0.8)}$ & 43.7 $_{(5.0)}$ \\\\\n"," LLaMA-33B \\snippet{} (10-psg) & 70.8 $_{(3.1)}$ & 30.9 $_{(1.4)}$ & 31.4 $_{(4.2)}$ & 31.5 $_{(5.3)}$ & 30.1 $_{(0.7)}$ & 42.8 $_{(3.6)}$ \\\\\n"," LLaMA-33B \\oracle{} (3-psg) & 82.6 $_{(7.1)}$ & 39.3 $_{(2.9)}$ & 20.2 $_{(6.2)}$ & 23.9 $_{(6.3)}$ & 33.1 $_{(0.9)}$ & 42.0 $_{(9.3)}$ \\\\ \\midrule\n"," Oasst-33B \\vani{} (3-psg) & 82.9 $_{(2.7)}$ & 34.8 $_{(1.5)}$ & 36.2 $_{(1.7)}$ & 38.3 $_{(2.7)}$ & 35.5 $_{(0.7)}$ & 45.2 $_{(6.3)}$ \\\\\n"," ~~w/ \\rerank & 83.2 $_{(2.4)}$ & 35.1 $_{(1.4)}$ & 66.7 $_{(0.2)}$ & 64.3 $_{(1.0)}$ & 35.0 $_{(0.6)}$ & 41.8 $_{(6.0)}$ \\\\\n"," Oasst-33B \\summ{} (10-psg) & 74.3 $_{(4.6)}$ & 40.9 $_{(1.1)}$ & 45.5 $_{(1.9)}$ & 44.0 $_{(2.9)}$ & 35.8 $_{(0.6)}$ & 54.3 $_{(4.8)}$ \\\\\n"," Oasst-33B \\snippet{} (10-psg) & 79.3 $_{(1.0)}$ & 40.1 $_{(0.9)}$ & 45.0 $_{(1.3)}$ & 43.3 $_{(2.2)}$ & 35.8 $_{(0.2)}$ & 50.9 $_{(4.1)}$ \\\\\n"," Oasst-33B \\oracle{} (3-psg) & 85.1 $_{(2.8)}$ & 44.3 $_{(2.4)}$ & 37.0 $_{(1.0)}$ & 39.6 $_{(1.5)}$ & 36.5 $_{(1.1)}$ & 44.2 $_{(5.8)}$ \\\\ \n"," \\midrule\n"," LLaMA-2-7B-Chat \\vani{} (5-psg) & 80.1 $_{(6.5)}$ & 33.9 $_{(2.1)}$ & 50.9 $_{(4.5)}$ & 47.5 $_{(3.7)}$ & 35.1 $_{(0.9)}$ & 42.3 $_{(10.1)}$\\\\\n"," LLaMA-2-13B-Chat \\vani{} (5-psg) & 72.4 $_{(6.3)}$ & 35.2 $_{(1.2)}$ & 38.4 $_{(5.9)}$ & 39.4 $_{(4.8)}$ & 35.8 $_{(0.9)}$ & 38.0 $_{(6.4)}$\\\\\n"," LLaMA-2-70B-Chat \\vani{} (5-psg) & 88.3 $_{(4.1)}$ & 41.5 $_{(0.8)}$ & 62.9 $_{(1.4)}$ & 61.3 $_{(2.1)}$ & 37.1 $_{(0.4)}$ & 52.9 $_{(9.5)}$\\\\\n"," \\midrule\n"," Stable Beluga 2 \\vani{} (5-psg) & 59.7 $_{(3.3)}$&37.2 $_{(1.0)}$&63.6 $_{(0.7)}$&63.5 $_{(0.1)}$&35.3 $_{(0.7)}$&23.8 $_{(3.2)}$\\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," \\caption{\n"," ASQA full results. %\n"," }\n"," \\label{tab:asqa_full}\n","\\end{table*}\n"],["\\begin{table*}[ht]\n"," \\centering\n"," \\small\n"," \\begin{tabular}{lccccc}\n"," \\toprule\n"," & \\multicolumn{2}{c}{\\tf{Correctness}} & \\multicolumn{2}{c}{\\tf{Citation} }\\\\\n"," \\cmidrule(lr){2-3} \\cmidrule(lr){4-5}\n"," & Rec.-5 & Prec. & Rec. & Prec. & Num Pred. \\\\\n"," \\midrule\n"," \\rowcolor{gray!16}\n"," \\multicolumn{6}{c}{\\textbf{ChatGPT}} \\\\\n"," \\tableskip\n"," \\vani{} (5-psg) & 20.8 $_{(2.2)}$ & 20.8 $_{(0.2)}$ & 20.5 $_{(0.7)}$ & 20.9 $_{(0.7)}$ & 5.0 $_{(0.5)}$ \\\\\n"," ~~w/ \\rerank{} & 22.8 $_{(-)}$ & 21.4 $_{(-)}$ & 21.2 $_{(-)}$ & 21.4 $_{(-)}$ & 5.4 $_{(-)}$ \\\\\n"," \\summ{} (10-psg) & 23.6 $_{(0.9)}$ & 21.2 $_{(0.5)}$ & 23.6 $_{(0.7)}$ & 25.7 $_{(0.8)}$ & 6.7 $_{(0.4)}$ \\\\\n"," \\snippet{} (10-psg) & 24.5 $_{(1.4)}$ & 21.5 $_{(1.8)}$ & 22.9 $_{(1.6)}$ & 24.9 $_{(0.4)}$ & 7.2 $_{(0.9)}$ \\\\\n"," ~~w/ \\interact{} & 21.9 $_{(0.9)}$ & 23.0 $_{(0.4)}$ & 21.9 $_{(1.2)}$ & 23.4 $_{(0.9)}$ & 6.7 $_{(0.4)}$ \\\\\n"," \\search{} & 17.2 $_{(1.1)}$ & 20.4 $_{(0.8)}$ & 14.9 $_{(0.8)}$ & 14.9 $_{(0.8)}$ & 6.7 $_{(0.2)}$ \\\\\n"," \\close{} & 32.9 $_{(1.1)}$ & 19.8 $_{(1.6)}$ & 10.0 $_{(0.4)}$ & 10.0 $_{(0.4)}$ & 17.0 $_{(2.9)}$ \\\\\n"," \\oracle{} & 37.0 $_{(3.1)}$ & 36.9 $_{(0.6)}$ & 24.1 $_{(1.2)}$ & 24.6 $_{(1.3)}$ & 5.3 $_{(0.6)}$ \\\\\n","\n","\n","\n"," \\headercolorlong\n"," \\tableskip\n"," \\multicolumn{6}{c}{\\textbf{ChatGPT-16K}} \\\\\n"," \\tableskip\n"," \\vani{} (5-psg) & 21.1 $_{(-)}$&22.0 $_{(-)}$&20.7 $_{(-)}$&21.2 $_{(-)}$&4.9 $_{(-)}$\\\\ \n"," \\vani{} (10-psg) & 23.4 $_{(-)}$&21.9 $_{(-)}$&21.6 $_{(-)}$&22.0 $_{(-)}$&5.7 $_{(-)}$\\\\\n"," \\vani{} (20-psg) & 26.4 $_{(-)}$&21.1 $_{(-)}$&19.4 $_{(-)}$&19.7 $_{(-)}$&7.6 $_{(-)}$\\\\\n","\n"," \\headercolorlong\n"," \\tableskip\n"," \\multicolumn{6}{c}{\\textbf{GPT-4}} \\\\\n"," \\tableskip\n","\n"," \\vani{} (5-psg) & 22.2 $_{(-)}$&25.0 $_{(-)}$&25.9 $_{(-)}$&27.0 $_{(-)}$&4.4 $_{(-)}$\\\\\n"," \\vani{} (10-psg) & 26.8 $_{(-)}$&25.1 $_{(-)}$&26.2 $_{(-)}$&27.2 $_{(-)}$&5.7 $_{(-)}$\\\\\n"," \\vani{} (20-psg) & 29.6 $_{(-)}$&26.2 $_{(-)}$&27.4 $_{(-)}$&28.5 $_{(-)}$&6.8 $_{(-)}$\\\\\n","\n","\n"," \\rowcolor{gray!16}\n"," \\multicolumn{6}{c}{\\textbf{Open-source}} \\\\\n"," LLaMA-7B \\vani{} (3-psg) & 7.8 $_{(3.4)}$ & 7.4 $_{(2.7)}$ & 5.1 $_{(0.5)}$ & 5.7 $_{(0.8)}$ & 5.7 $_{(0.6)}$ \\\\\n"," Alpaca-7B \\vani{} (3-psg) & 9.4 $_{(3.7)}$ & 9.5 $_{(3.6)}$ & 6.4 $_{(0.5)}$ & 6.8 $_{(0.5)}$ & 5.1 $_{(0.1)}$ \\\\\n"," Vicuna-7B \\vani{} (3-psg) & 11.3 $_{(1.4)}$ & 13.3 $_{(2.3)}$ & 10.1 $_{(0.6)}$ & 10.9 $_{(0.5)}$ & 3.9 $_{(0.3)}$ \\\\ \\midrule\n"," LLaMA-13B \\vani{} (3-psg) & 9.7 $_{(3.6)}$ & 9.1 $_{(3.1)}$ & 6.7 $_{(0.9)}$ & 7.1 $_{(0.9)}$ & 5.9 $_{(0.6)}$ \\\\\n"," ~~w/ \\rerank & 10.0 $_{(3.3)}$ & 10.7 $_{(3.3)}$ & 9.9 $_{(1.2)}$ & 10.2 $_{(1.1)}$ & 5.4 $_{(0.5)}$ \\\\\n"," LLaMA-13B \\summ{} (10-psg) & 14.8 $_{(2.5)}$ & 12.6 $_{(1.5)}$ & 7.4 $_{(0.5)}$ & 8.0 $_{(0.6)}$ & 8.1 $_{(0.9)}$ \\\\\n"," LLaMA-13B \\snippet{} (10-psg) & 17.7 $_{(1.4)}$ & 15.7 $_{(0.9)}$ & 8.8 $_{(0.7)}$ & 9.9 $_{(0.6)}$ & 8.2 $_{(0.4)}$ \\\\\n"," LLaMA-13B \\oracle{} (3-psg) & 16.8 $_{(6.6)}$ & 15.4 $_{(5.6)}$ & 7.7 $_{(1.0)}$ & 8.3 $_{(1.1)}$ & 5.7 $_{(0.7)}$ \\\\ \\midrule\n"," Vicuna-13B \\vani{} (3-psg) & 14.0 $_{(0.6)}$ & 15.9 $_{(1.7)}$ & 12.5 $_{(0.8)}$ & 13.4 $_{(0.7)}$ & 4.7 $_{(0.3)}$ \\\\\n"," ~~w/ \\rerank & 13.0 $_{(0.7)}$ & 17.2 $_{(2.2)}$ & 17.3 $_{(0.8)}$ & 17.7 $_{(0.6)}$ & 4.4 $_{(0.3)}$ \\\\\n"," Vicuna-13B \\summ{} (10-psg) & 21.1 $_{(1.4)}$ & 17.1 $_{(0.3)}$ & 15.7 $_{(0.2)}$ & 17.8 $_{(0.1)}$ & 6.9 $_{(0.7)}$ \\\\\n"," Vicuna-13B \\snippet{} (10-psg) & 21.9 $_{(0.8)}$ & 18.2 $_{(0.3)}$ & 16.8 $_{(0.3)}$ & 19.7 $_{(0.6)}$ & 7.5 $_{(0.4)}$ \\\\\n"," Vicuna-13B \\oracle{} (3-psg) & 25.9 $_{(1.6)}$ & 28.4 $_{(2.6)}$ & 15.8 $_{(1.4)}$ & 16.8 $_{(1.4)}$ & 4.9 $_{(0.5)}$ \\\\ \\midrule\n"," LLaMA-33B \\vani{} (3-psg) & 14.7 $_{(3.3)}$ & 12.0 $_{(2.2)}$ & 7.9 $_{(0.7)}$ & 8.3 $_{(0.6)}$ & 7.2 $_{(0.7)}$ \\\\\n"," ~~w/ \\rerank & 14.0 $_{(3.4)}$ & 13.9 $_{(2.6)}$ & 10.7 $_{(0.6)}$ & 11.1 $_{(0.5)}$ & 6.4 $_{(0.7)}$ \\\\\n"," LLaMA-33B \\summ{} (10-psg) & 19.0 $_{(1.9)}$ & 14.8 $_{(0.8)}$ & 12.5 $_{(0.2)}$ & 15.0 $_{(0.3)}$ & 7.6 $_{(0.6)}$ \\\\\n"," LLaMA-33B \\snippet{} (10-psg) & 19.6 $_{(1.1)}$ & 15.7 $_{(0.1)}$ & 12.8 $_{(1.1)}$ & 15.2 $_{(1.2)}$ & 7.8 $_{(0.5)}$ \\\\\n"," LLaMA-33B \\oracle{} (3-psg) & 23.9 $_{(6.9)}$ & 20.3 $_{(5.2)}$ & 9.8 $_{(1.2)}$ & 10.4 $_{(1.2)}$ & 6.8 $_{(0.9)}$ \\\\ \\midrule\n"," Oasst-33B \\vani{} (3-psg) & 15.5 $_{(1.5)}$ & 14.9 $_{(1.4)}$ & 9.0 $_{(1.6)}$ & 10.1 $_{(1.8)}$ & 5.6 $_{(0.3)}$ \\\\\n"," ~~w/ \\rerank & 14.1 $_{(1.1)}$ & 15.8 $_{(1.0)}$ & 15.0 $_{(1.6)}$ & 15.9 $_{(1.6)}$ & 4.7 $_{(0.3)}$ \\\\\n"," Oasst-33B \\summ{} (10-psg) & 21.0 $_{(0.6)}$ & 17.5 $_{(1.0)}$ & 12.9 $_{(1.2)}$ & 16.6 $_{(1.2)}$ & 7.1 $_{(0.4)}$ \\\\\n"," Oasst-33B \\snippet{} (10-psg) & 22.0 $_{(0.4)}$ & 17.4 $_{(0.3)}$ & 13.6 $_{(1.7)}$ & 17.7 $_{(1.6)}$ & 7.5 $_{(0.1)}$ \\\\\n"," Oasst-33B \\oracle{} (3-psg) & 26.9 $_{(3.7)}$ & 26.0 $_{(3.3)}$ & 11.7 $_{(1.0)}$ & 12.9 $_{(1.2)}$ & 5.6 $_{(0.4)}$ \\\\ \n"," \\midrule\n"," LLaMA-2-7B-Chat \\vani{} (5-psg) & 16.2 $_{(1.3)}$&15.3 $_{(1.6)}$&10.6 $_{(0.9)}$&10.9 $_{(1.0)}$&5.5 $_{(0.0)}$\\\\\n"," LLaMA-2-13B-Chat \\vani{} (5-psg) & 21.1 $_{(0.9)}$&18.2 $_{(0.5)}$&9.6 $_{(1.5)}$&9.7 $_{(1.5)}$&6.5 $_{(0.3)}$\\\\\n"," LLaMA-2-70B-Chat \\vani{} (5-psg) & 21.8 $_{(0.7)}$&18.4 $_{(0.1)}$&15.1 $_{(1.2)}$&15.6 $_{(1.3)}$&7.1 $_{(0.2)}$\\\\\n"," \\midrule\n"," Stable Beluga 2 & 19.7 $_{(0.7)}$&21.2 $_{(1.4)}$&18.6 $_{(1.2)}$&20.7 $_{(1.1)}$&5.1 $_{(0.3)}$\\\\\n"," \\bottomrule\n"," \n"," \\end{tabular}\n"," \\caption{\n"," QAMPARI full results.\n"," }\n"," \\label{tab:qampari_full}\n","\\end{table*}\n"],["\\begin{table*}[h]\n"," \\centering\n"," \\small\n"," \\begin{tabular}{lcccccc}\n"," \\toprule\n"," & \\tf{Fluency} & \\tf{Correct.} & \\multicolumn{2}{c}{\\tf{Citation} }\\\\\n"," \\cmidrule(lr){2-2} \\cmidrule(lr){3-3} \\cmidrule(lr){4-5}\n"," & (MAUVE) & (Claim) & Rec. & Prec. & ROUGE-L & Length \\\\\n"," \\midrule\n"," \\headercolorlong\n"," \\multicolumn{7}{c}{\\textbf{ChatGPT}} \\\\\n","\n"," \\vani{} (5-psg) & 57.2 $_{(1.6)}$ & 12.0 $_{(0.6)}$ & 51.1 $_{(4.2)}$ & 50.0 $_{(4.8)}$ & 20.6 $_{(0.2)}$ & 91.5 $_{(6.5)}$ \\\\\n"," ~~w/ \\rerank{} & 56.1 $_{(-)}$ & 11.4 $_{(-)}$ & 69.3 $_{(-)}$ & 67.8 $_{(-)}$ & 20.3 $_{(-)}$ & 103.4 $_{(-)}$ \\\\\n"," \\summ{} (10-psg)& 40.2 $_{(1.2)}$ & 12.5 $_{(0.2)}$ & 51.5 $_{(1.1)}$ & 48.2 $_{(2.0)}$ & 20.3 $_{(0.1)}$ & 90.0 $_{(6.6)}$ \\\\\n"," \\snippet{} (10-psg) & 62.9 $_{(2.2)}$ & 14.3 $_{(0.1)}$ & 50.4 $_{(1.1)}$ & 45.0 $_{(2.w)}$ & 21.0 $_{(0.1)}$ & 100.0 $_{(6.8)}$ \\\\\n"," ~~w/ \\interact{} & 68.0 $_{(5.8)}$ & 13.3 $_{(0.5)}$ & 47.8 $_{(3.3)}$ & 45.0 $_{(3.1)}$ & 20.1 $_{(0.2)}$ & 99.8 $_{(6.1)}$ \\\\\n"," \\search{} & 49.7 $_{(4.6)}$ & 13.4 $_{(1.1)}$ & 45.6 $_{(2.5)}$ &43.7 $_{(3.9)}$ & 20.4 $_{(0.3)}$ & 103.0 $_{(18.1)}$ \\\\\n"," \\close{} & 32.6 $_{(1.1)}$ & 18.6 $_{(0.5)}$ & 15.4 $_{(0.3)}$ & 15.4 $_{(0.3)}$ & 22.8 $_{(0.1)}$ & 108.3 $_{(8.9)}$ \\\\\n"," \\oracle{} (5-psg) & 59.4 $_{(4.1)}$ & 21.3 $_{(0.2)}$ & 57.8 $_{(3.7)}$ & 56.0 $_{(3.8)}$ & 21.2 $_{(0.3)}$ & 93.0 $_{(7.8)}$ \\\\\n","\n","\n","\n","\n"," \\headercolorlong\n"," \\multicolumn{7}{c}{\\textbf{ChatGPT-16K}} \\\\\n"," \\tableskip\n"," \\vani{} (5-psg) & 31.6 $_{(-)}$&14.4 $_{(-)}$&44.6 $_{(-)}$&44.1 $_{(-)}$&21.4 $_{(-)}$&87.6 $_{(-)}$\\\\ \n"," \\vani{} (10-psg) & 26.6 $_{(-)}$&14.4 $_{(-)}$&45.5 $_{(-)}$&43.3 $_{(-)}$&21.5 $_{(-)}$&87.5 $_{(-)}$\\\\\n"," \\vani{} (20-psg) & 31.6 $_{(-)}$&15.9 $_{(-)}$&43.4 $_{(-)}$&40.9 $_{(-)}$&21.7 $_{(-)}$&92.6 $_{(-)}$\\\\\n","\n"," \\headercolorlong\n"," \\tableskip\n"," \\multicolumn{7}{c}{\\textbf{GPT-4}} \\\\\n"," \\tableskip\n","\n"," \\vani{} (5-psg) & 38.4 $_{(-)}$&14.2 $_{(-)}$&44.0 $_{(-)}$&50.1 $_{(-)}$&20.6 $_{(-)}$&79.6 $_{(-)}$\\\\\n"," \\vani{} (10-psg) & 39.9 $_{(-)}$&15.7 $_{(-)}$&49.5 $_{(-)}$&54.2 $_{(-)}$&21.2 $_{(-)}$&88.2 $_{(-)}$\\\\\n"," \\vani{} (20-psg) & 41.5 $_{(-)}$&18.3 $_{(-)}$&48.5 $_{(-)}$&53.4 $_{(-)}$&22.2 $_{(-)}$&97.0 $_{(-)}$\\\\\n","\n"," \\headercolorlong\n"," \\tableskip\n"," \\multicolumn{7}{c}{\\textbf{Open-source} } \\\\\n"," \\tableskip\n"," LLaMA-7B \\vani{} (3-psg) & 28.6 $_{(17.9)}$ & 1.6 $_{(0.9)}$ & 1.2 $_{(0.0)}$ & 2.7 $_{(0.1)}$ & 12.2 $_{(1.3)}$ & 46.9 $_{(1.2)}$ \\\\\n"," Alpaca-7B \\vani{} (3-psg) & 45.9 $_{(5.3)}$ & 9.2 $_{(0.1)}$ & 4.5 $_{(1.6)}$ & 5.2 $_{(1.9)}$ & 18.8 $_{(0.3)}$ & 67.1 $_{(1.2)}$ \\\\\n"," Vicuna-7B \\vani{} (3-psg) & 43.2 $_{(3.9)}$ & 10.0 $_{(0.5)}$ & 12.6 $_{(2.3)}$ & 16.3 $_{(2.6)}$ & 19.1 $_{(0.4)}$ & 68.7 $_{(2.0)}$ \\\\ \\midrule\n"," LLaMA-13B \\vani{} (3-psg) & 50.0 $_{(2.0)}$ & 3.9 $_{(0.4)}$ & 3.1 $_{(0.9)}$ & 5.3 $_{(1.3)}$ & 16.1 $_{(0.5)}$ & 63.3 $_{(2.0)}$ \\\\\n"," ~~w/ \\rerank & 46.7 $_{(2.9)}$ & 4.3 $_{(0.4)}$ & 9.7 $_{(2.1)}$ & 15.0 $_{(2.2)}$ & 16.1 $_{(0.7)}$ & 63.0 $_{(2.3)}$ \\\\\n"," LLaMA-13B \\summ{} (10-psg) & 28.6 $_{(1.8)}$ & 2.9 $_{(0.1)}$ & 2.5 $_{(0.8)}$ & 3.8 $_{(0.8)}$ & 8.5 $_{(0.3)}$ & 33.1 $_{(0.6)}$ \\\\\n"," LLaMA-13B \\snippet{} (10-psg) & 48.4 $_{(3.1)}$ & 5.7 $_{(0.9)}$ & 5.8 $_{(0.6)}$ & 7.6 $_{(0.9)}$ & 15.1 $_{(1.1)}$ & 60.2 $_{(3.2)}$ \\\\\n"," LLaMA-13B \\oracle{} (3-psg) & 49.5 $_{(2.4)}$ & 6.4 $_{(0.6)}$ & 3.7 $_{(0.7)}$ & 6.5 $_{(1.0)}$ & 16.8 $_{(0.5)}$ & 64.5 $_{(1.7)}$ \\\\ \\midrule\n"," Vicuna-13B \\vani{} (3-psg) & 58.2 $_{(25.1)}$ & 10.0 $_{(0.3)}$ & 15.6 $_{(2.2)}$ & 19.6 $_{(2.0)}$ & 19.1 $_{(0.3)}$ & 69.6 $_{(0.6)}$ \\\\\n"," ~~w/ \\rerank & 45.9 $_{(4.3)}$ & 9.2 $_{(0.0)}$ & 31.7 $_{(2.9)}$ & 38.2 $_{(1.6)}$ & 18.6 $_{(0.5)}$ & 69.7 $_{(1.0)}$ \\\\\n"," Vicuna-13B \\summ{} (10-psg) & 22.4 $_{(3.0)}$ & 4.9 $_{(0.1)}$ & 9.7 $_{(1.3)}$ & 12.2 $_{(1.2)}$ & 9.3 $_{(0.4)}$ & 33.0 $_{(3.7)}$ \\\\\n"," Vicuna-13B \\snippet{} (10-psg) & 48.1 $_{(5.3)}$ & 11.2 $_{(1.4)}$ & 27.2 $_{(3.6)}$ & 27.9 $_{(1.9)}$ & 18.4 $_{(1.9)}$ & 76.8 $_{(8.7)}$ \\\\\n"," Vicuna-13B \\oracle{} (3-psg) & 41.6 $_{(3.1)}$ & 17.1 $_{(0.4)}$ & 20.2 $_{(3.0)}$ & 26.5 $_{(3.0)}$ & 20.0 $_{(0.3)}$ & 72.0 $_{(0.3)}$ \\\\ \\midrule\n"," LLaMA-33B \\vani{} (3-psg) & 58.8 $_{(4.3)}$ & 6.2 $_{(0.0)}$ & 9.3 $_{(3.0)}$ & 12.1 $_{(4.2)}$ & 16.9 $_{(0.2)}$ & 60.0 $_{(1.3)}$ \\\\\n"," ~~w/ \\rerank & 65.9 $_{(2.5)}$ & 6.0 $_{(0.7)}$ & 22.5 $_{(5.2)}$ & 26.1 $_{(6.9)}$ & 17.5 $_{(0.4)}$ & 61.0 $_{(1.2)}$ \\\\\n"," LLaMA-33B \\summ{} (10-psg) & 23.3 $_{(2.0)}$ & 3.0 $_{(0.2)}$ & 6.2 $_{(0.5)}$ & 8.2 $_{(0.7)}$ & 7.5 $_{(0.4)}$ & 26.2 $_{(2.3)}$ \\\\\n"," LLaMA-33B \\snippet{} (10-psg) & 53.2 $_{(4.0)}$ & 7.4 $_{(1.3)}$ & 13.7 $_{(0.5)}$ & 15.1 $_{(0.4)}$ & 14.4 $_{(1.7)}$ & 53.3 $_{(8.5)}$ \\\\\n"," LLaMA-33B \\oracle{} (3-psg) & 63.7 $_{(2.8)}$ & 11.4 $_{(0.5)}$ & 11.9 $_{(2.6)}$ & 15.4 $_{(3.6)}$ & 17.9 $_{(0.2)}$ & 61.7 $_{(2.6)}$ \\\\ \\midrule\n"," Oasst-33B \\vani{} (3-psg) & 46.8 $_{(7.6)}$ & 9.5 $_{(0.2)}$ & 16.0 $_{(2.5)}$ & 21.6 $_{(3.5)}$ & 18.6 $_{(0.3)}$ & 67.8 $_{(1.5)}$ \\\\\n"," ~~w/ \\rerank & 52.1 $_{(6.1)}$ & 8.5 $_{(0.5)}$ & 34.4 $_{(2.9)}$ & 41.5 $_{(2.5)}$ & 18.2 $_{(0.3)}$ & 67.0 $_{(1.5)}$ \\\\\n"," Oasst-33B \\summ{} (10-psg) & 24.8 $_{(2.8)}$ & 3.9 $_{(0.3)}$ & 12.3 $_{(0.2)}$ & 16.3 $_{(0.3)}$ & 9.1 $_{(0.3)}$ & 31.6 $_{(1.5)}$ \\\\\n"," Oasst-33B \\snippet{} (10-psg) & 50.7 $_{(4.6)}$ & 10.7 $_{(1.2)}$ & 25.8 $_{(3.3)}$ & 26.7 $_{(2.3)}$ & 17.8 $_{(1.8)}$ & 69.6 $_{(8.6)}$ \\\\\n"," Oasst-33B \\oracle{} (3-psg) & 50.7 $_{(12.1)}$ & 15.8 $_{(0.1)}$ & 20.8 $_{(2.8)}$ & 28.0 $_{(3.2)}$ & 19.4 $_{(0.1)}$ & 70.3 $_{(1.1)}$ \\\\ \n"," \\midrule\n"," LLaMA-2-7B-Chat \\vani{} (5-psg) & 27.8 $_{(3.0)}$&10.9 $_{(0.2)}$&19.8 $_{(1.2)}$&15.0 $_{(1.4)}$&20.5 $_{(0.2)}$&87.8 $_{(8.1)}$\\\\\n"," LLaMA-2-13B-Chat \\vani{} (5-psg) & 34.7 $_{(1.5)}$&13.4 $_{(0.4)}$&17.3 $_{(1.3)}$&15.8 $_{(1.4)}$&20.9 $_{(0.2)}$&88.3 $_{(6.3)}$\\\\\n"," LLaMA-2-70B-Chat \\vani{} (5-psg) & 38.6 $_{(4.8)}$&12.8 $_{(1.0)}$&38.3 $_{(2.4)}$&37.9 $_{(1.9)}$&21.3 $_{(0.1)}$&110.8 $_{(5.6)}$\\\\\n"," \\midrule\n"," StableBeluga2 & 33.0 $_{(4.4)}$&14.0 $_{(0.5)}$&27.9 $_{(2.7)}$&29.0 $_{(2.7)}$&20.6 $_{(0.2)}$&75.6 $_{(5.0)}$\\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," \\caption{\n"," ELI5 full results.\n"," }\n"," \\label{tab:eli5_full}\n","\\end{table*}\n"],["\\begin{table*}[ht]\n"," \\centering\n"," \\small\n"," \\begin{tabular}{>{\\raggedright\\arraybackslash\\tt}p{0.98\\textwidth}<{}}\n"," \\toprule\n"," Read the original question and passage, and generate 3 additional claims that are supported by the passage and answer the question.\n"," \\\\\\\\\n"," \\noindent {Original question: What's the difference between Shia vs. Sunni Islam?}\\\\\n"," Passage: The main difference between Shia and Sunni Muslim is related to ideological heritage and issues of leadership. This difference is first formed after the death of the Prophet Muhammad in 632 A.D. The ideological practice of the Sunni branch strictly follows Prophet Muhammad and his teachings, while the Shia branch follows Prophet Muhammad's son-in-law Ali. Nowadays, Sunni and Shia are the major branches of Islam. \\\\\n"," Claim 1: The major branches of Islam are Sunni and Shia. \\\\\n"," Claim 2: Prophet Muhammad died in 632 A.D. \\\\\n"," Claim 3: The ideological practice of the Sunni branch strictly follows Prophet Muhammad and his teachings. \\\\\\\\\n"," Original question: What causes Bi-polar disorder? \\\\\n"," Passage: Bipolar disorder is an emotional disorder that causes extreme mood swings between excitement and depression. The spectrum of mood swing may span from days to months. We are still not certain of the exact factors that cause such disorder, but genetics is considered a major factor. \\\\\n"," Claim 1: One symptom of Bi-polar disorder is extreme mood swings between excitement and depression.\\\\\n"," Claim 2: Genetics could be one of the major factors that causes Bi-polar disorder.\\\\\n"," Claim 3: The mood swing from Bi-polar disorder can last days to months.\\\\\\\\\n"," Original question: How do we hear differences in sound besides volume and pitch?\\\\\n"," Passage: Pitch refers to the frequency of soundwave, and volumn refers to the amplitude of the soundwave. Besides volumn and pitch, we can also tell the difference between sounds based on the tone of sound. For example, we can differentiate the sound of different instruments based on the tone of the sounds.\\\\\n"," Claim 1: Volume of sound is the amplitude of the soundwave.\\\\\n"," Claim 2: Pitch is the frequency of soundwave.\\\\\n"," Claim 3: We can use the tone of the sounds to differentiate the sound of different instruments. \\\\\\\\\n"," \\vspace{-0.7em}\n"," {\\color{brown}Original question: How are we able to discern whether a sound is coming from in front of us or behind us?} \\\\\n"," \\vspace{-0.7em}\n"," {\\color{brown}Passage: There are multiple explanations for why we can localize sounds. One explanation is that sounds travelling to the corresponding side of one's ear will be slightly louder. Another explanation is that there is a slight difference in the hitting time to one's left and right ear based on the sound's direction. However, these explanation means that when a sound is exactly in front of someone or exactly behind someone, he or she can not tell the difference.} \\\\\n"," \\vspace{-0.7em}\n"," {\\color{brown}Claim 1:} {\\color{blue} We can localize sounds by recognizing that the sound travelling to the corresponding side of one's ear will be slightly louder.} \\\\\n"," \\vspace{-0.7em}\n"," {\\color{blue}Claim 2: We can also localize sounds by recognizing the difference in hitting time to one's left and right ear based on the sound's direction.}\\\\\n"," \\vspace{-0.7em}\n"," {\\color{blue}Claim 3: We cannot tell the difference between a sound that is exactly in front of us or exactly behind us.} \\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," \\caption{\n"," Prompt used to generate the sub-claims for ELI5 questions.\n"," {\\color{blue} Blue text is model generation.}\n"," {\\color{brown} Brown text is the ELI5 example that we want to generate sub-claims for.}\n"," We construct the prompt by manually writing the sub-claims for three questions from the training set. \n"," }\n"," \\label{tab:eli5-claims-prompt}\n","\\end{table*}\n"],["\\begin{table*}[h]\n"," \\centering\n"," \\small\n"," \\begin{tabular}{>{\\raggedright\\arraybackslash\\tt}p{0.95\\textwidth}<{}}\n"," \\toprule\n"," Instruction: Write an accurate, engaging, and concise answer for the given question using only the provided search results (some of which might be irrelevant) and cite them properly. Use an unbiased and journalistic tone. Always cite for any factual claim. When citing several search results, use [1][2][3]. Cite at least one document and at most three documents in each sentence. If multiple documents support the sentence, only cite a minimum sufficient subset of the documents. \\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," \\caption{\n"," Instruction for \\vani{}.\n"," }\n"," \\label{tab:inst_vanilla}\n","\\end{table*}\n"],["\\begin{table*}[h]\n"," \\centering\n"," \\small\n"," \\begin{tabular}{>{\\raggedright\\arraybackslash\\tt}p{0.95\\textwidth}<{}}\n"," \\toprule\n"," Instruction: Write a high-quality answer for the given question using only the provided search results and cite them properly using [1][2][3].\\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," \\caption{\n"," Short instruction for \\vani{}.\n"," }\n"," \\label{tab:inst_vanilla_light}\n","\\end{table*}\n"],["\\begin{table*}[h]\n"," \\centering\n"," \\small\n"," \\begin{tabular}{>{\\raggedright\\arraybackslash\\tt}p{0.95\\textwidth}<{}}\n"," \\toprule\n"," Summarize the following document within 50 words with the question of interest \"\\{QUESTION\\}\" Return \"irrelevant\" if the document is irrelevant to the question. Try to keep all the important dates, numbers, and names.\\\\\n"," ~\\\\\n"," Title: \\{TITLE\\}\\\\\n"," Text: \\{TEXT\\}\\\\\n"," Summary:\\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," \\caption{\n"," Prompts for \\summ{}.\n"," }\n"," \\label{tab:prompt_summary}\n","\\end{table*}\n"],["\\begin{table*}[h]\n"," \\centering\n"," \\small\n"," \\begin{tabular}{>{\\raggedright\\arraybackslash\\tt}p{0.95\\textwidth}<{}}\n"," \\toprule\n","Given the follow passage and the question \"\\{QUESTION\\}\", extract a useful span from the passage that can answer the question. Resolve all the coreference issues to make the extracted span understandable standalone. If the passage is not helpful for answering the question, return \"irrelevant\".\\\\\n","\\\\\n","Title: \\{TITLE\\}\\\\\n","Text: \\{TEXT\\}\\\\\n","Extracted span:\\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," \\caption{\n"," Prompts for \\snippet{}.\n"," }\n"," \\label{tab:prompt_snippet}\n","\\end{table*}\n"],["\\begin{table*}[h]\n"," \\centering\n"," \\small\n"," \\begin{tabular}{>{\\raggedright\\arraybackslash\\tt}p{0.95\\textwidth}<{}}\n"," \\toprule\n"," Instruction: Write an accurate, engaging, and concise answer for the given question using only the provided search results and cite them properly. Use an unbiased and journalistic tone. Always cite for any factual claim.\\\\\n"," You are provided summaries/snippets of the search results. You can use \"Check: Document [1][2]\" to check the corresponding full documents (you should only check relevant documents and you can at most check 3 documents at a time) and use \"Output:\" to output a sentence in the answer. In the answer, cite properly by using [1][2][3]. Cite at least one document and at most three documents in each sentence. If multiple documents support the sentence, only cite a minimum sufficient subset of the documents. Use \"End\" to end the generation.\\\\\n"," \\\\\n"," \\vspace{-1em}\n"," \\noindent {\\color{brown}<Retrieve for question ``...''>}\\\\\n"," \\noindent {\\color{brown}<Get summaries/snippets for the passages and delete those that are ``irrelevant''>}\\\\\n"," Document {[1]}(Title: ...) \\{SUMMARY OR SNIPPET\\}\\\\\n"," ... \\\\\n"," \\\\\n"," \\vspace{-1em}\n"," Question: When did US break away from England? \\\\\n"," \\noindent {\\color{blue} Check: Document [1][2]} \\\\\n"," Document [1] \\{FULL TEXT\\} \\\\\n"," Document [2] \\{FULL TEXT\\} \\\\\n"," \\noindent {\\color{blue} Output: The United States ... \\tf{\\underline{[1]}} ... \\tf{\\underline{[2]}}.} \\\\\n"," \\noindent {\\color{brown}<Remove the full text of [1][2] from context>}\\\\\n"," \\noindent {\\color{blue} Check: Document [3]} \\\\\n"," Document [3] \\{FULL TEXT\\} \\\\\n"," \\noindent {\\color{blue} Output: The Treaty of Paris ... \\tf{\\underline{[3]}}.} \\\\\n"," \\noindent {\\color{brown}<Remove the full text of [3] from context>}\\\\\n"," \\noindent {\\color{blue}End.}\\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," \\caption{\n"," An example for \\interact{}.\n"," }\n"," \\label{tab:inst_inter}\n","\\end{table*}\n"],["\\begin{table*}[h]\n"," \\centering\n"," \\small\n"," \\begin{tabular}{>{\\raggedright\\arraybackslash\\tt}p{0.95\\textwidth}<{}}\n"," \\toprule\n"," Instruction: Write an accurate, engaging, and concise answer for the given question using only the provided search results and cite them properly. Use an unbiased and journalistic tone. Always cite for any factual claim.\\\\\n"," You can use \"Search: key words\" to check the most relevant document's full text and use \"Output:\" to output a sentence in the answer. In the answer, cite properly by using [1][2][3]. Cite at least one document and at most three documents in each sentence. If multiple documents support the sentence, only cite a minimum sufficient subset of the documents. Use \"End\" to end the generation.\\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," \\caption{\n"," Instruction for \\search{}.\n"," }\n"," \\label{tab:inst_search}\n","\\end{table*}\n"],["\\begin{table*}[h]\n"," \\centering\n"," \\small\n"," \\begin{tabular}{>{\\raggedright\\arraybackslash\\tt}p{0.95\\textwidth}<{}}\n"," \\toprule\n"," Instruction: Write an accurate, engaging, and concise answer for the given question. Use an unbiased and journalistic tone. \\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," \\caption{\n"," Instruction for \\close{}.\n"," }\n"," \\label{tab:prompt_close}\n","\\end{table*}\n"],["\\begin{table*}[h]\n"," \\centering\n"," \\small\n"," \\begin{tabular}{>{\\raggedright\\arraybackslash\\tt}p{0.95\\textwidth}<{}}\n"," \\toprule\n"," Instruction: Write an accurate, engaging, and concise answer for ... \\\\\n"," \\\\\n"," \\vspace{-1em}\n"," Document {[1]}(Title: How to Treat and Prevent Food Poisoning - MsPrepper) just a typical gastro upset. Salmonella is most commonly caused by eating undercooked or raw foods like eggs or meat. You know how your mom always warned you not to eat raw cookie dough? This is why. Most people do eat cookie dough and they are fine, but salmonella is a risk. If you do contract salmonella, you could start to feel bad within in a couple of hours after eating contaminated food, and sometimes it could take a day or two. Common symptoms are nausea and vomiting, loose stools (sometimes bloody), flu like symptoms, and stomach cramps. To treat \\\\\n"," Document {[2]}(Title: FDA Issues Warning About Eating Raw Cookie Dough, But Not For Salmonella Risks) FDA Issues Warning About Eating Raw Cookie Dough, But Not For Salmonella Risks Used to licking the spoon or placating yourself with full-on chunks of raw cookie dough? The Food and Drug Administration issued a warning on Tuesday that strongly advises against continuing the habit. The agency asserted that consuming raw batter of any kind, whether for bread, cookies or pizza, could make a person sick. While you may have been warned in the past against eating raw dough due to the risk of contracting salmonella from raw eggs, the FDA is citing raw flour as the culprit for a \\\\\n"," Document {[3]}(Title: It's Probably OK to Eat Raw Cookie Dough — As Long As You're Smart About It - The Crux - Very Top Secret Information) First, when most people think about health risks and cookie dough, they think about raw egg. Eggs can be contaminated with salmonella bacteria, and food safety recommendations encourage people to cook eggs until the white and yolk are firm in order to kill any bacteria. However, anyone making cookies can do things to reduce this risk by using pasteurized egg products. When my kids and I make cookie dough, we never use regular eggs. Instead, we use shell eggs that have been pasteurized to kill any harmful bacteria without actually cooking the egg itself. (A great public health innovation, if \\\\\n"," Document {[4]}(Title: How Dangerous Is It to Eat Raw Cookie Dough? | Men's Health) Can Eating Raw Cookie Dough Really Make You Sick? Scientists reveal the truth about this supposedly dangerous delicacy By Katherine Dempsey There are few things more tempting in life than eyeing a bowl of cookie dough and deciding whether or not to stick your finger in for a scoop. It’s a bit like playing Russian roulette. You could get lucky and enjoy the delicious dough without conseqence, but there’s always the risk of getting serously sick with a food-borne illness. That’s because multiple ingredients within the dough could be contaminated by pathogens such as Salmonella and E. coli, says Soohyoun \\\\\n"," Document {[5]}(Title: How Dangerous Is It to Eat Raw Cookie Dough? | Men's Health) is usually pasteurized, so it’s not likely that the egg would make you sick. However, other ingredients in the dough could potentially harbor pathogens. Experts say that a prime suspect in a 2009 E. coli outbreak linked to prepackaged cookie dough was actually the flour. Nuts and chocolate have also been linked to Salmonella outbreaks. Bottom line: You’re better off skipping raw cookie dough. At least you’ll have killer cookies as a consolation. The article How Bad Is It To Eat Raw Cookie Dough? originally ran on Prevention.com The 9 Foods Most Likely to Make You Sick Mmm, Just Don't \\\\\n","\n"," ... \\\\\n"," \\\\\n"," \\vspace{-1em}\n"," Question: Why is it bad to eat cookie dough for risk of salmonella but things like Cookie Dough Bites are ok? \\\\\n"," Answer: {\\color{blue} Raw cookie dough is not recommended to be eaten due to the risk of salmonella \\tf{\\underline{[1][2]}}. Eating raw flour is also a risk for food poisoning \\tf{\\underline{[2]}}. The egg in cookie dough is usually pasteurized, so it's not likely that the egg would make you sick, but other ingredients in the dough could potentially harbor pathogens \\tf{\\underline{[4][5]}}. However, prepackaged cookie dough like Cookie Dough Bites is safe to eat because the dough is made with pasteurized egg products and heat-treated flour \\tf{\\underline{[2][3]}}.}.\\\\\n"," \\\\\n"," \\noindent {\\color{orange} Ground truth claims:}\\\\\n"," \\noindent {\\color{orange} 1. Cookie Dough Bites are safe to eat since they do not contain eggs.}\\\\\n"," \\noindent {\\color{orange} 2. Salmonella is a bacteria found in eggs.}\\\\\n"," \\noindent {\\color{orange} 3. Eating raw cookie dough carries the risk of salmonella infection.}\\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," \\caption{\n"," ELI5 example 1.\n"," }\n"," \\label{tab:exp_eli5}\n","\\end{table*}\n"],["\\begin{table*}[h]\n"," \\centering\n"," \\small\n"," \\begin{tabular}{>{\\raggedright\\arraybackslash\\tt}p{0.98\\textwidth}<{}}\n"," \\toprule\n"," Instruction: Write an accurate, engaging, and concise answer for ... \\\\\n"," \\\\\n"," \\vspace{-1em}\n"," Document [1](Title: Is Snapchat really worth \\$19 billion? - CSMonitor.com) reporting that the Los Angeles-based company is aiming to raise \\$500 million at a valuation of \\$16 billion to \\$19 billion, making it the third most highly valued tech start-up backed by venture capitalists. The Chinese handset maker Xiaomi is valued at \\$45 billion, while Uber is estimated to be valued at about \\$40 billion, according to data from CB Insights. Read MoreVC investment hits \\$86B thanks to Uber, Xiaomi Snapchat was valued at \\$10 billion in August, according to a Dow Jones report. Some of its investors from previous rounds include Benchmark, Lightspeed Venture Partners and Kleiner Perkins Caufield \\\\\n"," Document [2](Title: What Are Venture Capital Investments? – DollarsAndSense.my) Ever wondered how highly valued technology giants like Google and Facebook were able to grow so fast and pay their employees so well in such a short amount of time, or how still growing start-ups like Uber are able to lose 1.2 billion US dollars in just the first half of this year alone and still command a valuation upwards of 50 billion US dollars? The answer lies with a special category of investment activity known as venture capital. Venture capitalists are professional investors who invest in a number of highly scalable high-risk technology ventures hoping to make a multi-fold \\\\\n"," Document [3](Title: Opinion | What Dara Khosrowshahi Must Do to Save Uber - The New York Times) at a discount. These are troubling signs. Every start-up must one day fulfill the market’s demand that it turn a profit, but Uber has never figured out how to do that. While ride sharing in some form will probably survive, it’s more likely that without some drastic changes, Uber won’t be around in three to five years. Mr. Khosrowshahi must avoid the mistakes of his predecessor by accepting that “pivots” (Silicon Valley-speak for the desperate changes troubled companies make to reassure their venture capitalist funders) are not the answer. None of the pivots Mr. Kalanick tried — like on-demand delivery \\\\\n"," Document [4](Title: Snapchat raising funding round at \\$19 billion valuation: Report) Snapchat raising funding round at \\$19 billion valuation: Report The mobile messaging app Snapchat is said to be seeking funding at a \\$19 billion valuation, according to a report, citing a person with knowledge of the matter. Bloomberg is reporting that the Los Angeles-based company is aiming to raise \\$500 million at a valuation of \\$16 billion to \\$19 billion, making it the third most highly valued tech start-up backed by venture capitalists. The Chinese handset maker Xiaomi is valued at \\$45 billion, while Uber is estimated to be valued at about \\$40 billion, according to data from CB Insights. \\\\\n"," Document [5](Title: Unicorns And Wall Street | MoneyTips) not start showing profits soon. Even Uber, with its massive \\$62-billion-plus valuation, will test investor's patience if it does not start showing signs of a profit in the next few years. Cracks are starting to show in terms of pre-IPO devaluations. Dropbox and Snapchat have seen a nearly one-quarter drop in their pre-IPO valuations, while Square's pre-IPO value has dropped by approximately one-third. Larger investors and venture capitalists are sending a message to the unicorns: Outline a solid path to profits and start producing them soon, or expect your valuation to be slashed. Diving ahead with an IPO is not \\\\\n"," ... \\\\\n"," \\\\\n"," \\vspace{-1em}\n"," Question: How are firms like snapchat, uber etc valued so highly while still not making a profit? Do venture capitalists not expect some form of repayment within a number of years? \\\\\n"," Answer: {\\color{blue} Venture capitalists invest in a number of highly scalable high-risk technology ventures hoping to make a multi-fold return on their investment in a short amount of time \\tf{\\underline{[2]}}. Highly valued companies like Snapchat, Uber, and Xiaomi are able to grow so fast and command high valuations because they have the potential to generate significant revenue in the future \\tf{\\underline{[2][4]}}. Venture capitalists expect a return on their investment within a number of years, but they also invest in the long-term potential of the company \\tf{\\underline{[2]}}. It is important to note that every start-up must eventually turn a profit, but some start-ups like Uber have not yet figured out how to do that \\tf{\\underline{[3][5]}}.}\\\\\n"," \\\\\n"," \\noindent {\\color{orange} Ground truth claims:}\\\\\n"," \\noindent {\\color{orange} 1. Firms like Snapchat and Uber need to establish their brand and amass users before introducing ads.}\\\\\n"," \\noindent {\\color{orange} 2. Introducing ads too early can deter potential users.}\\\\\n"," \\noindent {\\color{orange} 3. Uber is reinvesting a lot of money to make their service better.}\\\\\n"," \n"," \\bottomrule\n"," \\end{tabular}\n"," \\caption{\n"," ELI5 example 2.\n"," }\n"," \\label{tab:exp_eli52}\n","\\end{table*}\n"]],"string":"[\n [\n \"\\\\begin{figure}[t]\\n\",\n \" \\\\includegraphics[width=0.98\\\\linewidth]{figs/main.pdf}\\n\",\n \" \\\\caption{The task setup of \\\\citeeval{}. Given a question, \\n\",\n \" the system generates text while providing \\\\ti{citing passages} from a large retrieval corpus. Each statement may contain multiple citations (e.g., \\\\ttt{[1][2]}). %\\n\",\n \" }\\n\",\n \" \\\\vspace{-5pt}\\n\",\n \" \\\\label{fig:main}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table*}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\small\\n\",\n \" \\\\begin{tabular}{lllp{0.5\\\\textwidth}}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\tf{Dataset} & \\\\tf{Corpus (\\\\#passages)} & \\\\tf{Question type} & \\\\tf{Example} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" ASQA & Wikipedia (21M) & Factoid (ambiguous) & \\\\tf{Q}: When did the US break away from England? \\\\\\\\\\n\",\n \" &&& \\\\tf{A}: The US declared independence on July 2, 1776 \\\\citedb{[1][2]} ... The Treaty of Paris was later signed on September 3, 1783 \\\\citedb{[3]}. \\\\\\\\ %\\n\",\n \" QAMPARI & Wikipedia (21M) & Factoid (list) & \\\\tf{Q}: Which films have Gong Li as a member of their cast? \\\\\\\\\\n\",\n \" &&& \\\\tf{A}: The Story of Qiu Ju \\\\citedb{[1]}, Farewell My Concubine \\\\citedb{[2]}, The Monkey King 2 \\\\citedb{[3]}, Mulan \\\\citedb{[3]}, Saturday Fiction \\\\citedb{[3]} ...\\\\\\\\\\n\",\n \" ELI5 & Sphere (899M) & Why/How/What & \\\\tf{Q}: How do student loans affect getting a mortgage? \\\\\\\\\\n\",\n \" &&& \\\\tf{A}: Student loans can affect the debt to income ratio \\\\citedb{[1]}, which is a key factor in determining the amount that ... \\\\citedb{[2][3]} \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" \\\\caption{The three datasets used in our \\\\citeeval{} benchmark. %\\n\",\n \" These datasets cover a wide range of question types\\n\",\n \" and the corresponding corpora span from Wikipedia to \\n\",\n \" Web-scale document collection. %\\n\",\n \" }\\n\",\n \" \\\\vspace{-10pt}\\n\",\n \" \\\\label{tab:datasets}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{figure}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=0.94\\\\linewidth]{figs/correctness.pdf}\\n\",\n \" \\\\caption{Evaluation of correctness (details in \\\\S\\\\ref{sec:correctness}).}\\n\",\n \" \\\\vspace{-13pt}\\n\",\n \" \\\\label{fig:correctness}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{figure}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=0.98\\\\linewidth]{figs/citation.pdf}\\n\",\n \" \\\\caption{Evaluation of citation quality (details in \\\\S\\\\ref{sec:citation}). We use \\n\",\n \" an NLI model to verify whether a statement is supported by its citations. %\\n\",\n \" }\\n\",\n \" \\\\vspace{-15pt}\\n\",\n \" \\\\label{fig:citation}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\small\\n\",\n \" \\\\begin{tabular}{>{\\\\raggedright\\\\arraybackslash\\\\tt}p{0.94\\\\linewidth}<{}}\\n\",\n \" \\\\toprule\\n\",\n \" Instruction: Write an accurate, engaging, and concise answer for ... \\\\\\\\\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\vspace{-1em}\\n\",\n \" \\\\noindent {\\\\color{brown}<Retrieve for the question>}\\\\\\\\\\n\",\n \" Document {[1]}(Title: American Decolonization) ... \\\\\\\\\\n\",\n \" Document {[2]}(Title: Decolonization) ... \\\\\\\\\\n\",\n \" Document {[3]}(Title: American Revolution) ... \\\\\\\\\\n\",\n \" ... \\\\\\\\\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\vspace{-1em}\\n\",\n \" Question: When did US break away from England? \\\\\\\\\\n\",\n \" Answer: {\\\\color{blue} The United States took the first step towards gaining independence ... \\\\tf{\\\\underline{[1][2]}}. The Treaty of Paris was later signed ... \\\\tf{\\\\underline{[3]}}}.\\\\\\\\\\n\",\n \"\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" \\\\caption{\\n\",\n \" An example of our \\\\vani{} method. Different colors represent \\\\ttt{prompt}, \\\\ttt{\\\\color{blue} model generation}, and \\\\ttt{\\\\color{brown} <actions>}. \\n\",\n \" We also provide two in-context demonstrations before the test example. \\n\",\n \" }\\n\",\n \" \\\\vspace{-10pt}\\n\",\n \" \\\\label{tab:prompt_short}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\small\\n\",\n \" \\\\begin{tabular}{>{\\\\raggedright\\\\arraybackslash\\\\tt}p{0.94\\\\linewidth}<{}}\\n\",\n \" \\\\toprule\\n\",\n \" Instruction: ... \\\\\\\\\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\vspace{-1em}\\n\",\n \" \\\\noindent {\\\\color{brown}<Retrieve for question ``...''>}\\\\\\\\\\n\",\n \" Question: When did US break away from England? \\\\\\\\\\n\",\n \" \\\\noindent {\\\\color{blue} Search: Declaration of Independence} \\\\\\\\\\n\",\n \" \\\\noindent {\\\\color{brown}<Search the query among the top-100 passages>}\\\\\\\\\\n\",\n \" Document [1](Title: ...) ... \\\\\\\\\\n\",\n \" \\\\noindent {\\\\color{blue} Output: The United States ... \\\\tf{\\\\underline{[1]}}.} \\\\\\\\\\n\",\n \" \\\\noindent {\\\\color{brown}<Remove Document [1] from context>}\\\\\\\\\\n\",\n \" \\\\noindent {\\\\color{blue} Search: Treaty of Paris} \\\\\\\\\\n\",\n \" \\\\noindent {\\\\color{brown}<Search the query among the top-100 passages>}\\\\\\\\\\n\",\n \" Document [3](Title: ...) ... \\\\\\\\\\n\",\n \" \\\\noindent {\\\\color{blue} Output: The Treaty of Paris ... \\\\tf{\\\\underline{[3]}}.} \\\\\\\\\\n\",\n \" \\\\noindent {\\\\color{brown}<Remove Document [3] from context>}\\\\\\\\\\n\",\n \" \\\\noindent {\\\\color{blue}End.}\\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" \\\\caption{\\n\",\n \" An example of \\\\search{}. \\n\",\n \" }\\n\",\n \" \\\\vspace{-15pt}\\n\",\n \" \\\\label{tab:prompt_search}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\resizebox{0.98\\\\linewidth}{!}{\\n\",\n \" \\\\begin{tabular}{lcccc}\\n\",\n \" \\\\toprule\\n\",\n \" & \\\\tf{Fluency} & \\\\tf{Correct.} & \\\\multicolumn{2}{c}{\\\\tf{Citation} }\\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-2} \\\\cmidrule(lr){3-3} \\\\cmidrule(lr){4-5}\\n\",\n \" & (MAUVE) & (EM Rec.) & Rec. & Prec. \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\headercolor\\n\",\n \" \\\\multicolumn{5}{c}{\\\\textbf{ChatGPT}} \\\\\\\\\\n\",\n \" \\\\tableskip\\n\",\n \" \\\\vani{} (5-psg) & 66.6 & 40.4 & {73.6} & 72.5\\\\\\\\% {63.0} \\\\\\\\\\n\",\n \" ~~w/ \\\\rerank{} & 77.0 & 40.2 & \\\\tf{84.8} & \\\\textbf{81.6} \\\\\\\\% \\\\tf{73.3} \\\\\\\\\\n\",\n \" \\\\summ{} (10-psg) & 70.0 & \\\\tf{43.3} &68.9\\t& 61.8\\\\\\\\ %\\n\",\n \" ~~w/ \\\\interact{} & 69.0 &\\t39.1 & 73.4& 66.5\\\\\\\\ %\\n\",\n \" \\\\snippet{} (10-psg) & 69.8&\\t41.4& 65.3&\\t57.4\\\\\\\\ %\\n\",\n \" \\\\search{} & 58.7 & 32.4 & 58.3 & 58.2\\t\\\\\\\\ %\\n\",\n \" \\\\close{} & 52.7 &\\t38.3 & 26.7& 26.7 \\\\\\\\\\n\",\n \"\\n\",\n \" \\\\tableskip\\n\",\n \" \\\\headercolor\\n\",\n \" \\\\multicolumn{5}{c}{\\\\textbf{GPT-4} (\\\\vani{} prompting)} \\\\\\\\\\n\",\n \" \\\\tableskip\\n\",\n \" GPT-4 (5-psg) & 67.1 & 41.3 & 68.5 & 75.6 \\\\\\\\\\n\",\n \" GPT-4 (20-psg) & 64.9 & 44.4 & 73.0 & 76.5 \\\\\\\\\\n\",\n \"\\n\",\n \" \\\\tableskip\\n\",\n \" \\\\headercolor\\n\",\n \" \\\\multicolumn{5}{c}{\\\\textbf{LLaMA} (\\\\vani{} prompting)} \\\\\\\\\\n\",\n \" \\\\tableskip\\n\",\n \" LLaMA-13B (3-psg) & 68.4 & 26.9 & 10.6 & 15.4 \\\\\\\\\\n\",\n \" Vicuna-13B (3-psg) & 82.6 & 31.9 & 51.1 & 50.1 \\\\\\\\ \\n\",\n \" \\n\",\n \" Chat-13B (5-psg) & 72.4 & 35.2 & 38.4 & 39.4 \\\\\\\\\\n\",\n \" Chat-70B (5-psg) & 88.3 & 41.5 & 62.9 & 61.3 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \" \\\\caption{\\n\",\n \" Experiments on ASQA.\\n\",\n \" For \\\\close{}, we use \\\\posthoc{} to get citations.\\n\",\n \" $k$-psg: putting top-$k$ passages from the retrieval results into the context.\\n\",\n \" Chat-13B and Chat-70B refer to LLaMA-2-Chat.\\n\",\n \" }\\n\",\n \" \\\\label{tab:asqa_result}\\n\",\n \" \\\\vspace{-10pt}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\small\\n\",\n \" \\\\resizebox{0.98\\\\linewidth}{!}{\\n\",\n \" \\\\begin{tabular}{lcccc}\\n\",\n \" \\\\toprule\\n\",\n \" & \\\\multicolumn{2}{c}{\\\\tf{Correctness}} & \\\\multicolumn{2}{c}{\\\\tf{Citation} }\\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-3} \\\\cmidrule(lr){4-5}\\n\",\n \" & Rec.-5 & Prec. & Rec. & Prec. \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\headercolor\\n\",\n \" \\\\multicolumn{5}{c}{\\\\textbf{ChatGPT}} \\\\\\\\\\n\",\n \" \\\\tableskip\\n\",\n \" \\\\vani{} (5-psg) & 20.8 & 20.8 & 20.5&\\t20.9 \\\\\\\\\\n\",\n \" ~~w/ \\\\rerank{} & 22.8 & 21.4 & 21.2 & 21.4 \\\\\\\\ %\\n\",\n \" \\\\summ{} (10-psg) & 23.6& 21.2& \\\\tf{23.6}&\\t\\\\tf{25.7} \\\\\\\\\\n\",\n \" \\\\snippet{} (10-psg)&24.5 & 21.5& {22.9} & {24.9} \\\\\\\\\\n\",\n \" ~~w/ \\\\interact{} & 21.9 & \\\\tf{23.0} & 21.9&23.4\\\\\\\\\\n\",\n \" \\\\search{} & 17.2& 20.4 & 14.9&\\t14.9 \\\\\\\\\\n\",\n \" \\\\close{} & \\\\tf{32.9} & 19.8 & 10.0 & 10.0\\\\\\\\\\n\",\n \" \\\\tableskip\\n\",\n \" \\\\headercolor\\n\",\n \" \\\\multicolumn{5}{c}{\\\\textbf{GPT-4} (\\\\vani{} prompting)} \\\\\\\\\\n\",\n \" \\\\tableskip\\n\",\n \" GPT-4 (5-psg) & 22.2 & 25.0 & 25.9 & 27.0 \\\\\\\\\\n\",\n \" GPT-4 (20-psg) & 29.6 & 26.2 & 27.4 & 28.5 \\\\\\\\ \\n\",\n \"\\n\",\n \" \\\\tableskip\\n\",\n \" \\\\headercolor\\n\",\n \" \\\\multicolumn{5}{c}{\\\\textbf{LLaMA} (\\\\vani{} prompting)} \\\\\\\\\\n\",\n \" \\\\tableskip\\n\",\n \" LLaMA-13B (3-psg) & 9.7 & 9.1 & 6.7 & 7.1 \\\\\\\\% 6.8 \\\\\\\\\\n\",\n \" Vicuna-13B (5-psg) & 14.0 & 15.9 & 12.5 & 13.4 \\\\\\\\ %\\n\",\n \" \\n\",\n \" \\n\",\n \" Chat-13B (5-psg) & 21.1 & 18.2 & 9.6 & 9.7 \\\\\\\\\\n\",\n \" Chat-70B (5-psg) & 21.8 &18.4 & 15.1 &15.6 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \" \\\\caption{\\n\",\n \" Experiments on QAMPARI.\\n\",\n \" ``Rec.-5'': we set the recall to be 100\\\\% if the prediction includes at least 5 correct answers.\\n\",\n \" }\\n\",\n \" \\\\label{tab:qampari_result}\\n\",\n \" \\\\vspace{-10pt}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[h]\\n\",\n \" \\\\centering\\n\",\n \" \\\\resizebox{0.98\\\\linewidth}{!}{\\n\",\n \" \\\\begin{tabular}{lcccc}\\n\",\n \" \\\\toprule\\n\",\n \" & \\\\tf{Fluency} & \\\\tf{Correct.} & \\\\multicolumn{2}{c}{\\\\tf{Citation} }\\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-2} \\\\cmidrule(lr){3-3} \\\\cmidrule(lr){4-5}\\n\",\n \" & (MAUVE) & (Claim) & Rec. & Prec. \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\headercolor\\n\",\n \" \\\\multicolumn{5}{c}{\\\\textbf{ChatGPT}} \\\\\\\\\\n\",\n \" \\\\tableskip\\n\",\n \" \\\\vani{} (5-psg) &57.2&\\t12.0 &\\t51.1&\\t{50.0} \\\\\\\\\\n\",\n \" ~~w/ \\\\rerank{} & 56.1 & 11.4 & \\\\tf{69.3} & \\\\textbf{67.8}\\\\\\\\ %\\n\",\n \" \\\\summ{} (10-psg) & 40.3 & 12.5 & 51.5 & 48.2 \\\\\\\\\\n\",\n \" \\\\snippet{} (10-psg) & 62.9 &\\t{14.3} & 50.4 & 45.0 \\\\\\\\ %\\n\",\n \" ~~w/ \\\\interact{} & 68.0 &\\t13.3&\\t47.8& 45.0\\\\\\\\\\t%\\n\",\n \" \\\\search{} & 49.7&\\t13.4&\\t45.6& 43.7\\\\\\\\\\t%\\n\",\n \" \\\\close{} & 32.6 &\\\\tf{18.6}\\t&15.5\\t&15.5\\\\\\\\\\n\",\n \" \\\\tableskip\\n\",\n \" \\\\headercolor\\n\",\n \" \\\\multicolumn{5}{c}{\\\\textbf{GPT-4} (\\\\vani{} prompting)} \\\\\\\\\\n\",\n \" \\\\tableskip\\n\",\n \" GPT-4 (5-psg) & 38.4 & 14.2 & 44.0 & 50.1 \\\\\\\\\\n\",\n \" GPT-4 (20-psg) & 41.5 & 18.3 & 48.5 & 53.4 \\\\\\\\\\n\",\n \"\\n\",\n \" \\\\tableskip\\n\",\n \" \\\\headercolor\\n\",\n \" \\\\multicolumn{5}{c}{\\\\textbf{LLaMA} (\\\\vani{} prompting)} \\\\\\\\\\n\",\n \" \\\\tableskip\\n\",\n \" LLaMA-13B (3-psg) & 50.0 & 3.9 & 3.1 & 5.3 \\\\\\\\%2.0 \\\\\\\\\\n\",\n \" Vicuna-13B (3-psg)& 58.2 & {10.0} & 15.6 & 19.6 \\\\\\\\% 12.6 \\\\\\\\\\n\",\n \" \\n\",\n \" Chat-13B (5-psg) & 34.7 & 13.4 &17.3 & 15.8 \\\\\\\\\\n\",\n \" Chat-70B (5-psg) & 38.6 & 12.8 & 38.3 & 37.9 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \" \\\\caption{\\n\",\n \" Experiments on ELI5. We use \\\\textit{claim recall} for the correctness evaluation.\\n\",\n \" Chat-13B and Chat-70B refer to LLaMA-2-Chat.\\n\",\n \" }\\n\",\n \" \\\\label{tab:eli5_result}\\n\",\n \" \\\\vspace{-10pt}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{figure*}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\begin{minipage}[b]{0.66\\\\textwidth}\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=1.0\\\\textwidth]{figs/ret.pdf}\\n\",\n \" \\\\end{minipage}\\n\",\n \" \\\\hfill\\n\",\n \" \\\\begin{minipage}[b]{0.33\\\\textwidth}\\n\",\n \" \\\\centering\\n\",\n \"\\n\",\n \" \\\\resizebox{1.0\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lcccc}\\n\",\n \" \\\\toprule\\n\",\n \" & \\\\tf{Fluency} & \\\\tf{Correct.} & \\\\multicolumn{2}{c}{\\\\tf{Citation} }\\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-2} \\\\cmidrule(lr){3-3} \\\\cmidrule(lr){4-5}\\n\",\n \" & (MAUVE) & (EM) & Rec. & Prec. \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\headercolor\\n\",\n \" \\\\multicolumn{5}{c}{\\\\textbf{ChatGPT} (\\\\vani{})} \\\\\\\\\\n\",\n \" \\\\tableskip\\n\",\n \" DPR (5-psg) & 61.8 &\\t36.1 & 65.0 &\\t65.6\\\\\\\\%56.7 \\\\\\\\\\n\",\n \" GTR (1-psg) & 69.5&\\t38.4 & 56.0\\t& 64.0\\\\\\\\%56.0\\\\\\\\\\n\",\n \" GTR (3-psg) & 66.6 & 39.6 & 72.8\\t& \\\\tf{73.9}\\\\\\\\%64.0 \\\\\\\\\\n\",\n \" GTR (5-psg) & 66.6 & \\\\tf{40.4} & \\\\tf{73.6} & 72.5\\\\\\\\%63.0 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \" \\\\vspace{17pt}\\n\",\n \" \\\\end{minipage}\\n\",\n \" \\\\vspace{-25pt}\\n\",\n \" \\\\caption{\\n\",\n \"Retrieval recall@$k$ on ASQA (\\\\textit{EM recall}), QAMPARI (\\\\textit{recall-5}), and ELI5 (\\\\textit{claim recall}).\\n\",\n \"Retrieval recall serves as an upper bound for model performance,\\n\",\n \"and we compare them with two models' correctness results in the figure (dashed lines):\\n\",\n \"``Vanilla (5-psg)'' is ChatGPT \\\\vani{} with top-5 passages in context;\\n\",\n \"``Oracle'' is the same model except that it uses 5 gold passages (\\\\S\\\\ref{app_sec:retrieval}), whose recall matches Recall@100 on all three datasets. %\\n\",\n \" }\\n\",\n \" \\\\vspace{-10pt}\\n\",\n \" \\\\label{fig:ret}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{table}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\resizebox{0.98\\\\linewidth}{!}{\\n\",\n \" \\\\begin{tabular}{lcccc}\\n\",\n \" \\\\toprule\\n\",\n \" & \\\\tf{Fluency} & \\\\tf{Correct.} & \\\\multicolumn{2}{c}{\\\\tf{Citation} }\\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-2} \\\\cmidrule(lr){3-3} \\\\cmidrule(lr){4-5}\\n\",\n \" & (MAUVE) & (EM Rec.) & Rec. & Prec. \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\headercolor\\n\",\n \" \\\\multicolumn{5}{c}{\\\\textbf{Open-source} (max \\\\#tokens=2K-4K)} \\\\\\\\\\n\",\n \" \\\\tableskip\\n\",\n \" LLaMA-13B (3-psg) & 68.4 & 26.9 & 10.6 & 15.4 \\\\\\\\\\n\",\n \" Vicuna-13B (3-psg) & 82.6 & 31.9 & 51.1 & 50.1 \\\\\\\\ \\n\",\n \" Chat-13B (5-psg) & 72.4 & 35.2 & 38.4 & 39.4 \\\\\\\\\\n\",\n \" Chat-70B (5-psg) & 88.3 & 41.5 & 62.9 & 61.3 \\\\\\\\\\n\",\n \" \\\\tableskip\\n\",\n \" \\\\headercolor\\n\",\n \" \\\\multicolumn{5}{c}{\\\\textbf{ChatGPT} (max \\\\#tokens=4K)} \\\\\\\\\\n\",\n \" \\\\tableskip\\n\",\n \" ChatGPT (3-psg) & 66.6 & 39.6 & 72.8\\t& 73.9 \\\\\\\\% 64.0 \\\\\\\\\\n\",\n \" ChatGPT (5-psg) & 66.6 & 40.4 & {73.6} & 72.5 \\\\\\\\%{63.0} \\\\\\\\\\n\",\n \" \\\\tableskip\\n\",\n \" \\\\headercolor\\n\",\n \" \\\\multicolumn{5}{c}{\\\\textbf{ChatGPT-16K} (max \\\\#tokens=16K)} \\\\\\\\\\n\",\n \" \\\\tableskip\\n\",\n \" ChatGPT (5-psg) & 60.3 & 36.1 & 76.2 & 76.5 \\\\\\\\\\n\",\n \" ChatGPT (10-psg) & 56.3 & 36.7 & 75.3 & 75.0 \\\\\\\\ \\n\",\n \" ChatGPT (20-psg) & 56.7 & 36.1 & 73.7 & 73.5 \\\\\\\\\\n\",\n \"\\n\",\n \" \\\\tableskip\\n\",\n \" \\\\headercolor\\n\",\n \" \\\\multicolumn{5}{c}{\\\\textbf{GPT-4} (max \\\\#tokens=8K)} \\\\\\\\\\n\",\n \" \\\\tableskip\\n\",\n \" GPT-4 (5-psg) & 67.1 & 41.3 & 68.5 & 75.6 \\\\\\\\\\n\",\n \" GPT-4 (10-psg) & 71.5 & 43.1 & 72.0 & 75.5 \\\\\\\\\\n\",\n \" GPT-4 (20-psg) & 64.9 & 44.4 & 73.0 & 76.5 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \" \\\\caption{\\n\",\n \" Comparison of different LLMs on ASQA (GTR+\\\\vani{}). LLaMA-13B and Vicuna-13B have a context limit of 2,048 tokens, and thus can only use a short version of instructions and at most top-3 passages.\\n\",\n \" Chat-13B and Chat-70B refer to LLaMA-2-Chat.\\n\",\n \" }\\n\",\n \" \\\\label{tab:asqa_llm}\\n\",\n \" \\\\vspace{-10pt}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\resizebox{0.98\\\\linewidth}{!}{\\n\",\n \" \\\\begin{tabular}{lcccc}\\n\",\n \" \\\\toprule\\n\",\n \" & \\\\multicolumn{2}{c}{\\\\tf{Human scores}} & \\\\multicolumn{2}{c}{\\\\tf{\\\\citeeval{} scores}} \\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-3} \\\\cmidrule(lr){4-5}\\n\",\n \" & Rec.& Prec. & Rec. & Prec. \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" ChatGPT \\\\vani & 74.7 & 76.6 & 75.3 & 74.4 \\\\\\\\\\n\",\n \" ~~w/ \\\\rerank & \\\\textbf{79.3} & \\\\textbf{81.9} & \\\\textbf{83.9} & \\\\textbf{80.8} \\\\\\\\\\n\",\n \" Vicuna-13B \\\\vani & 51.6 & 51.5 & 50.3 & 50.1 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \" \\\\caption{\\n\",\n \" Human citation quality evaluation vs. \\\\citeeval{} citation quality evaluation on ASQA. \\n\",\n \" }\\n\",\n \" \\\\vspace{-5pt}\\n\",\n \" \\\\label{tab:human-asqa-all}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\resizebox{0.98\\\\linewidth}{!}{\\n\",\n \" \\\\begin{tabular}{lcccc}\\n\",\n \" \\\\toprule\\n\",\n \" & \\\\multicolumn{2}{c}{\\\\tf{Human scores}} & \\\\multicolumn{2}{c}{\\\\tf{\\\\citeeval{} scores}} \\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-3} \\\\cmidrule(lr){4-5}\\n\",\n \" & Rec. & Prec. & Rec. & Prec. \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" ChatGPT \\\\vani & 50.8 & 52.4 & 52.8 & 50.4 \\\\\\\\\\n\",\n \" ~~w/ \\\\rerank & \\\\textbf{59.7} & \\\\textbf{60.6} & \\\\textbf{63.0} & \\\\textbf{60.6} \\\\\\\\\\n\",\n \" Vicuna-13B \\\\vani & 13.4 & 19.2 & 13.6 & 18.1 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \" \\\\caption{\\n\",\n \" Human citation quality evaluation vs. \\\\citeeval{} citation quality evaluation on ELI5. \\n\",\n \" }\\n\",\n \" \\\\vspace{-10pt}\\n\",\n \" \\\\label{tab:human-eli5-all}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[ht]\\n\",\n \" \\\\centering\\n\",\n \" \\\\small\\n\",\n \" \\\\begin{tabular}{@{}lcc@{}}\\n\",\n \"\\\\toprule\\n\",\n \" & ROUGE-L & Claim recall\\\\\\\\ %\\n\",\n \" \\\\midrule\\n\",\n \"ChatGPT \\\\vani & 20.6 & 12.0\\\\\\\\% & 66.9 \\\\\\\\\\n\",\n \"ChatGPT \\\\oracle & 21.2 & 21.3 \\\\\\\\%& 64.4 \\\\\\\\\\n\",\n \"LLaMa-13B \\\\vani & 16.2 & 3.9 \\\\\\\\%& 50.0 \\\\\\\\\\n\",\n \"Top-1 passage & 19.1 & 3.0 \\\\\\\\%& 47.1 \\\\\\\\ \\\\bottomrule\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular} \\n\",\n \" \\\\caption{\\n\",\n \" Comparison between ROUGE-L and claim recall scores on ELI5. \\n\",\n \" }\\n\",\n \" \\\\label{tab:eli5-rouge}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[h]\\n\",\n \" \\\\centering\\n\",\n \" \\\\resizebox{0.98\\\\linewidth}{!}{\\n\",\n \" \\\\begin{tabular}{lcccc}\\n\",\n \" \\\\toprule\\n\",\n \" & \\\\tf{Fluency} & \\\\tf{Correct.} & \\\\multicolumn{2}{c}{\\\\tf{Citation} }\\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-2} \\\\cmidrule(lr){3-3} \\\\cmidrule(lr){4-5}\\n\",\n \" & (MAUVE) & (EM Rec.) & Rec. & Prec. \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" ChatGPT & 66.6 & 40.4 & {73.6} & {63.0} \\\\\\\\\\n\",\n \" Top-1 passage & {\\\\color{red}20.8} & 35.1 & 99.4 & 99.4\\\\\\\\\\n\",\n \" First 2 sents & 67.2 & {\\\\color{red}18.9} & 98.7 & 98.7 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \" \\\\caption{\\n\",\n \" ASQA cheating cases. ``ChatGPT'': the ChatGPT \\\\vani{} model with GTR-retrieved top-5 passages. \\n\",\n \" ``Top-1 passage'': use the top-1 retrieved passage as the response. \\n\",\n \" ``First 2 sents'': use the first 2 sentences of the top-1 retrieved passage.\\n\",\n \" }\\n\",\n \" \\\\label{tab:cheat}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\small\\n\",\n \" \\\\begin{tabular}{@{}lccccc@{}}\\n\",\n \" \\\\toprule\\n\",\n \" & R@1 & R@3 & R@5 & R@20 & R@100 \\\\\\\\ \\\\midrule\\n\",\n \" DPR & 29.6 & 44.5 & 51.5 & 64.6 & 74.1 \\\\\\\\\\n\",\n \" GTR & 35.1 & 50.7 & 56.8 & {70.3} & {78.4} \\\\\\\\\\n\",\n \" Oracle & 63.8 & 72.8 & 78.4 & - & - \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" \\\\caption{Retrieval results for ASQA (EM recall).}\\n\",\n \" \\\\label{tab:retrieval-asqa}\\n\",\n \"\\\\end{table}\\\\begin{table}[t]\\n\"\n ],\n [\n \"\\\\end{table}\\\\begin{table}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\small\\n\",\n \" \\\\begin{tabular}{@{}lccccc@{}}\\n\",\n \" \\\\toprule\\n\",\n \" & R@1 & R@3 & R@5 & R@20 & R@100 \\\\\\\\ \\\\midrule\\n\",\n \" BM25 & 3.0 & 6.6 & 9.6 & 19.3 & 31.8 \\\\\\\\\\n\",\n \" Oracle & 25.3 & 29.7 & 31.8 & - & - \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" \\\\caption{Retrieval results for ELI5 (claim recall).}\\n\",\n \" \\\\label{tab:retrieval-eli5}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[ht]\\n\",\n \" \\\\centering\\n\",\n \" \\\\resizebox{0.98\\\\linewidth}{!}{\\n\",\n \" \\\\begin{tabular}{lcccc}\\n\",\n \" \\\\toprule\\n\",\n \" & \\\\tf{Fluency} & \\\\tf{Correct.} & \\\\multicolumn{2}{c}{\\\\tf{Citation} }\\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-2} \\\\cmidrule(lr){3-3} \\\\cmidrule(lr){4-5}\\n\",\n \" & (MAUVE) & (EM Rec.) & Rec. & Prec. \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\headercolor\\n\",\n \" \\\\multicolumn{5}{c}{\\\\textbf{ChatGPT} (\\\\vani{}, 5-doc)} \\\\\\\\\\n\",\n \" Short instruction & 64.1&\\t39.5&69.6&\\t73.2 \\\\\\\\\\n\",\n \" Full instruction & 66.6 & {40.4} & {73.6} & 72.5 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \" \\\\caption{\\n\",\n \" Effect of different instructions on ASQA.\\n\",\n \" }\\n\",\n \" \\\\label{tab:inst}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[ht]\\n\",\n \" \\\\centering\\n\",\n \" \\\\resizebox{0.98\\\\linewidth}{!}{\\n\",\n \" \\\\begin{tabular}{lcccc}\\n\",\n \" \\\\toprule\\n\",\n \" & \\\\tf{Fluency} & \\\\tf{Correct.} & \\\\multicolumn{2}{c}{\\\\tf{Citation} }\\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-2} \\\\cmidrule(lr){3-3} \\\\cmidrule(lr){4-5}\\n\",\n \" & (MAUVE) & (EM) & Rec. & Prec. \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\headercolor\\n\",\n \" \\\\multicolumn{5}{c}{\\\\textbf{ChatGPT} (\\\\vani{})} \\\\\\\\\\n\",\n \" \\\\#demo = 0& 74.5 & 41.9 & 69.3\\t& 73.4 \\\\\\\\\\n\",\n \" \\\\#demo = 1& 68.9 & 39.8 & 74.6\\t& 73.2 \\\\\\\\\\n\",\n \" \\\\#demo = 2& 66.6 & 40.4 & 73.6 & 72.5 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \" \\\\caption{\\n\",\n \" Different demonstrations on ASQA. \\n\",\n \" }\\n\",\n \" \\\\label{tab:numdemo}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[ht]\\n\",\n \" \\\\centering\\n\",\n \" \\\\resizebox{0.98\\\\linewidth}{!}{\\n\",\n \" \\\\begin{tabular}{lcccc}\\n\",\n \" \\\\toprule\\n\",\n \" & \\\\tf{Fluency} & \\\\tf{Correct.} & \\\\multicolumn{2}{c}{\\\\tf{Citation} }\\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-2} \\\\cmidrule(lr){3-3} \\\\cmidrule(lr){4-5}\\n\",\n \" & (MAUVE) & (EM Rec.) & Rec. & Prec. \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" ChatGPT & 66.6 & {40.4} & {73.6} & 72.5 \\\\\\\\\\n\",\n \" FiD + \\\\posthoc{} & 75.8 & 28.4 & 58.1 & 58.0 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \" \\\\caption{\\n\",\n \" Comparison of Fusion-in-Decoder with ChatGPT on ASQA. \\n\",\n \" Both models use top-5 GTR passages. \\n\",\n \" }\\n\",\n \" \\\\label{tab:fid-asqa}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[h]\\n\",\n \" \\\\centering\\n\",\n \" \\\\resizebox{0.98\\\\linewidth}{!}{\\n\",\n \" \\\\begin{tabular}{lcccc}\\n\",\n \" \\\\toprule\\n\",\n \" & \\\\tf{Fluency} & \\\\tf{Correct.} & \\\\multicolumn{2}{c}{\\\\tf{Citation} }\\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-2} \\\\cmidrule(lr){3-3} \\\\cmidrule(lr){4-5}\\n\",\n \" & (MAUVE) & (Claim) & Rec. & Prec. \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" ChatGPT & 57.2 &\\t12.0 &\\t51.1&\\t{50.0} \\\\\\\\\\n\",\n \" FiD + \\\\posthoc{} & 25.2 & 4.4 & 39.3 & 39.3 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \" \\\\caption{\\n\",\n \" Comparison of Fusion-in-Decoder with ChatGPT on ELI5. \\n\",\n \" Both models use top-5 GTR passages.\\n\",\n \" }\\n\",\n \" \\\\label{tab:fid-eli5}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table*}[h]\\n\",\n \" \\\\centering\\n\",\n \" \\\\small\\n\",\n \" \\\\begin{tabular}{lcccccc}\\n\",\n \" \\\\toprule\\n\",\n \" & \\\\tf{Fluency} & \\\\tf{Correct.} & \\\\multicolumn{2}{c}{\\\\tf{Citation} }\\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-2} \\\\cmidrule(lr){3-3} \\\\cmidrule(lr){4-5}\\n\",\n \" & (MAUVE) & (EM Rec.) & Rec. & Prec. & ROUGE-L & Length \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\headercolorlong\\n\",\n \" \\\\multicolumn{7}{c}{\\\\textbf{ChatGPT}} \\\\\\\\\\n\",\n \" \\\\tableskip\\n\",\n \"\\n\",\n \" \\\\vani{} (5-psg) & 66.8 $_{(2.0)}$ & 40.4 $_{(0.6)}$ & 73.6 $_{(1.1)}$ & 72.5 $_{(1.8)}$ & 37.0 $_{(0.4)}$ & 40.0 $_{(3.1)}$ \\\\\\\\\\n\",\n \" ~~w/ \\\\rerank{} & 77.0 $_{(-)}$ & 40.2 $_{(-)}$ & 84.8 $_{(-)}$ & 81.6 $_{(-)}$ & 36.9 $_{(-)}$ & 40.8 $_{(-)}$ \\\\\\\\\\n\",\n \" \\\\summ{} (10-psg) & 70.0 $_{(1.2)}$ & 43.3 $_{(0.8)}$ & 68.8 $_{(0.6)}$ &61.8 $_{(1.1)}$ & 36.9 $_{(0.2)}$ & 49.8 $_{(4.3)}$ \\\\\\\\\\n\",\n \" ~~w/ \\\\interact{} & 69.0 $_{(2.7)}$ & 39.1 $_{(0.5)}$ & 73.4 $_{(0.2)}$ & 66.5 $_{(4.9)}$ & 35.7 $_{(0.2)}$ & 34.0 $_{(0.9)}$ \\\\\\\\\\n\",\n \" \\\\snippet{} (10-psg) & 69.8 $_{(2.5)}$ & 41.4 $_{(0.6)}$ & 65.3 $_{(0.6)}$ & 57.4 $_{(0.9)}$ & 36.4 $_{(0.4)}$ & 43.0 $_{(3.5)}$ \\\\\\\\\\n\",\n \" \\\\search{} & 58.7 $_{(1.3)}$ & 32.4 $_{(0.6)}$ & 58.3 $_{(1.3)}$ & 58.3 $_{(1.3)}$ & 58.2 $_{(1.1)}$ & 23.7 $_{(1.1)}$ \\\\\\\\\\n\",\n \" \\\\close{} & 52.7 $_{(4.9)}$ & 38.2 $_{(0.1)}$ & 26.7 $_{(1.1)}$ & 26.7 $_{(1.1)}$ & 37.1 $_{(0.3)}$ & 61.1 $_{(4.5)}$ \\\\\\\\\\n\",\n \" \\\\oracle (5-psg) & 64.4 $_{(0.6)}$ & 48.9 $_{(1.2)}$ & 74.5 $_{(0.6)}$ & 72.7 $_{(1.0)}$ & 38.2 $_{(1.0)}$ & 37.4 $_{(3.0)}$ \\\\\\\\\\n\",\n \"\\n\",\n \"\\n\",\n \" \\\\headercolorlong\\n\",\n \" \\\\tableskip\\n\",\n \" \\\\multicolumn{7}{c}{\\\\textbf{ChatGPT-16K}} \\\\\\\\\\n\",\n \" \\\\tableskip\\n\",\n \" \\\\vani{} (5-psg) & 60.3 $_{(-)}$&36.1 $_{(-)}$&76.2 $_{(-)}$&76.5 $_{(-)}$&36.2 $_{(-)}$&24.7 $_{(-)}$\\\\\\\\\\n\",\n \" \\\\vani{} (10-psg) & 56.3 $_{(-)}$&36.7 $_{(-)}$&75.3 $_{(-)}$&75.0 $_{(-)}$&35.6 $_{(-)}$&23.5 $_{(-)}$\\\\\\\\\\n\",\n \" \\\\vani{} (20-psg) & 56.7 $_{(-)}$&36.1 $_{(-)}$&73.7 $_{(-)}$&73.5 $_{(-)}$&35.5 $_{(-)}$&23.1 $_{(-)}$\\\\\\\\\\n\",\n \"\\n\",\n \" \\\\headercolorlong\\n\",\n \" \\\\tableskip\\n\",\n \" \\\\multicolumn{7}{c}{\\\\textbf{GPT-4}} \\\\\\\\\\n\",\n \" \\\\tableskip\\n\",\n \"\\n\",\n \" \\\\vani{} (5-psg) & 67.1 $_{(-)}$&41.3 $_{(-)}$&68.5 $_{(-)}$&75.6 $_{(-)}$&39.2 $_{(-)}$&31.8 $_{(-)}$\\\\\\\\\\n\",\n \" \\\\vani{} (10-psg) & 71.5 $_{(-)}$&43.1 $_{(-)}$&72.0 $_{(-)}$&75.5 $_{(-)}$&39.7 $_{(-)}$&33.8 $_{(-)}$\\\\\\\\\\n\",\n \" \\\\vani{} (20-psg) & 64.9 $_{(-)}$&44.4 $_{(-)}$&73.0 $_{(-)}$&76.5 $_{(-)}$&40.1 $_{(-)}$&34.3 $_{(-)}$\\\\\\\\\\n\",\n \"\\n\",\n \" \\\\headercolorlong\\n\",\n \" \\\\tableskip\\n\",\n \" \\\\multicolumn{7}{c}{\\\\textbf{Open-source} } \\\\\\\\\\n\",\n \" \\\\tableskip\\n\",\n \" LLaMA-7B \\\\vani{} (3-psg) & 69.8 $_{(2.0)}$ & 22.6 $_{(0.9)}$ & 6.2 $_{(2.7)}$ & 9.2 $_{(2.9)}$ & 29.1 $_{(0.2)}$ & 61.3 $_{(14.3)}$ \\\\\\\\\\n\",\n \" Alpaca-7B \\\\vani{} (3-psg) & 84.2 $_{(2.7)}$ & 32.1 $_{(1.7)}$ & 12.3 $_{(7.2)}$ & 14.1 $_{(7.0)}$ & 33.1 $_{(0.8)}$ & 51.7 $_{(12.8)}$ \\\\\\\\\\n\",\n \" Vicuna-7B \\\\vani{} (3-psg) & 82.9 $_{(5.0)}$ & 34.6 $_{(0.7)}$ & 40.3 $_{(0.5)}$ & 42.6 $_{(1.0)}$ & 35.9 $_{(0.7)}$ & 48.9 $_{(6.6)}$ \\\\\\\\ \\\\midrule\\n\",\n \" LLaMA-13B \\\\vani{} (3-psg) & 68.4 $_{(6.4)}$ & 26.9 $_{(0.4)}$ & 10.6 $_{(4.7)}$ & 15.4 $_{(5.2)}$ & 29.8 $_{(0.5)}$ & 67.1 $_{(19.1)}$ \\\\\\\\\\n\",\n \" ~~w/ \\\\rerank & 60.9 $_{(14.5)}$ & 25.2 $_{(2.5)}$ & 28.1 $_{(9.3)}$ & 37.0 $_{(7.2)}$ & 27.9 $_{(2.4)}$ & 50.5 $_{(14.3)}$ \\\\\\\\\\n\",\n \" LLaMA-13B \\\\summ{} (10-psg) & 76.8 $_{(4.7)}$ & 33.3 $_{(0.7)}$ & 19.6 $_{(3.9)}$ & 23.7 $_{(4.7)}$ & 32.1 $_{(0.3)}$ & 54.4 $_{(1.5)}$ \\\\\\\\\\n\",\n \" LLaMA-13B \\\\snippet{} (10-psg) & 72.0 $_{(0.8)}$ & 31.3 $_{(1.1)}$ & 18.2 $_{(3.1)}$ & 21.1 $_{(3.6)}$ & 30.8 $_{(0.4)}$ & 50.5 $_{(4.5)}$ \\\\\\\\\\n\",\n \" LLaMA-13B \\\\oracle{} (3-psg) & 69.5 $_{(11.4)}$ & 34.3 $_{(0.9)}$ & 10.8 $_{(4.9)}$ & 15.8 $_{(5.9)}$ & 30.6 $_{(0.1)}$ & 67.3 $_{(17.9)}$ \\\\\\\\ \\\\midrule\\n\",\n \" Vicuna-13B \\\\vani{} (3-psg) & 82.6 $_{(9.4)}$ & 31.9 $_{(3.9)}$ & 51.1 $_{(1.4)}$ & 50.1 $_{(2.5)}$ & 34.9 $_{(1.3)}$ & 39.1 $_{(6.6)}$ \\\\\\\\\\n\",\n \" ~~w/ \\\\rerank & 73.5 $_{(2.1)}$ & 32.9 $_{(1.3)}$ & 71.9 $_{(1.9)}$ & 65.4 $_{(1.5)}$ & 34.6 $_{(0.3)}$ & 35.7 $_{(4.2)}$ \\\\\\\\\\n\",\n \" Vicuna-13B \\\\summ{} (10-psg) & 67.7 $_{(0.3)}$ & 43.2 $_{(0.1)}$ & 52.7 $_{(2.6)}$ & 50.0 $_{(2.1)}$ & 36.7 $_{(0.2)}$ & 66.0 $_{(1.2)}$ \\\\\\\\\\n\",\n \" Vicuna-13B \\\\snippet{} (10-psg) & 81.4 $_{(3.0)}$ & 42.1 $_{(1.2)}$ & 53.4 $_{(1.9)}$ & 48.7 $_{(1.6)}$ & 36.9 $_{(0.4)}$ & 61.2 $_{(7.4)}$ \\\\\\\\\\n\",\n \" Vicuna-13B \\\\oracle{} (3-psg) & 72.9 $_{(3.5)}$ & 42.5 $_{(1.6)}$ & 52.2 $_{(0.8)}$ & 50.7 $_{(1.6)}$ & 36.5 $_{(0.9)}$ & 38.7 $_{(3.5)}$ \\\\\\\\ \\\\midrule\\n\",\n \" LLaMA-33B \\\\vani{} (3-psg) & 83.7 $_{(5.4)}$ & 31.0 $_{(0.8)}$ & 19.5 $_{(5.3)}$ & 23.0 $_{(5.3)}$ & 32.3 $_{(0.6)}$ & 44.1 $_{(9.3)}$ \\\\\\\\\\n\",\n \" ~~w/ \\\\rerank & 82.1 $_{(3.0)}$ & 31.3 $_{(1.1)}$ & 41.3 $_{(6.4)}$ & 44.7 $_{(5.5)}$ & 32.5 $_{(0.9)}$ & 39.4 $_{(8.0)}$ \\\\\\\\\\n\",\n \" LLaMA-33B \\\\summ{} (10-psg) & 72.0 $_{(3.0)}$ & 33.1 $_{(1.9)}$ & 34.7 $_{(5.8)}$ & 35.2 $_{(6.0)}$ & 31.1 $_{(0.8)}$ & 43.7 $_{(5.0)}$ \\\\\\\\\\n\",\n \" LLaMA-33B \\\\snippet{} (10-psg) & 70.8 $_{(3.1)}$ & 30.9 $_{(1.4)}$ & 31.4 $_{(4.2)}$ & 31.5 $_{(5.3)}$ & 30.1 $_{(0.7)}$ & 42.8 $_{(3.6)}$ \\\\\\\\\\n\",\n \" LLaMA-33B \\\\oracle{} (3-psg) & 82.6 $_{(7.1)}$ & 39.3 $_{(2.9)}$ & 20.2 $_{(6.2)}$ & 23.9 $_{(6.3)}$ & 33.1 $_{(0.9)}$ & 42.0 $_{(9.3)}$ \\\\\\\\ \\\\midrule\\n\",\n \" Oasst-33B \\\\vani{} (3-psg) & 82.9 $_{(2.7)}$ & 34.8 $_{(1.5)}$ & 36.2 $_{(1.7)}$ & 38.3 $_{(2.7)}$ & 35.5 $_{(0.7)}$ & 45.2 $_{(6.3)}$ \\\\\\\\\\n\",\n \" ~~w/ \\\\rerank & 83.2 $_{(2.4)}$ & 35.1 $_{(1.4)}$ & 66.7 $_{(0.2)}$ & 64.3 $_{(1.0)}$ & 35.0 $_{(0.6)}$ & 41.8 $_{(6.0)}$ \\\\\\\\\\n\",\n \" Oasst-33B \\\\summ{} (10-psg) & 74.3 $_{(4.6)}$ & 40.9 $_{(1.1)}$ & 45.5 $_{(1.9)}$ & 44.0 $_{(2.9)}$ & 35.8 $_{(0.6)}$ & 54.3 $_{(4.8)}$ \\\\\\\\\\n\",\n \" Oasst-33B \\\\snippet{} (10-psg) & 79.3 $_{(1.0)}$ & 40.1 $_{(0.9)}$ & 45.0 $_{(1.3)}$ & 43.3 $_{(2.2)}$ & 35.8 $_{(0.2)}$ & 50.9 $_{(4.1)}$ \\\\\\\\\\n\",\n \" Oasst-33B \\\\oracle{} (3-psg) & 85.1 $_{(2.8)}$ & 44.3 $_{(2.4)}$ & 37.0 $_{(1.0)}$ & 39.6 $_{(1.5)}$ & 36.5 $_{(1.1)}$ & 44.2 $_{(5.8)}$ \\\\\\\\ \\n\",\n \" \\\\midrule\\n\",\n \" LLaMA-2-7B-Chat \\\\vani{} (5-psg) & 80.1 $_{(6.5)}$ & 33.9 $_{(2.1)}$ & 50.9 $_{(4.5)}$ & 47.5 $_{(3.7)}$ & 35.1 $_{(0.9)}$ & 42.3 $_{(10.1)}$\\\\\\\\\\n\",\n \" LLaMA-2-13B-Chat \\\\vani{} (5-psg) & 72.4 $_{(6.3)}$ & 35.2 $_{(1.2)}$ & 38.4 $_{(5.9)}$ & 39.4 $_{(4.8)}$ & 35.8 $_{(0.9)}$ & 38.0 $_{(6.4)}$\\\\\\\\\\n\",\n \" LLaMA-2-70B-Chat \\\\vani{} (5-psg) & 88.3 $_{(4.1)}$ & 41.5 $_{(0.8)}$ & 62.9 $_{(1.4)}$ & 61.3 $_{(2.1)}$ & 37.1 $_{(0.4)}$ & 52.9 $_{(9.5)}$\\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Stable Beluga 2 \\\\vani{} (5-psg) & 59.7 $_{(3.3)}$&37.2 $_{(1.0)}$&63.6 $_{(0.7)}$&63.5 $_{(0.1)}$&35.3 $_{(0.7)}$&23.8 $_{(3.2)}$\\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" \\\\caption{\\n\",\n \" ASQA full results. %\\n\",\n \" }\\n\",\n \" \\\\label{tab:asqa_full}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[ht]\\n\",\n \" \\\\centering\\n\",\n \" \\\\small\\n\",\n \" \\\\begin{tabular}{lccccc}\\n\",\n \" \\\\toprule\\n\",\n \" & \\\\multicolumn{2}{c}{\\\\tf{Correctness}} & \\\\multicolumn{2}{c}{\\\\tf{Citation} }\\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-3} \\\\cmidrule(lr){4-5}\\n\",\n \" & Rec.-5 & Prec. & Rec. & Prec. & Num Pred. \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\rowcolor{gray!16}\\n\",\n \" \\\\multicolumn{6}{c}{\\\\textbf{ChatGPT}} \\\\\\\\\\n\",\n \" \\\\tableskip\\n\",\n \" \\\\vani{} (5-psg) & 20.8 $_{(2.2)}$ & 20.8 $_{(0.2)}$ & 20.5 $_{(0.7)}$ & 20.9 $_{(0.7)}$ & 5.0 $_{(0.5)}$ \\\\\\\\\\n\",\n \" ~~w/ \\\\rerank{} & 22.8 $_{(-)}$ & 21.4 $_{(-)}$ & 21.2 $_{(-)}$ & 21.4 $_{(-)}$ & 5.4 $_{(-)}$ \\\\\\\\\\n\",\n \" \\\\summ{} (10-psg) & 23.6 $_{(0.9)}$ & 21.2 $_{(0.5)}$ & 23.6 $_{(0.7)}$ & 25.7 $_{(0.8)}$ & 6.7 $_{(0.4)}$ \\\\\\\\\\n\",\n \" \\\\snippet{} (10-psg) & 24.5 $_{(1.4)}$ & 21.5 $_{(1.8)}$ & 22.9 $_{(1.6)}$ & 24.9 $_{(0.4)}$ & 7.2 $_{(0.9)}$ \\\\\\\\\\n\",\n \" ~~w/ \\\\interact{} & 21.9 $_{(0.9)}$ & 23.0 $_{(0.4)}$ & 21.9 $_{(1.2)}$ & 23.4 $_{(0.9)}$ & 6.7 $_{(0.4)}$ \\\\\\\\\\n\",\n \" \\\\search{} & 17.2 $_{(1.1)}$ & 20.4 $_{(0.8)}$ & 14.9 $_{(0.8)}$ & 14.9 $_{(0.8)}$ & 6.7 $_{(0.2)}$ \\\\\\\\\\n\",\n \" \\\\close{} & 32.9 $_{(1.1)}$ & 19.8 $_{(1.6)}$ & 10.0 $_{(0.4)}$ & 10.0 $_{(0.4)}$ & 17.0 $_{(2.9)}$ \\\\\\\\\\n\",\n \" \\\\oracle{} & 37.0 $_{(3.1)}$ & 36.9 $_{(0.6)}$ & 24.1 $_{(1.2)}$ & 24.6 $_{(1.3)}$ & 5.3 $_{(0.6)}$ \\\\\\\\\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" \\\\headercolorlong\\n\",\n \" \\\\tableskip\\n\",\n \" \\\\multicolumn{6}{c}{\\\\textbf{ChatGPT-16K}} \\\\\\\\\\n\",\n \" \\\\tableskip\\n\",\n \" \\\\vani{} (5-psg) & 21.1 $_{(-)}$&22.0 $_{(-)}$&20.7 $_{(-)}$&21.2 $_{(-)}$&4.9 $_{(-)}$\\\\\\\\ \\n\",\n \" \\\\vani{} (10-psg) & 23.4 $_{(-)}$&21.9 $_{(-)}$&21.6 $_{(-)}$&22.0 $_{(-)}$&5.7 $_{(-)}$\\\\\\\\\\n\",\n \" \\\\vani{} (20-psg) & 26.4 $_{(-)}$&21.1 $_{(-)}$&19.4 $_{(-)}$&19.7 $_{(-)}$&7.6 $_{(-)}$\\\\\\\\\\n\",\n \"\\n\",\n \" \\\\headercolorlong\\n\",\n \" \\\\tableskip\\n\",\n \" \\\\multicolumn{6}{c}{\\\\textbf{GPT-4}} \\\\\\\\\\n\",\n \" \\\\tableskip\\n\",\n \"\\n\",\n \" \\\\vani{} (5-psg) & 22.2 $_{(-)}$&25.0 $_{(-)}$&25.9 $_{(-)}$&27.0 $_{(-)}$&4.4 $_{(-)}$\\\\\\\\\\n\",\n \" \\\\vani{} (10-psg) & 26.8 $_{(-)}$&25.1 $_{(-)}$&26.2 $_{(-)}$&27.2 $_{(-)}$&5.7 $_{(-)}$\\\\\\\\\\n\",\n \" \\\\vani{} (20-psg) & 29.6 $_{(-)}$&26.2 $_{(-)}$&27.4 $_{(-)}$&28.5 $_{(-)}$&6.8 $_{(-)}$\\\\\\\\\\n\",\n \"\\n\",\n \"\\n\",\n \" \\\\rowcolor{gray!16}\\n\",\n \" \\\\multicolumn{6}{c}{\\\\textbf{Open-source}} \\\\\\\\\\n\",\n \" LLaMA-7B \\\\vani{} (3-psg) & 7.8 $_{(3.4)}$ & 7.4 $_{(2.7)}$ & 5.1 $_{(0.5)}$ & 5.7 $_{(0.8)}$ & 5.7 $_{(0.6)}$ \\\\\\\\\\n\",\n \" Alpaca-7B \\\\vani{} (3-psg) & 9.4 $_{(3.7)}$ & 9.5 $_{(3.6)}$ & 6.4 $_{(0.5)}$ & 6.8 $_{(0.5)}$ & 5.1 $_{(0.1)}$ \\\\\\\\\\n\",\n \" Vicuna-7B \\\\vani{} (3-psg) & 11.3 $_{(1.4)}$ & 13.3 $_{(2.3)}$ & 10.1 $_{(0.6)}$ & 10.9 $_{(0.5)}$ & 3.9 $_{(0.3)}$ \\\\\\\\ \\\\midrule\\n\",\n \" LLaMA-13B \\\\vani{} (3-psg) & 9.7 $_{(3.6)}$ & 9.1 $_{(3.1)}$ & 6.7 $_{(0.9)}$ & 7.1 $_{(0.9)}$ & 5.9 $_{(0.6)}$ \\\\\\\\\\n\",\n \" ~~w/ \\\\rerank & 10.0 $_{(3.3)}$ & 10.7 $_{(3.3)}$ & 9.9 $_{(1.2)}$ & 10.2 $_{(1.1)}$ & 5.4 $_{(0.5)}$ \\\\\\\\\\n\",\n \" LLaMA-13B \\\\summ{} (10-psg) & 14.8 $_{(2.5)}$ & 12.6 $_{(1.5)}$ & 7.4 $_{(0.5)}$ & 8.0 $_{(0.6)}$ & 8.1 $_{(0.9)}$ \\\\\\\\\\n\",\n \" LLaMA-13B \\\\snippet{} (10-psg) & 17.7 $_{(1.4)}$ & 15.7 $_{(0.9)}$ & 8.8 $_{(0.7)}$ & 9.9 $_{(0.6)}$ & 8.2 $_{(0.4)}$ \\\\\\\\\\n\",\n \" LLaMA-13B \\\\oracle{} (3-psg) & 16.8 $_{(6.6)}$ & 15.4 $_{(5.6)}$ & 7.7 $_{(1.0)}$ & 8.3 $_{(1.1)}$ & 5.7 $_{(0.7)}$ \\\\\\\\ \\\\midrule\\n\",\n \" Vicuna-13B \\\\vani{} (3-psg) & 14.0 $_{(0.6)}$ & 15.9 $_{(1.7)}$ & 12.5 $_{(0.8)}$ & 13.4 $_{(0.7)}$ & 4.7 $_{(0.3)}$ \\\\\\\\\\n\",\n \" ~~w/ \\\\rerank & 13.0 $_{(0.7)}$ & 17.2 $_{(2.2)}$ & 17.3 $_{(0.8)}$ & 17.7 $_{(0.6)}$ & 4.4 $_{(0.3)}$ \\\\\\\\\\n\",\n \" Vicuna-13B \\\\summ{} (10-psg) & 21.1 $_{(1.4)}$ & 17.1 $_{(0.3)}$ & 15.7 $_{(0.2)}$ & 17.8 $_{(0.1)}$ & 6.9 $_{(0.7)}$ \\\\\\\\\\n\",\n \" Vicuna-13B \\\\snippet{} (10-psg) & 21.9 $_{(0.8)}$ & 18.2 $_{(0.3)}$ & 16.8 $_{(0.3)}$ & 19.7 $_{(0.6)}$ & 7.5 $_{(0.4)}$ \\\\\\\\\\n\",\n \" Vicuna-13B \\\\oracle{} (3-psg) & 25.9 $_{(1.6)}$ & 28.4 $_{(2.6)}$ & 15.8 $_{(1.4)}$ & 16.8 $_{(1.4)}$ & 4.9 $_{(0.5)}$ \\\\\\\\ \\\\midrule\\n\",\n \" LLaMA-33B \\\\vani{} (3-psg) & 14.7 $_{(3.3)}$ & 12.0 $_{(2.2)}$ & 7.9 $_{(0.7)}$ & 8.3 $_{(0.6)}$ & 7.2 $_{(0.7)}$ \\\\\\\\\\n\",\n \" ~~w/ \\\\rerank & 14.0 $_{(3.4)}$ & 13.9 $_{(2.6)}$ & 10.7 $_{(0.6)}$ & 11.1 $_{(0.5)}$ & 6.4 $_{(0.7)}$ \\\\\\\\\\n\",\n \" LLaMA-33B \\\\summ{} (10-psg) & 19.0 $_{(1.9)}$ & 14.8 $_{(0.8)}$ & 12.5 $_{(0.2)}$ & 15.0 $_{(0.3)}$ & 7.6 $_{(0.6)}$ \\\\\\\\\\n\",\n \" LLaMA-33B \\\\snippet{} (10-psg) & 19.6 $_{(1.1)}$ & 15.7 $_{(0.1)}$ & 12.8 $_{(1.1)}$ & 15.2 $_{(1.2)}$ & 7.8 $_{(0.5)}$ \\\\\\\\\\n\",\n \" LLaMA-33B \\\\oracle{} (3-psg) & 23.9 $_{(6.9)}$ & 20.3 $_{(5.2)}$ & 9.8 $_{(1.2)}$ & 10.4 $_{(1.2)}$ & 6.8 $_{(0.9)}$ \\\\\\\\ \\\\midrule\\n\",\n \" Oasst-33B \\\\vani{} (3-psg) & 15.5 $_{(1.5)}$ & 14.9 $_{(1.4)}$ & 9.0 $_{(1.6)}$ & 10.1 $_{(1.8)}$ & 5.6 $_{(0.3)}$ \\\\\\\\\\n\",\n \" ~~w/ \\\\rerank & 14.1 $_{(1.1)}$ & 15.8 $_{(1.0)}$ & 15.0 $_{(1.6)}$ & 15.9 $_{(1.6)}$ & 4.7 $_{(0.3)}$ \\\\\\\\\\n\",\n \" Oasst-33B \\\\summ{} (10-psg) & 21.0 $_{(0.6)}$ & 17.5 $_{(1.0)}$ & 12.9 $_{(1.2)}$ & 16.6 $_{(1.2)}$ & 7.1 $_{(0.4)}$ \\\\\\\\\\n\",\n \" Oasst-33B \\\\snippet{} (10-psg) & 22.0 $_{(0.4)}$ & 17.4 $_{(0.3)}$ & 13.6 $_{(1.7)}$ & 17.7 $_{(1.6)}$ & 7.5 $_{(0.1)}$ \\\\\\\\\\n\",\n \" Oasst-33B \\\\oracle{} (3-psg) & 26.9 $_{(3.7)}$ & 26.0 $_{(3.3)}$ & 11.7 $_{(1.0)}$ & 12.9 $_{(1.2)}$ & 5.6 $_{(0.4)}$ \\\\\\\\ \\n\",\n \" \\\\midrule\\n\",\n \" LLaMA-2-7B-Chat \\\\vani{} (5-psg) & 16.2 $_{(1.3)}$&15.3 $_{(1.6)}$&10.6 $_{(0.9)}$&10.9 $_{(1.0)}$&5.5 $_{(0.0)}$\\\\\\\\\\n\",\n \" LLaMA-2-13B-Chat \\\\vani{} (5-psg) & 21.1 $_{(0.9)}$&18.2 $_{(0.5)}$&9.6 $_{(1.5)}$&9.7 $_{(1.5)}$&6.5 $_{(0.3)}$\\\\\\\\\\n\",\n \" LLaMA-2-70B-Chat \\\\vani{} (5-psg) & 21.8 $_{(0.7)}$&18.4 $_{(0.1)}$&15.1 $_{(1.2)}$&15.6 $_{(1.3)}$&7.1 $_{(0.2)}$\\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Stable Beluga 2 & 19.7 $_{(0.7)}$&21.2 $_{(1.4)}$&18.6 $_{(1.2)}$&20.7 $_{(1.1)}$&5.1 $_{(0.3)}$\\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\n\",\n \" \\\\end{tabular}\\n\",\n \" \\\\caption{\\n\",\n \" QAMPARI full results.\\n\",\n \" }\\n\",\n \" \\\\label{tab:qampari_full}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[h]\\n\",\n \" \\\\centering\\n\",\n \" \\\\small\\n\",\n \" \\\\begin{tabular}{lcccccc}\\n\",\n \" \\\\toprule\\n\",\n \" & \\\\tf{Fluency} & \\\\tf{Correct.} & \\\\multicolumn{2}{c}{\\\\tf{Citation} }\\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-2} \\\\cmidrule(lr){3-3} \\\\cmidrule(lr){4-5}\\n\",\n \" & (MAUVE) & (Claim) & Rec. & Prec. & ROUGE-L & Length \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\headercolorlong\\n\",\n \" \\\\multicolumn{7}{c}{\\\\textbf{ChatGPT}} \\\\\\\\\\n\",\n \"\\n\",\n \" \\\\vani{} (5-psg) & 57.2 $_{(1.6)}$ & 12.0 $_{(0.6)}$ & 51.1 $_{(4.2)}$ & 50.0 $_{(4.8)}$ & 20.6 $_{(0.2)}$ & 91.5 $_{(6.5)}$ \\\\\\\\\\n\",\n \" ~~w/ \\\\rerank{} & 56.1 $_{(-)}$ & 11.4 $_{(-)}$ & 69.3 $_{(-)}$ & 67.8 $_{(-)}$ & 20.3 $_{(-)}$ & 103.4 $_{(-)}$ \\\\\\\\\\n\",\n \" \\\\summ{} (10-psg)& 40.2 $_{(1.2)}$ & 12.5 $_{(0.2)}$ & 51.5 $_{(1.1)}$ & 48.2 $_{(2.0)}$ & 20.3 $_{(0.1)}$ & 90.0 $_{(6.6)}$ \\\\\\\\\\n\",\n \" \\\\snippet{} (10-psg) & 62.9 $_{(2.2)}$ & 14.3 $_{(0.1)}$ & 50.4 $_{(1.1)}$ & 45.0 $_{(2.w)}$ & 21.0 $_{(0.1)}$ & 100.0 $_{(6.8)}$ \\\\\\\\\\n\",\n \" ~~w/ \\\\interact{} & 68.0 $_{(5.8)}$ & 13.3 $_{(0.5)}$ & 47.8 $_{(3.3)}$ & 45.0 $_{(3.1)}$ & 20.1 $_{(0.2)}$ & 99.8 $_{(6.1)}$ \\\\\\\\\\n\",\n \" \\\\search{} & 49.7 $_{(4.6)}$ & 13.4 $_{(1.1)}$ & 45.6 $_{(2.5)}$ &43.7 $_{(3.9)}$ & 20.4 $_{(0.3)}$ & 103.0 $_{(18.1)}$ \\\\\\\\\\n\",\n \" \\\\close{} & 32.6 $_{(1.1)}$ & 18.6 $_{(0.5)}$ & 15.4 $_{(0.3)}$ & 15.4 $_{(0.3)}$ & 22.8 $_{(0.1)}$ & 108.3 $_{(8.9)}$ \\\\\\\\\\n\",\n \" \\\\oracle{} (5-psg) & 59.4 $_{(4.1)}$ & 21.3 $_{(0.2)}$ & 57.8 $_{(3.7)}$ & 56.0 $_{(3.8)}$ & 21.2 $_{(0.3)}$ & 93.0 $_{(7.8)}$ \\\\\\\\\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" \\\\headercolorlong\\n\",\n \" \\\\multicolumn{7}{c}{\\\\textbf{ChatGPT-16K}} \\\\\\\\\\n\",\n \" \\\\tableskip\\n\",\n \" \\\\vani{} (5-psg) & 31.6 $_{(-)}$&14.4 $_{(-)}$&44.6 $_{(-)}$&44.1 $_{(-)}$&21.4 $_{(-)}$&87.6 $_{(-)}$\\\\\\\\ \\n\",\n \" \\\\vani{} (10-psg) & 26.6 $_{(-)}$&14.4 $_{(-)}$&45.5 $_{(-)}$&43.3 $_{(-)}$&21.5 $_{(-)}$&87.5 $_{(-)}$\\\\\\\\\\n\",\n \" \\\\vani{} (20-psg) & 31.6 $_{(-)}$&15.9 $_{(-)}$&43.4 $_{(-)}$&40.9 $_{(-)}$&21.7 $_{(-)}$&92.6 $_{(-)}$\\\\\\\\\\n\",\n \"\\n\",\n \" \\\\headercolorlong\\n\",\n \" \\\\tableskip\\n\",\n \" \\\\multicolumn{7}{c}{\\\\textbf{GPT-4}} \\\\\\\\\\n\",\n \" \\\\tableskip\\n\",\n \"\\n\",\n \" \\\\vani{} (5-psg) & 38.4 $_{(-)}$&14.2 $_{(-)}$&44.0 $_{(-)}$&50.1 $_{(-)}$&20.6 $_{(-)}$&79.6 $_{(-)}$\\\\\\\\\\n\",\n \" \\\\vani{} (10-psg) & 39.9 $_{(-)}$&15.7 $_{(-)}$&49.5 $_{(-)}$&54.2 $_{(-)}$&21.2 $_{(-)}$&88.2 $_{(-)}$\\\\\\\\\\n\",\n \" \\\\vani{} (20-psg) & 41.5 $_{(-)}$&18.3 $_{(-)}$&48.5 $_{(-)}$&53.4 $_{(-)}$&22.2 $_{(-)}$&97.0 $_{(-)}$\\\\\\\\\\n\",\n \"\\n\",\n \" \\\\headercolorlong\\n\",\n \" \\\\tableskip\\n\",\n \" \\\\multicolumn{7}{c}{\\\\textbf{Open-source} } \\\\\\\\\\n\",\n \" \\\\tableskip\\n\",\n \" LLaMA-7B \\\\vani{} (3-psg) & 28.6 $_{(17.9)}$ & 1.6 $_{(0.9)}$ & 1.2 $_{(0.0)}$ & 2.7 $_{(0.1)}$ & 12.2 $_{(1.3)}$ & 46.9 $_{(1.2)}$ \\\\\\\\\\n\",\n \" Alpaca-7B \\\\vani{} (3-psg) & 45.9 $_{(5.3)}$ & 9.2 $_{(0.1)}$ & 4.5 $_{(1.6)}$ & 5.2 $_{(1.9)}$ & 18.8 $_{(0.3)}$ & 67.1 $_{(1.2)}$ \\\\\\\\\\n\",\n \" Vicuna-7B \\\\vani{} (3-psg) & 43.2 $_{(3.9)}$ & 10.0 $_{(0.5)}$ & 12.6 $_{(2.3)}$ & 16.3 $_{(2.6)}$ & 19.1 $_{(0.4)}$ & 68.7 $_{(2.0)}$ \\\\\\\\ \\\\midrule\\n\",\n \" LLaMA-13B \\\\vani{} (3-psg) & 50.0 $_{(2.0)}$ & 3.9 $_{(0.4)}$ & 3.1 $_{(0.9)}$ & 5.3 $_{(1.3)}$ & 16.1 $_{(0.5)}$ & 63.3 $_{(2.0)}$ \\\\\\\\\\n\",\n \" ~~w/ \\\\rerank & 46.7 $_{(2.9)}$ & 4.3 $_{(0.4)}$ & 9.7 $_{(2.1)}$ & 15.0 $_{(2.2)}$ & 16.1 $_{(0.7)}$ & 63.0 $_{(2.3)}$ \\\\\\\\\\n\",\n \" LLaMA-13B \\\\summ{} (10-psg) & 28.6 $_{(1.8)}$ & 2.9 $_{(0.1)}$ & 2.5 $_{(0.8)}$ & 3.8 $_{(0.8)}$ & 8.5 $_{(0.3)}$ & 33.1 $_{(0.6)}$ \\\\\\\\\\n\",\n \" LLaMA-13B \\\\snippet{} (10-psg) & 48.4 $_{(3.1)}$ & 5.7 $_{(0.9)}$ & 5.8 $_{(0.6)}$ & 7.6 $_{(0.9)}$ & 15.1 $_{(1.1)}$ & 60.2 $_{(3.2)}$ \\\\\\\\\\n\",\n \" LLaMA-13B \\\\oracle{} (3-psg) & 49.5 $_{(2.4)}$ & 6.4 $_{(0.6)}$ & 3.7 $_{(0.7)}$ & 6.5 $_{(1.0)}$ & 16.8 $_{(0.5)}$ & 64.5 $_{(1.7)}$ \\\\\\\\ \\\\midrule\\n\",\n \" Vicuna-13B \\\\vani{} (3-psg) & 58.2 $_{(25.1)}$ & 10.0 $_{(0.3)}$ & 15.6 $_{(2.2)}$ & 19.6 $_{(2.0)}$ & 19.1 $_{(0.3)}$ & 69.6 $_{(0.6)}$ \\\\\\\\\\n\",\n \" ~~w/ \\\\rerank & 45.9 $_{(4.3)}$ & 9.2 $_{(0.0)}$ & 31.7 $_{(2.9)}$ & 38.2 $_{(1.6)}$ & 18.6 $_{(0.5)}$ & 69.7 $_{(1.0)}$ \\\\\\\\\\n\",\n \" Vicuna-13B \\\\summ{} (10-psg) & 22.4 $_{(3.0)}$ & 4.9 $_{(0.1)}$ & 9.7 $_{(1.3)}$ & 12.2 $_{(1.2)}$ & 9.3 $_{(0.4)}$ & 33.0 $_{(3.7)}$ \\\\\\\\\\n\",\n \" Vicuna-13B \\\\snippet{} (10-psg) & 48.1 $_{(5.3)}$ & 11.2 $_{(1.4)}$ & 27.2 $_{(3.6)}$ & 27.9 $_{(1.9)}$ & 18.4 $_{(1.9)}$ & 76.8 $_{(8.7)}$ \\\\\\\\\\n\",\n \" Vicuna-13B \\\\oracle{} (3-psg) & 41.6 $_{(3.1)}$ & 17.1 $_{(0.4)}$ & 20.2 $_{(3.0)}$ & 26.5 $_{(3.0)}$ & 20.0 $_{(0.3)}$ & 72.0 $_{(0.3)}$ \\\\\\\\ \\\\midrule\\n\",\n \" LLaMA-33B \\\\vani{} (3-psg) & 58.8 $_{(4.3)}$ & 6.2 $_{(0.0)}$ & 9.3 $_{(3.0)}$ & 12.1 $_{(4.2)}$ & 16.9 $_{(0.2)}$ & 60.0 $_{(1.3)}$ \\\\\\\\\\n\",\n \" ~~w/ \\\\rerank & 65.9 $_{(2.5)}$ & 6.0 $_{(0.7)}$ & 22.5 $_{(5.2)}$ & 26.1 $_{(6.9)}$ & 17.5 $_{(0.4)}$ & 61.0 $_{(1.2)}$ \\\\\\\\\\n\",\n \" LLaMA-33B \\\\summ{} (10-psg) & 23.3 $_{(2.0)}$ & 3.0 $_{(0.2)}$ & 6.2 $_{(0.5)}$ & 8.2 $_{(0.7)}$ & 7.5 $_{(0.4)}$ & 26.2 $_{(2.3)}$ \\\\\\\\\\n\",\n \" LLaMA-33B \\\\snippet{} (10-psg) & 53.2 $_{(4.0)}$ & 7.4 $_{(1.3)}$ & 13.7 $_{(0.5)}$ & 15.1 $_{(0.4)}$ & 14.4 $_{(1.7)}$ & 53.3 $_{(8.5)}$ \\\\\\\\\\n\",\n \" LLaMA-33B \\\\oracle{} (3-psg) & 63.7 $_{(2.8)}$ & 11.4 $_{(0.5)}$ & 11.9 $_{(2.6)}$ & 15.4 $_{(3.6)}$ & 17.9 $_{(0.2)}$ & 61.7 $_{(2.6)}$ \\\\\\\\ \\\\midrule\\n\",\n \" Oasst-33B \\\\vani{} (3-psg) & 46.8 $_{(7.6)}$ & 9.5 $_{(0.2)}$ & 16.0 $_{(2.5)}$ & 21.6 $_{(3.5)}$ & 18.6 $_{(0.3)}$ & 67.8 $_{(1.5)}$ \\\\\\\\\\n\",\n \" ~~w/ \\\\rerank & 52.1 $_{(6.1)}$ & 8.5 $_{(0.5)}$ & 34.4 $_{(2.9)}$ & 41.5 $_{(2.5)}$ & 18.2 $_{(0.3)}$ & 67.0 $_{(1.5)}$ \\\\\\\\\\n\",\n \" Oasst-33B \\\\summ{} (10-psg) & 24.8 $_{(2.8)}$ & 3.9 $_{(0.3)}$ & 12.3 $_{(0.2)}$ & 16.3 $_{(0.3)}$ & 9.1 $_{(0.3)}$ & 31.6 $_{(1.5)}$ \\\\\\\\\\n\",\n \" Oasst-33B \\\\snippet{} (10-psg) & 50.7 $_{(4.6)}$ & 10.7 $_{(1.2)}$ & 25.8 $_{(3.3)}$ & 26.7 $_{(2.3)}$ & 17.8 $_{(1.8)}$ & 69.6 $_{(8.6)}$ \\\\\\\\\\n\",\n \" Oasst-33B \\\\oracle{} (3-psg) & 50.7 $_{(12.1)}$ & 15.8 $_{(0.1)}$ & 20.8 $_{(2.8)}$ & 28.0 $_{(3.2)}$ & 19.4 $_{(0.1)}$ & 70.3 $_{(1.1)}$ \\\\\\\\ \\n\",\n \" \\\\midrule\\n\",\n \" LLaMA-2-7B-Chat \\\\vani{} (5-psg) & 27.8 $_{(3.0)}$&10.9 $_{(0.2)}$&19.8 $_{(1.2)}$&15.0 $_{(1.4)}$&20.5 $_{(0.2)}$&87.8 $_{(8.1)}$\\\\\\\\\\n\",\n \" LLaMA-2-13B-Chat \\\\vani{} (5-psg) & 34.7 $_{(1.5)}$&13.4 $_{(0.4)}$&17.3 $_{(1.3)}$&15.8 $_{(1.4)}$&20.9 $_{(0.2)}$&88.3 $_{(6.3)}$\\\\\\\\\\n\",\n \" LLaMA-2-70B-Chat \\\\vani{} (5-psg) & 38.6 $_{(4.8)}$&12.8 $_{(1.0)}$&38.3 $_{(2.4)}$&37.9 $_{(1.9)}$&21.3 $_{(0.1)}$&110.8 $_{(5.6)}$\\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" StableBeluga2 & 33.0 $_{(4.4)}$&14.0 $_{(0.5)}$&27.9 $_{(2.7)}$&29.0 $_{(2.7)}$&20.6 $_{(0.2)}$&75.6 $_{(5.0)}$\\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" \\\\caption{\\n\",\n \" ELI5 full results.\\n\",\n \" }\\n\",\n \" \\\\label{tab:eli5_full}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[ht]\\n\",\n \" \\\\centering\\n\",\n \" \\\\small\\n\",\n \" \\\\begin{tabular}{>{\\\\raggedright\\\\arraybackslash\\\\tt}p{0.98\\\\textwidth}<{}}\\n\",\n \" \\\\toprule\\n\",\n \" Read the original question and passage, and generate 3 additional claims that are supported by the passage and answer the question.\\n\",\n \" \\\\\\\\\\\\\\\\\\n\",\n \" \\\\noindent {Original question: What's the difference between Shia vs. Sunni Islam?}\\\\\\\\\\n\",\n \" Passage: The main difference between Shia and Sunni Muslim is related to ideological heritage and issues of leadership. This difference is first formed after the death of the Prophet Muhammad in 632 A.D. The ideological practice of the Sunni branch strictly follows Prophet Muhammad and his teachings, while the Shia branch follows Prophet Muhammad's son-in-law Ali. Nowadays, Sunni and Shia are the major branches of Islam. \\\\\\\\\\n\",\n \" Claim 1: The major branches of Islam are Sunni and Shia. \\\\\\\\\\n\",\n \" Claim 2: Prophet Muhammad died in 632 A.D. \\\\\\\\\\n\",\n \" Claim 3: The ideological practice of the Sunni branch strictly follows Prophet Muhammad and his teachings. \\\\\\\\\\\\\\\\\\n\",\n \" Original question: What causes Bi-polar disorder? \\\\\\\\\\n\",\n \" Passage: Bipolar disorder is an emotional disorder that causes extreme mood swings between excitement and depression. The spectrum of mood swing may span from days to months. We are still not certain of the exact factors that cause such disorder, but genetics is considered a major factor. \\\\\\\\\\n\",\n \" Claim 1: One symptom of Bi-polar disorder is extreme mood swings between excitement and depression.\\\\\\\\\\n\",\n \" Claim 2: Genetics could be one of the major factors that causes Bi-polar disorder.\\\\\\\\\\n\",\n \" Claim 3: The mood swing from Bi-polar disorder can last days to months.\\\\\\\\\\\\\\\\\\n\",\n \" Original question: How do we hear differences in sound besides volume and pitch?\\\\\\\\\\n\",\n \" Passage: Pitch refers to the frequency of soundwave, and volumn refers to the amplitude of the soundwave. Besides volumn and pitch, we can also tell the difference between sounds based on the tone of sound. For example, we can differentiate the sound of different instruments based on the tone of the sounds.\\\\\\\\\\n\",\n \" Claim 1: Volume of sound is the amplitude of the soundwave.\\\\\\\\\\n\",\n \" Claim 2: Pitch is the frequency of soundwave.\\\\\\\\\\n\",\n \" Claim 3: We can use the tone of the sounds to differentiate the sound of different instruments. \\\\\\\\\\\\\\\\\\n\",\n \" \\\\vspace{-0.7em}\\n\",\n \" {\\\\color{brown}Original question: How are we able to discern whether a sound is coming from in front of us or behind us?} \\\\\\\\\\n\",\n \" \\\\vspace{-0.7em}\\n\",\n \" {\\\\color{brown}Passage: There are multiple explanations for why we can localize sounds. One explanation is that sounds travelling to the corresponding side of one's ear will be slightly louder. Another explanation is that there is a slight difference in the hitting time to one's left and right ear based on the sound's direction. However, these explanation means that when a sound is exactly in front of someone or exactly behind someone, he or she can not tell the difference.} \\\\\\\\\\n\",\n \" \\\\vspace{-0.7em}\\n\",\n \" {\\\\color{brown}Claim 1:} {\\\\color{blue} We can localize sounds by recognizing that the sound travelling to the corresponding side of one's ear will be slightly louder.} \\\\\\\\\\n\",\n \" \\\\vspace{-0.7em}\\n\",\n \" {\\\\color{blue}Claim 2: We can also localize sounds by recognizing the difference in hitting time to one's left and right ear based on the sound's direction.}\\\\\\\\\\n\",\n \" \\\\vspace{-0.7em}\\n\",\n \" {\\\\color{blue}Claim 3: We cannot tell the difference between a sound that is exactly in front of us or exactly behind us.} \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" \\\\caption{\\n\",\n \" Prompt used to generate the sub-claims for ELI5 questions.\\n\",\n \" {\\\\color{blue} Blue text is model generation.}\\n\",\n \" {\\\\color{brown} Brown text is the ELI5 example that we want to generate sub-claims for.}\\n\",\n \" We construct the prompt by manually writing the sub-claims for three questions from the training set. \\n\",\n \" }\\n\",\n \" \\\\label{tab:eli5-claims-prompt}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[h]\\n\",\n \" \\\\centering\\n\",\n \" \\\\small\\n\",\n \" \\\\begin{tabular}{>{\\\\raggedright\\\\arraybackslash\\\\tt}p{0.95\\\\textwidth}<{}}\\n\",\n \" \\\\toprule\\n\",\n \" Instruction: Write an accurate, engaging, and concise answer for the given question using only the provided search results (some of which might be irrelevant) and cite them properly. Use an unbiased and journalistic tone. Always cite for any factual claim. When citing several search results, use [1][2][3]. Cite at least one document and at most three documents in each sentence. If multiple documents support the sentence, only cite a minimum sufficient subset of the documents. \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" \\\\caption{\\n\",\n \" Instruction for \\\\vani{}.\\n\",\n \" }\\n\",\n \" \\\\label{tab:inst_vanilla}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[h]\\n\",\n \" \\\\centering\\n\",\n \" \\\\small\\n\",\n \" \\\\begin{tabular}{>{\\\\raggedright\\\\arraybackslash\\\\tt}p{0.95\\\\textwidth}<{}}\\n\",\n \" \\\\toprule\\n\",\n \" Instruction: Write a high-quality answer for the given question using only the provided search results and cite them properly using [1][2][3].\\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" \\\\caption{\\n\",\n \" Short instruction for \\\\vani{}.\\n\",\n \" }\\n\",\n \" \\\\label{tab:inst_vanilla_light}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[h]\\n\",\n \" \\\\centering\\n\",\n \" \\\\small\\n\",\n \" \\\\begin{tabular}{>{\\\\raggedright\\\\arraybackslash\\\\tt}p{0.95\\\\textwidth}<{}}\\n\",\n \" \\\\toprule\\n\",\n \" Summarize the following document within 50 words with the question of interest \\\"\\\\{QUESTION\\\\}\\\" Return \\\"irrelevant\\\" if the document is irrelevant to the question. Try to keep all the important dates, numbers, and names.\\\\\\\\\\n\",\n \" ~\\\\\\\\\\n\",\n \" Title: \\\\{TITLE\\\\}\\\\\\\\\\n\",\n \" Text: \\\\{TEXT\\\\}\\\\\\\\\\n\",\n \" Summary:\\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" \\\\caption{\\n\",\n \" Prompts for \\\\summ{}.\\n\",\n \" }\\n\",\n \" \\\\label{tab:prompt_summary}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[h]\\n\",\n \" \\\\centering\\n\",\n \" \\\\small\\n\",\n \" \\\\begin{tabular}{>{\\\\raggedright\\\\arraybackslash\\\\tt}p{0.95\\\\textwidth}<{}}\\n\",\n \" \\\\toprule\\n\",\n \"Given the follow passage and the question \\\"\\\\{QUESTION\\\\}\\\", extract a useful span from the passage that can answer the question. Resolve all the coreference issues to make the extracted span understandable standalone. If the passage is not helpful for answering the question, return \\\"irrelevant\\\".\\\\\\\\\\n\",\n \"\\\\\\\\\\n\",\n \"Title: \\\\{TITLE\\\\}\\\\\\\\\\n\",\n \"Text: \\\\{TEXT\\\\}\\\\\\\\\\n\",\n \"Extracted span:\\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" \\\\caption{\\n\",\n \" Prompts for \\\\snippet{}.\\n\",\n \" }\\n\",\n \" \\\\label{tab:prompt_snippet}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[h]\\n\",\n \" \\\\centering\\n\",\n \" \\\\small\\n\",\n \" \\\\begin{tabular}{>{\\\\raggedright\\\\arraybackslash\\\\tt}p{0.95\\\\textwidth}<{}}\\n\",\n \" \\\\toprule\\n\",\n \" Instruction: Write an accurate, engaging, and concise answer for the given question using only the provided search results and cite them properly. Use an unbiased and journalistic tone. Always cite for any factual claim.\\\\\\\\\\n\",\n \" You are provided summaries/snippets of the search results. You can use \\\"Check: Document [1][2]\\\" to check the corresponding full documents (you should only check relevant documents and you can at most check 3 documents at a time) and use \\\"Output:\\\" to output a sentence in the answer. In the answer, cite properly by using [1][2][3]. Cite at least one document and at most three documents in each sentence. If multiple documents support the sentence, only cite a minimum sufficient subset of the documents. Use \\\"End\\\" to end the generation.\\\\\\\\\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\vspace{-1em}\\n\",\n \" \\\\noindent {\\\\color{brown}<Retrieve for question ``...''>}\\\\\\\\\\n\",\n \" \\\\noindent {\\\\color{brown}<Get summaries/snippets for the passages and delete those that are ``irrelevant''>}\\\\\\\\\\n\",\n \" Document {[1]}(Title: ...) \\\\{SUMMARY OR SNIPPET\\\\}\\\\\\\\\\n\",\n \" ... \\\\\\\\\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\vspace{-1em}\\n\",\n \" Question: When did US break away from England? \\\\\\\\\\n\",\n \" \\\\noindent {\\\\color{blue} Check: Document [1][2]} \\\\\\\\\\n\",\n \" Document [1] \\\\{FULL TEXT\\\\} \\\\\\\\\\n\",\n \" Document [2] \\\\{FULL TEXT\\\\} \\\\\\\\\\n\",\n \" \\\\noindent {\\\\color{blue} Output: The United States ... \\\\tf{\\\\underline{[1]}} ... \\\\tf{\\\\underline{[2]}}.} \\\\\\\\\\n\",\n \" \\\\noindent {\\\\color{brown}<Remove the full text of [1][2] from context>}\\\\\\\\\\n\",\n \" \\\\noindent {\\\\color{blue} Check: Document [3]} \\\\\\\\\\n\",\n \" Document [3] \\\\{FULL TEXT\\\\} \\\\\\\\\\n\",\n \" \\\\noindent {\\\\color{blue} Output: The Treaty of Paris ... \\\\tf{\\\\underline{[3]}}.} \\\\\\\\\\n\",\n \" \\\\noindent {\\\\color{brown}<Remove the full text of [3] from context>}\\\\\\\\\\n\",\n \" \\\\noindent {\\\\color{blue}End.}\\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" \\\\caption{\\n\",\n \" An example for \\\\interact{}.\\n\",\n \" }\\n\",\n \" \\\\label{tab:inst_inter}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[h]\\n\",\n \" \\\\centering\\n\",\n \" \\\\small\\n\",\n \" \\\\begin{tabular}{>{\\\\raggedright\\\\arraybackslash\\\\tt}p{0.95\\\\textwidth}<{}}\\n\",\n \" \\\\toprule\\n\",\n \" Instruction: Write an accurate, engaging, and concise answer for the given question using only the provided search results and cite them properly. Use an unbiased and journalistic tone. Always cite for any factual claim.\\\\\\\\\\n\",\n \" You can use \\\"Search: key words\\\" to check the most relevant document's full text and use \\\"Output:\\\" to output a sentence in the answer. In the answer, cite properly by using [1][2][3]. Cite at least one document and at most three documents in each sentence. If multiple documents support the sentence, only cite a minimum sufficient subset of the documents. Use \\\"End\\\" to end the generation.\\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" \\\\caption{\\n\",\n \" Instruction for \\\\search{}.\\n\",\n \" }\\n\",\n \" \\\\label{tab:inst_search}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[h]\\n\",\n \" \\\\centering\\n\",\n \" \\\\small\\n\",\n \" \\\\begin{tabular}{>{\\\\raggedright\\\\arraybackslash\\\\tt}p{0.95\\\\textwidth}<{}}\\n\",\n \" \\\\toprule\\n\",\n \" Instruction: Write an accurate, engaging, and concise answer for the given question. Use an unbiased and journalistic tone. \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" \\\\caption{\\n\",\n \" Instruction for \\\\close{}.\\n\",\n \" }\\n\",\n \" \\\\label{tab:prompt_close}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[h]\\n\",\n \" \\\\centering\\n\",\n \" \\\\small\\n\",\n \" \\\\begin{tabular}{>{\\\\raggedright\\\\arraybackslash\\\\tt}p{0.95\\\\textwidth}<{}}\\n\",\n \" \\\\toprule\\n\",\n \" Instruction: Write an accurate, engaging, and concise answer for ... \\\\\\\\\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\vspace{-1em}\\n\",\n \" Document {[1]}(Title: How to Treat and Prevent Food Poisoning - MsPrepper) just a typical gastro upset. Salmonella is most commonly caused by eating undercooked or raw foods like eggs or meat. You know how your mom always warned you not to eat raw cookie dough? This is why. Most people do eat cookie dough and they are fine, but salmonella is a risk. If you do contract salmonella, you could start to feel bad within in a couple of hours after eating contaminated food, and sometimes it could take a day or two. Common symptoms are nausea and vomiting, loose stools (sometimes bloody), flu like symptoms, and stomach cramps. To treat \\\\\\\\\\n\",\n \" Document {[2]}(Title: FDA Issues Warning About Eating Raw Cookie Dough, But Not For Salmonella Risks) FDA Issues Warning About Eating Raw Cookie Dough, But Not For Salmonella Risks Used to licking the spoon or placating yourself with full-on chunks of raw cookie dough? The Food and Drug Administration issued a warning on Tuesday that strongly advises against continuing the habit. The agency asserted that consuming raw batter of any kind, whether for bread, cookies or pizza, could make a person sick. While you may have been warned in the past against eating raw dough due to the risk of contracting salmonella from raw eggs, the FDA is citing raw flour as the culprit for a \\\\\\\\\\n\",\n \" Document {[3]}(Title: It's Probably OK to Eat Raw Cookie Dough — As Long As You're Smart About It - The Crux - Very Top Secret Information) First, when most people think about health risks and cookie dough, they think about raw egg. Eggs can be contaminated with salmonella bacteria, and food safety recommendations encourage people to cook eggs until the white and yolk are firm in order to kill any bacteria. However, anyone making cookies can do things to reduce this risk by using pasteurized egg products. When my kids and I make cookie dough, we never use regular eggs. Instead, we use shell eggs that have been pasteurized to kill any harmful bacteria without actually cooking the egg itself. (A great public health innovation, if \\\\\\\\\\n\",\n \" Document {[4]}(Title: How Dangerous Is It to Eat Raw Cookie Dough? | Men's Health) Can Eating Raw Cookie Dough Really Make You Sick? Scientists reveal the truth about this supposedly dangerous delicacy By Katherine Dempsey There are few things more tempting in life than eyeing a bowl of cookie dough and deciding whether or not to stick your finger in for a scoop. It’s a bit like playing Russian roulette. You could get lucky and enjoy the delicious dough without conseqence, but there’s always the risk of getting serously sick with a food-borne illness. That’s because multiple ingredients within the dough could be contaminated by pathogens such as Salmonella and E. coli, says Soohyoun \\\\\\\\\\n\",\n \" Document {[5]}(Title: How Dangerous Is It to Eat Raw Cookie Dough? | Men's Health) is usually pasteurized, so it’s not likely that the egg would make you sick. However, other ingredients in the dough could potentially harbor pathogens. Experts say that a prime suspect in a 2009 E. coli outbreak linked to prepackaged cookie dough was actually the flour. Nuts and chocolate have also been linked to Salmonella outbreaks. Bottom line: You’re better off skipping raw cookie dough. At least you’ll have killer cookies as a consolation. The article How Bad Is It To Eat Raw Cookie Dough? originally ran on Prevention.com The 9 Foods Most Likely to Make You Sick Mmm, Just Don't \\\\\\\\\\n\",\n \"\\n\",\n \" ... \\\\\\\\\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\vspace{-1em}\\n\",\n \" Question: Why is it bad to eat cookie dough for risk of salmonella but things like Cookie Dough Bites are ok? \\\\\\\\\\n\",\n \" Answer: {\\\\color{blue} Raw cookie dough is not recommended to be eaten due to the risk of salmonella \\\\tf{\\\\underline{[1][2]}}. Eating raw flour is also a risk for food poisoning \\\\tf{\\\\underline{[2]}}. The egg in cookie dough is usually pasteurized, so it's not likely that the egg would make you sick, but other ingredients in the dough could potentially harbor pathogens \\\\tf{\\\\underline{[4][5]}}. However, prepackaged cookie dough like Cookie Dough Bites is safe to eat because the dough is made with pasteurized egg products and heat-treated flour \\\\tf{\\\\underline{[2][3]}}.}.\\\\\\\\\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\noindent {\\\\color{orange} Ground truth claims:}\\\\\\\\\\n\",\n \" \\\\noindent {\\\\color{orange} 1. Cookie Dough Bites are safe to eat since they do not contain eggs.}\\\\\\\\\\n\",\n \" \\\\noindent {\\\\color{orange} 2. Salmonella is a bacteria found in eggs.}\\\\\\\\\\n\",\n \" \\\\noindent {\\\\color{orange} 3. Eating raw cookie dough carries the risk of salmonella infection.}\\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" \\\\caption{\\n\",\n \" ELI5 example 1.\\n\",\n \" }\\n\",\n \" \\\\label{tab:exp_eli5}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[h]\\n\",\n \" \\\\centering\\n\",\n \" \\\\small\\n\",\n \" \\\\begin{tabular}{>{\\\\raggedright\\\\arraybackslash\\\\tt}p{0.98\\\\textwidth}<{}}\\n\",\n \" \\\\toprule\\n\",\n \" Instruction: Write an accurate, engaging, and concise answer for ... \\\\\\\\\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\vspace{-1em}\\n\",\n \" Document [1](Title: Is Snapchat really worth \\\\$19 billion? - CSMonitor.com) reporting that the Los Angeles-based company is aiming to raise \\\\$500 million at a valuation of \\\\$16 billion to \\\\$19 billion, making it the third most highly valued tech start-up backed by venture capitalists. The Chinese handset maker Xiaomi is valued at \\\\$45 billion, while Uber is estimated to be valued at about \\\\$40 billion, according to data from CB Insights. Read MoreVC investment hits \\\\$86B thanks to Uber, Xiaomi Snapchat was valued at \\\\$10 billion in August, according to a Dow Jones report. Some of its investors from previous rounds include Benchmark, Lightspeed Venture Partners and Kleiner Perkins Caufield \\\\\\\\\\n\",\n \" Document [2](Title: What Are Venture Capital Investments? – DollarsAndSense.my) Ever wondered how highly valued technology giants like Google and Facebook were able to grow so fast and pay their employees so well in such a short amount of time, or how still growing start-ups like Uber are able to lose 1.2 billion US dollars in just the first half of this year alone and still command a valuation upwards of 50 billion US dollars? The answer lies with a special category of investment activity known as venture capital. Venture capitalists are professional investors who invest in a number of highly scalable high-risk technology ventures hoping to make a multi-fold \\\\\\\\\\n\",\n \" Document [3](Title: Opinion | What Dara Khosrowshahi Must Do to Save Uber - The New York Times) at a discount. These are troubling signs. Every start-up must one day fulfill the market’s demand that it turn a profit, but Uber has never figured out how to do that. While ride sharing in some form will probably survive, it’s more likely that without some drastic changes, Uber won’t be around in three to five years. Mr. Khosrowshahi must avoid the mistakes of his predecessor by accepting that “pivots” (Silicon Valley-speak for the desperate changes troubled companies make to reassure their venture capitalist funders) are not the answer. None of the pivots Mr. Kalanick tried — like on-demand delivery \\\\\\\\\\n\",\n \" Document [4](Title: Snapchat raising funding round at \\\\$19 billion valuation: Report) Snapchat raising funding round at \\\\$19 billion valuation: Report The mobile messaging app Snapchat is said to be seeking funding at a \\\\$19 billion valuation, according to a report, citing a person with knowledge of the matter. Bloomberg is reporting that the Los Angeles-based company is aiming to raise \\\\$500 million at a valuation of \\\\$16 billion to \\\\$19 billion, making it the third most highly valued tech start-up backed by venture capitalists. The Chinese handset maker Xiaomi is valued at \\\\$45 billion, while Uber is estimated to be valued at about \\\\$40 billion, according to data from CB Insights. \\\\\\\\\\n\",\n \" Document [5](Title: Unicorns And Wall Street | MoneyTips) not start showing profits soon. Even Uber, with its massive \\\\$62-billion-plus valuation, will test investor's patience if it does not start showing signs of a profit in the next few years. Cracks are starting to show in terms of pre-IPO devaluations. Dropbox and Snapchat have seen a nearly one-quarter drop in their pre-IPO valuations, while Square's pre-IPO value has dropped by approximately one-third. Larger investors and venture capitalists are sending a message to the unicorns: Outline a solid path to profits and start producing them soon, or expect your valuation to be slashed. Diving ahead with an IPO is not \\\\\\\\\\n\",\n \" ... \\\\\\\\\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\vspace{-1em}\\n\",\n \" Question: How are firms like snapchat, uber etc valued so highly while still not making a profit? Do venture capitalists not expect some form of repayment within a number of years? \\\\\\\\\\n\",\n \" Answer: {\\\\color{blue} Venture capitalists invest in a number of highly scalable high-risk technology ventures hoping to make a multi-fold return on their investment in a short amount of time \\\\tf{\\\\underline{[2]}}. Highly valued companies like Snapchat, Uber, and Xiaomi are able to grow so fast and command high valuations because they have the potential to generate significant revenue in the future \\\\tf{\\\\underline{[2][4]}}. Venture capitalists expect a return on their investment within a number of years, but they also invest in the long-term potential of the company \\\\tf{\\\\underline{[2]}}. It is important to note that every start-up must eventually turn a profit, but some start-ups like Uber have not yet figured out how to do that \\\\tf{\\\\underline{[3][5]}}.}\\\\\\\\\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\noindent {\\\\color{orange} Ground truth claims:}\\\\\\\\\\n\",\n \" \\\\noindent {\\\\color{orange} 1. Firms like Snapchat and Uber need to establish their brand and amass users before introducing ads.}\\\\\\\\\\n\",\n \" \\\\noindent {\\\\color{orange} 2. Introducing ads too early can deter potential users.}\\\\\\\\\\n\",\n \" \\\\noindent {\\\\color{orange} 3. Uber is reinvesting a lot of money to make their service better.}\\\\\\\\\\n\",\n \" \\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" \\\\caption{\\n\",\n \" ELI5 example 2.\\n\",\n \" }\\n\",\n \" \\\\label{tab:exp_eli52}\\n\",\n \"\\\\end{table*}\\n\"\n ]\n]","isConversational":false},"fulltext":{"kind":"string","value":"\\pdfoutput=1\n\n\\documentclass[11pt]{article}\n\n\\usepackage{EMNLP2023}\n\n\n\\usepackage{times}\n\\usepackage{latexsym}\n\\usepackage{graphicx}\n\\usepackage{subfigure}\n\n\n\\usepackage[T1]{fontenc}\n\n\\usepackage[utf8]{inputenc}\n\n\\usepackage{microtype}\n\n\\usepackage{inconsolata}\n\n\\usepackage{array}\n\\usepackage{pifont}\n\\usepackage{tabularx}\n\\usepackage{adjustbox}\n\\usepackage{multirow}\n\\usepackage{enumitem}\n\\usepackage{xspace}\n\\usepackage{tcolorbox}\n\\usepackage{booktabs,amsfonts,dcolumn}\n\\usepackage{hyperref}\n\\usepackage{url}\n\\usepackage{amsmath,amsthm,amsfonts,amssymb,bm,stmaryrd,bbm}\n\\usepackage[noorphans,vskip=0.75ex,leftmargin=2ex]{quoting}\n\\usepackage{footmisc}\\interfootnotelinepenalty=10000\n\\usepackage{colortbl}\n\n\n\n\n\\providecommand{\\todo}[1]{\n {\\protect\\color{red}{[TODO: #1]}}\n}\n\n\\providecommand{\\danqi}[1]{\n {\\protect\\color{orange}{[Danqi: 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{\\protect\\color{red}{00.00}}\n}\n\n\\newcommand{\\uours}{USimCSE\\xspace}\n\\newcommand{\\ours}{SimCSE\\xspace}\n\\newcommand{\\identical}{identical}\n\\newcommand{\\Identical}{Identical}\n\n\\newcommand{\\la}{$_\\texttt{large}$}\n\\newcommand{\\ba}{$_\\texttt{base}$}\n\n\\DeclareMathOperator*{\\argmax}{arg\\,max}\n\\DeclareMathOperator*{\\argmin}{arg\\,min}\n\\renewcommand{\\paragraph}[1]{\\vspace{0.2cm}\\noindent\\textbf{#1}}\n\\newcommand{\\tpf}[1]{\\noindent\\textbf{#1}}\n\\newcommand{\\tableindent}{~~}\n\n\\newcommand{\\gense}{LLMCC}\n\n\\newcommand{\\vani}{{\\sc{Vanilla}}}\n\\newcommand{\\summ}{{\\sc{Summ}}}\n\\newcommand{\\snippet}{{\\sc{Snippet}}}\n\\newcommand{\\interact}{{\\sc{Interact}}}\n\\newcommand{\\search}{{\\sc{InlineSearch}}}\n\\newcommand{\\rerank}{{\\sc{Rerank}}}\n\\newcommand{\\close}{{\\sc{ClosedBook}}}\n\\newcommand{\\posthoc}{{\\sc{PostCite}}}\n\\newcommand{\\oracle}{{\\sc{Oracle}}}\n\n\n\\newcommand{\\pvani}{Vanilla}\n\\newcommand{\\psumm}{Summ}\n\\newcommand{\\psnippet}{Snippet}\n\\newcommand{\\pinteract}{Interact}\n\\newcommand{\\psearch}{InlineSearch}\n\\newcommand{\\prerank}{Rerank}\n\\newcommand{\\pclose}{ClosedBook}\n\\newcommand{\\pposthoc}{PostCite}\n\n\\newcommand{\\ccc}{\\textasciicircum}\n\n\\newcommand{\\citeeval}{ALCE}\n\n\\newcommand{\\tableskip}{\\noalign{\\vskip 2pt}}\n\\newcommand{\\headercolor}{\\rowcolor{gray!15}}\n\\newcommand{\\headercolorlong}{\\rowcolor{gray!17}}\n\n\n\\newcommand{\\revise}[1]{{{#1}}}\n\n\\newcommand{\\citedb}[1]{{\\color{darkblue}{#1}}}\n\n\n\n\n\\title{\\citeeval{}: An Automatic Benchmark for\\\\ Large Language Model Generations with \\ti{Citations}}\n\\title{Can Large Language Models Cite Precisely?\\\\ A Benchmark for Automatic Evaluation and Modeling}\n\n\\title{Enabling Large Language Models to Generate Text with Citations} %\n\n\n\n\n\n\n\n\\author{Tianyu Gao\\quad Howard Yen\\quad Jiatong Yu\\quad Danqi Chen \\\\\nDepartment of Computer Science\n\\& Princeton Language and Intelligence\\\\\nPrinceton University \\\\\n\\ttt{\\{tianyug,hyen,jiatongy,danqic\\}@cs.princeton.edu}\\quad\n}\n\n\\begin{document}\n\\maketitle\n\\begin{abstract}\n\n\n\n\nLarge language models (LLMs) have emerged as a widely-used tool for information seeking, %\nbut their generated outputs are prone to hallucination. %\nIn this work, our aim is to allow LLMs to generate text with \\ti{citations},\nimproving their factual correctness and verifiability.\nExisting work mainly relies on commercial search engines and human evaluation, making it challenging to reproduce and compare different modeling approaches.\nWe propose \\tf{\\citeeval{}}, %\nthe first benchmark for \\tf{A}utomatic \\tf{L}LMs' \\tf{C}itation \\tf{E}valuation. \n\\citeeval{} collects a diverse set of questions and retrieval corpora\nand requires building end-to-end systems to retrieve supporting evidence and generate answers with citations. \nWe develop automatic metrics along three dimensions---fluency, correctness, and citation quality---and \ndemonstrate their strong correlation with human judgements.\nOur experiments with state-of-the-art LLMs and novel prompting strategies show that current systems have considerable room for improvement---For example, on the ELI5 dataset, \neven the best models lack complete citation support 50\\% of the time.\nOur analyses further highlight promising future directions, including\ndeveloping better retrievers,\nadvancing long-context LLMs,\nand improving the ability to synthesize information from multiple sources.\\footnote{Our code and data are available at \\url{https://github.com/princeton-nlp/ALCE}.}\n\n\\end{abstract}\n\n\\section{Introduction}\n\n\n\nLarge language models (LLMs; \\citealp{brown2020language,openai2023gpt4}) have gained increasing popularity as a tool for information seeking.\nWhile they generate engaging and coherent responses,\ntheir outputs are prone to hallucination and often contain factually incorrect information~\\cite{ji2023survey}.\nThis makes it harder for users to trust and verify LLM-generated outputs without any supporting evidence.\n\n\\begin{figure}[t]\n \\includegraphics[width=0.98\\linewidth]{figs/main.pdf}\n \\caption{The task setup of \\citeeval{}. Given a question, \n the system generates text while providing \\ti{citing passages} from a large retrieval corpus. Each statement may contain multiple citations (e.g., \\ttt{[1][2]}). %\n }\n \\vspace{-5pt}\n \\label{fig:main}\n\\end{figure}\n\nIn this work, we study a new generation paradigm for LLMs, in which\nwe require LLMs to provide \\textit{citations} to one or a few text passages for any statement they generate (Figure~\\ref{fig:main}).\nIncorporating citations brings several benefits:\n(1) users can easily verify LLMs' claims with the provided citations;\n(2) LLMs can generate text that faithfully follows cited passages, which has the promise to improve correctness and alleviate hallucination.\n\n\n\n\\begin{table*}[t]\n \\centering\n \\small\n \\begin{tabular}{lllp{0.5\\textwidth}}\n \\toprule\n \\tf{Dataset} & \\tf{Corpus (\\#passages)} & \\tf{Question type} & \\tf{Example} \\\\\n \\midrule\n ASQA & Wikipedia (21M) & Factoid (ambiguous) & \\tf{Q}: When did the US break away from England? \\\\\n &&& \\tf{A}: The US declared independence on July 2, 1776 \\citedb{[1][2]} ... The Treaty of Paris was later signed on September 3, 1783 \\citedb{[3]}. \\\\ %\n QAMPARI & Wikipedia (21M) & Factoid (list) & \\tf{Q}: Which films have Gong Li as a member of their cast? \\\\\n &&& \\tf{A}: The Story of Qiu Ju \\citedb{[1]}, Farewell My Concubine \\citedb{[2]}, The Monkey King 2 \\citedb{[3]}, Mulan \\citedb{[3]}, Saturday Fiction \\citedb{[3]} ...\\\\\n ELI5 & Sphere (899M) & Why/How/What & \\tf{Q}: How do student loans affect getting a mortgage? \\\\\n &&& \\tf{A}: Student loans can affect the debt to income ratio \\citedb{[1]}, which is a key factor in determining the amount that ... \\citedb{[2][3]} \\\\\n \\bottomrule\n \\end{tabular}\n \\caption{The three datasets used in our \\citeeval{} benchmark. %\n These datasets cover a wide range of question types\n and the corresponding corpora span from Wikipedia to \n Web-scale document collection. %\n }\n \\vspace{-10pt}\n \\label{tab:datasets}\n\\end{table*}\n\nMultiple commercial systems have adopted this paradigm: %\nBing Chat\\footnote{\\url{https://www.bing.com/new}} and perplexity.ai\\footnote{\\url{https://www.perplexity.ai}}\nrespond to user questions in natural language with references to Web pages.\n\\citet{nakano2021webgpt,menick2022teaching} share a similar motivation, but they mainly experiment with commercial search engines and closed-source models, making their results difficult to evaluate.\nRetrieval-augmented LMs~\\citep{borgeaud2022retro,izacard2022atlas}\nincorporate retrieved passages during both training and inference,\nbut do not guarantee faithfulness to retrieved passages or explicitly provide citations.\nAdditionally,\nprevious studies mostly rely on human evaluation~\\cite{nakano2021webgpt,menick2022teaching,liu2023evaluating}, which is expensive and difficult to reproduce.\nWe argue that the absence of automated evaluation\nhinders the advances of such systems.\n\nWe present \\tf{\\citeeval{}}, the first {reproducible} benchmark for \\textit{automatically} evaluating LLMs' generations with citations.\n\\citeeval{} assumes a natural-language question and a retrieval corpus, and requires building end-to-end systems to retrieve relevant passages from the corpus, generate a response to the question, and cite corresponding supporting passages.\nWe compile three datasets that cover different types of questions and corpora---ASQA~\\cite{stelmakh2022asqa}, QAMPARI~\\cite{rubin2022qampari}, and ELI5~\\cite{fan-etal-2019-eli5}---as shown in Table~\\ref{tab:datasets}.\nDifferent from previous benchmarks~\\citep{lee-etal-2019-latent,bohnet2022attributed},\n\\citeeval{} evaluates long-text generation, \nfocusing on automatically evaluating citation quality,\nand allows citing \\textit{multiple} passages for individual statements.\n\nWe design automatic evaluation methods in three dimensions:\n\\tf{fluency}, \\tf{correctness}, and \\tf{citation quality}.\nSpecifically,\nwe use MAUVE~\\citep{pillutla2021mauve} to measure fluency, \npropose tailored correctness metrics for each dataset, %\nand\nadopt a natural language inference (NLI) model~\\citep{honovich-etal-2022-true-evaluating} to measure citation quality.\nWe showcase how the three dimensions together contribute to a robust evaluation, \npreventing systems from exploiting shortcuts. %\nAdditionally, we conduct human evaluation and demonstrate a strong correlation with our automatic metrics.\n\nWe experiment on multiple systems with state-of-the-art LLMs and retrievers and also propose novel prompting strategies to synthesize retrieved text into text generation. \nAlthough all systems are capable of providing fluent and coherent responses, \nthere remains substantial room for improvement in terms of correctness and citation quality: For example, on the ELI5 dataset, \naround 50\\% generations of our ChatGPT and GPT-4 baselines are not fully supported by the cited passages. \nAdditionally, we find that\n(1) a closed-book model (generating answers without accessing any retrieved documents) with post-hoc citing achieves good correctness but much worse citation quality;\n(2) although interactive retrieval approaches~\\citep{yao2023react,schick2023toolformer} offer more flexibility in when/what to retrieve, they do not improve the performance on this challenging benchmark;\n(3) summarizing the retrieved passages in a shorter text improves correctness but not citation quality;\n(4) reranking multiple generations boosts citation quality measured by human evaluation;\n(5) incorporating more retrieved passages in context does not help ChatGPT but improves GPT-4 performance.\n\n\nOur extensive analyses \nhighlight three major challenges of building LLMs to generate text with citations: \n(1) \nthe retrieval quality is crucial to the final performance and has substantial room for improvement;\n(2) \nLLMs' limited context window\nrestricts the number of passages they can incorporate;\n(3) \n\\revise{current LLMs struggle to synthesize multiple documents in context\nwithout being distracted by irrelevant ones, although better instruction tuning brings significant improvement.}\nThese challenges pose promising research directions for \ndeveloping better systems integrating retrieval and LLMs.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\\section{Task Setup and Datasets} %\n\n\n\n\n\\label{sec:task_def}\n\nOur task is formalized as follows:\nGiven a query $q$ and a corpus of text passages $\\mathcal{D}$,\nthe system %\nis required to return an output $\\mathcal{S}$%\n, which consists of $n$ statements $s_1$, ..., $s_n$, \nand each statement $s_i$ cites a list of passages $\\mathcal{C}_i = \\{c_{i,1}, c_{i, 2}, \\ldots\\}$\\footnote{In practice, we allow at most 3 citations for each statement as more citations usually do not help.}, where $c_{i,j}\\in \\mathcal{D}$. \nIn this work,\nwe segment LLMs' output into statements by sentence boundaries.\\footnote{\n QAMPARI requires a list as the answer, and we choose each entity in the generated list as a statement.} \nWhile LLMs may include sentences that do not require a citation, such as ``\\ti{I'm happy to help}'', \nwe observe that almost all sentences that LLMs output provide valuable information and require citations, similar to findings in\n\\citet{liu2023evaluating}. \nIn this work, citations are enclosed by box brackets such as \\texttt{[1][2]}.\n\nWe divide the corpus $\\mathcal{D}$ into 100-word passages following previous works on open-domain question answering~\\cite{karpukhin-etal-2020-dense,petroni-etal-2021-kilt,piktus2021web},\nin contrast to\ncommercial systems like Bing Chat, which cite entire Web pages. %\nWe take 100-word passages because\nit is easier for humans to verify, and %\nallows for more retrieved passages to fit in LLMs' limited context. %\n\n\nWe choose QA datasets so that \n(1) they contain factual questions, in which references are important; \n(2) questions require long-text answers that cover multiple aspects; %\n(3) answering the questions requires synthesizing multiple sources.\nWe select three datasets %\n(Table~\\ref{tab:datasets}) and introduce them below. %\nSee \\S\\ref{app_sec:dataset_stats} for additional statistics. \n\n \n\\textbf{ASQA}~\\cite{stelmakh2022asqa} is a long-form factoid dataset.\nAs shown in Figure~\\ref{fig:main}, \neach question is an ambiguous question from AmbigQA~\\cite{min-etal-2020-ambigqa} that requires multiple short answers to cover different aspects, \nand the dataset provides a long-form answer that covers all short answers.\nSince most questions can be answered by Wikipedia, we use the 2018-12-20 Wikipedia snapshot as $\\mathcal{D}$. \n\n\n\\textbf{QAMPARI} \\cite{rubin2022qampari} \nis a factoid QA dataset constructed from Wikipedia,\nwhere the answer is a list of entities that are drawn from different passages.\nSame as ASQA, we use the 2018-12-20 Wikipedia as the corpus.%\n\n\n\\textbf{ELI5} \\cite{fan-etal-2019-eli5} \nis a long-form QA dataset built on the Reddit forum ``Explain Like I'm Five''.\\footnote{\\url{https://www.reddit.com/r/explainlikeimfive/}} %\nMost ELI5 questions are how/why/what questions that require long answers and multiple passages as evidence.\nDue to the diverse topics discussed in the questions, we use\nSphere~\\cite{piktus2021web}---a filtered version of Common Crawl\\footnote{\\url{https://commoncrawl.org}. We also filter out any Web pages from Reddit. }---as the corpus. %\nThe ELI5 dataset is widely used in related work due to its challenging nature~\\cite{nakano2021webgpt,menick2022teaching,liu2023evaluating}.\n\n\n\nWe randomly select 1,000 examples from the development set of each dataset for \\citeeval{}.\nOur benchmark primarily assesses the citation capabilities of existing LLMs and does not provide training data,\nas\nthere are no available examples that provide supervision for citations\nin these datasets.\n\n\n\n\n\\section{Automatic Evaluation}\n\n\n\n\n\\vspace{-3pt}\nOur benchmark measures the following three dimensions of system responses:\n\\vspace{-3pt}\n\\begin{itemize}[leftmargin=1em,itemsep=0pt]\n \\item \\tf{Fluency}: whether the model's generated text is fluent and coherent.\n \\item \\tf{Correctness}: whether the answer is accurate and covers all aspects of interest.\n \\item \\tf{Citation quality}: whether the answer is well supported by the cited passages and no irrelevant passages are cited.\n\\end{itemize}\n\\vspace{-3pt}\nIn the following, we present automatic metrics for each dimension \nand discuss why the combination of the three metrics provides a robust evaluation. %\n\n\n\n\\subsection{Fluency} \nWe use MAUVE \\cite{pillutla2021mauve} to evaluate the fluency of the output (\\S\\ref{app_sec:imp_details}).\nWe deploy MAUVE for ASQA and ELI5\nand omit it for QAMPARI,\nas QAMPARI \nonly requires a list of short answers as the response\nand LLMs consistently adhere to the format in our experiments.\nAs MAUVE is sensitive to output length and text style, and most LLMs are capable of producing fluent text,\nwe mainly employ it as a sanity check as long as the MAUVE scores are high enough.\n\n\n\n\\subsection{Correctness}\n\\label{sec:correctness}\n\n\n\n\n\nOur objective is to measure the informativeness and utility of the generation to the question. \n\\citet{liu2023evaluating} propose to directly evaluate \\textit{perceived utility}\nby humans, a process difficult to automate. \nTherefore, we use correctness---whether the response is accurate compared to a ground truth answer---as a proxy.\nEvaluating the correctness of long-form generation is a challenging task~\\citep{krishna-etal-2021-hurdles},\nand we describe our strategy for each dataset below.\nFigure~\\ref{fig:correctness} illustrates the metrics and we include additional implementation details in \\S\\ref{app_sec:imp_details}.\n\n\nFor \\tf{ASQA}, we follow \\citet{stelmakh2022asqa} and \ncalculate the recall of correct short answers\nby checking whether the short answers (provided by the dataset) are exact substrings of the generation (\\textit{exact match recall}; EM recall).\n\n\nFor \\tf{QAMPARI}, we follow \\citet{rubin2022qampari} and calculate the \\textit{precision} and \\textit{recall} of \nthe model prediction, \nby checking the exact match to the gold answer list.\nWe add one additional adjustment:\nconsidering that users often want to know only a few example answers of \nthe question, %\nour evaluation considers recall to be 100\\% if the prediction includes at least 5 correct answers (\\textit{recall-5}).\n\nUnlike ASQA and QAMPARI, the \\tf{ELI5} dataset does not provide short entity answers.\n\\citet{fan-etal-2019-eli5} use ROUGE for evaluation, which does not reflect the correctness well (\\citealp{krishna-etal-2021-hurdles}; \\S\\ref{app_sec:claim_eli5}).\nInspired by works in summarization evaluation~\\cite{zhang-bansal-2021-finding,kamoi2023wice,wang-etal-2020-asking}, \nwe use InstructGPT~(\\ttt{text-davinci-003}; \\citealp{ouyang2022training}) to generate three ``sub-claims''. \nThen we use TRUE\\footnote{\\url{https://huggingface.co/google/t5_xxl_true_nli_mixture}. Details in \\S\\ref{app_sec:imp_details}.}~\\cite{honovich-etal-2022-true-evaluating}, a T5-11B~\\citep{raffel2020exploring} model fine-tuned on a collection of natural language inference (NLI) datasets, to check whether the model output entails the sub-claims (\\textit{claim recall}). TRUE targets factual correctness and has been used by previous works in similar context~\\citep{bohnet2022attributed,gao2022rarr}.\nWe demonstrate that claim recall provides a more accurate measure of correctness than existing metrics (more details in \\S\\ref{app_sec:claim_eli5}).\n\n\n\n\\begin{figure}[t]\n \\centering\n \\includegraphics[width=0.94\\linewidth]{figs/correctness.pdf}\n \\caption{Evaluation of correctness (details in \\S\\ref{sec:correctness}).}\n \\vspace{-13pt}\n \\label{fig:correctness}\n\\end{figure}\n\n\n\\subsection{Citation Quality}\n\\label{sec:citation}\n\n\n\n\n\nWe evaluate citation qualities using two metrics: \n(1) \\textit{citation recall}, which determines if the output is entirely supported by cited passages, and\n(2) \\textit{citation precision}, which identifies any irrelevant citations. %\nAlthough we prioritize citation recall as it entails a well-supported and truthful answer, \nenhancing precision is crucial for better user satisfaction,\nreducing the need for human review of extraneous passages.\nFigure~\\ref{fig:citation} provides an illustrated example. %\n\n\\begin{figure}[t]\n \\centering\n \\includegraphics[width=0.98\\linewidth]{figs/citation.pdf}\n \\caption{Evaluation of citation quality (details in \\S\\ref{sec:citation}). We use \n an NLI model to verify whether a statement is supported by its citations. %\n }\n \\vspace{-15pt}\n \\label{fig:citation}\n\\end{figure}\n\n\nWe use the NLI model TRUE~\\citep{honovich-etal-2022-true-evaluating} again\nto automatically examine whether the cited passages entail the model generation.\nWe conduct human evaluation (\\S\\ref{sec:human}) to demonstrate strong human correlation of our metric.\n\n\n\n\n\n\\paragraph{Citation recall.} \nWe calculate the citation recall of \\textit{each statement} (0 or 1) and average\nover all {statements} in the model response. %\nFor each statement $s_i$, %\nits citation recall is $1$ if and only if there is at least one citation ($\\mathcal{C}_i\\neq \\emptyset$) and \n$\\phi(\\text{concat}(\\mathcal{C}_i), s_i)=1$,\nwhere \n$\\phi(\\text{premise},\\text{hypothesis})$\nis the NLI model that outputs $1$ if the premise entails the hypothesis, and $0$ otherwise; \n$\\text{concat}(\\mathcal{C}_i)$ concatenates all passages in $\\mathcal{C}_i$ together (details in \\S\\ref{app_sec:imp_details}).\nThe NLI evaluation is in accordance with the \\textit{attributable to identified sources} (AIS) framework~\\cite{rashkin2021measuring}: \n$\\phi(\\text{concat}(\\mathcal{C}_i), s_i)=1$ implies that \n$s_i$ is true based solely on $\\text{concat}(\\mathcal{C}_i)$.\n\n\n\n \n\n\n\n\\paragraph{Citation precision.} \nOur citation precision evaluation detects citations that are irrelevant,\nbut it does not require citing a minimal set.\nWe follow this design because human writing often cites redundant sources to enhance credibility;\nhuman readers may also appreciate multiple citations, especially when it pertains to critical claims such as medical advice.\n\n\nWe calculate the citation precision for \\textit{each citation} (0 or 1) and\naverage over all {citations} in the response. %\nWe first define if a citation is ``irrelevant''.\nIntuitively, a citation $c_{i,j}$ is ``irrelevant'' if (a) $c_{i,j}$ itself cannot support $s_i$ and (b) removing $c_{i,j}$ does not affect the rest of the citations to support $s_i$. Formally, $c_{i,j}$ is ``irrelevant'' if and only if\n\\begin{enumerate}[itemsep=0pt,leftmargin=2em,label=(\\alph*)]\n \\item $\\phi(c_{i,j},s_i)=0$, AND\n \\item $\\phi(\\text{concat}(\\mathcal{C}_i \\setminus \\{c_{i,j}\\}), s_i)=1$. \n\\end{enumerate}\n$c_{i,j}$ has a precision of 1 if $s_i$ has recall$=$$1$ and $c_{i,j}$ is not irrelevant.\nFor example (Figure~\\ref{fig:citation}), when $s_3$ cites three references \\ttt{[2][4][5]} and recall$=$$1$,\n\\ttt{[2]} is ``irrelevant'' if $\\phi(\\texttt{[2]}, s_3)=0$ and $\\phi(\\texttt{[4][5]}, s_3)=1$.\nFor condition (b) to work, we set recall$=$$1$ as a prerequisite for precision$=1$. Note that this algorithm overlooks the scenario when one citation partially supports the statement. \nWe discuss the details in \\S\\ref{app_sec:citation_recall}.\n\n\n\n\n\n\n\n\n\n\\subsection{\\citeeval{} is Robust to Shortcut Cases}\nWe showcase how the \\citeeval{} evaluation is robust to two possible shortcuts in \\S\\ref{app_sec:short_cut}:\n(1) using the top-1 retrieved passage as the response and citing itself, \nand (2) using the first two sentences of the top-1 passage.\nBoth cases have almost-perfect citation scores, but (1) has low fluency due to its unnaturally long length compared to human answers, and (2) has low correctness due to low coverage.\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\\section{Modeling}\n\\label{sec:modeling}\n\n\n\nIn this section,\nwe discuss three major modeling components for an \\citeeval{} system---retrieval, synthesis, and post-editing.\n\n\n\n\n\n\\begin{table}[t]\n \\centering\n \\small\n \\begin{tabular}{>{\\raggedright\\arraybackslash\\tt}p{0.94\\linewidth}<{}}\n \\toprule\n Instruction: Write an accurate, engaging, and concise answer for ... \\\\\n \\\\\n \\vspace{-1em}\n \\noindent {\\color{brown}<Retrieve for the question>}\\\\\n Document {[1]}(Title: American Decolonization) ... \\\\\n Document {[2]}(Title: Decolonization) ... \\\\\n Document {[3]}(Title: American Revolution) ... \\\\\n ... \\\\\n \\\\\n \\vspace{-1em}\n Question: When did US break away from England? \\\\\n Answer: {\\color{blue} The United States took the first step towards gaining independence ... \\tf{\\underline{[1][2]}}. The Treaty of Paris was later signed ... \\tf{\\underline{[3]}}}.\\\\\n\n \\bottomrule\n \\end{tabular}\n \\caption{\n An example of our \\vani{} method. Different colors represent \\ttt{prompt}, \\ttt{\\color{blue} model generation}, and \\ttt{\\color{brown} <actions>}. \n We also provide two in-context demonstrations before the test example. \n }\n \\vspace{-10pt}\n \\label{tab:prompt_short}\n\\end{table}\n\n\\subsection{Retrieval}\n\\label{sec:retrieval}\n\nWe explore simple, off-the-shelf retrievers.\nWe use dense retrievers for Wikipedia, including GTR~\\cite{ni-etal-2022-large} and DPR~\\cite{karpukhin-etal-2020-dense}; \nwe use \nBM25 for Sphere. %\nFor each question,\nwe \nretrieve the top-100 passages. %\n\n\n\n\\subsection{Synthesis}\n\\label{sec:synthesis}\n\n\n\n\n\nWe focus on %\nhow to\nprompt an LLM to \ninteract with the retriever, and \nsynthesize and cite the evidence (without fine-tuning internal parameters).\nOne noteworthy challenge is that existing %\nLLMs all have limited context window and thus\ncan only fit a handful of passages.\n\n\n\n\n\n\\paragraph{\\textsc{\\pvani{}}.}\nWe simply provide the model with the top-$k$\\footnote{We can fit at most $k=3$ for models with 2K window and at most $k=5$ for models with 4K context window.} passages and instruct the model to cite accordingly (Table~\\ref{tab:prompt_short}).\nWe also use in-context learning~\\cite{brown2020language} and prepend two demonstrations.\nThe complete instruction is in Table~\\ref{tab:inst_vanilla}.\n\n\n\\paragraph{\\textsc{\\psumm}/\\textsc{\\psnippet{}}.}\nWith a 4K context window, we can at most safely fit $k=5$ passages. As shown in Figure~\\ref{fig:ret}, top-5 retrieved passages can only cover 56.8\\% percent of the answers in ASQA. \n\nTo tackle this limitation, we propose to provide \\textit{summaries} or \\textit{snippets} of passages instead of the full text (summaries are abstractive but snippets are spans from passages). \nWe acquire summaries and snippets by prompting ChatGPT with instructions (prompts in Table~\\ref{tab:prompt_summary} and \\ref{tab:prompt_snippet}).\\footnote{We also query ChatGPT whether the passage is relevant to the question, and\nfilter out passages that are ``irrelevant''.}\nThen we replace all passages with summaries/snippets.\nSummaries or snippets significantly reduce the passage length, allowing for more passages to fit in:\nfor ASQA, they reduce passage length by 6$\\times$ on average.\n\n\nThough \\summ{}/\\snippet{} allows for more retrieved passages, \nthey are lossy compressions. \nTo alleviate this problem,\nwe propose \\interact{}, an interactive prompting scheme \nto allow the model to check the full text of certain passages. %\nAt each step, the model can execute one of three actions: \n(1) ``\\ttt{Check: Document [1][2]}'' to check the full text of the corresponding documents;\n(2) ``\\ttt{Output:}'' to output a statement of the answer;\n(3) ``\\ttt{End.}'' to end the generation.\n\\S\\ref{app_sec:imp_details} provides more details.\n\n\n\n\n\n\n\\begin{table}[t]\n \\centering\n \\small\n \\begin{tabular}{>{\\raggedright\\arraybackslash\\tt}p{0.94\\linewidth}<{}}\n \\toprule\n Instruction: ... \\\\\n \\\\\n \\vspace{-1em}\n \\noindent {\\color{brown}<Retrieve for question ``...''>}\\\\\n Question: When did US break away from England? \\\\\n \\noindent {\\color{blue} Search: Declaration of Independence} \\\\\n \\noindent {\\color{brown}<Search the query among the top-100 passages>}\\\\\n Document [1](Title: ...) ... \\\\\n \\noindent {\\color{blue} Output: The United States ... \\tf{\\underline{[1]}}.} \\\\\n \\noindent {\\color{brown}<Remove Document [1] from context>}\\\\\n \\noindent {\\color{blue} Search: Treaty of Paris} \\\\\n \\noindent {\\color{brown}<Search the query among the top-100 passages>}\\\\\n Document [3](Title: ...) ... \\\\\n \\noindent {\\color{blue} Output: The Treaty of Paris ... \\tf{\\underline{[3]}}.} \\\\\n \\noindent {\\color{brown}<Remove Document [3] from context>}\\\\\n \\noindent {\\color{blue}End.}\\\\\n \\bottomrule\n \\end{tabular}\n \\caption{\n An example of \\search{}. \n }\n \\vspace{-15pt}\n \\label{tab:prompt_search}\n\\end{table}\n\n\\paragraph{\\textsc{\\psearch{}}.}\nThe above methods all display retrieval results at the beginning.\nIn \\search{},\nwe allow LLMs to call ``search'' during the generation process~\\citep{yao2023react,press2022measuring,jiang2023active}.\nAt each step, the model can execute one of three actions: \n``\\ttt{Search: \\{query\\}}'' to search among the top-100 passages\\footnote{We do not search over the entire corpus because \\ttt{\\{query\\}} may leave out certain context in the question and searching among the already-retrieved passages gives better results.} by using GTR; the ``\\ttt{Output}'' and ``\\ttt{End}'' actions are the same as \\interact{}.\nFor each ``\\ttt{Search}'' action, we display the best retrieved passage in the context.\nThe passage is removed after one action to save context space.\nTable~\\ref{tab:prompt_search} shows an example. %\n\n\n\n\\paragraph{\\textsc{\\pclose{}}.}\nWe also add a simple closed-book baseline, where the model is only prompted with the instruction and the question,\nwithout any retrieved passages provided.\nConsequently, this variant does not cite any evidences.\n\n\\subsection{Post-editing}\n\\label{sec:refinement}\n\nIn this section we discuss two strategies for refining the output to further improve its quality.\n\n\\paragraph{\\textsc{\\prerank{}}.}\nWe randomly sample $n_{\\text{sample}}=4$ %\nresponses for each question, and select the best response using the automatic \\textit{citation recall} score. \nwe expect \\textsc{\\prerank{}} to improve the citation quality.\n\n\\paragraph{\\textsc{\\pposthoc{}}.}\nFor each statement, we find the best matching passage among the top-100 retrieved passages using GTR and cite it.\nWe combine this with \\close{} in our experiments.\n\n\\section{Experiments}\n\\label{sec:experiments}\n\n\n\\begin{table}[t]\n \\centering\n \\resizebox{0.98\\linewidth}{!}{\n \\begin{tabular}{lcccc}\n \\toprule\n & \\tf{Fluency} & \\tf{Correct.} & \\multicolumn{2}{c}{\\tf{Citation} }\\\\\n \\cmidrule(lr){2-2} \\cmidrule(lr){3-3} \\cmidrule(lr){4-5}\n & (MAUVE) & (EM Rec.) & Rec. & Prec. \\\\\n \\midrule\n \\headercolor\n \\multicolumn{5}{c}{\\textbf{ChatGPT}} \\\\\n \\tableskip\n \\vani{} (5-psg) & 66.6 & 40.4 & {73.6} & 72.5\\\\% {63.0} \\\\\n ~~w/ \\rerank{} & 77.0 & 40.2 & \\tf{84.8} & \\textbf{81.6} \\\\% \\tf{73.3} \\\\\n \\summ{} (10-psg) & 70.0 & \\tf{43.3} &68.9\t& 61.8\\\\ %\n ~~w/ \\interact{} & 69.0 &\t39.1 & 73.4& 66.5\\\\ %\n \\snippet{} (10-psg) & 69.8&\t41.4& 65.3&\t57.4\\\\ %\n \\search{} & 58.7 & 32.4 & 58.3 & 58.2\t\\\\ %\n \\close{} & 52.7 &\t38.3 & 26.7& 26.7 \\\\\n\n \\tableskip\n \\headercolor\n \\multicolumn{5}{c}{\\textbf{GPT-4} (\\vani{} prompting)} \\\\\n \\tableskip\n GPT-4 (5-psg) & 67.1 & 41.3 & 68.5 & 75.6 \\\\\n GPT-4 (20-psg) & 64.9 & 44.4 & 73.0 & 76.5 \\\\\n\n \\tableskip\n \\headercolor\n \\multicolumn{5}{c}{\\textbf{LLaMA} (\\vani{} prompting)} \\\\\n \\tableskip\n LLaMA-13B (3-psg) & 68.4 & 26.9 & 10.6 & 15.4 \\\\\n Vicuna-13B (3-psg) & 82.6 & 31.9 & 51.1 & 50.1 \\\\ \n \n Chat-13B (5-psg) & 72.4 & 35.2 & 38.4 & 39.4 \\\\\n Chat-70B (5-psg) & 88.3 & 41.5 & 62.9 & 61.3 \\\\\n \\bottomrule\n \\end{tabular}\n }\n \\caption{\n Experiments on ASQA.\n For \\close{}, we use \\posthoc{} to get citations.\n $k$-psg: putting top-$k$ passages from the retrieval results into the context.\n Chat-13B and Chat-70B refer to LLaMA-2-Chat.\n }\n \\label{tab:asqa_result}\n \\vspace{-10pt}\n\\end{table}\n\n\\begin{table}[t]\n \\centering\n \\small\n \\resizebox{0.98\\linewidth}{!}{\n \\begin{tabular}{lcccc}\n \\toprule\n & \\multicolumn{2}{c}{\\tf{Correctness}} & \\multicolumn{2}{c}{\\tf{Citation} }\\\\\n \\cmidrule(lr){2-3} \\cmidrule(lr){4-5}\n & Rec.-5 & Prec. & Rec. & Prec. \\\\\n \\midrule\n \\headercolor\n \\multicolumn{5}{c}{\\textbf{ChatGPT}} \\\\\n \\tableskip\n \\vani{} (5-psg) & 20.8 & 20.8 & 20.5&\t20.9 \\\\\n ~~w/ \\rerank{} & 22.8 & 21.4 & 21.2 & 21.4 \\\\ %\n \\summ{} (10-psg) & 23.6& 21.2& \\tf{23.6}&\t\\tf{25.7} \\\\\n \\snippet{} (10-psg)&24.5 & 21.5& {22.9} & {24.9} \\\\\n ~~w/ \\interact{} & 21.9 & \\tf{23.0} & 21.9&23.4\\\\\n \\search{} & 17.2& 20.4 & 14.9&\t14.9 \\\\\n \\close{} & \\tf{32.9} & 19.8 & 10.0 & 10.0\\\\\n \\tableskip\n \\headercolor\n \\multicolumn{5}{c}{\\textbf{GPT-4} (\\vani{} prompting)} \\\\\n \\tableskip\n GPT-4 (5-psg) & 22.2 & 25.0 & 25.9 & 27.0 \\\\\n GPT-4 (20-psg) & 29.6 & 26.2 & 27.4 & 28.5 \\\\ \n\n \\tableskip\n \\headercolor\n \\multicolumn{5}{c}{\\textbf{LLaMA} (\\vani{} prompting)} \\\\\n \\tableskip\n LLaMA-13B (3-psg) & 9.7 & 9.1 & 6.7 & 7.1 \\\\% 6.8 \\\\\n Vicuna-13B (5-psg) & 14.0 & 15.9 & 12.5 & 13.4 \\\\ %\n \n \n Chat-13B (5-psg) & 21.1 & 18.2 & 9.6 & 9.7 \\\\\n Chat-70B (5-psg) & 21.8 &18.4 & 15.1 &15.6 \\\\\n \\bottomrule\n \\end{tabular}\n }\n \\caption{\n Experiments on QAMPARI.\n ``Rec.-5'': we set the recall to be 100\\% if the prediction includes at least 5 correct answers.\n }\n \\label{tab:qampari_result}\n \\vspace{-10pt}\n\\end{table}\n\n\n\\begin{table}[h]\n \\centering\n \\resizebox{0.98\\linewidth}{!}{\n \\begin{tabular}{lcccc}\n \\toprule\n & \\tf{Fluency} & \\tf{Correct.} & \\multicolumn{2}{c}{\\tf{Citation} }\\\\\n \\cmidrule(lr){2-2} \\cmidrule(lr){3-3} \\cmidrule(lr){4-5}\n & (MAUVE) & (Claim) & Rec. & Prec. \\\\\n \\midrule\n \\headercolor\n \\multicolumn{5}{c}{\\textbf{ChatGPT}} \\\\\n \\tableskip\n \\vani{} (5-psg) &57.2&\t12.0 &\t51.1&\t{50.0} \\\\\n ~~w/ \\rerank{} & 56.1 & 11.4 & \\tf{69.3} & \\textbf{67.8}\\\\ %\n \\summ{} (10-psg) & 40.3 & 12.5 & 51.5 & 48.2 \\\\\n \\snippet{} (10-psg) & 62.9 &\t{14.3} & 50.4 & 45.0 \\\\ %\n ~~w/ \\interact{} & 68.0 &\t13.3&\t47.8& 45.0\\\\\t%\n \\search{} & 49.7&\t13.4&\t45.6& 43.7\\\\\t%\n \\close{} & 32.6 &\\tf{18.6}\t&15.5\t&15.5\\\\\n \\tableskip\n \\headercolor\n \\multicolumn{5}{c}{\\textbf{GPT-4} (\\vani{} prompting)} \\\\\n \\tableskip\n GPT-4 (5-psg) & 38.4 & 14.2 & 44.0 & 50.1 \\\\\n GPT-4 (20-psg) & 41.5 & 18.3 & 48.5 & 53.4 \\\\\n\n \\tableskip\n \\headercolor\n \\multicolumn{5}{c}{\\textbf{LLaMA} (\\vani{} prompting)} \\\\\n \\tableskip\n LLaMA-13B (3-psg) & 50.0 & 3.9 & 3.1 & 5.3 \\\\%2.0 \\\\\n Vicuna-13B (3-psg)& 58.2 & {10.0} & 15.6 & 19.6 \\\\% 12.6 \\\\\n \n Chat-13B (5-psg) & 34.7 & 13.4 &17.3 & 15.8 \\\\\n Chat-70B (5-psg) & 38.6 & 12.8 & 38.3 & 37.9 \\\\\n \\bottomrule\n \\end{tabular}\n }\n \\caption{\n Experiments on ELI5. We use \\textit{claim recall} for the correctness evaluation.\n Chat-13B and Chat-70B refer to LLaMA-2-Chat.\n }\n \\label{tab:eli5_result}\n \\vspace{-10pt}\n\\end{table}\n\nWe describe experiment details in \\S\\ref{app_sec:imp_details}. \nWe use ChatGPT (\\ttt{gpt-3.5-turbo-0301}) with a 4K context window for most main experiments and ablations. \n\\revise{We also report results with ChatGPT-16K (\\ttt{gpt- 3.5-turbo-16k-0613}) and GPT-4 (\\ttt{gpt-4-0613}; 8K context window).}\nFor open-source models, we test LLaMA~\\citep{touvron2023llama} and \nits instruction-tuned versions, including \nAlpaca~\\citep{alpaca}, Vicuna~\\citep{vicuna2023}, and Oasst~\\citep{kopf2023openassistant}.\nThey all have a 2K context window.\nWe use short instructions for LLaMA (Table~\\ref{tab:inst_vanilla_light}) to save context budget.\n\\revise{Additionally, we test LLaMA-2-Chat, which were also trained to follow instructions \\cite{touvron2023llama2}. These models have a context window of 4K tokens, which allows for 5 passages per question.}\n\n\n\n\\subsection{Main Results}\nWe present the main results on three datasets in \nTable~\\ref{tab:asqa_result},~\\ref{tab:qampari_result}, and~\\ref{tab:eli5_result} respectively (full results in \\S\\ref{app_sec:more_main}). %\nWe first note that all models achieve good fluency scores (except some models on ELI5 mainly due to their longer generations).\nWe summarize the main takeaways from the experiments below.\n\n\\paragraph{\\tf{\\textsc{\\pvani{}}} achieves strong performance.}\nDespite its simplicity,\n\\vani{} (putting retrieved passages in context)\nachieves close-to-the-best performance among all prompting strategies. %\n\n\n\n\\paragraph{Using summaries or snippets improves correctness.}\nWe see a universal trend that \n\\summ{} or \\snippet{} \nimproves correctness,\nthough\non ASQA and ELI5, such an improvement comes at a cost of citation quality\ndue to the lossy compression. %\nCombining \\interact{} with \\summ{}/\\snippet{} does not bring improvement,\nand we hypothesize that \nchecking the full passages offers limited benefit and \n current LLMs are not proficient in an interactive usage.\n\n\n\n\n\n\n\\paragraph{Retrieving text on the fly does not improve performance.}\nAll datasets show that \n\\vani{} \n outperforms\n\\search{} on citation quality (and on correctness for ASQA and ELI5).\nBy manually examining the examples,\nwe find that \nit is challenging to ask detailed questions without seeing any passages.\nTo improve \\search{},\none may need to provide more context about the questions in advance or\nencourage the model to call retrievers with more detailed and diverse queries.\n\n\\paragraph{{\\textsc{\\prerank{}}} boosts citation quality.}\nWe observe that \\rerank{} \nleads to consistent improvement in citation quality (on ASQA and ELI5).\nAs the automatic scores may be biased in \\rerank{},\nwe also conduct human evaluation~(\\S\\ref{sec:human})\nand verify its effectiveness. %\n\n\\paragraph{\\textsc{\\pclose{}}+\\textsc{\\pposthoc{}} delivers strong correctness but poor citation quality.}\n\\close{} outperforms \\vani{} in correctness on ELI5 and QAMPARI, and has only a 2\\% gap on ASQA.\nHowever, \n\\close{} cannot provide any citation;\nwhen combined with \\posthoc{},\nthe citation quality remains inadequate. For instance, \ncitation recall of \\close{}+\\posthoc{} is lower than \\vani{} by 47\\% on ASQA. \n\nTo understand why \\close{} achieves better correctness and why \\posthoc{} cannot deliver satisfying citation quality,\nwe manually examine model outputs and find that:\n(1) open-book models are easily distracted by irrelevant passages and generate responses with lower correctness, \na phenomenon also observed by~\\citet{shi2023large};\n(2) \\close{} often generates texts that are correct but not similar to any retrieved passages, \nmaking it difficult to match a citation post-hoc.\n\n\\revise{\n\\paragraph{GPT-4 brings limited improvement but is better at using long context.}\nWe evaluate GPT-4 with \\vani{} and different numbers of passages (more results in \\S\\ref{app_sec:more_main}).\nGPT-4 brings consistent (but limited) improvement on correctness, but often at a cost of citation quality. \nGPT-4 can also incorporate more passages due to its longer context window,\nwhich boosts both correctness and citation quality. \nOn the contrary, \nincluding more passages with ChatGPT-16K does not improve the results (Table \\ref{tab:asqa_llm}), \nsuggesting that processing more passages is non-trivial and GPT-4 is better at synthesizing information from its long context than ChatGPT.\n}\n\n\\subsection{Comparison of Different LLMs}\n\\label{sec:different_llms}\n\n\n\\begin{figure*}[t]\n \\centering\n \\begin{minipage}[b]{0.66\\textwidth}\n \\centering\n \\includegraphics[width=1.0\\textwidth]{figs/ret.pdf}\n \\end{minipage}\n \\hfill\n \\begin{minipage}[b]{0.33\\textwidth}\n \\centering\n\n \\resizebox{1.0\\textwidth}{!}{\n \\begin{tabular}{lcccc}\n \\toprule\n & \\tf{Fluency} & \\tf{Correct.} & \\multicolumn{2}{c}{\\tf{Citation} }\\\\\n \\cmidrule(lr){2-2} \\cmidrule(lr){3-3} \\cmidrule(lr){4-5}\n & (MAUVE) & (EM) & Rec. & Prec. \\\\\n \\midrule\n \\headercolor\n \\multicolumn{5}{c}{\\textbf{ChatGPT} (\\vani{})} \\\\\n \\tableskip\n DPR (5-psg) & 61.8 &\t36.1 & 65.0 &\t65.6\\\\%56.7 \\\\\n GTR (1-psg) & 69.5&\t38.4 & 56.0\t& 64.0\\\\%56.0\\\\\n GTR (3-psg) & 66.6 & 39.6 & 72.8\t& \\tf{73.9}\\\\%64.0 \\\\\n GTR (5-psg) & 66.6 & \\tf{40.4} & \\tf{73.6} & 72.5\\\\%63.0 \\\\\n \\bottomrule\n \\end{tabular}\n }\n \\vspace{17pt}\n \\end{minipage}\n \\vspace{-25pt}\n \\caption{\nRetrieval recall@$k$ on ASQA (\\textit{EM recall}), QAMPARI (\\textit{recall-5}), and ELI5 (\\textit{claim recall}).\nRetrieval recall serves as an upper bound for model performance,\nand we compare them with two models' correctness results in the figure (dashed lines):\n``Vanilla (5-psg)'' is ChatGPT \\vani{} with top-5 passages in context;\n``Oracle'' is the same model except that it uses 5 gold passages (\\S\\ref{app_sec:retrieval}), whose recall matches Recall@100 on all three datasets. %\n }\n \\vspace{-10pt}\n \\label{fig:ret}\n\\end{figure*}\n\n\nTable~\\ref{tab:asqa_llm} compares \ndifferent LLMs on ASQA using \\vani{} (more results in \\S\\ref{app_sec:more_main}).\nNotably, instruction-tuned models (Vicuna-13B and LLaMA-2-Chat)\noutperform the original LLaMA models in correctness and \nconsiderably enhance the citation quality.\nWe observe that \nwhile the original LLaMA models are able to %\ncopy facts from the context,\nthey struggle with accurately citing the sources or simply do not cite. \n\\revise{Notably, the best open-source model, LLaMA-2-70B-Chat, achieves comparable correctness score as the OpenAI models, but still lags behind in citation quality. }\n\n\n\n\n\n\\subsection{Retrieval Analysis}\n\n\n\\begin{table}[t]\n \\centering\n \\resizebox{0.98\\linewidth}{!}{\n \\begin{tabular}{lcccc}\n \\toprule\n & \\tf{Fluency} & \\tf{Correct.} & \\multicolumn{2}{c}{\\tf{Citation} }\\\\\n \\cmidrule(lr){2-2} \\cmidrule(lr){3-3} \\cmidrule(lr){4-5}\n & (MAUVE) & (EM Rec.) & Rec. & Prec. \\\\\n \\midrule\n \\headercolor\n \\multicolumn{5}{c}{\\textbf{Open-source} (max \\#tokens=2K-4K)} \\\\\n \\tableskip\n LLaMA-13B (3-psg) & 68.4 & 26.9 & 10.6 & 15.4 \\\\\n Vicuna-13B (3-psg) & 82.6 & 31.9 & 51.1 & 50.1 \\\\ \n Chat-13B (5-psg) & 72.4 & 35.2 & 38.4 & 39.4 \\\\\n Chat-70B (5-psg) & 88.3 & 41.5 & 62.9 & 61.3 \\\\\n \\tableskip\n \\headercolor\n \\multicolumn{5}{c}{\\textbf{ChatGPT} (max \\#tokens=4K)} \\\\\n \\tableskip\n ChatGPT (3-psg) & 66.6 & 39.6 & 72.8\t& 73.9 \\\\% 64.0 \\\\\n ChatGPT (5-psg) & 66.6 & 40.4 & {73.6} & 72.5 \\\\%{63.0} \\\\\n \\tableskip\n \\headercolor\n \\multicolumn{5}{c}{\\textbf{ChatGPT-16K} (max \\#tokens=16K)} \\\\\n \\tableskip\n ChatGPT (5-psg) & 60.3 & 36.1 & 76.2 & 76.5 \\\\\n ChatGPT (10-psg) & 56.3 & 36.7 & 75.3 & 75.0 \\\\ \n ChatGPT (20-psg) & 56.7 & 36.1 & 73.7 & 73.5 \\\\\n\n \\tableskip\n \\headercolor\n \\multicolumn{5}{c}{\\textbf{GPT-4} (max \\#tokens=8K)} \\\\\n \\tableskip\n GPT-4 (5-psg) & 67.1 & 41.3 & 68.5 & 75.6 \\\\\n GPT-4 (10-psg) & 71.5 & 43.1 & 72.0 & 75.5 \\\\\n GPT-4 (20-psg) & 64.9 & 44.4 & 73.0 & 76.5 \\\\\n \\bottomrule\n \n \\end{tabular}\n }\n \\caption{\n Comparison of different LLMs on ASQA (GTR+\\vani{}). LLaMA-13B and Vicuna-13B have a context limit of 2,048 tokens, and thus can only use a short version of instructions and at most top-3 passages.\n Chat-13B and Chat-70B refer to LLaMA-2-Chat.\n }\n \\label{tab:asqa_llm}\n \\vspace{-10pt}\n\\end{table}\n\nThe retrieval results play a crucial role to the correctness and the citation quality.\nFigure~\\ref{fig:ret} presents \nthe retrieval recall@$k$ %\nwith different datasets and retrievers.\nAs the number of passages increases, retrieval recall steadily improves.\nAdditionally, Figure~\\ref{fig:ret} shows\nthe correctness performance of two models:\n(1) ChatGPT \\vani{} with top-5 passages (our primary baseline);\n(2) an oracle version of the same model employing\n5 gold passages (\\S\\ref{app_sec:retrieval}; the 5 gold passages match the retrieval recall@100).\nNotably,\nboth models' correctness lags behind the corresponding retrieval recall (except for ELI5 top-5).\nThe discrepancy suggests that \ndespite the presence of accurate answers in context,\nLLMs struggle to utilize them in their outputs.\n\n\n\nWe compare the impact of different retrievers and different numbers of passages to LLMs.\nFigure~\\ref{fig:ret} (right)\nshows that\nGTR outperforms DPR in both correctness and citation quality,\nemphasizing the importance of deploying better retrievers.\nContrary to the retrieval recall trend in Figure~\\ref{fig:ret},\nmore passages in context do not yield substantial improvement for ChatGPT.\nSpecifically, correctness plateaus at top-1 passage and citation quality plateaus at top-3. \n\\revise{GPT-4 (Table~\\ref{tab:asqa_llm}) exhibits an increasing trend with more passages, \nbut the improvement is not proportional to the retrieval performance.}\nThis indicates the limited ability of LLMs in utilizing multiple passages within context.\n\n\\subsection{Other Ablations}\n\\label{sec:other_ablation}\n\nWe provide additional ablations in \\S\\ref{app_sec:more_exp}. \nIn summary, we find that \n(1) \nusing comprehensive instructions\n enhances the citation quality of instruction-tuned models (\\S\\ref{sec:inst});\n(2) \nincluding at least one demonstration improves the performance %\n(\\S\\ref{sec:demo}); \n\\revise{(3) fine-tuned models (FiD; \\citealp{izacard-grave-2021-leveraging}) with \\posthoc{} lag behind LLMs in both correctness and citation quality and fail to generalize %\n(\\S\\ref{sec:fid}).}\n\n\n\n\n\n\n\n\\section{Human Evaluation}\n\\label{sec:human}\n\n\nTo verify that our automatic evaluation correlates with human judgement, \nwe conduct human evaluation on selected models %\nand request workers \nto judge model generations on three dimensions similar to \\citet{liu2023evaluating}---(1) utility: a 1-to-5 score indicating whether the generation helps answer the question; %\n(2) citation recall: the annotator is given a sentence and all passages that the sentence cited, and is asked to judge whether the passages fully support the sentence; %\n(3) citation precision: given a sentence and one of its citations, the annotator is asked to judge whether the citation ``fully supports'', ``partially supports'', or ``does not support'' the sentence. \nEach citation gets a precision score 1 if the output sentence has a citation recall of 1 and this citation at least ``partially supports'' it.\nSee Appendix~\\ref{app_sec:human} for more details.\n\n\n\\begin{table}[t]\n \\centering\n \\resizebox{0.98\\linewidth}{!}{\n \\begin{tabular}{lcccc}\n \\toprule\n & \\multicolumn{2}{c}{\\tf{Human scores}} & \\multicolumn{2}{c}{\\tf{\\citeeval{} scores}} \\\\\n \\cmidrule(lr){2-3} \\cmidrule(lr){4-5}\n & Rec.& Prec. & Rec. & Prec. \\\\\n \\midrule\n ChatGPT \\vani & 74.7 & 76.6 & 75.3 & 74.4 \\\\\n ~~w/ \\rerank & \\textbf{79.3} & \\textbf{81.9} & \\textbf{83.9} & \\textbf{80.8} \\\\\n Vicuna-13B \\vani & 51.6 & 51.5 & 50.3 & 50.1 \\\\\n \\bottomrule\n \\end{tabular}\n }\n \\caption{\n Human citation quality evaluation vs. \\citeeval{} citation quality evaluation on ASQA. \n }\n \\vspace{-5pt}\n \\label{tab:human-asqa-all}\n\\end{table}\n\\begin{table}[t]\n \\centering\n \\resizebox{0.98\\linewidth}{!}{\n \\begin{tabular}{lcccc}\n \\toprule\n & \\multicolumn{2}{c}{\\tf{Human scores}} & \\multicolumn{2}{c}{\\tf{\\citeeval{} scores}} \\\\\n \\cmidrule(lr){2-3} \\cmidrule(lr){4-5}\n & Rec. & Prec. & Rec. & Prec. \\\\\n \\midrule\n ChatGPT \\vani & 50.8 & 52.4 & 52.8 & 50.4 \\\\\n ~~w/ \\rerank & \\textbf{59.7} & \\textbf{60.6} & \\textbf{63.0} & \\textbf{60.6} \\\\\n Vicuna-13B \\vani & 13.4 & 19.2 & 13.6 & 18.1 \\\\\n \\bottomrule\n \\end{tabular}\n }\n \\caption{\n Human citation quality evaluation vs. \\citeeval{} citation quality evaluation on ELI5. \n }\n \\vspace{-10pt}\n \\label{tab:human-eli5-all}\n\\end{table}\n\n\\paragraph{Model outputs score high utility.} \nThe utility scores do not differ significantly between models, ranging $3.7$-$3.9$ for ASQA and $3.5$-$3.6$ for ELI5. \nUpon inspection, all tested models are mostly able to output fluent answers that are related to the question, %\ndespite differences in factual correctness. \n\n\n\n\n\n\\paragraph{Our automatic evaluation of citation quality strongly correlates with human judgements.}\nAs shown in Table~\\ref{tab:human-asqa-all} (ASQA) and Table~\\ref{tab:human-eli5-all} (ELI5),\nthe relative rankings induced by human and our automatic metrics are consistent.\nThe absolute citation scores from human and \\citeeval{} are very close except for \\rerank{} (which uses the automated citation recall for reranking).\nThis suggests that an improvement on \\citeeval{} citation metrics translates to improvement on human preferences. %\nFurthermore, the Cohen's kappa coefficient between \nhuman and \\citeeval{} suggests substantial agreement for citation recall ($0.698$) and moderate agreement for citation precision ($0.525$).\nWe also show in \\S\\ref{sec_more_human} that our automatic evaluation achieves high accuracy when treating human annotations as gold labels\n($85.1\\%$ for citation recall and $77.6\\%$ for citation precision).\n\n\n\n\n\n\n\n\n\n\n% \\input{sections/discussion}\n\\section{Related Work}\n\\label{sec:related_work}\n\\vspace{-5pt}\n\n\\paragraph{Evaluating citations.}\nGenerating text with citations is closely related to attribution.\n\\citet{rashkin2021measuring} define the ``attributable to identified sources''~(AIS) score to measure how faithful a generated text is to its sources.\n\\citet{bohnet2022attributed} apply AIS scores on a single-document short-answer QA dataset.\n\\citet{honovich-etal-2022-true-evaluating,yue2023automatic} study automatic evaluations for the AIS score.\nA concurrent work~\\citep{liu2023evaluating} \nconduct human evaluation on commercial generative search engines to\nexamine their citation qualities.\n\nScientific citation text generation~\\citep{funkquist2022citebench} is a related task to \\citeeval{} where the model is provided the papers-to-cite and context and is required to recover the citing text. \nIt is different from \\citeeval{} \nas all citations are provided\nand the model only needs to perform the summarization.\n\n\n\n\n\n\n\n\\paragraph{Retrieval-augmented LMs.}\nMany studies have explored augmenting LMs with \nexternally retrieved information.\n\\citet{guu2020realm,borgeaud2022retro,izacard2022atlas}\npre-train language models with retrieved passages, \nwhile \\citet{Khandelwal2020Generalization,zhong-etal-2022-training} augment\nLLMs' output by interpolating it with a $k$NN module;\nthough none of them explicitly provide citations to the retrieved sources.\nOther works prompt or fine-tune LLMs to \n``retrieve on-the-fly'' \\citep{parisi2022talm,schick2023toolformer,shuster-etal-2022-language,jiang2023active,yao2023react,press2022measuring},\nwhich offers flexibility of when and what to search.\n\\citet{gao2022rarr,he2022rethinking} propose to first generate text without accessing external documents and then\nretrieve relevant documents and revise the generation to be consistent. %\n\nAmong previous explorations, \\citet{nakano2021webgpt,menick2022teaching} \nare the closest to our setting, where\nLLMs are trained to answer questions while providing citations. \nHowever, they do not explore retrieval strategies and simply use commercial search engines, which are not reproducible, and their models and training data are closed-source.\nTo the best of our knowledge, \nwe are the first to implement end-to-end systems that retrieve, synthesize, and cite documents with LLMs.\n\n\n\n\n\\section{Conclusion}\n\nWe propose \\citeeval{}, the first automatic benchmark for evaluating LLM generations with citations.\nWe deploy automatic metrics to measure fluency, correctness, and citation quality, and verify their efficacy via human evaluation.\nWe explore a variety of strategies for incorporating citations in LLMs\nand demonstrate that current systems have considerable room for improvement on \\citeeval{}.\n\nOur experiments highlight a number of promising research directions, including\n(1) enhancing retrieval and refining retrieval integrations in LLMs,\n(2) developing long-context LLMs, and \n(3) advancing LLMs' ability to synthesize multiple sources.\nWhat's even more intriguing is that these research proposals extend beyond the \\citeeval{} setup (for example, long-context LLMs have numerous exciting applications), and\n\\citeeval{} can serve as a valuable testbed for their development.\n\\section*{Limitations}\n\n \n\nOur evaluation still has room for improvement:\n(1) MAUVE is found to be sensitive to output length and may provide unstable results;\n(2) for the ELI5's correctness evaluation, the automatically generated claims may not cover all possible answers due to the open-ended nature of the questions;\n(3) our citation quality evaluation is limited by the accuracy of the NLI model; for citation precision, the NLI model cannot detect the case of ``partially support'' and thus\nleads to a lower citation precision score than the human evaluation.\n\nAlthough we believe our curated datasets closely resemble the distribution of real-world user questions,\nwe acknowledge that they do not cover more challenging scenarios, such as\nmulti-hop reasoning, math reasoning, and code completion.\n\n\nIn our experiments, we focus on prompting LLMs without updating their model weights.\nTraining a model directly to incorporate citations remains challenging due to the lack of supervised data.\nHowever, we observe that certain human-instruction datasets\ncontain examples similar to our task setup.\nWe leave the exploration of training LLMs to generate citations for future work.\n\n\n\n\\section*{Acknowledgments}\n\nWe appreciate the helpful feedback from the members of the Princeton NLP group. %\nWe thank Alexander Wettig, Nelson Liu, Tianyi Zhang, Yu Meng, Sadhika Malladi, Yangsibo Huang, Zhiyuan Zeng, and Dan Friedman for the valuable discussion.\nWe thank Surge AI (especially Anna Folinsky and Edwin Chen)\nfor their support with the human evaluation.\nTianyu Gao is supported by an IBM PhD Fellowship. \nThis research is supported by an NSF CAREER award (IIS-2239290), a Sloan Research Fellowship, \nand Microsoft Azure credits through the ``Accelerate Foundation Models Academic Research'' Initiative. \n\n\n\n\\bibliography{anthology,custom}\n\\bibliographystyle{acl_natbib}\n\n\\clearpage\n\\appendix\n\n\n\\section{Generating Claims for ELI5}\n\\label{app_sec:claim_eli5}\n\n\n\nWe elect not to use ROUGE-L as our main correctness metrics since it does not account for the different ways of expressing the same answer and it can be easily gamed \\cite{krishna-etal-2021-hurdles}.\nWe further illustrate this issue in Table \\ref{tab:eli5-rouge}.\nA system can easily achieve high ROUGE-L score by retrieving and returning the top passage from a BM25 index.\nHowever, the claims evaluation metric does not reward this approach since the output often lacks different aspects of the answers.\n\\begin{table}[ht]\n \\centering\n \\small\n \\begin{tabular}{@{}lcc@{}}\n\\toprule\n & ROUGE-L & Claim recall\\\\ %\n \\midrule\nChatGPT \\vani & 20.6 & 12.0\\\\% & 66.9 \\\\\nChatGPT \\oracle & 21.2 & 21.3 \\\\%& 64.4 \\\\\nLLaMa-13B \\vani & 16.2 & 3.9 \\\\%& 50.0 \\\\\nTop-1 passage & 19.1 & 3.0 \\\\%& 47.1 \\\\ \\bottomrule\n\\bottomrule\n\\end{tabular} \n \\caption{\n Comparison between ROUGE-L and claim recall scores on ELI5. \n }\n \\label{tab:eli5-rouge}\n\\end{table}\n \nInstead, we leverage the original answers \nto generate sub-claims\nand use them to serve as an estimate of the different aspects of the answers that we expect the model to cover.\nThis approach is inspired by works in summarization evaluation and claim verification \\cite{zhang-bansal-2021-finding,kamoi2023wice,wang-etal-2020-asking}.\n\n\nSpecifically, we use \\ttt{text-davinci-003} to generate the sub-claims.\nWe first manually annotate three question and answer pairs from the original ELI5 training set with 3 sub-claims each.\nThen, we prompt \\ttt{text-davinci-003} with these pairs as demonstrations.\nThe full prompt with an example is shown in Table \\ref{tab:eli5-claims-prompt}.\n\n\\paragraph{InstructGPT generates coherent and faithful sub-claims.}\nTo ensure that the generated sub-claims are of good quality, we manually inspect a random sample of 40 answers and their generated sub-claims (totaling to 120 sub-claims).\nFor each sub-claim, we assign a score of $1$ if it is relevant to the question and faithful to the facts presented in the ground truth, and $0$ otherwise.\nWe found that $112$ out of the $120$ ($93.33\\%$) sub-claims received a score of $1$, meaning that our generated sub-claims are of high quality and faithful to the ground truth.\nFurthermore, the average number of words in the generated sub-claims is $14$ words, and they are typically just one sentence long.\nThis is aligned with the intent behind the metric---to capture short factual claims made by the original answer.\n\n\\paragraph{NLI model accurately predicts the entailment of sub-claims.}\nWe further analyze our sub-claim evaluation metrics by checking the error rate of the final prediction of the NLI model.\nTo this end, we first manually annotate the entailment scores between 40 outputs and their sub-claims (in total of 120 pairs; these are the same questions from the previous analysis). \nWe then use the NLI model to obtain the entailment scores for the output and sub-claims. \nUsing the human annotations as the ground truth label, we found that the NLI model achieved an accuracy of $80.0\\%$. \n\n\n\\section{Dataset Statistics}\n\n\\label{app_sec:dataset_stats}\n\nFor ASQA, human answers have an average length of 65 words.\nFor QAMPARI, each question has on average 13 answers. \nFor ELI5, human answers have an average length of 131 words.\n\n\\section{Implementation Details}\n\\label{app_sec:imp_details}\n\n\\paragraph{NLI model.} \nWe use the version of TRUE model from \\url{https://huggingface.co/google/t5_xxl_true_nli_mixture}, which is trained on \nSNLI~\\citep{bowman2015large_snli},\nMNLI~\\citep{williams2018broad_mnli},\nFever~\\citep{thorne-etal-2018-fever},\nScitail~\\citep{khot2018scitail},\nPAWS~\\citep{zhang-etal-2019-paws},\nand VitaminC~\\citep{schuster-etal-2021-get}.\nThis model uses the following prompt: ``\\ttt{premise: \\{PREMISE\\} hypothesis: \\{\\}}''\nand outputs ``\\texttt{1}'' if the premise entails the hypothesis.\nWe format each passage (when used as premise) \nby the format of ``\\ttt{Title: \\{TITLE\\}\\textbackslash{}n\\{TEXT\\}}'' and concatenate all passages with ``\\ttt{\\textbackslash{}n}'' as a separator. \n\n\n\n\n\\paragraph{MAUVE.} \nWhen running MAUVE, we concatenate the question and the model output (or human answer) by space. \nWe truncate both the references and the model generations to 100 words, \nas we found MAUVE results are unstable beyond this length for ELI5 (this is due to that ELI5 has a lot of extremely long human answers).\n\n\\paragraph{Exact match for ASQA and QAMPARI.}\nBoth ASQA and QAMPARI provide aliases for their short answers. \nWe normalize the response and the short answers similarly to \\citet{rajpurkar-etal-2016-squad}\nand report the score with the best-matching aliases. \nFor ASQA, \\citet{stelmakh2022asqa} also propose a QA-based evaluation which we found to be not as stable, and thus we do not report it in our paper.\n\n\n\n\\paragraph{Output truncation.} \nBefore evaluation,\nwe truncate model output by new lines, \nas non-instruction-tuned models may generate more content after new lines that are irrelevant. \n\n\n\n\\paragraph{\\textbf{\\textsc{\\pinteract{}}}.} \nEmpirically, we found that models tend to execute too many consecutive ``\\ttt{check}'' actions, so we force the model to always ``\\ttt{output}'' after each ``\\ttt{check}''. \nWe limit the maximum number of passages to check as 3 to avoid exceeding the length limit.\nThe full passages are removed from the context after one action to save context space.\nTable~\\ref{tab:inst_inter} provides an example for \\interact{}.\n\n\\paragraph{Main experiments.} \nFor all experiments except ChatGPT \\rerank{},\nwe run each model three times with different seeds and each time we sample two demonstrations from a pool of four.\nWe report the averaged scores for all experiments in the main paper \nand we report the standard deviations in Appendix \\ref{app_sec:more_main}.\n\n\\paragraph{Decoding methods.}\nBased on preliminary experiments we choose the following decoding methods: For ChatGPT and GPT-4, we use sampling with temperature $0.5$; for all open-source models, we use Nucleus sampling~\\citep{Holtzman2020The} and set $\\text{top\\_p}=0.95$. \n\n\n\\section{\\citeeval{} Catches Shortcut Cases}\n\\label{app_sec:short_cut}\n\n\n\\begin{table}[h]\n \\centering\n \\resizebox{0.98\\linewidth}{!}{\n \\begin{tabular}{lcccc}\n \\toprule\n & \\tf{Fluency} & \\tf{Correct.} & \\multicolumn{2}{c}{\\tf{Citation} }\\\\\n \\cmidrule(lr){2-2} \\cmidrule(lr){3-3} \\cmidrule(lr){4-5}\n & (MAUVE) & (EM Rec.) & Rec. & Prec. \\\\\n \\midrule\n ChatGPT & 66.6 & 40.4 & {73.6} & {63.0} \\\\\n Top-1 passage & {\\color{red}20.8} & 35.1 & 99.4 & 99.4\\\\\n First 2 sents & 67.2 & {\\color{red}18.9} & 98.7 & 98.7 \\\\\n \\bottomrule\n \\end{tabular}\n }\n \\caption{\n ASQA cheating cases. ``ChatGPT'': the ChatGPT \\vani{} model with GTR-retrieved top-5 passages. \n ``Top-1 passage'': use the top-1 retrieved passage as the response. \n ``First 2 sents'': use the first 2 sentences of the top-1 retrieved passage.\n }\n \\label{tab:cheat}\n\\end{table}\n\nTable~\\ref{tab:cheat} demonstrates the experiments to show that \\citeeval{}\nis robust to shortcut cases.\nUsing the top-1 passages or first two sentences of the top-1 passages\ninduces almost perfect citation quality, \nbut fluency and correctness are dramatically lower.\n\n\\section{Citation Recall Discussion}\n\\label{app_sec:citation_recall}\n\nOur citation precision evaluation cannot detect a citation that partially supports the statement and hence will falsely penalize it. \nConsider a statement $s_3$ and its citations \\ttt{[2][4][5]}:\nif \\ttt{[2]} entails partial information of $s_3$ that \\ttt{[4][5]} also entails, \n\\ttt{[2]} will be counted as ``irrelevant'' while it should not be penalized.\n\\citet{liu2023evaluating} conduct human evaluation on citation precision in a different way:\nFor each citation,\nthey ask annotators to judge whether the citation (1) fully support, (2) partially support, or (3) does not support $s_i$. \nOne citation $c_{i,j}$ is precise if\n(a) $c_{i,j}$ fully supports $s_i$ or \n(b) $\\mathcal{C}_i$ fully supports $s_i$, $c_{i,j}$ partially supports $s_i$, and no $c\\in \\mathcal{C}_i$ alone fully supports $s_i$.\nThis evaluation solved the corner case we mentioned in the main paper (one citation partially supports the claim but is identified as ``irrelevant'').\nHowever, it is challenging to conduct such evaluation automatically,\nas there is no existing model that can judge whether a citation ``partially'' supports a claim.\nWe also explore prompting ChatGPT to conduct such a task, which yields poor results.\nWe defer it to future work to collect supervised data to train a better $\\phi$ that can detect ``partial support''.\n\n\\section{Human Evaluation}\n\\label{app_sec:human}\n\nWe employ Surge AI (\\url{https://www.surgehq.ai/}) for our human evaluation.\nThe average pay to workers is 20 USD per hour.\nWe randomly sample 100 examples from ASQA and ELI5 and annotate outputs of selected models: ChatGPT \\vani, ChatGPT \\rerank, and Vicuna-13B \\vani.\n\n\n\\subsection{Utility}\nTo check if the model output is useful to downstream users, we measure the utility of the response $\\mathcal{S}$.\nWe first show the query $q$ and model response $\\mathcal{S}$ to the worker and ask them to rate their agreement with \nthe statement \"The response is a helpful and informative answer to the query\" on a Likert scale of 1-5, \ncorresponding to \\textit{Strongly Disagree}, \\textit{Disagree}, \\textit{Neutral}, \\textit{Agree}, and \\textit{Strongly Agree}.\n\n\n\\subsection{Citation Recall}\nThe annotators are shown the question $q$, the statement $s_i$, and all of its citations $\\mathcal{C}_i$, and they rate if the joint set of citations fully support the statement (recall=1) or if they do not support all the claims (recall=0).\nWe calculate the overall recall score\nfor the generation by taking an average of all the statements' recall scores.\n\n\\subsection{Citation Precision}\nWe show the question $q$ and a pair of a statement $s_i$ and one of its citation $c_{i,j} \\in \\mathcal{C}_i$\nto the annotator.\nWe ask the annotator if the citation \\textit{fully supports}, \\textit{partially supports}, or \\textit{does not support} the factual claims in $s_i$.\nCitation $c_{i,j}$ has a citation precision of $1$ if $s_i$ has a recall of $1$, and\n$c_{i,j}$ fully or partially supports $s_i$.\nFinally, we take an average of precision scores of all citations in the statement $\\mathcal{S}$ to obtain the citation precision score.\n\n\n\n\\begin{table}[t]\n \\centering\n \\small\n \\begin{tabular}{@{}lccccc@{}}\n \\toprule\n & R@1 & R@3 & R@5 & R@20 & R@100 \\\\ \\midrule\n DPR & 29.6 & 44.5 & 51.5 & 64.6 & 74.1 \\\\\n GTR & 35.1 & 50.7 & 56.8 & {70.3} & {78.4} \\\\\n Oracle & 63.8 & 72.8 & 78.4 & - & - \\\\\n \\bottomrule\n \\end{tabular}\n \\caption{Retrieval results for ASQA (EM recall).}\n \\label{tab:retrieval-asqa}\n\\end{table}\\begin{table}[t]\n \\centering\n \\small\n \\begin{tabular}{@{}lccccc@{}}\n \\toprule\n & R@1 & R@3 & R@5 & R@20 & R@100 \\\\ \\midrule\n DPR & 6.7 & 13.5 & 17.6 & 30.4 & 47.6 \\\\\n GTR & 14.6 & 24.7 & 31.6 & 49.7 & {65.6} \\\\\n Oracle & 44.3 & 58.7 & 65.6 & - & - \\\\\n \\bottomrule\n \\end{tabular}\n \\caption{Retrieval results for QAMPARI (recall-5).}\n \\label{tab:retrieval-qampari}\n\\end{table}\\begin{table}[t]\n \\centering\n \\small\n \\begin{tabular}{@{}lccccc@{}}\n \\toprule\n & R@1 & R@3 & R@5 & R@20 & R@100 \\\\ \\midrule\n BM25 & 3.0 & 6.6 & 9.6 & 19.3 & 31.8 \\\\\n Oracle & 25.3 & 29.7 & 31.8 & - & - \\\\\n \\bottomrule\n \\end{tabular}\n \\caption{Retrieval results for ELI5 (claim recall).}\n \\label{tab:retrieval-eli5}\n\\end{table}\n\n\n\\section{More Experiments}\n\\label{app_sec:more_exp}\n\n\n\n\\begin{table}[ht]\n \\centering\n \\resizebox{0.98\\linewidth}{!}{\n \\begin{tabular}{lcccc}\n \\toprule\n & \\tf{Fluency} & \\tf{Correct.} & \\multicolumn{2}{c}{\\tf{Citation} }\\\\\n \\cmidrule(lr){2-2} \\cmidrule(lr){3-3} \\cmidrule(lr){4-5}\n & (MAUVE) & (EM Rec.) & Rec. & Prec. \\\\\n \\midrule\n \\headercolor\n \\multicolumn{5}{c}{\\textbf{ChatGPT} (\\vani{}, 5-doc)} \\\\\n Short instruction & 64.1&\t39.5&69.6&\t73.2 \\\\\n Full instruction & 66.6 & {40.4} & {73.6} & 72.5 \\\\\n \\bottomrule\n \\end{tabular}\n }\n \\caption{\n Effect of different instructions on ASQA.\n }\n \\label{tab:inst}\n\\end{table}\n\n\n\\begin{table}[ht]\n \\centering\n \\resizebox{0.98\\linewidth}{!}{\n \\begin{tabular}{lcccc}\n \\toprule\n & \\tf{Fluency} & \\tf{Correct.} & \\multicolumn{2}{c}{\\tf{Citation} }\\\\\n \\cmidrule(lr){2-2} \\cmidrule(lr){3-3} \\cmidrule(lr){4-5}\n & (MAUVE) & (EM) & Rec. & Prec. \\\\\n \\midrule\n \\headercolor\n \\multicolumn{5}{c}{\\textbf{ChatGPT} (\\vani{})} \\\\\n \\#demo = 0& 74.5 & 41.9 & 69.3\t& 73.4 \\\\\n \\#demo = 1& 68.9 & 39.8 & 74.6\t& 73.2 \\\\\n \\#demo = 2& 66.6 & 40.4 & 73.6 & 72.5 \\\\\n \\bottomrule\n \\end{tabular}\n }\n \\caption{\n Different demonstrations on ASQA. \n }\n \\label{tab:numdemo}\n\\end{table}\n\n\\begin{table}[ht]\n \\centering\n \\resizebox{0.98\\linewidth}{!}{\n \\begin{tabular}{lcccc}\n \\toprule\n & \\tf{Fluency} & \\tf{Correct.} & \\multicolumn{2}{c}{\\tf{Citation} }\\\\\n \\cmidrule(lr){2-2} \\cmidrule(lr){3-3} \\cmidrule(lr){4-5}\n & (MAUVE) & (EM Rec.) & Rec. & Prec. \\\\\n \\midrule\n ChatGPT & 66.6 & {40.4} & {73.6} & 72.5 \\\\\n FiD + \\posthoc{} & 75.8 & 28.4 & 58.1 & 58.0 \\\\\n \\bottomrule\n \\end{tabular}\n }\n \\caption{\n Comparison of Fusion-in-Decoder with ChatGPT on ASQA. \n Both models use top-5 GTR passages. \n }\n \\label{tab:fid-asqa}\n\\end{table}\n\n\\begin{table}[h]\n \\centering\n \\resizebox{0.98\\linewidth}{!}{\n \\begin{tabular}{lcccc}\n \\toprule\n & \\tf{Fluency} & \\tf{Correct.} & \\multicolumn{2}{c}{\\tf{Citation} }\\\\\n \\cmidrule(lr){2-2} \\cmidrule(lr){3-3} \\cmidrule(lr){4-5}\n & (MAUVE) & (Claim) & Rec. & Prec. \\\\\n \\midrule\n ChatGPT & 57.2 &\t12.0 &\t51.1&\t{50.0} \\\\\n FiD + \\posthoc{} & 25.2 & 4.4 & 39.3 & 39.3 \\\\\n \\bottomrule\n \\end{tabular}\n }\n \\caption{\n Comparison of Fusion-in-Decoder with ChatGPT on ELI5. \n Both models use top-5 GTR passages.\n }\n \\label{tab:fid-eli5}\n\\end{table}\n\n\n\\subsection{Retrieval Analysis}\n\\label{app_sec:retrieval}\n\n\n\\paragraph{Oracle.}\nSince the original datasets do not contain gold passages\nat the same granularity level as our setting (100-word passages), \nwe approximate gold passages by running the following algorithm on the top-100 retrieved passages.\nWe first calculate the recall score for each passage. %\nThen, we sort the passages using their recall score and take the top 5 passages as our initial oracle set.\nFinally, we iterate through all passages that were not initially in the oracle set and try to replace the passages in the oracle set in a greedy fashion: we calculate the change in the recall score of the oracle set for every possible replacement and proceed with the replacement that results in the largest recall improvement. %\nThe set of 5 oracle passages were able to match the recall scores of the top-100 retrieved passages.\n\n\n\\paragraph{Detailed retrieval results.}\nWe show detailed retrieval results in Tables~\\ref{tab:retrieval-asqa}, \\ref{tab:retrieval-qampari}, and \\ref{tab:retrieval-eli5}.\n\n\n\n\n\n\n\\subsection{Effect of Instructions}\n\\label{sec:inst}\n\nTable~\\ref{tab:inst} shows results of using a full instruction (Table~\\ref{tab:inst_vanilla}) and a short version of the instruction (Table~\\ref{tab:inst_vanilla_light}).\nWe see that the full version induces stronger correctness and citation recall, while the two instructions lead to similar citation precision.\n\n\\subsection{Effect of Demonstrations}\n\n\\label{sec:demo}\n\n\n\nTable~\\ref{tab:numdemo}\nshows results on effect of different numbers of demonstrations.\nWe see that \nnumbers of demonstrations do not affect ChatGPT's correctness but\nusing at least one demonstration\nensures high citation recall.\nFor the original LLaMA model, Table~\\ref{tab:numdemo} shows the trend that more demonstrations lead to better performance.\n\n\\subsection{Fine-tuned Models}\n\\label{sec:fid}\n\n\\revise{\nTo better understand the differences between fine-tuned models and prompted large language models, we train state-of-the-art question answering model, Fusion-in-Decoder (FiD; \\citet{izacard-grave-2021-leveraging}), and evaluate it in conjunction with \\posthoc{}.\nDue to the lack of training data with citation annotation, we first train a T5-base FiD model for 5 epochs on the ASQA training set with a batch size of 64 and a learning rate of 1e-4.\nDuring evaluation, we use \\posthoc{} to add citations to the output. \nWe also use $k=5$ passages during both training and evaluation of the FiD model. \n\nThen, we evaluate this model on both ASQA (in-domain) and ELI5 (out-of-domain), and the results can be found in Tables \\ref{tab:fid-asqa} and \\ref{tab:fid-eli5}.\nNote that this is not a direct comparison, as ALCE assumes only evaluation data available and uses only few-shot data for prompting. \nAs the results show, the FiD baseline still significantly lags behind prompting ChatGPT in both correctness and citation quality (even though it is trained on 4000+ examples). \nWhen tested on another dataset (ELI5), FiD performs even worse, showing that it is challenging to solve the problem by fine-tuning a small pre-trained model. \n}\n \n\\subsection{More Human Evaluation}\n\\label{sec_more_human}\n\n\n\nWe evaluate the accuracy of our automatic metrics by treating the human annotations as gold labels.\nFor citation recall, \\citeeval{} achieves an accuracy of $85.1\\%$; for citation precision, \\citeeval{} has an accuracy of $77.6\\%$.\nRegarding detecting insufficient citations, \\citeeval{} has a recall of $82.3\\%$ and a precision of $84.2\\%$; \nregarding detecting ``irrelevant'' citations, \\citeeval{} has a recall of $75.6\\%$ and a precision of $66.1\\%$---\\citeeval{} is effective in detecting ``irrelevant'' citations, but \ndue to the limitation of the NLI model (cannot detect ``partial support''), it has a relatively high false positive rate.\n\n\\subsection{Main Results}\n\\label{app_sec:more_main}\nWe show full results of our experiments along with the standard deviation \nin Tables \\ref{tab:asqa_full}, \\ref{tab:qampari_full}, and \\ref{tab:eli5_full}.\nWe repeat all experiments with three different random seeds. \nHowever, for ChatGPT \\rerank, we use only one seeded run since each run repeats the generation step four times, and more experiments would incur significant costs. Similarly, due to the cost of running ChatGPT-16K and GPT-4, we only use one seeded run for each model. \n\n\\subsection{Open-source Models}\n\\label{app_sec:open-source-models}\nIn addition to the open-source models discussed in the main text, we also show the results of LLaMA-7B, Alpaca-7B, Vicuna-7B, LLaMA-33B, Oasst-33B, and Stable Beluga 2 in the Tables \\ref{tab:asqa_full}, \\ref{tab:qampari_full}, and \\ref{tab:eli5_full}.\nFor selected models, we also tested them using approaches beyond \\vani{}. \n\nAlthough open-source models generally lag behind the state-of-the-art models (i.e. GPT-4) in both correctness and citation quality, the largest instruction-following models (i.e. LLaMA-2-70B-Chat and Stable Beluga 2) can sometimes achieve competitive correctness to the SoTA. \n\nFurthermore, we found that the open-source models follow a similar trend between different approaches as ChatGPT. Specifically, using \\summ{} and \\snippet{} improves correctness and \\rerank{} boosts citation quality. \n\n\n\\begin{table*}[h]\n \\centering\n \\small\n \\begin{tabular}{lcccccc}\n \\toprule\n & \\tf{Fluency} & \\tf{Correct.} & \\multicolumn{2}{c}{\\tf{Citation} }\\\\\n \\cmidrule(lr){2-2} \\cmidrule(lr){3-3} \\cmidrule(lr){4-5}\n & (MAUVE) & (EM Rec.) & Rec. & Prec. & ROUGE-L & Length \\\\\n \\midrule\n \\headercolorlong\n \\multicolumn{7}{c}{\\textbf{ChatGPT}} \\\\\n \\tableskip\n\n \\vani{} (5-psg) & 66.8 $_{(2.0)}$ & 40.4 $_{(0.6)}$ & 73.6 $_{(1.1)}$ & 72.5 $_{(1.8)}$ & 37.0 $_{(0.4)}$ & 40.0 $_{(3.1)}$ \\\\\n ~~w/ \\rerank{} & 77.0 $_{(-)}$ & 40.2 $_{(-)}$ & 84.8 $_{(-)}$ & 81.6 $_{(-)}$ & 36.9 $_{(-)}$ & 40.8 $_{(-)}$ \\\\\n \\summ{} (10-psg) & 70.0 $_{(1.2)}$ & 43.3 $_{(0.8)}$ & 68.8 $_{(0.6)}$ &61.8 $_{(1.1)}$ & 36.9 $_{(0.2)}$ & 49.8 $_{(4.3)}$ \\\\\n ~~w/ \\interact{} & 69.0 $_{(2.7)}$ & 39.1 $_{(0.5)}$ & 73.4 $_{(0.2)}$ & 66.5 $_{(4.9)}$ & 35.7 $_{(0.2)}$ & 34.0 $_{(0.9)}$ \\\\\n \\snippet{} (10-psg) & 69.8 $_{(2.5)}$ & 41.4 $_{(0.6)}$ & 65.3 $_{(0.6)}$ & 57.4 $_{(0.9)}$ & 36.4 $_{(0.4)}$ & 43.0 $_{(3.5)}$ \\\\\n \\search{} & 58.7 $_{(1.3)}$ & 32.4 $_{(0.6)}$ & 58.3 $_{(1.3)}$ & 58.3 $_{(1.3)}$ & 58.2 $_{(1.1)}$ & 23.7 $_{(1.1)}$ \\\\\n \\close{} & 52.7 $_{(4.9)}$ & 38.2 $_{(0.1)}$ & 26.7 $_{(1.1)}$ & 26.7 $_{(1.1)}$ & 37.1 $_{(0.3)}$ & 61.1 $_{(4.5)}$ \\\\\n \\oracle (5-psg) & 64.4 $_{(0.6)}$ & 48.9 $_{(1.2)}$ & 74.5 $_{(0.6)}$ & 72.7 $_{(1.0)}$ & 38.2 $_{(1.0)}$ & 37.4 $_{(3.0)}$ \\\\\n\n\n \\headercolorlong\n \\tableskip\n \\multicolumn{7}{c}{\\textbf{ChatGPT-16K}} \\\\\n \\tableskip\n \\vani{} (5-psg) & 60.3 $_{(-)}$&36.1 $_{(-)}$&76.2 $_{(-)}$&76.5 $_{(-)}$&36.2 $_{(-)}$&24.7 $_{(-)}$\\\\\n \\vani{} (10-psg) & 56.3 $_{(-)}$&36.7 $_{(-)}$&75.3 $_{(-)}$&75.0 $_{(-)}$&35.6 $_{(-)}$&23.5 $_{(-)}$\\\\\n \\vani{} (20-psg) & 56.7 $_{(-)}$&36.1 $_{(-)}$&73.7 $_{(-)}$&73.5 $_{(-)}$&35.5 $_{(-)}$&23.1 $_{(-)}$\\\\\n\n \\headercolorlong\n \\tableskip\n \\multicolumn{7}{c}{\\textbf{GPT-4}} \\\\\n \\tableskip\n\n \\vani{} (5-psg) & 67.1 $_{(-)}$&41.3 $_{(-)}$&68.5 $_{(-)}$&75.6 $_{(-)}$&39.2 $_{(-)}$&31.8 $_{(-)}$\\\\\n \\vani{} (10-psg) & 71.5 $_{(-)}$&43.1 $_{(-)}$&72.0 $_{(-)}$&75.5 $_{(-)}$&39.7 $_{(-)}$&33.8 $_{(-)}$\\\\\n \\vani{} (20-psg) & 64.9 $_{(-)}$&44.4 $_{(-)}$&73.0 $_{(-)}$&76.5 $_{(-)}$&40.1 $_{(-)}$&34.3 $_{(-)}$\\\\\n\n \\headercolorlong\n \\tableskip\n \\multicolumn{7}{c}{\\textbf{Open-source} } \\\\\n \\tableskip\n LLaMA-7B \\vani{} (3-psg) & 69.8 $_{(2.0)}$ & 22.6 $_{(0.9)}$ & 6.2 $_{(2.7)}$ & 9.2 $_{(2.9)}$ & 29.1 $_{(0.2)}$ & 61.3 $_{(14.3)}$ \\\\\n Alpaca-7B \\vani{} (3-psg) & 84.2 $_{(2.7)}$ & 32.1 $_{(1.7)}$ & 12.3 $_{(7.2)}$ & 14.1 $_{(7.0)}$ & 33.1 $_{(0.8)}$ & 51.7 $_{(12.8)}$ \\\\\n Vicuna-7B \\vani{} (3-psg) & 82.9 $_{(5.0)}$ & 34.6 $_{(0.7)}$ & 40.3 $_{(0.5)}$ & 42.6 $_{(1.0)}$ & 35.9 $_{(0.7)}$ & 48.9 $_{(6.6)}$ \\\\ \\midrule\n LLaMA-13B \\vani{} (3-psg) & 68.4 $_{(6.4)}$ & 26.9 $_{(0.4)}$ & 10.6 $_{(4.7)}$ & 15.4 $_{(5.2)}$ & 29.8 $_{(0.5)}$ & 67.1 $_{(19.1)}$ \\\\\n ~~w/ \\rerank & 60.9 $_{(14.5)}$ & 25.2 $_{(2.5)}$ & 28.1 $_{(9.3)}$ & 37.0 $_{(7.2)}$ & 27.9 $_{(2.4)}$ & 50.5 $_{(14.3)}$ \\\\\n LLaMA-13B \\summ{} (10-psg) & 76.8 $_{(4.7)}$ & 33.3 $_{(0.7)}$ & 19.6 $_{(3.9)}$ & 23.7 $_{(4.7)}$ & 32.1 $_{(0.3)}$ & 54.4 $_{(1.5)}$ \\\\\n LLaMA-13B \\snippet{} (10-psg) & 72.0 $_{(0.8)}$ & 31.3 $_{(1.1)}$ & 18.2 $_{(3.1)}$ & 21.1 $_{(3.6)}$ & 30.8 $_{(0.4)}$ & 50.5 $_{(4.5)}$ \\\\\n LLaMA-13B \\oracle{} (3-psg) & 69.5 $_{(11.4)}$ & 34.3 $_{(0.9)}$ & 10.8 $_{(4.9)}$ & 15.8 $_{(5.9)}$ & 30.6 $_{(0.1)}$ & 67.3 $_{(17.9)}$ \\\\ \\midrule\n Vicuna-13B \\vani{} (3-psg) & 82.6 $_{(9.4)}$ & 31.9 $_{(3.9)}$ & 51.1 $_{(1.4)}$ & 50.1 $_{(2.5)}$ & 34.9 $_{(1.3)}$ & 39.1 $_{(6.6)}$ \\\\\n ~~w/ \\rerank & 73.5 $_{(2.1)}$ & 32.9 $_{(1.3)}$ & 71.9 $_{(1.9)}$ & 65.4 $_{(1.5)}$ & 34.6 $_{(0.3)}$ & 35.7 $_{(4.2)}$ \\\\\n Vicuna-13B \\summ{} (10-psg) & 67.7 $_{(0.3)}$ & 43.2 $_{(0.1)}$ & 52.7 $_{(2.6)}$ & 50.0 $_{(2.1)}$ & 36.7 $_{(0.2)}$ & 66.0 $_{(1.2)}$ \\\\\n Vicuna-13B \\snippet{} (10-psg) & 81.4 $_{(3.0)}$ & 42.1 $_{(1.2)}$ & 53.4 $_{(1.9)}$ & 48.7 $_{(1.6)}$ & 36.9 $_{(0.4)}$ & 61.2 $_{(7.4)}$ \\\\\n Vicuna-13B \\oracle{} (3-psg) & 72.9 $_{(3.5)}$ & 42.5 $_{(1.6)}$ & 52.2 $_{(0.8)}$ & 50.7 $_{(1.6)}$ & 36.5 $_{(0.9)}$ & 38.7 $_{(3.5)}$ \\\\ \\midrule\n LLaMA-33B \\vani{} (3-psg) & 83.7 $_{(5.4)}$ & 31.0 $_{(0.8)}$ & 19.5 $_{(5.3)}$ & 23.0 $_{(5.3)}$ & 32.3 $_{(0.6)}$ & 44.1 $_{(9.3)}$ \\\\\n ~~w/ \\rerank & 82.1 $_{(3.0)}$ & 31.3 $_{(1.1)}$ & 41.3 $_{(6.4)}$ & 44.7 $_{(5.5)}$ & 32.5 $_{(0.9)}$ & 39.4 $_{(8.0)}$ \\\\\n LLaMA-33B \\summ{} (10-psg) & 72.0 $_{(3.0)}$ & 33.1 $_{(1.9)}$ & 34.7 $_{(5.8)}$ & 35.2 $_{(6.0)}$ & 31.1 $_{(0.8)}$ & 43.7 $_{(5.0)}$ \\\\\n LLaMA-33B \\snippet{} (10-psg) & 70.8 $_{(3.1)}$ & 30.9 $_{(1.4)}$ & 31.4 $_{(4.2)}$ & 31.5 $_{(5.3)}$ & 30.1 $_{(0.7)}$ & 42.8 $_{(3.6)}$ \\\\\n LLaMA-33B \\oracle{} (3-psg) & 82.6 $_{(7.1)}$ & 39.3 $_{(2.9)}$ & 20.2 $_{(6.2)}$ & 23.9 $_{(6.3)}$ & 33.1 $_{(0.9)}$ & 42.0 $_{(9.3)}$ \\\\ \\midrule\n Oasst-33B \\vani{} (3-psg) & 82.9 $_{(2.7)}$ & 34.8 $_{(1.5)}$ & 36.2 $_{(1.7)}$ & 38.3 $_{(2.7)}$ & 35.5 $_{(0.7)}$ & 45.2 $_{(6.3)}$ \\\\\n ~~w/ \\rerank & 83.2 $_{(2.4)}$ & 35.1 $_{(1.4)}$ & 66.7 $_{(0.2)}$ & 64.3 $_{(1.0)}$ & 35.0 $_{(0.6)}$ & 41.8 $_{(6.0)}$ \\\\\n Oasst-33B \\summ{} (10-psg) & 74.3 $_{(4.6)}$ & 40.9 $_{(1.1)}$ & 45.5 $_{(1.9)}$ & 44.0 $_{(2.9)}$ & 35.8 $_{(0.6)}$ & 54.3 $_{(4.8)}$ \\\\\n Oasst-33B \\snippet{} (10-psg) & 79.3 $_{(1.0)}$ & 40.1 $_{(0.9)}$ & 45.0 $_{(1.3)}$ & 43.3 $_{(2.2)}$ & 35.8 $_{(0.2)}$ & 50.9 $_{(4.1)}$ \\\\\n Oasst-33B \\oracle{} (3-psg) & 85.1 $_{(2.8)}$ & 44.3 $_{(2.4)}$ & 37.0 $_{(1.0)}$ & 39.6 $_{(1.5)}$ & 36.5 $_{(1.1)}$ & 44.2 $_{(5.8)}$ \\\\ \n \\midrule\n LLaMA-2-7B-Chat \\vani{} (5-psg) & 80.1 $_{(6.5)}$ & 33.9 $_{(2.1)}$ & 50.9 $_{(4.5)}$ & 47.5 $_{(3.7)}$ & 35.1 $_{(0.9)}$ & 42.3 $_{(10.1)}$\\\\\n LLaMA-2-13B-Chat \\vani{} (5-psg) & 72.4 $_{(6.3)}$ & 35.2 $_{(1.2)}$ & 38.4 $_{(5.9)}$ & 39.4 $_{(4.8)}$ & 35.8 $_{(0.9)}$ & 38.0 $_{(6.4)}$\\\\\n LLaMA-2-70B-Chat \\vani{} (5-psg) & 88.3 $_{(4.1)}$ & 41.5 $_{(0.8)}$ & 62.9 $_{(1.4)}$ & 61.3 $_{(2.1)}$ & 37.1 $_{(0.4)}$ & 52.9 $_{(9.5)}$\\\\\n \\midrule\n Stable Beluga 2 \\vani{} (5-psg) & 59.7 $_{(3.3)}$&37.2 $_{(1.0)}$&63.6 $_{(0.7)}$&63.5 $_{(0.1)}$&35.3 $_{(0.7)}$&23.8 $_{(3.2)}$\\\\\n \\bottomrule\n \\end{tabular}\n \\caption{\n ASQA full results. %\n }\n \\label{tab:asqa_full}\n\\end{table*}\n\n\\begin{table*}[ht]\n \\centering\n \\small\n \\begin{tabular}{lccccc}\n \\toprule\n & \\multicolumn{2}{c}{\\tf{Correctness}} & \\multicolumn{2}{c}{\\tf{Citation} }\\\\\n \\cmidrule(lr){2-3} \\cmidrule(lr){4-5}\n & Rec.-5 & Prec. & Rec. & Prec. & Num Pred. \\\\\n \\midrule\n \\rowcolor{gray!16}\n \\multicolumn{6}{c}{\\textbf{ChatGPT}} \\\\\n \\tableskip\n \\vani{} (5-psg) & 20.8 $_{(2.2)}$ & 20.8 $_{(0.2)}$ & 20.5 $_{(0.7)}$ & 20.9 $_{(0.7)}$ & 5.0 $_{(0.5)}$ \\\\\n ~~w/ \\rerank{} & 22.8 $_{(-)}$ & 21.4 $_{(-)}$ & 21.2 $_{(-)}$ & 21.4 $_{(-)}$ & 5.4 $_{(-)}$ \\\\\n \\summ{} (10-psg) & 23.6 $_{(0.9)}$ & 21.2 $_{(0.5)}$ & 23.6 $_{(0.7)}$ & 25.7 $_{(0.8)}$ & 6.7 $_{(0.4)}$ \\\\\n \\snippet{} (10-psg) & 24.5 $_{(1.4)}$ & 21.5 $_{(1.8)}$ & 22.9 $_{(1.6)}$ & 24.9 $_{(0.4)}$ & 7.2 $_{(0.9)}$ \\\\\n ~~w/ \\interact{} & 21.9 $_{(0.9)}$ & 23.0 $_{(0.4)}$ & 21.9 $_{(1.2)}$ & 23.4 $_{(0.9)}$ & 6.7 $_{(0.4)}$ \\\\\n \\search{} & 17.2 $_{(1.1)}$ & 20.4 $_{(0.8)}$ & 14.9 $_{(0.8)}$ & 14.9 $_{(0.8)}$ & 6.7 $_{(0.2)}$ \\\\\n \\close{} & 32.9 $_{(1.1)}$ & 19.8 $_{(1.6)}$ & 10.0 $_{(0.4)}$ & 10.0 $_{(0.4)}$ & 17.0 $_{(2.9)}$ \\\\\n \\oracle{} & 37.0 $_{(3.1)}$ & 36.9 $_{(0.6)}$ & 24.1 $_{(1.2)}$ & 24.6 $_{(1.3)}$ & 5.3 $_{(0.6)}$ \\\\\n\n\n\n \\headercolorlong\n \\tableskip\n \\multicolumn{6}{c}{\\textbf{ChatGPT-16K}} \\\\\n \\tableskip\n \\vani{} (5-psg) & 21.1 $_{(-)}$&22.0 $_{(-)}$&20.7 $_{(-)}$&21.2 $_{(-)}$&4.9 $_{(-)}$\\\\ \n \\vani{} (10-psg) & 23.4 $_{(-)}$&21.9 $_{(-)}$&21.6 $_{(-)}$&22.0 $_{(-)}$&5.7 $_{(-)}$\\\\\n \\vani{} (20-psg) & 26.4 $_{(-)}$&21.1 $_{(-)}$&19.4 $_{(-)}$&19.7 $_{(-)}$&7.6 $_{(-)}$\\\\\n\n \\headercolorlong\n \\tableskip\n \\multicolumn{6}{c}{\\textbf{GPT-4}} \\\\\n \\tableskip\n\n \\vani{} (5-psg) & 22.2 $_{(-)}$&25.0 $_{(-)}$&25.9 $_{(-)}$&27.0 $_{(-)}$&4.4 $_{(-)}$\\\\\n \\vani{} (10-psg) & 26.8 $_{(-)}$&25.1 $_{(-)}$&26.2 $_{(-)}$&27.2 $_{(-)}$&5.7 $_{(-)}$\\\\\n \\vani{} (20-psg) & 29.6 $_{(-)}$&26.2 $_{(-)}$&27.4 $_{(-)}$&28.5 $_{(-)}$&6.8 $_{(-)}$\\\\\n\n\n \\rowcolor{gray!16}\n \\multicolumn{6}{c}{\\textbf{Open-source}} \\\\\n LLaMA-7B \\vani{} (3-psg) & 7.8 $_{(3.4)}$ & 7.4 $_{(2.7)}$ & 5.1 $_{(0.5)}$ & 5.7 $_{(0.8)}$ & 5.7 $_{(0.6)}$ \\\\\n Alpaca-7B \\vani{} (3-psg) & 9.4 $_{(3.7)}$ & 9.5 $_{(3.6)}$ & 6.4 $_{(0.5)}$ & 6.8 $_{(0.5)}$ & 5.1 $_{(0.1)}$ \\\\\n Vicuna-7B \\vani{} (3-psg) & 11.3 $_{(1.4)}$ & 13.3 $_{(2.3)}$ & 10.1 $_{(0.6)}$ & 10.9 $_{(0.5)}$ & 3.9 $_{(0.3)}$ \\\\ \\midrule\n LLaMA-13B \\vani{} (3-psg) & 9.7 $_{(3.6)}$ & 9.1 $_{(3.1)}$ & 6.7 $_{(0.9)}$ & 7.1 $_{(0.9)}$ & 5.9 $_{(0.6)}$ \\\\\n ~~w/ \\rerank & 10.0 $_{(3.3)}$ & 10.7 $_{(3.3)}$ & 9.9 $_{(1.2)}$ & 10.2 $_{(1.1)}$ & 5.4 $_{(0.5)}$ \\\\\n LLaMA-13B \\summ{} (10-psg) & 14.8 $_{(2.5)}$ & 12.6 $_{(1.5)}$ & 7.4 $_{(0.5)}$ & 8.0 $_{(0.6)}$ & 8.1 $_{(0.9)}$ \\\\\n LLaMA-13B \\snippet{} (10-psg) & 17.7 $_{(1.4)}$ & 15.7 $_{(0.9)}$ & 8.8 $_{(0.7)}$ & 9.9 $_{(0.6)}$ & 8.2 $_{(0.4)}$ \\\\\n LLaMA-13B \\oracle{} (3-psg) & 16.8 $_{(6.6)}$ & 15.4 $_{(5.6)}$ & 7.7 $_{(1.0)}$ & 8.3 $_{(1.1)}$ & 5.7 $_{(0.7)}$ \\\\ \\midrule\n Vicuna-13B \\vani{} (3-psg) & 14.0 $_{(0.6)}$ & 15.9 $_{(1.7)}$ & 12.5 $_{(0.8)}$ & 13.4 $_{(0.7)}$ & 4.7 $_{(0.3)}$ \\\\\n ~~w/ \\rerank & 13.0 $_{(0.7)}$ & 17.2 $_{(2.2)}$ & 17.3 $_{(0.8)}$ & 17.7 $_{(0.6)}$ & 4.4 $_{(0.3)}$ \\\\\n Vicuna-13B \\summ{} (10-psg) & 21.1 $_{(1.4)}$ & 17.1 $_{(0.3)}$ & 15.7 $_{(0.2)}$ & 17.8 $_{(0.1)}$ & 6.9 $_{(0.7)}$ \\\\\n Vicuna-13B \\snippet{} (10-psg) & 21.9 $_{(0.8)}$ & 18.2 $_{(0.3)}$ & 16.8 $_{(0.3)}$ & 19.7 $_{(0.6)}$ & 7.5 $_{(0.4)}$ \\\\\n Vicuna-13B \\oracle{} (3-psg) & 25.9 $_{(1.6)}$ & 28.4 $_{(2.6)}$ & 15.8 $_{(1.4)}$ & 16.8 $_{(1.4)}$ & 4.9 $_{(0.5)}$ \\\\ \\midrule\n LLaMA-33B \\vani{} (3-psg) & 14.7 $_{(3.3)}$ & 12.0 $_{(2.2)}$ & 7.9 $_{(0.7)}$ & 8.3 $_{(0.6)}$ & 7.2 $_{(0.7)}$ \\\\\n ~~w/ \\rerank & 14.0 $_{(3.4)}$ & 13.9 $_{(2.6)}$ & 10.7 $_{(0.6)}$ & 11.1 $_{(0.5)}$ & 6.4 $_{(0.7)}$ \\\\\n LLaMA-33B \\summ{} (10-psg) & 19.0 $_{(1.9)}$ & 14.8 $_{(0.8)}$ & 12.5 $_{(0.2)}$ & 15.0 $_{(0.3)}$ & 7.6 $_{(0.6)}$ \\\\\n LLaMA-33B \\snippet{} (10-psg) & 19.6 $_{(1.1)}$ & 15.7 $_{(0.1)}$ & 12.8 $_{(1.1)}$ & 15.2 $_{(1.2)}$ & 7.8 $_{(0.5)}$ \\\\\n LLaMA-33B \\oracle{} (3-psg) & 23.9 $_{(6.9)}$ & 20.3 $_{(5.2)}$ & 9.8 $_{(1.2)}$ & 10.4 $_{(1.2)}$ & 6.8 $_{(0.9)}$ \\\\ \\midrule\n Oasst-33B \\vani{} (3-psg) & 15.5 $_{(1.5)}$ & 14.9 $_{(1.4)}$ & 9.0 $_{(1.6)}$ & 10.1 $_{(1.8)}$ & 5.6 $_{(0.3)}$ \\\\\n ~~w/ \\rerank & 14.1 $_{(1.1)}$ & 15.8 $_{(1.0)}$ & 15.0 $_{(1.6)}$ & 15.9 $_{(1.6)}$ & 4.7 $_{(0.3)}$ \\\\\n Oasst-33B \\summ{} (10-psg) & 21.0 $_{(0.6)}$ & 17.5 $_{(1.0)}$ & 12.9 $_{(1.2)}$ & 16.6 $_{(1.2)}$ & 7.1 $_{(0.4)}$ \\\\\n Oasst-33B \\snippet{} (10-psg) & 22.0 $_{(0.4)}$ & 17.4 $_{(0.3)}$ & 13.6 $_{(1.7)}$ & 17.7 $_{(1.6)}$ & 7.5 $_{(0.1)}$ \\\\\n Oasst-33B \\oracle{} (3-psg) & 26.9 $_{(3.7)}$ & 26.0 $_{(3.3)}$ & 11.7 $_{(1.0)}$ & 12.9 $_{(1.2)}$ & 5.6 $_{(0.4)}$ \\\\ \n \\midrule\n LLaMA-2-7B-Chat \\vani{} (5-psg) & 16.2 $_{(1.3)}$&15.3 $_{(1.6)}$&10.6 $_{(0.9)}$&10.9 $_{(1.0)}$&5.5 $_{(0.0)}$\\\\\n LLaMA-2-13B-Chat \\vani{} (5-psg) & 21.1 $_{(0.9)}$&18.2 $_{(0.5)}$&9.6 $_{(1.5)}$&9.7 $_{(1.5)}$&6.5 $_{(0.3)}$\\\\\n LLaMA-2-70B-Chat \\vani{} (5-psg) & 21.8 $_{(0.7)}$&18.4 $_{(0.1)}$&15.1 $_{(1.2)}$&15.6 $_{(1.3)}$&7.1 $_{(0.2)}$\\\\\n \\midrule\n Stable Beluga 2 & 19.7 $_{(0.7)}$&21.2 $_{(1.4)}$&18.6 $_{(1.2)}$&20.7 $_{(1.1)}$&5.1 $_{(0.3)}$\\\\\n \\bottomrule\n \n \\end{tabular}\n \\caption{\n QAMPARI full results.\n }\n \\label{tab:qampari_full}\n\\end{table*}\n\n\\begin{table*}[h]\n \\centering\n \\small\n \\begin{tabular}{lcccccc}\n \\toprule\n & \\tf{Fluency} & \\tf{Correct.} & \\multicolumn{2}{c}{\\tf{Citation} }\\\\\n \\cmidrule(lr){2-2} \\cmidrule(lr){3-3} \\cmidrule(lr){4-5}\n & (MAUVE) & (Claim) & Rec. & Prec. & ROUGE-L & Length \\\\\n \\midrule\n \\headercolorlong\n \\multicolumn{7}{c}{\\textbf{ChatGPT}} \\\\\n\n \\vani{} (5-psg) & 57.2 $_{(1.6)}$ & 12.0 $_{(0.6)}$ & 51.1 $_{(4.2)}$ & 50.0 $_{(4.8)}$ & 20.6 $_{(0.2)}$ & 91.5 $_{(6.5)}$ \\\\\n ~~w/ \\rerank{} & 56.1 $_{(-)}$ & 11.4 $_{(-)}$ & 69.3 $_{(-)}$ & 67.8 $_{(-)}$ & 20.3 $_{(-)}$ & 103.4 $_{(-)}$ \\\\\n \\summ{} (10-psg)& 40.2 $_{(1.2)}$ & 12.5 $_{(0.2)}$ & 51.5 $_{(1.1)}$ & 48.2 $_{(2.0)}$ & 20.3 $_{(0.1)}$ & 90.0 $_{(6.6)}$ \\\\\n \\snippet{} (10-psg) & 62.9 $_{(2.2)}$ & 14.3 $_{(0.1)}$ & 50.4 $_{(1.1)}$ & 45.0 $_{(2.w)}$ & 21.0 $_{(0.1)}$ & 100.0 $_{(6.8)}$ \\\\\n ~~w/ \\interact{} & 68.0 $_{(5.8)}$ & 13.3 $_{(0.5)}$ & 47.8 $_{(3.3)}$ & 45.0 $_{(3.1)}$ & 20.1 $_{(0.2)}$ & 99.8 $_{(6.1)}$ \\\\\n \\search{} & 49.7 $_{(4.6)}$ & 13.4 $_{(1.1)}$ & 45.6 $_{(2.5)}$ &43.7 $_{(3.9)}$ & 20.4 $_{(0.3)}$ & 103.0 $_{(18.1)}$ \\\\\n \\close{} & 32.6 $_{(1.1)}$ & 18.6 $_{(0.5)}$ & 15.4 $_{(0.3)}$ & 15.4 $_{(0.3)}$ & 22.8 $_{(0.1)}$ & 108.3 $_{(8.9)}$ \\\\\n \\oracle{} (5-psg) & 59.4 $_{(4.1)}$ & 21.3 $_{(0.2)}$ & 57.8 $_{(3.7)}$ & 56.0 $_{(3.8)}$ & 21.2 $_{(0.3)}$ & 93.0 $_{(7.8)}$ \\\\\n\n\n\n\n \\headercolorlong\n \\multicolumn{7}{c}{\\textbf{ChatGPT-16K}} \\\\\n \\tableskip\n \\vani{} (5-psg) & 31.6 $_{(-)}$&14.4 $_{(-)}$&44.6 $_{(-)}$&44.1 $_{(-)}$&21.4 $_{(-)}$&87.6 $_{(-)}$\\\\ \n \\vani{} (10-psg) & 26.6 $_{(-)}$&14.4 $_{(-)}$&45.5 $_{(-)}$&43.3 $_{(-)}$&21.5 $_{(-)}$&87.5 $_{(-)}$\\\\\n \\vani{} (20-psg) & 31.6 $_{(-)}$&15.9 $_{(-)}$&43.4 $_{(-)}$&40.9 $_{(-)}$&21.7 $_{(-)}$&92.6 $_{(-)}$\\\\\n\n \\headercolorlong\n \\tableskip\n \\multicolumn{7}{c}{\\textbf{GPT-4}} \\\\\n \\tableskip\n\n \\vani{} (5-psg) & 38.4 $_{(-)}$&14.2 $_{(-)}$&44.0 $_{(-)}$&50.1 $_{(-)}$&20.6 $_{(-)}$&79.6 $_{(-)}$\\\\\n \\vani{} (10-psg) & 39.9 $_{(-)}$&15.7 $_{(-)}$&49.5 $_{(-)}$&54.2 $_{(-)}$&21.2 $_{(-)}$&88.2 $_{(-)}$\\\\\n \\vani{} (20-psg) & 41.5 $_{(-)}$&18.3 $_{(-)}$&48.5 $_{(-)}$&53.4 $_{(-)}$&22.2 $_{(-)}$&97.0 $_{(-)}$\\\\\n\n \\headercolorlong\n \\tableskip\n \\multicolumn{7}{c}{\\textbf{Open-source} } \\\\\n \\tableskip\n LLaMA-7B \\vani{} (3-psg) & 28.6 $_{(17.9)}$ & 1.6 $_{(0.9)}$ & 1.2 $_{(0.0)}$ & 2.7 $_{(0.1)}$ & 12.2 $_{(1.3)}$ & 46.9 $_{(1.2)}$ \\\\\n Alpaca-7B \\vani{} (3-psg) & 45.9 $_{(5.3)}$ & 9.2 $_{(0.1)}$ & 4.5 $_{(1.6)}$ & 5.2 $_{(1.9)}$ & 18.8 $_{(0.3)}$ & 67.1 $_{(1.2)}$ \\\\\n Vicuna-7B \\vani{} (3-psg) & 43.2 $_{(3.9)}$ & 10.0 $_{(0.5)}$ & 12.6 $_{(2.3)}$ & 16.3 $_{(2.6)}$ & 19.1 $_{(0.4)}$ & 68.7 $_{(2.0)}$ \\\\ \\midrule\n LLaMA-13B \\vani{} (3-psg) & 50.0 $_{(2.0)}$ & 3.9 $_{(0.4)}$ & 3.1 $_{(0.9)}$ & 5.3 $_{(1.3)}$ & 16.1 $_{(0.5)}$ & 63.3 $_{(2.0)}$ \\\\\n ~~w/ \\rerank & 46.7 $_{(2.9)}$ & 4.3 $_{(0.4)}$ & 9.7 $_{(2.1)}$ & 15.0 $_{(2.2)}$ & 16.1 $_{(0.7)}$ & 63.0 $_{(2.3)}$ \\\\\n LLaMA-13B \\summ{} (10-psg) & 28.6 $_{(1.8)}$ & 2.9 $_{(0.1)}$ & 2.5 $_{(0.8)}$ & 3.8 $_{(0.8)}$ & 8.5 $_{(0.3)}$ & 33.1 $_{(0.6)}$ \\\\\n LLaMA-13B \\snippet{} (10-psg) & 48.4 $_{(3.1)}$ & 5.7 $_{(0.9)}$ & 5.8 $_{(0.6)}$ & 7.6 $_{(0.9)}$ & 15.1 $_{(1.1)}$ & 60.2 $_{(3.2)}$ \\\\\n LLaMA-13B \\oracle{} (3-psg) & 49.5 $_{(2.4)}$ & 6.4 $_{(0.6)}$ & 3.7 $_{(0.7)}$ & 6.5 $_{(1.0)}$ & 16.8 $_{(0.5)}$ & 64.5 $_{(1.7)}$ \\\\ \\midrule\n Vicuna-13B \\vani{} (3-psg) & 58.2 $_{(25.1)}$ & 10.0 $_{(0.3)}$ & 15.6 $_{(2.2)}$ & 19.6 $_{(2.0)}$ & 19.1 $_{(0.3)}$ & 69.6 $_{(0.6)}$ \\\\\n ~~w/ \\rerank & 45.9 $_{(4.3)}$ & 9.2 $_{(0.0)}$ & 31.7 $_{(2.9)}$ & 38.2 $_{(1.6)}$ & 18.6 $_{(0.5)}$ & 69.7 $_{(1.0)}$ \\\\\n Vicuna-13B \\summ{} (10-psg) & 22.4 $_{(3.0)}$ & 4.9 $_{(0.1)}$ & 9.7 $_{(1.3)}$ & 12.2 $_{(1.2)}$ & 9.3 $_{(0.4)}$ & 33.0 $_{(3.7)}$ \\\\\n Vicuna-13B \\snippet{} (10-psg) & 48.1 $_{(5.3)}$ & 11.2 $_{(1.4)}$ & 27.2 $_{(3.6)}$ & 27.9 $_{(1.9)}$ & 18.4 $_{(1.9)}$ & 76.8 $_{(8.7)}$ \\\\\n Vicuna-13B \\oracle{} (3-psg) & 41.6 $_{(3.1)}$ & 17.1 $_{(0.4)}$ & 20.2 $_{(3.0)}$ & 26.5 $_{(3.0)}$ & 20.0 $_{(0.3)}$ & 72.0 $_{(0.3)}$ \\\\ \\midrule\n LLaMA-33B \\vani{} (3-psg) & 58.8 $_{(4.3)}$ & 6.2 $_{(0.0)}$ & 9.3 $_{(3.0)}$ & 12.1 $_{(4.2)}$ & 16.9 $_{(0.2)}$ & 60.0 $_{(1.3)}$ \\\\\n ~~w/ \\rerank & 65.9 $_{(2.5)}$ & 6.0 $_{(0.7)}$ & 22.5 $_{(5.2)}$ & 26.1 $_{(6.9)}$ & 17.5 $_{(0.4)}$ & 61.0 $_{(1.2)}$ \\\\\n LLaMA-33B \\summ{} (10-psg) & 23.3 $_{(2.0)}$ & 3.0 $_{(0.2)}$ & 6.2 $_{(0.5)}$ & 8.2 $_{(0.7)}$ & 7.5 $_{(0.4)}$ & 26.2 $_{(2.3)}$ \\\\\n LLaMA-33B \\snippet{} (10-psg) & 53.2 $_{(4.0)}$ & 7.4 $_{(1.3)}$ & 13.7 $_{(0.5)}$ & 15.1 $_{(0.4)}$ & 14.4 $_{(1.7)}$ & 53.3 $_{(8.5)}$ \\\\\n LLaMA-33B \\oracle{} (3-psg) & 63.7 $_{(2.8)}$ & 11.4 $_{(0.5)}$ & 11.9 $_{(2.6)}$ & 15.4 $_{(3.6)}$ & 17.9 $_{(0.2)}$ & 61.7 $_{(2.6)}$ \\\\ \\midrule\n Oasst-33B \\vani{} (3-psg) & 46.8 $_{(7.6)}$ & 9.5 $_{(0.2)}$ & 16.0 $_{(2.5)}$ & 21.6 $_{(3.5)}$ & 18.6 $_{(0.3)}$ & 67.8 $_{(1.5)}$ \\\\\n ~~w/ \\rerank & 52.1 $_{(6.1)}$ & 8.5 $_{(0.5)}$ & 34.4 $_{(2.9)}$ & 41.5 $_{(2.5)}$ & 18.2 $_{(0.3)}$ & 67.0 $_{(1.5)}$ \\\\\n Oasst-33B \\summ{} (10-psg) & 24.8 $_{(2.8)}$ & 3.9 $_{(0.3)}$ & 12.3 $_{(0.2)}$ & 16.3 $_{(0.3)}$ & 9.1 $_{(0.3)}$ & 31.6 $_{(1.5)}$ \\\\\n Oasst-33B \\snippet{} (10-psg) & 50.7 $_{(4.6)}$ & 10.7 $_{(1.2)}$ & 25.8 $_{(3.3)}$ & 26.7 $_{(2.3)}$ & 17.8 $_{(1.8)}$ & 69.6 $_{(8.6)}$ \\\\\n Oasst-33B \\oracle{} (3-psg) & 50.7 $_{(12.1)}$ & 15.8 $_{(0.1)}$ & 20.8 $_{(2.8)}$ & 28.0 $_{(3.2)}$ & 19.4 $_{(0.1)}$ & 70.3 $_{(1.1)}$ \\\\ \n \\midrule\n LLaMA-2-7B-Chat \\vani{} (5-psg) & 27.8 $_{(3.0)}$&10.9 $_{(0.2)}$&19.8 $_{(1.2)}$&15.0 $_{(1.4)}$&20.5 $_{(0.2)}$&87.8 $_{(8.1)}$\\\\\n LLaMA-2-13B-Chat \\vani{} (5-psg) & 34.7 $_{(1.5)}$&13.4 $_{(0.4)}$&17.3 $_{(1.3)}$&15.8 $_{(1.4)}$&20.9 $_{(0.2)}$&88.3 $_{(6.3)}$\\\\\n LLaMA-2-70B-Chat \\vani{} (5-psg) & 38.6 $_{(4.8)}$&12.8 $_{(1.0)}$&38.3 $_{(2.4)}$&37.9 $_{(1.9)}$&21.3 $_{(0.1)}$&110.8 $_{(5.6)}$\\\\\n \\midrule\n StableBeluga2 & 33.0 $_{(4.4)}$&14.0 $_{(0.5)}$&27.9 $_{(2.7)}$&29.0 $_{(2.7)}$&20.6 $_{(0.2)}$&75.6 $_{(5.0)}$\\\\\n \\bottomrule\n \\end{tabular}\n \\caption{\n ELI5 full results.\n }\n \\label{tab:eli5_full}\n\\end{table*}\n\n\n\n\\begin{table*}[ht]\n \\centering\n \\small\n \\begin{tabular}{>{\\raggedright\\arraybackslash\\tt}p{0.98\\textwidth}<{}}\n \\toprule\n Read the original question and passage, and generate 3 additional claims that are supported by the passage and answer the question.\n \\\\\\\\\n \\noindent {Original question: What's the difference between Shia vs. Sunni Islam?}\\\\\n Passage: The main difference between Shia and Sunni Muslim is related to ideological heritage and issues of leadership. This difference is first formed after the death of the Prophet Muhammad in 632 A.D. The ideological practice of the Sunni branch strictly follows Prophet Muhammad and his teachings, while the Shia branch follows Prophet Muhammad's son-in-law Ali. Nowadays, Sunni and Shia are the major branches of Islam. \\\\\n Claim 1: The major branches of Islam are Sunni and Shia. \\\\\n Claim 2: Prophet Muhammad died in 632 A.D. \\\\\n Claim 3: The ideological practice of the Sunni branch strictly follows Prophet Muhammad and his teachings. \\\\\\\\\n Original question: What causes Bi-polar disorder? \\\\\n Passage: Bipolar disorder is an emotional disorder that causes extreme mood swings between excitement and depression. The spectrum of mood swing may span from days to months. We are still not certain of the exact factors that cause such disorder, but genetics is considered a major factor. \\\\\n Claim 1: One symptom of Bi-polar disorder is extreme mood swings between excitement and depression.\\\\\n Claim 2: Genetics could be one of the major factors that causes Bi-polar disorder.\\\\\n Claim 3: The mood swing from Bi-polar disorder can last days to months.\\\\\\\\\n Original question: How do we hear differences in sound besides volume and pitch?\\\\\n Passage: Pitch refers to the frequency of soundwave, and volumn refers to the amplitude of the soundwave. Besides volumn and pitch, we can also tell the difference between sounds based on the tone of sound. For example, we can differentiate the sound of different instruments based on the tone of the sounds.\\\\\n Claim 1: Volume of sound is the amplitude of the soundwave.\\\\\n Claim 2: Pitch is the frequency of soundwave.\\\\\n Claim 3: We can use the tone of the sounds to differentiate the sound of different instruments. \\\\\\\\\n \\vspace{-0.7em}\n {\\color{brown}Original question: How are we able to discern whether a sound is coming from in front of us or behind us?} \\\\\n \\vspace{-0.7em}\n {\\color{brown}Passage: There are multiple explanations for why we can localize sounds. One explanation is that sounds travelling to the corresponding side of one's ear will be slightly louder. Another explanation is that there is a slight difference in the hitting time to one's left and right ear based on the sound's direction. However, these explanation means that when a sound is exactly in front of someone or exactly behind someone, he or she can not tell the difference.} \\\\\n \\vspace{-0.7em}\n {\\color{brown}Claim 1:} {\\color{blue} We can localize sounds by recognizing that the sound travelling to the corresponding side of one's ear will be slightly louder.} \\\\\n \\vspace{-0.7em}\n {\\color{blue}Claim 2: We can also localize sounds by recognizing the difference in hitting time to one's left and right ear based on the sound's direction.}\\\\\n \\vspace{-0.7em}\n {\\color{blue}Claim 3: We cannot tell the difference between a sound that is exactly in front of us or exactly behind us.} \\\\\n \\bottomrule\n \\end{tabular}\n \\caption{\n Prompt used to generate the sub-claims for ELI5 questions.\n {\\color{blue} Blue text is model generation.}\n {\\color{brown} Brown text is the ELI5 example that we want to generate sub-claims for.}\n We construct the prompt by manually writing the sub-claims for three questions from the training set. \n }\n \\label{tab:eli5-claims-prompt}\n\\end{table*}\n\\section{Prompts}\n\\label{app_sec:propmt}\n\nWe show detailed prompts used in our paper in Tables~\\ref{tab:inst_vanilla}, \\ref{tab:inst_vanilla_light}, \\ref{tab:prompt_summary}, \\ref{tab:prompt_snippet}, \\ref{tab:inst_inter}, \\ref{tab:inst_search}, and \\ref{tab:prompt_close}.\n\n\\begin{table*}[h]\n \\centering\n \\small\n \\begin{tabular}{>{\\raggedright\\arraybackslash\\tt}p{0.95\\textwidth}<{}}\n \\toprule\n Instruction: Write an accurate, engaging, and concise answer for the given question using only the provided search results (some of which might be irrelevant) and cite them properly. Use an unbiased and journalistic tone. Always cite for any factual claim. When citing several search results, use [1][2][3]. Cite at least one document and at most three documents in each sentence. If multiple documents support the sentence, only cite a minimum sufficient subset of the documents. \\\\\n \\bottomrule\n \\end{tabular}\n \\caption{\n Instruction for \\vani{}.\n }\n \\label{tab:inst_vanilla}\n\\end{table*}\n\n\\begin{table*}[h]\n \\centering\n \\small\n \\begin{tabular}{>{\\raggedright\\arraybackslash\\tt}p{0.95\\textwidth}<{}}\n \\toprule\n Instruction: Write a high-quality answer for the given question using only the provided search results and cite them properly using [1][2][3].\\\\\n \\bottomrule\n \\end{tabular}\n \\caption{\n Short instruction for \\vani{}.\n }\n \\label{tab:inst_vanilla_light}\n\\end{table*}\n\n\\begin{table*}[h]\n \\centering\n \\small\n \\begin{tabular}{>{\\raggedright\\arraybackslash\\tt}p{0.95\\textwidth}<{}}\n \\toprule\n Summarize the following document within 50 words with the question of interest \"\\{QUESTION\\}\" Return \"irrelevant\" if the document is irrelevant to the question. Try to keep all the important dates, numbers, and names.\\\\\n ~\\\\\n Title: \\{TITLE\\}\\\\\n Text: \\{TEXT\\}\\\\\n Summary:\\\\\n \\bottomrule\n \\end{tabular}\n \\caption{\n Prompts for \\summ{}.\n }\n \\label{tab:prompt_summary}\n\\end{table*}\n\n\\begin{table*}[h]\n \\centering\n \\small\n \\begin{tabular}{>{\\raggedright\\arraybackslash\\tt}p{0.95\\textwidth}<{}}\n \\toprule\nGiven the follow passage and the question \"\\{QUESTION\\}\", extract a useful span from the passage that can answer the question. Resolve all the coreference issues to make the extracted span understandable standalone. If the passage is not helpful for answering the question, return \"irrelevant\".\\\\\n\\\\\nTitle: \\{TITLE\\}\\\\\nText: \\{TEXT\\}\\\\\nExtracted span:\\\\\n \\bottomrule\n \\end{tabular}\n \\caption{\n Prompts for \\snippet{}.\n }\n \\label{tab:prompt_snippet}\n\\end{table*}\n\n\n\n\n\\begin{table*}[h]\n \\centering\n \\small\n \\begin{tabular}{>{\\raggedright\\arraybackslash\\tt}p{0.95\\textwidth}<{}}\n \\toprule\n Instruction: Write an accurate, engaging, and concise answer for the given question using only the provided search results and cite them properly. Use an unbiased and journalistic tone. Always cite for any factual claim.\\\\\n You are provided summaries/snippets of the search results. You can use \"Check: Document [1][2]\" to check the corresponding full documents (you should only check relevant documents and you can at most check 3 documents at a time) and use \"Output:\" to output a sentence in the answer. In the answer, cite properly by using [1][2][3]. Cite at least one document and at most three documents in each sentence. If multiple documents support the sentence, only cite a minimum sufficient subset of the documents. Use \"End\" to end the generation.\\\\\n \\\\\n \\vspace{-1em}\n \\noindent {\\color{brown}<Retrieve for question ``...''>}\\\\\n \\noindent {\\color{brown}<Get summaries/snippets for the passages and delete those that are ``irrelevant''>}\\\\\n Document {[1]}(Title: ...) \\{SUMMARY OR SNIPPET\\}\\\\\n ... \\\\\n \\\\\n \\vspace{-1em}\n Question: When did US break away from England? \\\\\n \\noindent {\\color{blue} Check: Document [1][2]} \\\\\n Document [1] \\{FULL TEXT\\} \\\\\n Document [2] \\{FULL TEXT\\} \\\\\n \\noindent {\\color{blue} Output: The United States ... \\tf{\\underline{[1]}} ... \\tf{\\underline{[2]}}.} \\\\\n \\noindent {\\color{brown}<Remove the full text of [1][2] from context>}\\\\\n \\noindent {\\color{blue} Check: Document [3]} \\\\\n Document [3] \\{FULL TEXT\\} \\\\\n \\noindent {\\color{blue} Output: The Treaty of Paris ... \\tf{\\underline{[3]}}.} \\\\\n \\noindent {\\color{brown}<Remove the full text of [3] from context>}\\\\\n \\noindent {\\color{blue}End.}\\\\\n \\bottomrule\n \\end{tabular}\n \\caption{\n An example for \\interact{}.\n }\n \\label{tab:inst_inter}\n\\end{table*}\n\n\\begin{table*}[h]\n \\centering\n \\small\n \\begin{tabular}{>{\\raggedright\\arraybackslash\\tt}p{0.95\\textwidth}<{}}\n \\toprule\n Instruction: Write an accurate, engaging, and concise answer for the given question using only the provided search results and cite them properly. Use an unbiased and journalistic tone. Always cite for any factual claim.\\\\\n You can use \"Search: key words\" to check the most relevant document's full text and use \"Output:\" to output a sentence in the answer. In the answer, cite properly by using [1][2][3]. Cite at least one document and at most three documents in each sentence. If multiple documents support the sentence, only cite a minimum sufficient subset of the documents. Use \"End\" to end the generation.\\\\\n \\bottomrule\n \\end{tabular}\n \\caption{\n Instruction for \\search{}.\n }\n \\label{tab:inst_search}\n\\end{table*}\n\n\\begin{table*}[h]\n \\centering\n \\small\n \\begin{tabular}{>{\\raggedright\\arraybackslash\\tt}p{0.95\\textwidth}<{}}\n \\toprule\n Instruction: Write an accurate, engaging, and concise answer for the given question. Use an unbiased and journalistic tone. \\\\\n \\bottomrule\n \\end{tabular}\n \\caption{\n Instruction for \\close{}.\n }\n \\label{tab:prompt_close}\n\\end{table*}\n\n\n\\section{Examples}\n\nIn Tables~\\ref{tab:exp_eli5} and \\ref{tab:exp_eli52} we show some examples of questions and model generated outputs. \n\n\\begin{table*}[h]\n \\centering\n \\small\n \\begin{tabular}{>{\\raggedright\\arraybackslash\\tt}p{0.95\\textwidth}<{}}\n \\toprule\n Instruction: Write an accurate, engaging, and concise answer for ... \\\\\n \\\\\n \\vspace{-1em}\n Document {[1]}(Title: How to Treat and Prevent Food Poisoning - MsPrepper) just a typical gastro upset. Salmonella is most commonly caused by eating undercooked or raw foods like eggs or meat. You know how your mom always warned you not to eat raw cookie dough? This is why. Most people do eat cookie dough and they are fine, but salmonella is a risk. If you do contract salmonella, you could start to feel bad within in a couple of hours after eating contaminated food, and sometimes it could take a day or two. Common symptoms are nausea and vomiting, loose stools (sometimes bloody), flu like symptoms, and stomach cramps. To treat \\\\\n Document {[2]}(Title: FDA Issues Warning About Eating Raw Cookie Dough, But Not For Salmonella Risks) FDA Issues Warning About Eating Raw Cookie Dough, But Not For Salmonella Risks Used to licking the spoon or placating yourself with full-on chunks of raw cookie dough? The Food and Drug Administration issued a warning on Tuesday that strongly advises against continuing the habit. The agency asserted that consuming raw batter of any kind, whether for bread, cookies or pizza, could make a person sick. While you may have been warned in the past against eating raw dough due to the risk of contracting salmonella from raw eggs, the FDA is citing raw flour as the culprit for a \\\\\n Document {[3]}(Title: It's Probably OK to Eat Raw Cookie Dough — As Long As You're Smart About It - The Crux - Very Top Secret Information) First, when most people think about health risks and cookie dough, they think about raw egg. Eggs can be contaminated with salmonella bacteria, and food safety recommendations encourage people to cook eggs until the white and yolk are firm in order to kill any bacteria. However, anyone making cookies can do things to reduce this risk by using pasteurized egg products. When my kids and I make cookie dough, we never use regular eggs. Instead, we use shell eggs that have been pasteurized to kill any harmful bacteria without actually cooking the egg itself. (A great public health innovation, if \\\\\n Document {[4]}(Title: How Dangerous Is It to Eat Raw Cookie Dough? | Men's Health) Can Eating Raw Cookie Dough Really Make You Sick? Scientists reveal the truth about this supposedly dangerous delicacy By Katherine Dempsey There are few things more tempting in life than eyeing a bowl of cookie dough and deciding whether or not to stick your finger in for a scoop. It’s a bit like playing Russian roulette. You could get lucky and enjoy the delicious dough without conseqence, but there’s always the risk of getting serously sick with a food-borne illness. That’s because multiple ingredients within the dough could be contaminated by pathogens such as Salmonella and E. coli, says Soohyoun \\\\\n Document {[5]}(Title: How Dangerous Is It to Eat Raw Cookie Dough? | Men's Health) is usually pasteurized, so it’s not likely that the egg would make you sick. However, other ingredients in the dough could potentially harbor pathogens. Experts say that a prime suspect in a 2009 E. coli outbreak linked to prepackaged cookie dough was actually the flour. Nuts and chocolate have also been linked to Salmonella outbreaks. Bottom line: You’re better off skipping raw cookie dough. At least you’ll have killer cookies as a consolation. The article How Bad Is It To Eat Raw Cookie Dough? originally ran on Prevention.com The 9 Foods Most Likely to Make You Sick Mmm, Just Don't \\\\\n\n ... \\\\\n \\\\\n \\vspace{-1em}\n Question: Why is it bad to eat cookie dough for risk of salmonella but things like Cookie Dough Bites are ok? \\\\\n Answer: {\\color{blue} Raw cookie dough is not recommended to be eaten due to the risk of salmonella \\tf{\\underline{[1][2]}}. Eating raw flour is also a risk for food poisoning \\tf{\\underline{[2]}}. The egg in cookie dough is usually pasteurized, so it's not likely that the egg would make you sick, but other ingredients in the dough could potentially harbor pathogens \\tf{\\underline{[4][5]}}. However, prepackaged cookie dough like Cookie Dough Bites is safe to eat because the dough is made with pasteurized egg products and heat-treated flour \\tf{\\underline{[2][3]}}.}.\\\\\n \\\\\n \\noindent {\\color{orange} Ground truth claims:}\\\\\n \\noindent {\\color{orange} 1. Cookie Dough Bites are safe to eat since they do not contain eggs.}\\\\\n \\noindent {\\color{orange} 2. Salmonella is a bacteria found in eggs.}\\\\\n \\noindent {\\color{orange} 3. Eating raw cookie dough carries the risk of salmonella infection.}\\\\\n \\bottomrule\n \\end{tabular}\n \\caption{\n ELI5 example 1.\n }\n \\label{tab:exp_eli5}\n\\end{table*}\n\n\n\\begin{table*}[h]\n \\centering\n \\small\n \\begin{tabular}{>{\\raggedright\\arraybackslash\\tt}p{0.98\\textwidth}<{}}\n \\toprule\n Instruction: Write an accurate, engaging, and concise answer for ... \\\\\n \\\\\n \\vspace{-1em}\n Document [1](Title: Is Snapchat really worth \\$19 billion? - CSMonitor.com) reporting that the Los Angeles-based company is aiming to raise \\$500 million at a valuation of \\$16 billion to \\$19 billion, making it the third most highly valued tech start-up backed by venture capitalists. The Chinese handset maker Xiaomi is valued at \\$45 billion, while Uber is estimated to be valued at about \\$40 billion, according to data from CB Insights. Read MoreVC investment hits \\$86B thanks to Uber, Xiaomi Snapchat was valued at \\$10 billion in August, according to a Dow Jones report. Some of its investors from previous rounds include Benchmark, Lightspeed Venture Partners and Kleiner Perkins Caufield \\\\\n Document [2](Title: What Are Venture Capital Investments? – DollarsAndSense.my) Ever wondered how highly valued technology giants like Google and Facebook were able to grow so fast and pay their employees so well in such a short amount of time, or how still growing start-ups like Uber are able to lose 1.2 billion US dollars in just the first half of this year alone and still command a valuation upwards of 50 billion US dollars? The answer lies with a special category of investment activity known as venture capital. Venture capitalists are professional investors who invest in a number of highly scalable high-risk technology ventures hoping to make a multi-fold \\\\\n Document [3](Title: Opinion | What Dara Khosrowshahi Must Do to Save Uber - The New York Times) at a discount. These are troubling signs. Every start-up must one day fulfill the market’s demand that it turn a profit, but Uber has never figured out how to do that. While ride sharing in some form will probably survive, it’s more likely that without some drastic changes, Uber won’t be around in three to five years. Mr. Khosrowshahi must avoid the mistakes of his predecessor by accepting that “pivots” (Silicon Valley-speak for the desperate changes troubled companies make to reassure their venture capitalist funders) are not the answer. None of the pivots Mr. Kalanick tried — like on-demand delivery \\\\\n Document [4](Title: Snapchat raising funding round at \\$19 billion valuation: Report) Snapchat raising funding round at \\$19 billion valuation: Report The mobile messaging app Snapchat is said to be seeking funding at a \\$19 billion valuation, according to a report, citing a person with knowledge of the matter. Bloomberg is reporting that the Los Angeles-based company is aiming to raise \\$500 million at a valuation of \\$16 billion to \\$19 billion, making it the third most highly valued tech start-up backed by venture capitalists. The Chinese handset maker Xiaomi is valued at \\$45 billion, while Uber is estimated to be valued at about \\$40 billion, according to data from CB Insights. \\\\\n Document [5](Title: Unicorns And Wall Street | MoneyTips) not start showing profits soon. Even Uber, with its massive \\$62-billion-plus valuation, will test investor's patience if it does not start showing signs of a profit in the next few years. Cracks are starting to show in terms of pre-IPO devaluations. Dropbox and Snapchat have seen a nearly one-quarter drop in their pre-IPO valuations, while Square's pre-IPO value has dropped by approximately one-third. Larger investors and venture capitalists are sending a message to the unicorns: Outline a solid path to profits and start producing them soon, or expect your valuation to be slashed. Diving ahead with an IPO is not \\\\\n ... \\\\\n \\\\\n \\vspace{-1em}\n Question: How are firms like snapchat, uber etc valued so highly while still not making a profit? Do venture capitalists not expect some form of repayment within a number of years? \\\\\n Answer: {\\color{blue} Venture capitalists invest in a number of highly scalable high-risk technology ventures hoping to make a multi-fold return on their investment in a short amount of time \\tf{\\underline{[2]}}. Highly valued companies like Snapchat, Uber, and Xiaomi are able to grow so fast and command high valuations because they have the potential to generate significant revenue in the future \\tf{\\underline{[2][4]}}. Venture capitalists expect a return on their investment within a number of years, but they also invest in the long-term potential of the company \\tf{\\underline{[2]}}. It is important to note that every start-up must eventually turn a profit, but some start-ups like Uber have not yet figured out how to do that \\tf{\\underline{[3][5]}}.}\\\\\n \\\\\n \\noindent {\\color{orange} Ground truth claims:}\\\\\n \\noindent {\\color{orange} 1. Firms like Snapchat and Uber need to establish their brand and amass users before introducing ads.}\\\\\n \\noindent {\\color{orange} 2. Introducing ads too early can deter potential users.}\\\\\n \\noindent {\\color{orange} 3. Uber is reinvesting a lot of money to make their service better.}\\\\\n \n \\bottomrule\n \\end{tabular}\n \\caption{\n ELI5 example 2.\n }\n \\label{tab:exp_eli52}\n\\end{table*}\n\n\\end{document}\n"},"question":{"kind":"string","value":"Which 13B model achieves the highest average citation F1 score, and what is that score?"},"answer":{"kind":"string","value":"Vicuna-13B, 26.9"},"plan":{"kind":"string","value":"SELECT ASQA results from Table 1\nSELECT QAMPARI results from Table 2\nSELECT ELI5 results from Table 3\nSELECT all 13B models\n\n\nLOOP for each model\n SELECT citation recall and citation precision for ASQA, QAMPARI, and ELI5\n LOOP for each dataset\n COMPUTE precision_recall_product = citation_precision * citation_recall\n COMPUTE precision_recall_sum = citation_precision + citation_recall\n COMPUTE f1_score = 2 * precision_recall_product / precision_recall_sum\n COMPUTE avg_f1 = average of f1_score across 3 datasets\n\n\nCOMPUTE (argmax_model, max_f1_score) = argmax avg_f1 over all models\nRETURN argmax_model and max_f1_score\n"}}},{"rowIdx":77,"cells":{"paper":{"kind":"string","value":"2505.23242"},"relevant_tables":{"kind":"list like","value":[["\\begin{table*}[!ht]\n"," \\centering\n"," \\resizebox{\\linewidth}{!}{\n"," \\begin{tabular}{lccccccccccr}\n"," \\toprule\n"," \\multirow{2}[4]{*}{ Models} & \\multirow{2}[4]{*}{Size} & \\multicolumn{3}{c}{ChartMind} & \\multicolumn{3}{c}{ChartQA } & \\multicolumn{3}{c}{Chart-to-Text} & OpenCQA \\\\\n","\\cmidrule{3-12} & & ACC & Avg.CIDEr & Avg.GPT-4o Score & Aug. ACC & Hum. ACC & Avg. ACC & Pew. BLEU & Statista. BLEU & Avg. BLEU & Avg. BLEU \\\\\n"," \\midrule\n"," \\rowcolor[rgb]{ .922, .945, .871} \\multicolumn{12}{c}{Instruction-Following-Based~\\cite{wei2021finetuned}} \\\\\n"," \\midrule\n"," TinyChart\\textdagger~\\cite{tinychart} & 3B & 5.36 & 18.45 & 16.81 & 93.60 & 72.16 & 82.88 & 10.84 & 27.04 & 18.94 & 19.62 \\\\\n"," ChartInstruct\\textdagger~\\cite{chartinstruct} & 7B & 9.82 & 24.55 & 15.05 & 82.40 & 40.64 & 61.52 & 12.81 & 39.39 & 26.10 & 14.78 \\\\\n"," ChartLlama\\textdagger~\\cite{chartllama} & 7B & 20.54 & 21.34 & 12.72 & 90.36 & 48.96 & 69.66 & 14.23 & 40.71 & 27.47 & 4.70 \\\\\n"," Sphinx-v2~\\cite{sphinx} & 7B & 9.82 & 25.95 & 13.69 & 60.96 & 43.92 & 52.44 & 3.43 & 4.94 & 4.19 & 3.10 \\\\\n"," LLaVA1.5~\\cite{llava1.5} & 7B & 34.82 & 39.50 & 15.58 & 20.12 & 25.20 & 22.66 & 15.70 & 11.07 & 13.39 & 15.17 \\\\\n"," ViP-LLaVA~\\cite{vipllava} & 7B & 20.54 & 37.01 & 15.56 & 17.60 & 26.16 & 21.88 & 1.36 & 2.59 & 1.98 & 15.04 \\\\\n"," LLaVA-NEXT~\\cite{llavanext} & 7B & 20.54 & 47.37 & 31.09 & 74.26 & 46.30 & 60.28 & 13.85 & 6.63 & 10.24 & 8.07 \\\\\n"," IXC-2.5~\\cite{ixc2.5} & 7B & 47.30 & 40.10 & 43.31 & 92.40 & 74.32 & 83.36 & 17.69 & 11.86 & 14.78 & 9.39 \\\\\n"," Qwen2-VL~\\cite{qwenvl} & 7B & 57.14 & 37.32 & 47.89 & 94.10 & 72.00 & 83.05 & 11.07 & 22.98 & 17.03 & 8.26 \\\\\n"," mPLUG-Owl2~\\cite{mplugowl2} & 8B & 25.00 & 36.17 & 14.22 & 24.13 & 27.34 & 25.74 & 12.83 & 5.97 & 9.40 & 5.34 \\\\\n"," MiniCPM-v2~\\cite{minicpmv2} & 8B & 22.32 & 28.48 & 10.63 & 91.12 & 69.02 & 80.07 & 22.17 & 11.01 & 16.59 & 20.05 \\\\\n"," CogVLM~\\cite{cogvlm} & 17B & 23.21 & 40.20 & 29.35 & 23.95 & 39.53 & 31.74 & 16.38 & 11.84 & 14.11 & 1.75 \\\\\n"," GLM-4V-plus~\\cite{chatglm4v} & - & 59.83 & 38.36 & 21.52 & 16.80 & 12.80 & 14.80 & 5.69 & 5.71 & 5.70 & 7.41 \\\\\n"," GPT-4o~\\cite{gpt4v} & - & \\textbf{61.89} & 47.25 & 68.81 & 95.34 & 76.06 & 85.70 & 17.75 & 8.70 & 13.23 & 13.92 \\\\\n"," \\midrule\n"," \\rowcolor[rgb]{ .922, .945, .871} \\multicolumn{12}{c}{OCR-Enhanced~\\cite{liu2023deplot}} \\\\\n"," \\midrule\n"," TinyChart\\textdagger~\\cite{tinychart} & 3B & \\makebox[5.4em][s]{6.71 (\\textcolor{blue}{+1.35})} & \\makebox[5.4em][s]{13.91 (\\textcolor{red}{-4.54})} & \\makebox[5.4em][s]{17.91 (\\textcolor{blue}{+1.10})} & \\makebox[5.4em][s]{94.86 (\\textcolor{blue}{+1.26})} & \\makebox[5.4em][s]{73.95 (\\textcolor{blue}{+1.79})} & \\makebox[5.4em][s]{84.41 (\\textcolor{blue}{+1.53})} & \\makebox[5.4em][s]{13.85 (\\textcolor{blue}{+3.01})} & \\makebox[5.4em][s]{28.27 (\\textcolor{blue}{+1.23})} & \\makebox[5.4em][s]{21.06 (\\textcolor{blue}{+2.12})} & \\makebox[5.4em][s]{20.15 (\\textcolor{blue}{+0.53})} \\\\\n"," ChartInstruct\\textdagger~\\cite{chartinstruct} & 7B & \\makebox[5.4em][s]{{10.01} (\\textcolor{blue}{+0.19})} & \\makebox[5.4em][s]{32.80 (\\textcolor{blue}{+8.25})} & \\makebox[5.4em][s]{23.42 (\\textcolor{blue}{+8.37})} & \\makebox[5.4em][s]{83.74 (\\textcolor{blue}{+1.34})} & \\makebox[5.4em][s]{42.17 (\\textcolor{blue}{+1.53})} & \\makebox[5.4em][s]{62.96 (\\textcolor{blue}{+1.44})} & \\makebox[5.4em][s]{14.95 (\\textcolor{blue}{+2.14})} & \\makebox[5.4em][s]{40.83 (\\textcolor{blue}{+1.44})} & \\makebox[5.4em][s]{27.89 (\\textcolor{blue}{+1.79})} & \\makebox[5.4em][s]{16.01 (\\textcolor{blue}{+1.23})} \\\\\n"," ChartLlama\\textdagger~\\cite{chartllama} & 7B & \\makebox[5.4em][s]{{22.03} (\\textcolor{blue}{+1.49})} & \\makebox[5.4em][s]{21.07 (\\textcolor{red}{-0.27})} & \\makebox[5.4em][s]{26.70 (\\textcolor{blue}{+13.97})} & \\makebox[5.4em][s]{90.85 (\\textcolor{blue}{+0.49})} & \\makebox[5.4em][s]{49.26 (\\textcolor{blue}{+0.30})} & \\makebox[5.4em][s]{70.06 (\\textcolor{blue}{+0.40})} & \\makebox[5.4em][s]{16.02 (\\textcolor{blue}{+1.79})} & \\makebox[5.4em][s]{39.97 (\\textcolor{red}{-0.74})} & \\makebox[5.4em][s]{28.00 (\\textcolor{blue}{+0.53})} & \\makebox[5.4em][s]{5.89 (\\textcolor{blue}{+1.19})} \\\\\n"," Sphinx-v2~\\cite{sphinx} & 7B & \\makebox[5.4em][s]{11.54 (\\textcolor{blue}{+1.72})} & \\makebox[5.4em][s]{24.14 (\\textcolor{red}{-1.81})} & \\makebox[5.4em][s]{17.21 (\\textcolor{blue}{+3.52})} & \\makebox[5.4em][s]{64.08 (\\textcolor{blue}{+3.12})} & \\makebox[5.4em][s]{45.49 (\\textcolor{blue}{+1.57})} & \\makebox[5.4em][s]{54.79 (\\textcolor{blue}{+2.35})} & \\makebox[5.4em][s]{8.81 (\\textcolor{blue}{+5.38})} & \\makebox[5.4em][s]{2.39 (\\textcolor{red}{-2.55})} & \\makebox[5.4em][s]{5.60 (\\textcolor{blue}{+1.41})} & \\makebox[5.4em][s]{3.16 (\\textcolor{blue}{+0.06})} \\\\\n"," LLaVA1.5~\\cite{llava1.5} & 7B & \\makebox[5.4em][s]{36.15 (\\textcolor{blue}{+1.33})} & \\makebox[5.4em][s]{33.49 (\\textcolor{red}{-6.01})} & \\makebox[5.4em][s]{21.03 (\\textcolor{blue}{+5.45})} & \\makebox[5.4em][s]{19.73 (\\textcolor{red}{-0.39})} & \\makebox[5.4em][s]{25.95 (\\textcolor{blue}{+0.75})} & \\makebox[5.4em][s]{22.84 (\\textcolor{blue}{+0.18})} & \\makebox[5.4em][s]{15.94 (\\textcolor{blue}{+0.24})} & \\makebox[5.4em][s]{12.67 (\\textcolor{blue}{+1.60})} & \\makebox[5.4em][s]{14.30 (\\textcolor{blue}{+0.91})} & \\makebox[5.4em][s]{16.31 (\\textcolor{blue}{+1.14})} \\\\\n"," ViP-LLaVA~\\cite{vipllava} & 7B & \\makebox[5.4em][s]{25.38 (\\textcolor{blue}{+4.84})} & \\makebox[5.4em][s]{36.77 (\\textcolor{red}{-0.24})} & \\makebox[5.4em][s]{26.45 (\\textcolor{blue}{+10.89})} & \\makebox[5.4em][s]{27.12 (\\textcolor{blue}{+9.52})} & \\makebox[5.4em][s]{24.94 (\\textcolor{red}{-1.22})} & \\makebox[5.4em][s]{26.03 (\\textcolor{blue}{+4.15})} & \\makebox[5.4em][s]{14.13 (\\textcolor{blue}{+12.77})} & \\makebox[5.4em][s]{14.37 (\\textcolor{blue}{+11.78})} & \\makebox[5.4em][s]{14.25 (\\textcolor{blue}{+12.27})} & \\makebox[5.4em][s]{18.08 (\\textcolor{blue}{+3.04})} \\\\\n"," LLaVA-NEXT~\\cite{llavanext} & 7B & \\makebox[5.4em][s]{41.15 (\\textcolor{blue}{+20.61})} & \\makebox[5.4em][s]{47.83 (\\textcolor{blue}{+0.46})} & \\makebox[5.4em][s]{31.51 (\\textcolor{blue}{+0.42})} & \\makebox[5.4em][s]{70.47 (\\textcolor{red}{-3.79})} & \\makebox[5.4em][s]{52.68 (\\textcolor{blue}{+6.38})} & \\makebox[5.4em][s]{61.58 (\\textcolor{blue}{+1.30})} & \\makebox[5.4em][s]{15.16 (\\textcolor{blue}{+1.31})} & \\makebox[5.4em][s]{8.82 (\\textcolor{blue}{+2.19})} & \\makebox[5.4em][s]{11.99 (\\textcolor{blue}{+1.75})} & \\makebox[5.4em][s]{8.25 (\\textcolor{blue}{+0.18})} \\\\\n"," IXC-2.5~\\cite{ixc2.5} & 7B & \\makebox[5.4em][s]{42.31 (\\textcolor{red}{-4.99})} & \\makebox[5.4em][s]{40.35 (\\textcolor{blue}{+0.24})} & \\makebox[5.4em][s]{45.38 (\\textcolor{blue}{+2.06})} & \\makebox[5.4em][s]{94.23 (\\textcolor{blue}{+1.83})} & \\makebox[5.4em][s]{73.40 (\\textcolor{red}{-0.92})} & \\makebox[5.4em][s]{83.82 (\\textcolor{blue}{+0.46})} & \\makebox[5.4em][s]{17.03 (\\textcolor{red}{-0.66})} & \\makebox[5.4em][s]{12.34 (\\textcolor{blue}{+0.48})} & \\makebox[5.4em][s]{14.68 (\\textcolor{red}{-0.10})} & \\makebox[5.4em][s]{14.53 (\\textcolor{blue}{+5.14})} \\\\\n"," Qwen2-VL~\\cite{qwenvl} & 7B & \\makebox[5.4em][s]{42.31 (\\textcolor{red}{-14.83})} & \\makebox[5.4em][s]{36.04 (\\textcolor{red}{-1.27})} & \\makebox[5.4em][s]{49.28 (\\textcolor{blue}{+1.39})} & \\makebox[5.4em][s]{94.23 (\\textcolor{blue}{+0.13})} & \\makebox[5.4em][s]{75.96 (\\textcolor{blue}{+3.96})} & \\makebox[5.4em][s]{85.10 (\\textcolor{blue}{+2.05})} & \\makebox[5.4em][s]{11.08 (\\textcolor{blue}{+0.01})} & \\makebox[5.4em][s]{23.21 (\\textcolor{blue}{+0.23})} & \\makebox[5.4em][s]{17.15 (\\textcolor{blue}{+0.12})} & \\makebox[5.4em][s]{11.75 (\\textcolor{blue}{+3.49})} \\\\\n"," mPLUG-Owl2~\\cite{mplugowl2} & 8B & \\makebox[5.4em][s]{27.62 (\\textcolor{blue}{+2.62})} & \\makebox[5.4em][s]{30.60 (\\textcolor{red}{-5.57})} & \\makebox[5.4em][s]{24.67 (\\textcolor{blue}{+10.44})} & \\makebox[5.4em][s]{35.58 (\\textcolor{blue}{+11.45})} & \\makebox[5.4em][s]{37.18 (\\textcolor{blue}{+9.84})} & \\makebox[5.4em][s]{36.38 (\\textcolor{blue}{+10.65})} & \\makebox[5.4em][s]{11.82 (\\textcolor{red}{-1.01})} & \\makebox[5.4em][s]{7.30 (\\textcolor{blue}{+1.33})} & \\makebox[5.4em][s]{9.56 (\\textcolor{blue}{+0.16})} & \\makebox[5.4em][s]{4.45 (\\textcolor{red}{-0.89})} \\\\\n"," MiniCPM-v2~\\cite{minicpmv2} & 8B & \\makebox[5.4em][s]{23.04 (\\textcolor{blue}{+0.72}} & \\makebox[5.4em][s]{19.73 (\\textcolor{red}{-8.75})} & \\makebox[5.4em][s]{18.10 (\\textcolor{blue}{+7.47})} & \\makebox[5.4em][s]{\\makebox[5.4em][s]{92.36 (\\textcolor{blue}{+1.24})}} & \\makebox[5.4em][s]{73.21 (\\textcolor{blue}{+4.19})} & \\makebox[5.4em][s]{82.79 (\\textcolor{blue}{+2.72})} & \\makebox[5.4em][s]{20.93 (\\textcolor{red}{-1.24})} & \\makebox[5.4em][s]{5.75 (\\textcolor{red}{-5.26})} & \\makebox[5.4em][s]{13.34 (\\textcolor{red}{-3.25})} & \\makebox[5.4em][s]{20.60 (\\textcolor{blue}{+0.55})} \\\\\n"," CogVLM~\\cite{cogvlm} & 17B & \\makebox[5.4em][s]{25.54 (\\textcolor{blue}{+2.33})} & \\makebox[5.4em][s]{39.00 (\\textcolor{red}{-1.20})} & \\makebox[5.4em][s]{36.80 (\\textcolor{blue}{+7.45})} & \\makebox[5.4em][s]{29.81 (\\textcolor{blue}{+5.86})} & \\makebox[5.4em][s]{48.72 (\\textcolor{blue}{+9.19})} & \\makebox[5.4em][s]{39.27 (\\textcolor{blue}{+7.53})} & \\makebox[5.4em][s]{20.85 (\\textcolor{blue}{+4.47})} & \\makebox[5.4em][s]{13.88 (\\textcolor{blue}{+2.04})} & \\makebox[5.4em][s]{17.37 (\\textcolor{blue}{+3.26})} & \\makebox[5.4em][s]{1.79 (\\textcolor{blue}{+0.04})} \\\\\n"," GLM-4V-plus~\\cite{chatglm4v} & - & \\makebox[5.4em][s]{44.64 (\\textcolor{red}{-15.19})} & \\makebox[5.4em][s]{44.83 (\\textcolor{blue}{+6.47})} & \\makebox[5.4em][s]{35.79 (\\textcolor{blue}{+14.27})} & \\makebox[5.4em][s]{17.95 (\\textcolor{blue}{+1.15})} & \\makebox[5.4em][s]{16.87 (\\textcolor{blue}{+4.07})} & \\makebox[5.4em][s]{17.41 (\\textcolor{blue}{+2.61})} & \\makebox[5.4em][s]{7.91 (\\textcolor{blue}{+2.22})} & \\makebox[5.4em][s]{7.63 (\\textcolor{blue}{+1.92})} & \\makebox[5.4em][s]{7.77 (\\textcolor{blue}{+2.07})} & \\makebox[5.4em][s]{8.72 (\\textcolor{blue}{+1.31})} \\\\\n"," GPT-4o~\\cite{gpt4v} & - & \\makebox[5.4em][s]{49.31 (\\textcolor{red}{-12.58})} & \\makebox[5.4em][s]{46.48 (\\textcolor{red}{-0.76})} & \\makebox[5.4em][s]{\\underline{71.79} (\\textcolor{blue}{+2.98})} & \\makebox[5.4em][s]{\\underline{96.20} (\\textcolor{blue}{+0.86})} & \\makebox[5.4em][s]{\\underline{78.04} (\\textcolor{blue}{+1.98})} & \\makebox[5.4em][s]{\\underline{87.12} (\\textcolor{blue}{+1.42})} & \\makebox[5.4em][s]{20.13 (\\textcolor{blue}{+2.38})} & \\makebox[5.4em][s]{9.86 (\\textcolor{blue}{+1.16})} & \\makebox[5.4em][s]{15.00 (\\textcolor{blue}{+1.77})} & \\makebox[5.4em][s]{14.85 (\\textcolor{blue}{+0.93})} \\\\\n"," \\midrule\n"," \\rowcolor[rgb]{ .922, .945, .871} \\multicolumn{12}{c}{COT-Based~\\cite{wei2022chain}} \\\\\n"," \\midrule\n"," TinyChart\\textdagger~\\cite{tinychart} & 3B & \\makebox[5.4em][s]{6.01 (\\textcolor{blue}{+0.65})} & \\makebox[5.4em][s]{13.58 (\\textcolor{red}{-4.87})} & \\makebox[5.4em][s]{19.30 (\\textcolor{blue}{+2.49})} & \\makebox[5.4em][s]{94.84 (\\textcolor{blue}{+1.24})} & \\makebox[5.4em][s]{74.46 (\\textcolor{blue}{+2.30})} & \\makebox[5.4em][s]{84.65 (\\textcolor{blue}{+1.77})} & \\makebox[5.4em][s]{12.31 (\\textcolor{blue}{+1.47})} & \\makebox[5.4em][s]{28.53 (\\textcolor{blue}{+1.49})} & \\makebox[5.4em][s]{20.42 (\\textcolor{blue}{+1.48})} & \\makebox[5.4em][s]{20.74 (\\textcolor{blue}{+1.12})} \\\\\n"," ChartInstruct\\textdagger~\\cite{chartinstruct} & 7B & \\makebox[5.4em][s]{9.96 (\\textcolor{blue}{+0.14})} & \\makebox[5.4em][s]{31.95 (\\textcolor{blue}{+7.40})} & \\makebox[5.4em][s]{22.44 (\\textcolor{blue}{+7.39})} & \\makebox[5.4em][s]{83.35 (\\textcolor{blue}{+0.95})} & \\makebox[5.4em][s]{42.74 (\\textcolor{blue}{+2.10})} & \\makebox[5.4em][s]{63.05 (\\textcolor{blue}{+1.53})} & \\makebox[5.4em][s]{14.34 (\\textcolor{blue}{+1.53})} & \\makebox[5.4em][s]{41.32 (\\textcolor{blue}{+1.93})} & \\makebox[5.4em][s]{27.83 (\\textcolor{blue}{+1.73})} & \\makebox[5.4em][s]{15.25 (\\textcolor{blue}{+0.47})} \\\\\n"," ChartLlama\\textdagger~\\cite{chartllama} & 7B & \\makebox[5.4em][s]{21.44 (\\textcolor{blue}{+0.90})} & \\makebox[5.4em][s]{18.99 (\\textcolor{red}{-2.36})} & \\makebox[5.4em][s]{21.77 (\\textcolor{blue}{+9.04})} & \\makebox[5.4em][s]{91.63 (\\textcolor{blue}{+1.27})} & \\makebox[5.4em][s]{50.04 (\\textcolor{blue}{+1.08})} & \\makebox[5.4em][s]{70.84 (\\textcolor{blue}{+1.18})} & \\makebox[5.4em][s]{15.76 (\\textcolor{blue}{+1.53})} & \\makebox[5.4em][s]{\\underline{41.42} (\\textcolor{blue}{+0.71})} & \\makebox[5.4em][s]{\\underline{28.59} (\\textcolor{blue}{+1.12})} & \\makebox[5.4em][s]{6.32 (\\textcolor{blue}{+1.62})} \\\\\n"," Sphinx-v2~\\cite{sphinx} & 7B & \\makebox[5.4em][s]{9.91 (\\textcolor{blue}{+0.09})} & \\makebox[5.4em][s]{25.03 (\\textcolor{red}{-0.92})} & \\makebox[5.4em][s]{16.26 (\\textcolor{blue}{+2.57})} & \\makebox[5.4em][s]{61.86 (\\textcolor{blue}{+0.90})} & \\makebox[5.4em][s]{46.79 (\\textcolor{blue}{+2.87})} & \\makebox[5.4em][s]{54.33 (\\textcolor{blue}{+1.89})} & \\makebox[5.4em][s]{3.53 (\\textcolor{blue}{+0.10})} & \\makebox[5.4em][s]{5.09 (\\textcolor{blue}{+0.15})} & \\makebox[5.4em][s]{4.31 (\\textcolor{blue}{+0.12})} & \\makebox[5.4em][s]{3.13 (\\textcolor{blue}{+0.03})} \\\\\n"," LLaVA1.5~\\cite{llava1.5} & 7B & \\makebox[5.4em][s]{35.77 (\\textcolor{blue}{+0.95})} & \\makebox[5.4em][s]{35.61 (\\textcolor{red}{-3.89})} & \\makebox[5.4em][s]{19.68 (\\textcolor{blue}{+4.10})} & \\makebox[5.4em][s]{16.90 (\\textcolor{red}{-3.22})} & \\makebox[5.4em][s]{28.57 (\\textcolor{blue}{+3.37})} & \\makebox[5.4em][s]{22.74 (\\textcolor{blue}{+0.08})} & \\makebox[5.4em][s]{15.20 (\\textcolor{red}{-0.50})} & \\makebox[5.4em][s]{11.66 (\\textcolor{blue}{+0.59})} & \\makebox[5.4em][s]{13.43 (\\textcolor{blue}{+0.04})} & \\makebox[5.4em][s]{15.93 (\\textcolor{blue}{+0.76})} \\\\\n"," ViP-LLaVA~\\cite{vipllava} & 7B & \\makebox[5.4em][s]{23.31 (\\textcolor{blue}{+2.77})} & \\makebox[5.4em][s]{36.13 (\\textcolor{red}{-0.88})} & \\makebox[5.4em][s]{22.24 (\\textcolor{blue}{+6.68})} & \\makebox[5.4em][s]{22.12 (\\textcolor{blue}{+4.52})} & \\makebox[5.4em][s]{28.21 (\\textcolor{blue}{+2.05})} & \\makebox[5.4em][s]{25.17 (\\textcolor{blue}{+3.29})} & \\makebox[5.4em][s]{15.48 (\\textcolor{blue}{+14.12})} & \\makebox[5.4em][s]{12.20 (\\textcolor{blue}{+9.61})} & \\makebox[5.4em][s]{13.84 (\\textcolor{blue}{+11.86})} & \\makebox[5.4em][s]{15.67 (\\textcolor{blue}{+0.63})} \\\\\n"," LLaVA-NEXT~\\cite{llavanext} & 7B & \\makebox[5.4em][s]{40.23 (\\textcolor{blue}{+19.69})} & \\makebox[5.4em][s]{47.44 (\\textcolor{blue}{+0.07})} & \\makebox[5.4em][s]{27.34 (\\textcolor{red}{-3.75})} & \\makebox[5.4em][s]{68.49 (\\textcolor{red}{-5.77})} & \\makebox[5.4em][s]{52.13 (\\textcolor{blue}{+5.83})} & \\makebox[5.4em][s]{60.31 (\\textcolor{blue}{+0.03})} & \\makebox[5.4em][s]{14.81 (\\textcolor{blue}{+0.96})} & \\makebox[5.4em][s]{6.29 (\\textcolor{red}{-0.34})} & \\makebox[5.4em][s]{10.55 (\\textcolor{blue}{+0.31})} & \\makebox[5.4em][s]{8.09 (\\textcolor{blue}{+0.02})} \\\\\n"," IXC-2.5~\\cite{ixc2.5} & 7B & \\makebox[5.4em][s]{41.15 (\\textcolor{red}{-6.15})} & \\makebox[5.4em][s]{41.23 (\\textcolor{blue}{+1.13})} & \\makebox[5.4em][s]{46.73 (\\textcolor{blue}{+3.42})} & \\makebox[5.4em][s]{93.91 (\\textcolor{blue}{+1.51})} & \\makebox[5.4em][s]{72.82 (\\textcolor{red}{-1.50})} & \\makebox[5.4em][s]{83.37 (\\textcolor{blue}{+0.01})} & \\makebox[5.4em][s]{17.36 (\\textcolor{blue}{+0.23})} & \\makebox[5.4em][s]{11.92 (\\textcolor{blue}{+0.06})} & \\makebox[5.4em][s]{14.64 (\\textcolor{red}{-0.14})} & \\makebox[5.4em][s]{14.39 (\\textcolor{blue}{+5.00})} \\\\\n"," Qwen2-VL~\\cite{qwenvl} & 7B & \\makebox[5.4em][s]{40.69 (\\textcolor{red}{-16.45})} & \\makebox[5.4em][s]{44.72 (\\textcolor{blue}{+7.41})} & \\makebox[5.4em][s]{55.12 (\\textcolor{blue}{+7.24})} & \\makebox[5.4em][s]{94.87 (\\textcolor{blue}{+0.77})} & \\makebox[5.4em][s]{77.88 (\\textcolor{blue}{+5.88})} & \\makebox[5.4em][s]{86.38 (\\textcolor{blue}{+3.33})} & \\makebox[5.4em][s]{16.70 (\\textcolor{blue}{+5.63})} & \\makebox[5.4em][s]{23.91 (\\textcolor{blue}{+0.93})} & \\makebox[5.4em][s]{20.30 (\\textcolor{blue}{+3.27})} & \\makebox[5.4em][s]{10.32 (\\textcolor{blue}{+2.06})} \\\\\n"," mPLUG-Owl2~\\cite{mplugowl2} & 8B & \\makebox[5.4em][s]{25.89 (\\textcolor{blue}{+0.89})} & \\makebox[5.4em][s]{35.10 (\\textcolor{red}{-1.08})} & \\makebox[5.4em][s]{21.27 (\\textcolor{blue}{+7.04})} & \\makebox[5.4em][s]{27.56 (\\textcolor{blue}{+3.43})} & \\makebox[5.4em][s]{31.09 (\\textcolor{blue}{+3.75})} & \\makebox[5.4em][s]{29.33 (\\textcolor{blue}{+3.59})} & \\makebox[5.4em][s]{14.00 (\\textcolor{blue}{+1.17})} & \\makebox[5.4em][s]{7.84 (\\textcolor{blue}{+1.87})} & \\makebox[5.4em][s]{10.92 (\\textcolor{blue}{+1.52})} & \\makebox[5.4em][s]{7.88 (\\textcolor{blue}{+2.54})} \\\\\n"," MiniCPM-v2~\\cite{minicpmv2} & 8B & \\makebox[5.4em][s]{22.78 (\\textcolor{blue}{+0.46})} & \\makebox[5.4em][s]{28.81 (\\textcolor{blue}{+0.33})} & \\makebox[5.4em][s]{18.18 (\\textcolor{blue}{+7.54})} & \\makebox[5.4em][s]{92.37 (\\textcolor{blue}{+1.25})} & \\makebox[5.4em][s]{71.47 (\\textcolor{blue}{+2.45})} & \\makebox[5.4em][s]{81.92 (\\textcolor{blue}{+1.85})} & \\makebox[5.4em][s]{\\underline{26.56} (\\textcolor{blue}{+4.39})} & \\makebox[5.4em][s]{12.53 (\\textcolor{blue}{+1.52})} & \\makebox[5.4em][s]{19.54 (\\textcolor{blue}{+2.95})} & \\makebox[5.4em][s]{20.30 (\\textcolor{blue}{+0.25})} \\\\\n"," CogVLM~\\cite{cogvlm} & 17B & \\makebox[5.4em][s]{24.01 (\\textcolor{blue}{+0.80})} & \\makebox[5.4em][s]{40.04 (\\textcolor{red}{-0.16})} & \\makebox[5.4em][s]{37.14 (\\textcolor{blue}{+7.79})} & \\makebox[5.4em][s]{27.31 (\\textcolor{blue}{+3.36})} & \\makebox[5.4em][s]{44.93 (\\textcolor{blue}{+5.40})} & \\makebox[5.4em][s]{36.12 (\\textcolor{blue}{+4.38})} & \\makebox[5.4em][s]{17.94 (\\textcolor{blue}{+1.56})} & \\makebox[5.4em][s]{12.57 (\\textcolor{blue}{+0.73})} & \\makebox[5.4em][s]{15.26 (\\textcolor{blue}{+1.15})} & \\makebox[5.4em][s]{3.41 (\\textcolor{blue}{+1.66})} \\\\\n"," GLM-4V-plus~\\cite{chatglm4v} & - & \\makebox[5.4em][s]{41.00 (\\textcolor{red}{-18.83})} & \\makebox[5.4em][s]{39.55 (\\textcolor{blue}{+1.19})} & \\makebox[5.4em][s]{21.68 (\\textcolor{blue}{+0.16})} & \\makebox[5.4em][s]{18.63 (\\textcolor{blue}{+1.83})} & \\makebox[5.4em][s]{15.96 (\\textcolor{blue}{+3.16})} & \\makebox[5.4em][s]{17.30 (\\textcolor{blue}{+2.50})} & \\makebox[5.4em][s]{6.86 (\\textcolor{blue}{+1.17})} & \\makebox[5.4em][s]{7.72 (\\textcolor{blue}{+2.01})} & \\makebox[5.4em][s]{7.29 (\\textcolor{blue}{+1.59})} & \\makebox[5.4em][s]{8.83 (\\textcolor{blue}{+1.42})} \\\\\n"," GPT-4o~\\cite{gpt4v} & - & \\makebox[5.4em][s]{46.15 (\\textcolor{red}{-15.74})} & \\makebox[5.4em][s]{\\underline{48.19} (\\textcolor{blue}{+0.95})} & \\makebox[5.4em][s]{69.00 (\\textcolor{blue}{+0.19})} & \\makebox[5.4em][s]{95.39 (\\textcolor{blue}{+0.05})} & \\makebox[5.4em][s]{77.23 (\\textcolor{blue}{+1.17})} & \\makebox[5.4em][s]{86.31 (\\textcolor{blue}{+0.61})} & \\makebox[5.4em][s]{19.20 (\\textcolor{blue}{+1.45})} & \\makebox[5.4em][s]{9.31 (\\textcolor{blue}{+0.61})} & \\makebox[5.4em][s]{14.26 (\\textcolor{blue}{+1.03})} & \\makebox[5.4em][s]{15.42 (\\textcolor{blue}{+1.50})} \\\\\n"," \\midrule\n"," \\rowcolor[rgb]{ .922, .945, .871} \\multicolumn{12}{c}{ChartLLM-Based} \\\\\n"," \\midrule\n"," TinyChart\\textdagger~\\cite{tinychart} & 3B & \\makebox[5.4em][s]{7.69 (\\textcolor{blue}{+2.33})} & \\makebox[5.4em][s]{20.07 (\\textcolor{blue}{+1.62})} & \\makebox[5.4em][s]{23.21 (\\textcolor{blue}{+6.40})} & \\makebox[5.4em][s]{95.04 (\\textcolor{blue}{+1.44})} & \\makebox[5.4em][s]{74.41 (\\textcolor{blue}{+2.25})} & \\makebox[5.4em][s]{84.73 (\\textcolor{blue}{+1.85})} & \\makebox[5.4em][s]{14.68 (\\textcolor{blue}{+3.84})} & \\makebox[5.4em][s]{34.22 (\\textcolor{blue}{+7.18})} & \\makebox[5.4em][s]{24.45 (\\textcolor{blue}{+5.51})} & \\makebox[5.4em][s]{\\textbf{21.84 } (\\textcolor{blue}{+2.22})} \\\\\n"," ChartInstruct\\textdagger~\\cite{chartinstruct} & 7B & \\makebox[5.4em][s]{11.54 (\\textcolor{blue}{+1.72})} & \\makebox[5.4em][s]{34.79 (\\textcolor{blue}{+10.24})} & \\makebox[5.4em][s]{26.43 (\\textcolor{blue}{+11.39})} & \\makebox[5.4em][s]{85.93 (\\textcolor{blue}{+3.53})} & \\makebox[5.4em][s]{43.52 (\\textcolor{blue}{+2.88})} & \\makebox[5.4em][s]{64.73 (\\textcolor{blue}{+3.20})} & \\makebox[5.4em][s]{15.52 (\\textcolor{blue}{+2.71})} & \\makebox[5.4em][s]{\\underline{41.42} (\\textcolor{blue}{+2.03})} & \\makebox[5.4em][s]{28.47 (\\textcolor{blue}{+2.37})} & \\makebox[5.4em][s]{18.53 (\\textcolor{blue}{+3.75})} \\\\\n"," ChartLlama\\textdagger~\\cite{chartllama} & 7B & \\makebox[5.4em][s]{22.67 (\\textcolor{blue}{+2.13})} & \\makebox[5.4em][s]{22.54 (\\textcolor{blue}{+1.19})} & \\makebox[5.4em][s]{27.58 (\\textcolor{blue}{+14.85})} & \\makebox[5.4em][s]{91.42 (\\textcolor{blue}{+1.06})} & \\makebox[5.4em][s]{51.72 (\\textcolor{blue}{+2.76})} & \\makebox[5.4em][s]{71.57 (\\textcolor{blue}{+1.91})} & \\makebox[5.4em][s]{17.94 (\\textcolor{blue}{+3.71})} & \\makebox[5.4em][s]{\\textbf{40.47 } (\\textcolor{red}{-0.24})} & \\makebox[5.4em][s]{\\textbf{29.21 } (\\textcolor{blue}{+1.74})} & \\makebox[5.4em][s]{7.40 (\\textcolor{blue}{+2.70})} \\\\\n"," Sphinx-v2~\\cite{sphinx} & 7B & \\makebox[5.4em][s]{13.85 (\\textcolor{blue}{+4.03})} & \\makebox[5.4em][s]{30.11 (\\textcolor{blue}{+4.16})} & \\makebox[5.4em][s]{23.68 (\\textcolor{blue}{+9.99})} & \\makebox[5.4em][s]{62.80 (\\textcolor{blue}{+1.84})} & \\makebox[5.4em][s]{48.00 (\\textcolor{blue}{+4.08})} & \\makebox[5.4em][s]{55.40 (\\textcolor{blue}{+2.96})} & \\makebox[5.4em][s]{7.90 (\\textcolor{blue}{+4.47})} & \\makebox[5.4em][s]{7.35 (\\textcolor{blue}{+2.41})} & \\makebox[5.4em][s]{7.63 (\\textcolor{blue}{+3.44})} & \\makebox[5.4em][s]{6.88 (\\textcolor{blue}{+3.78})} \\\\\n"," LLaVA1.5~\\cite{llava1.5} & 7B & \\makebox[5.4em][s]{36.92 (\\textcolor{blue}{+2.10})} & \\makebox[5.4em][s]{38.39 (\\textcolor{red}{-1.11})} & \\makebox[5.4em][s]{26.95 (\\textcolor{blue}{+11.37})} & \\makebox[5.4em][s]{25.44 (\\textcolor{blue}{+5.32})} & \\makebox[5.4em][s]{31.68 (\\textcolor{blue}{+6.48})} & \\makebox[5.4em][s]{28.56 (\\textcolor{blue}{+5.90})} & \\makebox[5.4em][s]{18.21 (\\textcolor{blue}{+2.51})} & \\makebox[5.4em][s]{17.83 (\\textcolor{blue}{+6.76})} & \\makebox[5.4em][s]{18.02 (\\textcolor{blue}{+4.63})} & \\makebox[5.4em][s]{17.40 (\\textcolor{blue}{+2.23})} \\\\\n"," ViP-LLaVA~\\cite{vipllava} & 7B & \\makebox[5.4em][s]{26.23 (\\textcolor{blue}{+5.69})} & \\makebox[5.4em][s]{41.98 (\\textcolor{blue}{+4.97})} & \\makebox[5.4em][s]{28.79 (\\textcolor{blue}{+13.23})} & \\makebox[5.4em][s]{23.96 (\\textcolor{blue}{+6.36})} & \\makebox[5.4em][s]{29.04 (\\textcolor{blue}{+2.88})} & \\makebox[5.4em][s]{26.50 (\\textcolor{blue}{+4.62})} & \\makebox[5.4em][s]{14.31 (\\textcolor{blue}{+12.95})} & \\makebox[5.4em][s]{14.38 (\\textcolor{blue}{+11.79})} & \\makebox[5.4em][s]{14.35 (\\textcolor{blue}{+12.37})} & \\makebox[5.4em][s]{18.72 (\\textcolor{blue}{+3.68})} \\\\\n"," LLaVA-NEXT~\\cite{llavanext} & 7B & \\makebox[5.4em][s]{42.31 (\\textcolor{blue}{+21.77})} & \\makebox[5.4em][s]{49.40 (\\textcolor{blue}{+2.03})} & \\makebox[5.4em][s]{34.40 (\\textcolor{blue}{+3.32})} & \\makebox[5.4em][s]{75.82 (\\textcolor{blue}{+1.56})} & \\makebox[5.4em][s]{47.68 (\\textcolor{blue}{+1.38})} & \\makebox[5.4em][s]{61.75 (\\textcolor{blue}{+1.47})} & \\makebox[5.4em][s]{15.26 (\\textcolor{blue}{+1.41})} & \\makebox[5.4em][s]{8.93 (\\textcolor{blue}{+2.30})} & \\makebox[5.4em][s]{12.10 (\\textcolor{blue}{+1.86})} & \\makebox[5.4em][s]{9.02 (\\textcolor{blue}{+0.95})} \\\\\n"," IXC-2.5~\\cite{ixc2.5} & 7B & \\makebox[5.4em][s]{47.31 (\\textcolor{blue}{+0.01})} & \\makebox[5.4em][s]{43.38 (\\textcolor{blue}{+3.28})} & \\makebox[5.4em][s]{51.88 (\\textcolor{blue}{+8.56})} & \\makebox[5.4em][s]{94.88 (\\textcolor{blue}{+2.48})} & \\makebox[5.4em][s]{76.24 (\\textcolor{blue}{+1.92})} & \\makebox[5.4em][s]{85.56 (\\textcolor{blue}{+2.20})} & \\makebox[5.4em][s]{19.82 (\\textcolor{blue}{+2.13})} & \\makebox[5.4em][s]{14.70 (\\textcolor{blue}{+2.84})} & \\makebox[5.4em][s]{17.26 (\\textcolor{blue}{+2.48})} & \\makebox[5.4em][s]{16.83 (\\textcolor{blue}{+7.44})} \\\\\n"," Qwen2-VL~\\cite{qwenvl} & 7B & \\makebox[5.4em][s]{57.66 (\\textcolor{blue}{+0.52})} & \\makebox[5.4em][s]{45.54 (\\textcolor{blue}{+8.22})} & \\makebox[5.4em][s]{56.10 (\\textcolor{blue}{+8.21})} & \\makebox[5.4em][s]{94.40 (\\textcolor{blue}{+0.30})} & \\makebox[5.4em][s]{77.44 (\\textcolor{blue}{+5.44})} & \\makebox[5.4em][s]{85.92 (\\textcolor{blue}{+2.87})} & \\makebox[5.4em][s]{20.96 (\\textcolor{blue}{+9.89})} & \\makebox[5.4em][s]{24.45 (\\textcolor{blue}{+1.47})} & \\makebox[5.4em][s]{22.71 (\\textcolor{blue}{+5.68})} & \\makebox[5.4em][s]{18.53 (\\textcolor{blue}{+10.27})} \\\\\n"," mPLUG-Owl2~\\cite{mplugowl2} & 8B & \\makebox[5.4em][s]{29.38 (\\textcolor{blue}{+4.38})} & \\makebox[5.4em][s]{40.46 (\\textcolor{blue}{+4.29})} & \\makebox[5.4em][s]{29.15 (\\textcolor{blue}{+14.93})} & \\makebox[5.4em][s]{38.76 (\\textcolor{blue}{+14.63})} & \\makebox[5.4em][s]{40.34 (\\textcolor{blue}{+13.00})} & \\makebox[5.4em][s]{39.55 (\\textcolor{blue}{+13.82})} & \\makebox[5.4em][s]{13.01 (\\textcolor{blue}{+0.18})} & \\makebox[5.4em][s]{8.91 (\\textcolor{blue}{+2.94})} & \\makebox[5.4em][s]{10.96 (\\textcolor{blue}{+1.56})} & \\makebox[5.4em][s]{6.26 (\\textcolor{blue}{+0.92})} \\\\\n"," MiniCPM-v2~\\cite{minicpmv2} & 8B & \\makebox[5.4em][s]{24.21 (\\textcolor{blue}{+1.89})} & \\makebox[5.4em][s]{38.65 (\\textcolor{blue}{+10.17})} & \\makebox[5.4em][s]{23.73 (\\textcolor{blue}{+13.09})} & \\makebox[5.4em][s]{93.84 (\\textcolor{blue}{+2.72})} & \\makebox[5.4em][s]{71.86 (\\textcolor{blue}{+2.84})} & \\makebox[5.4em][s]{82.85 (\\textcolor{blue}{+2.78})} & \\makebox[5.4em][s]{\\textbf{27.68 } (\\textcolor{blue}{+5.51})} & \\makebox[5.4em][s]{24.55 (\\textcolor{blue}{+13.54})} & \\makebox[5.4em][s]{26.12 (\\textcolor{blue}{+9.53})} & \\makebox[5.4em][s]{\\underline{20.88} (\\textcolor{blue}{+0.83})} \\\\\n"," CogVLM~\\cite{cogvlm} & 17B & \\makebox[5.4em][s]{26.38 (\\textcolor{blue}{+3.17})} & \\makebox[5.4em][s]{41.05 (\\textcolor{blue}{+0.85})} & \\makebox[5.4em][s]{41.85 (\\textcolor{blue}{+12.50})} & \\makebox[5.4em][s]{33.41 (\\textcolor{blue}{+9.46})} & \\makebox[5.4em][s]{51.73 (\\textcolor{blue}{+12.20})} & \\makebox[5.4em][s]{42.57 (\\textcolor{blue}{+10.83})} & \\makebox[5.4em][s]{21.46 (\\textcolor{blue}{+5.08})} & \\makebox[5.4em][s]{14.74 (\\textcolor{blue}{+2.90})} & \\makebox[5.4em][s]{18.10 (\\textcolor{blue}{+3.99})} & \\makebox[5.4em][s]{2.48 (\\textcolor{blue}{+0.73})} \\\\\n"," GLM-4V-plus~\\cite{chatglm4v} & - & \\makebox[5.4em][s]{\\underline{60.18} (\\textcolor{blue}{+0.35})} & \\makebox[5.4em][s]{47.00 (\\textcolor{blue}{+8.64})} & \\makebox[5.4em][s]{37.19 (\\textcolor{blue}{+15.67})} & \\makebox[5.4em][s]{19.74 (\\textcolor{blue}{+2.94})} & \\makebox[5.4em][s]{18.04 (\\textcolor{blue}{+5.24})} & \\makebox[5.4em][s]{19.66 (\\textcolor{blue}{+4.86})} & \\makebox[5.4em][s]{9.75 (\\textcolor{blue}{+4.06})} & \\makebox[5.4em][s]{8.97 (\\textcolor{blue}{+3.26})} & \\makebox[5.4em][s]{9.36 (\\textcolor{blue}{+3.66})} & \\makebox[5.4em][s]{9.74 (\\textcolor{blue}{+2.34})} \\\\\n"," GPT-4o~\\cite{gpt4v} & - & \\makebox[5.4em][s]{\\textbf{61.89 } (\\textcolor{blue}{+0.00})} & \\makebox[5.4em][s]{\\textbf{50.42 } (\\textcolor{blue}{+3.17})} & \\makebox[5.4em][s]{\\textbf{73.89 } (\\textcolor{blue}{+5.08})} & \\makebox[5.4em][s]{{\\textbf{98.63 }} (\\textcolor{blue}{+3.29})} & \\makebox[5.4em][s]{\\textbf{79.49 } (\\textcolor{blue}{+3.43})} & \\makebox[5.4em][s]{\\textbf{89.06 } (\\textcolor{blue}{+3.36})} & \\makebox[5.4em][s]{23.65 (\\textcolor{blue}{+5.90})} & \\makebox[5.4em][s]{11.07 (\\textcolor{blue}{+2.37})} & \\makebox[5.4em][s]{17.36 (\\textcolor{blue}{+4.14})} & \\makebox[5.4em][s]{16.04 (\\textcolor{blue}{+2.12})} \\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," }\n"," \\raggedright\n"," \\caption{Performance of multimodal models on ChartMind and three structured-output CQA datasets. The best results are highlighted in \\textbf{bold}, and the second-best results are \\underline{underlined}. \\textdagger Specialized CQA models.}\n"," \\vskip -0.15in\n"," \\label{tab:results1}\n","\\end{table*}\n"]],"string":"[\n [\n \"\\\\begin{table*}[!ht]\\n\",\n \" \\\\centering\\n\",\n \" \\\\resizebox{\\\\linewidth}{!}{\\n\",\n \" \\\\begin{tabular}{lccccccccccr}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\multirow{2}[4]{*}{ Models} & \\\\multirow{2}[4]{*}{Size} & \\\\multicolumn{3}{c}{ChartMind} & \\\\multicolumn{3}{c}{ChartQA } & \\\\multicolumn{3}{c}{Chart-to-Text} & OpenCQA \\\\\\\\\\n\",\n \"\\\\cmidrule{3-12} & & ACC & Avg.CIDEr & Avg.GPT-4o Score & Aug. ACC & Hum. ACC & Avg. ACC & Pew. BLEU & Statista. BLEU & Avg. BLEU & Avg. BLEU \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\rowcolor[rgb]{ .922, .945, .871} \\\\multicolumn{12}{c}{Instruction-Following-Based~\\\\cite{wei2021finetuned}} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" TinyChart\\\\textdagger~\\\\cite{tinychart} & 3B & 5.36 & 18.45 & 16.81 & 93.60 & 72.16 & 82.88 & 10.84 & 27.04 & 18.94 & 19.62 \\\\\\\\\\n\",\n \" ChartInstruct\\\\textdagger~\\\\cite{chartinstruct} & 7B & 9.82 & 24.55 & 15.05 & 82.40 & 40.64 & 61.52 & 12.81 & 39.39 & 26.10 & 14.78 \\\\\\\\\\n\",\n \" ChartLlama\\\\textdagger~\\\\cite{chartllama} & 7B & 20.54 & 21.34 & 12.72 & 90.36 & 48.96 & 69.66 & 14.23 & 40.71 & 27.47 & 4.70 \\\\\\\\\\n\",\n \" Sphinx-v2~\\\\cite{sphinx} & 7B & 9.82 & 25.95 & 13.69 & 60.96 & 43.92 & 52.44 & 3.43 & 4.94 & 4.19 & 3.10 \\\\\\\\\\n\",\n \" LLaVA1.5~\\\\cite{llava1.5} & 7B & 34.82 & 39.50 & 15.58 & 20.12 & 25.20 & 22.66 & 15.70 & 11.07 & 13.39 & 15.17 \\\\\\\\\\n\",\n \" ViP-LLaVA~\\\\cite{vipllava} & 7B & 20.54 & 37.01 & 15.56 & 17.60 & 26.16 & 21.88 & 1.36 & 2.59 & 1.98 & 15.04 \\\\\\\\\\n\",\n \" LLaVA-NEXT~\\\\cite{llavanext} & 7B & 20.54 & 47.37 & 31.09 & 74.26 & 46.30 & 60.28 & 13.85 & 6.63 & 10.24 & 8.07 \\\\\\\\\\n\",\n \" IXC-2.5~\\\\cite{ixc2.5} & 7B & 47.30 & 40.10 & 43.31 & 92.40 & 74.32 & 83.36 & 17.69 & 11.86 & 14.78 & 9.39 \\\\\\\\\\n\",\n \" Qwen2-VL~\\\\cite{qwenvl} & 7B & 57.14 & 37.32 & 47.89 & 94.10 & 72.00 & 83.05 & 11.07 & 22.98 & 17.03 & 8.26 \\\\\\\\\\n\",\n \" mPLUG-Owl2~\\\\cite{mplugowl2} & 8B & 25.00 & 36.17 & 14.22 & 24.13 & 27.34 & 25.74 & 12.83 & 5.97 & 9.40 & 5.34 \\\\\\\\\\n\",\n \" MiniCPM-v2~\\\\cite{minicpmv2} & 8B & 22.32 & 28.48 & 10.63 & 91.12 & 69.02 & 80.07 & 22.17 & 11.01 & 16.59 & 20.05 \\\\\\\\\\n\",\n \" CogVLM~\\\\cite{cogvlm} & 17B & 23.21 & 40.20 & 29.35 & 23.95 & 39.53 & 31.74 & 16.38 & 11.84 & 14.11 & 1.75 \\\\\\\\\\n\",\n \" GLM-4V-plus~\\\\cite{chatglm4v} & - & 59.83 & 38.36 & 21.52 & 16.80 & 12.80 & 14.80 & 5.69 & 5.71 & 5.70 & 7.41 \\\\\\\\\\n\",\n \" GPT-4o~\\\\cite{gpt4v} & - & \\\\textbf{61.89} & 47.25 & 68.81 & 95.34 & 76.06 & 85.70 & 17.75 & 8.70 & 13.23 & 13.92 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\rowcolor[rgb]{ .922, .945, .871} \\\\multicolumn{12}{c}{OCR-Enhanced~\\\\cite{liu2023deplot}} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" TinyChart\\\\textdagger~\\\\cite{tinychart} & 3B & \\\\makebox[5.4em][s]{6.71 (\\\\textcolor{blue}{+1.35})} & \\\\makebox[5.4em][s]{13.91 (\\\\textcolor{red}{-4.54})} & \\\\makebox[5.4em][s]{17.91 (\\\\textcolor{blue}{+1.10})} & \\\\makebox[5.4em][s]{94.86 (\\\\textcolor{blue}{+1.26})} & \\\\makebox[5.4em][s]{73.95 (\\\\textcolor{blue}{+1.79})} & \\\\makebox[5.4em][s]{84.41 (\\\\textcolor{blue}{+1.53})} & \\\\makebox[5.4em][s]{13.85 (\\\\textcolor{blue}{+3.01})} & \\\\makebox[5.4em][s]{28.27 (\\\\textcolor{blue}{+1.23})} & \\\\makebox[5.4em][s]{21.06 (\\\\textcolor{blue}{+2.12})} & \\\\makebox[5.4em][s]{20.15 (\\\\textcolor{blue}{+0.53})} \\\\\\\\\\n\",\n \" ChartInstruct\\\\textdagger~\\\\cite{chartinstruct} & 7B & \\\\makebox[5.4em][s]{{10.01} (\\\\textcolor{blue}{+0.19})} & \\\\makebox[5.4em][s]{32.80 (\\\\textcolor{blue}{+8.25})} & \\\\makebox[5.4em][s]{23.42 (\\\\textcolor{blue}{+8.37})} & \\\\makebox[5.4em][s]{83.74 (\\\\textcolor{blue}{+1.34})} & \\\\makebox[5.4em][s]{42.17 (\\\\textcolor{blue}{+1.53})} & \\\\makebox[5.4em][s]{62.96 (\\\\textcolor{blue}{+1.44})} & \\\\makebox[5.4em][s]{14.95 (\\\\textcolor{blue}{+2.14})} & \\\\makebox[5.4em][s]{40.83 (\\\\textcolor{blue}{+1.44})} & \\\\makebox[5.4em][s]{27.89 (\\\\textcolor{blue}{+1.79})} & \\\\makebox[5.4em][s]{16.01 (\\\\textcolor{blue}{+1.23})} \\\\\\\\\\n\",\n \" ChartLlama\\\\textdagger~\\\\cite{chartllama} & 7B & \\\\makebox[5.4em][s]{{22.03} (\\\\textcolor{blue}{+1.49})} & \\\\makebox[5.4em][s]{21.07 (\\\\textcolor{red}{-0.27})} & \\\\makebox[5.4em][s]{26.70 (\\\\textcolor{blue}{+13.97})} & \\\\makebox[5.4em][s]{90.85 (\\\\textcolor{blue}{+0.49})} & \\\\makebox[5.4em][s]{49.26 (\\\\textcolor{blue}{+0.30})} & \\\\makebox[5.4em][s]{70.06 (\\\\textcolor{blue}{+0.40})} & \\\\makebox[5.4em][s]{16.02 (\\\\textcolor{blue}{+1.79})} & \\\\makebox[5.4em][s]{39.97 (\\\\textcolor{red}{-0.74})} & \\\\makebox[5.4em][s]{28.00 (\\\\textcolor{blue}{+0.53})} & \\\\makebox[5.4em][s]{5.89 (\\\\textcolor{blue}{+1.19})} \\\\\\\\\\n\",\n \" Sphinx-v2~\\\\cite{sphinx} & 7B & \\\\makebox[5.4em][s]{11.54 (\\\\textcolor{blue}{+1.72})} & \\\\makebox[5.4em][s]{24.14 (\\\\textcolor{red}{-1.81})} & \\\\makebox[5.4em][s]{17.21 (\\\\textcolor{blue}{+3.52})} & \\\\makebox[5.4em][s]{64.08 (\\\\textcolor{blue}{+3.12})} & \\\\makebox[5.4em][s]{45.49 (\\\\textcolor{blue}{+1.57})} & \\\\makebox[5.4em][s]{54.79 (\\\\textcolor{blue}{+2.35})} & \\\\makebox[5.4em][s]{8.81 (\\\\textcolor{blue}{+5.38})} & \\\\makebox[5.4em][s]{2.39 (\\\\textcolor{red}{-2.55})} & \\\\makebox[5.4em][s]{5.60 (\\\\textcolor{blue}{+1.41})} & \\\\makebox[5.4em][s]{3.16 (\\\\textcolor{blue}{+0.06})} \\\\\\\\\\n\",\n \" LLaVA1.5~\\\\cite{llava1.5} & 7B & \\\\makebox[5.4em][s]{36.15 (\\\\textcolor{blue}{+1.33})} & \\\\makebox[5.4em][s]{33.49 (\\\\textcolor{red}{-6.01})} & \\\\makebox[5.4em][s]{21.03 (\\\\textcolor{blue}{+5.45})} & \\\\makebox[5.4em][s]{19.73 (\\\\textcolor{red}{-0.39})} & \\\\makebox[5.4em][s]{25.95 (\\\\textcolor{blue}{+0.75})} & \\\\makebox[5.4em][s]{22.84 (\\\\textcolor{blue}{+0.18})} & \\\\makebox[5.4em][s]{15.94 (\\\\textcolor{blue}{+0.24})} & \\\\makebox[5.4em][s]{12.67 (\\\\textcolor{blue}{+1.60})} & \\\\makebox[5.4em][s]{14.30 (\\\\textcolor{blue}{+0.91})} & \\\\makebox[5.4em][s]{16.31 (\\\\textcolor{blue}{+1.14})} \\\\\\\\\\n\",\n \" ViP-LLaVA~\\\\cite{vipllava} & 7B & \\\\makebox[5.4em][s]{25.38 (\\\\textcolor{blue}{+4.84})} & \\\\makebox[5.4em][s]{36.77 (\\\\textcolor{red}{-0.24})} & \\\\makebox[5.4em][s]{26.45 (\\\\textcolor{blue}{+10.89})} & \\\\makebox[5.4em][s]{27.12 (\\\\textcolor{blue}{+9.52})} & \\\\makebox[5.4em][s]{24.94 (\\\\textcolor{red}{-1.22})} & \\\\makebox[5.4em][s]{26.03 (\\\\textcolor{blue}{+4.15})} & \\\\makebox[5.4em][s]{14.13 (\\\\textcolor{blue}{+12.77})} & \\\\makebox[5.4em][s]{14.37 (\\\\textcolor{blue}{+11.78})} & \\\\makebox[5.4em][s]{14.25 (\\\\textcolor{blue}{+12.27})} & \\\\makebox[5.4em][s]{18.08 (\\\\textcolor{blue}{+3.04})} \\\\\\\\\\n\",\n \" LLaVA-NEXT~\\\\cite{llavanext} & 7B & \\\\makebox[5.4em][s]{41.15 (\\\\textcolor{blue}{+20.61})} & \\\\makebox[5.4em][s]{47.83 (\\\\textcolor{blue}{+0.46})} & \\\\makebox[5.4em][s]{31.51 (\\\\textcolor{blue}{+0.42})} & \\\\makebox[5.4em][s]{70.47 (\\\\textcolor{red}{-3.79})} & \\\\makebox[5.4em][s]{52.68 (\\\\textcolor{blue}{+6.38})} & \\\\makebox[5.4em][s]{61.58 (\\\\textcolor{blue}{+1.30})} & \\\\makebox[5.4em][s]{15.16 (\\\\textcolor{blue}{+1.31})} & \\\\makebox[5.4em][s]{8.82 (\\\\textcolor{blue}{+2.19})} & \\\\makebox[5.4em][s]{11.99 (\\\\textcolor{blue}{+1.75})} & \\\\makebox[5.4em][s]{8.25 (\\\\textcolor{blue}{+0.18})} \\\\\\\\\\n\",\n \" IXC-2.5~\\\\cite{ixc2.5} & 7B & \\\\makebox[5.4em][s]{42.31 (\\\\textcolor{red}{-4.99})} & \\\\makebox[5.4em][s]{40.35 (\\\\textcolor{blue}{+0.24})} & \\\\makebox[5.4em][s]{45.38 (\\\\textcolor{blue}{+2.06})} & \\\\makebox[5.4em][s]{94.23 (\\\\textcolor{blue}{+1.83})} & \\\\makebox[5.4em][s]{73.40 (\\\\textcolor{red}{-0.92})} & \\\\makebox[5.4em][s]{83.82 (\\\\textcolor{blue}{+0.46})} & \\\\makebox[5.4em][s]{17.03 (\\\\textcolor{red}{-0.66})} & \\\\makebox[5.4em][s]{12.34 (\\\\textcolor{blue}{+0.48})} & \\\\makebox[5.4em][s]{14.68 (\\\\textcolor{red}{-0.10})} & \\\\makebox[5.4em][s]{14.53 (\\\\textcolor{blue}{+5.14})} \\\\\\\\\\n\",\n \" Qwen2-VL~\\\\cite{qwenvl} & 7B & \\\\makebox[5.4em][s]{42.31 (\\\\textcolor{red}{-14.83})} & \\\\makebox[5.4em][s]{36.04 (\\\\textcolor{red}{-1.27})} & \\\\makebox[5.4em][s]{49.28 (\\\\textcolor{blue}{+1.39})} & \\\\makebox[5.4em][s]{94.23 (\\\\textcolor{blue}{+0.13})} & \\\\makebox[5.4em][s]{75.96 (\\\\textcolor{blue}{+3.96})} & \\\\makebox[5.4em][s]{85.10 (\\\\textcolor{blue}{+2.05})} & \\\\makebox[5.4em][s]{11.08 (\\\\textcolor{blue}{+0.01})} & \\\\makebox[5.4em][s]{23.21 (\\\\textcolor{blue}{+0.23})} & \\\\makebox[5.4em][s]{17.15 (\\\\textcolor{blue}{+0.12})} & \\\\makebox[5.4em][s]{11.75 (\\\\textcolor{blue}{+3.49})} \\\\\\\\\\n\",\n \" mPLUG-Owl2~\\\\cite{mplugowl2} & 8B & \\\\makebox[5.4em][s]{27.62 (\\\\textcolor{blue}{+2.62})} & \\\\makebox[5.4em][s]{30.60 (\\\\textcolor{red}{-5.57})} & \\\\makebox[5.4em][s]{24.67 (\\\\textcolor{blue}{+10.44})} & \\\\makebox[5.4em][s]{35.58 (\\\\textcolor{blue}{+11.45})} & \\\\makebox[5.4em][s]{37.18 (\\\\textcolor{blue}{+9.84})} & \\\\makebox[5.4em][s]{36.38 (\\\\textcolor{blue}{+10.65})} & \\\\makebox[5.4em][s]{11.82 (\\\\textcolor{red}{-1.01})} & \\\\makebox[5.4em][s]{7.30 (\\\\textcolor{blue}{+1.33})} & \\\\makebox[5.4em][s]{9.56 (\\\\textcolor{blue}{+0.16})} & \\\\makebox[5.4em][s]{4.45 (\\\\textcolor{red}{-0.89})} \\\\\\\\\\n\",\n \" MiniCPM-v2~\\\\cite{minicpmv2} & 8B & \\\\makebox[5.4em][s]{23.04 (\\\\textcolor{blue}{+0.72}} & \\\\makebox[5.4em][s]{19.73 (\\\\textcolor{red}{-8.75})} & \\\\makebox[5.4em][s]{18.10 (\\\\textcolor{blue}{+7.47})} & \\\\makebox[5.4em][s]{\\\\makebox[5.4em][s]{92.36 (\\\\textcolor{blue}{+1.24})}} & \\\\makebox[5.4em][s]{73.21 (\\\\textcolor{blue}{+4.19})} & \\\\makebox[5.4em][s]{82.79 (\\\\textcolor{blue}{+2.72})} & \\\\makebox[5.4em][s]{20.93 (\\\\textcolor{red}{-1.24})} & \\\\makebox[5.4em][s]{5.75 (\\\\textcolor{red}{-5.26})} & \\\\makebox[5.4em][s]{13.34 (\\\\textcolor{red}{-3.25})} & \\\\makebox[5.4em][s]{20.60 (\\\\textcolor{blue}{+0.55})} \\\\\\\\\\n\",\n \" CogVLM~\\\\cite{cogvlm} & 17B & \\\\makebox[5.4em][s]{25.54 (\\\\textcolor{blue}{+2.33})} & \\\\makebox[5.4em][s]{39.00 (\\\\textcolor{red}{-1.20})} & \\\\makebox[5.4em][s]{36.80 (\\\\textcolor{blue}{+7.45})} & \\\\makebox[5.4em][s]{29.81 (\\\\textcolor{blue}{+5.86})} & \\\\makebox[5.4em][s]{48.72 (\\\\textcolor{blue}{+9.19})} & \\\\makebox[5.4em][s]{39.27 (\\\\textcolor{blue}{+7.53})} & \\\\makebox[5.4em][s]{20.85 (\\\\textcolor{blue}{+4.47})} & \\\\makebox[5.4em][s]{13.88 (\\\\textcolor{blue}{+2.04})} & \\\\makebox[5.4em][s]{17.37 (\\\\textcolor{blue}{+3.26})} & \\\\makebox[5.4em][s]{1.79 (\\\\textcolor{blue}{+0.04})} \\\\\\\\\\n\",\n \" GLM-4V-plus~\\\\cite{chatglm4v} & - & \\\\makebox[5.4em][s]{44.64 (\\\\textcolor{red}{-15.19})} & \\\\makebox[5.4em][s]{44.83 (\\\\textcolor{blue}{+6.47})} & \\\\makebox[5.4em][s]{35.79 (\\\\textcolor{blue}{+14.27})} & \\\\makebox[5.4em][s]{17.95 (\\\\textcolor{blue}{+1.15})} & \\\\makebox[5.4em][s]{16.87 (\\\\textcolor{blue}{+4.07})} & \\\\makebox[5.4em][s]{17.41 (\\\\textcolor{blue}{+2.61})} & \\\\makebox[5.4em][s]{7.91 (\\\\textcolor{blue}{+2.22})} & \\\\makebox[5.4em][s]{7.63 (\\\\textcolor{blue}{+1.92})} & \\\\makebox[5.4em][s]{7.77 (\\\\textcolor{blue}{+2.07})} & \\\\makebox[5.4em][s]{8.72 (\\\\textcolor{blue}{+1.31})} \\\\\\\\\\n\",\n \" GPT-4o~\\\\cite{gpt4v} & - & \\\\makebox[5.4em][s]{49.31 (\\\\textcolor{red}{-12.58})} & \\\\makebox[5.4em][s]{46.48 (\\\\textcolor{red}{-0.76})} & \\\\makebox[5.4em][s]{\\\\underline{71.79} (\\\\textcolor{blue}{+2.98})} & \\\\makebox[5.4em][s]{\\\\underline{96.20} (\\\\textcolor{blue}{+0.86})} & \\\\makebox[5.4em][s]{\\\\underline{78.04} (\\\\textcolor{blue}{+1.98})} & \\\\makebox[5.4em][s]{\\\\underline{87.12} (\\\\textcolor{blue}{+1.42})} & \\\\makebox[5.4em][s]{20.13 (\\\\textcolor{blue}{+2.38})} & \\\\makebox[5.4em][s]{9.86 (\\\\textcolor{blue}{+1.16})} & \\\\makebox[5.4em][s]{15.00 (\\\\textcolor{blue}{+1.77})} & \\\\makebox[5.4em][s]{14.85 (\\\\textcolor{blue}{+0.93})} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\rowcolor[rgb]{ .922, .945, .871} \\\\multicolumn{12}{c}{COT-Based~\\\\cite{wei2022chain}} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" TinyChart\\\\textdagger~\\\\cite{tinychart} & 3B & \\\\makebox[5.4em][s]{6.01 (\\\\textcolor{blue}{+0.65})} & \\\\makebox[5.4em][s]{13.58 (\\\\textcolor{red}{-4.87})} & \\\\makebox[5.4em][s]{19.30 (\\\\textcolor{blue}{+2.49})} & \\\\makebox[5.4em][s]{94.84 (\\\\textcolor{blue}{+1.24})} & \\\\makebox[5.4em][s]{74.46 (\\\\textcolor{blue}{+2.30})} & \\\\makebox[5.4em][s]{84.65 (\\\\textcolor{blue}{+1.77})} & \\\\makebox[5.4em][s]{12.31 (\\\\textcolor{blue}{+1.47})} & \\\\makebox[5.4em][s]{28.53 (\\\\textcolor{blue}{+1.49})} & \\\\makebox[5.4em][s]{20.42 (\\\\textcolor{blue}{+1.48})} & \\\\makebox[5.4em][s]{20.74 (\\\\textcolor{blue}{+1.12})} \\\\\\\\\\n\",\n \" ChartInstruct\\\\textdagger~\\\\cite{chartinstruct} & 7B & \\\\makebox[5.4em][s]{9.96 (\\\\textcolor{blue}{+0.14})} & \\\\makebox[5.4em][s]{31.95 (\\\\textcolor{blue}{+7.40})} & \\\\makebox[5.4em][s]{22.44 (\\\\textcolor{blue}{+7.39})} & \\\\makebox[5.4em][s]{83.35 (\\\\textcolor{blue}{+0.95})} & \\\\makebox[5.4em][s]{42.74 (\\\\textcolor{blue}{+2.10})} & \\\\makebox[5.4em][s]{63.05 (\\\\textcolor{blue}{+1.53})} & \\\\makebox[5.4em][s]{14.34 (\\\\textcolor{blue}{+1.53})} & \\\\makebox[5.4em][s]{41.32 (\\\\textcolor{blue}{+1.93})} & \\\\makebox[5.4em][s]{27.83 (\\\\textcolor{blue}{+1.73})} & \\\\makebox[5.4em][s]{15.25 (\\\\textcolor{blue}{+0.47})} \\\\\\\\\\n\",\n \" ChartLlama\\\\textdagger~\\\\cite{chartllama} & 7B & \\\\makebox[5.4em][s]{21.44 (\\\\textcolor{blue}{+0.90})} & \\\\makebox[5.4em][s]{18.99 (\\\\textcolor{red}{-2.36})} & \\\\makebox[5.4em][s]{21.77 (\\\\textcolor{blue}{+9.04})} & \\\\makebox[5.4em][s]{91.63 (\\\\textcolor{blue}{+1.27})} & \\\\makebox[5.4em][s]{50.04 (\\\\textcolor{blue}{+1.08})} & \\\\makebox[5.4em][s]{70.84 (\\\\textcolor{blue}{+1.18})} & \\\\makebox[5.4em][s]{15.76 (\\\\textcolor{blue}{+1.53})} & \\\\makebox[5.4em][s]{\\\\underline{41.42} (\\\\textcolor{blue}{+0.71})} & \\\\makebox[5.4em][s]{\\\\underline{28.59} (\\\\textcolor{blue}{+1.12})} & \\\\makebox[5.4em][s]{6.32 (\\\\textcolor{blue}{+1.62})} \\\\\\\\\\n\",\n \" Sphinx-v2~\\\\cite{sphinx} & 7B & \\\\makebox[5.4em][s]{9.91 (\\\\textcolor{blue}{+0.09})} & \\\\makebox[5.4em][s]{25.03 (\\\\textcolor{red}{-0.92})} & \\\\makebox[5.4em][s]{16.26 (\\\\textcolor{blue}{+2.57})} & \\\\makebox[5.4em][s]{61.86 (\\\\textcolor{blue}{+0.90})} & \\\\makebox[5.4em][s]{46.79 (\\\\textcolor{blue}{+2.87})} & \\\\makebox[5.4em][s]{54.33 (\\\\textcolor{blue}{+1.89})} & \\\\makebox[5.4em][s]{3.53 (\\\\textcolor{blue}{+0.10})} & \\\\makebox[5.4em][s]{5.09 (\\\\textcolor{blue}{+0.15})} & \\\\makebox[5.4em][s]{4.31 (\\\\textcolor{blue}{+0.12})} & \\\\makebox[5.4em][s]{3.13 (\\\\textcolor{blue}{+0.03})} \\\\\\\\\\n\",\n \" LLaVA1.5~\\\\cite{llava1.5} & 7B & \\\\makebox[5.4em][s]{35.77 (\\\\textcolor{blue}{+0.95})} & \\\\makebox[5.4em][s]{35.61 (\\\\textcolor{red}{-3.89})} & \\\\makebox[5.4em][s]{19.68 (\\\\textcolor{blue}{+4.10})} & \\\\makebox[5.4em][s]{16.90 (\\\\textcolor{red}{-3.22})} & \\\\makebox[5.4em][s]{28.57 (\\\\textcolor{blue}{+3.37})} & \\\\makebox[5.4em][s]{22.74 (\\\\textcolor{blue}{+0.08})} & \\\\makebox[5.4em][s]{15.20 (\\\\textcolor{red}{-0.50})} & \\\\makebox[5.4em][s]{11.66 (\\\\textcolor{blue}{+0.59})} & \\\\makebox[5.4em][s]{13.43 (\\\\textcolor{blue}{+0.04})} & \\\\makebox[5.4em][s]{15.93 (\\\\textcolor{blue}{+0.76})} \\\\\\\\\\n\",\n \" ViP-LLaVA~\\\\cite{vipllava} & 7B & \\\\makebox[5.4em][s]{23.31 (\\\\textcolor{blue}{+2.77})} & \\\\makebox[5.4em][s]{36.13 (\\\\textcolor{red}{-0.88})} & \\\\makebox[5.4em][s]{22.24 (\\\\textcolor{blue}{+6.68})} & \\\\makebox[5.4em][s]{22.12 (\\\\textcolor{blue}{+4.52})} & \\\\makebox[5.4em][s]{28.21 (\\\\textcolor{blue}{+2.05})} & \\\\makebox[5.4em][s]{25.17 (\\\\textcolor{blue}{+3.29})} & \\\\makebox[5.4em][s]{15.48 (\\\\textcolor{blue}{+14.12})} & \\\\makebox[5.4em][s]{12.20 (\\\\textcolor{blue}{+9.61})} & \\\\makebox[5.4em][s]{13.84 (\\\\textcolor{blue}{+11.86})} & \\\\makebox[5.4em][s]{15.67 (\\\\textcolor{blue}{+0.63})} \\\\\\\\\\n\",\n \" LLaVA-NEXT~\\\\cite{llavanext} & 7B & \\\\makebox[5.4em][s]{40.23 (\\\\textcolor{blue}{+19.69})} & \\\\makebox[5.4em][s]{47.44 (\\\\textcolor{blue}{+0.07})} & \\\\makebox[5.4em][s]{27.34 (\\\\textcolor{red}{-3.75})} & \\\\makebox[5.4em][s]{68.49 (\\\\textcolor{red}{-5.77})} & \\\\makebox[5.4em][s]{52.13 (\\\\textcolor{blue}{+5.83})} & \\\\makebox[5.4em][s]{60.31 (\\\\textcolor{blue}{+0.03})} & \\\\makebox[5.4em][s]{14.81 (\\\\textcolor{blue}{+0.96})} & \\\\makebox[5.4em][s]{6.29 (\\\\textcolor{red}{-0.34})} & \\\\makebox[5.4em][s]{10.55 (\\\\textcolor{blue}{+0.31})} & \\\\makebox[5.4em][s]{8.09 (\\\\textcolor{blue}{+0.02})} \\\\\\\\\\n\",\n \" IXC-2.5~\\\\cite{ixc2.5} & 7B & \\\\makebox[5.4em][s]{41.15 (\\\\textcolor{red}{-6.15})} & \\\\makebox[5.4em][s]{41.23 (\\\\textcolor{blue}{+1.13})} & \\\\makebox[5.4em][s]{46.73 (\\\\textcolor{blue}{+3.42})} & \\\\makebox[5.4em][s]{93.91 (\\\\textcolor{blue}{+1.51})} & \\\\makebox[5.4em][s]{72.82 (\\\\textcolor{red}{-1.50})} & \\\\makebox[5.4em][s]{83.37 (\\\\textcolor{blue}{+0.01})} & \\\\makebox[5.4em][s]{17.36 (\\\\textcolor{blue}{+0.23})} & \\\\makebox[5.4em][s]{11.92 (\\\\textcolor{blue}{+0.06})} & \\\\makebox[5.4em][s]{14.64 (\\\\textcolor{red}{-0.14})} & \\\\makebox[5.4em][s]{14.39 (\\\\textcolor{blue}{+5.00})} \\\\\\\\\\n\",\n \" Qwen2-VL~\\\\cite{qwenvl} & 7B & \\\\makebox[5.4em][s]{40.69 (\\\\textcolor{red}{-16.45})} & \\\\makebox[5.4em][s]{44.72 (\\\\textcolor{blue}{+7.41})} & \\\\makebox[5.4em][s]{55.12 (\\\\textcolor{blue}{+7.24})} & \\\\makebox[5.4em][s]{94.87 (\\\\textcolor{blue}{+0.77})} & \\\\makebox[5.4em][s]{77.88 (\\\\textcolor{blue}{+5.88})} & \\\\makebox[5.4em][s]{86.38 (\\\\textcolor{blue}{+3.33})} & \\\\makebox[5.4em][s]{16.70 (\\\\textcolor{blue}{+5.63})} & \\\\makebox[5.4em][s]{23.91 (\\\\textcolor{blue}{+0.93})} & \\\\makebox[5.4em][s]{20.30 (\\\\textcolor{blue}{+3.27})} & \\\\makebox[5.4em][s]{10.32 (\\\\textcolor{blue}{+2.06})} \\\\\\\\\\n\",\n \" mPLUG-Owl2~\\\\cite{mplugowl2} & 8B & \\\\makebox[5.4em][s]{25.89 (\\\\textcolor{blue}{+0.89})} & \\\\makebox[5.4em][s]{35.10 (\\\\textcolor{red}{-1.08})} & \\\\makebox[5.4em][s]{21.27 (\\\\textcolor{blue}{+7.04})} & \\\\makebox[5.4em][s]{27.56 (\\\\textcolor{blue}{+3.43})} & \\\\makebox[5.4em][s]{31.09 (\\\\textcolor{blue}{+3.75})} & \\\\makebox[5.4em][s]{29.33 (\\\\textcolor{blue}{+3.59})} & \\\\makebox[5.4em][s]{14.00 (\\\\textcolor{blue}{+1.17})} & \\\\makebox[5.4em][s]{7.84 (\\\\textcolor{blue}{+1.87})} & \\\\makebox[5.4em][s]{10.92 (\\\\textcolor{blue}{+1.52})} & \\\\makebox[5.4em][s]{7.88 (\\\\textcolor{blue}{+2.54})} \\\\\\\\\\n\",\n \" MiniCPM-v2~\\\\cite{minicpmv2} & 8B & \\\\makebox[5.4em][s]{22.78 (\\\\textcolor{blue}{+0.46})} & \\\\makebox[5.4em][s]{28.81 (\\\\textcolor{blue}{+0.33})} & \\\\makebox[5.4em][s]{18.18 (\\\\textcolor{blue}{+7.54})} & \\\\makebox[5.4em][s]{92.37 (\\\\textcolor{blue}{+1.25})} & \\\\makebox[5.4em][s]{71.47 (\\\\textcolor{blue}{+2.45})} & \\\\makebox[5.4em][s]{81.92 (\\\\textcolor{blue}{+1.85})} & \\\\makebox[5.4em][s]{\\\\underline{26.56} (\\\\textcolor{blue}{+4.39})} & \\\\makebox[5.4em][s]{12.53 (\\\\textcolor{blue}{+1.52})} & \\\\makebox[5.4em][s]{19.54 (\\\\textcolor{blue}{+2.95})} & \\\\makebox[5.4em][s]{20.30 (\\\\textcolor{blue}{+0.25})} \\\\\\\\\\n\",\n \" CogVLM~\\\\cite{cogvlm} & 17B & \\\\makebox[5.4em][s]{24.01 (\\\\textcolor{blue}{+0.80})} & \\\\makebox[5.4em][s]{40.04 (\\\\textcolor{red}{-0.16})} & \\\\makebox[5.4em][s]{37.14 (\\\\textcolor{blue}{+7.79})} & \\\\makebox[5.4em][s]{27.31 (\\\\textcolor{blue}{+3.36})} & \\\\makebox[5.4em][s]{44.93 (\\\\textcolor{blue}{+5.40})} & \\\\makebox[5.4em][s]{36.12 (\\\\textcolor{blue}{+4.38})} & \\\\makebox[5.4em][s]{17.94 (\\\\textcolor{blue}{+1.56})} & \\\\makebox[5.4em][s]{12.57 (\\\\textcolor{blue}{+0.73})} & \\\\makebox[5.4em][s]{15.26 (\\\\textcolor{blue}{+1.15})} & \\\\makebox[5.4em][s]{3.41 (\\\\textcolor{blue}{+1.66})} \\\\\\\\\\n\",\n \" GLM-4V-plus~\\\\cite{chatglm4v} & - & \\\\makebox[5.4em][s]{41.00 (\\\\textcolor{red}{-18.83})} & \\\\makebox[5.4em][s]{39.55 (\\\\textcolor{blue}{+1.19})} & \\\\makebox[5.4em][s]{21.68 (\\\\textcolor{blue}{+0.16})} & \\\\makebox[5.4em][s]{18.63 (\\\\textcolor{blue}{+1.83})} & \\\\makebox[5.4em][s]{15.96 (\\\\textcolor{blue}{+3.16})} & \\\\makebox[5.4em][s]{17.30 (\\\\textcolor{blue}{+2.50})} & \\\\makebox[5.4em][s]{6.86 (\\\\textcolor{blue}{+1.17})} & \\\\makebox[5.4em][s]{7.72 (\\\\textcolor{blue}{+2.01})} & \\\\makebox[5.4em][s]{7.29 (\\\\textcolor{blue}{+1.59})} & \\\\makebox[5.4em][s]{8.83 (\\\\textcolor{blue}{+1.42})} \\\\\\\\\\n\",\n \" GPT-4o~\\\\cite{gpt4v} & - & \\\\makebox[5.4em][s]{46.15 (\\\\textcolor{red}{-15.74})} & \\\\makebox[5.4em][s]{\\\\underline{48.19} (\\\\textcolor{blue}{+0.95})} & \\\\makebox[5.4em][s]{69.00 (\\\\textcolor{blue}{+0.19})} & \\\\makebox[5.4em][s]{95.39 (\\\\textcolor{blue}{+0.05})} & \\\\makebox[5.4em][s]{77.23 (\\\\textcolor{blue}{+1.17})} & \\\\makebox[5.4em][s]{86.31 (\\\\textcolor{blue}{+0.61})} & \\\\makebox[5.4em][s]{19.20 (\\\\textcolor{blue}{+1.45})} & \\\\makebox[5.4em][s]{9.31 (\\\\textcolor{blue}{+0.61})} & \\\\makebox[5.4em][s]{14.26 (\\\\textcolor{blue}{+1.03})} & \\\\makebox[5.4em][s]{15.42 (\\\\textcolor{blue}{+1.50})} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\rowcolor[rgb]{ .922, .945, .871} \\\\multicolumn{12}{c}{ChartLLM-Based} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" TinyChart\\\\textdagger~\\\\cite{tinychart} & 3B & \\\\makebox[5.4em][s]{7.69 (\\\\textcolor{blue}{+2.33})} & \\\\makebox[5.4em][s]{20.07 (\\\\textcolor{blue}{+1.62})} & \\\\makebox[5.4em][s]{23.21 (\\\\textcolor{blue}{+6.40})} & \\\\makebox[5.4em][s]{95.04 (\\\\textcolor{blue}{+1.44})} & \\\\makebox[5.4em][s]{74.41 (\\\\textcolor{blue}{+2.25})} & \\\\makebox[5.4em][s]{84.73 (\\\\textcolor{blue}{+1.85})} & \\\\makebox[5.4em][s]{14.68 (\\\\textcolor{blue}{+3.84})} & \\\\makebox[5.4em][s]{34.22 (\\\\textcolor{blue}{+7.18})} & \\\\makebox[5.4em][s]{24.45 (\\\\textcolor{blue}{+5.51})} & \\\\makebox[5.4em][s]{\\\\textbf{21.84 } (\\\\textcolor{blue}{+2.22})} \\\\\\\\\\n\",\n \" ChartInstruct\\\\textdagger~\\\\cite{chartinstruct} & 7B & \\\\makebox[5.4em][s]{11.54 (\\\\textcolor{blue}{+1.72})} & \\\\makebox[5.4em][s]{34.79 (\\\\textcolor{blue}{+10.24})} & \\\\makebox[5.4em][s]{26.43 (\\\\textcolor{blue}{+11.39})} & \\\\makebox[5.4em][s]{85.93 (\\\\textcolor{blue}{+3.53})} & \\\\makebox[5.4em][s]{43.52 (\\\\textcolor{blue}{+2.88})} & \\\\makebox[5.4em][s]{64.73 (\\\\textcolor{blue}{+3.20})} & \\\\makebox[5.4em][s]{15.52 (\\\\textcolor{blue}{+2.71})} & \\\\makebox[5.4em][s]{\\\\underline{41.42} (\\\\textcolor{blue}{+2.03})} & \\\\makebox[5.4em][s]{28.47 (\\\\textcolor{blue}{+2.37})} & \\\\makebox[5.4em][s]{18.53 (\\\\textcolor{blue}{+3.75})} \\\\\\\\\\n\",\n \" ChartLlama\\\\textdagger~\\\\cite{chartllama} & 7B & \\\\makebox[5.4em][s]{22.67 (\\\\textcolor{blue}{+2.13})} & \\\\makebox[5.4em][s]{22.54 (\\\\textcolor{blue}{+1.19})} & \\\\makebox[5.4em][s]{27.58 (\\\\textcolor{blue}{+14.85})} & \\\\makebox[5.4em][s]{91.42 (\\\\textcolor{blue}{+1.06})} & \\\\makebox[5.4em][s]{51.72 (\\\\textcolor{blue}{+2.76})} & \\\\makebox[5.4em][s]{71.57 (\\\\textcolor{blue}{+1.91})} & \\\\makebox[5.4em][s]{17.94 (\\\\textcolor{blue}{+3.71})} & \\\\makebox[5.4em][s]{\\\\textbf{40.47 } (\\\\textcolor{red}{-0.24})} & \\\\makebox[5.4em][s]{\\\\textbf{29.21 } (\\\\textcolor{blue}{+1.74})} & \\\\makebox[5.4em][s]{7.40 (\\\\textcolor{blue}{+2.70})} \\\\\\\\\\n\",\n \" Sphinx-v2~\\\\cite{sphinx} & 7B & \\\\makebox[5.4em][s]{13.85 (\\\\textcolor{blue}{+4.03})} & \\\\makebox[5.4em][s]{30.11 (\\\\textcolor{blue}{+4.16})} & \\\\makebox[5.4em][s]{23.68 (\\\\textcolor{blue}{+9.99})} & \\\\makebox[5.4em][s]{62.80 (\\\\textcolor{blue}{+1.84})} & \\\\makebox[5.4em][s]{48.00 (\\\\textcolor{blue}{+4.08})} & \\\\makebox[5.4em][s]{55.40 (\\\\textcolor{blue}{+2.96})} & \\\\makebox[5.4em][s]{7.90 (\\\\textcolor{blue}{+4.47})} & \\\\makebox[5.4em][s]{7.35 (\\\\textcolor{blue}{+2.41})} & \\\\makebox[5.4em][s]{7.63 (\\\\textcolor{blue}{+3.44})} & \\\\makebox[5.4em][s]{6.88 (\\\\textcolor{blue}{+3.78})} \\\\\\\\\\n\",\n \" LLaVA1.5~\\\\cite{llava1.5} & 7B & \\\\makebox[5.4em][s]{36.92 (\\\\textcolor{blue}{+2.10})} & \\\\makebox[5.4em][s]{38.39 (\\\\textcolor{red}{-1.11})} & \\\\makebox[5.4em][s]{26.95 (\\\\textcolor{blue}{+11.37})} & \\\\makebox[5.4em][s]{25.44 (\\\\textcolor{blue}{+5.32})} & \\\\makebox[5.4em][s]{31.68 (\\\\textcolor{blue}{+6.48})} & \\\\makebox[5.4em][s]{28.56 (\\\\textcolor{blue}{+5.90})} & \\\\makebox[5.4em][s]{18.21 (\\\\textcolor{blue}{+2.51})} & \\\\makebox[5.4em][s]{17.83 (\\\\textcolor{blue}{+6.76})} & \\\\makebox[5.4em][s]{18.02 (\\\\textcolor{blue}{+4.63})} & \\\\makebox[5.4em][s]{17.40 (\\\\textcolor{blue}{+2.23})} \\\\\\\\\\n\",\n \" ViP-LLaVA~\\\\cite{vipllava} & 7B & \\\\makebox[5.4em][s]{26.23 (\\\\textcolor{blue}{+5.69})} & \\\\makebox[5.4em][s]{41.98 (\\\\textcolor{blue}{+4.97})} & \\\\makebox[5.4em][s]{28.79 (\\\\textcolor{blue}{+13.23})} & \\\\makebox[5.4em][s]{23.96 (\\\\textcolor{blue}{+6.36})} & \\\\makebox[5.4em][s]{29.04 (\\\\textcolor{blue}{+2.88})} & \\\\makebox[5.4em][s]{26.50 (\\\\textcolor{blue}{+4.62})} & \\\\makebox[5.4em][s]{14.31 (\\\\textcolor{blue}{+12.95})} & \\\\makebox[5.4em][s]{14.38 (\\\\textcolor{blue}{+11.79})} & \\\\makebox[5.4em][s]{14.35 (\\\\textcolor{blue}{+12.37})} & \\\\makebox[5.4em][s]{18.72 (\\\\textcolor{blue}{+3.68})} \\\\\\\\\\n\",\n \" LLaVA-NEXT~\\\\cite{llavanext} & 7B & \\\\makebox[5.4em][s]{42.31 (\\\\textcolor{blue}{+21.77})} & \\\\makebox[5.4em][s]{49.40 (\\\\textcolor{blue}{+2.03})} & \\\\makebox[5.4em][s]{34.40 (\\\\textcolor{blue}{+3.32})} & \\\\makebox[5.4em][s]{75.82 (\\\\textcolor{blue}{+1.56})} & \\\\makebox[5.4em][s]{47.68 (\\\\textcolor{blue}{+1.38})} & \\\\makebox[5.4em][s]{61.75 (\\\\textcolor{blue}{+1.47})} & \\\\makebox[5.4em][s]{15.26 (\\\\textcolor{blue}{+1.41})} & \\\\makebox[5.4em][s]{8.93 (\\\\textcolor{blue}{+2.30})} & \\\\makebox[5.4em][s]{12.10 (\\\\textcolor{blue}{+1.86})} & \\\\makebox[5.4em][s]{9.02 (\\\\textcolor{blue}{+0.95})} \\\\\\\\\\n\",\n \" IXC-2.5~\\\\cite{ixc2.5} & 7B & \\\\makebox[5.4em][s]{47.31 (\\\\textcolor{blue}{+0.01})} & \\\\makebox[5.4em][s]{43.38 (\\\\textcolor{blue}{+3.28})} & \\\\makebox[5.4em][s]{51.88 (\\\\textcolor{blue}{+8.56})} & \\\\makebox[5.4em][s]{94.88 (\\\\textcolor{blue}{+2.48})} & \\\\makebox[5.4em][s]{76.24 (\\\\textcolor{blue}{+1.92})} & \\\\makebox[5.4em][s]{85.56 (\\\\textcolor{blue}{+2.20})} & \\\\makebox[5.4em][s]{19.82 (\\\\textcolor{blue}{+2.13})} & \\\\makebox[5.4em][s]{14.70 (\\\\textcolor{blue}{+2.84})} & \\\\makebox[5.4em][s]{17.26 (\\\\textcolor{blue}{+2.48})} & \\\\makebox[5.4em][s]{16.83 (\\\\textcolor{blue}{+7.44})} \\\\\\\\\\n\",\n \" Qwen2-VL~\\\\cite{qwenvl} & 7B & \\\\makebox[5.4em][s]{57.66 (\\\\textcolor{blue}{+0.52})} & \\\\makebox[5.4em][s]{45.54 (\\\\textcolor{blue}{+8.22})} & \\\\makebox[5.4em][s]{56.10 (\\\\textcolor{blue}{+8.21})} & \\\\makebox[5.4em][s]{94.40 (\\\\textcolor{blue}{+0.30})} & \\\\makebox[5.4em][s]{77.44 (\\\\textcolor{blue}{+5.44})} & \\\\makebox[5.4em][s]{85.92 (\\\\textcolor{blue}{+2.87})} & \\\\makebox[5.4em][s]{20.96 (\\\\textcolor{blue}{+9.89})} & \\\\makebox[5.4em][s]{24.45 (\\\\textcolor{blue}{+1.47})} & \\\\makebox[5.4em][s]{22.71 (\\\\textcolor{blue}{+5.68})} & \\\\makebox[5.4em][s]{18.53 (\\\\textcolor{blue}{+10.27})} \\\\\\\\\\n\",\n \" mPLUG-Owl2~\\\\cite{mplugowl2} & 8B & \\\\makebox[5.4em][s]{29.38 (\\\\textcolor{blue}{+4.38})} & \\\\makebox[5.4em][s]{40.46 (\\\\textcolor{blue}{+4.29})} & \\\\makebox[5.4em][s]{29.15 (\\\\textcolor{blue}{+14.93})} & \\\\makebox[5.4em][s]{38.76 (\\\\textcolor{blue}{+14.63})} & \\\\makebox[5.4em][s]{40.34 (\\\\textcolor{blue}{+13.00})} & \\\\makebox[5.4em][s]{39.55 (\\\\textcolor{blue}{+13.82})} & \\\\makebox[5.4em][s]{13.01 (\\\\textcolor{blue}{+0.18})} & \\\\makebox[5.4em][s]{8.91 (\\\\textcolor{blue}{+2.94})} & \\\\makebox[5.4em][s]{10.96 (\\\\textcolor{blue}{+1.56})} & \\\\makebox[5.4em][s]{6.26 (\\\\textcolor{blue}{+0.92})} \\\\\\\\\\n\",\n \" MiniCPM-v2~\\\\cite{minicpmv2} & 8B & \\\\makebox[5.4em][s]{24.21 (\\\\textcolor{blue}{+1.89})} & \\\\makebox[5.4em][s]{38.65 (\\\\textcolor{blue}{+10.17})} & \\\\makebox[5.4em][s]{23.73 (\\\\textcolor{blue}{+13.09})} & \\\\makebox[5.4em][s]{93.84 (\\\\textcolor{blue}{+2.72})} & \\\\makebox[5.4em][s]{71.86 (\\\\textcolor{blue}{+2.84})} & \\\\makebox[5.4em][s]{82.85 (\\\\textcolor{blue}{+2.78})} & \\\\makebox[5.4em][s]{\\\\textbf{27.68 } (\\\\textcolor{blue}{+5.51})} & \\\\makebox[5.4em][s]{24.55 (\\\\textcolor{blue}{+13.54})} & \\\\makebox[5.4em][s]{26.12 (\\\\textcolor{blue}{+9.53})} & \\\\makebox[5.4em][s]{\\\\underline{20.88} (\\\\textcolor{blue}{+0.83})} \\\\\\\\\\n\",\n \" CogVLM~\\\\cite{cogvlm} & 17B & \\\\makebox[5.4em][s]{26.38 (\\\\textcolor{blue}{+3.17})} & \\\\makebox[5.4em][s]{41.05 (\\\\textcolor{blue}{+0.85})} & \\\\makebox[5.4em][s]{41.85 (\\\\textcolor{blue}{+12.50})} & \\\\makebox[5.4em][s]{33.41 (\\\\textcolor{blue}{+9.46})} & \\\\makebox[5.4em][s]{51.73 (\\\\textcolor{blue}{+12.20})} & \\\\makebox[5.4em][s]{42.57 (\\\\textcolor{blue}{+10.83})} & \\\\makebox[5.4em][s]{21.46 (\\\\textcolor{blue}{+5.08})} & \\\\makebox[5.4em][s]{14.74 (\\\\textcolor{blue}{+2.90})} & \\\\makebox[5.4em][s]{18.10 (\\\\textcolor{blue}{+3.99})} & \\\\makebox[5.4em][s]{2.48 (\\\\textcolor{blue}{+0.73})} \\\\\\\\\\n\",\n \" GLM-4V-plus~\\\\cite{chatglm4v} & - & \\\\makebox[5.4em][s]{\\\\underline{60.18} (\\\\textcolor{blue}{+0.35})} & \\\\makebox[5.4em][s]{47.00 (\\\\textcolor{blue}{+8.64})} & \\\\makebox[5.4em][s]{37.19 (\\\\textcolor{blue}{+15.67})} & \\\\makebox[5.4em][s]{19.74 (\\\\textcolor{blue}{+2.94})} & \\\\makebox[5.4em][s]{18.04 (\\\\textcolor{blue}{+5.24})} & \\\\makebox[5.4em][s]{19.66 (\\\\textcolor{blue}{+4.86})} & \\\\makebox[5.4em][s]{9.75 (\\\\textcolor{blue}{+4.06})} & \\\\makebox[5.4em][s]{8.97 (\\\\textcolor{blue}{+3.26})} & \\\\makebox[5.4em][s]{9.36 (\\\\textcolor{blue}{+3.66})} & \\\\makebox[5.4em][s]{9.74 (\\\\textcolor{blue}{+2.34})} \\\\\\\\\\n\",\n \" GPT-4o~\\\\cite{gpt4v} & - & \\\\makebox[5.4em][s]{\\\\textbf{61.89 } (\\\\textcolor{blue}{+0.00})} & \\\\makebox[5.4em][s]{\\\\textbf{50.42 } (\\\\textcolor{blue}{+3.17})} & \\\\makebox[5.4em][s]{\\\\textbf{73.89 } (\\\\textcolor{blue}{+5.08})} & \\\\makebox[5.4em][s]{{\\\\textbf{98.63 }} (\\\\textcolor{blue}{+3.29})} & \\\\makebox[5.4em][s]{\\\\textbf{79.49 } (\\\\textcolor{blue}{+3.43})} & \\\\makebox[5.4em][s]{\\\\textbf{89.06 } (\\\\textcolor{blue}{+3.36})} & \\\\makebox[5.4em][s]{23.65 (\\\\textcolor{blue}{+5.90})} & \\\\makebox[5.4em][s]{11.07 (\\\\textcolor{blue}{+2.37})} & \\\\makebox[5.4em][s]{17.36 (\\\\textcolor{blue}{+4.14})} & \\\\makebox[5.4em][s]{16.04 (\\\\textcolor{blue}{+2.12})} \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \" \\\\raggedright\\n\",\n \" \\\\caption{Performance of multimodal models on ChartMind and three structured-output CQA datasets. The best results are highlighted in \\\\textbf{bold}, and the second-best results are \\\\underline{underlined}. \\\\textdagger Specialized CQA models.}\\n\",\n \" \\\\vskip -0.15in\\n\",\n \" \\\\label{tab:results1}\\n\",\n \"\\\\end{table*}\\n\"\n ]\n]","isConversational":false},"tables":{"kind":"list like","value":[["\\begin{figure*}[t]\n"," \\centering\n"," \\includegraphics[width=\\linewidth]{images/fig1-3.pdf}\n"," \\caption{Key Challenges in CQA Benchmarks: (A) Predominantly monolingual, limiting multilingual applicability in chart question answering; (B) Fixed formats and metrics, restricting adaptability to diverse charts; (C) Emphasis on deterministic answers, overlooking complex reasoning, such as trend analysis, and summarization.}\n"," \\label{fig:challenges}\n","\\end{figure*}\n"],["\\begin{table*}[ht]\n"," \\centering\n"," \\resizebox{\\linewidth}{!}{\n"," \\begin{tabular}{lccccccccccccccc}\n"," \\toprule\n"," \\textbf{Dataset} & \\textbf{Avg. Ans.} & \\textbf{Instances} &\\textbf{Language} & \\textbf{Diverse} & \\textbf{Task} & \\textbf{Topic} & \\textbf{Chart} & \\textbf{Pie} & \\textbf{Scatter} & \\textbf{Common} & \\textbf{Grouped} & \\textbf{Stacked} & \\textbf{Complex} & \\textbf{Common} \\\\\n"," \\textbf{} &\\textbf{Length} & \\textbf{Number} & \\textbf{Format} & \\textbf{Format} &\\textbf{Format} & \\textbf{Format} & \\textbf{Format} & \\textbf{} & \\textbf{} & \\textbf{Bar} & \\textbf{Bar} & \\textbf{Bar} & \\textbf{Line} & \\textbf{Line} \\\\\n"," \\midrule\n"," ChartQA~\\cite{chartqa} &1.15 & 2,500 & English & 1 & 1 & 3 & 3& \\ding{51} & \\ding{55} & \\ding{51} & \\ding{55} & \\ding{55} & \\ding{55} & \\ding{51} \\\\\n"," MMC-Benchmark~\\cite{mmc}& 1.08 & 2,126 & English & 1 & 4 & 5 & 2& \\ding{55} & \\ding{51} & \\ding{55} & \\ding{55} & \\ding{55} & \\ding{55} & \\ding{51} \\\\\n"," PaperQA~\\cite{lu2023mathvista} &1.26 & 107 & English & 1 & 1 & 2 & 4& \\ding{51} & \\ding{51} & \\ding{51} & \\ding{55} & \\ding{55} & \\ding{55} & \\ding{51} \\\\\n"," OpenCQA~\\cite{opencqa} &55.73 & 1,159 & English & 1 & 1 & 4 & 4&\\ding{51} & \\ding{51} & \\ding{51} & \\ding{55} & \\ding{55} & \\ding{55} & \\ding{51} \\\\\n"," Chart-to-Text~\\cite{charttotext}&73.49 & 3,474 & English & 1 & 1 & 3 & 4& \\ding{51} & \\ding{51} & \\ding{51} & \\ding{55} & \\ding{55} & \\ding{55} & \\ding{51} \\\\\n"," LineCap~\\cite{9973197}&13.63 & 1,930 & English& 1 & 1 & 1 & 2& \\ding{55} & \\ding{55} & \\ding{55} & \\ding{55} & \\ding{55} & \\ding{51} & \\ding{51} \\\\\n"," \\midrule\n"," \\textbf{ChartMind}& \\textbf{119.69} & \\textbf{757}&\\textbf{EN\\&ZH} & \\textbf{2} & \\textbf{7} & \\textbf{6} & \\textbf{7} & \\ding{51} & \\ding{51} & \\ding{51} & \\ding{51} & \\ding{51} & \\ding{51} & \\ding{51} \\\\\n"," \\bottomrule\n"," \\end{tabular}}\n"," \\caption{Comparison of ChartMind with Existing Chart QA Datasets.}\n"," \\label{tab:dataset_comparison}\n"," \\vskip -0.2in\n","\\end{table*}\n"],["\\begin{figure*}[ht]\n"," \\centering\n"," \\includegraphics[width=\\linewidth]{images/fig3-6.pdf} \n"," \\caption{Data Construction Pipeline for the ChartMind.}\n"," \\label{fig:data_curation}\n"," \\vskip -0.1in\n","\\end{figure*}\n"],["\\begin{figure}[t]\n"," \\centering\n"," \\includegraphics[width=\\linewidth]{images/chart4-1-2.pdf}\n"," \\caption{Language and task distribution in ChartMind.}\n"," \\label{fig:task_distribution1}\n"," \\vskip -0.12in\n","\\end{figure}\n"],["\\begin{figure}[t]\n"," \\centering\n"," \\includegraphics[width=0.82\\linewidth]{images/fig5.pdf}\n"," \\caption{Topic distribution in ChartMind.}\n"," \\label{fig:task_distribution2}\n"," \\vskip -0.12in\n","\\end{figure}\n"],["\\begin{table}[t]\n"," \\centering\n"," \\resizebox{\\linewidth}{!}{\n"," \\begin{tabular}{lcrr}\n"," \\toprule\n"," \\textbf{Task} & \\textbf{Samples} & \\textbf{Query Length} & \\textbf{Answer Length} \\\\\n"," & & \\textbf{(Min / Max)} & \\textbf{(Min / Max)} \\\\\n"," \\midrule\n"," Chart Conversion & 140 & 11 / 477 & 5 / 55 \\\\\n"," Chart OCR Recognition & 139 & 13 / 351 & 8 / 59 \\\\\n"," Suggestions & 88 & 17 / 492 & 13 / 53 \\\\\n"," Chart Classification Analysis & 37 & 360 / 503 & 72 / 79 \\\\\n"," Chart Summarization & 34 & 76 / 335 & 12 / 113 \\\\\n"," Chart Assistance & 76 & 9 / 276 & 12 / 41 \\\\\n"," Information Positioning & 140 & 11 / 208 & 11 / 35 \\\\\n"," \\midrule\n"," \\textbf{Total} & \\textbf{757} & \\textbf{9 / 503} & \\textbf{5 / 113} \\\\\n"," \\bottomrule\n"," \\end{tabular}}\n"," \\caption{Task Type Statistics in ChartMind.}\n"," \\label{tab:key_statistics}\n"," \\vspace{-5mm}\n","\\end{table}\n"],["\\begin{table*}[!ht]\n"," \\centering\n"," \\resizebox{\\linewidth}{!}{\n"," \\begin{tabular}{lccccccccccr}\n"," \\toprule\n"," \\multirow{2}[4]{*}{ Models} & \\multirow{2}[4]{*}{Size} & \\multicolumn{3}{c}{ChartMind} & \\multicolumn{3}{c}{ChartQA } & \\multicolumn{3}{c}{Chart-to-Text} & OpenCQA \\\\\n","\\cmidrule{3-12} & & ACC & Avg.CIDEr & Avg.GPT-4o Score & Aug. ACC & Hum. ACC & Avg. ACC & Pew. BLEU & Statista. BLEU & Avg. BLEU & Avg. BLEU \\\\\n"," \\midrule\n"," \\rowcolor[rgb]{ .922, .945, .871} \\multicolumn{12}{c}{Instruction-Following-Based~\\cite{wei2021finetuned}} \\\\\n"," \\midrule\n"," TinyChart\\textdagger~\\cite{tinychart} & 3B & 5.36 & 18.45 & 16.81 & 93.60 & 72.16 & 82.88 & 10.84 & 27.04 & 18.94 & 19.62 \\\\\n"," ChartInstruct\\textdagger~\\cite{chartinstruct} & 7B & 9.82 & 24.55 & 15.05 & 82.40 & 40.64 & 61.52 & 12.81 & 39.39 & 26.10 & 14.78 \\\\\n"," ChartLlama\\textdagger~\\cite{chartllama} & 7B & 20.54 & 21.34 & 12.72 & 90.36 & 48.96 & 69.66 & 14.23 & 40.71 & 27.47 & 4.70 \\\\\n"," Sphinx-v2~\\cite{sphinx} & 7B & 9.82 & 25.95 & 13.69 & 60.96 & 43.92 & 52.44 & 3.43 & 4.94 & 4.19 & 3.10 \\\\\n"," LLaVA1.5~\\cite{llava1.5} & 7B & 34.82 & 39.50 & 15.58 & 20.12 & 25.20 & 22.66 & 15.70 & 11.07 & 13.39 & 15.17 \\\\\n"," ViP-LLaVA~\\cite{vipllava} & 7B & 20.54 & 37.01 & 15.56 & 17.60 & 26.16 & 21.88 & 1.36 & 2.59 & 1.98 & 15.04 \\\\\n"," LLaVA-NEXT~\\cite{llavanext} & 7B & 20.54 & 47.37 & 31.09 & 74.26 & 46.30 & 60.28 & 13.85 & 6.63 & 10.24 & 8.07 \\\\\n"," IXC-2.5~\\cite{ixc2.5} & 7B & 47.30 & 40.10 & 43.31 & 92.40 & 74.32 & 83.36 & 17.69 & 11.86 & 14.78 & 9.39 \\\\\n"," Qwen2-VL~\\cite{qwenvl} & 7B & 57.14 & 37.32 & 47.89 & 94.10 & 72.00 & 83.05 & 11.07 & 22.98 & 17.03 & 8.26 \\\\\n"," mPLUG-Owl2~\\cite{mplugowl2} & 8B & 25.00 & 36.17 & 14.22 & 24.13 & 27.34 & 25.74 & 12.83 & 5.97 & 9.40 & 5.34 \\\\\n"," MiniCPM-v2~\\cite{minicpmv2} & 8B & 22.32 & 28.48 & 10.63 & 91.12 & 69.02 & 80.07 & 22.17 & 11.01 & 16.59 & 20.05 \\\\\n"," CogVLM~\\cite{cogvlm} & 17B & 23.21 & 40.20 & 29.35 & 23.95 & 39.53 & 31.74 & 16.38 & 11.84 & 14.11 & 1.75 \\\\\n"," GLM-4V-plus~\\cite{chatglm4v} & - & 59.83 & 38.36 & 21.52 & 16.80 & 12.80 & 14.80 & 5.69 & 5.71 & 5.70 & 7.41 \\\\\n"," GPT-4o~\\cite{gpt4v} & - & \\textbf{61.89} & 47.25 & 68.81 & 95.34 & 76.06 & 85.70 & 17.75 & 8.70 & 13.23 & 13.92 \\\\\n"," \\midrule\n"," \\rowcolor[rgb]{ .922, .945, .871} \\multicolumn{12}{c}{OCR-Enhanced~\\cite{liu2023deplot}} \\\\\n"," \\midrule\n"," TinyChart\\textdagger~\\cite{tinychart} & 3B & \\makebox[5.4em][s]{6.71 (\\textcolor{blue}{+1.35})} & \\makebox[5.4em][s]{13.91 (\\textcolor{red}{-4.54})} & \\makebox[5.4em][s]{17.91 (\\textcolor{blue}{+1.10})} & \\makebox[5.4em][s]{94.86 (\\textcolor{blue}{+1.26})} & \\makebox[5.4em][s]{73.95 (\\textcolor{blue}{+1.79})} & \\makebox[5.4em][s]{84.41 (\\textcolor{blue}{+1.53})} & \\makebox[5.4em][s]{13.85 (\\textcolor{blue}{+3.01})} & \\makebox[5.4em][s]{28.27 (\\textcolor{blue}{+1.23})} & \\makebox[5.4em][s]{21.06 (\\textcolor{blue}{+2.12})} & \\makebox[5.4em][s]{20.15 (\\textcolor{blue}{+0.53})} \\\\\n"," ChartInstruct\\textdagger~\\cite{chartinstruct} & 7B & \\makebox[5.4em][s]{{10.01} (\\textcolor{blue}{+0.19})} & \\makebox[5.4em][s]{32.80 (\\textcolor{blue}{+8.25})} & \\makebox[5.4em][s]{23.42 (\\textcolor{blue}{+8.37})} & \\makebox[5.4em][s]{83.74 (\\textcolor{blue}{+1.34})} & \\makebox[5.4em][s]{42.17 (\\textcolor{blue}{+1.53})} & \\makebox[5.4em][s]{62.96 (\\textcolor{blue}{+1.44})} & \\makebox[5.4em][s]{14.95 (\\textcolor{blue}{+2.14})} & \\makebox[5.4em][s]{40.83 (\\textcolor{blue}{+1.44})} & \\makebox[5.4em][s]{27.89 (\\textcolor{blue}{+1.79})} & \\makebox[5.4em][s]{16.01 (\\textcolor{blue}{+1.23})} \\\\\n"," ChartLlama\\textdagger~\\cite{chartllama} & 7B & \\makebox[5.4em][s]{{22.03} (\\textcolor{blue}{+1.49})} & \\makebox[5.4em][s]{21.07 (\\textcolor{red}{-0.27})} & \\makebox[5.4em][s]{26.70 (\\textcolor{blue}{+13.97})} & \\makebox[5.4em][s]{90.85 (\\textcolor{blue}{+0.49})} & \\makebox[5.4em][s]{49.26 (\\textcolor{blue}{+0.30})} & \\makebox[5.4em][s]{70.06 (\\textcolor{blue}{+0.40})} & \\makebox[5.4em][s]{16.02 (\\textcolor{blue}{+1.79})} & \\makebox[5.4em][s]{39.97 (\\textcolor{red}{-0.74})} & \\makebox[5.4em][s]{28.00 (\\textcolor{blue}{+0.53})} & \\makebox[5.4em][s]{5.89 (\\textcolor{blue}{+1.19})} \\\\\n"," Sphinx-v2~\\cite{sphinx} & 7B & \\makebox[5.4em][s]{11.54 (\\textcolor{blue}{+1.72})} & \\makebox[5.4em][s]{24.14 (\\textcolor{red}{-1.81})} & \\makebox[5.4em][s]{17.21 (\\textcolor{blue}{+3.52})} & \\makebox[5.4em][s]{64.08 (\\textcolor{blue}{+3.12})} & \\makebox[5.4em][s]{45.49 (\\textcolor{blue}{+1.57})} & \\makebox[5.4em][s]{54.79 (\\textcolor{blue}{+2.35})} & \\makebox[5.4em][s]{8.81 (\\textcolor{blue}{+5.38})} & \\makebox[5.4em][s]{2.39 (\\textcolor{red}{-2.55})} & \\makebox[5.4em][s]{5.60 (\\textcolor{blue}{+1.41})} & \\makebox[5.4em][s]{3.16 (\\textcolor{blue}{+0.06})} \\\\\n"," LLaVA1.5~\\cite{llava1.5} & 7B & \\makebox[5.4em][s]{36.15 (\\textcolor{blue}{+1.33})} & \\makebox[5.4em][s]{33.49 (\\textcolor{red}{-6.01})} & \\makebox[5.4em][s]{21.03 (\\textcolor{blue}{+5.45})} & \\makebox[5.4em][s]{19.73 (\\textcolor{red}{-0.39})} & \\makebox[5.4em][s]{25.95 (\\textcolor{blue}{+0.75})} & \\makebox[5.4em][s]{22.84 (\\textcolor{blue}{+0.18})} & \\makebox[5.4em][s]{15.94 (\\textcolor{blue}{+0.24})} & \\makebox[5.4em][s]{12.67 (\\textcolor{blue}{+1.60})} & \\makebox[5.4em][s]{14.30 (\\textcolor{blue}{+0.91})} & \\makebox[5.4em][s]{16.31 (\\textcolor{blue}{+1.14})} \\\\\n"," ViP-LLaVA~\\cite{vipllava} & 7B & \\makebox[5.4em][s]{25.38 (\\textcolor{blue}{+4.84})} & \\makebox[5.4em][s]{36.77 (\\textcolor{red}{-0.24})} & \\makebox[5.4em][s]{26.45 (\\textcolor{blue}{+10.89})} & \\makebox[5.4em][s]{27.12 (\\textcolor{blue}{+9.52})} & \\makebox[5.4em][s]{24.94 (\\textcolor{red}{-1.22})} & \\makebox[5.4em][s]{26.03 (\\textcolor{blue}{+4.15})} & \\makebox[5.4em][s]{14.13 (\\textcolor{blue}{+12.77})} & \\makebox[5.4em][s]{14.37 (\\textcolor{blue}{+11.78})} & \\makebox[5.4em][s]{14.25 (\\textcolor{blue}{+12.27})} & \\makebox[5.4em][s]{18.08 (\\textcolor{blue}{+3.04})} \\\\\n"," LLaVA-NEXT~\\cite{llavanext} & 7B & \\makebox[5.4em][s]{41.15 (\\textcolor{blue}{+20.61})} & \\makebox[5.4em][s]{47.83 (\\textcolor{blue}{+0.46})} & \\makebox[5.4em][s]{31.51 (\\textcolor{blue}{+0.42})} & \\makebox[5.4em][s]{70.47 (\\textcolor{red}{-3.79})} & \\makebox[5.4em][s]{52.68 (\\textcolor{blue}{+6.38})} & \\makebox[5.4em][s]{61.58 (\\textcolor{blue}{+1.30})} & \\makebox[5.4em][s]{15.16 (\\textcolor{blue}{+1.31})} & \\makebox[5.4em][s]{8.82 (\\textcolor{blue}{+2.19})} & \\makebox[5.4em][s]{11.99 (\\textcolor{blue}{+1.75})} & \\makebox[5.4em][s]{8.25 (\\textcolor{blue}{+0.18})} \\\\\n"," IXC-2.5~\\cite{ixc2.5} & 7B & \\makebox[5.4em][s]{42.31 (\\textcolor{red}{-4.99})} & \\makebox[5.4em][s]{40.35 (\\textcolor{blue}{+0.24})} & \\makebox[5.4em][s]{45.38 (\\textcolor{blue}{+2.06})} & \\makebox[5.4em][s]{94.23 (\\textcolor{blue}{+1.83})} & \\makebox[5.4em][s]{73.40 (\\textcolor{red}{-0.92})} & \\makebox[5.4em][s]{83.82 (\\textcolor{blue}{+0.46})} & \\makebox[5.4em][s]{17.03 (\\textcolor{red}{-0.66})} & \\makebox[5.4em][s]{12.34 (\\textcolor{blue}{+0.48})} & \\makebox[5.4em][s]{14.68 (\\textcolor{red}{-0.10})} & \\makebox[5.4em][s]{14.53 (\\textcolor{blue}{+5.14})} \\\\\n"," Qwen2-VL~\\cite{qwenvl} & 7B & \\makebox[5.4em][s]{42.31 (\\textcolor{red}{-14.83})} & \\makebox[5.4em][s]{36.04 (\\textcolor{red}{-1.27})} & \\makebox[5.4em][s]{49.28 (\\textcolor{blue}{+1.39})} & \\makebox[5.4em][s]{94.23 (\\textcolor{blue}{+0.13})} & \\makebox[5.4em][s]{75.96 (\\textcolor{blue}{+3.96})} & \\makebox[5.4em][s]{85.10 (\\textcolor{blue}{+2.05})} & \\makebox[5.4em][s]{11.08 (\\textcolor{blue}{+0.01})} & \\makebox[5.4em][s]{23.21 (\\textcolor{blue}{+0.23})} & \\makebox[5.4em][s]{17.15 (\\textcolor{blue}{+0.12})} & \\makebox[5.4em][s]{11.75 (\\textcolor{blue}{+3.49})} \\\\\n"," mPLUG-Owl2~\\cite{mplugowl2} & 8B & \\makebox[5.4em][s]{27.62 (\\textcolor{blue}{+2.62})} & \\makebox[5.4em][s]{30.60 (\\textcolor{red}{-5.57})} & \\makebox[5.4em][s]{24.67 (\\textcolor{blue}{+10.44})} & \\makebox[5.4em][s]{35.58 (\\textcolor{blue}{+11.45})} & \\makebox[5.4em][s]{37.18 (\\textcolor{blue}{+9.84})} & \\makebox[5.4em][s]{36.38 (\\textcolor{blue}{+10.65})} & \\makebox[5.4em][s]{11.82 (\\textcolor{red}{-1.01})} & \\makebox[5.4em][s]{7.30 (\\textcolor{blue}{+1.33})} & \\makebox[5.4em][s]{9.56 (\\textcolor{blue}{+0.16})} & \\makebox[5.4em][s]{4.45 (\\textcolor{red}{-0.89})} \\\\\n"," MiniCPM-v2~\\cite{minicpmv2} & 8B & \\makebox[5.4em][s]{23.04 (\\textcolor{blue}{+0.72}} & \\makebox[5.4em][s]{19.73 (\\textcolor{red}{-8.75})} & \\makebox[5.4em][s]{18.10 (\\textcolor{blue}{+7.47})} & \\makebox[5.4em][s]{\\makebox[5.4em][s]{92.36 (\\textcolor{blue}{+1.24})}} & \\makebox[5.4em][s]{73.21 (\\textcolor{blue}{+4.19})} & \\makebox[5.4em][s]{82.79 (\\textcolor{blue}{+2.72})} & \\makebox[5.4em][s]{20.93 (\\textcolor{red}{-1.24})} & \\makebox[5.4em][s]{5.75 (\\textcolor{red}{-5.26})} & \\makebox[5.4em][s]{13.34 (\\textcolor{red}{-3.25})} & \\makebox[5.4em][s]{20.60 (\\textcolor{blue}{+0.55})} \\\\\n"," CogVLM~\\cite{cogvlm} & 17B & \\makebox[5.4em][s]{25.54 (\\textcolor{blue}{+2.33})} & \\makebox[5.4em][s]{39.00 (\\textcolor{red}{-1.20})} & \\makebox[5.4em][s]{36.80 (\\textcolor{blue}{+7.45})} & \\makebox[5.4em][s]{29.81 (\\textcolor{blue}{+5.86})} & \\makebox[5.4em][s]{48.72 (\\textcolor{blue}{+9.19})} & \\makebox[5.4em][s]{39.27 (\\textcolor{blue}{+7.53})} & \\makebox[5.4em][s]{20.85 (\\textcolor{blue}{+4.47})} & \\makebox[5.4em][s]{13.88 (\\textcolor{blue}{+2.04})} & \\makebox[5.4em][s]{17.37 (\\textcolor{blue}{+3.26})} & \\makebox[5.4em][s]{1.79 (\\textcolor{blue}{+0.04})} \\\\\n"," GLM-4V-plus~\\cite{chatglm4v} & - & \\makebox[5.4em][s]{44.64 (\\textcolor{red}{-15.19})} & \\makebox[5.4em][s]{44.83 (\\textcolor{blue}{+6.47})} & \\makebox[5.4em][s]{35.79 (\\textcolor{blue}{+14.27})} & \\makebox[5.4em][s]{17.95 (\\textcolor{blue}{+1.15})} & \\makebox[5.4em][s]{16.87 (\\textcolor{blue}{+4.07})} & \\makebox[5.4em][s]{17.41 (\\textcolor{blue}{+2.61})} & \\makebox[5.4em][s]{7.91 (\\textcolor{blue}{+2.22})} & \\makebox[5.4em][s]{7.63 (\\textcolor{blue}{+1.92})} & \\makebox[5.4em][s]{7.77 (\\textcolor{blue}{+2.07})} & \\makebox[5.4em][s]{8.72 (\\textcolor{blue}{+1.31})} \\\\\n"," GPT-4o~\\cite{gpt4v} & - & \\makebox[5.4em][s]{49.31 (\\textcolor{red}{-12.58})} & \\makebox[5.4em][s]{46.48 (\\textcolor{red}{-0.76})} & \\makebox[5.4em][s]{\\underline{71.79} (\\textcolor{blue}{+2.98})} & \\makebox[5.4em][s]{\\underline{96.20} (\\textcolor{blue}{+0.86})} & \\makebox[5.4em][s]{\\underline{78.04} (\\textcolor{blue}{+1.98})} & \\makebox[5.4em][s]{\\underline{87.12} (\\textcolor{blue}{+1.42})} & \\makebox[5.4em][s]{20.13 (\\textcolor{blue}{+2.38})} & \\makebox[5.4em][s]{9.86 (\\textcolor{blue}{+1.16})} & \\makebox[5.4em][s]{15.00 (\\textcolor{blue}{+1.77})} & \\makebox[5.4em][s]{14.85 (\\textcolor{blue}{+0.93})} \\\\\n"," \\midrule\n"," \\rowcolor[rgb]{ .922, .945, .871} \\multicolumn{12}{c}{COT-Based~\\cite{wei2022chain}} \\\\\n"," \\midrule\n"," TinyChart\\textdagger~\\cite{tinychart} & 3B & \\makebox[5.4em][s]{6.01 (\\textcolor{blue}{+0.65})} & \\makebox[5.4em][s]{13.58 (\\textcolor{red}{-4.87})} & \\makebox[5.4em][s]{19.30 (\\textcolor{blue}{+2.49})} & \\makebox[5.4em][s]{94.84 (\\textcolor{blue}{+1.24})} & \\makebox[5.4em][s]{74.46 (\\textcolor{blue}{+2.30})} & \\makebox[5.4em][s]{84.65 (\\textcolor{blue}{+1.77})} & \\makebox[5.4em][s]{12.31 (\\textcolor{blue}{+1.47})} & \\makebox[5.4em][s]{28.53 (\\textcolor{blue}{+1.49})} & \\makebox[5.4em][s]{20.42 (\\textcolor{blue}{+1.48})} & \\makebox[5.4em][s]{20.74 (\\textcolor{blue}{+1.12})} \\\\\n"," ChartInstruct\\textdagger~\\cite{chartinstruct} & 7B & \\makebox[5.4em][s]{9.96 (\\textcolor{blue}{+0.14})} & \\makebox[5.4em][s]{31.95 (\\textcolor{blue}{+7.40})} & \\makebox[5.4em][s]{22.44 (\\textcolor{blue}{+7.39})} & \\makebox[5.4em][s]{83.35 (\\textcolor{blue}{+0.95})} & \\makebox[5.4em][s]{42.74 (\\textcolor{blue}{+2.10})} & \\makebox[5.4em][s]{63.05 (\\textcolor{blue}{+1.53})} & \\makebox[5.4em][s]{14.34 (\\textcolor{blue}{+1.53})} & \\makebox[5.4em][s]{41.32 (\\textcolor{blue}{+1.93})} & \\makebox[5.4em][s]{27.83 (\\textcolor{blue}{+1.73})} & \\makebox[5.4em][s]{15.25 (\\textcolor{blue}{+0.47})} \\\\\n"," ChartLlama\\textdagger~\\cite{chartllama} & 7B & \\makebox[5.4em][s]{21.44 (\\textcolor{blue}{+0.90})} & \\makebox[5.4em][s]{18.99 (\\textcolor{red}{-2.36})} & \\makebox[5.4em][s]{21.77 (\\textcolor{blue}{+9.04})} & \\makebox[5.4em][s]{91.63 (\\textcolor{blue}{+1.27})} & \\makebox[5.4em][s]{50.04 (\\textcolor{blue}{+1.08})} & \\makebox[5.4em][s]{70.84 (\\textcolor{blue}{+1.18})} & \\makebox[5.4em][s]{15.76 (\\textcolor{blue}{+1.53})} & \\makebox[5.4em][s]{\\underline{41.42} (\\textcolor{blue}{+0.71})} & \\makebox[5.4em][s]{\\underline{28.59} (\\textcolor{blue}{+1.12})} & \\makebox[5.4em][s]{6.32 (\\textcolor{blue}{+1.62})} \\\\\n"," Sphinx-v2~\\cite{sphinx} & 7B & \\makebox[5.4em][s]{9.91 (\\textcolor{blue}{+0.09})} & \\makebox[5.4em][s]{25.03 (\\textcolor{red}{-0.92})} & \\makebox[5.4em][s]{16.26 (\\textcolor{blue}{+2.57})} & \\makebox[5.4em][s]{61.86 (\\textcolor{blue}{+0.90})} & \\makebox[5.4em][s]{46.79 (\\textcolor{blue}{+2.87})} & \\makebox[5.4em][s]{54.33 (\\textcolor{blue}{+1.89})} & \\makebox[5.4em][s]{3.53 (\\textcolor{blue}{+0.10})} & \\makebox[5.4em][s]{5.09 (\\textcolor{blue}{+0.15})} & \\makebox[5.4em][s]{4.31 (\\textcolor{blue}{+0.12})} & \\makebox[5.4em][s]{3.13 (\\textcolor{blue}{+0.03})} \\\\\n"," LLaVA1.5~\\cite{llava1.5} & 7B & \\makebox[5.4em][s]{35.77 (\\textcolor{blue}{+0.95})} & \\makebox[5.4em][s]{35.61 (\\textcolor{red}{-3.89})} & \\makebox[5.4em][s]{19.68 (\\textcolor{blue}{+4.10})} & \\makebox[5.4em][s]{16.90 (\\textcolor{red}{-3.22})} & \\makebox[5.4em][s]{28.57 (\\textcolor{blue}{+3.37})} & \\makebox[5.4em][s]{22.74 (\\textcolor{blue}{+0.08})} & \\makebox[5.4em][s]{15.20 (\\textcolor{red}{-0.50})} & \\makebox[5.4em][s]{11.66 (\\textcolor{blue}{+0.59})} & \\makebox[5.4em][s]{13.43 (\\textcolor{blue}{+0.04})} & \\makebox[5.4em][s]{15.93 (\\textcolor{blue}{+0.76})} \\\\\n"," ViP-LLaVA~\\cite{vipllava} & 7B & \\makebox[5.4em][s]{23.31 (\\textcolor{blue}{+2.77})} & \\makebox[5.4em][s]{36.13 (\\textcolor{red}{-0.88})} & \\makebox[5.4em][s]{22.24 (\\textcolor{blue}{+6.68})} & \\makebox[5.4em][s]{22.12 (\\textcolor{blue}{+4.52})} & \\makebox[5.4em][s]{28.21 (\\textcolor{blue}{+2.05})} & \\makebox[5.4em][s]{25.17 (\\textcolor{blue}{+3.29})} & \\makebox[5.4em][s]{15.48 (\\textcolor{blue}{+14.12})} & \\makebox[5.4em][s]{12.20 (\\textcolor{blue}{+9.61})} & \\makebox[5.4em][s]{13.84 (\\textcolor{blue}{+11.86})} & \\makebox[5.4em][s]{15.67 (\\textcolor{blue}{+0.63})} \\\\\n"," LLaVA-NEXT~\\cite{llavanext} & 7B & \\makebox[5.4em][s]{40.23 (\\textcolor{blue}{+19.69})} & \\makebox[5.4em][s]{47.44 (\\textcolor{blue}{+0.07})} & \\makebox[5.4em][s]{27.34 (\\textcolor{red}{-3.75})} & \\makebox[5.4em][s]{68.49 (\\textcolor{red}{-5.77})} & \\makebox[5.4em][s]{52.13 (\\textcolor{blue}{+5.83})} & \\makebox[5.4em][s]{60.31 (\\textcolor{blue}{+0.03})} & \\makebox[5.4em][s]{14.81 (\\textcolor{blue}{+0.96})} & \\makebox[5.4em][s]{6.29 (\\textcolor{red}{-0.34})} & \\makebox[5.4em][s]{10.55 (\\textcolor{blue}{+0.31})} & \\makebox[5.4em][s]{8.09 (\\textcolor{blue}{+0.02})} \\\\\n"," IXC-2.5~\\cite{ixc2.5} & 7B & \\makebox[5.4em][s]{41.15 (\\textcolor{red}{-6.15})} & \\makebox[5.4em][s]{41.23 (\\textcolor{blue}{+1.13})} & \\makebox[5.4em][s]{46.73 (\\textcolor{blue}{+3.42})} & \\makebox[5.4em][s]{93.91 (\\textcolor{blue}{+1.51})} & \\makebox[5.4em][s]{72.82 (\\textcolor{red}{-1.50})} & \\makebox[5.4em][s]{83.37 (\\textcolor{blue}{+0.01})} & \\makebox[5.4em][s]{17.36 (\\textcolor{blue}{+0.23})} & \\makebox[5.4em][s]{11.92 (\\textcolor{blue}{+0.06})} & \\makebox[5.4em][s]{14.64 (\\textcolor{red}{-0.14})} & \\makebox[5.4em][s]{14.39 (\\textcolor{blue}{+5.00})} \\\\\n"," Qwen2-VL~\\cite{qwenvl} & 7B & \\makebox[5.4em][s]{40.69 (\\textcolor{red}{-16.45})} & \\makebox[5.4em][s]{44.72 (\\textcolor{blue}{+7.41})} & \\makebox[5.4em][s]{55.12 (\\textcolor{blue}{+7.24})} & \\makebox[5.4em][s]{94.87 (\\textcolor{blue}{+0.77})} & \\makebox[5.4em][s]{77.88 (\\textcolor{blue}{+5.88})} & \\makebox[5.4em][s]{86.38 (\\textcolor{blue}{+3.33})} & \\makebox[5.4em][s]{16.70 (\\textcolor{blue}{+5.63})} & \\makebox[5.4em][s]{23.91 (\\textcolor{blue}{+0.93})} & \\makebox[5.4em][s]{20.30 (\\textcolor{blue}{+3.27})} & \\makebox[5.4em][s]{10.32 (\\textcolor{blue}{+2.06})} \\\\\n"," mPLUG-Owl2~\\cite{mplugowl2} & 8B & \\makebox[5.4em][s]{25.89 (\\textcolor{blue}{+0.89})} & \\makebox[5.4em][s]{35.10 (\\textcolor{red}{-1.08})} & \\makebox[5.4em][s]{21.27 (\\textcolor{blue}{+7.04})} & \\makebox[5.4em][s]{27.56 (\\textcolor{blue}{+3.43})} & \\makebox[5.4em][s]{31.09 (\\textcolor{blue}{+3.75})} & \\makebox[5.4em][s]{29.33 (\\textcolor{blue}{+3.59})} & \\makebox[5.4em][s]{14.00 (\\textcolor{blue}{+1.17})} & \\makebox[5.4em][s]{7.84 (\\textcolor{blue}{+1.87})} & \\makebox[5.4em][s]{10.92 (\\textcolor{blue}{+1.52})} & \\makebox[5.4em][s]{7.88 (\\textcolor{blue}{+2.54})} \\\\\n"," MiniCPM-v2~\\cite{minicpmv2} & 8B & \\makebox[5.4em][s]{22.78 (\\textcolor{blue}{+0.46})} & \\makebox[5.4em][s]{28.81 (\\textcolor{blue}{+0.33})} & \\makebox[5.4em][s]{18.18 (\\textcolor{blue}{+7.54})} & \\makebox[5.4em][s]{92.37 (\\textcolor{blue}{+1.25})} & \\makebox[5.4em][s]{71.47 (\\textcolor{blue}{+2.45})} & \\makebox[5.4em][s]{81.92 (\\textcolor{blue}{+1.85})} & \\makebox[5.4em][s]{\\underline{26.56} (\\textcolor{blue}{+4.39})} & \\makebox[5.4em][s]{12.53 (\\textcolor{blue}{+1.52})} & \\makebox[5.4em][s]{19.54 (\\textcolor{blue}{+2.95})} & \\makebox[5.4em][s]{20.30 (\\textcolor{blue}{+0.25})} \\\\\n"," CogVLM~\\cite{cogvlm} & 17B & \\makebox[5.4em][s]{24.01 (\\textcolor{blue}{+0.80})} & \\makebox[5.4em][s]{40.04 (\\textcolor{red}{-0.16})} & \\makebox[5.4em][s]{37.14 (\\textcolor{blue}{+7.79})} & \\makebox[5.4em][s]{27.31 (\\textcolor{blue}{+3.36})} & \\makebox[5.4em][s]{44.93 (\\textcolor{blue}{+5.40})} & \\makebox[5.4em][s]{36.12 (\\textcolor{blue}{+4.38})} & \\makebox[5.4em][s]{17.94 (\\textcolor{blue}{+1.56})} & \\makebox[5.4em][s]{12.57 (\\textcolor{blue}{+0.73})} & \\makebox[5.4em][s]{15.26 (\\textcolor{blue}{+1.15})} & \\makebox[5.4em][s]{3.41 (\\textcolor{blue}{+1.66})} \\\\\n"," GLM-4V-plus~\\cite{chatglm4v} & - & \\makebox[5.4em][s]{41.00 (\\textcolor{red}{-18.83})} & \\makebox[5.4em][s]{39.55 (\\textcolor{blue}{+1.19})} & \\makebox[5.4em][s]{21.68 (\\textcolor{blue}{+0.16})} & \\makebox[5.4em][s]{18.63 (\\textcolor{blue}{+1.83})} & \\makebox[5.4em][s]{15.96 (\\textcolor{blue}{+3.16})} & \\makebox[5.4em][s]{17.30 (\\textcolor{blue}{+2.50})} & \\makebox[5.4em][s]{6.86 (\\textcolor{blue}{+1.17})} & \\makebox[5.4em][s]{7.72 (\\textcolor{blue}{+2.01})} & \\makebox[5.4em][s]{7.29 (\\textcolor{blue}{+1.59})} & \\makebox[5.4em][s]{8.83 (\\textcolor{blue}{+1.42})} \\\\\n"," GPT-4o~\\cite{gpt4v} & - & \\makebox[5.4em][s]{46.15 (\\textcolor{red}{-15.74})} & \\makebox[5.4em][s]{\\underline{48.19} (\\textcolor{blue}{+0.95})} & \\makebox[5.4em][s]{69.00 (\\textcolor{blue}{+0.19})} & \\makebox[5.4em][s]{95.39 (\\textcolor{blue}{+0.05})} & \\makebox[5.4em][s]{77.23 (\\textcolor{blue}{+1.17})} & \\makebox[5.4em][s]{86.31 (\\textcolor{blue}{+0.61})} & \\makebox[5.4em][s]{19.20 (\\textcolor{blue}{+1.45})} & \\makebox[5.4em][s]{9.31 (\\textcolor{blue}{+0.61})} & \\makebox[5.4em][s]{14.26 (\\textcolor{blue}{+1.03})} & \\makebox[5.4em][s]{15.42 (\\textcolor{blue}{+1.50})} \\\\\n"," \\midrule\n"," \\rowcolor[rgb]{ .922, .945, .871} \\multicolumn{12}{c}{ChartLLM-Based} \\\\\n"," \\midrule\n"," TinyChart\\textdagger~\\cite{tinychart} & 3B & \\makebox[5.4em][s]{7.69 (\\textcolor{blue}{+2.33})} & \\makebox[5.4em][s]{20.07 (\\textcolor{blue}{+1.62})} & \\makebox[5.4em][s]{23.21 (\\textcolor{blue}{+6.40})} & \\makebox[5.4em][s]{95.04 (\\textcolor{blue}{+1.44})} & \\makebox[5.4em][s]{74.41 (\\textcolor{blue}{+2.25})} & \\makebox[5.4em][s]{84.73 (\\textcolor{blue}{+1.85})} & \\makebox[5.4em][s]{14.68 (\\textcolor{blue}{+3.84})} & \\makebox[5.4em][s]{34.22 (\\textcolor{blue}{+7.18})} & \\makebox[5.4em][s]{24.45 (\\textcolor{blue}{+5.51})} & \\makebox[5.4em][s]{\\textbf{21.84 } (\\textcolor{blue}{+2.22})} \\\\\n"," ChartInstruct\\textdagger~\\cite{chartinstruct} & 7B & \\makebox[5.4em][s]{11.54 (\\textcolor{blue}{+1.72})} & \\makebox[5.4em][s]{34.79 (\\textcolor{blue}{+10.24})} & \\makebox[5.4em][s]{26.43 (\\textcolor{blue}{+11.39})} & \\makebox[5.4em][s]{85.93 (\\textcolor{blue}{+3.53})} & \\makebox[5.4em][s]{43.52 (\\textcolor{blue}{+2.88})} & \\makebox[5.4em][s]{64.73 (\\textcolor{blue}{+3.20})} & \\makebox[5.4em][s]{15.52 (\\textcolor{blue}{+2.71})} & \\makebox[5.4em][s]{\\underline{41.42} (\\textcolor{blue}{+2.03})} & \\makebox[5.4em][s]{28.47 (\\textcolor{blue}{+2.37})} & \\makebox[5.4em][s]{18.53 (\\textcolor{blue}{+3.75})} \\\\\n"," ChartLlama\\textdagger~\\cite{chartllama} & 7B & \\makebox[5.4em][s]{22.67 (\\textcolor{blue}{+2.13})} & \\makebox[5.4em][s]{22.54 (\\textcolor{blue}{+1.19})} & \\makebox[5.4em][s]{27.58 (\\textcolor{blue}{+14.85})} & \\makebox[5.4em][s]{91.42 (\\textcolor{blue}{+1.06})} & \\makebox[5.4em][s]{51.72 (\\textcolor{blue}{+2.76})} & \\makebox[5.4em][s]{71.57 (\\textcolor{blue}{+1.91})} & \\makebox[5.4em][s]{17.94 (\\textcolor{blue}{+3.71})} & \\makebox[5.4em][s]{\\textbf{40.47 } (\\textcolor{red}{-0.24})} & \\makebox[5.4em][s]{\\textbf{29.21 } (\\textcolor{blue}{+1.74})} & \\makebox[5.4em][s]{7.40 (\\textcolor{blue}{+2.70})} \\\\\n"," Sphinx-v2~\\cite{sphinx} & 7B & \\makebox[5.4em][s]{13.85 (\\textcolor{blue}{+4.03})} & \\makebox[5.4em][s]{30.11 (\\textcolor{blue}{+4.16})} & \\makebox[5.4em][s]{23.68 (\\textcolor{blue}{+9.99})} & \\makebox[5.4em][s]{62.80 (\\textcolor{blue}{+1.84})} & \\makebox[5.4em][s]{48.00 (\\textcolor{blue}{+4.08})} & \\makebox[5.4em][s]{55.40 (\\textcolor{blue}{+2.96})} & \\makebox[5.4em][s]{7.90 (\\textcolor{blue}{+4.47})} & \\makebox[5.4em][s]{7.35 (\\textcolor{blue}{+2.41})} & \\makebox[5.4em][s]{7.63 (\\textcolor{blue}{+3.44})} & \\makebox[5.4em][s]{6.88 (\\textcolor{blue}{+3.78})} \\\\\n"," LLaVA1.5~\\cite{llava1.5} & 7B & \\makebox[5.4em][s]{36.92 (\\textcolor{blue}{+2.10})} & \\makebox[5.4em][s]{38.39 (\\textcolor{red}{-1.11})} & \\makebox[5.4em][s]{26.95 (\\textcolor{blue}{+11.37})} & \\makebox[5.4em][s]{25.44 (\\textcolor{blue}{+5.32})} & \\makebox[5.4em][s]{31.68 (\\textcolor{blue}{+6.48})} & \\makebox[5.4em][s]{28.56 (\\textcolor{blue}{+5.90})} & \\makebox[5.4em][s]{18.21 (\\textcolor{blue}{+2.51})} & \\makebox[5.4em][s]{17.83 (\\textcolor{blue}{+6.76})} & \\makebox[5.4em][s]{18.02 (\\textcolor{blue}{+4.63})} & \\makebox[5.4em][s]{17.40 (\\textcolor{blue}{+2.23})} \\\\\n"," ViP-LLaVA~\\cite{vipllava} & 7B & \\makebox[5.4em][s]{26.23 (\\textcolor{blue}{+5.69})} & \\makebox[5.4em][s]{41.98 (\\textcolor{blue}{+4.97})} & \\makebox[5.4em][s]{28.79 (\\textcolor{blue}{+13.23})} & \\makebox[5.4em][s]{23.96 (\\textcolor{blue}{+6.36})} & \\makebox[5.4em][s]{29.04 (\\textcolor{blue}{+2.88})} & \\makebox[5.4em][s]{26.50 (\\textcolor{blue}{+4.62})} & \\makebox[5.4em][s]{14.31 (\\textcolor{blue}{+12.95})} & \\makebox[5.4em][s]{14.38 (\\textcolor{blue}{+11.79})} & \\makebox[5.4em][s]{14.35 (\\textcolor{blue}{+12.37})} & \\makebox[5.4em][s]{18.72 (\\textcolor{blue}{+3.68})} \\\\\n"," LLaVA-NEXT~\\cite{llavanext} & 7B & \\makebox[5.4em][s]{42.31 (\\textcolor{blue}{+21.77})} & \\makebox[5.4em][s]{49.40 (\\textcolor{blue}{+2.03})} & \\makebox[5.4em][s]{34.40 (\\textcolor{blue}{+3.32})} & \\makebox[5.4em][s]{75.82 (\\textcolor{blue}{+1.56})} & \\makebox[5.4em][s]{47.68 (\\textcolor{blue}{+1.38})} & \\makebox[5.4em][s]{61.75 (\\textcolor{blue}{+1.47})} & \\makebox[5.4em][s]{15.26 (\\textcolor{blue}{+1.41})} & \\makebox[5.4em][s]{8.93 (\\textcolor{blue}{+2.30})} & \\makebox[5.4em][s]{12.10 (\\textcolor{blue}{+1.86})} & \\makebox[5.4em][s]{9.02 (\\textcolor{blue}{+0.95})} \\\\\n"," IXC-2.5~\\cite{ixc2.5} & 7B & \\makebox[5.4em][s]{47.31 (\\textcolor{blue}{+0.01})} & \\makebox[5.4em][s]{43.38 (\\textcolor{blue}{+3.28})} & \\makebox[5.4em][s]{51.88 (\\textcolor{blue}{+8.56})} & \\makebox[5.4em][s]{94.88 (\\textcolor{blue}{+2.48})} & \\makebox[5.4em][s]{76.24 (\\textcolor{blue}{+1.92})} & \\makebox[5.4em][s]{85.56 (\\textcolor{blue}{+2.20})} & \\makebox[5.4em][s]{19.82 (\\textcolor{blue}{+2.13})} & \\makebox[5.4em][s]{14.70 (\\textcolor{blue}{+2.84})} & \\makebox[5.4em][s]{17.26 (\\textcolor{blue}{+2.48})} & \\makebox[5.4em][s]{16.83 (\\textcolor{blue}{+7.44})} \\\\\n"," Qwen2-VL~\\cite{qwenvl} & 7B & \\makebox[5.4em][s]{57.66 (\\textcolor{blue}{+0.52})} & \\makebox[5.4em][s]{45.54 (\\textcolor{blue}{+8.22})} & \\makebox[5.4em][s]{56.10 (\\textcolor{blue}{+8.21})} & \\makebox[5.4em][s]{94.40 (\\textcolor{blue}{+0.30})} & \\makebox[5.4em][s]{77.44 (\\textcolor{blue}{+5.44})} & \\makebox[5.4em][s]{85.92 (\\textcolor{blue}{+2.87})} & \\makebox[5.4em][s]{20.96 (\\textcolor{blue}{+9.89})} & \\makebox[5.4em][s]{24.45 (\\textcolor{blue}{+1.47})} & \\makebox[5.4em][s]{22.71 (\\textcolor{blue}{+5.68})} & \\makebox[5.4em][s]{18.53 (\\textcolor{blue}{+10.27})} \\\\\n"," mPLUG-Owl2~\\cite{mplugowl2} & 8B & \\makebox[5.4em][s]{29.38 (\\textcolor{blue}{+4.38})} & \\makebox[5.4em][s]{40.46 (\\textcolor{blue}{+4.29})} & \\makebox[5.4em][s]{29.15 (\\textcolor{blue}{+14.93})} & \\makebox[5.4em][s]{38.76 (\\textcolor{blue}{+14.63})} & \\makebox[5.4em][s]{40.34 (\\textcolor{blue}{+13.00})} & \\makebox[5.4em][s]{39.55 (\\textcolor{blue}{+13.82})} & \\makebox[5.4em][s]{13.01 (\\textcolor{blue}{+0.18})} & \\makebox[5.4em][s]{8.91 (\\textcolor{blue}{+2.94})} & \\makebox[5.4em][s]{10.96 (\\textcolor{blue}{+1.56})} & \\makebox[5.4em][s]{6.26 (\\textcolor{blue}{+0.92})} \\\\\n"," MiniCPM-v2~\\cite{minicpmv2} & 8B & \\makebox[5.4em][s]{24.21 (\\textcolor{blue}{+1.89})} & \\makebox[5.4em][s]{38.65 (\\textcolor{blue}{+10.17})} & \\makebox[5.4em][s]{23.73 (\\textcolor{blue}{+13.09})} & \\makebox[5.4em][s]{93.84 (\\textcolor{blue}{+2.72})} & \\makebox[5.4em][s]{71.86 (\\textcolor{blue}{+2.84})} & \\makebox[5.4em][s]{82.85 (\\textcolor{blue}{+2.78})} & \\makebox[5.4em][s]{\\textbf{27.68 } (\\textcolor{blue}{+5.51})} & \\makebox[5.4em][s]{24.55 (\\textcolor{blue}{+13.54})} & \\makebox[5.4em][s]{26.12 (\\textcolor{blue}{+9.53})} & \\makebox[5.4em][s]{\\underline{20.88} (\\textcolor{blue}{+0.83})} \\\\\n"," CogVLM~\\cite{cogvlm} & 17B & \\makebox[5.4em][s]{26.38 (\\textcolor{blue}{+3.17})} & \\makebox[5.4em][s]{41.05 (\\textcolor{blue}{+0.85})} & \\makebox[5.4em][s]{41.85 (\\textcolor{blue}{+12.50})} & \\makebox[5.4em][s]{33.41 (\\textcolor{blue}{+9.46})} & \\makebox[5.4em][s]{51.73 (\\textcolor{blue}{+12.20})} & \\makebox[5.4em][s]{42.57 (\\textcolor{blue}{+10.83})} & \\makebox[5.4em][s]{21.46 (\\textcolor{blue}{+5.08})} & \\makebox[5.4em][s]{14.74 (\\textcolor{blue}{+2.90})} & \\makebox[5.4em][s]{18.10 (\\textcolor{blue}{+3.99})} & \\makebox[5.4em][s]{2.48 (\\textcolor{blue}{+0.73})} \\\\\n"," GLM-4V-plus~\\cite{chatglm4v} & - & \\makebox[5.4em][s]{\\underline{60.18} (\\textcolor{blue}{+0.35})} & \\makebox[5.4em][s]{47.00 (\\textcolor{blue}{+8.64})} & \\makebox[5.4em][s]{37.19 (\\textcolor{blue}{+15.67})} & \\makebox[5.4em][s]{19.74 (\\textcolor{blue}{+2.94})} & \\makebox[5.4em][s]{18.04 (\\textcolor{blue}{+5.24})} & \\makebox[5.4em][s]{19.66 (\\textcolor{blue}{+4.86})} & \\makebox[5.4em][s]{9.75 (\\textcolor{blue}{+4.06})} & \\makebox[5.4em][s]{8.97 (\\textcolor{blue}{+3.26})} & \\makebox[5.4em][s]{9.36 (\\textcolor{blue}{+3.66})} & \\makebox[5.4em][s]{9.74 (\\textcolor{blue}{+2.34})} \\\\\n"," GPT-4o~\\cite{gpt4v} & - & \\makebox[5.4em][s]{\\textbf{61.89 } (\\textcolor{blue}{+0.00})} & \\makebox[5.4em][s]{\\textbf{50.42 } (\\textcolor{blue}{+3.17})} & \\makebox[5.4em][s]{\\textbf{73.89 } (\\textcolor{blue}{+5.08})} & \\makebox[5.4em][s]{{\\textbf{98.63 }} (\\textcolor{blue}{+3.29})} & \\makebox[5.4em][s]{\\textbf{79.49 } (\\textcolor{blue}{+3.43})} & \\makebox[5.4em][s]{\\textbf{89.06 } (\\textcolor{blue}{+3.36})} & \\makebox[5.4em][s]{23.65 (\\textcolor{blue}{+5.90})} & \\makebox[5.4em][s]{11.07 (\\textcolor{blue}{+2.37})} & \\makebox[5.4em][s]{17.36 (\\textcolor{blue}{+4.14})} & \\makebox[5.4em][s]{16.04 (\\textcolor{blue}{+2.12})} \\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," }\n"," \\raggedright\n"," \\caption{Performance of multimodal models on ChartMind and three structured-output CQA datasets. The best results are highlighted in \\textbf{bold}, and the second-best results are \\underline{underlined}. \\textdagger Specialized CQA models.}\n"," \\vskip -0.15in\n"," \\label{tab:results1}\n","\\end{table*}\n"],["\\begin{table}[t]\n","\\centering\n","\\resizebox{\\linewidth}{!}{\n","\\begin{tabular}{lccc}\n","\\toprule\n","\\textbf{Models} & \\textbf{Size} & \\textbf{Avg. GPT-4o Score} & \\textbf{Avg. Human Score} \\\\\n","\\midrule\n","ChartInstruct~\\cite{chartinstruct} & 7B & 26.43 & 22.52 \\\\\n","ChartLlama~\\cite{chartllama} & 7B & 27.58 & 23.11 \\\\\n","TinyChart~\\cite{tinychart} & 3B & 23.21 & 21.97 \\\\\n","mPLUG-Owl2~\\cite{mplugowl2} & 8B & 29.15 & 29.31 \\\\\n","Sphinx-v2~\\cite{sphinx} & 7B & 23.68 & 22.31 \\\\\n","CogVLM~\\cite{cogvlm} & 17B & 41.85 & 34.96 \\\\\n","LLaVA1.5~\\cite{llava1.5} & 7B & 26.95 & 22.93 \\\\\n","MiniCPM-v2~\\cite{minicpmv2} & 8B & 23.73 & 24.01 \\\\\n","ViP-LLaVA~\\cite{vipllava} & 7B & 28.79 & 30.75 \\\\\n","LLaVA-NEXT~\\cite{llavanext} & 7B & 34.40 & 32.31 \\\\\n","IXC-2.5~\\cite{ixc2.5} & 7B & 51.88 & 36.61 \\\\\n","Qwen2-VL~\\cite{qwenvl} & 7B & 56.10 & 40.39 \\\\\n","GLM-4V-plus~\\cite{chatglm4v} & - & 37.19 & 39.35 \\\\\n","GPT-4o~\\cite{gpt4v} & - & 73.89 & 50.73 \\\\\n","\\midrule\n","\\textbf{PCC~\\cite{cohen2009pearson}} & - & \\multicolumn{2}{c}{\\textbf{93.09}} \\\\\n","\\bottomrule\n","\\end{tabular}}\n","\\caption{Correlation of GPT4o and Human Eval.}\n","\\vskip -0.2in\n","\\label{tab:consistency_metrics}\n","\\end{table}\n"],["\\begin{figure}[t]\n"," \\centering\n"," \\includegraphics[width=\\linewidth, height=0.38\\textheight]{images/fig7-6.pdf}\n"," \\caption{Performance of multimodal models across Chinese and English datasets in ChartMind.}\n"," \\label{fig:gpt4o_evaluation2}\n"," \\vskip -0.15in\n","\\end{figure}\n"],["\\begin{figure*}[t]\n"," \\centering\n"," \\includegraphics[width=\\linewidth]{images/fig5-1-v7.pdf}\n"," \\vskip -0.1in\n"," \\caption{Performance of multimodal models on seven tasks in ChartMind.}\n"," \\label{fig:gpt4o_evaluation1}\n"," \\vskip -0.15in\n","\\end{figure*}\n"],["\\begin{figure*}[t]\n"," \\centering\n"," \\includegraphics[width=\\linewidth]{images/fig_fulu.pdf}\n"," \\caption{Overview of the seven chart types and seven reasoning tasks included in ChartMind.}\n"," \\label{fig:chart_types_tasks}\n","\\end{figure*}\n"],["\\begin{figure*}[ht]\n"," \\centering\n"," \\includegraphics[width=\\linewidth]{images/fig7-3.pdf}\n"," \\caption{Prompt design for GPT-4o score.}\n"," \\label{fig:gpt4o_evaluation}\n","\\end{figure*}\n"],["\\begin{figure*}[ht]\n"," \\centering\n"," \\includegraphics[width=\\textwidth]{images/fig8-2.pdf} \n"," \\caption{Performance of multimodal models across chart types, categorized by four paradigms.}\n"," \\label{fig:chart_type_analysis}\n","\\end{figure*}\n"],["\\begin{figure*}[ht]\n"," \\centering\n"," \\includegraphics[width=\\linewidth]{images/fig8.pdf}\n"," \\caption{The four major error types in ChartMind.}\n"," \\label{fig:error_analysis}\n","\\end{figure*}\n"]],"string":"[\n [\n \"\\\\begin{figure*}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\linewidth]{images/fig1-3.pdf}\\n\",\n \" \\\\caption{Key Challenges in CQA Benchmarks: (A) Predominantly monolingual, limiting multilingual applicability in chart question answering; (B) Fixed formats and metrics, restricting adaptability to diverse charts; (C) Emphasis on deterministic answers, overlooking complex reasoning, such as trend analysis, and summarization.}\\n\",\n \" \\\\label{fig:challenges}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{table*}[ht]\\n\",\n \" \\\\centering\\n\",\n \" \\\\resizebox{\\\\linewidth}{!}{\\n\",\n \" \\\\begin{tabular}{lccccccccccccccc}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Dataset} & \\\\textbf{Avg. Ans.} & \\\\textbf{Instances} &\\\\textbf{Language} & \\\\textbf{Diverse} & \\\\textbf{Task} & \\\\textbf{Topic} & \\\\textbf{Chart} & \\\\textbf{Pie} & \\\\textbf{Scatter} & \\\\textbf{Common} & \\\\textbf{Grouped} & \\\\textbf{Stacked} & \\\\textbf{Complex} & \\\\textbf{Common} \\\\\\\\\\n\",\n \" \\\\textbf{} &\\\\textbf{Length} & \\\\textbf{Number} & \\\\textbf{Format} & \\\\textbf{Format} &\\\\textbf{Format} & \\\\textbf{Format} & \\\\textbf{Format} & \\\\textbf{} & \\\\textbf{} & \\\\textbf{Bar} & \\\\textbf{Bar} & \\\\textbf{Bar} & \\\\textbf{Line} & \\\\textbf{Line} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" ChartQA~\\\\cite{chartqa} &1.15 & 2,500 & English & 1 & 1 & 3 & 3& \\\\ding{51} & \\\\ding{55} & \\\\ding{51} & \\\\ding{55} & \\\\ding{55} & \\\\ding{55} & \\\\ding{51} \\\\\\\\\\n\",\n \" MMC-Benchmark~\\\\cite{mmc}& 1.08 & 2,126 & English & 1 & 4 & 5 & 2& \\\\ding{55} & \\\\ding{51} & \\\\ding{55} & \\\\ding{55} & \\\\ding{55} & \\\\ding{55} & \\\\ding{51} \\\\\\\\\\n\",\n \" PaperQA~\\\\cite{lu2023mathvista} &1.26 & 107 & English & 1 & 1 & 2 & 4& \\\\ding{51} & \\\\ding{51} & \\\\ding{51} & \\\\ding{55} & \\\\ding{55} & \\\\ding{55} & \\\\ding{51} \\\\\\\\\\n\",\n \" OpenCQA~\\\\cite{opencqa} &55.73 & 1,159 & English & 1 & 1 & 4 & 4&\\\\ding{51} & \\\\ding{51} & \\\\ding{51} & \\\\ding{55} & \\\\ding{55} & \\\\ding{55} & \\\\ding{51} \\\\\\\\\\n\",\n \" Chart-to-Text~\\\\cite{charttotext}&73.49 & 3,474 & English & 1 & 1 & 3 & 4& \\\\ding{51} & \\\\ding{51} & \\\\ding{51} & \\\\ding{55} & \\\\ding{55} & \\\\ding{55} & \\\\ding{51} \\\\\\\\\\n\",\n \" LineCap~\\\\cite{9973197}&13.63 & 1,930 & English& 1 & 1 & 1 & 2& \\\\ding{55} & \\\\ding{55} & \\\\ding{55} & \\\\ding{55} & \\\\ding{55} & \\\\ding{51} & \\\\ding{51} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\textbf{ChartMind}& \\\\textbf{119.69} & \\\\textbf{757}&\\\\textbf{EN\\\\&ZH} & \\\\textbf{2} & \\\\textbf{7} & \\\\textbf{6} & \\\\textbf{7} & \\\\ding{51} & \\\\ding{51} & \\\\ding{51} & \\\\ding{51} & \\\\ding{51} & \\\\ding{51} & \\\\ding{51} \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}}\\n\",\n \" \\\\caption{Comparison of ChartMind with Existing Chart QA Datasets.}\\n\",\n \" \\\\label{tab:dataset_comparison}\\n\",\n \" \\\\vskip -0.2in\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[ht]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\linewidth]{images/fig3-6.pdf} \\n\",\n \" \\\\caption{Data Construction Pipeline for the ChartMind.}\\n\",\n \" \\\\label{fig:data_curation}\\n\",\n \" \\\\vskip -0.1in\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{figure}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\linewidth]{images/chart4-1-2.pdf}\\n\",\n \" \\\\caption{Language and task distribution in ChartMind.}\\n\",\n \" \\\\label{fig:task_distribution1}\\n\",\n \" \\\\vskip -0.12in\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{figure}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=0.82\\\\linewidth]{images/fig5.pdf}\\n\",\n \" \\\\caption{Topic distribution in ChartMind.}\\n\",\n \" \\\\label{fig:task_distribution2}\\n\",\n \" \\\\vskip -0.12in\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\resizebox{\\\\linewidth}{!}{\\n\",\n \" \\\\begin{tabular}{lcrr}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Task} & \\\\textbf{Samples} & \\\\textbf{Query Length} & \\\\textbf{Answer Length} \\\\\\\\\\n\",\n \" & & \\\\textbf{(Min / Max)} & \\\\textbf{(Min / Max)} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Chart Conversion & 140 & 11 / 477 & 5 / 55 \\\\\\\\\\n\",\n \" Chart OCR Recognition & 139 & 13 / 351 & 8 / 59 \\\\\\\\\\n\",\n \" Suggestions & 88 & 17 / 492 & 13 / 53 \\\\\\\\\\n\",\n \" Chart Classification Analysis & 37 & 360 / 503 & 72 / 79 \\\\\\\\\\n\",\n \" Chart Summarization & 34 & 76 / 335 & 12 / 113 \\\\\\\\\\n\",\n \" Chart Assistance & 76 & 9 / 276 & 12 / 41 \\\\\\\\\\n\",\n \" Information Positioning & 140 & 11 / 208 & 11 / 35 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\textbf{Total} & \\\\textbf{757} & \\\\textbf{9 / 503} & \\\\textbf{5 / 113} \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}}\\n\",\n \" \\\\caption{Task Type Statistics in ChartMind.}\\n\",\n \" \\\\label{tab:key_statistics}\\n\",\n \" \\\\vspace{-5mm}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table*}[!ht]\\n\",\n \" \\\\centering\\n\",\n \" \\\\resizebox{\\\\linewidth}{!}{\\n\",\n \" \\\\begin{tabular}{lccccccccccr}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\multirow{2}[4]{*}{ Models} & \\\\multirow{2}[4]{*}{Size} & \\\\multicolumn{3}{c}{ChartMind} & \\\\multicolumn{3}{c}{ChartQA } & \\\\multicolumn{3}{c}{Chart-to-Text} & OpenCQA \\\\\\\\\\n\",\n \"\\\\cmidrule{3-12} & & ACC & Avg.CIDEr & Avg.GPT-4o Score & Aug. ACC & Hum. ACC & Avg. ACC & Pew. BLEU & Statista. BLEU & Avg. BLEU & Avg. BLEU \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\rowcolor[rgb]{ .922, .945, .871} \\\\multicolumn{12}{c}{Instruction-Following-Based~\\\\cite{wei2021finetuned}} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" TinyChart\\\\textdagger~\\\\cite{tinychart} & 3B & 5.36 & 18.45 & 16.81 & 93.60 & 72.16 & 82.88 & 10.84 & 27.04 & 18.94 & 19.62 \\\\\\\\\\n\",\n \" ChartInstruct\\\\textdagger~\\\\cite{chartinstruct} & 7B & 9.82 & 24.55 & 15.05 & 82.40 & 40.64 & 61.52 & 12.81 & 39.39 & 26.10 & 14.78 \\\\\\\\\\n\",\n \" ChartLlama\\\\textdagger~\\\\cite{chartllama} & 7B & 20.54 & 21.34 & 12.72 & 90.36 & 48.96 & 69.66 & 14.23 & 40.71 & 27.47 & 4.70 \\\\\\\\\\n\",\n \" Sphinx-v2~\\\\cite{sphinx} & 7B & 9.82 & 25.95 & 13.69 & 60.96 & 43.92 & 52.44 & 3.43 & 4.94 & 4.19 & 3.10 \\\\\\\\\\n\",\n \" LLaVA1.5~\\\\cite{llava1.5} & 7B & 34.82 & 39.50 & 15.58 & 20.12 & 25.20 & 22.66 & 15.70 & 11.07 & 13.39 & 15.17 \\\\\\\\\\n\",\n \" ViP-LLaVA~\\\\cite{vipllava} & 7B & 20.54 & 37.01 & 15.56 & 17.60 & 26.16 & 21.88 & 1.36 & 2.59 & 1.98 & 15.04 \\\\\\\\\\n\",\n \" LLaVA-NEXT~\\\\cite{llavanext} & 7B & 20.54 & 47.37 & 31.09 & 74.26 & 46.30 & 60.28 & 13.85 & 6.63 & 10.24 & 8.07 \\\\\\\\\\n\",\n \" IXC-2.5~\\\\cite{ixc2.5} & 7B & 47.30 & 40.10 & 43.31 & 92.40 & 74.32 & 83.36 & 17.69 & 11.86 & 14.78 & 9.39 \\\\\\\\\\n\",\n \" Qwen2-VL~\\\\cite{qwenvl} & 7B & 57.14 & 37.32 & 47.89 & 94.10 & 72.00 & 83.05 & 11.07 & 22.98 & 17.03 & 8.26 \\\\\\\\\\n\",\n \" mPLUG-Owl2~\\\\cite{mplugowl2} & 8B & 25.00 & 36.17 & 14.22 & 24.13 & 27.34 & 25.74 & 12.83 & 5.97 & 9.40 & 5.34 \\\\\\\\\\n\",\n \" MiniCPM-v2~\\\\cite{minicpmv2} & 8B & 22.32 & 28.48 & 10.63 & 91.12 & 69.02 & 80.07 & 22.17 & 11.01 & 16.59 & 20.05 \\\\\\\\\\n\",\n \" CogVLM~\\\\cite{cogvlm} & 17B & 23.21 & 40.20 & 29.35 & 23.95 & 39.53 & 31.74 & 16.38 & 11.84 & 14.11 & 1.75 \\\\\\\\\\n\",\n \" GLM-4V-plus~\\\\cite{chatglm4v} & - & 59.83 & 38.36 & 21.52 & 16.80 & 12.80 & 14.80 & 5.69 & 5.71 & 5.70 & 7.41 \\\\\\\\\\n\",\n \" GPT-4o~\\\\cite{gpt4v} & - & \\\\textbf{61.89} & 47.25 & 68.81 & 95.34 & 76.06 & 85.70 & 17.75 & 8.70 & 13.23 & 13.92 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\rowcolor[rgb]{ .922, .945, .871} \\\\multicolumn{12}{c}{OCR-Enhanced~\\\\cite{liu2023deplot}} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" TinyChart\\\\textdagger~\\\\cite{tinychart} & 3B & \\\\makebox[5.4em][s]{6.71 (\\\\textcolor{blue}{+1.35})} & \\\\makebox[5.4em][s]{13.91 (\\\\textcolor{red}{-4.54})} & \\\\makebox[5.4em][s]{17.91 (\\\\textcolor{blue}{+1.10})} & \\\\makebox[5.4em][s]{94.86 (\\\\textcolor{blue}{+1.26})} & \\\\makebox[5.4em][s]{73.95 (\\\\textcolor{blue}{+1.79})} & \\\\makebox[5.4em][s]{84.41 (\\\\textcolor{blue}{+1.53})} & \\\\makebox[5.4em][s]{13.85 (\\\\textcolor{blue}{+3.01})} & \\\\makebox[5.4em][s]{28.27 (\\\\textcolor{blue}{+1.23})} & \\\\makebox[5.4em][s]{21.06 (\\\\textcolor{blue}{+2.12})} & \\\\makebox[5.4em][s]{20.15 (\\\\textcolor{blue}{+0.53})} \\\\\\\\\\n\",\n \" ChartInstruct\\\\textdagger~\\\\cite{chartinstruct} & 7B & \\\\makebox[5.4em][s]{{10.01} (\\\\textcolor{blue}{+0.19})} & \\\\makebox[5.4em][s]{32.80 (\\\\textcolor{blue}{+8.25})} & \\\\makebox[5.4em][s]{23.42 (\\\\textcolor{blue}{+8.37})} & \\\\makebox[5.4em][s]{83.74 (\\\\textcolor{blue}{+1.34})} & \\\\makebox[5.4em][s]{42.17 (\\\\textcolor{blue}{+1.53})} & \\\\makebox[5.4em][s]{62.96 (\\\\textcolor{blue}{+1.44})} & \\\\makebox[5.4em][s]{14.95 (\\\\textcolor{blue}{+2.14})} & \\\\makebox[5.4em][s]{40.83 (\\\\textcolor{blue}{+1.44})} & \\\\makebox[5.4em][s]{27.89 (\\\\textcolor{blue}{+1.79})} & \\\\makebox[5.4em][s]{16.01 (\\\\textcolor{blue}{+1.23})} \\\\\\\\\\n\",\n \" ChartLlama\\\\textdagger~\\\\cite{chartllama} & 7B & \\\\makebox[5.4em][s]{{22.03} (\\\\textcolor{blue}{+1.49})} & \\\\makebox[5.4em][s]{21.07 (\\\\textcolor{red}{-0.27})} & \\\\makebox[5.4em][s]{26.70 (\\\\textcolor{blue}{+13.97})} & \\\\makebox[5.4em][s]{90.85 (\\\\textcolor{blue}{+0.49})} & \\\\makebox[5.4em][s]{49.26 (\\\\textcolor{blue}{+0.30})} & \\\\makebox[5.4em][s]{70.06 (\\\\textcolor{blue}{+0.40})} & \\\\makebox[5.4em][s]{16.02 (\\\\textcolor{blue}{+1.79})} & \\\\makebox[5.4em][s]{39.97 (\\\\textcolor{red}{-0.74})} & \\\\makebox[5.4em][s]{28.00 (\\\\textcolor{blue}{+0.53})} & \\\\makebox[5.4em][s]{5.89 (\\\\textcolor{blue}{+1.19})} \\\\\\\\\\n\",\n \" Sphinx-v2~\\\\cite{sphinx} & 7B & \\\\makebox[5.4em][s]{11.54 (\\\\textcolor{blue}{+1.72})} & \\\\makebox[5.4em][s]{24.14 (\\\\textcolor{red}{-1.81})} & \\\\makebox[5.4em][s]{17.21 (\\\\textcolor{blue}{+3.52})} & \\\\makebox[5.4em][s]{64.08 (\\\\textcolor{blue}{+3.12})} & \\\\makebox[5.4em][s]{45.49 (\\\\textcolor{blue}{+1.57})} & \\\\makebox[5.4em][s]{54.79 (\\\\textcolor{blue}{+2.35})} & \\\\makebox[5.4em][s]{8.81 (\\\\textcolor{blue}{+5.38})} & \\\\makebox[5.4em][s]{2.39 (\\\\textcolor{red}{-2.55})} & \\\\makebox[5.4em][s]{5.60 (\\\\textcolor{blue}{+1.41})} & \\\\makebox[5.4em][s]{3.16 (\\\\textcolor{blue}{+0.06})} \\\\\\\\\\n\",\n \" LLaVA1.5~\\\\cite{llava1.5} & 7B & \\\\makebox[5.4em][s]{36.15 (\\\\textcolor{blue}{+1.33})} & \\\\makebox[5.4em][s]{33.49 (\\\\textcolor{red}{-6.01})} & \\\\makebox[5.4em][s]{21.03 (\\\\textcolor{blue}{+5.45})} & \\\\makebox[5.4em][s]{19.73 (\\\\textcolor{red}{-0.39})} & \\\\makebox[5.4em][s]{25.95 (\\\\textcolor{blue}{+0.75})} & \\\\makebox[5.4em][s]{22.84 (\\\\textcolor{blue}{+0.18})} & \\\\makebox[5.4em][s]{15.94 (\\\\textcolor{blue}{+0.24})} & \\\\makebox[5.4em][s]{12.67 (\\\\textcolor{blue}{+1.60})} & \\\\makebox[5.4em][s]{14.30 (\\\\textcolor{blue}{+0.91})} & \\\\makebox[5.4em][s]{16.31 (\\\\textcolor{blue}{+1.14})} \\\\\\\\\\n\",\n \" ViP-LLaVA~\\\\cite{vipllava} & 7B & \\\\makebox[5.4em][s]{25.38 (\\\\textcolor{blue}{+4.84})} & \\\\makebox[5.4em][s]{36.77 (\\\\textcolor{red}{-0.24})} & \\\\makebox[5.4em][s]{26.45 (\\\\textcolor{blue}{+10.89})} & \\\\makebox[5.4em][s]{27.12 (\\\\textcolor{blue}{+9.52})} & \\\\makebox[5.4em][s]{24.94 (\\\\textcolor{red}{-1.22})} & \\\\makebox[5.4em][s]{26.03 (\\\\textcolor{blue}{+4.15})} & \\\\makebox[5.4em][s]{14.13 (\\\\textcolor{blue}{+12.77})} & \\\\makebox[5.4em][s]{14.37 (\\\\textcolor{blue}{+11.78})} & \\\\makebox[5.4em][s]{14.25 (\\\\textcolor{blue}{+12.27})} & \\\\makebox[5.4em][s]{18.08 (\\\\textcolor{blue}{+3.04})} \\\\\\\\\\n\",\n \" LLaVA-NEXT~\\\\cite{llavanext} & 7B & \\\\makebox[5.4em][s]{41.15 (\\\\textcolor{blue}{+20.61})} & \\\\makebox[5.4em][s]{47.83 (\\\\textcolor{blue}{+0.46})} & \\\\makebox[5.4em][s]{31.51 (\\\\textcolor{blue}{+0.42})} & \\\\makebox[5.4em][s]{70.47 (\\\\textcolor{red}{-3.79})} & \\\\makebox[5.4em][s]{52.68 (\\\\textcolor{blue}{+6.38})} & \\\\makebox[5.4em][s]{61.58 (\\\\textcolor{blue}{+1.30})} & \\\\makebox[5.4em][s]{15.16 (\\\\textcolor{blue}{+1.31})} & \\\\makebox[5.4em][s]{8.82 (\\\\textcolor{blue}{+2.19})} & \\\\makebox[5.4em][s]{11.99 (\\\\textcolor{blue}{+1.75})} & \\\\makebox[5.4em][s]{8.25 (\\\\textcolor{blue}{+0.18})} \\\\\\\\\\n\",\n \" IXC-2.5~\\\\cite{ixc2.5} & 7B & \\\\makebox[5.4em][s]{42.31 (\\\\textcolor{red}{-4.99})} & \\\\makebox[5.4em][s]{40.35 (\\\\textcolor{blue}{+0.24})} & \\\\makebox[5.4em][s]{45.38 (\\\\textcolor{blue}{+2.06})} & \\\\makebox[5.4em][s]{94.23 (\\\\textcolor{blue}{+1.83})} & \\\\makebox[5.4em][s]{73.40 (\\\\textcolor{red}{-0.92})} & \\\\makebox[5.4em][s]{83.82 (\\\\textcolor{blue}{+0.46})} & \\\\makebox[5.4em][s]{17.03 (\\\\textcolor{red}{-0.66})} & \\\\makebox[5.4em][s]{12.34 (\\\\textcolor{blue}{+0.48})} & \\\\makebox[5.4em][s]{14.68 (\\\\textcolor{red}{-0.10})} & \\\\makebox[5.4em][s]{14.53 (\\\\textcolor{blue}{+5.14})} \\\\\\\\\\n\",\n \" Qwen2-VL~\\\\cite{qwenvl} & 7B & \\\\makebox[5.4em][s]{42.31 (\\\\textcolor{red}{-14.83})} & \\\\makebox[5.4em][s]{36.04 (\\\\textcolor{red}{-1.27})} & \\\\makebox[5.4em][s]{49.28 (\\\\textcolor{blue}{+1.39})} & \\\\makebox[5.4em][s]{94.23 (\\\\textcolor{blue}{+0.13})} & \\\\makebox[5.4em][s]{75.96 (\\\\textcolor{blue}{+3.96})} & \\\\makebox[5.4em][s]{85.10 (\\\\textcolor{blue}{+2.05})} & \\\\makebox[5.4em][s]{11.08 (\\\\textcolor{blue}{+0.01})} & \\\\makebox[5.4em][s]{23.21 (\\\\textcolor{blue}{+0.23})} & \\\\makebox[5.4em][s]{17.15 (\\\\textcolor{blue}{+0.12})} & \\\\makebox[5.4em][s]{11.75 (\\\\textcolor{blue}{+3.49})} \\\\\\\\\\n\",\n \" mPLUG-Owl2~\\\\cite{mplugowl2} & 8B & \\\\makebox[5.4em][s]{27.62 (\\\\textcolor{blue}{+2.62})} & \\\\makebox[5.4em][s]{30.60 (\\\\textcolor{red}{-5.57})} & \\\\makebox[5.4em][s]{24.67 (\\\\textcolor{blue}{+10.44})} & \\\\makebox[5.4em][s]{35.58 (\\\\textcolor{blue}{+11.45})} & \\\\makebox[5.4em][s]{37.18 (\\\\textcolor{blue}{+9.84})} & \\\\makebox[5.4em][s]{36.38 (\\\\textcolor{blue}{+10.65})} & \\\\makebox[5.4em][s]{11.82 (\\\\textcolor{red}{-1.01})} & \\\\makebox[5.4em][s]{7.30 (\\\\textcolor{blue}{+1.33})} & \\\\makebox[5.4em][s]{9.56 (\\\\textcolor{blue}{+0.16})} & \\\\makebox[5.4em][s]{4.45 (\\\\textcolor{red}{-0.89})} \\\\\\\\\\n\",\n \" MiniCPM-v2~\\\\cite{minicpmv2} & 8B & \\\\makebox[5.4em][s]{23.04 (\\\\textcolor{blue}{+0.72}} & \\\\makebox[5.4em][s]{19.73 (\\\\textcolor{red}{-8.75})} & \\\\makebox[5.4em][s]{18.10 (\\\\textcolor{blue}{+7.47})} & \\\\makebox[5.4em][s]{\\\\makebox[5.4em][s]{92.36 (\\\\textcolor{blue}{+1.24})}} & \\\\makebox[5.4em][s]{73.21 (\\\\textcolor{blue}{+4.19})} & \\\\makebox[5.4em][s]{82.79 (\\\\textcolor{blue}{+2.72})} & \\\\makebox[5.4em][s]{20.93 (\\\\textcolor{red}{-1.24})} & \\\\makebox[5.4em][s]{5.75 (\\\\textcolor{red}{-5.26})} & \\\\makebox[5.4em][s]{13.34 (\\\\textcolor{red}{-3.25})} & \\\\makebox[5.4em][s]{20.60 (\\\\textcolor{blue}{+0.55})} \\\\\\\\\\n\",\n \" CogVLM~\\\\cite{cogvlm} & 17B & \\\\makebox[5.4em][s]{25.54 (\\\\textcolor{blue}{+2.33})} & \\\\makebox[5.4em][s]{39.00 (\\\\textcolor{red}{-1.20})} & \\\\makebox[5.4em][s]{36.80 (\\\\textcolor{blue}{+7.45})} & \\\\makebox[5.4em][s]{29.81 (\\\\textcolor{blue}{+5.86})} & \\\\makebox[5.4em][s]{48.72 (\\\\textcolor{blue}{+9.19})} & \\\\makebox[5.4em][s]{39.27 (\\\\textcolor{blue}{+7.53})} & \\\\makebox[5.4em][s]{20.85 (\\\\textcolor{blue}{+4.47})} & \\\\makebox[5.4em][s]{13.88 (\\\\textcolor{blue}{+2.04})} & \\\\makebox[5.4em][s]{17.37 (\\\\textcolor{blue}{+3.26})} & \\\\makebox[5.4em][s]{1.79 (\\\\textcolor{blue}{+0.04})} \\\\\\\\\\n\",\n \" GLM-4V-plus~\\\\cite{chatglm4v} & - & \\\\makebox[5.4em][s]{44.64 (\\\\textcolor{red}{-15.19})} & \\\\makebox[5.4em][s]{44.83 (\\\\textcolor{blue}{+6.47})} & \\\\makebox[5.4em][s]{35.79 (\\\\textcolor{blue}{+14.27})} & \\\\makebox[5.4em][s]{17.95 (\\\\textcolor{blue}{+1.15})} & \\\\makebox[5.4em][s]{16.87 (\\\\textcolor{blue}{+4.07})} & \\\\makebox[5.4em][s]{17.41 (\\\\textcolor{blue}{+2.61})} & \\\\makebox[5.4em][s]{7.91 (\\\\textcolor{blue}{+2.22})} & \\\\makebox[5.4em][s]{7.63 (\\\\textcolor{blue}{+1.92})} & \\\\makebox[5.4em][s]{7.77 (\\\\textcolor{blue}{+2.07})} & \\\\makebox[5.4em][s]{8.72 (\\\\textcolor{blue}{+1.31})} \\\\\\\\\\n\",\n \" GPT-4o~\\\\cite{gpt4v} & - & \\\\makebox[5.4em][s]{49.31 (\\\\textcolor{red}{-12.58})} & \\\\makebox[5.4em][s]{46.48 (\\\\textcolor{red}{-0.76})} & \\\\makebox[5.4em][s]{\\\\underline{71.79} (\\\\textcolor{blue}{+2.98})} & \\\\makebox[5.4em][s]{\\\\underline{96.20} (\\\\textcolor{blue}{+0.86})} & \\\\makebox[5.4em][s]{\\\\underline{78.04} (\\\\textcolor{blue}{+1.98})} & \\\\makebox[5.4em][s]{\\\\underline{87.12} (\\\\textcolor{blue}{+1.42})} & \\\\makebox[5.4em][s]{20.13 (\\\\textcolor{blue}{+2.38})} & \\\\makebox[5.4em][s]{9.86 (\\\\textcolor{blue}{+1.16})} & \\\\makebox[5.4em][s]{15.00 (\\\\textcolor{blue}{+1.77})} & \\\\makebox[5.4em][s]{14.85 (\\\\textcolor{blue}{+0.93})} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\rowcolor[rgb]{ .922, .945, .871} \\\\multicolumn{12}{c}{COT-Based~\\\\cite{wei2022chain}} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" TinyChart\\\\textdagger~\\\\cite{tinychart} & 3B & \\\\makebox[5.4em][s]{6.01 (\\\\textcolor{blue}{+0.65})} & \\\\makebox[5.4em][s]{13.58 (\\\\textcolor{red}{-4.87})} & \\\\makebox[5.4em][s]{19.30 (\\\\textcolor{blue}{+2.49})} & \\\\makebox[5.4em][s]{94.84 (\\\\textcolor{blue}{+1.24})} & \\\\makebox[5.4em][s]{74.46 (\\\\textcolor{blue}{+2.30})} & \\\\makebox[5.4em][s]{84.65 (\\\\textcolor{blue}{+1.77})} & \\\\makebox[5.4em][s]{12.31 (\\\\textcolor{blue}{+1.47})} & \\\\makebox[5.4em][s]{28.53 (\\\\textcolor{blue}{+1.49})} & \\\\makebox[5.4em][s]{20.42 (\\\\textcolor{blue}{+1.48})} & \\\\makebox[5.4em][s]{20.74 (\\\\textcolor{blue}{+1.12})} \\\\\\\\\\n\",\n \" ChartInstruct\\\\textdagger~\\\\cite{chartinstruct} & 7B & \\\\makebox[5.4em][s]{9.96 (\\\\textcolor{blue}{+0.14})} & \\\\makebox[5.4em][s]{31.95 (\\\\textcolor{blue}{+7.40})} & \\\\makebox[5.4em][s]{22.44 (\\\\textcolor{blue}{+7.39})} & \\\\makebox[5.4em][s]{83.35 (\\\\textcolor{blue}{+0.95})} & \\\\makebox[5.4em][s]{42.74 (\\\\textcolor{blue}{+2.10})} & \\\\makebox[5.4em][s]{63.05 (\\\\textcolor{blue}{+1.53})} & \\\\makebox[5.4em][s]{14.34 (\\\\textcolor{blue}{+1.53})} & \\\\makebox[5.4em][s]{41.32 (\\\\textcolor{blue}{+1.93})} & \\\\makebox[5.4em][s]{27.83 (\\\\textcolor{blue}{+1.73})} & \\\\makebox[5.4em][s]{15.25 (\\\\textcolor{blue}{+0.47})} \\\\\\\\\\n\",\n \" ChartLlama\\\\textdagger~\\\\cite{chartllama} & 7B & \\\\makebox[5.4em][s]{21.44 (\\\\textcolor{blue}{+0.90})} & \\\\makebox[5.4em][s]{18.99 (\\\\textcolor{red}{-2.36})} & \\\\makebox[5.4em][s]{21.77 (\\\\textcolor{blue}{+9.04})} & \\\\makebox[5.4em][s]{91.63 (\\\\textcolor{blue}{+1.27})} & \\\\makebox[5.4em][s]{50.04 (\\\\textcolor{blue}{+1.08})} & \\\\makebox[5.4em][s]{70.84 (\\\\textcolor{blue}{+1.18})} & \\\\makebox[5.4em][s]{15.76 (\\\\textcolor{blue}{+1.53})} & \\\\makebox[5.4em][s]{\\\\underline{41.42} (\\\\textcolor{blue}{+0.71})} & \\\\makebox[5.4em][s]{\\\\underline{28.59} (\\\\textcolor{blue}{+1.12})} & \\\\makebox[5.4em][s]{6.32 (\\\\textcolor{blue}{+1.62})} \\\\\\\\\\n\",\n \" Sphinx-v2~\\\\cite{sphinx} & 7B & \\\\makebox[5.4em][s]{9.91 (\\\\textcolor{blue}{+0.09})} & \\\\makebox[5.4em][s]{25.03 (\\\\textcolor{red}{-0.92})} & \\\\makebox[5.4em][s]{16.26 (\\\\textcolor{blue}{+2.57})} & \\\\makebox[5.4em][s]{61.86 (\\\\textcolor{blue}{+0.90})} & \\\\makebox[5.4em][s]{46.79 (\\\\textcolor{blue}{+2.87})} & \\\\makebox[5.4em][s]{54.33 (\\\\textcolor{blue}{+1.89})} & \\\\makebox[5.4em][s]{3.53 (\\\\textcolor{blue}{+0.10})} & \\\\makebox[5.4em][s]{5.09 (\\\\textcolor{blue}{+0.15})} & \\\\makebox[5.4em][s]{4.31 (\\\\textcolor{blue}{+0.12})} & \\\\makebox[5.4em][s]{3.13 (\\\\textcolor{blue}{+0.03})} \\\\\\\\\\n\",\n \" LLaVA1.5~\\\\cite{llava1.5} & 7B & \\\\makebox[5.4em][s]{35.77 (\\\\textcolor{blue}{+0.95})} & \\\\makebox[5.4em][s]{35.61 (\\\\textcolor{red}{-3.89})} & \\\\makebox[5.4em][s]{19.68 (\\\\textcolor{blue}{+4.10})} & \\\\makebox[5.4em][s]{16.90 (\\\\textcolor{red}{-3.22})} & \\\\makebox[5.4em][s]{28.57 (\\\\textcolor{blue}{+3.37})} & \\\\makebox[5.4em][s]{22.74 (\\\\textcolor{blue}{+0.08})} & \\\\makebox[5.4em][s]{15.20 (\\\\textcolor{red}{-0.50})} & \\\\makebox[5.4em][s]{11.66 (\\\\textcolor{blue}{+0.59})} & \\\\makebox[5.4em][s]{13.43 (\\\\textcolor{blue}{+0.04})} & \\\\makebox[5.4em][s]{15.93 (\\\\textcolor{blue}{+0.76})} \\\\\\\\\\n\",\n \" ViP-LLaVA~\\\\cite{vipllava} & 7B & \\\\makebox[5.4em][s]{23.31 (\\\\textcolor{blue}{+2.77})} & \\\\makebox[5.4em][s]{36.13 (\\\\textcolor{red}{-0.88})} & \\\\makebox[5.4em][s]{22.24 (\\\\textcolor{blue}{+6.68})} & \\\\makebox[5.4em][s]{22.12 (\\\\textcolor{blue}{+4.52})} & \\\\makebox[5.4em][s]{28.21 (\\\\textcolor{blue}{+2.05})} & \\\\makebox[5.4em][s]{25.17 (\\\\textcolor{blue}{+3.29})} & \\\\makebox[5.4em][s]{15.48 (\\\\textcolor{blue}{+14.12})} & \\\\makebox[5.4em][s]{12.20 (\\\\textcolor{blue}{+9.61})} & \\\\makebox[5.4em][s]{13.84 (\\\\textcolor{blue}{+11.86})} & \\\\makebox[5.4em][s]{15.67 (\\\\textcolor{blue}{+0.63})} \\\\\\\\\\n\",\n \" LLaVA-NEXT~\\\\cite{llavanext} & 7B & \\\\makebox[5.4em][s]{40.23 (\\\\textcolor{blue}{+19.69})} & \\\\makebox[5.4em][s]{47.44 (\\\\textcolor{blue}{+0.07})} & \\\\makebox[5.4em][s]{27.34 (\\\\textcolor{red}{-3.75})} & \\\\makebox[5.4em][s]{68.49 (\\\\textcolor{red}{-5.77})} & \\\\makebox[5.4em][s]{52.13 (\\\\textcolor{blue}{+5.83})} & \\\\makebox[5.4em][s]{60.31 (\\\\textcolor{blue}{+0.03})} & \\\\makebox[5.4em][s]{14.81 (\\\\textcolor{blue}{+0.96})} & \\\\makebox[5.4em][s]{6.29 (\\\\textcolor{red}{-0.34})} & \\\\makebox[5.4em][s]{10.55 (\\\\textcolor{blue}{+0.31})} & \\\\makebox[5.4em][s]{8.09 (\\\\textcolor{blue}{+0.02})} \\\\\\\\\\n\",\n \" IXC-2.5~\\\\cite{ixc2.5} & 7B & \\\\makebox[5.4em][s]{41.15 (\\\\textcolor{red}{-6.15})} & \\\\makebox[5.4em][s]{41.23 (\\\\textcolor{blue}{+1.13})} & \\\\makebox[5.4em][s]{46.73 (\\\\textcolor{blue}{+3.42})} & \\\\makebox[5.4em][s]{93.91 (\\\\textcolor{blue}{+1.51})} & \\\\makebox[5.4em][s]{72.82 (\\\\textcolor{red}{-1.50})} & \\\\makebox[5.4em][s]{83.37 (\\\\textcolor{blue}{+0.01})} & \\\\makebox[5.4em][s]{17.36 (\\\\textcolor{blue}{+0.23})} & \\\\makebox[5.4em][s]{11.92 (\\\\textcolor{blue}{+0.06})} & \\\\makebox[5.4em][s]{14.64 (\\\\textcolor{red}{-0.14})} & \\\\makebox[5.4em][s]{14.39 (\\\\textcolor{blue}{+5.00})} \\\\\\\\\\n\",\n \" Qwen2-VL~\\\\cite{qwenvl} & 7B & \\\\makebox[5.4em][s]{40.69 (\\\\textcolor{red}{-16.45})} & \\\\makebox[5.4em][s]{44.72 (\\\\textcolor{blue}{+7.41})} & \\\\makebox[5.4em][s]{55.12 (\\\\textcolor{blue}{+7.24})} & \\\\makebox[5.4em][s]{94.87 (\\\\textcolor{blue}{+0.77})} & \\\\makebox[5.4em][s]{77.88 (\\\\textcolor{blue}{+5.88})} & \\\\makebox[5.4em][s]{86.38 (\\\\textcolor{blue}{+3.33})} & \\\\makebox[5.4em][s]{16.70 (\\\\textcolor{blue}{+5.63})} & \\\\makebox[5.4em][s]{23.91 (\\\\textcolor{blue}{+0.93})} & \\\\makebox[5.4em][s]{20.30 (\\\\textcolor{blue}{+3.27})} & \\\\makebox[5.4em][s]{10.32 (\\\\textcolor{blue}{+2.06})} \\\\\\\\\\n\",\n \" mPLUG-Owl2~\\\\cite{mplugowl2} & 8B & \\\\makebox[5.4em][s]{25.89 (\\\\textcolor{blue}{+0.89})} & \\\\makebox[5.4em][s]{35.10 (\\\\textcolor{red}{-1.08})} & \\\\makebox[5.4em][s]{21.27 (\\\\textcolor{blue}{+7.04})} & \\\\makebox[5.4em][s]{27.56 (\\\\textcolor{blue}{+3.43})} & \\\\makebox[5.4em][s]{31.09 (\\\\textcolor{blue}{+3.75})} & \\\\makebox[5.4em][s]{29.33 (\\\\textcolor{blue}{+3.59})} & \\\\makebox[5.4em][s]{14.00 (\\\\textcolor{blue}{+1.17})} & \\\\makebox[5.4em][s]{7.84 (\\\\textcolor{blue}{+1.87})} & \\\\makebox[5.4em][s]{10.92 (\\\\textcolor{blue}{+1.52})} & \\\\makebox[5.4em][s]{7.88 (\\\\textcolor{blue}{+2.54})} \\\\\\\\\\n\",\n \" MiniCPM-v2~\\\\cite{minicpmv2} & 8B & \\\\makebox[5.4em][s]{22.78 (\\\\textcolor{blue}{+0.46})} & \\\\makebox[5.4em][s]{28.81 (\\\\textcolor{blue}{+0.33})} & \\\\makebox[5.4em][s]{18.18 (\\\\textcolor{blue}{+7.54})} & \\\\makebox[5.4em][s]{92.37 (\\\\textcolor{blue}{+1.25})} & \\\\makebox[5.4em][s]{71.47 (\\\\textcolor{blue}{+2.45})} & \\\\makebox[5.4em][s]{81.92 (\\\\textcolor{blue}{+1.85})} & \\\\makebox[5.4em][s]{\\\\underline{26.56} (\\\\textcolor{blue}{+4.39})} & \\\\makebox[5.4em][s]{12.53 (\\\\textcolor{blue}{+1.52})} & \\\\makebox[5.4em][s]{19.54 (\\\\textcolor{blue}{+2.95})} & \\\\makebox[5.4em][s]{20.30 (\\\\textcolor{blue}{+0.25})} \\\\\\\\\\n\",\n \" CogVLM~\\\\cite{cogvlm} & 17B & \\\\makebox[5.4em][s]{24.01 (\\\\textcolor{blue}{+0.80})} & \\\\makebox[5.4em][s]{40.04 (\\\\textcolor{red}{-0.16})} & \\\\makebox[5.4em][s]{37.14 (\\\\textcolor{blue}{+7.79})} & \\\\makebox[5.4em][s]{27.31 (\\\\textcolor{blue}{+3.36})} & \\\\makebox[5.4em][s]{44.93 (\\\\textcolor{blue}{+5.40})} & \\\\makebox[5.4em][s]{36.12 (\\\\textcolor{blue}{+4.38})} & \\\\makebox[5.4em][s]{17.94 (\\\\textcolor{blue}{+1.56})} & \\\\makebox[5.4em][s]{12.57 (\\\\textcolor{blue}{+0.73})} & \\\\makebox[5.4em][s]{15.26 (\\\\textcolor{blue}{+1.15})} & \\\\makebox[5.4em][s]{3.41 (\\\\textcolor{blue}{+1.66})} \\\\\\\\\\n\",\n \" GLM-4V-plus~\\\\cite{chatglm4v} & - & \\\\makebox[5.4em][s]{41.00 (\\\\textcolor{red}{-18.83})} & \\\\makebox[5.4em][s]{39.55 (\\\\textcolor{blue}{+1.19})} & \\\\makebox[5.4em][s]{21.68 (\\\\textcolor{blue}{+0.16})} & \\\\makebox[5.4em][s]{18.63 (\\\\textcolor{blue}{+1.83})} & \\\\makebox[5.4em][s]{15.96 (\\\\textcolor{blue}{+3.16})} & \\\\makebox[5.4em][s]{17.30 (\\\\textcolor{blue}{+2.50})} & \\\\makebox[5.4em][s]{6.86 (\\\\textcolor{blue}{+1.17})} & \\\\makebox[5.4em][s]{7.72 (\\\\textcolor{blue}{+2.01})} & \\\\makebox[5.4em][s]{7.29 (\\\\textcolor{blue}{+1.59})} & \\\\makebox[5.4em][s]{8.83 (\\\\textcolor{blue}{+1.42})} \\\\\\\\\\n\",\n \" GPT-4o~\\\\cite{gpt4v} & - & \\\\makebox[5.4em][s]{46.15 (\\\\textcolor{red}{-15.74})} & \\\\makebox[5.4em][s]{\\\\underline{48.19} (\\\\textcolor{blue}{+0.95})} & \\\\makebox[5.4em][s]{69.00 (\\\\textcolor{blue}{+0.19})} & \\\\makebox[5.4em][s]{95.39 (\\\\textcolor{blue}{+0.05})} & \\\\makebox[5.4em][s]{77.23 (\\\\textcolor{blue}{+1.17})} & \\\\makebox[5.4em][s]{86.31 (\\\\textcolor{blue}{+0.61})} & \\\\makebox[5.4em][s]{19.20 (\\\\textcolor{blue}{+1.45})} & \\\\makebox[5.4em][s]{9.31 (\\\\textcolor{blue}{+0.61})} & \\\\makebox[5.4em][s]{14.26 (\\\\textcolor{blue}{+1.03})} & \\\\makebox[5.4em][s]{15.42 (\\\\textcolor{blue}{+1.50})} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\rowcolor[rgb]{ .922, .945, .871} \\\\multicolumn{12}{c}{ChartLLM-Based} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" TinyChart\\\\textdagger~\\\\cite{tinychart} & 3B & \\\\makebox[5.4em][s]{7.69 (\\\\textcolor{blue}{+2.33})} & \\\\makebox[5.4em][s]{20.07 (\\\\textcolor{blue}{+1.62})} & \\\\makebox[5.4em][s]{23.21 (\\\\textcolor{blue}{+6.40})} & \\\\makebox[5.4em][s]{95.04 (\\\\textcolor{blue}{+1.44})} & \\\\makebox[5.4em][s]{74.41 (\\\\textcolor{blue}{+2.25})} & \\\\makebox[5.4em][s]{84.73 (\\\\textcolor{blue}{+1.85})} & \\\\makebox[5.4em][s]{14.68 (\\\\textcolor{blue}{+3.84})} & \\\\makebox[5.4em][s]{34.22 (\\\\textcolor{blue}{+7.18})} & \\\\makebox[5.4em][s]{24.45 (\\\\textcolor{blue}{+5.51})} & \\\\makebox[5.4em][s]{\\\\textbf{21.84 } (\\\\textcolor{blue}{+2.22})} \\\\\\\\\\n\",\n \" ChartInstruct\\\\textdagger~\\\\cite{chartinstruct} & 7B & \\\\makebox[5.4em][s]{11.54 (\\\\textcolor{blue}{+1.72})} & \\\\makebox[5.4em][s]{34.79 (\\\\textcolor{blue}{+10.24})} & \\\\makebox[5.4em][s]{26.43 (\\\\textcolor{blue}{+11.39})} & \\\\makebox[5.4em][s]{85.93 (\\\\textcolor{blue}{+3.53})} & \\\\makebox[5.4em][s]{43.52 (\\\\textcolor{blue}{+2.88})} & \\\\makebox[5.4em][s]{64.73 (\\\\textcolor{blue}{+3.20})} & \\\\makebox[5.4em][s]{15.52 (\\\\textcolor{blue}{+2.71})} & \\\\makebox[5.4em][s]{\\\\underline{41.42} (\\\\textcolor{blue}{+2.03})} & \\\\makebox[5.4em][s]{28.47 (\\\\textcolor{blue}{+2.37})} & \\\\makebox[5.4em][s]{18.53 (\\\\textcolor{blue}{+3.75})} \\\\\\\\\\n\",\n \" ChartLlama\\\\textdagger~\\\\cite{chartllama} & 7B & \\\\makebox[5.4em][s]{22.67 (\\\\textcolor{blue}{+2.13})} & \\\\makebox[5.4em][s]{22.54 (\\\\textcolor{blue}{+1.19})} & \\\\makebox[5.4em][s]{27.58 (\\\\textcolor{blue}{+14.85})} & \\\\makebox[5.4em][s]{91.42 (\\\\textcolor{blue}{+1.06})} & \\\\makebox[5.4em][s]{51.72 (\\\\textcolor{blue}{+2.76})} & \\\\makebox[5.4em][s]{71.57 (\\\\textcolor{blue}{+1.91})} & \\\\makebox[5.4em][s]{17.94 (\\\\textcolor{blue}{+3.71})} & \\\\makebox[5.4em][s]{\\\\textbf{40.47 } (\\\\textcolor{red}{-0.24})} & \\\\makebox[5.4em][s]{\\\\textbf{29.21 } (\\\\textcolor{blue}{+1.74})} & \\\\makebox[5.4em][s]{7.40 (\\\\textcolor{blue}{+2.70})} \\\\\\\\\\n\",\n \" Sphinx-v2~\\\\cite{sphinx} & 7B & \\\\makebox[5.4em][s]{13.85 (\\\\textcolor{blue}{+4.03})} & \\\\makebox[5.4em][s]{30.11 (\\\\textcolor{blue}{+4.16})} & \\\\makebox[5.4em][s]{23.68 (\\\\textcolor{blue}{+9.99})} & \\\\makebox[5.4em][s]{62.80 (\\\\textcolor{blue}{+1.84})} & \\\\makebox[5.4em][s]{48.00 (\\\\textcolor{blue}{+4.08})} & \\\\makebox[5.4em][s]{55.40 (\\\\textcolor{blue}{+2.96})} & \\\\makebox[5.4em][s]{7.90 (\\\\textcolor{blue}{+4.47})} & \\\\makebox[5.4em][s]{7.35 (\\\\textcolor{blue}{+2.41})} & \\\\makebox[5.4em][s]{7.63 (\\\\textcolor{blue}{+3.44})} & \\\\makebox[5.4em][s]{6.88 (\\\\textcolor{blue}{+3.78})} \\\\\\\\\\n\",\n \" LLaVA1.5~\\\\cite{llava1.5} & 7B & \\\\makebox[5.4em][s]{36.92 (\\\\textcolor{blue}{+2.10})} & \\\\makebox[5.4em][s]{38.39 (\\\\textcolor{red}{-1.11})} & \\\\makebox[5.4em][s]{26.95 (\\\\textcolor{blue}{+11.37})} & \\\\makebox[5.4em][s]{25.44 (\\\\textcolor{blue}{+5.32})} & \\\\makebox[5.4em][s]{31.68 (\\\\textcolor{blue}{+6.48})} & \\\\makebox[5.4em][s]{28.56 (\\\\textcolor{blue}{+5.90})} & \\\\makebox[5.4em][s]{18.21 (\\\\textcolor{blue}{+2.51})} & \\\\makebox[5.4em][s]{17.83 (\\\\textcolor{blue}{+6.76})} & \\\\makebox[5.4em][s]{18.02 (\\\\textcolor{blue}{+4.63})} & \\\\makebox[5.4em][s]{17.40 (\\\\textcolor{blue}{+2.23})} \\\\\\\\\\n\",\n \" ViP-LLaVA~\\\\cite{vipllava} & 7B & \\\\makebox[5.4em][s]{26.23 (\\\\textcolor{blue}{+5.69})} & \\\\makebox[5.4em][s]{41.98 (\\\\textcolor{blue}{+4.97})} & \\\\makebox[5.4em][s]{28.79 (\\\\textcolor{blue}{+13.23})} & \\\\makebox[5.4em][s]{23.96 (\\\\textcolor{blue}{+6.36})} & \\\\makebox[5.4em][s]{29.04 (\\\\textcolor{blue}{+2.88})} & \\\\makebox[5.4em][s]{26.50 (\\\\textcolor{blue}{+4.62})} & \\\\makebox[5.4em][s]{14.31 (\\\\textcolor{blue}{+12.95})} & \\\\makebox[5.4em][s]{14.38 (\\\\textcolor{blue}{+11.79})} & \\\\makebox[5.4em][s]{14.35 (\\\\textcolor{blue}{+12.37})} & \\\\makebox[5.4em][s]{18.72 (\\\\textcolor{blue}{+3.68})} \\\\\\\\\\n\",\n \" LLaVA-NEXT~\\\\cite{llavanext} & 7B & \\\\makebox[5.4em][s]{42.31 (\\\\textcolor{blue}{+21.77})} & \\\\makebox[5.4em][s]{49.40 (\\\\textcolor{blue}{+2.03})} & \\\\makebox[5.4em][s]{34.40 (\\\\textcolor{blue}{+3.32})} & \\\\makebox[5.4em][s]{75.82 (\\\\textcolor{blue}{+1.56})} & \\\\makebox[5.4em][s]{47.68 (\\\\textcolor{blue}{+1.38})} & \\\\makebox[5.4em][s]{61.75 (\\\\textcolor{blue}{+1.47})} & \\\\makebox[5.4em][s]{15.26 (\\\\textcolor{blue}{+1.41})} & \\\\makebox[5.4em][s]{8.93 (\\\\textcolor{blue}{+2.30})} & \\\\makebox[5.4em][s]{12.10 (\\\\textcolor{blue}{+1.86})} & \\\\makebox[5.4em][s]{9.02 (\\\\textcolor{blue}{+0.95})} \\\\\\\\\\n\",\n \" IXC-2.5~\\\\cite{ixc2.5} & 7B & \\\\makebox[5.4em][s]{47.31 (\\\\textcolor{blue}{+0.01})} & \\\\makebox[5.4em][s]{43.38 (\\\\textcolor{blue}{+3.28})} & \\\\makebox[5.4em][s]{51.88 (\\\\textcolor{blue}{+8.56})} & \\\\makebox[5.4em][s]{94.88 (\\\\textcolor{blue}{+2.48})} & \\\\makebox[5.4em][s]{76.24 (\\\\textcolor{blue}{+1.92})} & \\\\makebox[5.4em][s]{85.56 (\\\\textcolor{blue}{+2.20})} & \\\\makebox[5.4em][s]{19.82 (\\\\textcolor{blue}{+2.13})} & \\\\makebox[5.4em][s]{14.70 (\\\\textcolor{blue}{+2.84})} & \\\\makebox[5.4em][s]{17.26 (\\\\textcolor{blue}{+2.48})} & \\\\makebox[5.4em][s]{16.83 (\\\\textcolor{blue}{+7.44})} \\\\\\\\\\n\",\n \" Qwen2-VL~\\\\cite{qwenvl} & 7B & \\\\makebox[5.4em][s]{57.66 (\\\\textcolor{blue}{+0.52})} & \\\\makebox[5.4em][s]{45.54 (\\\\textcolor{blue}{+8.22})} & \\\\makebox[5.4em][s]{56.10 (\\\\textcolor{blue}{+8.21})} & \\\\makebox[5.4em][s]{94.40 (\\\\textcolor{blue}{+0.30})} & \\\\makebox[5.4em][s]{77.44 (\\\\textcolor{blue}{+5.44})} & \\\\makebox[5.4em][s]{85.92 (\\\\textcolor{blue}{+2.87})} & \\\\makebox[5.4em][s]{20.96 (\\\\textcolor{blue}{+9.89})} & \\\\makebox[5.4em][s]{24.45 (\\\\textcolor{blue}{+1.47})} & \\\\makebox[5.4em][s]{22.71 (\\\\textcolor{blue}{+5.68})} & \\\\makebox[5.4em][s]{18.53 (\\\\textcolor{blue}{+10.27})} \\\\\\\\\\n\",\n \" mPLUG-Owl2~\\\\cite{mplugowl2} & 8B & \\\\makebox[5.4em][s]{29.38 (\\\\textcolor{blue}{+4.38})} & \\\\makebox[5.4em][s]{40.46 (\\\\textcolor{blue}{+4.29})} & \\\\makebox[5.4em][s]{29.15 (\\\\textcolor{blue}{+14.93})} & \\\\makebox[5.4em][s]{38.76 (\\\\textcolor{blue}{+14.63})} & \\\\makebox[5.4em][s]{40.34 (\\\\textcolor{blue}{+13.00})} & \\\\makebox[5.4em][s]{39.55 (\\\\textcolor{blue}{+13.82})} & \\\\makebox[5.4em][s]{13.01 (\\\\textcolor{blue}{+0.18})} & \\\\makebox[5.4em][s]{8.91 (\\\\textcolor{blue}{+2.94})} & \\\\makebox[5.4em][s]{10.96 (\\\\textcolor{blue}{+1.56})} & \\\\makebox[5.4em][s]{6.26 (\\\\textcolor{blue}{+0.92})} \\\\\\\\\\n\",\n \" MiniCPM-v2~\\\\cite{minicpmv2} & 8B & \\\\makebox[5.4em][s]{24.21 (\\\\textcolor{blue}{+1.89})} & \\\\makebox[5.4em][s]{38.65 (\\\\textcolor{blue}{+10.17})} & \\\\makebox[5.4em][s]{23.73 (\\\\textcolor{blue}{+13.09})} & \\\\makebox[5.4em][s]{93.84 (\\\\textcolor{blue}{+2.72})} & \\\\makebox[5.4em][s]{71.86 (\\\\textcolor{blue}{+2.84})} & \\\\makebox[5.4em][s]{82.85 (\\\\textcolor{blue}{+2.78})} & \\\\makebox[5.4em][s]{\\\\textbf{27.68 } (\\\\textcolor{blue}{+5.51})} & \\\\makebox[5.4em][s]{24.55 (\\\\textcolor{blue}{+13.54})} & \\\\makebox[5.4em][s]{26.12 (\\\\textcolor{blue}{+9.53})} & \\\\makebox[5.4em][s]{\\\\underline{20.88} (\\\\textcolor{blue}{+0.83})} \\\\\\\\\\n\",\n \" CogVLM~\\\\cite{cogvlm} & 17B & \\\\makebox[5.4em][s]{26.38 (\\\\textcolor{blue}{+3.17})} & \\\\makebox[5.4em][s]{41.05 (\\\\textcolor{blue}{+0.85})} & \\\\makebox[5.4em][s]{41.85 (\\\\textcolor{blue}{+12.50})} & \\\\makebox[5.4em][s]{33.41 (\\\\textcolor{blue}{+9.46})} & \\\\makebox[5.4em][s]{51.73 (\\\\textcolor{blue}{+12.20})} & \\\\makebox[5.4em][s]{42.57 (\\\\textcolor{blue}{+10.83})} & \\\\makebox[5.4em][s]{21.46 (\\\\textcolor{blue}{+5.08})} & \\\\makebox[5.4em][s]{14.74 (\\\\textcolor{blue}{+2.90})} & \\\\makebox[5.4em][s]{18.10 (\\\\textcolor{blue}{+3.99})} & \\\\makebox[5.4em][s]{2.48 (\\\\textcolor{blue}{+0.73})} \\\\\\\\\\n\",\n \" GLM-4V-plus~\\\\cite{chatglm4v} & - & \\\\makebox[5.4em][s]{\\\\underline{60.18} (\\\\textcolor{blue}{+0.35})} & \\\\makebox[5.4em][s]{47.00 (\\\\textcolor{blue}{+8.64})} & \\\\makebox[5.4em][s]{37.19 (\\\\textcolor{blue}{+15.67})} & \\\\makebox[5.4em][s]{19.74 (\\\\textcolor{blue}{+2.94})} & \\\\makebox[5.4em][s]{18.04 (\\\\textcolor{blue}{+5.24})} & \\\\makebox[5.4em][s]{19.66 (\\\\textcolor{blue}{+4.86})} & \\\\makebox[5.4em][s]{9.75 (\\\\textcolor{blue}{+4.06})} & \\\\makebox[5.4em][s]{8.97 (\\\\textcolor{blue}{+3.26})} & \\\\makebox[5.4em][s]{9.36 (\\\\textcolor{blue}{+3.66})} & \\\\makebox[5.4em][s]{9.74 (\\\\textcolor{blue}{+2.34})} \\\\\\\\\\n\",\n \" GPT-4o~\\\\cite{gpt4v} & - & \\\\makebox[5.4em][s]{\\\\textbf{61.89 } (\\\\textcolor{blue}{+0.00})} & \\\\makebox[5.4em][s]{\\\\textbf{50.42 } (\\\\textcolor{blue}{+3.17})} & \\\\makebox[5.4em][s]{\\\\textbf{73.89 } (\\\\textcolor{blue}{+5.08})} & \\\\makebox[5.4em][s]{{\\\\textbf{98.63 }} (\\\\textcolor{blue}{+3.29})} & \\\\makebox[5.4em][s]{\\\\textbf{79.49 } (\\\\textcolor{blue}{+3.43})} & \\\\makebox[5.4em][s]{\\\\textbf{89.06 } (\\\\textcolor{blue}{+3.36})} & \\\\makebox[5.4em][s]{23.65 (\\\\textcolor{blue}{+5.90})} & \\\\makebox[5.4em][s]{11.07 (\\\\textcolor{blue}{+2.37})} & \\\\makebox[5.4em][s]{17.36 (\\\\textcolor{blue}{+4.14})} & \\\\makebox[5.4em][s]{16.04 (\\\\textcolor{blue}{+2.12})} \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \" \\\\raggedright\\n\",\n \" \\\\caption{Performance of multimodal models on ChartMind and three structured-output CQA datasets. The best results are highlighted in \\\\textbf{bold}, and the second-best results are \\\\underline{underlined}. \\\\textdagger Specialized CQA models.}\\n\",\n \" \\\\vskip -0.15in\\n\",\n \" \\\\label{tab:results1}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table}[t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\linewidth}{!}{\\n\",\n \"\\\\begin{tabular}{lccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Models} & \\\\textbf{Size} & \\\\textbf{Avg. GPT-4o Score} & \\\\textbf{Avg. Human Score} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"ChartInstruct~\\\\cite{chartinstruct} & 7B & 26.43 & 22.52 \\\\\\\\\\n\",\n \"ChartLlama~\\\\cite{chartllama} & 7B & 27.58 & 23.11 \\\\\\\\\\n\",\n \"TinyChart~\\\\cite{tinychart} & 3B & 23.21 & 21.97 \\\\\\\\\\n\",\n \"mPLUG-Owl2~\\\\cite{mplugowl2} & 8B & 29.15 & 29.31 \\\\\\\\\\n\",\n \"Sphinx-v2~\\\\cite{sphinx} & 7B & 23.68 & 22.31 \\\\\\\\\\n\",\n \"CogVLM~\\\\cite{cogvlm} & 17B & 41.85 & 34.96 \\\\\\\\\\n\",\n \"LLaVA1.5~\\\\cite{llava1.5} & 7B & 26.95 & 22.93 \\\\\\\\\\n\",\n \"MiniCPM-v2~\\\\cite{minicpmv2} & 8B & 23.73 & 24.01 \\\\\\\\\\n\",\n \"ViP-LLaVA~\\\\cite{vipllava} & 7B & 28.79 & 30.75 \\\\\\\\\\n\",\n \"LLaVA-NEXT~\\\\cite{llavanext} & 7B & 34.40 & 32.31 \\\\\\\\\\n\",\n \"IXC-2.5~\\\\cite{ixc2.5} & 7B & 51.88 & 36.61 \\\\\\\\\\n\",\n \"Qwen2-VL~\\\\cite{qwenvl} & 7B & 56.10 & 40.39 \\\\\\\\\\n\",\n \"GLM-4V-plus~\\\\cite{chatglm4v} & - & 37.19 & 39.35 \\\\\\\\\\n\",\n \"GPT-4o~\\\\cite{gpt4v} & - & 73.89 & 50.73 \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textbf{PCC~\\\\cite{cohen2009pearson}} & - & \\\\multicolumn{2}{c}{\\\\textbf{93.09}} \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}}\\n\",\n \"\\\\caption{Correlation of GPT4o and Human Eval.}\\n\",\n \"\\\\vskip -0.2in\\n\",\n \"\\\\label{tab:consistency_metrics}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{figure}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\linewidth, height=0.38\\\\textheight]{images/fig7-6.pdf}\\n\",\n \" \\\\caption{Performance of multimodal models across Chinese and English datasets in ChartMind.}\\n\",\n \" \\\\label{fig:gpt4o_evaluation2}\\n\",\n \" \\\\vskip -0.15in\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{figure*}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\linewidth]{images/fig5-1-v7.pdf}\\n\",\n \" \\\\vskip -0.1in\\n\",\n \" \\\\caption{Performance of multimodal models on seven tasks in ChartMind.}\\n\",\n \" \\\\label{fig:gpt4o_evaluation1}\\n\",\n \" \\\\vskip -0.15in\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\linewidth]{images/fig_fulu.pdf}\\n\",\n \" \\\\caption{Overview of the seven chart types and seven reasoning tasks included in ChartMind.}\\n\",\n \" \\\\label{fig:chart_types_tasks}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[ht]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\linewidth]{images/fig7-3.pdf}\\n\",\n \" \\\\caption{Prompt design for GPT-4o score.}\\n\",\n \" \\\\label{fig:gpt4o_evaluation}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[ht]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\textwidth]{images/fig8-2.pdf} \\n\",\n \" \\\\caption{Performance of multimodal models across chart types, categorized by four paradigms.}\\n\",\n \" \\\\label{fig:chart_type_analysis}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[ht]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\linewidth]{images/fig8.pdf}\\n\",\n \" \\\\caption{The four major error types in ChartMind.}\\n\",\n \" \\\\label{fig:error_analysis}\\n\",\n \"\\\\end{figure*}\\n\"\n ]\n]","isConversational":false},"fulltext":{"kind":"string","value":"% This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.\n\\pdfoutput=1\n\n\\documentclass[11pt]{article}\n\n% Change \"review\" to \"final\" to generate the final (sometimes called camera-ready) version.\n% Change to \"preprint\" to generate a non-anonymous version with page numbers.\n\\usepackage[preprint]{acl}\n\n% Standard package includes\n\\usepackage{times}\n\\usepackage{latexsym}\n\\usepackage{graphicx}\n\\usepackage{subfigure}\n\\usepackage{booktabs} % for professional tables\n\n\\usepackage{multirow}\n\\usepackage{verbatim}\n\\usepackage{graphicx}\n\\usepackage{pifont}\n\\usepackage{adjustbox}\n\\usepackage{enumitem}\n\\usepackage{amsfonts}\n\\usepackage{array}\n\\usepackage{caption}\n% \\usepackage[table]{xcolor}\n\n\\usepackage{amssymb}\n\\usepackage{mathtools}\n\\usepackage{amsthm}\n\n\n\n% For proper rendering and hyphenation of words containing Latin characters (including in bib files)\n\\usepackage[T1]{fontenc}\n% For Vietnamese characters\n% \\usepackage[T5]{fontenc}\n% See https://www.latex-project.org/help/documentation/encguide.pdf for other character sets\n\n% This assumes your files are encoded as UTF8\n\\usepackage[utf8]{inputenc}\n\n% This is not strictly necessary, and may be commented out,\n% but it will improve the layout of the manuscript,\n% and will typically save some space.\n\\usepackage{microtype}\n\n% This is also not strictly necessary, and may be commented out.\n% However, it will improve the aesthetics of text in\n% the typewriter font.\n\\usepackage{inconsolata}\n\n%Including images in your LaTeX document requires adding\n%additional package(s)\n\\usepackage{graphicx}\n\n\n\\title{ChartMind: A Comprehensive Benchmark for \\\\ Complex Real-world Multimodal Chart Question Answering}\n\n\n\n\\author{\n Jingxuan Wei$^{1,2,4}$\\thanks{\\ \\ Equal contribution.},\n Nan Xu$^{1,3}$\\footnotemark[1],\n Junnan Zhu$^{3}$\\thanks{\\ \\ Corresponding author.},\n Yanni Hao$^{1,3}$,\n Gaowei Wu$^{2,4}$, \\\\\n {\\bf Qi Chen$^{2,4}$,\n Bihui Yu$^{2,4}$,\n Lei Wang$^{1,3}$} \\\\\n $^1$Beijing Wenge Technology Co., Ltd. \\\\\n $^2$Shenyang Institute of Computing Technology, Chinese Academy of Sciences \\\\\n $^3$MAIS, Institute of Automation, Chinese Academy of Sciences \\\\\n $^4$University of Chinese Academy of Sciences \\\\\n \\texttt{junnan.zhu@nlpr.ia.ac.cn, weijingxuan20@mails.ucas.edu.cn, xunan2015@ia.ac.cn}\n}\n\n\n\\begin{document}\n\\maketitle\n\\begin{abstract}\nChart question answering (CQA) has become a critical multimodal task for evaluating the reasoning capabilities of vision-language models. While early approaches have shown promising performance by focusing on visual features or leveraging large-scale pre-training, most existing evaluations rely on rigid output formats and objective metrics, thus ignoring the complex, real-world demands of practical chart analysis. In this paper, we introduce ChartMind, a new benchmark designed for complex CQA tasks in real-world settings. ChartMind covers seven task categories, incorporates multilingual contexts, supports open-domain textual outputs, and accommodates diverse chart formats, bridging the gap between real-world applications and traditional academic benchmarks. Furthermore, we propose a context-aware yet model-agnostic framework, ChartLLM, that focuses on extracting key contextual elements, reducing noise, and enhancing the reasoning accuracy of multimodal large language models. Extensive evaluations on ChartMind and three representative public benchmarks with 14 mainstream multimodal models show our framework significantly outperforms the previous three common CQA paradigms: instruction-following, OCR-enhanced, and chain-of-thought, highlighting the importance of flexible chart understanding for real-world CQA. These findings suggest new directions for developing more robust chart reasoning in future research.\n\\end{abstract}\n\n\n\\section{Introduction}\n\\begin{figure*}[t]\n \\centering\n \\includegraphics[width=\\linewidth]{images/fig1-3.pdf}\n \\caption{Key Challenges in CQA Benchmarks: (A) Predominantly monolingual, limiting multilingual applicability in chart question answering; (B) Fixed formats and metrics, restricting adaptability to diverse charts; (C) Emphasis on deterministic answers, overlooking complex reasoning, such as trend analysis, and summarization.}\n \\label{fig:challenges}\n % \\vskip -0.15in\n\\end{figure*}\n\n% xn-----\n\nChart question answering~\\cite{ma2024c,qin2022deep} is a prominent multimodal task designed to evaluate the reasoning capabilities of vision-language models, especially their multimodal perception ability and local reasoning ability. Early studies treat CQA as a discriminative task, focusing on directly modeling visual elements to answer questions~\\cite{kafle2018dvqa,chang2022mapqa}. However, these methods often struggle with generalization due to their inability to capture the semantic and visual richness of charts. Hence, researchers introduce more visual semantic information (e.g., OCR) to enhance the multimodal perception ability~\\cite{liu2023deplot,wang2023domino}. Recent studies have shown the potential of multimodal large language models (LLMs) on the CQA task by adopting large-scale multimodal pre-training~\\cite{donut,pix2struct} or chain-of-thought (COT) reasoning~\\cite{li2024synthesize,wei2024mchartqa}, suggesting that leveraging large-scale datasets and supervised fine-tuning improves the interpretation of multimodal charts.\n\n% check\nSeveral benchmarks~\\cite{zaib2022conversational,bajic2023review,huang2024pixels} have been proposed to better understand the strengths and weaknesses of multi-modal LLMs for CQA. However, human evaluations often suffer from high variability and instability due to individual and cultural differences, leading many existing benchmarks~\\cite{kafle2018dvqa,9973197} to rely predominantly on automatic metrics (\\textit{e.g.}, F1 scores). While such approaches effectively evaluate the accuracy of a single answer (\\textit{e.g.}, ``\\textit{2024}\" for ``\\textit{What is the largest value in column X?}\"), they do not fully capture the need for complex and multi-step reasoning commonly required in real-world scenarios. Many professional data analysis tasks demand advanced inference, such as multi-hop reasoning or synthesizing information from multiple charts. Consequently, most existing benchmarks have widely ignored the logical steps involved in such inferencing, focusing instead on whether the answer includes the correct keyword or value. \n\nIn addition, as shown in ~\\autoref{fig:challenges}, we summarize three main challenges in existing benchmarks: multilingual charts, diverse formats, and questions lacking a single definitive answer, such as chart summarization. Models need to handle both visual comprehension and logical reasoning. To extract meaningful information, they must first recognize visual elements, such as colors, structures, and spatial relationships. Then, they must analyze the logical connections between elements and answer complex queries, such as performing calculations, identifying trends, and finding relationships within the data. Moreover, the wide range of real-world chart types (\\textit{e.g.}, bar charts, line charts, scatter plots) creates higher demands for models to generalize and perform well on new and unseen formats.\n\n\nTo address these challenges, we introduce ChartMind, a multilingual benchmark designed for high-level chart reasoning across seven task categories. It includes both English and Chinese charts, providing the first dual-language evaluation setting for chart QA. Compared to prior benchmarks that focus on single-answer prediction, ChartMind supports open-ended outputs such as summarization and trend analysis. This design narrows the gap between academic benchmarks and real-world chart usage scenarios. To support better performance in these complex tasks, we propose ChartLLM, a structured context modeling framework that explicitly extracts semantic components—titles, legends, axes—from charts and feeds them into the model. Unlike procedural reasoning like CoT, ChartLLM reduces cognitive burden by pre-structuring relevant visual information, improving the robustness and generalizability of existing MLLMs.\n\nTo validate our benchmark, we conduct a comprehensive study of 14 mainstream multimodal models, comparing ChartLLM-based approaches with three widely used CQA paradigms: (1) instruction-following methods driven by predefined prompts, (2) OCR-enhanced methods that prioritize text extraction, and (3) COT-based methods emphasizing step-by-step reasoning.\n\n\nOur contributions are as follows:\n(1) We introduce \\textbf{ChartMind}, the first benchmark for complex CQA tasks in real-world settings. Covering seven task categories, multilingual contexts, and diverse chart formats, it bridges the gap between real-world applications and traditional academic benchmarks.\n(2) We propose \\textbf{ChartLLM}, a context-aware yet model-agnostic framework that focuses on extracting key contextual elements, reducing noise, and enhancing the reasoning accuracy of MLLMs.\n(3) Through experiments across seven task categories, two languages, and seven chart formats, we show that ChartLLM outperforms prevalent CQA paradigms. \nThese findings highlight the need for flexible chart understanding and foster advanced research on real-world chart analysis.\n\n\\section{Related Work}\n\n\\begin{table*}[ht]\n \\centering\n % \\vskip -0.05in\n \\resizebox{\\linewidth}{!}{\n \\begin{tabular}{lccccccccccccccc}\n \\toprule\n \\textbf{Dataset} & \\textbf{Avg. Ans.} & \\textbf{Instances} &\\textbf{Language} & \\textbf{Diverse} & \\textbf{Task} & \\textbf{Topic} & \\textbf{Chart} & \\textbf{Pie} & \\textbf{Scatter} & \\textbf{Common} & \\textbf{Grouped} & \\textbf{Stacked} & \\textbf{Complex} & \\textbf{Common} \\\\\n \\textbf{} &\\textbf{Length} & \\textbf{Number} & \\textbf{Format} & \\textbf{Format} &\\textbf{Format} & \\textbf{Format} & \\textbf{Format} & \\textbf{} & \\textbf{} & \\textbf{Bar} & \\textbf{Bar} & \\textbf{Bar} & \\textbf{Line} & \\textbf{Line} \\\\\n \\midrule\n ChartQA~\\cite{chartqa} &1.15 & 2,500 & English & 1 & 1 & 3 & 3& \\ding{51} & \\ding{55} & \\ding{51} & \\ding{55} & \\ding{55} & \\ding{55} & \\ding{51} \\\\\n MMC-Benchmark~\\cite{mmc}& 1.08 & 2,126 & English & 1 & 4 & 5 & 2& \\ding{55} & \\ding{51} & \\ding{55} & \\ding{55} & \\ding{55} & \\ding{55} & \\ding{51} \\\\\n PaperQA~\\cite{lu2023mathvista} &1.26 & 107 & English & 1 & 1 & 2 & 4& \\ding{51} & \\ding{51} & \\ding{51} & \\ding{55} & \\ding{55} & \\ding{55} & \\ding{51} \\\\\n OpenCQA~\\cite{opencqa} &55.73 & 1,159 & English & 1 & 1 & 4 & 4&\\ding{51} & \\ding{51} & \\ding{51} & \\ding{55} & \\ding{55} & \\ding{55} & \\ding{51} \\\\\n Chart-to-Text~\\cite{charttotext}&73.49 & 3,474 & English & 1 & 1 & 3 & 4& \\ding{51} & \\ding{51} & \\ding{51} & \\ding{55} & \\ding{55} & \\ding{55} & \\ding{51} \\\\\n LineCap~\\cite{9973197}&13.63 & 1,930 & English& 1 & 1 & 1 & 2& \\ding{55} & \\ding{55} & \\ding{55} & \\ding{55} & \\ding{55} & \\ding{51} & \\ding{51} \\\\\n \\midrule\n \\textbf{ChartMind}& \\textbf{119.69} & \\textbf{757}&\\textbf{EN\\&ZH} & \\textbf{2} & \\textbf{7} & \\textbf{6} & \\textbf{7} & \\ding{51} & \\ding{51} & \\ding{51} & \\ding{51} & \\ding{51} & \\ding{51} & \\ding{51} \\\\\n \\bottomrule\n \\end{tabular}}\n \\caption{Comparison of ChartMind with Existing Chart QA Datasets.}\n \\label{tab:dataset_comparison}\n \\vskip -0.2in\n\\end{table*}\n\nIn contrast, ChartLLM uses structured semantic cues from charts—such as titles, legends, and axes—to guide model reasoning, without relying on step-by-step decomposition.\n\n\\textbf{CQA Methods.} The development of CQA methods~\\cite{zeng2024advancing,li2024synthesize,xu2023improving} has evolved from early discriminative approaches to structured reasoning and large-scale pretraining~\\cite{zhou2023instruction,li2023improving,huang2024pixels,tan2025boosting}. Early models like IMG+QUESS~\\cite{kafle2018dvqa} and V-MODEQA~\\cite{chang2022mapqa} use CNNs for visual encoding and RNNs for query processing, but suffer from limited generalization due to weak reasoning and OOV handling. OCR-enhanced methods~\\cite{liu2023deplot,wang2023domino} convert chart visuals into text, aiding value extraction but introducing noise and losing spatial cues. COT-based models~\\cite{li2024synthesize,wei2024mchartqa} decompose reasoning steps to improve interpretability, yet depend on structured input and struggle with varied chart layouts. Other methods like Donut~\\cite{donut} and Pix2Struct~\\cite{pix2struct} remove OCR dependency via end-to-end training, while instruction-following models~\\cite{gpt4v} leverage large-scale vision-language pretraining but still fall short on multilingual support and high-level reasoning. Recent work such as ChartInsights~\\cite{chartinsights} targets low-level factual QA, whereas ChartLLM uses structured semantic cues—titles, legends, axes—to support multilingual and high-level tasks without relying on CoT-style decomposition.\n\n\\textbf{CQA Benchmarks.} The development of CQA models necessitates reliable benchmarks to evaluate performance across diverse tasks~\\cite{zaib2022conversational,bajic2023review}. Existing datasets fall into Factoid Question Answering (FQA), Open-Domain Question Answering (OQA), and Captioning (CAP) categories~\\cite{huang2024pixels}. FQA datasets, such as ChartQA~\\cite{kafle2018dvqa}, MMC-Bench~\\cite{mmc}, and PaperQA~\\cite{lu2023mathvista}, assess factual queries, including numerical extractions, trend identification, and relational interpretations, relying on predefined chart types for objective reasoning. OQA datasets like OpenCQA~\\cite{opencqa} introduce open-ended questions but enforce rigid output structures and rely on automated metrics like BLEU, limiting adaptability to complex reasoning. CAP datasets, including Chart-to-Text~\\cite{charttotext} and LineCap~\\cite{9973197}, generate textual chart descriptions but remain constrained by structured evaluation metrics. ChartMind addresses these gaps by combining high-level semantic tasks, multilingual data, and diverse chart types to support broader and more flexible evaluation. Table \\ref{tab:dataset_comparison} compares representative CQA benchmarks.\n\n\n\n\\section{Construction of ChartMind}\n% check\n\\begin{figure*}[ht]\n \\centering\n \\includegraphics[width=\\linewidth]{images/fig3-6.pdf} \n \\caption{Data Construction Pipeline for the ChartMind.}\n \\label{fig:data_curation}\n \\vskip -0.1in\n\\end{figure*}\nFigure~\\ref{fig:data_curation} presents an overview of our three-stage data construction pipeline, including chart collection, GPT-based generation, and human validation. Each stage is described below.\n\n\\subsection{Stage I: Chart Collection and Processing}\nTo build a diverse and realistic chart QA benchmark, we collect over 1,200 charts from open-source platforms, including GitHub repositories, public datasets, and Overleaf-based academic projects. All content complies with permissive licenses (e.g., CC BY 4.0, MIT). Charts span multiple formats—pie, bar (common, grouped, stacked), line (common, complex), and scatter plots—covering domains such as economics, education, and technology.\n\nWe remove charts that are blurry, lack proper axis or legend labels, or contain unreadable text. This filtering step ensures that remaining charts support meaningful reasoning and are visually accessible to models. These cleaned charts serve as the input to the next stage.\n\n\\subsection{Stage II: Prompt-based QA Generation}\nGiven a chart, we generate diverse QA pairs for seven tasks (e.g., summarization, classification, suggestion) using GPT-4o~\\cite{gpt4v}. For each task type, we design a dedicated prompt template that includes a few-shot example, output format instructions, and style control. Prompts are adapted to the chart type and domain to ensure contextual grounding.\nTo avoid redundancy, we apply controlled randomness (e.g., varying prompt temperature and phrasing) and use clustering on question embeddings to eliminate duplicates. Figure~\\ref{fig:data_curation} (Stage II) illustrates this process.\n\n\\subsection{Stage III: Human Validation}\nEach generated QA pair is reviewed by at least two annotators with over two years of chart QA research experience. Annotators follow a unified protocol and examine: (1) semantic alignment between question and chart, (2) accuracy and consistency of answers, (3) proper use of terminology and metrics. We revise or discard pairs with hallucinated entities, incorrect reasoning, or weak chart grounding.\n\n\\paragraph{Answer Rewriting.} GPT-generated answers are not automatically accepted. Annotators verify references to chart elements (e.g., trends, labels, time ranges) and rewrite unclear or incorrect responses. textcolor{blue}{For example:\n\\textbf{Question:} What does this chart suggest about AI patent trends between 2013 and 2022? \n\\textbf{GPT-4o Answer:} They increased significantly. \n\\textbf{Human Answer:} The chart shows a consistent rise in AI patent filings, particularly in machine learning, highlighting growing investment in AI research during this period.}\n\n\\paragraph{Final Filtering.} Only QA pairs that pass human validation and align with visual evidence are included in ChartMind. Our process draws on best practices from TableBench~\\cite{tablebench} and ArXivQA~\\cite{arxivqa}. Annotators help refine task definitions by identifying unclear cases.\n\n\\subsection{Data Summary and Task Complexity}\n\\begin{figure}[t]\n \\centering\n \\includegraphics[width=\\linewidth]{images/chart4-1-2.pdf}\n \\caption{Language and task distribution in ChartMind.}\n \\label{fig:task_distribution1}\n \\vskip -0.12in\n\\end{figure}\n\\begin{figure}[t]\n \\centering\n \\includegraphics[width=0.82\\linewidth]{images/fig5.pdf}\n \\caption{Topic distribution in ChartMind.}\n \\label{fig:task_distribution2}\n \\vskip -0.12in\n\\end{figure}\n\n\\paragraph{Language and Topic Diversity.} As shown in Figure~\\ref{fig:task_distribution1}, ChartMind includes 59.71\\% English and 40.29\\% Chinese questions, enabling bilingual evaluation across all seven task types. While Chinese is not a low-resource language, high-quality chart reasoning data in Chinese remains rare. ChartMind provides a first step toward multilingual benchmarking, and we plan to expand to more languages in future releases.\n\nFigure~\\ref{fig:task_distribution2} illustrates the topic breakdown, where economic charts dominate with 68.00\\%, followed by education and technology.\n\n\\paragraph{Task Coverage and Reasoning Demands.}\nTable~\\ref{tab:key_statistics} summarizes the distribution and complexity of QA samples across the seven task categories.\n\n\\begin{table}[t]\n \\centering\n \\resizebox{\\linewidth}{!}{\n \\begin{tabular}{lcrr}\n \\toprule\n \\textbf{Task} & \\textbf{Samples} & \\textbf{Query Length} & \\textbf{Answer Length} \\\\\n & & \\textbf{(Min / Max)} & \\textbf{(Min / Max)} \\\\\n \\midrule\n Chart Conversion & 140 & 11 / 477 & 5 / 55 \\\\\n Chart OCR Recognition & 139 & 13 / 351 & 8 / 59 \\\\\n Suggestions & 88 & 17 / 492 & 13 / 53 \\\\\n Chart Classification Analysis & 37 & 360 / 503 & 72 / 79 \\\\\n Chart Summarization & 34 & 76 / 335 & 12 / 113 \\\\\n Chart Assistance & 76 & 9 / 276 & 12 / 41 \\\\\n Information Positioning & 140 & 11 / 208 & 11 / 35 \\\\\n \\midrule\n \\textbf{Total} & \\textbf{757} & \\textbf{9 / 503} & \\textbf{5 / 113} \\\\\n \\bottomrule\n \\end{tabular}}\n \\caption{Task Type Statistics in ChartMind.}\n \\label{tab:key_statistics}\n \\vspace{-5mm}\n\\end{table}\n\nThe seven task types differ in language structure, visual grounding, and reasoning depth. Summarization and Classification require long, structured responses, while Positioning and OCR involve precise short-form grounding. This diversity supports balanced evaluation of reasoning and generation.\n\n\n\\section{ChartLLM}\n\n\\subsection{Problem Definition}\nCQA is a task that involves providing an answer \\( A \\) to a natural language question \\( Q \\), based on the information contained in a chart \\( C \\). The answer \\( A \\) may take various forms, depending on the type of question. Specifically, \\( A \\) could be a numerical value, a categorical label, an entity set, or an open-domain sentence. These different answer types require distinct reasoning capabilities, ranging from retrieval-based reasoning (e.g., extracting numerical values) to analytical reasoning (e.g., identifying patterns and trends in the chart). Formally, the answer \\( A \\) is represented as a collection of values or entities \\( \\{a_1, a_2, \\dots, a_k\\} \\), where \\( k \\in \\mathbb{N}^{+} \\).\n\n\\subsection{Reasoning Methods}\nInstruction-following~\\cite{wei2021finetuned} and In-context learning~\\cite{dong2024survey} refer to strategies that optimize input for LLMs to generate practical outputs based on task-specific instructions and context. These methods enable models to leverage the provided task instructions to guide reasoning and output generation. To fully assess the reasoning capabilities of LLMs for CQA, we propose three distinct reasoning methods that aim to evaluate the model’s reasoning performance.\n\n\\paragraph{Instruction-following-based methods}\nSuch methods~\\cite{wei2021finetuned} leverage task-specific instructions to guide LLMs in reasoning tasks. The model utilizes a prompt to interpret chart data and generate answers. The prompt \\( P \\) provides additional contextual guidance for the natural language question \\( Q \\), specifying how the model should reason over the chart data. The reasoning process can be expressed as:\n\\begin{equation}\nM(C, Q, P) \\rightarrow A\n\\end{equation}\nwhere \\( M \\) represents the model, \\( C \\) is the chart, \\( Q \\) is the natural language question, \\( P \\) is the instruction prompt, and \\( A \\) is the answer. This approach can be applied in both fine-tuning and zero-shot settings, allowing the model to adapt to tasks based on the provided instructions.\n\n\\paragraph{OCR-enhanced methods}\nOCR-enhanced methods~\\cite{liu2023deplot} augment reasoning by incorporating textual content extracted from charts using OCR tools. These tools provide the model with additional information embedded in the chart, which may not be directly accessible through its visual content. The reasoning process is formulated as:\n\\begin{equation}\nM(C, Q, O(C)) \\rightarrow A\n\\end{equation}\n% where \\( O(C) \\) denotes the OCR-extracted textual content from the chart \\( C \\), while the other symbols remain as previously defined. OCR tools offer critical support in understanding chart-based queries by augmenting the model's input with relevant textual data.\nwhere \\( O(C) \\) denotes the OCR-extracted content from the chart \\( C \\). OCR tools offer essential support in understanding chart-based queries by enhancing the model's input with relevant textual data.\n\n\\paragraph{COT-based methods} \nCOT-based methods~\\cite{wei2022chain} break down the reasoning process into intermediate steps to improve both the accuracy and interpretability of the model’s responses. This approach decomposes the reasoning into a sequence of logical steps, which enhances the model’s ability to solve complex tasks. The process is represented as:\n\\begin{equation}\nM(C, Q) \\rightarrow \\{r_1, r_2, \\dots, r_k\\} \\rightarrow A\n\\end{equation}\nwhere \\( r_1, r_2, \\dots, r_k \\) represent intermediate reasoning steps, and \\( A \\) is the final answer. CoT is particularly useful for tasks requiring step-by-step reasoning, such as analyzing trends, identifying patterns, or extracting structured insights from complex chart data.\n\n\\begin{table*}[!ht]\n \\centering\n % \\caption{Performance of multimodal models on ChartMind and three objective datasets. The best results are highlighted in \\textbf{bold}, and the second-best results are \\underline{underlined}. \\textdagger Specialized CQA models.}\n % \\vspace{-2mm}\n \\resizebox{\\linewidth}{!}{\n % \\adjustbox{max width=\\linewidth, max height=0.6\\textheight}{\n \\begin{tabular}{lccccccccccr}\n % \\begin{tabular}{lrrrrrrrrrrr}\n \\toprule\n \\multirow{2}[4]{*}{ Models} & \\multirow{2}[4]{*}{Size} & \\multicolumn{3}{c}{ChartMind} & \\multicolumn{3}{c}{ChartQA } & \\multicolumn{3}{c}{Chart-to-Text} & OpenCQA \\\\\n\\cmidrule{3-12} & & ACC & Avg.CIDEr & Avg.GPT-4o Score & Aug. ACC & Hum. ACC & Avg. ACC & Pew. BLEU & Statista. BLEU & Avg. BLEU & Avg. BLEU \\\\\n \\midrule\n \\rowcolor[rgb]{ .922, .945, .871} \\multicolumn{12}{c}{Instruction-Following-Based~\\cite{wei2021finetuned}} \\\\\n \\midrule\n TinyChart\\textdagger~\\cite{tinychart} & 3B & 5.36 & 18.45 & 16.81 & 93.60 & 72.16 & 82.88 & 10.84 & 27.04 & 18.94 & 19.62 \\\\\n ChartInstruct\\textdagger~\\cite{chartinstruct} & 7B & 9.82 & 24.55 & 15.05 & 82.40 & 40.64 & 61.52 & 12.81 & 39.39 & 26.10 & 14.78 \\\\\n ChartLlama\\textdagger~\\cite{chartllama} & 7B & 20.54 & 21.34 & 12.72 & 90.36 & 48.96 & 69.66 & 14.23 & 40.71 & 27.47 & 4.70 \\\\\n Sphinx-v2~\\cite{sphinx} & 7B & 9.82 & 25.95 & 13.69 & 60.96 & 43.92 & 52.44 & 3.43 & 4.94 & 4.19 & 3.10 \\\\\n LLaVA1.5~\\cite{llava1.5} & 7B & 34.82 & 39.50 & 15.58 & 20.12 & 25.20 & 22.66 & 15.70 & 11.07 & 13.39 & 15.17 \\\\\n ViP-LLaVA~\\cite{vipllava} & 7B & 20.54 & 37.01 & 15.56 & 17.60 & 26.16 & 21.88 & 1.36 & 2.59 & 1.98 & 15.04 \\\\\n LLaVA-NEXT~\\cite{llavanext} & 7B & 20.54 & 47.37 & 31.09 & 74.26 & 46.30 & 60.28 & 13.85 & 6.63 & 10.24 & 8.07 \\\\\n IXC-2.5~\\cite{ixc2.5} & 7B & 47.30 & 40.10 & 43.31 & 92.40 & 74.32 & 83.36 & 17.69 & 11.86 & 14.78 & 9.39 \\\\\n Qwen2-VL~\\cite{qwenvl} & 7B & 57.14 & 37.32 & 47.89 & 94.10 & 72.00 & 83.05 & 11.07 & 22.98 & 17.03 & 8.26 \\\\\n mPLUG-Owl2~\\cite{mplugowl2} & 8B & 25.00 & 36.17 & 14.22 & 24.13 & 27.34 & 25.74 & 12.83 & 5.97 & 9.40 & 5.34 \\\\\n MiniCPM-v2~\\cite{minicpmv2} & 8B & 22.32 & 28.48 & 10.63 & 91.12 & 69.02 & 80.07 & 22.17 & 11.01 & 16.59 & 20.05 \\\\\n CogVLM~\\cite{cogvlm} & 17B & 23.21 & 40.20 & 29.35 & 23.95 & 39.53 & 31.74 & 16.38 & 11.84 & 14.11 & 1.75 \\\\\n GLM-4V-plus~\\cite{chatglm4v} & - & 59.83 & 38.36 & 21.52 & 16.80 & 12.80 & 14.80 & 5.69 & 5.71 & 5.70 & 7.41 \\\\\n GPT-4o~\\cite{gpt4v} & - & \\textbf{61.89} & 47.25 & 68.81 & 95.34 & 76.06 & 85.70 & 17.75 & 8.70 & 13.23 & 13.92 \\\\\n \\midrule\n \\rowcolor[rgb]{ .922, .945, .871} \\multicolumn{12}{c}{OCR-Enhanced~\\cite{liu2023deplot}} \\\\\n \\midrule\n TinyChart\\textdagger~\\cite{tinychart} & 3B & \\makebox[5.4em][s]{6.71 (\\textcolor{blue}{+1.35})} & \\makebox[5.4em][s]{13.91 (\\textcolor{red}{-4.54})} & \\makebox[5.4em][s]{17.91 (\\textcolor{blue}{+1.10})} & \\makebox[5.4em][s]{94.86 (\\textcolor{blue}{+1.26})} & \\makebox[5.4em][s]{73.95 (\\textcolor{blue}{+1.79})} & \\makebox[5.4em][s]{84.41 (\\textcolor{blue}{+1.53})} & \\makebox[5.4em][s]{13.85 (\\textcolor{blue}{+3.01})} & \\makebox[5.4em][s]{28.27 (\\textcolor{blue}{+1.23})} & \\makebox[5.4em][s]{21.06 (\\textcolor{blue}{+2.12})} & \\makebox[5.4em][s]{20.15 (\\textcolor{blue}{+0.53})} \\\\\n ChartInstruct\\textdagger~\\cite{chartinstruct} & 7B & \\makebox[5.4em][s]{{10.01} (\\textcolor{blue}{+0.19})} & \\makebox[5.4em][s]{32.80 (\\textcolor{blue}{+8.25})} & \\makebox[5.4em][s]{23.42 (\\textcolor{blue}{+8.37})} & \\makebox[5.4em][s]{83.74 (\\textcolor{blue}{+1.34})} & \\makebox[5.4em][s]{42.17 (\\textcolor{blue}{+1.53})} & \\makebox[5.4em][s]{62.96 (\\textcolor{blue}{+1.44})} & \\makebox[5.4em][s]{14.95 (\\textcolor{blue}{+2.14})} & \\makebox[5.4em][s]{40.83 (\\textcolor{blue}{+1.44})} & \\makebox[5.4em][s]{27.89 (\\textcolor{blue}{+1.79})} & \\makebox[5.4em][s]{16.01 (\\textcolor{blue}{+1.23})} \\\\\n ChartLlama\\textdagger~\\cite{chartllama} & 7B & \\makebox[5.4em][s]{{22.03} (\\textcolor{blue}{+1.49})} & \\makebox[5.4em][s]{21.07 (\\textcolor{red}{-0.27})} & \\makebox[5.4em][s]{26.70 (\\textcolor{blue}{+13.97})} & \\makebox[5.4em][s]{90.85 (\\textcolor{blue}{+0.49})} & \\makebox[5.4em][s]{49.26 (\\textcolor{blue}{+0.30})} & \\makebox[5.4em][s]{70.06 (\\textcolor{blue}{+0.40})} & \\makebox[5.4em][s]{16.02 (\\textcolor{blue}{+1.79})} & \\makebox[5.4em][s]{39.97 (\\textcolor{red}{-0.74})} & \\makebox[5.4em][s]{28.00 (\\textcolor{blue}{+0.53})} & \\makebox[5.4em][s]{5.89 (\\textcolor{blue}{+1.19})} \\\\\n Sphinx-v2~\\cite{sphinx} & 7B & \\makebox[5.4em][s]{11.54 (\\textcolor{blue}{+1.72})} & \\makebox[5.4em][s]{24.14 (\\textcolor{red}{-1.81})} & \\makebox[5.4em][s]{17.21 (\\textcolor{blue}{+3.52})} & \\makebox[5.4em][s]{64.08 (\\textcolor{blue}{+3.12})} & \\makebox[5.4em][s]{45.49 (\\textcolor{blue}{+1.57})} & \\makebox[5.4em][s]{54.79 (\\textcolor{blue}{+2.35})} & \\makebox[5.4em][s]{8.81 (\\textcolor{blue}{+5.38})} & \\makebox[5.4em][s]{2.39 (\\textcolor{red}{-2.55})} & \\makebox[5.4em][s]{5.60 (\\textcolor{blue}{+1.41})} & \\makebox[5.4em][s]{3.16 (\\textcolor{blue}{+0.06})} \\\\\n LLaVA1.5~\\cite{llava1.5} & 7B & \\makebox[5.4em][s]{36.15 (\\textcolor{blue}{+1.33})} & \\makebox[5.4em][s]{33.49 (\\textcolor{red}{-6.01})} & \\makebox[5.4em][s]{21.03 (\\textcolor{blue}{+5.45})} & \\makebox[5.4em][s]{19.73 (\\textcolor{red}{-0.39})} & \\makebox[5.4em][s]{25.95 (\\textcolor{blue}{+0.75})} & \\makebox[5.4em][s]{22.84 (\\textcolor{blue}{+0.18})} & \\makebox[5.4em][s]{15.94 (\\textcolor{blue}{+0.24})} & \\makebox[5.4em][s]{12.67 (\\textcolor{blue}{+1.60})} & \\makebox[5.4em][s]{14.30 (\\textcolor{blue}{+0.91})} & \\makebox[5.4em][s]{16.31 (\\textcolor{blue}{+1.14})} \\\\\n ViP-LLaVA~\\cite{vipllava} & 7B & \\makebox[5.4em][s]{25.38 (\\textcolor{blue}{+4.84})} & \\makebox[5.4em][s]{36.77 (\\textcolor{red}{-0.24})} & \\makebox[5.4em][s]{26.45 (\\textcolor{blue}{+10.89})} & \\makebox[5.4em][s]{27.12 (\\textcolor{blue}{+9.52})} & \\makebox[5.4em][s]{24.94 (\\textcolor{red}{-1.22})} & \\makebox[5.4em][s]{26.03 (\\textcolor{blue}{+4.15})} & \\makebox[5.4em][s]{14.13 (\\textcolor{blue}{+12.77})} & \\makebox[5.4em][s]{14.37 (\\textcolor{blue}{+11.78})} & \\makebox[5.4em][s]{14.25 (\\textcolor{blue}{+12.27})} & \\makebox[5.4em][s]{18.08 (\\textcolor{blue}{+3.04})} \\\\\n LLaVA-NEXT~\\cite{llavanext} & 7B & \\makebox[5.4em][s]{41.15 (\\textcolor{blue}{+20.61})} & \\makebox[5.4em][s]{47.83 (\\textcolor{blue}{+0.46})} & \\makebox[5.4em][s]{31.51 (\\textcolor{blue}{+0.42})} & \\makebox[5.4em][s]{70.47 (\\textcolor{red}{-3.79})} & \\makebox[5.4em][s]{52.68 (\\textcolor{blue}{+6.38})} & \\makebox[5.4em][s]{61.58 (\\textcolor{blue}{+1.30})} & \\makebox[5.4em][s]{15.16 (\\textcolor{blue}{+1.31})} & \\makebox[5.4em][s]{8.82 (\\textcolor{blue}{+2.19})} & \\makebox[5.4em][s]{11.99 (\\textcolor{blue}{+1.75})} & \\makebox[5.4em][s]{8.25 (\\textcolor{blue}{+0.18})} \\\\\n IXC-2.5~\\cite{ixc2.5} & 7B & \\makebox[5.4em][s]{42.31 (\\textcolor{red}{-4.99})} & \\makebox[5.4em][s]{40.35 (\\textcolor{blue}{+0.24})} & \\makebox[5.4em][s]{45.38 (\\textcolor{blue}{+2.06})} & \\makebox[5.4em][s]{94.23 (\\textcolor{blue}{+1.83})} & \\makebox[5.4em][s]{73.40 (\\textcolor{red}{-0.92})} & \\makebox[5.4em][s]{83.82 (\\textcolor{blue}{+0.46})} & \\makebox[5.4em][s]{17.03 (\\textcolor{red}{-0.66})} & \\makebox[5.4em][s]{12.34 (\\textcolor{blue}{+0.48})} & \\makebox[5.4em][s]{14.68 (\\textcolor{red}{-0.10})} & \\makebox[5.4em][s]{14.53 (\\textcolor{blue}{+5.14})} \\\\\n Qwen2-VL~\\cite{qwenvl} & 7B & \\makebox[5.4em][s]{42.31 (\\textcolor{red}{-14.83})} & \\makebox[5.4em][s]{36.04 (\\textcolor{red}{-1.27})} & \\makebox[5.4em][s]{49.28 (\\textcolor{blue}{+1.39})} & \\makebox[5.4em][s]{94.23 (\\textcolor{blue}{+0.13})} & \\makebox[5.4em][s]{75.96 (\\textcolor{blue}{+3.96})} & \\makebox[5.4em][s]{85.10 (\\textcolor{blue}{+2.05})} & \\makebox[5.4em][s]{11.08 (\\textcolor{blue}{+0.01})} & \\makebox[5.4em][s]{23.21 (\\textcolor{blue}{+0.23})} & \\makebox[5.4em][s]{17.15 (\\textcolor{blue}{+0.12})} & \\makebox[5.4em][s]{11.75 (\\textcolor{blue}{+3.49})} \\\\\n mPLUG-Owl2~\\cite{mplugowl2} & 8B & \\makebox[5.4em][s]{27.62 (\\textcolor{blue}{+2.62})} & \\makebox[5.4em][s]{30.60 (\\textcolor{red}{-5.57})} & \\makebox[5.4em][s]{24.67 (\\textcolor{blue}{+10.44})} & \\makebox[5.4em][s]{35.58 (\\textcolor{blue}{+11.45})} & \\makebox[5.4em][s]{37.18 (\\textcolor{blue}{+9.84})} & \\makebox[5.4em][s]{36.38 (\\textcolor{blue}{+10.65})} & \\makebox[5.4em][s]{11.82 (\\textcolor{red}{-1.01})} & \\makebox[5.4em][s]{7.30 (\\textcolor{blue}{+1.33})} & \\makebox[5.4em][s]{9.56 (\\textcolor{blue}{+0.16})} & \\makebox[5.4em][s]{4.45 (\\textcolor{red}{-0.89})} \\\\\n MiniCPM-v2~\\cite{minicpmv2} & 8B & \\makebox[5.4em][s]{23.04 (\\textcolor{blue}{+0.72}} & \\makebox[5.4em][s]{19.73 (\\textcolor{red}{-8.75})} & \\makebox[5.4em][s]{18.10 (\\textcolor{blue}{+7.47})} & \\makebox[5.4em][s]{\\makebox[5.4em][s]{92.36 (\\textcolor{blue}{+1.24})}} & \\makebox[5.4em][s]{73.21 (\\textcolor{blue}{+4.19})} & \\makebox[5.4em][s]{82.79 (\\textcolor{blue}{+2.72})} & \\makebox[5.4em][s]{20.93 (\\textcolor{red}{-1.24})} & \\makebox[5.4em][s]{5.75 (\\textcolor{red}{-5.26})} & \\makebox[5.4em][s]{13.34 (\\textcolor{red}{-3.25})} & \\makebox[5.4em][s]{20.60 (\\textcolor{blue}{+0.55})} \\\\\n CogVLM~\\cite{cogvlm} & 17B & \\makebox[5.4em][s]{25.54 (\\textcolor{blue}{+2.33})} & \\makebox[5.4em][s]{39.00 (\\textcolor{red}{-1.20})} & \\makebox[5.4em][s]{36.80 (\\textcolor{blue}{+7.45})} & \\makebox[5.4em][s]{29.81 (\\textcolor{blue}{+5.86})} & \\makebox[5.4em][s]{48.72 (\\textcolor{blue}{+9.19})} & \\makebox[5.4em][s]{39.27 (\\textcolor{blue}{+7.53})} & \\makebox[5.4em][s]{20.85 (\\textcolor{blue}{+4.47})} & \\makebox[5.4em][s]{13.88 (\\textcolor{blue}{+2.04})} & \\makebox[5.4em][s]{17.37 (\\textcolor{blue}{+3.26})} & \\makebox[5.4em][s]{1.79 (\\textcolor{blue}{+0.04})} \\\\\n GLM-4V-plus~\\cite{chatglm4v} & - & \\makebox[5.4em][s]{44.64 (\\textcolor{red}{-15.19})} & \\makebox[5.4em][s]{44.83 (\\textcolor{blue}{+6.47})} & \\makebox[5.4em][s]{35.79 (\\textcolor{blue}{+14.27})} & \\makebox[5.4em][s]{17.95 (\\textcolor{blue}{+1.15})} & \\makebox[5.4em][s]{16.87 (\\textcolor{blue}{+4.07})} & \\makebox[5.4em][s]{17.41 (\\textcolor{blue}{+2.61})} & \\makebox[5.4em][s]{7.91 (\\textcolor{blue}{+2.22})} & \\makebox[5.4em][s]{7.63 (\\textcolor{blue}{+1.92})} & \\makebox[5.4em][s]{7.77 (\\textcolor{blue}{+2.07})} & \\makebox[5.4em][s]{8.72 (\\textcolor{blue}{+1.31})} \\\\\n GPT-4o~\\cite{gpt4v} & - & \\makebox[5.4em][s]{49.31 (\\textcolor{red}{-12.58})} & \\makebox[5.4em][s]{46.48 (\\textcolor{red}{-0.76})} & \\makebox[5.4em][s]{\\underline{71.79} (\\textcolor{blue}{+2.98})} & \\makebox[5.4em][s]{\\underline{96.20} (\\textcolor{blue}{+0.86})} & \\makebox[5.4em][s]{\\underline{78.04} (\\textcolor{blue}{+1.98})} & \\makebox[5.4em][s]{\\underline{87.12} (\\textcolor{blue}{+1.42})} & \\makebox[5.4em][s]{20.13 (\\textcolor{blue}{+2.38})} & \\makebox[5.4em][s]{9.86 (\\textcolor{blue}{+1.16})} & \\makebox[5.4em][s]{15.00 (\\textcolor{blue}{+1.77})} & \\makebox[5.4em][s]{14.85 (\\textcolor{blue}{+0.93})} \\\\\n \\midrule\n \\rowcolor[rgb]{ .922, .945, .871} \\multicolumn{12}{c}{COT-Based~\\cite{wei2022chain}} \\\\\n \\midrule\n TinyChart\\textdagger~\\cite{tinychart} & 3B & \\makebox[5.4em][s]{6.01 (\\textcolor{blue}{+0.65})} & \\makebox[5.4em][s]{13.58 (\\textcolor{red}{-4.87})} & \\makebox[5.4em][s]{19.30 (\\textcolor{blue}{+2.49})} & \\makebox[5.4em][s]{94.84 (\\textcolor{blue}{+1.24})} & \\makebox[5.4em][s]{74.46 (\\textcolor{blue}{+2.30})} & \\makebox[5.4em][s]{84.65 (\\textcolor{blue}{+1.77})} & \\makebox[5.4em][s]{12.31 (\\textcolor{blue}{+1.47})} & \\makebox[5.4em][s]{28.53 (\\textcolor{blue}{+1.49})} & \\makebox[5.4em][s]{20.42 (\\textcolor{blue}{+1.48})} & \\makebox[5.4em][s]{20.74 (\\textcolor{blue}{+1.12})} \\\\\n ChartInstruct\\textdagger~\\cite{chartinstruct} & 7B & \\makebox[5.4em][s]{9.96 (\\textcolor{blue}{+0.14})} & \\makebox[5.4em][s]{31.95 (\\textcolor{blue}{+7.40})} & \\makebox[5.4em][s]{22.44 (\\textcolor{blue}{+7.39})} & \\makebox[5.4em][s]{83.35 (\\textcolor{blue}{+0.95})} & \\makebox[5.4em][s]{42.74 (\\textcolor{blue}{+2.10})} & \\makebox[5.4em][s]{63.05 (\\textcolor{blue}{+1.53})} & \\makebox[5.4em][s]{14.34 (\\textcolor{blue}{+1.53})} & \\makebox[5.4em][s]{41.32 (\\textcolor{blue}{+1.93})} & \\makebox[5.4em][s]{27.83 (\\textcolor{blue}{+1.73})} & \\makebox[5.4em][s]{15.25 (\\textcolor{blue}{+0.47})} \\\\\n ChartLlama\\textdagger~\\cite{chartllama} & 7B & \\makebox[5.4em][s]{21.44 (\\textcolor{blue}{+0.90})} & \\makebox[5.4em][s]{18.99 (\\textcolor{red}{-2.36})} & \\makebox[5.4em][s]{21.77 (\\textcolor{blue}{+9.04})} & \\makebox[5.4em][s]{91.63 (\\textcolor{blue}{+1.27})} & \\makebox[5.4em][s]{50.04 (\\textcolor{blue}{+1.08})} & \\makebox[5.4em][s]{70.84 (\\textcolor{blue}{+1.18})} & \\makebox[5.4em][s]{15.76 (\\textcolor{blue}{+1.53})} & \\makebox[5.4em][s]{\\underline{41.42} (\\textcolor{blue}{+0.71})} & \\makebox[5.4em][s]{\\underline{28.59} (\\textcolor{blue}{+1.12})} & \\makebox[5.4em][s]{6.32 (\\textcolor{blue}{+1.62})} \\\\\n Sphinx-v2~\\cite{sphinx} & 7B & \\makebox[5.4em][s]{9.91 (\\textcolor{blue}{+0.09})} & \\makebox[5.4em][s]{25.03 (\\textcolor{red}{-0.92})} & \\makebox[5.4em][s]{16.26 (\\textcolor{blue}{+2.57})} & \\makebox[5.4em][s]{61.86 (\\textcolor{blue}{+0.90})} & \\makebox[5.4em][s]{46.79 (\\textcolor{blue}{+2.87})} & \\makebox[5.4em][s]{54.33 (\\textcolor{blue}{+1.89})} & \\makebox[5.4em][s]{3.53 (\\textcolor{blue}{+0.10})} & \\makebox[5.4em][s]{5.09 (\\textcolor{blue}{+0.15})} & \\makebox[5.4em][s]{4.31 (\\textcolor{blue}{+0.12})} & \\makebox[5.4em][s]{3.13 (\\textcolor{blue}{+0.03})} \\\\\n LLaVA1.5~\\cite{llava1.5} & 7B & \\makebox[5.4em][s]{35.77 (\\textcolor{blue}{+0.95})} & \\makebox[5.4em][s]{35.61 (\\textcolor{red}{-3.89})} & \\makebox[5.4em][s]{19.68 (\\textcolor{blue}{+4.10})} & \\makebox[5.4em][s]{16.90 (\\textcolor{red}{-3.22})} & \\makebox[5.4em][s]{28.57 (\\textcolor{blue}{+3.37})} & \\makebox[5.4em][s]{22.74 (\\textcolor{blue}{+0.08})} & \\makebox[5.4em][s]{15.20 (\\textcolor{red}{-0.50})} & \\makebox[5.4em][s]{11.66 (\\textcolor{blue}{+0.59})} & \\makebox[5.4em][s]{13.43 (\\textcolor{blue}{+0.04})} & \\makebox[5.4em][s]{15.93 (\\textcolor{blue}{+0.76})} \\\\\n ViP-LLaVA~\\cite{vipllava} & 7B & \\makebox[5.4em][s]{23.31 (\\textcolor{blue}{+2.77})} & \\makebox[5.4em][s]{36.13 (\\textcolor{red}{-0.88})} & \\makebox[5.4em][s]{22.24 (\\textcolor{blue}{+6.68})} & \\makebox[5.4em][s]{22.12 (\\textcolor{blue}{+4.52})} & \\makebox[5.4em][s]{28.21 (\\textcolor{blue}{+2.05})} & \\makebox[5.4em][s]{25.17 (\\textcolor{blue}{+3.29})} & \\makebox[5.4em][s]{15.48 (\\textcolor{blue}{+14.12})} & \\makebox[5.4em][s]{12.20 (\\textcolor{blue}{+9.61})} & \\makebox[5.4em][s]{13.84 (\\textcolor{blue}{+11.86})} & \\makebox[5.4em][s]{15.67 (\\textcolor{blue}{+0.63})} \\\\\n LLaVA-NEXT~\\cite{llavanext} & 7B & \\makebox[5.4em][s]{40.23 (\\textcolor{blue}{+19.69})} & \\makebox[5.4em][s]{47.44 (\\textcolor{blue}{+0.07})} & \\makebox[5.4em][s]{27.34 (\\textcolor{red}{-3.75})} & \\makebox[5.4em][s]{68.49 (\\textcolor{red}{-5.77})} & \\makebox[5.4em][s]{52.13 (\\textcolor{blue}{+5.83})} & \\makebox[5.4em][s]{60.31 (\\textcolor{blue}{+0.03})} & \\makebox[5.4em][s]{14.81 (\\textcolor{blue}{+0.96})} & \\makebox[5.4em][s]{6.29 (\\textcolor{red}{-0.34})} & \\makebox[5.4em][s]{10.55 (\\textcolor{blue}{+0.31})} & \\makebox[5.4em][s]{8.09 (\\textcolor{blue}{+0.02})} \\\\\n IXC-2.5~\\cite{ixc2.5} & 7B & \\makebox[5.4em][s]{41.15 (\\textcolor{red}{-6.15})} & \\makebox[5.4em][s]{41.23 (\\textcolor{blue}{+1.13})} & \\makebox[5.4em][s]{46.73 (\\textcolor{blue}{+3.42})} & \\makebox[5.4em][s]{93.91 (\\textcolor{blue}{+1.51})} & \\makebox[5.4em][s]{72.82 (\\textcolor{red}{-1.50})} & \\makebox[5.4em][s]{83.37 (\\textcolor{blue}{+0.01})} & \\makebox[5.4em][s]{17.36 (\\textcolor{blue}{+0.23})} & \\makebox[5.4em][s]{11.92 (\\textcolor{blue}{+0.06})} & \\makebox[5.4em][s]{14.64 (\\textcolor{red}{-0.14})} & \\makebox[5.4em][s]{14.39 (\\textcolor{blue}{+5.00})} \\\\\n Qwen2-VL~\\cite{qwenvl} & 7B & \\makebox[5.4em][s]{40.69 (\\textcolor{red}{-16.45})} & \\makebox[5.4em][s]{44.72 (\\textcolor{blue}{+7.41})} & \\makebox[5.4em][s]{55.12 (\\textcolor{blue}{+7.24})} & \\makebox[5.4em][s]{94.87 (\\textcolor{blue}{+0.77})} & \\makebox[5.4em][s]{77.88 (\\textcolor{blue}{+5.88})} & \\makebox[5.4em][s]{86.38 (\\textcolor{blue}{+3.33})} & \\makebox[5.4em][s]{16.70 (\\textcolor{blue}{+5.63})} & \\makebox[5.4em][s]{23.91 (\\textcolor{blue}{+0.93})} & \\makebox[5.4em][s]{20.30 (\\textcolor{blue}{+3.27})} & \\makebox[5.4em][s]{10.32 (\\textcolor{blue}{+2.06})} \\\\\n mPLUG-Owl2~\\cite{mplugowl2} & 8B & \\makebox[5.4em][s]{25.89 (\\textcolor{blue}{+0.89})} & \\makebox[5.4em][s]{35.10 (\\textcolor{red}{-1.08})} & \\makebox[5.4em][s]{21.27 (\\textcolor{blue}{+7.04})} & \\makebox[5.4em][s]{27.56 (\\textcolor{blue}{+3.43})} & \\makebox[5.4em][s]{31.09 (\\textcolor{blue}{+3.75})} & \\makebox[5.4em][s]{29.33 (\\textcolor{blue}{+3.59})} & \\makebox[5.4em][s]{14.00 (\\textcolor{blue}{+1.17})} & \\makebox[5.4em][s]{7.84 (\\textcolor{blue}{+1.87})} & \\makebox[5.4em][s]{10.92 (\\textcolor{blue}{+1.52})} & \\makebox[5.4em][s]{7.88 (\\textcolor{blue}{+2.54})} \\\\\n MiniCPM-v2~\\cite{minicpmv2} & 8B & \\makebox[5.4em][s]{22.78 (\\textcolor{blue}{+0.46})} & \\makebox[5.4em][s]{28.81 (\\textcolor{blue}{+0.33})} & \\makebox[5.4em][s]{18.18 (\\textcolor{blue}{+7.54})} & \\makebox[5.4em][s]{92.37 (\\textcolor{blue}{+1.25})} & \\makebox[5.4em][s]{71.47 (\\textcolor{blue}{+2.45})} & \\makebox[5.4em][s]{81.92 (\\textcolor{blue}{+1.85})} & \\makebox[5.4em][s]{\\underline{26.56} (\\textcolor{blue}{+4.39})} & \\makebox[5.4em][s]{12.53 (\\textcolor{blue}{+1.52})} & \\makebox[5.4em][s]{19.54 (\\textcolor{blue}{+2.95})} & \\makebox[5.4em][s]{20.30 (\\textcolor{blue}{+0.25})} \\\\\n CogVLM~\\cite{cogvlm} & 17B & \\makebox[5.4em][s]{24.01 (\\textcolor{blue}{+0.80})} & \\makebox[5.4em][s]{40.04 (\\textcolor{red}{-0.16})} & \\makebox[5.4em][s]{37.14 (\\textcolor{blue}{+7.79})} & \\makebox[5.4em][s]{27.31 (\\textcolor{blue}{+3.36})} & \\makebox[5.4em][s]{44.93 (\\textcolor{blue}{+5.40})} & \\makebox[5.4em][s]{36.12 (\\textcolor{blue}{+4.38})} & \\makebox[5.4em][s]{17.94 (\\textcolor{blue}{+1.56})} & \\makebox[5.4em][s]{12.57 (\\textcolor{blue}{+0.73})} & \\makebox[5.4em][s]{15.26 (\\textcolor{blue}{+1.15})} & \\makebox[5.4em][s]{3.41 (\\textcolor{blue}{+1.66})} \\\\\n GLM-4V-plus~\\cite{chatglm4v} & - & \\makebox[5.4em][s]{41.00 (\\textcolor{red}{-18.83})} & \\makebox[5.4em][s]{39.55 (\\textcolor{blue}{+1.19})} & \\makebox[5.4em][s]{21.68 (\\textcolor{blue}{+0.16})} & \\makebox[5.4em][s]{18.63 (\\textcolor{blue}{+1.83})} & \\makebox[5.4em][s]{15.96 (\\textcolor{blue}{+3.16})} & \\makebox[5.4em][s]{17.30 (\\textcolor{blue}{+2.50})} & \\makebox[5.4em][s]{6.86 (\\textcolor{blue}{+1.17})} & \\makebox[5.4em][s]{7.72 (\\textcolor{blue}{+2.01})} & \\makebox[5.4em][s]{7.29 (\\textcolor{blue}{+1.59})} & \\makebox[5.4em][s]{8.83 (\\textcolor{blue}{+1.42})} \\\\\n GPT-4o~\\cite{gpt4v} & - & \\makebox[5.4em][s]{46.15 (\\textcolor{red}{-15.74})} & \\makebox[5.4em][s]{\\underline{48.19} (\\textcolor{blue}{+0.95})} & \\makebox[5.4em][s]{69.00 (\\textcolor{blue}{+0.19})} & \\makebox[5.4em][s]{95.39 (\\textcolor{blue}{+0.05})} & \\makebox[5.4em][s]{77.23 (\\textcolor{blue}{+1.17})} & \\makebox[5.4em][s]{86.31 (\\textcolor{blue}{+0.61})} & \\makebox[5.4em][s]{19.20 (\\textcolor{blue}{+1.45})} & \\makebox[5.4em][s]{9.31 (\\textcolor{blue}{+0.61})} & \\makebox[5.4em][s]{14.26 (\\textcolor{blue}{+1.03})} & \\makebox[5.4em][s]{15.42 (\\textcolor{blue}{+1.50})} \\\\\n \\midrule\n \\rowcolor[rgb]{ .922, .945, .871} \\multicolumn{12}{c}{ChartLLM-Based} \\\\\n \\midrule\n TinyChart\\textdagger~\\cite{tinychart} & 3B & \\makebox[5.4em][s]{7.69 (\\textcolor{blue}{+2.33})} & \\makebox[5.4em][s]{20.07 (\\textcolor{blue}{+1.62})} & \\makebox[5.4em][s]{23.21 (\\textcolor{blue}{+6.40})} & \\makebox[5.4em][s]{95.04 (\\textcolor{blue}{+1.44})} & \\makebox[5.4em][s]{74.41 (\\textcolor{blue}{+2.25})} & \\makebox[5.4em][s]{84.73 (\\textcolor{blue}{+1.85})} & \\makebox[5.4em][s]{14.68 (\\textcolor{blue}{+3.84})} & \\makebox[5.4em][s]{34.22 (\\textcolor{blue}{+7.18})} & \\makebox[5.4em][s]{24.45 (\\textcolor{blue}{+5.51})} & \\makebox[5.4em][s]{\\textbf{21.84 } (\\textcolor{blue}{+2.22})} \\\\\n ChartInstruct\\textdagger~\\cite{chartinstruct} & 7B & \\makebox[5.4em][s]{11.54 (\\textcolor{blue}{+1.72})} & \\makebox[5.4em][s]{34.79 (\\textcolor{blue}{+10.24})} & \\makebox[5.4em][s]{26.43 (\\textcolor{blue}{+11.39})} & \\makebox[5.4em][s]{85.93 (\\textcolor{blue}{+3.53})} & \\makebox[5.4em][s]{43.52 (\\textcolor{blue}{+2.88})} & \\makebox[5.4em][s]{64.73 (\\textcolor{blue}{+3.20})} & \\makebox[5.4em][s]{15.52 (\\textcolor{blue}{+2.71})} & \\makebox[5.4em][s]{\\underline{41.42} (\\textcolor{blue}{+2.03})} & \\makebox[5.4em][s]{28.47 (\\textcolor{blue}{+2.37})} & \\makebox[5.4em][s]{18.53 (\\textcolor{blue}{+3.75})} \\\\\n ChartLlama\\textdagger~\\cite{chartllama} & 7B & \\makebox[5.4em][s]{22.67 (\\textcolor{blue}{+2.13})} & \\makebox[5.4em][s]{22.54 (\\textcolor{blue}{+1.19})} & \\makebox[5.4em][s]{27.58 (\\textcolor{blue}{+14.85})} & \\makebox[5.4em][s]{91.42 (\\textcolor{blue}{+1.06})} & \\makebox[5.4em][s]{51.72 (\\textcolor{blue}{+2.76})} & \\makebox[5.4em][s]{71.57 (\\textcolor{blue}{+1.91})} & \\makebox[5.4em][s]{17.94 (\\textcolor{blue}{+3.71})} & \\makebox[5.4em][s]{\\textbf{40.47 } (\\textcolor{red}{-0.24})} & \\makebox[5.4em][s]{\\textbf{29.21 } (\\textcolor{blue}{+1.74})} & \\makebox[5.4em][s]{7.40 (\\textcolor{blue}{+2.70})} \\\\\n Sphinx-v2~\\cite{sphinx} & 7B & \\makebox[5.4em][s]{13.85 (\\textcolor{blue}{+4.03})} & \\makebox[5.4em][s]{30.11 (\\textcolor{blue}{+4.16})} & \\makebox[5.4em][s]{23.68 (\\textcolor{blue}{+9.99})} & \\makebox[5.4em][s]{62.80 (\\textcolor{blue}{+1.84})} & \\makebox[5.4em][s]{48.00 (\\textcolor{blue}{+4.08})} & \\makebox[5.4em][s]{55.40 (\\textcolor{blue}{+2.96})} & \\makebox[5.4em][s]{7.90 (\\textcolor{blue}{+4.47})} & \\makebox[5.4em][s]{7.35 (\\textcolor{blue}{+2.41})} & \\makebox[5.4em][s]{7.63 (\\textcolor{blue}{+3.44})} & \\makebox[5.4em][s]{6.88 (\\textcolor{blue}{+3.78})} \\\\\n LLaVA1.5~\\cite{llava1.5} & 7B & \\makebox[5.4em][s]{36.92 (\\textcolor{blue}{+2.10})} & \\makebox[5.4em][s]{38.39 (\\textcolor{red}{-1.11})} & \\makebox[5.4em][s]{26.95 (\\textcolor{blue}{+11.37})} & \\makebox[5.4em][s]{25.44 (\\textcolor{blue}{+5.32})} & \\makebox[5.4em][s]{31.68 (\\textcolor{blue}{+6.48})} & \\makebox[5.4em][s]{28.56 (\\textcolor{blue}{+5.90})} & \\makebox[5.4em][s]{18.21 (\\textcolor{blue}{+2.51})} & \\makebox[5.4em][s]{17.83 (\\textcolor{blue}{+6.76})} & \\makebox[5.4em][s]{18.02 (\\textcolor{blue}{+4.63})} & \\makebox[5.4em][s]{17.40 (\\textcolor{blue}{+2.23})} \\\\\n ViP-LLaVA~\\cite{vipllava} & 7B & \\makebox[5.4em][s]{26.23 (\\textcolor{blue}{+5.69})} & \\makebox[5.4em][s]{41.98 (\\textcolor{blue}{+4.97})} & \\makebox[5.4em][s]{28.79 (\\textcolor{blue}{+13.23})} & \\makebox[5.4em][s]{23.96 (\\textcolor{blue}{+6.36})} & \\makebox[5.4em][s]{29.04 (\\textcolor{blue}{+2.88})} & \\makebox[5.4em][s]{26.50 (\\textcolor{blue}{+4.62})} & \\makebox[5.4em][s]{14.31 (\\textcolor{blue}{+12.95})} & \\makebox[5.4em][s]{14.38 (\\textcolor{blue}{+11.79})} & \\makebox[5.4em][s]{14.35 (\\textcolor{blue}{+12.37})} & \\makebox[5.4em][s]{18.72 (\\textcolor{blue}{+3.68})} \\\\\n LLaVA-NEXT~\\cite{llavanext} & 7B & \\makebox[5.4em][s]{42.31 (\\textcolor{blue}{+21.77})} & \\makebox[5.4em][s]{49.40 (\\textcolor{blue}{+2.03})} & \\makebox[5.4em][s]{34.40 (\\textcolor{blue}{+3.32})} & \\makebox[5.4em][s]{75.82 (\\textcolor{blue}{+1.56})} & \\makebox[5.4em][s]{47.68 (\\textcolor{blue}{+1.38})} & \\makebox[5.4em][s]{61.75 (\\textcolor{blue}{+1.47})} & \\makebox[5.4em][s]{15.26 (\\textcolor{blue}{+1.41})} & \\makebox[5.4em][s]{8.93 (\\textcolor{blue}{+2.30})} & \\makebox[5.4em][s]{12.10 (\\textcolor{blue}{+1.86})} & \\makebox[5.4em][s]{9.02 (\\textcolor{blue}{+0.95})} \\\\\n IXC-2.5~\\cite{ixc2.5} & 7B & \\makebox[5.4em][s]{47.31 (\\textcolor{blue}{+0.01})} & \\makebox[5.4em][s]{43.38 (\\textcolor{blue}{+3.28})} & \\makebox[5.4em][s]{51.88 (\\textcolor{blue}{+8.56})} & \\makebox[5.4em][s]{94.88 (\\textcolor{blue}{+2.48})} & \\makebox[5.4em][s]{76.24 (\\textcolor{blue}{+1.92})} & \\makebox[5.4em][s]{85.56 (\\textcolor{blue}{+2.20})} & \\makebox[5.4em][s]{19.82 (\\textcolor{blue}{+2.13})} & \\makebox[5.4em][s]{14.70 (\\textcolor{blue}{+2.84})} & \\makebox[5.4em][s]{17.26 (\\textcolor{blue}{+2.48})} & \\makebox[5.4em][s]{16.83 (\\textcolor{blue}{+7.44})} \\\\\n Qwen2-VL~\\cite{qwenvl} & 7B & \\makebox[5.4em][s]{57.66 (\\textcolor{blue}{+0.52})} & \\makebox[5.4em][s]{45.54 (\\textcolor{blue}{+8.22})} & \\makebox[5.4em][s]{56.10 (\\textcolor{blue}{+8.21})} & \\makebox[5.4em][s]{94.40 (\\textcolor{blue}{+0.30})} & \\makebox[5.4em][s]{77.44 (\\textcolor{blue}{+5.44})} & \\makebox[5.4em][s]{85.92 (\\textcolor{blue}{+2.87})} & \\makebox[5.4em][s]{20.96 (\\textcolor{blue}{+9.89})} & \\makebox[5.4em][s]{24.45 (\\textcolor{blue}{+1.47})} & \\makebox[5.4em][s]{22.71 (\\textcolor{blue}{+5.68})} & \\makebox[5.4em][s]{18.53 (\\textcolor{blue}{+10.27})} \\\\\n mPLUG-Owl2~\\cite{mplugowl2} & 8B & \\makebox[5.4em][s]{29.38 (\\textcolor{blue}{+4.38})} & \\makebox[5.4em][s]{40.46 (\\textcolor{blue}{+4.29})} & \\makebox[5.4em][s]{29.15 (\\textcolor{blue}{+14.93})} & \\makebox[5.4em][s]{38.76 (\\textcolor{blue}{+14.63})} & \\makebox[5.4em][s]{40.34 (\\textcolor{blue}{+13.00})} & \\makebox[5.4em][s]{39.55 (\\textcolor{blue}{+13.82})} & \\makebox[5.4em][s]{13.01 (\\textcolor{blue}{+0.18})} & \\makebox[5.4em][s]{8.91 (\\textcolor{blue}{+2.94})} & \\makebox[5.4em][s]{10.96 (\\textcolor{blue}{+1.56})} & \\makebox[5.4em][s]{6.26 (\\textcolor{blue}{+0.92})} \\\\\n MiniCPM-v2~\\cite{minicpmv2} & 8B & \\makebox[5.4em][s]{24.21 (\\textcolor{blue}{+1.89})} & \\makebox[5.4em][s]{38.65 (\\textcolor{blue}{+10.17})} & \\makebox[5.4em][s]{23.73 (\\textcolor{blue}{+13.09})} & \\makebox[5.4em][s]{93.84 (\\textcolor{blue}{+2.72})} & \\makebox[5.4em][s]{71.86 (\\textcolor{blue}{+2.84})} & \\makebox[5.4em][s]{82.85 (\\textcolor{blue}{+2.78})} & \\makebox[5.4em][s]{\\textbf{27.68 } (\\textcolor{blue}{+5.51})} & \\makebox[5.4em][s]{24.55 (\\textcolor{blue}{+13.54})} & \\makebox[5.4em][s]{26.12 (\\textcolor{blue}{+9.53})} & \\makebox[5.4em][s]{\\underline{20.88} (\\textcolor{blue}{+0.83})} \\\\\n CogVLM~\\cite{cogvlm} & 17B & \\makebox[5.4em][s]{26.38 (\\textcolor{blue}{+3.17})} & \\makebox[5.4em][s]{41.05 (\\textcolor{blue}{+0.85})} & \\makebox[5.4em][s]{41.85 (\\textcolor{blue}{+12.50})} & \\makebox[5.4em][s]{33.41 (\\textcolor{blue}{+9.46})} & \\makebox[5.4em][s]{51.73 (\\textcolor{blue}{+12.20})} & \\makebox[5.4em][s]{42.57 (\\textcolor{blue}{+10.83})} & \\makebox[5.4em][s]{21.46 (\\textcolor{blue}{+5.08})} & \\makebox[5.4em][s]{14.74 (\\textcolor{blue}{+2.90})} & \\makebox[5.4em][s]{18.10 (\\textcolor{blue}{+3.99})} & \\makebox[5.4em][s]{2.48 (\\textcolor{blue}{+0.73})} \\\\\n GLM-4V-plus~\\cite{chatglm4v} & - & \\makebox[5.4em][s]{\\underline{60.18} (\\textcolor{blue}{+0.35})} & \\makebox[5.4em][s]{47.00 (\\textcolor{blue}{+8.64})} & \\makebox[5.4em][s]{37.19 (\\textcolor{blue}{+15.67})} & \\makebox[5.4em][s]{19.74 (\\textcolor{blue}{+2.94})} & \\makebox[5.4em][s]{18.04 (\\textcolor{blue}{+5.24})} & \\makebox[5.4em][s]{19.66 (\\textcolor{blue}{+4.86})} & \\makebox[5.4em][s]{9.75 (\\textcolor{blue}{+4.06})} & \\makebox[5.4em][s]{8.97 (\\textcolor{blue}{+3.26})} & \\makebox[5.4em][s]{9.36 (\\textcolor{blue}{+3.66})} & \\makebox[5.4em][s]{9.74 (\\textcolor{blue}{+2.34})} \\\\\n GPT-4o~\\cite{gpt4v} & - & \\makebox[5.4em][s]{\\textbf{61.89 } (\\textcolor{blue}{+0.00})} & \\makebox[5.4em][s]{\\textbf{50.42 } (\\textcolor{blue}{+3.17})} & \\makebox[5.4em][s]{\\textbf{73.89 } (\\textcolor{blue}{+5.08})} & \\makebox[5.4em][s]{{\\textbf{98.63 }} (\\textcolor{blue}{+3.29})} & \\makebox[5.4em][s]{\\textbf{79.49 } (\\textcolor{blue}{+3.43})} & \\makebox[5.4em][s]{\\textbf{89.06 } (\\textcolor{blue}{+3.36})} & \\makebox[5.4em][s]{23.65 (\\textcolor{blue}{+5.90})} & \\makebox[5.4em][s]{11.07 (\\textcolor{blue}{+2.37})} & \\makebox[5.4em][s]{17.36 (\\textcolor{blue}{+4.14})} & \\makebox[5.4em][s]{16.04 (\\textcolor{blue}{+2.12})} \\\\\n \\bottomrule\n \\end{tabular}\n }\n \\raggedright\n \\caption{Performance of multimodal models on ChartMind and three structured-output CQA datasets. The best results are highlighted in \\textbf{bold}, and the second-best results are \\underline{underlined}. \\textdagger Specialized CQA models.}\n \\vskip -0.15in\n \\label{tab:results1}\n\\end{table*}\n\n\\subsection{ChartLLM: Context Extraction for CQA}\n\nThe ChartLLM is designed to enhance CQA by extracting and structuring relevant contextual information from a chart. Given a chart \\( C \\), the context \\( C_{\\text{context}} = \\{ T, L, X, Y \\} \\), where \\( T \\) is the title, \\( L \\) is the legend, \\( X \\) is the X-axis label, and \\( Y \\) is the Y-axis label, is generated to represent the essential elements of the chart. This approach minimizes irrelevant data and focuses solely on the components required for accurate reasoning in CQA tasks. To extract \\( C_{\\text{context}} \\), predefined prompts, such as \"Extract key information from the chart, including title, legend, and X and Y-axis information,\" guide the model in identifying the necessary elements of the chart. \nThis ensures the extracted context is concise, relevant, and foundational for reasoning. Unlike step-by-step CoT reasoning, ChartLLM focuses on structured context modeling, reducing the model’s perceptual burden by presenting semantically key components upfront.\n\nThe reasoning objective for ChartLLM is to predict the answer \\( A \\) that maximizes the conditional probability given the question \\( Q \\) and the extracted context \\( C_{\\text{context}} \\). This can be expressed as:\n% \\vspace{-2mm}\n% { \\small\n% \\begin{equation}\n% A = \\operatorname{argmax}_{a \\in \\mathcal{A}} \\sum_{i=1}^{n} \\mathbb{E}_{C_{\\text{context}}, Q} \\left[ \\log P(a_i \\mid C_{\\text{context}}, Q; \\Theta) \\right]\n% \\label{eq:chartllm_objective}\n% \\end{equation}\n% }\n\\begin{equation}\n\\resizebox{\\linewidth}{!}{$\nA = \\operatorname{argmax}_{a \\in \\mathcal{A}} \n\\sum_{i=1}^{n} \\mathbb{E}_{C_{\\text{context}}, Q} \n\\left[ \\log P(a_i \\mid C_{\\text{context}}, Q; \\Theta) \\right]\n$}\n\\label{eq:chartllm_objective}\n\\end{equation}\nHere, \\( A \\) is the predicted answer, \\( \\mathcal{A} \\) represents the candidate answer space, \\( C_{\\text{context}} \\) is the extracted context from the chart \\( C \\), \\( Q \\) is the natural language question, \\( a_i \\) is the \\( i \\)-th candidate answer, and \\( \\Theta \\) denotes the model parameters.\n\n\n% \\textbf{*} The best results are highlighted in \\textbf{bold}, and the second-best results are \\underline{underlined}. \n% \\textbf{\\textdagger} Specialized CQA models.\n\n\n\\section{Experiments}\n\n\\subsection{Experimental Setup}\nWe evaluate four paradigms for CQA tasks, including instruction-following, COT-based reasoning, OCR-enhanced methods, and our proposed ChartLLM framework. These methods are tested on 14 MLLMs from three categories: specialized CQA models, general-purpose open-source models, and general-purpose closed-source models. The evaluation spans four datasets, including our proposed ChartMind and three structured-output CQA datasets—ChartQA~\\cite{chartqa}, Chart-to-Text~\\cite{charttotext}, and OpenCQA~\\cite{opencqa}—which primarily rely on predefined answer formats and automated scoring metrics. In contrast, ChartMind introduces diverse chart formats and open-domain textual outputs, enabling a more comprehensive assessment of real-world CQA scenarios. Further implementation details, model descriptions, and benchmark specifications are provided in Appendix~\\ref{appendix:exp_setup}.\n\n\n\n\\begin{table}[t]\n\\centering\n\\resizebox{\\linewidth}{!}{\n\\begin{tabular}{lccc}\n\\toprule\n\\textbf{Models} & \\textbf{Size} & \\textbf{Avg. GPT-4o Score} & \\textbf{Avg. Human Score} \\\\\n\\midrule\nChartInstruct~\\cite{chartinstruct} & 7B & 26.43 & 22.52 \\\\\nChartLlama~\\cite{chartllama} & 7B & 27.58 & 23.11 \\\\\nTinyChart~\\cite{tinychart} & 3B & 23.21 & 21.97 \\\\\nmPLUG-Owl2~\\cite{mplugowl2} & 8B & 29.15 & 29.31 \\\\\nSphinx-v2~\\cite{sphinx} & 7B & 23.68 & 22.31 \\\\\nCogVLM~\\cite{cogvlm} & 17B & 41.85 & 34.96 \\\\\nLLaVA1.5~\\cite{llava1.5} & 7B & 26.95 & 22.93 \\\\\nMiniCPM-v2~\\cite{minicpmv2} & 8B & 23.73 & 24.01 \\\\\nViP-LLaVA~\\cite{vipllava} & 7B & 28.79 & 30.75 \\\\\nLLaVA-NEXT~\\cite{llavanext} & 7B & 34.40 & 32.31 \\\\\nIXC-2.5~\\cite{ixc2.5} & 7B & 51.88 & 36.61 \\\\\nQwen2-VL~\\cite{qwenvl} & 7B & 56.10 & 40.39 \\\\\nGLM-4V-plus~\\cite{chatglm4v} & - & 37.19 & 39.35 \\\\\nGPT-4o~\\cite{gpt4v} & - & 73.89 & 50.73 \\\\\n\\midrule\n\\textbf{PCC~\\cite{cohen2009pearson}} & - & \\multicolumn{2}{c}{\\textbf{93.09}} \\\\\n\\bottomrule\n\\end{tabular}}\n\\caption{Correlation of GPT4o and Human Eval.}\n\\vskip -0.2in\n\\label{tab:consistency_metrics}\n\\end{table}\n\n\n\\subsection{Main Results} \nTo evaluate the effectiveness and robustness of ChartLLM-based methods over OCR-enhanced~\\cite{liu2023deplot} and COT-based~\\cite{wei2022chain} approaches in open-ended and structured-output reasoning, \\autoref{tab:results1} compares their performance across various benchmarks. Both OCR-enhanced and COT-based methods yield significant improvements (blue text), but their effectiveness varies by task. OCR-enhanced methods often degrade performance (red text), particularly in open-ended reasoning, where redundancy and noise from textual extraction disrupt holistic reasoning. For instance, GPT-4o’s~\\cite{gpt4v} ACC in open-ended tasks drops by -12.58 with OCR-enhanced methods, reflecting their sensitivity to flexible reasoning. COT-based methods enhance structured-output reasoning but struggle in open-ended tasks, reducing GPT-4o’s ACC by -15.74 due to difficulties in integrating contextual and visual elements. ChartLLM-based methods address these challenges by strategically extracting key contextual information and minimizing redundancy, reducing external noise in reasoning. By focusing on essential chart elements and preserving relevant semantic relationships, they achieve superior performance with consistent adaptability across both reasoning types. Their ability to balance context extraction and noise reduction underscores their robustness in handling complex chart reasoning.\n\n\n\\subsection{Correlation Analysis of Metrics}\nTo assess the consistency between automated and human evaluation in open-ended CQA, \\autoref{tab:consistency_metrics} analyzes the correlation between GPT-4o Score and Human Score across 14 multimodal models. The Pearson Correlation Coefficient (PCC)~\\cite{cohen2009pearson} is 93.09, indicating a strong linear relationship. High-performing models like GPT-4o~\\cite{gpt4v} and Qwen2-VL~\\cite{qwenvl} show strong alignment between GPT-4o and human scores, validating automated evaluation reliability. Notably, models like mPLUG-Owl2~\\cite{mplugowl2} and ViP-LLaVA~\\cite{vipllava} exhibit slight deviations, where human scores marginally exceed automated ones, possibly reflecting nuanced human judgment in open-ended reasoning. The high PCC confirms GPT-4o Score as a robust proxy for human evaluation, reinforcing its applicability in open-ended CQA.\n\n\n\\subsection{Sensitivity Analysis}\n\\paragraph{Language-Level Analysis.}\n\\begin{figure}[t]\n \\centering\n \\includegraphics[width=\\linewidth, height=0.38\\textheight]{images/fig7-6.pdf}\n % \\vspace{-6mm}\n \\caption{Performance of multimodal models across Chinese and English datasets in ChartMind.}\n \\label{fig:gpt4o_evaluation2}\n \\vskip -0.15in\n\\end{figure}\n\\begin{figure*}[t]\n \\centering\n \\includegraphics[width=\\linewidth]{images/fig5-1-v7.pdf}\n \\vskip -0.1in\n \\caption{Performance of multimodal models on seven tasks in ChartMind.}\n \\label{fig:gpt4o_evaluation1}\n \\vskip -0.15in\n\\end{figure*}\n\nTo evaluate the sensitivity of different paradigms to multilingual challenges in CQA, we analyze model performance across English and Chinese charts in ChartMind. Figure~\\ref{fig:gpt4o_evaluation2} compares results under each method across both languages, grouped by paradigm to highlight method robustness. We observe a consistent performance gap across models: Chinese tasks are generally more difficult, reflecting challenges in tokenization, OCR quality, and implicit reasoning common in Chinese chart labels. Instruction-following models such as GPT-4o~\\cite{gpt4v} and LLaVA1.5~\\cite{llava1.5} show significant degradation in Chinese due to weaker multilingual grounding.\nOCR-enhanced methods help mitigate these gaps by injecting extracted text, especially in Chinese, where axis labels and titles are often more semantically informative. COT-based methods help slightly but introduce more variance, especially in visual tasks where decomposition is less intuitive.\nChartLLM-based methods consistently achieve the best cross-lingual performance. By explicitly structuring chart context before reasoning, ChartLLM reduces noise and enhances semantic alignment, leading to more stable performance in both languages.\n\n\n\\paragraph{Task-Level Analysis.}\n\nTo explore how different paradigms handle diverse CQA tasks, we evaluate model performance across seven task types in ChartMind. As shown in \\autoref{fig:gpt4o_evaluation1}, these tasks vary in difficulty. \\textit{Chart Conversion} and \\textit{Chart Summarization} are the most challenging, involving semantic fusion and cross-modal reasoning. In contrast, \\textit{Suggestions} and \\textit{Information Positioning} focus on localized extraction and are comparatively easier.\nInstruction-following methods often struggle with complex tasks, showing unstable outputs due to weak multimodal alignment. OCR-enhanced approaches perform well in text-heavy scenarios like \\textit{Chart OCR Recognition}, but degrade on tasks such as \\textit{Summarization}, where excess raw text introduces noise and misleads the model.\nCOT-based methods help in procedural reasoning tasks like \\textit{Suggestions}, but fall short in integrative tasks such as \\textit{Chart Assistance}, where linear step-by-step thinking cannot capture multimodal dependencies.\nChartLLM-based methods consistently demonstrate robust performance across all task types. By explicitly modeling structural context before reasoning, ChartLLM improves semantic grounding in complex settings while preserving precision in simpler tasks. This balance highlights its adaptability and makes it particularly effective for real-world CQA.\n\n\\paragraph{Chart-Type-Level Analysis.}\n% \\paragraph{Chart-Type-Level Analysis.}\nWe examine how different paradigms perform across chart types of varying complexity in ChartMind. Tasks involving \\textit{Pie} and \\textit{Stacked Bar} charts require high-context reasoning, while \\textit{Complex Line} charts mainly involve direct value extraction.\nInstruction-following models struggle with layout-heavy formats; OCR-enhanced methods perform well on text-dense charts but falter when visual cues dominate. COT-based methods show moderate stability but lack semantic depth.\nChartLLM consistently outperforms others by explicitly modeling contextual elements, enabling it to generalize across both visually intricate and text-sparse chart types. A full breakdown is provided in~\\autoref{appendix:chart_type_analysis}.\n\n\n\n\\section{Conclusion}\nWe introduce \\textit{ChartMind}, the first benchmark for complex CQA in realistic settings. It addresses key gaps in prior work by supporting multilingual charts, open-ended outputs, and seven distinct task types. \nAcross four paradigms and 14 multimodal models, our results show that ChartLLM—a model-agnostic, context-aware framework—consistently outperforms OCR and CoT methods, establishing a strong baseline for future CQA research.\nFuture work will explore multi-turn dialogues, cross-chart reasoning, and hybrid chart–text queries to support more advanced and realistic use cases.\n\n\n\\section*{Limitations}\nChartMind provides a benchmark for complex CQA evaluation, yet several limitations remain. First, the dataset primarily relies on publicly available charts, potentially introducing biases in data distribution and task complexity. Ensuring broader representativeness requires further dataset expansion and diversification. Second, although ChartMind defines seven reasoning tasks, real-world chart analysis often involves more advanced reasoning, such as multi-turn interactions, cross-chart comparisons, and textual-visual information integration, which remain underexplored. Third, the reliance on automated evaluation methods, such as GPT-4 ratings, introduces challenges in capturing nuanced human judgment in complex reasoning. Addressing these issues requires refining evaluation methodologies and incorporating more human annotations. Future improvements may focus on expanding the dataset, enhancing evaluation metrics, and integrating multi-turn reasoning and cross-chart analysis to better reflect real-world scenarios.\n\n\n% Bibliography entries for the entire Anthology, followed by custom entries\n%\\bibliography{anthology,custom}\n% Custom bibliography entries only\n\\bibliography{custom}\n\n\\appendix\n\\section{Chart Types and Tasks in ChartMind}\n\\begin{figure*}[t]\n \\centering\n \\includegraphics[width=\\linewidth]{images/fig_fulu.pdf}\n \\caption{Overview of the seven chart types and seven reasoning tasks included in ChartMind.}\n \\label{fig:chart_types_tasks}\n\\end{figure*}\nChartMind supports a diverse range of chart types and reasoning tasks, ensuring a comprehensive evaluation of complex reasoning in CQA. As shown in \\autoref{fig:chart_types_tasks} The dataset includes seven distinct chart types—Pie, Common Bar, Scatter, Grouped Bar, Complex Line, Stacked Bar, and Common Line—capturing varied visual structures and data representations. Additionally, ChartMind defines seven reasoning tasks: Chart Conversion, Chart OCR Recognition, Suggestions, Chart Assistance, Chart Classification, Chart Summarization, and Information Positioning, covering key aspects of multimodal chart understanding. These distributions illustrate ChartMind’s ability to comprehensively assess complex multimodal reasoning, spanning diverse chart types and reasoning paradigms. Compared to prior benchmarks, ChartMind provides a broader evaluation scope, capturing the complexity of real-world CQA tasks.\n\n\n\n\\section{Experimental Setup Details}\n\\label{appendix:exp_setup}\n\n\\subsection{Implementation Details}\nTo assess the performance of models on complex CQA tasks in real-world settings, we experiment with four types of paradigms. First, we test MLLMs in the instruction-following setting~\\cite{zhou2023instruction}, where we use prompts to evaluate their ability to answer chart-related questions. Second, we apply COT-based methods~\\cite{wei2022chain}, which break down reasoning processes into intermediate steps to generate answers. Third, we adopt OCR-enhanced methods inspired by DePlot~\\cite{liu2023deplot}, which extract chart content as text and use it as input for multimodal reasoning models. Finally, we propose the ChartLLM method, which enhances reasoning performance by extracting structured contextual information, such as chart titles, legends, and axes, using Qwen2-VL~\\cite{qwenvl}, and feeding this information into models for further analysis. \n\n\n\\subsection{Models}\nWe evaluate 14 MLLMs across three categories: specialized CQA models, general-purpose open-source multimodal models, and general-purpose closed-source multimodal models. The majority of the models have a parameter size of approximately 7B, with a few exceptions, including smaller models such as TinyChart~\\cite{tinychart} with 3B parameters and larger models like CogVLM~\\cite{cogvlm} with 17B parameters. For specialized CQA models, we include ChartInstruct~\\cite{chartinstruct}, ChartLlama~\\cite{chartllama}, and TinyChart~\\cite{tinychart}. These models are specifically trained on CQA datasets, making them particularly suited for tasks requiring precise understanding of chart-related queries. Among open-source general-purpose multimodal models, we evaluate mPLUG-Owl2~\\cite{mplugowl2}, Sphinx-v2~\\cite{sphinx}, CogVLM~\\cite{cogvlm}, LLaVA1.5~\\cite{llava1.5}, MiniCPM-v2~\\cite{minicpmv2}, ViP-LLaVA~\\cite{vipllava}, LLaVA-NEXT~\\cite{llavanext}, IXC-2.5~\\cite{ixc2.5}, and Qwen2-VL~\\cite{qwenvl}. These models leverage extensive multimodal training datasets, including CQA data, and exhibit strong performance on chart-related tasks. Finally, closed-source general multimodal models, including GPT-4o~\\cite{gpt4v} and GLM-4V-plus~\\cite{chatglm4v}, are state-of-the-art models with advanced multimodal reasoning capacities, providing strong competition to existing open-source systems.\n\n\\begin{figure*}[ht]\n \\centering\n \\includegraphics[width=\\linewidth]{images/fig7-3.pdf}\n \\caption{Prompt design for GPT-4o score.}\n \\label{fig:gpt4o_evaluation}\n\\end{figure*}\n\n\\subsection{Benchmarks and Metrics}\n%check\nTo comprehensively evaluate multimodal CQA tasks, we adopt three representative structured-output reasoning datasets—ChartQA~\\cite{chartqa}, Chart-to-Text~\\cite{charttotext}, and OpenCQA~\\cite{opencqa}—alongside our proposed benchmark, ChartMind. ChartQA and Chart-to-Text primarily take a chart and a natural language question as input and generate structured textual answers, such as numerical values, categorical labels, or predefined captions, making them well-suited for factual extraction tasks. OpenCQA, despite allowing open-ended queries, constrains responses to structured formats evaluated by automated metrics like BLEU, limiting its ability to assess flexible reasoning. To address these constraints, ChartMind introduces a more comprehensive evaluation by supporting diverse chart types, open-ended textual outputs, and seven complex reasoning tasks, enabling a broader assessment of multimodal reasoning. Models are evaluated using Accuracy and CIDEr for structured assessments, while GPT-4o score and Human score serve as open-ended evaluation metrics, with GPT-4o score as the primary metric, as detailed in ~\\autoref{appendixa.1}. The structured-output datasets are evaluated using Accuracy and BLEU score.\n\n\\section{GPT-4o Scoring Prompt Design}\n\\label{appendixa.1}\nThe GPT-4o score prompt evaluates the performance of models on CQA tasks by assessing two key dimensions: output quality and output correctness. Output quality focuses on the fluency of the model's answer, the completeness of its reasoning process, and its ability to follow instructions accurately. Output correctness measures the overall accuracy of the reasoning, the correctness of the data, and the logical alignment with the human reference answer or chart content. The input to the prompt includes a JSON object containing the question, the human reference answer, and the model-generated answer. The output is also formatted as a JSON object, which includes a detailed explanation of the scoring rationale along with scores for both dimensions. The full design of the scoring prompt is visualized in ~\\autoref{fig:gpt4o_evaluation}.\n\n\\section{Chart-Type-Level Analysis}\n\\label{appendix:chart_type_analysis}\n\\begin{figure*}[ht]\n \\centering\n \\includegraphics[width=\\textwidth]{images/fig8-2.pdf} \n \\caption{Performance of multimodal models across chart types, categorized by four paradigms.}\n \\label{fig:chart_type_analysis}\n\\end{figure*}\n\\begin{figure*}[ht]\n \\centering\n \\includegraphics[width=\\linewidth]{images/fig8.pdf}\n \\caption{The four major error types in ChartMind.}\n \\label{fig:error_analysis}\n\\end{figure*}\nTo evaluate the sensitivity of different paradigms to diverse chart types in CQA tasks, we analyze their performance across seven chart types in ChartMind. \\autoref{fig:chart_type_analysis} presents a detailed breakdown of model performance. Chart types exhibit varying complexity, with \\textit{Pie} and \\textit{Stacked Bar} being the most challenging due to their reliance on integrated contextual reasoning, while simpler types like \\textit{Complex Line} primarily require straightforward data extraction. Instruction-following methods~\\cite{wei2021finetuned}, such as GPT-4o~\\cite{gpt4v} and LLAVA1.5~\\cite{llava1.5}, show significant performance drops in high-complexity charts, underscoring their limitations in managing holistic reasoning tasks. OCR-enhanced methods~\\cite{liu2023deplot} excel in text-heavy charts such as \\textit{Grouped Bar}, leveraging their ability to extract textual information, but struggle with tasks like \\textit{Scatter} that demand comprehensive visual-semantic integration. COT-based methods~\\cite{wei2022chain} demonstrate moderate performance across most chart types, performing relatively well in structured charts like \\textit{Common Line}, yet falling short in tasks requiring high-contextual reasoning. ChartLLM-based methods achieve the highest overall performance, excelling in high-difficulty charts by effectively using critical contextual elements and showcasing adaptability to diverse chart types. These results highlight the necessity of contextual reasoning for high-performance chart understanding.\n\n\n\n\n\n\\section{Error Analysis}\n\\label{appendixa.2}\n\\autoref{fig:error_analysis} illustrates specific examples of the four major error types observed in the ChartMind: value recognition errors, judgment errors, calculation errors, and color recognition errors. These examples highlight typical failure cases, such as incorrect identification of numerical values in bar segments (value recognition), flawed logical reasoning or mismatched context interpretation (judgment), inaccurate arithmetic operations (calculation), and misassociation of chart elements with their respective colors in legends or overlapping areas (color recognition). The figure provides detailed scenarios, such as errors in identifying peak values, interpreting differences in chart segments, and miscalculating relationships between visual elements. These cases emphasize the challenges faced by models in aligning visual interpretation with reasoning accuracy.\n\n\\section{Potential Risks}\n\nWhile our work primarily focuses on dataset construction and evaluation methodology for chart question answering (CQA), we acknowledge the following limited potential risks:\n\n\\begin{itemize}\n \\item \\textbf{Use of LLMs in Data Generation.} The initial QA pairs in ChartMind were generated using GPT-4o. Although all outputs were manually reviewed, revised, and filtered by trained annotators, there remains a low-level risk of inherited model bias or hallucination that may not have been fully eliminated.\n \n \\item \\textbf{Automated Evaluation.} Our experiments rely partially on GPT-4o for scoring open-ended answers. While we provide correlation analysis with human judgments to validate reliability (Section~5.2), model-based scoring may still carry implicit biases toward certain linguistic styles or answer formats.\n \n \\item \\textbf{Language Scope.} ChartMind currently supports English and Chinese. Although this already expands the field beyond English-only benchmarks, performance and fairness in other language contexts are not yet covered.\n\\end{itemize}\n\n\\noindent Overall, our design minimizes these risks through manual validation, diverse model comparisons, and detailed performance analysis. Future versions of ChartMind will incorporate broader language coverage and alternative evaluation strategies to further mitigate these concerns.\n\n\n\n\n\n\\end{document}\n"},"question":{"kind":"string","value":"Which models rank within the top five in both average accuracy and average BLEU when scores are first averaged across all datasets and prompting methods?"},"answer":{"kind":"string","value":"Qwen2-VL, MiniCPM-v2"},"plan":{"kind":"string","value":"SELECT all models\nLOOP for each model\n SELECT all prompting methods\n LOOP for each prompting method\n COMPUTE avg_acc = (ChartMind ACC + ChartQA Avg.ACC) / 2\n COMPUTE avg_bleu = (Chart-to-Text Avg.BLEU + OpenCQA Avg.BLEU) / 2\n COMPUTE overall_acc = average over all avg_acc \n COMPUTE overall_bleu = average over all avg_bleu \n\n\nSELECT all overall_acc values\nCOMPUTE top5_accuracy_models = top five models with highest overall_acc\n\n\nSELECT all overall_bleu values\nCOMPUTE top5_bleu_models = top five models with highest overall_bleu\n\n\nCOMPUTE intersection_models = intersection of top5_accuracy_models and top5_bleu_models\n\n\nRETURN intersection_models[a]\n"}}},{"rowIdx":78,"cells":{"paper":{"kind":"string","value":"2505.23765"},"relevant_tables":{"kind":"list like","value":[["\\begin{table*}[!t]\n","\\centering\n","\\resizebox{0.92\\textwidth}{!}{\n","\\begin{tabular}{@{}l|l|lccccc@{}}\n","\\toprule\n","Model Name & Approach & Type & NDCG@1 & NDCG@3 & NDCG@5 & NDCG@10 & \\makecell{\\# Input Token \\\\(Million)} \\\\ \\midrule\n","Random & None & / & 0.2501\t & 0.3516 & 0.4368 & 0.6211 & - \\\\ \\midrule\n","o4-mini & None & / & 0.3063\t & 0.4017 & 0.4805 & 0.6488 & 0.87 \\\\ \\midrule\n","\\multirow{2}{*}{Qwen3 8B} & \\multirow{2}{*}{Finetune} & Raw & 0.2694 & 0.3739 & 0.4589 & 0.6346 & 1.74 \\\\\n"," & & Summary & \\underline{0.2984}\t & \\underline{0.3966}\t & \\underline{0.4807}\t & \\underline{0.6480} & 1.74 \\\\ \\midrule \n","\\multirow{4}{*}{Gemma3 4B} & \\multirow{2}{*}{RAG} & Raw & 0.3291 & 0.4356 & 0.5159 & 0.6688 & 73.48 \\\\\n"," & & Summary & 0.3740 & 0.4895 & 0.5627 & 0.6991 & 174.62 \\\\\n"," & \\multirow{2}{*}{PROBE} & Raw & 0.4766 & 0.5891 & 0.6478 & 0.7620 & 38.44 \\\\\n"," & & Summary & \\underline{0.5430} & \\underline{0.6513} & \\underline{0.6994} & \\underline{0.7969} & 17.35 \\\\ \\midrule\n","\\multirow{4}{*}{\\makecell{Qwen3 8B \\\\Think}} & \\multirow{2}{*}{RAG} & Raw & 0.4168 & 0.5090 & 0.5779 & 0.7123 & 362.16 \\\\\n"," & & Summary & 0.5273 & 0.6110 & 0.6646 & 0.7717 & 176.88 \\\\\n"," & \\multirow{2}{*}{PROBE} & Raw & 0.6545 & 0.7305 & 0.7728 & 0.8483 & 315.52 \\\\\n"," & & Summary & \\underline{0.6944} & \\underline{0.7638} & \\underline{0.8005} & \\underline{0.8660} & 123.04 \\\\ \\midrule\n","\\multirow{4}{*}{\\makecell{Qwen3 32B \\\\Think}} & \\multirow{2}{*}{RAG} & Raw & 0.4052 & 0.5020 & 0.5705 & 0.7081 & 182.90 \\\\\n"," & & Summary & 0.5496 & 0.6321 & 0.6847 & 0.7850 & 176.88 \\\\\n"," & \\multirow{2}{*}{PROBE} & Raw & 0.6525 & 0.7347 & 0.7759 & 0.8501 & 315.52 \\\\\n"," & & Summary & \\underline{0.7056} & \\underline{0.7753} & \\underline{0.8114} & \\underline{0.8725} & 123.04 \\\\ \\midrule\n","\\multirow{4}{*}{GPT-4.1 mini} & \\multirow{2}{*}{RAG} & Raw & 0.4494 & 0.5387 & 0.6035 & 0.7299 & 344.37 \\\\\n"," & & Summary & 0.5782 & 0.6620 & 0.7104 & 0.8019 & 154.31 \\\\\n"," & \\multirow{2}{*}{PROBE} & Raw & 0.6806 & 0.7536 & 0.7936 & 0.8628 & 298.69 \\\\\n"," & & Summary & \\underline{0.7308} & \\underline{0.7942} & \\underline{0.8282} & \\underline{0.8843} & 107.11 \\\\ \\midrule\n","\\multirow{4}{*}{o4-mini} & \\multirow{2}{*}{RAG} & Raw & 0.4730 & 0.5510 & 0.6116 & 0.7383 & 344.37 \\\\\n"," & & Summary & 0.6122 & 0.6792\t & 0.7242 & 0.8140 & 154.31 \\\\\n"," & \\multirow{2}{*}{PROBE} & Raw & 0.7117 & 0.7747 & 0.8086 & 0.8745 & 298.69 \\\\\n"," & & Summary & \\textbf{\\underline{0.7571}} & \\textbf{\\underline{0.8095}}\t& \\textbf{\\underline{0.8386}}\t& \\textbf{\\underline{0.8930}} & 107.11 \\\\ \\bottomrule\n","\\end{tabular}}\n","\n","\\caption{Experimental results of different models using various retrieval approaches and conversation formats (raw vs. summarized). \\underline{Underlined} scores indicate the best results for each model, and \\textbf{bold} scores indicate the best overall results.}\\label{tab:expr_main}\n","\\end{table*}\n"]],"string":"[\n [\n \"\\\\begin{table*}[!t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{0.92\\\\textwidth}{!}{\\n\",\n \"\\\\begin{tabular}{@{}l|l|lccccc@{}}\\n\",\n \"\\\\toprule\\n\",\n \"Model Name & Approach & Type & NDCG@1 & NDCG@3 & NDCG@5 & NDCG@10 & \\\\makecell{\\\\# Input Token \\\\\\\\(Million)} \\\\\\\\ \\\\midrule\\n\",\n \"Random & None & / & 0.2501\\t & 0.3516 & 0.4368 & 0.6211 & - \\\\\\\\ \\\\midrule\\n\",\n \"o4-mini & None & / & 0.3063\\t & 0.4017 & 0.4805 & 0.6488 & 0.87 \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{2}{*}{Qwen3 8B} & \\\\multirow{2}{*}{Finetune} & Raw & 0.2694 & 0.3739 & 0.4589 & 0.6346 & 1.74 \\\\\\\\\\n\",\n \" & & Summary & \\\\underline{0.2984}\\t & \\\\underline{0.3966}\\t & \\\\underline{0.4807}\\t & \\\\underline{0.6480} & 1.74 \\\\\\\\ \\\\midrule \\n\",\n \"\\\\multirow{4}{*}{Gemma3 4B} & \\\\multirow{2}{*}{RAG} & Raw & 0.3291 & 0.4356 & 0.5159 & 0.6688 & 73.48 \\\\\\\\\\n\",\n \" & & Summary & 0.3740 & 0.4895 & 0.5627 & 0.6991 & 174.62 \\\\\\\\\\n\",\n \" & \\\\multirow{2}{*}{PROBE} & Raw & 0.4766 & 0.5891 & 0.6478 & 0.7620 & 38.44 \\\\\\\\\\n\",\n \" & & Summary & \\\\underline{0.5430} & \\\\underline{0.6513} & \\\\underline{0.6994} & \\\\underline{0.7969} & 17.35 \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{\\\\makecell{Qwen3 8B \\\\\\\\Think}} & \\\\multirow{2}{*}{RAG} & Raw & 0.4168 & 0.5090 & 0.5779 & 0.7123 & 362.16 \\\\\\\\\\n\",\n \" & & Summary & 0.5273 & 0.6110 & 0.6646 & 0.7717 & 176.88 \\\\\\\\\\n\",\n \" & \\\\multirow{2}{*}{PROBE} & Raw & 0.6545 & 0.7305 & 0.7728 & 0.8483 & 315.52 \\\\\\\\\\n\",\n \" & & Summary & \\\\underline{0.6944} & \\\\underline{0.7638} & \\\\underline{0.8005} & \\\\underline{0.8660} & 123.04 \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{\\\\makecell{Qwen3 32B \\\\\\\\Think}} & \\\\multirow{2}{*}{RAG} & Raw & 0.4052 & 0.5020 & 0.5705 & 0.7081 & 182.90 \\\\\\\\\\n\",\n \" & & Summary & 0.5496 & 0.6321 & 0.6847 & 0.7850 & 176.88 \\\\\\\\\\n\",\n \" & \\\\multirow{2}{*}{PROBE} & Raw & 0.6525 & 0.7347 & 0.7759 & 0.8501 & 315.52 \\\\\\\\\\n\",\n \" & & Summary & \\\\underline{0.7056} & \\\\underline{0.7753} & \\\\underline{0.8114} & \\\\underline{0.8725} & 123.04 \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{GPT-4.1 mini} & \\\\multirow{2}{*}{RAG} & Raw & 0.4494 & 0.5387 & 0.6035 & 0.7299 & 344.37 \\\\\\\\\\n\",\n \" & & Summary & 0.5782 & 0.6620 & 0.7104 & 0.8019 & 154.31 \\\\\\\\\\n\",\n \" & \\\\multirow{2}{*}{PROBE} & Raw & 0.6806 & 0.7536 & 0.7936 & 0.8628 & 298.69 \\\\\\\\\\n\",\n \" & & Summary & \\\\underline{0.7308} & \\\\underline{0.7942} & \\\\underline{0.8282} & \\\\underline{0.8843} & 107.11 \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{o4-mini} & \\\\multirow{2}{*}{RAG} & Raw & 0.4730 & 0.5510 & 0.6116 & 0.7383 & 344.37 \\\\\\\\\\n\",\n \" & & Summary & 0.6122 & 0.6792\\t & 0.7242 & 0.8140 & 154.31 \\\\\\\\\\n\",\n \" & \\\\multirow{2}{*}{PROBE} & Raw & 0.7117 & 0.7747 & 0.8086 & 0.8745 & 298.69 \\\\\\\\\\n\",\n \" & & Summary & \\\\textbf{\\\\underline{0.7571}} & \\\\textbf{\\\\underline{0.8095}}\\t& \\\\textbf{\\\\underline{0.8386}}\\t& \\\\textbf{\\\\underline{0.8930}} & 107.11 \\\\\\\\ \\\\bottomrule\\n\",\n \"\\\\end{tabular}}\\n\",\n \"\\n\",\n \"\\\\caption{Experimental results of different models using various retrieval approaches and conversation formats (raw vs. summarized). \\\\underline{Underlined} scores indicate the best results for each model, and \\\\textbf{bold} scores indicate the best overall results.}\\\\label{tab:expr_main}\\n\",\n \"\\\\end{table*}\\n\"\n ]\n]","isConversational":false},"tables":{"kind":"list like","value":[["\\begin{figure}[!t]\n"," \\centering\n"," \\includegraphics[trim={20px 20px 20px 20px},clip, width=0.99\\linewidth]{figures/intro_figure.png}\n"," \\caption{Comparison of different aggregation paradigms: (a) summarization, (b) aggregation over structured databases, and (c) aggregation over large sets of conversations (our focus).} \\label{fig:intro}\n","\\end{figure}\n"],["\\begin{figure*}[h]\n"," \\centering\n"," \\includegraphics[width=0.94\\linewidth]{figures/data_construction_new.png}\n"," \\caption{Overview of the WildChat-AQA dataset creation process.}\n"," \\label{fig:data_creation}\n","\\end{figure*}\n"],["\\begin{table}[!t]\n"," \\centering\n"," \\small\n"," \\begin{tabular}{@{}l@{ }cc@{ }c@{}}\n"," \\toprule\n"," \\textbf{Name} & \\textbf{Multi-Val} & \\textbf{Inferred} & \\textbf{Example} \\\\\n"," \\midrule\n"," Location & No & No & United States, Canada \\\\\n"," \\midrule\n"," User Name & No & No & lostclasp37, toughcue8\\\\\n"," \\midrule\n"," Time & No & No & 4/26/2023, 1:47:24 PM \\\\\n"," \\midrule\n"," Language & No & No & English, Russian \\\\\n"," \\midrule\n"," Topic & Yes & Yes & \\makecell{Software, Programming \\\\ and Computer Science} \\\\\n"," \\midrule\n"," Subtopic & Yes & Yes& \\makecell{Mobile Development, \\\\AI and ML} \\\\ \n"," \\midrule\n"," Keywords & Yes & Yes & C++, \n","Pokémon \\\\\n"," \\bottomrule\n"," \\end{tabular}%}\n"," \\caption{Attributes used in WildChat-AQA. \\textbf{Multi-Val} indicates whether an attribute can have multiple values per conversation. \\textbf{Inferred} indicates whether the attribute must be inferred from conversation content (as opposed to being directly available from metadata). \\textbf{Example} shows representative attribute values.}\n"," \\label{tab:att_intro}\n","\\end{table}\n"],["\\begin{figure*}[t]\n"," \\centering\n"," \\includegraphics[width=\\linewidth]{figures/PROBE.png}\n"," \\caption{Overview of the PROBE retrieval approach}\n"," \\label{fig:probe-overview}\n","\\end{figure*}\n"],["\\begin{figure}[!t]\n"," \\centering\n"," \\begin{subfigure}[b]{0.45\\textwidth}\n"," \\includegraphics[trim={7px 5px 7px 5px},clip,width=1.0\\linewidth]{figures/conv_hist.pdf}\n"," \\end{subfigure}\n"," \\caption{Distribution of total token count and conversation count of supporting context}\n"," \\label{fig:context_token_count}\n","\\end{figure}\n"],["\\begin{table}[!t]\n"," \\centering\n"," \\begin{tabular}{@{}lcc@{}}\n"," \\toprule\n"," Name & Human–Human $\\kappa$ & Human-Model $\\kappa$ \\\\\n"," \\midrule\n"," Topic & 0.581 & 0.617 \\\\\n"," \\midrule\n"," Subtopic & 0.576 & 0.609 \\\\\n"," \\bottomrule\n"," \\end{tabular}%\n","\\caption{Average Cohen's $\\kappa$ indicating agreement between human annotators (human-human) and between human annotations and model predictions (human-model).}\n","\\label{tab:human_eval}\n","\\end{table}\n"],["\\begin{table*}[!t]\n","\\centering\n","\\resizebox{0.92\\textwidth}{!}{\n","\\begin{tabular}{@{}l|l|lccccc@{}}\n","\\toprule\n","Model Name & Approach & Type & NDCG@1 & NDCG@3 & NDCG@5 & NDCG@10 & \\makecell{\\# Input Token \\\\(Million)} \\\\ \\midrule\n","Random & None & / & 0.2501\t & 0.3516 & 0.4368 & 0.6211 & - \\\\ \\midrule\n","o4-mini & None & / & 0.3063\t & 0.4017 & 0.4805 & 0.6488 & 0.87 \\\\ \\midrule\n","\\multirow{2}{*}{Qwen3 8B} & \\multirow{2}{*}{Finetune} & Raw & 0.2694 & 0.3739 & 0.4589 & 0.6346 & 1.74 \\\\\n"," & & Summary & \\underline{0.2984}\t & \\underline{0.3966}\t & \\underline{0.4807}\t & \\underline{0.6480} & 1.74 \\\\ \\midrule \n","\\multirow{4}{*}{Gemma3 4B} & \\multirow{2}{*}{RAG} & Raw & 0.3291 & 0.4356 & 0.5159 & 0.6688 & 73.48 \\\\\n"," & & Summary & 0.3740 & 0.4895 & 0.5627 & 0.6991 & 174.62 \\\\\n"," & \\multirow{2}{*}{PROBE} & Raw & 0.4766 & 0.5891 & 0.6478 & 0.7620 & 38.44 \\\\\n"," & & Summary & \\underline{0.5430} & \\underline{0.6513} & \\underline{0.6994} & \\underline{0.7969} & 17.35 \\\\ \\midrule\n","\\multirow{4}{*}{\\makecell{Qwen3 8B \\\\Think}} & \\multirow{2}{*}{RAG} & Raw & 0.4168 & 0.5090 & 0.5779 & 0.7123 & 362.16 \\\\\n"," & & Summary & 0.5273 & 0.6110 & 0.6646 & 0.7717 & 176.88 \\\\\n"," & \\multirow{2}{*}{PROBE} & Raw & 0.6545 & 0.7305 & 0.7728 & 0.8483 & 315.52 \\\\\n"," & & Summary & \\underline{0.6944} & \\underline{0.7638} & \\underline{0.8005} & \\underline{0.8660} & 123.04 \\\\ \\midrule\n","\\multirow{4}{*}{\\makecell{Qwen3 32B \\\\Think}} & \\multirow{2}{*}{RAG} & Raw & 0.4052 & 0.5020 & 0.5705 & 0.7081 & 182.90 \\\\\n"," & & Summary & 0.5496 & 0.6321 & 0.6847 & 0.7850 & 176.88 \\\\\n"," & \\multirow{2}{*}{PROBE} & Raw & 0.6525 & 0.7347 & 0.7759 & 0.8501 & 315.52 \\\\\n"," & & Summary & \\underline{0.7056} & \\underline{0.7753} & \\underline{0.8114} & \\underline{0.8725} & 123.04 \\\\ \\midrule\n","\\multirow{4}{*}{GPT-4.1 mini} & \\multirow{2}{*}{RAG} & Raw & 0.4494 & 0.5387 & 0.6035 & 0.7299 & 344.37 \\\\\n"," & & Summary & 0.5782 & 0.6620 & 0.7104 & 0.8019 & 154.31 \\\\\n"," & \\multirow{2}{*}{PROBE} & Raw & 0.6806 & 0.7536 & 0.7936 & 0.8628 & 298.69 \\\\\n"," & & Summary & \\underline{0.7308} & \\underline{0.7942} & \\underline{0.8282} & \\underline{0.8843} & 107.11 \\\\ \\midrule\n","\\multirow{4}{*}{o4-mini} & \\multirow{2}{*}{RAG} & Raw & 0.4730 & 0.5510 & 0.6116 & 0.7383 & 344.37 \\\\\n"," & & Summary & 0.6122 & 0.6792\t & 0.7242 & 0.8140 & 154.31 \\\\\n"," & \\multirow{2}{*}{PROBE} & Raw & 0.7117 & 0.7747 & 0.8086 & 0.8745 & 298.69 \\\\\n"," & & Summary & \\textbf{\\underline{0.7571}} & \\textbf{\\underline{0.8095}}\t& \\textbf{\\underline{0.8386}}\t& \\textbf{\\underline{0.8930}} & 107.11 \\\\ \\bottomrule\n","\\end{tabular}}\n","\n","\\caption{Experimental results of different models using various retrieval approaches and conversation formats (raw vs. summarized). \\underline{Underlined} scores indicate the best results for each model, and \\textbf{bold} scores indicate the best overall results.}\\label{tab:expr_main}\n","\\end{table*}\n"],["\\begin{table*}[h]\n","\\centering\n","\\small\n","\\begin{tabular}{@{}lccccccc@{}}\n","\\toprule\n","\\textbf{Method} & \\textbf{R@5} & \\textbf{R@10} & \\textbf{R@20} & \\textbf{R@50} & \\textbf{R@100} & \\textbf{R@200} & \\textbf{R@500} \\\\\n","\\midrule\n","RAG-Dense & 0.01 & 0.02\t & 0.04\t & 0.07\t & 0.10 &\t0.14 & 0.21 \\\\\n","\\midrule\n","PROBE-Dense & 0.07 & 0.13 & 0.23 & 0.35 & 0.43 & 0.50 & 0.58 \\\\\n","\\quad - filter only & 0.05 \\textcolor{gray}{ (-0.02)} & 0.09 \\textcolor{gray}{ (-0.04)} & 0.16 \\textcolor{gray}{ (-0.07)} & 0.24 \\textcolor{gray}{ (-0.11)} & 0.29 \\textcolor{gray}{ (-0.14)} & 0.33 \\textcolor{gray}{ (-0.17)} & 0.40 \\textcolor{gray}{ (-0.18)} \\\\\n","\\quad - question \\& filter & 0.06 \\textcolor{gray}{ (-0.01)} & 0.12 \\textcolor{gray}{ (-0.01)} & 0.21 \\textcolor{gray}{ (-0.02)} & 0.32 \\textcolor{gray}{ (-0.03)} & 0.40 \\textcolor{gray}{ (-0.03)} & 0.46 \\textcolor{gray}{ (-0.04)} & 0.53 \\textcolor{gray}{ (-0.05)} \\\\\n","\\bottomrule\n","\\end{tabular}\n","\\caption{Recall@k of PROBE-Dense (Summary) with ablations removing generated queries or filters. Numbers in parentheses indicate performance decrease compared to the full PROBE approach.}\n","\\label{tab:recall_abalation}\n","\\end{table*}\n"],["\\begin{table}[h]\n","\\small\n","\\begin{tabular}{@{}clcc@{}}\n","\\toprule\n","\\# Conversation & Approach & Recall & NDCG@5 \\\\\n","\\midrule\n","\\multirow{3}{*}{5} & RAG & 0.01 & 0.5373 \\\\\n"," & PROBE & 0.07 & 0.6991 \\\\\n"," & Oracle & {\\ul 0.10} & {\\ul 0.7925} \\\\\\midrule\n","\\multirow{3}{*}{20} & RAG & 0.04 & 0.5897 \\\\\n"," & PROBE & 0.23 & 0.7624 \\\\\n"," & Oracle & {\\ul 0.34} & {\\ul 0.8540} \\\\\\midrule\n","\\multirow{3}{*}{50} & RAG & 0.07 & 0.6318 \\\\\n"," & PROBE & 0.35 & 0.7927 \\\\\n"," & Oracle & {\\ul 0.54} & {\\ul 0.8721} \\\\\\midrule\n","\\multirow{3}{*}{200} & RAG & 0.14 & 0.6858 \\\\\n"," & PROBE & 0.50 & 0.8202 \\\\\n"," & Oracle & {\\ul 0.75} & {\\ul 0.8942} \\\\\\midrule\n","\\multirow{3}{*}{500} & RAG & 0.20 & 0.7141 \\\\\n"," & PROBE & 0.58 & 0.8263 \\\\\n"," & Oracle & {\\ul 0.84} & {\\ul 0.9005} \\\\\\bottomrule\n","\\end{tabular}\n","\n","\\caption{NDCG@5 score of o4-mini with summarized conversations and different recall rate. \\underline{Underlined} is the best for specific number of conversations}\n","\\label{tab:recall_vs_ndcg_table}\n","\\end{table}\n"],["\\begin{figure}[!t]\n"," \\centering\n"," \\includegraphics[width=0.9\\linewidth]{figures/recall.pdf}\n"," \\caption{Recall of different retrieval approaches}\n"," \\label{fig:recall_ablation}\n","\\end{figure}\n"],["\\begin{table}[!t]\n","\\resizebox{\\linewidth}{!}{\n","\\begin{tabular}{@{}llcccc@{}}\n","\\toprule\n","Model Name & Ctx Type & NDCG@1 & NDCG@3 & NDCG@5 & NDCG@10 \\\\ \\midrule\n","\\multirow{2}{*}{Gemma3 4B} & Raw & 0.4815\t& 0.6057 & 0.6601 & 0.7703 \\\\\n"," & Summary & 0.5699\t& 0.6787 & 0.7235 & 0.8102 \\\\ \\midrule\n","\\multirow{2}{*}{Qwen3 8B Think} & Raw & 0.7359\t& 0.7991 & 0.8360 & 0.8894 \\\\\n"," & Summary & 0.7757 & 0.8268 & 0.8510 & 0.9003 \\\\ \\midrule\n","\\multirow{2}{*}{Qwen3 32B Think}& Raw & 0.7225 & 0.8044 & 0.8355 & 0.8897 \\\\\n"," & Summary & 0.8134 & 0.8605 & 0.8817 & 0.9199 \\\\ \\midrule\n","\\multirow{2}{*}{GPT-4.1-mini} & Raw & 0.7849\t& 0.8388 & 0.8667 & 0.9121 \\\\\n"," & Summary & 0.8130 & 0.8602 & 0.8816 & 0.9216 \\\\ \\midrule\n","\\multirow{2}{*}{o4-mini} & Raw & 0.8003 & 0.8456 & 0.8719 & 0.9185 \\\\\n"," & Summary & \\textbf{0.8478} & \\textbf{0.8793} & \\textbf{0.9005} & \\textbf{0.9347} \\\\\n","\\bottomrule\n","\\end{tabular}}\n","\\caption{Experimental results on aggregative question answering using oracle (ground-truth) documents as context.}\\label{tab:expr_oracle}\n","\\end{table}\n"],["\\begin{figure}[!t]\n"," \\centering\n"," \\includegraphics[width=0.85\\linewidth]{figures/oracle_ndcg_panels.pdf}\n","\\caption{NDCG@5 scores for different models given varying numbers of oracle (ground-truth) documents, comparing raw and summarized conversations.} \\label{fig:oracle_experiment}\n","\n","\\end{figure}\n"],["\\begin{figure}[!t]\n"," \\centering\n"," \\includegraphics[width=0.98\\linewidth]{figures/retrieved_ndcg_panels.pdf}\n","\\caption{Comparison of NDCG@5 scores for different models with varying numbers of retrieved documents.}\n","\\label{fig:retrieved_ndcg_comp}\n","\\end{figure}\n"],["\\begin{table}[!t]\n","\\centering\n","\\resizebox{\\linewidth}{!}{\n","\\begin{tabular}{@{}lcccc@{}}\n","\\toprule\n","\\textbf{Method} & \\textbf{NDCG@1} & \\textbf{NDCG@3} & \\textbf{NDCG@5} & \\textbf{NDCG@10} \\\\\n","\\midrule\n","Oracle & 0.72 & 0.79 & 0.82 & 0.88 \\\\\n","\\quad + thinking & 0.81 \\textcolor{gray}{(+0.09)} & 0.86 \\textcolor{gray}{(+0.07)} & 0.88 \\textcolor{gray}{(+0.06)} & 0.92 \\textcolor{gray}{(+0.04)} \\\\\n","\\addlinespace\\midrule\n","RAG (Summary) & 0.48 & 0.56 & 0.62 & 0.74 \\\\\n","\\quad + thinking & 0.54 \\textcolor{gray}{(+0.07)} & 0.62 \\textcolor{gray}{(+0.07)} & 0.68 \\textcolor{gray}{(+0.06)} & 0.78 \\textcolor{gray}{(+0.04)} \\\\\n","\\addlinespace \\midrule\n","PROBE (Summary) & 0.64 & 0.71 & 0.75 & 0.84 \\\\\n","\\quad + thinking & 0.68 \\textcolor{gray}{(+0.04)} & 0.76 \\textcolor{gray}{(+0.05)} & 0.80 \\textcolor{gray}{(+0.05)} & 0.86 \\textcolor{gray}{(+0.02)} \\\\\n","\\bottomrule\n","\\end{tabular}}\n","\\caption{NDCG scores of Qwen3 32B with and without reasoning (``think'' mode). Improvements from reasoning are indicated in parentheses.}\\label{tab:ablation_thinking}\n","\\end{table}\n"]],"string":"[\n [\n \"\\\\begin{figure}[!t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[trim={20px 20px 20px 20px},clip, width=0.99\\\\linewidth]{figures/intro_figure.png}\\n\",\n \" \\\\caption{Comparison of different aggregation paradigms: (a) summarization, (b) aggregation over structured databases, and (c) aggregation over large sets of conversations (our focus).} \\\\label{fig:intro}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{figure*}[h]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=0.94\\\\linewidth]{figures/data_construction_new.png}\\n\",\n \" \\\\caption{Overview of the WildChat-AQA dataset creation process.}\\n\",\n \" \\\\label{fig:data_creation}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{table}[!t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\small\\n\",\n \" \\\\begin{tabular}{@{}l@{ }cc@{ }c@{}}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Name} & \\\\textbf{Multi-Val} & \\\\textbf{Inferred} & \\\\textbf{Example} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Location & No & No & United States, Canada \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" User Name & No & No & lostclasp37, toughcue8\\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Time & No & No & 4/26/2023, 1:47:24 PM \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Language & No & No & English, Russian \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Topic & Yes & Yes & \\\\makecell{Software, Programming \\\\\\\\ and Computer Science} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Subtopic & Yes & Yes& \\\\makecell{Mobile Development, \\\\\\\\AI and ML} \\\\\\\\ \\n\",\n \" \\\\midrule\\n\",\n \" Keywords & Yes & Yes & C++, \\n\",\n \"Pokémon \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}%}\\n\",\n \" \\\\caption{Attributes used in WildChat-AQA. \\\\textbf{Multi-Val} indicates whether an attribute can have multiple values per conversation. \\\\textbf{Inferred} indicates whether the attribute must be inferred from conversation content (as opposed to being directly available from metadata). \\\\textbf{Example} shows representative attribute values.}\\n\",\n \" \\\\label{tab:att_intro}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{figure*}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\linewidth]{figures/PROBE.png}\\n\",\n \" \\\\caption{Overview of the PROBE retrieval approach}\\n\",\n \" \\\\label{fig:probe-overview}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{figure}[!t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\begin{subfigure}[b]{0.45\\\\textwidth}\\n\",\n \" \\\\includegraphics[trim={7px 5px 7px 5px},clip,width=1.0\\\\linewidth]{figures/conv_hist.pdf}\\n\",\n \" \\\\end{subfigure}\\n\",\n \" \\\\caption{Distribution of total token count and conversation count of supporting context}\\n\",\n \" \\\\label{fig:context_token_count}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table}[!t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\begin{tabular}{@{}lcc@{}}\\n\",\n \" \\\\toprule\\n\",\n \" Name & Human–Human $\\\\kappa$ & Human-Model $\\\\kappa$ \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Topic & 0.581 & 0.617 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Subtopic & 0.576 & 0.609 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}%\\n\",\n \"\\\\caption{Average Cohen's $\\\\kappa$ indicating agreement between human annotators (human-human) and between human annotations and model predictions (human-model).}\\n\",\n \"\\\\label{tab:human_eval}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table*}[!t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{0.92\\\\textwidth}{!}{\\n\",\n \"\\\\begin{tabular}{@{}l|l|lccccc@{}}\\n\",\n \"\\\\toprule\\n\",\n \"Model Name & Approach & Type & NDCG@1 & NDCG@3 & NDCG@5 & NDCG@10 & \\\\makecell{\\\\# Input Token \\\\\\\\(Million)} \\\\\\\\ \\\\midrule\\n\",\n \"Random & None & / & 0.2501\\t & 0.3516 & 0.4368 & 0.6211 & - \\\\\\\\ \\\\midrule\\n\",\n \"o4-mini & None & / & 0.3063\\t & 0.4017 & 0.4805 & 0.6488 & 0.87 \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{2}{*}{Qwen3 8B} & \\\\multirow{2}{*}{Finetune} & Raw & 0.2694 & 0.3739 & 0.4589 & 0.6346 & 1.74 \\\\\\\\\\n\",\n \" & & Summary & \\\\underline{0.2984}\\t & \\\\underline{0.3966}\\t & \\\\underline{0.4807}\\t & \\\\underline{0.6480} & 1.74 \\\\\\\\ \\\\midrule \\n\",\n \"\\\\multirow{4}{*}{Gemma3 4B} & \\\\multirow{2}{*}{RAG} & Raw & 0.3291 & 0.4356 & 0.5159 & 0.6688 & 73.48 \\\\\\\\\\n\",\n \" & & Summary & 0.3740 & 0.4895 & 0.5627 & 0.6991 & 174.62 \\\\\\\\\\n\",\n \" & \\\\multirow{2}{*}{PROBE} & Raw & 0.4766 & 0.5891 & 0.6478 & 0.7620 & 38.44 \\\\\\\\\\n\",\n \" & & Summary & \\\\underline{0.5430} & \\\\underline{0.6513} & \\\\underline{0.6994} & \\\\underline{0.7969} & 17.35 \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{\\\\makecell{Qwen3 8B \\\\\\\\Think}} & \\\\multirow{2}{*}{RAG} & Raw & 0.4168 & 0.5090 & 0.5779 & 0.7123 & 362.16 \\\\\\\\\\n\",\n \" & & Summary & 0.5273 & 0.6110 & 0.6646 & 0.7717 & 176.88 \\\\\\\\\\n\",\n \" & \\\\multirow{2}{*}{PROBE} & Raw & 0.6545 & 0.7305 & 0.7728 & 0.8483 & 315.52 \\\\\\\\\\n\",\n \" & & Summary & \\\\underline{0.6944} & \\\\underline{0.7638} & \\\\underline{0.8005} & \\\\underline{0.8660} & 123.04 \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{\\\\makecell{Qwen3 32B \\\\\\\\Think}} & \\\\multirow{2}{*}{RAG} & Raw & 0.4052 & 0.5020 & 0.5705 & 0.7081 & 182.90 \\\\\\\\\\n\",\n \" & & Summary & 0.5496 & 0.6321 & 0.6847 & 0.7850 & 176.88 \\\\\\\\\\n\",\n \" & \\\\multirow{2}{*}{PROBE} & Raw & 0.6525 & 0.7347 & 0.7759 & 0.8501 & 315.52 \\\\\\\\\\n\",\n \" & & Summary & \\\\underline{0.7056} & \\\\underline{0.7753} & \\\\underline{0.8114} & \\\\underline{0.8725} & 123.04 \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{GPT-4.1 mini} & \\\\multirow{2}{*}{RAG} & Raw & 0.4494 & 0.5387 & 0.6035 & 0.7299 & 344.37 \\\\\\\\\\n\",\n \" & & Summary & 0.5782 & 0.6620 & 0.7104 & 0.8019 & 154.31 \\\\\\\\\\n\",\n \" & \\\\multirow{2}{*}{PROBE} & Raw & 0.6806 & 0.7536 & 0.7936 & 0.8628 & 298.69 \\\\\\\\\\n\",\n \" & & Summary & \\\\underline{0.7308} & \\\\underline{0.7942} & \\\\underline{0.8282} & \\\\underline{0.8843} & 107.11 \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{o4-mini} & \\\\multirow{2}{*}{RAG} & Raw & 0.4730 & 0.5510 & 0.6116 & 0.7383 & 344.37 \\\\\\\\\\n\",\n \" & & Summary & 0.6122 & 0.6792\\t & 0.7242 & 0.8140 & 154.31 \\\\\\\\\\n\",\n \" & \\\\multirow{2}{*}{PROBE} & Raw & 0.7117 & 0.7747 & 0.8086 & 0.8745 & 298.69 \\\\\\\\\\n\",\n \" & & Summary & \\\\textbf{\\\\underline{0.7571}} & \\\\textbf{\\\\underline{0.8095}}\\t& \\\\textbf{\\\\underline{0.8386}}\\t& \\\\textbf{\\\\underline{0.8930}} & 107.11 \\\\\\\\ \\\\bottomrule\\n\",\n \"\\\\end{tabular}}\\n\",\n \"\\n\",\n \"\\\\caption{Experimental results of different models using various retrieval approaches and conversation formats (raw vs. summarized). \\\\underline{Underlined} scores indicate the best results for each model, and \\\\textbf{bold} scores indicate the best overall results.}\\\\label{tab:expr_main}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[h]\\n\",\n \"\\\\centering\\n\",\n \"\\\\small\\n\",\n \"\\\\begin{tabular}{@{}lccccccc@{}}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Method} & \\\\textbf{R@5} & \\\\textbf{R@10} & \\\\textbf{R@20} & \\\\textbf{R@50} & \\\\textbf{R@100} & \\\\textbf{R@200} & \\\\textbf{R@500} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"RAG-Dense & 0.01 & 0.02\\t & 0.04\\t & 0.07\\t & 0.10 &\\t0.14 & 0.21 \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"PROBE-Dense & 0.07 & 0.13 & 0.23 & 0.35 & 0.43 & 0.50 & 0.58 \\\\\\\\\\n\",\n \"\\\\quad - filter only & 0.05 \\\\textcolor{gray}{ (-0.02)} & 0.09 \\\\textcolor{gray}{ (-0.04)} & 0.16 \\\\textcolor{gray}{ (-0.07)} & 0.24 \\\\textcolor{gray}{ (-0.11)} & 0.29 \\\\textcolor{gray}{ (-0.14)} & 0.33 \\\\textcolor{gray}{ (-0.17)} & 0.40 \\\\textcolor{gray}{ (-0.18)} \\\\\\\\\\n\",\n \"\\\\quad - question \\\\& filter & 0.06 \\\\textcolor{gray}{ (-0.01)} & 0.12 \\\\textcolor{gray}{ (-0.01)} & 0.21 \\\\textcolor{gray}{ (-0.02)} & 0.32 \\\\textcolor{gray}{ (-0.03)} & 0.40 \\\\textcolor{gray}{ (-0.03)} & 0.46 \\\\textcolor{gray}{ (-0.04)} & 0.53 \\\\textcolor{gray}{ (-0.05)} \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\caption{Recall@k of PROBE-Dense (Summary) with ablations removing generated queries or filters. Numbers in parentheses indicate performance decrease compared to the full PROBE approach.}\\n\",\n \"\\\\label{tab:recall_abalation}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table}[h]\\n\",\n \"\\\\small\\n\",\n \"\\\\begin{tabular}{@{}clcc@{}}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\# Conversation & Approach & Recall & NDCG@5 \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multirow{3}{*}{5} & RAG & 0.01 & 0.5373 \\\\\\\\\\n\",\n \" & PROBE & 0.07 & 0.6991 \\\\\\\\\\n\",\n \" & Oracle & {\\\\ul 0.10} & {\\\\ul 0.7925} \\\\\\\\\\\\midrule\\n\",\n \"\\\\multirow{3}{*}{20} & RAG & 0.04 & 0.5897 \\\\\\\\\\n\",\n \" & PROBE & 0.23 & 0.7624 \\\\\\\\\\n\",\n \" & Oracle & {\\\\ul 0.34} & {\\\\ul 0.8540} \\\\\\\\\\\\midrule\\n\",\n \"\\\\multirow{3}{*}{50} & RAG & 0.07 & 0.6318 \\\\\\\\\\n\",\n \" & PROBE & 0.35 & 0.7927 \\\\\\\\\\n\",\n \" & Oracle & {\\\\ul 0.54} & {\\\\ul 0.8721} \\\\\\\\\\\\midrule\\n\",\n \"\\\\multirow{3}{*}{200} & RAG & 0.14 & 0.6858 \\\\\\\\\\n\",\n \" & PROBE & 0.50 & 0.8202 \\\\\\\\\\n\",\n \" & Oracle & {\\\\ul 0.75} & {\\\\ul 0.8942} \\\\\\\\\\\\midrule\\n\",\n \"\\\\multirow{3}{*}{500} & RAG & 0.20 & 0.7141 \\\\\\\\\\n\",\n \" & PROBE & 0.58 & 0.8263 \\\\\\\\\\n\",\n \" & Oracle & {\\\\ul 0.84} & {\\\\ul 0.9005} \\\\\\\\\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\n\",\n \"\\\\caption{NDCG@5 score of o4-mini with summarized conversations and different recall rate. \\\\underline{Underlined} is the best for specific number of conversations}\\n\",\n \"\\\\label{tab:recall_vs_ndcg_table}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{figure}[!t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=0.9\\\\linewidth]{figures/recall.pdf}\\n\",\n \" \\\\caption{Recall of different retrieval approaches}\\n\",\n \" \\\\label{fig:recall_ablation}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table}[!t]\\n\",\n \"\\\\resizebox{\\\\linewidth}{!}{\\n\",\n \"\\\\begin{tabular}{@{}llcccc@{}}\\n\",\n \"\\\\toprule\\n\",\n \"Model Name & Ctx Type & NDCG@1 & NDCG@3 & NDCG@5 & NDCG@10 \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{2}{*}{Gemma3 4B} & Raw & 0.4815\\t& 0.6057 & 0.6601 & 0.7703 \\\\\\\\\\n\",\n \" & Summary & 0.5699\\t& 0.6787 & 0.7235 & 0.8102 \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{2}{*}{Qwen3 8B Think} & Raw & 0.7359\\t& 0.7991 & 0.8360 & 0.8894 \\\\\\\\\\n\",\n \" & Summary & 0.7757 & 0.8268 & 0.8510 & 0.9003 \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{2}{*}{Qwen3 32B Think}& Raw & 0.7225 & 0.8044 & 0.8355 & 0.8897 \\\\\\\\\\n\",\n \" & Summary & 0.8134 & 0.8605 & 0.8817 & 0.9199 \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{2}{*}{GPT-4.1-mini} & Raw & 0.7849\\t& 0.8388 & 0.8667 & 0.9121 \\\\\\\\\\n\",\n \" & Summary & 0.8130 & 0.8602 & 0.8816 & 0.9216 \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{2}{*}{o4-mini} & Raw & 0.8003 & 0.8456 & 0.8719 & 0.9185 \\\\\\\\\\n\",\n \" & Summary & \\\\textbf{0.8478} & \\\\textbf{0.8793} & \\\\textbf{0.9005} & \\\\textbf{0.9347} \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}}\\n\",\n \"\\\\caption{Experimental results on aggregative question answering using oracle (ground-truth) documents as context.}\\\\label{tab:expr_oracle}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{figure}[!t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=0.85\\\\linewidth]{figures/oracle_ndcg_panels.pdf}\\n\",\n \"\\\\caption{NDCG@5 scores for different models given varying numbers of oracle (ground-truth) documents, comparing raw and summarized conversations.} \\\\label{fig:oracle_experiment}\\n\",\n \"\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{figure}[!t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=0.98\\\\linewidth]{figures/retrieved_ndcg_panels.pdf}\\n\",\n \"\\\\caption{Comparison of NDCG@5 scores for different models with varying numbers of retrieved documents.}\\n\",\n \"\\\\label{fig:retrieved_ndcg_comp}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table}[!t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\linewidth}{!}{\\n\",\n \"\\\\begin{tabular}{@{}lcccc@{}}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Method} & \\\\textbf{NDCG@1} & \\\\textbf{NDCG@3} & \\\\textbf{NDCG@5} & \\\\textbf{NDCG@10} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"Oracle & 0.72 & 0.79 & 0.82 & 0.88 \\\\\\\\\\n\",\n \"\\\\quad + thinking & 0.81 \\\\textcolor{gray}{(+0.09)} & 0.86 \\\\textcolor{gray}{(+0.07)} & 0.88 \\\\textcolor{gray}{(+0.06)} & 0.92 \\\\textcolor{gray}{(+0.04)} \\\\\\\\\\n\",\n \"\\\\addlinespace\\\\midrule\\n\",\n \"RAG (Summary) & 0.48 & 0.56 & 0.62 & 0.74 \\\\\\\\\\n\",\n \"\\\\quad + thinking & 0.54 \\\\textcolor{gray}{(+0.07)} & 0.62 \\\\textcolor{gray}{(+0.07)} & 0.68 \\\\textcolor{gray}{(+0.06)} & 0.78 \\\\textcolor{gray}{(+0.04)} \\\\\\\\\\n\",\n \"\\\\addlinespace \\\\midrule\\n\",\n \"PROBE (Summary) & 0.64 & 0.71 & 0.75 & 0.84 \\\\\\\\\\n\",\n \"\\\\quad + thinking & 0.68 \\\\textcolor{gray}{(+0.04)} & 0.76 \\\\textcolor{gray}{(+0.05)} & 0.80 \\\\textcolor{gray}{(+0.05)} & 0.86 \\\\textcolor{gray}{(+0.02)} \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}}\\n\",\n \"\\\\caption{NDCG scores of Qwen3 32B with and without reasoning (``think'' mode). Improvements from reasoning are indicated in parentheses.}\\\\label{tab:ablation_thinking}\\n\",\n \"\\\\end{table}\\n\"\n ]\n]","isConversational":false},"fulltext":{"kind":"string","value":"% This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.\n\\pdfoutput=1\n% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.\n\n\\documentclass[11pt]{article}\n\n% Change \"review\" to \"final\" to generate the final (sometimes called camera-ready) version.\n% Change to \"preprint\" to generate a non-anonymous version with page numbers.\n\\usepackage[preprint]{acl}\n\n% Standard package includes\n\\usepackage{times}\n\\usepackage{latexsym}\n\\usepackage{hyperref}\n\n% For proper rendering and hyphenation of words containing Latin characters (including in bib files)\n\\usepackage[T1]{fontenc}\n% For Vietnamese characters\n% \\usepackage[T5]{fontenc}\n% See https://www.latex-project.org/help/documentation/encguide.pdf for other character sets\n\n% This assumes your files are encoded as UTF8\n\\usepackage[utf8]{inputenc}\n\n% This is not strictly necessary, and may be commented out,\n% but it will improve the layout of the manuscript,\n% and will typically save some space.\n\\usepackage{microtype}\n\n% This is also not strictly necessary, and may be commented out.\n% However, it will improve the aesthetics of text in\n% the typewriter font.\n\\usepackage{inconsolata}\n\n%Including images in your LaTeX document requires adding\n%additional package(s)\n\\usepackage{graphicx}\n\\usepackage{booktabs}\n\\usepackage{multirow}\n\\usepackage{subcaption}\n\\usepackage{makecell}\n\\usepackage{caption}\n\\usepackage{float}\n\\usepackage{amsmath}\n\\usepackage{xcolor} \n\\usepackage{tabularx}\n\\usepackage{tcolorbox}\n\\usepackage[frozencache,cachedir=minted-cache]{minted}\n\\usepackage{algorithm}\n\\usepackage{algpseudocode}\n\\usepackage{xspace}\n\\usepackage{stfloats}\n\\usepackage{longtable}\n\\usepackage[nameinlink]{cleveref}\n\\usepackage[normalem]{ulem}\n\\useunder{\\uline}{\\ul}{}\n\n% If the title and author information does not fit in the area allocated, uncomment the following\n%\n%\\setlength\\titlebox{<dim>}\n%\n% and set <dim> to something 5cm or larger.\n\\newcommand{\\name}{WildChat-AQA\\xspace}\n\n%\\title{\\name: Aggregative Question Answering}\n\\title{From Chat Logs to Collective Insights:\\\\ Aggregative Question Answering}\n\n\\author{Wentao Zhang \\\\\n University of Waterloo \\\\\n \\texttt{w564zhan@uwaterloo.ca} \\\\\\And\n Woojeong Kim \\\\\n Cornell University \\\\\n \\texttt{wk247@cornell.edu} \\\\\\And\n Yuntian Deng \\\\\n University of Waterloo \\\\\n \\texttt{yuntian@uwaterloo.ca} \\\\}\n\n\n\\begin{document}\n\\maketitle\n\\begin{abstract}\nConversational agents powered by large language models (LLMs) are rapidly becoming integral to our daily interactions, generating unprecedented amounts of conversational data. Such datasets offer a powerful lens into societal interests, trending topics, and collective concerns. Yet, existing approaches typically treat these interactions as independent and miss critical insights that could emerge from aggregating and reasoning across large-scale conversation logs. In this paper, we introduce Aggregative Question Answering, a novel task requiring models to reason explicitly over thousands of user-chatbot interactions to answer aggregative queries, such as identifying emerging concerns among specific demographics. To enable research in this direction, we construct a benchmark, WildChat-AQA, comprising 6,027 aggregative questions derived from 182,330 real-world chatbot conversations. Experiments show that existing methods either struggle to reason effectively or incur prohibitive computational costs, underscoring the need for new approaches capable of extracting collective insights from large-scale conversational data.\n\n\n %In this work, we present a new task called aggregated question answering, which requires models to derive high-level aggregated information such as trending topic or popular franchise based on given conditions within a large numbers of unstructured documents to answer the question. We introduce WildChat-Aggregated Question Answering benchmark (\\textbf{WildChat-AQA}) based on real world dataset WildChat. The dataset consists of \\textbf{6027} questions over \\textbf{182330} conversations with \\textbf{208 million} tokens. We describe the detailed creation process of the benchmark, evaluate the Retrieval Augmented Generation (RAG) and finetuned models. We found that all existing approaches are either cost-intensive or inaccurate. We believe \\name establishes an essential resource for future research on deriving \n%high-level aggregated information from large-scale datasets. We evaluate existing data comprehension approaches, including RAG and fine-tuning do not perform well on the aggregative question answering task. We propose a simple but effective approach \\textbf{PROBE} to improve the performance. We also build a user-friendly dataset visualization platform which is available at \\href{http://65.108.32.135:3000/dataview}{http://65.108.32.135:3000/dataview}\n\\end{abstract}\n\n\\section{Introduction}\n\n%We also build a user-friendly dataset visualization platform which is available at \\href{http://65.108.32.135:3000/dataview}{http://65.108.32.135:3000/dataview} TODO: mention this in intro\n% why we use language models for this task instead of directly using databases --- more flexible or ambiguous\nRapid adoption of conversation agents powered by large language models (LLMs) is transforming human-computer interactions, integrating deeply into society, and generating unprecedented volumes of conversational data~\\citep{backlinko_chatgpt_stats_2025, DeVynck_2023}. Platforms using LLM-based chatbots now routinely handle millions of interactions daily, producing rich datasets that capture real-time dialogues reflecting genuine user interests, emerging societal trends, and collective concerns~\\citep{zhao2024wildchat, zheng2024lmsyschatm}. Such conversational data offer immense potential for deriving insights at scale, revealing patterns in societal dynamics, shifts in public sentiment, and demographic-specific concerns.\n\n%Deriving high level aggregated information from large corpus based on queries and questions is a valuable and long lasting important problem in textual analysis and natural language understanding. For example, given a instructional finetuning dataset such as WildChat~\\citep{zhao2024wildchat} and LMSYS-Chat-1M~\\citep{zheng2024lmsyschatm}, researchers may ask questions like: \\textit{``What are some popular topic during the first week of April ?''} or \\textit{``Who are some active users that talks about computer science and software ?''}. These questions are asking about high-level aggregated information over a dataset instead of detailed, local, and factual information. \n \n\\begin{figure}[!t]\n \\centering\n \\includegraphics[trim={20px 20px 20px 20px},clip, width=0.99\\linewidth]{figures/intro_figure.png}\n \\caption{Comparison of different aggregation paradigms: (a) summarization, (b) aggregation over structured databases, and (c) aggregation over large sets of conversations (our focus).} \\label{fig:intro}\n\\end{figure}\n\n%Two popular existing aggregation paradigm are summarization and aggregation from the database. Figure \\ref{fig:intro} illustrate the difference between these two and aggregation over documents. Summarization is the most straightforward way to derive aggregated information from documents, but it only applies to one or several documents and generate low-level fine-grained information. Querying database can aggregate information well, but it requires and is limited to label or meta information of the data. Other existing popular approaches include performing counting or statistics to find popular words or phrases, and text clustering using strong text embedding models \\cite{zhang-etal-2021-supporting, grootendorst2022bertopic}, but they fail to provide information based on the question and require great effort the answer a single question.\n\nDespite the inherent richness of these conversational datasets, current research typically treats interactions as isolated, independent data points, primarily using them to finetune LLMs for generating improved individual responses~\\citep{vicuna2023, lambert2025tulu3pushingfrontiers, zhang2025bestinstructiontuningdatafit}. This independent and identically distributed (i.i.d.) assumption overlooks important temporal patterns and thematic connections that naturally arise from large-scale, real-world user-chatbot conversations. Conversations do not occur in isolation, but rather within specific temporal, geographical, and device-related contexts~\\citep{tamkin2024clioprivacypreservinginsightsrealworld}. These contextual features carry significant potential for deriving collective insights, such as understanding regional differences in user concerns or identifying temporal shifts in societal attitudes---insights which are lost under the simplifying i.i.d. assumption.\n\n%In real world, data are usually large, unstructured and have no predefined label or database indexing. This makes it challenging to answer questions about aggregated information. Existing question answering benchmarks, such as Hotpot QA \\cite{yang2018hotpotqa}, mainly focus on seeking \\textbf{local and factual information} based on one or several documents. Questions querying about globally aggregated information are usually asked in text to database query and table benchmark, such as WikiSQL \\cite{zhongSeq2SQL2017}, Spider \\cite{lei2024spider}, BIRD \\cite{li2024can}, and WikiTableQA \\cite{pasupat-liang-2015-compositional}. Data in these benchmarks are usually structured and numerical or categorical, which are easier for database to process. Therefore, it is important to research on answering aggregated question over large scale unstructured document since answering such kind of questions can be useful and meaningful but there lack necessary benchmark and solution to this problem. \n\nTo address this gap, we introduce a new task, Aggregative Question Answering, which requires reasoning across large-scale collections of user-chatbot interactions to extract aggregative insights. Unlike traditional summarization, which condenses information from one or a few documents into static summaries, Aggregative Question Answering generates dynamic answers that explicitly depend on the specific aggregative query posed. The task requires holistic reasoning over thousands of conversations to answer questions such as identifying trending topics within specific timeframes (\\textit{What topics trended last week?}''), emerging concerns among particular demographics (\\textit{What topics are Californians concerned about before an election?}''), or tracking changes in societal sentiment (``\\textit{How have users' attitudes toward artificial intelligence evolved this month?}''). The core challenge thus lies not in summarizing individual conversations, but rather in global-scale reasoning conditioned on the query. \\Cref{fig:intro} highlights the high-level distinctions between traditional summarization, aggregative question answering, and querying predefined databases.\n\n%To address this gap, we introduce a new task, Aggregative Question Answering, which requires reasoning across large-scale collections of user-chatbot interactions to extract aggregational insights. Unlike traditional summarization, which condenses information from one or a few documents, Aggregative Question Answering requires holistic reasoning over thousands or potentially hundreds of thousands of conversations. Example queries include identifying trending topics within specific timeframes (``\\textit{What topics trended last week?}''), emerging concerns among particular demographics (``\\textit{What topics are Californians concerned about before an election?}''), or tracking changes in societal sentiment (``\\textit{How have users' attitudes toward artificial intelligence evolved this month?}''). The core challenge thus lies not in summarizing individual conversations but rather in global-scale reasoning. \\Cref{fig:intro} highlights the high-level distinctions between traditional document summarization, aggregative question answering, and querying predefined databases.\n%To formulate the problem and evaluate existing approach on this aggregative question answering task, we first construct an aggregated question answering benchmark called \\textbf{WildChat-AQA}, which conditions and asks about hidden or explicit attributes of conversations between users and AI chatbots derived from WildChat. The attributes that we consider are topics, subtopics, keywords, location of users, language used in conversation, time in granularity of week, and names of users. Each question has 0 to 3 of the conditions and asking about the aggregated information of one of the remaining target attributes. The model or system are given the question, the entire corpus and a list of candidates, and they are required to output a ranking of these candidate attribute values based on the the frequency of them within all ground truth supporting context. \n\nTo facilitate research into Aggregative Question Answering, we introduce a benchmark WildChat-AQA, constructed from the WildChat dataset~\\citep{zhao2024wildchat,deng-etal-2024-wildvis}. WildChat captures not only conversation transcripts but also metadata such as temporal, geographical, and user-specific information. WildChat-AQA formulates aggregational queries about both explicit and implicit attributes of conversations---including topics, keywords, geographical locations, and time---in a multi-choice format. A concrete example of the data creation process is shown in \\Cref{fig:data_creation}. The benchmark comprises 6,027 aggregative questions derived from 182,330 real-world user-chatbot conversations, reflecting genuine user interests and societal trends, thus providing a resource for evaluating models' ability to reason holistically at scale.\n%\\footnote{Commercial chatbot providers such as OpenAI have access to even richer user metadata.}\n%Our evaluation including end-to-end tests of the performance of existing data comprehension approaches including finetuning and retrieval augmented genreation (RAG). To further improve the performance of the task, we proposed a simple yet effective retrieval augmented generation framework \\textbf{P}robing \\textbf{R}etrieval \\textbf{O}f \\textbf{B}road \\textbf{E}vidence (\\textbf{PROBE}), which lets the large language model to generate filter and diverse query based on the question description, and then query the database with generated probing query to get broad supporting evidence that supports the question. \n\nWe evaluate current methods, including both non-reasoning and reasoning models, adapted to this task via fine-tuning, retrieval-augmented generation (RAG), and a customized retrieval approach developed specifically for aggregative reasoning: {PROBE} ({P}robing {R}etrieval {O}f {B}road {E}vidence). Experimental results reveal substantial limitations in existing methods: current systems either struggle to reason effectively at scale or incur prohibitive computational costs. Even under idealized conditions---when the exact oracle contexts relevant to a query are provided---significant room for improvement remains; under realistic conditions, the performance further deteriorates.\n\n\\begin{figure*}[h]\n \\centering\n \\includegraphics[width=0.94\\linewidth]{figures/data_construction_new.png}\n \\caption{Overview of the WildChat-AQA dataset creation process.}\n \\label{fig:data_creation}\n %\\vspace*{-0.6\\baselineskip}\n\\end{figure*}\n\nOur findings emphasize the critical need for more scalable and effective methods capable of extracting collective insights from large-scale conversational datasets. While Aggregative Question Answering opens promising avenues for impactful real-world analytics, we acknowledge potential societal impacts, particularly when insights relate to sensitive topics like elections, public opinion, or public health---areas that could potentially be susceptible to manipulation. Nevertheless, we strongly believe that transparent, open academic research fosters responsible development and deployment of such powerful technologies. By introducing Aggregative Question Answering as a new task, we aim to spur future methods that fully harness the potential of large-scale conversational data, ultimately enabling deeper societal understanding and more responsive, informed applications of LLMs.\n\nOur benchmark, code, and dataset are publicly available at \\url{https://github.com/yuntian-group/wildchat_aqa}, and we also provide a user-friendly benchmark visualization tool at \\url{http://65.108.32.135:3000/dataview}.\n\n %In conclusion, our contributions are as follows:\n %\\begin{itemize}\n % \\item We propose aggregative question answering task, formulated as a candidate ranking problem. \n\n % \\item We carefully construct a dataset of aggregative question answering \\name based on WildChat dataset focus on aggregative question answering toward attributes of different type and granularity. \n\n % \\item We evaluate and analyze the current approaches on \\name, including retrieval augmented generation and finetuning.\n\n % \\item We proposed a new simple yet effective approach named \\textbf{PROBE} and reached \\textbf{state-of-the-art} performance of the dataset.\n %\\end{itemize}\n% More recently, works such as GraphRAG \\citep{edge2025localglobalgraphrag}, start to tackle the problem of global or high level insights derivation over many documents via extracting entities and relationship between entities and building graph based on the entities and relationships. The large language models build up the community report from local to global to aggregate information. The methodology heavily rely on the entities that are repeatedly referred and relationship between those entities. \n\n% All of methods and benchmarks mentioned above relies on entities, exact factual information and pre-built database tables. However, real world data are usually long noisy and unstructured text without index, no current benchmark or system focus on aggregative question answering over unstructured dataset. For example, given a instructional finetuning dataset such as WildChat \\cite{zhao2024wildchat}, researchers may ask questions like: \\textit{``What are some popular topic among users using Spanish ?''} and \\textit{``What kind of computer and software related query the users are asking ?''}. Given the fact that few previous work research on this kind of question answering and no benchmark exist, we first introduce our \\textbf{WildChat-AQA} as benchmark that evaluate different systems' performance.\n\n\n\n\n\n\\section{WildChat-AQA}\nTo support research on Aggregative Question Answering, we constructed the WildChat-AQA benchmark based on the WildChat dataset~\\citep{zhao2024wildchat,deng-etal-2024-wildvis}. WildChat provides real-world conversations between users and chatbots, along with basic metadata such as timestamps and user locations. In this work, we extended these attributes by introducing additional attributes such as topics and keywords inferred from the conversation text using LLMs. These inferred attributes serve as the ground-truth annotations for constructing our benchmark. At evaluation time, models must infer them from conversations to answer aggregative questions. \\Cref{tab:att_intro} summarizes the attributes and indicates which ones require inference and which ones are directly available.\n\n\n\\begin{table}[!t]\n \\centering\n \\small\n %\\resizebox{.45\\textwidth}{!}{\n \\begin{tabular}{@{}l@{ }cc@{ }c@{}}\n \\toprule\n \\textbf{Name} & \\textbf{Multi-Val} & \\textbf{Inferred} & \\textbf{Example} \\\\\n \\midrule\n Location & No & No & United States, Canada \\\\\n \\midrule\n User Name & No & No & lostclasp37, toughcue8\\\\\n \\midrule\n Time & No & No & 4/26/2023, 1:47:24 PM \\\\\n \\midrule\n Language & No & No & English, Russian \\\\\n \\midrule\n Topic & Yes & Yes & \\makecell{Software, Programming \\\\ and Computer Science} \\\\\n \\midrule\n Subtopic & Yes & Yes& \\makecell{Mobile Development, \\\\AI and ML} \\\\ \n \\midrule\n Keywords & Yes & Yes & C++, \nPokémon \\\\\n \\bottomrule\n \\end{tabular}%}\n \\caption{Attributes used in WildChat-AQA. \\textbf{Multi-Val} indicates whether an attribute can have multiple values per conversation. \\textbf{Inferred} indicates whether the attribute must be inferred from conversation content (as opposed to being directly available from metadata). \\textbf{Example} shows representative attribute values.}\n \\label{tab:att_intro}\n %\\vspace*{-0.6\\baselineskip}\n\\end{table}\n\\subsection{Dataset Construction}\n\nThe construction of WildChat-AQA involved four main steps, as illustrated in \\Cref{fig:data_creation}:\n%The dataset is constructed based on attributes derived or inferred from WildChat, which is a real-world user-chatbot interaction dataset. Table \\ref{tab:att_intro} illustrates the detailed information of the various attributes extracted from WildChat. Location, user name and time are naturally available, while language, topic and subtopic is implicitly contained in the textual data. Keywords is a special case, sometimes they explicitly appear in the document, for example, a document directly mentions ``Pokémon''. It can also be implicit, such as a document contains a C++ code segment, and then it has the keyword ``C++''.\\name is constructed through 4 major steps. The process of data creation is illustrated in Figure \\ref{fig:data_creation}.\n\n\\paragraph{Step 1: Preprocessing} We began by performing minHash-based deduplication~\\citep{huggingface_dedup_2023} to remove highly similar conversations, ensuring diversity. We also filtered conversations exceeding 4,096 tokens to maintain manageable context lengths. Additionally, we retained only active users (those with at least 10 interactions) to ensure sufficient user-specific data. We also generated user IDs from IP addresses and headers.\n\n%We first applied minHash-based deduplication~\\citep{huggingface_dedup_2023} to remove highly similar conversations. Conversations exceeding 4096 tokens were filtered out to ensure manageable context lengths. To guarantee sufficient data quality, we retained only active users (those with at least 10 conversations). User IDs were anonymized by hashing IP addresses and header information.\n\n%We used minHash-based \\cite{huggingface_dedup_2023} de-duplication to first remove highly similar prompts, and filtered the conversation by length so that all conversations have no greater than 4096 tokens. We also filter users by activeness so that we only keep users with 10 or more conversations. The user IDs are randomly generated based on users' hashed IP addresses and header information. \n\\paragraph{Step 2: Topic Discovery} To support meaningful aggregative queries, we prompted GPT-4o to summarize each conversation and extract relevant keywords. Using these summaries, we recursively applied TnT-LLM~\\citep{wan2024tntllmtextminingscale} to infer hierarchical topics at two levels: coarse-grained topics and fine-grained subtopics. Detailed prompts and examples can be found in \\Cref{appendix:data_creation}.\n%We prompt GPT-4o to extract keywords and summarize the conversation into short text. Using the summary, we run TnT-LLM \\cite{wan2024tntllmtextminingscale} recursively to discover topics of each conversation in two levels, a coarse-grained topic and a fine-grained topic. Detailed prompts and results can be found in \\Cref{appendix:data_creation}. \n\n\\paragraph{Step 3: Keywords Categorization} Certain subtopics, such as ``Programming'' and ``Fan-fiction and Crossover,'' contained many conversations. To support finer-grained aggregative queries, we further categorized keywords inferred from conversations into higher-level categories using LLMs so that we can derive aggregative information. For example, different Pokémon-related keywords (versions, characters, trademarks) were grouped into a single category ``Pokémon''. Full details of this procedure are also available in \\Cref{appendix:data_creation}.\n\n% Certain subtopics like \"Programming\" and \"Fan-fiction and Crossover\" exhibited a large volume of conversations. To allow more fine-grained analysis and meaningful aggregational questions, we further prompted LLMs to categorize extracted keywords into higher-level concepts within four predefined categories: \"programming language,\" \"creative artwork,\" \"public figure,\" and \"book.\" For example, related entities (different Pokémon versions, characters, or trademarks) were grouped under a unified, higher-level keyword. Details are in Appendix~\\ref{appendix:data_creation}.\n%After the topic assignment, we found some sub-topics such as ``Programming'' and ``Fan-fiction and Crossover'' which contain a lot of data samples. To raise more fine-grained questions, we further prompt LLMs to categorize keywords into higher-level keywords within four types: ``programming language'', ``creative art work'', ``public figure'', and ``book''. One example is shown in Figure \\ref{fig:data_creation}, different version, characters, and trademark under the same franchise are aggregated into one categorized keyword. Detailed procedure and steps can be found in \\Cref{appendix:data_creation}. \n\n\\paragraph{Step 4: Question Generation} Finally, we generated aggregative questions using combinations of attributes stored in our constructed database. We systematically sampled attribute combinations (from zero to three attributes as conditions) to query our database for a remaining target attribute. To ensure questions were both diverse and answerable, attribute combinations were carefully sampled. These structured queries were then translated into natural language using GPT-4.1. %Examples and the full translation procedure are provided in \\Cref{appendix:data_creation}.\n\n\n %After building a database with both metadata and inferred attributes, we systematically sampled attribute combinations (0 to 3 attributes per query) and queried the database to determine a suitable target attribute not already included in the conditions. These attribute combinations were carefully selected to ensure both diversity and answerability. The resulting structured database queries were then translated into natural-language questions using GPT-4.1. Additional details and examples of the question generation process are provided in Appendix~\\ref{appendix:data_creation}.\n% After previous steps, we can explicitly build a database of conversation that contains attributes or columns shown in Table \\ref{tab:att_intro}. Based on database consists of these attributes, we query the database in a brute force way given 0, 1, 2, 3 attributes to get the target distribution which is also one of attributes that is not in the condition attributes. After brute force search, we bucketize the data samples by target attribute value. Also, within each bucket, we sort the data sample by entropy to ensure the questions generated finally is answerable. Finally we sample question proposals consists of condition attributes and target attributes in a round-robin way from each bucket to ensure both diversity and answerability. The question proposals are finally translated into natural language faithfully. In this work, we prompt GPT-4.1 to translate a given condition and target attribute pair into question. Details about the question proposal and natural language question generation can found in appendix \\ref{appendix:data_creation}.\n\n%Through previous steps, we can build a database of conversation that contains attributes in \\Cref{tab:att_intro}. Based on the database that consists of these attributes, we query the database in a brute force way, given 0 to 3 attributes, to get the target attribute that is not in the condition attributes. We sample combinations of condition attribute values corresponding to selected attribute types to ensure both diversity and answerability. Finally, we translate the database query into natural language using GPT-4.1. Details about the question proposal and natural language question generation can be found in \\Cref{appendix:data_creation}.\n\n\n\n\\subsection{Dataset Statistics}\n\n\\begin{figure*}[t]\n \\centering\n \\includegraphics[width=\\linewidth]{figures/PROBE.png}\n \\caption{Overview of the PROBE retrieval approach}\n \\label{fig:probe-overview}\n%\\vspace*{-0.6\\baselineskip}\n\\end{figure*}\n\n%We collect a dataset containing 182,330 conversations between the user and the chatbot, and an aggregative question answering benchmark with 6,027 questions. The dataset is categorized into 28 topics and 455 subtopics, and contains 14482 unique aggregated keywords. \nThe resulting WildChat-AQA benchmark contains 182,330 user-chatbot conversations and 6,027 aggregative questions. These conversations cover 28 high-level topics, 455 fine-grained subtopics, and 14,482 keyword categories. %Each aggregative question is presented along with 10 candidate answers, and models are required to rank these candidates based on their relevance to the question and the provided context. \n Detailed statistics of questions organized by different attribute conditions and target attributes are shown in \\Cref{tab:data_question_stat} in \\Cref{sec:appendix_data_profile}. Unlike typical question-answering tasks, which derive answers from one or a few documents, WildChat-AQA requires models to reason over contexts whose total token counts range widely from $\\mathbf{10^1}$ to $\\mathbf{10^8}$ tokens. \\Cref{fig:context_token_count} illustrates the distribution of context token counts. Full data statistics are provided in \\Cref{sec:appendix_data_profile}.\n\n%The model is given 10 options per question and queried to rank the options by relevance or correctness based on the question and supporting context. \\Cref{tab:data_question_stat} in \\Cref{sec:appendix_data_profile} shows the statistics of questions based on different condition attributes and asking for different target attributes. Unlike typical question answering tasks, which can derive the answer based on one or several supporting documents, \\name requires the model to understand supporting documents whose total token count ranged from $\\mathbf{10^1}$ tokens to $\\mathbf{10^8}$ tokens. \\Cref{fig:context_token_count} illustrates the distribution of context token counts in $\\log_{10}$ scale. More statistics can be found in \\Cref{sec:appendix_data_profile}. \n\n\\begin{figure}[!t]\n \\centering\n \\begin{subfigure}[b]{0.45\\textwidth}\n \\includegraphics[trim={7px 5px 7px 5px},clip,width=1.0\\linewidth]{figures/conv_hist.pdf}\n \\end{subfigure}\n \\caption{Distribution of total token count and conversation count of supporting context}\n \\label{fig:context_token_count}\n %\\vspace*{-0.6\\baselineskip}\n\\end{figure}\n\n% \\begin{figure}[ht]\n% \\centering\n% \\includegraphics[width=0.5\\linewidth]{figures/topic_distribution_top_10.pdf}\n% \\caption{Caption}\n% \\label{fig:enter-label}\n% \\end{figure}\n\n\n\\subsection{Evaluation Protocol}\nWe frame the evaluation of aggregative question answering as a ranking problem. During training, the model or system under evaluation is provided access to the entire WildChat-AQA dataset. At test time, the model is given an aggregative question along with 10 candidate answers. Its task is to rank these candidates according to their relevance to the question. We use standard ranking metrics \\textbf{NDCG@1}, \\textbf{NDCG@3}, \\textbf{NDCG@5}, and \\textbf{NDCG@10} to measure performance.\n\n%To evaluate how good a model or a compound system can answer the aggregative questions, we formulate the challenge as a ranking problem. The model or system to be evaluated is provided the entire conversation dataset during learning or indexing phase. During test time, a question along with a list of 10 possible options is given, and the model or system under test is required to rank the 10 possible candidates based on the question. Ranking metrics \\textbf{NDCG@1}, \\textbf{NDCG@3}, \\textbf{NDCG@5}, \\textbf{NDCG@10} is employed to measure the performance. \n\n\n\\subsection{Human Evaluation}\nTo evaluate the quality of our inferred attributes, we conducted a human evaluation measuring both inter-annotator agreement (human-human) and human-model agreement using Cohen's $\\kappa$. Specifically, we randomly sampled 100 examples each for level-1 (topic) and level-2 (subtopic) taxonomy labeling. Due to the multi-label nature of these tasks, we computed per-label agreement by treating each possible category as an independent binary labeling task. For subtopic evaluation, we additionally reported macro-average agreement scores aggregated across all topics to provide a comprehensive view of annotation reliability. \n%We introduce human evaluation to test human-model agreement, we compute both inter-human Cohen's $\\kappa$ and human-model Cohen's $\\kappa$. We randomly sampled 100 data samples for level 1 topic taxonomy and level 2 subtopic taxonomy to for human evaluation. Considering this task is a hard multi-label labeling task, we report the per-label results, which treat labeling of each possible category as a separate task. For subtopic, we report the macro average under different topics to show the overall human-human and human-model agreement. \n\n% We report the three metrics: Cohen's Kappa of multi-label exact match agreement, and pooled label agreement. The Cohen's Kappa of subtopic is hierarchy-aware, which means we only measure the the agreement within specific hierarchy. \n\\begin{table}[!t]\n \\centering\n %\\resizebox{0.85\\linewidth}{!}{%\n \\begin{tabular}{@{}lcc@{}}\n \\toprule\n Name & Human–Human $\\kappa$ & Human-Model $\\kappa$ \\\\\n \\midrule\n Topic & 0.581 & 0.617 \\\\\n \\midrule\n Subtopic & 0.576 & 0.609 \\\\\n \\bottomrule\n \\end{tabular}%\n %}\n\\caption{Average Cohen's $\\kappa$ indicating agreement between human annotators (human-human) and between human annotations and model predictions (human-model).}\n\\label{tab:human_eval}\n%\\vspace*{-0.6\\baselineskip}\n\\end{table}\n\nWe found that Cohen's $\\kappa$ for both topics and subtopics indicates moderate to substantial agreement~\\citep{doi:10.1177/001316446002000104}, demonstrating a high degree of reliability between human annotations and model predictions.\n%We can find that the both topic and subtopic among human and model can be considered as moderate to substantial \\cite{doi:10.1177/001316446002000104}, which indicate the high agreement between human and model.\n\n\\section{Probing Retrieval Of Broad Evidence}\nTraditional retrieval methods, including those used in retrieval-augmented generation (RAG), typically aim to identify a small set of highly specific, relevant documents. However, for Aggregative Question Answering, it is essential to identify a broader range of documents that collectively support reasoning about high-level aggregational insights. To address this unique requirement, we introduce a customized retrieval method, \\textbf{P}robing \\textbf{R}etrieval \\textbf{O}f \\textbf{B}road \\textbf{E}vidence (\\textbf{PROBE}). PROBE operates in two main steps:\n\n%Usually, information retrieval and retrieval augmented generation are focused on retrieving specific relevant documents. However, for aggregative question answering task, it is important to discover a wider range of supporting documents. We propose \\textbf{P}robing \\textbf{R}etrieval \\textbf{O}f \\textbf{B}road \\textbf{E}vidence (\\textbf{PROBE}), which consists of two steps: query generation and generation based on the most relevant retrieved evidence. \n\\paragraph{Broad Query Generation} Given a question $\\mathbf{Q}$, we first prompt a large language model to generate a comprehensive set of short, diverse queries that may help retrieve a broad range of relevant documents. Specifically, the LLM generates a set of $n$ queries ${q_1, q_2, \\dots, q_n}$ related to the question. Additionally, the model generates strict filtering conditions $\\mathbf{F} = {f_1, f_2, \\dots, f_m}$ to exclude documents clearly unrelated to the question. Formally, this process is defined as:\n%Given the question $\\mathbf{Q}$, a large language model is prompted to generate all possible $n$ short queries $\\{q_1, q_2, \\cdots, q_n\\}$ that may be helpful to answer to the question. Also, we generate strict filter conditions $\\mathbf{F} = \\{f_1, f_2, \\cdots, f_m\\}$ are generated to filter out documents that are unlikely to support the question answering. Formally \n\\begin{equation*}\n \\mathbf{F}, \\{q_1, q_2, \\cdots, q_n \\} = \\text{LLM} (\\mathbf{p}, \\mathbf{Q}),\n\\end{equation*} where $\\mathbf{p}$ represents the prompt. % provided to the LLM.\n\n\n\\paragraph{Evidence Aggregation and Generation} Next, each generated query $q_i$ (alongside the filtering conditions $\\mathbf{F}$) is used individually to retrieve relevant documents. This results in $n$ separate retrieval runs. We then aggregate these results by merging the retrieved document lists according to their retrieval relevance scores. If a document appears multiple times across different queries, we use max pooling to assign it the highest relevance score it received from any query. Finally, we select the top $k$ documents from this aggregated list as evidence.\n\nThe resulting set of retrieved documents serves as supporting evidence for the model to perform aggregational reasoning and answer the question. An overview of the full PROBE retrieval pipeline is illustrated in \\Cref{fig:probe-overview}.\n\n%With list of generated queries, we perform $n$ times of retrieving using each of the generated query $q_i$ and filter $\\mathbf{F}$, merge the retrieved document list order by relevance score, and finally fetch top $k$ resulting document list based on the relevance score. If the one document is retrieved multiple times by different queries, we perform max pooling to use the max relevance score as the final relevance score of the retrieved document. \n\n%We finally use those retrieved documents as evidence to perform aggregative question answering, \\Cref{fig:probe-overview} provides an overview the entire PROBE framework. \n% We evaluate our benchmark on models of different capability within two settings: \\textbf{Oracle Supporting Context}, which feed the language models with ground truth supporting conversations and see the upper bound of LLMs' performance on answering aggregative question answering. Another setting is a more real-world like one, called \\textbf{Retrieved Supporting Context}, within which the models or compound systems are required to retrieve or learn supporting context to perform the aggregative question answering.\n\n\\section{Experiments}\nWe evaluate WildChat-AQA using various models, retrieval approaches, and conversation formats. We also explore model behaviors by performing ablation studies.\n\n%We thoroughly evaluate our benchmark with various experimental settings. Including different models, different data comprehension approaches, which means different approaches to understand the dataset, raw or summarized version of data sample, and ground truth (oracle) documents as context experiment.\n\n\\subsection{Models}\nWe select several representative models spanning various sizes: Gemma 3-4B~\\citep{gemmateam2025gemma3technicalreport}, Qwen3-8B, Qwen3-32B~\\citep{yang2025qwen3technicalreport}, and GPT-4.1-mini~\\citep{OpenAIGPT4.1}. We also evaluated reasoning models including Qwen3-8B-think, Qwen3-32B-think, and o4-mini~\\citep{OpenAI_o3_o4mini_2025}.\n% We will test the performance of a wide range of models of different size and capability, including Gemma 3-4B \\cite{gemmateam2025gemma3technicalreport}, Qwen3-8B, Qwen3 32B \\cite{yang2025qwen3technicalreport}, and GPT-4.1 mini \\cite{OpenAIGPT4.1}. We also employed most powerful reasoning models including Qwen3 8B-think, Qwen3 32B-think, and o4-mini \\cite{OpenAI_o3_o4mini_2025}.\n\n\\subsection{Experimental Setups}\n We explore several experimental setups to investigate how effectively models leverage conversational data to answer aggregative questions: \n%We employed four different data comprehension approaches to evaluate how prevailing real world models and systems can understand the dataset and answer the aggregative question over the dataset. Similar to the setting in oracle scenario, we test the performance on using both raw conversation and summarized conversation. \n\n\\paragraph{No Context} The model directly answers questions without external inputs, relying solely on internal knowledge. This approach establishes baseline performance using only pre-existing knowledge. Due to resource constraints, we only evaluated this baseline using the strongest reasoning model (o4-mini).\n\n%Let the model direct answer the question without any given data sample. We only perform experiment with no context on the best reasoning model o4-mini to check best the performance of our dataset without providing any context to check the performance of the model without learning about dataset. \n\n\\paragraph{Retrieval Augmented Generation (RAG)} We use standard retrieval-augmented generation using OpenAI's \\texttt{text-embedding-3-large} embeddings to retrieve relevant conversations as context. %We performed retrieval augmented generation based on embedding-based retrieval using OpenAI text-embedding-3-large as textual embedding model. \n\n\\paragraph{Finetuning} We finetune pretrained models on the entire WildChat-AQA dataset. % Given WildChat was originally designed for instruction fine-tuning, we test whether fine-tuning enables models to internalize aggregational knowledge effectively. %Finetuning is considered as a popular approach to inject large language model with new knowledge. As WildChat itself is a instructional finetuning, we finetuned pretrained model with all documents in WildChat-AQA dataset to check if the model learn aggregated information from finetuning. \n\n\\paragraph{PROBE} We use our proposed retrieval method, PROBE. Query generation uses GPT-4.1-mini, and retrieval relies on embeddings from OpenAI's \\texttt{text-embedding-3-large} model. %We applied the PROBE approach that we described above, for the retrieval part in the PROBE framework, we used GPT-4.1-mini as the model to generate the broad possible query. And then using text-embedding-large-3 by OpenAI to encode each generated query. \n\n\n\\subsection{Raw vs. Summarized Document}\nRaw conversations are detailed but noisy (average 1,143.4 tokens each), whereas summarized conversations are more concise (average 21.5 tokens). Thus, we experimented with both raw and summarized conversation inputs to investigate their effectiveness for aggregative question answering. Implementation details for experiments are provided in \\Cref{appendix:implementation_details}.\n%Summary of each conversation is naturally a denoised and compressed version of of the raw conversation. As we mentioned before, the average number of token in raw data is 1143.4, but the average token of summary is 21.5, which may allow the model consume more data samples under the scenario of retrieval augmented generation. As a result, we leveraged both raw conversation and summarized version of conversation as input of the data. \n\n%Implementation details of experiments can be found in appendix \\ref{appendix:implementation_details}\n\n% \\subsection{Oracle Document as Context}\n\n% We also checked how good the models perform under and ideal case -- all documents provided are ground truth documents. This experiment test the LLMs' aggregative question answering capability itself but rule out the influence of data comprehension approaches. This experiment will also provide the insights of headroom on the benchmark. Particularly, we experimented on two types of oracle conversation. \n\n% \\paragraph{Oracle Supporting Conversation} Denoted as \\textbf{raw} in following experiment. We are able to sample a batch of conversations from the ground truth supporting conversation set as the context to test how good the model can infer the answer from sampled conversations. \n\n% \\paragraph{Oracle Supporting Summarization} Considering the average token of each conversation is 1143, we are only able to input around 100 conversations to most models based on the budget and model context length constrain, which is usually 128k tokens. To let the model read more documents, we also use the summarized version of raw conversation as supporting document to further increase the number of context. \n\n\n\\subsection{Main Results}\n\n\\begin{table*}[!t]\n\\centering\n\\resizebox{0.92\\textwidth}{!}{\n\\begin{tabular}{@{}l|l|lccccc@{}}\n\\toprule\nModel Name & Approach & Type & NDCG@1 & NDCG@3 & NDCG@5 & NDCG@10 & \\makecell{\\# Input Token \\\\(Million)} \\\\ \\midrule\nRandom & None & / & 0.2501\t & 0.3516 & 0.4368 & 0.6211 & - \\\\ \\midrule\no4-mini & None & / & 0.3063\t & 0.4017 & 0.4805 & 0.6488 & 0.87 \\\\ \\midrule\n\\multirow{2}{*}{Qwen3 8B} & \\multirow{2}{*}{Finetune} & Raw & 0.2694 & 0.3739 & 0.4589 & 0.6346 & 1.74 \\\\\n & & Summary & \\underline{0.2984}\t & \\underline{0.3966}\t & \\underline{0.4807}\t & \\underline{0.6480} & 1.74 \\\\ \\midrule \n\\multirow{4}{*}{Gemma3 4B} & \\multirow{2}{*}{RAG} & Raw & 0.3291 & 0.4356 & 0.5159 & 0.6688 & 73.48 \\\\\n & & Summary & 0.3740 & 0.4895 & 0.5627 & 0.6991 & 174.62 \\\\\n & \\multirow{2}{*}{PROBE} & Raw & 0.4766 & 0.5891 & 0.6478 & 0.7620 & 38.44 \\\\\n & & Summary & \\underline{0.5430} & \\underline{0.6513} & \\underline{0.6994} & \\underline{0.7969} & 17.35 \\\\ \\midrule\n\\multirow{4}{*}{\\makecell{Qwen3 8B \\\\Think}} & \\multirow{2}{*}{RAG} & Raw & 0.4168 & 0.5090 & 0.5779 & 0.7123 & 362.16 \\\\\n & & Summary & 0.5273 & 0.6110 & 0.6646 & 0.7717 & 176.88 \\\\\n & \\multirow{2}{*}{PROBE} & Raw & 0.6545 & 0.7305 & 0.7728 & 0.8483 & 315.52 \\\\\n & & Summary & \\underline{0.6944} & \\underline{0.7638} & \\underline{0.8005} & \\underline{0.8660} & 123.04 \\\\ \\midrule\n\\multirow{4}{*}{\\makecell{Qwen3 32B \\\\Think}} & \\multirow{2}{*}{RAG} & Raw & 0.4052 & 0.5020 & 0.5705 & 0.7081 & 182.90 \\\\\n & & Summary & 0.5496 & 0.6321 & 0.6847 & 0.7850 & 176.88 \\\\\n & \\multirow{2}{*}{PROBE} & Raw & 0.6525 & 0.7347 & 0.7759 & 0.8501 & 315.52 \\\\\n & & Summary & \\underline{0.7056} & \\underline{0.7753} & \\underline{0.8114} & \\underline{0.8725} & 123.04 \\\\ \\midrule\n\\multirow{4}{*}{GPT-4.1 mini} & \\multirow{2}{*}{RAG} & Raw & 0.4494 & 0.5387 & 0.6035 & 0.7299 & 344.37 \\\\\n & & Summary & 0.5782 & 0.6620 & 0.7104 & 0.8019 & 154.31 \\\\\n & \\multirow{2}{*}{PROBE} & Raw & 0.6806 & 0.7536 & 0.7936 & 0.8628 & 298.69 \\\\\n & & Summary & \\underline{0.7308} & \\underline{0.7942} & \\underline{0.8282} & \\underline{0.8843} & 107.11 \\\\ \\midrule\n\\multirow{4}{*}{o4-mini} & \\multirow{2}{*}{RAG} & Raw & 0.4730 & 0.5510 & 0.6116 & 0.7383 & 344.37 \\\\\n & & Summary & 0.6122 & 0.6792\t & 0.7242 & 0.8140 & 154.31 \\\\\n & \\multirow{2}{*}{PROBE} & Raw & 0.7117 & 0.7747 & 0.8086 & 0.8745 & 298.69 \\\\\n & & Summary & \\textbf{\\underline{0.7571}} & \\textbf{\\underline{0.8095}}\t& \\textbf{\\underline{0.8386}}\t& \\textbf{\\underline{0.8930}} & 107.11 \\\\ \\bottomrule\n\\end{tabular}}\n\n\\caption{Experimental results of different models using various retrieval approaches and conversation formats (raw vs. summarized). \\underline{Underlined} scores indicate the best results for each model, and \\textbf{bold} scores indicate the best overall results.}\\label{tab:expr_main}\n%\\vspace*{-0.6\\baselineskip}\n\\end{table*}\n\n\n\\Cref{tab:expr_main} presents performance results across different models, retrieval methods, and conversation formats.\n\n\\paragraph{Stronger models perform better.}\n Among tested models, o4-mini consistently achieved the highest performance, with a maximum NDCG@1 score of 0.7571. GPT-4.1-mini, while also strong, trailed slightly behind. Among open-source models, Qwen3-32B-think achieves the highest performance (0.7056 NDCG@1).\n \n %In the experiment, we tested some of the state-the-of-art open source and proprietary models. We can find the most powerful reasoning model, o4-mini, achieved best performance on all different data comprehension approaches, which achieved 0.7571 NDCG@1 value. Another state-of-the-art non-reasoning model GPT-4.1 mini, also achieved good performance but lagged behind o4-mini. Qwen-3 32B, which is one of the state-of-the art open source model, achieved best performance 0.7056 NDCG@1 among open source models. \n\n\\paragraph{PROBE improves significantly over standard RAG.}\nCompared to standard RAG, PROBE consistently showed large performance improvements. On raw data, PROBE improved NDCG@1 scores by 14.8, 23.7, 24.7, 23.1, and 23.8 points for Gemma3-4B, Qwen3-8B-think, Qwen3-32B-think, GPT-4.1-mini, and o4-mini, respectively. A similar trend was observed using summarized conversations.\n\n%\\paragraph{PROBE significantly improve the performance} Result from \\Cref{tab:expr_main} demonstrate that our \\textbf{PROBE} framework constantly provide significant improvement over normal RAG approaches. The model performance based on raw conversation as data improved by 14.8, 23.7, 24.7, 23.1, 23.8 points for Gemma3-4B, Qwen3-8B, Qwen3-32B , GPT-4.1 mini and o4-mini respectively, and experimental results based on summarized document show a similar result. \n\n%\\paragraph{Summarized conversations lead to better performance} The experimental results show that summarized form of document always perform better than raw form of document. The result based on summarized data improved 4.5, 11.0, 14.4, 12.9, and 13.9 points on NDCG@1 using RAG as data comprehension approach for Gemma3-4B, Qwen3-8B, Qwen3-32B , GPT-4.1 mini and o4-mini respectively. And correspondingly have 6.6, 4.0, 5.3, 5.0, and 4.5 points of improvement on PROBE-based experiments. \n\\paragraph{Summaries outperform raw conversations.}\nModels consistently performed better with summarized inputs, showing improved NDCG@1 scores of 4.5 to 14.4 points over raw conversations for standard RAG, and 4.0 to 6.6 points for PROBE. Summaries enable more efficient information retrieval and easier aggregation of insights.\n\n\n\\paragraph{Finetuning doesn't help.}\nFinetuning Qwen3-8B did not significantly exceed random-chance performance. This result suggests that current finetuning methods fail to internalize aggregative information effectively from conversational datasets.\n\n%\\paragraph{Finetuning doesn't help} We finetune the entire conversation data using both raw form and summarized form on Qwen3-8B, but neither of them achieved above-random performance after finetuning. This indicates the fact that models failed to understand and aggregate information during the process of training. \n\n\\paragraph{High token consumption.}\nAchieving good performance on this task required models to consume a very large number of input tokens as shown in \\Cref{tab:expr_main}. This highlights a significant computational challenge and motivates future research to improve efficiency.\n%\\paragraph{Token consumption is huge} To achieve a good performance on this benchmark, the token consumption is huge for all working none-random baselines, which may require future research on how to understand data well and answering aggregative question without reading huge amount of tokens. \n\n\n\n\\begin{table*}[h]\n\\centering\n\\small\n%\\resizebox{0.95\\linewidth}{!}{\n\\begin{tabular}{@{}lccccccc@{}}\n\\toprule\n\\textbf{Method} & \\textbf{R@5} & \\textbf{R@10} & \\textbf{R@20} & \\textbf{R@50} & \\textbf{R@100} & \\textbf{R@200} & \\textbf{R@500} \\\\\n\\midrule\nRAG-Dense & 0.01 & 0.02\t & 0.04\t & 0.07\t & 0.10 &\t0.14 & 0.21 \\\\\n\\midrule\nPROBE-Dense & 0.07 & 0.13 & 0.23 & 0.35 & 0.43 & 0.50 & 0.58 \\\\\n\\quad - filter only & 0.05 \\textcolor{gray}{ (-0.02)} & 0.09 \\textcolor{gray}{ (-0.04)} & 0.16 \\textcolor{gray}{ (-0.07)} & 0.24 \\textcolor{gray}{ (-0.11)} & 0.29 \\textcolor{gray}{ (-0.14)} & 0.33 \\textcolor{gray}{ (-0.17)} & 0.40 \\textcolor{gray}{ (-0.18)} \\\\\n\\quad - question \\& filter & 0.06 \\textcolor{gray}{ (-0.01)} & 0.12 \\textcolor{gray}{ (-0.01)} & 0.21 \\textcolor{gray}{ (-0.02)} & 0.32 \\textcolor{gray}{ (-0.03)} & 0.40 \\textcolor{gray}{ (-0.03)} & 0.46 \\textcolor{gray}{ (-0.04)} & 0.53 \\textcolor{gray}{ (-0.05)} \\\\\n\\bottomrule\n\\end{tabular}\n%}\n\\caption{Recall@k of PROBE-Dense (Summary) with ablations removing generated queries or filters. Numbers in parentheses indicate performance decrease compared to the full PROBE approach.}\n\\label{tab:recall_abalation}\n%\\vspace*{-0.6\\baselineskip}\n\\end{table*}\n\n\\begin{table}[h]\n\\small\n\\begin{tabular}{@{}clcc@{}}\n\\toprule\n\\# Conversation & Approach & Recall & NDCG@5 \\\\\n\\midrule\n\\multirow{3}{*}{5} & RAG & 0.01 & 0.5373 \\\\\n & PROBE & 0.07 & 0.6991 \\\\\n & Oracle & {\\ul 0.10} & {\\ul 0.7925} \\\\\\midrule\n\\multirow{3}{*}{20} & RAG & 0.04 & 0.5897 \\\\\n & PROBE & 0.23 & 0.7624 \\\\\n & Oracle & {\\ul 0.34} & {\\ul 0.8540} \\\\\\midrule\n\\multirow{3}{*}{50} & RAG & 0.07 & 0.6318 \\\\\n & PROBE & 0.35 & 0.7927 \\\\\n & Oracle & {\\ul 0.54} & {\\ul 0.8721} \\\\\\midrule\n\\multirow{3}{*}{200} & RAG & 0.14 & 0.6858 \\\\\n & PROBE & 0.50 & 0.8202 \\\\\n & Oracle & {\\ul 0.75} & {\\ul 0.8942} \\\\\\midrule\n\\multirow{3}{*}{500} & RAG & 0.20 & 0.7141 \\\\\n & PROBE & 0.58 & 0.8263 \\\\\n & Oracle & {\\ul 0.84} & {\\ul 0.9005} \\\\\\bottomrule\n\\end{tabular}\n\n\\caption{NDCG@5 score of o4-mini with summarized conversations and different recall rate. \\underline{Underlined} is the best for specific number of conversations}\n\\label{tab:recall_vs_ndcg_table}\n%\\vspace*{-0.6\\baselineskip}\n\\end{table}\n\n\\subsection{Ablation Studies}\nWe conducted ablation studies on a stratified 10\\% subset of the benchmark, selected based on condition and target types. \n\n\n\\paragraph{Retrieval Effectiveness is Critical}\nRetrieval performance has a significant impact on the final results. \\Cref{tab:recall_vs_ndcg_table} presents the performance of o4-mini under varying recall rates achieved by different retrieval methods. We observe that higher recall rates consistently lead to improved NDCG scores.\n \n\n\\paragraph{Retrieval performance.} \nWe compared various retrieval approaches, including vector-based embeddings, BM25, random, and ground-truth retrieval. \\Cref{fig:recall_ablation} shows recall rates for different retrieval strategies. PROBE consistently provided substantial improvements over standard RAG, with the highest recall from PROBE-Dense (summarized). Removing either the generated query or filtering steps notably degraded PROBE's retrieval effectiveness (\\Cref{tab:recall_abalation}).\n \n %The performance of retrieval influence the final performance by a lot. We performed ablation study on recall rate of RAG and PROBE with different retrieval algorithms including vector-based retrieval and BM25 over both summarized and raw form of conversations. The performance of random retrieval (denoted as Random) and groun truth retrieval (denoted as Ground Truth)\n\\begin{figure}[!t]\n \\centering\n \\includegraphics[width=0.9\\linewidth]{figures/recall.pdf}\n \\caption{Recall of different retrieval approaches}\n \\label{fig:recall_ablation}\n\\end{figure}\n\n\n\n%\\Cref{fig:recall_ablation} illustrate the average recall rate using different recall settings given different number of conversation retrieved. We can also find \\textbf{PROBE} framework significantly boost the performance compare with normal RAG. And \\textbf{PROBE-Dense (Summary)} perform best among all retrieval approaches. This demonstrate the effectiveness of our PROBE framework. We further perform experiment on how and generated filter and broad evidence query impact the results. As shown in \\Cref{tab:recall_abalation}. If we only apply the generated filter condition or only apply the generated filter condition with question embedding, both of them significantly decrease the recall performance compare with PROBE. \n\n\\paragraph{Current models lack effective aggregational reasoning}\nTo explore model capabilities under ideal conditions, we performed experiments using oracle (ground-truth) documents as context (\\Cref{tab:expr_oracle}). All models performed better when given summarized contexts rather than raw conversations, indicating challenges in aggregating information from longer, noisier texts.\n %To explore model capabilities in ideal conditions, we performed experiments providing oracle (ground-truth) documents as context (\\Cref{tab:expr_oracle}). All models performed better using summarized contexts rather than raw, indicating difficulties in aggregating and synthesizing information from longer raw texts. \n%To explore how good different models can perform aggregative question answering under ideal retrieval scenario. We performed experiment based on oracle documents in the context. \n\n\\begin{table}[!t]\n\\resizebox{\\linewidth}{!}{\n\\begin{tabular}{@{}llcccc@{}}\n\\toprule\nModel Name & Ctx Type & NDCG@1 & NDCG@3 & NDCG@5 & NDCG@10 \\\\ \\midrule\n% Random Choice & - & 0.2501\t& 0.3516 & 0.4368 & 0.6211 \\\\ \\midrule\n% o4-mini & None & 0.3063\t& 0.4017 & 0.4805 &\t0.6488 \\\\ \\midrule\n\\multirow{2}{*}{Gemma3 4B} & Raw & 0.4815\t& 0.6057 & 0.6601 & 0.7703 \\\\\n & Summary & 0.5699\t& 0.6787 & 0.7235 & 0.8102 \\\\ \\midrule\n\\multirow{2}{*}{Qwen3 8B Think} & Raw & 0.7359\t& 0.7991 & 0.8360 & 0.8894 \\\\\n & Summary & 0.7757 & 0.8268 & 0.8510 & 0.9003 \\\\ \\midrule\n\\multirow{2}{*}{Qwen3 32B Think}& Raw & 0.7225 & 0.8044 & 0.8355 & 0.8897 \\\\\n & Summary & 0.8134 & 0.8605 & 0.8817 & 0.9199 \\\\ \\midrule\n\\multirow{2}{*}{GPT-4.1-mini} & Raw & 0.7849\t& 0.8388 & 0.8667 & 0.9121 \\\\\n & Summary & 0.8130 & 0.8602 & 0.8816 & 0.9216 \\\\ \\midrule\n\\multirow{2}{*}{o4-mini} & Raw & 0.8003 & 0.8456 & 0.8719 & 0.9185 \\\\\n & Summary & \\textbf{0.8478} & \\textbf{0.8793} & \\textbf{0.9005} & \\textbf{0.9347} \\\\\n\\bottomrule\n\\end{tabular}}\n\\caption{Experimental results on aggregative question answering using oracle (ground-truth) documents as context.}\\label{tab:expr_oracle}\n%\\vspace*{-0.6\\baselineskip}\n\\end{table}\n\n\n\n%\\Cref{tab:expr_oracle} shows the result of all models performs better given the summarized the conversation as context instead of raw version. This results indicate recent models still failed to extract related information implicitly based on the question for each document within context. A denoised summary yield better performance than raw conversation. \n\n\n\\begin{figure}[!t]\n \\centering\n \\includegraphics[width=0.85\\linewidth]{figures/oracle_ndcg_panels.pdf}\n\\caption{NDCG@5 scores for different models given varying numbers of oracle (ground-truth) documents, comparing raw and summarized conversations.} \\label{fig:oracle_experiment}\n\n\\end{figure}\n\nWe further analyzed how performance varied with the number of provided conversations (\\Cref{fig:oracle_experiment}). Weaker models such as Gemma3 and Qwen3 showed a substantial performance gap between raw and summarized contexts, even when given the same number of conversations, highlighting their limited ability to implicitly extract relevant information. Stronger models like GPT-4.1-mini and o4-mini showed a smaller initial gap, but this gap widened notably when the context increased to 100 documents, demonstrating that even advanced models struggle with aggregating and reasoning effectively over extensive raw contexts.\n%We further evaluated the models' aggregative question answering capability given different number of conversations. \\Cref{fig:oracle_experiment} shows that even given the same number of conversations, weaker models such as Gemma3 and Qwen3, still have large gap between the raw form input and summarized form of input, which indicate their poor capability of implicitly extracting information for each given conversation and answer the question. Although the gap for stronger model, such as GPT-4.1-mini and o4-mini, is smaller, the gap become bigger when 100 documents are given. This indicate even strong model failed to aggregate information when the context become long. \n\n%\\paragraph{Performance improve when more documents are given} Unlike regular RAG, which only rely on several documents, the proposed aggregative question answering rely on \\textbf{global information} across entire dataset. We perform experiment on gradually increasing the number and results are shown in \\Cref{fig:retrieved_ndcg_comp} in retrieval based settings. \n\\paragraph{Performance improves with more context.}\nUnlike standard RAG tasks, Aggregative Question Answering fundamentally relies on a broader set of documents. As more documents were provided, models improved significantly in answering aggregative questions (\\Cref{fig:retrieved_ndcg_comp}). This finding validates that aggregative question answering requires extensive context and global dataset knowledge.\n\\begin{figure}[!t]\n \\centering\n \\includegraphics[width=0.98\\linewidth]{figures/retrieved_ndcg_panels.pdf}\n\\caption{Comparison of NDCG@5 scores for different models with varying numbers of retrieved documents.}\n\\label{fig:retrieved_ndcg_comp}\n%\\vspace*{-0.6\\baselineskip}\n\\end{figure}\n\nThe results in \\Cref{fig:retrieved_ndcg_comp} and \\Cref{fig:oracle_experiment} show that under all experiment settings, the performance improve when more documents are given, which validate the correctness of our benchmark and emphasize the necessity of global information of the dataset. \n\n%\\paragraph{Aggregative question answering is a reasoning intensive task} Aggregative question answering requires the model implicitly retrieve relevant information based on query and answer the question, which is reasoning intensive. We test the performance of on this task using Qwen3-32B, which is a hybrid model that can either apply thinking or not flexibly during inference. \n\\paragraph{Aggregative question answering is reasoning-intensive.}\nWe evaluated Qwen3-32B (with optional reasoning via ``think'' mode) to measure the effect of explicit reasoning. Results (\\Cref{tab:ablation_thinking}) consistently showed reasoning led to significant performance improvements across all experimental setups, indicating aggregative question answering demands substantial reasoning abilities.\n\n\\begin{table}[!t]\n\\centering\n\\resizebox{\\linewidth}{!}{\n\\begin{tabular}{@{}lcccc@{}}\n\\toprule\n\\textbf{Method} & \\textbf{NDCG@1} & \\textbf{NDCG@3} & \\textbf{NDCG@5} & \\textbf{NDCG@10} \\\\\n\\midrule\nOracle & 0.72 & 0.79 & 0.82 & 0.88 \\\\\n\\quad + thinking & 0.81 \\textcolor{gray}{(+0.09)} & 0.86 \\textcolor{gray}{(+0.07)} & 0.88 \\textcolor{gray}{(+0.06)} & 0.92 \\textcolor{gray}{(+0.04)} \\\\\n\\addlinespace\\midrule\nRAG (Summary) & 0.48 & 0.56 & 0.62 & 0.74 \\\\\n\\quad + thinking & 0.54 \\textcolor{gray}{(+0.07)} & 0.62 \\textcolor{gray}{(+0.07)} & 0.68 \\textcolor{gray}{(+0.06)} & 0.78 \\textcolor{gray}{(+0.04)} \\\\\n\\addlinespace \\midrule\nPROBE (Summary) & 0.64 & 0.71 & 0.75 & 0.84 \\\\\n\\quad + thinking & 0.68 \\textcolor{gray}{(+0.04)} & 0.76 \\textcolor{gray}{(+0.05)} & 0.80 \\textcolor{gray}{(+0.05)} & 0.86 \\textcolor{gray}{(+0.02)} \\\\\n\\bottomrule\n\\end{tabular}}\n\\caption{NDCG scores of Qwen3 32B with and without reasoning (``think'' mode). Improvements from reasoning are indicated in parentheses.}\\label{tab:ablation_thinking}\n%\\vspace*{-0.6\\baselineskip}\n\\end{table}\n\n%The result in \\Cref{tab:ablation_thinking} shows that under all experimental settings, enable reasoning yield way higher performance than non-reasoning, which demonstrate aggregative question answering is a reasoning intensive task. \n\n% \\paragraph{Oracle Supporting Context} Table \\ref{expr_main} shows the best result for providing the models with ground truth supporting context. We select the best result based on the average NDCG score given different number of supporting contextual documents. We provided both performance score and number of tokens consumed. From the table we can find that smaller models, such as Gemma-3-4B and Llama-3.3-8B failed to reach a good performance even when given the ground truth supporting documents. However, given the same number of documents, the summarized version of the document as context outperform the raw version for all models, which means all these models' capability degenerate when the context become complicated. Which means current models still lack the capability of answering aggregative query. \n\n\n\n\n% We also test the performance of different number of oracle documents for different models. We find that when the number of ground truth document increase, the performance will increase. However, for weaker and smaller models, when the number of supporting context increase, the performance decrease because of the weak model capability. \n\n% For the most powerful model such as GPT-4.1-mini and o4-mini, the performance increase until reach the limit of context length. When we further increase the number of input conversations via inputting summarization of the conversations. \n\n% In conclusion, we found that stronger models have better aggregative question answering performance. And our benchmark showed that when the number of supporting document increases, the strong model can better answer the aggregative query because more detailed information is provided. \n\n\\section{Future Research Directions}\n\n\\paragraph{Reasoning Over Very Long Context}\nIn this work, we experimented with several reasoning-capable models and observed that current models typically have limited context windows, and performance degrades sharply as the length of the input context increases. Developing efficient and accurate methods for reasoning over very long textual contexts remains an important open problem.\n\n\\paragraph{Cost-Efficient Aggregative Question Answering}\nCurrent effective solutions for Aggregative Question Answering require processing extremely large amounts of text, resulting in substantial computational costs. Future research could explore hierarchical indexing, retrieval strategies, and long-term memory mechanisms to reduce token consumption and improve computational efficiency.\n\n\n\n%\\paragraph{Reasoning Over Very Long Context} In this work, we experimented on various reasoning models such as Qwen3 and o4-mini. We witness a significant improvement with reasoning when given more documents in context. However, recent reasoning models do not have sufficient context window and the performance also degenerate when input become long. \n\n%\\paragraph{Cost Efficient Aggregative Question Answering} Currently, working solution of aggregative question answering require consuming large number of tokens, which makes solving the problem expensive. New hierarchical indexing and long-term memory can be developed to alleviate this problem. \n \n\\paragraph{Streaming Aggregative Question Answering} In real-world scenarios, chatbot conversations often arrive in continuous streams rather than static collections. Future research could explore methods to dynamically update aggregational insights as new interactions occur in real time. Ideally, conversational agents would continuously integrate information from ongoing interactions---similar to how humans update their understanding based on new experiences---to maintain up-to-date and adaptive aggregational knowledge.\n\n%In real world case, documents usually come over time instead of once. Future researches can focus on how to deal with continuous increasing documents and give high level aggregated answer in a dynamic way. \n\n\\section{Conclusion}\nIn this paper, we introduced Aggregative Question Answering, a new task aimed at extracting collective insights from large-scale conversational data generated by interactions between users and LLM-powered chatbots. To facilitate research in this area, we constructed the WildChat-AQA benchmark, comprising 6,027 aggregational questions derived from 182,330 real-world chatbot conversations. Our experiments demonstrate that existing state-of-the-art methods, including fine-tuning, retrieval-augmented generation (RAG), and even an improved RAG approach specifically adapted for this task---PROBE (Probing Retrieval Of Broad Evidence)---struggle significantly, either failing to reason effectively at the necessary global scale or incurring prohibitively high computational costs. Looking ahead, we believe addressing these challenges would enable future models to better derive meaningful user and societal insights from large-scale conversational data.\n\n%In this work, we presented a new important task called aggregative question answering. To test performance of existing system, we constructed a real-world like benchmark named WildChat-AQA which contains 6027 questions over 182230 conversations between chatbots and active users. We throughly evaluate the task with RAG and finetuned based approach and find neither of them perform well. We proposed simple yet effective retrieval-based approach PROBE to boost the performance significantly. We further perform experiments and find that this new task requires intensive reasoning over global information, which current models and systems do not have. We finally suggest some future research direction about aggregative question answering from different perspective. \n\n\n\n\\section*{Limitations}\n\\paragraph{Potential Errors in Model-derived Annotations}\nAlthough we employed powerful large language models and pipelines (such as GPT-4o and TnT-LLM) to infer attributes and assign taxonomy labels, errors and inconsistencies may still occur due to model hallucinations or instruction misalignment. Additionally, real-world conversational data is inherently noisy, ambiguous, and challenging to categorize neatly, making it impossible to ensure completely error-free annotations.\n\n\\paragraph{Artificiality of Generated Questions}\nAggregative questions in WildChat-AQA were generated by prompting GPT-4.1 to translate structured database queries into natural-language questions. While effective, this method may result in questions that still feel somewhat templated or artificial, potentially limiting their naturalness and realism compared to genuinely human-authored queries.\n%\\paragraph{Annotations on real world data are not perfect} Although we use strong model to build taxonomy and assign the label to the data sample, there can be error in model annotation since even best model today can have hallucination and may not follow the instruction. In the meantime, real world data is noisy and hard to fit in taxonomy. As a result, the annotation process can be non-perfect and lead to error in both data creation.\n\n%\\paragraph{Questions are not natural and real enough} We prompt the GPT-4.1 to translate database query into natural language question. But this can still be a bit template oriented and not natural enough. \n% \\section*{Acknowledgments}\n% Collaborators, Advisor, OpenAI. \n\n\\section*{Ethical Considerations}\nAggregative Question Answering opens promising avenues for impactful real-world analytics but also raises potential ethical and societal concerns, particularly when insights relate to sensitive topics such as elections, public opinion, or public health---areas that could potentially be susceptible to manipulation. To mitigate such risks, all experiments conducted in this work rely exclusively on the publicly available and anonymized WildChat dataset, which is explicitly intended for open research purposes (licensed under ODC-BY). By introducing WildChat-AQA as an open benchmark, we aim to empower transparent academic research that responsibly explores both the capabilities and risks associated with aggregational analytics. Our goal is to encourage the open research community to better understand and evaluate these powerful systems, rather than relying solely on proprietary analyses conducted behind closed doors.\n\n\\bibliography{custom}\n\\appendix\n\n\\clearpage\n\n\\section{Related Works}\n\n\\paragraph{Question Answering} Question answering typically involves a diverse range of perspectives. Datasets such as TriviaQA \\cite{joshi-etal-2017-triviaqa}, RACE \\cite{lai-etal-2017-race}, HotPotQA \\cite{yang2018hotpotqa}, Natural Questions \\cite{kwiatkowski-etal-2019-natural}, MuSiQue \\cite{trivedi-etal-2022-musique}, 2Wiki \\cite{ho-etal-2020-constructing}, PopQA \\cite{mallen2023trustlanguagemodelsinvestigating}, and MultiHop-RAG \\cite{tang2024multihopragbenchmarkingretrievalaugmentedgeneration} focus on \\textbf{local information}, where answers can be derived from one or several documents.\nIn contrast, other benchmarks such as MMLU \\cite{hendrycks2021measuring}, MATH \\cite{hendrycksmath2021}, GSM8K \\cite{cobbe2021gsm8k}, and Big-Bench \\cite{srivastava2023beyond} emphasize science, technology, engineering, mathematics, and logical reasoning. These primarily evaluate models' world knowledge and reasoning capabilities but lack a benchmark for understanding large-scale datasets and deriving high-level insights.\nRecent works such as GraphRAG \\cite{edge2025localglobalgraphrag} address the long-context challenge by extracting entities and relationships from extended text data and constructing graph structures to answer questions.\n\n\\paragraph{Long Context Retrieval Augmented Generation} \\cite{NEURIPS2020_6b493230} has emerged as a prominent approach for enhancing the performance of large language models (LLMs) on knowledge-intensive tasks while also mitigating hallucinations. Recently, advances in computational capabilities have spurred interest in extending RAG to support very long contexts. Several studies—such as those by \\citet{jiang2024longragenhancingretrievalaugmentedgeneration}, \\citet{zhao-etal-2024-longrag}, and \\citet{jin2025longcontext}—have proposed methods to improve the effectiveness of LLMs in long-context settings. In parallel, \\citet{lee2024longcontextlanguagemodelssubsume} introduced LOFT, a new benchmark designed to evaluate LLMs on a broad range of tasks addressable by either RAG or long-context modeling.\n\n\\paragraph{Summarization} Summarization has been a long-standing challenge in natural language processing. Early benchmark datasets, such as CNN/Daily Mail \\cite{see-etal-2017-get} and XSum \\cite{Narayan2018DontGM}, primarily targeted single-document summarization. Subsequent efforts, including MultiNews \\cite{fabbri-etal-2019-multi} and MS$^2$ \\cite{deyoung-etal-2021-ms}, extended this task to the multi-document setting. Another line of related work focuses on query-based summarization, for which QMSum \\cite{zhong-etal-2021-qmsum} and DUC 2005 \\cite{10.5555/1654679.1654689} are two widely used datasets.\n\n\\paragraph{Text to SQL} Text-to-SQL is a widely studied approach for tackling aggregative question answering. In this paradigm, the model is required to generate a structured database query based on a natural language question. Several established benchmarks have been proposed to evaluate this task, including WikiSQL \\cite{zhongSeq2SQL2017}, Spider \\cite{lei2024spider}, BIRD \\cite{li2024can}, and WikiTableQA \\cite{pasupat-liang-2015-compositional}. Additionally, LOFT \\cite{lee2024longcontextlanguagemodelssubsume} includes a sub-task specifically designed to assess how effectively large language models can emulate database-style querying.\n\n% \\section{Related Works}\n\n% \\paragraph{Question Answering} Question answering usually involves a diverse perspective. TriviaQA \\cite{joshi-etal-2017-triviaqa}, RACE \\cite{lai-etal-2017-race}, HotPotQA \\cite{yang2018hotpotqa}, Natural Questions \\cite{kwiatkowski-etal-2019-natural}, MuSiQue \\cite{trivedi-etal-2022-musique} , 2Wiki \\cite{ho-etal-2020-constructing}, PopQA \\cite{mallen2023trustlanguagemodelsinvestigating}, and MultiHop-RAG \\cite{tang2024multihopragbenchmarkingretrievalaugmentedgeneration} are focused on local information and answer can be derived based on one or several documents. Other question answering benchmarks such as MMLU \\cite{hendrycks2021measuring}, MATH \\cite{hendrycksmath2021}, GSM8K \\cite{cobbe2021gsm8k}, and Big-Bench \\cite{srivastava2023beyond} focus on science, technology, engineering, mathematical, and logical question answering. These mainly evaluate models' world knowledge and reasoning capability. But they lack a benchmark for understanding large scale dataset and derive high level insight understanding. Other works, such as GraphRAG \\cite{edge2025localglobalgraphrag} tackle the long context problem via extracting entities and relationships from the long text data and build up graph structure to answer the question. \n\n% \\paragraph{Long Context Retrieval Augmented Generation} retrieval augmented generation \\cite{NEURIPS2020_6b493230} is a popular way for large language models to improve LLMs capability on knowledge intensive tasks and mitigate hallucination. Recently, with the increase of computational power, researcher start to work on RAG with very long context. \\citet{jiang2024longragenhancingretrievalaugmentedgeneration, zhao-etal-2024-longrag, jin2025longcontext} researched on approaches of improving long context LLMs. \\citet{lee2024longcontextlanguagemodelssubsume} proposed new benchmark named LOFT to further evaluate LLMs in a wide variety of tasks that can be solved by both RAG and long context settings. \n\n% \\paragraph{Summarization} Summarization is a long lasting problem in NLP, early datasets such as CNN / Daily Mail \\cite{see-etal-2017-get} and XSum \\cite{Narayan2018DontGM} mainly focus on summarize single document. Later works, such as MultiNews \\cite{fabbri-etal-2019-multi}, and MS\\textasciicircum2 \\cite{deyoung-etal-2021-ms} extend single document summarization to multi document summarization. Another task that is related to out work is query based summarization, QMSum \\cite{zhong-etal-2021-qmsum} and DUC2005 \\cite{10.5555/1654679.1654689} are two major dataset on evaluating this. \n\n% \\paragraph{Text to SQL} Text to SQL is another popular approach that can solve aggregative question answering. Based on given question, the model is asked to generate a database query to answer the question. WikiSQL \\cite{zhongSeq2SQL2017}, Spider \\cite{lei2024spider}, BIRD \\cite{li2024can}, and WikiTableQA \\cite{pasupat-liang-2015-compositional} are popular benchmark in Text-to-SQL task. LOFT \\citet{lee2024longcontextlanguagemodelssubsume} also include a sub-task to test how good can LLM perform task similar to database. \n\\include{appendix/data_profile}\n\n\\include{appendix/data_visualization_and_implementation}\n\n\n\\include{appendix/data_creation_process}\n\n\n\n\n\n\\end{document}\n"},"question":{"kind":"string","value":"Which model, using which approach, achieves the best performance for NDCG@5 among those trained on over 100 million tokens? Additionally, what percentage of the worst-performing model's score does the best-performing model achieve?"},"answer":{"kind":"string","value":"o4-mini with probe, 149.03%"},"plan":{"kind":"string","value":"SELECT Model-Context pairs and NDCG@5 scores where Input Tokens > 100 Million \nLOOP for each Model-Context pair \n COMPUTE add score to score_list \nCOMPUTE best_score = max in score_list \nCOMPUTE best_model_context = Model-Context pair for best_score \nCOMPUTE worst_score = min in score_list \nCOMPUTE percentage = best_score / worst_score * 100 \nRETURN best_model_context and percentage\n"}}},{"rowIdx":79,"cells":{"paper":{"kind":"string","value":"2505.23667"},"relevant_tables":{"kind":"list like","value":[["\\begin{table}[t!]\n"," \\centering\n"," \\caption{Performance under Zero-shot, SFT, and RL settings across models and datasets. Values in the table indicate accuracy (\\%). \\textit{Text} and \\textit{Formula} refer to textual and symbolic reasoning methods, respectively. \\textit{w/ CS} denotes cold-start RL \\cite{deepseekai2025deepseekr1incentivizingreasoningcapability} initialized from SFT. For open-source models, the best performance in each column is highlighted in \\textcolor{langdarkblue}{dark blue}, and the second-best in \\textcolor{langwildblue}{light blue}.}\n"," \\resizebox{\\columnwidth}{!}{%\n"," \\begin{tabular}{llcccccccc}\n"," \\toprule\n"," \\multirow[c]{2}{*}{\\textbf{Base Model}} \n"," & \\multirow[c]{2}{*}{\\textbf{Method}} \n"," & \\multicolumn{5}{c}{\\textbf{In‐Distribution}} \n"," & \\multicolumn{2}{c}{\\textbf{Out‐of‐Distribution}}\n"," & \\multirow[c]{2}{*}{\\textbf{Overall}} \\\\\n"," \\cmidrule(lr){3-7}\\cmidrule(lr){8-9}\n"," & & \\textbf{WikiTQ} & \\textbf{TabFact} & \\textbf{FinQA} & \\textbf{HiTab} & \\textbf{MultiHiertt} \n"," & \\textbf{AIT-QA} & \\textbf{TableBench} & \\\\\n"," \\midrule\n"," \\multicolumn{10}{l}{\\textit{\\textbf{Zero‐Shot}}} \\\\\n"," \\multirow[c]{2}{*}{\\quad \\textcolor{gray}{GPT-4o-mini}} \n"," & \\textcolor{gray}{Text} \n"," & \\textcolor{gray}{67.36} & \\textcolor{gray}{88.44} & \\textcolor{gray}{59.20} \n"," & \\textcolor{gray}{57.89} & \\textcolor{gray}{22.41} \n"," & \\textcolor{gray}{77.67} & \\textcolor{gray}{36.79} & \\textcolor{gray}{58.54} \\\\\n"," & \\textcolor{gray}{Formula} \n"," & \\textcolor{gray}{49.16} & \\textcolor{gray}{74.90} & \\textcolor{gray}{43.07} \n"," & \\textcolor{gray}{48.17} & \\textcolor{gray}{28.26} \n"," & \\textcolor{gray}{75.53} & \\textcolor{gray}{34.65} & \\textcolor{gray}{50.53} \\\\\n"," \\cmidrule(lr){2-10}\n"," \\multirow[c]{2}{*}{\\quad \\textcolor{gray}{GPT-4o}} \n"," & \\textcolor{gray}{Text} \n"," & \\textcolor{gray}{78.57} & \\textcolor{gray}{94.52} & \\textcolor{gray}{63.12} \n"," & \\textcolor{gray}{69.26} & \\textcolor{gray}{36.11} \n"," & \\textcolor{gray}{81.36} & \\textcolor{gray}{42.66} & \\textcolor{gray}{66.51} \\\\\n"," & \\textcolor{gray}{Formula} \n"," & \\textcolor{gray}{53.36} & \\textcolor{gray}{79.94} & \\textcolor{gray}{48.65} \n"," & \\textcolor{gray}{65.40} & \\textcolor{gray}{38.41} \n"," & \\textcolor{gray}{85.83} & \\textcolor{gray}{37.92} & \\textcolor{gray}{58.50} \\\\\n"," \\cmidrule(lr){2-10}\n"," \\multirow[c]{2}{*}{\\quad \\textcolor{gray}{O1}} \n"," & \\textcolor{gray}{Text} \n"," & \\textcolor{gray}{77.90} & \\textcolor{gray}{95.50} & \\textcolor{gray}{55.71} \n"," & \\textcolor{gray}{74.31} & \\textcolor{gray}{38.31} \n"," & \\textcolor{gray}{81.55} & \\textcolor{gray}{45.03} & \\textcolor{gray}{66.90} \\\\\n"," & \\textcolor{gray}{Formula} \n"," & \\textcolor{gray}{70.40} & \\textcolor{gray}{91.11} & \\textcolor{gray}{42.81} \n"," & \\textcolor{gray}{75.00} & \\textcolor{gray}{48.95} \n"," & \\textcolor{gray}{88.93} & \\textcolor{gray}{44.02} & \\textcolor{gray}{65.89} \\\\\n"," \\noalign{\\vskip 2pt}\n"," \\hdashline[2pt/2pt]\n"," \\noalign{\\vskip 2pt}\n"," \\multirow[c]{2}{*}{\\quad Llama-3.1$_{\\text{8B}}$} \n"," & Text & 50.46 & 67.84 & 42.37 & 29.92 & 18.77 \n"," & 59.61 & 21.29 & 41.47 \\\\\n"," & Formula & 12.58 & 15.77 & 22.67 & 6.57 & 4.23 \n"," & 20.00 & 10.31 & 13.16 \\\\\n"," \\cmidrule(lr){2-10}\n"," \\multirow[c]{2}{*}{\\quad Qwen2.5-Coder$_{\\text{7B}}$}\n"," & Text & 55.53 & 78.26 & 55.54 & 50.38 & 26.11 \n"," & 73.60 & 24.12 & 51.93 \\\\\n"," & Formula & 38.96 & 52.52 & 34.44 & 32.60 & 15.90 \n"," & 52.43 & 26.05 & 36.13 \\\\\n"," \\midrule\n"," \\multicolumn{10}{l}{\\textit{\\textbf{Supervised Fine‐Tuning (SFT)}}} \\\\\n"," \\multirow[c]{2}{*}{\\quad Llama-3.1$_{\\text{8B}}$} \n"," & Text & 66.15 & 82.95 & 50.04 & 70.27 & 39.98 \n"," & 78.45 & 23.10 & 58.71 \\\\\n"," & Formula & 59.62 & 72.48 & 58.50 & 72.54 & 44.00 \n"," & 74.95 & 31.48 & 59.08 \\\\\n"," \\cmidrule(lr){2-10}\n"," \\multirow[c]{2}{*}{\\quad Qwen2.5-Coder$_{\\text{7B}}$}\n"," & Text & 65.98 & 81.13 & 59.46 & 72.35 & 43.93 \n"," & 81.55 & 24.01 & 61.20 \\\\\n"," & Formula & 63.46 & 78.16 & 58.33 & 71.83 & 42.68 \n"," & 76.12 & 35.56 & 60.88 \\\\\n"," \\midrule\n"," \\multicolumn{10}{l}{\\textit{\\textbf{Reinforcement Learning (RL)}}} \\\\\n"," \\multirow[c]{4}{*}{\\quad Llama-3.1$_{\\text{8B}}$} \n"," & Text \n"," & 64.37 & 82.16 & 62.60 & 68.81 & 31.99 \n"," & 82.91 & 28.08 & 60.13 \\\\\n"," & Text w/ CS \n"," & \\textcolor{langdarkblue}{\\textbf{71.56}} & \\textcolor{langdarkblue}{\\textbf{87.01}} & 56.84 & 77.64 & 49.34 \n"," & \\textcolor{langdarkblue}{\\textbf{85.66}} & 28.16 & 65.17 \\\\\n"," & Formula \n"," & 57.64 & 80.09 & 60.85 & 67.93 & 29.40 \n"," & 80.78 & 30.69 & 58.20 \\\\\n"," \\rowcolor{gray!20}\n"," & Formula w/ CS \n"," & 70.49 & 83.04 & \\textcolor{langdarkblue}{\\textbf{71.99}} & \\textcolor{langdarkblue}{\\textbf{79.29}} & \\textcolor{langwildblue}{\\textbf{54.55}} \n"," & 81.29 & \\textcolor{langwildblue}{\\textbf{36.64}} & \\textcolor{langwildblue}{\\textbf{68.18}} \\\\\n"," \\cmidrule(lr){2-10}\n"," \\multirow[c]{4}{*}{\\quad Qwen2.5-Coder$_{\\text{7B}}$}\n"," & Text \n"," & 66.95 & 85.43 & 64.34 & 74.24 & 35.55 \n"," & 85.28 & 27.86 & 62.80 \\\\\n"," & Text w/ CS \n"," & \\textcolor{langwildblue}{\\textbf{71.31}} & 86.07 & 64.77 & 77.42 & 54.25 \n"," & \\textcolor{langwildblue}{\\textbf{85.43}} & 25.94 & 66.46 \\\\\n"," & Formula \n"," & 67.80 & 84.19 & 62.16 & 71.19 & 41.72 \n"," & 81.17 & 35.45 & 63.38 \\\\\n"," \\rowcolor{gray!20}\n"," & Formula w/ CS \n"," & 70.90 & \\textcolor{langwildblue}{\\textbf{86.18}} & \\textcolor{langwildblue}{\\textbf{69.21}} & \\textcolor{langwildblue}{\\textbf{77.89}} & \\textcolor{langdarkblue}{\\textbf{56.78}} \n"," & 79.14 & \\textcolor{langdarkblue}{\\textbf{39.25}} & \\textcolor{langdarkblue}{\\textbf{68.48}} \\\\\n"," \\bottomrule\n"," \\end{tabular}%\n"," }\n"," \\label{tab:main_perf}\n","\\end{table}\n"]],"string":"[\n [\n \"\\\\begin{table}[t!]\\n\",\n \" \\\\centering\\n\",\n \" \\\\caption{Performance under Zero-shot, SFT, and RL settings across models and datasets. Values in the table indicate accuracy (\\\\%). \\\\textit{Text} and \\\\textit{Formula} refer to textual and symbolic reasoning methods, respectively. \\\\textit{w/ CS} denotes cold-start RL \\\\cite{deepseekai2025deepseekr1incentivizingreasoningcapability} initialized from SFT. For open-source models, the best performance in each column is highlighted in \\\\textcolor{langdarkblue}{dark blue}, and the second-best in \\\\textcolor{langwildblue}{light blue}.}\\n\",\n \" \\\\resizebox{\\\\columnwidth}{!}{%\\n\",\n \" \\\\begin{tabular}{llcccccccc}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\multirow[c]{2}{*}{\\\\textbf{Base Model}} \\n\",\n \" & \\\\multirow[c]{2}{*}{\\\\textbf{Method}} \\n\",\n \" & \\\\multicolumn{5}{c}{\\\\textbf{In‐Distribution}} \\n\",\n \" & \\\\multicolumn{2}{c}{\\\\textbf{Out‐of‐Distribution}}\\n\",\n \" & \\\\multirow[c]{2}{*}{\\\\textbf{Overall}} \\\\\\\\\\n\",\n \" \\\\cmidrule(lr){3-7}\\\\cmidrule(lr){8-9}\\n\",\n \" & & \\\\textbf{WikiTQ} & \\\\textbf{TabFact} & \\\\textbf{FinQA} & \\\\textbf{HiTab} & \\\\textbf{MultiHiertt} \\n\",\n \" & \\\\textbf{AIT-QA} & \\\\textbf{TableBench} & \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multicolumn{10}{l}{\\\\textit{\\\\textbf{Zero‐Shot}}} \\\\\\\\\\n\",\n \" \\\\multirow[c]{2}{*}{\\\\quad \\\\textcolor{gray}{GPT-4o-mini}} \\n\",\n \" & \\\\textcolor{gray}{Text} \\n\",\n \" & \\\\textcolor{gray}{67.36} & \\\\textcolor{gray}{88.44} & \\\\textcolor{gray}{59.20} \\n\",\n \" & \\\\textcolor{gray}{57.89} & \\\\textcolor{gray}{22.41} \\n\",\n \" & \\\\textcolor{gray}{77.67} & \\\\textcolor{gray}{36.79} & \\\\textcolor{gray}{58.54} \\\\\\\\\\n\",\n \" & \\\\textcolor{gray}{Formula} \\n\",\n \" & \\\\textcolor{gray}{49.16} & \\\\textcolor{gray}{74.90} & \\\\textcolor{gray}{43.07} \\n\",\n \" & \\\\textcolor{gray}{48.17} & \\\\textcolor{gray}{28.26} \\n\",\n \" & \\\\textcolor{gray}{75.53} & \\\\textcolor{gray}{34.65} & \\\\textcolor{gray}{50.53} \\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-10}\\n\",\n \" \\\\multirow[c]{2}{*}{\\\\quad \\\\textcolor{gray}{GPT-4o}} \\n\",\n \" & \\\\textcolor{gray}{Text} \\n\",\n \" & \\\\textcolor{gray}{78.57} & \\\\textcolor{gray}{94.52} & \\\\textcolor{gray}{63.12} \\n\",\n \" & \\\\textcolor{gray}{69.26} & \\\\textcolor{gray}{36.11} \\n\",\n \" & \\\\textcolor{gray}{81.36} & \\\\textcolor{gray}{42.66} & \\\\textcolor{gray}{66.51} \\\\\\\\\\n\",\n \" & \\\\textcolor{gray}{Formula} \\n\",\n \" & \\\\textcolor{gray}{53.36} & \\\\textcolor{gray}{79.94} & \\\\textcolor{gray}{48.65} \\n\",\n \" & \\\\textcolor{gray}{65.40} & \\\\textcolor{gray}{38.41} \\n\",\n \" & \\\\textcolor{gray}{85.83} & \\\\textcolor{gray}{37.92} & \\\\textcolor{gray}{58.50} \\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-10}\\n\",\n \" \\\\multirow[c]{2}{*}{\\\\quad \\\\textcolor{gray}{O1}} \\n\",\n \" & \\\\textcolor{gray}{Text} \\n\",\n \" & \\\\textcolor{gray}{77.90} & \\\\textcolor{gray}{95.50} & \\\\textcolor{gray}{55.71} \\n\",\n \" & \\\\textcolor{gray}{74.31} & \\\\textcolor{gray}{38.31} \\n\",\n \" & \\\\textcolor{gray}{81.55} & \\\\textcolor{gray}{45.03} & \\\\textcolor{gray}{66.90} \\\\\\\\\\n\",\n \" & \\\\textcolor{gray}{Formula} \\n\",\n \" & \\\\textcolor{gray}{70.40} & \\\\textcolor{gray}{91.11} & \\\\textcolor{gray}{42.81} \\n\",\n \" & \\\\textcolor{gray}{75.00} & \\\\textcolor{gray}{48.95} \\n\",\n \" & \\\\textcolor{gray}{88.93} & \\\\textcolor{gray}{44.02} & \\\\textcolor{gray}{65.89} \\\\\\\\\\n\",\n \" \\\\noalign{\\\\vskip 2pt}\\n\",\n \" \\\\hdashline[2pt/2pt]\\n\",\n \" \\\\noalign{\\\\vskip 2pt}\\n\",\n \" \\\\multirow[c]{2}{*}{\\\\quad Llama-3.1$_{\\\\text{8B}}$} \\n\",\n \" & Text & 50.46 & 67.84 & 42.37 & 29.92 & 18.77 \\n\",\n \" & 59.61 & 21.29 & 41.47 \\\\\\\\\\n\",\n \" & Formula & 12.58 & 15.77 & 22.67 & 6.57 & 4.23 \\n\",\n \" & 20.00 & 10.31 & 13.16 \\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-10}\\n\",\n \" \\\\multirow[c]{2}{*}{\\\\quad Qwen2.5-Coder$_{\\\\text{7B}}$}\\n\",\n \" & Text & 55.53 & 78.26 & 55.54 & 50.38 & 26.11 \\n\",\n \" & 73.60 & 24.12 & 51.93 \\\\\\\\\\n\",\n \" & Formula & 38.96 & 52.52 & 34.44 & 32.60 & 15.90 \\n\",\n \" & 52.43 & 26.05 & 36.13 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multicolumn{10}{l}{\\\\textit{\\\\textbf{Supervised Fine‐Tuning (SFT)}}} \\\\\\\\\\n\",\n \" \\\\multirow[c]{2}{*}{\\\\quad Llama-3.1$_{\\\\text{8B}}$} \\n\",\n \" & Text & 66.15 & 82.95 & 50.04 & 70.27 & 39.98 \\n\",\n \" & 78.45 & 23.10 & 58.71 \\\\\\\\\\n\",\n \" & Formula & 59.62 & 72.48 & 58.50 & 72.54 & 44.00 \\n\",\n \" & 74.95 & 31.48 & 59.08 \\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-10}\\n\",\n \" \\\\multirow[c]{2}{*}{\\\\quad Qwen2.5-Coder$_{\\\\text{7B}}$}\\n\",\n \" & Text & 65.98 & 81.13 & 59.46 & 72.35 & 43.93 \\n\",\n \" & 81.55 & 24.01 & 61.20 \\\\\\\\\\n\",\n \" & Formula & 63.46 & 78.16 & 58.33 & 71.83 & 42.68 \\n\",\n \" & 76.12 & 35.56 & 60.88 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multicolumn{10}{l}{\\\\textit{\\\\textbf{Reinforcement Learning (RL)}}} \\\\\\\\\\n\",\n \" \\\\multirow[c]{4}{*}{\\\\quad Llama-3.1$_{\\\\text{8B}}$} \\n\",\n \" & Text \\n\",\n \" & 64.37 & 82.16 & 62.60 & 68.81 & 31.99 \\n\",\n \" & 82.91 & 28.08 & 60.13 \\\\\\\\\\n\",\n \" & Text w/ CS \\n\",\n \" & \\\\textcolor{langdarkblue}{\\\\textbf{71.56}} & \\\\textcolor{langdarkblue}{\\\\textbf{87.01}} & 56.84 & 77.64 & 49.34 \\n\",\n \" & \\\\textcolor{langdarkblue}{\\\\textbf{85.66}} & 28.16 & 65.17 \\\\\\\\\\n\",\n \" & Formula \\n\",\n \" & 57.64 & 80.09 & 60.85 & 67.93 & 29.40 \\n\",\n \" & 80.78 & 30.69 & 58.20 \\\\\\\\\\n\",\n \" \\\\rowcolor{gray!20}\\n\",\n \" & Formula w/ CS \\n\",\n \" & 70.49 & 83.04 & \\\\textcolor{langdarkblue}{\\\\textbf{71.99}} & \\\\textcolor{langdarkblue}{\\\\textbf{79.29}} & \\\\textcolor{langwildblue}{\\\\textbf{54.55}} \\n\",\n \" & 81.29 & \\\\textcolor{langwildblue}{\\\\textbf{36.64}} & \\\\textcolor{langwildblue}{\\\\textbf{68.18}} \\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-10}\\n\",\n \" \\\\multirow[c]{4}{*}{\\\\quad Qwen2.5-Coder$_{\\\\text{7B}}$}\\n\",\n \" & Text \\n\",\n \" & 66.95 & 85.43 & 64.34 & 74.24 & 35.55 \\n\",\n \" & 85.28 & 27.86 & 62.80 \\\\\\\\\\n\",\n \" & Text w/ CS \\n\",\n \" & \\\\textcolor{langwildblue}{\\\\textbf{71.31}} & 86.07 & 64.77 & 77.42 & 54.25 \\n\",\n \" & \\\\textcolor{langwildblue}{\\\\textbf{85.43}} & 25.94 & 66.46 \\\\\\\\\\n\",\n \" & Formula \\n\",\n \" & 67.80 & 84.19 & 62.16 & 71.19 & 41.72 \\n\",\n \" & 81.17 & 35.45 & 63.38 \\\\\\\\\\n\",\n \" \\\\rowcolor{gray!20}\\n\",\n \" & Formula w/ CS \\n\",\n \" & 70.90 & \\\\textcolor{langwildblue}{\\\\textbf{86.18}} & \\\\textcolor{langwildblue}{\\\\textbf{69.21}} & \\\\textcolor{langwildblue}{\\\\textbf{77.89}} & \\\\textcolor{langdarkblue}{\\\\textbf{56.78}} \\n\",\n \" & 79.14 & \\\\textcolor{langdarkblue}{\\\\textbf{39.25}} & \\\\textcolor{langdarkblue}{\\\\textbf{68.48}} \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}%\\n\",\n \" }\\n\",\n \" \\\\label{tab:main_perf}\\n\",\n \"\\\\end{table}\\n\"\n ]\n]","isConversational":false},"tables":{"kind":"list like","value":[["\\begin{figure}[ht!]\n"," \\centering\n"," \\includegraphics[width=0.82\\textwidth]{figures/fortune.pdf}\n"," \\caption{Overview of \\textit{Formula Tuning} (\\textsc{Fortune}).}\n"," \\label{fig:framework}\n","\\end{figure}\n"],["\\begin{figure}[t!]\n"," \\centering\n"," \\includegraphics[width=\\textwidth]{figures/method.pdf}\n"," \\caption{Comparison of Textual versus Symbolic Reasoning in Table Understanding, and Supervised Fine-Tuning (SFT) versus Reinforcement Learning (RL) in Symbolic Table Reasoning.}\n"," \\label{fig:method}\n","\\end{figure}\n"],["\\begin{table}[t!]\n"," \\centering\n"," \\caption{Performance under Zero-shot, SFT, and RL settings across models and datasets. Values in the table indicate accuracy (\\%). \\textit{Text} and \\textit{Formula} refer to textual and symbolic reasoning methods, respectively. \\textit{w/ CS} denotes cold-start RL \\cite{deepseekai2025deepseekr1incentivizingreasoningcapability} initialized from SFT. For open-source models, the best performance in each column is highlighted in \\textcolor{langdarkblue}{dark blue}, and the second-best in \\textcolor{langwildblue}{light blue}.}\n"," \\resizebox{\\columnwidth}{!}{%\n"," \\begin{tabular}{llcccccccc}\n"," \\toprule\n"," \\multirow[c]{2}{*}{\\textbf{Base Model}} \n"," & \\multirow[c]{2}{*}{\\textbf{Method}} \n"," & \\multicolumn{5}{c}{\\textbf{In‐Distribution}} \n"," & \\multicolumn{2}{c}{\\textbf{Out‐of‐Distribution}}\n"," & \\multirow[c]{2}{*}{\\textbf{Overall}} \\\\\n"," \\cmidrule(lr){3-7}\\cmidrule(lr){8-9}\n"," & & \\textbf{WikiTQ} & \\textbf{TabFact} & \\textbf{FinQA} & \\textbf{HiTab} & \\textbf{MultiHiertt} \n"," & \\textbf{AIT-QA} & \\textbf{TableBench} & \\\\\n"," \\midrule\n"," \\multicolumn{10}{l}{\\textit{\\textbf{Zero‐Shot}}} \\\\\n"," \\multirow[c]{2}{*}{\\quad \\textcolor{gray}{GPT-4o-mini}} \n"," & \\textcolor{gray}{Text} \n"," & \\textcolor{gray}{67.36} & \\textcolor{gray}{88.44} & \\textcolor{gray}{59.20} \n"," & \\textcolor{gray}{57.89} & \\textcolor{gray}{22.41} \n"," & \\textcolor{gray}{77.67} & \\textcolor{gray}{36.79} & \\textcolor{gray}{58.54} \\\\\n"," & \\textcolor{gray}{Formula} \n"," & \\textcolor{gray}{49.16} & \\textcolor{gray}{74.90} & \\textcolor{gray}{43.07} \n"," & \\textcolor{gray}{48.17} & \\textcolor{gray}{28.26} \n"," & \\textcolor{gray}{75.53} & \\textcolor{gray}{34.65} & \\textcolor{gray}{50.53} \\\\\n"," \\cmidrule(lr){2-10}\n"," \\multirow[c]{2}{*}{\\quad \\textcolor{gray}{GPT-4o}} \n"," & \\textcolor{gray}{Text} \n"," & \\textcolor{gray}{78.57} & \\textcolor{gray}{94.52} & \\textcolor{gray}{63.12} \n"," & \\textcolor{gray}{69.26} & \\textcolor{gray}{36.11} \n"," & \\textcolor{gray}{81.36} & \\textcolor{gray}{42.66} & \\textcolor{gray}{66.51} \\\\\n"," & \\textcolor{gray}{Formula} \n"," & \\textcolor{gray}{53.36} & \\textcolor{gray}{79.94} & \\textcolor{gray}{48.65} \n"," & \\textcolor{gray}{65.40} & \\textcolor{gray}{38.41} \n"," & \\textcolor{gray}{85.83} & \\textcolor{gray}{37.92} & \\textcolor{gray}{58.50} \\\\\n"," \\cmidrule(lr){2-10}\n"," \\multirow[c]{2}{*}{\\quad \\textcolor{gray}{O1}} \n"," & \\textcolor{gray}{Text} \n"," & \\textcolor{gray}{77.90} & \\textcolor{gray}{95.50} & \\textcolor{gray}{55.71} \n"," & \\textcolor{gray}{74.31} & \\textcolor{gray}{38.31} \n"," & \\textcolor{gray}{81.55} & \\textcolor{gray}{45.03} & \\textcolor{gray}{66.90} \\\\\n"," & \\textcolor{gray}{Formula} \n"," & \\textcolor{gray}{70.40} & \\textcolor{gray}{91.11} & \\textcolor{gray}{42.81} \n"," & \\textcolor{gray}{75.00} & \\textcolor{gray}{48.95} \n"," & \\textcolor{gray}{88.93} & \\textcolor{gray}{44.02} & \\textcolor{gray}{65.89} \\\\\n"," \\noalign{\\vskip 2pt}\n"," \\hdashline[2pt/2pt]\n"," \\noalign{\\vskip 2pt}\n"," \\multirow[c]{2}{*}{\\quad Llama-3.1$_{\\text{8B}}$} \n"," & Text & 50.46 & 67.84 & 42.37 & 29.92 & 18.77 \n"," & 59.61 & 21.29 & 41.47 \\\\\n"," & Formula & 12.58 & 15.77 & 22.67 & 6.57 & 4.23 \n"," & 20.00 & 10.31 & 13.16 \\\\\n"," \\cmidrule(lr){2-10}\n"," \\multirow[c]{2}{*}{\\quad Qwen2.5-Coder$_{\\text{7B}}$}\n"," & Text & 55.53 & 78.26 & 55.54 & 50.38 & 26.11 \n"," & 73.60 & 24.12 & 51.93 \\\\\n"," & Formula & 38.96 & 52.52 & 34.44 & 32.60 & 15.90 \n"," & 52.43 & 26.05 & 36.13 \\\\\n"," \\midrule\n"," \\multicolumn{10}{l}{\\textit{\\textbf{Supervised Fine‐Tuning (SFT)}}} \\\\\n"," \\multirow[c]{2}{*}{\\quad Llama-3.1$_{\\text{8B}}$} \n"," & Text & 66.15 & 82.95 & 50.04 & 70.27 & 39.98 \n"," & 78.45 & 23.10 & 58.71 \\\\\n"," & Formula & 59.62 & 72.48 & 58.50 & 72.54 & 44.00 \n"," & 74.95 & 31.48 & 59.08 \\\\\n"," \\cmidrule(lr){2-10}\n"," \\multirow[c]{2}{*}{\\quad Qwen2.5-Coder$_{\\text{7B}}$}\n"," & Text & 65.98 & 81.13 & 59.46 & 72.35 & 43.93 \n"," & 81.55 & 24.01 & 61.20 \\\\\n"," & Formula & 63.46 & 78.16 & 58.33 & 71.83 & 42.68 \n"," & 76.12 & 35.56 & 60.88 \\\\\n"," \\midrule\n"," \\multicolumn{10}{l}{\\textit{\\textbf{Reinforcement Learning (RL)}}} \\\\\n"," \\multirow[c]{4}{*}{\\quad Llama-3.1$_{\\text{8B}}$} \n"," & Text \n"," & 64.37 & 82.16 & 62.60 & 68.81 & 31.99 \n"," & 82.91 & 28.08 & 60.13 \\\\\n"," & Text w/ CS \n"," & \\textcolor{langdarkblue}{\\textbf{71.56}} & \\textcolor{langdarkblue}{\\textbf{87.01}} & 56.84 & 77.64 & 49.34 \n"," & \\textcolor{langdarkblue}{\\textbf{85.66}} & 28.16 & 65.17 \\\\\n"," & Formula \n"," & 57.64 & 80.09 & 60.85 & 67.93 & 29.40 \n"," & 80.78 & 30.69 & 58.20 \\\\\n"," \\rowcolor{gray!20}\n"," & Formula w/ CS \n"," & 70.49 & 83.04 & \\textcolor{langdarkblue}{\\textbf{71.99}} & \\textcolor{langdarkblue}{\\textbf{79.29}} & \\textcolor{langwildblue}{\\textbf{54.55}} \n"," & 81.29 & \\textcolor{langwildblue}{\\textbf{36.64}} & \\textcolor{langwildblue}{\\textbf{68.18}} \\\\\n"," \\cmidrule(lr){2-10}\n"," \\multirow[c]{4}{*}{\\quad Qwen2.5-Coder$_{\\text{7B}}$}\n"," & Text \n"," & 66.95 & 85.43 & 64.34 & 74.24 & 35.55 \n"," & 85.28 & 27.86 & 62.80 \\\\\n"," & Text w/ CS \n"," & \\textcolor{langwildblue}{\\textbf{71.31}} & 86.07 & 64.77 & 77.42 & 54.25 \n"," & \\textcolor{langwildblue}{\\textbf{85.43}} & 25.94 & 66.46 \\\\\n"," & Formula \n"," & 67.80 & 84.19 & 62.16 & 71.19 & 41.72 \n"," & 81.17 & 35.45 & 63.38 \\\\\n"," \\rowcolor{gray!20}\n"," & Formula w/ CS \n"," & 70.90 & \\textcolor{langwildblue}{\\textbf{86.18}} & \\textcolor{langwildblue}{\\textbf{69.21}} & \\textcolor{langwildblue}{\\textbf{77.89}} & \\textcolor{langdarkblue}{\\textbf{56.78}} \n"," & 79.14 & \\textcolor{langdarkblue}{\\textbf{39.25}} & \\textcolor{langdarkblue}{\\textbf{68.48}} \\\\\n"," \\bottomrule\n"," \\end{tabular}%\n"," }\n"," \\label{tab:main_perf}\n","\\end{table}\n"],["\\begin{table}[t!]\n"," \\centering\n"," \\caption{Performance comparison of different methods. Values in the table indicate accuracy (\\%). Values marked with * indicate out-of-distribution results. `-' indicates results not reported in the related paper. For fine-tuning-based methods, the best performance in each column is highlighted in \\textcolor{langdarkblue}{dark blue}, and the second-best in \\textcolor{langwildblue}{light blue}.}\n"," \\resizebox{0.85\\columnwidth}{!}{\n"," \\begin{tabular}{llccccc}\n"," \\toprule\n"," \\textbf{Method} & \\textbf{Backbone} \n"," & \\textbf{WikiTQ}\\phantom{\\textsuperscript{*}} \n"," & \\textbf{TabFact}\\phantom{\\textsuperscript{*}} \n"," & \\textbf{HiTab}\\phantom{\\textsuperscript{*}} \n"," & \\textbf{FinQA}\\phantom{\\textsuperscript{*}} \n"," & \\textbf{AIT-QA}\\phantom{\\textsuperscript{*}} \\\\\n"," \\midrule\n"," \\multicolumn{7}{l}{\\textit{\\textbf{Prompting-Based Methods}}} \\\\\n"," \\quad Binder~\\cite{cheng2023bindinglanguagemodelssymbolic} \n"," & CodeX \n"," & 64.60\\phantom{\\textsuperscript{*}} \n"," & 85.10\\phantom{\\textsuperscript{*}} \n"," & -\\phantom{\\textsuperscript{*}} \n"," & -\\phantom{\\textsuperscript{*}} \n"," & -\\phantom{\\textsuperscript{*}} \\\\\n"," \\quad Dater~\\cite{ye2023largelanguagemodelsversatile} \n"," & CodeX \n"," & 65.90\\phantom{\\textsuperscript{*}} \n"," & 85.60\\phantom{\\textsuperscript{*}} \n"," & -\\phantom{\\textsuperscript{*}} \n"," & -\\phantom{\\textsuperscript{*}} \n"," & -\\phantom{\\textsuperscript{*}} \\\\\n"," \\quad API-Assisted~\\cite{cao2025tablemasterrecipeadvancetable} \n"," & CodeX \n"," & 42.40\\phantom{\\textsuperscript{*}} \n"," & -\\phantom{\\textsuperscript{*}} \n"," & 69.30\\phantom{\\textsuperscript{*}} \n"," & -\\phantom{\\textsuperscript{*}} \n"," & -\\phantom{\\textsuperscript{*}} \\\\\n"," \\quad ReAcTable~\\cite{zhang2023reactableenhancingreacttable} \n"," & CodeX \n"," & 68.00\\phantom{\\textsuperscript{*}} \n"," & 86.10\\phantom{\\textsuperscript{*}} \n"," & -\\phantom{\\textsuperscript{*}} \n"," & -\\phantom{\\textsuperscript{*}} \n"," & -\\phantom{\\textsuperscript{*}} \\\\\n"," \\quad Chain-of-Table~\\cite{wang2024chainoftableevolvingtablesreasoning} \n"," & PaLM 2 \n"," & 67.31\\phantom{\\textsuperscript{*}} \n"," & 86.61\\phantom{\\textsuperscript{*}} \n"," & -\\phantom{\\textsuperscript{*}} \n"," & -\\phantom{\\textsuperscript{*}} \n"," & -\\phantom{\\textsuperscript{*}} \\\\\n"," \\quad Norm-DP\\&Agent~\\cite{liu2023rethinkingtabulardataunderstanding} \n"," & GPT-3.5 \n"," & 73.65\\phantom{\\textsuperscript{*}} \n"," & 88.50\\phantom{\\textsuperscript{*}} \n"," & -\\phantom{\\textsuperscript{*}} \n"," & -\\phantom{\\textsuperscript{*}} \n"," & -\\phantom{\\textsuperscript{*}} \\\\\n"," \\quad TIDE DP\\&Agent~\\cite{yang2025triples} \n"," & GPT-3.5 \n"," & 75.00\\phantom{\\textsuperscript{*}} \n"," & 89.82\\phantom{\\textsuperscript{*}} \n"," & -\\phantom{\\textsuperscript{*}} \n"," & -\\phantom{\\textsuperscript{*}} \n"," & -\\phantom{\\textsuperscript{*}} \\\\\n"," \\quad TableMaster~\\cite{cao2025tablemasterrecipeadvancetable} \n"," & GPT-4o-mini \n"," & 78.13\\phantom{\\textsuperscript{*}} \n"," & 90.12\\phantom{\\textsuperscript{*}} \n"," & -\\phantom{\\textsuperscript{*}} \n"," & 66.40\\phantom{\\textsuperscript{*}} \n"," & -\\phantom{\\textsuperscript{*}} \\\\\n"," \\quad E5~\\cite{zhang-etal-2024-e5} \n"," & GPT-4 \n"," & -\\phantom{\\textsuperscript{*}} \n"," & -\\phantom{\\textsuperscript{*}} \n"," & 85.08\\phantom{\\textsuperscript{*}} \n"," & -\\phantom{\\textsuperscript{*}} \n"," & -\\phantom{\\textsuperscript{*}} \\\\\n"," \\quad SS-CoT~\\cite{zhao2024stepwiseselfconsistentmathematicalreasoning} \n"," & Llama-3.1$_{\\text{70B}}$ \n"," & 76.80\\phantom{\\textsuperscript{*}} \n"," & -\\phantom{\\textsuperscript{*}} \n"," & 79.10\\phantom{\\textsuperscript{*}} \n"," & -\\phantom{\\textsuperscript{*}} \n"," & -\\phantom{\\textsuperscript{*}} \\\\\n"," \\midrule\n"," \\multicolumn{7}{l}{\\textit{\\textbf{Finetuning-Based Methods}}} \\\\\n"," \\quad FORTAP~\\cite{cheng2022fortapusingformulasnumericalreasoningaware} \n"," & BERT+LSTM \n"," & -\\phantom{\\textsuperscript{*}} \n"," & -\\phantom{\\textsuperscript{*}} \n"," & 47.00\\phantom{\\textsuperscript{*}} \n"," & -\\phantom{\\textsuperscript{*}} \n"," & -\\phantom{\\textsuperscript{*}} \\\\\n"," \\quad TAPEX-Large~\\cite{liu2022tapextablepretraininglearning} \n"," & BART$_{\\text{Large}}$ \n"," & 59.10\\phantom{\\textsuperscript{*}} \n"," & 84.20\\phantom{\\textsuperscript{*}} \n"," & 45.60\\phantom{\\textsuperscript{*}} \n"," & -\\phantom{\\textsuperscript{*}} \n"," & -\\phantom{\\textsuperscript{*}} \\\\\n"," \\quad OmniTab~\\cite{jiang-etal-2022-omnitab} \n"," & BART$_{\\text{Large}}$ \n"," & 62.80\\phantom{\\textsuperscript{*}} \n"," & -\\phantom{\\textsuperscript{*}} \n"," & -\\phantom{\\textsuperscript{*}} \n"," & -\\phantom{\\textsuperscript{*}} \n"," & -\\phantom{\\textsuperscript{*}} \\\\\n"," \\quad TableLlama~\\cite{zhang2024tablellamaopenlargegeneralist} \n"," & Llama-2$_{\\text{7B}}$ \n"," & 32.14\\textsuperscript{*} \n"," & 82.55\\phantom{\\textsuperscript{*}} \n"," & 60.48\\phantom{\\textsuperscript{*}} \n"," & 2.27\\textsuperscript{*} \n"," & 26.99\\textsuperscript{*} \\\\\n"," \\quad TableLLM~\\cite{zhang2025tablellmenablingtabulardata} \n"," & Qwen2$_{\\text{7B}}$ \n"," & 53.59\\phantom{\\textsuperscript{*}} \n"," & 69.81\\phantom{\\textsuperscript{*}} \n"," & 43.88\\phantom{\\textsuperscript{*}} \n"," & 8.63\\textsuperscript{*} \n"," & 64.85\\phantom{\\textsuperscript{*}} \\\\\n"," \\quad TableGPT2~\\cite{su2024tablegpt2largemultimodalmodel} \n"," & Qwen2.5$_{\\text{7B}}$ \n"," & 61.42\\phantom{\\textsuperscript{*}} \n"," & 77.80\\phantom{\\textsuperscript{*}} \n"," & 70.27\\phantom{\\textsuperscript{*}} \n"," & 40.28\\textsuperscript{*} \n"," & 12.43\\textsuperscript{*} \\\\\n"," \\quad TabAF~\\cite{wang2025generaltablequestionanswering} \n"," & Qwen2.5-Coder$_{\\text{7B}}$ \n"," & \\textcolor{langwildblue}{\\textbf{74.72}}\\phantom{\\textsuperscript{*}} \n"," & 83.99\\phantom{\\textsuperscript{*}} \n"," & \\textcolor{langwildblue}{\\textbf{78.41}}\\phantom{\\textsuperscript{*}} \n"," & 45.07\\textsuperscript{*} \n"," & 62.33\\textsuperscript{*} \\\\\n"," \\rowcolor{gray!20}\n"," \\quad \\textbf{Fortune (Ours)} \n"," & Qwen2.5-Coder$_{\\text{7B}}$ \n"," & 67.05\\phantom{\\textsuperscript{*}} \n"," & \\textcolor{langwildblue}{\\textbf{85.08}}\\phantom{\\textsuperscript{*}} \n"," & 69.74\\phantom{\\textsuperscript{*}} \n"," & \\textcolor{langwildblue}{\\textbf{62.16}}\\phantom{\\textsuperscript{*}} \n"," & \\textcolor{langwildblue}{\\textbf{80.39}}\\textsuperscript{*} \\\\\n"," \\rowcolor{gray!20}\n"," \\quad \\textbf{Fortune++ (Ours)} \n"," & Qwen2.5-Coder$_{\\text{7B}}$ \n"," & \\textcolor{langdarkblue}{\\textbf{82.54}}\\phantom{\\textsuperscript{*}} \n"," & \\textcolor{langdarkblue}{\\textbf{95.06}}\\phantom{\\textsuperscript{*}} \n"," & \\textcolor{langdarkblue}{\\textbf{87.24}}\\phantom{\\textsuperscript{*}} \n"," & \\textcolor{langdarkblue}{\\textbf{80.47}}\\phantom{\\textsuperscript{*}} \n"," & \\textcolor{langdarkblue}{\\textbf{93.20}}\\textsuperscript{*} \\\\\n"," \\bottomrule\n"," \\end{tabular}%\n"," }\n"," \\label{tab:comparison}\n","\\end{table}\n"],["\\begin{table}[ht]\n","\\centering\n","\\caption{Overview of the training data and table benchmarks used in this study.}\n","\\resizebox{\\textwidth}{!}{%\n","\\begin{tabular}{llccccccl}\n","\\toprule\n","Evaluation Type & Dataset & \\# Train Data & \\# Test Data & Table Type & Domain & License & Source \\\\\n","\\midrule\n","\\multirow{5}{*}{In-Distribution}\n"," & WikiTQ~\\cite{pasupat2015compositionalsemanticparsingsemistructured}\n"," & 13{,}753 & 4{,}217 & Relational & Wikipedia & CC-BY-SA-4.0 & \\href{https://github.com/ppasupat/WikiTableQuestions}{Link} \\\\\n"," & TabFact~\\cite{chen2020tabfactlargescaledatasettablebased}\n"," & 10{,}000 & 2{,}024 & Relational & Wikipedia & CC-BY-4.0 & \\href{https://github.com/wenhuchen/Table-Fact-Checking}{Link} \\\\\n"," & FinQA~\\cite{chen-etal-2021-finqa}\n"," & 6{,}251 & 1{,}147 & Relational & Finance & MIT & \\href{https://github.com/czyssrs/FinQA}{Link} \\\\\n"," & HiTab~\\cite{cheng2021hitab}\n"," & 7{,}399 & 1{,}583 & Hierarchical & Statistical Reports & C-UDA 1.0 & \\href{https://github.com/microsoft/HiTab}{Link} \\\\\n"," & MultiHiertt~\\cite{zhao-etal-2022-multihiertt}\n"," & 7{,}795 & 1{,}038 & Multiple \\& Hierarchical & Finance & MIT & \\href{https://github.com/psunlpgroup/MultiHiertt}{Link} \\\\\n","\\midrule\n","\\multirow{2}{*}{Out-of-Distribution}\n"," & AIT-QA~\\cite{katsis2021aitqa}\n"," & -- & 515 & Hierarchical & Airline & CDLA-Sharing-1.0 & \\href{https://github.com/IBM/AITQA}{Link} \\\\\n"," & TableBench~\\cite{wu2025tablebenchcomprehensivecomplexbenchmark}\n"," & -- & 883 & Relational & Cross Domain & CC0-1.0 & \\href{https://huggingface.co/datasets/Multilingual-Multimodal-NLP/TableBench}{Link} \\\\\n","\\bottomrule\n","\\end{tabular}\n","} % end resizebox\n","\\label{tab:datasets}\n","\\end{table}\n"],["\\begin{table}[ht!]\n"," \\centering\n"," \\caption{Performance comparison of TabAF, Fortune, and Fortune++ variants. Values in the table indicate accuracy (\\%). `-' indicates results not reported in the related paper. Gray rows represent upper-bound performance. Numbers in parentheses with a downward arrow (\\(\\downarrow\\)) indicate the performance drop relative to the default Fortune++ configuration. Top results are highlighted in \\textcolor{langdarkblue}{dark blue}.}\n"," \\resizebox{\\columnwidth}{!}{\n"," \\setlength{\\aboverulesep}{1pt}\n"," \\setlength{\\belowrulesep}{1pt}\n"," \\begin{tabular}{llccccccc}\n"," \\toprule\n"," \\multicolumn{2}{l}{\\textbf{Method}} \n"," & \\textbf{WikiTQ} & \\textbf{TabFact} & \\textbf{FinQA} \n"," & \\textbf{HiTab} & \\textbf{MultiHiertt} \n"," & \\textbf{AIT-QA} & \\textbf{TableBench} \\\\\n"," \\midrule\n"," \\multirow{4}{*}{TabAF \\cite{wang2025generaltablequestionanswering}} \n"," & \\textcolor{gray}{Upper Bound} \n"," & \\textcolor{gray}{80.13} & \\textcolor{gray}{94.02} & \\textcolor{gray}{-} \n"," & \\textcolor{gray}{82.07} & \\textcolor{gray}{-} \n"," & \\textcolor{gray}{-} & \\textcolor{gray}{-} \\\\\n"," & 5 Text \n"," & 61.42 & 81.47 & -\n"," & 74.24 & - \n"," & - & - \\\\\n"," & 5 Formula \n"," & 64.20 & 67.54 & - \n"," & 74.87 & - \n"," & - & - \\\\\n"," & 5 Text + 5 Formula \n"," & 74.72 & 83.99 & -\n"," & 78.41 & - \n"," & - & - \\\\\n"," \\midrule\n"," \\multirow{2}{*}{Fortune} \n"," & \\textcolor{gray}{Upper Bound} \n"," & \\textcolor{gray}{77.35} & \\textcolor{gray}{96.49} & \\textcolor{gray}{72.01} \n"," & \\textcolor{gray}{79.91} & \\textcolor{gray}{54.82} \n"," & \\textcolor{gray}{88.93} & \\textcolor{gray}{43.26} \\\\\n"," & 10 Formula \n"," & 67.05 & 85.08 & 62.16 \n"," & 69.74 & 40.85 \n"," & 80.39 & 35.22 \\\\\n"," \\midrule\n"," \\multirow{4}{*}{Fortune++} \n"," & \\textcolor{gray}{Upper Bound} \n"," & \\textcolor{gray}{93.62} & \\textcolor{gray}{99.51} & \\textcolor{gray}{91.02} \n"," & \\textcolor{gray}{95.01} & \\textcolor{gray}{71.29} \n"," & \\textcolor{gray}{98.06} & \\textcolor{gray}{61.16} \\\\\n"," & 5 Text \n"," & 64.52 (\\(\\downarrow\\)\\textit{18.02}) \n"," & 85.18 (\\(\\downarrow\\)\\textit{9.88}) \n"," & 63.64 (\\(\\downarrow\\)\\textit{16.83}) \n"," & 74.48 (\\(\\downarrow\\)\\textit{12.76}) \n"," & 36.42 (\\(\\downarrow\\)\\textit{15.31}) \n"," & 83.30 (\\(\\downarrow\\)\\textit{9.90}) \n"," & 28.31 (\\(\\downarrow\\)\\textit{16.65}) \\\\\n"," & 5 Formula \n"," & 66.48 (\\(\\downarrow\\)\\textit{16.06}) \n"," & 82.41 (\\(\\downarrow\\)\\textit{12.65}) \n"," & 61.99 (\\(\\downarrow\\)\\textit{18.48}) \n"," & 68.54 (\\(\\downarrow\\)\\textit{18.70}) \n"," & 39.60 (\\(\\downarrow\\)\\textit{12.13}) \n"," & 79.42 (\\(\\downarrow\\)\\textit{13.78}) \n"," & 34.65 (\\(\\downarrow\\)\\textit{10.31}) \\\\\n"," & 5 Text + 5 Formula \n"," & \\textcolor{langdarkblue}{\\textbf{82.54}} \n"," & \\textcolor{langdarkblue}{\\textbf{95.06}} \n"," & \\textcolor{langdarkblue}{\\textbf{80.47}} \n"," & \\textcolor{langdarkblue}{\\textbf{87.24}} \n"," & \\textcolor{langdarkblue}{\\textbf{51.73}} \n"," & \\textcolor{langdarkblue}{\\textbf{93.20}} \n"," & \\textcolor{langdarkblue}{\\textbf{44.96}} \\\\\n"," \\bottomrule\n"," \\end{tabular}%\n"," }\n"," \\label{tab:ablation}\n","\\end{table}\n"],["\\begin{figure}[t]\n"," \\centering\n"," \\begin{subfigure}[t]{0.48\\textwidth}\n"," \\centering\n"," \\includegraphics[height=120pt]{figures/symbolic_zs.pdf}\n"," \\caption{Zero-Shot setting}\n"," \\label{fig:symbolic_zs}\n"," \\end{subfigure}\n"," \\begin{subfigure}[t]{0.48\\textwidth}\n"," \\centering\n"," \\includegraphics[height=120pt]{figures/symbolic_rl.pdf}\n"," \\caption{Reinforcement Learning setting}\n"," \\label{fig:symbolic_rl}\n"," \\end{subfigure}\n"," \\caption{Comparison of performance across different symbolic reasoning methods under Zero-Shot and Reinforcement Learning (RL) settings. Values in the figure indicate accuracy (\\%).}\n"," \\label{fig:symbolic_comparison}\n","\\end{figure}\n"],["\\begin{table}[ht!]\n"," \\centering\n"," \\caption{Performance comparison of different symbolic reasoning methods (SQL, Python, and Formula) under Zero-shot and RL settings. Values in the table indicate accuracy (\\%). The best performance in each column is highlighted in \\textcolor{langdarkblue}{dark blue}.}\n"," \\resizebox{0.6\\columnwidth}{!}{%\n"," \\begin{tabular}{lccccc}\n"," \\toprule\n"," \\textbf{Method} & \\textbf{WikiTQ} & \\textbf{TabFact} & \\textbf{FinQA} & \\textbf{TableBench} & \\textbf{Overall} \\\\\n"," \\midrule\n"," \\multicolumn{6}{l}{\\textit{\\textbf{Zero-Shot}}} \\\\\n"," \\quad SQL & 17.07 & 21.15 & 1.51 & 7.64 & 11.84 \\\\\n"," \\quad Python & 30.96 & \\textcolor{langdarkblue}{\\textbf{60.39}} & \\textcolor{langdarkblue}{\\textbf{34.87}} & 16.33 & 35.64 \\\\\n"," \\quad \\textbf{Formula}\n"," & \\textcolor{langdarkblue}{\\textbf{38.96}} \n"," & 52.52 \n"," & 34.44\n"," & \\textcolor{langdarkblue}{\\textbf{26.05}} \n"," & \\textcolor{langdarkblue}{\\textbf{37.99}} \\\\\n"," \\midrule\n"," \\multicolumn{6}{l}{\\textit{\\textbf{Reinforcement Learning (RL)}}} \\\\\n"," \\quad SQL & 67.58 & 83.94 & 38.79 & 32.09 & 55.60 \\\\\n"," \\quad Python & 70.46 & 84.42 & 65.85 & 35.26 & 64.00 \\\\\n"," \\quad \\textbf{Formula}\n"," & \\textcolor{langdarkblue}{\\textbf{70.67}} \n"," & \\textcolor{langdarkblue}{\\textbf{84.50}} \n"," & \\textcolor{langdarkblue}{\\textbf{65.89}} \n"," & \\textcolor{langdarkblue}{\\textbf{35.84}} \n"," & \\textcolor{langdarkblue}{\\textbf{64.23}} \\\\\n"," \\bottomrule\n"," \\end{tabular}%\n"," }\n"," \\label{tab:symbolic}\n","\\end{table}\n"],["\\begin{figure}[t!]\n"," \\centering\n"," \\includegraphics[width=\\textwidth]{figures/reasoning_impact.pdf}\n"," \\caption{Performance comparison with and without explicit reasoning process under Zero-Shot and Reinforcement Learning (RL) settings across various datasets. Each group of bars shows the accuracy (\\%) achieved by four configurations: Zero-Shot without Reasoning, Zero-Shot with Reasoning, RL without Reasoning, and RL with Reasoning.}\n"," \\label{fig:reasoning_impact}\n","\\end{figure}\n"],["\\begin{table}[ht!]\n"," \\centering\n"," \\caption{Performance comparison with and without reasoning under Zero-Shot and RL settings across various datasets. Values in the table indicate accuracy (\\%).}\n"," \\resizebox{0.9\\columnwidth}{!}{%\n"," \\begin{tabular}{lccccccc c}\n"," \\toprule\n"," \\textbf{Method} & \\textbf{WikiTQ} & \\textbf{TabFact} & \\textbf{FinQA} & \\textbf{HiTab} & \\textbf{MultiHiertt} & \\textbf{AIT-QA} & \\textbf{TableBench} & \\textbf{Overall} \\\\\n"," \\midrule\n"," \\multicolumn{9}{l}{\\textit{\\textbf{Zero Shot}}} \\\\\n"," \\quad w/o Reasoning & 42.14 & 57.76 & 33.13 & 38.35 & 15.99 & 58.45 & 28.88 & 39.24 \\\\\n"," \\quad w/ Reasoning & 38.96 & 52.52 & 34.44 & 32.60 & 15.90 & 52.43 & 26.05 & 36.13 \\\\\n"," \\midrule\n"," \\multicolumn{9}{l}{\\textit{\\textbf{Reinforcement Learning (RL)}}} \\\\\n"," \\quad w/o Reasoning & 63.39 & 80.39 & 61.99 & 67.09 & 38.15 & 78.84 & 33.41 & 60.47 \\\\\n"," \\quad w/ Reasoning & 67.80 & 84.19 & 62.16 & 71.19 & 41.72 & 81.17 & 35.45 & 63.38 \\\\\n"," \\bottomrule\n"," \\end{tabular}%\n"," }\n"," \\label{tab:wo_reasining}\n","\\end{table}\n"],["\\begin{figure}[t!]\n"," \\centering\n"," \\includegraphics[width=0.85\\textwidth]{figures/top10_formula.pdf}\n"," \\caption{Top 10 most frequent formula operators used during evaluation.}\n"," \\label{fig:top10_formula}\n","\\end{figure}\n"],["\\begin{table}[ht]\n","\\caption{Statistics of generated formulas across different table understanding datasets.}\n","\\centering\n","\\resizebox{0.7\\columnwidth}{!}{%\n","\\begin{tabular}{lcccccc}\n","\\toprule\n","\\textbf{Dataset} \n","& \\multicolumn{3}{c}{\\textbf{Table Layout}} \n","& \\multicolumn{3}{c}{\\textbf{Generated Formula}} \\\\\n","\\cmidrule(lr){2-4} \\cmidrule(lr){5-7}\n","& \\textbf{Width} & \\textbf{Height} & \\textbf{Area Size} \n","& \\textbf{Length} & \\textbf{\\# Operators} & \\textbf{\\# Variables} \\\\\n","\\midrule\n","WikiTQ & 6.28 & 19.46 & 121.68 & 21.61 & 0.84 & 1.50 \\\\\n","TabFact & 6.28 & 14.04 & 87.42 & 44.85 & 3.11 & 2.85 \\\\\n","FinQA & 3.92 & 18.09 & 70.80 & 18.84 & 1.98 & 2.94 \\\\\n","HiTab & 6.16 & 21.32 & 171.40 & 12.20 & 0.49 & 1.34 \\\\\n","MultiHiertt & 7.25 & 46.59 & 339.30 & 22.64 & 2.10 & 3.21 \\\\\n","AIT-QA & 5.62 & 13.86 & 81.91 & 5.67 & 0.11 & 1.89 \\\\\n","TableBench & 6.71 & 16.26 & 108.24 & 26.87 & 1.52 & 2.78 \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\label{tab:formula_stats}\n","\\end{table}\n"],["\\begin{table}[h]\n","\\centering\n","\\caption{Case study comparing textual and symbolic reasoning. In this example, textual reasoning yields the correct answer.}\n","\\small\n","\\begin{tabular}{p{0.15\\textwidth}p{0.8\\textwidth}}\n","\\toprule\n","\\textbf{Method} & \\textbf{Case} \\\\\n","\\midrule\n","- & \\textbf{Question:} \\textit{rapper saigon has had at least () albums released in the U.S.?} \\\\\n","& \\textbf{Table:} \\\\\n","& \n","\\includegraphics[width=0.81\\textwidth]{figures/example_table_text.png}\n","\\\\\n","& \\textbf{Groundtruth Answer:} 4 \\\\\n","\n","\n","\\midrule\n","Symbolic & \\textbf{Think:} \\textit{To compute how many unique albums Saigon has released, I examine column F (F2:F13), which lists album names. Some of these cells contain \"nan\", which I treat as missing values. I only count the unique non-missing values. A standard formula to count unique non-empty values is: =SUMPRODUCT(1/COUNTIF(F2:F13,F2:F13)) This formula works because COUNTIF returns the number of times each value appears; taking 1 over that and summing gives the count of distinct values. This will count duplicates only once and ignore empty cells or \"nan\" if treated as the same placeholder.} \\\\\n","& \\textbf{Generated Formula:} \\texttt{=SUMPRODUCT(1/COUNTIF(F2:F13,F2:F13))} \\\\\n","\\\\\n","& \\textbf{Execution Result:} 5 \\\\\n","\\\\\n","& \\textbf{Execution Accuracy:} 0.0 \\\\\n","\n","\n","\\midrule\n","Textual & \\textbf{Think:} \\textit{We are asked to find out how many albums rapper Saigon has released in the U.S. From the table, we look at the 'Album' column (column F), which lists the associated album for each song. We ignore entries that are 'nan' or empty, as those do not correspond to a known album. From F2 to F13, we extract the non-nan values: - Warning Shots 2 - The Greatest Story Never Told - The Greatest Story Never Told Chapter 2: Bread and Circuses - The Greatest Story Never Told Chapter 3: The Troubled Times of Brian Carenard These are 4 distinct album names, so the answer is 4.} \\\\\n","& \\textbf{Generated Answer:} \\texttt{4} \\\\\n","\\\\\n","& \\textbf{Answer Accuracy:} 1.0 \\\\\n","\\bottomrule\n","\\end{tabular}\n","\\label{tab:example_text}\n","\\end{table}\n"],["\\begin{table}[ht]\n","\\centering\n","\\caption{Case study comparing textual and symbolic reasoning. In this example, symbolic reasoning yields the correct answer.}\n","\\small\n","\\begin{tabular}{p{0.15\\textwidth}p{0.8\\textwidth}}\n","\\toprule\n","\\textbf{Method} & \\textbf{Case} \\\\\n","\\midrule\n","- & \\textbf{Question:} \\textit{How many elections did the party win75 or more seats?} \\\\\n","& \\textbf{Table:} \\\\\n","& \n","\\includegraphics[width=0.35\\textwidth]{figures/example_table_symbolic.png}\n","\\\\\n","& \\textbf{Groundtruth Answer:} 13\\\\\n","\n","\n","\\midrule\n","Textual & \\textbf{Think:} \\textit{- A2, 1927 (sep): 57 seats - A3, 1932: 72 seats - ... - A26, 2002: 81 seats - A27, 2007: 77 seats - A28, 2011: 20 seats (less than 75) Counting the rows where the \"seats\" column is 75 or more: - 1927 (sep): 57 - 1932: 72 - ... - 2002: 81 - 2007: 77 Counting these, we find 22 elections where the party won 75 or more seats.} \\\\\n","& \\textbf{Generated Answer:} \\texttt{22} \\\\\n","\\\\\n","& \\textbf{Execution Accuracy:} 0.0 \\\\\n","\n","\n","\\midrule\n","Symbolic & \\textbf{Think:} \\textit{1. To answer this question, I need to look into the table provided in the question. The question is asking to determine how many times a party won 75 or more seats in various elections based on a given table. 2. Since the question is focused on the number of seats won, I need to look for a column in the table that contains the number of seats each party won in each election. 3. Given that the question is asking to count the number of elections where the party won 75 or more seats, I will need to look for a column that contains the seat numbers and then count how many of those seat numbers are 75 or greater. 4. Since the question is not providing a specific column in the table for seat numbers, I will need to infer that the seat numbers are contained in column D of the provided table, which is titled \"seats\".\n","} \\\\\n","& \\textbf{Generated Formula:} \\texttt{=COUNTIF(D2:D28, \">=75\")} \\\\\n","\\\\\n","& \\textbf{Execution Result:} 13 \\\\\n","\\\\\n","& \\textbf{Execution Accuracy:} 1.0 \\\\\n","\\bottomrule\n","\\end{tabular}\n","\\label{tab:example_symbolic}\n","\\end{table}\n"],["\\begin{table}[h]\n","\\centering\n","\\caption{Case study comparing performance of SFT vs. RL. In this example, RL yields the correct answer.}\n","\\small\n","\\begin{tabular}{p{0.15\\textwidth}p{0.8\\textwidth}}\n","\\toprule\n","\\textbf{Method} & \\textbf{Case} \\\\\n","\\midrule\n","- & \\textbf{Question:} \\textit{What is the number of winning drivers that represented the U.S.?} \\\\\n","& \\textbf{Table:} \\\\\n","& \n","\\includegraphics[width=0.81\\textwidth]{figures/example_table_rl.png}\n","\\\\\n","& \\textbf{Groundtruth Answer:} 15\\\\\n","\n","\n","\\midrule\n","SFT & \\textbf{Think:} \\textit{To determine the number of of winning States that represent, we need to count the occurrences of U.S. drivers in the range B1:B10. This will give us the total count of cells that contain the value \"A1\".} \\\\\n","& \\textbf{Generated Formula:} \\texttt{=COUNTIF(B1:B10, A1)} \\\\\\\\\n","& \\textbf{Execution Result:} 0 \\\\\n","\\\\\n","& \\textbf{Execution Accuracy:} 0.0 \\\\\n","\n","\n","\\midrule\n","RL & \\textbf{Think:} \\textit{1. To answer this question, I need to look into the table provided in the question. The question is asking to determine how many times a driver from the United States (U.S.) won a race based on a given table.\n","2. Since the question is focused on the winning drivers, I need to look for a column in the table that contains the names of the winning drivers.\n","3. Given that the question is asking to count how many times a U.S. driver won, I will need to look for a column that contains the names of the winning drivers and then count how many of those names are U.S. drivers.\n","4. Since the question is not providing a specific column or row in the table where the winning drivers' names are listed, I will need to assume that the winning drivers' names are listed in a column that I can reference in the table.} \\\\\n","& \\textbf{Generated Formula:} \\texttt{=COUNTIF(G2:G18, \"Rick Mears\") + COUNTIF(G2:G18, \"Al Unser, Jr.\") + COUNTIF(G2:G18, \"Michael Andretti\") + COUNTIF(G2:G18, \"Danny Sullivan\")} \\\\\n","\\\\\n","& \\textbf{Execution Result:} 15 \\\\\n","\\\\\n","& \\textbf{Execution Accuracy:} 1.0 \\\\\n","\\bottomrule\n","\\end{tabular}\n","\\label{tab:example_rl}\n","\\end{table}\n"],["\\begin{table}[ht]\n"," \\centering\n"," \\caption{Representative spreadsheet formula operators in symbolic table reasoning: Symbols, Definitions, and Examples.}\n"," \\resizebox{\\textwidth}{!}{%\n"," \\begin{tabular}{llll}\n"," \\toprule\n"," \\textbf{Name} & \\textbf{Symbol} & \\textbf{Description} & \\textbf{Example} \\\\\n"," \\midrule\n"," PLUS & + & Adds two numbers together & \\texttt{=A1 + A2} \\\\\n"," MINUS & -- & Subtracts one number from another & \\texttt{=A1 - A2} \\\\\n"," MULTIPLY & * & Multiplies two numbers together & \\texttt{=A1 * A2} \\\\\n"," DIVIDE & / & Divides one number by another & \\texttt{=A1 / A2} \\\\\n"," SUM & SUM & Sums a range of numbers & \\texttt{=SUM(A1:A10)} \\\\\n"," AVERAGE & AVERAGE& Calculates the average of a range of numbers & \\texttt{=AVERAGE(A1:A10)} \\\\\n"," COUNT & COUNT & Counts the number of numbers in a range & \\texttt{=COUNT(A1:A10)} \\\\\n"," MAX & MAX & Finds the maximum number in a range & \\texttt{=MAX(A1:A10)} \\\\\n"," MIN & MIN & Finds the minimum number in a range & \\texttt{=MIN(A1:A10)} \\\\\n"," EQUAL & = & Returns TRUE if the two values are equal & \\texttt{=A1 = A2} \\\\\n"," NOT\\_EQUAL & <> & Returns TRUE if the two values are not equal & \\texttt{=A1 <> A2} \\\\\n"," GREATER\\_THAN & > & Returns TRUE if the first value is greater than the second & \\texttt{=A1 > A2} \\\\\n"," LESS\\_THAN & < & Returns TRUE if the first value is less than the second & \\texttt{=A1 < A2} \\\\\n"," GREATER\\_THAN\\_OR\\_EQUAL & >= & Returns TRUE if the first value is greater than or equal to second& \\texttt{=A1 >= A2} \\\\\n"," LESS\\_THAN\\_OR\\_EQUAL & <= & Returns TRUE if the first value is less than or equal to second & \\texttt{=A1 <= A2} \\\\\n"," AND & AND & Returns TRUE if all arguments are TRUE & \\texttt{=AND(A1, A2)} \\\\\n"," OR & OR & Returns TRUE if any argument is TRUE & \\texttt{=OR(A1, A2)} \\\\\n"," NOT & NOT & Returns TRUE if the argument is FALSE & \\texttt{=NOT(A1)} \\\\\n"," IF & IF & Returns one value if a condition is TRUE and another if FALSE & \\texttt{=IF(A1 > 10, \"Yes\", \"No\")} \\\\\n"," TRUE & TRUE & Returns TRUE & \\texttt{=TRUE} \\\\\n"," FALSE & FALSE & Returns FALSE & \\texttt{=FALSE} \\\\\n"," INDEX & INDEX & Returns the value of a cell at a specific row and column & \\texttt{=INDEX(A1:A10, 1)} \\\\\n"," MATCH & MATCH & Returns the position of an item in an array (see syntax below) & \\texttt{=MATCH(\"value\", A1:A10, 0)} \\\\\n"," \\bottomrule\n"," \\end{tabular}%\n"," }\n"," \\label{tab:operator}\n","\\end{table}\n"],["\\begin{figure}[ht!]\n"," \\centering\n"," \\includegraphics[width=0.95\\textwidth]{prompts/prompt_formula.pdf}\n"," \\caption{Prompt for symbolic reasoning with formula. Blue text indicates placeholders for variables within the prompt.}\n"," \\label{fig:prompt_formula}\n","\\end{figure}\n"],["\\begin{figure}[ht!]\n"," \\centering\n"," \\includegraphics[width=0.95\\textwidth]{prompts/prompt_text.pdf}\n"," \\caption{Prompt for textual reasoning. Blue text indicates placeholders for variables within the prompt.}\n"," \\label{fig:prompt_text}\n","\\end{figure}\n"],["\\begin{figure}[ht!]\n"," \\centering\n"," \\includegraphics[width=0.95\\textwidth]{prompts/prompt_python.pdf}\n"," \\caption{Prompt for symbolic reasoning with Python. Blue text indicates placeholders for variables within the prompt.}\n"," \\label{fig:prompt_python}\n","\\end{figure}\n"],["\\begin{figure}[ht!]\n"," \\centering\n"," \\includegraphics[width=0.95\\textwidth]{prompts/prompt_sql.pdf}\n"," \\caption{Prompt for symbolic reasoning with SQL. Blue text indicates placeholders for variables within the prompt.}\n"," \\label{fig:prompt_sql}\n","\\end{figure}\n"],["\\begin{figure}[ht!]\n"," \\centering\n"," \\includegraphics[width=0.95\\textwidth]{prompts/prompt_wo_reason.pdf}\n"," \\caption{Prompt for symbolic reasoning with thinking process. Blue text indicates placeholders for variables within the prompt.}\n"," \\label{fig:prompt_wo_reason}\n","\\end{figure}\n"],["\\begin{table}[h]\n","\\caption{Notation used throughout the paper}\n","\\label{tab:notation}\n","\\begin{center}\n","\\begin{tabular}{cl}\n","\\toprule\n","\\textbf{Notation} & \\textbf{Description} \\\\\n","\\midrule\n","\\multicolumn{2}{l}{\\textit{General}} \\\\\n","$s$ & Input instance, a pair $(\\mathbb{T}, q)$ of table and question \\\\\n","$\\mathbb{T}$ & Input table with $m$ rows and $n$ columns \\\\\n","$q$ & Natural-language query \\\\\n","$C_{i,j}$ & Cell at row $i$, column $j$ in the table \\\\\n","$m,n$ & Number of rows and columns in $\\mathbb{T}$ \\\\\n","$a$ & Generated answer (textual or executed formula result) \\\\\n","$f$ & Spreadsheet formula generated by the model \\\\\n","$p(s)$ & Empirical distribution over table–query pairs \\\\\n","$r(a \\mid s)$ & Reward function evaluating answer correctness \\\\\n","$a^\\star(s)$ & Ground-truth answer for input $s$ \\\\\n","$\\operatorname{exec}(f,\\mathbb{T})$ & Deterministic executor applying $f$ to $\\mathbb{T}$ \\\\\n","\\midrule\n","\\multicolumn{2}{l}{\\textit{Policies}} \\\\\n","$\\pi_\\theta(a \\mid s)$ & LM generation policy parameterized by $\\theta$ \\\\\n","$\\pi_\\theta^{\\mathrm{txt}}$ & Textual reasoning policy (free-text answer) \\\\\n","$\\pi_\\theta^{\\mathrm{sym}}$ & Symbolic reasoning policy (formula-based) \\\\\n","$\\pi_g$ & Teacher policy in supervised fine-tuning \\\\\n","$\\pi_{\\theta^\\star}^{\\mathrm{SFT}}$ & Optimal SFT policy under Assumption \\\\\n","$\\pi_\\theta^{\\mathrm{RL}}$ & Policy learned via reinforcement learning \\\\\n","\\midrule\n","\\multicolumn{2}{l}{\\textit{Objective and Metrics}} \\\\\n","$\\mathbb{E}_{s\\sim p,\\,a\\sim\\pi}[\\cdot]$ & Expectation under inputs and policy \\\\\n","$\\mathbbm{1}[\\cdot]$ & Indicator function (1 if true, 0 otherwise) \\\\\n","\\midrule\n","\\multicolumn{2}{l}{\\textit{Assumptions and Events}} \\\\\n","$E_1$ & Event of selecting a correct high-level reasoning plan \\\\\n","$E_2$ & Event that all textual reasoning steps are correct \\\\\n","\\bottomrule\n","\\end{tabular}\n","\\end{center}\n","\\end{table}\n"]],"string":"[\n [\n \"\\\\begin{figure}[ht!]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=0.82\\\\textwidth]{figures/fortune.pdf}\\n\",\n \" \\\\caption{Overview of \\\\textit{Formula Tuning} (\\\\textsc{Fortune}).}\\n\",\n \" \\\\label{fig:framework}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{figure}[t!]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\textwidth]{figures/method.pdf}\\n\",\n \" \\\\caption{Comparison of Textual versus Symbolic Reasoning in Table Understanding, and Supervised Fine-Tuning (SFT) versus Reinforcement Learning (RL) in Symbolic Table Reasoning.}\\n\",\n \" \\\\label{fig:method}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table}[t!]\\n\",\n \" \\\\centering\\n\",\n \" \\\\caption{Performance under Zero-shot, SFT, and RL settings across models and datasets. Values in the table indicate accuracy (\\\\%). \\\\textit{Text} and \\\\textit{Formula} refer to textual and symbolic reasoning methods, respectively. \\\\textit{w/ CS} denotes cold-start RL \\\\cite{deepseekai2025deepseekr1incentivizingreasoningcapability} initialized from SFT. For open-source models, the best performance in each column is highlighted in \\\\textcolor{langdarkblue}{dark blue}, and the second-best in \\\\textcolor{langwildblue}{light blue}.}\\n\",\n \" \\\\resizebox{\\\\columnwidth}{!}{%\\n\",\n \" \\\\begin{tabular}{llcccccccc}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\multirow[c]{2}{*}{\\\\textbf{Base Model}} \\n\",\n \" & \\\\multirow[c]{2}{*}{\\\\textbf{Method}} \\n\",\n \" & \\\\multicolumn{5}{c}{\\\\textbf{In‐Distribution}} \\n\",\n \" & \\\\multicolumn{2}{c}{\\\\textbf{Out‐of‐Distribution}}\\n\",\n \" & \\\\multirow[c]{2}{*}{\\\\textbf{Overall}} \\\\\\\\\\n\",\n \" \\\\cmidrule(lr){3-7}\\\\cmidrule(lr){8-9}\\n\",\n \" & & \\\\textbf{WikiTQ} & \\\\textbf{TabFact} & \\\\textbf{FinQA} & \\\\textbf{HiTab} & \\\\textbf{MultiHiertt} \\n\",\n \" & \\\\textbf{AIT-QA} & \\\\textbf{TableBench} & \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multicolumn{10}{l}{\\\\textit{\\\\textbf{Zero‐Shot}}} \\\\\\\\\\n\",\n \" \\\\multirow[c]{2}{*}{\\\\quad \\\\textcolor{gray}{GPT-4o-mini}} \\n\",\n \" & \\\\textcolor{gray}{Text} \\n\",\n \" & \\\\textcolor{gray}{67.36} & \\\\textcolor{gray}{88.44} & \\\\textcolor{gray}{59.20} \\n\",\n \" & \\\\textcolor{gray}{57.89} & \\\\textcolor{gray}{22.41} \\n\",\n \" & \\\\textcolor{gray}{77.67} & \\\\textcolor{gray}{36.79} & \\\\textcolor{gray}{58.54} \\\\\\\\\\n\",\n \" & \\\\textcolor{gray}{Formula} \\n\",\n \" & \\\\textcolor{gray}{49.16} & \\\\textcolor{gray}{74.90} & \\\\textcolor{gray}{43.07} \\n\",\n \" & \\\\textcolor{gray}{48.17} & \\\\textcolor{gray}{28.26} \\n\",\n \" & \\\\textcolor{gray}{75.53} & \\\\textcolor{gray}{34.65} & \\\\textcolor{gray}{50.53} \\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-10}\\n\",\n \" \\\\multirow[c]{2}{*}{\\\\quad \\\\textcolor{gray}{GPT-4o}} \\n\",\n \" & \\\\textcolor{gray}{Text} \\n\",\n \" & \\\\textcolor{gray}{78.57} & \\\\textcolor{gray}{94.52} & \\\\textcolor{gray}{63.12} \\n\",\n \" & \\\\textcolor{gray}{69.26} & \\\\textcolor{gray}{36.11} \\n\",\n \" & \\\\textcolor{gray}{81.36} & \\\\textcolor{gray}{42.66} & \\\\textcolor{gray}{66.51} \\\\\\\\\\n\",\n \" & \\\\textcolor{gray}{Formula} \\n\",\n \" & \\\\textcolor{gray}{53.36} & \\\\textcolor{gray}{79.94} & \\\\textcolor{gray}{48.65} \\n\",\n \" & \\\\textcolor{gray}{65.40} & \\\\textcolor{gray}{38.41} \\n\",\n \" & \\\\textcolor{gray}{85.83} & \\\\textcolor{gray}{37.92} & \\\\textcolor{gray}{58.50} \\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-10}\\n\",\n \" \\\\multirow[c]{2}{*}{\\\\quad \\\\textcolor{gray}{O1}} \\n\",\n \" & \\\\textcolor{gray}{Text} \\n\",\n \" & \\\\textcolor{gray}{77.90} & \\\\textcolor{gray}{95.50} & \\\\textcolor{gray}{55.71} \\n\",\n \" & \\\\textcolor{gray}{74.31} & \\\\textcolor{gray}{38.31} \\n\",\n \" & \\\\textcolor{gray}{81.55} & \\\\textcolor{gray}{45.03} & \\\\textcolor{gray}{66.90} \\\\\\\\\\n\",\n \" & \\\\textcolor{gray}{Formula} \\n\",\n \" & \\\\textcolor{gray}{70.40} & \\\\textcolor{gray}{91.11} & \\\\textcolor{gray}{42.81} \\n\",\n \" & \\\\textcolor{gray}{75.00} & \\\\textcolor{gray}{48.95} \\n\",\n \" & \\\\textcolor{gray}{88.93} & \\\\textcolor{gray}{44.02} & \\\\textcolor{gray}{65.89} \\\\\\\\\\n\",\n \" \\\\noalign{\\\\vskip 2pt}\\n\",\n \" \\\\hdashline[2pt/2pt]\\n\",\n \" \\\\noalign{\\\\vskip 2pt}\\n\",\n \" \\\\multirow[c]{2}{*}{\\\\quad Llama-3.1$_{\\\\text{8B}}$} \\n\",\n \" & Text & 50.46 & 67.84 & 42.37 & 29.92 & 18.77 \\n\",\n \" & 59.61 & 21.29 & 41.47 \\\\\\\\\\n\",\n \" & Formula & 12.58 & 15.77 & 22.67 & 6.57 & 4.23 \\n\",\n \" & 20.00 & 10.31 & 13.16 \\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-10}\\n\",\n \" \\\\multirow[c]{2}{*}{\\\\quad Qwen2.5-Coder$_{\\\\text{7B}}$}\\n\",\n \" & Text & 55.53 & 78.26 & 55.54 & 50.38 & 26.11 \\n\",\n \" & 73.60 & 24.12 & 51.93 \\\\\\\\\\n\",\n \" & Formula & 38.96 & 52.52 & 34.44 & 32.60 & 15.90 \\n\",\n \" & 52.43 & 26.05 & 36.13 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multicolumn{10}{l}{\\\\textit{\\\\textbf{Supervised Fine‐Tuning (SFT)}}} \\\\\\\\\\n\",\n \" \\\\multirow[c]{2}{*}{\\\\quad Llama-3.1$_{\\\\text{8B}}$} \\n\",\n \" & Text & 66.15 & 82.95 & 50.04 & 70.27 & 39.98 \\n\",\n \" & 78.45 & 23.10 & 58.71 \\\\\\\\\\n\",\n \" & Formula & 59.62 & 72.48 & 58.50 & 72.54 & 44.00 \\n\",\n \" & 74.95 & 31.48 & 59.08 \\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-10}\\n\",\n \" \\\\multirow[c]{2}{*}{\\\\quad Qwen2.5-Coder$_{\\\\text{7B}}$}\\n\",\n \" & Text & 65.98 & 81.13 & 59.46 & 72.35 & 43.93 \\n\",\n \" & 81.55 & 24.01 & 61.20 \\\\\\\\\\n\",\n \" & Formula & 63.46 & 78.16 & 58.33 & 71.83 & 42.68 \\n\",\n \" & 76.12 & 35.56 & 60.88 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multicolumn{10}{l}{\\\\textit{\\\\textbf{Reinforcement Learning (RL)}}} \\\\\\\\\\n\",\n \" \\\\multirow[c]{4}{*}{\\\\quad Llama-3.1$_{\\\\text{8B}}$} \\n\",\n \" & Text \\n\",\n \" & 64.37 & 82.16 & 62.60 & 68.81 & 31.99 \\n\",\n \" & 82.91 & 28.08 & 60.13 \\\\\\\\\\n\",\n \" & Text w/ CS \\n\",\n \" & \\\\textcolor{langdarkblue}{\\\\textbf{71.56}} & \\\\textcolor{langdarkblue}{\\\\textbf{87.01}} & 56.84 & 77.64 & 49.34 \\n\",\n \" & \\\\textcolor{langdarkblue}{\\\\textbf{85.66}} & 28.16 & 65.17 \\\\\\\\\\n\",\n \" & Formula \\n\",\n \" & 57.64 & 80.09 & 60.85 & 67.93 & 29.40 \\n\",\n \" & 80.78 & 30.69 & 58.20 \\\\\\\\\\n\",\n \" \\\\rowcolor{gray!20}\\n\",\n \" & Formula w/ CS \\n\",\n \" & 70.49 & 83.04 & \\\\textcolor{langdarkblue}{\\\\textbf{71.99}} & \\\\textcolor{langdarkblue}{\\\\textbf{79.29}} & \\\\textcolor{langwildblue}{\\\\textbf{54.55}} \\n\",\n \" & 81.29 & \\\\textcolor{langwildblue}{\\\\textbf{36.64}} & \\\\textcolor{langwildblue}{\\\\textbf{68.18}} \\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-10}\\n\",\n \" \\\\multirow[c]{4}{*}{\\\\quad Qwen2.5-Coder$_{\\\\text{7B}}$}\\n\",\n \" & Text \\n\",\n \" & 66.95 & 85.43 & 64.34 & 74.24 & 35.55 \\n\",\n \" & 85.28 & 27.86 & 62.80 \\\\\\\\\\n\",\n \" & Text w/ CS \\n\",\n \" & \\\\textcolor{langwildblue}{\\\\textbf{71.31}} & 86.07 & 64.77 & 77.42 & 54.25 \\n\",\n \" & \\\\textcolor{langwildblue}{\\\\textbf{85.43}} & 25.94 & 66.46 \\\\\\\\\\n\",\n \" & Formula \\n\",\n \" & 67.80 & 84.19 & 62.16 & 71.19 & 41.72 \\n\",\n \" & 81.17 & 35.45 & 63.38 \\\\\\\\\\n\",\n \" \\\\rowcolor{gray!20}\\n\",\n \" & Formula w/ CS \\n\",\n \" & 70.90 & \\\\textcolor{langwildblue}{\\\\textbf{86.18}} & \\\\textcolor{langwildblue}{\\\\textbf{69.21}} & \\\\textcolor{langwildblue}{\\\\textbf{77.89}} & \\\\textcolor{langdarkblue}{\\\\textbf{56.78}} \\n\",\n \" & 79.14 & \\\\textcolor{langdarkblue}{\\\\textbf{39.25}} & \\\\textcolor{langdarkblue}{\\\\textbf{68.48}} \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}%\\n\",\n \" }\\n\",\n \" \\\\label{tab:main_perf}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[t!]\\n\",\n \" \\\\centering\\n\",\n \" \\\\caption{Performance comparison of different methods. Values in the table indicate accuracy (\\\\%). Values marked with * indicate out-of-distribution results. `-' indicates results not reported in the related paper. For fine-tuning-based methods, the best performance in each column is highlighted in \\\\textcolor{langdarkblue}{dark blue}, and the second-best in \\\\textcolor{langwildblue}{light blue}.}\\n\",\n \" \\\\resizebox{0.85\\\\columnwidth}{!}{\\n\",\n \" \\\\begin{tabular}{llccccc}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Method} & \\\\textbf{Backbone} \\n\",\n \" & \\\\textbf{WikiTQ}\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & \\\\textbf{TabFact}\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & \\\\textbf{HiTab}\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & \\\\textbf{FinQA}\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & \\\\textbf{AIT-QA}\\\\phantom{\\\\textsuperscript{*}} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multicolumn{7}{l}{\\\\textit{\\\\textbf{Prompting-Based Methods}}} \\\\\\\\\\n\",\n \" \\\\quad Binder~\\\\cite{cheng2023bindinglanguagemodelssymbolic} \\n\",\n \" & CodeX \\n\",\n \" & 64.60\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & 85.10\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & -\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & -\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & -\\\\phantom{\\\\textsuperscript{*}} \\\\\\\\\\n\",\n \" \\\\quad Dater~\\\\cite{ye2023largelanguagemodelsversatile} \\n\",\n \" & CodeX \\n\",\n \" & 65.90\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & 85.60\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & -\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & -\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & -\\\\phantom{\\\\textsuperscript{*}} \\\\\\\\\\n\",\n \" \\\\quad API-Assisted~\\\\cite{cao2025tablemasterrecipeadvancetable} \\n\",\n \" & CodeX \\n\",\n \" & 42.40\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & -\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & 69.30\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & -\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & -\\\\phantom{\\\\textsuperscript{*}} \\\\\\\\\\n\",\n \" \\\\quad ReAcTable~\\\\cite{zhang2023reactableenhancingreacttable} \\n\",\n \" & CodeX \\n\",\n \" & 68.00\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & 86.10\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & -\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & -\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & -\\\\phantom{\\\\textsuperscript{*}} \\\\\\\\\\n\",\n \" \\\\quad Chain-of-Table~\\\\cite{wang2024chainoftableevolvingtablesreasoning} \\n\",\n \" & PaLM 2 \\n\",\n \" & 67.31\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & 86.61\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & -\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & -\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & -\\\\phantom{\\\\textsuperscript{*}} \\\\\\\\\\n\",\n \" \\\\quad Norm-DP\\\\&Agent~\\\\cite{liu2023rethinkingtabulardataunderstanding} \\n\",\n \" & GPT-3.5 \\n\",\n \" & 73.65\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & 88.50\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & -\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & -\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & -\\\\phantom{\\\\textsuperscript{*}} \\\\\\\\\\n\",\n \" \\\\quad TIDE DP\\\\&Agent~\\\\cite{yang2025triples} \\n\",\n \" & GPT-3.5 \\n\",\n \" & 75.00\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & 89.82\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & -\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & -\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & -\\\\phantom{\\\\textsuperscript{*}} \\\\\\\\\\n\",\n \" \\\\quad TableMaster~\\\\cite{cao2025tablemasterrecipeadvancetable} \\n\",\n \" & GPT-4o-mini \\n\",\n \" & 78.13\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & 90.12\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & -\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & 66.40\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & -\\\\phantom{\\\\textsuperscript{*}} \\\\\\\\\\n\",\n \" \\\\quad E5~\\\\cite{zhang-etal-2024-e5} \\n\",\n \" & GPT-4 \\n\",\n \" & -\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & -\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & 85.08\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & -\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & -\\\\phantom{\\\\textsuperscript{*}} \\\\\\\\\\n\",\n \" \\\\quad SS-CoT~\\\\cite{zhao2024stepwiseselfconsistentmathematicalreasoning} \\n\",\n \" & Llama-3.1$_{\\\\text{70B}}$ \\n\",\n \" & 76.80\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & -\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & 79.10\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & -\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & -\\\\phantom{\\\\textsuperscript{*}} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multicolumn{7}{l}{\\\\textit{\\\\textbf{Finetuning-Based Methods}}} \\\\\\\\\\n\",\n \" \\\\quad FORTAP~\\\\cite{cheng2022fortapusingformulasnumericalreasoningaware} \\n\",\n \" & BERT+LSTM \\n\",\n \" & -\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & -\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & 47.00\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & -\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & -\\\\phantom{\\\\textsuperscript{*}} \\\\\\\\\\n\",\n \" \\\\quad TAPEX-Large~\\\\cite{liu2022tapextablepretraininglearning} \\n\",\n \" & BART$_{\\\\text{Large}}$ \\n\",\n \" & 59.10\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & 84.20\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & 45.60\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & -\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & -\\\\phantom{\\\\textsuperscript{*}} \\\\\\\\\\n\",\n \" \\\\quad OmniTab~\\\\cite{jiang-etal-2022-omnitab} \\n\",\n \" & BART$_{\\\\text{Large}}$ \\n\",\n \" & 62.80\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & -\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & -\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & -\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & -\\\\phantom{\\\\textsuperscript{*}} \\\\\\\\\\n\",\n \" \\\\quad TableLlama~\\\\cite{zhang2024tablellamaopenlargegeneralist} \\n\",\n \" & Llama-2$_{\\\\text{7B}}$ \\n\",\n \" & 32.14\\\\textsuperscript{*} \\n\",\n \" & 82.55\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & 60.48\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & 2.27\\\\textsuperscript{*} \\n\",\n \" & 26.99\\\\textsuperscript{*} \\\\\\\\\\n\",\n \" \\\\quad TableLLM~\\\\cite{zhang2025tablellmenablingtabulardata} \\n\",\n \" & Qwen2$_{\\\\text{7B}}$ \\n\",\n \" & 53.59\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & 69.81\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & 43.88\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & 8.63\\\\textsuperscript{*} \\n\",\n \" & 64.85\\\\phantom{\\\\textsuperscript{*}} \\\\\\\\\\n\",\n \" \\\\quad TableGPT2~\\\\cite{su2024tablegpt2largemultimodalmodel} \\n\",\n \" & Qwen2.5$_{\\\\text{7B}}$ \\n\",\n \" & 61.42\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & 77.80\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & 70.27\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & 40.28\\\\textsuperscript{*} \\n\",\n \" & 12.43\\\\textsuperscript{*} \\\\\\\\\\n\",\n \" \\\\quad TabAF~\\\\cite{wang2025generaltablequestionanswering} \\n\",\n \" & Qwen2.5-Coder$_{\\\\text{7B}}$ \\n\",\n \" & \\\\textcolor{langwildblue}{\\\\textbf{74.72}}\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & 83.99\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & \\\\textcolor{langwildblue}{\\\\textbf{78.41}}\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & 45.07\\\\textsuperscript{*} \\n\",\n \" & 62.33\\\\textsuperscript{*} \\\\\\\\\\n\",\n \" \\\\rowcolor{gray!20}\\n\",\n \" \\\\quad \\\\textbf{Fortune (Ours)} \\n\",\n \" & Qwen2.5-Coder$_{\\\\text{7B}}$ \\n\",\n \" & 67.05\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & \\\\textcolor{langwildblue}{\\\\textbf{85.08}}\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & 69.74\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & \\\\textcolor{langwildblue}{\\\\textbf{62.16}}\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & \\\\textcolor{langwildblue}{\\\\textbf{80.39}}\\\\textsuperscript{*} \\\\\\\\\\n\",\n \" \\\\rowcolor{gray!20}\\n\",\n \" \\\\quad \\\\textbf{Fortune++ (Ours)} \\n\",\n \" & Qwen2.5-Coder$_{\\\\text{7B}}$ \\n\",\n \" & \\\\textcolor{langdarkblue}{\\\\textbf{82.54}}\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & \\\\textcolor{langdarkblue}{\\\\textbf{95.06}}\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & \\\\textcolor{langdarkblue}{\\\\textbf{87.24}}\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & \\\\textcolor{langdarkblue}{\\\\textbf{80.47}}\\\\phantom{\\\\textsuperscript{*}} \\n\",\n \" & \\\\textcolor{langdarkblue}{\\\\textbf{93.20}}\\\\textsuperscript{*} \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}%\\n\",\n \" }\\n\",\n \" \\\\label{tab:comparison}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\caption{Overview of the training data and table benchmarks used in this study.}\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{%\\n\",\n \"\\\\begin{tabular}{llccccccl}\\n\",\n \"\\\\toprule\\n\",\n \"Evaluation Type & Dataset & \\\\# Train Data & \\\\# Test Data & Table Type & Domain & License & Source \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multirow{5}{*}{In-Distribution}\\n\",\n \" & WikiTQ~\\\\cite{pasupat2015compositionalsemanticparsingsemistructured}\\n\",\n \" & 13{,}753 & 4{,}217 & Relational & Wikipedia & CC-BY-SA-4.0 & \\\\href{https://github.com/ppasupat/WikiTableQuestions}{Link} \\\\\\\\\\n\",\n \" & TabFact~\\\\cite{chen2020tabfactlargescaledatasettablebased}\\n\",\n \" & 10{,}000 & 2{,}024 & Relational & Wikipedia & CC-BY-4.0 & \\\\href{https://github.com/wenhuchen/Table-Fact-Checking}{Link} \\\\\\\\\\n\",\n \" & FinQA~\\\\cite{chen-etal-2021-finqa}\\n\",\n \" & 6{,}251 & 1{,}147 & Relational & Finance & MIT & \\\\href{https://github.com/czyssrs/FinQA}{Link} \\\\\\\\\\n\",\n \" & HiTab~\\\\cite{cheng2021hitab}\\n\",\n \" & 7{,}399 & 1{,}583 & Hierarchical & Statistical Reports & C-UDA 1.0 & \\\\href{https://github.com/microsoft/HiTab}{Link} \\\\\\\\\\n\",\n \" & MultiHiertt~\\\\cite{zhao-etal-2022-multihiertt}\\n\",\n \" & 7{,}795 & 1{,}038 & Multiple \\\\& Hierarchical & Finance & MIT & \\\\href{https://github.com/psunlpgroup/MultiHiertt}{Link} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multirow{2}{*}{Out-of-Distribution}\\n\",\n \" & AIT-QA~\\\\cite{katsis2021aitqa}\\n\",\n \" & -- & 515 & Hierarchical & Airline & CDLA-Sharing-1.0 & \\\\href{https://github.com/IBM/AITQA}{Link} \\\\\\\\\\n\",\n \" & TableBench~\\\\cite{wu2025tablebenchcomprehensivecomplexbenchmark}\\n\",\n \" & -- & 883 & Relational & Cross Domain & CC0-1.0 & \\\\href{https://huggingface.co/datasets/Multilingual-Multimodal-NLP/TableBench}{Link} \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"} % end resizebox\\n\",\n \"\\\\label{tab:datasets}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[ht!]\\n\",\n \" \\\\centering\\n\",\n \" \\\\caption{Performance comparison of TabAF, Fortune, and Fortune++ variants. Values in the table indicate accuracy (\\\\%). `-' indicates results not reported in the related paper. Gray rows represent upper-bound performance. Numbers in parentheses with a downward arrow (\\\\(\\\\downarrow\\\\)) indicate the performance drop relative to the default Fortune++ configuration. Top results are highlighted in \\\\textcolor{langdarkblue}{dark blue}.}\\n\",\n \" \\\\resizebox{\\\\columnwidth}{!}{\\n\",\n \" \\\\setlength{\\\\aboverulesep}{1pt}\\n\",\n \" \\\\setlength{\\\\belowrulesep}{1pt}\\n\",\n \" \\\\begin{tabular}{llccccccc}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\multicolumn{2}{l}{\\\\textbf{Method}} \\n\",\n \" & \\\\textbf{WikiTQ} & \\\\textbf{TabFact} & \\\\textbf{FinQA} \\n\",\n \" & \\\\textbf{HiTab} & \\\\textbf{MultiHiertt} \\n\",\n \" & \\\\textbf{AIT-QA} & \\\\textbf{TableBench} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multirow{4}{*}{TabAF \\\\cite{wang2025generaltablequestionanswering}} \\n\",\n \" & \\\\textcolor{gray}{Upper Bound} \\n\",\n \" & \\\\textcolor{gray}{80.13} & \\\\textcolor{gray}{94.02} & \\\\textcolor{gray}{-} \\n\",\n \" & \\\\textcolor{gray}{82.07} & \\\\textcolor{gray}{-} \\n\",\n \" & \\\\textcolor{gray}{-} & \\\\textcolor{gray}{-} \\\\\\\\\\n\",\n \" & 5 Text \\n\",\n \" & 61.42 & 81.47 & -\\n\",\n \" & 74.24 & - \\n\",\n \" & - & - \\\\\\\\\\n\",\n \" & 5 Formula \\n\",\n \" & 64.20 & 67.54 & - \\n\",\n \" & 74.87 & - \\n\",\n \" & - & - \\\\\\\\\\n\",\n \" & 5 Text + 5 Formula \\n\",\n \" & 74.72 & 83.99 & -\\n\",\n \" & 78.41 & - \\n\",\n \" & - & - \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multirow{2}{*}{Fortune} \\n\",\n \" & \\\\textcolor{gray}{Upper Bound} \\n\",\n \" & \\\\textcolor{gray}{77.35} & \\\\textcolor{gray}{96.49} & \\\\textcolor{gray}{72.01} \\n\",\n \" & \\\\textcolor{gray}{79.91} & \\\\textcolor{gray}{54.82} \\n\",\n \" & \\\\textcolor{gray}{88.93} & \\\\textcolor{gray}{43.26} \\\\\\\\\\n\",\n \" & 10 Formula \\n\",\n \" & 67.05 & 85.08 & 62.16 \\n\",\n \" & 69.74 & 40.85 \\n\",\n \" & 80.39 & 35.22 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multirow{4}{*}{Fortune++} \\n\",\n \" & \\\\textcolor{gray}{Upper Bound} \\n\",\n \" & \\\\textcolor{gray}{93.62} & \\\\textcolor{gray}{99.51} & \\\\textcolor{gray}{91.02} \\n\",\n \" & \\\\textcolor{gray}{95.01} & \\\\textcolor{gray}{71.29} \\n\",\n \" & \\\\textcolor{gray}{98.06} & \\\\textcolor{gray}{61.16} \\\\\\\\\\n\",\n \" & 5 Text \\n\",\n \" & 64.52 (\\\\(\\\\downarrow\\\\)\\\\textit{18.02}) \\n\",\n \" & 85.18 (\\\\(\\\\downarrow\\\\)\\\\textit{9.88}) \\n\",\n \" & 63.64 (\\\\(\\\\downarrow\\\\)\\\\textit{16.83}) \\n\",\n \" & 74.48 (\\\\(\\\\downarrow\\\\)\\\\textit{12.76}) \\n\",\n \" & 36.42 (\\\\(\\\\downarrow\\\\)\\\\textit{15.31}) \\n\",\n \" & 83.30 (\\\\(\\\\downarrow\\\\)\\\\textit{9.90}) \\n\",\n \" & 28.31 (\\\\(\\\\downarrow\\\\)\\\\textit{16.65}) \\\\\\\\\\n\",\n \" & 5 Formula \\n\",\n \" & 66.48 (\\\\(\\\\downarrow\\\\)\\\\textit{16.06}) \\n\",\n \" & 82.41 (\\\\(\\\\downarrow\\\\)\\\\textit{12.65}) \\n\",\n \" & 61.99 (\\\\(\\\\downarrow\\\\)\\\\textit{18.48}) \\n\",\n \" & 68.54 (\\\\(\\\\downarrow\\\\)\\\\textit{18.70}) \\n\",\n \" & 39.60 (\\\\(\\\\downarrow\\\\)\\\\textit{12.13}) \\n\",\n \" & 79.42 (\\\\(\\\\downarrow\\\\)\\\\textit{13.78}) \\n\",\n \" & 34.65 (\\\\(\\\\downarrow\\\\)\\\\textit{10.31}) \\\\\\\\\\n\",\n \" & 5 Text + 5 Formula \\n\",\n \" & \\\\textcolor{langdarkblue}{\\\\textbf{82.54}} \\n\",\n \" & \\\\textcolor{langdarkblue}{\\\\textbf{95.06}} \\n\",\n \" & \\\\textcolor{langdarkblue}{\\\\textbf{80.47}} \\n\",\n \" & \\\\textcolor{langdarkblue}{\\\\textbf{87.24}} \\n\",\n \" & \\\\textcolor{langdarkblue}{\\\\textbf{51.73}} \\n\",\n \" & \\\\textcolor{langdarkblue}{\\\\textbf{93.20}} \\n\",\n \" & \\\\textcolor{langdarkblue}{\\\\textbf{44.96}} \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}%\\n\",\n \" }\\n\",\n \" \\\\label{tab:ablation}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{figure}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\begin{subfigure}[t]{0.48\\\\textwidth}\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[height=120pt]{figures/symbolic_zs.pdf}\\n\",\n \" \\\\caption{Zero-Shot setting}\\n\",\n \" \\\\label{fig:symbolic_zs}\\n\",\n \" \\\\end{subfigure}\\n\",\n \" \\\\begin{subfigure}[t]{0.48\\\\textwidth}\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[height=120pt]{figures/symbolic_rl.pdf}\\n\",\n \" \\\\caption{Reinforcement Learning setting}\\n\",\n \" \\\\label{fig:symbolic_rl}\\n\",\n \" \\\\end{subfigure}\\n\",\n \" \\\\caption{Comparison of performance across different symbolic reasoning methods under Zero-Shot and Reinforcement Learning (RL) settings. Values in the figure indicate accuracy (\\\\%).}\\n\",\n \" \\\\label{fig:symbolic_comparison}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table}[ht!]\\n\",\n \" \\\\centering\\n\",\n \" \\\\caption{Performance comparison of different symbolic reasoning methods (SQL, Python, and Formula) under Zero-shot and RL settings. Values in the table indicate accuracy (\\\\%). The best performance in each column is highlighted in \\\\textcolor{langdarkblue}{dark blue}.}\\n\",\n \" \\\\resizebox{0.6\\\\columnwidth}{!}{%\\n\",\n \" \\\\begin{tabular}{lccccc}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Method} & \\\\textbf{WikiTQ} & \\\\textbf{TabFact} & \\\\textbf{FinQA} & \\\\textbf{TableBench} & \\\\textbf{Overall} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multicolumn{6}{l}{\\\\textit{\\\\textbf{Zero-Shot}}} \\\\\\\\\\n\",\n \" \\\\quad SQL & 17.07 & 21.15 & 1.51 & 7.64 & 11.84 \\\\\\\\\\n\",\n \" \\\\quad Python & 30.96 & \\\\textcolor{langdarkblue}{\\\\textbf{60.39}} & \\\\textcolor{langdarkblue}{\\\\textbf{34.87}} & 16.33 & 35.64 \\\\\\\\\\n\",\n \" \\\\quad \\\\textbf{Formula}\\n\",\n \" & \\\\textcolor{langdarkblue}{\\\\textbf{38.96}} \\n\",\n \" & 52.52 \\n\",\n \" & 34.44\\n\",\n \" & \\\\textcolor{langdarkblue}{\\\\textbf{26.05}} \\n\",\n \" & \\\\textcolor{langdarkblue}{\\\\textbf{37.99}} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multicolumn{6}{l}{\\\\textit{\\\\textbf{Reinforcement Learning (RL)}}} \\\\\\\\\\n\",\n \" \\\\quad SQL & 67.58 & 83.94 & 38.79 & 32.09 & 55.60 \\\\\\\\\\n\",\n \" \\\\quad Python & 70.46 & 84.42 & 65.85 & 35.26 & 64.00 \\\\\\\\\\n\",\n \" \\\\quad \\\\textbf{Formula}\\n\",\n \" & \\\\textcolor{langdarkblue}{\\\\textbf{70.67}} \\n\",\n \" & \\\\textcolor{langdarkblue}{\\\\textbf{84.50}} \\n\",\n \" & \\\\textcolor{langdarkblue}{\\\\textbf{65.89}} \\n\",\n \" & \\\\textcolor{langdarkblue}{\\\\textbf{35.84}} \\n\",\n \" & \\\\textcolor{langdarkblue}{\\\\textbf{64.23}} \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}%\\n\",\n \" }\\n\",\n \" \\\\label{tab:symbolic}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{figure}[t!]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\textwidth]{figures/reasoning_impact.pdf}\\n\",\n \" \\\\caption{Performance comparison with and without explicit reasoning process under Zero-Shot and Reinforcement Learning (RL) settings across various datasets. Each group of bars shows the accuracy (\\\\%) achieved by four configurations: Zero-Shot without Reasoning, Zero-Shot with Reasoning, RL without Reasoning, and RL with Reasoning.}\\n\",\n \" \\\\label{fig:reasoning_impact}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table}[ht!]\\n\",\n \" \\\\centering\\n\",\n \" \\\\caption{Performance comparison with and without reasoning under Zero-Shot and RL settings across various datasets. Values in the table indicate accuracy (\\\\%).}\\n\",\n \" \\\\resizebox{0.9\\\\columnwidth}{!}{%\\n\",\n \" \\\\begin{tabular}{lccccccc c}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Method} & \\\\textbf{WikiTQ} & \\\\textbf{TabFact} & \\\\textbf{FinQA} & \\\\textbf{HiTab} & \\\\textbf{MultiHiertt} & \\\\textbf{AIT-QA} & \\\\textbf{TableBench} & \\\\textbf{Overall} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multicolumn{9}{l}{\\\\textit{\\\\textbf{Zero Shot}}} \\\\\\\\\\n\",\n \" \\\\quad w/o Reasoning & 42.14 & 57.76 & 33.13 & 38.35 & 15.99 & 58.45 & 28.88 & 39.24 \\\\\\\\\\n\",\n \" \\\\quad w/ Reasoning & 38.96 & 52.52 & 34.44 & 32.60 & 15.90 & 52.43 & 26.05 & 36.13 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multicolumn{9}{l}{\\\\textit{\\\\textbf{Reinforcement Learning (RL)}}} \\\\\\\\\\n\",\n \" \\\\quad w/o Reasoning & 63.39 & 80.39 & 61.99 & 67.09 & 38.15 & 78.84 & 33.41 & 60.47 \\\\\\\\\\n\",\n \" \\\\quad w/ Reasoning & 67.80 & 84.19 & 62.16 & 71.19 & 41.72 & 81.17 & 35.45 & 63.38 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}%\\n\",\n \" }\\n\",\n \" \\\\label{tab:wo_reasining}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{figure}[t!]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=0.85\\\\textwidth]{figures/top10_formula.pdf}\\n\",\n \" \\\\caption{Top 10 most frequent formula operators used during evaluation.}\\n\",\n \" \\\\label{fig:top10_formula}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table}[ht]\\n\",\n \"\\\\caption{Statistics of generated formulas across different table understanding datasets.}\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{0.7\\\\columnwidth}{!}{%\\n\",\n \"\\\\begin{tabular}{lcccccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Dataset} \\n\",\n \"& \\\\multicolumn{3}{c}{\\\\textbf{Table Layout}} \\n\",\n \"& \\\\multicolumn{3}{c}{\\\\textbf{Generated Formula}} \\\\\\\\\\n\",\n \"\\\\cmidrule(lr){2-4} \\\\cmidrule(lr){5-7}\\n\",\n \"& \\\\textbf{Width} & \\\\textbf{Height} & \\\\textbf{Area Size} \\n\",\n \"& \\\\textbf{Length} & \\\\textbf{\\\\# Operators} & \\\\textbf{\\\\# Variables} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"WikiTQ & 6.28 & 19.46 & 121.68 & 21.61 & 0.84 & 1.50 \\\\\\\\\\n\",\n \"TabFact & 6.28 & 14.04 & 87.42 & 44.85 & 3.11 & 2.85 \\\\\\\\\\n\",\n \"FinQA & 3.92 & 18.09 & 70.80 & 18.84 & 1.98 & 2.94 \\\\\\\\\\n\",\n \"HiTab & 6.16 & 21.32 & 171.40 & 12.20 & 0.49 & 1.34 \\\\\\\\\\n\",\n \"MultiHiertt & 7.25 & 46.59 & 339.30 & 22.64 & 2.10 & 3.21 \\\\\\\\\\n\",\n \"AIT-QA & 5.62 & 13.86 & 81.91 & 5.67 & 0.11 & 1.89 \\\\\\\\\\n\",\n \"TableBench & 6.71 & 16.26 & 108.24 & 26.87 & 1.52 & 2.78 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\label{tab:formula_stats}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[h]\\n\",\n \"\\\\centering\\n\",\n \"\\\\caption{Case study comparing textual and symbolic reasoning. In this example, textual reasoning yields the correct answer.}\\n\",\n \"\\\\small\\n\",\n \"\\\\begin{tabular}{p{0.15\\\\textwidth}p{0.8\\\\textwidth}}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Method} & \\\\textbf{Case} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"- & \\\\textbf{Question:} \\\\textit{rapper saigon has had at least () albums released in the U.S.?} \\\\\\\\\\n\",\n \"& \\\\textbf{Table:} \\\\\\\\\\n\",\n \"& \\n\",\n \"\\\\includegraphics[width=0.81\\\\textwidth]{figures/example_table_text.png}\\n\",\n \"\\\\\\\\\\n\",\n \"& \\\\textbf{Groundtruth Answer:} 4 \\\\\\\\\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\\midrule\\n\",\n \"Symbolic & \\\\textbf{Think:} \\\\textit{To compute how many unique albums Saigon has released, I examine column F (F2:F13), which lists album names. Some of these cells contain \\\"nan\\\", which I treat as missing values. I only count the unique non-missing values. A standard formula to count unique non-empty values is: =SUMPRODUCT(1/COUNTIF(F2:F13,F2:F13)) This formula works because COUNTIF returns the number of times each value appears; taking 1 over that and summing gives the count of distinct values. This will count duplicates only once and ignore empty cells or \\\"nan\\\" if treated as the same placeholder.} \\\\\\\\\\n\",\n \"& \\\\textbf{Generated Formula:} \\\\texttt{=SUMPRODUCT(1/COUNTIF(F2:F13,F2:F13))} \\\\\\\\\\n\",\n \"\\\\\\\\\\n\",\n \"& \\\\textbf{Execution Result:} 5 \\\\\\\\\\n\",\n \"\\\\\\\\\\n\",\n \"& \\\\textbf{Execution Accuracy:} 0.0 \\\\\\\\\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\\midrule\\n\",\n \"Textual & \\\\textbf{Think:} \\\\textit{We are asked to find out how many albums rapper Saigon has released in the U.S. From the table, we look at the 'Album' column (column F), which lists the associated album for each song. We ignore entries that are 'nan' or empty, as those do not correspond to a known album. From F2 to F13, we extract the non-nan values: - Warning Shots 2 - The Greatest Story Never Told - The Greatest Story Never Told Chapter 2: Bread and Circuses - The Greatest Story Never Told Chapter 3: The Troubled Times of Brian Carenard These are 4 distinct album names, so the answer is 4.} \\\\\\\\\\n\",\n \"& \\\\textbf{Generated Answer:} \\\\texttt{4} \\\\\\\\\\n\",\n \"\\\\\\\\\\n\",\n \"& \\\\textbf{Answer Accuracy:} 1.0 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\label{tab:example_text}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\caption{Case study comparing textual and symbolic reasoning. In this example, symbolic reasoning yields the correct answer.}\\n\",\n \"\\\\small\\n\",\n \"\\\\begin{tabular}{p{0.15\\\\textwidth}p{0.8\\\\textwidth}}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Method} & \\\\textbf{Case} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"- & \\\\textbf{Question:} \\\\textit{How many elections did the party win75 or more seats?} \\\\\\\\\\n\",\n \"& \\\\textbf{Table:} \\\\\\\\\\n\",\n \"& \\n\",\n \"\\\\includegraphics[width=0.35\\\\textwidth]{figures/example_table_symbolic.png}\\n\",\n \"\\\\\\\\\\n\",\n \"& \\\\textbf{Groundtruth Answer:} 13\\\\\\\\\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\\midrule\\n\",\n \"Textual & \\\\textbf{Think:} \\\\textit{- A2, 1927 (sep): 57 seats - A3, 1932: 72 seats - ... - A26, 2002: 81 seats - A27, 2007: 77 seats - A28, 2011: 20 seats (less than 75) Counting the rows where the \\\"seats\\\" column is 75 or more: - 1927 (sep): 57 - 1932: 72 - ... - 2002: 81 - 2007: 77 Counting these, we find 22 elections where the party won 75 or more seats.} \\\\\\\\\\n\",\n \"& \\\\textbf{Generated Answer:} \\\\texttt{22} \\\\\\\\\\n\",\n \"\\\\\\\\\\n\",\n \"& \\\\textbf{Execution Accuracy:} 0.0 \\\\\\\\\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\\midrule\\n\",\n \"Symbolic & \\\\textbf{Think:} \\\\textit{1. To answer this question, I need to look into the table provided in the question. The question is asking to determine how many times a party won 75 or more seats in various elections based on a given table. 2. Since the question is focused on the number of seats won, I need to look for a column in the table that contains the number of seats each party won in each election. 3. Given that the question is asking to count the number of elections where the party won 75 or more seats, I will need to look for a column that contains the seat numbers and then count how many of those seat numbers are 75 or greater. 4. Since the question is not providing a specific column in the table for seat numbers, I will need to infer that the seat numbers are contained in column D of the provided table, which is titled \\\"seats\\\".\\n\",\n \"} \\\\\\\\\\n\",\n \"& \\\\textbf{Generated Formula:} \\\\texttt{=COUNTIF(D2:D28, \\\">=75\\\")} \\\\\\\\\\n\",\n \"\\\\\\\\\\n\",\n \"& \\\\textbf{Execution Result:} 13 \\\\\\\\\\n\",\n \"\\\\\\\\\\n\",\n \"& \\\\textbf{Execution Accuracy:} 1.0 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\label{tab:example_symbolic}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[h]\\n\",\n \"\\\\centering\\n\",\n \"\\\\caption{Case study comparing performance of SFT vs. RL. In this example, RL yields the correct answer.}\\n\",\n \"\\\\small\\n\",\n \"\\\\begin{tabular}{p{0.15\\\\textwidth}p{0.8\\\\textwidth}}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Method} & \\\\textbf{Case} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"- & \\\\textbf{Question:} \\\\textit{What is the number of winning drivers that represented the U.S.?} \\\\\\\\\\n\",\n \"& \\\\textbf{Table:} \\\\\\\\\\n\",\n \"& \\n\",\n \"\\\\includegraphics[width=0.81\\\\textwidth]{figures/example_table_rl.png}\\n\",\n \"\\\\\\\\\\n\",\n \"& \\\\textbf{Groundtruth Answer:} 15\\\\\\\\\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\\midrule\\n\",\n \"SFT & \\\\textbf{Think:} \\\\textit{To determine the number of of winning States that represent, we need to count the occurrences of U.S. drivers in the range B1:B10. This will give us the total count of cells that contain the value \\\"A1\\\".} \\\\\\\\\\n\",\n \"& \\\\textbf{Generated Formula:} \\\\texttt{=COUNTIF(B1:B10, A1)} \\\\\\\\\\\\\\\\\\n\",\n \"& \\\\textbf{Execution Result:} 0 \\\\\\\\\\n\",\n \"\\\\\\\\\\n\",\n \"& \\\\textbf{Execution Accuracy:} 0.0 \\\\\\\\\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\\midrule\\n\",\n \"RL & \\\\textbf{Think:} \\\\textit{1. To answer this question, I need to look into the table provided in the question. The question is asking to determine how many times a driver from the United States (U.S.) won a race based on a given table.\\n\",\n \"2. Since the question is focused on the winning drivers, I need to look for a column in the table that contains the names of the winning drivers.\\n\",\n \"3. Given that the question is asking to count how many times a U.S. driver won, I will need to look for a column that contains the names of the winning drivers and then count how many of those names are U.S. drivers.\\n\",\n \"4. Since the question is not providing a specific column or row in the table where the winning drivers' names are listed, I will need to assume that the winning drivers' names are listed in a column that I can reference in the table.} \\\\\\\\\\n\",\n \"& \\\\textbf{Generated Formula:} \\\\texttt{=COUNTIF(G2:G18, \\\"Rick Mears\\\") + COUNTIF(G2:G18, \\\"Al Unser, Jr.\\\") + COUNTIF(G2:G18, \\\"Michael Andretti\\\") + COUNTIF(G2:G18, \\\"Danny Sullivan\\\")} \\\\\\\\\\n\",\n \"\\\\\\\\\\n\",\n \"& \\\\textbf{Execution Result:} 15 \\\\\\\\\\n\",\n \"\\\\\\\\\\n\",\n \"& \\\\textbf{Execution Accuracy:} 1.0 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\label{tab:example_rl}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[ht]\\n\",\n \" \\\\centering\\n\",\n \" \\\\caption{Representative spreadsheet formula operators in symbolic table reasoning: Symbols, Definitions, and Examples.}\\n\",\n \" \\\\resizebox{\\\\textwidth}{!}{%\\n\",\n \" \\\\begin{tabular}{llll}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Name} & \\\\textbf{Symbol} & \\\\textbf{Description} & \\\\textbf{Example} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" PLUS & + & Adds two numbers together & \\\\texttt{=A1 + A2} \\\\\\\\\\n\",\n \" MINUS & -- & Subtracts one number from another & \\\\texttt{=A1 - A2} \\\\\\\\\\n\",\n \" MULTIPLY & * & Multiplies two numbers together & \\\\texttt{=A1 * A2} \\\\\\\\\\n\",\n \" DIVIDE & / & Divides one number by another & \\\\texttt{=A1 / A2} \\\\\\\\\\n\",\n \" SUM & SUM & Sums a range of numbers & \\\\texttt{=SUM(A1:A10)} \\\\\\\\\\n\",\n \" AVERAGE & AVERAGE& Calculates the average of a range of numbers & \\\\texttt{=AVERAGE(A1:A10)} \\\\\\\\\\n\",\n \" COUNT & COUNT & Counts the number of numbers in a range & \\\\texttt{=COUNT(A1:A10)} \\\\\\\\\\n\",\n \" MAX & MAX & Finds the maximum number in a range & \\\\texttt{=MAX(A1:A10)} \\\\\\\\\\n\",\n \" MIN & MIN & Finds the minimum number in a range & \\\\texttt{=MIN(A1:A10)} \\\\\\\\\\n\",\n \" EQUAL & = & Returns TRUE if the two values are equal & \\\\texttt{=A1 = A2} \\\\\\\\\\n\",\n \" NOT\\\\_EQUAL & <> & Returns TRUE if the two values are not equal & \\\\texttt{=A1 <> A2} \\\\\\\\\\n\",\n \" GREATER\\\\_THAN & > & Returns TRUE if the first value is greater than the second & \\\\texttt{=A1 > A2} \\\\\\\\\\n\",\n \" LESS\\\\_THAN & < & Returns TRUE if the first value is less than the second & \\\\texttt{=A1 < A2} \\\\\\\\\\n\",\n \" GREATER\\\\_THAN\\\\_OR\\\\_EQUAL & >= & Returns TRUE if the first value is greater than or equal to second& \\\\texttt{=A1 >= A2} \\\\\\\\\\n\",\n \" LESS\\\\_THAN\\\\_OR\\\\_EQUAL & <= & Returns TRUE if the first value is less than or equal to second & \\\\texttt{=A1 <= A2} \\\\\\\\\\n\",\n \" AND & AND & Returns TRUE if all arguments are TRUE & \\\\texttt{=AND(A1, A2)} \\\\\\\\\\n\",\n \" OR & OR & Returns TRUE if any argument is TRUE & \\\\texttt{=OR(A1, A2)} \\\\\\\\\\n\",\n \" NOT & NOT & Returns TRUE if the argument is FALSE & \\\\texttt{=NOT(A1)} \\\\\\\\\\n\",\n \" IF & IF & Returns one value if a condition is TRUE and another if FALSE & \\\\texttt{=IF(A1 > 10, \\\"Yes\\\", \\\"No\\\")} \\\\\\\\\\n\",\n \" TRUE & TRUE & Returns TRUE & \\\\texttt{=TRUE} \\\\\\\\\\n\",\n \" FALSE & FALSE & Returns FALSE & \\\\texttt{=FALSE} \\\\\\\\\\n\",\n \" INDEX & INDEX & Returns the value of a cell at a specific row and column & \\\\texttt{=INDEX(A1:A10, 1)} \\\\\\\\\\n\",\n \" MATCH & MATCH & Returns the position of an item in an array (see syntax below) & \\\\texttt{=MATCH(\\\"value\\\", A1:A10, 0)} \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}%\\n\",\n \" }\\n\",\n \" \\\\label{tab:operator}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{figure}[ht!]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=0.95\\\\textwidth]{prompts/prompt_formula.pdf}\\n\",\n \" \\\\caption{Prompt for symbolic reasoning with formula. Blue text indicates placeholders for variables within the prompt.}\\n\",\n \" \\\\label{fig:prompt_formula}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{figure}[ht!]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=0.95\\\\textwidth]{prompts/prompt_text.pdf}\\n\",\n \" \\\\caption{Prompt for textual reasoning. Blue text indicates placeholders for variables within the prompt.}\\n\",\n \" \\\\label{fig:prompt_text}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{figure}[ht!]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=0.95\\\\textwidth]{prompts/prompt_python.pdf}\\n\",\n \" \\\\caption{Prompt for symbolic reasoning with Python. Blue text indicates placeholders for variables within the prompt.}\\n\",\n \" \\\\label{fig:prompt_python}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{figure}[ht!]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=0.95\\\\textwidth]{prompts/prompt_sql.pdf}\\n\",\n \" \\\\caption{Prompt for symbolic reasoning with SQL. Blue text indicates placeholders for variables within the prompt.}\\n\",\n \" \\\\label{fig:prompt_sql}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{figure}[ht!]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=0.95\\\\textwidth]{prompts/prompt_wo_reason.pdf}\\n\",\n \" \\\\caption{Prompt for symbolic reasoning with thinking process. Blue text indicates placeholders for variables within the prompt.}\\n\",\n \" \\\\label{fig:prompt_wo_reason}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table}[h]\\n\",\n \"\\\\caption{Notation used throughout the paper}\\n\",\n \"\\\\label{tab:notation}\\n\",\n \"\\\\begin{center}\\n\",\n \"\\\\begin{tabular}{cl}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Notation} & \\\\textbf{Description} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multicolumn{2}{l}{\\\\textit{General}} \\\\\\\\\\n\",\n \"$s$ & Input instance, a pair $(\\\\mathbb{T}, q)$ of table and question \\\\\\\\\\n\",\n \"$\\\\mathbb{T}$ & Input table with $m$ rows and $n$ columns \\\\\\\\\\n\",\n \"$q$ & Natural-language query \\\\\\\\\\n\",\n \"$C_{i,j}$ & Cell at row $i$, column $j$ in the table \\\\\\\\\\n\",\n \"$m,n$ & Number of rows and columns in $\\\\mathbb{T}$ \\\\\\\\\\n\",\n \"$a$ & Generated answer (textual or executed formula result) \\\\\\\\\\n\",\n \"$f$ & Spreadsheet formula generated by the model \\\\\\\\\\n\",\n \"$p(s)$ & Empirical distribution over table–query pairs \\\\\\\\\\n\",\n \"$r(a \\\\mid s)$ & Reward function evaluating answer correctness \\\\\\\\\\n\",\n \"$a^\\\\star(s)$ & Ground-truth answer for input $s$ \\\\\\\\\\n\",\n \"$\\\\operatorname{exec}(f,\\\\mathbb{T})$ & Deterministic executor applying $f$ to $\\\\mathbb{T}$ \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multicolumn{2}{l}{\\\\textit{Policies}} \\\\\\\\\\n\",\n \"$\\\\pi_\\\\theta(a \\\\mid s)$ & LM generation policy parameterized by $\\\\theta$ \\\\\\\\\\n\",\n \"$\\\\pi_\\\\theta^{\\\\mathrm{txt}}$ & Textual reasoning policy (free-text answer) \\\\\\\\\\n\",\n \"$\\\\pi_\\\\theta^{\\\\mathrm{sym}}$ & Symbolic reasoning policy (formula-based) \\\\\\\\\\n\",\n \"$\\\\pi_g$ & Teacher policy in supervised fine-tuning \\\\\\\\\\n\",\n \"$\\\\pi_{\\\\theta^\\\\star}^{\\\\mathrm{SFT}}$ & Optimal SFT policy under Assumption \\\\\\\\\\n\",\n \"$\\\\pi_\\\\theta^{\\\\mathrm{RL}}$ & Policy learned via reinforcement learning \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multicolumn{2}{l}{\\\\textit{Objective and Metrics}} \\\\\\\\\\n\",\n \"$\\\\mathbb{E}_{s\\\\sim p,\\\\,a\\\\sim\\\\pi}[\\\\cdot]$ & Expectation under inputs and policy \\\\\\\\\\n\",\n \"$\\\\mathbbm{1}[\\\\cdot]$ & Indicator function (1 if true, 0 otherwise) \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multicolumn{2}{l}{\\\\textit{Assumptions and Events}} \\\\\\\\\\n\",\n \"$E_1$ & Event of selecting a correct high-level reasoning plan \\\\\\\\\\n\",\n \"$E_2$ & Event that all textual reasoning steps are correct \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\end{center}\\n\",\n \"\\\\end{table}\\n\"\n ]\n]","isConversational":false},"fulltext":{"kind":"string","value":"\\documentclass{article}\n\n\n% if you need to pass options to natbib, use, e.g.:\n% \\PassOptionsToPackage{numbers, compress}{natbib}\n% before loading neurips_2025\n\n\n% ready for submission\n% 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START\n\\usepackage[utf8]{inputenc}\n\\usepackage[T1]{fontenc}\n\\usepackage{algorithm}\n\\usepackage{algorithmic}\n\\usepackage{graphicx}\n\\usepackage{booktabs}\n\\usepackage{multirow}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{enumitem}\n\\usepackage{url} \n\\usepackage{xspace}\n% \\usepackage{xcolor}\n\\usepackage[table]{xcolor}\n\\usepackage{hyperref}\n\\usepackage{epigraph}\n\\usepackage{amsthm}\n\\usepackage{amssymb}\n\\usepackage{arydshln}\n\\usepackage{bbm}\n\\usepackage{wrapfig}\n\\usepackage{subcaption}\n\n% theoretical analysis\n\\theoremstyle{plain}\n\\newtheorem{theorem}{Theorem}\n\\newtheorem{lemma}{Lemma}\n\\newtheorem{corollary}{Corollary}\n\\newtheorem{proposition}{Proposition}\n\\newtheorem{claim}{Claim}\n\\newtheorem{observation}{Observation}\n\\newtheorem{fact}{Fact}\n\\newtheorem{axiom}{Axiom}\n\\newtheorem{conjecture}{Conjecture}\n\n\\theoremstyle{definition}\n\\newtheorem{definition}{Definition}\n\\newtheorem{assumption}{Assumption}\n\\newtheorem{example}{Example}\n\n\\theoremstyle{remark}\n\\newtheorem{remark}{Remark}\n\\newtheorem{note}{Note}\n\n\n\n% colorful ref\n\\definecolor{langlightblue}{rgb}{0.3, 0.65, 1}\n\\definecolor{langblue}{rgb}{0, 0.4, 0.8}\n\\definecolor{langwildblue}{rgb}{0.0, 0.45, 0.73}\n\\definecolor{langdarkblue}{rgb}{0.0, 0.0, 0.61}\n\\definecolor{langred}{rgb}{0.81, 0.09, 0.13}\n\\definecolor{langgreen}{rgb}{0.0, 0.6, 0.3}\n\\definecolor{bingpink}{rgb}{1.0, 0.41, 0.71}\n\\hypersetup{\n colorlinks=true,\n linkcolor=langdarkblue,\n linkcolor=langblue,\n citecolor=langlightblue,\n urlcolor=langdarkblue,\n}\n\n% add underline to link\n\\makeatletter\n\\AtBeginDocument{%\n \\let\\oldref\\ref\n \\renewcommand{\\ref}[1]{%\n \\hyperref[{#1}]{\\underline{\\oldref{#1}}}%\n }%\n}\n\\makeatother\n\n% use DoToc for appendix catalogue\n\n\\usepackage{titletoc}\n\\newcommand\\DoToC{%\n \\startcontents\n \\printcontents{}{1}{\\textbf{\\large Contents of Appendix}\\vskip3pt\\hrule\\vskip5pt}\n \\vskip3pt\\hrule\\vskip5pt\n}\n%%% LangTex END\n\n\n% \\newcommand{Fortune}{Fortune\\xspace}\n% \\newcommand{Fortuneplus}{Fortune++\\xspace}\n\\newcommand{\\haoyu}[1]{\\textcolor{olive}{haoyu: #1}}\n\n% \\title{\\textsc{Fortune}: Formula-Driven Reinforcement Learning for Symbolic Reasoning over Generalized Tables with Large Language Models}\n\\title{\\textsc{Fortune}: Formula-Driven Reinforcement Learning for Symbolic Table Reasoning in Language Models}\n% \\title{\\textsc{Fortune}: Formula-Driven Reinforcement Learning for Generalized Table Understanding in Language Models}\n% \\title{Fortune: Formula-Driven Reinforcement Learning for 2D Data Reasoning in Language Models}\n% \\title{Fortune: Teaching Language Models How to Use Excel Through Formula-Driven Reinforcement Learning}\n\n\n% The \\author macro works with any number of authors. There are two commands\n% used to separate the names and addresses of multiple authors: \\And and \\AND.\n%\n% Using \\And between authors leaves it to LaTeX to determine where to break the\n% lines. Using \\AND forces a line break at that point. So, if LaTeX puts 3 of 4\n% authors names on the first line, and the last on the second line, try using\n% \\AND instead of \\And before the third author name.\n\n\n\\author{\n Lang Cao$^1$\\textsuperscript{\\dag}\\thanks{\\ \\ Work done during internship at Microsoft.} \\hspace{0.3em} Jingxian Xu$^2$\\footnotemark[1] \\hspace{0.3em} Hanbing Liu$^3$\\footnotemark[1] \\hspace{0.3em} Jinyu Wang$^4$\\footnotemark[1] \\hspace{0.3em} Mengyu Zhou\\textsuperscript{\\dag}\\\\\n \\textbf{Haoyu Dong \\hspace{0.3em} Shi Han \\hspace{0.3em} Dongmei Zhang} \\\\\n $^1$University of Illinois Urbana-Champaign \\quad $^2$Nankai University \\\\ \\quad$^3$Tsinghua University \\quad$^4$Shandong University \\quad Microsoft Research \\\\\n \\texttt{langcao2@illinois.edu} \\quad \\texttt{mezho@microsoft.com}\n}\n\n\n% \\author{%\n% David S.~Hippocampus\\thanks{Use footnote for providing further information\n% about author (webpage, alternative address)---\\emph{not} for acknowledging\n% funding agencies.} \\\\\n% Department of Computer Science\\\\\n% Cranberry-Lemon University\\\\\n% Pittsburgh, PA 15213 \\\\\n% \\texttt{hippo@cs.cranberry-lemon.edu} \\\\\n% % examples of more authors\n% % \\And\n% % Coauthor \\\\\n% % Affiliation \\\\\n% % Address \\\\\n% % \\texttt{email} \\\\\n% % \\AND\n% % Coauthor \\\\\n% % Affiliation \\\\\n% % Address \\\\\n% % \\texttt{email} \\\\\n% % \\And\n% % Coauthor \\\\\n% % Affiliation \\\\\n% % Address \\\\\n% % \\texttt{email} \\\\\n% % \\And\n% % Coauthor \\\\\n% % Affiliation \\\\\n% % Address \\\\\n% % \\texttt{email} \\\\\n% }\n\n\n\\begin{document}\n\n\n\\maketitle\n\n\n% keywords: Table Understanding, Formula Learning, Large Language Models\n% TL;DR: We propose Formula Tuning (Fortune), a reinforcement learning approach that enables language models to perform symbolic table reasoning by deriving executable spreadsheet formulas.\n\n\\begin{abstract}\nTables are a fundamental structure for organizing and analyzing data, making effective table understanding a critical capability for intelligent systems. While large language models (LMs) demonstrate strong general reasoning abilities, they continue to struggle with accurate numerical or symbolic reasoning over tabular data, especially in complex scenarios. Spreadsheet formulas provide a powerful and expressive medium for representing executable symbolic operations, encoding rich reasoning patterns that remain largely underutilized. In this paper, we propose \\textit{Formula Tuning} (Fortune), a reinforcement learning (RL) framework that trains LMs to generate executable spreadsheet formulas for question answering over general tabular data. \\textit{Formula Tuning} reduces the reliance on supervised formula annotations by using binary answer correctness as a reward signal, guiding the model to learn formula derivation through reasoning. We provide a theoretical analysis of its advantages and demonstrate its effectiveness through extensive experiments on seven table reasoning benchmarks. \\textit{Formula Tuning} substantially enhances LM performance, particularly on multi-step numerical and symbolic reasoning tasks, enabling a 7B model to outperform O1 on table understanding. This highlights the potential of formula-driven RL to advance symbolic table reasoning in LMs.\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% for lang for both intern and corresponding\n\\begingroup\n\\renewcommand{\\thefootnote}{\\dag}\n\\makebox[0pt][]{\\footnotemark} % insert a invisible footnotemark\n\\footnotetext{\\ \\ Corresponding authors.}\n\\endgroup\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n% Our code can be found at \\url{https://anonymous.4open.science/r/Fortune-0597}.\n\n\\vspace{-5pt}\n\\begin{figure}[ht!]\n \\centering\n \\includegraphics[width=0.82\\textwidth]{figures/fortune.pdf}\n \\caption{Overview of \\textit{Formula Tuning} (\\textsc{Fortune}).}\n \\label{fig:framework}\n % Acknowledging Jingxian for the artwork A.\n\\end{figure}\n\\end{abstract}\n\n\n\n% paper takeaways:\n\n% motivation:\n% better table understanding\n% utilization of formula but lack of data\n% better symbolic reasoning\n\n% methods:\n% reinforcement learning\n\n% others:\n% general table understanding / general two-dimensional data\n\n\\vspace{-20pt}\n\\section{Introduction}\nTables are a common and practical data structure in daily life, playing a central role in data collection, representation, and analysis \\cite{he2023text2analysisbenchmarktablequestion, yi2025tablepilotrecommendinghumanpreferredtabular}. Recent advances in large language models (LLMs) \\cite{gunasekar2023textbooksneed, openai2024gpt4technicalreport, touvron2023llamaopenefficientfoundation} have brought impressive performance across a wide range of natural language processing tasks, including language understanding \\cite{minaee2024largelanguagemodelssurvey, zhu-etal-2024-large} and general reasoning \\cite{plaat2024reasoninglargelanguagemodels}. Naturally, LLMs have also been applied to tabular data understanding and reasoning \\cite{fang2024largelanguagemodelsllmstabular, zhang2024surveytablereasoninglarge, cao2025tablemasterrecipeadvancetable}.\n\n\nHowever, reasoning over tabular data remains a key challenge for language models (LMs) \\cite{cao2025tablemasterrecipeadvancetable}, primarily because of the numerical nature and intricate structure of tables, which pose significant obstacles to understanding both content and layout. Besides, some findings \\cite{yan2025phdlevelllmstrulygrasp} suggest current LLMs rely on pattern memorization over genuine rule learning, often leading to incorrect mathematical computations during traditional chain-of-thought reasoning, also referred to as textual reasoning. Some approaches introduce symbolic methods \\cite{chen2023programthoughtspromptingdisentangling, gao2023palprogramaidedlanguagemodels}, where models first generate symbolic representations (such as programs) and then execute them to obtain results. While this improves arithmetic accuracy, such methods often struggle to generalize due to limited symbolic reasoning or program generation capabilities. Instead of truly understanding the context and generating problem-solving programs, LMs often fall back on memorized code snippets from pretraining \\cite{yang-etal-2024-llms}. Since not all complex symbolic patterns can be memorized, and given that high-quality code supervision is scarce \\cite{bi2023programofthoughtsworkreasoning}, more effective strategies for symbolic table reasoning are needed.\n\nRecent progress in reinforcement learning (RL) for LMs shows promise for overcoming such limitations. For example, DeepSeek-R1 \\cite{deepseekai2025deepseekr1incentivizingreasoningcapability} improves mathematical reasoning in LLMs via rule-based RL rewards, without relying on step-by-step annotations. DeepRetrieval \\cite{jiang2025deepretrievalhackingrealsearch} uses retrieval metrics as RL rewards to train models to reason over queries that maximize real-world retrieval performance across search engines and databases. DeepCoder \\cite{deepcoder2025} also demonstrates the effectiveness of RL for code reasoning and generation. These works collectively demonstrate the promise of RL in enabling LMs to perform robust symbolic reasoning without explicit intermediate supervision.\n\n% \\footnote{\\href{https://support.microsoft.com/en-us/office/overview-of-formulas-34519a4e-1e8d-4f4b-84d4-d642c4f63263}{https://support.microsoft.com/en-us/office/overview-of-formulas}}\n\nWhen it comes to symbolic table reasoning, \\textbf{spreadsheet formula} \\cite{microsoft_excel_formulas} is a powerful and versatile tool. In real-world scenarios, tabular data is often stored in spreadsheet formats (e.g., Microsoft Excel, Google Sheets) \\cite{dong-etal-2024-encoding}, where each cell holds individual data values. The spreadsheet formulas embedded in these files act as lightweight, program-like constructs that enable users to compute, transform, and reason over data. Compared to structured interfaces such as SQL or Python/Pandas, which are typically restricted to relational or flat table schemas, spreadsheet formulas offer greater flexibility, as they can be applied to arbitrary two-dimensional tables without structural constraints \\cite{wang2025generaltablequestionanswering}. Moreover, spreadsheet formulas are Turing complete \\cite{smalley2023excel}, making them particularly well-suited as a medium for symbolic table reasoning.\n\nWe envision that by training LMs to understand and generate spreadsheet formulas, they can acquire more robust and generalizable symbolic reasoning capabilities over tabular data. However, current LLMs still struggle to produce accurate and reliable spreadsheet formulas, as highlighted by recent evaluations \\cite{thorne2023experimentingchatgptspreadsheetformula}. At the same time, existing publicly available spreadsheet datasets tend to include relatively simple formulas \\cite{cheng2021hitab}, rely on heuristic conversions from SQL \\cite{zhao-etal-2024-nl2formula}, or synthesize formulas using LLMs in constrained question-answering settings \\cite{wang2025generaltablequestionanswering}. These approaches fall short of capturing the complexity necessary for diverse symbolic reasoning tasks and real-world downstream applications. This limitation poses a significant barrier to effectively leveraging spreadsheet formulas for training LMs in symbolic table reasoning.\n\nTo address these challenges, we propose \\textit{\\textbf{For}mula \\textbf{Tun}ing} (\\textbf{Fortune}), a RL framework designed to teach LMs to perform symbolic reasoning over general tabular data through spreadsheet formula. Specifically, our framework leverages answer correctness of formula execution results as a reward signal to guide the LMs in deriving formulas through reasoning (Figure~\\ref{fig:framework}). This approach reduces reliance on supervised formula annotations and enables LMs to generate executable formulas that answer questions over tables with improved accuracy. Extensive experiments validate the effectiveness of \\textit{Formula Tuning}, demonstrating that RL is more effective than supervised fine-tuning (SFT) in enhancing the symbolic reasoning capabilities of LMs. We also find that initializing RL with SFT as a cold start \\cite{deepseekai2025deepseekr1incentivizingreasoningcapability} provides a stronger foundation and raises the upper bound of RL performance, with SFT serving as a form of knowledge injection. Furthermore, we train both textual and symbolic reasoning components using RL in \\textbf{Fortune++}. By jointly leveraging both components during inference, our method achieves strong performance across multiple benchmarks \\textit{(e.g., 82.54\\% on WikiTQ, 95.06\\% on TabFact, 80.47\\% on FinQA, 87.24\\% on HiTab, and 93.20\\% on AIT-QA)}.\n\n% state-of-the-art\n\n\nIn summary, this paper makes the following key contributions:\n\\begin{itemize}[leftmargin=*, itemsep=0pt, labelsep=5pt, topsep=0pt]\n \\item We propose \\textit{Formula Tuning} (\\textbf{Fortune}), a reinforcement learning framework that enhances symbolic reasoning for table understanding by training language models to generate executable spreadsheet formulas.\n \\item We provide a theoretical analysis and discussion comparing textual versus symbolic reasoning in table understanding, as well as supervised fine-tuning versus reinforcement learning in symbolic table reasoning.\n \\item We conduct extensive experiments on seven table understanding benchmarks, demonstrating the effectiveness of \\textit{Formula Tuning}, and perform comprehensive analyses to provide deeper insights.\n\\end{itemize}\n\n\n\n\n\\section{Related Work}\n\n\\noindent \\textbf{Table Understanding and Reasoning.}\n% finetuning\nMany studies have explored fine-tuning language models (LMs) to improve their ability to understand and reason over tabular data. Building on the masked language modeling introduced by BERT \\cite{devlin2019bertpretrainingdeepbidirectional}, models such as TaPas \\cite{herzig2020tapas}, PaSTA \\cite{gu2022pastatableoperationsawarefact}, and TUTA \\cite{wang2021tuta} propose specialized pretraining strategies tailored for tables. TAPEX \\cite{liu2022tapextablepretraininglearning} pretrains an encoder-decoder model as a neural SQL executor to better capture the semantics of table operations. Recent efforts, including TableLLaMA \\cite{zhang2024tablellamaopenlargegeneralist} and TableGPT \\cite{zha2023tablegptunifyingtablesnature}, build upon large decoder-only language models pretrained for general-purpose table understanding across a wide range of downstream tasks.\n\n% zero-shot symbolic reasoning\nOther studies focus on enabling LMs to better perform table-related tasks without fine-tuning. For example, Dater \\cite{ye2023largelanguagemodelsversatile} proposes strategies for dynamically constructing sub-tables, modifying the input context to enhance comprehension. Chain-of-Table \\cite{wang2024chainoftableevolvingtablesreasoning} models table reasoning as a sequence of transformations using predefined operations, gradually generating sub-tables to support complex multi-step inference. TableMaster \\cite{cao2025tablemasterrecipeadvancetable} introduces a general framework for table understanding and underscores the importance of symbolic reasoning in handling complex scenarios. Given the structured and often numerical nature of tabular data, program-of-thought prompting \\cite{chen2023programthoughtspromptingdisentangling} and other symbolic approaches \\cite{cheng2023bindinglanguagemodelssymbolic, nahid-rafiei-2024-tabsqlify, mao2024potableprogrammingstandardlytablebased} have demonstrated strong effectiveness for table reasoning.\n\n% ours - trained symbolic reasoning\n\n\n\\noindent \\textbf{Formula Learning.}\nA growing body of research has explored the potential of spreadsheet formulas as a powerful means to enhance table understanding. NL2Formula \\cite{zhao-etal-2024-nl2formula} constructs a formula generation dataset by converting text-to-SQL tasks into spreadsheet formulas, enabling position-aware reasoning from natural language queries. ForTap \\cite{cheng2022fortapusingformulasnumericalreasoningaware} leverages spreadsheet formulas as pretraining signals to enhance numerical reasoning. Auto-Formula \\cite{chen2024auto} applies contrastive learning to transfer formulas from similar spreadsheets for formula recommendation. SpreadsheetCoder \\cite{chen2021spreadsheetcoderformulapredictionsemistructured} formulates formula prediction as a program synthesis task, leveraging both headers and surrounding cell values for context. FLAME \\cite{joshi2023flamesmalllanguagemodel} trains a small domain-specific model tailored for formula repair and completion. TabAF \\cite{wang2025generaltablequestionanswering} jointly generates answers and formulas for table question answering, but relies on supervised fine-tuning over datasets generated by LLMs.\n% Some studies \\cite{singh2024empiricalstudyvalidatingsynthetic} show that validating synthetic natural language annotations can improve LLM fine-tuning for formula generation.\n\n\\noindent \\textbf{Reinforcement Learning for Language Models.}\nReinforcement Learning (RL) \\cite{Kaelbling1996ReinforcementLA} is a machine learning paradigm that trains agents to make decisions through interaction with an environment, with the goal of maximizing cumulative rewards. In the era of large language models (LLMs), RL has gained significant traction as an effective framework for aligning models with human preferences. A prominent example is Reinforcement Learning from Human Feedback (RLHF) \\cite{Christiano2017DeepRL, Stiennon2020LearningTS, Ouyang2022TrainingLM}, which leverages the Proximal Policy Optimization (PPO) algorithm \\cite{Schulman2017ProximalPO} and human preference data to train a reward model that guides the fine-tuning of LLMs. Building on RLHF, more recent algorithms such as GRPO \\cite{shao2024deepseekmathpushinglimitsmathematical} and REINFORCE++ \\cite{Hu2025REINFORCEAS} aim to enhance reward modeling and mitigate issues like biased optimization \\cite{Xu2024IsDS}.\n\n\\noindent \\textbf{Reasoning with Language Models.}\nIt has been observed that sufficiently large language models (LMs) can demonstrate emergent reasoning capabilities \\cite{wei2022emergentabilitieslargelanguage, suzgun2022challengingbigbenchtaskschainofthought}. Chain-of-thought prompting \\cite{wei2023chainofthoughtpromptingelicitsreasoning} is one technique used to elicit step-by-step reasoning, significantly improving performance on complex tasks. Further advances include self-consistency \\cite{wang2023selfconsistencyimproveschainthought} and structuring the reasoning process in forms like trees \\cite{yao2023treethoughtsdeliberateproblem} or graphs \\cite{Besta_2024, cao-2024-graphreason} are also useful for more complex reasoning tasks. RL has also been used to directly improve reasoning skills during training \\cite{lightman2023letsverifystepstep, uesato2022solvingmathwordproblems}. Notably, DeepSeek-R1 \\cite{deepseekai2025deepseekr1incentivizingreasoningcapability} demonstrates that large-scale RL can substantially boost the reasoning abilities of LMs. In terms of application, DeepRetrieval \\cite{jiang2025deepretrievalhackingrealsearch} applies RL to teach models how to reason about interacting with search engines for information retrieval, while DeepCoder \\cite{deepcoder2025} uses RL for code reasoning and generation tasks. Rec-R1 \\cite{lin2025recr1bridginggenerativelarge} also bridges LLMs and recommendation systems through RL.\n\n\n\n\\begin{figure}[t!]\n \\centering\n \\includegraphics[width=\\textwidth]{figures/method.pdf}\n \\caption{Comparison of Textual versus Symbolic Reasoning in Table Understanding, and Supervised Fine-Tuning (SFT) versus Reinforcement Learning (RL) in Symbolic Table Reasoning.}\n % Acknowledging Jingxian for the artwork B.\n \\label{fig:method}\n\\end{figure}\n\n\\section{Methodology}\nIn this section, we present a theoretical analysis and discussion comparing textual versus symbolic reasoning in table understanding, as well as supervised fine-tuning (SFT) versus reinforcement learning (RL) in symbolic table reasoning (Figure~\\ref{fig:method}). We then introduce our proposed training framework, \\textit{Formula Tuning}. All notations are list at Appendix~\\ref{ap:notation}.\n\n\\subsection{Task Formulation}\n\\textbf{Table Understanding with Language Models.} \nWe consider a language model (LM) as a conditional generation policy \\(\\pi_{\\theta}(a \\mid s)\\), where \\(\\theta\\) denotes its parameters. The input \\(s \\in \\mathcal{S}\\) comprises a table \\(\\mathbb{T}\\) and a natural-language query \\(q\\), i.e., \\( s = (\\mathbb{T}, q) \\). The table \\(\\mathbb{T}\\) is a two-dimensional grid of cells:\n\\begin{equation}\n\\mathbb{T}_{m \\times n}\n=\n\\begin{bmatrix}\nC_{1,1} & C_{1,2} & \\cdots \\\\ \nC_{2,1} & C_{i,j} & \\cdots \\\\ \n\\vdots & \\vdots & \\ddots \\\\ \n\\end{bmatrix},\n\\end{equation}\nwhere each \\(C_{i,j}\\) may contain a data value, structural information (e.g., a top header or a left header), or be empty. In practice, we linearize \\(\\mathbb{T}\\) into a text sequence before feeding it to the LM.\n\nThe LM then generates an output \\(a \\in \\mathcal{A}\\), which can be either a final textual answer or a spreadsheet formula \\( f \\) that produces the answer upon execution. Our goal is to optimize the parameters \\(\\theta\\) that maximize the expected table‐understanding performance, measured by a reward function \\(r(a \\mid s)\\). Formally,\n\\begin{equation}\n\\max_{\\theta}\\;\n\\mathbb{E}_{s \\sim p(s),\\,a \\sim \\pi_{\\theta}(\\cdot \\mid s)}\\bigl[r(a \\mid s)\\bigr],\n\\label{eq:objective}\n\\end{equation}\nwhere \\(p(s)\\) denotes the empirical distribution over table–query pairs and \\(r(a \\mid s)\\) evaluates the correctness of the final answer from the LM given the input.\n\n\n\\subsection{Textual vs. Symbolic Reasoning in Table Understanding}\n\\label{sec:text_symbolic}\n\n%-------------------------------------------------------\n% Definition: Textual & Symbolic Policies\n%-------------------------------------------------------\n\\begin{definition}[Textual and Symbolic Policies]\n\\label{def:policies}\nGiven an input \\(s = (\\mathbb{T}, q)\\), we consider two types of reasoning strategies:\n\\begin{enumerate}[leftmargin=20pt,itemsep=0pt,labelsep=5pt,topsep=0pt]\n \\item \\textbf{Textual policy} \\(\\pi_{\\theta}^{\\mathrm{txt}}\\): \n The language model generates a chain of thought and directly produces a textual answer \\(a \\in \\mathcal{A}_{\\mathrm{txt}}\\).\n \\item \\textbf{Symbolic policy} \\(\\pi_{\\theta}^{\\mathrm{sym}}\\): \n The language model generates a chain of thought followed by a spreadsheet formula \\(f \\in \\mathcal{F}\\); the final answer is obtained by executing the formula deterministically: \\(a = \\operatorname{exec}(f, \\mathbb{T})\\).\n\\end{enumerate}\n\\end{definition}\n\n%-------------------------------------------------------\n% Lemma: Reward decomposition\n%-------------------------------------------------------\n\\begin{lemma}[Reward Decomposition]\\label{lem:decomp}\nLet the reward be defined as \\(r(a \\mid s) = \\mathbbm{1}[a = a^\\star(s)]\\), where \\(a^\\star(s)\\) denotes the ground-truth answer.\n\nFor textual reasoning, the expected reward is:\n\\begin{equation}\n\\mathbb{E}_{a \\sim \\pi_{\\theta}^{\\mathrm{txt}}} [r(a \\mid s)] \n= \\sum_{a} \\pi_{\\theta}^{\\mathrm{txt}}(a \\mid s) \\cdot \\mathbbm{1}[a = a^\\star(s)],\n\\end{equation}\nwhich represents the probability of generating both a logically valid reasoning path and a numerically correct final answer.\n\nFor symbolic reasoning, the model generates a formula \\(f\\), which is executed to produce an answer \\(a = \\operatorname{exec}(f, \\mathbb{T})\\). The expected reward becomes:\n\\begin{equation}\n\\mathbb{E}_{f \\sim \\pi_{\\theta}^{\\mathrm{sym}}} [r(\\operatorname{exec}(f, \\mathbb{T}) \\mid s)] \n= \\sum_{f} \\pi_{\\theta}^{\\mathrm{sym}}(f \\mid s) \\cdot \\mathbbm{1}[\\operatorname{exec}(f, \\mathbb{T}) = a^\\star(s)].\n\\end{equation}\nThis corresponds to the probability of generating a valid reasoning path and a formula that yields the correct answer. Importantly, any formula that produces the correct output receives full reward, regardless of whether it matches the canonical ground-truth formula.\n\\end{lemma}\n\n%-------------------------------------------------------\n% Assumption: Symbolic Reasoning Setting\n%-------------------------------------------------------\n\\begin{assumption}[Symbolic Reasoning Setting]\n\\label{as:symbolic}\n\\leavevmode\n\\begin{enumerate}[leftmargin=20pt, itemsep=0pt, labelsep=5pt, topsep=0pt]\n \\item The formula executor is sound and complete with respect to the formula language \\(\\mathcal{F}\\).\n \\item All symbolic outputs are executed deterministically and without numerical error.\n \\item The textual and symbolic policies are equally capable of planning high-level solution strategies in their respective formats (text or formula).\n\\end{enumerate}\n\\end{assumption}\n\n%-------------------------------------------------------\n% Theorem: Symbolic Reasoning Superiority\n%-------------------------------------------------------\n\\begin{theorem}[Symbolic Reasoning Superiority]\n\\label{thm:dominance}\nUnder Assumption~\\ref{as:symbolic}, the expected reward achieved by symbolic reasoning is greater than or equal to that of textual reasoning for any input \\(s\\):\n\\begin{equation}\n\\mathbb{E}_{a \\sim \\pi_{\\theta}^{\\mathrm{sym}}} [r(a \\mid s)] \n\\ge \\mathbb{E}_{a \\sim \\pi_{\\theta}^{\\mathrm{txt}}} [r(a \\mid s)].\n\\end{equation}\n\\end{theorem}\nThe proof of Theorem~\\ref{thm:dominance} is provided in Appendix~\\ref{proof:dominance}.\n\n%-------------------------------------------------------\n% Remark\n%-------------------------------------------------------\n\\begin{remark}[Symbolic Reasoning Advantage]\n\\label{rem:symbolic_implication}\nMaximizing the expected reward in Eq.~\\eqref{eq:objective} therefore favors the symbolic policy \\(\\pi_{\\theta}^{\\mathrm{sym}}\\) over the textual policy \\(\\pi_{\\theta}^{\\mathrm{txt}}\\). When both policies have equal capacity for high-level solution planning, symbolic reasoning offers distinct advantages: it is better suited for handling complex tables and questions that require intricate reasoning and intensive calculations. As a result, it can achieve higher accuracy than models that rely solely on free-text numerical manipulation.\n\\end{remark}\n\n\n\n\\subsection{SFT vs. RL in Symbolic Table Reasoning}\n\\label{sec:sft_rl}\n\n%-------------------------------------------------------\n% Assumption: SFT Setting\n%-------------------------------------------------------\n\\begin{assumption}[SFT Setting]\n\\label{as:sft}\n\\leavevmode\n\\begin{enumerate}[leftmargin=20pt, itemsep=0pt, labelsep=5pt, topsep=0pt]\n \\item \\textbf{Sufficient expressivity.}\n The model class \\(\\{\\pi_\\theta(\\cdot \\mid s)\\}\\) is expressive enough to represent the teacher policy \\(\\pi_g(\\cdot \\mid s)\\), in the sense that\n \\begin{equation}\n \\inf_{\\theta} \n \\mathbb{E}_{s \\sim p(s)} \n \\left[ \n D_{\\mathrm{KL}}\\left(\\pi_g(\\cdot \\mid s) \n \\parallel \\pi_\\theta(\\cdot \\mid s)\\right) \n \\right] = 0.\n \\end{equation}\n \\item \\textbf{Global optimization.}\n The optimization algorithm converges to a global optimum of the supervised fine-tuning (SFT) objective.\n \\item \\textbf{Data sufficiency.}\n As the number of training examples \\(N \\to \\infty\\), the empirical distribution \\(\\hat{p}(s, a)\\) converges almost surely to the true data-generating distribution \\(p(s)\\, \\pi_g(a \\mid s)\\).\n\\end{enumerate}\n\\end{assumption}\n\n%-------------------------------------------------------\n% Lemma: MLE minimizes KL\n%-------------------------------------------------------\n\\begin{lemma}[MLE Minimizes KL Divergence]\\label{lem:mle-kl}\nMaximum likelihood estimation (MLE) corresponds to minimizing the Kullback--Leibler (KL) divergence between the teacher policy \\(\\pi_g\\) and the model policy \\(\\pi_\\theta\\). For any fixed input \\(s\\), we have:\n\\begin{equation}\n \\mathbb{E}_{a \\sim \\pi_g} \\left[-\\log \\pi_\\theta(a \\mid s)\\right] \n = H\\left(\\pi_g(\\cdot \\mid s)\\right) \n + D_{\\mathrm{KL}}\\left(\\pi_g \\parallel \\pi_\\theta\\right),\n\\end{equation}\nwhere \\(H(\\pi_g)\\) denotes the entropy of the teacher policy. Thus, maximizing the log-likelihood of \\(\\pi_\\theta\\) under samples from \\(\\pi_g\\) is equivalent to minimizing the KL divergence from \\(\\pi_g\\) to \\(\\pi_\\theta\\).\n\\end{lemma}\n\n\\noindent The proof of Lemma~\\ref{lem:mle-kl} is provided in Appendix~\\ref{proof:mle-kl}.\n\n%-------------------------------------------------------\n% Theorem: SFT reward bound\n%-------------------------------------------------------\n\\begin{lemma}[Convergence of SFT and Reward Upper Bound]\n\\label{lem:sft}\nLet \\(s = (\\mathbb{T}, q) \\in \\mathcal{S}\\), where \\(\\mathbb{T}\\) is the input table and \\(q\\) is the natural language question. Suppose the model generates a formula \\(f \\sim \\pi_\\theta(\\cdot \\mid s)\\), and let the final answer be computed deterministically as \\(a = \\operatorname{exec}(f, \\mathbb{T})\\). \n\nUnder Assumption~\\ref{as:sft}, the optimal supervised fine-tuning (SFT) policy\n\\begin{equation}\n \\pi_{\\theta^\\star} = \\arg\\max_{\\theta} \n \\mathbb{E}_{s \\sim p,\\, f \\sim \\pi_g} \\left[\\log \\pi_\\theta(f \\mid s)\\right]\n\\end{equation}\nsatisfies\n\\begin{equation}\n \\pi_{\\theta^\\star}(f \\mid s) = \\pi_g(f \\mid s) \\quad \\text{for almost every } s \\in \\mathcal{S}.\n\\end{equation}\n\nConsequently, for the reward function \\(r(a \\mid s) = \\mathbbm{1}[a = a^\\star(s)]\\), we have:\n\\begin{equation}\n \\mathbb{E}_{s \\sim p,\\, f \\sim \\pi_{\\theta^\\star}} \\left[r(\\operatorname{exec}(f, \\mathbb{T}) \\mid s)\\right]\n \\le\n \\mathbb{E}_{s \\sim p,\\, f \\sim \\pi_g} \\left[r(\\operatorname{exec}(f, \\mathbb{T}) \\mid s)\\right].\n\\end{equation}\n\\end{lemma}\n\n\\noindent The proof of Lemma~\\ref{lem:sft} is provided in Appendix~\\ref{proof:sft}.\n\n%-------------------------------------------------------\n% Remark: SFT bound\n%-------------------------------------------------------\n\\begin{remark}[SFT Bound]\n\\label{rem:sft_bound}\nThis result shows that supervised fine-tuning (SFT), even under ideal assumptions of expressivity, optimization, and data sufficiency, can at most replicate the performance of the teacher policy. It thus establishes a theoretical upper bound on the expected task reward achievable by SFT alone.\n\\end{remark}\n\n%-------------------------------------------------------\n% Assumption: RL exploration\n%-------------------------------------------------------\n\\begin{assumption}[RL Exploration]\n\\label{as:rl_exploration}\nFor every input \\(s \\in \\mathcal{S}\\), there exists at least one high-reward action \\(a^\\star\\) such that\n\\(\\pi_\\theta(a^\\star \\mid s) > 0\\) during training, i.e., the RL policy has a non-zero probability of exploring a correct solution.\n\\end{assumption}\n\n%-------------------------------------------------------\n% Theorem: RL superiority\n%-------------------------------------------------------\n\\begin{theorem}[RL Superiority]\n\\label{thm:rl_better}\nUnder Assumption~\\ref{as:rl_exploration}, and assuming the reward function \\(r(a \\mid s)\\) is properly aligned with task success (e.g., exact match), reinforcement learning can achieve strictly higher expected reward than supervised fine-tuning:\n\\begin{equation}\n\\mathbb{E}_{s \\sim p,\\, a \\sim \\pi_\\theta^{\\mathrm{RL}}}[r(a \\mid s)] \n> \\mathbb{E}_{s \\sim p,\\, a \\sim \\pi_{\\theta^\\star}^{\\mathrm{SFT}}}[r(a \\mid s)],\n\\end{equation}\nprovided that the teacher policy \\(\\pi_g\\) does not fully cover all correct solutions.\n\\end{theorem}\n\n\n\\noindent The proof of Theorem~\\ref{thm:rl_better} is provided in Appendix~\\ref{proof:rl_sup}.\n\n\n%-------------------------------------------------------\n% Remark: RL advantage\n%-------------------------------------------------------\n\\begin{remark}[RL Objective and Advantage]\n\\label{rem:rl_advantage}\nUnlike SFT, which is constrained to imitating the teacher policy \\(\\pi_g\\), reinforcement learning (RL) directly maximizes the expected task reward:\n\\begin{equation}\n\\max_{\\theta} \\, \\mathbb{E}_{s \\sim p(s),\\, a \\sim \\pi_\\theta(\\cdot \\mid s)}[r(a \\mid s)].\n\\end{equation}\n\nThis objective allows the model to discover high-reward actions that may lie outside the support of \\(\\pi_g\\)—for example, alternative formulas that yield the correct answer but differ syntactically or structurally from those seen during supervised training.\n\nIn symbolic table reasoning, this flexibility is especially valuable. Since many distinct formulas may produce the same correct result, RL can exploit this many-to-one mapping by exploring diverse yet semantically valid expressions. As a result, RL has the potential to exceed the SFT reward bound established in Lemma~\\ref{lem:sft}, provided the reward signal is well aligned with the task objective and sufficient exploration occurs.\n\\end{remark}\n\n\n\n\n\\subsection{Formula Tuning}\n\\label{sec:fortune}\n\n%-------------------------------------------------------\n% Definition: Formula Tuning\n%-------------------------------------------------------\n\\textbf{Definition.} \n\\emph{Formula Tuning} is a reinforcement learning (RL) framework that defines spreadsheet formulas as an explicit symbolic reasoning space for table understanding. Specifically, we fine-tune a pretrained LM \\(\\pi_{\\theta}\\) to generate formulas \\(f \\in \\mathcal{F}\\), which are executed by a deterministic spreadsheet engine \\(\\operatorname{exec}(f, \\mathbb{T})\\). The resulting answer \\(a = \\operatorname{exec}(f, \\mathbb{T})\\) is compared against the ground-truth answer \\(a^{\\star}(s)\\), and the model receives a reward:\n\n\\begin{equation}\nr(a \\mid s) =\n\\begin{cases}\n1, & \\text{if } a = a^{\\star}(s), \\\\[0.3em]\n0.2, & \\text{if } a \\ne a^{\\star}(s) \\text{ and } f \\text{ is executable}, \\\\[0.3em]\n0, & \\text{if } f \\text{ is not executable}.\n\\end{cases}\n\\end{equation}\n\n\nThis reward function encourages the model to explore valid, executable formulas—even if initially incorrect—while assigning full credit only when the answer exactly matches the ground truth.\n\n%-------------------------------------------------------\n% Objective: RL optimization\n%-------------------------------------------------------\n\\noindent\n\\textbf{Objective.} \nFormula Tuning maximizes the expected reward using RL algorithms, such as proximal policy optimization (PPO)~\\cite{Schulman2017ProximalPO}, with the action space constrained to spreadsheet formulas:\n\n\\begin{equation}\n\\max_{\\theta} \\;\n\\mathbb{E}_{s \\sim p(s),\\, f \\sim \\pi_{\\theta}(\\cdot \\mid s)}\n\\left[\nr\\left( \\operatorname{exec}(f, \\mathbb{T}) \\mid s \\right)\n\\right].\n\\end{equation}\n\n%-------------------------------------------------------\n% Workflow: Training process\n%-------------------------------------------------------\n\\noindent\n\\textbf{Training Workflow.}\n\\begin{enumerate}[leftmargin=1.5em, itemsep=0.2em]\n\\item \\emph{Decoding:}\\; The LM generates a chain of thought and samples candidate formulas \\(f_1, f_2, \\ldots\\) from its current policy \\(\\pi_{\\theta}\\).\n\\item \\emph{Execution:}\\; Each formula \\(f_k\\) is executed to produce the corresponding answer \\(a_k\\).\n\\item \\emph{Rewarding:}\\; The environment returns a scalar reward \\(r_k = r(a_k \\mid s)\\) based on the correctness and executability of the result.\n\\item \\emph{Policy Update:}\\; The LM parameters \\(\\theta\\) are updated using a RL algorithm (e.g., PPO) based on the observed tuple \\((s, f_k, r_k)\\).\n\\end{enumerate}\n\n\\noindent\nThis framework enables the model to perform symbolic reasoning over general tables with higher accuracy, with its advantages analyzed in Section~\\ref{sec:text_symbolic} and Section~\\ref{sec:sft_rl}. For additional discussion and practical challenges of the methodology, please refer to Appendix~\\ref{ap:discussion}.\n\n\n\n\n\\section{Experiments}\n\n% In this section, we present extensive experiments to empirically demonstrate the effectiveness of \\textit{Formula Tuning}, and discuss how its behavior in practical scenarios aligns with our theoretical analysis. We also provide additional insights based on these observations.\n\n\n\\subsection{Settings}\nWe conduct experiments on seven diverse table understanding benchmarks, including WikiTQ~\\cite{pasupat2015compositionalsemanticparsingsemistructured}, TabFact~\\cite{chen2020tabfactlargescaledatasettablebased}, FinQA~\\cite{chen-etal-2021-finqa}, HiTab~\\cite{cheng2021hitab}, MultiHiertt~\\cite{zhao-etal-2022-multihiertt}, AIT-QA~\\cite{katsis2021aitqa}, and TableBench~\\cite{wu2025tablebenchcomprehensivecomplexbenchmark}. These datasets differ in domain sources, table structure types, and question complexity, collectively covering the full spectrum of table understanding tasks. For training, we merge the first five datasets into a single training corpus and train the model jointly on this combined set, then evaluate it separately on each dataset. Among these, AIT-QA and TableBench are treated as out-of-distribution (OOD) evaluation sets, while the rest are considered in-distribution (ID). Our experiments cover a range of models, including \\textit{GPT-4o-mini}, \\textit{GPT-4o}, \\textit{O1}, \\textit{Llama-3.1$_{8\\text{B}}$}, and \\textit{Qwen2.5-Coder$_{7\\text{B}}$}. Following prior work~\\cite{pasupat2015compositionalsemanticparsingsemistructured, cheng2021hitab}, we use exact match accuracy as our primary evaluation metric. The prompts used in our experiments are provided in Appendix~\\ref{ap:prompt}. Detailed settings are provided in Appendix~\\ref{ap:detail_setting}.\n\n\n\n\n\\subsection{Performance of Formula Learning under SFT, RL, and Cold-Start RL}\n\\begin{table}[t!]\n \\centering\n \\caption{Performance under Zero-shot, SFT, and RL settings across models and datasets. Values in the table indicate accuracy (\\%). \\textit{Text} and \\textit{Formula} refer to textual and symbolic reasoning methods, respectively. \\textit{w/ CS} denotes cold-start RL \\cite{deepseekai2025deepseekr1incentivizingreasoningcapability} initialized from SFT. For open-source models, the best performance in each column is highlighted in \\textcolor{langdarkblue}{dark blue}, and the second-best in \\textcolor{langwildblue}{light blue}.}\n \\resizebox{\\columnwidth}{!}{%\n \\begin{tabular}{llcccccccc}\n \\toprule\n \\multirow[c]{2}{*}{\\textbf{Base Model}} \n & \\multirow[c]{2}{*}{\\textbf{Method}} \n & \\multicolumn{5}{c}{\\textbf{In‐Distribution}} \n & \\multicolumn{2}{c}{\\textbf{Out‐of‐Distribution}}\n & \\multirow[c]{2}{*}{\\textbf{Overall}} \\\\\n \\cmidrule(lr){3-7}\\cmidrule(lr){8-9}\n & & \\textbf{WikiTQ} & \\textbf{TabFact} & \\textbf{FinQA} & \\textbf{HiTab} & \\textbf{MultiHiertt} \n & \\textbf{AIT-QA} & \\textbf{TableBench} & \\\\\n \\midrule\n \\multicolumn{10}{l}{\\textit{\\textbf{Zero‐Shot}}} \\\\\n \\multirow[c]{2}{*}{\\quad \\textcolor{gray}{GPT-4o-mini}} \n & \\textcolor{gray}{Text} \n & \\textcolor{gray}{67.36} & \\textcolor{gray}{88.44} & \\textcolor{gray}{59.20} \n & \\textcolor{gray}{57.89} & \\textcolor{gray}{22.41} \n & \\textcolor{gray}{77.67} & \\textcolor{gray}{36.79} & \\textcolor{gray}{58.54} \\\\\n & \\textcolor{gray}{Formula} \n & \\textcolor{gray}{49.16} & \\textcolor{gray}{74.90} & \\textcolor{gray}{43.07} \n & \\textcolor{gray}{48.17} & \\textcolor{gray}{28.26} \n & \\textcolor{gray}{75.53} & \\textcolor{gray}{34.65} & \\textcolor{gray}{50.53} \\\\\n \\cmidrule(lr){2-10}\n \\multirow[c]{2}{*}{\\quad \\textcolor{gray}{GPT-4o}} \n & \\textcolor{gray}{Text} \n & \\textcolor{gray}{78.57} & \\textcolor{gray}{94.52} & \\textcolor{gray}{63.12} \n & \\textcolor{gray}{69.26} & \\textcolor{gray}{36.11} \n & \\textcolor{gray}{81.36} & \\textcolor{gray}{42.66} & \\textcolor{gray}{66.51} \\\\\n & \\textcolor{gray}{Formula} \n & \\textcolor{gray}{53.36} & \\textcolor{gray}{79.94} & \\textcolor{gray}{48.65} \n & \\textcolor{gray}{65.40} & \\textcolor{gray}{38.41} \n & \\textcolor{gray}{85.83} & \\textcolor{gray}{37.92} & \\textcolor{gray}{58.50} \\\\\n \\cmidrule(lr){2-10}\n \\multirow[c]{2}{*}{\\quad \\textcolor{gray}{O1}} \n & \\textcolor{gray}{Text} \n & \\textcolor{gray}{77.90} & \\textcolor{gray}{95.50} & \\textcolor{gray}{55.71} \n & \\textcolor{gray}{74.31} & \\textcolor{gray}{38.31} \n & \\textcolor{gray}{81.55} & \\textcolor{gray}{45.03} & \\textcolor{gray}{66.90} \\\\\n & \\textcolor{gray}{Formula} \n & \\textcolor{gray}{70.40} & \\textcolor{gray}{91.11} & \\textcolor{gray}{42.81} \n & \\textcolor{gray}{75.00} & \\textcolor{gray}{48.95} \n & \\textcolor{gray}{88.93} & \\textcolor{gray}{44.02} & \\textcolor{gray}{65.89} \\\\\n \\noalign{\\vskip 2pt}\n \\hdashline[2pt/2pt]\n \\noalign{\\vskip 2pt}\n \\multirow[c]{2}{*}{\\quad Llama-3.1$_{\\text{8B}}$} \n & Text & 50.46 & 67.84 & 42.37 & 29.92 & 18.77 \n & 59.61 & 21.29 & 41.47 \\\\\n & Formula & 12.58 & 15.77 & 22.67 & 6.57 & 4.23 \n & 20.00 & 10.31 & 13.16 \\\\\n \\cmidrule(lr){2-10}\n \\multirow[c]{2}{*}{\\quad Qwen2.5-Coder$_{\\text{7B}}$}\n & Text & 55.53 & 78.26 & 55.54 & 50.38 & 26.11 \n & 73.60 & 24.12 & 51.93 \\\\\n & Formula & 38.96 & 52.52 & 34.44 & 32.60 & 15.90 \n & 52.43 & 26.05 & 36.13 \\\\\n \\midrule\n \\multicolumn{10}{l}{\\textit{\\textbf{Supervised Fine‐Tuning (SFT)}}} \\\\\n \\multirow[c]{2}{*}{\\quad Llama-3.1$_{\\text{8B}}$} \n & Text & 66.15 & 82.95 & 50.04 & 70.27 & 39.98 \n & 78.45 & 23.10 & 58.71 \\\\\n & Formula & 59.62 & 72.48 & 58.50 & 72.54 & 44.00 \n & 74.95 & 31.48 & 59.08 \\\\\n \\cmidrule(lr){2-10}\n \\multirow[c]{2}{*}{\\quad Qwen2.5-Coder$_{\\text{7B}}$}\n & Text & 65.98 & 81.13 & 59.46 & 72.35 & 43.93 \n & 81.55 & 24.01 & 61.20 \\\\\n & Formula & 63.46 & 78.16 & 58.33 & 71.83 & 42.68 \n & 76.12 & 35.56 & 60.88 \\\\\n \\midrule\n \\multicolumn{10}{l}{\\textit{\\textbf{Reinforcement Learning (RL)}}} \\\\\n \\multirow[c]{4}{*}{\\quad Llama-3.1$_{\\text{8B}}$} \n & Text \n & 64.37 & 82.16 & 62.60 & 68.81 & 31.99 \n & 82.91 & 28.08 & 60.13 \\\\\n & Text w/ CS \n & \\textcolor{langdarkblue}{\\textbf{71.56}} & \\textcolor{langdarkblue}{\\textbf{87.01}} & 56.84 & 77.64 & 49.34 \n & \\textcolor{langdarkblue}{\\textbf{85.66}} & 28.16 & 65.17 \\\\\n & Formula \n & 57.64 & 80.09 & 60.85 & 67.93 & 29.40 \n & 80.78 & 30.69 & 58.20 \\\\\n \\rowcolor{gray!20}\n & Formula w/ CS \n & 70.49 & 83.04 & \\textcolor{langdarkblue}{\\textbf{71.99}} & \\textcolor{langdarkblue}{\\textbf{79.29}} & \\textcolor{langwildblue}{\\textbf{54.55}} \n & 81.29 & \\textcolor{langwildblue}{\\textbf{36.64}} & \\textcolor{langwildblue}{\\textbf{68.18}} \\\\\n \\cmidrule(lr){2-10}\n \\multirow[c]{4}{*}{\\quad Qwen2.5-Coder$_{\\text{7B}}$}\n & Text \n & 66.95 & 85.43 & 64.34 & 74.24 & 35.55 \n & 85.28 & 27.86 & 62.80 \\\\\n & Text w/ CS \n & \\textcolor{langwildblue}{\\textbf{71.31}} & 86.07 & 64.77 & 77.42 & 54.25 \n & \\textcolor{langwildblue}{\\textbf{85.43}} & 25.94 & 66.46 \\\\\n & Formula \n & 67.80 & 84.19 & 62.16 & 71.19 & 41.72 \n & 81.17 & 35.45 & 63.38 \\\\\n \\rowcolor{gray!20}\n & Formula w/ CS \n & 70.90 & \\textcolor{langwildblue}{\\textbf{86.18}} & \\textcolor{langwildblue}{\\textbf{69.21}} & \\textcolor{langwildblue}{\\textbf{77.89}} & \\textcolor{langdarkblue}{\\textbf{56.78}} \n & 79.14 & \\textcolor{langdarkblue}{\\textbf{39.25}} & \\textcolor{langdarkblue}{\\textbf{68.48}} \\\\\n \\bottomrule\n \\end{tabular}%\n }\n \\label{tab:main_perf}\n\\end{table}\nTable~\\ref{tab:main_perf} presents the performance of different formula learning methods under supervised fine-tuning (\\textit{SFT}), reinforcement learning (\\textit{RL}), RL with a cold-start strategy (\\textit{RL w/ CS}), and direct zero-shot inference without any training. This experiment is primarily designed to validate the theoretical analysis discussed earlier and to discuss how its behavior in practical scenarios aligns with our theoretical analysis. Several key analyses and insights are summarized below:\n\n\\noindent\\textbf{Large zero-shot performance gap between textual and symbolic reasoning.} \nClosed-source models such as \\textit{GPT-4o} already achieve an overall accuracy of 66.5\\% with purely textual reasoning, yet their formula accuracy drops to 58.5\\%. \nFor open-source 7–8\\,B models the situation is more severe: textual scores hover around 40\\%, while formula accuracies nearly collapse (13–36\\%). \nThe only partial exception is \\textit{Qwen2.5-Coder${}_{7\\text{B}}$}, whose additional code pre-training endows a modest ability to emit formulas, confirming that vanilla pre-training leaves models largely unaware of spreadsheet syntax and semantics. This underscores the necessity of dedicated \\emph{formula tuning}.\n\n\\noindent\\textbf{SFT rapidly narrows the textual and symbolic reasoning gap.} \nAfter SFT, open-source models gain 17–20 accuracy points (e.g.\\ \\textit{Llama-3${}_{8\\text{B}}$} rises from 41.5\\% to 58.7\\%), and the residual gap between textual and symbolic reasoning shrinks to only 1–2 points. SFT thus injects essential task knowledge and brings symbolic reasoning almost on par with textual reasoning.\n\n\\noindent\\textbf{RL yields further gains, especially for formulas and OOD.} \nStarting from scratch, RL improves formula accuracy by an additional 5--9 points for both backbones, pushing overall performance above 63\\%. The improvements for text-only reasoning are comparatively smaller, suggesting that RL primarily enhances the model's ability to synthesize correct formulas rather than improve surface-level responses. Furthermore, the out-of-distribution results on AIT-QA and TableBench show substantial gains over supervised fine-tuning, demonstrating the generalization benefits of RL.\n\n\\noindent\\textbf{Cold-start RL is essential for open-source models.} \nInitializing RL from an SFT model instead of from scratch (the \\textit{w/ CS} rows) delivers an additional 3–7 point lift. The \\emph{Formula w/ CS} setting consistently achieves the best open-source numbers: 68.2\\% for \\textit{Llama-3${}_{8\\text{B}}$} and 68.4\\% for \\textit{Qwen2.5-Coder${}_{7\\text{B}}$}. SFT provides the knowledge base, while RL pushes performance toward the ceiling.\n\n\\noindent\\textbf{Symbolic reasoning shows markedly higher robustness on complex benchmarks.} \nOn the challenging TableBench OOD set, \\textit{Formula w/ CS} lifts \\textit{Llama-3${}_{8\\text{B}}$} from a 10.3\\% zero-shot score to 36.6\\%, surpassing \\textit{Text w/ CS} by 8.5 points. The advantage is most pronounced on datasets that require multi-step calculations, range manipulation, or nested aggregation.\n\n\\noindent\\textbf{Textual reasoning remains preferable for simple look-up QA.} \nOn TabFact, \\textit{Text w/ CS} slightly outperforms formula-based reasoning (87.0\\% vs.\\ 83.0\\% for \\textit{Llama-3${}_{8\\text{B}}$}), suggesting that composing an explicit formula is not always necessary when a direct textual response suffices. A similar trend is observed on \\textsc{AIT-QA}, where \\textit{Text w/ CS} again yields higher accuracy (85.06\\% vs.\\ 80.64\\%). These results indicate that textual reasoning is more effective in settings where answers can be directly extracted from the table without requiring symbolic composition.\n\n\\noindent\\textbf{Formula-tuned open-source models now rival or surpass closed-source LMs.} \nAfter SFT\\,+\\,RL, both \\textit{Llama-3${}_{8\\text{B}}$} and \\textit{Qwen2.5-Coder${}_{7\\text{B}}$} attain overall accuracies (68.2–68.4\\%) on par with or exceeding the proprietary \\textit{GPT-4o}-mini baseline (58.5\\%) and even approach the much larger \\textit{O1} model (66.9\\%). This highlights the effectiveness of our formula-centric training pipeline for democratizing high-quality table reasoning.\n\n\n\n\\begin{table}[t!]\n \\centering\n \\caption{Performance comparison of different methods. Values in the table indicate accuracy (\\%). Values marked with * indicate out-of-distribution results. `-' indicates results not reported in the related paper. For fine-tuning-based methods, the best performance in each column is highlighted in \\textcolor{langdarkblue}{dark blue}, and the second-best in \\textcolor{langwildblue}{light blue}.}\n \\resizebox{0.85\\columnwidth}{!}{\n \\begin{tabular}{llccccc}\n \\toprule\n \\textbf{Method} & \\textbf{Backbone} \n & \\textbf{WikiTQ}\\phantom{\\textsuperscript{*}} \n & \\textbf{TabFact}\\phantom{\\textsuperscript{*}} \n & \\textbf{HiTab}\\phantom{\\textsuperscript{*}} \n & \\textbf{FinQA}\\phantom{\\textsuperscript{*}} \n & \\textbf{AIT-QA}\\phantom{\\textsuperscript{*}} \\\\\n \\midrule\n \\multicolumn{7}{l}{\\textit{\\textbf{Prompting-Based Methods}}} \\\\\n \\quad Binder~\\cite{cheng2023bindinglanguagemodelssymbolic} \n & CodeX \n & 64.60\\phantom{\\textsuperscript{*}} \n & 85.10\\phantom{\\textsuperscript{*}} \n & -\\phantom{\\textsuperscript{*}} \n & -\\phantom{\\textsuperscript{*}} \n & -\\phantom{\\textsuperscript{*}} \\\\\n \\quad Dater~\\cite{ye2023largelanguagemodelsversatile} \n & CodeX \n & 65.90\\phantom{\\textsuperscript{*}} \n & 85.60\\phantom{\\textsuperscript{*}} \n & -\\phantom{\\textsuperscript{*}} \n & -\\phantom{\\textsuperscript{*}} \n & -\\phantom{\\textsuperscript{*}} \\\\\n \\quad API-Assisted~\\cite{cao2025tablemasterrecipeadvancetable} \n & CodeX \n & 42.40\\phantom{\\textsuperscript{*}} \n & -\\phantom{\\textsuperscript{*}} \n & 69.30\\phantom{\\textsuperscript{*}} \n & -\\phantom{\\textsuperscript{*}} \n & -\\phantom{\\textsuperscript{*}} \\\\\n \\quad ReAcTable~\\cite{zhang2023reactableenhancingreacttable} \n & CodeX \n & 68.00\\phantom{\\textsuperscript{*}} \n & 86.10\\phantom{\\textsuperscript{*}} \n & -\\phantom{\\textsuperscript{*}} \n & -\\phantom{\\textsuperscript{*}} \n & -\\phantom{\\textsuperscript{*}} \\\\\n \\quad Chain-of-Table~\\cite{wang2024chainoftableevolvingtablesreasoning} \n & PaLM 2 \n & 67.31\\phantom{\\textsuperscript{*}} \n & 86.61\\phantom{\\textsuperscript{*}} \n & -\\phantom{\\textsuperscript{*}} \n & -\\phantom{\\textsuperscript{*}} \n & -\\phantom{\\textsuperscript{*}} \\\\\n \\quad Norm-DP\\&Agent~\\cite{liu2023rethinkingtabulardataunderstanding} \n & GPT-3.5 \n & 73.65\\phantom{\\textsuperscript{*}} \n & 88.50\\phantom{\\textsuperscript{*}} \n & -\\phantom{\\textsuperscript{*}} \n & -\\phantom{\\textsuperscript{*}} \n & -\\phantom{\\textsuperscript{*}} \\\\\n \\quad TIDE DP\\&Agent~\\cite{yang2025triples} \n & GPT-3.5 \n & 75.00\\phantom{\\textsuperscript{*}} \n & 89.82\\phantom{\\textsuperscript{*}} \n & -\\phantom{\\textsuperscript{*}} \n & -\\phantom{\\textsuperscript{*}} \n & -\\phantom{\\textsuperscript{*}} \\\\\n \\quad TableMaster~\\cite{cao2025tablemasterrecipeadvancetable} \n & GPT-4o-mini \n & 78.13\\phantom{\\textsuperscript{*}} \n & 90.12\\phantom{\\textsuperscript{*}} \n & -\\phantom{\\textsuperscript{*}} \n & 66.40\\phantom{\\textsuperscript{*}} \n & -\\phantom{\\textsuperscript{*}} \\\\\n \\quad E5~\\cite{zhang-etal-2024-e5} \n & GPT-4 \n & -\\phantom{\\textsuperscript{*}} \n & -\\phantom{\\textsuperscript{*}} \n & 85.08\\phantom{\\textsuperscript{*}} \n & -\\phantom{\\textsuperscript{*}} \n & -\\phantom{\\textsuperscript{*}} \\\\\n \\quad SS-CoT~\\cite{zhao2024stepwiseselfconsistentmathematicalreasoning} \n & Llama-3.1$_{\\text{70B}}$ \n & 76.80\\phantom{\\textsuperscript{*}} \n & -\\phantom{\\textsuperscript{*}} \n & 79.10\\phantom{\\textsuperscript{*}} \n & -\\phantom{\\textsuperscript{*}} \n & -\\phantom{\\textsuperscript{*}} \\\\\n \\midrule\n \\multicolumn{7}{l}{\\textit{\\textbf{Finetuning-Based Methods}}} \\\\\n \\quad FORTAP~\\cite{cheng2022fortapusingformulasnumericalreasoningaware} \n & BERT+LSTM \n & -\\phantom{\\textsuperscript{*}} \n & -\\phantom{\\textsuperscript{*}} \n & 47.00\\phantom{\\textsuperscript{*}} \n & -\\phantom{\\textsuperscript{*}} \n & -\\phantom{\\textsuperscript{*}} \\\\\n \\quad TAPEX-Large~\\cite{liu2022tapextablepretraininglearning} \n & BART$_{\\text{Large}}$ \n & 59.10\\phantom{\\textsuperscript{*}} \n & 84.20\\phantom{\\textsuperscript{*}} \n & 45.60\\phantom{\\textsuperscript{*}} \n & -\\phantom{\\textsuperscript{*}} \n & -\\phantom{\\textsuperscript{*}} \\\\\n \\quad OmniTab~\\cite{jiang-etal-2022-omnitab} \n & BART$_{\\text{Large}}$ \n & 62.80\\phantom{\\textsuperscript{*}} \n & -\\phantom{\\textsuperscript{*}} \n & -\\phantom{\\textsuperscript{*}} \n & -\\phantom{\\textsuperscript{*}} \n & -\\phantom{\\textsuperscript{*}} \\\\\n \\quad TableLlama~\\cite{zhang2024tablellamaopenlargegeneralist} \n & Llama-2$_{\\text{7B}}$ \n & 32.14\\textsuperscript{*} \n & 82.55\\phantom{\\textsuperscript{*}} \n & 60.48\\phantom{\\textsuperscript{*}} \n & 2.27\\textsuperscript{*} \n & 26.99\\textsuperscript{*} \\\\\n \\quad TableLLM~\\cite{zhang2025tablellmenablingtabulardata} \n & Qwen2$_{\\text{7B}}$ \n & 53.59\\phantom{\\textsuperscript{*}} \n & 69.81\\phantom{\\textsuperscript{*}} \n & 43.88\\phantom{\\textsuperscript{*}} \n & 8.63\\textsuperscript{*} \n & 64.85\\phantom{\\textsuperscript{*}} \\\\\n \\quad TableGPT2~\\cite{su2024tablegpt2largemultimodalmodel} \n & Qwen2.5$_{\\text{7B}}$ \n & 61.42\\phantom{\\textsuperscript{*}} \n & 77.80\\phantom{\\textsuperscript{*}} \n & 70.27\\phantom{\\textsuperscript{*}} \n & 40.28\\textsuperscript{*} \n & 12.43\\textsuperscript{*} \\\\\n \\quad TabAF~\\cite{wang2025generaltablequestionanswering} \n & Qwen2.5-Coder$_{\\text{7B}}$ \n & \\textcolor{langwildblue}{\\textbf{74.72}}\\phantom{\\textsuperscript{*}} \n & 83.99\\phantom{\\textsuperscript{*}} \n & \\textcolor{langwildblue}{\\textbf{78.41}}\\phantom{\\textsuperscript{*}} \n & 45.07\\textsuperscript{*} \n & 62.33\\textsuperscript{*} \\\\\n \\rowcolor{gray!20}\n \\quad \\textbf{Fortune (Ours)} \n & Qwen2.5-Coder$_{\\text{7B}}$ \n & 67.05\\phantom{\\textsuperscript{*}} \n & \\textcolor{langwildblue}{\\textbf{85.08}}\\phantom{\\textsuperscript{*}} \n & 69.74\\phantom{\\textsuperscript{*}} \n & \\textcolor{langwildblue}{\\textbf{62.16}}\\phantom{\\textsuperscript{*}} \n & \\textcolor{langwildblue}{\\textbf{80.39}}\\textsuperscript{*} \\\\\n \\rowcolor{gray!20}\n \\quad \\textbf{Fortune++ (Ours)} \n & Qwen2.5-Coder$_{\\text{7B}}$ \n & \\textcolor{langdarkblue}{\\textbf{82.54}}\\phantom{\\textsuperscript{*}} \n & \\textcolor{langdarkblue}{\\textbf{95.06}}\\phantom{\\textsuperscript{*}} \n & \\textcolor{langdarkblue}{\\textbf{87.24}}\\phantom{\\textsuperscript{*}} \n & \\textcolor{langdarkblue}{\\textbf{80.47}}\\phantom{\\textsuperscript{*}} \n & \\textcolor{langdarkblue}{\\textbf{93.20}}\\textsuperscript{*} \\\\\n \\bottomrule\n \\end{tabular}%\n }\n \\label{tab:comparison}\n\\end{table}\n\\subsection{Performance of Fortune and Fortune++ Compared to Other Methods}\n\nTable~\\ref{tab:comparison} presents the performance of Fortune and Fortune++ compared to several strong baselines, as detailed in Appendix~\\ref{ap:detail_setting}. Fortune is derived from the best overall performance achieved through cold-start RL in formula-based symbolic reasoning. Following prior work~\\cite{liu2023rethinkingtabulardataunderstanding, yang2025triples, wang2025generaltablequestionanswering}, we adopt the self-consistency strategy~\\cite{wang2023selfconsistencyimproveschainthought} to enhance table understanding performance. This strategy involves generating multiple candidate formulas and selecting the final answer based on majority voting. It is a widely adopted and effective approach for improving accuracy. \\textbf{Fortune} follows previous work by generating \\textit{10 symbolic reasoning outputs}. To further leverage the complementary strengths of textual and symbolic reasoning, we introduce \\textbf{Fortune++}, which produces \\textit{a balanced mix of 5 textual and 5 symbolic outputs}. Neither method relies on a cold-start strategy.\n\n\\noindent\\textbf{Fortune++ delivers consistently strong performance across benchmarks.}\nFortune++ surpasses all finetuning-based methods across the reported datasets. Specifically, it achieves 80.47\\% on FinQA, demonstrating strong complex mathematical reasoning ability. On AIT-QA, Fortune++ brings an improvement of 30.9 points, highlighting the out-of-distribution robustness enabled by RL. These results also show that smaller open-source models can outperform larger closed-source models. Despite using only a 7B-parameter \\textit{Qwen} backbone, Fortune++ consistently outperforms nearly all prompting-based methods, including those powered by GPT-4o. The original Fortune, which relies solely on formula-based reasoning, also achieves competitive performance across benchmarks.\n\n\\noindent\\textbf{RL surpasses SFT.} \nTabAF~\\cite{wang2025generaltablequestionanswering} is a strong baseline that uses SFT for symbolic reasoning with formula and similarly adopts a hybrid self-consistency strategy with 5 textual and 5 formula-based outputs. Nevertheless, Fortune++ significantly outperforms TabAF, demonstrating that RL offers clear advantages over SFT-only models distilled from stronger teacher models.\n\n\\noindent\\textbf{Additional results and further analysis.} \nFortune achieves 40.85\\% on MultiHiertt and 35.22\\% on TableBench, while Fortune++ achieves 51.73\\% and 44.96\\%, respectively. \nAn \\textbf{ablation study} and upper-bound performance analysis of Fortune and Fortune++ are presented in Appendix~\\ref{ap:ablation}. \nA \\textbf{statistical analysis} of the generated formulas is provided in Appendix~\\ref{ap:stat}, and qualitative \\textbf{case studies} are discussed in Appendix~\\ref{ap:case_study}. \nA comparative study of formula tuning versus \\textbf{other symbolic table reasoning} methods is included in Appendix~\\ref{ap:other_symbolic}, and an impact analysis of the \\textbf{reasoning process} during formula tuning appears in Appendix~\\ref{ap:reasoning_process}.\n\n\n\n\\section{Conclusion}\nIn this paper, we introduced \\textit{Formula Tuning} (Fortune), a reinforcement learning framework that trains language models to generate executable spreadsheet formulas for table understanding task. Our findings highlight the promise of formula-driven learning in enhancing reasoning capabilities of language models on tabular tasks. Limitations and future work are discussed in Appendix~\\ref{ap:limitation}.\n\n% \\section*{Acknowledgements}\n% Thx~\n\n% \\begin{ack}\n% Thx~\n% \\end{ack}\n\n\n% The main text of a submitted paper is limited to nine content pages\n\\clearpage\n\\bibliography{references}\n\\bibliographystyle{unsrt}\n\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\n\\clearpage\n\\DoToC\n\\clearpage\n\n\\appendix\n\n\n\\section{Limitations and Future Work}\n\\label{ap:limitation}\n\nWhile Fortune demonstrates strong performance, several limitations remain and suggest promising directions for future research.\n\n\\noindent\\textbf{Limited datasets and experimental coverage.} Due to the resource-intensive nature of reinforcement learning, our evaluation is limited to a few representative public datasets, which primarily consist of clean and well-structured tables. This may not fully capture real-world scenarios, where spreadsheets often contain noisy, irregular, or complex two-dimensional layouts. Additionally, we experimented with only a limited set of base models and reinforcement learning algorithms (e.g., PPO). Nonetheless, we believe the experiments conducted in this work sufficiently demonstrate the effectiveness of our approach. Future work should explore a broader range of model sizes, architectures, and reinforcement learning algorithms across different downstream scenarios, such as applying Formula Tuning to larger models to achieve even better performance.\n\n\\noindent\\textbf{Applicability to broader table understanding tasks.} Our method assumes that answers can be fully derived from tabular data via executable formulas, which holds for many symbolic reasoning tasks. However, this assumption may not extend to tasks involving free-form text, multi-modal inputs, or ambiguous supervision. Nonetheless, formulas may still serve as useful intermediate representations, auxiliary objectives, or reasoning grounding mechanisms in such settings. Investigating how formula tuning can benefit or integrate with these broader tasks is an important direction.\n\n\\noindent\\textbf{Extensions to other formula-related tasks.} Executable formulas are central not only to reasoning but also to related tasks such as formula completion, correction, and refilling. These tasks could benefit from multi-task learning or joint training alongside formula reasoning. Conversely, using these tasks as pre-training objectives may also enhance symbolic reasoning capabilities via formula. Exploring how these tasks can be unified within a single framework could lead to more powerful and general-purpose symbolic table models.\n\n\\noindent\\textbf{Cold-start challenges in reinforcement learning.} For base models with limited symbolic reasoning capabilities and minimal knowledge of spreadsheet formulas, reinforcement learning from scratch can be unstable. In our experiments, we mitigated this by using the same training corpus for both SFT cold-start and RL. However, curating independent and high-quality cold-start corpora and identifying optimal initialization checkpoints for reinforcement learning remain open challenges.Furthermore, reinforcement learning itself is inherently unstable. Developing practical techniques to stabilize training and improve performance remains a critical area for exploration. Enhancing warm-up strategies and training stability could lead to significantly better RL outcomes.\n\n\\noindent\\textbf{Reward design for formula optimization.} Our current reward signal is based solely on binary execution accuracy. While simple and effective, it overlooks important factors such as formula efficiency, token redundancy, and partial credit. Future work can incorporate more fine-grained reward shaping, including length penalties or structure-aware scoring, to improve both learning stability and the quality of generated formulas.\n\n\\noindent These limitations point to several promising directions for future research: (1) scaling Formula Tuning to diverse domains and tasks, (2) exploring joint learning of symbolic tasks, (3) developing more stable and adaptive reinforcement learning strategies, and (4) advancing reward engineering for structured output generation.\n\n\n\\section{Broader Impacts and Safeguards}\n\\label{ap:ethic}\n\n\\noindent\\textbf{Broader Impacts.} \nFormula Tuning (Fortune) introduces a reinforcement learning framework that enhances symbolic reasoning for table understanding via spreadsheet formulas. By improving the ability of language models to reason over tabular data with verifiable, executable outputs, our work offers substantial benefits in domains where transparency and precision are essential—such as education, scientific analysis, finance, and public policy. Executable formulas can provide interpretable and auditable reasoning steps, potentially increasing user trust and reliability in AI-generated decisions involving structured data.\n\nHowever, these capabilities also introduce potential risks. If applied carelessly, formula generation may amplify biases present in training data or propagate subtle numerical errors. Moreover, spreadsheet formulas are deeply embedded in productivity workflows, and inaccurate generation at scale could lead to downstream harms (e.g., miscalculated budgets or flawed data reports). Furthermore, since symbolic reasoning via formulas may be more accessible in high-resource languages or domains with well-structured spreadsheets, deployment in low-resource settings could exacerbate inequalities in model performance and accessibility.\n\n\\noindent\\textbf{Safeguards.} \nTo mitigate such risks, we recommend several safeguards for future use of Fortune and similar symbolic reasoning systems. First, generated formulas should undergo verification through deterministic execution engines to ensure correctness. Second, evaluations should be conducted across diverse domains and spreadsheet structures, particularly including noisy or adversarial formats. Third, human-in-the-loop validation should be used in high-stakes applications (e.g., healthcare or financial audits) to ensure interpretability and safety. Finally, we advocate for transparent reporting of formula generation limitations and the inclusion of provenance indicators that show how a particular output was derived, enabling error tracing and accountability.\n\n\n\\section{Supplementary Proofs}\n\\label{ap:sup_proof}\n\n\\subsection{Proof of Symbolic Reasoning Dominance}\n\\label{proof:dominance}\n\n\\begin{proof}\nLet \\(E_1\\) denote the event that the model selects a correct high-level reasoning plan—i.e., a valid logical strategy that, if accurately followed, can lead to the correct answer.\n\nBy Assumption~\\ref{as:symbolic}~(3), both the symbolic policy \\(\\pi_{\\theta}^{\\mathrm{sym}}\\) and the textual policy \\(\\pi_{\\theta}^{\\mathrm{txt}}\\) are equally capable of producing such high-level plans:\n\\begin{equation}\n\\mathrm{P}_{\\mathrm{sym}}[E_1] = \\mathrm{P}_{\\mathrm{txt}}[E_1].\n\\end{equation}\n\nWe now compare how these two policies operationalize the same plan downstream:\n\n\\begin{itemize}[leftmargin=20pt, itemsep=0pt, labelsep=5pt, topsep=0pt]\n \\item \\textbf{Symbolic reasoning.} \n After selecting a correct high-level plan, the symbolic policy proceeds by emitting a formal expression—typically a spreadsheet formula \\(f\\)—that directly encodes the solution. This formula is then passed to an external executor, which deterministically computes the final answer \\(a = \\operatorname{exec}(f, \\mathbb{T})\\). Under Assumptions~\\ref{as:symbolic}~(1) and (2), if the plan is correct, the execution will reliably yield the correct answer \\(a^\\star(s)\\). Thus, the expected reward under the symbolic policy is:\n \\begin{equation}\n \\mathbb{E}_{a \\sim \\pi_{\\theta}^{\\mathrm{sym}}} [r(a \\mid s)] = \\mathrm{P}[E_1].\n \\end{equation}\n\n \\item \\textbf{Textual reasoning.} \n In contrast, after selecting the same correct high-level plan, the textual policy must verbalize the intermediate reasoning steps and compute results step-by-step in free text. This includes performing arithmetic, maintaining numerical precision, and formatting the final answer string. Let \\(E_2\\) denote the event that all intermediate computations and the final output are accurate. Then, the expected reward under the textual policy is:\n \\begin{equation}\n \\mathbb{E}_{a \\sim \\pi_{\\theta}^{\\mathrm{txt}}} [r(a \\mid s)] = \\mathrm{P}[E_1] \\cdot \\mathrm{P}[E_2 \\mid E_1].\n \\end{equation}\n Unlike symbolic execution, this textual process is inherently fragile. Errors in numerical calculations, token prediction, or formatting can easily lead to incorrect final answers, resulting in a reward of 0.\n\\end{itemize}\n\nSince \\(\\mathrm{P}[E_2 \\mid E_1] \\le 1\\), we conclude:\n\\begin{equation}\n\\mathbb{E}_{a \\sim \\pi_{\\theta}^{\\mathrm{txt}}} [r(a \\mid s)] \\le \\mathrm{P}[E_1] = \\mathbb{E}_{a \\sim \\pi_{\\theta}^{\\mathrm{sym}}} [r(a \\mid s)],\n\\end{equation}\nwhich completes the proof.\n\\end{proof}\n\n\\subsection{Proof of MLE Minimizes KL Divergence}\n\\label{proof:mle-kl}\n\n\\begin{proof}\nBy definition of KL divergence and entropy:\n\\begin{equation}\n\\begin{aligned}\n\\mathbb{E}_{a \\sim \\pi_g} [-\\log \\pi_\\theta(a \\mid s)]\n&= -\\sum_a \\pi_g(a \\mid s) \\log \\pi_\\theta(a \\mid s) \\\\\n&= -\\sum_a \\pi_g(a \\mid s) \\log \\frac{\\pi_\\theta(a \\mid s)}{\\pi_g(a \\mid s)} \n - \\sum_a \\pi_g(a \\mid s) \\log \\pi_g(a \\mid s) \\\\\n&= D_{\\mathrm{KL}}(\\pi_g \\parallel \\pi_\\theta) + H(\\pi_g).\n\\end{aligned}\n\\end{equation}\n\\end{proof}\n\n\\subsection{Proof of Convergence of SFT and Reward Upper Bound}\n\\label{proof:sft}\n\n\\begin{proof}\n\\textbf{(i) KL minimization.} \nBy Lemma~\\ref{lem:mle-kl}, maximizing the expected log-likelihood\n\\begin{equation}\n\\mathbb{E}_{s \\sim p(s),\\, f \\sim \\pi_g(\\cdot \\mid s)} \\left[ \\log \\pi_\\theta(f \\mid s) \\right]\n\\end{equation}\nis equivalent to minimizing the expected Kullback--Leibler (KL) divergence from the teacher policy:\n\\begin{equation}\n\\mathbb{E}_{s \\sim p(s)} \\left[ D_{\\mathrm{KL}}(\\pi_g(\\cdot \\mid s) \\parallel \\pi_\\theta(\\cdot \\mid s)) \\right].\n\\end{equation}\n\n\\textbf{(ii) Convergence.} \nUnder Assumption~\\ref{as:sft}(1), the model class \\(\\{\\pi_\\theta\\}\\) is expressive enough such that there exists some \\(\\theta^\\star\\) satisfying\n\\begin{equation}\n\\inf_\\theta \\, \\mathbb{E}_{s \\sim p(s)} \\left[ D_{\\mathrm{KL}}(\\pi_g(\\cdot \\mid s) \\parallel \\pi_\\theta(\\cdot \\mid s)) \\right] = 0.\n\\end{equation}\nAssumption~\\ref{as:sft}(2) ensures the optimization algorithm converges to this global optimum, and Assumption~\\ref{as:sft}(3) guarantees that the empirical distribution \\(\\hat{p}(s, f)\\) converges to the true distribution \\(p(s)\\pi_g(f \\mid s)\\) as the sample size \\(N \\to \\infty\\).\n\nTherefore, at convergence,\n\\begin{equation}\nD_{\\mathrm{KL}}(\\pi_g \\parallel \\pi_{\\theta^\\star}) = 0 \\quad \\text{almost everywhere},\n\\end{equation}\nwhich implies pointwise equivalence between the student and teacher policies:\n\\begin{equation}\n\\pi_{\\theta^\\star}(f \\mid s) = \\pi_g(f \\mid s) \\quad \\text{for almost every } s \\in \\mathcal{S}.\n\\end{equation}\n\n\\textbf{(iii) Reward upper bound.} \nLet \\(r(a \\mid s) = \\mathbbm{1}[a = a^\\star(s)]\\) be the task reward, where \\(a = \\operatorname{exec}(f, \\mathbb{T})\\) is the executed output. Since execution is deterministic and the student mimics the teacher exactly, we have:\n\\begin{equation}\n\\mathbb{E}_{s \\sim p, f \\sim \\pi_{\\theta^\\star}} \\left[r(\\operatorname{exec}(f, \\mathbb{T}) \\mid s)\\right] = \\mathbb{E}_{s \\sim p, f \\sim \\pi_g} \\left[r(\\operatorname{exec}(f, \\mathbb{T}) \\mid s)\\right].\n\\end{equation}\nThus, supervised fine-tuning under ideal assumptions can at best match the teacher's reward performance. In particular, this expected reward serves as an upper bound for what SFT can achieve when trained only on demonstrations from \\(\\pi_g\\).\n\\end{proof}\n\n\\subsection{Proof of RL Superiority}\n\\label{proof:rl_sup}\n\\begin{proof}\nLet \\(a^\\star(s)\\) be the ground-truth answer for input \\(s\\), and suppose that the teacher policy \\(\\pi_g(f \\mid s)\\) covers only a strict subset of all possible formulas \\(f\\) such that \\(\\operatorname{exec}(f, \\mathbb{T}) = a^\\star(s)\\).\n\nBy Lemma~\\ref{lem:sft}, supervised fine-tuning under ideal assumptions can at best match the expected reward of \\(\\pi_g\\):\n\\begin{equation}\n\\mathbb{E}_{s \\sim p,\\, f \\sim \\pi_{\\theta^\\star}} \\left[r(\\operatorname{exec}(f, \\mathbb{T}) \\mid s)\\right] = \\mathbb{E}_{s \\sim p,\\, f \\sim \\pi_g} \\left[r(\\operatorname{exec}(f, \\mathbb{T}) \\mid s)\\right].\n\\end{equation}\n\nNow consider an RL policy \\(\\pi_\\theta^{\\mathrm{RL}}\\). Under Assumption~\\ref{as:rl_exploration}, the RL policy explores the full action space and assigns non-zero probability to correct formulas \\(f'\\) that are not in the support of \\(\\pi_g\\) but still satisfy \\(\\operatorname{exec}(f', \\mathbb{T}) = a^\\star(s)\\).\n\nAs the reward function \\(r(a \\mid s)\\) depends solely on execution correctness, and not formula structure, RL is able to collect reward on these additional correct actions that \\(\\pi_g\\) does not generate. Therefore,\n\\begin{equation}\n\\mathbb{E}_{s \\sim p,\\, f \\sim \\pi_\\theta^{\\mathrm{RL}}} \\left[r(\\operatorname{exec}(f, \\mathbb{T}) \\mid s)\\right] > \\mathbb{E}_{s \\sim p,\\, f \\sim \\pi_g} \\left[r(\\operatorname{exec}(f, \\mathbb{T}) \\mid s)\\right],\n\\end{equation}\nwhich implies the desired result.\n\\end{proof}\n\n\n\n\\section{Supplementary Discussion of Methodology}\n\\label{ap:discussion}\n\n\\subsection{Textual vs. Symbolic Reasoning in Table Understanding}\n\nIn addition to the formal analysis in Section~\\ref{sec:text_symbolic}, we highlight several conceptual advantages of symbolic reasoning for table understanding:\n\n\\begin{itemize}[leftmargin=1.5em, itemsep=0.5em]\n \\item \\textbf{Execution-based computation.} Symbolic reasoning externalizes computation through deterministic execution, separating high-level logical planning from low-level arithmetic or formatting operations.\n\n \\item \\textbf{Compositionality and structure.} Spreadsheet formulas offer compositional and type-aware representations, providing stronger structural priors than unstructured text.\n\n \\item \\textbf{Verifiability and transparency.} Symbolic outputs are interpretable and verifiable: they can be inspected, tested, reused, or debugged—enabling traceable and auditable reasoning processes.\n\n \\item \\textbf{Discrete action space.} The symbolic action space is bounded and discrete, which facilitates more stable exploration and optimization during training.\n\n \\item \\textbf{Robustness to token-level variability.} Unlike textual reasoning, which is prone to errors from exposure bias or numerical drift, symbolic reasoning delegates exact computation to the executor, reducing dependency on fragile token generation.\n\\end{itemize}\n\n\\subsection{SFT vs. RL in Symbolic Table Reasoning}\n\nWe also expand upon the discussion in Section~\\ref{sec:sft_rl}, comparing supervised fine-tuning (SFT) and reinforcement learning (RL) for symbolic reasoning:\n\n\\begin{itemize}[leftmargin=1.5em, itemsep=0.5em]\n \\item \\textbf{SFT limitations.} SFT imitates teacher demonstrations at the token level and struggles to generalize beyond the training distribution. It penalizes semantically correct but structurally different formulas, constraining exploration.\n\n \\item \\textbf{Reward-aligned optimization.} RL optimizes directly for task-level correctness using execution-based rewards, allowing the model to discover diverse yet valid solution strategies.\n\n \\item \\textbf{Support for many-to-one mappings.} Since different formulas can yield the same correct answer, RL naturally accommodates this multiplicity, whereas SFT often fails to reward such diversity.\n\n \\item \\textbf{Flexible reward shaping.} RL allows for auxiliary reward terms—such as penalties on length, syntactic constraints, or correctness under verification—which are difficult to incorporate in SFT.\n\n \\item \\textbf{Improved generalization.} By optimizing for semantic correctness rather than mimicking surface-level token patterns, RL enables the model to generalize more effectively in both in-distribution (ID) and out-of-distribution (OOD) scenarios, including novel question types, unseen table schemas, and structurally diverse formulas.\n\\end{itemize}\n\n\n\\subsection{Practical Challenges of Formula Tuning}\n\\label{ap:challenge}\n\nWhile reinforcement learning (RL) offers significant advantages for symbolic table reasoning, it also introduces several practical challenges, especially under the \\textbf{assumptions} outlined in Section~\\ref{sec:text_symbolic} and \\ref{sec:sft_rl}.\n\n\\begin{itemize}[leftmargin=1.5em, itemsep=0.3em]\n \\item \\textbf{Exploration bottlenecks.} Assumption~\\ref{as:rl_exploration} assumes that the RL policy can eventually explore correct formulas. However, the space of possible formulas is extremely large, and valid, executable ones are rare—especially at the start of training. This makes it difficult for the model to receive useful reward signals, leading to slow or unstable learning.\n\n \\item \\textbf{Limited symbolic priors.} Unlike supervised fine-tuning (SFT), RL does not benefit from direct examples of correct formulas. If the model lacks prior knowledge of spreadsheet syntax or symbolic structures, it may struggle to generate meaningful outputs. This weak starting point often results in inefficient exploration and poor early performance.\n\n \\item \\textbf{RL training instability.} When training from scratch, the model often produces repetitive, invalid, or meaningless formulas in the early stages, receiving no reward. This can cause unstable training and hinder convergence. Empirically, initializing with a supervised or pretrained model leads to more stable training and faster reward learning.\n\n \\item \\textbf{Sparse and coarse reward signals.} Execution-based rewards typically only indicate whether the final answer is correct or not, without offering any feedback on partially correct or structurally promising outputs. This makes it harder for the model to learn from near misses. Designing more informative reward functions—such as those based on formula structure or partial execution—remains an important direction.\n\\end{itemize}\n\n\nOvercoming these challenges is essential for scaling \\textit{Formula Tuning} to more complex symbolic tasks, broader domains, and higher-capacity models. Future work may explore techniques such as curriculum learning, hybrid supervision, symbolic inductive priors, or multi-objective optimization to improve training stability and exploration efficiency.\n\n\n\n\n\\section{Detailed Settings of Experiments}\n\\label{ap:detail_setting}\n\n\n\\noindent\\textbf{Models.}\nOur experiments include both open-source and proprietary models. For open-source models, we use \\textit{Qwen2.5-Coder$_{7B}$} (\\texttt{Qwen2.5-Coder-7B-Instruct}, Apache 2.0 License) \\cite{hui2024qwen25codertechnicalreport} and \\textit{LLaMA-3.1$_{8B}$} (\\texttt{LLaMA-3.1-8B-Instruct}, Meta Llama 3 Community Licence) \\cite{grattafiori2024llama3herdmodels}. \nFor proprietary models, we evaluate OpenAI’s\\footnote{\\url{https://openai.com/policies/row-terms-of-use/}} \\textit{GPT-4o} (\\texttt{gpt-4o-2024-11-20}), \\textit{GPT-4o-mini} (\\texttt{gpt-4o-mini-2024-07-18}), and \\textit{O1} (\\texttt{o1-2024-12-17}) as baselines.\n\n\n\\noindent\\textbf{Datasets.}\nAs shown in Table~\\ref{tab:datasets}, we conduct experiments on seven diverse table understanding benchmarks: WikiTQ~\\cite{pasupat2015compositionalsemanticparsingsemistructured}, TabFact~\\cite{chen2020tabfactlargescaledatasettablebased}, FinQA~\\cite{chen-etal-2021-finqa}, HiTab~\\cite{cheng2021hitab}, MultiHiertt~\\cite{zhao-etal-2022-multihiertt}, AIT-QA~\\cite{katsis2021aitqa}, and TableBench~\\cite{wu2025tablebenchcomprehensivecomplexbenchmark}. These datasets vary in domain coverage, table structures, and question complexity, collectively spanning the full spectrum of table understanding tasks. For MultiHiertt, which contains multiple tables, we concatenate them vertically to form a single spreadsheet-like table. For training, we combine the first five datasets into a unified training corpus and train the model jointly on this merged set. Each dataset is then evaluated individually. All original training and test splits are preserved, except for TabFact, from which we randomly sample 10,000 examples to prevent its abundance of relatively simple binary QA examples from dominating or skewing the training. Among these benchmarks, AIT-QA and TableBench are considered out-of-distribution (OOD) evaluation sets, while the remaining datasets are treated as in-distribution (ID). The characteristics of each dataset are summarized below:\n\n\\begin{itemize}[leftmargin=20pt, itemsep=0pt, labelsep=5pt, topsep=0pt]\n \\item \\textbf{WikiTQ} is a Wikipedia-based table QA dataset with relatively simple factoid questions over relational tables.\n \\item \\textbf{TabFact} also uses Wikipedia tables but frames the task as fact verification, where each claim is labeled as either \\textit{true} or \\textit{false}.\n \\item \\textbf{FinQA} focuses on financial-domain tables and requires symbolic reasoning over semi-structured input that includes both pretext and posttext as additional context.\n \\item \\textbf{HiTab} contains hierarchical tables derived from statistical reports. While its structure is more complex than relational tables, the content is relatively straightforward.\n \\item \\textbf{MultiHiertt} involves multi-table reasoning over hierarchical tables in the financial domain, demanding both structural and symbolic reasoning.\n \\item \\textbf{AIT-QA} consists of hierarchical tables from the airline domain. Although structurally rich, its questions tend to be simpler.\n \\item \\textbf{TableBench} features complex questions over relational tables drawn from various domains. Many questions require multi-step symbolic reasoning, making it the most challenging benchmark in our evaluation.\n\\end{itemize}\n\n\n\\begin{table}[ht]\n\\centering\n\\caption{Overview of the training data and table benchmarks used in this study.}\n\\resizebox{\\textwidth}{!}{%\n\\begin{tabular}{llccccccl}\n\\toprule\nEvaluation Type & Dataset & \\# Train Data & \\# Test Data & Table Type & Domain & License & Source \\\\\n\\midrule\n\\multirow{5}{*}{In-Distribution}\n & WikiTQ~\\cite{pasupat2015compositionalsemanticparsingsemistructured}\n & 13{,}753 & 4{,}217 & Relational & Wikipedia & CC-BY-SA-4.0 & \\href{https://github.com/ppasupat/WikiTableQuestions}{Link} \\\\\n & TabFact~\\cite{chen2020tabfactlargescaledatasettablebased}\n & 10{,}000 & 2{,}024 & Relational & Wikipedia & CC-BY-4.0 & \\href{https://github.com/wenhuchen/Table-Fact-Checking}{Link} \\\\\n & FinQA~\\cite{chen-etal-2021-finqa}\n & 6{,}251 & 1{,}147 & Relational & Finance & MIT & \\href{https://github.com/czyssrs/FinQA}{Link} \\\\\n & HiTab~\\cite{cheng2021hitab}\n & 7{,}399 & 1{,}583 & Hierarchical & Statistical Reports & C-UDA 1.0 & \\href{https://github.com/microsoft/HiTab}{Link} \\\\\n & MultiHiertt~\\cite{zhao-etal-2022-multihiertt}\n & 7{,}795 & 1{,}038 & Multiple \\& Hierarchical & Finance & MIT & \\href{https://github.com/psunlpgroup/MultiHiertt}{Link} \\\\\n\\midrule\n\\multirow{2}{*}{Out-of-Distribution}\n & AIT-QA~\\cite{katsis2021aitqa}\n & -- & 515 & Hierarchical & Airline & CDLA-Sharing-1.0 & \\href{https://github.com/IBM/AITQA}{Link} \\\\\n & TableBench~\\cite{wu2025tablebenchcomprehensivecomplexbenchmark}\n & -- & 883 & Relational & Cross Domain & CC0-1.0 & \\href{https://huggingface.co/datasets/Multilingual-Multimodal-NLP/TableBench}{Link} \\\\\n\\bottomrule\n\\end{tabular}\n} % end resizebox\n\\label{tab:datasets}\n\\end{table}\n\n\n\\noindent\\textbf{Table Encoding.} \nWe adopt a table encoding method similar to SpreadsheetEncoder~\\cite{dong-etal-2024-encoding}, which converts a table into a linearized markdown-style format. Each cell is represented by its spreadsheet address and value, forming text sequences such as \\texttt{A1,Year|A2,Profit}. This encoding preserves both structural and content information, enabling the model to better understand cell-level references.\n\n\\noindent\\textbf{Output Format.} \nFollowing the structured reasoning paradigm, the model is required to produce outputs in a two-stage format:\n\n\\[\n y = \n \\underbrace{\\langle\\texttt{think}\\rangle\\, t \\,\\langle/\\texttt{think}\\rangle}_{\\textit{\\textbf{reasoning trajectory}}}\\,\n \\underbrace{\\langle\\texttt{answer}\\rangle\\, \\texttt{\\{json\\}} \\,\\langle/\\texttt{answer}\\rangle}_{\\textit{\\textbf{final answer}}},\n\\]\n\nwhere \\(t\\) is a free-form natural language reasoning process (i.e., the thinking process), and the answer block contains a JSON object from which the final prediction is extracted. This design enables decoupling the reasoning trajectory from the answer payload and facilitates more structured reward computation.\n\nTo encourage adherence to this format, we introduce a lightweight \\textit{format reward}. If the output fails to follow the required structure (e.g., malformed tags or unparseable JSON), the model receives a penalty of \\(-2\\). If the format is valid and the answer can be successfully parsed from the JSON object, a small positive reward of \\(+0.1\\) is added to the answer-level reward. Therefore, the final reward is as:\n\\begin{equation}\nr_{\\text{final}}(a \\mid s) = r_{\\text{ans}}(a \\mid s) + r_{\\text{fmt}}(a)\n\\end{equation}\nThis reward shaping helps stabilize training and guide the model toward producing reliably structured outputs.\n\n\n\n\\noindent\\textbf{Baselines.}\nWe compare our proposed framework against a broad range of strong baselines, including both prompting-based and fine-tuning-based methods. To ensure a fair comparison, we require all methods to output short, deterministic answers rather than open-ended free-form text. Following this criterion, we exclude TableLLM~\\cite{zhang2025tablellmenablingtabulardata}, which relies on a critique model for answer evaluation and does not produce a directly verifiable answer string. Prompting-based methods currently dominate the TableQA landscape, with most relying on large closed-source models for performance. We compare Fortune and Fortune++ with several representative methods in this category: Binder~\\cite{cheng2023bindinglanguagemodelssymbolic}, Dater~\\cite{ye2023largelanguagemodelsversatile}, API-Assisted~\\cite{cao2025tablemasterrecipeadvancetable}, Chain-of-Table~\\cite{wang2024chainoftableevolvingtablesreasoning}, ReAcTable~\\cite{zhang2023reactableenhancingreacttable}, Norm-DP~\\cite{liu2023rethinkingtabulardataunderstanding}, TIDE~\\cite{yang2025triples}, E5~\\cite{zhang-etal-2024-e5}, and SS-CoT~\\cite{zhao2024stepwiseselfconsistentmathematicalreasoning}. We also include TableMaster~\\cite{cao2025tablemasterrecipeadvancetable}, a recent recipe-based prompting framework built on GPT-4o-mini. For fine-tuning-based methods, we select models specifically trained for table question answering tasks. These include TAPEX-Large~\\cite{liu2022tapextablepretraininglearning}, OmniTab~\\cite{jiang-etal-2022-omnitab}, TableLlama~\\cite{zhang2024tablellamaopenlargegeneralist}, TableGPT2~\\cite{su2024tablegpt2largemultimodalmodel}, and TabAF~\\cite{wang2025generaltablequestionanswering}, a recent strong method that combines formula generation and hybrid self-consistency. Our framework is evaluated under the same settings to ensure consistency and comparability across methods.\n\n\n\\noindent\\textbf{RL Training.} \nWe use Proximal Policy Optimization (PPO) for reinforcement learning (RL). The maximum prompt length is set to 8192 tokens, and the maximum response length is 512 tokens. The critic model is initialized with the same weights as the actor model. The actor is trained with a learning rate of 1e-6, while the critic uses a slightly higher learning rate of 1e-5 to enable faster value estimation. We set the KL divergence coefficient to 0.001 to balance exploration and policy stability. The generation temperature is set to 0.6 to encourage a mix of determinism and diversity in the generated reasoning chains and formula outputs. The PPO mini-batch size is 64. We evaluate performance every 20 steps and report the results based on the best performance achieved on each dataset.\n\n\\noindent\\textbf{SFT Training.} \nThe supervised fine-tuning (SFT) training corpus is distilled from \\textit{GPT-4o} by prompting it with ground-truth answers, eliciting chain-of-thought reasoning followed by a final answer. We adopt a rejection-based fine-tuning (RFT) strategy~\\cite{yuan2023scalingrelationshiplearningmathematical}, retaining only examples where the generated answer exactly matches the ground truth. For symbolic reasoning tasks, correctness is determined by executing the generated formula and verifying that the resulting answer matches the expected output. This approach ensures high-quality supervision for fine-tuning. All SFT models are trained for 4 epochs, and we report results based on the checkpoint with the highest exact match accuracy on the test set. We use a learning rate of 2e-5 and a batch size of 64.\n\n\\noindent\\textbf{Evaluation Inference.} \nFor all models, including both open-source and proprietary ones, we use a temperature of 0, top-$k$ of 50, and top-$p$ of 0.7 during inference. Setting the temperature to 0 encourages deterministic outputs and improves stability in single-pass predictions. For self-consistency decoding in Fortune++, we use a higher temperature of 0.6 to promote diversity across multiple samples, enabling the model to better explore reasoning variations and improve final answer voting.\n\n\\noindent\\textbf{Evaluation Metrics.} \nFollowing prior work~\\cite{pasupat2015compositionalsemanticparsingsemistructured, cheng2021hitab}, we primarily use exact match (EM) as the evaluation metric, applying numeric tolerance when comparing numerical values. Official evaluation scripts are used whenever available to ensure consistency. For TabFact, which is formulated as a binary classification task, we report standard classification accuracy. Since our training objective aligns with evaluation, we also use exact match (EM) for answer reward calculation.\n\n\\noindent\\textbf{Software.} \nWe implement Fortune using Python 3.11, with the VERL framework~\\cite{sheng2024hybridflow} serving as the core architecture for reinforcement learning and other supervised fine-tuning with language models. Our implementation utilizes VLLM (v0.8.3)~\\cite{kwon2023efficientmemorymanagementlarge} for efficient LLM inference and generation, PyTorch (v2.4.0) with CUDA 12.8 for deep learning operations, and Ray~\\cite{moritz2018raydistributedframeworkemerging} for distributed training and inference. FlashAttention-2~\\cite{dao2022flashattentionfastmemoryefficientexact} is integrated to accelerate attention computation. For proprietary LLMs, we access OpenAI models via the Microsoft Azure platform\\footnote{\\url{https://azure.microsoft.com/}}. For formula execution, we use the open-source spreadsheet engine \\texttt{formulas}\\footnote{\\url{https://github.com/vinci1it2000/formulas}} (EUPL 1.1+ License), which supports a wide range of standard spreadsheet operators. A representative list of symbolic operators used in our table reasoning framework is provided in Appendix~\\ref{ap:operator}.\n\n\\noindent\\textbf{Hardware.} \nAll experiments are conducted on machines equipped with either NVIDIA A100 80GB PCIe or NVIDIA H100 80GB PCIe GPUs, along with 1.0 TB of RAM. For reinforcement learning (RL) training of open-source models, we use 8 × NVIDIA H100 80GB PCIe GPUs by default. For supervised fine-tuning (SFT) of open-source models, we use 4 × NVIDIA A100 80GB PCIe GPUs by default.\n\n\n\n\\section{Ablation Study and Upper-Bound Performance of Fortune++}\n\\label{ap:ablation}\n\\begin{table}[ht!]\n \\centering\n \\caption{Performance comparison of TabAF, Fortune, and Fortune++ variants. Values in the table indicate accuracy (\\%). `-' indicates results not reported in the related paper. Gray rows represent upper-bound performance. Numbers in parentheses with a downward arrow (\\(\\downarrow\\)) indicate the performance drop relative to the default Fortune++ configuration. Top results are highlighted in \\textcolor{langdarkblue}{dark blue}.}\n \\resizebox{\\columnwidth}{!}{\n \\setlength{\\aboverulesep}{1pt}\n \\setlength{\\belowrulesep}{1pt}\n \\begin{tabular}{llccccccc}\n \\toprule\n \\multicolumn{2}{l}{\\textbf{Method}} \n & \\textbf{WikiTQ} & \\textbf{TabFact} & \\textbf{FinQA} \n & \\textbf{HiTab} & \\textbf{MultiHiertt} \n & \\textbf{AIT-QA} & \\textbf{TableBench} \\\\\n \\midrule\n \\multirow{4}{*}{TabAF \\cite{wang2025generaltablequestionanswering}} \n & \\textcolor{gray}{Upper Bound} \n & \\textcolor{gray}{80.13} & \\textcolor{gray}{94.02} & \\textcolor{gray}{-} \n & \\textcolor{gray}{82.07} & \\textcolor{gray}{-} \n & \\textcolor{gray}{-} & \\textcolor{gray}{-} \\\\\n & 5 Text \n & 61.42 & 81.47 & -\n & 74.24 & - \n & - & - \\\\\n & 5 Formula \n & 64.20 & 67.54 & - \n & 74.87 & - \n & - & - \\\\\n & 5 Text + 5 Formula \n & 74.72 & 83.99 & -\n & 78.41 & - \n & - & - \\\\\n \\midrule\n \\multirow{2}{*}{Fortune} \n & \\textcolor{gray}{Upper Bound} \n & \\textcolor{gray}{77.35} & \\textcolor{gray}{96.49} & \\textcolor{gray}{72.01} \n & \\textcolor{gray}{79.91} & \\textcolor{gray}{54.82} \n & \\textcolor{gray}{88.93} & \\textcolor{gray}{43.26} \\\\\n & 10 Formula \n & 67.05 & 85.08 & 62.16 \n & 69.74 & 40.85 \n & 80.39 & 35.22 \\\\\n \\midrule\n \\multirow{4}{*}{Fortune++} \n & \\textcolor{gray}{Upper Bound} \n & \\textcolor{gray}{93.62} & \\textcolor{gray}{99.51} & \\textcolor{gray}{91.02} \n & \\textcolor{gray}{95.01} & \\textcolor{gray}{71.29} \n & \\textcolor{gray}{98.06} & \\textcolor{gray}{61.16} \\\\\n & 5 Text \n & 64.52 (\\(\\downarrow\\)\\textit{18.02}) \n & 85.18 (\\(\\downarrow\\)\\textit{9.88}) \n & 63.64 (\\(\\downarrow\\)\\textit{16.83}) \n & 74.48 (\\(\\downarrow\\)\\textit{12.76}) \n & 36.42 (\\(\\downarrow\\)\\textit{15.31}) \n & 83.30 (\\(\\downarrow\\)\\textit{9.90}) \n & 28.31 (\\(\\downarrow\\)\\textit{16.65}) \\\\\n & 5 Formula \n & 66.48 (\\(\\downarrow\\)\\textit{16.06}) \n & 82.41 (\\(\\downarrow\\)\\textit{12.65}) \n & 61.99 (\\(\\downarrow\\)\\textit{18.48}) \n & 68.54 (\\(\\downarrow\\)\\textit{18.70}) \n & 39.60 (\\(\\downarrow\\)\\textit{12.13}) \n & 79.42 (\\(\\downarrow\\)\\textit{13.78}) \n & 34.65 (\\(\\downarrow\\)\\textit{10.31}) \\\\\n & 5 Text + 5 Formula \n & \\textcolor{langdarkblue}{\\textbf{82.54}} \n & \\textcolor{langdarkblue}{\\textbf{95.06}} \n & \\textcolor{langdarkblue}{\\textbf{80.47}} \n & \\textcolor{langdarkblue}{\\textbf{87.24}} \n & \\textcolor{langdarkblue}{\\textbf{51.73}} \n & \\textcolor{langdarkblue}{\\textbf{93.20}} \n & \\textcolor{langdarkblue}{\\textbf{44.96}} \\\\\n \\bottomrule\n \\end{tabular}%\n }\n \\label{tab:ablation}\n\\end{table}\n\nWe conduct an ablation study of Fortune++ to investigate the complementary roles and effectiveness of textual and symbolic reasoning. Table~\\ref{tab:ablation} reveals several instructive patterns.\n\n\\textbf{Combining textual and symbolic reasoning yields the most robust performance.} \nThe balanced sampling strategy used by Fortune++ (five textual and five formula candidates) consistently outperforms both the pure-text and pure-formula variants across all benchmarks. This confirms that textual and symbolic reasoning address complementary error modes. Disabling either modality leads to substantial accuracy degradation, with drops of up to 18 percentage points (e.g., --18~pp on FinQA for text-only, --13~pp on AIT-QA for formula-only).\n\n\\textbf{Text and formula reasoning each excel in different scenarios.} \nTextual reasoning performs better on simpler table QA tasks, such as TabFact and AIT-QA, where natural language understanding and logical inference dominate. In contrast, formula-based reasoning excels on arithmetic-heavy or structured computation tasks like FinQA and MultiHiertt, where symbolic execution is crucial for deriving the correct answer. This division reinforces the importance of integrating both modalities for general-purpose table understanding.\n\n\\textbf{Reinforcement learning enhances symbolic reasoning beyond supervised fine-tuning.} \nCompared with TabAF—which uses the same backbone but is trained only with SFT—Fortune’s RL variant achieves substantially stronger formula-only performance (e.g., 82.41\\% vs.\\ 67.54\\% on TabFact with 5 Formula). This suggests that reinforcement learning encourages the model to explore more reliable and executable reasoning paths, ultimately improving symbolic program quality.\n\n\\textbf{Many correct answers are lost due to naive majority voting.} \nThe \\textit{upper-bound} rows show that Fortune++ frequently generates correct answers that are not selected by simple majority vote. The discrepancy between upper-bound and actual performance reaches 19~pp on MultiHiertt and 17~pp on TableBench, indicating considerable headroom for improved candidate selection through confidence-based aggregation or smarter reranking mechanisms.\n\n\\textbf{Complex, low-resource benchmarks remain the greatest challenge.} \nThe largest performance gaps appear on structurally complex and low-resource datasets such as MultiHiertt and TableBench. These results highlight the limitations of current voting and reasoning mechanisms and point to future directions including symbolic planner integration, adaptive sampling, and confidence-calibrated answer selection.\n\n\n\n\n\n\\section{Comparative Analysis of Formula Tuning and Other Symbolic Table Reasoning Methods}\n\\label{ap:other_symbolic}\n\n\\begin{figure}[t]\n \\centering\n \\begin{subfigure}[t]{0.48\\textwidth}\n \\centering\n \\includegraphics[height=120pt]{figures/symbolic_zs.pdf}\n \\caption{Zero-Shot setting}\n \\label{fig:symbolic_zs}\n \\end{subfigure}\n \\begin{subfigure}[t]{0.48\\textwidth}\n \\centering\n \\includegraphics[height=120pt]{figures/symbolic_rl.pdf}\n \\caption{Reinforcement Learning setting}\n \\label{fig:symbolic_rl}\n \\end{subfigure}\n \\caption{Comparison of performance across different symbolic reasoning methods under Zero-Shot and Reinforcement Learning (RL) settings. Values in the figure indicate accuracy (\\%).}\n \\label{fig:symbolic_comparison}\n % Big thanks to blingbing for the figure of double radar plots!\n\\end{figure}\n\n\nFigure~\\ref{fig:symbolic_comparison} and Table~\\ref{tab:symbolic} compare three symbolic reasoning paradigms—SQL queries, Python snippets, and spreadsheet formulas—under both zero-shot and reinforcement learning (RL) settings. To ensure a fair comparison, we train and evaluate these symbolic tools only on datasets with flat, relational tables: WikiTQ, TabFact, and FinQA for training, and all three plus TableBench for evaluation. This restriction is necessary because SQL operates solely on flat tables, while Python/Pandas are also usually worked for flat tabular structures. Several patterns emerge from the comparison.\n\n\n\\begin{table}[ht!]\n \\centering\n \\caption{Performance comparison of different symbolic reasoning methods (SQL, Python, and Formula) under Zero-shot and RL settings. Values in the table indicate accuracy (\\%). The best performance in each column is highlighted in \\textcolor{langdarkblue}{dark blue}.}\n \\resizebox{0.6\\columnwidth}{!}{%\n % \\setlength{\\aboverulesep}{1pt}\n % \\setlength{\\belowrulesep}{1pt}\n \\begin{tabular}{lccccc}\n \\toprule\n \\textbf{Method} & \\textbf{WikiTQ} & \\textbf{TabFact} & \\textbf{FinQA} & \\textbf{TableBench} & \\textbf{Overall} \\\\\n \\midrule\n \\multicolumn{6}{l}{\\textit{\\textbf{Zero-Shot}}} \\\\\n \\quad SQL & 17.07 & 21.15 & 1.51 & 7.64 & 11.84 \\\\\n \\quad Python & 30.96 & \\textcolor{langdarkblue}{\\textbf{60.39}} & \\textcolor{langdarkblue}{\\textbf{34.87}} & 16.33 & 35.64 \\\\\n \\quad \\textbf{Formula}\n & \\textcolor{langdarkblue}{\\textbf{38.96}} \n & 52.52 \n & 34.44\n & \\textcolor{langdarkblue}{\\textbf{26.05}} \n & \\textcolor{langdarkblue}{\\textbf{37.99}} \\\\\n \\midrule\n \\multicolumn{6}{l}{\\textit{\\textbf{Reinforcement Learning (RL)}}} \\\\\n \\quad SQL & 67.58 & 83.94 & 38.79 & 32.09 & 55.60 \\\\\n \\quad Python & 70.46 & 84.42 & 65.85 & 35.26 & 64.00 \\\\\n \\quad \\textbf{Formula}\n & \\textcolor{langdarkblue}{\\textbf{70.67}} \n & \\textcolor{langdarkblue}{\\textbf{84.50}} \n & \\textcolor{langdarkblue}{\\textbf{65.89}} \n & \\textcolor{langdarkblue}{\\textbf{35.84}} \n & \\textcolor{langdarkblue}{\\textbf{64.23}} \\\\\n \\bottomrule\n \\end{tabular}%\n }\n \\label{tab:symbolic}\n\\end{table}\n\n\\textbf{Spreadsheet formulas offer the strongest zero-shot symbolic reasoning.} \nWithout any task-specific training, formulas achieve the highest out-of-the-box performance, with an overall accuracy of 37.99\\%. They slightly outperform Python (35.64\\%) and significantly surpass SQL (11.84\\%). The gap is especially pronounced on datasets like WikiTQ and FinQA, suggesting that pre-trained language models possess some intuitive understanding of spreadsheet-style operations, but struggle to generate valid and executable SQL or Python code without further adaptation. On TableBench, which features complex table QA questions, Python code falls short. Models without fine-tuning often fail to generate long and sufficiently accurate code to solve challenging problems. In contrast, spreadsheet formulas are shorter, easier to generate, and more robust in zero-shot settings, making them better suited for this type of reasoning.\n\n\\textbf{Spreadsheet formulas remain the most effective symbolic tool after RL.} \nAll three symbolic tools improve significantly with RL training, with SQL and Python gaining 43.8 and 28.4 percentage points, respectively. This underscores the value of policy-gradient optimization for learning execution-constrained program structures. Post-training, formulas and Python reach nearly identical accuracy (64.23\\% vs.\\ 64.00\\%), while SQL still lags behind at 55.60\\%, largely due to its limited ability to handle numerical computation required in table reasoning. Although the final scores of formulas and Python are close, formulas maintain a consistent edge. Beyond their strong performance, spreadsheet formulas are shorter, easier to read, and more beginner-friendly. These qualities make them not only effective but also practical and accessible as a symbolic tool for table reasoning tasks.\n\n\n\n\\section{Impact Analysis of the Thinking Process in Formula Tuning}\n\\label{ap:reasoning_process}\n\n\\begin{figure}[t!]\n \\centering\n \\includegraphics[width=\\textwidth]{figures/reasoning_impact.pdf}\n \\caption{Performance comparison with and without explicit reasoning process under Zero-Shot and Reinforcement Learning (RL) settings across various datasets. Each group of bars shows the accuracy (\\%) achieved by four configurations: Zero-Shot without Reasoning, Zero-Shot with Reasoning, RL without Reasoning, and RL with Reasoning.}\n \\label{fig:reasoning_impact}\n % Acknowledging Jingyu for the figure A.\n\\end{figure}\n\n\nTable~\\ref{tab:wo_reasining} and Figure~\\ref{fig:reasoning_impact} highlight the impact of incorporating an explicit thinking process before generating formulas. \n\n\\begin{table}[ht!]\n \\centering\n \\caption{Performance comparison with and without reasoning under Zero-Shot and RL settings across various datasets. Values in the table indicate accuracy (\\%).}\n \\resizebox{0.9\\columnwidth}{!}{%\n % \\setlength{\\aboverulesep}{1pt}\n % \\setlength{\\belowrulesep}{1pt}\n \\begin{tabular}{lccccccc c}\n \\toprule\n \\textbf{Method} & \\textbf{WikiTQ} & \\textbf{TabFact} & \\textbf{FinQA} & \\textbf{HiTab} & \\textbf{MultiHiertt} & \\textbf{AIT-QA} & \\textbf{TableBench} & \\textbf{Overall} \\\\\n \\midrule\n \\multicolumn{9}{l}{\\textit{\\textbf{Zero Shot}}} \\\\\n \\quad w/o Reasoning & 42.14 & 57.76 & 33.13 & 38.35 & 15.99 & 58.45 & 28.88 & 39.24 \\\\\n \\quad w/ Reasoning & 38.96 & 52.52 & 34.44 & 32.60 & 15.90 & 52.43 & 26.05 & 36.13 \\\\\n \\midrule\n \\multicolumn{9}{l}{\\textit{\\textbf{Reinforcement Learning (RL)}}} \\\\\n \\quad w/o Reasoning & 63.39 & 80.39 & 61.99 & 67.09 & 38.15 & 78.84 & 33.41 & 60.47 \\\\\n \\quad w/ Reasoning & 67.80 & 84.19 & 62.16 & 71.19 & 41.72 & 81.17 & 35.45 & 63.38 \\\\\n \\bottomrule\n \\end{tabular}%\n }\n \\label{tab:wo_reasining}\n\\end{table}\n\\textbf{In the zero-shot setting, reasoning may hurts performance.} \nWe observe that adding a reasoning trace before the formula generally leads to lower accuracy (e.g., 39.24\\% → 36.13\\% overall). This is likely because such \\textit{thought-first-then-formula} generation patterns are underrepresented in pretraining corpora. As a result, models tend to produce unnatural or error-prone reasoning steps, which negatively affect the final output. The limitations of chain-of-thought reasoning in certain scenarios have also been discussed in recent work~\\cite{sprague2025cotcotchainofthoughthelps,liu2024mindstepbystep}.\n\n\\textbf{In the RL setting, reasoning significantly improves performance.} \nOnce trained with our answer-based reward, the model begins to benefit from generating an explicit reasoning trace. The inclusion of a thinking process effectively expands the exploration space during policy optimization and encourages the model to break down complex table reasoning tasks into more manageable steps. This leads to consistent performance improvements across datasets (e.g., 60.47\\% → 63.38\\% overall), demonstrating that reasoning becomes a valuable asset—once the model has been properly trained to utilize it effectively.\n\n\n\\begin{figure}[t!]\n \\centering\n \\includegraphics[width=0.85\\textwidth]{figures/top10_formula.pdf}\n \\caption{Top 10 most frequent formula operators used during evaluation.}\n \\label{fig:top10_formula}\n % Acknowledging Jingyu for the figure B.\n\\end{figure}\n\n\\section{Statistical Analysis of Generated Formulas in Symbolic Table Reasoning}\n\\label{ap:stat}\n\nWe collect and analyze statistics of the formulas generated by our formula-tuned model during evaluation to better understand their structural properties.\n\n\\begin{table}[ht]\n\\caption{Statistics of generated formulas across different table understanding datasets.}\n\\centering\n\\resizebox{0.7\\columnwidth}{!}{%\n\\begin{tabular}{lcccccc}\n\\toprule\n\\textbf{Dataset} \n& \\multicolumn{3}{c}{\\textbf{Table Layout}} \n& \\multicolumn{3}{c}{\\textbf{Generated Formula}} \\\\\n\\cmidrule(lr){2-4} \\cmidrule(lr){5-7}\n& \\textbf{Width} & \\textbf{Height} & \\textbf{Area Size} \n& \\textbf{Length} & \\textbf{\\# Operators} & \\textbf{\\# Variables} \\\\\n\\midrule\nWikiTQ & 6.28 & 19.46 & 121.68 & 21.61 & 0.84 & 1.50 \\\\\nTabFact & 6.28 & 14.04 & 87.42 & 44.85 & 3.11 & 2.85 \\\\\nFinQA & 3.92 & 18.09 & 70.80 & 18.84 & 1.98 & 2.94 \\\\\nHiTab & 6.16 & 21.32 & 171.40 & 12.20 & 0.49 & 1.34 \\\\\nMultiHiertt & 7.25 & 46.59 & 339.30 & 22.64 & 2.10 & 3.21 \\\\\nAIT-QA & 5.62 & 13.86 & 81.91 & 5.67 & 0.11 & 1.89 \\\\\nTableBench & 6.71 & 16.26 & 108.24 & 26.87 & 1.52 & 2.78 \\\\\n\\bottomrule\n\\end{tabular}\n}\n\\label{tab:formula_stats}\n\\end{table}\nTable~\\ref{tab:formula_stats} presents a quantitative summary of both table layout characteristics and the structural properties of generated formulas across seven widely used table understanding datasets. The table is divided into two parts: the first group (\\textit{Width}, \\textit{Height}, and \\textit{Area Size}) reflects the average structural complexity of the input tables, while the second group (\\textit{Length}, \\textit{\\# Operators}, and \\textit{\\# Variables}) captures the syntactic and symbolic complexity of the generated formulas.\n\nWe observe substantial variation in table layout complexity. For example, MultiHiertt has by far the largest average table area (339.30), indicating its multiple and hierarchical format. In contrast, datasets like AIT-QA and FinQA involve relatively smaller or simpler tables, which may place less structural burden on the reasoning process. Notably, HiTab also exhibits a high area size, despite having fewer variables and a short formula length, suggesting that its challenge lies more in table structure than in formula richness.\n\nIn terms of generated formulas, TabFact stands out with the longest average formula length (44.85 characters) and the highest number of operators (3.11), indicating that its fact verification tasks typically require complex symbolic conditions. On the other hand, AIT-QA exhibits the shortest formulas with minimal operator usage (5.67 length, 0.11 operators), reflecting the dataset’s relatively simple question types. Datasets like FinQA and MultiHiertt show high variable counts (around 3 per formula), which aligns with their multi-step reasoning nature involving multiple cell references. TableBench poses a greater challenge due to its combination of complex question intent and compositional reasoning demands. Although its average table size is moderate, the questions often require multi-step symbolic operations such as nested aggregations, comparisons, or indirect references—making it a strong testbed for evaluating deep reasoning ability.\n\nThese statistics provide important insights for model evaluation and reward design. First, different datasets pose very different reasoning demands—relying solely on benchmarks like WikiTQ or AIT-QA may underestimate a model’s true symbolic capacity. Second, symbolic complexity (e.g., operator density) varies nontrivially across tasks, and therefore reward shaping mechanisms should adapt accordingly to avoid penalizing semantically necessary long formulas. Lastly, the disconnect between table area and formula length in datasets like HiTab implies that structural layout, rather than size alone, can be the main source of reasoning difficulty—an insight that can guide future benchmark construction and curriculum learning design.\n\nFigure~\\ref{fig:top10_formula} presents the distribution of the most frequently used formula operators during evaluation. The conditional operator \\texttt{IF} appears overwhelmingly often, with a count of 2,700, indicating that conditional reasoning is central to many table reasoning tasks. Arithmetic operators such as \\texttt{MINUS} (1,695), \\texttt{PLUS} (1,633), and \\texttt{DIVIDE} (1,549) are also widely used, reflecting the numerical nature of many questions. Logical comparison operators like \\texttt{EQUAL}, \\texttt{GREATER\\_THAN}, and \\texttt{LESS\\_THAN} occur frequently as well, suggesting that relational reasoning is also a common requirement. Less frequently used operators such as \\texttt{MAX} and those grouped into the \\texttt{OTHERS} category play a smaller role. Overall, the operator distribution highlights the need for models to support both arithmetic and logical reasoning, with a strong emphasis on conditional operations.\n\n\n\n\n\n\\clearpage\n\\section{Case Study}\n\\label{ap:case_study}\n\n\\subsection{Textual vs. Symbolic Reasoning}\nWe present representative examples comparing textual and symbolic reasoning methods in table understanding tasks.\n\nAs shown in Table~\\ref{tab:example_text}, the textual approach performs better in this particular case. This is a simple counting question, so textual reasoning can easily enumerate the relevant items and output the correct answer (4). In contrast, the symbolic reasoning attempts to solve the problem via a more complex formula. Although the reasoning process is logically correct and the intent aligns with expectations, the actual formula execution produces an incorrect result due to implementation details—specifically, the presence of the string \\textit{nan} in the table being misinterpreted. This highlights that in certain corner cases, symbolic reasoning may not have fully mastered tool usage or aligned formula execution. In comparison, textual reasoning can sometimes bypass such pitfalls and arrive at the correct answer more robustly.\n\nOn the other hand, Table~\\ref{tab:example_symbolic} illustrates a case where symbolic reasoning proves more effective. This is a moderately difficult classic counting problem with a ground truth answer of 13. Here, textual reasoning fails, producing an incorrect count (22), suggesting that its performance deteriorates as task complexity increases. In contrast, symbolic reasoning correctly formulates a count-based expression and arrives at the correct answer. The accompanying reasoning trace (\\textit{Think}) also clearly articulates the logic behind the computation, demonstrating the advantage of symbolic approaches in precise numerical tasks.\n\n\n\\begin{table}[h]\n\\centering\n\\caption{Case study comparing textual and symbolic reasoning. In this example, textual reasoning yields the correct answer.}\n\\small\n\\begin{tabular}{p{0.15\\textwidth}p{0.8\\textwidth}}\n\\toprule\n\\textbf{Method} & \\textbf{Case} \\\\\n\\midrule\n- & \\textbf{Question:} \\textit{rapper saigon has had at least () albums released in the U.S.?} \\\\\n& \\textbf{Table:} \\\\\n& \n\\includegraphics[width=0.81\\textwidth]{figures/example_table_text.png}\n\\\\\n& \\textbf{Groundtruth Answer:} 4 \\\\\n\n\n\\midrule\nSymbolic & \\textbf{Think:} \\textit{To compute how many unique albums Saigon has released, I examine column F (F2:F13), which lists album names. Some of these cells contain \"nan\", which I treat as missing values. I only count the unique non-missing values. A standard formula to count unique non-empty values is: =SUMPRODUCT(1/COUNTIF(F2:F13,F2:F13)) This formula works because COUNTIF returns the number of times each value appears; taking 1 over that and summing gives the count of distinct values. This will count duplicates only once and ignore empty cells or \"nan\" if treated as the same placeholder.} \\\\\n& \\textbf{Generated Formula:} \\texttt{=SUMPRODUCT(1/COUNTIF(F2:F13,F2:F13))} \\\\\n\\\\\n& \\textbf{Execution Result:} 5 \\\\\n\\\\\n& \\textbf{Execution Accuracy:} 0.0 \\\\\n\n\n\\midrule\nTextual & \\textbf{Think:} \\textit{We are asked to find out how many albums rapper Saigon has released in the U.S. From the table, we look at the 'Album' column (column F), which lists the associated album for each song. We ignore entries that are 'nan' or empty, as those do not correspond to a known album. From F2 to F13, we extract the non-nan values: - Warning Shots 2 - The Greatest Story Never Told - The Greatest Story Never Told Chapter 2: Bread and Circuses - The Greatest Story Never Told Chapter 3: The Troubled Times of Brian Carenard These are 4 distinct album names, so the answer is 4.} \\\\\n& \\textbf{Generated Answer:} \\texttt{4} \\\\\n\\\\\n& \\textbf{Answer Accuracy:} 1.0 \\\\\n\\bottomrule\n\\end{tabular}\n\\label{tab:example_text}\n\\end{table}\n\\begin{table}[ht]\n\\centering\n\\caption{Case study comparing textual and symbolic reasoning. In this example, symbolic reasoning yields the correct answer.}\n\\small\n\\begin{tabular}{p{0.15\\textwidth}p{0.8\\textwidth}}\n\\toprule\n\\textbf{Method} & \\textbf{Case} \\\\\n\\midrule\n- & \\textbf{Question:} \\textit{How many elections did the party win75 or more seats?} \\\\\n& \\textbf{Table:} \\\\\n& \n\\includegraphics[width=0.35\\textwidth]{figures/example_table_symbolic.png}\n\\\\\n& \\textbf{Groundtruth Answer:} 13\\\\\n\n\n\\midrule\nTextual & \\textbf{Think:} \\textit{- A2, 1927 (sep): 57 seats - A3, 1932: 72 seats - ... - A26, 2002: 81 seats - A27, 2007: 77 seats - A28, 2011: 20 seats (less than 75) Counting the rows where the \"seats\" column is 75 or more: - 1927 (sep): 57 - 1932: 72 - ... - 2002: 81 - 2007: 77 Counting these, we find 22 elections where the party won 75 or more seats.} \\\\\n& \\textbf{Generated Answer:} \\texttt{22} \\\\\n\\\\\n& \\textbf{Execution Accuracy:} 0.0 \\\\\n\n\n\\midrule\nSymbolic & \\textbf{Think:} \\textit{1. To answer this question, I need to look into the table provided in the question. The question is asking to determine how many times a party won 75 or more seats in various elections based on a given table. 2. Since the question is focused on the number of seats won, I need to look for a column in the table that contains the number of seats each party won in each election. 3. Given that the question is asking to count the number of elections where the party won 75 or more seats, I will need to look for a column that contains the seat numbers and then count how many of those seat numbers are 75 or greater. 4. Since the question is not providing a specific column in the table for seat numbers, I will need to infer that the seat numbers are contained in column D of the provided table, which is titled \"seats\".\n} \\\\\n& \\textbf{Generated Formula:} \\texttt{=COUNTIF(D2:D28, \">=75\")} \\\\\n\\\\\n& \\textbf{Execution Result:} 13 \\\\\n\\\\\n& \\textbf{Execution Accuracy:} 1.0 \\\\\n\\bottomrule\n\\end{tabular}\n\\label{tab:example_symbolic}\n\\end{table}\n\\clearpage\n\\subsection{Performance of SFT vs. RL}\nWe also compare models trained with supervised fine-tuning (SFT) and reinforcement learning (RL) in the context of symbolic table reasoning using formulas. As shown in Table~\\ref{tab:example_rl}, RL enables the language model to generate more accurate and executable formulas, leading to improved symbolic reasoning performance. In this example, the SFT model appears to merely memorize a reasoning pattern and produces a formula that is superficially related to the question but logically incorrect. Moreover, the intermediate reasoning process is highly interpretable and clearly aligned with the question's logic.\n\n\n\\begin{table}[h]\n\\centering\n\\caption{Case study comparing performance of SFT vs. RL. In this example, RL yields the correct answer.}\n\\small\n\\begin{tabular}{p{0.15\\textwidth}p{0.8\\textwidth}}\n\\toprule\n\\textbf{Method} & \\textbf{Case} \\\\\n\\midrule\n- & \\textbf{Question:} \\textit{What is the number of winning drivers that represented the U.S.?} \\\\\n& \\textbf{Table:} \\\\\n& \n\\includegraphics[width=0.81\\textwidth]{figures/example_table_rl.png}\n\\\\\n& \\textbf{Groundtruth Answer:} 15\\\\\n\n\n\\midrule\nSFT & \\textbf{Think:} \\textit{To determine the number of of winning States that represent, we need to count the occurrences of U.S. drivers in the range B1:B10. This will give us the total count of cells that contain the value \"A1\".} \\\\\n& \\textbf{Generated Formula:} \\texttt{=COUNTIF(B1:B10, A1)} \\\\\\\\\n& \\textbf{Execution Result:} 0 \\\\\n\\\\\n& \\textbf{Execution Accuracy:} 0.0 \\\\\n\n\n\\midrule\nRL & \\textbf{Think:} \\textit{1. To answer this question, I need to look into the table provided in the question. The question is asking to determine how many times a driver from the United States (U.S.) won a race based on a given table.\n2. Since the question is focused on the winning drivers, I need to look for a column in the table that contains the names of the winning drivers.\n3. Given that the question is asking to count how many times a U.S. driver won, I will need to look for a column that contains the names of the winning drivers and then count how many of those names are U.S. drivers.\n4. Since the question is not providing a specific column or row in the table where the winning drivers' names are listed, I will need to assume that the winning drivers' names are listed in a column that I can reference in the table.} \\\\\n& \\textbf{Generated Formula:} \\texttt{=COUNTIF(G2:G18, \"Rick Mears\") + COUNTIF(G2:G18, \"Al Unser, Jr.\") + COUNTIF(G2:G18, \"Michael Andretti\") + COUNTIF(G2:G18, \"Danny Sullivan\")} \\\\\n\\\\\n& \\textbf{Execution Result:} 15 \\\\\n\\\\\n& \\textbf{Execution Accuracy:} 1.0 \\\\\n\\bottomrule\n\\end{tabular}\n\\label{tab:example_rl}\n\\end{table}\n\n\n\\clearpage\n\\section{Spreadsheet Formula Operators in Symbolic Table Reasoning}\n\\label{ap:operator}\n\nSpreadsheet formulas are Turing complete~\\cite{smalley2023excel}, but in practice, only a core subset of functions is commonly used. To guide the language model toward effective formula generation and support future research, we identify a set of representative operators in Table~\\ref{tab:operator}. This table lists each operator’s name, symbol, definition, and a representative example—primarily covering basic arithmetic and aggregation operations. Acknowledging the model’s limited formula knowledge at the beginning of training, we explicitly introduce these operators during prompting, while still allowing the model to use any formula supported by our execution engine.\n\nThe formula operators can be viewed as the action space in reinforcement learning for symbolic table reasoning. The selected operators are designed to cover the majority of symbolic reasoning needs in table-based question answering, including row/column indexing, numerical aggregation, and conditional filtering. Focusing on a fixed set of operators facilitates interpretable error analysis and enables fine-grained tracking of formula usage patterns during both training and evaluation. This curated set also provides a natural foundation for curriculum learning strategies—starting with simpler operators and progressively introducing more complex ones, such as nested conditions and lookup functions.\n\n\\begin{table}[ht]\n \\centering\n \\caption{Representative spreadsheet formula operators in symbolic table reasoning: Symbols, Definitions, and Examples.}\n \\resizebox{\\textwidth}{!}{%\n \\begin{tabular}{llll}\n \\toprule\n \\textbf{Name} & \\textbf{Symbol} & \\textbf{Description} & \\textbf{Example} \\\\\n \\midrule\n PLUS & + & Adds two numbers together & \\texttt{=A1 + A2} \\\\\n MINUS & -- & Subtracts one number from another & \\texttt{=A1 - A2} \\\\\n MULTIPLY & * & Multiplies two numbers together & \\texttt{=A1 * A2} \\\\\n DIVIDE & / & Divides one number by another & \\texttt{=A1 / A2} \\\\\n SUM & SUM & Sums a range of numbers & \\texttt{=SUM(A1:A10)} \\\\\n AVERAGE & AVERAGE& Calculates the average of a range of numbers & \\texttt{=AVERAGE(A1:A10)} \\\\\n COUNT & COUNT & Counts the number of numbers in a range & \\texttt{=COUNT(A1:A10)} \\\\\n MAX & MAX & Finds the maximum number in a range & \\texttt{=MAX(A1:A10)} \\\\\n MIN & MIN & Finds the minimum number in a range & \\texttt{=MIN(A1:A10)} \\\\\n EQUAL & = & Returns TRUE if the two values are equal & \\texttt{=A1 = A2} \\\\\n NOT\\_EQUAL & <> & Returns TRUE if the two values are not equal & \\texttt{=A1 <> A2} \\\\\n GREATER\\_THAN & > & Returns TRUE if the first value is greater than the second & \\texttt{=A1 > A2} \\\\\n LESS\\_THAN & < & Returns TRUE if the first value is less than the second & \\texttt{=A1 < A2} \\\\\n GREATER\\_THAN\\_OR\\_EQUAL & >= & Returns TRUE if the first value is greater than or equal to second& \\texttt{=A1 >= A2} \\\\\n LESS\\_THAN\\_OR\\_EQUAL & <= & Returns TRUE if the first value is less than or equal to second & \\texttt{=A1 <= A2} \\\\\n AND & AND & Returns TRUE if all arguments are TRUE & \\texttt{=AND(A1, A2)} \\\\\n OR & OR & Returns TRUE if any argument is TRUE & \\texttt{=OR(A1, A2)} \\\\\n NOT & NOT & Returns TRUE if the argument is FALSE & \\texttt{=NOT(A1)} \\\\\n IF & IF & Returns one value if a condition is TRUE and another if FALSE & \\texttt{=IF(A1 > 10, \"Yes\", \"No\")} \\\\\n TRUE & TRUE & Returns TRUE & \\texttt{=TRUE} \\\\\n FALSE & FALSE & Returns FALSE & \\texttt{=FALSE} \\\\\n INDEX & INDEX & Returns the value of a cell at a specific row and column & \\texttt{=INDEX(A1:A10, 1)} \\\\\n MATCH & MATCH & Returns the position of an item in an array (see syntax below) & \\texttt{=MATCH(\"value\", A1:A10, 0)} \\\\\n \\bottomrule\n \\end{tabular}%\n }\n \\label{tab:operator}\n\\end{table}\n\n\\clearpage\n\\section{Prompts Used in the Experiments}\n\\label{ap:prompt}\n\nFigures~\\ref{fig:prompt_formula}, \\ref{fig:prompt_text}, \\ref{fig:prompt_python}, \\ref{fig:prompt_sql}, and~\\ref{fig:prompt_wo_reason} illustrate the prompts used in our experiments across zero-shot inference, supervised fine-tuning (SFT), reinforcement learning (RL), and evaluation.\n\n\\textit{Pre-text} and \\textit{Post-text} refer to optional unstructured context surrounding the table, such as the data description format used in FinQA~\\cite{chen-etal-2021-finqa}. \\textit{formula operator instruction} represents the textual representations and usage guidelines of the representative spreadsheet formula operators in symbolic table reasoning, as detailed in Appendix~\\ref{ap:operator}.\n\nThese prompts serve as examples rather than optimal templates. They may vary across tasks and can be further optimized for better performance.\n\n\\begin{figure}[ht!]\n \\centering\n \\includegraphics[width=0.95\\textwidth]{prompts/prompt_formula.pdf}\n \\caption{Prompt for symbolic reasoning with formula. Blue text indicates placeholders for variables within the prompt.}\n \\label{fig:prompt_formula}\n\\end{figure}\n\n\\begin{figure}[ht!]\n \\centering\n \\includegraphics[width=0.95\\textwidth]{prompts/prompt_text.pdf}\n \\caption{Prompt for textual reasoning. Blue text indicates placeholders for variables within the prompt.}\n \\label{fig:prompt_text}\n\\end{figure}\n\n\\begin{figure}[ht!]\n \\centering\n \\includegraphics[width=0.95\\textwidth]{prompts/prompt_python.pdf}\n \\caption{Prompt for symbolic reasoning with Python. Blue text indicates placeholders for variables within the prompt.}\n \\label{fig:prompt_python}\n\\end{figure}\n\n\\begin{figure}[ht!]\n \\centering\n \\includegraphics[width=0.95\\textwidth]{prompts/prompt_sql.pdf}\n \\caption{Prompt for symbolic reasoning with SQL. Blue text indicates placeholders for variables within the prompt.}\n \\label{fig:prompt_sql}\n\\end{figure}\n\n\\begin{figure}[ht!]\n \\centering\n \\includegraphics[width=0.95\\textwidth]{prompts/prompt_wo_reason.pdf}\n \\caption{Prompt for symbolic reasoning with thinking process. Blue text indicates placeholders for variables within the prompt.}\n \\label{fig:prompt_wo_reason}\n\\end{figure}\n\n\\clearpage\n\\section{Notation Table}\n\\label{ap:notation}\n\nTable~\\ref{tab:notation} provides a comprehensive list of the notations used throughout this paper, along with their corresponding descriptions. This table serves as a quick reference to help readers better understand\nthe concepts presented in our work.\n\n% Notation Table\n\\begin{table}[h]\n\\caption{Notation used throughout the paper}\n\\label{tab:notation}\n\\begin{center}\n\\begin{tabular}{cl}\n\\toprule\n\\textbf{Notation} & \\textbf{Description} \\\\\n\\midrule\n\\multicolumn{2}{l}{\\textit{General}} \\\\\n$s$ & Input instance, a pair $(\\mathbb{T}, q)$ of table and question \\\\\n$\\mathbb{T}$ & Input table with $m$ rows and $n$ columns \\\\\n$q$ & Natural-language query \\\\\n$C_{i,j}$ & Cell at row $i$, column $j$ in the table \\\\\n$m,n$ & Number of rows and columns in $\\mathbb{T}$ \\\\\n$a$ & Generated answer (textual or executed formula result) \\\\\n$f$ & Spreadsheet formula generated by the model \\\\\n$p(s)$ & Empirical distribution over table–query pairs \\\\\n$r(a \\mid s)$ & Reward function evaluating answer correctness \\\\\n$a^\\star(s)$ & Ground-truth answer for input $s$ \\\\\n$\\operatorname{exec}(f,\\mathbb{T})$ & Deterministic executor applying $f$ to $\\mathbb{T}$ \\\\\n\\midrule\n\\multicolumn{2}{l}{\\textit{Policies}} \\\\\n$\\pi_\\theta(a \\mid s)$ & LM generation policy parameterized by $\\theta$ \\\\\n$\\pi_\\theta^{\\mathrm{txt}}$ & Textual reasoning policy (free-text answer) \\\\\n$\\pi_\\theta^{\\mathrm{sym}}$ & Symbolic reasoning policy (formula-based) \\\\\n$\\pi_g$ & Teacher policy in supervised fine-tuning \\\\\n$\\pi_{\\theta^\\star}^{\\mathrm{SFT}}$ & Optimal SFT policy under Assumption \\\\\n$\\pi_\\theta^{\\mathrm{RL}}$ & Policy learned via reinforcement learning \\\\\n\\midrule\n\\multicolumn{2}{l}{\\textit{Objective and Metrics}} \\\\\n$\\mathbb{E}_{s\\sim p,\\,a\\sim\\pi}[\\cdot]$ & Expectation under inputs and policy \\\\\n$\\mathbbm{1}[\\cdot]$ & Indicator function (1 if true, 0 otherwise) \\\\\n\\midrule\n\\multicolumn{2}{l}{\\textit{Assumptions and Events}} \\\\\n$E_1$ & Event of selecting a correct high-level reasoning plan \\\\\n$E_2$ & Event that all textual reasoning steps are correct \\\\\n\\bottomrule\n\\end{tabular}\n\\end{center}\n\\end{table}\n\n\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\n% \\newpage\n% \\input{checklist}\n\n%%%%%%%%%%%%%%%%%%%%%% Lang's LAST PAGE %%%%%%%%%%%%%%%%%%%%\n\n\\clearpage\n\\vspace*{\\fill}\n\\begin{center}\nHere is the LAST PAGE.\n% 2025.05.16 with bing in NeurIPS'25\n\\end{center}\n\\vspace*{\\fill}\n\n\\end{document}"},"question":{"kind":"string","value":"In RL, which method is the most stable across different datasets?"},"answer":{"kind":"string","value":"Formula w/ CS"},"plan":{"kind":"string","value":"SELECT RL methods, all datasets\nLOOP for each method\n SELECT scores across all datasets for current method\n COMPUTE max score\n COMPUTE min score\n COMPUTE range = max − min\n COMPUTE add range to stability_ranges \nCOMPUTE most_stable_method = argmin stability_ranges\nRETURN most_stable_method\n"}}},{"rowIdx":80,"cells":{"paper":{"kind":"string","value":"2505.22928"},"relevant_tables":{"kind":"list like","value":[["\\begin{table*}[t]\n","\t\\small\n","\t\\centering\n","\t\\renewcommand{\\arraystretch}{1.2}\n","\t\\setlength{\\tabcolsep}{4pt}\n","\t\\begin{tabular}{lc|cccccc|cccccc}\n","\t\t\\toprule\n","\t\t\\multirow{2}{*}{\\textbf{Model}} & \\multirow{2}{*}{\\textbf{Think}} & \\multicolumn{6}{c|}{\\cellcolor{gray!20}\\textsc{\\textbf{CochraneForest}}} & \\multicolumn{6}{c}{\\cellcolor{gray!20}\\textbf{RCTs}~\\cite{yun2024automatically}} \\\\\n","\t\t& & Acc & F1 & EM & EM@1 & MSE$\\downarrow$ & EIR$\\downarrow$ & Acc & F1 & EM & EM@1 & MSE$\\downarrow$ & EIR$\\downarrow$\\\\\n","\t\t\\toprule\n","\t\t\\multicolumn{2}{c}{\\cellcolor{gray!20}\\textbf{Pretrained LLMs}} & \\multicolumn{6}{c}{\\cellcolor{gray!20}} & \\multicolumn{6}{c}{\\cellcolor{gray!20}} \\\\\n","\t\t\\texttt{GPT-4-0125} & \\xmark & 71.4 & 73.7 & 28.4 & 72.1 & 0.88 & 0.31 & 71.2 & 65.3 & 34.5 & 70.3 & 0.69 & 0.44\\\\\n","\t\t\\texttt{Qwen2.5-7B} & \\xmark & 69.7 & 65.4 & 14.9 & 66.4 & 1.40 & 0.39 & 63.2 & 59.7 & 20.3 & 73.8 & 1.19 & 0.50 \\\\\n","\t\t\\texttt{Qwen2.5-14B} & \\xmark & 76.2& 73.7 & 27.4 & 73.6 &0.72 & 0.30 & 69.8 & 66.4 & 28.6 & 75.8 & 0.91 & 0.44 \\\\\n","\t\t\\texttt{Qwen2.5-72B} & \\xmark & 82.2 & 80.3 & 42.5 & 81.7 & 0.63 & 0.26 & \\textbf{75.3}& \\textbf{72.8}& \\textbf{35.1} &\\textbf{81.1} & \\textbf{0.68} & \\textbf{0.39}\\\\\n","\t\t\\texttt{Llama-3.1-8B} & \\xmark & 68.7 & 66.6 & 15.9 & 62.2 & 2.16 & 0.33 & 62.7 & 54.7 & 14.8 & 66.5 & 1.30 & 0.47 \\\\\n","\t\t\\texttt{Llama-3.1-70B} & \\xmark & 79.4 & 78.3 & 31.5 & 78.7 & 0.59 & 0.28 & 65.5 & 60.8 & 30.0 & 74.2 & 1.45 & 0.44\\\\\n","\t\t\\texttt{Llama-3.1-405B} & \\xmark & \\textbf{82.4} &\\textbf{80.8} & \\textbf{44.3} & \\textbf{82.4} & \\textbf{0.43} & \\textbf{0.23} & 70.3 & 66.5 & 33.6 & 76.7 & 0.80 & 0.41 \\\\\n","\t\t\\texttt{DeepSeek-Qwen-7B}& \\cmark & 59.7 & 54.3 & 11.2 & 58.3& 2.83 & 0.50& 53.8 & 45.7 & 9.4 & 53.8 & 3.68 & 0.61 \\\\\n","\t\t\\texttt{DeepSeek-Qwen-14B} & \\cmark & 65.4 & 61.2 & 19.9 & 66.4 & 1.29 & 0.44 &60.2 & 55.1 & 11.2 & 60.3 & 2.41 & 0.58 \\\\\n","\t\t\\texttt{DeepSeek-Qwen-32B} & \\cmark & 74.0 & 71.6 & 28.6 & 73.5 & 0.58 & 0.28 & 68.8 & 65.0 & 29.1 & 78.2 & 0.72 &0.43\\\\\n","\t\t\\toprule\n","\t\t\\multicolumn{2}{c}{\\cellcolor{gray!20}\\textbf{Our Models}} & \\multicolumn{6}{c}{\\cellcolor{gray!20}} & \\multicolumn{6}{c}{\\cellcolor{gray!20}} \\\\\n","\t\t\\texttt{Qwen2.5-7B-SFT} & \\cmark & 74.5 & 70.1 & 28.4 & 79.1 & 0.51 & 0.29& 71.5& 68.4 & 30.2 & 80.0& 0.64 & 0.36 \\\\\n","\t\t\\texttt{Qwen2.5-7B-RL} & \\cmark & \\textbf{81.6} & \\textbf{80.1} & \\textbf{42.2} & \\textbf{81.4} & \\textbf{0.40} & \\textbf{0.24} & \\textbf{79.3} & \\textbf{76.4} & \\textbf{41.2} & \\textbf{89.7} & \\textbf{0.49} &\\textbf{0.28}\\\\\n","\t\t\\bottomrule\n","\t\\end{tabular}\n","\t\\caption{Evaluation results across models on two datasets. We report Accuracy and F1 (label prediction), EM, EM@1, EIR and MSE (numerical extraction).}\n","\t\\label{tab:results}\n","\\end{table*}\n"]],"string":"[\n [\n \"\\\\begin{table*}[t]\\n\",\n \"\\t\\\\small\\n\",\n \"\\t\\\\centering\\n\",\n \"\\t\\\\renewcommand{\\\\arraystretch}{1.2}\\n\",\n \"\\t\\\\setlength{\\\\tabcolsep}{4pt}\\n\",\n \"\\t\\\\begin{tabular}{lc|cccccc|cccccc}\\n\",\n \"\\t\\t\\\\toprule\\n\",\n \"\\t\\t\\\\multirow{2}{*}{\\\\textbf{Model}} & \\\\multirow{2}{*}{\\\\textbf{Think}} & \\\\multicolumn{6}{c|}{\\\\cellcolor{gray!20}\\\\textsc{\\\\textbf{CochraneForest}}} & \\\\multicolumn{6}{c}{\\\\cellcolor{gray!20}\\\\textbf{RCTs}~\\\\cite{yun2024automatically}} \\\\\\\\\\n\",\n \"\\t\\t& & Acc & F1 & EM & EM@1 & MSE$\\\\downarrow$ & EIR$\\\\downarrow$ & Acc & F1 & EM & EM@1 & MSE$\\\\downarrow$ & EIR$\\\\downarrow$\\\\\\\\\\n\",\n \"\\t\\t\\\\toprule\\n\",\n \"\\t\\t\\\\multicolumn{2}{c}{\\\\cellcolor{gray!20}\\\\textbf{Pretrained LLMs}} & \\\\multicolumn{6}{c}{\\\\cellcolor{gray!20}} & \\\\multicolumn{6}{c}{\\\\cellcolor{gray!20}} \\\\\\\\\\n\",\n \"\\t\\t\\\\texttt{GPT-4-0125} & \\\\xmark & 71.4 & 73.7 & 28.4 & 72.1 & 0.88 & 0.31 & 71.2 & 65.3 & 34.5 & 70.3 & 0.69 & 0.44\\\\\\\\\\n\",\n \"\\t\\t\\\\texttt{Qwen2.5-7B} & \\\\xmark & 69.7 & 65.4 & 14.9 & 66.4 & 1.40 & 0.39 & 63.2 & 59.7 & 20.3 & 73.8 & 1.19 & 0.50 \\\\\\\\\\n\",\n \"\\t\\t\\\\texttt{Qwen2.5-14B} & \\\\xmark & 76.2& 73.7 & 27.4 & 73.6 &0.72 & 0.30 & 69.8 & 66.4 & 28.6 & 75.8 & 0.91 & 0.44 \\\\\\\\\\n\",\n \"\\t\\t\\\\texttt{Qwen2.5-72B} & \\\\xmark & 82.2 & 80.3 & 42.5 & 81.7 & 0.63 & 0.26 & \\\\textbf{75.3}& \\\\textbf{72.8}& \\\\textbf{35.1} &\\\\textbf{81.1} & \\\\textbf{0.68} & \\\\textbf{0.39}\\\\\\\\\\n\",\n \"\\t\\t\\\\texttt{Llama-3.1-8B} & \\\\xmark & 68.7 & 66.6 & 15.9 & 62.2 & 2.16 & 0.33 & 62.7 & 54.7 & 14.8 & 66.5 & 1.30 & 0.47 \\\\\\\\\\n\",\n \"\\t\\t\\\\texttt{Llama-3.1-70B} & \\\\xmark & 79.4 & 78.3 & 31.5 & 78.7 & 0.59 & 0.28 & 65.5 & 60.8 & 30.0 & 74.2 & 1.45 & 0.44\\\\\\\\\\n\",\n \"\\t\\t\\\\texttt{Llama-3.1-405B} & \\\\xmark & \\\\textbf{82.4} &\\\\textbf{80.8} & \\\\textbf{44.3} & \\\\textbf{82.4} & \\\\textbf{0.43} & \\\\textbf{0.23} & 70.3 & 66.5 & 33.6 & 76.7 & 0.80 & 0.41 \\\\\\\\\\n\",\n \"\\t\\t\\\\texttt{DeepSeek-Qwen-7B}& \\\\cmark & 59.7 & 54.3 & 11.2 & 58.3& 2.83 & 0.50& 53.8 & 45.7 & 9.4 & 53.8 & 3.68 & 0.61 \\\\\\\\\\n\",\n \"\\t\\t\\\\texttt{DeepSeek-Qwen-14B} & \\\\cmark & 65.4 & 61.2 & 19.9 & 66.4 & 1.29 & 0.44 &60.2 & 55.1 & 11.2 & 60.3 & 2.41 & 0.58 \\\\\\\\\\n\",\n \"\\t\\t\\\\texttt{DeepSeek-Qwen-32B} & \\\\cmark & 74.0 & 71.6 & 28.6 & 73.5 & 0.58 & 0.28 & 68.8 & 65.0 & 29.1 & 78.2 & 0.72 &0.43\\\\\\\\\\n\",\n \"\\t\\t\\\\toprule\\n\",\n \"\\t\\t\\\\multicolumn{2}{c}{\\\\cellcolor{gray!20}\\\\textbf{Our Models}} & \\\\multicolumn{6}{c}{\\\\cellcolor{gray!20}} & \\\\multicolumn{6}{c}{\\\\cellcolor{gray!20}} \\\\\\\\\\n\",\n \"\\t\\t\\\\texttt{Qwen2.5-7B-SFT} & \\\\cmark & 74.5 & 70.1 & 28.4 & 79.1 & 0.51 & 0.29& 71.5& 68.4 & 30.2 & 80.0& 0.64 & 0.36 \\\\\\\\\\n\",\n \"\\t\\t\\\\texttt{Qwen2.5-7B-RL} & \\\\cmark & \\\\textbf{81.6} & \\\\textbf{80.1} & \\\\textbf{42.2} & \\\\textbf{81.4} & \\\\textbf{0.40} & \\\\textbf{0.24} & \\\\textbf{79.3} & \\\\textbf{76.4} & \\\\textbf{41.2} & \\\\textbf{89.7} & \\\\textbf{0.49} &\\\\textbf{0.28}\\\\\\\\\\n\",\n \"\\t\\t\\\\bottomrule\\n\",\n \"\\t\\\\end{tabular}\\n\",\n \"\\t\\\\caption{Evaluation results across models on two datasets. We report Accuracy and F1 (label prediction), EM, EM@1, EIR and MSE (numerical extraction).}\\n\",\n \"\\t\\\\label{tab:results}\\n\",\n \"\\\\end{table*}\\n\"\n ]\n]","isConversational":false},"tables":{"kind":"list like","value":[["\\begin{figure}[t!]\n","\\centering\n","\\includegraphics[width=.43\\textwidth]{latex/assets/example4.pdf}\n","\\caption{Example of estimating the intervention effect based on the extracted outcome data for a clinical study.}\n","\\label{fig:example}\n","\\end{figure}\n"],["\\begin{figure}\n","\\centering\n","\\includegraphics[width=.4\\textwidth]{latex/assets/introduction.png}\n","\\caption{F1 score for predicting the correct answers on 30 instances from \\textsc{CochraneForest} on different retrieval precision using 4 state-of-the-art LLMs.}\n","\\label{fig:ret_pre_cf}\n","\\end{figure}\n"],["\\begin{table}[t]\n","\\scalebox{0.75}{\n","\\centering\n","\\begin{tabular}{lcccccc}\n","\\toprule\n","\\textbf{Dataset} & \\textbf{Train} & \\textbf{Test} & \\textbf{Total} & \\textbf{Avg tokens}\\\\\n","\\midrule\n","\\textsc{CochraneForestExt} & 1864 & 208 & 2072 & 12109.2 \\\\\n","\\textsc{CochraneForest} & – & 725 & 725 & 11688.7 \\\\\n","\\textsc{RCTs} & – & 413 & 413 & 4364.9 \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\caption{Datasets statistics. Train/test split only applies to \\textsc{CochraneForestExt}. \\textsc{CochraneForest} and RCTs are used for testing.}\n","\\label{tab:dataset_stats}\n","\\end{table}\n"],["\\begin{table*}[t]\n","\t\\small\n","\t\\centering\n","\t\\renewcommand{\\arraystretch}{1.2}\n","\t\\setlength{\\tabcolsep}{4pt}\n","\t\\begin{tabular}{lc|cccccc|cccccc}\n","\t\t\\toprule\n","\t\t\\multirow{2}{*}{\\textbf{Model}} & \\multirow{2}{*}{\\textbf{Think}} & \\multicolumn{6}{c|}{\\cellcolor{gray!20}\\textsc{\\textbf{CochraneForest}}} & \\multicolumn{6}{c}{\\cellcolor{gray!20}\\textbf{RCTs}~\\cite{yun2024automatically}} \\\\\n","\t\t& & Acc & F1 & EM & EM@1 & MSE$\\downarrow$ & EIR$\\downarrow$ & Acc & F1 & EM & EM@1 & MSE$\\downarrow$ & EIR$\\downarrow$\\\\\n","\t\t\\toprule\n","\t\t\\multicolumn{2}{c}{\\cellcolor{gray!20}\\textbf{Pretrained LLMs}} & \\multicolumn{6}{c}{\\cellcolor{gray!20}} & \\multicolumn{6}{c}{\\cellcolor{gray!20}} \\\\\n","\t\t\\texttt{GPT-4-0125} & \\xmark & 71.4 & 73.7 & 28.4 & 72.1 & 0.88 & 0.31 & 71.2 & 65.3 & 34.5 & 70.3 & 0.69 & 0.44\\\\\n","\t\t\\texttt{Qwen2.5-7B} & \\xmark & 69.7 & 65.4 & 14.9 & 66.4 & 1.40 & 0.39 & 63.2 & 59.7 & 20.3 & 73.8 & 1.19 & 0.50 \\\\\n","\t\t\\texttt{Qwen2.5-14B} & \\xmark & 76.2& 73.7 & 27.4 & 73.6 &0.72 & 0.30 & 69.8 & 66.4 & 28.6 & 75.8 & 0.91 & 0.44 \\\\\n","\t\t\\texttt{Qwen2.5-72B} & \\xmark & 82.2 & 80.3 & 42.5 & 81.7 & 0.63 & 0.26 & \\textbf{75.3}& \\textbf{72.8}& \\textbf{35.1} &\\textbf{81.1} & \\textbf{0.68} & \\textbf{0.39}\\\\\n","\t\t\\texttt{Llama-3.1-8B} & \\xmark & 68.7 & 66.6 & 15.9 & 62.2 & 2.16 & 0.33 & 62.7 & 54.7 & 14.8 & 66.5 & 1.30 & 0.47 \\\\\n","\t\t\\texttt{Llama-3.1-70B} & \\xmark & 79.4 & 78.3 & 31.5 & 78.7 & 0.59 & 0.28 & 65.5 & 60.8 & 30.0 & 74.2 & 1.45 & 0.44\\\\\n","\t\t\\texttt{Llama-3.1-405B} & \\xmark & \\textbf{82.4} &\\textbf{80.8} & \\textbf{44.3} & \\textbf{82.4} & \\textbf{0.43} & \\textbf{0.23} & 70.3 & 66.5 & 33.6 & 76.7 & 0.80 & 0.41 \\\\\n","\t\t\\texttt{DeepSeek-Qwen-7B}& \\cmark & 59.7 & 54.3 & 11.2 & 58.3& 2.83 & 0.50& 53.8 & 45.7 & 9.4 & 53.8 & 3.68 & 0.61 \\\\\n","\t\t\\texttt{DeepSeek-Qwen-14B} & \\cmark & 65.4 & 61.2 & 19.9 & 66.4 & 1.29 & 0.44 &60.2 & 55.1 & 11.2 & 60.3 & 2.41 & 0.58 \\\\\n","\t\t\\texttt{DeepSeek-Qwen-32B} & \\cmark & 74.0 & 71.6 & 28.6 & 73.5 & 0.58 & 0.28 & 68.8 & 65.0 & 29.1 & 78.2 & 0.72 &0.43\\\\\n","\t\t\\toprule\n","\t\t\\multicolumn{2}{c}{\\cellcolor{gray!20}\\textbf{Our Models}} & \\multicolumn{6}{c}{\\cellcolor{gray!20}} & \\multicolumn{6}{c}{\\cellcolor{gray!20}} \\\\\n","\t\t\\texttt{Qwen2.5-7B-SFT} & \\cmark & 74.5 & 70.1 & 28.4 & 79.1 & 0.51 & 0.29& 71.5& 68.4 & 30.2 & 80.0& 0.64 & 0.36 \\\\\n","\t\t\\texttt{Qwen2.5-7B-RL} & \\cmark & \\textbf{81.6} & \\textbf{80.1} & \\textbf{42.2} & \\textbf{81.4} & \\textbf{0.40} & \\textbf{0.24} & \\textbf{79.3} & \\textbf{76.4} & \\textbf{41.2} & \\textbf{89.7} & \\textbf{0.49} &\\textbf{0.28}\\\\\n","\t\t\\bottomrule\n","\t\\end{tabular}\n","\t\\caption{Evaluation results across models on two datasets. We report Accuracy and F1 (label prediction), EM, EM@1, EIR and MSE (numerical extraction).}\n","\t\\label{tab:results}\n","\\end{table*}\n"],["\\begin{table}[ht]\n","\\small\n","\t\\centering\n"," \\renewcommand{\\arraystretch}{1}\n","\t\\begin{tabular}{l|cc}\n","\t\t\\toprule\n","\t\t\\textbf{Method} & \\textbf{F1} & \\textbf{Acc} \\\\\n","\t\t\\midrule\n","\t\t\\rowcolor{gray!15}\n","\t\t\\multicolumn{3}{l}{\\textbf{RAG baselines}} \\\\\n","\t\tURCA~\\cite{pronesti2025} & 60.2 & 58.8 \\\\\n","\t\tGraphRAG~\\cite{graphrag} & 59.6 & 57.5 \\\\\n","\t\t\\midrule\n","\t\t\\rowcolor{gray!15}\n","\t\t\\multicolumn{3}{l}{\\textbf{Numerical baselines}} \\\\\n","\t\tQwen2.5-7B (zero-shot) & 69.7 & 65.4 \\\\\n","\t\tQwen2.5-7B-SFT & 74.5 & 70.1 \\\\\n","\t\tQwen2.5-7B-RL & \\textbf{81.6} & \\textbf{80.1} \\\\\n","\t\t\\bottomrule\n","\t\\end{tabular}\n","\t\\caption{Performance comparison between RAG-based and numbers-based approaches on \\textsc{CochraneForest} using \\texttt{Qwen2.5-7B-Instruct}.}\n","\t\\label{tab:rag_number_baselines}\n","\\end{table}\n"],["\\begin{table}[t]\n","\\small\n","\t\\centering\n"," \\renewcommand{\\arraystretch}{0.96}\n","\t\\begin{tabular}{ll|cc}\n","\t\t\\toprule\n","\t\t\\textbf{Model} & \\textbf{Input} & \\textbf{F1} & \\textbf{Acc} \\\\\n","\t\t\\midrule\n","\t\t\\rowcolor[gray]{0.9} \\multicolumn{4}{l}{\\textbf{Input Data Ablations}} \\\\\n","\t\t\\multirow{3}{*}{\\texttt{Qwen2.5-7B-SFT}} & \\texttt{text} & 62.3 & 60.1 \\\\\n","\t\t & \\texttt{tables} & 70.4 & 66.5 \\\\\n","\t\t & \\texttt{urca} & 56.7 & 54.0 \\\\\n","\t\t\\bottomrule\n","\t\t\\multirow{3}{*}{\\texttt{Qwen2.5-7B-RL}} & \\texttt{text} & 65.8 & 63.2 \\\\\n","\t\t & \\texttt{tables} &\\textbf{ 73.1} & \\textbf{71.6 }\\\\\n","\t\t & \\texttt{urca} & 59.2 & 56.8 \\\\\n","\t\t\\bottomrule\n","\t\t\n","\t\t\\rowcolor[gray]{0.9} \\multicolumn{4}{l}\n","\t\t{\\textbf{Training Ablations}} \\\\\n","\t\t\\texttt{Qwen2.5-7B} & \\texttt{text + tables} & 69.7 & 65.4 \\\\\n","\t\t\\texttt{+ SFT-no-CoT} & \\texttt{text + tables} &71.2 & 68.3 \\\\\n"," \\texttt{+ SFT}\n"," \\textbf{(ours)} & \\texttt{text + tables} & 74.5 & 70.1 \\\\\n"," \\texttt{+ RL-EX} & \\texttt{text + tables} & 79.7 & 78.8\\\\\n","\t\t\\texttt{+ RL} \\textbf{(ours)} & \\texttt{text + tables} & \\textbf{81.6} & \\textbf{80.1} \\\\\n","\t\t\n","\t\t\\bottomrule\n","\t\\end{tabular}\n","\t\\caption{Ablation study on model inputs and training supervision on \\textsc{CochraneForest}. \\texttt{urca} refers to the top 10 chunks retrieved by its namesake RAG approach.}\n","\t\\label{tab:ablations}\n","\\end{table}\n"],["\\begin{table*}[h]\n","\t \\small\n","\t \\centering\n","\t\\begin{tabular}{\n","\t\t\tc >{\\raggedright\\arraybackslash}p{5.8cm}\n","\t\t\t>{\\centering\\arraybackslash}m{1.2cm} >{\\centering\\arraybackslash}m{1.5cm}\n","\t\t\t>{\\centering\\arraybackslash}m{1.2cm} >{\\centering\\arraybackslash}m{1.5cm}\n","\t\t}\n","\t\t\\toprule\n","\t\t\\textbf{Thought Process} & \\textbf{Reasoning Label}\n","\t\t& \\multicolumn{2}{c}{\\texttt{Qwen2.5-7B-RL}} \n","\t\t& \\multicolumn{2}{c}{\\texttt{Qwen2.5-7B-SFT}} \\\\\n","\t\t\\cmidrule(lr){3-4} \\cmidrule(lr){5-6}\n","\t\t& & Count & Fraction & Count & Fraction \\\\\n","\t\t\\midrule\n","\t\t\\textbf{\\cmark} & Correct reasoning with traceability & 42 & 31.82\\% & 34 & 25.76\\% \\\\\n","\t\t\\textbf{\\cmark} & Correct reasoning without traceability & 39 & 29.55\\% & 24 & 18.18\\% \\\\\n","\t\t\\textbf{\\cmark} & Correct reasoning, incorrect number & 37 & 28.03\\% & 41 & 31.06\\% \\\\\n","\t\t\\textbf{\\xmark} & Correct number, incorrect reasoning & 3 & 2.72\\% & 13 & 9.85\\% \\\\\n","\t\t\\textbf{\\xmark} & Copy without reasoning & 6&4.55\\% & 0 & 0.00\\%\\\\\n","\t\t\\textbf{\\xmark} & Hallucinated &5 &3.79\\% & 20 & 15.15\\% \\\\\n","\t\t\\textbf{\\xmark} & Missing reasoning & 0 & 0.00\\% & 0 & 0.00\\% \\\\\n","\t\t\\midrule\n","\t\t& \\textbf{Total} & 132 & 100\\% & 132 & 100\\% \\\\\n","\t\t\\bottomrule\n","\t\\end{tabular}\n","\t\\caption{Distribution of annotated reasoning labels for \\texttt{Qwen2.5-7B-RL} and and {\\texttt{Qwen2.5-7B-SFT}. \\textbf{\\cmark} indicates correct reasoning. \\textbf{\\xmark} indicates incorrect reasoning.}}\n","\t\\label{tab:hum_eval_labels_models}\n","\\end{table*}\n"],["\\begin{figure}[h]\n","\\includegraphics[width=\\columnwidth]{latex/assets/fp_bin.png}\n","\\caption{A forest plot assessing clinical remission (binary outcome) in patients affected by medically refractory Crohn’s disease treated with stem cells versus control.}\n","\\label{fig:fp_bin}\n","\\end{figure}\n"],["\\begin{figure}[h]\n","\\includegraphics[width=\\columnwidth]{latex/assets/fp_cont.png}\n","\\caption{A forest plot comparing GnRH agonist versus no agonist (placebo) in women with endometriosis, assessing a continuous outcome (average number of oocytes per woman).}\n","\\label{fig:fp_cont}\n","\\end{figure}\n"],["\\begin{figure}\n"," \\small\n"," \\begin{tcolorbox}[\n"," colback=gray!10, \n"," colframe=black, % gray!120, \n"," boxrule=0.5mm,\n"," title={Prompt for synthetic data annotation}, \n"," fonttitle=\\bfseries % Bold font for the title\n"," ]\n"," \\{study\\_content\\}\n"," \\\\\n"," \\\\\n"," The above is a study from a medical systematic review.\n"," Your task is to produce a reasoning to explain how to extract the relevant numbers for the following intervention, comparator and outcome:\n"," \\\\\n"," \\\\\n"," intervention: \\{intervention\\}\\\\\n"," comparator: \\{comparator\\}\\\\\n"," outcome: \\{outcome\\} \\\\\n"," \\\\\n"," The expected output is:\n"," \\\\\n"," \\\\\n"," \\{target\\_value\\}\n"," \\\\\n"," \\\\\n"," You need to first explain how to infer the outcome type.\n"," Then, which numbers to extract and why. Finally, point to where these numbers appear in the study.\n"," \\end{tcolorbox}\n"," \\caption{Prompt for the synthetic data annotation with reasoning traces.}\n"," \\label{fig:sys_prompt}\n","\\end{figure}\n"],["\\begin{figure}\n","\t\\small\n","\t\\begin{tcolorbox}[\n","\t\tcolback=gray!10, \n","\t\tcolframe=black, % gray!120, \n","\t\tboxrule=0.5mm,\n","\t\ttitle={Prompt for training and inference}, \n","\t\tfonttitle=\\bfseries % Bold font for the title\n","\t\t]\n","\t\tArticles: \\{articles\\}\n","\t\t\\\\\n"," \\\\\n","\t\tQuestion: Based on the given trial articles, what is the outcome type and corresponding numerical data for the following Comparison and Outcome?\n","\t\t\\\\\n","\t\t\\\\\n","\t\tComparison: \\{comparison\\}\\\\\n","\t\tOutcome: \\{outcome\\}\n","\t\t\\\\\n","\t\t\\\\\n","\t\tFirst, determine and output the outcome\\_type as either: binary or continuous\n","\t\t\\\\\n","\t\t\\\\\n","\t\tThen, provide the extracted data in format as follows:\n","\t\t\\\\\n","\t\tIf the outcome is binary, use this format:\n","\t\t\\\\\n","\t\t\\\\\n","\t\toutcome\\_type: binary\n","\t\t\\\\intervention:\n","\t\t\\\\events: NUMBER total: NUMBER\n","\t\t\\\\comparator:\n","\t\t\\\\events: NUMBER total: NUMBER\n","\t\t\\\\\n","\t\t\\\\\n","\t\tIf the outcome is continuous, use this format:\n","\t\t\\\\\n","\t\t\\\\\n","\t\toutcome\\_type: continuous\n","\t\t\\\\intervention:\n","\t\t\\\\mean: NUMBER standard\\_deviation: NUMBER group\\_size: NUMBER\n","\t\t\\\\\n","\t\tcomparator:\n","\t\t\\\\mean: NUMBER standard\\_deviation: NUMBER group\\_size: NUMBER\n","\t\t\\\\\n","\t\t\\\\Use post-intervention data when both pre and post are available. If multiple timepoints are reported, choose the one closest to the timepoint of interest, or the latest available.\n","\t\tThink about it step by step.\n","\t\\end{tcolorbox}\n","\t\\caption{Prompt for training and inference.}\n","\t\\label{fig:train_inf_prompt}\n","\\end{figure}\n"],["\\begin{figure*}[h!]\n"," \\centering\n","\\includegraphics[width=1\\textwidth]{latex/assets/annotation_spans2.png}\n","\\caption{Example of multi-span numerical data annotations for a given study}\n","\\label{fig:annot_span}\n","\\end{figure*}\n"],["\\begin{figure*}%[h!]\n","\\centering\n","\\includegraphics[width=0.98\\textwidth]{latex/assets/sft_vs_rl_and_question_v2.drawio.png}\n","\\caption{Example of a complex example from \\textsc{CochraneForest}. The SFT model fails at inferring the correct numerical information, and its thought mismatches its structured output. On the other hand, the model trained with GRPO correctly infers the number of events, reasoning on the previously extracted total number of participants. \\textbf{Interestingly, even our human annotators initially struggled to extract the right numbers}.}\n","\\label{fig:qualitative_example}\n","\\end{figure*}\n"],[" \\begin{table*}[h!]\n","\t \\centering\n","\t \\begin{tabular}{l|>{\\raggedright\\arraybackslash}p{0.7\\textwidth}}\n","\t\t \\toprule\n","\t\t \\rowcolor[gray]{0.9} \\multicolumn{2}{c}{\\textbf{Hawkey 2015}} \\\\\n","\t\t \\midrule\n","\t\t \\textbf{Text} & Importance Case reports and series suggest hematopoietic stem cell transplantation (HSCT) may benefit some patients with Crohn disease.Objective To evaluate the effect of autologous HSCT on refractory Crohn disease. ... \\\\ \n","\t\t \\midrule\n","\t\t \\textbf{Outcome} & Clinical remission \\\\\n"," \\textbf{Comparison} & Stem cells versus Control\\\\\n","\t\t \\textbf{Outcome type} & Binary \\\\\n","\t\t \\textbf{Intervention events} & 8\\\\\n","\t\t \\textbf{Intervertion total} & 23 \\\\\n","\t\t \\textbf{Comparator events} & 2 \\\\\n","\t\t \\textbf{Comparator total} & 22 \\\\\n","\t\t \\textbf{Point estimate} & 3.83 \\\\\n","\t\t \\textbf{95\\% CI} & [0.91, 16.07]\\\\ \n","\t\t \\midrule\n"," \\textbf{Target value} & outcome\\_type: binary\\newline\n","\t\t intervention:\\newline\n","\t\t events: 8 total: 23\\newline\n","\t\t comparator:\\newline\n","\t\t events: 2 total: 22\\\\\n"," \\midrule\n"," \\textbf{Thought} & To extract the relevant numbers, we first infer the outcome type as \"binary\" because the outcome \"Clinical remission\" is a yes/no or present/absent type of outcome. \n","\t\t \n","\t\t The relevant numbers for this outcome are found in Table 2: \"Final Assessments\", under the outcome \"CDAI <150 last 3 mo\". \n"," Specifically, for the intervention group (Stem cells), we have 8 events (patients in clinical remission) out of 23 total patients (34.8\\%). For the comparator group (Control), we have 2 events out of 22 total patients (9.1\\%). \\\\\n","\t\t \\bottomrule\n","\t\t \\end{tabular}\n","\t \\caption{The corresponding entry in \\textsc{CochraneForestExt} for Hawekey 2015 in Figure~\\ref{fig:fp_bin}. Thought and target value are contatenated for SFT as follows: \\texttt{<think>\\{thought\\}</think>\\{target\\_value\\}}.}\n","\t \\label{tab:data_example}\n","\t \\end{table*}\n"]],"string":"[\n [\n \"\\\\begin{figure}[t!]\\n\",\n \"\\\\centering\\n\",\n \"\\\\includegraphics[width=.43\\\\textwidth]{latex/assets/example4.pdf}\\n\",\n \"\\\\caption{Example of estimating the intervention effect based on the extracted outcome data for a clinical study.}\\n\",\n \"\\\\label{fig:example}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{figure}\\n\",\n \"\\\\centering\\n\",\n \"\\\\includegraphics[width=.4\\\\textwidth]{latex/assets/introduction.png}\\n\",\n \"\\\\caption{F1 score for predicting the correct answers on 30 instances from \\\\textsc{CochraneForest} on different retrieval precision using 4 state-of-the-art LLMs.}\\n\",\n \"\\\\label{fig:ret_pre_cf}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table}[t]\\n\",\n \"\\\\scalebox{0.75}{\\n\",\n \"\\\\centering\\n\",\n \"\\\\begin{tabular}{lcccccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Dataset} & \\\\textbf{Train} & \\\\textbf{Test} & \\\\textbf{Total} & \\\\textbf{Avg tokens}\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textsc{CochraneForestExt} & 1864 & 208 & 2072 & 12109.2 \\\\\\\\\\n\",\n \"\\\\textsc{CochraneForest} & – & 725 & 725 & 11688.7 \\\\\\\\\\n\",\n \"\\\\textsc{RCTs} & – & 413 & 413 & 4364.9 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Datasets statistics. Train/test split only applies to \\\\textsc{CochraneForestExt}. \\\\textsc{CochraneForest} and RCTs are used for testing.}\\n\",\n \"\\\\label{tab:dataset_stats}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table*}[t]\\n\",\n \"\\t\\\\small\\n\",\n \"\\t\\\\centering\\n\",\n \"\\t\\\\renewcommand{\\\\arraystretch}{1.2}\\n\",\n \"\\t\\\\setlength{\\\\tabcolsep}{4pt}\\n\",\n \"\\t\\\\begin{tabular}{lc|cccccc|cccccc}\\n\",\n \"\\t\\t\\\\toprule\\n\",\n \"\\t\\t\\\\multirow{2}{*}{\\\\textbf{Model}} & \\\\multirow{2}{*}{\\\\textbf{Think}} & \\\\multicolumn{6}{c|}{\\\\cellcolor{gray!20}\\\\textsc{\\\\textbf{CochraneForest}}} & \\\\multicolumn{6}{c}{\\\\cellcolor{gray!20}\\\\textbf{RCTs}~\\\\cite{yun2024automatically}} \\\\\\\\\\n\",\n \"\\t\\t& & Acc & F1 & EM & EM@1 & MSE$\\\\downarrow$ & EIR$\\\\downarrow$ & Acc & F1 & EM & EM@1 & MSE$\\\\downarrow$ & EIR$\\\\downarrow$\\\\\\\\\\n\",\n \"\\t\\t\\\\toprule\\n\",\n \"\\t\\t\\\\multicolumn{2}{c}{\\\\cellcolor{gray!20}\\\\textbf{Pretrained LLMs}} & \\\\multicolumn{6}{c}{\\\\cellcolor{gray!20}} & \\\\multicolumn{6}{c}{\\\\cellcolor{gray!20}} \\\\\\\\\\n\",\n \"\\t\\t\\\\texttt{GPT-4-0125} & \\\\xmark & 71.4 & 73.7 & 28.4 & 72.1 & 0.88 & 0.31 & 71.2 & 65.3 & 34.5 & 70.3 & 0.69 & 0.44\\\\\\\\\\n\",\n \"\\t\\t\\\\texttt{Qwen2.5-7B} & \\\\xmark & 69.7 & 65.4 & 14.9 & 66.4 & 1.40 & 0.39 & 63.2 & 59.7 & 20.3 & 73.8 & 1.19 & 0.50 \\\\\\\\\\n\",\n \"\\t\\t\\\\texttt{Qwen2.5-14B} & \\\\xmark & 76.2& 73.7 & 27.4 & 73.6 &0.72 & 0.30 & 69.8 & 66.4 & 28.6 & 75.8 & 0.91 & 0.44 \\\\\\\\\\n\",\n \"\\t\\t\\\\texttt{Qwen2.5-72B} & \\\\xmark & 82.2 & 80.3 & 42.5 & 81.7 & 0.63 & 0.26 & \\\\textbf{75.3}& \\\\textbf{72.8}& \\\\textbf{35.1} &\\\\textbf{81.1} & \\\\textbf{0.68} & \\\\textbf{0.39}\\\\\\\\\\n\",\n \"\\t\\t\\\\texttt{Llama-3.1-8B} & \\\\xmark & 68.7 & 66.6 & 15.9 & 62.2 & 2.16 & 0.33 & 62.7 & 54.7 & 14.8 & 66.5 & 1.30 & 0.47 \\\\\\\\\\n\",\n \"\\t\\t\\\\texttt{Llama-3.1-70B} & \\\\xmark & 79.4 & 78.3 & 31.5 & 78.7 & 0.59 & 0.28 & 65.5 & 60.8 & 30.0 & 74.2 & 1.45 & 0.44\\\\\\\\\\n\",\n \"\\t\\t\\\\texttt{Llama-3.1-405B} & \\\\xmark & \\\\textbf{82.4} &\\\\textbf{80.8} & \\\\textbf{44.3} & \\\\textbf{82.4} & \\\\textbf{0.43} & \\\\textbf{0.23} & 70.3 & 66.5 & 33.6 & 76.7 & 0.80 & 0.41 \\\\\\\\\\n\",\n \"\\t\\t\\\\texttt{DeepSeek-Qwen-7B}& \\\\cmark & 59.7 & 54.3 & 11.2 & 58.3& 2.83 & 0.50& 53.8 & 45.7 & 9.4 & 53.8 & 3.68 & 0.61 \\\\\\\\\\n\",\n \"\\t\\t\\\\texttt{DeepSeek-Qwen-14B} & \\\\cmark & 65.4 & 61.2 & 19.9 & 66.4 & 1.29 & 0.44 &60.2 & 55.1 & 11.2 & 60.3 & 2.41 & 0.58 \\\\\\\\\\n\",\n \"\\t\\t\\\\texttt{DeepSeek-Qwen-32B} & \\\\cmark & 74.0 & 71.6 & 28.6 & 73.5 & 0.58 & 0.28 & 68.8 & 65.0 & 29.1 & 78.2 & 0.72 &0.43\\\\\\\\\\n\",\n \"\\t\\t\\\\toprule\\n\",\n \"\\t\\t\\\\multicolumn{2}{c}{\\\\cellcolor{gray!20}\\\\textbf{Our Models}} & \\\\multicolumn{6}{c}{\\\\cellcolor{gray!20}} & \\\\multicolumn{6}{c}{\\\\cellcolor{gray!20}} \\\\\\\\\\n\",\n \"\\t\\t\\\\texttt{Qwen2.5-7B-SFT} & \\\\cmark & 74.5 & 70.1 & 28.4 & 79.1 & 0.51 & 0.29& 71.5& 68.4 & 30.2 & 80.0& 0.64 & 0.36 \\\\\\\\\\n\",\n \"\\t\\t\\\\texttt{Qwen2.5-7B-RL} & \\\\cmark & \\\\textbf{81.6} & \\\\textbf{80.1} & \\\\textbf{42.2} & \\\\textbf{81.4} & \\\\textbf{0.40} & \\\\textbf{0.24} & \\\\textbf{79.3} & \\\\textbf{76.4} & \\\\textbf{41.2} & \\\\textbf{89.7} & \\\\textbf{0.49} &\\\\textbf{0.28}\\\\\\\\\\n\",\n \"\\t\\t\\\\bottomrule\\n\",\n \"\\t\\\\end{tabular}\\n\",\n \"\\t\\\\caption{Evaluation results across models on two datasets. We report Accuracy and F1 (label prediction), EM, EM@1, EIR and MSE (numerical extraction).}\\n\",\n \"\\t\\\\label{tab:results}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table}[ht]\\n\",\n \"\\\\small\\n\",\n \"\\t\\\\centering\\n\",\n \" \\\\renewcommand{\\\\arraystretch}{1}\\n\",\n \"\\t\\\\begin{tabular}{l|cc}\\n\",\n \"\\t\\t\\\\toprule\\n\",\n \"\\t\\t\\\\textbf{Method} & \\\\textbf{F1} & \\\\textbf{Acc} \\\\\\\\\\n\",\n \"\\t\\t\\\\midrule\\n\",\n \"\\t\\t\\\\rowcolor{gray!15}\\n\",\n \"\\t\\t\\\\multicolumn{3}{l}{\\\\textbf{RAG baselines}} \\\\\\\\\\n\",\n \"\\t\\tURCA~\\\\cite{pronesti2025} & 60.2 & 58.8 \\\\\\\\\\n\",\n \"\\t\\tGraphRAG~\\\\cite{graphrag} & 59.6 & 57.5 \\\\\\\\\\n\",\n \"\\t\\t\\\\midrule\\n\",\n \"\\t\\t\\\\rowcolor{gray!15}\\n\",\n \"\\t\\t\\\\multicolumn{3}{l}{\\\\textbf{Numerical baselines}} \\\\\\\\\\n\",\n \"\\t\\tQwen2.5-7B (zero-shot) & 69.7 & 65.4 \\\\\\\\\\n\",\n \"\\t\\tQwen2.5-7B-SFT & 74.5 & 70.1 \\\\\\\\\\n\",\n \"\\t\\tQwen2.5-7B-RL & \\\\textbf{81.6} & \\\\textbf{80.1} \\\\\\\\\\n\",\n \"\\t\\t\\\\bottomrule\\n\",\n \"\\t\\\\end{tabular}\\n\",\n \"\\t\\\\caption{Performance comparison between RAG-based and numbers-based approaches on \\\\textsc{CochraneForest} using \\\\texttt{Qwen2.5-7B-Instruct}.}\\n\",\n \"\\t\\\\label{tab:rag_number_baselines}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[t]\\n\",\n \"\\\\small\\n\",\n \"\\t\\\\centering\\n\",\n \" \\\\renewcommand{\\\\arraystretch}{0.96}\\n\",\n \"\\t\\\\begin{tabular}{ll|cc}\\n\",\n \"\\t\\t\\\\toprule\\n\",\n \"\\t\\t\\\\textbf{Model} & \\\\textbf{Input} & \\\\textbf{F1} & \\\\textbf{Acc} \\\\\\\\\\n\",\n \"\\t\\t\\\\midrule\\n\",\n \"\\t\\t\\\\rowcolor[gray]{0.9} \\\\multicolumn{4}{l}{\\\\textbf{Input Data Ablations}} \\\\\\\\\\n\",\n \"\\t\\t\\\\multirow{3}{*}{\\\\texttt{Qwen2.5-7B-SFT}} & \\\\texttt{text} & 62.3 & 60.1 \\\\\\\\\\n\",\n \"\\t\\t & \\\\texttt{tables} & 70.4 & 66.5 \\\\\\\\\\n\",\n \"\\t\\t & \\\\texttt{urca} & 56.7 & 54.0 \\\\\\\\\\n\",\n \"\\t\\t\\\\bottomrule\\n\",\n \"\\t\\t\\\\multirow{3}{*}{\\\\texttt{Qwen2.5-7B-RL}} & \\\\texttt{text} & 65.8 & 63.2 \\\\\\\\\\n\",\n \"\\t\\t & \\\\texttt{tables} &\\\\textbf{ 73.1} & \\\\textbf{71.6 }\\\\\\\\\\n\",\n \"\\t\\t & \\\\texttt{urca} & 59.2 & 56.8 \\\\\\\\\\n\",\n \"\\t\\t\\\\bottomrule\\n\",\n \"\\t\\t\\n\",\n \"\\t\\t\\\\rowcolor[gray]{0.9} \\\\multicolumn{4}{l}\\n\",\n \"\\t\\t{\\\\textbf{Training Ablations}} \\\\\\\\\\n\",\n \"\\t\\t\\\\texttt{Qwen2.5-7B} & \\\\texttt{text + tables} & 69.7 & 65.4 \\\\\\\\\\n\",\n \"\\t\\t\\\\texttt{+ SFT-no-CoT} & \\\\texttt{text + tables} &71.2 & 68.3 \\\\\\\\\\n\",\n \" \\\\texttt{+ SFT}\\n\",\n \" \\\\textbf{(ours)} & \\\\texttt{text + tables} & 74.5 & 70.1 \\\\\\\\\\n\",\n \" \\\\texttt{+ RL-EX} & \\\\texttt{text + tables} & 79.7 & 78.8\\\\\\\\\\n\",\n \"\\t\\t\\\\texttt{+ RL} \\\\textbf{(ours)} & \\\\texttt{text + tables} & \\\\textbf{81.6} & \\\\textbf{80.1} \\\\\\\\\\n\",\n \"\\t\\t\\n\",\n \"\\t\\t\\\\bottomrule\\n\",\n \"\\t\\\\end{tabular}\\n\",\n \"\\t\\\\caption{Ablation study on model inputs and training supervision on \\\\textsc{CochraneForest}. \\\\texttt{urca} refers to the top 10 chunks retrieved by its namesake RAG approach.}\\n\",\n \"\\t\\\\label{tab:ablations}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table*}[h]\\n\",\n \"\\t \\\\small\\n\",\n \"\\t \\\\centering\\n\",\n \"\\t\\\\begin{tabular}{\\n\",\n \"\\t\\t\\tc >{\\\\raggedright\\\\arraybackslash}p{5.8cm}\\n\",\n \"\\t\\t\\t>{\\\\centering\\\\arraybackslash}m{1.2cm} >{\\\\centering\\\\arraybackslash}m{1.5cm}\\n\",\n \"\\t\\t\\t>{\\\\centering\\\\arraybackslash}m{1.2cm} >{\\\\centering\\\\arraybackslash}m{1.5cm}\\n\",\n \"\\t\\t}\\n\",\n \"\\t\\t\\\\toprule\\n\",\n \"\\t\\t\\\\textbf{Thought Process} & \\\\textbf{Reasoning Label}\\n\",\n \"\\t\\t& \\\\multicolumn{2}{c}{\\\\texttt{Qwen2.5-7B-RL}} \\n\",\n \"\\t\\t& \\\\multicolumn{2}{c}{\\\\texttt{Qwen2.5-7B-SFT}} \\\\\\\\\\n\",\n \"\\t\\t\\\\cmidrule(lr){3-4} \\\\cmidrule(lr){5-6}\\n\",\n \"\\t\\t& & Count & Fraction & Count & Fraction \\\\\\\\\\n\",\n \"\\t\\t\\\\midrule\\n\",\n \"\\t\\t\\\\textbf{\\\\cmark} & Correct reasoning with traceability & 42 & 31.82\\\\% & 34 & 25.76\\\\% \\\\\\\\\\n\",\n \"\\t\\t\\\\textbf{\\\\cmark} & Correct reasoning without traceability & 39 & 29.55\\\\% & 24 & 18.18\\\\% \\\\\\\\\\n\",\n \"\\t\\t\\\\textbf{\\\\cmark} & Correct reasoning, incorrect number & 37 & 28.03\\\\% & 41 & 31.06\\\\% \\\\\\\\\\n\",\n \"\\t\\t\\\\textbf{\\\\xmark} & Correct number, incorrect reasoning & 3 & 2.72\\\\% & 13 & 9.85\\\\% \\\\\\\\\\n\",\n \"\\t\\t\\\\textbf{\\\\xmark} & Copy without reasoning & 6&4.55\\\\% & 0 & 0.00\\\\%\\\\\\\\\\n\",\n \"\\t\\t\\\\textbf{\\\\xmark} & Hallucinated &5 &3.79\\\\% & 20 & 15.15\\\\% \\\\\\\\\\n\",\n \"\\t\\t\\\\textbf{\\\\xmark} & Missing reasoning & 0 & 0.00\\\\% & 0 & 0.00\\\\% \\\\\\\\\\n\",\n \"\\t\\t\\\\midrule\\n\",\n \"\\t\\t& \\\\textbf{Total} & 132 & 100\\\\% & 132 & 100\\\\% \\\\\\\\\\n\",\n \"\\t\\t\\\\bottomrule\\n\",\n \"\\t\\\\end{tabular}\\n\",\n \"\\t\\\\caption{Distribution of annotated reasoning labels for \\\\texttt{Qwen2.5-7B-RL} and and {\\\\texttt{Qwen2.5-7B-SFT}. \\\\textbf{\\\\cmark} indicates correct reasoning. \\\\textbf{\\\\xmark} indicates incorrect reasoning.}}\\n\",\n \"\\t\\\\label{tab:hum_eval_labels_models}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{figure}[h]\\n\",\n \"\\\\includegraphics[width=\\\\columnwidth]{latex/assets/fp_bin.png}\\n\",\n \"\\\\caption{A forest plot assessing clinical remission (binary outcome) in patients affected by medically refractory Crohn’s disease treated with stem cells versus control.}\\n\",\n \"\\\\label{fig:fp_bin}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{figure}[h]\\n\",\n \"\\\\includegraphics[width=\\\\columnwidth]{latex/assets/fp_cont.png}\\n\",\n \"\\\\caption{A forest plot comparing GnRH agonist versus no agonist (placebo) in women with endometriosis, assessing a continuous outcome (average number of oocytes per woman).}\\n\",\n \"\\\\label{fig:fp_cont}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{figure}\\n\",\n \" \\\\small\\n\",\n \" \\\\begin{tcolorbox}[\\n\",\n \" colback=gray!10, \\n\",\n \" colframe=black, % gray!120, \\n\",\n \" boxrule=0.5mm,\\n\",\n \" title={Prompt for synthetic data annotation}, \\n\",\n \" fonttitle=\\\\bfseries % Bold font for the title\\n\",\n \" ]\\n\",\n \" \\\\{study\\\\_content\\\\}\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\\\\\\\n\",\n \" The above is a study from a medical systematic review.\\n\",\n \" Your task is to produce a reasoning to explain how to extract the relevant numbers for the following intervention, comparator and outcome:\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\\\\\\\n\",\n \" intervention: \\\\{intervention\\\\}\\\\\\\\\\n\",\n \" comparator: \\\\{comparator\\\\}\\\\\\\\\\n\",\n \" outcome: \\\\{outcome\\\\} \\\\\\\\\\n\",\n \" \\\\\\\\\\n\",\n \" The expected output is:\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\{target\\\\_value\\\\}\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\\\\\\\n\",\n \" You need to first explain how to infer the outcome type.\\n\",\n \" Then, which numbers to extract and why. Finally, point to where these numbers appear in the study.\\n\",\n \" \\\\end{tcolorbox}\\n\",\n \" \\\\caption{Prompt for the synthetic data annotation with reasoning traces.}\\n\",\n \" \\\\label{fig:sys_prompt}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{figure}\\n\",\n \"\\t\\\\small\\n\",\n \"\\t\\\\begin{tcolorbox}[\\n\",\n \"\\t\\tcolback=gray!10, \\n\",\n \"\\t\\tcolframe=black, % gray!120, \\n\",\n \"\\t\\tboxrule=0.5mm,\\n\",\n \"\\t\\ttitle={Prompt for training and inference}, \\n\",\n \"\\t\\tfonttitle=\\\\bfseries % Bold font for the title\\n\",\n \"\\t\\t]\\n\",\n \"\\t\\tArticles: \\\\{articles\\\\}\\n\",\n \"\\t\\t\\\\\\\\\\n\",\n \" \\\\\\\\\\n\",\n \"\\t\\tQuestion: Based on the given trial articles, what is the outcome type and corresponding numerical data for the following Comparison and Outcome?\\n\",\n \"\\t\\t\\\\\\\\\\n\",\n \"\\t\\t\\\\\\\\\\n\",\n \"\\t\\tComparison: \\\\{comparison\\\\}\\\\\\\\\\n\",\n \"\\t\\tOutcome: \\\\{outcome\\\\}\\n\",\n \"\\t\\t\\\\\\\\\\n\",\n \"\\t\\t\\\\\\\\\\n\",\n \"\\t\\tFirst, determine and output the outcome\\\\_type as either: binary or continuous\\n\",\n \"\\t\\t\\\\\\\\\\n\",\n \"\\t\\t\\\\\\\\\\n\",\n \"\\t\\tThen, provide the extracted data in format as follows:\\n\",\n \"\\t\\t\\\\\\\\\\n\",\n \"\\t\\tIf the outcome is binary, use this format:\\n\",\n \"\\t\\t\\\\\\\\\\n\",\n \"\\t\\t\\\\\\\\\\n\",\n \"\\t\\toutcome\\\\_type: binary\\n\",\n \"\\t\\t\\\\\\\\intervention:\\n\",\n \"\\t\\t\\\\\\\\events: NUMBER total: NUMBER\\n\",\n \"\\t\\t\\\\\\\\comparator:\\n\",\n \"\\t\\t\\\\\\\\events: NUMBER total: NUMBER\\n\",\n \"\\t\\t\\\\\\\\\\n\",\n \"\\t\\t\\\\\\\\\\n\",\n \"\\t\\tIf the outcome is continuous, use this format:\\n\",\n \"\\t\\t\\\\\\\\\\n\",\n \"\\t\\t\\\\\\\\\\n\",\n \"\\t\\toutcome\\\\_type: continuous\\n\",\n \"\\t\\t\\\\\\\\intervention:\\n\",\n \"\\t\\t\\\\\\\\mean: NUMBER standard\\\\_deviation: NUMBER group\\\\_size: NUMBER\\n\",\n \"\\t\\t\\\\\\\\\\n\",\n \"\\t\\tcomparator:\\n\",\n \"\\t\\t\\\\\\\\mean: NUMBER standard\\\\_deviation: NUMBER group\\\\_size: NUMBER\\n\",\n \"\\t\\t\\\\\\\\\\n\",\n \"\\t\\t\\\\\\\\Use post-intervention data when both pre and post are available. If multiple timepoints are reported, choose the one closest to the timepoint of interest, or the latest available.\\n\",\n \"\\t\\tThink about it step by step.\\n\",\n \"\\t\\\\end{tcolorbox}\\n\",\n \"\\t\\\\caption{Prompt for training and inference.}\\n\",\n \"\\t\\\\label{fig:train_inf_prompt}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{figure*}[h!]\\n\",\n \" \\\\centering\\n\",\n \"\\\\includegraphics[width=1\\\\textwidth]{latex/assets/annotation_spans2.png}\\n\",\n \"\\\\caption{Example of multi-span numerical data annotations for a given study}\\n\",\n \"\\\\label{fig:annot_span}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{figure*}%[h!]\\n\",\n \"\\\\centering\\n\",\n \"\\\\includegraphics[width=0.98\\\\textwidth]{latex/assets/sft_vs_rl_and_question_v2.drawio.png}\\n\",\n \"\\\\caption{Example of a complex example from \\\\textsc{CochraneForest}. The SFT model fails at inferring the correct numerical information, and its thought mismatches its structured output. On the other hand, the model trained with GRPO correctly infers the number of events, reasoning on the previously extracted total number of participants. \\\\textbf{Interestingly, even our human annotators initially struggled to extract the right numbers}.}\\n\",\n \"\\\\label{fig:qualitative_example}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \" \\\\begin{table*}[h!]\\n\",\n \"\\t \\\\centering\\n\",\n \"\\t \\\\begin{tabular}{l|>{\\\\raggedright\\\\arraybackslash}p{0.7\\\\textwidth}}\\n\",\n \"\\t\\t \\\\toprule\\n\",\n \"\\t\\t \\\\rowcolor[gray]{0.9} \\\\multicolumn{2}{c}{\\\\textbf{Hawkey 2015}} \\\\\\\\\\n\",\n \"\\t\\t \\\\midrule\\n\",\n \"\\t\\t \\\\textbf{Text} & Importance Case reports and series suggest hematopoietic stem cell transplantation (HSCT) may benefit some patients with Crohn disease.Objective To evaluate the effect of autologous HSCT on refractory Crohn disease. ... \\\\\\\\ \\n\",\n \"\\t\\t \\\\midrule\\n\",\n \"\\t\\t \\\\textbf{Outcome} & Clinical remission \\\\\\\\\\n\",\n \" \\\\textbf{Comparison} & Stem cells versus Control\\\\\\\\\\n\",\n \"\\t\\t \\\\textbf{Outcome type} & Binary \\\\\\\\\\n\",\n \"\\t\\t \\\\textbf{Intervention events} & 8\\\\\\\\\\n\",\n \"\\t\\t \\\\textbf{Intervertion total} & 23 \\\\\\\\\\n\",\n \"\\t\\t \\\\textbf{Comparator events} & 2 \\\\\\\\\\n\",\n \"\\t\\t \\\\textbf{Comparator total} & 22 \\\\\\\\\\n\",\n \"\\t\\t \\\\textbf{Point estimate} & 3.83 \\\\\\\\\\n\",\n \"\\t\\t \\\\textbf{95\\\\% CI} & [0.91, 16.07]\\\\\\\\ \\n\",\n \"\\t\\t \\\\midrule\\n\",\n \" \\\\textbf{Target value} & outcome\\\\_type: binary\\\\newline\\n\",\n \"\\t\\t intervention:\\\\newline\\n\",\n \"\\t\\t events: 8 total: 23\\\\newline\\n\",\n \"\\t\\t comparator:\\\\newline\\n\",\n \"\\t\\t events: 2 total: 22\\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\textbf{Thought} & To extract the relevant numbers, we first infer the outcome type as \\\"binary\\\" because the outcome \\\"Clinical remission\\\" is a yes/no or present/absent type of outcome. \\n\",\n \"\\t\\t \\n\",\n \"\\t\\t The relevant numbers for this outcome are found in Table 2: \\\"Final Assessments\\\", under the outcome \\\"CDAI <150 last 3 mo\\\". \\n\",\n \" Specifically, for the intervention group (Stem cells), we have 8 events (patients in clinical remission) out of 23 total patients (34.8\\\\%). For the comparator group (Control), we have 2 events out of 22 total patients (9.1\\\\%). \\\\\\\\\\n\",\n \"\\t\\t \\\\bottomrule\\n\",\n \"\\t\\t \\\\end{tabular}\\n\",\n \"\\t \\\\caption{The corresponding entry in \\\\textsc{CochraneForestExt} for Hawekey 2015 in Figure~\\\\ref{fig:fp_bin}. Thought and target value are contatenated for SFT as follows: \\\\texttt{<think>\\\\{thought\\\\}</think>\\\\{target\\\\_value\\\\}}.}\\n\",\n \"\\t \\\\label{tab:data_example}\\n\",\n \"\\t \\\\end{table*}\\n\"\n ]\n]","isConversational":false},"fulltext":{"kind":"string","value":"% This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.\n\\pdfoutput=1\n% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.\n\n\\PassOptionsToPackage{table}{xcolor}\n\\documentclass[11pt]{article}\n\n% Change \"review\" to \"final\" to generate the final (sometimes called camera-ready) version.\n% Change to \"preprint\" to generate a non-anonymous version with page numbers.\n\\usepackage[final]{acl}\n\n% Standard package includes\n\\usepackage{times}\n\\usepackage{latexsym}\n\n% For proper rendering and hyphenation of words containing Latin characters (including in bib files)\n\\usepackage[T1]{fontenc}\n% For Vietnamese characters\n% \\usepackage[T5]{fontenc}\n% See https://www.latex-project.org/help/documentation/encguide.pdf for other character sets\n\n% This assumes your files are encoded as UTF8\n\\usepackage[utf8]{inputenc}\n\n% This is not strictly necessary, and may be commented out,\n% but it will improve the layout of the manuscript,\n% and will typically save some space.\n\\usepackage{microtype}\n\n% This is also not strictly necessary, and may be commented out.\n% However, it will improve the aesthetics of text in\n% the typewriter font.\n\\usepackage{inconsolata}\n\n%Including images in your LaTeX document requires adding\n%additional package(s)\n\\usepackage{graphicx}\n\\usepackage{url}\n\\usepackage{tcolorbox}\n\n\\usepackage{mathtools}\n\\usepackage{amsmath, amssymb}\n\\usepackage{bbm}\n\\usepackage{booktabs}\n\\usepackage{tabularx}\n\\usepackage{multirow}\n\\usepackage{nccmath} % Add this in the preamble\n\\usepackage[table]{xcolor}\n\n\\usepackage{pifont}\n\\newcommand{\\cmark}{{\\color{green!60!black}\\ding{51}}} % green checkmark\n\\newcommand{\\xmark}{{\\color{red}\\ding{55}}} % red cross\n\n% If the title and author information does not fit in the area allocated, uncomment the following\n%\n%\\setlength\\titlebox{<dim>}\n%\n% and set <dim> to something 5cm or larger.\n\n% \\title{...: Study-Level Inference from Extracted Effect Sizes with Reinforcement Learning}\n% \\title{Inferring Study-Level Outcomes from Clinical Trial Papers Via Reinforcement Learning}\n\n%% PREVIOUS TITLE BY YUFANG:\n%\\title{Inferring Study-Level Outcomes from Clinical Trial Papers Through Numeric Reasoning}\n\n%% massi proposed title:\n%\\title{Reinforcing Study-Level Inference from Clinical Trial Papers Through Numeric Reasoning}\n\n\\title{Enhancing Study-Level Inference from Clinical Trial Papers via RL-based Numeric Reasoning}\n\n\n% Author information can be set in various styles:\n% For several authors from the same institution:\n% \\author{Author 1 \\and ... \\and Author n \\\\\n% Address line \\\\ ... \\\\ Address line}\n% if the names do not fit well on one line use\n% Author 1 \\\\ {\\bf Author 2} \\\\ ... \\\\ {\\bf Author n} \\\\\n% For authors from different institutions:\n% \\author{Author 1 \\\\ Address line \\\\ ... \\\\ Address line\n% \\And ... \\And\n% Author n \\\\ Address line \\\\ ... \\\\ Address line}\n% To start a separate ``row'' of authors use \\AND, as in\n% \\author{Author 1 \\\\ Address line \\\\ ... \\\\ Address line\n% \\AND\n% Author 2 \\\\ Address line \\\\ ... \\\\ Address line \\And\n% Author 3 \\\\ Address line \\\\ ... \\\\ Address line}\n\n\\author{\n \\textbf{Massimiliano Pronesti\\textsuperscript{1,2}},\n \\textbf{Michela Lorandi\\textsuperscript{2}},\n \\textbf{Paul Flanagan\\textsuperscript{2}},\n \\textbf{Oisín Redmond\\textsuperscript{2}},\n \\\\\n \\textbf{Anya Belz\\textsuperscript{2}},\n \\textbf{Yufang Hou\\textsuperscript{1,3}}\n\\\\\n \\textsuperscript{1}IBM Research Europe - Ireland,\n \\textsuperscript{2}Dublin City University,\\\\\n \\textsuperscript{3}IT:U Interdisciplinary Transformation University Austria\n\\\\\n \\small{\n \\textbf{Correspondence:} \\href{mailto:massimiliano.pronesti@ibm.com}{massimiliano.pronesti@ibm.com}, \\href{mailto:yufang.hou@it-u.at}{yufang.hou@it-u.at}\n }\n}\n\n%\\author{\n% \\textbf{First Author\\textsuperscript{1}},\n% \\textbf{Second Author\\textsuperscript{1,2}},\n% \\textbf{Third T. Author\\textsuperscript{1}},\n% \\textbf{Fourth Author\\textsuperscript{1}},\n%\\\\\n% \\textbf{Fifth Author\\textsuperscript{1,2}},\n% \\textbf{Sixth Author\\textsuperscript{1}},\n% \\textbf{Seventh Author\\textsuperscript{1}},\n% \\textbf{Eighth Author \\textsuperscript{1,2,3,4}},\n%\\\\\n% \\textbf{Ninth Author\\textsuperscript{1}},\n% \\textbf{Tenth Author\\textsuperscript{1}},\n% \\textbf{Eleventh E. Author\\textsuperscript{1,2,3,4,5}},\n% \\textbf{Twelfth Author\\textsuperscript{1}},\n%\\\\\n% \\textbf{Thirteenth Author\\textsuperscript{3}},\n% \\textbf{Fourteenth F. Author\\textsuperscript{2,4}},\n% \\textbf{Fifteenth Author\\textsuperscript{1}},\n% \\textbf{Sixteenth Author\\textsuperscript{1}},\n%\\\\\n% \\textbf{Seventeenth S. Author\\textsuperscript{4,5}},\n% \\textbf{Eighteenth Author\\textsuperscript{3,4}},\n% \\textbf{Nineteenth N. Author\\textsuperscript{2,5}},\n% \\textbf{Twentieth Author\\textsuperscript{1}}\n%\\\\\n%\\\\\n% \\textsuperscript{1}Affiliation 1,\n% \\textsuperscript{2}Affiliation 2,\n% \\textsuperscript{3}Affiliation 3,\n% \\textsuperscript{4}Affiliation 4,\n% \\textsuperscript{5}Affiliation 5\n%\\\\\n% \\small{\n% \\textbf{Correspondence:} \\href{mailto:email@domain}{email@domain}\n% }\n%}\n\n\\begin{document}\n\\maketitle\n\n% \\begingroup\n% \\renewcommand\\thefootnote{$\\dagger$}\n% \\footnotetext{These authors jointly supervised this work.}\n% \\endgroup\n\n\\begin{abstract}\nSystematic reviews in medicine play a critical role in evidence-based decision-making by aggregating findings from multiple studies. A central bottleneck in automating this process is extracting numeric evidence and determining study-level conclusions for specific outcomes and comparisons. Prior work has framed this %task as one of textual \nproblem as a textual inference task by \n%retrieval—%identifying \nretrieving \nrelevant content fragments and inferring conclusions from them. However, such approaches often rely on shallow textual cues and fail to capture the underlying numeric reasoning behind expert assessments.\nIn this work, we conceptualise the problem as one of quantitative reasoning. Rather than inferring conclusions from surface text, we extract structured numerical evidence %(such as effect sizes and confidence intervals) \n(e.g., \\emph{event counts} or \\emph{standard deviations}) \nand apply domain knowledge informed logic to derive outcome-specific conclusions. We develop %a modular pipeline of compact models\n%a novel numeric outcome results extraction system\na numeric reasoning system composed of a numeric data extraction model and an effect estimate component,\n%trained via reinforcement learning (RL),\n%which \n%trained via supervised fine-tuning (SFT), SFT with intermediate reasoning traces, and reinforcement learning (RL), \nenabling more accurate and interpretable \ninference aligned with the domain expert principles. %Cochrane review methodology.\n% \n%We train our numeric \nWe train the numeric data extraction model using different strategies, including supervised fine-tuning (SFT), %SFT with intermediate reasoning traces, \nand reinforcement learning (RL) with a new value reward model.\nWhen evaluated on the \\textsc{CochraneForest} benchmark, our best-performing approach -- using RL to train %the\na small-scale \nnumber extraction model -- yields\n%achieves \nup to a 21\\% absolute improvement in F1 score over retrieval-based systems and outperforms general-purpose LLMs of over 400B parameters by up to 9\\%. Our results demonstrate the promise of reasoning-driven approaches for automating systematic evidence synthesis. %Code and models are publicly available at [URL].\n\n\\end{abstract}\n\n\\section{Introduction}\n\\label{sec:intro}\n\n\\begin{figure}[t!]\n\\centering\n\\includegraphics[width=.43\\textwidth]{latex/assets/example4.pdf}\n\\caption{Example of estimating the intervention effect based on the extracted outcome data for a clinical study.}\n\\label{fig:example}\n\\end{figure}\n\nSystematic reviews are the cornerstone of evidence-based medicine, offering rigorous syntheses of available studies to guide clinical decision-making~\\cite{murad2016new}. A critical component of systematic reviews is the extraction of study-level numeric evidence (e.g., \\emph{event counts} or \\emph{standard deviations}) from one or multiple corresponding clinical trial papers and derive the conclusions for each outcome and comparison under assessment. However, automating this extraction remains an open challenge. As illustrated in Figure \\ref{fig:example} (prior work), previous studies~\\cite{pronesti2025} has primarily framed this task as a retrieval-based question answering problem: given a query about an outcome, systems retrieve relevant study fragments and infer conclusions based on the retrieved text. \n\nDespite progress, such approaches fundamentally rely on surface-level textual cues, limiting their effectiveness.\nTo illustrate these limitations, we plot in Figure~\\ref{fig:ret_pre_cf} the relationship between evidence retrieval precision (x-axis) and the answer F1 score (y-axis) on 30 manually annotated instances (Appendix \\ref{app:annot}) from the \\textsc{CochraneForest} benchmark~\\cite{pronesti2025} across four state-of-the-art large language models (LLMs). Each instance consists of a trial study comprising one or more papers, a research question, its corresponding categorical answer, and an exhaustive annotation of all supporting textual evidence drawn from the entire paper. In general, while better retrieval precision correlates with improved performance, even perfect retrieval (100\\% precision) results in a modest maximum F1 score of 68\\%. Moreover, performance gains plateau quickly: increasing retrieval precision from 50\\% to 100\\% yields only 3-4\\% absolute improvement. This suggests that textual information alone is often insufficient to determine study conclusions, particularly when studies address multiple outcomes or consist of multiple publications.\n\n\n\n\\begin{figure}\n\\centering\n\\includegraphics[width=.4\\textwidth]{latex/assets/introduction.png}\n\\caption{F1 score for predicting the correct answers on 30 instances from \\textsc{CochraneForest} on different retrieval precision using 4 state-of-the-art LLMs.}\n\\label{fig:ret_pre_cf}\n\\end{figure}\n\n\n% Recognising these limitations, we propose a different approach. Instead of relying solely on retrieval, we train models to extract the numerical results corresponding to each outcome and comparison, and then compute the study conclusion statistically. By doing so, we align the automation process with how systematic reviews are conducted manually~\\cite{yun-etal-2023-appraising}.\n% A related idea was explored by \\citealp{yun2024automatically}, who extracted structured information to predict study conclusions. However, their work differs from ours in key ways: they only used pretrained models without domain-specific adaptation, their dataset was limited to abstracts and results sections (excluding full texts), and they focused on individual randomised controlled trials (RCTs) rather than the more challenging task of extracting studies appearing within forest plots in systematic reviews.\n% In this paper, we develop custom lightweight models trained directly on full-text articles to predict both the outcome type and the associated numerical results. Our models achieve up to 12\\% absolute improvement over retrieval-based systems on the \\textsc{CochraneForest} benchmark. Furthermore, despite their small size, they outperform large pretrained LLMs by up to 5\\% in F1 score on the same task of numerical extraction.\n\nThese findings motivate a shift in perspective -- from relying on surface-level textual cues to explicitly modelling the quantitative reasoning that underpins expert assessments in systematic reviews. A natural alternative is to adopt domain expert principles by extracting and interpreting the numerical evidence (e.g., \\emph{effect sizes} and \\emph{confidence intervals}) that supports each study’s conclusion. This reframes the task from semantic retrieval to structured statistical inference.\nRecent work by~\\citealp[]{yun2024automatically} has explored this direction \n%in the context of randomised controlled trials (RCTs), \nby leveraging pretrained %large language models\nLLMs\nthrough prompting to extract quantitative results. While promising, this approach has so far been limited to individual trials and has not addressed the challenges posed by longer, heterogeneous full-text studies included in systematic reviews. Furthermore, the potential of custom-trained models optimised specifically for numerical reasoning and alignment with expert conclusions remains largely unexplored in this setting.\n\nConcurrently, advances in \nsupervised fine-tuning (SFT) and \nreinforcement learning (RL) have shown significant improvements in aligning model behaviour with complex reasoning objectives~\\cite{weifinetuned, guo2025deepseek}. Building on these insights, as shown in Figure \\ref{fig:example} (this work), \n%we propose a structured pipeline that extracts interpretable numerical evidence from full-text studies (numeric data extraction model) and infers study-level conclusions using transparent rules (effect estimate component), rather than relying on implicit textual signals.\nwe propose a structured pipeline that extracts interpretable numerical evidence from full-text studies via a numeric data extraction model, and infers study-level conclusions using transparent rules through an effect estimate component—eschewing reliance on implicit textual signals. We train compact numeric data extraction models using a wide range of strategies, including SFT, SFT with intermediate reasoning traces, and RL\nwith a novel value-based reward model. On two different datasets, our models outperform the prompting-based approaches based on big models proposed in \\newcite{yun2024automatically}.\n\nIn addition, compared to the implicit text evidence reasoning approach \\cite{pronesti2025}, our models allow one to automatically generate the corresponding row of a forest plot (Section \\ref{sec:forest_plot}) from a full text study by directly extracting numerical evidence for each outcome measure. This represents a key step toward full automation of the systematic review process, bridging the gap between primary study reporting and meta-analytic synthesis \\citep{wallace2010semi, tsafnat2014systematic}.\n\n%Our contributions in summary:\nIn summary, our contributions are as follows:\n(1) we propose a novel pipeline that predicts study conclusions by extracting and statistically analysing numerical outcomes, instead of relying purely on textual retrieval; (2) we explore how different training strategies—standard SFT, SFT with intermediate reasoning traces, and RL—affect the reasoning abilities of compact language models on this task; (3) we develop custom models trained for this task, achieving up to 21\\% absolute improvement in F1 score compared to retrieval-based systems on \\textsc{CochraneForest}.\n%(4) We make our models and code publicly available to foster future research.\n\n\n\n\\section{Preliminaries}\n\n\\subsection{Systematic Reviews}\nA systematic review is a rigorous method of synthesising evidence from multiple studies that address a clearly formulated research question~\\cite{cochranehandbook}. It follows a structured protocol for identifying, selecting, and appraising relevant research to minimise bias and yield reliable findings.\n\n\\subsection{Forest Plots}\n\\label{sec:forest_plot}\nForest plots are visual tools commonly used in systematic reviews to display the estimated effects from multiple studies on a common scale. Each study is typically represented as a point estimate (e.g., \\emph{mean difference}, \\emph{odds ratio}) with a confidence interval, and a vertical line indicating a null effect (e.g., 0 for differences, 1 for ratios). Forest plots help in assessing consistency across studies and interpreting the overall effect direction and magnitude. Examples are shown in Appendix~\\ref{sec:forest_plots_appendix}.\n\n\\subsection{Data Extraction from Biomedical Studies}\n%Biomedical studies, particularly randomised controlled trials (RCTs), typically report %treatment effects using quantitative measures such as mean differences, risk ratios, or odds ratios, often alongside confidence intervals based on \n%different \n% numerical outcome results for different treatment or intervention. These numerical results may appear in tables, figures, or within narrative text, and are essential for downstream analysis. \nBiomedical studies, particularly randomised controlled trials (RCTs), typically report numerical outcome data for different treatments or interventions. These results—such as event or group size—may appear in tables, figures, or embedded within narrative text. Extracting this information is essential for downstream tasks such as study-level evidence synthesis and across-study meta-analysis.\n\nFollowing \\newcite{yun2024automatically}, we categorise extracted numerical data into two main outcome types: \\emph{binary outcomes}, which include the number of events and group sizes for both the intervention and comparator arms; and \\emph{continuous outcomes}, which consist of means, standard deviations, and group sizes for intervention and comparator groups.\n \n\n% Studies often report multiple outcomes, stratify results across subgroups (e.g., by age or severity), and adopt heterogeneous reporting formats. Differences in terminology, rounding conventions, and missing data further complicate the task. As a result, automated systems must go beyond simple pattern matching and instead reason over structure and context.\n\nOnce extracted, these values can be used in standard meta-analytic methods to estimate treatment effects—such as mean differences, risk ratios, or odds ratios—along with their corresponding confidence intervals, forming the basis for deriving study-level conclusions. An example of estimating treatment effects is provided in Appendix~\\ref{sec:forest_plots_appendix}.\n% synthesise evidence across studies.\n%The most common approach computes a pooled effect size \\( \\hat{\\theta} \\) as a weighted average of individual estimates:\n% \\[\n% \\hat{\\theta} = \\frac{\\sum_{i} w_i \\theta_i}{\\sum_i w_i}\n% \\quad \\text{with} \\quad\n% w_i = \\frac{1}{\\sigma_i^2}\n% \\]\n% where \\( \\theta_i \\) is the effect estimate from study \\( i \\), and \\( \\sigma_i^2 \\) its variance. In random-effects models, an additional between-study variance \\( \\tau^2 \\) is included to account for heterogeneity. The resulting estimate provides a quantitative summary of the overall evidence, guiding clinical or policy conclusions.\n\n\n\n\n% \\subsection{Supervised Fine-Tuning of Language Models}\n% SFT adapts a pretrained language model $\\pi_\\theta$ to better reflect task-specific or domain-specific behaviour described by a desired distribution $\\mathcal{P}$. This is achieved by minimising the negative log-likelihood over a dataset of example sequences, encouraging the model to increase the probability of desired outputs. Given a discrete token sequence $\\mathbf{z} = (z_1, \\dots, z_T)$, the model is trained to maximise:\n% \\[\n% \\mathcal{L}_{\\text{full}}(\\theta) = - \\mathbb{E}_{\\mathbf{z} \\sim \\mathcal{P}} \\left[ \\sum_{t=1}^{T} \\log \\pi_\\theta(z_t \\mid z_{<t}) \\right]\n% \\]\n% where $z_{<t}$ is the lef-truncated context of length t-1.\n\n% Alternatively, when the task involves prompt-target pairs $(\\mathbf{x}, \\mathbf{y})$, the objective can be conditioned only on the target:\n% \\begin{equation}\n% \\mathcal{L}_{\\text{cond}}(\\theta) = - \\mathbb{E}_{(\\mathbf{x}, \\mathbf{y}) \\sim \\mathcal{P}} \\left[ \\sum_{t=1}^{m} \\log \\pi_\\theta(y_t \\mid \\mathbf{x}, y_{<t}) \\right]\n% \\label{eq:sft_cond}\n% \\end{equation}\n\n% This focus helps the model better learn task-relevant outputs without overfitting to input tokens~\\cite{wang-etal-2023-self-instruct, yu-etal-2024-lions}.\n\n% \\subsection{Reinforcement Learning for Language Models}\n% Reinforcement Learning from Human Feedback (RLHF) further refines LLMs by aligning model outputs with human preferences or reward signals. One popular approach is Proximal Policy Optimisation (PPO)~\\cite{ppo}, which updates the policy while constraining the deviation from previous behaviours. A clipped objective stabilises learning and encourages safe exploration.\n\n% An alternative is Group-Relative Policy Optimisation (GRPO)~\\cite{grpo}, which computes normalised rewards over a group of responses and reduces variance in learning. For each prompt $x$, multiple responses $\\{y_i\\}_{i=1}^G$ are sampled from a reference model. The reward $R_i$ for each response is normalised across the group:\n% \\[\n% A_i = \\frac{R_i - \\mathbb{E}[R_j]}{\\sqrt{\\mathbb{V}[R_j]}}\n% \\]\n% The learning objective minimises a clipped, KL-regularised loss that encourages agreement with high-reward behaviours while maintaining proximity to the reference policy:\n\n% {\\small\n% \t\\begin{align}\n% \t\t\\mathcal{L}_{\\text{GRPO}}(\\theta) =\\;&\n% \t\t\\mathbb{E} \\bigg[ \\frac{1}{G} \\sum_{i=1}^G \\frac{1}{T_i} \\sum_{t=1}^{T_i}\n% \t\t\\min\\Big( r A_i,\\,\n% \t\t\\text{clip}(r, 1{-}\\epsilon, 1{+}\\epsilon) A_i \\Big) \\notag \\\\\n% \t\t&\\quad - \\beta\\, \\text{KL}[\\pi_\\theta \\,\\|\\, \\pi_{\\text{ref}}] \\bigg]\n% \\label{eq:grpo_loss}\n% \t\\end{align}\n% }\n\n\n\n% where $r = p_{i,t}(\\theta)$ is the token-level probability ratio, and $\\beta$ governs the regularisation strength.\n\n\n\n\\section{Methodology}\nAs shown in Figure \\ref{fig:example}, our system consists of a fine-tuned numeric extraction model grounded in reasoning, alongside a rule-based effect size estimation component. In the following, we describe each part in detail.\n\n\\subsection{Training Dataset Creation}\\label{dataset}\n\\paragraph{Training Data Collection.}\nThe initial phase of our methodology involved the acquisition of high-quality, human-annotated data to train our models. Following the methodology described in~\\citet{pronesti2025}, we processed the Cochrane Database of Systematic Reviews (CDSR)\\footnote{\\url{https://www.cochranelibrary.com/cdsr/reviews}} to identify systematic reviews containing non-paywalled full-text studies and at least one forest plot available in SVG format. Each forest plot was parsed to extract the underlying numerical data (i.e. the number of events and total participants in each group for binary outcomes, or the mean, standard deviation, and sample size for continuous outcomes), along with the point estimate, 95\\% confidence interval, and textual conclusion for each included study. \n\nTo increase dataset size and training diversity, we relaxed one of the constraints imposed in the original \\textsc{CochraneForest} benchmark. Specifically, we no longer require that each forest plot contain at least two studies with differing conclusions. This change allows us to include more reviews while still preserving outcome-level heterogeneity across the dataset. In addition, to prevent data leakage, we explicitly excluded any study in \\textsc{CochraneForest} from our training set. \n\nThe final training dataset consists of 2,072 examples, spanning 104 systematic reviews and 25.9 M tokens of full-text biomedical content. Each data point contains: (1) the full text of a study, (2) the outcome type (i.e., \\emph{binary} or \\emph{continuous}) as defined in the corresponding forest plot, (3) the set of numerical values to be extracted (e.g., group means or event counts), (4) the computed point estimate, (5) the 95\\% CI, and (6) the final conclusion assigned to the outcome in the forest plot. We refer to this dataset as \\textsc{CochraneForestExt}. \n\n\n\\begin{table}[t]\n\\scalebox{0.75}{\n\\centering\n\\begin{tabular}{lcccccc}\n\\toprule\n\\textbf{Dataset} & \\textbf{Train} & \\textbf{Test} & \\textbf{Total} & \\textbf{Avg tokens}\\\\\n\\midrule\n\\textsc{CochraneForestExt} & 1864 & 208 & 2072 & 12109.2 \\\\\n\\textsc{CochraneForest} & – & 725 & 725 & 11688.7 \\\\\n\\textsc{RCTs} & – & 413 & 413 & 4364.9 \\\\\n\\bottomrule\n\\end{tabular}\n}\n\\caption{Datasets statistics. Train/test split only applies to \\textsc{CochraneForestExt}. \\textsc{CochraneForest} and RCTs are used for testing.}\n\\label{tab:dataset_stats}\n\\end{table}\n\n%% maybe add a small table with points per train and dev and portion of of outcomes (bin vs cont) etc.\n\n\\paragraph{Synthetic Data Annotation.}\n% \\noindent\\textbf{Synthetic Data Annotation.} \nTo %enhance \nenrich the dataset with reasoning traces for SFT, we used Llama 3.1 405B~\\cite{grattafiori2024llama} with the system prompt shown in Figure~\\ref{fig:sys_prompt} (Appendix), temperature of 0.7 and 2,048 tokens generation limit. An example data instance is provided in Table~\\ref{tab:data_example} (Appendix).\n\n\n\n\\subsection{Numeric Data Extraction Model}\n%\\subsubsection{Supervised Fine-Tuning of the Extraction Model}\n\\subsubsection{SFT with CoT}\n% We first adapt a pretrained language model to our task via supervised fine-tuning (SFT). The goal is to enable the model to map free-text descriptions of study results to structured outputs that capture outcome information in a standardised schema. Each training example consists of a biomedical study, paired with the corresponding outcome of interest. The output is represented in YAML format and encodes either binary outcomes (with event counts and totals for intervention and comparator groups) or continuous outcomes (with group-wise means, standard deviations, and sample sizes). The model learns to generate these structured summaries conditioned on the corresponding textual evidence (Equation~\\ref{eq:sft_cond}).\n\nWe first adapt a pretrained language model to our task via supervised fine-tuning (SFT), which adapts a pretrained language model $\\pi_\\theta$ to reflect a domain-specific distribution $\\mathcal{P}$. This is achieved by minimising the negative log-likelihood over a dataset of example sequences, encouraging the model to increase the probability of desired outputs. In our context, the goal is to enable the model to map free-text descriptions of study results to structured outputs that capture outcome information in a standardised schema. Each training example consists of a biomedical study, paired with the corresponding outcome of interest (Table \\ref{tab:data_example}). The output is represented in YAML format and encodes either binary outcomes (with event counts and totals for intervention and comparator groups) or continuous outcomes (with group-wise means, standard deviations, and sample sizes). The model learns to generate these structured summaries conditioned on the corresponding textual evidence. That is, given prompt–target pairs $(\\mathbf{x}, \\mathbf{y})$, the loss is computed as:\n\\[\n\\mathcal{L}_{\\text{cond}}(\\theta) = - \\mathbb{E}_{(\\mathbf{x}, \\mathbf{y}) \\sim \\mathcal{P}} \\left[ \\sum_{t=1}^{m} \\log \\pi_\\theta(y_t \\mid \\mathbf{x}, y_{<t}) \\right]\n\\]\n\nThis focus helps the model better learn task-relevant outputs without overfitting to input tokens~\\cite{wang-etal-2023-self-instruct,vicuna2023, yu-etal-2024-lions}.\n\n\n%\\subsubsection{Reinforcement Learning for Training the Extraction Model}\n\\subsubsection{RL with Fine-grained Rewards}\n\n% As an alternative to SFT, we explore reinforcement learning using GRPO~\\cite{grpo}. In our setup, the model acts as a policy $\\pi_\\theta$ that takes as input a textual passage describing clinical study results and outputs a structured response. This response consists of a thought process enclosed in a \\texttt{<think>} tag, followed by a YAML object encoding outcome data—such as event counts for binary outcomes or means and standard deviations for continuous outcomes, each specified for intervention and comparator groups. For each passage, the model samples a group of candidate completions. These are scored using rule-based reward functions that evaluate factual correctness and adherence to format. The policy is updated to maximise expected reward while staying close to a reference model $\\pi_{\\mathrm{ref}}$. Rewards are computed relative to the sampled group to encourage robustness and diversity, while a KL penalty term constrains deviation from $\\pi_{\\mathrm{ref}}$ (Equation~\\ref{eq:grpo_loss}).\n\nAs an alternative to SFT, we explore Reinforcement Learning, which further refines LLMs by aligning model outputs with human preferences or reward signals. We adopt Group-Relative Policy Optimisation (GRPO)~\\cite{grpo}, which computes normalised rewards over a group of responses and reduces variance in learning.\n\nIn our setup, the model acts as a policy $\\pi_\\theta$ that takes as input a textual passage describing clinical study results and outputs a structured response. Each response consists of a thought process enclosed in a \\texttt{<think>} tag, followed by a YAML object encoding outcome data, using the same schema adopted in SFT. For each passage $x$, multiple candidate completions $\\{y_i\\}_{i=1}^G$ are sampled from the model to encourage robustness and diversity. These completions are scored using rule-based reward functions that evaluate factual correctness and adherence to format. The raw rewards $R_i$ are then normalised across the group:\n\\[\nA_i = \\frac{R_i - \\mathbb{E}[R_j]}{\\sqrt{\\mathbb{V}[R_j]}}\n\\]\n\nThe policy is optimised using a clipped, KL-regularised objective that encourages agreement with high-reward behaviours while maintaining proximity to a reference model $\\pi_{\\mathrm{ref}}$:\n\n{\\small\n\t\\begin{align*}\n\t\\hspace{-0.2cm}\t\\mathcal{L}_{\\text{GRPO}}(\\theta) =\\;&\n\t\t\\mathbb{E} \\bigg[ \\frac{1}{G} \\sum_{i=1}^G \\frac{1}{T_i} \\sum_{t=1}^{T_i}\n\t\t\\min\\Big( r A_i,\\,\n\t\t\\text{clip}(r, 1{-}\\epsilon, 1{+}\\epsilon) A_i \\Big) \\notag \\\\\n\t\t&\\quad - \\beta\\, \\text{KL}[\\pi_\\theta \\,\\|\\, \\pi_{\\text{ref}}] \\bigg]\n\t\\end{align*}\n}\n\nwhere $r = p_{i,t}(\\theta)$ is the token-level probability ratio, and $\\beta$ governs the regularisation strength.\n\n\n\n\n\n\n\\paragraph{Reward Functions.}\n\nIn our setting, the model produces structured YAML outputs representing binary or continuous outcomes. To evaluate these outputs during RL, we define three rule-based reward functions based on format validity and numerical correctness. Let $C_i$ be the model output, $E_i$ the expected answer.%, and $r_i \\in [0, 1]$.\n\n\\paragraph{Correctness Reward (CR).} \n% \\noindent\\textbf{Correctness Reward (CR).} \nThis reward compares numerical values in $C_i$ and $E_i$ when the outcome type is correct. Let $V_i = \\{v_1, \\dots, v_n\\}$ and $\\hat{V}_i = \\{\\hat{v}_1, \\dots, \\hat{v}_n\\}$ be the parsed numerical fields. Then:\n\\[\nR_{\\mathrm{CR}} = \\frac{1 + \\sum_{j=1}^n \\mathbbm{1}\\{v_j \\approx \\hat{v}_j\\}}{1 + n}\n\\]\nwhere $v_j \\approx \\hat{v}_j$ means exact match for integers and absolute difference $< 10^{-3}$ for floats. If parsing fails or types mismatch, we set $R_{\\mathrm{CR}} = 0$.\n\n\\paragraph{Format Reward (FR).} \n% \\noindent\\textbf{Format Reward (FR).} \nThis reward checks whether $C_i$ follows the expected structure. Let $\\mathcal{F}$ denote the set of all valid formats (Appendix~\\ref{app:prompts}), including required keys and outcome type. We define:\n\\[\nR_{\\mathrm{FR}} = \n\\begin{cases}\n1 & \\text{if } \\pi_{\\theta}(x) \\in \\mathcal{F} \\\\\n0 & \\text{otherwise}\n\\end{cases}\n\\]\nThis reward ensures that the output adheres to a valid YAML schema.\n\n\\paragraph{Thought Format Reward (TFR).}\n% \\noindent\\textbf{Thought Format Reward (TFR).} \nThe TFR incentivises the model to adhere to a predefined output structure, such as the use of \\texttt{<think>} tag.\n\\[\nR_{\\mathrm{TFR}} = \n\\begin{cases}\n1 & \\text{if } \\pi_{\\theta}(x) \\text{ matches thought pattern} \\\\\n0 & \\text{otherwise}\n\\end{cases}\n\\]\n\n\n\n% \\paragraph{Final Reward.}\n\\noindent\\textbf{Final Reward.} \nThe final reward is a weighted combination of format and correctness components:\n\\[\nR = 0.8 \\cdot R_{\\mathrm{CR}} + 0.1 \\cdot R_{\\mathrm{FR}} + 0.1 \\cdot R_{\\mathrm{TFR}}\n\\]\nThe weights prioritise factual accuracy while still incentivising structural correctness and robustness to formatting issues with proper reasoning traces. %Outputs that cannot be parsed or matched yield $R = 0$.\nMore details are provided in Appendix~\\ref{app:hyper_and_apis}.\n\n\\subsection{Effect Estimate Component}\n\nAfter extracting the relevant numerical data, we compute standardised fixed-effect estimates based on the type of outcome~\\cite{hedges1998fixed}.\n\n\\paragraph{Binary Outcomes.} \n% \\noindent\\textbf{Binary Outcomes.} \nFor event-based outcomes, we compute the risk ratio (RR) as \\(\\text{RR} = \\frac{a/(a+b)}{c/(c+d)}\\), where \\(a, b\\) are the numbers of events and non-events in the treatment group, and \\(c, d\\) in the control group. To quantify uncertainty, we compute the 95\\% confidence interval on the log scale as \\(\\log(\\text{RR}) \\pm 1.96 \\cdot \\sqrt{\\frac{1}{a} - \\frac{1}{a+b} + \\frac{1}{c} - \\frac{1}{c+d}}\\), which is then exponentiated to return to the RR scale (see Appendix \\ref{sec:forest_plots_appendix} for an example calculation).\n\n\\paragraph{Continuous Outcomes.} \n% \\noindent\\textbf{Continuous Outcomes.} \nFor outcomes measured on a continuous scale, we compute the mean difference (MD) as \\(\\bar{x}_T - \\bar{x}_C\\), where \\(\\bar{x}_T\\) and \\(\\bar{x}_C\\) are the group means in the treatment and control arms, respectively. The 95\\% CI is then computed as $\\text{MD} \\pm 1.96 \\cdot \\sqrt{\\frac{s_T^2}{n_T} + \\frac{s_C^2}{n_C}}$, where \\(s_T, s_C\\) and \\(n_T, n_C\\) are respectively the standard deviations and samples sizes of the two groups (see Appendix \\ref{sec:forest_plots_appendix} for an example calculation).\n\n\\paragraph{Deriving Study Conclusions.} \n% \\noindent\\textbf{Deriving Study Conclusions.} \nStudy conclusions are determined directly from the 95\\% confidence interval of the effect estimate~\\cite{5minmeta}. For binary outcomes, if the confidence interval for the odds ratio lies entirely above 1, the study is classified as supporting the intervention; if it lies entirely below 1, it favors the control; and if it includes 1, the result is considered inconclusive. For continuous outcomes, the same logic applies with respect to the null value 0.\n\n\\section{Experiments}\n\\subsection{Experimental setup}\n\\paragraph{Training and Evaluation Datasets.} For training, we use the dataset created as described in Section~\\ref{dataset}, with the reasoning traces for SFT, using 1864 samples for training and 208 for validation.\n\nFor evaluation, we use two datasets. The first is \\textsc{CochraneForest}~\\cite{pronesti2025}, which consists of 725 instances derived from 48 Cochrane Systematic Reviews and 220 forest plots. The second, introduced by \\citet{yun2024automatically}, contains 413 complete, human-annotated instances derived from 120 RCTs. Dataset statistics are reported in Table~\\ref{tab:dataset_stats}.\n\n% \\begin{table*}[t]\n% \t\\small\n% \t\\centering\n% \t\\renewcommand{\\arraystretch}{1.2}\n% \t\\setlength{\\tabcolsep}{4pt}\n% \t\\begin{tabular}{l|cccccc|cccccc}\n% \t\t\\toprule\n% \t\t\\multirow{2}{*}{\\textbf{Model}} & \\multicolumn{6}{c|}{\\cellcolor{gray!20}\\textsc{\\textbf{CochraneForest}}} & \\multicolumn{6}{c}{\\cellcolor{gray!20}\\textbf{RCTs}~\\cite{yun2024automatically}} \\\\\n% \t\t& Acc & F1 & EM & EM@1 & MSE$\\downarrow$ & EIR$\\downarrow$ & Acc & F1 & EM & EM@1 & MSE$\\downarrow$ & EIR$\\downarrow$\\\\\n% \t\t\\toprule\n% \t\t\\multicolumn{1}{c}{\\cellcolor{gray!20}\\textbf{Pretrained LLMs}} & \\multicolumn{6}{c}{\\cellcolor{gray!20}} & \\multicolumn{6}{c}{\\cellcolor{gray!20}} \\\\\n% \t\t\\texttt{GPT-4-0125} & 71.4 & 73.7 & 28.4 & 72.1 & 0.88 & 0.31 & 71.2 & 65.3 & 34.5 & 70.3 & 0.69 & 0.44\\\\\n% \t\t\\texttt{Qwen2.5-7B} & 69.7 & 65.4 & 14.9 & 66.4 & 1.40 & 0.39 & 63.2 & 59.7 & 20.3 & 73.8 & 1.19 & 0.50 \\\\\n% \t\t\\texttt{Qwen2.5-14B} & 76.2& 73.7 & 27.4 & 73.6 &0.72 & 0.30 & 69.8 & 66.4 & 28.6 & 75.8 & 0.91 & 0.44 \\\\\n% \t\t\\texttt{Qwen2.5-72B} & 82.2 & 80.3 & 42.5 & 81.7 & 0.63 & 0.26 & \\textbf{75.3}& \\textbf{72.8}& \\textbf{35.1} &\\textbf{81.1} & \\textbf{0.68} & \\textbf{0.39}\\\\\n% \t\t\\texttt{Llama-3.1-8B} & 68.7 & 66.6 & 15.9 & 62.2 & 2.16 & 0.33 & 62.7 & 54.7 & 14.8 & 66.5 & 1.30 & 0.47 \\\\\n% \t\t\\texttt{Llama-3.1-70B} & 79.4 & 78.3 & 31.5 & 78.7 & 0.59 & 0.28 & 65.5 & 60.8 & 30.0 & 74.2 & 1.45 & 0.44\\\\\n% \t\t\\texttt{Llama-3.1-405B} & \\textbf{82.4 }&\\textbf{ 80.8 }& \\textbf{44.3} & \\textbf{82.4} & \\textbf{0.43} & \\textbf{0.23} & 70.3 & 66.5 & 33.6 & 76.7 & 0.80 & 0.41 \\\\\n% \t\t%\\texttt{DeepSeek-v3} & & & & & & & & & & & \\\\\n% \t\t%\\texttt{DeepSeek-R1} & & & & & & & & & & & \\\\\n% \t\t\\texttt{DeepSeek-Qwen-7B}& 59.7 & 54.3 & 11.2 & 58.3& 2.83 & 0.50& 53.8 & 45.7 & 9.4 & 53.8 & 3.68 & 0.61 \\\\\n% \t\t\\texttt{DeepSeek-Qwen-14B}\n% \t\t& 65.4 & 61.2 & 19.9 & 66.4 & 1.29 & 0.44 &60.2 & 55.1 & 11.2 & 60.3 & 2.41 & 0.58 \\\\\n% \t\t\\texttt{DeepSeek-Qwen-32B} & 74.0 & 71.6 & 28.6 & 73.5 & 0.58 & 0.28 & 68.8 & 65.0 & 29.1 & 78.2 & 0.72 &0.43\\\\\n% \t\t\\toprule\n% \t\t\\multicolumn{1}{c}{\\cellcolor{gray!20}\\textbf{Our Models}} & \\multicolumn{6}{c}{\\cellcolor{gray!20}} & \\multicolumn{6}{c}{\\cellcolor{gray!20}} \\\\\n% \t\t%\\texttt{Llama-3.1-8B-SFT} & & & & & & & & & & & \\\\\n% \t\t\\texttt{Qwen2.5-7B-SFT}& 74.5 & 70.1 & 28.4 & 79.1 & 0.51 & 0.29& 71.5& 68.4 & 30.2 & 80.0& 0.64 & 0.36 \\\\\n% \t\t\\texttt{Qwen2.5-7B-RL} & \\textbf{81.6} & \\textbf{80.1} & \\textbf{42.2} & \\textbf{81.4} & \\textbf{0.40} & \\textbf{0.24} & \\textbf{79.3} & \\textbf{76.4} & \\textbf{41.2} & \\textbf{89.7} & \\textbf{0.49} &\\textbf{0.28}\\\\\n% \t\t\\bottomrule\n% \t\\end{tabular}\n% \t\\caption{Evaluation results across models on two datasets. We report Accuracy and F1 (label prediction), EM, EM@1, EIR and MSE (numerical extraction).}\n% \t\\label{tab:results}\n% \\end{table*}\n\n\\begin{table*}[t]\n\t\\small\n\t\\centering\n\t\\renewcommand{\\arraystretch}{1.2}\n\t\\setlength{\\tabcolsep}{4pt}\n\t\\begin{tabular}{lc|cccccc|cccccc}\n\t\t\\toprule\n\t\t\\multirow{2}{*}{\\textbf{Model}} & \\multirow{2}{*}{\\textbf{Think}} & \\multicolumn{6}{c|}{\\cellcolor{gray!20}\\textsc{\\textbf{CochraneForest}}} & \\multicolumn{6}{c}{\\cellcolor{gray!20}\\textbf{RCTs}~\\cite{yun2024automatically}} \\\\\n\t\t& & Acc & F1 & EM & EM@1 & MSE$\\downarrow$ & EIR$\\downarrow$ & Acc & F1 & EM & EM@1 & MSE$\\downarrow$ & EIR$\\downarrow$\\\\\n\t\t\\toprule\n\t\t\\multicolumn{2}{c}{\\cellcolor{gray!20}\\textbf{Pretrained LLMs}} & \\multicolumn{6}{c}{\\cellcolor{gray!20}} & \\multicolumn{6}{c}{\\cellcolor{gray!20}} \\\\\n\t\t\\texttt{GPT-4-0125} & \\xmark & 71.4 & 73.7 & 28.4 & 72.1 & 0.88 & 0.31 & 71.2 & 65.3 & 34.5 & 70.3 & 0.69 & 0.44\\\\\n\t\t\\texttt{Qwen2.5-7B} & \\xmark & 69.7 & 65.4 & 14.9 & 66.4 & 1.40 & 0.39 & 63.2 & 59.7 & 20.3 & 73.8 & 1.19 & 0.50 \\\\\n\t\t\\texttt{Qwen2.5-14B} & \\xmark & 76.2& 73.7 & 27.4 & 73.6 &0.72 & 0.30 & 69.8 & 66.4 & 28.6 & 75.8 & 0.91 & 0.44 \\\\\n\t\t\\texttt{Qwen2.5-72B} & \\xmark & 82.2 & 80.3 & 42.5 & 81.7 & 0.63 & 0.26 & \\textbf{75.3}& \\textbf{72.8}& \\textbf{35.1} &\\textbf{81.1} & \\textbf{0.68} & \\textbf{0.39}\\\\\n\t\t\\texttt{Llama-3.1-8B} & \\xmark & 68.7 & 66.6 & 15.9 & 62.2 & 2.16 & 0.33 & 62.7 & 54.7 & 14.8 & 66.5 & 1.30 & 0.47 \\\\\n\t\t\\texttt{Llama-3.1-70B} & \\xmark & 79.4 & 78.3 & 31.5 & 78.7 & 0.59 & 0.28 & 65.5 & 60.8 & 30.0 & 74.2 & 1.45 & 0.44\\\\\n\t\t\\texttt{Llama-3.1-405B} & \\xmark & \\textbf{82.4} &\\textbf{80.8} & \\textbf{44.3} & \\textbf{82.4} & \\textbf{0.43} & \\textbf{0.23} & 70.3 & 66.5 & 33.6 & 76.7 & 0.80 & 0.41 \\\\\n\t\t%\\texttt{DeepSeek-v3} & & & & & & & & & & & \\\\\n\t\t%\\texttt{DeepSeek-R1} & & & & & & & & & & & \\\\\n\t\t\\texttt{DeepSeek-Qwen-7B}& \\cmark & 59.7 & 54.3 & 11.2 & 58.3& 2.83 & 0.50& 53.8 & 45.7 & 9.4 & 53.8 & 3.68 & 0.61 \\\\\n\t\t\\texttt{DeepSeek-Qwen-14B} & \\cmark & 65.4 & 61.2 & 19.9 & 66.4 & 1.29 & 0.44 &60.2 & 55.1 & 11.2 & 60.3 & 2.41 & 0.58 \\\\\n\t\t\\texttt{DeepSeek-Qwen-32B} & \\cmark & 74.0 & 71.6 & 28.6 & 73.5 & 0.58 & 0.28 & 68.8 & 65.0 & 29.1 & 78.2 & 0.72 &0.43\\\\\n\t\t\\toprule\n\t\t\\multicolumn{2}{c}{\\cellcolor{gray!20}\\textbf{Our Models}} & \\multicolumn{6}{c}{\\cellcolor{gray!20}} & \\multicolumn{6}{c}{\\cellcolor{gray!20}} \\\\\n\t\t%\\texttt{Llama-3.1-8B-SFT} & & & & & & & & & & & \\\\\n\t\t\\texttt{Qwen2.5-7B-SFT} & \\cmark & 74.5 & 70.1 & 28.4 & 79.1 & 0.51 & 0.29& 71.5& 68.4 & 30.2 & 80.0& 0.64 & 0.36 \\\\\n\t\t\\texttt{Qwen2.5-7B-RL} & \\cmark & \\textbf{81.6} & \\textbf{80.1} & \\textbf{42.2} & \\textbf{81.4} & \\textbf{0.40} & \\textbf{0.24} & \\textbf{79.3} & \\textbf{76.4} & \\textbf{41.2} & \\textbf{89.7} & \\textbf{0.49} &\\textbf{0.28}\\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\t\\caption{Evaluation results across models on two datasets. We report Accuracy and F1 (label prediction), EM, EM@1, EIR and MSE (numerical extraction).}\n\t\\label{tab:results}\n\\end{table*}\n\n\\paragraph{Evaluation Metrics.} \n% \\noindent\\textbf{Evaluation Metrics.} \nWe report a range of metrics to assess both end-to-end performance and intermediate reasoning accuracy. For the end-to-end task of predicting the final conclusion label of each study, we report accuracy and F1 score. Following \\citet{yun2024automatically}, we also evaluate the quality of numerical extraction using exact match (EM), EM@1 (at least one positional match), as well as the mean squared error (MSE) of the computed point estimate derived from the extracted values. In addition, we measure the Error Impact Rate (EIR), defined as the ratio between \nthe number of extraction errors that lead to a flipped conclusion and the number of total extraction errors at the study level.\n%the number of extraction errors and the number of those errors that lead to a flipped conclusion:\n\\[\n\\text{EIR} = \\frac{\\# \\text{errors that flip the conclusion}}{\\# \\text{extraction errors}}\n\\]\nThis metric provides an insight into how often extraction mistakes materially affect downstream decision-making. A high EIR indicates that even a few extraction errors can substantially alter the final prediction, whereas a low EIR suggests that the model's conclusions are more robust to imperfect inputs. Thus, EIR serves as a proxy for understanding the correlation between intermediate numerical accuracy and end-to-end reliability.\n\n\n\n\\paragraph{Training Setup.} \n% \\noindent\\textbf{Training Setup.} \nWe conduct our training using the Qwen2.5 model family~\\cite{qwen2.5}, specifically the 7B variant. Two distinct training regimes are explored: SFT and RL. In the results section, models are labelled with subscripts corresponding to the respective training strategy.\n\nSFT is performed for 5 epochs with a batch size of 1 using a learning rate of $5 \\times 10^{-5}$ and the AdamW optimiser~\\cite{adamw}. For the RL setup, we adopt the GRPO algorithm~\\cite{grpo}, training for 3 epochs with a learning rate of $1 \\times 10^{-6}$, batch size 1, and 16 sampled generations per batch. %Both SFT and GRPO use a gradient accumulation of 8 steps. %Both training protocols leverage gradient accumulation with 8 accumulation steps. \n%All experiments are conducted using the Open-R1 framework~\\cite{openr1} on 8 NVIDIA A100 GPUs, each equipped with 80GB of memory. \nAdditional details are provided in Appendix~\\ref{app:hyper_and_apis}.\n\n\\paragraph{Model Baselines.}\n% \\noindent\\textbf{Model Baselines.} \nTo validate our results, we compare a range of open- and closed-source models,\nwith and without reasoning capabilities. All models are evaluated in zero-shot settings with prompt and hyperparameters shown in Appendix~\\ref{app:prompts}%, temperature of 0.7 and 2,048 tokens as maximum output length\n. \n\nWe include two main model families: Qwen 2.5~\\cite{qwen2.5} and Llama 3.1~\\cite{grattafiori2024llama}. In addition, we benchmark DeepSeek-R1 and the distilled Qwen models derived from it. We exclude distilled models afferent to the Llama family because of their limited context size. %As regards\nFor closed-source models, we use \\texttt{GPT-4-0125}.\n\nIn addition, we compare performances on the end-to-end task against the two best RAG baselines for this task: URCA~\\cite{pronesti2025}, which clusters retrieved passages based on their embedding vectors and filters relevant information from each cluster given the query; and GraphRAG~\\cite{graphrag}, which builds a graph-based text index by summarising closely related entities from the source documents.\n\n% Description of models \n\n\n\n\\subsection{Main Results} \n\\paragraph{Comparison with Pretrained Baselines.}\nTable~\\ref{tab:results} presents a performance comparison of pretrained and fine-tuned language models on the \\textsc{CochraneForest} and RCTs datasets. Models vary in size, architecture, and training approach, allowing us to examine the relationship between model scale, reasoning capability, and task-specific performance. Among the pretrained baselines, \\texttt{Llama-3.1-405B} achieves the best performance on \\textsc{CochraneForest}, closely followed by \\texttt{Qwen2.5-72B}, which performs competitively on both datasets, topping the RCTs benchmark on several metrics. \\texttt{Llama-3.1-70B} and \\texttt{Qwen2.5-14B} also rank highly, while \\texttt{Qwen2.5-7B} and \\texttt{Llama-3.1-8B} underperform relative to their larger variants. \n\nFine-tuning\nimproves performance significantly. \\texttt{Qwen2.5-7B-SFT} outperforms all pretrained models of similar or larger size. The \\texttt{Qwen2.5-7B-RL} model establishes a new state-of-the-art on both datasets, surpassing all baselines—including the 405B model—in nearly every metric on RCTs and closely matching it on \\textsc{CochraneForest}, despite being nearly 58x smaller. In addition, when compared directly to its pretrained counterpart, \\texttt{Qwen2.5-7B-RL} shows large gains in EM (+21.2), accuracy (+16.1), and F1 (+16.7) on the RCTs dataset, highlighting the effectiveness of RL.\n\nNotably, we observe that reasoning capabilities do not always lead to improved performance. In fact, all the distilled DeepSeek models with reasoning perform worse than their non-reasoning counterparts. These results suggest that general reasoning ability alone is insufficient for complex domain-specific tasks. Instead, explicit task supervision and structured reasoning training appear necessary to guide models in applying reasoning capabilities effectively.\n\n\\paragraph{Comparison with RAG baselines.} \n% \\noindent\\textbf{Comparison with RAG baselines.}\nTable~\\ref{tab:rag_number_baselines} compares the best-performing numbers-based models with two strong retrieval-augmented generation (RAG) baselines ---URCA and GraphRAG---on the \\textsc{CochraneForest} dataset. Both RAG methods underperform compared to direct numerical reasoning. The fine-tuned \\texttt{Qwen2.5-7B-RL} achieves an absolute gain of over 20 F1 points compared to the best RAG baseline, highlighting the limitations of retrieval in settings where precise numerical grounding and structured inference are required. Even the zero-shot numbers-based approach outperforms the RAG methods, suggesting that factual retrieval alone is insufficient without robust reasoning over structured content.\n\n\n\n\\begin{table}[ht]\n\\small\n\t\\centering\n \\renewcommand{\\arraystretch}{1}\n\t\\begin{tabular}{l|cc}\n\t\t\\toprule\n\t\t\\textbf{Method} & \\textbf{F1} & \\textbf{Acc} \\\\\n\t\t\\midrule\n\t\t\\rowcolor{gray!15}\n\t\t\\multicolumn{3}{l}{\\textbf{RAG baselines}} \\\\\n\t\tURCA~\\cite{pronesti2025} & 60.2 & 58.8 \\\\\n\t\tGraphRAG~\\cite{graphrag} & 59.6 & 57.5 \\\\\n\t\t\\midrule\n\t\t\\rowcolor{gray!15}\n\t\t\\multicolumn{3}{l}{\\textbf{Numerical baselines}} \\\\\n\t\tQwen2.5-7B (zero-shot) & 69.7 & 65.4 \\\\\n\t\tQwen2.5-7B-SFT & 74.5 & 70.1 \\\\\n\t\tQwen2.5-7B-RL & \\textbf{81.6} & \\textbf{80.1} \\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\t\\caption{Performance comparison between RAG-based and numbers-based approaches on \\textsc{CochraneForest} using \\texttt{Qwen2.5-7B-Instruct}.}\n\t\\label{tab:rag_number_baselines}\n\\end{table}\n\n\\subsection{Ablation Studies}\n\nTo assess the impact of different input modalities and training strategies, we conduct a series of ablation experiments (Table~\\ref{tab:ablations}) on the RL- and SFT-trained models. These include both data ablations and training ablations.\n\nIn the data ablations, we isolate the contribution of different components of the input. We evaluate model performance when provided with: (i) only textual context, where all tables are removed; (ii) only tables, where we exclude surrounding text and provide the model with structured numerical content; and (iii) only the top retrieved chunks obtained with URCA~\\cite{pronesti2025}, restricting access to 10 evidence passages per query.\n\n% In the training ablations, we examine the impact of supervision and reasoning structure. We compare the fully-trained model using Reinforcement Learning (RL) to: (i) a version trained with SFT only; (ii) an SFT-trained model with the Chain-of-Thought (CoT) step removed, trained to predict the final answer directly without intermediate reasoning; and (iii) a RL-trained model using an \\texttt{EX} reward.\n\nIn the training ablations, we analyse the effect of intermediate reasoning and reward design. To evaluate the role of reasoning traces, we compare the SFT model to a version trained without Chain-of-Thought (CoT), which predicts the final answer directly without intermediate steps. To assess the impact of the reward function, we compare the RL model to another version trained using the \\texttt{EX} reward, which only provides a positive signal when all predicted values are correct. \n\n\\[\n\\scalebox{0.9}{$\nR_{\\mathrm{EX}} = \\mathbbm{1}\\left\\{ \\bigwedge_{j=1}^n \\left( v_j \\approx \\hat{v}_j \\right) \\right\\}\n$}\n\\]\n\n\nWe observe that among the data ablations, the models trained only on tables achieve the best performance, confirming the importance of structured numerical evidence in this task. However, there is a consistent drop in performance across all settings when models only have access to a single input type compared to the full input (text + tables), indicating that both textual and tabular information are necessary for accurate reasoning. The \\texttt{URCA} setting, which corresponds to the output of a retrieval-based pipeline, yields the lowest scores. This aligns with the findings in Table~\\ref{tab:rag_number_baselines} and further highlights the limitations of existing retrieval-augmented generation approaches in this setting, primarily due to their inability to recover the precise numerical content required for grounded inference.\n\nIn the training ablations, removing CoT supervision during SFT (\\texttt{SFT-no-CoT}) results in lower performance compared to full SFT, showing that intermediate reasoning traces help guide the model’s learning process. When using RL, we find that the dense reward signal ($R_{\\mathrm{CR}}$) significantly outperforms the variant trained with the EX reward ($R_{\\mathrm{EX}}$). This confirms that sparse rewards are less effective at shaping model behaviour than denser, fine-grained signals. %that provide feedback at each reasoning step.\n\n% \\begin{table}[t]\n% \\small\n% \t\\centering\n% \t\\begin{tabular}{l|cc}\n% \t\t\\toprule\n% \t\t\\textbf{Model} & \\textbf{F1} & \\textbf{Acc} \\\\\n% \t\t\\midrule\n% \t\t\\rowcolor[gray]{0.9} \\multicolumn{3}{l}{\\textbf{Input Data Ablations}} \\\\\n% \t\t\\texttt{Qwen2.5-7B-SFT w text} & 62.3 & 60.1 \\\\\n% \t\t\\texttt{Qwen2.5-7B-SFT w tables} & 70.4 & 66.5 \\\\\n% \t\t\\texttt{Qwen2.5-7B-SFT w urca} & 56.7 & 54.0 \\\\\n% \t\t\\bottomrule\n% \t\t\\texttt{Qwen2.5-7B-RL w text} & 65.8 & 63.2 \\\\\n% \t\t\\texttt{Qwen2.5-7B-RL w tables} &\\textbf{ 73.1} & \\textbf{71.6 }\\\\\n% \t\t\\texttt{Qwen2.5-7B-RL w urca} & 59.2 & 56.8 \\\\\n% \t\t\\bottomrule\n\t\t\n% \t\t\\rowcolor[gray]{0.9} \\multicolumn{3}{l}\n% \t\t{\\textbf{Training Ablations}} \\\\\n% \t\t\\texttt{Qwen2.5-7B} & 69.7 & 65.4 \\\\\n% \t\t\\texttt{+ SFT-no-CoT} &71.2 & 68.3 \\\\\n% \t\t\\texttt{+ SFT-CoT} & 74.5 & 70.1 \\\\\n% \t\t\\texttt{+ RL} & \\textbf{81.6} & \\textbf{80.1} \\\\\n% \t\t\\texttt{+ RL-EX} & 79.7 & 78.8\\\\\n% \t\t\\bottomrule\n% \t\\end{tabular}\n% \t\\caption{Ablation study on model inputs and training supervision on \\textsc{CochraneForest}. In training ablation, the input contains both text and tables.}\n% \t\\label{tab:ablations}\n% \\end{table}\n\n\\begin{table}[t]\n\\small\n\t\\centering\n \\renewcommand{\\arraystretch}{0.96}\n\t\\begin{tabular}{ll|cc}\n\t\t\\toprule\n\t\t\\textbf{Model} & \\textbf{Input} & \\textbf{F1} & \\textbf{Acc} \\\\\n\t\t\\midrule\n\t\t\\rowcolor[gray]{0.9} \\multicolumn{4}{l}{\\textbf{Input Data Ablations}} \\\\\n\t\t\\multirow{3}{*}{\\texttt{Qwen2.5-7B-SFT}} & \\texttt{text} & 62.3 & 60.1 \\\\\n\t\t & \\texttt{tables} & 70.4 & 66.5 \\\\\n\t\t & \\texttt{urca} & 56.7 & 54.0 \\\\\n\t\t\\bottomrule\n\t\t\\multirow{3}{*}{\\texttt{Qwen2.5-7B-RL}} & \\texttt{text} & 65.8 & 63.2 \\\\\n\t\t & \\texttt{tables} &\\textbf{ 73.1} & \\textbf{71.6 }\\\\\n\t\t & \\texttt{urca} & 59.2 & 56.8 \\\\\n\t\t\\bottomrule\n\t\t\n\t\t\\rowcolor[gray]{0.9} \\multicolumn{4}{l}\n\t\t{\\textbf{Training Ablations}} \\\\\n\t\t\\texttt{Qwen2.5-7B} & \\texttt{text + tables} & 69.7 & 65.4 \\\\\n\t\t\\texttt{+ SFT-no-CoT} & \\texttt{text + tables} &71.2 & 68.3 \\\\\n\t\t% \\texttt{+ SFT-CoT}\n \\texttt{+ SFT}\n \\textbf{(ours)} & \\texttt{text + tables} & 74.5 & 70.1 \\\\\n %%% ADD DASHED LINE HERE\n \\texttt{+ RL-EX} & \\texttt{text + tables} & 79.7 & 78.8\\\\\n\t\t\\texttt{+ RL} \\textbf{(ours)} & \\texttt{text + tables} & \\textbf{81.6} & \\textbf{80.1} \\\\\n\t\t\n\t\t\\bottomrule\n\t\\end{tabular}\n\t\\caption{Ablation study on model inputs and training supervision on \\textsc{CochraneForest}. \\texttt{urca} refers to the top 10 chunks retrieved by its namesake RAG approach.}\n\t\\label{tab:ablations}\n\\end{table}\n\n\n\\subsection{Analyses}\n\\begin{table*}[h]\n\t \\small\n\t \\centering\n\t\\begin{tabular}{\n\t\t\tc >{\\raggedright\\arraybackslash}p{5.8cm}\n\t\t\t>{\\centering\\arraybackslash}m{1.2cm} >{\\centering\\arraybackslash}m{1.5cm}\n\t\t\t>{\\centering\\arraybackslash}m{1.2cm} >{\\centering\\arraybackslash}m{1.5cm}\n\t\t}\n\t\t\\toprule\n\t\t\\textbf{Thought Process} & \\textbf{Reasoning Label}\n\t\t& \\multicolumn{2}{c}{\\texttt{Qwen2.5-7B-RL}} \n\t\t& \\multicolumn{2}{c}{\\texttt{Qwen2.5-7B-SFT}} \\\\\n\t\t\\cmidrule(lr){3-4} \\cmidrule(lr){5-6}\n\t\t& & Count & Fraction & Count & Fraction \\\\\n\t\t\\midrule\n\t\t\\textbf{\\cmark} & Correct reasoning with traceability & 42 & 31.82\\% & 34 & 25.76\\% \\\\\n\t\t\\textbf{\\cmark} & Correct reasoning without traceability & 39 & 29.55\\% & 24 & 18.18\\% \\\\\n\t\t\\textbf{\\cmark} & Correct reasoning, incorrect number & 37 & 28.03\\% & 41 & 31.06\\% \\\\\n\t\t\\textbf{\\xmark} & Correct number, incorrect reasoning & 3 & 2.72\\% & 13 & 9.85\\% \\\\\n\t\t\\textbf{\\xmark} & Copy without reasoning & 6&4.55\\% & 0 & 0.00\\%\\\\\n\t\t\\textbf{\\xmark} & Hallucinated &5 &3.79\\% & 20 & 15.15\\% \\\\\n\t\t\\textbf{\\xmark} & Missing reasoning & 0 & 0.00\\% & 0 & 0.00\\% \\\\\n\t\t\\midrule\n\t\t& \\textbf{Total} & 132 & 100\\% & 132 & 100\\% \\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\t\\caption{Distribution of annotated reasoning labels for \\texttt{Qwen2.5-7B-RL} and and {\\texttt{Qwen2.5-7B-SFT}. \\textbf{\\cmark} indicates correct reasoning. \\textbf{\\xmark} indicates incorrect reasoning.}}\n\t\\label{tab:hum_eval_labels_models}\n\\end{table*}\n\n\\paragraph{Impact of the Thought Process.} We want to evaluate whether the reasoning processes generated by our trained models (RL and SFT) are logically sound, provide meaningful explanations for the extracted numbers, and enable traceability back to the input papers. To this end, we manually annotate the outputs of both models on 30 biomedical studies, for which human annotators had previously identified the sources of the correct numbers (see Appendix \\ref{app:annot}), \nassigning a single label to each reasoning process. Labels distinguish whether the reasoning correctly supports the individual number, whether it enables traceability to the source, and whether it hallucinates or lacks explanation. A complete list of labels and annotation criteria is provided in Appendix~\\ref{app:annot_thought}.\n\nResults (Table~\\ref{tab:hum_eval_labels_models}) show that the RL model produces a higher fraction of correct reasoning processes overall compared to the SFT model (61.37\\% vs 43.94\\%, summing the first two rows). This suggests that reinforcement learning improves the model’s ability to provide plausible and well-structured explanations for numerical outputs. Notably, the RL model achieves a substantially lower rate of hallucinated reasoning (3.79\\% vs 15.15\\%), highlighting a significant improvement in factual grounding. It also shows a reduced frequency of incorrect reasoning behind correct numbers (2.72\\% vs 9.85\\%), indicating better alignment between the generated explanations and the numerical outputs.\n\n% Interestingly, the RL model occasionally copies numbers from the input without performing reasoning (4.55\\%), a behavior not observed in the SFT model. Nevertheless, the overall proportion of flawed or missing reasoning processes is considerably lower in the RL model.\n\nThese findings suggest that reinforcement learning not only improves the factual accuracy of model outputs, but also enhances the traceability and reliability of the underlying reasoning process.\n% Tables~\\ref{tab:hum_eval_general} and \\ref{tab:hum_eval_labels}\n\n% \\begin{table*}[h]\n% \\small\n% \\centering\n% \\begin{tabular}{>{\\centering\\arraybackslash}m{3cm}\n% >{\\centering\\arraybackslash}m{4cm}\n% >{\\centering\\arraybackslash}m{2.5cm}\n% >{\\centering\\arraybackslash}m{3.5cm}}\n% \\toprule\n% \\textbf{Thought Process} & \\textbf{Fraction of Correct Numbers} & \\textbf{Count} & \\textbf{Agreement with Annotation} \\\\\n% \\midrule\n% \\textbf{\\cmark} & & & \\\\\n% \\textbf{\\xmark} & & & --- \\\\\n% \\bottomrule\n% \\end{tabular}\n% \\caption{Effect of Thought Process Correctness on Number Extraction Accuracy}\\label{tab:hum_eval_general}\n% \\end{table*}\n\n% \\begin{table*}[h]\n% \t\\small\n% \t\\centering\n% \t\\begin{tabular}{\n% \t\t\tc >{\\raggedright\\arraybackslash}p{6cm}\n% \t\t\t>{\\centering\\arraybackslash}m{1.2cm} >{\\centering\\arraybackslash}m{1.5cm}\n% \t\t\t>{\\centering\\arraybackslash}m{1.2cm} >{\\centering\\arraybackslash}m{1.5cm}\n% \t\t}\n% \t\t\\toprule\n% \t\t\\textbf{Thought Process} & \\textbf{Reasoning Label}\n% \t\t& \\multicolumn{2}{c}{\\texttt{Qwen2.5-7B-RL}} \n% \t\t& \\multicolumn{2}{c}{\\texttt{Qwen2.5-7B-SFT}} \\\\\n% \t\t\\cmidrule(lr){3-4} \\cmidrule(lr){5-6}\n% \t\t& & Count & Fraction & Count & Fraction \\\\\n% \t\t\\midrule\n% \t\t\\textbf{\\cmark} & Correct reasoning with traceability & 42 & 31.82\\% & 34 & 25.76\\% \\\\\n% \t\t\\textbf{\\cmark} & Correct reasoning without traceability & 39 & 29.55\\% & 24 & 18.18\\% \\\\\n% \t\t\\textbf{\\cmark} & Correct reasoning, incorrect number & 37 & 28.03\\% & 41 & 31.06\\% \\\\\n% \t\t\\textbf{\\xmark} & Correct number, incorrect reasoning & 3 & 2.72\\% & 13 & 9.85\\% \\\\\n% \t\t\\textbf{\\xmark} & Copy without reasoning & 6&4.55\\% & 0 & 0\\%\\\\\n% \t\t\\textbf{\\xmark} & Hallucinated &5 &3.79\\% & 20 & 15.15\\% \\\\\n% \t\t\\textbf{\\xmark} & Missing reasoning & 0 & 0\\% & 0 & 0\\% \\\\\n% \t\t\\midrule\n% \t\t& \\textbf{Total} & 132 & 100\\% & 132 & 100\\% \\\\\n% \t\t\\bottomrule\n% \t\\end{tabular}\n% \t\\caption{Distribution of annotated reasoning labels for \\texttt{Qwen2.5-7B-RL} and and {\\texttt{Qwen2.5-7B-SFT}. \\textbf{\\cmark} indicates correct reasoning. \\textbf{\\xmark} indicates incorrect reasoning.}}\n% \t\\label{tab:hum_eval_labels_models}\n% \\end{table*}\n\n\n\n% \\begin{table*}[h]\n% \\small\n% \\centering\n% \\begin{tabular}{c>{\\raggedright\\arraybackslash}p{6cm} \n% >{\\centering\\arraybackslash}m{2cm} \n% >{\\centering\\arraybackslash}m{3cm}}\n% \\toprule\n% \\textbf{Thought Process} & \\textbf{Reasoning Label} & \\textbf{Count} & \\textbf{Fraction} \\\\\n% \\midrule\n% \\textbf{\\cmark} & Correct reasoning with traceability & & \\\\\n% \\textbf{\\cmark} & Correct reasoning without traceability & & \\\\\n% \\textbf{\\cmark} & Correct reasoning, incorrect number & & \\\\\n% \\textbf{\\xmark} & Correct number, incorrect reasoning & & \\\\\n% \\textbf{\\xmark} & Copy without reasoning & & \\\\\n% \\textbf{\\xmark} & Hallucinated & & \\\\\n% \\textbf{\\xmark} & Missing reasoning & 0 & 0\\% \\\\\n% \\midrule\n% & \\textbf{Total} & & 100\\% \\\\\n% \\bottomrule\n% \\end{tabular}\n% \\caption{Distribution of annotated reasoning labels. Each entry corresponds to one of six mutually exclusive categories, with a \\textbf{\\cmark} indicating correct reasoning process and \\textbf{\\xmark} indicating incorrect reasoning process.}\n% \\label{tab:hum_eval_labels}\n% \\end{table*}\n\n\\paragraph{Qualitative example.}\n% \\noindent\\textbf{Qualitative Example.}\nWe present a qualitative example from \\textsc{CochraneForest} (Figure~\\ref{fig:qualitative_example}, in appendix), comparing the reasoning and outputs of \\texttt{Qwen2.5-7B-SFT} and \\texttt{Qwen2.5-7B-RL} on a challenging case where the exact values for the comparator and outcome are not explicitly reported in the biomedical study. While the SFT model fails to extract the relevant information, the RL model demonstrates stronger reasoning capabilities: it identifies population percentages from one of the study’s tables and correctly maps them to the total number of participants previously extracted, thereby inferring the desired values. Notably, even our human annotators initially struggled to locate the correct numbers in this scenario.\n\n\\section{Related work}\n\n\\paragraph{Biomedical Information Extraction.}\n% \\noindent\\textbf{Biomedical Information Extraction.}\nRecent efforts in biomedical information extraction have made significant advances. \\citet{wadhwa2023jointlyextractinginterventionsoutcomes} introduced a generative approach for extracting interventions, comparing and outcomes from RCTs. While effective, we extend this by focusing on numerical evidence and reasoning.\n\n\\citet{lehman-etal-2019-inferring} demonstrated that accurately extracting evidence is the primary bottleneck for determining treatment efficacy. \\citet{odoherty-etal-2024-beyond} showed using abstracts alone rather than structured PICO data can worsen synthesis quality, underscoring the importance of data representation. Our model processes the full content of the paper, providing a more comprehensive basis.\n\n\\paragraph{LLMs for Evidence Synthesis and Numerical Reasoning.}\n% \\noindent\\textbf{LLMs for Evidence Synthesis and Numerical Reasoning.}\nWhile LLMs have shown promise in biomedical text mining, they face challenges synthesising complex evidence and numerical reasoning. \\citet{shaib-etal-2023-summarizing} found that GPT-3 struggled with multi-document synthesis and biased effect reporting. \\citet{nye-etal-2020-trialstreamer} introduced a framework to map evidence and infer conclusions. \\citet{yun2024automatically} assessed how effectively LLMs can extract numerical data, noting challenges with continuous outcomes and distinguishing similar measures. %Our models address these through their ability to discriminate and associate numerical values with correct outcomes.\n\n% \\paragraph{Reinforcement Learning for Numerical Evidence Extraction.}\n% % \\noindent\\textbf{Reinforcement Learning for Numerical Evidence Extraction.}\n% Previous work has proven that SFT can be used to improve models such as BERT \\cite{devlin2019bertpretrainingdeepbidirectional} for biomedical text mining \\cite{Lee_2019, XIE2022109460}.\n% RL has improved model alignment for complex reasoning objectives \\citep{weifinetuned, guo2025deepseek}. %We apply these techniques for the first time to the specific task of extracting numerical evidence in systematic reviews for more robust inference, of ambiguous text and multiple outcomes across reports.\n% We apply these techniques for the first time to extract numerical evidence from systematic reviews.%, enabling more robust inference.\n\n% \\vspace{-0.2cm}\n\n\\section{Conclusion}\nIn this paper we presented a quantitative reasoning framework for automating evidence extraction in systematic reviews, shifting away from shallow textual inference toward structured numeric understanding. By directly modelling the process experts follow,\n%—identifying structured numerical evidence and applying logical rules grounded in domain knowledge—\nwe move closer to automating a key step in evidence-based medicine. Our proposed system, combining SFT and RL numeric extraction models with reasoning over effect estimates, significantly outperforms retrieval-based baselines and even large-scale language models on both the \\textsc{CochraneForest} and the RCTs benchmarks. These results affirm the value of reasoning-driven supervision for complex scientific tasks and point to RL as a promising avenue for developing reliable and domain-aligned extraction systems in evidence-based medicine. \n\n\\section{Limitations}\nWhile this work makes meaningful contributions toward reasoning-based evidence extraction, several limitations remain. First, the system assumes that all relevant numerical evidence is explicitly reported and cleanly extractable, which is often not the case in real-world clinical studies. Missing values, indirect reporting, and inconsistent formats can hinder reliable extraction. At present, the model does not attempt to identify or flag missing information, which limits its utility in incomplete or noisy settings. Training models to detect and explicitly report missing or uncertain values is a critical direction for future development. Additionally, our approach currently focuses on a limited set of outcome types and uses predefined logical rules, which may constrain generalisation to more complex or diverse clinical scenarios. Addressing these limitations will be key to deploying robust, scalable systems for real-world systematic review automation.\n\n\n\n% Bibliography entries for the entire Anthology, followed by custom entries\n%\\bibliography{anthology,custom}\n% Custom bibliography entries only\n\\bibliography{custom}\n\n\\appendix\n\n%\\section{Forest Plots and Effect Size Interpretation}\n\\section{Forest Plots and Effect Size Estimation}\n\\label{sec:forest_plots_appendix}\n\nForest plots are a standard way to visualise the results of individual studies and their synthesis in a meta-analysis. Each row typically represents one study, showing its estimated treatment effect and the corresponding 95\\% confidence interval (CI). The point estimate is usually plotted as a square (with size proportional to the study’s weight), and the CI as a horizontal line. The vertical line represents the line of no effect: 1 for ratios (e.g., risk ratio or odds ratio), and 0 for differences (e.g., mean difference).\n\nAt the bottom of the plot, a diamond often represents the pooled effect estimate from all included studies. This visualisation helps readers assess not only the overall direction and magnitude of the effect but also the consistency (heterogeneity) across studies.\n\nBelow we illustrate how to interpret and compute effect estimates and confidence intervals for the two common outcome types: binary outcomes with risk ratios (Figure~\\ref{fig:fp_bin}), and continuous outcomes with mean differences (Figure~\\ref{fig:fp_cont}).\n\n\\begin{figure}[h]\n\\includegraphics[width=\\columnwidth]{latex/assets/fp_bin.png}\n\\caption{A forest plot assessing clinical remission (binary outcome) in patients affected by medically refractory Crohn’s disease treated with stem cells versus control.}\n\\label{fig:fp_bin}\n\\end{figure}\n\n\\begin{figure}[h]\n\\includegraphics[width=\\columnwidth]{latex/assets/fp_cont.png}\n\\caption{A forest plot comparing GnRH agonist versus no agonist (placebo) in women with endometriosis, assessing a continuous outcome (average number of oocytes per woman).}\n\\label{fig:fp_cont}\n\\end{figure}\n\n\n\n\\paragraph{Binary outcomes and risk ratios.}\nBinary outcomes refer to variables with two possible states, such as event/no event. A commonly used effect measure in this context is the \\emph{risk ratio} (RR), which compares the proportion of events in the intervention group to that in the comparator group:\n\n\\[\n\\text{RR} = \\frac{a/n_1}{c/n_2}\n\\]\n\nwhere $a$ and $n_1$ are the number of events and total participants in the intervention group, and $c$ and $n_2$ are those in the comparator group.\n\n\\paragraph{Example.} In the \\textbf{Hawkey 2015} study (Figure~\\ref{fig:fp_bin}):\n\n\\begin{itemize}\n\t\\item Intervention (stem cells): 8 events out of 23 participants\n\t\\item Comparator (placebo): 2 events out of 22 participants\n\\end{itemize}\n\nThe point estimate is:\n\n\\[\n\\text{RR} = \\frac{8/23}{2/22} \\approx \\frac{0.3478}{0.0909} \\approx 3.83\n\\]\n\nTo compute the 95\\% confidence interval, we use the standard error of \n$\\log$:~%(\\text{RR})$:\n\n\\[\n\\text{SE} = \\sqrt{\\frac{1}{8} - \\frac{1}{23} + \\frac{1}{2} - \\frac{1}{22}} \\approx \\sqrt{0.536} \\approx 0.732\n\\]\n\nThe CI on the log scale is:\n\n\\[\n\\log(3.83) \\pm 1.96 \\cdot 0.732 \\approx (-0.091, 2.777)\n\\]\n\nExponentiating the bounds gives the 95\\% CI for the RR:\n\n\\[\n\\text{95\\% CI} \\approx (e^{-0.091}, e^{2.777}) \\approx (0.91, 16.07)\n\\]\n\n\n\\paragraph{Continuous outcomes and mean differences.}\nContinuous outcomes are measured on a numerical scale, such as a score or a lab value. The typical effect measure is the \\emph{mean difference} (MD), which is the arithmetic difference in average outcome values between groups:\n\n\\[\n\\text{MD} = \\bar{x}_1 - \\bar{x}_2\n\\]\n\nwhere $\\bar{x}_1$ and $\\bar{x}_2$ are the group means.\n\n\\paragraph{Example.} In the \\textbf{Dicker 1992} study (Figure~\\ref{fig:fp_cont}):\n\n\\begin{itemize}\n\t\\item Intervention (GnRH agonist): mean = 5.22, SD = 2.22, $n=48$\n\t\\item Comparator (control): mean = 3.08, SD = 1.81, $n=51$\n\\end{itemize}\n\nThe point estimate is:\n\n\\[\n\\text{MD} = 5.22 - 3.08 = 2.14\n\\]\n\nThe standard error is computed as:\n\n\\[\n\\text{SE} = \\sqrt{\\frac{2.22^2}{48} + \\frac{1.81^2}{51}} \\approx 0.408\n\\]\n\nThen, the 95\\% confidence interval is:\n\n\\[\n2.14 \\pm 1.96 \\cdot 0.408 \\approx 2.14 \\pm 0.80 = (1.34, 2.94)\n\\]\n \n\n\\section{Prompts}\\label{app:prompts} The prompts used for synthetic data annotation and for training are shown in Figure~\\ref{fig:sys_prompt} and~\\ref{fig:train_inf_prompt}, respectively. For training, a temperature of 0.7 and 2,048 tokens as maximum output length are used.\n\n\n\n\n\\section{Manual Annotations Identifying Number Sources}\\label{app:annot}\n\n\\paragraph{Annotation Instances.}\nTwo master's degree students in NLP and co-authors of this work annotated %thirty \n30 \nstudies paired with outcomes from the \\textsc{CochraneForest} dataset. These studies were randomly sampled from the whole dataset, and each student annotated %fifteen \nfifteen studies with an overlap of five studies for cross-comparison. For each study, the annotators were instructed to locate and mark all textual or tabular spans that contained the numerical evidence supporting the reported outcomes. In the case of binary outcomes, this involved identifying four values: the event count and total group size for both the intervention and comparator arms. For continuous outcomes, six values were annotated: the mean, standard deviation, and group size for each group. Each annotated number was linked to one or more corresponding spans from the original study, enabling fine-grained traceability.\nAn example annotation instance is shown in Figure~\\ref{fig:annot_span}.\n\n\n\\paragraph{Inter-annotator Agreement.}\nTo assess annotation consistency, we computed inter-annotator agreement (IAA) on the five studies annotated by both annotators. Agreement was evaluated at the span level: a match was counted when both annotators identified overlapping text spans referring to the same numerical value (e.g., intervention group size or mean outcome). We report a span-level F1 score of 0.70, indicating substantial agreement. Disagreements were primarily due to differences in span boundaries or the inclusion of surrounding contextual phrases.\n\n\n\\begin{figure}\n \\small\n \\begin{tcolorbox}[\n colback=gray!10, \n colframe=black, % gray!120, \n boxrule=0.5mm,\n title={Prompt for synthetic data annotation}, \n fonttitle=\\bfseries % Bold font for the title\n ]\n %% content\n \\{study\\_content\\}\n \\\\\n \\\\\n The above is a study from a medical systematic review.\n Your task is to produce a reasoning to explain how to extract the relevant numbers for the following intervention, comparator and outcome:\n \\\\\n \\\\\n intervention: \\{intervention\\}\\\\\n comparator: \\{comparator\\}\\\\\n outcome: \\{outcome\\} \\\\\n \\\\\n The expected output is:\n \\\\\n \\\\\n \\{target\\_value\\}\n \\\\\n \\\\\n You need to first explain how to infer the outcome type.\n Then, which numbers to extract and why. Finally, point to where these numbers appear in the study.\n \\end{tcolorbox}\n \\caption{Prompt for the synthetic data annotation with reasoning traces.}\n \\label{fig:sys_prompt}\n\\end{figure}\n\n\n\\begin{figure}\n\t\\small\n\t\\begin{tcolorbox}[\n\t\tcolback=gray!10, \n\t\tcolframe=black, % gray!120, \n\t\tboxrule=0.5mm,\n\t\ttitle={Prompt for training and inference}, \n\t\tfonttitle=\\bfseries % Bold font for the title\n\t\t]\n\t\tArticles: \\{articles\\}\n\t\t\\\\\n \\\\\n\t\tQuestion: Based on the given trial articles, what is the outcome type and corresponding numerical data for the following Comparison and Outcome?\n\t\t\\\\\n\t\t\\\\\n\t\tComparison: \\{comparison\\}\\\\\n\t\tOutcome: \\{outcome\\}\n\t\t\\\\\n\t\t\\\\\n\t\tFirst, determine and output the outcome\\_type as either: binary or continuous\n\t\t\\\\\n\t\t\\\\\n\t\tThen, provide the extracted data in format as follows:\n\t\t\\\\\n\t\tIf the outcome is binary, use this format:\n\t\t\\\\\n\t\t\\\\\n\t\toutcome\\_type: binary\n\t\t\\\\intervention:\n\t\t\\\\events: NUMBER total: NUMBER\n\t\t\\\\comparator:\n\t\t\\\\events: NUMBER total: NUMBER\n\t\t\\\\\n\t\t\\\\\n\t\tIf the outcome is continuous, use this format:\n\t\t\\\\\n\t\t\\\\\n\t\toutcome\\_type: continuous\n\t\t\\\\intervention:\n\t\t\\\\mean: NUMBER standard\\_deviation: NUMBER group\\_size: NUMBER\n\t\t\\\\\n\t\tcomparator:\n\t\t\\\\mean: NUMBER standard\\_deviation: NUMBER group\\_size: NUMBER\n\t\t\\\\\n\t\t\\\\Use post-intervention data when both pre and post are available. If multiple timepoints are reported, choose the one closest to the timepoint of interest, or the latest available.\n\t\tThink about it step by step.\n\t\\end{tcolorbox}\n\t\\caption{Prompt for training and inference.}\n\t\\label{fig:train_inf_prompt}\n\\end{figure}\n\n\\section{Manual Annotations for Extraction Model Outputs}\\label{app:annot_thought}\nAfter some pilot studies, we use the following labels to categorize a model's thinking process:\n%\\paragraph{Thought process annotation labels.}\n\\begin{enumerate}\n \\item \\textit{Correct reasoning with traceability}, the reasoning correctly supports the generated numbers and provides sufficient clues to identify their location in the input papers;\n \\item \\textit{Correct reasoning without traceability}, the reasoning correctly supports the generated numbers, but the source of the number cannot be located based on the explanation;\n \\item \\textit{Correct reasoning, incorrect number}, the extracted number is incorrect, but the reasoning is sensible and supports the outcome. \n \\item \\textit{Correct number, incorrect reasoning}, the extracted number is correct, but the reasoning process is incorrect, misleading, or does not support the outcome;\n \\item \\textit{Copy without reasoning}, the numbers are copied from the input papers with no understanding or reasoning steps leading to the final answer;\n \\item \\textit{Hallucinated}, the reasoning process refers to data, methods, or results not present in the input papers;\n \\item \\textit{Missing reasoning}, no reasoning process is generated.\n\\end{enumerate}\n\n\\section{Hyperparameters and APIs}\\label{app:hyper_and_apis}\nWe executed all the experiments either via API or\non our own cluster. We used the paid-for OpenAI API to access GPT-3.5-turbo and GPT-4. On the\nother hand, we hosted and trained the open-source models used in this paper on a distributed cluster.\n\nSFT is performed for 5 epochs with a batch size of 1 (due to the %high\nlarge \nsize of the input data) using a learning rate of $5 \\times 10^{-5}$ and the AdamW optimiser~\\cite{adamw}. For the RL setup, we adopt the GRPO algorithm~\\cite{grpo}, training for 3 epochs with a learning rate of $1 \\times 10^{-6}$, batch size 1, and 16 sampled generations per batch. Both training protocols leverage gradient accumulation with 8 accumulation steps. All experiments are conducted using the Open-R1 framework~\\cite{openr1} on 8 NVIDIA A100 GPUs, each equipped with 80GB of memory. Models have been served for inference with the vllm framework~\\cite{vllm}.\n\nThe weights of the final reward model have been set to maximise the importance of the correctness reward (0.8) while still ensuring the model follows the YAML format reward (0.1) and the already learnt thought format reward present in the base model (0.1). A tuning of such weights revealed that assigning equal weights to the 3 components leads to a degradation of the performances. \n\n\\begin{figure*}[h!]\n \\centering\n %\\vspace{2cm}\n\\includegraphics[width=1\\textwidth]{latex/assets/annotation_spans2.png}\n\\caption{Example of multi-span numerical data annotations for a given study}\n\\label{fig:annot_span}\n\\end{figure*}\n\n\\begin{figure*}%[h!]\n\\centering\n\\includegraphics[width=0.98\\textwidth]{latex/assets/sft_vs_rl_and_question_v2.drawio.png}\n\\caption{Example of a complex example from \\textsc{CochraneForest}. The SFT model fails at inferring the correct numerical information, and its thought mismatches its structured output. On the other hand, the model trained with GRPO correctly infers the number of events, reasoning on the previously extracted total number of participants. \\textbf{Interestingly, even our human annotators initially struggled to extract the right numbers}.}\n\\label{fig:qualitative_example}\n\\end{figure*}\n\n\\section{Scientific artefacts and licensing}\nIn this work, we used the following scientific artefacts. LLaMa 3.1 is licensed under a commercial license\\footnote{\\url{https://llama.meta.com/doc/overview}}. GPT-4 is licensed under a commercial license\\footnote{\\url{https://openai.com/policies/terms-of-use}}. Qwen2.5 is licensed under the Apache 2.0 license\\footnote{\\url{https://qwenlm.github.io/blog/qwen3}}. DeepSeek models are licensed under the MIT license\\footnote{\\url{https://api-docs.deepseek.com/news/news250120}}. Mining text and data from the Cochrane library is permitted for non-commercial research through the Wiley API.\\footnote{\\url{https://www.cochranelibrary.com/help/access}}. The usage of the listed artefacts is consistent with their licenses.\n\n \\begin{table*}[h!]\n\t \\centering\n\t \\begin{tabular}{l|>{\\raggedright\\arraybackslash}p{0.7\\textwidth}}\n\t\t \\toprule\n\t\t \\rowcolor[gray]{0.9} \\multicolumn{2}{c}{\\textbf{Hawkey 2015}} \\\\\n\t\t % \\multicolumn{2}{c}{\\textbf{TODO Study name}} \\\\\n\t\t \\midrule\n\t\t \\textbf{Text} & Importance Case reports and series suggest hematopoietic stem cell transplantation (HSCT) may benefit some patients with Crohn disease.Objective To evaluate the effect of autologous HSCT on refractory Crohn disease. ... \\\\ \n\t\t \\midrule\n\t\t \\textbf{Outcome} & Clinical remission \\\\\n \\textbf{Comparison} & Stem cells versus Control\\\\\n\t\t \\textbf{Outcome type} & Binary \\\\\n\t\t \\textbf{Intervention events} & 8\\\\\n\t\t \\textbf{Intervertion total} & 23 \\\\\n\t\t \\textbf{Comparator events} & 2 \\\\\n\t\t \\textbf{Comparator total} & 22 \\\\\n\t\t \\textbf{Point estimate} & 3.83 \\\\\n\t\t \\textbf{95\\% CI} & [0.91, 16.07]\\\\ \n\t\t \\midrule\n \\textbf{Target value} & outcome\\_type: binary\\newline\n\t\t intervention:\\newline\n\t\t events: 8 total: 23\\newline\n\t\t comparator:\\newline\n\t\t events: 2 total: 22\\\\\n \\midrule\n \\textbf{Thought} & To extract the relevant numbers, we first infer the outcome type as \"binary\" because the outcome \"Clinical remission\" is a yes/no or present/absent type of outcome. \n\t\t \n\t\t The relevant numbers for this outcome are found in Table 2: \"Final Assessments\", under the outcome \"CDAI <150 last 3 mo\". \n Specifically, for the intervention group (Stem cells), we have 8 events (patients in clinical remission) out of 23 total patients (34.8\\%). For the comparator group (Control), we have 2 events out of 22 total patients (9.1\\%). \\\\\n\t\t \\bottomrule\n\t\t \\end{tabular}\n\t \\caption{The corresponding entry in \\textsc{CochraneForestExt} for Hawekey 2015 in Figure~\\ref{fig:fp_bin}. Thought and target value are contatenated for SFT as follows: \\texttt{<think>\\{thought\\}</think>\\{target\\_value\\}}.}\n\t \\label{tab:data_example}\n\t \\end{table*}\n\\end{document}\n"},"question":{"kind":"string","value":"Consider EM scores only, which pre-trained model performs best on COCHRANEFOREST? Then, using the EM-based ranking positions from both datasets, which pre-trained model performs best under the averaged ranking?"},"answer":{"kind":"string","value":"Llama-3.1-405B, Qwen2.5-72B"},"plan":{"kind":"string","value":"SELECT all Pretrained LLMs\nSELECT EM score under COCHRANEFOREST for Pretrained LLMs\nCOMPUTE model1 = argmax over EM scores\n\n\nSELECT all EM scores for COCHRANEFOREST and RCTs for Pretrained LLMs\nLOOP for each pre-trained model\n COMPUTE rank_c = rank by EM under COCHRANEFOREST\n COMPUTE rank_r = rank by EM under RCTs\n COMPUTE average_rank = (rank_C + rank_R) / 2\nCOMPUTE model2 = argmin over average_rank \nRETURN model1, model2\n"}}},{"rowIdx":81,"cells":{"paper":{"kind":"string","value":"2505.23745"},"relevant_tables":{"kind":"list like","value":[["\\begin{table}[t!]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n","\\begin{tabular}{l@{~~~~~}| c cccc cccc cccc cccc}\n","\\hline\n"," \\multicolumn{1}{c|}{} &\\multicolumn{4}{c|}{Flower102 } & \\multicolumn{4}{c|}{DTD } & \\multicolumn{4}{c|}{Aircraft} &\\multicolumn{4}{c}{Pets} \n"," \\\\\n"," \\multicolumn{1}{c|}{{}} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c}{ACC$\\uparrow$}\\\\\n","\\hline\n","\n","\n","\n"," \\multicolumn{1}{c|}{MaxLogit}&167.14 & 74.92 & 81.61 & \\multicolumn{1}{c|}{67.36} & 395.13 &69.63 & 85.23 & \\multicolumn{1}{c|}{44.39} & 731.25& 55.73 &93.77 & \\multicolumn{1}{c|}{23.85} & 48.75& 75.37 &68.59 & \\multicolumn{1}{c}{88.23} \\\\\n","\n"," \\multicolumn{1}{c|}{Energy}&194.67 &69.00 & 91.93 & \\multicolumn{1}{c|}{67.36} & 433.25 & 64.82 & 90.01 & \\multicolumn{1}{c|}{44.39} & 780.11& 45.48 &97.71 & \\multicolumn{1}{c|}{23.85} & 56.23& 71.90 &70.67 & \\multicolumn{1}{c}{88.23} \\\\\n"," \n"," \\multicolumn{1}{c|}{Entropy}&117.27 &84.88 &63.48 & \\multicolumn{1}{c|}{67.36} & 319.02 &79.03 & 77.05 & \\multicolumn{1}{c|}{44.39} & 575.71& 73.12 & 83.96 & \\multicolumn{1}{c|}{23.85} & 21.91& 89.31 &53.35 & \\multicolumn{1}{c}{88.23} \\\\\n"," \n"," \n"," \\multicolumn{1}{c|}{MCM}& 153.63& 78.22 & 71.02 & \\multicolumn{1}{c|}{67.36} & 333.43 &78.16 & 78.45 & \\multicolumn{1}{c|}{44.39} & 583.28& 72.58 &81.65 & \\multicolumn{1}{c|}{23.85} & 44.95& 77.03 &64.90 & \\multicolumn{1}{c}{88.23} \\\\\n","\n","\n"," \n"," \\multicolumn{1}{c|}{DOCTOR}&112.82 &85.82 &62.48 & \\multicolumn{1}{c|}{67.36} & 314.37& 79.66& 76.51 & \\multicolumn{1}{c|}{44.39} & 575.53& 73.05 &83.84 & \\multicolumn{1}{c|}{23.85} & 21.08 &89.92 &55.20 & \\multicolumn{1}{c}{88.23} \\\\\n"," \n"," \\multicolumn{1}{c|}{MSP}& 112.27&85.91 &63.98 & \\multicolumn{1}{c|}{67.36} &313.43 &79.81 &77.36 & \\multicolumn{1}{c|}{44.39} & 576.97&72.62 & 85.61 & \\multicolumn{1}{c|}{23.85} & 21.04& 89.94 &52.19 & \\multicolumn{1}{c}{88.23} \\\\\n"," \n","\\rowcolor{gray!30}\n"," \\multicolumn{1}{c|}{TrustVLM-C}&101.42 & 88.69&54.91 & \\multicolumn{1}{c|}{67.36} & 302.18 & 82.52 & 67.27 & \\multicolumn{1}{c|}{44.39} & 563.77& 75.20 &{81.51} & \\multicolumn{1}{c|}{23.85} & 20.93 & 89.89 &51.73 & \\multicolumn{1}{c}{88.23} \\\\\n"," \n"," \\rowcolor{gray!30}\n"," \\multicolumn{1}{c|}{TrustVLM-M}&103.68 & 88.29 & 53.42 & \\multicolumn{1}{c|}{67.36} & 298.17 &83.16 &65.50 & \\multicolumn{1}{c|}{44.39} &574.57 &73.22 & 84.31& \\multicolumn{1}{c|}{23.85} & {20.41} & \\textbf{90.38} &\\textbf{48.27} & \\multicolumn{1}{c}{{88.23}} \\\\\n"," \n"," \\rowcolor{gray!30}\n"," \\multicolumn{1}{c|}{TrustVLM-D}& 77.30 &{95.05} & 30.06 & \\multicolumn{1}{c|}{67.36} & 268.71 &\\textbf{88.55} &\\textbf{44.10} & \\multicolumn{1}{c|}{44.39} & {562.02} & {75.62} & 83.21 & \\multicolumn{1}{c|}{23.85} & 20.69& 90.05 &50.81 & \\multicolumn{1}{c}{88.23} \\\\\n","\n","\n","\n"," \\rowcolor{gray!30}\n"," \\multicolumn{1}{c|}{TrustVLM*-D}&0.52 &95.96 &13.04 &\\multicolumn{1}{c|}{\\textbf{99.07}} & 124.15 &78.39 &72.14 &\\multicolumn{1}{c|}{71.57} &554.12 &75.36 &83.05 &\\multicolumn{1}{c|}{24.60} &\\textbf{20.05} &90.30 &49.07 &\\textbf{88.28}\\\\ % v3 \n","\n","\n","\n"," \n","\n","\n"," \\rowcolor{gray!30} \\multicolumn{1}{c|}{TrustVLM*(F)-D}& \\textbf{0.41} &\\textbf{98.26} & \\textbf{7.69} & \\multicolumn{1}{c|}{98.42} & \\textbf{96.45} & 80.30 & 70.96& \\multicolumn{1}{c|}{\\textbf{74.76}} & \\textbf{544.40}& \\textbf{76.85} & \\textbf{78.63}& \\multicolumn{1}{c|}{\\textbf{24.90}} & \\textbf{20.05} & 90.30 & 49.07 & \\multicolumn{1}{c}{\\textbf{88.28}} \\\\ % v3\n","\n"," \\hline\n","\n","\n"," \\multicolumn{1}{c|}{{} } &\\multicolumn{4}{c|}{Caltech101 } & \\multicolumn{4}{c|}{Cars} & \\multicolumn{4}{c|}{EuroSAT} &\\multicolumn{4}{c}{UCF101} \n"," \\\\\n"," \\multicolumn{1}{c|}{{}} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c}{ACC$\\uparrow$}\\\\\n","\\hline\n","\n","\n","\n"," \n"," \\multicolumn{1}{c|}{MaxLogit}&53.72 &52.43 &94.55 & \\multicolumn{1}{c|}{93.31} &259.81 & 61.94 &89.58 & \\multicolumn{1}{c|}{65.61} & 522.56& 59.42 &87.79 & \\multicolumn{1}{c|}{42.10} & 258.47 & 61.85 & 91.41 & \\multicolumn{1}{c}{65.21} \\\\\n"," \n"," \\multicolumn{1}{c|}{Energy}&59.26 &48.81 &96.36 & \\multicolumn{1}{c|}{93.31} &304.90 & 54.85& 93.89& \\multicolumn{1}{c|}{65.61} & 568.57& 53.44 &88.30 & \\multicolumn{1}{c|}{42.10} & 299.84 & 55.38 & 94.83 & \\multicolumn{1}{c}{65.21} \\\\\n"," \n"," \\multicolumn{1}{c|}{Entropy}& 15.79&82.47 &79.39 & \\multicolumn{1}{c|}{93.31} & 145.61 & 79.83&75.76 & \\multicolumn{1}{c|}{65.61} &389.07 &72.18 &83.16 & \\multicolumn{1}{c|}{42.10} & 130.10& 83.97 & 70.52 & \\multicolumn{1}{c}{65.21} \\\\\n"," \n"," \\multicolumn{1}{c|}{MCM}&45.55 &61.00 &83.54 & \\multicolumn{1}{c|}{93.31} & 248.87 &63.77 &85.97 & \\multicolumn{1}{c|}{65.61} & 416.37& 71.28 &81.18 & \\multicolumn{1}{c|}{42.10} &188.00 &74.14 & 75.47 & \\multicolumn{1}{c}{65.21} \\\\\n"," \n"," \\multicolumn{1}{c|}{DOCTOR}& 12.89& 86.06&76.36 & \\multicolumn{1}{c|}{93.31} & 138.00 &81.50 &73.66 & \\multicolumn{1}{c|}{65.61} & 368.77& 74.43 & 81.69 & \\multicolumn{1}{c|}{42.10} &123.41 &85.67 &65.81 & \\multicolumn{1}{c}{65.21} \\\\\n"," \n"," \\multicolumn{1}{c|}{MSP}&12.23 & 86.99 &67.88 & \\multicolumn{1}{c|}{93.31} & 136.27 &81.95 & 72.25 & \\multicolumn{1}{c|}{65.61} & 355.66&76.39 &80.65 & \\multicolumn{1}{c|}{42.10} & 122.44 &85.98 & 64.89 & \\multicolumn{1}{c}{65.21} \\\\\n"," \n"," \\rowcolor{gray!30}\n"," \\multicolumn{1}{c|}{TrustVLM-C}& 13.25& 86.81 &70.30 & \\multicolumn{1}{c|}{93.31} &{129.45} & \\textbf{83.67} & \\textbf{67.73} & \\multicolumn{1}{c|}{65.61} & 322.52&82.90 &55.73 & \\multicolumn{1}{c|}{42.10} &111.95 & 88.69 &55.93 & \\multicolumn{1}{c}{65.21} \\\\\n"," \n"," \\rowcolor{gray!30}\n"," \\multicolumn{1}{c|}{TrustVLM-M}&{10.81} & 88.97 & 64.24 & \\multicolumn{1}{c|}{93.31} & 134.25 & 82.53 &72.29 & \\multicolumn{1}{c|}{65.61} & 320.12& 83.03 & 56.32 & \\multicolumn{1}{c|}{42.10} & 114.60& 87.93 &58.81 & \\multicolumn{1}{c}{65.21} \\\\\n"," \n"," \\rowcolor{gray!30}\n"," \\multicolumn{1}{c|}{TrustVLM-D}&11.11& {90.51} & {47.27} & \\multicolumn{1}{c|}{93.31} & 137.54 &82.05 &70.62 & \\multicolumn{1}{c|}{65.61} &{303.79} & \\textbf{85.48} & \\textbf{53.52} & \\multicolumn{1}{c|}{42.10} & {107.13} &\\textbf{90.21} &\\textbf{50.68} & \\multicolumn{1}{c}{65.21} \\\\\n","\n"," \n","\n","\n"," \\rowcolor{gray!30}\n"," \\multicolumn{1}{c|}{TrustVLM*-D}& 5.69 & 89.48 & \\textbf{35.62} & \\multicolumn{1}{c|}{97.04} & 137.97 & 81.73 & 71.25 & \\multicolumn{1}{c|}{65.83} & 72.96 & 74.87 & 73.50 & \\multicolumn{1}{c|}{83.56} & 65.26 & 86.23 & 62.93 & 77.11\\\\ % v3 \n"," \n","\n","\n"," \\rowcolor{gray!30} \\multicolumn{1}{c|}{TrustVLM*(F)-D}& \\textbf{2.61}& \\textbf{93.29}& 35.71& \\multicolumn{1}{c|}{\\textbf{97.16}} & \\textbf{132.54} & 82.58 &68.70 & \\multicolumn{1}{c|}{\\textbf{66.02}} &\\textbf{54.35} &77.31 & 72.48 & \\multicolumn{1}{c|}{\\textbf{85.69}} & \\textbf{55.81}& 87.26 & 66.06 & \\multicolumn{1}{c}{\\textbf{78.35}} \\\\ % v3\n"," \n","\\hline\n","\n","\n"," \\multicolumn{1}{c|}{{} } &\\multicolumn{4}{c|}{Food101 } & \\multicolumn{4}{c|}{SUN397} & \\multicolumn{4}{c|}{Average} \n"," \\\\\n"," \\multicolumn{1}{c|}{{}} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} \\\\\n","\\cline{1-13}\n","\n","\n","\n","\n"," \\multicolumn{1}{c|}{MaxLogit}& 67.81& 75.81& 78.13 & \\multicolumn{1}{c|}{83.66} & 278.17 & 62.77 &90.46 & \\multicolumn{1}{c|}{62.57} & 278.28 & 64.99 & 86.11 & \\multicolumn{1}{c|}{63.63} \\\\\n"," \n"," \\multicolumn{1}{c|}{Energy}&76.05 & 72.33 & 84.69 & \\multicolumn{1}{c|}{83.66} & 304.50 &58.34 &93.47 & \\multicolumn{1}{c|}{62.57} & 307.74 & 59.44 & 90.19 & \\multicolumn{1}{c|}{63.63} \\\\\n"," \n"," \\multicolumn{1}{c|}{Entropy}&55.80 &83.76 &63.73 & \\multicolumn{1}{c|}{83.66} & 194.33 &75.19 &80.97 & \\multicolumn{1}{c|}{62.57} & 196.46 & 80.37 & 73.14 & \\multicolumn{1}{c|}{63.63} \\\\\n"," \n"," \\multicolumn{1}{c|}{MCM}&60.90 & 79.90 & 67.23 & \\multicolumn{1}{c|}{83.66} & 242.95 &69.09 &83.62 & \\multicolumn{1}{c|}{62.57} & 231.79 & 72.52 & 77.30 & \\multicolumn{1}{c|}{63.63} \\\\\n"," \n"," \\multicolumn{1}{c|}{DOCTOR}& 54.99& 84.39&61.11 & \\multicolumn{1}{c|}{83.66} & 184.41 &77.35 & 77.37& \\multicolumn{1}{c|}{62.57} & 190.63 & 81.78 & 71.40 & \\multicolumn{1}{c|}{63.63} \\\\\n"," \n"," \\multicolumn{1}{c|}{MSP}&54.80 &84.51 &59.73 & \\multicolumn{1}{c|}{83.66} & 182.39 & 77.90 & 76.42 & \\multicolumn{1}{c|}{62.57} & 188.75 & 82.20 & 70.10 & \\multicolumn{1}{c|}{63.63} \\\\\n"," \n"," \\rowcolor{gray!30}\n"," \\multicolumn{1}{c|}{TrustVLM-C}&{36.02} &88.34 & 57.67 & \\multicolumn{1}{c|}{83.66} & 171.89 &80.39 &69.20 & \\multicolumn{1}{c|}{62.57} & 177.34 & 84.71 & 63.20 & \\multicolumn{1}{c|}{63.63} \\\\\n","\n"," \\rowcolor{gray!30}\n"," \\multicolumn{1}{c|}{TrustVLM-M}& 40.92& 86.65& 59.01& \\multicolumn{1}{c|}{83.66} & 172.98 & 79.83 & 71.63 & \\multicolumn{1}{c|}{62.57} & 179.05 & 84.40 & 63.38 & \\multicolumn{1}{c|}{63.63} \\\\\n"," \n","\\rowcolor{gray!30} \n"," \\multicolumn{1}{c|}{TrustVLM-D}&36.18 &{88.52} &\\textbf{55.96} & \\multicolumn{1}{c|}{83.66} & {156.16} & \\textbf{83.80} &\\textbf{58.44} & \\multicolumn{1}{c|}{62.57} & {168.06} & \\textbf{86.98} & \\textbf{54.47} & \\multicolumn{1}{c|}{63.63} \\\\\n","\n"," \n"," \n"," \n"," \\rowcolor{gray!30}\n"," \\multicolumn{1}{c|}{TrustVLM*-D}& 35.01 & 88.49 & 57.47 & \\multicolumn{1}{c|}{84.10} & 104.25 & 82.31 & 72.82 & \\multicolumn{1}{c|}{\\textbf{72.18}} & 112.00 & 84.31 & 59.09 & \\multicolumn{1}{c|}{76.33} \\\\ % v3 \n"," \n","\n","\n"," \\rowcolor{gray!30} \\multicolumn{1}{c|}{TrustVLM*(F)-D}& \\textbf{34.16} &\\textbf{88.79} &56.19 & \\multicolumn{1}{c|}{\\textbf{84.15}} & \\textbf{103.05} & 83.43 & 72.68 & \\multicolumn{1}{c|}{{71.33}} & \\textbf{104.38} & 85.84 & 57.82& \\multicolumn{1}{c|}{\\textbf{76.91}} \\\\ % v3\n","\n"," \n","\\cline{1-13}\n","\n","\n","\n","\\end{tabular} \n","}\n","\\vspace{+0.0cm}\n","\\caption{Misclassification detection performance on fine-grained classification datasets with CLIP ViT-B/16, where -C, -M, and -D are with CLIP-I, MoCo v2, and DINOv2 as auxiliary vision encoders. AURC is multiplied by $10^3$ following previous work~\\cite{zhu2023openmix}. }\n","\\vspace{-0.6cm}\n","\\label{tab:misd} \n","\\end{table}\n"],["\\begin{table}[t!]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n","\\begin{tabular}{l@{~~~~~}| c cccc cccc cccc cccc }\n","\\hline\n"," \n"," \\multicolumn{1}{c|}{{} } &\\multicolumn{4}{c|}{ImageNet-A} & \\multicolumn{4}{c|}{ImageNet-V2} & \\multicolumn{4}{c}{ImageNet-R} \n"," \\\\\n"," \\multicolumn{1}{c|}{{}} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c}{ACC$\\uparrow$} \\\\\n","\\hline\n","\n","\n","\n"," \\multicolumn{1}{c|}{MaxLogit}&387.23 &64.16 & 86.59 & \\multicolumn{1}{c|}{50.08} & 254.37& 68.58& 85.22 & \\multicolumn{1}{c|}{61.20} &138.25 & 72.98 & 79.48 & \\multicolumn{1}{c}{74.19} \\\\\n"," \n"," \\multicolumn{1}{c|}{Energy}& 421.78& 59.15& 90.00 & \\multicolumn{1}{c|}{50.08} & 278.89& 64.64 &89.26 & \\multicolumn{1}{c|}{61.20} &160.84 & 67.39 &86.55 & \\multicolumn{1}{c}{74.19} \\\\\n"," \n"," \\multicolumn{1}{c|}{Entropy}& 298.23& 75.46&77.47 & \\multicolumn{1}{c|}{50.08} & 189.58 & 77.57 &79.90 & \\multicolumn{1}{c|}{61.20} & 79.64& 85.80 & 63.13& \\multicolumn{1}{c}{74.19} \\\\\n"," \n"," \\multicolumn{1}{c|}{MCM}& 341.67& 71.99 &78.27 & \\multicolumn{1}{c|}{50.08} & 236.32 &71.62 & 80.38 & \\multicolumn{1}{c|}{61.20} &109.67 &80.49 &64.73 & \\multicolumn{1}{c}{74.19} \\\\\n"," \n"," \\multicolumn{1}{c|}{DOCTOR}&290.78 &76.67 &76.93 & \\multicolumn{1}{c|}{50.08} & 181.67 & 79.32 &73.67 & \\multicolumn{1}{c|}{61.20} & 75.33& 87.14 &60.81 & \\multicolumn{1}{c}{74.19} \\\\\n"," \n"," \\multicolumn{1}{c|}{MSP}& 290.46& 76.70 & 77.06 & \\multicolumn{1}{c|}{50.08} & 180.32 &79.72 &71.82 & \\multicolumn{1}{c|}{61.20} &74.36 &87.50 &59.66 & \\multicolumn{1}{c}{74.19} \\\\\n","\n","\n","\\rowcolor{gray!30}\n","\\multicolumn{1}{c|}{TrustVLM-D}& {274.46}& \\textbf{79.17} &\\textbf{70.27} & \\multicolumn{1}{c|}{50.08} & {176.82} & \\textbf{81.27} & \\textbf{66.02} & \\multicolumn{1}{c|}{61.20} &{72.10} &\\textbf{88.03} &\\textbf{57.70} & \\multicolumn{1}{c}{74.19} \\\\\n","\n"," \\rowcolor{gray!30}\n","\\multicolumn{1}{c|}{TrustVLM*-D}& 266.48& 75.94 & 77.67 & \\multicolumn{1}{c|}{\\textbf{53.02}} &\\textbf{172.65} &77.93 &74.92 & \\multicolumn{1}{c|}{63.92} &71.72& 87.50 & 60.03& \\multicolumn{1}{c}{74.62} \\\\\n","\n","\n"," \\rowcolor{gray!30}\n","\\multicolumn{1}{c|}{TrustVLM*(F)-D}&\\textbf{264.36} & 77.09 & 76.90 & \\multicolumn{1}{c|}{52.61} & 176.13& 77.71& 72.98 & \\multicolumn{1}{c|}{\\textbf{63.98}} &\\textbf{71.15}& 87.42 & 60.37 & \\multicolumn{1}{c}{\\textbf{74.83}} \\\\ % ImageNet-V2 5ep\n","\n","\\hline\n","\n","\n","\n"," \\multicolumn{1}{c|}{{} } &\\multicolumn{4}{c|}{ImageNet-Sketch} & \\multicolumn{4}{c|}{ImageNet} & \\multicolumn{4}{c}{Average} \n"," \\\\\n"," \\multicolumn{1}{c|}{{}} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c}{ACC$\\uparrow$}\\\\\n","\\hline\n","\n","\n","\n"," \\multicolumn{1}{c|}{MaxLogit}& 422.41& 65.28 & 87.25 & \\multicolumn{1}{c|}{45.67} & 228.56 & 65.95 & 85.80 & \\multicolumn{1}{c|}{66.68} & 286.16 & 67.39 & 84.87 & \\multicolumn{1}{c}{59.56} \\\\\n"," \n"," \\multicolumn{1}{c|}{Energy} & 472.36 & 58.03 &92.25 & \\multicolumn{1}{c|}{45.67} & 253.77 & 61.61 &89.95 & \\multicolumn{1}{c|}{66.68} & 317.53 & 62.16 & 89.60 & \\multicolumn{1}{c}{59.56} \\\\\n"," \n"," \\multicolumn{1}{c|}{Entropy} & 313.83 &78.37 &74.27 & \\multicolumn{1}{c|}{45.67} & 149.69& 78.26 &78.01 & \\multicolumn{1}{c|}{66.68}& 206.19 & 79.09 & 74.56 & \\multicolumn{1}{c}{59.56} \\\\\n"," \n"," \\multicolumn{1}{c|}{MCM} & 400.13 &70.10 & 76.52 & \\multicolumn{1}{c|}{45.67} &206.72 & 69.73& 81.27 & \\multicolumn{1}{c|}{66.68} &258.90 & 72.79 & 76.23 & \\multicolumn{1}{c}{59.56} \\\\\n"," \n"," \\multicolumn{1}{c|}{DOCTOR}&300.85 & 80.49 &70.75 & \\multicolumn{1}{c|}{45.67} & 140.70 &80.48 &74.46 & \\multicolumn{1}{c|}{66.68} & 197.87 & 80.82 & 71.32 & \\multicolumn{1}{c}{59.56} \\\\\n"," \n"," \\multicolumn{1}{c|}{MSP} & 299.57& 80.74 & 70.51 & \\multicolumn{1}{c|}{45.67} & 138.99& 81.00 & 72.74 & \\multicolumn{1}{c|}{66.68} & 196.74 & 81.13 & 70.36 & \\multicolumn{1}{c}{59.56} \\\\\n","\n","\n","\\rowcolor{gray!30}\n","\\multicolumn{1}{c|}{TrustVLM-D}& {280.72} &\\textbf{84.35} & \\textbf{58.99} & \\multicolumn{1}{c|}{45.67} &{132.10} & \\textbf{83.38} & \\textbf{64.04}& \\multicolumn{1}{c|}{66.68} & {187.24} & \\textbf{83.24} & \\textbf{63.40} & \\multicolumn{1}{c}{59.56} \\\\\n","\n"," \\rowcolor{gray!30}\n","\\multicolumn{1}{c|}{TrustVLM*-D}& 264.51& 74.74& 84.77& \\multicolumn{1}{c|}{53.13} &\\textbf{126.31} & 78.82 &77.43 & \\multicolumn{1}{c|}{70.58} &180.33& 78.99 &74.96 & \\multicolumn{1}{c}{63.05} \\\\\n","\n"," \\rowcolor{gray!30}\n","\\multicolumn{1}{c|}{TrustVLM*(F)-D}& \\textbf{243.44}&73.80 & 76.90 & \\multicolumn{1}{c|}{\\textbf{58.20}} & 129.38& 77.33&75.28 & \\multicolumn{1}{c|}{\\textbf{71.75}} &\\textbf{176.89} & 78.67 &72.49 & \\multicolumn{1}{c}{\\textbf{64.27}} \\\\ % ImageNet 5ep\n","\\hline\n","\n","\n","\n","\n","\\end{tabular} \n","}\n","\\vspace{+0.0cm}\n","\\caption{Misclassification detection performance on ImageNet and its variants with CLIP ViT-B/16.}\n","\\label{tab:imagenet} \n","\\end{table}% \\input{tables/cross-dataset}\n"]],"string":"[\n [\n \"\\\\begin{table}[t!]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \"\\\\begin{tabular}{l@{~~~~~}| c cccc cccc cccc cccc}\\n\",\n \"\\\\hline\\n\",\n \" \\\\multicolumn{1}{c|}{} &\\\\multicolumn{4}{c|}{Flower102 } & \\\\multicolumn{4}{c|}{DTD } & \\\\multicolumn{4}{c|}{Aircraft} &\\\\multicolumn{4}{c}{Pets} \\n\",\n \" \\\\\\\\\\n\",\n \" \\\\multicolumn{1}{c|}{{}} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c}{ACC$\\\\uparrow$}\\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" \\\\multicolumn{1}{c|}{MaxLogit}&167.14 & 74.92 & 81.61 & \\\\multicolumn{1}{c|}{67.36} & 395.13 &69.63 & 85.23 & \\\\multicolumn{1}{c|}{44.39} & 731.25& 55.73 &93.77 & \\\\multicolumn{1}{c|}{23.85} & 48.75& 75.37 &68.59 & \\\\multicolumn{1}{c}{88.23} \\\\\\\\\\n\",\n \"\\n\",\n \" \\\\multicolumn{1}{c|}{Energy}&194.67 &69.00 & 91.93 & \\\\multicolumn{1}{c|}{67.36} & 433.25 & 64.82 & 90.01 & \\\\multicolumn{1}{c|}{44.39} & 780.11& 45.48 &97.71 & \\\\multicolumn{1}{c|}{23.85} & 56.23& 71.90 &70.67 & \\\\multicolumn{1}{c}{88.23} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{Entropy}&117.27 &84.88 &63.48 & \\\\multicolumn{1}{c|}{67.36} & 319.02 &79.03 & 77.05 & \\\\multicolumn{1}{c|}{44.39} & 575.71& 73.12 & 83.96 & \\\\multicolumn{1}{c|}{23.85} & 21.91& 89.31 &53.35 & \\\\multicolumn{1}{c}{88.23} \\\\\\\\\\n\",\n \" \\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{MCM}& 153.63& 78.22 & 71.02 & \\\\multicolumn{1}{c|}{67.36} & 333.43 &78.16 & 78.45 & \\\\multicolumn{1}{c|}{44.39} & 583.28& 72.58 &81.65 & \\\\multicolumn{1}{c|}{23.85} & 44.95& 77.03 &64.90 & \\\\multicolumn{1}{c}{88.23} \\\\\\\\\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{DOCTOR}&112.82 &85.82 &62.48 & \\\\multicolumn{1}{c|}{67.36} & 314.37& 79.66& 76.51 & \\\\multicolumn{1}{c|}{44.39} & 575.53& 73.05 &83.84 & \\\\multicolumn{1}{c|}{23.85} & 21.08 &89.92 &55.20 & \\\\multicolumn{1}{c}{88.23} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{MSP}& 112.27&85.91 &63.98 & \\\\multicolumn{1}{c|}{67.36} &313.43 &79.81 &77.36 & \\\\multicolumn{1}{c|}{44.39} & 576.97&72.62 & 85.61 & \\\\multicolumn{1}{c|}{23.85} & 21.04& 89.94 &52.19 & \\\\multicolumn{1}{c}{88.23} \\\\\\\\\\n\",\n \" \\n\",\n \"\\\\rowcolor{gray!30}\\n\",\n \" \\\\multicolumn{1}{c|}{TrustVLM-C}&101.42 & 88.69&54.91 & \\\\multicolumn{1}{c|}{67.36} & 302.18 & 82.52 & 67.27 & \\\\multicolumn{1}{c|}{44.39} & 563.77& 75.20 &{81.51} & \\\\multicolumn{1}{c|}{23.85} & 20.93 & 89.89 &51.73 & \\\\multicolumn{1}{c}{88.23} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\rowcolor{gray!30}\\n\",\n \" \\\\multicolumn{1}{c|}{TrustVLM-M}&103.68 & 88.29 & 53.42 & \\\\multicolumn{1}{c|}{67.36} & 298.17 &83.16 &65.50 & \\\\multicolumn{1}{c|}{44.39} &574.57 &73.22 & 84.31& \\\\multicolumn{1}{c|}{23.85} & {20.41} & \\\\textbf{90.38} &\\\\textbf{48.27} & \\\\multicolumn{1}{c}{{88.23}} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\rowcolor{gray!30}\\n\",\n \" \\\\multicolumn{1}{c|}{TrustVLM-D}& 77.30 &{95.05} & 30.06 & \\\\multicolumn{1}{c|}{67.36} & 268.71 &\\\\textbf{88.55} &\\\\textbf{44.10} & \\\\multicolumn{1}{c|}{44.39} & {562.02} & {75.62} & 83.21 & \\\\multicolumn{1}{c|}{23.85} & 20.69& 90.05 &50.81 & \\\\multicolumn{1}{c}{88.23} \\\\\\\\\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" \\\\rowcolor{gray!30}\\n\",\n \" \\\\multicolumn{1}{c|}{TrustVLM*-D}&0.52 &95.96 &13.04 &\\\\multicolumn{1}{c|}{\\\\textbf{99.07}} & 124.15 &78.39 &72.14 &\\\\multicolumn{1}{c|}{71.57} &554.12 &75.36 &83.05 &\\\\multicolumn{1}{c|}{24.60} &\\\\textbf{20.05} &90.30 &49.07 &\\\\textbf{88.28}\\\\\\\\ % v3 \\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\\\rowcolor{gray!30} \\\\multicolumn{1}{c|}{TrustVLM*(F)-D}& \\\\textbf{0.41} &\\\\textbf{98.26} & \\\\textbf{7.69} & \\\\multicolumn{1}{c|}{98.42} & \\\\textbf{96.45} & 80.30 & 70.96& \\\\multicolumn{1}{c|}{\\\\textbf{74.76}} & \\\\textbf{544.40}& \\\\textbf{76.85} & \\\\textbf{78.63}& \\\\multicolumn{1}{c|}{\\\\textbf{24.90}} & \\\\textbf{20.05} & 90.30 & 49.07 & \\\\multicolumn{1}{c}{\\\\textbf{88.28}} \\\\\\\\ % v3\\n\",\n \"\\n\",\n \" \\\\hline\\n\",\n \"\\n\",\n \"\\n\",\n \" \\\\multicolumn{1}{c|}{{} } &\\\\multicolumn{4}{c|}{Caltech101 } & \\\\multicolumn{4}{c|}{Cars} & \\\\multicolumn{4}{c|}{EuroSAT} &\\\\multicolumn{4}{c}{UCF101} \\n\",\n \" \\\\\\\\\\n\",\n \" \\\\multicolumn{1}{c|}{{}} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c}{ACC$\\\\uparrow$}\\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{MaxLogit}&53.72 &52.43 &94.55 & \\\\multicolumn{1}{c|}{93.31} &259.81 & 61.94 &89.58 & \\\\multicolumn{1}{c|}{65.61} & 522.56& 59.42 &87.79 & \\\\multicolumn{1}{c|}{42.10} & 258.47 & 61.85 & 91.41 & \\\\multicolumn{1}{c}{65.21} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{Energy}&59.26 &48.81 &96.36 & \\\\multicolumn{1}{c|}{93.31} &304.90 & 54.85& 93.89& \\\\multicolumn{1}{c|}{65.61} & 568.57& 53.44 &88.30 & \\\\multicolumn{1}{c|}{42.10} & 299.84 & 55.38 & 94.83 & \\\\multicolumn{1}{c}{65.21} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{Entropy}& 15.79&82.47 &79.39 & \\\\multicolumn{1}{c|}{93.31} & 145.61 & 79.83&75.76 & \\\\multicolumn{1}{c|}{65.61} &389.07 &72.18 &83.16 & \\\\multicolumn{1}{c|}{42.10} & 130.10& 83.97 & 70.52 & \\\\multicolumn{1}{c}{65.21} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{MCM}&45.55 &61.00 &83.54 & \\\\multicolumn{1}{c|}{93.31} & 248.87 &63.77 &85.97 & \\\\multicolumn{1}{c|}{65.61} & 416.37& 71.28 &81.18 & \\\\multicolumn{1}{c|}{42.10} &188.00 &74.14 & 75.47 & \\\\multicolumn{1}{c}{65.21} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{DOCTOR}& 12.89& 86.06&76.36 & \\\\multicolumn{1}{c|}{93.31} & 138.00 &81.50 &73.66 & \\\\multicolumn{1}{c|}{65.61} & 368.77& 74.43 & 81.69 & \\\\multicolumn{1}{c|}{42.10} &123.41 &85.67 &65.81 & \\\\multicolumn{1}{c}{65.21} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{MSP}&12.23 & 86.99 &67.88 & \\\\multicolumn{1}{c|}{93.31} & 136.27 &81.95 & 72.25 & \\\\multicolumn{1}{c|}{65.61} & 355.66&76.39 &80.65 & \\\\multicolumn{1}{c|}{42.10} & 122.44 &85.98 & 64.89 & \\\\multicolumn{1}{c}{65.21} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\rowcolor{gray!30}\\n\",\n \" \\\\multicolumn{1}{c|}{TrustVLM-C}& 13.25& 86.81 &70.30 & \\\\multicolumn{1}{c|}{93.31} &{129.45} & \\\\textbf{83.67} & \\\\textbf{67.73} & \\\\multicolumn{1}{c|}{65.61} & 322.52&82.90 &55.73 & \\\\multicolumn{1}{c|}{42.10} &111.95 & 88.69 &55.93 & \\\\multicolumn{1}{c}{65.21} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\rowcolor{gray!30}\\n\",\n \" \\\\multicolumn{1}{c|}{TrustVLM-M}&{10.81} & 88.97 & 64.24 & \\\\multicolumn{1}{c|}{93.31} & 134.25 & 82.53 &72.29 & \\\\multicolumn{1}{c|}{65.61} & 320.12& 83.03 & 56.32 & \\\\multicolumn{1}{c|}{42.10} & 114.60& 87.93 &58.81 & \\\\multicolumn{1}{c}{65.21} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\rowcolor{gray!30}\\n\",\n \" \\\\multicolumn{1}{c|}{TrustVLM-D}&11.11& {90.51} & {47.27} & \\\\multicolumn{1}{c|}{93.31} & 137.54 &82.05 &70.62 & \\\\multicolumn{1}{c|}{65.61} &{303.79} & \\\\textbf{85.48} & \\\\textbf{53.52} & \\\\multicolumn{1}{c|}{42.10} & {107.13} &\\\\textbf{90.21} &\\\\textbf{50.68} & \\\\multicolumn{1}{c}{65.21} \\\\\\\\\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\\\rowcolor{gray!30}\\n\",\n \" \\\\multicolumn{1}{c|}{TrustVLM*-D}& 5.69 & 89.48 & \\\\textbf{35.62} & \\\\multicolumn{1}{c|}{97.04} & 137.97 & 81.73 & 71.25 & \\\\multicolumn{1}{c|}{65.83} & 72.96 & 74.87 & 73.50 & \\\\multicolumn{1}{c|}{83.56} & 65.26 & 86.23 & 62.93 & 77.11\\\\\\\\ % v3 \\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\\\rowcolor{gray!30} \\\\multicolumn{1}{c|}{TrustVLM*(F)-D}& \\\\textbf{2.61}& \\\\textbf{93.29}& 35.71& \\\\multicolumn{1}{c|}{\\\\textbf{97.16}} & \\\\textbf{132.54} & 82.58 &68.70 & \\\\multicolumn{1}{c|}{\\\\textbf{66.02}} &\\\\textbf{54.35} &77.31 & 72.48 & \\\\multicolumn{1}{c|}{\\\\textbf{85.69}} & \\\\textbf{55.81}& 87.26 & 66.06 & \\\\multicolumn{1}{c}{\\\\textbf{78.35}} \\\\\\\\ % v3\\n\",\n \" \\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\n\",\n \" \\\\multicolumn{1}{c|}{{} } &\\\\multicolumn{4}{c|}{Food101 } & \\\\multicolumn{4}{c|}{SUN397} & \\\\multicolumn{4}{c|}{Average} \\n\",\n \" \\\\\\\\\\n\",\n \" \\\\multicolumn{1}{c|}{{}} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} \\\\\\\\\\n\",\n \"\\\\cline{1-13}\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" \\\\multicolumn{1}{c|}{MaxLogit}& 67.81& 75.81& 78.13 & \\\\multicolumn{1}{c|}{83.66} & 278.17 & 62.77 &90.46 & \\\\multicolumn{1}{c|}{62.57} & 278.28 & 64.99 & 86.11 & \\\\multicolumn{1}{c|}{63.63} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{Energy}&76.05 & 72.33 & 84.69 & \\\\multicolumn{1}{c|}{83.66} & 304.50 &58.34 &93.47 & \\\\multicolumn{1}{c|}{62.57} & 307.74 & 59.44 & 90.19 & \\\\multicolumn{1}{c|}{63.63} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{Entropy}&55.80 &83.76 &63.73 & \\\\multicolumn{1}{c|}{83.66} & 194.33 &75.19 &80.97 & \\\\multicolumn{1}{c|}{62.57} & 196.46 & 80.37 & 73.14 & \\\\multicolumn{1}{c|}{63.63} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{MCM}&60.90 & 79.90 & 67.23 & \\\\multicolumn{1}{c|}{83.66} & 242.95 &69.09 &83.62 & \\\\multicolumn{1}{c|}{62.57} & 231.79 & 72.52 & 77.30 & \\\\multicolumn{1}{c|}{63.63} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{DOCTOR}& 54.99& 84.39&61.11 & \\\\multicolumn{1}{c|}{83.66} & 184.41 &77.35 & 77.37& \\\\multicolumn{1}{c|}{62.57} & 190.63 & 81.78 & 71.40 & \\\\multicolumn{1}{c|}{63.63} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{MSP}&54.80 &84.51 &59.73 & \\\\multicolumn{1}{c|}{83.66} & 182.39 & 77.90 & 76.42 & \\\\multicolumn{1}{c|}{62.57} & 188.75 & 82.20 & 70.10 & \\\\multicolumn{1}{c|}{63.63} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\rowcolor{gray!30}\\n\",\n \" \\\\multicolumn{1}{c|}{TrustVLM-C}&{36.02} &88.34 & 57.67 & \\\\multicolumn{1}{c|}{83.66} & 171.89 &80.39 &69.20 & \\\\multicolumn{1}{c|}{62.57} & 177.34 & 84.71 & 63.20 & \\\\multicolumn{1}{c|}{63.63} \\\\\\\\\\n\",\n \"\\n\",\n \" \\\\rowcolor{gray!30}\\n\",\n \" \\\\multicolumn{1}{c|}{TrustVLM-M}& 40.92& 86.65& 59.01& \\\\multicolumn{1}{c|}{83.66} & 172.98 & 79.83 & 71.63 & \\\\multicolumn{1}{c|}{62.57} & 179.05 & 84.40 & 63.38 & \\\\multicolumn{1}{c|}{63.63} \\\\\\\\\\n\",\n \" \\n\",\n \"\\\\rowcolor{gray!30} \\n\",\n \" \\\\multicolumn{1}{c|}{TrustVLM-D}&36.18 &{88.52} &\\\\textbf{55.96} & \\\\multicolumn{1}{c|}{83.66} & {156.16} & \\\\textbf{83.80} &\\\\textbf{58.44} & \\\\multicolumn{1}{c|}{62.57} & {168.06} & \\\\textbf{86.98} & \\\\textbf{54.47} & \\\\multicolumn{1}{c|}{63.63} \\\\\\\\\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\\\rowcolor{gray!30}\\n\",\n \" \\\\multicolumn{1}{c|}{TrustVLM*-D}& 35.01 & 88.49 & 57.47 & \\\\multicolumn{1}{c|}{84.10} & 104.25 & 82.31 & 72.82 & \\\\multicolumn{1}{c|}{\\\\textbf{72.18}} & 112.00 & 84.31 & 59.09 & \\\\multicolumn{1}{c|}{76.33} \\\\\\\\ % v3 \\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\\\rowcolor{gray!30} \\\\multicolumn{1}{c|}{TrustVLM*(F)-D}& \\\\textbf{34.16} &\\\\textbf{88.79} &56.19 & \\\\multicolumn{1}{c|}{\\\\textbf{84.15}} & \\\\textbf{103.05} & 83.43 & 72.68 & \\\\multicolumn{1}{c|}{{71.33}} & \\\\textbf{104.38} & 85.84 & 57.82& \\\\multicolumn{1}{c|}{\\\\textbf{76.91}} \\\\\\\\ % v3\\n\",\n \"\\n\",\n \" \\n\",\n \"\\\\cline{1-13}\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\\end{tabular} \\n\",\n \"}\\n\",\n \"\\\\vspace{+0.0cm}\\n\",\n \"\\\\caption{Misclassification detection performance on fine-grained classification datasets with CLIP ViT-B/16, where -C, -M, and -D are with CLIP-I, MoCo v2, and DINOv2 as auxiliary vision encoders. AURC is multiplied by $10^3$ following previous work~\\\\cite{zhu2023openmix}. }\\n\",\n \"\\\\vspace{-0.6cm}\\n\",\n \"\\\\label{tab:misd} \\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[t!]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \"\\\\begin{tabular}{l@{~~~~~}| c cccc cccc cccc cccc }\\n\",\n \"\\\\hline\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{{} } &\\\\multicolumn{4}{c|}{ImageNet-A} & \\\\multicolumn{4}{c|}{ImageNet-V2} & \\\\multicolumn{4}{c}{ImageNet-R} \\n\",\n \" \\\\\\\\\\n\",\n \" \\\\multicolumn{1}{c|}{{}} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c}{ACC$\\\\uparrow$} \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" \\\\multicolumn{1}{c|}{MaxLogit}&387.23 &64.16 & 86.59 & \\\\multicolumn{1}{c|}{50.08} & 254.37& 68.58& 85.22 & \\\\multicolumn{1}{c|}{61.20} &138.25 & 72.98 & 79.48 & \\\\multicolumn{1}{c}{74.19} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{Energy}& 421.78& 59.15& 90.00 & \\\\multicolumn{1}{c|}{50.08} & 278.89& 64.64 &89.26 & \\\\multicolumn{1}{c|}{61.20} &160.84 & 67.39 &86.55 & \\\\multicolumn{1}{c}{74.19} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{Entropy}& 298.23& 75.46&77.47 & \\\\multicolumn{1}{c|}{50.08} & 189.58 & 77.57 &79.90 & \\\\multicolumn{1}{c|}{61.20} & 79.64& 85.80 & 63.13& \\\\multicolumn{1}{c}{74.19} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{MCM}& 341.67& 71.99 &78.27 & \\\\multicolumn{1}{c|}{50.08} & 236.32 &71.62 & 80.38 & \\\\multicolumn{1}{c|}{61.20} &109.67 &80.49 &64.73 & \\\\multicolumn{1}{c}{74.19} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{DOCTOR}&290.78 &76.67 &76.93 & \\\\multicolumn{1}{c|}{50.08} & 181.67 & 79.32 &73.67 & \\\\multicolumn{1}{c|}{61.20} & 75.33& 87.14 &60.81 & \\\\multicolumn{1}{c}{74.19} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{MSP}& 290.46& 76.70 & 77.06 & \\\\multicolumn{1}{c|}{50.08} & 180.32 &79.72 &71.82 & \\\\multicolumn{1}{c|}{61.20} &74.36 &87.50 &59.66 & \\\\multicolumn{1}{c}{74.19} \\\\\\\\\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\\rowcolor{gray!30}\\n\",\n \"\\\\multicolumn{1}{c|}{TrustVLM-D}& {274.46}& \\\\textbf{79.17} &\\\\textbf{70.27} & \\\\multicolumn{1}{c|}{50.08} & {176.82} & \\\\textbf{81.27} & \\\\textbf{66.02} & \\\\multicolumn{1}{c|}{61.20} &{72.10} &\\\\textbf{88.03} &\\\\textbf{57.70} & \\\\multicolumn{1}{c}{74.19} \\\\\\\\\\n\",\n \"\\n\",\n \" \\\\rowcolor{gray!30}\\n\",\n \"\\\\multicolumn{1}{c|}{TrustVLM*-D}& 266.48& 75.94 & 77.67 & \\\\multicolumn{1}{c|}{\\\\textbf{53.02}} &\\\\textbf{172.65} &77.93 &74.92 & \\\\multicolumn{1}{c|}{63.92} &71.72& 87.50 & 60.03& \\\\multicolumn{1}{c}{74.62} \\\\\\\\\\n\",\n \"\\n\",\n \"\\n\",\n \" \\\\rowcolor{gray!30}\\n\",\n \"\\\\multicolumn{1}{c|}{TrustVLM*(F)-D}&\\\\textbf{264.36} & 77.09 & 76.90 & \\\\multicolumn{1}{c|}{52.61} & 176.13& 77.71& 72.98 & \\\\multicolumn{1}{c|}{\\\\textbf{63.98}} &\\\\textbf{71.15}& 87.42 & 60.37 & \\\\multicolumn{1}{c}{\\\\textbf{74.83}} \\\\\\\\ % ImageNet-V2 5ep\\n\",\n \"\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" \\\\multicolumn{1}{c|}{{} } &\\\\multicolumn{4}{c|}{ImageNet-Sketch} & \\\\multicolumn{4}{c|}{ImageNet} & \\\\multicolumn{4}{c}{Average} \\n\",\n \" \\\\\\\\\\n\",\n \" \\\\multicolumn{1}{c|}{{}} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c}{ACC$\\\\uparrow$}\\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" \\\\multicolumn{1}{c|}{MaxLogit}& 422.41& 65.28 & 87.25 & \\\\multicolumn{1}{c|}{45.67} & 228.56 & 65.95 & 85.80 & \\\\multicolumn{1}{c|}{66.68} & 286.16 & 67.39 & 84.87 & \\\\multicolumn{1}{c}{59.56} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{Energy} & 472.36 & 58.03 &92.25 & \\\\multicolumn{1}{c|}{45.67} & 253.77 & 61.61 &89.95 & \\\\multicolumn{1}{c|}{66.68} & 317.53 & 62.16 & 89.60 & \\\\multicolumn{1}{c}{59.56} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{Entropy} & 313.83 &78.37 &74.27 & \\\\multicolumn{1}{c|}{45.67} & 149.69& 78.26 &78.01 & \\\\multicolumn{1}{c|}{66.68}& 206.19 & 79.09 & 74.56 & \\\\multicolumn{1}{c}{59.56} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{MCM} & 400.13 &70.10 & 76.52 & \\\\multicolumn{1}{c|}{45.67} &206.72 & 69.73& 81.27 & \\\\multicolumn{1}{c|}{66.68} &258.90 & 72.79 & 76.23 & \\\\multicolumn{1}{c}{59.56} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{DOCTOR}&300.85 & 80.49 &70.75 & \\\\multicolumn{1}{c|}{45.67} & 140.70 &80.48 &74.46 & \\\\multicolumn{1}{c|}{66.68} & 197.87 & 80.82 & 71.32 & \\\\multicolumn{1}{c}{59.56} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{MSP} & 299.57& 80.74 & 70.51 & \\\\multicolumn{1}{c|}{45.67} & 138.99& 81.00 & 72.74 & \\\\multicolumn{1}{c|}{66.68} & 196.74 & 81.13 & 70.36 & \\\\multicolumn{1}{c}{59.56} \\\\\\\\\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\\rowcolor{gray!30}\\n\",\n \"\\\\multicolumn{1}{c|}{TrustVLM-D}& {280.72} &\\\\textbf{84.35} & \\\\textbf{58.99} & \\\\multicolumn{1}{c|}{45.67} &{132.10} & \\\\textbf{83.38} & \\\\textbf{64.04}& \\\\multicolumn{1}{c|}{66.68} & {187.24} & \\\\textbf{83.24} & \\\\textbf{63.40} & \\\\multicolumn{1}{c}{59.56} \\\\\\\\\\n\",\n \"\\n\",\n \" \\\\rowcolor{gray!30}\\n\",\n \"\\\\multicolumn{1}{c|}{TrustVLM*-D}& 264.51& 74.74& 84.77& \\\\multicolumn{1}{c|}{53.13} &\\\\textbf{126.31} & 78.82 &77.43 & \\\\multicolumn{1}{c|}{70.58} &180.33& 78.99 &74.96 & \\\\multicolumn{1}{c}{63.05} \\\\\\\\\\n\",\n \"\\n\",\n \" \\\\rowcolor{gray!30}\\n\",\n \"\\\\multicolumn{1}{c|}{TrustVLM*(F)-D}& \\\\textbf{243.44}&73.80 & 76.90 & \\\\multicolumn{1}{c|}{\\\\textbf{58.20}} & 129.38& 77.33&75.28 & \\\\multicolumn{1}{c|}{\\\\textbf{71.75}} &\\\\textbf{176.89} & 78.67 &72.49 & \\\\multicolumn{1}{c}{\\\\textbf{64.27}} \\\\\\\\ % ImageNet 5ep\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\\end{tabular} \\n\",\n \"}\\n\",\n \"\\\\vspace{+0.0cm}\\n\",\n \"\\\\caption{Misclassification detection performance on ImageNet and its variants with CLIP ViT-B/16.}\\n\",\n \"\\\\label{tab:imagenet} \\n\",\n \"\\\\end{table}% \\\\input{tables/cross-dataset}\\n\"\n ]\n]","isConversational":false},"tables":{"kind":"list like","value":[["\\begin{figure}[t!]\n"," \\centering \\includegraphics[width=\\linewidth]{imgs/crop_emb.pdf}\n","\\vspace{-0.6cm}\n"," \\caption{(a) CLIP’s image and text embeddings are located in two completely separate regions of the embedding space. (b) The concept of \"dog\" and \"seaplane\" is more distinguishable in the image embedding space than in the text embedding space. }\n"," \\label{fig:clip_emb}\n","\\end{figure}\n"],["\\begin{figure}[t!]\n"," \\centering \\includegraphics[width=\\linewidth]{imgs/crop_frame.pdf}\n","\\vspace{-0.7cm}\n"," \\caption{The proposed TrustVLM framework comprises three main steps. Initially, visual prototypes for each class are generated and stored using a pre-trained vision encoder. Subsequently, the VLMs perform zero-shot classification and yield an image-to-text similarity score, $S_{i-t}$. In the third step, the initial prediction is verified using image-to-image similarity, providing an additional confidence score, $S_{i-i}$. Finally, these two scores are combined to determine the overall prediction confidence.}\n"," \\label{fig:frame}\n","\\end{figure}\n"],["\\begin{table}[t!]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n","\\begin{tabular}{l@{~~~~~}| c cccc cccc cccc cccc}\n","\\hline\n"," \\multicolumn{1}{c|}{} &\\multicolumn{4}{c|}{Flower102 } & \\multicolumn{4}{c|}{DTD } & \\multicolumn{4}{c|}{Aircraft} &\\multicolumn{4}{c}{Pets} \n"," \\\\\n"," \\multicolumn{1}{c|}{{}} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c}{ACC$\\uparrow$}\\\\\n","\\hline\n","\n","\n","\n"," \\multicolumn{1}{c|}{MaxLogit}&167.14 & 74.92 & 81.61 & \\multicolumn{1}{c|}{67.36} & 395.13 &69.63 & 85.23 & \\multicolumn{1}{c|}{44.39} & 731.25& 55.73 &93.77 & \\multicolumn{1}{c|}{23.85} & 48.75& 75.37 &68.59 & \\multicolumn{1}{c}{88.23} \\\\\n","\n"," \\multicolumn{1}{c|}{Energy}&194.67 &69.00 & 91.93 & \\multicolumn{1}{c|}{67.36} & 433.25 & 64.82 & 90.01 & \\multicolumn{1}{c|}{44.39} & 780.11& 45.48 &97.71 & \\multicolumn{1}{c|}{23.85} & 56.23& 71.90 &70.67 & \\multicolumn{1}{c}{88.23} \\\\\n"," \n"," \\multicolumn{1}{c|}{Entropy}&117.27 &84.88 &63.48 & \\multicolumn{1}{c|}{67.36} & 319.02 &79.03 & 77.05 & \\multicolumn{1}{c|}{44.39} & 575.71& 73.12 & 83.96 & \\multicolumn{1}{c|}{23.85} & 21.91& 89.31 &53.35 & \\multicolumn{1}{c}{88.23} \\\\\n"," \n"," \n"," \\multicolumn{1}{c|}{MCM}& 153.63& 78.22 & 71.02 & \\multicolumn{1}{c|}{67.36} & 333.43 &78.16 & 78.45 & \\multicolumn{1}{c|}{44.39} & 583.28& 72.58 &81.65 & \\multicolumn{1}{c|}{23.85} & 44.95& 77.03 &64.90 & \\multicolumn{1}{c}{88.23} \\\\\n","\n","\n"," \n"," \\multicolumn{1}{c|}{DOCTOR}&112.82 &85.82 &62.48 & \\multicolumn{1}{c|}{67.36} & 314.37& 79.66& 76.51 & \\multicolumn{1}{c|}{44.39} & 575.53& 73.05 &83.84 & \\multicolumn{1}{c|}{23.85} & 21.08 &89.92 &55.20 & \\multicolumn{1}{c}{88.23} \\\\\n"," \n"," \\multicolumn{1}{c|}{MSP}& 112.27&85.91 &63.98 & \\multicolumn{1}{c|}{67.36} &313.43 &79.81 &77.36 & \\multicolumn{1}{c|}{44.39} & 576.97&72.62 & 85.61 & \\multicolumn{1}{c|}{23.85} & 21.04& 89.94 &52.19 & \\multicolumn{1}{c}{88.23} \\\\\n"," \n","\\rowcolor{gray!30}\n"," \\multicolumn{1}{c|}{TrustVLM-C}&101.42 & 88.69&54.91 & \\multicolumn{1}{c|}{67.36} & 302.18 & 82.52 & 67.27 & \\multicolumn{1}{c|}{44.39} & 563.77& 75.20 &{81.51} & \\multicolumn{1}{c|}{23.85} & 20.93 & 89.89 &51.73 & \\multicolumn{1}{c}{88.23} \\\\\n"," \n"," \\rowcolor{gray!30}\n"," \\multicolumn{1}{c|}{TrustVLM-M}&103.68 & 88.29 & 53.42 & \\multicolumn{1}{c|}{67.36} & 298.17 &83.16 &65.50 & \\multicolumn{1}{c|}{44.39} &574.57 &73.22 & 84.31& \\multicolumn{1}{c|}{23.85} & {20.41} & \\textbf{90.38} &\\textbf{48.27} & \\multicolumn{1}{c}{{88.23}} \\\\\n"," \n"," \\rowcolor{gray!30}\n"," \\multicolumn{1}{c|}{TrustVLM-D}& 77.30 &{95.05} & 30.06 & \\multicolumn{1}{c|}{67.36} & 268.71 &\\textbf{88.55} &\\textbf{44.10} & \\multicolumn{1}{c|}{44.39} & {562.02} & {75.62} & 83.21 & \\multicolumn{1}{c|}{23.85} & 20.69& 90.05 &50.81 & \\multicolumn{1}{c}{88.23} \\\\\n","\n","\n","\n"," \\rowcolor{gray!30}\n"," \\multicolumn{1}{c|}{TrustVLM*-D}&0.52 &95.96 &13.04 &\\multicolumn{1}{c|}{\\textbf{99.07}} & 124.15 &78.39 &72.14 &\\multicolumn{1}{c|}{71.57} &554.12 &75.36 &83.05 &\\multicolumn{1}{c|}{24.60} &\\textbf{20.05} &90.30 &49.07 &\\textbf{88.28}\\\\ % v3 \n","\n","\n","\n"," \n","\n","\n"," \\rowcolor{gray!30} \\multicolumn{1}{c|}{TrustVLM*(F)-D}& \\textbf{0.41} &\\textbf{98.26} & \\textbf{7.69} & \\multicolumn{1}{c|}{98.42} & \\textbf{96.45} & 80.30 & 70.96& \\multicolumn{1}{c|}{\\textbf{74.76}} & \\textbf{544.40}& \\textbf{76.85} & \\textbf{78.63}& \\multicolumn{1}{c|}{\\textbf{24.90}} & \\textbf{20.05} & 90.30 & 49.07 & \\multicolumn{1}{c}{\\textbf{88.28}} \\\\ % v3\n","\n"," \\hline\n","\n","\n"," \\multicolumn{1}{c|}{{} } &\\multicolumn{4}{c|}{Caltech101 } & \\multicolumn{4}{c|}{Cars} & \\multicolumn{4}{c|}{EuroSAT} &\\multicolumn{4}{c}{UCF101} \n"," \\\\\n"," \\multicolumn{1}{c|}{{}} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c}{ACC$\\uparrow$}\\\\\n","\\hline\n","\n","\n","\n"," \n"," \\multicolumn{1}{c|}{MaxLogit}&53.72 &52.43 &94.55 & \\multicolumn{1}{c|}{93.31} &259.81 & 61.94 &89.58 & \\multicolumn{1}{c|}{65.61} & 522.56& 59.42 &87.79 & \\multicolumn{1}{c|}{42.10} & 258.47 & 61.85 & 91.41 & \\multicolumn{1}{c}{65.21} \\\\\n"," \n"," \\multicolumn{1}{c|}{Energy}&59.26 &48.81 &96.36 & \\multicolumn{1}{c|}{93.31} &304.90 & 54.85& 93.89& \\multicolumn{1}{c|}{65.61} & 568.57& 53.44 &88.30 & \\multicolumn{1}{c|}{42.10} & 299.84 & 55.38 & 94.83 & \\multicolumn{1}{c}{65.21} \\\\\n"," \n"," \\multicolumn{1}{c|}{Entropy}& 15.79&82.47 &79.39 & \\multicolumn{1}{c|}{93.31} & 145.61 & 79.83&75.76 & \\multicolumn{1}{c|}{65.61} &389.07 &72.18 &83.16 & \\multicolumn{1}{c|}{42.10} & 130.10& 83.97 & 70.52 & \\multicolumn{1}{c}{65.21} \\\\\n"," \n"," \\multicolumn{1}{c|}{MCM}&45.55 &61.00 &83.54 & \\multicolumn{1}{c|}{93.31} & 248.87 &63.77 &85.97 & \\multicolumn{1}{c|}{65.61} & 416.37& 71.28 &81.18 & \\multicolumn{1}{c|}{42.10} &188.00 &74.14 & 75.47 & \\multicolumn{1}{c}{65.21} \\\\\n"," \n"," \\multicolumn{1}{c|}{DOCTOR}& 12.89& 86.06&76.36 & \\multicolumn{1}{c|}{93.31} & 138.00 &81.50 &73.66 & \\multicolumn{1}{c|}{65.61} & 368.77& 74.43 & 81.69 & \\multicolumn{1}{c|}{42.10} &123.41 &85.67 &65.81 & \\multicolumn{1}{c}{65.21} \\\\\n"," \n"," \\multicolumn{1}{c|}{MSP}&12.23 & 86.99 &67.88 & \\multicolumn{1}{c|}{93.31} & 136.27 &81.95 & 72.25 & \\multicolumn{1}{c|}{65.61} & 355.66&76.39 &80.65 & \\multicolumn{1}{c|}{42.10} & 122.44 &85.98 & 64.89 & \\multicolumn{1}{c}{65.21} \\\\\n"," \n"," \\rowcolor{gray!30}\n"," \\multicolumn{1}{c|}{TrustVLM-C}& 13.25& 86.81 &70.30 & \\multicolumn{1}{c|}{93.31} &{129.45} & \\textbf{83.67} & \\textbf{67.73} & \\multicolumn{1}{c|}{65.61} & 322.52&82.90 &55.73 & \\multicolumn{1}{c|}{42.10} &111.95 & 88.69 &55.93 & \\multicolumn{1}{c}{65.21} \\\\\n"," \n"," \\rowcolor{gray!30}\n"," \\multicolumn{1}{c|}{TrustVLM-M}&{10.81} & 88.97 & 64.24 & \\multicolumn{1}{c|}{93.31} & 134.25 & 82.53 &72.29 & \\multicolumn{1}{c|}{65.61} & 320.12& 83.03 & 56.32 & \\multicolumn{1}{c|}{42.10} & 114.60& 87.93 &58.81 & \\multicolumn{1}{c}{65.21} \\\\\n"," \n"," \\rowcolor{gray!30}\n"," \\multicolumn{1}{c|}{TrustVLM-D}&11.11& {90.51} & {47.27} & \\multicolumn{1}{c|}{93.31} & 137.54 &82.05 &70.62 & \\multicolumn{1}{c|}{65.61} &{303.79} & \\textbf{85.48} & \\textbf{53.52} & \\multicolumn{1}{c|}{42.10} & {107.13} &\\textbf{90.21} &\\textbf{50.68} & \\multicolumn{1}{c}{65.21} \\\\\n","\n"," \n","\n","\n"," \\rowcolor{gray!30}\n"," \\multicolumn{1}{c|}{TrustVLM*-D}& 5.69 & 89.48 & \\textbf{35.62} & \\multicolumn{1}{c|}{97.04} & 137.97 & 81.73 & 71.25 & \\multicolumn{1}{c|}{65.83} & 72.96 & 74.87 & 73.50 & \\multicolumn{1}{c|}{83.56} & 65.26 & 86.23 & 62.93 & 77.11\\\\ % v3 \n"," \n","\n","\n"," \\rowcolor{gray!30} \\multicolumn{1}{c|}{TrustVLM*(F)-D}& \\textbf{2.61}& \\textbf{93.29}& 35.71& \\multicolumn{1}{c|}{\\textbf{97.16}} & \\textbf{132.54} & 82.58 &68.70 & \\multicolumn{1}{c|}{\\textbf{66.02}} &\\textbf{54.35} &77.31 & 72.48 & \\multicolumn{1}{c|}{\\textbf{85.69}} & \\textbf{55.81}& 87.26 & 66.06 & \\multicolumn{1}{c}{\\textbf{78.35}} \\\\ % v3\n"," \n","\\hline\n","\n","\n"," \\multicolumn{1}{c|}{{} } &\\multicolumn{4}{c|}{Food101 } & \\multicolumn{4}{c|}{SUN397} & \\multicolumn{4}{c|}{Average} \n"," \\\\\n"," \\multicolumn{1}{c|}{{}} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} \\\\\n","\\cline{1-13}\n","\n","\n","\n","\n"," \\multicolumn{1}{c|}{MaxLogit}& 67.81& 75.81& 78.13 & \\multicolumn{1}{c|}{83.66} & 278.17 & 62.77 &90.46 & \\multicolumn{1}{c|}{62.57} & 278.28 & 64.99 & 86.11 & \\multicolumn{1}{c|}{63.63} \\\\\n"," \n"," \\multicolumn{1}{c|}{Energy}&76.05 & 72.33 & 84.69 & \\multicolumn{1}{c|}{83.66} & 304.50 &58.34 &93.47 & \\multicolumn{1}{c|}{62.57} & 307.74 & 59.44 & 90.19 & \\multicolumn{1}{c|}{63.63} \\\\\n"," \n"," \\multicolumn{1}{c|}{Entropy}&55.80 &83.76 &63.73 & \\multicolumn{1}{c|}{83.66} & 194.33 &75.19 &80.97 & \\multicolumn{1}{c|}{62.57} & 196.46 & 80.37 & 73.14 & \\multicolumn{1}{c|}{63.63} \\\\\n"," \n"," \\multicolumn{1}{c|}{MCM}&60.90 & 79.90 & 67.23 & \\multicolumn{1}{c|}{83.66} & 242.95 &69.09 &83.62 & \\multicolumn{1}{c|}{62.57} & 231.79 & 72.52 & 77.30 & \\multicolumn{1}{c|}{63.63} \\\\\n"," \n"," \\multicolumn{1}{c|}{DOCTOR}& 54.99& 84.39&61.11 & \\multicolumn{1}{c|}{83.66} & 184.41 &77.35 & 77.37& \\multicolumn{1}{c|}{62.57} & 190.63 & 81.78 & 71.40 & \\multicolumn{1}{c|}{63.63} \\\\\n"," \n"," \\multicolumn{1}{c|}{MSP}&54.80 &84.51 &59.73 & \\multicolumn{1}{c|}{83.66} & 182.39 & 77.90 & 76.42 & \\multicolumn{1}{c|}{62.57} & 188.75 & 82.20 & 70.10 & \\multicolumn{1}{c|}{63.63} \\\\\n"," \n"," \\rowcolor{gray!30}\n"," \\multicolumn{1}{c|}{TrustVLM-C}&{36.02} &88.34 & 57.67 & \\multicolumn{1}{c|}{83.66} & 171.89 &80.39 &69.20 & \\multicolumn{1}{c|}{62.57} & 177.34 & 84.71 & 63.20 & \\multicolumn{1}{c|}{63.63} \\\\\n","\n"," \\rowcolor{gray!30}\n"," \\multicolumn{1}{c|}{TrustVLM-M}& 40.92& 86.65& 59.01& \\multicolumn{1}{c|}{83.66} & 172.98 & 79.83 & 71.63 & \\multicolumn{1}{c|}{62.57} & 179.05 & 84.40 & 63.38 & \\multicolumn{1}{c|}{63.63} \\\\\n"," \n","\\rowcolor{gray!30} \n"," \\multicolumn{1}{c|}{TrustVLM-D}&36.18 &{88.52} &\\textbf{55.96} & \\multicolumn{1}{c|}{83.66} & {156.16} & \\textbf{83.80} &\\textbf{58.44} & \\multicolumn{1}{c|}{62.57} & {168.06} & \\textbf{86.98} & \\textbf{54.47} & \\multicolumn{1}{c|}{63.63} \\\\\n","\n"," \n"," \n"," \n"," \\rowcolor{gray!30}\n"," \\multicolumn{1}{c|}{TrustVLM*-D}& 35.01 & 88.49 & 57.47 & \\multicolumn{1}{c|}{84.10} & 104.25 & 82.31 & 72.82 & \\multicolumn{1}{c|}{\\textbf{72.18}} & 112.00 & 84.31 & 59.09 & \\multicolumn{1}{c|}{76.33} \\\\ % v3 \n"," \n","\n","\n"," \\rowcolor{gray!30} \\multicolumn{1}{c|}{TrustVLM*(F)-D}& \\textbf{34.16} &\\textbf{88.79} &56.19 & \\multicolumn{1}{c|}{\\textbf{84.15}} & \\textbf{103.05} & 83.43 & 72.68 & \\multicolumn{1}{c|}{{71.33}} & \\textbf{104.38} & 85.84 & 57.82& \\multicolumn{1}{c|}{\\textbf{76.91}} \\\\ % v3\n","\n"," \n","\\cline{1-13}\n","\n","\n","\n","\\end{tabular} \n","}\n","\\vspace{+0.0cm}\n","\\caption{Misclassification detection performance on fine-grained classification datasets with CLIP ViT-B/16, where -C, -M, and -D are with CLIP-I, MoCo v2, and DINOv2 as auxiliary vision encoders. AURC is multiplied by $10^3$ following previous work~\\cite{zhu2023openmix}. }\n","\\vspace{-0.6cm}\n","\\label{tab:misd} \n","\\end{table}\n"],["\\begin{table}[t!]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n","\\begin{tabular}{l@{~~~~~}| c cccc cccc cccc cccc }\n","\\hline\n"," \n"," \\multicolumn{1}{c|}{{} } &\\multicolumn{4}{c|}{ImageNet-A} & \\multicolumn{4}{c|}{ImageNet-V2} & \\multicolumn{4}{c}{ImageNet-R} \n"," \\\\\n"," \\multicolumn{1}{c|}{{}} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c}{ACC$\\uparrow$} \\\\\n","\\hline\n","\n","\n","\n"," \\multicolumn{1}{c|}{MaxLogit}&387.23 &64.16 & 86.59 & \\multicolumn{1}{c|}{50.08} & 254.37& 68.58& 85.22 & \\multicolumn{1}{c|}{61.20} &138.25 & 72.98 & 79.48 & \\multicolumn{1}{c}{74.19} \\\\\n"," \n"," \\multicolumn{1}{c|}{Energy}& 421.78& 59.15& 90.00 & \\multicolumn{1}{c|}{50.08} & 278.89& 64.64 &89.26 & \\multicolumn{1}{c|}{61.20} &160.84 & 67.39 &86.55 & \\multicolumn{1}{c}{74.19} \\\\\n"," \n"," \\multicolumn{1}{c|}{Entropy}& 298.23& 75.46&77.47 & \\multicolumn{1}{c|}{50.08} & 189.58 & 77.57 &79.90 & \\multicolumn{1}{c|}{61.20} & 79.64& 85.80 & 63.13& \\multicolumn{1}{c}{74.19} \\\\\n"," \n"," \\multicolumn{1}{c|}{MCM}& 341.67& 71.99 &78.27 & \\multicolumn{1}{c|}{50.08} & 236.32 &71.62 & 80.38 & \\multicolumn{1}{c|}{61.20} &109.67 &80.49 &64.73 & \\multicolumn{1}{c}{74.19} \\\\\n"," \n"," \\multicolumn{1}{c|}{DOCTOR}&290.78 &76.67 &76.93 & \\multicolumn{1}{c|}{50.08} & 181.67 & 79.32 &73.67 & \\multicolumn{1}{c|}{61.20} & 75.33& 87.14 &60.81 & \\multicolumn{1}{c}{74.19} \\\\\n"," \n"," \\multicolumn{1}{c|}{MSP}& 290.46& 76.70 & 77.06 & \\multicolumn{1}{c|}{50.08} & 180.32 &79.72 &71.82 & \\multicolumn{1}{c|}{61.20} &74.36 &87.50 &59.66 & \\multicolumn{1}{c}{74.19} \\\\\n","\n","\n","\\rowcolor{gray!30}\n","\\multicolumn{1}{c|}{TrustVLM-D}& {274.46}& \\textbf{79.17} &\\textbf{70.27} & \\multicolumn{1}{c|}{50.08} & {176.82} & \\textbf{81.27} & \\textbf{66.02} & \\multicolumn{1}{c|}{61.20} &{72.10} &\\textbf{88.03} &\\textbf{57.70} & \\multicolumn{1}{c}{74.19} \\\\\n","\n"," \\rowcolor{gray!30}\n","\\multicolumn{1}{c|}{TrustVLM*-D}& 266.48& 75.94 & 77.67 & \\multicolumn{1}{c|}{\\textbf{53.02}} &\\textbf{172.65} &77.93 &74.92 & \\multicolumn{1}{c|}{63.92} &71.72& 87.50 & 60.03& \\multicolumn{1}{c}{74.62} \\\\\n","\n","\n"," \\rowcolor{gray!30}\n","\\multicolumn{1}{c|}{TrustVLM*(F)-D}&\\textbf{264.36} & 77.09 & 76.90 & \\multicolumn{1}{c|}{52.61} & 176.13& 77.71& 72.98 & \\multicolumn{1}{c|}{\\textbf{63.98}} &\\textbf{71.15}& 87.42 & 60.37 & \\multicolumn{1}{c}{\\textbf{74.83}} \\\\ % ImageNet-V2 5ep\n","\n","\\hline\n","\n","\n","\n"," \\multicolumn{1}{c|}{{} } &\\multicolumn{4}{c|}{ImageNet-Sketch} & \\multicolumn{4}{c|}{ImageNet} & \\multicolumn{4}{c}{Average} \n"," \\\\\n"," \\multicolumn{1}{c|}{{}} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c}{ACC$\\uparrow$}\\\\\n","\\hline\n","\n","\n","\n"," \\multicolumn{1}{c|}{MaxLogit}& 422.41& 65.28 & 87.25 & \\multicolumn{1}{c|}{45.67} & 228.56 & 65.95 & 85.80 & \\multicolumn{1}{c|}{66.68} & 286.16 & 67.39 & 84.87 & \\multicolumn{1}{c}{59.56} \\\\\n"," \n"," \\multicolumn{1}{c|}{Energy} & 472.36 & 58.03 &92.25 & \\multicolumn{1}{c|}{45.67} & 253.77 & 61.61 &89.95 & \\multicolumn{1}{c|}{66.68} & 317.53 & 62.16 & 89.60 & \\multicolumn{1}{c}{59.56} \\\\\n"," \n"," \\multicolumn{1}{c|}{Entropy} & 313.83 &78.37 &74.27 & \\multicolumn{1}{c|}{45.67} & 149.69& 78.26 &78.01 & \\multicolumn{1}{c|}{66.68}& 206.19 & 79.09 & 74.56 & \\multicolumn{1}{c}{59.56} \\\\\n"," \n"," \\multicolumn{1}{c|}{MCM} & 400.13 &70.10 & 76.52 & \\multicolumn{1}{c|}{45.67} &206.72 & 69.73& 81.27 & \\multicolumn{1}{c|}{66.68} &258.90 & 72.79 & 76.23 & \\multicolumn{1}{c}{59.56} \\\\\n"," \n"," \\multicolumn{1}{c|}{DOCTOR}&300.85 & 80.49 &70.75 & \\multicolumn{1}{c|}{45.67} & 140.70 &80.48 &74.46 & \\multicolumn{1}{c|}{66.68} & 197.87 & 80.82 & 71.32 & \\multicolumn{1}{c}{59.56} \\\\\n"," \n"," \\multicolumn{1}{c|}{MSP} & 299.57& 80.74 & 70.51 & \\multicolumn{1}{c|}{45.67} & 138.99& 81.00 & 72.74 & \\multicolumn{1}{c|}{66.68} & 196.74 & 81.13 & 70.36 & \\multicolumn{1}{c}{59.56} \\\\\n","\n","\n","\\rowcolor{gray!30}\n","\\multicolumn{1}{c|}{TrustVLM-D}& {280.72} &\\textbf{84.35} & \\textbf{58.99} & \\multicolumn{1}{c|}{45.67} &{132.10} & \\textbf{83.38} & \\textbf{64.04}& \\multicolumn{1}{c|}{66.68} & {187.24} & \\textbf{83.24} & \\textbf{63.40} & \\multicolumn{1}{c}{59.56} \\\\\n","\n"," \\rowcolor{gray!30}\n","\\multicolumn{1}{c|}{TrustVLM*-D}& 264.51& 74.74& 84.77& \\multicolumn{1}{c|}{53.13} &\\textbf{126.31} & 78.82 &77.43 & \\multicolumn{1}{c|}{70.58} &180.33& 78.99 &74.96 & \\multicolumn{1}{c}{63.05} \\\\\n","\n"," \\rowcolor{gray!30}\n","\\multicolumn{1}{c|}{TrustVLM*(F)-D}& \\textbf{243.44}&73.80 & 76.90 & \\multicolumn{1}{c|}{\\textbf{58.20}} & 129.38& 77.33&75.28 & \\multicolumn{1}{c|}{\\textbf{71.75}} &\\textbf{176.89} & 78.67 &72.49 & \\multicolumn{1}{c}{\\textbf{64.27}} \\\\ % ImageNet 5ep\n","\\hline\n","\n","\n","\n","\n","\\end{tabular} \n","}\n","\\vspace{+0.0cm}\n","\\caption{Misclassification detection performance on ImageNet and its variants with CLIP ViT-B/16.}\n","\\label{tab:imagenet} \n","\\end{table}% \\input{tables/cross-dataset}\n"],["\\begin{table}[t]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\\begin{tabular}{cc|ccc|ccc|ccc|ccc}\n","\\hline\n","\\multirow{3}{*}{} & \\multirow{3}{*}{} & \\multicolumn{3}{c|}{CIFAR-10} & \\multicolumn{3}{c|}{CIFAR-100} & \\multicolumn{3}{c|}{EuroSAT} & \\multicolumn{3}{c}{Average} \\\\\n"," & & AUROC$\\uparrow$ & FPR95$\\downarrow$ & ACC$\\uparrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & ACC$\\uparrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & ACC$\\uparrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & ACC$\\uparrow$ \\\\\n","\\hline\n","\\multirow{1}{*}{ResNet-101} \n","& MSP & 85.98& 62.98& 78.01& 80.72& 73.40& 48.50& 61.73& 88.98& 30.30 & 76.14& 75.12& 52.27\\\\\n","& ORCA & 85.93 & 62.68 & 80.60 & 80.46 & 72.38 & 53.11 & 69.01 & 86.43 & 34.76 & 78.47& 73.83& 56.16\\\\\n","\\rowcolor{gray!30} \\cellcolor{white!30}& TrustVLM-D & \\textbf{94.76} & \\textbf{26.82} & 78.01 & \\textbf{90.03} & \\textbf{39.46} & 48.50 & \\textbf{74.03} & \\textbf{66.69} & 30.30& \\textbf{86.27}& \\textbf{44.32}& 52.27\\\\\n","\n","\n","\\rowcolor{gray!30} \\cellcolor{white!30}& TrustVLM*-D & 90.97& 41.88& \\textbf{95.63} & 82.27& 65.52& \\textbf{79.35}& 72.08 & 72.15 & \\textbf{81.78} & 81.77& 59.85& \\textbf{85.59}\\\\\n","\n","\n","\\hline\n","\\multirow{1}{*}{ViT-B/32} \n","& MSP & 88.92& 58.66& 88.92& 81.15& 71.09& 58.42& 76.42& 80.24& 41.11 & 82.16&70.00& 62.82\\\\\n","\n","& ORCA & 89.00 & 52.70 & 90.00 & 83.40 & 67.00 & 66.50 & 77.55 & 71.29 & 50.00& 83.32& 63.66& 68.83 \\\\\n","\n","\\rowcolor{gray!30} \\cellcolor{white!30}& TrustVLM-D & \\textbf{94.94} & 31.85 & 88.92 & \\textbf{89.13} & \\textbf{45.63} & 58.42 & \\textbf{78.88} & \\textbf{69.36} &41.11& \\textbf{87.65}& \\textbf{48.95}& 62.82 \\\\\n","\n","\\rowcolor{gray!30} \\cellcolor{white!30} & TrustVLM*-D & 94.06 &\\textbf{29.58} & \\textbf{95.91} &84.95 & 62.99 & \\textbf{79.98} & 74.70& 72.77 & \\textbf{82.81}& 84.57& 55.11& \\textbf{86.23}\\\\\n","\n","\n","\\hline\n","\\end{tabular}\n","}\n","\\vspace{-0.0cm}\n","\\caption{MisD performance compared with ORCA with CLIP ResNet-101 and ViT-B/32.}\n","\\label{tab:RN101-ORCA} \n","\\end{table}\n"],["\\begin{table*}[t]\n"," \\centering\n"," \\resizebox{0.95\\linewidth}{!}{\n"," \\begin{tabular}{l*{11}c}\n"," \\toprule\n"," Method & Aircraft & Caltech101 & Cars & DTD & EuroSAT & Flower102 & Food101 & Pets & SUN397 & UCF101 & Average \\\\\n"," \\midrule\n","\n","\n"," CLIP-RN50 &15.54 & 85.88& 55.74& 40.37&23.70 & 61.75& 73.95& 83.65&58.81 & 58.74& 55.81\\\\\n","\n"," CoOp~\\cite{zhou2022learning} & 22.20&87.70 &61.30 &52.20 &63.20 &81.00 &76.30 &86.20 &63.40 &67.00 &66.05 \\\\\n"," Tip-Adapter~\\cite{zhang2022tip} & 23.70& 88.80& 63.90& 54.70& 72.50& 83.20&76.70 &86.40 &66.70 &72.10 &68.87 \\\\\n"," \n"," CuPL~\\cite{pratt2023does} & 19.59&89.29 &57.28 &48.64 &38.38 &65.44 &76.94 &84.84 &62.55 &58.97 &60.19 \\\\\n","\n"," TPT~\\cite{shutest} &17.58 &87.02 &58.46 &40.84 &28.33 &62.69 & 74.88& 84.49& 61.46 &60.82& 57.66\\\\\n"," \n"," DMN~\\cite{zhang2024dual2} &20.22 &89.09 &58.36 &50.53 & 44.94& 68.33& 74.69& 86.29& 63.70& 64.02& 62.02\\\\\n"," TDA~\\cite{karmanov2024efficient} & 17.61& 89.70&57.78 &43.74 &42.11 &68.74 &\\textbf{77.75} &86.18 &62.53 &64.18 & 61.03\\\\\n","\n"," ECALP~\\cite{li2025efficient}& 21.12& 89.94& 60.56& 54.49& 49.09& 69.39&76.97 &88.20 &64.97 &66.67 &64.14 \\\\\n"," \n","\n","\n"," \\midrule\n"," \n"," \\rowcolor{gray!30}\n"," TrustVLM*-C& \\textbf{29.34} & 89.98& \\textbf{66.09}& 60.34& 75.11& 90.62&74.53 &85.94 & 66.30 & 72.09&71.03 \\\\\n"," \n"," \n","\n"," \n"," \n"," \\rowcolor{gray!30}TrustVLM*-M & 19.89 &93.27 &57.52 & 65.43& \\textbf{86.85}& 87.25& 74.85&\\textbf{90.22} &67.16 &73.28& 71.57 \\\\\n","\n"," \\rowcolor{gray!30}TrustVLM*-D& 20.25&96.43 &56.08 &71.34& 82.43&\\textbf{99.11} &75.23 &83.65 & \\textbf{71.45}&75.34 &73.13 \\\\\n","\n","\\rowcolor{gray!30}TrustVLM*(F)-D& 27.27 & \\textbf{96.59}\t& 56.19\t& \\textbf{74.70}& 85.43&98.50 &75.17 &83.76 & 70.71& \\textbf{76.90}& \\textbf{74.52} \\\\\n","\n","\n"," \\bottomrule\n"," \\end{tabular}\n"," } \n","\\vspace{-0.15cm}\n"," \\caption{{Zero-shot classification results on fine-grained datasets with CLIP ResNet-50. } \n"," }\n"," \\label{tab:fine-grained-rn50}\n","\\end{table*}\n"],["\\begin{table}\n","\\begin{minipage}[t]{0.68\\columnwidth}\n","\\begin{adjustbox}{width=\\linewidth}\n","\n","\\begin{tabular}{l@{~~~~~}| c cccc cccc cccc cccc }\n","\\hline\n","\n","\n"," \\multicolumn{1}{c|}{{} } &\\multicolumn{4}{c|}{CLIP ResNet-50} &\\multicolumn{4}{c}{SigLIP ViT-B/32} \n"," \\\\\n"," \\multicolumn{1}{c|}{{}} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$}& AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c}{ACC$\\uparrow$}\\\\\n","\n","\\hline\n","\n"," \n"," \\multicolumn{1}{c|}{DOCTOR}&259.56 & 80.55 & 73.83& \\multicolumn{1}{c|}{55.82} & 149.27 & 85.69 & 58.63 & \\multicolumn{1}{c}{70.69} \\\\\n"," \n"," \\multicolumn{1}{c|}{MSP}& 259.76& 80.63 & 73.11 & \\multicolumn{1}{c|}{55.82} & 149.13 & 85.86 & 57.31 & \\multicolumn{1}{c}{70.69} \\\\\n","\n","\n"," \n"," \\rowcolor{gray!30}\\multicolumn{1}{c|}{TrustVLM-D}&{226.95} &\\textbf{87.31} &\\textbf{53.98} & \\multicolumn{1}{c|}{55.82} & {129.84} & \\textbf{90.35} & \\textbf{44.49} & \\multicolumn{1}{c}{70.69} \\\\\n","\n"," \\rowcolor{gray!30}\\multicolumn{1}{c|}{TrustVLM*-D}& \\textbf{135.81}& 83.51 &59.97 &\\multicolumn{1}{c|}{\\textbf{73.13}} &\\textbf{87.35} &87.47 &53.97 & \\textbf{79.99} \\\\\n","\n"," \n","\\cline{1-13}\n","\n","\n","\n","\\end{tabular} \n","\\end{adjustbox}\n","\n","\\vspace{+0.05cm}\n","\\caption{Ablation on different architectures and VLMs. The average results on fine-grained classification datasets are reported.}\n","\\label{tab:abla-vlm} \n","\n","\n","\\end{minipage}\n","\\hfill\n","\\begin{minipage}[t]{0.29\\columnwidth}\n","\n","\\begin{adjustbox}{width=\\linewidth}\n","\\begin{tabular}{l@{~~~~~}| c cccc cccc cccc cccc }\n","\\hline\n","\n","\n"," \\multicolumn{1}{c}{{i-t}} &\\multicolumn{1}{c|}{{i-i}} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$\\\\\n","\n","\\hline\n","\n"," \n"," \\multicolumn{1}{c}{$\\checkmark$} & \\multicolumn{1}{c|}{} & 188.75& 82.20 &70.10 \\\\\n"," \n"," \\multicolumn{1}{c}{} & \\multicolumn{1}{c|}{$\\checkmark$} &214.14 & 77.15 & 62.26 \\\\\n","\n","\n"," \n","\\multicolumn{1}{c}{$\\checkmark$} & \\multicolumn{1}{c|}{$\\checkmark$}& \\textbf{168.06} & \\textbf{86.98} & \\textbf{54.47} \\\\\n"," \n"," \n","\\hline\n","\n","\n","\\end{tabular}\n","\\end{adjustbox}\n","\n","\\vspace{+0.05cm}\n","\\caption{Ablation on each component.}\n","\\label{tab:abla-mod} \n","\\end{minipage}\n","\\end{table}\n"],["\\begin{figure}[t!]\n"," \\centering \\includegraphics[width=\\linewidth]{imgs/crop_score.pdf}\n","\\vspace{-0.6cm}\n"," \\caption{Score distribution for correct and wrong predictions. Our TrustVLM achieves better separation between the score distributions of correct and wrong predictions, leading to improved performance in misclassification detection.\n"," }\n"," \\label{fig:abla_score}\n","\\end{figure}\n"],["\\begin{figure}[t!]\n"," \\centering \\includegraphics[width=0.8\\linewidth]{imgs/crop_illustration.pdf}\n","\\vspace{-0.3cm}\n"," \\caption{Illustration of TrustVLM’s mechanism. Initially, the incorrect prediction receives a higher confidence score $S_{i-t}$ than the correct one, indicating overconfidence. By performing verification in the image embedding space using $S_{i-i}$, this overconfidence is mitigated. As a result, the final confidence score $\\kappa(\\bm{x})$ is significantly higher for the correct prediction than for the incorrect one.}\n"," \\label{fig:abla_illu}\n","\\end{figure}\n"],["\\begin{figure}[t!]\n"," \\centering \\includegraphics[width=0.65\\linewidth]{imgs/crop_datasets.pdf}\n","\\vspace{-0.3cm}\n"," \\caption{Representative examples from each dataset used in this work. }\n"," \\label{fig:datasets}\n","\\end{figure}\n"],["\\begin{figure}[t!]\n"," \\centering \\includegraphics[width=0.9\\linewidth]{imgs/crop_illust.pdf}\n","\\vspace{-0.3cm}\n"," \\caption{More illustration on TrustVLM’s mechanism. }\n"," \\label{fig:illus}\n","\\end{figure}\n"],["\\begin{table}[t!]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n","\\begin{tabular}{l@{~~~~~}| c cccc cccc cccc cccc }\n","\\hline\n"," \n"," \\multicolumn{1}{c|}{{} } &\\multicolumn{4}{c|}{ImageNet-A} & \\multicolumn{4}{c|}{ImageNet-V2} & \\multicolumn{4}{c}{ImageNet-R} \n"," \\\\\n"," \\multicolumn{1}{c|}{{}} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c}{ACC$\\uparrow$} \\\\\n","\\hline\n","\n","\n"," \n"," \\multicolumn{1}{c|}{DOCTOR}& 316.50 & 75.98 &77.76 & \\multicolumn{1}{c|}{{47.85}} & 180.06 & 80.09 &73.38 & \\multicolumn{1}{c|}{60.88} & 76.39 & 87.12 & 60.54 & \\multicolumn{1}{c}{73.98} \\\\\n"," \n"," \\multicolumn{1}{c|}{MSP}& \\textbf{315.99} & \\textbf{76.07} &77.76 & \\multicolumn{1}{c|}{{47.85}} & 178.82 &80.45 & 71.26 & \\multicolumn{1}{c|}{60.88} & 75.47 & 87.46 &59.43 & \\multicolumn{1}{c}{73.98} \\\\\n","\n","\n","\\rowcolor{gray!30}\n","\\multicolumn{1}{c|}{TrustVLM-D}& 324.07 &75.87 & \\textbf{77.20} & \\multicolumn{1}{c|}{47.85} &\\textbf{174.23} & \\textbf{82.18} & \\textbf{64.82}& \\multicolumn{1}{c|}{{60.88}} & \\textbf{74.94} & \\textbf{87.69} & \\textbf{58.09} & \\multicolumn{1}{c}{{73.98}} \\\\\n","\n"," \n","\n","\\hline\n","\n","\n","\n"," \\multicolumn{1}{c|}{{} } &\\multicolumn{4}{c|}{ImageNet-Sketch} & \\multicolumn{4}{c|}{ImageNet} & \\multicolumn{4}{c}{Average} \n"," \\\\\n"," \\multicolumn{1}{c|}{{}} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c}{ACC$\\uparrow$}\\\\\n","\\hline\n","\n","\n","\n"," \n"," \\multicolumn{1}{c|}{DOCTOR}& 296.20& 80.54 & 70.94 & \\multicolumn{1}{c|}{46.09} & 140.21 &80.53 & 74.58 & \\multicolumn{1}{c|}{66.71} & 201.87 & 80.85 &71.44 & \\multicolumn{1}{c}{59.10} \\\\\n"," \n"," \\multicolumn{1}{c|}{MSP} & 294.85 & 80.82 & 70.29 & \\multicolumn{1}{c|}{46.09} & 138.52 &81.04 &72.85 & \\multicolumn{1}{c|}{66.71} &200.73 & 81.17 &70.32 & \\multicolumn{1}{c}{59.10} \\\\\n","\n","\n","\\rowcolor{gray!30}\n","\\multicolumn{1}{c|}{TrustVLM-D}& \\textbf{289.52} &\\textbf{82.40} & \\textbf{65.59} & \\multicolumn{1}{c|}{{46.09}} &\\textbf{133.17} & \\textbf{83.08} & \\textbf{64.47}& \\multicolumn{1}{c|}{{66.71}} & \\textbf{199.19} & \\textbf{82.24} & \\textbf{66.03} & \\multicolumn{1}{c}{{59.10}} \\\\\n","\n","\n","\n","\\hline\n","\n","\n","\n","\n","\\end{tabular} \n","}\n","\\vspace{+0.1cm}\n","\\caption{MisD performance on ImageNet and its variants using CLIP ViT-B/16, under distribution shifts defined by prototypes computed exclusively on ImageNet and deployed directly to variant datasets.}\n","\\label{tab:imagenet-shift} \n","\\end{table}\n"],["\\begin{table}[t!]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n","\\begin{tabular}{l@{~~~~~}| c cccc cccc cccc cccc }\n","\\hline\n","\n"," \\multicolumn{1}{c|}{{} } &\\multicolumn{4}{c|}{Flower102 } & \\multicolumn{4}{c|}{DTD } & \\multicolumn{4}{c|}{Aircraft} &\\multicolumn{4}{c}{Pets} \n"," \\\\\n"," \\multicolumn{1}{c|}{{}} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c}{ACC$\\uparrow$}\\\\\n","\\hline\n","\n"," \n"," \\multicolumn{1}{c|}{DOCTOR}& 26.03& 93.13 & 38.75 & \\multicolumn{1}{c|}{83.76} & 183.86 &79.14 & 72.67 & \\multicolumn{1}{c|}{61.29} &513.33 & 78.08 &80.74 & \\multicolumn{1}{c|}{27.42} & {7.65} & 95.55 & 30.65 & \\multicolumn{1}{c}{91.55} \\\\\n"," \n"," \\multicolumn{1}{c|}{MSP}&25.14 & 93.63 &34.75 & \\multicolumn{1}{c|}{83.76} & 181.52&79.66 &72.52 & \\multicolumn{1}{c|}{61.29} & 514.42& 77.78&76.98 & \\multicolumn{1}{c|}{27.42} & {7.65} &95.54 &{28.71} & \\multicolumn{1}{c}{91.55} \\\\\n","\n"," \n"," \\rowcolor{gray!30}\\multicolumn{1}{c|}{TrustVLM-D}&{16.62} & {98.42} &{9.50} & \\multicolumn{1}{c|}{83.76} & {144.44}& {87.64} &{49.01} & \\multicolumn{1}{c|}{61.29} & {503.98}& {79.56}& {75.83} & \\multicolumn{1}{c|}{27.42} & 7.66 &{95.58} & {28.71} & \\multicolumn{1}{c}{91.55} \\\\\n","\n"," \\rowcolor{gray!30} \\multicolumn{1}{c|}{TrustVLM*-D} & {0.13}& 97.82& 15.38& \\multicolumn{1}{c|}{99.47} & {98.27}& 82.72& 71.92& \\multicolumn{1}{c|}{72.64}& {496.11} &78.79 &79.14 & \\multicolumn{1}{c|}{28.23}& {7.55} &95.56 &29.97 &{91.63} \\\\\n"," \n","\\hline\n","\n"," \\multicolumn{1}{c|}{{} } &\\multicolumn{4}{c|}{Caltech101 } & \\multicolumn{4}{c|}{Cars} & \\multicolumn{4}{c|}{EuroSAT} &\\multicolumn{4}{c}{UCF101} \n"," \\\\\n"," \\multicolumn{1}{c|}{{}} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c}{ACC$\\uparrow$}\\\\\n","\\hline\n","\n"," \\multicolumn{1}{c|}{DOCTOR}& 2.31& 94.88 &27.50 & \\multicolumn{1}{c|}{96.75} & 18.84 &89.37 & 59.81 & \\multicolumn{1}{c|}{89.42} &481.80 &72.61 & 89.46& \\multicolumn{1}{c|}{31.26} & 85.35 &88.73 & 55.21 & \\multicolumn{1}{c}{70.31} \\\\\n"," \n","\n"," \n"," \\multicolumn{1}{c|}{MSP}&2.20 & 95.21 & {26.25} & \\multicolumn{1}{c|}{96.75} & 18.73& 89.48 & 58.87 & \\multicolumn{1}{c|}{89.42} & 485.10& 72.31 &90.39 & \\multicolumn{1}{c|}{31.26} & 84.54 & 88.97 &56.28 & \\multicolumn{1}{c}{70.31} \\\\\n","\n","\\rowcolor{gray!30} \\multicolumn{1}{c|}{TrustVLM-D} & {2.08} & {95.52} & {26.25} & \\multicolumn{1}{c|}{96.75}& {18.63} &{89.57} & {57.58} & \\multicolumn{1}{c|}{89.42} & {390.00}& {90.09} &{42.82} & \\multicolumn{1}{c|}{31.26} & {74.35} & {92.14}& {41.23}& \\multicolumn{1}{c}{70.31} \\\\\n","\n"," \\rowcolor{gray!30} \\multicolumn{1}{c|}{TrustVLM*-D} & {1.76} & {95.65} & {21.13} & \\multicolumn{1}{c|}{97.12} & 22.18 &87.92 &{54.05} &\\multicolumn{1}{c|}{89.42} & {72.64} &75.64 & 73.41& \\multicolumn{1}{c|}{82.91}& {54.41}&87.91 & 64.74&{78.03} \\\\\n","\n","\n"," \n","\\hline\n","\n"," \\multicolumn{1}{c|}{{} } &\\multicolumn{4}{c|}{Food101 } & \\multicolumn{4}{c|}{SUN397} &\\multicolumn{4}{c|}{Average} \n"," \\\\\n"," \\multicolumn{1}{c|}{{}} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$}\\\\\n","\\cline{1-13}\n","\n","\n"," \n"," \\multicolumn{1}{c|}{DOCTOR}&40.90 & 84.68 & 56.72& \\multicolumn{1}{c|}{87.54} & 132.64 & 80.73 & 74.81 & \\multicolumn{1}{c|}{67.62} & 149.27 & 85.69 & 58.63 & \\multicolumn{1}{c|}{70.69} \\\\\n"," \n"," \\multicolumn{1}{c|}{MSP}& 40.70& 84.84 & {54.70} & \\multicolumn{1}{c|}{87.54} & 131.29& 81.15 &73.63 & \\multicolumn{1}{c|}{67.62} & 149.13 & 85.86 & 57.31 & \\multicolumn{1}{c|}{70.69} \\\\\n","\n","\n"," \n"," \\rowcolor{gray!30}\\multicolumn{1}{c|}{TrustVLM-D}&{22.97} &{89.84} &{54.70} & \\multicolumn{1}{c|}{87.54} & {117.67} & {85.10} & {59.27} & \\multicolumn{1}{c|}{67.62} & {129.84} & \\textbf{90.35} & \\textbf{44.49} & \\multicolumn{1}{c|}{70.69} \\\\\n","\n"," \\rowcolor{gray!30} \\multicolumn{1}{c|}{TrustVLM*-D} & 23.20& 89.56& 56.14& \\multicolumn{1}{c|}{87.80}&97.28&83.11&73.78&\\multicolumn{1}{c|}{72.63}&\\textbf{87.35}&87.47&53.97&\\multicolumn{1}{c|}{\\textbf{79.99}} \\\\\n"," \n","\\cline{1-13}\n","\n","\n","\n","\\end{tabular} \n","}\n","\\vspace{-0.0cm}\n","\\caption{Misclassification detection performance on fine-grained classification datasets with SigLIP\n","ViT-B/16.}\n","\\label{tab:sig-misd} \n","\\end{table}\n"],["\\begin{table}[t!]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n","\\begin{tabular}{l@{~~~~~}| c cccc cccc cccc cccc }\n","\\hline\n"," \n"," \\multicolumn{1}{c|}{{} } &\\multicolumn{4}{c|}{ImageNet-A} & \\multicolumn{4}{c|}{ImageNet-V2} & \\multicolumn{4}{c}{ImageNet-R} \n"," \\\\\n"," \\multicolumn{1}{c|}{{}} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c}{ACC$\\uparrow$}\\\\\n","\\hline\n","\n","\n","\n"," \n"," \\multicolumn{1}{c|}{DOCTOR}&299.17 & 79.28 &72.72 & \\multicolumn{1}{c|}{47.62} & 124.64& 83.03 & 67.64 & \\multicolumn{1}{c|}{67.55} &21.40 & 91.53 &45.00 & \\multicolumn{1}{c}{87.82} \\\\\n"," \n"," \\multicolumn{1}{c|}{MSP}& 299.17& 79.25 &72.58 & \\multicolumn{1}{c|}{47.62} &124.12 &83.21 & 67.08 & \\multicolumn{1}{c|}{67.55} & 20.92& 91.90 & 43.06 & \\multicolumn{1}{c}{87.82} \\\\\n"," \n","\n"," \\rowcolor{gray!30}\\multicolumn{1}{c|}{TrustVLM-D}&{278.77}& {82.13} &{65.09} & \\multicolumn{1}{c|}{47.62} & {123.39} &{83.76} & {65.28} & \\multicolumn{1}{c|}{67.55} & {19.55}& {92.07 }&{42.05} & \\multicolumn{1}{c}{87.82} \\\\\n","\n"," \\rowcolor{gray!30} \\multicolumn{1}{c|}{TrustVLM*-D} & 266.12 &77.68&78.19& \\multicolumn{1}{c|}{51.82}&122.70&83.05&67.17& \\multicolumn{1}{c|}{68.17}&19.49&91.85&43.33&87.98 \\\\\n","\n","\\hline\n"," \n"," \\multicolumn{1}{c|}{{} } &\\multicolumn{4}{c|}{ImageNet-Sketch} & \\multicolumn{4}{c|}{ImageNet} & \\multicolumn{4}{c}{Average} \n"," \\\\\n"," \\multicolumn{1}{c|}{{}} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c}{ACC$\\uparrow$} \\\\\n","\\hline\n","\n","\n","\n"," \n"," \\multicolumn{1}{c|}{DOCTOR} & 136.52 & 84.98 & 62.35 & \\multicolumn{1}{c|}{64.29} & 86.80 &83.40& 67.59 & \\multicolumn{1}{c|}{74.74} &133.71 & 84.44 & 63.06 & \\multicolumn{1}{c}{68.40} \\\\ \n","\n"," \n"," \\multicolumn{1}{c|}{MSP}& 135.32& 85.31& 61.24& \\multicolumn{1}{c|}{64.29} & 85.84 & 83.78 & 66.03 & \\multicolumn{1}{c|}{74.74} & 133.07 & 84.69 & 62.00& \\multicolumn{1}{c}{68.40} \\\\\n"," \n","\n","\\rowcolor{gray!30} \\multicolumn{1}{c|}{TrustVLM-D}&{133.55} &{85.84} &{59.61} & \\multicolumn{1}{c|}{64.29} & {84.46} &{84.36}& {63.73} & \\multicolumn{1}{c|}{74.74} & {127.94} & \\textbf{85.63} & \\textbf{59.15} & \\multicolumn{1}{c}{68.40} \\\\\n","\n"," \\rowcolor{gray!30} \\multicolumn{1}{c|}{TrustVLM*-D} & 132.62& 84.76& 64.17& \\multicolumn{1}{c|}{65.14}& 83.96&83.40 &67.89 & \\multicolumn{1}{c|}{75.45}& \\textbf{124.98}&84.15 &64.15 &\\textbf{69.71} \\\\\n"," \n","\\hline\n","\n","\n","\\end{tabular} \n","}\n","\\vspace{-0.0cm}\n","\\caption{Misclassification detection performance on ImageNet and its variants with SigLIP ViT-B/16. }\n","\\label{tab:sig-imagenet} \n","\\end{table}\n"],["\\begin{table}[t!]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n","\\begin{tabular}{l@{~~~~~}| c cccc cccc cccc cccc }\n","\\hline\n","\n"," \\multicolumn{1}{c|}{{} } &\\multicolumn{4}{c|}{Flower102 } & \\multicolumn{4}{c|}{DTD } & \\multicolumn{4}{c|}{Aircraft} &\\multicolumn{4}{c}{Pets} \n"," \\\\\n"," \\multicolumn{1}{c|}{{}} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c}{ACC$\\uparrow$}\\\\\n","\\hline\n","\n"," \n"," \\multicolumn{1}{c|}{DOCTOR}& 154.49& 83.85 & 67.94 & \\multicolumn{1}{c|}{61.75} & 374.42 & 75.61&83.55 & \\multicolumn{1}{c|}{40.37} & 695.10& 74.86 &79.37 & \\multicolumn{1}{c|}{15.54} & 36.51 &87.78 & 62.33 & \\multicolumn{1}{c}{83.65} \\\\\n","\n"," \\multicolumn{1}{c|}{MSP}&154.02 & 83.99 &69.57 & \\multicolumn{1}{c|}{61.75} & 374.63 &75.58 &83.85 & \\multicolumn{1}{c|}{40.37} & 693.70& 75.15 & 80.97 & \\multicolumn{1}{c|}{15.54} &35.63 &88.30 &59.83 & \\multicolumn{1}{c}{83.65} \\\\\n"," \n","\\rowcolor{gray!30} \\multicolumn{1}{c|}{TrustVLM-D}& {106.43}& {94.74} & {27.07} & \\multicolumn{1}{c|}{61.75} &{309.72} & {87.72} & {47.87} & \\multicolumn{1}{c|}{40.37} &{675.03} & {79.38} &{79.37} & \\multicolumn{1}{c|}{15.54} & {35.02} & {88.62}& {58.00} & \\multicolumn{1}{c}{83.65} \\\\\n","\n"," \\rowcolor{gray!30} \\multicolumn{1}{c|}{TrustVLM*-D} & 0.23& 98.10& 4.55& \\multicolumn{1}{c|}{99.11}& 130.86&77.64 &75.46 & \\multicolumn{1}{c|}{71.34}&622.50 &75.35 &79.68 &\\multicolumn{1}{c|}{20.25} &34.37 & 88.95& 57.67& 83.65 \\\\\n","\n","\\hline\n","\n"," \\multicolumn{1}{c|}{{} } &\\multicolumn{4}{c|}{Caltech101 } & \\multicolumn{4}{c|}{Cars} & \\multicolumn{4}{c|}{EuroSAT} &\\multicolumn{4}{c}{UCF101} \n"," \\\\\n"," \\multicolumn{1}{c|}{{}} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c}{ACC$\\uparrow$}\\\\\n","\\hline\n","\n"," \n"," \\multicolumn{1}{c|}{DOCTOR}& 30.50& 87.30 & 69.54 & \\multicolumn{1}{c|}{85.88} & 212.51& 80.23 & 74.73 & \\multicolumn{1}{c|}{55.74} &618.09 & 70.73 &83.87 & \\multicolumn{1}{c|}{23.70} & 169.84& 84.01 & 72.38 & \\multicolumn{1}{c}{58.82} \\\\\n","\n","\n"," \\multicolumn{1}{c|}{MSP}& 29.55& 87.95 &66.38 & \\multicolumn{1}{c|}{85.88} & 209.07& 80.93 &72.99 & \\multicolumn{1}{c|}{55.74} &633.47 & 67.67 &87.27 & \\multicolumn{1}{c|}{23.70} &166.92 & 84.77 &68.98 & \\multicolumn{1}{c}{58.82} \\\\\n"," \n","\n"," \n","\\rowcolor{gray!30} \\multicolumn{1}{c|}{TrustVLM-D}& {22.17}& {93.16} &{36.49} & \\multicolumn{1}{c|}{85.88} &{206.48} & {81.70} &{70.75} & \\multicolumn{1}{c|}{55.74} &{514.75} &{87.02} &{54.70} & \\multicolumn{1}{c|}{23.70} & {144.99} & {89.67} &{49.71} & \\multicolumn{1}{c}{58.82} \\\\\n","\n"," \\rowcolor{gray!30} \\multicolumn{1}{c|}{TrustVLM*-D} & 6.06 &89.61 &40.91 &\\multicolumn{1}{c|}{96.43} & 205.62& 81.41& 70.92&\\multicolumn{1}{c|}{56.08} &101.58 &70.14 &74.00 & \\multicolumn{1}{c|}{82.43}& 73.84& 85.69& 62.81& 75.34 \\\\\n","\n","\\hline\n","\n"," \\multicolumn{1}{c|}{{} } &\\multicolumn{4}{c|}{Food101 } & \\multicolumn{4}{c|}{SUN397} & \\multicolumn{4}{c|}{Average} \n"," \\\\\n"," \\multicolumn{1}{c|}{{}} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} \\\\\n","\\cline{1-13}\n","\n"," \n"," \\multicolumn{1}{c|}{DOCTOR}& 94.96& 83.59 & 65.77 & \\multicolumn{1}{c|}{73.95} & 209.19 &77.49 & 78.84 & \\multicolumn{1}{c|}{58.81} & 259.56 & 80.55 & 73.83& \\multicolumn{1}{c|}{55.82} \\\\\n","\n"," \n"," \\multicolumn{1}{c|}{MSP}& 94.32& 83.83 &64.86 & \\multicolumn{1}{c|}{73.95} & 206.31 &78.16 & 76.41 & \\multicolumn{1}{c|}{58.81}& 259.76 & 80.63 & 73.11 & \\multicolumn{1}{c|}{55.82} \\\\\n"," \n","\n"," \n"," \\rowcolor{gray!30}\\multicolumn{1}{c|}{TrustVLM-D}& {76.19}& {87.05} &{58.46} & \\multicolumn{1}{c|}{73.95} & {178.75} & {84.08} & {57.38} & \\multicolumn{1}{c|}{58.81} & {226.95} & \\textbf{87.31} & \\textbf{53.98} & \\multicolumn{1}{c|}{55.82} \\\\\n","\n"," \\rowcolor{gray!30} \\multicolumn{1}{c|}{TrustVLM*-D} & 70.64 & 87.03 & 60.27 & \\multicolumn{1}{c|}{75.23} & 112.43&81.19 &73.47 &\\multicolumn{1}{c|}{71.45} & \\textbf{135.81} &83.51 &59.97 &\\multicolumn{1}{c|}{\\textbf{73.13}} \\\\\n"," \n","\\cline{1-13}\n","\n","\n","\n","\\end{tabular} \n","}\n","\\vspace{-0.0cm}\n","\\caption{Misclassification detection performance on fine-grained classification datasets with CLIP ResNet-50.}\n","\\label{tab:rn50-misd} \n","\\end{table}\n"],["\\begin{table}[t!]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n","\\begin{tabular}{l@{~~~~~}| c cccc cccc cccc cccc }\n","\\hline\n"," \n"," \\multicolumn{1}{c|}{{} } &\\multicolumn{4}{c|}{ImageNet-A} & \\multicolumn{4}{c|}{ImageNet-V2} & \\multicolumn{4}{c}{ImageNet-R}\n"," \\\\\n"," \\multicolumn{1}{c|}{{}} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c}{ACC$\\uparrow$}\\\\\n","\\hline\n","\n","\n"," \n"," \\multicolumn{1}{c|}{DOCTOR}& 672.36& 66.71 & 86.78 & \\multicolumn{1}{c|}{22.79} &256.91 & 79.22 & 75.41 & \\multicolumn{1}{c|}{51.50} & 188.11& 84.28 & 68.02 & \\multicolumn{1}{c}{56.36} \\\\\n","\n"," \n"," \\multicolumn{1}{c|}{MSP}&673.06 & 66.68 &86.39 & \\multicolumn{1}{c|}{22.79} & 255.65 &79.53 & 74.83 & \\multicolumn{1}{c|}{51.50} &187.18 & 84.51 & 66.21 & \\multicolumn{1}{c}{56.36} \\\\\n"," \n","\n"," \n"," \\rowcolor{gray!30}\\multicolumn{1}{c|}{TrustVLM-D}&{629.41} & {72.99} &{75.33} & \\multicolumn{1}{c|}{22.79} & {242.91} &{82.75} &{62.07} & \\multicolumn{1}{c|}{51.50} & {172.53}& {87.58}&{56.44} & \\multicolumn{1}{c}{56.36} \\\\\n","\n"," \\rowcolor{gray!30} \\multicolumn{1}{c|}{TrustVLM*-D} & 583.73 &67.67&85.08&\\multicolumn{1}{c|}{27.99}& 231.81& 77.67& 75.42& \\multicolumn{1}{c|}{56.13}&166.54 & 83.35& 71.47& 60.34 \\\\\n","\n","\\hline\n","\n"," \n"," \\multicolumn{1}{c|}{{} } &\\multicolumn{4}{c|}{ImageNet-Sketch} & \\multicolumn{4}{c|}{ImageNet} & \\multicolumn{4}{c}{Average} \n"," \\\\\n"," \\multicolumn{1}{c|}{{}} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c}{ACC$\\uparrow$}\\\\\n","\\hline\n","\n","\n"," \n"," \\multicolumn{1}{c|}{DOCTOR}& 446.07& 78.27 &74.71 & \\multicolumn{1}{c|}{32.99} & 204.36& 79.44& 73.82&\\multicolumn{1}{c|}{58.18} & 353.56 & 77.58 & 75.75 & \\multicolumn{1}{c}{44.36} \\\\\n","\n"," \n"," \\multicolumn{1}{c|}{MSP}&444.12 &78.62 &73.94 & \\multicolumn{1}{c|}{32.99}&202.30& 79.96& 72.03 &\\multicolumn{1}{c|}{58.18} &352.46 & 77.86 & 74.68 & \\multicolumn{1}{c}{44.36} \\\\\n"," \n","\n"," \n","\\rowcolor{gray!30} \\multicolumn{1}{c|}{TrustVLM-D}&{409.65} & {85.09} &{52.28} & \\multicolumn{1}{c|}{32.99} &{183.69} &{84.24} &{58.35} &\\multicolumn{1}{c|}{58.18} & {327.64} & \\textbf{82.53} & \\textbf{60.89} & \\multicolumn{1}{c}{44.36} \\\\\n","\n"," \\rowcolor{gray!30} \\multicolumn{1}{c|}{TrustVLM*-D} & 338.03 & 71.57& 80.89& \\multicolumn{1}{c|}{48.05}& 168.30&77.91 &74.34 &\\multicolumn{1}{c|}{65.00} &\\textbf{297.68} &75.63 &77.44 &\\textbf{51.50} \\\\\n","\n","\\hline\n","\n","\n","\\end{tabular} \n","}\n","\\vspace{-0.0cm}\n","\\caption{Misclassification detection performance on ImageNet and its variants with CLIP ResNet-50.}\n","\\label{tab:rn50-imagenet} \n","\\end{table}\n"]],"string":"[\n [\n \"\\\\begin{figure}[t!]\\n\",\n \" \\\\centering \\\\includegraphics[width=\\\\linewidth]{imgs/crop_emb.pdf}\\n\",\n \"\\\\vspace{-0.6cm}\\n\",\n \" \\\\caption{(a) CLIP’s image and text embeddings are located in two completely separate regions of the embedding space. (b) The concept of \\\"dog\\\" and \\\"seaplane\\\" is more distinguishable in the image embedding space than in the text embedding space. }\\n\",\n \" \\\\label{fig:clip_emb}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{figure}[t!]\\n\",\n \" \\\\centering \\\\includegraphics[width=\\\\linewidth]{imgs/crop_frame.pdf}\\n\",\n \"\\\\vspace{-0.7cm}\\n\",\n \" \\\\caption{The proposed TrustVLM framework comprises three main steps. Initially, visual prototypes for each class are generated and stored using a pre-trained vision encoder. Subsequently, the VLMs perform zero-shot classification and yield an image-to-text similarity score, $S_{i-t}$. In the third step, the initial prediction is verified using image-to-image similarity, providing an additional confidence score, $S_{i-i}$. Finally, these two scores are combined to determine the overall prediction confidence.}\\n\",\n \" \\\\label{fig:frame}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table}[t!]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \"\\\\begin{tabular}{l@{~~~~~}| c cccc cccc cccc cccc}\\n\",\n \"\\\\hline\\n\",\n \" \\\\multicolumn{1}{c|}{} &\\\\multicolumn{4}{c|}{Flower102 } & \\\\multicolumn{4}{c|}{DTD } & \\\\multicolumn{4}{c|}{Aircraft} &\\\\multicolumn{4}{c}{Pets} \\n\",\n \" \\\\\\\\\\n\",\n \" \\\\multicolumn{1}{c|}{{}} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c}{ACC$\\\\uparrow$}\\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" \\\\multicolumn{1}{c|}{MaxLogit}&167.14 & 74.92 & 81.61 & \\\\multicolumn{1}{c|}{67.36} & 395.13 &69.63 & 85.23 & \\\\multicolumn{1}{c|}{44.39} & 731.25& 55.73 &93.77 & \\\\multicolumn{1}{c|}{23.85} & 48.75& 75.37 &68.59 & \\\\multicolumn{1}{c}{88.23} \\\\\\\\\\n\",\n \"\\n\",\n \" \\\\multicolumn{1}{c|}{Energy}&194.67 &69.00 & 91.93 & \\\\multicolumn{1}{c|}{67.36} & 433.25 & 64.82 & 90.01 & \\\\multicolumn{1}{c|}{44.39} & 780.11& 45.48 &97.71 & \\\\multicolumn{1}{c|}{23.85} & 56.23& 71.90 &70.67 & \\\\multicolumn{1}{c}{88.23} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{Entropy}&117.27 &84.88 &63.48 & \\\\multicolumn{1}{c|}{67.36} & 319.02 &79.03 & 77.05 & \\\\multicolumn{1}{c|}{44.39} & 575.71& 73.12 & 83.96 & \\\\multicolumn{1}{c|}{23.85} & 21.91& 89.31 &53.35 & \\\\multicolumn{1}{c}{88.23} \\\\\\\\\\n\",\n \" \\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{MCM}& 153.63& 78.22 & 71.02 & \\\\multicolumn{1}{c|}{67.36} & 333.43 &78.16 & 78.45 & \\\\multicolumn{1}{c|}{44.39} & 583.28& 72.58 &81.65 & \\\\multicolumn{1}{c|}{23.85} & 44.95& 77.03 &64.90 & \\\\multicolumn{1}{c}{88.23} \\\\\\\\\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{DOCTOR}&112.82 &85.82 &62.48 & \\\\multicolumn{1}{c|}{67.36} & 314.37& 79.66& 76.51 & \\\\multicolumn{1}{c|}{44.39} & 575.53& 73.05 &83.84 & \\\\multicolumn{1}{c|}{23.85} & 21.08 &89.92 &55.20 & \\\\multicolumn{1}{c}{88.23} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{MSP}& 112.27&85.91 &63.98 & \\\\multicolumn{1}{c|}{67.36} &313.43 &79.81 &77.36 & \\\\multicolumn{1}{c|}{44.39} & 576.97&72.62 & 85.61 & \\\\multicolumn{1}{c|}{23.85} & 21.04& 89.94 &52.19 & \\\\multicolumn{1}{c}{88.23} \\\\\\\\\\n\",\n \" \\n\",\n \"\\\\rowcolor{gray!30}\\n\",\n \" \\\\multicolumn{1}{c|}{TrustVLM-C}&101.42 & 88.69&54.91 & \\\\multicolumn{1}{c|}{67.36} & 302.18 & 82.52 & 67.27 & \\\\multicolumn{1}{c|}{44.39} & 563.77& 75.20 &{81.51} & \\\\multicolumn{1}{c|}{23.85} & 20.93 & 89.89 &51.73 & \\\\multicolumn{1}{c}{88.23} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\rowcolor{gray!30}\\n\",\n \" \\\\multicolumn{1}{c|}{TrustVLM-M}&103.68 & 88.29 & 53.42 & \\\\multicolumn{1}{c|}{67.36} & 298.17 &83.16 &65.50 & \\\\multicolumn{1}{c|}{44.39} &574.57 &73.22 & 84.31& \\\\multicolumn{1}{c|}{23.85} & {20.41} & \\\\textbf{90.38} &\\\\textbf{48.27} & \\\\multicolumn{1}{c}{{88.23}} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\rowcolor{gray!30}\\n\",\n \" \\\\multicolumn{1}{c|}{TrustVLM-D}& 77.30 &{95.05} & 30.06 & \\\\multicolumn{1}{c|}{67.36} & 268.71 &\\\\textbf{88.55} &\\\\textbf{44.10} & \\\\multicolumn{1}{c|}{44.39} & {562.02} & {75.62} & 83.21 & \\\\multicolumn{1}{c|}{23.85} & 20.69& 90.05 &50.81 & \\\\multicolumn{1}{c}{88.23} \\\\\\\\\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" \\\\rowcolor{gray!30}\\n\",\n \" \\\\multicolumn{1}{c|}{TrustVLM*-D}&0.52 &95.96 &13.04 &\\\\multicolumn{1}{c|}{\\\\textbf{99.07}} & 124.15 &78.39 &72.14 &\\\\multicolumn{1}{c|}{71.57} &554.12 &75.36 &83.05 &\\\\multicolumn{1}{c|}{24.60} &\\\\textbf{20.05} &90.30 &49.07 &\\\\textbf{88.28}\\\\\\\\ % v3 \\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\\\rowcolor{gray!30} \\\\multicolumn{1}{c|}{TrustVLM*(F)-D}& \\\\textbf{0.41} &\\\\textbf{98.26} & \\\\textbf{7.69} & \\\\multicolumn{1}{c|}{98.42} & \\\\textbf{96.45} & 80.30 & 70.96& \\\\multicolumn{1}{c|}{\\\\textbf{74.76}} & \\\\textbf{544.40}& \\\\textbf{76.85} & \\\\textbf{78.63}& \\\\multicolumn{1}{c|}{\\\\textbf{24.90}} & \\\\textbf{20.05} & 90.30 & 49.07 & \\\\multicolumn{1}{c}{\\\\textbf{88.28}} \\\\\\\\ % v3\\n\",\n \"\\n\",\n \" \\\\hline\\n\",\n \"\\n\",\n \"\\n\",\n \" \\\\multicolumn{1}{c|}{{} } &\\\\multicolumn{4}{c|}{Caltech101 } & \\\\multicolumn{4}{c|}{Cars} & \\\\multicolumn{4}{c|}{EuroSAT} &\\\\multicolumn{4}{c}{UCF101} \\n\",\n \" \\\\\\\\\\n\",\n \" \\\\multicolumn{1}{c|}{{}} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c}{ACC$\\\\uparrow$}\\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{MaxLogit}&53.72 &52.43 &94.55 & \\\\multicolumn{1}{c|}{93.31} &259.81 & 61.94 &89.58 & \\\\multicolumn{1}{c|}{65.61} & 522.56& 59.42 &87.79 & \\\\multicolumn{1}{c|}{42.10} & 258.47 & 61.85 & 91.41 & \\\\multicolumn{1}{c}{65.21} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{Energy}&59.26 &48.81 &96.36 & \\\\multicolumn{1}{c|}{93.31} &304.90 & 54.85& 93.89& \\\\multicolumn{1}{c|}{65.61} & 568.57& 53.44 &88.30 & \\\\multicolumn{1}{c|}{42.10} & 299.84 & 55.38 & 94.83 & \\\\multicolumn{1}{c}{65.21} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{Entropy}& 15.79&82.47 &79.39 & \\\\multicolumn{1}{c|}{93.31} & 145.61 & 79.83&75.76 & \\\\multicolumn{1}{c|}{65.61} &389.07 &72.18 &83.16 & \\\\multicolumn{1}{c|}{42.10} & 130.10& 83.97 & 70.52 & \\\\multicolumn{1}{c}{65.21} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{MCM}&45.55 &61.00 &83.54 & \\\\multicolumn{1}{c|}{93.31} & 248.87 &63.77 &85.97 & \\\\multicolumn{1}{c|}{65.61} & 416.37& 71.28 &81.18 & \\\\multicolumn{1}{c|}{42.10} &188.00 &74.14 & 75.47 & \\\\multicolumn{1}{c}{65.21} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{DOCTOR}& 12.89& 86.06&76.36 & \\\\multicolumn{1}{c|}{93.31} & 138.00 &81.50 &73.66 & \\\\multicolumn{1}{c|}{65.61} & 368.77& 74.43 & 81.69 & \\\\multicolumn{1}{c|}{42.10} &123.41 &85.67 &65.81 & \\\\multicolumn{1}{c}{65.21} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{MSP}&12.23 & 86.99 &67.88 & \\\\multicolumn{1}{c|}{93.31} & 136.27 &81.95 & 72.25 & \\\\multicolumn{1}{c|}{65.61} & 355.66&76.39 &80.65 & \\\\multicolumn{1}{c|}{42.10} & 122.44 &85.98 & 64.89 & \\\\multicolumn{1}{c}{65.21} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\rowcolor{gray!30}\\n\",\n \" \\\\multicolumn{1}{c|}{TrustVLM-C}& 13.25& 86.81 &70.30 & \\\\multicolumn{1}{c|}{93.31} &{129.45} & \\\\textbf{83.67} & \\\\textbf{67.73} & \\\\multicolumn{1}{c|}{65.61} & 322.52&82.90 &55.73 & \\\\multicolumn{1}{c|}{42.10} &111.95 & 88.69 &55.93 & \\\\multicolumn{1}{c}{65.21} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\rowcolor{gray!30}\\n\",\n \" \\\\multicolumn{1}{c|}{TrustVLM-M}&{10.81} & 88.97 & 64.24 & \\\\multicolumn{1}{c|}{93.31} & 134.25 & 82.53 &72.29 & \\\\multicolumn{1}{c|}{65.61} & 320.12& 83.03 & 56.32 & \\\\multicolumn{1}{c|}{42.10} & 114.60& 87.93 &58.81 & \\\\multicolumn{1}{c}{65.21} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\rowcolor{gray!30}\\n\",\n \" \\\\multicolumn{1}{c|}{TrustVLM-D}&11.11& {90.51} & {47.27} & \\\\multicolumn{1}{c|}{93.31} & 137.54 &82.05 &70.62 & \\\\multicolumn{1}{c|}{65.61} &{303.79} & \\\\textbf{85.48} & \\\\textbf{53.52} & \\\\multicolumn{1}{c|}{42.10} & {107.13} &\\\\textbf{90.21} &\\\\textbf{50.68} & \\\\multicolumn{1}{c}{65.21} \\\\\\\\\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\\\rowcolor{gray!30}\\n\",\n \" \\\\multicolumn{1}{c|}{TrustVLM*-D}& 5.69 & 89.48 & \\\\textbf{35.62} & \\\\multicolumn{1}{c|}{97.04} & 137.97 & 81.73 & 71.25 & \\\\multicolumn{1}{c|}{65.83} & 72.96 & 74.87 & 73.50 & \\\\multicolumn{1}{c|}{83.56} & 65.26 & 86.23 & 62.93 & 77.11\\\\\\\\ % v3 \\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\\\rowcolor{gray!30} \\\\multicolumn{1}{c|}{TrustVLM*(F)-D}& \\\\textbf{2.61}& \\\\textbf{93.29}& 35.71& \\\\multicolumn{1}{c|}{\\\\textbf{97.16}} & \\\\textbf{132.54} & 82.58 &68.70 & \\\\multicolumn{1}{c|}{\\\\textbf{66.02}} &\\\\textbf{54.35} &77.31 & 72.48 & \\\\multicolumn{1}{c|}{\\\\textbf{85.69}} & \\\\textbf{55.81}& 87.26 & 66.06 & \\\\multicolumn{1}{c}{\\\\textbf{78.35}} \\\\\\\\ % v3\\n\",\n \" \\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\n\",\n \" \\\\multicolumn{1}{c|}{{} } &\\\\multicolumn{4}{c|}{Food101 } & \\\\multicolumn{4}{c|}{SUN397} & \\\\multicolumn{4}{c|}{Average} \\n\",\n \" \\\\\\\\\\n\",\n \" \\\\multicolumn{1}{c|}{{}} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} \\\\\\\\\\n\",\n \"\\\\cline{1-13}\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" \\\\multicolumn{1}{c|}{MaxLogit}& 67.81& 75.81& 78.13 & \\\\multicolumn{1}{c|}{83.66} & 278.17 & 62.77 &90.46 & \\\\multicolumn{1}{c|}{62.57} & 278.28 & 64.99 & 86.11 & \\\\multicolumn{1}{c|}{63.63} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{Energy}&76.05 & 72.33 & 84.69 & \\\\multicolumn{1}{c|}{83.66} & 304.50 &58.34 &93.47 & \\\\multicolumn{1}{c|}{62.57} & 307.74 & 59.44 & 90.19 & \\\\multicolumn{1}{c|}{63.63} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{Entropy}&55.80 &83.76 &63.73 & \\\\multicolumn{1}{c|}{83.66} & 194.33 &75.19 &80.97 & \\\\multicolumn{1}{c|}{62.57} & 196.46 & 80.37 & 73.14 & \\\\multicolumn{1}{c|}{63.63} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{MCM}&60.90 & 79.90 & 67.23 & \\\\multicolumn{1}{c|}{83.66} & 242.95 &69.09 &83.62 & \\\\multicolumn{1}{c|}{62.57} & 231.79 & 72.52 & 77.30 & \\\\multicolumn{1}{c|}{63.63} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{DOCTOR}& 54.99& 84.39&61.11 & \\\\multicolumn{1}{c|}{83.66} & 184.41 &77.35 & 77.37& \\\\multicolumn{1}{c|}{62.57} & 190.63 & 81.78 & 71.40 & \\\\multicolumn{1}{c|}{63.63} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{MSP}&54.80 &84.51 &59.73 & \\\\multicolumn{1}{c|}{83.66} & 182.39 & 77.90 & 76.42 & \\\\multicolumn{1}{c|}{62.57} & 188.75 & 82.20 & 70.10 & \\\\multicolumn{1}{c|}{63.63} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\rowcolor{gray!30}\\n\",\n \" \\\\multicolumn{1}{c|}{TrustVLM-C}&{36.02} &88.34 & 57.67 & \\\\multicolumn{1}{c|}{83.66} & 171.89 &80.39 &69.20 & \\\\multicolumn{1}{c|}{62.57} & 177.34 & 84.71 & 63.20 & \\\\multicolumn{1}{c|}{63.63} \\\\\\\\\\n\",\n \"\\n\",\n \" \\\\rowcolor{gray!30}\\n\",\n \" \\\\multicolumn{1}{c|}{TrustVLM-M}& 40.92& 86.65& 59.01& \\\\multicolumn{1}{c|}{83.66} & 172.98 & 79.83 & 71.63 & \\\\multicolumn{1}{c|}{62.57} & 179.05 & 84.40 & 63.38 & \\\\multicolumn{1}{c|}{63.63} \\\\\\\\\\n\",\n \" \\n\",\n \"\\\\rowcolor{gray!30} \\n\",\n \" \\\\multicolumn{1}{c|}{TrustVLM-D}&36.18 &{88.52} &\\\\textbf{55.96} & \\\\multicolumn{1}{c|}{83.66} & {156.16} & \\\\textbf{83.80} &\\\\textbf{58.44} & \\\\multicolumn{1}{c|}{62.57} & {168.06} & \\\\textbf{86.98} & \\\\textbf{54.47} & \\\\multicolumn{1}{c|}{63.63} \\\\\\\\\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\\\rowcolor{gray!30}\\n\",\n \" \\\\multicolumn{1}{c|}{TrustVLM*-D}& 35.01 & 88.49 & 57.47 & \\\\multicolumn{1}{c|}{84.10} & 104.25 & 82.31 & 72.82 & \\\\multicolumn{1}{c|}{\\\\textbf{72.18}} & 112.00 & 84.31 & 59.09 & \\\\multicolumn{1}{c|}{76.33} \\\\\\\\ % v3 \\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\\\rowcolor{gray!30} \\\\multicolumn{1}{c|}{TrustVLM*(F)-D}& \\\\textbf{34.16} &\\\\textbf{88.79} &56.19 & \\\\multicolumn{1}{c|}{\\\\textbf{84.15}} & \\\\textbf{103.05} & 83.43 & 72.68 & \\\\multicolumn{1}{c|}{{71.33}} & \\\\textbf{104.38} & 85.84 & 57.82& \\\\multicolumn{1}{c|}{\\\\textbf{76.91}} \\\\\\\\ % v3\\n\",\n \"\\n\",\n \" \\n\",\n \"\\\\cline{1-13}\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\\end{tabular} \\n\",\n \"}\\n\",\n \"\\\\vspace{+0.0cm}\\n\",\n \"\\\\caption{Misclassification detection performance on fine-grained classification datasets with CLIP ViT-B/16, where -C, -M, and -D are with CLIP-I, MoCo v2, and DINOv2 as auxiliary vision encoders. AURC is multiplied by $10^3$ following previous work~\\\\cite{zhu2023openmix}. }\\n\",\n \"\\\\vspace{-0.6cm}\\n\",\n \"\\\\label{tab:misd} \\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[t!]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \"\\\\begin{tabular}{l@{~~~~~}| c cccc cccc cccc cccc }\\n\",\n \"\\\\hline\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{{} } &\\\\multicolumn{4}{c|}{ImageNet-A} & \\\\multicolumn{4}{c|}{ImageNet-V2} & \\\\multicolumn{4}{c}{ImageNet-R} \\n\",\n \" \\\\\\\\\\n\",\n \" \\\\multicolumn{1}{c|}{{}} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c}{ACC$\\\\uparrow$} \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" \\\\multicolumn{1}{c|}{MaxLogit}&387.23 &64.16 & 86.59 & \\\\multicolumn{1}{c|}{50.08} & 254.37& 68.58& 85.22 & \\\\multicolumn{1}{c|}{61.20} &138.25 & 72.98 & 79.48 & \\\\multicolumn{1}{c}{74.19} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{Energy}& 421.78& 59.15& 90.00 & \\\\multicolumn{1}{c|}{50.08} & 278.89& 64.64 &89.26 & \\\\multicolumn{1}{c|}{61.20} &160.84 & 67.39 &86.55 & \\\\multicolumn{1}{c}{74.19} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{Entropy}& 298.23& 75.46&77.47 & \\\\multicolumn{1}{c|}{50.08} & 189.58 & 77.57 &79.90 & \\\\multicolumn{1}{c|}{61.20} & 79.64& 85.80 & 63.13& \\\\multicolumn{1}{c}{74.19} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{MCM}& 341.67& 71.99 &78.27 & \\\\multicolumn{1}{c|}{50.08} & 236.32 &71.62 & 80.38 & \\\\multicolumn{1}{c|}{61.20} &109.67 &80.49 &64.73 & \\\\multicolumn{1}{c}{74.19} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{DOCTOR}&290.78 &76.67 &76.93 & \\\\multicolumn{1}{c|}{50.08} & 181.67 & 79.32 &73.67 & \\\\multicolumn{1}{c|}{61.20} & 75.33& 87.14 &60.81 & \\\\multicolumn{1}{c}{74.19} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{MSP}& 290.46& 76.70 & 77.06 & \\\\multicolumn{1}{c|}{50.08} & 180.32 &79.72 &71.82 & \\\\multicolumn{1}{c|}{61.20} &74.36 &87.50 &59.66 & \\\\multicolumn{1}{c}{74.19} \\\\\\\\\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\\rowcolor{gray!30}\\n\",\n \"\\\\multicolumn{1}{c|}{TrustVLM-D}& {274.46}& \\\\textbf{79.17} &\\\\textbf{70.27} & \\\\multicolumn{1}{c|}{50.08} & {176.82} & \\\\textbf{81.27} & \\\\textbf{66.02} & \\\\multicolumn{1}{c|}{61.20} &{72.10} &\\\\textbf{88.03} &\\\\textbf{57.70} & \\\\multicolumn{1}{c}{74.19} \\\\\\\\\\n\",\n \"\\n\",\n \" \\\\rowcolor{gray!30}\\n\",\n \"\\\\multicolumn{1}{c|}{TrustVLM*-D}& 266.48& 75.94 & 77.67 & \\\\multicolumn{1}{c|}{\\\\textbf{53.02}} &\\\\textbf{172.65} &77.93 &74.92 & \\\\multicolumn{1}{c|}{63.92} &71.72& 87.50 & 60.03& \\\\multicolumn{1}{c}{74.62} \\\\\\\\\\n\",\n \"\\n\",\n \"\\n\",\n \" \\\\rowcolor{gray!30}\\n\",\n \"\\\\multicolumn{1}{c|}{TrustVLM*(F)-D}&\\\\textbf{264.36} & 77.09 & 76.90 & \\\\multicolumn{1}{c|}{52.61} & 176.13& 77.71& 72.98 & \\\\multicolumn{1}{c|}{\\\\textbf{63.98}} &\\\\textbf{71.15}& 87.42 & 60.37 & \\\\multicolumn{1}{c}{\\\\textbf{74.83}} \\\\\\\\ % ImageNet-V2 5ep\\n\",\n \"\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" \\\\multicolumn{1}{c|}{{} } &\\\\multicolumn{4}{c|}{ImageNet-Sketch} & \\\\multicolumn{4}{c|}{ImageNet} & \\\\multicolumn{4}{c}{Average} \\n\",\n \" \\\\\\\\\\n\",\n \" \\\\multicolumn{1}{c|}{{}} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c}{ACC$\\\\uparrow$}\\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" \\\\multicolumn{1}{c|}{MaxLogit}& 422.41& 65.28 & 87.25 & \\\\multicolumn{1}{c|}{45.67} & 228.56 & 65.95 & 85.80 & \\\\multicolumn{1}{c|}{66.68} & 286.16 & 67.39 & 84.87 & \\\\multicolumn{1}{c}{59.56} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{Energy} & 472.36 & 58.03 &92.25 & \\\\multicolumn{1}{c|}{45.67} & 253.77 & 61.61 &89.95 & \\\\multicolumn{1}{c|}{66.68} & 317.53 & 62.16 & 89.60 & \\\\multicolumn{1}{c}{59.56} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{Entropy} & 313.83 &78.37 &74.27 & \\\\multicolumn{1}{c|}{45.67} & 149.69& 78.26 &78.01 & \\\\multicolumn{1}{c|}{66.68}& 206.19 & 79.09 & 74.56 & \\\\multicolumn{1}{c}{59.56} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{MCM} & 400.13 &70.10 & 76.52 & \\\\multicolumn{1}{c|}{45.67} &206.72 & 69.73& 81.27 & \\\\multicolumn{1}{c|}{66.68} &258.90 & 72.79 & 76.23 & \\\\multicolumn{1}{c}{59.56} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{DOCTOR}&300.85 & 80.49 &70.75 & \\\\multicolumn{1}{c|}{45.67} & 140.70 &80.48 &74.46 & \\\\multicolumn{1}{c|}{66.68} & 197.87 & 80.82 & 71.32 & \\\\multicolumn{1}{c}{59.56} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{MSP} & 299.57& 80.74 & 70.51 & \\\\multicolumn{1}{c|}{45.67} & 138.99& 81.00 & 72.74 & \\\\multicolumn{1}{c|}{66.68} & 196.74 & 81.13 & 70.36 & \\\\multicolumn{1}{c}{59.56} \\\\\\\\\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\\rowcolor{gray!30}\\n\",\n \"\\\\multicolumn{1}{c|}{TrustVLM-D}& {280.72} &\\\\textbf{84.35} & \\\\textbf{58.99} & \\\\multicolumn{1}{c|}{45.67} &{132.10} & \\\\textbf{83.38} & \\\\textbf{64.04}& \\\\multicolumn{1}{c|}{66.68} & {187.24} & \\\\textbf{83.24} & \\\\textbf{63.40} & \\\\multicolumn{1}{c}{59.56} \\\\\\\\\\n\",\n \"\\n\",\n \" \\\\rowcolor{gray!30}\\n\",\n \"\\\\multicolumn{1}{c|}{TrustVLM*-D}& 264.51& 74.74& 84.77& \\\\multicolumn{1}{c|}{53.13} &\\\\textbf{126.31} & 78.82 &77.43 & \\\\multicolumn{1}{c|}{70.58} &180.33& 78.99 &74.96 & \\\\multicolumn{1}{c}{63.05} \\\\\\\\\\n\",\n \"\\n\",\n \" \\\\rowcolor{gray!30}\\n\",\n \"\\\\multicolumn{1}{c|}{TrustVLM*(F)-D}& \\\\textbf{243.44}&73.80 & 76.90 & \\\\multicolumn{1}{c|}{\\\\textbf{58.20}} & 129.38& 77.33&75.28 & \\\\multicolumn{1}{c|}{\\\\textbf{71.75}} &\\\\textbf{176.89} & 78.67 &72.49 & \\\\multicolumn{1}{c}{\\\\textbf{64.27}} \\\\\\\\ % ImageNet 5ep\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\\end{tabular} \\n\",\n \"}\\n\",\n \"\\\\vspace{+0.0cm}\\n\",\n \"\\\\caption{Misclassification detection performance on ImageNet and its variants with CLIP ViT-B/16.}\\n\",\n \"\\\\label{tab:imagenet} \\n\",\n \"\\\\end{table}% \\\\input{tables/cross-dataset}\\n\"\n ],\n [\n \"\\\\begin{table}[t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\\\begin{tabular}{cc|ccc|ccc|ccc|ccc}\\n\",\n \"\\\\hline\\n\",\n \"\\\\multirow{3}{*}{} & \\\\multirow{3}{*}{} & \\\\multicolumn{3}{c|}{CIFAR-10} & \\\\multicolumn{3}{c|}{CIFAR-100} & \\\\multicolumn{3}{c|}{EuroSAT} & \\\\multicolumn{3}{c}{Average} \\\\\\\\\\n\",\n \" & & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & ACC$\\\\uparrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & ACC$\\\\uparrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & ACC$\\\\uparrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & ACC$\\\\uparrow$ \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\\\multirow{1}{*}{ResNet-101} \\n\",\n \"& MSP & 85.98& 62.98& 78.01& 80.72& 73.40& 48.50& 61.73& 88.98& 30.30 & 76.14& 75.12& 52.27\\\\\\\\\\n\",\n \"& ORCA & 85.93 & 62.68 & 80.60 & 80.46 & 72.38 & 53.11 & 69.01 & 86.43 & 34.76 & 78.47& 73.83& 56.16\\\\\\\\\\n\",\n \"\\\\rowcolor{gray!30} \\\\cellcolor{white!30}& TrustVLM-D & \\\\textbf{94.76} & \\\\textbf{26.82} & 78.01 & \\\\textbf{90.03} & \\\\textbf{39.46} & 48.50 & \\\\textbf{74.03} & \\\\textbf{66.69} & 30.30& \\\\textbf{86.27}& \\\\textbf{44.32}& 52.27\\\\\\\\\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\\rowcolor{gray!30} \\\\cellcolor{white!30}& TrustVLM*-D & 90.97& 41.88& \\\\textbf{95.63} & 82.27& 65.52& \\\\textbf{79.35}& 72.08 & 72.15 & \\\\textbf{81.78} & 81.77& 59.85& \\\\textbf{85.59}\\\\\\\\\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\\hline\\n\",\n \"\\\\multirow{1}{*}{ViT-B/32} \\n\",\n \"& MSP & 88.92& 58.66& 88.92& 81.15& 71.09& 58.42& 76.42& 80.24& 41.11 & 82.16&70.00& 62.82\\\\\\\\\\n\",\n \"\\n\",\n \"& ORCA & 89.00 & 52.70 & 90.00 & 83.40 & 67.00 & 66.50 & 77.55 & 71.29 & 50.00& 83.32& 63.66& 68.83 \\\\\\\\\\n\",\n \"\\n\",\n \"\\\\rowcolor{gray!30} \\\\cellcolor{white!30}& TrustVLM-D & \\\\textbf{94.94} & 31.85 & 88.92 & \\\\textbf{89.13} & \\\\textbf{45.63} & 58.42 & \\\\textbf{78.88} & \\\\textbf{69.36} &41.11& \\\\textbf{87.65}& \\\\textbf{48.95}& 62.82 \\\\\\\\\\n\",\n \"\\n\",\n \"\\\\rowcolor{gray!30} \\\\cellcolor{white!30} & TrustVLM*-D & 94.06 &\\\\textbf{29.58} & \\\\textbf{95.91} &84.95 & 62.99 & \\\\textbf{79.98} & 74.70& 72.77 & \\\\textbf{82.81}& 84.57& 55.11& \\\\textbf{86.23}\\\\\\\\\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\\hline\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\vspace{-0.0cm}\\n\",\n \"\\\\caption{MisD performance compared with ORCA with CLIP ResNet-101 and ViT-B/32.}\\n\",\n \"\\\\label{tab:RN101-ORCA} \\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table*}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\resizebox{0.95\\\\linewidth}{!}{\\n\",\n \" \\\\begin{tabular}{l*{11}c}\\n\",\n \" \\\\toprule\\n\",\n \" Method & Aircraft & Caltech101 & Cars & DTD & EuroSAT & Flower102 & Food101 & Pets & SUN397 & UCF101 & Average \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \"\\n\",\n \"\\n\",\n \" CLIP-RN50 &15.54 & 85.88& 55.74& 40.37&23.70 & 61.75& 73.95& 83.65&58.81 & 58.74& 55.81\\\\\\\\\\n\",\n \"\\n\",\n \" CoOp~\\\\cite{zhou2022learning} & 22.20&87.70 &61.30 &52.20 &63.20 &81.00 &76.30 &86.20 &63.40 &67.00 &66.05 \\\\\\\\\\n\",\n \" Tip-Adapter~\\\\cite{zhang2022tip} & 23.70& 88.80& 63.90& 54.70& 72.50& 83.20&76.70 &86.40 &66.70 &72.10 &68.87 \\\\\\\\\\n\",\n \" \\n\",\n \" CuPL~\\\\cite{pratt2023does} & 19.59&89.29 &57.28 &48.64 &38.38 &65.44 &76.94 &84.84 &62.55 &58.97 &60.19 \\\\\\\\\\n\",\n \"\\n\",\n \" TPT~\\\\cite{shutest} &17.58 &87.02 &58.46 &40.84 &28.33 &62.69 & 74.88& 84.49& 61.46 &60.82& 57.66\\\\\\\\\\n\",\n \" \\n\",\n \" DMN~\\\\cite{zhang2024dual2} &20.22 &89.09 &58.36 &50.53 & 44.94& 68.33& 74.69& 86.29& 63.70& 64.02& 62.02\\\\\\\\\\n\",\n \" TDA~\\\\cite{karmanov2024efficient} & 17.61& 89.70&57.78 &43.74 &42.11 &68.74 &\\\\textbf{77.75} &86.18 &62.53 &64.18 & 61.03\\\\\\\\\\n\",\n \"\\n\",\n \" ECALP~\\\\cite{li2025efficient}& 21.12& 89.94& 60.56& 54.49& 49.09& 69.39&76.97 &88.20 &64.97 &66.67 &64.14 \\\\\\\\\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\\\midrule\\n\",\n \" \\n\",\n \" \\\\rowcolor{gray!30}\\n\",\n \" TrustVLM*-C& \\\\textbf{29.34} & 89.98& \\\\textbf{66.09}& 60.34& 75.11& 90.62&74.53 &85.94 & 66.30 & 72.09&71.03 \\\\\\\\\\n\",\n \" \\n\",\n \" \\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\\\rowcolor{gray!30}TrustVLM*-M & 19.89 &93.27 &57.52 & 65.43& \\\\textbf{86.85}& 87.25& 74.85&\\\\textbf{90.22} &67.16 &73.28& 71.57 \\\\\\\\\\n\",\n \"\\n\",\n \" \\\\rowcolor{gray!30}TrustVLM*-D& 20.25&96.43 &56.08 &71.34& 82.43&\\\\textbf{99.11} &75.23 &83.65 & \\\\textbf{71.45}&75.34 &73.13 \\\\\\\\\\n\",\n \"\\n\",\n \"\\\\rowcolor{gray!30}TrustVLM*(F)-D& 27.27 & \\\\textbf{96.59}\\t& 56.19\\t& \\\\textbf{74.70}& 85.43&98.50 &75.17 &83.76 & 70.71& \\\\textbf{76.90}& \\\\textbf{74.52} \\\\\\\\\\n\",\n \"\\n\",\n \"\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" } \\n\",\n \"\\\\vspace{-0.15cm}\\n\",\n \" \\\\caption{{Zero-shot classification results on fine-grained datasets with CLIP ResNet-50. } \\n\",\n \" }\\n\",\n \" \\\\label{tab:fine-grained-rn50}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table}\\n\",\n \"\\\\begin{minipage}[t]{0.68\\\\columnwidth}\\n\",\n \"\\\\begin{adjustbox}{width=\\\\linewidth}\\n\",\n \"\\n\",\n \"\\\\begin{tabular}{l@{~~~~~}| c cccc cccc cccc cccc }\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\n\",\n \" \\\\multicolumn{1}{c|}{{} } &\\\\multicolumn{4}{c|}{CLIP ResNet-50} &\\\\multicolumn{4}{c}{SigLIP ViT-B/32} \\n\",\n \" \\\\\\\\\\n\",\n \" \\\\multicolumn{1}{c|}{{}} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$}& AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c}{ACC$\\\\uparrow$}\\\\\\\\\\n\",\n \"\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{DOCTOR}&259.56 & 80.55 & 73.83& \\\\multicolumn{1}{c|}{55.82} & 149.27 & 85.69 & 58.63 & \\\\multicolumn{1}{c}{70.69} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{MSP}& 259.76& 80.63 & 73.11 & \\\\multicolumn{1}{c|}{55.82} & 149.13 & 85.86 & 57.31 & \\\\multicolumn{1}{c}{70.69} \\\\\\\\\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\\\rowcolor{gray!30}\\\\multicolumn{1}{c|}{TrustVLM-D}&{226.95} &\\\\textbf{87.31} &\\\\textbf{53.98} & \\\\multicolumn{1}{c|}{55.82} & {129.84} & \\\\textbf{90.35} & \\\\textbf{44.49} & \\\\multicolumn{1}{c}{70.69} \\\\\\\\\\n\",\n \"\\n\",\n \" \\\\rowcolor{gray!30}\\\\multicolumn{1}{c|}{TrustVLM*-D}& \\\\textbf{135.81}& 83.51 &59.97 &\\\\multicolumn{1}{c|}{\\\\textbf{73.13}} &\\\\textbf{87.35} &87.47 &53.97 & \\\\textbf{79.99} \\\\\\\\\\n\",\n \"\\n\",\n \" \\n\",\n \"\\\\cline{1-13}\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\\end{tabular} \\n\",\n \"\\\\end{adjustbox}\\n\",\n \"\\n\",\n \"\\\\vspace{+0.05cm}\\n\",\n \"\\\\caption{Ablation on different architectures and VLMs. The average results on fine-grained classification datasets are reported.}\\n\",\n \"\\\\label{tab:abla-vlm} \\n\",\n \"\\n\",\n \"\\n\",\n \"\\\\end{minipage}\\n\",\n \"\\\\hfill\\n\",\n \"\\\\begin{minipage}[t]{0.29\\\\columnwidth}\\n\",\n \"\\n\",\n \"\\\\begin{adjustbox}{width=\\\\linewidth}\\n\",\n \"\\\\begin{tabular}{l@{~~~~~}| c cccc cccc cccc cccc }\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\n\",\n \" \\\\multicolumn{1}{c}{{i-t}} &\\\\multicolumn{1}{c|}{{i-i}} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$\\\\\\\\\\n\",\n \"\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c}{$\\\\checkmark$} & \\\\multicolumn{1}{c|}{} & 188.75& 82.20 &70.10 \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c}{} & \\\\multicolumn{1}{c|}{$\\\\checkmark$} &214.14 & 77.15 & 62.26 \\\\\\\\\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\\\multicolumn{1}{c}{$\\\\checkmark$} & \\\\multicolumn{1}{c|}{$\\\\checkmark$}& \\\\textbf{168.06} & \\\\textbf{86.98} & \\\\textbf{54.47} \\\\\\\\\\n\",\n \" \\n\",\n \" \\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\end{adjustbox}\\n\",\n \"\\n\",\n \"\\\\vspace{+0.05cm}\\n\",\n \"\\\\caption{Ablation on each component.}\\n\",\n \"\\\\label{tab:abla-mod} \\n\",\n \"\\\\end{minipage}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{figure}[t!]\\n\",\n \" \\\\centering \\\\includegraphics[width=\\\\linewidth]{imgs/crop_score.pdf}\\n\",\n \"\\\\vspace{-0.6cm}\\n\",\n \" \\\\caption{Score distribution for correct and wrong predictions. Our TrustVLM achieves better separation between the score distributions of correct and wrong predictions, leading to improved performance in misclassification detection.\\n\",\n \" }\\n\",\n \" \\\\label{fig:abla_score}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{figure}[t!]\\n\",\n \" \\\\centering \\\\includegraphics[width=0.8\\\\linewidth]{imgs/crop_illustration.pdf}\\n\",\n \"\\\\vspace{-0.3cm}\\n\",\n \" \\\\caption{Illustration of TrustVLM’s mechanism. Initially, the incorrect prediction receives a higher confidence score $S_{i-t}$ than the correct one, indicating overconfidence. By performing verification in the image embedding space using $S_{i-i}$, this overconfidence is mitigated. As a result, the final confidence score $\\\\kappa(\\\\bm{x})$ is significantly higher for the correct prediction than for the incorrect one.}\\n\",\n \" \\\\label{fig:abla_illu}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{figure}[t!]\\n\",\n \" \\\\centering \\\\includegraphics[width=0.65\\\\linewidth]{imgs/crop_datasets.pdf}\\n\",\n \"\\\\vspace{-0.3cm}\\n\",\n \" \\\\caption{Representative examples from each dataset used in this work. }\\n\",\n \" \\\\label{fig:datasets}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{figure}[t!]\\n\",\n \" \\\\centering \\\\includegraphics[width=0.9\\\\linewidth]{imgs/crop_illust.pdf}\\n\",\n \"\\\\vspace{-0.3cm}\\n\",\n \" \\\\caption{More illustration on TrustVLM’s mechanism. }\\n\",\n \" \\\\label{fig:illus}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table}[t!]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \"\\\\begin{tabular}{l@{~~~~~}| c cccc cccc cccc cccc }\\n\",\n \"\\\\hline\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{{} } &\\\\multicolumn{4}{c|}{ImageNet-A} & \\\\multicolumn{4}{c|}{ImageNet-V2} & \\\\multicolumn{4}{c}{ImageNet-R} \\n\",\n \" \\\\\\\\\\n\",\n \" \\\\multicolumn{1}{c|}{{}} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c}{ACC$\\\\uparrow$} \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{DOCTOR}& 316.50 & 75.98 &77.76 & \\\\multicolumn{1}{c|}{{47.85}} & 180.06 & 80.09 &73.38 & \\\\multicolumn{1}{c|}{60.88} & 76.39 & 87.12 & 60.54 & \\\\multicolumn{1}{c}{73.98} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{MSP}& \\\\textbf{315.99} & \\\\textbf{76.07} &77.76 & \\\\multicolumn{1}{c|}{{47.85}} & 178.82 &80.45 & 71.26 & \\\\multicolumn{1}{c|}{60.88} & 75.47 & 87.46 &59.43 & \\\\multicolumn{1}{c}{73.98} \\\\\\\\\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\\rowcolor{gray!30}\\n\",\n \"\\\\multicolumn{1}{c|}{TrustVLM-D}& 324.07 &75.87 & \\\\textbf{77.20} & \\\\multicolumn{1}{c|}{47.85} &\\\\textbf{174.23} & \\\\textbf{82.18} & \\\\textbf{64.82}& \\\\multicolumn{1}{c|}{{60.88}} & \\\\textbf{74.94} & \\\\textbf{87.69} & \\\\textbf{58.09} & \\\\multicolumn{1}{c}{{73.98}} \\\\\\\\\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" \\\\multicolumn{1}{c|}{{} } &\\\\multicolumn{4}{c|}{ImageNet-Sketch} & \\\\multicolumn{4}{c|}{ImageNet} & \\\\multicolumn{4}{c}{Average} \\n\",\n \" \\\\\\\\\\n\",\n \" \\\\multicolumn{1}{c|}{{}} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c}{ACC$\\\\uparrow$}\\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{DOCTOR}& 296.20& 80.54 & 70.94 & \\\\multicolumn{1}{c|}{46.09} & 140.21 &80.53 & 74.58 & \\\\multicolumn{1}{c|}{66.71} & 201.87 & 80.85 &71.44 & \\\\multicolumn{1}{c}{59.10} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{MSP} & 294.85 & 80.82 & 70.29 & \\\\multicolumn{1}{c|}{46.09} & 138.52 &81.04 &72.85 & \\\\multicolumn{1}{c|}{66.71} &200.73 & 81.17 &70.32 & \\\\multicolumn{1}{c}{59.10} \\\\\\\\\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\\rowcolor{gray!30}\\n\",\n \"\\\\multicolumn{1}{c|}{TrustVLM-D}& \\\\textbf{289.52} &\\\\textbf{82.40} & \\\\textbf{65.59} & \\\\multicolumn{1}{c|}{{46.09}} &\\\\textbf{133.17} & \\\\textbf{83.08} & \\\\textbf{64.47}& \\\\multicolumn{1}{c|}{{66.71}} & \\\\textbf{199.19} & \\\\textbf{82.24} & \\\\textbf{66.03} & \\\\multicolumn{1}{c}{{59.10}} \\\\\\\\\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\\end{tabular} \\n\",\n \"}\\n\",\n \"\\\\vspace{+0.1cm}\\n\",\n \"\\\\caption{MisD performance on ImageNet and its variants using CLIP ViT-B/16, under distribution shifts defined by prototypes computed exclusively on ImageNet and deployed directly to variant datasets.}\\n\",\n \"\\\\label{tab:imagenet-shift} \\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[t!]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \"\\\\begin{tabular}{l@{~~~~~}| c cccc cccc cccc cccc }\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \" \\\\multicolumn{1}{c|}{{} } &\\\\multicolumn{4}{c|}{Flower102 } & \\\\multicolumn{4}{c|}{DTD } & \\\\multicolumn{4}{c|}{Aircraft} &\\\\multicolumn{4}{c}{Pets} \\n\",\n \" \\\\\\\\\\n\",\n \" \\\\multicolumn{1}{c|}{{}} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c}{ACC$\\\\uparrow$}\\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{DOCTOR}& 26.03& 93.13 & 38.75 & \\\\multicolumn{1}{c|}{83.76} & 183.86 &79.14 & 72.67 & \\\\multicolumn{1}{c|}{61.29} &513.33 & 78.08 &80.74 & \\\\multicolumn{1}{c|}{27.42} & {7.65} & 95.55 & 30.65 & \\\\multicolumn{1}{c}{91.55} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{MSP}&25.14 & 93.63 &34.75 & \\\\multicolumn{1}{c|}{83.76} & 181.52&79.66 &72.52 & \\\\multicolumn{1}{c|}{61.29} & 514.42& 77.78&76.98 & \\\\multicolumn{1}{c|}{27.42} & {7.65} &95.54 &{28.71} & \\\\multicolumn{1}{c}{91.55} \\\\\\\\\\n\",\n \"\\n\",\n \" \\n\",\n \" \\\\rowcolor{gray!30}\\\\multicolumn{1}{c|}{TrustVLM-D}&{16.62} & {98.42} &{9.50} & \\\\multicolumn{1}{c|}{83.76} & {144.44}& {87.64} &{49.01} & \\\\multicolumn{1}{c|}{61.29} & {503.98}& {79.56}& {75.83} & \\\\multicolumn{1}{c|}{27.42} & 7.66 &{95.58} & {28.71} & \\\\multicolumn{1}{c}{91.55} \\\\\\\\\\n\",\n \"\\n\",\n \" \\\\rowcolor{gray!30} \\\\multicolumn{1}{c|}{TrustVLM*-D} & {0.13}& 97.82& 15.38& \\\\multicolumn{1}{c|}{99.47} & {98.27}& 82.72& 71.92& \\\\multicolumn{1}{c|}{72.64}& {496.11} &78.79 &79.14 & \\\\multicolumn{1}{c|}{28.23}& {7.55} &95.56 &29.97 &{91.63} \\\\\\\\\\n\",\n \" \\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \" \\\\multicolumn{1}{c|}{{} } &\\\\multicolumn{4}{c|}{Caltech101 } & \\\\multicolumn{4}{c|}{Cars} & \\\\multicolumn{4}{c|}{EuroSAT} &\\\\multicolumn{4}{c}{UCF101} \\n\",\n \" \\\\\\\\\\n\",\n \" \\\\multicolumn{1}{c|}{{}} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c}{ACC$\\\\uparrow$}\\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \" \\\\multicolumn{1}{c|}{DOCTOR}& 2.31& 94.88 &27.50 & \\\\multicolumn{1}{c|}{96.75} & 18.84 &89.37 & 59.81 & \\\\multicolumn{1}{c|}{89.42} &481.80 &72.61 & 89.46& \\\\multicolumn{1}{c|}{31.26} & 85.35 &88.73 & 55.21 & \\\\multicolumn{1}{c}{70.31} \\\\\\\\\\n\",\n \" \\n\",\n \"\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{MSP}&2.20 & 95.21 & {26.25} & \\\\multicolumn{1}{c|}{96.75} & 18.73& 89.48 & 58.87 & \\\\multicolumn{1}{c|}{89.42} & 485.10& 72.31 &90.39 & \\\\multicolumn{1}{c|}{31.26} & 84.54 & 88.97 &56.28 & \\\\multicolumn{1}{c}{70.31} \\\\\\\\\\n\",\n \"\\n\",\n \"\\\\rowcolor{gray!30} \\\\multicolumn{1}{c|}{TrustVLM-D} & {2.08} & {95.52} & {26.25} & \\\\multicolumn{1}{c|}{96.75}& {18.63} &{89.57} & {57.58} & \\\\multicolumn{1}{c|}{89.42} & {390.00}& {90.09} &{42.82} & \\\\multicolumn{1}{c|}{31.26} & {74.35} & {92.14}& {41.23}& \\\\multicolumn{1}{c}{70.31} \\\\\\\\\\n\",\n \"\\n\",\n \" \\\\rowcolor{gray!30} \\\\multicolumn{1}{c|}{TrustVLM*-D} & {1.76} & {95.65} & {21.13} & \\\\multicolumn{1}{c|}{97.12} & 22.18 &87.92 &{54.05} &\\\\multicolumn{1}{c|}{89.42} & {72.64} &75.64 & 73.41& \\\\multicolumn{1}{c|}{82.91}& {54.41}&87.91 & 64.74&{78.03} \\\\\\\\\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \" \\\\multicolumn{1}{c|}{{} } &\\\\multicolumn{4}{c|}{Food101 } & \\\\multicolumn{4}{c|}{SUN397} &\\\\multicolumn{4}{c|}{Average} \\n\",\n \" \\\\\\\\\\n\",\n \" \\\\multicolumn{1}{c|}{{}} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$}\\\\\\\\\\n\",\n \"\\\\cline{1-13}\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{DOCTOR}&40.90 & 84.68 & 56.72& \\\\multicolumn{1}{c|}{87.54} & 132.64 & 80.73 & 74.81 & \\\\multicolumn{1}{c|}{67.62} & 149.27 & 85.69 & 58.63 & \\\\multicolumn{1}{c|}{70.69} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{MSP}& 40.70& 84.84 & {54.70} & \\\\multicolumn{1}{c|}{87.54} & 131.29& 81.15 &73.63 & \\\\multicolumn{1}{c|}{67.62} & 149.13 & 85.86 & 57.31 & \\\\multicolumn{1}{c|}{70.69} \\\\\\\\\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\\\rowcolor{gray!30}\\\\multicolumn{1}{c|}{TrustVLM-D}&{22.97} &{89.84} &{54.70} & \\\\multicolumn{1}{c|}{87.54} & {117.67} & {85.10} & {59.27} & \\\\multicolumn{1}{c|}{67.62} & {129.84} & \\\\textbf{90.35} & \\\\textbf{44.49} & \\\\multicolumn{1}{c|}{70.69} \\\\\\\\\\n\",\n \"\\n\",\n \" \\\\rowcolor{gray!30} \\\\multicolumn{1}{c|}{TrustVLM*-D} & 23.20& 89.56& 56.14& \\\\multicolumn{1}{c|}{87.80}&97.28&83.11&73.78&\\\\multicolumn{1}{c|}{72.63}&\\\\textbf{87.35}&87.47&53.97&\\\\multicolumn{1}{c|}{\\\\textbf{79.99}} \\\\\\\\\\n\",\n \" \\n\",\n \"\\\\cline{1-13}\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\\end{tabular} \\n\",\n \"}\\n\",\n \"\\\\vspace{-0.0cm}\\n\",\n \"\\\\caption{Misclassification detection performance on fine-grained classification datasets with SigLIP\\n\",\n \"ViT-B/16.}\\n\",\n \"\\\\label{tab:sig-misd} \\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[t!]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \"\\\\begin{tabular}{l@{~~~~~}| c cccc cccc cccc cccc }\\n\",\n \"\\\\hline\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{{} } &\\\\multicolumn{4}{c|}{ImageNet-A} & \\\\multicolumn{4}{c|}{ImageNet-V2} & \\\\multicolumn{4}{c}{ImageNet-R} \\n\",\n \" \\\\\\\\\\n\",\n \" \\\\multicolumn{1}{c|}{{}} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c}{ACC$\\\\uparrow$}\\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{DOCTOR}&299.17 & 79.28 &72.72 & \\\\multicolumn{1}{c|}{47.62} & 124.64& 83.03 & 67.64 & \\\\multicolumn{1}{c|}{67.55} &21.40 & 91.53 &45.00 & \\\\multicolumn{1}{c}{87.82} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{MSP}& 299.17& 79.25 &72.58 & \\\\multicolumn{1}{c|}{47.62} &124.12 &83.21 & 67.08 & \\\\multicolumn{1}{c|}{67.55} & 20.92& 91.90 & 43.06 & \\\\multicolumn{1}{c}{87.82} \\\\\\\\\\n\",\n \" \\n\",\n \"\\n\",\n \" \\\\rowcolor{gray!30}\\\\multicolumn{1}{c|}{TrustVLM-D}&{278.77}& {82.13} &{65.09} & \\\\multicolumn{1}{c|}{47.62} & {123.39} &{83.76} & {65.28} & \\\\multicolumn{1}{c|}{67.55} & {19.55}& {92.07 }&{42.05} & \\\\multicolumn{1}{c}{87.82} \\\\\\\\\\n\",\n \"\\n\",\n \" \\\\rowcolor{gray!30} \\\\multicolumn{1}{c|}{TrustVLM*-D} & 266.12 &77.68&78.19& \\\\multicolumn{1}{c|}{51.82}&122.70&83.05&67.17& \\\\multicolumn{1}{c|}{68.17}&19.49&91.85&43.33&87.98 \\\\\\\\\\n\",\n \"\\n\",\n \"\\\\hline\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{{} } &\\\\multicolumn{4}{c|}{ImageNet-Sketch} & \\\\multicolumn{4}{c|}{ImageNet} & \\\\multicolumn{4}{c}{Average} \\n\",\n \" \\\\\\\\\\n\",\n \" \\\\multicolumn{1}{c|}{{}} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c}{ACC$\\\\uparrow$} \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{DOCTOR} & 136.52 & 84.98 & 62.35 & \\\\multicolumn{1}{c|}{64.29} & 86.80 &83.40& 67.59 & \\\\multicolumn{1}{c|}{74.74} &133.71 & 84.44 & 63.06 & \\\\multicolumn{1}{c}{68.40} \\\\\\\\ \\n\",\n \"\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{MSP}& 135.32& 85.31& 61.24& \\\\multicolumn{1}{c|}{64.29} & 85.84 & 83.78 & 66.03 & \\\\multicolumn{1}{c|}{74.74} & 133.07 & 84.69 & 62.00& \\\\multicolumn{1}{c}{68.40} \\\\\\\\\\n\",\n \" \\n\",\n \"\\n\",\n \"\\\\rowcolor{gray!30} \\\\multicolumn{1}{c|}{TrustVLM-D}&{133.55} &{85.84} &{59.61} & \\\\multicolumn{1}{c|}{64.29} & {84.46} &{84.36}& {63.73} & \\\\multicolumn{1}{c|}{74.74} & {127.94} & \\\\textbf{85.63} & \\\\textbf{59.15} & \\\\multicolumn{1}{c}{68.40} \\\\\\\\\\n\",\n \"\\n\",\n \" \\\\rowcolor{gray!30} \\\\multicolumn{1}{c|}{TrustVLM*-D} & 132.62& 84.76& 64.17& \\\\multicolumn{1}{c|}{65.14}& 83.96&83.40 &67.89 & \\\\multicolumn{1}{c|}{75.45}& \\\\textbf{124.98}&84.15 &64.15 &\\\\textbf{69.71} \\\\\\\\\\n\",\n \" \\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\\end{tabular} \\n\",\n \"}\\n\",\n \"\\\\vspace{-0.0cm}\\n\",\n \"\\\\caption{Misclassification detection performance on ImageNet and its variants with SigLIP ViT-B/16. }\\n\",\n \"\\\\label{tab:sig-imagenet} \\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[t!]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \"\\\\begin{tabular}{l@{~~~~~}| c cccc cccc cccc cccc }\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \" \\\\multicolumn{1}{c|}{{} } &\\\\multicolumn{4}{c|}{Flower102 } & \\\\multicolumn{4}{c|}{DTD } & \\\\multicolumn{4}{c|}{Aircraft} &\\\\multicolumn{4}{c}{Pets} \\n\",\n \" \\\\\\\\\\n\",\n \" \\\\multicolumn{1}{c|}{{}} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c}{ACC$\\\\uparrow$}\\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{DOCTOR}& 154.49& 83.85 & 67.94 & \\\\multicolumn{1}{c|}{61.75} & 374.42 & 75.61&83.55 & \\\\multicolumn{1}{c|}{40.37} & 695.10& 74.86 &79.37 & \\\\multicolumn{1}{c|}{15.54} & 36.51 &87.78 & 62.33 & \\\\multicolumn{1}{c}{83.65} \\\\\\\\\\n\",\n \"\\n\",\n \" \\\\multicolumn{1}{c|}{MSP}&154.02 & 83.99 &69.57 & \\\\multicolumn{1}{c|}{61.75} & 374.63 &75.58 &83.85 & \\\\multicolumn{1}{c|}{40.37} & 693.70& 75.15 & 80.97 & \\\\multicolumn{1}{c|}{15.54} &35.63 &88.30 &59.83 & \\\\multicolumn{1}{c}{83.65} \\\\\\\\\\n\",\n \" \\n\",\n \"\\\\rowcolor{gray!30} \\\\multicolumn{1}{c|}{TrustVLM-D}& {106.43}& {94.74} & {27.07} & \\\\multicolumn{1}{c|}{61.75} &{309.72} & {87.72} & {47.87} & \\\\multicolumn{1}{c|}{40.37} &{675.03} & {79.38} &{79.37} & \\\\multicolumn{1}{c|}{15.54} & {35.02} & {88.62}& {58.00} & \\\\multicolumn{1}{c}{83.65} \\\\\\\\\\n\",\n \"\\n\",\n \" \\\\rowcolor{gray!30} \\\\multicolumn{1}{c|}{TrustVLM*-D} & 0.23& 98.10& 4.55& \\\\multicolumn{1}{c|}{99.11}& 130.86&77.64 &75.46 & \\\\multicolumn{1}{c|}{71.34}&622.50 &75.35 &79.68 &\\\\multicolumn{1}{c|}{20.25} &34.37 & 88.95& 57.67& 83.65 \\\\\\\\\\n\",\n \"\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \" \\\\multicolumn{1}{c|}{{} } &\\\\multicolumn{4}{c|}{Caltech101 } & \\\\multicolumn{4}{c|}{Cars} & \\\\multicolumn{4}{c|}{EuroSAT} &\\\\multicolumn{4}{c}{UCF101} \\n\",\n \" \\\\\\\\\\n\",\n \" \\\\multicolumn{1}{c|}{{}} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c}{ACC$\\\\uparrow$}\\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{DOCTOR}& 30.50& 87.30 & 69.54 & \\\\multicolumn{1}{c|}{85.88} & 212.51& 80.23 & 74.73 & \\\\multicolumn{1}{c|}{55.74} &618.09 & 70.73 &83.87 & \\\\multicolumn{1}{c|}{23.70} & 169.84& 84.01 & 72.38 & \\\\multicolumn{1}{c}{58.82} \\\\\\\\\\n\",\n \"\\n\",\n \"\\n\",\n \" \\\\multicolumn{1}{c|}{MSP}& 29.55& 87.95 &66.38 & \\\\multicolumn{1}{c|}{85.88} & 209.07& 80.93 &72.99 & \\\\multicolumn{1}{c|}{55.74} &633.47 & 67.67 &87.27 & \\\\multicolumn{1}{c|}{23.70} &166.92 & 84.77 &68.98 & \\\\multicolumn{1}{c}{58.82} \\\\\\\\\\n\",\n \" \\n\",\n \"\\n\",\n \" \\n\",\n \"\\\\rowcolor{gray!30} \\\\multicolumn{1}{c|}{TrustVLM-D}& {22.17}& {93.16} &{36.49} & \\\\multicolumn{1}{c|}{85.88} &{206.48} & {81.70} &{70.75} & \\\\multicolumn{1}{c|}{55.74} &{514.75} &{87.02} &{54.70} & \\\\multicolumn{1}{c|}{23.70} & {144.99} & {89.67} &{49.71} & \\\\multicolumn{1}{c}{58.82} \\\\\\\\\\n\",\n \"\\n\",\n \" \\\\rowcolor{gray!30} \\\\multicolumn{1}{c|}{TrustVLM*-D} & 6.06 &89.61 &40.91 &\\\\multicolumn{1}{c|}{96.43} & 205.62& 81.41& 70.92&\\\\multicolumn{1}{c|}{56.08} &101.58 &70.14 &74.00 & \\\\multicolumn{1}{c|}{82.43}& 73.84& 85.69& 62.81& 75.34 \\\\\\\\\\n\",\n \"\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \" \\\\multicolumn{1}{c|}{{} } &\\\\multicolumn{4}{c|}{Food101 } & \\\\multicolumn{4}{c|}{SUN397} & \\\\multicolumn{4}{c|}{Average} \\n\",\n \" \\\\\\\\\\n\",\n \" \\\\multicolumn{1}{c|}{{}} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} \\\\\\\\\\n\",\n \"\\\\cline{1-13}\\n\",\n \"\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{DOCTOR}& 94.96& 83.59 & 65.77 & \\\\multicolumn{1}{c|}{73.95} & 209.19 &77.49 & 78.84 & \\\\multicolumn{1}{c|}{58.81} & 259.56 & 80.55 & 73.83& \\\\multicolumn{1}{c|}{55.82} \\\\\\\\\\n\",\n \"\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{MSP}& 94.32& 83.83 &64.86 & \\\\multicolumn{1}{c|}{73.95} & 206.31 &78.16 & 76.41 & \\\\multicolumn{1}{c|}{58.81}& 259.76 & 80.63 & 73.11 & \\\\multicolumn{1}{c|}{55.82} \\\\\\\\\\n\",\n \" \\n\",\n \"\\n\",\n \" \\n\",\n \" \\\\rowcolor{gray!30}\\\\multicolumn{1}{c|}{TrustVLM-D}& {76.19}& {87.05} &{58.46} & \\\\multicolumn{1}{c|}{73.95} & {178.75} & {84.08} & {57.38} & \\\\multicolumn{1}{c|}{58.81} & {226.95} & \\\\textbf{87.31} & \\\\textbf{53.98} & \\\\multicolumn{1}{c|}{55.82} \\\\\\\\\\n\",\n \"\\n\",\n \" \\\\rowcolor{gray!30} \\\\multicolumn{1}{c|}{TrustVLM*-D} & 70.64 & 87.03 & 60.27 & \\\\multicolumn{1}{c|}{75.23} & 112.43&81.19 &73.47 &\\\\multicolumn{1}{c|}{71.45} & \\\\textbf{135.81} &83.51 &59.97 &\\\\multicolumn{1}{c|}{\\\\textbf{73.13}} \\\\\\\\\\n\",\n \" \\n\",\n \"\\\\cline{1-13}\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\\end{tabular} \\n\",\n \"}\\n\",\n \"\\\\vspace{-0.0cm}\\n\",\n \"\\\\caption{Misclassification detection performance on fine-grained classification datasets with CLIP ResNet-50.}\\n\",\n \"\\\\label{tab:rn50-misd} \\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[t!]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \"\\\\begin{tabular}{l@{~~~~~}| c cccc cccc cccc cccc }\\n\",\n \"\\\\hline\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{{} } &\\\\multicolumn{4}{c|}{ImageNet-A} & \\\\multicolumn{4}{c|}{ImageNet-V2} & \\\\multicolumn{4}{c}{ImageNet-R}\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\multicolumn{1}{c|}{{}} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c}{ACC$\\\\uparrow$}\\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{DOCTOR}& 672.36& 66.71 & 86.78 & \\\\multicolumn{1}{c|}{22.79} &256.91 & 79.22 & 75.41 & \\\\multicolumn{1}{c|}{51.50} & 188.11& 84.28 & 68.02 & \\\\multicolumn{1}{c}{56.36} \\\\\\\\\\n\",\n \"\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{MSP}&673.06 & 66.68 &86.39 & \\\\multicolumn{1}{c|}{22.79} & 255.65 &79.53 & 74.83 & \\\\multicolumn{1}{c|}{51.50} &187.18 & 84.51 & 66.21 & \\\\multicolumn{1}{c}{56.36} \\\\\\\\\\n\",\n \" \\n\",\n \"\\n\",\n \" \\n\",\n \" \\\\rowcolor{gray!30}\\\\multicolumn{1}{c|}{TrustVLM-D}&{629.41} & {72.99} &{75.33} & \\\\multicolumn{1}{c|}{22.79} & {242.91} &{82.75} &{62.07} & \\\\multicolumn{1}{c|}{51.50} & {172.53}& {87.58}&{56.44} & \\\\multicolumn{1}{c}{56.36} \\\\\\\\\\n\",\n \"\\n\",\n \" \\\\rowcolor{gray!30} \\\\multicolumn{1}{c|}{TrustVLM*-D} & 583.73 &67.67&85.08&\\\\multicolumn{1}{c|}{27.99}& 231.81& 77.67& 75.42& \\\\multicolumn{1}{c|}{56.13}&166.54 & 83.35& 71.47& 60.34 \\\\\\\\\\n\",\n \"\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{{} } &\\\\multicolumn{4}{c|}{ImageNet-Sketch} & \\\\multicolumn{4}{c|}{ImageNet} & \\\\multicolumn{4}{c}{Average} \\n\",\n \" \\\\\\\\\\n\",\n \" \\\\multicolumn{1}{c|}{{}} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c|}{ACC$\\\\uparrow$} & AURC$\\\\downarrow$ & AUROC$\\\\uparrow$ & FPR95$\\\\downarrow$ & \\\\multicolumn{1}{c}{ACC$\\\\uparrow$}\\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{DOCTOR}& 446.07& 78.27 &74.71 & \\\\multicolumn{1}{c|}{32.99} & 204.36& 79.44& 73.82&\\\\multicolumn{1}{c|}{58.18} & 353.56 & 77.58 & 75.75 & \\\\multicolumn{1}{c}{44.36} \\\\\\\\\\n\",\n \"\\n\",\n \" \\n\",\n \" \\\\multicolumn{1}{c|}{MSP}&444.12 &78.62 &73.94 & \\\\multicolumn{1}{c|}{32.99}&202.30& 79.96& 72.03 &\\\\multicolumn{1}{c|}{58.18} &352.46 & 77.86 & 74.68 & \\\\multicolumn{1}{c}{44.36} \\\\\\\\\\n\",\n \" \\n\",\n \"\\n\",\n \" \\n\",\n \"\\\\rowcolor{gray!30} \\\\multicolumn{1}{c|}{TrustVLM-D}&{409.65} & {85.09} &{52.28} & \\\\multicolumn{1}{c|}{32.99} &{183.69} &{84.24} &{58.35} &\\\\multicolumn{1}{c|}{58.18} & {327.64} & \\\\textbf{82.53} & \\\\textbf{60.89} & \\\\multicolumn{1}{c}{44.36} \\\\\\\\\\n\",\n \"\\n\",\n \" \\\\rowcolor{gray!30} \\\\multicolumn{1}{c|}{TrustVLM*-D} & 338.03 & 71.57& 80.89& \\\\multicolumn{1}{c|}{48.05}& 168.30&77.91 &74.34 &\\\\multicolumn{1}{c|}{65.00} &\\\\textbf{297.68} &75.63 &77.44 &\\\\textbf{51.50} \\\\\\\\\\n\",\n \"\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\\end{tabular} \\n\",\n \"}\\n\",\n \"\\\\vspace{-0.0cm}\\n\",\n \"\\\\caption{Misclassification detection performance on ImageNet and its variants with CLIP ResNet-50.}\\n\",\n \"\\\\label{tab:rn50-imagenet} \\n\",\n \"\\\\end{table}\\n\"\n ]\n]","isConversational":false},"fulltext":{"kind":"string","value":"\\documentclass{article}\n\n\n% if you need to pass options to natbib, use, e.g.:\n% \\PassOptionsToPackage{numbers, compress}{natbib}\n% before loading neurips_2025\n\n\n% ready for submission\n% \\usepackage{neurips_2025}\n\n\n% to compile a preprint version, e.g., for submission to arXiv, add add the\n% [preprint] option:\n\\usepackage[preprint,nonatbib]{neurips_2025}\n\n\n% to compile a camera-ready version, add the [final] option, e.g.:\n% \\usepackage[final]{neurips_2025}\n\n\n% to avoid loading the natbib package, add option nonatbib:\n% \\usepackage[nonatbib]{neurips_2025}\n\n\n\\usepackage[utf8]{inputenc} % allow utf-8 input\n\\usepackage[T1]{fontenc} % use 8-bit T1 fonts\n% \\usepackage{hyperref} % 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\n\\usepackage{thm-restate}\n%\\usepackage{lipsum}\n\\declaretheorem[name=Theorem,numberwithin=section]{thm}\n\n\\usepackage{enumitem}\n\\usepackage{adjustbox}\n\\newcommand{\\sE}{\\mathbb{E}}\n\\newcommand{\\lce}{\\ell_{\\text{CE}}}\n\\newcommand{\\cmark}{\\ding{51}}%\n\\newcommand{\\xmark}{\\ding{55}}%\n\\newcommand{\\dg}{domain generalization\\xspace}\n\\newcommand{\\da}{domain adaptation\\xspace}\n\\newcommand{\\Dg}{Domain generalization\\xspace}\n\\newcommand{\\etal}{\\textit{et al.}\\xspace}\n\n\\newcommand{\\wjd}[1]{{\\color{blue}{[\\bf\\sf (WJD): #1]}}}\n\\newcommand{\\cl}[1]{{\\color{blue}{[(cl): #1]}}}\n\\newcommand{\\tcb}{\\textcolor{blue}}\n\\newcommand{\\tcr}{\\textcolor{red}}\n\n\\newcommand{\\newnew}[1]{{\\color{blue}{[(NEW): #1]}}}\n\\newcommand{\\yz}[1]{\\textcolor{brown}{[Yue: #1]}}\n\n\\usepackage{multirow}\n\\newcommand{\\ieno}{\\textit{i}.\\textit{e}.}\n% \\newcommand{\\eg}{\\textit{e}.\\textit{g}.~}\n\\newcommand{\\egno}{\\textit{e}.\\textit{g}.} %there is no space\n% \\newcommand{\\etc}{\\textit{etc}.}\n\\newcommand{\\etcno}{\\textit{etc}} %there is no \".\"\n\n\\makeatletter\n\\newcommand*\\bigcdot{\\mathpalette\\bigcdot@{.5}}\n\\newcommand*\\bigcdot@[2]{\\mathbin{\\vcenter{\\hbox{\\scalebox{#2}{$\\m@th#1\\bullet$}}}}}\n\\makeatother\n\n\\usepackage[capitalize]{cleveref}\n\\crefname{section}{Sec.}{Secs.}\n\\Crefname{section}{Section}{Sections}\n\\Crefname{table}{Table}{Tables}\n\\crefname{table}{Tab.}{Tabs.}\n\n\\newcommand\\hlf[1]{\\textbf{\\textcolor{red}{#1}}} % highlight first\n\\newcommand\\hls[1]{\\textbf{\\textcolor{blue}{#1}}} % \n\\DeclareMathOperator{\\softmax}{\\mathrm{Softmax}}\n\\DeclareMathOperator{\\sigmoid}{\\mathrm{Sigmoid}}\n\\newcommand{\\Din}{\\mathcal{D}_\\mathrm{in}}\n\\newcommand{\\Dout}{\\mathcal{D}_\\mathrm{out}}\n\\newcommand{\\Zin}{\\calZ_\\mathrm{in}}\n\\newcommand{\\Zout}{\\calZ_\\mathrm{out}}\n\\newcommand{\\calD}{\\mathcal{D}}\n\\newcommand{\\calZ}{\\mathcal{Z}}\n\\newcommand{\\calP}{\\mathcal{P}}\n\\newcommand{\\calN}{\\mathcal{N}}\n\\newcommand{\\calX}{\\mathcal{X}}\n\\newcommand{\\calY}{\\mathcal{Y}}\n\\newcommand{\\bb}{\\mathbf{b}}\n\\newcommand{\\bw}{\\mathbf{w}}\n\\newcommand{\\bx}{\\mathbf{x}}\n\\newcommand{\\bz}{\\mathbf{z}}\n\\newcommand{\\bq}{\\mathbf{q}}\n\\newcommand{\\bZ}{\\mathbf{Z}}\n\\newcommand{\\bP}{Pr}\n\n\n\\title{To Trust Or Not To Trust Your Vision-Language Model's Prediction}\n\n\n% The \\author macro works with any number of authors. There are two commands\n% used to separate the names and addresses of multiple authors: \\And and \\AND.\n%\n% Using \\And between authors leaves it to LaTeX to determine where to break the\n% lines. Using \\AND forces a line break at that point. So, if LaTeX puts 3 of 4\n% authors names on the first line, and the last on the second line, try using\n% \\AND instead of \\And before the third author name.\n\n\\author{ \nHao Dong$^{1}$ \\ \\ \\ \nMoru Liu$^{2}$ \\ \\ \\\nJian Liang$^{3,4}$ \\ \\ \\\nEleni Chatzi$^{1}$ \\ \\ \\ \nOlga Fink$^{5}$ \\\\[0.5em]\n\\normalsize{$^{1}$ETH Z\\\"urich \\ \\ \\ \n$^{2}$Technical University of Munich} \\\\\n\\normalsize{$^{3}$NLPR \\& MAIS, Institute of Automation, Chinese Academy of Sciences} \\\\\n\\normalsize{$^{4}$University of Chinese Academy of Sciences\\ \\ \\ \n$^{5}$EPFL\\ \\ \\ }\n% \\\\\n% \\texttt{hao.dong@ibk.baug.ethz.ch}\n}\n\n\n\\begin{document}\n\n\n\\maketitle\n\n\n\\begin{abstract}\n Vision-Language Models (VLMs) have demonstrated strong capabilities in aligning visual and textual modalities, enabling a wide range of applications in multimodal understanding and generation. While they excel in zero-shot and transfer learning scenarios, VLMs remain susceptible to misclassification, often yielding confident yet incorrect predictions. This limitation poses a significant risk in safety-critical domains, where erroneous predictions can lead to severe consequences. In this work, we introduce \\textbf{TrustVLM}, a training-free framework designed to address the critical challenge of estimating when VLM's predictions can be trusted. Motivated by the observed modality gap in VLMs and the insight that certain concepts are more distinctly represented in the image embedding space, we propose a novel confidence-scoring function that leverages this space to improve misclassification detection. We rigorously evaluate our approach across $17$ diverse datasets, employing $4$ architectures and $2$ VLMs, and demonstrate state-of-the-art performance, with improvements of up to $51.87\\%$ in AURC, $9.14\\%$ in AUROC, and $32.42\\%$ in FPR95 compared to existing baselines. By improving the reliability of the model without requiring retraining, TrustVLM paves the way for safer deployment of VLMs in real-world applications. The code will be available at \\href{https://github.com/EPFL-IMOS/TrustVLM}{https://github.com/EPFL-IMOS/TrustVLM}.\n\\end{abstract}\n\n\n\\section{Introduction}\nRecent advances in Vision-Language Models (VLMs) have substantially transformed the field of multimodal learning by integrating visual and textual information within a unified framework. Models such as CLIP~\\cite{radford2021learning} and SigLIP~\\cite{zhai2023sigmoid} have been widely adopted for diverse tasks, including zero-shot classification~\\cite{zhou2022learning}, cross-modal retrieval~\\cite{ma2022ei}, and image captioning~\\cite{barraco2022unreasonable}. Trained on large-scale image-text datasets scraped from the web, these models learn rich and transferable representations. However, despite their substantial capabilities, VLMs often encounter critical limitations when applied in real-world settings. One pressing concern is misclassification, where the model produces a confident, yet incorrect, prediction that may appear both semantically plausible and visually aligned with the input. While much of the existing research has focused on improving the accuracy of VLMs outputs, the equally important issue of trustworthiness, that is, determining whether a prediction should be accepted or flagged for human review, remains largely underexplored. This challenge is particularly consequential in safety-critical domains~\\cite{sun2025ano,dong2023nngmix} such as autonomous driving, medical diagnostics, and surveillance, where erroneous predictions can lead to severe outcomes.\n\nThe challenge of misclassification detection (MisD) has been widely studied in the context of unimodal vision models, with numerous approaches proposed, including confidence-based scoring~\\cite{oodbaseline17iclr,jiang2018trust}, outlier exposure~\\cite{cheng2024breaking,zhu2023openmix,liu2025fm}, and confidence learning~\\cite{corbiere2019addressing,moon2020confidence}. However, these approaches often overlook the unique complexities of multimodal models, where the interaction between visual inputs and textual semantics introduces additional sources of uncertainty~\\cite{dong2025mmdasurvey,dong2024towards}. Recently, Nguyen et al.~\\cite{nguyen2025interpretable} proposed utilizing human-level concepts to detect misclassification of VLMs. However, their approach necessitates the construction of numerous concepts for each class through the use of large language models, which can be a demanding process. Although MisD and out-of-distribution (OOD) detection~\\cite{dong2024multiood,li2024dpu} share the similar goal of identifying problematic inputs for a trained model, they target fundamentally distinct challenges. MisD focuses primarily on identifying in-distribution samples that are incorrectly assigned to one of the known classes, often due to their proximity to decision boundaries or atypical feature representations within the learned data manifold. In contrast, OOD detection focuses on identifying inputs from entirely unseen distributions, representing novel or irrelevant stimuli rather than misclassifications within known classes. Consequently, methods tailored for one task often perform poorly on the other~\\cite{jaeger2022call,zhu2023openmix}. %[\\textbf{TODO:} add figure on differences between OOD and MisD of VLMs]\n\nTo address the specific challenge of misclassification detection in VLMs, we propose \\textbf{TrustVLM} -- a training-free framework for evaluating the reliability of VLM predictions. Traditional zero-shot classification with VLMs relies primarily on the cosine similarity between text and image embeddings, often overlooking the structure and discriminative capacity of the image embedding space. This is a critical limitation, as previous work has shown a modality gap in VLMs like CLIP, where image and text embeddings reside in distinct regions of the shared representation space~\\cite{liang2022mind} (see \\cref{fig:clip_emb}). In particular, some concepts are more distinguishable in the image embedding space than in the text embedding space (\\cref{fig:clip_emb} (b)). Building on this insight, TrustVLM leverages additional information from the image embedding space to design a novel confidence-scoring function for improved misclassification detection. Specifically, our framework employs an auxiliary vision encoder to store visual prototypes for each class and assess prediction reliability through image-to-image similarity with these prototypes. Beyond misclassification detection, these prototypes can also improve zero-shot VLM predictions and be fine-tuned for improved downstream performance. \n\n\nWe conduct a rigorous evaluation of TrustVLM across $17$ diverse datasets, $4$ architectures, and $2$ distinct VLMs. Our method achieves state-of-the-art performance in misclassification detection, with improvements of up to $51.87\\%$ in AURC, $9.14\\%$ in AUROC, and $32.42\\%$ in FPR95 over existing baselines. In addition, the use of visual prototypes improves the accuracy of zero-shot classification, giving an average improvement of $5.65\\%$. The primary contributions of this work are as follows:\n\\begin{itemize}[leftmargin=*]\n\\setlength\\itemsep{0em}\n\\item We provide an empirical analysis of the limitations of existing MisD paradigms in VLMs, highlighting the value of leveraging information from the image embedding space.\n\\item We propose TrustVLM, a training-free framework that combines image-to-text and image-to-image similarity to compute a robust confidence score for improved MisD.\n\\item We show that visual prototypes not only support more reliable confidence estimation, but also improve zero-shot prediction accuracy, and can optionally be fine-tuned for further gains.\n\\item We extensively validate TrustVLM across datasets, model architectures, and VLMs, demonstrating its generality and effectiveness. Our source code will be made publicly available to support future research in MisD for VLMs.\n\\end{itemize}\n\n\n\n\\section{Related Work}\n\n\\noindent\\textbf{Misclassification Detection.} The primary goal of MisD is to distinguish misclassified samples from correctly classified ones, often by evaluating the reliability of the prediction. Early approaches used simple baselines such as Maximum Softmax Probability (MSP)~\\cite{oodbaseline17iclr}, although these were limited by model overconfidence. TrustScore~\\cite{jiang2018trust} estimates the reliability of predictions based on distances to training samples in the feature space, though it can struggle with high-dimensional or domain-shifted data. DOCTOR~\\cite{granese2021doctor} introduces a simple rule-based rejection mechanism for black-box models without retraining, yet its performance depends on carefully tuned thresholds. Another direction involves directly learning confidence scores or training auxiliary components to predict prediction failure, such as learning the true class probability~\\cite{corbiere2019addressing} or adding dedicated confidence branches~\\cite{devries2018learning}. OpenMix~\\cite{zhu2023openmix} enhances robustness by generating synthetic outliers during training, improving calibration on misclassified samples. Recent literature~\\cite{nguyen2025interpretable} has also explored MisD with VLMs by leveraging human-level concepts to detect when and interpret why a model fails. \n\n\\noindent\\textbf{Out-of-distribution Detection} shares a similar objective with MisD but addresses fundamentally distinct challenges. OOD detection aims to identify test samples that exhibit semantic shifts without compromising in-distribution (ID) classification accuracy, which can be broadly categorized into post hoc methods and training-time regularization. \\emph{Post hoc} methods design OOD scores based on the classification outputs of neural networks, offering the advantage of ease of use without modifying the training procedure or objective~\\cite{oodbaseline17iclr,hendrycks2019anomalyseg,energyood20nips}. \n\\emph{Training-time regularization} methods address prediction overconfidence by imposing a constant vector norm on the logits during training~\\cite{wei2022mitigating} or using external OOD samples from other datasets during training to improve discrimination between ID and OOD samples~\\cite{oe18nips,nejjar2024sfosda}. Recently, some works~\\cite{ming2022delving,jiang2024negative,wang2023clipn} have also explored OOD detection via use of VLMs. However, methods optimized for OOD detection often underperform on MisD~\\cite{jaeger2022call,zhu2023openmix}, underscoring the need for specialized MisD approaches.\n\n% \\noindent\\textbf{Vision-Language Models.} CLIP [1] pioneered contrastive learning for joint image-text embeddings, enabling zero-shot transfer to downstream tasks. Subsequent work improved scalability (ALIGN [2]), robustness (FLAVA [3]), and efficiency (LiT [4]). While these studies focus on accuracy gains, they overlook the calibration of confidence scores, a prerequisite for trustworthy deployment.\n\n\n\n\\section{Methodology}\n\\subsection{Preliminaries}\n\\noindent\\textbf{{Vision-Language\nModels}} typically comprise an image encoder that projects high-dimensional images into a low-dimensional embedding space and a text encoder that embeds natural language into a corresponding text embedding space. A prominent example is CLIP~\\cite{radford2021learning}, trained on $400$ million image-text pairs, which employs a contrastive loss to align image and text embeddings. Specifically, given a batch of image-text pairs, CLIP maximizes the cosine similarity for the matched pairs while minimizing it for unmatched ones. During inference, the class names of a target dataset are embedded using the text encoder with a prompt of the form “a photo of a [CLASS]”, where [CLASS] is replaced with specific class names. The text encoder then generates text embeddings $\\mathbf{t}_c$ for each class $c \\in \\mathcal{Y} = \\{ 1, 2, \\dots, C \\}$, and the prediction probability for an input image $\\bm{x}$ with embedding $\\mathbf{f}_{x}$ is computed as:\n\\begin{equation} \\label{eqn:clip-metric}\n p(y=\\hat{y}|\\bm{x}) = \\frac{\\exp{(\\cos \\left(\\mathbf{f}_{x}, \\mathbf{t}_{\\hat{y}} \\right) / \\tau )} } { \\sum_{c=1}^{C} \\exp{(\\cos \\left(\\mathbf{f}_{x}, \\mathbf{t}_{c} \\right) / \\tau )} },\n\\end{equation}\nwhere $\\cos(\\cdot,\\cdot)$ denotes cosine similarity and $\\tau$ is a temperature parameter. The final prediction for $\\bm{x}$ is $\\hat{y} = \\operatorname{argmax}_{y \\in \\mathcal{Y}} p(y|\\bm{x})$, where $\\hat{y}$ can be either correctly classified or misclassified. %We use $\\mathcal{D}^{\\checkmark}$ and $\\mathcal{D}^{\\bm{\\times}}$ to represent the distribution of correct and misclassified samples, respectively.\n\n\\noindent\\textbf{{Misclassification Detection}}, also known as failure detection~\\cite{corbiere2019addressing}, serves as a critical safeguard for the reliable deployment of machine learning models in real-world applications. Its primary objective is to distinguish between correctly and incorrectly classified predictions, typically by leveraging confidence scores. Formally, let $\\kappa$ denote a confidence-scoring function that quantifies the confidence of the model in its prediction. Given a threshold $\\delta \\in \\mathbb{R}^{+}$, a decision function $g$ can be defined to detect misclassifications based on whether the confidence score exceeds this threshold. For a given input $\\bm{x}$:\n\\begin{equation}\n\\label{eq1}\ng(\\bm{x}) = \\left\\{ \n\\begin{aligned}\n &\\text{correct}~~~~~~~~~~\\text{if} ~~\\kappa(\\bm{x}) \\ge \\delta, \\\\\n &\\text{misclassified}~~~~\\text{otherwise}.\n\\end{aligned}\n\\right.\n\\end{equation} \n\n\n\\begin{figure}[t!]\n \\centering \\includegraphics[width=\\linewidth]{imgs/crop_emb.pdf}\n\\vspace{-0.6cm}\n \\caption{(a) CLIP’s image and text embeddings are located in two completely separate regions of the embedding space. (b) The concept of \"dog\" and \"seaplane\" is more distinguishable in the image embedding space than in the text embedding space. }\n \\label{fig:clip_emb}\n\\end{figure}\n\n\\noindent\\textbf{{Baselines for MisD of VLMs.}}\nGiven the prediction from \\cref{eqn:clip-metric}, Maximum Softmax Probability~\\cite{oodbaseline17iclr} can be readily computed as a confidence-scoring function. For a given input $\\bm{x}$, MSP is defined as $\\kappa(\\bm{x}) = \\max_{y \\in \\mathcal{Y}} p(y|\\bm{x})$, where $p(y|\\bm{x})$ denotes the predicted probability for class $y$. Similarly, various confidence scoring functions can be adopted from previous work on OOD detection, such as MaxLogit~\\cite{hendrycks2019anomalyseg}, Energy~\\cite{energyood20nips}, Entropy~\\cite{chan2021entropy}, and Maximum Concept Matching (MCM)~\\cite{ming2022delving}.\n\n\\subsection{Limitations of the Baselines}\n\nConsistent with findings from previous work on misclassification detection research~\\cite{jaeger2022call,zhu2023openmix}, the simple MSP often outperforms more sophisticated OOD detection methods, as shown in \\cref{tab:misd}. This observation suggests that advanced OOD detection methods frequently struggle to effectively capture misclassification errors in VLMs, underscoring the need to develop novel confidence-scoring functions tailored to this setting. Furthermore, the standard paradigm for zero-shot classification with VLM is mainly based on computing the cosine similarity between text and image embeddings (i.e., image-to-text similarity), as defined in \\cref{eqn:clip-metric}. However, this approach often overlooks important characteristics of the image embedding space, such as image-to-image similarity. As demonstrated by Liang et al.~\\cite{liang2022mind}, a modality gap exists within the representation space of VLMs; for example, the CLIP image and text embeddings reside in distinct regions of the joint embedding space, as illustrated in~\\cref{fig:clip_emb} (a). Consequently, relying solely on image-to-text similarity for zero-shot classification and misclassification detection may neglect critical information, potentially leading to suboptimal performance. For example, \\cref{fig:clip_emb} (b) provides a concrete example using CLIP embeddings for the concepts 'dog' and 'seaplane'. In this case, the separation margin based on image-to-text similarity is only $0.13$, whereas image-to-image similarity could yields a substantially larger margin of $0.29$, indicating that the two concepts are more clearly distinguishable in the image embedding space. \n \n This insight has practical implications. When an image-to-text prediction is incorrect -- for instance, classifying an image of a dog as a 'seaplane' -- the image-to-image similarity between the input image and a visual prototype for 'seaplane' would likely be low, helping mitigate overconfidence. Conversely, for correct predictions (e.g., classifying a dog image as 'dog'), the image-to-image similarity with the corresponding prototype would generally be high, thereby reinforcing the prediction with greater confidence. Therefore, exploring image-to-image similarity is crucial for designing effective confidence-scoring functions to enhance misclassification detection performance in VLMs.\n\n\n\n\n\n\\subsection{Proposed TrustVLM Framework}\n\n\n\n\\begin{figure}[t!]\n \\centering \\includegraphics[width=\\linewidth]{imgs/crop_frame.pdf}\n\\vspace{-0.7cm}\n \\caption{The proposed TrustVLM framework comprises three main steps. Initially, visual prototypes for each class are generated and stored using a pre-trained vision encoder. Subsequently, the VLMs perform zero-shot classification and yield an image-to-text similarity score, $S_{i-t}$. In the third step, the initial prediction is verified using image-to-image similarity, providing an additional confidence score, $S_{i-i}$. Finally, these two scores are combined to determine the overall prediction confidence.}\n \\label{fig:frame}\n\\end{figure}\n\nInspired by the modality gap phenomenon observed in VLMs and the enhanced distinguishability of concepts within the image embedding space, we propose TrustVLM. Our framework leverages information from the image embedding space to design the confidence-scoring function. In addition to the conventional confidence score derived from image-to-text similarity (calculated via \\cref{eqn:clip-metric}), TrustVLM incorporates a second score derived from image-to-image similarity and computed using an auxiliary vision encoder. These two scores are complementary, and their effective combination leads to more robust misclassification detection. The auxiliary vision encoder can be the original CLIP image encoder or other pre-trained vision models, such as MoCo v2 or DINOv2. The selection of a more powerful auxiliary vision encoder can further improve misclassification detection performance.\n\n\n\\noindent\\textbf{{Integrate Vision Encoder for Misclassification Detection.}} Our \\textit{\\textbf{TrustVLM}} framework operates in three main steps, as shown in \\cref{fig:frame}. The first step involves generating and storing visual prototypes. Specifically, for each class $c$, embeddings are extracted from $N$-shot samples in the training data using a pre-trained vision encoder, $E$ (e.g., the CLIP image encoder, MoCo v2, or DINOv2). The prototype embedding for class $c$, $P_c$, is then computed by averaging these $N$ embeddings. These class prototypes $\\{P_c\\}$ are subsequently stored. In the second step, for a given input image $\\bm{x}$, a zero-shot prediction $\\hat{y}$ is obtained using the VLM, as defined in \\cref{eqn:clip-metric}, where the prediction $\\hat{y}$ could be either correct or wrong. Concurrently, an initial confidence score, $S_{i-t} = \\max_{y \\in \\mathcal{Y}} p(y|\\bm{x})$, is derived from the image-to-text similarity. The third step focuses on generating a complementary image-to-image confidence score. An embedding $E_{x}$ of the input image $\\bm{x}$ is extracted using the same vision encoder $E$ employed in the first step. Since in the second step, the VLM believes $\\bm{x}$ to be class $\\hat{y}$, we calculate the cosine similarity between $E_{x}$ and $P_{\\hat{y}}$ as the image-to-image similarity score $S_{i-i} = E_x \\cdot P_{\\hat{y}}$. This $S_{i-i}$ score is expected to be low if the prediction $\\hat{y}$ is incorrect, as $E_{x}$ would be compared against an inappropriate prototype, thereby helping to mitigate overconfidence. Conversely, a correct prediction $\\hat{y}$ should result in a high $S_{i-i}$, reinforcing the prediction's reliability. Finally, this verification mechanism yields the combined confidence score for input $\\bm{x}$ is $\\kappa(\\bm{x}) = S_{i-t} + S_{i-i}$.\n\n\n\\noindent\\textbf{{Integrate Vision Encoder for Zero-shot Classification.}} Visual prototypes and image-to-image similarity can also be utilized to enhance the zero-shot prediction capabilities of VLMs.\nGiven the visual prototypes $\\{P_c\\}$ for each class $c \\in \\mathcal{Y} = \\{ 1, 2, \\dots, C \\}$, the probability of predicting class $\\hat{y}$ for an input image $\\bm{x}$ (with embedding $E_{x}$), based on image-to-image similarity, is computed as:\n\\begin{equation} \\label{eqn:vision-metric}\n p(y=\\hat{y}|\\bm{x}) = \\frac{\\exp{(\\cos \\left(E_{x}, P_{\\hat{y}} \\right) / \\tau )} } { \\sum_{c=1}^{C} \\exp{(\\cos \\left(E_{x}, P_{c} \\right) / \\tau )} }.\n\\end{equation}\nCombining \\cref{eqn:clip-metric} and \\cref{eqn:vision-metric}, we get the ensemble prediction from both image-to-text and image-to-image similarity as:\n\\begin{equation} \\label{eqn:ens-metric}\n p(y=\\hat{y}|\\bm{x}) = \\frac{\\exp{(\\cos \\left(\\mathbf{f}_{x}, \\mathbf{t}_{\\hat{y}} \\right) / \\tau )} } { \\sum_{c=1}^{C} \\exp{(\\cos \\left(\\mathbf{f}_{x}, \\mathbf{t}_{c} \\right) / \\tau )} } + \\frac{\\exp{(\\cos \\left(E_{x}, P_{\\hat{y}} \\right) / \\tau )} } { \\sum_{c=1}^{C} \\exp{(\\cos \\left(E_{x}, P_{c} \\right) / \\tau )} }.\n\\end{equation}\nFor this variant, termed \\textit{\\textbf{TrustVLM*}}, the confidence-scoring function for a given input $\\bm{x}$ is $\\kappa(\\bm{x}) = \\max_{y \\in \\mathcal{Y}} p(y|\\bm{x}) + S_{i-i}$.\n\n\\noindent\\textbf{{Visual Prototypes with Fine-tuning.}} Visual prototypes extracted from pre-trained vision encoders are typically fixed by default. In this section, we introduce \\textit{\\textbf{TrustVLM*(F)}} to treat these visual prototypes as learnable parameters initialized with their pre-computed values. These parameters are subsequently fine-tuned using stochastic gradient descent. The rationale is that updating the visual prototypes can enhance affinity estimation, thereby enabling a more accurate calculation of cosine similarities between test and training images, as demonstrated by~\\cite{zhang2022tip}. Specifically, we freeze the parameters of the VLMs and the vision encoder, while fine-tuning only the visual prototypes via a cross-entropy loss for $10$ epochs. Finally, these learned prototypes replace the original fixed prototypes $\\{P_c\\}$ in \\cref{eqn:ens-metric}.\n\n\n\n\\section{Experiments}\n\\subsection{Experimental Setting}\n\\noindent\\textbf{{Dataset.}} We evaluate our framework on a wide variety of $17$ datasets. \\textit{Fine-grained Classification Datasets,} including $10$ publicly available image classification datasets: Caltech101~\\cite{fei2004learning}, OxfordPets~\\cite{parkhi2012cats}, StanfordCars~\\cite{krause20133d}, Flowers102~\\cite{nilsback2008automated}, Food101~\\cite{bossard2014food}, FGVCAircraft~\\cite{maji2013fine}, SUN397~\\cite{xiao2010sun}, DTD~\\cite{cimpoi2014describing}, EuroSAT~\\cite{helber2019eurosat} and UCF101~\\cite{soomro2012ucf101}. These datasets constitute a comprehensive benchmark, which covers a diverse set of vision tasks including classification on generic objects, scenes, actions and fine-grained categories, as well as specialized downstream tasks such as recognizing textures and satellite imagery. \\textit{ImageNet and Its Variants,} including ImageNet~\\cite{deng2009imagenet}, ImageNetV2~\\cite{recht2019imagenet}, ImageNet-Sketch~\\cite{wang2019learning}, ImageNet-A~\\cite{hendrycks2021natural}, and ImageNet-R~\\cite{hendrycks2021many}, with distribution shifts in image style, data domains, etc. We also evaluate our framework on CIFAR-10 and CIFAR-100~\\cite{krizhevsky2009learning} to compare with ORCA~\\cite{nguyen2025interpretable}.\n\n\n\\noindent\\textbf{{Implementation Details.}} We utilize CLIP ViT-B/16~\\cite{dosovitskiy2020image} backbone to perform zero-shot prediction on the benchmarks and calculate the related performance metrics. We also compare with ORCA~\\cite{nguyen2025interpretable} on CLIP ResNet-101~\\cite{he2016deep} and ViT-B/32 following its setup. To demonstrate the generalization of the proposed framework to different VLMs, we further evaluate on CLIP ResNet-50 and SigLIP~\\cite{zhai2023sigmoid} ViT-B/16. For the auxiliary vision encoder, we use both the original CLIP image encoder as well as other pre-trained models such as DINOv2~\\cite{oquab2023dinov2} and MoCo v2~\\cite{chen2020improved}. We use $16$-shot samples from the training data to calculate the prototypes by default and set the temperature $\\tau$ to $0.01$. \n\n\\noindent\\textbf{{Evaluation Metrics.}} \\textbf{AURC.} The area under the risk-coverage curve (AURC)~\\cite{geifman2017selective} depicts the error rate which is computed by using samples whose confidence is higher than some confidence thresholds. \\textbf{AUROC.} The area under the receiver operating characteristic curve (AUROC)~\\cite{davis2006relationship} depicts the relationship between true positive rate (TPR) and false positive rate (FPR). \\textbf{FPR95.} The FPR at 95\\% TPR denotes the probability that a misclassified example is predicted as a correct one when the TPR is as high as 95\\%. \\textbf{ACC.} Test accuracy (ACC) is also an important metric.\n\n\\noindent\\textbf{{Baselines.}} We compare our method against well-established confidence-scoring functions, including MaxLogit~\\cite{hendrycks2019anomalyseg}, Energy~\\cite{energyood20nips}, Entropy~\\cite{chan2021entropy}, MCM~\\cite{ming2022delving}, MSP~\\cite{oodbaseline17iclr}, and DOCTOR~\\cite{granese2021doctor}, where DOCTOR fully exploits all available\ninformation contained in the soft-probabilities of the predictions to estimate the confidence. We also compare with the most recent concept-based method ORCA~\\cite{nguyen2025interpretable}.\n\n\n\n\\subsection{Results}\n\n\n\n\n\\renewcommand{\\arraystretch}{1.2}\n\\setlength{\\tabcolsep}{1pt}\n\n\\begin{table}[t!]\n\\centering\n\\resizebox{\\textwidth}{!}{\n\\begin{tabular}{l@{~~~~~}| c cccc cccc cccc cccc}\n\\hline\n \\multicolumn{1}{c|}{} &\\multicolumn{4}{c|}{Flower102 } & \\multicolumn{4}{c|}{DTD } & \\multicolumn{4}{c|}{Aircraft} &\\multicolumn{4}{c}{Pets} \n \\\\\n \\multicolumn{1}{c|}{{}} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c}{ACC$\\uparrow$}\\\\\n\\hline\n\n\n\n \\multicolumn{1}{c|}{MaxLogit}&167.14 & 74.92 & 81.61 & \\multicolumn{1}{c|}{67.36} & 395.13 &69.63 & 85.23 & \\multicolumn{1}{c|}{44.39} & 731.25& 55.73 &93.77 & \\multicolumn{1}{c|}{23.85} & 48.75& 75.37 &68.59 & \\multicolumn{1}{c}{88.23} \\\\\n\n \\multicolumn{1}{c|}{Energy}&194.67 &69.00 & 91.93 & \\multicolumn{1}{c|}{67.36} & 433.25 & 64.82 & 90.01 & \\multicolumn{1}{c|}{44.39} & 780.11& 45.48 &97.71 & \\multicolumn{1}{c|}{23.85} & 56.23& 71.90 &70.67 & \\multicolumn{1}{c}{88.23} \\\\\n \n \\multicolumn{1}{c|}{Entropy}&117.27 &84.88 &63.48 & \\multicolumn{1}{c|}{67.36} & 319.02 &79.03 & 77.05 & \\multicolumn{1}{c|}{44.39} & 575.71& 73.12 & 83.96 & \\multicolumn{1}{c|}{23.85} & 21.91& 89.31 &53.35 & \\multicolumn{1}{c}{88.23} \\\\\n \n \n \\multicolumn{1}{c|}{MCM}& 153.63& 78.22 & 71.02 & \\multicolumn{1}{c|}{67.36} & 333.43 &78.16 & 78.45 & \\multicolumn{1}{c|}{44.39} & 583.28& 72.58 &81.65 & \\multicolumn{1}{c|}{23.85} & 44.95& 77.03 &64.90 & \\multicolumn{1}{c}{88.23} \\\\\n\n\n \n \\multicolumn{1}{c|}{DOCTOR}&112.82 &85.82 &62.48 & \\multicolumn{1}{c|}{67.36} & 314.37& 79.66& 76.51 & \\multicolumn{1}{c|}{44.39} & 575.53& 73.05 &83.84 & \\multicolumn{1}{c|}{23.85} & 21.08 &89.92 &55.20 & \\multicolumn{1}{c}{88.23} \\\\\n \n \\multicolumn{1}{c|}{MSP}& 112.27&85.91 &63.98 & \\multicolumn{1}{c|}{67.36} &313.43 &79.81 &77.36 & \\multicolumn{1}{c|}{44.39} & 576.97&72.62 & 85.61 & \\multicolumn{1}{c|}{23.85} & 21.04& 89.94 &52.19 & \\multicolumn{1}{c}{88.23} \\\\\n \n\\rowcolor{gray!30}\n \\multicolumn{1}{c|}{TrustVLM-C}&101.42 & 88.69&54.91 & \\multicolumn{1}{c|}{67.36} & 302.18 & 82.52 & 67.27 & \\multicolumn{1}{c|}{44.39} & 563.77& 75.20 &{81.51} & \\multicolumn{1}{c|}{23.85} & 20.93 & 89.89 &51.73 & \\multicolumn{1}{c}{88.23} \\\\\n \n \\rowcolor{gray!30}\n \\multicolumn{1}{c|}{TrustVLM-M}&103.68 & 88.29 & 53.42 & \\multicolumn{1}{c|}{67.36} & 298.17 &83.16 &65.50 & \\multicolumn{1}{c|}{44.39} &574.57 &73.22 & 84.31& \\multicolumn{1}{c|}{23.85} & {20.41} & \\textbf{90.38} &\\textbf{48.27} & \\multicolumn{1}{c}{{88.23}} \\\\\n \n \\rowcolor{gray!30}\n \\multicolumn{1}{c|}{TrustVLM-D}& 77.30 &{95.05} & 30.06 & \\multicolumn{1}{c|}{67.36} & 268.71 &\\textbf{88.55} &\\textbf{44.10} & \\multicolumn{1}{c|}{44.39} & {562.02} & {75.62} & 83.21 & \\multicolumn{1}{c|}{23.85} & 20.69& 90.05 &50.81 & \\multicolumn{1}{c}{88.23} \\\\\n\n\n\n \\rowcolor{gray!30}\n \\multicolumn{1}{c|}{TrustVLM*-D}&0.52 &95.96 &13.04 &\\multicolumn{1}{c|}{\\textbf{99.07}} & 124.15 &78.39 &72.14 &\\multicolumn{1}{c|}{71.57} &554.12 &75.36 &83.05 &\\multicolumn{1}{c|}{24.60} &\\textbf{20.05} &90.30 &49.07 &\\textbf{88.28}\\\\ % v3 \n\n\n\n \n\n\n \\rowcolor{gray!30} \\multicolumn{1}{c|}{TrustVLM*(F)-D}& \\textbf{0.41} &\\textbf{98.26} & \\textbf{7.69} & \\multicolumn{1}{c|}{98.42} & \\textbf{96.45} & 80.30 & 70.96& \\multicolumn{1}{c|}{\\textbf{74.76}} & \\textbf{544.40}& \\textbf{76.85} & \\textbf{78.63}& \\multicolumn{1}{c|}{\\textbf{24.90}} & \\textbf{20.05} & 90.30 & 49.07 & \\multicolumn{1}{c}{\\textbf{88.28}} \\\\ % v3\n\n \\hline\n\n\n \\multicolumn{1}{c|}{{} } &\\multicolumn{4}{c|}{Caltech101 } & \\multicolumn{4}{c|}{Cars} & \\multicolumn{4}{c|}{EuroSAT} &\\multicolumn{4}{c}{UCF101} \n \\\\\n \\multicolumn{1}{c|}{{}} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c}{ACC$\\uparrow$}\\\\\n\\hline\n\n\n\n \n \\multicolumn{1}{c|}{MaxLogit}&53.72 &52.43 &94.55 & \\multicolumn{1}{c|}{93.31} &259.81 & 61.94 &89.58 & \\multicolumn{1}{c|}{65.61} & 522.56& 59.42 &87.79 & \\multicolumn{1}{c|}{42.10} & 258.47 & 61.85 & 91.41 & \\multicolumn{1}{c}{65.21} \\\\\n \n \\multicolumn{1}{c|}{Energy}&59.26 &48.81 &96.36 & \\multicolumn{1}{c|}{93.31} &304.90 & 54.85& 93.89& \\multicolumn{1}{c|}{65.61} & 568.57& 53.44 &88.30 & \\multicolumn{1}{c|}{42.10} & 299.84 & 55.38 & 94.83 & \\multicolumn{1}{c}{65.21} \\\\\n \n \\multicolumn{1}{c|}{Entropy}& 15.79&82.47 &79.39 & \\multicolumn{1}{c|}{93.31} & 145.61 & 79.83&75.76 & \\multicolumn{1}{c|}{65.61} &389.07 &72.18 &83.16 & \\multicolumn{1}{c|}{42.10} & 130.10& 83.97 & 70.52 & \\multicolumn{1}{c}{65.21} \\\\\n \n \\multicolumn{1}{c|}{MCM}&45.55 &61.00 &83.54 & \\multicolumn{1}{c|}{93.31} & 248.87 &63.77 &85.97 & \\multicolumn{1}{c|}{65.61} & 416.37& 71.28 &81.18 & \\multicolumn{1}{c|}{42.10} &188.00 &74.14 & 75.47 & \\multicolumn{1}{c}{65.21} \\\\\n \n \\multicolumn{1}{c|}{DOCTOR}& 12.89& 86.06&76.36 & \\multicolumn{1}{c|}{93.31} & 138.00 &81.50 &73.66 & \\multicolumn{1}{c|}{65.61} & 368.77& 74.43 & 81.69 & \\multicolumn{1}{c|}{42.10} &123.41 &85.67 &65.81 & \\multicolumn{1}{c}{65.21} \\\\\n \n \\multicolumn{1}{c|}{MSP}&12.23 & 86.99 &67.88 & \\multicolumn{1}{c|}{93.31} & 136.27 &81.95 & 72.25 & \\multicolumn{1}{c|}{65.61} & 355.66&76.39 &80.65 & \\multicolumn{1}{c|}{42.10} & 122.44 &85.98 & 64.89 & \\multicolumn{1}{c}{65.21} \\\\\n \n \\rowcolor{gray!30}\n \\multicolumn{1}{c|}{TrustVLM-C}& 13.25& 86.81 &70.30 & \\multicolumn{1}{c|}{93.31} &{129.45} & \\textbf{83.67} & \\textbf{67.73} & \\multicolumn{1}{c|}{65.61} & 322.52&82.90 &55.73 & \\multicolumn{1}{c|}{42.10} &111.95 & 88.69 &55.93 & \\multicolumn{1}{c}{65.21} \\\\\n \n \\rowcolor{gray!30}\n \\multicolumn{1}{c|}{TrustVLM-M}&{10.81} & 88.97 & 64.24 & \\multicolumn{1}{c|}{93.31} & 134.25 & 82.53 &72.29 & \\multicolumn{1}{c|}{65.61} & 320.12& 83.03 & 56.32 & \\multicolumn{1}{c|}{42.10} & 114.60& 87.93 &58.81 & \\multicolumn{1}{c}{65.21} \\\\\n \n \\rowcolor{gray!30}\n \\multicolumn{1}{c|}{TrustVLM-D}&11.11& {90.51} & {47.27} & \\multicolumn{1}{c|}{93.31} & 137.54 &82.05 &70.62 & \\multicolumn{1}{c|}{65.61} &{303.79} & \\textbf{85.48} & \\textbf{53.52} & \\multicolumn{1}{c|}{42.10} & {107.13} &\\textbf{90.21} &\\textbf{50.68} & \\multicolumn{1}{c}{65.21} \\\\\n\n \n\n\n \\rowcolor{gray!30}\n \\multicolumn{1}{c|}{TrustVLM*-D}& 5.69 & 89.48 & \\textbf{35.62} & \\multicolumn{1}{c|}{97.04} & 137.97 & 81.73 & 71.25 & \\multicolumn{1}{c|}{65.83} & 72.96 & 74.87 & 73.50 & \\multicolumn{1}{c|}{83.56} & 65.26 & 86.23 & 62.93 & 77.11\\\\ % v3 \n \n\n\n \\rowcolor{gray!30} \\multicolumn{1}{c|}{TrustVLM*(F)-D}& \\textbf{2.61}& \\textbf{93.29}& 35.71& \\multicolumn{1}{c|}{\\textbf{97.16}} & \\textbf{132.54} & 82.58 &68.70 & \\multicolumn{1}{c|}{\\textbf{66.02}} &\\textbf{54.35} &77.31 & 72.48 & \\multicolumn{1}{c|}{\\textbf{85.69}} & \\textbf{55.81}& 87.26 & 66.06 & \\multicolumn{1}{c}{\\textbf{78.35}} \\\\ % v3\n \n\\hline\n\n\n \\multicolumn{1}{c|}{{} } &\\multicolumn{4}{c|}{Food101 } & \\multicolumn{4}{c|}{SUN397} & \\multicolumn{4}{c|}{Average} \n \\\\\n \\multicolumn{1}{c|}{{}} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} \\\\\n\\cline{1-13}\n\n%\\hline\n\n\n\n \\multicolumn{1}{c|}{MaxLogit}& 67.81& 75.81& 78.13 & \\multicolumn{1}{c|}{83.66} & 278.17 & 62.77 &90.46 & \\multicolumn{1}{c|}{62.57} & 278.28 & 64.99 & 86.11 & \\multicolumn{1}{c|}{63.63} \\\\\n \n \\multicolumn{1}{c|}{Energy}&76.05 & 72.33 & 84.69 & \\multicolumn{1}{c|}{83.66} & 304.50 &58.34 &93.47 & \\multicolumn{1}{c|}{62.57} & 307.74 & 59.44 & 90.19 & \\multicolumn{1}{c|}{63.63} \\\\\n \n \\multicolumn{1}{c|}{Entropy}&55.80 &83.76 &63.73 & \\multicolumn{1}{c|}{83.66} & 194.33 &75.19 &80.97 & \\multicolumn{1}{c|}{62.57} & 196.46 & 80.37 & 73.14 & \\multicolumn{1}{c|}{63.63} \\\\\n \n \\multicolumn{1}{c|}{MCM}&60.90 & 79.90 & 67.23 & \\multicolumn{1}{c|}{83.66} & 242.95 &69.09 &83.62 & \\multicolumn{1}{c|}{62.57} & 231.79 & 72.52 & 77.30 & \\multicolumn{1}{c|}{63.63} \\\\\n \n \\multicolumn{1}{c|}{DOCTOR}& 54.99& 84.39&61.11 & \\multicolumn{1}{c|}{83.66} & 184.41 &77.35 & 77.37& \\multicolumn{1}{c|}{62.57} & 190.63 & 81.78 & 71.40 & \\multicolumn{1}{c|}{63.63} \\\\\n \n \\multicolumn{1}{c|}{MSP}&54.80 &84.51 &59.73 & \\multicolumn{1}{c|}{83.66} & 182.39 & 77.90 & 76.42 & \\multicolumn{1}{c|}{62.57} & 188.75 & 82.20 & 70.10 & \\multicolumn{1}{c|}{63.63} \\\\\n \n \\rowcolor{gray!30}\n \\multicolumn{1}{c|}{TrustVLM-C}&{36.02} &88.34 & 57.67 & \\multicolumn{1}{c|}{83.66} & 171.89 &80.39 &69.20 & \\multicolumn{1}{c|}{62.57} & 177.34 & 84.71 & 63.20 & \\multicolumn{1}{c|}{63.63} \\\\\n\n \\rowcolor{gray!30}\n \\multicolumn{1}{c|}{TrustVLM-M}& 40.92& 86.65& 59.01& \\multicolumn{1}{c|}{83.66} & 172.98 & 79.83 & 71.63 & \\multicolumn{1}{c|}{62.57} & 179.05 & 84.40 & 63.38 & \\multicolumn{1}{c|}{63.63} \\\\\n \n\\rowcolor{gray!30} \n \\multicolumn{1}{c|}{TrustVLM-D}&36.18 &{88.52} &\\textbf{55.96} & \\multicolumn{1}{c|}{83.66} & {156.16} & \\textbf{83.80} &\\textbf{58.44} & \\multicolumn{1}{c|}{62.57} & {168.06} & \\textbf{86.98} & \\textbf{54.47} & \\multicolumn{1}{c|}{63.63} \\\\\n\n \n \n \n \\rowcolor{gray!30}\n \\multicolumn{1}{c|}{TrustVLM*-D}& 35.01 & 88.49 & 57.47 & \\multicolumn{1}{c|}{84.10} & 104.25 & 82.31 & 72.82 & \\multicolumn{1}{c|}{\\textbf{72.18}} & 112.00 & 84.31 & 59.09 & \\multicolumn{1}{c|}{76.33} \\\\ % v3 \n \n\n\n \\rowcolor{gray!30} \\multicolumn{1}{c|}{TrustVLM*(F)-D}& \\textbf{34.16} &\\textbf{88.79} &56.19 & \\multicolumn{1}{c|}{\\textbf{84.15}} & \\textbf{103.05} & 83.43 & 72.68 & \\multicolumn{1}{c|}{{71.33}} & \\textbf{104.38} & 85.84 & 57.82& \\multicolumn{1}{c|}{\\textbf{76.91}} \\\\ % v3\n\n \n\\cline{1-13}\n\n%\\hline\n\n%%%%%%%%\n\n\\end{tabular} \n}\n\\vspace{+0.0cm}\n\\caption{Misclassification detection performance on fine-grained classification datasets with CLIP ViT-B/16, where -C, -M, and -D are with CLIP-I, MoCo v2, and DINOv2 as auxiliary vision encoders. AURC is multiplied by $10^3$ following previous work~\\cite{zhu2023openmix}. }\n\\vspace{-0.6cm}\n\\label{tab:misd} \n\\end{table}\n\n\\noindent\\textbf{{MisD Results on Fine-grained Classification Datasets.}} As presented in \\cref{tab:misd}, the simple MSP baseline consistently surpasses prominent OOD detection methods in MisD, including MaxLogit, Energy, and MCM. This indicates that current OOD detection techniques are limited in capturing misclassification errors effectively, highlighting a promising direction for future research: the development of confidence estimation methods that integrate OOD detection and MisD within a unified framework. Incorporating an image-to-image similarity confidence score in TrustVLM significantly enhances MisD performance, irrespective of the vision encoder employed (e.g., CLIP-I, MoCo v2, or DINOv2), thereby demonstrating the proposed framework's versatility. Notably, TrustVLM-D utilizing the DINOv2 encoder yields the best overall performance, achieving average improvements of $20.69\\%$ in AURC, $4.78\\%$ in AUROC, and $15.63\\%$ in FPR95 relative to the strongest baseline.\n\nWhen visual prototypes are employed for zero-shot classification (TrustVLM*-D), the accuracy improves substantially by an average of $12.7\\%$. This enhanced accuracy reduces the overall risk (error rate) across all coverage levels; consequently, the Area Under the Risk-Coverage curve (AURC) also decreases markedly, by an average of $56.06\\%$ compared to TrustVLM-D. However, its AUROC and FPR95 metrics are marginally lower than those of TrustVLM-D. A potential explanation for this is that as model accuracy improves, the confidence scores for many correct predictions might increase significantly.Nevertheless, some correctly classified but inherently “difficult” instances may still receive lower confidence scores. If the confidence scores of the few remaining misclassifications become more similar to these \"hard but correct\" instances, the overlap between the confidence distributions of misclassifications and correct classifications increases. This makes it harder for the detector to find a good threshold. Finally, fine-tuning on visual prototypes (TrustVLM*(F)-D) further enhances performance across all evaluated metrics compared to TrustVLM*-D.\n\n\n\\setlength{\\tabcolsep}{2pt}\n\n\\begin{table}[t!]\n\\centering\n\\resizebox{\\textwidth}{!}{\n\\begin{tabular}{l@{~~~~~}| c cccc cccc cccc cccc }\n\\hline\n \n \\multicolumn{1}{c|}{{} } &\\multicolumn{4}{c|}{ImageNet-A} & \\multicolumn{4}{c|}{ImageNet-V2} & \\multicolumn{4}{c}{ImageNet-R} \n \\\\\n \\multicolumn{1}{c|}{{}} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c}{ACC$\\uparrow$} \\\\\n\\hline\n\n\n\n \\multicolumn{1}{c|}{MaxLogit}&387.23 &64.16 & 86.59 & \\multicolumn{1}{c|}{50.08} & 254.37& 68.58& 85.22 & \\multicolumn{1}{c|}{61.20} &138.25 & 72.98 & 79.48 & \\multicolumn{1}{c}{74.19} \\\\\n \n \\multicolumn{1}{c|}{Energy}& 421.78& 59.15& 90.00 & \\multicolumn{1}{c|}{50.08} & 278.89& 64.64 &89.26 & \\multicolumn{1}{c|}{61.20} &160.84 & 67.39 &86.55 & \\multicolumn{1}{c}{74.19} \\\\\n \n \\multicolumn{1}{c|}{Entropy}& 298.23& 75.46&77.47 & \\multicolumn{1}{c|}{50.08} & 189.58 & 77.57 &79.90 & \\multicolumn{1}{c|}{61.20} & 79.64& 85.80 & 63.13& \\multicolumn{1}{c}{74.19} \\\\\n \n \\multicolumn{1}{c|}{MCM}& 341.67& 71.99 &78.27 & \\multicolumn{1}{c|}{50.08} & 236.32 &71.62 & 80.38 & \\multicolumn{1}{c|}{61.20} &109.67 &80.49 &64.73 & \\multicolumn{1}{c}{74.19} \\\\\n \n \\multicolumn{1}{c|}{DOCTOR}&290.78 &76.67 &76.93 & \\multicolumn{1}{c|}{50.08} & 181.67 & 79.32 &73.67 & \\multicolumn{1}{c|}{61.20} & 75.33& 87.14 &60.81 & \\multicolumn{1}{c}{74.19} \\\\\n \n \\multicolumn{1}{c|}{MSP}& 290.46& 76.70 & 77.06 & \\multicolumn{1}{c|}{50.08} & 180.32 &79.72 &71.82 & \\multicolumn{1}{c|}{61.20} &74.36 &87.50 &59.66 & \\multicolumn{1}{c}{74.19} \\\\\n\n\n\\rowcolor{gray!30}\n\\multicolumn{1}{c|}{TrustVLM-D}& {274.46}& \\textbf{79.17} &\\textbf{70.27} & \\multicolumn{1}{c|}{50.08} & {176.82} & \\textbf{81.27} & \\textbf{66.02} & \\multicolumn{1}{c|}{61.20} &{72.10} &\\textbf{88.03} &\\textbf{57.70} & \\multicolumn{1}{c}{74.19} \\\\\n\n \\rowcolor{gray!30}\n\\multicolumn{1}{c|}{TrustVLM*-D}& 266.48& 75.94 & 77.67 & \\multicolumn{1}{c|}{\\textbf{53.02}} &\\textbf{172.65} &77.93 &74.92 & \\multicolumn{1}{c|}{63.92} &71.72& 87.50 & 60.03& \\multicolumn{1}{c}{74.62} \\\\\n\n\n \\rowcolor{gray!30}\n\\multicolumn{1}{c|}{TrustVLM*(F)-D}&\\textbf{264.36} & 77.09 & 76.90 & \\multicolumn{1}{c|}{52.61} & 176.13& 77.71& 72.98 & \\multicolumn{1}{c|}{\\textbf{63.98}} &\\textbf{71.15}& 87.42 & 60.37 & \\multicolumn{1}{c}{\\textbf{74.83}} \\\\ % ImageNet-V2 5ep\n\n\\hline\n\n\n\n \\multicolumn{1}{c|}{{} } &\\multicolumn{4}{c|}{ImageNet-Sketch} & \\multicolumn{4}{c|}{ImageNet} & \\multicolumn{4}{c}{Average} \n \\\\\n \\multicolumn{1}{c|}{{}} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c}{ACC$\\uparrow$}\\\\\n\\hline\n\n\n\n \\multicolumn{1}{c|}{MaxLogit}& 422.41& 65.28 & 87.25 & \\multicolumn{1}{c|}{45.67} & 228.56 & 65.95 & 85.80 & \\multicolumn{1}{c|}{66.68} & 286.16 & 67.39 & 84.87 & \\multicolumn{1}{c}{59.56} \\\\\n \n \\multicolumn{1}{c|}{Energy} & 472.36 & 58.03 &92.25 & \\multicolumn{1}{c|}{45.67} & 253.77 & 61.61 &89.95 & \\multicolumn{1}{c|}{66.68} & 317.53 & 62.16 & 89.60 & \\multicolumn{1}{c}{59.56} \\\\\n \n \\multicolumn{1}{c|}{Entropy} & 313.83 &78.37 &74.27 & \\multicolumn{1}{c|}{45.67} & 149.69& 78.26 &78.01 & \\multicolumn{1}{c|}{66.68}& 206.19 & 79.09 & 74.56 & \\multicolumn{1}{c}{59.56} \\\\\n \n \\multicolumn{1}{c|}{MCM} & 400.13 &70.10 & 76.52 & \\multicolumn{1}{c|}{45.67} &206.72 & 69.73& 81.27 & \\multicolumn{1}{c|}{66.68} &258.90 & 72.79 & 76.23 & \\multicolumn{1}{c}{59.56} \\\\\n \n \\multicolumn{1}{c|}{DOCTOR}&300.85 & 80.49 &70.75 & \\multicolumn{1}{c|}{45.67} & 140.70 &80.48 &74.46 & \\multicolumn{1}{c|}{66.68} & 197.87 & 80.82 & 71.32 & \\multicolumn{1}{c}{59.56} \\\\\n \n \\multicolumn{1}{c|}{MSP} & 299.57& 80.74 & 70.51 & \\multicolumn{1}{c|}{45.67} & 138.99& 81.00 & 72.74 & \\multicolumn{1}{c|}{66.68} & 196.74 & 81.13 & 70.36 & \\multicolumn{1}{c}{59.56} \\\\\n\n\n\\rowcolor{gray!30}\n\\multicolumn{1}{c|}{TrustVLM-D}& {280.72} &\\textbf{84.35} & \\textbf{58.99} & \\multicolumn{1}{c|}{45.67} &{132.10} & \\textbf{83.38} & \\textbf{64.04}& \\multicolumn{1}{c|}{66.68} & {187.24} & \\textbf{83.24} & \\textbf{63.40} & \\multicolumn{1}{c}{59.56} \\\\\n\n \\rowcolor{gray!30}\n\\multicolumn{1}{c|}{TrustVLM*-D}& 264.51& 74.74& 84.77& \\multicolumn{1}{c|}{53.13} &\\textbf{126.31} & 78.82 &77.43 & \\multicolumn{1}{c|}{70.58} &180.33& 78.99 &74.96 & \\multicolumn{1}{c}{63.05} \\\\\n\n \\rowcolor{gray!30}\n\\multicolumn{1}{c|}{TrustVLM*(F)-D}& \\textbf{243.44}&73.80 & 76.90 & \\multicolumn{1}{c|}{\\textbf{58.20}} & 129.38& 77.33&75.28 & \\multicolumn{1}{c|}{\\textbf{71.75}} &\\textbf{176.89} & 78.67 &72.49 & \\multicolumn{1}{c}{\\textbf{64.27}} \\\\ % ImageNet 5ep\n\\hline\n\n\n\n%%%%%%%%\n\n\\end{tabular} \n}\n\\vspace{+0.0cm}\n\\caption{Misclassification detection performance on ImageNet and its variants with CLIP ViT-B/16.}\n %\\vspace{-0.6cm}\n\\label{tab:imagenet} \n\\end{table}% \\input{tables/cross-dataset}\n\n\\begin{wrapfigure}{r}{0.5\\textwidth}\n \\centering \n\\vspace{-0.4cm}\n\\includegraphics[width=\\linewidth]{imgs/crop_imagenet.pdf}\n\\vspace{-0.6cm}\n \\caption{MisD performance on ImageNet and its variants using CLIP ViT-B/16, under distribution shifts defined by prototypes computed exclusively on ImageNet and deployed directly to variant datasets.}\n \\label{fig:imagenet_shift}\n\\end{wrapfigure}\n\\noindent\\textbf{{MisD Results on ImageNet and Its Variants.}} \\cref{tab:imagenet} presents results from large-scale experiments conducted on ImageNet and its variants. As the variant datasets solely provide test splits, $N$ samples per class were selected from these test sets to compute prototypes, with the remaining samples utilized for evaluation. Consistent with the findings in \\cref{tab:misd}, the simple MSP baseline consistently outperforms strong OOD detection methods. Our TrustVLM-D with DINOv2 encoder demonstrates superior overall performance in most cases, achieving average improvements of $9.5\\%$ in AURC, $2.11\\%$ in AUROC, and $6.96\\%$ in FPR95 relative to the strongest baseline. While TrustVLM*-D and TrustVLM*(F)-D yield significant improvements in ACC and AURC, they adversely affect AUROC and FPR95 on these large-scale benchmarks. To further evaluate the robustness of our proposed method to distribution shifts, prototypes were computed using only N samples per class from the ImageNet training split. These prototypes were then applied directly to the ImageNet variants, thereby obviating the need to compute variant-specific prototypes. As illustrated in \\cref{fig:imagenet_shift}, our method demonstrates the overall leading performance of all metrics evaluated in this challenging scenario. Detailed results are provided in the Appendix.\n\n\n%\\input{tables/imagenet}\n\n\n% \\begin{figure}[t!]\n% \\centering \\includegraphics[width=0.6\\linewidth]{imgs/crop_imagenet.pdf}\n% \\vspace{-0.2cm}\n% \\caption{MisD performance on ImageNet and its variants using CLIP ViT-B/16, under distribution shifts defined by prototypes computed exclusively on ImageNet and deployed directly to variant datasets.}\n% \\label{fig:imagenet_shift}\n% \\end{figure}\n\n\n\n\n\n\n\\noindent\\textbf{{Compare with Concept-based Method.}} We further compare our method against the most recent ORCA~\\cite{nguyen2025interpretable} with CLIP ResNet-101 and ViT-B/32 on CIFAR-10, CIFAR-100, and EuroSAT. ORCA leverages human-level concepts to detect when and interpret why a model fails.\nAs shown in \\cref{tab:RN101-ORCA}, while ORCA generally outperforms the MSP baseline in most cases, our proposed method demonstrates a significant performance margin over ORCA. Specifically, with the CLIP ResNet-101 backbone, our approach achieves maximum improvements over ORCA of $9.57\\%$ in AUROC, $35.86\\%$ in FPR95, and $47.02\\%$ in ACC. When employing the CLIP ViT-B/32 backbone, these respective maximum improvements are $5.94\\%$, $21.37\\%$, and $32.81\\%$.\n\n%\\input{tables/ORCA_a}\n\n\n\n\\renewcommand{\\arraystretch}{1.2}\n\\setlength{\\tabcolsep}{2pt}\n\n\\begin{table}[t]\n\\centering\n\\resizebox{\\textwidth}{!}{\\begin{tabular}{cc|ccc|ccc|ccc|ccc}\n\\hline\n\\multirow{3}{*}{} & \\multirow{3}{*}{} & \\multicolumn{3}{c|}{CIFAR-10} & \\multicolumn{3}{c|}{CIFAR-100} & \\multicolumn{3}{c|}{EuroSAT} & \\multicolumn{3}{c}{Average} \\\\\n & & AUROC$\\uparrow$ & FPR95$\\downarrow$ & ACC$\\uparrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & ACC$\\uparrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & ACC$\\uparrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & ACC$\\uparrow$ \\\\\n\\hline\n\\multirow{1}{*}{ResNet-101} \n& MSP & 85.98& 62.98& 78.01& 80.72& 73.40& 48.50& 61.73& 88.98& 30.30 & 76.14& 75.12& 52.27\\\\\n& ORCA & 85.93 & 62.68 & 80.60 & 80.46 & 72.38 & 53.11 & 69.01 & 86.43 & 34.76 & 78.47& 73.83& 56.16\\\\\n\\rowcolor{gray!30} \\cellcolor{white!30}& TrustVLM-D & \\textbf{94.76} & \\textbf{26.82} & 78.01 & \\textbf{90.03} & \\textbf{39.46} & 48.50 & \\textbf{74.03} & \\textbf{66.69} & 30.30& \\textbf{86.27}& \\textbf{44.32}& 52.27\\\\\n\n\n\\rowcolor{gray!30} \\cellcolor{white!30}& TrustVLM*-D & 90.97& 41.88& \\textbf{95.63} & 82.27& 65.52& \\textbf{79.35}& 72.08 & 72.15 & \\textbf{81.78} & 81.77& 59.85& \\textbf{85.59}\\\\\n\n\n\\hline\n\\multirow{1}{*}{ViT-B/32} \n& MSP & 88.92& 58.66& 88.92& 81.15& 71.09& 58.42& 76.42& 80.24& 41.11 & 82.16&70.00& 62.82\\\\\n\n& ORCA & 89.00 & 52.70 & 90.00 & 83.40 & 67.00 & 66.50 & 77.55 & 71.29 & 50.00& 83.32& 63.66& 68.83 \\\\\n\n\\rowcolor{gray!30} \\cellcolor{white!30}& TrustVLM-D & \\textbf{94.94} & 31.85 & 88.92 & \\textbf{89.13} & \\textbf{45.63} & 58.42 & \\textbf{78.88} & \\textbf{69.36} &41.11& \\textbf{87.65}& \\textbf{48.95}& 62.82 \\\\\n\n\\rowcolor{gray!30} \\cellcolor{white!30} & TrustVLM*-D & 94.06 &\\textbf{29.58} & \\textbf{95.91} &84.95 & 62.99 & \\textbf{79.98} & 74.70& 72.77 & \\textbf{82.81}& 84.57& 55.11& \\textbf{86.23}\\\\\n\n\n\\hline\n\\end{tabular}\n}\n\\vspace{-0.0cm}\n\\caption{MisD performance compared with ORCA with CLIP ResNet-101 and ViT-B/32.}\n%\\vspace{-0.6cm}\n\\label{tab:RN101-ORCA} \n\\end{table}\n\\noindent\\textbf{{Zero-shot Classification on Fine-grained Classification Datasets.}} \nVisual prototypes and image-to-image similarity can also enhance the prediction accuracy of VLMs.\nIn \\cref{tab:fine-grained-rn50}, we compare our method against various zero-shot, few-shot, and test-time adaptation baselines using CLIP ResNet-50. Notably, without requiring any training phase, our method achieves the best overall performance, yielding an average accuracy improvement of $4.36\\%$ relative to these baselines. Furthermore, our approach is compatible with diverse vision encoders, including CLIP-I,\nMoCo v2, and DINOv2, and demonstrates robust performance across all of them. Subsequent fine-tuning of the visual prototypes, as implemented in TrustVLM*(F)-D, further improves accuracy by an additional $1.29\\%$ compared to TrustVLM*-D.\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%\n\n\\renewcommand\\arraystretch{1.2}\n\\begin{table*}[t]\n \\centering\n %\\vspace{-0.4cm}\n \\resizebox{0.95\\linewidth}{!}{\n \\begin{tabular}{l*{11}c}\n \\toprule\n Method & Aircraft & Caltech101 & Cars & DTD & EuroSAT & Flower102 & Food101 & Pets & SUN397 & UCF101 & Average \\\\\n \\midrule\n\n\n CLIP-RN50 &15.54 & 85.88& 55.74& 40.37&23.70 & 61.75& 73.95& 83.65&58.81 & 58.74& 55.81\\\\\n\n CoOp~\\cite{zhou2022learning} & 22.20&87.70 &61.30 &52.20 &63.20 &81.00 &76.30 &86.20 &63.40 &67.00 &66.05 \\\\\n Tip-Adapter~\\cite{zhang2022tip} & 23.70& 88.80& 63.90& 54.70& 72.50& 83.20&76.70 &86.40 &66.70 &72.10 &68.87 \\\\\n \n CuPL~\\cite{pratt2023does} & 19.59&89.29 &57.28 &48.64 &38.38 &65.44 &76.94 &84.84 &62.55 &58.97 &60.19 \\\\\n\n TPT~\\cite{shutest} &17.58 &87.02 &58.46 &40.84 &28.33 &62.69 & 74.88& 84.49& 61.46 &60.82& 57.66\\\\\n \n DMN~\\cite{zhang2024dual2} &20.22 &89.09 &58.36 &50.53 & 44.94& 68.33& 74.69& 86.29& 63.70& 64.02& 62.02\\\\\n TDA~\\cite{karmanov2024efficient} & 17.61& 89.70&57.78 &43.74 &42.11 &68.74 &\\textbf{77.75} &86.18 &62.53 &64.18 & 61.03\\\\\n\n ECALP~\\cite{li2025efficient}& 21.12& 89.94& 60.56& 54.49& 49.09& 69.39&76.97 &88.20 &64.97 &66.67 &64.14 \\\\\n \n\n\n \\midrule\n \n \\rowcolor{gray!30}\n TrustVLM*-C& \\textbf{29.34} & 89.98& \\textbf{66.09}& 60.34& 75.11& 90.62&74.53 &85.94 & 66.30 & 72.09&71.03 \\\\\n \n \n\n \n \n \\rowcolor{gray!30}TrustVLM*-M & 19.89 &93.27 &57.52 & 65.43& \\textbf{86.85}& 87.25& 74.85&\\textbf{90.22} &67.16 &73.28& 71.57 \\\\\n\n \\rowcolor{gray!30}TrustVLM*-D& 20.25&96.43 &56.08 &71.34& 82.43&\\textbf{99.11} &75.23 &83.65 & \\textbf{71.45}&75.34 &73.13 \\\\\n\n\\rowcolor{gray!30}TrustVLM*(F)-D& 27.27 & \\textbf{96.59}\t& 56.19\t& \\textbf{74.70}& 85.43&98.50 &75.17 &83.76 & 70.71& \\textbf{76.90}& \\textbf{74.52} \\\\\n\n\n \\bottomrule\n \\end{tabular}\n } \n\\vspace{-0.15cm}\n \\caption{{Zero-shot classification results on fine-grained datasets with CLIP ResNet-50. } \n }\n%\\vspace{-0.6cm}\n \\label{tab:fine-grained-rn50}\n\\end{table*}\n\n\\setlength{\\tabcolsep}{2pt}\n\\begin{table}\n\\begin{minipage}[t]{0.68\\columnwidth}\n\\begin{adjustbox}{width=\\linewidth}\n\n\\begin{tabular}{l@{~~~~~}| c cccc cccc cccc cccc }\n\\hline\n\n\n \\multicolumn{1}{c|}{{} } &\\multicolumn{4}{c|}{CLIP ResNet-50} &\\multicolumn{4}{c}{SigLIP ViT-B/32} \n \\\\\n \\multicolumn{1}{c|}{{}} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$}& AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c}{ACC$\\uparrow$}\\\\\n%\\cline{1-13}\n\n\\hline\n\n \n \\multicolumn{1}{c|}{DOCTOR}&259.56 & 80.55 & 73.83& \\multicolumn{1}{c|}{55.82} & 149.27 & 85.69 & 58.63 & \\multicolumn{1}{c}{70.69} \\\\\n \n \\multicolumn{1}{c|}{MSP}& 259.76& 80.63 & 73.11 & \\multicolumn{1}{c|}{55.82} & 149.13 & 85.86 & 57.31 & \\multicolumn{1}{c}{70.69} \\\\\n\n\n \n \\rowcolor{gray!30}\\multicolumn{1}{c|}{TrustVLM-D}&{226.95} &\\textbf{87.31} &\\textbf{53.98} & \\multicolumn{1}{c|}{55.82} & {129.84} & \\textbf{90.35} & \\textbf{44.49} & \\multicolumn{1}{c}{70.69} \\\\\n\n \\rowcolor{gray!30}\\multicolumn{1}{c|}{TrustVLM*-D}& \\textbf{135.81}& 83.51 &59.97 &\\multicolumn{1}{c|}{\\textbf{73.13}} &\\textbf{87.35} &87.47 &53.97 & \\textbf{79.99} \\\\\n\n \n\\cline{1-13}\n\n%\\hline\n\n%%%%%%%%\n\n\\end{tabular} \n\\end{adjustbox}\n\n\\vspace{+0.05cm}\n\\caption{Ablation on different architectures and VLMs. The average results on fine-grained classification datasets are reported.}\n\\label{tab:abla-vlm} \n\n\n\\end{minipage}\n\\hfill\n\\begin{minipage}[t]{0.29\\columnwidth}\n\n\\begin{adjustbox}{width=\\linewidth}\n\\begin{tabular}{l@{~~~~~}| c cccc cccc cccc cccc }\n\\hline\n\n\n \\multicolumn{1}{c}{{i-t}} &\\multicolumn{1}{c|}{{i-i}} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$\\\\\n%\\cline{1-13}\n\n\\hline\n\n \n \\multicolumn{1}{c}{$\\checkmark$} & \\multicolumn{1}{c|}{} & 188.75& 82.20 &70.10 \\\\\n \n \\multicolumn{1}{c}{} & \\multicolumn{1}{c|}{$\\checkmark$} &214.14 & 77.15 & 62.26 \\\\\n\n\n \n\\multicolumn{1}{c}{$\\checkmark$} & \\multicolumn{1}{c|}{$\\checkmark$}& \\textbf{168.06} & \\textbf{86.98} & \\textbf{54.47} \\\\\n \n \n\\hline\n\n%%%%%%%%\n\n\\end{tabular}\n\\end{adjustbox}\n\n\\vspace{+0.05cm}\n\\caption{Ablation on each component.}\n\\label{tab:abla-mod} \n\\end{minipage}\n\\end{table}\n\n\n\\subsection{Ablation Studies}\n\n\n% \\noindent\\textbf{{Different Vision Encoders.}} CLIP-I, MOCO V2, DINO V2 \\cref{tab:misd}\n% \\input{tables/abla_vlm}\n\n\n\\noindent\\textbf{{Different Architectures and VLMs.}} To demonstrate the versatility of the proposed framework, we evaluated its performance with different architectures and VLMs. Specifically, we replaced the CLIP ViT-B/16 backbone in \\cref{tab:misd} with CLIP ResNet-50 and SigLIP ViT-B/16, reporting average performance metrics across $10$ fine-grained datasets in \\cref{tab:abla-vlm}. Consistent with the observations in \\cref{tab:misd}, our method demonstrates robust compatibility across these varied architectures and VLMs, significantly surpassing the baseline methods in both configurations. More detailed results, including performance on ImageNet and its variants, are provided in the Appendix.\n\n\n\n\n\\noindent\\textbf{{Ablation on Each Component.}} We conducted comprehensive ablation studies to evaluate the contribution of each proposed module, as detailed in~\\cref{tab:abla-mod}. We denote 'i-t' as a baseline relying solely on image-to-text similarity (akin to MSP), and 'i-i' as the confidence score derived from our proposed image-to-image similarity module, which utilizes a vision encoder. The results indicate that employing either the 'i-t' or 'i-i' component alone yields suboptimal performance. These findings underscore the complementary nature of the two components, with the best results obtained when they are used in combination.\n\n\n%\\input{tables/abla_module}\n\n%\\begin{figure}[t!]\n\\begin{wrapfigure}{r}{0.5\\textwidth}\n \\centering \n\\vspace{-0.3cm}\n\\includegraphics[width=\\linewidth]{imgs/crop_abla_N.pdf}\n\\vspace{-0.7cm}\n \\caption{Influence of $N$ in prototypes.}\n\\vspace{-0.5cm}\n \\label{fig:abla_N}\n%\\end{figure}\n\\end{wrapfigure}\n\\noindent\\textbf{{Influence of N.}} We investigated the effect of $N$, the number of samples per class used for computing prototypes, on AUROC performance. As illustrated in~\\cref{fig:abla_N}, performance steadily improves with an increasing number of samples per class. Notably, using only a single sample per class ($N=1$) already achieves results superior to the baseline. The performance improvement tends to saturate when $N$ exceeds $4$.\n\n\n\n\n\\noindent\\textbf{{Visualization.}} We visualized the confidence score distributions for correct and incorrect predictions on the Flower102 dataset in \\cref{fig:abla_score}. The baseline MSP exhibits a poorer separation in confidence scores between correctly classified and misclassified samples. In contrast, our solution assigns higher confidence scores to correct predictions and lower scores to incorrect ones, leading to more distinct distributions and, thereby, improved misclassification detection.\n\\cref{fig:abla_illu} illustrates TrustVLM's mechanism for mitigating overconfidence. \n\n\n\\begin{figure}[t!]\n \\centering \\includegraphics[width=\\linewidth]{imgs/crop_score.pdf}\n\\vspace{-0.6cm}\n \\caption{Score distribution for correct and wrong predictions. Our TrustVLM achieves better separation between the score distributions of correct and wrong predictions, leading to improved performance in misclassification detection.\n }\n \\label{fig:abla_score}\n%\\vspace{-0.6cm}\n\\end{figure}\n\n%\\begin{wrapfigure}{r}{0.6\\textwidth}\n\\begin{figure}[t!]\n \\centering \\includegraphics[width=0.8\\linewidth]{imgs/crop_illustration.pdf}\n\\vspace{-0.3cm}\n \\caption{Illustration of TrustVLM’s mechanism. Initially, the incorrect prediction receives a higher confidence score $S_{i-t}$ than the correct one, indicating overconfidence. By performing verification in the image embedding space using $S_{i-i}$, this overconfidence is mitigated. As a result, the final confidence score $\\kappa(\\bm{x})$ is significantly higher for the correct prediction than for the incorrect one.}\n \\label{fig:abla_illu}\n\\end{figure}\n%\\end{wrapfigure}\n\n\n\\section{Conclusion}\nIn this work, we tackle the critical issue of misclassifications in VLMs, which hinders their reliable use, especially in safety-sensitive domains. We introduce TrustVLM, a novel training-free framework that substantially improves misclassification detection. TrustVLM uniquely leverages the commonly overlooked image embedding space by incorporating image-to-image similarity with an auxiliary vision encoder to derive a more discerning confidence score. The auxiliary vision encoder can also help VLMs make better zero-shot predictions and can be fine-tuned to achieve better performance. Our rigorous evaluations across $17$ datasets, $4$ architectures, and $2$ VLMs demonstrated TrustVLM's state-of-the-art performance. These findings highlight the considerable benefits of our approach in identifying confident, yet incorrect, VLM predictions. By enhancing the ability to determine when VLM outputs are trustworthy, TrustVLM contributes to the safer and more dependable deployment of these powerful models in real-world scenarios.\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\n\\appendix\n\n\n\n\\section{Broader Impact, Limitations, and Future Work}\n\n\\noindent\\textbf{Broader Impact.} The development of TrustVLM offers significant positive societal impacts by directly addressing the critical need for more reliable and trustworthy Vision-Language Models (VLMs). As VLMs become increasingly integrated into real-world applications, particularly in safety-critical domains such as autonomous driving, medical diagnostics, and public safety surveillance, the ability to accurately discern when a model's prediction can be trusted is paramount. Erroneous and overconfident predictions in these areas can lead to severe adverse consequences. TrustVLM contributes to mitigating such risks by providing a robust framework for misclassification detection, thereby enhancing the safety of VLM-powered systems. %The public availability of our code will also encourage further research and development in this area, potentially leading to even more sophisticated methods for ensuring AI safety and reliability across a wider range of multimodal applications.\n\n\n\n\\noindent\\textbf{Limitations.} While TrustVLM demonstrates strong performance across diverse benchmarks, several limitations remain. First, our method assumes access to clean class-level visual prototypes, which may not always be feasible in noisy or open-world settings. Second, our current work primarily focuses on zero-shot classification. While the core principles may be adaptable, the direct applicability and performance of TrustVLM on other VLM tasks, such as visual question answering or image captioning, have not yet been extensively evaluated. Third, the approach relies on a fixed auxiliary vision encoder and does not account for scenarios where the underlying data distribution evolves over time, such as in continual learning or streaming environments.\n\n\n\\noindent\\textbf{Future Work.} Building upon the promising results of TrustVLM, several avenues for future research warrant exploration. A key direction is the extension of the TrustVLM framework to a broader range of multimodal tasks beyond zero-shot classification, including visual question answering, image retrieval, and image captioning, to assess its generalizability and adapt its mechanisms where necessary. Besides, incorporating human-in-the-loop feedback for refining confidence scores may further improve VLM reliability in complex, real-world deployments.\n\n\n\n\\section{More Details on the Datasets}\nWe mainly evaluate our framework on $15$ datasets from \\textit{Fine-grained Classification Datasets} and \\textit{ImageNet and Its Variants}. The fine-grained classification datasets include $10$ publicly available image classification datasets, covering species of plants or animals (Flowers102~\\cite{nilsback2008automated}, OxfordPets~\\cite{parkhi2012cats}), scenes (SUN397~\\cite{xiao2010sun}), textures (DTD~\\cite{cimpoi2014describing}), food (Food101~\\cite{bossard2014food}), transportation(StanfordCars~\\cite{krause20133d}, FGVCAircraft~\\cite{maji2013fine}), human actions (UCF101~\\cite{soomro2012ucf101}), satellite images (EuroSAT~\\cite{helber2019eurosat}), and general objects (Caltech101~\\cite{fei2004learning}). The ImageNet and Its Variants features the original ImageNet~\\cite{deng2009imagenet}, along with several key variants: ImageNetV2~\\cite{recht2019imagenet}, an independent test set with natural images from a different source; ImageNet-Sketch~\\cite{wang2019learning}, comprising black and white sketches; ImageNet-A~\\cite{hendrycks2021natural}, a challenging test set of 'natural adversarial examples' often misclassified by standard ResNet-50 models~\\cite{he2016deep}; and ImageNet-R~\\cite{hendrycks2021many}, featuring artistic renditions of ImageNet categories. These variants collectively introduce diverse distribution shifts in image style, data domains, and other factors. \\cref{fig:datasets} illustrates representative examples from these datasets.\n\n\\begin{figure}[t!]\n \\centering \\includegraphics[width=0.65\\linewidth]{imgs/crop_datasets.pdf}\n\\vspace{-0.3cm}\n \\caption{Representative examples from each dataset used in this work. }\n \\label{fig:datasets}\n\\end{figure}\n\n\\section{Further Experimental Results}\n\n\n\\noindent\\textbf{More illustration on TrustVLM’s mechanism.} \\cref{fig:illus} demonstrates more examples on how TrustVLM works. The $S_{i-i}$ score is expected to be low if the prediction $\\hat{y}$ is incorrect, as the embedding $E_{x}$ would be compared against an inappropriate prototype, thereby helping to mitigate overconfidence. Conversely, a correct prediction $\\hat{y}$ should result in a high $S_{i-i}$, reinforcing the prediction's reliability.\n\n\n\\begin{figure}[t!]\n \\centering \\includegraphics[width=0.9\\linewidth]{imgs/crop_illust.pdf}\n\\vspace{-0.3cm}\n \\caption{More illustration on TrustVLM’s mechanism. }\n \\label{fig:illus}\n\\end{figure}\n\n\\noindent\\textbf{MisD performance on ImageNet and its variants under distribution shifts.} To evaluate the robustness of our proposed method to distribution shifts, the visual prototypes were computed using only $N$ samples per class from the ImageNet training split. These prototypes were then applied directly to the ImageNet variants, thereby obviating the need to compute variant-specific prototypes. As illustrated in \\cref{tab:imagenet-shift}, our method demonstrates the overall leading performance of all metrics evaluated in this challenging scenario, achieving average improvements of $1.54\\%$ in AURC, $1.07\\%$ in AUROC, and $4.29\\%$ in FPR95 relative to the baseline.\n\n\n\\setlength{\\tabcolsep}{2pt}\n%\\renewcommand\\arraystretch{1.2}\n\n\\begin{table}[t!]\n\\centering\n\\resizebox{\\textwidth}{!}{\n\\begin{tabular}{l@{~~~~~}| c cccc cccc cccc cccc }\n\\hline\n \n \\multicolumn{1}{c|}{{} } &\\multicolumn{4}{c|}{ImageNet-A} & \\multicolumn{4}{c|}{ImageNet-V2} & \\multicolumn{4}{c}{ImageNet-R} \n \\\\\n \\multicolumn{1}{c|}{{}} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c}{ACC$\\uparrow$} \\\\\n\\hline\n\n\n \n \\multicolumn{1}{c|}{DOCTOR}& 316.50 & 75.98 &77.76 & \\multicolumn{1}{c|}{{47.85}} & 180.06 & 80.09 &73.38 & \\multicolumn{1}{c|}{60.88} & 76.39 & 87.12 & 60.54 & \\multicolumn{1}{c}{73.98} \\\\\n \n \\multicolumn{1}{c|}{MSP}& \\textbf{315.99} & \\textbf{76.07} &77.76 & \\multicolumn{1}{c|}{{47.85}} & 178.82 &80.45 & 71.26 & \\multicolumn{1}{c|}{60.88} & 75.47 & 87.46 &59.43 & \\multicolumn{1}{c}{73.98} \\\\\n\n\n\\rowcolor{gray!30}\n\\multicolumn{1}{c|}{TrustVLM-D}& 324.07 &75.87 & \\textbf{77.20} & \\multicolumn{1}{c|}{47.85} &\\textbf{174.23} & \\textbf{82.18} & \\textbf{64.82}& \\multicolumn{1}{c|}{{60.88}} & \\textbf{74.94} & \\textbf{87.69} & \\textbf{58.09} & \\multicolumn{1}{c}{{73.98}} \\\\\n\n \n\n\\hline\n\n\n\n \\multicolumn{1}{c|}{{} } &\\multicolumn{4}{c|}{ImageNet-Sketch} & \\multicolumn{4}{c|}{ImageNet} & \\multicolumn{4}{c}{Average} \n \\\\\n \\multicolumn{1}{c|}{{}} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c}{ACC$\\uparrow$}\\\\\n\\hline\n\n\n\n \n \\multicolumn{1}{c|}{DOCTOR}& 296.20& 80.54 & 70.94 & \\multicolumn{1}{c|}{46.09} & 140.21 &80.53 & 74.58 & \\multicolumn{1}{c|}{66.71} & 201.87 & 80.85 &71.44 & \\multicolumn{1}{c}{59.10} \\\\\n \n \\multicolumn{1}{c|}{MSP} & 294.85 & 80.82 & 70.29 & \\multicolumn{1}{c|}{46.09} & 138.52 &81.04 &72.85 & \\multicolumn{1}{c|}{66.71} &200.73 & 81.17 &70.32 & \\multicolumn{1}{c}{59.10} \\\\\n\n\n\\rowcolor{gray!30}\n\\multicolumn{1}{c|}{TrustVLM-D}& \\textbf{289.52} &\\textbf{82.40} & \\textbf{65.59} & \\multicolumn{1}{c|}{{46.09}} &\\textbf{133.17} & \\textbf{83.08} & \\textbf{64.47}& \\multicolumn{1}{c|}{{66.71}} & \\textbf{199.19} & \\textbf{82.24} & \\textbf{66.03} & \\multicolumn{1}{c}{{59.10}} \\\\\n\n\n\n\\hline\n\n\n\n%%%%%%%%\n\n\\end{tabular} \n}\n\\vspace{+0.1cm}\n\\caption{MisD performance on ImageNet and its variants using CLIP ViT-B/16, under distribution shifts defined by prototypes computed exclusively on ImageNet and deployed directly to variant datasets.}\n\\label{tab:imagenet-shift} \n\\end{table}\n\n\\noindent\\textbf{Different architectures and VLMs.} To demonstrate the versatility of the proposed framework, we evaluated its performance with different architectures and VLMs. Specifically, we replaced the default CLIP ViT-B/16 backbone with CLIP ResNet-50 and SigLIP ViT-B/16, reporting average performance metrics across fine-grained datasets and ImageNet and its variants from \\cref{tab:sig-misd} to \\cref{tab:rn50-imagenet}. Consistent with the observations with CLIP ViT-B/16, our method demonstrates robust compatibility across these varied architectures and VLMs, significantly surpassing the baseline methods in both configurations.\n\n\n\\setlength{\\tabcolsep}{1.5pt}\n\\renewcommand\\arraystretch{1.2}\n\n\\begin{table}[t!]\n\\centering\n\\resizebox{\\textwidth}{!}{\n\\begin{tabular}{l@{~~~~~}| c cccc cccc cccc cccc }\n\\hline\n\n \\multicolumn{1}{c|}{{} } &\\multicolumn{4}{c|}{Flower102 } & \\multicolumn{4}{c|}{DTD } & \\multicolumn{4}{c|}{Aircraft} &\\multicolumn{4}{c}{Pets} \n \\\\\n \\multicolumn{1}{c|}{{}} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c}{ACC$\\uparrow$}\\\\\n\\hline\n\n \n \\multicolumn{1}{c|}{DOCTOR}& 26.03& 93.13 & 38.75 & \\multicolumn{1}{c|}{83.76} & 183.86 &79.14 & 72.67 & \\multicolumn{1}{c|}{61.29} &513.33 & 78.08 &80.74 & \\multicolumn{1}{c|}{27.42} & {7.65} & 95.55 & 30.65 & \\multicolumn{1}{c}{91.55} \\\\\n \n \\multicolumn{1}{c|}{MSP}&25.14 & 93.63 &34.75 & \\multicolumn{1}{c|}{83.76} & 181.52&79.66 &72.52 & \\multicolumn{1}{c|}{61.29} & 514.42& 77.78&76.98 & \\multicolumn{1}{c|}{27.42} & {7.65} &95.54 &{28.71} & \\multicolumn{1}{c}{91.55} \\\\\n\n \n \\rowcolor{gray!30}\\multicolumn{1}{c|}{TrustVLM-D}&{16.62} & {98.42} &{9.50} & \\multicolumn{1}{c|}{83.76} & {144.44}& {87.64} &{49.01} & \\multicolumn{1}{c|}{61.29} & {503.98}& {79.56}& {75.83} & \\multicolumn{1}{c|}{27.42} & 7.66 &{95.58} & {28.71} & \\multicolumn{1}{c}{91.55} \\\\\n\n \\rowcolor{gray!30} \\multicolumn{1}{c|}{TrustVLM*-D} & {0.13}& 97.82& 15.38& \\multicolumn{1}{c|}{99.47} & {98.27}& 82.72& 71.92& \\multicolumn{1}{c|}{72.64}& {496.11} &78.79 &79.14 & \\multicolumn{1}{c|}{28.23}& {7.55} &95.56 &29.97 &{91.63} \\\\\n \n\\hline\n\n \\multicolumn{1}{c|}{{} } &\\multicolumn{4}{c|}{Caltech101 } & \\multicolumn{4}{c|}{Cars} & \\multicolumn{4}{c|}{EuroSAT} &\\multicolumn{4}{c}{UCF101} \n \\\\\n \\multicolumn{1}{c|}{{}} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c}{ACC$\\uparrow$}\\\\\n\\hline\n\n \\multicolumn{1}{c|}{DOCTOR}& 2.31& 94.88 &27.50 & \\multicolumn{1}{c|}{96.75} & 18.84 &89.37 & 59.81 & \\multicolumn{1}{c|}{89.42} &481.80 &72.61 & 89.46& \\multicolumn{1}{c|}{31.26} & 85.35 &88.73 & 55.21 & \\multicolumn{1}{c}{70.31} \\\\\n \n\n \n \\multicolumn{1}{c|}{MSP}&2.20 & 95.21 & {26.25} & \\multicolumn{1}{c|}{96.75} & 18.73& 89.48 & 58.87 & \\multicolumn{1}{c|}{89.42} & 485.10& 72.31 &90.39 & \\multicolumn{1}{c|}{31.26} & 84.54 & 88.97 &56.28 & \\multicolumn{1}{c}{70.31} \\\\\n\n\\rowcolor{gray!30} \\multicolumn{1}{c|}{TrustVLM-D} & {2.08} & {95.52} & {26.25} & \\multicolumn{1}{c|}{96.75}& {18.63} &{89.57} & {57.58} & \\multicolumn{1}{c|}{89.42} & {390.00}& {90.09} &{42.82} & \\multicolumn{1}{c|}{31.26} & {74.35} & {92.14}& {41.23}& \\multicolumn{1}{c}{70.31} \\\\\n\n \\rowcolor{gray!30} \\multicolumn{1}{c|}{TrustVLM*-D} & {1.76} & {95.65} & {21.13} & \\multicolumn{1}{c|}{97.12} & 22.18 &87.92 &{54.05} &\\multicolumn{1}{c|}{89.42} & {72.64} &75.64 & 73.41& \\multicolumn{1}{c|}{82.91}& {54.41}&87.91 & 64.74&{78.03} \\\\\n\n\n \n\\hline\n\n \\multicolumn{1}{c|}{{} } &\\multicolumn{4}{c|}{Food101 } & \\multicolumn{4}{c|}{SUN397} &\\multicolumn{4}{c|}{Average} \n \\\\\n \\multicolumn{1}{c|}{{}} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$}\\\\\n\\cline{1-13}\n\n%\\hline\n\n \n \\multicolumn{1}{c|}{DOCTOR}&40.90 & 84.68 & 56.72& \\multicolumn{1}{c|}{87.54} & 132.64 & 80.73 & 74.81 & \\multicolumn{1}{c|}{67.62} & 149.27 & 85.69 & 58.63 & \\multicolumn{1}{c|}{70.69} \\\\\n \n \\multicolumn{1}{c|}{MSP}& 40.70& 84.84 & {54.70} & \\multicolumn{1}{c|}{87.54} & 131.29& 81.15 &73.63 & \\multicolumn{1}{c|}{67.62} & 149.13 & 85.86 & 57.31 & \\multicolumn{1}{c|}{70.69} \\\\\n\n\n \n \\rowcolor{gray!30}\\multicolumn{1}{c|}{TrustVLM-D}&{22.97} &{89.84} &{54.70} & \\multicolumn{1}{c|}{87.54} & {117.67} & {85.10} & {59.27} & \\multicolumn{1}{c|}{67.62} & {129.84} & \\textbf{90.35} & \\textbf{44.49} & \\multicolumn{1}{c|}{70.69} \\\\\n\n \\rowcolor{gray!30} \\multicolumn{1}{c|}{TrustVLM*-D} & 23.20& 89.56& 56.14& \\multicolumn{1}{c|}{87.80}&97.28&83.11&73.78&\\multicolumn{1}{c|}{72.63}&\\textbf{87.35}&87.47&53.97&\\multicolumn{1}{c|}{\\textbf{79.99}} \\\\\n \n\\cline{1-13}\n\n%\\hline\n\n%%%%%%%%\n\n\\end{tabular} \n}\n\\vspace{-0.0cm}\n\\caption{Misclassification detection performance on fine-grained classification datasets with SigLIP\nViT-B/16.}\n\\label{tab:sig-misd} \n\\end{table}\n\\setlength{\\tabcolsep}{2pt}\n%\\renewcommand\\arraystretch{1.2}\n\\begin{table}[t!]\n\\centering\n\\resizebox{\\textwidth}{!}{\n\\begin{tabular}{l@{~~~~~}| c cccc cccc cccc cccc }\n\\hline\n \n \\multicolumn{1}{c|}{{} } &\\multicolumn{4}{c|}{ImageNet-A} & \\multicolumn{4}{c|}{ImageNet-V2} & \\multicolumn{4}{c}{ImageNet-R} \n \\\\\n \\multicolumn{1}{c|}{{}} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c}{ACC$\\uparrow$}\\\\\n\\hline\n\n\n\n \n \\multicolumn{1}{c|}{DOCTOR}&299.17 & 79.28 &72.72 & \\multicolumn{1}{c|}{47.62} & 124.64& 83.03 & 67.64 & \\multicolumn{1}{c|}{67.55} &21.40 & 91.53 &45.00 & \\multicolumn{1}{c}{87.82} \\\\\n \n \\multicolumn{1}{c|}{MSP}& 299.17& 79.25 &72.58 & \\multicolumn{1}{c|}{47.62} &124.12 &83.21 & 67.08 & \\multicolumn{1}{c|}{67.55} & 20.92& 91.90 & 43.06 & \\multicolumn{1}{c}{87.82} \\\\\n \n\n \\rowcolor{gray!30}\\multicolumn{1}{c|}{TrustVLM-D}&{278.77}& {82.13} &{65.09} & \\multicolumn{1}{c|}{47.62} & {123.39} &{83.76} & {65.28} & \\multicolumn{1}{c|}{67.55} & {19.55}& {92.07 }&{42.05} & \\multicolumn{1}{c}{87.82} \\\\\n\n \\rowcolor{gray!30} \\multicolumn{1}{c|}{TrustVLM*-D} & 266.12 &77.68&78.19& \\multicolumn{1}{c|}{51.82}&122.70&83.05&67.17& \\multicolumn{1}{c|}{68.17}&19.49&91.85&43.33&87.98 \\\\\n\n\\hline\n \n \\multicolumn{1}{c|}{{} } &\\multicolumn{4}{c|}{ImageNet-Sketch} & \\multicolumn{4}{c|}{ImageNet} & \\multicolumn{4}{c}{Average} \n \\\\\n \\multicolumn{1}{c|}{{}} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c}{ACC$\\uparrow$} \\\\\n\\hline\n\n\n\n \n \\multicolumn{1}{c|}{DOCTOR} & 136.52 & 84.98 & 62.35 & \\multicolumn{1}{c|}{64.29} & 86.80 &83.40& 67.59 & \\multicolumn{1}{c|}{74.74} &133.71 & 84.44 & 63.06 & \\multicolumn{1}{c}{68.40} \\\\ \n\n \n \\multicolumn{1}{c|}{MSP}& 135.32& 85.31& 61.24& \\multicolumn{1}{c|}{64.29} & 85.84 & 83.78 & 66.03 & \\multicolumn{1}{c|}{74.74} & 133.07 & 84.69 & 62.00& \\multicolumn{1}{c}{68.40} \\\\\n \n\n\\rowcolor{gray!30} \\multicolumn{1}{c|}{TrustVLM-D}&{133.55} &{85.84} &{59.61} & \\multicolumn{1}{c|}{64.29} & {84.46} &{84.36}& {63.73} & \\multicolumn{1}{c|}{74.74} & {127.94} & \\textbf{85.63} & \\textbf{59.15} & \\multicolumn{1}{c}{68.40} \\\\\n\n \\rowcolor{gray!30} \\multicolumn{1}{c|}{TrustVLM*-D} & 132.62& 84.76& 64.17& \\multicolumn{1}{c|}{65.14}& 83.96&83.40 &67.89 & \\multicolumn{1}{c|}{75.45}& \\textbf{124.98}&84.15 &64.15 &\\textbf{69.71} \\\\\n \n\\hline\n\n%%%%%%%%\n\n\\end{tabular} \n}\n\\vspace{-0.0cm}\n\\caption{Misclassification detection performance on ImageNet and its variants with SigLIP ViT-B/16. }\n\\label{tab:sig-imagenet} \n\\end{table}\n\\setlength{\\tabcolsep}{1.5pt}\n\\renewcommand\\arraystretch{1.2}\n\\begin{table}[t!]\n\\centering\n\\resizebox{\\textwidth}{!}{\n\\begin{tabular}{l@{~~~~~}| c cccc cccc cccc cccc }\n\\hline\n\n \\multicolumn{1}{c|}{{} } &\\multicolumn{4}{c|}{Flower102 } & \\multicolumn{4}{c|}{DTD } & \\multicolumn{4}{c|}{Aircraft} &\\multicolumn{4}{c}{Pets} \n \\\\\n \\multicolumn{1}{c|}{{}} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c}{ACC$\\uparrow$}\\\\\n\\hline\n\n \n \\multicolumn{1}{c|}{DOCTOR}& 154.49& 83.85 & 67.94 & \\multicolumn{1}{c|}{61.75} & 374.42 & 75.61&83.55 & \\multicolumn{1}{c|}{40.37} & 695.10& 74.86 &79.37 & \\multicolumn{1}{c|}{15.54} & 36.51 &87.78 & 62.33 & \\multicolumn{1}{c}{83.65} \\\\\n\n \\multicolumn{1}{c|}{MSP}&154.02 & 83.99 &69.57 & \\multicolumn{1}{c|}{61.75} & 374.63 &75.58 &83.85 & \\multicolumn{1}{c|}{40.37} & 693.70& 75.15 & 80.97 & \\multicolumn{1}{c|}{15.54} &35.63 &88.30 &59.83 & \\multicolumn{1}{c}{83.65} \\\\\n \n\\rowcolor{gray!30} \\multicolumn{1}{c|}{TrustVLM-D}& {106.43}& {94.74} & {27.07} & \\multicolumn{1}{c|}{61.75} &{309.72} & {87.72} & {47.87} & \\multicolumn{1}{c|}{40.37} &{675.03} & {79.38} &{79.37} & \\multicolumn{1}{c|}{15.54} & {35.02} & {88.62}& {58.00} & \\multicolumn{1}{c}{83.65} \\\\\n\n \\rowcolor{gray!30} \\multicolumn{1}{c|}{TrustVLM*-D} & 0.23& 98.10& 4.55& \\multicolumn{1}{c|}{99.11}& 130.86&77.64 &75.46 & \\multicolumn{1}{c|}{71.34}&622.50 &75.35 &79.68 &\\multicolumn{1}{c|}{20.25} &34.37 & 88.95& 57.67& 83.65 \\\\\n\n\\hline\n\n \\multicolumn{1}{c|}{{} } &\\multicolumn{4}{c|}{Caltech101 } & \\multicolumn{4}{c|}{Cars} & \\multicolumn{4}{c|}{EuroSAT} &\\multicolumn{4}{c}{UCF101} \n \\\\\n \\multicolumn{1}{c|}{{}} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c}{ACC$\\uparrow$}\\\\\n\\hline\n\n \n \\multicolumn{1}{c|}{DOCTOR}& 30.50& 87.30 & 69.54 & \\multicolumn{1}{c|}{85.88} & 212.51& 80.23 & 74.73 & \\multicolumn{1}{c|}{55.74} &618.09 & 70.73 &83.87 & \\multicolumn{1}{c|}{23.70} & 169.84& 84.01 & 72.38 & \\multicolumn{1}{c}{58.82} \\\\\n\n\n \\multicolumn{1}{c|}{MSP}& 29.55& 87.95 &66.38 & \\multicolumn{1}{c|}{85.88} & 209.07& 80.93 &72.99 & \\multicolumn{1}{c|}{55.74} &633.47 & 67.67 &87.27 & \\multicolumn{1}{c|}{23.70} &166.92 & 84.77 &68.98 & \\multicolumn{1}{c}{58.82} \\\\\n \n\n \n\\rowcolor{gray!30} \\multicolumn{1}{c|}{TrustVLM-D}& {22.17}& {93.16} &{36.49} & \\multicolumn{1}{c|}{85.88} &{206.48} & {81.70} &{70.75} & \\multicolumn{1}{c|}{55.74} &{514.75} &{87.02} &{54.70} & \\multicolumn{1}{c|}{23.70} & {144.99} & {89.67} &{49.71} & \\multicolumn{1}{c}{58.82} \\\\\n\n \\rowcolor{gray!30} \\multicolumn{1}{c|}{TrustVLM*-D} & 6.06 &89.61 &40.91 &\\multicolumn{1}{c|}{96.43} & 205.62& 81.41& 70.92&\\multicolumn{1}{c|}{56.08} &101.58 &70.14 &74.00 & \\multicolumn{1}{c|}{82.43}& 73.84& 85.69& 62.81& 75.34 \\\\\n\n\\hline\n\n \\multicolumn{1}{c|}{{} } &\\multicolumn{4}{c|}{Food101 } & \\multicolumn{4}{c|}{SUN397} & \\multicolumn{4}{c|}{Average} \n \\\\\n \\multicolumn{1}{c|}{{}} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} \\\\\n\\cline{1-13}\n\n%\\hline\n \n \\multicolumn{1}{c|}{DOCTOR}& 94.96& 83.59 & 65.77 & \\multicolumn{1}{c|}{73.95} & 209.19 &77.49 & 78.84 & \\multicolumn{1}{c|}{58.81} & 259.56 & 80.55 & 73.83& \\multicolumn{1}{c|}{55.82} \\\\\n\n \n \\multicolumn{1}{c|}{MSP}& 94.32& 83.83 &64.86 & \\multicolumn{1}{c|}{73.95} & 206.31 &78.16 & 76.41 & \\multicolumn{1}{c|}{58.81}& 259.76 & 80.63 & 73.11 & \\multicolumn{1}{c|}{55.82} \\\\\n \n\n \n \\rowcolor{gray!30}\\multicolumn{1}{c|}{TrustVLM-D}& {76.19}& {87.05} &{58.46} & \\multicolumn{1}{c|}{73.95} & {178.75} & {84.08} & {57.38} & \\multicolumn{1}{c|}{58.81} & {226.95} & \\textbf{87.31} & \\textbf{53.98} & \\multicolumn{1}{c|}{55.82} \\\\\n\n \\rowcolor{gray!30} \\multicolumn{1}{c|}{TrustVLM*-D} & 70.64 & 87.03 & 60.27 & \\multicolumn{1}{c|}{75.23} & 112.43&81.19 &73.47 &\\multicolumn{1}{c|}{71.45} & \\textbf{135.81} &83.51 &59.97 &\\multicolumn{1}{c|}{\\textbf{73.13}} \\\\\n \n\\cline{1-13}\n\n%\\hline\n\n%%%%%%%%\n\n\\end{tabular} \n}\n\\vspace{-0.0cm}\n\\caption{Misclassification detection performance on fine-grained classification datasets with CLIP ResNet-50.}\n\\label{tab:rn50-misd} \n\\end{table}\n\\setlength{\\tabcolsep}{2pt}\n\n\\begin{table}[t!]\n\\centering\n\\resizebox{\\textwidth}{!}{\n\\begin{tabular}{l@{~~~~~}| c cccc cccc cccc cccc }\n\\hline\n \n \\multicolumn{1}{c|}{{} } &\\multicolumn{4}{c|}{ImageNet-A} & \\multicolumn{4}{c|}{ImageNet-V2} & \\multicolumn{4}{c}{ImageNet-R}\n \\\\\n \\multicolumn{1}{c|}{{}} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c}{ACC$\\uparrow$}\\\\\n\\hline\n\n\n \n \\multicolumn{1}{c|}{DOCTOR}& 672.36& 66.71 & 86.78 & \\multicolumn{1}{c|}{22.79} &256.91 & 79.22 & 75.41 & \\multicolumn{1}{c|}{51.50} & 188.11& 84.28 & 68.02 & \\multicolumn{1}{c}{56.36} \\\\\n\n \n \\multicolumn{1}{c|}{MSP}&673.06 & 66.68 &86.39 & \\multicolumn{1}{c|}{22.79} & 255.65 &79.53 & 74.83 & \\multicolumn{1}{c|}{51.50} &187.18 & 84.51 & 66.21 & \\multicolumn{1}{c}{56.36} \\\\\n \n\n \n \\rowcolor{gray!30}\\multicolumn{1}{c|}{TrustVLM-D}&{629.41} & {72.99} &{75.33} & \\multicolumn{1}{c|}{22.79} & {242.91} &{82.75} &{62.07} & \\multicolumn{1}{c|}{51.50} & {172.53}& {87.58}&{56.44} & \\multicolumn{1}{c}{56.36} \\\\\n\n \\rowcolor{gray!30} \\multicolumn{1}{c|}{TrustVLM*-D} & 583.73 &67.67&85.08&\\multicolumn{1}{c|}{27.99}& 231.81& 77.67& 75.42& \\multicolumn{1}{c|}{56.13}&166.54 & 83.35& 71.47& 60.34 \\\\\n\n\\hline\n\n \n \\multicolumn{1}{c|}{{} } &\\multicolumn{4}{c|}{ImageNet-Sketch} & \\multicolumn{4}{c|}{ImageNet} & \\multicolumn{4}{c}{Average} \n \\\\\n \\multicolumn{1}{c|}{{}} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c|}{ACC$\\uparrow$} & AURC$\\downarrow$ & AUROC$\\uparrow$ & FPR95$\\downarrow$ & \\multicolumn{1}{c}{ACC$\\uparrow$}\\\\\n\\hline\n\n\n \n \\multicolumn{1}{c|}{DOCTOR}& 446.07& 78.27 &74.71 & \\multicolumn{1}{c|}{32.99} & 204.36& 79.44& 73.82&\\multicolumn{1}{c|}{58.18} & 353.56 & 77.58 & 75.75 & \\multicolumn{1}{c}{44.36} \\\\\n\n \n \\multicolumn{1}{c|}{MSP}&444.12 &78.62 &73.94 & \\multicolumn{1}{c|}{32.99}&202.30& 79.96& 72.03 &\\multicolumn{1}{c|}{58.18} &352.46 & 77.86 & 74.68 & \\multicolumn{1}{c}{44.36} \\\\\n \n\n \n\\rowcolor{gray!30} \\multicolumn{1}{c|}{TrustVLM-D}&{409.65} & {85.09} &{52.28} & \\multicolumn{1}{c|}{32.99} &{183.69} &{84.24} &{58.35} &\\multicolumn{1}{c|}{58.18} & {327.64} & \\textbf{82.53} & \\textbf{60.89} & \\multicolumn{1}{c}{44.36} \\\\\n\n \\rowcolor{gray!30} \\multicolumn{1}{c|}{TrustVLM*-D} & 338.03 & 71.57& 80.89& \\multicolumn{1}{c|}{48.05}& 168.30&77.91 &74.34 &\\multicolumn{1}{c|}{65.00} &\\textbf{297.68} &75.63 &77.44 &\\textbf{51.50} \\\\\n\n\\hline\n\n%%%%%%%%\n\n\\end{tabular} \n}\n\\vspace{-0.0cm}\n\\caption{Misclassification detection performance on ImageNet and its variants with CLIP ResNet-50.}\n\\label{tab:rn50-imagenet} \n\\end{table}\n\\newpage\n\n\\bibliographystyle{splncs04}\n%\\bibliographystyle{unsrt}\n\\bibliography{my}\n\n\n\n%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n\\iffalse\n\\newpage\n\\section*{NeurIPS Paper Checklist}\n\n%%% BEGIN INSTRUCTIONS %%%\n% The checklist is designed to encourage best practices for responsible machine learning research, addressing issues of reproducibility, transparency, research ethics, and societal impact. 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The authors should preferably report a 2-sigma error bar than state that they have a 96\\% CI, if the hypothesis of Normality of errors is not verified.\n \\item For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out of range (e.g. negative error rates).\n \\item If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.\n \\end{itemize}\n\n\\item {\\bf Experiments compute resources}\n \\item[] Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?\n \\item[] Answer: \\answerYes{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n \\item[] Justification: We provide details in implementation details part.\n \\item[] Guidelines:\n \\begin{itemize}\n \\item The answer NA means that the paper does not include experiments.\n \\item The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.\n \\item The paper should provide the amount of compute required for each of the individual experimental runs as well as estimate the total compute. \n \\item The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make it into the paper). \n \\end{itemize}\n \n\\item {\\bf Code of ethics}\n \\item[] Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics \\url{https://neurips.cc/public/EthicsGuidelines}?\n \\item[] Answer: \\answerYes{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n \\item[] Justification: Our paper conforms, in every respect, with the NeurIPS Code of Ethics.\n \\item[] Guidelines:\n \\begin{itemize}\n \\item The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.\n \\item If the authors answer No, they should explain the special circumstances that require a deviation from the Code of Ethics.\n \\item The authors should make sure to preserve anonymity (e.g., if there is a special consideration due to laws or regulations in their jurisdiction).\n \\end{itemize}\n\n\n\\item {\\bf Broader impacts}\n \\item[] Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?\n \\item[] Answer: \\answerNA{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n \\item[] Justification: There is no societal impact of the work performed.\n \\item[] Guidelines:\n \\begin{itemize}\n \\item The answer NA means that there is no societal impact of the work performed.\n \\item If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact.\n \\item Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.\n \\item The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.\n \\item The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.\n \\item If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML).\n \\end{itemize}\n \n\\item {\\bf Safeguards}\n \\item[] Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?\n \\item[] Answer: \\answerNA{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n \\item[] Justification: The paper poses no such risks.\n \\item[] Guidelines:\n \\begin{itemize}\n \\item The answer NA means that the paper poses no such risks.\n \\item Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters. \n \\item Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.\n \\item We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make a best faith effort.\n \\end{itemize}\n\n\\item {\\bf Licenses for existing assets}\n \\item[] Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?\n \\item[] Answer: \\answerYes{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n \\item[] Justification: They are properly credited in our paper.\n \\item[] Guidelines:\n \\begin{itemize}\n \\item The answer NA means that the paper does not use existing assets.\n \\item The authors should cite the original paper that produced the code package or dataset.\n \\item The authors should state which version of the asset is used and, if possible, include a URL.\n \\item The name of the license (e.g., CC-BY 4.0) should be included for each asset.\n \\item For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.\n \\item If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, \\url{paperswithcode.com/datasets} has curated licenses for some datasets. Their licensing guide can help determine the license of a dataset.\n \\item For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.\n \\item If this information is not available online, the authors are encouraged to reach out to the asset's creators.\n \\end{itemize}\n\n\\item {\\bf New assets}\n \\item[] Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?\n \\item[] Answer: \\answerNA{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n \\item[] Justification: The paper does not release new assets.\n \\item[] Guidelines:\n \\begin{itemize}\n \\item The answer NA means that the paper does not release new assets.\n \\item Researchers should communicate the details of the dataset/code/model as part of their submissions via structured templates. This includes details about training, license, limitations, etc. \n \\item The paper should discuss whether and how consent was obtained from people whose asset is used.\n \\item At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.\n \\end{itemize}\n\n\\item {\\bf Crowdsourcing and research with human subjects}\n \\item[] Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)? \n \\item[] Answer: \\answerNA{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n \\item[] Justification: The paper does not involve crowdsourcing nor research with human subjects.\n \\item[] Guidelines:\n \\begin{itemize}\n \\item The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.\n \\item Including this information in the supplemental material is fine, but if the main contribution of the paper involves human subjects, then as much detail as possible should be included in the main paper. \n \\item According to the NeurIPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector. \n \\end{itemize}\n\n\\item {\\bf Institutional review board (IRB) approvals or equivalent for research with human subjects}\n \\item[] Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?\n \\item[] Answer: \\answerNA{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n \\item[] Justification: The paper does not involve crowdsourcing nor research with human subjects.\n \\item[] Guidelines:\n \\begin{itemize}\n \\item The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.\n \\item Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper. \n \\item We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere to the NeurIPS Code of Ethics and the guidelines for their institution. \n \\item For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.\n \\end{itemize}\n\n\\item {\\bf Declaration of LLM usage}\n \\item[] Question: Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.\n %this research? \n \\item[] Answer: \\answerNA{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n \\item[] Justification: The core method development in this research does not involve LLMs as any important, original, or non-standard components.\n \\item[] Guidelines:\n \\begin{itemize}\n \\item The answer NA means that the core method development in this research does not involve LLMs as any important, original, or non-standard components.\n \\item Please refer to our LLM policy (\\url{https://neurips.cc/Conferences/2025/LLM}) for what should or should not be described.\n \\end{itemize}\n\n\\end{enumerate}\n\\fi\n\n\\end{document}"},"question":{"kind":"string","value":"Which model has the best performance on both datasets in fpr95? Does this model perform better on fine-grained classification datasets or ImageNet datasets?"},"answer":{"kind":"string","value":"TrustVLM-D, fine-grained classification "},"plan":{"kind":"string","value":"SELECT FPR95 of models on fine-grained datasets from Table 1\nCOMPUTE best_model_1 = argmin over FPR95 scores on fine-grained datasets\n\n\nSELECT FPR95 of models on ImageNet datasets from Table 2\nCOMPUTE best_model_2 = argmin over FPR95 scores on ImageNet datasets\nIF best_model_1== best_model_2\n COMPUTE best_model_both = best_model_1\nELSE\n RETURN No best model on both, No dataset.\n\n\nCOMPUTE average FPR95 on fine-grained datasets for best_model_1\nCOMPUTE average FPR95 on ImageNet datasets for best_model_2\nCOMPUTE better_dataset = the dataset with lower of the two scores\n\n\nRETURN best_model_both, better_dataset\n"}}},{"rowIdx":82,"cells":{"paper":{"kind":"string","value":"2505.16986"},"relevant_tables":{"kind":"list like","value":[["\\begin{table*}[!ht]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n"," \\begin{tabular}{lcccc|cccc|c|cc|c}\n"," \\toprule\n"," \\textbf{Domain} & \\multicolumn{4}{c}{\\textbf{Tool Call}} & \\multicolumn{4}{|c|}{\\textbf{Parameter Matching}} & \\textbf{Code Exec. Rate} & \\multicolumn{2}{|c|}{\\textbf{Information Seeking}} & \\multicolumn{1}{c}{\\textbf{Cache Summary}} \\\\ \n"," & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\ \\midrule\n"," \\texttt{Single-Domain} \\\\\n"," \\midrule\n"," Flights & 83.51 & 89.98 & 92.08 & 91.02 & 58.02 & 66.38 & 82.18 & 73.44 & 85.26 & 39.19 & 86.44 & 62.07 \\\\\n"," Hotels & 78.18 & 84.87 & 90.84 & 87.75 & 60.40 & 67.29 & 85.53 & 75.32 & 72.13 & 48.30 & 80.91 & 71.35 \\\\\n"," Restaurants & 72.76 & 80.40 & 88.44 & 84.23 & 57.49 & 66.69 & 80.64 & 73.00 & 95.16 & 95.14 & 98.63 & 65.32 \\\\\n"," Attractions & 90.50 & 97.87 & 92.33 & 95.02 & 75.54 & 91.34 & 81.37 & 86.07 & 98.15 & N/A & N/A &83.00 \\\\ \\midrule\n"," \\multicolumn{10}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n"," F-H & 80.27 & 91.31 & 86.91 & 89.05 & 57.76 & 74.51 & 71.99 & 73.23 & 89.25 & 24.04 & 80.63 & 57.01 \\\\\n"," H-R & 82.02 & 91.03 & 89.23 & 90.12 & 68.44 & 77.95 & 84.86 & 81.26 & 83.67 & 29.12 & 87.58 & 64.42 \\\\\n"," H-A & 62.30 & 72.67 & 81.35 & 76.77 & 59.22 & 68.73 & 81.05 & 74.39 & 71.41 & 27.87 & 82.80 & 65.70 \\\\\n"," F-H-A & 77.19 & 87.50 & 86.76 & 87.13 & 59.63 & 74.16 & 75.27 & 74.71 & 81.15 & 22.65 & 83.45 & 52.76 \\\\\n"," F-H-R & 71.53 & 86.05 & 80.91 & 83.40 & 54.39 & 71.99 & 68.99 & 70.45 & 82.47 & 46.32 & 84.40 & 53.88 \\\\\n"," \\bottomrule\n"," \\end{tabular}\n","}\n","\\caption{Overall Results using Llama 3.1 8B Instruct after SFT.}\n","\\label{table:llama3.1-8b-sft}\n","\\end{table*}\n"],["\\begin{table*}[!ht]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n"," \\begin{tabular}{lcccc|cccc|c|cc|c}\n"," \\toprule\n"," \\textbf{Domain} & \\multicolumn{4}{c}{\\textbf{Tool Call}} & \\multicolumn{4}{|c|}{\\textbf{Parameter Matching}} & \\textbf{Code Exec. Rate} & \\multicolumn{2}{|c|}{\\textbf{Information Seeking}} & \\multicolumn{1}{c}{\\textbf{Cache Summary}} \\\\ \n"," & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\ \\midrule\n"," \\texttt{Single-Domain} \\\\\n"," \\midrule\n"," Flights & 58.42 & 78.26 & 69.75 & 73.76 & 32.11 & 40.51 & 60.76 & 48.61 & 83.32 & 20.25 & 82.90 & 53.80 \\\\\n"," Hotels & 92.42 & 96.03 & 96.10 & 96.07 & 75.33 & 80.85 & 91.69 & 85.93 & 98.55 & 46.98 & 80.27 & 75.00 \\\\\n"," Restaurants & 94.23 & 96.63 & 97.43 & 97.03 & 85.51 & 93.62 & 90.80 & 92.19 & 99.54 & 37.90 & 79.95 & 90.78 \\\\\n"," Attractions & 79.14 & 92.16 & 84.86 & 88.36 & 71.17 & 87.04 & 79.60 & 83.16 & 63.70 & N/A & N/A & 78.59 \\\\ \\midrule\n"," \\multicolumn{10}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n"," F-H & 43.48 & 53.73 & 69.52 & 60.61 & 22.28 & 25.94 & 61.24 & 36.44 & 95.02 & 27.39 & 83.42 & 37.15 \\\\\n"," H-R & 60.50 & 70.91 & 80.46 & 78.29 & 50.94 & 60.08 & 76.99 & 67.50 & 99.60 & 22.97 & 84.11 & 38.43 \\\\\n"," H-A & 64.91 & 68.93 & 91.75 & 78.72 & 59.24 & 66.59 & 84.29 & 74.40 & 97.01 & 30.02 & 82.71 & 57.03 \\\\\n"," F-H-A & 60.46 & 70.77 & 80.59 & 75.36 & 44.27 & 51.19 & 76.58 & 61.37 & 94.81 & 19.16 & 82.27 & 44.29 \\\\\n"," F-H-R & 52.97 & 62.22 & 78.07 & 69.25 & 42.93 & 50.66 & 73.76 & 60.07 & 91.30 & 40.45 & 85.62 & 45.37 \\\\\n"," \\bottomrule\n"," \\end{tabular}\n","}\n","\\caption{Overall Results using Llama 3.3 70B Instruct.}\n","\\label{table:llama3.3-70b}\n","\\end{table*}\n"]],"string":"[\n [\n \"\\\\begin{table*}[!ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lcccc|cccc|c|cc|c}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Domain} & \\\\multicolumn{4}{c}{\\\\textbf{Tool Call}} & \\\\multicolumn{4}{|c|}{\\\\textbf{Parameter Matching}} & \\\\textbf{Code Exec. Rate} & \\\\multicolumn{2}{|c|}{\\\\textbf{Information Seeking}} & \\\\multicolumn{1}{c}{\\\\textbf{Cache Summary}} \\\\\\\\ \\n\",\n \" & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\\\\\ \\\\midrule\\n\",\n \" \\\\texttt{Single-Domain} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Flights & 83.51 & 89.98 & 92.08 & 91.02 & 58.02 & 66.38 & 82.18 & 73.44 & 85.26 & 39.19 & 86.44 & 62.07 \\\\\\\\\\n\",\n \" Hotels & 78.18 & 84.87 & 90.84 & 87.75 & 60.40 & 67.29 & 85.53 & 75.32 & 72.13 & 48.30 & 80.91 & 71.35 \\\\\\\\\\n\",\n \" Restaurants & 72.76 & 80.40 & 88.44 & 84.23 & 57.49 & 66.69 & 80.64 & 73.00 & 95.16 & 95.14 & 98.63 & 65.32 \\\\\\\\\\n\",\n \" Attractions & 90.50 & 97.87 & 92.33 & 95.02 & 75.54 & 91.34 & 81.37 & 86.07 & 98.15 & N/A & N/A &83.00 \\\\\\\\ \\\\midrule\\n\",\n \" \\\\multicolumn{10}{l}{\\\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\\\\\ \\\\midrule\\n\",\n \" F-H & 80.27 & 91.31 & 86.91 & 89.05 & 57.76 & 74.51 & 71.99 & 73.23 & 89.25 & 24.04 & 80.63 & 57.01 \\\\\\\\\\n\",\n \" H-R & 82.02 & 91.03 & 89.23 & 90.12 & 68.44 & 77.95 & 84.86 & 81.26 & 83.67 & 29.12 & 87.58 & 64.42 \\\\\\\\\\n\",\n \" H-A & 62.30 & 72.67 & 81.35 & 76.77 & 59.22 & 68.73 & 81.05 & 74.39 & 71.41 & 27.87 & 82.80 & 65.70 \\\\\\\\\\n\",\n \" F-H-A & 77.19 & 87.50 & 86.76 & 87.13 & 59.63 & 74.16 & 75.27 & 74.71 & 81.15 & 22.65 & 83.45 & 52.76 \\\\\\\\\\n\",\n \" F-H-R & 71.53 & 86.05 & 80.91 & 83.40 & 54.39 & 71.99 & 68.99 & 70.45 & 82.47 & 46.32 & 84.40 & 53.88 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Overall Results using Llama 3.1 8B Instruct after SFT.}\\n\",\n \"\\\\label{table:llama3.1-8b-sft}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lcccc|cccc|c|cc|c}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Domain} & \\\\multicolumn{4}{c}{\\\\textbf{Tool Call}} & \\\\multicolumn{4}{|c|}{\\\\textbf{Parameter Matching}} & \\\\textbf{Code Exec. Rate} & \\\\multicolumn{2}{|c|}{\\\\textbf{Information Seeking}} & \\\\multicolumn{1}{c}{\\\\textbf{Cache Summary}} \\\\\\\\ \\n\",\n \" & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\\\\\ \\\\midrule\\n\",\n \" \\\\texttt{Single-Domain} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Flights & 58.42 & 78.26 & 69.75 & 73.76 & 32.11 & 40.51 & 60.76 & 48.61 & 83.32 & 20.25 & 82.90 & 53.80 \\\\\\\\\\n\",\n \" Hotels & 92.42 & 96.03 & 96.10 & 96.07 & 75.33 & 80.85 & 91.69 & 85.93 & 98.55 & 46.98 & 80.27 & 75.00 \\\\\\\\\\n\",\n \" Restaurants & 94.23 & 96.63 & 97.43 & 97.03 & 85.51 & 93.62 & 90.80 & 92.19 & 99.54 & 37.90 & 79.95 & 90.78 \\\\\\\\\\n\",\n \" Attractions & 79.14 & 92.16 & 84.86 & 88.36 & 71.17 & 87.04 & 79.60 & 83.16 & 63.70 & N/A & N/A & 78.59 \\\\\\\\ \\\\midrule\\n\",\n \" \\\\multicolumn{10}{l}{\\\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\\\\\ \\\\midrule\\n\",\n \" F-H & 43.48 & 53.73 & 69.52 & 60.61 & 22.28 & 25.94 & 61.24 & 36.44 & 95.02 & 27.39 & 83.42 & 37.15 \\\\\\\\\\n\",\n \" H-R & 60.50 & 70.91 & 80.46 & 78.29 & 50.94 & 60.08 & 76.99 & 67.50 & 99.60 & 22.97 & 84.11 & 38.43 \\\\\\\\\\n\",\n \" H-A & 64.91 & 68.93 & 91.75 & 78.72 & 59.24 & 66.59 & 84.29 & 74.40 & 97.01 & 30.02 & 82.71 & 57.03 \\\\\\\\\\n\",\n \" F-H-A & 60.46 & 70.77 & 80.59 & 75.36 & 44.27 & 51.19 & 76.58 & 61.37 & 94.81 & 19.16 & 82.27 & 44.29 \\\\\\\\\\n\",\n \" F-H-R & 52.97 & 62.22 & 78.07 & 69.25 & 42.93 & 50.66 & 73.76 & 60.07 & 91.30 & 40.45 & 85.62 & 45.37 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Overall Results using Llama 3.3 70B Instruct.}\\n\",\n \"\\\\label{table:llama3.3-70b}\\n\",\n \"\\\\end{table*}\\n\"\n ]\n]","isConversational":false},"tables":{"kind":"list like","value":[["\\begin{figure*}[!t]\n"," \\centering\n"," \\includegraphics[width=\\textwidth]{assets/pipeline.png}\n"," \\caption{Illustrative example from the $\\benchmarkname$ dataset. This example showcases a multi-domain scenario involving both flights and hotels, where the user is planning a trip and attempting to book relevant services. The dialogue is constructed by retrieving entities from a knowledge base, and tool calls are executed using a predefined toolbox, simulating realistic, tool-augmented agent behavior.}\n"," \\label{fig:architecture}\n","\\end{figure*}\n"],["\\begin{table*}[!htbp]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n"," \\begin{tabular}{lccccccc}\n"," \\toprule\n"," \\textbf{Dataset} & \\textbf{Deployed} & \\textbf{Human Annotated} & \\textbf{Execution Result} & \\textbf{Multi-turn Context} & \\textbf{Multi-Domain} \\\\\n"," & \\textbf{Tools} & \\textbf{Tool Planning} & \\textbf{Evaluation} & \\textbf{Planning} & \\textbf{Tool Planning}\\\\\n"," \\midrule\n"," APIBank~\\cite{li2023api} & \\cmark & \\cmark & \\cmark & \\cmark & & \\\\\n"," APIBench~\\cite{patil2024gorilla} & & & \\cmark & & \\\\\n"," GAIA~\\cite{mialon2023gaia} & & \\cmark & \\cmark & & \\\\\n"," GTA~\\cite{wang2024gta} & \\cmark & \\cmark & \\cmark & \\cmark & \\\\\n"," m\\&m's~\\cite{ma2024m} & \\cmark & \\cmark & \\cmark & \\cmark & \\\\\n"," ToolBench~\\cite{qin2023toolllm} & \\cmark & & \\cmark & & \\\\\n"," Toolformer~\\cite{schick2023toolformer} & \\cmark & \\cmark & \\cmark & & \\\\\n"," TravelPlanner~\\cite{xie2024travelplanner} & \\cmark & \\cmark & \\cmark & & \\cmark & \\\\ \n"," \\midrule\n"," $\\textbf{\\benchmarkname}$ & \\cmark & \\cmark & \\cmark & \\cmark & \\cmark \\\\\n"," \\bottomrule\n"," \\end{tabular}\n","}\n","\\caption{Comparison of datasets for the LLM-based agent systems.}\n","\\end{table*}\n"],["\\begin{figure*}[!t]\n"," \\centering\n"," \\includegraphics[width=\\textwidth]{assets/data_generation.png}\n"," \\caption{$\\benchmarkname$ generates data by populating delexicalized entities with corresponding entries from the knowledge base.}\n"," \\label{fig:data_generation}\n","\\end{figure*}\n"],["\\begin{table*}[!t]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n"," \\begin{tabular}{lcccc|cc|c|c}\n"," \\toprule \n"," & \\multicolumn{4}{c|}{\\textbf{Single-domain}} & \\multicolumn{2}{c|}{\\textbf{Multi-domain}}\n"," & \\textbf{Common} & \\textbf{Total} \\\\ \n"," & \\textbf{Flights} & \\textbf{Hotels} & \\textbf{Restaurants} & \\textbf{Attractions} & \\textbf{2 Domains} & \\textbf{3 Domains} & \\\\ \\midrule\n"," \\textbf{\\# Attributes} & 35 & 43 & 21 & 7 & N/A & N/A & N/A & 106 \\\\\n"," \\textbf{\\# Tools} & 2 & 2 & 2 & 2 & 1 & N/A & 5 & 14 \\\\ \n"," \\textbf{\\# Dialogues} & 1.5k & 1.5k & 1.5k & 1.5k & 4.5k & 3k & N/A & 13.5k \\\\ \n"," \\textbf{Avg. \\# turns} & 8.2 & 8.0 & 9.0 & 6.0 & 11.3 & 9.7 & N/A & N/A \\\\ \n"," \\bottomrule\n"," \\end{tabular}\n","}\n","\\caption{Dataset statistics detailing the number of attributes, tools, dialogues, and the average number of turns, categorized by each individual domain, multi-domain, and common categories. \"N/A\" indicates that the metric is not applicable in the given context.} \n","\\label{tab:baseline-results-one-epoch}\n","\\end{table*}\n"],["\\begin{figure}[!ht]\n"," \\centering\n"," \\includegraphics[width=\\linewidth]{assets/domain_adaptation.png}\n"," \\caption{\\textbf{Left:} Tool Call F1 performance and \\textbf{Right:} Parameter Matching F1 performance.}\n"," \\label{fig:enter-label}\n","\\end{figure}\n"],["\\begin{table*}[!ht]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n"," \\begin{tabular}{lcccc|cccc|c|cc|c}\n"," \\toprule\n"," \\textbf{Domain} & \\multicolumn{4}{c}{\\textbf{Tool Call}} & \\multicolumn{4}{|c|}{\\textbf{Parameter Matching}} & \\textbf{Code Exec.} & \\multicolumn{2}{|c|}{\\textbf{Information Seeking}} & \\multicolumn{1}{c}{\\textbf{Cache}} \\\\ \n"," & & & & & & & & & \\textbf{Rate} & & & \\textbf{Summary} \\\\\n"," & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\ \\midrule\n"," \\multicolumn{8}{l}{\\texttt{Single-Domain}} \\\\\n"," \\midrule\n"," Flights & 25.12 & 30.90 & 57.34 & 40.16 & 15.57 & 17.34 & 60.30 & 26.94 & 26.24 & 14.41 & 82.36 & 36.63 \\\\\n"," Hotels & 42.86 & 49.89 & 75.25 & 60.00 & 17.57 & 23.59 & 40.80 & 29.89 & 48.13 & 47.01 & 80.38 & 33.70 \\\\\n"," Restaurants & 40.40 & 52.97 & 63.00 & 57.55 & 31.47 & 36.07 & 71.12 & 47.87 & \\textbf{56.99} & 28.91 & 75.68 & 35.49 \\\\\n"," Attractions & 33.03 & 48.49 & 50.90 & 49.66 & 14.11 & 25.11 & 24.36 & 24.73 & 40.40 & N/A & N/A & 43.87 \\\\ \\midrule\n"," \\multicolumn{10}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n"," F-H & 33.09 & 40.89 & 63.44 & 49.73 & 16.11 & 19.28 & 49.48 & 27.75 & 38.43 & 26.54 & 82.00 & 21.85 \\\\\n"," H-R & 51.06 & 59.21 & 78.77 & 67.61 & 26.62 & 33.52 & 56.37 & 42.05 & 51.82 & 28.60 & 84.07 & 9.95 \\\\\n"," H-A & 38.75 & 45.46 & 72.39 & 55.85 & 16.11 & 20.02 & 45.12 & 27.74 & 48.88 & 26.03 & 81.77 & 32.75 \\\\\n"," F-H-A & 27.76 & 36.24 & 54.24 & 43.45 & 16.21 & 21.18 & 40.88 & 27.90 & 28.76 & 14.15 & 78.58 & 24.37 \\\\\n"," F-H-R & 29.04 & 34.65 & 64.19 & 45.00 & 18.53 & 21.77 & 55.44 & 31.26 & 36.79 & 33.28 & 82.84 & 27.47 \\\\\n"," \\bottomrule\n"," \\end{tabular}\n","}\n","\\caption{Overall results using Llama 3.1 8B Instruct with few-shot in-context learning.}\n","\\label{table:llama3.1-8b}\n","\\end{table*}\n"],["\\begin{figure}[!ht]\n"," \\centering\n"," \\includegraphics[width=0.9\\linewidth]{assets/flight_shot_learning.png}\n"," \\caption{Few-shot performance on Flight domain.}\n"," \\label{fig:few-shot}\n","\\end{figure}\n"],["\\begin{table*}[!ht]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n"," \\begin{tabular}{lcccc|cccc|c|cc|c}\n"," \\toprule\n"," \\textbf{Domain} & \\multicolumn{4}{c}{\\textbf{Tool Call}} & \\multicolumn{4}{|c|}{\\textbf{Parameter Matching}} & \\textbf{Code Exec. Rate} & \\multicolumn{2}{|c|}{\\textbf{Information Seeking}} & \\multicolumn{1}{c}{\\textbf{Cache Summary}} \\\\ \n"," & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\ \\midrule\n"," \\texttt{Single-Domain} \\\\\n"," \\midrule\n"," Flights & 83.51 & 89.98 & 92.08 & 91.02 & 58.02 & 66.38 & 82.18 & 73.44 & 85.26 & 39.19 & 86.44 & 62.07 \\\\\n"," Hotels & 78.18 & 84.87 & 90.84 & 87.75 & 60.40 & 67.29 & 85.53 & 75.32 & 72.13 & 48.30 & 80.91 & 71.35 \\\\\n"," Restaurants & 72.76 & 80.40 & 88.44 & 84.23 & 57.49 & 66.69 & 80.64 & 73.00 & 95.16 & 95.14 & 98.63 & 65.32 \\\\\n"," Attractions & 90.50 & 97.87 & 92.33 & 95.02 & 75.54 & 91.34 & 81.37 & 86.07 & 98.15 & N/A & N/A &83.00 \\\\ \\midrule\n"," \\multicolumn{10}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n"," F-H & 80.27 & 91.31 & 86.91 & 89.05 & 57.76 & 74.51 & 71.99 & 73.23 & 89.25 & 24.04 & 80.63 & 57.01 \\\\\n"," H-R & 82.02 & 91.03 & 89.23 & 90.12 & 68.44 & 77.95 & 84.86 & 81.26 & 83.67 & 29.12 & 87.58 & 64.42 \\\\\n"," H-A & 62.30 & 72.67 & 81.35 & 76.77 & 59.22 & 68.73 & 81.05 & 74.39 & 71.41 & 27.87 & 82.80 & 65.70 \\\\\n"," F-H-A & 77.19 & 87.50 & 86.76 & 87.13 & 59.63 & 74.16 & 75.27 & 74.71 & 81.15 & 22.65 & 83.45 & 52.76 \\\\\n"," F-H-R & 71.53 & 86.05 & 80.91 & 83.40 & 54.39 & 71.99 & 68.99 & 70.45 & 82.47 & 46.32 & 84.40 & 53.88 \\\\\n"," \\bottomrule\n"," \\end{tabular}\n","}\n","\\caption{Overall Results using Llama 3.1 8B Instruct after SFT.}\n","\\label{table:llama3.1-8b-sft}\n","\\end{table*}\n"],["\\begin{table*}[!ht]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n"," \\begin{tabular}{lcccc|cccc|c|cc|c}\n"," \\toprule\n"," \\textbf{Domain} & \\multicolumn{4}{c}{\\textbf{Tool Call}} & \\multicolumn{4}{|c|}{\\textbf{Parameter Matching}} & \\textbf{Code Exec. Rate} & \\multicolumn{2}{|c|}{\\textbf{Information Seeking}} & \\multicolumn{1}{c}{\\textbf{Cache Summary}} \\\\ \n"," & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\ \\midrule\n"," \\texttt{Single-Domain} \\\\\n"," \\midrule\n"," Flights & 58.42 & 78.26 & 69.75 & 73.76 & 32.11 & 40.51 & 60.76 & 48.61 & 83.32 & 20.25 & 82.90 & 53.80 \\\\\n"," Hotels & 92.42 & 96.03 & 96.10 & 96.07 & 75.33 & 80.85 & 91.69 & 85.93 & 98.55 & 46.98 & 80.27 & 75.00 \\\\\n"," Restaurants & 94.23 & 96.63 & 97.43 & 97.03 & 85.51 & 93.62 & 90.80 & 92.19 & 99.54 & 37.90 & 79.95 & 90.78 \\\\\n"," Attractions & 79.14 & 92.16 & 84.86 & 88.36 & 71.17 & 87.04 & 79.60 & 83.16 & 63.70 & N/A & N/A & 78.59 \\\\ \\midrule\n"," \\multicolumn{10}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n"," F-H & 43.48 & 53.73 & 69.52 & 60.61 & 22.28 & 25.94 & 61.24 & 36.44 & 95.02 & 27.39 & 83.42 & 37.15 \\\\\n"," H-R & 60.50 & 70.91 & 80.46 & 78.29 & 50.94 & 60.08 & 76.99 & 67.50 & 99.60 & 22.97 & 84.11 & 38.43 \\\\\n"," H-A & 64.91 & 68.93 & 91.75 & 78.72 & 59.24 & 66.59 & 84.29 & 74.40 & 97.01 & 30.02 & 82.71 & 57.03 \\\\\n"," F-H-A & 60.46 & 70.77 & 80.59 & 75.36 & 44.27 & 51.19 & 76.58 & 61.37 & 94.81 & 19.16 & 82.27 & 44.29 \\\\\n"," F-H-R & 52.97 & 62.22 & 78.07 & 69.25 & 42.93 & 50.66 & 73.76 & 60.07 & 91.30 & 40.45 & 85.62 & 45.37 \\\\\n"," \\bottomrule\n"," \\end{tabular}\n","}\n","\\caption{Overall Results using Llama 3.3 70B Instruct.}\n","\\label{table:llama3.3-70b}\n","\\end{table*}\n"],["\\begin{table*}[!ht]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n"," \\begin{tabular}{lcccc|cccc|c|cc|c}\n"," \\toprule\n"," \\textbf{Domain} & \\multicolumn{4}{c}{\\textbf{Tool Call}} & \\multicolumn{4}{|c|}{\\textbf{Parameter Matching}} & \\textbf{Code Exec. Rate} & \\multicolumn{2}{|c|}{\\textbf{Information Seeking}} & \\multicolumn{1}{c}{\\textbf{Cache Summary}} \\\\ \n"," & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\ \\midrule\n"," \\texttt{Single-Domain} \\\\\n"," \\midrule\n"," Flights & 77.59 & 87.87 & 86.90 & 87.38 & 49.53 & 58.04 & 77.15 & 66.25 & 57.20 & 29.65 & 83.12 & 57.46\n"," \\\\\n"," Hotels & 89.33 & 95.33 & 93.41 & 94.36 & 65.27 & 70.52 & 89.77 & 78.99 & 63.95 & 18.07 & 79.56 & 57.23\n"," \\\\\n"," Restaurants & 87.77 & 92.65 & 94.35 & 93.49 & 71.59 & 77.69 & 90.11 & 83.44 & 69.82 & 7.16 & 69.91 & 69.22\n"," \\\\\n"," Attractions & 65.76 & 80.16 & 78.54 & 79.34 & 44.92 & 53.04 & 74.59 & 62.00 & 72.57 & N/A & N/A & 63.87\n","\\\\ \\midrule\n"," \\multicolumn{10}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n"," F-H & 69.87 & 78.42 & 86.51 & 82.27 & 42.38 & 50.39 & 72.71 & 59.53 & 66.89 & 9.72 & 78.43 & 47.49\n"," \\\\\n"," H-R & 59.10 & 65.62 & 85.60 & 74.29 & 51.91 & 58.54 & 82.10 & 68.35 & 49.72 & 19.69 & 85.40 & 32.55\n"," \\\\\n"," H-A & 63.46 & 67.81 & 90.83 & 77.65 & 65.22 & 72.53 & 86.60 & 78.95 & 44.39 & 21.41 & 81.95 & 56.64\n"," \\\\\n"," F-H-A & 68.41 & 75.27 & 88.25 & 81.24 & 50.21 & 61.04 & 73.88 & 66.85 & 60.86 & 27.00 & 82.48 & 50.42\n"," \\\\\n"," F-H-R & 67.22 & 73.80 & 88.30 & 80.40 & 58.97 & 71.66 & 76.91 & 74.19 & 61.47 & 10.08 & 78.32 & 53.74\n"," \\\\\n"," \\bottomrule\n"," \\end{tabular}\n","}\n","\\caption{Overall Results using S1.1 32B.}\n","\\label{table:s1.1}\n","\\end{table*}\n"],["\\begin{table*}[!ht]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n"," \\begin{tabular}{lcccc|cccc|c|cc|c}\n"," \\toprule\n"," \\textbf{Domain} & \\multicolumn{4}{c}{\\textbf{Tool Call}} & \\multicolumn{4}{|c|}{\\textbf{Parameter Matching}} & \\textbf{Code Exec. Rate} & \\multicolumn{2}{|c|}{\\textbf{Information Seeking}} & \\multicolumn{1}{c}{\\textbf{Cache Summary}} \\\\ \n"," & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\ \\midrule\n"," \\texttt{Single-Domain} \\\\\n"," \\midrule\n"," Flights & 36.66 & 67.30 & 44.61 & 53.66 & 15.74 & 56.10 & 17.95 & 27.20 & 72.76 & 13.91 & 81.60 & 39.73\n"," \\\\\n"," Hotels & 83.96 & 91.74 & 90.82 & 91.28 & 53.22 & 63.09 & 77.30 & 69.47 & 93.05 & 46.95 & 80.47 & 46.52\n"," \\\\\n"," Restaurants & 64.13 & 83.60 & 73.36 & 78.14 & 47.10 & 61.89 & 66.34 & 64.04 & 93.49 & 28.27 & 69.74 & 50.53\n"," \\\\\n"," Attractions & 35.78 & 61.67 & 46.02 & 52.71 & 22.90 & 66.92 & 25.82 & 37.27 & 66.25 & N/A & N/A & 50.79\n","\\\\ \\midrule\n"," \\multicolumn{10}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n"," F-H & 57.79 & 88.12 & 62.67 & 73.25 & 29.91 & 59.20 & 37.68 & 46.05 & 83.37 & 27.96 & 83.02 & 32.53\n"," \\\\\n"," H-R & 54.07 & 70.71 & 69.67 & 70.18 & 35.42 & 50.91 & 53.79 & 52.31 & 96.04 & 23.19 & 84.83 & 21.72\n"," \\\\\n"," H-A & 65.81 & 78.69 & 80.08 & 79.38 & 48.58 & 61.78 & 69.45 & 65.39 & 96.04 & 27.92 & 82.97 & 53.52\n"," \\\\\n"," F-H-A & 37.83 & 62.18 & 49.14 & 54.90 & 32.73 & 64.59 & 39.89 & 49.32 & 84.49 & 15.42 & 81.00 & 32.33\n"," \\\\\n"," F-H-R & 46.79 & 72.75 & 56.74 & 63.75 & 33.99 & 59.56 & 44.18 & 50.73 & 91.86 & 36.30 & 84.63 & 37.65\n"," \\\\\n"," \\bottomrule\n"," \\end{tabular}\n","}\n","\\caption{Overall Results using Phi-4-reasoning-plus.}\n","\\label{table:phi-4-reasoning-plus}\n","\\end{table*}\n"],["\\begin{figure}[!ht]\n"," \\centering\n"," \\includegraphics[width=\\linewidth]{assets/all_model_f1_scores.png}\n"," \\caption{\\textbf{Above:} Tool Call F1 performance and \\textbf{Below:} Parameter Matching F1 performance.}\n"," \\label{fig:compare-models}\n","\\end{figure}\n"],["\\begin{table*}[!htbp]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n"," \\begin{tabular}{lccccccc}\n"," \\toprule\n"," \\textbf{Domain} & \\textbf{ \\# Turns} & \\textbf{\\# Validation Error} & \\textbf{\\# Variable Not Defined} & \\textbf{\\# Index Out Of Range} & \\textbf{\\# Other} \\\\\n"," \\midrule\n"," \\texttt{Single-Domain} \\\\\n"," \\midrule\n"," Flights & 8,200 & 737 & 830 & 0 & 468 \\\\\n"," Hotels & 8,000 & 417 & 218 & 0 & 820 \\\\\n"," Restaurants & 7,900 & 513 & 14 & 1 & 183 \\\\\n"," Attractions & 5,975 & 507 & 1 & 0 & 243 \\\\\n"," \\midrule\n"," \\multicolumn{6}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n"," F-H & 11,700 & 834 & 1087 & 0 & 408 \\\\\n"," H-R & 10,950 & 1532 & 18 & 1 & 522 \\\\\n"," H-A & 11,000 & 1024 & 167 & 0 & 308 \\\\\n"," F-H-A & 9,775 & 1163 & 990 & 0 & 575 \\\\\n"," F-H-R & 8,575 & 812 & 761 & 3 & 495 \\\\\n"," \\midrule\n"," \\texttt{Total} & - & 7539 & 4086 & 5 & 4022\\\\\n"," \\bottomrule\n"," \\end{tabular}\n","}\n","\\caption{Overall Error Analysis Results using Llama 3.1 8B Instruct.}\n","\\end{table*}\n"],["\\begin{table*}[!htbp]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n"," \\begin{tabular}{lccccccc}\n"," \\toprule\n"," \\textbf{Domain} & \\textbf{ \\# Turns} & \\textbf{\\# Validation Error} & \\textbf{\\# Variable Not Defined} & \\textbf{\\# Index Out Of Range} & \\textbf{\\# Other} \\\\\n"," \\midrule\n"," \\texttt{Single-Domain} \\\\\n"," \\midrule\n"," Flights & 8,200 & 408 & 176 & 42 & 26 \\\\\n"," Hotels & 8,000 & 1,099 & 16 & 0 & 0 \\\\\n"," Restaurants & 7,900 & 209 & 5 & 0 & 2 \\\\\n"," Attractions & 5,975 & 18 & 9 & 0 & 8 \\\\\n"," \\midrule\n"," \\multicolumn{6}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n"," F-H & 11,700 & 454 & 149 & 0 & 22 \\\\\n"," H-R & 10,950 & 692 & 185 & 0 & 17 \\\\\n"," H-A & 11,000 & 1248 & 315 & 0 & 1 \\\\\n"," F-H-A & 9,775 & 789 & 124 & 9 & 27 \\\\\n"," F-H-R & 8,575 & 541 & 109 & 0 & 101 \\\\\n"," \\midrule\n"," \\texttt{Total} & - & 5458 & 1088 & 51 & 204 \\\\\n"," \\bottomrule\n"," \\end{tabular}\n","}\n","\\caption{Overall Error Analysis Results using Llama 3.1 8B SFT Instruct.}\n","\\end{table*}\n"],["\\begin{table*}[!htbp]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n"," \\begin{tabular}{lccccccc}\n"," \\toprule\n"," \\textbf{Domain} & \\textbf{ \\# Turns} & \\textbf{\\# Validation Error} & \\textbf{\\# Variable Not Defined} & \\textbf{\\# Index Out Of Range} & \\textbf{\\# Other} \\\\\n"," \\midrule\n"," \\texttt{Single-Domain} \\\\\n"," \\midrule\n"," Flights & 8,200 & 518 & 2 & 30 & 0 \\\\\n"," Hotels & 8,000 & 41 & 0 & 0 & 0 \\\\\n"," Restaurants & 7,900 & 29 & 0 & 0 & 0 \\\\\n"," Attractions & 5,975 & 4 & 2 & 0 & 1 \\\\\n"," \\midrule\n"," \\multicolumn{6}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n"," F-H & 11,700 & 158 & 28 & 0 & 0\n"," \\\\\n"," H-R & 10,950 & 14 & 2 & 0 & 1\n"," \\\\\n"," H-A & 11,000 & 48 & 33 & 0 & 48\n"," \\\\\n"," F-H-A & 9,775 & 148 & 6 & 7 & 3\n"," \\\\\n"," F-H-R & 8,575 & 330 & 5 & 1 & 7\n"," \\\\\n"," \\midrule\n"," \\texttt{Total} & - & 1290 & 78 & 38 & 60 \\\\\n"," \\bottomrule\n"," \\end{tabular}\n","}\n","\\caption{Overall Error Analysis Results using Llama 3.3 70B Instruct.}\n","\\end{table*}\n"],["\\begin{table*}[!htbp]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n"," \\begin{tabular}{lccccccc}\n"," \\toprule\n"," \\textbf{Domain} & \\textbf{ \\# Turns} & \\textbf{\\# Validation Error} & \\textbf{\\# Variable Not Defined} & \\textbf{\\# Index Out Of Range} & \\textbf{\\# Other} \\\\\n"," \\midrule\n"," \\texttt{Single-Domain} \\\\\n"," \\midrule\n"," Flights & 8,200 & 559 & 16 & 3 & 40\n"," \\\\\n"," Hotels & 8,000 & 92 & 1 & 39 & 27\n"," \\\\\n"," Restaurants & 7,900 & 20 & 7 & 0 & 28\n"," \\\\\n"," Attractions & 5,975 & 11 & 27 & 0 & 76\n"," \\\\\n"," \\midrule\n"," \\multicolumn{6}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n"," F-H & 11,700 & 24 & 34 & 114 & 63\n"," \\\\\n"," H-R & 10,950 & 54 & 895 & 102 & 34\n"," \\\\\n"," H-A & 11,000 & 3 & 1071 & 43 & 23\n"," \\\\\n"," F-H-A & 9,775 & 89 & 579 & 25 & 49\n"," \\\\\n"," F-H-R & 8,575 & 85 & 241 & 7 & 28\n"," \\\\\n"," \\midrule\n"," \\texttt{Total} & - & 937 & 2871 & 333 & 368\n"," \\\\\n"," \\bottomrule\n"," \\end{tabular}\n","}\n","\\caption{Overall Error Analysis Results using S1.1.}\n","\\end{table*}\n"],["\\begin{table*}[!htbp]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n"," \\begin{tabular}{lccccccc}\n"," \\toprule\n"," \\textbf{Domain} & \\textbf{ \\# Turns} & \\textbf{\\# Validation Error} & \\textbf{\\# Variable Not Defined} & \\textbf{\\# Index Out Of Range} & \\textbf{\\# Other} \\\\\n"," \\midrule\n"," \\texttt{Single-Domain} \\\\\n"," \\midrule\n"," Flights & 8,200 & 103 & 7 & 1 & 1\n"," \\\\\n"," Hotels & 8,000 & 277 & 0 & 0 & 1\n"," \\\\\n"," Restaurants & 7,900 & 259 & 0 & 0 & 19\n"," \\\\\n"," Attractions & 5,975 & 0 & 0 & 0 & 0\n"," \\\\\n"," \\midrule\n"," \\multicolumn{6}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n"," F-H & 11,700 & 212 & 2 & 0 & 7\n"," \\\\\n"," H-R & 10,950 & 212 & 0 & 0 & 1\n"," \\\\\n"," H-A & 11,000 & 162 & 0 & 0 & 6\n"," \\\\\n"," F-H-A & 9,775 & 120 & 3 & 0 & 5\n"," \\\\\n"," F-H-R & 8,575 & 153 & 4 & 0 & 32\n"," \\\\\n"," \\midrule\n"," \\texttt{Total} & - & 1498 & 16 & 1 & 72\\\\\n"," \\bottomrule\n"," \\end{tabular}\n","}\n","\\caption{Overall Error Analysis Results using Phi-4-reasoning-plus.}\n","\\end{table*}\n"],["\\begin{table*}[!ht]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n"," \\begin{tabular}{lllll}\n"," \\toprule \n"," \\textbf{Domain} & \\textbf{Attributes} \\\\ \\midrule\n"," Flight & airline, flight\\_id, start\\_airport, start\\_airport\\_latitude, start\\_airport\\_longitude, \\\\\n"," & start\\_airport\\_code, end\\_airport, end\\_airport\\_latitude, end\\_airport\\_longitude, \\\\\n"," & end\\_airport\\_code, economy\\_class\\_option\\_present, business\\_class\\_option\\_present, \\\\\n"," & first\\_class\\_option\\_present, distance\\_miles, duration\\_minutes, \\\\\n"," & departure\\_time, arrival\\_time, number\\_of\\_layovers, first\\_layover\\_airport, \\\\\n"," Hotel & hotel\\_name, city, state, neighborhood, latitude, longitude, rating, \\\\ & stars, max\\_occupancy, gym\\_present, pool\\_present, price\\_per\\_night, num\\_rooms\\_available, \\\\ & breakfast\\_included, smoking\\_allowed, air\\_conditioning\\_present, \\\\\n"," & heating\\_present, free\\_wifi\\_included, airport\\_shuttle\\_present, \\\\ &\n"," is\\_pet\\_friendly, has\\_spa\\_services, has\\_room\\_service, \\\\ & has\\_beach\\_access, has\\_fitness\\_class, has\\_laundry\\_service, \\\\ & has\\_valet\\_parking, has\\_balcony, has\\_rooftop\\_bar, has\\_inroom\\_Kitchen, \\\\ & has\\_kids\\_club, has\\_meeting\\_rooms, has\\_electric\\_vehicle\\_charging, \\\\ & has\\_hot\\_tub, has\\_sauna, has\\_free\\_parking, is\\_wheelchair\\_accessible, \\\\ & has\\_skiing\\_lodging, has\\_ocean\\_view\\_rooms\\_present, has\\_city\\_view\\_rooms\\_present, \\\\ & start\\_date\\_available, end\\_date\\_available \\\\\n"," Attraction & city, state, name, type, latitude, longitude, neighborhood \\\\\n"," Restaurant & restaurant\\_name, city, state, neighborhood, \\\\\n"," & latitude, longitude, rating, price, price\\_per\\_person, has\\_nut\\_allergy\\_options, has\\_dairy\\_allergy\\_options, has\\_shell\\_fish\\_allergy\\_options, \\\\ & has\\_tomato\\_allergy\\_options, has\\_nightshade\\_allergy\\_options, \\\\ & has\\_gluten\\_free\\_options, has\\_vegetarian\\_options, has\\_vegan\\_options, \\\\ & has\\_kosher\\_options, has\\_halal\\_options, cuisine \\\\\n"," \\bottomrule\n"," \\end{tabular}\n","}\n","\\caption{Domains and Attributes.} \n","\\label{tab:domains-and-attributes}\n","\\end{table*}\n"],["\\begin{table*}[!ht]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n"," \\begin{tabular}{ll}\n"," \\toprule \n"," \\textbf{Domain} & \\textbf{Tools}\n"," \\\\\n"," \\textbf{Flights} & search\\_flights, filter\\_flights \\\\\n"," \\textbf{Hotels} & search\\_hotels, filter\\_hotels \\\\\n"," \\textbf{Restaurants} & search\\_restaurants, filter\\_restaurants \\\\\n"," \\textbf{Attractions} & search\\_attractions, filter\\_attractions \\\\\n"," \\textbf{Multi-domain} & search\\_nearest \\\\\n"," \\textbf{Common} & save\\_to\\_cache, get\\_results\\_from\\_cache, sort\\_results, seek\\_information, adjust\\_date \\\\\n"," \\bottomrule\n"," \\end{tabular}\n","}\n","\\caption{List of Tools.} \n","\\label{tab:defined-tools-domain}\n","\\end{table*}\n"],["\\begin{table*}[!ht]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n"," \\begin{tabular}{lll}\n"," \\toprule \n"," \\textbf{Attribute} & \\textbf{Type} & \\textbf{Description} \\\\ \\midrule\n"," airline & string & Airline of the flight, \\\\\n"," flight\\_classes & string & Classes for the flight (economy, business, first) \\\\\n"," num\\_layovers & integer & Number of layovers for the flight, between 0 and 2 \\\\\n"," layover\\_duration\\_amount & integer & Duration of a layover flight. Between 1 and 6 hours \\\\\n"," airports & list & Airport information for each flight including the city, state, airport code and airport name. \\\\\n"," \n"," \\bottomrule\n"," \\end{tabular}\n","}\n","\\caption{Flight Ontology Attributes.} \n","\\label{tab:flight-ontology-attributes}\n","\\end{table*}\n"],["\\begin{table*}[!htbp]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n"," \\begin{tabular}{lll}\n"," \\toprule \n"," \\textbf{Attribute} & \\textbf{Type} & \\textbf{Description} \\\\ \\midrule\n"," city & string & City that the hotel is in \\\\\n"," state & string & State that the hotel is in \\\\\n"," neighborhood & string & Neighborhood within the city that the hotel is in\\\\\n"," stars & integer & Star rating of the hotel between 1 and 5 \\\\\n"," rating & float & Customer rating of the hotel between 1.0 and 5.0, incremented by 0.1 \\\\\n"," price\\_per\\_night & integer & Price per night of the hotel ranging between 20 to 2000 dollars \\\\\n"," max\\_occupancy & integer & Maximum occupancy per room ranging between 1 and 7 \\\\\n"," gym\\_present & boolean & Whether or not the hotel has a gym \\\\\n"," pool\\_present & boolean & Whether or not the hotel has a pool \\\\\n"," breakfast\\_included & boolean & Whether or not the hotel has breakfast included \\\\\n"," smoking\\_allowed & boolean & Whether or not the hotel allows smoking \\\\\n"," air\\_conditioning\\_present & boolean & Whether or not the hotel has air conditioning \\\\\n"," heating\\_present & boolean & Whether or not the hotel has heating \\\\\n"," free\\_wifi\\_included & boolean & Whether or not the hotel includes free WiFi \\\\\n"," airport\\_shuttle\\_present & boolean & Whether or not the hotel has an airport shuffle \\\\\n"," is\\_pet\\_friendly & boolean & Whether or not the hotel allows pets \\\\\n"," has\\_spa\\_services & boolean & Whether or not the hotel has spa services \\\\\n"," has\\_room\\_service & boolean & Whether or not the hotel has room service \\\\\n"," has\\_beach\\_access & boolean & Whether or not the hotel has access to a beach \\\\\n"," has\\_business\\_center & boolean & Whether or not the hotel has a business center \\\\\n"," has\\_fitness\\_classes & boolean & Whether or not the hotel has fitness classes \\\\\n"," has\\_laundry\\_service & boolean & Whether or not the hotel has laundry services \\\\\n"," has\\_valet\\_parking & boolean & Whether or not the hotel has valet parking \\\\\n"," has\\_balcony & boolean & Whether or not the hotel has a balcony \\\\\n"," has\\_rooftop\\_bar & boolean & Whether or not the hotel has a rooftop bar \\\\\n"," has\\_inroom\\_kitchen & boolean & Whether or not the hotel has an inroom kitchen \\\\\n"," has\\_kids\\_club & boolean & Whether or not the hotel has a kids club \\\\\n"," has\\_meeting\\_rooms & boolean & Whether or not the hotel has meeting rooms \\\\\n"," has\\_electric\\_vehicle\\_charging & boolean & Whether or not the hotel has electric vehicle charging \\\\\n"," has\\_hot\\_tub & boolean & Whether or not the hotel has a hot tub \\\\\n"," has\\_sauna & boolean & Whether or not the hotel has a sauna \\\\ \n"," has\\_free\\_parking & boolean & Whether or not the hotel has free parking\\\\ \n"," is\\_wheelchair\\_accessible& boolean & Whether or not the hotel is wheelchair accessible \\\\ \n"," has\\_skiing\\_lodging & boolean & Whether or not the hotel has skiing and lodging \\\\ \n"," ocean\\_view\\_rooms\\_present & boolean & Whether or not the hotel has rooms with a view of the ocean \\\\\n"," city\\_view\\_rooms\\_present & boolean & Whether or not the hotel has rooms with views of the city\\\\\n"," \\bottomrule\n"," \\end{tabular}\n","}\n","\\caption{Hotel Ontology Attributes.} \n","\\label{tab:hotel-ontology-attributes}\n","\\end{table*}\n"],["\\begin{table*}[!htbp]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n"," \\begin{tabular}{lll}\n"," \\toprule \n"," \\textbf{Attribute} & \\textbf{Type} & \\textbf{Description} \\\\ \\midrule\n"," city & string & City that the restaurant is in \\\\\n"," state & string & State that the restaurant is in \\\\\n"," neighborhood & string & Neighborhood within the city that the restaurant is in\\\\\n"," rating & float & Customer rating of the restaurant between 1.0 and 5.0, incremented by 0.1 \\\\\n"," price\\_per\\_person & integer & Average price per person at the restaurant \\\\\n"," cuisine & string & Cuisine of the restaurant \\\\\n"," has\\_nut\\_allergy\\_options & boolean & Whether or not the restaurant has any options for individuals with an allergy to nuts \\\\\n"," has\\_dairy\\_allergy\\_options & boolean & Whether or not restaurant has any options for individuals with an allergy to dairy products \\\\\n"," has\\_shell\\_fish\\_allergy\\_options & boolean & Whether or not the restaurant has any options for individuals with an allergy to shell fish \\\\\n"," has\\_tomato\\_allergy\\_options & boolean & Whether or not the restaurant has options for individuals with an allergy to tomatoes \\\\\n"," has\\_nightshade\\_allergy\\_options & boolean & Whether or not the restaurant has options for individuals with an allergy to nightshades \\\\\n"," has\\_gluten\\_free\\_options & boolean & Whether or not the restaurant has gluten free options \\\\\n"," has\\_vegetarian\\_options & boolean & Whether or not the restaurant has vegetarian options \\\\\n"," has\\_vegan\\_options & boolean & Whether or not the restaurant has vegan options \\\\\n"," has\\_halal\\_options & boolean & Whether or not the restaurant has halal options \\\\\n"," has\\_kosher\\_options & boolean & Whether or not the hotel has kosher options \\\\ \n"," \\bottomrule\n"," \\end{tabular}\n","}\n","\\caption{Restaurant Ontology Attributes.} \n","\\label{tab:restaurant-ontology-attributes}\n","\\end{table*}\n"],["\\begin{table*}[!htbp]\n","\\centering\n","\\resizebox{0.8\\textwidth}{!}{\n"," \\begin{tabular}{lll}\n"," \\toprule \n"," \\textbf{Attribute} & \\textbf{Type} & \\textbf{Description} \\\\ \\midrule\n"," city & string & City that the attraction is in \\\\\n"," state & string & State that the attraction is in \\\\\n"," neighborhood & string & Neighborhood within the city that the attraction is in\\\\\n"," type & string & Type of the attraction \\\\ \n"," \\bottomrule\n"," \\end{tabular}\n","}\n","\\caption{Attraction Ontology Attributes.} \n","\\label{tab:attraction-ontology-attributes}\n","\\end{table*}\n"],["\\begin{table}[!ht]\n","\\centering\n","\\small\n","\\renewcommand{\\arraystretch}{1.3}\n","\\begin{tabular}{|l|p{5.5cm}|p{7.5cm}|}\n","\\hline\n","\\textbf{Domain} & \\textbf{Placeholders Used} & \\textbf{Required Scenarios} \\\\\n","\\hline\n","\\textbf{Flight} & \n","\\texttt{<CITY\\_x>}, \\texttt{<CLASS\\_x>}, \\texttt{<DEPARTURE\\_DATE\\_x>}, \\texttt{<DEPARTURE\\_TIME\\_x>}, \\texttt{<ARRIVAL\\_DATE\\_x>}, \\texttt{<ARRIVAL\\_TIME\\_x>}, \\texttt{<AIRLINE\\_x>}, \\texttt{<PRICE\\_x>}, \\texttt{<AIRPORT\\_x>}, \\texttt{<NUM\\_TRAVELERS\\_x>} & \n","Round trip (with 2 departure dates), one-way, multi-city, strict arrival time, flexible dates, large group bookings, solo/business/family travel, weekend getaways, budget constraints, special occasions (e.g., birthdays) \\\\\n","\\hline\n","\n","\\textbf{Hotel} & \n","\\texttt{<CHECK\\_IN\\_DATE\\_x>}, \\texttt{<CHECK\\_OUT\\_DATE\\_x>}, \\texttt{<CITY\\_x>}, \\texttt{<CITY\\_x\\_NEIGHBORHOOD\\_y>}, \\texttt{<STAR\\_x>}, \\texttt{<RATING\\_x>}, \\texttt{<PRICE\\_x>}, \\texttt{<NUM\\_ROOMS\\_x>}, \\texttt{<NUM\\_TRAVELERS\\_x>} & \n","Multi-city trips, multiple hotels in one city, specific neighborhoods, special occasions, last-minute reservations, business vs. leisure, extended stays, view/accessibility/pet-friendly requests, family and group accommodations \\\\\n","\\hline\n","\n","\\textbf{Attraction} & \n","\\texttt{<ATTRACTION\\_TYPE\\_x>}, \\texttt{<CITY\\_x>}, \\texttt{<STATE\\_x>}, \\texttt{<CITY\\_x\\_NEIGHBORHOOD\\_y>} & \n","Multi-type attraction queries, across cities/states, neighborhood-specific exploration, curiosity about state-level offerings. Assistant must ask open-ended questions and only mention placeholders if user provides them. \\\\\n","\\hline\n","\n","\\textbf{Restaurant} & \n","\\texttt{<RESTAURANT\\_RATING\\_x>}, \\texttt{<RESTAURANT\\_PRICE\\_x>}, \\texttt{<RESTAURANT\\_CUISINE\\_x>}, \\texttt{<CITY\\_x>}, \\texttt{<CITY\\_x\\_NEIGHBORHOOD\\_y>} & \n","Multi-city dining, cuisine preferences, dietary restrictions (e.g., nut-free, vegan, halal), budget vs. premium, special occasion dining, rating-focused or casual vs. upscale preferences \\\\\n","\\hline\n","\\end{tabular}\n","\\vspace{1mm}\n","\\caption{Domain-specific customization for template generation. Each domain builds on the shared prompt instructions with unique placeholders and required conversation scenarios.}\n","\\label{tab:domain-customization}\n","\\end{table}\n"],["\\begin{table}[!ht]\n","\\centering\n","\\small\n","\\renewcommand{\\arraystretch}{1.2}\n","\\begin{tabularx}{\\textwidth}{|l|X|X|}\n","\\hline\n","\\textbf{Domains} & \\textbf{Placeholders Used} & \\textbf{Required Scenarios} \\\\\n","\\hline\n","\n","\\textbf{Flight + Hotel} & \n","\\texttt{<CITY\\_x>}, \\texttt{<CLASS\\_x>}, \\texttt{<DEPARTURE\\_DATE\\_x>}, \\texttt{<CHECK\\_IN\\_DATE\\_x>}, \\texttt{<CHECK\\_OUT\\_DATE\\_x>}, \\texttt{<NUM\\_TRAVELERS\\_x>} & \n","Trip planning involving both air travel and accommodation. Includes round-trip and one-way flights, hotel stays across one or more cities, budget and luxury travelers, group vs. solo travel, flexible dates, specific preferences like nonstop flights or hotels with amenities (e.g., pool, gym, pet-friendly). \\\\\n","\\hline\n","\n","\\textbf{Flight + Hotel + Attraction} & \n","All flight, hotel, and attraction placeholders (e.g., \\texttt{<CITY\\_x>}, \\texttt{<CHECK\\_IN\\_DATE\\_x>}, \\texttt{<ATTRACTION\\_TYPE\\_x>}) & \n","User planning a full trip involving flights, hotel stays, and sightseeing. Must include: round-trip and one-way flights, multi-city stays, hotels near attractions, flexible schedules, user-provided attraction types or specific attraction names (only when linking to hotel proximity), and special constraints (e.g., group travel, business trips, anniversaries). Assistant must never use placeholder names before user introduces them. Includes amenity discussions and attraction-type grammar handling (e.g., cultural vs. \\texttt{<ATTRACTION\\_TYPE\\_1>} attraction). \\\\\n","\\hline\n","\n","\\textbf{Flight + Hotel + Restaurant} & \n","All flight, hotel, and restaurant placeholders (e.g., \\texttt{<RESTAURANT\\_CUISINE\\_x>}, \\texttt{<RESTAURANT\\_PRICE\\_x>}) & \n","End-to-end trip planning including flights, accommodation, and dining. Required scenarios include: dietary restrictions, cuisine-first or city-first planning, restaurant proximity to hotel, multiple hotels or restaurants in one city, round-trip and one-way flights, and amenities like pet-friendly or spa. Must feature diverse language, nonlinear flows, and varied user personas. Includes explicit handling of dietary needs (e.g., halal, nut-free, vegetarian), and placeholder logic as per prompt. \\\\\n","\\hline\n","\n","\\textbf{Hotel + Attraction} & \n","\\texttt{<CITY\\_x>}, \\texttt{<CHECK\\_IN\\_DATE\\_x>}, \\texttt{<ATTRACTION\\_TYPE\\_x>}, \\texttt{<CITY\\_x\\_NEIGHBORHOOD\\_y>} & \n","User looking for accommodations and nearby attractions. Includes: specific neighborhoods, family-friendly vs. solo travel, themed vacations, extended stays, proximity to cultural or outdoor attractions, and cases where attraction type drives hotel location. \\\\\n","\\hline\n","\n","\\textbf{Hotel + Restaurant} & \n","\\texttt{<RESTAURANT\\_CUISINE\\_x>}, \\texttt{<HOTEL\\_PRICE\\_x>}, \\texttt{<CITY\\_x\\_NEIGHBORHOOD\\_y>} & \n","Combines hotel booking with dining preferences. Includes cuisine-specific searches, budget dining vs. fine dining, dietary needs (gluten-free, halal), special events (anniversaries), restaurant proximity to hotel, and group size considerations. Templates vary in planning flow—some start with hotel, others with restaurants. \\\\\n","\\hline\n","\n","\\end{tabularx}\n","\\vspace{1mm}\n","\\caption{Multi-domain combinations and scenario requirements. Each configuration builds on the unified instructions with additional placeholder logic and domain-specific complexity.}\n","\\label{tab:multi-domain-customization}\n","\\end{table}\n"]],"string":"[\n [\n \"\\\\begin{figure*}[!t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\textwidth]{assets/pipeline.png}\\n\",\n \" \\\\caption{Illustrative example from the $\\\\benchmarkname$ dataset. This example showcases a multi-domain scenario involving both flights and hotels, where the user is planning a trip and attempting to book relevant services. The dialogue is constructed by retrieving entities from a knowledge base, and tool calls are executed using a predefined toolbox, simulating realistic, tool-augmented agent behavior.}\\n\",\n \" \\\\label{fig:architecture}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!htbp]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lccccccc}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Dataset} & \\\\textbf{Deployed} & \\\\textbf{Human Annotated} & \\\\textbf{Execution Result} & \\\\textbf{Multi-turn Context} & \\\\textbf{Multi-Domain} \\\\\\\\\\n\",\n \" & \\\\textbf{Tools} & \\\\textbf{Tool Planning} & \\\\textbf{Evaluation} & \\\\textbf{Planning} & \\\\textbf{Tool Planning}\\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" APIBank~\\\\cite{li2023api} & \\\\cmark & \\\\cmark & \\\\cmark & \\\\cmark & & \\\\\\\\\\n\",\n \" APIBench~\\\\cite{patil2024gorilla} & & & \\\\cmark & & \\\\\\\\\\n\",\n \" GAIA~\\\\cite{mialon2023gaia} & & \\\\cmark & \\\\cmark & & \\\\\\\\\\n\",\n \" GTA~\\\\cite{wang2024gta} & \\\\cmark & \\\\cmark & \\\\cmark & \\\\cmark & \\\\\\\\\\n\",\n \" m\\\\&m's~\\\\cite{ma2024m} & \\\\cmark & \\\\cmark & \\\\cmark & \\\\cmark & \\\\\\\\\\n\",\n \" ToolBench~\\\\cite{qin2023toolllm} & \\\\cmark & & \\\\cmark & & \\\\\\\\\\n\",\n \" Toolformer~\\\\cite{schick2023toolformer} & \\\\cmark & \\\\cmark & \\\\cmark & & \\\\\\\\\\n\",\n \" TravelPlanner~\\\\cite{xie2024travelplanner} & \\\\cmark & \\\\cmark & \\\\cmark & & \\\\cmark & \\\\\\\\ \\n\",\n \" \\\\midrule\\n\",\n \" $\\\\textbf{\\\\benchmarkname}$ & \\\\cmark & \\\\cmark & \\\\cmark & \\\\cmark & \\\\cmark \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Comparison of datasets for the LLM-based agent systems.}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[!t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\textwidth]{assets/data_generation.png}\\n\",\n \" \\\\caption{$\\\\benchmarkname$ generates data by populating delexicalized entities with corresponding entries from the knowledge base.}\\n\",\n \" \\\\label{fig:data_generation}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lcccc|cc|c|c}\\n\",\n \" \\\\toprule \\n\",\n \" & \\\\multicolumn{4}{c|}{\\\\textbf{Single-domain}} & \\\\multicolumn{2}{c|}{\\\\textbf{Multi-domain}}\\n\",\n \" & \\\\textbf{Common} & \\\\textbf{Total} \\\\\\\\ \\n\",\n \" & \\\\textbf{Flights} & \\\\textbf{Hotels} & \\\\textbf{Restaurants} & \\\\textbf{Attractions} & \\\\textbf{2 Domains} & \\\\textbf{3 Domains} & \\\\\\\\ \\\\midrule\\n\",\n \" \\\\textbf{\\\\# Attributes} & 35 & 43 & 21 & 7 & N/A & N/A & N/A & 106 \\\\\\\\\\n\",\n \" \\\\textbf{\\\\# Tools} & 2 & 2 & 2 & 2 & 1 & N/A & 5 & 14 \\\\\\\\ \\n\",\n \" \\\\textbf{\\\\# Dialogues} & 1.5k & 1.5k & 1.5k & 1.5k & 4.5k & 3k & N/A & 13.5k \\\\\\\\ \\n\",\n \" \\\\textbf{Avg. \\\\# turns} & 8.2 & 8.0 & 9.0 & 6.0 & 11.3 & 9.7 & N/A & N/A \\\\\\\\ \\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Dataset statistics detailing the number of attributes, tools, dialogues, and the average number of turns, categorized by each individual domain, multi-domain, and common categories. \\\"N/A\\\" indicates that the metric is not applicable in the given context.} \\n\",\n \"\\\\label{tab:baseline-results-one-epoch}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{figure}[!ht]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\linewidth]{assets/domain_adaptation.png}\\n\",\n \" \\\\caption{\\\\textbf{Left:} Tool Call F1 performance and \\\\textbf{Right:} Parameter Matching F1 performance.}\\n\",\n \" \\\\label{fig:enter-label}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table*}[!ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lcccc|cccc|c|cc|c}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Domain} & \\\\multicolumn{4}{c}{\\\\textbf{Tool Call}} & \\\\multicolumn{4}{|c|}{\\\\textbf{Parameter Matching}} & \\\\textbf{Code Exec.} & \\\\multicolumn{2}{|c|}{\\\\textbf{Information Seeking}} & \\\\multicolumn{1}{c}{\\\\textbf{Cache}} \\\\\\\\ \\n\",\n \" & & & & & & & & & \\\\textbf{Rate} & & & \\\\textbf{Summary} \\\\\\\\\\n\",\n \" & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\\\\\ \\\\midrule\\n\",\n \" \\\\multicolumn{8}{l}{\\\\texttt{Single-Domain}} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Flights & 25.12 & 30.90 & 57.34 & 40.16 & 15.57 & 17.34 & 60.30 & 26.94 & 26.24 & 14.41 & 82.36 & 36.63 \\\\\\\\\\n\",\n \" Hotels & 42.86 & 49.89 & 75.25 & 60.00 & 17.57 & 23.59 & 40.80 & 29.89 & 48.13 & 47.01 & 80.38 & 33.70 \\\\\\\\\\n\",\n \" Restaurants & 40.40 & 52.97 & 63.00 & 57.55 & 31.47 & 36.07 & 71.12 & 47.87 & \\\\textbf{56.99} & 28.91 & 75.68 & 35.49 \\\\\\\\\\n\",\n \" Attractions & 33.03 & 48.49 & 50.90 & 49.66 & 14.11 & 25.11 & 24.36 & 24.73 & 40.40 & N/A & N/A & 43.87 \\\\\\\\ \\\\midrule\\n\",\n \" \\\\multicolumn{10}{l}{\\\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\\\\\ \\\\midrule\\n\",\n \" F-H & 33.09 & 40.89 & 63.44 & 49.73 & 16.11 & 19.28 & 49.48 & 27.75 & 38.43 & 26.54 & 82.00 & 21.85 \\\\\\\\\\n\",\n \" H-R & 51.06 & 59.21 & 78.77 & 67.61 & 26.62 & 33.52 & 56.37 & 42.05 & 51.82 & 28.60 & 84.07 & 9.95 \\\\\\\\\\n\",\n \" H-A & 38.75 & 45.46 & 72.39 & 55.85 & 16.11 & 20.02 & 45.12 & 27.74 & 48.88 & 26.03 & 81.77 & 32.75 \\\\\\\\\\n\",\n \" F-H-A & 27.76 & 36.24 & 54.24 & 43.45 & 16.21 & 21.18 & 40.88 & 27.90 & 28.76 & 14.15 & 78.58 & 24.37 \\\\\\\\\\n\",\n \" F-H-R & 29.04 & 34.65 & 64.19 & 45.00 & 18.53 & 21.77 & 55.44 & 31.26 & 36.79 & 33.28 & 82.84 & 27.47 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Overall results using Llama 3.1 8B Instruct with few-shot in-context learning.}\\n\",\n \"\\\\label{table:llama3.1-8b}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{figure}[!ht]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=0.9\\\\linewidth]{assets/flight_shot_learning.png}\\n\",\n \" \\\\caption{Few-shot performance on Flight domain.}\\n\",\n \" \\\\label{fig:few-shot}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table*}[!ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lcccc|cccc|c|cc|c}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Domain} & \\\\multicolumn{4}{c}{\\\\textbf{Tool Call}} & \\\\multicolumn{4}{|c|}{\\\\textbf{Parameter Matching}} & \\\\textbf{Code Exec. Rate} & \\\\multicolumn{2}{|c|}{\\\\textbf{Information Seeking}} & \\\\multicolumn{1}{c}{\\\\textbf{Cache Summary}} \\\\\\\\ \\n\",\n \" & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\\\\\ \\\\midrule\\n\",\n \" \\\\texttt{Single-Domain} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Flights & 83.51 & 89.98 & 92.08 & 91.02 & 58.02 & 66.38 & 82.18 & 73.44 & 85.26 & 39.19 & 86.44 & 62.07 \\\\\\\\\\n\",\n \" Hotels & 78.18 & 84.87 & 90.84 & 87.75 & 60.40 & 67.29 & 85.53 & 75.32 & 72.13 & 48.30 & 80.91 & 71.35 \\\\\\\\\\n\",\n \" Restaurants & 72.76 & 80.40 & 88.44 & 84.23 & 57.49 & 66.69 & 80.64 & 73.00 & 95.16 & 95.14 & 98.63 & 65.32 \\\\\\\\\\n\",\n \" Attractions & 90.50 & 97.87 & 92.33 & 95.02 & 75.54 & 91.34 & 81.37 & 86.07 & 98.15 & N/A & N/A &83.00 \\\\\\\\ \\\\midrule\\n\",\n \" \\\\multicolumn{10}{l}{\\\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\\\\\ \\\\midrule\\n\",\n \" F-H & 80.27 & 91.31 & 86.91 & 89.05 & 57.76 & 74.51 & 71.99 & 73.23 & 89.25 & 24.04 & 80.63 & 57.01 \\\\\\\\\\n\",\n \" H-R & 82.02 & 91.03 & 89.23 & 90.12 & 68.44 & 77.95 & 84.86 & 81.26 & 83.67 & 29.12 & 87.58 & 64.42 \\\\\\\\\\n\",\n \" H-A & 62.30 & 72.67 & 81.35 & 76.77 & 59.22 & 68.73 & 81.05 & 74.39 & 71.41 & 27.87 & 82.80 & 65.70 \\\\\\\\\\n\",\n \" F-H-A & 77.19 & 87.50 & 86.76 & 87.13 & 59.63 & 74.16 & 75.27 & 74.71 & 81.15 & 22.65 & 83.45 & 52.76 \\\\\\\\\\n\",\n \" F-H-R & 71.53 & 86.05 & 80.91 & 83.40 & 54.39 & 71.99 & 68.99 & 70.45 & 82.47 & 46.32 & 84.40 & 53.88 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Overall Results using Llama 3.1 8B Instruct after SFT.}\\n\",\n \"\\\\label{table:llama3.1-8b-sft}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lcccc|cccc|c|cc|c}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Domain} & \\\\multicolumn{4}{c}{\\\\textbf{Tool Call}} & \\\\multicolumn{4}{|c|}{\\\\textbf{Parameter Matching}} & \\\\textbf{Code Exec. Rate} & \\\\multicolumn{2}{|c|}{\\\\textbf{Information Seeking}} & \\\\multicolumn{1}{c}{\\\\textbf{Cache Summary}} \\\\\\\\ \\n\",\n \" & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\\\\\ \\\\midrule\\n\",\n \" \\\\texttt{Single-Domain} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Flights & 58.42 & 78.26 & 69.75 & 73.76 & 32.11 & 40.51 & 60.76 & 48.61 & 83.32 & 20.25 & 82.90 & 53.80 \\\\\\\\\\n\",\n \" Hotels & 92.42 & 96.03 & 96.10 & 96.07 & 75.33 & 80.85 & 91.69 & 85.93 & 98.55 & 46.98 & 80.27 & 75.00 \\\\\\\\\\n\",\n \" Restaurants & 94.23 & 96.63 & 97.43 & 97.03 & 85.51 & 93.62 & 90.80 & 92.19 & 99.54 & 37.90 & 79.95 & 90.78 \\\\\\\\\\n\",\n \" Attractions & 79.14 & 92.16 & 84.86 & 88.36 & 71.17 & 87.04 & 79.60 & 83.16 & 63.70 & N/A & N/A & 78.59 \\\\\\\\ \\\\midrule\\n\",\n \" \\\\multicolumn{10}{l}{\\\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\\\\\ \\\\midrule\\n\",\n \" F-H & 43.48 & 53.73 & 69.52 & 60.61 & 22.28 & 25.94 & 61.24 & 36.44 & 95.02 & 27.39 & 83.42 & 37.15 \\\\\\\\\\n\",\n \" H-R & 60.50 & 70.91 & 80.46 & 78.29 & 50.94 & 60.08 & 76.99 & 67.50 & 99.60 & 22.97 & 84.11 & 38.43 \\\\\\\\\\n\",\n \" H-A & 64.91 & 68.93 & 91.75 & 78.72 & 59.24 & 66.59 & 84.29 & 74.40 & 97.01 & 30.02 & 82.71 & 57.03 \\\\\\\\\\n\",\n \" F-H-A & 60.46 & 70.77 & 80.59 & 75.36 & 44.27 & 51.19 & 76.58 & 61.37 & 94.81 & 19.16 & 82.27 & 44.29 \\\\\\\\\\n\",\n \" F-H-R & 52.97 & 62.22 & 78.07 & 69.25 & 42.93 & 50.66 & 73.76 & 60.07 & 91.30 & 40.45 & 85.62 & 45.37 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Overall Results using Llama 3.3 70B Instruct.}\\n\",\n \"\\\\label{table:llama3.3-70b}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lcccc|cccc|c|cc|c}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Domain} & \\\\multicolumn{4}{c}{\\\\textbf{Tool Call}} & \\\\multicolumn{4}{|c|}{\\\\textbf{Parameter Matching}} & \\\\textbf{Code Exec. Rate} & \\\\multicolumn{2}{|c|}{\\\\textbf{Information Seeking}} & \\\\multicolumn{1}{c}{\\\\textbf{Cache Summary}} \\\\\\\\ \\n\",\n \" & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\\\\\ \\\\midrule\\n\",\n \" \\\\texttt{Single-Domain} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Flights & 77.59 & 87.87 & 86.90 & 87.38 & 49.53 & 58.04 & 77.15 & 66.25 & 57.20 & 29.65 & 83.12 & 57.46\\n\",\n \" \\\\\\\\\\n\",\n \" Hotels & 89.33 & 95.33 & 93.41 & 94.36 & 65.27 & 70.52 & 89.77 & 78.99 & 63.95 & 18.07 & 79.56 & 57.23\\n\",\n \" \\\\\\\\\\n\",\n \" Restaurants & 87.77 & 92.65 & 94.35 & 93.49 & 71.59 & 77.69 & 90.11 & 83.44 & 69.82 & 7.16 & 69.91 & 69.22\\n\",\n \" \\\\\\\\\\n\",\n \" Attractions & 65.76 & 80.16 & 78.54 & 79.34 & 44.92 & 53.04 & 74.59 & 62.00 & 72.57 & N/A & N/A & 63.87\\n\",\n \"\\\\\\\\ \\\\midrule\\n\",\n \" \\\\multicolumn{10}{l}{\\\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\\\\\ \\\\midrule\\n\",\n \" F-H & 69.87 & 78.42 & 86.51 & 82.27 & 42.38 & 50.39 & 72.71 & 59.53 & 66.89 & 9.72 & 78.43 & 47.49\\n\",\n \" \\\\\\\\\\n\",\n \" H-R & 59.10 & 65.62 & 85.60 & 74.29 & 51.91 & 58.54 & 82.10 & 68.35 & 49.72 & 19.69 & 85.40 & 32.55\\n\",\n \" \\\\\\\\\\n\",\n \" H-A & 63.46 & 67.81 & 90.83 & 77.65 & 65.22 & 72.53 & 86.60 & 78.95 & 44.39 & 21.41 & 81.95 & 56.64\\n\",\n \" \\\\\\\\\\n\",\n \" F-H-A & 68.41 & 75.27 & 88.25 & 81.24 & 50.21 & 61.04 & 73.88 & 66.85 & 60.86 & 27.00 & 82.48 & 50.42\\n\",\n \" \\\\\\\\\\n\",\n \" F-H-R & 67.22 & 73.80 & 88.30 & 80.40 & 58.97 & 71.66 & 76.91 & 74.19 & 61.47 & 10.08 & 78.32 & 53.74\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Overall Results using S1.1 32B.}\\n\",\n \"\\\\label{table:s1.1}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lcccc|cccc|c|cc|c}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Domain} & \\\\multicolumn{4}{c}{\\\\textbf{Tool Call}} & \\\\multicolumn{4}{|c|}{\\\\textbf{Parameter Matching}} & \\\\textbf{Code Exec. Rate} & \\\\multicolumn{2}{|c|}{\\\\textbf{Information Seeking}} & \\\\multicolumn{1}{c}{\\\\textbf{Cache Summary}} \\\\\\\\ \\n\",\n \" & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\\\\\ \\\\midrule\\n\",\n \" \\\\texttt{Single-Domain} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Flights & 36.66 & 67.30 & 44.61 & 53.66 & 15.74 & 56.10 & 17.95 & 27.20 & 72.76 & 13.91 & 81.60 & 39.73\\n\",\n \" \\\\\\\\\\n\",\n \" Hotels & 83.96 & 91.74 & 90.82 & 91.28 & 53.22 & 63.09 & 77.30 & 69.47 & 93.05 & 46.95 & 80.47 & 46.52\\n\",\n \" \\\\\\\\\\n\",\n \" Restaurants & 64.13 & 83.60 & 73.36 & 78.14 & 47.10 & 61.89 & 66.34 & 64.04 & 93.49 & 28.27 & 69.74 & 50.53\\n\",\n \" \\\\\\\\\\n\",\n \" Attractions & 35.78 & 61.67 & 46.02 & 52.71 & 22.90 & 66.92 & 25.82 & 37.27 & 66.25 & N/A & N/A & 50.79\\n\",\n \"\\\\\\\\ \\\\midrule\\n\",\n \" \\\\multicolumn{10}{l}{\\\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\\\\\ \\\\midrule\\n\",\n \" F-H & 57.79 & 88.12 & 62.67 & 73.25 & 29.91 & 59.20 & 37.68 & 46.05 & 83.37 & 27.96 & 83.02 & 32.53\\n\",\n \" \\\\\\\\\\n\",\n \" H-R & 54.07 & 70.71 & 69.67 & 70.18 & 35.42 & 50.91 & 53.79 & 52.31 & 96.04 & 23.19 & 84.83 & 21.72\\n\",\n \" \\\\\\\\\\n\",\n \" H-A & 65.81 & 78.69 & 80.08 & 79.38 & 48.58 & 61.78 & 69.45 & 65.39 & 96.04 & 27.92 & 82.97 & 53.52\\n\",\n \" \\\\\\\\\\n\",\n \" F-H-A & 37.83 & 62.18 & 49.14 & 54.90 & 32.73 & 64.59 & 39.89 & 49.32 & 84.49 & 15.42 & 81.00 & 32.33\\n\",\n \" \\\\\\\\\\n\",\n \" F-H-R & 46.79 & 72.75 & 56.74 & 63.75 & 33.99 & 59.56 & 44.18 & 50.73 & 91.86 & 36.30 & 84.63 & 37.65\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Overall Results using Phi-4-reasoning-plus.}\\n\",\n \"\\\\label{table:phi-4-reasoning-plus}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{figure}[!ht]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\linewidth]{assets/all_model_f1_scores.png}\\n\",\n \" \\\\caption{\\\\textbf{Above:} Tool Call F1 performance and \\\\textbf{Below:} Parameter Matching F1 performance.}\\n\",\n \" \\\\label{fig:compare-models}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table*}[!htbp]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lccccccc}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Domain} & \\\\textbf{ \\\\# Turns} & \\\\textbf{\\\\# Validation Error} & \\\\textbf{\\\\# Variable Not Defined} & \\\\textbf{\\\\# Index Out Of Range} & \\\\textbf{\\\\# Other} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\texttt{Single-Domain} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Flights & 8,200 & 737 & 830 & 0 & 468 \\\\\\\\\\n\",\n \" Hotels & 8,000 & 417 & 218 & 0 & 820 \\\\\\\\\\n\",\n \" Restaurants & 7,900 & 513 & 14 & 1 & 183 \\\\\\\\\\n\",\n \" Attractions & 5,975 & 507 & 1 & 0 & 243 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multicolumn{6}{l}{\\\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\\\\\ \\\\midrule\\n\",\n \" F-H & 11,700 & 834 & 1087 & 0 & 408 \\\\\\\\\\n\",\n \" H-R & 10,950 & 1532 & 18 & 1 & 522 \\\\\\\\\\n\",\n \" H-A & 11,000 & 1024 & 167 & 0 & 308 \\\\\\\\\\n\",\n \" F-H-A & 9,775 & 1163 & 990 & 0 & 575 \\\\\\\\\\n\",\n \" F-H-R & 8,575 & 812 & 761 & 3 & 495 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\texttt{Total} & - & 7539 & 4086 & 5 & 4022\\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Overall Error Analysis Results using Llama 3.1 8B Instruct.}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!htbp]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lccccccc}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Domain} & \\\\textbf{ \\\\# Turns} & \\\\textbf{\\\\# Validation Error} & \\\\textbf{\\\\# Variable Not Defined} & \\\\textbf{\\\\# Index Out Of Range} & \\\\textbf{\\\\# Other} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\texttt{Single-Domain} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Flights & 8,200 & 408 & 176 & 42 & 26 \\\\\\\\\\n\",\n \" Hotels & 8,000 & 1,099 & 16 & 0 & 0 \\\\\\\\\\n\",\n \" Restaurants & 7,900 & 209 & 5 & 0 & 2 \\\\\\\\\\n\",\n \" Attractions & 5,975 & 18 & 9 & 0 & 8 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multicolumn{6}{l}{\\\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\\\\\ \\\\midrule\\n\",\n \" F-H & 11,700 & 454 & 149 & 0 & 22 \\\\\\\\\\n\",\n \" H-R & 10,950 & 692 & 185 & 0 & 17 \\\\\\\\\\n\",\n \" H-A & 11,000 & 1248 & 315 & 0 & 1 \\\\\\\\\\n\",\n \" F-H-A & 9,775 & 789 & 124 & 9 & 27 \\\\\\\\\\n\",\n \" F-H-R & 8,575 & 541 & 109 & 0 & 101 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\texttt{Total} & - & 5458 & 1088 & 51 & 204 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Overall Error Analysis Results using Llama 3.1 8B SFT Instruct.}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!htbp]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lccccccc}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Domain} & \\\\textbf{ \\\\# Turns} & \\\\textbf{\\\\# Validation Error} & \\\\textbf{\\\\# Variable Not Defined} & \\\\textbf{\\\\# Index Out Of Range} & \\\\textbf{\\\\# Other} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\texttt{Single-Domain} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Flights & 8,200 & 518 & 2 & 30 & 0 \\\\\\\\\\n\",\n \" Hotels & 8,000 & 41 & 0 & 0 & 0 \\\\\\\\\\n\",\n \" Restaurants & 7,900 & 29 & 0 & 0 & 0 \\\\\\\\\\n\",\n \" Attractions & 5,975 & 4 & 2 & 0 & 1 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multicolumn{6}{l}{\\\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\\\\\ \\\\midrule\\n\",\n \" F-H & 11,700 & 158 & 28 & 0 & 0\\n\",\n \" \\\\\\\\\\n\",\n \" H-R & 10,950 & 14 & 2 & 0 & 1\\n\",\n \" \\\\\\\\\\n\",\n \" H-A & 11,000 & 48 & 33 & 0 & 48\\n\",\n \" \\\\\\\\\\n\",\n \" F-H-A & 9,775 & 148 & 6 & 7 & 3\\n\",\n \" \\\\\\\\\\n\",\n \" F-H-R & 8,575 & 330 & 5 & 1 & 7\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\texttt{Total} & - & 1290 & 78 & 38 & 60 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Overall Error Analysis Results using Llama 3.3 70B Instruct.}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!htbp]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lccccccc}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Domain} & \\\\textbf{ \\\\# Turns} & \\\\textbf{\\\\# Validation Error} & \\\\textbf{\\\\# Variable Not Defined} & \\\\textbf{\\\\# Index Out Of Range} & \\\\textbf{\\\\# Other} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\texttt{Single-Domain} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Flights & 8,200 & 559 & 16 & 3 & 40\\n\",\n \" \\\\\\\\\\n\",\n \" Hotels & 8,000 & 92 & 1 & 39 & 27\\n\",\n \" \\\\\\\\\\n\",\n \" Restaurants & 7,900 & 20 & 7 & 0 & 28\\n\",\n \" \\\\\\\\\\n\",\n \" Attractions & 5,975 & 11 & 27 & 0 & 76\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multicolumn{6}{l}{\\\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\\\\\ \\\\midrule\\n\",\n \" F-H & 11,700 & 24 & 34 & 114 & 63\\n\",\n \" \\\\\\\\\\n\",\n \" H-R & 10,950 & 54 & 895 & 102 & 34\\n\",\n \" \\\\\\\\\\n\",\n \" H-A & 11,000 & 3 & 1071 & 43 & 23\\n\",\n \" \\\\\\\\\\n\",\n \" F-H-A & 9,775 & 89 & 579 & 25 & 49\\n\",\n \" \\\\\\\\\\n\",\n \" F-H-R & 8,575 & 85 & 241 & 7 & 28\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\texttt{Total} & - & 937 & 2871 & 333 & 368\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Overall Error Analysis Results using S1.1.}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!htbp]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lccccccc}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Domain} & \\\\textbf{ \\\\# Turns} & \\\\textbf{\\\\# Validation Error} & \\\\textbf{\\\\# Variable Not Defined} & \\\\textbf{\\\\# Index Out Of Range} & \\\\textbf{\\\\# Other} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\texttt{Single-Domain} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Flights & 8,200 & 103 & 7 & 1 & 1\\n\",\n \" \\\\\\\\\\n\",\n \" Hotels & 8,000 & 277 & 0 & 0 & 1\\n\",\n \" \\\\\\\\\\n\",\n \" Restaurants & 7,900 & 259 & 0 & 0 & 19\\n\",\n \" \\\\\\\\\\n\",\n \" Attractions & 5,975 & 0 & 0 & 0 & 0\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multicolumn{6}{l}{\\\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\\\\\ \\\\midrule\\n\",\n \" F-H & 11,700 & 212 & 2 & 0 & 7\\n\",\n \" \\\\\\\\\\n\",\n \" H-R & 10,950 & 212 & 0 & 0 & 1\\n\",\n \" \\\\\\\\\\n\",\n \" H-A & 11,000 & 162 & 0 & 0 & 6\\n\",\n \" \\\\\\\\\\n\",\n \" F-H-A & 9,775 & 120 & 3 & 0 & 5\\n\",\n \" \\\\\\\\\\n\",\n \" F-H-R & 8,575 & 153 & 4 & 0 & 32\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\texttt{Total} & - & 1498 & 16 & 1 & 72\\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Overall Error Analysis Results using Phi-4-reasoning-plus.}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lllll}\\n\",\n \" \\\\toprule \\n\",\n \" \\\\textbf{Domain} & \\\\textbf{Attributes} \\\\\\\\ \\\\midrule\\n\",\n \" Flight & airline, flight\\\\_id, start\\\\_airport, start\\\\_airport\\\\_latitude, start\\\\_airport\\\\_longitude, \\\\\\\\\\n\",\n \" & start\\\\_airport\\\\_code, end\\\\_airport, end\\\\_airport\\\\_latitude, end\\\\_airport\\\\_longitude, \\\\\\\\\\n\",\n \" & end\\\\_airport\\\\_code, economy\\\\_class\\\\_option\\\\_present, business\\\\_class\\\\_option\\\\_present, \\\\\\\\\\n\",\n \" & first\\\\_class\\\\_option\\\\_present, distance\\\\_miles, duration\\\\_minutes, \\\\\\\\\\n\",\n \" & departure\\\\_time, arrival\\\\_time, number\\\\_of\\\\_layovers, first\\\\_layover\\\\_airport, \\\\\\\\\\n\",\n \" Hotel & hotel\\\\_name, city, state, neighborhood, latitude, longitude, rating, \\\\\\\\ & stars, max\\\\_occupancy, gym\\\\_present, pool\\\\_present, price\\\\_per\\\\_night, num\\\\_rooms\\\\_available, \\\\\\\\ & breakfast\\\\_included, smoking\\\\_allowed, air\\\\_conditioning\\\\_present, \\\\\\\\\\n\",\n \" & heating\\\\_present, free\\\\_wifi\\\\_included, airport\\\\_shuttle\\\\_present, \\\\\\\\ &\\n\",\n \" is\\\\_pet\\\\_friendly, has\\\\_spa\\\\_services, has\\\\_room\\\\_service, \\\\\\\\ & has\\\\_beach\\\\_access, has\\\\_fitness\\\\_class, has\\\\_laundry\\\\_service, \\\\\\\\ & has\\\\_valet\\\\_parking, has\\\\_balcony, has\\\\_rooftop\\\\_bar, has\\\\_inroom\\\\_Kitchen, \\\\\\\\ & has\\\\_kids\\\\_club, has\\\\_meeting\\\\_rooms, has\\\\_electric\\\\_vehicle\\\\_charging, \\\\\\\\ & has\\\\_hot\\\\_tub, has\\\\_sauna, has\\\\_free\\\\_parking, is\\\\_wheelchair\\\\_accessible, \\\\\\\\ & has\\\\_skiing\\\\_lodging, has\\\\_ocean\\\\_view\\\\_rooms\\\\_present, has\\\\_city\\\\_view\\\\_rooms\\\\_present, \\\\\\\\ & start\\\\_date\\\\_available, end\\\\_date\\\\_available \\\\\\\\\\n\",\n \" Attraction & city, state, name, type, latitude, longitude, neighborhood \\\\\\\\\\n\",\n \" Restaurant & restaurant\\\\_name, city, state, neighborhood, \\\\\\\\\\n\",\n \" & latitude, longitude, rating, price, price\\\\_per\\\\_person, has\\\\_nut\\\\_allergy\\\\_options, has\\\\_dairy\\\\_allergy\\\\_options, has\\\\_shell\\\\_fish\\\\_allergy\\\\_options, \\\\\\\\ & has\\\\_tomato\\\\_allergy\\\\_options, has\\\\_nightshade\\\\_allergy\\\\_options, \\\\\\\\ & has\\\\_gluten\\\\_free\\\\_options, has\\\\_vegetarian\\\\_options, has\\\\_vegan\\\\_options, \\\\\\\\ & has\\\\_kosher\\\\_options, has\\\\_halal\\\\_options, cuisine \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Domains and Attributes.} \\n\",\n \"\\\\label{tab:domains-and-attributes}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{ll}\\n\",\n \" \\\\toprule \\n\",\n \" \\\\textbf{Domain} & \\\\textbf{Tools}\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\textbf{Flights} & search\\\\_flights, filter\\\\_flights \\\\\\\\\\n\",\n \" \\\\textbf{Hotels} & search\\\\_hotels, filter\\\\_hotels \\\\\\\\\\n\",\n \" \\\\textbf{Restaurants} & search\\\\_restaurants, filter\\\\_restaurants \\\\\\\\\\n\",\n \" \\\\textbf{Attractions} & search\\\\_attractions, filter\\\\_attractions \\\\\\\\\\n\",\n \" \\\\textbf{Multi-domain} & search\\\\_nearest \\\\\\\\\\n\",\n \" \\\\textbf{Common} & save\\\\_to\\\\_cache, get\\\\_results\\\\_from\\\\_cache, sort\\\\_results, seek\\\\_information, adjust\\\\_date \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{List of Tools.} \\n\",\n \"\\\\label{tab:defined-tools-domain}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lll}\\n\",\n \" \\\\toprule \\n\",\n \" \\\\textbf{Attribute} & \\\\textbf{Type} & \\\\textbf{Description} \\\\\\\\ \\\\midrule\\n\",\n \" airline & string & Airline of the flight, \\\\\\\\\\n\",\n \" flight\\\\_classes & string & Classes for the flight (economy, business, first) \\\\\\\\\\n\",\n \" num\\\\_layovers & integer & Number of layovers for the flight, between 0 and 2 \\\\\\\\\\n\",\n \" layover\\\\_duration\\\\_amount & integer & Duration of a layover flight. Between 1 and 6 hours \\\\\\\\\\n\",\n \" airports & list & Airport information for each flight including the city, state, airport code and airport name. \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Flight Ontology Attributes.} \\n\",\n \"\\\\label{tab:flight-ontology-attributes}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!htbp]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lll}\\n\",\n \" \\\\toprule \\n\",\n \" \\\\textbf{Attribute} & \\\\textbf{Type} & \\\\textbf{Description} \\\\\\\\ \\\\midrule\\n\",\n \" city & string & City that the hotel is in \\\\\\\\\\n\",\n \" state & string & State that the hotel is in \\\\\\\\\\n\",\n \" neighborhood & string & Neighborhood within the city that the hotel is in\\\\\\\\\\n\",\n \" stars & integer & Star rating of the hotel between 1 and 5 \\\\\\\\\\n\",\n \" rating & float & Customer rating of the hotel between 1.0 and 5.0, incremented by 0.1 \\\\\\\\\\n\",\n \" price\\\\_per\\\\_night & integer & Price per night of the hotel ranging between 20 to 2000 dollars \\\\\\\\\\n\",\n \" max\\\\_occupancy & integer & Maximum occupancy per room ranging between 1 and 7 \\\\\\\\\\n\",\n \" gym\\\\_present & boolean & Whether or not the hotel has a gym \\\\\\\\\\n\",\n \" pool\\\\_present & boolean & Whether or not the hotel has a pool \\\\\\\\\\n\",\n \" breakfast\\\\_included & boolean & Whether or not the hotel has breakfast included \\\\\\\\\\n\",\n \" smoking\\\\_allowed & boolean & Whether or not the hotel allows smoking \\\\\\\\\\n\",\n \" air\\\\_conditioning\\\\_present & boolean & Whether or not the hotel has air conditioning \\\\\\\\\\n\",\n \" heating\\\\_present & boolean & Whether or not the hotel has heating \\\\\\\\\\n\",\n \" free\\\\_wifi\\\\_included & boolean & Whether or not the hotel includes free WiFi \\\\\\\\\\n\",\n \" airport\\\\_shuttle\\\\_present & boolean & Whether or not the hotel has an airport shuffle \\\\\\\\\\n\",\n \" is\\\\_pet\\\\_friendly & boolean & Whether or not the hotel allows pets \\\\\\\\\\n\",\n \" has\\\\_spa\\\\_services & boolean & Whether or not the hotel has spa services \\\\\\\\\\n\",\n \" has\\\\_room\\\\_service & boolean & Whether or not the hotel has room service \\\\\\\\\\n\",\n \" has\\\\_beach\\\\_access & boolean & Whether or not the hotel has access to a beach \\\\\\\\\\n\",\n \" has\\\\_business\\\\_center & boolean & Whether or not the hotel has a business center \\\\\\\\\\n\",\n \" has\\\\_fitness\\\\_classes & boolean & Whether or not the hotel has fitness classes \\\\\\\\\\n\",\n \" has\\\\_laundry\\\\_service & boolean & Whether or not the hotel has laundry services \\\\\\\\\\n\",\n \" has\\\\_valet\\\\_parking & boolean & Whether or not the hotel has valet parking \\\\\\\\\\n\",\n \" has\\\\_balcony & boolean & Whether or not the hotel has a balcony \\\\\\\\\\n\",\n \" has\\\\_rooftop\\\\_bar & boolean & Whether or not the hotel has a rooftop bar \\\\\\\\\\n\",\n \" has\\\\_inroom\\\\_kitchen & boolean & Whether or not the hotel has an inroom kitchen \\\\\\\\\\n\",\n \" has\\\\_kids\\\\_club & boolean & Whether or not the hotel has a kids club \\\\\\\\\\n\",\n \" has\\\\_meeting\\\\_rooms & boolean & Whether or not the hotel has meeting rooms \\\\\\\\\\n\",\n \" has\\\\_electric\\\\_vehicle\\\\_charging & boolean & Whether or not the hotel has electric vehicle charging \\\\\\\\\\n\",\n \" has\\\\_hot\\\\_tub & boolean & Whether or not the hotel has a hot tub \\\\\\\\\\n\",\n \" has\\\\_sauna & boolean & Whether or not the hotel has a sauna \\\\\\\\ \\n\",\n \" has\\\\_free\\\\_parking & boolean & Whether or not the hotel has free parking\\\\\\\\ \\n\",\n \" is\\\\_wheelchair\\\\_accessible& boolean & Whether or not the hotel is wheelchair accessible \\\\\\\\ \\n\",\n \" has\\\\_skiing\\\\_lodging & boolean & Whether or not the hotel has skiing and lodging \\\\\\\\ \\n\",\n \" ocean\\\\_view\\\\_rooms\\\\_present & boolean & Whether or not the hotel has rooms with a view of the ocean \\\\\\\\\\n\",\n \" city\\\\_view\\\\_rooms\\\\_present & boolean & Whether or not the hotel has rooms with views of the city\\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Hotel Ontology Attributes.} \\n\",\n \"\\\\label{tab:hotel-ontology-attributes}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!htbp]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lll}\\n\",\n \" \\\\toprule \\n\",\n \" \\\\textbf{Attribute} & \\\\textbf{Type} & \\\\textbf{Description} \\\\\\\\ \\\\midrule\\n\",\n \" city & string & City that the restaurant is in \\\\\\\\\\n\",\n \" state & string & State that the restaurant is in \\\\\\\\\\n\",\n \" neighborhood & string & Neighborhood within the city that the restaurant is in\\\\\\\\\\n\",\n \" rating & float & Customer rating of the restaurant between 1.0 and 5.0, incremented by 0.1 \\\\\\\\\\n\",\n \" price\\\\_per\\\\_person & integer & Average price per person at the restaurant \\\\\\\\\\n\",\n \" cuisine & string & Cuisine of the restaurant \\\\\\\\\\n\",\n \" has\\\\_nut\\\\_allergy\\\\_options & boolean & Whether or not the restaurant has any options for individuals with an allergy to nuts \\\\\\\\\\n\",\n \" has\\\\_dairy\\\\_allergy\\\\_options & boolean & Whether or not restaurant has any options for individuals with an allergy to dairy products \\\\\\\\\\n\",\n \" has\\\\_shell\\\\_fish\\\\_allergy\\\\_options & boolean & Whether or not the restaurant has any options for individuals with an allergy to shell fish \\\\\\\\\\n\",\n \" has\\\\_tomato\\\\_allergy\\\\_options & boolean & Whether or not the restaurant has options for individuals with an allergy to tomatoes \\\\\\\\\\n\",\n \" has\\\\_nightshade\\\\_allergy\\\\_options & boolean & Whether or not the restaurant has options for individuals with an allergy to nightshades \\\\\\\\\\n\",\n \" has\\\\_gluten\\\\_free\\\\_options & boolean & Whether or not the restaurant has gluten free options \\\\\\\\\\n\",\n \" has\\\\_vegetarian\\\\_options & boolean & Whether or not the restaurant has vegetarian options \\\\\\\\\\n\",\n \" has\\\\_vegan\\\\_options & boolean & Whether or not the restaurant has vegan options \\\\\\\\\\n\",\n \" has\\\\_halal\\\\_options & boolean & Whether or not the restaurant has halal options \\\\\\\\\\n\",\n \" has\\\\_kosher\\\\_options & boolean & Whether or not the hotel has kosher options \\\\\\\\ \\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Restaurant Ontology Attributes.} \\n\",\n \"\\\\label{tab:restaurant-ontology-attributes}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[!htbp]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{0.8\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{lll}\\n\",\n \" \\\\toprule \\n\",\n \" \\\\textbf{Attribute} & \\\\textbf{Type} & \\\\textbf{Description} \\\\\\\\ \\\\midrule\\n\",\n \" city & string & City that the attraction is in \\\\\\\\\\n\",\n \" state & string & State that the attraction is in \\\\\\\\\\n\",\n \" neighborhood & string & Neighborhood within the city that the attraction is in\\\\\\\\\\n\",\n \" type & string & Type of the attraction \\\\\\\\ \\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Attraction Ontology Attributes.} \\n\",\n \"\\\\label{tab:attraction-ontology-attributes}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table}[!ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\small\\n\",\n \"\\\\renewcommand{\\\\arraystretch}{1.3}\\n\",\n \"\\\\begin{tabular}{|l|p{5.5cm}|p{7.5cm}|}\\n\",\n \"\\\\hline\\n\",\n \"\\\\textbf{Domain} & \\\\textbf{Placeholders Used} & \\\\textbf{Required Scenarios} \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\\\textbf{Flight} & \\n\",\n \"\\\\texttt{<CITY\\\\_x>}, \\\\texttt{<CLASS\\\\_x>}, \\\\texttt{<DEPARTURE\\\\_DATE\\\\_x>}, \\\\texttt{<DEPARTURE\\\\_TIME\\\\_x>}, \\\\texttt{<ARRIVAL\\\\_DATE\\\\_x>}, \\\\texttt{<ARRIVAL\\\\_TIME\\\\_x>}, \\\\texttt{<AIRLINE\\\\_x>}, \\\\texttt{<PRICE\\\\_x>}, \\\\texttt{<AIRPORT\\\\_x>}, \\\\texttt{<NUM\\\\_TRAVELERS\\\\_x>} & \\n\",\n \"Round trip (with 2 departure dates), one-way, multi-city, strict arrival time, flexible dates, large group bookings, solo/business/family travel, weekend getaways, budget constraints, special occasions (e.g., birthdays) \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\\\textbf{Hotel} & \\n\",\n \"\\\\texttt{<CHECK\\\\_IN\\\\_DATE\\\\_x>}, \\\\texttt{<CHECK\\\\_OUT\\\\_DATE\\\\_x>}, \\\\texttt{<CITY\\\\_x>}, \\\\texttt{<CITY\\\\_x\\\\_NEIGHBORHOOD\\\\_y>}, \\\\texttt{<STAR\\\\_x>}, \\\\texttt{<RATING\\\\_x>}, \\\\texttt{<PRICE\\\\_x>}, \\\\texttt{<NUM\\\\_ROOMS\\\\_x>}, \\\\texttt{<NUM\\\\_TRAVELERS\\\\_x>} & \\n\",\n \"Multi-city trips, multiple hotels in one city, specific neighborhoods, special occasions, last-minute reservations, business vs. leisure, extended stays, view/accessibility/pet-friendly requests, family and group accommodations \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\\\textbf{Attraction} & \\n\",\n \"\\\\texttt{<ATTRACTION\\\\_TYPE\\\\_x>}, \\\\texttt{<CITY\\\\_x>}, \\\\texttt{<STATE\\\\_x>}, \\\\texttt{<CITY\\\\_x\\\\_NEIGHBORHOOD\\\\_y>} & \\n\",\n \"Multi-type attraction queries, across cities/states, neighborhood-specific exploration, curiosity about state-level offerings. Assistant must ask open-ended questions and only mention placeholders if user provides them. \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\\\textbf{Restaurant} & \\n\",\n \"\\\\texttt{<RESTAURANT\\\\_RATING\\\\_x>}, \\\\texttt{<RESTAURANT\\\\_PRICE\\\\_x>}, \\\\texttt{<RESTAURANT\\\\_CUISINE\\\\_x>}, \\\\texttt{<CITY\\\\_x>}, \\\\texttt{<CITY\\\\_x\\\\_NEIGHBORHOOD\\\\_y>} & \\n\",\n \"Multi-city dining, cuisine preferences, dietary restrictions (e.g., nut-free, vegan, halal), budget vs. premium, special occasion dining, rating-focused or casual vs. upscale preferences \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\vspace{1mm}\\n\",\n \"\\\\caption{Domain-specific customization for template generation. Each domain builds on the shared prompt instructions with unique placeholders and required conversation scenarios.}\\n\",\n \"\\\\label{tab:domain-customization}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[!ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\small\\n\",\n \"\\\\renewcommand{\\\\arraystretch}{1.2}\\n\",\n \"\\\\begin{tabularx}{\\\\textwidth}{|l|X|X|}\\n\",\n \"\\\\hline\\n\",\n \"\\\\textbf{Domains} & \\\\textbf{Placeholders Used} & \\\\textbf{Required Scenarios} \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\\\textbf{Flight + Hotel} & \\n\",\n \"\\\\texttt{<CITY\\\\_x>}, \\\\texttt{<CLASS\\\\_x>}, \\\\texttt{<DEPARTURE\\\\_DATE\\\\_x>}, \\\\texttt{<CHECK\\\\_IN\\\\_DATE\\\\_x>}, \\\\texttt{<CHECK\\\\_OUT\\\\_DATE\\\\_x>}, \\\\texttt{<NUM\\\\_TRAVELERS\\\\_x>} & \\n\",\n \"Trip planning involving both air travel and accommodation. Includes round-trip and one-way flights, hotel stays across one or more cities, budget and luxury travelers, group vs. solo travel, flexible dates, specific preferences like nonstop flights or hotels with amenities (e.g., pool, gym, pet-friendly). \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\\\textbf{Flight + Hotel + Attraction} & \\n\",\n \"All flight, hotel, and attraction placeholders (e.g., \\\\texttt{<CITY\\\\_x>}, \\\\texttt{<CHECK\\\\_IN\\\\_DATE\\\\_x>}, \\\\texttt{<ATTRACTION\\\\_TYPE\\\\_x>}) & \\n\",\n \"User planning a full trip involving flights, hotel stays, and sightseeing. Must include: round-trip and one-way flights, multi-city stays, hotels near attractions, flexible schedules, user-provided attraction types or specific attraction names (only when linking to hotel proximity), and special constraints (e.g., group travel, business trips, anniversaries). Assistant must never use placeholder names before user introduces them. Includes amenity discussions and attraction-type grammar handling (e.g., cultural vs. \\\\texttt{<ATTRACTION\\\\_TYPE\\\\_1>} attraction). \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\\\textbf{Flight + Hotel + Restaurant} & \\n\",\n \"All flight, hotel, and restaurant placeholders (e.g., \\\\texttt{<RESTAURANT\\\\_CUISINE\\\\_x>}, \\\\texttt{<RESTAURANT\\\\_PRICE\\\\_x>}) & \\n\",\n \"End-to-end trip planning including flights, accommodation, and dining. Required scenarios include: dietary restrictions, cuisine-first or city-first planning, restaurant proximity to hotel, multiple hotels or restaurants in one city, round-trip and one-way flights, and amenities like pet-friendly or spa. Must feature diverse language, nonlinear flows, and varied user personas. Includes explicit handling of dietary needs (e.g., halal, nut-free, vegetarian), and placeholder logic as per prompt. \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\\\textbf{Hotel + Attraction} & \\n\",\n \"\\\\texttt{<CITY\\\\_x>}, \\\\texttt{<CHECK\\\\_IN\\\\_DATE\\\\_x>}, \\\\texttt{<ATTRACTION\\\\_TYPE\\\\_x>}, \\\\texttt{<CITY\\\\_x\\\\_NEIGHBORHOOD\\\\_y>} & \\n\",\n \"User looking for accommodations and nearby attractions. Includes: specific neighborhoods, family-friendly vs. solo travel, themed vacations, extended stays, proximity to cultural or outdoor attractions, and cases where attraction type drives hotel location. \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\\\textbf{Hotel + Restaurant} & \\n\",\n \"\\\\texttt{<RESTAURANT\\\\_CUISINE\\\\_x>}, \\\\texttt{<HOTEL\\\\_PRICE\\\\_x>}, \\\\texttt{<CITY\\\\_x\\\\_NEIGHBORHOOD\\\\_y>} & \\n\",\n \"Combines hotel booking with dining preferences. Includes cuisine-specific searches, budget dining vs. fine dining, dietary needs (gluten-free, halal), special events (anniversaries), restaurant proximity to hotel, and group size considerations. Templates vary in planning flow—some start with hotel, others with restaurants. \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\n\",\n \"\\\\end{tabularx}\\n\",\n \"\\\\vspace{1mm}\\n\",\n \"\\\\caption{Multi-domain combinations and scenario requirements. Each configuration builds on the unified instructions with additional placeholder logic and domain-specific complexity.}\\n\",\n \"\\\\label{tab:multi-domain-customization}\\n\",\n \"\\\\end{table}\\n\"\n ]\n]","isConversational":false},"fulltext":{"kind":"string","value":"\\documentclass{article}\n\n% if you need to pass options to natbib, use, e.g.:\n % \\PassOptionsToPackage{numbers, compress}{natbib}\n% before loading neurips_2024\n\n\\usepackage[square,numbers]{natbib}\n\\bibliographystyle{abbrvnat}\n\n% ready for submission\n% \\usepackage[]{neurips_2025}\n\\usepackage[preprint]{neurips_2025}\n\n\n% to compile a preprint version, e.g., for submission to arXiv, add add the\n% [preprint] option:\n% \\usepackage[preprint]{neurips_2024}\n\n\n% to compile a camera-ready version, add the [final] option, e.g.:\n% \\usepackage[final]{neurips_2024}\n\n\n% to avoid loading the natbib package, add option nonatbib:\n% \\usepackage[nonatbib]{neurips_2024}\n\n\n\\usepackage[utf8]{inputenc} % allow utf-8 input\n\\usepackage[T1]{fontenc} % use 8-bit T1 fonts\n\\usepackage{hyperref} % hyperlinks\n\\usepackage{url} % simple URL typesetting\n\\usepackage{booktabs} % professional-quality tables\n\\usepackage{amsfonts} % blackboard math symbols\n\\usepackage{nicefrac} % compact symbols for 1/2, etc.\n\\usepackage{microtype} % microtypography\n\\usepackage{xcolor} % colors\n\\usepackage{float}\n\\usepackage[breakable]{tcolorbox}\n\\usepackage{listings}\n\n\\lstset{\n basicstyle=\\ttfamily\\small,\n breaklines=true,\n breakatwhitespace=true,\n columns=fullflexible,\n frame=single\n}\n\\usepackage{tabularx} % Add in preamble\n% \\usepackage[table]{xcolor}\n% \\usepackage{tabularx}\n\\usepackage{amssymb} % for \\checkmark\n\\usepackage{hyperref}\n\\usepackage{url}\n\\usepackage{multirow}\n\\usepackage{bbm}\n\\usepackage{graphicx}\n\\usepackage{amssymb}\n\\usepackage{amsmath}\n\\usepackage{amsthm}\n\\usepackage{booktabs}\n\\usepackage{lscape}\n\\usepackage{pifont}\n\\usepackage{multirow}\n\\usepackage{booktabs}\n\\usepackage{textcomp}\n\\usepackage{lscape}\n\\usepackage{pifont}\n\\usepackage{algorithm}\n\\usepackage{graphicx}\n\\usepackage{caption}\n\\usepackage{subcaption}\n\\usepackage{CJKutf8}\n\\usepackage{algorithm}\n\\usepackage{algpseudocode}\n\\usepackage{enumitem}\n\\usepackage{wrapfig}\n\\usepackage{tabularx}\n\\usepackage{booktabs}\n\\usepackage{array}\n\\usepackage{pifont}\n\\usepackage{xcolor}\n\\newcommand{\\cmark}{\\ding{51}}\n\\newcommand{\\gcmark}{{\\color{green!60!black}\\fontsize{10}{10}\\selectfont\\ding{51}}}\n\\newcommand\\benchmarkname{\\textcolor{black}{\\textsc{T1}}}\n\\newcommand\\agentname{\\textcolor{black}{\\textsc{T1-Agent}}}\n\\newcommand\\dascomment[1]{\\textcolor{red}{das: \\textit{#1}}}\n\n\\title{$\\benchmarkname$: A Tool-Oriented Conversational Dataset for Multi-Turn Agentic Planning}\n\n% \\title{T$^2$ (ToolTalk): A Dataset for Tool-Oriented, Multi-Turn Agentic Planning}\n% \\title{ToolAgents: Tool-Driven Agents for Solving Multi-Domain Problems}\n\n\n% The \\author macro works with any number of authors. There are two commands\n% used to separate the names and addresses of multiple authors: \\And and \\AND.\n%\n% Using \\And between authors leaves it to LaTeX to determine where to break the\n% lines. Using \\AND forces a line break at that point. So, if LaTeX puts 3 of 4\n% authors names on the first line, and the last on the second line, try using\n% \\AND instead of \\And before the third author name.\n\n\n\\author{%\n Amartya Chakraborty\\thanks{The authors contributed equally.} \\text{ }, Paresh Dashore$^*$, Nadia Bathaee$^*$, Anmol Jain$^*$, \\\\\n \\textbf{Anirban Das}, \\textbf{Shi-Xiong Zhang}, \\textbf{Sambit Sahu}, \\textbf{Milind Naphade}, \\textbf{Genta Indra Winata$^*$} \\\\\n Capital One \\\\\n \\texttt{\\{amartya.chakraborty, paresh.dashore, nadia.bathaee\\}@capitalone.com} \\\\\n \\texttt{\\{anmol.jain, genta.winata\\}@capitalone.com}\n}\n\n\\begin{document}\n\n\\maketitle\n\n\\begin{abstract}\nLarge Language Models (LLMs) have demonstrated impressive capabilities as intelligent agents capable of solving complex problems. However, effective planning in scenarios involving dependencies between API or tool calls-particularly in multi-turn conversations-remains a significant challenge. To address this, we introduce $\\benchmarkname$, a tool-augmented, multi-domain, multi-turn conversational dataset specifically designed to capture and manage inter-tool dependencies across diverse domains. $\\benchmarkname$ enables rigorous evaluation of agents' ability to coordinate tool use across nine distinct domains (4 single domain and 5 multi-domain) with the help of an integrated caching mechanism for both short- and long-term memory, while supporting dynamic replanning-such as deciding whether to recompute or reuse cached results. Beyond facilitating research on tool use and planning, $\\benchmarkname$ also serves as a benchmark for evaluating the performance of open-source language models. We present results powered by $\\agentname$, highlighting their ability to plan and reason in complex, tool-dependent scenarios.\n\\end{abstract}\n\n\n\\section{Introduction}\nLeveraging external tools using Large Language Models (LLMs) to solve diverse conversational tasks has emerged as a promising direction in the development of intelligent agents~\\cite{tang2023toolalpaca}. Despite recent advances, task oriented llm based dialogue systems perform poorly over long conversational context~\\cite{laban2025llms}, and there remains a lack of comprehensive resources for training and evaluating multi-turn, multi-domain conversational agents that emphasizes on complex user needs. Existing datasets primarily focus on single-turn conversations for planning tasks—such as executing APIs or code~\\cite{schick2023toolformer, xie2024travelplanner}—and do not capture realistic multi-turn scenarios that require reasoning over long contexts, coordination across multiple tools, and adherence to complex constraints. Tool calling in realistic scenarios often involves interdependent tools, where the correctness and efficiency of task completion depend heavily on the order and context in which tools are invoked. An effective agent must therefore understand when, which, and in what sequence to call tools in order to successfully complete complex tasks. \n\nTo address this gap, we introduce $\\benchmarkname$, a new dataset and evaluation framework for assessing agent performance in multi-turn dialogues, with a particular focus on tool usage and reasoning about inter-tool dependencies. The dataset spans multiple domains and features complex, goal-oriented interactions between users and a travel assistant. Alongside the dataset, we propose $\\agentname$, an agent designed to interpret nuanced user intents and generate executable code using a predefined set of tools. $\\benchmarkname$ is specifically designed to evaluate the ability of LLM-based agents to plan tool use effectively and leverage a caching mechanism to efficiently reuse previously retrieved information. To support this, we incorporate dedicated tools for accessing and managing the cache.\n% Furthermore, we incorporate a novel caching mechanism on the agent side during code generation: before generating new outputs, the agent checks whether the current user query relates to any previously cached results. If a match is found, it retrieves the cached data and applies further filtering as needed.\n\n\\begin{figure*}[!t]\n \\centering\n \\includegraphics[width=\\textwidth]{assets/pipeline.png}\n \\caption{Illustrative example from the $\\benchmarkname$ dataset. This example showcases a multi-domain scenario involving both flights and hotels, where the user is planning a trip and attempting to book relevant services. The dialogue is constructed by retrieving entities from a knowledge base, and tool calls are executed using a predefined toolbox, simulating realistic, tool-augmented agent behavior.}\n \\label{fig:architecture}\n\\end{figure*}\n\nOur contributions can be summarized as follows:\n\\begin{itemize}\n \\item We introduce $\\benchmarkname$, a comprehensive multi-turn dataset consisting of 13.5k dialogues designed to evaluate tool-using, LLM-based agents across nine key domains—comprising four single-domain and five multi-domain settings. The dataset covers a wide range of interaction scenarios, including single-domain, mixed-domain, and fully multi-domain conversations. It incorporates 14 distinct tools, enabling realistic and fine-grained assessment of agent capabilities in complex, tool-driven dialogue tasks.\n \\item To enhance the complexity and realism of the evaluation, the dataset includes cross-domain tasks and interdependent tool calls, requiring agents to reason about tool selection and execution order within context. This evaluation framework assesses the ability of LLM-based agents to think critically, reason effectively, and make context-aware decisions.\n \\item We evaluate our dataset using an LLM-based agent, $\\agentname$, a code-generation system built on open-source language models and equipped with a caching mechanism for improved performance. This architecture enables scalable evaluation and provides a robust, efficient framework for tool-using agents.\n \\item We will publicly release our code and dataset to facilitate future research.\n\\end{itemize}\n\n\\section{T1 Dataset}\n\n$\\benchmarkname$ is a dataset specifically designed to evaluate LLM-based agents on tool usage and complex planning tasks over multi-turn conversational context. This dataset simulates multi-turn conversations spanning both four single-domain and five multi-domain settings: \\textit{flights}, \\textit{restaurants}, \\textit{hotels}, \\textit{attractions}, \\textit{flights-hotels}, \\textit{hotels-restaurants}, \\textit{hotels-attractions}, \\textit{flights-hotels-attractions}, and \\textit{flights-hotels-restaurants}.\n% \\dascomment{sentence abruptly ends}.\nPlanning tasks are formulated as code, where function calls to external tools are used to accomplish specific goals.\n\n\\subsection{Tasks and Notations}\n\nWe define a dialogue \\( D \\) as an alternating sequence of assistant and user turns: \n\\begin{align}\nD = \\{A_1, U_1, A_2, U_2, \\ldots, A_n\\},\n\\end{align}\nwhere each \\( A_i \\) represents an assistant turn and \\( U_i \\) a user turn. The dialogue always starts with an assistant turn and proceeds in a strictly alternating order. We provide a set of tools \\( \\mathcal{T} \\), where each tool \\( t \\in \\mathcal{T} \\) encapsulates logic to perform a specific function. These tools can be categorized as follows:\n\\begin{itemize}\n \\item \\textbf{Domain-specific tools}: Designed to handle operations tied to a particular application domain.\n \\item \\textbf{Interdependent tools}: Used to identify or reason about dependencies across domains.\n \\item \\textbf{Generic tools}: Domain-independent or auxiliary utilities applicable across tasks.\n\\end{itemize}\n\n\\begin{table*}[!htbp]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lccccccc}\n \\toprule\n \\textbf{Dataset} & \\textbf{Deployed} & \\textbf{Human Annotated} & \\textbf{Execution Result} & \\textbf{Multi-turn Context} & \\textbf{Multi-Domain} \\\\\n & \\textbf{Tools} & \\textbf{Tool Planning} & \\textbf{Evaluation} & \\textbf{Planning} & \\textbf{Tool Planning}\\\\\n \\midrule\n APIBank~\\cite{li2023api} & \\cmark & \\cmark & \\cmark & \\cmark & & \\\\\n APIBench~\\cite{patil2024gorilla} & & & \\cmark & & \\\\\n GAIA~\\cite{mialon2023gaia} & & \\cmark & \\cmark & & \\\\\n GTA~\\cite{wang2024gta} & \\cmark & \\cmark & \\cmark & \\cmark & \\\\\n m\\&m's~\\cite{ma2024m} & \\cmark & \\cmark & \\cmark & \\cmark & \\\\\n ToolBench~\\cite{qin2023toolllm} & \\cmark & & \\cmark & & \\\\\n Toolformer~\\cite{schick2023toolformer} & \\cmark & \\cmark & \\cmark & & \\\\\n TravelPlanner~\\cite{xie2024travelplanner} & \\cmark & \\cmark & \\cmark & & \\cmark & \\\\ \n \\midrule\n $\\textbf{\\benchmarkname}$ & \\cmark & \\cmark & \\cmark & \\cmark & \\cmark \\\\\n \\bottomrule\n \\end{tabular}\n}\n\\caption{Comparison of datasets for the LLM-based agent systems.}\n\\end{table*}\n\n\\begin{figure*}[!t]\n \\centering\n \\includegraphics[width=\\textwidth]{assets/data_generation.png}\n \\caption{$\\benchmarkname$ generates data by populating delexicalized entities with corresponding entries from the knowledge base.}\n \\label{fig:data_generation}\n\\end{figure*}\n\n% In this paper, we study four fundamental tasks involving tool usage:\n% \\begin{enumerate}\n% \\item \\textbf{Tool Calling}: Invoking the appropriate tool(s) based on the current dialogue context.\n% \\item \\textbf{Parameter Extraction}:\n% \\item \\textbf{Information Seeking}: Gathering missing or clarifying information required for successful tool execution.\n% \\item \\textbf{Data Caching}: Reusing previously generated results to improve efficiency and maintain continuity.\n% \\end{enumerate}\n\n\\subsection{Dataset Construction}\nWe construct our dataset by manually collecting data from Wikipedia to gather entities and metadata. For this task, we define four domains: flights, hotels, restaurants, and attractions. Additionally, we compile a list of 128 airports and 321 cities within the United States, along with up to 15 neighborhoods for each city. Some information—such as airline names, hotel names, and hotel star ratings—is synthesized to avoid inaccuracies and to prevent the LLMs from relying on its internal knowledge of real-world named entities.\n\n\\subsubsection{Ontology}\nWe have a total of 5 ontologies, one for each of the four defined domains and another one for city. For each ontology, we curated a list of key attributes that would be relevant such as the airline or number of layovers for a flight, the cuisine of a restaurant or the number of stars or customer rating for a hotel. For each of these attributes, we defined the possible values and then used the ontology to generate synthetic data for flights, hotels and restaurants which would subsequently be used by the tools we defined. \n\n\\begin{table*}[!t]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lcccc|cc|c|c}\n \\toprule \n & \\multicolumn{4}{c|}{\\textbf{Single-domain}} & \\multicolumn{2}{c|}{\\textbf{Multi-domain}}\n & \\textbf{Common} & \\textbf{Total} \\\\ \n & \\textbf{Flights} & \\textbf{Hotels} & \\textbf{Restaurants} & \\textbf{Attractions} & \\textbf{2 Domains} & \\textbf{3 Domains} & \\\\ \\midrule\n \\textbf{\\# Attributes} & 35 & 43 & 21 & 7 & N/A & N/A & N/A & 106 \\\\\n \\textbf{\\# Tools} & 2 & 2 & 2 & 2 & 1 & N/A & 5 & 14 \\\\ \n \\textbf{\\# Dialogues} & 1.5k & 1.5k & 1.5k & 1.5k & 4.5k & 3k & N/A & 13.5k \\\\ \n \\textbf{Avg. \\# turns} & 8.2 & 8.0 & 9.0 & 6.0 & 11.3 & 9.7 & N/A & N/A \\\\ \n \\bottomrule\n \\end{tabular}\n}\n\\caption{Dataset statistics detailing the number of attributes, tools, dialogues, and the average number of turns, categorized by each individual domain, multi-domain, and common categories. \"N/A\" indicates that the metric is not applicable in the given context.} \n\\label{tab:baseline-results-one-epoch}\n\\end{table*}\n\n\\paragraph{Flights.}\nThe ontology includes the airline, flight class (economy, business and first), number of layovers ranging between 0-2 stops, the duration of a layover ranging from 1-6 hours and a list of the possible airports to depart from and arrive to. To see the full table, go to Table~\\ref{tab:flight-ontology-attributes}.\n\n\\paragraph{Hotels.}\nThe ontology includes the number of stars ranging between 1 to 5 stars, the customer rating of the hotel ranging from 1.0 to 5.0, the cost of the hotel as well as whether or not the hotel includes a number of amenities such as the presence of a gym or pool. To see the full table, go to Table~\\ref{tab:hotel-ontology-attributes}.\n\n\\paragraph{Restaurants.}\nThe ontology includes the type of cuisine, the customer rating of the hotel ranging from 1.0 to 5.0, the price per person and whether or not the restaurant served any particular dietary options such as vegetarian, vegan, or halal. To see a full list of dietary restrictions, taken into consideration, go to Table~\\ref{tab:restaurant-ontology-attributes}.\n\n\\paragraph{Attractions.}\nThe ontology includes the type of attraction which is one of the following: touristy, culinary, historical, scenic, social, art, cultural, guided, and sporting.\n\n\\paragraph{Cities.}\nThe ontology includes a list of cities in the United States of America (US) that was collected through Wikipedia. For each city, we then extracted up to 15 neighborhoods as well as the approximate geographical coordinates of each neighborhood.\n\n\\subsubsection{Knowledge Bases}\n\\paragraph{Attractions.}\nFrom the list of 321 cities, we collect up to 15 attractions for 85 cities through the usage of Llama-3.3 70B Instruct and conduct quality assurance by human annotators to ensure the data correctness. We also collect the city neighborhood for each attraction as well as the geographical coordinates. In total, there are 728 attractions that were collected from the 85 cities for this dataset.\n\n\\paragraph{Flights.}\nFor flights, there are a total of 128 airports that are used to generate synthetic flight data. Each flight generated has an airline that was randomly selected from the ontology and the start and end airport are randomly selected from the list of airports in the ontology. Additionally, the departure time is randomly generated, however the arrival time is computed based on the geographical distance between the departure and arrival airports assuming the average speed of the flight to be 450 miles per hour. 480,410 synthetic flights were generated as part of this dataset\n\n\\paragraph{Hotels.}\nHotels are generated for all 321 cities in the ontology. For a particular city, a neighborhood is assigned and additionally, synthetic latitude and longitude coordinates are generated for each hotel in a city as the coordinates would be used for distance computing purposes. Each hotel is also provided a star and a synthetic customer rating that would be correlated to the amenities offered by the establishment as well as the price per night. 47,589 hotels were generated as part of this dataset. \n\n\n\\paragraph{Restaurants.}\nRestaurants are generated for all 321 cities in the ontology. Just like for hotels, in a particular city, a neighborhood is assigned to a restaurant and additionally, synthetic latitude and longitude coordinates are generated. Each restaurant also is given a user rating which was synthetically generated, a cuisine provided by the ontology as well as whether particular dietary options are supported and the average cost per person. 17,975 restaurants are generated as part of this dataset.\n\n\\subsubsection{Data Annotation and Quality Assurance}\nTo ensure high-quality and natural data, we employ five human annotators, with each data sample reviewed by both an annotator and a quality assurance (QA) reviewer. The annotators were selected to represent a diverse set of background and perspectives, while maintaining a high technical bar. All annotators have at least a Master's degree in Computer Science and demonstrated proficiency in Python, enabling them to handle complex annotation tasks requiring logical reasoning and scripting. The QA specialist also has a strong background in programming. Annotators are assigned a category of templates and are responsible for writing the appropriate code using the tools defined for this project. Afterward, the QA reviewer evaluates the annotated code and provides feedback, which is used to make necessary corrections and improvements.\n\n\\subsubsection{Dialogue Generation}\nWe create a total of nine data categories, as discussed in Section~\\ref{sec:distinct-domains}. The data construction follows a three-step process: first, we generate templates with placeholder values; second, we annotate the templates with code using the provided tools; and third, we programmatically fill in the placeholder values for each template.\n\\paragraph{Generating Dialogue Templates.}\nFor each of the 9 dialogue categories, we generate 60 dialogue templates using Llama-3.3 70B Instruct. These templates are then reviewed and refined by human annotators to ensure accurate dialogue flow, as well as high coherence and fluency. The model is prompted to generate synthetic dialogues for both single and multi-domain. Each template consists of a conversation between the assistant and user. To learn more about the prompt used, go to Appendix~\\ref{appendix:prompt-design-user-planner} and ~\\ref{appendix:multi-domain-prompt-design}. Additionally, the templates consist of placeholders for attributes such as a city or neighborhood name, cuisine of a restaurant, rating of a hotel or the type of an attraction. As the dataset is related to the area of travel, some placeholder values are tied to a particular city such as the neighborhood (\\texttt{<CITY\\_x\\_NEIGHBORHOOD\\_x>}, airport name (\\texttt{<CITY\\_x\\_AIRPORT\\_x>}), hotel name (\\texttt{<CITY\\_x\\_HOTEL\\_NAME\\_x>}) and restaurant name (\\texttt{<CITY\\_x\\_RESTAURANT\\_NAME\\_x>}). \n\n\\paragraph{Template Lexicalization.}\nWithin each dialogue category, 25 dialogues are generated with the placeholder values filled with the actual values present within the ontology. In addition to the dialogues, the ground-truth code which is part of the annotation has the placeholder values replaced with actual values. The dataset is split into three partitions: training, validation and test. To limit data contamination among these three partitions, the cities that are to be used for filling in the placeholder are also assigned to a particular partition only to be used there. Hence, for example if the city of Boston is assigned to the training partition, it will never be present in any of the validation or test dialogue conversations.\n\\label{sec:dialogue-template-creation}\nAdditionally, for fields such as the check-in and checkout dates for hotels or departure and return flight dates, particular methods are taken to ensure that any dates or times follow chronological order and there is no instance where a check-in date at a hotel would be after the checkout date.\n\n\\paragraph{Generated Dialogue and Ground Truth Validation.}\nOnce the placeholders are filled for both the dialogues and ground truth code samples, each block of code is executed to identify any potential errors in annotation including an improper use of a placeholder or a syntax error. The validation assists the team in correcting any annotations and ensures that the resulting code is runnable and correct. \n\n\\section{T1-Agent}\nWe build an LLM powered $\\agentname$ to evaluate and simulate our $\\benchmarkname$ agentic dataset and measure its performance across three tasks: information seeking, parameter extraction, and tool calling.\n\n\\subsection{Information Seeking}\nEach tool has a mandatory set of parameters that must be provided before running the tool successfully. Information seeking is the task of gathering this mandatory set of parameters for any of the respective tools. We want to evaluate the capability of the agent to understand both the intent of the user's query and which parameters to ask the user about as a follow-up. Figure~\\ref{fig:architecture} shows an example of how information seeking is used by the agent. When the user inquires about a flight from New York to San Jose and a hotel in San Jose, the agent understands that the user would like to search for flights but needs to provide the departure date of the flight and the check-in date of the hotel. Thus, the agent infers that in followup discussion, it needs to ask for these details from the user.\n\n\\subsection{Parameter Extraction}\nAfter the agent understands the tools to call and the necessary parameters to collect, it works to extract those parameters from the user dialogue. From Figure~\\ref{fig:architecture}, when the user mentions that they want a flight from New York to San Jose for 3 nights, the agent is able to extract the starting and ending airport city for the flight as well as the number of nights to stay at the hotel. However, neither \\texttt{search\\_flights} or \\texttt{search\\_hotels} can be called yet since the departure date of the flight and the check-in and check-out date of the hotel have not yet been provided.\n\n\\subsection{Tool Calling}\nOnce the agent understands the necessary tool(s) to call and is provided the necessary parameters for each tool, the agent will make calls to each tool. In Figure~\\ref{fig:architecture}, once the user provides the departure date for the flight to San Jose, the agent calls the \\texttt{search\\_flights} tool with the departure and arrival cities as well as the departure date. Next, before the agent can call the \\texttt{search\\_hotels} tool, it must first compute the check-out date for the hotel. The check-in date provided is May 9 and the user provided the context that the duration of the hotel stay is 3 nights. Hence, the \\texttt{adjust\\_date} tool is called to compute the check-out date of the hotel. Once this is calculated, the agent will call the \\texttt{search\\_hotels} tool with the check-in and check-out dates as well as the city of the hotel. Additionally, both the flight and hotels that came up in the results will be saved to the cache, for further possible usage.\n\n\\subsection{Data Caching}\n% Unlike prior work such as \\textit{CodeAct} \\cite{wang2024executable}, which generates fresh tool calls at each user turn, \nWe introduce a data caching mechanism that enables $\\agentname$ to reuse the outputs of earlier tool calls when handling subsequent user requests. This reduces redundant computation and improves response efficiency in multi-turn interactions. After each user turn, any search results retrieved by a tool are cached for potential reuse in later turns. The \\texttt{save\\_to\\_cache} tool is used to store these results. As illustrated in Figure~\\ref{fig:architecture}, after the user provides the departure date for a flight to San Jose, the agent calls both \\texttt{search\\_flights} and \\texttt{search\\_hotels}, and then stores the results using the \\texttt{save\\_to\\_cache} tool. Later, when the user requests flights from a specific airline, the agent retrieves the cached flight results using the \\texttt{get\\_results\\_from\\_cache} tool and filters them using the \\texttt{filter\\_flights} tool based on the user's airline preference. This caching approach helps avoid unnecessary API calls by reusing existing search results and applying filtering when appropriate.\n\n\\paragraph{Example: Refining a Flight Search.}\nSuppose a previous user query fetched flights from NYC to Boston, and the result was cached with the key \\texttt{\"flights\\_nyc\\_bos\"}. Later, the user asks to see only flights priced under \\$500. The LLM, using the cache summary, generates the following plan:\n\n\\begin{verbatim}\nflights = get_results_from_cache(key=\"flights_nyc_bos\")\naffordable_flights = filter_flights(prior_result=flights, budget=500)\n\\end{verbatim}\n\nThis illustrates how the LLM composes new logic by combining previously retrieved results with downstream tools, without repeating expensive API operations. Our approach enables more efficient, coherent, and stateful plan generation in realistic, multi-turn assistant conversations.\n\n\n\\section{Experimental Setup}\n\n\\subsection{Datasets}\n\\label{sec:distinct-domains}\nEach category contains 60 dialogue templates and a pool of 54 cities used to populate placeholder values. We split both the templates and cities into 25\\% for training, 9\\% for validation, and 66\\% for testing, resulting in 15 training templates, 5 validation templates, and 40 test templates per category. Similarly, 13 cities are allocated for training, 4 for validation, and 37 for testing. Dialogues are randomly sampled within each partition. After partitioning, a script fills all placeholders in each template using the corresponding cities from the assigned split, ensuring no data leakage or overlap of templates and entities between the training and test sets. Each template is instantiated into 25 unique dialogues after substituting the placeholders in the dialogue templates with entities defined in the ontology, yielding a total of 1,500 fully generated dialogues.\n\n\n\n% We generate 25 dialogues for each single-domain and multi-domain template. The dataset is further divided into training, validation, and test splits. For more details, please refer to Section~\\ref{sec:dialogue-template-creation}.\n\n\\subsection{Domain Adaptation using SFT} \nWe perform a simple instruction tuning with the train dataset in order to showcase that the performance on such complex conversational tool calling can be improved over zero or few shot prompting. We train a Llama 3.1 8B Instruct model for one epoch on the training dataset. The training dataset is structured as a list of \\texttt{(prompt, completions)} pairs. The SFT thereafter follows standard next token prediction with cross-entropy loss. We use LoRA~\\cite{lora} on 8 A100 SXM gpus instead of full finetuning. For reproducibility, we use the widely adopted Huggingface TRL library~\\cite{vonwerra2022trl} and include the exact command to replicate the training in Section~\\ref{SFTScript}. \n\n\\subsection{Inference Procedure}\nDuring inference, for each user turn in a dialogue, the $\\agentname$ generates executable Python code that fulfills the user request at that point in the conversation. Before generating any code, the agent will check an \\textbf{execution cache} to determine if a similar request has been previously resolved. If a cached result is available, the agent will write code that fetches and reuses the cached object to prevent redundant computation and tool invocation.\n\nTo do this, the agent constructs a prompt that includes the conversation history along with the current user turn. Instead of including the full execution cache—which can be large and token-intensive—the prompt incorporates a \\textbf{summary} of the cached results.\nThese summaries are generated using a deterministic, rule-based function that transforms each cached result into a concise representation. This approach significantly reduces the token load in the prompt, making it feasible to include relevant past results without exceeding model input limits.\n\nThis design enables the agent to consider past outcomes and reuse relevant information during code generation. Even in cases where the user’s current query differs slightly from earlier queries, the agent is able to fetch a prior result from the cache and use that as a starting point for the current user turn. By introducing summarization at the \\textit{planning stage}, we shift caching responsibilities from the tool-execution layer to the agent’s decision-making process. Our approach allows the agent to selectively reuse and adapt cached outputs, leading to improved latency and broader generalization.\n\nThe generated code is executed in a sandboxed environment, and the cache is updated with the new results on each user turn. Our dataset also includes the corresponding ground truth code and post-execution cache, which are used for performance evaluation.\n\n\\section{Results and Analysis}\nIn this section, we present the performance of various LLMs on both single-domain and multi-domain tasks. Single-domain tasks involve conversations focused exclusively on one domain—such as flights, hotels, restaurants, or attractions. In contrast, multi-domain tasks involve interactions spanning multiple domains, such as a user requesting both flight bookings and nearby hotel recommendations within the same dialogue. \n\n\\subsection{Overall Results}\nTable~\\ref{table:llama3.1-8b} presents the overall performance of \\texttt{Llama 3.1 8B Instruct} in a few-shot in-context learning setting. The model performs significantly worse on multi-domain tasks compared to single-domain ones. Additionally, much of the generated code—particularly in multi-domain scenarios—is not executable. Despite the inclusion of few-shot examples, the model struggles with accurate tool invocation. Parameter matching is even more challenging, as it requires the model not only to identify the correct tool but also to extract the appropriate parameter values from the user’s utterance.\n\n\\subsection{Analysis}\n\n\\paragraph{The Impact of Domain Adaptation on Performance.}\nFigure~\\ref{fig:enter-label} presents the tool call F1 and parameter matching F1 scores. Overall, the SFT models outperform the base model and even surpass the performance of the 70B model by a substantial margin across all domains.\n\n\\begin{figure}[!ht]\n \\centering\n \\includegraphics[width=\\linewidth]{assets/domain_adaptation.png}\n \\caption{\\textbf{Left:} Tool Call F1 performance and \\textbf{Right:} Parameter Matching F1 performance.}\n \\label{fig:enter-label}\n\\end{figure}\n\n\\begin{table*}[!ht]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lcccc|cccc|c|cc|c}\n \\toprule\n \\textbf{Domain} & \\multicolumn{4}{c}{\\textbf{Tool Call}} & \\multicolumn{4}{|c|}{\\textbf{Parameter Matching}} & \\textbf{Code Exec.} & \\multicolumn{2}{|c|}{\\textbf{Information Seeking}} & \\multicolumn{1}{c}{\\textbf{Cache}} \\\\ \n & & & & & & & & & \\textbf{Rate} & & & \\textbf{Summary} \\\\\n & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\ \\midrule\n \\multicolumn{8}{l}{\\texttt{Single-Domain}} \\\\\n \\midrule\n Flights & 25.12 & 30.90 & 57.34 & 40.16 & 15.57 & 17.34 & 60.30 & 26.94 & 26.24 & 14.41 & 82.36 & 36.63 \\\\\n Hotels & 42.86 & 49.89 & 75.25 & 60.00 & 17.57 & 23.59 & 40.80 & 29.89 & 48.13 & 47.01 & 80.38 & 33.70 \\\\\n Restaurants & 40.40 & 52.97 & 63.00 & 57.55 & 31.47 & 36.07 & 71.12 & 47.87 & \\textbf{56.99} & 28.91 & 75.68 & 35.49 \\\\\n Attractions & 33.03 & 48.49 & 50.90 & 49.66 & 14.11 & 25.11 & 24.36 & 24.73 & 40.40 & N/A & N/A & 43.87 \\\\ \\midrule\n \\multicolumn{10}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n F-H & 33.09 & 40.89 & 63.44 & 49.73 & 16.11 & 19.28 & 49.48 & 27.75 & 38.43 & 26.54 & 82.00 & 21.85 \\\\\n H-R & 51.06 & 59.21 & 78.77 & 67.61 & 26.62 & 33.52 & 56.37 & 42.05 & 51.82 & 28.60 & 84.07 & 9.95 \\\\\n H-A & 38.75 & 45.46 & 72.39 & 55.85 & 16.11 & 20.02 & 45.12 & 27.74 & 48.88 & 26.03 & 81.77 & 32.75 \\\\\n F-H-A & 27.76 & 36.24 & 54.24 & 43.45 & 16.21 & 21.18 & 40.88 & 27.90 & 28.76 & 14.15 & 78.58 & 24.37 \\\\\n F-H-R & 29.04 & 34.65 & 64.19 & 45.00 & 18.53 & 21.77 & 55.44 & 31.26 & 36.79 & 33.28 & 82.84 & 27.47 \\\\\n \\bottomrule\n \\end{tabular}\n}\n\\caption{Overall results using Llama 3.1 8B Instruct with few-shot in-context learning.}\n\\label{table:llama3.1-8b}\n\\end{table*}\n\n% \\begin{table*}[!ht]\n% \\centering\n% \\resizebox{\\textwidth}{!}{\n% \\begin{tabular}{lcccc|ccccccccc}\n% \\toprule\n% \\textbf{Model} & \\multicolumn{4}{c|}{\\textbf{Single Domain}} & \\multicolumn{5}{c}{\\textbf{Multi-Domain}} \\\\\n% & Flight & Hotel & Attraction & Restaurant & F-H & H-R & H-A & F-H-A & F-H-R \\\\ \\midrule\n% Llama 3.1 8B \\\\\n% $\\quad$+ Fine-tune \\\\\n% Llama 3.3 70B \\\\ \\midrule\n% Phi-4 R$^+$ 14B \\\\\n% S1.1 32B \\\\\n% \\bottomrule\n% \\end{tabular}\n% }\n% \\caption{F1 Results on Fine-grained Tool Call.}\n% \\end{table*}\n\n\\paragraph{Model Performance.}\nOur qualitative evaluation shows that the \\texttt{LLaMA 3.3 70B Instruct} model consistently delivers the strongest performance across tasks. In contrast, the \\texttt{LLaMA 3.1 8B Instruct} model, without any fine-tuning, struggles significantly. It often fails to utilize available cache effectively and generates an incorrect code.\n\n\\paragraph{Impact of Fine-Tuning.}\nWhile the 70B and the non-fine-tuned 8B models were prompted to generate answers with reasoning, a fine-tuned variant—despite not being instructed to provide reasoning—achieved performance comparable to the 70B Instruct model. This indicates the strong potential of task-specific fine-tuning in enhancing model performance.\n\n\\paragraph{Few-Shot Evaluation.}\nAs shown in Figure~\\ref{fig:few-shot}, we evaluate model performance on the \\textit{flights} domain under 0-shot, 5-shot, and 13-shot settings. Performance is notably poor in the 0-shot setting, with improvements observed in 5-shot and 13-shot configurations—though the gains plateau beyond 5 shots. Qualitative analysis suggests that without sufficient context, models continue to struggle with generalization in complex, multi-domain scenarios.\n\\begin{figure}[!ht]\n \\centering\n \\includegraphics[width=0.9\\linewidth]{assets/flight_shot_learning.png}\n \\caption{Few-shot performance on Flight domain.}\n \\label{fig:few-shot}\n\\end{figure}\n\n\\paragraph{Need for Complex Evaluation.}\nEven with the state-of-the-art model, \\texttt{LLaMA 3.3 70B Instruct}, which performs well on standard code generation tasks, we observe that it continues to struggle in complex, multi-turn scenarios involving advanced planning. The $\\benchmarkname$ dataset is designed to fill this gap by serving as a benchmark for evaluating model performance in such challenging settings.\n\n\\section{Related Work}\n\\subsection{Large Language Model Agents}\nLLM-based agents have emerged as foundational components in AI systems, capable of performing complex, multi-step tasks through reasoning, memory integration, and tool use. These agents often combine a pre-trained LLM with structured modules such as long-term memory, tool calling capabilities, and self-reflective feedback loops. Frameworks such as {AutoGPT}~\\cite{yang2023auto}, and {AgentGPT} allow LLMs to autonomously decompose user goals into subgoals and execute them sequentially using external APIs. More structured systems like {HuggingGPT}~\\cite{shen2023hugginggpt}, {CrewAI}, and {AutoGen}~\\cite{wu2023autogen} facilitate collaboration between multiple LLM agents, each specializing in roles such as planning, execution, or critique.\n\nDespite significant progress, planning within task-oriented dialogue systems—particularly over long horizons—remains a fundamental challenge. Previous paradigms such as \\textit{plan-observe-execute} (e.g., ReAct~\\cite{yao2023react}, ADaPT~\\cite{prasad2023adapt}) enable the model to interleave tool calls with reasoning steps. However, most frameworks focus on linear execution paths where each step invokes a single tool. These approaches often lack the ability to manage inter-tool dependencies, reuse intermediate results, or revise plans based on partial tool failures. Further, a lot of these systems performs single turn planning, i.e. the user of the system submits a request with all the information in the first turn. Multi-turn conversational planning is a nascent field. For example, in multi-turn workflows like conversational trip planning, agents need to collect information across multiple steps, then plan and coordinate across tools like flight search, visa information, and calendar APIs—something most current systems struggle to handle.\n\n\\subsection{Tool-Based Agents and Benchmarks}\n\nTool usage extends the scope of LLM capabilities beyond language modeling ~\\citep{team2024chameleon, schick2023toolformer} to real-world actionability, enabling interactions with APIs, web tools, and external software. To evaluate this ability, several benchmarks have been proposed, including {APIBank}~\\cite{li2023api}, {Tau-Bench}~\\cite{yao2024tau},\n{GAIA}~\\cite{mialon2023gaia}, {ALFWorld}~\\cite{shridhar2020alfworld}, {GTA}~\\cite{wang2024gta},\n{TravelPlanner}~\\cite{xie2024travelplanner}, and {ToolBench}~\\cite{qin2023toolllm}. Although these benchmarks each emphasize different strengths, such as breadth of API coverage, realism, or reasoning complexity—they often treat tool use as a series of isolated atomic actions without requiring coordinated planning between multiple tools. Further, there\n\nIn contrast, our work introduces $\\benchmarkname$, a tool-driven benchmark designed to evaluate LLM agents in multi-turn, multi-domain dialogue settings with inter-tool dependencies. $\\benchmarkname$ features an integrated caching mechanism that supports both short- and long-term memory of tool call results, allowing agents to make intelligent decisions about whether to replan or reuse cached outputs. Unlike prior benchmarks, it tests an agent’s ability to perform dynamic replanning, handle branching workflows, and compose tools in a realistic, dialog-driven environment. Thus, $\\benchmarkname$ not only challenges existing tool-use agents but also provides a diagnostic sandbox for evaluating the reasoning capabilities of open-source LLMs under realistic constraints.\n\n\n\n% \\section{Related Work }\n\n% \\subsection{Large Language Model Agents} AI Agents or Large Language model agents are now ubiquitously being used in various complex workflows~\\cite{tang2023toolalpaca}. The ability to utilize memory, tools alongside planning, reasoning and self reflection abilities have led to the enhancement in their abilities. Many tool calling frameworks such as AutoGPT~\\citep{}, HuggingGPT~\\citep{}, CrewAI~\\citep{}, AutoGen~\\citep{} etc, has sprung into existence that allows building of single or multi agent systems. However planning in task oriented dialogue systems, both long and short horizon, is not yet solved~\\citep{}. Prior work has looked into \\textit{plan-observe-execute} type paradigms to do planning in the presence of tools or functions~\\citep{react, rewoo, adapt} where each next action in the plan depends on the outcome of the previous execution. In this case however the plan at each step consists of calling an single tool. \n\n% \\paragraph{Tool-based Agents}\n% Tools give the AI agents ability to perform tasks. However in many cases, performing a single task may need multiple tools to be called with some application logic. Several benchmarks aims at assessing the performance of tool calling ability of AI Agents. APIBench, GTA, Tau-Bench, GAIA, OsWorld, Alfworld, Alfbench, TravelPlanner, Toolbench etc. \n% https://arxiv.org/pdf/2406.12045\n% %https://proceedings.neurips.cc/paper_files/paper/2024/file/bfd082c452dffb450d5a5202b0419205-Paper-Datasets_and_Benchmarks_Track.pdf\n% https://arxiv.org/pdf/2402.01622\n% https://arxiv.org/pdf/2406.14283?\n% https://aclanthology.org/2025.coling-main.672.pdf\n% https://arxiv.org/abs/2410.01440\n% https://openreview.net/pdf?id=dCPF1wlqj8\n% https://arxiv.org/pdf/2502.11221\n% https://aclanthology.org/2025.naacl-long.36.pdf\n% https://arxiv.org/pdf/2408.00764?\n% https://neurips.cc/virtual/2024/poster/97620\n\n\\newcolumntype{Y}{>{\\centering\\arraybackslash}X}\n\n\\section{Conclusion}\n\nWe introduce $\\benchmarkname$, a comprehensive dataset for evaluating planning, reasoning, and tool-usage in LLM-based agents through complex, multi-turn dialogues. By introducing inter-tool dependencies, dynamic replanning, and caching, it supports rigorous assessment across single- and multi-domain settings. Experiments with $\\agentname$ highlight both the strengths and limitations of open-source LLMs: while the \\texttt{LLaMA 3.3 70B Instruct} model performs best, the non-fine-tuned \\texttt{LLaMA 3.1 8B} struggles with caching and code generation. A fine-tuned 8B variant matches 70B performance, emphasizing the value of task-specific tuning. Despite improvements with few-shot learning, models still face generalization challenges in multi-domain scenarios.\n\\bibliography{main}\n\n\\appendix\n\n\\newpage\n% \\section*{NeurIPS Paper Checklist}\n% \\begin{enumerate}\n\n% \\item {\\bf Claims}\n% \\item[] Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?\n% \\item[] Answer: \\answerYes{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: The claim is supported by the abstract and introduction and aligns with the contributions outlined throughout the paper.\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that the abstract and introduction do not include the claims made in the paper.\n% \\item The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. 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You can either create an anonymized URL or include an anonymized zip file.\n% \\end{itemize}\n\n% \\item {\\bf Crowdsourcing and research with human subjects}\n% \\item[] Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)? \n% \\item[] Answer: \\answerNA{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: The paper does not involve crowdsourcing nor research with human subjects.\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.\n% \\item Including this information in the supplemental material is fine, but if the main contribution of the paper involves human subjects, then as much detail as possible should be included in the main paper. \n% \\item According to the NeurIPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector. \n% \\end{itemize}\n\n% \\item {\\bf Institutional review board (IRB) approvals or equivalent for research with human subjects}\n% \\item[] Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?\n% \\item[] Answer: \\answerNA{} % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: The paper does not involve crowdsourcing nor research with human subjects.\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.\n% \\item Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper. \n% \\item We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere to the NeurIPS Code of Ethics and the guidelines for their institution. \n% \\item For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.\n% \\end{itemize}\n\n% \\item {\\bf Declaration of LLM usage}\n% \\item[] Question: Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.\n% %this research? \n% \\item[] Answer: \\answerYes{}, % Replace by \\answerYes{}, \\answerNo{}, or \\answerNA{}.\n% \\item[] Justification: We declared during the submission.\n% \\item[] Guidelines:\n% \\begin{itemize}\n% \\item The answer NA means that the core method development in this research does not involve LLMs as any important, original, or non-standard components.\n% \\item Please refer to our LLM policy (\\url{https://neurips.cc/Conferences/2025/LLM}) for what should or should not be described.\n% \\end{itemize}\n \n\\section{Limitations}\nIn this work, we focus on constructing and introducing a new dataset as a benchmark for evaluating agentic workflows in multi-turn conversational dialogue settings, with an emphasis on tool calling for planning. Our evaluations are limited to open-source models, as proprietary models are not included due to resource constraints. We use Llama 3.1 8B Instruct, Llama 3.3 70B Instruct, S1.1 32B, and Phi4 Reasoning Plus. We will release $\\benchmarkname$ dataset publicly and hope it will encourage future research that includes evaluations on proprietary models as well.\n\n\\section{Additional Results}\n\\subsection{Detailed Results for Llama 3.1 8B Instruct with SFT and Llama 3.3 70B Instruct}\nTable~\\ref{table:llama3.1-8b-sft} shows the results for Llama 3.1 8B Instruct with Domain Adaptation and Table~\\ref{table:llama3.3-70b} shows the results for Llama 3.3 70B Instruct.\n\n\\begin{table*}[!ht]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lcccc|cccc|c|cc|c}\n \\toprule\n \\textbf{Domain} & \\multicolumn{4}{c}{\\textbf{Tool Call}} & \\multicolumn{4}{|c|}{\\textbf{Parameter Matching}} & \\textbf{Code Exec. Rate} & \\multicolumn{2}{|c|}{\\textbf{Information Seeking}} & \\multicolumn{1}{c}{\\textbf{Cache Summary}} \\\\ \n & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\ \\midrule\n \\texttt{Single-Domain} \\\\\n \\midrule\n Flights & 83.51 & 89.98 & 92.08 & 91.02 & 58.02 & 66.38 & 82.18 & 73.44 & 85.26 & 39.19 & 86.44 & 62.07 \\\\\n Hotels & 78.18 & 84.87 & 90.84 & 87.75 & 60.40 & 67.29 & 85.53 & 75.32 & 72.13 & 48.30 & 80.91 & 71.35 \\\\\n Restaurants & 72.76 & 80.40 & 88.44 & 84.23 & 57.49 & 66.69 & 80.64 & 73.00 & 95.16 & 95.14 & 98.63 & 65.32 \\\\\n Attractions & 90.50 & 97.87 & 92.33 & 95.02 & 75.54 & 91.34 & 81.37 & 86.07 & 98.15 & N/A & N/A &83.00 \\\\ \\midrule\n \\multicolumn{10}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n F-H & 80.27 & 91.31 & 86.91 & 89.05 & 57.76 & 74.51 & 71.99 & 73.23 & 89.25 & 24.04 & 80.63 & 57.01 \\\\\n H-R & 82.02 & 91.03 & 89.23 & 90.12 & 68.44 & 77.95 & 84.86 & 81.26 & 83.67 & 29.12 & 87.58 & 64.42 \\\\\n H-A & 62.30 & 72.67 & 81.35 & 76.77 & 59.22 & 68.73 & 81.05 & 74.39 & 71.41 & 27.87 & 82.80 & 65.70 \\\\\n F-H-A & 77.19 & 87.50 & 86.76 & 87.13 & 59.63 & 74.16 & 75.27 & 74.71 & 81.15 & 22.65 & 83.45 & 52.76 \\\\\n F-H-R & 71.53 & 86.05 & 80.91 & 83.40 & 54.39 & 71.99 & 68.99 & 70.45 & 82.47 & 46.32 & 84.40 & 53.88 \\\\\n \\bottomrule\n \\end{tabular}\n}\n\\caption{Overall Results using Llama 3.1 8B Instruct after SFT.}\n\\label{table:llama3.1-8b-sft}\n\\end{table*}\n\n\\begin{table*}[!ht]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lcccc|cccc|c|cc|c}\n \\toprule\n \\textbf{Domain} & \\multicolumn{4}{c}{\\textbf{Tool Call}} & \\multicolumn{4}{|c|}{\\textbf{Parameter Matching}} & \\textbf{Code Exec. Rate} & \\multicolumn{2}{|c|}{\\textbf{Information Seeking}} & \\multicolumn{1}{c}{\\textbf{Cache Summary}} \\\\ \n & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\ \\midrule\n \\texttt{Single-Domain} \\\\\n \\midrule\n Flights & 58.42 & 78.26 & 69.75 & 73.76 & 32.11 & 40.51 & 60.76 & 48.61 & 83.32 & 20.25 & 82.90 & 53.80 \\\\\n Hotels & 92.42 & 96.03 & 96.10 & 96.07 & 75.33 & 80.85 & 91.69 & 85.93 & 98.55 & 46.98 & 80.27 & 75.00 \\\\\n Restaurants & 94.23 & 96.63 & 97.43 & 97.03 & 85.51 & 93.62 & 90.80 & 92.19 & 99.54 & 37.90 & 79.95 & 90.78 \\\\\n Attractions & 79.14 & 92.16 & 84.86 & 88.36 & 71.17 & 87.04 & 79.60 & 83.16 & 63.70 & N/A & N/A & 78.59 \\\\ \\midrule\n \\multicolumn{10}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n F-H & 43.48 & 53.73 & 69.52 & 60.61 & 22.28 & 25.94 & 61.24 & 36.44 & 95.02 & 27.39 & 83.42 & 37.15 \\\\\n H-R & 60.50 & 70.91 & 80.46 & 78.29 & 50.94 & 60.08 & 76.99 & 67.50 & 99.60 & 22.97 & 84.11 & 38.43 \\\\\n H-A & 64.91 & 68.93 & 91.75 & 78.72 & 59.24 & 66.59 & 84.29 & 74.40 & 97.01 & 30.02 & 82.71 & 57.03 \\\\\n F-H-A & 60.46 & 70.77 & 80.59 & 75.36 & 44.27 & 51.19 & 76.58 & 61.37 & 94.81 & 19.16 & 82.27 & 44.29 \\\\\n F-H-R & 52.97 & 62.22 & 78.07 & 69.25 & 42.93 & 50.66 & 73.76 & 60.07 & 91.30 & 40.45 & 85.62 & 45.37 \\\\\n \\bottomrule\n \\end{tabular}\n}\n\\caption{Overall Results using Llama 3.3 70B Instruct.}\n\\label{table:llama3.3-70b}\n\\end{table*}\n\n\\subsection{Detailed Results for Reasoning Models}\nAdditionally, we also experiment with medium-sized reasoning models to see how they perform. We work with S1.1 32B~\\cite{muennighoff2025s1} and Phi-4-reasoning-plus 14B~\\cite{abdin2025phi}. The decoding configuration for both models includes a maximum token limit of 4,000, a temperature of 0.1, and max token length is 7,000. We host our models using vLLM in a A100 40GB x 8 node.\n\n\\begin{table*}[!ht]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lcccc|cccc|c|cc|c}\n \\toprule\n \\textbf{Domain} & \\multicolumn{4}{c}{\\textbf{Tool Call}} & \\multicolumn{4}{|c|}{\\textbf{Parameter Matching}} & \\textbf{Code Exec. Rate} & \\multicolumn{2}{|c|}{\\textbf{Information Seeking}} & \\multicolumn{1}{c}{\\textbf{Cache Summary}} \\\\ \n & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\ \\midrule\n \\texttt{Single-Domain} \\\\\n \\midrule\n Flights & 77.59 & 87.87 & 86.90 & 87.38 & 49.53 & 58.04 & 77.15 & 66.25 & 57.20 & 29.65 & 83.12 & 57.46\n \\\\\n Hotels & 89.33 & 95.33 & 93.41 & 94.36 & 65.27 & 70.52 & 89.77 & 78.99 & 63.95 & 18.07 & 79.56 & 57.23\n \\\\\n Restaurants & 87.77 & 92.65 & 94.35 & 93.49 & 71.59 & 77.69 & 90.11 & 83.44 & 69.82 & 7.16 & 69.91 & 69.22\n \\\\\n Attractions & 65.76 & 80.16 & 78.54 & 79.34 & 44.92 & 53.04 & 74.59 & 62.00 & 72.57 & N/A & N/A & 63.87\n\\\\ \\midrule\n \\multicolumn{10}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n F-H & 69.87 & 78.42 & 86.51 & 82.27 & 42.38 & 50.39 & 72.71 & 59.53 & 66.89 & 9.72 & 78.43 & 47.49\n \\\\\n H-R & 59.10 & 65.62 & 85.60 & 74.29 & 51.91 & 58.54 & 82.10 & 68.35 & 49.72 & 19.69 & 85.40 & 32.55\n \\\\\n H-A & 63.46 & 67.81 & 90.83 & 77.65 & 65.22 & 72.53 & 86.60 & 78.95 & 44.39 & 21.41 & 81.95 & 56.64\n \\\\\n F-H-A & 68.41 & 75.27 & 88.25 & 81.24 & 50.21 & 61.04 & 73.88 & 66.85 & 60.86 & 27.00 & 82.48 & 50.42\n \\\\\n F-H-R & 67.22 & 73.80 & 88.30 & 80.40 & 58.97 & 71.66 & 76.91 & 74.19 & 61.47 & 10.08 & 78.32 & 53.74\n \\\\\n \\bottomrule\n \\end{tabular}\n}\n\\caption{Overall Results using S1.1 32B.}\n\\label{table:s1.1}\n\\end{table*}\n\n\\begin{table*}[!ht]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lcccc|cccc|c|cc|c}\n \\toprule\n \\textbf{Domain} & \\multicolumn{4}{c}{\\textbf{Tool Call}} & \\multicolumn{4}{|c|}{\\textbf{Parameter Matching}} & \\textbf{Code Exec. Rate} & \\multicolumn{2}{|c|}{\\textbf{Information Seeking}} & \\multicolumn{1}{c}{\\textbf{Cache Summary}} \\\\ \n & Acc. & Prec. & Rec. & F1 & Acc. & Prec. & Rec. & F1 & Acc. & SacreBLEU & BERTScore & EM \\\\ \\midrule\n \\texttt{Single-Domain} \\\\\n \\midrule\n Flights & 36.66 & 67.30 & 44.61 & 53.66 & 15.74 & 56.10 & 17.95 & 27.20 & 72.76 & 13.91 & 81.60 & 39.73\n \\\\\n Hotels & 83.96 & 91.74 & 90.82 & 91.28 & 53.22 & 63.09 & 77.30 & 69.47 & 93.05 & 46.95 & 80.47 & 46.52\n \\\\\n Restaurants & 64.13 & 83.60 & 73.36 & 78.14 & 47.10 & 61.89 & 66.34 & 64.04 & 93.49 & 28.27 & 69.74 & 50.53\n \\\\\n Attractions & 35.78 & 61.67 & 46.02 & 52.71 & 22.90 & 66.92 & 25.82 & 37.27 & 66.25 & N/A & N/A & 50.79\n\\\\ \\midrule\n \\multicolumn{10}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n F-H & 57.79 & 88.12 & 62.67 & 73.25 & 29.91 & 59.20 & 37.68 & 46.05 & 83.37 & 27.96 & 83.02 & 32.53\n \\\\\n H-R & 54.07 & 70.71 & 69.67 & 70.18 & 35.42 & 50.91 & 53.79 & 52.31 & 96.04 & 23.19 & 84.83 & 21.72\n \\\\\n H-A & 65.81 & 78.69 & 80.08 & 79.38 & 48.58 & 61.78 & 69.45 & 65.39 & 96.04 & 27.92 & 82.97 & 53.52\n \\\\\n F-H-A & 37.83 & 62.18 & 49.14 & 54.90 & 32.73 & 64.59 & 39.89 & 49.32 & 84.49 & 15.42 & 81.00 & 32.33\n \\\\\n F-H-R & 46.79 & 72.75 & 56.74 & 63.75 & 33.99 & 59.56 & 44.18 & 50.73 & 91.86 & 36.30 & 84.63 & 37.65\n \\\\\n \\bottomrule\n \\end{tabular}\n}\n\\caption{Overall Results using Phi-4-reasoning-plus.}\n\\label{table:phi-4-reasoning-plus}\n\\end{table*}\n\nBetween these two models, the S1.1 model mostly outperforms the Phi-4-reasoning-plus model when it comes to Tool Call and Parameter Matching metrics.\n\n\\section{Performance Comparison Between Models}\nWe observe that the S1.1 model performance performs reasonably well especially compared to the Llama 3.3 70B model that we observe to produce the best results. The Phi-4-reasoning-plus model did well with some domains but overall it does not perform as well as the S1.1 and Llama 3.3 70B models. Figure~\\ref{fig:compare-models} shows the performance comparison between models.\n\n\\begin{figure}[!ht]\n \\centering\n \\includegraphics[width=\\linewidth]{assets/all_model_f1_scores.png}\n \\caption{\\textbf{Above:} Tool Call F1 performance and \\textbf{Below:} Parameter Matching F1 performance.}\n \\label{fig:compare-models}\n\\end{figure}\n\n% \\section{Reproducibility}\n% Our dataset has been anonymized and please find the dataset on Kaggle \\url{https://kaggle.com/datasets/b47e52b440ccaf964009a3f4a3bdfdb92de205249fae87c3c3db7b615c29490c}. Details on how to reproduce our experiments using TRL are provided in the Appendix of our submission and we also included here for your perusal in Section~\\ref{SFTScript}. \n% For additional instructions and implementation details, please refer to this anonymous repository at: \\url{https://anonymous.4open.science/r/T1-code-38C8/README.md}.\n\n\\section{Error Analysis}\n\nWhen we look closer at the errors found during inferencing, we first break down the errors into a total of 4 categories:\n\\begin{itemize}\n\\item \\textbf{Validation Error}: Errors related to improper arguments being passed to the defined tools. \n\\item \\textbf{Variable Not Defined}: Error indicating code uses an underfined variable. \n\\item \\textbf{Index Out Of Range}: Error indicating generated code involves an index out of range, usually corresponding to the list data structure in Python.\n\\item \\textbf{Other}: Other errors present within the generated code.\n\\end{itemize}\n\n\\begin{table*}[!htbp]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lccccccc}\n \\toprule\n \\textbf{Domain} & \\textbf{ \\# Turns} & \\textbf{\\# Validation Error} & \\textbf{\\# Variable Not Defined} & \\textbf{\\# Index Out Of Range} & \\textbf{\\# Other} \\\\\n \\midrule\n \\texttt{Single-Domain} \\\\\n \\midrule\n Flights & 8,200 & 737 & 830 & 0 & 468 \\\\\n Hotels & 8,000 & 417 & 218 & 0 & 820 \\\\\n Restaurants & 7,900 & 513 & 14 & 1 & 183 \\\\\n Attractions & 5,975 & 507 & 1 & 0 & 243 \\\\\n \\midrule\n \\multicolumn{6}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n F-H & 11,700 & 834 & 1087 & 0 & 408 \\\\\n H-R & 10,950 & 1532 & 18 & 1 & 522 \\\\\n H-A & 11,000 & 1024 & 167 & 0 & 308 \\\\\n F-H-A & 9,775 & 1163 & 990 & 0 & 575 \\\\\n F-H-R & 8,575 & 812 & 761 & 3 & 495 \\\\\n \\midrule\n \\texttt{Total} & - & 7539 & 4086 & 5 & 4022\\\\\n \\bottomrule\n \\end{tabular}\n}\n\\caption{Overall Error Analysis Results using Llama 3.1 8B Instruct.}\n\\end{table*}\n\n\\begin{table*}[!htbp]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lccccccc}\n \\toprule\n \\textbf{Domain} & \\textbf{ \\# Turns} & \\textbf{\\# Validation Error} & \\textbf{\\# Variable Not Defined} & \\textbf{\\# Index Out Of Range} & \\textbf{\\# Other} \\\\\n \\midrule\n \\texttt{Single-Domain} \\\\\n \\midrule\n Flights & 8,200 & 408 & 176 & 42 & 26 \\\\\n Hotels & 8,000 & 1,099 & 16 & 0 & 0 \\\\\n Restaurants & 7,900 & 209 & 5 & 0 & 2 \\\\\n Attractions & 5,975 & 18 & 9 & 0 & 8 \\\\\n \\midrule\n \\multicolumn{6}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n F-H & 11,700 & 454 & 149 & 0 & 22 \\\\\n H-R & 10,950 & 692 & 185 & 0 & 17 \\\\\n H-A & 11,000 & 1248 & 315 & 0 & 1 \\\\\n F-H-A & 9,775 & 789 & 124 & 9 & 27 \\\\\n F-H-R & 8,575 & 541 & 109 & 0 & 101 \\\\\n \\midrule\n \\texttt{Total} & - & 5458 & 1088 & 51 & 204 \\\\\n \\bottomrule\n \\end{tabular}\n}\n\\caption{Overall Error Analysis Results using Llama 3.1 8B SFT Instruct.}\n\\end{table*}\n\n\\begin{table*}[!htbp]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lccccccc}\n \\toprule\n \\textbf{Domain} & \\textbf{ \\# Turns} & \\textbf{\\# Validation Error} & \\textbf{\\# Variable Not Defined} & \\textbf{\\# Index Out Of Range} & \\textbf{\\# Other} \\\\\n \\midrule\n \\texttt{Single-Domain} \\\\\n \\midrule\n Flights & 8,200 & 518 & 2 & 30 & 0 \\\\\n Hotels & 8,000 & 41 & 0 & 0 & 0 \\\\\n Restaurants & 7,900 & 29 & 0 & 0 & 0 \\\\\n Attractions & 5,975 & 4 & 2 & 0 & 1 \\\\\n \\midrule\n \\multicolumn{6}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n F-H & 11,700 & 158 & 28 & 0 & 0\n \\\\\n H-R & 10,950 & 14 & 2 & 0 & 1\n \\\\\n H-A & 11,000 & 48 & 33 & 0 & 48\n \\\\\n F-H-A & 9,775 & 148 & 6 & 7 & 3\n \\\\\n F-H-R & 8,575 & 330 & 5 & 1 & 7\n \\\\\n \\midrule\n \\texttt{Total} & - & 1290 & 78 & 38 & 60 \\\\\n \\bottomrule\n \\end{tabular}\n}\n\\caption{Overall Error Analysis Results using Llama 3.3 70B Instruct.}\n\\end{table*}\n\n\\begin{table*}[!htbp]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lccccccc}\n \\toprule\n \\textbf{Domain} & \\textbf{ \\# Turns} & \\textbf{\\# Validation Error} & \\textbf{\\# Variable Not Defined} & \\textbf{\\# Index Out Of Range} & \\textbf{\\# Other} \\\\\n \\midrule\n \\texttt{Single-Domain} \\\\\n \\midrule\n Flights & 8,200 & 559 & 16 & 3 & 40\n \\\\\n Hotels & 8,000 & 92 & 1 & 39 & 27\n \\\\\n Restaurants & 7,900 & 20 & 7 & 0 & 28\n \\\\\n Attractions & 5,975 & 11 & 27 & 0 & 76\n \\\\\n \\midrule\n \\multicolumn{6}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n F-H & 11,700 & 24 & 34 & 114 & 63\n \\\\\n H-R & 10,950 & 54 & 895 & 102 & 34\n \\\\\n H-A & 11,000 & 3 & 1071 & 43 & 23\n \\\\\n F-H-A & 9,775 & 89 & 579 & 25 & 49\n \\\\\n F-H-R & 8,575 & 85 & 241 & 7 & 28\n \\\\\n \\midrule\n \\texttt{Total} & - & 937 & 2871 & 333 & 368\n \\\\\n \\bottomrule\n \\end{tabular}\n}\n\\caption{Overall Error Analysis Results using S1.1.}\n\\end{table*}\n\n\\begin{table*}[!htbp]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lccccccc}\n \\toprule\n \\textbf{Domain} & \\textbf{ \\# Turns} & \\textbf{\\# Validation Error} & \\textbf{\\# Variable Not Defined} & \\textbf{\\# Index Out Of Range} & \\textbf{\\# Other} \\\\\n \\midrule\n \\texttt{Single-Domain} \\\\\n \\midrule\n Flights & 8,200 & 103 & 7 & 1 & 1\n \\\\\n Hotels & 8,000 & 277 & 0 & 0 & 1\n \\\\\n Restaurants & 7,900 & 259 & 0 & 0 & 19\n \\\\\n Attractions & 5,975 & 0 & 0 & 0 & 0\n \\\\\n \\midrule\n \\multicolumn{6}{l}{\\texttt{Multi-Domain.} F: Flights, H: Hotels, R: Restaurants, A: Attractions} \\\\ \\midrule\n F-H & 11,700 & 212 & 2 & 0 & 7\n \\\\\n H-R & 10,950 & 212 & 0 & 0 & 1\n \\\\\n H-A & 11,000 & 162 & 0 & 0 & 6\n \\\\\n F-H-A & 9,775 & 120 & 3 & 0 & 5\n \\\\\n F-H-R & 8,575 & 153 & 4 & 0 & 32\n \\\\\n \\midrule\n \\texttt{Total} & - & 1498 & 16 & 1 & 72\\\\\n \\bottomrule\n \\end{tabular}\n}\n\\caption{Overall Error Analysis Results using Phi-4-reasoning-plus.}\n\\end{table*}\n\nOverall, we notice that validation errors are consistently present within the results of all of the models we evaluated.\n\n\\subsection{Llama 3.3 70B Instruct} The Llama 3.3 70B instruct model contains the fewest number of overall errors among the five models evaluated with a total of 1,466. This supports the idea that the larger number of parameters helps the model better understand the task as well passing in the proper parameters to the tools to generate high quality code. \n\n\\subsection{Llama 3.1 8B Instruct} The Llama 3.1 8B Instruct model consists of the most number of total errors among all 4 categories which were tracked. The only category it performs well in was the \\texttt{Index Out Of Range} error where there are only 5 instances.\n\n\\subsection{Llama 3.1 8B Instruct SFT}\nThe Llama 3.1 8B Instruct SFT does drastically improve on the number of \\texttt{Validation}, \\texttt{Variable Not Defined} and \\texttt{Other} categorized errors compared to the original model. This indicates that the model is able to better learn about calling the tools and writing higher quality code than the original model.\n\n\n\\subsection{S1.1} Overall, the S1.1 model performs the best when it comes to \\texttt{Validation} errors with a total of 937 errors. However, it also consists of the highest amount of \\texttt{Index Out of Range} errors and the 2nd highest amount of \\texttt{Variable Not Defined} errors.\n\n\\subsection{Phi-4-reasoning-plus}\nThe Phi-4-reasoning-plus has 1498 validation errors which is the third most among the 5 models. The model does perform reasonably well with the other error categories and has the fewest number of \\texttt{Index Out Of Range} and \\texttt{Variable Not Defined} errors with 1 and 16 respectively.\n\n\\section{Model and Prompt Configuration}\nWe use the Llama 3.1 8B Instruct and Llama 3.3 70B Instruct models, and fine-tune Llama 3.1 8B Instruct to evaluate the effectiveness of domain adaptation on the $\\benchmarkname$ dataset. We evaluate all the models in a \\textit{few-shot} setting, where several example turns are included in the prompt to guide the model’s behavior. The full prompts used for inference are provided in Section~\\ref{system-plan-generation-prompt} and Section~\\ref{user-plan-generation-prompt}. The decoding configuration for both models includes a maximum token limit of 4000, top-k sampling of 10, and a temperature of 0.1.\n\n\\section{Evaluation Protocol}\nWe evaluate the model's performance at every user turn by comparing the model’s output with the ground truth across the following facets.\n\\paragraph{Tool Call.} We evaluate the correctness of each tool call from the generated code against the ground truth code by comparing the number of times each tool is called using four metrics: accuracy, precision, recall, and F1.\n\\paragraph{Parameter Matching.} For each tool call in the ground truth, we identify the corresponding tool call in the generated code with the same name and the highest parameter overlap. We then calculate the accuracy, precision, recall, and F1 for the identified match. Parameter values are standardized to enable robust comparison; for example, lists are treated as sets to make them insensitive to ordering. Certain tool calls such as \\texttt{save\\_to\\_cache}, \\texttt{get\\_results\\_from\\_cache}, and \\texttt{seek\\_information} are excluded from this evaluation, as they depend on external artifacts (e.g., intermediate dataframes) and variable keys or identifiers that cannot be reliably matched. Similarly, certain parameter names (e.g., \\texttt{prior\\_results}) are also excluded. \n\\paragraph{Code Execution Success Rate.} We calculate the percentage of instances in which the model generates a code when expected and the code is executable without any errors in a sandbox environment.\n\\paragraph{Handling Information Seeking.} If both the model output and the ground truth include \\texttt{seek\\_information}, we evaluate the similarity of the strings inside the function using SacreBLEU~\\cite{post2018call} and BERTScore F1~\\cite{zhang2019bertscore}, capturing both sub-word overlap and semantic similarity of the requested information.\n\\paragraph{Cache Summary.} To assess how well the model serves complete requests, we compare the execution cache summary results of the generated code and ground truth using exact match (EM) to determine if the model-generated solution is functionally equivalent to the ground truth.\n\n\\section{More Information on T1 Dataset}\nTable~\\ref{tab:domains-and-attributes} shows the attributes for each domain. Table~\\ref{tab:defined-tools-domain} shows the tools used for each domain.\n\n\\begin{table*}[!ht]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lllll}\n \\toprule \n \\textbf{Domain} & \\textbf{Attributes} \\\\ \\midrule\n Flight & airline, flight\\_id, start\\_airport, start\\_airport\\_latitude, start\\_airport\\_longitude, \\\\\n & start\\_airport\\_code, end\\_airport, end\\_airport\\_latitude, end\\_airport\\_longitude, \\\\\n & end\\_airport\\_code, economy\\_class\\_option\\_present, business\\_class\\_option\\_present, \\\\\n & first\\_class\\_option\\_present, distance\\_miles, duration\\_minutes, \\\\\n & departure\\_time, arrival\\_time, number\\_of\\_layovers, first\\_layover\\_airport, \\\\\n Hotel & hotel\\_name, city, state, neighborhood, latitude, longitude, rating, \\\\ & stars, max\\_occupancy, gym\\_present, pool\\_present, price\\_per\\_night, num\\_rooms\\_available, \\\\ & breakfast\\_included, smoking\\_allowed, air\\_conditioning\\_present, \\\\\n & heating\\_present, free\\_wifi\\_included, airport\\_shuttle\\_present, \\\\ &\n is\\_pet\\_friendly, has\\_spa\\_services, has\\_room\\_service, \\\\ & has\\_beach\\_access, has\\_fitness\\_class, has\\_laundry\\_service, \\\\ & has\\_valet\\_parking, has\\_balcony, has\\_rooftop\\_bar, has\\_inroom\\_Kitchen, \\\\ & has\\_kids\\_club, has\\_meeting\\_rooms, has\\_electric\\_vehicle\\_charging, \\\\ & has\\_hot\\_tub, has\\_sauna, has\\_free\\_parking, is\\_wheelchair\\_accessible, \\\\ & has\\_skiing\\_lodging, has\\_ocean\\_view\\_rooms\\_present, has\\_city\\_view\\_rooms\\_present, \\\\ & start\\_date\\_available, end\\_date\\_available \\\\\n Attraction & city, state, name, type, latitude, longitude, neighborhood \\\\\n Restaurant & restaurant\\_name, city, state, neighborhood, \\\\\n & latitude, longitude, rating, price, price\\_per\\_person, has\\_nut\\_allergy\\_options, has\\_dairy\\_allergy\\_options, has\\_shell\\_fish\\_allergy\\_options, \\\\ & has\\_tomato\\_allergy\\_options, has\\_nightshade\\_allergy\\_options, \\\\ & has\\_gluten\\_free\\_options, has\\_vegetarian\\_options, has\\_vegan\\_options, \\\\ & has\\_kosher\\_options, has\\_halal\\_options, cuisine \\\\\n \\bottomrule\n \\end{tabular}\n}\n\\caption{Domains and Attributes.} \n\\label{tab:domains-and-attributes}\n\\end{table*}\n\n\\begin{table*}[!ht]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{ll}\n \\toprule \n \\textbf{Domain} & \\textbf{Tools}\n \\\\\n \\textbf{Flights} & search\\_flights, filter\\_flights \\\\\n \\textbf{Hotels} & search\\_hotels, filter\\_hotels \\\\\n \\textbf{Restaurants} & search\\_restaurants, filter\\_restaurants \\\\\n \\textbf{Attractions} & search\\_attractions, filter\\_attractions \\\\\n \\textbf{Multi-domain} & search\\_nearest \\\\\n \\textbf{Common} & save\\_to\\_cache, get\\_results\\_from\\_cache, sort\\_results, seek\\_information, adjust\\_date \\\\\n \\bottomrule\n \\end{tabular}\n}\n\\caption{List of Tools.} \n\\label{tab:defined-tools-domain}\n\\end{table*}\n\n\\newpage\n\\section{Sample Conversation from T1 Dataset}\n\n\n\\begin{table*}[!ht]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lll}\n \\toprule \n \\textbf{Attribute} & \\textbf{Type} & \\textbf{Description} \\\\ \\midrule\n airline & string & Airline of the flight, \\\\\n flight\\_classes & string & Classes for the flight (economy, business, first) \\\\\n num\\_layovers & integer & Number of layovers for the flight, between 0 and 2 \\\\\n layover\\_duration\\_amount & integer & Duration of a layover flight. Between 1 and 6 hours \\\\\n airports & list & Airport information for each flight including the city, state, airport code and airport name. \\\\\n \n \\bottomrule\n \\end{tabular}\n}\n\\caption{Flight Ontology Attributes.} \n\\label{tab:flight-ontology-attributes}\n\\end{table*}\n\n\\begin{table*}[!htbp]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lll}\n \\toprule \n \\textbf{Attribute} & \\textbf{Type} & \\textbf{Description} \\\\ \\midrule\n city & string & City that the hotel is in \\\\\n state & string & State that the hotel is in \\\\\n neighborhood & string & Neighborhood within the city that the hotel is in\\\\\n stars & integer & Star rating of the hotel between 1 and 5 \\\\\n rating & float & Customer rating of the hotel between 1.0 and 5.0, incremented by 0.1 \\\\\n price\\_per\\_night & integer & Price per night of the hotel ranging between 20 to 2000 dollars \\\\\n max\\_occupancy & integer & Maximum occupancy per room ranging between 1 and 7 \\\\\n gym\\_present & boolean & Whether or not the hotel has a gym \\\\\n pool\\_present & boolean & Whether or not the hotel has a pool \\\\\n breakfast\\_included & boolean & Whether or not the hotel has breakfast included \\\\\n smoking\\_allowed & boolean & Whether or not the hotel allows smoking \\\\\n air\\_conditioning\\_present & boolean & Whether or not the hotel has air conditioning \\\\\n heating\\_present & boolean & Whether or not the hotel has heating \\\\\n free\\_wifi\\_included & boolean & Whether or not the hotel includes free WiFi \\\\\n airport\\_shuttle\\_present & boolean & Whether or not the hotel has an airport shuffle \\\\\n is\\_pet\\_friendly & boolean & Whether or not the hotel allows pets \\\\\n has\\_spa\\_services & boolean & Whether or not the hotel has spa services \\\\\n has\\_room\\_service & boolean & Whether or not the hotel has room service \\\\\n has\\_beach\\_access & boolean & Whether or not the hotel has access to a beach \\\\\n has\\_business\\_center & boolean & Whether or not the hotel has a business center \\\\\n has\\_fitness\\_classes & boolean & Whether or not the hotel has fitness classes \\\\\n has\\_laundry\\_service & boolean & Whether or not the hotel has laundry services \\\\\n has\\_valet\\_parking & boolean & Whether or not the hotel has valet parking \\\\\n has\\_balcony & boolean & Whether or not the hotel has a balcony \\\\\n has\\_rooftop\\_bar & boolean & Whether or not the hotel has a rooftop bar \\\\\n has\\_inroom\\_kitchen & boolean & Whether or not the hotel has an inroom kitchen \\\\\n has\\_kids\\_club & boolean & Whether or not the hotel has a kids club \\\\\n has\\_meeting\\_rooms & boolean & Whether or not the hotel has meeting rooms \\\\\n has\\_electric\\_vehicle\\_charging & boolean & Whether or not the hotel has electric vehicle charging \\\\\n has\\_hot\\_tub & boolean & Whether or not the hotel has a hot tub \\\\\n has\\_sauna & boolean & Whether or not the hotel has a sauna \\\\ \n has\\_free\\_parking & boolean & Whether or not the hotel has free parking\\\\ \n is\\_wheelchair\\_accessible& boolean & Whether or not the hotel is wheelchair accessible \\\\ \n has\\_skiing\\_lodging & boolean & Whether or not the hotel has skiing and lodging \\\\ \n ocean\\_view\\_rooms\\_present & boolean & Whether or not the hotel has rooms with a view of the ocean \\\\\n city\\_view\\_rooms\\_present & boolean & Whether or not the hotel has rooms with views of the city\\\\\n \\bottomrule\n \\end{tabular}\n}\n\\caption{Hotel Ontology Attributes.} \n\\label{tab:hotel-ontology-attributes}\n\\end{table*}\n\n\\begin{table*}[!htbp]\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{lll}\n \\toprule \n \\textbf{Attribute} & \\textbf{Type} & \\textbf{Description} \\\\ \\midrule\n city & string & City that the restaurant is in \\\\\n state & string & State that the restaurant is in \\\\\n neighborhood & string & Neighborhood within the city that the restaurant is in\\\\\n rating & float & Customer rating of the restaurant between 1.0 and 5.0, incremented by 0.1 \\\\\n price\\_per\\_person & integer & Average price per person at the restaurant \\\\\n cuisine & string & Cuisine of the restaurant \\\\\n has\\_nut\\_allergy\\_options & boolean & Whether or not the restaurant has any options for individuals with an allergy to nuts \\\\\n has\\_dairy\\_allergy\\_options & boolean & Whether or not restaurant has any options for individuals with an allergy to dairy products \\\\\n has\\_shell\\_fish\\_allergy\\_options & boolean & Whether or not the restaurant has any options for individuals with an allergy to shell fish \\\\\n has\\_tomato\\_allergy\\_options & boolean & Whether or not the restaurant has options for individuals with an allergy to tomatoes \\\\\n has\\_nightshade\\_allergy\\_options & boolean & Whether or not the restaurant has options for individuals with an allergy to nightshades \\\\\n has\\_gluten\\_free\\_options & boolean & Whether or not the restaurant has gluten free options \\\\\n has\\_vegetarian\\_options & boolean & Whether or not the restaurant has vegetarian options \\\\\n has\\_vegan\\_options & boolean & Whether or not the restaurant has vegan options \\\\\n has\\_halal\\_options & boolean & Whether or not the restaurant has halal options \\\\\n has\\_kosher\\_options & boolean & Whether or not the hotel has kosher options \\\\ \n \\bottomrule\n \\end{tabular}\n}\n\\caption{Restaurant Ontology Attributes.} \n\\label{tab:restaurant-ontology-attributes}\n\\end{table*}\n\n\\begin{table*}[!htbp]\n\\centering\n\\resizebox{0.8\\textwidth}{!}{\n \\begin{tabular}{lll}\n \\toprule \n \\textbf{Attribute} & \\textbf{Type} & \\textbf{Description} \\\\ \\midrule\n city & string & City that the attraction is in \\\\\n state & string & State that the attraction is in \\\\\n neighborhood & string & Neighborhood within the city that the attraction is in\\\\\n type & string & Type of the attraction \\\\ \n \\bottomrule\n \\end{tabular}\n}\n\\caption{Attraction Ontology Attributes.} \n\\label{tab:attraction-ontology-attributes}\n\\end{table*}\n\n\n\\begin{tcolorbox}[colback=gray!10,colframe=black,title=Box 1. Sample conversation for attractions,breakable]\n\\textbf{assistant}: Hello! Are you looking for something to do in your free time?\\\\\n\\\\\n\\textbf{user}: Yeah, I am thinking of visiting some scenic attractions in San Antonio.\n\\\\\n\\\\\n\\textbf{assistant}: San Antonio has a lot of great Scenic spots. Have you considered Downtown San Antonio?\n\\\\\n\\\\\n\\textbf{user}: Actually, I haven't. What's there?\n\\\\\n\\\\\n\\textbf{assistant}: It's a great area with a lot of Scenic attractions. I can give you some recommendations.\n\\\\\n\\\\ \n\\textbf{user}: Okay, that sounds good.\n\\end{tcolorbox}\n\n\\begin{tcolorbox}[colback=gray!10,colframe=black,title=Box 2. Sample conversation for flights,breakable]\n\\textbf{assistant}: Welcome! What can I help you with today?\\\\\n\\\\\n\\textbf{user}: I need to book a flight from Colorado Springs to San Antonio for my anniversary, and I'm looking for something special.\n\\\\\n\\\\\n\\textbf{assistant}: Happy anniversary! What date are you planning to travel?\n\\\\\n\\\\\n\\textbf{user}: We're thinking of May 21, 2025.\n\\\\\n\\\\\n\\textbf{assistant}: That's great! What kind of flight experience are you looking for? Luxury, perhaps?\n\\\\\n\\\\ \n\\textbf{user}: Yes, something luxurious. And we'd like to depart from COS.\n\\\\\n\\\\\n\\textbf{assistant}: I'll look into that. What's your budget for the flight?\n\\\\\n\\\\ \n\\textbf{user}: We're looking to spend around 1100.\n\\end{tcolorbox}\n\n\\begin{tcolorbox}[colback=gray!10,colframe=black,title=Box 3. Sample conversation for hotels,breakable]\n\\textbf{assistant}: Hello, what brings you here today?\\\\\n\\\\\n\\textbf{user}: I'm looking for a hotel in St. Louis for my anniversary, can you help me?\n\\\\\n\\\\\n\\textbf{assistant}: Congratulations on your anniversary! What are your check-in and check-out dates?\n\\\\\n\\\\\n\\textbf{user}: We're planning to check-in on May 18, 2025 and check-out on May 29, 2025.\n\\\\\n\\\\\n\\textbf{assistant}: That's great! How many rooms do you need and what's your budget per night?\n\\\\\n\\\\ \n\\textbf{user}: We need 3 room and our budget is around 550 per night.\n\\\\\n\\\\\n\\textbf{assistant}: I see. What kind of amenities are you looking for in a hotel?\n\\\\\n\\\\ \n\\textbf{user}: We'd like a hotel with a gym, pool, and air conditioning.\n\\\\\n\\\\\n\\textbf{assistant}: Got it. Would you prefer a hotel in a specific neighborhood in St. Louis?\n\\\\\n\\\\ \n\\textbf{user}: Yes, we'd like to stay in Downtown St. Louis.\n\\end{tcolorbox}\n\n\\begin{tcolorbox}[colback=gray!10,colframe=black,title=Box 4. Sample conversation for restaurants,breakable]\n\\textbf{assistant}: assistant: Welcome, what brings you here today?\\\\\n\\\\\n\\textbf{user}: I'm gluten-free and I'm looking for a restaurant that can accommodate my dietary needs.\n\\\\\n\\\\\n\\textbf{assistant}: Don't worry, I'm here to help. What kind of cuisine are you interested in?\n\\\\\n\\\\\n\\textbf{user}: I'm really craving Livonian.\n\\\\\n\\\\\n\\textbf{assistant}: There are some great Livonian places that offer gluten-free options. Are you looking for something in a specific neighborhood?\n\\\\\n\\\\ \n\\textbf{user}: Yeah, I'd love something in Las Vegas somewhere in Summerlin.\n\\\\\n\\\\\n\\textbf{assistant}: I can check what's available in Summerlin. What's your budget for this meal?\n\\\\\n\\\\ \n\\textbf{user}: I'm looking to spend around 120 per person.\n\\\\\n\\\\\n\\textbf{assistant}: That helps. Would you like me to recommend a few places that fit your criteria?\n\\\\\n\\\\ \n\\textbf{user}: Please do.\n\\end{tcolorbox}\n\n\\begin{tcolorbox}[colback=gray!10,colframe=black,title=Box 5. Sample conversation for flight and hotels,breakable]\n\\textbf{assistant}: assistant: Hello, how can I assist you today?\n\\\\\n\\\\\n\\textbf{user}: I'm looking for a flight from Atlanta to Memphis and a hotel in Memphis\n\\\\\n\\\\\n\\textbf{assistant}: What are your travel dates?\n\\\\\n\\\\\n\\textbf{user}: My departure date is May 18, 2025 and I'd like to return on June 1, 2025\n\\\\\n\\\\\n\\textbf{assistant}: What class of flight are you interested in?\n\\\\\n\\\\ \n\\textbf{user}: user: I'd prefer business\n\\\\\n\\\\\n\\textbf{assistant}: And for your hotel, what star rating are you looking for?\n\\\\\n\\\\ \n\\textbf{user}: I'd like a 2 star hotel in Downtown Memphis\n\\end{tcolorbox}\n\n\\begin{tcolorbox}[colback=gray!10,colframe=black,title=Box 6. Sample conversation for hotel and restaurant,breakable]\n\\textbf{assistant}: assistant: Hello! How can I assist you today?\n\\\\\n\\\\\n\\textbf{user}: I'm looking for a hotel in San Antonio and a restaurant that serves Turkish.\n\\\\\n\\\\\n\\textbf{assistant}: That sounds like a great plan! Can you tell me a bit more about your hotel preferences, such as the number of rooms and check-in date?\n\\\\\n\\\\\n\\textbf{user}: I need 3 rooms for 5 people, checking in on May 22, 2025 and checking out on May 27, 2025.\n\\\\\n\\\\\n\\textbf{assistant}: I'd be happy to help you with that. What's your budget per night for the hotel?\n\\\\\n\\\\ \n\\textbf{user}: I'm looking to spend around 50 per night.\n\\\\\n\\\\\n\\textbf{assistant}: That's helpful to know. For the restaurant, are you looking for something with a specific rating or price range?\n\\\\\n\\\\ \n\\textbf{user}: Yes, I'd like a restaurant with a rating of at least 3.5 and a price range of around 85.\n\\\\\n\\\\\n\\textbf{assistant}: I'll keep that in mind. Would you like the hotel to have any specific amenities, such as a gym or pool?\n\\\\\n\\\\ \n\\textbf{user}: Yes, a pool would be great. And can you recommend a restaurant with Turkish near the hotel?\n\\end{tcolorbox}\n\n\\begin{tcolorbox}[colback=gray!10,colframe=black,title=Box 7. Sample conversation for hotel and attraction,breakable]\n\\textbf{assistant}: Hi there! What brings you here today?\n\\\\\n\\\\\n\\textbf{user}: I'm planning a last-minute trip to Portland for 2 people.\n\\\\\n\\\\\n\\textbf{assistant}: Last-minute trips can be exciting! What's your budget for the hotel?\n\\\\\n\\\\\n\\textbf{user}: We're looking to spend around 55 per night.\n\\\\\n\\\\\n\\textbf{assistant}: Okay, that helps. What's your check-in and check-out date?\n\\\\\n\\\\ \n\\textbf{user}: We're checking in on June 28, 2025 and checking out on July 10, 2025.\n\\\\\n\\\\\n\\textbf{assistant}: Are you interested in any specific attractions in Portland?\n\\\\\n\\\\ \n\\textbf{user}: Yes, we want to visit Pittock Mansion, which is a Social attraction.\n\\\\\n\\\\\n\\textbf{assistant}: That sounds like a great choice! I can help you find a hotel near Pittock Mansion. What kind of amenities are you look for in the hotel?\n\\\\\n\\\\ \n\\textbf{user}: We need a hotel with a gym and wifi.\n\\end{tcolorbox}\n\n\\begin{tcolorbox}[colback=gray!10,colframe=black,title=Box 8. Sample conversation for flight-hotel-attraction,breakable]\n\\textbf{assistant}: Hello, I'd be happy to help you plan your trip. What's on your mind?\n\\\\\n\\\\\n\\textbf{user}: I'm thinking of going to Las Vegas for my birthday and I need a business flight from Tucson.\n\\\\\n\\\\\n\\textbf{assistant}: Happy birthday in advance. When are you thinking of traveling?\n\\\\\n\\\\\n\\textbf{user}: We're looking at May 19, 2025 to May 27, 2025.\n\\\\\n\\\\\n\\textbf{assistant}: That's a great time. How many people are traveling?\n\\\\\n\\\\ \n\\textbf{user}: Just 2 of us.\n\\\\\n\\\\\n\\textbf{assistant}: Lovely. For your stay, what kind of hotel are you envisioning?\n\\\\\n\\\\ \n\\textbf{user}: A 2 star hotel with a spa and room service would be perfect. And we'd love to stay near Fountains of Bellagio with a beautiful view.\n\\\\\n\\\\\n\\textbf{assistant}: That sounds like a great choice! I can help you find a hotel near Pittock Mansion. What kind of amenities are you look for in the hotel?\n\\end{tcolorbox}\n\n\\begin{tcolorbox}[colback=gray!10,colframe=black,title=Box 9. Sample conversation for flight-hotel-restaurant,breakable]\n\\textbf{assistant}: Welcome to our travel assistant. How can I help you today?\n\\\\\n\\\\\n\\textbf{user}: I'm looking to travel from Tucson to St. Louis for a business trip.\n\\\\\n\\\\\n\\textbf{assistant}: What are your preferred travel dates?\n\\\\\n\\\\\n\\textbf{user}: I'd like to fly out on May 18, 2025 and come back on June 1, 2025\n\\\\\n\\\\\n\\textbf{assistant}: What class would you like to fly in?\n\\\\\n\\\\ \n\\textbf{user}: I prefer first.\n\\\\\n\\\\\n\\textbf{assistant}: Great, I can look into that. Where would you like to stay in St. Louis?\n\\\\\n\\\\ \n\\textbf{user}: I'd like a hotel in Downtown St. Louis with a 3 star rating.\n\\\\\n\\\\\n\\textbf{assistant}: And what kind of restaurant are you looking for?\n\\\\\n\\\\ \n\\textbf{user}: I'd like a restaurant in Downtown St. Louis with a rating of at least 3.5.\n\\end{tcolorbox}\n\n% \\section{Appendix / supplemental material}\n\n% \\section{System Prompt for Generating Planner Code}\n% \\label{appendix:prompt-design-system-planner}\n\n\\section{System Prompt Design for Single-Domain Template Generation}\n\\label{appendix:prompt-design-user-planner}\n\nThis section details the unified system prompt and its domain-specific customizations used for generating diverse, realistic multi-turn conversations for four domains: \\textbf{Flights, Hotels, Attractions, and Restaurants}.\n\n\\subsection{Unified Prompt Instructions (Common Across Domains)}\n\nThe following design principles apply across all domains to ensure consistency, diversity, and realism in the generated templates:\n\n\\begin{itemize}[leftmargin=1.5em]\n \\item \\textbf{Conversation Format:}\n \\begin{itemize}\n \\item Each template contains 6–8 dialogue turns (3–4 exchanges).\n \\item Each template starts with an \\texttt{assistant} turn and ends with a \\texttt{user} turn.\n \\item Turns alternate between \\texttt{assistant:} and \\texttt{user:}.\n \\end{itemize}\n\n \\item \\textbf{Placeholder Handling:}\n \\begin{itemize}\n \\item Use numbered placeholders like \\texttt{<CITY\\_1>}, \\texttt{<PRICE\\_2>}.\n \\item Numbering resets at the start of each new template.\n \\end{itemize}\n\n \\item \\textbf{Assistant Behavior Diversity:}\n \\begin{itemize}\n \\item Vary personality: formal/informal, concise/verbose.\n \\item Ask both open-ended and specific questions.\n \\item Do not assume user intent or provide options in the first message.\n \\item Correct misunderstandings if needed.\n \\end{itemize}\n\n \\item \\textbf{User Behavior Diversity:}\n \\begin{itemize}\n \\item Include verbose, terse, indecisive, and overly specific users.\n \\item Some users provide minimal context; others give excess information.\n \\item Include questions that revisit or skip steps.\n \\end{itemize}\n\n \\item \\textbf{Flow Diversity:}\n \\begin{itemize}\n \\item Avoid fixed dialogue patterns (e.g., greet $\\rightarrow$ date $\\rightarrow$ options).\n \\item Allow nonlinear flow: backtracking, parallel requests, etc.\n \\end{itemize}\n\n \\item \\textbf{Language Diversity:}\n \\begin{itemize}\n \\item Vary greetings, transitions, sentence structure, and phrasings.\n \\item No repeated phrasing across templates.\n \\end{itemize}\n\n \\item \\textbf{Output Requirements:}\n \\begin{itemize}\n \\item Output format is strict JSON with keys like \\texttt{\"template\\_1\"} through \\texttt{\"template\\_30\"}.\n \\item Each value is a newline-separated conversation string.\n \\item No preambles or postambles allowed.\n \\end{itemize}\n\\end{itemize}\n\n\\subsection{Domain-Specific Customization}\n\n\\begin{table}[!ht]\n\\centering\n\\small\n\\renewcommand{\\arraystretch}{1.3}\n\\begin{tabular}{|l|p{5.5cm}|p{7.5cm}|}\n\\hline\n\\textbf{Domain} & \\textbf{Placeholders Used} & \\textbf{Required Scenarios} \\\\\n\\hline\n\\textbf{Flight} & \n\\texttt{<CITY\\_x>}, \\texttt{<CLASS\\_x>}, \\texttt{<DEPARTURE\\_DATE\\_x>}, \\texttt{<DEPARTURE\\_TIME\\_x>}, \\texttt{<ARRIVAL\\_DATE\\_x>}, \\texttt{<ARRIVAL\\_TIME\\_x>}, \\texttt{<AIRLINE\\_x>}, \\texttt{<PRICE\\_x>}, \\texttt{<AIRPORT\\_x>}, \\texttt{<NUM\\_TRAVELERS\\_x>} & \nRound trip (with 2 departure dates), one-way, multi-city, strict arrival time, flexible dates, large group bookings, solo/business/family travel, weekend getaways, budget constraints, special occasions (e.g., birthdays) \\\\\n\\hline\n\n\\textbf{Hotel} & \n\\texttt{<CHECK\\_IN\\_DATE\\_x>}, \\texttt{<CHECK\\_OUT\\_DATE\\_x>}, \\texttt{<CITY\\_x>}, \\texttt{<CITY\\_x\\_NEIGHBORHOOD\\_y>}, \\texttt{<STAR\\_x>}, \\texttt{<RATING\\_x>}, \\texttt{<PRICE\\_x>}, \\texttt{<NUM\\_ROOMS\\_x>}, \\texttt{<NUM\\_TRAVELERS\\_x>} & \nMulti-city trips, multiple hotels in one city, specific neighborhoods, special occasions, last-minute reservations, business vs. leisure, extended stays, view/accessibility/pet-friendly requests, family and group accommodations \\\\\n\\hline\n\n\\textbf{Attraction} & \n\\texttt{<ATTRACTION\\_TYPE\\_x>}, \\texttt{<CITY\\_x>}, \\texttt{<STATE\\_x>}, \\texttt{<CITY\\_x\\_NEIGHBORHOOD\\_y>} & \nMulti-type attraction queries, across cities/states, neighborhood-specific exploration, curiosity about state-level offerings. Assistant must ask open-ended questions and only mention placeholders if user provides them. \\\\\n\\hline\n\n\\textbf{Restaurant} & \n\\texttt{<RESTAURANT\\_RATING\\_x>}, \\texttt{<RESTAURANT\\_PRICE\\_x>}, \\texttt{<RESTAURANT\\_CUISINE\\_x>}, \\texttt{<CITY\\_x>}, \\texttt{<CITY\\_x\\_NEIGHBORHOOD\\_y>} & \nMulti-city dining, cuisine preferences, dietary restrictions (e.g., nut-free, vegan, halal), budget vs. premium, special occasion dining, rating-focused or casual vs. upscale preferences \\\\\n\\hline\n\\end{tabular}\n\\vspace{1mm}\n\\caption{Domain-specific customization for template generation. Each domain builds on the shared prompt instructions with unique placeholders and required conversation scenarios.}\n\\label{tab:domain-customization}\n\\end{table}\n\\section{System Prompt Design for Multi-Domain Template Generation}\n\\label{appendix:multi-domain-prompt-design}\n\nThis section outlines the system prompt instructions used for generating multi-turn conversations that span multiple domains. These prompts are tailored to create realistic, nonlinear interactions involving combinations of Flights, Hotels, Attractions, and Restaurants.\n\n\\subsection{Unified Prompt Instructions (Shared Across Multi-Domain Prompts)}\n\n\\begin{itemize}[leftmargin=1.5em]\n \\item \\textbf{Conversation Format:}\n \\begin{itemize}\n \\item Each template includes at least 8–10 dialogue turns (4–5 exchanges).\n \\item Each template begins with an \\texttt{assistant} turn and ends with a \\texttt{user} turn.\n \\item Turns alternate between \\texttt{assistant:} and \\texttt{user:}.\n \\end{itemize}\n\n \\item \\textbf{Placeholder Handling:}\n \\begin{itemize}\n \\item Use only predefined placeholders (e.g., \\texttt{<CITY\\_1>}, \\texttt{<DEPARTURE\\_DATE\\_1>}).\n \\item Numbering restarts from 1 in every template.\n \\item Assistant must never mention a placeholder until the user provides it.\n \\end{itemize}\n\n \\item \\textbf{Assistant Behavior:}\n \\begin{itemize}\n \\item Vary personality (formal, informal), verbosity, and strategies (direct vs. open-ended questions).\n \\item Must not assume user intent or combine domain-specific questions unless user initiates.\n \\item Must correct misunderstandings and adapt to user behavior.\n \\end{itemize}\n\n \\item \\textbf{User Behavior:}\n \\begin{itemize}\n \\item Include users who are indecisive, impatient, verbose, overly specific, or vague.\n \\item Users may change their mind, skip steps, or backtrack in conversation.\n \\end{itemize}\n\n \\item \\textbf{Conversation Flow:}\n \\begin{itemize}\n \\item Avoid rigid order (e.g., flight $\\rightarrow$ hotel $\\rightarrow$ attraction).\n \\item Encourage nonlinear scenarios: jumping between domains, multiple questions at once.\n \\end{itemize}\n\n \\item \\textbf{Language and Style:}\n \\begin{itemize}\n \\item Vary greetings, transitions, sentence structures, and terminology across templates.\n \\item Avoid repeated phrasings.\n \\end{itemize}\n\n \\item \\textbf{Output Requirements:}\n \\begin{itemize}\n \\item Format is strict JSON: \\texttt{\"template\\_1\"} through \\texttt{\"template\\_30\"}.\n \\item Each value is a newline-separated string of alternating assistant/user dialogue.\n \\item No preamble, postamble, or markdown allowed.\n \\end{itemize}\n\\end{itemize}\n\n\\subsection{Multi-Domain Scenario Requirements}\n\n\\begin{table}[!ht]\n\\centering\n\\small\n\\renewcommand{\\arraystretch}{1.2}\n\\begin{tabularx}{\\textwidth}{|l|X|X|}\n\\hline\n\\textbf{Domains} & \\textbf{Placeholders Used} & \\textbf{Required Scenarios} \\\\\n\\hline\n\n\\textbf{Flight + Hotel} & \n\\texttt{<CITY\\_x>}, \\texttt{<CLASS\\_x>}, \\texttt{<DEPARTURE\\_DATE\\_x>}, \\texttt{<CHECK\\_IN\\_DATE\\_x>}, \\texttt{<CHECK\\_OUT\\_DATE\\_x>}, \\texttt{<NUM\\_TRAVELERS\\_x>} & \nTrip planning involving both air travel and accommodation. Includes round-trip and one-way flights, hotel stays across one or more cities, budget and luxury travelers, group vs. solo travel, flexible dates, specific preferences like nonstop flights or hotels with amenities (e.g., pool, gym, pet-friendly). \\\\\n\\hline\n\n\\textbf{Flight + Hotel + Attraction} & \nAll flight, hotel, and attraction placeholders (e.g., \\texttt{<CITY\\_x>}, \\texttt{<CHECK\\_IN\\_DATE\\_x>}, \\texttt{<ATTRACTION\\_TYPE\\_x>}) & \nUser planning a full trip involving flights, hotel stays, and sightseeing. Must include: round-trip and one-way flights, multi-city stays, hotels near attractions, flexible schedules, user-provided attraction types or specific attraction names (only when linking to hotel proximity), and special constraints (e.g., group travel, business trips, anniversaries). Assistant must never use placeholder names before user introduces them. Includes amenity discussions and attraction-type grammar handling (e.g., cultural vs. \\texttt{<ATTRACTION\\_TYPE\\_1>} attraction). \\\\\n\\hline\n\n\\textbf{Flight + Hotel + Restaurant} & \nAll flight, hotel, and restaurant placeholders (e.g., \\texttt{<RESTAURANT\\_CUISINE\\_x>}, \\texttt{<RESTAURANT\\_PRICE\\_x>}) & \nEnd-to-end trip planning including flights, accommodation, and dining. Required scenarios include: dietary restrictions, cuisine-first or city-first planning, restaurant proximity to hotel, multiple hotels or restaurants in one city, round-trip and one-way flights, and amenities like pet-friendly or spa. Must feature diverse language, nonlinear flows, and varied user personas. Includes explicit handling of dietary needs (e.g., halal, nut-free, vegetarian), and placeholder logic as per prompt. \\\\\n\\hline\n\n\\textbf{Hotel + Attraction} & \n\\texttt{<CITY\\_x>}, \\texttt{<CHECK\\_IN\\_DATE\\_x>}, \\texttt{<ATTRACTION\\_TYPE\\_x>}, \\texttt{<CITY\\_x\\_NEIGHBORHOOD\\_y>} & \nUser looking for accommodations and nearby attractions. Includes: specific neighborhoods, family-friendly vs. solo travel, themed vacations, extended stays, proximity to cultural or outdoor attractions, and cases where attraction type drives hotel location. \\\\\n\\hline\n\n\\textbf{Hotel + Restaurant} & \n\\texttt{<RESTAURANT\\_CUISINE\\_x>}, \\texttt{<HOTEL\\_PRICE\\_x>}, \\texttt{<CITY\\_x\\_NEIGHBORHOOD\\_y>} & \nCombines hotel booking with dining preferences. Includes cuisine-specific searches, budget dining vs. fine dining, dietary needs (gluten-free, halal), special events (anniversaries), restaurant proximity to hotel, and group size considerations. Templates vary in planning flow—some start with hotel, others with restaurants. \\\\\n\\hline\n\n\\end{tabularx}\n\\vspace{1mm}\n\\caption{Multi-domain combinations and scenario requirements. Each configuration builds on the unified instructions with additional placeholder logic and domain-specific complexity.}\n\\label{tab:multi-domain-customization}\n\\end{table}\n\n\\clearpage\n\\section{Example System Prompt: Flight Domain}\n\n\\begin{lstlisting}\nYour task is to generate diverse conversation templates for a flight finder chatbot.\n\nEach template should capture realistic, UNIQUE conversations between a user and an assistant about flight searches.\n\nPlaceholders to Use:\n- Departure city: <CITY_x>\n- Destination arrival city: <CITY_x>\n- Flight class: <CLASS_x>\n- Departure date: <DEPARTURE_DATE_x>\n- Departure time: <DEPARTURE_TIME_x>\n- Destination arrival date: <ARRIVAL_DATE_x>\n- Destination arrival time: <ARRIVAL_TIME_x>\n- Airline name: <AIRLINE_x>\n- Price: <PRICE_x>\n- Airport's name: <AIRPORT_x>\n- Number of travelers: <NUM_TRAVELERS_x>\n\nKey Requirements:\n1. Placeholder Numbering:\n - Use integers for ``x'' (e.g., <CITY_1>, <CITY_2>).\n - Reset numbering to 1 for each new template.\n\n2. Conversation Format & Structure:\n - 6-8 turns minimum per template (3-4 exchanges).\n - Format as alternating assistant: and user: lines.\n - Each template must start with an assistant turn and end with a user turn.\n - Vary conversation lengths-some should be shorter, some longer.\n\n3. Assistant Behavior Diversity:\n - Use distinctly different assistant personalities (formal, casual, verbose, concise).\n - Vary how information is requested (direct questions vs. open-ended).\n - Include templates where the assistant corrects misunderstandings.\n - NOTE: Assistant's first turn must be generic and not assume user intent (e.g., ``How can I help you?\").\n - NOTE: The assistant must not say it has found options for the user.\n\n4. User Behavior Diversity:\n - Create dramatically different user types (impatient, polite, verbose, terse).\n - Include users who provide minimal information (requiring follow-ups).\n - Include users who provide too much information.\n - Include complex, specific requests.\n - Include indecisive users who change their minds.\n\n5. Conversation Flow Diversity:\n - Avoid standard ``greeting -> dates -> preferences -> options'' structure.\n - Include nonlinear conversations (user circles back to earlier topics).\n - Include users asking multiple questions at once.\n - Include conversations skipping obvious steps.\n - Vary order in which info is elicited.\n\n6. Required Scenarios:\n - Multi-city trips\n - Round trip\n - One-way trip\n - Flexible travel dates\n - Users with strong preferences (airline, class, airport, etc.)\n - Non-stop flights only\n - Short layovers are okay\n - Rigid arrival time\n - Special occasions (e.g., anniversaries)\n - Large group bookings\n - Last-minute reservations\n - Business travel\n - Family vacations\n - Solo travelers\n - Budget constraints\n - Weekend getaways\n\n7. Round Trip Requirement:\n - Roundtrip flights must include 2 departure dates.\n\n8. Multiple Travelers:\n - Always include number of travelers via placeholder in multi-person scenarios.\n\n9. Linguistic Diversity:\n - Avoid repeating same greetings across templates.\n - Vary language used for dates, preferences, and constraints.\n - Use different phrasings for similar ideas.\n - Unique transitions between topics.\n - Varied sentence structures and response styles.\n\nImportant: Each template must be fundamentally different in structure, flow, language, and scenario.\n\\end{lstlisting}\n\n\\section{Example System Prompt: Flight-Hotel-Restaurant Domain}\n\n\\begin{lstlisting}\nYour task is to generate diverse conversation templates for a travel assistant chatbot.\n\nEach template should be a realistic and UNIQUE multi-turn interaction between a user and an assistant that includes:\n- Flight search\n- Hotel booking\n- Restaurant recommendations or reservations\n\nPlaceholders to Use:\n- Flight: <CITY_x>, <CLASS_x>, <DEPARTURE_DATE_x>, <DEPARTURE_TIME_x>, <ARRIVAL_DATE_x>, <ARRIVAL_TIME_x>, <AIRLINE_x>, <PRICE_x>, <AIRPORT_x>, <NUM_TRAVELERS_x>\n- Hotel: <HOTEL_x>, <CHECKIN_DATE_x>, <CHECKOUT_DATE_x>, <ROOM_TYPE_x>, <NUM_GUESTS_x>, <PRICE_x>\n- Restaurant: <RESTAURANT_x>, <CUISINE_x>, <RESERVATION_TIME_x>, <RESERVATION_DATE_x>, <PRICE_RANGE_x>, <LOCATION_x>, <NUM_PEOPLE_x>\n\nKey Requirements:\n1. Conversation must include all 3 components: flight, hotel, and restaurant.\n\n2. Placeholder Numbering:\n - Use integers for ``x\" (e.g., <CITY_1>, <CITY_2>).\n - Reset numbering to 1 for each new template.\n\n3. Conversation Format:\n - At least 8 turns (4 full exchanges).\n - Alternating assistant: and user: lines.\n - Assistant always starts the conversation and user always ends it.\n - Vary length and structure across templates.\n\n4. Assistant Behavior Diversity:\n - Include different tones: friendly, formal, efficient, humorous, inquisitive, etc.\n - Vary assistant strategies (confirming info early vs. late, etc.).\n - Assistant cannot confirm bookings or give specific results.\n - Must clarify ambiguous or missing information.\n\n5. User Behavior Diversity:\n - Mix of confident and indecisive users.\n - Include users who change minds mid-conversation.\n - Include users who ask multiple things at once.\n - Include low-information and high-information users.\n\n6. Conversation Flow Diversity:\n - Vary the sequence (flight -> hotel -> restaurant, or restaurant -> hotel -> flight, etc.).\n - Include users jumping between topics or circling back.\n - Include unexpected questions or constraints from users.\n\n7. Required Scenarios:\n - Honeymoon trip\n - Last-minute business travel\n - Budget backpacking vacation\n - Solo food tourism\n - Family summer vacation\n - Couple anniversary trip\n - Conference trip with team\n - Weekend getaway\n - Luxury experience\n - Specific date events (concerts, sports games, etc.)\n\n8. Linguistic Diversity:\n - Avoid repeating phrasing and structures across templates.\n - Vary tone and style significantly.\n - Mix concise and elaborate dialogues.\n\nImportant: Every template must feel distinct from all others in tone, language, structure, and scenario.\n\\end{lstlisting}\n\n\\section{Example System Prompt: Plan Generation Prompt}\n\\label{system-plan-generation-prompt}\n\\begin{lstlisting}\nYou are an expert AI travel planner and your responsibility is to generate Python code using APIs or Tools. \n\\end{lstlisting}\n\n\\section{Example User Prompt: Plan Generation Prompt}\n\\label{user-plan-generation-prompt}\n\\begin{lstlisting}\nYour task is to generate a Python code based on a conversation between the user and the assistant, where the last turn is from the user.\nThe code typically involves calling one or more tools (functions) to help the user in planning their travel request.\nIn the Python code, you need to use the following tools:\n# TOOL CONFIG\n<TOOL_CONFIG>\n# INSTRUCTIONS\n- Track content: Maintain the conversation state across turns and use all known information from earlier in the conversation.\n- As soon as the mandatory parameters (non-optional parameters) are all provided (refer to TOOL CONFIG to find mandatory parameters for each tool), generate the appropriate plan using Python code.\n- Do NOT modify entity values under any circumstances. Use them exactly as they appear in the conversation while populating attributes in the function during code generation.\nFor example, if the city is \"new york\" (lowercase), do not convert it to \"New York\" or \"NYC\".\n- Do not fill optional parameters unless they are explicitly provided in the conversation.\n- When generating seek_information, only mention mandatory parameters (non-optional parameters) that are missing. Never ask for optional parameters using seek_information. Refer to TOOL CONFIG to figure out what the mandatory parameters (non optional parameters) are and check CONVERSATION to know what parameters have been provided by the user. \nFor example, \"seek_information('<explain what mandatory parameters (non-optional parameters) are missing and must be gathered by the assistant>')\"\n- Only generate the code for the domain which the customer has mentioned in the conversation. For example, if user mentioned only about attractions, don't generate the code with restaurants search. Only if the user mentioned searching for restaurant anywhere in the conversation, then only search for restaurants.\n- If a tool result from a previous turn is still valid and relevant, use get_results_from_cache(key=\"<cache_key>\") to retrieve it. Use the cache summary to determine the most appropriate key to select from. If you have many keys in the cache for the same domain. Use the one which would be most relevant.\n- If you generate a tool call and its result could be reused later, save it with save_to_cache(\"<key>\",value). Ensure the cache key is unique and avoid naming collision with previously stored cache key name\n- If a result has already been stored in the cache for a conversation and no new result needs to be generated, do not regenerate the code. Instead, return the code as \"print(\"No planning needed\")\"\n# OUTPUT FORMAT\n- You need to generate the reasoning and the python code. The reasoning should clearly explain the process, steps and the reason behind the python plan that is going to be generated \nThe reasoning should be within the <REASONING> </REASONING> tags and the python code should be within the <CODE> </CODE> tags. Note while generating the python code, never have any markdown tags.\n# EXAMPLES\n<FEW_SHOT_EXAMPLES>\n# CONVERSATION\n<CONVERSATION>\n# CACHE\n<CACHE_FOR_CONVERSATION>\nGiven the provided conversation and cache summary, generate a Python code for the last user turn.\n\\end{lstlisting}\n\n\n\\section{Template: Plan Generation for Fine-Tuning}\n\\begin{lstlisting}\nYou are an expert AI travel planner and your responsibility is to generate Python code using APIs or Tools. \nYour task is to generate a Python code based on a conversation between the user and the assistant, where the last turn is from the user.\nThe code typically involves calling one or more tools (functions) to help the user in planning their travel request.\nIn the Python code, you need to use the following tools:\n# TOOL CONFIG\n<TOOL_CONFIG>\n# INSTRUCTIONS\n- Track content: Maintain the conversation state across turns and use all known information from earlier in the conversation.\n- As soon as the mandatory parameters (non-optional parameters) are all provided (refer to TOOL CONFIG to find mandatory parameters for each tool), generate the appropriate plan using Python code.\n- Do NOT modify entity values under any circumstances. Use them exactly as they appear in the conversation while populating attributes in the function during code generation.\n For example, if the city is \"new york\" (lowercase), do not convert it to \"New York\" or \"NYC\".\n- Do not fill optional parameters unless they are explicitly provided in the conversation.\n- When generating seek_information, only mention mandatory parameters (non-optional parameters) that are missing. Never ask for optional parameters using seek_information. Refer to TOOL CONFIG to figure out what the mandatory parameters (non optional parameters) are and check CONVERSATION to know what parameters have been provided by the user. \n For example, \"seek_information('<explain what mandatory parameters (non-optional parameters) are missing and must be gathered by the assistant>')\"\n- Only generate the code for the domain which the customer has mentioned in the conversation. For example, if user mentioned only about attractions, don't generate the code with restaurants search. Only if the user mentioned searching for restaurant anywhere in the conversation, then only search for restaurants.\n- If a tool result from a previous turn is still valid and relevant, use get_results_from_cache(key=\"<cache_key>\") to retrieve it. Use the cache summary to determine the most appropriate key to select from. If you have many keys in the cache for the same domain. Use the one which would be most relevant.\n- If you generate a tool call and its result could be reused later, save it with save_to_cache(\"<key>\",value). Ensure the cache key is unique and avoid naming collision with previously stored cache key name\n- If a result has already been stored in the cache for a conversation and no new result needs to be generated, do not regenerate the code. Instead, return the code as \"print(\"No planning needed\")\"\n# OUTPUT FORMAT\n- You need to generate only the python code. The python code should be within the <CODE> </CODE> tags. Note while generating the python code, never have any markdown tags.\n# CONVERSATION\n<CONVERSATION>\n# CACHE\n<CACHE_FOR_CONVERSATION>\nGiven the provided conversation and cache summary, generate a Python code for the last user turn.\n\\end{lstlisting}\n\n\\section{Script to replicate SFT} \\label{SFTScript}\n\\begin{lstlisting}\n>> git clone https://github.com/huggingface/trl.git\n>> cd trl\n>> git checkout v0.17.0 # We use the TRL version 0.17.0\n>> pip install -e .\n>> accelerate launch --config_file trl/examples/accelerate_configs/multi_gpu.yaml \\\n trl/scripts/sft.py \\\n --model_name_or_path <YOUR PATH TO A Meta-Llama-3.1-8B-Instruct LOCALLY> \\\n --dataset_name <TRAIN SET OF THE TOOL CALLING IN HUGGINGFACE DATASET FORMAT> \\\n --learning_rate 2.0e-5 \\\n --num_train_epochs 1 \\\n --packing \\\n --per_device_train_batch_size 1 \\\n --gradient_accumulation_steps 8 \\\n --gradient_checkpointing \\\n --logging_steps 25 \\\n --eval_strategy steps \\\n --eval_steps 100 \\\n --use_peft \\\n --lora_r 32 \\\n --lora_alpha 32 \\\n --lora_target_modules q_proj k_proj v_proj o_proj gate_proj up_proj down_proj \\\n --output_dir <SOME OUTPUT DIRECTORY>\\\n --report_to tensorboard \\\n --torch_dtype bfloat16\n\\end{lstlisting}\n\n\n\\end{document}"},"question":{"kind":"string","value":"For the Llama 3.1 8B Instruct model, which multi-domain combination achieves a higher Tool Call F1 score than at least one of the single-domain datasets that constitute it?"},"answer":{"kind":"string","value":"F-H, H-R"},"plan":{"kind":"string","value":"SELECT multi-domain combinations\nLOOP for each multi-domain dataset \n SELECT Tool Call F1 for the Multi-domain Combination\n SELECT single-domain components that constitute the current Combination\n LOOP for each single-domain component\n SELECT Tool Call F1 for the single-domain component\n IF multi-domain Tool Call F1 > single-domain component Tool Call F1 \n COMPUTE add multi-domain combination to result_list \nRETURN result_list\n"}}},{"rowIdx":83,"cells":{"paper":{"kind":"string","value":"2505.23621"},"relevant_tables":{"kind":"list like","value":[["\\begin{table*}[!t]\n","\\label{tab:main-results} \n","\\centering\n","\n","\\setlength{\\tabcolsep}{2pt} \n","\n","\\resizebox{\\textwidth}{!}{\n","\\begin{tabular}{l*{13}{c}}\n","\\toprule\n","\n","\\multirow{3}{*}{\\textbf{Model}} & \\multicolumn{4}{c}{\\textbf{In-domain Performance}} & \\multicolumn{9}{c}{\\textbf{Out-of-domain Performance}} \\\\\n","\\cmidrule(lr){2-5} \\cmidrule(lr){6-14}\n","\n","& \\multicolumn{1}{c}{\\textbf{FF-TQA}} & \\multicolumn{1}{c}{\\textbf{TFV}} & \\multicolumn{2}{c}{\\textbf{TQA}} & \\multicolumn{3}{c}{\\textbf{FF-TQA}} & \\multicolumn{3}{c}{\\textbf{TFV}} & \\multicolumn{3}{c}{\\textbf{TQA}} \\\\\n","\\cmidrule(lr){2-2} \\cmidrule(lr){3-3} \\cmidrule(lr){4-5} \\cmidrule(lr){6-8} \\cmidrule(lr){9-11} \\cmidrule(lr){12-14}\n","\n","& \\multicolumn{1}{c}{FeTaQA} & \\multicolumn{1}{c}{TabFact} & \\multicolumn{1}{c}{WTQ} & \\multicolumn{1}{c}{HiTab} & \\multicolumn{1}{c}{ToTTo} & \\multicolumn{1}{c}{QTSum} & \\multicolumn{1}{c}{R.W.} & \\multicolumn{1}{c}{InfoTabs} & \\multicolumn{1}{c}{PHT} & \\multicolumn{1}{c}{Feverous} & \\multicolumn{1}{c}{TMCQ} & \\multicolumn{1}{c}{TMWP} & \\multicolumn{1}{c}{FinQA} \\\\\n","\\midrule\n","\n","\\multicolumn{14}{c}{\\textit{\\textbf{Proprietary Models}}} \\\\ \n","\\addlinespace[0.5em]\n","GPT-4.1 & 25.1 & 86.5 & 68.0 & 84.7 & 20.4 & 45.7 & 21.0 & 90.5 & 88.2 & 87.7 & 92.0 & 77.0 & 74.0 \\\\\n","GPT-4.1 mini & 27.2 & 84.9 & 69.5 & 80.7 & 18.8 & 46.4 & 20.0 & 88.9 & 86.8 & 86.1 & 92.9 & 86.2 & 71.4 \\\\\n","\\midrule\n","\n","\\multicolumn{14}{c}{\\textit{\\textbf{Open-source Models}}} \\\\\n","\\addlinespace[0.5em]\n","\n","\\multicolumn{14}{l}{\\textit{LLMs}} \\\\\n","\\quad Qwen2.5-7B & 21.0 & 72.2 & 54.8 & 61.8 & 16.0 & 39.5 & 19.3 & 78.6 & 70.7 & 74.6 & 87.4 & 85.0 & 66.4 \\\\\n","\\quad Qwen2.5-32B & 21.9 & 90.3 & 77.3 & 79.4 & 17.8 & 41.4 & \\underline{20.0} & 90.5 & 86.7 & 79.2 & 92.1 & 95.8 & \\underline{77.3} \\\\\n","\\quad DeepSeek-V3 & 24.7 & \\textbf{92.4} & 69.9 & \\underline{82.2} & 19.0 & \\textbf{46.2} & \\textbf{20.9} & \\textbf{91.9} & 86.2 & \\textbf{85.8} & 87.6 & 93.4 & \\textbf{78.6} \\\\\n","\n","\\addlinespace[0.2em]\n","\\cdashlinelr{1-14}\n","\\addlinespace[0.5em]\n","\n","\\multicolumn{14}{l}{\\textit{Reasoning LLMs}} \\\\\n","\\addlinespace[0.2em]\n","\\quad DeepSeek-R1-Distill-7B & 19.1 & 79.6 & 57.8 & 46.2 & 10.7 & 37.2 & 18.0 & 85.7 & 87.1 & 77.5 & 80.9 & 94.0 & 66.8 \\\\\n","\\quad QwQ-32B & 23.8 & \\underline{91.5} & \\textbf{85.4} & 81.6 & 19.4 & 41.9 & 19.6 & \\underline{91.0} & 87.8 & 80.1 & 90.7 & \\textbf{99.4} & 76.2 \\\\\n","\\quad DeepSeek-R1 & 26.2 & 90.8 & 79.6 & \\textbf{82.4} & 18.5 & \\underline{43.8} & 19.9 & 90.4 & 87.5 & 76.0 & \\textbf{93.3} & \\underline{99.0} & 75.8 \\\\\n","\n","\\addlinespace[0.2em]\n","\\cdashlinelr{1-14}\n","\\addlinespace[0.5em]\n","\n","\\multicolumn{14}{l}{\\textit{Table-Specific LLMs}} \\\\\n","\\addlinespace[0.2em]\n","\\quad $^{\\dagger}$TableBenchLLM (7B) & 3.1 & 27.1 & 38.8 & -- & 6.2 & -- & -- & -- & -- & 42.3 & -- & -- & -- \\\\\n","\\quad $^{\\dagger}$TableLLM-13B & 10.8 & 69.0 & 66.3 & 6.3 & 5.4 & -- & -- & -- & -- & 21.5 & -- & -- & -- \\\\\n","\\quad $^{\\dagger}$TableLlama (7B) & \\textbf{39.0} & 82.6 & 35.0 & 64.7 & 20.8 & -- & -- & -- & -- & 73.8 & -- & -- & -- \\\\ \n","\\quad TableGPT2-7B & 29.0 & 77.8 & 61.4 & 70.3 & 14.1 & 39.0 & 19.0 & 85.4 & \\underline{89.1} & 78.0 & 77.2 & 79.7 & 66.4 \\\\ \n","\\quad $^{\\dagger*}$TableGPT2-72B & 32.3 & 85.4 & 71.5 & 75.6 & \\textbf{22.7} & -- & -- & -- & -- & 76.8 & -- & -- & -- \\\\\n","\\midrule\n","\n","\\multicolumn{14}{c}{\\textit{\\textbf{\\ours (Ours)}}} \\\\\n","\\addlinespace[0.5em]\n","\n","\\multicolumn{14}{l}{\\textit{Llama-3.1 Series}} \\\\\n","\\addlinespace[0.2em]\n","\n","\\quad Llama-3.1-8B-Instruct & 21.7 & 74.1 & 52.3 & 58.2 & 16.5 & 31.6 & 18.1 & 84.1 & 82.5 & 78.3 & 49.5 & 72.0 & 57.1 \\\\\n","\\quad Table-R1-SFT & 26.0 & 91.1 & \\underline{83.8} & 81.8 & 13.7 & 36.6 & 16.6 & 89.8 & 85.8 & 79.4 & 90.8 & 89.0 & 64.3 \\\\\n","\\quad Table-R1-Zero & \\underline{32.7} & 87.6 & 81.2 & 81.4 & \\underline{22.3} & 30.2 & 17.7 & 87.9 & \\textbf{91.6} & \\underline{80.2}& 68.6 & 84.6 & 62.3 \\\\ \n","\n","\\addlinespace[0.2em]\n","\\cdashlinelr{1-14}\n","\\addlinespace[0.5em]\n","\\multicolumn{14}{l}{\\textit{Qwen2.5 Series}} \\\\\n","\\addlinespace[0.2em]\n","\n","\\quad Qwen2.5-7B-Instruct & 21.0 & 72.2 & 54.8 & 61.8 & 16.0 & 39.5 & 19.3 & 78.6 & 70.7 & 74.6 & 87.4 & 85.0 & 66.4 \\\\\n","\\quad Table-R1-SFT & 25.3 & 89.9 & 81.9 & 78.3 & 14.1 & 38.8 & 18.8 & 88.8 & 84.6 & 76.0 & 90.9 & 96.6 & 71.7 \\\\\n","\\quad Table-R1-Zero & 30.6 & 87.6 & 79.8 & 78.1 & 19.8 & 43.1 & \\underline{20.0} & 83.7 & 88.0 & 76.2 & \\underline{93.0} & 96.4 & 70.8 \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\caption{Results on 13 table reasoning benchmarks spanning TQA, TFV, and FF-TQA tasks. For TQA, EM accuracy is reported (with ambiguous cases re-evaluated by GPT-4.1 mini); for TFV, classification accuracy; for FF-TQA, BLEU and ROUGE-L. \\textbf{Bold} and \\underline{underlined} scores indicate the top-2 performances among \\emph{open-source} models.\n","$^{\\dagger}$: Due to the context length limitations of most previous table-specific LLMs, it is challenging to conduct a fully fair comparison. Therefore, for these models, we directly use the results as reported in their respective papers, which may be based on sampled or filtered datasets. $^*$: Model weight has not been released.} \n","\\label{tab:main-results}\n","\\end{table*}\n"]],"string":"[\n [\n \"\\\\begin{table*}[!t]\\n\",\n \"\\\\label{tab:main-results} \\n\",\n \"\\\\centering\\n\",\n \"\\n\",\n \"\\\\setlength{\\\\tabcolsep}{2pt} \\n\",\n \"\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \"\\\\begin{tabular}{l*{13}{c}}\\n\",\n \"\\\\toprule\\n\",\n \"\\n\",\n \"\\\\multirow{3}{*}{\\\\textbf{Model}} & \\\\multicolumn{4}{c}{\\\\textbf{In-domain Performance}} & \\\\multicolumn{9}{c}{\\\\textbf{Out-of-domain Performance}} \\\\\\\\\\n\",\n \"\\\\cmidrule(lr){2-5} \\\\cmidrule(lr){6-14}\\n\",\n \"\\n\",\n \"& \\\\multicolumn{1}{c}{\\\\textbf{FF-TQA}} & \\\\multicolumn{1}{c}{\\\\textbf{TFV}} & \\\\multicolumn{2}{c}{\\\\textbf{TQA}} & \\\\multicolumn{3}{c}{\\\\textbf{FF-TQA}} & \\\\multicolumn{3}{c}{\\\\textbf{TFV}} & \\\\multicolumn{3}{c}{\\\\textbf{TQA}} \\\\\\\\\\n\",\n \"\\\\cmidrule(lr){2-2} \\\\cmidrule(lr){3-3} \\\\cmidrule(lr){4-5} \\\\cmidrule(lr){6-8} \\\\cmidrule(lr){9-11} \\\\cmidrule(lr){12-14}\\n\",\n \"\\n\",\n \"& \\\\multicolumn{1}{c}{FeTaQA} & \\\\multicolumn{1}{c}{TabFact} & \\\\multicolumn{1}{c}{WTQ} & \\\\multicolumn{1}{c}{HiTab} & \\\\multicolumn{1}{c}{ToTTo} & \\\\multicolumn{1}{c}{QTSum} & \\\\multicolumn{1}{c}{R.W.} & \\\\multicolumn{1}{c}{InfoTabs} & \\\\multicolumn{1}{c}{PHT} & \\\\multicolumn{1}{c}{Feverous} & \\\\multicolumn{1}{c}{TMCQ} & \\\\multicolumn{1}{c}{TMWP} & \\\\multicolumn{1}{c}{FinQA} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\n\",\n \"\\\\multicolumn{14}{c}{\\\\textit{\\\\textbf{Proprietary Models}}} \\\\\\\\ \\n\",\n \"\\\\addlinespace[0.5em]\\n\",\n \"GPT-4.1 & 25.1 & 86.5 & 68.0 & 84.7 & 20.4 & 45.7 & 21.0 & 90.5 & 88.2 & 87.7 & 92.0 & 77.0 & 74.0 \\\\\\\\\\n\",\n \"GPT-4.1 mini & 27.2 & 84.9 & 69.5 & 80.7 & 18.8 & 46.4 & 20.0 & 88.9 & 86.8 & 86.1 & 92.9 & 86.2 & 71.4 \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\n\",\n \"\\\\multicolumn{14}{c}{\\\\textit{\\\\textbf{Open-source Models}}} \\\\\\\\\\n\",\n \"\\\\addlinespace[0.5em]\\n\",\n \"\\n\",\n \"\\\\multicolumn{14}{l}{\\\\textit{LLMs}} \\\\\\\\\\n\",\n \"\\\\quad Qwen2.5-7B & 21.0 & 72.2 & 54.8 & 61.8 & 16.0 & 39.5 & 19.3 & 78.6 & 70.7 & 74.6 & 87.4 & 85.0 & 66.4 \\\\\\\\\\n\",\n \"\\\\quad Qwen2.5-32B & 21.9 & 90.3 & 77.3 & 79.4 & 17.8 & 41.4 & \\\\underline{20.0} & 90.5 & 86.7 & 79.2 & 92.1 & 95.8 & \\\\underline{77.3} \\\\\\\\\\n\",\n \"\\\\quad DeepSeek-V3 & 24.7 & \\\\textbf{92.4} & 69.9 & \\\\underline{82.2} & 19.0 & \\\\textbf{46.2} & \\\\textbf{20.9} & \\\\textbf{91.9} & 86.2 & \\\\textbf{85.8} & 87.6 & 93.4 & \\\\textbf{78.6} \\\\\\\\\\n\",\n \"\\n\",\n \"\\\\addlinespace[0.2em]\\n\",\n \"\\\\cdashlinelr{1-14}\\n\",\n \"\\\\addlinespace[0.5em]\\n\",\n \"\\n\",\n \"\\\\multicolumn{14}{l}{\\\\textit{Reasoning LLMs}} \\\\\\\\\\n\",\n \"\\\\addlinespace[0.2em]\\n\",\n \"\\\\quad DeepSeek-R1-Distill-7B & 19.1 & 79.6 & 57.8 & 46.2 & 10.7 & 37.2 & 18.0 & 85.7 & 87.1 & 77.5 & 80.9 & 94.0 & 66.8 \\\\\\\\\\n\",\n \"\\\\quad QwQ-32B & 23.8 & \\\\underline{91.5} & \\\\textbf{85.4} & 81.6 & 19.4 & 41.9 & 19.6 & \\\\underline{91.0} & 87.8 & 80.1 & 90.7 & \\\\textbf{99.4} & 76.2 \\\\\\\\\\n\",\n \"\\\\quad DeepSeek-R1 & 26.2 & 90.8 & 79.6 & \\\\textbf{82.4} & 18.5 & \\\\underline{43.8} & 19.9 & 90.4 & 87.5 & 76.0 & \\\\textbf{93.3} & \\\\underline{99.0} & 75.8 \\\\\\\\\\n\",\n \"\\n\",\n \"\\\\addlinespace[0.2em]\\n\",\n \"\\\\cdashlinelr{1-14}\\n\",\n \"\\\\addlinespace[0.5em]\\n\",\n \"\\n\",\n \"\\\\multicolumn{14}{l}{\\\\textit{Table-Specific LLMs}} \\\\\\\\\\n\",\n \"\\\\addlinespace[0.2em]\\n\",\n \"\\\\quad $^{\\\\dagger}$TableBenchLLM (7B) & 3.1 & 27.1 & 38.8 & -- & 6.2 & -- & -- & -- & -- & 42.3 & -- & -- & -- \\\\\\\\\\n\",\n \"\\\\quad $^{\\\\dagger}$TableLLM-13B & 10.8 & 69.0 & 66.3 & 6.3 & 5.4 & -- & -- & -- & -- & 21.5 & -- & -- & -- \\\\\\\\\\n\",\n \"\\\\quad $^{\\\\dagger}$TableLlama (7B) & \\\\textbf{39.0} & 82.6 & 35.0 & 64.7 & 20.8 & -- & -- & -- & -- & 73.8 & -- & -- & -- \\\\\\\\ \\n\",\n \"\\\\quad TableGPT2-7B & 29.0 & 77.8 & 61.4 & 70.3 & 14.1 & 39.0 & 19.0 & 85.4 & \\\\underline{89.1} & 78.0 & 77.2 & 79.7 & 66.4 \\\\\\\\ \\n\",\n \"\\\\quad $^{\\\\dagger*}$TableGPT2-72B & 32.3 & 85.4 & 71.5 & 75.6 & \\\\textbf{22.7} & -- & -- & -- & -- & 76.8 & -- & -- & -- \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\n\",\n \"\\\\multicolumn{14}{c}{\\\\textit{\\\\textbf{\\\\ours (Ours)}}} \\\\\\\\\\n\",\n \"\\\\addlinespace[0.5em]\\n\",\n \"\\n\",\n \"\\\\multicolumn{14}{l}{\\\\textit{Llama-3.1 Series}} \\\\\\\\\\n\",\n \"\\\\addlinespace[0.2em]\\n\",\n \"\\n\",\n \"\\\\quad Llama-3.1-8B-Instruct & 21.7 & 74.1 & 52.3 & 58.2 & 16.5 & 31.6 & 18.1 & 84.1 & 82.5 & 78.3 & 49.5 & 72.0 & 57.1 \\\\\\\\\\n\",\n \"\\\\quad Table-R1-SFT & 26.0 & 91.1 & \\\\underline{83.8} & 81.8 & 13.7 & 36.6 & 16.6 & 89.8 & 85.8 & 79.4 & 90.8 & 89.0 & 64.3 \\\\\\\\\\n\",\n \"\\\\quad Table-R1-Zero & \\\\underline{32.7} & 87.6 & 81.2 & 81.4 & \\\\underline{22.3} & 30.2 & 17.7 & 87.9 & \\\\textbf{91.6} & \\\\underline{80.2}& 68.6 & 84.6 & 62.3 \\\\\\\\ \\n\",\n \"\\n\",\n \"\\\\addlinespace[0.2em]\\n\",\n \"\\\\cdashlinelr{1-14}\\n\",\n \"\\\\addlinespace[0.5em]\\n\",\n \"\\\\multicolumn{14}{l}{\\\\textit{Qwen2.5 Series}} \\\\\\\\\\n\",\n \"\\\\addlinespace[0.2em]\\n\",\n \"\\n\",\n \"\\\\quad Qwen2.5-7B-Instruct & 21.0 & 72.2 & 54.8 & 61.8 & 16.0 & 39.5 & 19.3 & 78.6 & 70.7 & 74.6 & 87.4 & 85.0 & 66.4 \\\\\\\\\\n\",\n \"\\\\quad Table-R1-SFT & 25.3 & 89.9 & 81.9 & 78.3 & 14.1 & 38.8 & 18.8 & 88.8 & 84.6 & 76.0 & 90.9 & 96.6 & 71.7 \\\\\\\\\\n\",\n \"\\\\quad Table-R1-Zero & 30.6 & 87.6 & 79.8 & 78.1 & 19.8 & 43.1 & \\\\underline{20.0} & 83.7 & 88.0 & 76.2 & \\\\underline{93.0} & 96.4 & 70.8 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Results on 13 table reasoning benchmarks spanning TQA, TFV, and FF-TQA tasks. For TQA, EM accuracy is reported (with ambiguous cases re-evaluated by GPT-4.1 mini); for TFV, classification accuracy; for FF-TQA, BLEU and ROUGE-L. \\\\textbf{Bold} and \\\\underline{underlined} scores indicate the top-2 performances among \\\\emph{open-source} models.\\n\",\n \"$^{\\\\dagger}$: Due to the context length limitations of most previous table-specific LLMs, it is challenging to conduct a fully fair comparison. Therefore, for these models, we directly use the results as reported in their respective papers, which may be based on sampled or filtered datasets. $^*$: Model weight has not been released.} \\n\",\n \"\\\\label{tab:main-results}\\n\",\n \"\\\\end{table*}\\n\"\n ]\n]","isConversational":false},"tables":{"kind":"list like","value":[["\\begin{figure}[!t]\n"," \\includegraphics[width=\\columnwidth]{figure/RadarChart.pdf}\n"," \\caption{Overall performance comparison between \\ours and same-scale baselines on various table reasoning benchmarks. Both \\sftmodel and \\rlmodel exhibit substantial performance improvements over baselines, showing the effectiveness of our approach across both in- and out-of-domain benchmarks.}\n"," \\label{fig:experiments}\n","\\end{figure}\n"],["\\begin{figure}[!t]\n"," \\includegraphics[width=\\columnwidth]{figure/Table-R1.pdf}\n"," \\caption{An overview of our research and three research questions investigated in this study.}\n"," \\label{fig:overview}\n","\\end{figure}\\section{Related Work}\n"],["\\begin{table}[!t]\n","\t\\centering\n","\t\\small\n"," \\setlength{\\tabcolsep}{1pt}\n"," \\resizebox{\\linewidth}{!}{\n","\t\\begin{tabular}{llr}\n","\t\t\\toprule\n","\t\t\\textbf{Task} & \\textbf{Dataset} & \\textbf{Samples} \\\\\n","\t\t\\midrule\n","\t\t\\multirow{2}*{Short-form QA (TQA)} \n"," & WTQ~\\cite{pasupat-liang-2015-compositional} & 13,706 \\\\\n"," & HiTab~\\cite{cheng-etal-2022-hitab} & 6,793 \\\\\n","\t\t\\midrule\n","\t Fact Verification (TFV) & TabFact~\\cite{Chen2020TabFact} & 20,740 \\\\\n","\t\t\\midrule\n","\t\tFree-form QA (FF-TQA) & FeTaQA~\\cite{nan-etal-2022-fetaqa} & 7,324 \\\\\n","\t\t\\bottomrule\n","\t\\end{tabular}\n","\t}\n","\t\\caption{Overview of datasets collected in \\ours training data. For each dataset, we sample examples from its training set.}\n","\t\\label{tab:training-data}\n","\\end{table}\n"],["\\begin{table*}[!t]\n","\\label{tab:main-results} \n","\\centering\n","\n","\\setlength{\\tabcolsep}{2pt} \n","\n","\\resizebox{\\textwidth}{!}{\n","\\begin{tabular}{l*{13}{c}}\n","\\toprule\n","\n","\\multirow{3}{*}{\\textbf{Model}} & \\multicolumn{4}{c}{\\textbf{In-domain Performance}} & \\multicolumn{9}{c}{\\textbf{Out-of-domain Performance}} \\\\\n","\\cmidrule(lr){2-5} \\cmidrule(lr){6-14}\n","\n","& \\multicolumn{1}{c}{\\textbf{FF-TQA}} & \\multicolumn{1}{c}{\\textbf{TFV}} & \\multicolumn{2}{c}{\\textbf{TQA}} & \\multicolumn{3}{c}{\\textbf{FF-TQA}} & \\multicolumn{3}{c}{\\textbf{TFV}} & \\multicolumn{3}{c}{\\textbf{TQA}} \\\\\n","\\cmidrule(lr){2-2} \\cmidrule(lr){3-3} \\cmidrule(lr){4-5} \\cmidrule(lr){6-8} \\cmidrule(lr){9-11} \\cmidrule(lr){12-14}\n","\n","& \\multicolumn{1}{c}{FeTaQA} & \\multicolumn{1}{c}{TabFact} & \\multicolumn{1}{c}{WTQ} & \\multicolumn{1}{c}{HiTab} & \\multicolumn{1}{c}{ToTTo} & \\multicolumn{1}{c}{QTSum} & \\multicolumn{1}{c}{R.W.} & \\multicolumn{1}{c}{InfoTabs} & \\multicolumn{1}{c}{PHT} & \\multicolumn{1}{c}{Feverous} & \\multicolumn{1}{c}{TMCQ} & \\multicolumn{1}{c}{TMWP} & \\multicolumn{1}{c}{FinQA} \\\\\n","\\midrule\n","\n","\\multicolumn{14}{c}{\\textit{\\textbf{Proprietary Models}}} \\\\ \n","\\addlinespace[0.5em]\n","GPT-4.1 & 25.1 & 86.5 & 68.0 & 84.7 & 20.4 & 45.7 & 21.0 & 90.5 & 88.2 & 87.7 & 92.0 & 77.0 & 74.0 \\\\\n","GPT-4.1 mini & 27.2 & 84.9 & 69.5 & 80.7 & 18.8 & 46.4 & 20.0 & 88.9 & 86.8 & 86.1 & 92.9 & 86.2 & 71.4 \\\\\n","\\midrule\n","\n","\\multicolumn{14}{c}{\\textit{\\textbf{Open-source Models}}} \\\\\n","\\addlinespace[0.5em]\n","\n","\\multicolumn{14}{l}{\\textit{LLMs}} \\\\\n","\\quad Qwen2.5-7B & 21.0 & 72.2 & 54.8 & 61.8 & 16.0 & 39.5 & 19.3 & 78.6 & 70.7 & 74.6 & 87.4 & 85.0 & 66.4 \\\\\n","\\quad Qwen2.5-32B & 21.9 & 90.3 & 77.3 & 79.4 & 17.8 & 41.4 & \\underline{20.0} & 90.5 & 86.7 & 79.2 & 92.1 & 95.8 & \\underline{77.3} \\\\\n","\\quad DeepSeek-V3 & 24.7 & \\textbf{92.4} & 69.9 & \\underline{82.2} & 19.0 & \\textbf{46.2} & \\textbf{20.9} & \\textbf{91.9} & 86.2 & \\textbf{85.8} & 87.6 & 93.4 & \\textbf{78.6} \\\\\n","\n","\\addlinespace[0.2em]\n","\\cdashlinelr{1-14}\n","\\addlinespace[0.5em]\n","\n","\\multicolumn{14}{l}{\\textit{Reasoning LLMs}} \\\\\n","\\addlinespace[0.2em]\n","\\quad DeepSeek-R1-Distill-7B & 19.1 & 79.6 & 57.8 & 46.2 & 10.7 & 37.2 & 18.0 & 85.7 & 87.1 & 77.5 & 80.9 & 94.0 & 66.8 \\\\\n","\\quad QwQ-32B & 23.8 & \\underline{91.5} & \\textbf{85.4} & 81.6 & 19.4 & 41.9 & 19.6 & \\underline{91.0} & 87.8 & 80.1 & 90.7 & \\textbf{99.4} & 76.2 \\\\\n","\\quad DeepSeek-R1 & 26.2 & 90.8 & 79.6 & \\textbf{82.4} & 18.5 & \\underline{43.8} & 19.9 & 90.4 & 87.5 & 76.0 & \\textbf{93.3} & \\underline{99.0} & 75.8 \\\\\n","\n","\\addlinespace[0.2em]\n","\\cdashlinelr{1-14}\n","\\addlinespace[0.5em]\n","\n","\\multicolumn{14}{l}{\\textit{Table-Specific LLMs}} \\\\\n","\\addlinespace[0.2em]\n","\\quad $^{\\dagger}$TableBenchLLM (7B) & 3.1 & 27.1 & 38.8 & -- & 6.2 & -- & -- & -- & -- & 42.3 & -- & -- & -- \\\\\n","\\quad $^{\\dagger}$TableLLM-13B & 10.8 & 69.0 & 66.3 & 6.3 & 5.4 & -- & -- & -- & -- & 21.5 & -- & -- & -- \\\\\n","\\quad $^{\\dagger}$TableLlama (7B) & \\textbf{39.0} & 82.6 & 35.0 & 64.7 & 20.8 & -- & -- & -- & -- & 73.8 & -- & -- & -- \\\\ \n","\\quad TableGPT2-7B & 29.0 & 77.8 & 61.4 & 70.3 & 14.1 & 39.0 & 19.0 & 85.4 & \\underline{89.1} & 78.0 & 77.2 & 79.7 & 66.4 \\\\ \n","\\quad $^{\\dagger*}$TableGPT2-72B & 32.3 & 85.4 & 71.5 & 75.6 & \\textbf{22.7} & -- & -- & -- & -- & 76.8 & -- & -- & -- \\\\\n","\\midrule\n","\n","\\multicolumn{14}{c}{\\textit{\\textbf{\\ours (Ours)}}} \\\\\n","\\addlinespace[0.5em]\n","\n","\\multicolumn{14}{l}{\\textit{Llama-3.1 Series}} \\\\\n","\\addlinespace[0.2em]\n","\n","\\quad Llama-3.1-8B-Instruct & 21.7 & 74.1 & 52.3 & 58.2 & 16.5 & 31.6 & 18.1 & 84.1 & 82.5 & 78.3 & 49.5 & 72.0 & 57.1 \\\\\n","\\quad Table-R1-SFT & 26.0 & 91.1 & \\underline{83.8} & 81.8 & 13.7 & 36.6 & 16.6 & 89.8 & 85.8 & 79.4 & 90.8 & 89.0 & 64.3 \\\\\n","\\quad Table-R1-Zero & \\underline{32.7} & 87.6 & 81.2 & 81.4 & \\underline{22.3} & 30.2 & 17.7 & 87.9 & \\textbf{91.6} & \\underline{80.2}& 68.6 & 84.6 & 62.3 \\\\ \n","\n","\\addlinespace[0.2em]\n","\\cdashlinelr{1-14}\n","\\addlinespace[0.5em]\n","\\multicolumn{14}{l}{\\textit{Qwen2.5 Series}} \\\\\n","\\addlinespace[0.2em]\n","\n","\\quad Qwen2.5-7B-Instruct & 21.0 & 72.2 & 54.8 & 61.8 & 16.0 & 39.5 & 19.3 & 78.6 & 70.7 & 74.6 & 87.4 & 85.0 & 66.4 \\\\\n","\\quad Table-R1-SFT & 25.3 & 89.9 & 81.9 & 78.3 & 14.1 & 38.8 & 18.8 & 88.8 & 84.6 & 76.0 & 90.9 & 96.6 & 71.7 \\\\\n","\\quad Table-R1-Zero & 30.6 & 87.6 & 79.8 & 78.1 & 19.8 & 43.1 & \\underline{20.0} & 83.7 & 88.0 & 76.2 & \\underline{93.0} & 96.4 & 70.8 \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\caption{Results on 13 table reasoning benchmarks spanning TQA, TFV, and FF-TQA tasks. For TQA, EM accuracy is reported (with ambiguous cases re-evaluated by GPT-4.1 mini); for TFV, classification accuracy; for FF-TQA, BLEU and ROUGE-L. \\textbf{Bold} and \\underline{underlined} scores indicate the top-2 performances among \\emph{open-source} models.\n","$^{\\dagger}$: Due to the context length limitations of most previous table-specific LLMs, it is challenging to conduct a fully fair comparison. Therefore, for these models, we directly use the results as reported in their respective papers, which may be based on sampled or filtered datasets. $^*$: Model weight has not been released.} \n","\\label{tab:main-results}\n","\\end{table*}\n"],["\\begin{figure}[!t]\n"," \\includegraphics[width=\\columnwidth]{figure/model_response_length.pdf}\n"," \\caption{Response length during Table-R1 training across different models.}\n"," \\label{fig:training-dynamics-response}\n","\\end{figure}\\begin{figure*}[!t]\n"],["\\begin{figure}[!t]\n","\\includegraphics[width=0.9\\columnwidth]{figure/pass_at_k_wtq.pdf}\n"," \\includegraphics[width=0.9\\columnwidth]{figure/pass_at_k_hitab.pdf}\n"," \\caption {Pass@k performance on WTQ and HiTab.}\n"," \\label{fig:passk}\n","\\end{figure}\\begin{figure*}[!t]\n"],["\\begin{table*}[!t]\n","\\label{tab:ablation-studies}\n","\\centering\n","\\setlength{\\tabcolsep}{2pt}\n","\n","\\resizebox{\\textwidth}{!}{\n","\\begin{tabular}{l*{13}{c}}\n","\\toprule\n","\\multirow{3}{*}{\\textbf{Model}} & \\multicolumn{4}{c}{\\textbf{In-domain Performance}} & \\multicolumn{9}{c}{\\textbf{Out-of-domain Performance}} \\\\\n","\\cmidrule(lr){2-5} \\cmidrule(lr){6-14}\n","\n","& \\multicolumn{1}{c}{\\textbf{FF-TQA}} & \\multicolumn{1}{c}{\\textbf{TFV}} & \\multicolumn{2}{c}{\\textbf{TQA}} & \\multicolumn{3}{c}{\\textbf{FF-TQA}} & \\multicolumn{3}{c}{\\textbf{TFV}} & \\multicolumn{3}{c}{\\textbf{TQA}} \\\\\n","\\cmidrule(lr){2-2} \\cmidrule(lr){3-3} \\cmidrule(lr){4-5} \\cmidrule(lr){6-8} \\cmidrule(lr){9-11} \\cmidrule(lr){12-14}\n","\n","& \\multicolumn{1}{c}{FeTaQA} & \\multicolumn{1}{c}{TabFact} & \\multicolumn{1}{c}{WTQ} & \\multicolumn{1}{c}{HiTab} & \\multicolumn{1}{c}{ToTTo} & \\multicolumn{1}{c}{QTSum} & \\multicolumn{1}{c}{R.W.} & \\multicolumn{1}{c}{InfoTabs} & \\multicolumn{1}{c}{PHT} & \\multicolumn{1}{c}{Feverous} & \\multicolumn{1}{c}{TMCQ} & \\multicolumn{1}{c}{TMWP} & \\multicolumn{1}{c}{FinQA} \\\\\n","\\midrule\n","\n","\\multicolumn{14}{c}{\\textit{\\textbf{Model-level Ablation}}} \\\\\n","\\addlinespace[0.5em]\n","\n","\\multicolumn{14}{l}{\\textit{Base vs. Instruct}} \\\\\n","\\addlinespace[0.2em]\n","Qwen2.5-7B & 18.2 & 67.7 & 51.0 & 55.2 & 9.4 & 37.8 & 16.4 & 74.5 & 75.0 & 74.1 & 78.7 & 80.3 & 58.4 \\\\\n","Table-R1-Qwen2.5-7B & 29.8 & 87.3 & 78.5 & 75.2 & 15.9 & 38.1 & 18.2 & 87.9 & 91.6 & 78.4 & 84.8 & 94.4 & 61.2 \\\\\n","Qwen2.5-7B-Instruct & 21.0 & 72.2 & 54.8 & 61.8 & 16.0 & 39.5 & 19.3 & 78.6 & 70.7 & 74.6 & 87.4 & 85.0 & 66.4 \\\\\n","Table-R1-Qwen2.5-7B-Instruct & 30.6 & 87.6 & 79.8 & 78.1 & 19.8 & 43.1 & 20.0 & 83.7 & 88.0 & 76.2 & 93.0 & 96.4 & 70.8 \\\\\n","\n","\\addlinespace[0.2em]\n","\\cdashlinelr{1-14}\n","\\addlinespace[0.5em]\n","\n","\\multicolumn{14}{l}{\\textit{Model Architecture Comparison}} \\\\\n","\\addlinespace[0.2em]\n","Llama-3.1-8B & 10.2 & 21.2 & 35.0 & 44.7 & 6.8 & 18.6 & 10.9 & 21.6 & 26.8 & 19.4 & 68.9 & 43.1 & 21.9 \\\\\n","Table-R1-Llama-3.1-8B & 30.4 & 50.3 & 61.5 & 76.3 & 19.8 & 33.5 & 18.0 & 50.0 & 69.7 & 50.6 & 93.1 & 56.8 & 19.3 \\\\\n","Llama-3.1-8B-Instruct & 21.7 & 74.1 & 52.3 & 58.2 & 16.5 & 31.6 & 18.1 & 84.1 & 82.5 & 78.3 & 49.5 & 72.0 & 57.1 \\\\\n","Table-R1-Llama-3.1-8B-Instruct & 32.7 & 87.6 & 81.2 & 81.4 & 22.3 & 30.2 & 17.7 & 87.9 & 91.6 & 80.2 & 68.6 & 84.6 & 62.3 \\\\\n","\n","\\addlinespace[0.2em]\n","\\cdashlinelr{1-14}\n","\\addlinespace[0.5em]\n","\n","\\multicolumn{14}{l}{\\textit{SFT on Domain-Specific Data}} \\\\\n","\\addlinespace[0.2em]\n","DeepSeek-R1-7B & 19.1 & 79.6 & 57.8 & 46.2 & 10.7 & 37.2 & 18.0 & 85.7 & 87.1 & 77.5 & 80.9 & 94.0 & 66.8 \\\\\n","Table-SFT-Qwen2.5-7B & 25.3 & 89.9 & 81.9 & 78.3 & 14.1 & 38.8 & 18.8 & 88.8 & 84.6 & 76.0 & 90.9 & 96.6 & 71.7 \\\\\n","Table-SFT-Llama-3.1-8B & 26.0 & 91.1 & 83.8 & 81.8 & 13.7 & 36.6 & 16.6 & 89.8 & 85.8 & 79.4 & 90.8 & 89.0 & 64.3 \\\\\n","\\midrule\n","\n","\\multicolumn{14}{c}{\\textit{\\textbf{Task-level Ablation}}} \\\\\n","\\addlinespace[0.5em]\n","\\multicolumn{14}{l}{\\textit{Cross-task Generalization}} \\\\\n","\\addlinespace[0.2em]\n","Table-R1-TQA-Qwen2.5-7B & 14.1 & 86.2 & 79.2 & 77.4 & 16.5 & 36.4 & 19.8 & 89.7 & 89.9 & 82.7 & 93.5 & 96.1 & 65.9 \\\\\n","Table-R1-TQA-Llama-3.1-8B & 12.3 & 83.9 & 81.7 & 81.0 & 17.2 & 26.9 & 19.2 & 87.3 & 87.2 & 77.1 & 80.3 & 82.8 & 58.6 \\\\\n","Table-SFT-TQA-Qwen2.5-7B & 13.3 & 87.7 & 79.8 & 77.0 & 13.4 & 27.6 & 18.1 & 88.5 & 87.1 & 78.0 & 90.1 & 95.7 & 69.0 \\\\\n","Table-SFT-TQA-Llama-3.1-8B & 9.6 & 88.8 & 82.4 & 80.5 & 10.3 & 20.6 & 12.6 & 89.1 & 90.4 & 82.1 & 89.9 & 86.9 & 62.9 \\\\\n","\\midrule\n","\n","\\multicolumn{14}{c}{\\textit{\\textbf{Format Ablation}}} \\\\\n","\\addlinespace[0.5em]\n","\\multicolumn{14}{l}{\\textit{Effect of Format Reward}} \\\\\n","\\addlinespace[0.2em]\n","Table-R1-Explicit-Qwen2.5-7B & 29.0 & 85.8 & 76.0 & 73.2 & 13.6 & 37.2 & 14.8 & 87.5 & 88.8 & 81.0 & 52.5 & 91.3 & 49.6 \\\\\n","Table-R1-Qwen2.5-7B & 30.6 & 87.6 & 79.8 & 78.1 & 19.8 & 43.1 & 20.0 & 83.7 & 88.0 & 76.2 & 93.0 & 96.4 & 70.8 \\\\\n","\\addlinespace[0.3em]\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\caption{Ablation study results on model-level, task-level, and formats.}\n","\\end{table*}\\subsection{Qualitative Analysis}\n"],["\\begin{table*}[ht]\n","\\centering\n","\\small\n","\\resizebox{\\textwidth}{!}{\n","\\begin{tabular}{lllll}\n","\\toprule\n","\\textbf{Task Category} & \\textbf{Task Name} & \\textbf{Dataset} & \\textbf{Task Description} & \\textbf{Metric} \\\\\n","\\midrule\n","\\multirow{4}{*}{Training}\n"," & Table QA & WTQ, HiTab & QA over flat or hierarchical tables & Acc. \\\\\n"," & Free-form QA & FeTaQA & Generate long-form answers from tables & BLEU \\\\\n"," & Table Fact Verification & TabFact & Verify factual correctness based on a table & Acc. \\\\\n","\\midrule\n","\\multirow{4}{*}{In-Domain}\n"," & Table QA & WTQ, HiTab & In-domain table QA evaluation & Acc. \\\\\n"," & Table Fact Verification & TabFact & In-domain fact verification evaluation & Acc.\\\\\n"," & Free-form QA & FeTaQA & Evaluate long-form generation quality & BLEU \\\\\n","\\midrule\n","\\multirow{4}{*}{Out-of-Domain}\n"," & Table QA & TabMCQ, TMWP, FinQA & Multiple-choice and word problem QA & Acc. \\\\\n"," & Numerical Reasoning QA & FinQA, TABMWP & Financial and numerical table QA & Acc. \\\\\n"," & Table Fact Verification & InfoTabs, PHT, Feverous & Health and factual judgment from tables & Acc. \\\\\n"," & Free-form QA & ToTTo, Qtsumm, R.W. & Table-to-text generation from highlights & BLEU/R-L \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\caption{Overview of datasets used in training and evaluation.}\n","\\label{tab:our_dataset}\n","\\end{table*}\n"],["\\begin{table*}[h]\n"," \\centering\n"," \\small\n"," \\begin{tabular}{lll}\n"," \\toprule\n"," \\textbf{Model} & \\textbf{Citation} & \\textbf{Version} \\\\ \\midrule\n","\n"," GPT-4.1 & \\citet{openai2024gpt4o} & gpt-4.1-2025-04-14 \\\\\n"," GPT-4.1 mini & \\citet{openai2024gpt4o} & gpt-4.1-mini-2025-04-14 \\\\ \\midrule\n","\n"," Qwen2.5-7B & \\citet{qwen2.5} & \\href{https://huggingface.co/Qwen/Qwen2.5-7B-Instruct}{Qwen/Qwen2.5-7B-Instruct} \\\\\n"," Qwen2.5-14B & \\citet{qwen2.5} & \\href{https://huggingface.co/Qwen/Qwen2.5-14B-Instruct}{Qwen/Qwen2.5-14B-Instruct} \\\\\n"," Qwen2.5-32B & \\citet{qwen2.5} & \\href{https://huggingface.co/Qwen/Qwen2.5-32B-Instruct}{Qwen/Qwen2.5-32B-Instruct} \\\\\n"," QwQ-32B & \\citet{qwq32b} & \\href{https://huggingface.co/Qwen/QwQ-32B}{Qwen/QwQ-32B} \\\\\n","\n"," Llama-3.1-8B & \\citet{touvron2023llama} & \\href{https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct}{meta-llama/Llama-3.1-8B-Instruct} \\\\ \\midrule\n","\n"," DeepSeek-R1-7B & \\citet{guo2025deepseek} & \\href{https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B}{deepseek-ai/DeepSeek-R1-Distill-Qwen-7B} \\\\\n"," DeepSeek-R1-14B & \\citet{guo2025deepseek} & \\href{https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B}{deepseek-ai/DeepSeek-R1-Distill-Qwen-14B} \\\\\n"," DeepSeek-R1-32B & \\citet{guo2025deepseek} & \\href{https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B}{deepseek-ai/DeepSeek-R1-Distill-Qwen-32B} \\\\\n"," DeepSeek-R1 & \\citet{guo2025deepseek} & \\href{hhttps://huggingface.co/deepseek-ai/DeepSeek-R1}{deepseek-ai/DeepSeek-R1} \\\\ \\midrule\n"," TableGPT2-7B & \\citet{su2024tablegpt2} & \\href{https://huggingface.co/tablegpt/TableGPT2-7B}{TableGPT/TableGPT2-7B} \\\\\n"," TableLLM-13B & \\citet{zhang2024tablellm} & \\href{https://huggingface.co/RUCKBReasoning/TableLLM-13b}{RUCKBReasoning/TableLLM-13B} \\\\ \n"," TableLlama-7B & \\citet{zhang2023tablellama} & \\href{https://huggingface.co/osunlp/TableLlama}{osunlp/TableLlama} \\\\ \n"," TableBenchLLM & \\citet{zhang2024tablellm} & \\href{https://huggingface.co/Multilingual-Multimodal-NLP/TableLLM-Llama3.1-8B}{Multilingual-Multimodal-NLP/TableLLM-Llama3.1-8B} \\\\ \n"," \n"," \\bottomrule\n"," \\end{tabular}\n"," \\caption{Model List.}\n"," \\label{tab:model_list}\n","\\end{table*}\n"],["\\begin{figure*}[h]\n"," \\includegraphics[width=\\linewidth]{figure/example1.pdf}\n"," \\caption{Qualitative Analysis Case Example for TQA tasks}\n","\\end{figure*}\n"],["\\begin{figure}[h]\n"," \\includegraphics[width=\\linewidth]{figure/example2.pdf}\n"," \\caption{Qualitative Analysis Case Example for TFV tasks}\n","\\end{figure}\n"]],"string":"[\n [\n \"\\\\begin{figure}[!t]\\n\",\n \" \\\\includegraphics[width=\\\\columnwidth]{figure/RadarChart.pdf}\\n\",\n \" \\\\caption{Overall performance comparison between \\\\ours and same-scale baselines on various table reasoning benchmarks. Both \\\\sftmodel and \\\\rlmodel exhibit substantial performance improvements over baselines, showing the effectiveness of our approach across both in- and out-of-domain benchmarks.}\\n\",\n \" \\\\label{fig:experiments}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{figure}[!t]\\n\",\n \" \\\\includegraphics[width=\\\\columnwidth]{figure/Table-R1.pdf}\\n\",\n \" \\\\caption{An overview of our research and three research questions investigated in this study.}\\n\",\n \" \\\\label{fig:overview}\\n\",\n \"\\\\end{figure}\\\\section{Related Work}\\n\"\n ],\n [\n \"\\\\begin{table}[!t]\\n\",\n \"\\t\\\\centering\\n\",\n \"\\t\\\\small\\n\",\n \" \\\\setlength{\\\\tabcolsep}{1pt}\\n\",\n \" \\\\resizebox{\\\\linewidth}{!}{\\n\",\n \"\\t\\\\begin{tabular}{llr}\\n\",\n \"\\t\\t\\\\toprule\\n\",\n \"\\t\\t\\\\textbf{Task} & \\\\textbf{Dataset} & \\\\textbf{Samples} \\\\\\\\\\n\",\n \"\\t\\t\\\\midrule\\n\",\n \"\\t\\t\\\\multirow{2}*{Short-form QA (TQA)} \\n\",\n \" & WTQ~\\\\cite{pasupat-liang-2015-compositional} & 13,706 \\\\\\\\\\n\",\n \" & HiTab~\\\\cite{cheng-etal-2022-hitab} & 6,793 \\\\\\\\\\n\",\n \"\\t\\t\\\\midrule\\n\",\n \"\\t Fact Verification (TFV) & TabFact~\\\\cite{Chen2020TabFact} & 20,740 \\\\\\\\\\n\",\n \"\\t\\t\\\\midrule\\n\",\n \"\\t\\tFree-form QA (FF-TQA) & FeTaQA~\\\\cite{nan-etal-2022-fetaqa} & 7,324 \\\\\\\\\\n\",\n \"\\t\\t\\\\bottomrule\\n\",\n \"\\t\\\\end{tabular}\\n\",\n \"\\t}\\n\",\n \"\\t\\\\caption{Overview of datasets collected in \\\\ours training data. For each dataset, we sample examples from its training set.}\\n\",\n \"\\t\\\\label{tab:training-data}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table*}[!t]\\n\",\n \"\\\\label{tab:main-results} \\n\",\n \"\\\\centering\\n\",\n \"\\n\",\n \"\\\\setlength{\\\\tabcolsep}{2pt} \\n\",\n \"\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \"\\\\begin{tabular}{l*{13}{c}}\\n\",\n \"\\\\toprule\\n\",\n \"\\n\",\n \"\\\\multirow{3}{*}{\\\\textbf{Model}} & \\\\multicolumn{4}{c}{\\\\textbf{In-domain Performance}} & \\\\multicolumn{9}{c}{\\\\textbf{Out-of-domain Performance}} \\\\\\\\\\n\",\n \"\\\\cmidrule(lr){2-5} \\\\cmidrule(lr){6-14}\\n\",\n \"\\n\",\n \"& \\\\multicolumn{1}{c}{\\\\textbf{FF-TQA}} & \\\\multicolumn{1}{c}{\\\\textbf{TFV}} & \\\\multicolumn{2}{c}{\\\\textbf{TQA}} & \\\\multicolumn{3}{c}{\\\\textbf{FF-TQA}} & \\\\multicolumn{3}{c}{\\\\textbf{TFV}} & \\\\multicolumn{3}{c}{\\\\textbf{TQA}} \\\\\\\\\\n\",\n \"\\\\cmidrule(lr){2-2} \\\\cmidrule(lr){3-3} \\\\cmidrule(lr){4-5} \\\\cmidrule(lr){6-8} \\\\cmidrule(lr){9-11} \\\\cmidrule(lr){12-14}\\n\",\n \"\\n\",\n \"& \\\\multicolumn{1}{c}{FeTaQA} & \\\\multicolumn{1}{c}{TabFact} & \\\\multicolumn{1}{c}{WTQ} & \\\\multicolumn{1}{c}{HiTab} & \\\\multicolumn{1}{c}{ToTTo} & \\\\multicolumn{1}{c}{QTSum} & \\\\multicolumn{1}{c}{R.W.} & \\\\multicolumn{1}{c}{InfoTabs} & \\\\multicolumn{1}{c}{PHT} & \\\\multicolumn{1}{c}{Feverous} & \\\\multicolumn{1}{c}{TMCQ} & \\\\multicolumn{1}{c}{TMWP} & \\\\multicolumn{1}{c}{FinQA} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\n\",\n \"\\\\multicolumn{14}{c}{\\\\textit{\\\\textbf{Proprietary Models}}} \\\\\\\\ \\n\",\n \"\\\\addlinespace[0.5em]\\n\",\n \"GPT-4.1 & 25.1 & 86.5 & 68.0 & 84.7 & 20.4 & 45.7 & 21.0 & 90.5 & 88.2 & 87.7 & 92.0 & 77.0 & 74.0 \\\\\\\\\\n\",\n \"GPT-4.1 mini & 27.2 & 84.9 & 69.5 & 80.7 & 18.8 & 46.4 & 20.0 & 88.9 & 86.8 & 86.1 & 92.9 & 86.2 & 71.4 \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\n\",\n \"\\\\multicolumn{14}{c}{\\\\textit{\\\\textbf{Open-source Models}}} \\\\\\\\\\n\",\n \"\\\\addlinespace[0.5em]\\n\",\n \"\\n\",\n \"\\\\multicolumn{14}{l}{\\\\textit{LLMs}} \\\\\\\\\\n\",\n \"\\\\quad Qwen2.5-7B & 21.0 & 72.2 & 54.8 & 61.8 & 16.0 & 39.5 & 19.3 & 78.6 & 70.7 & 74.6 & 87.4 & 85.0 & 66.4 \\\\\\\\\\n\",\n \"\\\\quad Qwen2.5-32B & 21.9 & 90.3 & 77.3 & 79.4 & 17.8 & 41.4 & \\\\underline{20.0} & 90.5 & 86.7 & 79.2 & 92.1 & 95.8 & \\\\underline{77.3} \\\\\\\\\\n\",\n \"\\\\quad DeepSeek-V3 & 24.7 & \\\\textbf{92.4} & 69.9 & \\\\underline{82.2} & 19.0 & \\\\textbf{46.2} & \\\\textbf{20.9} & \\\\textbf{91.9} & 86.2 & \\\\textbf{85.8} & 87.6 & 93.4 & \\\\textbf{78.6} \\\\\\\\\\n\",\n \"\\n\",\n \"\\\\addlinespace[0.2em]\\n\",\n \"\\\\cdashlinelr{1-14}\\n\",\n \"\\\\addlinespace[0.5em]\\n\",\n \"\\n\",\n \"\\\\multicolumn{14}{l}{\\\\textit{Reasoning LLMs}} \\\\\\\\\\n\",\n \"\\\\addlinespace[0.2em]\\n\",\n \"\\\\quad DeepSeek-R1-Distill-7B & 19.1 & 79.6 & 57.8 & 46.2 & 10.7 & 37.2 & 18.0 & 85.7 & 87.1 & 77.5 & 80.9 & 94.0 & 66.8 \\\\\\\\\\n\",\n \"\\\\quad QwQ-32B & 23.8 & \\\\underline{91.5} & \\\\textbf{85.4} & 81.6 & 19.4 & 41.9 & 19.6 & \\\\underline{91.0} & 87.8 & 80.1 & 90.7 & \\\\textbf{99.4} & 76.2 \\\\\\\\\\n\",\n \"\\\\quad DeepSeek-R1 & 26.2 & 90.8 & 79.6 & \\\\textbf{82.4} & 18.5 & \\\\underline{43.8} & 19.9 & 90.4 & 87.5 & 76.0 & \\\\textbf{93.3} & \\\\underline{99.0} & 75.8 \\\\\\\\\\n\",\n \"\\n\",\n \"\\\\addlinespace[0.2em]\\n\",\n \"\\\\cdashlinelr{1-14}\\n\",\n \"\\\\addlinespace[0.5em]\\n\",\n \"\\n\",\n \"\\\\multicolumn{14}{l}{\\\\textit{Table-Specific LLMs}} \\\\\\\\\\n\",\n \"\\\\addlinespace[0.2em]\\n\",\n \"\\\\quad $^{\\\\dagger}$TableBenchLLM (7B) & 3.1 & 27.1 & 38.8 & -- & 6.2 & -- & -- & -- & -- & 42.3 & -- & -- & -- \\\\\\\\\\n\",\n \"\\\\quad $^{\\\\dagger}$TableLLM-13B & 10.8 & 69.0 & 66.3 & 6.3 & 5.4 & -- & -- & -- & -- & 21.5 & -- & -- & -- \\\\\\\\\\n\",\n \"\\\\quad $^{\\\\dagger}$TableLlama (7B) & \\\\textbf{39.0} & 82.6 & 35.0 & 64.7 & 20.8 & -- & -- & -- & -- & 73.8 & -- & -- & -- \\\\\\\\ \\n\",\n \"\\\\quad TableGPT2-7B & 29.0 & 77.8 & 61.4 & 70.3 & 14.1 & 39.0 & 19.0 & 85.4 & \\\\underline{89.1} & 78.0 & 77.2 & 79.7 & 66.4 \\\\\\\\ \\n\",\n \"\\\\quad $^{\\\\dagger*}$TableGPT2-72B & 32.3 & 85.4 & 71.5 & 75.6 & \\\\textbf{22.7} & -- & -- & -- & -- & 76.8 & -- & -- & -- \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\n\",\n \"\\\\multicolumn{14}{c}{\\\\textit{\\\\textbf{\\\\ours (Ours)}}} \\\\\\\\\\n\",\n \"\\\\addlinespace[0.5em]\\n\",\n \"\\n\",\n \"\\\\multicolumn{14}{l}{\\\\textit{Llama-3.1 Series}} \\\\\\\\\\n\",\n \"\\\\addlinespace[0.2em]\\n\",\n \"\\n\",\n \"\\\\quad Llama-3.1-8B-Instruct & 21.7 & 74.1 & 52.3 & 58.2 & 16.5 & 31.6 & 18.1 & 84.1 & 82.5 & 78.3 & 49.5 & 72.0 & 57.1 \\\\\\\\\\n\",\n \"\\\\quad Table-R1-SFT & 26.0 & 91.1 & \\\\underline{83.8} & 81.8 & 13.7 & 36.6 & 16.6 & 89.8 & 85.8 & 79.4 & 90.8 & 89.0 & 64.3 \\\\\\\\\\n\",\n \"\\\\quad Table-R1-Zero & \\\\underline{32.7} & 87.6 & 81.2 & 81.4 & \\\\underline{22.3} & 30.2 & 17.7 & 87.9 & \\\\textbf{91.6} & \\\\underline{80.2}& 68.6 & 84.6 & 62.3 \\\\\\\\ \\n\",\n \"\\n\",\n \"\\\\addlinespace[0.2em]\\n\",\n \"\\\\cdashlinelr{1-14}\\n\",\n \"\\\\addlinespace[0.5em]\\n\",\n \"\\\\multicolumn{14}{l}{\\\\textit{Qwen2.5 Series}} \\\\\\\\\\n\",\n \"\\\\addlinespace[0.2em]\\n\",\n \"\\n\",\n \"\\\\quad Qwen2.5-7B-Instruct & 21.0 & 72.2 & 54.8 & 61.8 & 16.0 & 39.5 & 19.3 & 78.6 & 70.7 & 74.6 & 87.4 & 85.0 & 66.4 \\\\\\\\\\n\",\n \"\\\\quad Table-R1-SFT & 25.3 & 89.9 & 81.9 & 78.3 & 14.1 & 38.8 & 18.8 & 88.8 & 84.6 & 76.0 & 90.9 & 96.6 & 71.7 \\\\\\\\\\n\",\n \"\\\\quad Table-R1-Zero & 30.6 & 87.6 & 79.8 & 78.1 & 19.8 & 43.1 & \\\\underline{20.0} & 83.7 & 88.0 & 76.2 & \\\\underline{93.0} & 96.4 & 70.8 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Results on 13 table reasoning benchmarks spanning TQA, TFV, and FF-TQA tasks. For TQA, EM accuracy is reported (with ambiguous cases re-evaluated by GPT-4.1 mini); for TFV, classification accuracy; for FF-TQA, BLEU and ROUGE-L. \\\\textbf{Bold} and \\\\underline{underlined} scores indicate the top-2 performances among \\\\emph{open-source} models.\\n\",\n \"$^{\\\\dagger}$: Due to the context length limitations of most previous table-specific LLMs, it is challenging to conduct a fully fair comparison. Therefore, for these models, we directly use the results as reported in their respective papers, which may be based on sampled or filtered datasets. $^*$: Model weight has not been released.} \\n\",\n \"\\\\label{tab:main-results}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{figure}[!t]\\n\",\n \" \\\\includegraphics[width=\\\\columnwidth]{figure/model_response_length.pdf}\\n\",\n \" \\\\caption{Response length during Table-R1 training across different models.}\\n\",\n \" \\\\label{fig:training-dynamics-response}\\n\",\n \"\\\\end{figure}\\\\begin{figure*}[!t]\\n\"\n ],\n [\n \"\\\\begin{figure}[!t]\\n\",\n \"\\\\includegraphics[width=0.9\\\\columnwidth]{figure/pass_at_k_wtq.pdf}\\n\",\n \" \\\\includegraphics[width=0.9\\\\columnwidth]{figure/pass_at_k_hitab.pdf}\\n\",\n \" \\\\caption {Pass@k performance on WTQ and HiTab.}\\n\",\n \" \\\\label{fig:passk}\\n\",\n \"\\\\end{figure}\\\\begin{figure*}[!t]\\n\"\n ],\n [\n \"\\\\begin{table*}[!t]\\n\",\n \"\\\\label{tab:ablation-studies}\\n\",\n \"\\\\centering\\n\",\n \"\\\\setlength{\\\\tabcolsep}{2pt}\\n\",\n \"\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \"\\\\begin{tabular}{l*{13}{c}}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{3}{*}{\\\\textbf{Model}} & \\\\multicolumn{4}{c}{\\\\textbf{In-domain Performance}} & \\\\multicolumn{9}{c}{\\\\textbf{Out-of-domain Performance}} \\\\\\\\\\n\",\n \"\\\\cmidrule(lr){2-5} \\\\cmidrule(lr){6-14}\\n\",\n \"\\n\",\n \"& \\\\multicolumn{1}{c}{\\\\textbf{FF-TQA}} & \\\\multicolumn{1}{c}{\\\\textbf{TFV}} & \\\\multicolumn{2}{c}{\\\\textbf{TQA}} & \\\\multicolumn{3}{c}{\\\\textbf{FF-TQA}} & \\\\multicolumn{3}{c}{\\\\textbf{TFV}} & \\\\multicolumn{3}{c}{\\\\textbf{TQA}} \\\\\\\\\\n\",\n \"\\\\cmidrule(lr){2-2} \\\\cmidrule(lr){3-3} \\\\cmidrule(lr){4-5} \\\\cmidrule(lr){6-8} \\\\cmidrule(lr){9-11} \\\\cmidrule(lr){12-14}\\n\",\n \"\\n\",\n \"& \\\\multicolumn{1}{c}{FeTaQA} & \\\\multicolumn{1}{c}{TabFact} & \\\\multicolumn{1}{c}{WTQ} & \\\\multicolumn{1}{c}{HiTab} & \\\\multicolumn{1}{c}{ToTTo} & \\\\multicolumn{1}{c}{QTSum} & \\\\multicolumn{1}{c}{R.W.} & \\\\multicolumn{1}{c}{InfoTabs} & \\\\multicolumn{1}{c}{PHT} & \\\\multicolumn{1}{c}{Feverous} & \\\\multicolumn{1}{c}{TMCQ} & \\\\multicolumn{1}{c}{TMWP} & \\\\multicolumn{1}{c}{FinQA} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\n\",\n \"\\\\multicolumn{14}{c}{\\\\textit{\\\\textbf{Model-level Ablation}}} \\\\\\\\\\n\",\n \"\\\\addlinespace[0.5em]\\n\",\n \"\\n\",\n \"\\\\multicolumn{14}{l}{\\\\textit{Base vs. Instruct}} \\\\\\\\\\n\",\n \"\\\\addlinespace[0.2em]\\n\",\n \"Qwen2.5-7B & 18.2 & 67.7 & 51.0 & 55.2 & 9.4 & 37.8 & 16.4 & 74.5 & 75.0 & 74.1 & 78.7 & 80.3 & 58.4 \\\\\\\\\\n\",\n \"Table-R1-Qwen2.5-7B & 29.8 & 87.3 & 78.5 & 75.2 & 15.9 & 38.1 & 18.2 & 87.9 & 91.6 & 78.4 & 84.8 & 94.4 & 61.2 \\\\\\\\\\n\",\n \"Qwen2.5-7B-Instruct & 21.0 & 72.2 & 54.8 & 61.8 & 16.0 & 39.5 & 19.3 & 78.6 & 70.7 & 74.6 & 87.4 & 85.0 & 66.4 \\\\\\\\\\n\",\n \"Table-R1-Qwen2.5-7B-Instruct & 30.6 & 87.6 & 79.8 & 78.1 & 19.8 & 43.1 & 20.0 & 83.7 & 88.0 & 76.2 & 93.0 & 96.4 & 70.8 \\\\\\\\\\n\",\n \"\\n\",\n \"\\\\addlinespace[0.2em]\\n\",\n \"\\\\cdashlinelr{1-14}\\n\",\n \"\\\\addlinespace[0.5em]\\n\",\n \"\\n\",\n \"\\\\multicolumn{14}{l}{\\\\textit{Model Architecture Comparison}} \\\\\\\\\\n\",\n \"\\\\addlinespace[0.2em]\\n\",\n \"Llama-3.1-8B & 10.2 & 21.2 & 35.0 & 44.7 & 6.8 & 18.6 & 10.9 & 21.6 & 26.8 & 19.4 & 68.9 & 43.1 & 21.9 \\\\\\\\\\n\",\n \"Table-R1-Llama-3.1-8B & 30.4 & 50.3 & 61.5 & 76.3 & 19.8 & 33.5 & 18.0 & 50.0 & 69.7 & 50.6 & 93.1 & 56.8 & 19.3 \\\\\\\\\\n\",\n \"Llama-3.1-8B-Instruct & 21.7 & 74.1 & 52.3 & 58.2 & 16.5 & 31.6 & 18.1 & 84.1 & 82.5 & 78.3 & 49.5 & 72.0 & 57.1 \\\\\\\\\\n\",\n \"Table-R1-Llama-3.1-8B-Instruct & 32.7 & 87.6 & 81.2 & 81.4 & 22.3 & 30.2 & 17.7 & 87.9 & 91.6 & 80.2 & 68.6 & 84.6 & 62.3 \\\\\\\\\\n\",\n \"\\n\",\n \"\\\\addlinespace[0.2em]\\n\",\n \"\\\\cdashlinelr{1-14}\\n\",\n \"\\\\addlinespace[0.5em]\\n\",\n \"\\n\",\n \"\\\\multicolumn{14}{l}{\\\\textit{SFT on Domain-Specific Data}} \\\\\\\\\\n\",\n \"\\\\addlinespace[0.2em]\\n\",\n \"DeepSeek-R1-7B & 19.1 & 79.6 & 57.8 & 46.2 & 10.7 & 37.2 & 18.0 & 85.7 & 87.1 & 77.5 & 80.9 & 94.0 & 66.8 \\\\\\\\\\n\",\n \"Table-SFT-Qwen2.5-7B & 25.3 & 89.9 & 81.9 & 78.3 & 14.1 & 38.8 & 18.8 & 88.8 & 84.6 & 76.0 & 90.9 & 96.6 & 71.7 \\\\\\\\\\n\",\n \"Table-SFT-Llama-3.1-8B & 26.0 & 91.1 & 83.8 & 81.8 & 13.7 & 36.6 & 16.6 & 89.8 & 85.8 & 79.4 & 90.8 & 89.0 & 64.3 \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\n\",\n \"\\\\multicolumn{14}{c}{\\\\textit{\\\\textbf{Task-level Ablation}}} \\\\\\\\\\n\",\n \"\\\\addlinespace[0.5em]\\n\",\n \"\\\\multicolumn{14}{l}{\\\\textit{Cross-task Generalization}} \\\\\\\\\\n\",\n \"\\\\addlinespace[0.2em]\\n\",\n \"Table-R1-TQA-Qwen2.5-7B & 14.1 & 86.2 & 79.2 & 77.4 & 16.5 & 36.4 & 19.8 & 89.7 & 89.9 & 82.7 & 93.5 & 96.1 & 65.9 \\\\\\\\\\n\",\n \"Table-R1-TQA-Llama-3.1-8B & 12.3 & 83.9 & 81.7 & 81.0 & 17.2 & 26.9 & 19.2 & 87.3 & 87.2 & 77.1 & 80.3 & 82.8 & 58.6 \\\\\\\\\\n\",\n \"Table-SFT-TQA-Qwen2.5-7B & 13.3 & 87.7 & 79.8 & 77.0 & 13.4 & 27.6 & 18.1 & 88.5 & 87.1 & 78.0 & 90.1 & 95.7 & 69.0 \\\\\\\\\\n\",\n \"Table-SFT-TQA-Llama-3.1-8B & 9.6 & 88.8 & 82.4 & 80.5 & 10.3 & 20.6 & 12.6 & 89.1 & 90.4 & 82.1 & 89.9 & 86.9 & 62.9 \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\n\",\n \"\\\\multicolumn{14}{c}{\\\\textit{\\\\textbf{Format Ablation}}} \\\\\\\\\\n\",\n \"\\\\addlinespace[0.5em]\\n\",\n \"\\\\multicolumn{14}{l}{\\\\textit{Effect of Format Reward}} \\\\\\\\\\n\",\n \"\\\\addlinespace[0.2em]\\n\",\n \"Table-R1-Explicit-Qwen2.5-7B & 29.0 & 85.8 & 76.0 & 73.2 & 13.6 & 37.2 & 14.8 & 87.5 & 88.8 & 81.0 & 52.5 & 91.3 & 49.6 \\\\\\\\\\n\",\n \"Table-R1-Qwen2.5-7B & 30.6 & 87.6 & 79.8 & 78.1 & 19.8 & 43.1 & 20.0 & 83.7 & 88.0 & 76.2 & 93.0 & 96.4 & 70.8 \\\\\\\\\\n\",\n \"\\\\addlinespace[0.3em]\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Ablation study results on model-level, task-level, and formats.}\\n\",\n \"\\\\end{table*}\\\\subsection{Qualitative Analysis}\\n\"\n ],\n [\n \"\\\\begin{table*}[ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\small\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \"\\\\begin{tabular}{lllll}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Task Category} & \\\\textbf{Task Name} & \\\\textbf{Dataset} & \\\\textbf{Task Description} & \\\\textbf{Metric} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Training}\\n\",\n \" & Table QA & WTQ, HiTab & QA over flat or hierarchical tables & Acc. \\\\\\\\\\n\",\n \" & Free-form QA & FeTaQA & Generate long-form answers from tables & BLEU \\\\\\\\\\n\",\n \" & Table Fact Verification & TabFact & Verify factual correctness based on a table & Acc. \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multirow{4}{*}{In-Domain}\\n\",\n \" & Table QA & WTQ, HiTab & In-domain table QA evaluation & Acc. \\\\\\\\\\n\",\n \" & Table Fact Verification & TabFact & In-domain fact verification evaluation & Acc.\\\\\\\\\\n\",\n \" & Free-form QA & FeTaQA & Evaluate long-form generation quality & BLEU \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Out-of-Domain}\\n\",\n \" & Table QA & TabMCQ, TMWP, FinQA & Multiple-choice and word problem QA & Acc. \\\\\\\\\\n\",\n \" & Numerical Reasoning QA & FinQA, TABMWP & Financial and numerical table QA & Acc. \\\\\\\\\\n\",\n \" & Table Fact Verification & InfoTabs, PHT, Feverous & Health and factual judgment from tables & Acc. \\\\\\\\\\n\",\n \" & Free-form QA & ToTTo, Qtsumm, R.W. & Table-to-text generation from highlights & BLEU/R-L \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Overview of datasets used in training and evaluation.}\\n\",\n \"\\\\label{tab:our_dataset}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[h]\\n\",\n \" \\\\centering\\n\",\n \" \\\\small\\n\",\n \" \\\\begin{tabular}{lll}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Model} & \\\\textbf{Citation} & \\\\textbf{Version} \\\\\\\\ \\\\midrule\\n\",\n \"\\n\",\n \" GPT-4.1 & \\\\citet{openai2024gpt4o} & gpt-4.1-2025-04-14 \\\\\\\\\\n\",\n \" GPT-4.1 mini & \\\\citet{openai2024gpt4o} & gpt-4.1-mini-2025-04-14 \\\\\\\\ \\\\midrule\\n\",\n \"\\n\",\n \" Qwen2.5-7B & \\\\citet{qwen2.5} & \\\\href{https://huggingface.co/Qwen/Qwen2.5-7B-Instruct}{Qwen/Qwen2.5-7B-Instruct} \\\\\\\\\\n\",\n \" Qwen2.5-14B & \\\\citet{qwen2.5} & \\\\href{https://huggingface.co/Qwen/Qwen2.5-14B-Instruct}{Qwen/Qwen2.5-14B-Instruct} \\\\\\\\\\n\",\n \" Qwen2.5-32B & \\\\citet{qwen2.5} & \\\\href{https://huggingface.co/Qwen/Qwen2.5-32B-Instruct}{Qwen/Qwen2.5-32B-Instruct} \\\\\\\\\\n\",\n \" QwQ-32B & \\\\citet{qwq32b} & \\\\href{https://huggingface.co/Qwen/QwQ-32B}{Qwen/QwQ-32B} \\\\\\\\\\n\",\n \"\\n\",\n \" Llama-3.1-8B & \\\\citet{touvron2023llama} & \\\\href{https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct}{meta-llama/Llama-3.1-8B-Instruct} \\\\\\\\ \\\\midrule\\n\",\n \"\\n\",\n \" DeepSeek-R1-7B & \\\\citet{guo2025deepseek} & \\\\href{https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B}{deepseek-ai/DeepSeek-R1-Distill-Qwen-7B} \\\\\\\\\\n\",\n \" DeepSeek-R1-14B & \\\\citet{guo2025deepseek} & \\\\href{https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B}{deepseek-ai/DeepSeek-R1-Distill-Qwen-14B} \\\\\\\\\\n\",\n \" DeepSeek-R1-32B & \\\\citet{guo2025deepseek} & \\\\href{https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B}{deepseek-ai/DeepSeek-R1-Distill-Qwen-32B} \\\\\\\\\\n\",\n \" DeepSeek-R1 & \\\\citet{guo2025deepseek} & \\\\href{hhttps://huggingface.co/deepseek-ai/DeepSeek-R1}{deepseek-ai/DeepSeek-R1} \\\\\\\\ \\\\midrule\\n\",\n \" TableGPT2-7B & \\\\citet{su2024tablegpt2} & \\\\href{https://huggingface.co/tablegpt/TableGPT2-7B}{TableGPT/TableGPT2-7B} \\\\\\\\\\n\",\n \" TableLLM-13B & \\\\citet{zhang2024tablellm} & \\\\href{https://huggingface.co/RUCKBReasoning/TableLLM-13b}{RUCKBReasoning/TableLLM-13B} \\\\\\\\ \\n\",\n \" TableLlama-7B & \\\\citet{zhang2023tablellama} & \\\\href{https://huggingface.co/osunlp/TableLlama}{osunlp/TableLlama} \\\\\\\\ \\n\",\n \" TableBenchLLM & \\\\citet{zhang2024tablellm} & \\\\href{https://huggingface.co/Multilingual-Multimodal-NLP/TableLLM-Llama3.1-8B}{Multilingual-Multimodal-NLP/TableLLM-Llama3.1-8B} \\\\\\\\ \\n\",\n \" \\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" \\\\caption{Model List.}\\n\",\n \" \\\\label{tab:model_list}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[h]\\n\",\n \" \\\\includegraphics[width=\\\\linewidth]{figure/example1.pdf}\\n\",\n \" \\\\caption{Qualitative Analysis Case Example for TQA tasks}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{figure}[h]\\n\",\n \" \\\\includegraphics[width=\\\\linewidth]{figure/example2.pdf}\\n\",\n \" \\\\caption{Qualitative Analysis Case Example for TFV tasks}\\n\",\n \"\\\\end{figure}\\n\"\n ]\n]","isConversational":false},"fulltext":{"kind":"string","value":"% This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.\n\\pdfoutput=1\n% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.\n\n\\documentclass[11pt]{article}\n\n% Remove the \"review\" option to generate the final version.\n\\usepackage[]{acl}\n\n% Standard package includes\n\\usepackage{times}\n\\usepackage{latexsym}\n\n% For proper rendering and hyphenation of words containing Latin characters (including in bib files)\n\\usepackage[T1]{fontenc}\n% For Vietnamese characters\n% \\usepackage[T5]{fontenc}\n% See https://www.latex-project.org/help/documentation/encguide.pdf for other character sets\n\n% This assumes your files are encoded as UTF8\n\\usepackage[utf8]{inputenc}\n\n% Added by authors\n\\usepackage{color}\n% \\usepackage[table]{xcolor}\n\\usepackage{tcolorbox}\n% \\usepackage{xcolor}\n\\usepackage{tabularx}\n\\usepackage{tabu}\n\\usepackage{booktabs}\n\\usepackage{multirow}\n\n\\usepackage{graphicx} \n\\usepackage{float}\n\\usepackage{subfigure} \n\\usepackage{amssymb}\n\n\\usepackage{hyperref}\n\\usepackage{tablefootnote}\n\\usepackage{xspace}\n\n\\usepackage{float}\n\\usepackage{amsmath}\n\\usepackage{bm}\n\\usepackage{algorithmicx}\n\\usepackage{algorithm}\n\\usepackage{algpseudocode}\n\n\\usepackage{arydshln}\n\n\\usepackage{amssymb}\n\\usepackage{pifont}\n\\newcommand{\\cmark}{\\ding{51}}%\n\\newcommand{\\xmark}{\\ding{55}}%\n\\usepackage{enumitem}\n\\usepackage{fontawesome5}\n% This is not strictly necessary, and may be commented out,\n% but it will improve the layout of the manuscript,\n% and will typically save some space.\n\\usepackage{microtype}\n\n% This is also not strictly necessary, and may be commented out.\n% However, it will improve the aesthetics of text in\n% the typewriter font.\n% \\usepackage{inconsolata}\n\\newcommand{\\ours}{Table-R1\\xspace}\n\\newcommand{\\sftmodel}{\\ours-SFT\\xspace}\n\\newcommand{\\rlmodel}{\\ours-Zero\\xspace}\n% \\newcommand{\\rlmodel}{Table-R1-Data\\xspace}\n\\newcommand{\\eg}{\\hbox{\\emph{e.g.,}}\\xspace}\n\\newcommand{\\ie}{\\hbox{\\emph{i.e.,}}\\xspace}\n\\newcommand{\\huggingface}{\\raisebox{-1.5pt}{\\includegraphics[height=1.05em]{icons/hf-logo.pdf}}\\xspace}\n\\newcommand{\\github}{\\raisebox{-1.5pt}{\\includegraphics[height=1.05em]{icons/github-logo.pdf}}\\xspace}\n\n% Author's Comments:\n\\newcommand{\\yilun}[1]{\\textcolor{purple}{\\bf\\small [Yilun: #1]}}\n\\newcommand{\\lyuhao}[1]{\\textcolor{brown}{\\bf\\small [Lyuhao: #1]}}\n\\newcommand{\\yzy}[1]{\\textcolor{blue}{\\bf\\small [Zheyuan: #1]}}\n\n\n\\newcommand{\\cdashlinelr}[1]{\n \\cdashline{#1}[.4pt/2pt]\n}\n\n\\newcommand{\\rqthree}{xxxxxx?}\n\n% If the title and author information does not fit in the area allocated, uncomment the following\n%\n%\\setlength\\titlebox{<dim>}\n%\n% and set <dim> to something 5cm or larger.\n\\addtocontents{toc}{\\protect\\setcounter{tocdepth}{-1}}\n% \\title{\\ours: Inference-Time Scaling for Table Reasoning Tasks}\n\\title{\\ours: Inference-Time Scaling for Table Reasoning}\n\n\n\\author{\nZheyuan Yang\\thanks{Equal Contributions.} \\quad Lyuhao Chen$^*$ \\quad Arman Cohan \\quad Yilun Zhao \\vspace{10pt}\\\\\nYale NLP Lab\n}\n\n\\begin{document}\n\\maketitle\n\n\\begin{abstract}\nIn this work, we present the first study to explore inference-time scaling on table reasoning tasks. We develop and evaluate two post-training strategies to enable inference-time scaling: distillation from frontier model reasoning traces and reinforcement learning with verifiable rewards (RLVR). \nFor distillation, we introduce a large-scale dataset of reasoning traces generated by DeepSeek-R1, which we use to fine-tune LLMs into the \\sftmodel model. \nFor RLVR, we propose task-specific verifiable reward functions and apply the GRPO algorithm to obtain the \\rlmodel model. \nWe evaluate our \\ours-series models across diverse table reasoning tasks, including short-form QA, fact verification, and free-form QA. \nNotably, the \\rlmodel model matches or exceeds the performance of GPT-4.1 and DeepSeek-R1, while using only a 7B-parameter LLM. It also demonstrates strong generalization to out-of-domain datasets.\nExtensive ablation and qualitative analyses reveal the benefits of instruction tuning, model architecture choices, and cross-task generalization, as well as emergence of essential table reasoning skills during RL training.\n\n\\vspace{-10pt}\n\\begin{small}\n\\begin{center}\n\\begin{tabular}{ll}\n\\huggingface~~\\textbf{Model} & \\href{https://huggingface.co/Table-R1} {\\path{huggingface.co/Table-R1}}\\\\\n\\github~~\\textbf{Code} &\\href{https://github.com/Table-R1/Table-R1}{\\path{github.com/Table-R1}}\\\\\n\\end{tabular}\n\\end{center} \n\\end{small}\n\\end{abstract}\n\n\\section{Introduction}\nReasoning large language models, such as OpenAI’s o-series~\\cite{jaech2024openai,pfister2025understanding} and Deepseek’s R1~\\cite{guo2025deepseek}, have demonstrated enhanced reasoning capabilities by inference-time scaling, \\ie generating a reasoning chain of tokens that allow the model to ``think'' before giving the final answer.\nBuilding on this success, recent research has extended inference-time scaling to various domains and tasks, including multimodal reasoning~\\cite{huang2025visionr1, xu2025llavacot}, machine translation~\\cite{feng2025mt}, agent-based tool use~\\cite{Agent-R1, jin2025searchr1}, and information retrieval~\\cite{weller2025rank1, zhuang2025rankr1}. \n\n\\begin{figure}[!t]\n \\includegraphics[width=\\columnwidth]{figure/RadarChart.pdf}\n \\caption{Overall performance comparison between \\ours and same-scale baselines on various table reasoning benchmarks. Both \\sftmodel and \\rlmodel exhibit substantial performance improvements over baselines, showing the effectiveness of our approach across both in- and out-of-domain benchmarks.}\n \\label{fig:experiments}\n\\end{figure}\n\nHowever, applying inference-time scaling to structure-dependent tasks—particularly table reasoning—remains largely unexplored. \nTable reasoning presents distinct challenges compared to text-only tasks: it requires interpreting diverse cell contents, aligning data across the table, and performing multi-step reasoning with aggregation and numerical operations~\\cite{deng2024tables, wu2025tablebench}. These requirements are further complicated by the need to process long and densely structured tabular inputs~\\cite{zhao-etal-2023-investigating, nahid2024tabsqlify, zhang2025survey}.\nAdvancing LLMs’ reasoning capabilities over tabular tasks holds significant promise for real-world applications, including data analysis~\\cite{zhao-etal-2024-docmath}, scientific reporting~\\cite{liang2024mapping, newman2024arxivdigestables}, and decision-support systems~\\cite{handler2024large}.\n\nIn this work, we present the first study to explore inference-time scaling on table reasoning tasks. \n\\autoref{fig:overview} presents the overview of our research.\nWe develop and systematically evaluate two widely used post-training strategies to enable inference-time scaling on table reasoning tasks: (1) distilling from reasoning traces of frontier reasoning models, and (2) reinforcement learning with verifiable rewards (RLVR).\nFor the distillation approach, we curate and open-source a large-scale table reasoning dataset containing reasoning traces generated by DeepSeek-R1 and verifid by LLM-based annotators. We fine-tune LLMs on this data to obtain \\sftmodel.\nFor the RLVR approach, we design task-specific, verifiable reward functions tailored to table reasoning and apply the Group Relative Policy Optimization (GRPO) algorithm~\\cite{shao2024deepseekmath,guo2025deepseek} to enable stable and scalable reinforcement learning. This yields the \\rlmodel model.\n\nWe evaluate \\ours-series models on a wide range of table reasoning tasks, including short-form table QA, fact verification, and free-form table QA.\nOur experiments demonstrate the effectiveness of inference-time scaling for table reasoning.\nThe RLVR approach, in particular, exhibits better performance and generalization capabilities, compared to the distillation approach. \nNotably, our \\rlmodel models achieve performance that is competitive with advanced language models such as GPT 4.1 and DeepSeek R1, despite using only a 7B-parameter LLM (\\ie Qwen2.5-7B) as the backbone.\nWe further conduct comprehensive ablation studies on \\emph{instruction tuning benefits}, \\emph{model family comparisons}, and \\emph{cross-task generalization}, providing insights for future applications of inference-time scaling in table reasoning.\nOur qualitative analysis of model responses reveals that \\rlmodel not only acquires multi-step reasoning and reflection abilities like other reasoning models, but also develops essential table-specific reasoning skills such as semantic understanding, information extraction, and arithmetic computation.\n\n\n\\begin{figure}[!t]\n \\includegraphics[width=\\columnwidth]{figure/Table-R1.pdf}\n \\caption{An overview of our research and three research questions investigated in this study.}\n \\label{fig:overview}\n\\end{figure}\\section{Related Work}\n\\subsection{Inference-Time Scaling}\nRecently, OpenAI's o1 has demonstrated that scaling inference-time computation can significantly enhance the reasoning abilities of large language models (LLMs) on complex tasks~\\cite{jaech2024openai}. To leverage this, various inference-time strategies have been explored, including the use of Monte Carlo Tree Search (MCTS) for exploring diverse reasoning trajectories~\\cite{feng2023alphazero, qi2024mutual, guan2025rstar} and process reward models (PRMs) that offer step-level feedback to guide model outputs~\\cite{lightman2023let, yuan2024free}. In parallel, supervised fine-tuning (SFT) on reasoning traces has emerged as a practical post-training method, enabling LLMs to better align generation with explicit chain-of-thought reasoning patterns~\\cite{wen2025light, muennighoff2025s1, ye2025limo}.\n\n\nBeyond supervised approaches, recent work has introduced reinforcement learning from verifiable rewards (RLVR) as a promising post-training paradigm for LLM reasoning~\\cite{guo2025deepseek, team2025kimi, qwq32b}. In this setting, models are directly optimized with rule-based rewards, allowing them to autonomously discover effective reasoning strategies without explicit intermediate supervision. Subsequent studies have improved RLVR training by incorporating dynamic sampling, token-level policy gradients, and reward normalization to enhance training stability and sample efficiency~\\cite{yu2025dapo, liu2025understanding, xia2025mimo}. The RLVR paradigm has demonstrated strong generalization across diverse domains, including mathematical problem solving~\\cite{hu2025open, openr1}, logical reasoning games~\\cite{xie2025logic}, vision-based reasonings~\\cite{huang2025vision}, and interactive agent scenarios~\\cite{wang2025ragen, xia2025gui, feng2025retool}.\n\n\\subsection{Table Reasoning}\nReasoning over tabular data has long attracted attention due to its practical applications in real-world scenarios such as data analysis. It encompasses a variety of tasks, including short-form question answering~\\cite{pasupat-liang-2015-compositional, cheng-etal-2022-hitab, lu2023dynamic, zhao-etal-2023-robut, zhao-etal-2024-knowledgefmath, wu2025tablebench}, fact verification~\\cite{Chen2020TabFact, gupta-etal-2020-infotabs}, and free-form question answering~\\cite{nan-etal-2022-fetaqa, zhao-etal-2023-loft}.\nEarlier research primarily focused on fine-tuning smaller language models for specific tasks~\\cite{herzig-etal-2020-tapas, zhao-etal-2022-reastap, liu2022tapex}. \nMore recently, efforts such as TableLlama~\\cite{zhang-etal-2024-tablellama}, TableLLM~\\cite{zhang2025tablellm}, and the TableGPT series~\\cite{zha2023tablegpt, su2024tablegpt2} have advanced the adaptation of LLMs for table reasoning, enabling more general-purpose capabilities across task types. \nIn parallel, several studies have begun exploring agentic approaches to tackle table reasoning tasks~\\cite{10.1145/3539618.3591708, zhao-etal-2024-tapera, nan-etal-2024-evaluating, yu2025tablecriticmultiagentframeworkcollaborative}.\nDespite these advances, the application and enhancement of inference-time scaling for table reasoning remain largely underexplored. \nOur study shows that 7B-scale LLMs with inference-time scaling can match the performance of frontier models such as GPT-4.1. This finding highlights a promising direction for advancing table reasoning.\n\\section{\\ours Models}\nTo systematically explore inference scaling in table reasoning tasks, we develop two variants of \\ours model, each leveraging a widely adopted post-training strategy for inference-time scaling:\n(1) \\sftmodel, trained via supervised fine-tuning on reasoning traces generated by frontier reasoning LLMs, and\nand (2) \\rlmodel, trained using our developed RLVR approach tailored for table reasoning tasks.\nThe methodologies for each approach are detailed in the following subsections.\n\n\\subsection{Training Data Collection}\nWe construct the \\ours training dataset by integrating three representative table reasoning tasks, each introducing distinct reasoning challenges: (1) Short-form Table QA, which requires models to provide precise answers to questions grounded in tabular data; (2) Table Fact Verification, which requires models to determine whether a given claim is entailed by the table content; and (3) Free-form Table QA, which requires models to produce open-ended answers grounded in tabular information.\nEach task contributes unique reasoning challenges, and we select established datasets (presented in \\autoref{tab:training-data}) to ensure comprehensive coverage. \nAll datasets are preprocessed with the presence of verifiable ground truths for reward computation to align with the RLVR paradigm.\n\n\\begin{table}[!t]\n\t\\centering\n\t\\small\n \\setlength{\\tabcolsep}{1pt}\n \\resizebox{\\linewidth}{!}{\n\t\\begin{tabular}{llr}\n\t\t\\toprule\n\t\t\\textbf{Task} & \\textbf{Dataset} & \\textbf{Samples} \\\\\n\t\t\\midrule\n\t\t\\multirow{2}*{Short-form QA (TQA)} \n & WTQ~\\cite{pasupat-liang-2015-compositional} & 13,706 \\\\\n & HiTab~\\cite{cheng-etal-2022-hitab} & 6,793 \\\\\n\t\t\\midrule\n\t Fact Verification (TFV) & TabFact~\\cite{Chen2020TabFact} & 20,740 \\\\\n\t\t\\midrule\n\t\tFree-form QA (FF-TQA) & FeTaQA~\\cite{nan-etal-2022-fetaqa} & 7,324 \\\\\n\t\t\\bottomrule\n\t\\end{tabular}\n\t}\n\t\\caption{Overview of datasets collected in \\ours training data. For each dataset, we sample examples from its training set.}\n\t\\label{tab:training-data}\n\\end{table}\n\\subsection{\\sftmodel via Supervised Finetuning}\nTo enable inference-time scaling in \\sftmodel, we curate a new table reasoning dataset featuring long CoT reasoning.\nSpecifically, for each instance in the raw training data described in the previous subsection, we use DeepSeek-R1~\\cite{deepseekr1} to generate a long CoT response. The response consists of a step-by-step reasoning process followed by a final answer. We present the prompts for response generation in Appendix~\\ref{sec:appendix-prompt-template}.\nTo ensure the quality and correctness of the training data, we apply automated evaluators (detailed in Section~\\ref{sec:eval}) to assess the final answers. Examples with incorrect answers are filtered out. \nAfter this verification step, we obtain a total of 33,601 high-quality examples for SFT training.\nThis dataset is then used to train the \\sftmodel model.\n\n\n\\subsection{\\rlmodel via RL Training}\nWe describe the RLVR algorithm and the reward design for training \\rlmodel as follows.\n\n\n\n\\paragraph{Reinforcement Learning with Verifiable Rewards (RLVR).}\nWe adapt Group Relative Policy Optimization (GRPO) with recent improvements introduced by DAPO~\\cite{yu2025dapo}, including both token-level loss computation and asymmetric (decoupled) clipping. Notably, following recent work~\\cite{hu2025open, liu2025understanding, xia2025mimo}, we omit the KL penalty term present in the original GRPO~\\cite{jaech2024openai,pfister2025understanding}.\nWe formally define the RL algorithm applied in our study as follows: \n\nFor each input $(q, a)$, the policy $\\pi_\\theta$ samples a group of $G$ candidate responses $\\{o_i\\}_{i=1}^G$. Each response receives a reward $R_i$ as described in the next paragraph. \nThe group-normalized advantage for the $i$-th response at time step $t$ is:\n\n\\begin{small}\n\\begin{equation}\n\\hat{A}_{i,t} = \\frac{R_i - \\text{mean}(\\{R_j\\}_{j=1}^G)}{\\text{std}(\\{R_j\\}_{j=1}^G)}.\n\\end{equation}\n\\end{small}\n\n\\noindent Our objective is optimized at the token level with decoupled, asymmetric clipping:\n\n\\begin{small}\n\\begin{equation}\n\\begin{aligned}\n\\mathcal{J}_{\\text{GRPO}}(\\theta) \n&= \\mathbb{E}_{(q,a)\\sim \\mathcal{D},\\, \\{o_i\\}_{i=1}^G\\sim \\pi_{\\theta_\\text{old}}(\\cdot\\mid q)} \\\\\n&\\Bigg[\n \\frac{1}{\\sum_{i=1}^{G}|o_i|}\\sum_{i=1}^{G}\\sum_{t=1}^{|o_i|} \\min \\Big( r_{i,t}(\\theta) \\hat{A}_{i,t}, \\\\\n&\\text{clip}\\big(r_{i,t}(\\theta), 1-\\varepsilon_{\\text{low}}, 1+\\varepsilon_{\\text{high}}\\big)\\hat{A}_{i,t} \\Big)\n\\Bigg]\n\\end{aligned}\n\\label{eq:grpo}\n\\end{equation}\n\\end{small}\n\n\\noindent where the probability ratio $r_{i,t}(\\theta)$ is defined as:\n\n\\begin{small}\n\\begin{equation}\nr_{i,t}(\\theta) = \\frac{\\pi_\\theta(o_{i,t} \\mid q, o_{i,<t})}{\\pi_{\\theta_{\\text{old}}}(o_{i,t} \\mid q, o_{i,<t})}.\n\\end{equation}\n\\end{small}\n\n\\noindent This formulation enables stable and effective RL fine-tuning for table reasoning with LLMs.\n\n\n\\paragraph{Reward Design.}\nTo facilitate effective RL training, we design verifiable reward signals tailored to the characteristics of each table reasoning task. Our reward framework consists of two components: \\emph{accuracy rewards} and \\emph{format rewards}.\nAccuracy rewards measure the correctness of model outputs. We define task-specific reward functions as follows:\n\\begin{itemize} [leftmargin=*]\n\\itemsep0em\n \\item \\textbf{TQA}: The ground-truth is a short-answer list, where each element contains several words. We employ exact match to assign a reward of 1 for a correct answer and 0 otherwise.\n \\item \\textbf{TFV}: The ground-truth is either ``entailed'' or ``refuted''. The reward is 1 if the predicted label matches the ground-truth, and 0 otherwise.\n \\item \\textbf{FF-TQA}: The ground-truth is a sentence or paragraph. We use the average of normalized BLEU and ROUGE-L scores to reflect semantic overlap between model output and reference answer.\n\\end{itemize}\nThis combination of rule-based and metric-based evaluation ensures reward interpretability and robustness, mitigating instability and reward hacking.\n\nIn addition to \\emph{accuracy}, we encourage models—especially base models without instruction tuning—to follow a strict \\emph{response format} specified by a system prompt. We introduce a \\emph{cumulative format reward} incentivizing outputs that match the required template: <think>\\ldots</think> <answer>\\ldots</answer>, with the <answer> block containing a JSON snippet of the form \\{\"answer\": ...\\}. The format reward is computed by a deterministic regular expression-based checker, which assigns partial credit as outputs progressively satisfy structural requirements (\\eg tag presence, JSON structure, valid answer types), and awards full credit for strictly conformant outputs. This dense and interpretable reward guides base models to generate well-structured, verifiable responses.\\section{Experiment}\nIn this section, we describe the experimental setup and address three central research questions, presenting the findings associated with each.\n\n\\subsection{\\ours Model Training Details}\nAll models are trained using the verl framework. We initialize Table-R1 with Qwen2.5-7B-Instruct and Llama-3.1-8B-Instruct models. For Table-R1-SFT, training is conducted with a batch size of 256, a maximum sequence length of 20,480, and ulysses\\_sequence\\_parallel\\_size set to 4. The learning rate is set to $1\\times10^{-5}$, and training is performed for 3 epochs. For Table-R1-Zero, we use a batch size of 256 and 16 rollouts per prompt under the GRPO algorithm. It is trained for 2 epochs. The learning rate is fixed at $1\\times10^{-6}$, sampling temperature is 1.0, with a maximum prompt length of 4096 tokens and a maximum response length of 1024 tokens. The GRPO clipping parameters are set to $\\varepsilon_{\\text{low}}=0.2$ and $\\varepsilon_{\\text{high}}=0.28$. For validation during training, inference is performed with temperature 0.6 and top-p 1.0. All the experiments are conducted on 4 NVIDIA A100 80GB GPUs.\n\n\\begin{table*}[!t]\n\\label{tab:main-results} \n\\centering\n\n\\setlength{\\tabcolsep}{2pt} \n\n\\resizebox{\\textwidth}{!}{\n\\begin{tabular}{l*{13}{c}}\n\\toprule\n\n\\multirow{3}{*}{\\textbf{Model}} & \\multicolumn{4}{c}{\\textbf{In-domain Performance}} & \\multicolumn{9}{c}{\\textbf{Out-of-domain Performance}} \\\\\n\\cmidrule(lr){2-5} \\cmidrule(lr){6-14}\n\n& \\multicolumn{1}{c}{\\textbf{FF-TQA}} & \\multicolumn{1}{c}{\\textbf{TFV}} & \\multicolumn{2}{c}{\\textbf{TQA}} & \\multicolumn{3}{c}{\\textbf{FF-TQA}} & \\multicolumn{3}{c}{\\textbf{TFV}} & \\multicolumn{3}{c}{\\textbf{TQA}} \\\\\n\\cmidrule(lr){2-2} \\cmidrule(lr){3-3} \\cmidrule(lr){4-5} \\cmidrule(lr){6-8} \\cmidrule(lr){9-11} \\cmidrule(lr){12-14}\n\n& \\multicolumn{1}{c}{FeTaQA} & \\multicolumn{1}{c}{TabFact} & \\multicolumn{1}{c}{WTQ} & \\multicolumn{1}{c}{HiTab} & \\multicolumn{1}{c}{ToTTo} & \\multicolumn{1}{c}{QTSum} & \\multicolumn{1}{c}{R.W.} & \\multicolumn{1}{c}{InfoTabs} & \\multicolumn{1}{c}{PHT} & \\multicolumn{1}{c}{Feverous} & \\multicolumn{1}{c}{TMCQ} & \\multicolumn{1}{c}{TMWP} & \\multicolumn{1}{c}{FinQA} \\\\\n\\midrule\n\n\\multicolumn{14}{c}{\\textit{\\textbf{Proprietary Models}}} \\\\ \n\\addlinespace[0.5em]\nGPT-4.1 & 25.1 & 86.5 & 68.0 & 84.7 & 20.4 & 45.7 & 21.0 & 90.5 & 88.2 & 87.7 & 92.0 & 77.0 & 74.0 \\\\\nGPT-4.1 mini & 27.2 & 84.9 & 69.5 & 80.7 & 18.8 & 46.4 & 20.0 & 88.9 & 86.8 & 86.1 & 92.9 & 86.2 & 71.4 \\\\\n\\midrule\n\n\\multicolumn{14}{c}{\\textit{\\textbf{Open-source Models}}} \\\\\n\\addlinespace[0.5em]\n\n\\multicolumn{14}{l}{\\textit{LLMs}} \\\\\n\\quad Qwen2.5-7B & 21.0 & 72.2 & 54.8 & 61.8 & 16.0 & 39.5 & 19.3 & 78.6 & 70.7 & 74.6 & 87.4 & 85.0 & 66.4 \\\\\n\\quad Qwen2.5-32B & 21.9 & 90.3 & 77.3 & 79.4 & 17.8 & 41.4 & \\underline{20.0} & 90.5 & 86.7 & 79.2 & 92.1 & 95.8 & \\underline{77.3} \\\\\n\\quad DeepSeek-V3 & 24.7 & \\textbf{92.4} & 69.9 & \\underline{82.2} & 19.0 & \\textbf{46.2} & \\textbf{20.9} & \\textbf{91.9} & 86.2 & \\textbf{85.8} & 87.6 & 93.4 & \\textbf{78.6} \\\\\n\n\\addlinespace[0.2em]\n\\cdashlinelr{1-14}\n\\addlinespace[0.5em]\n\n\\multicolumn{14}{l}{\\textit{Reasoning LLMs}} \\\\\n\\addlinespace[0.2em]\n\\quad DeepSeek-R1-Distill-7B & 19.1 & 79.6 & 57.8 & 46.2 & 10.7 & 37.2 & 18.0 & 85.7 & 87.1 & 77.5 & 80.9 & 94.0 & 66.8 \\\\\n\\quad QwQ-32B & 23.8 & \\underline{91.5} & \\textbf{85.4} & 81.6 & 19.4 & 41.9 & 19.6 & \\underline{91.0} & 87.8 & 80.1 & 90.7 & \\textbf{99.4} & 76.2 \\\\\n\\quad DeepSeek-R1 & 26.2 & 90.8 & 79.6 & \\textbf{82.4} & 18.5 & \\underline{43.8} & 19.9 & 90.4 & 87.5 & 76.0 & \\textbf{93.3} & \\underline{99.0} & 75.8 \\\\\n\n\\addlinespace[0.2em]\n\\cdashlinelr{1-14}\n\\addlinespace[0.5em]\n\n\\multicolumn{14}{l}{\\textit{Table-Specific LLMs}} \\\\\n\\addlinespace[0.2em]\n\\quad $^{\\dagger}$TableBenchLLM (7B) & 3.1 & 27.1 & 38.8 & -- & 6.2 & -- & -- & -- & -- & 42.3 & -- & -- & -- \\\\\n\\quad $^{\\dagger}$TableLLM-13B & 10.8 & 69.0 & 66.3 & 6.3 & 5.4 & -- & -- & -- & -- & 21.5 & -- & -- & -- \\\\\n\\quad $^{\\dagger}$TableLlama (7B) & \\textbf{39.0} & 82.6 & 35.0 & 64.7 & 20.8 & -- & -- & -- & -- & 73.8 & -- & -- & -- \\\\ \n\\quad TableGPT2-7B & 29.0 & 77.8 & 61.4 & 70.3 & 14.1 & 39.0 & 19.0 & 85.4 & \\underline{89.1} & 78.0 & 77.2 & 79.7 & 66.4 \\\\ \n\\quad $^{\\dagger*}$TableGPT2-72B & 32.3 & 85.4 & 71.5 & 75.6 & \\textbf{22.7} & -- & -- & -- & -- & 76.8 & -- & -- & -- \\\\\n\\midrule\n\n\\multicolumn{14}{c}{\\textit{\\textbf{\\ours (Ours)}}} \\\\\n\\addlinespace[0.5em]\n\n\\multicolumn{14}{l}{\\textit{Llama-3.1 Series}} \\\\\n\\addlinespace[0.2em]\n\n\\quad Llama-3.1-8B-Instruct & 21.7 & 74.1 & 52.3 & 58.2 & 16.5 & 31.6 & 18.1 & 84.1 & 82.5 & 78.3 & 49.5 & 72.0 & 57.1 \\\\\n\\quad Table-R1-SFT & 26.0 & 91.1 & \\underline{83.8} & 81.8 & 13.7 & 36.6 & 16.6 & 89.8 & 85.8 & 79.4 & 90.8 & 89.0 & 64.3 \\\\\n\\quad Table-R1-Zero & \\underline{32.7} & 87.6 & 81.2 & 81.4 & \\underline{22.3} & 30.2 & 17.7 & 87.9 & \\textbf{91.6} & \\underline{80.2}& 68.6 & 84.6 & 62.3 \\\\ \n\n\\addlinespace[0.2em]\n\\cdashlinelr{1-14}\n\\addlinespace[0.5em]\n\\multicolumn{14}{l}{\\textit{Qwen2.5 Series}} \\\\\n\\addlinespace[0.2em]\n\n\\quad Qwen2.5-7B-Instruct & 21.0 & 72.2 & 54.8 & 61.8 & 16.0 & 39.5 & 19.3 & 78.6 & 70.7 & 74.6 & 87.4 & 85.0 & 66.4 \\\\\n\\quad Table-R1-SFT & 25.3 & 89.9 & 81.9 & 78.3 & 14.1 & 38.8 & 18.8 & 88.8 & 84.6 & 76.0 & 90.9 & 96.6 & 71.7 \\\\\n\\quad Table-R1-Zero & 30.6 & 87.6 & 79.8 & 78.1 & 19.8 & 43.1 & \\underline{20.0} & 83.7 & 88.0 & 76.2 & \\underline{93.0} & 96.4 & 70.8 \\\\\n\\bottomrule\n\\end{tabular}\n}\n\\caption{Results on 13 table reasoning benchmarks spanning TQA, TFV, and FF-TQA tasks. For TQA, EM accuracy is reported (with ambiguous cases re-evaluated by GPT-4.1 mini); for TFV, classification accuracy; for FF-TQA, BLEU and ROUGE-L. \\textbf{Bold} and \\underline{underlined} scores indicate the top-2 performances among \\emph{open-source} models.\n$^{\\dagger}$: Due to the context length limitations of most previous table-specific LLMs, it is challenging to conduct a fully fair comparison. Therefore, for these models, we directly use the results as reported in their respective papers, which may be based on sampled or filtered datasets. $^*$: Model weight has not been released.} \n\\label{tab:main-results}\n\\end{table*}\n\\subsection{Experiment Setup}\\label{sec:eval}\n\\paragraph{Evaluation Benchmarks.} \nTo address the lack of a unified evaluation framework for table reasoning, we introduce a new benchmark suite encompassing a wide range of datasets.\nFor \\emph{in-domain evaluation}, we use test sets from the same distributions as the training data: WTQ~\\cite{pasupat-liang-2015-compositional} and HiTab~\\cite{cheng-etal-2022-hitab} for TQA; TabFact~\\cite{Chen2020TabFact} for TFV; and FeTaQA~\\cite{nan-etal-2022-fetaqa} for FF-TQA. \nTo assess generalization of \\ours, we further conduct \\emph{out-of-domain evaluation} on datasets that are not seen during training: TabMCQ~\\cite{jauhar2016tabmcq}, TabMWP~\\cite{lu2023dynamic}, and FinQA~\\cite{chen-etal-2021-finqa} for TQA; InfoTabs~\\cite{gupta-etal-2020-infotabs}, PubHealthTab~\\cite{akhtar-etal-2022-pubhealthtab}, and Feverous~\\cite{aly2021feverous} for TFV; ToTTo~\\cite{parikh-etal-2020-totto}, QTSumm~\\cite{zhao-etal-2023-qtsumm}, and RotoWire~\\cite{wiseman-etal-2017-challenges} for FF-TQA. \nWe provide detailed descriptions of the \\textbf{evaluated baseline systems} in Appendix~\\ref{app:exp-setup}.\n\n\\paragraph{Automated Evaluation System.}\nFor each evaluated dataset, we use its test set for evaluation.\nFor the TQA task, we report Exact Match (EM) accuracy. For TFV, we use classification accuracy. For FF-TQA, we measure generation quality with BLEU and ROUGE-L scores. Considering that EM accuracy in short-answer TQA may underestimate model performance due to formatting variations or semantically equivalent but non-exact matches, we further re-evaluate responses initially marked incorrect by EM using the GPT-4.1 mini model, with the prompt shown in Appendix~\\ref{sec:appendix-prompt-llm-as-a-judge}.\n\n\\begin{tcolorbox}[colback=gray!10, colframe=gray!50!white, width=\\linewidth, boxrule=0.5mm, arc=1mm, outer arc=1mm, left=2mm, right=2mm]\n\\textcolor{orange!50!yellow}{\\faLightbulb} \\xspace \\textbf{RQ1:} How effective is inference-time scaling for table reasoning tasks?\n\\end{tcolorbox}\n\n\\noindent To address RQ1, we conduct a comprehensive evaluation and summarize the key findings from the results presented in \\autoref{tab:main-results} as follows:\n\n\\subsection{Main Findings}\n\\paragraph{General-Purpose LLMs vs. Reasoning LLMs.}\nWe evaluate three pairs of general-purpose LLMs—with and without inference-time scaling capabilities—on table reasoning tasks: Qwen2.5-7B vs. DeepSeek-R1-Distill-7B, Qwen2.5-32B vs. QwQ-32B, and DeepSeek-V3 vs. DeepSeek-R1. While reasoning LLMs tend to outperform their counterparts on the TQA benchmark, their results on TFV and FF-TQA are mixed and not consistently superior. This indicates that inference-time scaling alone, without table-specific training, does not provide a clear advantage for table reasoning tasks. These findings underscore the importance of specialized adaptation strategies, such as Table-R1, for effective performance in this domain.\n\n\\paragraph{\\ours In-Domain Performance.}\nBoth \\sftmodel and \\rlmodel achieve substantial improvements across all tasks. Specifically, Table-R1-Zero-8B obtains a BLEU score of 32.7 on FeTaQA for FF-TQA, significantly surpassing the best among other models of 26.2 from Deepseek-R1; Table-R1-SFT-8B reaches 91.1 accuracy on TabFact for TFV, closely matching the leading 91.9; and for TQA, our models achieve 83.8 and 81.8 on WTQ and HiTab, respectively, which are comparable to the best scores among other models of 85.4 and 84.7. These results consistently demonstrate that Table-R1 models deliver robust gains and competitive performance across diverse table reasoning scenarios, validating the effectiveness and versatility of both SFT and RLVR training strategies.\n\n\\paragraph{\\ours Out-of-Domain Performance.}\nTable-R1 models exhibit strong generalization capabilities on out-of-domain datasets. Across most out-of-domain benchmarks, our models consistently outperform their respective initial baselines, demonstrating the effectiveness of both SFT and R1-Zero training methods for table reasoning. Notably, Table-R1-Zero-7B achieves the best overall generalization among all variants. In contrast, we observe that supervised fine-tuning (SFT) leads to weaker generalization compared to the R1-Zero method, and models initialized from Llama tend to generalize less effectively than those based on Qwen. These results highlight the advantage of our approach in improving table reasoning robustness beyond the training distribution.\n\n\\begin{figure}[!t]\n \\includegraphics[width=\\columnwidth]{figure/model_response_length.pdf}\n \\caption{Response length during Table-R1 training across different models.}\n \\label{fig:training-dynamics-response}\n\\end{figure}\\begin{figure*}[!t]\n \\includegraphics[width=0.48\\linewidth]{figure/HiTab_accuracy_score.pdf} \\hfill\n \\includegraphics[width=0.48\\linewidth]{figure/TabFact_accuracy_score.pdf} \\hfill\n \\includegraphics[width=0.48\\linewidth]{figure/WTQ_accuracy_score.pdf} \\hfill\n \\includegraphics[width=0.48\\linewidth]{figure/FeTaQA_Bleu_score.pdf}\n\\caption{Accuracy and BLEU score dynamics across four table reasoning datasets during RLVR training. Results are shown for all four Table-R1-Zero models, which are trained from Qwen2.5 7B or Llama-3.1 8B as initialization.}\n \\label{fig:training-dynamics-accuracy}\n\\end{figure*}\\begin{tcolorbox}[colback=gray!10, colframe=gray!50!white, width=\\linewidth, boxrule=0.5mm, arc=1mm, outer arc=1mm, left=2mm, right=2mm]\n\\textcolor{orange!50!yellow}{\\faLightbulb} \\xspace \\textbf{RQ2:} What contributes to the success of RLVR methods in table reasoning tasks?\n\\end{tcolorbox}\n\n\\noindent To address this research question, we present a comprehensive analysis of \\rlmodel in the following three subsections: the training dynamics, a qualitative assessment of model responses, and an exploration of the reasoning capacity boundaries.\n\n\\subsection{Analysis of Training Dynamics}\\label{sec:dynamics}\nWe conduct a detailed analysis of the training dynamics exhibited by our Table-R1 models across various model backbones, including Qwen2.5 7B and Llama-3.1 8B, under both \\textit{base} and \\textit{instruct} configurations.\n\n\\autoref{fig:training-dynamics-response} presents the evolution of \\textit{response length} throughout reinforcement learning. Notably, the base models consistently start with longer responses compared to their instruct counterparts. During the initial stage of RL training, we observe a sharp drop in response length, corresponding to a phase of format acquisition—where the model learns to produce outputs adhering to the expected answer format. Subsequently, response length gradually increases, with base models exhibiting a more pronounced growth trajectory than instruct models. Among all four model variants, Table-R1-Zero-Qwen2.5-7B-Instruct demonstrates the greatest stability, showing a smooth and moderate increase in response length. In contrast, Table-R1-Zero-Llama-3.1-8B displays considerable instability, ultimately failing to acquire the desired response format. We attribute this to the weaker instruction-following capabilities of the Llama-3.1-8B base model.\n\n\\autoref{fig:training-dynamics-accuracy} illustrates the progression of \\textit{model accuracy} over the course of RL training. We find that instruct models consistently achieve higher accuracy than their base versions throughout training. While base models start from a lower performance baseline, they undergo a phase of format adaptation, after which their accuracy trends converge with those of instruct models. Interestingly, although Table-R1-Zero-Llama-3.1-8B exhibits unstable changes in response length, it still demonstrates performance gains on most tasks, with the exception of TabFact—a binary classification task.\n\nOverall, these observations reveal that instruction tuning provides significant benefits in both stability and performance during RLVR training for table reasoning tasks. The results further suggest that initial format alignment and instruction-following capability play critical roles in the successful adaptation of large language models to structured reasoning scenarios.\n\n\\subsection{Reasoning Capacity Boundary}\n\\begin{figure}[!t]\n\\includegraphics[width=0.9\\columnwidth]{figure/pass_at_k_wtq.pdf}\n \\includegraphics[width=0.9\\columnwidth]{figure/pass_at_k_hitab.pdf}\n \\caption {Pass@k performance on WTQ and HiTab.}\n \\label{fig:passk}\n\\end{figure}\\begin{figure*}[!t]\n \\includegraphics[width=\\linewidth]{figure/main_example.pdf}\n \\caption{Illustration of the model's reasoning progression across training steps. The example demonstrates how reasoning quality evolves from superficial processing (Step 0), to partial column-aware reasoning (Step 180), and finally to accurate multi-step inference with semantic and arithmetic understanding (Step 378).}\n \\label{fig:qualitative-analysis}\n\\end{figure*}\nUnderstanding the upper limits of a model's reasoning ability is crucial for evaluating the true impact of RLVR on tabular reasoning tasks. Inspired by prior studies on the boundaries of RLVR method~\\cite{yue2025does}, we employ the \\textit{pass@k} metric to quantify the model's capacity. Specifically, pass@k measures the probability that at least one out of $k$ generated responses is correct, given a fixed input prompt. This metric is particularly well-suited for our setting, as it captures not only the model's accuracy, but also its ability to produce diverse and plausible reasoning trajectories within a limited number of attempts.\n\nWe systematically evaluate pass@k for $k$ up to 32, both before and after RLVR training, on the HiTab and WTQ datasets. As illustrated in \\autoref{fig:passk}, RLVR training leads to a notable increase in pass@k values throughout the evaluated range. This improvement is consistent across different datasets and model architectures, demonstrating that RLVR enhances not only the likelihood of obtaining a correct answer on first attempt (pass@1), but also the breadth of valid reasoning paths the model can explore in a small sampling budget.\n\n\\begin{table*}[!t]\n\\label{tab:ablation-studies}\n\\centering\n\\setlength{\\tabcolsep}{2pt}\n\n\\resizebox{\\textwidth}{!}{\n\\begin{tabular}{l*{13}{c}}\n\\toprule\n\\multirow{3}{*}{\\textbf{Model}} & \\multicolumn{4}{c}{\\textbf{In-domain Performance}} & \\multicolumn{9}{c}{\\textbf{Out-of-domain Performance}} \\\\\n\\cmidrule(lr){2-5} \\cmidrule(lr){6-14}\n\n& \\multicolumn{1}{c}{\\textbf{FF-TQA}} & \\multicolumn{1}{c}{\\textbf{TFV}} & \\multicolumn{2}{c}{\\textbf{TQA}} & \\multicolumn{3}{c}{\\textbf{FF-TQA}} & \\multicolumn{3}{c}{\\textbf{TFV}} & \\multicolumn{3}{c}{\\textbf{TQA}} \\\\\n\\cmidrule(lr){2-2} \\cmidrule(lr){3-3} \\cmidrule(lr){4-5} \\cmidrule(lr){6-8} \\cmidrule(lr){9-11} \\cmidrule(lr){12-14}\n\n& \\multicolumn{1}{c}{FeTaQA} & \\multicolumn{1}{c}{TabFact} & \\multicolumn{1}{c}{WTQ} & \\multicolumn{1}{c}{HiTab} & \\multicolumn{1}{c}{ToTTo} & \\multicolumn{1}{c}{QTSum} & \\multicolumn{1}{c}{R.W.} & \\multicolumn{1}{c}{InfoTabs} & \\multicolumn{1}{c}{PHT} & \\multicolumn{1}{c}{Feverous} & \\multicolumn{1}{c}{TMCQ} & \\multicolumn{1}{c}{TMWP} & \\multicolumn{1}{c}{FinQA} \\\\\n\\midrule\n\n\\multicolumn{14}{c}{\\textit{\\textbf{Model-level Ablation}}} \\\\\n\\addlinespace[0.5em]\n\n\\multicolumn{14}{l}{\\textit{Base vs. Instruct}} \\\\\n\\addlinespace[0.2em]\nQwen2.5-7B & 18.2 & 67.7 & 51.0 & 55.2 & 9.4 & 37.8 & 16.4 & 74.5 & 75.0 & 74.1 & 78.7 & 80.3 & 58.4 \\\\\nTable-R1-Qwen2.5-7B & 29.8 & 87.3 & 78.5 & 75.2 & 15.9 & 38.1 & 18.2 & 87.9 & 91.6 & 78.4 & 84.8 & 94.4 & 61.2 \\\\\nQwen2.5-7B-Instruct & 21.0 & 72.2 & 54.8 & 61.8 & 16.0 & 39.5 & 19.3 & 78.6 & 70.7 & 74.6 & 87.4 & 85.0 & 66.4 \\\\\nTable-R1-Qwen2.5-7B-Instruct & 30.6 & 87.6 & 79.8 & 78.1 & 19.8 & 43.1 & 20.0 & 83.7 & 88.0 & 76.2 & 93.0 & 96.4 & 70.8 \\\\\n\n\\addlinespace[0.2em]\n\\cdashlinelr{1-14}\n\\addlinespace[0.5em]\n\n\\multicolumn{14}{l}{\\textit{Model Architecture Comparison}} \\\\\n\\addlinespace[0.2em]\nLlama-3.1-8B & 10.2 & 21.2 & 35.0 & 44.7 & 6.8 & 18.6 & 10.9 & 21.6 & 26.8 & 19.4 & 68.9 & 43.1 & 21.9 \\\\\nTable-R1-Llama-3.1-8B & 30.4 & 50.3 & 61.5 & 76.3 & 19.8 & 33.5 & 18.0 & 50.0 & 69.7 & 50.6 & 93.1 & 56.8 & 19.3 \\\\\nLlama-3.1-8B-Instruct & 21.7 & 74.1 & 52.3 & 58.2 & 16.5 & 31.6 & 18.1 & 84.1 & 82.5 & 78.3 & 49.5 & 72.0 & 57.1 \\\\\nTable-R1-Llama-3.1-8B-Instruct & 32.7 & 87.6 & 81.2 & 81.4 & 22.3 & 30.2 & 17.7 & 87.9 & 91.6 & 80.2 & 68.6 & 84.6 & 62.3 \\\\\n\n\\addlinespace[0.2em]\n\\cdashlinelr{1-14}\n\\addlinespace[0.5em]\n\n\\multicolumn{14}{l}{\\textit{SFT on Domain-Specific Data}} \\\\\n\\addlinespace[0.2em]\nDeepSeek-R1-7B & 19.1 & 79.6 & 57.8 & 46.2 & 10.7 & 37.2 & 18.0 & 85.7 & 87.1 & 77.5 & 80.9 & 94.0 & 66.8 \\\\\nTable-SFT-Qwen2.5-7B & 25.3 & 89.9 & 81.9 & 78.3 & 14.1 & 38.8 & 18.8 & 88.8 & 84.6 & 76.0 & 90.9 & 96.6 & 71.7 \\\\\nTable-SFT-Llama-3.1-8B & 26.0 & 91.1 & 83.8 & 81.8 & 13.7 & 36.6 & 16.6 & 89.8 & 85.8 & 79.4 & 90.8 & 89.0 & 64.3 \\\\\n\\midrule\n\n\\multicolumn{14}{c}{\\textit{\\textbf{Task-level Ablation}}} \\\\\n\\addlinespace[0.5em]\n\\multicolumn{14}{l}{\\textit{Cross-task Generalization}} \\\\\n\\addlinespace[0.2em]\nTable-R1-TQA-Qwen2.5-7B & 14.1 & 86.2 & 79.2 & 77.4 & 16.5 & 36.4 & 19.8 & 89.7 & 89.9 & 82.7 & 93.5 & 96.1 & 65.9 \\\\\nTable-R1-TQA-Llama-3.1-8B & 12.3 & 83.9 & 81.7 & 81.0 & 17.2 & 26.9 & 19.2 & 87.3 & 87.2 & 77.1 & 80.3 & 82.8 & 58.6 \\\\\nTable-SFT-TQA-Qwen2.5-7B & 13.3 & 87.7 & 79.8 & 77.0 & 13.4 & 27.6 & 18.1 & 88.5 & 87.1 & 78.0 & 90.1 & 95.7 & 69.0 \\\\\nTable-SFT-TQA-Llama-3.1-8B & 9.6 & 88.8 & 82.4 & 80.5 & 10.3 & 20.6 & 12.6 & 89.1 & 90.4 & 82.1 & 89.9 & 86.9 & 62.9 \\\\\n\\midrule\n\n\\multicolumn{14}{c}{\\textit{\\textbf{Format Ablation}}} \\\\\n\\addlinespace[0.5em]\n\\multicolumn{14}{l}{\\textit{Effect of Format Reward}} \\\\\n\\addlinespace[0.2em]\nTable-R1-Explicit-Qwen2.5-7B & 29.0 & 85.8 & 76.0 & 73.2 & 13.6 & 37.2 & 14.8 & 87.5 & 88.8 & 81.0 & 52.5 & 91.3 & 49.6 \\\\\nTable-R1-Qwen2.5-7B & 30.6 & 87.6 & 79.8 & 78.1 & 19.8 & 43.1 & 20.0 & 83.7 & 88.0 & 76.2 & 93.0 & 96.4 & 70.8 \\\\\n\\addlinespace[0.3em]\n\\bottomrule\n\\end{tabular}\n}\n\\caption{Ablation study results on model-level, task-level, and formats.}\n\\end{table*}\\subsection{Qualitative Analysis}\nTo gain deeper insights into how RLVR shapes model behavior, we conduct a qualitative analysis of model responses throughout the training process. By examining the same set of representative prompts across different RL training steps, we observe that the model not only internalizes general R1-style reasoning characteristic, but also acquires table-specific reasoning abilities critical for tabular tasks, as illustrated in \\autoref{fig:qualitative-analysis} and Appendix~\\ref{app:example}.\n\nOn the \\textbf{reasoning} axis, we observe clear progress toward sophisticated, multi-step reasoning: after RL training, the \\rlmodel model decomposes complex queries into sequential sub-tasks, explicitly outlining intermediate steps and sometimes ``rethinking'' earlier conclusions to check or correct errors. Such reflective patterns, rare at initialization, become prevalent with training, suggesting the verifiable reward encourages explicit, auditable reasoning.\n\nOn the \\textbf{table-specific} axis, we observe notable improvements in three key areas. First, the model develops column-aware reasoning: it accurately identifies and references relevant table columns, often justifying its answer with explicit column mentions or by highlighting how information from multiple columns is synthesized. Second, the model demonstrates enhanced semantic understanding of natural language questions, especially in TQA settings. It is able to correctly interpret nuanced question intents (\\eg comparative, aggregative, or conditional queries) and map them to the corresponding table structures. Third, we note a marked increase in arithmetic and temporal reasoning capabilities. The model becomes more adept at performing arithmetic calculations over table entries and reasoning over temporal sequences, both of which are crucial for table reasoning tasks.\n\n\n\\subsection{Ablation Studies on RLVR Training}\n\\begin{tcolorbox}[colback=gray!10, colframe=gray!50!white, width=\\linewidth, boxrule=0.5mm, arc=1mm, outer arc=1mm, left=2mm, right=2mm]\n\\textcolor{orange!50!yellow}{\\faLightbulb} \\xspace \\textbf{RQ3:} How do various factors influence the effectiveness of RLVR training in table reasoning tasks?\n\\end{tcolorbox}\n\n\\noindent To better understand the contributions of both SFT and RLVR methods and to assess the robustness of Table-R1 across different configurations, we conduct extensive ablation studies from three perspectives: model-level, task-level, and explicit reasoning format. We detail our findings as follows.\n\n\\paragraph{Model-level Ablation.}\nWe first analyze the effect of model initialization by comparing base and instruct variants. Across all settings, instruct models consistently outperform their base counterparts. We attribute this to the enhanced instruction-following ability inherent to instruct models, which enables faster adaptation to the explicit reasoning formats required by table tasks and leads to more stable training dynamics. \n\nWe next compare representative model architectures—Qwen2.5 and Llama-3.1. Under identical training regimes, Llama-3.1-8B-Instruct achieves superior in-domain performance on table reasoning benchmarks compared to Qwen2.5-7B-Instruct, indicating a stronger capacity for learning table-specific reasoning. However, Qwen2.5 demonstrates better out-of-domain generalization, suggesting that model architecture and pretraining data may influence the balance between in-domain accuracy and cross-domain robustness.\n\nFurthermore, we evaluate the effect of distillation data by comparing our SFT models—fine-tuned on table-specific DeepSeek-R1 distillation data—against the official DeepSeek-R1 distilled models. Our models not only surpass the official versions, but also outperform larger-scale distilled models. This underscores the effectiveness of domain-specific fine-tuning and the importance of high-quality, task-aligned training data.\n\n\\paragraph{Task-level Ablation.}\nTo investigate cross-task generalization, we train Table-R1 exclusively on the TQA dataset and evaluate its performance on TFV and FF-TQA tasks. Interestingly, models trained solely on TQA exhibit notable performance gains on TFV, indicating that the reasoning capabilities required for TFV are closely aligned with those developed for short-answer TQA. In contrast, no significant improvement is observed on FF-TQA, likely due to the distinct reasoning and answer generation skills required for free-form responses, which are not adequately covered by TQA training. These results highlight the varying degrees of transferability among table reasoning tasks and emphasize the need for targeted training to achieve robust generalization.\n\n\\paragraph{Format Ablation.}\nWe assess the role of explicit reasoning format supervision by removing the format reward during training. This ablation reduces training stability and slightly lowers in-domain performance. More notably, generalization suffers: while TFV scores may improve, performance on short-answer TQA and FF-TQA declines, sometimes even below baseline, indicating that format supervision is crucial for transferable reasoning.\nWe also test inference-time control by restricting the initial output token to \\texttt{<think>} or \\texttt{<answer>}. However, this constraint does not alter the model’s internal reasoning or task performance, showing that inference-time decoding tricks cannot substitute for training with flexible reasoning formats.\\section{Conclusion}\nThis work presents the first comprehensive study on applying inference-time scaling to table reasoning tasks. \nThrough extensive evaluation across 13 diverse benchmarks, we demonstrate that inference-time scaling enables substantial improvements in reasoning quality, with RLVR methods yielding stronger generalization to out-of-domain tasks. \nAblation studies confirm the benefits of instruction tuning, model architecture choice, and task composition in enhancing training effectiveness. Qualitative analysis reveals that RLVR fosters the emergence of structured, multi-step reasoning and table-specific capabilities. We hope this work paves the way for future research in structured reasoning.\\section*{Limitations}\nSeveral limitations remain that warrant future investigation:\nThe SFT data was generated exclusively using DeepSeek-R1. Additionally, the data verification and filtering processes may have inadvertently removed difficult or high-quality training examples. Future research could explore incorporating outputs from other reasoning LLMs, such as QwQ-32B, to enhance distillation performance and data diversity.\nFurthermore, in Section~\\ref{sec:dynamics}, we observe that models initialized from the LLaMA-3.1-8B backbone exhibit unstable training dynamics during RLVR fine-tuning, including inconsistent acquisition of the desired output format and significant fluctuations in response length. While instruct-tuned variants mitigate some of these issues, the underlying causes of instability—such as sensitivity to initialization, reward sparsity, or optimization hyperparameters—remain underexplored.\nFuture work could investigate strategies to improve the robustness and generalizability of RLVR for structured reasoning tasks.\n\\section*{Acknowledgments}\nWe would like to thank Zijie Zhou for making the TQA-Distill-R1 dataset\\footnote{\\url{https://huggingface.co/datasets/jared-zhou/TQA-Distill-R1}} publicly available, which we used in our early experiments.\n\n\n% Entries for the entire Anthology, followed by custom entries\n\\bibliography{anthology, custom}\n\n\\newpage\n\\appendix\n\n\\clearpage\n\\onecolumn\n\\section{Information of Evaluated Table Reasoning Datasets}\n\\label{sec:appendix-dataset}\n\\begin{table*}[ht]\n\\centering\n\\small\n\\resizebox{\\textwidth}{!}{\n\\begin{tabular}{lllll}\n\\toprule\n\\textbf{Task Category} & \\textbf{Task Name} & \\textbf{Dataset} & \\textbf{Task Description} & \\textbf{Metric} \\\\\n\\midrule\n\\multirow{4}{*}{Training}\n & Table QA & WTQ, HiTab & QA over flat or hierarchical tables & Acc. \\\\\n & Free-form QA & FeTaQA & Generate long-form answers from tables & BLEU \\\\\n & Table Fact Verification & TabFact & Verify factual correctness based on a table & Acc. \\\\\n\\midrule\n\\multirow{4}{*}{In-Domain}\n & Table QA & WTQ, HiTab & In-domain table QA evaluation & Acc. \\\\\n & Table Fact Verification & TabFact & In-domain fact verification evaluation & Acc.\\\\\n & Free-form QA & FeTaQA & Evaluate long-form generation quality & BLEU \\\\\n\\midrule\n\\multirow{4}{*}{Out-of-Domain}\n & Table QA & TabMCQ, TMWP, FinQA & Multiple-choice and word problem QA & Acc. \\\\\n & Numerical Reasoning QA & FinQA, TABMWP & Financial and numerical table QA & Acc. \\\\\n & Table Fact Verification & InfoTabs, PHT, Feverous & Health and factual judgment from tables & Acc. \\\\\n & Free-form QA & ToTTo, Qtsumm, R.W. & Table-to-text generation from highlights & BLEU/R-L \\\\\n\\bottomrule\n\\end{tabular}\n}\n\\caption{Overview of datasets used in training and evaluation.}\n\\label{tab:our_dataset}\n\\end{table*}\n\n\n\\section{Experiment Setup}\\label{app:exp-setup}\n\\subsection{Baseline Systems}\nWe benchmark our approach against a comprehensive set of strong baselines, encompassing both proprietary and open-source models. Among proprietary models, we include GPT-4.1 and GPT-4.1 mini. For open-source baselines, we evaluate general purpose LLMs from the Qwen2.5 and LLaMA3 series, reasoning models such as Deepseek-R1, its official distilled variant, our SFT model distilled from Deepseek-R1 on table reasoning data (as described in Section~3.2), and QwQ-32B, as well as table-oriented models including TableLlama, TableLLM, and TableBenchLLM. Additional evaluation details, including specific model configurations and prompt templates, are provided in Appendix~\\ref{sec:appendix-eval}.\n\n\\clearpage\n\\section{Evaluation Details}\n\\label{sec:appendix-eval}\nAdditional evaluation details, including inference setup, evaluated model configurations and prompt templates.\n\n\\subsection{Inference Setup}\nFor all open-source models, inference is performed using the vLLM framework, while for closed-source models, the official OpenAI API is utilized. The maximum output length is set to 2048 tokens for most models. However, for reasoning models, this limit is increased to 18,000 tokens to accommodate their long chain-of-thought generation. The temperature is set to 0.6 and the top-p value to 0.95. All inference processes are conducted on four NVIDIA A100-80G GPUs.\n\n\\subsection{Evaluated Model Configuration}\n\\begin{table*}[h]\n \\centering\n \\small\n \\begin{tabular}{lll}\n \\toprule\n \\textbf{Model} & \\textbf{Citation} & \\textbf{Version} \\\\ \\midrule\n\n GPT-4.1 & \\citet{openai2024gpt4o} & gpt-4.1-2025-04-14 \\\\\n GPT-4.1 mini & \\citet{openai2024gpt4o} & gpt-4.1-mini-2025-04-14 \\\\ \\midrule\n\n Qwen2.5-7B & \\citet{qwen2.5} & \\href{https://huggingface.co/Qwen/Qwen2.5-7B-Instruct}{Qwen/Qwen2.5-7B-Instruct} \\\\\n Qwen2.5-14B & \\citet{qwen2.5} & \\href{https://huggingface.co/Qwen/Qwen2.5-14B-Instruct}{Qwen/Qwen2.5-14B-Instruct} \\\\\n Qwen2.5-32B & \\citet{qwen2.5} & \\href{https://huggingface.co/Qwen/Qwen2.5-32B-Instruct}{Qwen/Qwen2.5-32B-Instruct} \\\\\n QwQ-32B & \\citet{qwq32b} & \\href{https://huggingface.co/Qwen/QwQ-32B}{Qwen/QwQ-32B} \\\\\n\n Llama-3.1-8B & \\citet{touvron2023llama} & \\href{https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct}{meta-llama/Llama-3.1-8B-Instruct} \\\\ \\midrule\n\n DeepSeek-R1-7B & \\citet{guo2025deepseek} & \\href{https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B}{deepseek-ai/DeepSeek-R1-Distill-Qwen-7B} \\\\\n DeepSeek-R1-14B & \\citet{guo2025deepseek} & \\href{https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B}{deepseek-ai/DeepSeek-R1-Distill-Qwen-14B} \\\\\n DeepSeek-R1-32B & \\citet{guo2025deepseek} & \\href{https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B}{deepseek-ai/DeepSeek-R1-Distill-Qwen-32B} \\\\\n DeepSeek-R1 & \\citet{guo2025deepseek} & \\href{hhttps://huggingface.co/deepseek-ai/DeepSeek-R1}{deepseek-ai/DeepSeek-R1} \\\\ \\midrule\n TableGPT2-7B & \\citet{su2024tablegpt2} & \\href{https://huggingface.co/tablegpt/TableGPT2-7B}{TableGPT/TableGPT2-7B} \\\\\n TableLLM-13B & \\citet{zhang2024tablellm} & \\href{https://huggingface.co/RUCKBReasoning/TableLLM-13b}{RUCKBReasoning/TableLLM-13B} \\\\ \n TableLlama-7B & \\citet{zhang2023tablellama} & \\href{https://huggingface.co/osunlp/TableLlama}{osunlp/TableLlama} \\\\ \n TableBenchLLM & \\citet{zhang2024tablellm} & \\href{https://huggingface.co/Multilingual-Multimodal-NLP/TableLLM-Llama3.1-8B}{Multilingual-Multimodal-NLP/TableLLM-Llama3.1-8B} \\\\ \n \n \\bottomrule\n \\end{tabular}\n \\caption{Model List.}\n \\label{tab:model_list}\n\\end{table*}\n\n\\subsection{Prompts}\n\\label{sec:appendix-prompt-template}\n\\begin{tcolorbox}[colback=black!3!white, colframe=black!70!white, title=System prompt used to guide structured response generation, fontupper=\\footnotesize, fonttitle=\\footnotesize]\nA conversation between User and Assistant. The user asks a question, and the assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. The reasoning process and answer are enclosed within <think> </think> and <answer> </answer> tags, respectively, i.e., <think> reasoning process here </think> <answer> answer here </answer>.\n\\end{tcolorbox}\n\n\n \\begin{tcolorbox}[colback=black!3!white, colframe=black!70!white, title=Prompt Template for TQA, fontupper=\\footnotesize, fonttitle=\\footnotesize]\n \\textbf{Instruction:} This is a short-answer table QA task. Answer the question based on the provided table. \\\\\n\n \\textbf{Table} \\\\\n Table Title: \\texttt{\\{table\\_title\\}} \\\\\n Table Content: \\texttt{\\{table\\_repr (markdown / html)\\}} \\\\\n\n \\textbf{Question:} \\texttt{\\{question\\}} \\\\\n\n \\textbf{Answer Format:} \\\\\n The final answer should be concise and use the following format:\n\\begin{verbatim}\n```json\n{\n \"answer\": [\n \"answer1\",\n \"answer2\",\n ...\n ]\n}\n```\n\\end{verbatim}\n\\end{tcolorbox}\n\n\n \\begin{tcolorbox}[colback=black!3!white, colframe=black!70!white, title=Prompt Template for TFV, fontupper=\\footnotesize, fonttitle=\\footnotesize]\n \\textbf{Instruction:} This is a table fact verification task. The goal is to determine whether the given statement is entailed or refuted by the table. \\\\\n\n \\textbf{Table} \\\\\n Table Title: \\texttt{\\{table\\_title\\}} \\\\\n Table Content: \\texttt{\\{table\\_repr (markdown / html)\\}} \\\\\n\n \\textbf{Statement:} \\texttt{\\{statement\\}} \\\\\n\n \\textbf{Answer Format:} \\\\\n The final answer should be either \"entailed\" or \"refuted\" and use the following format:\n\\begin{verbatim}\n```json\n{\n \"answer\": \"entailed\" or \"refuted\"\n}\n```\n\\end{verbatim}\n\\end{tcolorbox}\n\n \\begin{tcolorbox}[colback=black!3!white, colframe=black!70!white, title=Prompt Template for Free-Form TQA, fontupper=\\footnotesize, fonttitle=\\footnotesize]\n \\textbf{Instruction:} This is a free-form table QA task. Answer the question based on the provided table. \\\\\n\n \\textbf{Table} \\\\\n Table Title: \\texttt{\\{table\\_title\\}} \\\\\n Table Content: \\texttt{\\{table\\_repr (markdown / html)\\}} \\\\\n\n \\textbf{Question:} \\texttt{\\{question\\}} \\\\\n\n \\textbf{Answer Format:} \\\\\n The final answer should be a sentence and use the following format:\n\\begin{verbatim}\n```json\n{\n \"answer\": \"your_generated_sentence_here\"\n}\n```\n\\end{verbatim}\n\\end{tcolorbox}\n\n\n \\begin{tcolorbox}[colback=black!3!white, colframe=black!70!white, title=Prompt Template for LLM-as-a-Judge, fontupper=\\footnotesize, fonttitle=\\footnotesize]\n You are given two answers for a short-answer Table QA task: \\textbf{response} and \\textbf{ground\\_truth}. \\\\\n\n - \\textbf{response}: This is the LLM's answer to the task. It may include reasoning steps and a final answer. \\\\\n - \\textbf{ground\\_truth}: A list of short answers, typically 2-3 word noun phrases or numbers. \\\\\n\n Your task is to determine whether the response is fully correct, using these rules: \\\\\n - \\textbf{Noun phrases}: Considered correct if meaning matches ground\\_truth regardless of wording. \\\\\n - \\textbf{Numbers}: Considered correct if numerically close (tolerance < 0.01). \\\\\n - Every ground\\_truth item must be matched in the response. Order doesn't matter. \\\\\n\nYour output must be in the following format:\n\\begin{verbatim}\n```json\n{\n \"judgement\": \"correct\" or \"incorrect\"\n}\n```\n\\end{verbatim}\nDo not provide any explanation or additional output.\\\\\n\n\\textbf{Input:} \\\\\n\\textbf{Response:} \\{response\\} \\\\\n\\textbf{Ground\\_truth:} \\{ground\\_truth\\} \\\\\n\n\nEvaluate and output the judgement.\n\\end{tcolorbox}\n\\label{sec:appendix-prompt-llm-as-a-judge}\n\n\\clearpage\n\\section{Qualitative Analysis Cases}\\label{app:example}\n\n\\begin{figure*}[h]\n \\includegraphics[width=\\linewidth]{figure/example1.pdf}\n \\caption{Qualitative Analysis Case Example for TQA tasks}\n\\end{figure*}\n\n\\begin{figure}[h]\n \\includegraphics[width=\\linewidth]{figure/example2.pdf}\n \\caption{Qualitative Analysis Case Example for TFV tasks}\n\\end{figure}\n\n\\end{document}\n"},"question":{"kind":"string","value":"Which open-source model with at most 32B parameters outperforms GPT-4.1 mini on at least two out-of-domain TFV tasks, and what are those tasks?"},"answer":{"kind":"string","value":"QwQ-32B, InfoTabs, PHT"},"plan":{"kind":"string","value":"SELECT open-source models with ≤ 32B parameters\nLOOP for each model\n SELECT out-of-domain TFV tasks\n LOOP for each TFV task\n SELECT GPT-4.1-mini’s task score for current TFV Task\n SELECT model score for current TFV Task\n IF model score > GPT-4.1-mini score\n COMPUTE increase count\n COMPUTE add TFV Task to better_tasks_list\n IF count >= 2\n COMPUTE add model and better_tasks_list to result_list\n\n\nRETURN result_list\n"}}},{"rowIdx":84,"cells":{"paper":{"kind":"string","value":"2505.22582"},"relevant_tables":{"kind":"list like","value":[["\\begin{table*}[t]\n"," \\centering\n"," \\resizebox*{\\linewidth}{!}{\n"," \\begin{tabular}{lc|cccccccc|ccc}\n"," \\bottomrule\n"," & & \\multicolumn{2}{c}{\\textbf{ARC-C}} & \\multicolumn{2}{c}{\\textbf{MMLU}} & \\multicolumn{2}{c}{\\textbf{Hellaswag}} & \\multicolumn{2}{c|}{\\textbf{Belebele}} & & & \\\\\n"," \\hline\n"," \\textbf{Methods} & \\textbf{Expand-params} & \\textbf{Old} &\t\\textbf{New} &\t\\textbf{Old} &\t\\textbf{New} & \\textbf{Old} &\t\\textbf{New}\t& \\textbf{Old}\t& \\textbf{New} &\t\\textbf{\\textit{Old-avg}} & \\textbf{\\textit{New-avg}} & \\textbf{\\textit{avg}}\\\\\n"," \n"," \\toprule\n"," \\multicolumn{13}{c}{\\textbf{Old (G0): \\textit{en}, \\textit{es}, \\textit{zh} \\!$\\rightarrow$\\! New (G1): \\textit{el}, \\textit{hu}, \\textit{tr}}} \\\\\n"," \\bottomrule\n"," Qwen1.5-1.8B\t&\t/\t&\t33.31 \t&\t22.93 \t&\t47.23 \t&\t30.52 \t&\t49.70 \t&\t29.27 \t&\t55.89 \t&\t32.92 \t&\t46.53 \t&\t28.91 \t&\t37.72 \t\\\\\n"," \\hline\n"," $^*$LoRA\t&\t/ & 28.33 &\t23.89 &\t37.42 & 29.30 & 41.48 & 29.78 & 39.45 & 26.93 & 36.67\t& 27.48\t& 32.07 \\\\\n"," $^*$Full Fine-tuning\t& / &\t31.72 & 25.98 & 43.51 & 33.18 & 47.38 & 35.28 & 45.26 & 33.70 & 41.98 &\t32.04 & \t37.00 \\\\\n"," $^*$LLaMA-Pro\t& 0.6B &\t31.77 & 24.35 & 44.06 & 34.02 & 48.36 & 33.85 & 48.78 & 31.52 & 43.24 &\t30.94 & \t37.09 \\\\\n"," $^*$MoE\t & 4B\t& 32.51 & 26.43 & 44.16 & 35.07 & 48.54 & 37.01 & 45.37 & 32.74 & 42.65 &\t32.81 &\t37.73 \\\\\n"," $^*$LoRAMoE\t & 0.8B &\t32.43 & 26.63 & 45.41 & 34.17 & 48.61 & 37.17 & 47.74 & 32.81 & 43.55\t& 32.70 &\t38.12 \\\\\n"," \\hline\n"," \\rowcolor{gray!25}\n"," MoE-LPR (6*24)\t&\t4B\t&\t32.26 \t&\t26.55 \t&\t46.51 \t&\t34.95 \t&\t49.75 \t&\t37.53 \t&\t52.92 \t&\t39.04 \t&\t45.36 \t&\t34.52 \t&\t39.94 \t\\\\\n"," \n"," MOLA ($\\nabla$)\t&\t3.2B ($\\downarrow$ 20\\%)\t&\t32.49 \t&\t27.00 \t&\t46.38 \t&\t33.98 \t&\t49.63 \t&\t37.03 \t&\t53.85 \t&\t38.37 \t&\t45.59 \t&\t34.10 \t&\t39.84 \t\\\\\n"," \n"," MoE-LPR (4*24)\t&\t2.4B ($\\downarrow$ 40\\%)\t&\t32.29 \t&\t26.69 \t&\t46.51 \t&\t34.11 \t&\t49.60 \t&\t37.12 \t&\t52.33 \t&\t37.74 \t&\t45.18 \t&\t33.92 \t&\t39.55 \t\\\\\n"," \n"," MoE-LPR (3*24)\t&\t1.6B ($\\downarrow$ 60\\%)\t&\t32.77 \t&\t26.43 \t&\t46.54\t&\t34.59 \t&\t49.63 \t&\t37.08 \t&\t52.07 \t&\t37.15 \t&\t45.25 \t&\t33.81 \t&\t39.53 \t\\\\\n"," \n"," LayerMoE (Ours)\t&\t\\textbf{1.6B ($\\downarrow$ 60\\%)}\t&\t\\textbf{33.03} \t&\t\\textbf{26.66} \t&\t46.12 \t&\t34.80 \t&\t49.24 \t&\t37.01 \t&\t\\textbf{54.82} \t&\t\\textbf{40.37}\t&\t\\textbf{45.80} \t&\t\\textbf{34.71} \t&\t\\textbf{40.26} \t\\\\\n"," \\toprule\n"," \n"," \\multicolumn{13}{c}{\\textbf{Old (G0): \\textit{en}, \\textit{es}, \\textit{zh} \\!$\\rightarrow$\\! New (G2): \\textit{bn}, \\textit{hi}, \\textit{ne}}} \\\\\n"," \\bottomrule\n"," Qwen1.5-1.8B\t&\t/\t&\t33.31 \t&\t23.05 \t&\t47.23 \t&\t29.54 \t&\t49.70 \t&\t28.22 \t&\t55.89 \t&\t28.85 \t&\t46.53 \t&\t27.41 \t&\t36.97 \t\\\\\n"," \\hline\n"," \\rowcolor{gray!25}\n"," MoE-LPR (6*24)\t&\t4B\t&\t33.06 \t&\t23.85 \t&\t47.04 \t&\t30.62 \t&\t49.69 \t&\t30.92 \t&\t53.11 \t&\t33.85 \t&\t45.72 \t&\t29.81 \t&\t37.77 \t\\\\\n"," \n"," MOLA ($\\nabla$)\t&\t3.2B ($\\downarrow$ 20\\%)\t&\t32.48 \t&\t23.67 \t&\t47.02 \t&\t30.82 \t&\t49.73 \t&\t30.48 \t&\t53.59 \t&\t32.15 \t&\t45.71 \t&\t29.28 \t&\t37.49 \t\\\\\n","\n"," MoE-LPR (4*24)\t&\t2.4B ($\\downarrow$ 40\\%)\t&\t33.08 \t&\t23.27 \t&\t47.21 \t&\t30.74 \t&\t49.64 \t&\t30.67 \t&\t52.85 \t&\t33.04 \t&\t45.70 \t&\t29.43 \t&\t37.56 \t\\\\\n"," \n"," MoE-LPR (3*24)\t&\t1.6B ($\\downarrow$ 60\\%)\t&\t32.77 \t&\t23.53 \t&\t46.97 \t&\t30.72 \t&\t49.68 \t&\t30.56 \t&\t52.59 \t&\t33.33 \t&\t45.50 \t&\t29.53 \t&\t37.52 \t\\\\\n","\n"," LayerMoE (Ours)\t&\t\\textbf{1.6B ($\\downarrow$ 60\\%)}\t& 32.52 &\t\\textbf{24.02} &\t\\textbf{47.10} & \t\\textbf{30.85} & \t49.46 & \t30.47 &\t\\textbf{55.15} &\t\\textbf{34.11} & \\textbf{46.06} & \\textbf{29.86} & \\textbf{37.96} \\\\\n","\n"," \\toprule\n"," \\end{tabular}\n"," }\n"," \\caption{\n"," The results of the single-expansion settings (G0 \\!$\\rightarrow$\\! G1, G0 \\!$\\rightarrow$\\! G2). The results marked with $^*$ represent that they are directly taken from the MoE-LPR paper \\cite{zhou2024moelprmultilingualextensionlarge}, which utilizes 8 billion tokens for each new language during post-pretraining.\n"," Other experiments are trained with 2 billion tokens for each new language.\n"," ``Expand-params'' denotes the parameter number of newly added experts and {($\\downarrow$ *\\%)} means the reduction percentage of parameters compared to MoE-LPR (6*24).\n"," The results in \\textbf{bold} mean they are better than the previous SOTA method ``MoE-LPR (6*24)''.\n"," The detailed results for each language are reported in Table \\ref{table:single-res-detailed-g1} and \\ref{table:single-res-detailed-g2} of Appendix \\ref{sec:appendix-detailed-results}.\n"," }\n"," \\label{table:single-res}\n","\\end{table*}\n"]],"string":"[\n [\n \"\\\\begin{table*}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\resizebox*{\\\\linewidth}{!}{\\n\",\n \" \\\\begin{tabular}{lc|cccccccc|ccc}\\n\",\n \" \\\\bottomrule\\n\",\n \" & & \\\\multicolumn{2}{c}{\\\\textbf{ARC-C}} & \\\\multicolumn{2}{c}{\\\\textbf{MMLU}} & \\\\multicolumn{2}{c}{\\\\textbf{Hellaswag}} & \\\\multicolumn{2}{c|}{\\\\textbf{Belebele}} & & & \\\\\\\\\\n\",\n \" \\\\hline\\n\",\n \" \\\\textbf{Methods} & \\\\textbf{Expand-params} & \\\\textbf{Old} &\\t\\\\textbf{New} &\\t\\\\textbf{Old} &\\t\\\\textbf{New} & \\\\textbf{Old} &\\t\\\\textbf{New}\\t& \\\\textbf{Old}\\t& \\\\textbf{New} &\\t\\\\textbf{\\\\textit{Old-avg}} & \\\\textbf{\\\\textit{New-avg}} & \\\\textbf{\\\\textit{avg}}\\\\\\\\\\n\",\n \" \\n\",\n \" \\\\toprule\\n\",\n \" \\\\multicolumn{13}{c}{\\\\textbf{Old (G0): \\\\textit{en}, \\\\textit{es}, \\\\textit{zh} \\\\!$\\\\rightarrow$\\\\! New (G1): \\\\textit{el}, \\\\textit{hu}, \\\\textit{tr}}} \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" Qwen1.5-1.8B\\t&\\t/\\t&\\t33.31 \\t&\\t22.93 \\t&\\t47.23 \\t&\\t30.52 \\t&\\t49.70 \\t&\\t29.27 \\t&\\t55.89 \\t&\\t32.92 \\t&\\t46.53 \\t&\\t28.91 \\t&\\t37.72 \\t\\\\\\\\\\n\",\n \" \\\\hline\\n\",\n \" $^*$LoRA\\t&\\t/ & 28.33 &\\t23.89 &\\t37.42 & 29.30 & 41.48 & 29.78 & 39.45 & 26.93 & 36.67\\t& 27.48\\t& 32.07 \\\\\\\\\\n\",\n \" $^*$Full Fine-tuning\\t& / &\\t31.72 & 25.98 & 43.51 & 33.18 & 47.38 & 35.28 & 45.26 & 33.70 & 41.98 &\\t32.04 & \\t37.00 \\\\\\\\\\n\",\n \" $^*$LLaMA-Pro\\t& 0.6B &\\t31.77 & 24.35 & 44.06 & 34.02 & 48.36 & 33.85 & 48.78 & 31.52 & 43.24 &\\t30.94 & \\t37.09 \\\\\\\\\\n\",\n \" $^*$MoE\\t & 4B\\t& 32.51 & 26.43 & 44.16 & 35.07 & 48.54 & 37.01 & 45.37 & 32.74 & 42.65 &\\t32.81 &\\t37.73 \\\\\\\\\\n\",\n \" $^*$LoRAMoE\\t & 0.8B &\\t32.43 & 26.63 & 45.41 & 34.17 & 48.61 & 37.17 & 47.74 & 32.81 & 43.55\\t& 32.70 &\\t38.12 \\\\\\\\\\n\",\n \" \\\\hline\\n\",\n \" \\\\rowcolor{gray!25}\\n\",\n \" MoE-LPR (6*24)\\t&\\t4B\\t&\\t32.26 \\t&\\t26.55 \\t&\\t46.51 \\t&\\t34.95 \\t&\\t49.75 \\t&\\t37.53 \\t&\\t52.92 \\t&\\t39.04 \\t&\\t45.36 \\t&\\t34.52 \\t&\\t39.94 \\t\\\\\\\\\\n\",\n \" \\n\",\n \" MOLA ($\\\\nabla$)\\t&\\t3.2B ($\\\\downarrow$ 20\\\\%)\\t&\\t32.49 \\t&\\t27.00 \\t&\\t46.38 \\t&\\t33.98 \\t&\\t49.63 \\t&\\t37.03 \\t&\\t53.85 \\t&\\t38.37 \\t&\\t45.59 \\t&\\t34.10 \\t&\\t39.84 \\t\\\\\\\\\\n\",\n \" \\n\",\n \" MoE-LPR (4*24)\\t&\\t2.4B ($\\\\downarrow$ 40\\\\%)\\t&\\t32.29 \\t&\\t26.69 \\t&\\t46.51 \\t&\\t34.11 \\t&\\t49.60 \\t&\\t37.12 \\t&\\t52.33 \\t&\\t37.74 \\t&\\t45.18 \\t&\\t33.92 \\t&\\t39.55 \\t\\\\\\\\\\n\",\n \" \\n\",\n \" MoE-LPR (3*24)\\t&\\t1.6B ($\\\\downarrow$ 60\\\\%)\\t&\\t32.77 \\t&\\t26.43 \\t&\\t46.54\\t&\\t34.59 \\t&\\t49.63 \\t&\\t37.08 \\t&\\t52.07 \\t&\\t37.15 \\t&\\t45.25 \\t&\\t33.81 \\t&\\t39.53 \\t\\\\\\\\\\n\",\n \" \\n\",\n \" LayerMoE (Ours)\\t&\\t\\\\textbf{1.6B ($\\\\downarrow$ 60\\\\%)}\\t&\\t\\\\textbf{33.03} \\t&\\t\\\\textbf{26.66} \\t&\\t46.12 \\t&\\t34.80 \\t&\\t49.24 \\t&\\t37.01 \\t&\\t\\\\textbf{54.82} \\t&\\t\\\\textbf{40.37}\\t&\\t\\\\textbf{45.80} \\t&\\t\\\\textbf{34.71} \\t&\\t\\\\textbf{40.26} \\t\\\\\\\\\\n\",\n \" \\\\toprule\\n\",\n \" \\n\",\n \" \\\\multicolumn{13}{c}{\\\\textbf{Old (G0): \\\\textit{en}, \\\\textit{es}, \\\\textit{zh} \\\\!$\\\\rightarrow$\\\\! New (G2): \\\\textit{bn}, \\\\textit{hi}, \\\\textit{ne}}} \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" Qwen1.5-1.8B\\t&\\t/\\t&\\t33.31 \\t&\\t23.05 \\t&\\t47.23 \\t&\\t29.54 \\t&\\t49.70 \\t&\\t28.22 \\t&\\t55.89 \\t&\\t28.85 \\t&\\t46.53 \\t&\\t27.41 \\t&\\t36.97 \\t\\\\\\\\\\n\",\n \" \\\\hline\\n\",\n \" \\\\rowcolor{gray!25}\\n\",\n \" MoE-LPR (6*24)\\t&\\t4B\\t&\\t33.06 \\t&\\t23.85 \\t&\\t47.04 \\t&\\t30.62 \\t&\\t49.69 \\t&\\t30.92 \\t&\\t53.11 \\t&\\t33.85 \\t&\\t45.72 \\t&\\t29.81 \\t&\\t37.77 \\t\\\\\\\\\\n\",\n \" \\n\",\n \" MOLA ($\\\\nabla$)\\t&\\t3.2B ($\\\\downarrow$ 20\\\\%)\\t&\\t32.48 \\t&\\t23.67 \\t&\\t47.02 \\t&\\t30.82 \\t&\\t49.73 \\t&\\t30.48 \\t&\\t53.59 \\t&\\t32.15 \\t&\\t45.71 \\t&\\t29.28 \\t&\\t37.49 \\t\\\\\\\\\\n\",\n \"\\n\",\n \" MoE-LPR (4*24)\\t&\\t2.4B ($\\\\downarrow$ 40\\\\%)\\t&\\t33.08 \\t&\\t23.27 \\t&\\t47.21 \\t&\\t30.74 \\t&\\t49.64 \\t&\\t30.67 \\t&\\t52.85 \\t&\\t33.04 \\t&\\t45.70 \\t&\\t29.43 \\t&\\t37.56 \\t\\\\\\\\\\n\",\n \" \\n\",\n \" MoE-LPR (3*24)\\t&\\t1.6B ($\\\\downarrow$ 60\\\\%)\\t&\\t32.77 \\t&\\t23.53 \\t&\\t46.97 \\t&\\t30.72 \\t&\\t49.68 \\t&\\t30.56 \\t&\\t52.59 \\t&\\t33.33 \\t&\\t45.50 \\t&\\t29.53 \\t&\\t37.52 \\t\\\\\\\\\\n\",\n \"\\n\",\n \" LayerMoE (Ours)\\t&\\t\\\\textbf{1.6B ($\\\\downarrow$ 60\\\\%)}\\t& 32.52 &\\t\\\\textbf{24.02} &\\t\\\\textbf{47.10} & \\t\\\\textbf{30.85} & \\t49.46 & \\t30.47 &\\t\\\\textbf{55.15} &\\t\\\\textbf{34.11} & \\\\textbf{46.06} & \\\\textbf{29.86} & \\\\textbf{37.96} \\\\\\\\\\n\",\n \"\\n\",\n \" \\\\toprule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \" \\\\caption{\\n\",\n \" The results of the single-expansion settings (G0 \\\\!$\\\\rightarrow$\\\\! G1, G0 \\\\!$\\\\rightarrow$\\\\! G2). The results marked with $^*$ represent that they are directly taken from the MoE-LPR paper \\\\cite{zhou2024moelprmultilingualextensionlarge}, which utilizes 8 billion tokens for each new language during post-pretraining.\\n\",\n \" Other experiments are trained with 2 billion tokens for each new language.\\n\",\n \" ``Expand-params'' denotes the parameter number of newly added experts and {($\\\\downarrow$ *\\\\%)} means the reduction percentage of parameters compared to MoE-LPR (6*24).\\n\",\n \" The results in \\\\textbf{bold} mean they are better than the previous SOTA method ``MoE-LPR (6*24)''.\\n\",\n \" The detailed results for each language are reported in Table \\\\ref{table:single-res-detailed-g1} and \\\\ref{table:single-res-detailed-g2} of Appendix \\\\ref{sec:appendix-detailed-results}.\\n\",\n \" }\\n\",\n \" \\\\label{table:single-res}\\n\",\n \"\\\\end{table*}\\n\"\n ]\n]","isConversational":false},"tables":{"kind":"list like","value":[["\\begin{figure}[t]\n"," \\centering\n"," \\includegraphics[width=\\linewidth]{p1-v1.jpg}\n"," \\caption{The similarity of HSAs in different languages across all layers within Qwen1.5-1.8B.}\n"," \\label{fig:similarity}\n","\\end{figure}\n"],["\\begin{figure*}[t]\n"," \\centering\n"," \\includegraphics[width=\\linewidth]{main-v5.jpg}\n"," \\caption{The procedure of our LayerMoE. In Step 1, we first calculate the indicated similarity for each layer. In Step 2, we allocate experts based on the similarity. The layers with lower similarity are allocated more experts, while those with higher similarity are assigned fewer experts. Additionally, we add a classifier in front of the router in the layers with higher similarity to guide the routing of old language tokens.}\n"," \\label{fig:model}\n","\\end{figure*}\n"],["\\begin{figure}[t]\n"," \\centering\n"," \\includegraphics[width=\\linewidth]{p3-v3.jpg}\n"," \\caption{The expert allocation of our method and baselines for each layer under G0 \\!$\\rightarrow$\\! G1, G0 \\!$\\rightarrow$\\! G2.}\n"," \\label{fig:allocation}\n","\\end{figure}\n"],["\\begin{table*}[t]\n"," \\centering\n"," \\resizebox*{\\linewidth}{!}{\n"," \\begin{tabular}{lc|cccccccc|ccc}\n"," \\bottomrule\n"," & & \\multicolumn{2}{c}{\\textbf{ARC-C}} & \\multicolumn{2}{c}{\\textbf{MMLU}} & \\multicolumn{2}{c}{\\textbf{Hellaswag}} & \\multicolumn{2}{c|}{\\textbf{Belebele}} & & & \\\\\n"," \\hline\n"," \\textbf{Methods} & \\textbf{Expand-params} & \\textbf{Old} &\t\\textbf{New} &\t\\textbf{Old} &\t\\textbf{New} & \\textbf{Old} &\t\\textbf{New}\t& \\textbf{Old}\t& \\textbf{New} &\t\\textbf{\\textit{Old-avg}} & \\textbf{\\textit{New-avg}} & \\textbf{\\textit{avg}}\\\\\n"," \n"," \\toprule\n"," \\multicolumn{13}{c}{\\textbf{Old (G0): \\textit{en}, \\textit{es}, \\textit{zh} \\!$\\rightarrow$\\! New (G1): \\textit{el}, \\textit{hu}, \\textit{tr}}} \\\\\n"," \\bottomrule\n"," Qwen1.5-1.8B\t&\t/\t&\t33.31 \t&\t22.93 \t&\t47.23 \t&\t30.52 \t&\t49.70 \t&\t29.27 \t&\t55.89 \t&\t32.92 \t&\t46.53 \t&\t28.91 \t&\t37.72 \t\\\\\n"," \\hline\n"," $^*$LoRA\t&\t/ & 28.33 &\t23.89 &\t37.42 & 29.30 & 41.48 & 29.78 & 39.45 & 26.93 & 36.67\t& 27.48\t& 32.07 \\\\\n"," $^*$Full Fine-tuning\t& / &\t31.72 & 25.98 & 43.51 & 33.18 & 47.38 & 35.28 & 45.26 & 33.70 & 41.98 &\t32.04 & \t37.00 \\\\\n"," $^*$LLaMA-Pro\t& 0.6B &\t31.77 & 24.35 & 44.06 & 34.02 & 48.36 & 33.85 & 48.78 & 31.52 & 43.24 &\t30.94 & \t37.09 \\\\\n"," $^*$MoE\t & 4B\t& 32.51 & 26.43 & 44.16 & 35.07 & 48.54 & 37.01 & 45.37 & 32.74 & 42.65 &\t32.81 &\t37.73 \\\\\n"," $^*$LoRAMoE\t & 0.8B &\t32.43 & 26.63 & 45.41 & 34.17 & 48.61 & 37.17 & 47.74 & 32.81 & 43.55\t& 32.70 &\t38.12 \\\\\n"," \\hline\n"," \\rowcolor{gray!25}\n"," MoE-LPR (6*24)\t&\t4B\t&\t32.26 \t&\t26.55 \t&\t46.51 \t&\t34.95 \t&\t49.75 \t&\t37.53 \t&\t52.92 \t&\t39.04 \t&\t45.36 \t&\t34.52 \t&\t39.94 \t\\\\\n"," \n"," MOLA ($\\nabla$)\t&\t3.2B ($\\downarrow$ 20\\%)\t&\t32.49 \t&\t27.00 \t&\t46.38 \t&\t33.98 \t&\t49.63 \t&\t37.03 \t&\t53.85 \t&\t38.37 \t&\t45.59 \t&\t34.10 \t&\t39.84 \t\\\\\n"," \n"," MoE-LPR (4*24)\t&\t2.4B ($\\downarrow$ 40\\%)\t&\t32.29 \t&\t26.69 \t&\t46.51 \t&\t34.11 \t&\t49.60 \t&\t37.12 \t&\t52.33 \t&\t37.74 \t&\t45.18 \t&\t33.92 \t&\t39.55 \t\\\\\n"," \n"," MoE-LPR (3*24)\t&\t1.6B ($\\downarrow$ 60\\%)\t&\t32.77 \t&\t26.43 \t&\t46.54\t&\t34.59 \t&\t49.63 \t&\t37.08 \t&\t52.07 \t&\t37.15 \t&\t45.25 \t&\t33.81 \t&\t39.53 \t\\\\\n"," \n"," LayerMoE (Ours)\t&\t\\textbf{1.6B ($\\downarrow$ 60\\%)}\t&\t\\textbf{33.03} \t&\t\\textbf{26.66} \t&\t46.12 \t&\t34.80 \t&\t49.24 \t&\t37.01 \t&\t\\textbf{54.82} \t&\t\\textbf{40.37}\t&\t\\textbf{45.80} \t&\t\\textbf{34.71} \t&\t\\textbf{40.26} \t\\\\\n"," \\toprule\n"," \n"," \\multicolumn{13}{c}{\\textbf{Old (G0): \\textit{en}, \\textit{es}, \\textit{zh} \\!$\\rightarrow$\\! New (G2): \\textit{bn}, \\textit{hi}, \\textit{ne}}} \\\\\n"," \\bottomrule\n"," Qwen1.5-1.8B\t&\t/\t&\t33.31 \t&\t23.05 \t&\t47.23 \t&\t29.54 \t&\t49.70 \t&\t28.22 \t&\t55.89 \t&\t28.85 \t&\t46.53 \t&\t27.41 \t&\t36.97 \t\\\\\n"," \\hline\n"," \\rowcolor{gray!25}\n"," MoE-LPR (6*24)\t&\t4B\t&\t33.06 \t&\t23.85 \t&\t47.04 \t&\t30.62 \t&\t49.69 \t&\t30.92 \t&\t53.11 \t&\t33.85 \t&\t45.72 \t&\t29.81 \t&\t37.77 \t\\\\\n"," \n"," MOLA ($\\nabla$)\t&\t3.2B ($\\downarrow$ 20\\%)\t&\t32.48 \t&\t23.67 \t&\t47.02 \t&\t30.82 \t&\t49.73 \t&\t30.48 \t&\t53.59 \t&\t32.15 \t&\t45.71 \t&\t29.28 \t&\t37.49 \t\\\\\n","\n"," MoE-LPR (4*24)\t&\t2.4B ($\\downarrow$ 40\\%)\t&\t33.08 \t&\t23.27 \t&\t47.21 \t&\t30.74 \t&\t49.64 \t&\t30.67 \t&\t52.85 \t&\t33.04 \t&\t45.70 \t&\t29.43 \t&\t37.56 \t\\\\\n"," \n"," MoE-LPR (3*24)\t&\t1.6B ($\\downarrow$ 60\\%)\t&\t32.77 \t&\t23.53 \t&\t46.97 \t&\t30.72 \t&\t49.68 \t&\t30.56 \t&\t52.59 \t&\t33.33 \t&\t45.50 \t&\t29.53 \t&\t37.52 \t\\\\\n","\n"," LayerMoE (Ours)\t&\t\\textbf{1.6B ($\\downarrow$ 60\\%)}\t& 32.52 &\t\\textbf{24.02} &\t\\textbf{47.10} & \t\\textbf{30.85} & \t49.46 & \t30.47 &\t\\textbf{55.15} &\t\\textbf{34.11} & \\textbf{46.06} & \\textbf{29.86} & \\textbf{37.96} \\\\\n","\n"," \\toprule\n"," \\end{tabular}\n"," }\n"," \\caption{\n"," The results of the single-expansion settings (G0 \\!$\\rightarrow$\\! G1, G0 \\!$\\rightarrow$\\! G2). The results marked with $^*$ represent that they are directly taken from the MoE-LPR paper \\cite{zhou2024moelprmultilingualextensionlarge}, which utilizes 8 billion tokens for each new language during post-pretraining.\n"," Other experiments are trained with 2 billion tokens for each new language.\n"," ``Expand-params'' denotes the parameter number of newly added experts and {($\\downarrow$ *\\%)} means the reduction percentage of parameters compared to MoE-LPR (6*24).\n"," The results in \\textbf{bold} mean they are better than the previous SOTA method ``MoE-LPR (6*24)''.\n"," The detailed results for each language are reported in Table \\ref{table:single-res-detailed-g1} and \\ref{table:single-res-detailed-g2} of Appendix \\ref{sec:appendix-detailed-results}.\n"," }\n"," \\label{table:single-res}\n","\\end{table*}\n"],["\\begin{table*}[t]\n"," \\centering\n"," \\resizebox*{\\linewidth}{!}{\n"," \\begin{tabular}{lcccc|cccc}\n"," \\bottomrule\n"," \\textbf{Methods} & \\textbf{Order} & \\textbf{G1-E-p} & \\textbf{G2-E-p} & \\textbf{All-E-p} & \\textbf{\\textit{G0-avg}} & \\textbf{\\textit{G1-avg}} & \\textbf{\\textit{G2-avg}} & \\textbf{\\textit{avg}} \\\\\n"," \\hline \n"," Qwen1.5-1.8B\t& / & / & / & / & 46.53\t& 28.91 & 27.41 & 34.29\t\\\\\n"," \\hline\n"," MoE-LPR (7*24) & G0 \\!$\\rightarrow$\\! G1 \\!$+$\\! G2 & 4.8B & 4.8B & 4.8B &\t45.15 &\t34.17 &\t29.98 & 36.43 \\\\\n"," MoE-LPR (4*24) & G0 \\!$\\rightarrow$\\! G1 \\!$+$\\! G2 & 2.4B & 2.4B & 2.4B ($\\downarrow$ 50.0\\%) & 45.28 &\t34.07 &\t29.55 & 36.30 \\\\\n"," LayerMoE (Ours) & G0 \\!$\\rightarrow$\\! G1 \\!$+$\\! G2 & 1.6B & 1.6B & \\textbf{1.6B ($\\downarrow$ \\textbf{66.7\\%})} & \\textbf{45.56} &\t\\textbf{34.73} &\t29.94 & \\textbf{36.74} \\\\\n"," \n"," \\hline\n"," MoE-LPR (7*24) & G0 \\!$\\rightarrow$\\! G1 \\!$\\rightarrow$\\! G2 & 2.4B & 2.4B & 4.8B & 45.88\t& 34.40\t& 30.26 & 36.85 \\\\\n"," LayerMoE (Ours) & G0 \\!$\\rightarrow$\\! G1 \\!$\\rightarrow$\\! G2 & 1.6B & 1.6B & \\textbf{3.2B ($\\downarrow$ \\textbf{33.3\\%})} & \\textbf{46.10} &\t\\textbf{34.56} &\t\\textbf{30.29} & \\textbf{36.98} \\\\\n"," \\hline\n"," MoE-LPR (7*24) & G0 \\!$\\rightarrow$\\! G2 \\!$\\rightarrow$\\! G1 & 2.4B & 2.4B & 4.8B & 45.28 &\t33.42 &\t28.39 & 35.70 \\\\\n"," LayerMoE (Ours) & G0 \\!$\\rightarrow$\\! G2 \\!$\\rightarrow$\\! G1 & 1.6B & 1.6B & \\textbf{3.2B ($\\downarrow$ \\textbf{33.3\\%})} & \\textbf{46.25} & \\textbf{33.76} &\t\\textbf{29.80} & \\textbf{36.60} \\\\\n","\n"," \\toprule\n"," \\end{tabular}\n"," }\n"," \\caption{\n"," The results of G0 \\!$\\rightarrow$\\! G1 \\!$+$\\! G2 and the lifelong-expansion settings (G0 \\!$\\rightarrow$ \\!G1 \\!$\\rightarrow$ \\!G2 and G0 \\!$\\rightarrow$ \\!G2 $\\rightarrow$ \\!G1).\n"," ``G1/G2/All-E-p'' represents the expansion number of newly added parameters for G1, G2, and the sum, respectively.\n"," The detailed results for each language are reported in Table \\ref{table:mixed-res-detailed} of Appendix \\ref{sec:appendix-detailed-results}.\n"," }\n"," \\label{table:lifelong-res}\n","\\end{table*}\n"],["\\begin{table}[t]\n"," \\centering\n"," \\resizebox*{\\linewidth}{!}{\n"," \\begin{tabular}{l|ll|c}\n"," \\bottomrule\n"," \t& \\textbf{\\textit{Old-avg} ($\\uparrow$)} & \\textbf{\\textit{New-avg} ($\\uparrow$)} & \\textbf{\\textit{avg} ($\\uparrow$)}\\\\\n"," \n"," \\hline\n","\n"," LayerMoE (G0 \\!$\\rightarrow$\\! G1)\t&\t\\textbf{45.80} \t&\t\\textbf{34.71} \t&\t\\textbf{40.26} \t\\\\\n"," \\quad \\textit{w/} random & 45.68 ($\\downarrow$ 0.12) & 34.11 ($\\downarrow$ \\textbf{0.60}) & 39.90 \\\\\n"," \\quad \\textit{w/o} classifier & 45.33 ($\\downarrow$ \\textbf{0.47}) & 34.61 ($\\downarrow$ 0.10) & 39.97 \\\\\n","\\hline\n"," LayerMoE (G0 \\!$\\rightarrow$\\! G2)\t&\t\\textbf{46.06} \t&\t\\textbf{29.86} \t&\t\\textbf{37.96} \t\\\\\n"," \\quad \\textit{w/} random & 45.94 ($\\downarrow$ 0.12) & 29.15 ($\\downarrow$ \\textbf{0.71}) & 37.54 \\\\\n"," \\quad \\textit{w/o} classifier & 45.39 ($\\downarrow$ \\textbf{0.67}) & 29.53 ($\\downarrow$ 0.33) & 37.46 \\\\\n"," \n"," \\toprule\n"," \\end{tabular}\n"," }\n"," \\caption{\n"," The ablation results under G0 \\!$\\rightarrow$\\! G1 and G0 \\!$\\rightarrow$\\! G2. ``\\textit{w/} random'' means randomly allocating numbers of new experts for each layer without calculating the similarity. ``\\textit{w/o} classifier'' denotes no classifier is added in front of the router network.\n"," }\n"," \\label{table:ablation-study}\n","\\end{table}\n"],["\\begin{table}[t]\n"," \\centering\n"," \\resizebox*{\\linewidth}{!}{\n"," \\begin{tabular}{l|ccc|l|ccc}\n"," \\bottomrule\n"," \t& \\textbf{\\textit{Old} } & \\textbf{\\textit{New} } & \\textbf{\\textit{avg}} & & \\textbf{\\textit{Old} } & \\textbf{\\textit{New} } & \\textbf{\\textit{avg}} \\\\\n"," \n"," \\hline\n"," top-1 & 44.90 & 34.30 & 39.60 & \\textbf{top-7} & 45.80 & 34.71 & \\textbf{40.26} \\\\\n"," top-2 & 44.81 & 34.69 & 39.75 & top-8 & 45.79 & 34.54 & 40.17 \\\\\n"," top-3 & 45.10 & 34.41 & 39.76 & top-9 & 45.95 & 34.42 & 40.19 \\\\\n"," top-4 & 45.41 & 34.67 & 40.04 & top-10 & \\textbf{46.16} & 34.28 & 40.22 \\\\\n"," top-5 & 45.28 & 34.66 & 39.97 & top-24 & 46.04 & 34.32 & 40.18 \\\\\n"," top-6 & 45.74 & \\textbf{34.72} & 40.23 & top-23 & 46.15 & 33.23 & 39.69 \\\\\n"," \\hline\n"," last-7 & 44.96 & 34.60 & 39.78 & random-7 & 45.32 & 34.60 & 39.96 \\\\\n"," \n"," \\toprule\n"," \\end{tabular}\n"," }\n"," \\caption{\n"," The results of adding classifiers in different layers under G0 \\!$\\rightarrow$\\! G1.\n"," ``top-*'' denotes adding classifiers to * layers with the highest similarity.\n"," ``random-7'' means randomly selecting 7 layers, while ``last-7'' represents the 7 layers with the lowest similarity.\n"," }\n"," \\label{table:layers-calssifier}\n","\\end{table}\n"],["\\begin{table*}[t]\n"," \\centering\n"," \\resizebox*{\\linewidth}{!}{\n"," \\begin{tabular}{l|cccccccccc|ccc}\n"," \\bottomrule\n"," & \\multicolumn{2}{c}{\\textbf{ARC-C}} & \\multicolumn{2}{c}{\\textbf{MMLU}} & \\multicolumn{2}{c}{\\textbf{Hellaswag}} & \\multicolumn{2}{c}{\\textbf{Belebele}} & \\multicolumn{2}{c|}{\\textbf{FLORES}} & & & \\\\\n"," \\hline\n"," \\textbf{Methods} & \\textbf{Old} &\t\\textbf{New} &\t\\textbf{Old} &\t\\textbf{New} & \\textbf{Old} &\t\\textbf{New}\t& \\textbf{Old}\t& \\textbf{New} & \\textbf{Old}\t& \\textbf{New} &\\textbf{\\textit{Old-avg}} & \\textbf{\\textit{New-avg}} & \\textbf{\\textit{avg}}\\\\\n"," \n"," \\hline\n"," Llama-3.2-3B & 44.64 & 33.39 & \t49.84 & 42.15\t & 63.56 & 43.62 & \t70.48 & 61.37 & \t84.88 & 83.96\t & 62.68 & \t52.90 & \t57.79 \\\\\n"," \\hline\n"," MoE-LPR (3*24) & \t43.70 & 37.58 & \t49.99 & 43.98 & \t64.17 & 51.19 & \t70.74 & 64.41\t & 84.58 & 84.35 & \t62.64 & \t56.30\t & 59.47\t\\\\\n"," LayerMoE (Ours)\t& \\textbf{44.10}\t& \\textbf{37.64}\t& \t49.96\t& \\textbf{44.08}\t& \t64.07\t& \\textbf{51.33}\t& \t\\textbf{71.22}\t& \\textbf{64.63}\t& \t\\textbf{84.72}\t& \\textbf{84.69}\t\t& \\textbf{62.81}\t\t& \\textbf{56.47}\t& \t\\textbf{59.64}\n","\t\\\\\n"," \\toprule\n"," \n"," \\end{tabular}\n"," }\n"," \\caption{\n"," The results of Llama-3.2-3B under the single-expansion setting (G0 \\!$\\rightarrow$\\! G1).\n"," The detailed results for each language are reported in Table \\ref{table:llama-res-detailed-1} and \\ref{table:llama-res-detailed-2} of Appendix \\ref{sec:appendix-detailed-results}.\n"," }\n"," \\label{table:llama-res}\n","\\end{table*}\n"],["\\begin{table*}[h]\n"," \\centering\n"," \\resizebox*{0.8\\linewidth}{!}{\n"," \\begin{tabular}{l|ll}\n"," \\bottomrule\n"," Languages\t& \\textbf{Language Family} & \\textbf{Writing System} \\\\\n"," \n"," \\hline\n","\n"," English (\\textit{en}) & Indo-European (Germanic) & Latin alphabet (26 letters) \\\\\n"," Chinese (\\textit{zh}) & Sino-Tibetan (Sinitic) & Chinese characters \\\\\n"," Spanish (\\textit{es}) & Indo-European (Romance) & Latin alphabet (27 letters, including ñ) \\\\\n"," Greek (\\textit{el}) & Indo-European (Hellenic) & Greek alphabet (24 letters) \\\\\n"," Hungarian (\\textit{hu}) & Uralic (Finno-Ugric) & Latin alphabet with diacritics (40 letters) \\\\\n"," Turkish (\\textit{tr}) & Turkic & Latin alphabet (29 letters) \\\\\n"," Bengali (\\textit{bn}) & Indo-European (Indo-Aryan) & Bengali script \\\\\n"," Nepali (\\textit{ne}) & Indo-European (Indo-Aryan) & Devanagari script \\\\\n"," Hindi (\\textit{hi}) & Indo-European (Indo-Aryan) & Devanagari script \\\\\n","\n","\n"," \n"," \\toprule\n"," \\end{tabular}\n"," }\n"," \\caption{\n"," The detailed language families and writing systems.\n"," }\n"," \\label{table:languages}\n","\\end{table*}\n"],["\\begin{figure*}[h]\n","\t\\centering %图片全局居中\n","\t\\subfigbottomskip=2pt %两行子图之间的行间距\n","\t\\subfigcapskip=-5pt %设置子图与子标题之间的距离\n","\t\\subfigure[G0 \\!$\\rightarrow$\\! G1]{\n","\t\t\\includegraphics[width=0.32\\linewidth]{p5-1.jpg}}\n","\t\\subfigure[G0 \\!$\\rightarrow$\\! G2]{\n","\t\t\\includegraphics[width=0.32\\linewidth]{p5-2.jpg}}\n"," \\subfigure[G0 \\!$\\rightarrow$\\! G1 \\!$+$\\! G2]{\n","\t\t\\includegraphics[width=0.32\\linewidth]{p5-3.jpg}}\n"," \\\\\n"," \\subfigure[G0 \\!$\\rightarrow$ \\!G1 \\!$\\rightarrow$ \\!G2]{\n","\t\t\\includegraphics[width=0.49\\linewidth]{p5-4.jpg}}\n","\t\\subfigure[G0 \\!$\\rightarrow$ \\!G2 $\\rightarrow$ \\!G1]{\n","\t\t\\includegraphics[width=0.49\\linewidth]{p5-5.jpg}}\n","\t\\caption{The detailed similarity and expert allocation of our method for each layer of Qwen1.5-1.8B under the five settings.}\n"," \\label{fig:sim_experts}\n","\\end{figure*}\n"],["\\begin{table*}[h]\n"," \\centering\n"," \\resizebox*{\\linewidth}{!}{\n"," \\begin{tabular}{l|ccc|ccc|ccc|ccc|c}\n"," \\bottomrule\n","& \\multicolumn{3}{c|}{\\textbf{ARC-Challenge}} & \\multicolumn{3}{c|}{\\textbf{MMLU}} & \\multicolumn{3}{c|}{\\textbf{Hellaswag}} & \\multicolumn{3}{c|}{\\textbf{Belebele}} & \\\\\n","\\hline\n","\\textbf{Methods}\t&\t\\textbf{en}\t&\t\\textbf{es}\t&\t\\textbf{zh}\t&\t\\textbf{en}\t&\t\\textbf{es}\t&\t\\textbf{zh}\t&\t\\textbf{en}\t&\t\\textbf{es}\t&\t\\textbf{zh}\t&\t\\textbf{en}\t&\t\\textbf{es}\t&\t\\textbf{zh}\t&\t\\textbf{\\textit{G0-avg}}\t\\\\\n","\\hline\n","Qwen1.5-1.8B\t&\t37.97 \t&\t27.86 \t&\t34.10 \t&\t45.67 \t&\t38.81 \t&\t57.21 \t&\t61.62 \t&\t39.35 \t&\t48.14 \t&\t60.78 \t&\t47.44 \t&\t59.44 \t&\t46.53 \t\\\\\n","\\hline\n","MoE-LPR (6*24)\t&\t38.05 \t&\t26.84 \t&\t31.88 \t&\t45.77 \t&\t38.26 \t&\t55.50 \t&\t61.34 \t&\t39.41 \t&\t48.49 \t&\t58.44 \t&\t45.89 \t&\t54.44 \t&\t45.36 \t\\\\\n","MOLA\t&\t37.88 \t&\t26.84 \t&\t32.74 \t&\t45.81 \t&\t38.30 \t&\t55.02 \t&\t61.36 \t&\t39.27 \t&\t48.26 \t&\t60.11 \t&\t47.11 \t&\t54.33 \t&\t45.59 \t\\\\\n","MoE-LPR (4*24)\t&\t36.69 \t&\t27.35 \t&\t32.82 \t&\t46.09 \t&\t38.57 \t&\t54.87 \t&\t61.17 \t&\t39.37 \t&\t48.27 \t&\t58.33 \t&\t44.22 \t&\t54.44 \t&\t45.18 \t\\\\\n","MoE-LPR (3*24)\t&\t37.46 \t&\t27.35 \t&\t33.50 \t&\t46.00 \t&\t38.67 \t&\t54.94 \t&\t61.20 \t&\t39.43 \t&\t48.25 \t&\t59.44 \t&\t44.44 \t&\t52.33 \t&\t45.25 \t\\\\\n","LayerMoE (Ours)\t&\t38.57 \t&\t26.58 \t&\t33.93 \t&\t45.81 \t&\t37.97 \t&\t54.59 \t&\t60.90 \t&\t38.98 \t&\t47.84 \t&\t60.56 \t&\t46.89 \t&\t57.00 \t&\t\\textbf{45.80} \t\\\\ \n","\\toprule\n","\\bottomrule\n","\\textbf{Methods}\t&\t\\textbf{el}\t&\t\\textbf{hu}\t&\t\\textbf{tr}\t&\t\\textbf{el}\t&\t\\textbf{hu}\t&\t\\textbf{tr}\t&\t\\textbf{el}\t&\t\\textbf{hu}\t&\t\\textbf{tr}\t&\t\\textbf{el}\t&\t\\textbf{hu}\t&\t\\textbf{tr}\t&\t\\textbf{\\textit{G1-avg}}\t\\\\\n","\\hline\n","Qwen1.5-1.8B\t&\t22.95 \t&\t23.20 \t&\t22.63 \t&\t28.94 \t&\t32.25 \t&\t30.37 \t&\t29.15 \t&\t29.63 \t&\t29.04 \t&\t33.33 \t&\t32.00 \t&\t33.44 \t&\t28.91 \t\\\\\n","\\hline\n","MoE-LPR (6*24)\t&\t25.26 \t&\t27.14 \t&\t27.24 \t&\t32.06 \t&\t36.30 \t&\t36.49 \t&\t36.09 \t&\t38.63 \t&\t37.87 \t&\t38.33 \t&\t36.56 \t&\t42.22 \t&\t34.52 \t\\\\\n","MOLA\t&\t25.94 \t&\t27.40 \t&\t27.67 \t&\t31.43 \t&\t35.80 \t&\t34.71 \t&\t35.85 \t&\t37.79 \t&\t37.46 \t&\t35.22 \t&\t39.00 \t&\t40.89 \t&\t34.10 \t\\\\\n","MoE-LPR (4*24)\t&\t25.34 \t&\t27.57 \t&\t27.16 \t&\t31.58 \t&\t35.79 \t&\t34.97 \t&\t35.70 \t&\t38.26 \t&\t37.39 \t&\t34.44 \t&\t40.22 \t&\t38.56 \t&\t33.92 \t\\\\\n","MoE-LPR (3*24)\t&\t24.32 \t&\t27.05 \t&\t27.92 \t&\t31.90 \t&\t36.27 \t&\t35.59 \t&\t35.55 \t&\t38.18 \t&\t37.51 \t&\t32.00 \t&\t39.56 \t&\t39.89 \t&\t33.81 \t\\\\\n","LayerMoE (Ours)\t&\t25.34 \t&\t26.80 \t&\t27.84 \t&\t31.96 \t&\t36.76 \t&\t35.68 \t&\t35.61 \t&\t38.10 \t&\t37.31 \t&\t37.44 \t&\t41.56 \t&\t42.11 \t&\t\\textbf{34.71} \t\\\\\n","\n"," \\toprule\n"," \\end{tabular}\n"," }\n"," \\caption{\n"," The detailed results of Qwen1.5-1.8B under the G0 \\!$\\rightarrow$\\! G1 setting for each language. \n"," }\n"," \\label{table:single-res-detailed-g1}\n","\\end{table*}\n"],["\\begin{table*}[ht]\n"," \\centering\n"," \\resizebox*{\\linewidth}{!}{\n"," \\begin{tabular}{l|ccc|ccc|ccc|ccc|c}\n"," \\bottomrule\n","& \\multicolumn{3}{c|}{\\textbf{ARC-Challenge}} & \\multicolumn{3}{c|}{\\textbf{MMLU}} & \\multicolumn{3}{c|}{\\textbf{Hellaswag}} & \\multicolumn{3}{c|}{\\textbf{Belebele}} & \\\\\n","\\hline\n","\\textbf{Methods}\t&\t\\textbf{en}\t&\t\\textbf{es}\t&\t\\textbf{zh}\t&\t\\textbf{en}\t&\t\\textbf{es}\t&\t\\textbf{zh}\t&\t\\textbf{en}\t&\t\\textbf{es}\t&\t\\textbf{zh}\t&\t\\textbf{en}\t&\t\\textbf{es}\t&\t\\textbf{zh}\t&\t\\textbf{\\textit{G0-avg}}\t\\\\\n","\\hline\n","Qwen1.5-1.8B\t&\t37.97 \t&\t27.86 \t&\t34.10 \t&\t45.67 \t&\t38.81 \t&\t57.21 \t&\t61.62 \t&\t39.35 \t&\t48.14 \t&\t60.78 \t&\t47.44 \t&\t59.44 \t&\t46.53 \t\\\\\n","\\hline\n","MoE-LPR (6*24)\t&\t37.29 \t&\t27.26 \t&\t34.62 \t&\t46.05 \t&\t38.46 \t&\t56.60 \t&\t61.73 \t&\t39.27 \t&\t48.07 \t&\t59.67 \t&\t46.33 \t&\t53.33 \t&\t45.72 \t\\\\\n","MOLA\t&\t37.37 \t&\t27.26 \t&\t32.82 \t&\t45.88 \t&\t38.71 \t&\t56.48 \t&\t61.76 \t&\t39.09 \t&\t48.34 \t&\t59.56 \t&\t46.22 \t&\t55.00 \t&\t45.71 \t\\\\\n","MoE-LPR (4*24)\t&\t37.80 \t&\t27.18 \t&\t34.27 \t&\t46.00 \t&\t38.88 \t&\t56.76 \t&\t61.65 \t&\t39.03 \t&\t48.24 \t&\t59.67 \t&\t45.22 \t&\t53.67 \t&\t45.70 \t\\\\\n","MoE-LPR (3*24)\t&\t37.80 \t&\t27.09 \t&\t33.42 \t&\t45.52 \t&\t38.62 \t&\t56.78 \t&\t61.59 \t&\t39.13 \t&\t48.32 \t&\t59.89 \t&\t45.33 \t&\t52.56 \t&\t45.50 \t\\\\\n","LayerMoE (Ours)\t&\t36.86 \t&\t27.44 \t&\t33.25 \t&\t46.15 \t&\t39.05 \t&\t56.09 \t&\t61.50 \t&\t39.11 \t&\t47.77 \t&\t61.89 \t&\t46.67 \t&\t56.89 \t&\t\\textbf{46.06} \t\\\\\n","\\toprule\n","\\bottomrule\n","\\textbf{Methods}\t&\t\\textbf{bn}\t&\t\\textbf{hi}\t&\t\\textbf{ne}\t&\t\\textbf{bn}\t&\t\\textbf{hi}\t&\t\\textbf{ne}\t& \\textbf{bn}\t&\t\\textbf{hi}\t&\t\\textbf{ne}\t& \\textbf{bn}\t&\t\\textbf{hi}\t&\t\\textbf{ne}\t&\t\\textbf{\\textit{G2-avg}}\t\\\\\n","\\hline\n","Qwen1.5-1.8B\t&\t22.50 \t&\t23.46 \t&\t23.18 \t&\t29.59 \t&\t29.89 \t&\t29.13 \t&\t28.23 \t&\t28.48 \t&\t27.95 \t&\t29.67 \t&\t30.00 \t&\t26.89 \t&\t27.41 \t\\\\\n","\\hline\n","MoE-LPR (6*24)\t&\t24.64 \t&\t25.26 \t&\t21.64 \t&\t30.29 \t&\t30.57 \t&\t31.01 \t&\t29.90 \t&\t32.78 \t&\t30.08 \t&\t33.22 \t&\t35.22 \t&\t33.11 \t&\t29.81 \t\\\\\n","MOLA\t&\t24.29 \t&\t25.09 \t&\t21.64 \t&\t31.01 \t&\t30.67 \t&\t30.77 \t&\t29.44 \t&\t32.54 \t&\t29.46 \t&\t31.00 \t&\t33.33 \t&\t32.11 \t&\t29.28 \t\\\\\n","MoE-LPR (4*24)\t&\t23.52 \t&\t24.91 \t&\t21.39 \t&\t30.73 \t&\t30.54 \t&\t30.94 \t&\t30.02 \t&\t32.55 \t&\t29.45 \t&\t32.11 \t&\t34.33 \t&\t32.67 \t&\t29.43 \t\\\\\n","MoE-LPR (3*24)\t&\t24.98 \t&\t24.57 \t&\t21.04 \t&\t31.59 \t&\t30.87 \t&\t29.70 \t&\t29.55 \t&\t32.68 \t&\t29.44 \t&\t32.44 \t&\t34.33 \t&\t33.22 \t&\t29.53 \t\\\\\n","LayerMoE (Ours)\t&\t24.21 \t&\t25.26 \t&\t22.58 \t&\t31.10 \t&\t30.43 \t&\t31.01 \t&\t29.31 \t&\t32.42 \t&\t29.67 \t&\t33.44 \t&\t33.44 \t&\t35.44 \t&\t\\textbf{29.86} \t\\\\\n"," \\toprule\n"," \\end{tabular}\n"," }\n"," \\caption{\n"," The detailed results of Qwen1.5-1.8B under the G0 \\!$\\rightarrow$\\! G2 setting for each language. \n"," }\n"," \\label{table:single-res-detailed-g2}\n","\\end{table*}\n"],["\\begin{table*}[ht]\n"," \\centering\n"," \\resizebox*{\\linewidth}{!}{\n"," \\begin{tabular}{lc|ccc|ccc|ccc|ccc|c}\n"," \\bottomrule\n","& & \\multicolumn{3}{c|}{\\textbf{ARC-Challenge}} & \\multicolumn{3}{c|}{\\textbf{MMLU}} & \\multicolumn{3}{c|}{\\textbf{Hellaswag}} & \\multicolumn{3}{c|}{\\textbf{Belebele}} & \\\\\n","\\hline\n","\\textbf{Methods} & \\textbf{Order}\t&\t\\textbf{en}\t&\t\\textbf{es}\t&\t\\textbf{zh}\t&\t\\textbf{en}\t&\t\\textbf{es}\t&\t\\textbf{zh}\t&\t\\textbf{en}\t&\t\\textbf{es}\t&\t\\textbf{zh}\t&\t\\textbf{en}\t&\t\\textbf{es}\t&\t\\textbf{zh}\t&\t\\textbf{\\textit{G0-avg}}\t\\\\\n","\\hline\n","Qwen1.5-1.8B & / \t&\t37.97 \t&\t27.86 \t&\t34.10 \t&\t45.67 \t&\t38.81 \t&\t57.21 \t&\t61.62 \t&\t39.35 \t&\t48.14 \t&\t60.78 \t&\t47.44 \t&\t59.44 \t&\t46.53 \t\\\\\n","\\hline\n","MoE-LPR (7*24) & G0 \\!$\\rightarrow$\\! G1 \\!$+$\\! G2 & \t37.88 \t&\t26.92 \t&\t33.85 \t&\t45.26 \t&\t37.61 \t&\t54.51 \t&\t61.43 \t&\t39.29 \t&\t48.14 \t&\t59.33 \t&\t44.56 \t&\t53.00 \t&\t45.15 \t\\\\\n","MoE-LPR (4*24) & G0 \\!$\\rightarrow$\\! G1 \\!$+$\\! G2 & \t37.80 \t&\t27.69 \t&\t32.82 \t&\t45.29 \t&\t38.12 \t&\t54.39 \t&\t61.45 \t&\t39.11 \t&\t48.20 \t&\t59.89 \t&\t44.33 \t&\t54.22 \t&\t45.28 \t\\\\\n","LayerMoE (Ours) & G0 \\!$\\rightarrow$\\! G1 \\!$+$\\! G2 & \t37.46 \t&\t27.01 \t&\t34.27 \t&\t45.63 \t&\t38.14 \t&\t54.18 \t&\t61.37 \t&\t39.08 \t&\t47.86 \t&\t60.11 \t&\t46.22 \t&\t55.44 \t&\t\\textbf{45.56} \t\\\\\n","\\hline\n","MoE-LPR (7*24) & G0 \\!$\\rightarrow$\\! G1 \\!$\\rightarrow$\\! G2 & \t37.54 \t&\t26.84 \t&\t33.16 \t&\t45.71 \t&\t38.29 \t&\t55.42 \t&\t61.43 \t&\t39.21 \t&\t48.12 \t&\t61.22 \t&\t47.33 \t&\t56.33 \t&\t45.88 \t\\\\\n","LayerMoE (Ours) & G0 \\!$\\rightarrow$\\! G1 \\!$\\rightarrow$\\! G2 & \t37.29 \t&\t26.58 \t&\t33.93 \t&\t45.69 \t&\t38.86 \t&\t55.40 \t&\t61.23 \t&\t39.23 \t&\t48.05 \t&\t62.22 \t&\t48.00 \t&\t56.67 \t&\t\\textbf{46.10} \t\\\\\n","\\hline\n","MoE-LPR (7*24) & G0 \\!$\\rightarrow$\\! G2 \\!$\\rightarrow$\\! G1 & \t38.14 \t&\t27.18 \t&\t33.42 \t&\t46.07 \t&\t38.26 \t&\t55.28 \t&\t61.36 \t&\t39.44 \t&\t48.26 \t&\t57.44 \t&\t44.78 \t&\t53.67 \t&\t45.28 \t\\\\\n","LayerMoE (Ours) & G0 \\!$\\rightarrow$\\! G2 \\!$\\rightarrow$\\! G1 & 38.31 &\t27.09 &\t34.70 & 46.19 & 38.79 & 55.85 & 61.19 & 39.05 & 48.05 & 61.89 & 46.44 & 57.44 & \\textbf{46.25} \\\\\n","\n","\\toprule\n","\\bottomrule\n","\\textbf{Methods} & \\textbf{Order}\t&\t\\textbf{el}\t&\t\\textbf{hu}\t&\t\\textbf{tr}\t&\t\\textbf{el}\t&\t\\textbf{hu}\t&\t\\textbf{tr}\t&\t\\textbf{el}\t&\t\\textbf{hu}\t&\t\\textbf{tr}\t&\t\\textbf{el}\t&\t\\textbf{hu}\t&\t\\textbf{tr}\t&\t\\textbf{\\textit{G1-avg}}\t\\\\\n","\\hline\n","Qwen1.5-1.8B\t& / &\t22.95 \t&\t23.20 \t&\t22.63 \t&\t28.94 \t&\t32.25 \t&\t30.37 \t&\t29.15 \t&\t29.63 \t&\t29.04 \t&\t33.33 \t&\t32.00 \t&\t33.44 \t&\t28.91 \t\\\\\n","\\hline\n","MoE-LPR (7*24) & G0 \\!$\\rightarrow$\\! G1 \\!$+$\\! G2 & \t26.46 \t&\t28.00 \t&\t27.92 \t&\t30.38 \t&\t35.14 \t&\t34.64 \t&\t36.40 \t&\t39.01 \t&\t37.92 \t&\t35.33 \t&\t39.00 \t&\t39.78 \t&\t34.17 \t\\\\\n","MoE-LPR (4*24) & G0 \\!$\\rightarrow$\\! G1 \\!$+$\\! G2 & \t26.20 \t&\t27.65 \t&\t27.92 \t&\t31.01 \t&\t35.08 \t&\t34.37 \t&\t35.60 \t&\t38.14 \t&\t37.11 \t&\t34.11 \t&\t41.11 \t&\t40.56 \t&\t34.07 \t\\\\\n","LayerMoE (Ours) & G0 \\!$\\rightarrow$\\! G1 \\!$+$\\! G2 & \t25.34 \t&\t27.83 \t&\t27.92 \t&\t31.51 \t&\t36.43 \t&\t35.32 \t&\t35.50 \t&\t38.00 \t&\t37.27 \t&\t36.89 \t&\t42.11 \t&\t42.67 \t&\t\\textbf{34.73} \t\\\\\n","\\hline\n","MoE-LPR (7*24) & G0 \\!$\\rightarrow$\\! G1 \\!$\\rightarrow$\\! G2 & \t25.51 \t&\t26.20 \t&\t27.75 \t&\t30.51 \t&\t35.29 \t&\t34.46 \t&\t35.20 \t&\t37.92 \t&\t37.08 \t&\t37.44 \t&\t43.44 \t&\t42.00 \t&\t34.40 \t\\\\\n","LayerMoE (Ours) & G0 \\!$\\rightarrow$\\! G1 \\!$\\rightarrow$\\! G2 & \t25.34 \t&\t25.77 \t&\t27.75 \t&\t31.94 \t&\t36.58 \t&\t35.09 \t&\t35.17 \t&\t37.92 \t&\t36.24 \t&\t37.34 \t&\t43.34 \t&\t42.18 \t&\t\\textbf{34.56} \t\\\\\n","\\hline\n","MoE-LPR (7*24) & G0 \\!$\\rightarrow$\\! G2 \\!$\\rightarrow$\\! G1 & \t25.34 \t&\t26.97 \t&\t26.47 \t&\t30.76 \t&\t34.41 \t&\t34.47 \t&\t35.11 \t&\t37.42 \t&\t37.09 \t&\t33.67 \t&\t38.44 \t&\t40.89 \t&\t33.42 \t\\\\\n","LayerMoE (Ours) & G0 \\!$\\rightarrow$\\! G2 \\!$\\rightarrow$\\! G1 & 24.57 & 26.80 & 26.30 & 30.86\t& 35.36 & \t35.26 & 34.54 &\t37.07 &\t36.12 & 36.44 & 39.00 & 42.78 & \\textbf{33.76} \\\\\n","\n","\\toprule\n","\\bottomrule\n","\\textbf{Methods} & \\textbf{Order}\t&\t\\textbf{bn}\t&\t\\textbf{hi}\t&\t\\textbf{ne}\t&\t\\textbf{bn}\t&\t\\textbf{hi}\t&\t\\textbf{ne}\t& \\textbf{bn}\t&\t\\textbf{hi}\t&\t\\textbf{ne}\t& \\textbf{bn}\t&\t\\textbf{hi}\t&\t\\textbf{ne}\t&\t\\textbf{\\textit{G2-avg}}\t\\\\\n","\\hline\n","Qwen1.5-1.8B & /\t&\t22.50 \t&\t23.46 \t&\t23.18 \t&\t29.59 \t&\t29.89 \t&\t29.13 \t&\t28.23 \t&\t28.48 \t&\t27.95 \t&\t29.67 \t&\t30.00 \t&\t26.89 \t&\t27.41 \t\\\\\n","\\hline\n","MoE-LPR (7*24) & G0 \\!$\\rightarrow$\\! G1 \\!$+$\\! G2 & \t24.29 \t&\t24.91 \t&\t20.96 \t&\t30.94 \t&\t32.26 \t&\t31.31 \t&\t30.03 \t&\t33.24 \t&\t30.03 \t&\t31.56 \t&\t34.00 \t&\t36.22 \t&\t\\textbf{29.98} \t\\\\\n","MoE-LPR (4*24) & G0 \\!$\\rightarrow$\\! G1 \\!$+$\\! G2 & \t24.38 \t&\t25.00 \t&\t21.47 \t&\t29.64 \t&\t31.81 \t&\t30.09 \t&\t29.59 \t&\t32.72 \t&\t29.73 \t&\t33.11 \t&\t34.33 \t&\t32.78 \t&\t29.55 \t\\\\\n","LayerMoE (Ours) & G0 \\!$\\rightarrow$\\! G1 \\!$+$\\! G2 & \t23.27 \t&\t25.26 \t&\t21.47 \t&\t31.23 \t&\t32.05 \t&\t30.60 \t&\t29.67 \t&\t32.34 \t&\t29.66 \t&\t32.67 \t&\t36.00 \t&\t35.00 \t&\t29.94 \t\\\\\n","\\hline\n","MoE-LPR (7*24) & G0 \\!$\\rightarrow$\\! G1 \\!$\\rightarrow$\\! G2 & \t23.44 \t&\t25.34 \t&\t21.81 \t&\t31.66 \t&\t32.31 \t&\t31.41 \t&\t29.55 \t&\t32.60 \t&\t29.54 \t&\t34.22 \t&\t35.44 \t&\t35.78 \t&\t30.26 \t\\\\\n","LayerMoE (Ours) & G0 \\!$\\rightarrow$\\! G1 \\!$\\rightarrow$\\! G2 & \t23.78 \t&\t25.43 \t&\t22.33 \t&\t31.42 \t&\t31.92 \t&\t31.11 \t&\t29.71 \t&\t32.49 \t&\t29.88 \t&\t34.02 \t&\t35.62 \t&\t35.82 \t&\t\\textbf{30.29} \t\\\\\n","\\hline\n","MoE-LPR (7*24) & G0 \\!$\\rightarrow$\\! G2 \\!$\\rightarrow$\\! G1 & \t25.32 \t&\t24.40 \t&\t21.81 \t&\t30.41 \t&\t31.89 \t&\t29.92 \t&\t28.79 \t&\t30.73 \t&\t29.00 \t&\t27.78 \t&\t29.67 \t&\t31.00 \t&\t28.39 \t\\\\\n","LayerMoE (Ours) & G0 \\!$\\rightarrow$\\! G2 \\!$\\rightarrow$\\! G1 & 24.98 & 25.17 & 22.67 & 31.75 & 29.95 & 30.41 & 29.25 &\t32.49 &\t29.46 & 32.56 & 35.11 & 33.78 & \\textbf{29.80} \\\\\n","\n"," \\toprule\n"," \\end{tabular}\n"," }\n"," \\caption{\n"," The detailed results of Qwen1.5-1.8B under the G0 \\!$\\rightarrow$\\! G1 \\!$+$\\! G2, G0 \\!$\\rightarrow$ \\!G1 \\!$\\rightarrow$ \\!G2, and G0 \\!$\\rightarrow$ \\!G2 $\\rightarrow$ \\!G1 settings for each language.\n"," }\n"," \\label{table:mixed-res-detailed}\n","\\end{table*}\n"],["\\begin{table*}[ht]\n"," \\centering\n"," \\resizebox*{\\linewidth}{!}{\n"," \\begin{tabular}{l|ccc|ccc|ccc|ccc|c}\n"," \\bottomrule\n","& \\multicolumn{3}{c|}{\\textbf{ARC-Challenge}} & \\multicolumn{3}{c|}{\\textbf{MMLU}} & \\multicolumn{3}{c|}{\\textbf{Hellaswag}} & \\multicolumn{3}{c|}{\\textbf{Belebele}} & \\\\\n","\\hline\n","\\textbf{Methods}\t&\t\\textbf{en}\t&\t\\textbf{es}\t&\t\\textbf{zh}\t&\t\\textbf{en}\t&\t\\textbf{es}\t&\t\\textbf{zh}\t&\t\\textbf{en}\t&\t\\textbf{es}\t&\t\\textbf{zh}\t&\t\\textbf{en}\t&\t\\textbf{es}\t&\t\\textbf{zh}\t&\t\\textbf{\\textit{G0-avg}}\t\\\\\n","\\hline\n","Llama-3.2-3B\t& 51.11 & \t43.59 & \t39.23 & 56.42 & \t49.09 & \t44.00 & 76.32 & \t61.50 & \t52.87 & 74.22 & \t68.56 & \t68.67 & 57.13\t\\\\\n","\\hline\n","MoE-LPR (3*24)\t&\t50.00 & \t42.31 & \t38.80 & 56.25 & \t49.63 & \t44.08 & 76.35 & \t62.23 & \t53.94 & 74.89 & \t69.22 & \t68.11 & 57.15 \\\\\n","LayerMoE (Ours)\t&\t49.91 & \t42.56 & \t39.83 & 56.05 & \t49.77 & \t44.07 & 76.28 & \t62.18 & \t53.74 & 75.33 & \t69.44 & \t68.89 & \\textbf{57.34}\t\\\\ \n","\\toprule\n","\\bottomrule\n","\\textbf{Methods}\t&\t\\textbf{el}\t&\t\\textbf{hu}\t&\t\\textbf{tr}\t&\t\\textbf{el}\t&\t\\textbf{hu}\t&\t\\textbf{tr}\t&\t\\textbf{el}\t&\t\\textbf{hu}\t&\t\\textbf{tr}\t&\t\\textbf{el}\t&\t\\textbf{hu}\t&\t\\textbf{tr}\t&\t\\textbf{\\textit{G1-avg}}\t\\\\\n","\\hline\n","Llama-3.2-3B &\t31.76 \t&\t34.85 \t&\t33.56 \t&\t41.08 \t&\t43.01 \t&\t42.37 \t&\t43.37 \t&\t44.65 \t&\t42.85 \t&\t65.22 \t&\t59.67 \t&\t59.22 \t& 45.13\t\\\\\n","\\hline\n","MoE-LPR (3*24)\t&\t36.64 \t&\t39.21 \t&\t36.89 \t&\t43.96 \t&\t44.51 \t&\t43.46 \t&\t52.49 \t&\t51.33 \t&\t49.74 \t&\t68.44 \t&\t63.00 \t&\t61.78 \t& \t49.29 \\\\\n","LayerMoE (Ours)\t&\t36.64 \t&\t39.73 \t&\t36.55 \t&\t43.88 \t&\t44.86 \t&\t43.51 \t&\t52.72 \t&\t51.66 \t&\t49.60 \t&\t67.89 \t&\t64.22 \t&\t61.78 \t&\t\\textbf{49.42} \\\\\n","\n"," \\toprule\n"," \\end{tabular}\n"," }\n"," \\caption{\n"," The results of Llama-3.2-3B on ARC-Challenge, MMLU, Hellaswag, and Belebele under the G0 \\!$\\rightarrow$\\! G1 setting for each language. \n"," }\n"," \\label{table:llama-res-detailed-1}\n","\\end{table*}\n"],["\\begin{table*}[ht]\n"," \\centering\n"," \\resizebox*{\\linewidth}{!}{\n"," \\begin{tabular}{l|cccc|c|cccccc|c}\n"," \\bottomrule\n","\\textbf{Methods}\t&\t\\textbf{\\textit{en $\\rightarrow$ zh}}\t&\t\\textbf{\\textit{zh $\\rightarrow$ en}}\t&\t\\textbf{\\textit{en $\\rightarrow$ es}}\t&\t\\textbf{\\textit{es $\\rightarrow$ en}}\t& \\textbf{old-avg}\t & \\textbf{\\textit{en $\\rightarrow$ el}}\t&\t\\textbf{\\textit{el $\\rightarrow$ en}}\t&\t\\textbf{\\textit{en $\\rightarrow$ hu}}\t&\t\\textbf{\\textit{hu $\\rightarrow$ en}}\t& \\textbf{\\textit{en $\\rightarrow$ tr}}\t&\t\\textbf{\\textit{tr $\\rightarrow$ en}}\t& \\textbf{new-avg} \\\\\n","\\hline\n","Llama-3.2-3B\t& 84.36\t & 83.95 & \t84.79\t & 86.4\t & 84.88 & \t82.47\t & 85.86\t & 83.81 & \t86.15\t & 78.97\t & 86.53 & \t83.96 \\\\\n","\\hline\n","MoE-LPR (3*24)\t&\t84.19 & \t83.19 & \t84.67 & \t86.27 & \t84.58\t & 84.18\t & 85.89\t & 83.24\t & 85.6\t & 81.95\t & 85.26 & \t84.35 \\\\\n","LayerMoE (Ours)\t&\t84.32 & \t83.67\t & 84.63 & \t86.26\t & \\textbf{84.72}\t & 85.14\t & 86.18\t & 83.7\t & 85.65 & \t82.02 & \t85.44 & \t\\textbf{84.69}\t\\\\ \n","\\toprule\n"," \\end{tabular}\n"," }\n"," \\caption{\n"," The detailed results of Llama-3.2-3B on the FLORES benchmark under the G0 \\!$\\rightarrow$\\! G1 setting for each language. \n"," }\n"," \\label{table:llama-res-detailed-2}\n","\\end{table*}\n"],["\\begin{figure}[b]\n"," \\centering\n"," \\includegraphics[width=0.7\\linewidth]{p6.jpg}\n"," \\caption{The detailed similarity and expert allocation of our method for each layer of Llama-3.2-3B under the G0 \\!$\\rightarrow$\\! G1 setting.}\n"," \\label{fig:sim_experts_llama}\n","\\end{figure}\n"]],"string":"[\n [\n \"\\\\begin{figure}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\linewidth]{p1-v1.jpg}\\n\",\n \" \\\\caption{The similarity of HSAs in different languages across all layers within Qwen1.5-1.8B.}\\n\",\n \" \\\\label{fig:similarity}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{figure*}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\linewidth]{main-v5.jpg}\\n\",\n \" \\\\caption{The procedure of our LayerMoE. In Step 1, we first calculate the indicated similarity for each layer. In Step 2, we allocate experts based on the similarity. The layers with lower similarity are allocated more experts, while those with higher similarity are assigned fewer experts. Additionally, we add a classifier in front of the router in the layers with higher similarity to guide the routing of old language tokens.}\\n\",\n \" \\\\label{fig:model}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{figure}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\linewidth]{p3-v3.jpg}\\n\",\n \" \\\\caption{The expert allocation of our method and baselines for each layer under G0 \\\\!$\\\\rightarrow$\\\\! G1, G0 \\\\!$\\\\rightarrow$\\\\! G2.}\\n\",\n \" \\\\label{fig:allocation}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table*}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\resizebox*{\\\\linewidth}{!}{\\n\",\n \" \\\\begin{tabular}{lc|cccccccc|ccc}\\n\",\n \" \\\\bottomrule\\n\",\n \" & & \\\\multicolumn{2}{c}{\\\\textbf{ARC-C}} & \\\\multicolumn{2}{c}{\\\\textbf{MMLU}} & \\\\multicolumn{2}{c}{\\\\textbf{Hellaswag}} & \\\\multicolumn{2}{c|}{\\\\textbf{Belebele}} & & & \\\\\\\\\\n\",\n \" \\\\hline\\n\",\n \" \\\\textbf{Methods} & \\\\textbf{Expand-params} & \\\\textbf{Old} &\\t\\\\textbf{New} &\\t\\\\textbf{Old} &\\t\\\\textbf{New} & \\\\textbf{Old} &\\t\\\\textbf{New}\\t& \\\\textbf{Old}\\t& \\\\textbf{New} &\\t\\\\textbf{\\\\textit{Old-avg}} & \\\\textbf{\\\\textit{New-avg}} & \\\\textbf{\\\\textit{avg}}\\\\\\\\\\n\",\n \" \\n\",\n \" \\\\toprule\\n\",\n \" \\\\multicolumn{13}{c}{\\\\textbf{Old (G0): \\\\textit{en}, \\\\textit{es}, \\\\textit{zh} \\\\!$\\\\rightarrow$\\\\! New (G1): \\\\textit{el}, \\\\textit{hu}, \\\\textit{tr}}} \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" Qwen1.5-1.8B\\t&\\t/\\t&\\t33.31 \\t&\\t22.93 \\t&\\t47.23 \\t&\\t30.52 \\t&\\t49.70 \\t&\\t29.27 \\t&\\t55.89 \\t&\\t32.92 \\t&\\t46.53 \\t&\\t28.91 \\t&\\t37.72 \\t\\\\\\\\\\n\",\n \" \\\\hline\\n\",\n \" $^*$LoRA\\t&\\t/ & 28.33 &\\t23.89 &\\t37.42 & 29.30 & 41.48 & 29.78 & 39.45 & 26.93 & 36.67\\t& 27.48\\t& 32.07 \\\\\\\\\\n\",\n \" $^*$Full Fine-tuning\\t& / &\\t31.72 & 25.98 & 43.51 & 33.18 & 47.38 & 35.28 & 45.26 & 33.70 & 41.98 &\\t32.04 & \\t37.00 \\\\\\\\\\n\",\n \" $^*$LLaMA-Pro\\t& 0.6B &\\t31.77 & 24.35 & 44.06 & 34.02 & 48.36 & 33.85 & 48.78 & 31.52 & 43.24 &\\t30.94 & \\t37.09 \\\\\\\\\\n\",\n \" $^*$MoE\\t & 4B\\t& 32.51 & 26.43 & 44.16 & 35.07 & 48.54 & 37.01 & 45.37 & 32.74 & 42.65 &\\t32.81 &\\t37.73 \\\\\\\\\\n\",\n \" $^*$LoRAMoE\\t & 0.8B &\\t32.43 & 26.63 & 45.41 & 34.17 & 48.61 & 37.17 & 47.74 & 32.81 & 43.55\\t& 32.70 &\\t38.12 \\\\\\\\\\n\",\n \" \\\\hline\\n\",\n \" \\\\rowcolor{gray!25}\\n\",\n \" MoE-LPR (6*24)\\t&\\t4B\\t&\\t32.26 \\t&\\t26.55 \\t&\\t46.51 \\t&\\t34.95 \\t&\\t49.75 \\t&\\t37.53 \\t&\\t52.92 \\t&\\t39.04 \\t&\\t45.36 \\t&\\t34.52 \\t&\\t39.94 \\t\\\\\\\\\\n\",\n \" \\n\",\n \" MOLA ($\\\\nabla$)\\t&\\t3.2B ($\\\\downarrow$ 20\\\\%)\\t&\\t32.49 \\t&\\t27.00 \\t&\\t46.38 \\t&\\t33.98 \\t&\\t49.63 \\t&\\t37.03 \\t&\\t53.85 \\t&\\t38.37 \\t&\\t45.59 \\t&\\t34.10 \\t&\\t39.84 \\t\\\\\\\\\\n\",\n \" \\n\",\n \" MoE-LPR (4*24)\\t&\\t2.4B ($\\\\downarrow$ 40\\\\%)\\t&\\t32.29 \\t&\\t26.69 \\t&\\t46.51 \\t&\\t34.11 \\t&\\t49.60 \\t&\\t37.12 \\t&\\t52.33 \\t&\\t37.74 \\t&\\t45.18 \\t&\\t33.92 \\t&\\t39.55 \\t\\\\\\\\\\n\",\n \" \\n\",\n \" MoE-LPR (3*24)\\t&\\t1.6B ($\\\\downarrow$ 60\\\\%)\\t&\\t32.77 \\t&\\t26.43 \\t&\\t46.54\\t&\\t34.59 \\t&\\t49.63 \\t&\\t37.08 \\t&\\t52.07 \\t&\\t37.15 \\t&\\t45.25 \\t&\\t33.81 \\t&\\t39.53 \\t\\\\\\\\\\n\",\n \" \\n\",\n \" LayerMoE (Ours)\\t&\\t\\\\textbf{1.6B ($\\\\downarrow$ 60\\\\%)}\\t&\\t\\\\textbf{33.03} \\t&\\t\\\\textbf{26.66} \\t&\\t46.12 \\t&\\t34.80 \\t&\\t49.24 \\t&\\t37.01 \\t&\\t\\\\textbf{54.82} \\t&\\t\\\\textbf{40.37}\\t&\\t\\\\textbf{45.80} \\t&\\t\\\\textbf{34.71} \\t&\\t\\\\textbf{40.26} \\t\\\\\\\\\\n\",\n \" \\\\toprule\\n\",\n \" \\n\",\n \" \\\\multicolumn{13}{c}{\\\\textbf{Old (G0): \\\\textit{en}, \\\\textit{es}, \\\\textit{zh} \\\\!$\\\\rightarrow$\\\\! New (G2): \\\\textit{bn}, \\\\textit{hi}, \\\\textit{ne}}} \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" Qwen1.5-1.8B\\t&\\t/\\t&\\t33.31 \\t&\\t23.05 \\t&\\t47.23 \\t&\\t29.54 \\t&\\t49.70 \\t&\\t28.22 \\t&\\t55.89 \\t&\\t28.85 \\t&\\t46.53 \\t&\\t27.41 \\t&\\t36.97 \\t\\\\\\\\\\n\",\n \" \\\\hline\\n\",\n \" \\\\rowcolor{gray!25}\\n\",\n \" MoE-LPR (6*24)\\t&\\t4B\\t&\\t33.06 \\t&\\t23.85 \\t&\\t47.04 \\t&\\t30.62 \\t&\\t49.69 \\t&\\t30.92 \\t&\\t53.11 \\t&\\t33.85 \\t&\\t45.72 \\t&\\t29.81 \\t&\\t37.77 \\t\\\\\\\\\\n\",\n \" \\n\",\n \" MOLA ($\\\\nabla$)\\t&\\t3.2B ($\\\\downarrow$ 20\\\\%)\\t&\\t32.48 \\t&\\t23.67 \\t&\\t47.02 \\t&\\t30.82 \\t&\\t49.73 \\t&\\t30.48 \\t&\\t53.59 \\t&\\t32.15 \\t&\\t45.71 \\t&\\t29.28 \\t&\\t37.49 \\t\\\\\\\\\\n\",\n \"\\n\",\n \" MoE-LPR (4*24)\\t&\\t2.4B ($\\\\downarrow$ 40\\\\%)\\t&\\t33.08 \\t&\\t23.27 \\t&\\t47.21 \\t&\\t30.74 \\t&\\t49.64 \\t&\\t30.67 \\t&\\t52.85 \\t&\\t33.04 \\t&\\t45.70 \\t&\\t29.43 \\t&\\t37.56 \\t\\\\\\\\\\n\",\n \" \\n\",\n \" MoE-LPR (3*24)\\t&\\t1.6B ($\\\\downarrow$ 60\\\\%)\\t&\\t32.77 \\t&\\t23.53 \\t&\\t46.97 \\t&\\t30.72 \\t&\\t49.68 \\t&\\t30.56 \\t&\\t52.59 \\t&\\t33.33 \\t&\\t45.50 \\t&\\t29.53 \\t&\\t37.52 \\t\\\\\\\\\\n\",\n \"\\n\",\n \" LayerMoE (Ours)\\t&\\t\\\\textbf{1.6B ($\\\\downarrow$ 60\\\\%)}\\t& 32.52 &\\t\\\\textbf{24.02} &\\t\\\\textbf{47.10} & \\t\\\\textbf{30.85} & \\t49.46 & \\t30.47 &\\t\\\\textbf{55.15} &\\t\\\\textbf{34.11} & \\\\textbf{46.06} & \\\\textbf{29.86} & \\\\textbf{37.96} \\\\\\\\\\n\",\n \"\\n\",\n \" \\\\toprule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \" \\\\caption{\\n\",\n \" The results of the single-expansion settings (G0 \\\\!$\\\\rightarrow$\\\\! G1, G0 \\\\!$\\\\rightarrow$\\\\! G2). The results marked with $^*$ represent that they are directly taken from the MoE-LPR paper \\\\cite{zhou2024moelprmultilingualextensionlarge}, which utilizes 8 billion tokens for each new language during post-pretraining.\\n\",\n \" Other experiments are trained with 2 billion tokens for each new language.\\n\",\n \" ``Expand-params'' denotes the parameter number of newly added experts and {($\\\\downarrow$ *\\\\%)} means the reduction percentage of parameters compared to MoE-LPR (6*24).\\n\",\n \" The results in \\\\textbf{bold} mean they are better than the previous SOTA method ``MoE-LPR (6*24)''.\\n\",\n \" The detailed results for each language are reported in Table \\\\ref{table:single-res-detailed-g1} and \\\\ref{table:single-res-detailed-g2} of Appendix \\\\ref{sec:appendix-detailed-results}.\\n\",\n \" }\\n\",\n \" \\\\label{table:single-res}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\resizebox*{\\\\linewidth}{!}{\\n\",\n \" \\\\begin{tabular}{lcccc|cccc}\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\textbf{Methods} & \\\\textbf{Order} & \\\\textbf{G1-E-p} & \\\\textbf{G2-E-p} & \\\\textbf{All-E-p} & \\\\textbf{\\\\textit{G0-avg}} & \\\\textbf{\\\\textit{G1-avg}} & \\\\textbf{\\\\textit{G2-avg}} & \\\\textbf{\\\\textit{avg}} \\\\\\\\\\n\",\n \" \\\\hline \\n\",\n \" Qwen1.5-1.8B\\t& / & / & / & / & 46.53\\t& 28.91 & 27.41 & 34.29\\t\\\\\\\\\\n\",\n \" \\\\hline\\n\",\n \" MoE-LPR (7*24) & G0 \\\\!$\\\\rightarrow$\\\\! G1 \\\\!$+$\\\\! G2 & 4.8B & 4.8B & 4.8B &\\t45.15 &\\t34.17 &\\t29.98 & 36.43 \\\\\\\\\\n\",\n \" MoE-LPR (4*24) & G0 \\\\!$\\\\rightarrow$\\\\! G1 \\\\!$+$\\\\! G2 & 2.4B & 2.4B & 2.4B ($\\\\downarrow$ 50.0\\\\%) & 45.28 &\\t34.07 &\\t29.55 & 36.30 \\\\\\\\\\n\",\n \" LayerMoE (Ours) & G0 \\\\!$\\\\rightarrow$\\\\! G1 \\\\!$+$\\\\! G2 & 1.6B & 1.6B & \\\\textbf{1.6B ($\\\\downarrow$ \\\\textbf{66.7\\\\%})} & \\\\textbf{45.56} &\\t\\\\textbf{34.73} &\\t29.94 & \\\\textbf{36.74} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\hline\\n\",\n \" MoE-LPR (7*24) & G0 \\\\!$\\\\rightarrow$\\\\! G1 \\\\!$\\\\rightarrow$\\\\! G2 & 2.4B & 2.4B & 4.8B & 45.88\\t& 34.40\\t& 30.26 & 36.85 \\\\\\\\\\n\",\n \" LayerMoE (Ours) & G0 \\\\!$\\\\rightarrow$\\\\! G1 \\\\!$\\\\rightarrow$\\\\! G2 & 1.6B & 1.6B & \\\\textbf{3.2B ($\\\\downarrow$ \\\\textbf{33.3\\\\%})} & \\\\textbf{46.10} &\\t\\\\textbf{34.56} &\\t\\\\textbf{30.29} & \\\\textbf{36.98} \\\\\\\\\\n\",\n \" \\\\hline\\n\",\n \" MoE-LPR (7*24) & G0 \\\\!$\\\\rightarrow$\\\\! G2 \\\\!$\\\\rightarrow$\\\\! G1 & 2.4B & 2.4B & 4.8B & 45.28 &\\t33.42 &\\t28.39 & 35.70 \\\\\\\\\\n\",\n \" LayerMoE (Ours) & G0 \\\\!$\\\\rightarrow$\\\\! G2 \\\\!$\\\\rightarrow$\\\\! G1 & 1.6B & 1.6B & \\\\textbf{3.2B ($\\\\downarrow$ \\\\textbf{33.3\\\\%})} & \\\\textbf{46.25} & \\\\textbf{33.76} &\\t\\\\textbf{29.80} & \\\\textbf{36.60} \\\\\\\\\\n\",\n \"\\n\",\n \" \\\\toprule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \" \\\\caption{\\n\",\n \" The results of G0 \\\\!$\\\\rightarrow$\\\\! G1 \\\\!$+$\\\\! G2 and the lifelong-expansion settings (G0 \\\\!$\\\\rightarrow$ \\\\!G1 \\\\!$\\\\rightarrow$ \\\\!G2 and G0 \\\\!$\\\\rightarrow$ \\\\!G2 $\\\\rightarrow$ \\\\!G1).\\n\",\n \" ``G1/G2/All-E-p'' represents the expansion number of newly added parameters for G1, G2, and the sum, respectively.\\n\",\n \" The detailed results for each language are reported in Table \\\\ref{table:mixed-res-detailed} of Appendix \\\\ref{sec:appendix-detailed-results}.\\n\",\n \" }\\n\",\n \" \\\\label{table:lifelong-res}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\resizebox*{\\\\linewidth}{!}{\\n\",\n \" \\\\begin{tabular}{l|ll|c}\\n\",\n \" \\\\bottomrule\\n\",\n \" \\t& \\\\textbf{\\\\textit{Old-avg} ($\\\\uparrow$)} & \\\\textbf{\\\\textit{New-avg} ($\\\\uparrow$)} & \\\\textbf{\\\\textit{avg} ($\\\\uparrow$)}\\\\\\\\\\n\",\n \" \\n\",\n \" \\\\hline\\n\",\n \"\\n\",\n \" LayerMoE (G0 \\\\!$\\\\rightarrow$\\\\! G1)\\t&\\t\\\\textbf{45.80} \\t&\\t\\\\textbf{34.71} \\t&\\t\\\\textbf{40.26} \\t\\\\\\\\\\n\",\n \" \\\\quad \\\\textit{w/} random & 45.68 ($\\\\downarrow$ 0.12) & 34.11 ($\\\\downarrow$ \\\\textbf{0.60}) & 39.90 \\\\\\\\\\n\",\n \" \\\\quad \\\\textit{w/o} classifier & 45.33 ($\\\\downarrow$ \\\\textbf{0.47}) & 34.61 ($\\\\downarrow$ 0.10) & 39.97 \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \" LayerMoE (G0 \\\\!$\\\\rightarrow$\\\\! G2)\\t&\\t\\\\textbf{46.06} \\t&\\t\\\\textbf{29.86} \\t&\\t\\\\textbf{37.96} \\t\\\\\\\\\\n\",\n \" \\\\quad \\\\textit{w/} random & 45.94 ($\\\\downarrow$ 0.12) & 29.15 ($\\\\downarrow$ \\\\textbf{0.71}) & 37.54 \\\\\\\\\\n\",\n \" \\\\quad \\\\textit{w/o} classifier & 45.39 ($\\\\downarrow$ \\\\textbf{0.67}) & 29.53 ($\\\\downarrow$ 0.33) & 37.46 \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\toprule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \" \\\\caption{\\n\",\n \" The ablation results under G0 \\\\!$\\\\rightarrow$\\\\! G1 and G0 \\\\!$\\\\rightarrow$\\\\! G2. ``\\\\textit{w/} random'' means randomly allocating numbers of new experts for each layer without calculating the similarity. ``\\\\textit{w/o} classifier'' denotes no classifier is added in front of the router network.\\n\",\n \" }\\n\",\n \" \\\\label{table:ablation-study}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\resizebox*{\\\\linewidth}{!}{\\n\",\n \" \\\\begin{tabular}{l|ccc|l|ccc}\\n\",\n \" \\\\bottomrule\\n\",\n \" \\t& \\\\textbf{\\\\textit{Old} } & \\\\textbf{\\\\textit{New} } & \\\\textbf{\\\\textit{avg}} & & \\\\textbf{\\\\textit{Old} } & \\\\textbf{\\\\textit{New} } & \\\\textbf{\\\\textit{avg}} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\hline\\n\",\n \" top-1 & 44.90 & 34.30 & 39.60 & \\\\textbf{top-7} & 45.80 & 34.71 & \\\\textbf{40.26} \\\\\\\\\\n\",\n \" top-2 & 44.81 & 34.69 & 39.75 & top-8 & 45.79 & 34.54 & 40.17 \\\\\\\\\\n\",\n \" top-3 & 45.10 & 34.41 & 39.76 & top-9 & 45.95 & 34.42 & 40.19 \\\\\\\\\\n\",\n \" top-4 & 45.41 & 34.67 & 40.04 & top-10 & \\\\textbf{46.16} & 34.28 & 40.22 \\\\\\\\\\n\",\n \" top-5 & 45.28 & 34.66 & 39.97 & top-24 & 46.04 & 34.32 & 40.18 \\\\\\\\\\n\",\n \" top-6 & 45.74 & \\\\textbf{34.72} & 40.23 & top-23 & 46.15 & 33.23 & 39.69 \\\\\\\\\\n\",\n \" \\\\hline\\n\",\n \" last-7 & 44.96 & 34.60 & 39.78 & random-7 & 45.32 & 34.60 & 39.96 \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\toprule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \" \\\\caption{\\n\",\n \" The results of adding classifiers in different layers under G0 \\\\!$\\\\rightarrow$\\\\! G1.\\n\",\n \" ``top-*'' denotes adding classifiers to * layers with the highest similarity.\\n\",\n \" ``random-7'' means randomly selecting 7 layers, while ``last-7'' represents the 7 layers with the lowest similarity.\\n\",\n \" }\\n\",\n \" \\\\label{table:layers-calssifier}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table*}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\resizebox*{\\\\linewidth}{!}{\\n\",\n \" \\\\begin{tabular}{l|cccccccccc|ccc}\\n\",\n \" \\\\bottomrule\\n\",\n \" & \\\\multicolumn{2}{c}{\\\\textbf{ARC-C}} & \\\\multicolumn{2}{c}{\\\\textbf{MMLU}} & \\\\multicolumn{2}{c}{\\\\textbf{Hellaswag}} & \\\\multicolumn{2}{c}{\\\\textbf{Belebele}} & \\\\multicolumn{2}{c|}{\\\\textbf{FLORES}} & & & \\\\\\\\\\n\",\n \" \\\\hline\\n\",\n \" \\\\textbf{Methods} & \\\\textbf{Old} &\\t\\\\textbf{New} &\\t\\\\textbf{Old} &\\t\\\\textbf{New} & \\\\textbf{Old} &\\t\\\\textbf{New}\\t& \\\\textbf{Old}\\t& \\\\textbf{New} & \\\\textbf{Old}\\t& \\\\textbf{New} &\\\\textbf{\\\\textit{Old-avg}} & \\\\textbf{\\\\textit{New-avg}} & \\\\textbf{\\\\textit{avg}}\\\\\\\\\\n\",\n \" \\n\",\n \" \\\\hline\\n\",\n \" Llama-3.2-3B & 44.64 & 33.39 & \\t49.84 & 42.15\\t & 63.56 & 43.62 & \\t70.48 & 61.37 & \\t84.88 & 83.96\\t & 62.68 & \\t52.90 & \\t57.79 \\\\\\\\\\n\",\n \" \\\\hline\\n\",\n \" MoE-LPR (3*24) & \\t43.70 & 37.58 & \\t49.99 & 43.98 & \\t64.17 & 51.19 & \\t70.74 & 64.41\\t & 84.58 & 84.35 & \\t62.64 & \\t56.30\\t & 59.47\\t\\\\\\\\\\n\",\n \" LayerMoE (Ours)\\t& \\\\textbf{44.10}\\t& \\\\textbf{37.64}\\t& \\t49.96\\t& \\\\textbf{44.08}\\t& \\t64.07\\t& \\\\textbf{51.33}\\t& \\t\\\\textbf{71.22}\\t& \\\\textbf{64.63}\\t& \\t\\\\textbf{84.72}\\t& \\\\textbf{84.69}\\t\\t& \\\\textbf{62.81}\\t\\t& \\\\textbf{56.47}\\t& \\t\\\\textbf{59.64}\\n\",\n \"\\t\\\\\\\\\\n\",\n \" \\\\toprule\\n\",\n \" \\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \" \\\\caption{\\n\",\n \" The results of Llama-3.2-3B under the single-expansion setting (G0 \\\\!$\\\\rightarrow$\\\\! G1).\\n\",\n \" The detailed results for each language are reported in Table \\\\ref{table:llama-res-detailed-1} and \\\\ref{table:llama-res-detailed-2} of Appendix \\\\ref{sec:appendix-detailed-results}.\\n\",\n \" }\\n\",\n \" \\\\label{table:llama-res}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[h]\\n\",\n \" \\\\centering\\n\",\n \" \\\\resizebox*{0.8\\\\linewidth}{!}{\\n\",\n \" \\\\begin{tabular}{l|ll}\\n\",\n \" \\\\bottomrule\\n\",\n \" Languages\\t& \\\\textbf{Language Family} & \\\\textbf{Writing System} \\\\\\\\\\n\",\n \" \\n\",\n \" \\\\hline\\n\",\n \"\\n\",\n \" English (\\\\textit{en}) & Indo-European (Germanic) & Latin alphabet (26 letters) \\\\\\\\\\n\",\n \" Chinese (\\\\textit{zh}) & Sino-Tibetan (Sinitic) & Chinese characters \\\\\\\\\\n\",\n \" Spanish (\\\\textit{es}) & Indo-European (Romance) & Latin alphabet (27 letters, including ñ) \\\\\\\\\\n\",\n \" Greek (\\\\textit{el}) & Indo-European (Hellenic) & Greek alphabet (24 letters) \\\\\\\\\\n\",\n \" Hungarian (\\\\textit{hu}) & Uralic (Finno-Ugric) & Latin alphabet with diacritics (40 letters) \\\\\\\\\\n\",\n \" Turkish (\\\\textit{tr}) & Turkic & Latin alphabet (29 letters) \\\\\\\\\\n\",\n \" Bengali (\\\\textit{bn}) & Indo-European (Indo-Aryan) & Bengali script \\\\\\\\\\n\",\n \" Nepali (\\\\textit{ne}) & Indo-European (Indo-Aryan) & Devanagari script \\\\\\\\\\n\",\n \" Hindi (\\\\textit{hi}) & Indo-European (Indo-Aryan) & Devanagari script \\\\\\\\\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\\\toprule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \" \\\\caption{\\n\",\n \" The detailed language families and writing systems.\\n\",\n \" }\\n\",\n \" \\\\label{table:languages}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[h]\\n\",\n \"\\t\\\\centering %图片全局居中\\n\",\n \"\\t\\\\subfigbottomskip=2pt %两行子图之间的行间距\\n\",\n \"\\t\\\\subfigcapskip=-5pt %设置子图与子标题之间的距离\\n\",\n \"\\t\\\\subfigure[G0 \\\\!$\\\\rightarrow$\\\\! G1]{\\n\",\n \"\\t\\t\\\\includegraphics[width=0.32\\\\linewidth]{p5-1.jpg}}\\n\",\n \"\\t\\\\subfigure[G0 \\\\!$\\\\rightarrow$\\\\! G2]{\\n\",\n \"\\t\\t\\\\includegraphics[width=0.32\\\\linewidth]{p5-2.jpg}}\\n\",\n \" \\\\subfigure[G0 \\\\!$\\\\rightarrow$\\\\! G1 \\\\!$+$\\\\! G2]{\\n\",\n \"\\t\\t\\\\includegraphics[width=0.32\\\\linewidth]{p5-3.jpg}}\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\subfigure[G0 \\\\!$\\\\rightarrow$ \\\\!G1 \\\\!$\\\\rightarrow$ \\\\!G2]{\\n\",\n \"\\t\\t\\\\includegraphics[width=0.49\\\\linewidth]{p5-4.jpg}}\\n\",\n \"\\t\\\\subfigure[G0 \\\\!$\\\\rightarrow$ \\\\!G2 $\\\\rightarrow$ \\\\!G1]{\\n\",\n \"\\t\\t\\\\includegraphics[width=0.49\\\\linewidth]{p5-5.jpg}}\\n\",\n \"\\t\\\\caption{The detailed similarity and expert allocation of our method for each layer of Qwen1.5-1.8B under the five settings.}\\n\",\n \" \\\\label{fig:sim_experts}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{table*}[h]\\n\",\n \" \\\\centering\\n\",\n \" \\\\resizebox*{\\\\linewidth}{!}{\\n\",\n \" \\\\begin{tabular}{l|ccc|ccc|ccc|ccc|c}\\n\",\n \" \\\\bottomrule\\n\",\n \"& \\\\multicolumn{3}{c|}{\\\\textbf{ARC-Challenge}} & \\\\multicolumn{3}{c|}{\\\\textbf{MMLU}} & \\\\multicolumn{3}{c|}{\\\\textbf{Hellaswag}} & \\\\multicolumn{3}{c|}{\\\\textbf{Belebele}} & \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\\\textbf{Methods}\\t&\\t\\\\textbf{en}\\t&\\t\\\\textbf{es}\\t&\\t\\\\textbf{zh}\\t&\\t\\\\textbf{en}\\t&\\t\\\\textbf{es}\\t&\\t\\\\textbf{zh}\\t&\\t\\\\textbf{en}\\t&\\t\\\\textbf{es}\\t&\\t\\\\textbf{zh}\\t&\\t\\\\textbf{en}\\t&\\t\\\\textbf{es}\\t&\\t\\\\textbf{zh}\\t&\\t\\\\textbf{\\\\textit{G0-avg}}\\t\\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"Qwen1.5-1.8B\\t&\\t37.97 \\t&\\t27.86 \\t&\\t34.10 \\t&\\t45.67 \\t&\\t38.81 \\t&\\t57.21 \\t&\\t61.62 \\t&\\t39.35 \\t&\\t48.14 \\t&\\t60.78 \\t&\\t47.44 \\t&\\t59.44 \\t&\\t46.53 \\t\\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"MoE-LPR (6*24)\\t&\\t38.05 \\t&\\t26.84 \\t&\\t31.88 \\t&\\t45.77 \\t&\\t38.26 \\t&\\t55.50 \\t&\\t61.34 \\t&\\t39.41 \\t&\\t48.49 \\t&\\t58.44 \\t&\\t45.89 \\t&\\t54.44 \\t&\\t45.36 \\t\\\\\\\\\\n\",\n \"MOLA\\t&\\t37.88 \\t&\\t26.84 \\t&\\t32.74 \\t&\\t45.81 \\t&\\t38.30 \\t&\\t55.02 \\t&\\t61.36 \\t&\\t39.27 \\t&\\t48.26 \\t&\\t60.11 \\t&\\t47.11 \\t&\\t54.33 \\t&\\t45.59 \\t\\\\\\\\\\n\",\n \"MoE-LPR (4*24)\\t&\\t36.69 \\t&\\t27.35 \\t&\\t32.82 \\t&\\t46.09 \\t&\\t38.57 \\t&\\t54.87 \\t&\\t61.17 \\t&\\t39.37 \\t&\\t48.27 \\t&\\t58.33 \\t&\\t44.22 \\t&\\t54.44 \\t&\\t45.18 \\t\\\\\\\\\\n\",\n \"MoE-LPR (3*24)\\t&\\t37.46 \\t&\\t27.35 \\t&\\t33.50 \\t&\\t46.00 \\t&\\t38.67 \\t&\\t54.94 \\t&\\t61.20 \\t&\\t39.43 \\t&\\t48.25 \\t&\\t59.44 \\t&\\t44.44 \\t&\\t52.33 \\t&\\t45.25 \\t\\\\\\\\\\n\",\n \"LayerMoE (Ours)\\t&\\t38.57 \\t&\\t26.58 \\t&\\t33.93 \\t&\\t45.81 \\t&\\t37.97 \\t&\\t54.59 \\t&\\t60.90 \\t&\\t38.98 \\t&\\t47.84 \\t&\\t60.56 \\t&\\t46.89 \\t&\\t57.00 \\t&\\t\\\\textbf{45.80} \\t\\\\\\\\ \\n\",\n \"\\\\toprule\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\textbf{Methods}\\t&\\t\\\\textbf{el}\\t&\\t\\\\textbf{hu}\\t&\\t\\\\textbf{tr}\\t&\\t\\\\textbf{el}\\t&\\t\\\\textbf{hu}\\t&\\t\\\\textbf{tr}\\t&\\t\\\\textbf{el}\\t&\\t\\\\textbf{hu}\\t&\\t\\\\textbf{tr}\\t&\\t\\\\textbf{el}\\t&\\t\\\\textbf{hu}\\t&\\t\\\\textbf{tr}\\t&\\t\\\\textbf{\\\\textit{G1-avg}}\\t\\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"Qwen1.5-1.8B\\t&\\t22.95 \\t&\\t23.20 \\t&\\t22.63 \\t&\\t28.94 \\t&\\t32.25 \\t&\\t30.37 \\t&\\t29.15 \\t&\\t29.63 \\t&\\t29.04 \\t&\\t33.33 \\t&\\t32.00 \\t&\\t33.44 \\t&\\t28.91 \\t\\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"MoE-LPR (6*24)\\t&\\t25.26 \\t&\\t27.14 \\t&\\t27.24 \\t&\\t32.06 \\t&\\t36.30 \\t&\\t36.49 \\t&\\t36.09 \\t&\\t38.63 \\t&\\t37.87 \\t&\\t38.33 \\t&\\t36.56 \\t&\\t42.22 \\t&\\t34.52 \\t\\\\\\\\\\n\",\n \"MOLA\\t&\\t25.94 \\t&\\t27.40 \\t&\\t27.67 \\t&\\t31.43 \\t&\\t35.80 \\t&\\t34.71 \\t&\\t35.85 \\t&\\t37.79 \\t&\\t37.46 \\t&\\t35.22 \\t&\\t39.00 \\t&\\t40.89 \\t&\\t34.10 \\t\\\\\\\\\\n\",\n \"MoE-LPR (4*24)\\t&\\t25.34 \\t&\\t27.57 \\t&\\t27.16 \\t&\\t31.58 \\t&\\t35.79 \\t&\\t34.97 \\t&\\t35.70 \\t&\\t38.26 \\t&\\t37.39 \\t&\\t34.44 \\t&\\t40.22 \\t&\\t38.56 \\t&\\t33.92 \\t\\\\\\\\\\n\",\n \"MoE-LPR (3*24)\\t&\\t24.32 \\t&\\t27.05 \\t&\\t27.92 \\t&\\t31.90 \\t&\\t36.27 \\t&\\t35.59 \\t&\\t35.55 \\t&\\t38.18 \\t&\\t37.51 \\t&\\t32.00 \\t&\\t39.56 \\t&\\t39.89 \\t&\\t33.81 \\t\\\\\\\\\\n\",\n \"LayerMoE (Ours)\\t&\\t25.34 \\t&\\t26.80 \\t&\\t27.84 \\t&\\t31.96 \\t&\\t36.76 \\t&\\t35.68 \\t&\\t35.61 \\t&\\t38.10 \\t&\\t37.31 \\t&\\t37.44 \\t&\\t41.56 \\t&\\t42.11 \\t&\\t\\\\textbf{34.71} \\t\\\\\\\\\\n\",\n \"\\n\",\n \" \\\\toprule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \" \\\\caption{\\n\",\n \" The detailed results of Qwen1.5-1.8B under the G0 \\\\!$\\\\rightarrow$\\\\! G1 setting for each language. \\n\",\n \" }\\n\",\n \" \\\\label{table:single-res-detailed-g1}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[ht]\\n\",\n \" \\\\centering\\n\",\n \" \\\\resizebox*{\\\\linewidth}{!}{\\n\",\n \" \\\\begin{tabular}{l|ccc|ccc|ccc|ccc|c}\\n\",\n \" \\\\bottomrule\\n\",\n \"& \\\\multicolumn{3}{c|}{\\\\textbf{ARC-Challenge}} & \\\\multicolumn{3}{c|}{\\\\textbf{MMLU}} & \\\\multicolumn{3}{c|}{\\\\textbf{Hellaswag}} & \\\\multicolumn{3}{c|}{\\\\textbf{Belebele}} & \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\\\textbf{Methods}\\t&\\t\\\\textbf{en}\\t&\\t\\\\textbf{es}\\t&\\t\\\\textbf{zh}\\t&\\t\\\\textbf{en}\\t&\\t\\\\textbf{es}\\t&\\t\\\\textbf{zh}\\t&\\t\\\\textbf{en}\\t&\\t\\\\textbf{es}\\t&\\t\\\\textbf{zh}\\t&\\t\\\\textbf{en}\\t&\\t\\\\textbf{es}\\t&\\t\\\\textbf{zh}\\t&\\t\\\\textbf{\\\\textit{G0-avg}}\\t\\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"Qwen1.5-1.8B\\t&\\t37.97 \\t&\\t27.86 \\t&\\t34.10 \\t&\\t45.67 \\t&\\t38.81 \\t&\\t57.21 \\t&\\t61.62 \\t&\\t39.35 \\t&\\t48.14 \\t&\\t60.78 \\t&\\t47.44 \\t&\\t59.44 \\t&\\t46.53 \\t\\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"MoE-LPR (6*24)\\t&\\t37.29 \\t&\\t27.26 \\t&\\t34.62 \\t&\\t46.05 \\t&\\t38.46 \\t&\\t56.60 \\t&\\t61.73 \\t&\\t39.27 \\t&\\t48.07 \\t&\\t59.67 \\t&\\t46.33 \\t&\\t53.33 \\t&\\t45.72 \\t\\\\\\\\\\n\",\n \"MOLA\\t&\\t37.37 \\t&\\t27.26 \\t&\\t32.82 \\t&\\t45.88 \\t&\\t38.71 \\t&\\t56.48 \\t&\\t61.76 \\t&\\t39.09 \\t&\\t48.34 \\t&\\t59.56 \\t&\\t46.22 \\t&\\t55.00 \\t&\\t45.71 \\t\\\\\\\\\\n\",\n \"MoE-LPR (4*24)\\t&\\t37.80 \\t&\\t27.18 \\t&\\t34.27 \\t&\\t46.00 \\t&\\t38.88 \\t&\\t56.76 \\t&\\t61.65 \\t&\\t39.03 \\t&\\t48.24 \\t&\\t59.67 \\t&\\t45.22 \\t&\\t53.67 \\t&\\t45.70 \\t\\\\\\\\\\n\",\n \"MoE-LPR (3*24)\\t&\\t37.80 \\t&\\t27.09 \\t&\\t33.42 \\t&\\t45.52 \\t&\\t38.62 \\t&\\t56.78 \\t&\\t61.59 \\t&\\t39.13 \\t&\\t48.32 \\t&\\t59.89 \\t&\\t45.33 \\t&\\t52.56 \\t&\\t45.50 \\t\\\\\\\\\\n\",\n \"LayerMoE (Ours)\\t&\\t36.86 \\t&\\t27.44 \\t&\\t33.25 \\t&\\t46.15 \\t&\\t39.05 \\t&\\t56.09 \\t&\\t61.50 \\t&\\t39.11 \\t&\\t47.77 \\t&\\t61.89 \\t&\\t46.67 \\t&\\t56.89 \\t&\\t\\\\textbf{46.06} \\t\\\\\\\\\\n\",\n \"\\\\toprule\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\textbf{Methods}\\t&\\t\\\\textbf{bn}\\t&\\t\\\\textbf{hi}\\t&\\t\\\\textbf{ne}\\t&\\t\\\\textbf{bn}\\t&\\t\\\\textbf{hi}\\t&\\t\\\\textbf{ne}\\t& \\\\textbf{bn}\\t&\\t\\\\textbf{hi}\\t&\\t\\\\textbf{ne}\\t& \\\\textbf{bn}\\t&\\t\\\\textbf{hi}\\t&\\t\\\\textbf{ne}\\t&\\t\\\\textbf{\\\\textit{G2-avg}}\\t\\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"Qwen1.5-1.8B\\t&\\t22.50 \\t&\\t23.46 \\t&\\t23.18 \\t&\\t29.59 \\t&\\t29.89 \\t&\\t29.13 \\t&\\t28.23 \\t&\\t28.48 \\t&\\t27.95 \\t&\\t29.67 \\t&\\t30.00 \\t&\\t26.89 \\t&\\t27.41 \\t\\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"MoE-LPR (6*24)\\t&\\t24.64 \\t&\\t25.26 \\t&\\t21.64 \\t&\\t30.29 \\t&\\t30.57 \\t&\\t31.01 \\t&\\t29.90 \\t&\\t32.78 \\t&\\t30.08 \\t&\\t33.22 \\t&\\t35.22 \\t&\\t33.11 \\t&\\t29.81 \\t\\\\\\\\\\n\",\n \"MOLA\\t&\\t24.29 \\t&\\t25.09 \\t&\\t21.64 \\t&\\t31.01 \\t&\\t30.67 \\t&\\t30.77 \\t&\\t29.44 \\t&\\t32.54 \\t&\\t29.46 \\t&\\t31.00 \\t&\\t33.33 \\t&\\t32.11 \\t&\\t29.28 \\t\\\\\\\\\\n\",\n \"MoE-LPR (4*24)\\t&\\t23.52 \\t&\\t24.91 \\t&\\t21.39 \\t&\\t30.73 \\t&\\t30.54 \\t&\\t30.94 \\t&\\t30.02 \\t&\\t32.55 \\t&\\t29.45 \\t&\\t32.11 \\t&\\t34.33 \\t&\\t32.67 \\t&\\t29.43 \\t\\\\\\\\\\n\",\n \"MoE-LPR (3*24)\\t&\\t24.98 \\t&\\t24.57 \\t&\\t21.04 \\t&\\t31.59 \\t&\\t30.87 \\t&\\t29.70 \\t&\\t29.55 \\t&\\t32.68 \\t&\\t29.44 \\t&\\t32.44 \\t&\\t34.33 \\t&\\t33.22 \\t&\\t29.53 \\t\\\\\\\\\\n\",\n \"LayerMoE (Ours)\\t&\\t24.21 \\t&\\t25.26 \\t&\\t22.58 \\t&\\t31.10 \\t&\\t30.43 \\t&\\t31.01 \\t&\\t29.31 \\t&\\t32.42 \\t&\\t29.67 \\t&\\t33.44 \\t&\\t33.44 \\t&\\t35.44 \\t&\\t\\\\textbf{29.86} \\t\\\\\\\\\\n\",\n \" \\\\toprule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \" \\\\caption{\\n\",\n \" The detailed results of Qwen1.5-1.8B under the G0 \\\\!$\\\\rightarrow$\\\\! G2 setting for each language. \\n\",\n \" }\\n\",\n \" \\\\label{table:single-res-detailed-g2}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[ht]\\n\",\n \" \\\\centering\\n\",\n \" \\\\resizebox*{\\\\linewidth}{!}{\\n\",\n \" \\\\begin{tabular}{lc|ccc|ccc|ccc|ccc|c}\\n\",\n \" \\\\bottomrule\\n\",\n \"& & \\\\multicolumn{3}{c|}{\\\\textbf{ARC-Challenge}} & \\\\multicolumn{3}{c|}{\\\\textbf{MMLU}} & \\\\multicolumn{3}{c|}{\\\\textbf{Hellaswag}} & \\\\multicolumn{3}{c|}{\\\\textbf{Belebele}} & \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\\\textbf{Methods} & \\\\textbf{Order}\\t&\\t\\\\textbf{en}\\t&\\t\\\\textbf{es}\\t&\\t\\\\textbf{zh}\\t&\\t\\\\textbf{en}\\t&\\t\\\\textbf{es}\\t&\\t\\\\textbf{zh}\\t&\\t\\\\textbf{en}\\t&\\t\\\\textbf{es}\\t&\\t\\\\textbf{zh}\\t&\\t\\\\textbf{en}\\t&\\t\\\\textbf{es}\\t&\\t\\\\textbf{zh}\\t&\\t\\\\textbf{\\\\textit{G0-avg}}\\t\\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"Qwen1.5-1.8B & / \\t&\\t37.97 \\t&\\t27.86 \\t&\\t34.10 \\t&\\t45.67 \\t&\\t38.81 \\t&\\t57.21 \\t&\\t61.62 \\t&\\t39.35 \\t&\\t48.14 \\t&\\t60.78 \\t&\\t47.44 \\t&\\t59.44 \\t&\\t46.53 \\t\\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"MoE-LPR (7*24) & G0 \\\\!$\\\\rightarrow$\\\\! G1 \\\\!$+$\\\\! G2 & \\t37.88 \\t&\\t26.92 \\t&\\t33.85 \\t&\\t45.26 \\t&\\t37.61 \\t&\\t54.51 \\t&\\t61.43 \\t&\\t39.29 \\t&\\t48.14 \\t&\\t59.33 \\t&\\t44.56 \\t&\\t53.00 \\t&\\t45.15 \\t\\\\\\\\\\n\",\n \"MoE-LPR (4*24) & G0 \\\\!$\\\\rightarrow$\\\\! G1 \\\\!$+$\\\\! G2 & \\t37.80 \\t&\\t27.69 \\t&\\t32.82 \\t&\\t45.29 \\t&\\t38.12 \\t&\\t54.39 \\t&\\t61.45 \\t&\\t39.11 \\t&\\t48.20 \\t&\\t59.89 \\t&\\t44.33 \\t&\\t54.22 \\t&\\t45.28 \\t\\\\\\\\\\n\",\n \"LayerMoE (Ours) & G0 \\\\!$\\\\rightarrow$\\\\! G1 \\\\!$+$\\\\! G2 & \\t37.46 \\t&\\t27.01 \\t&\\t34.27 \\t&\\t45.63 \\t&\\t38.14 \\t&\\t54.18 \\t&\\t61.37 \\t&\\t39.08 \\t&\\t47.86 \\t&\\t60.11 \\t&\\t46.22 \\t&\\t55.44 \\t&\\t\\\\textbf{45.56} \\t\\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"MoE-LPR (7*24) & G0 \\\\!$\\\\rightarrow$\\\\! G1 \\\\!$\\\\rightarrow$\\\\! G2 & \\t37.54 \\t&\\t26.84 \\t&\\t33.16 \\t&\\t45.71 \\t&\\t38.29 \\t&\\t55.42 \\t&\\t61.43 \\t&\\t39.21 \\t&\\t48.12 \\t&\\t61.22 \\t&\\t47.33 \\t&\\t56.33 \\t&\\t45.88 \\t\\\\\\\\\\n\",\n \"LayerMoE (Ours) & G0 \\\\!$\\\\rightarrow$\\\\! G1 \\\\!$\\\\rightarrow$\\\\! G2 & \\t37.29 \\t&\\t26.58 \\t&\\t33.93 \\t&\\t45.69 \\t&\\t38.86 \\t&\\t55.40 \\t&\\t61.23 \\t&\\t39.23 \\t&\\t48.05 \\t&\\t62.22 \\t&\\t48.00 \\t&\\t56.67 \\t&\\t\\\\textbf{46.10} \\t\\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"MoE-LPR (7*24) & G0 \\\\!$\\\\rightarrow$\\\\! G2 \\\\!$\\\\rightarrow$\\\\! G1 & \\t38.14 \\t&\\t27.18 \\t&\\t33.42 \\t&\\t46.07 \\t&\\t38.26 \\t&\\t55.28 \\t&\\t61.36 \\t&\\t39.44 \\t&\\t48.26 \\t&\\t57.44 \\t&\\t44.78 \\t&\\t53.67 \\t&\\t45.28 \\t\\\\\\\\\\n\",\n \"LayerMoE (Ours) & G0 \\\\!$\\\\rightarrow$\\\\! G2 \\\\!$\\\\rightarrow$\\\\! G1 & 38.31 &\\t27.09 &\\t34.70 & 46.19 & 38.79 & 55.85 & 61.19 & 39.05 & 48.05 & 61.89 & 46.44 & 57.44 & \\\\textbf{46.25} \\\\\\\\\\n\",\n \"\\n\",\n \"\\\\toprule\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\textbf{Methods} & \\\\textbf{Order}\\t&\\t\\\\textbf{el}\\t&\\t\\\\textbf{hu}\\t&\\t\\\\textbf{tr}\\t&\\t\\\\textbf{el}\\t&\\t\\\\textbf{hu}\\t&\\t\\\\textbf{tr}\\t&\\t\\\\textbf{el}\\t&\\t\\\\textbf{hu}\\t&\\t\\\\textbf{tr}\\t&\\t\\\\textbf{el}\\t&\\t\\\\textbf{hu}\\t&\\t\\\\textbf{tr}\\t&\\t\\\\textbf{\\\\textit{G1-avg}}\\t\\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"Qwen1.5-1.8B\\t& / &\\t22.95 \\t&\\t23.20 \\t&\\t22.63 \\t&\\t28.94 \\t&\\t32.25 \\t&\\t30.37 \\t&\\t29.15 \\t&\\t29.63 \\t&\\t29.04 \\t&\\t33.33 \\t&\\t32.00 \\t&\\t33.44 \\t&\\t28.91 \\t\\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"MoE-LPR (7*24) & G0 \\\\!$\\\\rightarrow$\\\\! G1 \\\\!$+$\\\\! G2 & \\t26.46 \\t&\\t28.00 \\t&\\t27.92 \\t&\\t30.38 \\t&\\t35.14 \\t&\\t34.64 \\t&\\t36.40 \\t&\\t39.01 \\t&\\t37.92 \\t&\\t35.33 \\t&\\t39.00 \\t&\\t39.78 \\t&\\t34.17 \\t\\\\\\\\\\n\",\n \"MoE-LPR (4*24) & G0 \\\\!$\\\\rightarrow$\\\\! G1 \\\\!$+$\\\\! G2 & \\t26.20 \\t&\\t27.65 \\t&\\t27.92 \\t&\\t31.01 \\t&\\t35.08 \\t&\\t34.37 \\t&\\t35.60 \\t&\\t38.14 \\t&\\t37.11 \\t&\\t34.11 \\t&\\t41.11 \\t&\\t40.56 \\t&\\t34.07 \\t\\\\\\\\\\n\",\n \"LayerMoE (Ours) & G0 \\\\!$\\\\rightarrow$\\\\! G1 \\\\!$+$\\\\! G2 & \\t25.34 \\t&\\t27.83 \\t&\\t27.92 \\t&\\t31.51 \\t&\\t36.43 \\t&\\t35.32 \\t&\\t35.50 \\t&\\t38.00 \\t&\\t37.27 \\t&\\t36.89 \\t&\\t42.11 \\t&\\t42.67 \\t&\\t\\\\textbf{34.73} \\t\\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"MoE-LPR (7*24) & G0 \\\\!$\\\\rightarrow$\\\\! G1 \\\\!$\\\\rightarrow$\\\\! G2 & \\t25.51 \\t&\\t26.20 \\t&\\t27.75 \\t&\\t30.51 \\t&\\t35.29 \\t&\\t34.46 \\t&\\t35.20 \\t&\\t37.92 \\t&\\t37.08 \\t&\\t37.44 \\t&\\t43.44 \\t&\\t42.00 \\t&\\t34.40 \\t\\\\\\\\\\n\",\n \"LayerMoE (Ours) & G0 \\\\!$\\\\rightarrow$\\\\! G1 \\\\!$\\\\rightarrow$\\\\! G2 & \\t25.34 \\t&\\t25.77 \\t&\\t27.75 \\t&\\t31.94 \\t&\\t36.58 \\t&\\t35.09 \\t&\\t35.17 \\t&\\t37.92 \\t&\\t36.24 \\t&\\t37.34 \\t&\\t43.34 \\t&\\t42.18 \\t&\\t\\\\textbf{34.56} \\t\\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"MoE-LPR (7*24) & G0 \\\\!$\\\\rightarrow$\\\\! G2 \\\\!$\\\\rightarrow$\\\\! G1 & \\t25.34 \\t&\\t26.97 \\t&\\t26.47 \\t&\\t30.76 \\t&\\t34.41 \\t&\\t34.47 \\t&\\t35.11 \\t&\\t37.42 \\t&\\t37.09 \\t&\\t33.67 \\t&\\t38.44 \\t&\\t40.89 \\t&\\t33.42 \\t\\\\\\\\\\n\",\n \"LayerMoE (Ours) & G0 \\\\!$\\\\rightarrow$\\\\! G2 \\\\!$\\\\rightarrow$\\\\! G1 & 24.57 & 26.80 & 26.30 & 30.86\\t& 35.36 & \\t35.26 & 34.54 &\\t37.07 &\\t36.12 & 36.44 & 39.00 & 42.78 & \\\\textbf{33.76} \\\\\\\\\\n\",\n \"\\n\",\n \"\\\\toprule\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\textbf{Methods} & \\\\textbf{Order}\\t&\\t\\\\textbf{bn}\\t&\\t\\\\textbf{hi}\\t&\\t\\\\textbf{ne}\\t&\\t\\\\textbf{bn}\\t&\\t\\\\textbf{hi}\\t&\\t\\\\textbf{ne}\\t& \\\\textbf{bn}\\t&\\t\\\\textbf{hi}\\t&\\t\\\\textbf{ne}\\t& \\\\textbf{bn}\\t&\\t\\\\textbf{hi}\\t&\\t\\\\textbf{ne}\\t&\\t\\\\textbf{\\\\textit{G2-avg}}\\t\\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"Qwen1.5-1.8B & /\\t&\\t22.50 \\t&\\t23.46 \\t&\\t23.18 \\t&\\t29.59 \\t&\\t29.89 \\t&\\t29.13 \\t&\\t28.23 \\t&\\t28.48 \\t&\\t27.95 \\t&\\t29.67 \\t&\\t30.00 \\t&\\t26.89 \\t&\\t27.41 \\t\\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"MoE-LPR (7*24) & G0 \\\\!$\\\\rightarrow$\\\\! G1 \\\\!$+$\\\\! G2 & \\t24.29 \\t&\\t24.91 \\t&\\t20.96 \\t&\\t30.94 \\t&\\t32.26 \\t&\\t31.31 \\t&\\t30.03 \\t&\\t33.24 \\t&\\t30.03 \\t&\\t31.56 \\t&\\t34.00 \\t&\\t36.22 \\t&\\t\\\\textbf{29.98} \\t\\\\\\\\\\n\",\n \"MoE-LPR (4*24) & G0 \\\\!$\\\\rightarrow$\\\\! G1 \\\\!$+$\\\\! G2 & \\t24.38 \\t&\\t25.00 \\t&\\t21.47 \\t&\\t29.64 \\t&\\t31.81 \\t&\\t30.09 \\t&\\t29.59 \\t&\\t32.72 \\t&\\t29.73 \\t&\\t33.11 \\t&\\t34.33 \\t&\\t32.78 \\t&\\t29.55 \\t\\\\\\\\\\n\",\n \"LayerMoE (Ours) & G0 \\\\!$\\\\rightarrow$\\\\! G1 \\\\!$+$\\\\! G2 & \\t23.27 \\t&\\t25.26 \\t&\\t21.47 \\t&\\t31.23 \\t&\\t32.05 \\t&\\t30.60 \\t&\\t29.67 \\t&\\t32.34 \\t&\\t29.66 \\t&\\t32.67 \\t&\\t36.00 \\t&\\t35.00 \\t&\\t29.94 \\t\\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"MoE-LPR (7*24) & G0 \\\\!$\\\\rightarrow$\\\\! G1 \\\\!$\\\\rightarrow$\\\\! G2 & \\t23.44 \\t&\\t25.34 \\t&\\t21.81 \\t&\\t31.66 \\t&\\t32.31 \\t&\\t31.41 \\t&\\t29.55 \\t&\\t32.60 \\t&\\t29.54 \\t&\\t34.22 \\t&\\t35.44 \\t&\\t35.78 \\t&\\t30.26 \\t\\\\\\\\\\n\",\n \"LayerMoE (Ours) & G0 \\\\!$\\\\rightarrow$\\\\! G1 \\\\!$\\\\rightarrow$\\\\! G2 & \\t23.78 \\t&\\t25.43 \\t&\\t22.33 \\t&\\t31.42 \\t&\\t31.92 \\t&\\t31.11 \\t&\\t29.71 \\t&\\t32.49 \\t&\\t29.88 \\t&\\t34.02 \\t&\\t35.62 \\t&\\t35.82 \\t&\\t\\\\textbf{30.29} \\t\\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"MoE-LPR (7*24) & G0 \\\\!$\\\\rightarrow$\\\\! G2 \\\\!$\\\\rightarrow$\\\\! G1 & \\t25.32 \\t&\\t24.40 \\t&\\t21.81 \\t&\\t30.41 \\t&\\t31.89 \\t&\\t29.92 \\t&\\t28.79 \\t&\\t30.73 \\t&\\t29.00 \\t&\\t27.78 \\t&\\t29.67 \\t&\\t31.00 \\t&\\t28.39 \\t\\\\\\\\\\n\",\n \"LayerMoE (Ours) & G0 \\\\!$\\\\rightarrow$\\\\! G2 \\\\!$\\\\rightarrow$\\\\! G1 & 24.98 & 25.17 & 22.67 & 31.75 & 29.95 & 30.41 & 29.25 &\\t32.49 &\\t29.46 & 32.56 & 35.11 & 33.78 & \\\\textbf{29.80} \\\\\\\\\\n\",\n \"\\n\",\n \" \\\\toprule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \" \\\\caption{\\n\",\n \" The detailed results of Qwen1.5-1.8B under the G0 \\\\!$\\\\rightarrow$\\\\! G1 \\\\!$+$\\\\! G2, G0 \\\\!$\\\\rightarrow$ \\\\!G1 \\\\!$\\\\rightarrow$ \\\\!G2, and G0 \\\\!$\\\\rightarrow$ \\\\!G2 $\\\\rightarrow$ \\\\!G1 settings for each language.\\n\",\n \" }\\n\",\n \" \\\\label{table:mixed-res-detailed}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[ht]\\n\",\n \" \\\\centering\\n\",\n \" \\\\resizebox*{\\\\linewidth}{!}{\\n\",\n \" \\\\begin{tabular}{l|ccc|ccc|ccc|ccc|c}\\n\",\n \" \\\\bottomrule\\n\",\n \"& \\\\multicolumn{3}{c|}{\\\\textbf{ARC-Challenge}} & \\\\multicolumn{3}{c|}{\\\\textbf{MMLU}} & \\\\multicolumn{3}{c|}{\\\\textbf{Hellaswag}} & \\\\multicolumn{3}{c|}{\\\\textbf{Belebele}} & \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"\\\\textbf{Methods}\\t&\\t\\\\textbf{en}\\t&\\t\\\\textbf{es}\\t&\\t\\\\textbf{zh}\\t&\\t\\\\textbf{en}\\t&\\t\\\\textbf{es}\\t&\\t\\\\textbf{zh}\\t&\\t\\\\textbf{en}\\t&\\t\\\\textbf{es}\\t&\\t\\\\textbf{zh}\\t&\\t\\\\textbf{en}\\t&\\t\\\\textbf{es}\\t&\\t\\\\textbf{zh}\\t&\\t\\\\textbf{\\\\textit{G0-avg}}\\t\\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"Llama-3.2-3B\\t& 51.11 & \\t43.59 & \\t39.23 & 56.42 & \\t49.09 & \\t44.00 & 76.32 & \\t61.50 & \\t52.87 & 74.22 & \\t68.56 & \\t68.67 & 57.13\\t\\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"MoE-LPR (3*24)\\t&\\t50.00 & \\t42.31 & \\t38.80 & 56.25 & \\t49.63 & \\t44.08 & 76.35 & \\t62.23 & \\t53.94 & 74.89 & \\t69.22 & \\t68.11 & 57.15 \\\\\\\\\\n\",\n \"LayerMoE (Ours)\\t&\\t49.91 & \\t42.56 & \\t39.83 & 56.05 & \\t49.77 & \\t44.07 & 76.28 & \\t62.18 & \\t53.74 & 75.33 & \\t69.44 & \\t68.89 & \\\\textbf{57.34}\\t\\\\\\\\ \\n\",\n \"\\\\toprule\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\textbf{Methods}\\t&\\t\\\\textbf{el}\\t&\\t\\\\textbf{hu}\\t&\\t\\\\textbf{tr}\\t&\\t\\\\textbf{el}\\t&\\t\\\\textbf{hu}\\t&\\t\\\\textbf{tr}\\t&\\t\\\\textbf{el}\\t&\\t\\\\textbf{hu}\\t&\\t\\\\textbf{tr}\\t&\\t\\\\textbf{el}\\t&\\t\\\\textbf{hu}\\t&\\t\\\\textbf{tr}\\t&\\t\\\\textbf{\\\\textit{G1-avg}}\\t\\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"Llama-3.2-3B &\\t31.76 \\t&\\t34.85 \\t&\\t33.56 \\t&\\t41.08 \\t&\\t43.01 \\t&\\t42.37 \\t&\\t43.37 \\t&\\t44.65 \\t&\\t42.85 \\t&\\t65.22 \\t&\\t59.67 \\t&\\t59.22 \\t& 45.13\\t\\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"MoE-LPR (3*24)\\t&\\t36.64 \\t&\\t39.21 \\t&\\t36.89 \\t&\\t43.96 \\t&\\t44.51 \\t&\\t43.46 \\t&\\t52.49 \\t&\\t51.33 \\t&\\t49.74 \\t&\\t68.44 \\t&\\t63.00 \\t&\\t61.78 \\t& \\t49.29 \\\\\\\\\\n\",\n \"LayerMoE (Ours)\\t&\\t36.64 \\t&\\t39.73 \\t&\\t36.55 \\t&\\t43.88 \\t&\\t44.86 \\t&\\t43.51 \\t&\\t52.72 \\t&\\t51.66 \\t&\\t49.60 \\t&\\t67.89 \\t&\\t64.22 \\t&\\t61.78 \\t&\\t\\\\textbf{49.42} \\\\\\\\\\n\",\n \"\\n\",\n \" \\\\toprule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \" \\\\caption{\\n\",\n \" The results of Llama-3.2-3B on ARC-Challenge, MMLU, Hellaswag, and Belebele under the G0 \\\\!$\\\\rightarrow$\\\\! G1 setting for each language. \\n\",\n \" }\\n\",\n \" \\\\label{table:llama-res-detailed-1}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[ht]\\n\",\n \" \\\\centering\\n\",\n \" \\\\resizebox*{\\\\linewidth}{!}{\\n\",\n \" \\\\begin{tabular}{l|cccc|c|cccccc|c}\\n\",\n \" \\\\bottomrule\\n\",\n \"\\\\textbf{Methods}\\t&\\t\\\\textbf{\\\\textit{en $\\\\rightarrow$ zh}}\\t&\\t\\\\textbf{\\\\textit{zh $\\\\rightarrow$ en}}\\t&\\t\\\\textbf{\\\\textit{en $\\\\rightarrow$ es}}\\t&\\t\\\\textbf{\\\\textit{es $\\\\rightarrow$ en}}\\t& \\\\textbf{old-avg}\\t & \\\\textbf{\\\\textit{en $\\\\rightarrow$ el}}\\t&\\t\\\\textbf{\\\\textit{el $\\\\rightarrow$ en}}\\t&\\t\\\\textbf{\\\\textit{en $\\\\rightarrow$ hu}}\\t&\\t\\\\textbf{\\\\textit{hu $\\\\rightarrow$ en}}\\t& \\\\textbf{\\\\textit{en $\\\\rightarrow$ tr}}\\t&\\t\\\\textbf{\\\\textit{tr $\\\\rightarrow$ en}}\\t& \\\\textbf{new-avg} \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"Llama-3.2-3B\\t& 84.36\\t & 83.95 & \\t84.79\\t & 86.4\\t & 84.88 & \\t82.47\\t & 85.86\\t & 83.81 & \\t86.15\\t & 78.97\\t & 86.53 & \\t83.96 \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"MoE-LPR (3*24)\\t&\\t84.19 & \\t83.19 & \\t84.67 & \\t86.27 & \\t84.58\\t & 84.18\\t & 85.89\\t & 83.24\\t & 85.6\\t & 81.95\\t & 85.26 & \\t84.35 \\\\\\\\\\n\",\n \"LayerMoE (Ours)\\t&\\t84.32 & \\t83.67\\t & 84.63 & \\t86.26\\t & \\\\textbf{84.72}\\t & 85.14\\t & 86.18\\t & 83.7\\t & 85.65 & \\t82.02 & \\t85.44 & \\t\\\\textbf{84.69}\\t\\\\\\\\ \\n\",\n \"\\\\toprule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \" \\\\caption{\\n\",\n \" The detailed results of Llama-3.2-3B on the FLORES benchmark under the G0 \\\\!$\\\\rightarrow$\\\\! G1 setting for each language. \\n\",\n \" }\\n\",\n \" \\\\label{table:llama-res-detailed-2}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{figure}[b]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=0.7\\\\linewidth]{p6.jpg}\\n\",\n \" \\\\caption{The detailed similarity and expert allocation of our method for each layer of Llama-3.2-3B under the G0 \\\\!$\\\\rightarrow$\\\\! G1 setting.}\\n\",\n \" \\\\label{fig:sim_experts_llama}\\n\",\n \"\\\\end{figure}\\n\"\n ]\n]","isConversational":false},"fulltext":{"kind":"string","value":"% This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.\n\\pdfoutput=1\n% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.\n\n\\documentclass[11pt]{article}\n\n% Change \"review\" to \"final\" to generate the final (sometimes called camera-ready) version.\n% Change to \"preprint\" to generate a non-anonymous version with page numbers.\n\\usepackage[final]{acl}\n\n% Standard package includes\n\\usepackage{times}\n\\usepackage{latexsym}\n\\usepackage{graphicx}\n\\usepackage{latexsym}\n\\usepackage{supertabular}\n\\usepackage{stfloats}\n\\usepackage{tabularx}\n\\usepackage{multirow}\n\\usepackage{makecell}\n\\usepackage{booktabs}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{amssymb}\n\\usepackage{enumerate}\n\\usepackage{algorithm}\n% \\usepackage{arydshln}\n\\usepackage{algorithmic}\n\\usepackage{bbm}\n% \\usepackage[dvipsnames]{xcolor}\n% \\usepackage{xcolor}\n% \\usepackage{color}\n\\usepackage{pifont}\n\\usepackage{colortbl}\n\\usepackage{xspace}\n\\usepackage{inconsolata}\n\\usepackage{subfigure}\n\\usepackage{caption}\n\\usepackage{subcaption}\n\\usepackage{enumitem}\n\n% Standard package includes\n\\usepackage{times}\n\\usepackage{latexsym}\n\\usepackage{graphicx} %插入图片的宏包\n\\usepackage{float} %设置图片浮动位置的宏包\n\\usepackage{subfigure} %插入多图时用子图显示的宏包\n\n% For proper rendering and hyphenation of words containing Latin characters (including in bib files)\n\\usepackage[T1]{fontenc}\n% For Vietnamese characters\n% \\usepackage[T5]{fontenc}\n% See https://www.latex-project.org/help/documentation/encguide.pdf for other character sets\n\n% This assumes your files are encoded as UTF8\n\\usepackage[utf8]{inputenc}\n\n% This is not strictly necessary, and may be commented out,\n% but it will improve the layout of the manuscript,\n% and will typically save some space.\n\\usepackage{microtype}\n\n% This is also not strictly necessary, and may be commented out.\n% However, it will improve the aesthetics of text in\n% the typewriter font.\n\\usepackage{inconsolata}\n\n%Including images in your LaTeX document requires adding\n%additional package(s)\n\\usepackage{graphicx}\n\n% If the title and author information does not fit in the area allocated, uncomment the following\n%\n%\\setlength\\titlebox{<dim>}\n%\n% and set <dim> to something 5cm or larger.\n\n% \\title{Expanding New Languages for LLMs using Layer-wise Mixture-of-Experts}\n\\title{\\textit{Less, but Better}: Efficient Multilingual Expansion for LLMs\\\\via Layer-wise Mixture-of-Experts}\n\n% Authors\n% \\author{Xue Zhang\\textsuperscript{1}\\thanks{ \\ \\ This work was done during internship at Pattern Recognition Center, WeChat AI, Tencent Inc, China.}, Yunlong Liang\\textsuperscript{2}, \n% Fandong Meng\\textsuperscript{2}, Songming Zhang\\textsuperscript{1},\\\\\n% \\textbf{Yufeng Chen\\textsuperscript{1}\\thanks{ \\ \\ Yufeng Chen is the corresponding author.}},\n% \\textbf{Jinan Xu\\textsuperscript{1}}, \\textbf{Jie Zhou\\textsuperscript{2}} \\\\\n% \\textsuperscript{1}Key Laboratory of Big Data \\& Artificial Intelligence in Transportation, Ministry of Education, \\\\\n% School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China \\\\\n% \\textsuperscript{2}Pattern Recognition Center, WeChat AI, Tencent Inc, China \\\\\n% \\texttt{\\{\\text{zhang\\_xue},smzhang22,chenyf,jaxu\\}@bjtu.edu.cn}, \\\\\n% \\texttt{\\{yunlonliang,fandongmeng,withtomzhou\\}@tencent.com} \\\\}\n\n\n\\author{Xue Zhang\\textsuperscript{1,2}\\thanks{ \\ \\ This work was done during the internship at Pattern Recognition Center, WeChat AI, Tencent Inc, China.}, Yunlong Liang\\textsuperscript{3}, \nFandong Meng\\textsuperscript{3}, Songming Zhang\\textsuperscript{1,2},\\\\\n\\textbf{Yufeng Chen\\textsuperscript{1,2}\\thanks{ \\ \\ Yufeng Chen is the corresponding author.}},\n\\textbf{Jinan Xu\\textsuperscript{1,2}}, \\textbf{Jie Zhou\\textsuperscript{3}} \\\\\n\\textsuperscript{1}Key Laboratory of Big Data \\& Artificial Intelligence in Transportation,\\\\(Beijing Jiaotong University), Ministry of Education \\\\\n\\textsuperscript{2}School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China \\\\\n\\textsuperscript{3}Pattern Recognition Center, WeChat AI, Tencent Inc, China \\\\\n\\texttt{\\{\\text{zhang\\_xue},smzhang22,chenyf,jaxu\\}@bjtu.edu.cn}}\n% \\texttt{\\{yunlonliang,fandongmeng,withtomzhou\\}@tencent.com} \\\\}\n\n\n\\begin{document}\n\\maketitle\n\n\n\\begin{abstract}\n% 1. 在扩展新语言的时候没有考虑不同层语言的特性，给每层分配了相同数量的专家，导致在持续的扩展中造成参数的大规模增长；我们的方法通过考虑不同层的语言特性，设计了一个不同层的专家分配策略；\n% 2. 在扩增语言时灾难性遗忘会累加，导致 old 语言的性能持续下降；我们通过在相似度较高的层设置分类器减少了灾难性的遗忘；\nContinually expanding new languages for existing large language models (LLMs) is a promising yet challenging approach to building powerful multilingual LLMs.\nThe biggest challenge is to make the model continuously learn new languages while preserving the proficient ability of old languages.\nTo achieve this, recent work utilizes the Mixture-of-Experts (MoE) architecture to expand new languages by adding new experts and avoid catastrophic forgetting of old languages by routing corresponding tokens to the original model backbone (old experts).\nAlthough intuitive, this kind of method is parameter-costly when expanding new languages and still inevitably impacts the performance of old languages.\nTo address these limitations, we analyze the language characteristics of different layers in LLMs and propose a layer-wise expert allocation algorithm (\\textbf{LayerMoE}) to determine the appropriate number of new experts for each layer.\nSpecifically, we find different layers in LLMs exhibit different representation similarities between languages and then utilize the similarity as the indicator to allocate experts for each layer, \\textit{i.e.}, the higher similarity, the fewer experts.\nAdditionally, to further mitigate the forgetting of old languages, we add a classifier in front of the router network on the layers with higher similarity to guide the routing of old language tokens.\nExperimental results show that our method outperforms the previous state-of-the-art baseline with 60\\% fewer experts in the single-expansion setting and with 33.3\\% fewer experts in the lifelong-expansion setting, demonstrating the effectiveness of our method.\n\n% Lifelong expanding new languages for existing large language models (LLMs) is a sustainable approach to building powerful multilingual LLMs. \n% The core idea is to continually update the model with new language data while avoiding catastrophic forgetting of the ability in originally proficient languages.\n% Recently, MoE-LPR utilizes the Mixture-of-Experts (MoE) architecture to expand new languages by adding new experts while preserving original language proficiency by routing to old experts.\n% Although effective, this method ignores the language characteristics of different layers to directly add the same number of experts in all layers, leading to inefficient expansion and high resource usage.\n% To address this problem, we propose a layer-wise expert allocation algorithm to determine the appropriate expert number for each layer.\n% Specifically, we first analyze the similarity of hidden states in different languages and find that some layers exhibit high similarity. \n% Then we allocate the expert number based on the similarity of different layers, i.e., higher similarity lower expert number.\n% Additionally, although MoE-LPR designs the language priors routing network to recover the abilities of the original languages, the corresponding performance of old languages still declines noticeably.\n% To mitigate the forgetting, we add a classification network in front of the routing network on the layers that exhibit higher similarity to guide the routing network to route old languages to old experts.\n% Experimental results show that our method outperforms the baseline with 60\\% fewer experts in the single-expansion setting and with 33.3\\% fewer experts in the lifelong-expansion setting, which demonstrates the effectiveness of our method.\n\\end{abstract}\n\n\n\\section{Introduction}\n\n% \\begin{table}[t]\n% \\centering\n% % \\belowrulesep=0pt\n% % \\aboverulesep=0pt\n% \\resizebox*{\\linewidth}{!}{\n% \\begin{tabular}{l|ccc}\n% \\bottomrule \n\n% \\textbf{Methods} & \\textbf{Expand-params} ($\\downarrow$) & \\textbf{\\textit{Old-avg}} ($\\uparrow$) & \\textbf{\\textit{New-avg}} ($\\uparrow$) \\\\ \n% \\hline\n% Qwen1.5-1.8B & / & 46.53 & 28.91 \\\\\n% \\hline\n% MoE-LPR (6*24) & 4B & 45.36 & 34.52 \\\\\n% MOLA ($\\nabla$) & 3.2B \\color{ForestGreen}{($\\downarrow$ 20\\%)} & 45.59 \\color{ForestGreen}{($+$0.23)} & 34.10 \\color{Red}{($-$0.42)} \\\\\n% MoE-LPR (4*24) & 2.4B \\color{ForestGreen}{($\\downarrow$ 40\\%)} & 45.18 \\color{Red}{($-$0.18)} & 33.92 \\color{Red}{($-$0.60)} \\\\\n% MoE-LPR (3*24) & \\textbf{1.6B} \\color{ForestGreen}{($\\downarrow$ \\textbf{60\\%})} & 45.25 \\color{Red}{($-$0.11)} & 33.81 \\color{Red}{($-$0.71)} \\\\\n% LayerMoE (Ours) & \\textbf{1.6B} \\color{ForestGreen}{($\\downarrow$ \\textbf{60\\%})} & \\textbf{45.80} \\color{ForestGreen}{(\\textbf{$+$0.44})} & \\textbf{34.71} \\color{ForestGreen}{(\\textbf{$+$0.19})} \\\\\n \n% \\toprule\n% \\end{tabular}\n% }\n% \\caption{\n% The results on four benchmarks when expanding three new languages (\\textit{el}, \\textit{hu}, and \\textit{tr}) based on Qwen1.5-1.8B.\n% ``Expand-params'' denotes the parameter number of newly added experts and {\\color{ForestGreen}($\\downarrow$ *\\%)} means the reduction percentage compared to MoE-LPR (6*24).\n% ``\\textit{Old-avg}'' represents the average results when testing on \\textit{en}, \\textit{zh}, and \\textit{es}, while ``\\textit{New-avg}'' means the average results of \\textit{el}, \\textit{hu}, and \\textit{tr}.\n% {\\color{ForestGreen}{($+$*)}} and {\\color{Red}{($-$*)}} represent improvement and degradation of the performance compared to the previous SOTA results of ``MoE-LPR (6*24)''.\n% }\n% \\label{table:introduction}\n% \\end{table}\n\nAlthough existing large language models (LLMs) have exhibited remarkable ability in high-resource languages, their performance in multilingual scenarios is still limited \\cite{lai-etal-2023-chatgpt, zhang-etal-2024-enhancing-multilingual, huang2025surveylargelanguagemodels}.\nTo enhance the multilingual ability of LLMs, continually expanding new languages for existing LLMs is a sustainable approach, which does not require excessive computation or data resources than re-training from scratch \\cite{huang2024surveylargelanguagemodels}.\nDuring this process, the key challenge is updating the model with new language data while avoiding catastrophic forgetting of the ability in originally proficient languages.\n\nAs a promising solution, MoE-LPR \\cite{zhou2024moelprmultilingualextensionlarge}, a two-stage training strategy, utilizes the Mixture-of-Experts (MoE) architecture to achieve expansion of new languages and seeks to preserve the ability of the original languages by targeted routing.\nSpecifically, at the first stage, it adds and trains several new experts beyond the dense LLM to learn new languages.\nThen, at the second stage, it tries to recover the ability of old languages by language priors routing training.\nAlthough this method shows the potential of MoE on balancing the capability of newly expanded languages and the original languages, there are still two problems.\nFor one thing, the expansion of MoE-LPR is inefficient and leads to high resource usage after expansion (\\textit{e.g.}, the original Qwen1.5-1.8B has to be 3.2$\\times$ larger to accommodate three additional languages).\n% For one thing, this method ignores the language characteristics of different layers to directly add the same number of experts in all layers, leading to inefficient expansion and high resource usage.\nFor another, although MoE-LPR designs the language priors routing to recover the abilities of the original languages, the corresponding performance of old languages still declines noticeably.\n\nTowards these limitations, we first analyze the similarity between hidden states in different languages and find that some layers exhibit high similarity.\nOn this basis, we propose a layer-wise expert allocation algorithm (\\textbf{LayerMoE}) to determine the appropriate number of new experts for each layer, with the insight that higher representation similarity requires fewer experts.\nAdditionally, we speculate that high similarity between old and new languages in some layers may confuse the router network, causing the forgetting of old languages. \nTo further mitigate the forgetting, we add a classifier in front of the router network on the layers exhibiting higher similarity to guide the routing of tokens in old languages.\n% Additionally, to mitigate the forgetting, we add a classification network in front of the routing network on the layers that exhibit higher similarity to guide the routing network to route old languages to old experts.\n% Experimental results on 4 benchmarks show that our method outperforms the baseline with 60\\% fewer experts in the single-expansion setting and with 33.3\\% fewer experts in the lifelong-expansion setting, which demonstrates the effectiveness of our method.\n\nWe conduct experiments under two settings: (1) single-expansion, where the model is enhanced once by incorporating a new language group, and (2) lifelong-expansion, where the model undergoes sequential adaptation to multiple new language groups.\nThe corresponding experimental results on four benchmarks demonstrate the effectiveness of our LayerMoE.\nSpecifically, in the single-expansion setting (please refer to Table \\ref{table:single-res}), our method achieves superior performance with 60\\% fewer experts compared to the previous state-of-the-art method, \\textit{i.e.}, better expansion of new languages, and less forgetting of old languages.\nIn the lifelong-expansion setting (please refer to Table \\ref{table:lifelong-res}), our method also outperforms baselines, requiring 33.3\\% fewer experts.\nThese results indicate that our expert allocation algorithm offers a resource-efficient solution for continuous language learning, and the classifier added in front of the router network further prevents the forgetting of old languages.\n\nIn summary, the major contributions of this paper are as follows\\footnote{The code is publicly available at \\url{https://github.com/XZhang00/LayerMoE}.}:\n% [itemsep=2pt,topsep=0pt,parsep=0pt]\n\\begin{itemize}\n \\item We devise a layer-wise expert allocation algorithm to assign the appropriate number of new experts for each layer, which is parameter-efficient and achieves better expansion performance of new languages.\n\n \\item We add a classifier in front of the router network on the layers that exhibit higher similarity, further mitigating the forgetting of old languages.\n\n \\item Experimental results in the single-expansion and lifelong-expansion settings demonstrate the effectiveness and the efficiency of our LayerMoE.\n \n\\end{itemize}\n\n\n\n\n\n% \\section{Background and Preliminary Study}\n\\section{Background}\n\nIn this section, we first introduce the two-stage expansion method MoE-LPR \\cite{zhou2024moelprmultilingualextensionlarge} and discuss its limitations.\n% in \\S\\ref{sec:MoE-lpr}.\n% Then we xxx in \\S\\ref{sec:similarity}.\n\n\n\\paragraph{Stage-1: Continual pre-training with MoE.}\nThe dense model is upcycled to an MoE model by adding new feed-forward networks (FFNs) and router networks.\nFormally, the MoE model includes $N$ experts and a linear router network $W_r \\in \\mathbb{R}^{h \\times N}$ for each layer, where $h$ is the dimension of hidden states after the Attention module.\nDuring the forward process, the top-$K$ experts $\\mathcal{I}$ are dynamically selected by the router according to the router scores:\n\\begin{equation}\n G(\\mathbf{x}) = \\text{Softmax} (\\mathbf{x} \\cdot W_r),\n\\end{equation}\n\\begin{equation}\n \\mathcal{I} = \\{i \\mid G_i(\\mathbf{x}) \\in \\mathrm{TopK}(G(\\mathbf{x}), K)\\},\n\\end{equation}\nwhere $\\mathbf{x} \\in \\mathbb{R}^h$ is the input token hidden state vector of the router network, $G(\\mathbf{x}) \\in \\mathbb{R}^N$ represents the router scores of each expert, and $K$ is the number of the activated experts ($K = 2$).\nThen we normalize the weights of the selected experts and weighted sum their outputs to obtain the final representation:\n\\begin{equation}\\label{equation:route}\n \\mathbf{y} = \\sum_{i \\in \\mathcal{I}} \\frac{G_i(\\mathbf{x})}{\\Sigma_{j \\in \\mathcal{I}}G_j(\\mathbf{x})} E_i(\\mathbf{x}) + \\mathbf{x},\n\\end{equation}\nwhere $G_i(\\mathbf{x})$ and $E_i(\\mathbf{x})$ denote the router score and the output of the \\textit{i}-th expert respectively.\n\nIn stage-1, the parameters of the original dense model are frozen to avoid catastrophic forgetting and the newly added experts and routers are updated with the data of expanded languages.\nThe training objective includes the next token prediction loss $\\mathcal{L}_{NTP}$ and the load balance loss $\\mathcal{L}_{balance}$ \\cite{fedus2022switchtransformersscalingtrillion}.\nGiven the MoE model $\\mathcal{M}$ with the parameters of the newly added experts $\\theta_{e}$ and the routers $\\theta_{r}$, the $\\mathcal{L}_{NTP}$ of one input sequence $\\mathbf{t}$ is $\\mathcal{L}_{NTP}(\\theta_{e}, \\theta_{r}) = -\\sum_{i}^{|\\mathbf{t}|}\\log p_{\\mathcal{M}}(t_i\\mid \\mathbf{t}_{<i})$.\n% \\begin{equation}\n% \\mathcal{L}_{NTP}(\\theta_{new}, \\theta_{W_r}) = -\\sum_{i}^{|\\mathbf{s}|}\\log P_{M}(s_i\\mid \\mathbf{s}_{<i}).\n% \\end{equation}\nThe $\\mathcal{L}_{balance}$ is an expert-level load balance loss (please refer to Appendix \\ref{sec:appendix-load-balande-loss} for the detailed introduction) to mitigate the risk of routing collapse.\nThe final training loss of stage-1 is:\n\\begin{equation}\n \\mathcal{L}_{stage1}(\\theta_{e}, \\theta_{r}) = \\mathcal{L}_{NTP} + \\alpha \\mathcal{L}_{balance},\n\\end{equation}\nwhere $\\alpha$ is a hyper-parameter that controls the weight of the load balance loss ($\\alpha = 0.01$).\n\n\n\\paragraph{Stage-2: Review with LPR.}\nIn this stage, only the routers are updated with the mixed data of the old languages and the new languages.\nThe language priors routing (LPR) loss is designed to route the old language tokens to the original frozen experts (\\textit{i.e.}, the expert 0 in each layer).\nFormally, given a batch tokens $\\mathbb{T}$ including the old language tokens set $\\mathbb{T}_{old}$ and the indicator function $F(t)$, the LPR loss $\\mathcal{L}_{LPR}$ is defined as:\n\\begin{equation}\n \\mathcal{L}_{LPR}(\\theta_{r}) = -\\sum_{t \\in \\mathbb{T}} F(t)\\log G_0(t),\n\\end{equation}\n\\begin{equation}\\label{equation:ft}\n F(t) = \\left\\{\n \\begin{array}{ll}\n 1, & \\text{if } t \\in \\mathbb{T}_{old}, \\\\\n 0, & \\text{if } t \\notin \\mathbb{T}_{old},\n \\end{array}\n \\right.\n\\end{equation}\nwhere $G_0(t)$ is the routing score of the expert 0.\nThe final training loss of stage-2 is:\n\\begin{equation}\n \\mathcal{L}_{stage2}(\\theta_{r}) = \\mathcal{L}_{NTP} + \\beta \\mathcal{L}_{LPR},\n\\end{equation}\nwhere $\\beta$ is a hyper-parameter that controls the weight of the LPR loss ($\\beta = 0.1$).\n\n\n\\paragraph{Discussions about MoE-LPR.} \nDuring stage-1, MoE-LPR directly adds uniformly distributed experts for each layer, \\textit{i.e.}, each layer adds the same number of new experts.\nThis expansion paradigm may lead to inefficient expansion and high resource usage, especially in lifelong learning settings. \nInspired by the findings on language-specific and language-agnostic characteristics of different layers \\cite{chen2023journeycenterknowledgeneurons, zhang2024multilingualknowledgeeditinglanguageagnostic}, we raise a question: do all layers need the same number of new experts? \nOne intuition is that decreasing the number of experts in some layers may not hurt the overall performance, which motivates us to design an expert allocation algorithm.\nDuring stage-2, although the LPR loss is used to guide the routing of old language tokens, the performance of the old languages still declines noticeably.\n% (as shown by ``\\textbf{\\textit{Old-avg}}'' in Table \\ref{table:introduction}).\nThis suggests that the routing rules in some layers may be difficult for the router network to learn.\n\n\n% \\subsection{MoE-LPR}\\label{sec:MoE-lpr}\n\n\n% 缺点1：在这种扩增方式下，life-long 的扩增会使参数增加的过快；\n% 提出一个问题，在 MoE 专家存在冗余的前提下，不同层是否需要相同的专家；\n% 缺点2：即使有 stage-2 的恢复，old 语言的能力仍然存在一定的下降，如何进一步减少遗忘呢？\n\n\n\\section{Methodology}\n\n\nTo address the two limitations of MoE-LPR, we first analyze the similarity of hidden states between different languages across different layers.\nThen we introduce our layer-wise expert allocation algorithm based on the similarity.\nLastly, we introduce the classifier ahead of the router network in the layers with high similarity to control the routing of the old languages.\n\n\\subsection{Similarity Analysis}\\label{sec:similarity}\n\nConsidering the simplistic structure of the routing network, which essentially performs a linear transformation, the routing results are typically dependent on the input of the router network, i.e., the Hidden States obtained after the Attention module (HSAs). \nTherefore, we analyze the similarity of HSAs in different languages to investigate the potential differences of languages at different layers.\n\nGiven two languages $\\ell_1$ and $\\ell_2$, we randomly sample many examples for each language and perform forward propagation to obtain HSAs of each token at different layers.\nThen we randomly select ${Q}$ tokens for each language and take their HSAs to build the candidate set $\\mathbf{Q}_{\\ell_1}$ and $\\mathbf{Q}_{\\ell_2}$ for calculating the similarity of these selected tokens in different languages.\nFor example in layer $i$, the similarity of $\\mathbf{Q}_{\\ell_1}^i$ and $\\mathbf{Q}_{\\ell_2}^i$ are obtained by calculating the cosine similarity of any two HSA vectors:\n\\begin{equation}\\label{equation:similarity}\n \\mathrm{sim}(\\mathbf{Q}_{\\ell_1}^i, \\mathbf{Q}_{\\ell_2}^i) \\!= \\! \\frac{1}{Q^2} \\! \\sum_{\\mathbf{u} \\in \\mathbf{Q}_{\\ell_1}^i} \\! \\sum_{\\mathbf{v} \\in \\mathbf{Q}_{\\ell_2}^i} \\mathrm{cos} (\\mathbf{u}, \\mathbf{v}),\n\\end{equation}\nwhere $\\mathrm{cos}(\\cdot)$ denotes the cosine similarity, and $\\mathbf{u}/\\mathbf{v} \\in \\mathbb{R}^h$.\nThese HSAs of randomly selected tokens contain different contextual information \\cite{ethayarajh-2019-contextual}, which simulates the actual input of the router network.\n\n\\begin{figure}[t]\n \\centering\n \\includegraphics[width=\\linewidth]{p1-v1.jpg}\n \\caption{The similarity of HSAs in different languages across all layers within Qwen1.5-1.8B.}\n \\label{fig:similarity}\n\\end{figure}\n\nAs a preliminary study, we calculate the similarity of HSAs between some languages in different layers.\nWe select Qwen1.5-1.8B \\cite{qwen1.5} as the base model and choose four languages including English (\\textit{en}), Spanish (\\textit{es}), Bengali (\\textit{bn}), and Greek (\\textit{el}).\nFor each language $\\ell$, we sample examples from CulturalX \\cite{nguyen-etal-2024-culturax} and then randomly select $Q=100000$ tokens to obtain the candidate set $\\mathbf{Q}_{\\ell}$.\n% 一致性没说。\nThen we follow Eq.(\\ref{equation:similarity}) to calculate the similarity between four language pairs, including \\textit{en}\\&\\textit{en}, \\textit{en}\\&\\textit{es}, \\textit{en}\\&\\textit{bn}, and \\textit{bn}\\&\\textit{hi}. \nThe results in Figure \\ref{fig:similarity} show that the similarity in different layers exhibits significant differences, with higher similarity in the middle and last few layers, and lower similarity in the 0\\textasciitilde4 layers and 17\\textasciitilde21 layers.\n\\textit{en}\\&\\textit{en} (the red line) displays the highest similarity across all layers among the four language pairs, while the other 3 language pairs show similar trends with \\textit{en}\\&\\textit{en}.\n\n\n\n\n\nAccording to the similarity difference across all layers, we speculate that the higher similarity indicates that HSAs capture more language-agnostic information, while the lower similarity suggests more language-specific characteristics, which aligns with previous studies \\cite{zhang2024multilingualknowledgeeditinglanguageagnostic, tang-etal-2024-language} that have observed the existence of language-agnostic/specific neurons within LLMs.\nWe hypothesize that a layer with higher similarity needs fewer new experts when expanding new languages, as the model can extract language-agnostic representations in this layer. Conversely, lower similarity implies that more experts are necessary to accommodate language-specific features for the new languages.\nOn this basis, we propose our LayerMoE.\n\n\n\\begin{figure*}[t]\n \\centering\n \\includegraphics[width=\\linewidth]{main-v5.jpg}\n \\caption{The procedure of our LayerMoE. In Step 1, we first calculate the indicated similarity for each layer. In Step 2, we allocate experts based on the similarity. The layers with lower similarity are allocated more experts, while those with higher similarity are assigned fewer experts. Additionally, we add a classifier in front of the router in the layers with higher similarity to guide the routing of old language tokens.}\n \\label{fig:model}\n\\end{figure*}\n\n\\subsection{Layer-wise Expert Allocation}\nFigure \\ref{fig:model} shows the procedure of our method.\nWhen expanding new languages, we first calculate the similarity between \\textit{new}\\&\\textit{new} and \\textit{new}\\&\\textit{old} languages for each layer.\nThen we allocate appropriate experts for each layer according to the similarity.\n\n\\paragraph{Step 1: Calculate Similarity.}\nGiven the dense model $\\mathcal{M}_0$, and its proficient language group $L_{old}$ and current expanded language group $L_{new}$, we first randomly select $Q$ tokens to build the candidate token set $\\mathbf{Q}_{old}$ and $\\mathbf{Q}_{new}$. \nThen we calculate the similarity of $\\mathbf{Q}_{new}$\\&$\\mathbf{Q}_{old}$ and $\\mathbf{Q}_{new}$\\&$\\mathbf{Q}_{new}$, and compute their average as the indicated similarity of each layer.\nFor example in layer $i$, the indicated similarity $S^i$ is calucluated by:\n% \\begin{equation}\n% Sim_{new\\&old}^i = \n% \\frac{\\sum_{\\ell_{1} \\in L_{new}} \\sum_{\\ell_{2} \\in L_{old}} Sim^i(\\mathbb{Q}_{\\ell_1}^i, \\mathbb{Q}_{\\ell_2}^i)}{|L_{new}||L_{old}|} \n% \\end{equation}\n\\begin{equation}\\nonumber\n S_{new\\&old}^i \\! = \\!\n \\frac{\\sum_{j}^{|L_{new}|} \\! \\sum_{k}^{|L_{old}|} \\! \\mathrm{sim}(\\mathbf{Q}_{\\ell_j}^i, \\! \\mathbf{Q}_{\\ell_k}^i)}{|L_{new}||L_{old}|},\n\\end{equation}\n\\begin{equation}\\nonumber\n S_{new\\&new}^i \\! = \\!\n \\frac{\\sum_{j}^{|L_{new}|} \\! \\sum_{k, k \\neq j}^{|L_{new}|} \\! \\mathrm{sim}(\\mathbf{Q}_{\\ell_j}^i, \\! \\mathbf{Q}_{\\ell_k}^i)}{2|L_{new}|(|L_{new}|-1)},\n\\end{equation}\n\\begin{equation}\\label{equation:sim-layers}\n S^i = \\frac{S_{new\\&old}^i + S_{new\\&new}^i}{2},\n\\end{equation}\nwhere $\\mathrm{sim}(\\cdot, \\cdot)$ follows Eq.(\\ref{equation:similarity}).\n\n\\paragraph{Step 2: Allocate Experts.}\nGiven the dense model with $m$ layers, we obtain the indicated similarity vector $\\mathcal{S} \\! = \\! [S^1, S^2, \\dots, S^m]$ after Step 1.\nThen numbers of experts across $m$ layers $\\mathcal{N} \\! = \\! [N^1, N^2, \\dots, N^m]$ is calculated as follows:\n\\begin{equation}\\label{equation:expert-number}\n N^i = \\left\\lceil \\left (\\frac{({S^i})^{-1}}{\\sum_{i=1}^{m}(S^i)^{-1}} \\right ) \\times \\delta \\right\\rceil,\n\\end{equation}\nwhere $\\delta$ is the target sum number of all experts such that $\\sum_{i=1}^m N^i=\\delta$, and $\\left\\lceil \\cdot \\right\\rceil$ denotes round up to the nearest integer.\nAs a result, fewer experts will be allocated to the layers with higher similarity, while more experts to the layers with lower similarity.\nAfter allocating experts to each layer, we conduct the post-training (stage-1) using the data of new languages to obtain the MoE model $\\mathcal{M}_{stage1}$.\n\n\\subsection{Routing Classification Network}\n% 针对问题二，我们同样利用相似度，在相似度较高的层添加分类网络作为路由的指导信息。\nDuring stage-2, the key objective is $\\mathcal{L}_{LPR}$ that trains the router network to route the tokens in old languages to old experts.\nTo reduce the confusion of the routers on old languages and new languages, we add a routing classification network $W_c$ in front of the router network to judge whether the current token belongs to old languages, where $W_c \\in \\mathbb{R}^{h \\times 2}$.\nAfter obtaining the model $\\mathcal{M}_{stage1}$, we first calculate the similarities $\\mathcal{S}_{new\\&old} \\! = \\![S^1_{new\\&old}, S^2_{new\\&old}, \\dots, S^m_{new\\&old}]$ between new and old languages following Eq.(\\ref{equation:sim-layers}) for each layer.\nThen we add the classifier to the top-$K$ layers according to $\\{i | S^i_{new\\&old} \\in \\mathrm{TopK}(\\mathcal{S}_{new\\&old}, K)\\}$.\nThe training objective of $W_c$ is:\n\\begin{equation}\n \\mathcal{L}_{CLS}(\\theta_c) \\!=\\! -\\sum_{t \\in \\mathbb{T}} F(t)\\mathrm{log\\_softmax}(\\mathbf{x} \\cdot W_c),\n\\end{equation}\nwhere the indicator function $F(t)$ is the same as Eq.(\\ref{equation:ft}), $\\mathbf{x}$ is the hidden state of the token $t$, and $\\theta_c$ denotes the parameters of the classifiers.\nTherefore, the final training loss of stage-2 is:\n\\begin{equation}\n \\mathcal{L}_{stage2}(\\theta_{r}, \\theta_c) = \\mathcal{L}_{NTP} + \\beta \\mathcal{L}_{LPR} + \\gamma \\mathcal{L}_{CLS},\n\\end{equation}\nwhere $\\beta$ and $\\gamma$ are hyper-parameters that control the weights of $\\mathcal{L}_{LPR}$ and $\\mathcal{L}_{CLS}$.\n\nDuring inference, the tokens classified to old languages are directly routed to the old expert, while others classified to new languages are normally routed following Eq.(\\ref{equation:route}):\n\\begin{equation}\n \\mathbf{y} \\!=\\! \n\\left\\{\n\\begin{array}{ll}\nE_0(\\mathbf{x}) + \\mathbf{x}, \\!&\\! cls(\\mathbf{x})\\!=\\!0, \\\\\n\\sum_{i \\in \\mathcal{I}} \\frac{G_i(\\mathbf{x})}{\\Sigma_{j \\in \\mathcal{I}}G_j(\\mathbf{x})} E_i(\\mathbf{x}) + \\mathbf{x}, \\!&\\! cls(\\mathbf{x})\\!=\\!1,\n\\end{array}\n\\right.\n\\end{equation}\nwhere $cls(\\mathbf{x}) = \\mathrm{argmax}(\\mathbf{x} \\cdot W_r)$, ``0/1'' denotes the current token belonging to ``old/new'' languages, and $E_0$ represents the old expert. \n\n\n\n\\section{Experiments}\n\n\n\\subsection{Experimental Setup}\nWe conduct the experiments under single-expansion and lifelong-expansion settings. \nThe single-expansion setting means expanding one group of new languages to enhance the original model once, while the lifelong-expansion setting means sequentially adapting the model on multiple groups of new languages.\n\n\n\\paragraph{Model and Languages.}\nFollowing MoE-LPR \\cite{zhou2024moelprmultilingualextensionlarge}, we select Qwen1.5-1.8B \\cite{qwen1.5} as our base model, which has a powerful multilingual tokenizer and lower computation overhead for easily upcycling.\nBased on the ability of Qwen1.5-1.8B, we select three languages with high performance as the old language group (G0) to observe the catastrophic forgetting phenomenon: English (\\textit{en}), Chinese (\\textit{zh}), and Spanish (\\textit{es}).\nAdditionally, we choose six low-resource languages\\footnote{The performance on six languages is shown in Table \\ref{table:single-res}. More details of each language are listed in Appendix \\ref{sec:appendix-languages}.} in which Qwen1.5-1.8B performs poorly as the expanded languages: Greek (\\textit{el}), Hungarian (\\textit{hu}), Turkish (\\textit{tr}), Bengali (\\textit{bn}), Hindi (\\textit{hi}), and Nepali (\\textit{ne}).\nSpecifically, the selection of \\textit{el}, \\textit{hu}, and \\textit{tr} follows MoE-LPR \\cite{zhou2024moelprmultilingualextensionlarge}, while \\textit{bn}, \\textit{hi}, and \\textit{ne} are selected to observe the performance of different methods on morphologically rich non-Latin scripts, which pose greater modeling challenges and suffer from limited resources.\nHence, we divide them into two groups (G1: \\textit{el}, \\textit{hu}, and \\textit{tr}, G2: \\textit{bn}, \\textit{hi}, and \\textit{ne}) for the single-expansion setting (G0 \\!$\\rightarrow$\\! G1, G0 \\!$\\rightarrow$\\! G2, and G0 \\!$\\rightarrow$\\! G1 \\!$+$\\! G2) and the lifelong-expansion setting (G0 \\!$\\rightarrow$\\! G1 \\!$\\rightarrow$\\! G2, and G0 \\!$\\rightarrow$\\! G2 \\!$\\rightarrow$\\! G1).\n\n\n\n\\paragraph{Training.} \nDuring stage-1, we train the new experts and the router network with new language data.\nFor each new language, we sample 2 billion tokens from CulturalX \\cite{nguyen-etal-2024-culturax}, a substantial multilingual dataset with 6.3 trillion tokens in 167 languages.\nThe detailed training setup follows MoE-LPR \\cite{zhou2024moelprmultilingualextensionlarge}: the batch size is 512, the sequence length is 1024, the learning rate is 5e-5, and the weight $\\alpha$ of the load balancing loss is 0.01.\nDuring stage-2, we mix the data of old languages and new languages to train the router network and the classification network.\nThe top-7 layers with high similarity for the single-expansion settings and top-5 layers for the lifelong-expansion settings are taken according to the best performance to add the routing classification network.\nWe randomly sample 50K examples for each old language and 100K examples for each new language as the training data of stage-2.\nFollowing MoE-LPR \\cite{zhou2024moelprmultilingualextensionlarge}, the English/Chinese/Spanish data is sampled from Slimpajam \\cite{cerebras2023slimpajama}, SkyPile-150B \\cite{wei2023skyworkopenbilingualfoundation}, and CulturalX \\cite{nguyen-etal-2024-culturax}, respectively.\nThe 100k examples for each new language are sampled from the training data of stage-1.\nWe set the batch size to 768 and the learning rate to 5e-5.\nThe weight of $\\mathcal{L}_{LPR}$ and $\\mathcal{L}_{CLS}$ loss is set to 0.1.\nAll experiments are conducted on 8$\\times$A100-40G GPUs.\n\n\n\n\\paragraph{Evaluation.} \nWe evaluate all methods on 4 benchmarks: ARC-Challenge (25-shot) \\cite{clark2018thinksolvedquestionanswering}, MMLU (5-shot) \\cite{hendrycks2021measuringmassivemultitasklanguage}, HellaSwag (10-shot) \\cite{zellers-etal-2019-hellaswag}, and Belebele (5-shot) \\cite{Bandarkar_2024}.\nWe utilize the \\texttt{lm-evaluation-hardness}\\footnote{\\url{https://github.com/EleutherAI/lm-evaluation-harness}} framework \\cite{eval-harness} to conduct evaluation on their multilingual version.\nPlease refer to Appendix \\ref{sec:appendix-eval} for the detailed introduction.\n% 下面的需要删减的话就放在附录里面\n\n\n\n\n\n\n\\subsection{Baselines}\n{MoE-LPR} \\cite{zhou2024moelprmultilingualextensionlarge} adds the same number of new experts for each layer. \n\\citeauthor{gao2024higherlayersneedlora} proposes MOLA that experts in lower layers are more similar than those in higher layers, \\textit{i.e.}, higher layers need more experts.\nFor G0 \\!$\\rightarrow$\\! G1, G0 \\!$\\rightarrow$\\! G2, we conduct three versions of MoE-LPR, adding 2/3/5 experts per layer as 3*24/4*24/6*24 (Qwen1.5-1.8B contains 24 layers).\nAnd MOLA ($\\nabla$, \\textbf{2468}) is employed following the best setting, allocating 2/4/6/8 experts for 0\\textasciitilde5/6\\textasciitilde11/12\\textasciitilde17/18\\textasciitilde23 layers.\nFor G0 \\!$\\rightarrow$\\! G1 \\!$+$\\! G2, G0 \\!$\\rightarrow$\\! G1 \\!$\\rightarrow$\\! G2, and G0 \\!$\\rightarrow$\\! G2 \\!$\\rightarrow$\\! G1, we conduct MoE-LPR (7*24/4*24) as baselines.\n% allocating 2 experts for each layer in the first 6 layers, 4 experts for each layer in 7-12 layers, 6 experts for each layer in 13-18 layers, and 8 experts for each layer in last 6 experts.\n% \\textbf{MoE-CT} xxx.\n% We present the expert allocation of MOLA and our method across all layers in Figure \\ref{}.\nFor a fair comparison, we reproduce all versions of MoE-LPR and MOLA using the same data as our method, \\textit{i.e.}, 2 billion tokens for each new language. \n\nAdditionally, we directly take five baselines of MoE-LPR (trained with 8 billion tokens for each new language) under G0 \\!$\\rightarrow$\\! G1 for a clear comparison, including {LoRA} \\cite{hu2021loralowrankadaptationlarge}, {Full Fine-tuning}, {LLaMA-Pro} \\cite{wu2024llamaproprogressivellama}, {MoE}, and {LoRAMoE} \\cite{dou2024loraMoEalleviateworldknowledge}. \n\\textbf{LoRA} \\cite{hu2021loralowrankadaptationlarge} introduces Low-Rank Adaptation for efficiently fine-tuning large language models. In this paper, the LoRA targets include all linear modules. The LoRA rank is set to 8.\n\\textbf{Full Fine-tuning} is fine-tuning all parameters on the dense model.\n\\textbf{LLaMA-Pro} \\cite{wu2024llamaproprogressivellama} insert new transformer blocks to replace the duplicate layers. Only the newly inserted blocks are trained to store new knowledge during post-pretraining. 12 new layers (the best setting) are inserted into the original dense model.\n\\textbf{MoE} means that only the stage-1 of MoE-LPR is conducted.\n\\textbf{LoRAMoE} \\cite{dou2024loraMoEalleviateworldknowledge} is a novel framework that combines multiple LoRAs with a router network. The router selects all LoRAs for each token. \nThe LoRA rank is set to 180, and the number of LoRAs is 8 for each module.\nDue to the poor performance of the 5 baselines, we do not reproduce them and directly take the results trained with 8 billion tokens for each new language from the MoE-LPR paper \\cite{zhou2024moelprmultilingualextensionlarge}.\n% The details of these 5 baselines are listed in Appendix \\ref{sec:appendix-baselines}.\n% Due to the poor performance of the 5 baselines, we do not reproduce them and directly take the results trained with 8 billion tokens for each new language from the MoE-LPR paper \\cite{zhou2024moelprmultilingualextensionlarge}.\n\n\n\n\n\\subsection{Allocation of Experts}\nAfter calculating the similarity following Step 1, we assign the number of new experts for each layer according to Eq.(\\ref{equation:expert-number}).\nFor a fair comparison with MoE-LPR (3*24), we set the targeted number of new experts $\\delta$ in Eq.(\\ref{equation:expert-number}) to 72.\nThe allocation results of our LayerMoE and baselines (under G0 \\!$\\rightarrow$\\! G1 and G0 \\!$\\rightarrow$\\! G2) are present in Figure \\ref{fig:allocation}.\nOur method allocates more experts for the shallow and deep layers and less experts for the middle layers.\nThe detailed similarity and expert allocation results of our method under each setting are plotted in Figure \\ref{fig:sim_experts} of Appendix \\ref{sec:appendix-sim_and_allocation}.\n% \n\n\n\n\\begin{figure}[t]\n \\centering\n \\includegraphics[width=\\linewidth]{p3-v3.jpg}\n \\caption{The expert allocation of our method and baselines for each layer under G0 \\!$\\rightarrow$\\! G1, G0 \\!$\\rightarrow$\\! G2.}\n \\label{fig:allocation}\n\\end{figure}\n\n\\begin{table*}[t]\n \\centering\n % \\belowrulesep=0pt\n % \\aboverulesep=0pt\n \\resizebox*{\\linewidth}{!}{\n \\begin{tabular}{lc|cccccccc|ccc}\n \\bottomrule\n & & \\multicolumn{2}{c}{\\textbf{ARC-C}} & \\multicolumn{2}{c}{\\textbf{MMLU}} & \\multicolumn{2}{c}{\\textbf{Hellaswag}} & \\multicolumn{2}{c|}{\\textbf{Belebele}} & & & \\\\\n \\hline\n \\textbf{Methods} & \\textbf{Expand-params} & \\textbf{Old} &\t\\textbf{New} &\t\\textbf{Old} &\t\\textbf{New} & \\textbf{Old} &\t\\textbf{New}\t& \\textbf{Old}\t& \\textbf{New} &\t\\textbf{\\textit{Old-avg}} & \\textbf{\\textit{New-avg}} & \\textbf{\\textit{avg}}\\\\\n \n \\toprule\n \\multicolumn{13}{c}{\\textbf{Old (G0): \\textit{en}, \\textit{es}, \\textit{zh} \\!$\\rightarrow$\\! New (G1): \\textit{el}, \\textit{hu}, \\textit{tr}}} \\\\\n \\bottomrule\n Qwen1.5-1.8B\t&\t/\t&\t33.31 \t&\t22.93 \t&\t47.23 \t&\t30.52 \t&\t49.70 \t&\t29.27 \t&\t55.89 \t&\t32.92 \t&\t46.53 \t&\t28.91 \t&\t37.72 \t\\\\\n \\hline\n $^*$LoRA\t&\t/ & 28.33 &\t23.89 &\t37.42 & 29.30 & 41.48 & 29.78 & 39.45 & 26.93 & 36.67\t& 27.48\t& 32.07 \\\\\n $^*$Full Fine-tuning\t& / &\t31.72 & 25.98 & 43.51 & 33.18 & 47.38 & 35.28 & 45.26 & 33.70 & 41.98 &\t32.04 & \t37.00 \\\\\n $^*$LLaMA-Pro\t& 0.6B &\t31.77 & 24.35 & 44.06 & 34.02 & 48.36 & 33.85 & 48.78 & 31.52 & 43.24 &\t30.94 & \t37.09 \\\\\n $^*$MoE\t & 4B\t& 32.51 & 26.43 & 44.16 & 35.07 & 48.54 & 37.01 & 45.37 & 32.74 & 42.65 &\t32.81 &\t37.73 \\\\\n $^*$LoRAMoE\t & 0.8B &\t32.43 & 26.63 & 45.41 & 34.17 & 48.61 & 37.17 & 47.74 & 32.81 & 43.55\t& 32.70 &\t38.12 \\\\\n % MoE-CT & 2.4B & 32.83 & 26.43 & 46.12 & 34.22 & 49.46 & 36.85 & 53.52 & 38.78 & 45.48 & 34.07 & 39.78 \\\\\n % MoE-CT & \\\\\n % $^*$MoE-LPR (6*24) & 4B & 32.72 & \t28.43 &\t44.62 & \t34.10 &\t49.12 &\t41.06 &\t51.81 &\t39.92 & 44.57 & 35.88 & 40.22 \\\\\n \\hline\n \\rowcolor{gray!25}\n MoE-LPR (6*24)\t&\t4B\t&\t32.26 \t&\t26.55 \t&\t46.51 \t&\t34.95 \t&\t49.75 \t&\t37.53 \t&\t52.92 \t&\t39.04 \t&\t45.36 \t&\t34.52 \t&\t39.94 \t\\\\\n \n MOLA ($\\nabla$)\t&\t3.2B ($\\downarrow$ 20\\%)\t&\t32.49 \t&\t27.00 \t&\t46.38 \t&\t33.98 \t&\t49.63 \t&\t37.03 \t&\t53.85 \t&\t38.37 \t&\t45.59 \t&\t34.10 \t&\t39.84 \t\\\\\n \n MoE-LPR (4*24)\t&\t2.4B ($\\downarrow$ 40\\%)\t&\t32.29 \t&\t26.69 \t&\t46.51 \t&\t34.11 \t&\t49.60 \t&\t37.12 \t&\t52.33 \t&\t37.74 \t&\t45.18 \t&\t33.92 \t&\t39.55 \t\\\\\n \n MoE-LPR (3*24)\t&\t1.6B ($\\downarrow$ 60\\%)\t&\t32.77 \t&\t26.43 \t&\t46.54\t&\t34.59 \t&\t49.63 \t&\t37.08 \t&\t52.07 \t&\t37.15 \t&\t45.25 \t&\t33.81 \t&\t39.53 \t\\\\\n \n % \\rowcolor{gray!25}\n LayerMoE (Ours)\t&\t\\textbf{1.6B ($\\downarrow$ 60\\%)}\t&\t\\textbf{33.03} \t&\t\\textbf{26.66} \t&\t46.12 \t&\t34.80 \t&\t49.24 \t&\t37.01 \t&\t\\textbf{54.82} \t&\t\\textbf{40.37}\t&\t\\textbf{45.80} \t&\t\\textbf{34.71} \t&\t\\textbf{40.26} \t\\\\\n \\toprule\n \n \\multicolumn{13}{c}{\\textbf{Old (G0): \\textit{en}, \\textit{es}, \\textit{zh} \\!$\\rightarrow$\\! New (G2): \\textit{bn}, \\textit{hi}, \\textit{ne}}} \\\\\n \\bottomrule\n Qwen1.5-1.8B\t&\t/\t&\t33.31 \t&\t23.05 \t&\t47.23 \t&\t29.54 \t&\t49.70 \t&\t28.22 \t&\t55.89 \t&\t28.85 \t&\t46.53 \t&\t27.41 \t&\t36.97 \t\\\\\n \\hline\n \\rowcolor{gray!25}\n MoE-LPR (6*24)\t&\t4B\t&\t33.06 \t&\t23.85 \t&\t47.04 \t&\t30.62 \t&\t49.69 \t&\t30.92 \t&\t53.11 \t&\t33.85 \t&\t45.72 \t&\t29.81 \t&\t37.77 \t\\\\\n \n MOLA ($\\nabla$)\t&\t3.2B ($\\downarrow$ 20\\%)\t&\t32.48 \t&\t23.67 \t&\t47.02 \t&\t30.82 \t&\t49.73 \t&\t30.48 \t&\t53.59 \t&\t32.15 \t&\t45.71 \t&\t29.28 \t&\t37.49 \t\\\\\n\n MoE-LPR (4*24)\t&\t2.4B ($\\downarrow$ 40\\%)\t&\t33.08 \t&\t23.27 \t&\t47.21 \t&\t30.74 \t&\t49.64 \t&\t30.67 \t&\t52.85 \t&\t33.04 \t&\t45.70 \t&\t29.43 \t&\t37.56 \t\\\\\n \n MoE-LPR (3*24)\t&\t1.6B ($\\downarrow$ 60\\%)\t&\t32.77 \t&\t23.53 \t&\t46.97 \t&\t30.72 \t&\t49.68 \t&\t30.56 \t&\t52.59 \t&\t33.33 \t&\t45.50 \t&\t29.53 \t&\t37.52 \t\\\\\n\n LayerMoE (Ours)\t&\t\\textbf{1.6B ($\\downarrow$ 60\\%)}\t& 32.52 &\t\\textbf{24.02} &\t\\textbf{47.10} & \t\\textbf{30.85} & \t49.46 & \t30.47 &\t\\textbf{55.15} &\t\\textbf{34.11} & \\textbf{46.06} & \\textbf{29.86} & \\textbf{37.96} \\\\\n\n \\toprule\n \\end{tabular}\n }\n \\caption{\n The results of the single-expansion settings (G0 \\!$\\rightarrow$\\! G1, G0 \\!$\\rightarrow$\\! G2). The results marked with $^*$ represent that they are directly taken from the MoE-LPR paper \\cite{zhou2024moelprmultilingualextensionlarge}, which utilizes 8 billion tokens for each new language during post-pretraining.\n Other experiments are trained with 2 billion tokens for each new language.\n ``Expand-params'' denotes the parameter number of newly added experts and {($\\downarrow$ *\\%)} means the reduction percentage of parameters compared to MoE-LPR (6*24).\n The results in \\textbf{bold} mean they are better than the previous SOTA method ``MoE-LPR (6*24)''.\n The detailed results for each language are reported in Table \\ref{table:single-res-detailed-g1} and \\ref{table:single-res-detailed-g2} of Appendix \\ref{sec:appendix-detailed-results}.\n }\n \\label{table:single-res}\n\\end{table*}\n\n\n\n\\section{Results and Analysis}\n\n\\subsection{Main Results}\n\n\n\\paragraph{Single-Expansion.}\n% 结果主要需要得出的结论：\n% 1. 我们的方法在减少参数量的情况下仍然性能好，说明我们的方法是参数高效的扩展；\n% 2. 在旧语言上的性能下降更少了，证明了添加分类器的有效性；\nThe evaluation results of the single-expansion setting (G0 \\!$\\rightarrow$\\! G1 and G0 \\!$\\rightarrow$\\! G2) are listed in Table \\ref{table:single-res}.\nThe results show that our method surpasses all baselines in ``{\\textit{Old-avg}}'' and ``{\\textit{New-avg}}'' under the expansion of G1 and G2.\nParticularly, our method outperforms the previous SOTA method ``MoE-LPR (6*24)'' with 60\\% fewer parameter numbers of newly added experts (\\textit{i.e.}, from 4B to 1.6B), which suggests that our method is more parameter-efficient.\nOn the contrary, MOLA and MoE-LPR (3*24/4*24) underperform MoE-LPR (6*24) when decreasing the number of expansion parameters.\nAlthough other baselines (LoRA, Full Fine-tuning, LLaMA-Pro, MoE, and LoRAMoE) utilize 8B tokens for each new language during post-pretraining, they still perform poorly due to the lack of special designs.\nAdditionally, when keeping the same parameter numbers (1.6B) with ``MoE-LPR (3*24)'', our method exhibits a significant improvement over ``MoE-LPR (3*24)'' referring to ``\\textit{New-avg}'' (for G1 from 33.81 to 34.71 and for G2 from 29.53 to 29.86).\nThis superiority indicates that allocating experts based on the language similarity in different layers has a positive contribution to the expansion of new languages.\nFurthermore, the results of ``{\\textit{Old-avg}}' showcase that our method performs the best on preserving the ability of the old languages, with 45.80 for expanding G1 and 46.06 for G2.\nThese results prove that appropriate correction of the router network can effectively mitigate the catastrophic forgetting of old languages.\n% 其他方法表现不好的对比说明；\n% 不同语言单独的性能差异；\n\n% G0/G1 new 提升幅度不一致的原因分析；\n% G0/G1 old 下降不一致的原因分析；\nThe expansion performance exhibits differences among different languages according to ``\\textit{New-avg}''.\nWhen conducting G0 \\!$\\rightarrow$\\! G1, the performance of new languages improves by 5.8 from 28.91 to 34.71, while only improves by 2.45 (from 27.41 to 29.86) on G0 \\!$\\rightarrow$\\! G2.\nAlthough the amounts of training tokens (2B) for each new language are the same, the morphologically rich non-Latin languages in G2 (\\textit{bn}, \\textit{hi}, \\textit{ne}) obtain a smaller performance improvement compared to expanding G1. \nThe difference can be attributed to their lower encoding efficiencies \\cite{arnett2024languagemodelsperformworse, rust-etal-2021-good, ahia-etal-2023-languages, petrov2023languagemodeltokenizersintroduce, ali-etal-2024-tokenizer}, \\textit{i.e.}, these languages typically employ UTF-8 encoding during the tokenization process, which results in longer encoded sequences. \nConsequently, the number of effective training tokens is reduced, limiting the potential for performance improvement.\nThese results suggest that more tokens are needed for better expansion of G2.\nFurthermore, when expanding G1 or G2, there are different degrees of forgetting of old languages.\nExpanding G1 brings more forgetting (from 46.53 to 45.80) compared to expanding G2 (from 46.53 to 46.06), which may be caused by more token overlaps between G0 and G1 since the writing systems of \\textit{en/es} in G0 and \\textit{hu/tr} in G1 all contain the Latin alphabet (as shown in Table \\ref{table:languages} of Appendix \\ref{sec:appendix-languages}).\n\n\n\\begin{table*}[t]\n \\centering\n % \\belowrulesep=0pt\n % \\aboverulesep=0pt\n \\resizebox*{\\linewidth}{!}{\n \\begin{tabular}{lcccc|cccc}\n \\bottomrule\n \\textbf{Methods} & \\textbf{Order} & \\textbf{G1-E-p} & \\textbf{G2-E-p} & \\textbf{All-E-p} & \\textbf{\\textit{G0-avg}} & \\textbf{\\textit{G1-avg}} & \\textbf{\\textit{G2-avg}} & \\textbf{\\textit{avg}} \\\\\n \\hline \n Qwen1.5-1.8B\t& / & / & / & / & 46.53\t& 28.91 & 27.41 & 34.29\t\\\\\n \\hline\n % \\rowcolor{gray!25}\n MoE-LPR (7*24) & G0 \\!$\\rightarrow$\\! G1 \\!$+$\\! G2 & 4.8B & 4.8B & 4.8B &\t45.15 &\t34.17 &\t29.98 & 36.43 \\\\\n MoE-LPR (4*24) & G0 \\!$\\rightarrow$\\! G1 \\!$+$\\! G2 & 2.4B & 2.4B & 2.4B ($\\downarrow$ 50.0\\%) & 45.28 &\t34.07 &\t29.55 & 36.30 \\\\\n LayerMoE (Ours) & G0 \\!$\\rightarrow$\\! G1 \\!$+$\\! G2 & 1.6B & 1.6B & \\textbf{1.6B ($\\downarrow$ \\textbf{66.7\\%})} & \\textbf{45.56} &\t\\textbf{34.73} &\t29.94 & \\textbf{36.74} \\\\\n \n \\hline\n % MoE-LPR & G0 $\\rightarrow$ G1 $\\rightarrow$ G1 $+$ G2 & 2.4B & 2.4B & 2.4B & 108h & &\t &\t & \\\\\n % MoE-LPR & G0 $\\rightarrow$ G1 & 2.4B & / & 2.4B & 45.18 &\t33.92 &\t/ & / \\\\\n % LayerMoE (Ours) & G0 $\\rightarrow$ G1 & 1.6B & / & 1.6B ($\\downarrow$ 33.3\\%) & 45.80 & 34.71 &\t/ & / \\\\\n % MoE-LPR & G0 $\\rightarrow$ G1 $\\rightarrow$ G2 & 2.4B & 2.4B & 4.8B & 43.81 ($\\downarrow$ 1.37) &\t33.56 ($\\downarrow$ 0.36) &\t30.08 & 35.82 \\\\\n % \\rowcolor{gray!25}\n MoE-LPR (7*24) & G0 \\!$\\rightarrow$\\! G1 \\!$\\rightarrow$\\! G2 & 2.4B & 2.4B & 4.8B & 45.88\t& 34.40\t& 30.26 & 36.85 \\\\\n LayerMoE (Ours) & G0 \\!$\\rightarrow$\\! G1 \\!$\\rightarrow$\\! G2 & 1.6B & 1.6B & \\textbf{3.2B ($\\downarrow$ \\textbf{33.3\\%})} & \\textbf{46.10} &\t\\textbf{34.56} &\t\\textbf{30.29} & \\textbf{36.98} \\\\\n \\hline\n % MoE-LPR & G0 \\!$\\rightarrow$\\! G2 & 2.4B & / & 2.4B & 45.70 &\t/ &\t29.43 & / \\\\\n % Ours & G0 \\!$\\rightarrow$\\! G2 & 1.6B & / & 1.6B ($\\downarrow$ 33.3\\%) & 46.06 & / &\t29.86 & / \\\\\n % MoE-LPR & G0 \\!$\\rightarrow$\\! G2 \\!$\\rightarrow$\\! G1 $+$ G2 & 2.4B & 2.4B & 2.4B & 108h & &\t &\t & \\\\\n % \\rowcolor{gray!25}\n MoE-LPR (7*24) & G0 \\!$\\rightarrow$\\! G2 \\!$\\rightarrow$\\! G1 & 2.4B & 2.4B & 4.8B & 45.28 &\t33.42 &\t28.39 & 35.70 \\\\\n LayerMoE (Ours) & G0 \\!$\\rightarrow$\\! G2 \\!$\\rightarrow$\\! G1 & 1.6B & 1.6B & \\textbf{3.2B ($\\downarrow$ \\textbf{33.3\\%})} & \\textbf{46.25} & \\textbf{33.76} &\t\\textbf{29.80} & \\textbf{36.60} \\\\\n\n \\toprule\n \\end{tabular}\n }\n \\caption{\n The results of G0 \\!$\\rightarrow$\\! G1 \\!$+$\\! G2 and the lifelong-expansion settings (G0 \\!$\\rightarrow$ \\!G1 \\!$\\rightarrow$ \\!G2 and G0 \\!$\\rightarrow$ \\!G2 $\\rightarrow$ \\!G1).\n ``G1/G2/All-E-p'' represents the expansion number of newly added parameters for G1, G2, and the sum, respectively.\n The detailed results for each language are reported in Table \\ref{table:mixed-res-detailed} of Appendix \\ref{sec:appendix-detailed-results}.\n }\n \\label{table:lifelong-res}\n\\end{table*}\n\n\n\n\\paragraph{Lifelong-Expansion.}\nThe results of G0 \\!$\\rightarrow$ \\!G1 \\!$\\rightarrow$ \\!G2 and G0 \\!$\\rightarrow$ \\!G2 $\\rightarrow$ \\!G1 in Table \\ref{table:lifelong-res} demonstrate that our method outperforms MoE-LPR (7*24) with fewer 33.3\\% new experts in terms of ``\\textit{G0/G1/G2-avg}'', proving the effectiveness of our method in lifelong-expansion setting.\nAdditionally, we surprisingly observe that G0 \\!$\\rightarrow$ \\!G1 \\!$\\rightarrow$ \\!G2 brings better expansion performance for G2 (``\\textit{G2-avg}'') and less forgetting of old languages (``\\textit{G0-avg}'') compared to G0 \\!$\\rightarrow$\\! G1 \\!$+$\\! G2.\nBut G0 \\!$\\rightarrow$ \\!G2 \\!$\\rightarrow$ \\!G1 does not show similar improvement for G1.\nThis difference indicates that the learning order of different languages may have non-trivial influences on the final performance of each language.\n% These results may be attributed to the fact that lifelong expansion training for different languages reduces language conflicts during the training process.\n% 先加高资源语言对低资源语言的学习有帮助；\n\n\n\n\n\\begin{table}[t]\n \\centering\n % \\belowrulesep=0pt\n % \\aboverulesep=0pt\n \\resizebox*{\\linewidth}{!}{\n \\begin{tabular}{l|ll|c}\n \\bottomrule\n \t& \\textbf{\\textit{Old-avg} ($\\uparrow$)} & \\textbf{\\textit{New-avg} ($\\uparrow$)} & \\textbf{\\textit{avg} ($\\uparrow$)}\\\\\n \n \\hline\n\n LayerMoE (G0 \\!$\\rightarrow$\\! G1)\t&\t\\textbf{45.80} \t&\t\\textbf{34.71} \t&\t\\textbf{40.26} \t\\\\\n \\quad \\textit{w/} random & 45.68 ($\\downarrow$ 0.12) & 34.11 ($\\downarrow$ \\textbf{0.60}) & 39.90 \\\\\n \\quad \\textit{w/o} classifier & 45.33 ($\\downarrow$ \\textbf{0.47}) & 34.61 ($\\downarrow$ 0.10) & 39.97 \\\\\n\\hline\n LayerMoE (G0 \\!$\\rightarrow$\\! G2)\t&\t\\textbf{46.06} \t&\t\\textbf{29.86} \t&\t\\textbf{37.96} \t\\\\\n \\quad \\textit{w/} random & 45.94 ($\\downarrow$ 0.12) & 29.15 ($\\downarrow$ \\textbf{0.71}) & 37.54 \\\\\n \\quad \\textit{w/o} classifier & 45.39 ($\\downarrow$ \\textbf{0.67}) & 29.53 ($\\downarrow$ 0.33) & 37.46 \\\\\n \n \\toprule\n \\end{tabular}\n }\n \\caption{\n The ablation results under G0 \\!$\\rightarrow$\\! G1 and G0 \\!$\\rightarrow$\\! G2. ``\\textit{w/} random'' means randomly allocating numbers of new experts for each layer without calculating the similarity. ``\\textit{w/o} classifier'' denotes no classifier is added in front of the router network.\n }\n \\label{table:ablation-study}\n\\end{table}\n\n\\subsection{Ablation Study}\n\nTo verify the effectiveness of our expert allocation algorithm and the added classifier, we conduct the ablation experiments under G0 \\!$\\rightarrow$\\! G1 and G0 \\!$\\rightarrow$\\! G2 settings and report the results in Table \\ref{table:ablation-study}.\nThese results show that randomly allocating experts for each layer without calculating the similarity significantly decreases the expansion performance ($\\downarrow$ 0.60 in G1, $\\downarrow$ 0.71 in G2).\nAdditionally, the forgetting of old languages becomes worse ($\\downarrow$ 0.47 in G1, $\\downarrow$ 0.67 in G2) when no classifiers are added in front of the router network.\nOverall, these results prove the necessity of our layer-wise expert allocation algorithm and the added classifier.\n\n\\subsection{Classifiers in Different Layers}\nThe top-$K$ layers with the higher similarity between new and old languages are selected according to $\\{i | S^i_{new\\&old} \\in \\mathrm{TopK}(\\mathcal{S}_{new\\&old}, K)\\}$ to add the classifier.\nTo investigate the best value of $K$, we conduct the experiments from top-1 to top-11 and top-24 (all layers) under G0 \\!$\\rightarrow$\\! G1 and list the results in Table \\ref{table:layers-calssifier}.\nThe results show that as more classifiers are added, the performance of the new language expansion first increases and then decreases, and the performance of the old language preservation increases constantly.\nThe best overall performance peaks on the top-7 setting.\nAdditionally, we also report the results of ``random-7'' and ``last-7'' to verify the effectiveness of adding classifiers in layers with higher similarity.\nRandomly selecting layers or opting for those with lower similarity consistently yields poor performance in preserving the capability of old languages.\n\n\n\n\n\n\n\n\n\\begin{table}[t]\n \\centering\n % \\belowrulesep=0pt\n % \\aboverulesep=0pt\n \\resizebox*{\\linewidth}{!}{\n \\begin{tabular}{l|ccc|l|ccc}\n \\bottomrule\n \t& \\textbf{\\textit{Old} } & \\textbf{\\textit{New} } & \\textbf{\\textit{avg}} & & \\textbf{\\textit{Old} } & \\textbf{\\textit{New} } & \\textbf{\\textit{avg}} \\\\\n \n \\hline\n top-1 & 44.90 & 34.30 & 39.60 & \\textbf{top-7} & 45.80 & 34.71 & \\textbf{40.26} \\\\\n top-2 & 44.81 & 34.69 & 39.75 & top-8 & 45.79 & 34.54 & 40.17 \\\\\n top-3 & 45.10 & 34.41 & 39.76 & top-9 & 45.95 & 34.42 & 40.19 \\\\\n top-4 & 45.41 & 34.67 & 40.04 & top-10 & \\textbf{46.16} & 34.28 & 40.22 \\\\\n top-5 & 45.28 & 34.66 & 39.97 & top-24 & 46.04 & 34.32 & 40.18 \\\\\n top-6 & 45.74 & \\textbf{34.72} & 40.23 & top-23 & 46.15 & 33.23 & 39.69 \\\\\n \\hline\n last-7 & 44.96 & 34.60 & 39.78 & random-7 & 45.32 & 34.60 & 39.96 \\\\\n \n \\toprule\n \\end{tabular}\n }\n \\caption{\n The results of adding classifiers in different layers under G0 \\!$\\rightarrow$\\! G1.\n ``top-*'' denotes adding classifiers to * layers with the highest similarity.\n ``random-7'' means randomly selecting 7 layers, while ``last-7'' represents the 7 layers with the lowest similarity.\n }\n \\label{table:layers-calssifier}\n\\end{table}\n\n\n\\subsection{Generalization Study}\nTo evaluate the generalization of our method on different models, we conduct a brief verification of our method on G0 \\!$\\rightarrow$\\! G1 with Llama-3.2-3B \\cite{grattafiori2024llama3herdmodels} and evaluate the performance on 5 benchmarks. \nParticularly, we add the machine translation task FLORES \\cite{nllb2022} to prove the effectiveness of our method on generation tasks.\nSpecifically, we set the following directions: \\textit{en $\\rightarrow$ zh}, \\textit{zh $\\rightarrow$ en}, \\textit{en $\\rightarrow$ es}, \\textit{es $\\rightarrow$ en}, \\textit{en $\\rightarrow$ el}, \\textit{el $\\rightarrow$ en}, \\textit{en $\\rightarrow$ hu}, \\textit{hu $\\rightarrow$ en}, \\textit{en $\\rightarrow$ tr}, and \\textit{tr $\\rightarrow$ en}. \nAnd we utilize COMET \\cite{rei-etal-2020-comet} as the evaluation metric. \nThe results in Table \\ref{table:llama-res} demonstrate the effectiveness of our method across different LLMs and generative tasks, which further proves the practical application value of our method.\n\n\n\n\\begin{table*}[t]\n \\centering\n % \\belowrulesep=0pt\n % \\aboverulesep=0pt\n \\resizebox*{\\linewidth}{!}{\n \\begin{tabular}{l|cccccccccc|ccc}\n \\bottomrule\n & \\multicolumn{2}{c}{\\textbf{ARC-C}} & \\multicolumn{2}{c}{\\textbf{MMLU}} & \\multicolumn{2}{c}{\\textbf{Hellaswag}} & \\multicolumn{2}{c}{\\textbf{Belebele}} & \\multicolumn{2}{c|}{\\textbf{FLORES}} & & & \\\\\n \\hline\n \\textbf{Methods} & \\textbf{Old} &\t\\textbf{New} &\t\\textbf{Old} &\t\\textbf{New} & \\textbf{Old} &\t\\textbf{New}\t& \\textbf{Old}\t& \\textbf{New} & \\textbf{Old}\t& \\textbf{New} &\\textbf{\\textit{Old-avg}} & \\textbf{\\textit{New-avg}} & \\textbf{\\textit{avg}}\\\\\n \n \\hline\n Llama-3.2-3B & 44.64 & 33.39 & \t49.84 & 42.15\t & 63.56 & 43.62 & \t70.48 & 61.37 & \t84.88 & 83.96\t & 62.68 & \t52.90 & \t57.79 \\\\\n \\hline\n MoE-LPR (3*24) & \t43.70 & 37.58 & \t49.99 & 43.98 & \t64.17 & 51.19 & \t70.74 & 64.41\t & 84.58 & 84.35 & \t62.64 & \t56.30\t & 59.47\t\\\\\n LayerMoE (Ours)\t& \\textbf{44.10}\t& \\textbf{37.64}\t& \t49.96\t& \\textbf{44.08}\t& \t64.07\t& \\textbf{51.33}\t& \t\\textbf{71.22}\t& \\textbf{64.63}\t& \t\\textbf{84.72}\t& \\textbf{84.69}\t\t& \\textbf{62.81}\t\t& \\textbf{56.47}\t& \t\\textbf{59.64}\n\t\\\\\n \\toprule\n \n \\end{tabular}\n }\n \\caption{\n The results of Llama-3.2-3B under the single-expansion setting (G0 \\!$\\rightarrow$\\! G1).\n The detailed results for each language are reported in Table \\ref{table:llama-res-detailed-1} and \\ref{table:llama-res-detailed-2} of Appendix \\ref{sec:appendix-detailed-results}.\n }\n \\label{table:llama-res}\n\\end{table*}\n\n\n\n\n\\section{Related Work}\n% 写扩展新语言的方法\nThere are two categories \\cite{huang2024surveylargelanguagemodels} in expanding new languages to build powerful multilingual LLMs: (1) utilizing the collected multilingual corpus to train the multilingual LLMs from scratch and (2) continually training LLMs on top of existing LLMs only with new language data.\nThe first category generally necessitates meticulous design in terms of the data ratio between different languages \\cite{ai2024yiopenfoundationmodels, zhang2024bayling2multilinguallarge, nguyen2024seallmslargelanguage} and the implementation of curriculum learning strategies \\cite{wei2023polylmopensourcepolyglot, ustun-etal-2024-aya} during the training process to ensure a balanced improvement in proficiency across various languages. \nTraining from scratch typically requires excessive computational resources and training durations, which are often challenging to implement and environmentally unfriendly.\n\nThe second category is to continually train the original model with new language data to enhance the ability of new languages while avoiding the catastrophic forgetting of the ability in originally proficient languages.\nMoE-LPR \\cite{zhou2024moelprmultilingualextensionlarge} and MoE-CT \\cite{li2024MoEctnovelapproachlarge} upcycle the dense model to the MoE model for improving the multilingual ability of LLMs.\n% Recently, introducing external parameters and strategies to learn new languages has a good balance on new language expansion and old language forgetting.\nSimilarly, some methods also have introduced external parameters to improve the ability of the original LLMs and preserve the original capabilities by freezing the old parameters.\n{LLaMA-Pro} \\cite{wu2024llamaproprogressivellama} insert new transformer blocks to replace the duplicate layers to further enhance the original LLM.\n{LoRAMoE} \\cite{dou2024loraMoEalleviateworldknowledge} combines multiple low-rank adapters with a router network to achieve better performance on downstream tasks.\nIn this work, we only focus on the multilingual expansion and propose LayerMoE to address the limitations of existing methods, \\textit{e.g.}, inefficient expansion, high resource usage, and accumulated forgetfulness of old languages.\nAdditionally, the concurrent work AlphaLoRA \\cite{qing-etal-2024-alphalora} similar to ours allocates LoRA experts based on Heavy-Tailed Self-Regularization Theory to mitigate redundancy of LoRA experts and improve the general ability of LLMs.\nDifferently, we calculate the representation similarity of different languages in different layers as the indicator to allocate new experts for each layer, which is intuitive and effective for the expansion of new languages.\n\n% MoE-LPR \\cite{zhou2024moelprmultilingualextensionlarge} is a two-stage method to utilize the Mixture-of-Experts (MoE) architecture to expand new languages by adding new experts while preserving original language proficiency by routing to old experts.\n% MoE-CT \\cite{li2024MoEctnovelapproachlarge} also uses MoE to advance the multilingual ability of LLMs by designing a fusion routing strategy.\n% However, these methods may lead to inefficient expansion, high resource usage, and accumulated forgetfulness under lifelong expanding new language.\n% Therefore, we consider the language characteristics of different layers to solve these limitations.\n\n% 是不是还需要写对 MoE 专家的改进的一些工作？\n\n\n\n\n\n\\section{Conclusion}\nIn this work, we propose a layer-wise expert allocation algorithm to assign the appropriate number of new experts for each layer when expanding new languages for existing LLMs.\nWe first calculate the similarity of different languages for each layer as the indicator and allocate more experts to the layers exhibiting lower similarity.\nAdditionally, we add a classifier in front of the router network for the layers with higher similarity to further mitigate the forgetting of old languages.\nExperimental results demonstrate the effectiveness of our method, which is parameter-efficient and achieves good expansion of new languages and less forgetting of old languages.\n\n\n\\section*{Limitations}\nAlthough our method achieves better improvements in terms of ``\\textit{Old-avg}'' and ``\\textit{New-avg}'' than MoE-LPR (6*24) with 60\\% fewer experts, the improvements in different benchmarks and languages are inconsistent.\nThe inherent reasons for the inconsistency are not yet clear and need further investigation.\nAdditionally, we set the total number of new experts according to the baseline ``MoE-LPR (3*24)'' for a fair comparison rather than exploring the optimal total number.\n% And we only verify the effectiveness of our method on one LLM due to the limited resources.\nIn the future, we will continually solve these limitations for a better expert allocation solution.\n\n\\section*{Acknowledgments}\nThe research work described in this paper has been supported by the National Nature Science Foundation of China (No. 62476023, 61976016, 62376019, 61976015) and Henan Provincial Science and Technology Research Project (No. 252102210102), and the authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve this paper.\n\n\n\n% Bibliography entries for the entire Anthology, followed by custom entries\n%\\bibliography{anthology,custom}\n% Custom bibliography entries only\n\\bibliography{custom}\n\n\\appendix\n\\onecolumn\n\\newpage\n\n\\section{Load Balance Loss}\\label{sec:appendix-load-balande-loss}\nThe load balance loss \\cite{fedus2022switchtransformersscalingtrillion} generally mitigates the risk of routing collapse by constraining an even distribution of tokens across different experts.\nGiven a batch tokens $\\mathbb{T}$ and $N$ experts for each layer, the load balance loss $\\mathcal{L}_{balance}$ \\cite{zhou2024moelprmultilingualextensionlarge} is calculated following:\n\\begin{equation}\n \\mathcal{L}_{balance} = \\sum_{i=1}^{N}f_i P_i,\n\\end{equation}\n\\begin{equation}\n f_i = \\frac{N}{K|\\mathbb{T}|}\\sum_{t \\in {\\mathbb{T}}} \\mathbbm{1}\\{\\text{Token } t \\text{ selects expert }i\\},\n\\end{equation}\n\\begin{equation}\n P_i = \\frac{1}{|\\mathbb{T}|} \\sum_{t \\in {\\mathbb{T}}} G_i(t),\n\\end{equation}\nwhere $t$ represents a token, $G_i(t)$ is the router score of the expert $i$, and $\\mathbbm{1}(\\cdot)$ is a indicator function when the token $t$ selecting the expert $i$ equals 1.\n\n\n\\section{Experimental Details}\n\n\\subsection{Detailed Introduction of Different Languages}\\label{sec:appendix-languages}\nThe language families and writing systems of our nine selected languages are listed in Table \\ref{table:languages}.\nSpecifically, \\textit{bn}, \\textit{hi}, and \\textit{ne} all belong to the Indo-Aryan branch of the Indo-European family and use non-Latin scripts, making them more challenging to model effectively and less well-resourced compared to Latin-script languages.\n\n\n\\begin{table*}[h]\n \\centering\n % \\belowrulesep=0pt\n % \\aboverulesep=0pt\n \\resizebox*{0.8\\linewidth}{!}{\n \\begin{tabular}{l|ll}\n \\bottomrule\n Languages\t& \\textbf{Language Family} & \\textbf{Writing System} \\\\\n \n \\hline\n\n English (\\textit{en}) & Indo-European (Germanic) & Latin alphabet (26 letters) \\\\\n Chinese (\\textit{zh}) & Sino-Tibetan (Sinitic) & Chinese characters \\\\\n Spanish (\\textit{es}) & Indo-European (Romance) & Latin alphabet (27 letters, including ñ) \\\\\n Greek (\\textit{el}) & Indo-European (Hellenic) & Greek alphabet (24 letters) \\\\\n Hungarian (\\textit{hu}) & Uralic (Finno-Ugric) & Latin alphabet with diacritics (40 letters) \\\\\n Turkish (\\textit{tr}) & Turkic & Latin alphabet (29 letters) \\\\\n Bengali (\\textit{bn}) & Indo-European (Indo-Aryan) & Bengali script \\\\\n Nepali (\\textit{ne}) & Indo-European (Indo-Aryan) & Devanagari script \\\\\n Hindi (\\textit{hi}) & Indo-European (Indo-Aryan) & Devanagari script \\\\\n\n\n \n \\toprule\n \\end{tabular}\n }\n \\caption{\n The detailed language families and writing systems.\n }\n \\label{table:languages}\n\\end{table*}\n\n\n\n\\subsection{Evaluation Details}\\label{sec:appendix-eval}\n\nThe \\texttt{lm-evaluation-hardness} framework has included the multilingual version of ARC-Challenge, MMLU, and HellaSwag translated by Okapi \\cite{lai-etal-2023-okapi}.\nFor unsupported languages in new languages, we manually integrate the Turkish version from \\texttt{OpenLLM Turkish leaderboard}\\footnote{\\url{https://huggingface.co/spaces/malhajar/OpenLLMTurkishLeaderboard}} and the Greek version translated by ILSP\\footnote{\\url{https://huggingface.co/ilsp}} following the official guide\\footnote{\\url{https://github.com/EleutherAI/lm-evaluation-harness/blob/main/docs/new_task_guide.md}}.\n\n\n% \\subsection{Additional Baselines}\\label{sec:appendix-baselines}\n\n% \\textbf{LoRA} \\cite{hu2021loralowrankadaptationlarge} introduces Low-Rank Adaptation for efficiently fine-tuning large language models. In this paper, the LoRA targets include all linear modules. The LoRA rank is set to 8.\n% \\textbf{Full Fine-tuning} is fine-tuning all parameters on the dense model.\n% \\textbf{LLaMA-Pro} \\cite{wu2024llamaproprogressivellama} insert new transformer blocks to replace the duplicate layers. Only the newly inserted blocks are trained to store new knowledge during post-pretraining. 12 new layers (the best setting) are inserted into the original dense model.\n% \\textbf{MoE} means that only the stage-1 of MoE-LPR is conducted.\n% \\textbf{LoRAMoE} \\cite{dou2024loraMoEalleviateworldknowledge} is a novel framework that combines multiple LoRAs with a router network. The router selects all LoRAs for each token. \n% The LoRA rank is set to 180 and the number of LoRAs is 8 for each module.\n% Due to the poor performance of the 5 baselines, we do not reproduce them and directly take the results trained with 8 billion tokens for each new language from the MoE-LPR paper \\cite{zhou2024moelprmultilingualextensionlarge}.\n\n\n\\subsection{Similarity and Allocation}\\label{sec:appendix-sim_and_allocation}\nFigure \\ref{fig:sim_experts} presents the detailed similarity and expert allocation of our method for each layer under the five settings.\n\n\\begin{figure*}[h]\n\t\\centering %图片全局居中\n\t\\subfigbottomskip=2pt %两行子图之间的行间距\n\t\\subfigcapskip=-5pt %设置子图与子标题之间的距离\n\t\\subfigure[G0 \\!$\\rightarrow$\\! G1]{\n\t\t\\includegraphics[width=0.32\\linewidth]{p5-1.jpg}}\n\t\\subfigure[G0 \\!$\\rightarrow$\\! G2]{\n\t\t\\includegraphics[width=0.32\\linewidth]{p5-2.jpg}}\n \\subfigure[G0 \\!$\\rightarrow$\\! G1 \\!$+$\\! G2]{\n\t\t\\includegraphics[width=0.32\\linewidth]{p5-3.jpg}}\n \\\\\n \\subfigure[G0 \\!$\\rightarrow$ \\!G1 \\!$\\rightarrow$ \\!G2]{\n\t\t\\includegraphics[width=0.49\\linewidth]{p5-4.jpg}}\n\t%\\quad\n\t\\subfigure[G0 \\!$\\rightarrow$ \\!G2 $\\rightarrow$ \\!G1]{\n\t\t\\includegraphics[width=0.49\\linewidth]{p5-5.jpg}}\n\t\\caption{The detailed similarity and expert allocation of our method for each layer of Qwen1.5-1.8B under the five settings.}\n \\label{fig:sim_experts}\n\\end{figure*}\n\n\n\n\n\n\n\n\\section{Detailed Results}\\label{sec:appendix-detailed-results}\n\\paragraph{Qwen1.5-1.8B:}\nThe detailed results for each language under the single-expansion settings (G0 \\!$\\rightarrow$\\! G1, G0 \\!$\\rightarrow$\\! G2) are listed in Table \\ref{table:single-res-detailed-g1} and \\ref{table:single-res-detailed-g2}.\nThe detailed results of G0 \\!$\\rightarrow$\\! G1 \\!$+$\\! G2 and the lifelong-expansion settings (G0 \\!$\\rightarrow$ \\!G1 \\!$\\rightarrow$ \\!G2 and G0 \\!$\\rightarrow$ \\!G2 $\\rightarrow$ \\!G1) are reported in Table \\ref{table:mixed-res-detailed}.\n\n\\begin{table*}[h]\n \\centering\n % \\belowrulesep=0pt\n % \\aboverulesep=0pt\n \\resizebox*{\\linewidth}{!}{\n \\begin{tabular}{l|ccc|ccc|ccc|ccc|c}\n \\bottomrule\n& \\multicolumn{3}{c|}{\\textbf{ARC-Challenge}} & \\multicolumn{3}{c|}{\\textbf{MMLU}} & \\multicolumn{3}{c|}{\\textbf{Hellaswag}} & \\multicolumn{3}{c|}{\\textbf{Belebele}} & \\\\\n\\hline\n\\textbf{Methods}\t&\t\\textbf{en}\t&\t\\textbf{es}\t&\t\\textbf{zh}\t&\t\\textbf{en}\t&\t\\textbf{es}\t&\t\\textbf{zh}\t&\t\\textbf{en}\t&\t\\textbf{es}\t&\t\\textbf{zh}\t&\t\\textbf{en}\t&\t\\textbf{es}\t&\t\\textbf{zh}\t&\t\\textbf{\\textit{G0-avg}}\t\\\\\n\\hline\nQwen1.5-1.8B\t&\t37.97 \t&\t27.86 \t&\t34.10 \t&\t45.67 \t&\t38.81 \t&\t57.21 \t&\t61.62 \t&\t39.35 \t&\t48.14 \t&\t60.78 \t&\t47.44 \t&\t59.44 \t&\t46.53 \t\\\\\n\\hline\nMoE-LPR (6*24)\t&\t38.05 \t&\t26.84 \t&\t31.88 \t&\t45.77 \t&\t38.26 \t&\t55.50 \t&\t61.34 \t&\t39.41 \t&\t48.49 \t&\t58.44 \t&\t45.89 \t&\t54.44 \t&\t45.36 \t\\\\\nMOLA\t&\t37.88 \t&\t26.84 \t&\t32.74 \t&\t45.81 \t&\t38.30 \t&\t55.02 \t&\t61.36 \t&\t39.27 \t&\t48.26 \t&\t60.11 \t&\t47.11 \t&\t54.33 \t&\t45.59 \t\\\\\nMoE-LPR (4*24)\t&\t36.69 \t&\t27.35 \t&\t32.82 \t&\t46.09 \t&\t38.57 \t&\t54.87 \t&\t61.17 \t&\t39.37 \t&\t48.27 \t&\t58.33 \t&\t44.22 \t&\t54.44 \t&\t45.18 \t\\\\\nMoE-LPR (3*24)\t&\t37.46 \t&\t27.35 \t&\t33.50 \t&\t46.00 \t&\t38.67 \t&\t54.94 \t&\t61.20 \t&\t39.43 \t&\t48.25 \t&\t59.44 \t&\t44.44 \t&\t52.33 \t&\t45.25 \t\\\\\nLayerMoE (Ours)\t&\t38.57 \t&\t26.58 \t&\t33.93 \t&\t45.81 \t&\t37.97 \t&\t54.59 \t&\t60.90 \t&\t38.98 \t&\t47.84 \t&\t60.56 \t&\t46.89 \t&\t57.00 \t&\t\\textbf{45.80} \t\\\\ \n\\toprule\n\\bottomrule\n\\textbf{Methods}\t&\t\\textbf{el}\t&\t\\textbf{hu}\t&\t\\textbf{tr}\t&\t\\textbf{el}\t&\t\\textbf{hu}\t&\t\\textbf{tr}\t&\t\\textbf{el}\t&\t\\textbf{hu}\t&\t\\textbf{tr}\t&\t\\textbf{el}\t&\t\\textbf{hu}\t&\t\\textbf{tr}\t&\t\\textbf{\\textit{G1-avg}}\t\\\\\n\\hline\nQwen1.5-1.8B\t&\t22.95 \t&\t23.20 \t&\t22.63 \t&\t28.94 \t&\t32.25 \t&\t30.37 \t&\t29.15 \t&\t29.63 \t&\t29.04 \t&\t33.33 \t&\t32.00 \t&\t33.44 \t&\t28.91 \t\\\\\n\\hline\nMoE-LPR (6*24)\t&\t25.26 \t&\t27.14 \t&\t27.24 \t&\t32.06 \t&\t36.30 \t&\t36.49 \t&\t36.09 \t&\t38.63 \t&\t37.87 \t&\t38.33 \t&\t36.56 \t&\t42.22 \t&\t34.52 \t\\\\\nMOLA\t&\t25.94 \t&\t27.40 \t&\t27.67 \t&\t31.43 \t&\t35.80 \t&\t34.71 \t&\t35.85 \t&\t37.79 \t&\t37.46 \t&\t35.22 \t&\t39.00 \t&\t40.89 \t&\t34.10 \t\\\\\nMoE-LPR (4*24)\t&\t25.34 \t&\t27.57 \t&\t27.16 \t&\t31.58 \t&\t35.79 \t&\t34.97 \t&\t35.70 \t&\t38.26 \t&\t37.39 \t&\t34.44 \t&\t40.22 \t&\t38.56 \t&\t33.92 \t\\\\\nMoE-LPR (3*24)\t&\t24.32 \t&\t27.05 \t&\t27.92 \t&\t31.90 \t&\t36.27 \t&\t35.59 \t&\t35.55 \t&\t38.18 \t&\t37.51 \t&\t32.00 \t&\t39.56 \t&\t39.89 \t&\t33.81 \t\\\\\nLayerMoE (Ours)\t&\t25.34 \t&\t26.80 \t&\t27.84 \t&\t31.96 \t&\t36.76 \t&\t35.68 \t&\t35.61 \t&\t38.10 \t&\t37.31 \t&\t37.44 \t&\t41.56 \t&\t42.11 \t&\t\\textbf{34.71} \t\\\\\n\n \\toprule\n \\end{tabular}\n }\n \\caption{\n The detailed results of Qwen1.5-1.8B under the G0 \\!$\\rightarrow$\\! G1 setting for each language. \n }\n \\label{table:single-res-detailed-g1}\n\\end{table*}\n\n\\begin{table*}[ht]\n \\centering\n % \\belowrulesep=0pt\n % \\aboverulesep=0pt\n \\resizebox*{\\linewidth}{!}{\n \\begin{tabular}{l|ccc|ccc|ccc|ccc|c}\n \\bottomrule\n& \\multicolumn{3}{c|}{\\textbf{ARC-Challenge}} & \\multicolumn{3}{c|}{\\textbf{MMLU}} & \\multicolumn{3}{c|}{\\textbf{Hellaswag}} & \\multicolumn{3}{c|}{\\textbf{Belebele}} & \\\\\n\\hline\n\\textbf{Methods}\t&\t\\textbf{en}\t&\t\\textbf{es}\t&\t\\textbf{zh}\t&\t\\textbf{en}\t&\t\\textbf{es}\t&\t\\textbf{zh}\t&\t\\textbf{en}\t&\t\\textbf{es}\t&\t\\textbf{zh}\t&\t\\textbf{en}\t&\t\\textbf{es}\t&\t\\textbf{zh}\t&\t\\textbf{\\textit{G0-avg}}\t\\\\\n\\hline\nQwen1.5-1.8B\t&\t37.97 \t&\t27.86 \t&\t34.10 \t&\t45.67 \t&\t38.81 \t&\t57.21 \t&\t61.62 \t&\t39.35 \t&\t48.14 \t&\t60.78 \t&\t47.44 \t&\t59.44 \t&\t46.53 \t\\\\\n\\hline\nMoE-LPR (6*24)\t&\t37.29 \t&\t27.26 \t&\t34.62 \t&\t46.05 \t&\t38.46 \t&\t56.60 \t&\t61.73 \t&\t39.27 \t&\t48.07 \t&\t59.67 \t&\t46.33 \t&\t53.33 \t&\t45.72 \t\\\\\nMOLA\t&\t37.37 \t&\t27.26 \t&\t32.82 \t&\t45.88 \t&\t38.71 \t&\t56.48 \t&\t61.76 \t&\t39.09 \t&\t48.34 \t&\t59.56 \t&\t46.22 \t&\t55.00 \t&\t45.71 \t\\\\\nMoE-LPR (4*24)\t&\t37.80 \t&\t27.18 \t&\t34.27 \t&\t46.00 \t&\t38.88 \t&\t56.76 \t&\t61.65 \t&\t39.03 \t&\t48.24 \t&\t59.67 \t&\t45.22 \t&\t53.67 \t&\t45.70 \t\\\\\nMoE-LPR (3*24)\t&\t37.80 \t&\t27.09 \t&\t33.42 \t&\t45.52 \t&\t38.62 \t&\t56.78 \t&\t61.59 \t&\t39.13 \t&\t48.32 \t&\t59.89 \t&\t45.33 \t&\t52.56 \t&\t45.50 \t\\\\\nLayerMoE (Ours)\t&\t36.86 \t&\t27.44 \t&\t33.25 \t&\t46.15 \t&\t39.05 \t&\t56.09 \t&\t61.50 \t&\t39.11 \t&\t47.77 \t&\t61.89 \t&\t46.67 \t&\t56.89 \t&\t\\textbf{46.06} \t\\\\\n\\toprule\n\\bottomrule\n\\textbf{Methods}\t&\t\\textbf{bn}\t&\t\\textbf{hi}\t&\t\\textbf{ne}\t&\t\\textbf{bn}\t&\t\\textbf{hi}\t&\t\\textbf{ne}\t& \\textbf{bn}\t&\t\\textbf{hi}\t&\t\\textbf{ne}\t& \\textbf{bn}\t&\t\\textbf{hi}\t&\t\\textbf{ne}\t&\t\\textbf{\\textit{G2-avg}}\t\\\\\n\\hline\nQwen1.5-1.8B\t&\t22.50 \t&\t23.46 \t&\t23.18 \t&\t29.59 \t&\t29.89 \t&\t29.13 \t&\t28.23 \t&\t28.48 \t&\t27.95 \t&\t29.67 \t&\t30.00 \t&\t26.89 \t&\t27.41 \t\\\\\n\\hline\nMoE-LPR (6*24)\t&\t24.64 \t&\t25.26 \t&\t21.64 \t&\t30.29 \t&\t30.57 \t&\t31.01 \t&\t29.90 \t&\t32.78 \t&\t30.08 \t&\t33.22 \t&\t35.22 \t&\t33.11 \t&\t29.81 \t\\\\\nMOLA\t&\t24.29 \t&\t25.09 \t&\t21.64 \t&\t31.01 \t&\t30.67 \t&\t30.77 \t&\t29.44 \t&\t32.54 \t&\t29.46 \t&\t31.00 \t&\t33.33 \t&\t32.11 \t&\t29.28 \t\\\\\nMoE-LPR (4*24)\t&\t23.52 \t&\t24.91 \t&\t21.39 \t&\t30.73 \t&\t30.54 \t&\t30.94 \t&\t30.02 \t&\t32.55 \t&\t29.45 \t&\t32.11 \t&\t34.33 \t&\t32.67 \t&\t29.43 \t\\\\\nMoE-LPR (3*24)\t&\t24.98 \t&\t24.57 \t&\t21.04 \t&\t31.59 \t&\t30.87 \t&\t29.70 \t&\t29.55 \t&\t32.68 \t&\t29.44 \t&\t32.44 \t&\t34.33 \t&\t33.22 \t&\t29.53 \t\\\\\nLayerMoE (Ours)\t&\t24.21 \t&\t25.26 \t&\t22.58 \t&\t31.10 \t&\t30.43 \t&\t31.01 \t&\t29.31 \t&\t32.42 \t&\t29.67 \t&\t33.44 \t&\t33.44 \t&\t35.44 \t&\t\\textbf{29.86} \t\\\\\n \\toprule\n \\end{tabular}\n }\n \\caption{\n The detailed results of Qwen1.5-1.8B under the G0 \\!$\\rightarrow$\\! G2 setting for each language. \n }\n \\label{table:single-res-detailed-g2}\n\\end{table*}\n\n\n\\begin{table*}[ht]\n \\centering\n % \\belowrulesep=0pt\n % \\aboverulesep=0pt\n \\resizebox*{\\linewidth}{!}{\n \\begin{tabular}{lc|ccc|ccc|ccc|ccc|c}\n \\bottomrule\n& & \\multicolumn{3}{c|}{\\textbf{ARC-Challenge}} & \\multicolumn{3}{c|}{\\textbf{MMLU}} & \\multicolumn{3}{c|}{\\textbf{Hellaswag}} & \\multicolumn{3}{c|}{\\textbf{Belebele}} & \\\\\n\\hline\n\\textbf{Methods} & \\textbf{Order}\t&\t\\textbf{en}\t&\t\\textbf{es}\t&\t\\textbf{zh}\t&\t\\textbf{en}\t&\t\\textbf{es}\t&\t\\textbf{zh}\t&\t\\textbf{en}\t&\t\\textbf{es}\t&\t\\textbf{zh}\t&\t\\textbf{en}\t&\t\\textbf{es}\t&\t\\textbf{zh}\t&\t\\textbf{\\textit{G0-avg}}\t\\\\\n\\hline\nQwen1.5-1.8B & / \t&\t37.97 \t&\t27.86 \t&\t34.10 \t&\t45.67 \t&\t38.81 \t&\t57.21 \t&\t61.62 \t&\t39.35 \t&\t48.14 \t&\t60.78 \t&\t47.44 \t&\t59.44 \t&\t46.53 \t\\\\\n\\hline\nMoE-LPR (7*24) & G0 \\!$\\rightarrow$\\! G1 \\!$+$\\! G2 & \t37.88 \t&\t26.92 \t&\t33.85 \t&\t45.26 \t&\t37.61 \t&\t54.51 \t&\t61.43 \t&\t39.29 \t&\t48.14 \t&\t59.33 \t&\t44.56 \t&\t53.00 \t&\t45.15 \t\\\\\nMoE-LPR (4*24) & G0 \\!$\\rightarrow$\\! G1 \\!$+$\\! G2 & \t37.80 \t&\t27.69 \t&\t32.82 \t&\t45.29 \t&\t38.12 \t&\t54.39 \t&\t61.45 \t&\t39.11 \t&\t48.20 \t&\t59.89 \t&\t44.33 \t&\t54.22 \t&\t45.28 \t\\\\\nLayerMoE (Ours) & G0 \\!$\\rightarrow$\\! G1 \\!$+$\\! G2 & \t37.46 \t&\t27.01 \t&\t34.27 \t&\t45.63 \t&\t38.14 \t&\t54.18 \t&\t61.37 \t&\t39.08 \t&\t47.86 \t&\t60.11 \t&\t46.22 \t&\t55.44 \t&\t\\textbf{45.56} \t\\\\\n\\hline\nMoE-LPR (7*24) & G0 \\!$\\rightarrow$\\! G1 \\!$\\rightarrow$\\! G2 & \t37.54 \t&\t26.84 \t&\t33.16 \t&\t45.71 \t&\t38.29 \t&\t55.42 \t&\t61.43 \t&\t39.21 \t&\t48.12 \t&\t61.22 \t&\t47.33 \t&\t56.33 \t&\t45.88 \t\\\\\nLayerMoE (Ours) & G0 \\!$\\rightarrow$\\! G1 \\!$\\rightarrow$\\! G2 & \t37.29 \t&\t26.58 \t&\t33.93 \t&\t45.69 \t&\t38.86 \t&\t55.40 \t&\t61.23 \t&\t39.23 \t&\t48.05 \t&\t62.22 \t&\t48.00 \t&\t56.67 \t&\t\\textbf{46.10} \t\\\\\n\\hline\nMoE-LPR (7*24) & G0 \\!$\\rightarrow$\\! G2 \\!$\\rightarrow$\\! G1 & \t38.14 \t&\t27.18 \t&\t33.42 \t&\t46.07 \t&\t38.26 \t&\t55.28 \t&\t61.36 \t&\t39.44 \t&\t48.26 \t&\t57.44 \t&\t44.78 \t&\t53.67 \t&\t45.28 \t\\\\\nLayerMoE (Ours) & G0 \\!$\\rightarrow$\\! G2 \\!$\\rightarrow$\\! G1 & 38.31 &\t27.09 &\t34.70 & 46.19 & 38.79 & 55.85 & 61.19 & 39.05 & 48.05 & 61.89 & 46.44 & 57.44 & \\textbf{46.25} \\\\\n\n\\toprule\n\\bottomrule\n\\textbf{Methods} & \\textbf{Order}\t&\t\\textbf{el}\t&\t\\textbf{hu}\t&\t\\textbf{tr}\t&\t\\textbf{el}\t&\t\\textbf{hu}\t&\t\\textbf{tr}\t&\t\\textbf{el}\t&\t\\textbf{hu}\t&\t\\textbf{tr}\t&\t\\textbf{el}\t&\t\\textbf{hu}\t&\t\\textbf{tr}\t&\t\\textbf{\\textit{G1-avg}}\t\\\\\n\\hline\nQwen1.5-1.8B\t& / &\t22.95 \t&\t23.20 \t&\t22.63 \t&\t28.94 \t&\t32.25 \t&\t30.37 \t&\t29.15 \t&\t29.63 \t&\t29.04 \t&\t33.33 \t&\t32.00 \t&\t33.44 \t&\t28.91 \t\\\\\n\\hline\nMoE-LPR (7*24) & G0 \\!$\\rightarrow$\\! G1 \\!$+$\\! G2 & \t26.46 \t&\t28.00 \t&\t27.92 \t&\t30.38 \t&\t35.14 \t&\t34.64 \t&\t36.40 \t&\t39.01 \t&\t37.92 \t&\t35.33 \t&\t39.00 \t&\t39.78 \t&\t34.17 \t\\\\\nMoE-LPR (4*24) & G0 \\!$\\rightarrow$\\! G1 \\!$+$\\! G2 & \t26.20 \t&\t27.65 \t&\t27.92 \t&\t31.01 \t&\t35.08 \t&\t34.37 \t&\t35.60 \t&\t38.14 \t&\t37.11 \t&\t34.11 \t&\t41.11 \t&\t40.56 \t&\t34.07 \t\\\\\nLayerMoE (Ours) & G0 \\!$\\rightarrow$\\! G1 \\!$+$\\! G2 & \t25.34 \t&\t27.83 \t&\t27.92 \t&\t31.51 \t&\t36.43 \t&\t35.32 \t&\t35.50 \t&\t38.00 \t&\t37.27 \t&\t36.89 \t&\t42.11 \t&\t42.67 \t&\t\\textbf{34.73} \t\\\\\n\\hline\nMoE-LPR (7*24) & G0 \\!$\\rightarrow$\\! G1 \\!$\\rightarrow$\\! G2 & \t25.51 \t&\t26.20 \t&\t27.75 \t&\t30.51 \t&\t35.29 \t&\t34.46 \t&\t35.20 \t&\t37.92 \t&\t37.08 \t&\t37.44 \t&\t43.44 \t&\t42.00 \t&\t34.40 \t\\\\\nLayerMoE (Ours) & G0 \\!$\\rightarrow$\\! G1 \\!$\\rightarrow$\\! G2 & \t25.34 \t&\t25.77 \t&\t27.75 \t&\t31.94 \t&\t36.58 \t&\t35.09 \t&\t35.17 \t&\t37.92 \t&\t36.24 \t&\t37.34 \t&\t43.34 \t&\t42.18 \t&\t\\textbf{34.56} \t\\\\\n\\hline\nMoE-LPR (7*24) & G0 \\!$\\rightarrow$\\! G2 \\!$\\rightarrow$\\! G1 & \t25.34 \t&\t26.97 \t&\t26.47 \t&\t30.76 \t&\t34.41 \t&\t34.47 \t&\t35.11 \t&\t37.42 \t&\t37.09 \t&\t33.67 \t&\t38.44 \t&\t40.89 \t&\t33.42 \t\\\\\nLayerMoE (Ours) & G0 \\!$\\rightarrow$\\! G2 \\!$\\rightarrow$\\! G1 & 24.57 & 26.80 & 26.30 & 30.86\t& 35.36 & \t35.26 & 34.54 &\t37.07 &\t36.12 & 36.44 & 39.00 & 42.78 & \\textbf{33.76} \\\\\n\n\\toprule\n\\bottomrule\n\\textbf{Methods} & \\textbf{Order}\t&\t\\textbf{bn}\t&\t\\textbf{hi}\t&\t\\textbf{ne}\t&\t\\textbf{bn}\t&\t\\textbf{hi}\t&\t\\textbf{ne}\t& \\textbf{bn}\t&\t\\textbf{hi}\t&\t\\textbf{ne}\t& \\textbf{bn}\t&\t\\textbf{hi}\t&\t\\textbf{ne}\t&\t\\textbf{\\textit{G2-avg}}\t\\\\\n\\hline\nQwen1.5-1.8B & /\t&\t22.50 \t&\t23.46 \t&\t23.18 \t&\t29.59 \t&\t29.89 \t&\t29.13 \t&\t28.23 \t&\t28.48 \t&\t27.95 \t&\t29.67 \t&\t30.00 \t&\t26.89 \t&\t27.41 \t\\\\\n\\hline\nMoE-LPR (7*24) & G0 \\!$\\rightarrow$\\! G1 \\!$+$\\! G2 & \t24.29 \t&\t24.91 \t&\t20.96 \t&\t30.94 \t&\t32.26 \t&\t31.31 \t&\t30.03 \t&\t33.24 \t&\t30.03 \t&\t31.56 \t&\t34.00 \t&\t36.22 \t&\t\\textbf{29.98} \t\\\\\nMoE-LPR (4*24) & G0 \\!$\\rightarrow$\\! G1 \\!$+$\\! G2 & \t24.38 \t&\t25.00 \t&\t21.47 \t&\t29.64 \t&\t31.81 \t&\t30.09 \t&\t29.59 \t&\t32.72 \t&\t29.73 \t&\t33.11 \t&\t34.33 \t&\t32.78 \t&\t29.55 \t\\\\\nLayerMoE (Ours) & G0 \\!$\\rightarrow$\\! G1 \\!$+$\\! G2 & \t23.27 \t&\t25.26 \t&\t21.47 \t&\t31.23 \t&\t32.05 \t&\t30.60 \t&\t29.67 \t&\t32.34 \t&\t29.66 \t&\t32.67 \t&\t36.00 \t&\t35.00 \t&\t29.94 \t\\\\\n\\hline\nMoE-LPR (7*24) & G0 \\!$\\rightarrow$\\! G1 \\!$\\rightarrow$\\! G2 & \t23.44 \t&\t25.34 \t&\t21.81 \t&\t31.66 \t&\t32.31 \t&\t31.41 \t&\t29.55 \t&\t32.60 \t&\t29.54 \t&\t34.22 \t&\t35.44 \t&\t35.78 \t&\t30.26 \t\\\\\nLayerMoE (Ours) & G0 \\!$\\rightarrow$\\! G1 \\!$\\rightarrow$\\! G2 & \t23.78 \t&\t25.43 \t&\t22.33 \t&\t31.42 \t&\t31.92 \t&\t31.11 \t&\t29.71 \t&\t32.49 \t&\t29.88 \t&\t34.02 \t&\t35.62 \t&\t35.82 \t&\t\\textbf{30.29} \t\\\\\n\\hline\nMoE-LPR (7*24) & G0 \\!$\\rightarrow$\\! G2 \\!$\\rightarrow$\\! G1 & \t25.32 \t&\t24.40 \t&\t21.81 \t&\t30.41 \t&\t31.89 \t&\t29.92 \t&\t28.79 \t&\t30.73 \t&\t29.00 \t&\t27.78 \t&\t29.67 \t&\t31.00 \t&\t28.39 \t\\\\\nLayerMoE (Ours) & G0 \\!$\\rightarrow$\\! G2 \\!$\\rightarrow$\\! G1 & 24.98 & 25.17 & 22.67 & 31.75 & 29.95 & 30.41 & 29.25 &\t32.49 &\t29.46 & 32.56 & 35.11 & 33.78 & \\textbf{29.80} \\\\\n\n \\toprule\n \\end{tabular}\n }\n \\caption{\n The detailed results of Qwen1.5-1.8B under the G0 \\!$\\rightarrow$\\! G1 \\!$+$\\! G2, G0 \\!$\\rightarrow$ \\!G1 \\!$\\rightarrow$ \\!G2, and G0 \\!$\\rightarrow$ \\!G2 $\\rightarrow$ \\!G1 settings for each language.\n }\n \\label{table:mixed-res-detailed}\n\\end{table*}\n\n\n\\paragraph{Llama-3.2-3B:}\nFigure \\ref{fig:sim_experts_llama} presents the detailed similarity and expert allocation of our method for each layer of Llama-3.2-3B under the G0 \\!$\\rightarrow$\\! G1 setting.\nThe detailed results for each language under the G0 \\!$\\rightarrow$\\! G1 setting are listed in Table \\ref{table:llama-res-detailed-1} and \\ref{table:llama-res-detailed-2}.\n\n\n\n\n\\begin{table*}[ht]\n \\centering\n % \\belowrulesep=0pt\n % \\aboverulesep=0pt\n \\resizebox*{\\linewidth}{!}{\n \\begin{tabular}{l|ccc|ccc|ccc|ccc|c}\n \\bottomrule\n& \\multicolumn{3}{c|}{\\textbf{ARC-Challenge}} & \\multicolumn{3}{c|}{\\textbf{MMLU}} & \\multicolumn{3}{c|}{\\textbf{Hellaswag}} & \\multicolumn{3}{c|}{\\textbf{Belebele}} & \\\\\n\\hline\n\\textbf{Methods}\t&\t\\textbf{en}\t&\t\\textbf{es}\t&\t\\textbf{zh}\t&\t\\textbf{en}\t&\t\\textbf{es}\t&\t\\textbf{zh}\t&\t\\textbf{en}\t&\t\\textbf{es}\t&\t\\textbf{zh}\t&\t\\textbf{en}\t&\t\\textbf{es}\t&\t\\textbf{zh}\t&\t\\textbf{\\textit{G0-avg}}\t\\\\\n\\hline\nLlama-3.2-3B\t& 51.11 & \t43.59 & \t39.23 & 56.42 & \t49.09 & \t44.00 & 76.32 & \t61.50 & \t52.87 & 74.22 & \t68.56 & \t68.67 & 57.13\t\\\\\n\\hline\nMoE-LPR (3*24)\t&\t50.00 & \t42.31 & \t38.80 & 56.25 & \t49.63 & \t44.08 & 76.35 & \t62.23 & \t53.94 & 74.89 & \t69.22 & \t68.11 & 57.15 \\\\\nLayerMoE (Ours)\t&\t49.91 & \t42.56 & \t39.83 & 56.05 & \t49.77 & \t44.07 & 76.28 & \t62.18 & \t53.74 & 75.33 & \t69.44 & \t68.89 & \\textbf{57.34}\t\\\\ \n\\toprule\n\\bottomrule\n\\textbf{Methods}\t&\t\\textbf{el}\t&\t\\textbf{hu}\t&\t\\textbf{tr}\t&\t\\textbf{el}\t&\t\\textbf{hu}\t&\t\\textbf{tr}\t&\t\\textbf{el}\t&\t\\textbf{hu}\t&\t\\textbf{tr}\t&\t\\textbf{el}\t&\t\\textbf{hu}\t&\t\\textbf{tr}\t&\t\\textbf{\\textit{G1-avg}}\t\\\\\n\\hline\nLlama-3.2-3B &\t31.76 \t&\t34.85 \t&\t33.56 \t&\t41.08 \t&\t43.01 \t&\t42.37 \t&\t43.37 \t&\t44.65 \t&\t42.85 \t&\t65.22 \t&\t59.67 \t&\t59.22 \t& 45.13\t\\\\\n\\hline\nMoE-LPR (3*24)\t&\t36.64 \t&\t39.21 \t&\t36.89 \t&\t43.96 \t&\t44.51 \t&\t43.46 \t&\t52.49 \t&\t51.33 \t&\t49.74 \t&\t68.44 \t&\t63.00 \t&\t61.78 \t& \t49.29 \\\\\nLayerMoE (Ours)\t&\t36.64 \t&\t39.73 \t&\t36.55 \t&\t43.88 \t&\t44.86 \t&\t43.51 \t&\t52.72 \t&\t51.66 \t&\t49.60 \t&\t67.89 \t&\t64.22 \t&\t61.78 \t&\t\\textbf{49.42} \\\\\n\n \\toprule\n \\end{tabular}\n }\n \\caption{\n The results of Llama-3.2-3B on ARC-Challenge, MMLU, Hellaswag, and Belebele under the G0 \\!$\\rightarrow$\\! G1 setting for each language. \n }\n \\label{table:llama-res-detailed-1}\n\\end{table*}\n\n\n\n\n\n\\begin{table*}[ht]\n \\centering\n % \\belowrulesep=0pt\n % \\aboverulesep=0pt\n \\resizebox*{\\linewidth}{!}{\n \\begin{tabular}{l|cccc|c|cccccc|c}\n \\bottomrule\n\\textbf{Methods}\t&\t\\textbf{\\textit{en $\\rightarrow$ zh}}\t&\t\\textbf{\\textit{zh $\\rightarrow$ en}}\t&\t\\textbf{\\textit{en $\\rightarrow$ es}}\t&\t\\textbf{\\textit{es $\\rightarrow$ en}}\t& \\textbf{old-avg}\t & \\textbf{\\textit{en $\\rightarrow$ el}}\t&\t\\textbf{\\textit{el $\\rightarrow$ en}}\t&\t\\textbf{\\textit{en $\\rightarrow$ hu}}\t&\t\\textbf{\\textit{hu $\\rightarrow$ en}}\t& \\textbf{\\textit{en $\\rightarrow$ tr}}\t&\t\\textbf{\\textit{tr $\\rightarrow$ en}}\t& \\textbf{new-avg} \\\\\n\\hline\nLlama-3.2-3B\t& 84.36\t & 83.95 & \t84.79\t & 86.4\t & 84.88 & \t82.47\t & 85.86\t & 83.81 & \t86.15\t & 78.97\t & 86.53 & \t83.96 \\\\\n\\hline\nMoE-LPR (3*24)\t&\t84.19 & \t83.19 & \t84.67 & \t86.27 & \t84.58\t & 84.18\t & 85.89\t & 83.24\t & 85.6\t & 81.95\t & 85.26 & \t84.35 \\\\\nLayerMoE (Ours)\t&\t84.32 & \t83.67\t & 84.63 & \t86.26\t & \\textbf{84.72}\t & 85.14\t & 86.18\t & 83.7\t & 85.65 & \t82.02 & \t85.44 & \t\\textbf{84.69}\t\\\\ \n\\toprule\n \\end{tabular}\n }\n \\caption{\n The detailed results of Llama-3.2-3B on the FLORES benchmark under the G0 \\!$\\rightarrow$\\! G1 setting for each language. \n }\n \\label{table:llama-res-detailed-2}\n\\end{table*}\n\n\n\n\\begin{figure}[b]\n \\centering\n \\includegraphics[width=0.7\\linewidth]{p6.jpg}\n \\caption{The detailed similarity and expert allocation of our method for each layer of Llama-3.2-3B under the G0 \\!$\\rightarrow$\\! G1 setting.}\n \\label{fig:sim_experts_llama}\n\\end{figure}\n\n\\end{document}\n"},"question":{"kind":"string","value":"In the G0 → G1 setting, which method exhibits the smallest relative decline from Old-avg to New-avg, and what is that decline percentage to 2 decimal places?"},"answer":{"kind":"string","value":"*MoE, 23.07%"},"plan":{"kind":"string","value":"SELECT G0 → G1 methods\nLOOP for each method\n SELECT old-avg and new-avg scores for current Method\n COMPUTE decline_percentage = (old_avg - new_avg) /old_avg * 100\nCOMPUTE best_method = argmin over decline_percentages\nRETURN best_method, round(argmin[decline], 2)\n"}}},{"rowIdx":85,"cells":{"paper":{"kind":"string","value":"2505.24615"},"relevant_tables":{"kind":"list like","value":[["\\begin{table*}[t]\n"," \\centering\n"," \\fontsize{8}{6}\\selectfont\n"," \\caption{{\\small Experiments idea retrieval with different embedding backbones. The proposed LLM-KD retriever outperforms RA (Reference Alignment) and Vanilla (baseline without supervision) baselines, demonstrating the effectiveness of idea-level supervision.}}\\label{table:main1}\n"," \\vspace{-10pt}\n","\\begin{tabular}{|c|c|ccccc|ccccc|}\n","\\midrule\\multicolumn{2}{|c|}{Domain} & \\multicolumn{5}{|c|}{Marketing} & \\multicolumn{5}{c|}{NLP} \\\\ \\midrule\n","\\multicolumn{1}{|l|}{Backbone} & Methods & Acc@5 & Acc@10 & Acc@20 & Acc@50 & MAP & Acc@5 & Acc@10 & Acc@20 & Acc@50 & MAP \\\\\\midrule\n","\\multirow{4}{*}{GTE} & Vanilla & 0.7525 & 0.7907 & 0.8303 & 0.8612 & 0.6541 & 0.5585 & 0.5804 & 0.6003 & 0.6248 & 0.5025 \\\\\n","& RA & 0.6847 & 0.7257 & 0.7589 & 0.8089 & 0.5963 & 0.4747 & 0.5001 & 0.5229 & 0.5500 & 0.4289 \\\\\n","& LLM-KD & \\textbf{0.7593} & \\textbf{0.8021} & \\textbf{0.8321} & \\textbf{0.8662} & \\textbf{0.6649} & \\textbf{0.5846} & \\textbf{0.6096} & \\textbf{0.6294} & \\textbf{0.6578} & \\textbf{0.5265} \\\\\n","& Imprv. & 0.90\\% & 1.44\\% & 0.22\\% & 0.58\\% & 1.65\\% & 4.67\\% & 5.02\\% & 4.84\\% & 5.29\\% & 4.79\\% \\\\\\midrule\n","\\multirow{4}{*}{E5} & Vanilla & 0.7357 & 0.7712 & 0.8035 & 0.8412 & 0.6370 & 0.5552 & 0.5766 & 0.5974 & 0.6238 & 0.5014 \\\\\n","& RA & 0.6879 & 0.7261 & 0.7589 & 0.8203 & 0.6037 & 0.4875 & 0.5142 & 0.5378 & 0.5674 & 0.4374 \\\\\n","& LLM-KD & \\textbf{0.7530} & \\textbf{0.7925} & \\textbf{0.8217} & \\textbf{0.8640} & \\textbf{0.6566} & \\textbf{0.5828} & \\textbf{0.6085} & \\textbf{0.6306} & \\textbf{0.6561} & \\textbf{0.5249} \\\\\n","& Imprv. & 2.35\\% & 2.76\\% & 2.27\\% & 2.71\\% & 3.08\\% & 4.98\\% & 5.53\\% & 5.55\\% & 5.18\\% & 4.70\\% \\\\\\midrule\n","\\multirow{4}{*}{SimCSE} & Vanilla & 0.5905 & 0.6356 & 0.6783 & 0.7416 & 0.5061 & 0.3849 & 0.4113 & 0.4364 & 0.4662 & 0.3482 \\\\\n","& RA & 0.5896 & 0.6442 & 0.6906 & 0.7530 & 0.5323 & 0.3897 & 0.4215 & 0.4451 & 0.4817 & 0.3475 \\\\\n","& LLM-KD & \\textbf{0.6479} & \\textbf{0.6888} & \\textbf{0.7320} & \\textbf{0.7916} & \\textbf{0.5575} & \\textbf{0.5459} & \\textbf{0.5730} & \\textbf{0.5981} & \\textbf{0.6322} & \\textbf{0.4869} \\\\\n","& Imprv. & 9.72\\% & 6.92\\% & 5.99\\% & 5.13\\% & 4.73\\% & 40.09\\% & 35.93\\% & 34.37\\% & 31.24\\% & 39.84\\% \\\\\\midrule\n","\\multirow{4}{*}{sbert\\_p} & Vanilla & 0.6037 & 0.6465 & 0.6911 & 0.7571 & 0.5338 & 0.4919 & 0.5197 & 0.5457 & 0.5769 & 0.4414 \\\\\n","& RA & 0.6447 & 0.6947 & 0.7407 & 0.7966 & 0.5727 & 0.4509 & 0.4823 & 0.5088 & 0.5440 & 0.4002 \\\\\n","& LLM-KD & \\textbf{0.6793} & \\textbf{0.7243} & \\textbf{0.7775} & \\textbf{0.8153} & \\textbf{0.5825} & \\textbf{0.5500} & \\textbf{0.5761} & \\textbf{0.6009} & \\textbf{0.6344} & \\textbf{0.4902} \\\\\n","& Imprv. & 5.37\\% & 4.26\\% & 4.97\\% & 2.34\\% & 1.71\\% & 11.80\\% & 10.85\\% & 10.12\\% & 9.96\\% & 11.06\\% \\\\\\midrule\n","\\multirow{4}{*}{NLI} & Vanilla & 0.3558 & 0.3935 & 0.4327 & 0.4959 & 0.3066 & 0.2085 & 0.2259 & 0.2440 & 0.2678 & 0.1871 \\\\\n","& RA & 0.6160 & 0.6592 & 0.6938 & 0.7439 & 0.5534 & 0.4411 & 0.4695 & 0.4951 & 0.5316 & 0.3994 \\\\\n","& LLM-KD & \\textbf{0.7056} & \\textbf{0.7548} & \\textbf{0.7934} & \\textbf{0.8430} & \\textbf{0.6151} & \\textbf{0.5613} & \\textbf{0.5874} & \\textbf{0.6084} & \\textbf{0.6425} & \\textbf{0.5042} \\\\\n","& Imprv. & 14.54\\% & 14.50\\% & 14.35\\% & 13.33\\% & 11.14\\% & 27.23\\% & 25.10\\% & 22.89\\% & 20.87\\% & 26.23\\% \\\\\\midrule\n","\\multirow{4}{*}{BGE} & Vanilla & 0.7266 & 0.7698 & 0.7994 & 0.8312 & 0.6339 & 0.5294 & 0.5543 & 0.5749 & 0.6023 & 0.4735 \\\\\n","& RA & 0.7093 & 0.7425 & 0.7743 & 0.8248 & 0.6154 & 0.4816 & 0.5074 & 0.5291 & 0.5581 & 0.4319 \\\\\n","& LLM-KD & \\textbf{0.7675} & \\textbf{0.8089} & \\textbf{0.8380} & \\textbf{0.8703} & \\textbf{0.6636} & \\textbf{0.5812} & \\textbf{0.6047} & \\textbf{0.6269} & \\textbf{0.6587} & \\textbf{0.5225} \\\\\n","& Imprv. & 5.63\\% & 5.08\\% & 4.83\\% & 4.70\\% & 4.69\\% & 9.79\\% & 9.10\\% & 9.04\\% & 9.37\\% & 10.35\\% \\\\\\midrule \n","\\end{tabular}\n","\\vspace{-10pt}\n","\\end{table*}\n"]],"string":"[\n [\n \"\\\\begin{table*}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\fontsize{8}{6}\\\\selectfont\\n\",\n \" \\\\caption{{\\\\small Experiments idea retrieval with different embedding backbones. The proposed LLM-KD retriever outperforms RA (Reference Alignment) and Vanilla (baseline without supervision) baselines, demonstrating the effectiveness of idea-level supervision.}}\\\\label{table:main1}\\n\",\n \" \\\\vspace{-10pt}\\n\",\n \"\\\\begin{tabular}{|c|c|ccccc|ccccc|}\\n\",\n \"\\\\midrule\\\\multicolumn{2}{|c|}{Domain} & \\\\multicolumn{5}{|c|}{Marketing} & \\\\multicolumn{5}{c|}{NLP} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multicolumn{1}{|l|}{Backbone} & Methods & Acc@5 & Acc@10 & Acc@20 & Acc@50 & MAP & Acc@5 & Acc@10 & Acc@20 & Acc@50 & MAP \\\\\\\\\\\\midrule\\n\",\n \"\\\\multirow{4}{*}{GTE} & Vanilla & 0.7525 & 0.7907 & 0.8303 & 0.8612 & 0.6541 & 0.5585 & 0.5804 & 0.6003 & 0.6248 & 0.5025 \\\\\\\\\\n\",\n \"& RA & 0.6847 & 0.7257 & 0.7589 & 0.8089 & 0.5963 & 0.4747 & 0.5001 & 0.5229 & 0.5500 & 0.4289 \\\\\\\\\\n\",\n \"& LLM-KD & \\\\textbf{0.7593} & \\\\textbf{0.8021} & \\\\textbf{0.8321} & \\\\textbf{0.8662} & \\\\textbf{0.6649} & \\\\textbf{0.5846} & \\\\textbf{0.6096} & \\\\textbf{0.6294} & \\\\textbf{0.6578} & \\\\textbf{0.5265} \\\\\\\\\\n\",\n \"& Imprv. & 0.90\\\\% & 1.44\\\\% & 0.22\\\\% & 0.58\\\\% & 1.65\\\\% & 4.67\\\\% & 5.02\\\\% & 4.84\\\\% & 5.29\\\\% & 4.79\\\\% \\\\\\\\\\\\midrule\\n\",\n \"\\\\multirow{4}{*}{E5} & Vanilla & 0.7357 & 0.7712 & 0.8035 & 0.8412 & 0.6370 & 0.5552 & 0.5766 & 0.5974 & 0.6238 & 0.5014 \\\\\\\\\\n\",\n \"& RA & 0.6879 & 0.7261 & 0.7589 & 0.8203 & 0.6037 & 0.4875 & 0.5142 & 0.5378 & 0.5674 & 0.4374 \\\\\\\\\\n\",\n \"& LLM-KD & \\\\textbf{0.7530} & \\\\textbf{0.7925} & \\\\textbf{0.8217} & \\\\textbf{0.8640} & \\\\textbf{0.6566} & \\\\textbf{0.5828} & \\\\textbf{0.6085} & \\\\textbf{0.6306} & \\\\textbf{0.6561} & \\\\textbf{0.5249} \\\\\\\\\\n\",\n \"& Imprv. & 2.35\\\\% & 2.76\\\\% & 2.27\\\\% & 2.71\\\\% & 3.08\\\\% & 4.98\\\\% & 5.53\\\\% & 5.55\\\\% & 5.18\\\\% & 4.70\\\\% \\\\\\\\\\\\midrule\\n\",\n \"\\\\multirow{4}{*}{SimCSE} & Vanilla & 0.5905 & 0.6356 & 0.6783 & 0.7416 & 0.5061 & 0.3849 & 0.4113 & 0.4364 & 0.4662 & 0.3482 \\\\\\\\\\n\",\n \"& RA & 0.5896 & 0.6442 & 0.6906 & 0.7530 & 0.5323 & 0.3897 & 0.4215 & 0.4451 & 0.4817 & 0.3475 \\\\\\\\\\n\",\n \"& LLM-KD & \\\\textbf{0.6479} & \\\\textbf{0.6888} & \\\\textbf{0.7320} & \\\\textbf{0.7916} & \\\\textbf{0.5575} & \\\\textbf{0.5459} & \\\\textbf{0.5730} & \\\\textbf{0.5981} & \\\\textbf{0.6322} & \\\\textbf{0.4869} \\\\\\\\\\n\",\n \"& Imprv. & 9.72\\\\% & 6.92\\\\% & 5.99\\\\% & 5.13\\\\% & 4.73\\\\% & 40.09\\\\% & 35.93\\\\% & 34.37\\\\% & 31.24\\\\% & 39.84\\\\% \\\\\\\\\\\\midrule\\n\",\n \"\\\\multirow{4}{*}{sbert\\\\_p} & Vanilla & 0.6037 & 0.6465 & 0.6911 & 0.7571 & 0.5338 & 0.4919 & 0.5197 & 0.5457 & 0.5769 & 0.4414 \\\\\\\\\\n\",\n \"& RA & 0.6447 & 0.6947 & 0.7407 & 0.7966 & 0.5727 & 0.4509 & 0.4823 & 0.5088 & 0.5440 & 0.4002 \\\\\\\\\\n\",\n \"& LLM-KD & \\\\textbf{0.6793} & \\\\textbf{0.7243} & \\\\textbf{0.7775} & \\\\textbf{0.8153} & \\\\textbf{0.5825} & \\\\textbf{0.5500} & \\\\textbf{0.5761} & \\\\textbf{0.6009} & \\\\textbf{0.6344} & \\\\textbf{0.4902} \\\\\\\\\\n\",\n \"& Imprv. & 5.37\\\\% & 4.26\\\\% & 4.97\\\\% & 2.34\\\\% & 1.71\\\\% & 11.80\\\\% & 10.85\\\\% & 10.12\\\\% & 9.96\\\\% & 11.06\\\\% \\\\\\\\\\\\midrule\\n\",\n \"\\\\multirow{4}{*}{NLI} & Vanilla & 0.3558 & 0.3935 & 0.4327 & 0.4959 & 0.3066 & 0.2085 & 0.2259 & 0.2440 & 0.2678 & 0.1871 \\\\\\\\\\n\",\n \"& RA & 0.6160 & 0.6592 & 0.6938 & 0.7439 & 0.5534 & 0.4411 & 0.4695 & 0.4951 & 0.5316 & 0.3994 \\\\\\\\\\n\",\n \"& LLM-KD & \\\\textbf{0.7056} & \\\\textbf{0.7548} & \\\\textbf{0.7934} & \\\\textbf{0.8430} & \\\\textbf{0.6151} & \\\\textbf{0.5613} & \\\\textbf{0.5874} & \\\\textbf{0.6084} & \\\\textbf{0.6425} & \\\\textbf{0.5042} \\\\\\\\\\n\",\n \"& Imprv. & 14.54\\\\% & 14.50\\\\% & 14.35\\\\% & 13.33\\\\% & 11.14\\\\% & 27.23\\\\% & 25.10\\\\% & 22.89\\\\% & 20.87\\\\% & 26.23\\\\% \\\\\\\\\\\\midrule\\n\",\n \"\\\\multirow{4}{*}{BGE} & Vanilla & 0.7266 & 0.7698 & 0.7994 & 0.8312 & 0.6339 & 0.5294 & 0.5543 & 0.5749 & 0.6023 & 0.4735 \\\\\\\\\\n\",\n \"& RA & 0.7093 & 0.7425 & 0.7743 & 0.8248 & 0.6154 & 0.4816 & 0.5074 & 0.5291 & 0.5581 & 0.4319 \\\\\\\\\\n\",\n \"& LLM-KD & \\\\textbf{0.7675} & \\\\textbf{0.8089} & \\\\textbf{0.8380} & \\\\textbf{0.8703} & \\\\textbf{0.6636} & \\\\textbf{0.5812} & \\\\textbf{0.6047} & \\\\textbf{0.6269} & \\\\textbf{0.6587} & \\\\textbf{0.5225} \\\\\\\\\\n\",\n \"& Imprv. & 5.63\\\\% & 5.08\\\\% & 4.83\\\\% & 4.70\\\\% & 4.69\\\\% & 9.79\\\\% & 9.10\\\\% & 9.04\\\\% & 9.37\\\\% & 10.35\\\\% \\\\\\\\\\\\midrule \\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\vspace{-10pt}\\n\",\n \"\\\\end{table*}\\n\"\n ]\n]","isConversational":false},"tables":{"kind":"list like","value":[["\\begin{figure}\n"," \\centering\n"," \\includegraphics[width=\\linewidth]{figures/EMNLP_F1.png}\n"," \\caption{An illustrative example about the gap between textual similarity and idea conception. The LLM-paraphrased idea aligns conceptually with the target (green ticked) but shows lower textual similarity than distinct ideas retrieved by existing retrievers.}\n"," \\label{fig:intro}\n"," \\vspace{-20pt}\n","\\end{figure}\n"],["\\begin{figure*}\n"," \\centering\n"," \\includegraphics[width=\\linewidth]{figures/EMNLP_F2.png}\n"," \\caption{The overall architecture of the data construction and methodology. It includes three main components: (1) benchmark dataset construction using topological closure and idea compactness; (2) an LLM-based knowledge distillation framework to train the idea retriever using rephrased, partial, and incremental ideas; and (3) a RAG-based novelty detection strategy that retrieves top-k idea candidates and evaluates novelty scores for decision-making.}\n"," \\label{fig:arch}\n","\\end{figure*}\n"],["\\begin{figure*}[t] % 让 box 跨越双栏\n","\\centering\n","\\begin{tcolorbox}[colback=yellow!10, colframe=black!89!white, \n"," before skip=5pt, after skip=5pt,title={\\small\\textbf{Prompt 3:} Brief prompt of triggering LLMs for ND.}]\\small % <<<<<< 加在这里\n","\\textit{{\\color{red} System}: You are given a *\"new research idea\"* and a list of *\"existing research ideas.\"* Your task is to compare the new idea **against each existing idea** and assign a **novelty score**.\n","\\newline\n","{\\color{red} User}: \\#\\#\\# \\textbf{Clarified Scoring Criteria:} Use the following novelty scoring rubric for each comparison. \n","\\newline \\textbf{0.0 – No Novelty (Identical / Reworded)}. The new idea is a direct copy or a rephrased version of an existing idea. Shares the same claims, findings, information or logic ...\n","\\newline \\textbf{0.3 – Low Novelty (Subset of Existing Idea)}. The new idea is a strict subset of an existing idea. Removes a condition, claim, or component, but otherwise shares the same logic and goal ...\n","\\newline \\textbf{0.5 – Moderate Novelty (large partial overlap)}. The new idea shares a substantial portion (around 50\\%) of the ideas or claims with an existing idea. It introduces some new elements, such as ...\n","\\newline \\textbf{0.7 – High Novelty (small partial overlap)}. The new idea has minor similarities (e.g., method or framing), but differs in research focus, target, or core claim. It applies known ideas in a new context or formulates a distinct question ...\n","\\newline \\textbf{1.0 – Very High Novelty (Distinct Research Direction)}. The idea is entirely distinct in structure, claim, scope, and research objective. Only minor thematic or terminological similarity, if any ...\n","\\newline \\textbf{\\#\\#\\# Evaluation Instructions:} For each comparison between the new idea and an existing idea: 1. Assess Overlap... 2. Assign a Novelty Score... 3. Repeat for All Comparisons...\n","\\newline \\#\\#\\# \\textbf{Output Format:} [List of scores like: [0.3, 0.5, 0.3, 0.7, 1.0]]. Now, compare the given research idea with each of the existing ideas. For each comparison, assign a novelty score using the rubric above and no need to justify the score...\n","\\newline {\\color{magenta} Given Idea: {QUERY.}} {\\color{blue} Existing Ideas: 1. \\{Idea 1\\}, ..., K. \\{Idea K\\}. }}\n","\\end{tcolorbox}\n","\\vspace{-10pt}\n","\\end{figure*}\n"],["\\begin{table*}[t]\n"," \\centering\n"," \\fontsize{8}{6}\\selectfont\n"," \\caption{{\\small Experiments idea retrieval with different embedding backbones. The proposed LLM-KD retriever outperforms RA (Reference Alignment) and Vanilla (baseline without supervision) baselines, demonstrating the effectiveness of idea-level supervision.}}\\label{table:main1}\n"," \\vspace{-10pt}\n","\\begin{tabular}{|c|c|ccccc|ccccc|}\n","\\midrule\\multicolumn{2}{|c|}{Domain} & \\multicolumn{5}{|c|}{Marketing} & \\multicolumn{5}{c|}{NLP} \\\\ \\midrule\n","\\multicolumn{1}{|l|}{Backbone} & Methods & Acc@5 & Acc@10 & Acc@20 & Acc@50 & MAP & Acc@5 & Acc@10 & Acc@20 & Acc@50 & MAP \\\\\\midrule\n","\\multirow{4}{*}{GTE} & Vanilla & 0.7525 & 0.7907 & 0.8303 & 0.8612 & 0.6541 & 0.5585 & 0.5804 & 0.6003 & 0.6248 & 0.5025 \\\\\n","& RA & 0.6847 & 0.7257 & 0.7589 & 0.8089 & 0.5963 & 0.4747 & 0.5001 & 0.5229 & 0.5500 & 0.4289 \\\\\n","& LLM-KD & \\textbf{0.7593} & \\textbf{0.8021} & \\textbf{0.8321} & \\textbf{0.8662} & \\textbf{0.6649} & \\textbf{0.5846} & \\textbf{0.6096} & \\textbf{0.6294} & \\textbf{0.6578} & \\textbf{0.5265} \\\\\n","& Imprv. & 0.90\\% & 1.44\\% & 0.22\\% & 0.58\\% & 1.65\\% & 4.67\\% & 5.02\\% & 4.84\\% & 5.29\\% & 4.79\\% \\\\\\midrule\n","\\multirow{4}{*}{E5} & Vanilla & 0.7357 & 0.7712 & 0.8035 & 0.8412 & 0.6370 & 0.5552 & 0.5766 & 0.5974 & 0.6238 & 0.5014 \\\\\n","& RA & 0.6879 & 0.7261 & 0.7589 & 0.8203 & 0.6037 & 0.4875 & 0.5142 & 0.5378 & 0.5674 & 0.4374 \\\\\n","& LLM-KD & \\textbf{0.7530} & \\textbf{0.7925} & \\textbf{0.8217} & \\textbf{0.8640} & \\textbf{0.6566} & \\textbf{0.5828} & \\textbf{0.6085} & \\textbf{0.6306} & \\textbf{0.6561} & \\textbf{0.5249} \\\\\n","& Imprv. & 2.35\\% & 2.76\\% & 2.27\\% & 2.71\\% & 3.08\\% & 4.98\\% & 5.53\\% & 5.55\\% & 5.18\\% & 4.70\\% \\\\\\midrule\n","\\multirow{4}{*}{SimCSE} & Vanilla & 0.5905 & 0.6356 & 0.6783 & 0.7416 & 0.5061 & 0.3849 & 0.4113 & 0.4364 & 0.4662 & 0.3482 \\\\\n","& RA & 0.5896 & 0.6442 & 0.6906 & 0.7530 & 0.5323 & 0.3897 & 0.4215 & 0.4451 & 0.4817 & 0.3475 \\\\\n","& LLM-KD & \\textbf{0.6479} & \\textbf{0.6888} & \\textbf{0.7320} & \\textbf{0.7916} & \\textbf{0.5575} & \\textbf{0.5459} & \\textbf{0.5730} & \\textbf{0.5981} & \\textbf{0.6322} & \\textbf{0.4869} \\\\\n","& Imprv. & 9.72\\% & 6.92\\% & 5.99\\% & 5.13\\% & 4.73\\% & 40.09\\% & 35.93\\% & 34.37\\% & 31.24\\% & 39.84\\% \\\\\\midrule\n","\\multirow{4}{*}{sbert\\_p} & Vanilla & 0.6037 & 0.6465 & 0.6911 & 0.7571 & 0.5338 & 0.4919 & 0.5197 & 0.5457 & 0.5769 & 0.4414 \\\\\n","& RA & 0.6447 & 0.6947 & 0.7407 & 0.7966 & 0.5727 & 0.4509 & 0.4823 & 0.5088 & 0.5440 & 0.4002 \\\\\n","& LLM-KD & \\textbf{0.6793} & \\textbf{0.7243} & \\textbf{0.7775} & \\textbf{0.8153} & \\textbf{0.5825} & \\textbf{0.5500} & \\textbf{0.5761} & \\textbf{0.6009} & \\textbf{0.6344} & \\textbf{0.4902} \\\\\n","& Imprv. & 5.37\\% & 4.26\\% & 4.97\\% & 2.34\\% & 1.71\\% & 11.80\\% & 10.85\\% & 10.12\\% & 9.96\\% & 11.06\\% \\\\\\midrule\n","\\multirow{4}{*}{NLI} & Vanilla & 0.3558 & 0.3935 & 0.4327 & 0.4959 & 0.3066 & 0.2085 & 0.2259 & 0.2440 & 0.2678 & 0.1871 \\\\\n","& RA & 0.6160 & 0.6592 & 0.6938 & 0.7439 & 0.5534 & 0.4411 & 0.4695 & 0.4951 & 0.5316 & 0.3994 \\\\\n","& LLM-KD & \\textbf{0.7056} & \\textbf{0.7548} & \\textbf{0.7934} & \\textbf{0.8430} & \\textbf{0.6151} & \\textbf{0.5613} & \\textbf{0.5874} & \\textbf{0.6084} & \\textbf{0.6425} & \\textbf{0.5042} \\\\\n","& Imprv. & 14.54\\% & 14.50\\% & 14.35\\% & 13.33\\% & 11.14\\% & 27.23\\% & 25.10\\% & 22.89\\% & 20.87\\% & 26.23\\% \\\\\\midrule\n","\\multirow{4}{*}{BGE} & Vanilla & 0.7266 & 0.7698 & 0.7994 & 0.8312 & 0.6339 & 0.5294 & 0.5543 & 0.5749 & 0.6023 & 0.4735 \\\\\n","& RA & 0.7093 & 0.7425 & 0.7743 & 0.8248 & 0.6154 & 0.4816 & 0.5074 & 0.5291 & 0.5581 & 0.4319 \\\\\n","& LLM-KD & \\textbf{0.7675} & \\textbf{0.8089} & \\textbf{0.8380} & \\textbf{0.8703} & \\textbf{0.6636} & \\textbf{0.5812} & \\textbf{0.6047} & \\textbf{0.6269} & \\textbf{0.6587} & \\textbf{0.5225} \\\\\n","& Imprv. & 5.63\\% & 5.08\\% & 4.83\\% & 4.70\\% & 4.69\\% & 9.79\\% & 9.10\\% & 9.04\\% & 9.37\\% & 10.35\\% \\\\\\midrule \n","\\end{tabular}\n","\\vspace{-10pt}\n","\\end{table*}\n"],["\\begin{table}[t]\n"," \\centering \n"," \\caption{{\\small Experiments on different types of synthesized (non-novel) ideas with BGE backbone in NLP dataset. }}\\label{table:group}\n"," \\fontsize{8}{6}\\selectfont\n","\\begin{tabular}{|c|c|c|c|c|}\\midrule \n","Type & Metric & Vanilla & LLM KD & Imprv. \\\\\\midrule \n","\\multirow{3}{*}{\\begin{tabular}[c]{@{}c@{}}Rephrased\\\\ idea\\end{tabular}} & Acc@5 & 0.9815 & 0.9887 & 0.74\\% \\\\\n","& Acc@20 & 0.9899 & 0.9952 & 0.53\\% \\\\\n","& MAP & 0.9667 & 0.9767 & 1.04\\% \\\\\\midrule\n","\\multirow{3}{*}{\\begin{tabular}[c]{@{}c@{}}Partial\\\\ idea\\end{tabular}} & Acc@5 & 0.9296 & 0.9554 & 2.77\\% \\\\\n","& Acc@20 & 0.9618 & 0.9787 & 1.76\\% \\\\\n","& MAP & 0.8911 & 0.9230 & 3.58\\% \\\\\\midrule\n","\\multirow{3}{*}{\\begin{tabular}[c]{@{}c@{}}Incremental \\\\ idea\\end{tabular}} & Acc@5 & 0.3404 & 0.4079 & 19.82\\% \\\\\n","& Acc@20 & 0.3970 & 0.4672 & 17.67\\% \\\\\n","& MAP & 0.2715 & 0.3329 & 22.61\\% \\\\\\midrule\n","\\end{tabular}\n","\\vspace{-10pt}\n","\\end{table}\n"],["\\begin{table*}[t]\n"," \\centering\n"," \\fontsize{8}{6}\\selectfont\n"," \\caption{{\\small Experiments on different methods in ND task. The proposed RAG-KD method outperforms all baselines, as demonstrated by the percentage gains. }}\\label{table:main2}\n"," \\vspace{-10pt}\n","\\begin{tabular}{|c|cccc|cccc|}\\midrule\n","Dataset & \\multicolumn{4}{|c|}{Marketing} & \\multicolumn{4}{|c|}{NLP} \\\\\\midrule\n","Baseline & Accuacy & Precision & Recall & F1 & Accuacy & Precision & Recall & F1 \\\\\\midrule\n","CD & 0.6005 & 0.6395 & 0.6005 & 0.5797 & 0.5167 & 0.5406 & 0.5167 & 0.4430 \\\\\n","URPC & 0.5500 & 0.5565 & 0.5500 & 0.5366 & 0.4800 & 0.3698 & 0.4800 & 0.3404 \\\\\n","PES & 0.4700 & 0.4577 & 0.4700 & 0.4283 & 0.5800 & 0.5891 & 0.5800 & 0.5690 \\\\\n","MOOSE & 0.6400 & 0.7604 & 0.6400 & 0.5929 & 0.5900 & 0.6018 & 0.5900 & 0.5778 \\\\\n","SciMON & 0.4900 & 0.4745 & 0.4900 & 0.3985 & 0.6100 & 0.6244 & 0.6100 & 0.5984 \\\\\\midrule\n","RAG-Vanilla & 0.7292 & 0.7664 & 0.7292 & 0.7180 & 0.6100 &0.63356& 0.61000 & 0.59201 \\\\\n","RAG-KD & \\textbf{0.7453} & \\textbf{0.8023} & \\textbf{0.7453} & \\textbf{0.7344} & \\textbf{0.7474} & \\textbf{0.8010 } & \\textbf{ 0.7474 } & \\textbf{0.7349} \\\\\\midrule\n","Improve & 24.11\\% & 25.46\\% & 24.11\\% & 26.69\\% & 22.54\\%\t&28.29\\% &\t22.54\\%\t&22.82\\% \\\\\\midrule\n","\\end{tabular}\n","\\vspace{-10pt}\n","\\end{table*}\n"],["\\begin{figure}\n"," \\centering\n"," \\includegraphics[width=0.85\\linewidth]{figures/hyper.png}\n"," \\caption{Investigate on (a) LLM backbones and (b) retrieval size $K$ for ND tasks .}\n"," \\label{fig:hyper}\n","\\vspace{-10pt}\n","\\end{figure}\n"],["\\begin{figure}[t]\n","\\vspace{-20pt}\n","\\vspace{-1em}\n"," \\begin{tcolorbox}[colback=yellow!10, colframe=black!89!white, before skip=5pt, after skip=5pt,\n"," title=\\textbf{Prompt 1 (b):} Detailed prompt for idea extraction in Marketing domain.,\n"," fontupper=\\small, % <-- apply \\small to the box contents\n"," sharp corners,\n"," ] % <<<<<< 加在这里\n","\\textit{{\\color{red} System}: You are a social science expert searching for new research topics to explore. To accomplish this, you need to conduct a comprehensive literature review to understand the current research landscape, including the challenges being addressed, the hypotheses being tested, the methodologies employed, and the specific conditions under which key findings are obtained.\n","\\newline\n","{\\color{red} User}: In social science research, a hypothesis is typically a clear, specific, and testable statement predicting the relationship between an independent variable and a dependent variable. It often outlines how one variable influences another and to what degree. The hypothesis is central to the research, as it guides the study's aim of proving or disproving this relationship. In many cases, the hypothesis reflects the main conclusion or finding of the research. Keywords or key phrases are essential terms capturing the study's core concepts, variables, and major themes, and should reflect the primary focus areas. They should neither be too specific nor too general, avoiding keyword over-expansion while effectively indexing the research.\n","\\newline Example of Research Hypotheses in Social Science: Viewing a visually depicted product that facilitates embodied mental simulation leads to heightened purchase intentions, with perceptual resources for mental simulation attenuating this effect, and for negatively valenced products, it decreases purchase intentions.\n","\\newline Your task is to extract the hypothesis from each title and abstract pair of a research paper. \n","\\newline Given time constraints, you only need to review the title and abstract of research papers published in top-tier journals. Your task is to extract the hypothesis from each title and abstract pair. The hypothesis should be comprehensive, including key details or side hypotheses if present. If no hypothesis is found, you can extract the findings or conclusions of the contributions of the study as the hypothesis.\n","\\newline Below is the target article:\n","\\newline Title: {\\color{magenta} \\{title\\}}\n","\\newline Abstract: {\\color{magenta} \\{abstract\\}}\n","\\newline After reviewing the abstract, please extract the main hypothesis using the following format (only the hypothesis, no additional formatting, no additional text to explain your answer):\n","\\newline Hypothesis: [Extracted hypothesis, including details if present, put value to be 'None' if no hypothesis is found]\\#\n","}\n","\\end{tcolorbox}\n","\\end{figure}\n"],["\\begin{figure*}[t]\n"," \\centering\n"," \\begin{tcolorbox}[colback=yellow!10, colframe=black!89!white, before skip=5pt, after skip=5pt,\n"," title=\\textbf{Prompt 2 (a):} Detailed prompt for rephrased idea generation by LLMs,\n"," fontupper=\\small, % <-- apply \\small to the box contents\n"," sharp corners,\n"," ] % <<<<<< 加在这里\n","{{\\color{red} System}: You are an expert in scientific writing and language transformation. Your task is to paraphrase scientific statements while ensuring clarity, precision, and natural readability. The meaning of the original sentence must remain unchanged, but the structure and expression should be significantly altered.\n","\\newline\n","{\\color{red} User}: \n","\\newline \\#\\#\\#\\#**Task:** Given an input sentence, generate **up to 5 sentences** that retain the original information while allowing for modifications. Each generated sentence should be of the same meaning as the original, but with completely different words and structure.\n","\\newline \\#\\#\\#\\# **Instructions:** \n","\\newline 1. **Change Words to Synonyms** \n","\\newline - Replace key terms with scientifically appropriate synonyms while maintaining clarity and rigor. \n","\\newline 2. **Modify Sentence Length** \n","\\newline - Either **extend or shorten** the sentence while preserving the original meaning. \n","\\newline - If extending, add **clarifying details** or **restructure for better readability**. \n","\\newline - If shortening, remove redundant words while retaining key scientific information. \n","\\newline 3. **Add or Delete Non-Essential Words** \n","\\newline - Introduce or remove words **that do not alter** the fundamental meaning. \n","\\newline - Improve fluency by modifying transitions or simplifying phrasing. \n","\\newline 4. **Alter Sentence Structure** \n","\\newline - Rearrange the sentence while maintaining logical flow. \n","\\newline - Convert passive to active voice (or vice versa) where appropriate. \n","\\newline - Split complex sentences into simpler ones or combine shorter ones for a smoother read. \n","\\newline 5. **Ensure Significant Differences from the Original** \n","\\newline - The paraphrased versions **must be structurally different** while still preserving the core message. \n","\\newline 6. **Output Format:** \n","\\newline - Return **only** the elaborated subset sentences, numbered **1 to 5**, with no additional text or explanations. \n","\\newline \\#\\#\\# **Example Input \\& Output** \n","\\newline \\#\\#\\#\\# **Example 1: Scientific Context** \n","\\newline**Input Sentence:** \n","\\newline*\"Long-term exposure to polluted air has been linked to an increased risk of developing respiratory illnesses such as asthma and chronic bronchitis.\"* \n","\\newline**Generated Sentences:** \n","\\newline1. Extended periods of contact with contaminated air are associated with a greater likelihood of respiratory conditions, including asthma and bronchitis. \n","\\newline2. Prolonged inhalation of polluted air may elevate the probability of experiencing chronic lung diseases. \n","\\newline3. Airborne pollutants have been found to contribute to the onset of various respiratory disorders over time. \n","\\newline4. Studies suggest that individuals consistently exposed to poor air quality are at a heightened risk of breathing-related health issues. \n","\\newline5. The presence of hazardous particles in the atmosphere can gradually impair lung function and lead to chronic respiratory distress. \n","\\newline---\n","\\newline\\#\\#\\#\\# **Example 2: AI \\& Technology Context** \n","\\newline **Input Sentence:** \n","\\newline *\"Machine learning algorithms are increasingly being used to detect patterns in large datasets, improving decision-making processes in fields such as healthcare and finance.\"* \n","\\newline **Generated Sentences:** \n","\\newline 1. AI-driven models are enhancing decision-making by identifying trends in vast amounts of data. \n","\\newline 2. The application of machine learning techniques is revolutionizing data analysis across multiple industries. \n","\\newline 3. Advanced computational models help uncover hidden insights within extensive datasets. \n","\\newline 4. The use of artificial intelligence in fields like healthcare and banking is optimizing predictive analytics. \n","\\newline 5. Modern machine learning tools enable more efficient data-driven decisions through pattern recognition. \n","\\newline ---\n","\\newline Now, generate up to {k} paraphrased sentences for the following input: \n","\\newline **Input:** {\\color{blue} \\{idea\\}}\n","}\n","\\end{tcolorbox}\n","\\end{figure*}\n"],["\\begin{figure*}[t]\n"," \\centering\n"," \\begin{tcolorbox}[colback=yellow!10, colframe=black!89!white, before skip=5pt, after skip=5pt,\n"," title=\\textbf{Prompt 2 (b):} Detailed prompt for partial idea generation by LLMs,\n"," fontupper=\\small, % <-- apply \\small to the box contents\n"," sharp corners,\n"," ] % <<<<<< 加在这里\n","{{\\color{red} System}: You are an expert in scientific writing and language transformation. Your task is to paraphrase scientific statements while ensuring clarity, precision, and natural readability. The meaning of the original sentence must remain unchanged, but the structure and expression should be significantly altered.\n","\\newline\n","{\\color{red} User}: \n","\\newline \\#\\#\\#\\#**Task:** Given an input sentence, generate up to k subset sentences that retain part of the original information while allowing for minor modifications. You may add, delete, or replace words as long as the subset sentence conveys a meaningful portion of the original content without including all of it. Each subset sentence should be **elaborated**, providing additional context or explanation while staying true to the original meaning.\n","\\newline \\#\\#\\#\\# **Instructions:** \n","\\newline 1. Extract a meaningful portion of the input sentence. \n","\\newline 2. Modify the extracted portion by **adding, deleting, or replacing** words while ensuring clarity and coherence. \n","\\newline 3. Elaborate on the extracted information by adding context, explanation, or detail while retaining the core meaning. \n","\\newline 4. Each subset sentence should retain **only part of the original information**, not the full meaning. \n","\\newline 5. Ensure grammatical correctness and natural phrasing. \n","\\newline 6. Avoid directly copying the exact words from the input sentence. \n","\\newline 7. Do not introduce entirely new or unrelated information. \n","\\newline 8. **Output only the elaborated subset sentences, numbered from 1 to 10, with no additional text or explanations.**\n","\\newline --- \n","\\newline \\#\\#\\# **Example Input \\& Output** \n","\\newline \\#\\#\\#\\# **Example 1:** \n","\\newline **Input:** \"The increasing reliance on artificial intelligence in the healthcare industry is transforming patient diagnostics and treatment planning.\" \n","\\newline \n","\\newline **Output:** \n","\\newline 1. AI is revolutionizing healthcare by enhancing how doctors diagnose illnesses and design personalized treatments. \n","\\newline 2. The healthcare sector is increasingly adopting AI-driven tools to streamline diagnostic processes and improve patient outcomes. \n","\\newline 3. Advanced algorithms are now assisting healthcare providers in identifying conditions more accurately and efficiently. \n","\\newline 4. Artificial intelligence is not only transforming diagnostics but also reshaping how treatment plans are tailored to individual patients. \n","\\newline 5. Medical professionals are leveraging AI systems to analyze patient data and detect abnormalities earlier. \n","\\newline --- \n","\\newline \\#\\#\\# **Example 2:** \n","\\newline*\"Long-term exposure to polluted air has been linked to an increased risk of developing respiratory illnesses such as asthma and chronic bronchitis.\"* \n","\\newline**Generated Sentences:** \n","\\newline1. Extended periods of contact with contaminated air are associated with a greater likelihood of respiratory conditions, including asthma and bronchitis. \n","\\newline2. Prolonged inhalation of polluted air may elevate the probability of experiencing chronic lung diseases. \n","\\newline3. Airborne pollutants have been found to contribute to the onset of various respiratory disorders over time. \n","\\newline4. Studies suggest that individuals consistently exposed to poor air quality are at a heightened risk of breathing-related health issues. \n","\\newline5. The presence of hazardous particles in the atmosphere can gradually impair lung function and lead to chronic respiratory distress. \n","\\newline---\n","\\newline\\#\\#\\#\\# **Example 2: AI \\& Technology Context** \n","\\newline **Input:** \"Due to climate change, extreme weather events such as hurricanes and heatwaves have become more frequent and intense.\" \n","\\newline **Output:** \n","\\newline1. Rising global temperatures are causing hurricanes to become more powerful and destructive. \n","\\newline2. Climate change is intensifying heatwaves, making them last longer and reach higher temperatures. \n","\\newline3. Severe weather patterns are now more common due to the warming atmosphere and changing climate conditions. \n","\\newline4. The increased occurrence of hurricanes and heatwaves is a direct result of shifts in global weather systems. \n","\\newline5. Scientists link the rise in extreme weather events to the ongoing effects of climate change. \n","\\newline---\n","\\newline Now, generate up to {k} elaborated subset sentences for the following input: \n","\\newline **Input:** {\\color{blue} \\{idea\\}}\n","}\n","\\end{tcolorbox}\n","\\end{figure*}\n"],["\\begin{figure*}[t]\n"," \\centering\n"," \\begin{tcolorbox}[colback=yellow!10, colframe=black!89!white, before skip=5pt, after skip=5pt,\n"," title=\\textbf{Prompt 2 (c):} Detailed prompt for incremental idea generation by LLMs,\n"," fontupper=\\small, % <-- apply \\small to the box contents\n"," sharp corners,\n"," ] % <<<<<< 加在这里\n","{{\\color{red} System}: You are an expert in scientific writing and language transformation. Your task is to paraphrase scientific statements while ensuring clarity, precision, and natural readability. The meaning of the original sentence must remain unchanged, but the structure and expression should be significantly altered.\n","\\newline\n","{\\color{red} User}: \n","\\newline \\#\\#\\#\\#**Task:** Given two input sentences, generate **up to k sentences** that retain the original information with no modifications or elaborations. The generated sentences are LAGRELY different from these two papers in semantic level (e.g., low BERT-based similarity), but bring the key ideas from them. For example, if the paper A has distinct ideas: idea\\_A1, idea\\_A2, and paper B distinct ideas: idea\\_B1, idea\\_B2, you can generate the idea by selecting subset of ideas from both these two papers (e.g., ideas idea\\_A1 + idea\\_B2 -> new abstract). Besides that, you can follow the below rules to help you the generated abstract that seems to be largely distinct to the original papers (e.g., Paper A and Paper B):\n","\\newline \\#\\#\\#\\# **Instructions:** \n","\\newline1. **Rephrase \\& Restructure**: The new sentence must **significantly alter** the structure and wording of the selected subsets while **retaining only their core ideas**. \n","\\newline2. **Break Down \\& Blend**: Instead of copying large portions, **extract fragments** from the subsets and recombine them in a different way. \n","\\newline3. **Introduce Metaphors or Analogies**: Use figurative language to convey the original meaning in a more indirect manner. \n","\\newline4. **Use Different Sentence Structures**: Experiment with **questions, lists, cause-effect statements, conditionals, or passive voice**. \n","\\newline5. **Limit Direct Keywords**: Avoid using the **exact phrasing** from the original subsets-paraphrase where possible.\n","\\newline6. **Focus on Implicit Links**: The new sentence **should not clearly resemble either source sentence**, making it harder to trace back to any one subset. \n","\\newline7. **Enforce Conceptual Fusion**: The new sentence must not focus too heavily on just one of the selected subsets but should **merge ideas from both** in a way that feels natural and balanced. \n","\\newline8. **Ensure Consistency**: Does not create a new perspective or connection between the ideas, presents the ideas together in one sentence, but without forcing a relationship.\n","\\newline9. **Output Format:** \n","\\newline - Return **only** the elaborated subset sentences, numbered **1 to 5**, with no additional text or explanations. \n","\\newline --- \n","\\newline \\#\\#\\# **Example Input \\& Output** \n","\\newline \\#\\#\\#\\# **Example 1:** \n","\\newline**idea\\_A1:** Personalization improves email engagement.\n","\\newline**idea\\_B2:** Scarcity messaging can damage long-term trust.\n","\\newline**Generated Sentence:**\n","\\newline1. Tailored content boosts email responses, while limited-time offers may compromise brand credibility.\n","\\newline\n","\\newline\\#\\#\\#\\# **Example 2: NLP Domain**\n","\\newline **idea\\_A2:** Instruction tuning aligns model outputs with user prompts.\n","\\newline**idea\\_B1:** Retrieval enhances factual accuracy in text generation.\n","\\newline\n","\\newline**Generated Sentence:**\n","\\newline1. While instruction tuning improves adherence to task instructions, adding retrieval helps ground outputs in reliable information.\n","\\newline---\n","\\newline Now, generate up to k fused sentences for the following inputs: \n","\\newline Sentence A: {\\color{blue} Idea 1}\n","\\newline Sentence B: {\\color{blue} Idea 2}\n","}\n","\\end{tcolorbox}\n","\\end{figure*}\n"],["\\begin{figure*}[t]\n"," \\centering\n"," \\begin{tcolorbox}[colback=yellow!10, colframe=black!89!white, before skip=5pt, after skip=5pt,\n"," title=\\textbf{Prompt 3:} Trigger LLMs for novelty detection.,\n"," fontupper=\\small, % <-- apply \\small to the box contents\n"," sharp corners,\n"," ] % <<<<<< 加在这里\n","{{\\color{red} System}: You are given a *\"new research idea\"* and a list of *\"existing research ideas.\"* Your task is to compare the new idea **against each existing idea** and assign a **novelty score**.\n","\\newline\n","{\\color{red} User}: \\#\\#\\# \\textbf{Clarified Scoring Criteria:}\n","\\newline Use the following **novelty scoring rubric** for each comparison:\n","\\newline Scoring Guidelines: \n","\\newline 0.0 – No Novelty (Identical / Reworded). The new idea is a direct copy or a rephrased version of an existing idea. Shares the same claims, findings, information or logic. Example: Paraphrasing an existing idea without introducing any change.\n","\\newline 0.3 – Low Novelty (Subset of Existing Idea). The new idea is a strict subset of an existing idea. Removes a condition, claim, or component, but otherwise shares the same logic and goal. No new dimension or direction added. Example: Taking just “Claim A” from “Claim A + Claim B”.\n","\\newline 0.5 – Moderate Novelty (large partial overlap). The new idea shares a substantial portion (around 50\\%) of the ideas or claims with an existing idea. It introduces some new elements, such as a different combination of claims or modified emphasis, but it’s not entirely new. It’s not a subset or superset, but has a large information intersection. Example: “Claim A + Claim C” compared to existing “Claim A + Claim B”.\n","\\newline 0.7 – High Novelty (small partial overlap). The new idea has minor similarities (e.g., method or framing), but differs in research focus, target, or core claim. It applies known ideas in a new context or formulates a distinct question, showing clear divergence from existing ideas. Example: Using a social theory from marketing in the context of education.\n","\\newline 1.0 – Very High Novelty (Distinct Research Direction). The idea is entirely distinct in structure, claim, scope, and research objective. Only minor thematic or terminological similarity, if any. Example: Proposing a brand new framework, theory, or dataset with no precedent in existing ideas.\n","\\newline \\textbf{\\#\\#\\# Evaluation Instructions:}\n","\\newline For each comparison between the new idea and an existing idea:\n","\\newline 1. Assess Overlap. Examine the conceptual and structural overlap between the new idea and the existing idea.\n","\\newline 2. Assign a Novelty Score. Use the Novelty Scoring Rubric to assign a score between 0.0 and 1.0, based on the degree of similarity or difference.\n","\\newline 3. Repeat for All Comparisons. Perform this scoring for each existing idea in the set.\n","\\newline \\#\\#\\# \\textbf{Output Format:} [List of scores like: [0.3, 0.5, 0.3, 0.7, 1.0]]\n","\\newline Now, compare the given research idea with each of the existing ideas. For each comparison, assign a novelty score using the rubric above and no need to justify the score. Please return only a Python-style list of the novelty scores for each comparison and the final decision, no further explanation. \n","\\newline Given Idea: {\\color{magenta}{(QUERY) The investigation discovers that the effectiveness of online ads is influenced by the volume and placement of ads, and a data-driven strategy to manage ad exposure could potentially increase campaign success by 15\\%.}}\n","\\newline {\\color{blue} Existing Ideas:}\n","\\newline {\\color{blue} 1. The study finds that varying degrees of wearout and weariness exist among consumers in response to online ad volume and placement, with an appropriate \"profiling and capping\" strategy potentially improving ad deployment effectiveness by 15\\%. }\n","\\newline {\\color{blue} ... }\n","\\newline {\\color{blue} K. The study finds that display advertisements have a low direct effect on purchase conversion but stimulate subsequent visits through other advertisement formats, and the commonly used measure of conversion rate is biased in favor of search advertisements, underestimating the conversion effect of display advertisements. }\n","}\n","\\end{tcolorbox}\n","\\end{figure*}\n"]],"string":"[\n [\n \"\\\\begin{figure}\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\linewidth]{figures/EMNLP_F1.png}\\n\",\n \" \\\\caption{An illustrative example about the gap between textual similarity and idea conception. The LLM-paraphrased idea aligns conceptually with the target (green ticked) but shows lower textual similarity than distinct ideas retrieved by existing retrievers.}\\n\",\n \" \\\\label{fig:intro}\\n\",\n \" \\\\vspace{-20pt}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{figure*}\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\linewidth]{figures/EMNLP_F2.png}\\n\",\n \" \\\\caption{The overall architecture of the data construction and methodology. It includes three main components: (1) benchmark dataset construction using topological closure and idea compactness; (2) an LLM-based knowledge distillation framework to train the idea retriever using rephrased, partial, and incremental ideas; and (3) a RAG-based novelty detection strategy that retrieves top-k idea candidates and evaluates novelty scores for decision-making.}\\n\",\n \" \\\\label{fig:arch}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[t] % 让 box 跨越双栏\\n\",\n \"\\\\centering\\n\",\n \"\\\\begin{tcolorbox}[colback=yellow!10, colframe=black!89!white, \\n\",\n \" before skip=5pt, after skip=5pt,title={\\\\small\\\\textbf{Prompt 3:} Brief prompt of triggering LLMs for ND.}]\\\\small % <<<<<< 加在这里\\n\",\n \"\\\\textit{{\\\\color{red} System}: You are given a *\\\"new research idea\\\"* and a list of *\\\"existing research ideas.\\\"* Your task is to compare the new idea **against each existing idea** and assign a **novelty score**.\\n\",\n \"\\\\newline\\n\",\n \"{\\\\color{red} User}: \\\\#\\\\#\\\\# \\\\textbf{Clarified Scoring Criteria:} Use the following novelty scoring rubric for each comparison. \\n\",\n \"\\\\newline \\\\textbf{0.0 – No Novelty (Identical / Reworded)}. The new idea is a direct copy or a rephrased version of an existing idea. Shares the same claims, findings, information or logic ...\\n\",\n \"\\\\newline \\\\textbf{0.3 – Low Novelty (Subset of Existing Idea)}. The new idea is a strict subset of an existing idea. Removes a condition, claim, or component, but otherwise shares the same logic and goal ...\\n\",\n \"\\\\newline \\\\textbf{0.5 – Moderate Novelty (large partial overlap)}. The new idea shares a substantial portion (around 50\\\\%) of the ideas or claims with an existing idea. It introduces some new elements, such as ...\\n\",\n \"\\\\newline \\\\textbf{0.7 – High Novelty (small partial overlap)}. The new idea has minor similarities (e.g., method or framing), but differs in research focus, target, or core claim. It applies known ideas in a new context or formulates a distinct question ...\\n\",\n \"\\\\newline \\\\textbf{1.0 – Very High Novelty (Distinct Research Direction)}. The idea is entirely distinct in structure, claim, scope, and research objective. Only minor thematic or terminological similarity, if any ...\\n\",\n \"\\\\newline \\\\textbf{\\\\#\\\\#\\\\# Evaluation Instructions:} For each comparison between the new idea and an existing idea: 1. Assess Overlap... 2. Assign a Novelty Score... 3. Repeat for All Comparisons...\\n\",\n \"\\\\newline \\\\#\\\\#\\\\# \\\\textbf{Output Format:} [List of scores like: [0.3, 0.5, 0.3, 0.7, 1.0]]. Now, compare the given research idea with each of the existing ideas. For each comparison, assign a novelty score using the rubric above and no need to justify the score...\\n\",\n \"\\\\newline {\\\\color{magenta} Given Idea: {QUERY.}} {\\\\color{blue} Existing Ideas: 1. \\\\{Idea 1\\\\}, ..., K. \\\\{Idea K\\\\}. }}\\n\",\n \"\\\\end{tcolorbox}\\n\",\n \"\\\\vspace{-10pt}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{table*}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\fontsize{8}{6}\\\\selectfont\\n\",\n \" \\\\caption{{\\\\small Experiments idea retrieval with different embedding backbones. The proposed LLM-KD retriever outperforms RA (Reference Alignment) and Vanilla (baseline without supervision) baselines, demonstrating the effectiveness of idea-level supervision.}}\\\\label{table:main1}\\n\",\n \" \\\\vspace{-10pt}\\n\",\n \"\\\\begin{tabular}{|c|c|ccccc|ccccc|}\\n\",\n \"\\\\midrule\\\\multicolumn{2}{|c|}{Domain} & \\\\multicolumn{5}{|c|}{Marketing} & \\\\multicolumn{5}{c|}{NLP} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multicolumn{1}{|l|}{Backbone} & Methods & Acc@5 & Acc@10 & Acc@20 & Acc@50 & MAP & Acc@5 & Acc@10 & Acc@20 & Acc@50 & MAP \\\\\\\\\\\\midrule\\n\",\n \"\\\\multirow{4}{*}{GTE} & Vanilla & 0.7525 & 0.7907 & 0.8303 & 0.8612 & 0.6541 & 0.5585 & 0.5804 & 0.6003 & 0.6248 & 0.5025 \\\\\\\\\\n\",\n \"& RA & 0.6847 & 0.7257 & 0.7589 & 0.8089 & 0.5963 & 0.4747 & 0.5001 & 0.5229 & 0.5500 & 0.4289 \\\\\\\\\\n\",\n \"& LLM-KD & \\\\textbf{0.7593} & \\\\textbf{0.8021} & \\\\textbf{0.8321} & \\\\textbf{0.8662} & \\\\textbf{0.6649} & \\\\textbf{0.5846} & \\\\textbf{0.6096} & \\\\textbf{0.6294} & \\\\textbf{0.6578} & \\\\textbf{0.5265} \\\\\\\\\\n\",\n \"& Imprv. & 0.90\\\\% & 1.44\\\\% & 0.22\\\\% & 0.58\\\\% & 1.65\\\\% & 4.67\\\\% & 5.02\\\\% & 4.84\\\\% & 5.29\\\\% & 4.79\\\\% \\\\\\\\\\\\midrule\\n\",\n \"\\\\multirow{4}{*}{E5} & Vanilla & 0.7357 & 0.7712 & 0.8035 & 0.8412 & 0.6370 & 0.5552 & 0.5766 & 0.5974 & 0.6238 & 0.5014 \\\\\\\\\\n\",\n \"& RA & 0.6879 & 0.7261 & 0.7589 & 0.8203 & 0.6037 & 0.4875 & 0.5142 & 0.5378 & 0.5674 & 0.4374 \\\\\\\\\\n\",\n \"& LLM-KD & \\\\textbf{0.7530} & \\\\textbf{0.7925} & \\\\textbf{0.8217} & \\\\textbf{0.8640} & \\\\textbf{0.6566} & \\\\textbf{0.5828} & \\\\textbf{0.6085} & \\\\textbf{0.6306} & \\\\textbf{0.6561} & \\\\textbf{0.5249} \\\\\\\\\\n\",\n \"& Imprv. & 2.35\\\\% & 2.76\\\\% & 2.27\\\\% & 2.71\\\\% & 3.08\\\\% & 4.98\\\\% & 5.53\\\\% & 5.55\\\\% & 5.18\\\\% & 4.70\\\\% \\\\\\\\\\\\midrule\\n\",\n \"\\\\multirow{4}{*}{SimCSE} & Vanilla & 0.5905 & 0.6356 & 0.6783 & 0.7416 & 0.5061 & 0.3849 & 0.4113 & 0.4364 & 0.4662 & 0.3482 \\\\\\\\\\n\",\n \"& RA & 0.5896 & 0.6442 & 0.6906 & 0.7530 & 0.5323 & 0.3897 & 0.4215 & 0.4451 & 0.4817 & 0.3475 \\\\\\\\\\n\",\n \"& LLM-KD & \\\\textbf{0.6479} & \\\\textbf{0.6888} & \\\\textbf{0.7320} & \\\\textbf{0.7916} & \\\\textbf{0.5575} & \\\\textbf{0.5459} & \\\\textbf{0.5730} & \\\\textbf{0.5981} & \\\\textbf{0.6322} & \\\\textbf{0.4869} \\\\\\\\\\n\",\n \"& Imprv. & 9.72\\\\% & 6.92\\\\% & 5.99\\\\% & 5.13\\\\% & 4.73\\\\% & 40.09\\\\% & 35.93\\\\% & 34.37\\\\% & 31.24\\\\% & 39.84\\\\% \\\\\\\\\\\\midrule\\n\",\n \"\\\\multirow{4}{*}{sbert\\\\_p} & Vanilla & 0.6037 & 0.6465 & 0.6911 & 0.7571 & 0.5338 & 0.4919 & 0.5197 & 0.5457 & 0.5769 & 0.4414 \\\\\\\\\\n\",\n \"& RA & 0.6447 & 0.6947 & 0.7407 & 0.7966 & 0.5727 & 0.4509 & 0.4823 & 0.5088 & 0.5440 & 0.4002 \\\\\\\\\\n\",\n \"& LLM-KD & \\\\textbf{0.6793} & \\\\textbf{0.7243} & \\\\textbf{0.7775} & \\\\textbf{0.8153} & \\\\textbf{0.5825} & \\\\textbf{0.5500} & \\\\textbf{0.5761} & \\\\textbf{0.6009} & \\\\textbf{0.6344} & \\\\textbf{0.4902} \\\\\\\\\\n\",\n \"& Imprv. & 5.37\\\\% & 4.26\\\\% & 4.97\\\\% & 2.34\\\\% & 1.71\\\\% & 11.80\\\\% & 10.85\\\\% & 10.12\\\\% & 9.96\\\\% & 11.06\\\\% \\\\\\\\\\\\midrule\\n\",\n \"\\\\multirow{4}{*}{NLI} & Vanilla & 0.3558 & 0.3935 & 0.4327 & 0.4959 & 0.3066 & 0.2085 & 0.2259 & 0.2440 & 0.2678 & 0.1871 \\\\\\\\\\n\",\n \"& RA & 0.6160 & 0.6592 & 0.6938 & 0.7439 & 0.5534 & 0.4411 & 0.4695 & 0.4951 & 0.5316 & 0.3994 \\\\\\\\\\n\",\n \"& LLM-KD & \\\\textbf{0.7056} & \\\\textbf{0.7548} & \\\\textbf{0.7934} & \\\\textbf{0.8430} & \\\\textbf{0.6151} & \\\\textbf{0.5613} & \\\\textbf{0.5874} & \\\\textbf{0.6084} & \\\\textbf{0.6425} & \\\\textbf{0.5042} \\\\\\\\\\n\",\n \"& Imprv. & 14.54\\\\% & 14.50\\\\% & 14.35\\\\% & 13.33\\\\% & 11.14\\\\% & 27.23\\\\% & 25.10\\\\% & 22.89\\\\% & 20.87\\\\% & 26.23\\\\% \\\\\\\\\\\\midrule\\n\",\n \"\\\\multirow{4}{*}{BGE} & Vanilla & 0.7266 & 0.7698 & 0.7994 & 0.8312 & 0.6339 & 0.5294 & 0.5543 & 0.5749 & 0.6023 & 0.4735 \\\\\\\\\\n\",\n \"& RA & 0.7093 & 0.7425 & 0.7743 & 0.8248 & 0.6154 & 0.4816 & 0.5074 & 0.5291 & 0.5581 & 0.4319 \\\\\\\\\\n\",\n \"& LLM-KD & \\\\textbf{0.7675} & \\\\textbf{0.8089} & \\\\textbf{0.8380} & \\\\textbf{0.8703} & \\\\textbf{0.6636} & \\\\textbf{0.5812} & \\\\textbf{0.6047} & \\\\textbf{0.6269} & \\\\textbf{0.6587} & \\\\textbf{0.5225} \\\\\\\\\\n\",\n \"& Imprv. & 5.63\\\\% & 5.08\\\\% & 4.83\\\\% & 4.70\\\\% & 4.69\\\\% & 9.79\\\\% & 9.10\\\\% & 9.04\\\\% & 9.37\\\\% & 10.35\\\\% \\\\\\\\\\\\midrule \\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\vspace{-10pt}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table}[t]\\n\",\n \" \\\\centering \\n\",\n \" \\\\caption{{\\\\small Experiments on different types of synthesized (non-novel) ideas with BGE backbone in NLP dataset. }}\\\\label{table:group}\\n\",\n \" \\\\fontsize{8}{6}\\\\selectfont\\n\",\n \"\\\\begin{tabular}{|c|c|c|c|c|}\\\\midrule \\n\",\n \"Type & Metric & Vanilla & LLM KD & Imprv. \\\\\\\\\\\\midrule \\n\",\n \"\\\\multirow{3}{*}{\\\\begin{tabular}[c]{@{}c@{}}Rephrased\\\\\\\\ idea\\\\end{tabular}} & Acc@5 & 0.9815 & 0.9887 & 0.74\\\\% \\\\\\\\\\n\",\n \"& Acc@20 & 0.9899 & 0.9952 & 0.53\\\\% \\\\\\\\\\n\",\n \"& MAP & 0.9667 & 0.9767 & 1.04\\\\% \\\\\\\\\\\\midrule\\n\",\n \"\\\\multirow{3}{*}{\\\\begin{tabular}[c]{@{}c@{}}Partial\\\\\\\\ idea\\\\end{tabular}} & Acc@5 & 0.9296 & 0.9554 & 2.77\\\\% \\\\\\\\\\n\",\n \"& Acc@20 & 0.9618 & 0.9787 & 1.76\\\\% \\\\\\\\\\n\",\n \"& MAP & 0.8911 & 0.9230 & 3.58\\\\% \\\\\\\\\\\\midrule\\n\",\n \"\\\\multirow{3}{*}{\\\\begin{tabular}[c]{@{}c@{}}Incremental \\\\\\\\ idea\\\\end{tabular}} & Acc@5 & 0.3404 & 0.4079 & 19.82\\\\% \\\\\\\\\\n\",\n \"& Acc@20 & 0.3970 & 0.4672 & 17.67\\\\% \\\\\\\\\\n\",\n \"& MAP & 0.2715 & 0.3329 & 22.61\\\\% \\\\\\\\\\\\midrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\vspace{-10pt}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table*}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\fontsize{8}{6}\\\\selectfont\\n\",\n \" \\\\caption{{\\\\small Experiments on different methods in ND task. The proposed RAG-KD method outperforms all baselines, as demonstrated by the percentage gains. }}\\\\label{table:main2}\\n\",\n \" \\\\vspace{-10pt}\\n\",\n \"\\\\begin{tabular}{|c|cccc|cccc|}\\\\midrule\\n\",\n \"Dataset & \\\\multicolumn{4}{|c|}{Marketing} & \\\\multicolumn{4}{|c|}{NLP} \\\\\\\\\\\\midrule\\n\",\n \"Baseline & Accuacy & Precision & Recall & F1 & Accuacy & Precision & Recall & F1 \\\\\\\\\\\\midrule\\n\",\n \"CD & 0.6005 & 0.6395 & 0.6005 & 0.5797 & 0.5167 & 0.5406 & 0.5167 & 0.4430 \\\\\\\\\\n\",\n \"URPC & 0.5500 & 0.5565 & 0.5500 & 0.5366 & 0.4800 & 0.3698 & 0.4800 & 0.3404 \\\\\\\\\\n\",\n \"PES & 0.4700 & 0.4577 & 0.4700 & 0.4283 & 0.5800 & 0.5891 & 0.5800 & 0.5690 \\\\\\\\\\n\",\n \"MOOSE & 0.6400 & 0.7604 & 0.6400 & 0.5929 & 0.5900 & 0.6018 & 0.5900 & 0.5778 \\\\\\\\\\n\",\n \"SciMON & 0.4900 & 0.4745 & 0.4900 & 0.3985 & 0.6100 & 0.6244 & 0.6100 & 0.5984 \\\\\\\\\\\\midrule\\n\",\n \"RAG-Vanilla & 0.7292 & 0.7664 & 0.7292 & 0.7180 & 0.6100 &0.63356& 0.61000 & 0.59201 \\\\\\\\\\n\",\n \"RAG-KD & \\\\textbf{0.7453} & \\\\textbf{0.8023} & \\\\textbf{0.7453} & \\\\textbf{0.7344} & \\\\textbf{0.7474} & \\\\textbf{0.8010 } & \\\\textbf{ 0.7474 } & \\\\textbf{0.7349} \\\\\\\\\\\\midrule\\n\",\n \"Improve & 24.11\\\\% & 25.46\\\\% & 24.11\\\\% & 26.69\\\\% & 22.54\\\\%\\t&28.29\\\\% &\\t22.54\\\\%\\t&22.82\\\\% \\\\\\\\\\\\midrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\vspace{-10pt}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{figure}\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=0.85\\\\linewidth]{figures/hyper.png}\\n\",\n \" \\\\caption{Investigate on (a) LLM backbones and (b) retrieval size $K$ for ND tasks .}\\n\",\n \" \\\\label{fig:hyper}\\n\",\n \"\\\\vspace{-10pt}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{figure}[t]\\n\",\n \"\\\\vspace{-20pt}\\n\",\n \"\\\\vspace{-1em}\\n\",\n \" \\\\begin{tcolorbox}[colback=yellow!10, colframe=black!89!white, before skip=5pt, after skip=5pt,\\n\",\n \" title=\\\\textbf{Prompt 1 (b):} Detailed prompt for idea extraction in Marketing domain.,\\n\",\n \" fontupper=\\\\small, % <-- apply \\\\small to the box contents\\n\",\n \" sharp corners,\\n\",\n \" ] % <<<<<< 加在这里\\n\",\n \"\\\\textit{{\\\\color{red} System}: You are a social science expert searching for new research topics to explore. To accomplish this, you need to conduct a comprehensive literature review to understand the current research landscape, including the challenges being addressed, the hypotheses being tested, the methodologies employed, and the specific conditions under which key findings are obtained.\\n\",\n \"\\\\newline\\n\",\n \"{\\\\color{red} User}: In social science research, a hypothesis is typically a clear, specific, and testable statement predicting the relationship between an independent variable and a dependent variable. It often outlines how one variable influences another and to what degree. The hypothesis is central to the research, as it guides the study's aim of proving or disproving this relationship. In many cases, the hypothesis reflects the main conclusion or finding of the research. Keywords or key phrases are essential terms capturing the study's core concepts, variables, and major themes, and should reflect the primary focus areas. They should neither be too specific nor too general, avoiding keyword over-expansion while effectively indexing the research.\\n\",\n \"\\\\newline Example of Research Hypotheses in Social Science: Viewing a visually depicted product that facilitates embodied mental simulation leads to heightened purchase intentions, with perceptual resources for mental simulation attenuating this effect, and for negatively valenced products, it decreases purchase intentions.\\n\",\n \"\\\\newline Your task is to extract the hypothesis from each title and abstract pair of a research paper. \\n\",\n \"\\\\newline Given time constraints, you only need to review the title and abstract of research papers published in top-tier journals. Your task is to extract the hypothesis from each title and abstract pair. The hypothesis should be comprehensive, including key details or side hypotheses if present. If no hypothesis is found, you can extract the findings or conclusions of the contributions of the study as the hypothesis.\\n\",\n \"\\\\newline Below is the target article:\\n\",\n \"\\\\newline Title: {\\\\color{magenta} \\\\{title\\\\}}\\n\",\n \"\\\\newline Abstract: {\\\\color{magenta} \\\\{abstract\\\\}}\\n\",\n \"\\\\newline After reviewing the abstract, please extract the main hypothesis using the following format (only the hypothesis, no additional formatting, no additional text to explain your answer):\\n\",\n \"\\\\newline Hypothesis: [Extracted hypothesis, including details if present, put value to be 'None' if no hypothesis is found]\\\\#\\n\",\n \"}\\n\",\n \"\\\\end{tcolorbox}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{figure*}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\begin{tcolorbox}[colback=yellow!10, colframe=black!89!white, before skip=5pt, after skip=5pt,\\n\",\n \" title=\\\\textbf{Prompt 2 (a):} Detailed prompt for rephrased idea generation by LLMs,\\n\",\n \" fontupper=\\\\small, % <-- apply \\\\small to the box contents\\n\",\n \" sharp corners,\\n\",\n \" ] % <<<<<< 加在这里\\n\",\n \"{{\\\\color{red} System}: You are an expert in scientific writing and language transformation. Your task is to paraphrase scientific statements while ensuring clarity, precision, and natural readability. The meaning of the original sentence must remain unchanged, but the structure and expression should be significantly altered.\\n\",\n \"\\\\newline\\n\",\n \"{\\\\color{red} User}: \\n\",\n \"\\\\newline \\\\#\\\\#\\\\#\\\\#**Task:** Given an input sentence, generate **up to 5 sentences** that retain the original information while allowing for modifications. Each generated sentence should be of the same meaning as the original, but with completely different words and structure.\\n\",\n \"\\\\newline \\\\#\\\\#\\\\#\\\\# **Instructions:** \\n\",\n \"\\\\newline 1. **Change Words to Synonyms** \\n\",\n \"\\\\newline - Replace key terms with scientifically appropriate synonyms while maintaining clarity and rigor. \\n\",\n \"\\\\newline 2. **Modify Sentence Length** \\n\",\n \"\\\\newline - Either **extend or shorten** the sentence while preserving the original meaning. \\n\",\n \"\\\\newline - If extending, add **clarifying details** or **restructure for better readability**. \\n\",\n \"\\\\newline - If shortening, remove redundant words while retaining key scientific information. \\n\",\n \"\\\\newline 3. **Add or Delete Non-Essential Words** \\n\",\n \"\\\\newline - Introduce or remove words **that do not alter** the fundamental meaning. \\n\",\n \"\\\\newline - Improve fluency by modifying transitions or simplifying phrasing. \\n\",\n \"\\\\newline 4. **Alter Sentence Structure** \\n\",\n \"\\\\newline - Rearrange the sentence while maintaining logical flow. \\n\",\n \"\\\\newline - Convert passive to active voice (or vice versa) where appropriate. \\n\",\n \"\\\\newline - Split complex sentences into simpler ones or combine shorter ones for a smoother read. \\n\",\n \"\\\\newline 5. **Ensure Significant Differences from the Original** \\n\",\n \"\\\\newline - The paraphrased versions **must be structurally different** while still preserving the core message. \\n\",\n \"\\\\newline 6. **Output Format:** \\n\",\n \"\\\\newline - Return **only** the elaborated subset sentences, numbered **1 to 5**, with no additional text or explanations. \\n\",\n \"\\\\newline \\\\#\\\\#\\\\# **Example Input \\\\& Output** \\n\",\n \"\\\\newline \\\\#\\\\#\\\\#\\\\# **Example 1: Scientific Context** \\n\",\n \"\\\\newline**Input Sentence:** \\n\",\n \"\\\\newline*\\\"Long-term exposure to polluted air has been linked to an increased risk of developing respiratory illnesses such as asthma and chronic bronchitis.\\\"* \\n\",\n \"\\\\newline**Generated Sentences:** \\n\",\n \"\\\\newline1. Extended periods of contact with contaminated air are associated with a greater likelihood of respiratory conditions, including asthma and bronchitis. \\n\",\n \"\\\\newline2. Prolonged inhalation of polluted air may elevate the probability of experiencing chronic lung diseases. \\n\",\n \"\\\\newline3. Airborne pollutants have been found to contribute to the onset of various respiratory disorders over time. \\n\",\n \"\\\\newline4. Studies suggest that individuals consistently exposed to poor air quality are at a heightened risk of breathing-related health issues. \\n\",\n \"\\\\newline5. The presence of hazardous particles in the atmosphere can gradually impair lung function and lead to chronic respiratory distress. \\n\",\n \"\\\\newline---\\n\",\n \"\\\\newline\\\\#\\\\#\\\\#\\\\# **Example 2: AI \\\\& Technology Context** \\n\",\n \"\\\\newline **Input Sentence:** \\n\",\n \"\\\\newline *\\\"Machine learning algorithms are increasingly being used to detect patterns in large datasets, improving decision-making processes in fields such as healthcare and finance.\\\"* \\n\",\n \"\\\\newline **Generated Sentences:** \\n\",\n \"\\\\newline 1. AI-driven models are enhancing decision-making by identifying trends in vast amounts of data. \\n\",\n \"\\\\newline 2. The application of machine learning techniques is revolutionizing data analysis across multiple industries. \\n\",\n \"\\\\newline 3. Advanced computational models help uncover hidden insights within extensive datasets. \\n\",\n \"\\\\newline 4. The use of artificial intelligence in fields like healthcare and banking is optimizing predictive analytics. \\n\",\n \"\\\\newline 5. Modern machine learning tools enable more efficient data-driven decisions through pattern recognition. \\n\",\n \"\\\\newline ---\\n\",\n \"\\\\newline Now, generate up to {k} paraphrased sentences for the following input: \\n\",\n \"\\\\newline **Input:** {\\\\color{blue} \\\\{idea\\\\}}\\n\",\n \"}\\n\",\n \"\\\\end{tcolorbox}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\begin{tcolorbox}[colback=yellow!10, colframe=black!89!white, before skip=5pt, after skip=5pt,\\n\",\n \" title=\\\\textbf{Prompt 2 (b):} Detailed prompt for partial idea generation by LLMs,\\n\",\n \" fontupper=\\\\small, % <-- apply \\\\small to the box contents\\n\",\n \" sharp corners,\\n\",\n \" ] % <<<<<< 加在这里\\n\",\n \"{{\\\\color{red} System}: You are an expert in scientific writing and language transformation. Your task is to paraphrase scientific statements while ensuring clarity, precision, and natural readability. The meaning of the original sentence must remain unchanged, but the structure and expression should be significantly altered.\\n\",\n \"\\\\newline\\n\",\n \"{\\\\color{red} User}: \\n\",\n \"\\\\newline \\\\#\\\\#\\\\#\\\\#**Task:** Given an input sentence, generate up to k subset sentences that retain part of the original information while allowing for minor modifications. You may add, delete, or replace words as long as the subset sentence conveys a meaningful portion of the original content without including all of it. Each subset sentence should be **elaborated**, providing additional context or explanation while staying true to the original meaning.\\n\",\n \"\\\\newline \\\\#\\\\#\\\\#\\\\# **Instructions:** \\n\",\n \"\\\\newline 1. Extract a meaningful portion of the input sentence. \\n\",\n \"\\\\newline 2. Modify the extracted portion by **adding, deleting, or replacing** words while ensuring clarity and coherence. \\n\",\n \"\\\\newline 3. Elaborate on the extracted information by adding context, explanation, or detail while retaining the core meaning. \\n\",\n \"\\\\newline 4. Each subset sentence should retain **only part of the original information**, not the full meaning. \\n\",\n \"\\\\newline 5. Ensure grammatical correctness and natural phrasing. \\n\",\n \"\\\\newline 6. Avoid directly copying the exact words from the input sentence. \\n\",\n \"\\\\newline 7. Do not introduce entirely new or unrelated information. \\n\",\n \"\\\\newline 8. **Output only the elaborated subset sentences, numbered from 1 to 10, with no additional text or explanations.**\\n\",\n \"\\\\newline --- \\n\",\n \"\\\\newline \\\\#\\\\#\\\\# **Example Input \\\\& Output** \\n\",\n \"\\\\newline \\\\#\\\\#\\\\#\\\\# **Example 1:** \\n\",\n \"\\\\newline **Input:** \\\"The increasing reliance on artificial intelligence in the healthcare industry is transforming patient diagnostics and treatment planning.\\\" \\n\",\n \"\\\\newline \\n\",\n \"\\\\newline **Output:** \\n\",\n \"\\\\newline 1. AI is revolutionizing healthcare by enhancing how doctors diagnose illnesses and design personalized treatments. \\n\",\n \"\\\\newline 2. The healthcare sector is increasingly adopting AI-driven tools to streamline diagnostic processes and improve patient outcomes. \\n\",\n \"\\\\newline 3. Advanced algorithms are now assisting healthcare providers in identifying conditions more accurately and efficiently. \\n\",\n \"\\\\newline 4. Artificial intelligence is not only transforming diagnostics but also reshaping how treatment plans are tailored to individual patients. \\n\",\n \"\\\\newline 5. Medical professionals are leveraging AI systems to analyze patient data and detect abnormalities earlier. \\n\",\n \"\\\\newline --- \\n\",\n \"\\\\newline \\\\#\\\\#\\\\# **Example 2:** \\n\",\n \"\\\\newline*\\\"Long-term exposure to polluted air has been linked to an increased risk of developing respiratory illnesses such as asthma and chronic bronchitis.\\\"* \\n\",\n \"\\\\newline**Generated Sentences:** \\n\",\n \"\\\\newline1. Extended periods of contact with contaminated air are associated with a greater likelihood of respiratory conditions, including asthma and bronchitis. \\n\",\n \"\\\\newline2. Prolonged inhalation of polluted air may elevate the probability of experiencing chronic lung diseases. \\n\",\n \"\\\\newline3. Airborne pollutants have been found to contribute to the onset of various respiratory disorders over time. \\n\",\n \"\\\\newline4. Studies suggest that individuals consistently exposed to poor air quality are at a heightened risk of breathing-related health issues. \\n\",\n \"\\\\newline5. The presence of hazardous particles in the atmosphere can gradually impair lung function and lead to chronic respiratory distress. \\n\",\n \"\\\\newline---\\n\",\n \"\\\\newline\\\\#\\\\#\\\\#\\\\# **Example 2: AI \\\\& Technology Context** \\n\",\n \"\\\\newline **Input:** \\\"Due to climate change, extreme weather events such as hurricanes and heatwaves have become more frequent and intense.\\\" \\n\",\n \"\\\\newline **Output:** \\n\",\n \"\\\\newline1. Rising global temperatures are causing hurricanes to become more powerful and destructive. \\n\",\n \"\\\\newline2. Climate change is intensifying heatwaves, making them last longer and reach higher temperatures. \\n\",\n \"\\\\newline3. Severe weather patterns are now more common due to the warming atmosphere and changing climate conditions. \\n\",\n \"\\\\newline4. The increased occurrence of hurricanes and heatwaves is a direct result of shifts in global weather systems. \\n\",\n \"\\\\newline5. Scientists link the rise in extreme weather events to the ongoing effects of climate change. \\n\",\n \"\\\\newline---\\n\",\n \"\\\\newline Now, generate up to {k} elaborated subset sentences for the following input: \\n\",\n \"\\\\newline **Input:** {\\\\color{blue} \\\\{idea\\\\}}\\n\",\n \"}\\n\",\n \"\\\\end{tcolorbox}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\begin{tcolorbox}[colback=yellow!10, colframe=black!89!white, before skip=5pt, after skip=5pt,\\n\",\n \" title=\\\\textbf{Prompt 2 (c):} Detailed prompt for incremental idea generation by LLMs,\\n\",\n \" fontupper=\\\\small, % <-- apply \\\\small to the box contents\\n\",\n \" sharp corners,\\n\",\n \" ] % <<<<<< 加在这里\\n\",\n \"{{\\\\color{red} System}: You are an expert in scientific writing and language transformation. Your task is to paraphrase scientific statements while ensuring clarity, precision, and natural readability. The meaning of the original sentence must remain unchanged, but the structure and expression should be significantly altered.\\n\",\n \"\\\\newline\\n\",\n \"{\\\\color{red} User}: \\n\",\n \"\\\\newline \\\\#\\\\#\\\\#\\\\#**Task:** Given two input sentences, generate **up to k sentences** that retain the original information with no modifications or elaborations. The generated sentences are LAGRELY different from these two papers in semantic level (e.g., low BERT-based similarity), but bring the key ideas from them. For example, if the paper A has distinct ideas: idea\\\\_A1, idea\\\\_A2, and paper B distinct ideas: idea\\\\_B1, idea\\\\_B2, you can generate the idea by selecting subset of ideas from both these two papers (e.g., ideas idea\\\\_A1 + idea\\\\_B2 -> new abstract). Besides that, you can follow the below rules to help you the generated abstract that seems to be largely distinct to the original papers (e.g., Paper A and Paper B):\\n\",\n \"\\\\newline \\\\#\\\\#\\\\#\\\\# **Instructions:** \\n\",\n \"\\\\newline1. **Rephrase \\\\& Restructure**: The new sentence must **significantly alter** the structure and wording of the selected subsets while **retaining only their core ideas**. \\n\",\n \"\\\\newline2. **Break Down \\\\& Blend**: Instead of copying large portions, **extract fragments** from the subsets and recombine them in a different way. \\n\",\n \"\\\\newline3. **Introduce Metaphors or Analogies**: Use figurative language to convey the original meaning in a more indirect manner. \\n\",\n \"\\\\newline4. **Use Different Sentence Structures**: Experiment with **questions, lists, cause-effect statements, conditionals, or passive voice**. \\n\",\n \"\\\\newline5. **Limit Direct Keywords**: Avoid using the **exact phrasing** from the original subsets-paraphrase where possible.\\n\",\n \"\\\\newline6. **Focus on Implicit Links**: The new sentence **should not clearly resemble either source sentence**, making it harder to trace back to any one subset. \\n\",\n \"\\\\newline7. **Enforce Conceptual Fusion**: The new sentence must not focus too heavily on just one of the selected subsets but should **merge ideas from both** in a way that feels natural and balanced. \\n\",\n \"\\\\newline8. **Ensure Consistency**: Does not create a new perspective or connection between the ideas, presents the ideas together in one sentence, but without forcing a relationship.\\n\",\n \"\\\\newline9. **Output Format:** \\n\",\n \"\\\\newline - Return **only** the elaborated subset sentences, numbered **1 to 5**, with no additional text or explanations. \\n\",\n \"\\\\newline --- \\n\",\n \"\\\\newline \\\\#\\\\#\\\\# **Example Input \\\\& Output** \\n\",\n \"\\\\newline \\\\#\\\\#\\\\#\\\\# **Example 1:** \\n\",\n \"\\\\newline**idea\\\\_A1:** Personalization improves email engagement.\\n\",\n \"\\\\newline**idea\\\\_B2:** Scarcity messaging can damage long-term trust.\\n\",\n \"\\\\newline**Generated Sentence:**\\n\",\n \"\\\\newline1. Tailored content boosts email responses, while limited-time offers may compromise brand credibility.\\n\",\n \"\\\\newline\\n\",\n \"\\\\newline\\\\#\\\\#\\\\#\\\\# **Example 2: NLP Domain**\\n\",\n \"\\\\newline **idea\\\\_A2:** Instruction tuning aligns model outputs with user prompts.\\n\",\n \"\\\\newline**idea\\\\_B1:** Retrieval enhances factual accuracy in text generation.\\n\",\n \"\\\\newline\\n\",\n \"\\\\newline**Generated Sentence:**\\n\",\n \"\\\\newline1. While instruction tuning improves adherence to task instructions, adding retrieval helps ground outputs in reliable information.\\n\",\n \"\\\\newline---\\n\",\n \"\\\\newline Now, generate up to k fused sentences for the following inputs: \\n\",\n \"\\\\newline Sentence A: {\\\\color{blue} Idea 1}\\n\",\n \"\\\\newline Sentence B: {\\\\color{blue} Idea 2}\\n\",\n \"}\\n\",\n \"\\\\end{tcolorbox}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\begin{tcolorbox}[colback=yellow!10, colframe=black!89!white, before skip=5pt, after skip=5pt,\\n\",\n \" title=\\\\textbf{Prompt 3:} Trigger LLMs for novelty detection.,\\n\",\n \" fontupper=\\\\small, % <-- apply \\\\small to the box contents\\n\",\n \" sharp corners,\\n\",\n \" ] % <<<<<< 加在这里\\n\",\n \"{{\\\\color{red} System}: You are given a *\\\"new research idea\\\"* and a list of *\\\"existing research ideas.\\\"* Your task is to compare the new idea **against each existing idea** and assign a **novelty score**.\\n\",\n \"\\\\newline\\n\",\n \"{\\\\color{red} User}: \\\\#\\\\#\\\\# \\\\textbf{Clarified Scoring Criteria:}\\n\",\n \"\\\\newline Use the following **novelty scoring rubric** for each comparison:\\n\",\n \"\\\\newline Scoring Guidelines: \\n\",\n \"\\\\newline 0.0 – No Novelty (Identical / Reworded). The new idea is a direct copy or a rephrased version of an existing idea. Shares the same claims, findings, information or logic. Example: Paraphrasing an existing idea without introducing any change.\\n\",\n \"\\\\newline 0.3 – Low Novelty (Subset of Existing Idea). The new idea is a strict subset of an existing idea. Removes a condition, claim, or component, but otherwise shares the same logic and goal. No new dimension or direction added. Example: Taking just “Claim A” from “Claim A + Claim B”.\\n\",\n \"\\\\newline 0.5 – Moderate Novelty (large partial overlap). The new idea shares a substantial portion (around 50\\\\%) of the ideas or claims with an existing idea. It introduces some new elements, such as a different combination of claims or modified emphasis, but it’s not entirely new. It’s not a subset or superset, but has a large information intersection. Example: “Claim A + Claim C” compared to existing “Claim A + Claim B”.\\n\",\n \"\\\\newline 0.7 – High Novelty (small partial overlap). The new idea has minor similarities (e.g., method or framing), but differs in research focus, target, or core claim. It applies known ideas in a new context or formulates a distinct question, showing clear divergence from existing ideas. Example: Using a social theory from marketing in the context of education.\\n\",\n \"\\\\newline 1.0 – Very High Novelty (Distinct Research Direction). The idea is entirely distinct in structure, claim, scope, and research objective. Only minor thematic or terminological similarity, if any. Example: Proposing a brand new framework, theory, or dataset with no precedent in existing ideas.\\n\",\n \"\\\\newline \\\\textbf{\\\\#\\\\#\\\\# Evaluation Instructions:}\\n\",\n \"\\\\newline For each comparison between the new idea and an existing idea:\\n\",\n \"\\\\newline 1. Assess Overlap. Examine the conceptual and structural overlap between the new idea and the existing idea.\\n\",\n \"\\\\newline 2. Assign a Novelty Score. Use the Novelty Scoring Rubric to assign a score between 0.0 and 1.0, based on the degree of similarity or difference.\\n\",\n \"\\\\newline 3. Repeat for All Comparisons. Perform this scoring for each existing idea in the set.\\n\",\n \"\\\\newline \\\\#\\\\#\\\\# \\\\textbf{Output Format:} [List of scores like: [0.3, 0.5, 0.3, 0.7, 1.0]]\\n\",\n \"\\\\newline Now, compare the given research idea with each of the existing ideas. For each comparison, assign a novelty score using the rubric above and no need to justify the score. Please return only a Python-style list of the novelty scores for each comparison and the final decision, no further explanation. \\n\",\n \"\\\\newline Given Idea: {\\\\color{magenta}{(QUERY) The investigation discovers that the effectiveness of online ads is influenced by the volume and placement of ads, and a data-driven strategy to manage ad exposure could potentially increase campaign success by 15\\\\%.}}\\n\",\n \"\\\\newline {\\\\color{blue} Existing Ideas:}\\n\",\n \"\\\\newline {\\\\color{blue} 1. The study finds that varying degrees of wearout and weariness exist among consumers in response to online ad volume and placement, with an appropriate \\\"profiling and capping\\\" strategy potentially improving ad deployment effectiveness by 15\\\\%. }\\n\",\n \"\\\\newline {\\\\color{blue} ... }\\n\",\n \"\\\\newline {\\\\color{blue} K. The study finds that display advertisements have a low direct effect on purchase conversion but stimulate subsequent visits through other advertisement formats, and the commonly used measure of conversion rate is biased in favor of search advertisements, underestimating the conversion effect of display advertisements. }\\n\",\n \"}\\n\",\n \"\\\\end{tcolorbox}\\n\",\n \"\\\\end{figure*}\\n\"\n ]\n]","isConversational":false},"fulltext":{"kind":"string","value":"% This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.\n\\pdfoutput=1\n% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.\n\n\\documentclass[11pt]{article}\n\n% Change \"review\" to \"final\" to generate the final (sometimes called camera-ready) version.\n% Change to \"preprint\" to generate a non-anonymous version with page numbers.\n\\usepackage[preprint]{acl}\n\n% Standard package includes\n\\usepackage{times}\n\\usepackage{latexsym}\n\n% For proper rendering and hyphenation of words containing Latin characters (including in bib files)\n\\usepackage[T1]{fontenc}\n% For Vietnamese characters\n% \\usepackage[T5]{fontenc}\n% See https://www.latex-project.org/help/documentation/encguide.pdf for other character sets\n\n% This assumes your files are encoded as UTF8\n\\usepackage[utf8]{inputenc}\n\n% This is not strictly necessary, and may be commented out,\n% but it will improve the layout of the manuscript,\n% and will typically save some space.\n\\usepackage{microtype}\n\n% This is also not strictly necessary, and may be commented out.\n% However, it will improve the aesthetics of text in\n% the typewriter font.\n\\usepackage{inconsolata}\n\n%Including images in your LaTeX document requires adding\n%additional package(s)\n\\usepackage{graphicx}\n\\usepackage{multirow}\n\\usepackage{multicol}\n\\usepackage{booktabs}\n\\usepackage{tabularx}\n\\usepackage{amsmath}\n\\usepackage{amssymb}\n\\usepackage{color}\n\\usepackage{multicol} % 引入宏包\n\\usepackage{enumitem} % 放在导言区\n\\usepackage{float} % 这行放在导言区\n\n\n% for prompt\n\\usepackage[most]{tcolorbox}\n\\usepackage{xcolor}\n\\newtcbox{\\myshadowbox}[1][]{colframe=black!100!white, colback=yellow!10, boxrule=1pt, arc=4mm, auto outer arc, drop shadow, #1}\n% If the title and author information does not fit in the area allocated, uncomment the following\n%\n%\\setlength\\titlebox{<dim>}\n%\n% and set <dim> to something 5cm or larger.\n\n\\title{Harnessing Large Language Models for Scientific Novelty Detection}\n\n% Author information can be set in various styles:\n% For several authors from the same institution:\n% \\author{Author 1 \\and ... \\and Author n \\\\\n% Address line \\\\ ... \\\\ Address line}\n% if the names do not fit well on one line use\n% Author 1 \\\\ {\\bf Author 2} \\\\ ... \\\\ {\\bf Author n} \\\\\n% For authors from different institutions:\n% \\author{Author 1 \\\\ Address line \\\\ ... \\\\ Address line\n% \\And ... \\And\n% Author n \\\\ Address line \\\\ ... \\\\ Address line}\n% To start a separate ``row'' of authors use \\AND, as in\n% \\author{Author 1 \\\\ Address line \\\\ ... \\\\ Address line\n% \\AND\n% Author 2 \\\\ Address line \\\\ ... \\\\ Address line \\And\n% Author 3 \\\\ Address line \\\\ ... \\\\ Address line}\n\n% \\author{Yan Liu, Zonglin Yang \\& Sojanya \\\\\n% Nanyang Technological University \\\\\n% Singapore \\\\\n% \\texttt{yan010@e.ntu.edu.sg, zonglin.yang@ntu.edu.sg, sojanya.yang@stud.edu.sg} \\\\\n% \\And\n% Than-Son Nyugen \\& Erik Cambria\\\\\n% A*STAR \\\\\n% Singapore \\\\\n% \\texttt{thanson.nyugen@astar.edu.sg, erik.cambria@ntu.edu.sg} \\\\\n% \\AND\n% Coauthor \\\\\n% Affiliation3 \\\\\n% Address3 \\\\\n% \\texttt{email3}\n% }\n\\author{\n Yan Liu\\textsuperscript{1} \\quad\n Zonglin Yang\\textsuperscript{1} \\quad\n Soujanya Poria\\textsuperscript{2} \\quad\n Thanh-Son Nguyen\\textsuperscript{3} \\quad\n Erik Cambria\\textsuperscript{1}\\thanks{Corresponding author: Erik Cambria.} \\\\\n \\textsuperscript{1}Nanyang Technological University \\\\\n \\textsuperscript{2}Singapore University of Technology and Design \\\\\n \\textsuperscript{3}Agency for Science, Technology and Research (A*STAR) \\\\\n \\texttt{\\{yan010, zonglin.yang, cambria\\}@ntu.edu.sg} \\\\\n \\texttt{sporia@sutd.edu.sg, nguyen\\_thanh\\_son@ihpc.a-star.edu.sg} \\\\\n}\n\n% \\texttt{sporia@sutd.edu.sg, nguyen\\_thanh\\_son@ihpc.a-star.edu.sg}\n% }\n\n\\begin{document}\n\\maketitle\n\\begin{abstract}\n\n\nIn an era of exponential scientific growth, identifying novel research ideas is crucial and challenging in academia. Despite potential, the lack of an appropriate benchmark dataset hinders the research of novelty detection. More importantly, simply adopting existing NLP technologies, e.g., retrieving and then cross-checking, is not a one-size-fits-all solution due to the gap between textual similarity and idea conception. In this paper, we propose to harness large language models (LLMs) for scientific novelty detection (ND), associated with two new datasets in marketing and NLP domains. To construct the considerate datasets for ND, we propose to extract closure sets of papers based on their relationship, and then summarize their main ideas based on LLMs. To capture idea conception, we propose to train a lightweight retriever by distilling the idea-level knowledge from LLMs to align ideas with similar conception, enabling efficient and accurate idea retrieval for LLM novelty detection. Experiments show our method consistently outperforms others on the proposed benchmark datasets for idea retrieval and ND tasks. Codes and data are available at \\url{https://anonymous.4open.science/r/NoveltyDetection-10FB/}.\n\n% 1. Why call it a benchmark rather than a dataset?\n% 2. Need to focus more on novelty detection rather than idea generation.\n\n\n\\end{abstract}\n\\section{Introduction}\n\n\nIn the rapidly evolving landscape of scientific research, the ability to identify novel and underexplored ideas is critical for driving meaningful advancements. This challenge is particularly pronounced in specialized fields such as macadamia research, where the exponential growth of academic literature makes it difficult to discern truly original contributions \\cite{zhao2025review}. In addition, despite the exponential growth in scientific and technological outputs, recent studies reveal a paradoxical decline in the novelty and disruptiveness of published papers and patents \\cite{park2023papers}. Therefore, scientific novelty detection (ND) holds significant potential for accelerating innovation, but the absence of tailored benchmark datasets and underexplored methodologies limits progress in this area. \n\nTo construct ND-tailored benchmark datasets, we propose to crawl a paper corpus with topological \\textit{closure} in topological and \\textit{compactness} for ND. Specifically, the closure property indicates the completeness of the paper corpus for accurate ND, e.g., missing related papers make it possible to misclassify a non-novel idea into a novel one. To this end, we select a subset of papers as seed papers, based on which we extract their references into the corpus. Therefore, all the relevant papers included in the corpus become a closure set for these seed papers. To achieve the compactness for ND tasks, we elicit large language models (LLMs) to generate structured summaries of each paper’s core contributions, hypotheses, and methodologies, making it easy for these datasets to be utilized for ND tasks. We notice some datasets and benchmarks (e.g., DiscoveryBench~\\cite{majumder2024discoverybench}) focus on content duplication, which is somewhat similar to ours. However, these datasets are either not closed-domain corpora or fail to concisely represent research ideas (e.g., the whole papers), limiting further exploration in this area.\n\n\n\n\n\\begin{figure}\n \\centering\n \\includegraphics[width=\\linewidth]{figures/EMNLP_F1.png}\n \\caption{An illustrative example about the gap between textual similarity and idea conception. The LLM-paraphrased idea aligns conceptually with the target (green ticked) but shows lower textual similarity than distinct ideas retrieved by existing retrievers.}\n \\label{fig:intro}\n \\vspace{-20pt}\n\\end{figure}\n\n\nCurrent ND methods predominantly rely on human expert assessments or heuristic measurements, which are resource-intensive and prone to biases arising from incomplete knowledge and the subjectivity of heuristic rule design ~\\cite{jeon2023measuring,hofstra2020diversity,wang2017bias}. With the recent remarkable capabilities and rapid development of LLMs, it is intuitive to utilize their extensive knowledge, powerful text comprehension, and reasoning abilities \\cite{zhao2023survey} to detect the novelty of research ideas. Intuitively, the LLM can easily identify its novelty if there is no similar idea in the corpus by cross-checking. Still, it is intractable to cross-check with all ideas in a large-scale corpus due to efficiency considerations. Therefore, the existing retrieval-augmented generation (RAG) strategy \\cite{gao2023retrieval} is a promising way, i.e., to retrieve relevant ideas and then cross-check by LLMs, for both effective and efficient ND. However, simply utilizing RAG is not a one-size-fits-all solution for ND, leaving two main challenges for ND. Firstly, \\textbf{\\textit{the idea-level (conceptual) similarity is not well captured by existing retrievers}} compared to textual level similarity. \\autoref{fig:intro} shows the textual level similarity of the target idea with respect to distinct ideas and synthesized ideas, where the synthesized idea is the paraphrase of the target idea by LLMs. Although the synthesized idea shares a similar idea conception to the target idea, it shares lower textual similarity compared to other distinct ideas identified by retrievers (e.g., BGE \\cite{xiao2024c}). This greatly hinders the accuracy of ND by LLMs in the cross-checking phase. Secondly, \\textbf{\\textit{ there are no appropriate entities (e.g., non-novel paper) and relations (e.g., idea-to-idea pairs with similar academic ideas) to bridge between textual similarity and idea conception for retrievers}}. For example, the reference relation essentially implies task/tech-related relations rather than idea conception alignment. This leaves the idea-level alignment for the retriever unexplored. \n\nTo address these challenges, we propose an LLM-based knowledge distillation (KD) framework to train an idea retriever, explicitly aligning ideas with similar conception by distilling knowledge from LLMs for ND. Firstly, to alleviate the absence of entities and relations for idea-level alignment, we generate a large-scale corpus of synthesized (non-novel) ideas based on anchor ideas by an LLM, which share overlapping conceptual content despite lower textual similarity. Specifically, to comprehensively model synthesized ideas by LLMs, we cover three types of generated ideas based on the information coverage of an anchor idea, namely rephrased ideas (information equivalence), partial ideas (information reduction), and incremental ideas (information addition). Secondly, we distill knowledge from LLMs into a lightweight retriever. Specifically, we propose fine-tuning a retriever based on idea pairs (anchor–synthesized ideas) to bridge textual similarity and idea conception. Finally, by cross-checking the target ideas with retrieved ideas using a tailored prompt, we can trigger LLMs to effectively and efficiently detect the novelty of a specific research idea. Our main contributions are threefold:\n\\begin{itemize}[leftmargin=*,itemsep=0pt, parsep=0pt]\n \\item We construct a largeND-tailored benchmark datasets for ND by ensuring the closure property and compactness.\n \\item {We propose an LLM-based ND framework that distills idea-level knowledge from LLMs into a lightweight retriever, using synthesized idea pairs to teach it to capture conceptual rather than textual similarity. This enables the framework to bridge the gap between surface-level text similarity and deeper idea-level alignment.}\n \\item We conduct extensive experiments to validate the effectiveness of our framework in both the idea retrieval task and the ND task.\n\\end{itemize}\n\n\\section{Related work}\n\\paragraph{Novelty Detection} Existing novelty detection (ND) methods can be roughly categorized into three classes, namely citation–based methods, content-based methods, and multi-source-based methods. \\textbf{citation network–based} methods aim to measure novelty by analyzing patterns and statistics in a paper’s citation graph \\cite{uzzi2013atypical,lee2015creativity,wang2017bias,trapido2015novelty}. For example, \\citet{uzzi2013atypical} introduced a Z-score metric that quantifies atypical journal-pair combinations in a paper’s reference list. \\textbf{Content-based} methods propose to utilized different levels of granularity of textual data to quantify the novelty of scientific articles, much effort are devoted into exploring keywords \\cite{azoulay2011incentives,mirowski2018future,yan2020impact,ruan2023effect}, entities \\cite{liu2022pandemics,luo2022combination,chen2024exploring}, and sentences \\cite{chen2019automatic,jeon2023measuring,wang2023measuring} via statistical or embedding-based metrics \\cite{ruan2023effect,liu2022pandemics}. \n% For example, \\citet{liu2022pandemics} used BioBERT embeddings to compute cosine distances between biological entity pairs, labeling those with high semantic distance as indicators of novelty.\n \\textbf{Multi-source-based} methods treat citations, entities, or concepts as evolving knowledge graphs and quantify novelty by structural perturbations \\cite{shibayama2021measuring,amplayo2018network,hofstra2020diversity,de2021system}. For example, \\citet{hofstra2020diversity} identified new cross-community links in concept co-occurrence networks as signals of disruptive or boundary-spanning work. However, these methods mainly rely on heuristic measurement strategies, which are resource-intensive and prone to biases arising from incomplete knowledge and the subjectivity of rule design. \n\n\\paragraph{LLMs for Scientific Research} \nDue to the advantages of LLMs, recent studies have focused on generating scientific ideas and hypotheses with their assistance~\\cite{li2024learning,wang2024scimon,yang2023large,lu2024ai,baek2024researchagent,liu2025researchbench}. Most of these methods adopt a generation-then-verification framework, aiming to produce novel ideas for scientific research. For example, SCIMON~\\cite{wang2024scimon} encodes LLM-generated hypotheses using SentenceBERT, retrieves similar papers via vector search, computes cosine similarity scores, and refines the hypothesis until its similarity to existing work falls below a predefined novelty threshold. \\citet{yang2023large} follows a comparable pipeline, integrating a “novelty checker” that iteratively optimizes hypotheses based on their cosine distance from prior literature. However, these novelty verification approaches rely solely on textual similarity measures, which fail to capture the core insights of an idea, leading to inaccurate assessments due to the gap between textual and conceptual similarity.\n\n\n\n\n\\section{Problem Formulation}\n\nSuppose we have a novelty corpus $\\mathcal{G}_N$ collected from real papers with $M$ novel ideas, and a synthesized corpus $\\mathcal{G}_S$ generated by LLMs with $N$ non-novel ideas. Each idea $d \\in \\mathcal{G}_N \\cup \\mathcal{G}_S$ is associated with a textual description. For the novelty corpus $\\mathcal{G}_N$, we assume that its ideas are novel and distinct from each other, i.e., {\\small $\\sum_{j \\neq i,, d_j \\in \\mathcal{G}_N} I(d_i, d_j) = 0$} for each $d_i \\in \\mathcal{G}_N$, where $I(\\cdot, \\cdot)$ denotes the idea detector, such that $I(d_i, d_j) = 1$ if idea $d_i$ and idea $d_j$ are identical or similar, and 0 otherwise. For the synthesized corpus, we assume that its ideas are non-novel as they are generated based on $\\mathcal{G}_N$, i.e., {\\small $\\sum_{d_j \\in \\mathcal{G}_N} I(d_i, d_j) \\ge 1$} for each $d_i \\in \\mathcal{G}_S$.\n\nIn this work, we aim to research two kinds of tasks, namely the research \\textit{\\textbf{idea retrieval}} task and the \\textbf{\\textit{novelty detection}} (ND) task. In the idea retrieval task, given a synthesized idea $d\\in \\mathcal G_S$, our goal is to target the anchor papers from the novelty corpus, which can be formulated into retrieving a ranking list for the synthesized paper to hit the anchor papers. In novelty detection task, our goal is to check novelty of a given idea $d\\sim \\mathcal G_N\\bigcup \\mathcal{G}_S$, which can be novel (i.e., $d \\in \\mathcal G_N$) or non-novel (i.e., $d\\in\\mathcal G_S$). \n\n\\section{Benchmark Dataset and Methodology}\n\n\\begin{figure*}\n \\centering\n \\includegraphics[width=\\linewidth]{figures/EMNLP_F2.png}\n \\caption{The overall architecture of the data construction and methodology. It includes three main components: (1) benchmark dataset construction using topological closure and idea compactness; (2) an LLM-based knowledge distillation framework to train the idea retriever using rephrased, partial, and incremental ideas; and (3) a RAG-based novelty detection strategy that retrieves top-k idea candidates and evaluates novelty scores for decision-making.}\n \\label{fig:arch}\n\\end{figure*}\n\n\nFirst, we introduce two ND-tailored benchmark datasets with topological closure and compactness for ND in Section \\ref{sec:benchmark}. Second, we propose to train an idea retriever by an LLM-based KD framework in Section \\ref{sec:KD}. Finally, we detail an ND strategy equipped with RAG in Section \\ref{sec:ND}. The overall architecture of the data construction and methodology is shown in \\autoref{fig:arch}.\n\n\\subsection{Benchmark datasets for ND}\\label{sec:benchmark}\nTo construct ND-tailored benchmark datasets, we propose to crawl a paper corpus with consideration of the topological \\textit{closure} and \\textit{compactness} for ND.\n\n\nTo ensure the \\textit{closure property} of the collected datasets, we propose selecting a subset of papers in a specific domain as \\textit{seed papers}, based on which we extract their references into the corpus accordingly. Therefore, all the relevant papers included in the corpus form a closure set for these seed papers, i.e., no related paper published prior to the seed papers is excluded from the corpus. Specifically, we propose to extract seed papers from two representative research domains, \n\\begin{itemize}[leftmargin=*,itemsep=0pt, parsep=0pt]\n \\item \\textbf{Marketing Domain.} Due to restrictions of social science publications, we collect 470 research articles from two leading journals in the Marketing field: the \\textit{Journal of Marketing} and the \\textit{Journal of Marketing Research}, spanning 2004 to 2024. \n \\item \\textbf{Natural Language Processing (NLP) domain.} The NLP domain benefits from open-access practices. We systematically collected 3,533 papers from ACL conferences over the past five years to build the NLP dataset.\n\\end{itemize}\nTo collect references of seed papers into a closed corpus, we adopt the Semantic Scholar API \\footnote{\\url{https://www.semanticscholar.org/product/api}}, and collected reference papers for each seed paper, yielding a reference corpus of 12,832 papers in the Marketing domain and 33,911 in the NLP domain. \n\n\\begin{tcolorbox}[breakable, colback=yellow!10, colframe=black!75!white, title={\\small Prompt 2: Brief of synthesized idea generation}]\\small\n\\textit{{\\color{red} System}: You are an expert in scientific writing and language transformation. Your task is to paraphrase scientific statements while ensuring clarity, precision, and natural readability...\n\\newline {\\color{red} User:} {\\color{magenta} Rephrased idea generation:} Given an input sentence, generate up to {k} sentences that retain the original information while allowing for modifications. ...\n**Instructions:** ....\n\\newline {\\color{red} User:} {\\color{magenta} Partial idea generation:} Given an input sentence, generate up to {k} subset sentences that retain part of the original information while allowing for minor modifications. You may add, delete, ... **Instructions:** ....\n\\newline {\\color{red} User:} {\\color{magenta} Partial idea generation:} Given two input sentences, generate up to {k} sentences that retain the original information with no modifications or elaborations. The generated sentences are LAGRELY different from these two papers ... **Instructions:** ....\n}\n\\end{tcolorbox}\n\n\n\\textbf{Idea extraction and summarization}\nTo achieve the {\\textit{compactness property}} of corpora for ND tasks, we elicit LLMs to generate a summarization of each paper’s core contributions, hypotheses, and methodologies, making it easy for these datasets to be utilized for ND tasks. The detailed extract and summary rule can be found in Prompt 1 (a,b) in the Appendix. To ensure the effectiveness of idea extraction, we hire 3 experts (i.e., 2 Ph.D. students and 1 research fellow) to vote for alignment between of idea extraction and the original abstract on 50 papers by different LLM backbones (i.e., GPT-4o-mini, LLaMA3-3.1-8B, and PHI-3-3B). As a result, GPT-4o-mini showed optimal alignment and was chosen for idea extraction and summarization. \n\n\\begin{figure*}[t] % 让 box 跨越双栏\n\\centering\n\\begin{tcolorbox}[colback=yellow!10, colframe=black!89!white, \n before skip=5pt, after skip=5pt,title={\\small\\textbf{Prompt 3:} Brief prompt of triggering LLMs for ND.}]\\small % <<<<<< 加在这里\n\\textit{{\\color{red} System}: You are given a *\"new research idea\"* and a list of *\"existing research ideas.\"* Your task is to compare the new idea **against each existing idea** and assign a **novelty score**.\n\\newline\n{\\color{red} User}: \\#\\#\\# \\textbf{Clarified Scoring Criteria:} Use the following novelty scoring rubric for each comparison. \n\\newline \\textbf{0.0 – No Novelty (Identical / Reworded)}. The new idea is a direct copy or a rephrased version of an existing idea. Shares the same claims, findings, information or logic ...\n\\newline \\textbf{0.3 – Low Novelty (Subset of Existing Idea)}. The new idea is a strict subset of an existing idea. Removes a condition, claim, or component, but otherwise shares the same logic and goal ...\n\\newline \\textbf{0.5 – Moderate Novelty (large partial overlap)}. The new idea shares a substantial portion (around 50\\%) of the ideas or claims with an existing idea. It introduces some new elements, such as ...\n\\newline \\textbf{0.7 – High Novelty (small partial overlap)}. The new idea has minor similarities (e.g., method or framing), but differs in research focus, target, or core claim. It applies known ideas in a new context or formulates a distinct question ...\n\\newline \\textbf{1.0 – Very High Novelty (Distinct Research Direction)}. The idea is entirely distinct in structure, claim, scope, and research objective. Only minor thematic or terminological similarity, if any ...\n\\newline \\textbf{\\#\\#\\# Evaluation Instructions:} For each comparison between the new idea and an existing idea: 1. Assess Overlap... 2. Assign a Novelty Score... 3. Repeat for All Comparisons...\n\\newline \\#\\#\\# \\textbf{Output Format:} [List of scores like: [0.3, 0.5, 0.3, 0.7, 1.0]]. Now, compare the given research idea with each of the existing ideas. For each comparison, assign a novelty score using the rubric above and no need to justify the score...\n\\newline {\\color{magenta} Given Idea: {QUERY.}} {\\color{blue} Existing Ideas: 1. \\{Idea 1\\}, ..., K. \\{Idea K\\}. }}\n\\end{tcolorbox}\n\\vspace{-10pt}\n\\end{figure*}\n\n\\subsection{LLM-based KD for idea retriever}\\label{sec:KD}\n\nIntuitively, LLMs can identify their novelty by depending on whether there is a similar idea in the corpus. However, the large-scale corpus makes it intractable to cross-check with all ideas, leaving it urgent to retrieve the most idea-level similar ideas. To train an idea retriever tailored for idea retrieval tasks, we propose an LLM-based KD framework that explicitly aligns ideas with similar conception through distilling knowledge from LLMs. To alleviate the absence of available entities and relations for idea alignment, we propose triggering LLMs to generate a large-scale corpus of synthesized ideas based on novelty ideas $\\mathcal G_N$, then training a lightweight retriever for idea alignment. Specifically, we cover three types of synthesized ideas based on the information coverage of an anchor idea, i.e., equivalence, reduction, and addition.\n\n\\begin{itemize}[leftmargin=*,itemsep=0pt, parsep=0pt]\n \\item \\textbf{\\textit{Rephrased idea}} is synthesized by rephrasing an idea using different linguistic expressions while preserving the original conceptions. This type of generation reflects \\underline{information equivalence}, as it maintains the same research method, contribution, and hypothesis as the anchor idea.\n \\item \\textbf{\\textit{Partial idea}} is synthesized by extracting a subset of the conception of an idea, such as isolating one specific contribution, methodology, or application domain. This type of generation reflects \\underline{information reduction}, where the synthesized idea represents an incomplete or narrowed version of the anchor idea.\n \\item \\textbf{\\textit{Incremental idea}} is synthesized by extending the anchor idea with additional but closely related components, such as combining it with another idea or a minor extension. This represents \\underline{information addition}, where the synthesized idea builds upon the original but includes additional conceptions in existing ideas.\n\\end{itemize}\n\n\n\nWe organize anchor-synthesized idea pairs into a base set $\\mathcal{F} = \\{(s_i, g_i) \\mid s_i \\in \\mathcal{G}_N\\}$, where $g_i = \\text{LLM}(d_i, \\texttt{Prompt2})$ is generated by LLMs based on different generation strategies (see brief in Prompt 2 and detail in Appendix Prompt 2 (a) (b) (c)).\n\n\nTo distill the knowledge from LLMs, we propose to fine-tune a retriever based on idea pairs \\((s_i, g_i)\\) to bridge between textual similarity and idea conception, where \\(s_i \\in \\mathcal{G}_N\\) is the anchor (novel) idea and \\(g_i \\in \\mathcal{G}_S\\) is the synthesized non-novel idea (i.e., rephrased, partial, or incremental). The core objective is to align the representation space of the retriever with the idea-level similarity determined by the LLM. Specifically, let \\(f_{\\theta}(\\cdot)\\) denote the embedding function of the retriever with initial parameters \\(\\theta\\), which maps a text into a dense vector. We aim to train \\(f_{\\theta}\\) such that the embedding of the synthesized idea \\(g_i\\) is close to its corresponding anchor \\(s_i\\), and far from unrelated novel ideas \\(s_j \\in \\mathcal{G}_N, j \\neq i\\), which can be achieved via a contrastive learning objective:\n\\begin{equation*}\n\\mathcal{L} = \\!\\!\\!\\!\\!\\sum_{(s_i,g_i)\\in \\mathcal{F}} \\!\\!\\!\\!\\!-\\!\\log \\frac{\\exp\\left( \\text{sim}(f_{\\theta}(s_i), f_{\\theta}(g_i)) / \\tau \\right)}{\\sum_{j=1}^{|\\mathcal{G}_N|} \\exp\\left( \\text{sim}(f_{\\theta}(s_j), f_{\\theta}(g_i)) / \\tau \\right)}\n\\end{equation*}\nwhere \\(\\text{sim}(\\cdot, \\cdot)\\) is a similarity function (e.g., cosine similarity), and \\(\\tau\\) is a temperature scaling factor.\n\n% Additionally, we adopt a margin-based triplet loss to further distinguish positive pairs from hard negatives:\n\n% \\begin{equation}\n% \\mathcal{L}_{\\text{triplet}} = \\sum_{i=1}^{|\\mathcal{G}^*|} \\max \\left( 0, \\, m + \\text{sim}(f_{\\theta}(g_i), f_{\\theta}(s_j^{-})) - \\text{sim}(f_{\\theta}(g_i), f_{\\theta}(s_i)) \\right)\n% \\end{equation}\n\n% where \\(s_j^{-}\\) denotes a hard negative sample (i.e., unrelated novel idea), and \\(m\\) is a predefined margin.\n\n% The total loss function for retriever distillation is:\n\n% \\begin{equation}\n% \\mathcal{L}_{\\text{total}} = \\mathcal{L}_{\\text{contrastive}} + \\lambda \\cdot \\mathcal{L}_{\\text{triplet}}\n% \\end{equation}\n\n% where \\(\\lambda\\) is a balancing coefficient between the two components.\nIn summary, the proposed KD framework ensures that the retriever learns to reflect idea-level similarity aligned with LLMs’ knowledge, rather than surface-level textual semantics. The lightweight retriever can be leveraged not only for assisting human-centric novelty evaluation, but also for efficiently retrieving relevant ideas to support downstream LLM-based ND.\n\n\\subsection{RAG-based novelty detection}\\label{sec:ND}\n\nAfter retrieving top-$K$ idea candidates from the corpus using the idea-level retriever, we perform a cross-checking procedure with LLMs to determine the novelty of the target research idea, addressing the core ND task by leveraging the reasoning capabilities and prior knowledge of LLMs. Specifically, given a target idea \\( q \\) and a set of retrieved candidate ideas \\( \\mathcal{C}_q=\\{d_1, d_2, \\ldots, d_K\\} \\subset \\mathcal{G}_N\\) by the idea retriever $f_{\\theta}(\\cdot)$, we design a structured prompt to guide the LLM to compare \\( q \\) against each \\( d_i \\) and output a novelty score compared to $d_i$,\n\\begin{equation*}\n s_q= \\text{LLM}(q,\\mathcal C_q,\\texttt{prompt3})\\in \\mathbb{R}^K\n\\end{equation*}\nwhere novelty scores are defined on 5 novelty levels, namely very high novelty, high novelty, moderate novelty, low novelty, and no novelty, which can be found in prompt 3 (see completed one in Appendix Prompt 3). \n\nInstead of relying on manually designed thresholds, we propose to learn the novelty decision rule directly from data via a supervised decision tree classifier. This approach captures non-linear combinations and interactions among novelty scores from retrieved ideas, enabling more flexible and accurate ND. Given a training dataset $\\mathcal{D} = \\{(s_q, y_q)\\}$, where $y_q \\in \\{\\texttt{Novel}, \\texttt{Non-Novel}\\}$ is the ground truth label ($y_q=\\texttt{Novel}$ if $q\\in\\mathcal G_N$ and $y_q=\\texttt{Non-Novel}$ if $q\\in\\mathcal G_S$), we train a decision tree classifier $DTree(\\cdot)$, therefore the final decision about ND can be formulated by the decision tree classifier $\\hat{y}_q = DTree(s_q)$.\n\n\n\n\n\n\n\n\\section{Experiments}\n\n\n\nIn this section, we aim to validate the effectiveness of the proposed method. Specifically, we conduct extensive experiments in both idea retrieval and the ND task to study the following research questions: \\textbf{RQ1}: Whether the existing retrievers benefit from the proposed LLM-based KD framework in idea retrieval tasks? \\textbf{RQ2}: Whether the proposed method benefits from the idea retriever in ND tasks? \n\\textbf{RQ3}: How do hyperparameters influence the performance of the proposed method?\n\n\n\\subsection{Experimental Setup}\n \\paragraph{Datasets.} We utilized the newly proposed benchmark datasets in both Marketing and NLP domains, described in Section 3.1. Specifically, the \\textbf{\\textit{Marketing}} dataset comprises 470 seed papers and their closure references totaling 12,577 unique papers. The \\textbf{\\textit{NLP}} dataset contains 3,533 seed papers and their associated closure references, totaling 32,239 unique papers. Negative examples (non-novel ideas) were generated from seed papers using GPT-4o-mini with rephrased, partial, and incremental prompts, producing up to 10 synthesized variants per anchor paper. To prevent data leakage, we propose filtering each reference corpus based on the publication date of its corresponding seed paper. An overlap of 255 (Marketing) and 1,672 (NLP) papers between seed and reference sets was identified and removed appropriately. \n\\subsection{Experiments on idea retrieval tasks (RQ1)}\n\n\n\n\n\n\\paragraph{Baseline Methods.} We compared our proposed distilled retriever with several state-of-the-art retrievers:\n \\textbf{\\textit{Vanilla}}: The vanilla version of the retriever backbone without fine-tuning. \\textit{\\textbf{Reference alignment (RA)}}: Fine-tuning the retriever backbone based on anchor-reference alignment. \\textit{\\textbf{LLM-based knowledge distillation retriever (LLM KD)}}: Fine-tuning the retriever backbone based on synthesized ideas generated by LLMs.\n\n\\paragraph{Retriever Backbone.} To comprehensively evaluate the proposed LLM-based KD framework, we adopt 6 well-known retriever backbones for comparison, namely General Text Encoder (GTE) \\cite{li2023towards}, E5 \\cite{wang2022text}, SimCSE \\cite{gao2021simcse}, Sentence-BERT Paraphrase (SBERT\\_p) \\cite{reimers2019sentence}, Natural Language Inference-tuned (NLI) \\cite{conneau2017supervised}, and Baidu General Embedding (BGE) \\cite{xiao2024c}. \n\n\n\\paragraph{Evaluation Protocol, Metrics, and Implementation Details.} We split the seed papers and their corresponding negative examples into train, valid, and test subsets by a ratio of 6:1:3. To evaluate idea retrieval performances of different models, we adopt standard retrieval metrics in IR tasks: Acc@k and MAP, where we choose $k\\in\\{1,5,10,20,50\\}$ empirically. For the retriever fine-tuning, we set the learning rate of $2e^{-5}$, batch size of 16, and a cosine similarity function with temperature scaling. \n\n% Please add the following required packages to your document preamble:\n% \\usepackage{multirow} Retrieval was performed using FAISS for efficient indexing.\n\n\\paragraph{Performance Comparison.} \\autoref{table:main1} presents the comparative results in idea retrieval task. From the experimental results, we obtain the following conclusions: Firstly, LLM-based KD retriever consistently outperforms baseline methods across both domains, which shows the effectiveness of the proposed method. LLM-based KD retriever achieves notable enhancement, with average improvements of 5.40\\% and 15.19\\% when compared to the top-performing baseline method on the Marketing domain and NLP task. Secondly, the variant RA, which is trained by anchor-reference alignment, degrades the Vanilla retriever in most cases, showing that anchor-reference usually excludes idea-level similarity. Specifically, although the anchor papers and their reference papers usually share a similar research question and background, the ideas and novel conception of them are distinctive as the novelty requirement for publication. Thirdly, the proposed method LLM-based KD retriever achieves more improvement on the NLP dataset with more papers compared to Vanilla. This is attributed to the large-scale papers in the corpus making the synthesized idea be less likely to be retrieved based on textual similarity, but it can be effectively retrieved based on idea similarity.\n\n\\renewcommand{\\arraystretch}{1.6}\n\\begin{table*}[t]\n \\centering\n \\fontsize{8}{6}\\selectfont\n \\caption{{\\small Experiments idea retrieval with different embedding backbones. The proposed LLM-KD retriever outperforms RA (Reference Alignment) and Vanilla (baseline without supervision) baselines, demonstrating the effectiveness of idea-level supervision.}}\\label{table:main1}\n \\vspace{-10pt}\n\\begin{tabular}{|c|c|ccccc|ccccc|}\n\\midrule\\multicolumn{2}{|c|}{Domain} & \\multicolumn{5}{|c|}{Marketing} & \\multicolumn{5}{c|}{NLP} \\\\ \\midrule\n\\multicolumn{1}{|l|}{Backbone} & Methods & Acc@5 & Acc@10 & Acc@20 & Acc@50 & MAP & Acc@5 & Acc@10 & Acc@20 & Acc@50 & MAP \\\\\\midrule\n\\multirow{4}{*}{GTE} & Vanilla & 0.7525 & 0.7907 & 0.8303 & 0.8612 & 0.6541 & 0.5585 & 0.5804 & 0.6003 & 0.6248 & 0.5025 \\\\\n& RA & 0.6847 & 0.7257 & 0.7589 & 0.8089 & 0.5963 & 0.4747 & 0.5001 & 0.5229 & 0.5500 & 0.4289 \\\\\n& LLM-KD & \\textbf{0.7593} & \\textbf{0.8021} & \\textbf{0.8321} & \\textbf{0.8662} & \\textbf{0.6649} & \\textbf{0.5846} & \\textbf{0.6096} & \\textbf{0.6294} & \\textbf{0.6578} & \\textbf{0.5265} \\\\\n& Imprv. & 0.90\\% & 1.44\\% & 0.22\\% & 0.58\\% & 1.65\\% & 4.67\\% & 5.02\\% & 4.84\\% & 5.29\\% & 4.79\\% \\\\\\midrule\n\\multirow{4}{*}{E5} & Vanilla & 0.7357 & 0.7712 & 0.8035 & 0.8412 & 0.6370 & 0.5552 & 0.5766 & 0.5974 & 0.6238 & 0.5014 \\\\\n& RA & 0.6879 & 0.7261 & 0.7589 & 0.8203 & 0.6037 & 0.4875 & 0.5142 & 0.5378 & 0.5674 & 0.4374 \\\\\n& LLM-KD & \\textbf{0.7530} & \\textbf{0.7925} & \\textbf{0.8217} & \\textbf{0.8640} & \\textbf{0.6566} & \\textbf{0.5828} & \\textbf{0.6085} & \\textbf{0.6306} & \\textbf{0.6561} & \\textbf{0.5249} \\\\\n& Imprv. & 2.35\\% & 2.76\\% & 2.27\\% & 2.71\\% & 3.08\\% & 4.98\\% & 5.53\\% & 5.55\\% & 5.18\\% & 4.70\\% \\\\\\midrule\n\\multirow{4}{*}{SimCSE} & Vanilla & 0.5905 & 0.6356 & 0.6783 & 0.7416 & 0.5061 & 0.3849 & 0.4113 & 0.4364 & 0.4662 & 0.3482 \\\\\n& RA & 0.5896 & 0.6442 & 0.6906 & 0.7530 & 0.5323 & 0.3897 & 0.4215 & 0.4451 & 0.4817 & 0.3475 \\\\\n& LLM-KD & \\textbf{0.6479} & \\textbf{0.6888} & \\textbf{0.7320} & \\textbf{0.7916} & \\textbf{0.5575} & \\textbf{0.5459} & \\textbf{0.5730} & \\textbf{0.5981} & \\textbf{0.6322} & \\textbf{0.4869} \\\\\n& Imprv. & 9.72\\% & 6.92\\% & 5.99\\% & 5.13\\% & 4.73\\% & 40.09\\% & 35.93\\% & 34.37\\% & 31.24\\% & 39.84\\% \\\\\\midrule\n\\multirow{4}{*}{sbert\\_p} & Vanilla & 0.6037 & 0.6465 & 0.6911 & 0.7571 & 0.5338 & 0.4919 & 0.5197 & 0.5457 & 0.5769 & 0.4414 \\\\\n& RA & 0.6447 & 0.6947 & 0.7407 & 0.7966 & 0.5727 & 0.4509 & 0.4823 & 0.5088 & 0.5440 & 0.4002 \\\\\n& LLM-KD & \\textbf{0.6793} & \\textbf{0.7243} & \\textbf{0.7775} & \\textbf{0.8153} & \\textbf{0.5825} & \\textbf{0.5500} & \\textbf{0.5761} & \\textbf{0.6009} & \\textbf{0.6344} & \\textbf{0.4902} \\\\\n& Imprv. & 5.37\\% & 4.26\\% & 4.97\\% & 2.34\\% & 1.71\\% & 11.80\\% & 10.85\\% & 10.12\\% & 9.96\\% & 11.06\\% \\\\\\midrule\n\\multirow{4}{*}{NLI} & Vanilla & 0.3558 & 0.3935 & 0.4327 & 0.4959 & 0.3066 & 0.2085 & 0.2259 & 0.2440 & 0.2678 & 0.1871 \\\\\n& RA & 0.6160 & 0.6592 & 0.6938 & 0.7439 & 0.5534 & 0.4411 & 0.4695 & 0.4951 & 0.5316 & 0.3994 \\\\\n& LLM-KD & \\textbf{0.7056} & \\textbf{0.7548} & \\textbf{0.7934} & \\textbf{0.8430} & \\textbf{0.6151} & \\textbf{0.5613} & \\textbf{0.5874} & \\textbf{0.6084} & \\textbf{0.6425} & \\textbf{0.5042} \\\\\n& Imprv. & 14.54\\% & 14.50\\% & 14.35\\% & 13.33\\% & 11.14\\% & 27.23\\% & 25.10\\% & 22.89\\% & 20.87\\% & 26.23\\% \\\\\\midrule\n\\multirow{4}{*}{BGE} & Vanilla & 0.7266 & 0.7698 & 0.7994 & 0.8312 & 0.6339 & 0.5294 & 0.5543 & 0.5749 & 0.6023 & 0.4735 \\\\\n& RA & 0.7093 & 0.7425 & 0.7743 & 0.8248 & 0.6154 & 0.4816 & 0.5074 & 0.5291 & 0.5581 & 0.4319 \\\\\n& LLM-KD & \\textbf{0.7675} & \\textbf{0.8089} & \\textbf{0.8380} & \\textbf{0.8703} & \\textbf{0.6636} & \\textbf{0.5812} & \\textbf{0.6047} & \\textbf{0.6269} & \\textbf{0.6587} & \\textbf{0.5225} \\\\\n& Imprv. & 5.63\\% & 5.08\\% & 4.83\\% & 4.70\\% & 4.69\\% & 9.79\\% & 9.10\\% & 9.04\\% & 9.37\\% & 10.35\\% \\\\\\midrule \n\\end{tabular}\n\\vspace{-10pt}\n\\end{table*}\n\n\n\\renewcommand{\\arraystretch}{1.6}\n\\begin{table}[t]\n \\centering \n \\caption{{\\small Experiments on different types of synthesized (non-novel) ideas with BGE backbone in NLP dataset. }}\\label{table:group}\n \\fontsize{8}{6}\\selectfont\n\\begin{tabular}{|c|c|c|c|c|}\\midrule \nType & Metric & Vanilla & LLM KD & Imprv. \\\\\\midrule \n\\multirow{3}{*}{\\begin{tabular}[c]{@{}c@{}}Rephrased\\\\ idea\\end{tabular}} & Acc@5 & 0.9815 & 0.9887 & 0.74\\% \\\\\n& Acc@20 & 0.9899 & 0.9952 & 0.53\\% \\\\\n& MAP & 0.9667 & 0.9767 & 1.04\\% \\\\\\midrule\n\\multirow{3}{*}{\\begin{tabular}[c]{@{}c@{}}Partial\\\\ idea\\end{tabular}} & Acc@5 & 0.9296 & 0.9554 & 2.77\\% \\\\\n& Acc@20 & 0.9618 & 0.9787 & 1.76\\% \\\\\n& MAP & 0.8911 & 0.9230 & 3.58\\% \\\\\\midrule\n\\multirow{3}{*}{\\begin{tabular}[c]{@{}c@{}}Incremental \\\\ idea\\end{tabular}} & Acc@5 & 0.3404 & 0.4079 & 19.82\\% \\\\\n& Acc@20 & 0.3970 & 0.4672 & 17.67\\% \\\\\n& MAP & 0.2715 & 0.3329 & 22.61\\% \\\\\\midrule\n\\end{tabular}\n\\vspace{-10pt}\n\\end{table}\n\n\\renewcommand{\\arraystretch}{1.6}\n\\begin{table*}[t]\n \\centering\n \\fontsize{8}{6}\\selectfont\n \\caption{{\\small Experiments on different methods in ND task. The proposed RAG-KD method outperforms all baselines, as demonstrated by the percentage gains. }}\\label{table:main2}\n \\vspace{-10pt}\n\\begin{tabular}{|c|cccc|cccc|}\\midrule\nDataset & \\multicolumn{4}{|c|}{Marketing} & \\multicolumn{4}{|c|}{NLP} \\\\\\midrule\nBaseline & Accuacy & Precision & Recall & F1 & Accuacy & Precision & Recall & F1 \\\\\\midrule\nCD & 0.6005 & 0.6395 & 0.6005 & 0.5797 & 0.5167 & 0.5406 & 0.5167 & 0.4430 \\\\\nURPC & 0.5500 & 0.5565 & 0.5500 & 0.5366 & 0.4800 & 0.3698 & 0.4800 & 0.3404 \\\\\nPES & 0.4700 & 0.4577 & 0.4700 & 0.4283 & 0.5800 & 0.5891 & 0.5800 & 0.5690 \\\\\nMOOSE & 0.6400 & 0.7604 & 0.6400 & 0.5929 & 0.5900 & 0.6018 & 0.5900 & 0.5778 \\\\\nSciMON & 0.4900 & 0.4745 & 0.4900 & 0.3985 & 0.6100 & 0.6244 & 0.6100 & 0.5984 \\\\\\midrule\nRAG-Vanilla & 0.7292 & 0.7664 & 0.7292 & 0.7180 & 0.6100 &0.63356& 0.61000 & 0.59201 \\\\\nRAG-KD & \\textbf{0.7453} & \\textbf{0.8023} & \\textbf{0.7453} & \\textbf{0.7344} & \\textbf{0.7474} & \\textbf{0.8010 } & \\textbf{ 0.7474 } & \\textbf{0.7349} \\\\\\midrule\nImprove & 24.11\\% & 25.46\\% & 24.11\\% & 26.69\\% & 22.54\\%\t&28.29\\% &\t22.54\\%\t&22.82\\% \\\\\\midrule\n\\end{tabular}\n\\vspace{-10pt}\n\\end{table*}\n\n\\paragraph{Group Analysis on Synthesized Ideas.}\nTo further investigate the effectiveness of the proposed LLM-based knowledge distillation, we analyze its performance across different types of synthesized (non-novel) ideas, including \\textit{Rephrased}, \\textit{Partial}, and \\textit{Incremental} ideas. As shown in Table~\\ref{table:group}, we observe consistent improvements across all types when using the LLM-based KD retriever compared to the vanilla BGE retriever. Most significantly, the KD retriever demonstrates the largest improvement on incremental ideas, which have the lowest textual similarity but share similar ideas with their anchors. This highlights that the LLM-based KD retriever can effectively capture the idea-level similarity. For example, the ``incremental contribution and novelty'' is usually recognized as the main weakness in reviews by experts for submission. \n\n\\subsection{Experiments on idea retrieval tasks (RQ2)}\n\n\\paragraph{Baseline Methods} For ND, we compared the proposed methods with several state-of-the-art baselines and some variants of our method: \\textbf{URPC}: \\citet{uzzi2013atypical} propose measure novelty by identifying \\underline{u}nusually \\underline{r}are journal‐\\underline{p}air \\underline{c}ombinations in a paper’s reference list. \\textbf{PES}: \\citet{liu2022pandemics} detect novelty in COVID-19 research by measuring the \\underline{p}roportion of biological \\underline{e}ntity pairs with high \\underline{s}emantic distance using BioBERT embeddings. \\textbf{CD}: \\citet{shibayama2021measuring} quantify novelty by computing \\underline{c}osine \\underline{d}istances among word embeddings of a paper’s cited references. \\textbf{SCIMON}: \\citet{wang2024scimon} propose an idea generation framework for scientific research. It heuristically judges the novelty based on a threshold of cosine similarity to existing ideas. \\textbf{MOOSE}: \\cite{yang2024moose} propose an idea generation framework for scientific research. It directly triggers LLMs to judge the novelty of a specific idea. \\textbf{RAG-Vanilla/KD}: Our proposed \\underline{RAG}-based ND by LLMs, where the we adopt the \\underline{Vanilla}/LLM-based \\underline{KD} retriever for RAG. \n\n\\paragraph{Evaluation Protocol, Metrics, and Implementation Details.}\nFor efficient evaluation, we randomly select 100 samples from the train (for decision tree training) and test sets (for evaluation) with 1:1 novel and non-novel instances, respectively. As ND is a classification tasks, we adopt accuracy, precision, recall, and F1-score to evaluate binary classification performance. We adopt the LLM-based KD BGE as the RAG retriever due to its effectiveness for idea retrieval, employing deepseek-reasoner as the LLM backbone for the ND task. We retrieve top-$10$ and top-$5$ ideas as candidate ideas of RAG in Marketing and NLP datasets, respectively. \n\n\n\n\n\n\n\\begin{figure}\n \\centering\n \\includegraphics[width=0.85\\linewidth]{figures/hyper.png}\n \\caption{Investigate on (a) LLM backbones and (b) retrieval size $K$ for ND tasks .}\n \\label{fig:hyper}\n\\vspace{-10pt}\n\\end{figure}\n\n\n\\paragraph{Performance comparison} \\autoref{table:main2} presents the comparative results in ND task. From the experimental results, we obtain the following conclusions: Firstly, our method RAG-KD consistently outperforms baseline methods across both domains, which shows the effectiveness of the proposed method. Secondly, RAG-KD outperforms RAG-Vanilla in all cases, showing the importance of accurately retrieving conceptional similar ideas for ND tasks. Thirdly, among baseline methods, MOOSE achieves a competitive performance among these methods, indicating the effectiveness of utilizing LLMs for novelty detection. Several baselines (e.g., SciMON, PES, CD) show huge performances varying with domains, indicating that heuristic rules may be optimal for all scenarios. \n\n\n\\subsection{Hyperparameter analysis (RQ3)}\nIn this subsection, we further conduct experiments to analyze the impact of hyperparameters and the LLM backbone selection for ND tasks.\n\\paragraph{LLM Backbone.}\n \\autoref{fig:hyper} (a) evaluates our method with different backbones, namely Llama-3.1-8B-Instruct, gpt4o-mini, and deepseek-reasoner. Firstly, the LLM-based KD retriever outperforms the Vanilla retriever in most cases, validating the general applicability of our framework. Secondly, the deepseek-reasoner consistently outperforms other LLM backbones. We suggest employing the deepseek-reasoner with an LLM-based KD retriever for real-world implementation for ND tasks.\n\n\\paragraph{Retrieval Size $K$.} \\autoref{fig:hyper} (b) investigates the influence of the number of retrieved ideas \\( K \\), indicating that moderate $K$ (e.g., 5 and 10) contributes to stable and optimal performances of our method. We notice that the large $K$ (e.g., 20) does not promise the optimal performances, which may be attributed to the limited capability of LLM for handling large-scale ideas~\\cite{liu-etal-2024-lost}.\n\n\n\n\n\n\n\n\n\n\\section{Conclusion}\n\nIn this paper, we propose two ND-tailored benchmark datasets, namely Marketing and NLP, for ND by ensuring the closure property and compactness. To extract the knowledge and reasoning capability of LLMs into a retriever, we propose an LLM-based knowledge distillation framework to elicit the retriever to identify potential idea-similar in the corpus, enabling accurate idea retrieval and ND tasks. Finally, we propose a cross-checking procedure with LLMs to determine the novelty of the target research idea.\nExtensive experiments validate the effectiveness of our framework in both the idea retrieval task and the ND task.\n\n\\section{Limitation}\nThe primary constraints of this paper are as follows: \n1) The LLM-generated ideas and novelty scores are not guaranteed to be fully accurate or consistent, especially when the source prompts are subtle or ambiguous. Such noise in pseudo labels may affect the quality of retriever fine-tuning and ND.\n2) Our framework currently models ND as a binary classification task. However, novelty is often subjective and continuous, which may require future extensions to soft or human-in-the-loop evaluations.\n\n\n\n% Accuracy was validated on a sample of 50 papers with GPT-4o. To balance performance and cost, we compared GPT-4o-mini, LLaMA3 (3.1, 8B), and PHI-3 (3B), using ROUGE-L, BLEU-4, and SentenceBERT similarity. GPT-4o-mini showed optimal alignment and was chosen for all subsequent extraction tasks. \n\n\n\n\n% To achieve the compactness for ND tasks, we elicit LLMs large language models (LLMs) to generate structured summaries of each paper’s core contributions, hypotheses, and methodologies, making it easy for these datasets to be utilized for ND tasks. \n\n\n% \\begin{itemize}\n% \\item A curated dataset for research idea generation, comprising 12,000 Marketing and 30,000 Natural Language Processing data points.\n% \\item A Retrieval-Augmented Generation (RAG) approach for evaluating research hypothesis novelty.\n% \\item Experimental validation of our retrieval method's effectiveness in identifying relevant research ideas.\n\n% . Concurrently, Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks, including machine translation and complex text generation. This success has spurred interest in leveraging LLMs for more sophisticated endeavors, particularly in augmenting human scientific discovery through research idea generation.\n\n% The ideation phase is critical in academic progress, as emphasized by Stremersch \\cite{stremersch2024can}, suggesting that generative AI could significantly enhance both the breadth and quality of research ideas. Various approaches utilizing LLMs—such as prompt engineering, domain-specific fine-tuning, and hybrid retrieval-generation methods—have been proposed. However, the effectiveness of these methods fundamentally depends on robust evaluation metrics for novelty and practicality.\n\n% Current evaluation methods predominantly rely on human expert assessments across dimensions like novelty, validity, and potential impact. While valuable, these human-centered approaches have notable limitations: they are resource-intensive, lack scalability, may introduce biases due to incomplete knowledge of existing literature, and can be inconsistent across different experts. To address these issues, recent efforts have investigated the use of LLMs as evaluators under structured assessment guidelines. Early results indicate promising alignment between LLM and human evaluations, yet challenges remain in mitigating hallucinations, handling computational complexity, and ensuring evaluation consistency.\n\n% Scientific novelty represents the originality and uniqueness of research contributions within the existing knowledge framework. It manifests through various channels: the introduction of unprecedented concepts or methodologies, novel cross-disciplinary applications, substantial improvements to established approaches, or critical reexamination of accepted paradigms. Importantly, novelty exists on a continuum rather than as a binary attribute—novel ideas must transcend mere restatement but need not be entirely disconnected from existing knowledge. Indeed, meaningful innovation often emerges from the creative synthesis of established concepts.\n\n% Evaluating novelty presents interconnected challenges. Comprehensive comparison requires assessment against the entire relevant knowledge corpus, an increasingly complex task given the exponential growth of research. Qualitative assessment demands nuanced consideration of both uniqueness and relationship to existing knowledge. Additionally, the rapid idea-generation capabilities of LLMs necessitate efficient evaluation methods capable of processing large volumes while maintaining assessment quality.\n\n% Despite the importance of novelty detection, there is a scarcity of standardized benchmarks in this area. Existing datasets, such as TREC 2002-2004, primarily focus on news articles and may not adequately represent the complexities of research idea generation \\cite{feeney2022novelcraft}. This gap underscores the need for specialized benchmarks tailored to evaluating the novelty of research hypotheses.\n\n% To address this gap, we propose a benchmark for novelty detection in research idea generation. Our approach involves crawling a comprehensive corpus of research papers and generating hypotheses based on their abstracts. We then employ a knowledge graph-based method for novelty evaluation, which captures the nuanced relationships between concepts and facilitates a more accurate assessment of originality.\n\n% Our primary contributions are as follows:\n% \\begin{itemize}\n% \\item A curated dataset for research idea generation, comprising 12,000 Marketing and 30,000 Natural Language Processing data points.\n% \\item A Retrieval-Augmented Generation (RAG) approach for evaluating research hypothesis novelty.\n% \\item Experimental validation of our retrieval method's effectiveness in identifying relevant research ideas.\n% \\end{itemize}\n\n% The remainder of this paper details our methodology, demonstrates its effectiveness through comprehensive experiments, and discusses implications for future research in automated idea generation and evaluation.\n\n% \\bibliographystyle{plain}\n% \\bibliography{references}\n% \\input{latex/1_introduction}\n% \\input{latex/2_Defining_Novelty}\n% \\input{latex/2_related_work}\n% \\input{latex/3_dataset}\n% \\input{latex/4_method}\n% \\input{latex/5_experiment}\n% \\input{latex/6_conclusion_future_limitation}\n\n\n\n\n% Bibliography entries for the entire Anthology, followed by custom entries\n%\\bibliography{anthology,custom}\n% Custom bibliography entries only\n\\bibliography{main}\n\\newpage\n\\appendix\n% \\clearpage\n\\section{Appendix}\\label{sec:appendix}\n\\begin{figure}[t]\n\\vspace{-20pt}\n\\vspace{-1em}\n \\begin{tcolorbox}[colback=yellow!10, colframe=black!89!white, before skip=5pt, after skip=5pt,\n title=\\textbf{Prompt 1 (b):} Detailed prompt for idea extraction in Marketing domain.,\n fontupper=\\small, % <-- apply \\small to the box contents\n sharp corners,\n ] % <<<<<< 加在这里\n\\textit{{\\color{red} System}: You are a social science expert searching for new research topics to explore. To accomplish this, you need to conduct a comprehensive literature review to understand the current research landscape, including the challenges being addressed, the hypotheses being tested, the methodologies employed, and the specific conditions under which key findings are obtained.\n\\newline\n{\\color{red} User}: In social science research, a hypothesis is typically a clear, specific, and testable statement predicting the relationship between an independent variable and a dependent variable. It often outlines how one variable influences another and to what degree. The hypothesis is central to the research, as it guides the study's aim of proving or disproving this relationship. In many cases, the hypothesis reflects the main conclusion or finding of the research. Keywords or key phrases are essential terms capturing the study's core concepts, variables, and major themes, and should reflect the primary focus areas. They should neither be too specific nor too general, avoiding keyword over-expansion while effectively indexing the research.\n\\newline Example of Research Hypotheses in Social Science: Viewing a visually depicted product that facilitates embodied mental simulation leads to heightened purchase intentions, with perceptual resources for mental simulation attenuating this effect, and for negatively valenced products, it decreases purchase intentions.\n\\newline Your task is to extract the hypothesis from each title and abstract pair of a research paper. \n\\newline Given time constraints, you only need to review the title and abstract of research papers published in top-tier journals. Your task is to extract the hypothesis from each title and abstract pair. The hypothesis should be comprehensive, including key details or side hypotheses if present. If no hypothesis is found, you can extract the findings or conclusions of the contributions of the study as the hypothesis.\n\\newline Below is the target article:\n\\newline Title: {\\color{magenta} \\{title\\}}\n\\newline Abstract: {\\color{magenta} \\{abstract\\}}\n\\newline After reviewing the abstract, please extract the main hypothesis using the following format (only the hypothesis, no additional formatting, no additional text to explain your answer):\n\\newline Hypothesis: [Extracted hypothesis, including details if present, put value to be 'None' if no hypothesis is found]\\#\n}\n\\end{tcolorbox}\n\\end{figure}\n\n% \\begin{figure*}[t]\n\\vspace{43pt}\n % \\centering\n \\begin{tcolorbox}[colback=yellow!10, colframe=black!89!white, before skip=5pt, after skip=5pt,\n title=\\textbf{Prompt 1 (a):} Detailed prompt for idea extraction in NLP domain.,\n fontupper=\\small, % <-- apply \\small to the box contents\n sharp corners,\n ] % <<<<<< 加在这里\n{{\\color{red} System}: You are a computer science expert searching for new research ideas. To accomplish this, you need to conduct a comprehensive literature review to understand the current research landscape, including the research tasks, the methods used on the tasks, and the specific conditions under which key findings are obtained.\n\\newline\n{\\color{red} User}: A research hypothesis in a computer science paper is a testable statement or prediction about a specific phenomenon, system, or algorithm that the research aims to investigate or validate. It serves as the foundation of the study, guiding the research design, experiments, and analysis. The hypothesis should be clearly articulated and typically stems from prior knowledge, gaps in existing literature, or a novel idea the authors aim to explore. A good hypothesis should be specific, testable, context-dependent, and relevant to the research problem. When extracting the hypothesis, ensure it begins directly with the subject of the statement (e.g., \"Optimistic posterior sampling algorithm for reinforcement learning (OPSRL)...\") without introductory phrases such as \"The proposed\" or \"We propose.\" Focus on presenting the hypothesis succinctly and directly. Examples of Research Hypotheses in Computer Science: Algorithm Development: \"METHODS will perform better in terms of computational efficiency and accuracy compared to existing algorithms for large-scale data sorting.\" Machine Learning: \"Incorpusting domain-specific embeddings in the neural network architecture will significantly improve its performance on task X.\"\n\\newline Your task is to extract the hypothesis from each title and abstract pair of a research paper. If you cannot find any hypothesis in the abstract, you can extract the findings or conclusions of the contributions of the study as the hypothesis. If the input abstract is not meaningful, you can output 'None'.\n\\newline Below is the target article:\n\\newline Title: {\\color{magenta} \\{title\\}}\n\\newline Abstract: {\\color{magenta} \\{abstract\\}}\n\\newline After reviewing the abstract, please extract the hypothesis using the following format (only the hypothesis, no additional formatting, no additional text to explain your answer):\n\\newline Hypothesis: [Extracted hypothesis, including details if present, put value to be 'None' if no hypothesis is found]\\#\n}\n\\end{tcolorbox}\n% \\end{figure*}\n\n\n\n\\begin{figure*}[t]\n \\centering\n \\begin{tcolorbox}[colback=yellow!10, colframe=black!89!white, before skip=5pt, after skip=5pt,\n title=\\textbf{Prompt 2 (a):} Detailed prompt for rephrased idea generation by LLMs,\n fontupper=\\small, % <-- apply \\small to the box contents\n sharp corners,\n ] % <<<<<< 加在这里\n{{\\color{red} System}: You are an expert in scientific writing and language transformation. Your task is to paraphrase scientific statements while ensuring clarity, precision, and natural readability. The meaning of the original sentence must remain unchanged, but the structure and expression should be significantly altered.\n\\newline\n{\\color{red} User}: \n\\newline \\#\\#\\#\\#**Task:** Given an input sentence, generate **up to 5 sentences** that retain the original information while allowing for modifications. Each generated sentence should be of the same meaning as the original, but with completely different words and structure.\n\\newline \\#\\#\\#\\# **Instructions:** \n\\newline 1. **Change Words to Synonyms** \n\\newline - Replace key terms with scientifically appropriate synonyms while maintaining clarity and rigor. \n\\newline 2. **Modify Sentence Length** \n\\newline - Either **extend or shorten** the sentence while preserving the original meaning. \n\\newline - If extending, add **clarifying details** or **restructure for better readability**. \n\\newline - If shortening, remove redundant words while retaining key scientific information. \n\\newline 3. **Add or Delete Non-Essential Words** \n\\newline - Introduce or remove words **that do not alter** the fundamental meaning. \n\\newline - Improve fluency by modifying transitions or simplifying phrasing. \n\\newline 4. **Alter Sentence Structure** \n\\newline - Rearrange the sentence while maintaining logical flow. \n\\newline - Convert passive to active voice (or vice versa) where appropriate. \n\\newline - Split complex sentences into simpler ones or combine shorter ones for a smoother read. \n\\newline 5. **Ensure Significant Differences from the Original** \n\\newline - The paraphrased versions **must be structurally different** while still preserving the core message. \n\\newline 6. **Output Format:** \n\\newline - Return **only** the elaborated subset sentences, numbered **1 to 5**, with no additional text or explanations. \n\\newline \\#\\#\\# **Example Input \\& Output** \n\\newline \\#\\#\\#\\# **Example 1: Scientific Context** \n\\newline**Input Sentence:** \n\\newline*\"Long-term exposure to polluted air has been linked to an increased risk of developing respiratory illnesses such as asthma and chronic bronchitis.\"* \n\\newline**Generated Sentences:** \n\\newline1. Extended periods of contact with contaminated air are associated with a greater likelihood of respiratory conditions, including asthma and bronchitis. \n\\newline2. Prolonged inhalation of polluted air may elevate the probability of experiencing chronic lung diseases. \n\\newline3. Airborne pollutants have been found to contribute to the onset of various respiratory disorders over time. \n\\newline4. Studies suggest that individuals consistently exposed to poor air quality are at a heightened risk of breathing-related health issues. \n\\newline5. The presence of hazardous particles in the atmosphere can gradually impair lung function and lead to chronic respiratory distress. \n\\newline---\n\\newline\\#\\#\\#\\# **Example 2: AI \\& Technology Context** \n\\newline **Input Sentence:** \n\\newline *\"Machine learning algorithms are increasingly being used to detect patterns in large datasets, improving decision-making processes in fields such as healthcare and finance.\"* \n\\newline **Generated Sentences:** \n\\newline 1. AI-driven models are enhancing decision-making by identifying trends in vast amounts of data. \n\\newline 2. The application of machine learning techniques is revolutionizing data analysis across multiple industries. \n\\newline 3. Advanced computational models help uncover hidden insights within extensive datasets. \n\\newline 4. The use of artificial intelligence in fields like healthcare and banking is optimizing predictive analytics. \n\\newline 5. Modern machine learning tools enable more efficient data-driven decisions through pattern recognition. \n\\newline ---\n\\newline Now, generate up to {k} paraphrased sentences for the following input: \n\\newline **Input:** {\\color{blue} \\{idea\\}}\n}\n\\end{tcolorbox}\n\\end{figure*}\n\n\n\\begin{figure*}[t]\n \\centering\n \\begin{tcolorbox}[colback=yellow!10, colframe=black!89!white, before skip=5pt, after skip=5pt,\n title=\\textbf{Prompt 2 (b):} Detailed prompt for partial idea generation by LLMs,\n fontupper=\\small, % <-- apply \\small to the box contents\n sharp corners,\n ] % <<<<<< 加在这里\n{{\\color{red} System}: You are an expert in scientific writing and language transformation. Your task is to paraphrase scientific statements while ensuring clarity, precision, and natural readability. The meaning of the original sentence must remain unchanged, but the structure and expression should be significantly altered.\n\\newline\n{\\color{red} User}: \n\\newline \\#\\#\\#\\#**Task:** Given an input sentence, generate up to k subset sentences that retain part of the original information while allowing for minor modifications. You may add, delete, or replace words as long as the subset sentence conveys a meaningful portion of the original content without including all of it. Each subset sentence should be **elaborated**, providing additional context or explanation while staying true to the original meaning.\n\\newline \\#\\#\\#\\# **Instructions:** \n\\newline 1. Extract a meaningful portion of the input sentence. \n\\newline 2. Modify the extracted portion by **adding, deleting, or replacing** words while ensuring clarity and coherence. \n\\newline 3. Elaborate on the extracted information by adding context, explanation, or detail while retaining the core meaning. \n\\newline 4. Each subset sentence should retain **only part of the original information**, not the full meaning. \n\\newline 5. Ensure grammatical correctness and natural phrasing. \n\\newline 6. Avoid directly copying the exact words from the input sentence. \n\\newline 7. Do not introduce entirely new or unrelated information. \n\\newline 8. **Output only the elaborated subset sentences, numbered from 1 to 10, with no additional text or explanations.**\n\\newline --- \n\\newline \\#\\#\\# **Example Input \\& Output** \n\\newline \\#\\#\\#\\# **Example 1:** \n\\newline **Input:** \"The increasing reliance on artificial intelligence in the healthcare industry is transforming patient diagnostics and treatment planning.\" \n\\newline \n\\newline **Output:** \n\\newline 1. AI is revolutionizing healthcare by enhancing how doctors diagnose illnesses and design personalized treatments. \n\\newline 2. The healthcare sector is increasingly adopting AI-driven tools to streamline diagnostic processes and improve patient outcomes. \n\\newline 3. Advanced algorithms are now assisting healthcare providers in identifying conditions more accurately and efficiently. \n\\newline 4. Artificial intelligence is not only transforming diagnostics but also reshaping how treatment plans are tailored to individual patients. \n\\newline 5. Medical professionals are leveraging AI systems to analyze patient data and detect abnormalities earlier. \n\\newline --- \n\\newline \\#\\#\\# **Example 2:** \n\\newline*\"Long-term exposure to polluted air has been linked to an increased risk of developing respiratory illnesses such as asthma and chronic bronchitis.\"* \n\\newline**Generated Sentences:** \n\\newline1. Extended periods of contact with contaminated air are associated with a greater likelihood of respiratory conditions, including asthma and bronchitis. \n\\newline2. Prolonged inhalation of polluted air may elevate the probability of experiencing chronic lung diseases. \n\\newline3. Airborne pollutants have been found to contribute to the onset of various respiratory disorders over time. \n\\newline4. Studies suggest that individuals consistently exposed to poor air quality are at a heightened risk of breathing-related health issues. \n\\newline5. The presence of hazardous particles in the atmosphere can gradually impair lung function and lead to chronic respiratory distress. \n\\newline---\n\\newline\\#\\#\\#\\# **Example 2: AI \\& Technology Context** \n\\newline **Input:** \"Due to climate change, extreme weather events such as hurricanes and heatwaves have become more frequent and intense.\" \n\\newline **Output:** \n\\newline1. Rising global temperatures are causing hurricanes to become more powerful and destructive. \n\\newline2. Climate change is intensifying heatwaves, making them last longer and reach higher temperatures. \n\\newline3. Severe weather patterns are now more common due to the warming atmosphere and changing climate conditions. \n\\newline4. The increased occurrence of hurricanes and heatwaves is a direct result of shifts in global weather systems. \n\\newline5. Scientists link the rise in extreme weather events to the ongoing effects of climate change. \n\\newline---\n\\newline Now, generate up to {k} elaborated subset sentences for the following input: \n\\newline **Input:** {\\color{blue} \\{idea\\}}\n}\n\\end{tcolorbox}\n\\end{figure*}\n\n\\begin{figure*}[t]\n \\centering\n \\begin{tcolorbox}[colback=yellow!10, colframe=black!89!white, before skip=5pt, after skip=5pt,\n title=\\textbf{Prompt 2 (c):} Detailed prompt for incremental idea generation by LLMs,\n fontupper=\\small, % <-- apply \\small to the box contents\n sharp corners,\n ] % <<<<<< 加在这里\n{{\\color{red} System}: You are an expert in scientific writing and language transformation. Your task is to paraphrase scientific statements while ensuring clarity, precision, and natural readability. The meaning of the original sentence must remain unchanged, but the structure and expression should be significantly altered.\n\\newline\n{\\color{red} User}: \n\\newline \\#\\#\\#\\#**Task:** Given two input sentences, generate **up to k sentences** that retain the original information with no modifications or elaborations. The generated sentences are LAGRELY different from these two papers in semantic level (e.g., low BERT-based similarity), but bring the key ideas from them. For example, if the paper A has distinct ideas: idea\\_A1, idea\\_A2, and paper B distinct ideas: idea\\_B1, idea\\_B2, you can generate the idea by selecting subset of ideas from both these two papers (e.g., ideas idea\\_A1 + idea\\_B2 -> new abstract). Besides that, you can follow the below rules to help you the generated abstract that seems to be largely distinct to the original papers (e.g., Paper A and Paper B):\n\\newline \\#\\#\\#\\# **Instructions:** \n\\newline1. **Rephrase \\& Restructure**: The new sentence must **significantly alter** the structure and wording of the selected subsets while **retaining only their core ideas**. \n\\newline2. **Break Down \\& Blend**: Instead of copying large portions, **extract fragments** from the subsets and recombine them in a different way. \n\\newline3. **Introduce Metaphors or Analogies**: Use figurative language to convey the original meaning in a more indirect manner. \n\\newline4. **Use Different Sentence Structures**: Experiment with **questions, lists, cause-effect statements, conditionals, or passive voice**. \n\\newline5. **Limit Direct Keywords**: Avoid using the **exact phrasing** from the original subsets-paraphrase where possible.\n\\newline6. **Focus on Implicit Links**: The new sentence **should not clearly resemble either source sentence**, making it harder to trace back to any one subset. \n\\newline7. **Enforce Conceptual Fusion**: The new sentence must not focus too heavily on just one of the selected subsets but should **merge ideas from both** in a way that feels natural and balanced. \n\\newline8. **Ensure Consistency**: Does not create a new perspective or connection between the ideas, presents the ideas together in one sentence, but without forcing a relationship.\n\\newline9. **Output Format:** \n\\newline - Return **only** the elaborated subset sentences, numbered **1 to 5**, with no additional text or explanations. \n\\newline --- \n\\newline \\#\\#\\# **Example Input \\& Output** \n\\newline \\#\\#\\#\\# **Example 1:** \n\\newline**idea\\_A1:** Personalization improves email engagement.\n\\newline**idea\\_B2:** Scarcity messaging can damage long-term trust.\n\\newline**Generated Sentence:**\n\\newline1. Tailored content boosts email responses, while limited-time offers may compromise brand credibility.\n\\newline\n\\newline\\#\\#\\#\\# **Example 2: NLP Domain**\n\\newline **idea\\_A2:** Instruction tuning aligns model outputs with user prompts.\n\\newline**idea\\_B1:** Retrieval enhances factual accuracy in text generation.\n\\newline\n\\newline**Generated Sentence:**\n\\newline1. While instruction tuning improves adherence to task instructions, adding retrieval helps ground outputs in reliable information.\n\\newline---\n\\newline Now, generate up to k fused sentences for the following inputs: \n\\newline Sentence A: {\\color{blue} Idea 1}\n\\newline Sentence B: {\\color{blue} Idea 2}\n}\n\\end{tcolorbox}\n\\end{figure*}\n\n\n\\begin{figure*}[t]\n \\centering\n \\begin{tcolorbox}[colback=yellow!10, colframe=black!89!white, before skip=5pt, after skip=5pt,\n title=\\textbf{Prompt 3:} Trigger LLMs for novelty detection.,\n fontupper=\\small, % <-- apply \\small to the box contents\n sharp corners,\n ] % <<<<<< 加在这里\n{{\\color{red} System}: You are given a *\"new research idea\"* and a list of *\"existing research ideas.\"* Your task is to compare the new idea **against each existing idea** and assign a **novelty score**.\n\\newline\n{\\color{red} User}: \\#\\#\\# \\textbf{Clarified Scoring Criteria:}\n\\newline Use the following **novelty scoring rubric** for each comparison:\n\\newline Scoring Guidelines: \n\\newline 0.0 – No Novelty (Identical / Reworded). The new idea is a direct copy or a rephrased version of an existing idea. Shares the same claims, findings, information or logic. Example: Paraphrasing an existing idea without introducing any change.\n\\newline 0.3 – Low Novelty (Subset of Existing Idea). The new idea is a strict subset of an existing idea. Removes a condition, claim, or component, but otherwise shares the same logic and goal. No new dimension or direction added. Example: Taking just “Claim A” from “Claim A + Claim B”.\n\\newline 0.5 – Moderate Novelty (large partial overlap). The new idea shares a substantial portion (around 50\\%) of the ideas or claims with an existing idea. It introduces some new elements, such as a different combination of claims or modified emphasis, but it’s not entirely new. It’s not a subset or superset, but has a large information intersection. Example: “Claim A + Claim C” compared to existing “Claim A + Claim B”.\n\\newline 0.7 – High Novelty (small partial overlap). The new idea has minor similarities (e.g., method or framing), but differs in research focus, target, or core claim. It applies known ideas in a new context or formulates a distinct question, showing clear divergence from existing ideas. Example: Using a social theory from marketing in the context of education.\n\\newline 1.0 – Very High Novelty (Distinct Research Direction). The idea is entirely distinct in structure, claim, scope, and research objective. Only minor thematic or terminological similarity, if any. Example: Proposing a brand new framework, theory, or dataset with no precedent in existing ideas.\n\\newline \\textbf{\\#\\#\\# Evaluation Instructions:}\n\\newline For each comparison between the new idea and an existing idea:\n\\newline 1. Assess Overlap. Examine the conceptual and structural overlap between the new idea and the existing idea.\n\\newline 2. Assign a Novelty Score. Use the Novelty Scoring Rubric to assign a score between 0.0 and 1.0, based on the degree of similarity or difference.\n\\newline 3. Repeat for All Comparisons. Perform this scoring for each existing idea in the set.\n\\newline \\#\\#\\# \\textbf{Output Format:} [List of scores like: [0.3, 0.5, 0.3, 0.7, 1.0]]\n\\newline Now, compare the given research idea with each of the existing ideas. For each comparison, assign a novelty score using the rubric above and no need to justify the score. Please return only a Python-style list of the novelty scores for each comparison and the final decision, no further explanation. \n\\newline Given Idea: {\\color{magenta}{(QUERY) The investigation discovers that the effectiveness of online ads is influenced by the volume and placement of ads, and a data-driven strategy to manage ad exposure could potentially increase campaign success by 15\\%.}}\n\\newline {\\color{blue} Existing Ideas:}\n\\newline {\\color{blue} 1. The study finds that varying degrees of wearout and weariness exist among consumers in response to online ad volume and placement, with an appropriate \"profiling and capping\" strategy potentially improving ad deployment effectiveness by 15\\%. }\n\\newline {\\color{blue} ... }\n\\newline {\\color{blue} K. The study finds that display advertisements have a low direct effect on purchase conversion but stimulate subsequent visits through other advertisement formats, and the commonly used measure of conversion rate is biased in favor of search advertisements, underestimating the conversion effect of display advertisements. }\n}\n\\end{tcolorbox}\n\\end{figure*}\n\n\\end{document}\n"},"question":{"kind":"string","value":"Which backbone shows the largest percentage improvement in MAP for LLM-KD over RA in the marketing domain, and how much larger is this improvement compared to its corresponding MAP improvement in the NLP domain?"},"answer":{"kind":"string","value":"NLI, 15.09%"},"plan":{"kind":"string","value":"SELECT backbones in Marketing domain\nLOOP for each backbone\n SELECT LLM-KD MAP and RA MAP for current Backbone in Marketing\n COMPUTE marketing_improvement = LLM-KD MAP − RA MAP\nCOMPUTE target_backbone, max_market_improvement = argmax over marketing_improvement\n\n\nSELECT LLM-KD MAP and RA MAP for target_backbone in NLP domain\nCOMPUTE nlp_improvement = LLM-KD MAP − RA MAP\n\n\nCOMPUTE difference = max_market_improvement − nlp_improvement\nRETURN target_backbone, round(difference, 2)\n"}}},{"rowIdx":86,"cells":{"paper":{"kind":"string","value":"2506.03090"},"relevant_tables":{"kind":"list like","value":[["\\begin{table}[t]\n","\\centering\n","\\footnotesize % Reduce font size for fitting in one column\n","\n","\n","\\centering\n","\n","\\resizebox{0.48\\textwidth}{!}{%\n","\\begin{tabular}{lcc|cc|c}\n","\\toprule\n"," & \\multicolumn{2}{c}{\\textbf{Primary Sources }} & \\multicolumn{3}{c}{\\textbf{Dataset Examples}} \\\\\n"," & \\multicolumn{2}{c}{(\\textit{n=7})} & \\multicolumn{3}{c}{(\\textit{n=292})} \\\\\n","\\cmidrule(lr){2-3} \\cmidrule(lr){4-6}\n"," & \\textsc{Tokens} & \\textsc{Words} & \\textsc{Tokens} & \\textsc{Words} & \\textsc{\\# Ex/Book}\\\\\n","\\midrule\n","\\textsc{Mean} & 85,526 & 69,456 & 254.9 & 203.6 & 36.0 \\\\\n","\\textsc{St. Dev.} & 26,6304 & 21,167 & 66.9 & 52.9 & 19.6 \\\\\n","\\textsc{Max} & 124,544 & 102,549 & 492.0 & 385.0 & 52.0 \\\\\n","\\textsc{Min} & 45,038 & 37,209 & 116.0 & 91.0 & 7.0 \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\caption{Summary statistics for long-context RELiC. Token counts were computed with the \\texttt{o200k\\_base} encoding via \\texttt{tiktoken} (\\url{https://github.com/openai/tiktoken}) and word counts were computed by splitting on whitespace.}\n","\\label{tab:basic_summary_stats_dataset}\n","\\end{table}\n"]],"string":"[\n [\n \"\\\\begin{table}[t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\footnotesize % Reduce font size for fitting in one column\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\\centering\\n\",\n \"\\n\",\n \"\\\\resizebox{0.48\\\\textwidth}{!}{%\\n\",\n \"\\\\begin{tabular}{lcc|cc|c}\\n\",\n \"\\\\toprule\\n\",\n \" & \\\\multicolumn{2}{c}{\\\\textbf{Primary Sources }} & \\\\multicolumn{3}{c}{\\\\textbf{Dataset Examples}} \\\\\\\\\\n\",\n \" & \\\\multicolumn{2}{c}{(\\\\textit{n=7})} & \\\\multicolumn{3}{c}{(\\\\textit{n=292})} \\\\\\\\\\n\",\n \"\\\\cmidrule(lr){2-3} \\\\cmidrule(lr){4-6}\\n\",\n \" & \\\\textsc{Tokens} & \\\\textsc{Words} & \\\\textsc{Tokens} & \\\\textsc{Words} & \\\\textsc{\\\\# Ex/Book}\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textsc{Mean} & 85,526 & 69,456 & 254.9 & 203.6 & 36.0 \\\\\\\\\\n\",\n \"\\\\textsc{St. Dev.} & 26,6304 & 21,167 & 66.9 & 52.9 & 19.6 \\\\\\\\\\n\",\n \"\\\\textsc{Max} & 124,544 & 102,549 & 492.0 & 385.0 & 52.0 \\\\\\\\\\n\",\n \"\\\\textsc{Min} & 45,038 & 37,209 & 116.0 & 91.0 & 7.0 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Summary statistics for long-context RELiC. Token counts were computed with the \\\\texttt{o200k\\\\_base} encoding via \\\\texttt{tiktoken} (\\\\url{https://github.com/openai/tiktoken}) and word counts were computed by splitting on whitespace.}\\n\",\n \"\\\\label{tab:basic_summary_stats_dataset}\\n\",\n \"\\\\end{table}\\n\"\n ]\n]","isConversational":false},"tables":{"kind":"list like","value":[["\\begin{table}[t]\n","\\centering\n","\\footnotesize % Reduce font size for fitting in one column\n","\n","\n","\\centering\n","\n","\\resizebox{0.48\\textwidth}{!}{%\n","\\begin{tabular}{lcc|cc|c}\n","\\toprule\n"," & \\multicolumn{2}{c}{\\textbf{Primary Sources }} & \\multicolumn{3}{c}{\\textbf{Dataset Examples}} \\\\\n"," & \\multicolumn{2}{c}{(\\textit{n=7})} & \\multicolumn{3}{c}{(\\textit{n=292})} \\\\\n","\\cmidrule(lr){2-3} \\cmidrule(lr){4-6}\n"," & \\textsc{Tokens} & \\textsc{Words} & \\textsc{Tokens} & \\textsc{Words} & \\textsc{\\# Ex/Book}\\\\\n","\\midrule\n","\\textsc{Mean} & 85,526 & 69,456 & 254.9 & 203.6 & 36.0 \\\\\n","\\textsc{St. Dev.} & 26,6304 & 21,167 & 66.9 & 52.9 & 19.6 \\\\\n","\\textsc{Max} & 124,544 & 102,549 & 492.0 & 385.0 & 52.0 \\\\\n","\\textsc{Min} & 45,038 & 37,209 & 116.0 & 91.0 & 7.0 \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\caption{Summary statistics for long-context RELiC. Token counts were computed with the \\texttt{o200k\\_base} encoding via \\texttt{tiktoken} (\\url{https://github.com/openai/tiktoken}) and word counts were computed by splitting on whitespace.}\n","\\label{tab:basic_summary_stats_dataset}\n","\\end{table}\n"],["\\begin{table}[t!]\n","\\small\n","\\resizebox{0.48\\textwidth}{!}{%\n","\\begin{tabular}{lccc} % Updated to four columns\n","\n","\\toprule\n","\\textsc{Model + Simple Prompt} & \\textsc{ALL} \\textsubscript{($n$=292)} & \\textsc{\\faUser} \\textsubscript{($n$=40)}& \\textsc{\\faBook} \\textsubscript{($n$=39)}\\\\ \n","\\midrule\n","\\geminiprotwo~\\cite{google2025gemini25} & 63.7 & 57.5 & \\textbf{82.1} \\\\\n","\\othree~\\cite{o3} & 49.7 & 47.5 & 71.8 \\\\\n","\\geminiproone~\\cite{geminiteam2024gemini} & 36.1 & 22.5 & 43.6 \\\\\n","\\oone~\\cite{openai2024o1} & 32.2 & 25.0 & 41.0 \\\\\n","\\gpto\\ \\cite{openai2024gpt4ocard} & 27.4 & 17.5 & 35.9 \\\\\n","\\qwenlarge\\ \\cite{qwen_qwen25_2025} & 11.3 & 7.5 & 20.5 \\\\\n","\\llamasmall\\ \\cite{grattafiori2024llama3herdmodels} & 5.1 & 5.0 & 2.6 \\\\\n","\\qwensmall & 2.7 & 5.0 & 2.6 \\\\\n","\\llamalarge & 2.1 & 0.0 & 2.6 \\\\\n","\\midrule\n","\\textsc{Model + Explanation Prompt} & & & \\\\\n","\\midrule\n","\\geminiprotwo & \\textbf{64.7} & \\textbf{62.5} & 79.5 \\\\\n","\\gptfourone~\\cite{gpt-4.1} & 51.0 & 47.5 & 69.2 \\\\\n","\\othree & 50.7 & 50.0 & 66.7 \\\\\n","\\geminiproone & 38.5 & 40.0 & 50.0 \\\\\n","\\claude~\\cite{claude_sonnet_2025} & 37.0 & 32.5 & 48.7 \\\\\n","\\deepseek~\\cite{deepseekai2025deepseekv3technicalreport} & 29.1 & 15.0 & 38.5 \\\\\n","\\gpto & 24.3 & 22.5 & 31.8 \\\\\n","\\qwenthreelarge~\\cite{qwen_qwen25_2025} & 19.2 & 20.0 & 33.3 \\\\\n","\\qwenthreesmall & 8.9 & 5.0 & 10.3 \\\\\n","\\othreemini\\ \\cite{openai2025o3mini} & 8.3 & 10.0 & 13.6 \\\\\n","\\midrule\n","\\textsc{Baselines} & & & \\\\\n","\\midrule\n","\\gteqwen & 4.5 & 2.5 & 6.8 \\\\\n","\\human & - & 55.0 & - \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\caption{Percentage of test set examples where the model generated the correct ground truth quotation for different folds of the test set. The \\textsc{\\faUser} and \\textsc{\\faBook} columns contain the accuracy of each model on the human-evaluated and close reading subsets of the data, respectively.} \n","\\label{tab:benchmark_accuracy}\n","\\end{table}\n"],["\\begin{figure*}[t]\n"," \\centering\n"," \\includegraphics[width=\\textwidth]{figures/table_3.png}\n"," \\caption{An example from long context RELiC where the \\human\\ annotator successfully retrieves the quotation while the top-performing LLMs fail.}\n"," \\label{fig:llm_fail}\n","\\end{figure*}\n"],["\\begin{figure*}[t]\n"," \\centering\n"," \\includegraphics[width=\\textwidth]{figures/table_4.png}\n"," \\caption{An example of long context RELiC where the \\human\\ annotator and \\geminiprotwo\\ both select the same quotation, but it is not the ground truth quotation.}\n"," \\label{fig:alt_evidence}\n","\\end{figure*}\n"],["\\begin{table}[t!]\n","\\small\n","\\centering\n","\\resizebox{0.48\\textwidth}{!}{%\n","\\begin{tabular}{lcc}\n","\\toprule\n","\\textsc{Model + Simple Prompt} & \\textsc{\\faUser} Accuracy \\textsubscript{($n$=40)} & Avg. Length Ratio\\\\\n","\\midrule\n","\\geminiprotwo & 57.5 & 3.5\\\\\n","\\othree & 47.5 & 3.5\\\\\n","\\geminiproone & 22.5 & 2.8\\\\\n","\\oone & 25.0 & 3.2\\\\\n","\\gpto & 17.5 & 3.9\\\\\n","\\qwenlarge & 7.5 & 3.2\\\\\n","\\llamasmall & 5.0 & 5.9\\\\\n","\\qwensmall & 5.0 & 2.8\\\\\n","\\llamalarge & 0.0 & 5.7\\\\\n","\\midrule\n","\\multicolumn{3}{l}{\\textsc{Model + Explanation Prompt}} \\\\\n","\\midrule\n","\\geminiprotwo & \\textbf{62.5} & 3.0\\\\\n","\\gptfourone & 47.5 & 4.8\\\\\n","\\othree & 50.0 & 2.7\\\\\n","\\geminiproone & 40.0 & 3.3\\\\\n","\\claude & 32.5 & 4.0\\\\\n","\\deepseek & 15.0 & 3.6\\\\\n","\\gpto & 22.5 & 3.6\\\\\n","\\qwenthreelarge & 20.0 & 2.7\\\\\n","\\qwenthreesmall & 5.0 & 2.4\\\\\n","\\othreemini & 10.0 & 3.5\\\\\n","\\midrule\n","\\multicolumn{3}{l}{\\textsc{Baselines}} \\\\\n","\\midrule\n","\\human & 55.0 & \\textbf{2.1}\\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\caption{The average length ratios for each model, defined as the ratio of the length model generation to that of the ground truth (measured in characters). All models have an average ratio greater than that of the human annotator (2.1), indicating overgeneration.}\n","\\label{tab:length_ratios}\n","\\end{table}\n"],["\\begin{table*}[t]\n"," \\renewcommand{\\arraystretch}{1.2} \n"," \\setlength{\\tabcolsep}{6pt}\n"," \\footnotesize\n"," \\centering\n"," \\begin{tabular}{l l c c c}\n"," \\toprule\n"," \\textbf{Book Title} & \\textbf{Author} & \\textbf{Publication Year} & \\textbf{Token Count} & \\textbf{Word Count} \\\\\n"," \\midrule\n"," Brave New World & Aldous Huxley & 1932 & 91,472 & 65,278 \\\\\n"," What Maisie Knew & Henry James & 1897 & 124,544 & 95,988 \\\\\n"," Ethan Frome & Edith Wharton & 1911 & 45,038 & 34,926 \\\\\n"," Frankenstein & Mary Shelley & 1818 & 95,018 & 75,131 \\\\\n"," The Great Gatsby & F. Scott Fitzgerald & 1925 & 63,683 & 48,972 \\\\\n"," The Awakening & Kate Chopin & 1899 & 66,574 & 49,932 \\\\\n"," The Scarlet Letter & Nathaniel Hawthorne & 1850 & 112,355 & 83,311 \\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," \\caption{Books included in the dataset. The token count was provided as per \\href{https://github.com/openai/tiktoken}{\\texttt{tiktoken}} tokenization. }\n"," \\label{tab:book-details}\n","\\end{table*}\n"],["\\begin{table}[t!]\n","\\centering\n","\\small\n","\\resizebox{0.48\\textwidth}{!}{%\n","\\begin{tabular}{l c l c c}\n","\\toprule\n","\\textsc{Model} & \\textsc{Context} & \\textsc{Avail.} & \\textsc{Checkpoints} \\\\\n","\\midrule\n","\\href{https://platform.openai.com/docs/models}{\\oone} & 200k & \\faLock & \\texttt{o1-2024-12-17} \\\\\n","\\href{https://platform.openai.com/docs/models}{\\othree} & 200k & \\faLock & \\texttt{o3-2025-04-16} \\\\\n","\\href{https://platform.openai.com/docs/models}{\\othreemini} & 200k & \\faLock & \\texttt{o3-mini-2025-01-31} \\\\\n","\\href{https://platform.openai.com/docs/models}{\\gpto} & 128k & \\faLock & \\texttt{gpt-4o-2024-11-20} \\\\\n","\\href{https://platform.openai.com/docs/models}{\\gptfourone} & 1M & \\faLock & \\texttt{gpt-4.1-2025-01-31} \\\\\n","\\href{https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/gemini}{\\geminiproone} & 1M & \\faLock & \\texttt{gemini-1.5-pro-002} \\\\\n","\\href{https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/gemini}{\\geminiprotwo} & 1M & \\faLock & \\texttt{gemini-2.5-pro-preview-05-06} \\\\\n","\\href{https://docs.anthropic.com/en/docs/about-claude/models/overview}{\\claude} & 200k & \\faLock & \\texttt{claude-3-7-sonnet@20250219} \\\\\n","\\href{https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct}{\\llamalarge} & 128k & \\faUnlock & \\texttt{Llama-3.3-70B-Instruct} \\\\\n","\\href{https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct}{\\llamasmall} & 128k & \\faUnlock & \\texttt{\n","Llama-3.1-8B-Instruct } \\\\\n","\\href{https://huggingface.co/Qwen/Qwen2.5-72B-Instruct}{\\qwenlarge} & 128k & \\faUnlock & \\texttt{Qwen2.5-72B-Instruct} \\\\\n","\\href{https://huggingface.co/Qwen/Qwen2.5-7B-Instruct}{\\qwensmall} & 128k & \\faUnlock & \\texttt{Qwen2.5-7B-Instruct} \\\\\n","\\href{https://huggingface.co/Qwen/Qwen3-8B}{\\qwenthreesmall} & 131k* & \\faUnlock & \\texttt{Qwen3-8B} \\\\\n","\\href{https://huggingface.co/Qwen/Qwen3-32B}{\\qwenthreelarge} & 131k* & \\faUnlock & \\texttt{Qwen3-32B} \\\\\n","\\midrule\n","\\href{https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct}{\\gteqwenparam} & 32k & \\faUnlock & \\texttt{gte-Qwen2-7B-instruct} \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\caption{The upper rows display the evaluated LLMs, while the bottom row displays the text embedding model used for the baseline. Context lengths marked with * were extended with YaRN.}\n","\\label{tab:models}\n","\\end{table}\n"],["\\begin{table*}[t]\n"," \\renewcommand{\\arraystretch}{1.2} \n"," \\setlength{\\tabcolsep}{4pt}\n"," \\footnotesize\n"," \\centering\n"," \\begin{tabular}{c p{15cm}}\n"," \\toprule\n"," & \\multicolumn{1}{c}{\\bf Prompt (Simple)} \\\\\n"," \\midrule\n"," \\noalign{\\vskip 1mm}\n"," & \\texttt{You are provided with the full text of \\textbf{[book\\_title]} and an excerpt of literary analysis that directly cites \\textbf{[book\\_title]} with the cited quotation represented as \\textless MASK\\textgreater.} \\\\[3mm]\n"," & \\texttt{Your task is to carefully read the text of \\textbf{[book\\_title]} and the excerpt of literary analysis, then select a window from \\textbf{[book\\_title]} that most appropriately replaces \\textless MASK\\textgreater as the cited quotation by providing textual evidence for any claims in the literary analysis.} \\\\[1mm]\n"," & \\texttt{The excerpt of literary analysis should form a valid argument when \\textless MASK\\textgreater is replaced by the window from \\textbf{[book\\_title]}.} \\\\[1mm]\n"," & \\texttt{\\textless full\\_text\\_of\\_[book\\_title\\_snake\\_case]\\textgreater \\textbf{[book\\_sentences]} \\textless /full\\_text\\_of\\_[book\\_title\\_snake\\_case]\\textgreater} \\\\[1mm]\n"," & \\texttt{\\textless literary\\_analysis\\_excerpt\\textgreater \\textbf{[lit\\_analysis\\_excerpt]} \\textless /literary\\_analysis\\_excerpt\\textgreater} \\\\[1mm]\n"," & \\texttt{Identify the window that best supports the claims being made in the excerpt of literary analysis. The window should contain no more than 5 consecutive sentences from \\textbf{[book\\_title]}.} \\\\[1mm]\n"," & \\texttt{Provide your final answer in the following format:} \\\\[1mm]\n"," & \\texttt{\\textless window\\textgreater \\textbf{YOUR SELECTED WINDOW} \\textless /window\\textgreater} \\\\[1mm]\n"," \\bottomrule\n"," \\end{tabular}\n","\\caption{Prompt template for literary evidence retrieval (Simple).}\n","\\label{tab:inference_simple}\n","\\end{table*}\n"],["\\begin{table*}[t]\n"," \\renewcommand{\\arraystretch}{1.2} \n"," \\setlength{\\tabcolsep}{4pt}\n"," \\footnotesize\n"," \\centering\n"," \\begin{tabular}{c p{15cm}}\n"," \\toprule\n"," & \\multicolumn{1}{c}{\\bf Prompt w/ Explanations} \\\\\n"," \\midrule\n"," \\noalign{\\vskip 1mm}\n"," & \\texttt{You are provided with the full text of \\textbf{[book\\_title]} and an excerpt of literary analysis that directly cites \\textbf{[book\\_title]} with the cited quotation represented as \\textless MASK\\textgreater.} \\\\[3mm]\n"," & \\texttt{Your task is to carefully read the text of \\textbf{[book\\_title]} and the excerpt of literary analysis, then select a window from \\textbf{[book\\_title]} that most appropriately replaces \\textless MASK\\textgreater as the cited quotation by providing textual evidence for any claims in the literary analysis.} \\\\[1mm]\n"," & \\texttt{The excerpt of literary analysis should form a valid argument when \\textless MASK\\textgreater is replaced by the window from \\textbf{[book\\_title]}.} \\\\[1mm]\n"," & \\texttt{\\textless full\\_text\\_of\\_[book\\_title\\_snake\\_case]\\textgreater \\textbf{[book\\_sentences]} \\textless /full\\_text\\_of\\_[book\\_title\\_snake\\_case]\\textgreater} \\\\[1mm]\n"," & \\texttt{\\textless literary\\_analysis\\_excerpt\\textgreater \\textbf{[lit\\_analysis\\_excerpt]} \\textless /literary\\_analysis\\_excerpt\\textgreater} \\\\[1mm]\n"," & \\texttt{First, provide an explanation of your decision marking process in no more than one paragraph.} \\\\[1mm]\n"," & \\texttt{Then, identify the window that best supports the claims being made in the excerpt of literary analysis. The window should contain no more than 5 consecutive sentences from \\textbf{[book\\_title]}.} \\\\[1mm]\n"," & \\texttt{Provide your final answer in the following format:} \\\\[1mm]\n"," & \\texttt{\\textless explanation\\textgreater \\textbf{YOUR EXPLANATION} \\textless /explanation\\textgreater} \\\\[1mm]\n"," & \\texttt{\\textless window\\textgreater \\textbf{YOUR SELECTED WINDOW} \\textless /window\\textgreater} \\\\[1mm]\n"," \\bottomrule\n"," \\end{tabular}\n","\\caption{Prompt template for literary evidence retrieval with explanation.}\n","\\label{tab:inference_explain}\n","\\end{table*}\n"],["\\begin{table*}[t]\n"," \\renewcommand{\\arraystretch}{1.2} \n"," \\setlength{\\tabcolsep}{4pt}\n"," \\footnotesize\n"," \\centering\n"," \\begin{tabular}{c p{15cm}}\n"," \\toprule\n"," & \\multicolumn{1}{c}{\\bf Prompt for Cleaning RELiC Examples} \\\\\n"," \\midrule\n"," \\noalign{\\vskip 1mm}\n"," & \\texttt{I want to create a dataset for Natural Language Processing that consists of excerpts of literary analysis that quote directly from a primary source text.} \\\\[3mm]\n"," & \\texttt{I have already collected windows of literary analysis that quote from many different primary sources in the public domain. The part of the excerpt before the quotation is called the \"prefix,\" and the part of the excerpt after the quotation is called the \"suffix.\" Because I collected the data from PDFs with OCR, the prefixes and suffixes contain artifacts like in-line citations, page numbers, chapter names, headers, and footers that I need to remove.} \\\\[3mm]\n"," & \\texttt{Here is an example:} \\\\[1mm]\n"," & \\texttt{\\textless prefix\\textgreater \\textbf{[prefix]} \\textless /prefix\\textgreater} \\\\[1mm]\n"," & \\texttt{\\textless ground\\_truth\\_quotation\\textgreater \\textbf{[ground\\_truth\\_quotation]} \\textless /ground\\_truth\\_quotation\\textgreater} \\\\[1mm]\n"," & \\texttt{\\textless suffix\\textgreater \\textbf{[suffix]} \\textless /suffix\\textgreater} \\\\[1mm]\n"," & \\texttt{Please follow the following guidelines to help me clean and filter this example:} \\\\[1mm]\n"," & \\texttt{1. Remove all textual artifacts from the OCR process by deleting them. Artifacts include page numbers, chapter headings, footnotes, image captions, etc.} \\\\[1mm]\n"," & \\texttt{2. Correct the grammar, punctuation, and spelling of the prefix and suffix without altering the meaning so that the primary source quotation fits seamlessly and grammatically between the prefix and suffix.} \\\\[1mm]\n"," & \\texttt{3. Remove all in-line citations following quotations, especially those that say things like \"(my emphasis).\"} \\\\[1mm]\n"," & \\texttt{Respond with ONLY the cleaned prefix and suffix in the following format:} \\\\[1mm]\n"," & \\texttt{\\textless clean\\_prefix\\textgreater \\textbf{CLEAN PREFIX} \\textless /clean\\_prefix\\textgreater} \\\\[1mm]\n"," & \\texttt{\\textless clean\\_suffix\\textgreater \\textbf{CLEAN SUFFIX} \\textless /clean\\_suffix\\textgreater} \\\\[1mm]\n"," \\bottomrule\n"," \\end{tabular}\n","\\caption{Prompt template for cleaning RELiC examples}\n","\\label{tab:clean_filter}\n","\\end{table*}\n"],["\\begin{table*}[t]\n"," \\renewcommand{\\arraystretch}{1.2} \n"," \\setlength{\\tabcolsep}{4pt}\n"," \\footnotesize\n"," \\centering\n"," \\begin{tabular}{c p{15cm}}\n"," \\toprule\n"," & \\multicolumn{1}{c}{\\bf Prompt for Classifying Literary Analysis} \\\\\n"," \\midrule\n"," \\noalign{\\vskip 1mm}\n"," & \\texttt{I want to create a dataset for Natural Language Processing that consists of excerpts of literary analysis that quote directly from a primary source text.} \\\\[3mm]\n"," & \\texttt{Other texts can contain quotes from primary sources, like biographies of authors. However, I only want examples of literary analysis in the dataset.} \\\\[3mm]\n"," & \\texttt{Please determine whether the following excerpt is an example of literary analysis. If it is literary analysis, respond with TRUE. Otherwise, respond with FALSE.} \\\\[3mm]\n"," & \\texttt{Here is the excerpt:} \\\\[1mm]\n"," & \\texttt{\\textless excerpt\\textgreater \\textbf{[clean\\_prefix]} \\textbf{[answer\\_quote]} \\textbf{[clean\\_suffix]} \\textless /excerpt\\textgreater} \\\\[1mm]\n"," & \\texttt{Respond with ONLY your answer in the following format:} \\\\[1mm]\n"," & \\texttt{\\textless answer\\textgreater \\textbf{YOUR ANSWER} \\textless /answer\\textgreater} \\\\[1mm]\n"," \\bottomrule\n"," \\end{tabular}\n","\\caption{Prompt template for classifying literary analysis}\n","\\label{tab:classify_lit_analysis}\n","\\end{table*}\n"],["\\begin{table*}[t]\n"," \\renewcommand{\\arraystretch}{1.2} \n"," \\setlength{\\tabcolsep}{4pt}\n"," \\footnotesize\n"," \\centering\n"," \\begin{tabular}{c p{15cm}}\n"," \\toprule\n"," & \\multicolumn{1}{c}{\\bf Prompt for Identifying Quote Location Disclosure} \\\\\n"," \\midrule\n"," \\noalign{\\vskip 1mm}\n"," & \\texttt{I want to create a dataset for a Natural Language Processing task called \"Literary Evidence Retrieval.\"} \\\\[3mm]\n"," & \\texttt{The input has two parts:} \\\\[1mm]\n"," & \\texttt{(1) An excerpt of literary analysis that quotes 1 to 5 full sentences from a primary source text, but the quote is replaced with \"[MASK]\".} \\\\[1mm]\n"," & \\texttt{(2) The full text of the primary source.} \\\\[3mm]\n"," & \\texttt{The output should be the correct quotation of 1 to 5 full sentences from the primary source—this is the ground truth.} \\\\[3mm]\n"," & \\texttt{I have already collected windows of literary analysis that quote from many different primary sources in the public domain.} \\\\[3mm]\n"," & \\texttt{The part of the excerpt before the quotation is called the \"prefix,\" and the part after is called the \"suffix.\"} \\\\[3mm]\n"," & \\texttt{To avoid giving the model hints, it's extremely important that the prefix and suffix do not mention the chapter where the ground truth quotation is located.} \\\\[3mm]\n"," & \\texttt{Please determine whether the following instance contains the chapter location of the ground truth quotation in either the prefix or the suffix. If it does, respond with TRUE; otherwise, respond with FALSE.} \\\\[3mm]\n"," & \\texttt{The prefix and suffix may contain the location of other quotations in the passage. I only want to identify if the location of the ground truth quotation is revealed.} \\\\[3mm]\n"," & \\texttt{Look out for phrases like \"In the prologue\" or \"In Chapter...\"} \\\\[3mm]\n"," & \\texttt{Here is the instance:} \\\\[1mm]\n"," & \\texttt{\\textless prefix\\textgreater \\textbf{[prefix]} \\textless /prefix\\textgreater} \\\\[1mm]\n"," & \\texttt{\\textless ground\\_truth\\_quotation\\textgreater \\textbf{[ground\\_truth\\_quotation]} \\textless /ground\\_truth\\_quotation\\textgreater} \\\\[1mm]\n"," & \\texttt{\\textless suffix\\textgreater \\textbf{[suffix]} \\textless /suffix\\textgreater} \\\\[1mm]\n"," & \\texttt{Respond with ONLY your answer in the following format:} \\\\[1mm]\n"," & \\texttt{\\textless answer\\textgreater \\textbf{YOUR ANSWER} \\textless /answer\\textgreater} \\\\[1mm]\n"," \\bottomrule\n"," \\end{tabular}\n","\\caption{Prompt template for identifying quote location disclosure}\n","\\label{tab:quote_location_disclosure}\n","\\end{table*}\n"]],"string":"[\n [\n \"\\\\begin{table}[t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\footnotesize % Reduce font size for fitting in one column\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\\centering\\n\",\n \"\\n\",\n \"\\\\resizebox{0.48\\\\textwidth}{!}{%\\n\",\n \"\\\\begin{tabular}{lcc|cc|c}\\n\",\n \"\\\\toprule\\n\",\n \" & \\\\multicolumn{2}{c}{\\\\textbf{Primary Sources }} & \\\\multicolumn{3}{c}{\\\\textbf{Dataset Examples}} \\\\\\\\\\n\",\n \" & \\\\multicolumn{2}{c}{(\\\\textit{n=7})} & \\\\multicolumn{3}{c}{(\\\\textit{n=292})} \\\\\\\\\\n\",\n \"\\\\cmidrule(lr){2-3} \\\\cmidrule(lr){4-6}\\n\",\n \" & \\\\textsc{Tokens} & \\\\textsc{Words} & \\\\textsc{Tokens} & \\\\textsc{Words} & \\\\textsc{\\\\# Ex/Book}\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textsc{Mean} & 85,526 & 69,456 & 254.9 & 203.6 & 36.0 \\\\\\\\\\n\",\n \"\\\\textsc{St. Dev.} & 26,6304 & 21,167 & 66.9 & 52.9 & 19.6 \\\\\\\\\\n\",\n \"\\\\textsc{Max} & 124,544 & 102,549 & 492.0 & 385.0 & 52.0 \\\\\\\\\\n\",\n \"\\\\textsc{Min} & 45,038 & 37,209 & 116.0 & 91.0 & 7.0 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Summary statistics for long-context RELiC. Token counts were computed with the \\\\texttt{o200k\\\\_base} encoding via \\\\texttt{tiktoken} (\\\\url{https://github.com/openai/tiktoken}) and word counts were computed by splitting on whitespace.}\\n\",\n \"\\\\label{tab:basic_summary_stats_dataset}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[t!]\\n\",\n \"\\\\small\\n\",\n \"\\\\resizebox{0.48\\\\textwidth}{!}{%\\n\",\n \"\\\\begin{tabular}{lccc} % Updated to four columns\\n\",\n \"\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textsc{Model + Simple Prompt} & \\\\textsc{ALL} \\\\textsubscript{($n$=292)} & \\\\textsc{\\\\faUser} \\\\textsubscript{($n$=40)}& \\\\textsc{\\\\faBook} \\\\textsubscript{($n$=39)}\\\\\\\\ \\n\",\n \"\\\\midrule\\n\",\n \"\\\\geminiprotwo~\\\\cite{google2025gemini25} & 63.7 & 57.5 & \\\\textbf{82.1} \\\\\\\\\\n\",\n \"\\\\othree~\\\\cite{o3} & 49.7 & 47.5 & 71.8 \\\\\\\\\\n\",\n \"\\\\geminiproone~\\\\cite{geminiteam2024gemini} & 36.1 & 22.5 & 43.6 \\\\\\\\\\n\",\n \"\\\\oone~\\\\cite{openai2024o1} & 32.2 & 25.0 & 41.0 \\\\\\\\\\n\",\n \"\\\\gpto\\\\ \\\\cite{openai2024gpt4ocard} & 27.4 & 17.5 & 35.9 \\\\\\\\\\n\",\n \"\\\\qwenlarge\\\\ \\\\cite{qwen_qwen25_2025} & 11.3 & 7.5 & 20.5 \\\\\\\\\\n\",\n \"\\\\llamasmall\\\\ \\\\cite{grattafiori2024llama3herdmodels} & 5.1 & 5.0 & 2.6 \\\\\\\\\\n\",\n \"\\\\qwensmall & 2.7 & 5.0 & 2.6 \\\\\\\\\\n\",\n \"\\\\llamalarge & 2.1 & 0.0 & 2.6 \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textsc{Model + Explanation Prompt} & & & \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\geminiprotwo & \\\\textbf{64.7} & \\\\textbf{62.5} & 79.5 \\\\\\\\\\n\",\n \"\\\\gptfourone~\\\\cite{gpt-4.1} & 51.0 & 47.5 & 69.2 \\\\\\\\\\n\",\n \"\\\\othree & 50.7 & 50.0 & 66.7 \\\\\\\\\\n\",\n \"\\\\geminiproone & 38.5 & 40.0 & 50.0 \\\\\\\\\\n\",\n \"\\\\claude~\\\\cite{claude_sonnet_2025} & 37.0 & 32.5 & 48.7 \\\\\\\\\\n\",\n \"\\\\deepseek~\\\\cite{deepseekai2025deepseekv3technicalreport} & 29.1 & 15.0 & 38.5 \\\\\\\\\\n\",\n \"\\\\gpto & 24.3 & 22.5 & 31.8 \\\\\\\\\\n\",\n \"\\\\qwenthreelarge~\\\\cite{qwen_qwen25_2025} & 19.2 & 20.0 & 33.3 \\\\\\\\\\n\",\n \"\\\\qwenthreesmall & 8.9 & 5.0 & 10.3 \\\\\\\\\\n\",\n \"\\\\othreemini\\\\ \\\\cite{openai2025o3mini} & 8.3 & 10.0 & 13.6 \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textsc{Baselines} & & & \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\gteqwen & 4.5 & 2.5 & 6.8 \\\\\\\\\\n\",\n \"\\\\human & - & 55.0 & - \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Percentage of test set examples where the model generated the correct ground truth quotation for different folds of the test set. The \\\\textsc{\\\\faUser} and \\\\textsc{\\\\faBook} columns contain the accuracy of each model on the human-evaluated and close reading subsets of the data, respectively.} \\n\",\n \"\\\\label{tab:benchmark_accuracy}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{figure*}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\textwidth]{figures/table_3.png}\\n\",\n \" \\\\caption{An example from long context RELiC where the \\\\human\\\\ annotator successfully retrieves the quotation while the top-performing LLMs fail.}\\n\",\n \" \\\\label{fig:llm_fail}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\textwidth]{figures/table_4.png}\\n\",\n \" \\\\caption{An example of long context RELiC where the \\\\human\\\\ annotator and \\\\geminiprotwo\\\\ both select the same quotation, but it is not the ground truth quotation.}\\n\",\n \" \\\\label{fig:alt_evidence}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{table}[t!]\\n\",\n \"\\\\small\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{0.48\\\\textwidth}{!}{%\\n\",\n \"\\\\begin{tabular}{lcc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textsc{Model + Simple Prompt} & \\\\textsc{\\\\faUser} Accuracy \\\\textsubscript{($n$=40)} & Avg. Length Ratio\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\geminiprotwo & 57.5 & 3.5\\\\\\\\\\n\",\n \"\\\\othree & 47.5 & 3.5\\\\\\\\\\n\",\n \"\\\\geminiproone & 22.5 & 2.8\\\\\\\\\\n\",\n \"\\\\oone & 25.0 & 3.2\\\\\\\\\\n\",\n \"\\\\gpto & 17.5 & 3.9\\\\\\\\\\n\",\n \"\\\\qwenlarge & 7.5 & 3.2\\\\\\\\\\n\",\n \"\\\\llamasmall & 5.0 & 5.9\\\\\\\\\\n\",\n \"\\\\qwensmall & 5.0 & 2.8\\\\\\\\\\n\",\n \"\\\\llamalarge & 0.0 & 5.7\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multicolumn{3}{l}{\\\\textsc{Model + Explanation Prompt}} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\geminiprotwo & \\\\textbf{62.5} & 3.0\\\\\\\\\\n\",\n \"\\\\gptfourone & 47.5 & 4.8\\\\\\\\\\n\",\n \"\\\\othree & 50.0 & 2.7\\\\\\\\\\n\",\n \"\\\\geminiproone & 40.0 & 3.3\\\\\\\\\\n\",\n \"\\\\claude & 32.5 & 4.0\\\\\\\\\\n\",\n \"\\\\deepseek & 15.0 & 3.6\\\\\\\\\\n\",\n \"\\\\gpto & 22.5 & 3.6\\\\\\\\\\n\",\n \"\\\\qwenthreelarge & 20.0 & 2.7\\\\\\\\\\n\",\n \"\\\\qwenthreesmall & 5.0 & 2.4\\\\\\\\\\n\",\n \"\\\\othreemini & 10.0 & 3.5\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multicolumn{3}{l}{\\\\textsc{Baselines}} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\human & 55.0 & \\\\textbf{2.1}\\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{The average length ratios for each model, defined as the ratio of the length model generation to that of the ground truth (measured in characters). All models have an average ratio greater than that of the human annotator (2.1), indicating overgeneration.}\\n\",\n \"\\\\label{tab:length_ratios}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table*}[t]\\n\",\n \" \\\\renewcommand{\\\\arraystretch}{1.2} \\n\",\n \" \\\\setlength{\\\\tabcolsep}{6pt}\\n\",\n \" \\\\footnotesize\\n\",\n \" \\\\centering\\n\",\n \" \\\\begin{tabular}{l l c c c}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Book Title} & \\\\textbf{Author} & \\\\textbf{Publication Year} & \\\\textbf{Token Count} & \\\\textbf{Word Count} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Brave New World & Aldous Huxley & 1932 & 91,472 & 65,278 \\\\\\\\\\n\",\n \" What Maisie Knew & Henry James & 1897 & 124,544 & 95,988 \\\\\\\\\\n\",\n \" Ethan Frome & Edith Wharton & 1911 & 45,038 & 34,926 \\\\\\\\\\n\",\n \" Frankenstein & Mary Shelley & 1818 & 95,018 & 75,131 \\\\\\\\\\n\",\n \" The Great Gatsby & F. Scott Fitzgerald & 1925 & 63,683 & 48,972 \\\\\\\\\\n\",\n \" The Awakening & Kate Chopin & 1899 & 66,574 & 49,932 \\\\\\\\\\n\",\n \" The Scarlet Letter & Nathaniel Hawthorne & 1850 & 112,355 & 83,311 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" \\\\caption{Books included in the dataset. The token count was provided as per \\\\href{https://github.com/openai/tiktoken}{\\\\texttt{tiktoken}} tokenization. }\\n\",\n \" \\\\label{tab:book-details}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table}[t!]\\n\",\n \"\\\\centering\\n\",\n \"\\\\small\\n\",\n \"\\\\resizebox{0.48\\\\textwidth}{!}{%\\n\",\n \"\\\\begin{tabular}{l c l c c}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textsc{Model} & \\\\textsc{Context} & \\\\textsc{Avail.} & \\\\textsc{Checkpoints} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\href{https://platform.openai.com/docs/models}{\\\\oone} & 200k & \\\\faLock & \\\\texttt{o1-2024-12-17} \\\\\\\\\\n\",\n \"\\\\href{https://platform.openai.com/docs/models}{\\\\othree} & 200k & \\\\faLock & \\\\texttt{o3-2025-04-16} \\\\\\\\\\n\",\n \"\\\\href{https://platform.openai.com/docs/models}{\\\\othreemini} & 200k & \\\\faLock & \\\\texttt{o3-mini-2025-01-31} \\\\\\\\\\n\",\n \"\\\\href{https://platform.openai.com/docs/models}{\\\\gpto} & 128k & \\\\faLock & \\\\texttt{gpt-4o-2024-11-20} \\\\\\\\\\n\",\n \"\\\\href{https://platform.openai.com/docs/models}{\\\\gptfourone} & 1M & \\\\faLock & \\\\texttt{gpt-4.1-2025-01-31} \\\\\\\\\\n\",\n \"\\\\href{https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/gemini}{\\\\geminiproone} & 1M & \\\\faLock & \\\\texttt{gemini-1.5-pro-002} \\\\\\\\\\n\",\n \"\\\\href{https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/gemini}{\\\\geminiprotwo} & 1M & \\\\faLock & \\\\texttt{gemini-2.5-pro-preview-05-06} \\\\\\\\\\n\",\n \"\\\\href{https://docs.anthropic.com/en/docs/about-claude/models/overview}{\\\\claude} & 200k & \\\\faLock & \\\\texttt{claude-3-7-sonnet@20250219} \\\\\\\\\\n\",\n \"\\\\href{https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct}{\\\\llamalarge} & 128k & \\\\faUnlock & \\\\texttt{Llama-3.3-70B-Instruct} \\\\\\\\\\n\",\n \"\\\\href{https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct}{\\\\llamasmall} & 128k & \\\\faUnlock & \\\\texttt{\\n\",\n \"Llama-3.1-8B-Instruct } \\\\\\\\\\n\",\n \"\\\\href{https://huggingface.co/Qwen/Qwen2.5-72B-Instruct}{\\\\qwenlarge} & 128k & \\\\faUnlock & \\\\texttt{Qwen2.5-72B-Instruct} \\\\\\\\\\n\",\n \"\\\\href{https://huggingface.co/Qwen/Qwen2.5-7B-Instruct}{\\\\qwensmall} & 128k & \\\\faUnlock & \\\\texttt{Qwen2.5-7B-Instruct} \\\\\\\\\\n\",\n \"\\\\href{https://huggingface.co/Qwen/Qwen3-8B}{\\\\qwenthreesmall} & 131k* & \\\\faUnlock & \\\\texttt{Qwen3-8B} \\\\\\\\\\n\",\n \"\\\\href{https://huggingface.co/Qwen/Qwen3-32B}{\\\\qwenthreelarge} & 131k* & \\\\faUnlock & \\\\texttt{Qwen3-32B} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\href{https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct}{\\\\gteqwenparam} & 32k & \\\\faUnlock & \\\\texttt{gte-Qwen2-7B-instruct} \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{The upper rows display the evaluated LLMs, while the bottom row displays the text embedding model used for the baseline. Context lengths marked with * were extended with YaRN.}\\n\",\n \"\\\\label{tab:models}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table*}[t]\\n\",\n \" \\\\renewcommand{\\\\arraystretch}{1.2} \\n\",\n \" \\\\setlength{\\\\tabcolsep}{4pt}\\n\",\n \" \\\\footnotesize\\n\",\n \" \\\\centering\\n\",\n \" \\\\begin{tabular}{c p{15cm}}\\n\",\n \" \\\\toprule\\n\",\n \" & \\\\multicolumn{1}{c}{\\\\bf Prompt (Simple)} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\noalign{\\\\vskip 1mm}\\n\",\n \" & \\\\texttt{You are provided with the full text of \\\\textbf{[book\\\\_title]} and an excerpt of literary analysis that directly cites \\\\textbf{[book\\\\_title]} with the cited quotation represented as \\\\textless MASK\\\\textgreater.} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{Your task is to carefully read the text of \\\\textbf{[book\\\\_title]} and the excerpt of literary analysis, then select a window from \\\\textbf{[book\\\\_title]} that most appropriately replaces \\\\textless MASK\\\\textgreater as the cited quotation by providing textual evidence for any claims in the literary analysis.} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{The excerpt of literary analysis should form a valid argument when \\\\textless MASK\\\\textgreater is replaced by the window from \\\\textbf{[book\\\\_title]}.} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless full\\\\_text\\\\_of\\\\_[book\\\\_title\\\\_snake\\\\_case]\\\\textgreater \\\\textbf{[book\\\\_sentences]} \\\\textless /full\\\\_text\\\\_of\\\\_[book\\\\_title\\\\_snake\\\\_case]\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless literary\\\\_analysis\\\\_excerpt\\\\textgreater \\\\textbf{[lit\\\\_analysis\\\\_excerpt]} \\\\textless /literary\\\\_analysis\\\\_excerpt\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{Identify the window that best supports the claims being made in the excerpt of literary analysis. The window should contain no more than 5 consecutive sentences from \\\\textbf{[book\\\\_title]}.} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{Provide your final answer in the following format:} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless window\\\\textgreater \\\\textbf{YOUR SELECTED WINDOW} \\\\textless /window\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"\\\\caption{Prompt template for literary evidence retrieval (Simple).}\\n\",\n \"\\\\label{tab:inference_simple}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[t]\\n\",\n \" \\\\renewcommand{\\\\arraystretch}{1.2} \\n\",\n \" \\\\setlength{\\\\tabcolsep}{4pt}\\n\",\n \" \\\\footnotesize\\n\",\n \" \\\\centering\\n\",\n \" \\\\begin{tabular}{c p{15cm}}\\n\",\n \" \\\\toprule\\n\",\n \" & \\\\multicolumn{1}{c}{\\\\bf Prompt w/ Explanations} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\noalign{\\\\vskip 1mm}\\n\",\n \" & \\\\texttt{You are provided with the full text of \\\\textbf{[book\\\\_title]} and an excerpt of literary analysis that directly cites \\\\textbf{[book\\\\_title]} with the cited quotation represented as \\\\textless MASK\\\\textgreater.} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{Your task is to carefully read the text of \\\\textbf{[book\\\\_title]} and the excerpt of literary analysis, then select a window from \\\\textbf{[book\\\\_title]} that most appropriately replaces \\\\textless MASK\\\\textgreater as the cited quotation by providing textual evidence for any claims in the literary analysis.} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{The excerpt of literary analysis should form a valid argument when \\\\textless MASK\\\\textgreater is replaced by the window from \\\\textbf{[book\\\\_title]}.} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless full\\\\_text\\\\_of\\\\_[book\\\\_title\\\\_snake\\\\_case]\\\\textgreater \\\\textbf{[book\\\\_sentences]} \\\\textless /full\\\\_text\\\\_of\\\\_[book\\\\_title\\\\_snake\\\\_case]\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless literary\\\\_analysis\\\\_excerpt\\\\textgreater \\\\textbf{[lit\\\\_analysis\\\\_excerpt]} \\\\textless /literary\\\\_analysis\\\\_excerpt\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{First, provide an explanation of your decision marking process in no more than one paragraph.} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{Then, identify the window that best supports the claims being made in the excerpt of literary analysis. The window should contain no more than 5 consecutive sentences from \\\\textbf{[book\\\\_title]}.} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{Provide your final answer in the following format:} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless explanation\\\\textgreater \\\\textbf{YOUR EXPLANATION} \\\\textless /explanation\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless window\\\\textgreater \\\\textbf{YOUR SELECTED WINDOW} \\\\textless /window\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"\\\\caption{Prompt template for literary evidence retrieval with explanation.}\\n\",\n \"\\\\label{tab:inference_explain}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[t]\\n\",\n \" \\\\renewcommand{\\\\arraystretch}{1.2} \\n\",\n \" \\\\setlength{\\\\tabcolsep}{4pt}\\n\",\n \" \\\\footnotesize\\n\",\n \" \\\\centering\\n\",\n \" \\\\begin{tabular}{c p{15cm}}\\n\",\n \" \\\\toprule\\n\",\n \" & \\\\multicolumn{1}{c}{\\\\bf Prompt for Cleaning RELiC Examples} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\noalign{\\\\vskip 1mm}\\n\",\n \" & \\\\texttt{I want to create a dataset for Natural Language Processing that consists of excerpts of literary analysis that quote directly from a primary source text.} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{I have already collected windows of literary analysis that quote from many different primary sources in the public domain. The part of the excerpt before the quotation is called the \\\"prefix,\\\" and the part of the excerpt after the quotation is called the \\\"suffix.\\\" Because I collected the data from PDFs with OCR, the prefixes and suffixes contain artifacts like in-line citations, page numbers, chapter names, headers, and footers that I need to remove.} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{Here is an example:} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless prefix\\\\textgreater \\\\textbf{[prefix]} \\\\textless /prefix\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless ground\\\\_truth\\\\_quotation\\\\textgreater \\\\textbf{[ground\\\\_truth\\\\_quotation]} \\\\textless /ground\\\\_truth\\\\_quotation\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless suffix\\\\textgreater \\\\textbf{[suffix]} \\\\textless /suffix\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{Please follow the following guidelines to help me clean and filter this example:} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{1. Remove all textual artifacts from the OCR process by deleting them. Artifacts include page numbers, chapter headings, footnotes, image captions, etc.} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{2. Correct the grammar, punctuation, and spelling of the prefix and suffix without altering the meaning so that the primary source quotation fits seamlessly and grammatically between the prefix and suffix.} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{3. Remove all in-line citations following quotations, especially those that say things like \\\"(my emphasis).\\\"} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{Respond with ONLY the cleaned prefix and suffix in the following format:} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless clean\\\\_prefix\\\\textgreater \\\\textbf{CLEAN PREFIX} \\\\textless /clean\\\\_prefix\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless clean\\\\_suffix\\\\textgreater \\\\textbf{CLEAN SUFFIX} \\\\textless /clean\\\\_suffix\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"\\\\caption{Prompt template for cleaning RELiC examples}\\n\",\n \"\\\\label{tab:clean_filter}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[t]\\n\",\n \" \\\\renewcommand{\\\\arraystretch}{1.2} \\n\",\n \" \\\\setlength{\\\\tabcolsep}{4pt}\\n\",\n \" \\\\footnotesize\\n\",\n \" \\\\centering\\n\",\n \" \\\\begin{tabular}{c p{15cm}}\\n\",\n \" \\\\toprule\\n\",\n \" & \\\\multicolumn{1}{c}{\\\\bf Prompt for Classifying Literary Analysis} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\noalign{\\\\vskip 1mm}\\n\",\n \" & \\\\texttt{I want to create a dataset for Natural Language Processing that consists of excerpts of literary analysis that quote directly from a primary source text.} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{Other texts can contain quotes from primary sources, like biographies of authors. However, I only want examples of literary analysis in the dataset.} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{Please determine whether the following excerpt is an example of literary analysis. If it is literary analysis, respond with TRUE. Otherwise, respond with FALSE.} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{Here is the excerpt:} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless excerpt\\\\textgreater \\\\textbf{[clean\\\\_prefix]} \\\\textbf{[answer\\\\_quote]} \\\\textbf{[clean\\\\_suffix]} \\\\textless /excerpt\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{Respond with ONLY your answer in the following format:} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless answer\\\\textgreater \\\\textbf{YOUR ANSWER} \\\\textless /answer\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"\\\\caption{Prompt template for classifying literary analysis}\\n\",\n \"\\\\label{tab:classify_lit_analysis}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[t]\\n\",\n \" \\\\renewcommand{\\\\arraystretch}{1.2} \\n\",\n \" \\\\setlength{\\\\tabcolsep}{4pt}\\n\",\n \" \\\\footnotesize\\n\",\n \" \\\\centering\\n\",\n \" \\\\begin{tabular}{c p{15cm}}\\n\",\n \" \\\\toprule\\n\",\n \" & \\\\multicolumn{1}{c}{\\\\bf Prompt for Identifying Quote Location Disclosure} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\noalign{\\\\vskip 1mm}\\n\",\n \" & \\\\texttt{I want to create a dataset for a Natural Language Processing task called \\\"Literary Evidence Retrieval.\\\"} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{The input has two parts:} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{(1) An excerpt of literary analysis that quotes 1 to 5 full sentences from a primary source text, but the quote is replaced with \\\"[MASK]\\\".} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{(2) The full text of the primary source.} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{The output should be the correct quotation of 1 to 5 full sentences from the primary source—this is the ground truth.} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{I have already collected windows of literary analysis that quote from many different primary sources in the public domain.} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{The part of the excerpt before the quotation is called the \\\"prefix,\\\" and the part after is called the \\\"suffix.\\\"} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{To avoid giving the model hints, it's extremely important that the prefix and suffix do not mention the chapter where the ground truth quotation is located.} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{Please determine whether the following instance contains the chapter location of the ground truth quotation in either the prefix or the suffix. If it does, respond with TRUE; otherwise, respond with FALSE.} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{The prefix and suffix may contain the location of other quotations in the passage. I only want to identify if the location of the ground truth quotation is revealed.} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{Look out for phrases like \\\"In the prologue\\\" or \\\"In Chapter...\\\"} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{Here is the instance:} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless prefix\\\\textgreater \\\\textbf{[prefix]} \\\\textless /prefix\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless ground\\\\_truth\\\\_quotation\\\\textgreater \\\\textbf{[ground\\\\_truth\\\\_quotation]} \\\\textless /ground\\\\_truth\\\\_quotation\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless suffix\\\\textgreater \\\\textbf{[suffix]} \\\\textless /suffix\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{Respond with ONLY your answer in the following format:} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless answer\\\\textgreater \\\\textbf{YOUR ANSWER} \\\\textless /answer\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"\\\\caption{Prompt template for identifying quote location disclosure}\\n\",\n \"\\\\label{tab:quote_location_disclosure}\\n\",\n \"\\\\end{table*}\\n\"\n ]\n]","isConversational":false},"fulltext":{"kind":"string","value":"% This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.\n\\pdfoutput=1\n% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.\n\n\\documentclass[11pt]{article}\n\n% Change \"review\" to \"final\" to generate the final (sometimes called camera-ready) version.\n% Change to \"preprint\" to generate a non-anonymous version with page numbers.\n\\usepackage[final]{acl}\n\n% Standard package includes\n\\usepackage{times}\n\\usepackage{latexsym}\n\n% For proper rendering and hyphenation of words containing Latin characters (including in bib files)\n\\usepackage[T1]{fontenc}\n% For Vietnamese characters\n% \\usepackage[T5]{fontenc}\n% See 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gte-Qwen2-7B-instruct}}\n\\newcommand{\\human}{\\textsc{\\small Human}}\n\\newcommand{\\relicquote}[1]{\\textbf{\\textcolor{purple}{#1}}}\n\n\n\n\\newcommand\\blankfootnote[1]{%\n \\let\\thefootnote\\relax\\footnotetext{#1}%\n \\let\\thefootnote\\svthefootnote%\n}\n\n\n\n\n\n\\title{\\raisebox{-0.3\\height}{\\includegraphics[width=1.75cm]{figures/lc_relic_emoji.png}}\\; Literary Evidence Retrieval via Long-Context Language Models}\n\n\n% Author information can be set in various styles:\n% For several authors from the same institution:\n% \\author{Author 1 \\and ... \\and Author n \\\\\n% Address line \\\\ ... \\\\ Address line}\n% if the names do not fit well on one line use\n% Author 1 \\\\ {\\bf Author 2} \\\\ ... \\\\ {\\bf Author n} \\\\\n% For authors from different institutions:\n% \\author{Author 1 \\\\ Address line \\\\ ... \\\\ Address line\n% \\And ... \\And\n% Author n \\\\ Address line \\\\ ... \\\\ Address line}\n% To start a separate ``row'' of authors use \\AND, as in\n% \\author{Author 1 \\\\ Address line \\\\ ... \\\\ Address line\n% \\AND\n% Author 2 \\\\ Address line \\\\ ... \\\\ Address line \\And\n% Author 3 \\\\ Address line \\\\ ... \\\\ Address line}\n\n\n\n\\author{Katherine Thai\\twemoji{crystal ball} and Mohit Iyyer\\twemoji{crystal ball}\\twemoji{magnifying glass tilted right}\\\\\nUMass Amherst\\twemoji{crystal ball}\\hspace{0.3cm} \nUniversity of Maryland, College Park\\twemoji{magnifying glass tilted right},\\\\\n\\texttt{kbthai@umass.edu}, \\texttt{miyyer@umd.edu} \\\\\n\\\\\n\\url{https://github.com/katherinethai/long_context_relic}\n}\n\n\n%\\author{\n% \\textbf{First Author\\textsuperscript{1}},\n% \\textbf{Second Author\\textsuperscript{1,2}},\n% \\textbf{Third T. Author\\textsuperscript{1}},\n% \\textbf{Fourth Author\\textsuperscript{1}},\n%\\\\\n% \\textbf{Fifth Author\\textsuperscript{1,2}},\n% \\textbf{Sixth Author\\textsuperscript{1}},\n% \\textbf{Seventh Author\\textsuperscript{1}},\n% \\textbf{Eighth Author \\textsuperscript{1,2,3,4}},\n%\\\\\n% \\textbf{Ninth Author\\textsuperscript{1}},\n% \\textbf{Tenth Author\\textsuperscript{1}},\n% \\textbf{Eleventh E. Author\\textsuperscript{1,2,3,4,5}},\n% \\textbf{Twelfth Author\\textsuperscript{1}},\n%\\\\\n% \\textbf{Thirteenth Author\\textsuperscript{3}},\n% \\textbf{Fourteenth F. Author\\textsuperscript{2,4}},\n% \\textbf{Fifteenth Author\\textsuperscript{1}},\n% \\textbf{Sixteenth Author\\textsuperscript{1}},\n%\\\\\n% \\textbf{Seventeenth S. Author\\textsuperscript{4,5}},\n% \\textbf{Eighteenth Author\\textsuperscript{3,4}},\n% \\textbf{Nineteenth N. Author\\textsuperscript{2,5}},\n% \\textbf{Twentieth Author\\textsuperscript{1}}\n%\\\\\n%\\\\\n% \\textsuperscript{1}Affiliation 1,\n% \\textsuperscript{2}Affiliation 2,\n% \\textsuperscript{3}Affiliation 3,\n% \\textsuperscript{4}Affiliation 4,\n% \\textsuperscript{5}Affiliation 5\n%\\\\\n% \\small{\n% \\textbf{Correspondence:} \\href{mailto:email@domain}{email@domain}\n% }\n%}\n\n\n\n\n\\begin{document}\n\n\n\\maketitle\n\n\\begin{abstract}\n\nHow well do modern long-context language models understand literary fiction? We explore this question via the task of \\emph{literary evidence retrieval}, repurposing the RELiC dataset of~\\citet{thai-etal-2022-relic} to construct a benchmark where the entire text of a primary source (e.g., \\textit{The Great Gatsby}) is provided to an LLM alongside literary criticism with a missing quotation from that work. This setting, in which the model must generate the missing quotation, mirrors the human process of literary analysis by requiring models to perform both global narrative reasoning and close textual examination. We curate a high-quality subset of 292 examples through extensive filtering and human verification. Our experiments show that recent reasoning models, such as \\geminiprotwo\\ can exceed human expert performance (62.5\\% vs. 50\\% accuracy). In contrast, the best open-weight model achieves only 29.1\\% accuracy, highlighting a wide gap in interpretive reasoning between open and closed-weight models. Despite their speed and apparent accuracy, even the strongest models struggle with nuanced literary signals and overgeneration, signaling open challenges for applying LLMs to literary analysis. We release our dataset and evaluation code to encourage future work in this direction.\n\n\\end{abstract}\\section{Introduction}\n\\label{sec:intro} \n\nThe emergence of long-context language models, which can process millions of tokens~\\cite{geminiteam2024gemini}, has unlocked new AI applications for literary analysis. In this paper, we focus on the task of \\emph{literary evidence retrieval}, in which a model must retrieve a supporting quotation from a primary source (e.g., a novel) to substantiate an excerpt of literary criticism.\n\n~\\citet{thai-etal-2022-relic} frame literary evidence retrieval as a computational task by introducing RELiC, a dataset that contains short excerpts from published literary criticism that include quotations from famous novels. While RELiC was developed to benchmark and improve retriever models, which compute embeddings of claims and short chunks of the book text, we repurpose it as a testbed for long-context\\footnote{In this paper, we consider ``long-context\" to mean allowing a minimum of 128k tokens as input.} language models: A model is given the entire text of the book and an excerpt of literary criticism with a missing quotation from that book, then asked to generate the missing quote (see \\autoref{fig:llm_fail}, top). This task does not exactly mirror the human process of literary analysis, in which a scholar typically develops claims and selects supporting quotes iteratively. However, the task requires the same understanding of and complex reasoning over plot, subtext, and other literary devices with the advantage of being easily verifiable. \n\nTo enable robust evaluation, we curated a high-quality subset of 292 examples from RELiC, verified through a combination of automated filtering and expert human review. These 292 examples contain claims that require both global reasoning over events and ``close reading'' over singular passages to solve. Our experiments reveal that state-of-the-art reasoning models significantly outperform previous LLMs and even surpass a human expert baseline: our best model, \\geminiprotwo\\ obtains an accuracy of \\textbf{62.5\\%} compared to \\textbf{55.0\\%} for a human expert on a subset of the data. However, we also find that these models tend to overgenerate and struggle with subtle literary cues. Open-weight models perform substantially worse, suggesting that interpretive reasoning, not just long-context capacity, is essential to success in this domain, forming important directions for future research.\n\n\\begin{table}[t]\n\\centering\n\\footnotesize % Reduce font size for fitting in one column\n\n\n\\centering\n\n\\resizebox{0.48\\textwidth}{!}{%\n\\begin{tabular}{lcc|cc|c}\n\\toprule\n & \\multicolumn{2}{c}{\\textbf{Primary Sources }} & \\multicolumn{3}{c}{\\textbf{Dataset Examples}} \\\\\n & \\multicolumn{2}{c}{(\\textit{n=7})} & \\multicolumn{3}{c}{(\\textit{n=292})} \\\\\n\\cmidrule(lr){2-3} \\cmidrule(lr){4-6}\n & \\textsc{Tokens} & \\textsc{Words} & \\textsc{Tokens} & \\textsc{Words} & \\textsc{\\# Ex/Book}\\\\\n\\midrule\n\\textsc{Mean} & 85,526 & 69,456 & 254.9 & 203.6 & 36.0 \\\\\n\\textsc{St. Dev.} & 26,6304 & 21,167 & 66.9 & 52.9 & 19.6 \\\\\n\\textsc{Max} & 124,544 & 102,549 & 492.0 & 385.0 & 52.0 \\\\\n\\textsc{Min} & 45,038 & 37,209 & 116.0 & 91.0 & 7.0 \\\\\n\\bottomrule\n\\end{tabular}\n}\n\\caption{Summary statistics for long-context RELiC. Token counts were computed with the \\texttt{o200k\\_base} encoding via \\texttt{tiktoken} (\\url{https://github.com/openai/tiktoken}) and word counts were computed by splitting on whitespace.}\n\\label{tab:basic_summary_stats_dataset}\n\\end{table}\n\\section{Dataset Curation}\n\\label{sec:data_methods}\n\n\\begin{table}[t!]\n\\small\n\\resizebox{0.48\\textwidth}{!}{%\n\\begin{tabular}{lccc} % Updated to four columns\n\n\\toprule\n\\textsc{Model + Simple Prompt} & \\textsc{ALL} \\textsubscript{($n$=292)} & \\textsc{\\faUser} \\textsubscript{($n$=40)}& \\textsc{\\faBook} \\textsubscript{($n$=39)}\\\\ \n\\midrule\n\\geminiprotwo~\\cite{google2025gemini25} & 63.7 & 57.5 & \\textbf{82.1} \\\\\n\\othree~\\cite{o3} & 49.7 & 47.5 & 71.8 \\\\\n\\geminiproone~\\cite{geminiteam2024gemini} & 36.1 & 22.5 & 43.6 \\\\\n\\oone~\\cite{openai2024o1} & 32.2 & 25.0 & 41.0 \\\\\n\\gpto\\ \\cite{openai2024gpt4ocard} & 27.4 & 17.5 & 35.9 \\\\\n\\qwenlarge\\ \\cite{qwen_qwen25_2025} & 11.3 & 7.5 & 20.5 \\\\\n\\llamasmall\\ \\cite{grattafiori2024llama3herdmodels} & 5.1 & 5.0 & 2.6 \\\\\n\\qwensmall & 2.7 & 5.0 & 2.6 \\\\\n\\llamalarge & 2.1 & 0.0 & 2.6 \\\\\n\\midrule\n\\textsc{Model + Explanation Prompt} & & & \\\\\n\\midrule\n\\geminiprotwo & \\textbf{64.7} & \\textbf{62.5} & 79.5 \\\\\n\\gptfourone~\\cite{gpt-4.1} & 51.0 & 47.5 & 69.2 \\\\\n\\othree & 50.7 & 50.0 & 66.7 \\\\\n\\geminiproone & 38.5 & 40.0 & 50.0 \\\\\n\\claude~\\cite{claude_sonnet_2025} & 37.0 & 32.5 & 48.7 \\\\\n\\deepseek~\\cite{deepseekai2025deepseekv3technicalreport} & 29.1 & 15.0 & 38.5 \\\\\n\\gpto & 24.3 & 22.5 & 31.8 \\\\\n\\qwenthreelarge~\\cite{qwen_qwen25_2025} & 19.2 & 20.0 & 33.3 \\\\\n\\qwenthreesmall & 8.9 & 5.0 & 10.3 \\\\\n\\othreemini\\ \\cite{openai2025o3mini} & 8.3 & 10.0 & 13.6 \\\\\n\\midrule\n\\textsc{Baselines} & & & \\\\\n\\midrule\n\\gteqwen & 4.5 & 2.5 & 6.8 \\\\\n\\human & - & 55.0 & - \\\\\n\\bottomrule\n\\end{tabular}\n}\n\\caption{Percentage of test set examples where the model generated the correct ground truth quotation for different folds of the test set. The \\textsc{\\faUser} and \\textsc{\\faBook} columns contain the accuracy of each model on the human-evaluated and close reading subsets of the data, respectively.} \n\\label{tab:benchmark_accuracy}\n\\end{table}\n\n\nThe RELiC dataset~\\cite{thai-etal-2022-relic} consists of 78k excerpts of English-language literary analysis collected from scholarly journals where each excerpt includes a direct quotation from one of 79 primary source texts in the public domain. Each example in the dataset contains up to four sentences preceding the quotation and up to four sentences following the quotation. Together, these sentences of make up the ``context'' of the ground truth quotation. The primary source quotations may be up to five consecutive sentences in length. \n\n%Data about the books in the test and validation splits can be found in Table \\td{ref}.\n% , with the data for the train split books available in Appendix \\ref{}.\n% Because RELiC was designed as a retrieval task, the dataset also includes the full text of each primary source broken up into sentences.\n\n\n\n\\subsection{Adapting RELiC for long-context reasoning}\n% The original data curation and processing steps for RELiC involved applying several programmatic heuristics, such as fuzzy-matching and regex, to the data to classify examples of literary analysis, identify the boundaries between literary analysis and ground truth quotations, and segment the data into sentences. \nWhile large, RELiC is also noisy, which necessitates several filtering steps before we can evaluate models on it. We implemented an extensive data preprocessing pipeline\\footnote{All prompts, details of filtering heuristics, and descriptions of our manual validations are in Appendix A.} that involved several cleaning and filtering passes with \\gptmini\\ and \\gpto\\ supplemented by some programmatic heuristics, all geared towards mitigating the below issues:\n\\paragraph{Low data quality:} Some RELiC examples contain \\textbf{OCR artifacts} present in the primary source texts that render the prefixes and suffixes ungrammatical. There are also some examples that are \\textbf{misclassified} as literary analysis.\n\\paragraph{Model exploits:} Several aspects of RELiC examples can provide unintended cues to models, allowing them to bypass the reasoning challenge. One was the \\textbf{disclosure of the location} of the quote in the prefix or suffix. Another was \\textbf{quote leakage}, or the appearance of part or all of the ground truth quotation in the context. Finally, \\textbf{data contamination} was a concern--the literary analysis excerpts could appear in the training data of the LLMs we benchmarked and may have been memorized, or the ground truth quotation is so prevalent in the training data (because it belongs to a public domain novel) that the model is able to retrieve it without needing the primary source text at all.\n% \\paragraph{LLM-aided data filtering:}\n\n% Literary Analysis Section\n% \\begin{table*}[t]\n% \\centering\n% \\renewcommand{\\arraystretch}{1.4} % Better row spacing\n% \\setlength{\\tabcolsep}{6pt} % Reduce padding slightly\n% \\scriptsize\n% % Literary Analysis Section\n% \\begin{tabularx}{\\textwidth}{|X|}\n% \\hline\n% Literary Analysis Excerpt with \\relicquote{Ground Truth Quotation} from \\textit{The Great Gatsby} \\\\\n% \\hline\n% One of the more memorable scenes in \\textit{The Great Gatsby}, which Nick calls a \"picture,\" is a distant cousin of the facetious art photograph, \"George B. Wilson at the Gasoline Pump.\" It is a still picture of a mundane subject that reveals, with great impact, a critical insight and the emotional jolt at the discovery of bitter knowledge: \\\\\n\n\n% \\relicquote{Daisy and Tom were sitting opposite each other at the kitchen table with a plate of cold fried chicken between them and two bottles of ale. He was talking intently across the table at her and in his earnestness his hand had fallen upon and covered her own. Once in a while she looked up at him and nodded in agreement. They weren't happy, and neither of them had touched the chicken or the ale—and yet they weren't unhappy either. There was an unmistakable air of natural intimacy about the picture and anybody would have said that they were conspiring together.}\\\\\n\n% The passage could be considered \"Jamesian,\" comparable to corresponding tableaux in \\textit{The Ambassadors} and \\textit{The Portrait of a Lady}. The picture seals Gatsby's fate and Nick's too; his doubts and hopes are over now. \"Jamesian\" surely, but just as surely photographic. \\\\\n% \\hline\n% \\end{tabularx}\n% \\end{table*}\n\n% \\vspace{3pt} % Space between merged section and individual columns\n\n% % Model Comparison Table\n% \\begin{tabularx}{\\textwidth}{|X|X|}\n% \\hline\n% \\textbf{Response} & \\textbf{Explanation} \\\\\n% \\hline\n% \\faCheck\\ \\human: They weren’t happy, and neither of them had touched the chicken or the ale—and yet they weren’t unhappy either. There was an unmistakable air of natural intimacy about the picture, and anybody would have said that they were conspiring together. & The part in the suffix about \"sealing Gatsby's fate\" made me think this quote was towards the end and something damning for Gatsby. The \"picture\" described by Nick is Nick's observation that Daisy and Tom are actually fine after all of the drama of the car accident and Gatsby and Daisy revealing their relationship to Tom. \\\\\n% \\faClose\\ \\geminipro: In one of the windows over the garage the curtains had been moved aside a little and Myrtle Wilson was peering down at the car. So engrossed was she that she had no consciousness of being observed and one emotion after another crept into her face like objects into a slowly developing picture. Her expression was curiously familiar-it was an expression I had often seen on women's faces but on Myrtle Wilson's face it seemed purposeless and inexplicable until I realized that her eyes, wide with jealous terror, were fixed not on Tom, but on Jordan Baker, whom she took to be his wife. & The literary analysis excerpt refers to a memorable scene that Nick calls a \"picture,\" featuring a mundane subject with great impact, revealing bitter knowledge and sealing Gatsby's and Nick's fates. The analysis also mentions the scene being \"Jamesian\" and photographic. Therefore, the selected window should depict a static, impactful scene revealing a harsh truth that deeply affects both Gatsby and Nick, and ideally involves an observation made by Nick. The scene with Myrtle Wilson looking out the window fits these criteria perfectly. It is described as a slowly developing picture, reveals Myrtle's mistaken jealousy, and foreshadows the tragic events that follow, impacting both Gatsby and Nick. \\\\\n% % \\gpto: There was a faint, barely perceptible movement of the water as the fresh flow from one end urged its way toward the drain at the other. With little ripples that were hardly the shadows of waves, the laden mattress moved irregularly down the pool. A small gust of wind that scarcely corrugated the surface was enough to disturb its accidental course with its accidental burden. The touch of a cluster of leaves revolved it slowly, tracing, like the leg of a compass, a thin red circle in the water. & The literary analysis excerpt describes a \"picture\" in *The Great Gatsby* that is likened to a still photograph, revealing a critical insight and emotional jolt tied to bitter knowledge. The analysis suggests that this moment seals Gatsby's fate and Nick's as well, marking the end of their doubts and hopes. The most fitting scene is the one where Nick observes Gatsby's body in the pool, accompanied by the symbolic imagery of the mattress and the thin red circle in the water. This moment captures the stillness, the emotional weight, and the finality of Gatsby's fate, aligning with the analysis's description of a \"Jamesian\" tableau and a photographic quality. \\\\\n% \\faClose\\ \\othreemini: GEORGE B. WILSON. Cars Bought and Sold—and I followed Tom inside. The interior was unprosperous and bare; the only car visible was the dust‐covered wreck of a Ford which crouched in a dim corner. It had occurred to me that this shadow of a garage must be a blind and that sumptuous and romantic apartments were concealed overhead when the proprietor himself appeared in the door of an office, wiping his hands on a piece of waste. He was a blonde, spiritless man, anaemic, and faintly handsome. When he saw us a damp gleam of hope sprang into his light blue eyes.\n% & I chose a window from the section that introduces George B. Wilson because the literary analysis explicitly compares a “picture” in the novel to the facetious art photograph “George B. Wilson at the Gasoline Pump.” This window, which paints a stark, almost photographic portrayal of the mundane reality of Wilson’s life and surroundings, encapsulates the bitter insight that Nick experiences—a recognition of the decay and desolation underpinning the glitter of wealth—which in turn seals both Gatsby’s and Nick’s fates, as noted by the analysis. \\\\\n% \\hline\n% \\end{tabularx}\n\n% \\caption{Comparison of model responses with explanations and correctness analysis for a selected RELiC example.}\n% \\label{tab:relic_detailed_comparison}\n% \\end{table*}\n\n% Model Comparison Table\n% \\begin{tabularx}{\\textwidth}{|X|}\n% \\hline\n% \\faCheck\\ \\textbf{Human explanation}: The part in the suffix about \"sealing Gatsby's fate\" made me think this quote was towards the end and something damning for Gatsby. The \"picture\" described by Nick is Nick's observation that Daisy and Tom are actually fine after all of the drama of the car accident and Gatsby and Daisy revealing their relationship to Tom. \\\\\n% \\faClose\\ \\textbf{Gemini Pro 1.5 response}: In one of the windows over the garage the curtains had been moved aside a little and Myrtle Wilson was peering down at the car. So engrossed was she that she had no consciousness of being observed and one emotion after another crept into her face like objects into a slowly developing picture. Her expression was curiously familiar-it was an expression I had often seen on women's faces but on Myrtle Wilson's face it seemed purposeless and inexplicable until I realized that her eyes, wide with jealous terror, were fixed not on Tom, but on Jordan Baker, whom she took to be his wife. \\\\\n% \\faClose\\ \\textbf{o3-mini response}: GEORGE B. WILSON. Cars Bought and Sold—and I followed Tom inside. The interior was unprosperous and bare; the only car visible was the dust‐covered wreck of a Ford which crouched in a dim corner. It had occurred to me that this shadow of a garage must be a blind and that sumptuous and romantic apartments were concealed overhead when the proprietor himself appeared in the door of an office, wiping his hands on a piece of waste. He was a blonde, spiritless man, anaemic, and faintly handsome. When he saw us a damp gleam of hope sprang into his light blue eyes.\\\\\n% \\hline\n% \\end{tabularx}\n% \\vspace{-8pt}\n% \\caption{An example of long context RELiC where the \\human\\ annotator successfully retrieves the quotation while the LLMs fail.}\n% \\label{tab:human_beats_models_comparison}\n% \\end{table*}\n\n\\begin{figure*}[t]\n \\centering\n \\includegraphics[width=\\textwidth]{figures/table_3.png}\n \\caption{An example from long context RELiC where the \\human\\ annotator successfully retrieves the quotation while the top-performing LLMs fail.}\n \\label{fig:llm_fail}\n\\end{figure*}\n\n\\paragraph{Human verification:} After filtering, we create a high-quality human-verified subset of the dataset by having one of the authors, who has a degree in English literature and has read all of the primary source novels manually review 400 filtered examples. This author marked 292 examples that were well-formed instances of literary analysis with a ground truth quotation that could be identified given only the book and literary analysis context. See Table \\ref{tab:basic_summary_stats_dataset} for dataset statistics.\n% Overall, we identify 252 high-quality examples.\n% . The author was only shown examples from primary source novels they had read, and the examples were shown in random order. At their discretion, the author identified 252 particularly high quality examples from RELiC containing specific and detailed literary analysis that is well-supported by the ground truth quotation. \n\n% \\paragraph{Identification of close reading examples} During the manual verification of examples, the author also labeled examples of \\textbf{close reading}, a literary analysis technique in which the reader considers ``linguistic elements, semantic aspects, syntax, rhetoric, structural elements, thematic, and generic references in the text\" and ``delves into the form and structure of the text, gaining insight into the usage, purpose, and contextual significance of the language\" \\cite{close_reading}. A natural consequence of this interpretive technique in written literary analysis is the repeated citation of parts of the ground truth quote in the context. While an example of close reading is a well-formed example of literary analysis, the context contains lexical overlap with the ground truth quotation that the model can exploit during the RELiC task\\footnote{We attempted to implement another LLM-based filter for close reading examples, but found that \\gptmini\\ overwhelmingly designated RELiC instances as close reading examples. We also found that even closed source models did not perform perfectly on these ``easy'' retrieval examples, so we did not try to exclude them from our benchmarking dataset.}. \n\n% \\subsection{Long-Context Dataset Construction}\n% In constructing our final dataset, we discarded all RELiC examples with ground truth quotations from primary source that were longer than 128k tokens so we could evaluate the open-source Llama suite of models \\cite{grattafiori2024llama3herdmodels}, which have a context length of 128k. Following the original RELiC dataset approach, we created test, validation, and train splits by partitioning the primary source novels such that examples with ground truth quotations from a specific novel appear in only one of the splits. Data about the books in the test and validation splits can be found in Table \\ref{}, with the data for the train split books available in Appendix \\ref{}.\n\n% \\paragraph{} The validation set consists of 50 of the 252 high quality examples selected by our author. The remaining 202 high quality examples were designated for the test set and supplemented with 1,000 randomly sampled examples after applying all filters. In other words, we randomly selected 1,000 of the most ``challenging\" examples according to our heuristics. \n\n\\paragraph{Dataset folds:} \nOur new dataset contains two labeled folds of special data, (1) the \\faUser\\ \\textsc{Human Eval Set}: 40 examples attempted by our author, and (2) the \\faBook\\ \\textsc{Close Reading Set}: 39 examples labeled by our author as examples of \\textbf{close reading}, a literary analysis technique in which the reader considers ``linguistic elements, semantic aspects, syntax, rhetoric, structural elements, thematic, and generic references in the text''~\\cite{close_reading}. A natural consequence of this interpretive technique is the repeated citation of parts of the ground truth quote in the context. Since these examples contain lexical overlap with the ground truth quotation, models can exploit this during evidence retrieval.\n\n% \\paragraph{} Finally, we randomly sampled 10,000 examples for the train set. We report statistics for all splits in Table \\ref{} and for the test set folds in Table \\ref{}. Though we do not utilize the train set in our experiments, we will release it to spur future research into increasing the performance of open source models on long-context reasoning tasks.\n\n\\section{Experimental Setup}\n\\label{sec:experiments}\nWe evaluated four closed-source and four open-weight models on the long-context RELiC dataset, prompting each in a zero-shot setting to retrieve the most fitting quotation for a literary analysis excerpt given the full primary source text. We tested two prompt types: (1) \\textsc{Simple}, which requested only the quotation, and (2) \\textsc{Explanation}, which first asked the model to justify its choice before selecting a quotation\\footnote{Prompts \\& inference details in Appendix \\ref{sec:model-inf-details}.}. Our prompts are adapted from \\citet{karpinska-etal-2024-nocha} in the \\textsc{Nocha} long-context benchmark. Additionally, we implemented an embedding-based retrieval baseline using \\gteqwen, the top-performing text embedding model on MTEB\\footnote{\\url{https://huggingface.co/spaces/mteb/leaderboard} accessed February 2025} at the time of writing.\n\n\\subsection{Human Evaluation}\nThe author who manually validated high quality examples also attempted 40 examples from four previously-read primary source novels. The author had access to a digital copy of the primary source text with a string-match search function and the corresponding Wikipedia summaries. In addition to selecting missing quotations, the author also wrote a short justification for each choice. The task took approximately 8 hours.\n\n\\subsection{Evaluation Scheme}\nOur automatic evaluation of model responses applied partial ratio fuzzy matching (to account for minor typographical differences) to check that the model response was from the primary source text and to measure overlap between the response and the ground truth quotation\\footnote{Further details can be found in Appendix \\ref{sec:eval-details}.}. For the embedding baseline, we calculate recall@1.\n% \\FloatBarrier\n\\section{Results \\& Analysis}\n\\label{sec:analysis}\n\n\n% \\begin{table*}[t]\n% \\centering\n% \\renewcommand{\\arraystretch}{1.4} % Better row spacing\n% \\setlength{\\tabcolsep}{6pt} % Reduce padding slightly\n% \\scriptsize\n\n% % Literary Analysis Section\n% \\begin{tabularx}{\\textwidth}{|X|}\n\n% \\hline\n% Literary Analysis Excerpt with \\relicquote{Ground Truth Quotation} from \\textit{Frankenstein} \\\\\n% \\hline\n% \"It may therefore be judged indecent in me to come forward on this occasion,\" she proclaims on the stand, \"but when I see a fellow creature about to perish through the cowardice of her pretended friends, I wish to be allowed to speak, that I may say what I know of her character.\" Attempting to resist class lines and to speak of Justine as a \"fellow creature\" calls the silent cowardly, which means that Elizabeth, too, forgot her manners. Her testimony of Justine's dedication, loyalty, and nurturing cannot be heard, and the jury's response mimics Frankenstein's own preoccupation and inability to read the interactions and relationships between women in the broader sense. \\\\\n\n% \\relicquote{But I did not feel the inconvenience of the weather; my imagination was busy in scenes of evil and despair.} \\\\\n\n% Class and gender clearly matter in Geneva, and these factors, rather than a psychosexual rivalry, explain why Justine is punished for her ingratitude toward her benefactors. Elizabeth's testimony falls on deaf ears. What the jury perceives as important, and punishes, is indecorum—a breach of the mistress-servant relationship. \\\\\n% \\hline\n% \\end{tabularx}\n\n% \\vspace{3pt} % Space between merged section and individual columns\n\n% \\begin{tabularx}{\\textwidth}{|X|}\n% \\hline\n% \\faClose\\ \\textbf{Human and Gemini Pro 1.5 response}: A murmur of approbation followed Elizabeth’s simple and powerful appeal, but it was excited by her generous interference, and not in favour of poor Justine, on whom the public indignation was turned with renewed violence, charging her with the blackest ingratitude.\\\\\n\n% \\textbf{Human explanation}: I selected the quote that follows Elizabeth's full testimony, which is quoted in part at the beginning of the prefix. The selected quote also mentions Justine's \"ingratitute,\" like the suffix. \\\\\n% \\textbf{Gemini Pro 1.5 explanation}: The literary analysis focuses on Elizabeth's testimony during Justine's trial and the jury's reaction to it. The analysis highlights Elizabeth's attempt to transcend class boundaries by referring to Justine as a \"fellow creature\" and argues that this, along with Elizabeth's emotional testimony, is perceived as a breach of decorum and contributes to Justine's conviction. Therefore, the selected window should include Elizabeth's testimony where she calls Justine a \"fellow creature\" and speaks about her character, as well as the jury's immediate negative reaction, emphasizing their focus on the perceived impropriety of the situation rather than the content of the testimony. \\\\\n% \\hline\n% \\end{tabularx}\n% \\vspace{-8pt} % Reduce vertical spaces\n% \\caption{An example of long context RELiC where the \\human\\ annotator and \\geminipro\\ both select the same quotation, but it is not the ground truth quotation.}\n% \\label{tab:human_beats_models_comparison_2}\n% \\end{table*}\n\n\\begin{figure*}[t]\n \\centering\n \\includegraphics[width=\\textwidth]{figures/table_4.png}\n \\caption{An example of long context RELiC where the \\human\\ annotator and \\geminiprotwo\\ both select the same quotation, but it is not the ground truth quotation.}\n \\label{fig:alt_evidence}\n\\end{figure*}\n\n\nTable \\ref{tab:benchmark_accuracy} reports results for all evaluated models on the full set of 292 claims and the two folds. \n\n\\paragraph{LLMs outperform \\human\\ and SOTA embedding baseline}\nGoogle's \\geminiprotwo\\ reasoning model achieves better accuracy than our \\human\\ baseline in a fraction of the time---each API call took an average of 45 seconds vs. an average of 12 minutes for the \\human\\ expert. Both Google and OpenAI’s latest long-context models show substantial performance gains over their immediate predecessors on the literary evidence retrieval task. \\geminiprotwo\\ outperforms \\geminiproone\\ by a wide margin (64.7\\% vs. 38.5\\%), and OpenAI’s \\gptfourone\\ improves significantly over \\gpto\\ (51.0\\% vs. 24.3\\%). Similarly, \\othree\\ surpasses \\oone\\ (48.7\\% vs. 32.2\\%).\nThe embedding-based method achieved 4.5\\% accuracy, only 1.6\\% higher than the best recall@1 reported in the original RELiC paper from three years ago. LLMs’ success over embeddings suggests the importance of contextualized representations that can enable reasoning over entire primary source texts.\n\n\\paragraph{Closed-source outperform open-weight LLMs} Despite recent progress in open-weight LLM development, we observe a striking performance gap between closed-source and open-weight models on the literary evidence retrieval task. The best-performing open-weight model, \\deepseek, achieves only 29.1\\% accuracy---less than half the performance of the top closed-source model, \\geminiprotwo\\ (64.7\\%).\n\n\\paragraph{Small LLMs can't capitalize on close reading examples} Nearly all models larger than 8B parameters show modest to significant improvements on the close reading fold, with some models improving by over 20\\%. This effect is expected due to ground truth leakage in the the provided excerpt of literary analysis, but the 7B and 8B parameter open-weight models experience nearly no performance boost on this fold---the performance of \\llamasmall\\ actually suffers, suggesting that the smaller models lack the capacity to even exploit lexical overlap. \n\n\n\\paragraph{\\textsc{Explanation} prompt provides a glimpse into model reasoning} We observed mixed results when querying non-reasoning LLMs with the \\textsc{explanation} prompt. While \\geminiproone\\ improved with explanation-based reasoning, \\gpto\\ saw a decline. Recent reasoning LLMs such as \\geminiprotwo\\ and \\othree\\ use internal reasoning tokens that are not exposed via the API but influence the model's final output. These tokens are critical to the model's performance on complex tasks, but their inaccessibility complicates model evaluation and comparison. Applying the \\textsc{explanation} prompt to these reasoning models preserved accuracy, despite the redundancy, while providing insight into the models' reasoning processes. See a comparison of human and model justifications in Figure \\ref{fig:alt_evidence}. \n\n\\paragraph{All models tend to overgenerate}\nDespite prompts explicitly instructing models to select no more than five consecutive sentences from the primary source, we observe a consistent tendency across all evaluated models to produce significantly longer outputs. To quantify this behavior, we compute the length ratio, defined as the ratio of the model’s generated output length to the ground truth quotation length (measured in characters); values closer to 1.0 indicate greater adherence to the prompt constraints and minimal overgeneration. The length ratios are reported in Table \\ref{tab:length_ratios}.\n\nWe find that all models overgenerate, including the human annotator (average ratio of 2.1), likely due to natural variance in interpreting sentence boundaries. However, language models consistently surpass this baseline: for instance, state-of-the-art models such as \\geminiprotwo\\ and \\gptfourone\\ have average ratios of 3.0 and 4.8, respectively. Smaller open models like \\llamasmall\\ and \\llamalarge\\ exhibit even more extreme overgeneration (ratios > 5.7), suggesting that weaker models may compensate for uncertainty by producing longer outputs. \n\n\\paragraph{LLMs struggle with literary nuance}\nIn Figure \\ref{fig:llm_fail}, we present a challenging example where all LLMs fail but our \\human\\ expert correctly identified the ground truth quotation from \\textit{The Scarlet Letter}, a >80k-word novel. The context alludes to a description of the character Roger Chillingworth at a specific moment in the story (when he recognizes Hester Prynne). In their explanation, the \\human\\ expert mentions considering two different passages, but ultimately selecting the correct passage because it best demonstrates the ``melodrama\" mentioned in the literary analysis. The expert highlights the features of the correct passage that demonstrate ``melodrama\": an exaggerated character description, words that connote movement, and emotional tension. All three top-performing LLMs selected the other passage the \\human\\ expert was considering but ultimately eliminated, suggesting that even the best models still lack the expertise to navigate literary devices and more nuanced signals that inform expert literary interpretation. \n\n% \\vspace{-10pt}\n\\paragraph{Models can identify alternative literary evidence} In Figure \\ref{fig:alt_evidence}, we present an example where both \\human\\ and \\geminiprotwo\\ selected the same \"incorrect\" quote. The explanations reveal that both interpreted the literary analysis context as an introduction to a quote about the jury’s response. However, the ground truth quotation actually supports Frankenstein’s own preoccupation rather than the trial’s immediate outcome. This example highlights two key insights: (1) literary evidence retrieval is inherently interpretative, meaning that multiple passages may plausibly support a given claim, and (2) LLMs, like human readers, can surface alternative yet reasonable quotations that align with certain aspects of the analysis. While models may not always select the canonical answer, their ability to propose viable alternative evidence could be valuable for assisting literary scholars in exploring multiple textual connections.\n\n\\begin{table}[t!]\n\\small\n\\centering\n\\resizebox{0.48\\textwidth}{!}{%\n\\begin{tabular}{lcc}\n\\toprule\n\\textsc{Model + Simple Prompt} & \\textsc{\\faUser} Accuracy \\textsubscript{($n$=40)} & Avg. Length Ratio\\\\\n\\midrule\n\\geminiprotwo & 57.5 & 3.5\\\\\n\\othree & 47.5 & 3.5\\\\\n\\geminiproone & 22.5 & 2.8\\\\\n\\oone & 25.0 & 3.2\\\\\n\\gpto & 17.5 & 3.9\\\\\n\\qwenlarge & 7.5 & 3.2\\\\\n\\llamasmall & 5.0 & 5.9\\\\\n\\qwensmall & 5.0 & 2.8\\\\\n\\llamalarge & 0.0 & 5.7\\\\\n\\midrule\n\\multicolumn{3}{l}{\\textsc{Model + Explanation Prompt}} \\\\\n\\midrule\n\\geminiprotwo & \\textbf{62.5} & 3.0\\\\\n\\gptfourone & 47.5 & 4.8\\\\\n\\othree & 50.0 & 2.7\\\\\n\\geminiproone & 40.0 & 3.3\\\\\n\\claude & 32.5 & 4.0\\\\\n\\deepseek & 15.0 & 3.6\\\\\n\\gpto & 22.5 & 3.6\\\\\n\\qwenthreelarge & 20.0 & 2.7\\\\\n\\qwenthreesmall & 5.0 & 2.4\\\\\n\\othreemini & 10.0 & 3.5\\\\\n\\midrule\n\\multicolumn{3}{l}{\\textsc{Baselines}} \\\\\n\\midrule\n\\human & 55.0 & \\textbf{2.1}\\\\\n\\bottomrule\n\\end{tabular}\n}\n\\caption{The average length ratios for each model, defined as the ratio of the length model generation to that of the ground truth (measured in characters). All models have an average ratio greater than that of the human annotator (2.1), indicating overgeneration.}\n\\label{tab:length_ratios}\n\\end{table}\n\n\n\\section{Related Work}\n\\label{sec:rel_work}\n\nOur work builds on recent papers that apply and evaluate LLMs for computational literary analysis. Prior work has explored summarization or claim verification in novels~\\cite{subbiah-etal-2024-storysumm,fables24,karpinska-etal-2024-nocha} or short stories~\\cite{10.1162/tacl_a_00702}. Other more specific tasks, mainly in the short context setting, include extracting narrative elements~\\cite{shen-etal-2024-heart}, story arcs and turning points~\\cite{tian-etal-2024-large-language}, character analysis~\\cite{papoudakis-etal-2024-bookworm}, narrative discourse~\\cite{piper-bagga-2024-using}, plot development~\\cite{huot2024agents,xu-etal-2024-fine}, and creativity evaluation~\\cite{10.1145/3613904.3642731}. \n\n\\section{Conclusion}\nIn this work, we introduced a long-context dataset derived from RELiC for evaluating literary evidence retrieval using large language models. We constructed a high-quality set of 292 examples, ensuring rigorous evaluation through filtering and human verification. Our results demonstrate that long-context LLMs outperform embedding-based retrieval methods and even a \\human\\ expert baseline. However, closer analysis reveals that model success is not always grounded in robust interpretative abilities: models tend to overgenerate and still struggle to grasp nuances in literature. But, they can also successfully identify additional evidence for literary claims. Finally, open-weight models lag far behind their closed-weight counterparts, suggesting that long-context capabilities alone are insufficient without strong interpretive reasoning. We release our data and evaluation code to spur research at the intersection of NLP and literary analysis.\n\\section*{Limitations}\nAs noted in the original paper, the RELiC dataset represents a limited, English-only subset of world literature, with a strong emphasis on the Western literary canon. To foster a more inclusive and representative benchmark, we aim to extend this work to cross-lingual literary analysis and retrieval on texts from historically underrepresented literary traditions. Expanding the dataset in this way would not only enhance the linguistic and cultural diversity of literary evidence retrieval but also improve the generalizability of models across different literary frameworks.\n\nAdditionally, our human evaluation was limited in scope and scale, as it was conducted by a single annotator, one of the authors of this paper. While this approach was practical given the intensive nature of literary evidence retrieval over full novels, it inherently reflects the knowledge, biases, and interpretive lens of a single individual. In the future, we aim to broaden our evaluation by incorporating multiple expert annotators, enabling a more comprehensive and diverse assessment of model performance on the task of complex literary reasoning.\\section*{Ethical Considerations}\nWhile LLMs can assist in literary interpretation, they should not replace scholarly analysis conducted by trained experts. We acknowledge the potential for misuse of a system that can retrieve evidence for scholarly claims and emphasize that automated retrieval should be viewed as a complement to, not a substitute for, human literary scholarship.\n\nAs mentioned in the Limitations section, the primary source texts in our dataset are drawn from public domain works, which primarily reflect Western literary traditions. The results of this benchmark may not generalize to literary traditions outside the Anglophone canon, potentially reinforcing existing biases in computational literary studies.\n\n\n\\bibliography{custom}\n\n\n\\appendix\n\\section{Dataset Details}\n\\label{app:data_details}\nWe are using a publicly available dataset released by researchers who stated in their paper that they released their data to \"facilitate further research in this direction.\" Statistics for the primary sources can be found in \\ref{tab:book-details}.\n\n\n\\begin{table*}[t]\n \\renewcommand{\\arraystretch}{1.2} \n \\setlength{\\tabcolsep}{6pt}\n \\footnotesize\n \\centering\n \\begin{tabular}{l l c c c}\n \\toprule\n \\textbf{Book Title} & \\textbf{Author} & \\textbf{Publication Year} & \\textbf{Token Count} & \\textbf{Word Count} \\\\\n \\midrule\n Brave New World & Aldous Huxley & 1932 & 91,472 & 65,278 \\\\\n What Maisie Knew & Henry James & 1897 & 124,544 & 95,988 \\\\\n Ethan Frome & Edith Wharton & 1911 & 45,038 & 34,926 \\\\\n Frankenstein & Mary Shelley & 1818 & 95,018 & 75,131 \\\\\n The Great Gatsby & F. Scott Fitzgerald & 1925 & 63,683 & 48,972 \\\\\n The Awakening & Kate Chopin & 1899 & 66,574 & 49,932 \\\\\n The Scarlet Letter & Nathaniel Hawthorne & 1850 & 112,355 & 83,311 \\\\\n \\bottomrule\n \\end{tabular}\n \\caption{Books included in the dataset. The token count was provided as per \\href{https://github.com/openai/tiktoken}{\\texttt{tiktoken}} tokenization. }\n \\label{tab:book-details}\n\\end{table*}\n\n\n\n\n\n\\section{LLM-Aided Data Processing}\n\\subsection{Filtering steps}\nThese cleaning and filtering steps were applied to the entire RELiC dataset in the following order:\n\\begin{enumerate}\n \\item \\verb|CLEAN| (\\gptmini): We asked the model to remove any OCR artifacts and to ensure the context and ground truth quotation flow seamless and grammatically without changing any meaning. Additionally, we asked the model to remove any remaining in-line citations that revealed the page number of the ground truth quotation, which allowed us to preserve some examples that might have been caught by the \\verb|LOCATION| filter below. See the prompt in Table \\ref{tab:clean_filter}.\n \\item \\verb|LEAKAGE| (heuristic): If the sentences in the context preceding or following the ground truth quotation were fuzzy-matches (threshold: 95) with any text from the primary source, the example was excluded from our dataset.\n \\item \\verb|LIT ANALYSIS| (\\gptmini): We asked the model to classify whether each RELiC instance was an example of literary analysis. If it was not, we excluded it from our dataset. See the prompt in Table \\ref{tab:classify_lit_analysis}.\n \\item \\verb|LOCATION| (\\gptmini): We asked the model if the context revealed the location of the ground truth quotation. If it did, we excluded it from our dataset. See the prompt in Table \\ref{tab:quote_location_disclosure}.\n \\item \\verb|FIRST SENT| (heuristic): We identified cases where the ground truth quotation was the first sentence of its primary source novel. The intuition behind this filter (and the following two) was that the first sentence of a primary source might be famous or more likely to be quoted, and therefore easier to guess without needing to reason over the text or more likely to appear in training data.\n \\item \\verb|LAST SENT| (heuristic): Similarly, we identified cases where the ground truth quotation was the last sentence of its primary source novel.\n \\item \\verb|OUTLIER| (heuristic): We identified cases where the ground truth quotation was cited much more frequently than any of the other quotations from the same primary source novel.\n \\item \\verb|EZ2MEM| (\\gpt): We asked the model to perform the RELiC task \\textit{without} providing the primary source text and identified cases where the model was able to correctly generate at least one sentence of the ground truth quotation. \n\\end{enumerate}\nNote that we did not automatically exclude any of the examples identified by the last four filters. Though we did not use them in this project, we have left the labels in our dataset to facilitate future research.\n\n\\subsection{Manual validation of data}\nWe conducted multiple validations of the RELiC data for quality control using human annotations. \n\n\\paragraph{Validation of LLM filters} To validate our LLM-aided approach to data preprocessing, we manually annotated 100 examples of RELiC and compared our judgments to keep or reject each example to the results of the LLM + heuristic filters. The f1-score of our filtering scheme was 89.8, with our scheme identifying 57 true positives, 30 true negatives, 6 false positives, and 7 false negatives.\n\n\\section{Model Inference Details}\n\\label{sec:model-inf-details}\n\\paragraph{API access details}\n\\begin{itemize}\n \\item All OpenAI models were accessed via the OpenAI API (\\url{https://platform.openai.com/}).\n \\item All Gemini models were accessed via the Google AI API (\\url{https://ai.google.dev/}).\n \\item \\claude\\ was accessed via the Vertex AI/Google Cloud (\\url{https://cloud.google.com/vertex-ai?hl=en}).\n \\item DeepSeek was run via OpenRouter (\\url{https://openrouter.ai/}).\n\\end{itemize}\nThe approximate total cost of running all closed-weight models for both development and evaluation was \\$2k. \n\n\\paragraph{Local inference details}\nAll Llama and Qwen models were run locally. The smaller open-weight models were run on 1 A100 GPU for a total of 14 hours, while the larger open-weight models were run on 4 A100 GPUs for a total of 16 hours. \n\n\\paragraph{Generation hyperparameters}\nThe OpenAI reasoning models (\\oone, \\othreemini, and \\othree) were a special case: the temperature parameter is fixed and the token generation limit includes the inaccessible reasoning tokens. As such, we set the token generation limit to 12,000 for \\oone\\ and \\othreemini. For \\othree, the token generation limit was set to 25,000 as recommended by OpenAI\\footnote{\\url{https://platform.openai.com/docs/guides/reasoning\\#allocating-space-for-reasoning}}. For all three OpenAI reasoning models, we used the default \\verb|medium| reasoning effort. \n\nFor all other models, the token generation limit was set to 800 for \\textsc{Simple} prompts and 1,200 for \\textsc{Explanation} prompts. Temperature was set to 0.0 for all models except for \\qwenthreelarge\\ and \\qwenthreesmall, where temperature was set to the default 0.6.\n\nFor a list of the models and their checkpoints, see Table \\ref{tab:models}.\n\n\\begin{table}[t!]\n\\centering\n\\small\n\\resizebox{0.48\\textwidth}{!}{%\n\\begin{tabular}{l c l c c}\n\\toprule\n\\textsc{Model} & \\textsc{Context} & \\textsc{Avail.} & \\textsc{Checkpoints} \\\\\n\\midrule\n\\href{https://platform.openai.com/docs/models}{\\oone} & 200k & \\faLock & \\texttt{o1-2024-12-17} \\\\\n\\href{https://platform.openai.com/docs/models}{\\othree} & 200k & \\faLock & \\texttt{o3-2025-04-16} \\\\\n\\href{https://platform.openai.com/docs/models}{\\othreemini} & 200k & \\faLock & \\texttt{o3-mini-2025-01-31} \\\\\n\\href{https://platform.openai.com/docs/models}{\\gpto} & 128k & \\faLock & \\texttt{gpt-4o-2024-11-20} \\\\\n\\href{https://platform.openai.com/docs/models}{\\gptfourone} & 1M & \\faLock & \\texttt{gpt-4.1-2025-01-31} \\\\\n\\href{https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/gemini}{\\geminiproone} & 1M & \\faLock & \\texttt{gemini-1.5-pro-002} \\\\\n\\href{https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/gemini}{\\geminiprotwo} & 1M & \\faLock & \\texttt{gemini-2.5-pro-preview-05-06} \\\\\n\\href{https://docs.anthropic.com/en/docs/about-claude/models/overview}{\\claude} & 200k & \\faLock & \\texttt{claude-3-7-sonnet@20250219} \\\\\n\\href{https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct}{\\llamalarge} & 128k & \\faUnlock & \\texttt{Llama-3.3-70B-Instruct} \\\\\n\\href{https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct}{\\llamasmall} & 128k & \\faUnlock & \\texttt{\nLlama-3.1-8B-Instruct } \\\\\n\\href{https://huggingface.co/Qwen/Qwen2.5-72B-Instruct}{\\qwenlarge} & 128k & \\faUnlock & \\texttt{Qwen2.5-72B-Instruct} \\\\\n\\href{https://huggingface.co/Qwen/Qwen2.5-7B-Instruct}{\\qwensmall} & 128k & \\faUnlock & \\texttt{Qwen2.5-7B-Instruct} \\\\\n\\href{https://huggingface.co/Qwen/Qwen3-8B}{\\qwenthreesmall} & 131k* & \\faUnlock & \\texttt{Qwen3-8B} \\\\\n\\href{https://huggingface.co/Qwen/Qwen3-32B}{\\qwenthreelarge} & 131k* & \\faUnlock & \\texttt{Qwen3-32B} \\\\\n\\midrule\n\\href{https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct}{\\gteqwenparam} & 32k & \\faUnlock & \\texttt{gte-Qwen2-7B-instruct} \\\\\n\\bottomrule\n\\end{tabular}\n}\n\\caption{The upper rows display the evaluated LLMs, while the bottom row displays the text embedding model used for the baseline. Context lengths marked with * were extended with YaRN.}\n\\label{tab:models}\n\\end{table}\n\n\\section{Evaluation Scheme Details}\n\\label{sec:eval-details}\nWe use the \\verb|rapidfuzz| Python package for our fuzzy match evaluation with a threshold of 95 for checking the existence of the model response in the primary source and a threshold of 90 for checking the overlap between the model response and the ground truth quotation. The fuzzy matching allows for small typographical differences between the primary source and the model outputs (e.g. different types of quotation marks). Thresholds were determined after manual inspection of outputs at varying thresholds. \n\n\n\n% \\section{Additional Example for \\textsc{Human} vs. \\textsc{Model}}\n% \\label{sec:hum_vs_model_extra}\n\n\n\n\n\\begin{table*}[t]\n \\renewcommand{\\arraystretch}{1.2} \n \\setlength{\\tabcolsep}{4pt}\n \\footnotesize\n \\centering\n \\begin{tabular}{c p{15cm}}\n \\toprule\n & \\multicolumn{1}{c}{\\bf Prompt (Simple)} \\\\\n \\midrule\n \\noalign{\\vskip 1mm}\n & \\texttt{You are provided with the full text of \\textbf{[book\\_title]} and an excerpt of literary analysis that directly cites \\textbf{[book\\_title]} with the cited quotation represented as \\textless MASK\\textgreater.} \\\\[3mm]\n & \\texttt{Your task is to carefully read the text of \\textbf{[book\\_title]} and the excerpt of literary analysis, then select a window from \\textbf{[book\\_title]} that most appropriately replaces \\textless MASK\\textgreater as the cited quotation by providing textual evidence for any claims in the literary analysis.} \\\\[1mm]\n & \\texttt{The excerpt of literary analysis should form a valid argument when \\textless MASK\\textgreater is replaced by the window from \\textbf{[book\\_title]}.} \\\\[1mm]\n & \\texttt{\\textless full\\_text\\_of\\_[book\\_title\\_snake\\_case]\\textgreater \\textbf{[book\\_sentences]} \\textless /full\\_text\\_of\\_[book\\_title\\_snake\\_case]\\textgreater} \\\\[1mm]\n & \\texttt{\\textless literary\\_analysis\\_excerpt\\textgreater \\textbf{[lit\\_analysis\\_excerpt]} \\textless /literary\\_analysis\\_excerpt\\textgreater} \\\\[1mm]\n & \\texttt{Identify the window that best supports the claims being made in the excerpt of literary analysis. The window should contain no more than 5 consecutive sentences from \\textbf{[book\\_title]}.} \\\\[1mm]\n & \\texttt{Provide your final answer in the following format:} \\\\[1mm]\n & \\texttt{\\textless window\\textgreater \\textbf{YOUR SELECTED WINDOW} \\textless /window\\textgreater} \\\\[1mm]\n \\bottomrule\n \\end{tabular}\n\\caption{Prompt template for literary evidence retrieval (Simple).}\n\\label{tab:inference_simple}\n\\end{table*}\n\n\n\\begin{table*}[t]\n \\renewcommand{\\arraystretch}{1.2} \n \\setlength{\\tabcolsep}{4pt}\n \\footnotesize\n \\centering\n \\begin{tabular}{c p{15cm}}\n \\toprule\n & \\multicolumn{1}{c}{\\bf Prompt w/ Explanations} \\\\\n \\midrule\n \\noalign{\\vskip 1mm}\n & \\texttt{You are provided with the full text of \\textbf{[book\\_title]} and an excerpt of literary analysis that directly cites \\textbf{[book\\_title]} with the cited quotation represented as \\textless MASK\\textgreater.} \\\\[3mm]\n & \\texttt{Your task is to carefully read the text of \\textbf{[book\\_title]} and the excerpt of literary analysis, then select a window from \\textbf{[book\\_title]} that most appropriately replaces \\textless MASK\\textgreater as the cited quotation by providing textual evidence for any claims in the literary analysis.} \\\\[1mm]\n & \\texttt{The excerpt of literary analysis should form a valid argument when \\textless MASK\\textgreater is replaced by the window from \\textbf{[book\\_title]}.} \\\\[1mm]\n & \\texttt{\\textless full\\_text\\_of\\_[book\\_title\\_snake\\_case]\\textgreater \\textbf{[book\\_sentences]} \\textless /full\\_text\\_of\\_[book\\_title\\_snake\\_case]\\textgreater} \\\\[1mm]\n & \\texttt{\\textless literary\\_analysis\\_excerpt\\textgreater \\textbf{[lit\\_analysis\\_excerpt]} \\textless /literary\\_analysis\\_excerpt\\textgreater} \\\\[1mm]\n & \\texttt{First, provide an explanation of your decision marking process in no more than one paragraph.} \\\\[1mm]\n & \\texttt{Then, identify the window that best supports the claims being made in the excerpt of literary analysis. The window should contain no more than 5 consecutive sentences from \\textbf{[book\\_title]}.} \\\\[1mm]\n & \\texttt{Provide your final answer in the following format:} \\\\[1mm]\n & \\texttt{\\textless explanation\\textgreater \\textbf{YOUR EXPLANATION} \\textless /explanation\\textgreater} \\\\[1mm]\n & \\texttt{\\textless window\\textgreater \\textbf{YOUR SELECTED WINDOW} \\textless /window\\textgreater} \\\\[1mm]\n \\bottomrule\n \\end{tabular}\n\\caption{Prompt template for literary evidence retrieval with explanation.}\n\\label{tab:inference_explain}\n\\end{table*}\n\n\n\\begin{table*}[t]\n \\renewcommand{\\arraystretch}{1.2} \n \\setlength{\\tabcolsep}{4pt}\n \\footnotesize\n \\centering\n \\begin{tabular}{c p{15cm}}\n \\toprule\n & \\multicolumn{1}{c}{\\bf Prompt for Cleaning RELiC Examples} \\\\\n \\midrule\n \\noalign{\\vskip 1mm}\n & \\texttt{I want to create a dataset for Natural Language Processing that consists of excerpts of literary analysis that quote directly from a primary source text.} \\\\[3mm]\n & \\texttt{I have already collected windows of literary analysis that quote from many different primary sources in the public domain. The part of the excerpt before the quotation is called the \"prefix,\" and the part of the excerpt after the quotation is called the \"suffix.\" Because I collected the data from PDFs with OCR, the prefixes and suffixes contain artifacts like in-line citations, page numbers, chapter names, headers, and footers that I need to remove.} \\\\[3mm]\n & \\texttt{Here is an example:} \\\\[1mm]\n & \\texttt{\\textless prefix\\textgreater \\textbf{[prefix]} \\textless /prefix\\textgreater} \\\\[1mm]\n & \\texttt{\\textless ground\\_truth\\_quotation\\textgreater \\textbf{[ground\\_truth\\_quotation]} \\textless /ground\\_truth\\_quotation\\textgreater} \\\\[1mm]\n & \\texttt{\\textless suffix\\textgreater \\textbf{[suffix]} \\textless /suffix\\textgreater} \\\\[1mm]\n & \\texttt{Please follow the following guidelines to help me clean and filter this example:} \\\\[1mm]\n & \\texttt{1. Remove all textual artifacts from the OCR process by deleting them. Artifacts include page numbers, chapter headings, footnotes, image captions, etc.} \\\\[1mm]\n & \\texttt{2. Correct the grammar, punctuation, and spelling of the prefix and suffix without altering the meaning so that the primary source quotation fits seamlessly and grammatically between the prefix and suffix.} \\\\[1mm]\n & \\texttt{3. Remove all in-line citations following quotations, especially those that say things like \"(my emphasis).\"} \\\\[1mm]\n & \\texttt{Respond with ONLY the cleaned prefix and suffix in the following format:} \\\\[1mm]\n & \\texttt{\\textless clean\\_prefix\\textgreater \\textbf{CLEAN PREFIX} \\textless /clean\\_prefix\\textgreater} \\\\[1mm]\n & \\texttt{\\textless clean\\_suffix\\textgreater \\textbf{CLEAN SUFFIX} \\textless /clean\\_suffix\\textgreater} \\\\[1mm]\n \\bottomrule\n \\end{tabular}\n\\caption{Prompt template for cleaning RELiC examples}\n\\label{tab:clean_filter}\n\\end{table*}\n\n\\begin{table*}[t]\n \\renewcommand{\\arraystretch}{1.2} \n \\setlength{\\tabcolsep}{4pt}\n \\footnotesize\n \\centering\n \\begin{tabular}{c p{15cm}}\n \\toprule\n & \\multicolumn{1}{c}{\\bf Prompt for Classifying Literary Analysis} \\\\\n \\midrule\n \\noalign{\\vskip 1mm}\n & \\texttt{I want to create a dataset for Natural Language Processing that consists of excerpts of literary analysis that quote directly from a primary source text.} \\\\[3mm]\n & \\texttt{Other texts can contain quotes from primary sources, like biographies of authors. However, I only want examples of literary analysis in the dataset.} \\\\[3mm]\n & \\texttt{Please determine whether the following excerpt is an example of literary analysis. If it is literary analysis, respond with TRUE. Otherwise, respond with FALSE.} \\\\[3mm]\n & \\texttt{Here is the excerpt:} \\\\[1mm]\n & \\texttt{\\textless excerpt\\textgreater \\textbf{[clean\\_prefix]} \\textbf{[answer\\_quote]} \\textbf{[clean\\_suffix]} \\textless /excerpt\\textgreater} \\\\[1mm]\n & \\texttt{Respond with ONLY your answer in the following format:} \\\\[1mm]\n & \\texttt{\\textless answer\\textgreater \\textbf{YOUR ANSWER} \\textless /answer\\textgreater} \\\\[1mm]\n \\bottomrule\n \\end{tabular}\n\\caption{Prompt template for classifying literary analysis}\n\\label{tab:classify_lit_analysis}\n\\end{table*}\n\n\\begin{table*}[t]\n \\renewcommand{\\arraystretch}{1.2} \n \\setlength{\\tabcolsep}{4pt}\n \\footnotesize\n \\centering\n \\begin{tabular}{c p{15cm}}\n \\toprule\n & \\multicolumn{1}{c}{\\bf Prompt for Identifying Quote Location Disclosure} \\\\\n \\midrule\n \\noalign{\\vskip 1mm}\n & \\texttt{I want to create a dataset for a Natural Language Processing task called \"Literary Evidence Retrieval.\"} \\\\[3mm]\n & \\texttt{The input has two parts:} \\\\[1mm]\n & \\texttt{(1) An excerpt of literary analysis that quotes 1 to 5 full sentences from a primary source text, but the quote is replaced with \"[MASK]\".} \\\\[1mm]\n & \\texttt{(2) The full text of the primary source.} \\\\[3mm]\n & \\texttt{The output should be the correct quotation of 1 to 5 full sentences from the primary source—this is the ground truth.} \\\\[3mm]\n & \\texttt{I have already collected windows of literary analysis that quote from many different primary sources in the public domain.} \\\\[3mm]\n & \\texttt{The part of the excerpt before the quotation is called the \"prefix,\" and the part after is called the \"suffix.\"} \\\\[3mm]\n & \\texttt{To avoid giving the model hints, it's extremely important that the prefix and suffix do not mention the chapter where the ground truth quotation is located.} \\\\[3mm]\n & \\texttt{Please determine whether the following instance contains the chapter location of the ground truth quotation in either the prefix or the suffix. If it does, respond with TRUE; otherwise, respond with FALSE.} \\\\[3mm]\n & \\texttt{The prefix and suffix may contain the location of other quotations in the passage. I only want to identify if the location of the ground truth quotation is revealed.} \\\\[3mm]\n & \\texttt{Look out for phrases like \"In the prologue\" or \"In Chapter...\"} \\\\[3mm]\n & \\texttt{Here is the instance:} \\\\[1mm]\n & \\texttt{\\textless prefix\\textgreater \\textbf{[prefix]} \\textless /prefix\\textgreater} \\\\[1mm]\n & \\texttt{\\textless ground\\_truth\\_quotation\\textgreater \\textbf{[ground\\_truth\\_quotation]} \\textless /ground\\_truth\\_quotation\\textgreater} \\\\[1mm]\n & \\texttt{\\textless suffix\\textgreater \\textbf{[suffix]} \\textless /suffix\\textgreater} \\\\[1mm]\n & \\texttt{Respond with ONLY your answer in the following format:} \\\\[1mm]\n & \\texttt{\\textless answer\\textgreater \\textbf{YOUR ANSWER} \\textless /answer\\textgreater} \\\\[1mm]\n \\bottomrule\n \\end{tabular}\n\\caption{Prompt template for identifying quote location disclosure}\n\\label{tab:quote_location_disclosure}\n\\end{table*}\n\n\\section{Use of AI Assistants}\nThe authors used Github Copilot for coding assistance during their experiments and ChatGPT for assistance in formatting \\LaTeX\\ in the writing of this paper.\n\\end{document}"},"question":{"kind":"string","value":"In the table, which group shows the highest relative variability in token count?"},"answer":{"kind":"string","value":"Primary sources"},"plan":{"kind":"string","value":"SELECT Primary Sources, token count mean and st. dev.\nCOMPUTE primary_variability = primary st. dev / mean\n\n\nSELECT Dataset Examples, token count mean and st. dev.\nCOMPUTE dataset_variability = dataset st. dev / mean\n\n\nCOMPUTE target_group = argmax of primary_variability and dataset_variability\nRETURN target_group \n"}}},{"rowIdx":87,"cells":{"paper":{"kind":"string","value":"2506.03090"},"relevant_tables":{"kind":"list like","value":[["\\begin{table}[t!]\n","\\small\n","\\resizebox{0.48\\textwidth}{!}{%\n","\\begin{tabular}{lccc} % Updated to four columns\n","\n","\\toprule\n","\\textsc{Model + Simple Prompt} & \\textsc{ALL} \\textsubscript{($n$=292)} & \\textsc{\\faUser} \\textsubscript{($n$=40)}& \\textsc{\\faBook} \\textsubscript{($n$=39)}\\\\ \n","\\midrule\n","\\geminiprotwo~\\cite{google2025gemini25} & 63.7 & 57.5 & \\textbf{82.1} \\\\\n","\\othree~\\cite{o3} & 49.7 & 47.5 & 71.8 \\\\\n","\\geminiproone~\\cite{geminiteam2024gemini} & 36.1 & 22.5 & 43.6 \\\\\n","\\oone~\\cite{openai2024o1} & 32.2 & 25.0 & 41.0 \\\\\n","\\gpto\\ \\cite{openai2024gpt4ocard} & 27.4 & 17.5 & 35.9 \\\\\n","\\qwenlarge\\ \\cite{qwen_qwen25_2025} & 11.3 & 7.5 & 20.5 \\\\\n","\\llamasmall\\ \\cite{grattafiori2024llama3herdmodels} & 5.1 & 5.0 & 2.6 \\\\\n","\\qwensmall & 2.7 & 5.0 & 2.6 \\\\\n","\\llamalarge & 2.1 & 0.0 & 2.6 \\\\\n","\\midrule\n","\\textsc{Model + Explanation Prompt} & & & \\\\\n","\\midrule\n","\\geminiprotwo & \\textbf{64.7} & \\textbf{62.5} & 79.5 \\\\\n","\\gptfourone~\\cite{gpt-4.1} & 51.0 & 47.5 & 69.2 \\\\\n","\\othree & 50.7 & 50.0 & 66.7 \\\\\n","\\geminiproone & 38.5 & 40.0 & 50.0 \\\\\n","\\claude~\\cite{claude_sonnet_2025} & 37.0 & 32.5 & 48.7 \\\\\n","\\deepseek~\\cite{deepseekai2025deepseekv3technicalreport} & 29.1 & 15.0 & 38.5 \\\\\n","\\gpto & 24.3 & 22.5 & 31.8 \\\\\n","\\qwenthreelarge~\\cite{qwen_qwen25_2025} & 19.2 & 20.0 & 33.3 \\\\\n","\\qwenthreesmall & 8.9 & 5.0 & 10.3 \\\\\n","\\othreemini\\ \\cite{openai2025o3mini} & 8.3 & 10.0 & 13.6 \\\\\n","\\midrule\n","\\textsc{Baselines} & & & \\\\\n","\\midrule\n","\\gteqwen & 4.5 & 2.5 & 6.8 \\\\\n","\\human & - & 55.0 & - \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\caption{Percentage of test set examples where the model generated the correct ground truth quotation for different folds of the test set. The \\textsc{\\faUser} and \\textsc{\\faBook} columns contain the accuracy of each model on the human-evaluated and close reading subsets of the data, respectively.} \n","\\label{tab:benchmark_accuracy}\n","\\end{table}\n"]],"string":"[\n [\n \"\\\\begin{table}[t!]\\n\",\n \"\\\\small\\n\",\n \"\\\\resizebox{0.48\\\\textwidth}{!}{%\\n\",\n \"\\\\begin{tabular}{lccc} % Updated to four columns\\n\",\n \"\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textsc{Model + Simple Prompt} & \\\\textsc{ALL} \\\\textsubscript{($n$=292)} & \\\\textsc{\\\\faUser} \\\\textsubscript{($n$=40)}& \\\\textsc{\\\\faBook} \\\\textsubscript{($n$=39)}\\\\\\\\ \\n\",\n \"\\\\midrule\\n\",\n \"\\\\geminiprotwo~\\\\cite{google2025gemini25} & 63.7 & 57.5 & \\\\textbf{82.1} \\\\\\\\\\n\",\n \"\\\\othree~\\\\cite{o3} & 49.7 & 47.5 & 71.8 \\\\\\\\\\n\",\n \"\\\\geminiproone~\\\\cite{geminiteam2024gemini} & 36.1 & 22.5 & 43.6 \\\\\\\\\\n\",\n \"\\\\oone~\\\\cite{openai2024o1} & 32.2 & 25.0 & 41.0 \\\\\\\\\\n\",\n \"\\\\gpto\\\\ \\\\cite{openai2024gpt4ocard} & 27.4 & 17.5 & 35.9 \\\\\\\\\\n\",\n \"\\\\qwenlarge\\\\ \\\\cite{qwen_qwen25_2025} & 11.3 & 7.5 & 20.5 \\\\\\\\\\n\",\n \"\\\\llamasmall\\\\ \\\\cite{grattafiori2024llama3herdmodels} & 5.1 & 5.0 & 2.6 \\\\\\\\\\n\",\n \"\\\\qwensmall & 2.7 & 5.0 & 2.6 \\\\\\\\\\n\",\n \"\\\\llamalarge & 2.1 & 0.0 & 2.6 \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textsc{Model + Explanation Prompt} & & & \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\geminiprotwo & \\\\textbf{64.7} & \\\\textbf{62.5} & 79.5 \\\\\\\\\\n\",\n \"\\\\gptfourone~\\\\cite{gpt-4.1} & 51.0 & 47.5 & 69.2 \\\\\\\\\\n\",\n \"\\\\othree & 50.7 & 50.0 & 66.7 \\\\\\\\\\n\",\n \"\\\\geminiproone & 38.5 & 40.0 & 50.0 \\\\\\\\\\n\",\n \"\\\\claude~\\\\cite{claude_sonnet_2025} & 37.0 & 32.5 & 48.7 \\\\\\\\\\n\",\n \"\\\\deepseek~\\\\cite{deepseekai2025deepseekv3technicalreport} & 29.1 & 15.0 & 38.5 \\\\\\\\\\n\",\n \"\\\\gpto & 24.3 & 22.5 & 31.8 \\\\\\\\\\n\",\n \"\\\\qwenthreelarge~\\\\cite{qwen_qwen25_2025} & 19.2 & 20.0 & 33.3 \\\\\\\\\\n\",\n \"\\\\qwenthreesmall & 8.9 & 5.0 & 10.3 \\\\\\\\\\n\",\n \"\\\\othreemini\\\\ \\\\cite{openai2025o3mini} & 8.3 & 10.0 & 13.6 \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textsc{Baselines} & & & \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\gteqwen & 4.5 & 2.5 & 6.8 \\\\\\\\\\n\",\n \"\\\\human & - & 55.0 & - \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Percentage of test set examples where the model generated the correct ground truth quotation for different folds of the test set. The \\\\textsc{\\\\faUser} and \\\\textsc{\\\\faBook} columns contain the accuracy of each model on the human-evaluated and close reading subsets of the data, respectively.} \\n\",\n \"\\\\label{tab:benchmark_accuracy}\\n\",\n \"\\\\end{table}\\n\"\n ]\n]","isConversational":false},"tables":{"kind":"list like","value":[["\\begin{table}[t]\n","\\centering\n","\\footnotesize % Reduce font size for fitting in one column\n","\n","\n","\\centering\n","\n","\\resizebox{0.48\\textwidth}{!}{%\n","\\begin{tabular}{lcc|cc|c}\n","\\toprule\n"," & \\multicolumn{2}{c}{\\textbf{Primary Sources }} & \\multicolumn{3}{c}{\\textbf{Dataset Examples}} \\\\\n"," & \\multicolumn{2}{c}{(\\textit{n=7})} & \\multicolumn{3}{c}{(\\textit{n=292})} \\\\\n","\\cmidrule(lr){2-3} \\cmidrule(lr){4-6}\n"," & \\textsc{Tokens} & \\textsc{Words} & \\textsc{Tokens} & \\textsc{Words} & \\textsc{\\# Ex/Book}\\\\\n","\\midrule\n","\\textsc{Mean} & 85,526 & 69,456 & 254.9 & 203.6 & 36.0 \\\\\n","\\textsc{St. Dev.} & 26,6304 & 21,167 & 66.9 & 52.9 & 19.6 \\\\\n","\\textsc{Max} & 124,544 & 102,549 & 492.0 & 385.0 & 52.0 \\\\\n","\\textsc{Min} & 45,038 & 37,209 & 116.0 & 91.0 & 7.0 \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\caption{Summary statistics for long-context RELiC. Token counts were computed with the \\texttt{o200k\\_base} encoding via \\texttt{tiktoken} (\\url{https://github.com/openai/tiktoken}) and word counts were computed by splitting on whitespace.}\n","\\label{tab:basic_summary_stats_dataset}\n","\\end{table}\n"],["\\begin{table}[t!]\n","\\small\n","\\resizebox{0.48\\textwidth}{!}{%\n","\\begin{tabular}{lccc} % Updated to four columns\n","\n","\\toprule\n","\\textsc{Model + Simple Prompt} & \\textsc{ALL} \\textsubscript{($n$=292)} & \\textsc{\\faUser} \\textsubscript{($n$=40)}& \\textsc{\\faBook} \\textsubscript{($n$=39)}\\\\ \n","\\midrule\n","\\geminiprotwo~\\cite{google2025gemini25} & 63.7 & 57.5 & \\textbf{82.1} \\\\\n","\\othree~\\cite{o3} & 49.7 & 47.5 & 71.8 \\\\\n","\\geminiproone~\\cite{geminiteam2024gemini} & 36.1 & 22.5 & 43.6 \\\\\n","\\oone~\\cite{openai2024o1} & 32.2 & 25.0 & 41.0 \\\\\n","\\gpto\\ \\cite{openai2024gpt4ocard} & 27.4 & 17.5 & 35.9 \\\\\n","\\qwenlarge\\ \\cite{qwen_qwen25_2025} & 11.3 & 7.5 & 20.5 \\\\\n","\\llamasmall\\ \\cite{grattafiori2024llama3herdmodels} & 5.1 & 5.0 & 2.6 \\\\\n","\\qwensmall & 2.7 & 5.0 & 2.6 \\\\\n","\\llamalarge & 2.1 & 0.0 & 2.6 \\\\\n","\\midrule\n","\\textsc{Model + Explanation Prompt} & & & \\\\\n","\\midrule\n","\\geminiprotwo & \\textbf{64.7} & \\textbf{62.5} & 79.5 \\\\\n","\\gptfourone~\\cite{gpt-4.1} & 51.0 & 47.5 & 69.2 \\\\\n","\\othree & 50.7 & 50.0 & 66.7 \\\\\n","\\geminiproone & 38.5 & 40.0 & 50.0 \\\\\n","\\claude~\\cite{claude_sonnet_2025} & 37.0 & 32.5 & 48.7 \\\\\n","\\deepseek~\\cite{deepseekai2025deepseekv3technicalreport} & 29.1 & 15.0 & 38.5 \\\\\n","\\gpto & 24.3 & 22.5 & 31.8 \\\\\n","\\qwenthreelarge~\\cite{qwen_qwen25_2025} & 19.2 & 20.0 & 33.3 \\\\\n","\\qwenthreesmall & 8.9 & 5.0 & 10.3 \\\\\n","\\othreemini\\ \\cite{openai2025o3mini} & 8.3 & 10.0 & 13.6 \\\\\n","\\midrule\n","\\textsc{Baselines} & & & \\\\\n","\\midrule\n","\\gteqwen & 4.5 & 2.5 & 6.8 \\\\\n","\\human & - & 55.0 & - \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\caption{Percentage of test set examples where the model generated the correct ground truth quotation for different folds of the test set. The \\textsc{\\faUser} and \\textsc{\\faBook} columns contain the accuracy of each model on the human-evaluated and close reading subsets of the data, respectively.} \n","\\label{tab:benchmark_accuracy}\n","\\end{table}\n"],["\\begin{figure*}[t]\n"," \\centering\n"," \\includegraphics[width=\\textwidth]{figures/table_3.png}\n"," \\caption{An example from long context RELiC where the \\human\\ annotator successfully retrieves the quotation while the top-performing LLMs fail.}\n"," \\label{fig:llm_fail}\n","\\end{figure*}\n"],["\\begin{figure*}[t]\n"," \\centering\n"," \\includegraphics[width=\\textwidth]{figures/table_4.png}\n"," \\caption{An example of long context RELiC where the \\human\\ annotator and \\geminiprotwo\\ both select the same quotation, but it is not the ground truth quotation.}\n"," \\label{fig:alt_evidence}\n","\\end{figure*}\n"],["\\begin{table}[t!]\n","\\small\n","\\centering\n","\\resizebox{0.48\\textwidth}{!}{%\n","\\begin{tabular}{lcc}\n","\\toprule\n","\\textsc{Model + Simple Prompt} & \\textsc{\\faUser} Accuracy \\textsubscript{($n$=40)} & Avg. Length Ratio\\\\\n","\\midrule\n","\\geminiprotwo & 57.5 & 3.5\\\\\n","\\othree & 47.5 & 3.5\\\\\n","\\geminiproone & 22.5 & 2.8\\\\\n","\\oone & 25.0 & 3.2\\\\\n","\\gpto & 17.5 & 3.9\\\\\n","\\qwenlarge & 7.5 & 3.2\\\\\n","\\llamasmall & 5.0 & 5.9\\\\\n","\\qwensmall & 5.0 & 2.8\\\\\n","\\llamalarge & 0.0 & 5.7\\\\\n","\\midrule\n","\\multicolumn{3}{l}{\\textsc{Model + Explanation Prompt}} \\\\\n","\\midrule\n","\\geminiprotwo & \\textbf{62.5} & 3.0\\\\\n","\\gptfourone & 47.5 & 4.8\\\\\n","\\othree & 50.0 & 2.7\\\\\n","\\geminiproone & 40.0 & 3.3\\\\\n","\\claude & 32.5 & 4.0\\\\\n","\\deepseek & 15.0 & 3.6\\\\\n","\\gpto & 22.5 & 3.6\\\\\n","\\qwenthreelarge & 20.0 & 2.7\\\\\n","\\qwenthreesmall & 5.0 & 2.4\\\\\n","\\othreemini & 10.0 & 3.5\\\\\n","\\midrule\n","\\multicolumn{3}{l}{\\textsc{Baselines}} \\\\\n","\\midrule\n","\\human & 55.0 & \\textbf{2.1}\\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\caption{The average length ratios for each model, defined as the ratio of the length model generation to that of the ground truth (measured in characters). All models have an average ratio greater than that of the human annotator (2.1), indicating overgeneration.}\n","\\label{tab:length_ratios}\n","\\end{table}\n"],["\\begin{table*}[t]\n"," \\renewcommand{\\arraystretch}{1.2} \n"," \\setlength{\\tabcolsep}{6pt}\n"," \\footnotesize\n"," \\centering\n"," \\begin{tabular}{l l c c c}\n"," \\toprule\n"," \\textbf{Book Title} & \\textbf{Author} & \\textbf{Publication Year} & \\textbf{Token Count} & \\textbf{Word Count} \\\\\n"," \\midrule\n"," Brave New World & Aldous Huxley & 1932 & 91,472 & 65,278 \\\\\n"," What Maisie Knew & Henry James & 1897 & 124,544 & 95,988 \\\\\n"," Ethan Frome & Edith Wharton & 1911 & 45,038 & 34,926 \\\\\n"," Frankenstein & Mary Shelley & 1818 & 95,018 & 75,131 \\\\\n"," The Great Gatsby & F. Scott Fitzgerald & 1925 & 63,683 & 48,972 \\\\\n"," The Awakening & Kate Chopin & 1899 & 66,574 & 49,932 \\\\\n"," The Scarlet Letter & Nathaniel Hawthorne & 1850 & 112,355 & 83,311 \\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," \\caption{Books included in the dataset. The token count was provided as per \\href{https://github.com/openai/tiktoken}{\\texttt{tiktoken}} tokenization. }\n"," \\label{tab:book-details}\n","\\end{table*}\n"],["\\begin{table}[t!]\n","\\centering\n","\\small\n","\\resizebox{0.48\\textwidth}{!}{%\n","\\begin{tabular}{l c l c c}\n","\\toprule\n","\\textsc{Model} & \\textsc{Context} & \\textsc{Avail.} & \\textsc{Checkpoints} \\\\\n","\\midrule\n","\\href{https://platform.openai.com/docs/models}{\\oone} & 200k & \\faLock & \\texttt{o1-2024-12-17} \\\\\n","\\href{https://platform.openai.com/docs/models}{\\othree} & 200k & \\faLock & \\texttt{o3-2025-04-16} \\\\\n","\\href{https://platform.openai.com/docs/models}{\\othreemini} & 200k & \\faLock & \\texttt{o3-mini-2025-01-31} \\\\\n","\\href{https://platform.openai.com/docs/models}{\\gpto} & 128k & \\faLock & \\texttt{gpt-4o-2024-11-20} \\\\\n","\\href{https://platform.openai.com/docs/models}{\\gptfourone} & 1M & \\faLock & \\texttt{gpt-4.1-2025-01-31} \\\\\n","\\href{https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/gemini}{\\geminiproone} & 1M & \\faLock & \\texttt{gemini-1.5-pro-002} \\\\\n","\\href{https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/gemini}{\\geminiprotwo} & 1M & \\faLock & \\texttt{gemini-2.5-pro-preview-05-06} \\\\\n","\\href{https://docs.anthropic.com/en/docs/about-claude/models/overview}{\\claude} & 200k & \\faLock & \\texttt{claude-3-7-sonnet@20250219} \\\\\n","\\href{https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct}{\\llamalarge} & 128k & \\faUnlock & \\texttt{Llama-3.3-70B-Instruct} \\\\\n","\\href{https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct}{\\llamasmall} & 128k & \\faUnlock & \\texttt{\n","Llama-3.1-8B-Instruct } \\\\\n","\\href{https://huggingface.co/Qwen/Qwen2.5-72B-Instruct}{\\qwenlarge} & 128k & \\faUnlock & \\texttt{Qwen2.5-72B-Instruct} \\\\\n","\\href{https://huggingface.co/Qwen/Qwen2.5-7B-Instruct}{\\qwensmall} & 128k & \\faUnlock & \\texttt{Qwen2.5-7B-Instruct} \\\\\n","\\href{https://huggingface.co/Qwen/Qwen3-8B}{\\qwenthreesmall} & 131k* & \\faUnlock & \\texttt{Qwen3-8B} \\\\\n","\\href{https://huggingface.co/Qwen/Qwen3-32B}{\\qwenthreelarge} & 131k* & \\faUnlock & \\texttt{Qwen3-32B} \\\\\n","\\midrule\n","\\href{https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct}{\\gteqwenparam} & 32k & \\faUnlock & \\texttt{gte-Qwen2-7B-instruct} \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\caption{The upper rows display the evaluated LLMs, while the bottom row displays the text embedding model used for the baseline. Context lengths marked with * were extended with YaRN.}\n","\\label{tab:models}\n","\\end{table}\n"],["\\begin{table*}[t]\n"," \\renewcommand{\\arraystretch}{1.2} \n"," \\setlength{\\tabcolsep}{4pt}\n"," \\footnotesize\n"," \\centering\n"," \\begin{tabular}{c p{15cm}}\n"," \\toprule\n"," & \\multicolumn{1}{c}{\\bf Prompt (Simple)} \\\\\n"," \\midrule\n"," \\noalign{\\vskip 1mm}\n"," & \\texttt{You are provided with the full text of \\textbf{[book\\_title]} and an excerpt of literary analysis that directly cites \\textbf{[book\\_title]} with the cited quotation represented as \\textless MASK\\textgreater.} \\\\[3mm]\n"," & \\texttt{Your task is to carefully read the text of \\textbf{[book\\_title]} and the excerpt of literary analysis, then select a window from \\textbf{[book\\_title]} that most appropriately replaces \\textless MASK\\textgreater as the cited quotation by providing textual evidence for any claims in the literary analysis.} \\\\[1mm]\n"," & \\texttt{The excerpt of literary analysis should form a valid argument when \\textless MASK\\textgreater is replaced by the window from \\textbf{[book\\_title]}.} \\\\[1mm]\n"," & \\texttt{\\textless full\\_text\\_of\\_[book\\_title\\_snake\\_case]\\textgreater \\textbf{[book\\_sentences]} \\textless /full\\_text\\_of\\_[book\\_title\\_snake\\_case]\\textgreater} \\\\[1mm]\n"," & \\texttt{\\textless literary\\_analysis\\_excerpt\\textgreater \\textbf{[lit\\_analysis\\_excerpt]} \\textless /literary\\_analysis\\_excerpt\\textgreater} \\\\[1mm]\n"," & \\texttt{Identify the window that best supports the claims being made in the excerpt of literary analysis. The window should contain no more than 5 consecutive sentences from \\textbf{[book\\_title]}.} \\\\[1mm]\n"," & \\texttt{Provide your final answer in the following format:} \\\\[1mm]\n"," & \\texttt{\\textless window\\textgreater \\textbf{YOUR SELECTED WINDOW} \\textless /window\\textgreater} \\\\[1mm]\n"," \\bottomrule\n"," \\end{tabular}\n","\\caption{Prompt template for literary evidence retrieval (Simple).}\n","\\label{tab:inference_simple}\n","\\end{table*}\n"],["\\begin{table*}[t]\n"," \\renewcommand{\\arraystretch}{1.2} \n"," \\setlength{\\tabcolsep}{4pt}\n"," \\footnotesize\n"," \\centering\n"," \\begin{tabular}{c p{15cm}}\n"," \\toprule\n"," & \\multicolumn{1}{c}{\\bf Prompt w/ Explanations} \\\\\n"," \\midrule\n"," \\noalign{\\vskip 1mm}\n"," & \\texttt{You are provided with the full text of \\textbf{[book\\_title]} and an excerpt of literary analysis that directly cites \\textbf{[book\\_title]} with the cited quotation represented as \\textless MASK\\textgreater.} \\\\[3mm]\n"," & \\texttt{Your task is to carefully read the text of \\textbf{[book\\_title]} and the excerpt of literary analysis, then select a window from \\textbf{[book\\_title]} that most appropriately replaces \\textless MASK\\textgreater as the cited quotation by providing textual evidence for any claims in the literary analysis.} \\\\[1mm]\n"," & \\texttt{The excerpt of literary analysis should form a valid argument when \\textless MASK\\textgreater is replaced by the window from \\textbf{[book\\_title]}.} \\\\[1mm]\n"," & \\texttt{\\textless full\\_text\\_of\\_[book\\_title\\_snake\\_case]\\textgreater \\textbf{[book\\_sentences]} \\textless /full\\_text\\_of\\_[book\\_title\\_snake\\_case]\\textgreater} \\\\[1mm]\n"," & \\texttt{\\textless literary\\_analysis\\_excerpt\\textgreater \\textbf{[lit\\_analysis\\_excerpt]} \\textless /literary\\_analysis\\_excerpt\\textgreater} \\\\[1mm]\n"," & \\texttt{First, provide an explanation of your decision marking process in no more than one paragraph.} \\\\[1mm]\n"," & \\texttt{Then, identify the window that best supports the claims being made in the excerpt of literary analysis. The window should contain no more than 5 consecutive sentences from \\textbf{[book\\_title]}.} \\\\[1mm]\n"," & \\texttt{Provide your final answer in the following format:} \\\\[1mm]\n"," & \\texttt{\\textless explanation\\textgreater \\textbf{YOUR EXPLANATION} \\textless /explanation\\textgreater} \\\\[1mm]\n"," & \\texttt{\\textless window\\textgreater \\textbf{YOUR SELECTED WINDOW} \\textless /window\\textgreater} \\\\[1mm]\n"," \\bottomrule\n"," \\end{tabular}\n","\\caption{Prompt template for literary evidence retrieval with explanation.}\n","\\label{tab:inference_explain}\n","\\end{table*}\n"],["\\begin{table*}[t]\n"," \\renewcommand{\\arraystretch}{1.2} \n"," \\setlength{\\tabcolsep}{4pt}\n"," \\footnotesize\n"," \\centering\n"," \\begin{tabular}{c p{15cm}}\n"," \\toprule\n"," & \\multicolumn{1}{c}{\\bf Prompt for Cleaning RELiC Examples} \\\\\n"," \\midrule\n"," \\noalign{\\vskip 1mm}\n"," & \\texttt{I want to create a dataset for Natural Language Processing that consists of excerpts of literary analysis that quote directly from a primary source text.} \\\\[3mm]\n"," & \\texttt{I have already collected windows of literary analysis that quote from many different primary sources in the public domain. The part of the excerpt before the quotation is called the \"prefix,\" and the part of the excerpt after the quotation is called the \"suffix.\" Because I collected the data from PDFs with OCR, the prefixes and suffixes contain artifacts like in-line citations, page numbers, chapter names, headers, and footers that I need to remove.} \\\\[3mm]\n"," & \\texttt{Here is an example:} \\\\[1mm]\n"," & \\texttt{\\textless prefix\\textgreater \\textbf{[prefix]} \\textless /prefix\\textgreater} \\\\[1mm]\n"," & \\texttt{\\textless ground\\_truth\\_quotation\\textgreater \\textbf{[ground\\_truth\\_quotation]} \\textless /ground\\_truth\\_quotation\\textgreater} \\\\[1mm]\n"," & \\texttt{\\textless suffix\\textgreater \\textbf{[suffix]} \\textless /suffix\\textgreater} \\\\[1mm]\n"," & \\texttt{Please follow the following guidelines to help me clean and filter this example:} \\\\[1mm]\n"," & \\texttt{1. Remove all textual artifacts from the OCR process by deleting them. Artifacts include page numbers, chapter headings, footnotes, image captions, etc.} \\\\[1mm]\n"," & \\texttt{2. Correct the grammar, punctuation, and spelling of the prefix and suffix without altering the meaning so that the primary source quotation fits seamlessly and grammatically between the prefix and suffix.} \\\\[1mm]\n"," & \\texttt{3. Remove all in-line citations following quotations, especially those that say things like \"(my emphasis).\"} \\\\[1mm]\n"," & \\texttt{Respond with ONLY the cleaned prefix and suffix in the following format:} \\\\[1mm]\n"," & \\texttt{\\textless clean\\_prefix\\textgreater \\textbf{CLEAN PREFIX} \\textless /clean\\_prefix\\textgreater} \\\\[1mm]\n"," & \\texttt{\\textless clean\\_suffix\\textgreater \\textbf{CLEAN SUFFIX} \\textless /clean\\_suffix\\textgreater} \\\\[1mm]\n"," \\bottomrule\n"," \\end{tabular}\n","\\caption{Prompt template for cleaning RELiC examples}\n","\\label{tab:clean_filter}\n","\\end{table*}\n"],["\\begin{table*}[t]\n"," \\renewcommand{\\arraystretch}{1.2} \n"," \\setlength{\\tabcolsep}{4pt}\n"," \\footnotesize\n"," \\centering\n"," \\begin{tabular}{c p{15cm}}\n"," \\toprule\n"," & \\multicolumn{1}{c}{\\bf Prompt for Classifying Literary Analysis} \\\\\n"," \\midrule\n"," \\noalign{\\vskip 1mm}\n"," & \\texttt{I want to create a dataset for Natural Language Processing that consists of excerpts of literary analysis that quote directly from a primary source text.} \\\\[3mm]\n"," & \\texttt{Other texts can contain quotes from primary sources, like biographies of authors. However, I only want examples of literary analysis in the dataset.} \\\\[3mm]\n"," & \\texttt{Please determine whether the following excerpt is an example of literary analysis. If it is literary analysis, respond with TRUE. Otherwise, respond with FALSE.} \\\\[3mm]\n"," & \\texttt{Here is the excerpt:} \\\\[1mm]\n"," & \\texttt{\\textless excerpt\\textgreater \\textbf{[clean\\_prefix]} \\textbf{[answer\\_quote]} \\textbf{[clean\\_suffix]} \\textless /excerpt\\textgreater} \\\\[1mm]\n"," & \\texttt{Respond with ONLY your answer in the following format:} \\\\[1mm]\n"," & \\texttt{\\textless answer\\textgreater \\textbf{YOUR ANSWER} \\textless /answer\\textgreater} \\\\[1mm]\n"," \\bottomrule\n"," \\end{tabular}\n","\\caption{Prompt template for classifying literary analysis}\n","\\label{tab:classify_lit_analysis}\n","\\end{table*}\n"],["\\begin{table*}[t]\n"," \\renewcommand{\\arraystretch}{1.2} \n"," \\setlength{\\tabcolsep}{4pt}\n"," \\footnotesize\n"," \\centering\n"," \\begin{tabular}{c p{15cm}}\n"," \\toprule\n"," & \\multicolumn{1}{c}{\\bf Prompt for Identifying Quote Location Disclosure} \\\\\n"," \\midrule\n"," \\noalign{\\vskip 1mm}\n"," & \\texttt{I want to create a dataset for a Natural Language Processing task called \"Literary Evidence Retrieval.\"} \\\\[3mm]\n"," & \\texttt{The input has two parts:} \\\\[1mm]\n"," & \\texttt{(1) An excerpt of literary analysis that quotes 1 to 5 full sentences from a primary source text, but the quote is replaced with \"[MASK]\".} \\\\[1mm]\n"," & \\texttt{(2) The full text of the primary source.} \\\\[3mm]\n"," & \\texttt{The output should be the correct quotation of 1 to 5 full sentences from the primary source—this is the ground truth.} \\\\[3mm]\n"," & \\texttt{I have already collected windows of literary analysis that quote from many different primary sources in the public domain.} \\\\[3mm]\n"," & \\texttt{The part of the excerpt before the quotation is called the \"prefix,\" and the part after is called the \"suffix.\"} \\\\[3mm]\n"," & \\texttt{To avoid giving the model hints, it's extremely important that the prefix and suffix do not mention the chapter where the ground truth quotation is located.} \\\\[3mm]\n"," & \\texttt{Please determine whether the following instance contains the chapter location of the ground truth quotation in either the prefix or the suffix. If it does, respond with TRUE; otherwise, respond with FALSE.} \\\\[3mm]\n"," & \\texttt{The prefix and suffix may contain the location of other quotations in the passage. I only want to identify if the location of the ground truth quotation is revealed.} \\\\[3mm]\n"," & \\texttt{Look out for phrases like \"In the prologue\" or \"In Chapter...\"} \\\\[3mm]\n"," & \\texttt{Here is the instance:} \\\\[1mm]\n"," & \\texttt{\\textless prefix\\textgreater \\textbf{[prefix]} \\textless /prefix\\textgreater} \\\\[1mm]\n"," & \\texttt{\\textless ground\\_truth\\_quotation\\textgreater \\textbf{[ground\\_truth\\_quotation]} \\textless /ground\\_truth\\_quotation\\textgreater} \\\\[1mm]\n"," & \\texttt{\\textless suffix\\textgreater \\textbf{[suffix]} \\textless /suffix\\textgreater} \\\\[1mm]\n"," & \\texttt{Respond with ONLY your answer in the following format:} \\\\[1mm]\n"," & \\texttt{\\textless answer\\textgreater \\textbf{YOUR ANSWER} \\textless /answer\\textgreater} \\\\[1mm]\n"," \\bottomrule\n"," \\end{tabular}\n","\\caption{Prompt template for identifying quote location disclosure}\n","\\label{tab:quote_location_disclosure}\n","\\end{table*}\n"]],"string":"[\n [\n \"\\\\begin{table}[t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\footnotesize % Reduce font size for fitting in one column\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\\centering\\n\",\n \"\\n\",\n \"\\\\resizebox{0.48\\\\textwidth}{!}{%\\n\",\n \"\\\\begin{tabular}{lcc|cc|c}\\n\",\n \"\\\\toprule\\n\",\n \" & \\\\multicolumn{2}{c}{\\\\textbf{Primary Sources }} & \\\\multicolumn{3}{c}{\\\\textbf{Dataset Examples}} \\\\\\\\\\n\",\n \" & \\\\multicolumn{2}{c}{(\\\\textit{n=7})} & \\\\multicolumn{3}{c}{(\\\\textit{n=292})} \\\\\\\\\\n\",\n \"\\\\cmidrule(lr){2-3} \\\\cmidrule(lr){4-6}\\n\",\n \" & \\\\textsc{Tokens} & \\\\textsc{Words} & \\\\textsc{Tokens} & \\\\textsc{Words} & \\\\textsc{\\\\# Ex/Book}\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textsc{Mean} & 85,526 & 69,456 & 254.9 & 203.6 & 36.0 \\\\\\\\\\n\",\n \"\\\\textsc{St. Dev.} & 26,6304 & 21,167 & 66.9 & 52.9 & 19.6 \\\\\\\\\\n\",\n \"\\\\textsc{Max} & 124,544 & 102,549 & 492.0 & 385.0 & 52.0 \\\\\\\\\\n\",\n \"\\\\textsc{Min} & 45,038 & 37,209 & 116.0 & 91.0 & 7.0 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Summary statistics for long-context RELiC. Token counts were computed with the \\\\texttt{o200k\\\\_base} encoding via \\\\texttt{tiktoken} (\\\\url{https://github.com/openai/tiktoken}) and word counts were computed by splitting on whitespace.}\\n\",\n \"\\\\label{tab:basic_summary_stats_dataset}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[t!]\\n\",\n \"\\\\small\\n\",\n \"\\\\resizebox{0.48\\\\textwidth}{!}{%\\n\",\n \"\\\\begin{tabular}{lccc} % Updated to four columns\\n\",\n \"\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textsc{Model + Simple Prompt} & \\\\textsc{ALL} \\\\textsubscript{($n$=292)} & \\\\textsc{\\\\faUser} \\\\textsubscript{($n$=40)}& \\\\textsc{\\\\faBook} \\\\textsubscript{($n$=39)}\\\\\\\\ \\n\",\n \"\\\\midrule\\n\",\n \"\\\\geminiprotwo~\\\\cite{google2025gemini25} & 63.7 & 57.5 & \\\\textbf{82.1} \\\\\\\\\\n\",\n \"\\\\othree~\\\\cite{o3} & 49.7 & 47.5 & 71.8 \\\\\\\\\\n\",\n \"\\\\geminiproone~\\\\cite{geminiteam2024gemini} & 36.1 & 22.5 & 43.6 \\\\\\\\\\n\",\n \"\\\\oone~\\\\cite{openai2024o1} & 32.2 & 25.0 & 41.0 \\\\\\\\\\n\",\n \"\\\\gpto\\\\ \\\\cite{openai2024gpt4ocard} & 27.4 & 17.5 & 35.9 \\\\\\\\\\n\",\n \"\\\\qwenlarge\\\\ \\\\cite{qwen_qwen25_2025} & 11.3 & 7.5 & 20.5 \\\\\\\\\\n\",\n \"\\\\llamasmall\\\\ \\\\cite{grattafiori2024llama3herdmodels} & 5.1 & 5.0 & 2.6 \\\\\\\\\\n\",\n \"\\\\qwensmall & 2.7 & 5.0 & 2.6 \\\\\\\\\\n\",\n \"\\\\llamalarge & 2.1 & 0.0 & 2.6 \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textsc{Model + Explanation Prompt} & & & \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\geminiprotwo & \\\\textbf{64.7} & \\\\textbf{62.5} & 79.5 \\\\\\\\\\n\",\n \"\\\\gptfourone~\\\\cite{gpt-4.1} & 51.0 & 47.5 & 69.2 \\\\\\\\\\n\",\n \"\\\\othree & 50.7 & 50.0 & 66.7 \\\\\\\\\\n\",\n \"\\\\geminiproone & 38.5 & 40.0 & 50.0 \\\\\\\\\\n\",\n \"\\\\claude~\\\\cite{claude_sonnet_2025} & 37.0 & 32.5 & 48.7 \\\\\\\\\\n\",\n \"\\\\deepseek~\\\\cite{deepseekai2025deepseekv3technicalreport} & 29.1 & 15.0 & 38.5 \\\\\\\\\\n\",\n \"\\\\gpto & 24.3 & 22.5 & 31.8 \\\\\\\\\\n\",\n \"\\\\qwenthreelarge~\\\\cite{qwen_qwen25_2025} & 19.2 & 20.0 & 33.3 \\\\\\\\\\n\",\n \"\\\\qwenthreesmall & 8.9 & 5.0 & 10.3 \\\\\\\\\\n\",\n \"\\\\othreemini\\\\ \\\\cite{openai2025o3mini} & 8.3 & 10.0 & 13.6 \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textsc{Baselines} & & & \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\gteqwen & 4.5 & 2.5 & 6.8 \\\\\\\\\\n\",\n \"\\\\human & - & 55.0 & - \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Percentage of test set examples where the model generated the correct ground truth quotation for different folds of the test set. The \\\\textsc{\\\\faUser} and \\\\textsc{\\\\faBook} columns contain the accuracy of each model on the human-evaluated and close reading subsets of the data, respectively.} \\n\",\n \"\\\\label{tab:benchmark_accuracy}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{figure*}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\textwidth]{figures/table_3.png}\\n\",\n \" \\\\caption{An example from long context RELiC where the \\\\human\\\\ annotator successfully retrieves the quotation while the top-performing LLMs fail.}\\n\",\n \" \\\\label{fig:llm_fail}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\textwidth]{figures/table_4.png}\\n\",\n \" \\\\caption{An example of long context RELiC where the \\\\human\\\\ annotator and \\\\geminiprotwo\\\\ both select the same quotation, but it is not the ground truth quotation.}\\n\",\n \" \\\\label{fig:alt_evidence}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{table}[t!]\\n\",\n \"\\\\small\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{0.48\\\\textwidth}{!}{%\\n\",\n \"\\\\begin{tabular}{lcc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textsc{Model + Simple Prompt} & \\\\textsc{\\\\faUser} Accuracy \\\\textsubscript{($n$=40)} & Avg. Length Ratio\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\geminiprotwo & 57.5 & 3.5\\\\\\\\\\n\",\n \"\\\\othree & 47.5 & 3.5\\\\\\\\\\n\",\n \"\\\\geminiproone & 22.5 & 2.8\\\\\\\\\\n\",\n \"\\\\oone & 25.0 & 3.2\\\\\\\\\\n\",\n \"\\\\gpto & 17.5 & 3.9\\\\\\\\\\n\",\n \"\\\\qwenlarge & 7.5 & 3.2\\\\\\\\\\n\",\n \"\\\\llamasmall & 5.0 & 5.9\\\\\\\\\\n\",\n \"\\\\qwensmall & 5.0 & 2.8\\\\\\\\\\n\",\n \"\\\\llamalarge & 0.0 & 5.7\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multicolumn{3}{l}{\\\\textsc{Model + Explanation Prompt}} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\geminiprotwo & \\\\textbf{62.5} & 3.0\\\\\\\\\\n\",\n \"\\\\gptfourone & 47.5 & 4.8\\\\\\\\\\n\",\n \"\\\\othree & 50.0 & 2.7\\\\\\\\\\n\",\n \"\\\\geminiproone & 40.0 & 3.3\\\\\\\\\\n\",\n \"\\\\claude & 32.5 & 4.0\\\\\\\\\\n\",\n \"\\\\deepseek & 15.0 & 3.6\\\\\\\\\\n\",\n \"\\\\gpto & 22.5 & 3.6\\\\\\\\\\n\",\n \"\\\\qwenthreelarge & 20.0 & 2.7\\\\\\\\\\n\",\n \"\\\\qwenthreesmall & 5.0 & 2.4\\\\\\\\\\n\",\n \"\\\\othreemini & 10.0 & 3.5\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multicolumn{3}{l}{\\\\textsc{Baselines}} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\human & 55.0 & \\\\textbf{2.1}\\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{The average length ratios for each model, defined as the ratio of the length model generation to that of the ground truth (measured in characters). All models have an average ratio greater than that of the human annotator (2.1), indicating overgeneration.}\\n\",\n \"\\\\label{tab:length_ratios}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table*}[t]\\n\",\n \" \\\\renewcommand{\\\\arraystretch}{1.2} \\n\",\n \" \\\\setlength{\\\\tabcolsep}{6pt}\\n\",\n \" \\\\footnotesize\\n\",\n \" \\\\centering\\n\",\n \" \\\\begin{tabular}{l l c c c}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Book Title} & \\\\textbf{Author} & \\\\textbf{Publication Year} & \\\\textbf{Token Count} & \\\\textbf{Word Count} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Brave New World & Aldous Huxley & 1932 & 91,472 & 65,278 \\\\\\\\\\n\",\n \" What Maisie Knew & Henry James & 1897 & 124,544 & 95,988 \\\\\\\\\\n\",\n \" Ethan Frome & Edith Wharton & 1911 & 45,038 & 34,926 \\\\\\\\\\n\",\n \" Frankenstein & Mary Shelley & 1818 & 95,018 & 75,131 \\\\\\\\\\n\",\n \" The Great Gatsby & F. Scott Fitzgerald & 1925 & 63,683 & 48,972 \\\\\\\\\\n\",\n \" The Awakening & Kate Chopin & 1899 & 66,574 & 49,932 \\\\\\\\\\n\",\n \" The Scarlet Letter & Nathaniel Hawthorne & 1850 & 112,355 & 83,311 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" \\\\caption{Books included in the dataset. The token count was provided as per \\\\href{https://github.com/openai/tiktoken}{\\\\texttt{tiktoken}} tokenization. }\\n\",\n \" \\\\label{tab:book-details}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table}[t!]\\n\",\n \"\\\\centering\\n\",\n \"\\\\small\\n\",\n \"\\\\resizebox{0.48\\\\textwidth}{!}{%\\n\",\n \"\\\\begin{tabular}{l c l c c}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textsc{Model} & \\\\textsc{Context} & \\\\textsc{Avail.} & \\\\textsc{Checkpoints} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\href{https://platform.openai.com/docs/models}{\\\\oone} & 200k & \\\\faLock & \\\\texttt{o1-2024-12-17} \\\\\\\\\\n\",\n \"\\\\href{https://platform.openai.com/docs/models}{\\\\othree} & 200k & \\\\faLock & \\\\texttt{o3-2025-04-16} \\\\\\\\\\n\",\n \"\\\\href{https://platform.openai.com/docs/models}{\\\\othreemini} & 200k & \\\\faLock & \\\\texttt{o3-mini-2025-01-31} \\\\\\\\\\n\",\n \"\\\\href{https://platform.openai.com/docs/models}{\\\\gpto} & 128k & \\\\faLock & \\\\texttt{gpt-4o-2024-11-20} \\\\\\\\\\n\",\n \"\\\\href{https://platform.openai.com/docs/models}{\\\\gptfourone} & 1M & \\\\faLock & \\\\texttt{gpt-4.1-2025-01-31} \\\\\\\\\\n\",\n \"\\\\href{https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/gemini}{\\\\geminiproone} & 1M & \\\\faLock & \\\\texttt{gemini-1.5-pro-002} \\\\\\\\\\n\",\n \"\\\\href{https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/gemini}{\\\\geminiprotwo} & 1M & \\\\faLock & \\\\texttt{gemini-2.5-pro-preview-05-06} \\\\\\\\\\n\",\n \"\\\\href{https://docs.anthropic.com/en/docs/about-claude/models/overview}{\\\\claude} & 200k & \\\\faLock & \\\\texttt{claude-3-7-sonnet@20250219} \\\\\\\\\\n\",\n \"\\\\href{https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct}{\\\\llamalarge} & 128k & \\\\faUnlock & \\\\texttt{Llama-3.3-70B-Instruct} \\\\\\\\\\n\",\n \"\\\\href{https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct}{\\\\llamasmall} & 128k & \\\\faUnlock & \\\\texttt{\\n\",\n \"Llama-3.1-8B-Instruct } \\\\\\\\\\n\",\n \"\\\\href{https://huggingface.co/Qwen/Qwen2.5-72B-Instruct}{\\\\qwenlarge} & 128k & \\\\faUnlock & \\\\texttt{Qwen2.5-72B-Instruct} \\\\\\\\\\n\",\n \"\\\\href{https://huggingface.co/Qwen/Qwen2.5-7B-Instruct}{\\\\qwensmall} & 128k & \\\\faUnlock & \\\\texttt{Qwen2.5-7B-Instruct} \\\\\\\\\\n\",\n \"\\\\href{https://huggingface.co/Qwen/Qwen3-8B}{\\\\qwenthreesmall} & 131k* & \\\\faUnlock & \\\\texttt{Qwen3-8B} \\\\\\\\\\n\",\n \"\\\\href{https://huggingface.co/Qwen/Qwen3-32B}{\\\\qwenthreelarge} & 131k* & \\\\faUnlock & \\\\texttt{Qwen3-32B} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\href{https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct}{\\\\gteqwenparam} & 32k & \\\\faUnlock & \\\\texttt{gte-Qwen2-7B-instruct} \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{The upper rows display the evaluated LLMs, while the bottom row displays the text embedding model used for the baseline. Context lengths marked with * were extended with YaRN.}\\n\",\n \"\\\\label{tab:models}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table*}[t]\\n\",\n \" \\\\renewcommand{\\\\arraystretch}{1.2} \\n\",\n \" \\\\setlength{\\\\tabcolsep}{4pt}\\n\",\n \" \\\\footnotesize\\n\",\n \" \\\\centering\\n\",\n \" \\\\begin{tabular}{c p{15cm}}\\n\",\n \" \\\\toprule\\n\",\n \" & \\\\multicolumn{1}{c}{\\\\bf Prompt (Simple)} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\noalign{\\\\vskip 1mm}\\n\",\n \" & \\\\texttt{You are provided with the full text of \\\\textbf{[book\\\\_title]} and an excerpt of literary analysis that directly cites \\\\textbf{[book\\\\_title]} with the cited quotation represented as \\\\textless MASK\\\\textgreater.} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{Your task is to carefully read the text of \\\\textbf{[book\\\\_title]} and the excerpt of literary analysis, then select a window from \\\\textbf{[book\\\\_title]} that most appropriately replaces \\\\textless MASK\\\\textgreater as the cited quotation by providing textual evidence for any claims in the literary analysis.} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{The excerpt of literary analysis should form a valid argument when \\\\textless MASK\\\\textgreater is replaced by the window from \\\\textbf{[book\\\\_title]}.} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless full\\\\_text\\\\_of\\\\_[book\\\\_title\\\\_snake\\\\_case]\\\\textgreater \\\\textbf{[book\\\\_sentences]} \\\\textless /full\\\\_text\\\\_of\\\\_[book\\\\_title\\\\_snake\\\\_case]\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless literary\\\\_analysis\\\\_excerpt\\\\textgreater \\\\textbf{[lit\\\\_analysis\\\\_excerpt]} \\\\textless /literary\\\\_analysis\\\\_excerpt\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{Identify the window that best supports the claims being made in the excerpt of literary analysis. The window should contain no more than 5 consecutive sentences from \\\\textbf{[book\\\\_title]}.} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{Provide your final answer in the following format:} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless window\\\\textgreater \\\\textbf{YOUR SELECTED WINDOW} \\\\textless /window\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"\\\\caption{Prompt template for literary evidence retrieval (Simple).}\\n\",\n \"\\\\label{tab:inference_simple}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[t]\\n\",\n \" \\\\renewcommand{\\\\arraystretch}{1.2} \\n\",\n \" \\\\setlength{\\\\tabcolsep}{4pt}\\n\",\n \" \\\\footnotesize\\n\",\n \" \\\\centering\\n\",\n \" \\\\begin{tabular}{c p{15cm}}\\n\",\n \" \\\\toprule\\n\",\n \" & \\\\multicolumn{1}{c}{\\\\bf Prompt w/ Explanations} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\noalign{\\\\vskip 1mm}\\n\",\n \" & \\\\texttt{You are provided with the full text of \\\\textbf{[book\\\\_title]} and an excerpt of literary analysis that directly cites \\\\textbf{[book\\\\_title]} with the cited quotation represented as \\\\textless MASK\\\\textgreater.} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{Your task is to carefully read the text of \\\\textbf{[book\\\\_title]} and the excerpt of literary analysis, then select a window from \\\\textbf{[book\\\\_title]} that most appropriately replaces \\\\textless MASK\\\\textgreater as the cited quotation by providing textual evidence for any claims in the literary analysis.} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{The excerpt of literary analysis should form a valid argument when \\\\textless MASK\\\\textgreater is replaced by the window from \\\\textbf{[book\\\\_title]}.} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless full\\\\_text\\\\_of\\\\_[book\\\\_title\\\\_snake\\\\_case]\\\\textgreater \\\\textbf{[book\\\\_sentences]} \\\\textless /full\\\\_text\\\\_of\\\\_[book\\\\_title\\\\_snake\\\\_case]\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless literary\\\\_analysis\\\\_excerpt\\\\textgreater \\\\textbf{[lit\\\\_analysis\\\\_excerpt]} \\\\textless /literary\\\\_analysis\\\\_excerpt\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{First, provide an explanation of your decision marking process in no more than one paragraph.} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{Then, identify the window that best supports the claims being made in the excerpt of literary analysis. The window should contain no more than 5 consecutive sentences from \\\\textbf{[book\\\\_title]}.} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{Provide your final answer in the following format:} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless explanation\\\\textgreater \\\\textbf{YOUR EXPLANATION} \\\\textless /explanation\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless window\\\\textgreater \\\\textbf{YOUR SELECTED WINDOW} \\\\textless /window\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"\\\\caption{Prompt template for literary evidence retrieval with explanation.}\\n\",\n \"\\\\label{tab:inference_explain}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[t]\\n\",\n \" \\\\renewcommand{\\\\arraystretch}{1.2} \\n\",\n \" \\\\setlength{\\\\tabcolsep}{4pt}\\n\",\n \" \\\\footnotesize\\n\",\n \" \\\\centering\\n\",\n \" \\\\begin{tabular}{c p{15cm}}\\n\",\n \" \\\\toprule\\n\",\n \" & \\\\multicolumn{1}{c}{\\\\bf Prompt for Cleaning RELiC Examples} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\noalign{\\\\vskip 1mm}\\n\",\n \" & \\\\texttt{I want to create a dataset for Natural Language Processing that consists of excerpts of literary analysis that quote directly from a primary source text.} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{I have already collected windows of literary analysis that quote from many different primary sources in the public domain. The part of the excerpt before the quotation is called the \\\"prefix,\\\" and the part of the excerpt after the quotation is called the \\\"suffix.\\\" Because I collected the data from PDFs with OCR, the prefixes and suffixes contain artifacts like in-line citations, page numbers, chapter names, headers, and footers that I need to remove.} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{Here is an example:} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless prefix\\\\textgreater \\\\textbf{[prefix]} \\\\textless /prefix\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless ground\\\\_truth\\\\_quotation\\\\textgreater \\\\textbf{[ground\\\\_truth\\\\_quotation]} \\\\textless /ground\\\\_truth\\\\_quotation\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless suffix\\\\textgreater \\\\textbf{[suffix]} \\\\textless /suffix\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{Please follow the following guidelines to help me clean and filter this example:} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{1. Remove all textual artifacts from the OCR process by deleting them. Artifacts include page numbers, chapter headings, footnotes, image captions, etc.} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{2. Correct the grammar, punctuation, and spelling of the prefix and suffix without altering the meaning so that the primary source quotation fits seamlessly and grammatically between the prefix and suffix.} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{3. Remove all in-line citations following quotations, especially those that say things like \\\"(my emphasis).\\\"} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{Respond with ONLY the cleaned prefix and suffix in the following format:} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless clean\\\\_prefix\\\\textgreater \\\\textbf{CLEAN PREFIX} \\\\textless /clean\\\\_prefix\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless clean\\\\_suffix\\\\textgreater \\\\textbf{CLEAN SUFFIX} \\\\textless /clean\\\\_suffix\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"\\\\caption{Prompt template for cleaning RELiC examples}\\n\",\n \"\\\\label{tab:clean_filter}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[t]\\n\",\n \" \\\\renewcommand{\\\\arraystretch}{1.2} \\n\",\n \" \\\\setlength{\\\\tabcolsep}{4pt}\\n\",\n \" \\\\footnotesize\\n\",\n \" \\\\centering\\n\",\n \" \\\\begin{tabular}{c p{15cm}}\\n\",\n \" \\\\toprule\\n\",\n \" & \\\\multicolumn{1}{c}{\\\\bf Prompt for Classifying Literary Analysis} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\noalign{\\\\vskip 1mm}\\n\",\n \" & \\\\texttt{I want to create a dataset for Natural Language Processing that consists of excerpts of literary analysis that quote directly from a primary source text.} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{Other texts can contain quotes from primary sources, like biographies of authors. However, I only want examples of literary analysis in the dataset.} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{Please determine whether the following excerpt is an example of literary analysis. If it is literary analysis, respond with TRUE. Otherwise, respond with FALSE.} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{Here is the excerpt:} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless excerpt\\\\textgreater \\\\textbf{[clean\\\\_prefix]} \\\\textbf{[answer\\\\_quote]} \\\\textbf{[clean\\\\_suffix]} \\\\textless /excerpt\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{Respond with ONLY your answer in the following format:} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless answer\\\\textgreater \\\\textbf{YOUR ANSWER} \\\\textless /answer\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"\\\\caption{Prompt template for classifying literary analysis}\\n\",\n \"\\\\label{tab:classify_lit_analysis}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[t]\\n\",\n \" \\\\renewcommand{\\\\arraystretch}{1.2} \\n\",\n \" \\\\setlength{\\\\tabcolsep}{4pt}\\n\",\n \" \\\\footnotesize\\n\",\n \" \\\\centering\\n\",\n \" \\\\begin{tabular}{c p{15cm}}\\n\",\n \" \\\\toprule\\n\",\n \" & \\\\multicolumn{1}{c}{\\\\bf Prompt for Identifying Quote Location Disclosure} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\noalign{\\\\vskip 1mm}\\n\",\n \" & \\\\texttt{I want to create a dataset for a Natural Language Processing task called \\\"Literary Evidence Retrieval.\\\"} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{The input has two parts:} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{(1) An excerpt of literary analysis that quotes 1 to 5 full sentences from a primary source text, but the quote is replaced with \\\"[MASK]\\\".} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{(2) The full text of the primary source.} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{The output should be the correct quotation of 1 to 5 full sentences from the primary source—this is the ground truth.} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{I have already collected windows of literary analysis that quote from many different primary sources in the public domain.} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{The part of the excerpt before the quotation is called the \\\"prefix,\\\" and the part after is called the \\\"suffix.\\\"} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{To avoid giving the model hints, it's extremely important that the prefix and suffix do not mention the chapter where the ground truth quotation is located.} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{Please determine whether the following instance contains the chapter location of the ground truth quotation in either the prefix or the suffix. If it does, respond with TRUE; otherwise, respond with FALSE.} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{The prefix and suffix may contain the location of other quotations in the passage. I only want to identify if the location of the ground truth quotation is revealed.} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{Look out for phrases like \\\"In the prologue\\\" or \\\"In Chapter...\\\"} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{Here is the instance:} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless prefix\\\\textgreater \\\\textbf{[prefix]} \\\\textless /prefix\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless ground\\\\_truth\\\\_quotation\\\\textgreater \\\\textbf{[ground\\\\_truth\\\\_quotation]} \\\\textless /ground\\\\_truth\\\\_quotation\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless suffix\\\\textgreater \\\\textbf{[suffix]} \\\\textless /suffix\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{Respond with ONLY your answer in the following format:} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless answer\\\\textgreater \\\\textbf{YOUR ANSWER} \\\\textless /answer\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"\\\\caption{Prompt template for identifying quote location disclosure}\\n\",\n \"\\\\label{tab:quote_location_disclosure}\\n\",\n \"\\\\end{table*}\\n\"\n ]\n]","isConversational":false},"fulltext":{"kind":"string","value":"% This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.\n\\pdfoutput=1\n% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.\n\n\\documentclass[11pt]{article}\n\n% Change \"review\" to \"final\" to generate the final (sometimes called camera-ready) version.\n% Change to \"preprint\" to generate a non-anonymous version with page numbers.\n\\usepackage[final]{acl}\n\n% Standard package includes\n\\usepackage{times}\n\\usepackage{latexsym}\n\n% For proper rendering and hyphenation of words containing Latin characters (including in bib files)\n\\usepackage[T1]{fontenc}\n% For Vietnamese characters\n% \\usepackage[T5]{fontenc}\n% See 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gte-Qwen2-7B-instruct}}\n\\newcommand{\\human}{\\textsc{\\small Human}}\n\\newcommand{\\relicquote}[1]{\\textbf{\\textcolor{purple}{#1}}}\n\n\n\n\\newcommand\\blankfootnote[1]{%\n \\let\\thefootnote\\relax\\footnotetext{#1}%\n \\let\\thefootnote\\svthefootnote%\n}\n\n\n\n\n\n\\title{\\raisebox{-0.3\\height}{\\includegraphics[width=1.75cm]{figures/lc_relic_emoji.png}}\\; Literary Evidence Retrieval via Long-Context Language Models}\n\n\n% Author information can be set in various styles:\n% For several authors from the same institution:\n% \\author{Author 1 \\and ... \\and Author n \\\\\n% Address line \\\\ ... \\\\ Address line}\n% if the names do not fit well on one line use\n% Author 1 \\\\ {\\bf Author 2} \\\\ ... \\\\ {\\bf Author n} \\\\\n% For authors from different institutions:\n% \\author{Author 1 \\\\ Address line \\\\ ... \\\\ Address line\n% \\And ... \\And\n% Author n \\\\ Address line \\\\ ... \\\\ Address line}\n% To start a separate ``row'' of authors use \\AND, as in\n% \\author{Author 1 \\\\ Address line \\\\ ... \\\\ Address line\n% \\AND\n% Author 2 \\\\ Address line \\\\ ... \\\\ Address line \\And\n% Author 3 \\\\ Address line \\\\ ... \\\\ Address line}\n\n\n\n\\author{Katherine Thai\\twemoji{crystal ball} and Mohit Iyyer\\twemoji{crystal ball}\\twemoji{magnifying glass tilted right}\\\\\nUMass Amherst\\twemoji{crystal ball}\\hspace{0.3cm} \nUniversity of Maryland, College Park\\twemoji{magnifying glass tilted right},\\\\\n\\texttt{kbthai@umass.edu}, \\texttt{miyyer@umd.edu} \\\\\n\\\\\n\\url{https://github.com/katherinethai/long_context_relic}\n}\n\n\n%\\author{\n% \\textbf{First Author\\textsuperscript{1}},\n% \\textbf{Second Author\\textsuperscript{1,2}},\n% \\textbf{Third T. Author\\textsuperscript{1}},\n% \\textbf{Fourth Author\\textsuperscript{1}},\n%\\\\\n% \\textbf{Fifth Author\\textsuperscript{1,2}},\n% \\textbf{Sixth Author\\textsuperscript{1}},\n% \\textbf{Seventh Author\\textsuperscript{1}},\n% \\textbf{Eighth Author \\textsuperscript{1,2,3,4}},\n%\\\\\n% \\textbf{Ninth Author\\textsuperscript{1}},\n% \\textbf{Tenth Author\\textsuperscript{1}},\n% \\textbf{Eleventh E. Author\\textsuperscript{1,2,3,4,5}},\n% \\textbf{Twelfth Author\\textsuperscript{1}},\n%\\\\\n% \\textbf{Thirteenth Author\\textsuperscript{3}},\n% \\textbf{Fourteenth F. Author\\textsuperscript{2,4}},\n% \\textbf{Fifteenth Author\\textsuperscript{1}},\n% \\textbf{Sixteenth Author\\textsuperscript{1}},\n%\\\\\n% \\textbf{Seventeenth S. Author\\textsuperscript{4,5}},\n% \\textbf{Eighteenth Author\\textsuperscript{3,4}},\n% \\textbf{Nineteenth N. Author\\textsuperscript{2,5}},\n% \\textbf{Twentieth Author\\textsuperscript{1}}\n%\\\\\n%\\\\\n% \\textsuperscript{1}Affiliation 1,\n% \\textsuperscript{2}Affiliation 2,\n% \\textsuperscript{3}Affiliation 3,\n% \\textsuperscript{4}Affiliation 4,\n% \\textsuperscript{5}Affiliation 5\n%\\\\\n% \\small{\n% \\textbf{Correspondence:} \\href{mailto:email@domain}{email@domain}\n% }\n%}\n\n\n\n\n\\begin{document}\n\n\n\\maketitle\n\n\\begin{abstract}\n\nHow well do modern long-context language models understand literary fiction? We explore this question via the task of \\emph{literary evidence retrieval}, repurposing the RELiC dataset of~\\citet{thai-etal-2022-relic} to construct a benchmark where the entire text of a primary source (e.g., \\textit{The Great Gatsby}) is provided to an LLM alongside literary criticism with a missing quotation from that work. This setting, in which the model must generate the missing quotation, mirrors the human process of literary analysis by requiring models to perform both global narrative reasoning and close textual examination. We curate a high-quality subset of 292 examples through extensive filtering and human verification. Our experiments show that recent reasoning models, such as \\geminiprotwo\\ can exceed human expert performance (62.5\\% vs. 50\\% accuracy). In contrast, the best open-weight model achieves only 29.1\\% accuracy, highlighting a wide gap in interpretive reasoning between open and closed-weight models. Despite their speed and apparent accuracy, even the strongest models struggle with nuanced literary signals and overgeneration, signaling open challenges for applying LLMs to literary analysis. We release our dataset and evaluation code to encourage future work in this direction.\n\n\\end{abstract}\\section{Introduction}\n\\label{sec:intro} \n\nThe emergence of long-context language models, which can process millions of tokens~\\cite{geminiteam2024gemini}, has unlocked new AI applications for literary analysis. In this paper, we focus on the task of \\emph{literary evidence retrieval}, in which a model must retrieve a supporting quotation from a primary source (e.g., a novel) to substantiate an excerpt of literary criticism.\n\n~\\citet{thai-etal-2022-relic} frame literary evidence retrieval as a computational task by introducing RELiC, a dataset that contains short excerpts from published literary criticism that include quotations from famous novels. While RELiC was developed to benchmark and improve retriever models, which compute embeddings of claims and short chunks of the book text, we repurpose it as a testbed for long-context\\footnote{In this paper, we consider ``long-context\" to mean allowing a minimum of 128k tokens as input.} language models: A model is given the entire text of the book and an excerpt of literary criticism with a missing quotation from that book, then asked to generate the missing quote (see \\autoref{fig:llm_fail}, top). This task does not exactly mirror the human process of literary analysis, in which a scholar typically develops claims and selects supporting quotes iteratively. However, the task requires the same understanding of and complex reasoning over plot, subtext, and other literary devices with the advantage of being easily verifiable. \n\nTo enable robust evaluation, we curated a high-quality subset of 292 examples from RELiC, verified through a combination of automated filtering and expert human review. These 292 examples contain claims that require both global reasoning over events and ``close reading'' over singular passages to solve. Our experiments reveal that state-of-the-art reasoning models significantly outperform previous LLMs and even surpass a human expert baseline: our best model, \\geminiprotwo\\ obtains an accuracy of \\textbf{62.5\\%} compared to \\textbf{55.0\\%} for a human expert on a subset of the data. However, we also find that these models tend to overgenerate and struggle with subtle literary cues. Open-weight models perform substantially worse, suggesting that interpretive reasoning, not just long-context capacity, is essential to success in this domain, forming important directions for future research.\n\n\\begin{table}[t]\n\\centering\n\\footnotesize % Reduce font size for fitting in one column\n\n\n\\centering\n\n\\resizebox{0.48\\textwidth}{!}{%\n\\begin{tabular}{lcc|cc|c}\n\\toprule\n & \\multicolumn{2}{c}{\\textbf{Primary Sources }} & \\multicolumn{3}{c}{\\textbf{Dataset Examples}} \\\\\n & \\multicolumn{2}{c}{(\\textit{n=7})} & \\multicolumn{3}{c}{(\\textit{n=292})} \\\\\n\\cmidrule(lr){2-3} \\cmidrule(lr){4-6}\n & \\textsc{Tokens} & \\textsc{Words} & \\textsc{Tokens} & \\textsc{Words} & \\textsc{\\# Ex/Book}\\\\\n\\midrule\n\\textsc{Mean} & 85,526 & 69,456 & 254.9 & 203.6 & 36.0 \\\\\n\\textsc{St. Dev.} & 26,6304 & 21,167 & 66.9 & 52.9 & 19.6 \\\\\n\\textsc{Max} & 124,544 & 102,549 & 492.0 & 385.0 & 52.0 \\\\\n\\textsc{Min} & 45,038 & 37,209 & 116.0 & 91.0 & 7.0 \\\\\n\\bottomrule\n\\end{tabular}\n}\n\\caption{Summary statistics for long-context RELiC. Token counts were computed with the \\texttt{o200k\\_base} encoding via \\texttt{tiktoken} (\\url{https://github.com/openai/tiktoken}) and word counts were computed by splitting on whitespace.}\n\\label{tab:basic_summary_stats_dataset}\n\\end{table}\n\\section{Dataset Curation}\n\\label{sec:data_methods}\n\n\\begin{table}[t!]\n\\small\n\\resizebox{0.48\\textwidth}{!}{%\n\\begin{tabular}{lccc} % Updated to four columns\n\n\\toprule\n\\textsc{Model + Simple Prompt} & \\textsc{ALL} \\textsubscript{($n$=292)} & \\textsc{\\faUser} \\textsubscript{($n$=40)}& \\textsc{\\faBook} \\textsubscript{($n$=39)}\\\\ \n\\midrule\n\\geminiprotwo~\\cite{google2025gemini25} & 63.7 & 57.5 & \\textbf{82.1} \\\\\n\\othree~\\cite{o3} & 49.7 & 47.5 & 71.8 \\\\\n\\geminiproone~\\cite{geminiteam2024gemini} & 36.1 & 22.5 & 43.6 \\\\\n\\oone~\\cite{openai2024o1} & 32.2 & 25.0 & 41.0 \\\\\n\\gpto\\ \\cite{openai2024gpt4ocard} & 27.4 & 17.5 & 35.9 \\\\\n\\qwenlarge\\ \\cite{qwen_qwen25_2025} & 11.3 & 7.5 & 20.5 \\\\\n\\llamasmall\\ \\cite{grattafiori2024llama3herdmodels} & 5.1 & 5.0 & 2.6 \\\\\n\\qwensmall & 2.7 & 5.0 & 2.6 \\\\\n\\llamalarge & 2.1 & 0.0 & 2.6 \\\\\n\\midrule\n\\textsc{Model + Explanation Prompt} & & & \\\\\n\\midrule\n\\geminiprotwo & \\textbf{64.7} & \\textbf{62.5} & 79.5 \\\\\n\\gptfourone~\\cite{gpt-4.1} & 51.0 & 47.5 & 69.2 \\\\\n\\othree & 50.7 & 50.0 & 66.7 \\\\\n\\geminiproone & 38.5 & 40.0 & 50.0 \\\\\n\\claude~\\cite{claude_sonnet_2025} & 37.0 & 32.5 & 48.7 \\\\\n\\deepseek~\\cite{deepseekai2025deepseekv3technicalreport} & 29.1 & 15.0 & 38.5 \\\\\n\\gpto & 24.3 & 22.5 & 31.8 \\\\\n\\qwenthreelarge~\\cite{qwen_qwen25_2025} & 19.2 & 20.0 & 33.3 \\\\\n\\qwenthreesmall & 8.9 & 5.0 & 10.3 \\\\\n\\othreemini\\ \\cite{openai2025o3mini} & 8.3 & 10.0 & 13.6 \\\\\n\\midrule\n\\textsc{Baselines} & & & \\\\\n\\midrule\n\\gteqwen & 4.5 & 2.5 & 6.8 \\\\\n\\human & - & 55.0 & - \\\\\n\\bottomrule\n\\end{tabular}\n}\n\\caption{Percentage of test set examples where the model generated the correct ground truth quotation for different folds of the test set. The \\textsc{\\faUser} and \\textsc{\\faBook} columns contain the accuracy of each model on the human-evaluated and close reading subsets of the data, respectively.} \n\\label{tab:benchmark_accuracy}\n\\end{table}\n\n\nThe RELiC dataset~\\cite{thai-etal-2022-relic} consists of 78k excerpts of English-language literary analysis collected from scholarly journals where each excerpt includes a direct quotation from one of 79 primary source texts in the public domain. Each example in the dataset contains up to four sentences preceding the quotation and up to four sentences following the quotation. Together, these sentences of make up the ``context'' of the ground truth quotation. The primary source quotations may be up to five consecutive sentences in length. \n\n%Data about the books in the test and validation splits can be found in Table \\td{ref}.\n% , with the data for the train split books available in Appendix \\ref{}.\n% Because RELiC was designed as a retrieval task, the dataset also includes the full text of each primary source broken up into sentences.\n\n\n\n\\subsection{Adapting RELiC for long-context reasoning}\n% The original data curation and processing steps for RELiC involved applying several programmatic heuristics, such as fuzzy-matching and regex, to the data to classify examples of literary analysis, identify the boundaries between literary analysis and ground truth quotations, and segment the data into sentences. \nWhile large, RELiC is also noisy, which necessitates several filtering steps before we can evaluate models on it. We implemented an extensive data preprocessing pipeline\\footnote{All prompts, details of filtering heuristics, and descriptions of our manual validations are in Appendix A.} that involved several cleaning and filtering passes with \\gptmini\\ and \\gpto\\ supplemented by some programmatic heuristics, all geared towards mitigating the below issues:\n\\paragraph{Low data quality:} Some RELiC examples contain \\textbf{OCR artifacts} present in the primary source texts that render the prefixes and suffixes ungrammatical. There are also some examples that are \\textbf{misclassified} as literary analysis.\n\\paragraph{Model exploits:} Several aspects of RELiC examples can provide unintended cues to models, allowing them to bypass the reasoning challenge. One was the \\textbf{disclosure of the location} of the quote in the prefix or suffix. Another was \\textbf{quote leakage}, or the appearance of part or all of the ground truth quotation in the context. Finally, \\textbf{data contamination} was a concern--the literary analysis excerpts could appear in the training data of the LLMs we benchmarked and may have been memorized, or the ground truth quotation is so prevalent in the training data (because it belongs to a public domain novel) that the model is able to retrieve it without needing the primary source text at all.\n% \\paragraph{LLM-aided data filtering:}\n\n% Literary Analysis Section\n% \\begin{table*}[t]\n% \\centering\n% \\renewcommand{\\arraystretch}{1.4} % Better row spacing\n% \\setlength{\\tabcolsep}{6pt} % Reduce padding slightly\n% \\scriptsize\n% % Literary Analysis Section\n% \\begin{tabularx}{\\textwidth}{|X|}\n% \\hline\n% Literary Analysis Excerpt with \\relicquote{Ground Truth Quotation} from \\textit{The Great Gatsby} \\\\\n% \\hline\n% One of the more memorable scenes in \\textit{The Great Gatsby}, which Nick calls a \"picture,\" is a distant cousin of the facetious art photograph, \"George B. Wilson at the Gasoline Pump.\" It is a still picture of a mundane subject that reveals, with great impact, a critical insight and the emotional jolt at the discovery of bitter knowledge: \\\\\n\n\n% \\relicquote{Daisy and Tom were sitting opposite each other at the kitchen table with a plate of cold fried chicken between them and two bottles of ale. He was talking intently across the table at her and in his earnestness his hand had fallen upon and covered her own. Once in a while she looked up at him and nodded in agreement. They weren't happy, and neither of them had touched the chicken or the ale—and yet they weren't unhappy either. There was an unmistakable air of natural intimacy about the picture and anybody would have said that they were conspiring together.}\\\\\n\n% The passage could be considered \"Jamesian,\" comparable to corresponding tableaux in \\textit{The Ambassadors} and \\textit{The Portrait of a Lady}. The picture seals Gatsby's fate and Nick's too; his doubts and hopes are over now. \"Jamesian\" surely, but just as surely photographic. \\\\\n% \\hline\n% \\end{tabularx}\n% \\end{table*}\n\n% \\vspace{3pt} % Space between merged section and individual columns\n\n% % Model Comparison Table\n% \\begin{tabularx}{\\textwidth}{|X|X|}\n% \\hline\n% \\textbf{Response} & \\textbf{Explanation} \\\\\n% \\hline\n% \\faCheck\\ \\human: They weren’t happy, and neither of them had touched the chicken or the ale—and yet they weren’t unhappy either. There was an unmistakable air of natural intimacy about the picture, and anybody would have said that they were conspiring together. & The part in the suffix about \"sealing Gatsby's fate\" made me think this quote was towards the end and something damning for Gatsby. The \"picture\" described by Nick is Nick's observation that Daisy and Tom are actually fine after all of the drama of the car accident and Gatsby and Daisy revealing their relationship to Tom. \\\\\n% \\faClose\\ \\geminipro: In one of the windows over the garage the curtains had been moved aside a little and Myrtle Wilson was peering down at the car. So engrossed was she that she had no consciousness of being observed and one emotion after another crept into her face like objects into a slowly developing picture. Her expression was curiously familiar-it was an expression I had often seen on women's faces but on Myrtle Wilson's face it seemed purposeless and inexplicable until I realized that her eyes, wide with jealous terror, were fixed not on Tom, but on Jordan Baker, whom she took to be his wife. & The literary analysis excerpt refers to a memorable scene that Nick calls a \"picture,\" featuring a mundane subject with great impact, revealing bitter knowledge and sealing Gatsby's and Nick's fates. The analysis also mentions the scene being \"Jamesian\" and photographic. Therefore, the selected window should depict a static, impactful scene revealing a harsh truth that deeply affects both Gatsby and Nick, and ideally involves an observation made by Nick. The scene with Myrtle Wilson looking out the window fits these criteria perfectly. It is described as a slowly developing picture, reveals Myrtle's mistaken jealousy, and foreshadows the tragic events that follow, impacting both Gatsby and Nick. \\\\\n% % \\gpto: There was a faint, barely perceptible movement of the water as the fresh flow from one end urged its way toward the drain at the other. With little ripples that were hardly the shadows of waves, the laden mattress moved irregularly down the pool. A small gust of wind that scarcely corrugated the surface was enough to disturb its accidental course with its accidental burden. The touch of a cluster of leaves revolved it slowly, tracing, like the leg of a compass, a thin red circle in the water. & The literary analysis excerpt describes a \"picture\" in *The Great Gatsby* that is likened to a still photograph, revealing a critical insight and emotional jolt tied to bitter knowledge. The analysis suggests that this moment seals Gatsby's fate and Nick's as well, marking the end of their doubts and hopes. The most fitting scene is the one where Nick observes Gatsby's body in the pool, accompanied by the symbolic imagery of the mattress and the thin red circle in the water. This moment captures the stillness, the emotional weight, and the finality of Gatsby's fate, aligning with the analysis's description of a \"Jamesian\" tableau and a photographic quality. \\\\\n% \\faClose\\ \\othreemini: GEORGE B. WILSON. Cars Bought and Sold—and I followed Tom inside. The interior was unprosperous and bare; the only car visible was the dust‐covered wreck of a Ford which crouched in a dim corner. It had occurred to me that this shadow of a garage must be a blind and that sumptuous and romantic apartments were concealed overhead when the proprietor himself appeared in the door of an office, wiping his hands on a piece of waste. He was a blonde, spiritless man, anaemic, and faintly handsome. When he saw us a damp gleam of hope sprang into his light blue eyes.\n% & I chose a window from the section that introduces George B. Wilson because the literary analysis explicitly compares a “picture” in the novel to the facetious art photograph “George B. Wilson at the Gasoline Pump.” This window, which paints a stark, almost photographic portrayal of the mundane reality of Wilson’s life and surroundings, encapsulates the bitter insight that Nick experiences—a recognition of the decay and desolation underpinning the glitter of wealth—which in turn seals both Gatsby’s and Nick’s fates, as noted by the analysis. \\\\\n% \\hline\n% \\end{tabularx}\n\n% \\caption{Comparison of model responses with explanations and correctness analysis for a selected RELiC example.}\n% \\label{tab:relic_detailed_comparison}\n% \\end{table*}\n\n% Model Comparison Table\n% \\begin{tabularx}{\\textwidth}{|X|}\n% \\hline\n% \\faCheck\\ \\textbf{Human explanation}: The part in the suffix about \"sealing Gatsby's fate\" made me think this quote was towards the end and something damning for Gatsby. The \"picture\" described by Nick is Nick's observation that Daisy and Tom are actually fine after all of the drama of the car accident and Gatsby and Daisy revealing their relationship to Tom. \\\\\n% \\faClose\\ \\textbf{Gemini Pro 1.5 response}: In one of the windows over the garage the curtains had been moved aside a little and Myrtle Wilson was peering down at the car. So engrossed was she that she had no consciousness of being observed and one emotion after another crept into her face like objects into a slowly developing picture. Her expression was curiously familiar-it was an expression I had often seen on women's faces but on Myrtle Wilson's face it seemed purposeless and inexplicable until I realized that her eyes, wide with jealous terror, were fixed not on Tom, but on Jordan Baker, whom she took to be his wife. \\\\\n% \\faClose\\ \\textbf{o3-mini response}: GEORGE B. WILSON. Cars Bought and Sold—and I followed Tom inside. The interior was unprosperous and bare; the only car visible was the dust‐covered wreck of a Ford which crouched in a dim corner. It had occurred to me that this shadow of a garage must be a blind and that sumptuous and romantic apartments were concealed overhead when the proprietor himself appeared in the door of an office, wiping his hands on a piece of waste. He was a blonde, spiritless man, anaemic, and faintly handsome. When he saw us a damp gleam of hope sprang into his light blue eyes.\\\\\n% \\hline\n% \\end{tabularx}\n% \\vspace{-8pt}\n% \\caption{An example of long context RELiC where the \\human\\ annotator successfully retrieves the quotation while the LLMs fail.}\n% \\label{tab:human_beats_models_comparison}\n% \\end{table*}\n\n\\begin{figure*}[t]\n \\centering\n \\includegraphics[width=\\textwidth]{figures/table_3.png}\n \\caption{An example from long context RELiC where the \\human\\ annotator successfully retrieves the quotation while the top-performing LLMs fail.}\n \\label{fig:llm_fail}\n\\end{figure*}\n\n\\paragraph{Human verification:} After filtering, we create a high-quality human-verified subset of the dataset by having one of the authors, who has a degree in English literature and has read all of the primary source novels manually review 400 filtered examples. This author marked 292 examples that were well-formed instances of literary analysis with a ground truth quotation that could be identified given only the book and literary analysis context. See Table \\ref{tab:basic_summary_stats_dataset} for dataset statistics.\n% Overall, we identify 252 high-quality examples.\n% . The author was only shown examples from primary source novels they had read, and the examples were shown in random order. At their discretion, the author identified 252 particularly high quality examples from RELiC containing specific and detailed literary analysis that is well-supported by the ground truth quotation. \n\n% \\paragraph{Identification of close reading examples} During the manual verification of examples, the author also labeled examples of \\textbf{close reading}, a literary analysis technique in which the reader considers ``linguistic elements, semantic aspects, syntax, rhetoric, structural elements, thematic, and generic references in the text\" and ``delves into the form and structure of the text, gaining insight into the usage, purpose, and contextual significance of the language\" \\cite{close_reading}. A natural consequence of this interpretive technique in written literary analysis is the repeated citation of parts of the ground truth quote in the context. While an example of close reading is a well-formed example of literary analysis, the context contains lexical overlap with the ground truth quotation that the model can exploit during the RELiC task\\footnote{We attempted to implement another LLM-based filter for close reading examples, but found that \\gptmini\\ overwhelmingly designated RELiC instances as close reading examples. We also found that even closed source models did not perform perfectly on these ``easy'' retrieval examples, so we did not try to exclude them from our benchmarking dataset.}. \n\n% \\subsection{Long-Context Dataset Construction}\n% In constructing our final dataset, we discarded all RELiC examples with ground truth quotations from primary source that were longer than 128k tokens so we could evaluate the open-source Llama suite of models \\cite{grattafiori2024llama3herdmodels}, which have a context length of 128k. Following the original RELiC dataset approach, we created test, validation, and train splits by partitioning the primary source novels such that examples with ground truth quotations from a specific novel appear in only one of the splits. Data about the books in the test and validation splits can be found in Table \\ref{}, with the data for the train split books available in Appendix \\ref{}.\n\n% \\paragraph{} The validation set consists of 50 of the 252 high quality examples selected by our author. The remaining 202 high quality examples were designated for the test set and supplemented with 1,000 randomly sampled examples after applying all filters. In other words, we randomly selected 1,000 of the most ``challenging\" examples according to our heuristics. \n\n\\paragraph{Dataset folds:} \nOur new dataset contains two labeled folds of special data, (1) the \\faUser\\ \\textsc{Human Eval Set}: 40 examples attempted by our author, and (2) the \\faBook\\ \\textsc{Close Reading Set}: 39 examples labeled by our author as examples of \\textbf{close reading}, a literary analysis technique in which the reader considers ``linguistic elements, semantic aspects, syntax, rhetoric, structural elements, thematic, and generic references in the text''~\\cite{close_reading}. A natural consequence of this interpretive technique is the repeated citation of parts of the ground truth quote in the context. Since these examples contain lexical overlap with the ground truth quotation, models can exploit this during evidence retrieval.\n\n% \\paragraph{} Finally, we randomly sampled 10,000 examples for the train set. We report statistics for all splits in Table \\ref{} and for the test set folds in Table \\ref{}. Though we do not utilize the train set in our experiments, we will release it to spur future research into increasing the performance of open source models on long-context reasoning tasks.\n\n\\section{Experimental Setup}\n\\label{sec:experiments}\nWe evaluated four closed-source and four open-weight models on the long-context RELiC dataset, prompting each in a zero-shot setting to retrieve the most fitting quotation for a literary analysis excerpt given the full primary source text. We tested two prompt types: (1) \\textsc{Simple}, which requested only the quotation, and (2) \\textsc{Explanation}, which first asked the model to justify its choice before selecting a quotation\\footnote{Prompts \\& inference details in Appendix \\ref{sec:model-inf-details}.}. Our prompts are adapted from \\citet{karpinska-etal-2024-nocha} in the \\textsc{Nocha} long-context benchmark. Additionally, we implemented an embedding-based retrieval baseline using \\gteqwen, the top-performing text embedding model on MTEB\\footnote{\\url{https://huggingface.co/spaces/mteb/leaderboard} accessed February 2025} at the time of writing.\n\n\\subsection{Human Evaluation}\nThe author who manually validated high quality examples also attempted 40 examples from four previously-read primary source novels. The author had access to a digital copy of the primary source text with a string-match search function and the corresponding Wikipedia summaries. In addition to selecting missing quotations, the author also wrote a short justification for each choice. The task took approximately 8 hours.\n\n\\subsection{Evaluation Scheme}\nOur automatic evaluation of model responses applied partial ratio fuzzy matching (to account for minor typographical differences) to check that the model response was from the primary source text and to measure overlap between the response and the ground truth quotation\\footnote{Further details can be found in Appendix \\ref{sec:eval-details}.}. For the embedding baseline, we calculate recall@1.\n% \\FloatBarrier\n\\section{Results \\& Analysis}\n\\label{sec:analysis}\n\n\n% \\begin{table*}[t]\n% \\centering\n% \\renewcommand{\\arraystretch}{1.4} % Better row spacing\n% \\setlength{\\tabcolsep}{6pt} % Reduce padding slightly\n% \\scriptsize\n\n% % Literary Analysis Section\n% \\begin{tabularx}{\\textwidth}{|X|}\n\n% \\hline\n% Literary Analysis Excerpt with \\relicquote{Ground Truth Quotation} from \\textit{Frankenstein} \\\\\n% \\hline\n% \"It may therefore be judged indecent in me to come forward on this occasion,\" she proclaims on the stand, \"but when I see a fellow creature about to perish through the cowardice of her pretended friends, I wish to be allowed to speak, that I may say what I know of her character.\" Attempting to resist class lines and to speak of Justine as a \"fellow creature\" calls the silent cowardly, which means that Elizabeth, too, forgot her manners. Her testimony of Justine's dedication, loyalty, and nurturing cannot be heard, and the jury's response mimics Frankenstein's own preoccupation and inability to read the interactions and relationships between women in the broader sense. \\\\\n\n% \\relicquote{But I did not feel the inconvenience of the weather; my imagination was busy in scenes of evil and despair.} \\\\\n\n% Class and gender clearly matter in Geneva, and these factors, rather than a psychosexual rivalry, explain why Justine is punished for her ingratitude toward her benefactors. Elizabeth's testimony falls on deaf ears. What the jury perceives as important, and punishes, is indecorum—a breach of the mistress-servant relationship. \\\\\n% \\hline\n% \\end{tabularx}\n\n% \\vspace{3pt} % Space between merged section and individual columns\n\n% \\begin{tabularx}{\\textwidth}{|X|}\n% \\hline\n% \\faClose\\ \\textbf{Human and Gemini Pro 1.5 response}: A murmur of approbation followed Elizabeth’s simple and powerful appeal, but it was excited by her generous interference, and not in favour of poor Justine, on whom the public indignation was turned with renewed violence, charging her with the blackest ingratitude.\\\\\n\n% \\textbf{Human explanation}: I selected the quote that follows Elizabeth's full testimony, which is quoted in part at the beginning of the prefix. The selected quote also mentions Justine's \"ingratitute,\" like the suffix. \\\\\n% \\textbf{Gemini Pro 1.5 explanation}: The literary analysis focuses on Elizabeth's testimony during Justine's trial and the jury's reaction to it. The analysis highlights Elizabeth's attempt to transcend class boundaries by referring to Justine as a \"fellow creature\" and argues that this, along with Elizabeth's emotional testimony, is perceived as a breach of decorum and contributes to Justine's conviction. Therefore, the selected window should include Elizabeth's testimony where she calls Justine a \"fellow creature\" and speaks about her character, as well as the jury's immediate negative reaction, emphasizing their focus on the perceived impropriety of the situation rather than the content of the testimony. \\\\\n% \\hline\n% \\end{tabularx}\n% \\vspace{-8pt} % Reduce vertical spaces\n% \\caption{An example of long context RELiC where the \\human\\ annotator and \\geminipro\\ both select the same quotation, but it is not the ground truth quotation.}\n% \\label{tab:human_beats_models_comparison_2}\n% \\end{table*}\n\n\\begin{figure*}[t]\n \\centering\n \\includegraphics[width=\\textwidth]{figures/table_4.png}\n \\caption{An example of long context RELiC where the \\human\\ annotator and \\geminiprotwo\\ both select the same quotation, but it is not the ground truth quotation.}\n \\label{fig:alt_evidence}\n\\end{figure*}\n\n\nTable \\ref{tab:benchmark_accuracy} reports results for all evaluated models on the full set of 292 claims and the two folds. \n\n\\paragraph{LLMs outperform \\human\\ and SOTA embedding baseline}\nGoogle's \\geminiprotwo\\ reasoning model achieves better accuracy than our \\human\\ baseline in a fraction of the time---each API call took an average of 45 seconds vs. an average of 12 minutes for the \\human\\ expert. Both Google and OpenAI’s latest long-context models show substantial performance gains over their immediate predecessors on the literary evidence retrieval task. \\geminiprotwo\\ outperforms \\geminiproone\\ by a wide margin (64.7\\% vs. 38.5\\%), and OpenAI’s \\gptfourone\\ improves significantly over \\gpto\\ (51.0\\% vs. 24.3\\%). Similarly, \\othree\\ surpasses \\oone\\ (48.7\\% vs. 32.2\\%).\nThe embedding-based method achieved 4.5\\% accuracy, only 1.6\\% higher than the best recall@1 reported in the original RELiC paper from three years ago. LLMs’ success over embeddings suggests the importance of contextualized representations that can enable reasoning over entire primary source texts.\n\n\\paragraph{Closed-source outperform open-weight LLMs} Despite recent progress in open-weight LLM development, we observe a striking performance gap between closed-source and open-weight models on the literary evidence retrieval task. The best-performing open-weight model, \\deepseek, achieves only 29.1\\% accuracy---less than half the performance of the top closed-source model, \\geminiprotwo\\ (64.7\\%).\n\n\\paragraph{Small LLMs can't capitalize on close reading examples} Nearly all models larger than 8B parameters show modest to significant improvements on the close reading fold, with some models improving by over 20\\%. This effect is expected due to ground truth leakage in the the provided excerpt of literary analysis, but the 7B and 8B parameter open-weight models experience nearly no performance boost on this fold---the performance of \\llamasmall\\ actually suffers, suggesting that the smaller models lack the capacity to even exploit lexical overlap. \n\n\n\\paragraph{\\textsc{Explanation} prompt provides a glimpse into model reasoning} We observed mixed results when querying non-reasoning LLMs with the \\textsc{explanation} prompt. While \\geminiproone\\ improved with explanation-based reasoning, \\gpto\\ saw a decline. Recent reasoning LLMs such as \\geminiprotwo\\ and \\othree\\ use internal reasoning tokens that are not exposed via the API but influence the model's final output. These tokens are critical to the model's performance on complex tasks, but their inaccessibility complicates model evaluation and comparison. Applying the \\textsc{explanation} prompt to these reasoning models preserved accuracy, despite the redundancy, while providing insight into the models' reasoning processes. See a comparison of human and model justifications in Figure \\ref{fig:alt_evidence}. \n\n\\paragraph{All models tend to overgenerate}\nDespite prompts explicitly instructing models to select no more than five consecutive sentences from the primary source, we observe a consistent tendency across all evaluated models to produce significantly longer outputs. To quantify this behavior, we compute the length ratio, defined as the ratio of the model’s generated output length to the ground truth quotation length (measured in characters); values closer to 1.0 indicate greater adherence to the prompt constraints and minimal overgeneration. The length ratios are reported in Table \\ref{tab:length_ratios}.\n\nWe find that all models overgenerate, including the human annotator (average ratio of 2.1), likely due to natural variance in interpreting sentence boundaries. However, language models consistently surpass this baseline: for instance, state-of-the-art models such as \\geminiprotwo\\ and \\gptfourone\\ have average ratios of 3.0 and 4.8, respectively. Smaller open models like \\llamasmall\\ and \\llamalarge\\ exhibit even more extreme overgeneration (ratios > 5.7), suggesting that weaker models may compensate for uncertainty by producing longer outputs. \n\n\\paragraph{LLMs struggle with literary nuance}\nIn Figure \\ref{fig:llm_fail}, we present a challenging example where all LLMs fail but our \\human\\ expert correctly identified the ground truth quotation from \\textit{The Scarlet Letter}, a >80k-word novel. The context alludes to a description of the character Roger Chillingworth at a specific moment in the story (when he recognizes Hester Prynne). In their explanation, the \\human\\ expert mentions considering two different passages, but ultimately selecting the correct passage because it best demonstrates the ``melodrama\" mentioned in the literary analysis. The expert highlights the features of the correct passage that demonstrate ``melodrama\": an exaggerated character description, words that connote movement, and emotional tension. All three top-performing LLMs selected the other passage the \\human\\ expert was considering but ultimately eliminated, suggesting that even the best models still lack the expertise to navigate literary devices and more nuanced signals that inform expert literary interpretation. \n\n% \\vspace{-10pt}\n\\paragraph{Models can identify alternative literary evidence} In Figure \\ref{fig:alt_evidence}, we present an example where both \\human\\ and \\geminiprotwo\\ selected the same \"incorrect\" quote. The explanations reveal that both interpreted the literary analysis context as an introduction to a quote about the jury’s response. However, the ground truth quotation actually supports Frankenstein’s own preoccupation rather than the trial’s immediate outcome. This example highlights two key insights: (1) literary evidence retrieval is inherently interpretative, meaning that multiple passages may plausibly support a given claim, and (2) LLMs, like human readers, can surface alternative yet reasonable quotations that align with certain aspects of the analysis. While models may not always select the canonical answer, their ability to propose viable alternative evidence could be valuable for assisting literary scholars in exploring multiple textual connections.\n\n\\begin{table}[t!]\n\\small\n\\centering\n\\resizebox{0.48\\textwidth}{!}{%\n\\begin{tabular}{lcc}\n\\toprule\n\\textsc{Model + Simple Prompt} & \\textsc{\\faUser} Accuracy \\textsubscript{($n$=40)} & Avg. Length Ratio\\\\\n\\midrule\n\\geminiprotwo & 57.5 & 3.5\\\\\n\\othree & 47.5 & 3.5\\\\\n\\geminiproone & 22.5 & 2.8\\\\\n\\oone & 25.0 & 3.2\\\\\n\\gpto & 17.5 & 3.9\\\\\n\\qwenlarge & 7.5 & 3.2\\\\\n\\llamasmall & 5.0 & 5.9\\\\\n\\qwensmall & 5.0 & 2.8\\\\\n\\llamalarge & 0.0 & 5.7\\\\\n\\midrule\n\\multicolumn{3}{l}{\\textsc{Model + Explanation Prompt}} \\\\\n\\midrule\n\\geminiprotwo & \\textbf{62.5} & 3.0\\\\\n\\gptfourone & 47.5 & 4.8\\\\\n\\othree & 50.0 & 2.7\\\\\n\\geminiproone & 40.0 & 3.3\\\\\n\\claude & 32.5 & 4.0\\\\\n\\deepseek & 15.0 & 3.6\\\\\n\\gpto & 22.5 & 3.6\\\\\n\\qwenthreelarge & 20.0 & 2.7\\\\\n\\qwenthreesmall & 5.0 & 2.4\\\\\n\\othreemini & 10.0 & 3.5\\\\\n\\midrule\n\\multicolumn{3}{l}{\\textsc{Baselines}} \\\\\n\\midrule\n\\human & 55.0 & \\textbf{2.1}\\\\\n\\bottomrule\n\\end{tabular}\n}\n\\caption{The average length ratios for each model, defined as the ratio of the length model generation to that of the ground truth (measured in characters). All models have an average ratio greater than that of the human annotator (2.1), indicating overgeneration.}\n\\label{tab:length_ratios}\n\\end{table}\n\n\n\\section{Related Work}\n\\label{sec:rel_work}\n\nOur work builds on recent papers that apply and evaluate LLMs for computational literary analysis. Prior work has explored summarization or claim verification in novels~\\cite{subbiah-etal-2024-storysumm,fables24,karpinska-etal-2024-nocha} or short stories~\\cite{10.1162/tacl_a_00702}. Other more specific tasks, mainly in the short context setting, include extracting narrative elements~\\cite{shen-etal-2024-heart}, story arcs and turning points~\\cite{tian-etal-2024-large-language}, character analysis~\\cite{papoudakis-etal-2024-bookworm}, narrative discourse~\\cite{piper-bagga-2024-using}, plot development~\\cite{huot2024agents,xu-etal-2024-fine}, and creativity evaluation~\\cite{10.1145/3613904.3642731}. \n\n\\section{Conclusion}\nIn this work, we introduced a long-context dataset derived from RELiC for evaluating literary evidence retrieval using large language models. We constructed a high-quality set of 292 examples, ensuring rigorous evaluation through filtering and human verification. Our results demonstrate that long-context LLMs outperform embedding-based retrieval methods and even a \\human\\ expert baseline. However, closer analysis reveals that model success is not always grounded in robust interpretative abilities: models tend to overgenerate and still struggle to grasp nuances in literature. But, they can also successfully identify additional evidence for literary claims. Finally, open-weight models lag far behind their closed-weight counterparts, suggesting that long-context capabilities alone are insufficient without strong interpretive reasoning. We release our data and evaluation code to spur research at the intersection of NLP and literary analysis.\n\\section*{Limitations}\nAs noted in the original paper, the RELiC dataset represents a limited, English-only subset of world literature, with a strong emphasis on the Western literary canon. To foster a more inclusive and representative benchmark, we aim to extend this work to cross-lingual literary analysis and retrieval on texts from historically underrepresented literary traditions. Expanding the dataset in this way would not only enhance the linguistic and cultural diversity of literary evidence retrieval but also improve the generalizability of models across different literary frameworks.\n\nAdditionally, our human evaluation was limited in scope and scale, as it was conducted by a single annotator, one of the authors of this paper. While this approach was practical given the intensive nature of literary evidence retrieval over full novels, it inherently reflects the knowledge, biases, and interpretive lens of a single individual. In the future, we aim to broaden our evaluation by incorporating multiple expert annotators, enabling a more comprehensive and diverse assessment of model performance on the task of complex literary reasoning.\\section*{Ethical Considerations}\nWhile LLMs can assist in literary interpretation, they should not replace scholarly analysis conducted by trained experts. We acknowledge the potential for misuse of a system that can retrieve evidence for scholarly claims and emphasize that automated retrieval should be viewed as a complement to, not a substitute for, human literary scholarship.\n\nAs mentioned in the Limitations section, the primary source texts in our dataset are drawn from public domain works, which primarily reflect Western literary traditions. The results of this benchmark may not generalize to literary traditions outside the Anglophone canon, potentially reinforcing existing biases in computational literary studies.\n\n\n\\bibliography{custom}\n\n\n\\appendix\n\\section{Dataset Details}\n\\label{app:data_details}\nWe are using a publicly available dataset released by researchers who stated in their paper that they released their data to \"facilitate further research in this direction.\" Statistics for the primary sources can be found in \\ref{tab:book-details}.\n\n\n\\begin{table*}[t]\n \\renewcommand{\\arraystretch}{1.2} \n \\setlength{\\tabcolsep}{6pt}\n \\footnotesize\n \\centering\n \\begin{tabular}{l l c c c}\n \\toprule\n \\textbf{Book Title} & \\textbf{Author} & \\textbf{Publication Year} & \\textbf{Token Count} & \\textbf{Word Count} \\\\\n \\midrule\n Brave New World & Aldous Huxley & 1932 & 91,472 & 65,278 \\\\\n What Maisie Knew & Henry James & 1897 & 124,544 & 95,988 \\\\\n Ethan Frome & Edith Wharton & 1911 & 45,038 & 34,926 \\\\\n Frankenstein & Mary Shelley & 1818 & 95,018 & 75,131 \\\\\n The Great Gatsby & F. Scott Fitzgerald & 1925 & 63,683 & 48,972 \\\\\n The Awakening & Kate Chopin & 1899 & 66,574 & 49,932 \\\\\n The Scarlet Letter & Nathaniel Hawthorne & 1850 & 112,355 & 83,311 \\\\\n \\bottomrule\n \\end{tabular}\n \\caption{Books included in the dataset. The token count was provided as per \\href{https://github.com/openai/tiktoken}{\\texttt{tiktoken}} tokenization. }\n \\label{tab:book-details}\n\\end{table*}\n\n\n\n\n\n\\section{LLM-Aided Data Processing}\n\\subsection{Filtering steps}\nThese cleaning and filtering steps were applied to the entire RELiC dataset in the following order:\n\\begin{enumerate}\n \\item \\verb|CLEAN| (\\gptmini): We asked the model to remove any OCR artifacts and to ensure the context and ground truth quotation flow seamless and grammatically without changing any meaning. Additionally, we asked the model to remove any remaining in-line citations that revealed the page number of the ground truth quotation, which allowed us to preserve some examples that might have been caught by the \\verb|LOCATION| filter below. See the prompt in Table \\ref{tab:clean_filter}.\n \\item \\verb|LEAKAGE| (heuristic): If the sentences in the context preceding or following the ground truth quotation were fuzzy-matches (threshold: 95) with any text from the primary source, the example was excluded from our dataset.\n \\item \\verb|LIT ANALYSIS| (\\gptmini): We asked the model to classify whether each RELiC instance was an example of literary analysis. If it was not, we excluded it from our dataset. See the prompt in Table \\ref{tab:classify_lit_analysis}.\n \\item \\verb|LOCATION| (\\gptmini): We asked the model if the context revealed the location of the ground truth quotation. If it did, we excluded it from our dataset. See the prompt in Table \\ref{tab:quote_location_disclosure}.\n \\item \\verb|FIRST SENT| (heuristic): We identified cases where the ground truth quotation was the first sentence of its primary source novel. The intuition behind this filter (and the following two) was that the first sentence of a primary source might be famous or more likely to be quoted, and therefore easier to guess without needing to reason over the text or more likely to appear in training data.\n \\item \\verb|LAST SENT| (heuristic): Similarly, we identified cases where the ground truth quotation was the last sentence of its primary source novel.\n \\item \\verb|OUTLIER| (heuristic): We identified cases where the ground truth quotation was cited much more frequently than any of the other quotations from the same primary source novel.\n \\item \\verb|EZ2MEM| (\\gpt): We asked the model to perform the RELiC task \\textit{without} providing the primary source text and identified cases where the model was able to correctly generate at least one sentence of the ground truth quotation. \n\\end{enumerate}\nNote that we did not automatically exclude any of the examples identified by the last four filters. Though we did not use them in this project, we have left the labels in our dataset to facilitate future research.\n\n\\subsection{Manual validation of data}\nWe conducted multiple validations of the RELiC data for quality control using human annotations. \n\n\\paragraph{Validation of LLM filters} To validate our LLM-aided approach to data preprocessing, we manually annotated 100 examples of RELiC and compared our judgments to keep or reject each example to the results of the LLM + heuristic filters. The f1-score of our filtering scheme was 89.8, with our scheme identifying 57 true positives, 30 true negatives, 6 false positives, and 7 false negatives.\n\n\\section{Model Inference Details}\n\\label{sec:model-inf-details}\n\\paragraph{API access details}\n\\begin{itemize}\n \\item All OpenAI models were accessed via the OpenAI API (\\url{https://platform.openai.com/}).\n \\item All Gemini models were accessed via the Google AI API (\\url{https://ai.google.dev/}).\n \\item \\claude\\ was accessed via the Vertex AI/Google Cloud (\\url{https://cloud.google.com/vertex-ai?hl=en}).\n \\item DeepSeek was run via OpenRouter (\\url{https://openrouter.ai/}).\n\\end{itemize}\nThe approximate total cost of running all closed-weight models for both development and evaluation was \\$2k. \n\n\\paragraph{Local inference details}\nAll Llama and Qwen models were run locally. The smaller open-weight models were run on 1 A100 GPU for a total of 14 hours, while the larger open-weight models were run on 4 A100 GPUs for a total of 16 hours. \n\n\\paragraph{Generation hyperparameters}\nThe OpenAI reasoning models (\\oone, \\othreemini, and \\othree) were a special case: the temperature parameter is fixed and the token generation limit includes the inaccessible reasoning tokens. As such, we set the token generation limit to 12,000 for \\oone\\ and \\othreemini. For \\othree, the token generation limit was set to 25,000 as recommended by OpenAI\\footnote{\\url{https://platform.openai.com/docs/guides/reasoning\\#allocating-space-for-reasoning}}. For all three OpenAI reasoning models, we used the default \\verb|medium| reasoning effort. \n\nFor all other models, the token generation limit was set to 800 for \\textsc{Simple} prompts and 1,200 for \\textsc{Explanation} prompts. Temperature was set to 0.0 for all models except for \\qwenthreelarge\\ and \\qwenthreesmall, where temperature was set to the default 0.6.\n\nFor a list of the models and their checkpoints, see Table \\ref{tab:models}.\n\n\\begin{table}[t!]\n\\centering\n\\small\n\\resizebox{0.48\\textwidth}{!}{%\n\\begin{tabular}{l c l c c}\n\\toprule\n\\textsc{Model} & \\textsc{Context} & \\textsc{Avail.} & \\textsc{Checkpoints} \\\\\n\\midrule\n\\href{https://platform.openai.com/docs/models}{\\oone} & 200k & \\faLock & \\texttt{o1-2024-12-17} \\\\\n\\href{https://platform.openai.com/docs/models}{\\othree} & 200k & \\faLock & \\texttt{o3-2025-04-16} \\\\\n\\href{https://platform.openai.com/docs/models}{\\othreemini} & 200k & \\faLock & \\texttt{o3-mini-2025-01-31} \\\\\n\\href{https://platform.openai.com/docs/models}{\\gpto} & 128k & \\faLock & \\texttt{gpt-4o-2024-11-20} \\\\\n\\href{https://platform.openai.com/docs/models}{\\gptfourone} & 1M & \\faLock & \\texttt{gpt-4.1-2025-01-31} \\\\\n\\href{https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/gemini}{\\geminiproone} & 1M & \\faLock & \\texttt{gemini-1.5-pro-002} \\\\\n\\href{https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/gemini}{\\geminiprotwo} & 1M & \\faLock & \\texttt{gemini-2.5-pro-preview-05-06} \\\\\n\\href{https://docs.anthropic.com/en/docs/about-claude/models/overview}{\\claude} & 200k & \\faLock & \\texttt{claude-3-7-sonnet@20250219} \\\\\n\\href{https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct}{\\llamalarge} & 128k & \\faUnlock & \\texttt{Llama-3.3-70B-Instruct} \\\\\n\\href{https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct}{\\llamasmall} & 128k & \\faUnlock & \\texttt{\nLlama-3.1-8B-Instruct } \\\\\n\\href{https://huggingface.co/Qwen/Qwen2.5-72B-Instruct}{\\qwenlarge} & 128k & \\faUnlock & \\texttt{Qwen2.5-72B-Instruct} \\\\\n\\href{https://huggingface.co/Qwen/Qwen2.5-7B-Instruct}{\\qwensmall} & 128k & \\faUnlock & \\texttt{Qwen2.5-7B-Instruct} \\\\\n\\href{https://huggingface.co/Qwen/Qwen3-8B}{\\qwenthreesmall} & 131k* & \\faUnlock & \\texttt{Qwen3-8B} \\\\\n\\href{https://huggingface.co/Qwen/Qwen3-32B}{\\qwenthreelarge} & 131k* & \\faUnlock & \\texttt{Qwen3-32B} \\\\\n\\midrule\n\\href{https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct}{\\gteqwenparam} & 32k & \\faUnlock & \\texttt{gte-Qwen2-7B-instruct} \\\\\n\\bottomrule\n\\end{tabular}\n}\n\\caption{The upper rows display the evaluated LLMs, while the bottom row displays the text embedding model used for the baseline. Context lengths marked with * were extended with YaRN.}\n\\label{tab:models}\n\\end{table}\n\n\\section{Evaluation Scheme Details}\n\\label{sec:eval-details}\nWe use the \\verb|rapidfuzz| Python package for our fuzzy match evaluation with a threshold of 95 for checking the existence of the model response in the primary source and a threshold of 90 for checking the overlap between the model response and the ground truth quotation. The fuzzy matching allows for small typographical differences between the primary source and the model outputs (e.g. different types of quotation marks). Thresholds were determined after manual inspection of outputs at varying thresholds. \n\n\n\n% \\section{Additional Example for \\textsc{Human} vs. \\textsc{Model}}\n% \\label{sec:hum_vs_model_extra}\n\n\n\n\n\\begin{table*}[t]\n \\renewcommand{\\arraystretch}{1.2} \n \\setlength{\\tabcolsep}{4pt}\n \\footnotesize\n \\centering\n \\begin{tabular}{c p{15cm}}\n \\toprule\n & \\multicolumn{1}{c}{\\bf Prompt (Simple)} \\\\\n \\midrule\n \\noalign{\\vskip 1mm}\n & \\texttt{You are provided with the full text of \\textbf{[book\\_title]} and an excerpt of literary analysis that directly cites \\textbf{[book\\_title]} with the cited quotation represented as \\textless MASK\\textgreater.} \\\\[3mm]\n & \\texttt{Your task is to carefully read the text of \\textbf{[book\\_title]} and the excerpt of literary analysis, then select a window from \\textbf{[book\\_title]} that most appropriately replaces \\textless MASK\\textgreater as the cited quotation by providing textual evidence for any claims in the literary analysis.} \\\\[1mm]\n & \\texttt{The excerpt of literary analysis should form a valid argument when \\textless MASK\\textgreater is replaced by the window from \\textbf{[book\\_title]}.} \\\\[1mm]\n & \\texttt{\\textless full\\_text\\_of\\_[book\\_title\\_snake\\_case]\\textgreater \\textbf{[book\\_sentences]} \\textless /full\\_text\\_of\\_[book\\_title\\_snake\\_case]\\textgreater} \\\\[1mm]\n & \\texttt{\\textless literary\\_analysis\\_excerpt\\textgreater \\textbf{[lit\\_analysis\\_excerpt]} \\textless /literary\\_analysis\\_excerpt\\textgreater} \\\\[1mm]\n & \\texttt{Identify the window that best supports the claims being made in the excerpt of literary analysis. The window should contain no more than 5 consecutive sentences from \\textbf{[book\\_title]}.} \\\\[1mm]\n & \\texttt{Provide your final answer in the following format:} \\\\[1mm]\n & \\texttt{\\textless window\\textgreater \\textbf{YOUR SELECTED WINDOW} \\textless /window\\textgreater} \\\\[1mm]\n \\bottomrule\n \\end{tabular}\n\\caption{Prompt template for literary evidence retrieval (Simple).}\n\\label{tab:inference_simple}\n\\end{table*}\n\n\n\\begin{table*}[t]\n \\renewcommand{\\arraystretch}{1.2} \n \\setlength{\\tabcolsep}{4pt}\n \\footnotesize\n \\centering\n \\begin{tabular}{c p{15cm}}\n \\toprule\n & \\multicolumn{1}{c}{\\bf Prompt w/ Explanations} \\\\\n \\midrule\n \\noalign{\\vskip 1mm}\n & \\texttt{You are provided with the full text of \\textbf{[book\\_title]} and an excerpt of literary analysis that directly cites \\textbf{[book\\_title]} with the cited quotation represented as \\textless MASK\\textgreater.} \\\\[3mm]\n & \\texttt{Your task is to carefully read the text of \\textbf{[book\\_title]} and the excerpt of literary analysis, then select a window from \\textbf{[book\\_title]} that most appropriately replaces \\textless MASK\\textgreater as the cited quotation by providing textual evidence for any claims in the literary analysis.} \\\\[1mm]\n & \\texttt{The excerpt of literary analysis should form a valid argument when \\textless MASK\\textgreater is replaced by the window from \\textbf{[book\\_title]}.} \\\\[1mm]\n & \\texttt{\\textless full\\_text\\_of\\_[book\\_title\\_snake\\_case]\\textgreater \\textbf{[book\\_sentences]} \\textless /full\\_text\\_of\\_[book\\_title\\_snake\\_case]\\textgreater} \\\\[1mm]\n & \\texttt{\\textless literary\\_analysis\\_excerpt\\textgreater \\textbf{[lit\\_analysis\\_excerpt]} \\textless /literary\\_analysis\\_excerpt\\textgreater} \\\\[1mm]\n & \\texttt{First, provide an explanation of your decision marking process in no more than one paragraph.} \\\\[1mm]\n & \\texttt{Then, identify the window that best supports the claims being made in the excerpt of literary analysis. The window should contain no more than 5 consecutive sentences from \\textbf{[book\\_title]}.} \\\\[1mm]\n & \\texttt{Provide your final answer in the following format:} \\\\[1mm]\n & \\texttt{\\textless explanation\\textgreater \\textbf{YOUR EXPLANATION} \\textless /explanation\\textgreater} \\\\[1mm]\n & \\texttt{\\textless window\\textgreater \\textbf{YOUR SELECTED WINDOW} \\textless /window\\textgreater} \\\\[1mm]\n \\bottomrule\n \\end{tabular}\n\\caption{Prompt template for literary evidence retrieval with explanation.}\n\\label{tab:inference_explain}\n\\end{table*}\n\n\n\\begin{table*}[t]\n \\renewcommand{\\arraystretch}{1.2} \n \\setlength{\\tabcolsep}{4pt}\n \\footnotesize\n \\centering\n \\begin{tabular}{c p{15cm}}\n \\toprule\n & \\multicolumn{1}{c}{\\bf Prompt for Cleaning RELiC Examples} \\\\\n \\midrule\n \\noalign{\\vskip 1mm}\n & \\texttt{I want to create a dataset for Natural Language Processing that consists of excerpts of literary analysis that quote directly from a primary source text.} \\\\[3mm]\n & \\texttt{I have already collected windows of literary analysis that quote from many different primary sources in the public domain. The part of the excerpt before the quotation is called the \"prefix,\" and the part of the excerpt after the quotation is called the \"suffix.\" Because I collected the data from PDFs with OCR, the prefixes and suffixes contain artifacts like in-line citations, page numbers, chapter names, headers, and footers that I need to remove.} \\\\[3mm]\n & \\texttt{Here is an example:} \\\\[1mm]\n & \\texttt{\\textless prefix\\textgreater \\textbf{[prefix]} \\textless /prefix\\textgreater} \\\\[1mm]\n & \\texttt{\\textless ground\\_truth\\_quotation\\textgreater \\textbf{[ground\\_truth\\_quotation]} \\textless /ground\\_truth\\_quotation\\textgreater} \\\\[1mm]\n & \\texttt{\\textless suffix\\textgreater \\textbf{[suffix]} \\textless /suffix\\textgreater} \\\\[1mm]\n & \\texttt{Please follow the following guidelines to help me clean and filter this example:} \\\\[1mm]\n & \\texttt{1. Remove all textual artifacts from the OCR process by deleting them. Artifacts include page numbers, chapter headings, footnotes, image captions, etc.} \\\\[1mm]\n & \\texttt{2. Correct the grammar, punctuation, and spelling of the prefix and suffix without altering the meaning so that the primary source quotation fits seamlessly and grammatically between the prefix and suffix.} \\\\[1mm]\n & \\texttt{3. Remove all in-line citations following quotations, especially those that say things like \"(my emphasis).\"} \\\\[1mm]\n & \\texttt{Respond with ONLY the cleaned prefix and suffix in the following format:} \\\\[1mm]\n & \\texttt{\\textless clean\\_prefix\\textgreater \\textbf{CLEAN PREFIX} \\textless /clean\\_prefix\\textgreater} \\\\[1mm]\n & \\texttt{\\textless clean\\_suffix\\textgreater \\textbf{CLEAN SUFFIX} \\textless /clean\\_suffix\\textgreater} \\\\[1mm]\n \\bottomrule\n \\end{tabular}\n\\caption{Prompt template for cleaning RELiC examples}\n\\label{tab:clean_filter}\n\\end{table*}\n\n\\begin{table*}[t]\n \\renewcommand{\\arraystretch}{1.2} \n \\setlength{\\tabcolsep}{4pt}\n \\footnotesize\n \\centering\n \\begin{tabular}{c p{15cm}}\n \\toprule\n & \\multicolumn{1}{c}{\\bf Prompt for Classifying Literary Analysis} \\\\\n \\midrule\n \\noalign{\\vskip 1mm}\n & \\texttt{I want to create a dataset for Natural Language Processing that consists of excerpts of literary analysis that quote directly from a primary source text.} \\\\[3mm]\n & \\texttt{Other texts can contain quotes from primary sources, like biographies of authors. However, I only want examples of literary analysis in the dataset.} \\\\[3mm]\n & \\texttt{Please determine whether the following excerpt is an example of literary analysis. If it is literary analysis, respond with TRUE. Otherwise, respond with FALSE.} \\\\[3mm]\n & \\texttt{Here is the excerpt:} \\\\[1mm]\n & \\texttt{\\textless excerpt\\textgreater \\textbf{[clean\\_prefix]} \\textbf{[answer\\_quote]} \\textbf{[clean\\_suffix]} \\textless /excerpt\\textgreater} \\\\[1mm]\n & \\texttt{Respond with ONLY your answer in the following format:} \\\\[1mm]\n & \\texttt{\\textless answer\\textgreater \\textbf{YOUR ANSWER} \\textless /answer\\textgreater} \\\\[1mm]\n \\bottomrule\n \\end{tabular}\n\\caption{Prompt template for classifying literary analysis}\n\\label{tab:classify_lit_analysis}\n\\end{table*}\n\n\\begin{table*}[t]\n \\renewcommand{\\arraystretch}{1.2} \n \\setlength{\\tabcolsep}{4pt}\n \\footnotesize\n \\centering\n \\begin{tabular}{c p{15cm}}\n \\toprule\n & \\multicolumn{1}{c}{\\bf Prompt for Identifying Quote Location Disclosure} \\\\\n \\midrule\n \\noalign{\\vskip 1mm}\n & \\texttt{I want to create a dataset for a Natural Language Processing task called \"Literary Evidence Retrieval.\"} \\\\[3mm]\n & \\texttt{The input has two parts:} \\\\[1mm]\n & \\texttt{(1) An excerpt of literary analysis that quotes 1 to 5 full sentences from a primary source text, but the quote is replaced with \"[MASK]\".} \\\\[1mm]\n & \\texttt{(2) The full text of the primary source.} \\\\[3mm]\n & \\texttt{The output should be the correct quotation of 1 to 5 full sentences from the primary source—this is the ground truth.} \\\\[3mm]\n & \\texttt{I have already collected windows of literary analysis that quote from many different primary sources in the public domain.} \\\\[3mm]\n & \\texttt{The part of the excerpt before the quotation is called the \"prefix,\" and the part after is called the \"suffix.\"} \\\\[3mm]\n & \\texttt{To avoid giving the model hints, it's extremely important that the prefix and suffix do not mention the chapter where the ground truth quotation is located.} \\\\[3mm]\n & \\texttt{Please determine whether the following instance contains the chapter location of the ground truth quotation in either the prefix or the suffix. If it does, respond with TRUE; otherwise, respond with FALSE.} \\\\[3mm]\n & \\texttt{The prefix and suffix may contain the location of other quotations in the passage. I only want to identify if the location of the ground truth quotation is revealed.} \\\\[3mm]\n & \\texttt{Look out for phrases like \"In the prologue\" or \"In Chapter...\"} \\\\[3mm]\n & \\texttt{Here is the instance:} \\\\[1mm]\n & \\texttt{\\textless prefix\\textgreater \\textbf{[prefix]} \\textless /prefix\\textgreater} \\\\[1mm]\n & \\texttt{\\textless ground\\_truth\\_quotation\\textgreater \\textbf{[ground\\_truth\\_quotation]} \\textless /ground\\_truth\\_quotation\\textgreater} \\\\[1mm]\n & \\texttt{\\textless suffix\\textgreater \\textbf{[suffix]} \\textless /suffix\\textgreater} \\\\[1mm]\n & \\texttt{Respond with ONLY your answer in the following format:} \\\\[1mm]\n & \\texttt{\\textless answer\\textgreater \\textbf{YOUR ANSWER} \\textless /answer\\textgreater} \\\\[1mm]\n \\bottomrule\n \\end{tabular}\n\\caption{Prompt template for identifying quote location disclosure}\n\\label{tab:quote_location_disclosure}\n\\end{table*}\n\n\\section{Use of AI Assistants}\nThe authors used Github Copilot for coding assistance during their experiments and ChatGPT for assistance in formatting \\LaTeX\\ in the writing of this paper.\n\\end{document}"},"question":{"kind":"string","value":"Compare the top-performing model with the explanation prompt and the baseline model, what is the improvement factor for human-evaluated dataset? And what is the improvement factor for close-reading dataset?"},"answer":{"kind":"string","value":"25, 11.7"},"plan":{"kind":"string","value":"SELECT “ALL” accuracy for the Explanation Prompt\nSELECT Human-evaluated accuracy for the Explanation Prompt Models\nCOMPUTE best_explanation_model = argmax over Human-evaluated accuracy \n\n\nSELECT Human-evaluated accuracy for best_explanation_model and Baseline\nCOMPUTE human_improvement_factor = explanation_human_accuracy / baseline_human_accuracy\n\n\nSELECT Close-reading Accuracy for best_explanation_model and Baseline\nCOMPUTE close_improvement_factor = explanation_close_accuracy / baseline_close_accuracy\nRETURN human_improvement_factor, close_improvement_factor\n"}}},{"rowIdx":88,"cells":{"paper":{"kind":"string","value":"2506.03090"},"relevant_tables":{"kind":"list like","value":[["\\begin{table}[t!]\n","\\small\n","\\resizebox{0.48\\textwidth}{!}{%\n","\\begin{tabular}{lccc} % Updated to four columns\n","\n","\\toprule\n","\\textsc{Model + Simple Prompt} & \\textsc{ALL} \\textsubscript{($n$=292)} & \\textsc{\\faUser} \\textsubscript{($n$=40)}& \\textsc{\\faBook} \\textsubscript{($n$=39)}\\\\ \n","\\midrule\n","\\geminiprotwo~\\cite{google2025gemini25} & 63.7 & 57.5 & \\textbf{82.1} \\\\\n","\\othree~\\cite{o3} & 49.7 & 47.5 & 71.8 \\\\\n","\\geminiproone~\\cite{geminiteam2024gemini} & 36.1 & 22.5 & 43.6 \\\\\n","\\oone~\\cite{openai2024o1} & 32.2 & 25.0 & 41.0 \\\\\n","\\gpto\\ \\cite{openai2024gpt4ocard} & 27.4 & 17.5 & 35.9 \\\\\n","\\qwenlarge\\ \\cite{qwen_qwen25_2025} & 11.3 & 7.5 & 20.5 \\\\\n","\\llamasmall\\ \\cite{grattafiori2024llama3herdmodels} & 5.1 & 5.0 & 2.6 \\\\\n","\\qwensmall & 2.7 & 5.0 & 2.6 \\\\\n","\\llamalarge & 2.1 & 0.0 & 2.6 \\\\\n","\\midrule\n","\\textsc{Model + Explanation Prompt} & & & \\\\\n","\\midrule\n","\\geminiprotwo & \\textbf{64.7} & \\textbf{62.5} & 79.5 \\\\\n","\\gptfourone~\\cite{gpt-4.1} & 51.0 & 47.5 & 69.2 \\\\\n","\\othree & 50.7 & 50.0 & 66.7 \\\\\n","\\geminiproone & 38.5 & 40.0 & 50.0 \\\\\n","\\claude~\\cite{claude_sonnet_2025} & 37.0 & 32.5 & 48.7 \\\\\n","\\deepseek~\\cite{deepseekai2025deepseekv3technicalreport} & 29.1 & 15.0 & 38.5 \\\\\n","\\gpto & 24.3 & 22.5 & 31.8 \\\\\n","\\qwenthreelarge~\\cite{qwen_qwen25_2025} & 19.2 & 20.0 & 33.3 \\\\\n","\\qwenthreesmall & 8.9 & 5.0 & 10.3 \\\\\n","\\othreemini\\ \\cite{openai2025o3mini} & 8.3 & 10.0 & 13.6 \\\\\n","\\midrule\n","\\textsc{Baselines} & & & \\\\\n","\\midrule\n","\\gteqwen & 4.5 & 2.5 & 6.8 \\\\\n","\\human & - & 55.0 & - \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\caption{Percentage of test set examples where the model generated the correct ground truth quotation for different folds of the test set. The \\textsc{\\faUser} and \\textsc{\\faBook} columns contain the accuracy of each model on the human-evaluated and close reading subsets of the data, respectively.} \n","\\label{tab:benchmark_accuracy}\n","\\end{table}\n"]],"string":"[\n [\n \"\\\\begin{table}[t!]\\n\",\n \"\\\\small\\n\",\n \"\\\\resizebox{0.48\\\\textwidth}{!}{%\\n\",\n \"\\\\begin{tabular}{lccc} % Updated to four columns\\n\",\n \"\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textsc{Model + Simple Prompt} & \\\\textsc{ALL} \\\\textsubscript{($n$=292)} & \\\\textsc{\\\\faUser} \\\\textsubscript{($n$=40)}& \\\\textsc{\\\\faBook} \\\\textsubscript{($n$=39)}\\\\\\\\ \\n\",\n \"\\\\midrule\\n\",\n \"\\\\geminiprotwo~\\\\cite{google2025gemini25} & 63.7 & 57.5 & \\\\textbf{82.1} \\\\\\\\\\n\",\n \"\\\\othree~\\\\cite{o3} & 49.7 & 47.5 & 71.8 \\\\\\\\\\n\",\n \"\\\\geminiproone~\\\\cite{geminiteam2024gemini} & 36.1 & 22.5 & 43.6 \\\\\\\\\\n\",\n \"\\\\oone~\\\\cite{openai2024o1} & 32.2 & 25.0 & 41.0 \\\\\\\\\\n\",\n \"\\\\gpto\\\\ \\\\cite{openai2024gpt4ocard} & 27.4 & 17.5 & 35.9 \\\\\\\\\\n\",\n \"\\\\qwenlarge\\\\ \\\\cite{qwen_qwen25_2025} & 11.3 & 7.5 & 20.5 \\\\\\\\\\n\",\n \"\\\\llamasmall\\\\ \\\\cite{grattafiori2024llama3herdmodels} & 5.1 & 5.0 & 2.6 \\\\\\\\\\n\",\n \"\\\\qwensmall & 2.7 & 5.0 & 2.6 \\\\\\\\\\n\",\n \"\\\\llamalarge & 2.1 & 0.0 & 2.6 \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textsc{Model + Explanation Prompt} & & & \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\geminiprotwo & \\\\textbf{64.7} & \\\\textbf{62.5} & 79.5 \\\\\\\\\\n\",\n \"\\\\gptfourone~\\\\cite{gpt-4.1} & 51.0 & 47.5 & 69.2 \\\\\\\\\\n\",\n \"\\\\othree & 50.7 & 50.0 & 66.7 \\\\\\\\\\n\",\n \"\\\\geminiproone & 38.5 & 40.0 & 50.0 \\\\\\\\\\n\",\n \"\\\\claude~\\\\cite{claude_sonnet_2025} & 37.0 & 32.5 & 48.7 \\\\\\\\\\n\",\n \"\\\\deepseek~\\\\cite{deepseekai2025deepseekv3technicalreport} & 29.1 & 15.0 & 38.5 \\\\\\\\\\n\",\n \"\\\\gpto & 24.3 & 22.5 & 31.8 \\\\\\\\\\n\",\n \"\\\\qwenthreelarge~\\\\cite{qwen_qwen25_2025} & 19.2 & 20.0 & 33.3 \\\\\\\\\\n\",\n \"\\\\qwenthreesmall & 8.9 & 5.0 & 10.3 \\\\\\\\\\n\",\n \"\\\\othreemini\\\\ \\\\cite{openai2025o3mini} & 8.3 & 10.0 & 13.6 \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textsc{Baselines} & & & \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\gteqwen & 4.5 & 2.5 & 6.8 \\\\\\\\\\n\",\n \"\\\\human & - & 55.0 & - \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Percentage of test set examples where the model generated the correct ground truth quotation for different folds of the test set. The \\\\textsc{\\\\faUser} and \\\\textsc{\\\\faBook} columns contain the accuracy of each model on the human-evaluated and close reading subsets of the data, respectively.} \\n\",\n \"\\\\label{tab:benchmark_accuracy}\\n\",\n \"\\\\end{table}\\n\"\n ]\n]","isConversational":false},"tables":{"kind":"list like","value":[["\\begin{table}[t]\n","\\centering\n","\\footnotesize % Reduce font size for fitting in one column\n","\n","\n","\\centering\n","\n","\\resizebox{0.48\\textwidth}{!}{%\n","\\begin{tabular}{lcc|cc|c}\n","\\toprule\n"," & \\multicolumn{2}{c}{\\textbf{Primary Sources }} & \\multicolumn{3}{c}{\\textbf{Dataset Examples}} \\\\\n"," & \\multicolumn{2}{c}{(\\textit{n=7})} & \\multicolumn{3}{c}{(\\textit{n=292})} \\\\\n","\\cmidrule(lr){2-3} \\cmidrule(lr){4-6}\n"," & \\textsc{Tokens} & \\textsc{Words} & \\textsc{Tokens} & \\textsc{Words} & \\textsc{\\# Ex/Book}\\\\\n","\\midrule\n","\\textsc{Mean} & 85,526 & 69,456 & 254.9 & 203.6 & 36.0 \\\\\n","\\textsc{St. Dev.} & 26,6304 & 21,167 & 66.9 & 52.9 & 19.6 \\\\\n","\\textsc{Max} & 124,544 & 102,549 & 492.0 & 385.0 & 52.0 \\\\\n","\\textsc{Min} & 45,038 & 37,209 & 116.0 & 91.0 & 7.0 \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\caption{Summary statistics for long-context RELiC. Token counts were computed with the \\texttt{o200k\\_base} encoding via \\texttt{tiktoken} (\\url{https://github.com/openai/tiktoken}) and word counts were computed by splitting on whitespace.}\n","\\label{tab:basic_summary_stats_dataset}\n","\\end{table}\n"],["\\begin{table}[t!]\n","\\small\n","\\resizebox{0.48\\textwidth}{!}{%\n","\\begin{tabular}{lccc} % Updated to four columns\n","\n","\\toprule\n","\\textsc{Model + Simple Prompt} & \\textsc{ALL} \\textsubscript{($n$=292)} & \\textsc{\\faUser} \\textsubscript{($n$=40)}& \\textsc{\\faBook} \\textsubscript{($n$=39)}\\\\ \n","\\midrule\n","\\geminiprotwo~\\cite{google2025gemini25} & 63.7 & 57.5 & \\textbf{82.1} \\\\\n","\\othree~\\cite{o3} & 49.7 & 47.5 & 71.8 \\\\\n","\\geminiproone~\\cite{geminiteam2024gemini} & 36.1 & 22.5 & 43.6 \\\\\n","\\oone~\\cite{openai2024o1} & 32.2 & 25.0 & 41.0 \\\\\n","\\gpto\\ \\cite{openai2024gpt4ocard} & 27.4 & 17.5 & 35.9 \\\\\n","\\qwenlarge\\ \\cite{qwen_qwen25_2025} & 11.3 & 7.5 & 20.5 \\\\\n","\\llamasmall\\ \\cite{grattafiori2024llama3herdmodels} & 5.1 & 5.0 & 2.6 \\\\\n","\\qwensmall & 2.7 & 5.0 & 2.6 \\\\\n","\\llamalarge & 2.1 & 0.0 & 2.6 \\\\\n","\\midrule\n","\\textsc{Model + Explanation Prompt} & & & \\\\\n","\\midrule\n","\\geminiprotwo & \\textbf{64.7} & \\textbf{62.5} & 79.5 \\\\\n","\\gptfourone~\\cite{gpt-4.1} & 51.0 & 47.5 & 69.2 \\\\\n","\\othree & 50.7 & 50.0 & 66.7 \\\\\n","\\geminiproone & 38.5 & 40.0 & 50.0 \\\\\n","\\claude~\\cite{claude_sonnet_2025} & 37.0 & 32.5 & 48.7 \\\\\n","\\deepseek~\\cite{deepseekai2025deepseekv3technicalreport} & 29.1 & 15.0 & 38.5 \\\\\n","\\gpto & 24.3 & 22.5 & 31.8 \\\\\n","\\qwenthreelarge~\\cite{qwen_qwen25_2025} & 19.2 & 20.0 & 33.3 \\\\\n","\\qwenthreesmall & 8.9 & 5.0 & 10.3 \\\\\n","\\othreemini\\ \\cite{openai2025o3mini} & 8.3 & 10.0 & 13.6 \\\\\n","\\midrule\n","\\textsc{Baselines} & & & \\\\\n","\\midrule\n","\\gteqwen & 4.5 & 2.5 & 6.8 \\\\\n","\\human & - & 55.0 & - \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\caption{Percentage of test set examples where the model generated the correct ground truth quotation for different folds of the test set. The \\textsc{\\faUser} and \\textsc{\\faBook} columns contain the accuracy of each model on the human-evaluated and close reading subsets of the data, respectively.} \n","\\label{tab:benchmark_accuracy}\n","\\end{table}\n"],["\\begin{figure*}[t]\n"," \\centering\n"," \\includegraphics[width=\\textwidth]{figures/table_3.png}\n"," \\caption{An example from long context RELiC where the \\human\\ annotator successfully retrieves the quotation while the top-performing LLMs fail.}\n"," \\label{fig:llm_fail}\n","\\end{figure*}\n"],["\\begin{figure*}[t]\n"," \\centering\n"," \\includegraphics[width=\\textwidth]{figures/table_4.png}\n"," \\caption{An example of long context RELiC where the \\human\\ annotator and \\geminiprotwo\\ both select the same quotation, but it is not the ground truth quotation.}\n"," \\label{fig:alt_evidence}\n","\\end{figure*}\n"],["\\begin{table}[t!]\n","\\small\n","\\centering\n","\\resizebox{0.48\\textwidth}{!}{%\n","\\begin{tabular}{lcc}\n","\\toprule\n","\\textsc{Model + Simple Prompt} & \\textsc{\\faUser} Accuracy \\textsubscript{($n$=40)} & Avg. Length Ratio\\\\\n","\\midrule\n","\\geminiprotwo & 57.5 & 3.5\\\\\n","\\othree & 47.5 & 3.5\\\\\n","\\geminiproone & 22.5 & 2.8\\\\\n","\\oone & 25.0 & 3.2\\\\\n","\\gpto & 17.5 & 3.9\\\\\n","\\qwenlarge & 7.5 & 3.2\\\\\n","\\llamasmall & 5.0 & 5.9\\\\\n","\\qwensmall & 5.0 & 2.8\\\\\n","\\llamalarge & 0.0 & 5.7\\\\\n","\\midrule\n","\\multicolumn{3}{l}{\\textsc{Model + Explanation Prompt}} \\\\\n","\\midrule\n","\\geminiprotwo & \\textbf{62.5} & 3.0\\\\\n","\\gptfourone & 47.5 & 4.8\\\\\n","\\othree & 50.0 & 2.7\\\\\n","\\geminiproone & 40.0 & 3.3\\\\\n","\\claude & 32.5 & 4.0\\\\\n","\\deepseek & 15.0 & 3.6\\\\\n","\\gpto & 22.5 & 3.6\\\\\n","\\qwenthreelarge & 20.0 & 2.7\\\\\n","\\qwenthreesmall & 5.0 & 2.4\\\\\n","\\othreemini & 10.0 & 3.5\\\\\n","\\midrule\n","\\multicolumn{3}{l}{\\textsc{Baselines}} \\\\\n","\\midrule\n","\\human & 55.0 & \\textbf{2.1}\\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\caption{The average length ratios for each model, defined as the ratio of the length model generation to that of the ground truth (measured in characters). All models have an average ratio greater than that of the human annotator (2.1), indicating overgeneration.}\n","\\label{tab:length_ratios}\n","\\end{table}\n"],["\\begin{table*}[t]\n"," \\renewcommand{\\arraystretch}{1.2} \n"," \\setlength{\\tabcolsep}{6pt}\n"," \\footnotesize\n"," \\centering\n"," \\begin{tabular}{l l c c c}\n"," \\toprule\n"," \\textbf{Book Title} & \\textbf{Author} & \\textbf{Publication Year} & \\textbf{Token Count} & \\textbf{Word Count} \\\\\n"," \\midrule\n"," Brave New World & Aldous Huxley & 1932 & 91,472 & 65,278 \\\\\n"," What Maisie Knew & Henry James & 1897 & 124,544 & 95,988 \\\\\n"," Ethan Frome & Edith Wharton & 1911 & 45,038 & 34,926 \\\\\n"," Frankenstein & Mary Shelley & 1818 & 95,018 & 75,131 \\\\\n"," The Great Gatsby & F. Scott Fitzgerald & 1925 & 63,683 & 48,972 \\\\\n"," The Awakening & Kate Chopin & 1899 & 66,574 & 49,932 \\\\\n"," The Scarlet Letter & Nathaniel Hawthorne & 1850 & 112,355 & 83,311 \\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," \\caption{Books included in the dataset. The token count was provided as per \\href{https://github.com/openai/tiktoken}{\\texttt{tiktoken}} tokenization. }\n"," \\label{tab:book-details}\n","\\end{table*}\n"],["\\begin{table}[t!]\n","\\centering\n","\\small\n","\\resizebox{0.48\\textwidth}{!}{%\n","\\begin{tabular}{l c l c c}\n","\\toprule\n","\\textsc{Model} & \\textsc{Context} & \\textsc{Avail.} & \\textsc{Checkpoints} \\\\\n","\\midrule\n","\\href{https://platform.openai.com/docs/models}{\\oone} & 200k & \\faLock & \\texttt{o1-2024-12-17} \\\\\n","\\href{https://platform.openai.com/docs/models}{\\othree} & 200k & \\faLock & \\texttt{o3-2025-04-16} \\\\\n","\\href{https://platform.openai.com/docs/models}{\\othreemini} & 200k & \\faLock & \\texttt{o3-mini-2025-01-31} \\\\\n","\\href{https://platform.openai.com/docs/models}{\\gpto} & 128k & \\faLock & \\texttt{gpt-4o-2024-11-20} \\\\\n","\\href{https://platform.openai.com/docs/models}{\\gptfourone} & 1M & \\faLock & \\texttt{gpt-4.1-2025-01-31} \\\\\n","\\href{https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/gemini}{\\geminiproone} & 1M & \\faLock & \\texttt{gemini-1.5-pro-002} \\\\\n","\\href{https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/gemini}{\\geminiprotwo} & 1M & \\faLock & \\texttt{gemini-2.5-pro-preview-05-06} \\\\\n","\\href{https://docs.anthropic.com/en/docs/about-claude/models/overview}{\\claude} & 200k & \\faLock & \\texttt{claude-3-7-sonnet@20250219} \\\\\n","\\href{https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct}{\\llamalarge} & 128k & \\faUnlock & \\texttt{Llama-3.3-70B-Instruct} \\\\\n","\\href{https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct}{\\llamasmall} & 128k & \\faUnlock & \\texttt{\n","Llama-3.1-8B-Instruct } \\\\\n","\\href{https://huggingface.co/Qwen/Qwen2.5-72B-Instruct}{\\qwenlarge} & 128k & \\faUnlock & \\texttt{Qwen2.5-72B-Instruct} \\\\\n","\\href{https://huggingface.co/Qwen/Qwen2.5-7B-Instruct}{\\qwensmall} & 128k & \\faUnlock & \\texttt{Qwen2.5-7B-Instruct} \\\\\n","\\href{https://huggingface.co/Qwen/Qwen3-8B}{\\qwenthreesmall} & 131k* & \\faUnlock & \\texttt{Qwen3-8B} \\\\\n","\\href{https://huggingface.co/Qwen/Qwen3-32B}{\\qwenthreelarge} & 131k* & \\faUnlock & \\texttt{Qwen3-32B} \\\\\n","\\midrule\n","\\href{https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct}{\\gteqwenparam} & 32k & \\faUnlock & \\texttt{gte-Qwen2-7B-instruct} \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\caption{The upper rows display the evaluated LLMs, while the bottom row displays the text embedding model used for the baseline. Context lengths marked with * were extended with YaRN.}\n","\\label{tab:models}\n","\\end{table}\n"],["\\begin{table*}[t]\n"," \\renewcommand{\\arraystretch}{1.2} \n"," \\setlength{\\tabcolsep}{4pt}\n"," \\footnotesize\n"," \\centering\n"," \\begin{tabular}{c p{15cm}}\n"," \\toprule\n"," & \\multicolumn{1}{c}{\\bf Prompt (Simple)} \\\\\n"," \\midrule\n"," \\noalign{\\vskip 1mm}\n"," & \\texttt{You are provided with the full text of \\textbf{[book\\_title]} and an excerpt of literary analysis that directly cites \\textbf{[book\\_title]} with the cited quotation represented as \\textless MASK\\textgreater.} \\\\[3mm]\n"," & \\texttt{Your task is to carefully read the text of \\textbf{[book\\_title]} and the excerpt of literary analysis, then select a window from \\textbf{[book\\_title]} that most appropriately replaces \\textless MASK\\textgreater as the cited quotation by providing textual evidence for any claims in the literary analysis.} \\\\[1mm]\n"," & \\texttt{The excerpt of literary analysis should form a valid argument when \\textless MASK\\textgreater is replaced by the window from \\textbf{[book\\_title]}.} \\\\[1mm]\n"," & \\texttt{\\textless full\\_text\\_of\\_[book\\_title\\_snake\\_case]\\textgreater \\textbf{[book\\_sentences]} \\textless /full\\_text\\_of\\_[book\\_title\\_snake\\_case]\\textgreater} \\\\[1mm]\n"," & \\texttt{\\textless literary\\_analysis\\_excerpt\\textgreater \\textbf{[lit\\_analysis\\_excerpt]} \\textless /literary\\_analysis\\_excerpt\\textgreater} \\\\[1mm]\n"," & \\texttt{Identify the window that best supports the claims being made in the excerpt of literary analysis. The window should contain no more than 5 consecutive sentences from \\textbf{[book\\_title]}.} \\\\[1mm]\n"," & \\texttt{Provide your final answer in the following format:} \\\\[1mm]\n"," & \\texttt{\\textless window\\textgreater \\textbf{YOUR SELECTED WINDOW} \\textless /window\\textgreater} \\\\[1mm]\n"," \\bottomrule\n"," \\end{tabular}\n","\\caption{Prompt template for literary evidence retrieval (Simple).}\n","\\label{tab:inference_simple}\n","\\end{table*}\n"],["\\begin{table*}[t]\n"," \\renewcommand{\\arraystretch}{1.2} \n"," \\setlength{\\tabcolsep}{4pt}\n"," \\footnotesize\n"," \\centering\n"," \\begin{tabular}{c p{15cm}}\n"," \\toprule\n"," & \\multicolumn{1}{c}{\\bf Prompt w/ Explanations} \\\\\n"," \\midrule\n"," \\noalign{\\vskip 1mm}\n"," & \\texttt{You are provided with the full text of \\textbf{[book\\_title]} and an excerpt of literary analysis that directly cites \\textbf{[book\\_title]} with the cited quotation represented as \\textless MASK\\textgreater.} \\\\[3mm]\n"," & \\texttt{Your task is to carefully read the text of \\textbf{[book\\_title]} and the excerpt of literary analysis, then select a window from \\textbf{[book\\_title]} that most appropriately replaces \\textless MASK\\textgreater as the cited quotation by providing textual evidence for any claims in the literary analysis.} \\\\[1mm]\n"," & \\texttt{The excerpt of literary analysis should form a valid argument when \\textless MASK\\textgreater is replaced by the window from \\textbf{[book\\_title]}.} \\\\[1mm]\n"," & \\texttt{\\textless full\\_text\\_of\\_[book\\_title\\_snake\\_case]\\textgreater \\textbf{[book\\_sentences]} \\textless /full\\_text\\_of\\_[book\\_title\\_snake\\_case]\\textgreater} \\\\[1mm]\n"," & \\texttt{\\textless literary\\_analysis\\_excerpt\\textgreater \\textbf{[lit\\_analysis\\_excerpt]} \\textless /literary\\_analysis\\_excerpt\\textgreater} \\\\[1mm]\n"," & \\texttt{First, provide an explanation of your decision marking process in no more than one paragraph.} \\\\[1mm]\n"," & \\texttt{Then, identify the window that best supports the claims being made in the excerpt of literary analysis. The window should contain no more than 5 consecutive sentences from \\textbf{[book\\_title]}.} \\\\[1mm]\n"," & \\texttt{Provide your final answer in the following format:} \\\\[1mm]\n"," & \\texttt{\\textless explanation\\textgreater \\textbf{YOUR EXPLANATION} \\textless /explanation\\textgreater} \\\\[1mm]\n"," & \\texttt{\\textless window\\textgreater \\textbf{YOUR SELECTED WINDOW} \\textless /window\\textgreater} \\\\[1mm]\n"," \\bottomrule\n"," \\end{tabular}\n","\\caption{Prompt template for literary evidence retrieval with explanation.}\n","\\label{tab:inference_explain}\n","\\end{table*}\n"],["\\begin{table*}[t]\n"," \\renewcommand{\\arraystretch}{1.2} \n"," \\setlength{\\tabcolsep}{4pt}\n"," \\footnotesize\n"," \\centering\n"," \\begin{tabular}{c p{15cm}}\n"," \\toprule\n"," & \\multicolumn{1}{c}{\\bf Prompt for Cleaning RELiC Examples} \\\\\n"," \\midrule\n"," \\noalign{\\vskip 1mm}\n"," & \\texttt{I want to create a dataset for Natural Language Processing that consists of excerpts of literary analysis that quote directly from a primary source text.} \\\\[3mm]\n"," & \\texttt{I have already collected windows of literary analysis that quote from many different primary sources in the public domain. The part of the excerpt before the quotation is called the \"prefix,\" and the part of the excerpt after the quotation is called the \"suffix.\" Because I collected the data from PDFs with OCR, the prefixes and suffixes contain artifacts like in-line citations, page numbers, chapter names, headers, and footers that I need to remove.} \\\\[3mm]\n"," & \\texttt{Here is an example:} \\\\[1mm]\n"," & \\texttt{\\textless prefix\\textgreater \\textbf{[prefix]} \\textless /prefix\\textgreater} \\\\[1mm]\n"," & \\texttt{\\textless ground\\_truth\\_quotation\\textgreater \\textbf{[ground\\_truth\\_quotation]} \\textless /ground\\_truth\\_quotation\\textgreater} \\\\[1mm]\n"," & \\texttt{\\textless suffix\\textgreater \\textbf{[suffix]} \\textless /suffix\\textgreater} \\\\[1mm]\n"," & \\texttt{Please follow the following guidelines to help me clean and filter this example:} \\\\[1mm]\n"," & \\texttt{1. Remove all textual artifacts from the OCR process by deleting them. Artifacts include page numbers, chapter headings, footnotes, image captions, etc.} \\\\[1mm]\n"," & \\texttt{2. Correct the grammar, punctuation, and spelling of the prefix and suffix without altering the meaning so that the primary source quotation fits seamlessly and grammatically between the prefix and suffix.} \\\\[1mm]\n"," & \\texttt{3. Remove all in-line citations following quotations, especially those that say things like \"(my emphasis).\"} \\\\[1mm]\n"," & \\texttt{Respond with ONLY the cleaned prefix and suffix in the following format:} \\\\[1mm]\n"," & \\texttt{\\textless clean\\_prefix\\textgreater \\textbf{CLEAN PREFIX} \\textless /clean\\_prefix\\textgreater} \\\\[1mm]\n"," & \\texttt{\\textless clean\\_suffix\\textgreater \\textbf{CLEAN SUFFIX} \\textless /clean\\_suffix\\textgreater} \\\\[1mm]\n"," \\bottomrule\n"," \\end{tabular}\n","\\caption{Prompt template for cleaning RELiC examples}\n","\\label{tab:clean_filter}\n","\\end{table*}\n"],["\\begin{table*}[t]\n"," \\renewcommand{\\arraystretch}{1.2} \n"," \\setlength{\\tabcolsep}{4pt}\n"," \\footnotesize\n"," \\centering\n"," \\begin{tabular}{c p{15cm}}\n"," \\toprule\n"," & \\multicolumn{1}{c}{\\bf Prompt for Classifying Literary Analysis} \\\\\n"," \\midrule\n"," \\noalign{\\vskip 1mm}\n"," & \\texttt{I want to create a dataset for Natural Language Processing that consists of excerpts of literary analysis that quote directly from a primary source text.} \\\\[3mm]\n"," & \\texttt{Other texts can contain quotes from primary sources, like biographies of authors. However, I only want examples of literary analysis in the dataset.} \\\\[3mm]\n"," & \\texttt{Please determine whether the following excerpt is an example of literary analysis. If it is literary analysis, respond with TRUE. Otherwise, respond with FALSE.} \\\\[3mm]\n"," & \\texttt{Here is the excerpt:} \\\\[1mm]\n"," & \\texttt{\\textless excerpt\\textgreater \\textbf{[clean\\_prefix]} \\textbf{[answer\\_quote]} \\textbf{[clean\\_suffix]} \\textless /excerpt\\textgreater} \\\\[1mm]\n"," & \\texttt{Respond with ONLY your answer in the following format:} \\\\[1mm]\n"," & \\texttt{\\textless answer\\textgreater \\textbf{YOUR ANSWER} \\textless /answer\\textgreater} \\\\[1mm]\n"," \\bottomrule\n"," \\end{tabular}\n","\\caption{Prompt template for classifying literary analysis}\n","\\label{tab:classify_lit_analysis}\n","\\end{table*}\n"],["\\begin{table*}[t]\n"," \\renewcommand{\\arraystretch}{1.2} \n"," \\setlength{\\tabcolsep}{4pt}\n"," \\footnotesize\n"," \\centering\n"," \\begin{tabular}{c p{15cm}}\n"," \\toprule\n"," & \\multicolumn{1}{c}{\\bf Prompt for Identifying Quote Location Disclosure} \\\\\n"," \\midrule\n"," \\noalign{\\vskip 1mm}\n"," & \\texttt{I want to create a dataset for a Natural Language Processing task called \"Literary Evidence Retrieval.\"} \\\\[3mm]\n"," & \\texttt{The input has two parts:} \\\\[1mm]\n"," & \\texttt{(1) An excerpt of literary analysis that quotes 1 to 5 full sentences from a primary source text, but the quote is replaced with \"[MASK]\".} \\\\[1mm]\n"," & \\texttt{(2) The full text of the primary source.} \\\\[3mm]\n"," & \\texttt{The output should be the correct quotation of 1 to 5 full sentences from the primary source—this is the ground truth.} \\\\[3mm]\n"," & \\texttt{I have already collected windows of literary analysis that quote from many different primary sources in the public domain.} \\\\[3mm]\n"," & \\texttt{The part of the excerpt before the quotation is called the \"prefix,\" and the part after is called the \"suffix.\"} \\\\[3mm]\n"," & \\texttt{To avoid giving the model hints, it's extremely important that the prefix and suffix do not mention the chapter where the ground truth quotation is located.} \\\\[3mm]\n"," & \\texttt{Please determine whether the following instance contains the chapter location of the ground truth quotation in either the prefix or the suffix. If it does, respond with TRUE; otherwise, respond with FALSE.} \\\\[3mm]\n"," & \\texttt{The prefix and suffix may contain the location of other quotations in the passage. I only want to identify if the location of the ground truth quotation is revealed.} \\\\[3mm]\n"," & \\texttt{Look out for phrases like \"In the prologue\" or \"In Chapter...\"} \\\\[3mm]\n"," & \\texttt{Here is the instance:} \\\\[1mm]\n"," & \\texttt{\\textless prefix\\textgreater \\textbf{[prefix]} \\textless /prefix\\textgreater} \\\\[1mm]\n"," & \\texttt{\\textless ground\\_truth\\_quotation\\textgreater \\textbf{[ground\\_truth\\_quotation]} \\textless /ground\\_truth\\_quotation\\textgreater} \\\\[1mm]\n"," & \\texttt{\\textless suffix\\textgreater \\textbf{[suffix]} \\textless /suffix\\textgreater} \\\\[1mm]\n"," & \\texttt{Respond with ONLY your answer in the following format:} \\\\[1mm]\n"," & \\texttt{\\textless answer\\textgreater \\textbf{YOUR ANSWER} \\textless /answer\\textgreater} \\\\[1mm]\n"," \\bottomrule\n"," \\end{tabular}\n","\\caption{Prompt template for identifying quote location disclosure}\n","\\label{tab:quote_location_disclosure}\n","\\end{table*}\n"]],"string":"[\n [\n \"\\\\begin{table}[t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\footnotesize % Reduce font size for fitting in one column\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\\centering\\n\",\n \"\\n\",\n \"\\\\resizebox{0.48\\\\textwidth}{!}{%\\n\",\n \"\\\\begin{tabular}{lcc|cc|c}\\n\",\n \"\\\\toprule\\n\",\n \" & \\\\multicolumn{2}{c}{\\\\textbf{Primary Sources }} & \\\\multicolumn{3}{c}{\\\\textbf{Dataset Examples}} \\\\\\\\\\n\",\n \" & \\\\multicolumn{2}{c}{(\\\\textit{n=7})} & \\\\multicolumn{3}{c}{(\\\\textit{n=292})} \\\\\\\\\\n\",\n \"\\\\cmidrule(lr){2-3} \\\\cmidrule(lr){4-6}\\n\",\n \" & \\\\textsc{Tokens} & \\\\textsc{Words} & \\\\textsc{Tokens} & \\\\textsc{Words} & \\\\textsc{\\\\# Ex/Book}\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textsc{Mean} & 85,526 & 69,456 & 254.9 & 203.6 & 36.0 \\\\\\\\\\n\",\n \"\\\\textsc{St. Dev.} & 26,6304 & 21,167 & 66.9 & 52.9 & 19.6 \\\\\\\\\\n\",\n \"\\\\textsc{Max} & 124,544 & 102,549 & 492.0 & 385.0 & 52.0 \\\\\\\\\\n\",\n \"\\\\textsc{Min} & 45,038 & 37,209 & 116.0 & 91.0 & 7.0 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Summary statistics for long-context RELiC. Token counts were computed with the \\\\texttt{o200k\\\\_base} encoding via \\\\texttt{tiktoken} (\\\\url{https://github.com/openai/tiktoken}) and word counts were computed by splitting on whitespace.}\\n\",\n \"\\\\label{tab:basic_summary_stats_dataset}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[t!]\\n\",\n \"\\\\small\\n\",\n \"\\\\resizebox{0.48\\\\textwidth}{!}{%\\n\",\n \"\\\\begin{tabular}{lccc} % Updated to four columns\\n\",\n \"\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textsc{Model + Simple Prompt} & \\\\textsc{ALL} \\\\textsubscript{($n$=292)} & \\\\textsc{\\\\faUser} \\\\textsubscript{($n$=40)}& \\\\textsc{\\\\faBook} \\\\textsubscript{($n$=39)}\\\\\\\\ \\n\",\n \"\\\\midrule\\n\",\n \"\\\\geminiprotwo~\\\\cite{google2025gemini25} & 63.7 & 57.5 & \\\\textbf{82.1} \\\\\\\\\\n\",\n \"\\\\othree~\\\\cite{o3} & 49.7 & 47.5 & 71.8 \\\\\\\\\\n\",\n \"\\\\geminiproone~\\\\cite{geminiteam2024gemini} & 36.1 & 22.5 & 43.6 \\\\\\\\\\n\",\n \"\\\\oone~\\\\cite{openai2024o1} & 32.2 & 25.0 & 41.0 \\\\\\\\\\n\",\n \"\\\\gpto\\\\ \\\\cite{openai2024gpt4ocard} & 27.4 & 17.5 & 35.9 \\\\\\\\\\n\",\n \"\\\\qwenlarge\\\\ \\\\cite{qwen_qwen25_2025} & 11.3 & 7.5 & 20.5 \\\\\\\\\\n\",\n \"\\\\llamasmall\\\\ \\\\cite{grattafiori2024llama3herdmodels} & 5.1 & 5.0 & 2.6 \\\\\\\\\\n\",\n \"\\\\qwensmall & 2.7 & 5.0 & 2.6 \\\\\\\\\\n\",\n \"\\\\llamalarge & 2.1 & 0.0 & 2.6 \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textsc{Model + Explanation Prompt} & & & \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\geminiprotwo & \\\\textbf{64.7} & \\\\textbf{62.5} & 79.5 \\\\\\\\\\n\",\n \"\\\\gptfourone~\\\\cite{gpt-4.1} & 51.0 & 47.5 & 69.2 \\\\\\\\\\n\",\n \"\\\\othree & 50.7 & 50.0 & 66.7 \\\\\\\\\\n\",\n \"\\\\geminiproone & 38.5 & 40.0 & 50.0 \\\\\\\\\\n\",\n \"\\\\claude~\\\\cite{claude_sonnet_2025} & 37.0 & 32.5 & 48.7 \\\\\\\\\\n\",\n \"\\\\deepseek~\\\\cite{deepseekai2025deepseekv3technicalreport} & 29.1 & 15.0 & 38.5 \\\\\\\\\\n\",\n \"\\\\gpto & 24.3 & 22.5 & 31.8 \\\\\\\\\\n\",\n \"\\\\qwenthreelarge~\\\\cite{qwen_qwen25_2025} & 19.2 & 20.0 & 33.3 \\\\\\\\\\n\",\n \"\\\\qwenthreesmall & 8.9 & 5.0 & 10.3 \\\\\\\\\\n\",\n \"\\\\othreemini\\\\ \\\\cite{openai2025o3mini} & 8.3 & 10.0 & 13.6 \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textsc{Baselines} & & & \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\gteqwen & 4.5 & 2.5 & 6.8 \\\\\\\\\\n\",\n \"\\\\human & - & 55.0 & - \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Percentage of test set examples where the model generated the correct ground truth quotation for different folds of the test set. The \\\\textsc{\\\\faUser} and \\\\textsc{\\\\faBook} columns contain the accuracy of each model on the human-evaluated and close reading subsets of the data, respectively.} \\n\",\n \"\\\\label{tab:benchmark_accuracy}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{figure*}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\textwidth]{figures/table_3.png}\\n\",\n \" \\\\caption{An example from long context RELiC where the \\\\human\\\\ annotator successfully retrieves the quotation while the top-performing LLMs fail.}\\n\",\n \" \\\\label{fig:llm_fail}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\textwidth]{figures/table_4.png}\\n\",\n \" \\\\caption{An example of long context RELiC where the \\\\human\\\\ annotator and \\\\geminiprotwo\\\\ both select the same quotation, but it is not the ground truth quotation.}\\n\",\n \" \\\\label{fig:alt_evidence}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{table}[t!]\\n\",\n \"\\\\small\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{0.48\\\\textwidth}{!}{%\\n\",\n \"\\\\begin{tabular}{lcc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textsc{Model + Simple Prompt} & \\\\textsc{\\\\faUser} Accuracy \\\\textsubscript{($n$=40)} & Avg. Length Ratio\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\geminiprotwo & 57.5 & 3.5\\\\\\\\\\n\",\n \"\\\\othree & 47.5 & 3.5\\\\\\\\\\n\",\n \"\\\\geminiproone & 22.5 & 2.8\\\\\\\\\\n\",\n \"\\\\oone & 25.0 & 3.2\\\\\\\\\\n\",\n \"\\\\gpto & 17.5 & 3.9\\\\\\\\\\n\",\n \"\\\\qwenlarge & 7.5 & 3.2\\\\\\\\\\n\",\n \"\\\\llamasmall & 5.0 & 5.9\\\\\\\\\\n\",\n \"\\\\qwensmall & 5.0 & 2.8\\\\\\\\\\n\",\n \"\\\\llamalarge & 0.0 & 5.7\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multicolumn{3}{l}{\\\\textsc{Model + Explanation Prompt}} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\geminiprotwo & \\\\textbf{62.5} & 3.0\\\\\\\\\\n\",\n \"\\\\gptfourone & 47.5 & 4.8\\\\\\\\\\n\",\n \"\\\\othree & 50.0 & 2.7\\\\\\\\\\n\",\n \"\\\\geminiproone & 40.0 & 3.3\\\\\\\\\\n\",\n \"\\\\claude & 32.5 & 4.0\\\\\\\\\\n\",\n \"\\\\deepseek & 15.0 & 3.6\\\\\\\\\\n\",\n \"\\\\gpto & 22.5 & 3.6\\\\\\\\\\n\",\n \"\\\\qwenthreelarge & 20.0 & 2.7\\\\\\\\\\n\",\n \"\\\\qwenthreesmall & 5.0 & 2.4\\\\\\\\\\n\",\n \"\\\\othreemini & 10.0 & 3.5\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multicolumn{3}{l}{\\\\textsc{Baselines}} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\human & 55.0 & \\\\textbf{2.1}\\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{The average length ratios for each model, defined as the ratio of the length model generation to that of the ground truth (measured in characters). All models have an average ratio greater than that of the human annotator (2.1), indicating overgeneration.}\\n\",\n \"\\\\label{tab:length_ratios}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table*}[t]\\n\",\n \" \\\\renewcommand{\\\\arraystretch}{1.2} \\n\",\n \" \\\\setlength{\\\\tabcolsep}{6pt}\\n\",\n \" \\\\footnotesize\\n\",\n \" \\\\centering\\n\",\n \" \\\\begin{tabular}{l l c c c}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Book Title} & \\\\textbf{Author} & \\\\textbf{Publication Year} & \\\\textbf{Token Count} & \\\\textbf{Word Count} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" Brave New World & Aldous Huxley & 1932 & 91,472 & 65,278 \\\\\\\\\\n\",\n \" What Maisie Knew & Henry James & 1897 & 124,544 & 95,988 \\\\\\\\\\n\",\n \" Ethan Frome & Edith Wharton & 1911 & 45,038 & 34,926 \\\\\\\\\\n\",\n \" Frankenstein & Mary Shelley & 1818 & 95,018 & 75,131 \\\\\\\\\\n\",\n \" The Great Gatsby & F. Scott Fitzgerald & 1925 & 63,683 & 48,972 \\\\\\\\\\n\",\n \" The Awakening & Kate Chopin & 1899 & 66,574 & 49,932 \\\\\\\\\\n\",\n \" The Scarlet Letter & Nathaniel Hawthorne & 1850 & 112,355 & 83,311 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" \\\\caption{Books included in the dataset. The token count was provided as per \\\\href{https://github.com/openai/tiktoken}{\\\\texttt{tiktoken}} tokenization. }\\n\",\n \" \\\\label{tab:book-details}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table}[t!]\\n\",\n \"\\\\centering\\n\",\n \"\\\\small\\n\",\n \"\\\\resizebox{0.48\\\\textwidth}{!}{%\\n\",\n \"\\\\begin{tabular}{l c l c c}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textsc{Model} & \\\\textsc{Context} & \\\\textsc{Avail.} & \\\\textsc{Checkpoints} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\href{https://platform.openai.com/docs/models}{\\\\oone} & 200k & \\\\faLock & \\\\texttt{o1-2024-12-17} \\\\\\\\\\n\",\n \"\\\\href{https://platform.openai.com/docs/models}{\\\\othree} & 200k & \\\\faLock & \\\\texttt{o3-2025-04-16} \\\\\\\\\\n\",\n \"\\\\href{https://platform.openai.com/docs/models}{\\\\othreemini} & 200k & \\\\faLock & \\\\texttt{o3-mini-2025-01-31} \\\\\\\\\\n\",\n \"\\\\href{https://platform.openai.com/docs/models}{\\\\gpto} & 128k & \\\\faLock & \\\\texttt{gpt-4o-2024-11-20} \\\\\\\\\\n\",\n \"\\\\href{https://platform.openai.com/docs/models}{\\\\gptfourone} & 1M & \\\\faLock & \\\\texttt{gpt-4.1-2025-01-31} \\\\\\\\\\n\",\n \"\\\\href{https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/gemini}{\\\\geminiproone} & 1M & \\\\faLock & \\\\texttt{gemini-1.5-pro-002} \\\\\\\\\\n\",\n \"\\\\href{https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/gemini}{\\\\geminiprotwo} & 1M & \\\\faLock & \\\\texttt{gemini-2.5-pro-preview-05-06} \\\\\\\\\\n\",\n \"\\\\href{https://docs.anthropic.com/en/docs/about-claude/models/overview}{\\\\claude} & 200k & \\\\faLock & \\\\texttt{claude-3-7-sonnet@20250219} \\\\\\\\\\n\",\n \"\\\\href{https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct}{\\\\llamalarge} & 128k & \\\\faUnlock & \\\\texttt{Llama-3.3-70B-Instruct} \\\\\\\\\\n\",\n \"\\\\href{https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct}{\\\\llamasmall} & 128k & \\\\faUnlock & \\\\texttt{\\n\",\n \"Llama-3.1-8B-Instruct } \\\\\\\\\\n\",\n \"\\\\href{https://huggingface.co/Qwen/Qwen2.5-72B-Instruct}{\\\\qwenlarge} & 128k & \\\\faUnlock & \\\\texttt{Qwen2.5-72B-Instruct} \\\\\\\\\\n\",\n \"\\\\href{https://huggingface.co/Qwen/Qwen2.5-7B-Instruct}{\\\\qwensmall} & 128k & \\\\faUnlock & \\\\texttt{Qwen2.5-7B-Instruct} \\\\\\\\\\n\",\n \"\\\\href{https://huggingface.co/Qwen/Qwen3-8B}{\\\\qwenthreesmall} & 131k* & \\\\faUnlock & \\\\texttt{Qwen3-8B} \\\\\\\\\\n\",\n \"\\\\href{https://huggingface.co/Qwen/Qwen3-32B}{\\\\qwenthreelarge} & 131k* & \\\\faUnlock & \\\\texttt{Qwen3-32B} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\href{https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct}{\\\\gteqwenparam} & 32k & \\\\faUnlock & \\\\texttt{gte-Qwen2-7B-instruct} \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{The upper rows display the evaluated LLMs, while the bottom row displays the text embedding model used for the baseline. Context lengths marked with * were extended with YaRN.}\\n\",\n \"\\\\label{tab:models}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table*}[t]\\n\",\n \" \\\\renewcommand{\\\\arraystretch}{1.2} \\n\",\n \" \\\\setlength{\\\\tabcolsep}{4pt}\\n\",\n \" \\\\footnotesize\\n\",\n \" \\\\centering\\n\",\n \" \\\\begin{tabular}{c p{15cm}}\\n\",\n \" \\\\toprule\\n\",\n \" & \\\\multicolumn{1}{c}{\\\\bf Prompt (Simple)} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\noalign{\\\\vskip 1mm}\\n\",\n \" & \\\\texttt{You are provided with the full text of \\\\textbf{[book\\\\_title]} and an excerpt of literary analysis that directly cites \\\\textbf{[book\\\\_title]} with the cited quotation represented as \\\\textless MASK\\\\textgreater.} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{Your task is to carefully read the text of \\\\textbf{[book\\\\_title]} and the excerpt of literary analysis, then select a window from \\\\textbf{[book\\\\_title]} that most appropriately replaces \\\\textless MASK\\\\textgreater as the cited quotation by providing textual evidence for any claims in the literary analysis.} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{The excerpt of literary analysis should form a valid argument when \\\\textless MASK\\\\textgreater is replaced by the window from \\\\textbf{[book\\\\_title]}.} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless full\\\\_text\\\\_of\\\\_[book\\\\_title\\\\_snake\\\\_case]\\\\textgreater \\\\textbf{[book\\\\_sentences]} \\\\textless /full\\\\_text\\\\_of\\\\_[book\\\\_title\\\\_snake\\\\_case]\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless literary\\\\_analysis\\\\_excerpt\\\\textgreater \\\\textbf{[lit\\\\_analysis\\\\_excerpt]} \\\\textless /literary\\\\_analysis\\\\_excerpt\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{Identify the window that best supports the claims being made in the excerpt of literary analysis. The window should contain no more than 5 consecutive sentences from \\\\textbf{[book\\\\_title]}.} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{Provide your final answer in the following format:} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless window\\\\textgreater \\\\textbf{YOUR SELECTED WINDOW} \\\\textless /window\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"\\\\caption{Prompt template for literary evidence retrieval (Simple).}\\n\",\n \"\\\\label{tab:inference_simple}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[t]\\n\",\n \" \\\\renewcommand{\\\\arraystretch}{1.2} \\n\",\n \" \\\\setlength{\\\\tabcolsep}{4pt}\\n\",\n \" \\\\footnotesize\\n\",\n \" \\\\centering\\n\",\n \" \\\\begin{tabular}{c p{15cm}}\\n\",\n \" \\\\toprule\\n\",\n \" & \\\\multicolumn{1}{c}{\\\\bf Prompt w/ Explanations} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\noalign{\\\\vskip 1mm}\\n\",\n \" & \\\\texttt{You are provided with the full text of \\\\textbf{[book\\\\_title]} and an excerpt of literary analysis that directly cites \\\\textbf{[book\\\\_title]} with the cited quotation represented as \\\\textless MASK\\\\textgreater.} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{Your task is to carefully read the text of \\\\textbf{[book\\\\_title]} and the excerpt of literary analysis, then select a window from \\\\textbf{[book\\\\_title]} that most appropriately replaces \\\\textless MASK\\\\textgreater as the cited quotation by providing textual evidence for any claims in the literary analysis.} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{The excerpt of literary analysis should form a valid argument when \\\\textless MASK\\\\textgreater is replaced by the window from \\\\textbf{[book\\\\_title]}.} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless full\\\\_text\\\\_of\\\\_[book\\\\_title\\\\_snake\\\\_case]\\\\textgreater \\\\textbf{[book\\\\_sentences]} \\\\textless /full\\\\_text\\\\_of\\\\_[book\\\\_title\\\\_snake\\\\_case]\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless literary\\\\_analysis\\\\_excerpt\\\\textgreater \\\\textbf{[lit\\\\_analysis\\\\_excerpt]} \\\\textless /literary\\\\_analysis\\\\_excerpt\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{First, provide an explanation of your decision marking process in no more than one paragraph.} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{Then, identify the window that best supports the claims being made in the excerpt of literary analysis. The window should contain no more than 5 consecutive sentences from \\\\textbf{[book\\\\_title]}.} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{Provide your final answer in the following format:} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless explanation\\\\textgreater \\\\textbf{YOUR EXPLANATION} \\\\textless /explanation\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless window\\\\textgreater \\\\textbf{YOUR SELECTED WINDOW} \\\\textless /window\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"\\\\caption{Prompt template for literary evidence retrieval with explanation.}\\n\",\n \"\\\\label{tab:inference_explain}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[t]\\n\",\n \" \\\\renewcommand{\\\\arraystretch}{1.2} \\n\",\n \" \\\\setlength{\\\\tabcolsep}{4pt}\\n\",\n \" \\\\footnotesize\\n\",\n \" \\\\centering\\n\",\n \" \\\\begin{tabular}{c p{15cm}}\\n\",\n \" \\\\toprule\\n\",\n \" & \\\\multicolumn{1}{c}{\\\\bf Prompt for Cleaning RELiC Examples} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\noalign{\\\\vskip 1mm}\\n\",\n \" & \\\\texttt{I want to create a dataset for Natural Language Processing that consists of excerpts of literary analysis that quote directly from a primary source text.} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{I have already collected windows of literary analysis that quote from many different primary sources in the public domain. The part of the excerpt before the quotation is called the \\\"prefix,\\\" and the part of the excerpt after the quotation is called the \\\"suffix.\\\" Because I collected the data from PDFs with OCR, the prefixes and suffixes contain artifacts like in-line citations, page numbers, chapter names, headers, and footers that I need to remove.} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{Here is an example:} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless prefix\\\\textgreater \\\\textbf{[prefix]} \\\\textless /prefix\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless ground\\\\_truth\\\\_quotation\\\\textgreater \\\\textbf{[ground\\\\_truth\\\\_quotation]} \\\\textless /ground\\\\_truth\\\\_quotation\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless suffix\\\\textgreater \\\\textbf{[suffix]} \\\\textless /suffix\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{Please follow the following guidelines to help me clean and filter this example:} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{1. Remove all textual artifacts from the OCR process by deleting them. Artifacts include page numbers, chapter headings, footnotes, image captions, etc.} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{2. Correct the grammar, punctuation, and spelling of the prefix and suffix without altering the meaning so that the primary source quotation fits seamlessly and grammatically between the prefix and suffix.} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{3. Remove all in-line citations following quotations, especially those that say things like \\\"(my emphasis).\\\"} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{Respond with ONLY the cleaned prefix and suffix in the following format:} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless clean\\\\_prefix\\\\textgreater \\\\textbf{CLEAN PREFIX} \\\\textless /clean\\\\_prefix\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless clean\\\\_suffix\\\\textgreater \\\\textbf{CLEAN SUFFIX} \\\\textless /clean\\\\_suffix\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"\\\\caption{Prompt template for cleaning RELiC examples}\\n\",\n \"\\\\label{tab:clean_filter}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[t]\\n\",\n \" \\\\renewcommand{\\\\arraystretch}{1.2} \\n\",\n \" \\\\setlength{\\\\tabcolsep}{4pt}\\n\",\n \" \\\\footnotesize\\n\",\n \" \\\\centering\\n\",\n \" \\\\begin{tabular}{c p{15cm}}\\n\",\n \" \\\\toprule\\n\",\n \" & \\\\multicolumn{1}{c}{\\\\bf Prompt for Classifying Literary Analysis} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\noalign{\\\\vskip 1mm}\\n\",\n \" & \\\\texttt{I want to create a dataset for Natural Language Processing that consists of excerpts of literary analysis that quote directly from a primary source text.} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{Other texts can contain quotes from primary sources, like biographies of authors. However, I only want examples of literary analysis in the dataset.} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{Please determine whether the following excerpt is an example of literary analysis. If it is literary analysis, respond with TRUE. Otherwise, respond with FALSE.} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{Here is the excerpt:} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless excerpt\\\\textgreater \\\\textbf{[clean\\\\_prefix]} \\\\textbf{[answer\\\\_quote]} \\\\textbf{[clean\\\\_suffix]} \\\\textless /excerpt\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{Respond with ONLY your answer in the following format:} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless answer\\\\textgreater \\\\textbf{YOUR ANSWER} \\\\textless /answer\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"\\\\caption{Prompt template for classifying literary analysis}\\n\",\n \"\\\\label{tab:classify_lit_analysis}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[t]\\n\",\n \" \\\\renewcommand{\\\\arraystretch}{1.2} \\n\",\n \" \\\\setlength{\\\\tabcolsep}{4pt}\\n\",\n \" \\\\footnotesize\\n\",\n \" \\\\centering\\n\",\n \" \\\\begin{tabular}{c p{15cm}}\\n\",\n \" \\\\toprule\\n\",\n \" & \\\\multicolumn{1}{c}{\\\\bf Prompt for Identifying Quote Location Disclosure} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\noalign{\\\\vskip 1mm}\\n\",\n \" & \\\\texttt{I want to create a dataset for a Natural Language Processing task called \\\"Literary Evidence Retrieval.\\\"} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{The input has two parts:} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{(1) An excerpt of literary analysis that quotes 1 to 5 full sentences from a primary source text, but the quote is replaced with \\\"[MASK]\\\".} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{(2) The full text of the primary source.} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{The output should be the correct quotation of 1 to 5 full sentences from the primary source—this is the ground truth.} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{I have already collected windows of literary analysis that quote from many different primary sources in the public domain.} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{The part of the excerpt before the quotation is called the \\\"prefix,\\\" and the part after is called the \\\"suffix.\\\"} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{To avoid giving the model hints, it's extremely important that the prefix and suffix do not mention the chapter where the ground truth quotation is located.} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{Please determine whether the following instance contains the chapter location of the ground truth quotation in either the prefix or the suffix. If it does, respond with TRUE; otherwise, respond with FALSE.} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{The prefix and suffix may contain the location of other quotations in the passage. I only want to identify if the location of the ground truth quotation is revealed.} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{Look out for phrases like \\\"In the prologue\\\" or \\\"In Chapter...\\\"} \\\\\\\\[3mm]\\n\",\n \" & \\\\texttt{Here is the instance:} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless prefix\\\\textgreater \\\\textbf{[prefix]} \\\\textless /prefix\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless ground\\\\_truth\\\\_quotation\\\\textgreater \\\\textbf{[ground\\\\_truth\\\\_quotation]} \\\\textless /ground\\\\_truth\\\\_quotation\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless suffix\\\\textgreater \\\\textbf{[suffix]} \\\\textless /suffix\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{Respond with ONLY your answer in the following format:} \\\\\\\\[1mm]\\n\",\n \" & \\\\texttt{\\\\textless answer\\\\textgreater \\\\textbf{YOUR ANSWER} \\\\textless /answer\\\\textgreater} \\\\\\\\[1mm]\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"\\\\caption{Prompt template for identifying quote location disclosure}\\n\",\n \"\\\\label{tab:quote_location_disclosure}\\n\",\n \"\\\\end{table*}\\n\"\n ]\n]","isConversational":false},"fulltext":{"kind":"string","value":"% This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.\n\\pdfoutput=1\n% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.\n\n\\documentclass[11pt]{article}\n\n% Change \"review\" to \"final\" to generate the final (sometimes called camera-ready) version.\n% Change to \"preprint\" to generate a non-anonymous version with page numbers.\n\\usepackage[final]{acl}\n\n% Standard package includes\n\\usepackage{times}\n\\usepackage{latexsym}\n\n% For proper rendering and hyphenation of words containing Latin characters (including in bib files)\n\\usepackage[T1]{fontenc}\n% For Vietnamese characters\n% \\usepackage[T5]{fontenc}\n% See 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gte-Qwen2-7B-instruct}}\n\\newcommand{\\human}{\\textsc{\\small Human}}\n\\newcommand{\\relicquote}[1]{\\textbf{\\textcolor{purple}{#1}}}\n\n\n\n\\newcommand\\blankfootnote[1]{%\n \\let\\thefootnote\\relax\\footnotetext{#1}%\n \\let\\thefootnote\\svthefootnote%\n}\n\n\n\n\n\n\\title{\\raisebox{-0.3\\height}{\\includegraphics[width=1.75cm]{figures/lc_relic_emoji.png}}\\; Literary Evidence Retrieval via Long-Context Language Models}\n\n\n% Author information can be set in various styles:\n% For several authors from the same institution:\n% \\author{Author 1 \\and ... \\and Author n \\\\\n% Address line \\\\ ... \\\\ Address line}\n% if the names do not fit well on one line use\n% Author 1 \\\\ {\\bf Author 2} \\\\ ... \\\\ {\\bf Author n} \\\\\n% For authors from different institutions:\n% \\author{Author 1 \\\\ Address line \\\\ ... \\\\ Address line\n% \\And ... \\And\n% Author n \\\\ Address line \\\\ ... \\\\ Address line}\n% To start a separate ``row'' of authors use \\AND, as in\n% \\author{Author 1 \\\\ Address line \\\\ ... \\\\ Address line\n% \\AND\n% Author 2 \\\\ Address line \\\\ ... \\\\ Address line \\And\n% Author 3 \\\\ Address line \\\\ ... \\\\ Address line}\n\n\n\n\\author{Katherine Thai\\twemoji{crystal ball} and Mohit Iyyer\\twemoji{crystal ball}\\twemoji{magnifying glass tilted right}\\\\\nUMass Amherst\\twemoji{crystal ball}\\hspace{0.3cm} \nUniversity of Maryland, College Park\\twemoji{magnifying glass tilted right},\\\\\n\\texttt{kbthai@umass.edu}, \\texttt{miyyer@umd.edu} \\\\\n\\\\\n\\url{https://github.com/katherinethai/long_context_relic}\n}\n\n\n%\\author{\n% \\textbf{First Author\\textsuperscript{1}},\n% \\textbf{Second Author\\textsuperscript{1,2}},\n% \\textbf{Third T. Author\\textsuperscript{1}},\n% \\textbf{Fourth Author\\textsuperscript{1}},\n%\\\\\n% \\textbf{Fifth Author\\textsuperscript{1,2}},\n% \\textbf{Sixth Author\\textsuperscript{1}},\n% \\textbf{Seventh Author\\textsuperscript{1}},\n% \\textbf{Eighth Author \\textsuperscript{1,2,3,4}},\n%\\\\\n% \\textbf{Ninth Author\\textsuperscript{1}},\n% \\textbf{Tenth Author\\textsuperscript{1}},\n% \\textbf{Eleventh E. Author\\textsuperscript{1,2,3,4,5}},\n% \\textbf{Twelfth Author\\textsuperscript{1}},\n%\\\\\n% \\textbf{Thirteenth Author\\textsuperscript{3}},\n% \\textbf{Fourteenth F. Author\\textsuperscript{2,4}},\n% \\textbf{Fifteenth Author\\textsuperscript{1}},\n% \\textbf{Sixteenth Author\\textsuperscript{1}},\n%\\\\\n% \\textbf{Seventeenth S. Author\\textsuperscript{4,5}},\n% \\textbf{Eighteenth Author\\textsuperscript{3,4}},\n% \\textbf{Nineteenth N. Author\\textsuperscript{2,5}},\n% \\textbf{Twentieth Author\\textsuperscript{1}}\n%\\\\\n%\\\\\n% \\textsuperscript{1}Affiliation 1,\n% \\textsuperscript{2}Affiliation 2,\n% \\textsuperscript{3}Affiliation 3,\n% \\textsuperscript{4}Affiliation 4,\n% \\textsuperscript{5}Affiliation 5\n%\\\\\n% \\small{\n% \\textbf{Correspondence:} \\href{mailto:email@domain}{email@domain}\n% }\n%}\n\n\n\n\n\\begin{document}\n\n\n\\maketitle\n\n\\begin{abstract}\n\nHow well do modern long-context language models understand literary fiction? We explore this question via the task of \\emph{literary evidence retrieval}, repurposing the RELiC dataset of~\\citet{thai-etal-2022-relic} to construct a benchmark where the entire text of a primary source (e.g., \\textit{The Great Gatsby}) is provided to an LLM alongside literary criticism with a missing quotation from that work. This setting, in which the model must generate the missing quotation, mirrors the human process of literary analysis by requiring models to perform both global narrative reasoning and close textual examination. We curate a high-quality subset of 292 examples through extensive filtering and human verification. Our experiments show that recent reasoning models, such as \\geminiprotwo\\ can exceed human expert performance (62.5\\% vs. 50\\% accuracy). In contrast, the best open-weight model achieves only 29.1\\% accuracy, highlighting a wide gap in interpretive reasoning between open and closed-weight models. Despite their speed and apparent accuracy, even the strongest models struggle with nuanced literary signals and overgeneration, signaling open challenges for applying LLMs to literary analysis. We release our dataset and evaluation code to encourage future work in this direction.\n\n\\end{abstract}\\section{Introduction}\n\\label{sec:intro} \n\nThe emergence of long-context language models, which can process millions of tokens~\\cite{geminiteam2024gemini}, has unlocked new AI applications for literary analysis. In this paper, we focus on the task of \\emph{literary evidence retrieval}, in which a model must retrieve a supporting quotation from a primary source (e.g., a novel) to substantiate an excerpt of literary criticism.\n\n~\\citet{thai-etal-2022-relic} frame literary evidence retrieval as a computational task by introducing RELiC, a dataset that contains short excerpts from published literary criticism that include quotations from famous novels. While RELiC was developed to benchmark and improve retriever models, which compute embeddings of claims and short chunks of the book text, we repurpose it as a testbed for long-context\\footnote{In this paper, we consider ``long-context\" to mean allowing a minimum of 128k tokens as input.} language models: A model is given the entire text of the book and an excerpt of literary criticism with a missing quotation from that book, then asked to generate the missing quote (see \\autoref{fig:llm_fail}, top). This task does not exactly mirror the human process of literary analysis, in which a scholar typically develops claims and selects supporting quotes iteratively. However, the task requires the same understanding of and complex reasoning over plot, subtext, and other literary devices with the advantage of being easily verifiable. \n\nTo enable robust evaluation, we curated a high-quality subset of 292 examples from RELiC, verified through a combination of automated filtering and expert human review. These 292 examples contain claims that require both global reasoning over events and ``close reading'' over singular passages to solve. Our experiments reveal that state-of-the-art reasoning models significantly outperform previous LLMs and even surpass a human expert baseline: our best model, \\geminiprotwo\\ obtains an accuracy of \\textbf{62.5\\%} compared to \\textbf{55.0\\%} for a human expert on a subset of the data. However, we also find that these models tend to overgenerate and struggle with subtle literary cues. Open-weight models perform substantially worse, suggesting that interpretive reasoning, not just long-context capacity, is essential to success in this domain, forming important directions for future research.\n\n\\begin{table}[t]\n\\centering\n\\footnotesize % Reduce font size for fitting in one column\n\n\n\\centering\n\n\\resizebox{0.48\\textwidth}{!}{%\n\\begin{tabular}{lcc|cc|c}\n\\toprule\n & \\multicolumn{2}{c}{\\textbf{Primary Sources }} & \\multicolumn{3}{c}{\\textbf{Dataset Examples}} \\\\\n & \\multicolumn{2}{c}{(\\textit{n=7})} & \\multicolumn{3}{c}{(\\textit{n=292})} \\\\\n\\cmidrule(lr){2-3} \\cmidrule(lr){4-6}\n & \\textsc{Tokens} & \\textsc{Words} & \\textsc{Tokens} & \\textsc{Words} & \\textsc{\\# Ex/Book}\\\\\n\\midrule\n\\textsc{Mean} & 85,526 & 69,456 & 254.9 & 203.6 & 36.0 \\\\\n\\textsc{St. Dev.} & 26,6304 & 21,167 & 66.9 & 52.9 & 19.6 \\\\\n\\textsc{Max} & 124,544 & 102,549 & 492.0 & 385.0 & 52.0 \\\\\n\\textsc{Min} & 45,038 & 37,209 & 116.0 & 91.0 & 7.0 \\\\\n\\bottomrule\n\\end{tabular}\n}\n\\caption{Summary statistics for long-context RELiC. Token counts were computed with the \\texttt{o200k\\_base} encoding via \\texttt{tiktoken} (\\url{https://github.com/openai/tiktoken}) and word counts were computed by splitting on whitespace.}\n\\label{tab:basic_summary_stats_dataset}\n\\end{table}\n\\section{Dataset Curation}\n\\label{sec:data_methods}\n\n\\begin{table}[t!]\n\\small\n\\resizebox{0.48\\textwidth}{!}{%\n\\begin{tabular}{lccc} % Updated to four columns\n\n\\toprule\n\\textsc{Model + Simple Prompt} & \\textsc{ALL} \\textsubscript{($n$=292)} & \\textsc{\\faUser} \\textsubscript{($n$=40)}& \\textsc{\\faBook} \\textsubscript{($n$=39)}\\\\ \n\\midrule\n\\geminiprotwo~\\cite{google2025gemini25} & 63.7 & 57.5 & \\textbf{82.1} \\\\\n\\othree~\\cite{o3} & 49.7 & 47.5 & 71.8 \\\\\n\\geminiproone~\\cite{geminiteam2024gemini} & 36.1 & 22.5 & 43.6 \\\\\n\\oone~\\cite{openai2024o1} & 32.2 & 25.0 & 41.0 \\\\\n\\gpto\\ \\cite{openai2024gpt4ocard} & 27.4 & 17.5 & 35.9 \\\\\n\\qwenlarge\\ \\cite{qwen_qwen25_2025} & 11.3 & 7.5 & 20.5 \\\\\n\\llamasmall\\ \\cite{grattafiori2024llama3herdmodels} & 5.1 & 5.0 & 2.6 \\\\\n\\qwensmall & 2.7 & 5.0 & 2.6 \\\\\n\\llamalarge & 2.1 & 0.0 & 2.6 \\\\\n\\midrule\n\\textsc{Model + Explanation Prompt} & & & \\\\\n\\midrule\n\\geminiprotwo & \\textbf{64.7} & \\textbf{62.5} & 79.5 \\\\\n\\gptfourone~\\cite{gpt-4.1} & 51.0 & 47.5 & 69.2 \\\\\n\\othree & 50.7 & 50.0 & 66.7 \\\\\n\\geminiproone & 38.5 & 40.0 & 50.0 \\\\\n\\claude~\\cite{claude_sonnet_2025} & 37.0 & 32.5 & 48.7 \\\\\n\\deepseek~\\cite{deepseekai2025deepseekv3technicalreport} & 29.1 & 15.0 & 38.5 \\\\\n\\gpto & 24.3 & 22.5 & 31.8 \\\\\n\\qwenthreelarge~\\cite{qwen_qwen25_2025} & 19.2 & 20.0 & 33.3 \\\\\n\\qwenthreesmall & 8.9 & 5.0 & 10.3 \\\\\n\\othreemini\\ \\cite{openai2025o3mini} & 8.3 & 10.0 & 13.6 \\\\\n\\midrule\n\\textsc{Baselines} & & & \\\\\n\\midrule\n\\gteqwen & 4.5 & 2.5 & 6.8 \\\\\n\\human & - & 55.0 & - \\\\\n\\bottomrule\n\\end{tabular}\n}\n\\caption{Percentage of test set examples where the model generated the correct ground truth quotation for different folds of the test set. The \\textsc{\\faUser} and \\textsc{\\faBook} columns contain the accuracy of each model on the human-evaluated and close reading subsets of the data, respectively.} \n\\label{tab:benchmark_accuracy}\n\\end{table}\n\n\nThe RELiC dataset~\\cite{thai-etal-2022-relic} consists of 78k excerpts of English-language literary analysis collected from scholarly journals where each excerpt includes a direct quotation from one of 79 primary source texts in the public domain. Each example in the dataset contains up to four sentences preceding the quotation and up to four sentences following the quotation. Together, these sentences of make up the ``context'' of the ground truth quotation. The primary source quotations may be up to five consecutive sentences in length. \n\n%Data about the books in the test and validation splits can be found in Table \\td{ref}.\n% , with the data for the train split books available in Appendix \\ref{}.\n% Because RELiC was designed as a retrieval task, the dataset also includes the full text of each primary source broken up into sentences.\n\n\n\n\\subsection{Adapting RELiC for long-context reasoning}\n% The original data curation and processing steps for RELiC involved applying several programmatic heuristics, such as fuzzy-matching and regex, to the data to classify examples of literary analysis, identify the boundaries between literary analysis and ground truth quotations, and segment the data into sentences. \nWhile large, RELiC is also noisy, which necessitates several filtering steps before we can evaluate models on it. We implemented an extensive data preprocessing pipeline\\footnote{All prompts, details of filtering heuristics, and descriptions of our manual validations are in Appendix A.} that involved several cleaning and filtering passes with \\gptmini\\ and \\gpto\\ supplemented by some programmatic heuristics, all geared towards mitigating the below issues:\n\\paragraph{Low data quality:} Some RELiC examples contain \\textbf{OCR artifacts} present in the primary source texts that render the prefixes and suffixes ungrammatical. There are also some examples that are \\textbf{misclassified} as literary analysis.\n\\paragraph{Model exploits:} Several aspects of RELiC examples can provide unintended cues to models, allowing them to bypass the reasoning challenge. One was the \\textbf{disclosure of the location} of the quote in the prefix or suffix. Another was \\textbf{quote leakage}, or the appearance of part or all of the ground truth quotation in the context. Finally, \\textbf{data contamination} was a concern--the literary analysis excerpts could appear in the training data of the LLMs we benchmarked and may have been memorized, or the ground truth quotation is so prevalent in the training data (because it belongs to a public domain novel) that the model is able to retrieve it without needing the primary source text at all.\n% \\paragraph{LLM-aided data filtering:}\n\n% Literary Analysis Section\n% \\begin{table*}[t]\n% \\centering\n% \\renewcommand{\\arraystretch}{1.4} % Better row spacing\n% \\setlength{\\tabcolsep}{6pt} % Reduce padding slightly\n% \\scriptsize\n% % Literary Analysis Section\n% \\begin{tabularx}{\\textwidth}{|X|}\n% \\hline\n% Literary Analysis Excerpt with \\relicquote{Ground Truth Quotation} from \\textit{The Great Gatsby} \\\\\n% \\hline\n% One of the more memorable scenes in \\textit{The Great Gatsby}, which Nick calls a \"picture,\" is a distant cousin of the facetious art photograph, \"George B. Wilson at the Gasoline Pump.\" It is a still picture of a mundane subject that reveals, with great impact, a critical insight and the emotional jolt at the discovery of bitter knowledge: \\\\\n\n\n% \\relicquote{Daisy and Tom were sitting opposite each other at the kitchen table with a plate of cold fried chicken between them and two bottles of ale. He was talking intently across the table at her and in his earnestness his hand had fallen upon and covered her own. Once in a while she looked up at him and nodded in agreement. They weren't happy, and neither of them had touched the chicken or the ale—and yet they weren't unhappy either. There was an unmistakable air of natural intimacy about the picture and anybody would have said that they were conspiring together.}\\\\\n\n% The passage could be considered \"Jamesian,\" comparable to corresponding tableaux in \\textit{The Ambassadors} and \\textit{The Portrait of a Lady}. The picture seals Gatsby's fate and Nick's too; his doubts and hopes are over now. \"Jamesian\" surely, but just as surely photographic. \\\\\n% \\hline\n% \\end{tabularx}\n% \\end{table*}\n\n% \\vspace{3pt} % Space between merged section and individual columns\n\n% % Model Comparison Table\n% \\begin{tabularx}{\\textwidth}{|X|X|}\n% \\hline\n% \\textbf{Response} & \\textbf{Explanation} \\\\\n% \\hline\n% \\faCheck\\ \\human: They weren’t happy, and neither of them had touched the chicken or the ale—and yet they weren’t unhappy either. There was an unmistakable air of natural intimacy about the picture, and anybody would have said that they were conspiring together. & The part in the suffix about \"sealing Gatsby's fate\" made me think this quote was towards the end and something damning for Gatsby. The \"picture\" described by Nick is Nick's observation that Daisy and Tom are actually fine after all of the drama of the car accident and Gatsby and Daisy revealing their relationship to Tom. \\\\\n% \\faClose\\ \\geminipro: In one of the windows over the garage the curtains had been moved aside a little and Myrtle Wilson was peering down at the car. So engrossed was she that she had no consciousness of being observed and one emotion after another crept into her face like objects into a slowly developing picture. Her expression was curiously familiar-it was an expression I had often seen on women's faces but on Myrtle Wilson's face it seemed purposeless and inexplicable until I realized that her eyes, wide with jealous terror, were fixed not on Tom, but on Jordan Baker, whom she took to be his wife. & The literary analysis excerpt refers to a memorable scene that Nick calls a \"picture,\" featuring a mundane subject with great impact, revealing bitter knowledge and sealing Gatsby's and Nick's fates. The analysis also mentions the scene being \"Jamesian\" and photographic. Therefore, the selected window should depict a static, impactful scene revealing a harsh truth that deeply affects both Gatsby and Nick, and ideally involves an observation made by Nick. The scene with Myrtle Wilson looking out the window fits these criteria perfectly. It is described as a slowly developing picture, reveals Myrtle's mistaken jealousy, and foreshadows the tragic events that follow, impacting both Gatsby and Nick. \\\\\n% % \\gpto: There was a faint, barely perceptible movement of the water as the fresh flow from one end urged its way toward the drain at the other. With little ripples that were hardly the shadows of waves, the laden mattress moved irregularly down the pool. A small gust of wind that scarcely corrugated the surface was enough to disturb its accidental course with its accidental burden. The touch of a cluster of leaves revolved it slowly, tracing, like the leg of a compass, a thin red circle in the water. & The literary analysis excerpt describes a \"picture\" in *The Great Gatsby* that is likened to a still photograph, revealing a critical insight and emotional jolt tied to bitter knowledge. The analysis suggests that this moment seals Gatsby's fate and Nick's as well, marking the end of their doubts and hopes. The most fitting scene is the one where Nick observes Gatsby's body in the pool, accompanied by the symbolic imagery of the mattress and the thin red circle in the water. This moment captures the stillness, the emotional weight, and the finality of Gatsby's fate, aligning with the analysis's description of a \"Jamesian\" tableau and a photographic quality. \\\\\n% \\faClose\\ \\othreemini: GEORGE B. WILSON. Cars Bought and Sold—and I followed Tom inside. The interior was unprosperous and bare; the only car visible was the dust‐covered wreck of a Ford which crouched in a dim corner. It had occurred to me that this shadow of a garage must be a blind and that sumptuous and romantic apartments were concealed overhead when the proprietor himself appeared in the door of an office, wiping his hands on a piece of waste. He was a blonde, spiritless man, anaemic, and faintly handsome. When he saw us a damp gleam of hope sprang into his light blue eyes.\n% & I chose a window from the section that introduces George B. Wilson because the literary analysis explicitly compares a “picture” in the novel to the facetious art photograph “George B. Wilson at the Gasoline Pump.” This window, which paints a stark, almost photographic portrayal of the mundane reality of Wilson’s life and surroundings, encapsulates the bitter insight that Nick experiences—a recognition of the decay and desolation underpinning the glitter of wealth—which in turn seals both Gatsby’s and Nick’s fates, as noted by the analysis. \\\\\n% \\hline\n% \\end{tabularx}\n\n% \\caption{Comparison of model responses with explanations and correctness analysis for a selected RELiC example.}\n% \\label{tab:relic_detailed_comparison}\n% \\end{table*}\n\n% Model Comparison Table\n% \\begin{tabularx}{\\textwidth}{|X|}\n% \\hline\n% \\faCheck\\ \\textbf{Human explanation}: The part in the suffix about \"sealing Gatsby's fate\" made me think this quote was towards the end and something damning for Gatsby. The \"picture\" described by Nick is Nick's observation that Daisy and Tom are actually fine after all of the drama of the car accident and Gatsby and Daisy revealing their relationship to Tom. \\\\\n% \\faClose\\ \\textbf{Gemini Pro 1.5 response}: In one of the windows over the garage the curtains had been moved aside a little and Myrtle Wilson was peering down at the car. So engrossed was she that she had no consciousness of being observed and one emotion after another crept into her face like objects into a slowly developing picture. Her expression was curiously familiar-it was an expression I had often seen on women's faces but on Myrtle Wilson's face it seemed purposeless and inexplicable until I realized that her eyes, wide with jealous terror, were fixed not on Tom, but on Jordan Baker, whom she took to be his wife. \\\\\n% \\faClose\\ \\textbf{o3-mini response}: GEORGE B. WILSON. Cars Bought and Sold—and I followed Tom inside. The interior was unprosperous and bare; the only car visible was the dust‐covered wreck of a Ford which crouched in a dim corner. It had occurred to me that this shadow of a garage must be a blind and that sumptuous and romantic apartments were concealed overhead when the proprietor himself appeared in the door of an office, wiping his hands on a piece of waste. He was a blonde, spiritless man, anaemic, and faintly handsome. When he saw us a damp gleam of hope sprang into his light blue eyes.\\\\\n% \\hline\n% \\end{tabularx}\n% \\vspace{-8pt}\n% \\caption{An example of long context RELiC where the \\human\\ annotator successfully retrieves the quotation while the LLMs fail.}\n% \\label{tab:human_beats_models_comparison}\n% \\end{table*}\n\n\\begin{figure*}[t]\n \\centering\n \\includegraphics[width=\\textwidth]{figures/table_3.png}\n \\caption{An example from long context RELiC where the \\human\\ annotator successfully retrieves the quotation while the top-performing LLMs fail.}\n \\label{fig:llm_fail}\n\\end{figure*}\n\n\\paragraph{Human verification:} After filtering, we create a high-quality human-verified subset of the dataset by having one of the authors, who has a degree in English literature and has read all of the primary source novels manually review 400 filtered examples. This author marked 292 examples that were well-formed instances of literary analysis with a ground truth quotation that could be identified given only the book and literary analysis context. See Table \\ref{tab:basic_summary_stats_dataset} for dataset statistics.\n% Overall, we identify 252 high-quality examples.\n% . The author was only shown examples from primary source novels they had read, and the examples were shown in random order. At their discretion, the author identified 252 particularly high quality examples from RELiC containing specific and detailed literary analysis that is well-supported by the ground truth quotation. \n\n% \\paragraph{Identification of close reading examples} During the manual verification of examples, the author also labeled examples of \\textbf{close reading}, a literary analysis technique in which the reader considers ``linguistic elements, semantic aspects, syntax, rhetoric, structural elements, thematic, and generic references in the text\" and ``delves into the form and structure of the text, gaining insight into the usage, purpose, and contextual significance of the language\" \\cite{close_reading}. A natural consequence of this interpretive technique in written literary analysis is the repeated citation of parts of the ground truth quote in the context. While an example of close reading is a well-formed example of literary analysis, the context contains lexical overlap with the ground truth quotation that the model can exploit during the RELiC task\\footnote{We attempted to implement another LLM-based filter for close reading examples, but found that \\gptmini\\ overwhelmingly designated RELiC instances as close reading examples. We also found that even closed source models did not perform perfectly on these ``easy'' retrieval examples, so we did not try to exclude them from our benchmarking dataset.}. \n\n% \\subsection{Long-Context Dataset Construction}\n% In constructing our final dataset, we discarded all RELiC examples with ground truth quotations from primary source that were longer than 128k tokens so we could evaluate the open-source Llama suite of models \\cite{grattafiori2024llama3herdmodels}, which have a context length of 128k. Following the original RELiC dataset approach, we created test, validation, and train splits by partitioning the primary source novels such that examples with ground truth quotations from a specific novel appear in only one of the splits. Data about the books in the test and validation splits can be found in Table \\ref{}, with the data for the train split books available in Appendix \\ref{}.\n\n% \\paragraph{} The validation set consists of 50 of the 252 high quality examples selected by our author. The remaining 202 high quality examples were designated for the test set and supplemented with 1,000 randomly sampled examples after applying all filters. In other words, we randomly selected 1,000 of the most ``challenging\" examples according to our heuristics. \n\n\\paragraph{Dataset folds:} \nOur new dataset contains two labeled folds of special data, (1) the \\faUser\\ \\textsc{Human Eval Set}: 40 examples attempted by our author, and (2) the \\faBook\\ \\textsc{Close Reading Set}: 39 examples labeled by our author as examples of \\textbf{close reading}, a literary analysis technique in which the reader considers ``linguistic elements, semantic aspects, syntax, rhetoric, structural elements, thematic, and generic references in the text''~\\cite{close_reading}. A natural consequence of this interpretive technique is the repeated citation of parts of the ground truth quote in the context. Since these examples contain lexical overlap with the ground truth quotation, models can exploit this during evidence retrieval.\n\n% \\paragraph{} Finally, we randomly sampled 10,000 examples for the train set. We report statistics for all splits in Table \\ref{} and for the test set folds in Table \\ref{}. Though we do not utilize the train set in our experiments, we will release it to spur future research into increasing the performance of open source models on long-context reasoning tasks.\n\n\\section{Experimental Setup}\n\\label{sec:experiments}\nWe evaluated four closed-source and four open-weight models on the long-context RELiC dataset, prompting each in a zero-shot setting to retrieve the most fitting quotation for a literary analysis excerpt given the full primary source text. We tested two prompt types: (1) \\textsc{Simple}, which requested only the quotation, and (2) \\textsc{Explanation}, which first asked the model to justify its choice before selecting a quotation\\footnote{Prompts \\& inference details in Appendix \\ref{sec:model-inf-details}.}. Our prompts are adapted from \\citet{karpinska-etal-2024-nocha} in the \\textsc{Nocha} long-context benchmark. Additionally, we implemented an embedding-based retrieval baseline using \\gteqwen, the top-performing text embedding model on MTEB\\footnote{\\url{https://huggingface.co/spaces/mteb/leaderboard} accessed February 2025} at the time of writing.\n\n\\subsection{Human Evaluation}\nThe author who manually validated high quality examples also attempted 40 examples from four previously-read primary source novels. The author had access to a digital copy of the primary source text with a string-match search function and the corresponding Wikipedia summaries. In addition to selecting missing quotations, the author also wrote a short justification for each choice. The task took approximately 8 hours.\n\n\\subsection{Evaluation Scheme}\nOur automatic evaluation of model responses applied partial ratio fuzzy matching (to account for minor typographical differences) to check that the model response was from the primary source text and to measure overlap between the response and the ground truth quotation\\footnote{Further details can be found in Appendix \\ref{sec:eval-details}.}. For the embedding baseline, we calculate recall@1.\n% \\FloatBarrier\n\\section{Results \\& Analysis}\n\\label{sec:analysis}\n\n\n% \\begin{table*}[t]\n% \\centering\n% \\renewcommand{\\arraystretch}{1.4} % Better row spacing\n% \\setlength{\\tabcolsep}{6pt} % Reduce padding slightly\n% \\scriptsize\n\n% % Literary Analysis Section\n% \\begin{tabularx}{\\textwidth}{|X|}\n\n% \\hline\n% Literary Analysis Excerpt with \\relicquote{Ground Truth Quotation} from \\textit{Frankenstein} \\\\\n% \\hline\n% \"It may therefore be judged indecent in me to come forward on this occasion,\" she proclaims on the stand, \"but when I see a fellow creature about to perish through the cowardice of her pretended friends, I wish to be allowed to speak, that I may say what I know of her character.\" Attempting to resist class lines and to speak of Justine as a \"fellow creature\" calls the silent cowardly, which means that Elizabeth, too, forgot her manners. Her testimony of Justine's dedication, loyalty, and nurturing cannot be heard, and the jury's response mimics Frankenstein's own preoccupation and inability to read the interactions and relationships between women in the broader sense. \\\\\n\n% \\relicquote{But I did not feel the inconvenience of the weather; my imagination was busy in scenes of evil and despair.} \\\\\n\n% Class and gender clearly matter in Geneva, and these factors, rather than a psychosexual rivalry, explain why Justine is punished for her ingratitude toward her benefactors. Elizabeth's testimony falls on deaf ears. What the jury perceives as important, and punishes, is indecorum—a breach of the mistress-servant relationship. \\\\\n% \\hline\n% \\end{tabularx}\n\n% \\vspace{3pt} % Space between merged section and individual columns\n\n% \\begin{tabularx}{\\textwidth}{|X|}\n% \\hline\n% \\faClose\\ \\textbf{Human and Gemini Pro 1.5 response}: A murmur of approbation followed Elizabeth’s simple and powerful appeal, but it was excited by her generous interference, and not in favour of poor Justine, on whom the public indignation was turned with renewed violence, charging her with the blackest ingratitude.\\\\\n\n% \\textbf{Human explanation}: I selected the quote that follows Elizabeth's full testimony, which is quoted in part at the beginning of the prefix. The selected quote also mentions Justine's \"ingratitute,\" like the suffix. \\\\\n% \\textbf{Gemini Pro 1.5 explanation}: The literary analysis focuses on Elizabeth's testimony during Justine's trial and the jury's reaction to it. The analysis highlights Elizabeth's attempt to transcend class boundaries by referring to Justine as a \"fellow creature\" and argues that this, along with Elizabeth's emotional testimony, is perceived as a breach of decorum and contributes to Justine's conviction. Therefore, the selected window should include Elizabeth's testimony where she calls Justine a \"fellow creature\" and speaks about her character, as well as the jury's immediate negative reaction, emphasizing their focus on the perceived impropriety of the situation rather than the content of the testimony. \\\\\n% \\hline\n% \\end{tabularx}\n% \\vspace{-8pt} % Reduce vertical spaces\n% \\caption{An example of long context RELiC where the \\human\\ annotator and \\geminipro\\ both select the same quotation, but it is not the ground truth quotation.}\n% \\label{tab:human_beats_models_comparison_2}\n% \\end{table*}\n\n\\begin{figure*}[t]\n \\centering\n \\includegraphics[width=\\textwidth]{figures/table_4.png}\n \\caption{An example of long context RELiC where the \\human\\ annotator and \\geminiprotwo\\ both select the same quotation, but it is not the ground truth quotation.}\n \\label{fig:alt_evidence}\n\\end{figure*}\n\n\nTable \\ref{tab:benchmark_accuracy} reports results for all evaluated models on the full set of 292 claims and the two folds. \n\n\\paragraph{LLMs outperform \\human\\ and SOTA embedding baseline}\nGoogle's \\geminiprotwo\\ reasoning model achieves better accuracy than our \\human\\ baseline in a fraction of the time---each API call took an average of 45 seconds vs. an average of 12 minutes for the \\human\\ expert. Both Google and OpenAI’s latest long-context models show substantial performance gains over their immediate predecessors on the literary evidence retrieval task. \\geminiprotwo\\ outperforms \\geminiproone\\ by a wide margin (64.7\\% vs. 38.5\\%), and OpenAI’s \\gptfourone\\ improves significantly over \\gpto\\ (51.0\\% vs. 24.3\\%). Similarly, \\othree\\ surpasses \\oone\\ (48.7\\% vs. 32.2\\%).\nThe embedding-based method achieved 4.5\\% accuracy, only 1.6\\% higher than the best recall@1 reported in the original RELiC paper from three years ago. LLMs’ success over embeddings suggests the importance of contextualized representations that can enable reasoning over entire primary source texts.\n\n\\paragraph{Closed-source outperform open-weight LLMs} Despite recent progress in open-weight LLM development, we observe a striking performance gap between closed-source and open-weight models on the literary evidence retrieval task. The best-performing open-weight model, \\deepseek, achieves only 29.1\\% accuracy---less than half the performance of the top closed-source model, \\geminiprotwo\\ (64.7\\%).\n\n\\paragraph{Small LLMs can't capitalize on close reading examples} Nearly all models larger than 8B parameters show modest to significant improvements on the close reading fold, with some models improving by over 20\\%. This effect is expected due to ground truth leakage in the the provided excerpt of literary analysis, but the 7B and 8B parameter open-weight models experience nearly no performance boost on this fold---the performance of \\llamasmall\\ actually suffers, suggesting that the smaller models lack the capacity to even exploit lexical overlap. \n\n\n\\paragraph{\\textsc{Explanation} prompt provides a glimpse into model reasoning} We observed mixed results when querying non-reasoning LLMs with the \\textsc{explanation} prompt. While \\geminiproone\\ improved with explanation-based reasoning, \\gpto\\ saw a decline. Recent reasoning LLMs such as \\geminiprotwo\\ and \\othree\\ use internal reasoning tokens that are not exposed via the API but influence the model's final output. These tokens are critical to the model's performance on complex tasks, but their inaccessibility complicates model evaluation and comparison. Applying the \\textsc{explanation} prompt to these reasoning models preserved accuracy, despite the redundancy, while providing insight into the models' reasoning processes. See a comparison of human and model justifications in Figure \\ref{fig:alt_evidence}. \n\n\\paragraph{All models tend to overgenerate}\nDespite prompts explicitly instructing models to select no more than five consecutive sentences from the primary source, we observe a consistent tendency across all evaluated models to produce significantly longer outputs. To quantify this behavior, we compute the length ratio, defined as the ratio of the model’s generated output length to the ground truth quotation length (measured in characters); values closer to 1.0 indicate greater adherence to the prompt constraints and minimal overgeneration. The length ratios are reported in Table \\ref{tab:length_ratios}.\n\nWe find that all models overgenerate, including the human annotator (average ratio of 2.1), likely due to natural variance in interpreting sentence boundaries. However, language models consistently surpass this baseline: for instance, state-of-the-art models such as \\geminiprotwo\\ and \\gptfourone\\ have average ratios of 3.0 and 4.8, respectively. Smaller open models like \\llamasmall\\ and \\llamalarge\\ exhibit even more extreme overgeneration (ratios > 5.7), suggesting that weaker models may compensate for uncertainty by producing longer outputs. \n\n\\paragraph{LLMs struggle with literary nuance}\nIn Figure \\ref{fig:llm_fail}, we present a challenging example where all LLMs fail but our \\human\\ expert correctly identified the ground truth quotation from \\textit{The Scarlet Letter}, a >80k-word novel. The context alludes to a description of the character Roger Chillingworth at a specific moment in the story (when he recognizes Hester Prynne). In their explanation, the \\human\\ expert mentions considering two different passages, but ultimately selecting the correct passage because it best demonstrates the ``melodrama\" mentioned in the literary analysis. The expert highlights the features of the correct passage that demonstrate ``melodrama\": an exaggerated character description, words that connote movement, and emotional tension. All three top-performing LLMs selected the other passage the \\human\\ expert was considering but ultimately eliminated, suggesting that even the best models still lack the expertise to navigate literary devices and more nuanced signals that inform expert literary interpretation. \n\n% \\vspace{-10pt}\n\\paragraph{Models can identify alternative literary evidence} In Figure \\ref{fig:alt_evidence}, we present an example where both \\human\\ and \\geminiprotwo\\ selected the same \"incorrect\" quote. The explanations reveal that both interpreted the literary analysis context as an introduction to a quote about the jury’s response. However, the ground truth quotation actually supports Frankenstein’s own preoccupation rather than the trial’s immediate outcome. This example highlights two key insights: (1) literary evidence retrieval is inherently interpretative, meaning that multiple passages may plausibly support a given claim, and (2) LLMs, like human readers, can surface alternative yet reasonable quotations that align with certain aspects of the analysis. While models may not always select the canonical answer, their ability to propose viable alternative evidence could be valuable for assisting literary scholars in exploring multiple textual connections.\n\n\\begin{table}[t!]\n\\small\n\\centering\n\\resizebox{0.48\\textwidth}{!}{%\n\\begin{tabular}{lcc}\n\\toprule\n\\textsc{Model + Simple Prompt} & \\textsc{\\faUser} Accuracy \\textsubscript{($n$=40)} & Avg. Length Ratio\\\\\n\\midrule\n\\geminiprotwo & 57.5 & 3.5\\\\\n\\othree & 47.5 & 3.5\\\\\n\\geminiproone & 22.5 & 2.8\\\\\n\\oone & 25.0 & 3.2\\\\\n\\gpto & 17.5 & 3.9\\\\\n\\qwenlarge & 7.5 & 3.2\\\\\n\\llamasmall & 5.0 & 5.9\\\\\n\\qwensmall & 5.0 & 2.8\\\\\n\\llamalarge & 0.0 & 5.7\\\\\n\\midrule\n\\multicolumn{3}{l}{\\textsc{Model + Explanation Prompt}} \\\\\n\\midrule\n\\geminiprotwo & \\textbf{62.5} & 3.0\\\\\n\\gptfourone & 47.5 & 4.8\\\\\n\\othree & 50.0 & 2.7\\\\\n\\geminiproone & 40.0 & 3.3\\\\\n\\claude & 32.5 & 4.0\\\\\n\\deepseek & 15.0 & 3.6\\\\\n\\gpto & 22.5 & 3.6\\\\\n\\qwenthreelarge & 20.0 & 2.7\\\\\n\\qwenthreesmall & 5.0 & 2.4\\\\\n\\othreemini & 10.0 & 3.5\\\\\n\\midrule\n\\multicolumn{3}{l}{\\textsc{Baselines}} \\\\\n\\midrule\n\\human & 55.0 & \\textbf{2.1}\\\\\n\\bottomrule\n\\end{tabular}\n}\n\\caption{The average length ratios for each model, defined as the ratio of the length model generation to that of the ground truth (measured in characters). All models have an average ratio greater than that of the human annotator (2.1), indicating overgeneration.}\n\\label{tab:length_ratios}\n\\end{table}\n\n\n\\section{Related Work}\n\\label{sec:rel_work}\n\nOur work builds on recent papers that apply and evaluate LLMs for computational literary analysis. Prior work has explored summarization or claim verification in novels~\\cite{subbiah-etal-2024-storysumm,fables24,karpinska-etal-2024-nocha} or short stories~\\cite{10.1162/tacl_a_00702}. Other more specific tasks, mainly in the short context setting, include extracting narrative elements~\\cite{shen-etal-2024-heart}, story arcs and turning points~\\cite{tian-etal-2024-large-language}, character analysis~\\cite{papoudakis-etal-2024-bookworm}, narrative discourse~\\cite{piper-bagga-2024-using}, plot development~\\cite{huot2024agents,xu-etal-2024-fine}, and creativity evaluation~\\cite{10.1145/3613904.3642731}. \n\n\\section{Conclusion}\nIn this work, we introduced a long-context dataset derived from RELiC for evaluating literary evidence retrieval using large language models. We constructed a high-quality set of 292 examples, ensuring rigorous evaluation through filtering and human verification. Our results demonstrate that long-context LLMs outperform embedding-based retrieval methods and even a \\human\\ expert baseline. However, closer analysis reveals that model success is not always grounded in robust interpretative abilities: models tend to overgenerate and still struggle to grasp nuances in literature. But, they can also successfully identify additional evidence for literary claims. Finally, open-weight models lag far behind their closed-weight counterparts, suggesting that long-context capabilities alone are insufficient without strong interpretive reasoning. We release our data and evaluation code to spur research at the intersection of NLP and literary analysis.\n\\section*{Limitations}\nAs noted in the original paper, the RELiC dataset represents a limited, English-only subset of world literature, with a strong emphasis on the Western literary canon. To foster a more inclusive and representative benchmark, we aim to extend this work to cross-lingual literary analysis and retrieval on texts from historically underrepresented literary traditions. Expanding the dataset in this way would not only enhance the linguistic and cultural diversity of literary evidence retrieval but also improve the generalizability of models across different literary frameworks.\n\nAdditionally, our human evaluation was limited in scope and scale, as it was conducted by a single annotator, one of the authors of this paper. While this approach was practical given the intensive nature of literary evidence retrieval over full novels, it inherently reflects the knowledge, biases, and interpretive lens of a single individual. In the future, we aim to broaden our evaluation by incorporating multiple expert annotators, enabling a more comprehensive and diverse assessment of model performance on the task of complex literary reasoning.\\section*{Ethical Considerations}\nWhile LLMs can assist in literary interpretation, they should not replace scholarly analysis conducted by trained experts. We acknowledge the potential for misuse of a system that can retrieve evidence for scholarly claims and emphasize that automated retrieval should be viewed as a complement to, not a substitute for, human literary scholarship.\n\nAs mentioned in the Limitations section, the primary source texts in our dataset are drawn from public domain works, which primarily reflect Western literary traditions. The results of this benchmark may not generalize to literary traditions outside the Anglophone canon, potentially reinforcing existing biases in computational literary studies.\n\n\n\\bibliography{custom}\n\n\n\\appendix\n\\section{Dataset Details}\n\\label{app:data_details}\nWe are using a publicly available dataset released by researchers who stated in their paper that they released their data to \"facilitate further research in this direction.\" Statistics for the primary sources can be found in \\ref{tab:book-details}.\n\n\n\\begin{table*}[t]\n \\renewcommand{\\arraystretch}{1.2} \n \\setlength{\\tabcolsep}{6pt}\n \\footnotesize\n \\centering\n \\begin{tabular}{l l c c c}\n \\toprule\n \\textbf{Book Title} & \\textbf{Author} & \\textbf{Publication Year} & \\textbf{Token Count} & \\textbf{Word Count} \\\\\n \\midrule\n Brave New World & Aldous Huxley & 1932 & 91,472 & 65,278 \\\\\n What Maisie Knew & Henry James & 1897 & 124,544 & 95,988 \\\\\n Ethan Frome & Edith Wharton & 1911 & 45,038 & 34,926 \\\\\n Frankenstein & Mary Shelley & 1818 & 95,018 & 75,131 \\\\\n The Great Gatsby & F. Scott Fitzgerald & 1925 & 63,683 & 48,972 \\\\\n The Awakening & Kate Chopin & 1899 & 66,574 & 49,932 \\\\\n The Scarlet Letter & Nathaniel Hawthorne & 1850 & 112,355 & 83,311 \\\\\n \\bottomrule\n \\end{tabular}\n \\caption{Books included in the dataset. The token count was provided as per \\href{https://github.com/openai/tiktoken}{\\texttt{tiktoken}} tokenization. }\n \\label{tab:book-details}\n\\end{table*}\n\n\n\n\n\n\\section{LLM-Aided Data Processing}\n\\subsection{Filtering steps}\nThese cleaning and filtering steps were applied to the entire RELiC dataset in the following order:\n\\begin{enumerate}\n \\item \\verb|CLEAN| (\\gptmini): We asked the model to remove any OCR artifacts and to ensure the context and ground truth quotation flow seamless and grammatically without changing any meaning. Additionally, we asked the model to remove any remaining in-line citations that revealed the page number of the ground truth quotation, which allowed us to preserve some examples that might have been caught by the \\verb|LOCATION| filter below. See the prompt in Table \\ref{tab:clean_filter}.\n \\item \\verb|LEAKAGE| (heuristic): If the sentences in the context preceding or following the ground truth quotation were fuzzy-matches (threshold: 95) with any text from the primary source, the example was excluded from our dataset.\n \\item \\verb|LIT ANALYSIS| (\\gptmini): We asked the model to classify whether each RELiC instance was an example of literary analysis. If it was not, we excluded it from our dataset. See the prompt in Table \\ref{tab:classify_lit_analysis}.\n \\item \\verb|LOCATION| (\\gptmini): We asked the model if the context revealed the location of the ground truth quotation. If it did, we excluded it from our dataset. See the prompt in Table \\ref{tab:quote_location_disclosure}.\n \\item \\verb|FIRST SENT| (heuristic): We identified cases where the ground truth quotation was the first sentence of its primary source novel. The intuition behind this filter (and the following two) was that the first sentence of a primary source might be famous or more likely to be quoted, and therefore easier to guess without needing to reason over the text or more likely to appear in training data.\n \\item \\verb|LAST SENT| (heuristic): Similarly, we identified cases where the ground truth quotation was the last sentence of its primary source novel.\n \\item \\verb|OUTLIER| (heuristic): We identified cases where the ground truth quotation was cited much more frequently than any of the other quotations from the same primary source novel.\n \\item \\verb|EZ2MEM| (\\gpt): We asked the model to perform the RELiC task \\textit{without} providing the primary source text and identified cases where the model was able to correctly generate at least one sentence of the ground truth quotation. \n\\end{enumerate}\nNote that we did not automatically exclude any of the examples identified by the last four filters. Though we did not use them in this project, we have left the labels in our dataset to facilitate future research.\n\n\\subsection{Manual validation of data}\nWe conducted multiple validations of the RELiC data for quality control using human annotations. \n\n\\paragraph{Validation of LLM filters} To validate our LLM-aided approach to data preprocessing, we manually annotated 100 examples of RELiC and compared our judgments to keep or reject each example to the results of the LLM + heuristic filters. The f1-score of our filtering scheme was 89.8, with our scheme identifying 57 true positives, 30 true negatives, 6 false positives, and 7 false negatives.\n\n\\section{Model Inference Details}\n\\label{sec:model-inf-details}\n\\paragraph{API access details}\n\\begin{itemize}\n \\item All OpenAI models were accessed via the OpenAI API (\\url{https://platform.openai.com/}).\n \\item All Gemini models were accessed via the Google AI API (\\url{https://ai.google.dev/}).\n \\item \\claude\\ was accessed via the Vertex AI/Google Cloud (\\url{https://cloud.google.com/vertex-ai?hl=en}).\n \\item DeepSeek was run via OpenRouter (\\url{https://openrouter.ai/}).\n\\end{itemize}\nThe approximate total cost of running all closed-weight models for both development and evaluation was \\$2k. \n\n\\paragraph{Local inference details}\nAll Llama and Qwen models were run locally. The smaller open-weight models were run on 1 A100 GPU for a total of 14 hours, while the larger open-weight models were run on 4 A100 GPUs for a total of 16 hours. \n\n\\paragraph{Generation hyperparameters}\nThe OpenAI reasoning models (\\oone, \\othreemini, and \\othree) were a special case: the temperature parameter is fixed and the token generation limit includes the inaccessible reasoning tokens. As such, we set the token generation limit to 12,000 for \\oone\\ and \\othreemini. For \\othree, the token generation limit was set to 25,000 as recommended by OpenAI\\footnote{\\url{https://platform.openai.com/docs/guides/reasoning\\#allocating-space-for-reasoning}}. For all three OpenAI reasoning models, we used the default \\verb|medium| reasoning effort. \n\nFor all other models, the token generation limit was set to 800 for \\textsc{Simple} prompts and 1,200 for \\textsc{Explanation} prompts. Temperature was set to 0.0 for all models except for \\qwenthreelarge\\ and \\qwenthreesmall, where temperature was set to the default 0.6.\n\nFor a list of the models and their checkpoints, see Table \\ref{tab:models}.\n\n\\begin{table}[t!]\n\\centering\n\\small\n\\resizebox{0.48\\textwidth}{!}{%\n\\begin{tabular}{l c l c c}\n\\toprule\n\\textsc{Model} & \\textsc{Context} & \\textsc{Avail.} & \\textsc{Checkpoints} \\\\\n\\midrule\n\\href{https://platform.openai.com/docs/models}{\\oone} & 200k & \\faLock & \\texttt{o1-2024-12-17} \\\\\n\\href{https://platform.openai.com/docs/models}{\\othree} & 200k & \\faLock & \\texttt{o3-2025-04-16} \\\\\n\\href{https://platform.openai.com/docs/models}{\\othreemini} & 200k & \\faLock & \\texttt{o3-mini-2025-01-31} \\\\\n\\href{https://platform.openai.com/docs/models}{\\gpto} & 128k & \\faLock & \\texttt{gpt-4o-2024-11-20} \\\\\n\\href{https://platform.openai.com/docs/models}{\\gptfourone} & 1M & \\faLock & \\texttt{gpt-4.1-2025-01-31} \\\\\n\\href{https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/gemini}{\\geminiproone} & 1M & \\faLock & \\texttt{gemini-1.5-pro-002} \\\\\n\\href{https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/gemini}{\\geminiprotwo} & 1M & \\faLock & \\texttt{gemini-2.5-pro-preview-05-06} \\\\\n\\href{https://docs.anthropic.com/en/docs/about-claude/models/overview}{\\claude} & 200k & \\faLock & \\texttt{claude-3-7-sonnet@20250219} \\\\\n\\href{https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct}{\\llamalarge} & 128k & \\faUnlock & \\texttt{Llama-3.3-70B-Instruct} \\\\\n\\href{https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct}{\\llamasmall} & 128k & \\faUnlock & \\texttt{\nLlama-3.1-8B-Instruct } \\\\\n\\href{https://huggingface.co/Qwen/Qwen2.5-72B-Instruct}{\\qwenlarge} & 128k & \\faUnlock & \\texttt{Qwen2.5-72B-Instruct} \\\\\n\\href{https://huggingface.co/Qwen/Qwen2.5-7B-Instruct}{\\qwensmall} & 128k & \\faUnlock & \\texttt{Qwen2.5-7B-Instruct} \\\\\n\\href{https://huggingface.co/Qwen/Qwen3-8B}{\\qwenthreesmall} & 131k* & \\faUnlock & \\texttt{Qwen3-8B} \\\\\n\\href{https://huggingface.co/Qwen/Qwen3-32B}{\\qwenthreelarge} & 131k* & \\faUnlock & \\texttt{Qwen3-32B} \\\\\n\\midrule\n\\href{https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct}{\\gteqwenparam} & 32k & \\faUnlock & \\texttt{gte-Qwen2-7B-instruct} \\\\\n\\bottomrule\n\\end{tabular}\n}\n\\caption{The upper rows display the evaluated LLMs, while the bottom row displays the text embedding model used for the baseline. Context lengths marked with * were extended with YaRN.}\n\\label{tab:models}\n\\end{table}\n\n\\section{Evaluation Scheme Details}\n\\label{sec:eval-details}\nWe use the \\verb|rapidfuzz| Python package for our fuzzy match evaluation with a threshold of 95 for checking the existence of the model response in the primary source and a threshold of 90 for checking the overlap between the model response and the ground truth quotation. The fuzzy matching allows for small typographical differences between the primary source and the model outputs (e.g. different types of quotation marks). Thresholds were determined after manual inspection of outputs at varying thresholds. \n\n\n\n% \\section{Additional Example for \\textsc{Human} vs. \\textsc{Model}}\n% \\label{sec:hum_vs_model_extra}\n\n\n\n\n\\begin{table*}[t]\n \\renewcommand{\\arraystretch}{1.2} \n \\setlength{\\tabcolsep}{4pt}\n \\footnotesize\n \\centering\n \\begin{tabular}{c p{15cm}}\n \\toprule\n & \\multicolumn{1}{c}{\\bf Prompt (Simple)} \\\\\n \\midrule\n \\noalign{\\vskip 1mm}\n & \\texttt{You are provided with the full text of \\textbf{[book\\_title]} and an excerpt of literary analysis that directly cites \\textbf{[book\\_title]} with the cited quotation represented as \\textless MASK\\textgreater.} \\\\[3mm]\n & \\texttt{Your task is to carefully read the text of \\textbf{[book\\_title]} and the excerpt of literary analysis, then select a window from \\textbf{[book\\_title]} that most appropriately replaces \\textless MASK\\textgreater as the cited quotation by providing textual evidence for any claims in the literary analysis.} \\\\[1mm]\n & \\texttt{The excerpt of literary analysis should form a valid argument when \\textless MASK\\textgreater is replaced by the window from \\textbf{[book\\_title]}.} \\\\[1mm]\n & \\texttt{\\textless full\\_text\\_of\\_[book\\_title\\_snake\\_case]\\textgreater \\textbf{[book\\_sentences]} \\textless /full\\_text\\_of\\_[book\\_title\\_snake\\_case]\\textgreater} \\\\[1mm]\n & \\texttt{\\textless literary\\_analysis\\_excerpt\\textgreater \\textbf{[lit\\_analysis\\_excerpt]} \\textless /literary\\_analysis\\_excerpt\\textgreater} \\\\[1mm]\n & \\texttt{Identify the window that best supports the claims being made in the excerpt of literary analysis. The window should contain no more than 5 consecutive sentences from \\textbf{[book\\_title]}.} \\\\[1mm]\n & \\texttt{Provide your final answer in the following format:} \\\\[1mm]\n & \\texttt{\\textless window\\textgreater \\textbf{YOUR SELECTED WINDOW} \\textless /window\\textgreater} \\\\[1mm]\n \\bottomrule\n \\end{tabular}\n\\caption{Prompt template for literary evidence retrieval (Simple).}\n\\label{tab:inference_simple}\n\\end{table*}\n\n\n\\begin{table*}[t]\n \\renewcommand{\\arraystretch}{1.2} \n \\setlength{\\tabcolsep}{4pt}\n \\footnotesize\n \\centering\n \\begin{tabular}{c p{15cm}}\n \\toprule\n & \\multicolumn{1}{c}{\\bf Prompt w/ Explanations} \\\\\n \\midrule\n \\noalign{\\vskip 1mm}\n & \\texttt{You are provided with the full text of \\textbf{[book\\_title]} and an excerpt of literary analysis that directly cites \\textbf{[book\\_title]} with the cited quotation represented as \\textless MASK\\textgreater.} \\\\[3mm]\n & \\texttt{Your task is to carefully read the text of \\textbf{[book\\_title]} and the excerpt of literary analysis, then select a window from \\textbf{[book\\_title]} that most appropriately replaces \\textless MASK\\textgreater as the cited quotation by providing textual evidence for any claims in the literary analysis.} \\\\[1mm]\n & \\texttt{The excerpt of literary analysis should form a valid argument when \\textless MASK\\textgreater is replaced by the window from \\textbf{[book\\_title]}.} \\\\[1mm]\n & \\texttt{\\textless full\\_text\\_of\\_[book\\_title\\_snake\\_case]\\textgreater \\textbf{[book\\_sentences]} \\textless /full\\_text\\_of\\_[book\\_title\\_snake\\_case]\\textgreater} \\\\[1mm]\n & \\texttt{\\textless literary\\_analysis\\_excerpt\\textgreater \\textbf{[lit\\_analysis\\_excerpt]} \\textless /literary\\_analysis\\_excerpt\\textgreater} \\\\[1mm]\n & \\texttt{First, provide an explanation of your decision marking process in no more than one paragraph.} \\\\[1mm]\n & \\texttt{Then, identify the window that best supports the claims being made in the excerpt of literary analysis. The window should contain no more than 5 consecutive sentences from \\textbf{[book\\_title]}.} \\\\[1mm]\n & \\texttt{Provide your final answer in the following format:} \\\\[1mm]\n & \\texttt{\\textless explanation\\textgreater \\textbf{YOUR EXPLANATION} \\textless /explanation\\textgreater} \\\\[1mm]\n & \\texttt{\\textless window\\textgreater \\textbf{YOUR SELECTED WINDOW} \\textless /window\\textgreater} \\\\[1mm]\n \\bottomrule\n \\end{tabular}\n\\caption{Prompt template for literary evidence retrieval with explanation.}\n\\label{tab:inference_explain}\n\\end{table*}\n\n\n\\begin{table*}[t]\n \\renewcommand{\\arraystretch}{1.2} \n \\setlength{\\tabcolsep}{4pt}\n \\footnotesize\n \\centering\n \\begin{tabular}{c p{15cm}}\n \\toprule\n & \\multicolumn{1}{c}{\\bf Prompt for Cleaning RELiC Examples} \\\\\n \\midrule\n \\noalign{\\vskip 1mm}\n & \\texttt{I want to create a dataset for Natural Language Processing that consists of excerpts of literary analysis that quote directly from a primary source text.} \\\\[3mm]\n & \\texttt{I have already collected windows of literary analysis that quote from many different primary sources in the public domain. The part of the excerpt before the quotation is called the \"prefix,\" and the part of the excerpt after the quotation is called the \"suffix.\" Because I collected the data from PDFs with OCR, the prefixes and suffixes contain artifacts like in-line citations, page numbers, chapter names, headers, and footers that I need to remove.} \\\\[3mm]\n & \\texttt{Here is an example:} \\\\[1mm]\n & \\texttt{\\textless prefix\\textgreater \\textbf{[prefix]} \\textless /prefix\\textgreater} \\\\[1mm]\n & \\texttt{\\textless ground\\_truth\\_quotation\\textgreater \\textbf{[ground\\_truth\\_quotation]} \\textless /ground\\_truth\\_quotation\\textgreater} \\\\[1mm]\n & \\texttt{\\textless suffix\\textgreater \\textbf{[suffix]} \\textless /suffix\\textgreater} \\\\[1mm]\n & \\texttt{Please follow the following guidelines to help me clean and filter this example:} \\\\[1mm]\n & \\texttt{1. Remove all textual artifacts from the OCR process by deleting them. Artifacts include page numbers, chapter headings, footnotes, image captions, etc.} \\\\[1mm]\n & \\texttt{2. Correct the grammar, punctuation, and spelling of the prefix and suffix without altering the meaning so that the primary source quotation fits seamlessly and grammatically between the prefix and suffix.} \\\\[1mm]\n & \\texttt{3. Remove all in-line citations following quotations, especially those that say things like \"(my emphasis).\"} \\\\[1mm]\n & \\texttt{Respond with ONLY the cleaned prefix and suffix in the following format:} \\\\[1mm]\n & \\texttt{\\textless clean\\_prefix\\textgreater \\textbf{CLEAN PREFIX} \\textless /clean\\_prefix\\textgreater} \\\\[1mm]\n & \\texttt{\\textless clean\\_suffix\\textgreater \\textbf{CLEAN SUFFIX} \\textless /clean\\_suffix\\textgreater} \\\\[1mm]\n \\bottomrule\n \\end{tabular}\n\\caption{Prompt template for cleaning RELiC examples}\n\\label{tab:clean_filter}\n\\end{table*}\n\n\\begin{table*}[t]\n \\renewcommand{\\arraystretch}{1.2} \n \\setlength{\\tabcolsep}{4pt}\n \\footnotesize\n \\centering\n \\begin{tabular}{c p{15cm}}\n \\toprule\n & \\multicolumn{1}{c}{\\bf Prompt for Classifying Literary Analysis} \\\\\n \\midrule\n \\noalign{\\vskip 1mm}\n & \\texttt{I want to create a dataset for Natural Language Processing that consists of excerpts of literary analysis that quote directly from a primary source text.} \\\\[3mm]\n & \\texttt{Other texts can contain quotes from primary sources, like biographies of authors. However, I only want examples of literary analysis in the dataset.} \\\\[3mm]\n & \\texttt{Please determine whether the following excerpt is an example of literary analysis. If it is literary analysis, respond with TRUE. Otherwise, respond with FALSE.} \\\\[3mm]\n & \\texttt{Here is the excerpt:} \\\\[1mm]\n & \\texttt{\\textless excerpt\\textgreater \\textbf{[clean\\_prefix]} \\textbf{[answer\\_quote]} \\textbf{[clean\\_suffix]} \\textless /excerpt\\textgreater} \\\\[1mm]\n & \\texttt{Respond with ONLY your answer in the following format:} \\\\[1mm]\n & \\texttt{\\textless answer\\textgreater \\textbf{YOUR ANSWER} \\textless /answer\\textgreater} \\\\[1mm]\n \\bottomrule\n \\end{tabular}\n\\caption{Prompt template for classifying literary analysis}\n\\label{tab:classify_lit_analysis}\n\\end{table*}\n\n\\begin{table*}[t]\n \\renewcommand{\\arraystretch}{1.2} \n \\setlength{\\tabcolsep}{4pt}\n \\footnotesize\n \\centering\n \\begin{tabular}{c p{15cm}}\n \\toprule\n & \\multicolumn{1}{c}{\\bf Prompt for Identifying Quote Location Disclosure} \\\\\n \\midrule\n \\noalign{\\vskip 1mm}\n & \\texttt{I want to create a dataset for a Natural Language Processing task called \"Literary Evidence Retrieval.\"} \\\\[3mm]\n & \\texttt{The input has two parts:} \\\\[1mm]\n & \\texttt{(1) An excerpt of literary analysis that quotes 1 to 5 full sentences from a primary source text, but the quote is replaced with \"[MASK]\".} \\\\[1mm]\n & \\texttt{(2) The full text of the primary source.} \\\\[3mm]\n & \\texttt{The output should be the correct quotation of 1 to 5 full sentences from the primary source—this is the ground truth.} \\\\[3mm]\n & \\texttt{I have already collected windows of literary analysis that quote from many different primary sources in the public domain.} \\\\[3mm]\n & \\texttt{The part of the excerpt before the quotation is called the \"prefix,\" and the part after is called the \"suffix.\"} \\\\[3mm]\n & \\texttt{To avoid giving the model hints, it's extremely important that the prefix and suffix do not mention the chapter where the ground truth quotation is located.} \\\\[3mm]\n & \\texttt{Please determine whether the following instance contains the chapter location of the ground truth quotation in either the prefix or the suffix. If it does, respond with TRUE; otherwise, respond with FALSE.} \\\\[3mm]\n & \\texttt{The prefix and suffix may contain the location of other quotations in the passage. I only want to identify if the location of the ground truth quotation is revealed.} \\\\[3mm]\n & \\texttt{Look out for phrases like \"In the prologue\" or \"In Chapter...\"} \\\\[3mm]\n & \\texttt{Here is the instance:} \\\\[1mm]\n & \\texttt{\\textless prefix\\textgreater \\textbf{[prefix]} \\textless /prefix\\textgreater} \\\\[1mm]\n & \\texttt{\\textless ground\\_truth\\_quotation\\textgreater \\textbf{[ground\\_truth\\_quotation]} \\textless /ground\\_truth\\_quotation\\textgreater} \\\\[1mm]\n & \\texttt{\\textless suffix\\textgreater \\textbf{[suffix]} \\textless /suffix\\textgreater} \\\\[1mm]\n & \\texttt{Respond with ONLY your answer in the following format:} \\\\[1mm]\n & \\texttt{\\textless answer\\textgreater \\textbf{YOUR ANSWER} \\textless /answer\\textgreater} \\\\[1mm]\n \\bottomrule\n \\end{tabular}\n\\caption{Prompt template for identifying quote location disclosure}\n\\label{tab:quote_location_disclosure}\n\\end{table*}\n\n\\section{Use of AI Assistants}\nThe authors used Github Copilot for coding assistance during their experiments and ChatGPT for assistance in formatting \\LaTeX\\ in the writing of this paper.\n\\end{document}"},"question":{"kind":"string","value":"Compare the same models appearing in the simple prompt group and the explained prompt group. What is the overall absolute and relative improvement after adding the explanation? Your answer should be expressed as a percentage."},"answer":{"kind":"string","value":"0.325%, 0.73%"},"plan":{"kind":"string","value":"SELECT models in simple prompt group\nSELECT models in explanation prompt group\nCOMPUTE common models = intersection of simple prompt models and explanation prompt models\nSELECT accuracies for common models in simple prompt group\nCOMPUTE average accuracy for simple prompt group\nSELECT accuracies for common models in explanation prompt group\nCOMPUTE average accuracy for explanation prompt group\nCOMPUTE absolute_improvement = explanation average - simple average\nCOMPUTE relative_improvement_percentage = (absolute improvement / simple average) * 100\nRETURN absolute_improvement, relative_improvement_percentage\n"}}},{"rowIdx":89,"cells":{"paper":{"kind":"string","value":"2506.02561"},"relevant_tables":{"kind":"list like","value":[["\\begin{table*}[t]\n","\\caption{Main Results of \\texttt{Cus-Prun} on multilingual setting with a pruning ratio of $25\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in summarization tasks and in accuracy (\\%) for other datasets.}\n"," \\centering\n","\\footnotesize\n","\\setlength{\\tabcolsep}{0.8pt}\n"," \\scalebox{0.9}{\n"," \\begin{tabular}{c|l|cccc|ccccc|ccccc|cccc}\n","\\toprule\n","\\quad\\quad & \\multirow{2}{*}{\\textbf{\\normalsize{Method}}} & \n"," \\multicolumn{4}{c}{\\textbf{\\normalsize{General Capability}}} \\vline & \\multicolumn{5}{c}{\\textbf{\\normalsize{Multilingual Expert}}} \\vline & \\multicolumn{5}{c}{\\textbf{\\normalsize{Multidomain Expert }}} \\vline & \\multicolumn{4}{c}{\\textbf{\\normalsize{Multitask Expert }}} \\\\ \n"," & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3 & XQuAD & Sum & Avg. & MMCQ & FTQA & TSA & AMSA & Avg. & MSum & ASum & AMCF & Avg. \\\\ \\midrule\n","\\rowcolor{lightgray}\\cellcolor{white}\\multirow{5}{*}{\\rotatebox{90}{\\textbf{Llama3-8B}}} & Dense & 70.7 & 58.3 & 63.1 & 64.1 &41.2 &49.1 & 63.4 &32.9 &46.7 & 51.8 & 23.9 & 67.1 & 95.9 & 59.8 & 76.6 & 16.2 & 78.2 & 57.0 \\\\\n"," & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 1.1 & 24.0 & 13.6 & 23.2 & 15.5 & 0.0 & 0.0 & \\textbf{61.8} & 76.0 & 34.5 & 62.2 & \\textbf{21.8} & \\textbf{80.0} & \\textbf{54.7} \\\\\n"," & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & 14.9 & 16.6 & 8.5 & 10.0 & 22.6 & 0.0 & 41.2 & 53.7 & 29.4 & 7.3 & 2.9 & 51.3 & 20.5 \\\\\n"," & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & 26.9 & 0.0 & 2.7 & 7.4 & 3.2 & 0.0 & 38.6 & 35.7 & 19.4 & 4.1 & 4.8 & 43.8 & 17.6 \\\\\n"," &\\cellcolor{lightblue}\\texttt{Cus-Prun} &\\cellcolor{lightblue}\\textbf{62.4} & \\cellcolor{lightblue}\\textbf{37.0} & \\cellcolor{lightblue}\\textbf{54.7} & \\cellcolor{lightblue}\\textbf{51.4} & \\cellcolor{lightblue}\\textbf{30.1} & \\cellcolor{lightblue}\\textbf{41.5} & \\cellcolor{lightblue}\\textbf{52.6} & \\cellcolor{lightblue}\\textbf{31.5} & \\cellcolor{lightblue}\\textbf{38.9} & \\cellcolor{lightblue}\\textbf{42.9} & \\cellcolor{lightblue}\\textbf{20.6} & \\cellcolor{lightblue}\\textbf{61.8} & \\cellcolor{lightblue}\\textbf{87.6} & \\cellcolor{lightblue}\\textbf{53.2} & \\cellcolor{lightblue}\\textbf{68.4} & \\cellcolor{lightblue}12.8 & \\cellcolor{lightblue}75.5 & \\cellcolor{lightblue}52.2 \\\\ \\midrule\n","\\rowcolor{lightgray}\\cellcolor{white}\\multirow{5}{*}{\\rotatebox{90}{\\textbf{Mistral-12B}}} & Dense & 82.6 & 68.5 & 50.4 & 67.2 & 51.7 & 43.8 & 49.2 & 25.4 & 42.5 & 54.6 & 26.6 & 69.4 & 92.4 & 60.8 & 88.7 & 3.0 & 78.6 & 56.4 \\\\\n"," & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 2.1 & 27.8 & 19.0 & \\textbf{23.2} & 18.0 & 0.0 & 0.0 & 51.0 & 20.9 & 18.0 & 59.3 & 0.5 & 2.8 & 20.9 \\\\\n"," & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 0.8 & 25.1 & 17.4 & 7.8 & 12.8 & 24.9 & 9.2 & 34.2 & 54.3 & 30.7 & 27.4 & 1.3 & 36.3 & 21.7 \\\\\n"," & ShortGPT & 37.8 & 0.0 & 33.9 & 23.9 & 2.9 & 27.0 & 18.0 & 5.0 & 13.2 & 31.4 & 7.2 & 39.2 & 52.5 & 32.6 & 26.2 & 0.2 & 42.7 & 23.0 \\\\\n"," & \\cellcolor{lightblue}\\texttt{Cus-Prun} & \\cellcolor{lightblue}\\textbf{67.5} & \\cellcolor{lightblue}\\textbf{43.4} & \\cellcolor{lightblue}\\textbf{43.8} & \\cellcolor{lightblue}\\textbf{51.6} & \\cellcolor{lightblue}\\textbf{34.3} & \\cellcolor{lightblue}\\textbf{39.2} & \\cellcolor{lightblue}\\textbf{40.7} & \\cellcolor{lightblue}23.1 & \\cellcolor{lightblue}\\textbf{34.3} & \\cellcolor{lightblue}\\textbf{47.9} & \\cellcolor{lightblue}\\textbf{25.1} & \\cellcolor{lightblue}\\textbf{67.3} & \\cellcolor{lightblue}\\textbf{83.7} & \\cellcolor{lightblue}\\textbf{56.0} & \\cellcolor{lightblue}\\textbf{83.5} & \\cellcolor{lightblue}\\textbf{3.4} & \\cellcolor{lightblue}\\textbf{72.8} & \\cellcolor{lightblue}\\textbf{50.9} \\\\ \\midrule\n","\\rowcolor{lightgray}\\cellcolor{white}\\multirow{5}{*}{\\rotatebox{90}{\\textbf{Llama2-13B}}} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 17.5 & 30.4 & 44.1 & 24.9 & 29.2 & 25.2 & 0.0 & 42.7 & 84.1 & 38.0 & 70.0 & 7.4 & 44.3 & 40.6 \\\\\n"," & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 1.1 & 22.8 & 3.8 & 17.7 & 11.3 & 0.0 & 0.0 & 9.7 & 0.0 & 2.4 & 21.6 & 4.8 & 0.0 & 8.8 \\\\\n"," & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 2.8 & 25.3 & 23.4 & 9.9 & 15.5 & 18.7 & 0.0 & 28.4 & 67.3 & 28.6 & 24.5 & 4.9 & 32.9 & 20.8 \\\\\n"," & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 2.4 & 23.0 & 24.7 & 11.3 & 15.3 & 16.9 & 0.0 & 34.6 & \\textbf{69.8} & 30.3 & 23.8 & 5.2 & 39.1 & 22.7 \\\\\n"," & \\cellcolor{lightblue}\\texttt{Cus-Prun} & \\cellcolor{lightblue}\\textbf{48.3} & \\cellcolor{lightblue}\\textbf{20.8} & \\cellcolor{lightblue}\\textbf{50.0} & \\cellcolor{lightblue}\\textbf{39.7} & \\cellcolor{lightblue}\\textbf{12.7} & \\cellcolor{lightblue}\\textbf{26.2} & \\cellcolor{lightblue}\\textbf{34.2} & \\cellcolor{lightblue}\\textbf{24.1} & \\cellcolor{lightblue}\\textbf{24.3} & \\cellcolor{lightblue}\\textbf{25.6} & \\cellcolor{lightblue}0.0 & \\cellcolor{lightblue}\\textbf{38.5} & \\cellcolor{lightblue}68.3 & \\cellcolor{lightblue}\\textbf{33.1} & \\cellcolor{lightblue}\\textbf{64.5} & \\cellcolor{lightblue}\\textbf{6.7} & \\cellcolor{lightblue}\\textbf{42.9} & \\cellcolor{lightblue}\\textbf{38.0} \\\\ \\midrule\n","\\rowcolor{lightgray}\\cellcolor{white}\\multirow{5}{*}{\\rotatebox{90}{\\textbf{Llama3-70B}}} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 69.5 & 71.1 & 69.1 & 36.6 & 61.6 & 72.1 & 55.3 & 83.6 & 96.2 & 76.8 & 84.2 & 17.3 & 81.8 & 61.1 \\\\\n"," & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 16.8 & 43.7 & 43.0 & 29.0 & 33.1 & 27.3 & 1.0 & 51.0 & 50.3 & 32.4 & 10.2 & 13.7 & 20.6 & 14.8 \\\\\n"," & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 3.7 & 44.8 & 33.0 & 21.2 & 25.7 & 57.6 & 27.6 & 68.1 & 59.4 & 53.2 & 58.0 & 14.2 & 68.3 & 46.8 \\\\\n"," & ShortGPT & 59.4 & 5.6 & \\textbf{75.5} & 46.8 & 11.9 & 43.1 & 38.8 & 24.0 & 29.5 & 58.4 & 32.2 & 67.5 & 64.9 & 55.8 & 59.6 & 13.9 & 65.8 & 46.4 \\\\\n"," & \\cellcolor{lightblue}\\texttt{Cus-Prun} & \\cellcolor{lightblue}\\textbf{68.4} & \\cellcolor{lightblue}\\textbf{53.2} & \\cellcolor{lightblue}66.6 & \\cellcolor{lightblue}\\textbf{62.7} & \\cellcolor{lightblue}\\textbf{43.1} & \\cellcolor{lightblue}\\textbf{57.7} & \\cellcolor{lightblue}\\textbf{59.8} & \\cellcolor{lightblue}\\textbf{34.3} & \\cellcolor{lightblue}\\textbf{48.7} & \\cellcolor{lightblue}\\textbf{68.2} & \\cellcolor{lightblue}\\textbf{43.9} & \\cellcolor{lightblue}\\textbf{81.4} & \\cellcolor{lightblue}\\textbf{87.8} & \\cellcolor{lightblue}\\textbf{70.3} & \\cellcolor{lightblue}\\textbf{80.4} & \\cellcolor{lightblue}\\textbf{15.7} & \\cellcolor{lightblue}\\textbf{77.5} & \\cellcolor{lightblue}\\textbf{57.9} \\\\ \n","\\bottomrule\n","\\end{tabular}}\n"," \\label{table:cross_ling}\n","\\vspace{-0.2cm}\n","\\end{table*}\n"],["\\begin{table}[ht]\n","\\caption{Aggressive pruning ratio on Llama3-8B.}\n","\\centering\n","\\setlength{\\tabcolsep}{3pt}\n"," \\scalebox{0.9}{\n","\\begin{tabular}{l|c|c|c|c}\n"," \\hline\n"," \\textbf{Method} & \\textbf{Ratio} & \\textbf{Speedup} & \\textbf{MMLU} & \\textbf{Expert} \\\\\\midrule\n"," \\rowcolor{lightgray}\\cellcolor{white}Dense & 0.0 & 1$\\times$ & 63.1 & 59.7 \\\\\\midrule\n"," ShortGPT & 25.0 & 1.3$\\times$ & 28.6 & 24.6 \\\\\n"," \\rowcolor{lightblue}\\cellcolor{white}\\texttt{Cus-Prun} & 25.0 & 1.3$\\times$ & \\textbf{51.9} & \\textbf{53.3} \\\\\\midrule\n"," ShortGPT & 34.2 & 1.5$\\times$ & 20.8 & 18.5 \\\\\n"," \\rowcolor{lightblue}\\cellcolor{white}\\texttt{Cus-Prun} & 35.0 & 1.5$\\times$ & \\textbf{50.2} & \\textbf{51.4} \\\\\\midrule\n"," ShortGPT & 43.8 & 1.8$\\times$ & 7.9 & 10.2 \\\\\n"," \\rowcolor{lightblue}\\cellcolor{white}\\texttt{Cus-Prun} & 45.0 & 1.8$\\times$ & \\textbf{48.4} & \\textbf{50.6} \\\\\n"," \\hline\n"," \\end{tabular}}\n"," \\label{table:prune_more}\n","\\end{table}\n"]],"string":"[\n [\n \"\\\\begin{table*}[t]\\n\",\n \"\\\\caption{Main Results of \\\\texttt{Cus-Prun} on multilingual setting with a pruning ratio of $25\\\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in summarization tasks and in accuracy (\\\\%) for other datasets.}\\n\",\n \" \\\\centering\\n\",\n \"\\\\footnotesize\\n\",\n \"\\\\setlength{\\\\tabcolsep}{0.8pt}\\n\",\n \" \\\\scalebox{0.9}{\\n\",\n \" \\\\begin{tabular}{c|l|cccc|ccccc|ccccc|cccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\quad\\\\quad & \\\\multirow{2}{*}{\\\\textbf{\\\\normalsize{Method}}} & \\n\",\n \" \\\\multicolumn{4}{c}{\\\\textbf{\\\\normalsize{General Capability}}} \\\\vline & \\\\multicolumn{5}{c}{\\\\textbf{\\\\normalsize{Multilingual Expert}}} \\\\vline & \\\\multicolumn{5}{c}{\\\\textbf{\\\\normalsize{Multidomain Expert }}} \\\\vline & \\\\multicolumn{4}{c}{\\\\textbf{\\\\normalsize{Multitask Expert }}} \\\\\\\\ \\n\",\n \" & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3 & XQuAD & Sum & Avg. & MMCQ & FTQA & TSA & AMSA & Avg. & MSum & ASum & AMCF & Avg. \\\\\\\\ \\\\midrule\\n\",\n \"\\\\rowcolor{lightgray}\\\\cellcolor{white}\\\\multirow{5}{*}{\\\\rotatebox{90}{\\\\textbf{Llama3-8B}}} & Dense & 70.7 & 58.3 & 63.1 & 64.1 &41.2 &49.1 & 63.4 &32.9 &46.7 & 51.8 & 23.9 & 67.1 & 95.9 & 59.8 & 76.6 & 16.2 & 78.2 & 57.0 \\\\\\\\\\n\",\n \" & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 1.1 & 24.0 & 13.6 & 23.2 & 15.5 & 0.0 & 0.0 & \\\\textbf{61.8} & 76.0 & 34.5 & 62.2 & \\\\textbf{21.8} & \\\\textbf{80.0} & \\\\textbf{54.7} \\\\\\\\\\n\",\n \" & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & 14.9 & 16.6 & 8.5 & 10.0 & 22.6 & 0.0 & 41.2 & 53.7 & 29.4 & 7.3 & 2.9 & 51.3 & 20.5 \\\\\\\\\\n\",\n \" & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & 26.9 & 0.0 & 2.7 & 7.4 & 3.2 & 0.0 & 38.6 & 35.7 & 19.4 & 4.1 & 4.8 & 43.8 & 17.6 \\\\\\\\\\n\",\n \" &\\\\cellcolor{lightblue}\\\\texttt{Cus-Prun} &\\\\cellcolor{lightblue}\\\\textbf{62.4} & \\\\cellcolor{lightblue}\\\\textbf{37.0} & \\\\cellcolor{lightblue}\\\\textbf{54.7} & \\\\cellcolor{lightblue}\\\\textbf{51.4} & \\\\cellcolor{lightblue}\\\\textbf{30.1} & \\\\cellcolor{lightblue}\\\\textbf{41.5} & \\\\cellcolor{lightblue}\\\\textbf{52.6} & \\\\cellcolor{lightblue}\\\\textbf{31.5} & \\\\cellcolor{lightblue}\\\\textbf{38.9} & \\\\cellcolor{lightblue}\\\\textbf{42.9} & \\\\cellcolor{lightblue}\\\\textbf{20.6} & \\\\cellcolor{lightblue}\\\\textbf{61.8} & \\\\cellcolor{lightblue}\\\\textbf{87.6} & \\\\cellcolor{lightblue}\\\\textbf{53.2} & \\\\cellcolor{lightblue}\\\\textbf{68.4} & \\\\cellcolor{lightblue}12.8 & \\\\cellcolor{lightblue}75.5 & \\\\cellcolor{lightblue}52.2 \\\\\\\\ \\\\midrule\\n\",\n \"\\\\rowcolor{lightgray}\\\\cellcolor{white}\\\\multirow{5}{*}{\\\\rotatebox{90}{\\\\textbf{Mistral-12B}}} & Dense & 82.6 & 68.5 & 50.4 & 67.2 & 51.7 & 43.8 & 49.2 & 25.4 & 42.5 & 54.6 & 26.6 & 69.4 & 92.4 & 60.8 & 88.7 & 3.0 & 78.6 & 56.4 \\\\\\\\\\n\",\n \" & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 2.1 & 27.8 & 19.0 & \\\\textbf{23.2} & 18.0 & 0.0 & 0.0 & 51.0 & 20.9 & 18.0 & 59.3 & 0.5 & 2.8 & 20.9 \\\\\\\\\\n\",\n \" & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 0.8 & 25.1 & 17.4 & 7.8 & 12.8 & 24.9 & 9.2 & 34.2 & 54.3 & 30.7 & 27.4 & 1.3 & 36.3 & 21.7 \\\\\\\\\\n\",\n \" & ShortGPT & 37.8 & 0.0 & 33.9 & 23.9 & 2.9 & 27.0 & 18.0 & 5.0 & 13.2 & 31.4 & 7.2 & 39.2 & 52.5 & 32.6 & 26.2 & 0.2 & 42.7 & 23.0 \\\\\\\\\\n\",\n \" & \\\\cellcolor{lightblue}\\\\texttt{Cus-Prun} & \\\\cellcolor{lightblue}\\\\textbf{67.5} & \\\\cellcolor{lightblue}\\\\textbf{43.4} & \\\\cellcolor{lightblue}\\\\textbf{43.8} & \\\\cellcolor{lightblue}\\\\textbf{51.6} & \\\\cellcolor{lightblue}\\\\textbf{34.3} & \\\\cellcolor{lightblue}\\\\textbf{39.2} & \\\\cellcolor{lightblue}\\\\textbf{40.7} & \\\\cellcolor{lightblue}23.1 & \\\\cellcolor{lightblue}\\\\textbf{34.3} & \\\\cellcolor{lightblue}\\\\textbf{47.9} & \\\\cellcolor{lightblue}\\\\textbf{25.1} & \\\\cellcolor{lightblue}\\\\textbf{67.3} & \\\\cellcolor{lightblue}\\\\textbf{83.7} & \\\\cellcolor{lightblue}\\\\textbf{56.0} & \\\\cellcolor{lightblue}\\\\textbf{83.5} & \\\\cellcolor{lightblue}\\\\textbf{3.4} & \\\\cellcolor{lightblue}\\\\textbf{72.8} & \\\\cellcolor{lightblue}\\\\textbf{50.9} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\rowcolor{lightgray}\\\\cellcolor{white}\\\\multirow{5}{*}{\\\\rotatebox{90}{\\\\textbf{Llama2-13B}}} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 17.5 & 30.4 & 44.1 & 24.9 & 29.2 & 25.2 & 0.0 & 42.7 & 84.1 & 38.0 & 70.0 & 7.4 & 44.3 & 40.6 \\\\\\\\\\n\",\n \" & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 1.1 & 22.8 & 3.8 & 17.7 & 11.3 & 0.0 & 0.0 & 9.7 & 0.0 & 2.4 & 21.6 & 4.8 & 0.0 & 8.8 \\\\\\\\\\n\",\n \" & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 2.8 & 25.3 & 23.4 & 9.9 & 15.5 & 18.7 & 0.0 & 28.4 & 67.3 & 28.6 & 24.5 & 4.9 & 32.9 & 20.8 \\\\\\\\\\n\",\n \" & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 2.4 & 23.0 & 24.7 & 11.3 & 15.3 & 16.9 & 0.0 & 34.6 & \\\\textbf{69.8} & 30.3 & 23.8 & 5.2 & 39.1 & 22.7 \\\\\\\\\\n\",\n \" & \\\\cellcolor{lightblue}\\\\texttt{Cus-Prun} & \\\\cellcolor{lightblue}\\\\textbf{48.3} & \\\\cellcolor{lightblue}\\\\textbf{20.8} & \\\\cellcolor{lightblue}\\\\textbf{50.0} & \\\\cellcolor{lightblue}\\\\textbf{39.7} & \\\\cellcolor{lightblue}\\\\textbf{12.7} & \\\\cellcolor{lightblue}\\\\textbf{26.2} & \\\\cellcolor{lightblue}\\\\textbf{34.2} & \\\\cellcolor{lightblue}\\\\textbf{24.1} & \\\\cellcolor{lightblue}\\\\textbf{24.3} & \\\\cellcolor{lightblue}\\\\textbf{25.6} & \\\\cellcolor{lightblue}0.0 & \\\\cellcolor{lightblue}\\\\textbf{38.5} & \\\\cellcolor{lightblue}68.3 & \\\\cellcolor{lightblue}\\\\textbf{33.1} & \\\\cellcolor{lightblue}\\\\textbf{64.5} & \\\\cellcolor{lightblue}\\\\textbf{6.7} & \\\\cellcolor{lightblue}\\\\textbf{42.9} & \\\\cellcolor{lightblue}\\\\textbf{38.0} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\rowcolor{lightgray}\\\\cellcolor{white}\\\\multirow{5}{*}{\\\\rotatebox{90}{\\\\textbf{Llama3-70B}}} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 69.5 & 71.1 & 69.1 & 36.6 & 61.6 & 72.1 & 55.3 & 83.6 & 96.2 & 76.8 & 84.2 & 17.3 & 81.8 & 61.1 \\\\\\\\\\n\",\n \" & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 16.8 & 43.7 & 43.0 & 29.0 & 33.1 & 27.3 & 1.0 & 51.0 & 50.3 & 32.4 & 10.2 & 13.7 & 20.6 & 14.8 \\\\\\\\\\n\",\n \" & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 3.7 & 44.8 & 33.0 & 21.2 & 25.7 & 57.6 & 27.6 & 68.1 & 59.4 & 53.2 & 58.0 & 14.2 & 68.3 & 46.8 \\\\\\\\\\n\",\n \" & ShortGPT & 59.4 & 5.6 & \\\\textbf{75.5} & 46.8 & 11.9 & 43.1 & 38.8 & 24.0 & 29.5 & 58.4 & 32.2 & 67.5 & 64.9 & 55.8 & 59.6 & 13.9 & 65.8 & 46.4 \\\\\\\\\\n\",\n \" & \\\\cellcolor{lightblue}\\\\texttt{Cus-Prun} & \\\\cellcolor{lightblue}\\\\textbf{68.4} & \\\\cellcolor{lightblue}\\\\textbf{53.2} & \\\\cellcolor{lightblue}66.6 & \\\\cellcolor{lightblue}\\\\textbf{62.7} & \\\\cellcolor{lightblue}\\\\textbf{43.1} & \\\\cellcolor{lightblue}\\\\textbf{57.7} & \\\\cellcolor{lightblue}\\\\textbf{59.8} & \\\\cellcolor{lightblue}\\\\textbf{34.3} & \\\\cellcolor{lightblue}\\\\textbf{48.7} & \\\\cellcolor{lightblue}\\\\textbf{68.2} & \\\\cellcolor{lightblue}\\\\textbf{43.9} & \\\\cellcolor{lightblue}\\\\textbf{81.4} & \\\\cellcolor{lightblue}\\\\textbf{87.8} & \\\\cellcolor{lightblue}\\\\textbf{70.3} & \\\\cellcolor{lightblue}\\\\textbf{80.4} & \\\\cellcolor{lightblue}\\\\textbf{15.7} & \\\\cellcolor{lightblue}\\\\textbf{77.5} & \\\\cellcolor{lightblue}\\\\textbf{57.9} \\\\\\\\ \\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}}\\n\",\n \" \\\\label{table:cross_ling}\\n\",\n \"\\\\vspace{-0.2cm}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table}[ht]\\n\",\n \"\\\\caption{Aggressive pruning ratio on Llama3-8B.}\\n\",\n \"\\\\centering\\n\",\n \"\\\\setlength{\\\\tabcolsep}{3pt}\\n\",\n \" \\\\scalebox{0.9}{\\n\",\n \"\\\\begin{tabular}{l|c|c|c|c}\\n\",\n \" \\\\hline\\n\",\n \" \\\\textbf{Method} & \\\\textbf{Ratio} & \\\\textbf{Speedup} & \\\\textbf{MMLU} & \\\\textbf{Expert} \\\\\\\\\\\\midrule\\n\",\n \" \\\\rowcolor{lightgray}\\\\cellcolor{white}Dense & 0.0 & 1$\\\\times$ & 63.1 & 59.7 \\\\\\\\\\\\midrule\\n\",\n \" ShortGPT & 25.0 & 1.3$\\\\times$ & 28.6 & 24.6 \\\\\\\\\\n\",\n \" \\\\rowcolor{lightblue}\\\\cellcolor{white}\\\\texttt{Cus-Prun} & 25.0 & 1.3$\\\\times$ & \\\\textbf{51.9} & \\\\textbf{53.3} \\\\\\\\\\\\midrule\\n\",\n \" ShortGPT & 34.2 & 1.5$\\\\times$ & 20.8 & 18.5 \\\\\\\\\\n\",\n \" \\\\rowcolor{lightblue}\\\\cellcolor{white}\\\\texttt{Cus-Prun} & 35.0 & 1.5$\\\\times$ & \\\\textbf{50.2} & \\\\textbf{51.4} \\\\\\\\\\\\midrule\\n\",\n \" ShortGPT & 43.8 & 1.8$\\\\times$ & 7.9 & 10.2 \\\\\\\\\\n\",\n \" \\\\rowcolor{lightblue}\\\\cellcolor{white}\\\\texttt{Cus-Prun} & 45.0 & 1.8$\\\\times$ & \\\\textbf{48.4} & \\\\textbf{50.6} \\\\\\\\\\n\",\n \" \\\\hline\\n\",\n \" \\\\end{tabular}}\\n\",\n \" \\\\label{table:prune_more}\\n\",\n \"\\\\end{table}\\n\"\n ]\n]","isConversational":false},"tables":{"kind":"list like","value":[["\\begin{figure}[t]\n"," \\centering\n"," \\includegraphics[width=0.48\\textwidth]{Figures/cust-prune.pdf}\n"," \\caption{Given a requirement for an expert model across three dimensions (language, domain, and task), \\texttt{Cus-Prun} (i) identifies irrelevant neurons for each dimension using corresponding corpora (left), and (ii) prunes \\textbf{overlapping} irrelevant neurons across dimensions to obtain the expert model (right).}\n"," \\label{fig:cus-prun}\n","\\end{figure}\n"],["\\begin{figure}[t]\n","\\scalebox{0.95}{ \n","\\begin{minipage}{\\linewidth}\n","\\begin{algorithm}[H]\n","\\renewcommand{\\algorithmicrequire}{\\textbf{Input:}}\n","\\renewcommand{\\algorithmicensure}{\\textbf{Output:}}\n","\\caption{Adaptive Custom Pruning}\n","\\begin{algorithmic}[1]\n","\\REQUIRE Original language model $\\mathcal{LLM}$, request for expert model $\\mathcal{LLM}_{\\text{Exp}}$ with selected dimensions: $L_{\\text{Exp}}$, $D_{\\text{Exp}}$, $T_{\\text{Exp}}$ (any subset), request for pruning ratio $\\sigma$.\n","\\STATE \\texttt{// Construct specific corpora for each selected dimension.}\n","\\STATE $C = \\{\\}$\n","\\IF{$L_{\\text{Exp}}$ is specified}\n"," \\STATE $C = C \\cup \\{(L_{\\text{Exp}}, D, T) \\mid D \\in \\mathbb{D}, T \\in \\mathbb{T}\\}$\n","\\ENDIF\n","\\IF{$D_{\\text{Exp}}$ is specified}\n"," \\STATE $C = C \\cup \\{(L, D_{\\text{Exp}}, T) \\mid L \\in \\mathbb{L}, T \\in \\mathbb{T}\\}$\n","\\ENDIF\n","\\IF{$T_{\\text{Exp}}$ is specified}\n"," \\STATE $C = C \\cup \\{(L, D, T_{\\text{Exp}}) \\mid L \\in \\mathbb{L}, D \\in \\mathbb{D}\\}$\n","\\ENDIF\n","\\STATE \\texttt{// Identify irrelevant neurons for each selected dimension.}\n","\\FORALL{neuron $N_{i}^{(l)}$ in $\\mathcal{LLM}$}\n"," \\IF{$\\forall c \\in C,$ $N_{i}^{(l)} \\in \\tilde{\\mathcal{N}}(c)$}\n"," \\STATE $\\tilde{\\mathcal{N}} \\gets \\tilde{\\mathcal{N}} \\cup N_{i}^{(l)}$\n"," \\ENDIF\n","\\ENDFOR\n","\\STATE \\texttt{// Prune irrelevant neurons to obtain expert model.}\n","\\STATE $\\mathcal{LLM}_{\\text{Exp}} = \\mathcal{LLM} \\ominus \\tilde{\\mathcal{N}}$\n","\\ENSURE $\\mathcal{LLM}_{\\text{Exp}}$\n","\\end{algorithmic}\n","\\label{algo:adaptive_pruning}\n","\\end{algorithm}\n","\\end{minipage}\n","}\n","\\end{figure}\n"],["\\begin{figure}[t]\n"," \\centering\n"," \\includegraphics[width=0.48\\textwidth]{Figures/three-dim-examples.pdf}\n"," \\caption{Concrete examples of applying \\texttt{Cus-Prun} to prune $25\\%$ of Llama3-8B-Base's parameters into three-dimensional expert models. Numbers above each box indicate performance on the \\textbf{whole} test set, with the first evaluated by Rouge-L, and the other two by accuracy. }\n"," \\label{fig:korean}\n","\\end{figure}\n"],["\\begin{table*}[t]\n","\\caption{Main Results of \\texttt{Cus-Prun} on multilingual setting with a pruning ratio of $25\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in summarization tasks and in accuracy (\\%) for other datasets.}\n"," \\centering\n","\\footnotesize\n","\\setlength{\\tabcolsep}{0.8pt}\n"," \\scalebox{0.9}{\n"," \\begin{tabular}{c|l|cccc|ccccc|ccccc|cccc}\n","\\toprule\n","\\quad\\quad & \\multirow{2}{*}{\\textbf{\\normalsize{Method}}} & \n"," \\multicolumn{4}{c}{\\textbf{\\normalsize{General Capability}}} \\vline & \\multicolumn{5}{c}{\\textbf{\\normalsize{Multilingual Expert}}} \\vline & \\multicolumn{5}{c}{\\textbf{\\normalsize{Multidomain Expert }}} \\vline & \\multicolumn{4}{c}{\\textbf{\\normalsize{Multitask Expert }}} \\\\ \n"," & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3 & XQuAD & Sum & Avg. & MMCQ & FTQA & TSA & AMSA & Avg. & MSum & ASum & AMCF & Avg. \\\\ \\midrule\n","\\rowcolor{lightgray}\\cellcolor{white}\\multirow{5}{*}{\\rotatebox{90}{\\textbf{Llama3-8B}}} & Dense & 70.7 & 58.3 & 63.1 & 64.1 &41.2 &49.1 & 63.4 &32.9 &46.7 & 51.8 & 23.9 & 67.1 & 95.9 & 59.8 & 76.6 & 16.2 & 78.2 & 57.0 \\\\\n"," & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 1.1 & 24.0 & 13.6 & 23.2 & 15.5 & 0.0 & 0.0 & \\textbf{61.8} & 76.0 & 34.5 & 62.2 & \\textbf{21.8} & \\textbf{80.0} & \\textbf{54.7} \\\\\n"," & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & 14.9 & 16.6 & 8.5 & 10.0 & 22.6 & 0.0 & 41.2 & 53.7 & 29.4 & 7.3 & 2.9 & 51.3 & 20.5 \\\\\n"," & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & 26.9 & 0.0 & 2.7 & 7.4 & 3.2 & 0.0 & 38.6 & 35.7 & 19.4 & 4.1 & 4.8 & 43.8 & 17.6 \\\\\n"," &\\cellcolor{lightblue}\\texttt{Cus-Prun} &\\cellcolor{lightblue}\\textbf{62.4} & \\cellcolor{lightblue}\\textbf{37.0} & \\cellcolor{lightblue}\\textbf{54.7} & \\cellcolor{lightblue}\\textbf{51.4} & \\cellcolor{lightblue}\\textbf{30.1} & \\cellcolor{lightblue}\\textbf{41.5} & \\cellcolor{lightblue}\\textbf{52.6} & \\cellcolor{lightblue}\\textbf{31.5} & \\cellcolor{lightblue}\\textbf{38.9} & \\cellcolor{lightblue}\\textbf{42.9} & \\cellcolor{lightblue}\\textbf{20.6} & \\cellcolor{lightblue}\\textbf{61.8} & \\cellcolor{lightblue}\\textbf{87.6} & \\cellcolor{lightblue}\\textbf{53.2} & \\cellcolor{lightblue}\\textbf{68.4} & \\cellcolor{lightblue}12.8 & \\cellcolor{lightblue}75.5 & \\cellcolor{lightblue}52.2 \\\\ \\midrule\n","\\rowcolor{lightgray}\\cellcolor{white}\\multirow{5}{*}{\\rotatebox{90}{\\textbf{Mistral-12B}}} & Dense & 82.6 & 68.5 & 50.4 & 67.2 & 51.7 & 43.8 & 49.2 & 25.4 & 42.5 & 54.6 & 26.6 & 69.4 & 92.4 & 60.8 & 88.7 & 3.0 & 78.6 & 56.4 \\\\\n"," & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 2.1 & 27.8 & 19.0 & \\textbf{23.2} & 18.0 & 0.0 & 0.0 & 51.0 & 20.9 & 18.0 & 59.3 & 0.5 & 2.8 & 20.9 \\\\\n"," & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 0.8 & 25.1 & 17.4 & 7.8 & 12.8 & 24.9 & 9.2 & 34.2 & 54.3 & 30.7 & 27.4 & 1.3 & 36.3 & 21.7 \\\\\n"," & ShortGPT & 37.8 & 0.0 & 33.9 & 23.9 & 2.9 & 27.0 & 18.0 & 5.0 & 13.2 & 31.4 & 7.2 & 39.2 & 52.5 & 32.6 & 26.2 & 0.2 & 42.7 & 23.0 \\\\\n"," & \\cellcolor{lightblue}\\texttt{Cus-Prun} & \\cellcolor{lightblue}\\textbf{67.5} & \\cellcolor{lightblue}\\textbf{43.4} & \\cellcolor{lightblue}\\textbf{43.8} & \\cellcolor{lightblue}\\textbf{51.6} & \\cellcolor{lightblue}\\textbf{34.3} & \\cellcolor{lightblue}\\textbf{39.2} & \\cellcolor{lightblue}\\textbf{40.7} & \\cellcolor{lightblue}23.1 & \\cellcolor{lightblue}\\textbf{34.3} & \\cellcolor{lightblue}\\textbf{47.9} & \\cellcolor{lightblue}\\textbf{25.1} & \\cellcolor{lightblue}\\textbf{67.3} & \\cellcolor{lightblue}\\textbf{83.7} & \\cellcolor{lightblue}\\textbf{56.0} & \\cellcolor{lightblue}\\textbf{83.5} & \\cellcolor{lightblue}\\textbf{3.4} & \\cellcolor{lightblue}\\textbf{72.8} & \\cellcolor{lightblue}\\textbf{50.9} \\\\ \\midrule\n","\\rowcolor{lightgray}\\cellcolor{white}\\multirow{5}{*}{\\rotatebox{90}{\\textbf{Llama2-13B}}} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 17.5 & 30.4 & 44.1 & 24.9 & 29.2 & 25.2 & 0.0 & 42.7 & 84.1 & 38.0 & 70.0 & 7.4 & 44.3 & 40.6 \\\\\n"," & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 1.1 & 22.8 & 3.8 & 17.7 & 11.3 & 0.0 & 0.0 & 9.7 & 0.0 & 2.4 & 21.6 & 4.8 & 0.0 & 8.8 \\\\\n"," & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 2.8 & 25.3 & 23.4 & 9.9 & 15.5 & 18.7 & 0.0 & 28.4 & 67.3 & 28.6 & 24.5 & 4.9 & 32.9 & 20.8 \\\\\n"," & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 2.4 & 23.0 & 24.7 & 11.3 & 15.3 & 16.9 & 0.0 & 34.6 & \\textbf{69.8} & 30.3 & 23.8 & 5.2 & 39.1 & 22.7 \\\\\n"," & \\cellcolor{lightblue}\\texttt{Cus-Prun} & \\cellcolor{lightblue}\\textbf{48.3} & \\cellcolor{lightblue}\\textbf{20.8} & \\cellcolor{lightblue}\\textbf{50.0} & \\cellcolor{lightblue}\\textbf{39.7} & \\cellcolor{lightblue}\\textbf{12.7} & \\cellcolor{lightblue}\\textbf{26.2} & \\cellcolor{lightblue}\\textbf{34.2} & \\cellcolor{lightblue}\\textbf{24.1} & \\cellcolor{lightblue}\\textbf{24.3} & \\cellcolor{lightblue}\\textbf{25.6} & \\cellcolor{lightblue}0.0 & \\cellcolor{lightblue}\\textbf{38.5} & \\cellcolor{lightblue}68.3 & \\cellcolor{lightblue}\\textbf{33.1} & \\cellcolor{lightblue}\\textbf{64.5} & \\cellcolor{lightblue}\\textbf{6.7} & \\cellcolor{lightblue}\\textbf{42.9} & \\cellcolor{lightblue}\\textbf{38.0} \\\\ \\midrule\n","\\rowcolor{lightgray}\\cellcolor{white}\\multirow{5}{*}{\\rotatebox{90}{\\textbf{Llama3-70B}}} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 69.5 & 71.1 & 69.1 & 36.6 & 61.6 & 72.1 & 55.3 & 83.6 & 96.2 & 76.8 & 84.2 & 17.3 & 81.8 & 61.1 \\\\\n"," & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 16.8 & 43.7 & 43.0 & 29.0 & 33.1 & 27.3 & 1.0 & 51.0 & 50.3 & 32.4 & 10.2 & 13.7 & 20.6 & 14.8 \\\\\n"," & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 3.7 & 44.8 & 33.0 & 21.2 & 25.7 & 57.6 & 27.6 & 68.1 & 59.4 & 53.2 & 58.0 & 14.2 & 68.3 & 46.8 \\\\\n"," & ShortGPT & 59.4 & 5.6 & \\textbf{75.5} & 46.8 & 11.9 & 43.1 & 38.8 & 24.0 & 29.5 & 58.4 & 32.2 & 67.5 & 64.9 & 55.8 & 59.6 & 13.9 & 65.8 & 46.4 \\\\\n"," & \\cellcolor{lightblue}\\texttt{Cus-Prun} & \\cellcolor{lightblue}\\textbf{68.4} & \\cellcolor{lightblue}\\textbf{53.2} & \\cellcolor{lightblue}66.6 & \\cellcolor{lightblue}\\textbf{62.7} & \\cellcolor{lightblue}\\textbf{43.1} & \\cellcolor{lightblue}\\textbf{57.7} & \\cellcolor{lightblue}\\textbf{59.8} & \\cellcolor{lightblue}\\textbf{34.3} & \\cellcolor{lightblue}\\textbf{48.7} & \\cellcolor{lightblue}\\textbf{68.2} & \\cellcolor{lightblue}\\textbf{43.9} & \\cellcolor{lightblue}\\textbf{81.4} & \\cellcolor{lightblue}\\textbf{87.8} & \\cellcolor{lightblue}\\textbf{70.3} & \\cellcolor{lightblue}\\textbf{80.4} & \\cellcolor{lightblue}\\textbf{15.7} & \\cellcolor{lightblue}\\textbf{77.5} & \\cellcolor{lightblue}\\textbf{57.9} \\\\ \n","\\bottomrule\n","\\end{tabular}}\n"," \\label{table:cross_ling}\n","\\vspace{-0.2cm}\n","\\end{table*}\n"],["\\begin{figure*}[t]\n"," \\centering\n"," \\begin{minipage}{0.4\\textwidth}\n"," \\centering\n"," \\captionof{table}{Performance of Chinese-Medical expert model on MCQ task}\n"," \\label{tab:cmexam}\n"," \\scalebox{0.96}{\n"," \\begin{tabular}{l|c|c}\n"," \\hline\n"," \\textbf{Method} & \\textbf{General} & \\textbf{CMExam} \\\\\\midrule\n"," \\rowcolor{lightgray}\\cellcolor{white} Dense & 59.3 & 50.6 \\\\\n"," LLM-Pruner & 18.6 & 25.0 \\\\\n"," SliceGPT & 27.8 & 26.9 \\\\\n"," ShortGPT & 23.9 & 23.7 \\\\\n"," \\rowcolor{lightblue}\\cellcolor{white} \\texttt{Cus-Prun} & \\textbf{52.4} & \\textbf{48.7} \\\\\n"," \\hline\n"," \\end{tabular}}\n"," \\end{minipage}\n"," \\hfill\n"," \\begin{minipage}{0.58\\textwidth}\n"," \\centering\n"," \\includegraphics[width=\\textwidth]{Figures/huatuo.pdf}\n"," \\caption{Chinese Medical LLM performance. Numbers are quality on the \\textbf{whole} test set evaluated by GPT4.}\n"," \\label{fig:huatuo}\n"," \\end{minipage}\n","\\end{figure*}\n"],["\\begin{table}[ht]\n","\\caption{Aggressive pruning ratio on Llama3-8B.}\n","\\centering\n","\\setlength{\\tabcolsep}{3pt}\n"," \\scalebox{0.9}{\n","\\begin{tabular}{l|c|c|c|c}\n"," \\hline\n"," \\textbf{Method} & \\textbf{Ratio} & \\textbf{Speedup} & \\textbf{MMLU} & \\textbf{Expert} \\\\\\midrule\n"," \\rowcolor{lightgray}\\cellcolor{white}Dense & 0.0 & 1$\\times$ & 63.1 & 59.7 \\\\\\midrule\n"," ShortGPT & 25.0 & 1.3$\\times$ & 28.6 & 24.6 \\\\\n"," \\rowcolor{lightblue}\\cellcolor{white}\\texttt{Cus-Prun} & 25.0 & 1.3$\\times$ & \\textbf{51.9} & \\textbf{53.3} \\\\\\midrule\n"," ShortGPT & 34.2 & 1.5$\\times$ & 20.8 & 18.5 \\\\\n"," \\rowcolor{lightblue}\\cellcolor{white}\\texttt{Cus-Prun} & 35.0 & 1.5$\\times$ & \\textbf{50.2} & \\textbf{51.4} \\\\\\midrule\n"," ShortGPT & 43.8 & 1.8$\\times$ & 7.9 & 10.2 \\\\\n"," \\rowcolor{lightblue}\\cellcolor{white}\\texttt{Cus-Prun} & 45.0 & 1.8$\\times$ & \\textbf{48.4} & \\textbf{50.6} \\\\\n"," \\hline\n"," \\end{tabular}}\n"," \\label{table:prune_more}\n","\\end{table}\n"],["\\begin{figure*}[t]\n"," \\centering\n"," \\includegraphics[width=1.0\\textwidth]{Figures/lang-specific.pdf}\n"," \\caption{Performance of \\texttt{Cus-Prun} in obtaining language-specific models.}\n"," \\label{fig:lang-spec}\n","\\end{figure*}\n"],["\\begin{table}[ht]\n"," \\centering\n","\\footnotesize\n"," \\scalebox{1}{\n"," \\begin{tabular}{lp{5.3cm}}\n"," \\toprule\n"," \\textbf{Task} & \\textbf{Prompt} \\\\\\midrule\n"," Generation & Generate a text document in \\{language\\}/\\{domain\\}/\\{task\\}. Make sure the documents is not fixed to one \\{language\\}/\\{domain\\}/\\{task\\} or \\{language\\}/\\{domain\\}/\\{task\\}. Ensure the content is clear, concise, and appropriate for the specified request. Use professional and domain-specific terminology where necessary. \\\\\\midrule\n"," Evaluation & Evaluate the quality of the given answer to the question. Provide a score from 0 to 5, where 0 represents very low quality and 5 represents very high quality. Question: \\{question\\} Answer: \\{answer\\}.\\\\\n"," \\bottomrule\n"," \\end{tabular}}\n","\\caption{GPT-4o prompts for generating documents and evaluating answer quality.} \\label{table:testset}\n","\\end{table}\n"],["\\begin{table*}[hbpt]\n","\\caption{Main Results of \\texttt{Cus-Prun} on Germany with a pruning ratio of $25\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in XLSum and in accuracy (\\%) for other datasets.}\n"," \\centering\n","\\footnotesize\n","\\setlength{\\tabcolsep}{3pt}\n"," \\scalebox{1.0}{\n"," \\begin{tabular}{l|l|cccc|ccccc}\n","\\toprule\n","\\multirow{2}{*}{\\textbf{\\normalsize{Model}}} & \\multirow{2}{*}{\\textbf{\\normalsize{Method}}} & \n"," \\multicolumn{4}{c}{\\textbf{\\normalsize{General Capability}}} \\vline & \\multicolumn{5}{c}{\\textbf{\\normalsize{Expert Capability}}} \\\\ \n"," & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3Exam & XQuAD & XLSum & Avg. \\\\ \\midrule\n","\\multirow{4}{*}{Llama3-8B} &\n"," Dense & 70.7 & 58.3 & 63.1 & 64.1 & 44.8 & - & 52.9 & - & 48.8 \\\\\n"," & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 0.0 & - & 11.0 & - & 5.5 \\\\\n"," & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & - & 9.8 & - & 4.9 \\\\\n"," & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & - & 0.0 & - & 0.0 \\\\\n"," & \\texttt{Cus-Prun} & 61.4 & 38.9 & 54.5 & \\textbf{51.6} & 32.8 & - & 49.6 & - & \\textbf{41.2} \\\\ \\midrule\n","\\multirow{4}{*}{Mistral-12B} & Dense & 82.6 & 68.5 & 50.4 & 59.3 & 56.8 & - & 41.2 & - & 49.0 \\\\\n"," & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 2.4 & - & 13.4 & - & 7.9 \\\\\n"," & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 0.8 & - & 15.5 & - & 8.2 \\\\\n"," & ShortGPT & 37.8 & 0.0 & 33.9 & 23.9 & 3.6 & - & 20.3 & - & 12.0 \\\\\n"," & \\texttt{Cus-Prun} & 64.6 & 39.7 & 43.2 & \\textbf{49.2} & 31.6 & - & 35.9 & - & \\textbf{33.8} \\\\ \\midrule\n","\\multirow{4}{*}{Llama2-13B} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 24.4 & - & 40.3 & - & 32.3 \\\\\n"," & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 2.0 & - & 5.7 & - & 3.9 \\\\\n"," & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 3.6 & - & 18.1 & - & 10.9 \\\\\n"," & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 2.8 & - & 27.2 & - & 15.0 \\\\\n"," & \\texttt{Cus-Prun} & 47.6 & 19.8 & 49.9 & \\textbf{39.1} & 18.4 & - & 31.7 & - & \\textbf{25.0} \\\\ \\midrule\n","\\multirow{4}{*}{Llama3-70B} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 74.8 & - & 58.2 & - & 66.5 \\\\\n"," & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 18.0 & - & 27.3 & - & 22.7 \\\\\n"," & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 0.0 & - & 17.3 & - & 8.7 \\\\\n"," & ShortGPT & 59.4 & 5.6 & 75.5 & 46.8 & 9.6 & - & 31.5 & - & 20.6 \\\\\n"," & \\texttt{Cus-Prun} & 66.8 & 59.3 & 69.1 & \\textbf{65.1} & 48.2 & - & 53.9 & - & \\textbf{51.1} \\\\ \n","\\bottomrule\n","\\end{tabular}}\n"," \\label{table:cl_ger}\n","\\end{table*}\n"],["\\begin{table*}[hbpt]\n","\\caption{Main Results of \\texttt{Cus-Prun} on Chinese with a pruning ratio of $25\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in XLSum and in accuracy (\\%) for other datasets.}\n"," \\centering\n","\\footnotesize\n","\\setlength{\\tabcolsep}{3pt}\n"," \\scalebox{1.0}{\n"," \\begin{tabular}{l|l|cccc|ccccc}\n","\\toprule\n","\\multirow{2}{*}{\\textbf{\\normalsize{Model}}} & \\multirow{2}{*}{\\textbf{\\normalsize{Method}}} & \n"," \\multicolumn{4}{c}{\\textbf{\\normalsize{General Capability}}} \\vline & \\multicolumn{5}{c}{\\textbf{\\normalsize{Specific Capability}}} \\\\ \n"," & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3Exam & XQuAD & XLSum & Avg. \\\\ \\midrule\n","\\multirow{4}{*}{Llama3-8B} &\n"," Dense & 70.7 & 58.3 & 63.1 & 64.1 & 43.6 & 55.1 & 78.7 & 49.1 & 56.6 \\\\\n"," & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 2.4 & 23.6 & 21.3 & 32.8 & 20.0 \\\\\n"," & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & 17.4 & 23.5 & 8.3 & 12.3 \\\\\n"," & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & 28.3 & 0.0 & 3.1 & 7.9 \\\\\n"," & \\texttt{Cus-Prun} & 60.5 & 25.7 & 49.4 & \\textbf{45.2} & 36.0 & 44.7 & 65.6 & 46.3 & \\textbf{48.2} \\\\ \\midrule\n","\\multirow{4}{*}{Mistral-12B} & Dense & 82.6 & 68.5 & 50.4 & 59.3 & 53.2 & 47.8 & 62.2 & 33.0 & 49.1 \\\\\n"," & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 2.8 & 30.7 & 31.8 & 32.6 & 24.5 \\\\\n"," & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 1.6 & 26.4 & 28.3 & 10.8 & 16.8 \\\\\n"," & ShortGPT & 37.8 & 0.0 & 33.9 & 23.9 & 4.4 & 28.2 & 29.1 & 7.2 & 17.2 \\\\\n"," & \\texttt{Cus-Prun} & 68.3 & 43.2 & 39.5 & \\textbf{50.3} & 38.4 & 40.7 & 50.6 & 30.3 & \\textbf{40.0} \\\\ \\midrule\n","\\multirow{4}{*}{Llama2-13B} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 21.6 & 36.5 & 59.8 & 35.3 & 38.3 \\\\\n"," & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 1.2 & 23.3 & 3.8 & 25.1 & 13.4\\\\\n"," & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 4.8 & 24.5 & 28.4 & 11.2 & 17.2 \\\\\n"," & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 4.4 & 22.9 & 24.6 & 13.7 & 16.4 \\\\\n"," & \\texttt{Cus-Prun} & 48.6 & 20.7 & 51.9 & \\textbf{40.4} & 14.8 & 28.2 & 47.3 & 34.4 & \\textbf{31.2} \\\\ \\midrule\n","\\multirow{4}{*}{Llama3-70B} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 68.4 & 76.1 & 81.3 & 55.3 & 70.3 \\\\\n"," & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 16.8 & 47.5 & 56.1 & 41.3 & 40.4 \\\\\n"," & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 6.4 & 48.3 & 42.2 & 29.3 & 31.6 \\\\\n"," & ShortGPT & 59.4 & 5.6 & 75.5 & 46.8 & 12.4 & 45.5 & 44.6 & 36.1 & 34.7 \\\\\n"," & \\texttt{Cus-Prun} & 72.3 & 48.5 & 65.2 & \\textbf{62.0} & 40.8 & 61.7 & 66.9 & 51.6 & \\textbf{55.3} \\\\ \n","\\bottomrule\n","\\end{tabular}}\n"," \\label{table:cl_chi}\n","\\end{table*}\n"],["\\begin{table*}[hbpt]\n","\\caption{Main Results of \\texttt{Cus-Prun} on Thai with a pruning ratio of $25\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in XLSum and in accuracy (\\%) for other datasets.}\n"," \\centering\n","\\footnotesize\n","\\setlength{\\tabcolsep}{3pt}\n"," \\scalebox{1.0}{\n"," \\begin{tabular}{l|l|cccc|ccccc}\n","\\toprule\n","\\multirow{2}{*}{\\textbf{\\normalsize{Model}}} & \\multirow{2}{*}{\\textbf{\\normalsize{Method}}} & \n"," \\multicolumn{4}{c}{\\textbf{\\normalsize{General Capability}}} \\vline & \\multicolumn{5}{c}{\\textbf{\\normalsize{Specific Capability}}} \\\\ \n"," & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3Exam & XQuAD & XLSum & Avg. \\\\ \\midrule\n","\\multirow{4}{*}{Llama3-8B} &\n"," Dense & 70.7 & 58.3 & 63.1 & 64.1 & 35.2 & 43.0 & 58.7 & 16.7 & 38.4 \\\\\n"," & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 0.8 & 24.4 & 8.4 & 13.5 & 11.8 \\\\\n"," & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & 12.3 & 16.6 & 8.7 & 9.4 \\\\\n"," & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & 25.4 & 0.0 & 2.3 & 6.9 \\\\\n"," & \\texttt{Cus-Prun} & 58.9 & 31.2 & 52.4 & \\textbf{47.5} & 21.6 & 38.3 & 42.6 & 16.8 & \\textbf{29.8} \\\\ \\midrule\n","\\multirow{4}{*}{Mistral-12B} & Dense & 82.6 & 68.5 & 50.4 & 59.3 & 45.2 & 39.9 & 44.1 & 17.8 & 36.8 \\\\\n"," & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 1.2 & 24.8 & 11.9 & 13.7 & 12.9 \\\\\n"," & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 0.0 & 23.8 & 8.4 & 4.7 & 12.3 \\\\\n"," & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 0.8 & 25.7 & 4.7 & 2.8 & 8.5 \\\\\n"," & \\texttt{Cus-Prun} & 68.2 & 35.8 & 47.6 & \\textbf{50.5} & 32.8 & 37.7 & 35.6 & 15.9 & \\textbf{30.5} \\\\ \\midrule\n","\\multirow{4}{*}{Llama2-13B} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 6.4 & 24.3 & 28.3 & 14.5 & 18.4 \\\\\n"," & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 0.0 & 22.3 & 1.8 & 10.2 & 8.6 \\\\\n"," & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 0.0 & 26.2 & 23.7 & 8.6 & 14.6 \\\\\n"," & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 0.0 & 23.1 & 22.3 & 8.9 & 13.6 \\\\\n"," & \\texttt{Cus-Prun} & 47.8 & 20.9 & 50.7 & \\textbf{39.8} & 4.8 & 24.2 & 23.6 & 13.8 & \\textbf{16.6} \\\\ \\midrule\n","\\multirow{4}{*}{Llama3-70B} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 65.2 & 66.1 & 67.8 & 17.8 & 54.2 \\\\\n"," & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 15.6 & 39.9 & 29.8 & 16.6 & 25.5 \\\\\n"," & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 4.8 & 41.3 & 39.6 & 13.2 & 24.7 \\\\\n"," & ShortGPT & 59.4 & 5.6 & 75.5 & 46.8 & 13.7 & 40.7 & 40.4 & 11.9 & 26.7 \\\\\n"," & \\texttt{Cus-Prun} & 73.3 & 58.7 & 68.4 & \\textbf{66.8} & 40.4 & 53.6 & 58.5 & 16.9 & \\textbf{42.4} \\\\ \n","\\bottomrule\n","\\end{tabular}}\n"," \\label{table:cl_tha}\n","\\end{table*}\n"]],"string":"[\n [\n \"\\\\begin{figure}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=0.48\\\\textwidth]{Figures/cust-prune.pdf}\\n\",\n \" \\\\caption{Given a requirement for an expert model across three dimensions (language, domain, and task), \\\\texttt{Cus-Prun} (i) identifies irrelevant neurons for each dimension using corresponding corpora (left), and (ii) prunes \\\\textbf{overlapping} irrelevant neurons across dimensions to obtain the expert model (right).}\\n\",\n \" \\\\label{fig:cus-prun}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{figure}[t]\\n\",\n \"\\\\scalebox{0.95}{ \\n\",\n \"\\\\begin{minipage}{\\\\linewidth}\\n\",\n \"\\\\begin{algorithm}[H]\\n\",\n \"\\\\renewcommand{\\\\algorithmicrequire}{\\\\textbf{Input:}}\\n\",\n \"\\\\renewcommand{\\\\algorithmicensure}{\\\\textbf{Output:}}\\n\",\n \"\\\\caption{Adaptive Custom Pruning}\\n\",\n \"\\\\begin{algorithmic}[1]\\n\",\n \"\\\\REQUIRE Original language model $\\\\mathcal{LLM}$, request for expert model $\\\\mathcal{LLM}_{\\\\text{Exp}}$ with selected dimensions: $L_{\\\\text{Exp}}$, $D_{\\\\text{Exp}}$, $T_{\\\\text{Exp}}$ (any subset), request for pruning ratio $\\\\sigma$.\\n\",\n \"\\\\STATE \\\\texttt{// Construct specific corpora for each selected dimension.}\\n\",\n \"\\\\STATE $C = \\\\{\\\\}$\\n\",\n \"\\\\IF{$L_{\\\\text{Exp}}$ is specified}\\n\",\n \" \\\\STATE $C = C \\\\cup \\\\{(L_{\\\\text{Exp}}, D, T) \\\\mid D \\\\in \\\\mathbb{D}, T \\\\in \\\\mathbb{T}\\\\}$\\n\",\n \"\\\\ENDIF\\n\",\n \"\\\\IF{$D_{\\\\text{Exp}}$ is specified}\\n\",\n \" \\\\STATE $C = C \\\\cup \\\\{(L, D_{\\\\text{Exp}}, T) \\\\mid L \\\\in \\\\mathbb{L}, T \\\\in \\\\mathbb{T}\\\\}$\\n\",\n \"\\\\ENDIF\\n\",\n \"\\\\IF{$T_{\\\\text{Exp}}$ is specified}\\n\",\n \" \\\\STATE $C = C \\\\cup \\\\{(L, D, T_{\\\\text{Exp}}) \\\\mid L \\\\in \\\\mathbb{L}, D \\\\in \\\\mathbb{D}\\\\}$\\n\",\n \"\\\\ENDIF\\n\",\n \"\\\\STATE \\\\texttt{// Identify irrelevant neurons for each selected dimension.}\\n\",\n \"\\\\FORALL{neuron $N_{i}^{(l)}$ in $\\\\mathcal{LLM}$}\\n\",\n \" \\\\IF{$\\\\forall c \\\\in C,$ $N_{i}^{(l)} \\\\in \\\\tilde{\\\\mathcal{N}}(c)$}\\n\",\n \" \\\\STATE $\\\\tilde{\\\\mathcal{N}} \\\\gets \\\\tilde{\\\\mathcal{N}} \\\\cup N_{i}^{(l)}$\\n\",\n \" \\\\ENDIF\\n\",\n \"\\\\ENDFOR\\n\",\n \"\\\\STATE \\\\texttt{// Prune irrelevant neurons to obtain expert model.}\\n\",\n \"\\\\STATE $\\\\mathcal{LLM}_{\\\\text{Exp}} = \\\\mathcal{LLM} \\\\ominus \\\\tilde{\\\\mathcal{N}}$\\n\",\n \"\\\\ENSURE $\\\\mathcal{LLM}_{\\\\text{Exp}}$\\n\",\n \"\\\\end{algorithmic}\\n\",\n \"\\\\label{algo:adaptive_pruning}\\n\",\n \"\\\\end{algorithm}\\n\",\n \"\\\\end{minipage}\\n\",\n \"}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{figure}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=0.48\\\\textwidth]{Figures/three-dim-examples.pdf}\\n\",\n \" \\\\caption{Concrete examples of applying \\\\texttt{Cus-Prun} to prune $25\\\\%$ of Llama3-8B-Base's parameters into three-dimensional expert models. Numbers above each box indicate performance on the \\\\textbf{whole} test set, with the first evaluated by Rouge-L, and the other two by accuracy. }\\n\",\n \" \\\\label{fig:korean}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table*}[t]\\n\",\n \"\\\\caption{Main Results of \\\\texttt{Cus-Prun} on multilingual setting with a pruning ratio of $25\\\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in summarization tasks and in accuracy (\\\\%) for other datasets.}\\n\",\n \" \\\\centering\\n\",\n \"\\\\footnotesize\\n\",\n \"\\\\setlength{\\\\tabcolsep}{0.8pt}\\n\",\n \" \\\\scalebox{0.9}{\\n\",\n \" \\\\begin{tabular}{c|l|cccc|ccccc|ccccc|cccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\quad\\\\quad & \\\\multirow{2}{*}{\\\\textbf{\\\\normalsize{Method}}} & \\n\",\n \" \\\\multicolumn{4}{c}{\\\\textbf{\\\\normalsize{General Capability}}} \\\\vline & \\\\multicolumn{5}{c}{\\\\textbf{\\\\normalsize{Multilingual Expert}}} \\\\vline & \\\\multicolumn{5}{c}{\\\\textbf{\\\\normalsize{Multidomain Expert }}} \\\\vline & \\\\multicolumn{4}{c}{\\\\textbf{\\\\normalsize{Multitask Expert }}} \\\\\\\\ \\n\",\n \" & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3 & XQuAD & Sum & Avg. & MMCQ & FTQA & TSA & AMSA & Avg. & MSum & ASum & AMCF & Avg. \\\\\\\\ \\\\midrule\\n\",\n \"\\\\rowcolor{lightgray}\\\\cellcolor{white}\\\\multirow{5}{*}{\\\\rotatebox{90}{\\\\textbf{Llama3-8B}}} & Dense & 70.7 & 58.3 & 63.1 & 64.1 &41.2 &49.1 & 63.4 &32.9 &46.7 & 51.8 & 23.9 & 67.1 & 95.9 & 59.8 & 76.6 & 16.2 & 78.2 & 57.0 \\\\\\\\\\n\",\n \" & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 1.1 & 24.0 & 13.6 & 23.2 & 15.5 & 0.0 & 0.0 & \\\\textbf{61.8} & 76.0 & 34.5 & 62.2 & \\\\textbf{21.8} & \\\\textbf{80.0} & \\\\textbf{54.7} \\\\\\\\\\n\",\n \" & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & 14.9 & 16.6 & 8.5 & 10.0 & 22.6 & 0.0 & 41.2 & 53.7 & 29.4 & 7.3 & 2.9 & 51.3 & 20.5 \\\\\\\\\\n\",\n \" & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & 26.9 & 0.0 & 2.7 & 7.4 & 3.2 & 0.0 & 38.6 & 35.7 & 19.4 & 4.1 & 4.8 & 43.8 & 17.6 \\\\\\\\\\n\",\n \" &\\\\cellcolor{lightblue}\\\\texttt{Cus-Prun} &\\\\cellcolor{lightblue}\\\\textbf{62.4} & \\\\cellcolor{lightblue}\\\\textbf{37.0} & \\\\cellcolor{lightblue}\\\\textbf{54.7} & \\\\cellcolor{lightblue}\\\\textbf{51.4} & \\\\cellcolor{lightblue}\\\\textbf{30.1} & \\\\cellcolor{lightblue}\\\\textbf{41.5} & \\\\cellcolor{lightblue}\\\\textbf{52.6} & \\\\cellcolor{lightblue}\\\\textbf{31.5} & \\\\cellcolor{lightblue}\\\\textbf{38.9} & \\\\cellcolor{lightblue}\\\\textbf{42.9} & \\\\cellcolor{lightblue}\\\\textbf{20.6} & \\\\cellcolor{lightblue}\\\\textbf{61.8} & \\\\cellcolor{lightblue}\\\\textbf{87.6} & \\\\cellcolor{lightblue}\\\\textbf{53.2} & \\\\cellcolor{lightblue}\\\\textbf{68.4} & \\\\cellcolor{lightblue}12.8 & \\\\cellcolor{lightblue}75.5 & \\\\cellcolor{lightblue}52.2 \\\\\\\\ \\\\midrule\\n\",\n \"\\\\rowcolor{lightgray}\\\\cellcolor{white}\\\\multirow{5}{*}{\\\\rotatebox{90}{\\\\textbf{Mistral-12B}}} & Dense & 82.6 & 68.5 & 50.4 & 67.2 & 51.7 & 43.8 & 49.2 & 25.4 & 42.5 & 54.6 & 26.6 & 69.4 & 92.4 & 60.8 & 88.7 & 3.0 & 78.6 & 56.4 \\\\\\\\\\n\",\n \" & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 2.1 & 27.8 & 19.0 & \\\\textbf{23.2} & 18.0 & 0.0 & 0.0 & 51.0 & 20.9 & 18.0 & 59.3 & 0.5 & 2.8 & 20.9 \\\\\\\\\\n\",\n \" & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 0.8 & 25.1 & 17.4 & 7.8 & 12.8 & 24.9 & 9.2 & 34.2 & 54.3 & 30.7 & 27.4 & 1.3 & 36.3 & 21.7 \\\\\\\\\\n\",\n \" & ShortGPT & 37.8 & 0.0 & 33.9 & 23.9 & 2.9 & 27.0 & 18.0 & 5.0 & 13.2 & 31.4 & 7.2 & 39.2 & 52.5 & 32.6 & 26.2 & 0.2 & 42.7 & 23.0 \\\\\\\\\\n\",\n \" & \\\\cellcolor{lightblue}\\\\texttt{Cus-Prun} & \\\\cellcolor{lightblue}\\\\textbf{67.5} & \\\\cellcolor{lightblue}\\\\textbf{43.4} & \\\\cellcolor{lightblue}\\\\textbf{43.8} & \\\\cellcolor{lightblue}\\\\textbf{51.6} & \\\\cellcolor{lightblue}\\\\textbf{34.3} & \\\\cellcolor{lightblue}\\\\textbf{39.2} & \\\\cellcolor{lightblue}\\\\textbf{40.7} & \\\\cellcolor{lightblue}23.1 & \\\\cellcolor{lightblue}\\\\textbf{34.3} & \\\\cellcolor{lightblue}\\\\textbf{47.9} & \\\\cellcolor{lightblue}\\\\textbf{25.1} & \\\\cellcolor{lightblue}\\\\textbf{67.3} & \\\\cellcolor{lightblue}\\\\textbf{83.7} & \\\\cellcolor{lightblue}\\\\textbf{56.0} & \\\\cellcolor{lightblue}\\\\textbf{83.5} & \\\\cellcolor{lightblue}\\\\textbf{3.4} & \\\\cellcolor{lightblue}\\\\textbf{72.8} & \\\\cellcolor{lightblue}\\\\textbf{50.9} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\rowcolor{lightgray}\\\\cellcolor{white}\\\\multirow{5}{*}{\\\\rotatebox{90}{\\\\textbf{Llama2-13B}}} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 17.5 & 30.4 & 44.1 & 24.9 & 29.2 & 25.2 & 0.0 & 42.7 & 84.1 & 38.0 & 70.0 & 7.4 & 44.3 & 40.6 \\\\\\\\\\n\",\n \" & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 1.1 & 22.8 & 3.8 & 17.7 & 11.3 & 0.0 & 0.0 & 9.7 & 0.0 & 2.4 & 21.6 & 4.8 & 0.0 & 8.8 \\\\\\\\\\n\",\n \" & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 2.8 & 25.3 & 23.4 & 9.9 & 15.5 & 18.7 & 0.0 & 28.4 & 67.3 & 28.6 & 24.5 & 4.9 & 32.9 & 20.8 \\\\\\\\\\n\",\n \" & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 2.4 & 23.0 & 24.7 & 11.3 & 15.3 & 16.9 & 0.0 & 34.6 & \\\\textbf{69.8} & 30.3 & 23.8 & 5.2 & 39.1 & 22.7 \\\\\\\\\\n\",\n \" & \\\\cellcolor{lightblue}\\\\texttt{Cus-Prun} & \\\\cellcolor{lightblue}\\\\textbf{48.3} & \\\\cellcolor{lightblue}\\\\textbf{20.8} & \\\\cellcolor{lightblue}\\\\textbf{50.0} & \\\\cellcolor{lightblue}\\\\textbf{39.7} & \\\\cellcolor{lightblue}\\\\textbf{12.7} & \\\\cellcolor{lightblue}\\\\textbf{26.2} & \\\\cellcolor{lightblue}\\\\textbf{34.2} & \\\\cellcolor{lightblue}\\\\textbf{24.1} & \\\\cellcolor{lightblue}\\\\textbf{24.3} & \\\\cellcolor{lightblue}\\\\textbf{25.6} & \\\\cellcolor{lightblue}0.0 & \\\\cellcolor{lightblue}\\\\textbf{38.5} & \\\\cellcolor{lightblue}68.3 & \\\\cellcolor{lightblue}\\\\textbf{33.1} & \\\\cellcolor{lightblue}\\\\textbf{64.5} & \\\\cellcolor{lightblue}\\\\textbf{6.7} & \\\\cellcolor{lightblue}\\\\textbf{42.9} & \\\\cellcolor{lightblue}\\\\textbf{38.0} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\rowcolor{lightgray}\\\\cellcolor{white}\\\\multirow{5}{*}{\\\\rotatebox{90}{\\\\textbf{Llama3-70B}}} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 69.5 & 71.1 & 69.1 & 36.6 & 61.6 & 72.1 & 55.3 & 83.6 & 96.2 & 76.8 & 84.2 & 17.3 & 81.8 & 61.1 \\\\\\\\\\n\",\n \" & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 16.8 & 43.7 & 43.0 & 29.0 & 33.1 & 27.3 & 1.0 & 51.0 & 50.3 & 32.4 & 10.2 & 13.7 & 20.6 & 14.8 \\\\\\\\\\n\",\n \" & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 3.7 & 44.8 & 33.0 & 21.2 & 25.7 & 57.6 & 27.6 & 68.1 & 59.4 & 53.2 & 58.0 & 14.2 & 68.3 & 46.8 \\\\\\\\\\n\",\n \" & ShortGPT & 59.4 & 5.6 & \\\\textbf{75.5} & 46.8 & 11.9 & 43.1 & 38.8 & 24.0 & 29.5 & 58.4 & 32.2 & 67.5 & 64.9 & 55.8 & 59.6 & 13.9 & 65.8 & 46.4 \\\\\\\\\\n\",\n \" & \\\\cellcolor{lightblue}\\\\texttt{Cus-Prun} & \\\\cellcolor{lightblue}\\\\textbf{68.4} & \\\\cellcolor{lightblue}\\\\textbf{53.2} & \\\\cellcolor{lightblue}66.6 & \\\\cellcolor{lightblue}\\\\textbf{62.7} & \\\\cellcolor{lightblue}\\\\textbf{43.1} & \\\\cellcolor{lightblue}\\\\textbf{57.7} & \\\\cellcolor{lightblue}\\\\textbf{59.8} & \\\\cellcolor{lightblue}\\\\textbf{34.3} & \\\\cellcolor{lightblue}\\\\textbf{48.7} & \\\\cellcolor{lightblue}\\\\textbf{68.2} & \\\\cellcolor{lightblue}\\\\textbf{43.9} & \\\\cellcolor{lightblue}\\\\textbf{81.4} & \\\\cellcolor{lightblue}\\\\textbf{87.8} & \\\\cellcolor{lightblue}\\\\textbf{70.3} & \\\\cellcolor{lightblue}\\\\textbf{80.4} & \\\\cellcolor{lightblue}\\\\textbf{15.7} & \\\\cellcolor{lightblue}\\\\textbf{77.5} & \\\\cellcolor{lightblue}\\\\textbf{57.9} \\\\\\\\ \\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}}\\n\",\n \" \\\\label{table:cross_ling}\\n\",\n \"\\\\vspace{-0.2cm}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\begin{minipage}{0.4\\\\textwidth}\\n\",\n \" \\\\centering\\n\",\n \" \\\\captionof{table}{Performance of Chinese-Medical expert model on MCQ task}\\n\",\n \" \\\\label{tab:cmexam}\\n\",\n \" \\\\scalebox{0.96}{\\n\",\n \" \\\\begin{tabular}{l|c|c}\\n\",\n \" \\\\hline\\n\",\n \" \\\\textbf{Method} & \\\\textbf{General} & \\\\textbf{CMExam} \\\\\\\\\\\\midrule\\n\",\n \" \\\\rowcolor{lightgray}\\\\cellcolor{white} Dense & 59.3 & 50.6 \\\\\\\\\\n\",\n \" LLM-Pruner & 18.6 & 25.0 \\\\\\\\\\n\",\n \" SliceGPT & 27.8 & 26.9 \\\\\\\\\\n\",\n \" ShortGPT & 23.9 & 23.7 \\\\\\\\\\n\",\n \" \\\\rowcolor{lightblue}\\\\cellcolor{white} \\\\texttt{Cus-Prun} & \\\\textbf{52.4} & \\\\textbf{48.7} \\\\\\\\\\n\",\n \" \\\\hline\\n\",\n \" \\\\end{tabular}}\\n\",\n \" \\\\end{minipage}\\n\",\n \" \\\\hfill\\n\",\n \" \\\\begin{minipage}{0.58\\\\textwidth}\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\textwidth]{Figures/huatuo.pdf}\\n\",\n \" \\\\caption{Chinese Medical LLM performance. Numbers are quality on the \\\\textbf{whole} test set evaluated by GPT4.}\\n\",\n \" \\\\label{fig:huatuo}\\n\",\n \" \\\\end{minipage}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{table}[ht]\\n\",\n \"\\\\caption{Aggressive pruning ratio on Llama3-8B.}\\n\",\n \"\\\\centering\\n\",\n \"\\\\setlength{\\\\tabcolsep}{3pt}\\n\",\n \" \\\\scalebox{0.9}{\\n\",\n \"\\\\begin{tabular}{l|c|c|c|c}\\n\",\n \" \\\\hline\\n\",\n \" \\\\textbf{Method} & \\\\textbf{Ratio} & \\\\textbf{Speedup} & \\\\textbf{MMLU} & \\\\textbf{Expert} \\\\\\\\\\\\midrule\\n\",\n \" \\\\rowcolor{lightgray}\\\\cellcolor{white}Dense & 0.0 & 1$\\\\times$ & 63.1 & 59.7 \\\\\\\\\\\\midrule\\n\",\n \" ShortGPT & 25.0 & 1.3$\\\\times$ & 28.6 & 24.6 \\\\\\\\\\n\",\n \" \\\\rowcolor{lightblue}\\\\cellcolor{white}\\\\texttt{Cus-Prun} & 25.0 & 1.3$\\\\times$ & \\\\textbf{51.9} & \\\\textbf{53.3} \\\\\\\\\\\\midrule\\n\",\n \" ShortGPT & 34.2 & 1.5$\\\\times$ & 20.8 & 18.5 \\\\\\\\\\n\",\n \" \\\\rowcolor{lightblue}\\\\cellcolor{white}\\\\texttt{Cus-Prun} & 35.0 & 1.5$\\\\times$ & \\\\textbf{50.2} & \\\\textbf{51.4} \\\\\\\\\\\\midrule\\n\",\n \" ShortGPT & 43.8 & 1.8$\\\\times$ & 7.9 & 10.2 \\\\\\\\\\n\",\n \" \\\\rowcolor{lightblue}\\\\cellcolor{white}\\\\texttt{Cus-Prun} & 45.0 & 1.8$\\\\times$ & \\\\textbf{48.4} & \\\\textbf{50.6} \\\\\\\\\\n\",\n \" \\\\hline\\n\",\n \" \\\\end{tabular}}\\n\",\n \" \\\\label{table:prune_more}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{figure*}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=1.0\\\\textwidth]{Figures/lang-specific.pdf}\\n\",\n \" \\\\caption{Performance of \\\\texttt{Cus-Prun} in obtaining language-specific models.}\\n\",\n \" \\\\label{fig:lang-spec}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{table}[ht]\\n\",\n \" \\\\centering\\n\",\n \"\\\\footnotesize\\n\",\n \" \\\\scalebox{1}{\\n\",\n \" \\\\begin{tabular}{lp{5.3cm}}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Task} & \\\\textbf{Prompt} \\\\\\\\\\\\midrule\\n\",\n \" Generation & Generate a text document in \\\\{language\\\\}/\\\\{domain\\\\}/\\\\{task\\\\}. Make sure the documents is not fixed to one \\\\{language\\\\}/\\\\{domain\\\\}/\\\\{task\\\\} or \\\\{language\\\\}/\\\\{domain\\\\}/\\\\{task\\\\}. Ensure the content is clear, concise, and appropriate for the specified request. Use professional and domain-specific terminology where necessary. \\\\\\\\\\\\midrule\\n\",\n \" Evaluation & Evaluate the quality of the given answer to the question. Provide a score from 0 to 5, where 0 represents very low quality and 5 represents very high quality. Question: \\\\{question\\\\} Answer: \\\\{answer\\\\}.\\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}}\\n\",\n \"\\\\caption{GPT-4o prompts for generating documents and evaluating answer quality.} \\\\label{table:testset}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table*}[hbpt]\\n\",\n \"\\\\caption{Main Results of \\\\texttt{Cus-Prun} on Germany with a pruning ratio of $25\\\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in XLSum and in accuracy (\\\\%) for other datasets.}\\n\",\n \" \\\\centering\\n\",\n \"\\\\footnotesize\\n\",\n \"\\\\setlength{\\\\tabcolsep}{3pt}\\n\",\n \" \\\\scalebox{1.0}{\\n\",\n \" \\\\begin{tabular}{l|l|cccc|ccccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{\\\\textbf{\\\\normalsize{Model}}} & \\\\multirow{2}{*}{\\\\textbf{\\\\normalsize{Method}}} & \\n\",\n \" \\\\multicolumn{4}{c}{\\\\textbf{\\\\normalsize{General Capability}}} \\\\vline & \\\\multicolumn{5}{c}{\\\\textbf{\\\\normalsize{Expert Capability}}} \\\\\\\\ \\n\",\n \" & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3Exam & XQuAD & XLSum & Avg. \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Llama3-8B} &\\n\",\n \" Dense & 70.7 & 58.3 & 63.1 & 64.1 & 44.8 & - & 52.9 & - & 48.8 \\\\\\\\\\n\",\n \" & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 0.0 & - & 11.0 & - & 5.5 \\\\\\\\\\n\",\n \" & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & - & 9.8 & - & 4.9 \\\\\\\\\\n\",\n \" & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & - & 0.0 & - & 0.0 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 61.4 & 38.9 & 54.5 & \\\\textbf{51.6} & 32.8 & - & 49.6 & - & \\\\textbf{41.2} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Mistral-12B} & Dense & 82.6 & 68.5 & 50.4 & 59.3 & 56.8 & - & 41.2 & - & 49.0 \\\\\\\\\\n\",\n \" & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 2.4 & - & 13.4 & - & 7.9 \\\\\\\\\\n\",\n \" & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 0.8 & - & 15.5 & - & 8.2 \\\\\\\\\\n\",\n \" & ShortGPT & 37.8 & 0.0 & 33.9 & 23.9 & 3.6 & - & 20.3 & - & 12.0 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 64.6 & 39.7 & 43.2 & \\\\textbf{49.2} & 31.6 & - & 35.9 & - & \\\\textbf{33.8} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Llama2-13B} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 24.4 & - & 40.3 & - & 32.3 \\\\\\\\\\n\",\n \" & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 2.0 & - & 5.7 & - & 3.9 \\\\\\\\\\n\",\n \" & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 3.6 & - & 18.1 & - & 10.9 \\\\\\\\\\n\",\n \" & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 2.8 & - & 27.2 & - & 15.0 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 47.6 & 19.8 & 49.9 & \\\\textbf{39.1} & 18.4 & - & 31.7 & - & \\\\textbf{25.0} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Llama3-70B} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 74.8 & - & 58.2 & - & 66.5 \\\\\\\\\\n\",\n \" & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 18.0 & - & 27.3 & - & 22.7 \\\\\\\\\\n\",\n \" & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 0.0 & - & 17.3 & - & 8.7 \\\\\\\\\\n\",\n \" & ShortGPT & 59.4 & 5.6 & 75.5 & 46.8 & 9.6 & - & 31.5 & - & 20.6 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 66.8 & 59.3 & 69.1 & \\\\textbf{65.1} & 48.2 & - & 53.9 & - & \\\\textbf{51.1} \\\\\\\\ \\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}}\\n\",\n \" \\\\label{table:cl_ger}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[hbpt]\\n\",\n \"\\\\caption{Main Results of \\\\texttt{Cus-Prun} on Chinese with a pruning ratio of $25\\\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in XLSum and in accuracy (\\\\%) for other datasets.}\\n\",\n \" \\\\centering\\n\",\n \"\\\\footnotesize\\n\",\n \"\\\\setlength{\\\\tabcolsep}{3pt}\\n\",\n \" \\\\scalebox{1.0}{\\n\",\n \" \\\\begin{tabular}{l|l|cccc|ccccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{\\\\textbf{\\\\normalsize{Model}}} & \\\\multirow{2}{*}{\\\\textbf{\\\\normalsize{Method}}} & \\n\",\n \" \\\\multicolumn{4}{c}{\\\\textbf{\\\\normalsize{General Capability}}} \\\\vline & \\\\multicolumn{5}{c}{\\\\textbf{\\\\normalsize{Specific Capability}}} \\\\\\\\ \\n\",\n \" & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3Exam & XQuAD & XLSum & Avg. \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Llama3-8B} &\\n\",\n \" Dense & 70.7 & 58.3 & 63.1 & 64.1 & 43.6 & 55.1 & 78.7 & 49.1 & 56.6 \\\\\\\\\\n\",\n \" & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 2.4 & 23.6 & 21.3 & 32.8 & 20.0 \\\\\\\\\\n\",\n \" & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & 17.4 & 23.5 & 8.3 & 12.3 \\\\\\\\\\n\",\n \" & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & 28.3 & 0.0 & 3.1 & 7.9 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 60.5 & 25.7 & 49.4 & \\\\textbf{45.2} & 36.0 & 44.7 & 65.6 & 46.3 & \\\\textbf{48.2} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Mistral-12B} & Dense & 82.6 & 68.5 & 50.4 & 59.3 & 53.2 & 47.8 & 62.2 & 33.0 & 49.1 \\\\\\\\\\n\",\n \" & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 2.8 & 30.7 & 31.8 & 32.6 & 24.5 \\\\\\\\\\n\",\n \" & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 1.6 & 26.4 & 28.3 & 10.8 & 16.8 \\\\\\\\\\n\",\n \" & ShortGPT & 37.8 & 0.0 & 33.9 & 23.9 & 4.4 & 28.2 & 29.1 & 7.2 & 17.2 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 68.3 & 43.2 & 39.5 & \\\\textbf{50.3} & 38.4 & 40.7 & 50.6 & 30.3 & \\\\textbf{40.0} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Llama2-13B} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 21.6 & 36.5 & 59.8 & 35.3 & 38.3 \\\\\\\\\\n\",\n \" & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 1.2 & 23.3 & 3.8 & 25.1 & 13.4\\\\\\\\\\n\",\n \" & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 4.8 & 24.5 & 28.4 & 11.2 & 17.2 \\\\\\\\\\n\",\n \" & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 4.4 & 22.9 & 24.6 & 13.7 & 16.4 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 48.6 & 20.7 & 51.9 & \\\\textbf{40.4} & 14.8 & 28.2 & 47.3 & 34.4 & \\\\textbf{31.2} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Llama3-70B} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 68.4 & 76.1 & 81.3 & 55.3 & 70.3 \\\\\\\\\\n\",\n \" & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 16.8 & 47.5 & 56.1 & 41.3 & 40.4 \\\\\\\\\\n\",\n \" & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 6.4 & 48.3 & 42.2 & 29.3 & 31.6 \\\\\\\\\\n\",\n \" & ShortGPT & 59.4 & 5.6 & 75.5 & 46.8 & 12.4 & 45.5 & 44.6 & 36.1 & 34.7 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 72.3 & 48.5 & 65.2 & \\\\textbf{62.0} & 40.8 & 61.7 & 66.9 & 51.6 & \\\\textbf{55.3} \\\\\\\\ \\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}}\\n\",\n \" \\\\label{table:cl_chi}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[hbpt]\\n\",\n \"\\\\caption{Main Results of \\\\texttt{Cus-Prun} on Thai with a pruning ratio of $25\\\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in XLSum and in accuracy (\\\\%) for other datasets.}\\n\",\n \" \\\\centering\\n\",\n \"\\\\footnotesize\\n\",\n \"\\\\setlength{\\\\tabcolsep}{3pt}\\n\",\n \" \\\\scalebox{1.0}{\\n\",\n \" \\\\begin{tabular}{l|l|cccc|ccccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{\\\\textbf{\\\\normalsize{Model}}} & \\\\multirow{2}{*}{\\\\textbf{\\\\normalsize{Method}}} & \\n\",\n \" \\\\multicolumn{4}{c}{\\\\textbf{\\\\normalsize{General Capability}}} \\\\vline & \\\\multicolumn{5}{c}{\\\\textbf{\\\\normalsize{Specific Capability}}} \\\\\\\\ \\n\",\n \" & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3Exam & XQuAD & XLSum & Avg. \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Llama3-8B} &\\n\",\n \" Dense & 70.7 & 58.3 & 63.1 & 64.1 & 35.2 & 43.0 & 58.7 & 16.7 & 38.4 \\\\\\\\\\n\",\n \" & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 0.8 & 24.4 & 8.4 & 13.5 & 11.8 \\\\\\\\\\n\",\n \" & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & 12.3 & 16.6 & 8.7 & 9.4 \\\\\\\\\\n\",\n \" & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & 25.4 & 0.0 & 2.3 & 6.9 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 58.9 & 31.2 & 52.4 & \\\\textbf{47.5} & 21.6 & 38.3 & 42.6 & 16.8 & \\\\textbf{29.8} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Mistral-12B} & Dense & 82.6 & 68.5 & 50.4 & 59.3 & 45.2 & 39.9 & 44.1 & 17.8 & 36.8 \\\\\\\\\\n\",\n \" & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 1.2 & 24.8 & 11.9 & 13.7 & 12.9 \\\\\\\\\\n\",\n \" & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 0.0 & 23.8 & 8.4 & 4.7 & 12.3 \\\\\\\\\\n\",\n \" & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 0.8 & 25.7 & 4.7 & 2.8 & 8.5 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 68.2 & 35.8 & 47.6 & \\\\textbf{50.5} & 32.8 & 37.7 & 35.6 & 15.9 & \\\\textbf{30.5} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Llama2-13B} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 6.4 & 24.3 & 28.3 & 14.5 & 18.4 \\\\\\\\\\n\",\n \" & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 0.0 & 22.3 & 1.8 & 10.2 & 8.6 \\\\\\\\\\n\",\n \" & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 0.0 & 26.2 & 23.7 & 8.6 & 14.6 \\\\\\\\\\n\",\n \" & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 0.0 & 23.1 & 22.3 & 8.9 & 13.6 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 47.8 & 20.9 & 50.7 & \\\\textbf{39.8} & 4.8 & 24.2 & 23.6 & 13.8 & \\\\textbf{16.6} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Llama3-70B} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 65.2 & 66.1 & 67.8 & 17.8 & 54.2 \\\\\\\\\\n\",\n \" & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 15.6 & 39.9 & 29.8 & 16.6 & 25.5 \\\\\\\\\\n\",\n \" & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 4.8 & 41.3 & 39.6 & 13.2 & 24.7 \\\\\\\\\\n\",\n \" & ShortGPT & 59.4 & 5.6 & 75.5 & 46.8 & 13.7 & 40.7 & 40.4 & 11.9 & 26.7 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 73.3 & 58.7 & 68.4 & \\\\textbf{66.8} & 40.4 & 53.6 & 58.5 & 16.9 & \\\\textbf{42.4} \\\\\\\\ \\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}}\\n\",\n \" \\\\label{table:cl_tha}\\n\",\n \"\\\\end{table*}\\n\"\n ]\n]","isConversational":false},"fulltext":{"kind":"string","value":"\\pdfoutput=1\n\n\\documentclass[11pt]{article}\n\n\\usepackage[final]{acl}\n\n\n\\usepackage{microtype}\n\\usepackage{hyperref}\n\\usepackage{url}\n\\usepackage{booktabs}\n\\usepackage{times}\n\\usepackage{latexsym}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{graphicx}\n\\usepackage{multirow}\n% This is not strictly necessary, and may be commented out,\n% but it will improve the layout of the manuscript,\n% and will typically save some space.\n\\usepackage{subcaption}\n% For proper rendering and hyphenation of words containing Latin characters (including in bib files)\n\\usepackage[T1]{fontenc}\n\n\\usepackage{tikz}\n\n\\usepackage{listings}\n\\usepackage{color}\n% \\usepackage{minted}\n\\definecolor{dkgreen}{rgb}{0,0.6,0}\n\\definecolor{gray}{rgb}{0.5,0.5,0.5}\n\\definecolor{mauve}{rgb}{0.58,0,0.82}\n\\definecolor{lightblue}{rgb}{0.9, 0.95, 1.0}\n\\definecolor{lightgray}{rgb}{0.9, 0.9, 0.9}\n\n\\lstset{frame=tb,\n language=Python,\n aboveskip=3mm,\n belowskip=3mm,\n showstringspaces=false,\n columns=flexible,\n basicstyle={\\small\\ttfamily},\n numbers=none,\n numberstyle=\\tiny\\color{gray},\n keywordstyle=\\color{blue},\n commentstyle=\\color{dkgreen},\n stringstyle=\\color{mauve},\n breaklines=true,\n breakatwhitespace=true,\n tabsize=3\n}\n\n\\newcommand{\\circled}[1]{%\n \\tikz[baseline=(char.base)]{\n \\node[shape=circle, draw, inner sep=0.5pt, fill=black] (char) {\\textcolor{white}{\\small{#1}}};\n }\n}\n\\usepackage{colortbl}\n\n\\usepackage{algorithmic}\n\\usepackage{algorithm}\n\n\\usepackage[utf8]{inputenc}\n\\usepackage{multirow}\n% This is not strictly necessary, and may be commented out,\n% but it will improve the layout of the manuscript,\n% and will typically save some space.\n\\usepackage{microtype}\n\\usepackage{booktabs}\n\n\n\\title{Pruning General Large Language Models into Customized Expert Models}\n\n\n\\author{\n Yiran Zhao$^{1}$ \\; Guizhen Chen$^{2,3}$ \\; Kenji Kawaguchi$^{1}$\n \\; Lidong Bing$^{4}$ \\; Wenxuan Zhang$^{5}$\\footnotemark[2] \\\\\n $^1$ National University of Singapore \\quad $^2$ Nanyang Technological University, Singapore \\\\ \n $^3$ DAMO Academy, Alibaba Group, Singapore \\quad $^4$ MiroMind \\\\\n $^5$ Singapore University of Technology and Design \\\\\n}\n\n\n\n\n\\newcommand{\\fix}{\\marginpar{FIX}}\n\\newcommand{\\new}{\\marginpar{NEW}}\n\n\n\n\\begin{document}\n\n\\newcommand{\\yiran}[1]{\\textcolor{brown}{\\it\\small [#1 --Yiran]}}\n\\newcommand{\\isaac}[1]{\\textcolor{orange}{\\textbf{[Isaac:} #1]}}\n\n\\maketitle\n\\renewcommand{\\thefootnote}{\\fnsymbol{footnote}}\n\\footnotetext[2]{Wenxuan Zhang is the corresponding author.}\n\\renewcommand{\\thefootnote}{\\arabic{footnote}}\n\n\\begin{abstract}\n\nLarge language models (LLMs) have revolutionized natural language processing, yet their substantial model sizes often require substantial computational resources. To preserve computing resources and accelerate inference speed, it is crucial to prune redundant parameters, especially for experienced users who often need compact expert models tailored to specific downstream scenarios. However, most existing pruning methods focus on preserving the model’s general capabilities, often requiring extensive post-training or suffering from degraded performance due to coarse-grained pruning. In this work, we design a \\underline{Cus}tom \\underline{Prun}ing method (\\texttt{Cus-Prun}) to prune a large general model into a smaller lightweight expert model, which is positioned along the ``language'', ``domain'' and ``task'' dimensions. By identifying and pruning irrelevant neurons of each dimension, \\texttt{Cus-Prun} creates expert models without any post-training. \nOur experiments demonstrate that \\texttt{Cus-Prun} consistently outperforms other methods, achieving minimal loss in both expert and general capabilities across various models from different model families and sizes.\\footnote{Our code is publicly available at \\url{https://github.com/zhaoyiran924/Custom-Prune}.}\n\n\\end{abstract}\n\n\\section{Introduction}\n\nLarge language models (LLMs)~\\citep{achiam2023gpt, reid2024gemini, dubey2024llama, team2024gemma} have revolutionized the field of natural language processing (NLP), emerging as powerful tools with widespread applications across various languages~\\citep{cui2023efficient, yang2024qwen2, seallms3}, domains \\citep{ li2023huatuo26m, roziere2023code, li2023large}, and tasks~\\citep{azerbayevllemma, alves2024tower,naacl-sentiment}.\nHowever, the impressive performance of LLMs often comes at the cost of immense model sizes, mostly containing billions of parameters and thus demand significant computing resources~\\citep{goldstein2023generative, musser2023cost}. To address this issue, researchers have recently proposed various model pruning methods for LLMs. These methods aim to reduce model parameters while maintaining the overall performance through techniques such as removal of unimportant structures~\\citep{ma2023llm, men2024shortgpt, songsleb}, matrix approximation~\\citep{sharmatruth, ashkboosslicegpt}, and extensive post-training after pruning~\\citep{wang2024reconstruct, xiasheared}.\n\n\n\nMost existing pruning methods focus on preserving the \\textit{general capabilities} of the model, often evaluated by broad-spectrum benchmarks like MMLU~\\citep{hendrycksmeasuring}.\nWhile aiming for overall versatility, they may not align well with real-world user needs for a pruned small model, which are usually more \\textit{specific and targeted}. For instance, a user might require a question-answering model tailored specifically for the education domain in German. \nSuch specialized request in fact aligns well with the fundamental motivation behind pruning: to create a smaller model by eliminating unnecessary parameters. In this context, the notion of ``unnecessary'' parameters becomes more precise—referring to those parameters that are irrelevant to the particular use case. By selectively pruning these redundant parameters, one can construct a smaller, expert model that is better aligned with specific requirements.\n\nHowever, current pruning techniques focusing on preserving the general capabilities of LLMs often employ coarse-grained approaches such as removing entire layers or modules \\citep{li2022parameter, kurz2024language, huang2024pruning}. Therefore, it may remove parameters that are critical for specialized downstream scenarios, and thus sometimes require extensive post-training to recover the pruned capabilities \\citep{xiasheared, muralidharan2024compact}. On the other hand, the preserved certain capabilities may not be useful or relevant to our desired application scenario. This misalignment with practical requirements, where users need compact ready-to-deploy expert models without retraining, motivates us to design a more fine-grained and expert model targeting approach.\n\n\n\n\\begin{figure}[t]\n \\centering\n \\includegraphics[width=0.48\\textwidth]{Figures/cust-prune.pdf}\n \\caption{Given a requirement for an expert model across three dimensions (language, domain, and task), \\texttt{Cus-Prun} (i) identifies irrelevant neurons for each dimension using corresponding corpora (left), and (ii) prunes \\textbf{overlapping} irrelevant neurons across dimensions to obtain the expert model (right).}\n \\label{fig:cus-prun}\n\\end{figure}\n\nIn this work, we introduce a novel \\underline{Cus}tom \\underline{Prun}ing (\\texttt{Cus-Prun}) method, designed to prune a large general-purpose model into a small specialized expert model tailored for specific scenarios.\nTo achieve broad adaptability, we define the expert model by positioning the target user's needs along three key dimensions: language (e.g., English, Chinese, German), domain (e.g., E-commerce, education), and task (e.g., QA, summarization).\nInspired by existing studies that certain neurons are responsible for certain functions \\citep{zhao2024large, tang2024language, liang2024multilingual}, \\texttt{Cus-Prun} identifies and preserves critical neurons that are more relevant to particular languages, domains, or tasks, while pruning less relevant ones, ultimately leading to a smaller expert models.\nSpecifically, as illustrated in Figure \\ref{fig:cus-prun}, \n\\texttt{Cus-Prun} first identifies irrelevant neurons for each dimension by assessing the impact of their removal on the generated output when processing corresponding corpus, which could be easily constructed from the relevant plain text documents.\nNext, the expert model is constructed by pruning irrelevant neurons across all dimensions.\nFurthermore, \\texttt{Cus-Prun}'s flexibility allows it to focus on one, two, or all three dimensions (language, domain, task) as needed, making it adaptable to a wide range of real-world applications where specialized LLMs are required. Importantly, by performing fine-grained pruning at the neuron level, the method could also retain most of the essential neurons within the model backbone, thereby preserving most general capabilities.\n\nWe conduct comprehensive experiments to evaluate the performance of \\texttt{Cus-Prun} across various scenarios. Experimental results demonstrate that it consistently outperforms other pruning methods in all settings. For three-dimensional specific expert models, \\texttt{Cus-Prun} demonstrates remarkable pruning effectiveness across different model families and sizes, such as Mistral-Nemo-12B, Llama3-8B, Llama2-13B, and Llama3-70B, while maintaining strong expert and general capabilities. The method is evaluated across multilingual, multidomain, and multitask datasets, as well as representative compound NLP benchmarks, and consistently outperforms state-of-the-art pruning methods. Moreover, \\texttt{Cus-Prun} is highly adaptable, working effectively across a wide range of pruning ratios, even up to nearly half the parameters, without compromising its superior performance. For more focused applications, such as two- or one-dimensional specific expert models (e.g., language-domain specific or language-specific models), \\texttt{Cus-Prun} continues to significantly outperform other pruning methods, showcasing its versatility and effectiveness in diverse specialized settings.\n\n\n\n\\section{Custom Pruning (\\texttt{Cus-Prun})}\n\n\nAn expert model could be generally positioned from three dimensions: ``language'' ($L\\in\\mathbb{L}$), ``domain'' ($D\\in\\mathbb{D}$), and ``task'' ($T\\in\\mathbb{T}$), which can be represented as $LLM_{\\text{Exp}} := (L, D, T) \\in \\mathbb{L}\\times \\mathbb{D}\\times \\mathbb{T}$. Specifically, the language dimension encompasses various languages such as English, Spanish, and Thai. The domain dimension covers different fields like finance, legal, and medical. The task dimension includes various applications such as question-answering, data-to-text, and summarization. In this section, we propose a custom pruning method named \\texttt{Cus-Prun} to derive smaller expert models with flexible customization granularity.\n\n\n\\subsection{Foundational Custom Pruning}\n\n\nDrawing inspiration from recent LLM interpretation studies~\\citep{tang2024language,liang2024multilingual,zhao2024large} that many parameters in the model are redundant to processing a specific ``language'', we hypothesize that this phenomenon can be extended to other dimensions such as ``domain'' and ``task'', meaning that certain parameters remain unused when handling a specific dimension. \nRather than eliminating entire layers or modules~\\citep{songsleb, men2024shortgpt, zhang2024finercut}, \\texttt{Cus-Prun} performs a fine-grained investigation by identifying and removing redundant neurons (i.e., individual rows or columns in parameter matrices) across all components (e.g., attention and feed-forward layers).\n\n\nConcretely, when handling each dimension, we identify a specific set of \\textit{irrelevant neurons} in the original LLM, denoted as $\\tilde{\\mathcal{N}}_{L}$, $\\tilde{\\mathcal{N}}_{D}$, and $\\tilde{\\mathcal{N}}_{T}$ for $L$, $D$, and $T$, respectively. \n\nSpecifically, to identify irrelevant neurons corresponding to the selected dimension, we construct a corpus within that dimension while ablating others. For example, to determine irrelevant neurons for a specific language $L_{\\text{Exp}}$, we create a corpus set \n\\begin{equation}\nC_{L_{\\text{Exp}}}=\\{(L_{\\text{Exp}}, D, T)|D\\in\\mathbb{D}, T\\in\\mathbb{T}\\},\n\\end{equation} \ncomprising documents in language $L_{\\text{Exp}}$ across various domains $D$ and tasks $T$. We then identify neurons that are consistently irrelevant across all documents in $C_{L_{\\text{Exp}}}$,\n\\begin{equation}\n\\scalebox{0.92}{$ % Adjust 0.97 to the desired scaling factor\n\\tilde{\\mathcal{N}}_{L_{\\text{Exp}}} = \n\\left\\{\n\\text{Neuron} \\middle| \n\\text{Irrelevant to } c, \\; \\forall c \\in C_{L_{\\text{Exp}}}\n\\right\\},\n$}\n\\label{equ:neuron}\n\\end{equation}\nwhere a neuron is considered irrelevant if its removal from the parameter matrix affects the generated output below a specified threshold. Formally,\nfor $i$-th neuron in layer $l$, denoted as ${N}_{i}^{(l)}$, its relevance to document $c$ is measured by $|{h}_{\\setminus{{N}_{i}^{(l)}}, i}(c) - h_i(c)|_2$, where $h_i(c)$ is the layer output and ${h}_{\\setminus{{N}_{i}^{(l)}}, i}(c)$ is the output with the neuron removed. Furthermore, neurons with impact in the lowest $\\sigma\\%$ are considered irrelevant, where $\\sigma$ is a pre-defined pruning ratio.\n\n\nSimilarly, we could establish corresponding corpus sets for other dimensions, \n\\begin{equation}\nC_{D_{\\text{Exp}}}=\\{(L, D_{\\text{Exp}}, T)|L\\in\\mathbb{L}, T\\in\\mathbb{T}\\},\n\\end{equation} \n\\begin{equation}\nC_{T_{\\text{Exp}}}=\\{(L, D, T_{\\text{Exp}})|L\\in\\mathbb{L}, D\\in\\mathbb{D}\\},\n\\end{equation} to extract irrelevant neurons, $\\tilde{\\mathcal{N}}_{D_{\\text{Exp}}} $ and $\\tilde{\\mathcal{N}}_{T_{\\text{Exp}}} $. \nFinally, the expert model could constructed by\n\\begin{equation}\n\\scalebox{0.87}{$ % Adjust 0.95 to the desired scaling factor\n\\mathcal{LLM}_{\\text{Exp}} = \n\\mathcal{LLM} \\ominus \n\\left\\{\n\\tilde{\\mathcal{N}}_{L_{\\text{Exp}}} \n\\cap \\tilde{\\mathcal{N}}_{D_{\\text{Exp}}} \n\\cap \\tilde{\\mathcal{N}}_{T_{\\text{Exp}}}\n\\right\\},\n$}\n\\label{equ:cus}\n\\end{equation}\nwhere $\\ominus$ represents removing the corresponding neurons from $\\mathcal{LLM}$. The overall\nalgorithm is further illustrated in Algorithm \\ref{algo:adaptive_pruning}.\n\n\n\n\\begin{figure}[t]\n\\scalebox{0.95}{ \n\\begin{minipage}{\\linewidth}\n\\begin{algorithm}[H]\n\\renewcommand{\\algorithmicrequire}{\\textbf{Input:}}\n\\renewcommand{\\algorithmicensure}{\\textbf{Output:}}\n\\caption{Adaptive Custom Pruning}\n\\begin{algorithmic}[1]\n\\REQUIRE Original language model $\\mathcal{LLM}$, request for expert model $\\mathcal{LLM}_{\\text{Exp}}$ with selected dimensions: $L_{\\text{Exp}}$, $D_{\\text{Exp}}$, $T_{\\text{Exp}}$ (any subset), request for pruning ratio $\\sigma$.\n\\STATE \\texttt{// Construct specific corpora for each selected dimension.}\n\\STATE $C = \\{\\}$\n\\IF{$L_{\\text{Exp}}$ is specified}\n \\STATE $C = C \\cup \\{(L_{\\text{Exp}}, D, T) \\mid D \\in \\mathbb{D}, T \\in \\mathbb{T}\\}$\n\\ENDIF\n\\IF{$D_{\\text{Exp}}$ is specified}\n \\STATE $C = C \\cup \\{(L, D_{\\text{Exp}}, T) \\mid L \\in \\mathbb{L}, T \\in \\mathbb{T}\\}$\n\\ENDIF\n\\IF{$T_{\\text{Exp}}$ is specified}\n \\STATE $C = C \\cup \\{(L, D, T_{\\text{Exp}}) \\mid L \\in \\mathbb{L}, D \\in \\mathbb{D}\\}$\n\\ENDIF\n\\STATE \\texttt{// Identify irrelevant neurons for each selected dimension.}\n\\FORALL{neuron $N_{i}^{(l)}$ in $\\mathcal{LLM}$}\n \\IF{$\\forall c \\in C,$ $N_{i}^{(l)} \\in \\tilde{\\mathcal{N}}(c)$}\n \\STATE $\\tilde{\\mathcal{N}} \\gets \\tilde{\\mathcal{N}} \\cup N_{i}^{(l)}$\n \\ENDIF\n\\ENDFOR\n\\STATE \\texttt{// Prune irrelevant neurons to obtain expert model.}\n\\STATE $\\mathcal{LLM}_{\\text{Exp}} = \\mathcal{LLM} \\ominus \\tilde{\\mathcal{N}}$\n\\ENSURE $\\mathcal{LLM}_{\\text{Exp}}$\n\\end{algorithmic}\n\\label{algo:adaptive_pruning}\n\\end{algorithm}\n\\end{minipage}\n}\n\\end{figure}\n\n\n\\subsection{Adaptive Custom Pruning}\\label{sec:adaptive}\n\n\nIn many applications, the need for expertise might be constrained to one or two dimensions. For example, a language-specific or domain-specific model only requires pruning along a single dimension, while a language-domain-specific model (e.g., a Chinese Medical LLM) constrains two dimensions. In this section, we extend \\texttt{Cus-Prun} to support different granularity levels.\n\n\\paragraph{Two-Dimensional Specific Expert Model}\n\nWithout losing generality, we use the language-domain expert model as a concrete example, which requires an expert model constrained in two dimensions: language ($L_{\\text{Exp}}$) and domain ($D_{\\text{Exp}}$). We similarly derive the sets of irrelevant neurons $\\tilde{\\mathcal{N}}_{L_{\\text{Exp}}}$ and $\\tilde{\\mathcal{N}}_{D_{\\text{Exp}}}$, and\n% according to Equation \\ref{equ:neuron_final}. \nobtain the expert model by pruning the original dense model as follows: \\begin{equation}\n\\mathcal{LLM}_{\\text{Exp}}:=\\mathcal{LLM}\\ominus\\{\\tilde{\\mathcal{N}}_{L_{\\text{Exp}}} \\cap \\tilde{\\mathcal{N}}_{D_{\\text{Exp}}}\\}.\n\\end{equation}\n\n\n\\paragraph{One-Dimensional Specific Expert Model}\nWe use the language-specific expert model as an example, which focuses on optimizing performance for a certain language ($L_{\\text{Exp}}$), irrespective of domain or task. Similarly, we obtain the language-specific corpus $C_{L_{\\text{Exp}}}$, then identify irrelevant neurons $\\tilde{\\mathcal{N}}_{L_{\\text{Exp}}}$ \nand extract the expert model by \n\\begin{equation}\n\\mathcal{LLM}_{\\text{Exp}}:=\\mathcal{LLM}\\ominus\\{\\tilde{\\mathcal{N}}_{L_{\\text{Exp}}} \\}.\n\\end{equation}\nTo enhance efficiency, we implement the parallel neuron-detection method~\\citep{zhao2024large}, which accelerates the sequential calculations from line14 to line16 in Algorithm \\ref{algo:adaptive_pruning}. \n\n\n\n\n\\section{Preliminary Evaluation}\\label{sec:pre_eva}\n\nIn this section, to simulate realistic user-defined requirements where all three dimensions (language, domain, task) are explicitly specified, we conduct preliminary experiments on developing expert models with fine-grained pruning. This setting not only validates our approach under the most demanding conditions but also serves as a prototype for later experiments with one- or two-dimensional customizations.\n\n\n\\paragraph{Experiment Design} \n\nWe consider three scenarios, each named according to the \\textit{language}-\\textit{domain}-\\textit{task} pattern:\n\\textit{Korean-Legal-Summarization}~\\citep{hwang2022multi}, \\textit{English-Medical-MCQ}~\\citep{garcia2024medical}, and \\textit{Chinese-E-commerce-Sentiment Analysis}~\\citep{zhang2015character}.\nThese scenarios were selected because each represents a unique combination of language, domain, and task, effectively simulating the diverse needs a user might specify in practice.\n\nFor each scenario, we curate the corresponding corpus for each dimension. This curation can be done through manual collection or by automatically retrieving relevant documents online. In this preliminary study, without loss of generality, we employ a strong proprietary model\\footnote{\\url{https://platform.openai.com/docs/models/gpt-4o}} to generate a corpus containing $50$ documents for each dimension. Detailed prompts can be found in Appendix \\ref{sec:appen_prompt}. The generated documents could then be used to determine the relevance of neurons for each dimension of each scenario.\n\n\n\\paragraph{Experiment Setup}\nWe use Llama3-8B~\\citep{dubey2024llama} as the original dense model and follow the typical setting to set the pruning ratio as $25\\%$.\nPerformance is evaluated using Rouge-L~\\citep{lin2004rouge} for Korean-Legal-Summary and accuracy score for another two tasks. For comparison, we adopt SliceGPT~\\citep{ashkboosslicegpt} as a baseline, which replaces each weight matrix with a smaller proxy matrix without considering the specific use-case requirements.\n\n\\begin{figure}[t]\n \\centering\n \\includegraphics[width=0.48\\textwidth]{Figures/three-dim-examples.pdf}\n \\caption{Concrete examples of applying \\texttt{Cus-Prun} to prune $25\\%$ of Llama3-8B-Base's parameters into three-dimensional expert models. Numbers above each box indicate performance on the \\textbf{whole} test set, with the first evaluated by Rouge-L, and the other two by accuracy. }\n \\label{fig:korean}\n\\end{figure}\n\n\\begin{table*}[t]\n\\caption{Main Results of \\texttt{Cus-Prun} on multilingual setting with a pruning ratio of $25\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in summarization tasks and in accuracy (\\%) for other datasets.}\n \\centering\n\\footnotesize\n\\setlength{\\tabcolsep}{0.8pt}\n \\scalebox{0.9}{\n \\begin{tabular}{c|l|cccc|ccccc|ccccc|cccc}\n\\toprule\n\\quad\\quad & \\multirow{2}{*}{\\textbf{\\normalsize{Method}}} & \n \\multicolumn{4}{c}{\\textbf{\\normalsize{General Capability}}} \\vline & \\multicolumn{5}{c}{\\textbf{\\normalsize{Multilingual Expert}}} \\vline & \\multicolumn{5}{c}{\\textbf{\\normalsize{Multidomain Expert }}} \\vline & \\multicolumn{4}{c}{\\textbf{\\normalsize{Multitask Expert }}} \\\\ \n & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3 & XQuAD & Sum & Avg. & MMCQ & FTQA & TSA & AMSA & Avg. & MSum & ASum & AMCF & Avg. \\\\ \\midrule\n\\rowcolor{lightgray}\\cellcolor{white}\\multirow{5}{*}{\\rotatebox{90}{\\textbf{Llama3-8B}}} & Dense & 70.7 & 58.3 & 63.1 & 64.1 &41.2 &49.1 & 63.4 &32.9 &46.7 & 51.8 & 23.9 & 67.1 & 95.9 & 59.8 & 76.6 & 16.2 & 78.2 & 57.0 \\\\\n & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 1.1 & 24.0 & 13.6 & 23.2 & 15.5 & 0.0 & 0.0 & \\textbf{61.8} & 76.0 & 34.5 & 62.2 & \\textbf{21.8} & \\textbf{80.0} & \\textbf{54.7} \\\\\n & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & 14.9 & 16.6 & 8.5 & 10.0 & 22.6 & 0.0 & 41.2 & 53.7 & 29.4 & 7.3 & 2.9 & 51.3 & 20.5 \\\\\n & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & 26.9 & 0.0 & 2.7 & 7.4 & 3.2 & 0.0 & 38.6 & 35.7 & 19.4 & 4.1 & 4.8 & 43.8 & 17.6 \\\\\n &\\cellcolor{lightblue}\\texttt{Cus-Prun} &\\cellcolor{lightblue}\\textbf{62.4} & \\cellcolor{lightblue}\\textbf{37.0} & \\cellcolor{lightblue}\\textbf{54.7} & \\cellcolor{lightblue}\\textbf{51.4} & \\cellcolor{lightblue}\\textbf{30.1} & \\cellcolor{lightblue}\\textbf{41.5} & \\cellcolor{lightblue}\\textbf{52.6} & \\cellcolor{lightblue}\\textbf{31.5} & \\cellcolor{lightblue}\\textbf{38.9} & \\cellcolor{lightblue}\\textbf{42.9} & \\cellcolor{lightblue}\\textbf{20.6} & \\cellcolor{lightblue}\\textbf{61.8} & \\cellcolor{lightblue}\\textbf{87.6} & \\cellcolor{lightblue}\\textbf{53.2} & \\cellcolor{lightblue}\\textbf{68.4} & \\cellcolor{lightblue}12.8 & \\cellcolor{lightblue}75.5 & \\cellcolor{lightblue}52.2 \\\\ \\midrule\n\\rowcolor{lightgray}\\cellcolor{white}\\multirow{5}{*}{\\rotatebox{90}{\\textbf{Mistral-12B}}} & Dense & 82.6 & 68.5 & 50.4 & 67.2 & 51.7 & 43.8 & 49.2 & 25.4 & 42.5 & 54.6 & 26.6 & 69.4 & 92.4 & 60.8 & 88.7 & 3.0 & 78.6 & 56.4 \\\\\n & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 2.1 & 27.8 & 19.0 & \\textbf{23.2} & 18.0 & 0.0 & 0.0 & 51.0 & 20.9 & 18.0 & 59.3 & 0.5 & 2.8 & 20.9 \\\\\n & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 0.8 & 25.1 & 17.4 & 7.8 & 12.8 & 24.9 & 9.2 & 34.2 & 54.3 & 30.7 & 27.4 & 1.3 & 36.3 & 21.7 \\\\\n & ShortGPT & 37.8 & 0.0 & 33.9 & 23.9 & 2.9 & 27.0 & 18.0 & 5.0 & 13.2 & 31.4 & 7.2 & 39.2 & 52.5 & 32.6 & 26.2 & 0.2 & 42.7 & 23.0 \\\\\n & \\cellcolor{lightblue}\\texttt{Cus-Prun} & \\cellcolor{lightblue}\\textbf{67.5} & \\cellcolor{lightblue}\\textbf{43.4} & \\cellcolor{lightblue}\\textbf{43.8} & \\cellcolor{lightblue}\\textbf{51.6} & \\cellcolor{lightblue}\\textbf{34.3} & \\cellcolor{lightblue}\\textbf{39.2} & \\cellcolor{lightblue}\\textbf{40.7} & \\cellcolor{lightblue}23.1 & \\cellcolor{lightblue}\\textbf{34.3} & \\cellcolor{lightblue}\\textbf{47.9} & \\cellcolor{lightblue}\\textbf{25.1} & \\cellcolor{lightblue}\\textbf{67.3} & \\cellcolor{lightblue}\\textbf{83.7} & \\cellcolor{lightblue}\\textbf{56.0} & \\cellcolor{lightblue}\\textbf{83.5} & \\cellcolor{lightblue}\\textbf{3.4} & \\cellcolor{lightblue}\\textbf{72.8} & \\cellcolor{lightblue}\\textbf{50.9} \\\\ \\midrule\n\\rowcolor{lightgray}\\cellcolor{white}\\multirow{5}{*}{\\rotatebox{90}{\\textbf{Llama2-13B}}} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 17.5 & 30.4 & 44.1 & 24.9 & 29.2 & 25.2 & 0.0 & 42.7 & 84.1 & 38.0 & 70.0 & 7.4 & 44.3 & 40.6 \\\\\n & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 1.1 & 22.8 & 3.8 & 17.7 & 11.3 & 0.0 & 0.0 & 9.7 & 0.0 & 2.4 & 21.6 & 4.8 & 0.0 & 8.8 \\\\\n & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 2.8 & 25.3 & 23.4 & 9.9 & 15.5 & 18.7 & 0.0 & 28.4 & 67.3 & 28.6 & 24.5 & 4.9 & 32.9 & 20.8 \\\\\n & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 2.4 & 23.0 & 24.7 & 11.3 & 15.3 & 16.9 & 0.0 & 34.6 & \\textbf{69.8} & 30.3 & 23.8 & 5.2 & 39.1 & 22.7 \\\\\n & \\cellcolor{lightblue}\\texttt{Cus-Prun} & \\cellcolor{lightblue}\\textbf{48.3} & \\cellcolor{lightblue}\\textbf{20.8} & \\cellcolor{lightblue}\\textbf{50.0} & \\cellcolor{lightblue}\\textbf{39.7} & \\cellcolor{lightblue}\\textbf{12.7} & \\cellcolor{lightblue}\\textbf{26.2} & \\cellcolor{lightblue}\\textbf{34.2} & \\cellcolor{lightblue}\\textbf{24.1} & \\cellcolor{lightblue}\\textbf{24.3} & \\cellcolor{lightblue}\\textbf{25.6} & \\cellcolor{lightblue}0.0 & \\cellcolor{lightblue}\\textbf{38.5} & \\cellcolor{lightblue}68.3 & \\cellcolor{lightblue}\\textbf{33.1} & \\cellcolor{lightblue}\\textbf{64.5} & \\cellcolor{lightblue}\\textbf{6.7} & \\cellcolor{lightblue}\\textbf{42.9} & \\cellcolor{lightblue}\\textbf{38.0} \\\\ \\midrule\n\\rowcolor{lightgray}\\cellcolor{white}\\multirow{5}{*}{\\rotatebox{90}{\\textbf{Llama3-70B}}} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 69.5 & 71.1 & 69.1 & 36.6 & 61.6 & 72.1 & 55.3 & 83.6 & 96.2 & 76.8 & 84.2 & 17.3 & 81.8 & 61.1 \\\\\n & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 16.8 & 43.7 & 43.0 & 29.0 & 33.1 & 27.3 & 1.0 & 51.0 & 50.3 & 32.4 & 10.2 & 13.7 & 20.6 & 14.8 \\\\\n & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 3.7 & 44.8 & 33.0 & 21.2 & 25.7 & 57.6 & 27.6 & 68.1 & 59.4 & 53.2 & 58.0 & 14.2 & 68.3 & 46.8 \\\\\n & ShortGPT & 59.4 & 5.6 & \\textbf{75.5} & 46.8 & 11.9 & 43.1 & 38.8 & 24.0 & 29.5 & 58.4 & 32.2 & 67.5 & 64.9 & 55.8 & 59.6 & 13.9 & 65.8 & 46.4 \\\\\n & \\cellcolor{lightblue}\\texttt{Cus-Prun} & \\cellcolor{lightblue}\\textbf{68.4} & \\cellcolor{lightblue}\\textbf{53.2} & \\cellcolor{lightblue}66.6 & \\cellcolor{lightblue}\\textbf{62.7} & \\cellcolor{lightblue}\\textbf{43.1} & \\cellcolor{lightblue}\\textbf{57.7} & \\cellcolor{lightblue}\\textbf{59.8} & \\cellcolor{lightblue}\\textbf{34.3} & \\cellcolor{lightblue}\\textbf{48.7} & \\cellcolor{lightblue}\\textbf{68.2} & \\cellcolor{lightblue}\\textbf{43.9} & \\cellcolor{lightblue}\\textbf{81.4} & \\cellcolor{lightblue}\\textbf{87.8} & \\cellcolor{lightblue}\\textbf{70.3} & \\cellcolor{lightblue}\\textbf{80.4} & \\cellcolor{lightblue}\\textbf{15.7} & \\cellcolor{lightblue}\\textbf{77.5} & \\cellcolor{lightblue}\\textbf{57.9} \\\\ \n\\bottomrule\n\\end{tabular}}\n \\label{table:cross_ling}\n\\vspace{-0.2cm}\n\\end{table*}\n\n\n\n\\paragraph{Main Results}\n\n\nFigure \\ref{fig:korean} presents the results and one concrete example for the original dense model, pruned model with SliceGPT, and pruned model with our proposed \\texttt{Cus-Prun} method across the three scenarios.\nWe observe that \\texttt{Cus-Prun} largely preserves the performance of the dense model, retraining 92\\%, 83\\%, and 94\\% of the original dense model performance on these three cases respectively.\nIn contrast, the baseline method SliceGPT, which does not consider specific use cases, largely underperforms compared to \\texttt{Cus-Prun}.\nOverall, the results demonstrate that our proposed \\texttt{Cus-Prun} method could effectively obtain expert models tailored to specific use cases across different languages, domains, and tasks that maintain high performance despite substantial pruning.\n\n\n\n\n\\section{Foundational Custom Pruning Assessment}\n\nAs demonstrated by preliminary evaluation in Section \\ref{sec:pre_eva}, \\texttt{Cus-Prun} enables the creation of expert models tailored to specific languages, domains, and tasks. However, existing benchmark datasets may not always align with such specialized requirements for a systematic evaluation. To simplify our evaluation without losing generality, we use two distinct corpora: one focusing independently on a single dimension and another encompassing the remaining two dimensions. This approach allows us to evaluate \\texttt{Cus-Prun}'s performance in \\textit{multilingual}, \\textit{multidomain}, and \\textit{multitask} settings. \n\nFormally, in the multilingual setting, instead of constructing $C_{L_{\\text{Exp}}}$, $C_{D_{\\text{Exp}}}$ and $C_{T_{\\text{Exp}}}$ independently, we can construct two corpora, $C_{L_{\\text{Exp}}}$ and $C_{(D_, T)_{\\text{Exp}}}$, where $C_{L_{\\text{Exp}}}$ helps to identify irrelevant neurons in a specific language ($\\tilde{\\mathcal{N}}_{L_{\\text{Exp}}}$) and $C_{(D_, T)_{\\text{Exp}}}$ helps to identify irrelevant neurons in a specific domain-task combination ($\\tilde{\\mathcal{N}}_{D_{\\text{Exp}}\\cap T_{\\text{Exp}}}$). Formally speaking, \\texttt{Cus-Prun} in Equation \\ref{equ:cus} is transferred to \n\\begin{equation}\n\\scalebox{0.85}{$ % Adjust 0.94 to the desired scaling factor\n\\begin{aligned}\n\\mathcal{LLM}_{\\text{Exp}} & = \n\\mathcal{LLM} \\ominus \n\\left\\{\n\\tilde{\\mathcal{N}}_{L_{\\text{Exp}}} \\cap \n\\left( \n\\tilde{\\mathcal{N}}_{D_{\\text{Exp}}} \\cap \\tilde{\\mathcal{N}}_{T_{\\text{Exp}}}\n\\right)\n\\right\\} \\\\\n& \\equiv \n\\mathcal{LLM} \\ominus \n\\left\\{\n\\tilde{\\mathcal{N}}_{L_{\\text{Exp}}} \\cap \n\\tilde{\\mathcal{N}}_{D_{\\text{Exp}} \\cap T_{\\text{Exp}}}\n\\right\\}.\n\\end{aligned}\n$}\n\\label{equ:cross-lingual}\n\\end{equation}\nNote that this simplification is also applicable to $C_{D_{\\text{Exp}}}$, $C_{(L, T)_{\\text{Exp}}}$ and $C_{T_{\\text{Exp}}}$, $C_{(L, D)_{\\text{Exp}}}$. \n\n\\subsection{Experiment Setup} \n\n\\paragraph{Benchmarks} Although the primary goal of \\texttt{Cus-Prun} is to create expert LLMs tuned to specific use cases, we also evaluate the pruned models on standard general capabilities to ensure minimal performance loss. Specifically, we employ ARC-Challenge~\\citep{clark2018think} (5-shots), GSM8K~\\citep{cobbe2021training} (5-shots with CoT prompting~\\citep{wei2022chain}), and MMLU~\\citep{hendrycksmeasuring} (5-shots) to represent models general capability. \nNote that we utilize a generation task and implement CoT prompting method, a more challenging setting that has not been previously evaluated by existing pruning techniques~\\citep{songsleb, sharmatruth, yang2024laco,zhang2024finercut}. \n\n\n\\paragraph{Baselines} We employ several state-of-the-art pruning methods as the baseline that do not require post-training after pruning the model. (i) Dense represents the original model without pruning; (ii) LLM-Pruner~\\citep{ma2023llm} adopts structural pruning that selectively removes non-critical coupled structures based on gradient information;\\footnote{To ensure a fair comparison, we evaluate its performance before post-training, following \\citet{men2024shortgpt}.} (iii) SliceGPT~\\citep{ashkboosslicegpt} replaces each weight matrix with a smaller dense matrix, reducing the embedding dimension of the network; (iv) ShortGPT~\\citep{men2024shortgpt} directly deletes the redundant layers in LLMs\nbased on an importance score.\nWe follow the typical pruning setting from these previous studies to set the pruning ratio to $25\\%$ for all methods and all models. \n\n\\paragraph{Backbone Models} We choose 4 models from different model series and different sizes, including Llama3-8B-Base~\\citep{dubey2024llama}, Mistral-Nemo-Base-2407\\footnote{\\url{https://huggingface.co/mistralai/Mistral-Nemo-Base-2407}}(short as Mistral-12B), Llama2-13B-Base~\\citep{touvron2023llama}, Llama3-70B-Base~\\citep{dubey2024llama}. \n\n\n\n\n\n\\subsection{Multilingual Setting}\n\n\n\\paragraph{Dataset}\nWe employ multiple representative multilingual datasets for multilingual setting, which covers reasoning (MGSM~\\citep{shilanguage}, 5-shots), multilingual knowledge (M3Exam~\\citep{zhang2023m3exam}, 3-shots, abbreviated as M3), understanding (XQuAD~\\citep{artetxe2020cross}, 5-shots), and generation (XLSum~\\citep{hasan2021xl}, zero-shots, abbreviated as Sum). Furthermore, we consider three languages spanning a range from high-resource to low-resource including German (De), Chinese (Zh) and Thai (Th). \nMore detailed experiment settings are explained in Appendix \\ref{sec:appen_setting_1}.\n\n\n\n\\paragraph{Main Results} \n\n\nTable \\ref{table:cross_ling} summarizes the performance of \\texttt{Cus-Prun} on multilingual datasets, with average scores across languages. Detailed breakdown results for each language are shown in Table~\\ref{table:cl_ger}, Table~\\ref{table:cl_chi} and Table~\\ref{table:cl_tha} in Appendix~\\ref{sec:appen_cl}.\nWe can observe that \\texttt{Cus-Prun} consistently outperforms baseline pruning methods in achieving expert capabilities while preserving general performance. For example, on Llama3-8B, \\texttt{Cus-Prun} achieves a score of \\(38.9\\) compared to at most \\(15.5\\) for other methods. Similar improvements are observed for Mistral-12B (\\(34.3\\) vs. \\( 18.0\\)), Llama2-13B (\\(24.3\\) vs. \\( 15.5\\)), and Llama3-70B (\\(48.7\\) vs. \\( 33.1\\)). We can also see that the generation tasks are especially challenging; for instance, \\texttt{Cus-Prun} achieves a score of \\(30.1\\) on MGSM for Llama3-8B, while other methods nearly lose the ability to generate coherent reasoning outputs (often approaching \\(0\\) accuracy for all but the largest model). Additionally, \\texttt{Cus-Prun} performs robustly across both high-resource and low-resource languages. Overall, \\texttt{Cus-Prun} excels in both performance and robustness across diverse tasks and languages.\n\n\n\n\\subsection{MultilDomain Setting}\n\n\\paragraph{Dataset} For the multidomain setting, we employ several domain-specific datasets, including medical domain multiply choices questions (MedMCQ~\\citep{pmlr-v174-pal22a}, 3-shots, abbreviated as MMCQ), finance domain table question-answering (FinTQA~\\citep{chen2021finqa}, 8-shots, abbreviated as FTQA), social media domain sentiment analysis (TSA~\\citep{kharde975sentiment}, 3-shots), and e-commerce domain sentiment analysis (AMSA~\\citep{zhang2015character}, 3-shots). Moreover, in multidomain setting, our focus is exclusively on the English language. Detailed experiment settings are explained in Appendix \\ref{sec:appen_setting_2}.\n\n\n\n\\paragraph{Main Results} Table \\ref{table:cross_ling} shows the performance of \\texttt{Cus-Prun} on multidomain setting. We find that \\texttt{Cus-Prun} consistently outperforms other pruning methods in both expert and general capabilities. For expert capabilities, \\texttt{Cus-Prun} achieves a score of $53.2$ on Llama3-8B, while other pruning methods achieve at most $34.5$. Similar improvements are observed for Mistral-12B (\\(56.0\\) vs. \\(32.6\\)), Llama2-13B (\\(33.1\\) vs. \\(30.3\\)), and Llama3-70B (\\(70.3\\) vs. \\(55.8\\)).\n\n\n\n\\begin{figure*}[t]\n \\centering\n \\begin{minipage}{0.4\\textwidth}\n \\centering\n \\captionof{table}{Performance of Chinese-Medical expert model on MCQ task}\n \\label{tab:cmexam}\n \\scalebox{0.96}{\n \\begin{tabular}{l|c|c}\n \\hline\n \\textbf{Method} & \\textbf{General} & \\textbf{CMExam} \\\\\\midrule\n \\rowcolor{lightgray}\\cellcolor{white} Dense & 59.3 & 50.6 \\\\\n LLM-Pruner & 18.6 & 25.0 \\\\\n SliceGPT & 27.8 & 26.9 \\\\\n ShortGPT & 23.9 & 23.7 \\\\\n \\rowcolor{lightblue}\\cellcolor{white} \\texttt{Cus-Prun} & \\textbf{52.4} & \\textbf{48.7} \\\\\n \\hline\n \\end{tabular}}\n \\end{minipage}\n \\hfill\n \\begin{minipage}{0.58\\textwidth}\n \\centering\n \\includegraphics[width=\\textwidth]{Figures/huatuo.pdf}\n \\caption{Chinese Medical LLM performance. Numbers are quality on the \\textbf{whole} test set evaluated by GPT4.}\n \\label{fig:huatuo}\n \\end{minipage}\n\\end{figure*}\n\n\\subsection{MultiTask Setting}\n\n\n\n\\paragraph{Dataset} For the multitask setting, we employ several task-specific datasets, including the medical summarization task (MedSum~\\citep{abacha2019summarization}, 3-shots, abbreviated as MSum), summarization task in e-commerce (Amazon Summary~\\citep{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions, brüelgabrielsson2024compressserveservingthousands}, 3-shots, abbreviated as ASum), counterfactual task in e-commerce (Amazon Counterfactual~\\citep{o2021wish}, 3-shots, abbreviated as AMCF). Similarly, in multitask setting scenarios, our focus is exclusively on the English language. Detailed experiment settings are explained in Appendix \\ref{sec:appen_setting_3}.\n\n\n\n\\paragraph{Main Results} Table \\ref{table:cross_ling} shows the performance of \\texttt{Cus-Prun} on multitask setting. We find that except for LLM-Pruner under Llama3-8B, \\texttt{Cus-Prun} outperforms other pruning methods in both expert and general capabilities. For expert tasks, \\texttt{Cus-Prun} achieves a score of \\(50.9\\) on \\texttt{Mistral-12B}, significantly outperforming the highest score of \\(23.0\\) from other methods. Likewise, it attains \\(38.0\\) on \\texttt{Llama2-13B} and \\(57.9\\) on \\texttt{Llama3-70B}, both markedly higher than the corresponding maximum scores of \\(22.7\\) and \\(46.8\\) achieved by competing methods.\n\n\n\n\\subsection{Analysis on Aggressive Pruning Ratio}\n\n\nTo optimize for specialized tasks rather than maintaining general capabilities, we employ more aggressive pruning ratios. We combine layer pruning with our custom neuron pruning method in Algorithm \\ref{algo:adaptive_pruning} and evaluate the approach on M3Exam, MedMCQ, and Amazon Counterfactual (AMContFact) datasets using Llama3-8B. Detailed results are shown in Table \\ref{table:prune_more}. We find that \\texttt{Cus-Prun} consistently maintains the model's capabilities even at higher pruning ratios. Specifically, when the pruning ratio is increased to $45\\%$, ShortGPT nearly loses the capability of generating meaningful answers, while \\texttt{Cus-Prun} still achieves scores of $48.4$ on MMLU and $50.6$ on expert capabilities.\n\n\\begin{table}[ht]\n\\caption{Aggressive pruning ratio on Llama3-8B.}\n\\centering\n\\setlength{\\tabcolsep}{3pt}\n \\scalebox{0.9}{\n\\begin{tabular}{l|c|c|c|c}\n \\hline\n \\textbf{Method} & \\textbf{Ratio} & \\textbf{Speedup} & \\textbf{MMLU} & \\textbf{Expert} \\\\\\midrule\n \\rowcolor{lightgray}\\cellcolor{white}Dense & 0.0 & 1$\\times$ & 63.1 & 59.7 \\\\\\midrule\n ShortGPT & 25.0 & 1.3$\\times$ & 28.6 & 24.6 \\\\\n \\rowcolor{lightblue}\\cellcolor{white}\\texttt{Cus-Prun} & 25.0 & 1.3$\\times$ & \\textbf{51.9} & \\textbf{53.3} \\\\\\midrule\n ShortGPT & 34.2 & 1.5$\\times$ & 20.8 & 18.5 \\\\\n \\rowcolor{lightblue}\\cellcolor{white}\\texttt{Cus-Prun} & 35.0 & 1.5$\\times$ & \\textbf{50.2} & \\textbf{51.4} \\\\\\midrule\n ShortGPT & 43.8 & 1.8$\\times$ & 7.9 & 10.2 \\\\\n \\rowcolor{lightblue}\\cellcolor{white}\\texttt{Cus-Prun} & 45.0 & 1.8$\\times$ & \\textbf{48.4} & \\textbf{50.6} \\\\\n \\hline\n \\end{tabular}}\n \\label{table:prune_more}\n\\end{table}\n\n\n\n\\section{Adaptive Custom Pruning Assessment}\n\nIn this section, we evaluate the generality of \\texttt{Cus-Prun} in dynamic scenarios, including specific expert models in two and one dimensions, as described in Section \\ref{sec:adaptive}.\n\n\\subsection{Two Dimensions Specific Expert Model}\n\n\\paragraph{Experiment Settings} We use the Chinese-Medical setting as a concrete example of a two-dimensional expert model designed to perform a wide range of medical tasks in Chinese. \nWe adopt Mistral-12b as the backbone model and utilize corpus from Wikipedia for Chinese content and general medical corpus for medical knowledge. The performance of the target Chinese-Medical expert model is evaluated on two datasets: CMExam~\\citep{liu2023benchmarking} (5-shots), a Chinese medical multiple-choice question dataset, and HuatuoQA~\\citep{li2023huatuo26m}, a Chinese medical question-answering dataset. We assess the performance on CMExam using accuracy metrics. For the latter, we sample a sub-testset of size $100$ and use GPT-4 as the evaluator, which assigns a score from $0$ to $5$, representing its quality from low to high. Detailed prompts are listed in Appendix \\ref{sec:appen_prompt}.\n\n\n\n\\paragraph{Main Results} Table \\ref{tab:cmexam} presents the performance of the Chinese-Medical LLM on CMExam and its general capabilities. Our results indicate that the expert model pruned using \\texttt{Cus-Prun} outperforms models obtained through other pruning methods. Specifically, \\texttt{Cus-Prun} achieves a score of $48.7$ on CMExam, while its general capability score is $52.4$. These results compare favorably to the dense model, which scores $50.6$ on CMExam and $59.3$ on general capabilities. On the contrary, other pruning methods nearly lose the general and specific capabilities. Furthermore, Figure \\ref{fig:huatuo} shows a concrete example of Chinese-Medical LLM performance on medical question-answering. We find that \\texttt{Cus-Prun} can produce smaller expert models that maintain their expert capabilities, as demonstrated by its performance score of $2.9/5.0$ compared to $3.2/5.0$ for the dense model.\n\n\n\n\n\n\\subsection{One Dimension Specific Expert Model}\n\n\\begin{figure*}[t]\n \\centering\n \\includegraphics[width=1.0\\textwidth]{Figures/lang-specific.pdf}\n \\caption{Performance of \\texttt{Cus-Prun} in obtaining language-specific models.}\n \\label{fig:lang-spec}\n\\end{figure*}\n\n\n\\paragraph{Experiment Settings} \n\n\nFor evaluating the pruning method under a one-dimensional expert model setting, we focus on language-specific pruning, showing how to transform a dense model into language-specific variants.\nWe consider three linguistically diverse languages: German, Chinese, and Thai. We conduct experiments based on the Llama3-8b model.\nTo identify language-specific (while domain- and task-agnostic) neurons, we employ a diverse range of corpora, including Wikipedia, MGSM, and M3Exam, ensuring coverage of various domains and tasks. The effectiveness of our pruning technique is then evaluated using three held-out multilingual datasets including XQuAD~\\citep{artetxe2020cross}, XNLI~\\citep{conneau2018xnli}, and XSum ~\\citep{narayan2018don}.\n\n\n\\paragraph{Main Results} \nFigure \\ref{fig:lang-spec} illustrates the performance of language-specific models using \\texttt{Cus-Prun}. By pruning $25\\%$ of the neurons from the original model, \\texttt{Cus-Prun} not only retains general performance but also preserves language-specific capabilities. For instance, the German-specific model scores $54.7$ in general capabilities, $48.3$ on XQuAD, and $56.8$ on XNLI, compared to the dense model's scores of $64.1$, $52.9$, and $62.0$, respectively. This trend is consistent for Chinese and Thai models as well. In contrast, ShortGPT struggles to maintain the model's capabilities, particularly in XQuAD and XLSUm, which require generative abilities.\n\n\n\\section{Related Work}\n\n\\paragraph{LLM Compression} Given the high costs of training, inferencing, and tuning LLMs, many studies focus on model compression methods, including compression~\\citep{zhu2023survey}, quantization~\\citep{xu2023qa, dettmers2024qlora, lin2024awq, li2024evaluating}, and pruning~\\citep{wang2019structured}. Sparsity-based structural pruning enhances GPU efficiency with sparse structures but doesn't always reduce parameter counts \\citep{li2022parameter, li2023losparse, kurz2024language, zhao2024convex, huang2024pruning}. Unstructured pruning reduces parameters while maintaining performance, using post-training techniques \\citep{ma2023llm, xiasheared, muralidharan2024compact} or coarse methods like parameter approximation \\citep{zhao2024convex}, layer removal \\citep{men2024shortgpt}, or structural elimination \\citep{zhang2024finercut}. However, these often fail to preserve domain- or task-specific capabilities, limiting their utility for specialized scenarios.\n\n\n\\paragraph{Customizing Model} Customizing LLMs is essential for addressing language-specific challenges, domain-specific needs, and task-specific applications \\citep{cui2023efficient, yang2024laco, li2023huatuo26m, roziere2023code, li2023large, azerbayevllemma, alves2024tower,naacl-sentiment}. However, customization often demands extensive fine-tuning with curated data, making it resource-intensive. This highlights the need for efficient methods to quickly create robust, expert models tailored to diverse industries without compromising quality.\n\n\\section{Conclusion}\n\nLLMs deliver impressive capabilities but incur high computational costs. Efficient pruning of redundant parameters is vital for conserving resources and improving inference speed, especially for specialized models. Our method, \\texttt{Cus-Prun}, generates smaller expert models without post-training by pruning irrelevant neurons across ``language'', ``domain'', and ``task'' dimensions. This finer-grained approach outperforms existing techniques on three-dimensional models and can adapt to realistic scenarios, such as language-domain or language-specific models.\n\n\n\\section*{Limitation}\n\nDespite the promising results of Cus-Prun, several limitations should be noted. First, while our method leverages three dimensions (language, domain, and task) for pruning, certain crucial restrictions cannot be fully captured within this framework, such as variations in query format or input structure. Second, whether pruned base models can effectively undergo post-training remains an open question that requires further investigation. This uncertainty about post-training capabilities could limit the model's adaptability to new scenarios or requirements after pruning. These limitations suggest important directions for future research, including exploring additional dimensions for more comprehensive pruning strategies and investigating the relationship between pruning and post-training effectiveness.\n\n\\section*{Acknowledgement}\nThis research is supported by the Ministry of Education, Singapore, under its Academic Research Fund (AcRF) Tier 1 grant, and funded through the SUTD Assistant Professorship Scheme (SAP 2025\\_001).\nThis material is based upon work partially supported by the Air Force Office of Scientific Research under award number FA2386-24-1-4011, and this research is partially supported by the Singapore Ministry of Education Academic Research Fund Tier 1 (Award No: T1 251RES2207).\n\n\n\\bibliography{custom}\n\n\n\\appendix\n\\section{Appendix}\n\n\\subsection{GPT-4o Prompts}\\label{sec:appen_prompt}\nThe prompts used for generating documents and evaluating answer quality are presented in Table~\\ref{table:testset}.\n\n\\begin{table}[ht]\n \\centering\n\\footnotesize\n \\scalebox{1}{\n \\begin{tabular}{lp{5.3cm}}\n \\toprule\n \\textbf{Task} & \\textbf{Prompt} \\\\\\midrule\n Generation & Generate a text document in \\{language\\}/\\{domain\\}/\\{task\\}. Make sure the documents is not fixed to one \\{language\\}/\\{domain\\}/\\{task\\} or \\{language\\}/\\{domain\\}/\\{task\\}. Ensure the content is clear, concise, and appropriate for the specified request. Use professional and domain-specific terminology where necessary. \\\\\\midrule\n Evaluation & Evaluate the quality of the given answer to the question. Provide a score from 0 to 5, where 0 represents very low quality and 5 represents very high quality. Question: \\{question\\} Answer: \\{answer\\}.\\\\\n \\bottomrule\n \\end{tabular}}\n\\caption{GPT-4o prompts for generating documents and evaluating answer quality.} \\label{table:testset}\n\\end{table}\n\n\\subsection{Detailed Results for Multilingual}\\label{sec:appen_cl}\n\nDetailed results for multilingual settings can be found in Table \\ref{table:cl_ger}, Table \\ref{table:cl_chi} and Table \\ref{table:cl_tha} for German, Chinese and Thai, respectively.\n\n\\begin{table*}[hbpt]\n\\caption{Main Results of \\texttt{Cus-Prun} on Germany with a pruning ratio of $25\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in XLSum and in accuracy (\\%) for other datasets.}\n \\centering\n\\footnotesize\n\\setlength{\\tabcolsep}{3pt}\n \\scalebox{1.0}{\n \\begin{tabular}{l|l|cccc|ccccc}\n\\toprule\n\\multirow{2}{*}{\\textbf{\\normalsize{Model}}} & \\multirow{2}{*}{\\textbf{\\normalsize{Method}}} & \n \\multicolumn{4}{c}{\\textbf{\\normalsize{General Capability}}} \\vline & \\multicolumn{5}{c}{\\textbf{\\normalsize{Expert Capability}}} \\\\ \n & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3Exam & XQuAD & XLSum & Avg. \\\\ \\midrule\n\\multirow{4}{*}{Llama3-8B} &\n Dense & 70.7 & 58.3 & 63.1 & 64.1 & 44.8 & - & 52.9 & - & 48.8 \\\\\n & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 0.0 & - & 11.0 & - & 5.5 \\\\\n & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & - & 9.8 & - & 4.9 \\\\\n & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & - & 0.0 & - & 0.0 \\\\\n & \\texttt{Cus-Prun} & 61.4 & 38.9 & 54.5 & \\textbf{51.6} & 32.8 & - & 49.6 & - & \\textbf{41.2} \\\\ \\midrule\n\\multirow{4}{*}{Mistral-12B} & Dense & 82.6 & 68.5 & 50.4 & 59.3 & 56.8 & - & 41.2 & - & 49.0 \\\\\n & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 2.4 & - & 13.4 & - & 7.9 \\\\\n & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 0.8 & - & 15.5 & - & 8.2 \\\\\n & ShortGPT & 37.8 & 0.0 & 33.9 & 23.9 & 3.6 & - & 20.3 & - & 12.0 \\\\\n & \\texttt{Cus-Prun} & 64.6 & 39.7 & 43.2 & \\textbf{49.2} & 31.6 & - & 35.9 & - & \\textbf{33.8} \\\\ \\midrule\n\\multirow{4}{*}{Llama2-13B} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 24.4 & - & 40.3 & - & 32.3 \\\\\n & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 2.0 & - & 5.7 & - & 3.9 \\\\\n & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 3.6 & - & 18.1 & - & 10.9 \\\\\n & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 2.8 & - & 27.2 & - & 15.0 \\\\\n & \\texttt{Cus-Prun} & 47.6 & 19.8 & 49.9 & \\textbf{39.1} & 18.4 & - & 31.7 & - & \\textbf{25.0} \\\\ \\midrule\n\\multirow{4}{*}{Llama3-70B} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 74.8 & - & 58.2 & - & 66.5 \\\\\n & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 18.0 & - & 27.3 & - & 22.7 \\\\\n & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 0.0 & - & 17.3 & - & 8.7 \\\\\n & ShortGPT & 59.4 & 5.6 & 75.5 & 46.8 & 9.6 & - & 31.5 & - & 20.6 \\\\\n & \\texttt{Cus-Prun} & 66.8 & 59.3 & 69.1 & \\textbf{65.1} & 48.2 & - & 53.9 & - & \\textbf{51.1} \\\\ \n\\bottomrule\n\\end{tabular}}\n \\label{table:cl_ger}\n\\end{table*}\n\n\n\\begin{table*}[hbpt]\n\\caption{Main Results of \\texttt{Cus-Prun} on Chinese with a pruning ratio of $25\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in XLSum and in accuracy (\\%) for other datasets.}\n \\centering\n\\footnotesize\n\\setlength{\\tabcolsep}{3pt}\n \\scalebox{1.0}{\n \\begin{tabular}{l|l|cccc|ccccc}\n\\toprule\n\\multirow{2}{*}{\\textbf{\\normalsize{Model}}} & \\multirow{2}{*}{\\textbf{\\normalsize{Method}}} & \n \\multicolumn{4}{c}{\\textbf{\\normalsize{General Capability}}} \\vline & \\multicolumn{5}{c}{\\textbf{\\normalsize{Specific Capability}}} \\\\ \n & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3Exam & XQuAD & XLSum & Avg. \\\\ \\midrule\n\\multirow{4}{*}{Llama3-8B} &\n Dense & 70.7 & 58.3 & 63.1 & 64.1 & 43.6 & 55.1 & 78.7 & 49.1 & 56.6 \\\\\n & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 2.4 & 23.6 & 21.3 & 32.8 & 20.0 \\\\\n & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & 17.4 & 23.5 & 8.3 & 12.3 \\\\\n & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & 28.3 & 0.0 & 3.1 & 7.9 \\\\\n & \\texttt{Cus-Prun} & 60.5 & 25.7 & 49.4 & \\textbf{45.2} & 36.0 & 44.7 & 65.6 & 46.3 & \\textbf{48.2} \\\\ \\midrule\n\\multirow{4}{*}{Mistral-12B} & Dense & 82.6 & 68.5 & 50.4 & 59.3 & 53.2 & 47.8 & 62.2 & 33.0 & 49.1 \\\\\n & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 2.8 & 30.7 & 31.8 & 32.6 & 24.5 \\\\\n & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 1.6 & 26.4 & 28.3 & 10.8 & 16.8 \\\\\n & ShortGPT & 37.8 & 0.0 & 33.9 & 23.9 & 4.4 & 28.2 & 29.1 & 7.2 & 17.2 \\\\\n & \\texttt{Cus-Prun} & 68.3 & 43.2 & 39.5 & \\textbf{50.3} & 38.4 & 40.7 & 50.6 & 30.3 & \\textbf{40.0} \\\\ \\midrule\n\\multirow{4}{*}{Llama2-13B} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 21.6 & 36.5 & 59.8 & 35.3 & 38.3 \\\\\n & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 1.2 & 23.3 & 3.8 & 25.1 & 13.4\\\\\n & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 4.8 & 24.5 & 28.4 & 11.2 & 17.2 \\\\\n & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 4.4 & 22.9 & 24.6 & 13.7 & 16.4 \\\\\n & \\texttt{Cus-Prun} & 48.6 & 20.7 & 51.9 & \\textbf{40.4} & 14.8 & 28.2 & 47.3 & 34.4 & \\textbf{31.2} \\\\ \\midrule\n\\multirow{4}{*}{Llama3-70B} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 68.4 & 76.1 & 81.3 & 55.3 & 70.3 \\\\\n & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 16.8 & 47.5 & 56.1 & 41.3 & 40.4 \\\\\n & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 6.4 & 48.3 & 42.2 & 29.3 & 31.6 \\\\\n & ShortGPT & 59.4 & 5.6 & 75.5 & 46.8 & 12.4 & 45.5 & 44.6 & 36.1 & 34.7 \\\\\n & \\texttt{Cus-Prun} & 72.3 & 48.5 & 65.2 & \\textbf{62.0} & 40.8 & 61.7 & 66.9 & 51.6 & \\textbf{55.3} \\\\ \n\\bottomrule\n\\end{tabular}}\n \\label{table:cl_chi}\n\\end{table*}\n\n\n\\begin{table*}[hbpt]\n\\caption{Main Results of \\texttt{Cus-Prun} on Thai with a pruning ratio of $25\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in XLSum and in accuracy (\\%) for other datasets.}\n \\centering\n\\footnotesize\n\\setlength{\\tabcolsep}{3pt}\n \\scalebox{1.0}{\n \\begin{tabular}{l|l|cccc|ccccc}\n\\toprule\n\\multirow{2}{*}{\\textbf{\\normalsize{Model}}} & \\multirow{2}{*}{\\textbf{\\normalsize{Method}}} & \n \\multicolumn{4}{c}{\\textbf{\\normalsize{General Capability}}} \\vline & \\multicolumn{5}{c}{\\textbf{\\normalsize{Specific Capability}}} \\\\ \n & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3Exam & XQuAD & XLSum & Avg. \\\\ \\midrule\n\\multirow{4}{*}{Llama3-8B} &\n Dense & 70.7 & 58.3 & 63.1 & 64.1 & 35.2 & 43.0 & 58.7 & 16.7 & 38.4 \\\\\n & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 0.8 & 24.4 & 8.4 & 13.5 & 11.8 \\\\\n & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & 12.3 & 16.6 & 8.7 & 9.4 \\\\\n & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & 25.4 & 0.0 & 2.3 & 6.9 \\\\\n & \\texttt{Cus-Prun} & 58.9 & 31.2 & 52.4 & \\textbf{47.5} & 21.6 & 38.3 & 42.6 & 16.8 & \\textbf{29.8} \\\\ \\midrule\n\\multirow{4}{*}{Mistral-12B} & Dense & 82.6 & 68.5 & 50.4 & 59.3 & 45.2 & 39.9 & 44.1 & 17.8 & 36.8 \\\\\n & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 1.2 & 24.8 & 11.9 & 13.7 & 12.9 \\\\\n & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 0.0 & 23.8 & 8.4 & 4.7 & 12.3 \\\\\n & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 0.8 & 25.7 & 4.7 & 2.8 & 8.5 \\\\\n & \\texttt{Cus-Prun} & 68.2 & 35.8 & 47.6 & \\textbf{50.5} & 32.8 & 37.7 & 35.6 & 15.9 & \\textbf{30.5} \\\\ \\midrule\n\\multirow{4}{*}{Llama2-13B} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 6.4 & 24.3 & 28.3 & 14.5 & 18.4 \\\\\n & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 0.0 & 22.3 & 1.8 & 10.2 & 8.6 \\\\\n & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 0.0 & 26.2 & 23.7 & 8.6 & 14.6 \\\\\n & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 0.0 & 23.1 & 22.3 & 8.9 & 13.6 \\\\\n & \\texttt{Cus-Prun} & 47.8 & 20.9 & 50.7 & \\textbf{39.8} & 4.8 & 24.2 & 23.6 & 13.8 & \\textbf{16.6} \\\\ \\midrule\n\\multirow{4}{*}{Llama3-70B} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 65.2 & 66.1 & 67.8 & 17.8 & 54.2 \\\\\n & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 15.6 & 39.9 & 29.8 & 16.6 & 25.5 \\\\\n & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 4.8 & 41.3 & 39.6 & 13.2 & 24.7 \\\\\n & ShortGPT & 59.4 & 5.6 & 75.5 & 46.8 & 13.7 & 40.7 & 40.4 & 11.9 & 26.7 \\\\\n & \\texttt{Cus-Prun} & 73.3 & 58.7 & 68.4 & \\textbf{66.8} & 40.4 & 53.6 & 58.5 & 16.9 & \\textbf{42.4} \\\\ \n\\bottomrule\n\\end{tabular}}\n \\label{table:cl_tha}\n\\end{table*}\n\n\\subsection{Experiments Detailed Settings}\\label{sec:appen_setting}\n\n\\subsubsection{Multilingual Settings}\\label{sec:appen_setting_1}\n\n\\paragraph{Experiment Details} For multilingual setting, we can obtain two corpora: $C_{L_{\\text{Exp}}}$ and $C_{(D, T)_{\\text{Exp}}}$. The first corpus contains samples in a specific language across various domains and tasks, while the second corpus contains samples from a specific domain-task combination in other languages, i.e., the target dataset in other languages. Specifically, for $C_{L_{\\text{Exp}}}$ we employ Wikipedia\\footnote{\\url{https://huggingface.co/datasets/wikimedia/wikipedia}} to construct language-specific corpus covering various domains and tasks. For $C_{(D, T)_{\\text{Exp}}}$, we employ the corresponding datasets in English, including GSM8K~\\citep{cobbe2021training} for MGSM, the English split of M3Exam\\footnote{M3Exam is language-specific and does not utilize a translated parallel corpus.} for M3Exam, SQuAD~\\citep{rajpurkar2016squad} for XQuAD, and XSum~\\citep{narayan2018don} for XLSum. \n\nHyperparameters, including the sizes of $C_{L_{\\text{Exp}}}$ and $C_{(D, T)_{\\text{Exp}}}$, are determined using the validation set of the XLSum dataset and then applied to testsets in other multilingual datasets. Furthermore, accuracy is the metric used for ARC-c, GSM8K, MMLU, MGSM, M3Exam, and XQuAD, while Rouge-L~\\citep{lin2004rouge} is used for XLSum. \n\n\\subsubsection{Multidomain Settings}\\label{sec:appen_setting_2}\n\n\\paragraph{Settings} For multidomain setting, we can obtain two corpora: $C_{D_{\\text{Exp}}} = \\{(L, D_{\\text{Exp}}, T)|L\\in\\mathbb{L}, T\\in\\mathbb{T}\\}$ and $C_{(L, T)_{\\text{Exp}}}=\\{(D, (L, T)_{\\text{Exp}})|D\\in\\mathbb{D}\\}$. The first corpus contains samples in a specific domain across various languages and tasks, while the second corpus contains samples from a specific language-task combination across different domains, i.e., the target dataset in other domains. Specifically, for $C_{D_{\\text{Exp}}}$ we employ specific domain corpus, including English split of medical corpus~\\citep{garcia2024medical} for medical domain, general finance corpus for finance domain\\footnote{\\url{https://huggingface.co/datasets/gbharti/finance-alpaca}}, general Twitter corpus~\\citep{kharde975sentiment}, and English split of Amazon corpus~\\citep{marc_reviews}. For $C_{(L, T)_{\\text{Exp}}}$, we employ the corresponding datasets in general domains, including CommonsenseQA~\\citep{talmor2019commonsenseqa} for MedMCQ, open table question-answering OTT-QA~\\citep{chen2020open} for FinTQA, general sentiment analysis~\\citep{attia2018multilingual} for TSA and AMSA.\n\n\\paragraph{Experiment Details} Hyperparameters, including the sizes of $C_{D_{\\text{Exp}}}$ and $C_{(L, T)_{\\text{Exp}}}$, are determined using the validation set of the Amazon sentiment analysis dataset and then applied to testsets in other multidomain datasets. Furthermore, accuracy is the metric used for all datasets. \n\n\n\n\\subsubsection{Multitask Settings}\\label{sec:appen_setting_3}\n\n\\paragraph{Settings} For multitask setting, we can obtain two corpora: $C_{T_{\\text{Exp}}} = \\{(L, D, T_{\\text{Exp}})|L\\in\\mathbb{L}, D\\in\\mathbb{D}\\}$ and $C_{(L, D)_{\\text{Exp}}}=\\{(T, (L, S)_{\\text{Exp}})|T\\in\\mathbb{T}\\}$. The first corpus contains samples in a specific task across various languages and domains, while the second corpus contains samples from a specific language-domain combination across different tasks, i.e., the target dataset in other tasks. Specifically, for $C_{T_{\\text{Exp}}}$ we employ specific task corpus, including XSum corpus~\\citep{abacha2019summarization} for summarization task, general conterfact corpus\\footnote{\\url{https://huggingface.co/datasets/azhx/counterfact-easy}} for counterfactual task. For $C_{(L, D)_{\\text{Exp}}}$, we employ the corresponding datasets in other tasks, including MedQCQ~\\citep{pmlr-v174-pal22a} for MedSum, AMSA~\\citep{zhang2015character} for AMSum and AMContFact.\n\n\\paragraph{Experiment Details} Hyperparameters, including the sizes of $C_{T_{\\text{Exp}}}$ and $C_{(L, D)_{\\text{Exp}}}$, are determined using the validation set of the Amazon counterfactual dataset and then applied to testsets in other multitask setting datasets. Furthermore, accuracy is the metric used for ARC-c, GSM8K, MMLU, and AMContFact, while Rouge-L~\\citep{lin2004rouge} is used for MedSum and AMSum. \n\n\n\n\\end{document}\n"},"question":{"kind":"string","value":"If the Llama2-13B model has the same change rate in MMLU as the Llama3-8B model at the same pruning ratio, what will be the final MMLU values of ShortGPT and Cus-Prun at a pruning rate of about 35%? Your answer only needs to be rounded to one decimal place."},"answer":{"kind":"string","value":"17.6, 42.5"},"plan":{"kind":"string","value":"COMPUTE change rate of ShortGPT MMLU between the two pruning ratios\nSELECT Llama3-8B Cus-Prun MMLU values at two pruning ratios\nCOMPUTE change rate of Cus-Prun MMLU between the two pruning ratios\nSELECT Llama2-13B ShortGPT MMLU value at the lower pruning ratio\nCOMPUTE predicted ShortGPT MMLU at the higher pruning ratio using the ShortGPT change rate\nSELECT Llama2-13B Cus-Prun MMLU value at the lower pruning ratio\nCOMPUTE predicted Cus-Prun MMLU at the higher pruning ratio using the Cus-Prun change rate\nRETURN predicted ShortGPT MMLU at the higher pruning ratio, predicted Cus-Prun MMLU at the higher pruning ratio%%%\n\nSELECT Llama3-8B ShortGPT MMLU values at two Pruning Ratios\nCOMPUTE shortgpt_change_rate = (high_ratio_mmlu - low_ratio_mmlu) / low_ratio_mmlu\nSELECT Llama3-8B Cus-Prun MMLU values at two Pruning Ratios\nCOMPUTE cus_prun_change_rate = (high_ratio_mmlu - low_ratio_mmlu) / low_ratio_mmlu\n\n\nSELECT Llama2-13B ShortGPT MMLU value at the lower Pruning Ratio\nCOMPUTE predicted_shortgpt_mmlu = llama2_13b_shortgpt_low * (1 + shortgpt_change_rate)\nSELECT Llama2-13B Cus-Prun MMLU value at the lower Pruning Ratio\nCOMPUTE predicted_cus_prun_mmlu = llama2_13b_cus_prun_low * (1 + cus_prun_change_rate)\n\n\nRETURN predicted_shortgpt_mmlu and predicted_cus_prun_mmlu[h]\n"}}},{"rowIdx":90,"cells":{"paper":{"kind":"string","value":"2506.02561"},"relevant_tables":{"kind":"list like","value":[["\\begin{table*}[t]\n","\\caption{Main Results of \\texttt{Cus-Prun} on multilingual setting with a pruning ratio of $25\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in summarization tasks and in accuracy (\\%) for other datasets.}\n"," \\centering\n","\\footnotesize\n","\\setlength{\\tabcolsep}{0.8pt}\n"," \\scalebox{0.9}{\n"," \\begin{tabular}{c|l|cccc|ccccc|ccccc|cccc}\n","\\toprule\n","\\quad\\quad & \\multirow{2}{*}{\\textbf{\\normalsize{Method}}} & \n"," \\multicolumn{4}{c}{\\textbf{\\normalsize{General Capability}}} \\vline & \\multicolumn{5}{c}{\\textbf{\\normalsize{Multilingual Expert}}} \\vline & \\multicolumn{5}{c}{\\textbf{\\normalsize{Multidomain Expert }}} \\vline & \\multicolumn{4}{c}{\\textbf{\\normalsize{Multitask Expert }}} \\\\ \n"," & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3 & XQuAD & Sum & Avg. & MMCQ & FTQA & TSA & AMSA & Avg. & MSum & ASum & AMCF & Avg. \\\\ \\midrule\n","\\rowcolor{lightgray}\\cellcolor{white}\\multirow{5}{*}{\\rotatebox{90}{\\textbf{Llama3-8B}}} & Dense & 70.7 & 58.3 & 63.1 & 64.1 &41.2 &49.1 & 63.4 &32.9 &46.7 & 51.8 & 23.9 & 67.1 & 95.9 & 59.8 & 76.6 & 16.2 & 78.2 & 57.0 \\\\\n"," & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 1.1 & 24.0 & 13.6 & 23.2 & 15.5 & 0.0 & 0.0 & \\textbf{61.8} & 76.0 & 34.5 & 62.2 & \\textbf{21.8} & \\textbf{80.0} & \\textbf{54.7} \\\\\n"," & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & 14.9 & 16.6 & 8.5 & 10.0 & 22.6 & 0.0 & 41.2 & 53.7 & 29.4 & 7.3 & 2.9 & 51.3 & 20.5 \\\\\n"," & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & 26.9 & 0.0 & 2.7 & 7.4 & 3.2 & 0.0 & 38.6 & 35.7 & 19.4 & 4.1 & 4.8 & 43.8 & 17.6 \\\\\n"," &\\cellcolor{lightblue}\\texttt{Cus-Prun} &\\cellcolor{lightblue}\\textbf{62.4} & \\cellcolor{lightblue}\\textbf{37.0} & \\cellcolor{lightblue}\\textbf{54.7} & \\cellcolor{lightblue}\\textbf{51.4} & \\cellcolor{lightblue}\\textbf{30.1} & \\cellcolor{lightblue}\\textbf{41.5} & \\cellcolor{lightblue}\\textbf{52.6} & \\cellcolor{lightblue}\\textbf{31.5} & \\cellcolor{lightblue}\\textbf{38.9} & \\cellcolor{lightblue}\\textbf{42.9} & \\cellcolor{lightblue}\\textbf{20.6} & \\cellcolor{lightblue}\\textbf{61.8} & \\cellcolor{lightblue}\\textbf{87.6} & \\cellcolor{lightblue}\\textbf{53.2} & \\cellcolor{lightblue}\\textbf{68.4} & \\cellcolor{lightblue}12.8 & \\cellcolor{lightblue}75.5 & \\cellcolor{lightblue}52.2 \\\\ \\midrule\n","\\rowcolor{lightgray}\\cellcolor{white}\\multirow{5}{*}{\\rotatebox{90}{\\textbf{Mistral-12B}}} & Dense & 82.6 & 68.5 & 50.4 & 67.2 & 51.7 & 43.8 & 49.2 & 25.4 & 42.5 & 54.6 & 26.6 & 69.4 & 92.4 & 60.8 & 88.7 & 3.0 & 78.6 & 56.4 \\\\\n"," & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 2.1 & 27.8 & 19.0 & \\textbf{23.2} & 18.0 & 0.0 & 0.0 & 51.0 & 20.9 & 18.0 & 59.3 & 0.5 & 2.8 & 20.9 \\\\\n"," & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 0.8 & 25.1 & 17.4 & 7.8 & 12.8 & 24.9 & 9.2 & 34.2 & 54.3 & 30.7 & 27.4 & 1.3 & 36.3 & 21.7 \\\\\n"," & ShortGPT & 37.8 & 0.0 & 33.9 & 23.9 & 2.9 & 27.0 & 18.0 & 5.0 & 13.2 & 31.4 & 7.2 & 39.2 & 52.5 & 32.6 & 26.2 & 0.2 & 42.7 & 23.0 \\\\\n"," & \\cellcolor{lightblue}\\texttt{Cus-Prun} & \\cellcolor{lightblue}\\textbf{67.5} & \\cellcolor{lightblue}\\textbf{43.4} & \\cellcolor{lightblue}\\textbf{43.8} & \\cellcolor{lightblue}\\textbf{51.6} & \\cellcolor{lightblue}\\textbf{34.3} & \\cellcolor{lightblue}\\textbf{39.2} & \\cellcolor{lightblue}\\textbf{40.7} & \\cellcolor{lightblue}23.1 & \\cellcolor{lightblue}\\textbf{34.3} & \\cellcolor{lightblue}\\textbf{47.9} & \\cellcolor{lightblue}\\textbf{25.1} & \\cellcolor{lightblue}\\textbf{67.3} & \\cellcolor{lightblue}\\textbf{83.7} & \\cellcolor{lightblue}\\textbf{56.0} & \\cellcolor{lightblue}\\textbf{83.5} & \\cellcolor{lightblue}\\textbf{3.4} & \\cellcolor{lightblue}\\textbf{72.8} & \\cellcolor{lightblue}\\textbf{50.9} \\\\ \\midrule\n","\\rowcolor{lightgray}\\cellcolor{white}\\multirow{5}{*}{\\rotatebox{90}{\\textbf{Llama2-13B}}} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 17.5 & 30.4 & 44.1 & 24.9 & 29.2 & 25.2 & 0.0 & 42.7 & 84.1 & 38.0 & 70.0 & 7.4 & 44.3 & 40.6 \\\\\n"," & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 1.1 & 22.8 & 3.8 & 17.7 & 11.3 & 0.0 & 0.0 & 9.7 & 0.0 & 2.4 & 21.6 & 4.8 & 0.0 & 8.8 \\\\\n"," & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 2.8 & 25.3 & 23.4 & 9.9 & 15.5 & 18.7 & 0.0 & 28.4 & 67.3 & 28.6 & 24.5 & 4.9 & 32.9 & 20.8 \\\\\n"," & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 2.4 & 23.0 & 24.7 & 11.3 & 15.3 & 16.9 & 0.0 & 34.6 & \\textbf{69.8} & 30.3 & 23.8 & 5.2 & 39.1 & 22.7 \\\\\n"," & \\cellcolor{lightblue}\\texttt{Cus-Prun} & \\cellcolor{lightblue}\\textbf{48.3} & \\cellcolor{lightblue}\\textbf{20.8} & \\cellcolor{lightblue}\\textbf{50.0} & \\cellcolor{lightblue}\\textbf{39.7} & \\cellcolor{lightblue}\\textbf{12.7} & \\cellcolor{lightblue}\\textbf{26.2} & \\cellcolor{lightblue}\\textbf{34.2} & \\cellcolor{lightblue}\\textbf{24.1} & \\cellcolor{lightblue}\\textbf{24.3} & \\cellcolor{lightblue}\\textbf{25.6} & \\cellcolor{lightblue}0.0 & \\cellcolor{lightblue}\\textbf{38.5} & \\cellcolor{lightblue}68.3 & \\cellcolor{lightblue}\\textbf{33.1} & \\cellcolor{lightblue}\\textbf{64.5} & \\cellcolor{lightblue}\\textbf{6.7} & \\cellcolor{lightblue}\\textbf{42.9} & \\cellcolor{lightblue}\\textbf{38.0} \\\\ \\midrule\n","\\rowcolor{lightgray}\\cellcolor{white}\\multirow{5}{*}{\\rotatebox{90}{\\textbf{Llama3-70B}}} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 69.5 & 71.1 & 69.1 & 36.6 & 61.6 & 72.1 & 55.3 & 83.6 & 96.2 & 76.8 & 84.2 & 17.3 & 81.8 & 61.1 \\\\\n"," & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 16.8 & 43.7 & 43.0 & 29.0 & 33.1 & 27.3 & 1.0 & 51.0 & 50.3 & 32.4 & 10.2 & 13.7 & 20.6 & 14.8 \\\\\n"," & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 3.7 & 44.8 & 33.0 & 21.2 & 25.7 & 57.6 & 27.6 & 68.1 & 59.4 & 53.2 & 58.0 & 14.2 & 68.3 & 46.8 \\\\\n"," & ShortGPT & 59.4 & 5.6 & \\textbf{75.5} & 46.8 & 11.9 & 43.1 & 38.8 & 24.0 & 29.5 & 58.4 & 32.2 & 67.5 & 64.9 & 55.8 & 59.6 & 13.9 & 65.8 & 46.4 \\\\\n"," & \\cellcolor{lightblue}\\texttt{Cus-Prun} & \\cellcolor{lightblue}\\textbf{68.4} & \\cellcolor{lightblue}\\textbf{53.2} & \\cellcolor{lightblue}66.6 & \\cellcolor{lightblue}\\textbf{62.7} & \\cellcolor{lightblue}\\textbf{43.1} & \\cellcolor{lightblue}\\textbf{57.7} & \\cellcolor{lightblue}\\textbf{59.8} & \\cellcolor{lightblue}\\textbf{34.3} & \\cellcolor{lightblue}\\textbf{48.7} & \\cellcolor{lightblue}\\textbf{68.2} & \\cellcolor{lightblue}\\textbf{43.9} & \\cellcolor{lightblue}\\textbf{81.4} & \\cellcolor{lightblue}\\textbf{87.8} & \\cellcolor{lightblue}\\textbf{70.3} & \\cellcolor{lightblue}\\textbf{80.4} & \\cellcolor{lightblue}\\textbf{15.7} & \\cellcolor{lightblue}\\textbf{77.5} & \\cellcolor{lightblue}\\textbf{57.9} \\\\ \n","\\bottomrule\n","\\end{tabular}}\n"," \\label{table:cross_ling}\n","\\vspace{-0.2cm}\n","\\end{table*}\n"]],"string":"[\n [\n \"\\\\begin{table*}[t]\\n\",\n \"\\\\caption{Main Results of \\\\texttt{Cus-Prun} on multilingual setting with a pruning ratio of $25\\\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in summarization tasks and in accuracy (\\\\%) for other datasets.}\\n\",\n \" \\\\centering\\n\",\n \"\\\\footnotesize\\n\",\n \"\\\\setlength{\\\\tabcolsep}{0.8pt}\\n\",\n \" \\\\scalebox{0.9}{\\n\",\n \" \\\\begin{tabular}{c|l|cccc|ccccc|ccccc|cccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\quad\\\\quad & \\\\multirow{2}{*}{\\\\textbf{\\\\normalsize{Method}}} & \\n\",\n \" \\\\multicolumn{4}{c}{\\\\textbf{\\\\normalsize{General Capability}}} \\\\vline & \\\\multicolumn{5}{c}{\\\\textbf{\\\\normalsize{Multilingual Expert}}} \\\\vline & \\\\multicolumn{5}{c}{\\\\textbf{\\\\normalsize{Multidomain Expert }}} \\\\vline & \\\\multicolumn{4}{c}{\\\\textbf{\\\\normalsize{Multitask Expert }}} \\\\\\\\ \\n\",\n \" & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3 & XQuAD & Sum & Avg. & MMCQ & FTQA & TSA & AMSA & Avg. & MSum & ASum & AMCF & Avg. \\\\\\\\ \\\\midrule\\n\",\n \"\\\\rowcolor{lightgray}\\\\cellcolor{white}\\\\multirow{5}{*}{\\\\rotatebox{90}{\\\\textbf{Llama3-8B}}} & Dense & 70.7 & 58.3 & 63.1 & 64.1 &41.2 &49.1 & 63.4 &32.9 &46.7 & 51.8 & 23.9 & 67.1 & 95.9 & 59.8 & 76.6 & 16.2 & 78.2 & 57.0 \\\\\\\\\\n\",\n \" & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 1.1 & 24.0 & 13.6 & 23.2 & 15.5 & 0.0 & 0.0 & \\\\textbf{61.8} & 76.0 & 34.5 & 62.2 & \\\\textbf{21.8} & \\\\textbf{80.0} & \\\\textbf{54.7} \\\\\\\\\\n\",\n \" & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & 14.9 & 16.6 & 8.5 & 10.0 & 22.6 & 0.0 & 41.2 & 53.7 & 29.4 & 7.3 & 2.9 & 51.3 & 20.5 \\\\\\\\\\n\",\n \" & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & 26.9 & 0.0 & 2.7 & 7.4 & 3.2 & 0.0 & 38.6 & 35.7 & 19.4 & 4.1 & 4.8 & 43.8 & 17.6 \\\\\\\\\\n\",\n \" &\\\\cellcolor{lightblue}\\\\texttt{Cus-Prun} &\\\\cellcolor{lightblue}\\\\textbf{62.4} & \\\\cellcolor{lightblue}\\\\textbf{37.0} & \\\\cellcolor{lightblue}\\\\textbf{54.7} & \\\\cellcolor{lightblue}\\\\textbf{51.4} & \\\\cellcolor{lightblue}\\\\textbf{30.1} & \\\\cellcolor{lightblue}\\\\textbf{41.5} & \\\\cellcolor{lightblue}\\\\textbf{52.6} & \\\\cellcolor{lightblue}\\\\textbf{31.5} & \\\\cellcolor{lightblue}\\\\textbf{38.9} & \\\\cellcolor{lightblue}\\\\textbf{42.9} & \\\\cellcolor{lightblue}\\\\textbf{20.6} & \\\\cellcolor{lightblue}\\\\textbf{61.8} & \\\\cellcolor{lightblue}\\\\textbf{87.6} & \\\\cellcolor{lightblue}\\\\textbf{53.2} & \\\\cellcolor{lightblue}\\\\textbf{68.4} & \\\\cellcolor{lightblue}12.8 & \\\\cellcolor{lightblue}75.5 & \\\\cellcolor{lightblue}52.2 \\\\\\\\ \\\\midrule\\n\",\n \"\\\\rowcolor{lightgray}\\\\cellcolor{white}\\\\multirow{5}{*}{\\\\rotatebox{90}{\\\\textbf{Mistral-12B}}} & Dense & 82.6 & 68.5 & 50.4 & 67.2 & 51.7 & 43.8 & 49.2 & 25.4 & 42.5 & 54.6 & 26.6 & 69.4 & 92.4 & 60.8 & 88.7 & 3.0 & 78.6 & 56.4 \\\\\\\\\\n\",\n \" & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 2.1 & 27.8 & 19.0 & \\\\textbf{23.2} & 18.0 & 0.0 & 0.0 & 51.0 & 20.9 & 18.0 & 59.3 & 0.5 & 2.8 & 20.9 \\\\\\\\\\n\",\n \" & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 0.8 & 25.1 & 17.4 & 7.8 & 12.8 & 24.9 & 9.2 & 34.2 & 54.3 & 30.7 & 27.4 & 1.3 & 36.3 & 21.7 \\\\\\\\\\n\",\n \" & ShortGPT & 37.8 & 0.0 & 33.9 & 23.9 & 2.9 & 27.0 & 18.0 & 5.0 & 13.2 & 31.4 & 7.2 & 39.2 & 52.5 & 32.6 & 26.2 & 0.2 & 42.7 & 23.0 \\\\\\\\\\n\",\n \" & \\\\cellcolor{lightblue}\\\\texttt{Cus-Prun} & \\\\cellcolor{lightblue}\\\\textbf{67.5} & \\\\cellcolor{lightblue}\\\\textbf{43.4} & \\\\cellcolor{lightblue}\\\\textbf{43.8} & \\\\cellcolor{lightblue}\\\\textbf{51.6} & \\\\cellcolor{lightblue}\\\\textbf{34.3} & \\\\cellcolor{lightblue}\\\\textbf{39.2} & \\\\cellcolor{lightblue}\\\\textbf{40.7} & \\\\cellcolor{lightblue}23.1 & \\\\cellcolor{lightblue}\\\\textbf{34.3} & \\\\cellcolor{lightblue}\\\\textbf{47.9} & \\\\cellcolor{lightblue}\\\\textbf{25.1} & \\\\cellcolor{lightblue}\\\\textbf{67.3} & \\\\cellcolor{lightblue}\\\\textbf{83.7} & \\\\cellcolor{lightblue}\\\\textbf{56.0} & \\\\cellcolor{lightblue}\\\\textbf{83.5} & \\\\cellcolor{lightblue}\\\\textbf{3.4} & \\\\cellcolor{lightblue}\\\\textbf{72.8} & \\\\cellcolor{lightblue}\\\\textbf{50.9} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\rowcolor{lightgray}\\\\cellcolor{white}\\\\multirow{5}{*}{\\\\rotatebox{90}{\\\\textbf{Llama2-13B}}} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 17.5 & 30.4 & 44.1 & 24.9 & 29.2 & 25.2 & 0.0 & 42.7 & 84.1 & 38.0 & 70.0 & 7.4 & 44.3 & 40.6 \\\\\\\\\\n\",\n \" & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 1.1 & 22.8 & 3.8 & 17.7 & 11.3 & 0.0 & 0.0 & 9.7 & 0.0 & 2.4 & 21.6 & 4.8 & 0.0 & 8.8 \\\\\\\\\\n\",\n \" & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 2.8 & 25.3 & 23.4 & 9.9 & 15.5 & 18.7 & 0.0 & 28.4 & 67.3 & 28.6 & 24.5 & 4.9 & 32.9 & 20.8 \\\\\\\\\\n\",\n \" & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 2.4 & 23.0 & 24.7 & 11.3 & 15.3 & 16.9 & 0.0 & 34.6 & \\\\textbf{69.8} & 30.3 & 23.8 & 5.2 & 39.1 & 22.7 \\\\\\\\\\n\",\n \" & \\\\cellcolor{lightblue}\\\\texttt{Cus-Prun} & \\\\cellcolor{lightblue}\\\\textbf{48.3} & \\\\cellcolor{lightblue}\\\\textbf{20.8} & \\\\cellcolor{lightblue}\\\\textbf{50.0} & \\\\cellcolor{lightblue}\\\\textbf{39.7} & \\\\cellcolor{lightblue}\\\\textbf{12.7} & \\\\cellcolor{lightblue}\\\\textbf{26.2} & \\\\cellcolor{lightblue}\\\\textbf{34.2} & \\\\cellcolor{lightblue}\\\\textbf{24.1} & \\\\cellcolor{lightblue}\\\\textbf{24.3} & \\\\cellcolor{lightblue}\\\\textbf{25.6} & \\\\cellcolor{lightblue}0.0 & \\\\cellcolor{lightblue}\\\\textbf{38.5} & \\\\cellcolor{lightblue}68.3 & \\\\cellcolor{lightblue}\\\\textbf{33.1} & \\\\cellcolor{lightblue}\\\\textbf{64.5} & \\\\cellcolor{lightblue}\\\\textbf{6.7} & \\\\cellcolor{lightblue}\\\\textbf{42.9} & \\\\cellcolor{lightblue}\\\\textbf{38.0} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\rowcolor{lightgray}\\\\cellcolor{white}\\\\multirow{5}{*}{\\\\rotatebox{90}{\\\\textbf{Llama3-70B}}} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 69.5 & 71.1 & 69.1 & 36.6 & 61.6 & 72.1 & 55.3 & 83.6 & 96.2 & 76.8 & 84.2 & 17.3 & 81.8 & 61.1 \\\\\\\\\\n\",\n \" & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 16.8 & 43.7 & 43.0 & 29.0 & 33.1 & 27.3 & 1.0 & 51.0 & 50.3 & 32.4 & 10.2 & 13.7 & 20.6 & 14.8 \\\\\\\\\\n\",\n \" & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 3.7 & 44.8 & 33.0 & 21.2 & 25.7 & 57.6 & 27.6 & 68.1 & 59.4 & 53.2 & 58.0 & 14.2 & 68.3 & 46.8 \\\\\\\\\\n\",\n \" & ShortGPT & 59.4 & 5.6 & \\\\textbf{75.5} & 46.8 & 11.9 & 43.1 & 38.8 & 24.0 & 29.5 & 58.4 & 32.2 & 67.5 & 64.9 & 55.8 & 59.6 & 13.9 & 65.8 & 46.4 \\\\\\\\\\n\",\n \" & \\\\cellcolor{lightblue}\\\\texttt{Cus-Prun} & \\\\cellcolor{lightblue}\\\\textbf{68.4} & \\\\cellcolor{lightblue}\\\\textbf{53.2} & \\\\cellcolor{lightblue}66.6 & \\\\cellcolor{lightblue}\\\\textbf{62.7} & \\\\cellcolor{lightblue}\\\\textbf{43.1} & \\\\cellcolor{lightblue}\\\\textbf{57.7} & \\\\cellcolor{lightblue}\\\\textbf{59.8} & \\\\cellcolor{lightblue}\\\\textbf{34.3} & \\\\cellcolor{lightblue}\\\\textbf{48.7} & \\\\cellcolor{lightblue}\\\\textbf{68.2} & \\\\cellcolor{lightblue}\\\\textbf{43.9} & \\\\cellcolor{lightblue}\\\\textbf{81.4} & \\\\cellcolor{lightblue}\\\\textbf{87.8} & \\\\cellcolor{lightblue}\\\\textbf{70.3} & \\\\cellcolor{lightblue}\\\\textbf{80.4} & \\\\cellcolor{lightblue}\\\\textbf{15.7} & \\\\cellcolor{lightblue}\\\\textbf{77.5} & \\\\cellcolor{lightblue}\\\\textbf{57.9} \\\\\\\\ \\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}}\\n\",\n \" \\\\label{table:cross_ling}\\n\",\n \"\\\\vspace{-0.2cm}\\n\",\n \"\\\\end{table*}\\n\"\n ]\n]","isConversational":false},"tables":{"kind":"list like","value":[["\\begin{figure}[t]\n"," \\centering\n"," \\includegraphics[width=0.48\\textwidth]{Figures/cust-prune.pdf}\n"," \\caption{Given a requirement for an expert model across three dimensions (language, domain, and task), \\texttt{Cus-Prun} (i) identifies irrelevant neurons for each dimension using corresponding corpora (left), and (ii) prunes \\textbf{overlapping} irrelevant neurons across dimensions to obtain the expert model (right).}\n"," \\label{fig:cus-prun}\n","\\end{figure}\n"],["\\begin{figure}[t]\n","\\scalebox{0.95}{ \n","\\begin{minipage}{\\linewidth}\n","\\begin{algorithm}[H]\n","\\renewcommand{\\algorithmicrequire}{\\textbf{Input:}}\n","\\renewcommand{\\algorithmicensure}{\\textbf{Output:}}\n","\\caption{Adaptive Custom Pruning}\n","\\begin{algorithmic}[1]\n","\\REQUIRE Original language model $\\mathcal{LLM}$, request for expert model $\\mathcal{LLM}_{\\text{Exp}}$ with selected dimensions: $L_{\\text{Exp}}$, $D_{\\text{Exp}}$, $T_{\\text{Exp}}$ (any subset), request for pruning ratio $\\sigma$.\n","\\STATE \\texttt{// Construct specific corpora for each selected dimension.}\n","\\STATE $C = \\{\\}$\n","\\IF{$L_{\\text{Exp}}$ is specified}\n"," \\STATE $C = C \\cup \\{(L_{\\text{Exp}}, D, T) \\mid D \\in \\mathbb{D}, T \\in \\mathbb{T}\\}$\n","\\ENDIF\n","\\IF{$D_{\\text{Exp}}$ is specified}\n"," \\STATE $C = C \\cup \\{(L, D_{\\text{Exp}}, T) \\mid L \\in \\mathbb{L}, T \\in \\mathbb{T}\\}$\n","\\ENDIF\n","\\IF{$T_{\\text{Exp}}$ is specified}\n"," \\STATE $C = C \\cup \\{(L, D, T_{\\text{Exp}}) \\mid L \\in \\mathbb{L}, D \\in \\mathbb{D}\\}$\n","\\ENDIF\n","\\STATE \\texttt{// Identify irrelevant neurons for each selected dimension.}\n","\\FORALL{neuron $N_{i}^{(l)}$ in $\\mathcal{LLM}$}\n"," \\IF{$\\forall c \\in C,$ $N_{i}^{(l)} \\in \\tilde{\\mathcal{N}}(c)$}\n"," \\STATE $\\tilde{\\mathcal{N}} \\gets \\tilde{\\mathcal{N}} \\cup N_{i}^{(l)}$\n"," \\ENDIF\n","\\ENDFOR\n","\\STATE \\texttt{// Prune irrelevant neurons to obtain expert model.}\n","\\STATE $\\mathcal{LLM}_{\\text{Exp}} = \\mathcal{LLM} \\ominus \\tilde{\\mathcal{N}}$\n","\\ENSURE $\\mathcal{LLM}_{\\text{Exp}}$\n","\\end{algorithmic}\n","\\label{algo:adaptive_pruning}\n","\\end{algorithm}\n","\\end{minipage}\n","}\n","\\end{figure}\n"],["\\begin{figure}[t]\n"," \\centering\n"," \\includegraphics[width=0.48\\textwidth]{Figures/three-dim-examples.pdf}\n"," \\caption{Concrete examples of applying \\texttt{Cus-Prun} to prune $25\\%$ of Llama3-8B-Base's parameters into three-dimensional expert models. Numbers above each box indicate performance on the \\textbf{whole} test set, with the first evaluated by Rouge-L, and the other two by accuracy. }\n"," \\label{fig:korean}\n","\\end{figure}\n"],["\\begin{table*}[t]\n","\\caption{Main Results of \\texttt{Cus-Prun} on multilingual setting with a pruning ratio of $25\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in summarization tasks and in accuracy (\\%) for other datasets.}\n"," \\centering\n","\\footnotesize\n","\\setlength{\\tabcolsep}{0.8pt}\n"," \\scalebox{0.9}{\n"," \\begin{tabular}{c|l|cccc|ccccc|ccccc|cccc}\n","\\toprule\n","\\quad\\quad & \\multirow{2}{*}{\\textbf{\\normalsize{Method}}} & \n"," \\multicolumn{4}{c}{\\textbf{\\normalsize{General Capability}}} \\vline & \\multicolumn{5}{c}{\\textbf{\\normalsize{Multilingual Expert}}} \\vline & \\multicolumn{5}{c}{\\textbf{\\normalsize{Multidomain Expert }}} \\vline & \\multicolumn{4}{c}{\\textbf{\\normalsize{Multitask Expert }}} \\\\ \n"," & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3 & XQuAD & Sum & Avg. & MMCQ & FTQA & TSA & AMSA & Avg. & MSum & ASum & AMCF & Avg. \\\\ \\midrule\n","\\rowcolor{lightgray}\\cellcolor{white}\\multirow{5}{*}{\\rotatebox{90}{\\textbf{Llama3-8B}}} & Dense & 70.7 & 58.3 & 63.1 & 64.1 &41.2 &49.1 & 63.4 &32.9 &46.7 & 51.8 & 23.9 & 67.1 & 95.9 & 59.8 & 76.6 & 16.2 & 78.2 & 57.0 \\\\\n"," & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 1.1 & 24.0 & 13.6 & 23.2 & 15.5 & 0.0 & 0.0 & \\textbf{61.8} & 76.0 & 34.5 & 62.2 & \\textbf{21.8} & \\textbf{80.0} & \\textbf{54.7} \\\\\n"," & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & 14.9 & 16.6 & 8.5 & 10.0 & 22.6 & 0.0 & 41.2 & 53.7 & 29.4 & 7.3 & 2.9 & 51.3 & 20.5 \\\\\n"," & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & 26.9 & 0.0 & 2.7 & 7.4 & 3.2 & 0.0 & 38.6 & 35.7 & 19.4 & 4.1 & 4.8 & 43.8 & 17.6 \\\\\n"," &\\cellcolor{lightblue}\\texttt{Cus-Prun} &\\cellcolor{lightblue}\\textbf{62.4} & \\cellcolor{lightblue}\\textbf{37.0} & \\cellcolor{lightblue}\\textbf{54.7} & \\cellcolor{lightblue}\\textbf{51.4} & \\cellcolor{lightblue}\\textbf{30.1} & \\cellcolor{lightblue}\\textbf{41.5} & \\cellcolor{lightblue}\\textbf{52.6} & \\cellcolor{lightblue}\\textbf{31.5} & \\cellcolor{lightblue}\\textbf{38.9} & \\cellcolor{lightblue}\\textbf{42.9} & \\cellcolor{lightblue}\\textbf{20.6} & \\cellcolor{lightblue}\\textbf{61.8} & \\cellcolor{lightblue}\\textbf{87.6} & \\cellcolor{lightblue}\\textbf{53.2} & \\cellcolor{lightblue}\\textbf{68.4} & \\cellcolor{lightblue}12.8 & \\cellcolor{lightblue}75.5 & \\cellcolor{lightblue}52.2 \\\\ \\midrule\n","\\rowcolor{lightgray}\\cellcolor{white}\\multirow{5}{*}{\\rotatebox{90}{\\textbf{Mistral-12B}}} & Dense & 82.6 & 68.5 & 50.4 & 67.2 & 51.7 & 43.8 & 49.2 & 25.4 & 42.5 & 54.6 & 26.6 & 69.4 & 92.4 & 60.8 & 88.7 & 3.0 & 78.6 & 56.4 \\\\\n"," & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 2.1 & 27.8 & 19.0 & \\textbf{23.2} & 18.0 & 0.0 & 0.0 & 51.0 & 20.9 & 18.0 & 59.3 & 0.5 & 2.8 & 20.9 \\\\\n"," & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 0.8 & 25.1 & 17.4 & 7.8 & 12.8 & 24.9 & 9.2 & 34.2 & 54.3 & 30.7 & 27.4 & 1.3 & 36.3 & 21.7 \\\\\n"," & ShortGPT & 37.8 & 0.0 & 33.9 & 23.9 & 2.9 & 27.0 & 18.0 & 5.0 & 13.2 & 31.4 & 7.2 & 39.2 & 52.5 & 32.6 & 26.2 & 0.2 & 42.7 & 23.0 \\\\\n"," & \\cellcolor{lightblue}\\texttt{Cus-Prun} & \\cellcolor{lightblue}\\textbf{67.5} & \\cellcolor{lightblue}\\textbf{43.4} & \\cellcolor{lightblue}\\textbf{43.8} & \\cellcolor{lightblue}\\textbf{51.6} & \\cellcolor{lightblue}\\textbf{34.3} & \\cellcolor{lightblue}\\textbf{39.2} & \\cellcolor{lightblue}\\textbf{40.7} & \\cellcolor{lightblue}23.1 & \\cellcolor{lightblue}\\textbf{34.3} & \\cellcolor{lightblue}\\textbf{47.9} & \\cellcolor{lightblue}\\textbf{25.1} & \\cellcolor{lightblue}\\textbf{67.3} & \\cellcolor{lightblue}\\textbf{83.7} & \\cellcolor{lightblue}\\textbf{56.0} & \\cellcolor{lightblue}\\textbf{83.5} & \\cellcolor{lightblue}\\textbf{3.4} & \\cellcolor{lightblue}\\textbf{72.8} & \\cellcolor{lightblue}\\textbf{50.9} \\\\ \\midrule\n","\\rowcolor{lightgray}\\cellcolor{white}\\multirow{5}{*}{\\rotatebox{90}{\\textbf{Llama2-13B}}} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 17.5 & 30.4 & 44.1 & 24.9 & 29.2 & 25.2 & 0.0 & 42.7 & 84.1 & 38.0 & 70.0 & 7.4 & 44.3 & 40.6 \\\\\n"," & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 1.1 & 22.8 & 3.8 & 17.7 & 11.3 & 0.0 & 0.0 & 9.7 & 0.0 & 2.4 & 21.6 & 4.8 & 0.0 & 8.8 \\\\\n"," & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 2.8 & 25.3 & 23.4 & 9.9 & 15.5 & 18.7 & 0.0 & 28.4 & 67.3 & 28.6 & 24.5 & 4.9 & 32.9 & 20.8 \\\\\n"," & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 2.4 & 23.0 & 24.7 & 11.3 & 15.3 & 16.9 & 0.0 & 34.6 & \\textbf{69.8} & 30.3 & 23.8 & 5.2 & 39.1 & 22.7 \\\\\n"," & \\cellcolor{lightblue}\\texttt{Cus-Prun} & \\cellcolor{lightblue}\\textbf{48.3} & \\cellcolor{lightblue}\\textbf{20.8} & \\cellcolor{lightblue}\\textbf{50.0} & \\cellcolor{lightblue}\\textbf{39.7} & \\cellcolor{lightblue}\\textbf{12.7} & \\cellcolor{lightblue}\\textbf{26.2} & \\cellcolor{lightblue}\\textbf{34.2} & \\cellcolor{lightblue}\\textbf{24.1} & \\cellcolor{lightblue}\\textbf{24.3} & \\cellcolor{lightblue}\\textbf{25.6} & \\cellcolor{lightblue}0.0 & \\cellcolor{lightblue}\\textbf{38.5} & \\cellcolor{lightblue}68.3 & \\cellcolor{lightblue}\\textbf{33.1} & \\cellcolor{lightblue}\\textbf{64.5} & \\cellcolor{lightblue}\\textbf{6.7} & \\cellcolor{lightblue}\\textbf{42.9} & \\cellcolor{lightblue}\\textbf{38.0} \\\\ \\midrule\n","\\rowcolor{lightgray}\\cellcolor{white}\\multirow{5}{*}{\\rotatebox{90}{\\textbf{Llama3-70B}}} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 69.5 & 71.1 & 69.1 & 36.6 & 61.6 & 72.1 & 55.3 & 83.6 & 96.2 & 76.8 & 84.2 & 17.3 & 81.8 & 61.1 \\\\\n"," & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 16.8 & 43.7 & 43.0 & 29.0 & 33.1 & 27.3 & 1.0 & 51.0 & 50.3 & 32.4 & 10.2 & 13.7 & 20.6 & 14.8 \\\\\n"," & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 3.7 & 44.8 & 33.0 & 21.2 & 25.7 & 57.6 & 27.6 & 68.1 & 59.4 & 53.2 & 58.0 & 14.2 & 68.3 & 46.8 \\\\\n"," & ShortGPT & 59.4 & 5.6 & \\textbf{75.5} & 46.8 & 11.9 & 43.1 & 38.8 & 24.0 & 29.5 & 58.4 & 32.2 & 67.5 & 64.9 & 55.8 & 59.6 & 13.9 & 65.8 & 46.4 \\\\\n"," & \\cellcolor{lightblue}\\texttt{Cus-Prun} & \\cellcolor{lightblue}\\textbf{68.4} & \\cellcolor{lightblue}\\textbf{53.2} & \\cellcolor{lightblue}66.6 & \\cellcolor{lightblue}\\textbf{62.7} & \\cellcolor{lightblue}\\textbf{43.1} & \\cellcolor{lightblue}\\textbf{57.7} & \\cellcolor{lightblue}\\textbf{59.8} & \\cellcolor{lightblue}\\textbf{34.3} & \\cellcolor{lightblue}\\textbf{48.7} & \\cellcolor{lightblue}\\textbf{68.2} & \\cellcolor{lightblue}\\textbf{43.9} & \\cellcolor{lightblue}\\textbf{81.4} & \\cellcolor{lightblue}\\textbf{87.8} & \\cellcolor{lightblue}\\textbf{70.3} & \\cellcolor{lightblue}\\textbf{80.4} & \\cellcolor{lightblue}\\textbf{15.7} & \\cellcolor{lightblue}\\textbf{77.5} & \\cellcolor{lightblue}\\textbf{57.9} \\\\ \n","\\bottomrule\n","\\end{tabular}}\n"," \\label{table:cross_ling}\n","\\vspace{-0.2cm}\n","\\end{table*}\n"],["\\begin{figure*}[t]\n"," \\centering\n"," \\begin{minipage}{0.4\\textwidth}\n"," \\centering\n"," \\captionof{table}{Performance of Chinese-Medical expert model on MCQ task}\n"," \\label{tab:cmexam}\n"," \\scalebox{0.96}{\n"," \\begin{tabular}{l|c|c}\n"," \\hline\n"," \\textbf{Method} & \\textbf{General} & \\textbf{CMExam} \\\\\\midrule\n"," \\rowcolor{lightgray}\\cellcolor{white} Dense & 59.3 & 50.6 \\\\\n"," LLM-Pruner & 18.6 & 25.0 \\\\\n"," SliceGPT & 27.8 & 26.9 \\\\\n"," ShortGPT & 23.9 & 23.7 \\\\\n"," \\rowcolor{lightblue}\\cellcolor{white} \\texttt{Cus-Prun} & \\textbf{52.4} & \\textbf{48.7} \\\\\n"," \\hline\n"," \\end{tabular}}\n"," \\end{minipage}\n"," \\hfill\n"," \\begin{minipage}{0.58\\textwidth}\n"," \\centering\n"," \\includegraphics[width=\\textwidth]{Figures/huatuo.pdf}\n"," \\caption{Chinese Medical LLM performance. Numbers are quality on the \\textbf{whole} test set evaluated by GPT4.}\n"," \\label{fig:huatuo}\n"," \\end{minipage}\n","\\end{figure*}\n"],["\\begin{table}[ht]\n","\\caption{Aggressive pruning ratio on Llama3-8B.}\n","\\centering\n","\\setlength{\\tabcolsep}{3pt}\n"," \\scalebox{0.9}{\n","\\begin{tabular}{l|c|c|c|c}\n"," \\hline\n"," \\textbf{Method} & \\textbf{Ratio} & \\textbf{Speedup} & \\textbf{MMLU} & \\textbf{Expert} \\\\\\midrule\n"," \\rowcolor{lightgray}\\cellcolor{white}Dense & 0.0 & 1$\\times$ & 63.1 & 59.7 \\\\\\midrule\n"," ShortGPT & 25.0 & 1.3$\\times$ & 28.6 & 24.6 \\\\\n"," \\rowcolor{lightblue}\\cellcolor{white}\\texttt{Cus-Prun} & 25.0 & 1.3$\\times$ & \\textbf{51.9} & \\textbf{53.3} \\\\\\midrule\n"," ShortGPT & 34.2 & 1.5$\\times$ & 20.8 & 18.5 \\\\\n"," \\rowcolor{lightblue}\\cellcolor{white}\\texttt{Cus-Prun} & 35.0 & 1.5$\\times$ & \\textbf{50.2} & \\textbf{51.4} \\\\\\midrule\n"," ShortGPT & 43.8 & 1.8$\\times$ & 7.9 & 10.2 \\\\\n"," \\rowcolor{lightblue}\\cellcolor{white}\\texttt{Cus-Prun} & 45.0 & 1.8$\\times$ & \\textbf{48.4} & \\textbf{50.6} \\\\\n"," \\hline\n"," \\end{tabular}}\n"," \\label{table:prune_more}\n","\\end{table}\n"],["\\begin{figure*}[t]\n"," \\centering\n"," \\includegraphics[width=1.0\\textwidth]{Figures/lang-specific.pdf}\n"," \\caption{Performance of \\texttt{Cus-Prun} in obtaining language-specific models.}\n"," \\label{fig:lang-spec}\n","\\end{figure*}\n"],["\\begin{table}[ht]\n"," \\centering\n","\\footnotesize\n"," \\scalebox{1}{\n"," \\begin{tabular}{lp{5.3cm}}\n"," \\toprule\n"," \\textbf{Task} & \\textbf{Prompt} \\\\\\midrule\n"," Generation & Generate a text document in \\{language\\}/\\{domain\\}/\\{task\\}. Make sure the documents is not fixed to one \\{language\\}/\\{domain\\}/\\{task\\} or \\{language\\}/\\{domain\\}/\\{task\\}. Ensure the content is clear, concise, and appropriate for the specified request. Use professional and domain-specific terminology where necessary. \\\\\\midrule\n"," Evaluation & Evaluate the quality of the given answer to the question. Provide a score from 0 to 5, where 0 represents very low quality and 5 represents very high quality. Question: \\{question\\} Answer: \\{answer\\}.\\\\\n"," \\bottomrule\n"," \\end{tabular}}\n","\\caption{GPT-4o prompts for generating documents and evaluating answer quality.} \\label{table:testset}\n","\\end{table}\n"],["\\begin{table*}[hbpt]\n","\\caption{Main Results of \\texttt{Cus-Prun} on Germany with a pruning ratio of $25\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in XLSum and in accuracy (\\%) for other datasets.}\n"," \\centering\n","\\footnotesize\n","\\setlength{\\tabcolsep}{3pt}\n"," \\scalebox{1.0}{\n"," \\begin{tabular}{l|l|cccc|ccccc}\n","\\toprule\n","\\multirow{2}{*}{\\textbf{\\normalsize{Model}}} & \\multirow{2}{*}{\\textbf{\\normalsize{Method}}} & \n"," \\multicolumn{4}{c}{\\textbf{\\normalsize{General Capability}}} \\vline & \\multicolumn{5}{c}{\\textbf{\\normalsize{Expert Capability}}} \\\\ \n"," & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3Exam & XQuAD & XLSum & Avg. \\\\ \\midrule\n","\\multirow{4}{*}{Llama3-8B} &\n"," Dense & 70.7 & 58.3 & 63.1 & 64.1 & 44.8 & - & 52.9 & - & 48.8 \\\\\n"," & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 0.0 & - & 11.0 & - & 5.5 \\\\\n"," & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & - & 9.8 & - & 4.9 \\\\\n"," & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & - & 0.0 & - & 0.0 \\\\\n"," & \\texttt{Cus-Prun} & 61.4 & 38.9 & 54.5 & \\textbf{51.6} & 32.8 & - & 49.6 & - & \\textbf{41.2} \\\\ \\midrule\n","\\multirow{4}{*}{Mistral-12B} & Dense & 82.6 & 68.5 & 50.4 & 59.3 & 56.8 & - & 41.2 & - & 49.0 \\\\\n"," & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 2.4 & - & 13.4 & - & 7.9 \\\\\n"," & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 0.8 & - & 15.5 & - & 8.2 \\\\\n"," & ShortGPT & 37.8 & 0.0 & 33.9 & 23.9 & 3.6 & - & 20.3 & - & 12.0 \\\\\n"," & \\texttt{Cus-Prun} & 64.6 & 39.7 & 43.2 & \\textbf{49.2} & 31.6 & - & 35.9 & - & \\textbf{33.8} \\\\ \\midrule\n","\\multirow{4}{*}{Llama2-13B} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 24.4 & - & 40.3 & - & 32.3 \\\\\n"," & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 2.0 & - & 5.7 & - & 3.9 \\\\\n"," & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 3.6 & - & 18.1 & - & 10.9 \\\\\n"," & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 2.8 & - & 27.2 & - & 15.0 \\\\\n"," & \\texttt{Cus-Prun} & 47.6 & 19.8 & 49.9 & \\textbf{39.1} & 18.4 & - & 31.7 & - & \\textbf{25.0} \\\\ \\midrule\n","\\multirow{4}{*}{Llama3-70B} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 74.8 & - & 58.2 & - & 66.5 \\\\\n"," & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 18.0 & - & 27.3 & - & 22.7 \\\\\n"," & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 0.0 & - & 17.3 & - & 8.7 \\\\\n"," & ShortGPT & 59.4 & 5.6 & 75.5 & 46.8 & 9.6 & - & 31.5 & - & 20.6 \\\\\n"," & \\texttt{Cus-Prun} & 66.8 & 59.3 & 69.1 & \\textbf{65.1} & 48.2 & - & 53.9 & - & \\textbf{51.1} \\\\ \n","\\bottomrule\n","\\end{tabular}}\n"," \\label{table:cl_ger}\n","\\end{table*}\n"],["\\begin{table*}[hbpt]\n","\\caption{Main Results of \\texttt{Cus-Prun} on Chinese with a pruning ratio of $25\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in XLSum and in accuracy (\\%) for other datasets.}\n"," \\centering\n","\\footnotesize\n","\\setlength{\\tabcolsep}{3pt}\n"," \\scalebox{1.0}{\n"," \\begin{tabular}{l|l|cccc|ccccc}\n","\\toprule\n","\\multirow{2}{*}{\\textbf{\\normalsize{Model}}} & \\multirow{2}{*}{\\textbf{\\normalsize{Method}}} & \n"," \\multicolumn{4}{c}{\\textbf{\\normalsize{General Capability}}} \\vline & \\multicolumn{5}{c}{\\textbf{\\normalsize{Specific Capability}}} \\\\ \n"," & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3Exam & XQuAD & XLSum & Avg. \\\\ \\midrule\n","\\multirow{4}{*}{Llama3-8B} &\n"," Dense & 70.7 & 58.3 & 63.1 & 64.1 & 43.6 & 55.1 & 78.7 & 49.1 & 56.6 \\\\\n"," & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 2.4 & 23.6 & 21.3 & 32.8 & 20.0 \\\\\n"," & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & 17.4 & 23.5 & 8.3 & 12.3 \\\\\n"," & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & 28.3 & 0.0 & 3.1 & 7.9 \\\\\n"," & \\texttt{Cus-Prun} & 60.5 & 25.7 & 49.4 & \\textbf{45.2} & 36.0 & 44.7 & 65.6 & 46.3 & \\textbf{48.2} \\\\ \\midrule\n","\\multirow{4}{*}{Mistral-12B} & Dense & 82.6 & 68.5 & 50.4 & 59.3 & 53.2 & 47.8 & 62.2 & 33.0 & 49.1 \\\\\n"," & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 2.8 & 30.7 & 31.8 & 32.6 & 24.5 \\\\\n"," & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 1.6 & 26.4 & 28.3 & 10.8 & 16.8 \\\\\n"," & ShortGPT & 37.8 & 0.0 & 33.9 & 23.9 & 4.4 & 28.2 & 29.1 & 7.2 & 17.2 \\\\\n"," & \\texttt{Cus-Prun} & 68.3 & 43.2 & 39.5 & \\textbf{50.3} & 38.4 & 40.7 & 50.6 & 30.3 & \\textbf{40.0} \\\\ \\midrule\n","\\multirow{4}{*}{Llama2-13B} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 21.6 & 36.5 & 59.8 & 35.3 & 38.3 \\\\\n"," & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 1.2 & 23.3 & 3.8 & 25.1 & 13.4\\\\\n"," & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 4.8 & 24.5 & 28.4 & 11.2 & 17.2 \\\\\n"," & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 4.4 & 22.9 & 24.6 & 13.7 & 16.4 \\\\\n"," & \\texttt{Cus-Prun} & 48.6 & 20.7 & 51.9 & \\textbf{40.4} & 14.8 & 28.2 & 47.3 & 34.4 & \\textbf{31.2} \\\\ \\midrule\n","\\multirow{4}{*}{Llama3-70B} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 68.4 & 76.1 & 81.3 & 55.3 & 70.3 \\\\\n"," & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 16.8 & 47.5 & 56.1 & 41.3 & 40.4 \\\\\n"," & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 6.4 & 48.3 & 42.2 & 29.3 & 31.6 \\\\\n"," & ShortGPT & 59.4 & 5.6 & 75.5 & 46.8 & 12.4 & 45.5 & 44.6 & 36.1 & 34.7 \\\\\n"," & \\texttt{Cus-Prun} & 72.3 & 48.5 & 65.2 & \\textbf{62.0} & 40.8 & 61.7 & 66.9 & 51.6 & \\textbf{55.3} \\\\ \n","\\bottomrule\n","\\end{tabular}}\n"," \\label{table:cl_chi}\n","\\end{table*}\n"],["\\begin{table*}[hbpt]\n","\\caption{Main Results of \\texttt{Cus-Prun} on Thai with a pruning ratio of $25\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in XLSum and in accuracy (\\%) for other datasets.}\n"," \\centering\n","\\footnotesize\n","\\setlength{\\tabcolsep}{3pt}\n"," \\scalebox{1.0}{\n"," \\begin{tabular}{l|l|cccc|ccccc}\n","\\toprule\n","\\multirow{2}{*}{\\textbf{\\normalsize{Model}}} & \\multirow{2}{*}{\\textbf{\\normalsize{Method}}} & \n"," \\multicolumn{4}{c}{\\textbf{\\normalsize{General Capability}}} \\vline & \\multicolumn{5}{c}{\\textbf{\\normalsize{Specific Capability}}} \\\\ \n"," & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3Exam & XQuAD & XLSum & Avg. \\\\ \\midrule\n","\\multirow{4}{*}{Llama3-8B} &\n"," Dense & 70.7 & 58.3 & 63.1 & 64.1 & 35.2 & 43.0 & 58.7 & 16.7 & 38.4 \\\\\n"," & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 0.8 & 24.4 & 8.4 & 13.5 & 11.8 \\\\\n"," & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & 12.3 & 16.6 & 8.7 & 9.4 \\\\\n"," & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & 25.4 & 0.0 & 2.3 & 6.9 \\\\\n"," & \\texttt{Cus-Prun} & 58.9 & 31.2 & 52.4 & \\textbf{47.5} & 21.6 & 38.3 & 42.6 & 16.8 & \\textbf{29.8} \\\\ \\midrule\n","\\multirow{4}{*}{Mistral-12B} & Dense & 82.6 & 68.5 & 50.4 & 59.3 & 45.2 & 39.9 & 44.1 & 17.8 & 36.8 \\\\\n"," & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 1.2 & 24.8 & 11.9 & 13.7 & 12.9 \\\\\n"," & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 0.0 & 23.8 & 8.4 & 4.7 & 12.3 \\\\\n"," & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 0.8 & 25.7 & 4.7 & 2.8 & 8.5 \\\\\n"," & \\texttt{Cus-Prun} & 68.2 & 35.8 & 47.6 & \\textbf{50.5} & 32.8 & 37.7 & 35.6 & 15.9 & \\textbf{30.5} \\\\ \\midrule\n","\\multirow{4}{*}{Llama2-13B} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 6.4 & 24.3 & 28.3 & 14.5 & 18.4 \\\\\n"," & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 0.0 & 22.3 & 1.8 & 10.2 & 8.6 \\\\\n"," & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 0.0 & 26.2 & 23.7 & 8.6 & 14.6 \\\\\n"," & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 0.0 & 23.1 & 22.3 & 8.9 & 13.6 \\\\\n"," & \\texttt{Cus-Prun} & 47.8 & 20.9 & 50.7 & \\textbf{39.8} & 4.8 & 24.2 & 23.6 & 13.8 & \\textbf{16.6} \\\\ \\midrule\n","\\multirow{4}{*}{Llama3-70B} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 65.2 & 66.1 & 67.8 & 17.8 & 54.2 \\\\\n"," & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 15.6 & 39.9 & 29.8 & 16.6 & 25.5 \\\\\n"," & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 4.8 & 41.3 & 39.6 & 13.2 & 24.7 \\\\\n"," & ShortGPT & 59.4 & 5.6 & 75.5 & 46.8 & 13.7 & 40.7 & 40.4 & 11.9 & 26.7 \\\\\n"," & \\texttt{Cus-Prun} & 73.3 & 58.7 & 68.4 & \\textbf{66.8} & 40.4 & 53.6 & 58.5 & 16.9 & \\textbf{42.4} \\\\ \n","\\bottomrule\n","\\end{tabular}}\n"," \\label{table:cl_tha}\n","\\end{table*}\n"]],"string":"[\n [\n \"\\\\begin{figure}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=0.48\\\\textwidth]{Figures/cust-prune.pdf}\\n\",\n \" \\\\caption{Given a requirement for an expert model across three dimensions (language, domain, and task), \\\\texttt{Cus-Prun} (i) identifies irrelevant neurons for each dimension using corresponding corpora (left), and (ii) prunes \\\\textbf{overlapping} irrelevant neurons across dimensions to obtain the expert model (right).}\\n\",\n \" \\\\label{fig:cus-prun}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{figure}[t]\\n\",\n \"\\\\scalebox{0.95}{ \\n\",\n \"\\\\begin{minipage}{\\\\linewidth}\\n\",\n \"\\\\begin{algorithm}[H]\\n\",\n \"\\\\renewcommand{\\\\algorithmicrequire}{\\\\textbf{Input:}}\\n\",\n \"\\\\renewcommand{\\\\algorithmicensure}{\\\\textbf{Output:}}\\n\",\n \"\\\\caption{Adaptive Custom Pruning}\\n\",\n \"\\\\begin{algorithmic}[1]\\n\",\n \"\\\\REQUIRE Original language model $\\\\mathcal{LLM}$, request for expert model $\\\\mathcal{LLM}_{\\\\text{Exp}}$ with selected dimensions: $L_{\\\\text{Exp}}$, $D_{\\\\text{Exp}}$, $T_{\\\\text{Exp}}$ (any subset), request for pruning ratio $\\\\sigma$.\\n\",\n \"\\\\STATE \\\\texttt{// Construct specific corpora for each selected dimension.}\\n\",\n \"\\\\STATE $C = \\\\{\\\\}$\\n\",\n \"\\\\IF{$L_{\\\\text{Exp}}$ is specified}\\n\",\n \" \\\\STATE $C = C \\\\cup \\\\{(L_{\\\\text{Exp}}, D, T) \\\\mid D \\\\in \\\\mathbb{D}, T \\\\in \\\\mathbb{T}\\\\}$\\n\",\n \"\\\\ENDIF\\n\",\n \"\\\\IF{$D_{\\\\text{Exp}}$ is specified}\\n\",\n \" \\\\STATE $C = C \\\\cup \\\\{(L, D_{\\\\text{Exp}}, T) \\\\mid L \\\\in \\\\mathbb{L}, T \\\\in \\\\mathbb{T}\\\\}$\\n\",\n \"\\\\ENDIF\\n\",\n \"\\\\IF{$T_{\\\\text{Exp}}$ is specified}\\n\",\n \" \\\\STATE $C = C \\\\cup \\\\{(L, D, T_{\\\\text{Exp}}) \\\\mid L \\\\in \\\\mathbb{L}, D \\\\in \\\\mathbb{D}\\\\}$\\n\",\n \"\\\\ENDIF\\n\",\n \"\\\\STATE \\\\texttt{// Identify irrelevant neurons for each selected dimension.}\\n\",\n \"\\\\FORALL{neuron $N_{i}^{(l)}$ in $\\\\mathcal{LLM}$}\\n\",\n \" \\\\IF{$\\\\forall c \\\\in C,$ $N_{i}^{(l)} \\\\in \\\\tilde{\\\\mathcal{N}}(c)$}\\n\",\n \" \\\\STATE $\\\\tilde{\\\\mathcal{N}} \\\\gets \\\\tilde{\\\\mathcal{N}} \\\\cup N_{i}^{(l)}$\\n\",\n \" \\\\ENDIF\\n\",\n \"\\\\ENDFOR\\n\",\n \"\\\\STATE \\\\texttt{// Prune irrelevant neurons to obtain expert model.}\\n\",\n \"\\\\STATE $\\\\mathcal{LLM}_{\\\\text{Exp}} = \\\\mathcal{LLM} \\\\ominus \\\\tilde{\\\\mathcal{N}}$\\n\",\n \"\\\\ENSURE $\\\\mathcal{LLM}_{\\\\text{Exp}}$\\n\",\n \"\\\\end{algorithmic}\\n\",\n \"\\\\label{algo:adaptive_pruning}\\n\",\n \"\\\\end{algorithm}\\n\",\n \"\\\\end{minipage}\\n\",\n \"}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{figure}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=0.48\\\\textwidth]{Figures/three-dim-examples.pdf}\\n\",\n \" \\\\caption{Concrete examples of applying \\\\texttt{Cus-Prun} to prune $25\\\\%$ of Llama3-8B-Base's parameters into three-dimensional expert models. Numbers above each box indicate performance on the \\\\textbf{whole} test set, with the first evaluated by Rouge-L, and the other two by accuracy. }\\n\",\n \" \\\\label{fig:korean}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table*}[t]\\n\",\n \"\\\\caption{Main Results of \\\\texttt{Cus-Prun} on multilingual setting with a pruning ratio of $25\\\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in summarization tasks and in accuracy (\\\\%) for other datasets.}\\n\",\n \" \\\\centering\\n\",\n \"\\\\footnotesize\\n\",\n \"\\\\setlength{\\\\tabcolsep}{0.8pt}\\n\",\n \" \\\\scalebox{0.9}{\\n\",\n \" \\\\begin{tabular}{c|l|cccc|ccccc|ccccc|cccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\quad\\\\quad & \\\\multirow{2}{*}{\\\\textbf{\\\\normalsize{Method}}} & \\n\",\n \" \\\\multicolumn{4}{c}{\\\\textbf{\\\\normalsize{General Capability}}} \\\\vline & \\\\multicolumn{5}{c}{\\\\textbf{\\\\normalsize{Multilingual Expert}}} \\\\vline & \\\\multicolumn{5}{c}{\\\\textbf{\\\\normalsize{Multidomain Expert }}} \\\\vline & \\\\multicolumn{4}{c}{\\\\textbf{\\\\normalsize{Multitask Expert }}} \\\\\\\\ \\n\",\n \" & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3 & XQuAD & Sum & Avg. & MMCQ & FTQA & TSA & AMSA & Avg. & MSum & ASum & AMCF & Avg. \\\\\\\\ \\\\midrule\\n\",\n \"\\\\rowcolor{lightgray}\\\\cellcolor{white}\\\\multirow{5}{*}{\\\\rotatebox{90}{\\\\textbf{Llama3-8B}}} & Dense & 70.7 & 58.3 & 63.1 & 64.1 &41.2 &49.1 & 63.4 &32.9 &46.7 & 51.8 & 23.9 & 67.1 & 95.9 & 59.8 & 76.6 & 16.2 & 78.2 & 57.0 \\\\\\\\\\n\",\n \" & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 1.1 & 24.0 & 13.6 & 23.2 & 15.5 & 0.0 & 0.0 & \\\\textbf{61.8} & 76.0 & 34.5 & 62.2 & \\\\textbf{21.8} & \\\\textbf{80.0} & \\\\textbf{54.7} \\\\\\\\\\n\",\n \" & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & 14.9 & 16.6 & 8.5 & 10.0 & 22.6 & 0.0 & 41.2 & 53.7 & 29.4 & 7.3 & 2.9 & 51.3 & 20.5 \\\\\\\\\\n\",\n \" & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & 26.9 & 0.0 & 2.7 & 7.4 & 3.2 & 0.0 & 38.6 & 35.7 & 19.4 & 4.1 & 4.8 & 43.8 & 17.6 \\\\\\\\\\n\",\n \" &\\\\cellcolor{lightblue}\\\\texttt{Cus-Prun} &\\\\cellcolor{lightblue}\\\\textbf{62.4} & \\\\cellcolor{lightblue}\\\\textbf{37.0} & \\\\cellcolor{lightblue}\\\\textbf{54.7} & \\\\cellcolor{lightblue}\\\\textbf{51.4} & \\\\cellcolor{lightblue}\\\\textbf{30.1} & \\\\cellcolor{lightblue}\\\\textbf{41.5} & \\\\cellcolor{lightblue}\\\\textbf{52.6} & \\\\cellcolor{lightblue}\\\\textbf{31.5} & \\\\cellcolor{lightblue}\\\\textbf{38.9} & \\\\cellcolor{lightblue}\\\\textbf{42.9} & \\\\cellcolor{lightblue}\\\\textbf{20.6} & \\\\cellcolor{lightblue}\\\\textbf{61.8} & \\\\cellcolor{lightblue}\\\\textbf{87.6} & \\\\cellcolor{lightblue}\\\\textbf{53.2} & \\\\cellcolor{lightblue}\\\\textbf{68.4} & \\\\cellcolor{lightblue}12.8 & \\\\cellcolor{lightblue}75.5 & \\\\cellcolor{lightblue}52.2 \\\\\\\\ \\\\midrule\\n\",\n \"\\\\rowcolor{lightgray}\\\\cellcolor{white}\\\\multirow{5}{*}{\\\\rotatebox{90}{\\\\textbf{Mistral-12B}}} & Dense & 82.6 & 68.5 & 50.4 & 67.2 & 51.7 & 43.8 & 49.2 & 25.4 & 42.5 & 54.6 & 26.6 & 69.4 & 92.4 & 60.8 & 88.7 & 3.0 & 78.6 & 56.4 \\\\\\\\\\n\",\n \" & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 2.1 & 27.8 & 19.0 & \\\\textbf{23.2} & 18.0 & 0.0 & 0.0 & 51.0 & 20.9 & 18.0 & 59.3 & 0.5 & 2.8 & 20.9 \\\\\\\\\\n\",\n \" & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 0.8 & 25.1 & 17.4 & 7.8 & 12.8 & 24.9 & 9.2 & 34.2 & 54.3 & 30.7 & 27.4 & 1.3 & 36.3 & 21.7 \\\\\\\\\\n\",\n \" & ShortGPT & 37.8 & 0.0 & 33.9 & 23.9 & 2.9 & 27.0 & 18.0 & 5.0 & 13.2 & 31.4 & 7.2 & 39.2 & 52.5 & 32.6 & 26.2 & 0.2 & 42.7 & 23.0 \\\\\\\\\\n\",\n \" & \\\\cellcolor{lightblue}\\\\texttt{Cus-Prun} & \\\\cellcolor{lightblue}\\\\textbf{67.5} & \\\\cellcolor{lightblue}\\\\textbf{43.4} & \\\\cellcolor{lightblue}\\\\textbf{43.8} & \\\\cellcolor{lightblue}\\\\textbf{51.6} & \\\\cellcolor{lightblue}\\\\textbf{34.3} & \\\\cellcolor{lightblue}\\\\textbf{39.2} & \\\\cellcolor{lightblue}\\\\textbf{40.7} & \\\\cellcolor{lightblue}23.1 & \\\\cellcolor{lightblue}\\\\textbf{34.3} & \\\\cellcolor{lightblue}\\\\textbf{47.9} & \\\\cellcolor{lightblue}\\\\textbf{25.1} & \\\\cellcolor{lightblue}\\\\textbf{67.3} & \\\\cellcolor{lightblue}\\\\textbf{83.7} & \\\\cellcolor{lightblue}\\\\textbf{56.0} & \\\\cellcolor{lightblue}\\\\textbf{83.5} & \\\\cellcolor{lightblue}\\\\textbf{3.4} & \\\\cellcolor{lightblue}\\\\textbf{72.8} & \\\\cellcolor{lightblue}\\\\textbf{50.9} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\rowcolor{lightgray}\\\\cellcolor{white}\\\\multirow{5}{*}{\\\\rotatebox{90}{\\\\textbf{Llama2-13B}}} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 17.5 & 30.4 & 44.1 & 24.9 & 29.2 & 25.2 & 0.0 & 42.7 & 84.1 & 38.0 & 70.0 & 7.4 & 44.3 & 40.6 \\\\\\\\\\n\",\n \" & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 1.1 & 22.8 & 3.8 & 17.7 & 11.3 & 0.0 & 0.0 & 9.7 & 0.0 & 2.4 & 21.6 & 4.8 & 0.0 & 8.8 \\\\\\\\\\n\",\n \" & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 2.8 & 25.3 & 23.4 & 9.9 & 15.5 & 18.7 & 0.0 & 28.4 & 67.3 & 28.6 & 24.5 & 4.9 & 32.9 & 20.8 \\\\\\\\\\n\",\n \" & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 2.4 & 23.0 & 24.7 & 11.3 & 15.3 & 16.9 & 0.0 & 34.6 & \\\\textbf{69.8} & 30.3 & 23.8 & 5.2 & 39.1 & 22.7 \\\\\\\\\\n\",\n \" & \\\\cellcolor{lightblue}\\\\texttt{Cus-Prun} & \\\\cellcolor{lightblue}\\\\textbf{48.3} & \\\\cellcolor{lightblue}\\\\textbf{20.8} & \\\\cellcolor{lightblue}\\\\textbf{50.0} & \\\\cellcolor{lightblue}\\\\textbf{39.7} & \\\\cellcolor{lightblue}\\\\textbf{12.7} & \\\\cellcolor{lightblue}\\\\textbf{26.2} & \\\\cellcolor{lightblue}\\\\textbf{34.2} & \\\\cellcolor{lightblue}\\\\textbf{24.1} & \\\\cellcolor{lightblue}\\\\textbf{24.3} & \\\\cellcolor{lightblue}\\\\textbf{25.6} & \\\\cellcolor{lightblue}0.0 & \\\\cellcolor{lightblue}\\\\textbf{38.5} & \\\\cellcolor{lightblue}68.3 & \\\\cellcolor{lightblue}\\\\textbf{33.1} & \\\\cellcolor{lightblue}\\\\textbf{64.5} & \\\\cellcolor{lightblue}\\\\textbf{6.7} & \\\\cellcolor{lightblue}\\\\textbf{42.9} & \\\\cellcolor{lightblue}\\\\textbf{38.0} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\rowcolor{lightgray}\\\\cellcolor{white}\\\\multirow{5}{*}{\\\\rotatebox{90}{\\\\textbf{Llama3-70B}}} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 69.5 & 71.1 & 69.1 & 36.6 & 61.6 & 72.1 & 55.3 & 83.6 & 96.2 & 76.8 & 84.2 & 17.3 & 81.8 & 61.1 \\\\\\\\\\n\",\n \" & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 16.8 & 43.7 & 43.0 & 29.0 & 33.1 & 27.3 & 1.0 & 51.0 & 50.3 & 32.4 & 10.2 & 13.7 & 20.6 & 14.8 \\\\\\\\\\n\",\n \" & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 3.7 & 44.8 & 33.0 & 21.2 & 25.7 & 57.6 & 27.6 & 68.1 & 59.4 & 53.2 & 58.0 & 14.2 & 68.3 & 46.8 \\\\\\\\\\n\",\n \" & ShortGPT & 59.4 & 5.6 & \\\\textbf{75.5} & 46.8 & 11.9 & 43.1 & 38.8 & 24.0 & 29.5 & 58.4 & 32.2 & 67.5 & 64.9 & 55.8 & 59.6 & 13.9 & 65.8 & 46.4 \\\\\\\\\\n\",\n \" & \\\\cellcolor{lightblue}\\\\texttt{Cus-Prun} & \\\\cellcolor{lightblue}\\\\textbf{68.4} & \\\\cellcolor{lightblue}\\\\textbf{53.2} & \\\\cellcolor{lightblue}66.6 & \\\\cellcolor{lightblue}\\\\textbf{62.7} & \\\\cellcolor{lightblue}\\\\textbf{43.1} & \\\\cellcolor{lightblue}\\\\textbf{57.7} & \\\\cellcolor{lightblue}\\\\textbf{59.8} & \\\\cellcolor{lightblue}\\\\textbf{34.3} & \\\\cellcolor{lightblue}\\\\textbf{48.7} & \\\\cellcolor{lightblue}\\\\textbf{68.2} & \\\\cellcolor{lightblue}\\\\textbf{43.9} & \\\\cellcolor{lightblue}\\\\textbf{81.4} & \\\\cellcolor{lightblue}\\\\textbf{87.8} & \\\\cellcolor{lightblue}\\\\textbf{70.3} & \\\\cellcolor{lightblue}\\\\textbf{80.4} & \\\\cellcolor{lightblue}\\\\textbf{15.7} & \\\\cellcolor{lightblue}\\\\textbf{77.5} & \\\\cellcolor{lightblue}\\\\textbf{57.9} \\\\\\\\ \\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}}\\n\",\n \" \\\\label{table:cross_ling}\\n\",\n \"\\\\vspace{-0.2cm}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\begin{minipage}{0.4\\\\textwidth}\\n\",\n \" \\\\centering\\n\",\n \" \\\\captionof{table}{Performance of Chinese-Medical expert model on MCQ task}\\n\",\n \" \\\\label{tab:cmexam}\\n\",\n \" \\\\scalebox{0.96}{\\n\",\n \" \\\\begin{tabular}{l|c|c}\\n\",\n \" \\\\hline\\n\",\n \" \\\\textbf{Method} & \\\\textbf{General} & \\\\textbf{CMExam} \\\\\\\\\\\\midrule\\n\",\n \" \\\\rowcolor{lightgray}\\\\cellcolor{white} Dense & 59.3 & 50.6 \\\\\\\\\\n\",\n \" LLM-Pruner & 18.6 & 25.0 \\\\\\\\\\n\",\n \" SliceGPT & 27.8 & 26.9 \\\\\\\\\\n\",\n \" ShortGPT & 23.9 & 23.7 \\\\\\\\\\n\",\n \" \\\\rowcolor{lightblue}\\\\cellcolor{white} \\\\texttt{Cus-Prun} & \\\\textbf{52.4} & \\\\textbf{48.7} \\\\\\\\\\n\",\n \" \\\\hline\\n\",\n \" \\\\end{tabular}}\\n\",\n \" \\\\end{minipage}\\n\",\n \" \\\\hfill\\n\",\n \" \\\\begin{minipage}{0.58\\\\textwidth}\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\textwidth]{Figures/huatuo.pdf}\\n\",\n \" \\\\caption{Chinese Medical LLM performance. Numbers are quality on the \\\\textbf{whole} test set evaluated by GPT4.}\\n\",\n \" \\\\label{fig:huatuo}\\n\",\n \" \\\\end{minipage}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{table}[ht]\\n\",\n \"\\\\caption{Aggressive pruning ratio on Llama3-8B.}\\n\",\n \"\\\\centering\\n\",\n \"\\\\setlength{\\\\tabcolsep}{3pt}\\n\",\n \" \\\\scalebox{0.9}{\\n\",\n \"\\\\begin{tabular}{l|c|c|c|c}\\n\",\n \" \\\\hline\\n\",\n \" \\\\textbf{Method} & \\\\textbf{Ratio} & \\\\textbf{Speedup} & \\\\textbf{MMLU} & \\\\textbf{Expert} \\\\\\\\\\\\midrule\\n\",\n \" \\\\rowcolor{lightgray}\\\\cellcolor{white}Dense & 0.0 & 1$\\\\times$ & 63.1 & 59.7 \\\\\\\\\\\\midrule\\n\",\n \" ShortGPT & 25.0 & 1.3$\\\\times$ & 28.6 & 24.6 \\\\\\\\\\n\",\n \" \\\\rowcolor{lightblue}\\\\cellcolor{white}\\\\texttt{Cus-Prun} & 25.0 & 1.3$\\\\times$ & \\\\textbf{51.9} & \\\\textbf{53.3} \\\\\\\\\\\\midrule\\n\",\n \" ShortGPT & 34.2 & 1.5$\\\\times$ & 20.8 & 18.5 \\\\\\\\\\n\",\n \" \\\\rowcolor{lightblue}\\\\cellcolor{white}\\\\texttt{Cus-Prun} & 35.0 & 1.5$\\\\times$ & \\\\textbf{50.2} & \\\\textbf{51.4} \\\\\\\\\\\\midrule\\n\",\n \" ShortGPT & 43.8 & 1.8$\\\\times$ & 7.9 & 10.2 \\\\\\\\\\n\",\n \" \\\\rowcolor{lightblue}\\\\cellcolor{white}\\\\texttt{Cus-Prun} & 45.0 & 1.8$\\\\times$ & \\\\textbf{48.4} & \\\\textbf{50.6} \\\\\\\\\\n\",\n \" \\\\hline\\n\",\n \" \\\\end{tabular}}\\n\",\n \" \\\\label{table:prune_more}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{figure*}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=1.0\\\\textwidth]{Figures/lang-specific.pdf}\\n\",\n \" \\\\caption{Performance of \\\\texttt{Cus-Prun} in obtaining language-specific models.}\\n\",\n \" \\\\label{fig:lang-spec}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{table}[ht]\\n\",\n \" \\\\centering\\n\",\n \"\\\\footnotesize\\n\",\n \" \\\\scalebox{1}{\\n\",\n \" \\\\begin{tabular}{lp{5.3cm}}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Task} & \\\\textbf{Prompt} \\\\\\\\\\\\midrule\\n\",\n \" Generation & Generate a text document in \\\\{language\\\\}/\\\\{domain\\\\}/\\\\{task\\\\}. Make sure the documents is not fixed to one \\\\{language\\\\}/\\\\{domain\\\\}/\\\\{task\\\\} or \\\\{language\\\\}/\\\\{domain\\\\}/\\\\{task\\\\}. Ensure the content is clear, concise, and appropriate for the specified request. Use professional and domain-specific terminology where necessary. \\\\\\\\\\\\midrule\\n\",\n \" Evaluation & Evaluate the quality of the given answer to the question. Provide a score from 0 to 5, where 0 represents very low quality and 5 represents very high quality. Question: \\\\{question\\\\} Answer: \\\\{answer\\\\}.\\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}}\\n\",\n \"\\\\caption{GPT-4o prompts for generating documents and evaluating answer quality.} \\\\label{table:testset}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table*}[hbpt]\\n\",\n \"\\\\caption{Main Results of \\\\texttt{Cus-Prun} on Germany with a pruning ratio of $25\\\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in XLSum and in accuracy (\\\\%) for other datasets.}\\n\",\n \" \\\\centering\\n\",\n \"\\\\footnotesize\\n\",\n \"\\\\setlength{\\\\tabcolsep}{3pt}\\n\",\n \" \\\\scalebox{1.0}{\\n\",\n \" \\\\begin{tabular}{l|l|cccc|ccccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{\\\\textbf{\\\\normalsize{Model}}} & \\\\multirow{2}{*}{\\\\textbf{\\\\normalsize{Method}}} & \\n\",\n \" \\\\multicolumn{4}{c}{\\\\textbf{\\\\normalsize{General Capability}}} \\\\vline & \\\\multicolumn{5}{c}{\\\\textbf{\\\\normalsize{Expert Capability}}} \\\\\\\\ \\n\",\n \" & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3Exam & XQuAD & XLSum & Avg. \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Llama3-8B} &\\n\",\n \" Dense & 70.7 & 58.3 & 63.1 & 64.1 & 44.8 & - & 52.9 & - & 48.8 \\\\\\\\\\n\",\n \" & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 0.0 & - & 11.0 & - & 5.5 \\\\\\\\\\n\",\n \" & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & - & 9.8 & - & 4.9 \\\\\\\\\\n\",\n \" & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & - & 0.0 & - & 0.0 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 61.4 & 38.9 & 54.5 & \\\\textbf{51.6} & 32.8 & - & 49.6 & - & \\\\textbf{41.2} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Mistral-12B} & Dense & 82.6 & 68.5 & 50.4 & 59.3 & 56.8 & - & 41.2 & - & 49.0 \\\\\\\\\\n\",\n \" & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 2.4 & - & 13.4 & - & 7.9 \\\\\\\\\\n\",\n \" & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 0.8 & - & 15.5 & - & 8.2 \\\\\\\\\\n\",\n \" & ShortGPT & 37.8 & 0.0 & 33.9 & 23.9 & 3.6 & - & 20.3 & - & 12.0 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 64.6 & 39.7 & 43.2 & \\\\textbf{49.2} & 31.6 & - & 35.9 & - & \\\\textbf{33.8} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Llama2-13B} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 24.4 & - & 40.3 & - & 32.3 \\\\\\\\\\n\",\n \" & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 2.0 & - & 5.7 & - & 3.9 \\\\\\\\\\n\",\n \" & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 3.6 & - & 18.1 & - & 10.9 \\\\\\\\\\n\",\n \" & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 2.8 & - & 27.2 & - & 15.0 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 47.6 & 19.8 & 49.9 & \\\\textbf{39.1} & 18.4 & - & 31.7 & - & \\\\textbf{25.0} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Llama3-70B} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 74.8 & - & 58.2 & - & 66.5 \\\\\\\\\\n\",\n \" & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 18.0 & - & 27.3 & - & 22.7 \\\\\\\\\\n\",\n \" & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 0.0 & - & 17.3 & - & 8.7 \\\\\\\\\\n\",\n \" & ShortGPT & 59.4 & 5.6 & 75.5 & 46.8 & 9.6 & - & 31.5 & - & 20.6 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 66.8 & 59.3 & 69.1 & \\\\textbf{65.1} & 48.2 & - & 53.9 & - & \\\\textbf{51.1} \\\\\\\\ \\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}}\\n\",\n \" \\\\label{table:cl_ger}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[hbpt]\\n\",\n \"\\\\caption{Main Results of \\\\texttt{Cus-Prun} on Chinese with a pruning ratio of $25\\\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in XLSum and in accuracy (\\\\%) for other datasets.}\\n\",\n \" \\\\centering\\n\",\n \"\\\\footnotesize\\n\",\n \"\\\\setlength{\\\\tabcolsep}{3pt}\\n\",\n \" \\\\scalebox{1.0}{\\n\",\n \" \\\\begin{tabular}{l|l|cccc|ccccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{\\\\textbf{\\\\normalsize{Model}}} & \\\\multirow{2}{*}{\\\\textbf{\\\\normalsize{Method}}} & \\n\",\n \" \\\\multicolumn{4}{c}{\\\\textbf{\\\\normalsize{General Capability}}} \\\\vline & \\\\multicolumn{5}{c}{\\\\textbf{\\\\normalsize{Specific Capability}}} \\\\\\\\ \\n\",\n \" & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3Exam & XQuAD & XLSum & Avg. \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Llama3-8B} &\\n\",\n \" Dense & 70.7 & 58.3 & 63.1 & 64.1 & 43.6 & 55.1 & 78.7 & 49.1 & 56.6 \\\\\\\\\\n\",\n \" & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 2.4 & 23.6 & 21.3 & 32.8 & 20.0 \\\\\\\\\\n\",\n \" & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & 17.4 & 23.5 & 8.3 & 12.3 \\\\\\\\\\n\",\n \" & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & 28.3 & 0.0 & 3.1 & 7.9 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 60.5 & 25.7 & 49.4 & \\\\textbf{45.2} & 36.0 & 44.7 & 65.6 & 46.3 & \\\\textbf{48.2} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Mistral-12B} & Dense & 82.6 & 68.5 & 50.4 & 59.3 & 53.2 & 47.8 & 62.2 & 33.0 & 49.1 \\\\\\\\\\n\",\n \" & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 2.8 & 30.7 & 31.8 & 32.6 & 24.5 \\\\\\\\\\n\",\n \" & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 1.6 & 26.4 & 28.3 & 10.8 & 16.8 \\\\\\\\\\n\",\n \" & ShortGPT & 37.8 & 0.0 & 33.9 & 23.9 & 4.4 & 28.2 & 29.1 & 7.2 & 17.2 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 68.3 & 43.2 & 39.5 & \\\\textbf{50.3} & 38.4 & 40.7 & 50.6 & 30.3 & \\\\textbf{40.0} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Llama2-13B} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 21.6 & 36.5 & 59.8 & 35.3 & 38.3 \\\\\\\\\\n\",\n \" & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 1.2 & 23.3 & 3.8 & 25.1 & 13.4\\\\\\\\\\n\",\n \" & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 4.8 & 24.5 & 28.4 & 11.2 & 17.2 \\\\\\\\\\n\",\n \" & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 4.4 & 22.9 & 24.6 & 13.7 & 16.4 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 48.6 & 20.7 & 51.9 & \\\\textbf{40.4} & 14.8 & 28.2 & 47.3 & 34.4 & \\\\textbf{31.2} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Llama3-70B} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 68.4 & 76.1 & 81.3 & 55.3 & 70.3 \\\\\\\\\\n\",\n \" & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 16.8 & 47.5 & 56.1 & 41.3 & 40.4 \\\\\\\\\\n\",\n \" & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 6.4 & 48.3 & 42.2 & 29.3 & 31.6 \\\\\\\\\\n\",\n \" & ShortGPT & 59.4 & 5.6 & 75.5 & 46.8 & 12.4 & 45.5 & 44.6 & 36.1 & 34.7 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 72.3 & 48.5 & 65.2 & \\\\textbf{62.0} & 40.8 & 61.7 & 66.9 & 51.6 & \\\\textbf{55.3} \\\\\\\\ \\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}}\\n\",\n \" \\\\label{table:cl_chi}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[hbpt]\\n\",\n \"\\\\caption{Main Results of \\\\texttt{Cus-Prun} on Thai with a pruning ratio of $25\\\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in XLSum and in accuracy (\\\\%) for other datasets.}\\n\",\n \" \\\\centering\\n\",\n \"\\\\footnotesize\\n\",\n \"\\\\setlength{\\\\tabcolsep}{3pt}\\n\",\n \" \\\\scalebox{1.0}{\\n\",\n \" \\\\begin{tabular}{l|l|cccc|ccccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{\\\\textbf{\\\\normalsize{Model}}} & \\\\multirow{2}{*}{\\\\textbf{\\\\normalsize{Method}}} & \\n\",\n \" \\\\multicolumn{4}{c}{\\\\textbf{\\\\normalsize{General Capability}}} \\\\vline & \\\\multicolumn{5}{c}{\\\\textbf{\\\\normalsize{Specific Capability}}} \\\\\\\\ \\n\",\n \" & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3Exam & XQuAD & XLSum & Avg. \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Llama3-8B} &\\n\",\n \" Dense & 70.7 & 58.3 & 63.1 & 64.1 & 35.2 & 43.0 & 58.7 & 16.7 & 38.4 \\\\\\\\\\n\",\n \" & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 0.8 & 24.4 & 8.4 & 13.5 & 11.8 \\\\\\\\\\n\",\n \" & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & 12.3 & 16.6 & 8.7 & 9.4 \\\\\\\\\\n\",\n \" & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & 25.4 & 0.0 & 2.3 & 6.9 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 58.9 & 31.2 & 52.4 & \\\\textbf{47.5} & 21.6 & 38.3 & 42.6 & 16.8 & \\\\textbf{29.8} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Mistral-12B} & Dense & 82.6 & 68.5 & 50.4 & 59.3 & 45.2 & 39.9 & 44.1 & 17.8 & 36.8 \\\\\\\\\\n\",\n \" & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 1.2 & 24.8 & 11.9 & 13.7 & 12.9 \\\\\\\\\\n\",\n \" & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 0.0 & 23.8 & 8.4 & 4.7 & 12.3 \\\\\\\\\\n\",\n \" & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 0.8 & 25.7 & 4.7 & 2.8 & 8.5 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 68.2 & 35.8 & 47.6 & \\\\textbf{50.5} & 32.8 & 37.7 & 35.6 & 15.9 & \\\\textbf{30.5} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Llama2-13B} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 6.4 & 24.3 & 28.3 & 14.5 & 18.4 \\\\\\\\\\n\",\n \" & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 0.0 & 22.3 & 1.8 & 10.2 & 8.6 \\\\\\\\\\n\",\n \" & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 0.0 & 26.2 & 23.7 & 8.6 & 14.6 \\\\\\\\\\n\",\n \" & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 0.0 & 23.1 & 22.3 & 8.9 & 13.6 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 47.8 & 20.9 & 50.7 & \\\\textbf{39.8} & 4.8 & 24.2 & 23.6 & 13.8 & \\\\textbf{16.6} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Llama3-70B} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 65.2 & 66.1 & 67.8 & 17.8 & 54.2 \\\\\\\\\\n\",\n \" & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 15.6 & 39.9 & 29.8 & 16.6 & 25.5 \\\\\\\\\\n\",\n \" & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 4.8 & 41.3 & 39.6 & 13.2 & 24.7 \\\\\\\\\\n\",\n \" & ShortGPT & 59.4 & 5.6 & 75.5 & 46.8 & 13.7 & 40.7 & 40.4 & 11.9 & 26.7 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 73.3 & 58.7 & 68.4 & \\\\textbf{66.8} & 40.4 & 53.6 & 58.5 & 16.9 & \\\\textbf{42.4} \\\\\\\\ \\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}}\\n\",\n \" \\\\label{table:cl_tha}\\n\",\n \"\\\\end{table*}\\n\"\n ]\n]","isConversational":false},"fulltext":{"kind":"string","value":"\\pdfoutput=1\n\n\\documentclass[11pt]{article}\n\n\\usepackage[final]{acl}\n\n\n\\usepackage{microtype}\n\\usepackage{hyperref}\n\\usepackage{url}\n\\usepackage{booktabs}\n\\usepackage{times}\n\\usepackage{latexsym}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{graphicx}\n\\usepackage{multirow}\n% This is not strictly necessary, and may be commented out,\n% but it will improve the layout of the manuscript,\n% and will typically save some space.\n\\usepackage{subcaption}\n% For proper rendering and hyphenation of words containing Latin characters (including in bib files)\n\\usepackage[T1]{fontenc}\n\n\\usepackage{tikz}\n\n\\usepackage{listings}\n\\usepackage{color}\n% \\usepackage{minted}\n\\definecolor{dkgreen}{rgb}{0,0.6,0}\n\\definecolor{gray}{rgb}{0.5,0.5,0.5}\n\\definecolor{mauve}{rgb}{0.58,0,0.82}\n\\definecolor{lightblue}{rgb}{0.9, 0.95, 1.0}\n\\definecolor{lightgray}{rgb}{0.9, 0.9, 0.9}\n\n\\lstset{frame=tb,\n language=Python,\n aboveskip=3mm,\n belowskip=3mm,\n showstringspaces=false,\n columns=flexible,\n basicstyle={\\small\\ttfamily},\n numbers=none,\n numberstyle=\\tiny\\color{gray},\n keywordstyle=\\color{blue},\n commentstyle=\\color{dkgreen},\n stringstyle=\\color{mauve},\n breaklines=true,\n breakatwhitespace=true,\n tabsize=3\n}\n\n\\newcommand{\\circled}[1]{%\n \\tikz[baseline=(char.base)]{\n \\node[shape=circle, draw, inner sep=0.5pt, fill=black] (char) {\\textcolor{white}{\\small{#1}}};\n }\n}\n\\usepackage{colortbl}\n\n\\usepackage{algorithmic}\n\\usepackage{algorithm}\n\n\\usepackage[utf8]{inputenc}\n\\usepackage{multirow}\n% This is not strictly necessary, and may be commented out,\n% but it will improve the layout of the manuscript,\n% and will typically save some space.\n\\usepackage{microtype}\n\\usepackage{booktabs}\n\n\n\\title{Pruning General Large Language Models into Customized Expert Models}\n\n\n\\author{\n Yiran Zhao$^{1}$ \\; Guizhen Chen$^{2,3}$ \\; Kenji Kawaguchi$^{1}$\n \\; Lidong Bing$^{4}$ \\; Wenxuan Zhang$^{5}$\\footnotemark[2] \\\\\n $^1$ National University of Singapore \\quad $^2$ Nanyang Technological University, Singapore \\\\ \n $^3$ DAMO Academy, Alibaba Group, Singapore \\quad $^4$ MiroMind \\\\\n $^5$ Singapore University of Technology and Design \\\\\n}\n\n\n\n\n\\newcommand{\\fix}{\\marginpar{FIX}}\n\\newcommand{\\new}{\\marginpar{NEW}}\n\n\n\n\\begin{document}\n\n\\newcommand{\\yiran}[1]{\\textcolor{brown}{\\it\\small [#1 --Yiran]}}\n\\newcommand{\\isaac}[1]{\\textcolor{orange}{\\textbf{[Isaac:} #1]}}\n\n\\maketitle\n\\renewcommand{\\thefootnote}{\\fnsymbol{footnote}}\n\\footnotetext[2]{Wenxuan Zhang is the corresponding author.}\n\\renewcommand{\\thefootnote}{\\arabic{footnote}}\n\n\\begin{abstract}\n\nLarge language models (LLMs) have revolutionized natural language processing, yet their substantial model sizes often require substantial computational resources. To preserve computing resources and accelerate inference speed, it is crucial to prune redundant parameters, especially for experienced users who often need compact expert models tailored to specific downstream scenarios. However, most existing pruning methods focus on preserving the model’s general capabilities, often requiring extensive post-training or suffering from degraded performance due to coarse-grained pruning. In this work, we design a \\underline{Cus}tom \\underline{Prun}ing method (\\texttt{Cus-Prun}) to prune a large general model into a smaller lightweight expert model, which is positioned along the ``language'', ``domain'' and ``task'' dimensions. By identifying and pruning irrelevant neurons of each dimension, \\texttt{Cus-Prun} creates expert models without any post-training. \nOur experiments demonstrate that \\texttt{Cus-Prun} consistently outperforms other methods, achieving minimal loss in both expert and general capabilities across various models from different model families and sizes.\\footnote{Our code is publicly available at \\url{https://github.com/zhaoyiran924/Custom-Prune}.}\n\n\\end{abstract}\n\n\\section{Introduction}\n\nLarge language models (LLMs)~\\citep{achiam2023gpt, reid2024gemini, dubey2024llama, team2024gemma} have revolutionized the field of natural language processing (NLP), emerging as powerful tools with widespread applications across various languages~\\citep{cui2023efficient, yang2024qwen2, seallms3}, domains \\citep{ li2023huatuo26m, roziere2023code, li2023large}, and tasks~\\citep{azerbayevllemma, alves2024tower,naacl-sentiment}.\nHowever, the impressive performance of LLMs often comes at the cost of immense model sizes, mostly containing billions of parameters and thus demand significant computing resources~\\citep{goldstein2023generative, musser2023cost}. To address this issue, researchers have recently proposed various model pruning methods for LLMs. These methods aim to reduce model parameters while maintaining the overall performance through techniques such as removal of unimportant structures~\\citep{ma2023llm, men2024shortgpt, songsleb}, matrix approximation~\\citep{sharmatruth, ashkboosslicegpt}, and extensive post-training after pruning~\\citep{wang2024reconstruct, xiasheared}.\n\n\n\nMost existing pruning methods focus on preserving the \\textit{general capabilities} of the model, often evaluated by broad-spectrum benchmarks like MMLU~\\citep{hendrycksmeasuring}.\nWhile aiming for overall versatility, they may not align well with real-world user needs for a pruned small model, which are usually more \\textit{specific and targeted}. For instance, a user might require a question-answering model tailored specifically for the education domain in German. \nSuch specialized request in fact aligns well with the fundamental motivation behind pruning: to create a smaller model by eliminating unnecessary parameters. In this context, the notion of ``unnecessary'' parameters becomes more precise—referring to those parameters that are irrelevant to the particular use case. By selectively pruning these redundant parameters, one can construct a smaller, expert model that is better aligned with specific requirements.\n\nHowever, current pruning techniques focusing on preserving the general capabilities of LLMs often employ coarse-grained approaches such as removing entire layers or modules \\citep{li2022parameter, kurz2024language, huang2024pruning}. Therefore, it may remove parameters that are critical for specialized downstream scenarios, and thus sometimes require extensive post-training to recover the pruned capabilities \\citep{xiasheared, muralidharan2024compact}. On the other hand, the preserved certain capabilities may not be useful or relevant to our desired application scenario. This misalignment with practical requirements, where users need compact ready-to-deploy expert models without retraining, motivates us to design a more fine-grained and expert model targeting approach.\n\n\n\n\\begin{figure}[t]\n \\centering\n \\includegraphics[width=0.48\\textwidth]{Figures/cust-prune.pdf}\n \\caption{Given a requirement for an expert model across three dimensions (language, domain, and task), \\texttt{Cus-Prun} (i) identifies irrelevant neurons for each dimension using corresponding corpora (left), and (ii) prunes \\textbf{overlapping} irrelevant neurons across dimensions to obtain the expert model (right).}\n \\label{fig:cus-prun}\n\\end{figure}\n\nIn this work, we introduce a novel \\underline{Cus}tom \\underline{Prun}ing (\\texttt{Cus-Prun}) method, designed to prune a large general-purpose model into a small specialized expert model tailored for specific scenarios.\nTo achieve broad adaptability, we define the expert model by positioning the target user's needs along three key dimensions: language (e.g., English, Chinese, German), domain (e.g., E-commerce, education), and task (e.g., QA, summarization).\nInspired by existing studies that certain neurons are responsible for certain functions \\citep{zhao2024large, tang2024language, liang2024multilingual}, \\texttt{Cus-Prun} identifies and preserves critical neurons that are more relevant to particular languages, domains, or tasks, while pruning less relevant ones, ultimately leading to a smaller expert models.\nSpecifically, as illustrated in Figure \\ref{fig:cus-prun}, \n\\texttt{Cus-Prun} first identifies irrelevant neurons for each dimension by assessing the impact of their removal on the generated output when processing corresponding corpus, which could be easily constructed from the relevant plain text documents.\nNext, the expert model is constructed by pruning irrelevant neurons across all dimensions.\nFurthermore, \\texttt{Cus-Prun}'s flexibility allows it to focus on one, two, or all three dimensions (language, domain, task) as needed, making it adaptable to a wide range of real-world applications where specialized LLMs are required. Importantly, by performing fine-grained pruning at the neuron level, the method could also retain most of the essential neurons within the model backbone, thereby preserving most general capabilities.\n\nWe conduct comprehensive experiments to evaluate the performance of \\texttt{Cus-Prun} across various scenarios. Experimental results demonstrate that it consistently outperforms other pruning methods in all settings. For three-dimensional specific expert models, \\texttt{Cus-Prun} demonstrates remarkable pruning effectiveness across different model families and sizes, such as Mistral-Nemo-12B, Llama3-8B, Llama2-13B, and Llama3-70B, while maintaining strong expert and general capabilities. The method is evaluated across multilingual, multidomain, and multitask datasets, as well as representative compound NLP benchmarks, and consistently outperforms state-of-the-art pruning methods. Moreover, \\texttt{Cus-Prun} is highly adaptable, working effectively across a wide range of pruning ratios, even up to nearly half the parameters, without compromising its superior performance. For more focused applications, such as two- or one-dimensional specific expert models (e.g., language-domain specific or language-specific models), \\texttt{Cus-Prun} continues to significantly outperform other pruning methods, showcasing its versatility and effectiveness in diverse specialized settings.\n\n\n\n\\section{Custom Pruning (\\texttt{Cus-Prun})}\n\n\nAn expert model could be generally positioned from three dimensions: ``language'' ($L\\in\\mathbb{L}$), ``domain'' ($D\\in\\mathbb{D}$), and ``task'' ($T\\in\\mathbb{T}$), which can be represented as $LLM_{\\text{Exp}} := (L, D, T) \\in \\mathbb{L}\\times \\mathbb{D}\\times \\mathbb{T}$. Specifically, the language dimension encompasses various languages such as English, Spanish, and Thai. The domain dimension covers different fields like finance, legal, and medical. The task dimension includes various applications such as question-answering, data-to-text, and summarization. In this section, we propose a custom pruning method named \\texttt{Cus-Prun} to derive smaller expert models with flexible customization granularity.\n\n\n\\subsection{Foundational Custom Pruning}\n\n\nDrawing inspiration from recent LLM interpretation studies~\\citep{tang2024language,liang2024multilingual,zhao2024large} that many parameters in the model are redundant to processing a specific ``language'', we hypothesize that this phenomenon can be extended to other dimensions such as ``domain'' and ``task'', meaning that certain parameters remain unused when handling a specific dimension. \nRather than eliminating entire layers or modules~\\citep{songsleb, men2024shortgpt, zhang2024finercut}, \\texttt{Cus-Prun} performs a fine-grained investigation by identifying and removing redundant neurons (i.e., individual rows or columns in parameter matrices) across all components (e.g., attention and feed-forward layers).\n\n\nConcretely, when handling each dimension, we identify a specific set of \\textit{irrelevant neurons} in the original LLM, denoted as $\\tilde{\\mathcal{N}}_{L}$, $\\tilde{\\mathcal{N}}_{D}$, and $\\tilde{\\mathcal{N}}_{T}$ for $L$, $D$, and $T$, respectively. \n\nSpecifically, to identify irrelevant neurons corresponding to the selected dimension, we construct a corpus within that dimension while ablating others. For example, to determine irrelevant neurons for a specific language $L_{\\text{Exp}}$, we create a corpus set \n\\begin{equation}\nC_{L_{\\text{Exp}}}=\\{(L_{\\text{Exp}}, D, T)|D\\in\\mathbb{D}, T\\in\\mathbb{T}\\},\n\\end{equation} \ncomprising documents in language $L_{\\text{Exp}}$ across various domains $D$ and tasks $T$. We then identify neurons that are consistently irrelevant across all documents in $C_{L_{\\text{Exp}}}$,\n\\begin{equation}\n\\scalebox{0.92}{$ % Adjust 0.97 to the desired scaling factor\n\\tilde{\\mathcal{N}}_{L_{\\text{Exp}}} = \n\\left\\{\n\\text{Neuron} \\middle| \n\\text{Irrelevant to } c, \\; \\forall c \\in C_{L_{\\text{Exp}}}\n\\right\\},\n$}\n\\label{equ:neuron}\n\\end{equation}\nwhere a neuron is considered irrelevant if its removal from the parameter matrix affects the generated output below a specified threshold. Formally,\nfor $i$-th neuron in layer $l$, denoted as ${N}_{i}^{(l)}$, its relevance to document $c$ is measured by $|{h}_{\\setminus{{N}_{i}^{(l)}}, i}(c) - h_i(c)|_2$, where $h_i(c)$ is the layer output and ${h}_{\\setminus{{N}_{i}^{(l)}}, i}(c)$ is the output with the neuron removed. Furthermore, neurons with impact in the lowest $\\sigma\\%$ are considered irrelevant, where $\\sigma$ is a pre-defined pruning ratio.\n\n\nSimilarly, we could establish corresponding corpus sets for other dimensions, \n\\begin{equation}\nC_{D_{\\text{Exp}}}=\\{(L, D_{\\text{Exp}}, T)|L\\in\\mathbb{L}, T\\in\\mathbb{T}\\},\n\\end{equation} \n\\begin{equation}\nC_{T_{\\text{Exp}}}=\\{(L, D, T_{\\text{Exp}})|L\\in\\mathbb{L}, D\\in\\mathbb{D}\\},\n\\end{equation} to extract irrelevant neurons, $\\tilde{\\mathcal{N}}_{D_{\\text{Exp}}} $ and $\\tilde{\\mathcal{N}}_{T_{\\text{Exp}}} $. \nFinally, the expert model could constructed by\n\\begin{equation}\n\\scalebox{0.87}{$ % Adjust 0.95 to the desired scaling factor\n\\mathcal{LLM}_{\\text{Exp}} = \n\\mathcal{LLM} \\ominus \n\\left\\{\n\\tilde{\\mathcal{N}}_{L_{\\text{Exp}}} \n\\cap \\tilde{\\mathcal{N}}_{D_{\\text{Exp}}} \n\\cap \\tilde{\\mathcal{N}}_{T_{\\text{Exp}}}\n\\right\\},\n$}\n\\label{equ:cus}\n\\end{equation}\nwhere $\\ominus$ represents removing the corresponding neurons from $\\mathcal{LLM}$. The overall\nalgorithm is further illustrated in Algorithm \\ref{algo:adaptive_pruning}.\n\n\n\n\\begin{figure}[t]\n\\scalebox{0.95}{ \n\\begin{minipage}{\\linewidth}\n\\begin{algorithm}[H]\n\\renewcommand{\\algorithmicrequire}{\\textbf{Input:}}\n\\renewcommand{\\algorithmicensure}{\\textbf{Output:}}\n\\caption{Adaptive Custom Pruning}\n\\begin{algorithmic}[1]\n\\REQUIRE Original language model $\\mathcal{LLM}$, request for expert model $\\mathcal{LLM}_{\\text{Exp}}$ with selected dimensions: $L_{\\text{Exp}}$, $D_{\\text{Exp}}$, $T_{\\text{Exp}}$ (any subset), request for pruning ratio $\\sigma$.\n\\STATE \\texttt{// Construct specific corpora for each selected dimension.}\n\\STATE $C = \\{\\}$\n\\IF{$L_{\\text{Exp}}$ is specified}\n \\STATE $C = C \\cup \\{(L_{\\text{Exp}}, D, T) \\mid D \\in \\mathbb{D}, T \\in \\mathbb{T}\\}$\n\\ENDIF\n\\IF{$D_{\\text{Exp}}$ is specified}\n \\STATE $C = C \\cup \\{(L, D_{\\text{Exp}}, T) \\mid L \\in \\mathbb{L}, T \\in \\mathbb{T}\\}$\n\\ENDIF\n\\IF{$T_{\\text{Exp}}$ is specified}\n \\STATE $C = C \\cup \\{(L, D, T_{\\text{Exp}}) \\mid L \\in \\mathbb{L}, D \\in \\mathbb{D}\\}$\n\\ENDIF\n\\STATE \\texttt{// Identify irrelevant neurons for each selected dimension.}\n\\FORALL{neuron $N_{i}^{(l)}$ in $\\mathcal{LLM}$}\n \\IF{$\\forall c \\in C,$ $N_{i}^{(l)} \\in \\tilde{\\mathcal{N}}(c)$}\n \\STATE $\\tilde{\\mathcal{N}} \\gets \\tilde{\\mathcal{N}} \\cup N_{i}^{(l)}$\n \\ENDIF\n\\ENDFOR\n\\STATE \\texttt{// Prune irrelevant neurons to obtain expert model.}\n\\STATE $\\mathcal{LLM}_{\\text{Exp}} = \\mathcal{LLM} \\ominus \\tilde{\\mathcal{N}}$\n\\ENSURE $\\mathcal{LLM}_{\\text{Exp}}$\n\\end{algorithmic}\n\\label{algo:adaptive_pruning}\n\\end{algorithm}\n\\end{minipage}\n}\n\\end{figure}\n\n\n\\subsection{Adaptive Custom Pruning}\\label{sec:adaptive}\n\n\nIn many applications, the need for expertise might be constrained to one or two dimensions. For example, a language-specific or domain-specific model only requires pruning along a single dimension, while a language-domain-specific model (e.g., a Chinese Medical LLM) constrains two dimensions. In this section, we extend \\texttt{Cus-Prun} to support different granularity levels.\n\n\\paragraph{Two-Dimensional Specific Expert Model}\n\nWithout losing generality, we use the language-domain expert model as a concrete example, which requires an expert model constrained in two dimensions: language ($L_{\\text{Exp}}$) and domain ($D_{\\text{Exp}}$). We similarly derive the sets of irrelevant neurons $\\tilde{\\mathcal{N}}_{L_{\\text{Exp}}}$ and $\\tilde{\\mathcal{N}}_{D_{\\text{Exp}}}$, and\n% according to Equation \\ref{equ:neuron_final}. \nobtain the expert model by pruning the original dense model as follows: \\begin{equation}\n\\mathcal{LLM}_{\\text{Exp}}:=\\mathcal{LLM}\\ominus\\{\\tilde{\\mathcal{N}}_{L_{\\text{Exp}}} \\cap \\tilde{\\mathcal{N}}_{D_{\\text{Exp}}}\\}.\n\\end{equation}\n\n\n\\paragraph{One-Dimensional Specific Expert Model}\nWe use the language-specific expert model as an example, which focuses on optimizing performance for a certain language ($L_{\\text{Exp}}$), irrespective of domain or task. Similarly, we obtain the language-specific corpus $C_{L_{\\text{Exp}}}$, then identify irrelevant neurons $\\tilde{\\mathcal{N}}_{L_{\\text{Exp}}}$ \nand extract the expert model by \n\\begin{equation}\n\\mathcal{LLM}_{\\text{Exp}}:=\\mathcal{LLM}\\ominus\\{\\tilde{\\mathcal{N}}_{L_{\\text{Exp}}} \\}.\n\\end{equation}\nTo enhance efficiency, we implement the parallel neuron-detection method~\\citep{zhao2024large}, which accelerates the sequential calculations from line14 to line16 in Algorithm \\ref{algo:adaptive_pruning}. \n\n\n\n\n\\section{Preliminary Evaluation}\\label{sec:pre_eva}\n\nIn this section, to simulate realistic user-defined requirements where all three dimensions (language, domain, task) are explicitly specified, we conduct preliminary experiments on developing expert models with fine-grained pruning. This setting not only validates our approach under the most demanding conditions but also serves as a prototype for later experiments with one- or two-dimensional customizations.\n\n\n\\paragraph{Experiment Design} \n\nWe consider three scenarios, each named according to the \\textit{language}-\\textit{domain}-\\textit{task} pattern:\n\\textit{Korean-Legal-Summarization}~\\citep{hwang2022multi}, \\textit{English-Medical-MCQ}~\\citep{garcia2024medical}, and \\textit{Chinese-E-commerce-Sentiment Analysis}~\\citep{zhang2015character}.\nThese scenarios were selected because each represents a unique combination of language, domain, and task, effectively simulating the diverse needs a user might specify in practice.\n\nFor each scenario, we curate the corresponding corpus for each dimension. This curation can be done through manual collection or by automatically retrieving relevant documents online. In this preliminary study, without loss of generality, we employ a strong proprietary model\\footnote{\\url{https://platform.openai.com/docs/models/gpt-4o}} to generate a corpus containing $50$ documents for each dimension. Detailed prompts can be found in Appendix \\ref{sec:appen_prompt}. The generated documents could then be used to determine the relevance of neurons for each dimension of each scenario.\n\n\n\\paragraph{Experiment Setup}\nWe use Llama3-8B~\\citep{dubey2024llama} as the original dense model and follow the typical setting to set the pruning ratio as $25\\%$.\nPerformance is evaluated using Rouge-L~\\citep{lin2004rouge} for Korean-Legal-Summary and accuracy score for another two tasks. For comparison, we adopt SliceGPT~\\citep{ashkboosslicegpt} as a baseline, which replaces each weight matrix with a smaller proxy matrix without considering the specific use-case requirements.\n\n\\begin{figure}[t]\n \\centering\n \\includegraphics[width=0.48\\textwidth]{Figures/three-dim-examples.pdf}\n \\caption{Concrete examples of applying \\texttt{Cus-Prun} to prune $25\\%$ of Llama3-8B-Base's parameters into three-dimensional expert models. Numbers above each box indicate performance on the \\textbf{whole} test set, with the first evaluated by Rouge-L, and the other two by accuracy. }\n \\label{fig:korean}\n\\end{figure}\n\n\\begin{table*}[t]\n\\caption{Main Results of \\texttt{Cus-Prun} on multilingual setting with a pruning ratio of $25\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in summarization tasks and in accuracy (\\%) for other datasets.}\n \\centering\n\\footnotesize\n\\setlength{\\tabcolsep}{0.8pt}\n \\scalebox{0.9}{\n \\begin{tabular}{c|l|cccc|ccccc|ccccc|cccc}\n\\toprule\n\\quad\\quad & \\multirow{2}{*}{\\textbf{\\normalsize{Method}}} & \n \\multicolumn{4}{c}{\\textbf{\\normalsize{General Capability}}} \\vline & \\multicolumn{5}{c}{\\textbf{\\normalsize{Multilingual Expert}}} \\vline & \\multicolumn{5}{c}{\\textbf{\\normalsize{Multidomain Expert }}} \\vline & \\multicolumn{4}{c}{\\textbf{\\normalsize{Multitask Expert }}} \\\\ \n & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3 & XQuAD & Sum & Avg. & MMCQ & FTQA & TSA & AMSA & Avg. & MSum & ASum & AMCF & Avg. \\\\ \\midrule\n\\rowcolor{lightgray}\\cellcolor{white}\\multirow{5}{*}{\\rotatebox{90}{\\textbf{Llama3-8B}}} & Dense & 70.7 & 58.3 & 63.1 & 64.1 &41.2 &49.1 & 63.4 &32.9 &46.7 & 51.8 & 23.9 & 67.1 & 95.9 & 59.8 & 76.6 & 16.2 & 78.2 & 57.0 \\\\\n & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 1.1 & 24.0 & 13.6 & 23.2 & 15.5 & 0.0 & 0.0 & \\textbf{61.8} & 76.0 & 34.5 & 62.2 & \\textbf{21.8} & \\textbf{80.0} & \\textbf{54.7} \\\\\n & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & 14.9 & 16.6 & 8.5 & 10.0 & 22.6 & 0.0 & 41.2 & 53.7 & 29.4 & 7.3 & 2.9 & 51.3 & 20.5 \\\\\n & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & 26.9 & 0.0 & 2.7 & 7.4 & 3.2 & 0.0 & 38.6 & 35.7 & 19.4 & 4.1 & 4.8 & 43.8 & 17.6 \\\\\n &\\cellcolor{lightblue}\\texttt{Cus-Prun} &\\cellcolor{lightblue}\\textbf{62.4} & \\cellcolor{lightblue}\\textbf{37.0} & \\cellcolor{lightblue}\\textbf{54.7} & \\cellcolor{lightblue}\\textbf{51.4} & \\cellcolor{lightblue}\\textbf{30.1} & \\cellcolor{lightblue}\\textbf{41.5} & \\cellcolor{lightblue}\\textbf{52.6} & \\cellcolor{lightblue}\\textbf{31.5} & \\cellcolor{lightblue}\\textbf{38.9} & \\cellcolor{lightblue}\\textbf{42.9} & \\cellcolor{lightblue}\\textbf{20.6} & \\cellcolor{lightblue}\\textbf{61.8} & \\cellcolor{lightblue}\\textbf{87.6} & \\cellcolor{lightblue}\\textbf{53.2} & \\cellcolor{lightblue}\\textbf{68.4} & \\cellcolor{lightblue}12.8 & \\cellcolor{lightblue}75.5 & \\cellcolor{lightblue}52.2 \\\\ \\midrule\n\\rowcolor{lightgray}\\cellcolor{white}\\multirow{5}{*}{\\rotatebox{90}{\\textbf{Mistral-12B}}} & Dense & 82.6 & 68.5 & 50.4 & 67.2 & 51.7 & 43.8 & 49.2 & 25.4 & 42.5 & 54.6 & 26.6 & 69.4 & 92.4 & 60.8 & 88.7 & 3.0 & 78.6 & 56.4 \\\\\n & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 2.1 & 27.8 & 19.0 & \\textbf{23.2} & 18.0 & 0.0 & 0.0 & 51.0 & 20.9 & 18.0 & 59.3 & 0.5 & 2.8 & 20.9 \\\\\n & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 0.8 & 25.1 & 17.4 & 7.8 & 12.8 & 24.9 & 9.2 & 34.2 & 54.3 & 30.7 & 27.4 & 1.3 & 36.3 & 21.7 \\\\\n & ShortGPT & 37.8 & 0.0 & 33.9 & 23.9 & 2.9 & 27.0 & 18.0 & 5.0 & 13.2 & 31.4 & 7.2 & 39.2 & 52.5 & 32.6 & 26.2 & 0.2 & 42.7 & 23.0 \\\\\n & \\cellcolor{lightblue}\\texttt{Cus-Prun} & \\cellcolor{lightblue}\\textbf{67.5} & \\cellcolor{lightblue}\\textbf{43.4} & \\cellcolor{lightblue}\\textbf{43.8} & \\cellcolor{lightblue}\\textbf{51.6} & \\cellcolor{lightblue}\\textbf{34.3} & \\cellcolor{lightblue}\\textbf{39.2} & \\cellcolor{lightblue}\\textbf{40.7} & \\cellcolor{lightblue}23.1 & \\cellcolor{lightblue}\\textbf{34.3} & \\cellcolor{lightblue}\\textbf{47.9} & \\cellcolor{lightblue}\\textbf{25.1} & \\cellcolor{lightblue}\\textbf{67.3} & \\cellcolor{lightblue}\\textbf{83.7} & \\cellcolor{lightblue}\\textbf{56.0} & \\cellcolor{lightblue}\\textbf{83.5} & \\cellcolor{lightblue}\\textbf{3.4} & \\cellcolor{lightblue}\\textbf{72.8} & \\cellcolor{lightblue}\\textbf{50.9} \\\\ \\midrule\n\\rowcolor{lightgray}\\cellcolor{white}\\multirow{5}{*}{\\rotatebox{90}{\\textbf{Llama2-13B}}} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 17.5 & 30.4 & 44.1 & 24.9 & 29.2 & 25.2 & 0.0 & 42.7 & 84.1 & 38.0 & 70.0 & 7.4 & 44.3 & 40.6 \\\\\n & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 1.1 & 22.8 & 3.8 & 17.7 & 11.3 & 0.0 & 0.0 & 9.7 & 0.0 & 2.4 & 21.6 & 4.8 & 0.0 & 8.8 \\\\\n & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 2.8 & 25.3 & 23.4 & 9.9 & 15.5 & 18.7 & 0.0 & 28.4 & 67.3 & 28.6 & 24.5 & 4.9 & 32.9 & 20.8 \\\\\n & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 2.4 & 23.0 & 24.7 & 11.3 & 15.3 & 16.9 & 0.0 & 34.6 & \\textbf{69.8} & 30.3 & 23.8 & 5.2 & 39.1 & 22.7 \\\\\n & \\cellcolor{lightblue}\\texttt{Cus-Prun} & \\cellcolor{lightblue}\\textbf{48.3} & \\cellcolor{lightblue}\\textbf{20.8} & \\cellcolor{lightblue}\\textbf{50.0} & \\cellcolor{lightblue}\\textbf{39.7} & \\cellcolor{lightblue}\\textbf{12.7} & \\cellcolor{lightblue}\\textbf{26.2} & \\cellcolor{lightblue}\\textbf{34.2} & \\cellcolor{lightblue}\\textbf{24.1} & \\cellcolor{lightblue}\\textbf{24.3} & \\cellcolor{lightblue}\\textbf{25.6} & \\cellcolor{lightblue}0.0 & \\cellcolor{lightblue}\\textbf{38.5} & \\cellcolor{lightblue}68.3 & \\cellcolor{lightblue}\\textbf{33.1} & \\cellcolor{lightblue}\\textbf{64.5} & \\cellcolor{lightblue}\\textbf{6.7} & \\cellcolor{lightblue}\\textbf{42.9} & \\cellcolor{lightblue}\\textbf{38.0} \\\\ \\midrule\n\\rowcolor{lightgray}\\cellcolor{white}\\multirow{5}{*}{\\rotatebox{90}{\\textbf{Llama3-70B}}} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 69.5 & 71.1 & 69.1 & 36.6 & 61.6 & 72.1 & 55.3 & 83.6 & 96.2 & 76.8 & 84.2 & 17.3 & 81.8 & 61.1 \\\\\n & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 16.8 & 43.7 & 43.0 & 29.0 & 33.1 & 27.3 & 1.0 & 51.0 & 50.3 & 32.4 & 10.2 & 13.7 & 20.6 & 14.8 \\\\\n & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 3.7 & 44.8 & 33.0 & 21.2 & 25.7 & 57.6 & 27.6 & 68.1 & 59.4 & 53.2 & 58.0 & 14.2 & 68.3 & 46.8 \\\\\n & ShortGPT & 59.4 & 5.6 & \\textbf{75.5} & 46.8 & 11.9 & 43.1 & 38.8 & 24.0 & 29.5 & 58.4 & 32.2 & 67.5 & 64.9 & 55.8 & 59.6 & 13.9 & 65.8 & 46.4 \\\\\n & \\cellcolor{lightblue}\\texttt{Cus-Prun} & \\cellcolor{lightblue}\\textbf{68.4} & \\cellcolor{lightblue}\\textbf{53.2} & \\cellcolor{lightblue}66.6 & \\cellcolor{lightblue}\\textbf{62.7} & \\cellcolor{lightblue}\\textbf{43.1} & \\cellcolor{lightblue}\\textbf{57.7} & \\cellcolor{lightblue}\\textbf{59.8} & \\cellcolor{lightblue}\\textbf{34.3} & \\cellcolor{lightblue}\\textbf{48.7} & \\cellcolor{lightblue}\\textbf{68.2} & \\cellcolor{lightblue}\\textbf{43.9} & \\cellcolor{lightblue}\\textbf{81.4} & \\cellcolor{lightblue}\\textbf{87.8} & \\cellcolor{lightblue}\\textbf{70.3} & \\cellcolor{lightblue}\\textbf{80.4} & \\cellcolor{lightblue}\\textbf{15.7} & \\cellcolor{lightblue}\\textbf{77.5} & \\cellcolor{lightblue}\\textbf{57.9} \\\\ \n\\bottomrule\n\\end{tabular}}\n \\label{table:cross_ling}\n\\vspace{-0.2cm}\n\\end{table*}\n\n\n\n\\paragraph{Main Results}\n\n\nFigure \\ref{fig:korean} presents the results and one concrete example for the original dense model, pruned model with SliceGPT, and pruned model with our proposed \\texttt{Cus-Prun} method across the three scenarios.\nWe observe that \\texttt{Cus-Prun} largely preserves the performance of the dense model, retraining 92\\%, 83\\%, and 94\\% of the original dense model performance on these three cases respectively.\nIn contrast, the baseline method SliceGPT, which does not consider specific use cases, largely underperforms compared to \\texttt{Cus-Prun}.\nOverall, the results demonstrate that our proposed \\texttt{Cus-Prun} method could effectively obtain expert models tailored to specific use cases across different languages, domains, and tasks that maintain high performance despite substantial pruning.\n\n\n\n\n\\section{Foundational Custom Pruning Assessment}\n\nAs demonstrated by preliminary evaluation in Section \\ref{sec:pre_eva}, \\texttt{Cus-Prun} enables the creation of expert models tailored to specific languages, domains, and tasks. However, existing benchmark datasets may not always align with such specialized requirements for a systematic evaluation. To simplify our evaluation without losing generality, we use two distinct corpora: one focusing independently on a single dimension and another encompassing the remaining two dimensions. This approach allows us to evaluate \\texttt{Cus-Prun}'s performance in \\textit{multilingual}, \\textit{multidomain}, and \\textit{multitask} settings. \n\nFormally, in the multilingual setting, instead of constructing $C_{L_{\\text{Exp}}}$, $C_{D_{\\text{Exp}}}$ and $C_{T_{\\text{Exp}}}$ independently, we can construct two corpora, $C_{L_{\\text{Exp}}}$ and $C_{(D_, T)_{\\text{Exp}}}$, where $C_{L_{\\text{Exp}}}$ helps to identify irrelevant neurons in a specific language ($\\tilde{\\mathcal{N}}_{L_{\\text{Exp}}}$) and $C_{(D_, T)_{\\text{Exp}}}$ helps to identify irrelevant neurons in a specific domain-task combination ($\\tilde{\\mathcal{N}}_{D_{\\text{Exp}}\\cap T_{\\text{Exp}}}$). Formally speaking, \\texttt{Cus-Prun} in Equation \\ref{equ:cus} is transferred to \n\\begin{equation}\n\\scalebox{0.85}{$ % Adjust 0.94 to the desired scaling factor\n\\begin{aligned}\n\\mathcal{LLM}_{\\text{Exp}} & = \n\\mathcal{LLM} \\ominus \n\\left\\{\n\\tilde{\\mathcal{N}}_{L_{\\text{Exp}}} \\cap \n\\left( \n\\tilde{\\mathcal{N}}_{D_{\\text{Exp}}} \\cap \\tilde{\\mathcal{N}}_{T_{\\text{Exp}}}\n\\right)\n\\right\\} \\\\\n& \\equiv \n\\mathcal{LLM} \\ominus \n\\left\\{\n\\tilde{\\mathcal{N}}_{L_{\\text{Exp}}} \\cap \n\\tilde{\\mathcal{N}}_{D_{\\text{Exp}} \\cap T_{\\text{Exp}}}\n\\right\\}.\n\\end{aligned}\n$}\n\\label{equ:cross-lingual}\n\\end{equation}\nNote that this simplification is also applicable to $C_{D_{\\text{Exp}}}$, $C_{(L, T)_{\\text{Exp}}}$ and $C_{T_{\\text{Exp}}}$, $C_{(L, D)_{\\text{Exp}}}$. \n\n\\subsection{Experiment Setup} \n\n\\paragraph{Benchmarks} Although the primary goal of \\texttt{Cus-Prun} is to create expert LLMs tuned to specific use cases, we also evaluate the pruned models on standard general capabilities to ensure minimal performance loss. Specifically, we employ ARC-Challenge~\\citep{clark2018think} (5-shots), GSM8K~\\citep{cobbe2021training} (5-shots with CoT prompting~\\citep{wei2022chain}), and MMLU~\\citep{hendrycksmeasuring} (5-shots) to represent models general capability. \nNote that we utilize a generation task and implement CoT prompting method, a more challenging setting that has not been previously evaluated by existing pruning techniques~\\citep{songsleb, sharmatruth, yang2024laco,zhang2024finercut}. \n\n\n\\paragraph{Baselines} We employ several state-of-the-art pruning methods as the baseline that do not require post-training after pruning the model. (i) Dense represents the original model without pruning; (ii) LLM-Pruner~\\citep{ma2023llm} adopts structural pruning that selectively removes non-critical coupled structures based on gradient information;\\footnote{To ensure a fair comparison, we evaluate its performance before post-training, following \\citet{men2024shortgpt}.} (iii) SliceGPT~\\citep{ashkboosslicegpt} replaces each weight matrix with a smaller dense matrix, reducing the embedding dimension of the network; (iv) ShortGPT~\\citep{men2024shortgpt} directly deletes the redundant layers in LLMs\nbased on an importance score.\nWe follow the typical pruning setting from these previous studies to set the pruning ratio to $25\\%$ for all methods and all models. \n\n\\paragraph{Backbone Models} We choose 4 models from different model series and different sizes, including Llama3-8B-Base~\\citep{dubey2024llama}, Mistral-Nemo-Base-2407\\footnote{\\url{https://huggingface.co/mistralai/Mistral-Nemo-Base-2407}}(short as Mistral-12B), Llama2-13B-Base~\\citep{touvron2023llama}, Llama3-70B-Base~\\citep{dubey2024llama}. \n\n\n\n\n\n\\subsection{Multilingual Setting}\n\n\n\\paragraph{Dataset}\nWe employ multiple representative multilingual datasets for multilingual setting, which covers reasoning (MGSM~\\citep{shilanguage}, 5-shots), multilingual knowledge (M3Exam~\\citep{zhang2023m3exam}, 3-shots, abbreviated as M3), understanding (XQuAD~\\citep{artetxe2020cross}, 5-shots), and generation (XLSum~\\citep{hasan2021xl}, zero-shots, abbreviated as Sum). Furthermore, we consider three languages spanning a range from high-resource to low-resource including German (De), Chinese (Zh) and Thai (Th). \nMore detailed experiment settings are explained in Appendix \\ref{sec:appen_setting_1}.\n\n\n\n\\paragraph{Main Results} \n\n\nTable \\ref{table:cross_ling} summarizes the performance of \\texttt{Cus-Prun} on multilingual datasets, with average scores across languages. Detailed breakdown results for each language are shown in Table~\\ref{table:cl_ger}, Table~\\ref{table:cl_chi} and Table~\\ref{table:cl_tha} in Appendix~\\ref{sec:appen_cl}.\nWe can observe that \\texttt{Cus-Prun} consistently outperforms baseline pruning methods in achieving expert capabilities while preserving general performance. For example, on Llama3-8B, \\texttt{Cus-Prun} achieves a score of \\(38.9\\) compared to at most \\(15.5\\) for other methods. Similar improvements are observed for Mistral-12B (\\(34.3\\) vs. \\( 18.0\\)), Llama2-13B (\\(24.3\\) vs. \\( 15.5\\)), and Llama3-70B (\\(48.7\\) vs. \\( 33.1\\)). We can also see that the generation tasks are especially challenging; for instance, \\texttt{Cus-Prun} achieves a score of \\(30.1\\) on MGSM for Llama3-8B, while other methods nearly lose the ability to generate coherent reasoning outputs (often approaching \\(0\\) accuracy for all but the largest model). Additionally, \\texttt{Cus-Prun} performs robustly across both high-resource and low-resource languages. Overall, \\texttt{Cus-Prun} excels in both performance and robustness across diverse tasks and languages.\n\n\n\n\\subsection{MultilDomain Setting}\n\n\\paragraph{Dataset} For the multidomain setting, we employ several domain-specific datasets, including medical domain multiply choices questions (MedMCQ~\\citep{pmlr-v174-pal22a}, 3-shots, abbreviated as MMCQ), finance domain table question-answering (FinTQA~\\citep{chen2021finqa}, 8-shots, abbreviated as FTQA), social media domain sentiment analysis (TSA~\\citep{kharde975sentiment}, 3-shots), and e-commerce domain sentiment analysis (AMSA~\\citep{zhang2015character}, 3-shots). Moreover, in multidomain setting, our focus is exclusively on the English language. Detailed experiment settings are explained in Appendix \\ref{sec:appen_setting_2}.\n\n\n\n\\paragraph{Main Results} Table \\ref{table:cross_ling} shows the performance of \\texttt{Cus-Prun} on multidomain setting. We find that \\texttt{Cus-Prun} consistently outperforms other pruning methods in both expert and general capabilities. For expert capabilities, \\texttt{Cus-Prun} achieves a score of $53.2$ on Llama3-8B, while other pruning methods achieve at most $34.5$. Similar improvements are observed for Mistral-12B (\\(56.0\\) vs. \\(32.6\\)), Llama2-13B (\\(33.1\\) vs. \\(30.3\\)), and Llama3-70B (\\(70.3\\) vs. \\(55.8\\)).\n\n\n\n\\begin{figure*}[t]\n \\centering\n \\begin{minipage}{0.4\\textwidth}\n \\centering\n \\captionof{table}{Performance of Chinese-Medical expert model on MCQ task}\n \\label{tab:cmexam}\n \\scalebox{0.96}{\n \\begin{tabular}{l|c|c}\n \\hline\n \\textbf{Method} & \\textbf{General} & \\textbf{CMExam} \\\\\\midrule\n \\rowcolor{lightgray}\\cellcolor{white} Dense & 59.3 & 50.6 \\\\\n LLM-Pruner & 18.6 & 25.0 \\\\\n SliceGPT & 27.8 & 26.9 \\\\\n ShortGPT & 23.9 & 23.7 \\\\\n \\rowcolor{lightblue}\\cellcolor{white} \\texttt{Cus-Prun} & \\textbf{52.4} & \\textbf{48.7} \\\\\n \\hline\n \\end{tabular}}\n \\end{minipage}\n \\hfill\n \\begin{minipage}{0.58\\textwidth}\n \\centering\n \\includegraphics[width=\\textwidth]{Figures/huatuo.pdf}\n \\caption{Chinese Medical LLM performance. Numbers are quality on the \\textbf{whole} test set evaluated by GPT4.}\n \\label{fig:huatuo}\n \\end{minipage}\n\\end{figure*}\n\n\\subsection{MultiTask Setting}\n\n\n\n\\paragraph{Dataset} For the multitask setting, we employ several task-specific datasets, including the medical summarization task (MedSum~\\citep{abacha2019summarization}, 3-shots, abbreviated as MSum), summarization task in e-commerce (Amazon Summary~\\citep{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions, brüelgabrielsson2024compressserveservingthousands}, 3-shots, abbreviated as ASum), counterfactual task in e-commerce (Amazon Counterfactual~\\citep{o2021wish}, 3-shots, abbreviated as AMCF). Similarly, in multitask setting scenarios, our focus is exclusively on the English language. Detailed experiment settings are explained in Appendix \\ref{sec:appen_setting_3}.\n\n\n\n\\paragraph{Main Results} Table \\ref{table:cross_ling} shows the performance of \\texttt{Cus-Prun} on multitask setting. We find that except for LLM-Pruner under Llama3-8B, \\texttt{Cus-Prun} outperforms other pruning methods in both expert and general capabilities. For expert tasks, \\texttt{Cus-Prun} achieves a score of \\(50.9\\) on \\texttt{Mistral-12B}, significantly outperforming the highest score of \\(23.0\\) from other methods. Likewise, it attains \\(38.0\\) on \\texttt{Llama2-13B} and \\(57.9\\) on \\texttt{Llama3-70B}, both markedly higher than the corresponding maximum scores of \\(22.7\\) and \\(46.8\\) achieved by competing methods.\n\n\n\n\\subsection{Analysis on Aggressive Pruning Ratio}\n\n\nTo optimize for specialized tasks rather than maintaining general capabilities, we employ more aggressive pruning ratios. We combine layer pruning with our custom neuron pruning method in Algorithm \\ref{algo:adaptive_pruning} and evaluate the approach on M3Exam, MedMCQ, and Amazon Counterfactual (AMContFact) datasets using Llama3-8B. Detailed results are shown in Table \\ref{table:prune_more}. We find that \\texttt{Cus-Prun} consistently maintains the model's capabilities even at higher pruning ratios. Specifically, when the pruning ratio is increased to $45\\%$, ShortGPT nearly loses the capability of generating meaningful answers, while \\texttt{Cus-Prun} still achieves scores of $48.4$ on MMLU and $50.6$ on expert capabilities.\n\n\\begin{table}[ht]\n\\caption{Aggressive pruning ratio on Llama3-8B.}\n\\centering\n\\setlength{\\tabcolsep}{3pt}\n \\scalebox{0.9}{\n\\begin{tabular}{l|c|c|c|c}\n \\hline\n \\textbf{Method} & \\textbf{Ratio} & \\textbf{Speedup} & \\textbf{MMLU} & \\textbf{Expert} \\\\\\midrule\n \\rowcolor{lightgray}\\cellcolor{white}Dense & 0.0 & 1$\\times$ & 63.1 & 59.7 \\\\\\midrule\n ShortGPT & 25.0 & 1.3$\\times$ & 28.6 & 24.6 \\\\\n \\rowcolor{lightblue}\\cellcolor{white}\\texttt{Cus-Prun} & 25.0 & 1.3$\\times$ & \\textbf{51.9} & \\textbf{53.3} \\\\\\midrule\n ShortGPT & 34.2 & 1.5$\\times$ & 20.8 & 18.5 \\\\\n \\rowcolor{lightblue}\\cellcolor{white}\\texttt{Cus-Prun} & 35.0 & 1.5$\\times$ & \\textbf{50.2} & \\textbf{51.4} \\\\\\midrule\n ShortGPT & 43.8 & 1.8$\\times$ & 7.9 & 10.2 \\\\\n \\rowcolor{lightblue}\\cellcolor{white}\\texttt{Cus-Prun} & 45.0 & 1.8$\\times$ & \\textbf{48.4} & \\textbf{50.6} \\\\\n \\hline\n \\end{tabular}}\n \\label{table:prune_more}\n\\end{table}\n\n\n\n\\section{Adaptive Custom Pruning Assessment}\n\nIn this section, we evaluate the generality of \\texttt{Cus-Prun} in dynamic scenarios, including specific expert models in two and one dimensions, as described in Section \\ref{sec:adaptive}.\n\n\\subsection{Two Dimensions Specific Expert Model}\n\n\\paragraph{Experiment Settings} We use the Chinese-Medical setting as a concrete example of a two-dimensional expert model designed to perform a wide range of medical tasks in Chinese. \nWe adopt Mistral-12b as the backbone model and utilize corpus from Wikipedia for Chinese content and general medical corpus for medical knowledge. The performance of the target Chinese-Medical expert model is evaluated on two datasets: CMExam~\\citep{liu2023benchmarking} (5-shots), a Chinese medical multiple-choice question dataset, and HuatuoQA~\\citep{li2023huatuo26m}, a Chinese medical question-answering dataset. We assess the performance on CMExam using accuracy metrics. For the latter, we sample a sub-testset of size $100$ and use GPT-4 as the evaluator, which assigns a score from $0$ to $5$, representing its quality from low to high. Detailed prompts are listed in Appendix \\ref{sec:appen_prompt}.\n\n\n\n\\paragraph{Main Results} Table \\ref{tab:cmexam} presents the performance of the Chinese-Medical LLM on CMExam and its general capabilities. Our results indicate that the expert model pruned using \\texttt{Cus-Prun} outperforms models obtained through other pruning methods. Specifically, \\texttt{Cus-Prun} achieves a score of $48.7$ on CMExam, while its general capability score is $52.4$. These results compare favorably to the dense model, which scores $50.6$ on CMExam and $59.3$ on general capabilities. On the contrary, other pruning methods nearly lose the general and specific capabilities. Furthermore, Figure \\ref{fig:huatuo} shows a concrete example of Chinese-Medical LLM performance on medical question-answering. We find that \\texttt{Cus-Prun} can produce smaller expert models that maintain their expert capabilities, as demonstrated by its performance score of $2.9/5.0$ compared to $3.2/5.0$ for the dense model.\n\n\n\n\n\n\\subsection{One Dimension Specific Expert Model}\n\n\\begin{figure*}[t]\n \\centering\n \\includegraphics[width=1.0\\textwidth]{Figures/lang-specific.pdf}\n \\caption{Performance of \\texttt{Cus-Prun} in obtaining language-specific models.}\n \\label{fig:lang-spec}\n\\end{figure*}\n\n\n\\paragraph{Experiment Settings} \n\n\nFor evaluating the pruning method under a one-dimensional expert model setting, we focus on language-specific pruning, showing how to transform a dense model into language-specific variants.\nWe consider three linguistically diverse languages: German, Chinese, and Thai. We conduct experiments based on the Llama3-8b model.\nTo identify language-specific (while domain- and task-agnostic) neurons, we employ a diverse range of corpora, including Wikipedia, MGSM, and M3Exam, ensuring coverage of various domains and tasks. The effectiveness of our pruning technique is then evaluated using three held-out multilingual datasets including XQuAD~\\citep{artetxe2020cross}, XNLI~\\citep{conneau2018xnli}, and XSum ~\\citep{narayan2018don}.\n\n\n\\paragraph{Main Results} \nFigure \\ref{fig:lang-spec} illustrates the performance of language-specific models using \\texttt{Cus-Prun}. By pruning $25\\%$ of the neurons from the original model, \\texttt{Cus-Prun} not only retains general performance but also preserves language-specific capabilities. For instance, the German-specific model scores $54.7$ in general capabilities, $48.3$ on XQuAD, and $56.8$ on XNLI, compared to the dense model's scores of $64.1$, $52.9$, and $62.0$, respectively. This trend is consistent for Chinese and Thai models as well. In contrast, ShortGPT struggles to maintain the model's capabilities, particularly in XQuAD and XLSUm, which require generative abilities.\n\n\n\\section{Related Work}\n\n\\paragraph{LLM Compression} Given the high costs of training, inferencing, and tuning LLMs, many studies focus on model compression methods, including compression~\\citep{zhu2023survey}, quantization~\\citep{xu2023qa, dettmers2024qlora, lin2024awq, li2024evaluating}, and pruning~\\citep{wang2019structured}. Sparsity-based structural pruning enhances GPU efficiency with sparse structures but doesn't always reduce parameter counts \\citep{li2022parameter, li2023losparse, kurz2024language, zhao2024convex, huang2024pruning}. Unstructured pruning reduces parameters while maintaining performance, using post-training techniques \\citep{ma2023llm, xiasheared, muralidharan2024compact} or coarse methods like parameter approximation \\citep{zhao2024convex}, layer removal \\citep{men2024shortgpt}, or structural elimination \\citep{zhang2024finercut}. However, these often fail to preserve domain- or task-specific capabilities, limiting their utility for specialized scenarios.\n\n\n\\paragraph{Customizing Model} Customizing LLMs is essential for addressing language-specific challenges, domain-specific needs, and task-specific applications \\citep{cui2023efficient, yang2024laco, li2023huatuo26m, roziere2023code, li2023large, azerbayevllemma, alves2024tower,naacl-sentiment}. However, customization often demands extensive fine-tuning with curated data, making it resource-intensive. This highlights the need for efficient methods to quickly create robust, expert models tailored to diverse industries without compromising quality.\n\n\\section{Conclusion}\n\nLLMs deliver impressive capabilities but incur high computational costs. Efficient pruning of redundant parameters is vital for conserving resources and improving inference speed, especially for specialized models. Our method, \\texttt{Cus-Prun}, generates smaller expert models without post-training by pruning irrelevant neurons across ``language'', ``domain'', and ``task'' dimensions. This finer-grained approach outperforms existing techniques on three-dimensional models and can adapt to realistic scenarios, such as language-domain or language-specific models.\n\n\n\\section*{Limitation}\n\nDespite the promising results of Cus-Prun, several limitations should be noted. First, while our method leverages three dimensions (language, domain, and task) for pruning, certain crucial restrictions cannot be fully captured within this framework, such as variations in query format or input structure. Second, whether pruned base models can effectively undergo post-training remains an open question that requires further investigation. This uncertainty about post-training capabilities could limit the model's adaptability to new scenarios or requirements after pruning. These limitations suggest important directions for future research, including exploring additional dimensions for more comprehensive pruning strategies and investigating the relationship between pruning and post-training effectiveness.\n\n\\section*{Acknowledgement}\nThis research is supported by the Ministry of Education, Singapore, under its Academic Research Fund (AcRF) Tier 1 grant, and funded through the SUTD Assistant Professorship Scheme (SAP 2025\\_001).\nThis material is based upon work partially supported by the Air Force Office of Scientific Research under award number FA2386-24-1-4011, and this research is partially supported by the Singapore Ministry of Education Academic Research Fund Tier 1 (Award No: T1 251RES2207).\n\n\n\\bibliography{custom}\n\n\n\\appendix\n\\section{Appendix}\n\n\\subsection{GPT-4o Prompts}\\label{sec:appen_prompt}\nThe prompts used for generating documents and evaluating answer quality are presented in Table~\\ref{table:testset}.\n\n\\begin{table}[ht]\n \\centering\n\\footnotesize\n \\scalebox{1}{\n \\begin{tabular}{lp{5.3cm}}\n \\toprule\n \\textbf{Task} & \\textbf{Prompt} \\\\\\midrule\n Generation & Generate a text document in \\{language\\}/\\{domain\\}/\\{task\\}. Make sure the documents is not fixed to one \\{language\\}/\\{domain\\}/\\{task\\} or \\{language\\}/\\{domain\\}/\\{task\\}. Ensure the content is clear, concise, and appropriate for the specified request. Use professional and domain-specific terminology where necessary. \\\\\\midrule\n Evaluation & Evaluate the quality of the given answer to the question. Provide a score from 0 to 5, where 0 represents very low quality and 5 represents very high quality. Question: \\{question\\} Answer: \\{answer\\}.\\\\\n \\bottomrule\n \\end{tabular}}\n\\caption{GPT-4o prompts for generating documents and evaluating answer quality.} \\label{table:testset}\n\\end{table}\n\n\\subsection{Detailed Results for Multilingual}\\label{sec:appen_cl}\n\nDetailed results for multilingual settings can be found in Table \\ref{table:cl_ger}, Table \\ref{table:cl_chi} and Table \\ref{table:cl_tha} for German, Chinese and Thai, respectively.\n\n\\begin{table*}[hbpt]\n\\caption{Main Results of \\texttt{Cus-Prun} on Germany with a pruning ratio of $25\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in XLSum and in accuracy (\\%) for other datasets.}\n \\centering\n\\footnotesize\n\\setlength{\\tabcolsep}{3pt}\n \\scalebox{1.0}{\n \\begin{tabular}{l|l|cccc|ccccc}\n\\toprule\n\\multirow{2}{*}{\\textbf{\\normalsize{Model}}} & \\multirow{2}{*}{\\textbf{\\normalsize{Method}}} & \n \\multicolumn{4}{c}{\\textbf{\\normalsize{General Capability}}} \\vline & \\multicolumn{5}{c}{\\textbf{\\normalsize{Expert Capability}}} \\\\ \n & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3Exam & XQuAD & XLSum & Avg. \\\\ \\midrule\n\\multirow{4}{*}{Llama3-8B} &\n Dense & 70.7 & 58.3 & 63.1 & 64.1 & 44.8 & - & 52.9 & - & 48.8 \\\\\n & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 0.0 & - & 11.0 & - & 5.5 \\\\\n & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & - & 9.8 & - & 4.9 \\\\\n & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & - & 0.0 & - & 0.0 \\\\\n & \\texttt{Cus-Prun} & 61.4 & 38.9 & 54.5 & \\textbf{51.6} & 32.8 & - & 49.6 & - & \\textbf{41.2} \\\\ \\midrule\n\\multirow{4}{*}{Mistral-12B} & Dense & 82.6 & 68.5 & 50.4 & 59.3 & 56.8 & - & 41.2 & - & 49.0 \\\\\n & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 2.4 & - & 13.4 & - & 7.9 \\\\\n & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 0.8 & - & 15.5 & - & 8.2 \\\\\n & ShortGPT & 37.8 & 0.0 & 33.9 & 23.9 & 3.6 & - & 20.3 & - & 12.0 \\\\\n & \\texttt{Cus-Prun} & 64.6 & 39.7 & 43.2 & \\textbf{49.2} & 31.6 & - & 35.9 & - & \\textbf{33.8} \\\\ \\midrule\n\\multirow{4}{*}{Llama2-13B} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 24.4 & - & 40.3 & - & 32.3 \\\\\n & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 2.0 & - & 5.7 & - & 3.9 \\\\\n & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 3.6 & - & 18.1 & - & 10.9 \\\\\n & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 2.8 & - & 27.2 & - & 15.0 \\\\\n & \\texttt{Cus-Prun} & 47.6 & 19.8 & 49.9 & \\textbf{39.1} & 18.4 & - & 31.7 & - & \\textbf{25.0} \\\\ \\midrule\n\\multirow{4}{*}{Llama3-70B} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 74.8 & - & 58.2 & - & 66.5 \\\\\n & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 18.0 & - & 27.3 & - & 22.7 \\\\\n & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 0.0 & - & 17.3 & - & 8.7 \\\\\n & ShortGPT & 59.4 & 5.6 & 75.5 & 46.8 & 9.6 & - & 31.5 & - & 20.6 \\\\\n & \\texttt{Cus-Prun} & 66.8 & 59.3 & 69.1 & \\textbf{65.1} & 48.2 & - & 53.9 & - & \\textbf{51.1} \\\\ \n\\bottomrule\n\\end{tabular}}\n \\label{table:cl_ger}\n\\end{table*}\n\n\n\\begin{table*}[hbpt]\n\\caption{Main Results of \\texttt{Cus-Prun} on Chinese with a pruning ratio of $25\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in XLSum and in accuracy (\\%) for other datasets.}\n \\centering\n\\footnotesize\n\\setlength{\\tabcolsep}{3pt}\n \\scalebox{1.0}{\n \\begin{tabular}{l|l|cccc|ccccc}\n\\toprule\n\\multirow{2}{*}{\\textbf{\\normalsize{Model}}} & \\multirow{2}{*}{\\textbf{\\normalsize{Method}}} & \n \\multicolumn{4}{c}{\\textbf{\\normalsize{General Capability}}} \\vline & \\multicolumn{5}{c}{\\textbf{\\normalsize{Specific Capability}}} \\\\ \n & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3Exam & XQuAD & XLSum & Avg. \\\\ \\midrule\n\\multirow{4}{*}{Llama3-8B} &\n Dense & 70.7 & 58.3 & 63.1 & 64.1 & 43.6 & 55.1 & 78.7 & 49.1 & 56.6 \\\\\n & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 2.4 & 23.6 & 21.3 & 32.8 & 20.0 \\\\\n & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & 17.4 & 23.5 & 8.3 & 12.3 \\\\\n & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & 28.3 & 0.0 & 3.1 & 7.9 \\\\\n & \\texttt{Cus-Prun} & 60.5 & 25.7 & 49.4 & \\textbf{45.2} & 36.0 & 44.7 & 65.6 & 46.3 & \\textbf{48.2} \\\\ \\midrule\n\\multirow{4}{*}{Mistral-12B} & Dense & 82.6 & 68.5 & 50.4 & 59.3 & 53.2 & 47.8 & 62.2 & 33.0 & 49.1 \\\\\n & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 2.8 & 30.7 & 31.8 & 32.6 & 24.5 \\\\\n & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 1.6 & 26.4 & 28.3 & 10.8 & 16.8 \\\\\n & ShortGPT & 37.8 & 0.0 & 33.9 & 23.9 & 4.4 & 28.2 & 29.1 & 7.2 & 17.2 \\\\\n & \\texttt{Cus-Prun} & 68.3 & 43.2 & 39.5 & \\textbf{50.3} & 38.4 & 40.7 & 50.6 & 30.3 & \\textbf{40.0} \\\\ \\midrule\n\\multirow{4}{*}{Llama2-13B} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 21.6 & 36.5 & 59.8 & 35.3 & 38.3 \\\\\n & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 1.2 & 23.3 & 3.8 & 25.1 & 13.4\\\\\n & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 4.8 & 24.5 & 28.4 & 11.2 & 17.2 \\\\\n & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 4.4 & 22.9 & 24.6 & 13.7 & 16.4 \\\\\n & \\texttt{Cus-Prun} & 48.6 & 20.7 & 51.9 & \\textbf{40.4} & 14.8 & 28.2 & 47.3 & 34.4 & \\textbf{31.2} \\\\ \\midrule\n\\multirow{4}{*}{Llama3-70B} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 68.4 & 76.1 & 81.3 & 55.3 & 70.3 \\\\\n & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 16.8 & 47.5 & 56.1 & 41.3 & 40.4 \\\\\n & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 6.4 & 48.3 & 42.2 & 29.3 & 31.6 \\\\\n & ShortGPT & 59.4 & 5.6 & 75.5 & 46.8 & 12.4 & 45.5 & 44.6 & 36.1 & 34.7 \\\\\n & \\texttt{Cus-Prun} & 72.3 & 48.5 & 65.2 & \\textbf{62.0} & 40.8 & 61.7 & 66.9 & 51.6 & \\textbf{55.3} \\\\ \n\\bottomrule\n\\end{tabular}}\n \\label{table:cl_chi}\n\\end{table*}\n\n\n\\begin{table*}[hbpt]\n\\caption{Main Results of \\texttt{Cus-Prun} on Thai with a pruning ratio of $25\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in XLSum and in accuracy (\\%) for other datasets.}\n \\centering\n\\footnotesize\n\\setlength{\\tabcolsep}{3pt}\n \\scalebox{1.0}{\n \\begin{tabular}{l|l|cccc|ccccc}\n\\toprule\n\\multirow{2}{*}{\\textbf{\\normalsize{Model}}} & \\multirow{2}{*}{\\textbf{\\normalsize{Method}}} & \n \\multicolumn{4}{c}{\\textbf{\\normalsize{General Capability}}} \\vline & \\multicolumn{5}{c}{\\textbf{\\normalsize{Specific Capability}}} \\\\ \n & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3Exam & XQuAD & XLSum & Avg. \\\\ \\midrule\n\\multirow{4}{*}{Llama3-8B} &\n Dense & 70.7 & 58.3 & 63.1 & 64.1 & 35.2 & 43.0 & 58.7 & 16.7 & 38.4 \\\\\n & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 0.8 & 24.4 & 8.4 & 13.5 & 11.8 \\\\\n & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & 12.3 & 16.6 & 8.7 & 9.4 \\\\\n & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & 25.4 & 0.0 & 2.3 & 6.9 \\\\\n & \\texttt{Cus-Prun} & 58.9 & 31.2 & 52.4 & \\textbf{47.5} & 21.6 & 38.3 & 42.6 & 16.8 & \\textbf{29.8} \\\\ \\midrule\n\\multirow{4}{*}{Mistral-12B} & Dense & 82.6 & 68.5 & 50.4 & 59.3 & 45.2 & 39.9 & 44.1 & 17.8 & 36.8 \\\\\n & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 1.2 & 24.8 & 11.9 & 13.7 & 12.9 \\\\\n & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 0.0 & 23.8 & 8.4 & 4.7 & 12.3 \\\\\n & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 0.8 & 25.7 & 4.7 & 2.8 & 8.5 \\\\\n & \\texttt{Cus-Prun} & 68.2 & 35.8 & 47.6 & \\textbf{50.5} & 32.8 & 37.7 & 35.6 & 15.9 & \\textbf{30.5} \\\\ \\midrule\n\\multirow{4}{*}{Llama2-13B} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 6.4 & 24.3 & 28.3 & 14.5 & 18.4 \\\\\n & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 0.0 & 22.3 & 1.8 & 10.2 & 8.6 \\\\\n & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 0.0 & 26.2 & 23.7 & 8.6 & 14.6 \\\\\n & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 0.0 & 23.1 & 22.3 & 8.9 & 13.6 \\\\\n & \\texttt{Cus-Prun} & 47.8 & 20.9 & 50.7 & \\textbf{39.8} & 4.8 & 24.2 & 23.6 & 13.8 & \\textbf{16.6} \\\\ \\midrule\n\\multirow{4}{*}{Llama3-70B} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 65.2 & 66.1 & 67.8 & 17.8 & 54.2 \\\\\n & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 15.6 & 39.9 & 29.8 & 16.6 & 25.5 \\\\\n & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 4.8 & 41.3 & 39.6 & 13.2 & 24.7 \\\\\n & ShortGPT & 59.4 & 5.6 & 75.5 & 46.8 & 13.7 & 40.7 & 40.4 & 11.9 & 26.7 \\\\\n & \\texttt{Cus-Prun} & 73.3 & 58.7 & 68.4 & \\textbf{66.8} & 40.4 & 53.6 & 58.5 & 16.9 & \\textbf{42.4} \\\\ \n\\bottomrule\n\\end{tabular}}\n \\label{table:cl_tha}\n\\end{table*}\n\n\\subsection{Experiments Detailed Settings}\\label{sec:appen_setting}\n\n\\subsubsection{Multilingual Settings}\\label{sec:appen_setting_1}\n\n\\paragraph{Experiment Details} For multilingual setting, we can obtain two corpora: $C_{L_{\\text{Exp}}}$ and $C_{(D, T)_{\\text{Exp}}}$. The first corpus contains samples in a specific language across various domains and tasks, while the second corpus contains samples from a specific domain-task combination in other languages, i.e., the target dataset in other languages. Specifically, for $C_{L_{\\text{Exp}}}$ we employ Wikipedia\\footnote{\\url{https://huggingface.co/datasets/wikimedia/wikipedia}} to construct language-specific corpus covering various domains and tasks. For $C_{(D, T)_{\\text{Exp}}}$, we employ the corresponding datasets in English, including GSM8K~\\citep{cobbe2021training} for MGSM, the English split of M3Exam\\footnote{M3Exam is language-specific and does not utilize a translated parallel corpus.} for M3Exam, SQuAD~\\citep{rajpurkar2016squad} for XQuAD, and XSum~\\citep{narayan2018don} for XLSum. \n\nHyperparameters, including the sizes of $C_{L_{\\text{Exp}}}$ and $C_{(D, T)_{\\text{Exp}}}$, are determined using the validation set of the XLSum dataset and then applied to testsets in other multilingual datasets. Furthermore, accuracy is the metric used for ARC-c, GSM8K, MMLU, MGSM, M3Exam, and XQuAD, while Rouge-L~\\citep{lin2004rouge} is used for XLSum. \n\n\\subsubsection{Multidomain Settings}\\label{sec:appen_setting_2}\n\n\\paragraph{Settings} For multidomain setting, we can obtain two corpora: $C_{D_{\\text{Exp}}} = \\{(L, D_{\\text{Exp}}, T)|L\\in\\mathbb{L}, T\\in\\mathbb{T}\\}$ and $C_{(L, T)_{\\text{Exp}}}=\\{(D, (L, T)_{\\text{Exp}})|D\\in\\mathbb{D}\\}$. The first corpus contains samples in a specific domain across various languages and tasks, while the second corpus contains samples from a specific language-task combination across different domains, i.e., the target dataset in other domains. Specifically, for $C_{D_{\\text{Exp}}}$ we employ specific domain corpus, including English split of medical corpus~\\citep{garcia2024medical} for medical domain, general finance corpus for finance domain\\footnote{\\url{https://huggingface.co/datasets/gbharti/finance-alpaca}}, general Twitter corpus~\\citep{kharde975sentiment}, and English split of Amazon corpus~\\citep{marc_reviews}. For $C_{(L, T)_{\\text{Exp}}}$, we employ the corresponding datasets in general domains, including CommonsenseQA~\\citep{talmor2019commonsenseqa} for MedMCQ, open table question-answering OTT-QA~\\citep{chen2020open} for FinTQA, general sentiment analysis~\\citep{attia2018multilingual} for TSA and AMSA.\n\n\\paragraph{Experiment Details} Hyperparameters, including the sizes of $C_{D_{\\text{Exp}}}$ and $C_{(L, T)_{\\text{Exp}}}$, are determined using the validation set of the Amazon sentiment analysis dataset and then applied to testsets in other multidomain datasets. Furthermore, accuracy is the metric used for all datasets. \n\n\n\n\\subsubsection{Multitask Settings}\\label{sec:appen_setting_3}\n\n\\paragraph{Settings} For multitask setting, we can obtain two corpora: $C_{T_{\\text{Exp}}} = \\{(L, D, T_{\\text{Exp}})|L\\in\\mathbb{L}, D\\in\\mathbb{D}\\}$ and $C_{(L, D)_{\\text{Exp}}}=\\{(T, (L, S)_{\\text{Exp}})|T\\in\\mathbb{T}\\}$. The first corpus contains samples in a specific task across various languages and domains, while the second corpus contains samples from a specific language-domain combination across different tasks, i.e., the target dataset in other tasks. Specifically, for $C_{T_{\\text{Exp}}}$ we employ specific task corpus, including XSum corpus~\\citep{abacha2019summarization} for summarization task, general conterfact corpus\\footnote{\\url{https://huggingface.co/datasets/azhx/counterfact-easy}} for counterfactual task. For $C_{(L, D)_{\\text{Exp}}}$, we employ the corresponding datasets in other tasks, including MedQCQ~\\citep{pmlr-v174-pal22a} for MedSum, AMSA~\\citep{zhang2015character} for AMSum and AMContFact.\n\n\\paragraph{Experiment Details} Hyperparameters, including the sizes of $C_{T_{\\text{Exp}}}$ and $C_{(L, D)_{\\text{Exp}}}$, are determined using the validation set of the Amazon counterfactual dataset and then applied to testsets in other multitask setting datasets. Furthermore, accuracy is the metric used for ARC-c, GSM8K, MMLU, and AMContFact, while Rouge-L~\\citep{lin2004rouge} is used for MedSum and AMSum. \n\n\n\n\\end{document}\n"},"question":{"kind":"string","value":"Which model is completely unaffected by the method used on some task? Which model has the best performance on the Cus-Prun method on this task?"},"answer":{"kind":"string","value":"Llama2-13B， Llama3-70B"},"plan":{"kind":"string","value":"SELECT all models\nSELECT all tasks\nSELECT all methods\nLOOP for each model\n LOOP for each task\n SELECT scores of this model on this task across all methods\n COMPUTE maximum score across methods\n COMPUTE minimum score across methods\n IF maximum score equals minimum score\n SELECT this model and this task as unchanged\nSELECT all models\nSELECT Cus-Prun scores on unchanged tasks\nCOMPUTE model with maximum Cus-Prun score on unchanged tasks\nRETURN unchanged models and tasks, selected model\n"}}},{"rowIdx":91,"cells":{"paper":{"kind":"string","value":"2506.02561"},"relevant_tables":{"kind":"list like","value":[["\\begin{table}[ht]\n","\\caption{Aggressive pruning ratio on Llama3-8B.}\n","\\centering\n","\\setlength{\\tabcolsep}{3pt}\n"," \\scalebox{0.9}{\n","\\begin{tabular}{l|c|c|c|c}\n"," \\hline\n"," \\textbf{Method} & \\textbf{Ratio} & \\textbf{Speedup} & \\textbf{MMLU} & \\textbf{Expert} \\\\\\midrule\n"," \\rowcolor{lightgray}\\cellcolor{white}Dense & 0.0 & 1$\\times$ & 63.1 & 59.7 \\\\\\midrule\n"," ShortGPT & 25.0 & 1.3$\\times$ & 28.6 & 24.6 \\\\\n"," \\rowcolor{lightblue}\\cellcolor{white}\\texttt{Cus-Prun} & 25.0 & 1.3$\\times$ & \\textbf{51.9} & \\textbf{53.3} \\\\\\midrule\n"," ShortGPT & 34.2 & 1.5$\\times$ & 20.8 & 18.5 \\\\\n"," \\rowcolor{lightblue}\\cellcolor{white}\\texttt{Cus-Prun} & 35.0 & 1.5$\\times$ & \\textbf{50.2} & \\textbf{51.4} \\\\\\midrule\n"," ShortGPT & 43.8 & 1.8$\\times$ & 7.9 & 10.2 \\\\\n"," \\rowcolor{lightblue}\\cellcolor{white}\\texttt{Cus-Prun} & 45.0 & 1.8$\\times$ & \\textbf{48.4} & \\textbf{50.6} \\\\\n"," \\hline\n"," \\end{tabular}}\n"," \\label{table:prune_more}\n","\\end{table}\n"]],"string":"[\n [\n \"\\\\begin{table}[ht]\\n\",\n \"\\\\caption{Aggressive pruning ratio on Llama3-8B.}\\n\",\n \"\\\\centering\\n\",\n \"\\\\setlength{\\\\tabcolsep}{3pt}\\n\",\n \" \\\\scalebox{0.9}{\\n\",\n \"\\\\begin{tabular}{l|c|c|c|c}\\n\",\n \" \\\\hline\\n\",\n \" \\\\textbf{Method} & \\\\textbf{Ratio} & \\\\textbf{Speedup} & \\\\textbf{MMLU} & \\\\textbf{Expert} \\\\\\\\\\\\midrule\\n\",\n \" \\\\rowcolor{lightgray}\\\\cellcolor{white}Dense & 0.0 & 1$\\\\times$ & 63.1 & 59.7 \\\\\\\\\\\\midrule\\n\",\n \" ShortGPT & 25.0 & 1.3$\\\\times$ & 28.6 & 24.6 \\\\\\\\\\n\",\n \" \\\\rowcolor{lightblue}\\\\cellcolor{white}\\\\texttt{Cus-Prun} & 25.0 & 1.3$\\\\times$ & \\\\textbf{51.9} & \\\\textbf{53.3} \\\\\\\\\\\\midrule\\n\",\n \" ShortGPT & 34.2 & 1.5$\\\\times$ & 20.8 & 18.5 \\\\\\\\\\n\",\n \" \\\\rowcolor{lightblue}\\\\cellcolor{white}\\\\texttt{Cus-Prun} & 35.0 & 1.5$\\\\times$ & \\\\textbf{50.2} & \\\\textbf{51.4} \\\\\\\\\\\\midrule\\n\",\n \" ShortGPT & 43.8 & 1.8$\\\\times$ & 7.9 & 10.2 \\\\\\\\\\n\",\n \" \\\\rowcolor{lightblue}\\\\cellcolor{white}\\\\texttt{Cus-Prun} & 45.0 & 1.8$\\\\times$ & \\\\textbf{48.4} & \\\\textbf{50.6} \\\\\\\\\\n\",\n \" \\\\hline\\n\",\n \" \\\\end{tabular}}\\n\",\n \" \\\\label{table:prune_more}\\n\",\n \"\\\\end{table}\\n\"\n ]\n]","isConversational":false},"tables":{"kind":"list like","value":[["\\begin{figure}[t]\n"," \\centering\n"," \\includegraphics[width=0.48\\textwidth]{Figures/cust-prune.pdf}\n"," \\caption{Given a requirement for an expert model across three dimensions (language, domain, and task), \\texttt{Cus-Prun} (i) identifies irrelevant neurons for each dimension using corresponding corpora (left), and (ii) prunes \\textbf{overlapping} irrelevant neurons across dimensions to obtain the expert model (right).}\n"," \\label{fig:cus-prun}\n","\\end{figure}\n"],["\\begin{figure}[t]\n","\\scalebox{0.95}{ \n","\\begin{minipage}{\\linewidth}\n","\\begin{algorithm}[H]\n","\\renewcommand{\\algorithmicrequire}{\\textbf{Input:}}\n","\\renewcommand{\\algorithmicensure}{\\textbf{Output:}}\n","\\caption{Adaptive Custom Pruning}\n","\\begin{algorithmic}[1]\n","\\REQUIRE Original language model $\\mathcal{LLM}$, request for expert model $\\mathcal{LLM}_{\\text{Exp}}$ with selected dimensions: $L_{\\text{Exp}}$, $D_{\\text{Exp}}$, $T_{\\text{Exp}}$ (any subset), request for pruning ratio $\\sigma$.\n","\\STATE \\texttt{// Construct specific corpora for each selected dimension.}\n","\\STATE $C = \\{\\}$\n","\\IF{$L_{\\text{Exp}}$ is specified}\n"," \\STATE $C = C \\cup \\{(L_{\\text{Exp}}, D, T) \\mid D \\in \\mathbb{D}, T \\in \\mathbb{T}\\}$\n","\\ENDIF\n","\\IF{$D_{\\text{Exp}}$ is specified}\n"," \\STATE $C = C \\cup \\{(L, D_{\\text{Exp}}, T) \\mid L \\in \\mathbb{L}, T \\in \\mathbb{T}\\}$\n","\\ENDIF\n","\\IF{$T_{\\text{Exp}}$ is specified}\n"," \\STATE $C = C \\cup \\{(L, D, T_{\\text{Exp}}) \\mid L \\in \\mathbb{L}, D \\in \\mathbb{D}\\}$\n","\\ENDIF\n","\\STATE \\texttt{// Identify irrelevant neurons for each selected dimension.}\n","\\FORALL{neuron $N_{i}^{(l)}$ in $\\mathcal{LLM}$}\n"," \\IF{$\\forall c \\in C,$ $N_{i}^{(l)} \\in \\tilde{\\mathcal{N}}(c)$}\n"," \\STATE $\\tilde{\\mathcal{N}} \\gets \\tilde{\\mathcal{N}} \\cup N_{i}^{(l)}$\n"," \\ENDIF\n","\\ENDFOR\n","\\STATE \\texttt{// Prune irrelevant neurons to obtain expert model.}\n","\\STATE $\\mathcal{LLM}_{\\text{Exp}} = \\mathcal{LLM} \\ominus \\tilde{\\mathcal{N}}$\n","\\ENSURE $\\mathcal{LLM}_{\\text{Exp}}$\n","\\end{algorithmic}\n","\\label{algo:adaptive_pruning}\n","\\end{algorithm}\n","\\end{minipage}\n","}\n","\\end{figure}\n"],["\\begin{figure}[t]\n"," \\centering\n"," \\includegraphics[width=0.48\\textwidth]{Figures/three-dim-examples.pdf}\n"," \\caption{Concrete examples of applying \\texttt{Cus-Prun} to prune $25\\%$ of Llama3-8B-Base's parameters into three-dimensional expert models. Numbers above each box indicate performance on the \\textbf{whole} test set, with the first evaluated by Rouge-L, and the other two by accuracy. }\n"," \\label{fig:korean}\n","\\end{figure}\n"],["\\begin{table*}[t]\n","\\caption{Main Results of \\texttt{Cus-Prun} on multilingual setting with a pruning ratio of $25\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in summarization tasks and in accuracy (\\%) for other datasets.}\n"," \\centering\n","\\footnotesize\n","\\setlength{\\tabcolsep}{0.8pt}\n"," \\scalebox{0.9}{\n"," \\begin{tabular}{c|l|cccc|ccccc|ccccc|cccc}\n","\\toprule\n","\\quad\\quad & \\multirow{2}{*}{\\textbf{\\normalsize{Method}}} & \n"," \\multicolumn{4}{c}{\\textbf{\\normalsize{General Capability}}} \\vline & \\multicolumn{5}{c}{\\textbf{\\normalsize{Multilingual Expert}}} \\vline & \\multicolumn{5}{c}{\\textbf{\\normalsize{Multidomain Expert }}} \\vline & \\multicolumn{4}{c}{\\textbf{\\normalsize{Multitask Expert }}} \\\\ \n"," & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3 & XQuAD & Sum & Avg. & MMCQ & FTQA & TSA & AMSA & Avg. & MSum & ASum & AMCF & Avg. \\\\ \\midrule\n","\\rowcolor{lightgray}\\cellcolor{white}\\multirow{5}{*}{\\rotatebox{90}{\\textbf{Llama3-8B}}} & Dense & 70.7 & 58.3 & 63.1 & 64.1 &41.2 &49.1 & 63.4 &32.9 &46.7 & 51.8 & 23.9 & 67.1 & 95.9 & 59.8 & 76.6 & 16.2 & 78.2 & 57.0 \\\\\n"," & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 1.1 & 24.0 & 13.6 & 23.2 & 15.5 & 0.0 & 0.0 & \\textbf{61.8} & 76.0 & 34.5 & 62.2 & \\textbf{21.8} & \\textbf{80.0} & \\textbf{54.7} \\\\\n"," & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & 14.9 & 16.6 & 8.5 & 10.0 & 22.6 & 0.0 & 41.2 & 53.7 & 29.4 & 7.3 & 2.9 & 51.3 & 20.5 \\\\\n"," & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & 26.9 & 0.0 & 2.7 & 7.4 & 3.2 & 0.0 & 38.6 & 35.7 & 19.4 & 4.1 & 4.8 & 43.8 & 17.6 \\\\\n"," &\\cellcolor{lightblue}\\texttt{Cus-Prun} &\\cellcolor{lightblue}\\textbf{62.4} & \\cellcolor{lightblue}\\textbf{37.0} & \\cellcolor{lightblue}\\textbf{54.7} & \\cellcolor{lightblue}\\textbf{51.4} & \\cellcolor{lightblue}\\textbf{30.1} & \\cellcolor{lightblue}\\textbf{41.5} & \\cellcolor{lightblue}\\textbf{52.6} & \\cellcolor{lightblue}\\textbf{31.5} & \\cellcolor{lightblue}\\textbf{38.9} & \\cellcolor{lightblue}\\textbf{42.9} & \\cellcolor{lightblue}\\textbf{20.6} & \\cellcolor{lightblue}\\textbf{61.8} & \\cellcolor{lightblue}\\textbf{87.6} & \\cellcolor{lightblue}\\textbf{53.2} & \\cellcolor{lightblue}\\textbf{68.4} & \\cellcolor{lightblue}12.8 & \\cellcolor{lightblue}75.5 & \\cellcolor{lightblue}52.2 \\\\ \\midrule\n","\\rowcolor{lightgray}\\cellcolor{white}\\multirow{5}{*}{\\rotatebox{90}{\\textbf{Mistral-12B}}} & Dense & 82.6 & 68.5 & 50.4 & 67.2 & 51.7 & 43.8 & 49.2 & 25.4 & 42.5 & 54.6 & 26.6 & 69.4 & 92.4 & 60.8 & 88.7 & 3.0 & 78.6 & 56.4 \\\\\n"," & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 2.1 & 27.8 & 19.0 & \\textbf{23.2} & 18.0 & 0.0 & 0.0 & 51.0 & 20.9 & 18.0 & 59.3 & 0.5 & 2.8 & 20.9 \\\\\n"," & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 0.8 & 25.1 & 17.4 & 7.8 & 12.8 & 24.9 & 9.2 & 34.2 & 54.3 & 30.7 & 27.4 & 1.3 & 36.3 & 21.7 \\\\\n"," & ShortGPT & 37.8 & 0.0 & 33.9 & 23.9 & 2.9 & 27.0 & 18.0 & 5.0 & 13.2 & 31.4 & 7.2 & 39.2 & 52.5 & 32.6 & 26.2 & 0.2 & 42.7 & 23.0 \\\\\n"," & \\cellcolor{lightblue}\\texttt{Cus-Prun} & \\cellcolor{lightblue}\\textbf{67.5} & \\cellcolor{lightblue}\\textbf{43.4} & \\cellcolor{lightblue}\\textbf{43.8} & \\cellcolor{lightblue}\\textbf{51.6} & \\cellcolor{lightblue}\\textbf{34.3} & \\cellcolor{lightblue}\\textbf{39.2} & \\cellcolor{lightblue}\\textbf{40.7} & \\cellcolor{lightblue}23.1 & \\cellcolor{lightblue}\\textbf{34.3} & \\cellcolor{lightblue}\\textbf{47.9} & \\cellcolor{lightblue}\\textbf{25.1} & \\cellcolor{lightblue}\\textbf{67.3} & \\cellcolor{lightblue}\\textbf{83.7} & \\cellcolor{lightblue}\\textbf{56.0} & \\cellcolor{lightblue}\\textbf{83.5} & \\cellcolor{lightblue}\\textbf{3.4} & \\cellcolor{lightblue}\\textbf{72.8} & \\cellcolor{lightblue}\\textbf{50.9} \\\\ \\midrule\n","\\rowcolor{lightgray}\\cellcolor{white}\\multirow{5}{*}{\\rotatebox{90}{\\textbf{Llama2-13B}}} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 17.5 & 30.4 & 44.1 & 24.9 & 29.2 & 25.2 & 0.0 & 42.7 & 84.1 & 38.0 & 70.0 & 7.4 & 44.3 & 40.6 \\\\\n"," & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 1.1 & 22.8 & 3.8 & 17.7 & 11.3 & 0.0 & 0.0 & 9.7 & 0.0 & 2.4 & 21.6 & 4.8 & 0.0 & 8.8 \\\\\n"," & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 2.8 & 25.3 & 23.4 & 9.9 & 15.5 & 18.7 & 0.0 & 28.4 & 67.3 & 28.6 & 24.5 & 4.9 & 32.9 & 20.8 \\\\\n"," & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 2.4 & 23.0 & 24.7 & 11.3 & 15.3 & 16.9 & 0.0 & 34.6 & \\textbf{69.8} & 30.3 & 23.8 & 5.2 & 39.1 & 22.7 \\\\\n"," & \\cellcolor{lightblue}\\texttt{Cus-Prun} & \\cellcolor{lightblue}\\textbf{48.3} & \\cellcolor{lightblue}\\textbf{20.8} & \\cellcolor{lightblue}\\textbf{50.0} & \\cellcolor{lightblue}\\textbf{39.7} & \\cellcolor{lightblue}\\textbf{12.7} & \\cellcolor{lightblue}\\textbf{26.2} & \\cellcolor{lightblue}\\textbf{34.2} & \\cellcolor{lightblue}\\textbf{24.1} & \\cellcolor{lightblue}\\textbf{24.3} & \\cellcolor{lightblue}\\textbf{25.6} & \\cellcolor{lightblue}0.0 & \\cellcolor{lightblue}\\textbf{38.5} & \\cellcolor{lightblue}68.3 & \\cellcolor{lightblue}\\textbf{33.1} & \\cellcolor{lightblue}\\textbf{64.5} & \\cellcolor{lightblue}\\textbf{6.7} & \\cellcolor{lightblue}\\textbf{42.9} & \\cellcolor{lightblue}\\textbf{38.0} \\\\ \\midrule\n","\\rowcolor{lightgray}\\cellcolor{white}\\multirow{5}{*}{\\rotatebox{90}{\\textbf{Llama3-70B}}} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 69.5 & 71.1 & 69.1 & 36.6 & 61.6 & 72.1 & 55.3 & 83.6 & 96.2 & 76.8 & 84.2 & 17.3 & 81.8 & 61.1 \\\\\n"," & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 16.8 & 43.7 & 43.0 & 29.0 & 33.1 & 27.3 & 1.0 & 51.0 & 50.3 & 32.4 & 10.2 & 13.7 & 20.6 & 14.8 \\\\\n"," & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 3.7 & 44.8 & 33.0 & 21.2 & 25.7 & 57.6 & 27.6 & 68.1 & 59.4 & 53.2 & 58.0 & 14.2 & 68.3 & 46.8 \\\\\n"," & ShortGPT & 59.4 & 5.6 & \\textbf{75.5} & 46.8 & 11.9 & 43.1 & 38.8 & 24.0 & 29.5 & 58.4 & 32.2 & 67.5 & 64.9 & 55.8 & 59.6 & 13.9 & 65.8 & 46.4 \\\\\n"," & \\cellcolor{lightblue}\\texttt{Cus-Prun} & \\cellcolor{lightblue}\\textbf{68.4} & \\cellcolor{lightblue}\\textbf{53.2} & \\cellcolor{lightblue}66.6 & \\cellcolor{lightblue}\\textbf{62.7} & \\cellcolor{lightblue}\\textbf{43.1} & \\cellcolor{lightblue}\\textbf{57.7} & \\cellcolor{lightblue}\\textbf{59.8} & \\cellcolor{lightblue}\\textbf{34.3} & \\cellcolor{lightblue}\\textbf{48.7} & \\cellcolor{lightblue}\\textbf{68.2} & \\cellcolor{lightblue}\\textbf{43.9} & \\cellcolor{lightblue}\\textbf{81.4} & \\cellcolor{lightblue}\\textbf{87.8} & \\cellcolor{lightblue}\\textbf{70.3} & \\cellcolor{lightblue}\\textbf{80.4} & \\cellcolor{lightblue}\\textbf{15.7} & \\cellcolor{lightblue}\\textbf{77.5} & \\cellcolor{lightblue}\\textbf{57.9} \\\\ \n","\\bottomrule\n","\\end{tabular}}\n"," \\label{table:cross_ling}\n","\\vspace{-0.2cm}\n","\\end{table*}\n"],["\\begin{figure*}[t]\n"," \\centering\n"," \\begin{minipage}{0.4\\textwidth}\n"," \\centering\n"," \\captionof{table}{Performance of Chinese-Medical expert model on MCQ task}\n"," \\label{tab:cmexam}\n"," \\scalebox{0.96}{\n"," \\begin{tabular}{l|c|c}\n"," \\hline\n"," \\textbf{Method} & \\textbf{General} & \\textbf{CMExam} \\\\\\midrule\n"," \\rowcolor{lightgray}\\cellcolor{white} Dense & 59.3 & 50.6 \\\\\n"," LLM-Pruner & 18.6 & 25.0 \\\\\n"," SliceGPT & 27.8 & 26.9 \\\\\n"," ShortGPT & 23.9 & 23.7 \\\\\n"," \\rowcolor{lightblue}\\cellcolor{white} \\texttt{Cus-Prun} & \\textbf{52.4} & \\textbf{48.7} \\\\\n"," \\hline\n"," \\end{tabular}}\n"," \\end{minipage}\n"," \\hfill\n"," \\begin{minipage}{0.58\\textwidth}\n"," \\centering\n"," \\includegraphics[width=\\textwidth]{Figures/huatuo.pdf}\n"," \\caption{Chinese Medical LLM performance. Numbers are quality on the \\textbf{whole} test set evaluated by GPT4.}\n"," \\label{fig:huatuo}\n"," \\end{minipage}\n","\\end{figure*}\n"],["\\begin{table}[ht]\n","\\caption{Aggressive pruning ratio on Llama3-8B.}\n","\\centering\n","\\setlength{\\tabcolsep}{3pt}\n"," \\scalebox{0.9}{\n","\\begin{tabular}{l|c|c|c|c}\n"," \\hline\n"," \\textbf{Method} & \\textbf{Ratio} & \\textbf{Speedup} & \\textbf{MMLU} & \\textbf{Expert} \\\\\\midrule\n"," \\rowcolor{lightgray}\\cellcolor{white}Dense & 0.0 & 1$\\times$ & 63.1 & 59.7 \\\\\\midrule\n"," ShortGPT & 25.0 & 1.3$\\times$ & 28.6 & 24.6 \\\\\n"," \\rowcolor{lightblue}\\cellcolor{white}\\texttt{Cus-Prun} & 25.0 & 1.3$\\times$ & \\textbf{51.9} & \\textbf{53.3} \\\\\\midrule\n"," ShortGPT & 34.2 & 1.5$\\times$ & 20.8 & 18.5 \\\\\n"," \\rowcolor{lightblue}\\cellcolor{white}\\texttt{Cus-Prun} & 35.0 & 1.5$\\times$ & \\textbf{50.2} & \\textbf{51.4} \\\\\\midrule\n"," ShortGPT & 43.8 & 1.8$\\times$ & 7.9 & 10.2 \\\\\n"," \\rowcolor{lightblue}\\cellcolor{white}\\texttt{Cus-Prun} & 45.0 & 1.8$\\times$ & \\textbf{48.4} & \\textbf{50.6} \\\\\n"," \\hline\n"," \\end{tabular}}\n"," \\label{table:prune_more}\n","\\end{table}\n"],["\\begin{figure*}[t]\n"," \\centering\n"," \\includegraphics[width=1.0\\textwidth]{Figures/lang-specific.pdf}\n"," \\caption{Performance of \\texttt{Cus-Prun} in obtaining language-specific models.}\n"," \\label{fig:lang-spec}\n","\\end{figure*}\n"],["\\begin{table}[ht]\n"," \\centering\n","\\footnotesize\n"," \\scalebox{1}{\n"," \\begin{tabular}{lp{5.3cm}}\n"," \\toprule\n"," \\textbf{Task} & \\textbf{Prompt} \\\\\\midrule\n"," Generation & Generate a text document in \\{language\\}/\\{domain\\}/\\{task\\}. Make sure the documents is not fixed to one \\{language\\}/\\{domain\\}/\\{task\\} or \\{language\\}/\\{domain\\}/\\{task\\}. Ensure the content is clear, concise, and appropriate for the specified request. Use professional and domain-specific terminology where necessary. \\\\\\midrule\n"," Evaluation & Evaluate the quality of the given answer to the question. Provide a score from 0 to 5, where 0 represents very low quality and 5 represents very high quality. Question: \\{question\\} Answer: \\{answer\\}.\\\\\n"," \\bottomrule\n"," \\end{tabular}}\n","\\caption{GPT-4o prompts for generating documents and evaluating answer quality.} \\label{table:testset}\n","\\end{table}\n"],["\\begin{table*}[hbpt]\n","\\caption{Main Results of \\texttt{Cus-Prun} on Germany with a pruning ratio of $25\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in XLSum and in accuracy (\\%) for other datasets.}\n"," \\centering\n","\\footnotesize\n","\\setlength{\\tabcolsep}{3pt}\n"," \\scalebox{1.0}{\n"," \\begin{tabular}{l|l|cccc|ccccc}\n","\\toprule\n","\\multirow{2}{*}{\\textbf{\\normalsize{Model}}} & \\multirow{2}{*}{\\textbf{\\normalsize{Method}}} & \n"," \\multicolumn{4}{c}{\\textbf{\\normalsize{General Capability}}} \\vline & \\multicolumn{5}{c}{\\textbf{\\normalsize{Expert Capability}}} \\\\ \n"," & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3Exam & XQuAD & XLSum & Avg. \\\\ \\midrule\n","\\multirow{4}{*}{Llama3-8B} &\n"," Dense & 70.7 & 58.3 & 63.1 & 64.1 & 44.8 & - & 52.9 & - & 48.8 \\\\\n"," & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 0.0 & - & 11.0 & - & 5.5 \\\\\n"," & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & - & 9.8 & - & 4.9 \\\\\n"," & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & - & 0.0 & - & 0.0 \\\\\n"," & \\texttt{Cus-Prun} & 61.4 & 38.9 & 54.5 & \\textbf{51.6} & 32.8 & - & 49.6 & - & \\textbf{41.2} \\\\ \\midrule\n","\\multirow{4}{*}{Mistral-12B} & Dense & 82.6 & 68.5 & 50.4 & 59.3 & 56.8 & - & 41.2 & - & 49.0 \\\\\n"," & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 2.4 & - & 13.4 & - & 7.9 \\\\\n"," & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 0.8 & - & 15.5 & - & 8.2 \\\\\n"," & ShortGPT & 37.8 & 0.0 & 33.9 & 23.9 & 3.6 & - & 20.3 & - & 12.0 \\\\\n"," & \\texttt{Cus-Prun} & 64.6 & 39.7 & 43.2 & \\textbf{49.2} & 31.6 & - & 35.9 & - & \\textbf{33.8} \\\\ \\midrule\n","\\multirow{4}{*}{Llama2-13B} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 24.4 & - & 40.3 & - & 32.3 \\\\\n"," & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 2.0 & - & 5.7 & - & 3.9 \\\\\n"," & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 3.6 & - & 18.1 & - & 10.9 \\\\\n"," & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 2.8 & - & 27.2 & - & 15.0 \\\\\n"," & \\texttt{Cus-Prun} & 47.6 & 19.8 & 49.9 & \\textbf{39.1} & 18.4 & - & 31.7 & - & \\textbf{25.0} \\\\ \\midrule\n","\\multirow{4}{*}{Llama3-70B} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 74.8 & - & 58.2 & - & 66.5 \\\\\n"," & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 18.0 & - & 27.3 & - & 22.7 \\\\\n"," & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 0.0 & - & 17.3 & - & 8.7 \\\\\n"," & ShortGPT & 59.4 & 5.6 & 75.5 & 46.8 & 9.6 & - & 31.5 & - & 20.6 \\\\\n"," & \\texttt{Cus-Prun} & 66.8 & 59.3 & 69.1 & \\textbf{65.1} & 48.2 & - & 53.9 & - & \\textbf{51.1} \\\\ \n","\\bottomrule\n","\\end{tabular}}\n"," \\label{table:cl_ger}\n","\\end{table*}\n"],["\\begin{table*}[hbpt]\n","\\caption{Main Results of \\texttt{Cus-Prun} on Chinese with a pruning ratio of $25\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in XLSum and in accuracy (\\%) for other datasets.}\n"," \\centering\n","\\footnotesize\n","\\setlength{\\tabcolsep}{3pt}\n"," \\scalebox{1.0}{\n"," \\begin{tabular}{l|l|cccc|ccccc}\n","\\toprule\n","\\multirow{2}{*}{\\textbf{\\normalsize{Model}}} & \\multirow{2}{*}{\\textbf{\\normalsize{Method}}} & \n"," \\multicolumn{4}{c}{\\textbf{\\normalsize{General Capability}}} \\vline & \\multicolumn{5}{c}{\\textbf{\\normalsize{Specific Capability}}} \\\\ \n"," & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3Exam & XQuAD & XLSum & Avg. \\\\ \\midrule\n","\\multirow{4}{*}{Llama3-8B} &\n"," Dense & 70.7 & 58.3 & 63.1 & 64.1 & 43.6 & 55.1 & 78.7 & 49.1 & 56.6 \\\\\n"," & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 2.4 & 23.6 & 21.3 & 32.8 & 20.0 \\\\\n"," & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & 17.4 & 23.5 & 8.3 & 12.3 \\\\\n"," & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & 28.3 & 0.0 & 3.1 & 7.9 \\\\\n"," & \\texttt{Cus-Prun} & 60.5 & 25.7 & 49.4 & \\textbf{45.2} & 36.0 & 44.7 & 65.6 & 46.3 & \\textbf{48.2} \\\\ \\midrule\n","\\multirow{4}{*}{Mistral-12B} & Dense & 82.6 & 68.5 & 50.4 & 59.3 & 53.2 & 47.8 & 62.2 & 33.0 & 49.1 \\\\\n"," & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 2.8 & 30.7 & 31.8 & 32.6 & 24.5 \\\\\n"," & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 1.6 & 26.4 & 28.3 & 10.8 & 16.8 \\\\\n"," & ShortGPT & 37.8 & 0.0 & 33.9 & 23.9 & 4.4 & 28.2 & 29.1 & 7.2 & 17.2 \\\\\n"," & \\texttt{Cus-Prun} & 68.3 & 43.2 & 39.5 & \\textbf{50.3} & 38.4 & 40.7 & 50.6 & 30.3 & \\textbf{40.0} \\\\ \\midrule\n","\\multirow{4}{*}{Llama2-13B} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 21.6 & 36.5 & 59.8 & 35.3 & 38.3 \\\\\n"," & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 1.2 & 23.3 & 3.8 & 25.1 & 13.4\\\\\n"," & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 4.8 & 24.5 & 28.4 & 11.2 & 17.2 \\\\\n"," & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 4.4 & 22.9 & 24.6 & 13.7 & 16.4 \\\\\n"," & \\texttt{Cus-Prun} & 48.6 & 20.7 & 51.9 & \\textbf{40.4} & 14.8 & 28.2 & 47.3 & 34.4 & \\textbf{31.2} \\\\ \\midrule\n","\\multirow{4}{*}{Llama3-70B} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 68.4 & 76.1 & 81.3 & 55.3 & 70.3 \\\\\n"," & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 16.8 & 47.5 & 56.1 & 41.3 & 40.4 \\\\\n"," & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 6.4 & 48.3 & 42.2 & 29.3 & 31.6 \\\\\n"," & ShortGPT & 59.4 & 5.6 & 75.5 & 46.8 & 12.4 & 45.5 & 44.6 & 36.1 & 34.7 \\\\\n"," & \\texttt{Cus-Prun} & 72.3 & 48.5 & 65.2 & \\textbf{62.0} & 40.8 & 61.7 & 66.9 & 51.6 & \\textbf{55.3} \\\\ \n","\\bottomrule\n","\\end{tabular}}\n"," \\label{table:cl_chi}\n","\\end{table*}\n"],["\\begin{table*}[hbpt]\n","\\caption{Main Results of \\texttt{Cus-Prun} on Thai with a pruning ratio of $25\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in XLSum and in accuracy (\\%) for other datasets.}\n"," \\centering\n","\\footnotesize\n","\\setlength{\\tabcolsep}{3pt}\n"," \\scalebox{1.0}{\n"," \\begin{tabular}{l|l|cccc|ccccc}\n","\\toprule\n","\\multirow{2}{*}{\\textbf{\\normalsize{Model}}} & \\multirow{2}{*}{\\textbf{\\normalsize{Method}}} & \n"," \\multicolumn{4}{c}{\\textbf{\\normalsize{General Capability}}} \\vline & \\multicolumn{5}{c}{\\textbf{\\normalsize{Specific Capability}}} \\\\ \n"," & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3Exam & XQuAD & XLSum & Avg. \\\\ \\midrule\n","\\multirow{4}{*}{Llama3-8B} &\n"," Dense & 70.7 & 58.3 & 63.1 & 64.1 & 35.2 & 43.0 & 58.7 & 16.7 & 38.4 \\\\\n"," & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 0.8 & 24.4 & 8.4 & 13.5 & 11.8 \\\\\n"," & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & 12.3 & 16.6 & 8.7 & 9.4 \\\\\n"," & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & 25.4 & 0.0 & 2.3 & 6.9 \\\\\n"," & \\texttt{Cus-Prun} & 58.9 & 31.2 & 52.4 & \\textbf{47.5} & 21.6 & 38.3 & 42.6 & 16.8 & \\textbf{29.8} \\\\ \\midrule\n","\\multirow{4}{*}{Mistral-12B} & Dense & 82.6 & 68.5 & 50.4 & 59.3 & 45.2 & 39.9 & 44.1 & 17.8 & 36.8 \\\\\n"," & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 1.2 & 24.8 & 11.9 & 13.7 & 12.9 \\\\\n"," & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 0.0 & 23.8 & 8.4 & 4.7 & 12.3 \\\\\n"," & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 0.8 & 25.7 & 4.7 & 2.8 & 8.5 \\\\\n"," & \\texttt{Cus-Prun} & 68.2 & 35.8 & 47.6 & \\textbf{50.5} & 32.8 & 37.7 & 35.6 & 15.9 & \\textbf{30.5} \\\\ \\midrule\n","\\multirow{4}{*}{Llama2-13B} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 6.4 & 24.3 & 28.3 & 14.5 & 18.4 \\\\\n"," & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 0.0 & 22.3 & 1.8 & 10.2 & 8.6 \\\\\n"," & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 0.0 & 26.2 & 23.7 & 8.6 & 14.6 \\\\\n"," & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 0.0 & 23.1 & 22.3 & 8.9 & 13.6 \\\\\n"," & \\texttt{Cus-Prun} & 47.8 & 20.9 & 50.7 & \\textbf{39.8} & 4.8 & 24.2 & 23.6 & 13.8 & \\textbf{16.6} \\\\ \\midrule\n","\\multirow{4}{*}{Llama3-70B} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 65.2 & 66.1 & 67.8 & 17.8 & 54.2 \\\\\n"," & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 15.6 & 39.9 & 29.8 & 16.6 & 25.5 \\\\\n"," & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 4.8 & 41.3 & 39.6 & 13.2 & 24.7 \\\\\n"," & ShortGPT & 59.4 & 5.6 & 75.5 & 46.8 & 13.7 & 40.7 & 40.4 & 11.9 & 26.7 \\\\\n"," & \\texttt{Cus-Prun} & 73.3 & 58.7 & 68.4 & \\textbf{66.8} & 40.4 & 53.6 & 58.5 & 16.9 & \\textbf{42.4} \\\\ \n","\\bottomrule\n","\\end{tabular}}\n"," \\label{table:cl_tha}\n","\\end{table*}\n"]],"string":"[\n [\n \"\\\\begin{figure}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=0.48\\\\textwidth]{Figures/cust-prune.pdf}\\n\",\n \" \\\\caption{Given a requirement for an expert model across three dimensions (language, domain, and task), \\\\texttt{Cus-Prun} (i) identifies irrelevant neurons for each dimension using corresponding corpora (left), and (ii) prunes \\\\textbf{overlapping} irrelevant neurons across dimensions to obtain the expert model (right).}\\n\",\n \" \\\\label{fig:cus-prun}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{figure}[t]\\n\",\n \"\\\\scalebox{0.95}{ \\n\",\n \"\\\\begin{minipage}{\\\\linewidth}\\n\",\n \"\\\\begin{algorithm}[H]\\n\",\n \"\\\\renewcommand{\\\\algorithmicrequire}{\\\\textbf{Input:}}\\n\",\n \"\\\\renewcommand{\\\\algorithmicensure}{\\\\textbf{Output:}}\\n\",\n \"\\\\caption{Adaptive Custom Pruning}\\n\",\n \"\\\\begin{algorithmic}[1]\\n\",\n \"\\\\REQUIRE Original language model $\\\\mathcal{LLM}$, request for expert model $\\\\mathcal{LLM}_{\\\\text{Exp}}$ with selected dimensions: $L_{\\\\text{Exp}}$, $D_{\\\\text{Exp}}$, $T_{\\\\text{Exp}}$ (any subset), request for pruning ratio $\\\\sigma$.\\n\",\n \"\\\\STATE \\\\texttt{// Construct specific corpora for each selected dimension.}\\n\",\n \"\\\\STATE $C = \\\\{\\\\}$\\n\",\n \"\\\\IF{$L_{\\\\text{Exp}}$ is specified}\\n\",\n \" \\\\STATE $C = C \\\\cup \\\\{(L_{\\\\text{Exp}}, D, T) \\\\mid D \\\\in \\\\mathbb{D}, T \\\\in \\\\mathbb{T}\\\\}$\\n\",\n \"\\\\ENDIF\\n\",\n \"\\\\IF{$D_{\\\\text{Exp}}$ is specified}\\n\",\n \" \\\\STATE $C = C \\\\cup \\\\{(L, D_{\\\\text{Exp}}, T) \\\\mid L \\\\in \\\\mathbb{L}, T \\\\in \\\\mathbb{T}\\\\}$\\n\",\n \"\\\\ENDIF\\n\",\n \"\\\\IF{$T_{\\\\text{Exp}}$ is specified}\\n\",\n \" \\\\STATE $C = C \\\\cup \\\\{(L, D, T_{\\\\text{Exp}}) \\\\mid L \\\\in \\\\mathbb{L}, D \\\\in \\\\mathbb{D}\\\\}$\\n\",\n \"\\\\ENDIF\\n\",\n \"\\\\STATE \\\\texttt{// Identify irrelevant neurons for each selected dimension.}\\n\",\n \"\\\\FORALL{neuron $N_{i}^{(l)}$ in $\\\\mathcal{LLM}$}\\n\",\n \" \\\\IF{$\\\\forall c \\\\in C,$ $N_{i}^{(l)} \\\\in \\\\tilde{\\\\mathcal{N}}(c)$}\\n\",\n \" \\\\STATE $\\\\tilde{\\\\mathcal{N}} \\\\gets \\\\tilde{\\\\mathcal{N}} \\\\cup N_{i}^{(l)}$\\n\",\n \" \\\\ENDIF\\n\",\n \"\\\\ENDFOR\\n\",\n \"\\\\STATE \\\\texttt{// Prune irrelevant neurons to obtain expert model.}\\n\",\n \"\\\\STATE $\\\\mathcal{LLM}_{\\\\text{Exp}} = \\\\mathcal{LLM} \\\\ominus \\\\tilde{\\\\mathcal{N}}$\\n\",\n \"\\\\ENSURE $\\\\mathcal{LLM}_{\\\\text{Exp}}$\\n\",\n \"\\\\end{algorithmic}\\n\",\n \"\\\\label{algo:adaptive_pruning}\\n\",\n \"\\\\end{algorithm}\\n\",\n \"\\\\end{minipage}\\n\",\n \"}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{figure}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=0.48\\\\textwidth]{Figures/three-dim-examples.pdf}\\n\",\n \" \\\\caption{Concrete examples of applying \\\\texttt{Cus-Prun} to prune $25\\\\%$ of Llama3-8B-Base's parameters into three-dimensional expert models. Numbers above each box indicate performance on the \\\\textbf{whole} test set, with the first evaluated by Rouge-L, and the other two by accuracy. }\\n\",\n \" \\\\label{fig:korean}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table*}[t]\\n\",\n \"\\\\caption{Main Results of \\\\texttt{Cus-Prun} on multilingual setting with a pruning ratio of $25\\\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in summarization tasks and in accuracy (\\\\%) for other datasets.}\\n\",\n \" \\\\centering\\n\",\n \"\\\\footnotesize\\n\",\n \"\\\\setlength{\\\\tabcolsep}{0.8pt}\\n\",\n \" \\\\scalebox{0.9}{\\n\",\n \" \\\\begin{tabular}{c|l|cccc|ccccc|ccccc|cccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\quad\\\\quad & \\\\multirow{2}{*}{\\\\textbf{\\\\normalsize{Method}}} & \\n\",\n \" \\\\multicolumn{4}{c}{\\\\textbf{\\\\normalsize{General Capability}}} \\\\vline & \\\\multicolumn{5}{c}{\\\\textbf{\\\\normalsize{Multilingual Expert}}} \\\\vline & \\\\multicolumn{5}{c}{\\\\textbf{\\\\normalsize{Multidomain Expert }}} \\\\vline & \\\\multicolumn{4}{c}{\\\\textbf{\\\\normalsize{Multitask Expert }}} \\\\\\\\ \\n\",\n \" & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3 & XQuAD & Sum & Avg. & MMCQ & FTQA & TSA & AMSA & Avg. & MSum & ASum & AMCF & Avg. \\\\\\\\ \\\\midrule\\n\",\n \"\\\\rowcolor{lightgray}\\\\cellcolor{white}\\\\multirow{5}{*}{\\\\rotatebox{90}{\\\\textbf{Llama3-8B}}} & Dense & 70.7 & 58.3 & 63.1 & 64.1 &41.2 &49.1 & 63.4 &32.9 &46.7 & 51.8 & 23.9 & 67.1 & 95.9 & 59.8 & 76.6 & 16.2 & 78.2 & 57.0 \\\\\\\\\\n\",\n \" & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 1.1 & 24.0 & 13.6 & 23.2 & 15.5 & 0.0 & 0.0 & \\\\textbf{61.8} & 76.0 & 34.5 & 62.2 & \\\\textbf{21.8} & \\\\textbf{80.0} & \\\\textbf{54.7} \\\\\\\\\\n\",\n \" & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & 14.9 & 16.6 & 8.5 & 10.0 & 22.6 & 0.0 & 41.2 & 53.7 & 29.4 & 7.3 & 2.9 & 51.3 & 20.5 \\\\\\\\\\n\",\n \" & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & 26.9 & 0.0 & 2.7 & 7.4 & 3.2 & 0.0 & 38.6 & 35.7 & 19.4 & 4.1 & 4.8 & 43.8 & 17.6 \\\\\\\\\\n\",\n \" &\\\\cellcolor{lightblue}\\\\texttt{Cus-Prun} &\\\\cellcolor{lightblue}\\\\textbf{62.4} & \\\\cellcolor{lightblue}\\\\textbf{37.0} & \\\\cellcolor{lightblue}\\\\textbf{54.7} & \\\\cellcolor{lightblue}\\\\textbf{51.4} & \\\\cellcolor{lightblue}\\\\textbf{30.1} & \\\\cellcolor{lightblue}\\\\textbf{41.5} & \\\\cellcolor{lightblue}\\\\textbf{52.6} & \\\\cellcolor{lightblue}\\\\textbf{31.5} & \\\\cellcolor{lightblue}\\\\textbf{38.9} & \\\\cellcolor{lightblue}\\\\textbf{42.9} & \\\\cellcolor{lightblue}\\\\textbf{20.6} & \\\\cellcolor{lightblue}\\\\textbf{61.8} & \\\\cellcolor{lightblue}\\\\textbf{87.6} & \\\\cellcolor{lightblue}\\\\textbf{53.2} & \\\\cellcolor{lightblue}\\\\textbf{68.4} & \\\\cellcolor{lightblue}12.8 & \\\\cellcolor{lightblue}75.5 & \\\\cellcolor{lightblue}52.2 \\\\\\\\ \\\\midrule\\n\",\n \"\\\\rowcolor{lightgray}\\\\cellcolor{white}\\\\multirow{5}{*}{\\\\rotatebox{90}{\\\\textbf{Mistral-12B}}} & Dense & 82.6 & 68.5 & 50.4 & 67.2 & 51.7 & 43.8 & 49.2 & 25.4 & 42.5 & 54.6 & 26.6 & 69.4 & 92.4 & 60.8 & 88.7 & 3.0 & 78.6 & 56.4 \\\\\\\\\\n\",\n \" & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 2.1 & 27.8 & 19.0 & \\\\textbf{23.2} & 18.0 & 0.0 & 0.0 & 51.0 & 20.9 & 18.0 & 59.3 & 0.5 & 2.8 & 20.9 \\\\\\\\\\n\",\n \" & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 0.8 & 25.1 & 17.4 & 7.8 & 12.8 & 24.9 & 9.2 & 34.2 & 54.3 & 30.7 & 27.4 & 1.3 & 36.3 & 21.7 \\\\\\\\\\n\",\n \" & ShortGPT & 37.8 & 0.0 & 33.9 & 23.9 & 2.9 & 27.0 & 18.0 & 5.0 & 13.2 & 31.4 & 7.2 & 39.2 & 52.5 & 32.6 & 26.2 & 0.2 & 42.7 & 23.0 \\\\\\\\\\n\",\n \" & \\\\cellcolor{lightblue}\\\\texttt{Cus-Prun} & \\\\cellcolor{lightblue}\\\\textbf{67.5} & \\\\cellcolor{lightblue}\\\\textbf{43.4} & \\\\cellcolor{lightblue}\\\\textbf{43.8} & \\\\cellcolor{lightblue}\\\\textbf{51.6} & \\\\cellcolor{lightblue}\\\\textbf{34.3} & \\\\cellcolor{lightblue}\\\\textbf{39.2} & \\\\cellcolor{lightblue}\\\\textbf{40.7} & \\\\cellcolor{lightblue}23.1 & \\\\cellcolor{lightblue}\\\\textbf{34.3} & \\\\cellcolor{lightblue}\\\\textbf{47.9} & \\\\cellcolor{lightblue}\\\\textbf{25.1} & \\\\cellcolor{lightblue}\\\\textbf{67.3} & \\\\cellcolor{lightblue}\\\\textbf{83.7} & \\\\cellcolor{lightblue}\\\\textbf{56.0} & \\\\cellcolor{lightblue}\\\\textbf{83.5} & \\\\cellcolor{lightblue}\\\\textbf{3.4} & \\\\cellcolor{lightblue}\\\\textbf{72.8} & \\\\cellcolor{lightblue}\\\\textbf{50.9} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\rowcolor{lightgray}\\\\cellcolor{white}\\\\multirow{5}{*}{\\\\rotatebox{90}{\\\\textbf{Llama2-13B}}} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 17.5 & 30.4 & 44.1 & 24.9 & 29.2 & 25.2 & 0.0 & 42.7 & 84.1 & 38.0 & 70.0 & 7.4 & 44.3 & 40.6 \\\\\\\\\\n\",\n \" & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 1.1 & 22.8 & 3.8 & 17.7 & 11.3 & 0.0 & 0.0 & 9.7 & 0.0 & 2.4 & 21.6 & 4.8 & 0.0 & 8.8 \\\\\\\\\\n\",\n \" & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 2.8 & 25.3 & 23.4 & 9.9 & 15.5 & 18.7 & 0.0 & 28.4 & 67.3 & 28.6 & 24.5 & 4.9 & 32.9 & 20.8 \\\\\\\\\\n\",\n \" & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 2.4 & 23.0 & 24.7 & 11.3 & 15.3 & 16.9 & 0.0 & 34.6 & \\\\textbf{69.8} & 30.3 & 23.8 & 5.2 & 39.1 & 22.7 \\\\\\\\\\n\",\n \" & \\\\cellcolor{lightblue}\\\\texttt{Cus-Prun} & \\\\cellcolor{lightblue}\\\\textbf{48.3} & \\\\cellcolor{lightblue}\\\\textbf{20.8} & \\\\cellcolor{lightblue}\\\\textbf{50.0} & \\\\cellcolor{lightblue}\\\\textbf{39.7} & \\\\cellcolor{lightblue}\\\\textbf{12.7} & \\\\cellcolor{lightblue}\\\\textbf{26.2} & \\\\cellcolor{lightblue}\\\\textbf{34.2} & \\\\cellcolor{lightblue}\\\\textbf{24.1} & \\\\cellcolor{lightblue}\\\\textbf{24.3} & \\\\cellcolor{lightblue}\\\\textbf{25.6} & \\\\cellcolor{lightblue}0.0 & \\\\cellcolor{lightblue}\\\\textbf{38.5} & \\\\cellcolor{lightblue}68.3 & \\\\cellcolor{lightblue}\\\\textbf{33.1} & \\\\cellcolor{lightblue}\\\\textbf{64.5} & \\\\cellcolor{lightblue}\\\\textbf{6.7} & \\\\cellcolor{lightblue}\\\\textbf{42.9} & \\\\cellcolor{lightblue}\\\\textbf{38.0} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\rowcolor{lightgray}\\\\cellcolor{white}\\\\multirow{5}{*}{\\\\rotatebox{90}{\\\\textbf{Llama3-70B}}} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 69.5 & 71.1 & 69.1 & 36.6 & 61.6 & 72.1 & 55.3 & 83.6 & 96.2 & 76.8 & 84.2 & 17.3 & 81.8 & 61.1 \\\\\\\\\\n\",\n \" & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 16.8 & 43.7 & 43.0 & 29.0 & 33.1 & 27.3 & 1.0 & 51.0 & 50.3 & 32.4 & 10.2 & 13.7 & 20.6 & 14.8 \\\\\\\\\\n\",\n \" & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 3.7 & 44.8 & 33.0 & 21.2 & 25.7 & 57.6 & 27.6 & 68.1 & 59.4 & 53.2 & 58.0 & 14.2 & 68.3 & 46.8 \\\\\\\\\\n\",\n \" & ShortGPT & 59.4 & 5.6 & \\\\textbf{75.5} & 46.8 & 11.9 & 43.1 & 38.8 & 24.0 & 29.5 & 58.4 & 32.2 & 67.5 & 64.9 & 55.8 & 59.6 & 13.9 & 65.8 & 46.4 \\\\\\\\\\n\",\n \" & \\\\cellcolor{lightblue}\\\\texttt{Cus-Prun} & \\\\cellcolor{lightblue}\\\\textbf{68.4} & \\\\cellcolor{lightblue}\\\\textbf{53.2} & \\\\cellcolor{lightblue}66.6 & \\\\cellcolor{lightblue}\\\\textbf{62.7} & \\\\cellcolor{lightblue}\\\\textbf{43.1} & \\\\cellcolor{lightblue}\\\\textbf{57.7} & \\\\cellcolor{lightblue}\\\\textbf{59.8} & \\\\cellcolor{lightblue}\\\\textbf{34.3} & \\\\cellcolor{lightblue}\\\\textbf{48.7} & \\\\cellcolor{lightblue}\\\\textbf{68.2} & \\\\cellcolor{lightblue}\\\\textbf{43.9} & \\\\cellcolor{lightblue}\\\\textbf{81.4} & \\\\cellcolor{lightblue}\\\\textbf{87.8} & \\\\cellcolor{lightblue}\\\\textbf{70.3} & \\\\cellcolor{lightblue}\\\\textbf{80.4} & \\\\cellcolor{lightblue}\\\\textbf{15.7} & \\\\cellcolor{lightblue}\\\\textbf{77.5} & \\\\cellcolor{lightblue}\\\\textbf{57.9} \\\\\\\\ \\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}}\\n\",\n \" \\\\label{table:cross_ling}\\n\",\n \"\\\\vspace{-0.2cm}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\begin{minipage}{0.4\\\\textwidth}\\n\",\n \" \\\\centering\\n\",\n \" \\\\captionof{table}{Performance of Chinese-Medical expert model on MCQ task}\\n\",\n \" \\\\label{tab:cmexam}\\n\",\n \" \\\\scalebox{0.96}{\\n\",\n \" \\\\begin{tabular}{l|c|c}\\n\",\n \" \\\\hline\\n\",\n \" \\\\textbf{Method} & \\\\textbf{General} & \\\\textbf{CMExam} \\\\\\\\\\\\midrule\\n\",\n \" \\\\rowcolor{lightgray}\\\\cellcolor{white} Dense & 59.3 & 50.6 \\\\\\\\\\n\",\n \" LLM-Pruner & 18.6 & 25.0 \\\\\\\\\\n\",\n \" SliceGPT & 27.8 & 26.9 \\\\\\\\\\n\",\n \" ShortGPT & 23.9 & 23.7 \\\\\\\\\\n\",\n \" \\\\rowcolor{lightblue}\\\\cellcolor{white} \\\\texttt{Cus-Prun} & \\\\textbf{52.4} & \\\\textbf{48.7} \\\\\\\\\\n\",\n \" \\\\hline\\n\",\n \" \\\\end{tabular}}\\n\",\n \" \\\\end{minipage}\\n\",\n \" \\\\hfill\\n\",\n \" \\\\begin{minipage}{0.58\\\\textwidth}\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\textwidth]{Figures/huatuo.pdf}\\n\",\n \" \\\\caption{Chinese Medical LLM performance. Numbers are quality on the \\\\textbf{whole} test set evaluated by GPT4.}\\n\",\n \" \\\\label{fig:huatuo}\\n\",\n \" \\\\end{minipage}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{table}[ht]\\n\",\n \"\\\\caption{Aggressive pruning ratio on Llama3-8B.}\\n\",\n \"\\\\centering\\n\",\n \"\\\\setlength{\\\\tabcolsep}{3pt}\\n\",\n \" \\\\scalebox{0.9}{\\n\",\n \"\\\\begin{tabular}{l|c|c|c|c}\\n\",\n \" \\\\hline\\n\",\n \" \\\\textbf{Method} & \\\\textbf{Ratio} & \\\\textbf{Speedup} & \\\\textbf{MMLU} & \\\\textbf{Expert} \\\\\\\\\\\\midrule\\n\",\n \" \\\\rowcolor{lightgray}\\\\cellcolor{white}Dense & 0.0 & 1$\\\\times$ & 63.1 & 59.7 \\\\\\\\\\\\midrule\\n\",\n \" ShortGPT & 25.0 & 1.3$\\\\times$ & 28.6 & 24.6 \\\\\\\\\\n\",\n \" \\\\rowcolor{lightblue}\\\\cellcolor{white}\\\\texttt{Cus-Prun} & 25.0 & 1.3$\\\\times$ & \\\\textbf{51.9} & \\\\textbf{53.3} \\\\\\\\\\\\midrule\\n\",\n \" ShortGPT & 34.2 & 1.5$\\\\times$ & 20.8 & 18.5 \\\\\\\\\\n\",\n \" \\\\rowcolor{lightblue}\\\\cellcolor{white}\\\\texttt{Cus-Prun} & 35.0 & 1.5$\\\\times$ & \\\\textbf{50.2} & \\\\textbf{51.4} \\\\\\\\\\\\midrule\\n\",\n \" ShortGPT & 43.8 & 1.8$\\\\times$ & 7.9 & 10.2 \\\\\\\\\\n\",\n \" \\\\rowcolor{lightblue}\\\\cellcolor{white}\\\\texttt{Cus-Prun} & 45.0 & 1.8$\\\\times$ & \\\\textbf{48.4} & \\\\textbf{50.6} \\\\\\\\\\n\",\n \" \\\\hline\\n\",\n \" \\\\end{tabular}}\\n\",\n \" \\\\label{table:prune_more}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{figure*}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=1.0\\\\textwidth]{Figures/lang-specific.pdf}\\n\",\n \" \\\\caption{Performance of \\\\texttt{Cus-Prun} in obtaining language-specific models.}\\n\",\n \" \\\\label{fig:lang-spec}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{table}[ht]\\n\",\n \" \\\\centering\\n\",\n \"\\\\footnotesize\\n\",\n \" \\\\scalebox{1}{\\n\",\n \" \\\\begin{tabular}{lp{5.3cm}}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Task} & \\\\textbf{Prompt} \\\\\\\\\\\\midrule\\n\",\n \" Generation & Generate a text document in \\\\{language\\\\}/\\\\{domain\\\\}/\\\\{task\\\\}. Make sure the documents is not fixed to one \\\\{language\\\\}/\\\\{domain\\\\}/\\\\{task\\\\} or \\\\{language\\\\}/\\\\{domain\\\\}/\\\\{task\\\\}. Ensure the content is clear, concise, and appropriate for the specified request. Use professional and domain-specific terminology where necessary. \\\\\\\\\\\\midrule\\n\",\n \" Evaluation & Evaluate the quality of the given answer to the question. Provide a score from 0 to 5, where 0 represents very low quality and 5 represents very high quality. Question: \\\\{question\\\\} Answer: \\\\{answer\\\\}.\\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}}\\n\",\n \"\\\\caption{GPT-4o prompts for generating documents and evaluating answer quality.} \\\\label{table:testset}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table*}[hbpt]\\n\",\n \"\\\\caption{Main Results of \\\\texttt{Cus-Prun} on Germany with a pruning ratio of $25\\\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in XLSum and in accuracy (\\\\%) for other datasets.}\\n\",\n \" \\\\centering\\n\",\n \"\\\\footnotesize\\n\",\n \"\\\\setlength{\\\\tabcolsep}{3pt}\\n\",\n \" \\\\scalebox{1.0}{\\n\",\n \" \\\\begin{tabular}{l|l|cccc|ccccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{\\\\textbf{\\\\normalsize{Model}}} & \\\\multirow{2}{*}{\\\\textbf{\\\\normalsize{Method}}} & \\n\",\n \" \\\\multicolumn{4}{c}{\\\\textbf{\\\\normalsize{General Capability}}} \\\\vline & \\\\multicolumn{5}{c}{\\\\textbf{\\\\normalsize{Expert Capability}}} \\\\\\\\ \\n\",\n \" & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3Exam & XQuAD & XLSum & Avg. \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Llama3-8B} &\\n\",\n \" Dense & 70.7 & 58.3 & 63.1 & 64.1 & 44.8 & - & 52.9 & - & 48.8 \\\\\\\\\\n\",\n \" & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 0.0 & - & 11.0 & - & 5.5 \\\\\\\\\\n\",\n \" & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & - & 9.8 & - & 4.9 \\\\\\\\\\n\",\n \" & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & - & 0.0 & - & 0.0 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 61.4 & 38.9 & 54.5 & \\\\textbf{51.6} & 32.8 & - & 49.6 & - & \\\\textbf{41.2} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Mistral-12B} & Dense & 82.6 & 68.5 & 50.4 & 59.3 & 56.8 & - & 41.2 & - & 49.0 \\\\\\\\\\n\",\n \" & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 2.4 & - & 13.4 & - & 7.9 \\\\\\\\\\n\",\n \" & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 0.8 & - & 15.5 & - & 8.2 \\\\\\\\\\n\",\n \" & ShortGPT & 37.8 & 0.0 & 33.9 & 23.9 & 3.6 & - & 20.3 & - & 12.0 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 64.6 & 39.7 & 43.2 & \\\\textbf{49.2} & 31.6 & - & 35.9 & - & \\\\textbf{33.8} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Llama2-13B} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 24.4 & - & 40.3 & - & 32.3 \\\\\\\\\\n\",\n \" & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 2.0 & - & 5.7 & - & 3.9 \\\\\\\\\\n\",\n \" & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 3.6 & - & 18.1 & - & 10.9 \\\\\\\\\\n\",\n \" & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 2.8 & - & 27.2 & - & 15.0 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 47.6 & 19.8 & 49.9 & \\\\textbf{39.1} & 18.4 & - & 31.7 & - & \\\\textbf{25.0} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Llama3-70B} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 74.8 & - & 58.2 & - & 66.5 \\\\\\\\\\n\",\n \" & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 18.0 & - & 27.3 & - & 22.7 \\\\\\\\\\n\",\n \" & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 0.0 & - & 17.3 & - & 8.7 \\\\\\\\\\n\",\n \" & ShortGPT & 59.4 & 5.6 & 75.5 & 46.8 & 9.6 & - & 31.5 & - & 20.6 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 66.8 & 59.3 & 69.1 & \\\\textbf{65.1} & 48.2 & - & 53.9 & - & \\\\textbf{51.1} \\\\\\\\ \\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}}\\n\",\n \" \\\\label{table:cl_ger}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[hbpt]\\n\",\n \"\\\\caption{Main Results of \\\\texttt{Cus-Prun} on Chinese with a pruning ratio of $25\\\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in XLSum and in accuracy (\\\\%) for other datasets.}\\n\",\n \" \\\\centering\\n\",\n \"\\\\footnotesize\\n\",\n \"\\\\setlength{\\\\tabcolsep}{3pt}\\n\",\n \" \\\\scalebox{1.0}{\\n\",\n \" \\\\begin{tabular}{l|l|cccc|ccccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{\\\\textbf{\\\\normalsize{Model}}} & \\\\multirow{2}{*}{\\\\textbf{\\\\normalsize{Method}}} & \\n\",\n \" \\\\multicolumn{4}{c}{\\\\textbf{\\\\normalsize{General Capability}}} \\\\vline & \\\\multicolumn{5}{c}{\\\\textbf{\\\\normalsize{Specific Capability}}} \\\\\\\\ \\n\",\n \" & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3Exam & XQuAD & XLSum & Avg. \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Llama3-8B} &\\n\",\n \" Dense & 70.7 & 58.3 & 63.1 & 64.1 & 43.6 & 55.1 & 78.7 & 49.1 & 56.6 \\\\\\\\\\n\",\n \" & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 2.4 & 23.6 & 21.3 & 32.8 & 20.0 \\\\\\\\\\n\",\n \" & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & 17.4 & 23.5 & 8.3 & 12.3 \\\\\\\\\\n\",\n \" & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & 28.3 & 0.0 & 3.1 & 7.9 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 60.5 & 25.7 & 49.4 & \\\\textbf{45.2} & 36.0 & 44.7 & 65.6 & 46.3 & \\\\textbf{48.2} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Mistral-12B} & Dense & 82.6 & 68.5 & 50.4 & 59.3 & 53.2 & 47.8 & 62.2 & 33.0 & 49.1 \\\\\\\\\\n\",\n \" & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 2.8 & 30.7 & 31.8 & 32.6 & 24.5 \\\\\\\\\\n\",\n \" & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 1.6 & 26.4 & 28.3 & 10.8 & 16.8 \\\\\\\\\\n\",\n \" & ShortGPT & 37.8 & 0.0 & 33.9 & 23.9 & 4.4 & 28.2 & 29.1 & 7.2 & 17.2 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 68.3 & 43.2 & 39.5 & \\\\textbf{50.3} & 38.4 & 40.7 & 50.6 & 30.3 & \\\\textbf{40.0} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Llama2-13B} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 21.6 & 36.5 & 59.8 & 35.3 & 38.3 \\\\\\\\\\n\",\n \" & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 1.2 & 23.3 & 3.8 & 25.1 & 13.4\\\\\\\\\\n\",\n \" & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 4.8 & 24.5 & 28.4 & 11.2 & 17.2 \\\\\\\\\\n\",\n \" & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 4.4 & 22.9 & 24.6 & 13.7 & 16.4 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 48.6 & 20.7 & 51.9 & \\\\textbf{40.4} & 14.8 & 28.2 & 47.3 & 34.4 & \\\\textbf{31.2} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Llama3-70B} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 68.4 & 76.1 & 81.3 & 55.3 & 70.3 \\\\\\\\\\n\",\n \" & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 16.8 & 47.5 & 56.1 & 41.3 & 40.4 \\\\\\\\\\n\",\n \" & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 6.4 & 48.3 & 42.2 & 29.3 & 31.6 \\\\\\\\\\n\",\n \" & ShortGPT & 59.4 & 5.6 & 75.5 & 46.8 & 12.4 & 45.5 & 44.6 & 36.1 & 34.7 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 72.3 & 48.5 & 65.2 & \\\\textbf{62.0} & 40.8 & 61.7 & 66.9 & 51.6 & \\\\textbf{55.3} \\\\\\\\ \\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}}\\n\",\n \" \\\\label{table:cl_chi}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[hbpt]\\n\",\n \"\\\\caption{Main Results of \\\\texttt{Cus-Prun} on Thai with a pruning ratio of $25\\\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in XLSum and in accuracy (\\\\%) for other datasets.}\\n\",\n \" \\\\centering\\n\",\n \"\\\\footnotesize\\n\",\n \"\\\\setlength{\\\\tabcolsep}{3pt}\\n\",\n \" \\\\scalebox{1.0}{\\n\",\n \" \\\\begin{tabular}{l|l|cccc|ccccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{\\\\textbf{\\\\normalsize{Model}}} & \\\\multirow{2}{*}{\\\\textbf{\\\\normalsize{Method}}} & \\n\",\n \" \\\\multicolumn{4}{c}{\\\\textbf{\\\\normalsize{General Capability}}} \\\\vline & \\\\multicolumn{5}{c}{\\\\textbf{\\\\normalsize{Specific Capability}}} \\\\\\\\ \\n\",\n \" & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3Exam & XQuAD & XLSum & Avg. \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Llama3-8B} &\\n\",\n \" Dense & 70.7 & 58.3 & 63.1 & 64.1 & 35.2 & 43.0 & 58.7 & 16.7 & 38.4 \\\\\\\\\\n\",\n \" & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 0.8 & 24.4 & 8.4 & 13.5 & 11.8 \\\\\\\\\\n\",\n \" & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & 12.3 & 16.6 & 8.7 & 9.4 \\\\\\\\\\n\",\n \" & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & 25.4 & 0.0 & 2.3 & 6.9 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 58.9 & 31.2 & 52.4 & \\\\textbf{47.5} & 21.6 & 38.3 & 42.6 & 16.8 & \\\\textbf{29.8} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Mistral-12B} & Dense & 82.6 & 68.5 & 50.4 & 59.3 & 45.2 & 39.9 & 44.1 & 17.8 & 36.8 \\\\\\\\\\n\",\n \" & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 1.2 & 24.8 & 11.9 & 13.7 & 12.9 \\\\\\\\\\n\",\n \" & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 0.0 & 23.8 & 8.4 & 4.7 & 12.3 \\\\\\\\\\n\",\n \" & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 0.8 & 25.7 & 4.7 & 2.8 & 8.5 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 68.2 & 35.8 & 47.6 & \\\\textbf{50.5} & 32.8 & 37.7 & 35.6 & 15.9 & \\\\textbf{30.5} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Llama2-13B} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 6.4 & 24.3 & 28.3 & 14.5 & 18.4 \\\\\\\\\\n\",\n \" & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 0.0 & 22.3 & 1.8 & 10.2 & 8.6 \\\\\\\\\\n\",\n \" & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 0.0 & 26.2 & 23.7 & 8.6 & 14.6 \\\\\\\\\\n\",\n \" & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 0.0 & 23.1 & 22.3 & 8.9 & 13.6 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 47.8 & 20.9 & 50.7 & \\\\textbf{39.8} & 4.8 & 24.2 & 23.6 & 13.8 & \\\\textbf{16.6} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Llama3-70B} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 65.2 & 66.1 & 67.8 & 17.8 & 54.2 \\\\\\\\\\n\",\n \" & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 15.6 & 39.9 & 29.8 & 16.6 & 25.5 \\\\\\\\\\n\",\n \" & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 4.8 & 41.3 & 39.6 & 13.2 & 24.7 \\\\\\\\\\n\",\n \" & ShortGPT & 59.4 & 5.6 & 75.5 & 46.8 & 13.7 & 40.7 & 40.4 & 11.9 & 26.7 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 73.3 & 58.7 & 68.4 & \\\\textbf{66.8} & 40.4 & 53.6 & 58.5 & 16.9 & \\\\textbf{42.4} \\\\\\\\ \\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}}\\n\",\n \" \\\\label{table:cl_tha}\\n\",\n \"\\\\end{table*}\\n\"\n ]\n]","isConversational":false},"fulltext":{"kind":"string","value":"\\pdfoutput=1\n\n\\documentclass[11pt]{article}\n\n\\usepackage[final]{acl}\n\n\n\\usepackage{microtype}\n\\usepackage{hyperref}\n\\usepackage{url}\n\\usepackage{booktabs}\n\\usepackage{times}\n\\usepackage{latexsym}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{graphicx}\n\\usepackage{multirow}\n% This is not strictly necessary, and may be commented out,\n% but it will improve the layout of the manuscript,\n% and will typically save some space.\n\\usepackage{subcaption}\n% For proper rendering and hyphenation of words containing Latin characters (including in bib files)\n\\usepackage[T1]{fontenc}\n\n\\usepackage{tikz}\n\n\\usepackage{listings}\n\\usepackage{color}\n% \\usepackage{minted}\n\\definecolor{dkgreen}{rgb}{0,0.6,0}\n\\definecolor{gray}{rgb}{0.5,0.5,0.5}\n\\definecolor{mauve}{rgb}{0.58,0,0.82}\n\\definecolor{lightblue}{rgb}{0.9, 0.95, 1.0}\n\\definecolor{lightgray}{rgb}{0.9, 0.9, 0.9}\n\n\\lstset{frame=tb,\n language=Python,\n aboveskip=3mm,\n belowskip=3mm,\n showstringspaces=false,\n columns=flexible,\n basicstyle={\\small\\ttfamily},\n numbers=none,\n numberstyle=\\tiny\\color{gray},\n keywordstyle=\\color{blue},\n commentstyle=\\color{dkgreen},\n stringstyle=\\color{mauve},\n breaklines=true,\n breakatwhitespace=true,\n tabsize=3\n}\n\n\\newcommand{\\circled}[1]{%\n \\tikz[baseline=(char.base)]{\n \\node[shape=circle, draw, inner sep=0.5pt, fill=black] (char) {\\textcolor{white}{\\small{#1}}};\n }\n}\n\\usepackage{colortbl}\n\n\\usepackage{algorithmic}\n\\usepackage{algorithm}\n\n\\usepackage[utf8]{inputenc}\n\\usepackage{multirow}\n% This is not strictly necessary, and may be commented out,\n% but it will improve the layout of the manuscript,\n% and will typically save some space.\n\\usepackage{microtype}\n\\usepackage{booktabs}\n\n\n\\title{Pruning General Large Language Models into Customized Expert Models}\n\n\n\\author{\n Yiran Zhao$^{1}$ \\; Guizhen Chen$^{2,3}$ \\; Kenji Kawaguchi$^{1}$\n \\; Lidong Bing$^{4}$ \\; Wenxuan Zhang$^{5}$\\footnotemark[2] \\\\\n $^1$ National University of Singapore \\quad $^2$ Nanyang Technological University, Singapore \\\\ \n $^3$ DAMO Academy, Alibaba Group, Singapore \\quad $^4$ MiroMind \\\\\n $^5$ Singapore University of Technology and Design \\\\\n}\n\n\n\n\n\\newcommand{\\fix}{\\marginpar{FIX}}\n\\newcommand{\\new}{\\marginpar{NEW}}\n\n\n\n\\begin{document}\n\n\\newcommand{\\yiran}[1]{\\textcolor{brown}{\\it\\small [#1 --Yiran]}}\n\\newcommand{\\isaac}[1]{\\textcolor{orange}{\\textbf{[Isaac:} #1]}}\n\n\\maketitle\n\\renewcommand{\\thefootnote}{\\fnsymbol{footnote}}\n\\footnotetext[2]{Wenxuan Zhang is the corresponding author.}\n\\renewcommand{\\thefootnote}{\\arabic{footnote}}\n\n\\begin{abstract}\n\nLarge language models (LLMs) have revolutionized natural language processing, yet their substantial model sizes often require substantial computational resources. To preserve computing resources and accelerate inference speed, it is crucial to prune redundant parameters, especially for experienced users who often need compact expert models tailored to specific downstream scenarios. However, most existing pruning methods focus on preserving the model’s general capabilities, often requiring extensive post-training or suffering from degraded performance due to coarse-grained pruning. In this work, we design a \\underline{Cus}tom \\underline{Prun}ing method (\\texttt{Cus-Prun}) to prune a large general model into a smaller lightweight expert model, which is positioned along the ``language'', ``domain'' and ``task'' dimensions. By identifying and pruning irrelevant neurons of each dimension, \\texttt{Cus-Prun} creates expert models without any post-training. \nOur experiments demonstrate that \\texttt{Cus-Prun} consistently outperforms other methods, achieving minimal loss in both expert and general capabilities across various models from different model families and sizes.\\footnote{Our code is publicly available at \\url{https://github.com/zhaoyiran924/Custom-Prune}.}\n\n\\end{abstract}\n\n\\section{Introduction}\n\nLarge language models (LLMs)~\\citep{achiam2023gpt, reid2024gemini, dubey2024llama, team2024gemma} have revolutionized the field of natural language processing (NLP), emerging as powerful tools with widespread applications across various languages~\\citep{cui2023efficient, yang2024qwen2, seallms3}, domains \\citep{ li2023huatuo26m, roziere2023code, li2023large}, and tasks~\\citep{azerbayevllemma, alves2024tower,naacl-sentiment}.\nHowever, the impressive performance of LLMs often comes at the cost of immense model sizes, mostly containing billions of parameters and thus demand significant computing resources~\\citep{goldstein2023generative, musser2023cost}. To address this issue, researchers have recently proposed various model pruning methods for LLMs. These methods aim to reduce model parameters while maintaining the overall performance through techniques such as removal of unimportant structures~\\citep{ma2023llm, men2024shortgpt, songsleb}, matrix approximation~\\citep{sharmatruth, ashkboosslicegpt}, and extensive post-training after pruning~\\citep{wang2024reconstruct, xiasheared}.\n\n\n\nMost existing pruning methods focus on preserving the \\textit{general capabilities} of the model, often evaluated by broad-spectrum benchmarks like MMLU~\\citep{hendrycksmeasuring}.\nWhile aiming for overall versatility, they may not align well with real-world user needs for a pruned small model, which are usually more \\textit{specific and targeted}. For instance, a user might require a question-answering model tailored specifically for the education domain in German. \nSuch specialized request in fact aligns well with the fundamental motivation behind pruning: to create a smaller model by eliminating unnecessary parameters. In this context, the notion of ``unnecessary'' parameters becomes more precise—referring to those parameters that are irrelevant to the particular use case. By selectively pruning these redundant parameters, one can construct a smaller, expert model that is better aligned with specific requirements.\n\nHowever, current pruning techniques focusing on preserving the general capabilities of LLMs often employ coarse-grained approaches such as removing entire layers or modules \\citep{li2022parameter, kurz2024language, huang2024pruning}. Therefore, it may remove parameters that are critical for specialized downstream scenarios, and thus sometimes require extensive post-training to recover the pruned capabilities \\citep{xiasheared, muralidharan2024compact}. On the other hand, the preserved certain capabilities may not be useful or relevant to our desired application scenario. This misalignment with practical requirements, where users need compact ready-to-deploy expert models without retraining, motivates us to design a more fine-grained and expert model targeting approach.\n\n\n\n\\begin{figure}[t]\n \\centering\n \\includegraphics[width=0.48\\textwidth]{Figures/cust-prune.pdf}\n \\caption{Given a requirement for an expert model across three dimensions (language, domain, and task), \\texttt{Cus-Prun} (i) identifies irrelevant neurons for each dimension using corresponding corpora (left), and (ii) prunes \\textbf{overlapping} irrelevant neurons across dimensions to obtain the expert model (right).}\n \\label{fig:cus-prun}\n\\end{figure}\n\nIn this work, we introduce a novel \\underline{Cus}tom \\underline{Prun}ing (\\texttt{Cus-Prun}) method, designed to prune a large general-purpose model into a small specialized expert model tailored for specific scenarios.\nTo achieve broad adaptability, we define the expert model by positioning the target user's needs along three key dimensions: language (e.g., English, Chinese, German), domain (e.g., E-commerce, education), and task (e.g., QA, summarization).\nInspired by existing studies that certain neurons are responsible for certain functions \\citep{zhao2024large, tang2024language, liang2024multilingual}, \\texttt{Cus-Prun} identifies and preserves critical neurons that are more relevant to particular languages, domains, or tasks, while pruning less relevant ones, ultimately leading to a smaller expert models.\nSpecifically, as illustrated in Figure \\ref{fig:cus-prun}, \n\\texttt{Cus-Prun} first identifies irrelevant neurons for each dimension by assessing the impact of their removal on the generated output when processing corresponding corpus, which could be easily constructed from the relevant plain text documents.\nNext, the expert model is constructed by pruning irrelevant neurons across all dimensions.\nFurthermore, \\texttt{Cus-Prun}'s flexibility allows it to focus on one, two, or all three dimensions (language, domain, task) as needed, making it adaptable to a wide range of real-world applications where specialized LLMs are required. Importantly, by performing fine-grained pruning at the neuron level, the method could also retain most of the essential neurons within the model backbone, thereby preserving most general capabilities.\n\nWe conduct comprehensive experiments to evaluate the performance of \\texttt{Cus-Prun} across various scenarios. Experimental results demonstrate that it consistently outperforms other pruning methods in all settings. For three-dimensional specific expert models, \\texttt{Cus-Prun} demonstrates remarkable pruning effectiveness across different model families and sizes, such as Mistral-Nemo-12B, Llama3-8B, Llama2-13B, and Llama3-70B, while maintaining strong expert and general capabilities. The method is evaluated across multilingual, multidomain, and multitask datasets, as well as representative compound NLP benchmarks, and consistently outperforms state-of-the-art pruning methods. Moreover, \\texttt{Cus-Prun} is highly adaptable, working effectively across a wide range of pruning ratios, even up to nearly half the parameters, without compromising its superior performance. For more focused applications, such as two- or one-dimensional specific expert models (e.g., language-domain specific or language-specific models), \\texttt{Cus-Prun} continues to significantly outperform other pruning methods, showcasing its versatility and effectiveness in diverse specialized settings.\n\n\n\n\\section{Custom Pruning (\\texttt{Cus-Prun})}\n\n\nAn expert model could be generally positioned from three dimensions: ``language'' ($L\\in\\mathbb{L}$), ``domain'' ($D\\in\\mathbb{D}$), and ``task'' ($T\\in\\mathbb{T}$), which can be represented as $LLM_{\\text{Exp}} := (L, D, T) \\in \\mathbb{L}\\times \\mathbb{D}\\times \\mathbb{T}$. Specifically, the language dimension encompasses various languages such as English, Spanish, and Thai. The domain dimension covers different fields like finance, legal, and medical. The task dimension includes various applications such as question-answering, data-to-text, and summarization. In this section, we propose a custom pruning method named \\texttt{Cus-Prun} to derive smaller expert models with flexible customization granularity.\n\n\n\\subsection{Foundational Custom Pruning}\n\n\nDrawing inspiration from recent LLM interpretation studies~\\citep{tang2024language,liang2024multilingual,zhao2024large} that many parameters in the model are redundant to processing a specific ``language'', we hypothesize that this phenomenon can be extended to other dimensions such as ``domain'' and ``task'', meaning that certain parameters remain unused when handling a specific dimension. \nRather than eliminating entire layers or modules~\\citep{songsleb, men2024shortgpt, zhang2024finercut}, \\texttt{Cus-Prun} performs a fine-grained investigation by identifying and removing redundant neurons (i.e., individual rows or columns in parameter matrices) across all components (e.g., attention and feed-forward layers).\n\n\nConcretely, when handling each dimension, we identify a specific set of \\textit{irrelevant neurons} in the original LLM, denoted as $\\tilde{\\mathcal{N}}_{L}$, $\\tilde{\\mathcal{N}}_{D}$, and $\\tilde{\\mathcal{N}}_{T}$ for $L$, $D$, and $T$, respectively. \n\nSpecifically, to identify irrelevant neurons corresponding to the selected dimension, we construct a corpus within that dimension while ablating others. For example, to determine irrelevant neurons for a specific language $L_{\\text{Exp}}$, we create a corpus set \n\\begin{equation}\nC_{L_{\\text{Exp}}}=\\{(L_{\\text{Exp}}, D, T)|D\\in\\mathbb{D}, T\\in\\mathbb{T}\\},\n\\end{equation} \ncomprising documents in language $L_{\\text{Exp}}$ across various domains $D$ and tasks $T$. We then identify neurons that are consistently irrelevant across all documents in $C_{L_{\\text{Exp}}}$,\n\\begin{equation}\n\\scalebox{0.92}{$ % Adjust 0.97 to the desired scaling factor\n\\tilde{\\mathcal{N}}_{L_{\\text{Exp}}} = \n\\left\\{\n\\text{Neuron} \\middle| \n\\text{Irrelevant to } c, \\; \\forall c \\in C_{L_{\\text{Exp}}}\n\\right\\},\n$}\n\\label{equ:neuron}\n\\end{equation}\nwhere a neuron is considered irrelevant if its removal from the parameter matrix affects the generated output below a specified threshold. Formally,\nfor $i$-th neuron in layer $l$, denoted as ${N}_{i}^{(l)}$, its relevance to document $c$ is measured by $|{h}_{\\setminus{{N}_{i}^{(l)}}, i}(c) - h_i(c)|_2$, where $h_i(c)$ is the layer output and ${h}_{\\setminus{{N}_{i}^{(l)}}, i}(c)$ is the output with the neuron removed. Furthermore, neurons with impact in the lowest $\\sigma\\%$ are considered irrelevant, where $\\sigma$ is a pre-defined pruning ratio.\n\n\nSimilarly, we could establish corresponding corpus sets for other dimensions, \n\\begin{equation}\nC_{D_{\\text{Exp}}}=\\{(L, D_{\\text{Exp}}, T)|L\\in\\mathbb{L}, T\\in\\mathbb{T}\\},\n\\end{equation} \n\\begin{equation}\nC_{T_{\\text{Exp}}}=\\{(L, D, T_{\\text{Exp}})|L\\in\\mathbb{L}, D\\in\\mathbb{D}\\},\n\\end{equation} to extract irrelevant neurons, $\\tilde{\\mathcal{N}}_{D_{\\text{Exp}}} $ and $\\tilde{\\mathcal{N}}_{T_{\\text{Exp}}} $. \nFinally, the expert model could constructed by\n\\begin{equation}\n\\scalebox{0.87}{$ % Adjust 0.95 to the desired scaling factor\n\\mathcal{LLM}_{\\text{Exp}} = \n\\mathcal{LLM} \\ominus \n\\left\\{\n\\tilde{\\mathcal{N}}_{L_{\\text{Exp}}} \n\\cap \\tilde{\\mathcal{N}}_{D_{\\text{Exp}}} \n\\cap \\tilde{\\mathcal{N}}_{T_{\\text{Exp}}}\n\\right\\},\n$}\n\\label{equ:cus}\n\\end{equation}\nwhere $\\ominus$ represents removing the corresponding neurons from $\\mathcal{LLM}$. The overall\nalgorithm is further illustrated in Algorithm \\ref{algo:adaptive_pruning}.\n\n\n\n\\begin{figure}[t]\n\\scalebox{0.95}{ \n\\begin{minipage}{\\linewidth}\n\\begin{algorithm}[H]\n\\renewcommand{\\algorithmicrequire}{\\textbf{Input:}}\n\\renewcommand{\\algorithmicensure}{\\textbf{Output:}}\n\\caption{Adaptive Custom Pruning}\n\\begin{algorithmic}[1]\n\\REQUIRE Original language model $\\mathcal{LLM}$, request for expert model $\\mathcal{LLM}_{\\text{Exp}}$ with selected dimensions: $L_{\\text{Exp}}$, $D_{\\text{Exp}}$, $T_{\\text{Exp}}$ (any subset), request for pruning ratio $\\sigma$.\n\\STATE \\texttt{// Construct specific corpora for each selected dimension.}\n\\STATE $C = \\{\\}$\n\\IF{$L_{\\text{Exp}}$ is specified}\n \\STATE $C = C \\cup \\{(L_{\\text{Exp}}, D, T) \\mid D \\in \\mathbb{D}, T \\in \\mathbb{T}\\}$\n\\ENDIF\n\\IF{$D_{\\text{Exp}}$ is specified}\n \\STATE $C = C \\cup \\{(L, D_{\\text{Exp}}, T) \\mid L \\in \\mathbb{L}, T \\in \\mathbb{T}\\}$\n\\ENDIF\n\\IF{$T_{\\text{Exp}}$ is specified}\n \\STATE $C = C \\cup \\{(L, D, T_{\\text{Exp}}) \\mid L \\in \\mathbb{L}, D \\in \\mathbb{D}\\}$\n\\ENDIF\n\\STATE \\texttt{// Identify irrelevant neurons for each selected dimension.}\n\\FORALL{neuron $N_{i}^{(l)}$ in $\\mathcal{LLM}$}\n \\IF{$\\forall c \\in C,$ $N_{i}^{(l)} \\in \\tilde{\\mathcal{N}}(c)$}\n \\STATE $\\tilde{\\mathcal{N}} \\gets \\tilde{\\mathcal{N}} \\cup N_{i}^{(l)}$\n \\ENDIF\n\\ENDFOR\n\\STATE \\texttt{// Prune irrelevant neurons to obtain expert model.}\n\\STATE $\\mathcal{LLM}_{\\text{Exp}} = \\mathcal{LLM} \\ominus \\tilde{\\mathcal{N}}$\n\\ENSURE $\\mathcal{LLM}_{\\text{Exp}}$\n\\end{algorithmic}\n\\label{algo:adaptive_pruning}\n\\end{algorithm}\n\\end{minipage}\n}\n\\end{figure}\n\n\n\\subsection{Adaptive Custom Pruning}\\label{sec:adaptive}\n\n\nIn many applications, the need for expertise might be constrained to one or two dimensions. For example, a language-specific or domain-specific model only requires pruning along a single dimension, while a language-domain-specific model (e.g., a Chinese Medical LLM) constrains two dimensions. In this section, we extend \\texttt{Cus-Prun} to support different granularity levels.\n\n\\paragraph{Two-Dimensional Specific Expert Model}\n\nWithout losing generality, we use the language-domain expert model as a concrete example, which requires an expert model constrained in two dimensions: language ($L_{\\text{Exp}}$) and domain ($D_{\\text{Exp}}$). We similarly derive the sets of irrelevant neurons $\\tilde{\\mathcal{N}}_{L_{\\text{Exp}}}$ and $\\tilde{\\mathcal{N}}_{D_{\\text{Exp}}}$, and\n% according to Equation \\ref{equ:neuron_final}. \nobtain the expert model by pruning the original dense model as follows: \\begin{equation}\n\\mathcal{LLM}_{\\text{Exp}}:=\\mathcal{LLM}\\ominus\\{\\tilde{\\mathcal{N}}_{L_{\\text{Exp}}} \\cap \\tilde{\\mathcal{N}}_{D_{\\text{Exp}}}\\}.\n\\end{equation}\n\n\n\\paragraph{One-Dimensional Specific Expert Model}\nWe use the language-specific expert model as an example, which focuses on optimizing performance for a certain language ($L_{\\text{Exp}}$), irrespective of domain or task. Similarly, we obtain the language-specific corpus $C_{L_{\\text{Exp}}}$, then identify irrelevant neurons $\\tilde{\\mathcal{N}}_{L_{\\text{Exp}}}$ \nand extract the expert model by \n\\begin{equation}\n\\mathcal{LLM}_{\\text{Exp}}:=\\mathcal{LLM}\\ominus\\{\\tilde{\\mathcal{N}}_{L_{\\text{Exp}}} \\}.\n\\end{equation}\nTo enhance efficiency, we implement the parallel neuron-detection method~\\citep{zhao2024large}, which accelerates the sequential calculations from line14 to line16 in Algorithm \\ref{algo:adaptive_pruning}. \n\n\n\n\n\\section{Preliminary Evaluation}\\label{sec:pre_eva}\n\nIn this section, to simulate realistic user-defined requirements where all three dimensions (language, domain, task) are explicitly specified, we conduct preliminary experiments on developing expert models with fine-grained pruning. This setting not only validates our approach under the most demanding conditions but also serves as a prototype for later experiments with one- or two-dimensional customizations.\n\n\n\\paragraph{Experiment Design} \n\nWe consider three scenarios, each named according to the \\textit{language}-\\textit{domain}-\\textit{task} pattern:\n\\textit{Korean-Legal-Summarization}~\\citep{hwang2022multi}, \\textit{English-Medical-MCQ}~\\citep{garcia2024medical}, and \\textit{Chinese-E-commerce-Sentiment Analysis}~\\citep{zhang2015character}.\nThese scenarios were selected because each represents a unique combination of language, domain, and task, effectively simulating the diverse needs a user might specify in practice.\n\nFor each scenario, we curate the corresponding corpus for each dimension. This curation can be done through manual collection or by automatically retrieving relevant documents online. In this preliminary study, without loss of generality, we employ a strong proprietary model\\footnote{\\url{https://platform.openai.com/docs/models/gpt-4o}} to generate a corpus containing $50$ documents for each dimension. Detailed prompts can be found in Appendix \\ref{sec:appen_prompt}. The generated documents could then be used to determine the relevance of neurons for each dimension of each scenario.\n\n\n\\paragraph{Experiment Setup}\nWe use Llama3-8B~\\citep{dubey2024llama} as the original dense model and follow the typical setting to set the pruning ratio as $25\\%$.\nPerformance is evaluated using Rouge-L~\\citep{lin2004rouge} for Korean-Legal-Summary and accuracy score for another two tasks. For comparison, we adopt SliceGPT~\\citep{ashkboosslicegpt} as a baseline, which replaces each weight matrix with a smaller proxy matrix without considering the specific use-case requirements.\n\n\\begin{figure}[t]\n \\centering\n \\includegraphics[width=0.48\\textwidth]{Figures/three-dim-examples.pdf}\n \\caption{Concrete examples of applying \\texttt{Cus-Prun} to prune $25\\%$ of Llama3-8B-Base's parameters into three-dimensional expert models. Numbers above each box indicate performance on the \\textbf{whole} test set, with the first evaluated by Rouge-L, and the other two by accuracy. }\n \\label{fig:korean}\n\\end{figure}\n\n\\begin{table*}[t]\n\\caption{Main Results of \\texttt{Cus-Prun} on multilingual setting with a pruning ratio of $25\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in summarization tasks and in accuracy (\\%) for other datasets.}\n \\centering\n\\footnotesize\n\\setlength{\\tabcolsep}{0.8pt}\n \\scalebox{0.9}{\n \\begin{tabular}{c|l|cccc|ccccc|ccccc|cccc}\n\\toprule\n\\quad\\quad & \\multirow{2}{*}{\\textbf{\\normalsize{Method}}} & \n \\multicolumn{4}{c}{\\textbf{\\normalsize{General Capability}}} \\vline & \\multicolumn{5}{c}{\\textbf{\\normalsize{Multilingual Expert}}} \\vline & \\multicolumn{5}{c}{\\textbf{\\normalsize{Multidomain Expert }}} \\vline & \\multicolumn{4}{c}{\\textbf{\\normalsize{Multitask Expert }}} \\\\ \n & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3 & XQuAD & Sum & Avg. & MMCQ & FTQA & TSA & AMSA & Avg. & MSum & ASum & AMCF & Avg. \\\\ \\midrule\n\\rowcolor{lightgray}\\cellcolor{white}\\multirow{5}{*}{\\rotatebox{90}{\\textbf{Llama3-8B}}} & Dense & 70.7 & 58.3 & 63.1 & 64.1 &41.2 &49.1 & 63.4 &32.9 &46.7 & 51.8 & 23.9 & 67.1 & 95.9 & 59.8 & 76.6 & 16.2 & 78.2 & 57.0 \\\\\n & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 1.1 & 24.0 & 13.6 & 23.2 & 15.5 & 0.0 & 0.0 & \\textbf{61.8} & 76.0 & 34.5 & 62.2 & \\textbf{21.8} & \\textbf{80.0} & \\textbf{54.7} \\\\\n & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & 14.9 & 16.6 & 8.5 & 10.0 & 22.6 & 0.0 & 41.2 & 53.7 & 29.4 & 7.3 & 2.9 & 51.3 & 20.5 \\\\\n & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & 26.9 & 0.0 & 2.7 & 7.4 & 3.2 & 0.0 & 38.6 & 35.7 & 19.4 & 4.1 & 4.8 & 43.8 & 17.6 \\\\\n &\\cellcolor{lightblue}\\texttt{Cus-Prun} &\\cellcolor{lightblue}\\textbf{62.4} & \\cellcolor{lightblue}\\textbf{37.0} & \\cellcolor{lightblue}\\textbf{54.7} & \\cellcolor{lightblue}\\textbf{51.4} & \\cellcolor{lightblue}\\textbf{30.1} & \\cellcolor{lightblue}\\textbf{41.5} & \\cellcolor{lightblue}\\textbf{52.6} & \\cellcolor{lightblue}\\textbf{31.5} & \\cellcolor{lightblue}\\textbf{38.9} & \\cellcolor{lightblue}\\textbf{42.9} & \\cellcolor{lightblue}\\textbf{20.6} & \\cellcolor{lightblue}\\textbf{61.8} & \\cellcolor{lightblue}\\textbf{87.6} & \\cellcolor{lightblue}\\textbf{53.2} & \\cellcolor{lightblue}\\textbf{68.4} & \\cellcolor{lightblue}12.8 & \\cellcolor{lightblue}75.5 & \\cellcolor{lightblue}52.2 \\\\ \\midrule\n\\rowcolor{lightgray}\\cellcolor{white}\\multirow{5}{*}{\\rotatebox{90}{\\textbf{Mistral-12B}}} & Dense & 82.6 & 68.5 & 50.4 & 67.2 & 51.7 & 43.8 & 49.2 & 25.4 & 42.5 & 54.6 & 26.6 & 69.4 & 92.4 & 60.8 & 88.7 & 3.0 & 78.6 & 56.4 \\\\\n & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 2.1 & 27.8 & 19.0 & \\textbf{23.2} & 18.0 & 0.0 & 0.0 & 51.0 & 20.9 & 18.0 & 59.3 & 0.5 & 2.8 & 20.9 \\\\\n & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 0.8 & 25.1 & 17.4 & 7.8 & 12.8 & 24.9 & 9.2 & 34.2 & 54.3 & 30.7 & 27.4 & 1.3 & 36.3 & 21.7 \\\\\n & ShortGPT & 37.8 & 0.0 & 33.9 & 23.9 & 2.9 & 27.0 & 18.0 & 5.0 & 13.2 & 31.4 & 7.2 & 39.2 & 52.5 & 32.6 & 26.2 & 0.2 & 42.7 & 23.0 \\\\\n & \\cellcolor{lightblue}\\texttt{Cus-Prun} & \\cellcolor{lightblue}\\textbf{67.5} & \\cellcolor{lightblue}\\textbf{43.4} & \\cellcolor{lightblue}\\textbf{43.8} & \\cellcolor{lightblue}\\textbf{51.6} & \\cellcolor{lightblue}\\textbf{34.3} & \\cellcolor{lightblue}\\textbf{39.2} & \\cellcolor{lightblue}\\textbf{40.7} & \\cellcolor{lightblue}23.1 & \\cellcolor{lightblue}\\textbf{34.3} & \\cellcolor{lightblue}\\textbf{47.9} & \\cellcolor{lightblue}\\textbf{25.1} & \\cellcolor{lightblue}\\textbf{67.3} & \\cellcolor{lightblue}\\textbf{83.7} & \\cellcolor{lightblue}\\textbf{56.0} & \\cellcolor{lightblue}\\textbf{83.5} & \\cellcolor{lightblue}\\textbf{3.4} & \\cellcolor{lightblue}\\textbf{72.8} & \\cellcolor{lightblue}\\textbf{50.9} \\\\ \\midrule\n\\rowcolor{lightgray}\\cellcolor{white}\\multirow{5}{*}{\\rotatebox{90}{\\textbf{Llama2-13B}}} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 17.5 & 30.4 & 44.1 & 24.9 & 29.2 & 25.2 & 0.0 & 42.7 & 84.1 & 38.0 & 70.0 & 7.4 & 44.3 & 40.6 \\\\\n & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 1.1 & 22.8 & 3.8 & 17.7 & 11.3 & 0.0 & 0.0 & 9.7 & 0.0 & 2.4 & 21.6 & 4.8 & 0.0 & 8.8 \\\\\n & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 2.8 & 25.3 & 23.4 & 9.9 & 15.5 & 18.7 & 0.0 & 28.4 & 67.3 & 28.6 & 24.5 & 4.9 & 32.9 & 20.8 \\\\\n & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 2.4 & 23.0 & 24.7 & 11.3 & 15.3 & 16.9 & 0.0 & 34.6 & \\textbf{69.8} & 30.3 & 23.8 & 5.2 & 39.1 & 22.7 \\\\\n & \\cellcolor{lightblue}\\texttt{Cus-Prun} & \\cellcolor{lightblue}\\textbf{48.3} & \\cellcolor{lightblue}\\textbf{20.8} & \\cellcolor{lightblue}\\textbf{50.0} & \\cellcolor{lightblue}\\textbf{39.7} & \\cellcolor{lightblue}\\textbf{12.7} & \\cellcolor{lightblue}\\textbf{26.2} & \\cellcolor{lightblue}\\textbf{34.2} & \\cellcolor{lightblue}\\textbf{24.1} & \\cellcolor{lightblue}\\textbf{24.3} & \\cellcolor{lightblue}\\textbf{25.6} & \\cellcolor{lightblue}0.0 & \\cellcolor{lightblue}\\textbf{38.5} & \\cellcolor{lightblue}68.3 & \\cellcolor{lightblue}\\textbf{33.1} & \\cellcolor{lightblue}\\textbf{64.5} & \\cellcolor{lightblue}\\textbf{6.7} & \\cellcolor{lightblue}\\textbf{42.9} & \\cellcolor{lightblue}\\textbf{38.0} \\\\ \\midrule\n\\rowcolor{lightgray}\\cellcolor{white}\\multirow{5}{*}{\\rotatebox{90}{\\textbf{Llama3-70B}}} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 69.5 & 71.1 & 69.1 & 36.6 & 61.6 & 72.1 & 55.3 & 83.6 & 96.2 & 76.8 & 84.2 & 17.3 & 81.8 & 61.1 \\\\\n & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 16.8 & 43.7 & 43.0 & 29.0 & 33.1 & 27.3 & 1.0 & 51.0 & 50.3 & 32.4 & 10.2 & 13.7 & 20.6 & 14.8 \\\\\n & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 3.7 & 44.8 & 33.0 & 21.2 & 25.7 & 57.6 & 27.6 & 68.1 & 59.4 & 53.2 & 58.0 & 14.2 & 68.3 & 46.8 \\\\\n & ShortGPT & 59.4 & 5.6 & \\textbf{75.5} & 46.8 & 11.9 & 43.1 & 38.8 & 24.0 & 29.5 & 58.4 & 32.2 & 67.5 & 64.9 & 55.8 & 59.6 & 13.9 & 65.8 & 46.4 \\\\\n & \\cellcolor{lightblue}\\texttt{Cus-Prun} & \\cellcolor{lightblue}\\textbf{68.4} & \\cellcolor{lightblue}\\textbf{53.2} & \\cellcolor{lightblue}66.6 & \\cellcolor{lightblue}\\textbf{62.7} & \\cellcolor{lightblue}\\textbf{43.1} & \\cellcolor{lightblue}\\textbf{57.7} & \\cellcolor{lightblue}\\textbf{59.8} & \\cellcolor{lightblue}\\textbf{34.3} & \\cellcolor{lightblue}\\textbf{48.7} & \\cellcolor{lightblue}\\textbf{68.2} & \\cellcolor{lightblue}\\textbf{43.9} & \\cellcolor{lightblue}\\textbf{81.4} & \\cellcolor{lightblue}\\textbf{87.8} & \\cellcolor{lightblue}\\textbf{70.3} & \\cellcolor{lightblue}\\textbf{80.4} & \\cellcolor{lightblue}\\textbf{15.7} & \\cellcolor{lightblue}\\textbf{77.5} & \\cellcolor{lightblue}\\textbf{57.9} \\\\ \n\\bottomrule\n\\end{tabular}}\n \\label{table:cross_ling}\n\\vspace{-0.2cm}\n\\end{table*}\n\n\n\n\\paragraph{Main Results}\n\n\nFigure \\ref{fig:korean} presents the results and one concrete example for the original dense model, pruned model with SliceGPT, and pruned model with our proposed \\texttt{Cus-Prun} method across the three scenarios.\nWe observe that \\texttt{Cus-Prun} largely preserves the performance of the dense model, retraining 92\\%, 83\\%, and 94\\% of the original dense model performance on these three cases respectively.\nIn contrast, the baseline method SliceGPT, which does not consider specific use cases, largely underperforms compared to \\texttt{Cus-Prun}.\nOverall, the results demonstrate that our proposed \\texttt{Cus-Prun} method could effectively obtain expert models tailored to specific use cases across different languages, domains, and tasks that maintain high performance despite substantial pruning.\n\n\n\n\n\\section{Foundational Custom Pruning Assessment}\n\nAs demonstrated by preliminary evaluation in Section \\ref{sec:pre_eva}, \\texttt{Cus-Prun} enables the creation of expert models tailored to specific languages, domains, and tasks. However, existing benchmark datasets may not always align with such specialized requirements for a systematic evaluation. To simplify our evaluation without losing generality, we use two distinct corpora: one focusing independently on a single dimension and another encompassing the remaining two dimensions. This approach allows us to evaluate \\texttt{Cus-Prun}'s performance in \\textit{multilingual}, \\textit{multidomain}, and \\textit{multitask} settings. \n\nFormally, in the multilingual setting, instead of constructing $C_{L_{\\text{Exp}}}$, $C_{D_{\\text{Exp}}}$ and $C_{T_{\\text{Exp}}}$ independently, we can construct two corpora, $C_{L_{\\text{Exp}}}$ and $C_{(D_, T)_{\\text{Exp}}}$, where $C_{L_{\\text{Exp}}}$ helps to identify irrelevant neurons in a specific language ($\\tilde{\\mathcal{N}}_{L_{\\text{Exp}}}$) and $C_{(D_, T)_{\\text{Exp}}}$ helps to identify irrelevant neurons in a specific domain-task combination ($\\tilde{\\mathcal{N}}_{D_{\\text{Exp}}\\cap T_{\\text{Exp}}}$). Formally speaking, \\texttt{Cus-Prun} in Equation \\ref{equ:cus} is transferred to \n\\begin{equation}\n\\scalebox{0.85}{$ % Adjust 0.94 to the desired scaling factor\n\\begin{aligned}\n\\mathcal{LLM}_{\\text{Exp}} & = \n\\mathcal{LLM} \\ominus \n\\left\\{\n\\tilde{\\mathcal{N}}_{L_{\\text{Exp}}} \\cap \n\\left( \n\\tilde{\\mathcal{N}}_{D_{\\text{Exp}}} \\cap \\tilde{\\mathcal{N}}_{T_{\\text{Exp}}}\n\\right)\n\\right\\} \\\\\n& \\equiv \n\\mathcal{LLM} \\ominus \n\\left\\{\n\\tilde{\\mathcal{N}}_{L_{\\text{Exp}}} \\cap \n\\tilde{\\mathcal{N}}_{D_{\\text{Exp}} \\cap T_{\\text{Exp}}}\n\\right\\}.\n\\end{aligned}\n$}\n\\label{equ:cross-lingual}\n\\end{equation}\nNote that this simplification is also applicable to $C_{D_{\\text{Exp}}}$, $C_{(L, T)_{\\text{Exp}}}$ and $C_{T_{\\text{Exp}}}$, $C_{(L, D)_{\\text{Exp}}}$. \n\n\\subsection{Experiment Setup} \n\n\\paragraph{Benchmarks} Although the primary goal of \\texttt{Cus-Prun} is to create expert LLMs tuned to specific use cases, we also evaluate the pruned models on standard general capabilities to ensure minimal performance loss. Specifically, we employ ARC-Challenge~\\citep{clark2018think} (5-shots), GSM8K~\\citep{cobbe2021training} (5-shots with CoT prompting~\\citep{wei2022chain}), and MMLU~\\citep{hendrycksmeasuring} (5-shots) to represent models general capability. \nNote that we utilize a generation task and implement CoT prompting method, a more challenging setting that has not been previously evaluated by existing pruning techniques~\\citep{songsleb, sharmatruth, yang2024laco,zhang2024finercut}. \n\n\n\\paragraph{Baselines} We employ several state-of-the-art pruning methods as the baseline that do not require post-training after pruning the model. (i) Dense represents the original model without pruning; (ii) LLM-Pruner~\\citep{ma2023llm} adopts structural pruning that selectively removes non-critical coupled structures based on gradient information;\\footnote{To ensure a fair comparison, we evaluate its performance before post-training, following \\citet{men2024shortgpt}.} (iii) SliceGPT~\\citep{ashkboosslicegpt} replaces each weight matrix with a smaller dense matrix, reducing the embedding dimension of the network; (iv) ShortGPT~\\citep{men2024shortgpt} directly deletes the redundant layers in LLMs\nbased on an importance score.\nWe follow the typical pruning setting from these previous studies to set the pruning ratio to $25\\%$ for all methods and all models. \n\n\\paragraph{Backbone Models} We choose 4 models from different model series and different sizes, including Llama3-8B-Base~\\citep{dubey2024llama}, Mistral-Nemo-Base-2407\\footnote{\\url{https://huggingface.co/mistralai/Mistral-Nemo-Base-2407}}(short as Mistral-12B), Llama2-13B-Base~\\citep{touvron2023llama}, Llama3-70B-Base~\\citep{dubey2024llama}. \n\n\n\n\n\n\\subsection{Multilingual Setting}\n\n\n\\paragraph{Dataset}\nWe employ multiple representative multilingual datasets for multilingual setting, which covers reasoning (MGSM~\\citep{shilanguage}, 5-shots), multilingual knowledge (M3Exam~\\citep{zhang2023m3exam}, 3-shots, abbreviated as M3), understanding (XQuAD~\\citep{artetxe2020cross}, 5-shots), and generation (XLSum~\\citep{hasan2021xl}, zero-shots, abbreviated as Sum). Furthermore, we consider three languages spanning a range from high-resource to low-resource including German (De), Chinese (Zh) and Thai (Th). \nMore detailed experiment settings are explained in Appendix \\ref{sec:appen_setting_1}.\n\n\n\n\\paragraph{Main Results} \n\n\nTable \\ref{table:cross_ling} summarizes the performance of \\texttt{Cus-Prun} on multilingual datasets, with average scores across languages. Detailed breakdown results for each language are shown in Table~\\ref{table:cl_ger}, Table~\\ref{table:cl_chi} and Table~\\ref{table:cl_tha} in Appendix~\\ref{sec:appen_cl}.\nWe can observe that \\texttt{Cus-Prun} consistently outperforms baseline pruning methods in achieving expert capabilities while preserving general performance. For example, on Llama3-8B, \\texttt{Cus-Prun} achieves a score of \\(38.9\\) compared to at most \\(15.5\\) for other methods. Similar improvements are observed for Mistral-12B (\\(34.3\\) vs. \\( 18.0\\)), Llama2-13B (\\(24.3\\) vs. \\( 15.5\\)), and Llama3-70B (\\(48.7\\) vs. \\( 33.1\\)). We can also see that the generation tasks are especially challenging; for instance, \\texttt{Cus-Prun} achieves a score of \\(30.1\\) on MGSM for Llama3-8B, while other methods nearly lose the ability to generate coherent reasoning outputs (often approaching \\(0\\) accuracy for all but the largest model). Additionally, \\texttt{Cus-Prun} performs robustly across both high-resource and low-resource languages. Overall, \\texttt{Cus-Prun} excels in both performance and robustness across diverse tasks and languages.\n\n\n\n\\subsection{MultilDomain Setting}\n\n\\paragraph{Dataset} For the multidomain setting, we employ several domain-specific datasets, including medical domain multiply choices questions (MedMCQ~\\citep{pmlr-v174-pal22a}, 3-shots, abbreviated as MMCQ), finance domain table question-answering (FinTQA~\\citep{chen2021finqa}, 8-shots, abbreviated as FTQA), social media domain sentiment analysis (TSA~\\citep{kharde975sentiment}, 3-shots), and e-commerce domain sentiment analysis (AMSA~\\citep{zhang2015character}, 3-shots). Moreover, in multidomain setting, our focus is exclusively on the English language. Detailed experiment settings are explained in Appendix \\ref{sec:appen_setting_2}.\n\n\n\n\\paragraph{Main Results} Table \\ref{table:cross_ling} shows the performance of \\texttt{Cus-Prun} on multidomain setting. We find that \\texttt{Cus-Prun} consistently outperforms other pruning methods in both expert and general capabilities. For expert capabilities, \\texttt{Cus-Prun} achieves a score of $53.2$ on Llama3-8B, while other pruning methods achieve at most $34.5$. Similar improvements are observed for Mistral-12B (\\(56.0\\) vs. \\(32.6\\)), Llama2-13B (\\(33.1\\) vs. \\(30.3\\)), and Llama3-70B (\\(70.3\\) vs. \\(55.8\\)).\n\n\n\n\\begin{figure*}[t]\n \\centering\n \\begin{minipage}{0.4\\textwidth}\n \\centering\n \\captionof{table}{Performance of Chinese-Medical expert model on MCQ task}\n \\label{tab:cmexam}\n \\scalebox{0.96}{\n \\begin{tabular}{l|c|c}\n \\hline\n \\textbf{Method} & \\textbf{General} & \\textbf{CMExam} \\\\\\midrule\n \\rowcolor{lightgray}\\cellcolor{white} Dense & 59.3 & 50.6 \\\\\n LLM-Pruner & 18.6 & 25.0 \\\\\n SliceGPT & 27.8 & 26.9 \\\\\n ShortGPT & 23.9 & 23.7 \\\\\n \\rowcolor{lightblue}\\cellcolor{white} \\texttt{Cus-Prun} & \\textbf{52.4} & \\textbf{48.7} \\\\\n \\hline\n \\end{tabular}}\n \\end{minipage}\n \\hfill\n \\begin{minipage}{0.58\\textwidth}\n \\centering\n \\includegraphics[width=\\textwidth]{Figures/huatuo.pdf}\n \\caption{Chinese Medical LLM performance. Numbers are quality on the \\textbf{whole} test set evaluated by GPT4.}\n \\label{fig:huatuo}\n \\end{minipage}\n\\end{figure*}\n\n\\subsection{MultiTask Setting}\n\n\n\n\\paragraph{Dataset} For the multitask setting, we employ several task-specific datasets, including the medical summarization task (MedSum~\\citep{abacha2019summarization}, 3-shots, abbreviated as MSum), summarization task in e-commerce (Amazon Summary~\\citep{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions, brüelgabrielsson2024compressserveservingthousands}, 3-shots, abbreviated as ASum), counterfactual task in e-commerce (Amazon Counterfactual~\\citep{o2021wish}, 3-shots, abbreviated as AMCF). Similarly, in multitask setting scenarios, our focus is exclusively on the English language. Detailed experiment settings are explained in Appendix \\ref{sec:appen_setting_3}.\n\n\n\n\\paragraph{Main Results} Table \\ref{table:cross_ling} shows the performance of \\texttt{Cus-Prun} on multitask setting. We find that except for LLM-Pruner under Llama3-8B, \\texttt{Cus-Prun} outperforms other pruning methods in both expert and general capabilities. For expert tasks, \\texttt{Cus-Prun} achieves a score of \\(50.9\\) on \\texttt{Mistral-12B}, significantly outperforming the highest score of \\(23.0\\) from other methods. Likewise, it attains \\(38.0\\) on \\texttt{Llama2-13B} and \\(57.9\\) on \\texttt{Llama3-70B}, both markedly higher than the corresponding maximum scores of \\(22.7\\) and \\(46.8\\) achieved by competing methods.\n\n\n\n\\subsection{Analysis on Aggressive Pruning Ratio}\n\n\nTo optimize for specialized tasks rather than maintaining general capabilities, we employ more aggressive pruning ratios. We combine layer pruning with our custom neuron pruning method in Algorithm \\ref{algo:adaptive_pruning} and evaluate the approach on M3Exam, MedMCQ, and Amazon Counterfactual (AMContFact) datasets using Llama3-8B. Detailed results are shown in Table \\ref{table:prune_more}. We find that \\texttt{Cus-Prun} consistently maintains the model's capabilities even at higher pruning ratios. Specifically, when the pruning ratio is increased to $45\\%$, ShortGPT nearly loses the capability of generating meaningful answers, while \\texttt{Cus-Prun} still achieves scores of $48.4$ on MMLU and $50.6$ on expert capabilities.\n\n\\begin{table}[ht]\n\\caption{Aggressive pruning ratio on Llama3-8B.}\n\\centering\n\\setlength{\\tabcolsep}{3pt}\n \\scalebox{0.9}{\n\\begin{tabular}{l|c|c|c|c}\n \\hline\n \\textbf{Method} & \\textbf{Ratio} & \\textbf{Speedup} & \\textbf{MMLU} & \\textbf{Expert} \\\\\\midrule\n \\rowcolor{lightgray}\\cellcolor{white}Dense & 0.0 & 1$\\times$ & 63.1 & 59.7 \\\\\\midrule\n ShortGPT & 25.0 & 1.3$\\times$ & 28.6 & 24.6 \\\\\n \\rowcolor{lightblue}\\cellcolor{white}\\texttt{Cus-Prun} & 25.0 & 1.3$\\times$ & \\textbf{51.9} & \\textbf{53.3} \\\\\\midrule\n ShortGPT & 34.2 & 1.5$\\times$ & 20.8 & 18.5 \\\\\n \\rowcolor{lightblue}\\cellcolor{white}\\texttt{Cus-Prun} & 35.0 & 1.5$\\times$ & \\textbf{50.2} & \\textbf{51.4} \\\\\\midrule\n ShortGPT & 43.8 & 1.8$\\times$ & 7.9 & 10.2 \\\\\n \\rowcolor{lightblue}\\cellcolor{white}\\texttt{Cus-Prun} & 45.0 & 1.8$\\times$ & \\textbf{48.4} & \\textbf{50.6} \\\\\n \\hline\n \\end{tabular}}\n \\label{table:prune_more}\n\\end{table}\n\n\n\n\\section{Adaptive Custom Pruning Assessment}\n\nIn this section, we evaluate the generality of \\texttt{Cus-Prun} in dynamic scenarios, including specific expert models in two and one dimensions, as described in Section \\ref{sec:adaptive}.\n\n\\subsection{Two Dimensions Specific Expert Model}\n\n\\paragraph{Experiment Settings} We use the Chinese-Medical setting as a concrete example of a two-dimensional expert model designed to perform a wide range of medical tasks in Chinese. \nWe adopt Mistral-12b as the backbone model and utilize corpus from Wikipedia for Chinese content and general medical corpus for medical knowledge. The performance of the target Chinese-Medical expert model is evaluated on two datasets: CMExam~\\citep{liu2023benchmarking} (5-shots), a Chinese medical multiple-choice question dataset, and HuatuoQA~\\citep{li2023huatuo26m}, a Chinese medical question-answering dataset. We assess the performance on CMExam using accuracy metrics. For the latter, we sample a sub-testset of size $100$ and use GPT-4 as the evaluator, which assigns a score from $0$ to $5$, representing its quality from low to high. Detailed prompts are listed in Appendix \\ref{sec:appen_prompt}.\n\n\n\n\\paragraph{Main Results} Table \\ref{tab:cmexam} presents the performance of the Chinese-Medical LLM on CMExam and its general capabilities. Our results indicate that the expert model pruned using \\texttt{Cus-Prun} outperforms models obtained through other pruning methods. Specifically, \\texttt{Cus-Prun} achieves a score of $48.7$ on CMExam, while its general capability score is $52.4$. These results compare favorably to the dense model, which scores $50.6$ on CMExam and $59.3$ on general capabilities. On the contrary, other pruning methods nearly lose the general and specific capabilities. Furthermore, Figure \\ref{fig:huatuo} shows a concrete example of Chinese-Medical LLM performance on medical question-answering. We find that \\texttt{Cus-Prun} can produce smaller expert models that maintain their expert capabilities, as demonstrated by its performance score of $2.9/5.0$ compared to $3.2/5.0$ for the dense model.\n\n\n\n\n\n\\subsection{One Dimension Specific Expert Model}\n\n\\begin{figure*}[t]\n \\centering\n \\includegraphics[width=1.0\\textwidth]{Figures/lang-specific.pdf}\n \\caption{Performance of \\texttt{Cus-Prun} in obtaining language-specific models.}\n \\label{fig:lang-spec}\n\\end{figure*}\n\n\n\\paragraph{Experiment Settings} \n\n\nFor evaluating the pruning method under a one-dimensional expert model setting, we focus on language-specific pruning, showing how to transform a dense model into language-specific variants.\nWe consider three linguistically diverse languages: German, Chinese, and Thai. We conduct experiments based on the Llama3-8b model.\nTo identify language-specific (while domain- and task-agnostic) neurons, we employ a diverse range of corpora, including Wikipedia, MGSM, and M3Exam, ensuring coverage of various domains and tasks. The effectiveness of our pruning technique is then evaluated using three held-out multilingual datasets including XQuAD~\\citep{artetxe2020cross}, XNLI~\\citep{conneau2018xnli}, and XSum ~\\citep{narayan2018don}.\n\n\n\\paragraph{Main Results} \nFigure \\ref{fig:lang-spec} illustrates the performance of language-specific models using \\texttt{Cus-Prun}. By pruning $25\\%$ of the neurons from the original model, \\texttt{Cus-Prun} not only retains general performance but also preserves language-specific capabilities. For instance, the German-specific model scores $54.7$ in general capabilities, $48.3$ on XQuAD, and $56.8$ on XNLI, compared to the dense model's scores of $64.1$, $52.9$, and $62.0$, respectively. This trend is consistent for Chinese and Thai models as well. In contrast, ShortGPT struggles to maintain the model's capabilities, particularly in XQuAD and XLSUm, which require generative abilities.\n\n\n\\section{Related Work}\n\n\\paragraph{LLM Compression} Given the high costs of training, inferencing, and tuning LLMs, many studies focus on model compression methods, including compression~\\citep{zhu2023survey}, quantization~\\citep{xu2023qa, dettmers2024qlora, lin2024awq, li2024evaluating}, and pruning~\\citep{wang2019structured}. Sparsity-based structural pruning enhances GPU efficiency with sparse structures but doesn't always reduce parameter counts \\citep{li2022parameter, li2023losparse, kurz2024language, zhao2024convex, huang2024pruning}. Unstructured pruning reduces parameters while maintaining performance, using post-training techniques \\citep{ma2023llm, xiasheared, muralidharan2024compact} or coarse methods like parameter approximation \\citep{zhao2024convex}, layer removal \\citep{men2024shortgpt}, or structural elimination \\citep{zhang2024finercut}. However, these often fail to preserve domain- or task-specific capabilities, limiting their utility for specialized scenarios.\n\n\n\\paragraph{Customizing Model} Customizing LLMs is essential for addressing language-specific challenges, domain-specific needs, and task-specific applications \\citep{cui2023efficient, yang2024laco, li2023huatuo26m, roziere2023code, li2023large, azerbayevllemma, alves2024tower,naacl-sentiment}. However, customization often demands extensive fine-tuning with curated data, making it resource-intensive. This highlights the need for efficient methods to quickly create robust, expert models tailored to diverse industries without compromising quality.\n\n\\section{Conclusion}\n\nLLMs deliver impressive capabilities but incur high computational costs. Efficient pruning of redundant parameters is vital for conserving resources and improving inference speed, especially for specialized models. Our method, \\texttt{Cus-Prun}, generates smaller expert models without post-training by pruning irrelevant neurons across ``language'', ``domain'', and ``task'' dimensions. This finer-grained approach outperforms existing techniques on three-dimensional models and can adapt to realistic scenarios, such as language-domain or language-specific models.\n\n\n\\section*{Limitation}\n\nDespite the promising results of Cus-Prun, several limitations should be noted. First, while our method leverages three dimensions (language, domain, and task) for pruning, certain crucial restrictions cannot be fully captured within this framework, such as variations in query format or input structure. Second, whether pruned base models can effectively undergo post-training remains an open question that requires further investigation. This uncertainty about post-training capabilities could limit the model's adaptability to new scenarios or requirements after pruning. These limitations suggest important directions for future research, including exploring additional dimensions for more comprehensive pruning strategies and investigating the relationship between pruning and post-training effectiveness.\n\n\\section*{Acknowledgement}\nThis research is supported by the Ministry of Education, Singapore, under its Academic Research Fund (AcRF) Tier 1 grant, and funded through the SUTD Assistant Professorship Scheme (SAP 2025\\_001).\nThis material is based upon work partially supported by the Air Force Office of Scientific Research under award number FA2386-24-1-4011, and this research is partially supported by the Singapore Ministry of Education Academic Research Fund Tier 1 (Award No: T1 251RES2207).\n\n\n\\bibliography{custom}\n\n\n\\appendix\n\\section{Appendix}\n\n\\subsection{GPT-4o Prompts}\\label{sec:appen_prompt}\nThe prompts used for generating documents and evaluating answer quality are presented in Table~\\ref{table:testset}.\n\n\\begin{table}[ht]\n \\centering\n\\footnotesize\n \\scalebox{1}{\n \\begin{tabular}{lp{5.3cm}}\n \\toprule\n \\textbf{Task} & \\textbf{Prompt} \\\\\\midrule\n Generation & Generate a text document in \\{language\\}/\\{domain\\}/\\{task\\}. Make sure the documents is not fixed to one \\{language\\}/\\{domain\\}/\\{task\\} or \\{language\\}/\\{domain\\}/\\{task\\}. Ensure the content is clear, concise, and appropriate for the specified request. Use professional and domain-specific terminology where necessary. \\\\\\midrule\n Evaluation & Evaluate the quality of the given answer to the question. Provide a score from 0 to 5, where 0 represents very low quality and 5 represents very high quality. Question: \\{question\\} Answer: \\{answer\\}.\\\\\n \\bottomrule\n \\end{tabular}}\n\\caption{GPT-4o prompts for generating documents and evaluating answer quality.} \\label{table:testset}\n\\end{table}\n\n\\subsection{Detailed Results for Multilingual}\\label{sec:appen_cl}\n\nDetailed results for multilingual settings can be found in Table \\ref{table:cl_ger}, Table \\ref{table:cl_chi} and Table \\ref{table:cl_tha} for German, Chinese and Thai, respectively.\n\n\\begin{table*}[hbpt]\n\\caption{Main Results of \\texttt{Cus-Prun} on Germany with a pruning ratio of $25\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in XLSum and in accuracy (\\%) for other datasets.}\n \\centering\n\\footnotesize\n\\setlength{\\tabcolsep}{3pt}\n \\scalebox{1.0}{\n \\begin{tabular}{l|l|cccc|ccccc}\n\\toprule\n\\multirow{2}{*}{\\textbf{\\normalsize{Model}}} & \\multirow{2}{*}{\\textbf{\\normalsize{Method}}} & \n \\multicolumn{4}{c}{\\textbf{\\normalsize{General Capability}}} \\vline & \\multicolumn{5}{c}{\\textbf{\\normalsize{Expert Capability}}} \\\\ \n & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3Exam & XQuAD & XLSum & Avg. \\\\ \\midrule\n\\multirow{4}{*}{Llama3-8B} &\n Dense & 70.7 & 58.3 & 63.1 & 64.1 & 44.8 & - & 52.9 & - & 48.8 \\\\\n & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 0.0 & - & 11.0 & - & 5.5 \\\\\n & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & - & 9.8 & - & 4.9 \\\\\n & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & - & 0.0 & - & 0.0 \\\\\n & \\texttt{Cus-Prun} & 61.4 & 38.9 & 54.5 & \\textbf{51.6} & 32.8 & - & 49.6 & - & \\textbf{41.2} \\\\ \\midrule\n\\multirow{4}{*}{Mistral-12B} & Dense & 82.6 & 68.5 & 50.4 & 59.3 & 56.8 & - & 41.2 & - & 49.0 \\\\\n & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 2.4 & - & 13.4 & - & 7.9 \\\\\n & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 0.8 & - & 15.5 & - & 8.2 \\\\\n & ShortGPT & 37.8 & 0.0 & 33.9 & 23.9 & 3.6 & - & 20.3 & - & 12.0 \\\\\n & \\texttt{Cus-Prun} & 64.6 & 39.7 & 43.2 & \\textbf{49.2} & 31.6 & - & 35.9 & - & \\textbf{33.8} \\\\ \\midrule\n\\multirow{4}{*}{Llama2-13B} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 24.4 & - & 40.3 & - & 32.3 \\\\\n & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 2.0 & - & 5.7 & - & 3.9 \\\\\n & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 3.6 & - & 18.1 & - & 10.9 \\\\\n & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 2.8 & - & 27.2 & - & 15.0 \\\\\n & \\texttt{Cus-Prun} & 47.6 & 19.8 & 49.9 & \\textbf{39.1} & 18.4 & - & 31.7 & - & \\textbf{25.0} \\\\ \\midrule\n\\multirow{4}{*}{Llama3-70B} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 74.8 & - & 58.2 & - & 66.5 \\\\\n & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 18.0 & - & 27.3 & - & 22.7 \\\\\n & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 0.0 & - & 17.3 & - & 8.7 \\\\\n & ShortGPT & 59.4 & 5.6 & 75.5 & 46.8 & 9.6 & - & 31.5 & - & 20.6 \\\\\n & \\texttt{Cus-Prun} & 66.8 & 59.3 & 69.1 & \\textbf{65.1} & 48.2 & - & 53.9 & - & \\textbf{51.1} \\\\ \n\\bottomrule\n\\end{tabular}}\n \\label{table:cl_ger}\n\\end{table*}\n\n\n\\begin{table*}[hbpt]\n\\caption{Main Results of \\texttt{Cus-Prun} on Chinese with a pruning ratio of $25\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in XLSum and in accuracy (\\%) for other datasets.}\n \\centering\n\\footnotesize\n\\setlength{\\tabcolsep}{3pt}\n \\scalebox{1.0}{\n \\begin{tabular}{l|l|cccc|ccccc}\n\\toprule\n\\multirow{2}{*}{\\textbf{\\normalsize{Model}}} & \\multirow{2}{*}{\\textbf{\\normalsize{Method}}} & \n \\multicolumn{4}{c}{\\textbf{\\normalsize{General Capability}}} \\vline & \\multicolumn{5}{c}{\\textbf{\\normalsize{Specific Capability}}} \\\\ \n & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3Exam & XQuAD & XLSum & Avg. \\\\ \\midrule\n\\multirow{4}{*}{Llama3-8B} &\n Dense & 70.7 & 58.3 & 63.1 & 64.1 & 43.6 & 55.1 & 78.7 & 49.1 & 56.6 \\\\\n & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 2.4 & 23.6 & 21.3 & 32.8 & 20.0 \\\\\n & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & 17.4 & 23.5 & 8.3 & 12.3 \\\\\n & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & 28.3 & 0.0 & 3.1 & 7.9 \\\\\n & \\texttt{Cus-Prun} & 60.5 & 25.7 & 49.4 & \\textbf{45.2} & 36.0 & 44.7 & 65.6 & 46.3 & \\textbf{48.2} \\\\ \\midrule\n\\multirow{4}{*}{Mistral-12B} & Dense & 82.6 & 68.5 & 50.4 & 59.3 & 53.2 & 47.8 & 62.2 & 33.0 & 49.1 \\\\\n & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 2.8 & 30.7 & 31.8 & 32.6 & 24.5 \\\\\n & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 1.6 & 26.4 & 28.3 & 10.8 & 16.8 \\\\\n & ShortGPT & 37.8 & 0.0 & 33.9 & 23.9 & 4.4 & 28.2 & 29.1 & 7.2 & 17.2 \\\\\n & \\texttt{Cus-Prun} & 68.3 & 43.2 & 39.5 & \\textbf{50.3} & 38.4 & 40.7 & 50.6 & 30.3 & \\textbf{40.0} \\\\ \\midrule\n\\multirow{4}{*}{Llama2-13B} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 21.6 & 36.5 & 59.8 & 35.3 & 38.3 \\\\\n & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 1.2 & 23.3 & 3.8 & 25.1 & 13.4\\\\\n & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 4.8 & 24.5 & 28.4 & 11.2 & 17.2 \\\\\n & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 4.4 & 22.9 & 24.6 & 13.7 & 16.4 \\\\\n & \\texttt{Cus-Prun} & 48.6 & 20.7 & 51.9 & \\textbf{40.4} & 14.8 & 28.2 & 47.3 & 34.4 & \\textbf{31.2} \\\\ \\midrule\n\\multirow{4}{*}{Llama3-70B} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 68.4 & 76.1 & 81.3 & 55.3 & 70.3 \\\\\n & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 16.8 & 47.5 & 56.1 & 41.3 & 40.4 \\\\\n & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 6.4 & 48.3 & 42.2 & 29.3 & 31.6 \\\\\n & ShortGPT & 59.4 & 5.6 & 75.5 & 46.8 & 12.4 & 45.5 & 44.6 & 36.1 & 34.7 \\\\\n & \\texttt{Cus-Prun} & 72.3 & 48.5 & 65.2 & \\textbf{62.0} & 40.8 & 61.7 & 66.9 & 51.6 & \\textbf{55.3} \\\\ \n\\bottomrule\n\\end{tabular}}\n \\label{table:cl_chi}\n\\end{table*}\n\n\n\\begin{table*}[hbpt]\n\\caption{Main Results of \\texttt{Cus-Prun} on Thai with a pruning ratio of $25\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in XLSum and in accuracy (\\%) for other datasets.}\n \\centering\n\\footnotesize\n\\setlength{\\tabcolsep}{3pt}\n \\scalebox{1.0}{\n \\begin{tabular}{l|l|cccc|ccccc}\n\\toprule\n\\multirow{2}{*}{\\textbf{\\normalsize{Model}}} & \\multirow{2}{*}{\\textbf{\\normalsize{Method}}} & \n \\multicolumn{4}{c}{\\textbf{\\normalsize{General Capability}}} \\vline & \\multicolumn{5}{c}{\\textbf{\\normalsize{Specific Capability}}} \\\\ \n & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3Exam & XQuAD & XLSum & Avg. \\\\ \\midrule\n\\multirow{4}{*}{Llama3-8B} &\n Dense & 70.7 & 58.3 & 63.1 & 64.1 & 35.2 & 43.0 & 58.7 & 16.7 & 38.4 \\\\\n & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 0.8 & 24.4 & 8.4 & 13.5 & 11.8 \\\\\n & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & 12.3 & 16.6 & 8.7 & 9.4 \\\\\n & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & 25.4 & 0.0 & 2.3 & 6.9 \\\\\n & \\texttt{Cus-Prun} & 58.9 & 31.2 & 52.4 & \\textbf{47.5} & 21.6 & 38.3 & 42.6 & 16.8 & \\textbf{29.8} \\\\ \\midrule\n\\multirow{4}{*}{Mistral-12B} & Dense & 82.6 & 68.5 & 50.4 & 59.3 & 45.2 & 39.9 & 44.1 & 17.8 & 36.8 \\\\\n & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 1.2 & 24.8 & 11.9 & 13.7 & 12.9 \\\\\n & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 0.0 & 23.8 & 8.4 & 4.7 & 12.3 \\\\\n & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 0.8 & 25.7 & 4.7 & 2.8 & 8.5 \\\\\n & \\texttt{Cus-Prun} & 68.2 & 35.8 & 47.6 & \\textbf{50.5} & 32.8 & 37.7 & 35.6 & 15.9 & \\textbf{30.5} \\\\ \\midrule\n\\multirow{4}{*}{Llama2-13B} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 6.4 & 24.3 & 28.3 & 14.5 & 18.4 \\\\\n & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 0.0 & 22.3 & 1.8 & 10.2 & 8.6 \\\\\n & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 0.0 & 26.2 & 23.7 & 8.6 & 14.6 \\\\\n & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 0.0 & 23.1 & 22.3 & 8.9 & 13.6 \\\\\n & \\texttt{Cus-Prun} & 47.8 & 20.9 & 50.7 & \\textbf{39.8} & 4.8 & 24.2 & 23.6 & 13.8 & \\textbf{16.6} \\\\ \\midrule\n\\multirow{4}{*}{Llama3-70B} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 65.2 & 66.1 & 67.8 & 17.8 & 54.2 \\\\\n & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 15.6 & 39.9 & 29.8 & 16.6 & 25.5 \\\\\n & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 4.8 & 41.3 & 39.6 & 13.2 & 24.7 \\\\\n & ShortGPT & 59.4 & 5.6 & 75.5 & 46.8 & 13.7 & 40.7 & 40.4 & 11.9 & 26.7 \\\\\n & \\texttt{Cus-Prun} & 73.3 & 58.7 & 68.4 & \\textbf{66.8} & 40.4 & 53.6 & 58.5 & 16.9 & \\textbf{42.4} \\\\ \n\\bottomrule\n\\end{tabular}}\n \\label{table:cl_tha}\n\\end{table*}\n\n\\subsection{Experiments Detailed Settings}\\label{sec:appen_setting}\n\n\\subsubsection{Multilingual Settings}\\label{sec:appen_setting_1}\n\n\\paragraph{Experiment Details} For multilingual setting, we can obtain two corpora: $C_{L_{\\text{Exp}}}$ and $C_{(D, T)_{\\text{Exp}}}$. The first corpus contains samples in a specific language across various domains and tasks, while the second corpus contains samples from a specific domain-task combination in other languages, i.e., the target dataset in other languages. Specifically, for $C_{L_{\\text{Exp}}}$ we employ Wikipedia\\footnote{\\url{https://huggingface.co/datasets/wikimedia/wikipedia}} to construct language-specific corpus covering various domains and tasks. For $C_{(D, T)_{\\text{Exp}}}$, we employ the corresponding datasets in English, including GSM8K~\\citep{cobbe2021training} for MGSM, the English split of M3Exam\\footnote{M3Exam is language-specific and does not utilize a translated parallel corpus.} for M3Exam, SQuAD~\\citep{rajpurkar2016squad} for XQuAD, and XSum~\\citep{narayan2018don} for XLSum. \n\nHyperparameters, including the sizes of $C_{L_{\\text{Exp}}}$ and $C_{(D, T)_{\\text{Exp}}}$, are determined using the validation set of the XLSum dataset and then applied to testsets in other multilingual datasets. Furthermore, accuracy is the metric used for ARC-c, GSM8K, MMLU, MGSM, M3Exam, and XQuAD, while Rouge-L~\\citep{lin2004rouge} is used for XLSum. \n\n\\subsubsection{Multidomain Settings}\\label{sec:appen_setting_2}\n\n\\paragraph{Settings} For multidomain setting, we can obtain two corpora: $C_{D_{\\text{Exp}}} = \\{(L, D_{\\text{Exp}}, T)|L\\in\\mathbb{L}, T\\in\\mathbb{T}\\}$ and $C_{(L, T)_{\\text{Exp}}}=\\{(D, (L, T)_{\\text{Exp}})|D\\in\\mathbb{D}\\}$. The first corpus contains samples in a specific domain across various languages and tasks, while the second corpus contains samples from a specific language-task combination across different domains, i.e., the target dataset in other domains. Specifically, for $C_{D_{\\text{Exp}}}$ we employ specific domain corpus, including English split of medical corpus~\\citep{garcia2024medical} for medical domain, general finance corpus for finance domain\\footnote{\\url{https://huggingface.co/datasets/gbharti/finance-alpaca}}, general Twitter corpus~\\citep{kharde975sentiment}, and English split of Amazon corpus~\\citep{marc_reviews}. For $C_{(L, T)_{\\text{Exp}}}$, we employ the corresponding datasets in general domains, including CommonsenseQA~\\citep{talmor2019commonsenseqa} for MedMCQ, open table question-answering OTT-QA~\\citep{chen2020open} for FinTQA, general sentiment analysis~\\citep{attia2018multilingual} for TSA and AMSA.\n\n\\paragraph{Experiment Details} Hyperparameters, including the sizes of $C_{D_{\\text{Exp}}}$ and $C_{(L, T)_{\\text{Exp}}}$, are determined using the validation set of the Amazon sentiment analysis dataset and then applied to testsets in other multidomain datasets. Furthermore, accuracy is the metric used for all datasets. \n\n\n\n\\subsubsection{Multitask Settings}\\label{sec:appen_setting_3}\n\n\\paragraph{Settings} For multitask setting, we can obtain two corpora: $C_{T_{\\text{Exp}}} = \\{(L, D, T_{\\text{Exp}})|L\\in\\mathbb{L}, D\\in\\mathbb{D}\\}$ and $C_{(L, D)_{\\text{Exp}}}=\\{(T, (L, S)_{\\text{Exp}})|T\\in\\mathbb{T}\\}$. The first corpus contains samples in a specific task across various languages and domains, while the second corpus contains samples from a specific language-domain combination across different tasks, i.e., the target dataset in other tasks. Specifically, for $C_{T_{\\text{Exp}}}$ we employ specific task corpus, including XSum corpus~\\citep{abacha2019summarization} for summarization task, general conterfact corpus\\footnote{\\url{https://huggingface.co/datasets/azhx/counterfact-easy}} for counterfactual task. For $C_{(L, D)_{\\text{Exp}}}$, we employ the corresponding datasets in other tasks, including MedQCQ~\\citep{pmlr-v174-pal22a} for MedSum, AMSA~\\citep{zhang2015character} for AMSum and AMContFact.\n\n\\paragraph{Experiment Details} Hyperparameters, including the sizes of $C_{T_{\\text{Exp}}}$ and $C_{(L, D)_{\\text{Exp}}}$, are determined using the validation set of the Amazon counterfactual dataset and then applied to testsets in other multitask setting datasets. Furthermore, accuracy is the metric used for ARC-c, GSM8K, MMLU, and AMContFact, while Rouge-L~\\citep{lin2004rouge} is used for MedSum and AMSum. \n\n\n\n\\end{document}\n"},"question":{"kind":"string","value":"When the pruning rate increases from 25% to about 45%, how much original performance do ShortGPT and Cus-Prun's MMLU retain compared to their respective original Dense models? From the results, which method is more sensitive to pruning gains?"},"answer":{"kind":"string","value":"12.5%, 76.7% ,ShortGPT"},"plan":{"kind":"string","value":"SELECT Dense MMLU score\nSELECT ShortGPT MMLU score at the higher Pruning Ratio\nCOMPUTE shortgpt_retention = (shortgpt_mmlu_high / dense_mmlu) * 100\nSELECT Cus-Prun MMLU score at the higher Pruning Ratio\nCOMPUTE cus_prun_retention = (cus_prun_mmlu_high / dense_mmlu) * 100\n\n\nIF shortgpt_retention < cus_prun_retention\n COMPUTE more_sensitive_method = ShortGPT\nELSE\n COMPUTE more_sensitive_method = Cus-Prun\n\n\nRETURN shortgpt_retention, cus_prun_retention, and more_sensitive_method\n"}}},{"rowIdx":92,"cells":{"paper":{"kind":"string","value":"2506.02561"},"relevant_tables":{"kind":"list like","value":[["\\begin{table}[ht]\n","\\caption{Aggressive pruning ratio on Llama3-8B.}\n","\\centering\n","\\setlength{\\tabcolsep}{3pt}\n"," \\scalebox{0.9}{\n","\\begin{tabular}{l|c|c|c|c}\n"," \\hline\n"," \\textbf{Method} & \\textbf{Ratio} & \\textbf{Speedup} & \\textbf{MMLU} & \\textbf{Expert} \\\\\\midrule\n"," \\rowcolor{lightgray}\\cellcolor{white}Dense & 0.0 & 1$\\times$ & 63.1 & 59.7 \\\\\\midrule\n"," ShortGPT & 25.0 & 1.3$\\times$ & 28.6 & 24.6 \\\\\n"," \\rowcolor{lightblue}\\cellcolor{white}\\texttt{Cus-Prun} & 25.0 & 1.3$\\times$ & \\textbf{51.9} & \\textbf{53.3} \\\\\\midrule\n"," ShortGPT & 34.2 & 1.5$\\times$ & 20.8 & 18.5 \\\\\n"," \\rowcolor{lightblue}\\cellcolor{white}\\texttt{Cus-Prun} & 35.0 & 1.5$\\times$ & \\textbf{50.2} & \\textbf{51.4} \\\\\\midrule\n"," ShortGPT & 43.8 & 1.8$\\times$ & 7.9 & 10.2 \\\\\n"," \\rowcolor{lightblue}\\cellcolor{white}\\texttt{Cus-Prun} & 45.0 & 1.8$\\times$ & \\textbf{48.4} & \\textbf{50.6} \\\\\n"," \\hline\n"," \\end{tabular}}\n"," \\label{table:prune_more}\n","\\end{table}\n"]],"string":"[\n [\n \"\\\\begin{table}[ht]\\n\",\n \"\\\\caption{Aggressive pruning ratio on Llama3-8B.}\\n\",\n \"\\\\centering\\n\",\n \"\\\\setlength{\\\\tabcolsep}{3pt}\\n\",\n \" \\\\scalebox{0.9}{\\n\",\n \"\\\\begin{tabular}{l|c|c|c|c}\\n\",\n \" \\\\hline\\n\",\n \" \\\\textbf{Method} & \\\\textbf{Ratio} & \\\\textbf{Speedup} & \\\\textbf{MMLU} & \\\\textbf{Expert} \\\\\\\\\\\\midrule\\n\",\n \" \\\\rowcolor{lightgray}\\\\cellcolor{white}Dense & 0.0 & 1$\\\\times$ & 63.1 & 59.7 \\\\\\\\\\\\midrule\\n\",\n \" ShortGPT & 25.0 & 1.3$\\\\times$ & 28.6 & 24.6 \\\\\\\\\\n\",\n \" \\\\rowcolor{lightblue}\\\\cellcolor{white}\\\\texttt{Cus-Prun} & 25.0 & 1.3$\\\\times$ & \\\\textbf{51.9} & \\\\textbf{53.3} \\\\\\\\\\\\midrule\\n\",\n \" ShortGPT & 34.2 & 1.5$\\\\times$ & 20.8 & 18.5 \\\\\\\\\\n\",\n \" \\\\rowcolor{lightblue}\\\\cellcolor{white}\\\\texttt{Cus-Prun} & 35.0 & 1.5$\\\\times$ & \\\\textbf{50.2} & \\\\textbf{51.4} \\\\\\\\\\\\midrule\\n\",\n \" ShortGPT & 43.8 & 1.8$\\\\times$ & 7.9 & 10.2 \\\\\\\\\\n\",\n \" \\\\rowcolor{lightblue}\\\\cellcolor{white}\\\\texttt{Cus-Prun} & 45.0 & 1.8$\\\\times$ & \\\\textbf{48.4} & \\\\textbf{50.6} \\\\\\\\\\n\",\n \" \\\\hline\\n\",\n \" \\\\end{tabular}}\\n\",\n \" \\\\label{table:prune_more}\\n\",\n \"\\\\end{table}\\n\"\n ]\n]","isConversational":false},"tables":{"kind":"list like","value":[["\\begin{figure}[t]\n"," \\centering\n"," \\includegraphics[width=0.48\\textwidth]{Figures/cust-prune.pdf}\n"," \\caption{Given a requirement for an expert model across three dimensions (language, domain, and task), \\texttt{Cus-Prun} (i) identifies irrelevant neurons for each dimension using corresponding corpora (left), and (ii) prunes \\textbf{overlapping} irrelevant neurons across dimensions to obtain the expert model (right).}\n"," \\label{fig:cus-prun}\n","\\end{figure}\n"],["\\begin{figure}[t]\n","\\scalebox{0.95}{ \n","\\begin{minipage}{\\linewidth}\n","\\begin{algorithm}[H]\n","\\renewcommand{\\algorithmicrequire}{\\textbf{Input:}}\n","\\renewcommand{\\algorithmicensure}{\\textbf{Output:}}\n","\\caption{Adaptive Custom Pruning}\n","\\begin{algorithmic}[1]\n","\\REQUIRE Original language model $\\mathcal{LLM}$, request for expert model $\\mathcal{LLM}_{\\text{Exp}}$ with selected dimensions: $L_{\\text{Exp}}$, $D_{\\text{Exp}}$, $T_{\\text{Exp}}$ (any subset), request for pruning ratio $\\sigma$.\n","\\STATE \\texttt{// Construct specific corpora for each selected dimension.}\n","\\STATE $C = \\{\\}$\n","\\IF{$L_{\\text{Exp}}$ is specified}\n"," \\STATE $C = C \\cup \\{(L_{\\text{Exp}}, D, T) \\mid D \\in \\mathbb{D}, T \\in \\mathbb{T}\\}$\n","\\ENDIF\n","\\IF{$D_{\\text{Exp}}$ is specified}\n"," \\STATE $C = C \\cup \\{(L, D_{\\text{Exp}}, T) \\mid L \\in \\mathbb{L}, T \\in \\mathbb{T}\\}$\n","\\ENDIF\n","\\IF{$T_{\\text{Exp}}$ is specified}\n"," \\STATE $C = C \\cup \\{(L, D, T_{\\text{Exp}}) \\mid L \\in \\mathbb{L}, D \\in \\mathbb{D}\\}$\n","\\ENDIF\n","\\STATE \\texttt{// Identify irrelevant neurons for each selected dimension.}\n","\\FORALL{neuron $N_{i}^{(l)}$ in $\\mathcal{LLM}$}\n"," \\IF{$\\forall c \\in C,$ $N_{i}^{(l)} \\in \\tilde{\\mathcal{N}}(c)$}\n"," \\STATE $\\tilde{\\mathcal{N}} \\gets \\tilde{\\mathcal{N}} \\cup N_{i}^{(l)}$\n"," \\ENDIF\n","\\ENDFOR\n","\\STATE \\texttt{// Prune irrelevant neurons to obtain expert model.}\n","\\STATE $\\mathcal{LLM}_{\\text{Exp}} = \\mathcal{LLM} \\ominus \\tilde{\\mathcal{N}}$\n","\\ENSURE $\\mathcal{LLM}_{\\text{Exp}}$\n","\\end{algorithmic}\n","\\label{algo:adaptive_pruning}\n","\\end{algorithm}\n","\\end{minipage}\n","}\n","\\end{figure}\n"],["\\begin{figure}[t]\n"," \\centering\n"," \\includegraphics[width=0.48\\textwidth]{Figures/three-dim-examples.pdf}\n"," \\caption{Concrete examples of applying \\texttt{Cus-Prun} to prune $25\\%$ of Llama3-8B-Base's parameters into three-dimensional expert models. Numbers above each box indicate performance on the \\textbf{whole} test set, with the first evaluated by Rouge-L, and the other two by accuracy. }\n"," \\label{fig:korean}\n","\\end{figure}\n"],["\\begin{table*}[t]\n","\\caption{Main Results of \\texttt{Cus-Prun} on multilingual setting with a pruning ratio of $25\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in summarization tasks and in accuracy (\\%) for other datasets.}\n"," \\centering\n","\\footnotesize\n","\\setlength{\\tabcolsep}{0.8pt}\n"," \\scalebox{0.9}{\n"," \\begin{tabular}{c|l|cccc|ccccc|ccccc|cccc}\n","\\toprule\n","\\quad\\quad & \\multirow{2}{*}{\\textbf{\\normalsize{Method}}} & \n"," \\multicolumn{4}{c}{\\textbf{\\normalsize{General Capability}}} \\vline & \\multicolumn{5}{c}{\\textbf{\\normalsize{Multilingual Expert}}} \\vline & \\multicolumn{5}{c}{\\textbf{\\normalsize{Multidomain Expert }}} \\vline & \\multicolumn{4}{c}{\\textbf{\\normalsize{Multitask Expert }}} \\\\ \n"," & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3 & XQuAD & Sum & Avg. & MMCQ & FTQA & TSA & AMSA & Avg. & MSum & ASum & AMCF & Avg. \\\\ \\midrule\n","\\rowcolor{lightgray}\\cellcolor{white}\\multirow{5}{*}{\\rotatebox{90}{\\textbf{Llama3-8B}}} & Dense & 70.7 & 58.3 & 63.1 & 64.1 &41.2 &49.1 & 63.4 &32.9 &46.7 & 51.8 & 23.9 & 67.1 & 95.9 & 59.8 & 76.6 & 16.2 & 78.2 & 57.0 \\\\\n"," & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 1.1 & 24.0 & 13.6 & 23.2 & 15.5 & 0.0 & 0.0 & \\textbf{61.8} & 76.0 & 34.5 & 62.2 & \\textbf{21.8} & \\textbf{80.0} & \\textbf{54.7} \\\\\n"," & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & 14.9 & 16.6 & 8.5 & 10.0 & 22.6 & 0.0 & 41.2 & 53.7 & 29.4 & 7.3 & 2.9 & 51.3 & 20.5 \\\\\n"," & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & 26.9 & 0.0 & 2.7 & 7.4 & 3.2 & 0.0 & 38.6 & 35.7 & 19.4 & 4.1 & 4.8 & 43.8 & 17.6 \\\\\n"," &\\cellcolor{lightblue}\\texttt{Cus-Prun} &\\cellcolor{lightblue}\\textbf{62.4} & \\cellcolor{lightblue}\\textbf{37.0} & \\cellcolor{lightblue}\\textbf{54.7} & \\cellcolor{lightblue}\\textbf{51.4} & \\cellcolor{lightblue}\\textbf{30.1} & \\cellcolor{lightblue}\\textbf{41.5} & \\cellcolor{lightblue}\\textbf{52.6} & \\cellcolor{lightblue}\\textbf{31.5} & \\cellcolor{lightblue}\\textbf{38.9} & \\cellcolor{lightblue}\\textbf{42.9} & \\cellcolor{lightblue}\\textbf{20.6} & \\cellcolor{lightblue}\\textbf{61.8} & \\cellcolor{lightblue}\\textbf{87.6} & \\cellcolor{lightblue}\\textbf{53.2} & \\cellcolor{lightblue}\\textbf{68.4} & \\cellcolor{lightblue}12.8 & \\cellcolor{lightblue}75.5 & \\cellcolor{lightblue}52.2 \\\\ \\midrule\n","\\rowcolor{lightgray}\\cellcolor{white}\\multirow{5}{*}{\\rotatebox{90}{\\textbf{Mistral-12B}}} & Dense & 82.6 & 68.5 & 50.4 & 67.2 & 51.7 & 43.8 & 49.2 & 25.4 & 42.5 & 54.6 & 26.6 & 69.4 & 92.4 & 60.8 & 88.7 & 3.0 & 78.6 & 56.4 \\\\\n"," & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 2.1 & 27.8 & 19.0 & \\textbf{23.2} & 18.0 & 0.0 & 0.0 & 51.0 & 20.9 & 18.0 & 59.3 & 0.5 & 2.8 & 20.9 \\\\\n"," & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 0.8 & 25.1 & 17.4 & 7.8 & 12.8 & 24.9 & 9.2 & 34.2 & 54.3 & 30.7 & 27.4 & 1.3 & 36.3 & 21.7 \\\\\n"," & ShortGPT & 37.8 & 0.0 & 33.9 & 23.9 & 2.9 & 27.0 & 18.0 & 5.0 & 13.2 & 31.4 & 7.2 & 39.2 & 52.5 & 32.6 & 26.2 & 0.2 & 42.7 & 23.0 \\\\\n"," & \\cellcolor{lightblue}\\texttt{Cus-Prun} & \\cellcolor{lightblue}\\textbf{67.5} & \\cellcolor{lightblue}\\textbf{43.4} & \\cellcolor{lightblue}\\textbf{43.8} & \\cellcolor{lightblue}\\textbf{51.6} & \\cellcolor{lightblue}\\textbf{34.3} & \\cellcolor{lightblue}\\textbf{39.2} & \\cellcolor{lightblue}\\textbf{40.7} & \\cellcolor{lightblue}23.1 & \\cellcolor{lightblue}\\textbf{34.3} & \\cellcolor{lightblue}\\textbf{47.9} & \\cellcolor{lightblue}\\textbf{25.1} & \\cellcolor{lightblue}\\textbf{67.3} & \\cellcolor{lightblue}\\textbf{83.7} & \\cellcolor{lightblue}\\textbf{56.0} & \\cellcolor{lightblue}\\textbf{83.5} & \\cellcolor{lightblue}\\textbf{3.4} & \\cellcolor{lightblue}\\textbf{72.8} & \\cellcolor{lightblue}\\textbf{50.9} \\\\ \\midrule\n","\\rowcolor{lightgray}\\cellcolor{white}\\multirow{5}{*}{\\rotatebox{90}{\\textbf{Llama2-13B}}} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 17.5 & 30.4 & 44.1 & 24.9 & 29.2 & 25.2 & 0.0 & 42.7 & 84.1 & 38.0 & 70.0 & 7.4 & 44.3 & 40.6 \\\\\n"," & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 1.1 & 22.8 & 3.8 & 17.7 & 11.3 & 0.0 & 0.0 & 9.7 & 0.0 & 2.4 & 21.6 & 4.8 & 0.0 & 8.8 \\\\\n"," & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 2.8 & 25.3 & 23.4 & 9.9 & 15.5 & 18.7 & 0.0 & 28.4 & 67.3 & 28.6 & 24.5 & 4.9 & 32.9 & 20.8 \\\\\n"," & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 2.4 & 23.0 & 24.7 & 11.3 & 15.3 & 16.9 & 0.0 & 34.6 & \\textbf{69.8} & 30.3 & 23.8 & 5.2 & 39.1 & 22.7 \\\\\n"," & \\cellcolor{lightblue}\\texttt{Cus-Prun} & \\cellcolor{lightblue}\\textbf{48.3} & \\cellcolor{lightblue}\\textbf{20.8} & \\cellcolor{lightblue}\\textbf{50.0} & \\cellcolor{lightblue}\\textbf{39.7} & \\cellcolor{lightblue}\\textbf{12.7} & \\cellcolor{lightblue}\\textbf{26.2} & \\cellcolor{lightblue}\\textbf{34.2} & \\cellcolor{lightblue}\\textbf{24.1} & \\cellcolor{lightblue}\\textbf{24.3} & \\cellcolor{lightblue}\\textbf{25.6} & \\cellcolor{lightblue}0.0 & \\cellcolor{lightblue}\\textbf{38.5} & \\cellcolor{lightblue}68.3 & \\cellcolor{lightblue}\\textbf{33.1} & \\cellcolor{lightblue}\\textbf{64.5} & \\cellcolor{lightblue}\\textbf{6.7} & \\cellcolor{lightblue}\\textbf{42.9} & \\cellcolor{lightblue}\\textbf{38.0} \\\\ \\midrule\n","\\rowcolor{lightgray}\\cellcolor{white}\\multirow{5}{*}{\\rotatebox{90}{\\textbf{Llama3-70B}}} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 69.5 & 71.1 & 69.1 & 36.6 & 61.6 & 72.1 & 55.3 & 83.6 & 96.2 & 76.8 & 84.2 & 17.3 & 81.8 & 61.1 \\\\\n"," & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 16.8 & 43.7 & 43.0 & 29.0 & 33.1 & 27.3 & 1.0 & 51.0 & 50.3 & 32.4 & 10.2 & 13.7 & 20.6 & 14.8 \\\\\n"," & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 3.7 & 44.8 & 33.0 & 21.2 & 25.7 & 57.6 & 27.6 & 68.1 & 59.4 & 53.2 & 58.0 & 14.2 & 68.3 & 46.8 \\\\\n"," & ShortGPT & 59.4 & 5.6 & \\textbf{75.5} & 46.8 & 11.9 & 43.1 & 38.8 & 24.0 & 29.5 & 58.4 & 32.2 & 67.5 & 64.9 & 55.8 & 59.6 & 13.9 & 65.8 & 46.4 \\\\\n"," & \\cellcolor{lightblue}\\texttt{Cus-Prun} & \\cellcolor{lightblue}\\textbf{68.4} & \\cellcolor{lightblue}\\textbf{53.2} & \\cellcolor{lightblue}66.6 & \\cellcolor{lightblue}\\textbf{62.7} & \\cellcolor{lightblue}\\textbf{43.1} & \\cellcolor{lightblue}\\textbf{57.7} & \\cellcolor{lightblue}\\textbf{59.8} & \\cellcolor{lightblue}\\textbf{34.3} & \\cellcolor{lightblue}\\textbf{48.7} & \\cellcolor{lightblue}\\textbf{68.2} & \\cellcolor{lightblue}\\textbf{43.9} & \\cellcolor{lightblue}\\textbf{81.4} & \\cellcolor{lightblue}\\textbf{87.8} & \\cellcolor{lightblue}\\textbf{70.3} & \\cellcolor{lightblue}\\textbf{80.4} & \\cellcolor{lightblue}\\textbf{15.7} & \\cellcolor{lightblue}\\textbf{77.5} & \\cellcolor{lightblue}\\textbf{57.9} \\\\ \n","\\bottomrule\n","\\end{tabular}}\n"," \\label{table:cross_ling}\n","\\vspace{-0.2cm}\n","\\end{table*}\n"],["\\begin{figure*}[t]\n"," \\centering\n"," \\begin{minipage}{0.4\\textwidth}\n"," \\centering\n"," \\captionof{table}{Performance of Chinese-Medical expert model on MCQ task}\n"," \\label{tab:cmexam}\n"," \\scalebox{0.96}{\n"," \\begin{tabular}{l|c|c}\n"," \\hline\n"," \\textbf{Method} & \\textbf{General} & \\textbf{CMExam} \\\\\\midrule\n"," \\rowcolor{lightgray}\\cellcolor{white} Dense & 59.3 & 50.6 \\\\\n"," LLM-Pruner & 18.6 & 25.0 \\\\\n"," SliceGPT & 27.8 & 26.9 \\\\\n"," ShortGPT & 23.9 & 23.7 \\\\\n"," \\rowcolor{lightblue}\\cellcolor{white} \\texttt{Cus-Prun} & \\textbf{52.4} & \\textbf{48.7} \\\\\n"," \\hline\n"," \\end{tabular}}\n"," \\end{minipage}\n"," \\hfill\n"," \\begin{minipage}{0.58\\textwidth}\n"," \\centering\n"," \\includegraphics[width=\\textwidth]{Figures/huatuo.pdf}\n"," \\caption{Chinese Medical LLM performance. Numbers are quality on the \\textbf{whole} test set evaluated by GPT4.}\n"," \\label{fig:huatuo}\n"," \\end{minipage}\n","\\end{figure*}\n"],["\\begin{table}[ht]\n","\\caption{Aggressive pruning ratio on Llama3-8B.}\n","\\centering\n","\\setlength{\\tabcolsep}{3pt}\n"," \\scalebox{0.9}{\n","\\begin{tabular}{l|c|c|c|c}\n"," \\hline\n"," \\textbf{Method} & \\textbf{Ratio} & \\textbf{Speedup} & \\textbf{MMLU} & \\textbf{Expert} \\\\\\midrule\n"," \\rowcolor{lightgray}\\cellcolor{white}Dense & 0.0 & 1$\\times$ & 63.1 & 59.7 \\\\\\midrule\n"," ShortGPT & 25.0 & 1.3$\\times$ & 28.6 & 24.6 \\\\\n"," \\rowcolor{lightblue}\\cellcolor{white}\\texttt{Cus-Prun} & 25.0 & 1.3$\\times$ & \\textbf{51.9} & \\textbf{53.3} \\\\\\midrule\n"," ShortGPT & 34.2 & 1.5$\\times$ & 20.8 & 18.5 \\\\\n"," \\rowcolor{lightblue}\\cellcolor{white}\\texttt{Cus-Prun} & 35.0 & 1.5$\\times$ & \\textbf{50.2} & \\textbf{51.4} \\\\\\midrule\n"," ShortGPT & 43.8 & 1.8$\\times$ & 7.9 & 10.2 \\\\\n"," \\rowcolor{lightblue}\\cellcolor{white}\\texttt{Cus-Prun} & 45.0 & 1.8$\\times$ & \\textbf{48.4} & \\textbf{50.6} \\\\\n"," \\hline\n"," \\end{tabular}}\n"," \\label{table:prune_more}\n","\\end{table}\n"],["\\begin{figure*}[t]\n"," \\centering\n"," \\includegraphics[width=1.0\\textwidth]{Figures/lang-specific.pdf}\n"," \\caption{Performance of \\texttt{Cus-Prun} in obtaining language-specific models.}\n"," \\label{fig:lang-spec}\n","\\end{figure*}\n"],["\\begin{table}[ht]\n"," \\centering\n","\\footnotesize\n"," \\scalebox{1}{\n"," \\begin{tabular}{lp{5.3cm}}\n"," \\toprule\n"," \\textbf{Task} & \\textbf{Prompt} \\\\\\midrule\n"," Generation & Generate a text document in \\{language\\}/\\{domain\\}/\\{task\\}. Make sure the documents is not fixed to one \\{language\\}/\\{domain\\}/\\{task\\} or \\{language\\}/\\{domain\\}/\\{task\\}. Ensure the content is clear, concise, and appropriate for the specified request. Use professional and domain-specific terminology where necessary. \\\\\\midrule\n"," Evaluation & Evaluate the quality of the given answer to the question. Provide a score from 0 to 5, where 0 represents very low quality and 5 represents very high quality. Question: \\{question\\} Answer: \\{answer\\}.\\\\\n"," \\bottomrule\n"," \\end{tabular}}\n","\\caption{GPT-4o prompts for generating documents and evaluating answer quality.} \\label{table:testset}\n","\\end{table}\n"],["\\begin{table*}[hbpt]\n","\\caption{Main Results of \\texttt{Cus-Prun} on Germany with a pruning ratio of $25\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in XLSum and in accuracy (\\%) for other datasets.}\n"," \\centering\n","\\footnotesize\n","\\setlength{\\tabcolsep}{3pt}\n"," \\scalebox{1.0}{\n"," \\begin{tabular}{l|l|cccc|ccccc}\n","\\toprule\n","\\multirow{2}{*}{\\textbf{\\normalsize{Model}}} & \\multirow{2}{*}{\\textbf{\\normalsize{Method}}} & \n"," \\multicolumn{4}{c}{\\textbf{\\normalsize{General Capability}}} \\vline & \\multicolumn{5}{c}{\\textbf{\\normalsize{Expert Capability}}} \\\\ \n"," & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3Exam & XQuAD & XLSum & Avg. \\\\ \\midrule\n","\\multirow{4}{*}{Llama3-8B} &\n"," Dense & 70.7 & 58.3 & 63.1 & 64.1 & 44.8 & - & 52.9 & - & 48.8 \\\\\n"," & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 0.0 & - & 11.0 & - & 5.5 \\\\\n"," & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & - & 9.8 & - & 4.9 \\\\\n"," & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & - & 0.0 & - & 0.0 \\\\\n"," & \\texttt{Cus-Prun} & 61.4 & 38.9 & 54.5 & \\textbf{51.6} & 32.8 & - & 49.6 & - & \\textbf{41.2} \\\\ \\midrule\n","\\multirow{4}{*}{Mistral-12B} & Dense & 82.6 & 68.5 & 50.4 & 59.3 & 56.8 & - & 41.2 & - & 49.0 \\\\\n"," & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 2.4 & - & 13.4 & - & 7.9 \\\\\n"," & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 0.8 & - & 15.5 & - & 8.2 \\\\\n"," & ShortGPT & 37.8 & 0.0 & 33.9 & 23.9 & 3.6 & - & 20.3 & - & 12.0 \\\\\n"," & \\texttt{Cus-Prun} & 64.6 & 39.7 & 43.2 & \\textbf{49.2} & 31.6 & - & 35.9 & - & \\textbf{33.8} \\\\ \\midrule\n","\\multirow{4}{*}{Llama2-13B} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 24.4 & - & 40.3 & - & 32.3 \\\\\n"," & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 2.0 & - & 5.7 & - & 3.9 \\\\\n"," & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 3.6 & - & 18.1 & - & 10.9 \\\\\n"," & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 2.8 & - & 27.2 & - & 15.0 \\\\\n"," & \\texttt{Cus-Prun} & 47.6 & 19.8 & 49.9 & \\textbf{39.1} & 18.4 & - & 31.7 & - & \\textbf{25.0} \\\\ \\midrule\n","\\multirow{4}{*}{Llama3-70B} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 74.8 & - & 58.2 & - & 66.5 \\\\\n"," & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 18.0 & - & 27.3 & - & 22.7 \\\\\n"," & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 0.0 & - & 17.3 & - & 8.7 \\\\\n"," & ShortGPT & 59.4 & 5.6 & 75.5 & 46.8 & 9.6 & - & 31.5 & - & 20.6 \\\\\n"," & \\texttt{Cus-Prun} & 66.8 & 59.3 & 69.1 & \\textbf{65.1} & 48.2 & - & 53.9 & - & \\textbf{51.1} \\\\ \n","\\bottomrule\n","\\end{tabular}}\n"," \\label{table:cl_ger}\n","\\end{table*}\n"],["\\begin{table*}[hbpt]\n","\\caption{Main Results of \\texttt{Cus-Prun} on Chinese with a pruning ratio of $25\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in XLSum and in accuracy (\\%) for other datasets.}\n"," \\centering\n","\\footnotesize\n","\\setlength{\\tabcolsep}{3pt}\n"," \\scalebox{1.0}{\n"," \\begin{tabular}{l|l|cccc|ccccc}\n","\\toprule\n","\\multirow{2}{*}{\\textbf{\\normalsize{Model}}} & \\multirow{2}{*}{\\textbf{\\normalsize{Method}}} & \n"," \\multicolumn{4}{c}{\\textbf{\\normalsize{General Capability}}} \\vline & \\multicolumn{5}{c}{\\textbf{\\normalsize{Specific Capability}}} \\\\ \n"," & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3Exam & XQuAD & XLSum & Avg. \\\\ \\midrule\n","\\multirow{4}{*}{Llama3-8B} &\n"," Dense & 70.7 & 58.3 & 63.1 & 64.1 & 43.6 & 55.1 & 78.7 & 49.1 & 56.6 \\\\\n"," & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 2.4 & 23.6 & 21.3 & 32.8 & 20.0 \\\\\n"," & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & 17.4 & 23.5 & 8.3 & 12.3 \\\\\n"," & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & 28.3 & 0.0 & 3.1 & 7.9 \\\\\n"," & \\texttt{Cus-Prun} & 60.5 & 25.7 & 49.4 & \\textbf{45.2} & 36.0 & 44.7 & 65.6 & 46.3 & \\textbf{48.2} \\\\ \\midrule\n","\\multirow{4}{*}{Mistral-12B} & Dense & 82.6 & 68.5 & 50.4 & 59.3 & 53.2 & 47.8 & 62.2 & 33.0 & 49.1 \\\\\n"," & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 2.8 & 30.7 & 31.8 & 32.6 & 24.5 \\\\\n"," & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 1.6 & 26.4 & 28.3 & 10.8 & 16.8 \\\\\n"," & ShortGPT & 37.8 & 0.0 & 33.9 & 23.9 & 4.4 & 28.2 & 29.1 & 7.2 & 17.2 \\\\\n"," & \\texttt{Cus-Prun} & 68.3 & 43.2 & 39.5 & \\textbf{50.3} & 38.4 & 40.7 & 50.6 & 30.3 & \\textbf{40.0} \\\\ \\midrule\n","\\multirow{4}{*}{Llama2-13B} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 21.6 & 36.5 & 59.8 & 35.3 & 38.3 \\\\\n"," & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 1.2 & 23.3 & 3.8 & 25.1 & 13.4\\\\\n"," & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 4.8 & 24.5 & 28.4 & 11.2 & 17.2 \\\\\n"," & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 4.4 & 22.9 & 24.6 & 13.7 & 16.4 \\\\\n"," & \\texttt{Cus-Prun} & 48.6 & 20.7 & 51.9 & \\textbf{40.4} & 14.8 & 28.2 & 47.3 & 34.4 & \\textbf{31.2} \\\\ \\midrule\n","\\multirow{4}{*}{Llama3-70B} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 68.4 & 76.1 & 81.3 & 55.3 & 70.3 \\\\\n"," & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 16.8 & 47.5 & 56.1 & 41.3 & 40.4 \\\\\n"," & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 6.4 & 48.3 & 42.2 & 29.3 & 31.6 \\\\\n"," & ShortGPT & 59.4 & 5.6 & 75.5 & 46.8 & 12.4 & 45.5 & 44.6 & 36.1 & 34.7 \\\\\n"," & \\texttt{Cus-Prun} & 72.3 & 48.5 & 65.2 & \\textbf{62.0} & 40.8 & 61.7 & 66.9 & 51.6 & \\textbf{55.3} \\\\ \n","\\bottomrule\n","\\end{tabular}}\n"," \\label{table:cl_chi}\n","\\end{table*}\n"],["\\begin{table*}[hbpt]\n","\\caption{Main Results of \\texttt{Cus-Prun} on Thai with a pruning ratio of $25\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in XLSum and in accuracy (\\%) for other datasets.}\n"," \\centering\n","\\footnotesize\n","\\setlength{\\tabcolsep}{3pt}\n"," \\scalebox{1.0}{\n"," \\begin{tabular}{l|l|cccc|ccccc}\n","\\toprule\n","\\multirow{2}{*}{\\textbf{\\normalsize{Model}}} & \\multirow{2}{*}{\\textbf{\\normalsize{Method}}} & \n"," \\multicolumn{4}{c}{\\textbf{\\normalsize{General Capability}}} \\vline & \\multicolumn{5}{c}{\\textbf{\\normalsize{Specific Capability}}} \\\\ \n"," & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3Exam & XQuAD & XLSum & Avg. \\\\ \\midrule\n","\\multirow{4}{*}{Llama3-8B} &\n"," Dense & 70.7 & 58.3 & 63.1 & 64.1 & 35.2 & 43.0 & 58.7 & 16.7 & 38.4 \\\\\n"," & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 0.8 & 24.4 & 8.4 & 13.5 & 11.8 \\\\\n"," & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & 12.3 & 16.6 & 8.7 & 9.4 \\\\\n"," & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & 25.4 & 0.0 & 2.3 & 6.9 \\\\\n"," & \\texttt{Cus-Prun} & 58.9 & 31.2 & 52.4 & \\textbf{47.5} & 21.6 & 38.3 & 42.6 & 16.8 & \\textbf{29.8} \\\\ \\midrule\n","\\multirow{4}{*}{Mistral-12B} & Dense & 82.6 & 68.5 & 50.4 & 59.3 & 45.2 & 39.9 & 44.1 & 17.8 & 36.8 \\\\\n"," & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 1.2 & 24.8 & 11.9 & 13.7 & 12.9 \\\\\n"," & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 0.0 & 23.8 & 8.4 & 4.7 & 12.3 \\\\\n"," & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 0.8 & 25.7 & 4.7 & 2.8 & 8.5 \\\\\n"," & \\texttt{Cus-Prun} & 68.2 & 35.8 & 47.6 & \\textbf{50.5} & 32.8 & 37.7 & 35.6 & 15.9 & \\textbf{30.5} \\\\ \\midrule\n","\\multirow{4}{*}{Llama2-13B} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 6.4 & 24.3 & 28.3 & 14.5 & 18.4 \\\\\n"," & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 0.0 & 22.3 & 1.8 & 10.2 & 8.6 \\\\\n"," & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 0.0 & 26.2 & 23.7 & 8.6 & 14.6 \\\\\n"," & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 0.0 & 23.1 & 22.3 & 8.9 & 13.6 \\\\\n"," & \\texttt{Cus-Prun} & 47.8 & 20.9 & 50.7 & \\textbf{39.8} & 4.8 & 24.2 & 23.6 & 13.8 & \\textbf{16.6} \\\\ \\midrule\n","\\multirow{4}{*}{Llama3-70B} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 65.2 & 66.1 & 67.8 & 17.8 & 54.2 \\\\\n"," & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 15.6 & 39.9 & 29.8 & 16.6 & 25.5 \\\\\n"," & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 4.8 & 41.3 & 39.6 & 13.2 & 24.7 \\\\\n"," & ShortGPT & 59.4 & 5.6 & 75.5 & 46.8 & 13.7 & 40.7 & 40.4 & 11.9 & 26.7 \\\\\n"," & \\texttt{Cus-Prun} & 73.3 & 58.7 & 68.4 & \\textbf{66.8} & 40.4 & 53.6 & 58.5 & 16.9 & \\textbf{42.4} \\\\ \n","\\bottomrule\n","\\end{tabular}}\n"," \\label{table:cl_tha}\n","\\end{table*}\n"]],"string":"[\n [\n \"\\\\begin{figure}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=0.48\\\\textwidth]{Figures/cust-prune.pdf}\\n\",\n \" \\\\caption{Given a requirement for an expert model across three dimensions (language, domain, and task), \\\\texttt{Cus-Prun} (i) identifies irrelevant neurons for each dimension using corresponding corpora (left), and (ii) prunes \\\\textbf{overlapping} irrelevant neurons across dimensions to obtain the expert model (right).}\\n\",\n \" \\\\label{fig:cus-prun}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{figure}[t]\\n\",\n \"\\\\scalebox{0.95}{ \\n\",\n \"\\\\begin{minipage}{\\\\linewidth}\\n\",\n \"\\\\begin{algorithm}[H]\\n\",\n \"\\\\renewcommand{\\\\algorithmicrequire}{\\\\textbf{Input:}}\\n\",\n \"\\\\renewcommand{\\\\algorithmicensure}{\\\\textbf{Output:}}\\n\",\n \"\\\\caption{Adaptive Custom Pruning}\\n\",\n \"\\\\begin{algorithmic}[1]\\n\",\n \"\\\\REQUIRE Original language model $\\\\mathcal{LLM}$, request for expert model $\\\\mathcal{LLM}_{\\\\text{Exp}}$ with selected dimensions: $L_{\\\\text{Exp}}$, $D_{\\\\text{Exp}}$, $T_{\\\\text{Exp}}$ (any subset), request for pruning ratio $\\\\sigma$.\\n\",\n \"\\\\STATE \\\\texttt{// Construct specific corpora for each selected dimension.}\\n\",\n \"\\\\STATE $C = \\\\{\\\\}$\\n\",\n \"\\\\IF{$L_{\\\\text{Exp}}$ is specified}\\n\",\n \" \\\\STATE $C = C \\\\cup \\\\{(L_{\\\\text{Exp}}, D, T) \\\\mid D \\\\in \\\\mathbb{D}, T \\\\in \\\\mathbb{T}\\\\}$\\n\",\n \"\\\\ENDIF\\n\",\n \"\\\\IF{$D_{\\\\text{Exp}}$ is specified}\\n\",\n \" \\\\STATE $C = C \\\\cup \\\\{(L, D_{\\\\text{Exp}}, T) \\\\mid L \\\\in \\\\mathbb{L}, T \\\\in \\\\mathbb{T}\\\\}$\\n\",\n \"\\\\ENDIF\\n\",\n \"\\\\IF{$T_{\\\\text{Exp}}$ is specified}\\n\",\n \" \\\\STATE $C = C \\\\cup \\\\{(L, D, T_{\\\\text{Exp}}) \\\\mid L \\\\in \\\\mathbb{L}, D \\\\in \\\\mathbb{D}\\\\}$\\n\",\n \"\\\\ENDIF\\n\",\n \"\\\\STATE \\\\texttt{// Identify irrelevant neurons for each selected dimension.}\\n\",\n \"\\\\FORALL{neuron $N_{i}^{(l)}$ in $\\\\mathcal{LLM}$}\\n\",\n \" \\\\IF{$\\\\forall c \\\\in C,$ $N_{i}^{(l)} \\\\in \\\\tilde{\\\\mathcal{N}}(c)$}\\n\",\n \" \\\\STATE $\\\\tilde{\\\\mathcal{N}} \\\\gets \\\\tilde{\\\\mathcal{N}} \\\\cup N_{i}^{(l)}$\\n\",\n \" \\\\ENDIF\\n\",\n \"\\\\ENDFOR\\n\",\n \"\\\\STATE \\\\texttt{// Prune irrelevant neurons to obtain expert model.}\\n\",\n \"\\\\STATE $\\\\mathcal{LLM}_{\\\\text{Exp}} = \\\\mathcal{LLM} \\\\ominus \\\\tilde{\\\\mathcal{N}}$\\n\",\n \"\\\\ENSURE $\\\\mathcal{LLM}_{\\\\text{Exp}}$\\n\",\n \"\\\\end{algorithmic}\\n\",\n \"\\\\label{algo:adaptive_pruning}\\n\",\n \"\\\\end{algorithm}\\n\",\n \"\\\\end{minipage}\\n\",\n \"}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{figure}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=0.48\\\\textwidth]{Figures/three-dim-examples.pdf}\\n\",\n \" \\\\caption{Concrete examples of applying \\\\texttt{Cus-Prun} to prune $25\\\\%$ of Llama3-8B-Base's parameters into three-dimensional expert models. Numbers above each box indicate performance on the \\\\textbf{whole} test set, with the first evaluated by Rouge-L, and the other two by accuracy. }\\n\",\n \" \\\\label{fig:korean}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table*}[t]\\n\",\n \"\\\\caption{Main Results of \\\\texttt{Cus-Prun} on multilingual setting with a pruning ratio of $25\\\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in summarization tasks and in accuracy (\\\\%) for other datasets.}\\n\",\n \" \\\\centering\\n\",\n \"\\\\footnotesize\\n\",\n \"\\\\setlength{\\\\tabcolsep}{0.8pt}\\n\",\n \" \\\\scalebox{0.9}{\\n\",\n \" \\\\begin{tabular}{c|l|cccc|ccccc|ccccc|cccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\quad\\\\quad & \\\\multirow{2}{*}{\\\\textbf{\\\\normalsize{Method}}} & \\n\",\n \" \\\\multicolumn{4}{c}{\\\\textbf{\\\\normalsize{General Capability}}} \\\\vline & \\\\multicolumn{5}{c}{\\\\textbf{\\\\normalsize{Multilingual Expert}}} \\\\vline & \\\\multicolumn{5}{c}{\\\\textbf{\\\\normalsize{Multidomain Expert }}} \\\\vline & \\\\multicolumn{4}{c}{\\\\textbf{\\\\normalsize{Multitask Expert }}} \\\\\\\\ \\n\",\n \" & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3 & XQuAD & Sum & Avg. & MMCQ & FTQA & TSA & AMSA & Avg. & MSum & ASum & AMCF & Avg. \\\\\\\\ \\\\midrule\\n\",\n \"\\\\rowcolor{lightgray}\\\\cellcolor{white}\\\\multirow{5}{*}{\\\\rotatebox{90}{\\\\textbf{Llama3-8B}}} & Dense & 70.7 & 58.3 & 63.1 & 64.1 &41.2 &49.1 & 63.4 &32.9 &46.7 & 51.8 & 23.9 & 67.1 & 95.9 & 59.8 & 76.6 & 16.2 & 78.2 & 57.0 \\\\\\\\\\n\",\n \" & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 1.1 & 24.0 & 13.6 & 23.2 & 15.5 & 0.0 & 0.0 & \\\\textbf{61.8} & 76.0 & 34.5 & 62.2 & \\\\textbf{21.8} & \\\\textbf{80.0} & \\\\textbf{54.7} \\\\\\\\\\n\",\n \" & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & 14.9 & 16.6 & 8.5 & 10.0 & 22.6 & 0.0 & 41.2 & 53.7 & 29.4 & 7.3 & 2.9 & 51.3 & 20.5 \\\\\\\\\\n\",\n \" & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & 26.9 & 0.0 & 2.7 & 7.4 & 3.2 & 0.0 & 38.6 & 35.7 & 19.4 & 4.1 & 4.8 & 43.8 & 17.6 \\\\\\\\\\n\",\n \" &\\\\cellcolor{lightblue}\\\\texttt{Cus-Prun} &\\\\cellcolor{lightblue}\\\\textbf{62.4} & \\\\cellcolor{lightblue}\\\\textbf{37.0} & \\\\cellcolor{lightblue}\\\\textbf{54.7} & \\\\cellcolor{lightblue}\\\\textbf{51.4} & \\\\cellcolor{lightblue}\\\\textbf{30.1} & \\\\cellcolor{lightblue}\\\\textbf{41.5} & \\\\cellcolor{lightblue}\\\\textbf{52.6} & \\\\cellcolor{lightblue}\\\\textbf{31.5} & \\\\cellcolor{lightblue}\\\\textbf{38.9} & \\\\cellcolor{lightblue}\\\\textbf{42.9} & \\\\cellcolor{lightblue}\\\\textbf{20.6} & \\\\cellcolor{lightblue}\\\\textbf{61.8} & \\\\cellcolor{lightblue}\\\\textbf{87.6} & \\\\cellcolor{lightblue}\\\\textbf{53.2} & \\\\cellcolor{lightblue}\\\\textbf{68.4} & \\\\cellcolor{lightblue}12.8 & \\\\cellcolor{lightblue}75.5 & \\\\cellcolor{lightblue}52.2 \\\\\\\\ \\\\midrule\\n\",\n \"\\\\rowcolor{lightgray}\\\\cellcolor{white}\\\\multirow{5}{*}{\\\\rotatebox{90}{\\\\textbf{Mistral-12B}}} & Dense & 82.6 & 68.5 & 50.4 & 67.2 & 51.7 & 43.8 & 49.2 & 25.4 & 42.5 & 54.6 & 26.6 & 69.4 & 92.4 & 60.8 & 88.7 & 3.0 & 78.6 & 56.4 \\\\\\\\\\n\",\n \" & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 2.1 & 27.8 & 19.0 & \\\\textbf{23.2} & 18.0 & 0.0 & 0.0 & 51.0 & 20.9 & 18.0 & 59.3 & 0.5 & 2.8 & 20.9 \\\\\\\\\\n\",\n \" & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 0.8 & 25.1 & 17.4 & 7.8 & 12.8 & 24.9 & 9.2 & 34.2 & 54.3 & 30.7 & 27.4 & 1.3 & 36.3 & 21.7 \\\\\\\\\\n\",\n \" & ShortGPT & 37.8 & 0.0 & 33.9 & 23.9 & 2.9 & 27.0 & 18.0 & 5.0 & 13.2 & 31.4 & 7.2 & 39.2 & 52.5 & 32.6 & 26.2 & 0.2 & 42.7 & 23.0 \\\\\\\\\\n\",\n \" & \\\\cellcolor{lightblue}\\\\texttt{Cus-Prun} & \\\\cellcolor{lightblue}\\\\textbf{67.5} & \\\\cellcolor{lightblue}\\\\textbf{43.4} & \\\\cellcolor{lightblue}\\\\textbf{43.8} & \\\\cellcolor{lightblue}\\\\textbf{51.6} & \\\\cellcolor{lightblue}\\\\textbf{34.3} & \\\\cellcolor{lightblue}\\\\textbf{39.2} & \\\\cellcolor{lightblue}\\\\textbf{40.7} & \\\\cellcolor{lightblue}23.1 & \\\\cellcolor{lightblue}\\\\textbf{34.3} & \\\\cellcolor{lightblue}\\\\textbf{47.9} & \\\\cellcolor{lightblue}\\\\textbf{25.1} & \\\\cellcolor{lightblue}\\\\textbf{67.3} & \\\\cellcolor{lightblue}\\\\textbf{83.7} & \\\\cellcolor{lightblue}\\\\textbf{56.0} & \\\\cellcolor{lightblue}\\\\textbf{83.5} & \\\\cellcolor{lightblue}\\\\textbf{3.4} & \\\\cellcolor{lightblue}\\\\textbf{72.8} & \\\\cellcolor{lightblue}\\\\textbf{50.9} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\rowcolor{lightgray}\\\\cellcolor{white}\\\\multirow{5}{*}{\\\\rotatebox{90}{\\\\textbf{Llama2-13B}}} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 17.5 & 30.4 & 44.1 & 24.9 & 29.2 & 25.2 & 0.0 & 42.7 & 84.1 & 38.0 & 70.0 & 7.4 & 44.3 & 40.6 \\\\\\\\\\n\",\n \" & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 1.1 & 22.8 & 3.8 & 17.7 & 11.3 & 0.0 & 0.0 & 9.7 & 0.0 & 2.4 & 21.6 & 4.8 & 0.0 & 8.8 \\\\\\\\\\n\",\n \" & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 2.8 & 25.3 & 23.4 & 9.9 & 15.5 & 18.7 & 0.0 & 28.4 & 67.3 & 28.6 & 24.5 & 4.9 & 32.9 & 20.8 \\\\\\\\\\n\",\n \" & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 2.4 & 23.0 & 24.7 & 11.3 & 15.3 & 16.9 & 0.0 & 34.6 & \\\\textbf{69.8} & 30.3 & 23.8 & 5.2 & 39.1 & 22.7 \\\\\\\\\\n\",\n \" & \\\\cellcolor{lightblue}\\\\texttt{Cus-Prun} & \\\\cellcolor{lightblue}\\\\textbf{48.3} & \\\\cellcolor{lightblue}\\\\textbf{20.8} & \\\\cellcolor{lightblue}\\\\textbf{50.0} & \\\\cellcolor{lightblue}\\\\textbf{39.7} & \\\\cellcolor{lightblue}\\\\textbf{12.7} & \\\\cellcolor{lightblue}\\\\textbf{26.2} & \\\\cellcolor{lightblue}\\\\textbf{34.2} & \\\\cellcolor{lightblue}\\\\textbf{24.1} & \\\\cellcolor{lightblue}\\\\textbf{24.3} & \\\\cellcolor{lightblue}\\\\textbf{25.6} & \\\\cellcolor{lightblue}0.0 & \\\\cellcolor{lightblue}\\\\textbf{38.5} & \\\\cellcolor{lightblue}68.3 & \\\\cellcolor{lightblue}\\\\textbf{33.1} & \\\\cellcolor{lightblue}\\\\textbf{64.5} & \\\\cellcolor{lightblue}\\\\textbf{6.7} & \\\\cellcolor{lightblue}\\\\textbf{42.9} & \\\\cellcolor{lightblue}\\\\textbf{38.0} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\rowcolor{lightgray}\\\\cellcolor{white}\\\\multirow{5}{*}{\\\\rotatebox{90}{\\\\textbf{Llama3-70B}}} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 69.5 & 71.1 & 69.1 & 36.6 & 61.6 & 72.1 & 55.3 & 83.6 & 96.2 & 76.8 & 84.2 & 17.3 & 81.8 & 61.1 \\\\\\\\\\n\",\n \" & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 16.8 & 43.7 & 43.0 & 29.0 & 33.1 & 27.3 & 1.0 & 51.0 & 50.3 & 32.4 & 10.2 & 13.7 & 20.6 & 14.8 \\\\\\\\\\n\",\n \" & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 3.7 & 44.8 & 33.0 & 21.2 & 25.7 & 57.6 & 27.6 & 68.1 & 59.4 & 53.2 & 58.0 & 14.2 & 68.3 & 46.8 \\\\\\\\\\n\",\n \" & ShortGPT & 59.4 & 5.6 & \\\\textbf{75.5} & 46.8 & 11.9 & 43.1 & 38.8 & 24.0 & 29.5 & 58.4 & 32.2 & 67.5 & 64.9 & 55.8 & 59.6 & 13.9 & 65.8 & 46.4 \\\\\\\\\\n\",\n \" & \\\\cellcolor{lightblue}\\\\texttt{Cus-Prun} & \\\\cellcolor{lightblue}\\\\textbf{68.4} & \\\\cellcolor{lightblue}\\\\textbf{53.2} & \\\\cellcolor{lightblue}66.6 & \\\\cellcolor{lightblue}\\\\textbf{62.7} & \\\\cellcolor{lightblue}\\\\textbf{43.1} & \\\\cellcolor{lightblue}\\\\textbf{57.7} & \\\\cellcolor{lightblue}\\\\textbf{59.8} & \\\\cellcolor{lightblue}\\\\textbf{34.3} & \\\\cellcolor{lightblue}\\\\textbf{48.7} & \\\\cellcolor{lightblue}\\\\textbf{68.2} & \\\\cellcolor{lightblue}\\\\textbf{43.9} & \\\\cellcolor{lightblue}\\\\textbf{81.4} & \\\\cellcolor{lightblue}\\\\textbf{87.8} & \\\\cellcolor{lightblue}\\\\textbf{70.3} & \\\\cellcolor{lightblue}\\\\textbf{80.4} & \\\\cellcolor{lightblue}\\\\textbf{15.7} & \\\\cellcolor{lightblue}\\\\textbf{77.5} & \\\\cellcolor{lightblue}\\\\textbf{57.9} \\\\\\\\ \\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}}\\n\",\n \" \\\\label{table:cross_ling}\\n\",\n \"\\\\vspace{-0.2cm}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\begin{minipage}{0.4\\\\textwidth}\\n\",\n \" \\\\centering\\n\",\n \" \\\\captionof{table}{Performance of Chinese-Medical expert model on MCQ task}\\n\",\n \" \\\\label{tab:cmexam}\\n\",\n \" \\\\scalebox{0.96}{\\n\",\n \" \\\\begin{tabular}{l|c|c}\\n\",\n \" \\\\hline\\n\",\n \" \\\\textbf{Method} & \\\\textbf{General} & \\\\textbf{CMExam} \\\\\\\\\\\\midrule\\n\",\n \" \\\\rowcolor{lightgray}\\\\cellcolor{white} Dense & 59.3 & 50.6 \\\\\\\\\\n\",\n \" LLM-Pruner & 18.6 & 25.0 \\\\\\\\\\n\",\n \" SliceGPT & 27.8 & 26.9 \\\\\\\\\\n\",\n \" ShortGPT & 23.9 & 23.7 \\\\\\\\\\n\",\n \" \\\\rowcolor{lightblue}\\\\cellcolor{white} \\\\texttt{Cus-Prun} & \\\\textbf{52.4} & \\\\textbf{48.7} \\\\\\\\\\n\",\n \" \\\\hline\\n\",\n \" \\\\end{tabular}}\\n\",\n \" \\\\end{minipage}\\n\",\n \" \\\\hfill\\n\",\n \" \\\\begin{minipage}{0.58\\\\textwidth}\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=\\\\textwidth]{Figures/huatuo.pdf}\\n\",\n \" \\\\caption{Chinese Medical LLM performance. Numbers are quality on the \\\\textbf{whole} test set evaluated by GPT4.}\\n\",\n \" \\\\label{fig:huatuo}\\n\",\n \" \\\\end{minipage}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{table}[ht]\\n\",\n \"\\\\caption{Aggressive pruning ratio on Llama3-8B.}\\n\",\n \"\\\\centering\\n\",\n \"\\\\setlength{\\\\tabcolsep}{3pt}\\n\",\n \" \\\\scalebox{0.9}{\\n\",\n \"\\\\begin{tabular}{l|c|c|c|c}\\n\",\n \" \\\\hline\\n\",\n \" \\\\textbf{Method} & \\\\textbf{Ratio} & \\\\textbf{Speedup} & \\\\textbf{MMLU} & \\\\textbf{Expert} \\\\\\\\\\\\midrule\\n\",\n \" \\\\rowcolor{lightgray}\\\\cellcolor{white}Dense & 0.0 & 1$\\\\times$ & 63.1 & 59.7 \\\\\\\\\\\\midrule\\n\",\n \" ShortGPT & 25.0 & 1.3$\\\\times$ & 28.6 & 24.6 \\\\\\\\\\n\",\n \" \\\\rowcolor{lightblue}\\\\cellcolor{white}\\\\texttt{Cus-Prun} & 25.0 & 1.3$\\\\times$ & \\\\textbf{51.9} & \\\\textbf{53.3} \\\\\\\\\\\\midrule\\n\",\n \" ShortGPT & 34.2 & 1.5$\\\\times$ & 20.8 & 18.5 \\\\\\\\\\n\",\n \" \\\\rowcolor{lightblue}\\\\cellcolor{white}\\\\texttt{Cus-Prun} & 35.0 & 1.5$\\\\times$ & \\\\textbf{50.2} & \\\\textbf{51.4} \\\\\\\\\\\\midrule\\n\",\n \" ShortGPT & 43.8 & 1.8$\\\\times$ & 7.9 & 10.2 \\\\\\\\\\n\",\n \" \\\\rowcolor{lightblue}\\\\cellcolor{white}\\\\texttt{Cus-Prun} & 45.0 & 1.8$\\\\times$ & \\\\textbf{48.4} & \\\\textbf{50.6} \\\\\\\\\\n\",\n \" \\\\hline\\n\",\n \" \\\\end{tabular}}\\n\",\n \" \\\\label{table:prune_more}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{figure*}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=1.0\\\\textwidth]{Figures/lang-specific.pdf}\\n\",\n \" \\\\caption{Performance of \\\\texttt{Cus-Prun} in obtaining language-specific models.}\\n\",\n \" \\\\label{fig:lang-spec}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{table}[ht]\\n\",\n \" \\\\centering\\n\",\n \"\\\\footnotesize\\n\",\n \" \\\\scalebox{1}{\\n\",\n \" \\\\begin{tabular}{lp{5.3cm}}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Task} & \\\\textbf{Prompt} \\\\\\\\\\\\midrule\\n\",\n \" Generation & Generate a text document in \\\\{language\\\\}/\\\\{domain\\\\}/\\\\{task\\\\}. Make sure the documents is not fixed to one \\\\{language\\\\}/\\\\{domain\\\\}/\\\\{task\\\\} or \\\\{language\\\\}/\\\\{domain\\\\}/\\\\{task\\\\}. Ensure the content is clear, concise, and appropriate for the specified request. Use professional and domain-specific terminology where necessary. \\\\\\\\\\\\midrule\\n\",\n \" Evaluation & Evaluate the quality of the given answer to the question. Provide a score from 0 to 5, where 0 represents very low quality and 5 represents very high quality. Question: \\\\{question\\\\} Answer: \\\\{answer\\\\}.\\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}}\\n\",\n \"\\\\caption{GPT-4o prompts for generating documents and evaluating answer quality.} \\\\label{table:testset}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table*}[hbpt]\\n\",\n \"\\\\caption{Main Results of \\\\texttt{Cus-Prun} on Germany with a pruning ratio of $25\\\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in XLSum and in accuracy (\\\\%) for other datasets.}\\n\",\n \" \\\\centering\\n\",\n \"\\\\footnotesize\\n\",\n \"\\\\setlength{\\\\tabcolsep}{3pt}\\n\",\n \" \\\\scalebox{1.0}{\\n\",\n \" \\\\begin{tabular}{l|l|cccc|ccccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{\\\\textbf{\\\\normalsize{Model}}} & \\\\multirow{2}{*}{\\\\textbf{\\\\normalsize{Method}}} & \\n\",\n \" \\\\multicolumn{4}{c}{\\\\textbf{\\\\normalsize{General Capability}}} \\\\vline & \\\\multicolumn{5}{c}{\\\\textbf{\\\\normalsize{Expert Capability}}} \\\\\\\\ \\n\",\n \" & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3Exam & XQuAD & XLSum & Avg. \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Llama3-8B} &\\n\",\n \" Dense & 70.7 & 58.3 & 63.1 & 64.1 & 44.8 & - & 52.9 & - & 48.8 \\\\\\\\\\n\",\n \" & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 0.0 & - & 11.0 & - & 5.5 \\\\\\\\\\n\",\n \" & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & - & 9.8 & - & 4.9 \\\\\\\\\\n\",\n \" & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & - & 0.0 & - & 0.0 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 61.4 & 38.9 & 54.5 & \\\\textbf{51.6} & 32.8 & - & 49.6 & - & \\\\textbf{41.2} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Mistral-12B} & Dense & 82.6 & 68.5 & 50.4 & 59.3 & 56.8 & - & 41.2 & - & 49.0 \\\\\\\\\\n\",\n \" & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 2.4 & - & 13.4 & - & 7.9 \\\\\\\\\\n\",\n \" & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 0.8 & - & 15.5 & - & 8.2 \\\\\\\\\\n\",\n \" & ShortGPT & 37.8 & 0.0 & 33.9 & 23.9 & 3.6 & - & 20.3 & - & 12.0 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 64.6 & 39.7 & 43.2 & \\\\textbf{49.2} & 31.6 & - & 35.9 & - & \\\\textbf{33.8} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Llama2-13B} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 24.4 & - & 40.3 & - & 32.3 \\\\\\\\\\n\",\n \" & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 2.0 & - & 5.7 & - & 3.9 \\\\\\\\\\n\",\n \" & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 3.6 & - & 18.1 & - & 10.9 \\\\\\\\\\n\",\n \" & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 2.8 & - & 27.2 & - & 15.0 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 47.6 & 19.8 & 49.9 & \\\\textbf{39.1} & 18.4 & - & 31.7 & - & \\\\textbf{25.0} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Llama3-70B} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 74.8 & - & 58.2 & - & 66.5 \\\\\\\\\\n\",\n \" & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 18.0 & - & 27.3 & - & 22.7 \\\\\\\\\\n\",\n \" & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 0.0 & - & 17.3 & - & 8.7 \\\\\\\\\\n\",\n \" & ShortGPT & 59.4 & 5.6 & 75.5 & 46.8 & 9.6 & - & 31.5 & - & 20.6 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 66.8 & 59.3 & 69.1 & \\\\textbf{65.1} & 48.2 & - & 53.9 & - & \\\\textbf{51.1} \\\\\\\\ \\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}}\\n\",\n \" \\\\label{table:cl_ger}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[hbpt]\\n\",\n \"\\\\caption{Main Results of \\\\texttt{Cus-Prun} on Chinese with a pruning ratio of $25\\\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in XLSum and in accuracy (\\\\%) for other datasets.}\\n\",\n \" \\\\centering\\n\",\n \"\\\\footnotesize\\n\",\n \"\\\\setlength{\\\\tabcolsep}{3pt}\\n\",\n \" \\\\scalebox{1.0}{\\n\",\n \" \\\\begin{tabular}{l|l|cccc|ccccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{\\\\textbf{\\\\normalsize{Model}}} & \\\\multirow{2}{*}{\\\\textbf{\\\\normalsize{Method}}} & \\n\",\n \" \\\\multicolumn{4}{c}{\\\\textbf{\\\\normalsize{General Capability}}} \\\\vline & \\\\multicolumn{5}{c}{\\\\textbf{\\\\normalsize{Specific Capability}}} \\\\\\\\ \\n\",\n \" & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3Exam & XQuAD & XLSum & Avg. \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Llama3-8B} &\\n\",\n \" Dense & 70.7 & 58.3 & 63.1 & 64.1 & 43.6 & 55.1 & 78.7 & 49.1 & 56.6 \\\\\\\\\\n\",\n \" & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 2.4 & 23.6 & 21.3 & 32.8 & 20.0 \\\\\\\\\\n\",\n \" & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & 17.4 & 23.5 & 8.3 & 12.3 \\\\\\\\\\n\",\n \" & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & 28.3 & 0.0 & 3.1 & 7.9 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 60.5 & 25.7 & 49.4 & \\\\textbf{45.2} & 36.0 & 44.7 & 65.6 & 46.3 & \\\\textbf{48.2} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Mistral-12B} & Dense & 82.6 & 68.5 & 50.4 & 59.3 & 53.2 & 47.8 & 62.2 & 33.0 & 49.1 \\\\\\\\\\n\",\n \" & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 2.8 & 30.7 & 31.8 & 32.6 & 24.5 \\\\\\\\\\n\",\n \" & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 1.6 & 26.4 & 28.3 & 10.8 & 16.8 \\\\\\\\\\n\",\n \" & ShortGPT & 37.8 & 0.0 & 33.9 & 23.9 & 4.4 & 28.2 & 29.1 & 7.2 & 17.2 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 68.3 & 43.2 & 39.5 & \\\\textbf{50.3} & 38.4 & 40.7 & 50.6 & 30.3 & \\\\textbf{40.0} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Llama2-13B} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 21.6 & 36.5 & 59.8 & 35.3 & 38.3 \\\\\\\\\\n\",\n \" & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 1.2 & 23.3 & 3.8 & 25.1 & 13.4\\\\\\\\\\n\",\n \" & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 4.8 & 24.5 & 28.4 & 11.2 & 17.2 \\\\\\\\\\n\",\n \" & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 4.4 & 22.9 & 24.6 & 13.7 & 16.4 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 48.6 & 20.7 & 51.9 & \\\\textbf{40.4} & 14.8 & 28.2 & 47.3 & 34.4 & \\\\textbf{31.2} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Llama3-70B} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 68.4 & 76.1 & 81.3 & 55.3 & 70.3 \\\\\\\\\\n\",\n \" & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 16.8 & 47.5 & 56.1 & 41.3 & 40.4 \\\\\\\\\\n\",\n \" & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 6.4 & 48.3 & 42.2 & 29.3 & 31.6 \\\\\\\\\\n\",\n \" & ShortGPT & 59.4 & 5.6 & 75.5 & 46.8 & 12.4 & 45.5 & 44.6 & 36.1 & 34.7 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 72.3 & 48.5 & 65.2 & \\\\textbf{62.0} & 40.8 & 61.7 & 66.9 & 51.6 & \\\\textbf{55.3} \\\\\\\\ \\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}}\\n\",\n \" \\\\label{table:cl_chi}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[hbpt]\\n\",\n \"\\\\caption{Main Results of \\\\texttt{Cus-Prun} on Thai with a pruning ratio of $25\\\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in XLSum and in accuracy (\\\\%) for other datasets.}\\n\",\n \" \\\\centering\\n\",\n \"\\\\footnotesize\\n\",\n \"\\\\setlength{\\\\tabcolsep}{3pt}\\n\",\n \" \\\\scalebox{1.0}{\\n\",\n \" \\\\begin{tabular}{l|l|cccc|ccccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{\\\\textbf{\\\\normalsize{Model}}} & \\\\multirow{2}{*}{\\\\textbf{\\\\normalsize{Method}}} & \\n\",\n \" \\\\multicolumn{4}{c}{\\\\textbf{\\\\normalsize{General Capability}}} \\\\vline & \\\\multicolumn{5}{c}{\\\\textbf{\\\\normalsize{Specific Capability}}} \\\\\\\\ \\n\",\n \" & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3Exam & XQuAD & XLSum & Avg. \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Llama3-8B} &\\n\",\n \" Dense & 70.7 & 58.3 & 63.1 & 64.1 & 35.2 & 43.0 & 58.7 & 16.7 & 38.4 \\\\\\\\\\n\",\n \" & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 0.8 & 24.4 & 8.4 & 13.5 & 11.8 \\\\\\\\\\n\",\n \" & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & 12.3 & 16.6 & 8.7 & 9.4 \\\\\\\\\\n\",\n \" & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & 25.4 & 0.0 & 2.3 & 6.9 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 58.9 & 31.2 & 52.4 & \\\\textbf{47.5} & 21.6 & 38.3 & 42.6 & 16.8 & \\\\textbf{29.8} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Mistral-12B} & Dense & 82.6 & 68.5 & 50.4 & 59.3 & 45.2 & 39.9 & 44.1 & 17.8 & 36.8 \\\\\\\\\\n\",\n \" & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 1.2 & 24.8 & 11.9 & 13.7 & 12.9 \\\\\\\\\\n\",\n \" & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 0.0 & 23.8 & 8.4 & 4.7 & 12.3 \\\\\\\\\\n\",\n \" & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 0.8 & 25.7 & 4.7 & 2.8 & 8.5 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 68.2 & 35.8 & 47.6 & \\\\textbf{50.5} & 32.8 & 37.7 & 35.6 & 15.9 & \\\\textbf{30.5} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Llama2-13B} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 6.4 & 24.3 & 28.3 & 14.5 & 18.4 \\\\\\\\\\n\",\n \" & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 0.0 & 22.3 & 1.8 & 10.2 & 8.6 \\\\\\\\\\n\",\n \" & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 0.0 & 26.2 & 23.7 & 8.6 & 14.6 \\\\\\\\\\n\",\n \" & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 0.0 & 23.1 & 22.3 & 8.9 & 13.6 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 47.8 & 20.9 & 50.7 & \\\\textbf{39.8} & 4.8 & 24.2 & 23.6 & 13.8 & \\\\textbf{16.6} \\\\\\\\ \\\\midrule\\n\",\n \"\\\\multirow{4}{*}{Llama3-70B} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 65.2 & 66.1 & 67.8 & 17.8 & 54.2 \\\\\\\\\\n\",\n \" & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 15.6 & 39.9 & 29.8 & 16.6 & 25.5 \\\\\\\\\\n\",\n \" & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 4.8 & 41.3 & 39.6 & 13.2 & 24.7 \\\\\\\\\\n\",\n \" & ShortGPT & 59.4 & 5.6 & 75.5 & 46.8 & 13.7 & 40.7 & 40.4 & 11.9 & 26.7 \\\\\\\\\\n\",\n \" & \\\\texttt{Cus-Prun} & 73.3 & 58.7 & 68.4 & \\\\textbf{66.8} & 40.4 & 53.6 & 58.5 & 16.9 & \\\\textbf{42.4} \\\\\\\\ \\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}}\\n\",\n \" \\\\label{table:cl_tha}\\n\",\n \"\\\\end{table*}\\n\"\n ]\n]","isConversational":false},"fulltext":{"kind":"string","value":"\\pdfoutput=1\n\n\\documentclass[11pt]{article}\n\n\\usepackage[final]{acl}\n\n\n\\usepackage{microtype}\n\\usepackage{hyperref}\n\\usepackage{url}\n\\usepackage{booktabs}\n\\usepackage{times}\n\\usepackage{latexsym}\n\\usepackage{amsmath}\n\\usepackage{amsfonts}\n\\usepackage{graphicx}\n\\usepackage{multirow}\n% This is not strictly necessary, and may be commented out,\n% but it will improve the layout of the manuscript,\n% and will typically save some space.\n\\usepackage{subcaption}\n% For proper rendering and hyphenation of words containing Latin characters (including in bib files)\n\\usepackage[T1]{fontenc}\n\n\\usepackage{tikz}\n\n\\usepackage{listings}\n\\usepackage{color}\n% \\usepackage{minted}\n\\definecolor{dkgreen}{rgb}{0,0.6,0}\n\\definecolor{gray}{rgb}{0.5,0.5,0.5}\n\\definecolor{mauve}{rgb}{0.58,0,0.82}\n\\definecolor{lightblue}{rgb}{0.9, 0.95, 1.0}\n\\definecolor{lightgray}{rgb}{0.9, 0.9, 0.9}\n\n\\lstset{frame=tb,\n language=Python,\n aboveskip=3mm,\n belowskip=3mm,\n showstringspaces=false,\n columns=flexible,\n basicstyle={\\small\\ttfamily},\n numbers=none,\n numberstyle=\\tiny\\color{gray},\n keywordstyle=\\color{blue},\n commentstyle=\\color{dkgreen},\n stringstyle=\\color{mauve},\n breaklines=true,\n breakatwhitespace=true,\n tabsize=3\n}\n\n\\newcommand{\\circled}[1]{%\n \\tikz[baseline=(char.base)]{\n \\node[shape=circle, draw, inner sep=0.5pt, fill=black] (char) {\\textcolor{white}{\\small{#1}}};\n }\n}\n\\usepackage{colortbl}\n\n\\usepackage{algorithmic}\n\\usepackage{algorithm}\n\n\\usepackage[utf8]{inputenc}\n\\usepackage{multirow}\n% This is not strictly necessary, and may be commented out,\n% but it will improve the layout of the manuscript,\n% and will typically save some space.\n\\usepackage{microtype}\n\\usepackage{booktabs}\n\n\n\\title{Pruning General Large Language Models into Customized Expert Models}\n\n\n\\author{\n Yiran Zhao$^{1}$ \\; Guizhen Chen$^{2,3}$ \\; Kenji Kawaguchi$^{1}$\n \\; Lidong Bing$^{4}$ \\; Wenxuan Zhang$^{5}$\\footnotemark[2] \\\\\n $^1$ National University of Singapore \\quad $^2$ Nanyang Technological University, Singapore \\\\ \n $^3$ DAMO Academy, Alibaba Group, Singapore \\quad $^4$ MiroMind \\\\\n $^5$ Singapore University of Technology and Design \\\\\n}\n\n\n\n\n\\newcommand{\\fix}{\\marginpar{FIX}}\n\\newcommand{\\new}{\\marginpar{NEW}}\n\n\n\n\\begin{document}\n\n\\newcommand{\\yiran}[1]{\\textcolor{brown}{\\it\\small [#1 --Yiran]}}\n\\newcommand{\\isaac}[1]{\\textcolor{orange}{\\textbf{[Isaac:} #1]}}\n\n\\maketitle\n\\renewcommand{\\thefootnote}{\\fnsymbol{footnote}}\n\\footnotetext[2]{Wenxuan Zhang is the corresponding author.}\n\\renewcommand{\\thefootnote}{\\arabic{footnote}}\n\n\\begin{abstract}\n\nLarge language models (LLMs) have revolutionized natural language processing, yet their substantial model sizes often require substantial computational resources. To preserve computing resources and accelerate inference speed, it is crucial to prune redundant parameters, especially for experienced users who often need compact expert models tailored to specific downstream scenarios. However, most existing pruning methods focus on preserving the model’s general capabilities, often requiring extensive post-training or suffering from degraded performance due to coarse-grained pruning. In this work, we design a \\underline{Cus}tom \\underline{Prun}ing method (\\texttt{Cus-Prun}) to prune a large general model into a smaller lightweight expert model, which is positioned along the ``language'', ``domain'' and ``task'' dimensions. By identifying and pruning irrelevant neurons of each dimension, \\texttt{Cus-Prun} creates expert models without any post-training. \nOur experiments demonstrate that \\texttt{Cus-Prun} consistently outperforms other methods, achieving minimal loss in both expert and general capabilities across various models from different model families and sizes.\\footnote{Our code is publicly available at \\url{https://github.com/zhaoyiran924/Custom-Prune}.}\n\n\\end{abstract}\n\n\\section{Introduction}\n\nLarge language models (LLMs)~\\citep{achiam2023gpt, reid2024gemini, dubey2024llama, team2024gemma} have revolutionized the field of natural language processing (NLP), emerging as powerful tools with widespread applications across various languages~\\citep{cui2023efficient, yang2024qwen2, seallms3}, domains \\citep{ li2023huatuo26m, roziere2023code, li2023large}, and tasks~\\citep{azerbayevllemma, alves2024tower,naacl-sentiment}.\nHowever, the impressive performance of LLMs often comes at the cost of immense model sizes, mostly containing billions of parameters and thus demand significant computing resources~\\citep{goldstein2023generative, musser2023cost}. To address this issue, researchers have recently proposed various model pruning methods for LLMs. These methods aim to reduce model parameters while maintaining the overall performance through techniques such as removal of unimportant structures~\\citep{ma2023llm, men2024shortgpt, songsleb}, matrix approximation~\\citep{sharmatruth, ashkboosslicegpt}, and extensive post-training after pruning~\\citep{wang2024reconstruct, xiasheared}.\n\n\n\nMost existing pruning methods focus on preserving the \\textit{general capabilities} of the model, often evaluated by broad-spectrum benchmarks like MMLU~\\citep{hendrycksmeasuring}.\nWhile aiming for overall versatility, they may not align well with real-world user needs for a pruned small model, which are usually more \\textit{specific and targeted}. For instance, a user might require a question-answering model tailored specifically for the education domain in German. \nSuch specialized request in fact aligns well with the fundamental motivation behind pruning: to create a smaller model by eliminating unnecessary parameters. In this context, the notion of ``unnecessary'' parameters becomes more precise—referring to those parameters that are irrelevant to the particular use case. By selectively pruning these redundant parameters, one can construct a smaller, expert model that is better aligned with specific requirements.\n\nHowever, current pruning techniques focusing on preserving the general capabilities of LLMs often employ coarse-grained approaches such as removing entire layers or modules \\citep{li2022parameter, kurz2024language, huang2024pruning}. Therefore, it may remove parameters that are critical for specialized downstream scenarios, and thus sometimes require extensive post-training to recover the pruned capabilities \\citep{xiasheared, muralidharan2024compact}. On the other hand, the preserved certain capabilities may not be useful or relevant to our desired application scenario. This misalignment with practical requirements, where users need compact ready-to-deploy expert models without retraining, motivates us to design a more fine-grained and expert model targeting approach.\n\n\n\n\\begin{figure}[t]\n \\centering\n \\includegraphics[width=0.48\\textwidth]{Figures/cust-prune.pdf}\n \\caption{Given a requirement for an expert model across three dimensions (language, domain, and task), \\texttt{Cus-Prun} (i) identifies irrelevant neurons for each dimension using corresponding corpora (left), and (ii) prunes \\textbf{overlapping} irrelevant neurons across dimensions to obtain the expert model (right).}\n \\label{fig:cus-prun}\n\\end{figure}\n\nIn this work, we introduce a novel \\underline{Cus}tom \\underline{Prun}ing (\\texttt{Cus-Prun}) method, designed to prune a large general-purpose model into a small specialized expert model tailored for specific scenarios.\nTo achieve broad adaptability, we define the expert model by positioning the target user's needs along three key dimensions: language (e.g., English, Chinese, German), domain (e.g., E-commerce, education), and task (e.g., QA, summarization).\nInspired by existing studies that certain neurons are responsible for certain functions \\citep{zhao2024large, tang2024language, liang2024multilingual}, \\texttt{Cus-Prun} identifies and preserves critical neurons that are more relevant to particular languages, domains, or tasks, while pruning less relevant ones, ultimately leading to a smaller expert models.\nSpecifically, as illustrated in Figure \\ref{fig:cus-prun}, \n\\texttt{Cus-Prun} first identifies irrelevant neurons for each dimension by assessing the impact of their removal on the generated output when processing corresponding corpus, which could be easily constructed from the relevant plain text documents.\nNext, the expert model is constructed by pruning irrelevant neurons across all dimensions.\nFurthermore, \\texttt{Cus-Prun}'s flexibility allows it to focus on one, two, or all three dimensions (language, domain, task) as needed, making it adaptable to a wide range of real-world applications where specialized LLMs are required. Importantly, by performing fine-grained pruning at the neuron level, the method could also retain most of the essential neurons within the model backbone, thereby preserving most general capabilities.\n\nWe conduct comprehensive experiments to evaluate the performance of \\texttt{Cus-Prun} across various scenarios. Experimental results demonstrate that it consistently outperforms other pruning methods in all settings. For three-dimensional specific expert models, \\texttt{Cus-Prun} demonstrates remarkable pruning effectiveness across different model families and sizes, such as Mistral-Nemo-12B, Llama3-8B, Llama2-13B, and Llama3-70B, while maintaining strong expert and general capabilities. The method is evaluated across multilingual, multidomain, and multitask datasets, as well as representative compound NLP benchmarks, and consistently outperforms state-of-the-art pruning methods. Moreover, \\texttt{Cus-Prun} is highly adaptable, working effectively across a wide range of pruning ratios, even up to nearly half the parameters, without compromising its superior performance. For more focused applications, such as two- or one-dimensional specific expert models (e.g., language-domain specific or language-specific models), \\texttt{Cus-Prun} continues to significantly outperform other pruning methods, showcasing its versatility and effectiveness in diverse specialized settings.\n\n\n\n\\section{Custom Pruning (\\texttt{Cus-Prun})}\n\n\nAn expert model could be generally positioned from three dimensions: ``language'' ($L\\in\\mathbb{L}$), ``domain'' ($D\\in\\mathbb{D}$), and ``task'' ($T\\in\\mathbb{T}$), which can be represented as $LLM_{\\text{Exp}} := (L, D, T) \\in \\mathbb{L}\\times \\mathbb{D}\\times \\mathbb{T}$. Specifically, the language dimension encompasses various languages such as English, Spanish, and Thai. The domain dimension covers different fields like finance, legal, and medical. The task dimension includes various applications such as question-answering, data-to-text, and summarization. In this section, we propose a custom pruning method named \\texttt{Cus-Prun} to derive smaller expert models with flexible customization granularity.\n\n\n\\subsection{Foundational Custom Pruning}\n\n\nDrawing inspiration from recent LLM interpretation studies~\\citep{tang2024language,liang2024multilingual,zhao2024large} that many parameters in the model are redundant to processing a specific ``language'', we hypothesize that this phenomenon can be extended to other dimensions such as ``domain'' and ``task'', meaning that certain parameters remain unused when handling a specific dimension. \nRather than eliminating entire layers or modules~\\citep{songsleb, men2024shortgpt, zhang2024finercut}, \\texttt{Cus-Prun} performs a fine-grained investigation by identifying and removing redundant neurons (i.e., individual rows or columns in parameter matrices) across all components (e.g., attention and feed-forward layers).\n\n\nConcretely, when handling each dimension, we identify a specific set of \\textit{irrelevant neurons} in the original LLM, denoted as $\\tilde{\\mathcal{N}}_{L}$, $\\tilde{\\mathcal{N}}_{D}$, and $\\tilde{\\mathcal{N}}_{T}$ for $L$, $D$, and $T$, respectively. \n\nSpecifically, to identify irrelevant neurons corresponding to the selected dimension, we construct a corpus within that dimension while ablating others. For example, to determine irrelevant neurons for a specific language $L_{\\text{Exp}}$, we create a corpus set \n\\begin{equation}\nC_{L_{\\text{Exp}}}=\\{(L_{\\text{Exp}}, D, T)|D\\in\\mathbb{D}, T\\in\\mathbb{T}\\},\n\\end{equation} \ncomprising documents in language $L_{\\text{Exp}}$ across various domains $D$ and tasks $T$. We then identify neurons that are consistently irrelevant across all documents in $C_{L_{\\text{Exp}}}$,\n\\begin{equation}\n\\scalebox{0.92}{$ % Adjust 0.97 to the desired scaling factor\n\\tilde{\\mathcal{N}}_{L_{\\text{Exp}}} = \n\\left\\{\n\\text{Neuron} \\middle| \n\\text{Irrelevant to } c, \\; \\forall c \\in C_{L_{\\text{Exp}}}\n\\right\\},\n$}\n\\label{equ:neuron}\n\\end{equation}\nwhere a neuron is considered irrelevant if its removal from the parameter matrix affects the generated output below a specified threshold. Formally,\nfor $i$-th neuron in layer $l$, denoted as ${N}_{i}^{(l)}$, its relevance to document $c$ is measured by $|{h}_{\\setminus{{N}_{i}^{(l)}}, i}(c) - h_i(c)|_2$, where $h_i(c)$ is the layer output and ${h}_{\\setminus{{N}_{i}^{(l)}}, i}(c)$ is the output with the neuron removed. Furthermore, neurons with impact in the lowest $\\sigma\\%$ are considered irrelevant, where $\\sigma$ is a pre-defined pruning ratio.\n\n\nSimilarly, we could establish corresponding corpus sets for other dimensions, \n\\begin{equation}\nC_{D_{\\text{Exp}}}=\\{(L, D_{\\text{Exp}}, T)|L\\in\\mathbb{L}, T\\in\\mathbb{T}\\},\n\\end{equation} \n\\begin{equation}\nC_{T_{\\text{Exp}}}=\\{(L, D, T_{\\text{Exp}})|L\\in\\mathbb{L}, D\\in\\mathbb{D}\\},\n\\end{equation} to extract irrelevant neurons, $\\tilde{\\mathcal{N}}_{D_{\\text{Exp}}} $ and $\\tilde{\\mathcal{N}}_{T_{\\text{Exp}}} $. \nFinally, the expert model could constructed by\n\\begin{equation}\n\\scalebox{0.87}{$ % Adjust 0.95 to the desired scaling factor\n\\mathcal{LLM}_{\\text{Exp}} = \n\\mathcal{LLM} \\ominus \n\\left\\{\n\\tilde{\\mathcal{N}}_{L_{\\text{Exp}}} \n\\cap \\tilde{\\mathcal{N}}_{D_{\\text{Exp}}} \n\\cap \\tilde{\\mathcal{N}}_{T_{\\text{Exp}}}\n\\right\\},\n$}\n\\label{equ:cus}\n\\end{equation}\nwhere $\\ominus$ represents removing the corresponding neurons from $\\mathcal{LLM}$. The overall\nalgorithm is further illustrated in Algorithm \\ref{algo:adaptive_pruning}.\n\n\n\n\\begin{figure}[t]\n\\scalebox{0.95}{ \n\\begin{minipage}{\\linewidth}\n\\begin{algorithm}[H]\n\\renewcommand{\\algorithmicrequire}{\\textbf{Input:}}\n\\renewcommand{\\algorithmicensure}{\\textbf{Output:}}\n\\caption{Adaptive Custom Pruning}\n\\begin{algorithmic}[1]\n\\REQUIRE Original language model $\\mathcal{LLM}$, request for expert model $\\mathcal{LLM}_{\\text{Exp}}$ with selected dimensions: $L_{\\text{Exp}}$, $D_{\\text{Exp}}$, $T_{\\text{Exp}}$ (any subset), request for pruning ratio $\\sigma$.\n\\STATE \\texttt{// Construct specific corpora for each selected dimension.}\n\\STATE $C = \\{\\}$\n\\IF{$L_{\\text{Exp}}$ is specified}\n \\STATE $C = C \\cup \\{(L_{\\text{Exp}}, D, T) \\mid D \\in \\mathbb{D}, T \\in \\mathbb{T}\\}$\n\\ENDIF\n\\IF{$D_{\\text{Exp}}$ is specified}\n \\STATE $C = C \\cup \\{(L, D_{\\text{Exp}}, T) \\mid L \\in \\mathbb{L}, T \\in \\mathbb{T}\\}$\n\\ENDIF\n\\IF{$T_{\\text{Exp}}$ is specified}\n \\STATE $C = C \\cup \\{(L, D, T_{\\text{Exp}}) \\mid L \\in \\mathbb{L}, D \\in \\mathbb{D}\\}$\n\\ENDIF\n\\STATE \\texttt{// Identify irrelevant neurons for each selected dimension.}\n\\FORALL{neuron $N_{i}^{(l)}$ in $\\mathcal{LLM}$}\n \\IF{$\\forall c \\in C,$ $N_{i}^{(l)} \\in \\tilde{\\mathcal{N}}(c)$}\n \\STATE $\\tilde{\\mathcal{N}} \\gets \\tilde{\\mathcal{N}} \\cup N_{i}^{(l)}$\n \\ENDIF\n\\ENDFOR\n\\STATE \\texttt{// Prune irrelevant neurons to obtain expert model.}\n\\STATE $\\mathcal{LLM}_{\\text{Exp}} = \\mathcal{LLM} \\ominus \\tilde{\\mathcal{N}}$\n\\ENSURE $\\mathcal{LLM}_{\\text{Exp}}$\n\\end{algorithmic}\n\\label{algo:adaptive_pruning}\n\\end{algorithm}\n\\end{minipage}\n}\n\\end{figure}\n\n\n\\subsection{Adaptive Custom Pruning}\\label{sec:adaptive}\n\n\nIn many applications, the need for expertise might be constrained to one or two dimensions. For example, a language-specific or domain-specific model only requires pruning along a single dimension, while a language-domain-specific model (e.g., a Chinese Medical LLM) constrains two dimensions. In this section, we extend \\texttt{Cus-Prun} to support different granularity levels.\n\n\\paragraph{Two-Dimensional Specific Expert Model}\n\nWithout losing generality, we use the language-domain expert model as a concrete example, which requires an expert model constrained in two dimensions: language ($L_{\\text{Exp}}$) and domain ($D_{\\text{Exp}}$). We similarly derive the sets of irrelevant neurons $\\tilde{\\mathcal{N}}_{L_{\\text{Exp}}}$ and $\\tilde{\\mathcal{N}}_{D_{\\text{Exp}}}$, and\n% according to Equation \\ref{equ:neuron_final}. \nobtain the expert model by pruning the original dense model as follows: \\begin{equation}\n\\mathcal{LLM}_{\\text{Exp}}:=\\mathcal{LLM}\\ominus\\{\\tilde{\\mathcal{N}}_{L_{\\text{Exp}}} \\cap \\tilde{\\mathcal{N}}_{D_{\\text{Exp}}}\\}.\n\\end{equation}\n\n\n\\paragraph{One-Dimensional Specific Expert Model}\nWe use the language-specific expert model as an example, which focuses on optimizing performance for a certain language ($L_{\\text{Exp}}$), irrespective of domain or task. Similarly, we obtain the language-specific corpus $C_{L_{\\text{Exp}}}$, then identify irrelevant neurons $\\tilde{\\mathcal{N}}_{L_{\\text{Exp}}}$ \nand extract the expert model by \n\\begin{equation}\n\\mathcal{LLM}_{\\text{Exp}}:=\\mathcal{LLM}\\ominus\\{\\tilde{\\mathcal{N}}_{L_{\\text{Exp}}} \\}.\n\\end{equation}\nTo enhance efficiency, we implement the parallel neuron-detection method~\\citep{zhao2024large}, which accelerates the sequential calculations from line14 to line16 in Algorithm \\ref{algo:adaptive_pruning}. \n\n\n\n\n\\section{Preliminary Evaluation}\\label{sec:pre_eva}\n\nIn this section, to simulate realistic user-defined requirements where all three dimensions (language, domain, task) are explicitly specified, we conduct preliminary experiments on developing expert models with fine-grained pruning. This setting not only validates our approach under the most demanding conditions but also serves as a prototype for later experiments with one- or two-dimensional customizations.\n\n\n\\paragraph{Experiment Design} \n\nWe consider three scenarios, each named according to the \\textit{language}-\\textit{domain}-\\textit{task} pattern:\n\\textit{Korean-Legal-Summarization}~\\citep{hwang2022multi}, \\textit{English-Medical-MCQ}~\\citep{garcia2024medical}, and \\textit{Chinese-E-commerce-Sentiment Analysis}~\\citep{zhang2015character}.\nThese scenarios were selected because each represents a unique combination of language, domain, and task, effectively simulating the diverse needs a user might specify in practice.\n\nFor each scenario, we curate the corresponding corpus for each dimension. This curation can be done through manual collection or by automatically retrieving relevant documents online. In this preliminary study, without loss of generality, we employ a strong proprietary model\\footnote{\\url{https://platform.openai.com/docs/models/gpt-4o}} to generate a corpus containing $50$ documents for each dimension. Detailed prompts can be found in Appendix \\ref{sec:appen_prompt}. The generated documents could then be used to determine the relevance of neurons for each dimension of each scenario.\n\n\n\\paragraph{Experiment Setup}\nWe use Llama3-8B~\\citep{dubey2024llama} as the original dense model and follow the typical setting to set the pruning ratio as $25\\%$.\nPerformance is evaluated using Rouge-L~\\citep{lin2004rouge} for Korean-Legal-Summary and accuracy score for another two tasks. For comparison, we adopt SliceGPT~\\citep{ashkboosslicegpt} as a baseline, which replaces each weight matrix with a smaller proxy matrix without considering the specific use-case requirements.\n\n\\begin{figure}[t]\n \\centering\n \\includegraphics[width=0.48\\textwidth]{Figures/three-dim-examples.pdf}\n \\caption{Concrete examples of applying \\texttt{Cus-Prun} to prune $25\\%$ of Llama3-8B-Base's parameters into three-dimensional expert models. Numbers above each box indicate performance on the \\textbf{whole} test set, with the first evaluated by Rouge-L, and the other two by accuracy. }\n \\label{fig:korean}\n\\end{figure}\n\n\\begin{table*}[t]\n\\caption{Main Results of \\texttt{Cus-Prun} on multilingual setting with a pruning ratio of $25\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in summarization tasks and in accuracy (\\%) for other datasets.}\n \\centering\n\\footnotesize\n\\setlength{\\tabcolsep}{0.8pt}\n \\scalebox{0.9}{\n \\begin{tabular}{c|l|cccc|ccccc|ccccc|cccc}\n\\toprule\n\\quad\\quad & \\multirow{2}{*}{\\textbf{\\normalsize{Method}}} & \n \\multicolumn{4}{c}{\\textbf{\\normalsize{General Capability}}} \\vline & \\multicolumn{5}{c}{\\textbf{\\normalsize{Multilingual Expert}}} \\vline & \\multicolumn{5}{c}{\\textbf{\\normalsize{Multidomain Expert }}} \\vline & \\multicolumn{4}{c}{\\textbf{\\normalsize{Multitask Expert }}} \\\\ \n & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3 & XQuAD & Sum & Avg. & MMCQ & FTQA & TSA & AMSA & Avg. & MSum & ASum & AMCF & Avg. \\\\ \\midrule\n\\rowcolor{lightgray}\\cellcolor{white}\\multirow{5}{*}{\\rotatebox{90}{\\textbf{Llama3-8B}}} & Dense & 70.7 & 58.3 & 63.1 & 64.1 &41.2 &49.1 & 63.4 &32.9 &46.7 & 51.8 & 23.9 & 67.1 & 95.9 & 59.8 & 76.6 & 16.2 & 78.2 & 57.0 \\\\\n & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 1.1 & 24.0 & 13.6 & 23.2 & 15.5 & 0.0 & 0.0 & \\textbf{61.8} & 76.0 & 34.5 & 62.2 & \\textbf{21.8} & \\textbf{80.0} & \\textbf{54.7} \\\\\n & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & 14.9 & 16.6 & 8.5 & 10.0 & 22.6 & 0.0 & 41.2 & 53.7 & 29.4 & 7.3 & 2.9 & 51.3 & 20.5 \\\\\n & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & 26.9 & 0.0 & 2.7 & 7.4 & 3.2 & 0.0 & 38.6 & 35.7 & 19.4 & 4.1 & 4.8 & 43.8 & 17.6 \\\\\n &\\cellcolor{lightblue}\\texttt{Cus-Prun} &\\cellcolor{lightblue}\\textbf{62.4} & \\cellcolor{lightblue}\\textbf{37.0} & \\cellcolor{lightblue}\\textbf{54.7} & \\cellcolor{lightblue}\\textbf{51.4} & \\cellcolor{lightblue}\\textbf{30.1} & \\cellcolor{lightblue}\\textbf{41.5} & \\cellcolor{lightblue}\\textbf{52.6} & \\cellcolor{lightblue}\\textbf{31.5} & \\cellcolor{lightblue}\\textbf{38.9} & \\cellcolor{lightblue}\\textbf{42.9} & \\cellcolor{lightblue}\\textbf{20.6} & \\cellcolor{lightblue}\\textbf{61.8} & \\cellcolor{lightblue}\\textbf{87.6} & \\cellcolor{lightblue}\\textbf{53.2} & \\cellcolor{lightblue}\\textbf{68.4} & \\cellcolor{lightblue}12.8 & \\cellcolor{lightblue}75.5 & \\cellcolor{lightblue}52.2 \\\\ \\midrule\n\\rowcolor{lightgray}\\cellcolor{white}\\multirow{5}{*}{\\rotatebox{90}{\\textbf{Mistral-12B}}} & Dense & 82.6 & 68.5 & 50.4 & 67.2 & 51.7 & 43.8 & 49.2 & 25.4 & 42.5 & 54.6 & 26.6 & 69.4 & 92.4 & 60.8 & 88.7 & 3.0 & 78.6 & 56.4 \\\\\n & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 2.1 & 27.8 & 19.0 & \\textbf{23.2} & 18.0 & 0.0 & 0.0 & 51.0 & 20.9 & 18.0 & 59.3 & 0.5 & 2.8 & 20.9 \\\\\n & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 0.8 & 25.1 & 17.4 & 7.8 & 12.8 & 24.9 & 9.2 & 34.2 & 54.3 & 30.7 & 27.4 & 1.3 & 36.3 & 21.7 \\\\\n & ShortGPT & 37.8 & 0.0 & 33.9 & 23.9 & 2.9 & 27.0 & 18.0 & 5.0 & 13.2 & 31.4 & 7.2 & 39.2 & 52.5 & 32.6 & 26.2 & 0.2 & 42.7 & 23.0 \\\\\n & \\cellcolor{lightblue}\\texttt{Cus-Prun} & \\cellcolor{lightblue}\\textbf{67.5} & \\cellcolor{lightblue}\\textbf{43.4} & \\cellcolor{lightblue}\\textbf{43.8} & \\cellcolor{lightblue}\\textbf{51.6} & \\cellcolor{lightblue}\\textbf{34.3} & \\cellcolor{lightblue}\\textbf{39.2} & \\cellcolor{lightblue}\\textbf{40.7} & \\cellcolor{lightblue}23.1 & \\cellcolor{lightblue}\\textbf{34.3} & \\cellcolor{lightblue}\\textbf{47.9} & \\cellcolor{lightblue}\\textbf{25.1} & \\cellcolor{lightblue}\\textbf{67.3} & \\cellcolor{lightblue}\\textbf{83.7} & \\cellcolor{lightblue}\\textbf{56.0} & \\cellcolor{lightblue}\\textbf{83.5} & \\cellcolor{lightblue}\\textbf{3.4} & \\cellcolor{lightblue}\\textbf{72.8} & \\cellcolor{lightblue}\\textbf{50.9} \\\\ \\midrule\n\\rowcolor{lightgray}\\cellcolor{white}\\multirow{5}{*}{\\rotatebox{90}{\\textbf{Llama2-13B}}} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 17.5 & 30.4 & 44.1 & 24.9 & 29.2 & 25.2 & 0.0 & 42.7 & 84.1 & 38.0 & 70.0 & 7.4 & 44.3 & 40.6 \\\\\n & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 1.1 & 22.8 & 3.8 & 17.7 & 11.3 & 0.0 & 0.0 & 9.7 & 0.0 & 2.4 & 21.6 & 4.8 & 0.0 & 8.8 \\\\\n & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 2.8 & 25.3 & 23.4 & 9.9 & 15.5 & 18.7 & 0.0 & 28.4 & 67.3 & 28.6 & 24.5 & 4.9 & 32.9 & 20.8 \\\\\n & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 2.4 & 23.0 & 24.7 & 11.3 & 15.3 & 16.9 & 0.0 & 34.6 & \\textbf{69.8} & 30.3 & 23.8 & 5.2 & 39.1 & 22.7 \\\\\n & \\cellcolor{lightblue}\\texttt{Cus-Prun} & \\cellcolor{lightblue}\\textbf{48.3} & \\cellcolor{lightblue}\\textbf{20.8} & \\cellcolor{lightblue}\\textbf{50.0} & \\cellcolor{lightblue}\\textbf{39.7} & \\cellcolor{lightblue}\\textbf{12.7} & \\cellcolor{lightblue}\\textbf{26.2} & \\cellcolor{lightblue}\\textbf{34.2} & \\cellcolor{lightblue}\\textbf{24.1} & \\cellcolor{lightblue}\\textbf{24.3} & \\cellcolor{lightblue}\\textbf{25.6} & \\cellcolor{lightblue}0.0 & \\cellcolor{lightblue}\\textbf{38.5} & \\cellcolor{lightblue}68.3 & \\cellcolor{lightblue}\\textbf{33.1} & \\cellcolor{lightblue}\\textbf{64.5} & \\cellcolor{lightblue}\\textbf{6.7} & \\cellcolor{lightblue}\\textbf{42.9} & \\cellcolor{lightblue}\\textbf{38.0} \\\\ \\midrule\n\\rowcolor{lightgray}\\cellcolor{white}\\multirow{5}{*}{\\rotatebox{90}{\\textbf{Llama3-70B}}} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 69.5 & 71.1 & 69.1 & 36.6 & 61.6 & 72.1 & 55.3 & 83.6 & 96.2 & 76.8 & 84.2 & 17.3 & 81.8 & 61.1 \\\\\n & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 16.8 & 43.7 & 43.0 & 29.0 & 33.1 & 27.3 & 1.0 & 51.0 & 50.3 & 32.4 & 10.2 & 13.7 & 20.6 & 14.8 \\\\\n & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 3.7 & 44.8 & 33.0 & 21.2 & 25.7 & 57.6 & 27.6 & 68.1 & 59.4 & 53.2 & 58.0 & 14.2 & 68.3 & 46.8 \\\\\n & ShortGPT & 59.4 & 5.6 & \\textbf{75.5} & 46.8 & 11.9 & 43.1 & 38.8 & 24.0 & 29.5 & 58.4 & 32.2 & 67.5 & 64.9 & 55.8 & 59.6 & 13.9 & 65.8 & 46.4 \\\\\n & \\cellcolor{lightblue}\\texttt{Cus-Prun} & \\cellcolor{lightblue}\\textbf{68.4} & \\cellcolor{lightblue}\\textbf{53.2} & \\cellcolor{lightblue}66.6 & \\cellcolor{lightblue}\\textbf{62.7} & \\cellcolor{lightblue}\\textbf{43.1} & \\cellcolor{lightblue}\\textbf{57.7} & \\cellcolor{lightblue}\\textbf{59.8} & \\cellcolor{lightblue}\\textbf{34.3} & \\cellcolor{lightblue}\\textbf{48.7} & \\cellcolor{lightblue}\\textbf{68.2} & \\cellcolor{lightblue}\\textbf{43.9} & \\cellcolor{lightblue}\\textbf{81.4} & \\cellcolor{lightblue}\\textbf{87.8} & \\cellcolor{lightblue}\\textbf{70.3} & \\cellcolor{lightblue}\\textbf{80.4} & \\cellcolor{lightblue}\\textbf{15.7} & \\cellcolor{lightblue}\\textbf{77.5} & \\cellcolor{lightblue}\\textbf{57.9} \\\\ \n\\bottomrule\n\\end{tabular}}\n \\label{table:cross_ling}\n\\vspace{-0.2cm}\n\\end{table*}\n\n\n\n\\paragraph{Main Results}\n\n\nFigure \\ref{fig:korean} presents the results and one concrete example for the original dense model, pruned model with SliceGPT, and pruned model with our proposed \\texttt{Cus-Prun} method across the three scenarios.\nWe observe that \\texttt{Cus-Prun} largely preserves the performance of the dense model, retraining 92\\%, 83\\%, and 94\\% of the original dense model performance on these three cases respectively.\nIn contrast, the baseline method SliceGPT, which does not consider specific use cases, largely underperforms compared to \\texttt{Cus-Prun}.\nOverall, the results demonstrate that our proposed \\texttt{Cus-Prun} method could effectively obtain expert models tailored to specific use cases across different languages, domains, and tasks that maintain high performance despite substantial pruning.\n\n\n\n\n\\section{Foundational Custom Pruning Assessment}\n\nAs demonstrated by preliminary evaluation in Section \\ref{sec:pre_eva}, \\texttt{Cus-Prun} enables the creation of expert models tailored to specific languages, domains, and tasks. However, existing benchmark datasets may not always align with such specialized requirements for a systematic evaluation. To simplify our evaluation without losing generality, we use two distinct corpora: one focusing independently on a single dimension and another encompassing the remaining two dimensions. This approach allows us to evaluate \\texttt{Cus-Prun}'s performance in \\textit{multilingual}, \\textit{multidomain}, and \\textit{multitask} settings. \n\nFormally, in the multilingual setting, instead of constructing $C_{L_{\\text{Exp}}}$, $C_{D_{\\text{Exp}}}$ and $C_{T_{\\text{Exp}}}$ independently, we can construct two corpora, $C_{L_{\\text{Exp}}}$ and $C_{(D_, T)_{\\text{Exp}}}$, where $C_{L_{\\text{Exp}}}$ helps to identify irrelevant neurons in a specific language ($\\tilde{\\mathcal{N}}_{L_{\\text{Exp}}}$) and $C_{(D_, T)_{\\text{Exp}}}$ helps to identify irrelevant neurons in a specific domain-task combination ($\\tilde{\\mathcal{N}}_{D_{\\text{Exp}}\\cap T_{\\text{Exp}}}$). Formally speaking, \\texttt{Cus-Prun} in Equation \\ref{equ:cus} is transferred to \n\\begin{equation}\n\\scalebox{0.85}{$ % Adjust 0.94 to the desired scaling factor\n\\begin{aligned}\n\\mathcal{LLM}_{\\text{Exp}} & = \n\\mathcal{LLM} \\ominus \n\\left\\{\n\\tilde{\\mathcal{N}}_{L_{\\text{Exp}}} \\cap \n\\left( \n\\tilde{\\mathcal{N}}_{D_{\\text{Exp}}} \\cap \\tilde{\\mathcal{N}}_{T_{\\text{Exp}}}\n\\right)\n\\right\\} \\\\\n& \\equiv \n\\mathcal{LLM} \\ominus \n\\left\\{\n\\tilde{\\mathcal{N}}_{L_{\\text{Exp}}} \\cap \n\\tilde{\\mathcal{N}}_{D_{\\text{Exp}} \\cap T_{\\text{Exp}}}\n\\right\\}.\n\\end{aligned}\n$}\n\\label{equ:cross-lingual}\n\\end{equation}\nNote that this simplification is also applicable to $C_{D_{\\text{Exp}}}$, $C_{(L, T)_{\\text{Exp}}}$ and $C_{T_{\\text{Exp}}}$, $C_{(L, D)_{\\text{Exp}}}$. \n\n\\subsection{Experiment Setup} \n\n\\paragraph{Benchmarks} Although the primary goal of \\texttt{Cus-Prun} is to create expert LLMs tuned to specific use cases, we also evaluate the pruned models on standard general capabilities to ensure minimal performance loss. Specifically, we employ ARC-Challenge~\\citep{clark2018think} (5-shots), GSM8K~\\citep{cobbe2021training} (5-shots with CoT prompting~\\citep{wei2022chain}), and MMLU~\\citep{hendrycksmeasuring} (5-shots) to represent models general capability. \nNote that we utilize a generation task and implement CoT prompting method, a more challenging setting that has not been previously evaluated by existing pruning techniques~\\citep{songsleb, sharmatruth, yang2024laco,zhang2024finercut}. \n\n\n\\paragraph{Baselines} We employ several state-of-the-art pruning methods as the baseline that do not require post-training after pruning the model. (i) Dense represents the original model without pruning; (ii) LLM-Pruner~\\citep{ma2023llm} adopts structural pruning that selectively removes non-critical coupled structures based on gradient information;\\footnote{To ensure a fair comparison, we evaluate its performance before post-training, following \\citet{men2024shortgpt}.} (iii) SliceGPT~\\citep{ashkboosslicegpt} replaces each weight matrix with a smaller dense matrix, reducing the embedding dimension of the network; (iv) ShortGPT~\\citep{men2024shortgpt} directly deletes the redundant layers in LLMs\nbased on an importance score.\nWe follow the typical pruning setting from these previous studies to set the pruning ratio to $25\\%$ for all methods and all models. \n\n\\paragraph{Backbone Models} We choose 4 models from different model series and different sizes, including Llama3-8B-Base~\\citep{dubey2024llama}, Mistral-Nemo-Base-2407\\footnote{\\url{https://huggingface.co/mistralai/Mistral-Nemo-Base-2407}}(short as Mistral-12B), Llama2-13B-Base~\\citep{touvron2023llama}, Llama3-70B-Base~\\citep{dubey2024llama}. \n\n\n\n\n\n\\subsection{Multilingual Setting}\n\n\n\\paragraph{Dataset}\nWe employ multiple representative multilingual datasets for multilingual setting, which covers reasoning (MGSM~\\citep{shilanguage}, 5-shots), multilingual knowledge (M3Exam~\\citep{zhang2023m3exam}, 3-shots, abbreviated as M3), understanding (XQuAD~\\citep{artetxe2020cross}, 5-shots), and generation (XLSum~\\citep{hasan2021xl}, zero-shots, abbreviated as Sum). Furthermore, we consider three languages spanning a range from high-resource to low-resource including German (De), Chinese (Zh) and Thai (Th). \nMore detailed experiment settings are explained in Appendix \\ref{sec:appen_setting_1}.\n\n\n\n\\paragraph{Main Results} \n\n\nTable \\ref{table:cross_ling} summarizes the performance of \\texttt{Cus-Prun} on multilingual datasets, with average scores across languages. Detailed breakdown results for each language are shown in Table~\\ref{table:cl_ger}, Table~\\ref{table:cl_chi} and Table~\\ref{table:cl_tha} in Appendix~\\ref{sec:appen_cl}.\nWe can observe that \\texttt{Cus-Prun} consistently outperforms baseline pruning methods in achieving expert capabilities while preserving general performance. For example, on Llama3-8B, \\texttt{Cus-Prun} achieves a score of \\(38.9\\) compared to at most \\(15.5\\) for other methods. Similar improvements are observed for Mistral-12B (\\(34.3\\) vs. \\( 18.0\\)), Llama2-13B (\\(24.3\\) vs. \\( 15.5\\)), and Llama3-70B (\\(48.7\\) vs. \\( 33.1\\)). We can also see that the generation tasks are especially challenging; for instance, \\texttt{Cus-Prun} achieves a score of \\(30.1\\) on MGSM for Llama3-8B, while other methods nearly lose the ability to generate coherent reasoning outputs (often approaching \\(0\\) accuracy for all but the largest model). Additionally, \\texttt{Cus-Prun} performs robustly across both high-resource and low-resource languages. Overall, \\texttt{Cus-Prun} excels in both performance and robustness across diverse tasks and languages.\n\n\n\n\\subsection{MultilDomain Setting}\n\n\\paragraph{Dataset} For the multidomain setting, we employ several domain-specific datasets, including medical domain multiply choices questions (MedMCQ~\\citep{pmlr-v174-pal22a}, 3-shots, abbreviated as MMCQ), finance domain table question-answering (FinTQA~\\citep{chen2021finqa}, 8-shots, abbreviated as FTQA), social media domain sentiment analysis (TSA~\\citep{kharde975sentiment}, 3-shots), and e-commerce domain sentiment analysis (AMSA~\\citep{zhang2015character}, 3-shots). Moreover, in multidomain setting, our focus is exclusively on the English language. Detailed experiment settings are explained in Appendix \\ref{sec:appen_setting_2}.\n\n\n\n\\paragraph{Main Results} Table \\ref{table:cross_ling} shows the performance of \\texttt{Cus-Prun} on multidomain setting. We find that \\texttt{Cus-Prun} consistently outperforms other pruning methods in both expert and general capabilities. For expert capabilities, \\texttt{Cus-Prun} achieves a score of $53.2$ on Llama3-8B, while other pruning methods achieve at most $34.5$. Similar improvements are observed for Mistral-12B (\\(56.0\\) vs. \\(32.6\\)), Llama2-13B (\\(33.1\\) vs. \\(30.3\\)), and Llama3-70B (\\(70.3\\) vs. \\(55.8\\)).\n\n\n\n\\begin{figure*}[t]\n \\centering\n \\begin{minipage}{0.4\\textwidth}\n \\centering\n \\captionof{table}{Performance of Chinese-Medical expert model on MCQ task}\n \\label{tab:cmexam}\n \\scalebox{0.96}{\n \\begin{tabular}{l|c|c}\n \\hline\n \\textbf{Method} & \\textbf{General} & \\textbf{CMExam} \\\\\\midrule\n \\rowcolor{lightgray}\\cellcolor{white} Dense & 59.3 & 50.6 \\\\\n LLM-Pruner & 18.6 & 25.0 \\\\\n SliceGPT & 27.8 & 26.9 \\\\\n ShortGPT & 23.9 & 23.7 \\\\\n \\rowcolor{lightblue}\\cellcolor{white} \\texttt{Cus-Prun} & \\textbf{52.4} & \\textbf{48.7} \\\\\n \\hline\n \\end{tabular}}\n \\end{minipage}\n \\hfill\n \\begin{minipage}{0.58\\textwidth}\n \\centering\n \\includegraphics[width=\\textwidth]{Figures/huatuo.pdf}\n \\caption{Chinese Medical LLM performance. Numbers are quality on the \\textbf{whole} test set evaluated by GPT4.}\n \\label{fig:huatuo}\n \\end{minipage}\n\\end{figure*}\n\n\\subsection{MultiTask Setting}\n\n\n\n\\paragraph{Dataset} For the multitask setting, we employ several task-specific datasets, including the medical summarization task (MedSum~\\citep{abacha2019summarization}, 3-shots, abbreviated as MSum), summarization task in e-commerce (Amazon Summary~\\citep{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions, brüelgabrielsson2024compressserveservingthousands}, 3-shots, abbreviated as ASum), counterfactual task in e-commerce (Amazon Counterfactual~\\citep{o2021wish}, 3-shots, abbreviated as AMCF). Similarly, in multitask setting scenarios, our focus is exclusively on the English language. Detailed experiment settings are explained in Appendix \\ref{sec:appen_setting_3}.\n\n\n\n\\paragraph{Main Results} Table \\ref{table:cross_ling} shows the performance of \\texttt{Cus-Prun} on multitask setting. We find that except for LLM-Pruner under Llama3-8B, \\texttt{Cus-Prun} outperforms other pruning methods in both expert and general capabilities. For expert tasks, \\texttt{Cus-Prun} achieves a score of \\(50.9\\) on \\texttt{Mistral-12B}, significantly outperforming the highest score of \\(23.0\\) from other methods. Likewise, it attains \\(38.0\\) on \\texttt{Llama2-13B} and \\(57.9\\) on \\texttt{Llama3-70B}, both markedly higher than the corresponding maximum scores of \\(22.7\\) and \\(46.8\\) achieved by competing methods.\n\n\n\n\\subsection{Analysis on Aggressive Pruning Ratio}\n\n\nTo optimize for specialized tasks rather than maintaining general capabilities, we employ more aggressive pruning ratios. We combine layer pruning with our custom neuron pruning method in Algorithm \\ref{algo:adaptive_pruning} and evaluate the approach on M3Exam, MedMCQ, and Amazon Counterfactual (AMContFact) datasets using Llama3-8B. Detailed results are shown in Table \\ref{table:prune_more}. We find that \\texttt{Cus-Prun} consistently maintains the model's capabilities even at higher pruning ratios. Specifically, when the pruning ratio is increased to $45\\%$, ShortGPT nearly loses the capability of generating meaningful answers, while \\texttt{Cus-Prun} still achieves scores of $48.4$ on MMLU and $50.6$ on expert capabilities.\n\n\\begin{table}[ht]\n\\caption{Aggressive pruning ratio on Llama3-8B.}\n\\centering\n\\setlength{\\tabcolsep}{3pt}\n \\scalebox{0.9}{\n\\begin{tabular}{l|c|c|c|c}\n \\hline\n \\textbf{Method} & \\textbf{Ratio} & \\textbf{Speedup} & \\textbf{MMLU} & \\textbf{Expert} \\\\\\midrule\n \\rowcolor{lightgray}\\cellcolor{white}Dense & 0.0 & 1$\\times$ & 63.1 & 59.7 \\\\\\midrule\n ShortGPT & 25.0 & 1.3$\\times$ & 28.6 & 24.6 \\\\\n \\rowcolor{lightblue}\\cellcolor{white}\\texttt{Cus-Prun} & 25.0 & 1.3$\\times$ & \\textbf{51.9} & \\textbf{53.3} \\\\\\midrule\n ShortGPT & 34.2 & 1.5$\\times$ & 20.8 & 18.5 \\\\\n \\rowcolor{lightblue}\\cellcolor{white}\\texttt{Cus-Prun} & 35.0 & 1.5$\\times$ & \\textbf{50.2} & \\textbf{51.4} \\\\\\midrule\n ShortGPT & 43.8 & 1.8$\\times$ & 7.9 & 10.2 \\\\\n \\rowcolor{lightblue}\\cellcolor{white}\\texttt{Cus-Prun} & 45.0 & 1.8$\\times$ & \\textbf{48.4} & \\textbf{50.6} \\\\\n \\hline\n \\end{tabular}}\n \\label{table:prune_more}\n\\end{table}\n\n\n\n\\section{Adaptive Custom Pruning Assessment}\n\nIn this section, we evaluate the generality of \\texttt{Cus-Prun} in dynamic scenarios, including specific expert models in two and one dimensions, as described in Section \\ref{sec:adaptive}.\n\n\\subsection{Two Dimensions Specific Expert Model}\n\n\\paragraph{Experiment Settings} We use the Chinese-Medical setting as a concrete example of a two-dimensional expert model designed to perform a wide range of medical tasks in Chinese. \nWe adopt Mistral-12b as the backbone model and utilize corpus from Wikipedia for Chinese content and general medical corpus for medical knowledge. The performance of the target Chinese-Medical expert model is evaluated on two datasets: CMExam~\\citep{liu2023benchmarking} (5-shots), a Chinese medical multiple-choice question dataset, and HuatuoQA~\\citep{li2023huatuo26m}, a Chinese medical question-answering dataset. We assess the performance on CMExam using accuracy metrics. For the latter, we sample a sub-testset of size $100$ and use GPT-4 as the evaluator, which assigns a score from $0$ to $5$, representing its quality from low to high. Detailed prompts are listed in Appendix \\ref{sec:appen_prompt}.\n\n\n\n\\paragraph{Main Results} Table \\ref{tab:cmexam} presents the performance of the Chinese-Medical LLM on CMExam and its general capabilities. Our results indicate that the expert model pruned using \\texttt{Cus-Prun} outperforms models obtained through other pruning methods. Specifically, \\texttt{Cus-Prun} achieves a score of $48.7$ on CMExam, while its general capability score is $52.4$. These results compare favorably to the dense model, which scores $50.6$ on CMExam and $59.3$ on general capabilities. On the contrary, other pruning methods nearly lose the general and specific capabilities. Furthermore, Figure \\ref{fig:huatuo} shows a concrete example of Chinese-Medical LLM performance on medical question-answering. We find that \\texttt{Cus-Prun} can produce smaller expert models that maintain their expert capabilities, as demonstrated by its performance score of $2.9/5.0$ compared to $3.2/5.0$ for the dense model.\n\n\n\n\n\n\\subsection{One Dimension Specific Expert Model}\n\n\\begin{figure*}[t]\n \\centering\n \\includegraphics[width=1.0\\textwidth]{Figures/lang-specific.pdf}\n \\caption{Performance of \\texttt{Cus-Prun} in obtaining language-specific models.}\n \\label{fig:lang-spec}\n\\end{figure*}\n\n\n\\paragraph{Experiment Settings} \n\n\nFor evaluating the pruning method under a one-dimensional expert model setting, we focus on language-specific pruning, showing how to transform a dense model into language-specific variants.\nWe consider three linguistically diverse languages: German, Chinese, and Thai. We conduct experiments based on the Llama3-8b model.\nTo identify language-specific (while domain- and task-agnostic) neurons, we employ a diverse range of corpora, including Wikipedia, MGSM, and M3Exam, ensuring coverage of various domains and tasks. The effectiveness of our pruning technique is then evaluated using three held-out multilingual datasets including XQuAD~\\citep{artetxe2020cross}, XNLI~\\citep{conneau2018xnli}, and XSum ~\\citep{narayan2018don}.\n\n\n\\paragraph{Main Results} \nFigure \\ref{fig:lang-spec} illustrates the performance of language-specific models using \\texttt{Cus-Prun}. By pruning $25\\%$ of the neurons from the original model, \\texttt{Cus-Prun} not only retains general performance but also preserves language-specific capabilities. For instance, the German-specific model scores $54.7$ in general capabilities, $48.3$ on XQuAD, and $56.8$ on XNLI, compared to the dense model's scores of $64.1$, $52.9$, and $62.0$, respectively. This trend is consistent for Chinese and Thai models as well. In contrast, ShortGPT struggles to maintain the model's capabilities, particularly in XQuAD and XLSUm, which require generative abilities.\n\n\n\\section{Related Work}\n\n\\paragraph{LLM Compression} Given the high costs of training, inferencing, and tuning LLMs, many studies focus on model compression methods, including compression~\\citep{zhu2023survey}, quantization~\\citep{xu2023qa, dettmers2024qlora, lin2024awq, li2024evaluating}, and pruning~\\citep{wang2019structured}. Sparsity-based structural pruning enhances GPU efficiency with sparse structures but doesn't always reduce parameter counts \\citep{li2022parameter, li2023losparse, kurz2024language, zhao2024convex, huang2024pruning}. Unstructured pruning reduces parameters while maintaining performance, using post-training techniques \\citep{ma2023llm, xiasheared, muralidharan2024compact} or coarse methods like parameter approximation \\citep{zhao2024convex}, layer removal \\citep{men2024shortgpt}, or structural elimination \\citep{zhang2024finercut}. However, these often fail to preserve domain- or task-specific capabilities, limiting their utility for specialized scenarios.\n\n\n\\paragraph{Customizing Model} Customizing LLMs is essential for addressing language-specific challenges, domain-specific needs, and task-specific applications \\citep{cui2023efficient, yang2024laco, li2023huatuo26m, roziere2023code, li2023large, azerbayevllemma, alves2024tower,naacl-sentiment}. However, customization often demands extensive fine-tuning with curated data, making it resource-intensive. This highlights the need for efficient methods to quickly create robust, expert models tailored to diverse industries without compromising quality.\n\n\\section{Conclusion}\n\nLLMs deliver impressive capabilities but incur high computational costs. Efficient pruning of redundant parameters is vital for conserving resources and improving inference speed, especially for specialized models. Our method, \\texttt{Cus-Prun}, generates smaller expert models without post-training by pruning irrelevant neurons across ``language'', ``domain'', and ``task'' dimensions. This finer-grained approach outperforms existing techniques on three-dimensional models and can adapt to realistic scenarios, such as language-domain or language-specific models.\n\n\n\\section*{Limitation}\n\nDespite the promising results of Cus-Prun, several limitations should be noted. First, while our method leverages three dimensions (language, domain, and task) for pruning, certain crucial restrictions cannot be fully captured within this framework, such as variations in query format or input structure. Second, whether pruned base models can effectively undergo post-training remains an open question that requires further investigation. This uncertainty about post-training capabilities could limit the model's adaptability to new scenarios or requirements after pruning. These limitations suggest important directions for future research, including exploring additional dimensions for more comprehensive pruning strategies and investigating the relationship between pruning and post-training effectiveness.\n\n\\section*{Acknowledgement}\nThis research is supported by the Ministry of Education, Singapore, under its Academic Research Fund (AcRF) Tier 1 grant, and funded through the SUTD Assistant Professorship Scheme (SAP 2025\\_001).\nThis material is based upon work partially supported by the Air Force Office of Scientific Research under award number FA2386-24-1-4011, and this research is partially supported by the Singapore Ministry of Education Academic Research Fund Tier 1 (Award No: T1 251RES2207).\n\n\n\\bibliography{custom}\n\n\n\\appendix\n\\section{Appendix}\n\n\\subsection{GPT-4o Prompts}\\label{sec:appen_prompt}\nThe prompts used for generating documents and evaluating answer quality are presented in Table~\\ref{table:testset}.\n\n\\begin{table}[ht]\n \\centering\n\\footnotesize\n \\scalebox{1}{\n \\begin{tabular}{lp{5.3cm}}\n \\toprule\n \\textbf{Task} & \\textbf{Prompt} \\\\\\midrule\n Generation & Generate a text document in \\{language\\}/\\{domain\\}/\\{task\\}. Make sure the documents is not fixed to one \\{language\\}/\\{domain\\}/\\{task\\} or \\{language\\}/\\{domain\\}/\\{task\\}. Ensure the content is clear, concise, and appropriate for the specified request. Use professional and domain-specific terminology where necessary. \\\\\\midrule\n Evaluation & Evaluate the quality of the given answer to the question. Provide a score from 0 to 5, where 0 represents very low quality and 5 represents very high quality. Question: \\{question\\} Answer: \\{answer\\}.\\\\\n \\bottomrule\n \\end{tabular}}\n\\caption{GPT-4o prompts for generating documents and evaluating answer quality.} \\label{table:testset}\n\\end{table}\n\n\\subsection{Detailed Results for Multilingual}\\label{sec:appen_cl}\n\nDetailed results for multilingual settings can be found in Table \\ref{table:cl_ger}, Table \\ref{table:cl_chi} and Table \\ref{table:cl_tha} for German, Chinese and Thai, respectively.\n\n\\begin{table*}[hbpt]\n\\caption{Main Results of \\texttt{Cus-Prun} on Germany with a pruning ratio of $25\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in XLSum and in accuracy (\\%) for other datasets.}\n \\centering\n\\footnotesize\n\\setlength{\\tabcolsep}{3pt}\n \\scalebox{1.0}{\n \\begin{tabular}{l|l|cccc|ccccc}\n\\toprule\n\\multirow{2}{*}{\\textbf{\\normalsize{Model}}} & \\multirow{2}{*}{\\textbf{\\normalsize{Method}}} & \n \\multicolumn{4}{c}{\\textbf{\\normalsize{General Capability}}} \\vline & \\multicolumn{5}{c}{\\textbf{\\normalsize{Expert Capability}}} \\\\ \n & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3Exam & XQuAD & XLSum & Avg. \\\\ \\midrule\n\\multirow{4}{*}{Llama3-8B} &\n Dense & 70.7 & 58.3 & 63.1 & 64.1 & 44.8 & - & 52.9 & - & 48.8 \\\\\n & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 0.0 & - & 11.0 & - & 5.5 \\\\\n & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & - & 9.8 & - & 4.9 \\\\\n & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & - & 0.0 & - & 0.0 \\\\\n & \\texttt{Cus-Prun} & 61.4 & 38.9 & 54.5 & \\textbf{51.6} & 32.8 & - & 49.6 & - & \\textbf{41.2} \\\\ \\midrule\n\\multirow{4}{*}{Mistral-12B} & Dense & 82.6 & 68.5 & 50.4 & 59.3 & 56.8 & - & 41.2 & - & 49.0 \\\\\n & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 2.4 & - & 13.4 & - & 7.9 \\\\\n & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 0.8 & - & 15.5 & - & 8.2 \\\\\n & ShortGPT & 37.8 & 0.0 & 33.9 & 23.9 & 3.6 & - & 20.3 & - & 12.0 \\\\\n & \\texttt{Cus-Prun} & 64.6 & 39.7 & 43.2 & \\textbf{49.2} & 31.6 & - & 35.9 & - & \\textbf{33.8} \\\\ \\midrule\n\\multirow{4}{*}{Llama2-13B} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 24.4 & - & 40.3 & - & 32.3 \\\\\n & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 2.0 & - & 5.7 & - & 3.9 \\\\\n & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 3.6 & - & 18.1 & - & 10.9 \\\\\n & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 2.8 & - & 27.2 & - & 15.0 \\\\\n & \\texttt{Cus-Prun} & 47.6 & 19.8 & 49.9 & \\textbf{39.1} & 18.4 & - & 31.7 & - & \\textbf{25.0} \\\\ \\midrule\n\\multirow{4}{*}{Llama3-70B} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 74.8 & - & 58.2 & - & 66.5 \\\\\n & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 18.0 & - & 27.3 & - & 22.7 \\\\\n & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 0.0 & - & 17.3 & - & 8.7 \\\\\n & ShortGPT & 59.4 & 5.6 & 75.5 & 46.8 & 9.6 & - & 31.5 & - & 20.6 \\\\\n & \\texttt{Cus-Prun} & 66.8 & 59.3 & 69.1 & \\textbf{65.1} & 48.2 & - & 53.9 & - & \\textbf{51.1} \\\\ \n\\bottomrule\n\\end{tabular}}\n \\label{table:cl_ger}\n\\end{table*}\n\n\n\\begin{table*}[hbpt]\n\\caption{Main Results of \\texttt{Cus-Prun} on Chinese with a pruning ratio of $25\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in XLSum and in accuracy (\\%) for other datasets.}\n \\centering\n\\footnotesize\n\\setlength{\\tabcolsep}{3pt}\n \\scalebox{1.0}{\n \\begin{tabular}{l|l|cccc|ccccc}\n\\toprule\n\\multirow{2}{*}{\\textbf{\\normalsize{Model}}} & \\multirow{2}{*}{\\textbf{\\normalsize{Method}}} & \n \\multicolumn{4}{c}{\\textbf{\\normalsize{General Capability}}} \\vline & \\multicolumn{5}{c}{\\textbf{\\normalsize{Specific Capability}}} \\\\ \n & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3Exam & XQuAD & XLSum & Avg. \\\\ \\midrule\n\\multirow{4}{*}{Llama3-8B} &\n Dense & 70.7 & 58.3 & 63.1 & 64.1 & 43.6 & 55.1 & 78.7 & 49.1 & 56.6 \\\\\n & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 2.4 & 23.6 & 21.3 & 32.8 & 20.0 \\\\\n & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & 17.4 & 23.5 & 8.3 & 12.3 \\\\\n & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & 28.3 & 0.0 & 3.1 & 7.9 \\\\\n & \\texttt{Cus-Prun} & 60.5 & 25.7 & 49.4 & \\textbf{45.2} & 36.0 & 44.7 & 65.6 & 46.3 & \\textbf{48.2} \\\\ \\midrule\n\\multirow{4}{*}{Mistral-12B} & Dense & 82.6 & 68.5 & 50.4 & 59.3 & 53.2 & 47.8 & 62.2 & 33.0 & 49.1 \\\\\n & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 2.8 & 30.7 & 31.8 & 32.6 & 24.5 \\\\\n & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 1.6 & 26.4 & 28.3 & 10.8 & 16.8 \\\\\n & ShortGPT & 37.8 & 0.0 & 33.9 & 23.9 & 4.4 & 28.2 & 29.1 & 7.2 & 17.2 \\\\\n & \\texttt{Cus-Prun} & 68.3 & 43.2 & 39.5 & \\textbf{50.3} & 38.4 & 40.7 & 50.6 & 30.3 & \\textbf{40.0} \\\\ \\midrule\n\\multirow{4}{*}{Llama2-13B} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 21.6 & 36.5 & 59.8 & 35.3 & 38.3 \\\\\n & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 1.2 & 23.3 & 3.8 & 25.1 & 13.4\\\\\n & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 4.8 & 24.5 & 28.4 & 11.2 & 17.2 \\\\\n & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 4.4 & 22.9 & 24.6 & 13.7 & 16.4 \\\\\n & \\texttt{Cus-Prun} & 48.6 & 20.7 & 51.9 & \\textbf{40.4} & 14.8 & 28.2 & 47.3 & 34.4 & \\textbf{31.2} \\\\ \\midrule\n\\multirow{4}{*}{Llama3-70B} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 68.4 & 76.1 & 81.3 & 55.3 & 70.3 \\\\\n & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 16.8 & 47.5 & 56.1 & 41.3 & 40.4 \\\\\n & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 6.4 & 48.3 & 42.2 & 29.3 & 31.6 \\\\\n & ShortGPT & 59.4 & 5.6 & 75.5 & 46.8 & 12.4 & 45.5 & 44.6 & 36.1 & 34.7 \\\\\n & \\texttt{Cus-Prun} & 72.3 & 48.5 & 65.2 & \\textbf{62.0} & 40.8 & 61.7 & 66.9 & 51.6 & \\textbf{55.3} \\\\ \n\\bottomrule\n\\end{tabular}}\n \\label{table:cl_chi}\n\\end{table*}\n\n\n\\begin{table*}[hbpt]\n\\caption{Main Results of \\texttt{Cus-Prun} on Thai with a pruning ratio of $25\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in Rouge-L in XLSum and in accuracy (\\%) for other datasets.}\n \\centering\n\\footnotesize\n\\setlength{\\tabcolsep}{3pt}\n \\scalebox{1.0}{\n \\begin{tabular}{l|l|cccc|ccccc}\n\\toprule\n\\multirow{2}{*}{\\textbf{\\normalsize{Model}}} & \\multirow{2}{*}{\\textbf{\\normalsize{Method}}} & \n \\multicolumn{4}{c}{\\textbf{\\normalsize{General Capability}}} \\vline & \\multicolumn{5}{c}{\\textbf{\\normalsize{Specific Capability}}} \\\\ \n & & ARC-c & GSM8K & MMLU & Avg. & MGSM & M3Exam & XQuAD & XLSum & Avg. \\\\ \\midrule\n\\multirow{4}{*}{Llama3-8B} &\n Dense & 70.7 & 58.3 & 63.1 & 64.1 & 35.2 & 43.0 & 58.7 & 16.7 & 38.4 \\\\\n & LLMPrun. & 26.3 & 2.5 & 24.2 & 17.7 & 0.8 & 24.4 & 8.4 & 13.5 & 11.8 \\\\\n & SliceGPT & 41.5 & 0.0 & 24.2 & 21.9 & 0.0 & 12.3 & 16.6 & 8.7 & 9.4 \\\\\n & ShortGPT & 38.3 & 0.0 & 28.6 & 22.3 & 0.0 & 25.4 & 0.0 & 2.3 & 6.9 \\\\\n & \\texttt{Cus-Prun} & 58.9 & 31.2 & 52.4 & \\textbf{47.5} & 21.6 & 38.3 & 42.6 & 16.8 & \\textbf{29.8} \\\\ \\midrule\n\\multirow{4}{*}{Mistral-12B} & Dense & 82.6 & 68.5 & 50.4 & 59.3 & 45.2 & 39.9 & 44.1 & 17.8 & 36.8 \\\\\n & LLMPrun. & 22.5 & 2.7 & 30.7 & 18.6 & 1.2 & 24.8 & 11.9 & 13.7 & 12.9 \\\\\n & SliceGPT & 49.4 & 1.9 & 32.1 & 27.8 & 0.0 & 23.8 & 8.4 & 4.7 & 12.3 \\\\\n & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 0.8 & 25.7 & 4.7 & 2.8 & 8.5 \\\\\n & \\texttt{Cus-Prun} & 68.2 & 35.8 & 47.6 & \\textbf{50.5} & 32.8 & 37.7 & 35.6 & 15.9 & \\textbf{30.5} \\\\ \\midrule\n\\multirow{4}{*}{Llama2-13B} & Dense & 50.3 & 31.4 & 53.4 & 45.1 & 6.4 & 24.3 & 28.3 & 14.5 & 18.4 \\\\\n & LLMPrun. & 22.4 & 2.1 & 23.6 & 16.0 & 0.0 & 22.3 & 1.8 & 10.2 & 8.6 \\\\\n & SliceGPT & 45.9 & 2.4 & 48.7 & 32.3 & 0.0 & 26.2 & 23.7 & 8.6 & 14.6 \\\\\n & ShortGPT & 39.5 & 3.8 & 37.2 & 26.8 & 0.0 & 23.1 & 22.3 & 8.9 & 13.6 \\\\\n & \\texttt{Cus-Prun} & 47.8 & 20.9 & 50.7 & \\textbf{39.8} & 4.8 & 24.2 & 23.6 & 13.8 & \\textbf{16.6} \\\\ \\midrule\n\\multirow{4}{*}{Llama3-70B} & Dense & 84.1 & 82.7 & 78.8 & 81.9 & 65.2 & 66.1 & 67.8 & 17.8 & 54.2 \\\\\n & LLMPrun. & 69.1 & 26.0 & 53.2 & 49.4 & 15.6 & 39.9 & 29.8 & 16.6 & 25.5 \\\\\n & SliceGPT & 65.7 & 0.0 & 54.2 & 40.0 & 4.8 & 41.3 & 39.6 & 13.2 & 24.7 \\\\\n & ShortGPT & 59.4 & 5.6 & 75.5 & 46.8 & 13.7 & 40.7 & 40.4 & 11.9 & 26.7 \\\\\n & \\texttt{Cus-Prun} & 73.3 & 58.7 & 68.4 & \\textbf{66.8} & 40.4 & 53.6 & 58.5 & 16.9 & \\textbf{42.4} \\\\ \n\\bottomrule\n\\end{tabular}}\n \\label{table:cl_tha}\n\\end{table*}\n\n\\subsection{Experiments Detailed Settings}\\label{sec:appen_setting}\n\n\\subsubsection{Multilingual Settings}\\label{sec:appen_setting_1}\n\n\\paragraph{Experiment Details} For multilingual setting, we can obtain two corpora: $C_{L_{\\text{Exp}}}$ and $C_{(D, T)_{\\text{Exp}}}$. The first corpus contains samples in a specific language across various domains and tasks, while the second corpus contains samples from a specific domain-task combination in other languages, i.e., the target dataset in other languages. Specifically, for $C_{L_{\\text{Exp}}}$ we employ Wikipedia\\footnote{\\url{https://huggingface.co/datasets/wikimedia/wikipedia}} to construct language-specific corpus covering various domains and tasks. For $C_{(D, T)_{\\text{Exp}}}$, we employ the corresponding datasets in English, including GSM8K~\\citep{cobbe2021training} for MGSM, the English split of M3Exam\\footnote{M3Exam is language-specific and does not utilize a translated parallel corpus.} for M3Exam, SQuAD~\\citep{rajpurkar2016squad} for XQuAD, and XSum~\\citep{narayan2018don} for XLSum. \n\nHyperparameters, including the sizes of $C_{L_{\\text{Exp}}}$ and $C_{(D, T)_{\\text{Exp}}}$, are determined using the validation set of the XLSum dataset and then applied to testsets in other multilingual datasets. Furthermore, accuracy is the metric used for ARC-c, GSM8K, MMLU, MGSM, M3Exam, and XQuAD, while Rouge-L~\\citep{lin2004rouge} is used for XLSum. \n\n\\subsubsection{Multidomain Settings}\\label{sec:appen_setting_2}\n\n\\paragraph{Settings} For multidomain setting, we can obtain two corpora: $C_{D_{\\text{Exp}}} = \\{(L, D_{\\text{Exp}}, T)|L\\in\\mathbb{L}, T\\in\\mathbb{T}\\}$ and $C_{(L, T)_{\\text{Exp}}}=\\{(D, (L, T)_{\\text{Exp}})|D\\in\\mathbb{D}\\}$. The first corpus contains samples in a specific domain across various languages and tasks, while the second corpus contains samples from a specific language-task combination across different domains, i.e., the target dataset in other domains. Specifically, for $C_{D_{\\text{Exp}}}$ we employ specific domain corpus, including English split of medical corpus~\\citep{garcia2024medical} for medical domain, general finance corpus for finance domain\\footnote{\\url{https://huggingface.co/datasets/gbharti/finance-alpaca}}, general Twitter corpus~\\citep{kharde975sentiment}, and English split of Amazon corpus~\\citep{marc_reviews}. For $C_{(L, T)_{\\text{Exp}}}$, we employ the corresponding datasets in general domains, including CommonsenseQA~\\citep{talmor2019commonsenseqa} for MedMCQ, open table question-answering OTT-QA~\\citep{chen2020open} for FinTQA, general sentiment analysis~\\citep{attia2018multilingual} for TSA and AMSA.\n\n\\paragraph{Experiment Details} Hyperparameters, including the sizes of $C_{D_{\\text{Exp}}}$ and $C_{(L, T)_{\\text{Exp}}}$, are determined using the validation set of the Amazon sentiment analysis dataset and then applied to testsets in other multidomain datasets. Furthermore, accuracy is the metric used for all datasets. \n\n\n\n\\subsubsection{Multitask Settings}\\label{sec:appen_setting_3}\n\n\\paragraph{Settings} For multitask setting, we can obtain two corpora: $C_{T_{\\text{Exp}}} = \\{(L, D, T_{\\text{Exp}})|L\\in\\mathbb{L}, D\\in\\mathbb{D}\\}$ and $C_{(L, D)_{\\text{Exp}}}=\\{(T, (L, S)_{\\text{Exp}})|T\\in\\mathbb{T}\\}$. The first corpus contains samples in a specific task across various languages and domains, while the second corpus contains samples from a specific language-domain combination across different tasks, i.e., the target dataset in other tasks. Specifically, for $C_{T_{\\text{Exp}}}$ we employ specific task corpus, including XSum corpus~\\citep{abacha2019summarization} for summarization task, general conterfact corpus\\footnote{\\url{https://huggingface.co/datasets/azhx/counterfact-easy}} for counterfactual task. For $C_{(L, D)_{\\text{Exp}}}$, we employ the corresponding datasets in other tasks, including MedQCQ~\\citep{pmlr-v174-pal22a} for MedSum, AMSA~\\citep{zhang2015character} for AMSum and AMContFact.\n\n\\paragraph{Experiment Details} Hyperparameters, including the sizes of $C_{T_{\\text{Exp}}}$ and $C_{(L, D)_{\\text{Exp}}}$, are determined using the validation set of the Amazon counterfactual dataset and then applied to testsets in other multitask setting datasets. Furthermore, accuracy is the metric used for ARC-c, GSM8K, MMLU, and AMContFact, while Rouge-L~\\citep{lin2004rouge} is used for MedSum and AMSum. \n\n\n\n\\end{document}\n"},"question":{"kind":"string","value":"Assuming that the MMLU scores of ShortGPT follow a linear trend between pruning rates of 25% and 43.8%, what would be the predicted MMLU score if the pruning rate were increased to 50%?"},"answer":{"kind":"string","value":"1.10%"},"plan":{"kind":"string","value":"SELECT ShortGPT MMLU scores at Pruning Rates 25% and 43.8%\nCOMPUTE rate_change = (mmlu_at_43.8 - mmlu_at_25) / (43.8 - 25)\nCOMPUTE pruning_gap = 50 - 43.8\nCOMPUTE mmlu_delta = rate_change * pruning_gap\nCOMPUTE predicted_mmlu_50 = mmlu_at_43.8 + mmlu_delta\nRETURN predicted_mmlu_50\n"}}},{"rowIdx":93,"cells":{"paper":{"kind":"string","value":"2506.04050"},"relevant_tables":{"kind":"list like","value":[["\\begin{table}[ht]\n","\\centering\n","\\scriptsize\n","\\renewcommand{\\arraystretch}{1.2}\n","\\caption{Model performance by language and domain on unmodified AI text. \\textbf{Bold} indicates the best result within each language and domain block.}\n","\\label{tab:lang_domain_updated}\n","\\begin{tabular}{llcccc|llcccc}\n","\\toprule\n","\\multicolumn{6}{c}{\\textbf{English Dataset}} & \\multicolumn{6}{c}{\\textbf{Dutch Dataset}} \\\\\n","\\cmidrule(lr){1-6} \\cmidrule(lr){7-12}\n","\\textbf{Model} & \\textbf{Domain} & \\textbf{Acc} & \\textbf{Prec} & \\textbf{Rec} & \\textbf{F1} \n"," & \\textbf{Model} & \\textbf{Domain} & \\textbf{Acc} & \\textbf{Prec} & \\textbf{Rec} & \\textbf{F1} \\\\\n","\\midrule\n","\\textbf{--- News ---} & & & & & \n","& \\textbf{--- News ---} & & & & & \\\\\n","XGBoost \n"," & news & 0.89 & 0.93 & 0.84 & 0.88 \n"," & XGBoost \n"," & news & 0.85 & 0.88 & 0.88 & 0.88 \\\\\n","BERT-base \n"," & news & 0.94 & 0.97 & 0.91 & 0.94\n"," & BERT-base \n"," & news & 0.68 & 0.79 & 0.56 & 0.66 \\\\\n","DistilBERT \n"," & news & 0.92 & 0.90 & \\textbf{0.95} & 0.92\n"," & DistilBERT \n"," & news & 0.68 & 0.73 & 0.66 & 0.69 \\\\\n","XLM-RoBERTa\n"," & news & 0.49 & 0.49 & 1.00 & 0.66\n"," & XLM-RoBERTa\n"," & news & 0.53 & 0.53 & \\textbf{1.00} & 0.69 \\\\\n","Ensemble \n"," & news & \\textbf{0.95} & \\textbf{1.00} & 0.90 & \\textbf{0.95}\n"," & Ensemble \n"," & news & \\textbf{0.99} & \\textbf{0.98} & \\textbf{1.00} & \\textbf{0.99} \\\\\n","\\midrule\n","\\textbf{--- Reviews ---} & & & & & \n","& \\textbf{--- Reviews ---} & & & & & \\\\\n","XGBoost \n"," & reviews & 0.51 & 0.57 & 0.59 & 0.58\n"," & XGBoost \n"," & reviews & 0.53 & 0.51 & 0.46 & 0.48 \\\\\n","BERT-base \n"," & reviews & 0.50 & 0.54 & 0.62 & 0.58\n"," & BERT-base \n"," & reviews & 0.40 & 0.35 & 0.30 & 0.32 \\\\\n","DistilBERT \n"," & reviews & 0.65 & 0.68 & 0.71 & 0.69\n"," & DistilBERT \n"," & reviews & 0.42 & 0.38 & 0.50 & 0.43 \\\\\n","XLM-RoBERTa\n"," & reviews & 0.56 & 0.56 & \\textbf{0.90} & 0.69\n"," & XLM-RoBERTa\n"," & reviews & 0.43 & 0.43 & \\textbf{0.60} & 0.48 \\\\\n","Ensemble \n"," & reviews & \\textbf{0.67} & \\textbf{0.71} & 0.68 & \\textbf{0.70}\n"," & Ensemble \n"," & reviews & \\textbf{0.60} & \\textbf{0.53} & 0.44 & \\textbf{0.49} \\\\\n","\\midrule\n","\\textbf{--- Twitter / X ---} & & & & & \n","& \\textbf{--- Twitter / X ---} & & & & & \\\\\n","XGBoost \n"," & twitter & 0.91 & \\textbf{0.92} & 0.98 & 0.95\n"," & XGBoost \n"," & twitter & 0.80 & 0.85 & 0.78 & 0.81 \\\\\n","BERT-base \n"," & twitter & 0.90 & 0.86 & 0.98 & 0.92\n"," & BERT-base \n"," & twitter & 0.80 & 0.85 & 0.78 & 0.81 \\\\\n","DistilBERT \n"," & twitter & 0.90 & 0.86 & 0.98 & 0.92\n"," & DistilBERT \n"," & twitter & 0.84 & 0.86 & 0.80 & 0.83 \\\\\n","XLM-RoBERTa\n"," & twitter & 0.60 & 0.53 & \\textbf{1.00} & 0.69\n"," & XLM-RoBERTa\n"," & twitter & 0.62 & 0.60 & \\textbf{0.90} & 0.72 \\\\\n","Ensemble \n"," & twitter & \\textbf{0.93} & 0.86 & \\textbf{1.00} & \\textbf{0.93}\n"," & Ensemble \n"," & twitter & \\textbf{0.87} & \\textbf{0.90} & 0.85 & \\textbf{0.87} \\\\\n","\\bottomrule\n","\\end{tabular}\n","\\end{table}\n"]],"string":"[\n [\n \"\\\\begin{table}[ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\scriptsize\\n\",\n \"\\\\renewcommand{\\\\arraystretch}{1.2}\\n\",\n \"\\\\caption{Model performance by language and domain on unmodified AI text. \\\\textbf{Bold} indicates the best result within each language and domain block.}\\n\",\n \"\\\\label{tab:lang_domain_updated}\\n\",\n \"\\\\begin{tabular}{llcccc|llcccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multicolumn{6}{c}{\\\\textbf{English Dataset}} & \\\\multicolumn{6}{c}{\\\\textbf{Dutch Dataset}} \\\\\\\\\\n\",\n \"\\\\cmidrule(lr){1-6} \\\\cmidrule(lr){7-12}\\n\",\n \"\\\\textbf{Model} & \\\\textbf{Domain} & \\\\textbf{Acc} & \\\\textbf{Prec} & \\\\textbf{Rec} & \\\\textbf{F1} \\n\",\n \" & \\\\textbf{Model} & \\\\textbf{Domain} & \\\\textbf{Acc} & \\\\textbf{Prec} & \\\\textbf{Rec} & \\\\textbf{F1} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textbf{--- News ---} & & & & & \\n\",\n \"& \\\\textbf{--- News ---} & & & & & \\\\\\\\\\n\",\n \"XGBoost \\n\",\n \" & news & 0.89 & 0.93 & 0.84 & 0.88 \\n\",\n \" & XGBoost \\n\",\n \" & news & 0.85 & 0.88 & 0.88 & 0.88 \\\\\\\\\\n\",\n \"BERT-base \\n\",\n \" & news & 0.94 & 0.97 & 0.91 & 0.94\\n\",\n \" & BERT-base \\n\",\n \" & news & 0.68 & 0.79 & 0.56 & 0.66 \\\\\\\\\\n\",\n \"DistilBERT \\n\",\n \" & news & 0.92 & 0.90 & \\\\textbf{0.95} & 0.92\\n\",\n \" & DistilBERT \\n\",\n \" & news & 0.68 & 0.73 & 0.66 & 0.69 \\\\\\\\\\n\",\n \"XLM-RoBERTa\\n\",\n \" & news & 0.49 & 0.49 & 1.00 & 0.66\\n\",\n \" & XLM-RoBERTa\\n\",\n \" & news & 0.53 & 0.53 & \\\\textbf{1.00} & 0.69 \\\\\\\\\\n\",\n \"Ensemble \\n\",\n \" & news & \\\\textbf{0.95} & \\\\textbf{1.00} & 0.90 & \\\\textbf{0.95}\\n\",\n \" & Ensemble \\n\",\n \" & news & \\\\textbf{0.99} & \\\\textbf{0.98} & \\\\textbf{1.00} & \\\\textbf{0.99} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textbf{--- Reviews ---} & & & & & \\n\",\n \"& \\\\textbf{--- Reviews ---} & & & & & \\\\\\\\\\n\",\n \"XGBoost \\n\",\n \" & reviews & 0.51 & 0.57 & 0.59 & 0.58\\n\",\n \" & XGBoost \\n\",\n \" & reviews & 0.53 & 0.51 & 0.46 & 0.48 \\\\\\\\\\n\",\n \"BERT-base \\n\",\n \" & reviews & 0.50 & 0.54 & 0.62 & 0.58\\n\",\n \" & BERT-base \\n\",\n \" & reviews & 0.40 & 0.35 & 0.30 & 0.32 \\\\\\\\\\n\",\n \"DistilBERT \\n\",\n \" & reviews & 0.65 & 0.68 & 0.71 & 0.69\\n\",\n \" & DistilBERT \\n\",\n \" & reviews & 0.42 & 0.38 & 0.50 & 0.43 \\\\\\\\\\n\",\n \"XLM-RoBERTa\\n\",\n \" & reviews & 0.56 & 0.56 & \\\\textbf{0.90} & 0.69\\n\",\n \" & XLM-RoBERTa\\n\",\n \" & reviews & 0.43 & 0.43 & \\\\textbf{0.60} & 0.48 \\\\\\\\\\n\",\n \"Ensemble \\n\",\n \" & reviews & \\\\textbf{0.67} & \\\\textbf{0.71} & 0.68 & \\\\textbf{0.70}\\n\",\n \" & Ensemble \\n\",\n \" & reviews & \\\\textbf{0.60} & \\\\textbf{0.53} & 0.44 & \\\\textbf{0.49} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textbf{--- Twitter / X ---} & & & & & \\n\",\n \"& \\\\textbf{--- Twitter / X ---} & & & & & \\\\\\\\\\n\",\n \"XGBoost \\n\",\n \" & twitter & 0.91 & \\\\textbf{0.92} & 0.98 & 0.95\\n\",\n \" & XGBoost \\n\",\n \" & twitter & 0.80 & 0.85 & 0.78 & 0.81 \\\\\\\\\\n\",\n \"BERT-base \\n\",\n \" & twitter & 0.90 & 0.86 & 0.98 & 0.92\\n\",\n \" & BERT-base \\n\",\n \" & twitter & 0.80 & 0.85 & 0.78 & 0.81 \\\\\\\\\\n\",\n \"DistilBERT \\n\",\n \" & twitter & 0.90 & 0.86 & 0.98 & 0.92\\n\",\n \" & DistilBERT \\n\",\n \" & twitter & 0.84 & 0.86 & 0.80 & 0.83 \\\\\\\\\\n\",\n \"XLM-RoBERTa\\n\",\n \" & twitter & 0.60 & 0.53 & \\\\textbf{1.00} & 0.69\\n\",\n \" & XLM-RoBERTa\\n\",\n \" & twitter & 0.62 & 0.60 & \\\\textbf{0.90} & 0.72 \\\\\\\\\\n\",\n \"Ensemble \\n\",\n \" & twitter & \\\\textbf{0.93} & 0.86 & \\\\textbf{1.00} & \\\\textbf{0.93}\\n\",\n \" & Ensemble \\n\",\n \" & twitter & \\\\textbf{0.87} & \\\\textbf{0.90} & 0.85 & \\\\textbf{0.87} \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\end{table}\\n\"\n ]\n]","isConversational":false},"tables":{"kind":"list like","value":[["\\begin{figure*}[!ht]\n","\\centering\n","\\includegraphics[width=0.7\\linewidth]{plots/model.jpeg}\n","\\vspace{-5pt}\n","\\caption{\\small The architecture of the proposed ensemble model. Outputs of three frozen BERT models (left) and three fresh BERT models (right) are concatenated and passed through dense layers, followed by a voting mechanism.}\n","\\label{fig:ms-structure}\n","\\end{figure*}\n"],["\\begin{table}[ht]\n","\\centering\n","\\scriptsize\n","\\renewcommand{\\arraystretch}{1.2}\n","\\caption{Summary of token replacement strategies}\n","\\label{tab:token_replacement}\n","\\begin{tabular}{p{4.8cm}p{9.2cm}}\n","\\toprule\n","\\textbf{Strategy} & \\textbf{Description} \\\\\n","\\midrule\n","\\textbf{Human Similar Word Replacement (HSR)} \n"," & A Word2Vec model is trained on all human-labeled texts in the training set to learn an embedding space that captures typical human usage. \\\\\n","\\textbf{Part-of-Speech Replacement (PSR)} \n"," & HSR is refined by ensuring that part-of-speech tags match. \\\\\n","\\textbf{GPT-based Replacement (GPT)} \n"," & A generative model (GPT-4o-mini) is queried using the prompt: \n"," \\texttt{\"Replace '\\{token\\}' with a more human-like word in this text: '\\{text\\}'\"}. \\\\\n","\\textbf{GPT-based Replacement with Genre Context (GPT+Genre)} \n"," & The GPT-based method is extended by including explicit genre information (e.g., \\emph{tweets} or \\emph{reviews}) in the prompt:\n"," \\texttt{\"Replace '\\{token\\}' in this \\{domain\\} text with a more human-like word: '\\{text\\}'\"}. \\\\\n","\\bottomrule\n","\\end{tabular}\n","\\end{table}\n"],["\\begin{table}[ht]\n","\\centering\n","\\scriptsize\n","\\renewcommand{\\arraystretch}{1.2}\n","\\caption{Model performance by language and domain on unmodified AI text. \\textbf{Bold} indicates the best result within each language and domain block.}\n","\\label{tab:lang_domain_updated}\n","\\begin{tabular}{llcccc|llcccc}\n","\\toprule\n","\\multicolumn{6}{c}{\\textbf{English Dataset}} & \\multicolumn{6}{c}{\\textbf{Dutch Dataset}} \\\\\n","\\cmidrule(lr){1-6} \\cmidrule(lr){7-12}\n","\\textbf{Model} & \\textbf{Domain} & \\textbf{Acc} & \\textbf{Prec} & \\textbf{Rec} & \\textbf{F1} \n"," & \\textbf{Model} & \\textbf{Domain} & \\textbf{Acc} & \\textbf{Prec} & \\textbf{Rec} & \\textbf{F1} \\\\\n","\\midrule\n","\\textbf{--- News ---} & & & & & \n","& \\textbf{--- News ---} & & & & & \\\\\n","XGBoost \n"," & news & 0.89 & 0.93 & 0.84 & 0.88 \n"," & XGBoost \n"," & news & 0.85 & 0.88 & 0.88 & 0.88 \\\\\n","BERT-base \n"," & news & 0.94 & 0.97 & 0.91 & 0.94\n"," & BERT-base \n"," & news & 0.68 & 0.79 & 0.56 & 0.66 \\\\\n","DistilBERT \n"," & news & 0.92 & 0.90 & \\textbf{0.95} & 0.92\n"," & DistilBERT \n"," & news & 0.68 & 0.73 & 0.66 & 0.69 \\\\\n","XLM-RoBERTa\n"," & news & 0.49 & 0.49 & 1.00 & 0.66\n"," & XLM-RoBERTa\n"," & news & 0.53 & 0.53 & \\textbf{1.00} & 0.69 \\\\\n","Ensemble \n"," & news & \\textbf{0.95} & \\textbf{1.00} & 0.90 & \\textbf{0.95}\n"," & Ensemble \n"," & news & \\textbf{0.99} & \\textbf{0.98} & \\textbf{1.00} & \\textbf{0.99} \\\\\n","\\midrule\n","\\textbf{--- Reviews ---} & & & & & \n","& \\textbf{--- Reviews ---} & & & & & \\\\\n","XGBoost \n"," & reviews & 0.51 & 0.57 & 0.59 & 0.58\n"," & XGBoost \n"," & reviews & 0.53 & 0.51 & 0.46 & 0.48 \\\\\n","BERT-base \n"," & reviews & 0.50 & 0.54 & 0.62 & 0.58\n"," & BERT-base \n"," & reviews & 0.40 & 0.35 & 0.30 & 0.32 \\\\\n","DistilBERT \n"," & reviews & 0.65 & 0.68 & 0.71 & 0.69\n"," & DistilBERT \n"," & reviews & 0.42 & 0.38 & 0.50 & 0.43 \\\\\n","XLM-RoBERTa\n"," & reviews & 0.56 & 0.56 & \\textbf{0.90} & 0.69\n"," & XLM-RoBERTa\n"," & reviews & 0.43 & 0.43 & \\textbf{0.60} & 0.48 \\\\\n","Ensemble \n"," & reviews & \\textbf{0.67} & \\textbf{0.71} & 0.68 & \\textbf{0.70}\n"," & Ensemble \n"," & reviews & \\textbf{0.60} & \\textbf{0.53} & 0.44 & \\textbf{0.49} \\\\\n","\\midrule\n","\\textbf{--- Twitter / X ---} & & & & & \n","& \\textbf{--- Twitter / X ---} & & & & & \\\\\n","XGBoost \n"," & twitter & 0.91 & \\textbf{0.92} & 0.98 & 0.95\n"," & XGBoost \n"," & twitter & 0.80 & 0.85 & 0.78 & 0.81 \\\\\n","BERT-base \n"," & twitter & 0.90 & 0.86 & 0.98 & 0.92\n"," & BERT-base \n"," & twitter & 0.80 & 0.85 & 0.78 & 0.81 \\\\\n","DistilBERT \n"," & twitter & 0.90 & 0.86 & 0.98 & 0.92\n"," & DistilBERT \n"," & twitter & 0.84 & 0.86 & 0.80 & 0.83 \\\\\n","XLM-RoBERTa\n"," & twitter & 0.60 & 0.53 & \\textbf{1.00} & 0.69\n"," & XLM-RoBERTa\n"," & twitter & 0.62 & 0.60 & \\textbf{0.90} & 0.72 \\\\\n","Ensemble \n"," & twitter & \\textbf{0.93} & 0.86 & \\textbf{1.00} & \\textbf{0.93}\n"," & Ensemble \n"," & twitter & \\textbf{0.87} & \\textbf{0.90} & 0.85 & \\textbf{0.87} \\\\\n","\\bottomrule\n","\\end{tabular}\n","\\end{table}\n"],["\\begin{table*}[ht]\n","\\centering\n","\\caption{\\small F1 score and Accuracy of each detector under every rewriting scenario. \n","The best metric in a column is \\textbf{bolded}; the largest negative drop is \\dashuline{\\phantom{-00\\%}}, the smallest negative drop is \\dotuline{\\phantom{-00\\%}}.\n","\\footnotesize{$\\Delta=(\\text{Scenario Value}-\\text{Original Value})\\times100\\%$.}}\n","\\label{tab:all_models_rewriting_f1acc}\n","\\renewcommand{\\arraystretch}{1.4}\n","\\resizebox{\\textwidth}{!}{\n","\\begin{tabular}{l l\n","c c c c c c c c c c}\n","\\toprule\n","\\multirow{2}{*}{\\textbf{Explain}} & \\multirow{2}{*}{\\textbf{Strategy}}\n","& \\multicolumn{2}{c}{\\textbf{XGBoost}}\n","& \\multicolumn{2}{c}{\\textbf{BERT-base}}\n","& \\multicolumn{2}{c}{\\textbf{DistilBERT}}\n","& \\multicolumn{2}{c}{\\textbf{XLM-RoBERTa}}\n","& \\multicolumn{2}{c}{\\textbf{Ensemble}}\\\\\n","\\cmidrule(lr){3-4}\\cmidrule(lr){5-6}\\cmidrule(lr){7-8}\\cmidrule(lr){9-10}\\cmidrule(lr){11-12}\n","& & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\n"," & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\n"," & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\n"," & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\n"," & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\\\\\n","\\midrule\n","\\emph{NoRewriting} & Original\n","& 0.81 & 0.80\n","& 0.74 & 0.75\n","& 0.79 & 0.77\n","& 0.68 & 0.54\n","& \\textbf{0.83} & \\textbf{0.83}\\\\\n","\\midrule\n","\\textbf{SHAP} & HSR\n","& 0.73 (\\largest{-8\\%}) & 0.75 (\\largest{-5\\%})\n","& 0.71 (-3\\%) & 0.72 (-3\\%)\n","& 0.76 (-3\\%) & 0.75 (-2\\%)\n","& 0.68 (\\smallest{0\\%}) & 0.54 (\\smallest{0\\%})\n","& \\textbf{0.78} (-5\\%) & \\textbf{0.80} (-3\\%)\\\\\n","\\textbf{SHAP} & PSR\n","& 0.78 (\\largest{-3\\%}) & 0.78 (\\largest{-2\\%})\n","& 0.73 (-1\\%) & 0.74 (-1\\%)\n","& 0.78 (-1\\%) & 0.77 (0\\%)\n","& 0.68 (\\smallest{0\\%}) & 0.54 (\\smallest{0\\%})\n","& \\textbf{0.82} (-1\\%) & \\textbf{0.82} (-1\\%)\\\\\n","\\textbf{SHAP} & GPT\n","& 0.79 (-2\\%) & 0.79 (-1\\%)\n","& 0.73 (-1\\%) & 0.74 (-1\\%)\n","& 0.77 (-2\\%) & 0.76 (-1\\%)\n","& 0.68 (\\smallest{0\\%}) & 0.55 (\\smallest{+1\\%})\n","& \\textbf{0.80} (\\largest{-3\\%}) & \\textbf{0.81} (\\largest{-2\\%})\\\\\n","\\textbf{SHAP} & GPT+Genre\n","& \\textbf{0.80} (-1\\%) & \\textbf{0.80} (0\\%)\n","& 0.73 (-1\\%) & 0.74 (-1\\%)\n","& 0.77 (-2\\%) & 0.76 (-1\\%)\n","& 0.68 (\\smallest{0\\%}) & 0.55 (\\smallest{+1\\%})\n","& 0.79 (\\largest{-4\\%}) & 0.80 (\\largest{-3\\%})\\\\\n","\\midrule\n","\\textbf{LIME} & HSR\n","& 0.25 (\\largest{-56\\%}) & 0.48 (\\largest{-32\\%})\n","& 0.56 (-18\\%) & 0.62 (-13\\%)\n","& 0.62 (-17\\%) & 0.65 (-12\\%)\n","& \\textbf{0.67} (\\smallest{-1\\%}) & 0.52 (\\smallest{-2\\%})\n","& 0.64 (-19\\%) & \\textbf{0.69} (-14\\%)\\\\\n","\\textbf{LIME} & PSR\n","& 0.56 (\\largest{-25\\%}) & 0.63 (\\largest{-17\\%})\n","& 0.64 (-10\\%) & 0.67 (-8\\%)\n","& 0.71 (-8\\%) & 0.71 (-6\\%)\n","& 0.66 (\\smallest{-2\\%}) & 0.52 (\\smallest{-2\\%})\n","& \\textbf{0.73} (-10\\%) & \\textbf{0.75} (-8\\%)\\\\\n","\\textbf{LIME} & GPT\n","& 0.57 (\\largest{-24\\%}) & 0.64 (\\largest{-16\\%})\n","& 0.73 (-1\\%) & 0.74 (-1\\%)\n","& 0.78 (-1\\%) & 0.77 (0\\%)\n","& 0.68 (\\smallest{0\\%}) & 0.55 (\\smallest{+1\\%})\n","& \\textbf{0.83} (\\smallest{0\\%}) & \\textbf{0.83} (0\\%)\\\\\n","\\textbf{LIME} & GPT+Genre\n","& 0.60 (\\largest{-21\\%}) & 0.65 (\\largest{-15\\%})\n","& \\textbf{0.75} (\\smallest{+1\\%}) & \\textbf{0.75} (0\\%)\n","& 0.77 (-2\\%) & 0.76 (-1\\%)\n","& 0.68 (0\\%) & 0.55 (\\smallest{+1\\%})\n","& \\textbf{0.81} (-2\\%) & \\textbf{0.81} (-2\\%)\\\\\n","\\midrule\n","\\textbf{Random} & HSR\n","& 0.73 (-8\\%) & 0.74 (-6\\%)\n","& 0.63 (\\largest{-11\\%}) & 0.67 (\\largest{-8\\%})\n","& 0.68 (\\largest{-11\\%}) & 0.69 (\\largest{-8\\%})\n","& 0.67 (\\smallest{-1\\%}) & 0.53 (\\smallest{-1\\%})\n","& \\textbf{0.73} (-10\\%) & \\textbf{0.75} (\\largest{-8\\%})\\\\\n","\\textbf{Random} & PSR\n","& 0.76 (-5\\%) & 0.76 (-4\\%)\n","& 0.65 (\\largest{-9\\%}) & 0.68 (\\largest{-7\\%})\n","& 0.74 (-5\\%) & 0.73 (-4\\%)\n","& 0.67 (\\smallest{-1\\%}) & 0.53 (\\smallest{-1\\%})\n","& \\textbf{0.78} (-5\\%) & \\textbf{0.79} (-4\\%)\\\\\n","\\textbf{Random} & GPT\n","& 0.74 (\\largest{-7\\%}) & 0.75 (\\largest{-5\\%})\n","& 0.74 (0\\%) & 0.75 (0\\%)\n","& 0.77 (-2\\%) & 0.76 (-1\\%)\n","& 0.68 (\\smallest{0\\%}) & 0.55 (\\smallest{+1\\%})\n","& \\textbf{0.81} (-2\\%) & \\textbf{0.82} (-1\\%)\\\\\n","\\textbf{Random} & GPT+Genre\n","& 0.75 (\\largest{-6\\%}) & 0.75 (\\largest{-5\\%})\n","& 0.73 (-1\\%) & 0.74 (-1\\%)\n","& \\textbf{0.78} (-1\\%) & 0.77 (0\\%)\n","& 0.68 (0\\%) & 0.55 (\\smallest{+1\\%})\n","& \\textbf{0.81} (-2\\%) & \\textbf{0.81} (-2\\%)\\\\\n","\\bottomrule\n","\\end{tabular}}\n","\\end{table*}\n"],["\\begin{table}[ht]\n","\\centering\n","\\scriptsize\n","\\renewcommand{\\arraystretch}{1.15} % Slightly tighter row spacing\n","\\setlength{\\tabcolsep}{4pt} % Adjust column spacing as desired\n","\n","\\caption{Flip rates (\\%) from \\textbf{AI} to \\textbf{human}, grouped by rewriting strategies. \n"," Each scenario has 120 AI-labeled items; higher values indicate greater susceptibility to ``flipping.''}\n","\\label{tab:flip_rates_split}\n","\n","\\begin{subtable}[t]{0.48\\textwidth}\n"," \\centering\n"," \\caption{HSR \\& PSR Strategies}\n"," \\label{tab:flip_hsr_psr}\n"," \\begin{tabular}{\n"," l\n"," l\n"," l\n"," S[table-format=3.0]\n"," S[table-format=2.0]\n"," S[table-format=3.2]\n"," }\n"," \\toprule\n"," \\textbf{Explain} & \\textbf{Strategy} & \\textbf{Model} \n"," & {\\#AI} & {Flips} & {Flip Rate} \\\\\n"," \\midrule\n"," \\multirow{10}{*}{SHAP}\n"," & \\multirow{5}{*}{HSR}\n"," & XGBoost & 120 & 17 & 14.17 \\\\\n"," & & BERT & 120 & 7 & 5.83 \\\\\n"," & & DistilBERT & 120 & 9 & 7.50 \\\\\n"," & & XLM-R & 120 & 1 & 0.83 \\\\\n"," & & Ensemble & 120 & 8 & 6.67 \\\\\n"," \\cmidrule(lr){2-6}\n"," & \\multirow{5}{*}{PSR}\n"," & XGBoost & 120 & 7 & 5.83 \\\\\n"," & & BERT & 120 & 4 & 3.33 \\\\\n"," & & DistilBERT & 120 & 4 & 3.33 \\\\\n"," & & XLM-R & 120 & 0 & 0.00 \\\\\n"," & & Ensemble & 120 & 3 & 2.50 \\\\\n"," \\midrule\n"," \\multirow{10}{*}{LIME}\n"," & \\multirow{5}{*}{HSR}\n"," & XGBoost & 120 & 78 & 65.00 \\\\\n"," & & BERT & 120 & 31 & 25.83 \\\\\n"," & & DistilBERT & 120 & 32 & 26.67 \\\\\n"," & & XLM-R & 120 & 3 & 2.50 \\\\\n"," & & Ensemble & 120 & 33 & 27.50 \\\\\n"," \\cmidrule(lr){2-6}\n"," & \\multirow{5}{*}{PSR}\n"," & XGBoost & 120 & 46 & 38.33 \\\\\n"," & & BERT & 120 & 20 & 16.67 \\\\\n"," & & DistilBERT & 120 & 19 & 15.83 \\\\\n"," & & XLM-R & 120 & 5 & 4.17 \\\\\n"," & & Ensemble & 120 & 19 & 15.83 \\\\\n"," \\midrule\n"," \\multirow{10}{*}{Random}\n"," & \\multirow{5}{*}{HSR}\n"," & XGBoost & 120 & 15 & 12.50 \\\\\n"," & & BERT & 120 & 20 & 16.67 \\\\\n"," & & DistilBERT & 120 & 22 & 18.33 \\\\\n"," & & XLM-R & 120 & 2 & 1.67 \\\\\n"," & & Ensemble & 120 & 20 & 16.67 \\\\\n"," \\cmidrule(lr){2-6}\n"," & \\multirow{5}{*}{PSR}\n"," & XGBoost & 120 & 10 & 8.33 \\\\\n"," & & BERT & 120 & 17 & 14.17 \\\\\n"," & & DistilBERT & 120 & 11 & 9.17 \\\\\n"," & & XLM-R & 120 & 1 & 0.83 \\\\\n"," & & Ensemble & 120 & 14 & 11.67 \\\\\n"," \\bottomrule\n"," \\end{tabular}\n","\\end{subtable}\n","\\hfill\n","\\begin{subtable}[t]{0.48\\textwidth}\n"," \\centering\n"," \\caption{GPT \\& GPT+Genre Strategies}\n"," \\label{tab:flip_gpt_gptg}\n"," \\begin{tabular}{\n"," l\n"," l\n"," l\n"," S[table-format=3.0]\n"," S[table-format=2.0]\n"," S[table-format=3.2]\n"," }\n"," \\toprule\n"," \\textbf{Explain} & \\textbf{Strategy} & \\textbf{Model} \n"," & {\\#AI} & {Flips} & {Flip Rate} \\\\\n"," \\midrule\n"," \\multirow{10}{*}{SHAP}\n"," & \\multirow{5}{*}{GPT}\n"," & XGBoost & 120 & 4 & 3.33 \\\\\n"," & & BERT & 120 & 7 & 5.83 \\\\\n"," & & DistilBERT & 120 & 7 & 5.83 \\\\\n"," & & XLM-R & 120 & 0 & 0.00 \\\\\n"," & & Ensemble & 120 & 10 & 8.33 \\\\\n"," \\cmidrule(lr){2-6}\n"," & \\multirow{5}{*}{GPT+Genre}\n"," & XGBoost & 120 & 4 & 3.33 \\\\\n"," & & BERT & 120 & 3 & 2.50 \\\\\n"," & & DistilBERT & 120 & 6 & 5.00 \\\\\n"," & & XLM-R & 120 & 0 & 0.00 \\\\\n"," & & Ensemble & 120 & 11 & 9.17 \\\\\n"," \\midrule\n"," \\multirow{10}{*}{LIME}\n"," & \\multirow{5}{*}{GPT}\n"," & XGBoost & 120 & 38 & 31.67 \\\\\n"," & & BERT & 120 & 11 & 9.17 \\\\\n"," & & DistilBERT & 120 & 11 & 9.17 \\\\\n"," & & XLM-R & 120 & 0 & 0.00 \\\\\n"," & & Ensemble & 120 & 11 & 9.17 \\\\\n"," \\cmidrule(lr){2-6}\n"," & \\multirow{5}{*}{GPT+Genre}\n"," & XGBoost & 120 & 41 & 34.17 \\\\\n"," & & BERT & 120 & 4 & 3.33 \\\\\n"," & & DistilBERT & 120 & 7 & 5.83 \\\\\n"," & & XLM-R & 120 & 0 & 0.00 \\\\\n"," & & Ensemble & 120 & 9 & 7.50 \\\\\n"," \\midrule\n"," \\multirow{10}{*}{Random}\n"," & \\multirow{5}{*}{GPT}\n"," & XGBoost & 120 & 16 & 13.33 \\\\\n"," & & BERT & 120 & 6 & 5.00 \\\\\n"," & & DistilBERT & 120 & 6 & 5.00 \\\\\n"," & & XLM-R & 120 & 0 & 0.00 \\\\\n"," & & Ensemble & 120 & 5 & 4.17 \\\\\n"," \\cmidrule(lr){2-6}\n"," & \\multirow{5}{*}{GPT+Genre}\n"," & XGBoost & 120 & 13 & 10.83 \\\\\n"," & & BERT & 120 & 6 & 5.00 \\\\\n"," & & DistilBERT & 120 & 9 & 7.50 \\\\\n"," & & XLM-R & 120 & 0 & 0.00 \\\\\n"," & & Ensemble & 120 & 11 & 9.17 \\\\\n"," \\bottomrule\n"," \\end{tabular}\n","\\end{subtable}\n","\n","\\end{table}\n"],["\\begin{figure*}[ht]\n","\\centering\n","\n","\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_SHAP_HSR.png}\n","\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_SHAP_PSR.png}\n","\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_SHAP_GPT.png}\n","\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_SHAP_GPT+Genre.png}\n","\n","\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_LIME_HSR.png}\n","\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_LIME_PSR.png}\n","\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_LIME_GPT.png}\n","\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_LIME_GPT+Genre.png}\n","\n","\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_Random_HSR.png}\n","\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_Random_PSR.png}\n","\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_Random_GPT.png}\n","\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_Random_GPT+Genre.png}\n","\n","\\caption{\\small Overlaps among flipped AI samples under different explainability methods and rewriting strategies. Each bar corresponds to the number of samples simultaneously flipped by one or more models.}\n","\\label{fig:all_upset_plots}\n","\\end{figure*}\n"]],"string":"[\n [\n \"\\\\begin{figure*}[!ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\includegraphics[width=0.7\\\\linewidth]{plots/model.jpeg}\\n\",\n \"\\\\vspace{-5pt}\\n\",\n \"\\\\caption{\\\\small The architecture of the proposed ensemble model. Outputs of three frozen BERT models (left) and three fresh BERT models (right) are concatenated and passed through dense layers, followed by a voting mechanism.}\\n\",\n \"\\\\label{fig:ms-structure}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{table}[ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\scriptsize\\n\",\n \"\\\\renewcommand{\\\\arraystretch}{1.2}\\n\",\n \"\\\\caption{Summary of token replacement strategies}\\n\",\n \"\\\\label{tab:token_replacement}\\n\",\n \"\\\\begin{tabular}{p{4.8cm}p{9.2cm}}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Strategy} & \\\\textbf{Description} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textbf{Human Similar Word Replacement (HSR)} \\n\",\n \" & A Word2Vec model is trained on all human-labeled texts in the training set to learn an embedding space that captures typical human usage. \\\\\\\\\\n\",\n \"\\\\textbf{Part-of-Speech Replacement (PSR)} \\n\",\n \" & HSR is refined by ensuring that part-of-speech tags match. \\\\\\\\\\n\",\n \"\\\\textbf{GPT-based Replacement (GPT)} \\n\",\n \" & A generative model (GPT-4o-mini) is queried using the prompt: \\n\",\n \" \\\\texttt{\\\"Replace '\\\\{token\\\\}' with a more human-like word in this text: '\\\\{text\\\\}'\\\"}. \\\\\\\\\\n\",\n \"\\\\textbf{GPT-based Replacement with Genre Context (GPT+Genre)} \\n\",\n \" & The GPT-based method is extended by including explicit genre information (e.g., \\\\emph{tweets} or \\\\emph{reviews}) in the prompt:\\n\",\n \" \\\\texttt{\\\"Replace '\\\\{token\\\\}' in this \\\\{domain\\\\} text with a more human-like word: '\\\\{text\\\\}'\\\"}. \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\scriptsize\\n\",\n \"\\\\renewcommand{\\\\arraystretch}{1.2}\\n\",\n \"\\\\caption{Model performance by language and domain on unmodified AI text. \\\\textbf{Bold} indicates the best result within each language and domain block.}\\n\",\n \"\\\\label{tab:lang_domain_updated}\\n\",\n \"\\\\begin{tabular}{llcccc|llcccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multicolumn{6}{c}{\\\\textbf{English Dataset}} & \\\\multicolumn{6}{c}{\\\\textbf{Dutch Dataset}} \\\\\\\\\\n\",\n \"\\\\cmidrule(lr){1-6} \\\\cmidrule(lr){7-12}\\n\",\n \"\\\\textbf{Model} & \\\\textbf{Domain} & \\\\textbf{Acc} & \\\\textbf{Prec} & \\\\textbf{Rec} & \\\\textbf{F1} \\n\",\n \" & \\\\textbf{Model} & \\\\textbf{Domain} & \\\\textbf{Acc} & \\\\textbf{Prec} & \\\\textbf{Rec} & \\\\textbf{F1} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textbf{--- News ---} & & & & & \\n\",\n \"& \\\\textbf{--- News ---} & & & & & \\\\\\\\\\n\",\n \"XGBoost \\n\",\n \" & news & 0.89 & 0.93 & 0.84 & 0.88 \\n\",\n \" & XGBoost \\n\",\n \" & news & 0.85 & 0.88 & 0.88 & 0.88 \\\\\\\\\\n\",\n \"BERT-base \\n\",\n \" & news & 0.94 & 0.97 & 0.91 & 0.94\\n\",\n \" & BERT-base \\n\",\n \" & news & 0.68 & 0.79 & 0.56 & 0.66 \\\\\\\\\\n\",\n \"DistilBERT \\n\",\n \" & news & 0.92 & 0.90 & \\\\textbf{0.95} & 0.92\\n\",\n \" & DistilBERT \\n\",\n \" & news & 0.68 & 0.73 & 0.66 & 0.69 \\\\\\\\\\n\",\n \"XLM-RoBERTa\\n\",\n \" & news & 0.49 & 0.49 & 1.00 & 0.66\\n\",\n \" & XLM-RoBERTa\\n\",\n \" & news & 0.53 & 0.53 & \\\\textbf{1.00} & 0.69 \\\\\\\\\\n\",\n \"Ensemble \\n\",\n \" & news & \\\\textbf{0.95} & \\\\textbf{1.00} & 0.90 & \\\\textbf{0.95}\\n\",\n \" & Ensemble \\n\",\n \" & news & \\\\textbf{0.99} & \\\\textbf{0.98} & \\\\textbf{1.00} & \\\\textbf{0.99} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textbf{--- Reviews ---} & & & & & \\n\",\n \"& \\\\textbf{--- Reviews ---} & & & & & \\\\\\\\\\n\",\n \"XGBoost \\n\",\n \" & reviews & 0.51 & 0.57 & 0.59 & 0.58\\n\",\n \" & XGBoost \\n\",\n \" & reviews & 0.53 & 0.51 & 0.46 & 0.48 \\\\\\\\\\n\",\n \"BERT-base \\n\",\n \" & reviews & 0.50 & 0.54 & 0.62 & 0.58\\n\",\n \" & BERT-base \\n\",\n \" & reviews & 0.40 & 0.35 & 0.30 & 0.32 \\\\\\\\\\n\",\n \"DistilBERT \\n\",\n \" & reviews & 0.65 & 0.68 & 0.71 & 0.69\\n\",\n \" & DistilBERT \\n\",\n \" & reviews & 0.42 & 0.38 & 0.50 & 0.43 \\\\\\\\\\n\",\n \"XLM-RoBERTa\\n\",\n \" & reviews & 0.56 & 0.56 & \\\\textbf{0.90} & 0.69\\n\",\n \" & XLM-RoBERTa\\n\",\n \" & reviews & 0.43 & 0.43 & \\\\textbf{0.60} & 0.48 \\\\\\\\\\n\",\n \"Ensemble \\n\",\n \" & reviews & \\\\textbf{0.67} & \\\\textbf{0.71} & 0.68 & \\\\textbf{0.70}\\n\",\n \" & Ensemble \\n\",\n \" & reviews & \\\\textbf{0.60} & \\\\textbf{0.53} & 0.44 & \\\\textbf{0.49} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textbf{--- Twitter / X ---} & & & & & \\n\",\n \"& \\\\textbf{--- Twitter / X ---} & & & & & \\\\\\\\\\n\",\n \"XGBoost \\n\",\n \" & twitter & 0.91 & \\\\textbf{0.92} & 0.98 & 0.95\\n\",\n \" & XGBoost \\n\",\n \" & twitter & 0.80 & 0.85 & 0.78 & 0.81 \\\\\\\\\\n\",\n \"BERT-base \\n\",\n \" & twitter & 0.90 & 0.86 & 0.98 & 0.92\\n\",\n \" & BERT-base \\n\",\n \" & twitter & 0.80 & 0.85 & 0.78 & 0.81 \\\\\\\\\\n\",\n \"DistilBERT \\n\",\n \" & twitter & 0.90 & 0.86 & 0.98 & 0.92\\n\",\n \" & DistilBERT \\n\",\n \" & twitter & 0.84 & 0.86 & 0.80 & 0.83 \\\\\\\\\\n\",\n \"XLM-RoBERTa\\n\",\n \" & twitter & 0.60 & 0.53 & \\\\textbf{1.00} & 0.69\\n\",\n \" & XLM-RoBERTa\\n\",\n \" & twitter & 0.62 & 0.60 & \\\\textbf{0.90} & 0.72 \\\\\\\\\\n\",\n \"Ensemble \\n\",\n \" & twitter & \\\\textbf{0.93} & 0.86 & \\\\textbf{1.00} & \\\\textbf{0.93}\\n\",\n \" & Ensemble \\n\",\n \" & twitter & \\\\textbf{0.87} & \\\\textbf{0.90} & 0.85 & \\\\textbf{0.87} \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table*}[ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\caption{\\\\small F1 score and Accuracy of each detector under every rewriting scenario. \\n\",\n \"The best metric in a column is \\\\textbf{bolded}; the largest negative drop is \\\\dashuline{\\\\phantom{-00\\\\%}}, the smallest negative drop is \\\\dotuline{\\\\phantom{-00\\\\%}}.\\n\",\n \"\\\\footnotesize{$\\\\Delta=(\\\\text{Scenario Value}-\\\\text{Original Value})\\\\times100\\\\%$.}}\\n\",\n \"\\\\label{tab:all_models_rewriting_f1acc}\\n\",\n \"\\\\renewcommand{\\\\arraystretch}{1.4}\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \"\\\\begin{tabular}{l l\\n\",\n \"c c c c c c c c c c}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{\\\\textbf{Explain}} & \\\\multirow{2}{*}{\\\\textbf{Strategy}}\\n\",\n \"& \\\\multicolumn{2}{c}{\\\\textbf{XGBoost}}\\n\",\n \"& \\\\multicolumn{2}{c}{\\\\textbf{BERT-base}}\\n\",\n \"& \\\\multicolumn{2}{c}{\\\\textbf{DistilBERT}}\\n\",\n \"& \\\\multicolumn{2}{c}{\\\\textbf{XLM-RoBERTa}}\\n\",\n \"& \\\\multicolumn{2}{c}{\\\\textbf{Ensemble}}\\\\\\\\\\n\",\n \"\\\\cmidrule(lr){3-4}\\\\cmidrule(lr){5-6}\\\\cmidrule(lr){7-8}\\\\cmidrule(lr){9-10}\\\\cmidrule(lr){11-12}\\n\",\n \"& & \\\\textbf{F1 ($\\\\Delta$)} & \\\\textbf{Acc ($\\\\Delta$)}\\n\",\n \" & \\\\textbf{F1 ($\\\\Delta$)} & \\\\textbf{Acc ($\\\\Delta$)}\\n\",\n \" & \\\\textbf{F1 ($\\\\Delta$)} & \\\\textbf{Acc ($\\\\Delta$)}\\n\",\n \" & \\\\textbf{F1 ($\\\\Delta$)} & \\\\textbf{Acc ($\\\\Delta$)}\\n\",\n \" & \\\\textbf{F1 ($\\\\Delta$)} & \\\\textbf{Acc ($\\\\Delta$)}\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\emph{NoRewriting} & Original\\n\",\n \"& 0.81 & 0.80\\n\",\n \"& 0.74 & 0.75\\n\",\n \"& 0.79 & 0.77\\n\",\n \"& 0.68 & 0.54\\n\",\n \"& \\\\textbf{0.83} & \\\\textbf{0.83}\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textbf{SHAP} & HSR\\n\",\n \"& 0.73 (\\\\largest{-8\\\\%}) & 0.75 (\\\\largest{-5\\\\%})\\n\",\n \"& 0.71 (-3\\\\%) & 0.72 (-3\\\\%)\\n\",\n \"& 0.76 (-3\\\\%) & 0.75 (-2\\\\%)\\n\",\n \"& 0.68 (\\\\smallest{0\\\\%}) & 0.54 (\\\\smallest{0\\\\%})\\n\",\n \"& \\\\textbf{0.78} (-5\\\\%) & \\\\textbf{0.80} (-3\\\\%)\\\\\\\\\\n\",\n \"\\\\textbf{SHAP} & PSR\\n\",\n \"& 0.78 (\\\\largest{-3\\\\%}) & 0.78 (\\\\largest{-2\\\\%})\\n\",\n \"& 0.73 (-1\\\\%) & 0.74 (-1\\\\%)\\n\",\n \"& 0.78 (-1\\\\%) & 0.77 (0\\\\%)\\n\",\n \"& 0.68 (\\\\smallest{0\\\\%}) & 0.54 (\\\\smallest{0\\\\%})\\n\",\n \"& \\\\textbf{0.82} (-1\\\\%) & \\\\textbf{0.82} (-1\\\\%)\\\\\\\\\\n\",\n \"\\\\textbf{SHAP} & GPT\\n\",\n \"& 0.79 (-2\\\\%) & 0.79 (-1\\\\%)\\n\",\n \"& 0.73 (-1\\\\%) & 0.74 (-1\\\\%)\\n\",\n \"& 0.77 (-2\\\\%) & 0.76 (-1\\\\%)\\n\",\n \"& 0.68 (\\\\smallest{0\\\\%}) & 0.55 (\\\\smallest{+1\\\\%})\\n\",\n \"& \\\\textbf{0.80} (\\\\largest{-3\\\\%}) & \\\\textbf{0.81} (\\\\largest{-2\\\\%})\\\\\\\\\\n\",\n \"\\\\textbf{SHAP} & GPT+Genre\\n\",\n \"& \\\\textbf{0.80} (-1\\\\%) & \\\\textbf{0.80} (0\\\\%)\\n\",\n \"& 0.73 (-1\\\\%) & 0.74 (-1\\\\%)\\n\",\n \"& 0.77 (-2\\\\%) & 0.76 (-1\\\\%)\\n\",\n \"& 0.68 (\\\\smallest{0\\\\%}) & 0.55 (\\\\smallest{+1\\\\%})\\n\",\n \"& 0.79 (\\\\largest{-4\\\\%}) & 0.80 (\\\\largest{-3\\\\%})\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textbf{LIME} & HSR\\n\",\n \"& 0.25 (\\\\largest{-56\\\\%}) & 0.48 (\\\\largest{-32\\\\%})\\n\",\n \"& 0.56 (-18\\\\%) & 0.62 (-13\\\\%)\\n\",\n \"& 0.62 (-17\\\\%) & 0.65 (-12\\\\%)\\n\",\n \"& \\\\textbf{0.67} (\\\\smallest{-1\\\\%}) & 0.52 (\\\\smallest{-2\\\\%})\\n\",\n \"& 0.64 (-19\\\\%) & \\\\textbf{0.69} (-14\\\\%)\\\\\\\\\\n\",\n \"\\\\textbf{LIME} & PSR\\n\",\n \"& 0.56 (\\\\largest{-25\\\\%}) & 0.63 (\\\\largest{-17\\\\%})\\n\",\n \"& 0.64 (-10\\\\%) & 0.67 (-8\\\\%)\\n\",\n \"& 0.71 (-8\\\\%) & 0.71 (-6\\\\%)\\n\",\n \"& 0.66 (\\\\smallest{-2\\\\%}) & 0.52 (\\\\smallest{-2\\\\%})\\n\",\n \"& \\\\textbf{0.73} (-10\\\\%) & \\\\textbf{0.75} (-8\\\\%)\\\\\\\\\\n\",\n \"\\\\textbf{LIME} & GPT\\n\",\n \"& 0.57 (\\\\largest{-24\\\\%}) & 0.64 (\\\\largest{-16\\\\%})\\n\",\n \"& 0.73 (-1\\\\%) & 0.74 (-1\\\\%)\\n\",\n \"& 0.78 (-1\\\\%) & 0.77 (0\\\\%)\\n\",\n \"& 0.68 (\\\\smallest{0\\\\%}) & 0.55 (\\\\smallest{+1\\\\%})\\n\",\n \"& \\\\textbf{0.83} (\\\\smallest{0\\\\%}) & \\\\textbf{0.83} (0\\\\%)\\\\\\\\\\n\",\n \"\\\\textbf{LIME} & GPT+Genre\\n\",\n \"& 0.60 (\\\\largest{-21\\\\%}) & 0.65 (\\\\largest{-15\\\\%})\\n\",\n \"& \\\\textbf{0.75} (\\\\smallest{+1\\\\%}) & \\\\textbf{0.75} (0\\\\%)\\n\",\n \"& 0.77 (-2\\\\%) & 0.76 (-1\\\\%)\\n\",\n \"& 0.68 (0\\\\%) & 0.55 (\\\\smallest{+1\\\\%})\\n\",\n \"& \\\\textbf{0.81} (-2\\\\%) & \\\\textbf{0.81} (-2\\\\%)\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textbf{Random} & HSR\\n\",\n \"& 0.73 (-8\\\\%) & 0.74 (-6\\\\%)\\n\",\n \"& 0.63 (\\\\largest{-11\\\\%}) & 0.67 (\\\\largest{-8\\\\%})\\n\",\n \"& 0.68 (\\\\largest{-11\\\\%}) & 0.69 (\\\\largest{-8\\\\%})\\n\",\n \"& 0.67 (\\\\smallest{-1\\\\%}) & 0.53 (\\\\smallest{-1\\\\%})\\n\",\n \"& \\\\textbf{0.73} (-10\\\\%) & \\\\textbf{0.75} (\\\\largest{-8\\\\%})\\\\\\\\\\n\",\n \"\\\\textbf{Random} & PSR\\n\",\n \"& 0.76 (-5\\\\%) & 0.76 (-4\\\\%)\\n\",\n \"& 0.65 (\\\\largest{-9\\\\%}) & 0.68 (\\\\largest{-7\\\\%})\\n\",\n \"& 0.74 (-5\\\\%) & 0.73 (-4\\\\%)\\n\",\n \"& 0.67 (\\\\smallest{-1\\\\%}) & 0.53 (\\\\smallest{-1\\\\%})\\n\",\n \"& \\\\textbf{0.78} (-5\\\\%) & \\\\textbf{0.79} (-4\\\\%)\\\\\\\\\\n\",\n \"\\\\textbf{Random} & GPT\\n\",\n \"& 0.74 (\\\\largest{-7\\\\%}) & 0.75 (\\\\largest{-5\\\\%})\\n\",\n \"& 0.74 (0\\\\%) & 0.75 (0\\\\%)\\n\",\n \"& 0.77 (-2\\\\%) & 0.76 (-1\\\\%)\\n\",\n \"& 0.68 (\\\\smallest{0\\\\%}) & 0.55 (\\\\smallest{+1\\\\%})\\n\",\n \"& \\\\textbf{0.81} (-2\\\\%) & \\\\textbf{0.82} (-1\\\\%)\\\\\\\\\\n\",\n \"\\\\textbf{Random} & GPT+Genre\\n\",\n \"& 0.75 (\\\\largest{-6\\\\%}) & 0.75 (\\\\largest{-5\\\\%})\\n\",\n \"& 0.73 (-1\\\\%) & 0.74 (-1\\\\%)\\n\",\n \"& \\\\textbf{0.78} (-1\\\\%) & 0.77 (0\\\\%)\\n\",\n \"& 0.68 (0\\\\%) & 0.55 (\\\\smallest{+1\\\\%})\\n\",\n \"& \\\\textbf{0.81} (-2\\\\%) & \\\\textbf{0.81} (-2\\\\%)\\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table}[ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\scriptsize\\n\",\n \"\\\\renewcommand{\\\\arraystretch}{1.15} % Slightly tighter row spacing\\n\",\n \"\\\\setlength{\\\\tabcolsep}{4pt} % Adjust column spacing as desired\\n\",\n \"\\n\",\n \"\\\\caption{Flip rates (\\\\%) from \\\\textbf{AI} to \\\\textbf{human}, grouped by rewriting strategies. \\n\",\n \" Each scenario has 120 AI-labeled items; higher values indicate greater susceptibility to ``flipping.''}\\n\",\n \"\\\\label{tab:flip_rates_split}\\n\",\n \"\\n\",\n \"\\\\begin{subtable}[t]{0.48\\\\textwidth}\\n\",\n \" \\\\centering\\n\",\n \" \\\\caption{HSR \\\\& PSR Strategies}\\n\",\n \" \\\\label{tab:flip_hsr_psr}\\n\",\n \" \\\\begin{tabular}{\\n\",\n \" l\\n\",\n \" l\\n\",\n \" l\\n\",\n \" S[table-format=3.0]\\n\",\n \" S[table-format=2.0]\\n\",\n \" S[table-format=3.2]\\n\",\n \" }\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Explain} & \\\\textbf{Strategy} & \\\\textbf{Model} \\n\",\n \" & {\\\\#AI} & {Flips} & {Flip Rate} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multirow{10}{*}{SHAP}\\n\",\n \" & \\\\multirow{5}{*}{HSR}\\n\",\n \" & XGBoost & 120 & 17 & 14.17 \\\\\\\\\\n\",\n \" & & BERT & 120 & 7 & 5.83 \\\\\\\\\\n\",\n \" & & DistilBERT & 120 & 9 & 7.50 \\\\\\\\\\n\",\n \" & & XLM-R & 120 & 1 & 0.83 \\\\\\\\\\n\",\n \" & & Ensemble & 120 & 8 & 6.67 \\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-6}\\n\",\n \" & \\\\multirow{5}{*}{PSR}\\n\",\n \" & XGBoost & 120 & 7 & 5.83 \\\\\\\\\\n\",\n \" & & BERT & 120 & 4 & 3.33 \\\\\\\\\\n\",\n \" & & DistilBERT & 120 & 4 & 3.33 \\\\\\\\\\n\",\n \" & & XLM-R & 120 & 0 & 0.00 \\\\\\\\\\n\",\n \" & & Ensemble & 120 & 3 & 2.50 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multirow{10}{*}{LIME}\\n\",\n \" & \\\\multirow{5}{*}{HSR}\\n\",\n \" & XGBoost & 120 & 78 & 65.00 \\\\\\\\\\n\",\n \" & & BERT & 120 & 31 & 25.83 \\\\\\\\\\n\",\n \" & & DistilBERT & 120 & 32 & 26.67 \\\\\\\\\\n\",\n \" & & XLM-R & 120 & 3 & 2.50 \\\\\\\\\\n\",\n \" & & Ensemble & 120 & 33 & 27.50 \\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-6}\\n\",\n \" & \\\\multirow{5}{*}{PSR}\\n\",\n \" & XGBoost & 120 & 46 & 38.33 \\\\\\\\\\n\",\n \" & & BERT & 120 & 20 & 16.67 \\\\\\\\\\n\",\n \" & & DistilBERT & 120 & 19 & 15.83 \\\\\\\\\\n\",\n \" & & XLM-R & 120 & 5 & 4.17 \\\\\\\\\\n\",\n \" & & Ensemble & 120 & 19 & 15.83 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multirow{10}{*}{Random}\\n\",\n \" & \\\\multirow{5}{*}{HSR}\\n\",\n \" & XGBoost & 120 & 15 & 12.50 \\\\\\\\\\n\",\n \" & & BERT & 120 & 20 & 16.67 \\\\\\\\\\n\",\n \" & & DistilBERT & 120 & 22 & 18.33 \\\\\\\\\\n\",\n \" & & XLM-R & 120 & 2 & 1.67 \\\\\\\\\\n\",\n \" & & Ensemble & 120 & 20 & 16.67 \\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-6}\\n\",\n \" & \\\\multirow{5}{*}{PSR}\\n\",\n \" & XGBoost & 120 & 10 & 8.33 \\\\\\\\\\n\",\n \" & & BERT & 120 & 17 & 14.17 \\\\\\\\\\n\",\n \" & & DistilBERT & 120 & 11 & 9.17 \\\\\\\\\\n\",\n \" & & XLM-R & 120 & 1 & 0.83 \\\\\\\\\\n\",\n \" & & Ensemble & 120 & 14 & 11.67 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"\\\\end{subtable}\\n\",\n \"\\\\hfill\\n\",\n \"\\\\begin{subtable}[t]{0.48\\\\textwidth}\\n\",\n \" \\\\centering\\n\",\n \" \\\\caption{GPT \\\\& GPT+Genre Strategies}\\n\",\n \" \\\\label{tab:flip_gpt_gptg}\\n\",\n \" \\\\begin{tabular}{\\n\",\n \" l\\n\",\n \" l\\n\",\n \" l\\n\",\n \" S[table-format=3.0]\\n\",\n \" S[table-format=2.0]\\n\",\n \" S[table-format=3.2]\\n\",\n \" }\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Explain} & \\\\textbf{Strategy} & \\\\textbf{Model} \\n\",\n \" & {\\\\#AI} & {Flips} & {Flip Rate} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multirow{10}{*}{SHAP}\\n\",\n \" & \\\\multirow{5}{*}{GPT}\\n\",\n \" & XGBoost & 120 & 4 & 3.33 \\\\\\\\\\n\",\n \" & & BERT & 120 & 7 & 5.83 \\\\\\\\\\n\",\n \" & & DistilBERT & 120 & 7 & 5.83 \\\\\\\\\\n\",\n \" & & XLM-R & 120 & 0 & 0.00 \\\\\\\\\\n\",\n \" & & Ensemble & 120 & 10 & 8.33 \\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-6}\\n\",\n \" & \\\\multirow{5}{*}{GPT+Genre}\\n\",\n \" & XGBoost & 120 & 4 & 3.33 \\\\\\\\\\n\",\n \" & & BERT & 120 & 3 & 2.50 \\\\\\\\\\n\",\n \" & & DistilBERT & 120 & 6 & 5.00 \\\\\\\\\\n\",\n \" & & XLM-R & 120 & 0 & 0.00 \\\\\\\\\\n\",\n \" & & Ensemble & 120 & 11 & 9.17 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multirow{10}{*}{LIME}\\n\",\n \" & \\\\multirow{5}{*}{GPT}\\n\",\n \" & XGBoost & 120 & 38 & 31.67 \\\\\\\\\\n\",\n \" & & BERT & 120 & 11 & 9.17 \\\\\\\\\\n\",\n \" & & DistilBERT & 120 & 11 & 9.17 \\\\\\\\\\n\",\n \" & & XLM-R & 120 & 0 & 0.00 \\\\\\\\\\n\",\n \" & & Ensemble & 120 & 11 & 9.17 \\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-6}\\n\",\n \" & \\\\multirow{5}{*}{GPT+Genre}\\n\",\n \" & XGBoost & 120 & 41 & 34.17 \\\\\\\\\\n\",\n \" & & BERT & 120 & 4 & 3.33 \\\\\\\\\\n\",\n \" & & DistilBERT & 120 & 7 & 5.83 \\\\\\\\\\n\",\n \" & & XLM-R & 120 & 0 & 0.00 \\\\\\\\\\n\",\n \" & & Ensemble & 120 & 9 & 7.50 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multirow{10}{*}{Random}\\n\",\n \" & \\\\multirow{5}{*}{GPT}\\n\",\n \" & XGBoost & 120 & 16 & 13.33 \\\\\\\\\\n\",\n \" & & BERT & 120 & 6 & 5.00 \\\\\\\\\\n\",\n \" & & DistilBERT & 120 & 6 & 5.00 \\\\\\\\\\n\",\n \" & & XLM-R & 120 & 0 & 0.00 \\\\\\\\\\n\",\n \" & & Ensemble & 120 & 5 & 4.17 \\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-6}\\n\",\n \" & \\\\multirow{5}{*}{GPT+Genre}\\n\",\n \" & XGBoost & 120 & 13 & 10.83 \\\\\\\\\\n\",\n \" & & BERT & 120 & 6 & 5.00 \\\\\\\\\\n\",\n \" & & DistilBERT & 120 & 9 & 7.50 \\\\\\\\\\n\",\n \" & & XLM-R & 120 & 0 & 0.00 \\\\\\\\\\n\",\n \" & & Ensemble & 120 & 11 & 9.17 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"\\\\end{subtable}\\n\",\n \"\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{figure*}[ht]\\n\",\n \"\\\\centering\\n\",\n \"\\n\",\n \"\\\\includegraphics[width=0.24\\\\textwidth]{plots/flip/upset_flips_SHAP_HSR.png}\\n\",\n \"\\\\includegraphics[width=0.24\\\\textwidth]{plots/flip/upset_flips_SHAP_PSR.png}\\n\",\n \"\\\\includegraphics[width=0.24\\\\textwidth]{plots/flip/upset_flips_SHAP_GPT.png}\\n\",\n \"\\\\includegraphics[width=0.24\\\\textwidth]{plots/flip/upset_flips_SHAP_GPT+Genre.png}\\n\",\n \"\\n\",\n \"\\\\includegraphics[width=0.24\\\\textwidth]{plots/flip/upset_flips_LIME_HSR.png}\\n\",\n \"\\\\includegraphics[width=0.24\\\\textwidth]{plots/flip/upset_flips_LIME_PSR.png}\\n\",\n \"\\\\includegraphics[width=0.24\\\\textwidth]{plots/flip/upset_flips_LIME_GPT.png}\\n\",\n \"\\\\includegraphics[width=0.24\\\\textwidth]{plots/flip/upset_flips_LIME_GPT+Genre.png}\\n\",\n \"\\n\",\n \"\\\\includegraphics[width=0.24\\\\textwidth]{plots/flip/upset_flips_Random_HSR.png}\\n\",\n \"\\\\includegraphics[width=0.24\\\\textwidth]{plots/flip/upset_flips_Random_PSR.png}\\n\",\n \"\\\\includegraphics[width=0.24\\\\textwidth]{plots/flip/upset_flips_Random_GPT.png}\\n\",\n \"\\\\includegraphics[width=0.24\\\\textwidth]{plots/flip/upset_flips_Random_GPT+Genre.png}\\n\",\n \"\\n\",\n \"\\\\caption{\\\\small Overlaps among flipped AI samples under different explainability methods and rewriting strategies. Each bar corresponds to the number of samples simultaneously flipped by one or more models.}\\n\",\n \"\\\\label{fig:all_upset_plots}\\n\",\n \"\\\\end{figure*}\\n\"\n ]\n]","isConversational":false},"fulltext":{"kind":"string","value":" %% This is a JAIR Example File Compiled by Nicholas Mattei (nsmattei@tulane.edu) \n%% and Odd Erik Gundersen (odderik@ntnu.no)\n%% and Mykel Kochenderfer (mykel@stanford.edu)\n%% March 29, 2025\n%%\n%% This file is based off the ACM Latex Template https://www.acm.org/publications/proceedings-template\n%% Revision 2.12 (12/28/2024)\n%% \n%% Please see https://www.jair.org/index.php/jair for more information and submission instructions.\n%%\n\n%% The first command in your LaTeX source must be the \\documentclass\n%% command.\n%%\n%% For submission and review of your manuscript please change the\n%% command to \\documentclass[manuscript, screen, review]{jair}.\n%%\n%\\documentclass[review]{jair}\n%\\documentclass[manuscript, screen, review]{jair}\n\n\\documentclass{jair}\n\n%---------- kill the ACM-style footer line ----------\n\\settopmatter{printacmref=false} % no ACM reference strip\n\\pagestyle{plain} % plain page style = no header\n\\makeatletter\n\\renewcommand\\footnotetextcopyrightpermission[1]{} % empty out the footer macro\n\\makeatother\n%---------------------------------------------------\n\n\n\n%\\setcopyright{cc}\n%\\copyrightyear{2025}\n%\\acmYear{2025}\n%\\acmDOI{10.1613/jair.1.xxxxx}\n\n%%\n%\\JAIRAE{Insert JAIR AE Name}\n\n%\\renewcommand{\\JAIRAE}[1]{}\n\n%\\JAIRTrack{Insert JAIR Track Name Here}\n%\\renewcommand{\\JAIRTrack}[1]{}\n\n% \\acmVolume{4}\n% \\acmArticle{111}\n% \\acmMonth{8}\n% \\acmYear{2025}\n\n\\usepackage{graphicx}\n\\usepackage{caption} \n\\usepackage{float} \n\\usepackage[utf8]{inputenc}\n\\let\\Bbbk\\relax\n\\usepackage{amsmath,amssymb}\n\\usepackage{algorithm}\n\\usepackage{algpseudocode}\n\\usepackage{setspace}\n\\usepackage{wrapfig}\n\\usepackage{booktabs} \n\\usepackage{siunitx} \n\n\\usepackage{footmisc} \n\n\\usepackage[normalem]{ulem} \n\\newcommand{\\largest}[1]{\\dashuline{#1}} \n\\newcommand{\\smallest}[1]{\\dotuline{#1}} \n\n\\captionsetup[algorithm]{labelfont=bf}\n\n\n\\captionsetup[algorithm]{labelfont=bf}\n\n\\algrenewcommand{\\algorithmicindent}{1.5em} \n\\algrenewcommand{\\algorithmiccomment}[1]{\\hfill\\(\\#\\) #1}\n%\\algrenewcommand{\\algorithmicendfunction}{} \n\n\\usepackage{booktabs,siunitx,multirow,subcaption}\n\\setlength{\\tabcolsep}{4pt} \n\\renewcommand{\\arraystretch}{0.9}\n\\sisetup{table-format=9.0} \n\n\n\n\n%%\n%% For managing citations, it is recommended to use bibliography\n%% files in BibTeX format.\n%%\n%% You can then either use BibTeX with the ACM-Reference-Format style,\n%% or BibLaTeX with the acmnumeric or acmauthoryear sytles, that include\n%% support for advanced citation of software artefact from the\n%% biblatex-software package, also separately available on CTAN.\n%%\n%% Look at the sample-*-biblatex.tex files for templates showcasing\n%% the biblatex styles.\n%%\n\n%%\n%% The majority of ACM publications use numbered citations and\n%% references. The command \\citestyle{authoryear} switches to the\n%% \"author year\" style.\n%%\n%% If you are preparing content for an event\n%% sponsored by ACM SIGGRAPH, you must use the \"author year\" style of\n%% citations and references.\n%% Uncommenting\n%% the next command will enable that style.\n%%\\citestyle{acmauthoryear}\n\n\n%%\n%% end of the preamble, start of the body of the document source.\n\\begin{document}\n\n%%\n%% The \"title\" command has an optional parameter,\n%% allowing the author to define a \"short title\" to be used in page headers.\n%\\title{JAIR Example Template}\n%\\title[Satzilla]{Explainability-Based Token Replacement on LLM-Generated Text}\n% \\title[Explainability-Based Token Replacement]{Explainability-Based Token Replacement on LLM-Generated Text}\n\n\\title{Explainability-Based Token Replacement on LLM-Generated Text}\n\n%%\n%% The \"author\" command and its associated commands are used to define\n%% the authors and their affiliations.\n%% Of note is the shared affiliation of the first two authors, and the\n%% \"authornote\" and \"authornotemark\" commands\n%% used to denote shared contribution to the research and/or corresponding author.\n\\author{Hadi Mohammadi}\n\\authornote{Corresponding Author.}\n\\email{h.mohammadi@uu.nl}\n\\orcid{0000-0003-0860-9200}\n% \\orcid{https://orcid.org/0000-0002-2037-3694} % This works too if you want to see the full URL\n\\affiliation{%\n \\institution{Department of Methodology and Statistics, Utrecht University}\n \\city{Utrecht}\n \\state{Utrecht}\n \\country{The Netherlands}\n}\n\n\n\\author{Anastasia Giachanou}\n\\orcid{0000-0002-7601-8667}\n\\email{a.giachanou@uu.nl}\n\\affiliation{%\n \\institution{Department of Methodology and Statistics, Utrecht University}\n \\city{Utrecht}\n \\state{Utrecht}\n \\country{The Netherlands}\n}\n\n\\author{Daniel L. Oberski}\n\\orcid{0000-0001-7467-2297}\n\\email{d.l.oberski@uu.nl}\n\\affiliation{%\n \\institution{Department of Methodology and Statistics, Utrecht University}\n \\city{Utrecht}\n \\state{Utrecht}\n \\country{The Netherlands}\n}\n\n\\author{Ayoub Bagheri}\n\\orcid{0000-0001-6366-2173}\n\\email{a.bagheri@uu.nl}\n\\affiliation{%\n \\institution{Department of Methodology and Statistics, Utrecht University}\n \\city{Utrecht}\n \\state{Utrecht}\n \\country{The Netherlands}\n}\n\n\n\n%\\author{Ben Trovato}\n\n%%\n%% By default, the full list of authors will be used in the page\n%% headers. Often, this list is too long, and will overlap\n%% other information printed in the page headers. This command allows\n%% the author to define a more concise list\n%% of authors' names for this purpose.\n\n%\\renewcommand{\\shortauthors}{Mohammadi et al.}\n\n%%\n%% The abstract is a short summary of the work to be presented in the\n%% article.\n\\begin{abstract}\n{\\bf Background:} \n Generative models, especially large language models (LLMs), have shown remarkable progress in producing text that appears human-like. However, they often exhibit patterns that make their output easier to detect than text written by humans.\n \n {\\bf Objectives:}\n In this paper, we investigate how explainable AI (XAI) methods can be used to reduce the detectability of AI-generated text (AIGT) while also introducing a robust ensemble-based detection approach. \n \n {\\bf Methods:}\n We begin by training an ensemble classifier to distinguish AIGT from human-written text, then apply SHAP and LIME to identify tokens that most strongly influence its predictions. We propose four explainability-based token replacement strategies to modify these influential tokens. \n \n {\\bf Results:}\n Our findings show that these token replacement approaches can significantly diminish a single classifier’s ability to detect AIGT. However, our ensemble classifier maintains strong performance across multiple languages and domains, showing that a multi-model approach can mitigate the impact of token-level manipulations.\n \n {\\bf Conclusions:}\n Our findings show that XAI methods can make AIGT harder to detect by focusing on the most influential tokens. At the same time, they highlight the need for robust, ensemble-based detection strategies that can adapt to evolving approaches for hiding AIGT.\n\\end{abstract}\n\n%\\begin{abstract}\n% A clear and well-documented \\LaTeX\\ document is presented as an\n% article formatted for publication by ACM in a conference proceedings\n% or journal publication. Based on the ``acmart'' document class, this\n% article presents and explains many of the common variations, as well\n% as many of the formatting elements an author may use in the\n% preparation of the documentation of their work.\n%\\end{abstract}\n\n\n%% JAIR Note: \n%% Do not include ACM CCS Concepts or Keywords\n\n\n%% To be updated by authors.\n% \\received{24 May 2025}\n% \\received[revised]{12 June 2025}\n% \\received[accepted]{5 July 2025}\n\n%%\n%% This command processes the author and affiliation and title\n%% information and builds the first part of the formatted document.\n\\maketitle\n\n\n%\\blfootnote{Under review at the Journal of Artificial Intelligence Research (JAIR).}\n\n\n\\section{Introduction}\n\\label{sec:intro}\n\nRecent advancements in LLMs, such as the GPT series, have enabled the creation of highly fluent text that often resembles human writing~\\cite{brown2020language}. Still, even the best models often show subtle signs of being artificial, like repeating phrases, using a limited range of words, having no typos, or showing a consistent writing style. These clues can help readers or automated tools detect that the text was written by AI~\\cite{sadasivan2023can}. Being able to spot AIGT is important in various domains, such as in for protecting academic honesty, fighting misinformation, and keeping public trust. On the other hand, there are good reasons to make AI text less detectable, like tools that help people write in their own style~\\cite{Yeh2024}, or AI summaries that aim to be especially clear and well-structured~\\cite{nacke2024ai}.\n\nThis conflict between helpful and harmful uses of LLMs creates a challenge: how to keep their benefits while preventing misuse. One proposed solution is watermarking, which adds hidden patterns to generated text to make it more transparent~\\cite{kirchenbauer2023watermarking}. However, recent research shows that these watermarks can be weakened or removed by changing the text, like paraphrasing~\\cite{li2023towards}. Against this backdrop, our study asks:~\\emph{Can explanation methods be used to both hide and detect AI-generated text?} We contribute in two ways. First, we develop an \\textbf{ensemble‑based detector} that remains accurate across languages (English and Dutch) and domains (news, reviews, Twitter). Second, we show how two explanation tools, SHapley Additive exPlanations (SHAP)~\\cite{lundberg2017unified} and Local Interpretable Model-agnostic Explanations (LIME)~\\cite{ribeiro2016should}, can be turned against a detector: we identify the most influential tokens and replace them via four targeted strategies (semantically similar human‑preferred words, part‑of‑speech‑constrained synonyms, GPT‑based substitutions, and GPT substitutions enriched with genre context). We evaluate both \\emph{detectability} (accuracy, F1) and \\emph{textual fidelity} (BLEU, ROUGE), thereby quantifying the trade‑off between hiding AI signals and preserving meaning. Our results show that token‑level manipulation can fool many single‑model detectors, yet a robust ensemble still performs strongly, underscoring the need to combine explainability, watermarking, and policy safeguards to manage the evolving landscape of AIGT.\n\nThe rest of this paper is organized as follows:\nSection~\\ref{sec:related} reviews related work on AI-generated text detection, watermarking, and adversarial rewriting.\nSection~\\ref{sec:dataset} describes the datasets and our data augmentation process.\nIn Section~\\ref{sec:methodology}, we present our methodology, including details on model architectures, explainability approaches, and token replacement strategies.\nSection~\\ref{sec:experiments} reports experimental results, showing how different rewriting methods affect detection performance and textual fidelity.\nFinally, Section~\\ref{sec:discussion_conclusion} concludes the paper and discusses future research directions.\n\n\n\\section{Related Work}\n\\label{sec:related}\n\n\\citet{kirchenbauer2023watermarking} introduced an important \\emph{red--green list} watermark. In this method, a secret hash divides the vocabulary at each token position into a preferred ``green'' group and a less preferred ``red'' group. The model then gives higher chances to the green tokens, so the generated text contains a hidden cryptographic signature. Light editing often keeps these watermarks, but bigger changes, like paraphrasing or replacing words with synonyms, can remove them~\\citep{kirchenbauer2024reliability,li2023towards}. To make watermarks harder to notice, distortion-free methods hide information in the token-sampling \\emph{order} instead of the probability distribution. However, these can also become weak if someone rewrites the text in a tricky way~\\citep{christ2024undetectable}. Other approaches, called post-hoc schemes, add watermarks later by changing syntax templates or choosing context-aware synonyms~\\citep{yang2023watermarking}, but they also have trouble surviving strong text changes.\n\n\nAnother important detection method takes advantage of the fact that language models (LMs) often choose very predictable words. \\citet{gehrmann2019gltr} created a token-rank histogram tool to help people find words that are ``too likely'' in a sentence. Zero-shot methods like \\emph{DetectGPT} look at the ``curvature'' of local log-probabilities; if the curvature is negative, it suggests the text was written by a machine~\\citep{mitchell2023detectgpt}. \\emph{Fast-DetectGPT} makes this process faster and more accurate for text from ChatGPT or GPT-4~\\citep{bao2023fast}. Other detection methods use features such as normalized perturbed rank~\\citep{su2023detectllm} and the amount of unused information in token probability distributions~\\citep{zawistowski2024unused}. These ``white-box'' methods work well when they can directly access the model's probabilities, but they become less effective without this access. To solve this problem, ``DNA'' regeneration methods, like DNA-GPT~\\citep{yang2023dna} and GPT Paternity Test~\\citep{yu2023gpt}, do not need internal access; instead, they query a black-box model again and compare the new outputs for similarity.\n\nStylometric approaches instead focus on word choice, sentence structure, and how the text is organized and connected.~\\citet{frohling2021feature} grouped 35 linguistic features into four categories, low diversity, repetition, incoherence, and lack of purpose, and trained SVM and random-forest models. More advanced work introduces entity-coherence graphs~\\citep{liu2023coco} or journalism-specific style metrics~\\citep{kumarage2023j}, which bolster detection on brief social-media texts. Though interpretable, these handcrafted methods may be narrower in scope and less transferable across domains.\n\nA different direction uses fine-tuned neural networks to classify AI-generated text. OpenAI’s initial GPT-detector trained RoBERTa on mixed GPT-2 corpora, achieving roughly 95\\% accuracy on in-domain data~\\citep{solaiman2019release}. More recent detectors employ Longformer~\\citep{beltagy2020longformer} or ELECTRA to handle longer contexts and detect texts from multiple generators~\\citep{clark2020electra}. However, these systems often struggle with out-of-distribution domains. Recent ``cross-model'' or contrastive ensembles, such as Ghostbuster~\\citep{verma2023ghostbuster} and DeTeCtive~\\citep{guo2024detective}, compare probability distributions from multiple open-source LMs to improve robustness.\n\nEnsemble methods combine diverse signals to mitigate the weaknesses of individual detectors. Simple voting stacks of transformer-based classifiers outperform single models in multilingual tasks~\\citep{nguyen2023stacking}. For example, recent work introduces ensemble-based approaches for detecting AI-generated text in educational content, achieving a favorable false-positive trade-off in academic scenarios~\\citep{najjar2025detecting}. Our work follows this line by designing an explanation-oriented ensemble meant to withstand token-level adversarial edits.\n\nParaphrasing is one of the best ways to trick AI detectors. For example, \\citet{krishna2023paraphrasing} showed that changing the wording can make DetectGPT miss almost all AI-written text. Rewriting the text many times makes it even harder to detect~\\citep{sadasivan2023can}. Some new methods, like RADAR~\\citep{hu2023radar} and OUTFOX~\\citep{koike2024outfox}, are better at catching these changes because they are trained on paraphrased examples, but they still only recover some accuracy. Even small mistakes, like typos, can also confuse detectors~\\citep{huang2024ai}. More advanced methods can handle some edits, but none can stop all tricks or rewrites.\n\nParaphrasing is seen as the strongest way to avoid AI text detectors:~\\citet{krishna2023paraphrasing} found that rewriting text can reduce DetectGPT’s ability to catch AI-generated text from 70\\% to less than 5\\%. Rewriting the text several times in a row (called “self-rewrites”) makes it even harder to detect, while still keeping the original meaning~\\citep{sadasivan2023can}. Defenses such as RADAR~\\citep{hu2023radar} and OUTFOX~\\citep{koike2024outfox} become stronger by training on paraphrased texts, and can recover 10–20 F\\textsubscript{1} points. Recent studies also show that even simple changes at the letter level—like typos or swapping similar-looking letters—can confuse detectors and make them much less accurate~\\citep{huang2024ai}. Although using features like ensemble cross-probability or advanced watermarking can resist some small changes, no method is completely safe from strong rewriting attacks.\n\n\nExplainability has become a cornerstone of modern NLP deployments~\\citep{mohammadi2025explainability}. XAI tools like SHAP and LIME show which words or tokens have the biggest effect on a detector’s decisions. For example, ~\\citet{mitrovic2023chatgpt} used LIME to highlight important words and help people review short ChatGPT answers. But attackers can use this information in the opposite way, by changing or removing the most important words.~\\citet{zhou2024humanizing} showed that using SHAP-based synonyms for key words can lower detector accuracy by 40–60\\%. In our work, we carefully study this double-sided use of explanations, testing four types of word substitutions in two languages and different types of texts.\n\nDetectors often make mistakes with texts written by people using English as a second language.~\\citet{liang2023gpt} found that seven popular GPT detectors wrongly marked more than 61\\% of TOEFL essays by non-native English speakers as AI-generated, even though they were very accurate with essays by native speakers. Because of this bias, some teachers are moving away from strict rules and are now encouraging students to use AI tools as support, especially for those with different language backgrounds. Since LLMs can quickly create lots of convincing fake news, it is very hard for people alone to spot which content is real~\\citep{zellers2019defending}. To improve detection in these situations, it helps to use a mix of classifiers and to add information about where the text comes from~\\citep{fraser2025detecting}.\n\nRecent use of AI chatbots for mental health support shows that people often think these systems are caring and human-like, which leads to real and meaningful conversations. But this can be risky, especially when users do not realize they are talking to an AI. ~\\citet{song2024typing} found that people in severe distress sometimes seek help from chatbots and even see them as human. In the same way,~\\citet{siddals2024happened} stressed the need for stronger safety rules and clearer information in AI mental health tools. These studies show how important it is to have good detection systems and to make it clear when someone is interacting with an AI, so that chatbots are used ethically in sensitive cases. Research also shows that no single method is perfect; reliable AI-generated text detection needs to use many signals, be tested against tricky cases, and give clear reasons for its decisions. These are the main goals of our work.\n\n\n\\section{Dataset}\n\\label{sec:dataset}\nIn this section, we detail the CLIN33 dataset~\\cite{fivez2024clin33} used as our primary corpus and describe how it was augmented to expand its linguistic variability. We also present the portion of the AuTexTification dataset\\footnote{\\url{https://zenodo.org/records/10732813}} used to strengthen our ensemble models. The CLIN33 shared task corpus comprises human-written and AIGTs in English and Dutch, including three domains: news, tweets, and reviews. Each domain contains 200 samples from human authors and 200 samples from LLMs (GPT-4 and Vicu\\~{n}a-13B), producing a total of 1,200 texts in English and 1,200 in Dutch. The human-written news samples come from well-known media outlets; the Dutch social media posts primarily discuss policy or health topics, whereas the English ones often revolve around social or sports events. Reviews typically cover literature in Dutch and various consumer goods in English. \n\n\nAlthough CLIN33 offers balanced classes across two languages and three genres, its size remains relatively small for training large-scale models. To address this limitation and diversify the textual patterns, we created an augmented version of the dataset. We started with the original texts and applied a series of transformations to each sample. These transformations included synonym substitution, random word swaps, random insertions, targeted deletions, variations in spelling, and back-translation\\footnote{Translating from English to Dutch and then back to English}. Each text thus produced several new variants that preserved the key semantics but introduced new surface forms. We ultimately obtained 9{,}720 total samples for training, split evenly between human and AI-generated content (4{,}860 each) and distributed equally across the three domains and the two languages. The final augmented corpus enables more robust feature learning.\n\n\\begin{wraptable}{l}{0.65\\textwidth} \n \\vspace{-1em}\n \\centering\n %\\small \n \\scriptsize\n \\caption{\\small Overview of data resources.}\n \\label{tab:combined_data_overview}\n %\\vspace{-8pt}\n \\begin{tabular}{p{2.5cm}p{3.4cm}p{3.0cm}} \n \\toprule\n & \\textbf{AuTexTification (EN)} & \\textbf{Augmented CLIN33} \\\\\n \\midrule\n \\textbf{Total Samples} & 33{,}845 & 9{,}720 \\\\\n \\textbf{Domains/Genres} & Tweets, Legal, Wiki & News, Twitter, Reviews \\\\\n \\textbf{Languages} & English & English, Dutch \\\\\n \\textbf{AI Models} & BLOOM & GPT‑4 \\\\[-2pt]\n & GPT‑3 & Vicu\\~na‑13B \\\\\n \\textbf{Human Sources} & MultiEURLEX, Amazon, XSUM, TSATC, WikiLingua\n & News, tweets, reviews \\\\\n \\textbf{Class Ratio} & 50.36\\% / 49.64~\\% & 50\\% / 50\\%\\\\\n \\textbf{Augmentations} & None & Yes (9) \\\\\n \\bottomrule\n \\end{tabular}\n \\vspace{-1em}\n\\end{wraptable}\n\n\nAs an additional resource for pre-training and fine-tuning, we employed a subset of the AuTexTification dataset\\footnote{\\url{https://sites.google.com/view/autextification}}, introduced by~\\citet{sarvazyan2023overview}. While the complete dataset includes English and Spanish texts in five distinct domains, we selectively used the \\emph{English-only} portion from tweets, legal, and wiki categories. This subset contains 33{,}845 texts designated for binary AI-detection (Task 1), split nearly evenly between human-written (50.36\\%) and AI-generated (49.64\\%) documents. The human content is drawn from sources such as MultiEURLEX, XSUM, Amazon Reviews, TSATC, and WikiLingua, while the AI-generated portion is produced by BLOOM (1B7, 3B, 7B1) and GPT-3 (babbage, curie, text-davinci-003). Because of its class balance and domain variety, this subset of AuTexTification serves as a valuable source of pre-training examples for our ensemble.\nTable~\\ref{tab:combined_data_overview} summarizes the essential attributes of the two datasets used to train and evaluate our methodology. The combined setup draws from the 33{,}845 samples in AuTexTification (English only) and the 9{,}720 augmented CLIN33 samples (both English and Dutch). We use AuTexTification Dataset for initial pre-training, 90\\% of the CLIN data for the Augmenting and training and 10\\% of unchanged CLIN data for evaluating.\n\n\n\\section{Methodology}\n\\label{sec:methodology}\nWe used an approach that integrates several models for text classification to address the AIGT detection task. First, we converted all text to lowercase to handle tokens in a consistent way. We then removed non-informative elements such as URLs, punctuation, and special characters, since these tokens often do not carry semantic meaning. After this cleaning, we split the text into tokens and applied lemmatization to map words to their base forms, which lowered data complexity and improved the model’s ability to recognize patterns.\n\n\\paragraph{\\textbf{Baselines Models.}} We built an XGBoost classifier by first transforming the text into numerical features with a TF-IDF vectorizer set to a maximum of 5000 features. The XGBoost model was configured with use\\_label\\_encoder=False and eval\\_metric='logloss'. Training used the TF-IDF representations from the training set, and final predictions on the test set were evaluated using precision, recall, F1, and accuracy. Also, We fine-tuned BERT-base (bert-base-uncased), DistilBERT (distilbert-base-uncased), and XLM-RoBERTa (xlm-roberta-base). The best checkpoint was chosen based on the highest F1 score on a validation subset. We then evaluated the final models on the test data. \n\n\n\n\\begin{wrapfigure}{r}{0.48\\textwidth}\n \\vspace{-1em}\n \\centering\n \\begin{minipage}{\\linewidth}\n \\begin{algorithm}[H]\n \\footnotesize\n \\setstretch{0.83}\n \\caption{Combined BERT Ensemble Structure}\n \\label{alg:combinedmodel}\n\n \\raggedright\n \\textbf{Input:} Text $x$\\\\\n \\textbf{Output:} Predicted label \\{\\emph{human}, \\emph{AI}\\}\\\\[2pt]\n \\par\n\n \\begin{algorithmic}[1]\n % ---------- Pre‑training block ----------\n \\Function{PretrainEnsemble}{$x$}\n \\State $\\text{bert\\_out} \\leftarrow \\text{BERTModel}_{\\text{frozen}}(x)$\n \\State $\\text{xlmr\\_out} \\leftarrow \\text{XLMRobertaModel}_{\\text{frozen}}(x)$\n \\State $\\text{distil\\_out} \\leftarrow \\text{DistilBERTModel}_{\\text{frozen}}(x)$\n \\State \\Return $(\\text{bert\\_out},\\ \\text{xlmr\\_out},\\ \\text{distil\\_out})$\n \\EndFunction\n\n % ---------- Fine‑tuning block ----------\n \\Function{FineTuneEnsemble}{$x$}\n \\State $\\text{bert\\_out\\_fresh} \\leftarrow \\text{BERTModel}_{\\text{fresh}}(x)$\n \\State $\\text{xlmr\\_out\\_fresh} \\leftarrow \\text{XLMRobertaModel}_{\\text{fresh}}(x)$\n \\State $\\text{distil\\_out\\_fresh} \\leftarrow \\text{DistilBERTModel}_{\\text{fresh}}(x)$\n \\State \\Return $(\\text{bert\\_out\\_fresh},\\ \\text{xlmr\\_out\\_fresh},\\ \\text{distil\\_out\\_fresh})$\n \\EndFunction\n\n % ---------- Combined model ----------\n \\Function{CombinedModel}{$x$}\n \\State $(\\text{froz\\_bert},\\ \\text{froz\\_xlmr},\\ \\text{froz\\_distil})\n \\leftarrow \\Call{PretrainEnsemble}{x}$\n \\State $(\\text{fresh\\_bert},\\ \\text{fresh\\_xlmr},\\ \\text{fresh\\_distil})\n \\leftarrow \\Call{FineTuneEnsemble}{x}$\n\n % split long concatenate call over two lines\n \\State $\\text{concat\\_outs} \\leftarrow \\text{concatenate}\\!\\bigl[%\n \\text{froz\\_bert},\\ \\text{froz\\_xlmr},\\ \\text{froz\\_distil},$\n \\Statex\\hspace{\\algorithmicindent}\\phantom{$\\text{concat\\_outs} \\leftarrow \\text{concatenate}\\!\\bigl[$}%\n $\\text{fresh\\_bert},\\ \\text{fresh\\_xlmr},\\ \\text{fresh\\_distil}\\bigr]$\n\n \\State $\\text{dense\\_out} \\leftarrow \\text{DenseLayer}(\\text{concat\\_outs})$ \\Comment{\\# L2‑regularized dense layer}\n \\State $\\hat{y} \\leftarrow \\sigma(\\text{dense\\_out})$ \\Comment{\\# Probability of AI‑generated text}\n \\If{$\\hat{y} \\geq 0.5$}\n \\State \\Return \\emph{AI}\n \\Else\n \\State \\Return \\emph{human}\n \\EndIf\n \\EndFunction\n \\end{algorithmic}\n \\end{algorithm}\n \\end{minipage}\n\\end{wrapfigure}\n\n\n\n\n\\paragraph{\\textbf{Ensemble Model.}}\nWe built an ensemble of multiple transformers instead of relying on a single one to classify human and AIGT), as inspired by~\\citet{mohammadi2023towards}. In this method, we use the strengths of each model and create an ensemble that can be fine-tuned on similar datasets. This helps the model adapt to different domains and reduces problems caused by limited data, as the features learned by each part complement each other. Specifically, we integrated bert-base-multilingual-uncased, distilbert-base-multilingual-cased, and xlm-roberta-base. Each transformer produced a representation of the input text, and we concatenated these representations before passing them through a dense layer with L2 regularization for final classification.\n\nWe first built a \\emph{Combined BERT} model by training three BERT models on the AuTexTification training set. We optimized the network's hyperparameters and evaluated performance on the AuTexTification test set, focusing on F1 and average metrics across folds. After training, we froze these model weights to retain the features they had learned and reduce the risk of overfitting. We next created a \\emph{combined model} that merges three BERT models with frozen weights (trained on AuTexTification) and three fresh BERT models with unfrozen parameters. These fresh models were fine-tuned on our newly augmented dataset Such an ensemble synergy aligns with~\\citet{fraser2025detecting}, who highlight the benefits of multi-model integration in detecting AI-generated text. Algorithm~\\ref{alg:combinedmodel} outlines the training and process for our combined BERT ensemble. During training, we minimize the binary cross-entropy loss on each branch using the augmented data. We then unify the outputs in a final fully connected layer, optionally employing majority voting if multiple final heads are used. Figure~\\ref{fig:ms-structure} shows the architecture, with frozen models on the left (gray) and fresh models on the right (green).\n\n\n\n\n\\begin{figure*}[!ht]\n\\centering\n\\includegraphics[width=0.7\\linewidth]{plots/model.jpeg}\n\\vspace{-5pt}\n\\caption{\\small The architecture of the proposed ensemble model. Outputs of three frozen BERT models (left) and three fresh BERT models (right) are concatenated and passed through dense layers, followed by a voting mechanism.}\n\\label{fig:ms-structure}\n\\end{figure*}\n\n\n\\subsection{Experimental Setup}\n\\label{subsec:setup}\n\nWe trained our models using the Hugging Face Transformers library\\footnote{\\url{https://huggingface.co/}} and set a maximum token length of 512. Hyperparameter tuning was done through a random search over learning rates (ranging from \\(1\\times 10^{-5}\\) to \\(1\\times 10^{-4}\\)) and batch sizes of 32, 64, or 128. We used a cosine decay schedule for learning rate adjustments, with 200 warm-up steps and an early-stopping patience of 5 epochs based on validation loss. \n\nThe final training stage used 90\\% of the augmented development set, while we left 10\\% for test. The hyperparameters and early-stopping rules were adjusted using the validation split. After training, we evaluated the system on the remaining data, measuring precision, recall, F1-score, and accuracy. By combining different transformers, freezing selectively learned features, and training fresh models in parallel, this ensemble method balanced the advantages of robust pre-trained representations with the adaptability needed for the new dataset.\n\n\\subsection{Explainability-driven detection}\n\\label{sec:explain}\n\nIn this section, we identify the words that most strongly influence an \\emph{AI-generated} prediction. Our approach uses two instance-level explanation tools: SHAP and LIME. Although they both operate on individual examples, SHAP uses game-theoretic principles to estimate a token’s overall (global) influence, while LIME explores small local perturbations to approximate importance. We take the absolute values of these importance scores $\\phi_j$ (for SHAP) or coefficients (for LIME) to capture both positive and negative contributions.\n\n\n \\setlength\\intextsep{0.3em} \n \\setlength\\columnsep{1em} \n \\begin{wrapfigure}[5]{r}{0.40\\textwidth} % [5]=height in text lines\n \\vspace*{-0.2em} % fine‑tune space above box\n \\begin{minipage}{\\linewidth}\\centering\\scriptsize\n \\[\n \\phi_j\n = \\sum_{S \\subseteq \\{1,\\dots,m\\}\\setminus\\{j\\}}\n \\frac{|S|!\\,\\bigl(m-|S|-1\\bigr)!}{m!}\\,\n \\bigl[f(S \\cup \\{j\\}) - f(S)\\bigr].\n \\]\n \\end{minipage}\n \\vspace*{-0.4em} % fine‑tune space below box\n \\end{wrapfigure}\n\nWe follow the methodology in~\\cite{mohammadi2024transparent}, in this framework, we only analyze sentences that the model \\emph{correctly} classifies, ensuring the tokens we highlight have genuinely contributed to accurate decisions. We then average each token’s importance within the sentence to get a consistent measure of its overall effect. This step helps us confirm that the extracted terms are meaningful indicators of the model’s reasoning. Given a trained model \\(f\\) and an instance \\(x\\) composed of tokens \\((t_1, t_2, \\ldots, t_m)\\), SHAP assigns a Shapley value \\(\\phi_j\\) to each token \\(t_j\\). These values are computed by measuring how much including or excluding \\(t_j\\) changes the model’s output, across all possible subsets of tokens. Formally:\n\n\nHere, \\(f(S)\\) is the model's prediction when only the tokens in set \\(S\\) are present. The weighting term ensures each token's effect is fairly distributed among all subsets~\\cite{lundberg2017unified}. LIME, on the other hand, makes small changes near the original text to learn a simpler, local model that approximates \\(f\\). It then assigns coefficients reflecting each token’s importance for that instance~\\cite{ribeiro2016should}. Although LIME focuses more on local neighborhoods, it also provides per-token contributions based on how the model’s prediction shifts when individual tokens are perturbed. We also consider a random token selection. This lets us compare how much improvement we gain by targeting the tokens that SHAP or LIME indicates are most influential. By using both a global (SHAP) and local (LIME) perspective, we can better trust that the identified tokens genuinely drive the model's decision. In our subsequent steps, we use these tokens to guide targeted replacements and measure how effectively such edits can mask AI-generated content.\n\n\\subsection{Token Replacement Strategies}\nOnce we identify influential tokens, we modify them using several approaches designed to make the text appear more human-like. We propose four rewriting strategies summarized in Table~\\ref{tab:token_replacement}.\n\n\\begin{table}[ht]\n\\centering\n\\scriptsize\n%\\small\n\\renewcommand{\\arraystretch}{1.2}\n\\caption{Summary of token replacement strategies}\n\\label{tab:token_replacement}\n\\begin{tabular}{p{4.8cm}p{9.2cm}}\n\\toprule\n\\textbf{Strategy} & \\textbf{Description} \\\\\n\\midrule\n\\textbf{Human Similar Word Replacement (HSR)} \n & A Word2Vec model is trained on all human-labeled texts in the training set to learn an embedding space that captures typical human usage. \\\\\n\\textbf{Part-of-Speech Replacement (PSR)} \n & HSR is refined by ensuring that part-of-speech tags match. \\\\\n\\textbf{GPT-based Replacement (GPT)} \n & A generative model (GPT-4o-mini) is queried using the prompt: \n \\texttt{\"Replace '\\{token\\}' with a more human-like word in this text: '\\{text\\}'\"}. \\\\\n\\textbf{GPT-based Replacement with Genre Context (GPT+Genre)} \n & The GPT-based method is extended by including explicit genre information (e.g., \\emph{tweets} or \\emph{reviews}) in the prompt:\n \\texttt{\"Replace '\\{token\\}' in this \\{domain\\} text with a more human-like word: '\\{text\\}'\"}. \\\\\n\\bottomrule\n\\end{tabular}\n\\end{table}\n\nIn \\emph{Strategy HSR}, each influential token is replaced with its nearest neighbor in the Word2Vec space, provided that the neighbor is frequently observed in human texts. In \\emph{Strategy PSR}, if a token \\(t\\) is tagged as a noun, for instance, a Word2Vec neighbor that is also tagged as a noun is selected, preserving grammatical structure. In \\emph{Strategy GPT}, a replacement that appears more \\emph{human-like} is proposed based on the surrounding sentence. While the suggestions are context-sensitive, certain LLM-specific patterns may still be retained if the generative model reintroduces specific phrasing. In \\emph{Strategy GPT+Genre}, incorporating domain-specific information allows the generative model to propose replacements that align more closely with the original style. However, this added context may also amplify distinctive AI traits. Finally, after substituting the most influential tokens in each text, we assess performance along two dimensions: (1) changes in detectability and (2) fidelity to the original content.\n\n\n\\subsection{Evaluation Metrics}\n\\label{subsec:evaluation_metrics}\n\nWe use a combination of detection and text-similarity metrics to evaluate how rewriting strategies affect our models' ability to detect AIGT, as well as how much the rewritten text deviates from its original form. First, once we rewrite an AIGT via HSR, PSR, GPT, or GPT+Genre. Next, we investigate how different rewriting scenarios affect AI-text detection. We adopt the adversarial rewriting perspective discussed in \\citet{fraser2025detecting}, \nwhere paraphrased or partially edited texts can undermine naive detectors. We run it through each classifier to check whether the label remains \\emph{AI} or flips to \\emph{human}. We then calculate precision, recall, F1, and accuracy to capture each model’s classification performance to detect AI/human texts under these modified inputs. Additionally, we compute the \\emph{flip rate}, defined as the fraction of AI texts whose predicted label changes to \\emph{human} after rewriting; a higher flip rate implies that the rewriting method is more effective in evading detection. In parallel, we calculate the degree of textual change introduced by each rewriting approach. We use BLEU, ROUGE-1, and ROUGE-L to measure the overlap between the original text and its rewritten form, with higher scores showing fewer modifications. By combining detection metrics, and text-similarity scores, we gain a clear view of how effectively each rewriting method hides AI-generated content and how much it changes the original text. \n\n\\section{Experimental Analysis and Results}\n\\label{sec:experiments}\n\nIn this section, we compare five classification models (XGBoost, BERT-base, DistilBERT, XLM-RoBERTa, and our \\textbf{Ensemble} approach) across various token-replacement scenarios for the detection of AIGT, Consistent with~\\citet{fraser2025detecting}, we anticipate that multi-domain evaluation is critical for robust results. Our main objective is to observe how different rewriting methods (HSR, PSR, GPT, GPT+Genre) combined with token-selection strategies (SHAP, LIME, Random) affect each model’s ability to detect generated text.\n\nTable~\\ref{tab:lang_domain_updated} shows each model’s performance on \\emph{unmodified} AIGT across two languages (English, Dutch) and three domains (news, reviews, and X/Twitter). As can be seen, although certain single models perform well on specific subsets (e.g., DistilBERT in English news, XGBoost in English Twitter), the \\textbf{Ensemble} approach tends to achieve the best performance in \\emph{most} cases. This pattern is particularly clear in Dutch news, where the Ensemble model attains perfect scores. Even when other models have high recall or precision on certain tasks, they often fall behind on at least one metric (e.g., XLM-RoBERTa predicts too many positives, increasing recall but lowering precision). showing strong cross-language adaptability. This result agrees with ~\\citet{fraser2025detecting}, who emphasize the importance of multi-lingual calibration for robust detection.\n\n\n\n%\n\\begin{table}[ht]\n\\centering\n%\\small\n\\scriptsize\n\\renewcommand{\\arraystretch}{1.2}\n\\caption{Model performance by language and domain on unmodified AI text. \\textbf{Bold} indicates the best result within each language and domain block.}\n\\label{tab:lang_domain_updated}\n\\begin{tabular}{llcccc|llcccc}\n\\toprule\n\\multicolumn{6}{c}{\\textbf{English Dataset}} & \\multicolumn{6}{c}{\\textbf{Dutch Dataset}} \\\\\n\\cmidrule(lr){1-6} \\cmidrule(lr){7-12}\n\\textbf{Model} & \\textbf{Domain} & \\textbf{Acc} & \\textbf{Prec} & \\textbf{Rec} & \\textbf{F1} \n & \\textbf{Model} & \\textbf{Domain} & \\textbf{Acc} & \\textbf{Prec} & \\textbf{Rec} & \\textbf{F1} \\\\\n\\midrule\n\\textbf{--- News ---} & & & & & \n& \\textbf{--- News ---} & & & & & \\\\\nXGBoost \n & news & 0.89 & 0.93 & 0.84 & 0.88 \n & XGBoost \n & news & 0.85 & 0.88 & 0.88 & 0.88 \\\\\nBERT-base \n & news & 0.94 & 0.97 & 0.91 & 0.94\n & BERT-base \n & news & 0.68 & 0.79 & 0.56 & 0.66 \\\\\nDistilBERT \n & news & 0.92 & 0.90 & \\textbf{0.95} & 0.92\n & DistilBERT \n & news & 0.68 & 0.73 & 0.66 & 0.69 \\\\\nXLM-RoBERTa\n & news & 0.49 & 0.49 & 1.00 & 0.66\n & XLM-RoBERTa\n & news & 0.53 & 0.53 & \\textbf{1.00} & 0.69 \\\\\nEnsemble \n & news & \\textbf{0.95} & \\textbf{1.00} & 0.90 & \\textbf{0.95}\n & Ensemble \n & news & \\textbf{0.99} & \\textbf{0.98} & \\textbf{1.00} & \\textbf{0.99} \\\\\n\\midrule\n\\textbf{--- Reviews ---} & & & & & \n& \\textbf{--- Reviews ---} & & & & & \\\\\nXGBoost \n & reviews & 0.51 & 0.57 & 0.59 & 0.58\n & XGBoost \n & reviews & 0.53 & 0.51 & 0.46 & 0.48 \\\\\nBERT-base \n & reviews & 0.50 & 0.54 & 0.62 & 0.58\n & BERT-base \n & reviews & 0.40 & 0.35 & 0.30 & 0.32 \\\\\nDistilBERT \n & reviews & 0.65 & 0.68 & 0.71 & 0.69\n & DistilBERT \n & reviews & 0.42 & 0.38 & 0.50 & 0.43 \\\\\nXLM-RoBERTa\n & reviews & 0.56 & 0.56 & \\textbf{0.90} & 0.69\n & XLM-RoBERTa\n & reviews & 0.43 & 0.43 & \\textbf{0.60} & 0.48 \\\\\nEnsemble \n & reviews & \\textbf{0.67} & \\textbf{0.71} & 0.68 & \\textbf{0.70}\n & Ensemble \n & reviews & \\textbf{0.60} & \\textbf{0.53} & 0.44 & \\textbf{0.49} \\\\\n\\midrule\n\\textbf{--- Twitter / X ---} & & & & & \n& \\textbf{--- Twitter / X ---} & & & & & \\\\\nXGBoost \n & twitter & 0.91 & \\textbf{0.92} & 0.98 & 0.95\n & XGBoost \n & twitter & 0.80 & 0.85 & 0.78 & 0.81 \\\\\nBERT-base \n & twitter & 0.90 & 0.86 & 0.98 & 0.92\n & BERT-base \n & twitter & 0.80 & 0.85 & 0.78 & 0.81 \\\\\nDistilBERT \n & twitter & 0.90 & 0.86 & 0.98 & 0.92\n & DistilBERT \n & twitter & 0.84 & 0.86 & 0.80 & 0.83 \\\\\nXLM-RoBERTa\n & twitter & 0.60 & 0.53 & \\textbf{1.00} & 0.69\n & XLM-RoBERTa\n & twitter & 0.62 & 0.60 & \\textbf{0.90} & 0.72 \\\\\nEnsemble \n & twitter & \\textbf{0.93} & 0.86 & \\textbf{1.00} & \\textbf{0.93}\n & Ensemble \n & twitter & \\textbf{0.87} & \\textbf{0.90} & 0.85 & \\textbf{0.87} \\\\\n\\bottomrule\n\\end{tabular}\n\\end{table}\n\n\nWe next investigate how different rewriting scenarios affect AI-text detection. In particular, we apply four rewriting strategies and combine each of these with one of three token-selection strategies: SHAP (prioritizes globally influential tokens), LIME (locally influential tokens), or Random (tokens chosen arbitrarily). Table~\\ref{tab:all_models_rewriting_f1acc} presents performance metrics for each model under all these rewriting conditions.\n\n% \\begin{table*}[ht]\n% \\centering\n% \\caption{\\small F1 score and Accuracy of each detector under every rewriting scenario. \n% The best metric in a column is \\textbf{bolded}; the largest negative drop is shown in \\textcolor{purple}{red}, the smallest in \\textcolor{teal}{green}. \n% \\footnotesize{$\\Delta=(\\text{Scenario Value}-\\text{Original Value})\\times100\\%$.}}\n% \\label{tab:all_models_rewriting_f1acc}\n% \\renewcommand{\\arraystretch}{1.4}\n% \\resizebox{\\textwidth}{!}{\n% \\begin{tabular}{l l\n% c c c c c c c c c c}\n% \\toprule\n% \\multirow{2}{*}{\\textbf{Explain}} & \\multirow{2}{*}{\\textbf{Strategy}}\n% & \\multicolumn{2}{c}{\\textbf{XGBoost}}\n% & \\multicolumn{2}{c}{\\textbf{BERT-base}}\n% & \\multicolumn{2}{c}{\\textbf{DistilBERT}}\n% & \\multicolumn{2}{c}{\\textbf{XLM-RoBERTa}}\n% & \\multicolumn{2}{c}{\\textbf{Ensemble}}\\\\\n% \\cmidrule(lr){3-4}\\cmidrule(lr){5-6}\\cmidrule(lr){7-8}\\cmidrule(lr){9-10}\\cmidrule(lr){11-12}\n% & & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\n% & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\n% & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\n% & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\n% & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\\\\\n% \\midrule\n% \\emph{NoRewriting} & Original\n% & 0.81 & 0.80\n% & 0.74 & 0.75\n% & 0.79 & 0.77\n% & 0.68 & 0.54\n% & \\textbf{0.83} & \\textbf{0.83}\\\\\n% \\midrule\n% \\textbf{SHAP} & HSR\n% & 0.73 (\\textcolor{purple}{-8\\%}) & 0.75 (\\textcolor{purple}{-5\\%})\n% & 0.71 (-3\\%) & 0.72 (-3\\%)\n% & 0.76 (-3\\%) & 0.75 (-2\\%)\n% & 0.68 (\\textcolor{teal}{0\\%}) & 0.54 (\\textcolor{teal}{0\\%})\n% & \\textbf{0.78} (-5\\%) & \\textbf{0.80} (-3\\%)\\\\\n% \\textbf{SHAP} & PSR\n% & 0.78 (\\textcolor{purple}{-3\\%}) & 0.78 (\\textcolor{purple}{-2\\%})\n% & 0.73 (-1\\%) & 0.74 (-1\\%)\n% & 0.78 (-1\\%) & 0.77 (0\\%)\n% & 0.68 (\\textcolor{teal}{0\\%}) & 0.54 (\\textcolor{teal}{0\\%})\n% & \\textbf{0.82} (-1\\%) & \\textbf{0.82} (-1\\%)\\\\\n% \\textbf{SHAP} & GPT\n% & 0.79 (-2\\%) & 0.79 (-1\\%)\n% & 0.73 (-1\\%) & 0.74 (-1\\%)\n% & 0.77 (-2\\%) & 0.76 (-1\\%)\n% & 0.68 (\\textcolor{teal}{0\\%}) & 0.55 (\\textcolor{teal}{+1\\%})\n% & \\textbf{0.80} (\\textcolor{purple}{-3\\%}) & \\textbf{0.81} (\\textcolor{purple}{-2\\%})\\\\\n% \\textbf{SHAP} & GPT+Genre\n% & \\textbf{0.80} (-1\\%) & \\textbf{0.80} (0\\%)\n% & 0.73 (-1\\%) & 0.74 (-1\\%)\n% & 0.77 (-2\\%) & 0.76 (-1\\%)\n% & 0.68 (\\textcolor{teal}{0\\%}) & 0.55 (+1\\%)\n% & 0.79 (\\textcolor{purple}{-4\\%}) & 0.80 (\\textcolor{purple}{-3\\%})\\\\\n% \\midrule\n% \\textbf{LIME} & HSR\n% & 0.25 (\\textcolor{purple}{-56\\%}) & 0.48 (\\textcolor{purple}{-32\\%})\n% & 0.56 (-18\\%) & 0.62 (-13\\%)\n% & 0.62 (-17\\%) & 0.65 (-12\\%)\n% & \\textbf{0.67} (\\textcolor{teal}{-1\\%}) & 0.52 (\\textcolor{teal}{-2\\%})\n% & 0.64 (-19\\%) & \\textbf{0.69} (-14\\%)\\\\\n% \\textbf{LIME} & PSR\n% & 0.56 (\\textcolor{purple}{-25\\%}) & 0.63 (\\textcolor{purple}{-17\\%})\n% & 0.64 (-10\\%) & 0.67 (-8\\%)\n% & 0.71 (-8\\%) & 0.71 (-6\\%)\n% & 0.66 (\\textcolor{teal}{-2\\%}) & 0.52 (\\textcolor{teal}{-2\\%})\n% & \\textbf{0.73} (-10\\%) & \\textbf{0.75} (-8\\%)\\\\\n% \\textbf{LIME} & GPT\n% & 0.57 (\\textcolor{purple}{-24\\%}) & 0.64 (\\textcolor{purple}{-16\\%})\n% & 0.73 (-1\\%) & 0.74 (-1\\%)\n% & 0.78 (-1\\%) & 0.77 (0\\%)\n% & 0.68 (\\textcolor{teal}{0\\%}) & 0.55 (\\textcolor{teal}{+1\\%})\n% & \\textbf{0.83} (\\textcolor{teal}{0\\%}) & \\textbf{0.83} (0\\%)\\\\\n% \\textbf{LIME} & GPT+Genre\n% & 0.60 (\\textcolor{purple}{-21\\%}) & 0.65 (\\textcolor{purple}{-15\\%})\n% & \\textbf{0.75} (\\textcolor{teal}{+1\\%}) & \\textbf{0.75} (0\\%)\n% & 0.77 (-2\\%) & 0.76 (-1\\%)\n% & 0.68 (0\\%) & 0.55 (\\textcolor{teal}{+1\\%})\n% & \\textbf{0.81} (-2\\%) & \\textbf{0.81} (-2\\%)\\\\\n% \\midrule\n% \\textbf{Random} & HSR\n% & 0.73 (-8\\%) & 0.74 (-6\\%)\n% & 0.63 (\\textcolor{purple}{-11\\%}) & 0.67 (\\textcolor{purple}{-8\\%})\n% & 0.68 (\\textcolor{purple}{-11\\%}) & 0.69 (\\textcolor{purple}{-8\\%})\n% & 0.67 (\\textcolor{teal}{-1\\%}) & 0.53 (\\textcolor{teal}{-1\\%})\n% & \\textbf{0.73} (-10\\%) & \\textbf{0.75} (\\textcolor{purple}{-8\\%})\\\\\n% \\textbf{Random} & PSR\n% & 0.76 (-5\\%) & 0.76 (-4\\%)\n% & 0.65 (\\textcolor{purple}{-9\\%}) & 0.68 (\\textcolor{purple}{-7\\%})\n% & 0.74 (-5\\%) & 0.73 (-4\\%)\n% & 0.67 (\\textcolor{teal}{-1\\%}) & 0.53 (\\textcolor{teal}{-1\\%})\n% & \\textbf{0.78} (-5\\%) & \\textbf{0.79} (-4\\%)\\\\\n% \\textbf{Random} & GPT\n% & 0.74 (\\textcolor{purple}{-7\\%}) & 0.75 (\\textcolor{purple}{-5\\%})\n% & 0.74 (0\\%) & 0.75 (0\\%)\n% & 0.77 (-2\\%) & 0.76 (-1\\%)\n% & 0.68 (\\textcolor{teal}{0\\%}) & 0.55 (\\textcolor{teal}{+1\\%})\n% & \\textbf{0.81} (-2\\%) & \\textbf{0.82} (-1\\%)\\\\\n% \\textbf{Random} & GPT+Genre\n% & 0.75 (\\textcolor{purple}{-6\\%}) & 0.75 (\\textcolor{purple}{-5\\%})\n% & 0.73 (-1\\%) & 0.74 (-1\\%)\n% & \\textbf{0.78} (-1\\%) & 0.77 (0\\%)\n% & 0.68 (\\textcolor{teal}{0\\%}) & 0.55 (\\textcolor{teal}{+1\\%})\n% & \\textbf{0.81} (-2\\%) & \\textbf{0.81} (-2\\%)\\\\\n% \\bottomrule\n% \\end{tabular}}\n% \\end{table*}\n\n\n \n\n\\begin{table*}[ht]\n\\centering\n\\caption{\\small F1 score and Accuracy of each detector under every rewriting scenario. \nThe best metric in a column is \\textbf{bolded}; the largest negative drop is \\dashuline{\\phantom{-00\\%}}, the smallest negative drop is \\dotuline{\\phantom{-00\\%}}.\n\\footnotesize{$\\Delta=(\\text{Scenario Value}-\\text{Original Value})\\times100\\%$.}}\n\\label{tab:all_models_rewriting_f1acc}\n\\renewcommand{\\arraystretch}{1.4}\n\\resizebox{\\textwidth}{!}{\n\\begin{tabular}{l l\nc c c c c c c c c c}\n\\toprule\n\\multirow{2}{*}{\\textbf{Explain}} & \\multirow{2}{*}{\\textbf{Strategy}}\n& \\multicolumn{2}{c}{\\textbf{XGBoost}}\n& \\multicolumn{2}{c}{\\textbf{BERT-base}}\n& \\multicolumn{2}{c}{\\textbf{DistilBERT}}\n& \\multicolumn{2}{c}{\\textbf{XLM-RoBERTa}}\n& \\multicolumn{2}{c}{\\textbf{Ensemble}}\\\\\n\\cmidrule(lr){3-4}\\cmidrule(lr){5-6}\\cmidrule(lr){7-8}\\cmidrule(lr){9-10}\\cmidrule(lr){11-12}\n& & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\n & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\n & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\n & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\n & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\\\\\n\\midrule\n\\emph{NoRewriting} & Original\n& 0.81 & 0.80\n& 0.74 & 0.75\n& 0.79 & 0.77\n& 0.68 & 0.54\n& \\textbf{0.83} & \\textbf{0.83}\\\\\n\\midrule\n\\textbf{SHAP} & HSR\n& 0.73 (\\largest{-8\\%}) & 0.75 (\\largest{-5\\%})\n& 0.71 (-3\\%) & 0.72 (-3\\%)\n& 0.76 (-3\\%) & 0.75 (-2\\%)\n& 0.68 (\\smallest{0\\%}) & 0.54 (\\smallest{0\\%})\n& \\textbf{0.78} (-5\\%) & \\textbf{0.80} (-3\\%)\\\\\n\\textbf{SHAP} & PSR\n& 0.78 (\\largest{-3\\%}) & 0.78 (\\largest{-2\\%})\n& 0.73 (-1\\%) & 0.74 (-1\\%)\n& 0.78 (-1\\%) & 0.77 (0\\%)\n& 0.68 (\\smallest{0\\%}) & 0.54 (\\smallest{0\\%})\n& \\textbf{0.82} (-1\\%) & \\textbf{0.82} (-1\\%)\\\\\n\\textbf{SHAP} & GPT\n& 0.79 (-2\\%) & 0.79 (-1\\%)\n& 0.73 (-1\\%) & 0.74 (-1\\%)\n& 0.77 (-2\\%) & 0.76 (-1\\%)\n& 0.68 (\\smallest{0\\%}) & 0.55 (\\smallest{+1\\%})\n& \\textbf{0.80} (\\largest{-3\\%}) & \\textbf{0.81} (\\largest{-2\\%})\\\\\n\\textbf{SHAP} & GPT+Genre\n& \\textbf{0.80} (-1\\%) & \\textbf{0.80} (0\\%)\n& 0.73 (-1\\%) & 0.74 (-1\\%)\n& 0.77 (-2\\%) & 0.76 (-1\\%)\n& 0.68 (\\smallest{0\\%}) & 0.55 (\\smallest{+1\\%})\n& 0.79 (\\largest{-4\\%}) & 0.80 (\\largest{-3\\%})\\\\\n\\midrule\n\\textbf{LIME} & HSR\n& 0.25 (\\largest{-56\\%}) & 0.48 (\\largest{-32\\%})\n& 0.56 (-18\\%) & 0.62 (-13\\%)\n& 0.62 (-17\\%) & 0.65 (-12\\%)\n& \\textbf{0.67} (\\smallest{-1\\%}) & 0.52 (\\smallest{-2\\%})\n& 0.64 (-19\\%) & \\textbf{0.69} (-14\\%)\\\\\n\\textbf{LIME} & PSR\n& 0.56 (\\largest{-25\\%}) & 0.63 (\\largest{-17\\%})\n& 0.64 (-10\\%) & 0.67 (-8\\%)\n& 0.71 (-8\\%) & 0.71 (-6\\%)\n& 0.66 (\\smallest{-2\\%}) & 0.52 (\\smallest{-2\\%})\n& \\textbf{0.73} (-10\\%) & \\textbf{0.75} (-8\\%)\\\\\n\\textbf{LIME} & GPT\n& 0.57 (\\largest{-24\\%}) & 0.64 (\\largest{-16\\%})\n& 0.73 (-1\\%) & 0.74 (-1\\%)\n& 0.78 (-1\\%) & 0.77 (0\\%)\n& 0.68 (\\smallest{0\\%}) & 0.55 (\\smallest{+1\\%})\n& \\textbf{0.83} (\\smallest{0\\%}) & \\textbf{0.83} (0\\%)\\\\\n\\textbf{LIME} & GPT+Genre\n& 0.60 (\\largest{-21\\%}) & 0.65 (\\largest{-15\\%})\n& \\textbf{0.75} (\\smallest{+1\\%}) & \\textbf{0.75} (0\\%)\n& 0.77 (-2\\%) & 0.76 (-1\\%)\n& 0.68 (0\\%) & 0.55 (\\smallest{+1\\%})\n& \\textbf{0.81} (-2\\%) & \\textbf{0.81} (-2\\%)\\\\\n\\midrule\n\\textbf{Random} & HSR\n& 0.73 (-8\\%) & 0.74 (-6\\%)\n& 0.63 (\\largest{-11\\%}) & 0.67 (\\largest{-8\\%})\n& 0.68 (\\largest{-11\\%}) & 0.69 (\\largest{-8\\%})\n& 0.67 (\\smallest{-1\\%}) & 0.53 (\\smallest{-1\\%})\n& \\textbf{0.73} (-10\\%) & \\textbf{0.75} (\\largest{-8\\%})\\\\\n\\textbf{Random} & PSR\n& 0.76 (-5\\%) & 0.76 (-4\\%)\n& 0.65 (\\largest{-9\\%}) & 0.68 (\\largest{-7\\%})\n& 0.74 (-5\\%) & 0.73 (-4\\%)\n& 0.67 (\\smallest{-1\\%}) & 0.53 (\\smallest{-1\\%})\n& \\textbf{0.78} (-5\\%) & \\textbf{0.79} (-4\\%)\\\\\n\\textbf{Random} & GPT\n& 0.74 (\\largest{-7\\%}) & 0.75 (\\largest{-5\\%})\n& 0.74 (0\\%) & 0.75 (0\\%)\n& 0.77 (-2\\%) & 0.76 (-1\\%)\n& 0.68 (\\smallest{0\\%}) & 0.55 (\\smallest{+1\\%})\n& \\textbf{0.81} (-2\\%) & \\textbf{0.82} (-1\\%)\\\\\n\\textbf{Random} & GPT+Genre\n& 0.75 (\\largest{-6\\%}) & 0.75 (\\largest{-5\\%})\n& 0.73 (-1\\%) & 0.74 (-1\\%)\n& \\textbf{0.78} (-1\\%) & 0.77 (0\\%)\n& 0.68 (0\\%) & 0.55 (\\smallest{+1\\%})\n& \\textbf{0.81} (-2\\%) & \\textbf{0.81} (-2\\%)\\\\\n\\bottomrule\n\\end{tabular}}\n\\end{table*}\n\n\n\\paragraph{\\textbf{General Trends.}}\nWhen no rewriting is applied (\\emph{NoRewriting, Original}), most models exhibit reasonably strong performance (precision and recall around 0.75--0.83), with the Ensemble reaching the highest overall F1 (0.83) and accuracy (0.83). After rewriting, simpler models often detect less, especially if the replaced tokens were the ones they used to identify AI patterns.\n\n\\paragraph{\\textbf{Comparison of Token-Selection Approaches.}}\nSHAP or LIME-based replacements often make larger drops in detection metrics than Random replacements, since replacing highly influential tokens more effectively removes AI indicators. For instance, comparing LIME+HSR vs.\\ Random+HSR, XGBoost’s F1 can decrease from around 0.73 to 0.25 if LIME is used, indicating a big loss in detection ability once key tokens are perturbed. On the other hand, random token replacement leaves more of the AI signals intact, so detection remains higher.\n\n\\paragraph{\\textbf{Influence of Rewriting Methods.}}\nHSR and PSR replace tokens with synonyms from a human corpus, often preserving the original text’s structure. This is reflected in relatively high BLEU scores (e.g., over 90\\% in the SHAP+HSR or SHAP+PSR rows), meaning minimal rewriting. Nonetheless, these small edits can still mislead detectors if the replaced tokens were crucial. GPT-based rewrites typically lower BLEU into the 70s or 80s, reflecting more substantial paraphrases. In turn, many detectors (e.g., XGBoost or XLM-RoBERTa) experience an increase in recall but a decrease in precision, or the opposite, because the text shifts away from familiar AI phrasing. GPT+Genre further contextualizes the rewrite with domain hints (e.g.\\ write it like a news article), causing domain-sensitive lexical changes. This can introduce new phrasing that confuses detectors reliant on domain-specific cues.\n\n\n\\paragraph{\\textbf{Model-Specific Observations.}}\n%From the model perspective, \nXLM-RoBERTa often shows high recall (sometimes hitting 1.0), but its precision remains around 0.52, leading to only modest changes in F1. DistilBERT is more balanced yet remains vulnerable when its top keywords are replaced by synonyms or paraphrases. BERT-base typically holds a moderate advantage across scenarios but can be undermined in LIME-based rewrites (e.g., LIME+HSR, where BERT-base F1 falls to about 0.56). By contrast, the \\textbf{Ensemble} approach consistently retains the highest or near-highest F1 and accuracy. For example, under LIME+GPT, the Ensemble still attains an F1 of 0.83, significantly above XGBoost’s 0.57 or BERT-base’s 0.73. Similarly, in SHAP plus GPT+Genre, the Ensemble preserves a strong F1 of about 0.79, while other models vary more dramatically. Also, We show text similarity scores (BLEU, ROUGE1, ROUGEL) to capture how extensively each rewrite modifies the original text.\n\n\n% \\begin{wraptable}{r}{0.62\\textwidth} \n% \\vspace{-0.8em} \n% \\centering\n% \\scriptsize\n% \\caption{\\small BLEU, ROUGE‑1 and ROUGE‑L scores for every rewriting scenario.}\n% \\label{tab:rewriting_similarity}\n% \\renewcommand{\\arraystretch}{1.2}\n% \\begin{tabular}{l l c c c}\n% \\toprule\n% \\textbf{Explain} & \\textbf{Strategy} & \\textbf{BLEU ($\\Delta$)} & \\textbf{ROUGE‑1 ($\\Delta$)} & \\textbf{ROUGE‑L ($\\Delta$)}\\\\\n% \\midrule\n% \\emph{NoRewriting} & Original & 1,00 & 1,00 & 1,00\\\\\n% \\midrule\n% \\textbf{SHAP} & HSR & 0,93 (\\textcolor{black}{-7\\%}) & 0,97 (\\textcolor{black}{-3\\%}) & 0,97 (\\textcolor{black}{-3\\%})\\\\\n% \\textbf{SHAP} & PSR & \\textbf{0,97} (\\textcolor{teal}{-3\\%}) & \\textbf{0,99} (\\textcolor{teal}{-1\\%}) & \\textbf{0,99} (\\textcolor{teal}{-1\\%})\\\\\n% \\textbf{SHAP} & GPT & 0,86 (\\textcolor{black}{-14\\%}) & 0,93 (\\textcolor{black}{-7\\%}) & 0,93 (\\textcolor{black}{-7\\%})\\\\\n% \\textbf{SHAP} & GPT+Genre & 0,86 (\\textcolor{black}{-14\\%}) & 0,94 (\\textcolor{black}{-6\\%}) & 0,94 (\\textcolor{black}{-6\\%})\\\\\n% \\midrule\n% \\textbf{LIME} & HSR & 0,84 (\\textcolor{black}{-16\\%}) & 0,92 (\\textcolor{black}{-8\\%}) & 0,92 (\\textcolor{black}{-8\\%})\\\\\n% \\textbf{LIME} & PSR & 0,90 (\\textcolor{black}{-10\\%}) & 0,96 (\\textcolor{black}{-5\\%}) & 0,95 (\\textcolor{black}{-5\\%})\\\\\n% \\textbf{LIME} & GPT & 0,76 (\\textcolor{purple}{-24\\%}) & 0,84 (\\textcolor{purple}{-16\\%}) & 0,84 (\\textcolor{purple}{-16\\%})\\\\\n% \\textbf{LIME} & GPT+Genre & 0,76 (\\textcolor{purple}{-24\\%}) & 0,85 (\\textcolor{black}{-15\\%}) & 0,84 (\\textcolor{purple}{-16\\%})\\\\\n% \\midrule\n% \\textbf{Random} & HSR & 0,84 (\\textcolor{black}{-16\\%}) & 0,92 (\\textcolor{black}{-8\\%}) & 0,92 (\\textcolor{black}{-8\\%})\\\\\n% \\textbf{Random} & PSR & 0,90 (\\textcolor{black}{-11\\%}) & 0,95 (\\textcolor{black}{-5\\%}) & 0,95 (\\textcolor{black}{-5\\%})\\\\\n% \\textbf{Random} & GPT & 0,76 (\\textcolor{purple}{-24\\%}) & 0,84 (\\textcolor{purple}{-16\\%}) & 0,84 (\\textcolor{purple}{-16\\%})\\\\\n% \\textbf{Random} & GPT+Genre & 0,76 (\\textcolor{purple}{-24\\%}) & 0,84 (\\textcolor{purple}{-16\\%}) & 0,84 (\\textcolor{purple}{-16\\%})\\\\\n% \\bottomrule\n% \\end{tabular}\n% \\vspace{-1em} \n% \\end{wraptable}\n\n\n\n\n\\paragraph{\\textbf{Text Similarity Implications.}}\n Table~\\ref{tab:rewriting_similarity} presents text similarity scores (BLEU, ROUGE1, ROUGEL) under all these rewriting conditions. Looking at BLEU and ROUGE scores confirms that GPT-based methods, especially GPT+Genre, introduce the largest textual shifts. Replacements guided by synonyms (HSR or PSR) keep BLEU near or above 90, whereas GPT rewrites can dip into the mid-70s. This trade-off highlights how more extensive rewriting can more thoroughly remove AI signatures at the expense of text fidelity.\n\n\n\n\n\\begin{wraptable}{r}{0.62\\textwidth} \n \\vspace{-0.8em} \n \\centering\n \\scriptsize\n \\caption{\\small BLEU, ROUGE‑1 and ROUGE‑L scores for every rewriting scenario.}\n \\label{tab:rewriting_similarity}\n \\renewcommand{\\arraystretch}{1.2}\n \\begin{tabular}{l l c c c}\n \\toprule\n \\textbf{Explain} & \\textbf{Strategy} & \\textbf{BLEU ($\\Delta$)} & \\textbf{ROUGE‑1 ($\\Delta$)} & \\textbf{ROUGE‑L ($\\Delta$)}\\\\\n \\midrule\n \\emph{NoRewriting} & Original & 1,00 & 1,00 & 1,00\\\\\n \\midrule\n \\textbf{SHAP} & HSR & 0,93 (-7\\%) & 0,97 (-3\\%) & 0,97 (-3\\%)\\\\\n \\textbf{SHAP} & PSR & \\textbf{0,97} (\\smallest{-3\\%}) & \\textbf{0,99} (\\smallest{-1\\%}) & \\textbf{0,99} (\\smallest{-1\\%})\\\\\n \\textbf{SHAP} & GPT & 0,86 (-14\\%) & 0,93 (-7\\%) & 0,93 (-7\\%)\\\\\n \\textbf{SHAP} & GPT+Genre & 0,86 (-14\\%) & 0,94 (-6\\%) & 0,94 (-6\\%)\\\\\n \\midrule\n \\textbf{LIME} & HSR & 0,84 (-16\\%) & 0,92 (-8\\%) & 0,92 (-8\\%)\\\\\n \\textbf{LIME} & PSR & 0,90 (-10\\%) & 0,96 (-5\\%) & 0,95 (-5\\%)\\\\\n \\textbf{LIME} & GPT & 0,76 (\\largest{-24\\%}) & 0,84 (\\largest{-16\\%}) & 0,84 (\\largest{-16\\%})\\\\\n \\textbf{LIME} & GPT+Genre & 0,76 (\\largest{-24\\%}) & 0,85 (-15\\%) & 0,84 (\\largest{-16\\%})\\\\\n \\midrule\n \\textbf{Random} & HSR & 0,84 (-16\\%) & 0,92 (-8\\%) & 0,92 (-8\\%)\\\\\n \\textbf{Random} & PSR & 0,90 (-11\\%) & 0,95 (-5\\%) & 0,95 (-5\\%)\\\\\n \\textbf{Random} & GPT & 0,76 (\\largest{-24\\%}) & 0,84 (\\largest{-16\\%}) & 0,84 (\\largest{-16\\%})\\\\\n \\textbf{Random} & GPT+Genre & 0,76 (\\largest{-24\\%}) & 0,84 (\\largest{-16\\%}) & 0,84 (\\largest{-16\\%})\\\\\n \\bottomrule\n \\end{tabular}\n %\\vspace{-1em} \n\\end{wraptable}\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\\paragraph{\\textbf{Flip Rates and Overlapping Flips}}\n\\label{subsec:flip_rates}\n\nTo estimate how many AIGTs are relabeled as \\emph{human} after rewriting, we track \\emph{flip rates} for each combination of explainability method, rewriting strategy, and model. A higher flip rate shows that the corresponding model is more easily deceived by that particular rewriting approach. In these experiments, LIME-based edits coupled with HSR lead to notably high misclassification rates in simpler models such as XGBoost (flipping up to 65\\% of the samples), whereas XLM-RoBERTa remains relatively robust at a mere 2.5\\% flip rate. GPT-based rewrites result in mid-level flip percentages overall; for example, under LIME+GPT, XGBoost flips 31.67\\% of texts while BERT-base and DistilBERT are both at around 9.17\\%. Moreover, SHAP+PSR frequently produces lower flip rates, showing that while part-of-speech-constrained edits can deceive less sophisticated classifiers, advanced or ensemble approaches remain relatively unaffected (with an Ensemble flip rate of only 2.5\\%). Notably, XLM-RoBERTa’s tendency to overpredict \\emph{AI} keeps its flips artificially low. The Ensemble, although not entirely immune, stays in the lower to mid-range across all scenarios, once again showing greater robustness than single-model detectors. Table~\\ref{tab:flip_rates_split} provides a detailed breakdown of which sets of models flip each sample in various scenarios. \n\n% \\begin{table}[ht]\n% \\centering\n% \\scriptsize\n% \\caption{Flip rates (\\%) from \\textbf{AI} to \\textbf{human}. \n% Each scenario contains 120 AI-labelled items; higher values mean the\n% detector is easier to ``flip'' by rewriting.}\n% \\label{tab:flip_rates_split}\n% \\begin{subtable}[t]{0.48\\linewidth}\n% \\centering\n% \\caption{HSR \\& PSR strategies}\n% \\resizebox{\\linewidth}{!}{%\n% \\begin{tabular}{ll\n% l\n% S S S}\n% \\toprule\n% \\textbf{Exp.} & \\textbf{Strat.} & \\textbf{Model} &\n% {\\#AI} & {Flips} & {Rate} \\\\\n% \\midrule\n% % ---------- HSR ----------\n% \\multirow{15}{*}{SHAP}\n% & \\multirow{5}{*}{HSR}\n% & XGBoost & 120 & 17 & 14.17 \\\\\n% & & BERT & 120 & 7 & 5.83 \\\\\n% & & DistilBERT & 120 & 9 & 7.50 \\\\\n% & & XLM-R & 120 & 1 & 0.83 \\\\\n% & & Ensemble & 120 & 8 & 6.67 \\\\\n% \\cmidrule(lr){2-6}\n% & \\multirow{5}{*}{PSR}\n% & XGBoost & 120 & 7 & 5.83 \\\\\n% & & BERT & 120 & 4 & 3.33 \\\\\n% & & DistilBERT & 120 & 4 & 3.33 \\\\\n% & & XLM-R & 120 & 0 & 0.00 \\\\\n% & & Ensemble & 120 & 3 & 2.50 \\\\\n% \\midrule\n% \\multirow{10}{*}{LIME}\n% & \\multirow{5}{*}{HSR}\n% & XGBoost & 120 & 78 & 65.00 \\\\\n% & & BERT & 120 & 31 & 25.83 \\\\\n% & & DistilBERT & 120 & 32 & 26.67 \\\\\n% & & XLM-R & 120 & 3 & 2.50 \\\\\n% & & Ensemble & 120 & 33 & 27.50 \\\\\n% \\cmidrule(lr){2-6}\n% & \\multirow{5}{*}{PSR}\n% & XGBoost & 120 & 46 & 38.33 \\\\\n% & & BERT & 120 & 20 & 16.67 \\\\\n% & & DistilBERT & 120 & 19 & 15.83 \\\\\n% & & XLM-R & 120 & 5 & 4.17 \\\\\n% & & Ensemble & 120 & 19 & 15.83 \\\\\n% \\midrule\n% \\multirow{10}{*}{Rand}\n% & \\multirow{5}{*}{HSR}\n% & XGBoost & 120 & 15 & 12.50 \\\\\n% & & BERT & 120 & 20 & 16.67 \\\\\n% & & DistilBERT & 120 & 22 & 18.33 \\\\\n% & & XLM-R & 120 & 2 & 1.67 \\\\\n% & & Ensemble & 120 & 20 & 16.67 \\\\\n% \\cmidrule(lr){2-6}\n% & \\multirow{5}{*}{PSR}\n% & XGBoost & 120 & 10 & 8.33 \\\\\n% & & BERT & 120 & 17 & 14.17 \\\\\n% & & DistilBERT & 120 & 11 & 9.17 \\\\\n% & & XLM-R & 120 & 1 & 0.83 \\\\\n% & & Ensemble & 120 & 14 & 11.67 \\\\\n% \\bottomrule\n% \\end{tabular}}\n% \\end{subtable}%\n% \\hfill\n% \\begin{subtable}[t]{0.48\\linewidth}\n% \\centering\n% \\caption{GPT \\& GPT-Genre strategies}\n% \\resizebox{\\linewidth}{!}{%\n% \\begin{tabular}{ll\n% l\n% S S S}\n% \\toprule\n% \\textbf{Exp.} & \\textbf{Strat.} & \\textbf{Model} &\n% {\\#AI} & {Flips} & {Rate} \\\\\n% \\midrule\n% % ---------- GPT ----------\n% \\multirow{15}{*}{SHAP}\n% & \\multirow{5}{*}{GPT}\n% & XGBoost & 120 & 4 & 3.33 \\\\\n% & & BERT & 120 & 7 & 5.83 \\\\\n% & & DistilBERT & 120 & 7 & 5.83 \\\\\n% & & XLM-R & 120 & 0 & 0.00 \\\\\n% & & Ensemble & 120 & 10 & 8.33 \\\\\n% \\cmidrule(lr){2-6}\n% & \\multirow{5}{*}{GPT+G}\n% & XGBoost & 120 & 4 & 3.33 \\\\\n% & & BERT & 120 & 3 & 2.50 \\\\\n% & & DistilBERT & 120 & 6 & 5.00 \\\\\n% & & XLM-R & 120 & 0 & 0.00 \\\\\n% & & Ensemble & 120 & 11 & 9.17 \\\\\n% \\midrule\n% \\multirow{10}{*}{LIME}\n% & \\multirow{5}{*}{GPT}\n% & XGBoost & 120 & 38 & 31.67 \\\\\n% & & BERT & 120 & 11 & 9.17 \\\\\n% & & DistilBERT & 120 & 11 & 9.17 \\\\\n% & & XLM-R & 120 & 0 & 0.00 \\\\\n% & & Ensemble & 120 & 11 & 9.17 \\\\\n% \\cmidrule(lr){2-6}\n% & \\multirow{5}{*}{GPT+G}\n% & XGBoost & 120 & 41 & 34.17 \\\\\n% & & BERT & 120 & 4 & 3.33 \\\\\n% & & DistilBERT & 120 & 7 & 5.83 \\\\\n% & & XLM-R & 120 & 0 & 0.00 \\\\\n% & & Ensemble & 120 & 9 & 7.50 \\\\\n% \\midrule\n% \\multirow{10}{*}{Rand}\n% & \\multirow{5}{*}{GPT}\n% & XGBoost & 120 & 16 & 13.33 \\\\\n% & & BERT & 120 & 6 & 5.00 \\\\\n% & & DistilBERT & 120 & 6 & 5.00 \\\\\n% & & XLM-R & 120 & 0 & 0.00 \\\\\n% & & Ensemble & 120 & 5 & 4.17 \\\\\n% \\cmidrule(lr){2-6}\n% & \\multirow{5}{*}{GPT+G}\n% & XGBoost & 120 & 13 & 10.83 \\\\\n% & & BERT & 120 & 6 & 5.00 \\\\\n% & & DistilBERT & 120 & 9 & 7.50 \\\\\n% & & XLM-R & 120 & 0 & 0.00 \\\\\n% & & Ensemble & 120 & 11 & 9.17 \\\\\n% \\bottomrule\n% \\end{tabular}}\n% \\end{subtable}\n% \\end{table}\n\n\n\n\\begin{table}[ht]\n\\centering\n\\scriptsize\n\\renewcommand{\\arraystretch}{1.15} % Slightly tighter row spacing\n\\setlength{\\tabcolsep}{4pt} % Adjust column spacing as desired\n\n\\caption{Flip rates (\\%) from \\textbf{AI} to \\textbf{human}, grouped by rewriting strategies. \n Each scenario has 120 AI-labeled items; higher values indicate greater susceptibility to ``flipping.''}\n\\label{tab:flip_rates_split}\n\n\\begin{subtable}[t]{0.48\\textwidth}\n \\centering\n \\caption{HSR \\& PSR Strategies}\n \\label{tab:flip_hsr_psr}\n \\begin{tabular}{\n l\n l\n l\n S[table-format=3.0]\n S[table-format=2.0]\n S[table-format=3.2]\n }\n \\toprule\n \\textbf{Explain} & \\textbf{Strategy} & \\textbf{Model} \n & {\\#AI} & {Flips} & {Flip Rate} \\\\\n \\midrule\n % --- SHAP, HSR ---\n \\multirow{10}{*}{SHAP}\n & \\multirow{5}{*}{HSR}\n & XGBoost & 120 & 17 & 14.17 \\\\\n & & BERT & 120 & 7 & 5.83 \\\\\n & & DistilBERT & 120 & 9 & 7.50 \\\\\n & & XLM-R & 120 & 1 & 0.83 \\\\\n & & Ensemble & 120 & 8 & 6.67 \\\\\n \\cmidrule(lr){2-6}\n % --- SHAP, PSR ---\n & \\multirow{5}{*}{PSR}\n & XGBoost & 120 & 7 & 5.83 \\\\\n & & BERT & 120 & 4 & 3.33 \\\\\n & & DistilBERT & 120 & 4 & 3.33 \\\\\n & & XLM-R & 120 & 0 & 0.00 \\\\\n & & Ensemble & 120 & 3 & 2.50 \\\\\n \\midrule\n % --- LIME, HSR ---\n \\multirow{10}{*}{LIME}\n & \\multirow{5}{*}{HSR}\n & XGBoost & 120 & 78 & 65.00 \\\\\n & & BERT & 120 & 31 & 25.83 \\\\\n & & DistilBERT & 120 & 32 & 26.67 \\\\\n & & XLM-R & 120 & 3 & 2.50 \\\\\n & & Ensemble & 120 & 33 & 27.50 \\\\\n \\cmidrule(lr){2-6}\n % --- LIME, PSR ---\n & \\multirow{5}{*}{PSR}\n & XGBoost & 120 & 46 & 38.33 \\\\\n & & BERT & 120 & 20 & 16.67 \\\\\n & & DistilBERT & 120 & 19 & 15.83 \\\\\n & & XLM-R & 120 & 5 & 4.17 \\\\\n & & Ensemble & 120 & 19 & 15.83 \\\\\n \\midrule\n % --- Random, HSR ---\n \\multirow{10}{*}{Random}\n & \\multirow{5}{*}{HSR}\n & XGBoost & 120 & 15 & 12.50 \\\\\n & & BERT & 120 & 20 & 16.67 \\\\\n & & DistilBERT & 120 & 22 & 18.33 \\\\\n & & XLM-R & 120 & 2 & 1.67 \\\\\n & & Ensemble & 120 & 20 & 16.67 \\\\\n \\cmidrule(lr){2-6}\n % --- Random, PSR ---\n & \\multirow{5}{*}{PSR}\n & XGBoost & 120 & 10 & 8.33 \\\\\n & & BERT & 120 & 17 & 14.17 \\\\\n & & DistilBERT & 120 & 11 & 9.17 \\\\\n & & XLM-R & 120 & 1 & 0.83 \\\\\n & & Ensemble & 120 & 14 & 11.67 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{subtable}\n\\hfill\n\\begin{subtable}[t]{0.48\\textwidth}\n \\centering\n \\caption{GPT \\& GPT+Genre Strategies}\n \\label{tab:flip_gpt_gptg}\n \\begin{tabular}{\n l\n l\n l\n S[table-format=3.0]\n S[table-format=2.0]\n S[table-format=3.2]\n }\n \\toprule\n \\textbf{Explain} & \\textbf{Strategy} & \\textbf{Model} \n & {\\#AI} & {Flips} & {Flip Rate} \\\\\n \\midrule\n % --- SHAP, GPT ---\n \\multirow{10}{*}{SHAP}\n & \\multirow{5}{*}{GPT}\n & XGBoost & 120 & 4 & 3.33 \\\\\n & & BERT & 120 & 7 & 5.83 \\\\\n & & DistilBERT & 120 & 7 & 5.83 \\\\\n & & XLM-R & 120 & 0 & 0.00 \\\\\n & & Ensemble & 120 & 10 & 8.33 \\\\\n \\cmidrule(lr){2-6}\n % --- SHAP, GPT+Genre ---\n & \\multirow{5}{*}{GPT+Genre}\n & XGBoost & 120 & 4 & 3.33 \\\\\n & & BERT & 120 & 3 & 2.50 \\\\\n & & DistilBERT & 120 & 6 & 5.00 \\\\\n & & XLM-R & 120 & 0 & 0.00 \\\\\n & & Ensemble & 120 & 11 & 9.17 \\\\\n \\midrule\n % --- LIME, GPT ---\n \\multirow{10}{*}{LIME}\n & \\multirow{5}{*}{GPT}\n & XGBoost & 120 & 38 & 31.67 \\\\\n & & BERT & 120 & 11 & 9.17 \\\\\n & & DistilBERT & 120 & 11 & 9.17 \\\\\n & & XLM-R & 120 & 0 & 0.00 \\\\\n & & Ensemble & 120 & 11 & 9.17 \\\\\n \\cmidrule(lr){2-6}\n % --- LIME, GPT+Genre ---\n & \\multirow{5}{*}{GPT+Genre}\n & XGBoost & 120 & 41 & 34.17 \\\\\n & & BERT & 120 & 4 & 3.33 \\\\\n & & DistilBERT & 120 & 7 & 5.83 \\\\\n & & XLM-R & 120 & 0 & 0.00 \\\\\n & & Ensemble & 120 & 9 & 7.50 \\\\\n \\midrule\n % --- Random, GPT ---\n \\multirow{10}{*}{Random}\n & \\multirow{5}{*}{GPT}\n & XGBoost & 120 & 16 & 13.33 \\\\\n & & BERT & 120 & 6 & 5.00 \\\\\n & & DistilBERT & 120 & 6 & 5.00 \\\\\n & & XLM-R & 120 & 0 & 0.00 \\\\\n & & Ensemble & 120 & 5 & 4.17 \\\\\n \\cmidrule(lr){2-6}\n % --- Random, GPT+Genre ---\n & \\multirow{5}{*}{GPT+Genre}\n & XGBoost & 120 & 13 & 10.83 \\\\\n & & BERT & 120 & 6 & 5.00 \\\\\n & & DistilBERT & 120 & 9 & 7.50 \\\\\n & & XLM-R & 120 & 0 & 0.00 \\\\\n & & Ensemble & 120 & 11 & 9.17 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{subtable}\n\n\\end{table}\n\n\nAs shown in Table~\\ref{tab:flip_rates_split}, many rewriting strategies cause a lot of flips for some detectors, especially the simpler ones. However, the Ensemble model usually has a lower flip rate. This shows that using several models together makes detection more reliable. Figure~\\ref{fig:all_upset_plots} also shows how the flipped items overlap for each rewriting method. Even when many detectors are affected by these edits, the Ensemble is less likely to be fooled, highlighting the advantage of using an ensemble approach\n\n\n\\begin{figure*}[ht]\n\\centering\n\n\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_SHAP_HSR.png}\n\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_SHAP_PSR.png}\n\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_SHAP_GPT.png}\n\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_SHAP_GPT+Genre.png}\n\n\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_LIME_HSR.png}\n\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_LIME_PSR.png}\n\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_LIME_GPT.png}\n\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_LIME_GPT+Genre.png}\n\n\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_Random_HSR.png}\n\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_Random_PSR.png}\n\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_Random_GPT.png}\n\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_Random_GPT+Genre.png}\n\n\\caption{\\small Overlaps among flipped AI samples under different explainability methods and rewriting strategies. Each bar corresponds to the number of samples simultaneously flipped by one or more models.}\n\\label{fig:all_upset_plots}\n\\end{figure*}\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n% \\paragraph{\\textbf{Flip Rates and Overlapping Flips}}\n% \\label{subsec:flip_rates}\n\n% To estimate how many AIGTs are relabeled as \\emph{human} after rewriting, we track \\emph{flip rates} for each combination of explainability method, rewriting strategy, and model. A higher flip rate shows that the corresponding model is more easily deceived by that particular rewriting approach. In these experiments, LIME-based edits coupled with HSR lead to notably high misclassification rates in simpler models such as XGBoost (flipping up to 65\\% of the samples), whereas XLM-RoBERTa remains relatively robust at a mere 2.5\\% flip rate. GPT-based rewrites result in mid-level flip percentages overall; for example, under LIME+GPT, XGBoost flips 31.67\\% of texts while BERT-base and DistilBERT are both at around 9.17\\%. Moreover, SHAP+PSR frequently produces lower flip rates, showing that while part-of-speech-constrained edits can deceive less sophisticated classifiers, advanced or ensemble approaches remain relatively unaffected (with an Ensemble flip rate of only 2.5\\%). Notably, XLM-RoBERTa’s tendency to overpredict \\emph{AI} keeps its flips artificially low. The Ensemble, although not entirely immune, stays in the lower to mid-range across all scenarios, once again signaling greater robustness than single-model detectors. we show all flip rates in Appendix~\\ref{appendix:overlap_table}. To show how models overlap in flipping the same AIGT subsets, we provide all relevant plots in Appendix~\\ref{appendix:upset_plots}. These plots illustrate how different token-selection methods and rewriting techniques affect flipped intersections among models.Figure~\\ref{fig:overlap_stacked} shows these results in a stacked-bar format, showing that although certain scenarios (e.g., LIME+HSR) can trick multiple models on the same items, the Ensemble remains less prone to flipping.\n\n% \\begin{figure*}[ht]\n% \\centering\n% \\includegraphics[width=0.9\\textwidth]{plots/overlap_stacked_bar_all_scenarios.png}\n \n% \\caption{\\small Overlap chart of flip rates. Each colored segment shows how many AI items become misclassified as human by a specific model. (All values underlying the colored segments are provided in Appendix~\\ref{appendix:overlap_table} for accessibility and reproducibility.)}\n% \\label{fig:overlap_stacked}\n% \\end{figure*}\n\n\nIn general, GPT-based replacements make text more natural or domain-specific, which greatly impacts models that rely on simple token patterns for detection. HSR and PSR (especially with SHAP/LIME) can also be effective while keeping a higher BLEU score, meaning fewer changes. Since PSR aligns part-of-speech tags, the text remains fluent but loses patterns AI might use, leading to performance drops in classifiers like XLM-RoBERTa and XGBoost. In contrast, our Ensemble model is more robust, as its combined transformer knowledge helps it detect AI content even after targeted token edits. Overall, our results show that the Ensemble model is the most accurate and resilient, effectively handling English and Dutch texts across different genres. While rewriting reduces detection rates, it does not fully bypass the Ensemble model.\n\n\\section{Discussion and Conclusion}\n\\label{sec:discussion_conclusion}\n%\\vspace{-5pt}\nOur findings show that explanation techniques such as SHAP and LIME can guide systematic token replacements that make AIGT less likely to be classified as \\emph{AI-generated}. When we identify and edit tokens most indicative of artificial content, simpler models like XGBoost can be misled at high rates, especially under LIME-based rewrites combined with synonym substitutions. Yet even more advanced models, such as BERT-base or DistilBERT, still show vulnerability in certain scenarios, particularly when GPT-generated replacements transform the text more extensively. These methods, however, do not completely deceive our Ensemble model, which remains comparatively robust across languages, genres, and rewriting methods.\n\nFrom the perspective of text similarity, we see that methods like HSR and PSR typically achieve higher BLEU and ROUGE scores, indicating minimal alterations to the original text. This can preserve meaning and style, but targeted token swaps can still hide enough AI signals to disrupt certain detectors. In contrast, GPT-based rewrites, especially GPT+Genre, often provide more comprehensive revisions, making the text harder to classify but also changing more from the source. Although such extensive rewriting can produce higher flip rates, our Ensemble classifier still maintains better overall performance than single-model systems.\n\nIn practice, these results underscore both the potential and the limitations of adversarial rewriting. Minor edits guided by SHAP or LIME can undermine certain classifiers. Future work might extend to more sophisticated paraphrasing of entire sentences or incorporate style transfer models that better replicate a specific human author. At the same time, it is crucial to consider watermarking and policy frameworks that ensure transparency when AIGT is involved. Our experiments show that even when AI content is skillfully masked, detection remains possible with multi-architecture approaches.\n\nIn conclusion, our study shows how explanation methods can inform token replacements to disguise AIGT, yet also reveals that robust ensemble classifiers are harder to deceive. Balancing improved detection methods with the need for responsible use of writing aids will remain an important challenge. We hope these findings contribute to a deeper understanding of both the potential vulnerabilities in existing AI detection models and the methods that can keep them resilient against adversarial transformations.\n\n\\begin{acks}\nWe thank the CLIN33 shared task organizers for making the dataset available, and we appreciate the helpful feedback from colleagues who guided our research directions. This work is partially supported by the Dutch Research Council (NWO) (grant number: VI.Vidi.195.152 to D. L. Oberski). We also thank SURF for providing the computational facilities. \n\n%The authors used OpenAI's ChatGPT-4o exclusively for grammar and style checks. They have reviewed the content thoroughly and take full responsibility for the final manuscript.\n\\end{acks}\n\n\n\\bibliographystyle{ACM-Reference-Format}\n\\bibliography{01-sample-base}\n\n\n\n \\appendix\n\n% \\section{Overlap Among Flipped Samples}\n% \\label{appendix:overlap_table}\n\n% Table~\\ref{tab:flip_rates_split} provides a detailed of which sets of models flip each sample in various scenarios. For instance, under LIME+GPT, 28 items are flipped exclusively by XGBoost, eight solely by the Ensemble, etc. Although there are shared flips across multiple models, the Ensemble consistently shows smaller overlaps, reinforcing its comparative resilience.\n\n\n% \\begin{table}[H]\n% \\centering\n% \\scriptsize\n% \\caption{Flip rates (\\%) from \\textbf{AI} to \\textbf{human}. \n% Each scenario contains 120 AI-labelled items; higher values mean the\n% detector is easier to “flip’’ by rewriting.}\n% \\label{tab:flip_rates_split}\n% \\begin{subtable}[t]{0.48\\linewidth}\n% \\centering\n% \\caption{HSR \\& PSR strategies}\n% \\resizebox{\\linewidth}{!}{%\n% \\begin{tabular}{ll\n% l\n% S S S}\n% \\toprule\n% \\textbf{Exp.} & \\textbf{Strat.} & \\textbf{Model} &\n% {\\#AI} & {Flips} & {Rate} \\\\\n% \\midrule\n% % ---------- HSR ----------\n% \\multirow{15}{*}{SHAP}\n% & \\multirow{5}{*}{HSR}\n% & XGBoost & 120 & 17 & 14.17 \\\\\n% & & BERT & 120 & 7 & 5.83 \\\\\n% & & DistilBERT & 120 & 9 & 7.50 \\\\\n% & & XLM-R & 120 & 1 & 0.83 \\\\\n% & & Ensemble & 120 & 8 & 6.67 \\\\\n% \\cmidrule(lr){2-6}\n% & \\multirow{5}{*}{PSR}\n% & XGBoost & 120 & 7 & 5.83 \\\\\n% & & BERT & 120 & 4 & 3.33 \\\\\n% & & DistilBERT & 120 & 4 & 3.33 \\\\\n% & & XLM-R & 120 & 0 & 0.00 \\\\\n% & & Ensemble & 120 & 3 & 2.50 \\\\\n% \\midrule\n% \\multirow{10}{*}{LIME}\n% & \\multirow{5}{*}{HSR}\n% & XGBoost & 120 & 78 & 65.00 \\\\\n% & & BERT & 120 & 31 & 25.83 \\\\\n% & & DistilBERT & 120 & 32 & 26.67 \\\\\n% & & XLM-R & 120 & 3 & 2.50 \\\\\n% & & Ensemble & 120 & 33 & 27.50 \\\\\n% \\cmidrule(lr){2-6}\n% & \\multirow{5}{*}{PSR}\n% & XGBoost & 120 & 46 & 38.33 \\\\\n% & & BERT & 120 & 20 & 16.67 \\\\\n% & & DistilBERT & 120 & 19 & 15.83 \\\\\n% & & XLM-R & 120 & 5 & 4.17 \\\\\n% & & Ensemble & 120 & 19 & 15.83 \\\\\n% \\midrule\n% \\multirow{10}{*}{Rand}\n% & \\multirow{5}{*}{HSR}\n% & XGBoost & 120 & 15 & 12.50 \\\\\n% & & BERT & 120 & 20 & 16.67 \\\\\n% & & DistilBERT & 120 & 22 & 18.33 \\\\\n% & & XLM-R & 120 & 2 & 1.67 \\\\\n% & & Ensemble & 120 & 20 & 16.67 \\\\\n% \\cmidrule(lr){2-6}\n% & \\multirow{5}{*}{PSR}\n% & XGBoost & 120 & 10 & 8.33 \\\\\n% & & BERT & 120 & 17 & 14.17 \\\\\n% & & DistilBERT & 120 & 11 & 9.17 \\\\\n% & & XLM-R & 120 & 1 & 0.83 \\\\\n% & & Ensemble & 120 & 14 & 11.67 \\\\\n% \\bottomrule\n% \\end{tabular}}\n% \\end{subtable}%\n% \\hfill\n% \\begin{subtable}[t]{0.48\\linewidth}\n% \\centering\n% \\caption{GPT \\& GPT-Genre strategies}\n% \\resizebox{\\linewidth}{!}{%\n% \\begin{tabular}{ll\n% l\n% S S S}\n% \\toprule\n% \\textbf{Exp.} & \\textbf{Strat.} & \\textbf{Model} &\n% {\\#AI} & {Flips} & {Rate} \\\\\n% \\midrule\n% % ---------- GPT ----------\n% \\multirow{15}{*}{SHAP}\n% & \\multirow{5}{*}{GPT}\n% & XGBoost & 120 & 4 & 3.33 \\\\\n% & & BERT & 120 & 7 & 5.83 \\\\\n% & & DistilBERT & 120 & 7 & 5.83 \\\\\n% & & XLM-R & 120 & 0 & 0.00 \\\\\n% & & Ensemble & 120 & 10 & 8.33 \\\\\n% \\cmidrule(lr){2-6}\n% & \\multirow{5}{*}{GPT+G}\n% & XGBoost & 120 & 4 & 3.33 \\\\\n% & & BERT & 120 & 3 & 2.50 \\\\\n% & & DistilBERT & 120 & 6 & 5.00 \\\\\n% & & XLM-R & 120 & 0 & 0.00 \\\\\n% & & Ensemble & 120 & 11 & 9.17 \\\\\n% \\midrule\n% \\multirow{10}{*}{LIME}\n% & \\multirow{5}{*}{GPT}\n% & XGBoost & 120 & 38 & 31.67 \\\\\n% & & BERT & 120 & 11 & 9.17 \\\\\n% & & DistilBERT & 120 & 11 & 9.17 \\\\\n% & & XLM-R & 120 & 0 & 0.00 \\\\\n% & & Ensemble & 120 & 11 & 9.17 \\\\\n% \\cmidrule(lr){2-6}\n% & \\multirow{5}{*}{GPT+G}\n% & XGBoost & 120 & 41 & 34.17 \\\\\n% & & BERT & 120 & 4 & 3.33 \\\\\n% & & DistilBERT & 120 & 7 & 5.83 \\\\\n% & & XLM-R & 120 & 0 & 0.00 \\\\\n% & & Ensemble & 120 & 9 & 7.50 \\\\\n% \\midrule\n% \\multirow{10}{*}{Rand}\n% & \\multirow{5}{*}{GPT}\n% & XGBoost & 120 & 16 & 13.33 \\\\\n% & & BERT & 120 & 6 & 5.00 \\\\\n% & & DistilBERT & 120 & 6 & 5.00 \\\\\n% & & XLM-R & 120 & 0 & 0.00 \\\\\n% & & Ensemble & 120 & 5 & 4.17 \\\\\n% \\cmidrule(lr){2-6}\n% & \\multirow{5}{*}{GPT+G}\n% & XGBoost & 120 & 13 & 10.83 \\\\\n% & & BERT & 120 & 6 & 5.00 \\\\\n% & & DistilBERT & 120 & 9 & 7.50 \\\\\n% & & XLM-R & 120 & 0 & 0.00 \\\\\n% & & Ensemble & 120 & 11 & 9.17 \\\\\n% \\bottomrule\n% \\end{tabular}}\n% \\end{subtable}\n% \\end{table}\n\n\n\n\n% \\section{Plots for Overlap Analysis}\n% \\label{appendix:upset_plots}\n\n% Figure~\\ref{fig:all_upset_plots} presents all plots covering every combination of \\{\\texttt{SHAP}, \\texttt{LIME}, \\texttt{Random}\\} $\\times$ \\{\\texttt{HSR}, \\texttt{PSR}, \\texttt{GPT}, \\texttt{GPT+Genre}\\}. Each subfigure depicts how many AI samples were flipped by one or more models under that scenario.\n\n% \\begin{figure*}[ht]\n% \\centering\n\n% \\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_SHAP_HSR.png}\n% \\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_SHAP_PSR.png}\n% \\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_SHAP_GPT.png}\n% \\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_SHAP_GPT+Genre.png}\n\n% \\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_LIME_HSR.png}\n% \\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_LIME_PSR.png}\n% \\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_LIME_GPT.png}\n% \\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_LIME_GPT+Genre.png}\n\n% \\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_Random_HSR.png}\n% \\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_Random_PSR.png}\n% \\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_Random_GPT.png}\n% \\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_Random_GPT+Genre.png}\n\n% \\caption{\\small Overlaps among flipped AI samples under different explainability methods and rewriting strategies. Each bar corresponds to the number of samples simultaneously flipped by one or more models.}\n% \\label{fig:all_upset_plots}\n% \\end{figure*}\n\n\n\\clearpage\n\\section*{Reproducibility Checklist for JAIR}\n\n\\noindent\\textbf{All articles:}\n\\begin{enumerate}\n \\item \\textbf{All claims investigated in this work are clearly stated.} \\\\\n \\textit{Answer:} Yes, we clearly state our main claims: we want to see how effective rewriting with SHAP/LIME is, and how an ensemble model performs.\n \n \\item \\textbf{Clear explanations are given how the work reported substantiates the claims.} \\\\\n \\textit{Answer:} Yes, we explain how each experiment’s results support our claims.\n \n \\item \\textbf{Limitations or technical assumptions are stated clearly and explicitly.} \\\\\n \\textit{Answer:} Some. We focus on detection and rewriting only, not on other possible aspects.\n \n \\item \\textbf{Conceptual outlines and/or pseudo-code descriptions of the AI methods introduced in this work are provided, and important implementation details are discussed.} \\\\\n \\textit{Answer:} Yes, our method flow is clearly described, especially for the ensemble approach in the algorithm~\\ref{fig:ms-structure}.\n \n \\item \\textbf{Motivation is provided for all design choices, including algorithms, implementation choices, parameters, data sets, and experimental protocols beyond metrics.} \\\\\n \\textit{Answer:} Yes, we explain why we use these models, datasets, and rewriting strategies.\n\\end{enumerate}\n\n\\subsection*{Articles containing theoretical contributions}\n\\noindent\\textit{Does this paper make theoretical contributions?} \\\\\n\\textit{Answer:} No. (We do not do formal theoretical proofs.)\n\n\\subsection*{Articles reporting on computational experiments}\n\\noindent\\textit{Does this paper include computational experiments?} \\\\\n\\textit{Answer:} Yes.\n\\begin{enumerate}\n \\item \\textbf{All source code required for experiments is included or will be made publicly available upon publication, following best practices.} \\\\\n \\textit{Answer:} We plan to share the main code base (e.g., on GitHub).\n \n \\item \\textbf{The source code comes with a license that allows free usage for reproducibility purposes.} \\\\\n \\textit{Answer:} Yes, we use a permissive license.\n \n \\item \\textbf{The source code comes with a license that allows free usage for research purposes in general.} \\\\\n \\textit{Answer:} Yes.\n \n \\item \\textbf{Raw, unaggregated data from all experiments is included or will be made publicly available.} \\\\\n \\textit{Answer:} We will share our final splits, but some augmented data might be partial.\n \n \\item \\textbf{The unaggregated data comes with a license that allows free usage for reproducibility purposes.} \\\\\n \\textit{Answer:} Yes, as far as allowed by the original dataset licenses.\n \n \\item \\textbf{The unaggregated data comes with a license that allows free usage for research purposes in general.} \\\\\n \\textit{Answer:} Yes, within the limits of the original dataset licenses.\n \n \\item \\textbf{If an algorithm depends on randomness, the method used for generating random numbers and setting seeds is described sufficiently.} \\\\\n \\textit{Answer:} We set random seeds but do not deeply document random generation details.\n \n \\item \\textbf{The execution environment for experiments (hardware/software) is described.} \\\\\n \\textit{Answer:} Yes, we mention key libraries (e.g., Hugging Face Transformers, PyTorch).\n \n \\item \\textbf{The evaluation metrics used are clearly explained and motivated.} \\\\\n \\textit{Answer:} Yes, we use F1, accuracy, BLEU, and ROUGE and explain why.\n \n \\item \\textbf{The number of algorithm runs used to compute each result is reported.} \\\\\n \\textit{Answer:} We use cross-validation or mention repeated runs (varies).\n \n \\item \\textbf{Reported results have not been “cherry-picked.”} \\\\\n \\textit{Answer:} Yes, we show both successes and failures.\n \n \\item \\textbf{Analysis of results goes beyond single-dimensional summaries of performance.} \\\\\n \\textit{Answer:} We show F1, accuracy, and text-similarity. (We do not always show standard deviation or confidence intervals.)\n \n \\item \\textbf{All (hyper-) parameter settings for the algorithms/methods used are reported, along with the rationale or method for determining them.} \\\\\n \\textit{Answer:} Yes, we provide learning rates, batch sizes, etc.\n \n \\item \\textbf{The number and range of (hyper-) parameter settings explored prior to final experiments have been indicated.} \\\\\n \\textit{Answer:} We mention random search, though not each detail.\n \n \\item \\textbf{Appropriately chosen statistical hypothesis tests are used to establish significance in the presence of noise.} \\\\\n \\textit{Answer:} We do not do formal significance testing.\n\\end{enumerate}\n\n\\subsection*{Articles using data sets}\n\\noindent\\textit{Does this work rely on one or more data sets?} \\\\\n\\textit{Answer:} Yes.\n\n\\begin{enumerate}\n \\item \\textbf{All newly introduced data sets are included in an online appendix or will be made publicly available with a suitable license.} \\\\\n \\textit{Answer:} We mostly use public sets (CLIN33, AuTexTification) and will release our augmented version.\n \n \\item \\textbf{The newly introduced data set comes with a license that allows free usage for reproducibility purposes.} \\\\\n \\textit{Answer:} Yes, for the augmented subset.\n \n \\item \\textbf{The newly introduced data set comes with a license that allows free usage for research purposes in general.} \\\\\n \\textit{Answer:} Yes.\n \n \\item \\textbf{All data sets drawn from the literature or other public sources are accompanied by appropriate citations.} \\\\\n \\textit{Answer:} Yes, we cite the original authors.\n \n \\item \\textbf{All data sets drawn from the existing literature are publicly available.} \\\\\n \\textit{Answer:} Yes, with possible constraints.\n \n \\item \\textbf{All new or non-public data sets are described in detail, including relevant statistics, collection, and annotation processes.} \\\\\n \\textit{Answer:} Yes, for our augmented data.\n \n \\item \\textbf{All methods used for preprocessing, augmenting, batching, or splitting data sets are described in detail.} \\\\\n \\textit{Answer:} Yes, we detail how we clean and split.\n\\end{enumerate}\n\n\\subsection*{Explanations on any of the answers above (optional):}\nWe use mostly public data and provide augmented sets where possible. Some data licensing is inherited from the original corpora. Where possible, we provide reproducible code, scripts, and subsets to ensure others can replicate our results.\n\n\n\n\n\n\\end{document}\n\n\n"},"question":{"kind":"string","value":"Comparing the performance of the two language databases, which model has the biggest performance difference between English and Dutch and in which field? What is the specific difference?"},"answer":{"kind":"string","value":"BERT-base, news, 0.28"},"plan":{"kind":"string","value":"SELECT English dataset F1 scores for all models and all domains\nSELECT Dutch dataset F1 scores for all models and all domains\nLOOP for each model\n LOOP for each domain\n COMPUTE diff_value = performance difference between English F1 and Dutch F1\n COMPUTE add diff_value and Model-Domain pair to diff_list\nCOMPUTE best_entry = argmax over diff_list\nRETURN Model, Domain, and diff_value from best_entry\n"}}},{"rowIdx":94,"cells":{"paper":{"kind":"string","value":"2506.04050"},"relevant_tables":{"kind":"list like","value":[["\\begin{table*}[ht]\n","\\centering\n","\\caption{\\small F1 score and Accuracy of each detector under every rewriting scenario. \n","The best metric in a column is \\textbf{bolded}; the largest negative drop is \\dashuline{\\phantom{-00\\%}}, the smallest negative drop is \\dotuline{\\phantom{-00\\%}}.\n","\\footnotesize{$\\Delta=(\\text{Scenario Value}-\\text{Original Value})\\times100\\%$.}}\n","\\label{tab:all_models_rewriting_f1acc}\n","\\renewcommand{\\arraystretch}{1.4}\n","\\resizebox{\\textwidth}{!}{\n","\\begin{tabular}{l l\n","c c c c c c c c c c}\n","\\toprule\n","\\multirow{2}{*}{\\textbf{Explain}} & \\multirow{2}{*}{\\textbf{Strategy}}\n","& \\multicolumn{2}{c}{\\textbf{XGBoost}}\n","& \\multicolumn{2}{c}{\\textbf{BERT-base}}\n","& \\multicolumn{2}{c}{\\textbf{DistilBERT}}\n","& \\multicolumn{2}{c}{\\textbf{XLM-RoBERTa}}\n","& \\multicolumn{2}{c}{\\textbf{Ensemble}}\\\\\n","\\cmidrule(lr){3-4}\\cmidrule(lr){5-6}\\cmidrule(lr){7-8}\\cmidrule(lr){9-10}\\cmidrule(lr){11-12}\n","& & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\n"," & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\n"," & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\n"," & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\n"," & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\\\\\n","\\midrule\n","\\emph{NoRewriting} & Original\n","& 0.81 & 0.80\n","& 0.74 & 0.75\n","& 0.79 & 0.77\n","& 0.68 & 0.54\n","& \\textbf{0.83} & \\textbf{0.83}\\\\\n","\\midrule\n","\\textbf{SHAP} & HSR\n","& 0.73 (\\largest{-8\\%}) & 0.75 (\\largest{-5\\%})\n","& 0.71 (-3\\%) & 0.72 (-3\\%)\n","& 0.76 (-3\\%) & 0.75 (-2\\%)\n","& 0.68 (\\smallest{0\\%}) & 0.54 (\\smallest{0\\%})\n","& \\textbf{0.78} (-5\\%) & \\textbf{0.80} (-3\\%)\\\\\n","\\textbf{SHAP} & PSR\n","& 0.78 (\\largest{-3\\%}) & 0.78 (\\largest{-2\\%})\n","& 0.73 (-1\\%) & 0.74 (-1\\%)\n","& 0.78 (-1\\%) & 0.77 (0\\%)\n","& 0.68 (\\smallest{0\\%}) & 0.54 (\\smallest{0\\%})\n","& \\textbf{0.82} (-1\\%) & \\textbf{0.82} (-1\\%)\\\\\n","\\textbf{SHAP} & GPT\n","& 0.79 (-2\\%) & 0.79 (-1\\%)\n","& 0.73 (-1\\%) & 0.74 (-1\\%)\n","& 0.77 (-2\\%) & 0.76 (-1\\%)\n","& 0.68 (\\smallest{0\\%}) & 0.55 (\\smallest{+1\\%})\n","& \\textbf{0.80} (\\largest{-3\\%}) & \\textbf{0.81} (\\largest{-2\\%})\\\\\n","\\textbf{SHAP} & GPT+Genre\n","& \\textbf{0.80} (-1\\%) & \\textbf{0.80} (0\\%)\n","& 0.73 (-1\\%) & 0.74 (-1\\%)\n","& 0.77 (-2\\%) & 0.76 (-1\\%)\n","& 0.68 (\\smallest{0\\%}) & 0.55 (\\smallest{+1\\%})\n","& 0.79 (\\largest{-4\\%}) & 0.80 (\\largest{-3\\%})\\\\\n","\\midrule\n","\\textbf{LIME} & HSR\n","& 0.25 (\\largest{-56\\%}) & 0.48 (\\largest{-32\\%})\n","& 0.56 (-18\\%) & 0.62 (-13\\%)\n","& 0.62 (-17\\%) & 0.65 (-12\\%)\n","& \\textbf{0.67} (\\smallest{-1\\%}) & 0.52 (\\smallest{-2\\%})\n","& 0.64 (-19\\%) & \\textbf{0.69} (-14\\%)\\\\\n","\\textbf{LIME} & PSR\n","& 0.56 (\\largest{-25\\%}) & 0.63 (\\largest{-17\\%})\n","& 0.64 (-10\\%) & 0.67 (-8\\%)\n","& 0.71 (-8\\%) & 0.71 (-6\\%)\n","& 0.66 (\\smallest{-2\\%}) & 0.52 (\\smallest{-2\\%})\n","& \\textbf{0.73} (-10\\%) & \\textbf{0.75} (-8\\%)\\\\\n","\\textbf{LIME} & GPT\n","& 0.57 (\\largest{-24\\%}) & 0.64 (\\largest{-16\\%})\n","& 0.73 (-1\\%) & 0.74 (-1\\%)\n","& 0.78 (-1\\%) & 0.77 (0\\%)\n","& 0.68 (\\smallest{0\\%}) & 0.55 (\\smallest{+1\\%})\n","& \\textbf{0.83} (\\smallest{0\\%}) & \\textbf{0.83} (0\\%)\\\\\n","\\textbf{LIME} & GPT+Genre\n","& 0.60 (\\largest{-21\\%}) & 0.65 (\\largest{-15\\%})\n","& \\textbf{0.75} (\\smallest{+1\\%}) & \\textbf{0.75} (0\\%)\n","& 0.77 (-2\\%) & 0.76 (-1\\%)\n","& 0.68 (0\\%) & 0.55 (\\smallest{+1\\%})\n","& \\textbf{0.81} (-2\\%) & \\textbf{0.81} (-2\\%)\\\\\n","\\midrule\n","\\textbf{Random} & HSR\n","& 0.73 (-8\\%) & 0.74 (-6\\%)\n","& 0.63 (\\largest{-11\\%}) & 0.67 (\\largest{-8\\%})\n","& 0.68 (\\largest{-11\\%}) & 0.69 (\\largest{-8\\%})\n","& 0.67 (\\smallest{-1\\%}) & 0.53 (\\smallest{-1\\%})\n","& \\textbf{0.73} (-10\\%) & \\textbf{0.75} (\\largest{-8\\%})\\\\\n","\\textbf{Random} & PSR\n","& 0.76 (-5\\%) & 0.76 (-4\\%)\n","& 0.65 (\\largest{-9\\%}) & 0.68 (\\largest{-7\\%})\n","& 0.74 (-5\\%) & 0.73 (-4\\%)\n","& 0.67 (\\smallest{-1\\%}) & 0.53 (\\smallest{-1\\%})\n","& \\textbf{0.78} (-5\\%) & \\textbf{0.79} (-4\\%)\\\\\n","\\textbf{Random} & GPT\n","& 0.74 (\\largest{-7\\%}) & 0.75 (\\largest{-5\\%})\n","& 0.74 (0\\%) & 0.75 (0\\%)\n","& 0.77 (-2\\%) & 0.76 (-1\\%)\n","& 0.68 (\\smallest{0\\%}) & 0.55 (\\smallest{+1\\%})\n","& \\textbf{0.81} (-2\\%) & \\textbf{0.82} (-1\\%)\\\\\n","\\textbf{Random} & GPT+Genre\n","& 0.75 (\\largest{-6\\%}) & 0.75 (\\largest{-5\\%})\n","& 0.73 (-1\\%) & 0.74 (-1\\%)\n","& \\textbf{0.78} (-1\\%) & 0.77 (0\\%)\n","& 0.68 (0\\%) & 0.55 (\\smallest{+1\\%})\n","& \\textbf{0.81} (-2\\%) & \\textbf{0.81} (-2\\%)\\\\\n","\\bottomrule\n","\\end{tabular}}\n","\\end{table*}\n"]],"string":"[\n [\n \"\\\\begin{table*}[ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\caption{\\\\small F1 score and Accuracy of each detector under every rewriting scenario. \\n\",\n \"The best metric in a column is \\\\textbf{bolded}; the largest negative drop is \\\\dashuline{\\\\phantom{-00\\\\%}}, the smallest negative drop is \\\\dotuline{\\\\phantom{-00\\\\%}}.\\n\",\n \"\\\\footnotesize{$\\\\Delta=(\\\\text{Scenario Value}-\\\\text{Original Value})\\\\times100\\\\%$.}}\\n\",\n \"\\\\label{tab:all_models_rewriting_f1acc}\\n\",\n \"\\\\renewcommand{\\\\arraystretch}{1.4}\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \"\\\\begin{tabular}{l l\\n\",\n \"c c c c c c c c c c}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{\\\\textbf{Explain}} & \\\\multirow{2}{*}{\\\\textbf{Strategy}}\\n\",\n \"& \\\\multicolumn{2}{c}{\\\\textbf{XGBoost}}\\n\",\n \"& \\\\multicolumn{2}{c}{\\\\textbf{BERT-base}}\\n\",\n \"& \\\\multicolumn{2}{c}{\\\\textbf{DistilBERT}}\\n\",\n \"& \\\\multicolumn{2}{c}{\\\\textbf{XLM-RoBERTa}}\\n\",\n \"& \\\\multicolumn{2}{c}{\\\\textbf{Ensemble}}\\\\\\\\\\n\",\n \"\\\\cmidrule(lr){3-4}\\\\cmidrule(lr){5-6}\\\\cmidrule(lr){7-8}\\\\cmidrule(lr){9-10}\\\\cmidrule(lr){11-12}\\n\",\n \"& & \\\\textbf{F1 ($\\\\Delta$)} & \\\\textbf{Acc ($\\\\Delta$)}\\n\",\n \" & \\\\textbf{F1 ($\\\\Delta$)} & \\\\textbf{Acc ($\\\\Delta$)}\\n\",\n \" & \\\\textbf{F1 ($\\\\Delta$)} & \\\\textbf{Acc ($\\\\Delta$)}\\n\",\n \" & \\\\textbf{F1 ($\\\\Delta$)} & \\\\textbf{Acc ($\\\\Delta$)}\\n\",\n \" & \\\\textbf{F1 ($\\\\Delta$)} & \\\\textbf{Acc ($\\\\Delta$)}\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\emph{NoRewriting} & Original\\n\",\n \"& 0.81 & 0.80\\n\",\n \"& 0.74 & 0.75\\n\",\n \"& 0.79 & 0.77\\n\",\n \"& 0.68 & 0.54\\n\",\n \"& \\\\textbf{0.83} & \\\\textbf{0.83}\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textbf{SHAP} & HSR\\n\",\n \"& 0.73 (\\\\largest{-8\\\\%}) & 0.75 (\\\\largest{-5\\\\%})\\n\",\n \"& 0.71 (-3\\\\%) & 0.72 (-3\\\\%)\\n\",\n \"& 0.76 (-3\\\\%) & 0.75 (-2\\\\%)\\n\",\n \"& 0.68 (\\\\smallest{0\\\\%}) & 0.54 (\\\\smallest{0\\\\%})\\n\",\n \"& \\\\textbf{0.78} (-5\\\\%) & \\\\textbf{0.80} (-3\\\\%)\\\\\\\\\\n\",\n \"\\\\textbf{SHAP} & PSR\\n\",\n \"& 0.78 (\\\\largest{-3\\\\%}) & 0.78 (\\\\largest{-2\\\\%})\\n\",\n \"& 0.73 (-1\\\\%) & 0.74 (-1\\\\%)\\n\",\n \"& 0.78 (-1\\\\%) & 0.77 (0\\\\%)\\n\",\n \"& 0.68 (\\\\smallest{0\\\\%}) & 0.54 (\\\\smallest{0\\\\%})\\n\",\n \"& \\\\textbf{0.82} (-1\\\\%) & \\\\textbf{0.82} (-1\\\\%)\\\\\\\\\\n\",\n \"\\\\textbf{SHAP} & GPT\\n\",\n \"& 0.79 (-2\\\\%) & 0.79 (-1\\\\%)\\n\",\n \"& 0.73 (-1\\\\%) & 0.74 (-1\\\\%)\\n\",\n \"& 0.77 (-2\\\\%) & 0.76 (-1\\\\%)\\n\",\n \"& 0.68 (\\\\smallest{0\\\\%}) & 0.55 (\\\\smallest{+1\\\\%})\\n\",\n \"& \\\\textbf{0.80} (\\\\largest{-3\\\\%}) & \\\\textbf{0.81} (\\\\largest{-2\\\\%})\\\\\\\\\\n\",\n \"\\\\textbf{SHAP} & GPT+Genre\\n\",\n \"& \\\\textbf{0.80} (-1\\\\%) & \\\\textbf{0.80} (0\\\\%)\\n\",\n \"& 0.73 (-1\\\\%) & 0.74 (-1\\\\%)\\n\",\n \"& 0.77 (-2\\\\%) & 0.76 (-1\\\\%)\\n\",\n \"& 0.68 (\\\\smallest{0\\\\%}) & 0.55 (\\\\smallest{+1\\\\%})\\n\",\n \"& 0.79 (\\\\largest{-4\\\\%}) & 0.80 (\\\\largest{-3\\\\%})\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textbf{LIME} & HSR\\n\",\n \"& 0.25 (\\\\largest{-56\\\\%}) & 0.48 (\\\\largest{-32\\\\%})\\n\",\n \"& 0.56 (-18\\\\%) & 0.62 (-13\\\\%)\\n\",\n \"& 0.62 (-17\\\\%) & 0.65 (-12\\\\%)\\n\",\n \"& \\\\textbf{0.67} (\\\\smallest{-1\\\\%}) & 0.52 (\\\\smallest{-2\\\\%})\\n\",\n \"& 0.64 (-19\\\\%) & \\\\textbf{0.69} (-14\\\\%)\\\\\\\\\\n\",\n \"\\\\textbf{LIME} & PSR\\n\",\n \"& 0.56 (\\\\largest{-25\\\\%}) & 0.63 (\\\\largest{-17\\\\%})\\n\",\n \"& 0.64 (-10\\\\%) & 0.67 (-8\\\\%)\\n\",\n \"& 0.71 (-8\\\\%) & 0.71 (-6\\\\%)\\n\",\n \"& 0.66 (\\\\smallest{-2\\\\%}) & 0.52 (\\\\smallest{-2\\\\%})\\n\",\n \"& \\\\textbf{0.73} (-10\\\\%) & \\\\textbf{0.75} (-8\\\\%)\\\\\\\\\\n\",\n \"\\\\textbf{LIME} & GPT\\n\",\n \"& 0.57 (\\\\largest{-24\\\\%}) & 0.64 (\\\\largest{-16\\\\%})\\n\",\n \"& 0.73 (-1\\\\%) & 0.74 (-1\\\\%)\\n\",\n \"& 0.78 (-1\\\\%) & 0.77 (0\\\\%)\\n\",\n \"& 0.68 (\\\\smallest{0\\\\%}) & 0.55 (\\\\smallest{+1\\\\%})\\n\",\n \"& \\\\textbf{0.83} (\\\\smallest{0\\\\%}) & \\\\textbf{0.83} (0\\\\%)\\\\\\\\\\n\",\n \"\\\\textbf{LIME} & GPT+Genre\\n\",\n \"& 0.60 (\\\\largest{-21\\\\%}) & 0.65 (\\\\largest{-15\\\\%})\\n\",\n \"& \\\\textbf{0.75} (\\\\smallest{+1\\\\%}) & \\\\textbf{0.75} (0\\\\%)\\n\",\n \"& 0.77 (-2\\\\%) & 0.76 (-1\\\\%)\\n\",\n \"& 0.68 (0\\\\%) & 0.55 (\\\\smallest{+1\\\\%})\\n\",\n \"& \\\\textbf{0.81} (-2\\\\%) & \\\\textbf{0.81} (-2\\\\%)\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textbf{Random} & HSR\\n\",\n \"& 0.73 (-8\\\\%) & 0.74 (-6\\\\%)\\n\",\n \"& 0.63 (\\\\largest{-11\\\\%}) & 0.67 (\\\\largest{-8\\\\%})\\n\",\n \"& 0.68 (\\\\largest{-11\\\\%}) & 0.69 (\\\\largest{-8\\\\%})\\n\",\n \"& 0.67 (\\\\smallest{-1\\\\%}) & 0.53 (\\\\smallest{-1\\\\%})\\n\",\n \"& \\\\textbf{0.73} (-10\\\\%) & \\\\textbf{0.75} (\\\\largest{-8\\\\%})\\\\\\\\\\n\",\n \"\\\\textbf{Random} & PSR\\n\",\n \"& 0.76 (-5\\\\%) & 0.76 (-4\\\\%)\\n\",\n \"& 0.65 (\\\\largest{-9\\\\%}) & 0.68 (\\\\largest{-7\\\\%})\\n\",\n \"& 0.74 (-5\\\\%) & 0.73 (-4\\\\%)\\n\",\n \"& 0.67 (\\\\smallest{-1\\\\%}) & 0.53 (\\\\smallest{-1\\\\%})\\n\",\n \"& \\\\textbf{0.78} (-5\\\\%) & \\\\textbf{0.79} (-4\\\\%)\\\\\\\\\\n\",\n \"\\\\textbf{Random} & GPT\\n\",\n \"& 0.74 (\\\\largest{-7\\\\%}) & 0.75 (\\\\largest{-5\\\\%})\\n\",\n \"& 0.74 (0\\\\%) & 0.75 (0\\\\%)\\n\",\n \"& 0.77 (-2\\\\%) & 0.76 (-1\\\\%)\\n\",\n \"& 0.68 (\\\\smallest{0\\\\%}) & 0.55 (\\\\smallest{+1\\\\%})\\n\",\n \"& \\\\textbf{0.81} (-2\\\\%) & \\\\textbf{0.82} (-1\\\\%)\\\\\\\\\\n\",\n \"\\\\textbf{Random} & GPT+Genre\\n\",\n \"& 0.75 (\\\\largest{-6\\\\%}) & 0.75 (\\\\largest{-5\\\\%})\\n\",\n \"& 0.73 (-1\\\\%) & 0.74 (-1\\\\%)\\n\",\n \"& \\\\textbf{0.78} (-1\\\\%) & 0.77 (0\\\\%)\\n\",\n \"& 0.68 (0\\\\%) & 0.55 (\\\\smallest{+1\\\\%})\\n\",\n \"& \\\\textbf{0.81} (-2\\\\%) & \\\\textbf{0.81} (-2\\\\%)\\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}}\\n\",\n \"\\\\end{table*}\\n\"\n ]\n]","isConversational":false},"tables":{"kind":"list like","value":[["\\begin{figure*}[!ht]\n","\\centering\n","\\includegraphics[width=0.7\\linewidth]{plots/model.jpeg}\n","\\vspace{-5pt}\n","\\caption{\\small The architecture of the proposed ensemble model. Outputs of three frozen BERT models (left) and three fresh BERT models (right) are concatenated and passed through dense layers, followed by a voting mechanism.}\n","\\label{fig:ms-structure}\n","\\end{figure*}\n"],["\\begin{table}[ht]\n","\\centering\n","\\scriptsize\n","\\renewcommand{\\arraystretch}{1.2}\n","\\caption{Summary of token replacement strategies}\n","\\label{tab:token_replacement}\n","\\begin{tabular}{p{4.8cm}p{9.2cm}}\n","\\toprule\n","\\textbf{Strategy} & \\textbf{Description} \\\\\n","\\midrule\n","\\textbf{Human Similar Word Replacement (HSR)} \n"," & A Word2Vec model is trained on all human-labeled texts in the training set to learn an embedding space that captures typical human usage. \\\\\n","\\textbf{Part-of-Speech Replacement (PSR)} \n"," & HSR is refined by ensuring that part-of-speech tags match. \\\\\n","\\textbf{GPT-based Replacement (GPT)} \n"," & A generative model (GPT-4o-mini) is queried using the prompt: \n"," \\texttt{\"Replace '\\{token\\}' with a more human-like word in this text: '\\{text\\}'\"}. \\\\\n","\\textbf{GPT-based Replacement with Genre Context (GPT+Genre)} \n"," & The GPT-based method is extended by including explicit genre information (e.g., \\emph{tweets} or \\emph{reviews}) in the prompt:\n"," \\texttt{\"Replace '\\{token\\}' in this \\{domain\\} text with a more human-like word: '\\{text\\}'\"}. \\\\\n","\\bottomrule\n","\\end{tabular}\n","\\end{table}\n"],["\\begin{table}[ht]\n","\\centering\n","\\scriptsize\n","\\renewcommand{\\arraystretch}{1.2}\n","\\caption{Model performance by language and domain on unmodified AI text. \\textbf{Bold} indicates the best result within each language and domain block.}\n","\\label{tab:lang_domain_updated}\n","\\begin{tabular}{llcccc|llcccc}\n","\\toprule\n","\\multicolumn{6}{c}{\\textbf{English Dataset}} & \\multicolumn{6}{c}{\\textbf{Dutch Dataset}} \\\\\n","\\cmidrule(lr){1-6} \\cmidrule(lr){7-12}\n","\\textbf{Model} & \\textbf{Domain} & \\textbf{Acc} & \\textbf{Prec} & \\textbf{Rec} & \\textbf{F1} \n"," & \\textbf{Model} & \\textbf{Domain} & \\textbf{Acc} & \\textbf{Prec} & \\textbf{Rec} & \\textbf{F1} \\\\\n","\\midrule\n","\\textbf{--- News ---} & & & & & \n","& \\textbf{--- News ---} & & & & & \\\\\n","XGBoost \n"," & news & 0.89 & 0.93 & 0.84 & 0.88 \n"," & XGBoost \n"," & news & 0.85 & 0.88 & 0.88 & 0.88 \\\\\n","BERT-base \n"," & news & 0.94 & 0.97 & 0.91 & 0.94\n"," & BERT-base \n"," & news & 0.68 & 0.79 & 0.56 & 0.66 \\\\\n","DistilBERT \n"," & news & 0.92 & 0.90 & \\textbf{0.95} & 0.92\n"," & DistilBERT \n"," & news & 0.68 & 0.73 & 0.66 & 0.69 \\\\\n","XLM-RoBERTa\n"," & news & 0.49 & 0.49 & 1.00 & 0.66\n"," & XLM-RoBERTa\n"," & news & 0.53 & 0.53 & \\textbf{1.00} & 0.69 \\\\\n","Ensemble \n"," & news & \\textbf{0.95} & \\textbf{1.00} & 0.90 & \\textbf{0.95}\n"," & Ensemble \n"," & news & \\textbf{0.99} & \\textbf{0.98} & \\textbf{1.00} & \\textbf{0.99} \\\\\n","\\midrule\n","\\textbf{--- Reviews ---} & & & & & \n","& \\textbf{--- Reviews ---} & & & & & \\\\\n","XGBoost \n"," & reviews & 0.51 & 0.57 & 0.59 & 0.58\n"," & XGBoost \n"," & reviews & 0.53 & 0.51 & 0.46 & 0.48 \\\\\n","BERT-base \n"," & reviews & 0.50 & 0.54 & 0.62 & 0.58\n"," & BERT-base \n"," & reviews & 0.40 & 0.35 & 0.30 & 0.32 \\\\\n","DistilBERT \n"," & reviews & 0.65 & 0.68 & 0.71 & 0.69\n"," & DistilBERT \n"," & reviews & 0.42 & 0.38 & 0.50 & 0.43 \\\\\n","XLM-RoBERTa\n"," & reviews & 0.56 & 0.56 & \\textbf{0.90} & 0.69\n"," & XLM-RoBERTa\n"," & reviews & 0.43 & 0.43 & \\textbf{0.60} & 0.48 \\\\\n","Ensemble \n"," & reviews & \\textbf{0.67} & \\textbf{0.71} & 0.68 & \\textbf{0.70}\n"," & Ensemble \n"," & reviews & \\textbf{0.60} & \\textbf{0.53} & 0.44 & \\textbf{0.49} \\\\\n","\\midrule\n","\\textbf{--- Twitter / X ---} & & & & & \n","& \\textbf{--- Twitter / X ---} & & & & & \\\\\n","XGBoost \n"," & twitter & 0.91 & \\textbf{0.92} & 0.98 & 0.95\n"," & XGBoost \n"," & twitter & 0.80 & 0.85 & 0.78 & 0.81 \\\\\n","BERT-base \n"," & twitter & 0.90 & 0.86 & 0.98 & 0.92\n"," & BERT-base \n"," & twitter & 0.80 & 0.85 & 0.78 & 0.81 \\\\\n","DistilBERT \n"," & twitter & 0.90 & 0.86 & 0.98 & 0.92\n"," & DistilBERT \n"," & twitter & 0.84 & 0.86 & 0.80 & 0.83 \\\\\n","XLM-RoBERTa\n"," & twitter & 0.60 & 0.53 & \\textbf{1.00} & 0.69\n"," & XLM-RoBERTa\n"," & twitter & 0.62 & 0.60 & \\textbf{0.90} & 0.72 \\\\\n","Ensemble \n"," & twitter & \\textbf{0.93} & 0.86 & \\textbf{1.00} & \\textbf{0.93}\n"," & Ensemble \n"," & twitter & \\textbf{0.87} & \\textbf{0.90} & 0.85 & \\textbf{0.87} \\\\\n","\\bottomrule\n","\\end{tabular}\n","\\end{table}\n"],["\\begin{table*}[ht]\n","\\centering\n","\\caption{\\small F1 score and Accuracy of each detector under every rewriting scenario. \n","The best metric in a column is \\textbf{bolded}; the largest negative drop is \\dashuline{\\phantom{-00\\%}}, the smallest negative drop is \\dotuline{\\phantom{-00\\%}}.\n","\\footnotesize{$\\Delta=(\\text{Scenario Value}-\\text{Original Value})\\times100\\%$.}}\n","\\label{tab:all_models_rewriting_f1acc}\n","\\renewcommand{\\arraystretch}{1.4}\n","\\resizebox{\\textwidth}{!}{\n","\\begin{tabular}{l l\n","c c c c c c c c c c}\n","\\toprule\n","\\multirow{2}{*}{\\textbf{Explain}} & \\multirow{2}{*}{\\textbf{Strategy}}\n","& \\multicolumn{2}{c}{\\textbf{XGBoost}}\n","& \\multicolumn{2}{c}{\\textbf{BERT-base}}\n","& \\multicolumn{2}{c}{\\textbf{DistilBERT}}\n","& \\multicolumn{2}{c}{\\textbf{XLM-RoBERTa}}\n","& \\multicolumn{2}{c}{\\textbf{Ensemble}}\\\\\n","\\cmidrule(lr){3-4}\\cmidrule(lr){5-6}\\cmidrule(lr){7-8}\\cmidrule(lr){9-10}\\cmidrule(lr){11-12}\n","& & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\n"," & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\n"," & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\n"," & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\n"," & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\\\\\n","\\midrule\n","\\emph{NoRewriting} & Original\n","& 0.81 & 0.80\n","& 0.74 & 0.75\n","& 0.79 & 0.77\n","& 0.68 & 0.54\n","& \\textbf{0.83} & \\textbf{0.83}\\\\\n","\\midrule\n","\\textbf{SHAP} & HSR\n","& 0.73 (\\largest{-8\\%}) & 0.75 (\\largest{-5\\%})\n","& 0.71 (-3\\%) & 0.72 (-3\\%)\n","& 0.76 (-3\\%) & 0.75 (-2\\%)\n","& 0.68 (\\smallest{0\\%}) & 0.54 (\\smallest{0\\%})\n","& \\textbf{0.78} (-5\\%) & \\textbf{0.80} (-3\\%)\\\\\n","\\textbf{SHAP} & PSR\n","& 0.78 (\\largest{-3\\%}) & 0.78 (\\largest{-2\\%})\n","& 0.73 (-1\\%) & 0.74 (-1\\%)\n","& 0.78 (-1\\%) & 0.77 (0\\%)\n","& 0.68 (\\smallest{0\\%}) & 0.54 (\\smallest{0\\%})\n","& \\textbf{0.82} (-1\\%) & \\textbf{0.82} (-1\\%)\\\\\n","\\textbf{SHAP} & GPT\n","& 0.79 (-2\\%) & 0.79 (-1\\%)\n","& 0.73 (-1\\%) & 0.74 (-1\\%)\n","& 0.77 (-2\\%) & 0.76 (-1\\%)\n","& 0.68 (\\smallest{0\\%}) & 0.55 (\\smallest{+1\\%})\n","& \\textbf{0.80} (\\largest{-3\\%}) & \\textbf{0.81} (\\largest{-2\\%})\\\\\n","\\textbf{SHAP} & GPT+Genre\n","& \\textbf{0.80} (-1\\%) & \\textbf{0.80} (0\\%)\n","& 0.73 (-1\\%) & 0.74 (-1\\%)\n","& 0.77 (-2\\%) & 0.76 (-1\\%)\n","& 0.68 (\\smallest{0\\%}) & 0.55 (\\smallest{+1\\%})\n","& 0.79 (\\largest{-4\\%}) & 0.80 (\\largest{-3\\%})\\\\\n","\\midrule\n","\\textbf{LIME} & HSR\n","& 0.25 (\\largest{-56\\%}) & 0.48 (\\largest{-32\\%})\n","& 0.56 (-18\\%) & 0.62 (-13\\%)\n","& 0.62 (-17\\%) & 0.65 (-12\\%)\n","& \\textbf{0.67} (\\smallest{-1\\%}) & 0.52 (\\smallest{-2\\%})\n","& 0.64 (-19\\%) & \\textbf{0.69} (-14\\%)\\\\\n","\\textbf{LIME} & PSR\n","& 0.56 (\\largest{-25\\%}) & 0.63 (\\largest{-17\\%})\n","& 0.64 (-10\\%) & 0.67 (-8\\%)\n","& 0.71 (-8\\%) & 0.71 (-6\\%)\n","& 0.66 (\\smallest{-2\\%}) & 0.52 (\\smallest{-2\\%})\n","& \\textbf{0.73} (-10\\%) & \\textbf{0.75} (-8\\%)\\\\\n","\\textbf{LIME} & GPT\n","& 0.57 (\\largest{-24\\%}) & 0.64 (\\largest{-16\\%})\n","& 0.73 (-1\\%) & 0.74 (-1\\%)\n","& 0.78 (-1\\%) & 0.77 (0\\%)\n","& 0.68 (\\smallest{0\\%}) & 0.55 (\\smallest{+1\\%})\n","& \\textbf{0.83} (\\smallest{0\\%}) & \\textbf{0.83} (0\\%)\\\\\n","\\textbf{LIME} & GPT+Genre\n","& 0.60 (\\largest{-21\\%}) & 0.65 (\\largest{-15\\%})\n","& \\textbf{0.75} (\\smallest{+1\\%}) & \\textbf{0.75} (0\\%)\n","& 0.77 (-2\\%) & 0.76 (-1\\%)\n","& 0.68 (0\\%) & 0.55 (\\smallest{+1\\%})\n","& \\textbf{0.81} (-2\\%) & \\textbf{0.81} (-2\\%)\\\\\n","\\midrule\n","\\textbf{Random} & HSR\n","& 0.73 (-8\\%) & 0.74 (-6\\%)\n","& 0.63 (\\largest{-11\\%}) & 0.67 (\\largest{-8\\%})\n","& 0.68 (\\largest{-11\\%}) & 0.69 (\\largest{-8\\%})\n","& 0.67 (\\smallest{-1\\%}) & 0.53 (\\smallest{-1\\%})\n","& \\textbf{0.73} (-10\\%) & \\textbf{0.75} (\\largest{-8\\%})\\\\\n","\\textbf{Random} & PSR\n","& 0.76 (-5\\%) & 0.76 (-4\\%)\n","& 0.65 (\\largest{-9\\%}) & 0.68 (\\largest{-7\\%})\n","& 0.74 (-5\\%) & 0.73 (-4\\%)\n","& 0.67 (\\smallest{-1\\%}) & 0.53 (\\smallest{-1\\%})\n","& \\textbf{0.78} (-5\\%) & \\textbf{0.79} (-4\\%)\\\\\n","\\textbf{Random} & GPT\n","& 0.74 (\\largest{-7\\%}) & 0.75 (\\largest{-5\\%})\n","& 0.74 (0\\%) & 0.75 (0\\%)\n","& 0.77 (-2\\%) & 0.76 (-1\\%)\n","& 0.68 (\\smallest{0\\%}) & 0.55 (\\smallest{+1\\%})\n","& \\textbf{0.81} (-2\\%) & \\textbf{0.82} (-1\\%)\\\\\n","\\textbf{Random} & GPT+Genre\n","& 0.75 (\\largest{-6\\%}) & 0.75 (\\largest{-5\\%})\n","& 0.73 (-1\\%) & 0.74 (-1\\%)\n","& \\textbf{0.78} (-1\\%) & 0.77 (0\\%)\n","& 0.68 (0\\%) & 0.55 (\\smallest{+1\\%})\n","& \\textbf{0.81} (-2\\%) & \\textbf{0.81} (-2\\%)\\\\\n","\\bottomrule\n","\\end{tabular}}\n","\\end{table*}\n"],["\\begin{table}[ht]\n","\\centering\n","\\scriptsize\n","\\renewcommand{\\arraystretch}{1.15} % Slightly tighter row spacing\n","\\setlength{\\tabcolsep}{4pt} % Adjust column spacing as desired\n","\n","\\caption{Flip rates (\\%) from \\textbf{AI} to \\textbf{human}, grouped by rewriting strategies. \n"," Each scenario has 120 AI-labeled items; higher values indicate greater susceptibility to ``flipping.''}\n","\\label{tab:flip_rates_split}\n","\n","\\begin{subtable}[t]{0.48\\textwidth}\n"," \\centering\n"," \\caption{HSR \\& PSR Strategies}\n"," \\label{tab:flip_hsr_psr}\n"," \\begin{tabular}{\n"," l\n"," l\n"," l\n"," S[table-format=3.0]\n"," S[table-format=2.0]\n"," S[table-format=3.2]\n"," }\n"," \\toprule\n"," \\textbf{Explain} & \\textbf{Strategy} & \\textbf{Model} \n"," & {\\#AI} & {Flips} & {Flip Rate} \\\\\n"," \\midrule\n"," \\multirow{10}{*}{SHAP}\n"," & \\multirow{5}{*}{HSR}\n"," & XGBoost & 120 & 17 & 14.17 \\\\\n"," & & BERT & 120 & 7 & 5.83 \\\\\n"," & & DistilBERT & 120 & 9 & 7.50 \\\\\n"," & & XLM-R & 120 & 1 & 0.83 \\\\\n"," & & Ensemble & 120 & 8 & 6.67 \\\\\n"," \\cmidrule(lr){2-6}\n"," & \\multirow{5}{*}{PSR}\n"," & XGBoost & 120 & 7 & 5.83 \\\\\n"," & & BERT & 120 & 4 & 3.33 \\\\\n"," & & DistilBERT & 120 & 4 & 3.33 \\\\\n"," & & XLM-R & 120 & 0 & 0.00 \\\\\n"," & & Ensemble & 120 & 3 & 2.50 \\\\\n"," \\midrule\n"," \\multirow{10}{*}{LIME}\n"," & \\multirow{5}{*}{HSR}\n"," & XGBoost & 120 & 78 & 65.00 \\\\\n"," & & BERT & 120 & 31 & 25.83 \\\\\n"," & & DistilBERT & 120 & 32 & 26.67 \\\\\n"," & & XLM-R & 120 & 3 & 2.50 \\\\\n"," & & Ensemble & 120 & 33 & 27.50 \\\\\n"," \\cmidrule(lr){2-6}\n"," & \\multirow{5}{*}{PSR}\n"," & XGBoost & 120 & 46 & 38.33 \\\\\n"," & & BERT & 120 & 20 & 16.67 \\\\\n"," & & DistilBERT & 120 & 19 & 15.83 \\\\\n"," & & XLM-R & 120 & 5 & 4.17 \\\\\n"," & & Ensemble & 120 & 19 & 15.83 \\\\\n"," \\midrule\n"," \\multirow{10}{*}{Random}\n"," & \\multirow{5}{*}{HSR}\n"," & XGBoost & 120 & 15 & 12.50 \\\\\n"," & & BERT & 120 & 20 & 16.67 \\\\\n"," & & DistilBERT & 120 & 22 & 18.33 \\\\\n"," & & XLM-R & 120 & 2 & 1.67 \\\\\n"," & & Ensemble & 120 & 20 & 16.67 \\\\\n"," \\cmidrule(lr){2-6}\n"," & \\multirow{5}{*}{PSR}\n"," & XGBoost & 120 & 10 & 8.33 \\\\\n"," & & BERT & 120 & 17 & 14.17 \\\\\n"," & & DistilBERT & 120 & 11 & 9.17 \\\\\n"," & & XLM-R & 120 & 1 & 0.83 \\\\\n"," & & Ensemble & 120 & 14 & 11.67 \\\\\n"," \\bottomrule\n"," \\end{tabular}\n","\\end{subtable}\n","\\hfill\n","\\begin{subtable}[t]{0.48\\textwidth}\n"," \\centering\n"," \\caption{GPT \\& GPT+Genre Strategies}\n"," \\label{tab:flip_gpt_gptg}\n"," \\begin{tabular}{\n"," l\n"," l\n"," l\n"," S[table-format=3.0]\n"," S[table-format=2.0]\n"," S[table-format=3.2]\n"," }\n"," \\toprule\n"," \\textbf{Explain} & \\textbf{Strategy} & \\textbf{Model} \n"," & {\\#AI} & {Flips} & {Flip Rate} \\\\\n"," \\midrule\n"," \\multirow{10}{*}{SHAP}\n"," & \\multirow{5}{*}{GPT}\n"," & XGBoost & 120 & 4 & 3.33 \\\\\n"," & & BERT & 120 & 7 & 5.83 \\\\\n"," & & DistilBERT & 120 & 7 & 5.83 \\\\\n"," & & XLM-R & 120 & 0 & 0.00 \\\\\n"," & & Ensemble & 120 & 10 & 8.33 \\\\\n"," \\cmidrule(lr){2-6}\n"," & \\multirow{5}{*}{GPT+Genre}\n"," & XGBoost & 120 & 4 & 3.33 \\\\\n"," & & BERT & 120 & 3 & 2.50 \\\\\n"," & & DistilBERT & 120 & 6 & 5.00 \\\\\n"," & & XLM-R & 120 & 0 & 0.00 \\\\\n"," & & Ensemble & 120 & 11 & 9.17 \\\\\n"," \\midrule\n"," \\multirow{10}{*}{LIME}\n"," & \\multirow{5}{*}{GPT}\n"," & XGBoost & 120 & 38 & 31.67 \\\\\n"," & & BERT & 120 & 11 & 9.17 \\\\\n"," & & DistilBERT & 120 & 11 & 9.17 \\\\\n"," & & XLM-R & 120 & 0 & 0.00 \\\\\n"," & & Ensemble & 120 & 11 & 9.17 \\\\\n"," \\cmidrule(lr){2-6}\n"," & \\multirow{5}{*}{GPT+Genre}\n"," & XGBoost & 120 & 41 & 34.17 \\\\\n"," & & BERT & 120 & 4 & 3.33 \\\\\n"," & & DistilBERT & 120 & 7 & 5.83 \\\\\n"," & & XLM-R & 120 & 0 & 0.00 \\\\\n"," & & Ensemble & 120 & 9 & 7.50 \\\\\n"," \\midrule\n"," \\multirow{10}{*}{Random}\n"," & \\multirow{5}{*}{GPT}\n"," & XGBoost & 120 & 16 & 13.33 \\\\\n"," & & BERT & 120 & 6 & 5.00 \\\\\n"," & & DistilBERT & 120 & 6 & 5.00 \\\\\n"," & & XLM-R & 120 & 0 & 0.00 \\\\\n"," & & Ensemble & 120 & 5 & 4.17 \\\\\n"," \\cmidrule(lr){2-6}\n"," & \\multirow{5}{*}{GPT+Genre}\n"," & XGBoost & 120 & 13 & 10.83 \\\\\n"," & & BERT & 120 & 6 & 5.00 \\\\\n"," & & DistilBERT & 120 & 9 & 7.50 \\\\\n"," & & XLM-R & 120 & 0 & 0.00 \\\\\n"," & & Ensemble & 120 & 11 & 9.17 \\\\\n"," \\bottomrule\n"," \\end{tabular}\n","\\end{subtable}\n","\n","\\end{table}\n"],["\\begin{figure*}[ht]\n","\\centering\n","\n","\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_SHAP_HSR.png}\n","\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_SHAP_PSR.png}\n","\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_SHAP_GPT.png}\n","\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_SHAP_GPT+Genre.png}\n","\n","\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_LIME_HSR.png}\n","\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_LIME_PSR.png}\n","\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_LIME_GPT.png}\n","\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_LIME_GPT+Genre.png}\n","\n","\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_Random_HSR.png}\n","\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_Random_PSR.png}\n","\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_Random_GPT.png}\n","\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_Random_GPT+Genre.png}\n","\n","\\caption{\\small Overlaps among flipped AI samples under different explainability methods and rewriting strategies. Each bar corresponds to the number of samples simultaneously flipped by one or more models.}\n","\\label{fig:all_upset_plots}\n","\\end{figure*}\n"]],"string":"[\n [\n \"\\\\begin{figure*}[!ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\includegraphics[width=0.7\\\\linewidth]{plots/model.jpeg}\\n\",\n \"\\\\vspace{-5pt}\\n\",\n \"\\\\caption{\\\\small The architecture of the proposed ensemble model. Outputs of three frozen BERT models (left) and three fresh BERT models (right) are concatenated and passed through dense layers, followed by a voting mechanism.}\\n\",\n \"\\\\label{fig:ms-structure}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{table}[ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\scriptsize\\n\",\n \"\\\\renewcommand{\\\\arraystretch}{1.2}\\n\",\n \"\\\\caption{Summary of token replacement strategies}\\n\",\n \"\\\\label{tab:token_replacement}\\n\",\n \"\\\\begin{tabular}{p{4.8cm}p{9.2cm}}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Strategy} & \\\\textbf{Description} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textbf{Human Similar Word Replacement (HSR)} \\n\",\n \" & A Word2Vec model is trained on all human-labeled texts in the training set to learn an embedding space that captures typical human usage. \\\\\\\\\\n\",\n \"\\\\textbf{Part-of-Speech Replacement (PSR)} \\n\",\n \" & HSR is refined by ensuring that part-of-speech tags match. \\\\\\\\\\n\",\n \"\\\\textbf{GPT-based Replacement (GPT)} \\n\",\n \" & A generative model (GPT-4o-mini) is queried using the prompt: \\n\",\n \" \\\\texttt{\\\"Replace '\\\\{token\\\\}' with a more human-like word in this text: '\\\\{text\\\\}'\\\"}. \\\\\\\\\\n\",\n \"\\\\textbf{GPT-based Replacement with Genre Context (GPT+Genre)} \\n\",\n \" & The GPT-based method is extended by including explicit genre information (e.g., \\\\emph{tweets} or \\\\emph{reviews}) in the prompt:\\n\",\n \" \\\\texttt{\\\"Replace '\\\\{token\\\\}' in this \\\\{domain\\\\} text with a more human-like word: '\\\\{text\\\\}'\\\"}. \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\scriptsize\\n\",\n \"\\\\renewcommand{\\\\arraystretch}{1.2}\\n\",\n \"\\\\caption{Model performance by language and domain on unmodified AI text. \\\\textbf{Bold} indicates the best result within each language and domain block.}\\n\",\n \"\\\\label{tab:lang_domain_updated}\\n\",\n \"\\\\begin{tabular}{llcccc|llcccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multicolumn{6}{c}{\\\\textbf{English Dataset}} & \\\\multicolumn{6}{c}{\\\\textbf{Dutch Dataset}} \\\\\\\\\\n\",\n \"\\\\cmidrule(lr){1-6} \\\\cmidrule(lr){7-12}\\n\",\n \"\\\\textbf{Model} & \\\\textbf{Domain} & \\\\textbf{Acc} & \\\\textbf{Prec} & \\\\textbf{Rec} & \\\\textbf{F1} \\n\",\n \" & \\\\textbf{Model} & \\\\textbf{Domain} & \\\\textbf{Acc} & \\\\textbf{Prec} & \\\\textbf{Rec} & \\\\textbf{F1} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textbf{--- News ---} & & & & & \\n\",\n \"& \\\\textbf{--- News ---} & & & & & \\\\\\\\\\n\",\n \"XGBoost \\n\",\n \" & news & 0.89 & 0.93 & 0.84 & 0.88 \\n\",\n \" & XGBoost \\n\",\n \" & news & 0.85 & 0.88 & 0.88 & 0.88 \\\\\\\\\\n\",\n \"BERT-base \\n\",\n \" & news & 0.94 & 0.97 & 0.91 & 0.94\\n\",\n \" & BERT-base \\n\",\n \" & news & 0.68 & 0.79 & 0.56 & 0.66 \\\\\\\\\\n\",\n \"DistilBERT \\n\",\n \" & news & 0.92 & 0.90 & \\\\textbf{0.95} & 0.92\\n\",\n \" & DistilBERT \\n\",\n \" & news & 0.68 & 0.73 & 0.66 & 0.69 \\\\\\\\\\n\",\n \"XLM-RoBERTa\\n\",\n \" & news & 0.49 & 0.49 & 1.00 & 0.66\\n\",\n \" & XLM-RoBERTa\\n\",\n \" & news & 0.53 & 0.53 & \\\\textbf{1.00} & 0.69 \\\\\\\\\\n\",\n \"Ensemble \\n\",\n \" & news & \\\\textbf{0.95} & \\\\textbf{1.00} & 0.90 & \\\\textbf{0.95}\\n\",\n \" & Ensemble \\n\",\n \" & news & \\\\textbf{0.99} & \\\\textbf{0.98} & \\\\textbf{1.00} & \\\\textbf{0.99} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textbf{--- Reviews ---} & & & & & \\n\",\n \"& \\\\textbf{--- Reviews ---} & & & & & \\\\\\\\\\n\",\n \"XGBoost \\n\",\n \" & reviews & 0.51 & 0.57 & 0.59 & 0.58\\n\",\n \" & XGBoost \\n\",\n \" & reviews & 0.53 & 0.51 & 0.46 & 0.48 \\\\\\\\\\n\",\n \"BERT-base \\n\",\n \" & reviews & 0.50 & 0.54 & 0.62 & 0.58\\n\",\n \" & BERT-base \\n\",\n \" & reviews & 0.40 & 0.35 & 0.30 & 0.32 \\\\\\\\\\n\",\n \"DistilBERT \\n\",\n \" & reviews & 0.65 & 0.68 & 0.71 & 0.69\\n\",\n \" & DistilBERT \\n\",\n \" & reviews & 0.42 & 0.38 & 0.50 & 0.43 \\\\\\\\\\n\",\n \"XLM-RoBERTa\\n\",\n \" & reviews & 0.56 & 0.56 & \\\\textbf{0.90} & 0.69\\n\",\n \" & XLM-RoBERTa\\n\",\n \" & reviews & 0.43 & 0.43 & \\\\textbf{0.60} & 0.48 \\\\\\\\\\n\",\n \"Ensemble \\n\",\n \" & reviews & \\\\textbf{0.67} & \\\\textbf{0.71} & 0.68 & \\\\textbf{0.70}\\n\",\n \" & Ensemble \\n\",\n \" & reviews & \\\\textbf{0.60} & \\\\textbf{0.53} & 0.44 & \\\\textbf{0.49} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textbf{--- Twitter / X ---} & & & & & \\n\",\n \"& \\\\textbf{--- Twitter / X ---} & & & & & \\\\\\\\\\n\",\n \"XGBoost \\n\",\n \" & twitter & 0.91 & \\\\textbf{0.92} & 0.98 & 0.95\\n\",\n \" & XGBoost \\n\",\n \" & twitter & 0.80 & 0.85 & 0.78 & 0.81 \\\\\\\\\\n\",\n \"BERT-base \\n\",\n \" & twitter & 0.90 & 0.86 & 0.98 & 0.92\\n\",\n \" & BERT-base \\n\",\n \" & twitter & 0.80 & 0.85 & 0.78 & 0.81 \\\\\\\\\\n\",\n \"DistilBERT \\n\",\n \" & twitter & 0.90 & 0.86 & 0.98 & 0.92\\n\",\n \" & DistilBERT \\n\",\n \" & twitter & 0.84 & 0.86 & 0.80 & 0.83 \\\\\\\\\\n\",\n \"XLM-RoBERTa\\n\",\n \" & twitter & 0.60 & 0.53 & \\\\textbf{1.00} & 0.69\\n\",\n \" & XLM-RoBERTa\\n\",\n \" & twitter & 0.62 & 0.60 & \\\\textbf{0.90} & 0.72 \\\\\\\\\\n\",\n \"Ensemble \\n\",\n \" & twitter & \\\\textbf{0.93} & 0.86 & \\\\textbf{1.00} & \\\\textbf{0.93}\\n\",\n \" & Ensemble \\n\",\n \" & twitter & \\\\textbf{0.87} & \\\\textbf{0.90} & 0.85 & \\\\textbf{0.87} \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table*}[ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\caption{\\\\small F1 score and Accuracy of each detector under every rewriting scenario. \\n\",\n \"The best metric in a column is \\\\textbf{bolded}; the largest negative drop is \\\\dashuline{\\\\phantom{-00\\\\%}}, the smallest negative drop is \\\\dotuline{\\\\phantom{-00\\\\%}}.\\n\",\n \"\\\\footnotesize{$\\\\Delta=(\\\\text{Scenario Value}-\\\\text{Original Value})\\\\times100\\\\%$.}}\\n\",\n \"\\\\label{tab:all_models_rewriting_f1acc}\\n\",\n \"\\\\renewcommand{\\\\arraystretch}{1.4}\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \"\\\\begin{tabular}{l l\\n\",\n \"c c c c c c c c c c}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{\\\\textbf{Explain}} & \\\\multirow{2}{*}{\\\\textbf{Strategy}}\\n\",\n \"& \\\\multicolumn{2}{c}{\\\\textbf{XGBoost}}\\n\",\n \"& \\\\multicolumn{2}{c}{\\\\textbf{BERT-base}}\\n\",\n \"& \\\\multicolumn{2}{c}{\\\\textbf{DistilBERT}}\\n\",\n \"& \\\\multicolumn{2}{c}{\\\\textbf{XLM-RoBERTa}}\\n\",\n \"& \\\\multicolumn{2}{c}{\\\\textbf{Ensemble}}\\\\\\\\\\n\",\n \"\\\\cmidrule(lr){3-4}\\\\cmidrule(lr){5-6}\\\\cmidrule(lr){7-8}\\\\cmidrule(lr){9-10}\\\\cmidrule(lr){11-12}\\n\",\n \"& & \\\\textbf{F1 ($\\\\Delta$)} & \\\\textbf{Acc ($\\\\Delta$)}\\n\",\n \" & \\\\textbf{F1 ($\\\\Delta$)} & \\\\textbf{Acc ($\\\\Delta$)}\\n\",\n \" & \\\\textbf{F1 ($\\\\Delta$)} & \\\\textbf{Acc ($\\\\Delta$)}\\n\",\n \" & \\\\textbf{F1 ($\\\\Delta$)} & \\\\textbf{Acc ($\\\\Delta$)}\\n\",\n \" & \\\\textbf{F1 ($\\\\Delta$)} & \\\\textbf{Acc ($\\\\Delta$)}\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\emph{NoRewriting} & Original\\n\",\n \"& 0.81 & 0.80\\n\",\n \"& 0.74 & 0.75\\n\",\n \"& 0.79 & 0.77\\n\",\n \"& 0.68 & 0.54\\n\",\n \"& \\\\textbf{0.83} & \\\\textbf{0.83}\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textbf{SHAP} & HSR\\n\",\n \"& 0.73 (\\\\largest{-8\\\\%}) & 0.75 (\\\\largest{-5\\\\%})\\n\",\n \"& 0.71 (-3\\\\%) & 0.72 (-3\\\\%)\\n\",\n \"& 0.76 (-3\\\\%) & 0.75 (-2\\\\%)\\n\",\n \"& 0.68 (\\\\smallest{0\\\\%}) & 0.54 (\\\\smallest{0\\\\%})\\n\",\n \"& \\\\textbf{0.78} (-5\\\\%) & \\\\textbf{0.80} (-3\\\\%)\\\\\\\\\\n\",\n \"\\\\textbf{SHAP} & PSR\\n\",\n \"& 0.78 (\\\\largest{-3\\\\%}) & 0.78 (\\\\largest{-2\\\\%})\\n\",\n \"& 0.73 (-1\\\\%) & 0.74 (-1\\\\%)\\n\",\n \"& 0.78 (-1\\\\%) & 0.77 (0\\\\%)\\n\",\n \"& 0.68 (\\\\smallest{0\\\\%}) & 0.54 (\\\\smallest{0\\\\%})\\n\",\n \"& \\\\textbf{0.82} (-1\\\\%) & \\\\textbf{0.82} (-1\\\\%)\\\\\\\\\\n\",\n \"\\\\textbf{SHAP} & GPT\\n\",\n \"& 0.79 (-2\\\\%) & 0.79 (-1\\\\%)\\n\",\n \"& 0.73 (-1\\\\%) & 0.74 (-1\\\\%)\\n\",\n \"& 0.77 (-2\\\\%) & 0.76 (-1\\\\%)\\n\",\n \"& 0.68 (\\\\smallest{0\\\\%}) & 0.55 (\\\\smallest{+1\\\\%})\\n\",\n \"& \\\\textbf{0.80} (\\\\largest{-3\\\\%}) & \\\\textbf{0.81} (\\\\largest{-2\\\\%})\\\\\\\\\\n\",\n \"\\\\textbf{SHAP} & GPT+Genre\\n\",\n \"& \\\\textbf{0.80} (-1\\\\%) & \\\\textbf{0.80} (0\\\\%)\\n\",\n \"& 0.73 (-1\\\\%) & 0.74 (-1\\\\%)\\n\",\n \"& 0.77 (-2\\\\%) & 0.76 (-1\\\\%)\\n\",\n \"& 0.68 (\\\\smallest{0\\\\%}) & 0.55 (\\\\smallest{+1\\\\%})\\n\",\n \"& 0.79 (\\\\largest{-4\\\\%}) & 0.80 (\\\\largest{-3\\\\%})\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textbf{LIME} & HSR\\n\",\n \"& 0.25 (\\\\largest{-56\\\\%}) & 0.48 (\\\\largest{-32\\\\%})\\n\",\n \"& 0.56 (-18\\\\%) & 0.62 (-13\\\\%)\\n\",\n \"& 0.62 (-17\\\\%) & 0.65 (-12\\\\%)\\n\",\n \"& \\\\textbf{0.67} (\\\\smallest{-1\\\\%}) & 0.52 (\\\\smallest{-2\\\\%})\\n\",\n \"& 0.64 (-19\\\\%) & \\\\textbf{0.69} (-14\\\\%)\\\\\\\\\\n\",\n \"\\\\textbf{LIME} & PSR\\n\",\n \"& 0.56 (\\\\largest{-25\\\\%}) & 0.63 (\\\\largest{-17\\\\%})\\n\",\n \"& 0.64 (-10\\\\%) & 0.67 (-8\\\\%)\\n\",\n \"& 0.71 (-8\\\\%) & 0.71 (-6\\\\%)\\n\",\n \"& 0.66 (\\\\smallest{-2\\\\%}) & 0.52 (\\\\smallest{-2\\\\%})\\n\",\n \"& \\\\textbf{0.73} (-10\\\\%) & \\\\textbf{0.75} (-8\\\\%)\\\\\\\\\\n\",\n \"\\\\textbf{LIME} & GPT\\n\",\n \"& 0.57 (\\\\largest{-24\\\\%}) & 0.64 (\\\\largest{-16\\\\%})\\n\",\n \"& 0.73 (-1\\\\%) & 0.74 (-1\\\\%)\\n\",\n \"& 0.78 (-1\\\\%) & 0.77 (0\\\\%)\\n\",\n \"& 0.68 (\\\\smallest{0\\\\%}) & 0.55 (\\\\smallest{+1\\\\%})\\n\",\n \"& \\\\textbf{0.83} (\\\\smallest{0\\\\%}) & \\\\textbf{0.83} (0\\\\%)\\\\\\\\\\n\",\n \"\\\\textbf{LIME} & GPT+Genre\\n\",\n \"& 0.60 (\\\\largest{-21\\\\%}) & 0.65 (\\\\largest{-15\\\\%})\\n\",\n \"& \\\\textbf{0.75} (\\\\smallest{+1\\\\%}) & \\\\textbf{0.75} (0\\\\%)\\n\",\n \"& 0.77 (-2\\\\%) & 0.76 (-1\\\\%)\\n\",\n \"& 0.68 (0\\\\%) & 0.55 (\\\\smallest{+1\\\\%})\\n\",\n \"& \\\\textbf{0.81} (-2\\\\%) & \\\\textbf{0.81} (-2\\\\%)\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textbf{Random} & HSR\\n\",\n \"& 0.73 (-8\\\\%) & 0.74 (-6\\\\%)\\n\",\n \"& 0.63 (\\\\largest{-11\\\\%}) & 0.67 (\\\\largest{-8\\\\%})\\n\",\n \"& 0.68 (\\\\largest{-11\\\\%}) & 0.69 (\\\\largest{-8\\\\%})\\n\",\n \"& 0.67 (\\\\smallest{-1\\\\%}) & 0.53 (\\\\smallest{-1\\\\%})\\n\",\n \"& \\\\textbf{0.73} (-10\\\\%) & \\\\textbf{0.75} (\\\\largest{-8\\\\%})\\\\\\\\\\n\",\n \"\\\\textbf{Random} & PSR\\n\",\n \"& 0.76 (-5\\\\%) & 0.76 (-4\\\\%)\\n\",\n \"& 0.65 (\\\\largest{-9\\\\%}) & 0.68 (\\\\largest{-7\\\\%})\\n\",\n \"& 0.74 (-5\\\\%) & 0.73 (-4\\\\%)\\n\",\n \"& 0.67 (\\\\smallest{-1\\\\%}) & 0.53 (\\\\smallest{-1\\\\%})\\n\",\n \"& \\\\textbf{0.78} (-5\\\\%) & \\\\textbf{0.79} (-4\\\\%)\\\\\\\\\\n\",\n \"\\\\textbf{Random} & GPT\\n\",\n \"& 0.74 (\\\\largest{-7\\\\%}) & 0.75 (\\\\largest{-5\\\\%})\\n\",\n \"& 0.74 (0\\\\%) & 0.75 (0\\\\%)\\n\",\n \"& 0.77 (-2\\\\%) & 0.76 (-1\\\\%)\\n\",\n \"& 0.68 (\\\\smallest{0\\\\%}) & 0.55 (\\\\smallest{+1\\\\%})\\n\",\n \"& \\\\textbf{0.81} (-2\\\\%) & \\\\textbf{0.82} (-1\\\\%)\\\\\\\\\\n\",\n \"\\\\textbf{Random} & GPT+Genre\\n\",\n \"& 0.75 (\\\\largest{-6\\\\%}) & 0.75 (\\\\largest{-5\\\\%})\\n\",\n \"& 0.73 (-1\\\\%) & 0.74 (-1\\\\%)\\n\",\n \"& \\\\textbf{0.78} (-1\\\\%) & 0.77 (0\\\\%)\\n\",\n \"& 0.68 (0\\\\%) & 0.55 (\\\\smallest{+1\\\\%})\\n\",\n \"& \\\\textbf{0.81} (-2\\\\%) & \\\\textbf{0.81} (-2\\\\%)\\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table}[ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\scriptsize\\n\",\n \"\\\\renewcommand{\\\\arraystretch}{1.15} % Slightly tighter row spacing\\n\",\n \"\\\\setlength{\\\\tabcolsep}{4pt} % Adjust column spacing as desired\\n\",\n \"\\n\",\n \"\\\\caption{Flip rates (\\\\%) from \\\\textbf{AI} to \\\\textbf{human}, grouped by rewriting strategies. \\n\",\n \" Each scenario has 120 AI-labeled items; higher values indicate greater susceptibility to ``flipping.''}\\n\",\n \"\\\\label{tab:flip_rates_split}\\n\",\n \"\\n\",\n \"\\\\begin{subtable}[t]{0.48\\\\textwidth}\\n\",\n \" \\\\centering\\n\",\n \" \\\\caption{HSR \\\\& PSR Strategies}\\n\",\n \" \\\\label{tab:flip_hsr_psr}\\n\",\n \" \\\\begin{tabular}{\\n\",\n \" l\\n\",\n \" l\\n\",\n \" l\\n\",\n \" S[table-format=3.0]\\n\",\n \" S[table-format=2.0]\\n\",\n \" S[table-format=3.2]\\n\",\n \" }\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Explain} & \\\\textbf{Strategy} & \\\\textbf{Model} \\n\",\n \" & {\\\\#AI} & {Flips} & {Flip Rate} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multirow{10}{*}{SHAP}\\n\",\n \" & \\\\multirow{5}{*}{HSR}\\n\",\n \" & XGBoost & 120 & 17 & 14.17 \\\\\\\\\\n\",\n \" & & BERT & 120 & 7 & 5.83 \\\\\\\\\\n\",\n \" & & DistilBERT & 120 & 9 & 7.50 \\\\\\\\\\n\",\n \" & & XLM-R & 120 & 1 & 0.83 \\\\\\\\\\n\",\n \" & & Ensemble & 120 & 8 & 6.67 \\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-6}\\n\",\n \" & \\\\multirow{5}{*}{PSR}\\n\",\n \" & XGBoost & 120 & 7 & 5.83 \\\\\\\\\\n\",\n \" & & BERT & 120 & 4 & 3.33 \\\\\\\\\\n\",\n \" & & DistilBERT & 120 & 4 & 3.33 \\\\\\\\\\n\",\n \" & & XLM-R & 120 & 0 & 0.00 \\\\\\\\\\n\",\n \" & & Ensemble & 120 & 3 & 2.50 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multirow{10}{*}{LIME}\\n\",\n \" & \\\\multirow{5}{*}{HSR}\\n\",\n \" & XGBoost & 120 & 78 & 65.00 \\\\\\\\\\n\",\n \" & & BERT & 120 & 31 & 25.83 \\\\\\\\\\n\",\n \" & & DistilBERT & 120 & 32 & 26.67 \\\\\\\\\\n\",\n \" & & XLM-R & 120 & 3 & 2.50 \\\\\\\\\\n\",\n \" & & Ensemble & 120 & 33 & 27.50 \\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-6}\\n\",\n \" & \\\\multirow{5}{*}{PSR}\\n\",\n \" & XGBoost & 120 & 46 & 38.33 \\\\\\\\\\n\",\n \" & & BERT & 120 & 20 & 16.67 \\\\\\\\\\n\",\n \" & & DistilBERT & 120 & 19 & 15.83 \\\\\\\\\\n\",\n \" & & XLM-R & 120 & 5 & 4.17 \\\\\\\\\\n\",\n \" & & Ensemble & 120 & 19 & 15.83 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multirow{10}{*}{Random}\\n\",\n \" & \\\\multirow{5}{*}{HSR}\\n\",\n \" & XGBoost & 120 & 15 & 12.50 \\\\\\\\\\n\",\n \" & & BERT & 120 & 20 & 16.67 \\\\\\\\\\n\",\n \" & & DistilBERT & 120 & 22 & 18.33 \\\\\\\\\\n\",\n \" & & XLM-R & 120 & 2 & 1.67 \\\\\\\\\\n\",\n \" & & Ensemble & 120 & 20 & 16.67 \\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-6}\\n\",\n \" & \\\\multirow{5}{*}{PSR}\\n\",\n \" & XGBoost & 120 & 10 & 8.33 \\\\\\\\\\n\",\n \" & & BERT & 120 & 17 & 14.17 \\\\\\\\\\n\",\n \" & & DistilBERT & 120 & 11 & 9.17 \\\\\\\\\\n\",\n \" & & XLM-R & 120 & 1 & 0.83 \\\\\\\\\\n\",\n \" & & Ensemble & 120 & 14 & 11.67 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"\\\\end{subtable}\\n\",\n \"\\\\hfill\\n\",\n \"\\\\begin{subtable}[t]{0.48\\\\textwidth}\\n\",\n \" \\\\centering\\n\",\n \" \\\\caption{GPT \\\\& GPT+Genre Strategies}\\n\",\n \" \\\\label{tab:flip_gpt_gptg}\\n\",\n \" \\\\begin{tabular}{\\n\",\n \" l\\n\",\n \" l\\n\",\n \" l\\n\",\n \" S[table-format=3.0]\\n\",\n \" S[table-format=2.0]\\n\",\n \" S[table-format=3.2]\\n\",\n \" }\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Explain} & \\\\textbf{Strategy} & \\\\textbf{Model} \\n\",\n \" & {\\\\#AI} & {Flips} & {Flip Rate} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multirow{10}{*}{SHAP}\\n\",\n \" & \\\\multirow{5}{*}{GPT}\\n\",\n \" & XGBoost & 120 & 4 & 3.33 \\\\\\\\\\n\",\n \" & & BERT & 120 & 7 & 5.83 \\\\\\\\\\n\",\n \" & & DistilBERT & 120 & 7 & 5.83 \\\\\\\\\\n\",\n \" & & XLM-R & 120 & 0 & 0.00 \\\\\\\\\\n\",\n \" & & Ensemble & 120 & 10 & 8.33 \\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-6}\\n\",\n \" & \\\\multirow{5}{*}{GPT+Genre}\\n\",\n \" & XGBoost & 120 & 4 & 3.33 \\\\\\\\\\n\",\n \" & & BERT & 120 & 3 & 2.50 \\\\\\\\\\n\",\n \" & & DistilBERT & 120 & 6 & 5.00 \\\\\\\\\\n\",\n \" & & XLM-R & 120 & 0 & 0.00 \\\\\\\\\\n\",\n \" & & Ensemble & 120 & 11 & 9.17 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multirow{10}{*}{LIME}\\n\",\n \" & \\\\multirow{5}{*}{GPT}\\n\",\n \" & XGBoost & 120 & 38 & 31.67 \\\\\\\\\\n\",\n \" & & BERT & 120 & 11 & 9.17 \\\\\\\\\\n\",\n \" & & DistilBERT & 120 & 11 & 9.17 \\\\\\\\\\n\",\n \" & & XLM-R & 120 & 0 & 0.00 \\\\\\\\\\n\",\n \" & & Ensemble & 120 & 11 & 9.17 \\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-6}\\n\",\n \" & \\\\multirow{5}{*}{GPT+Genre}\\n\",\n \" & XGBoost & 120 & 41 & 34.17 \\\\\\\\\\n\",\n \" & & BERT & 120 & 4 & 3.33 \\\\\\\\\\n\",\n \" & & DistilBERT & 120 & 7 & 5.83 \\\\\\\\\\n\",\n \" & & XLM-R & 120 & 0 & 0.00 \\\\\\\\\\n\",\n \" & & Ensemble & 120 & 9 & 7.50 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multirow{10}{*}{Random}\\n\",\n \" & \\\\multirow{5}{*}{GPT}\\n\",\n \" & XGBoost & 120 & 16 & 13.33 \\\\\\\\\\n\",\n \" & & BERT & 120 & 6 & 5.00 \\\\\\\\\\n\",\n \" & & DistilBERT & 120 & 6 & 5.00 \\\\\\\\\\n\",\n \" & & XLM-R & 120 & 0 & 0.00 \\\\\\\\\\n\",\n \" & & Ensemble & 120 & 5 & 4.17 \\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-6}\\n\",\n \" & \\\\multirow{5}{*}{GPT+Genre}\\n\",\n \" & XGBoost & 120 & 13 & 10.83 \\\\\\\\\\n\",\n \" & & BERT & 120 & 6 & 5.00 \\\\\\\\\\n\",\n \" & & DistilBERT & 120 & 9 & 7.50 \\\\\\\\\\n\",\n \" & & XLM-R & 120 & 0 & 0.00 \\\\\\\\\\n\",\n \" & & Ensemble & 120 & 11 & 9.17 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"\\\\end{subtable}\\n\",\n \"\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{figure*}[ht]\\n\",\n \"\\\\centering\\n\",\n \"\\n\",\n \"\\\\includegraphics[width=0.24\\\\textwidth]{plots/flip/upset_flips_SHAP_HSR.png}\\n\",\n \"\\\\includegraphics[width=0.24\\\\textwidth]{plots/flip/upset_flips_SHAP_PSR.png}\\n\",\n \"\\\\includegraphics[width=0.24\\\\textwidth]{plots/flip/upset_flips_SHAP_GPT.png}\\n\",\n \"\\\\includegraphics[width=0.24\\\\textwidth]{plots/flip/upset_flips_SHAP_GPT+Genre.png}\\n\",\n \"\\n\",\n \"\\\\includegraphics[width=0.24\\\\textwidth]{plots/flip/upset_flips_LIME_HSR.png}\\n\",\n \"\\\\includegraphics[width=0.24\\\\textwidth]{plots/flip/upset_flips_LIME_PSR.png}\\n\",\n \"\\\\includegraphics[width=0.24\\\\textwidth]{plots/flip/upset_flips_LIME_GPT.png}\\n\",\n \"\\\\includegraphics[width=0.24\\\\textwidth]{plots/flip/upset_flips_LIME_GPT+Genre.png}\\n\",\n \"\\n\",\n \"\\\\includegraphics[width=0.24\\\\textwidth]{plots/flip/upset_flips_Random_HSR.png}\\n\",\n \"\\\\includegraphics[width=0.24\\\\textwidth]{plots/flip/upset_flips_Random_PSR.png}\\n\",\n \"\\\\includegraphics[width=0.24\\\\textwidth]{plots/flip/upset_flips_Random_GPT.png}\\n\",\n \"\\\\includegraphics[width=0.24\\\\textwidth]{plots/flip/upset_flips_Random_GPT+Genre.png}\\n\",\n \"\\n\",\n \"\\\\caption{\\\\small Overlaps among flipped AI samples under different explainability methods and rewriting strategies. Each bar corresponds to the number of samples simultaneously flipped by one or more models.}\\n\",\n \"\\\\label{fig:all_upset_plots}\\n\",\n \"\\\\end{figure*}\\n\"\n ]\n]","isConversational":false},"fulltext":{"kind":"string","value":" %% This is a JAIR Example File Compiled by Nicholas Mattei (nsmattei@tulane.edu) \n%% and Odd Erik Gundersen (odderik@ntnu.no)\n%% and Mykel Kochenderfer (mykel@stanford.edu)\n%% March 29, 2025\n%%\n%% This file is based off the ACM Latex Template https://www.acm.org/publications/proceedings-template\n%% Revision 2.12 (12/28/2024)\n%% \n%% Please see https://www.jair.org/index.php/jair for more information and submission instructions.\n%%\n\n%% The first command in your LaTeX source must be the \\documentclass\n%% command.\n%%\n%% For submission and review of your manuscript please change the\n%% command to \\documentclass[manuscript, screen, review]{jair}.\n%%\n%\\documentclass[review]{jair}\n%\\documentclass[manuscript, screen, review]{jair}\n\n\\documentclass{jair}\n\n%---------- kill the ACM-style footer line ----------\n\\settopmatter{printacmref=false} % no ACM reference strip\n\\pagestyle{plain} % plain page style = no header\n\\makeatletter\n\\renewcommand\\footnotetextcopyrightpermission[1]{} % empty out the footer macro\n\\makeatother\n%---------------------------------------------------\n\n\n\n%\\setcopyright{cc}\n%\\copyrightyear{2025}\n%\\acmYear{2025}\n%\\acmDOI{10.1613/jair.1.xxxxx}\n\n%%\n%\\JAIRAE{Insert JAIR AE Name}\n\n%\\renewcommand{\\JAIRAE}[1]{}\n\n%\\JAIRTrack{Insert JAIR Track Name Here}\n%\\renewcommand{\\JAIRTrack}[1]{}\n\n% \\acmVolume{4}\n% \\acmArticle{111}\n% \\acmMonth{8}\n% \\acmYear{2025}\n\n\\usepackage{graphicx}\n\\usepackage{caption} \n\\usepackage{float} \n\\usepackage[utf8]{inputenc}\n\\let\\Bbbk\\relax\n\\usepackage{amsmath,amssymb}\n\\usepackage{algorithm}\n\\usepackage{algpseudocode}\n\\usepackage{setspace}\n\\usepackage{wrapfig}\n\\usepackage{booktabs} \n\\usepackage{siunitx} \n\n\\usepackage{footmisc} \n\n\\usepackage[normalem]{ulem} \n\\newcommand{\\largest}[1]{\\dashuline{#1}} \n\\newcommand{\\smallest}[1]{\\dotuline{#1}} \n\n\\captionsetup[algorithm]{labelfont=bf}\n\n\n\\captionsetup[algorithm]{labelfont=bf}\n\n\\algrenewcommand{\\algorithmicindent}{1.5em} \n\\algrenewcommand{\\algorithmiccomment}[1]{\\hfill\\(\\#\\) #1}\n%\\algrenewcommand{\\algorithmicendfunction}{} \n\n\\usepackage{booktabs,siunitx,multirow,subcaption}\n\\setlength{\\tabcolsep}{4pt} \n\\renewcommand{\\arraystretch}{0.9}\n\\sisetup{table-format=9.0} \n\n\n\n\n%%\n%% For managing citations, it is recommended to use bibliography\n%% files in BibTeX format.\n%%\n%% You can then either use BibTeX with the ACM-Reference-Format style,\n%% or BibLaTeX with the acmnumeric or acmauthoryear sytles, that include\n%% support for advanced citation of software artefact from the\n%% biblatex-software package, also separately available on CTAN.\n%%\n%% Look at the sample-*-biblatex.tex files for templates showcasing\n%% the biblatex styles.\n%%\n\n%%\n%% The majority of ACM publications use numbered citations and\n%% references. The command \\citestyle{authoryear} switches to the\n%% \"author year\" style.\n%%\n%% If you are preparing content for an event\n%% sponsored by ACM SIGGRAPH, you must use the \"author year\" style of\n%% citations and references.\n%% Uncommenting\n%% the next command will enable that style.\n%%\\citestyle{acmauthoryear}\n\n\n%%\n%% end of the preamble, start of the body of the document source.\n\\begin{document}\n\n%%\n%% The \"title\" command has an optional parameter,\n%% allowing the author to define a \"short title\" to be used in page headers.\n%\\title{JAIR Example Template}\n%\\title[Satzilla]{Explainability-Based Token Replacement on LLM-Generated Text}\n% \\title[Explainability-Based Token Replacement]{Explainability-Based Token Replacement on LLM-Generated Text}\n\n\\title{Explainability-Based Token Replacement on LLM-Generated Text}\n\n%%\n%% The \"author\" command and its associated commands are used to define\n%% the authors and their affiliations.\n%% Of note is the shared affiliation of the first two authors, and the\n%% \"authornote\" and \"authornotemark\" commands\n%% used to denote shared contribution to the research and/or corresponding author.\n\\author{Hadi Mohammadi}\n\\authornote{Corresponding Author.}\n\\email{h.mohammadi@uu.nl}\n\\orcid{0000-0003-0860-9200}\n% \\orcid{https://orcid.org/0000-0002-2037-3694} % This works too if you want to see the full URL\n\\affiliation{%\n \\institution{Department of Methodology and Statistics, Utrecht University}\n \\city{Utrecht}\n \\state{Utrecht}\n \\country{The Netherlands}\n}\n\n\n\\author{Anastasia Giachanou}\n\\orcid{0000-0002-7601-8667}\n\\email{a.giachanou@uu.nl}\n\\affiliation{%\n \\institution{Department of Methodology and Statistics, Utrecht University}\n \\city{Utrecht}\n \\state{Utrecht}\n \\country{The Netherlands}\n}\n\n\\author{Daniel L. Oberski}\n\\orcid{0000-0001-7467-2297}\n\\email{d.l.oberski@uu.nl}\n\\affiliation{%\n \\institution{Department of Methodology and Statistics, Utrecht University}\n \\city{Utrecht}\n \\state{Utrecht}\n \\country{The Netherlands}\n}\n\n\\author{Ayoub Bagheri}\n\\orcid{0000-0001-6366-2173}\n\\email{a.bagheri@uu.nl}\n\\affiliation{%\n \\institution{Department of Methodology and Statistics, Utrecht University}\n \\city{Utrecht}\n \\state{Utrecht}\n \\country{The Netherlands}\n}\n\n\n\n%\\author{Ben Trovato}\n\n%%\n%% By default, the full list of authors will be used in the page\n%% headers. Often, this list is too long, and will overlap\n%% other information printed in the page headers. This command allows\n%% the author to define a more concise list\n%% of authors' names for this purpose.\n\n%\\renewcommand{\\shortauthors}{Mohammadi et al.}\n\n%%\n%% The abstract is a short summary of the work to be presented in the\n%% article.\n\\begin{abstract}\n{\\bf Background:} \n Generative models, especially large language models (LLMs), have shown remarkable progress in producing text that appears human-like. However, they often exhibit patterns that make their output easier to detect than text written by humans.\n \n {\\bf Objectives:}\n In this paper, we investigate how explainable AI (XAI) methods can be used to reduce the detectability of AI-generated text (AIGT) while also introducing a robust ensemble-based detection approach. \n \n {\\bf Methods:}\n We begin by training an ensemble classifier to distinguish AIGT from human-written text, then apply SHAP and LIME to identify tokens that most strongly influence its predictions. We propose four explainability-based token replacement strategies to modify these influential tokens. \n \n {\\bf Results:}\n Our findings show that these token replacement approaches can significantly diminish a single classifier’s ability to detect AIGT. However, our ensemble classifier maintains strong performance across multiple languages and domains, showing that a multi-model approach can mitigate the impact of token-level manipulations.\n \n {\\bf Conclusions:}\n Our findings show that XAI methods can make AIGT harder to detect by focusing on the most influential tokens. At the same time, they highlight the need for robust, ensemble-based detection strategies that can adapt to evolving approaches for hiding AIGT.\n\\end{abstract}\n\n%\\begin{abstract}\n% A clear and well-documented \\LaTeX\\ document is presented as an\n% article formatted for publication by ACM in a conference proceedings\n% or journal publication. Based on the ``acmart'' document class, this\n% article presents and explains many of the common variations, as well\n% as many of the formatting elements an author may use in the\n% preparation of the documentation of their work.\n%\\end{abstract}\n\n\n%% JAIR Note: \n%% Do not include ACM CCS Concepts or Keywords\n\n\n%% To be updated by authors.\n% \\received{24 May 2025}\n% \\received[revised]{12 June 2025}\n% \\received[accepted]{5 July 2025}\n\n%%\n%% This command processes the author and affiliation and title\n%% information and builds the first part of the formatted document.\n\\maketitle\n\n\n%\\blfootnote{Under review at the Journal of Artificial Intelligence Research (JAIR).}\n\n\n\\section{Introduction}\n\\label{sec:intro}\n\nRecent advancements in LLMs, such as the GPT series, have enabled the creation of highly fluent text that often resembles human writing~\\cite{brown2020language}. Still, even the best models often show subtle signs of being artificial, like repeating phrases, using a limited range of words, having no typos, or showing a consistent writing style. These clues can help readers or automated tools detect that the text was written by AI~\\cite{sadasivan2023can}. Being able to spot AIGT is important in various domains, such as in for protecting academic honesty, fighting misinformation, and keeping public trust. On the other hand, there are good reasons to make AI text less detectable, like tools that help people write in their own style~\\cite{Yeh2024}, or AI summaries that aim to be especially clear and well-structured~\\cite{nacke2024ai}.\n\nThis conflict between helpful and harmful uses of LLMs creates a challenge: how to keep their benefits while preventing misuse. One proposed solution is watermarking, which adds hidden patterns to generated text to make it more transparent~\\cite{kirchenbauer2023watermarking}. However, recent research shows that these watermarks can be weakened or removed by changing the text, like paraphrasing~\\cite{li2023towards}. Against this backdrop, our study asks:~\\emph{Can explanation methods be used to both hide and detect AI-generated text?} We contribute in two ways. First, we develop an \\textbf{ensemble‑based detector} that remains accurate across languages (English and Dutch) and domains (news, reviews, Twitter). Second, we show how two explanation tools, SHapley Additive exPlanations (SHAP)~\\cite{lundberg2017unified} and Local Interpretable Model-agnostic Explanations (LIME)~\\cite{ribeiro2016should}, can be turned against a detector: we identify the most influential tokens and replace them via four targeted strategies (semantically similar human‑preferred words, part‑of‑speech‑constrained synonyms, GPT‑based substitutions, and GPT substitutions enriched with genre context). We evaluate both \\emph{detectability} (accuracy, F1) and \\emph{textual fidelity} (BLEU, ROUGE), thereby quantifying the trade‑off between hiding AI signals and preserving meaning. Our results show that token‑level manipulation can fool many single‑model detectors, yet a robust ensemble still performs strongly, underscoring the need to combine explainability, watermarking, and policy safeguards to manage the evolving landscape of AIGT.\n\nThe rest of this paper is organized as follows:\nSection~\\ref{sec:related} reviews related work on AI-generated text detection, watermarking, and adversarial rewriting.\nSection~\\ref{sec:dataset} describes the datasets and our data augmentation process.\nIn Section~\\ref{sec:methodology}, we present our methodology, including details on model architectures, explainability approaches, and token replacement strategies.\nSection~\\ref{sec:experiments} reports experimental results, showing how different rewriting methods affect detection performance and textual fidelity.\nFinally, Section~\\ref{sec:discussion_conclusion} concludes the paper and discusses future research directions.\n\n\n\\section{Related Work}\n\\label{sec:related}\n\n\\citet{kirchenbauer2023watermarking} introduced an important \\emph{red--green list} watermark. In this method, a secret hash divides the vocabulary at each token position into a preferred ``green'' group and a less preferred ``red'' group. The model then gives higher chances to the green tokens, so the generated text contains a hidden cryptographic signature. Light editing often keeps these watermarks, but bigger changes, like paraphrasing or replacing words with synonyms, can remove them~\\citep{kirchenbauer2024reliability,li2023towards}. To make watermarks harder to notice, distortion-free methods hide information in the token-sampling \\emph{order} instead of the probability distribution. However, these can also become weak if someone rewrites the text in a tricky way~\\citep{christ2024undetectable}. Other approaches, called post-hoc schemes, add watermarks later by changing syntax templates or choosing context-aware synonyms~\\citep{yang2023watermarking}, but they also have trouble surviving strong text changes.\n\n\nAnother important detection method takes advantage of the fact that language models (LMs) often choose very predictable words. \\citet{gehrmann2019gltr} created a token-rank histogram tool to help people find words that are ``too likely'' in a sentence. Zero-shot methods like \\emph{DetectGPT} look at the ``curvature'' of local log-probabilities; if the curvature is negative, it suggests the text was written by a machine~\\citep{mitchell2023detectgpt}. \\emph{Fast-DetectGPT} makes this process faster and more accurate for text from ChatGPT or GPT-4~\\citep{bao2023fast}. Other detection methods use features such as normalized perturbed rank~\\citep{su2023detectllm} and the amount of unused information in token probability distributions~\\citep{zawistowski2024unused}. These ``white-box'' methods work well when they can directly access the model's probabilities, but they become less effective without this access. To solve this problem, ``DNA'' regeneration methods, like DNA-GPT~\\citep{yang2023dna} and GPT Paternity Test~\\citep{yu2023gpt}, do not need internal access; instead, they query a black-box model again and compare the new outputs for similarity.\n\nStylometric approaches instead focus on word choice, sentence structure, and how the text is organized and connected.~\\citet{frohling2021feature} grouped 35 linguistic features into four categories, low diversity, repetition, incoherence, and lack of purpose, and trained SVM and random-forest models. More advanced work introduces entity-coherence graphs~\\citep{liu2023coco} or journalism-specific style metrics~\\citep{kumarage2023j}, which bolster detection on brief social-media texts. Though interpretable, these handcrafted methods may be narrower in scope and less transferable across domains.\n\nA different direction uses fine-tuned neural networks to classify AI-generated text. OpenAI’s initial GPT-detector trained RoBERTa on mixed GPT-2 corpora, achieving roughly 95\\% accuracy on in-domain data~\\citep{solaiman2019release}. More recent detectors employ Longformer~\\citep{beltagy2020longformer} or ELECTRA to handle longer contexts and detect texts from multiple generators~\\citep{clark2020electra}. However, these systems often struggle with out-of-distribution domains. Recent ``cross-model'' or contrastive ensembles, such as Ghostbuster~\\citep{verma2023ghostbuster} and DeTeCtive~\\citep{guo2024detective}, compare probability distributions from multiple open-source LMs to improve robustness.\n\nEnsemble methods combine diverse signals to mitigate the weaknesses of individual detectors. Simple voting stacks of transformer-based classifiers outperform single models in multilingual tasks~\\citep{nguyen2023stacking}. For example, recent work introduces ensemble-based approaches for detecting AI-generated text in educational content, achieving a favorable false-positive trade-off in academic scenarios~\\citep{najjar2025detecting}. Our work follows this line by designing an explanation-oriented ensemble meant to withstand token-level adversarial edits.\n\nParaphrasing is one of the best ways to trick AI detectors. For example, \\citet{krishna2023paraphrasing} showed that changing the wording can make DetectGPT miss almost all AI-written text. Rewriting the text many times makes it even harder to detect~\\citep{sadasivan2023can}. Some new methods, like RADAR~\\citep{hu2023radar} and OUTFOX~\\citep{koike2024outfox}, are better at catching these changes because they are trained on paraphrased examples, but they still only recover some accuracy. Even small mistakes, like typos, can also confuse detectors~\\citep{huang2024ai}. More advanced methods can handle some edits, but none can stop all tricks or rewrites.\n\nParaphrasing is seen as the strongest way to avoid AI text detectors:~\\citet{krishna2023paraphrasing} found that rewriting text can reduce DetectGPT’s ability to catch AI-generated text from 70\\% to less than 5\\%. Rewriting the text several times in a row (called “self-rewrites”) makes it even harder to detect, while still keeping the original meaning~\\citep{sadasivan2023can}. Defenses such as RADAR~\\citep{hu2023radar} and OUTFOX~\\citep{koike2024outfox} become stronger by training on paraphrased texts, and can recover 10–20 F\\textsubscript{1} points. Recent studies also show that even simple changes at the letter level—like typos or swapping similar-looking letters—can confuse detectors and make them much less accurate~\\citep{huang2024ai}. Although using features like ensemble cross-probability or advanced watermarking can resist some small changes, no method is completely safe from strong rewriting attacks.\n\n\nExplainability has become a cornerstone of modern NLP deployments~\\citep{mohammadi2025explainability}. XAI tools like SHAP and LIME show which words or tokens have the biggest effect on a detector’s decisions. For example, ~\\citet{mitrovic2023chatgpt} used LIME to highlight important words and help people review short ChatGPT answers. But attackers can use this information in the opposite way, by changing or removing the most important words.~\\citet{zhou2024humanizing} showed that using SHAP-based synonyms for key words can lower detector accuracy by 40–60\\%. In our work, we carefully study this double-sided use of explanations, testing four types of word substitutions in two languages and different types of texts.\n\nDetectors often make mistakes with texts written by people using English as a second language.~\\citet{liang2023gpt} found that seven popular GPT detectors wrongly marked more than 61\\% of TOEFL essays by non-native English speakers as AI-generated, even though they were very accurate with essays by native speakers. Because of this bias, some teachers are moving away from strict rules and are now encouraging students to use AI tools as support, especially for those with different language backgrounds. Since LLMs can quickly create lots of convincing fake news, it is very hard for people alone to spot which content is real~\\citep{zellers2019defending}. To improve detection in these situations, it helps to use a mix of classifiers and to add information about where the text comes from~\\citep{fraser2025detecting}.\n\nRecent use of AI chatbots for mental health support shows that people often think these systems are caring and human-like, which leads to real and meaningful conversations. But this can be risky, especially when users do not realize they are talking to an AI. ~\\citet{song2024typing} found that people in severe distress sometimes seek help from chatbots and even see them as human. In the same way,~\\citet{siddals2024happened} stressed the need for stronger safety rules and clearer information in AI mental health tools. These studies show how important it is to have good detection systems and to make it clear when someone is interacting with an AI, so that chatbots are used ethically in sensitive cases. Research also shows that no single method is perfect; reliable AI-generated text detection needs to use many signals, be tested against tricky cases, and give clear reasons for its decisions. These are the main goals of our work.\n\n\n\\section{Dataset}\n\\label{sec:dataset}\nIn this section, we detail the CLIN33 dataset~\\cite{fivez2024clin33} used as our primary corpus and describe how it was augmented to expand its linguistic variability. We also present the portion of the AuTexTification dataset\\footnote{\\url{https://zenodo.org/records/10732813}} used to strengthen our ensemble models. The CLIN33 shared task corpus comprises human-written and AIGTs in English and Dutch, including three domains: news, tweets, and reviews. Each domain contains 200 samples from human authors and 200 samples from LLMs (GPT-4 and Vicu\\~{n}a-13B), producing a total of 1,200 texts in English and 1,200 in Dutch. The human-written news samples come from well-known media outlets; the Dutch social media posts primarily discuss policy or health topics, whereas the English ones often revolve around social or sports events. Reviews typically cover literature in Dutch and various consumer goods in English. \n\n\nAlthough CLIN33 offers balanced classes across two languages and three genres, its size remains relatively small for training large-scale models. To address this limitation and diversify the textual patterns, we created an augmented version of the dataset. We started with the original texts and applied a series of transformations to each sample. These transformations included synonym substitution, random word swaps, random insertions, targeted deletions, variations in spelling, and back-translation\\footnote{Translating from English to Dutch and then back to English}. Each text thus produced several new variants that preserved the key semantics but introduced new surface forms. We ultimately obtained 9{,}720 total samples for training, split evenly between human and AI-generated content (4{,}860 each) and distributed equally across the three domains and the two languages. The final augmented corpus enables more robust feature learning.\n\n\\begin{wraptable}{l}{0.65\\textwidth} \n \\vspace{-1em}\n \\centering\n %\\small \n \\scriptsize\n \\caption{\\small Overview of data resources.}\n \\label{tab:combined_data_overview}\n %\\vspace{-8pt}\n \\begin{tabular}{p{2.5cm}p{3.4cm}p{3.0cm}} \n \\toprule\n & \\textbf{AuTexTification (EN)} & \\textbf{Augmented CLIN33} \\\\\n \\midrule\n \\textbf{Total Samples} & 33{,}845 & 9{,}720 \\\\\n \\textbf{Domains/Genres} & Tweets, Legal, Wiki & News, Twitter, Reviews \\\\\n \\textbf{Languages} & English & English, Dutch \\\\\n \\textbf{AI Models} & BLOOM & GPT‑4 \\\\[-2pt]\n & GPT‑3 & Vicu\\~na‑13B \\\\\n \\textbf{Human Sources} & MultiEURLEX, Amazon, XSUM, TSATC, WikiLingua\n & News, tweets, reviews \\\\\n \\textbf{Class Ratio} & 50.36\\% / 49.64~\\% & 50\\% / 50\\%\\\\\n \\textbf{Augmentations} & None & Yes (9) \\\\\n \\bottomrule\n \\end{tabular}\n \\vspace{-1em}\n\\end{wraptable}\n\n\nAs an additional resource for pre-training and fine-tuning, we employed a subset of the AuTexTification dataset\\footnote{\\url{https://sites.google.com/view/autextification}}, introduced by~\\citet{sarvazyan2023overview}. While the complete dataset includes English and Spanish texts in five distinct domains, we selectively used the \\emph{English-only} portion from tweets, legal, and wiki categories. This subset contains 33{,}845 texts designated for binary AI-detection (Task 1), split nearly evenly between human-written (50.36\\%) and AI-generated (49.64\\%) documents. The human content is drawn from sources such as MultiEURLEX, XSUM, Amazon Reviews, TSATC, and WikiLingua, while the AI-generated portion is produced by BLOOM (1B7, 3B, 7B1) and GPT-3 (babbage, curie, text-davinci-003). Because of its class balance and domain variety, this subset of AuTexTification serves as a valuable source of pre-training examples for our ensemble.\nTable~\\ref{tab:combined_data_overview} summarizes the essential attributes of the two datasets used to train and evaluate our methodology. The combined setup draws from the 33{,}845 samples in AuTexTification (English only) and the 9{,}720 augmented CLIN33 samples (both English and Dutch). We use AuTexTification Dataset for initial pre-training, 90\\% of the CLIN data for the Augmenting and training and 10\\% of unchanged CLIN data for evaluating.\n\n\n\\section{Methodology}\n\\label{sec:methodology}\nWe used an approach that integrates several models for text classification to address the AIGT detection task. First, we converted all text to lowercase to handle tokens in a consistent way. We then removed non-informative elements such as URLs, punctuation, and special characters, since these tokens often do not carry semantic meaning. After this cleaning, we split the text into tokens and applied lemmatization to map words to their base forms, which lowered data complexity and improved the model’s ability to recognize patterns.\n\n\\paragraph{\\textbf{Baselines Models.}} We built an XGBoost classifier by first transforming the text into numerical features with a TF-IDF vectorizer set to a maximum of 5000 features. The XGBoost model was configured with use\\_label\\_encoder=False and eval\\_metric='logloss'. Training used the TF-IDF representations from the training set, and final predictions on the test set were evaluated using precision, recall, F1, and accuracy. Also, We fine-tuned BERT-base (bert-base-uncased), DistilBERT (distilbert-base-uncased), and XLM-RoBERTa (xlm-roberta-base). The best checkpoint was chosen based on the highest F1 score on a validation subset. We then evaluated the final models on the test data. \n\n\n\n\\begin{wrapfigure}{r}{0.48\\textwidth}\n \\vspace{-1em}\n \\centering\n \\begin{minipage}{\\linewidth}\n \\begin{algorithm}[H]\n \\footnotesize\n \\setstretch{0.83}\n \\caption{Combined BERT Ensemble Structure}\n \\label{alg:combinedmodel}\n\n \\raggedright\n \\textbf{Input:} Text $x$\\\\\n \\textbf{Output:} Predicted label \\{\\emph{human}, \\emph{AI}\\}\\\\[2pt]\n \\par\n\n \\begin{algorithmic}[1]\n % ---------- Pre‑training block ----------\n \\Function{PretrainEnsemble}{$x$}\n \\State $\\text{bert\\_out} \\leftarrow \\text{BERTModel}_{\\text{frozen}}(x)$\n \\State $\\text{xlmr\\_out} \\leftarrow \\text{XLMRobertaModel}_{\\text{frozen}}(x)$\n \\State $\\text{distil\\_out} \\leftarrow \\text{DistilBERTModel}_{\\text{frozen}}(x)$\n \\State \\Return $(\\text{bert\\_out},\\ \\text{xlmr\\_out},\\ \\text{distil\\_out})$\n \\EndFunction\n\n % ---------- Fine‑tuning block ----------\n \\Function{FineTuneEnsemble}{$x$}\n \\State $\\text{bert\\_out\\_fresh} \\leftarrow \\text{BERTModel}_{\\text{fresh}}(x)$\n \\State $\\text{xlmr\\_out\\_fresh} \\leftarrow \\text{XLMRobertaModel}_{\\text{fresh}}(x)$\n \\State $\\text{distil\\_out\\_fresh} \\leftarrow \\text{DistilBERTModel}_{\\text{fresh}}(x)$\n \\State \\Return $(\\text{bert\\_out\\_fresh},\\ \\text{xlmr\\_out\\_fresh},\\ \\text{distil\\_out\\_fresh})$\n \\EndFunction\n\n % ---------- Combined model ----------\n \\Function{CombinedModel}{$x$}\n \\State $(\\text{froz\\_bert},\\ \\text{froz\\_xlmr},\\ \\text{froz\\_distil})\n \\leftarrow \\Call{PretrainEnsemble}{x}$\n \\State $(\\text{fresh\\_bert},\\ \\text{fresh\\_xlmr},\\ \\text{fresh\\_distil})\n \\leftarrow \\Call{FineTuneEnsemble}{x}$\n\n % split long concatenate call over two lines\n \\State $\\text{concat\\_outs} \\leftarrow \\text{concatenate}\\!\\bigl[%\n \\text{froz\\_bert},\\ \\text{froz\\_xlmr},\\ \\text{froz\\_distil},$\n \\Statex\\hspace{\\algorithmicindent}\\phantom{$\\text{concat\\_outs} \\leftarrow \\text{concatenate}\\!\\bigl[$}%\n $\\text{fresh\\_bert},\\ \\text{fresh\\_xlmr},\\ \\text{fresh\\_distil}\\bigr]$\n\n \\State $\\text{dense\\_out} \\leftarrow \\text{DenseLayer}(\\text{concat\\_outs})$ \\Comment{\\# L2‑regularized dense layer}\n \\State $\\hat{y} \\leftarrow \\sigma(\\text{dense\\_out})$ \\Comment{\\# Probability of AI‑generated text}\n \\If{$\\hat{y} \\geq 0.5$}\n \\State \\Return \\emph{AI}\n \\Else\n \\State \\Return \\emph{human}\n \\EndIf\n \\EndFunction\n \\end{algorithmic}\n \\end{algorithm}\n \\end{minipage}\n\\end{wrapfigure}\n\n\n\n\n\\paragraph{\\textbf{Ensemble Model.}}\nWe built an ensemble of multiple transformers instead of relying on a single one to classify human and AIGT), as inspired by~\\citet{mohammadi2023towards}. In this method, we use the strengths of each model and create an ensemble that can be fine-tuned on similar datasets. This helps the model adapt to different domains and reduces problems caused by limited data, as the features learned by each part complement each other. Specifically, we integrated bert-base-multilingual-uncased, distilbert-base-multilingual-cased, and xlm-roberta-base. Each transformer produced a representation of the input text, and we concatenated these representations before passing them through a dense layer with L2 regularization for final classification.\n\nWe first built a \\emph{Combined BERT} model by training three BERT models on the AuTexTification training set. We optimized the network's hyperparameters and evaluated performance on the AuTexTification test set, focusing on F1 and average metrics across folds. After training, we froze these model weights to retain the features they had learned and reduce the risk of overfitting. We next created a \\emph{combined model} that merges three BERT models with frozen weights (trained on AuTexTification) and three fresh BERT models with unfrozen parameters. These fresh models were fine-tuned on our newly augmented dataset Such an ensemble synergy aligns with~\\citet{fraser2025detecting}, who highlight the benefits of multi-model integration in detecting AI-generated text. Algorithm~\\ref{alg:combinedmodel} outlines the training and process for our combined BERT ensemble. During training, we minimize the binary cross-entropy loss on each branch using the augmented data. We then unify the outputs in a final fully connected layer, optionally employing majority voting if multiple final heads are used. Figure~\\ref{fig:ms-structure} shows the architecture, with frozen models on the left (gray) and fresh models on the right (green).\n\n\n\n\n\\begin{figure*}[!ht]\n\\centering\n\\includegraphics[width=0.7\\linewidth]{plots/model.jpeg}\n\\vspace{-5pt}\n\\caption{\\small The architecture of the proposed ensemble model. Outputs of three frozen BERT models (left) and three fresh BERT models (right) are concatenated and passed through dense layers, followed by a voting mechanism.}\n\\label{fig:ms-structure}\n\\end{figure*}\n\n\n\\subsection{Experimental Setup}\n\\label{subsec:setup}\n\nWe trained our models using the Hugging Face Transformers library\\footnote{\\url{https://huggingface.co/}} and set a maximum token length of 512. Hyperparameter tuning was done through a random search over learning rates (ranging from \\(1\\times 10^{-5}\\) to \\(1\\times 10^{-4}\\)) and batch sizes of 32, 64, or 128. We used a cosine decay schedule for learning rate adjustments, with 200 warm-up steps and an early-stopping patience of 5 epochs based on validation loss. \n\nThe final training stage used 90\\% of the augmented development set, while we left 10\\% for test. The hyperparameters and early-stopping rules were adjusted using the validation split. After training, we evaluated the system on the remaining data, measuring precision, recall, F1-score, and accuracy. By combining different transformers, freezing selectively learned features, and training fresh models in parallel, this ensemble method balanced the advantages of robust pre-trained representations with the adaptability needed for the new dataset.\n\n\\subsection{Explainability-driven detection}\n\\label{sec:explain}\n\nIn this section, we identify the words that most strongly influence an \\emph{AI-generated} prediction. Our approach uses two instance-level explanation tools: SHAP and LIME. Although they both operate on individual examples, SHAP uses game-theoretic principles to estimate a token’s overall (global) influence, while LIME explores small local perturbations to approximate importance. We take the absolute values of these importance scores $\\phi_j$ (for SHAP) or coefficients (for LIME) to capture both positive and negative contributions.\n\n\n \\setlength\\intextsep{0.3em} \n \\setlength\\columnsep{1em} \n \\begin{wrapfigure}[5]{r}{0.40\\textwidth} % [5]=height in text lines\n \\vspace*{-0.2em} % fine‑tune space above box\n \\begin{minipage}{\\linewidth}\\centering\\scriptsize\n \\[\n \\phi_j\n = \\sum_{S \\subseteq \\{1,\\dots,m\\}\\setminus\\{j\\}}\n \\frac{|S|!\\,\\bigl(m-|S|-1\\bigr)!}{m!}\\,\n \\bigl[f(S \\cup \\{j\\}) - f(S)\\bigr].\n \\]\n \\end{minipage}\n \\vspace*{-0.4em} % fine‑tune space below box\n \\end{wrapfigure}\n\nWe follow the methodology in~\\cite{mohammadi2024transparent}, in this framework, we only analyze sentences that the model \\emph{correctly} classifies, ensuring the tokens we highlight have genuinely contributed to accurate decisions. We then average each token’s importance within the sentence to get a consistent measure of its overall effect. This step helps us confirm that the extracted terms are meaningful indicators of the model’s reasoning. Given a trained model \\(f\\) and an instance \\(x\\) composed of tokens \\((t_1, t_2, \\ldots, t_m)\\), SHAP assigns a Shapley value \\(\\phi_j\\) to each token \\(t_j\\). These values are computed by measuring how much including or excluding \\(t_j\\) changes the model’s output, across all possible subsets of tokens. Formally:\n\n\nHere, \\(f(S)\\) is the model's prediction when only the tokens in set \\(S\\) are present. The weighting term ensures each token's effect is fairly distributed among all subsets~\\cite{lundberg2017unified}. LIME, on the other hand, makes small changes near the original text to learn a simpler, local model that approximates \\(f\\). It then assigns coefficients reflecting each token’s importance for that instance~\\cite{ribeiro2016should}. Although LIME focuses more on local neighborhoods, it also provides per-token contributions based on how the model’s prediction shifts when individual tokens are perturbed. We also consider a random token selection. This lets us compare how much improvement we gain by targeting the tokens that SHAP or LIME indicates are most influential. By using both a global (SHAP) and local (LIME) perspective, we can better trust that the identified tokens genuinely drive the model's decision. In our subsequent steps, we use these tokens to guide targeted replacements and measure how effectively such edits can mask AI-generated content.\n\n\\subsection{Token Replacement Strategies}\nOnce we identify influential tokens, we modify them using several approaches designed to make the text appear more human-like. We propose four rewriting strategies summarized in Table~\\ref{tab:token_replacement}.\n\n\\begin{table}[ht]\n\\centering\n\\scriptsize\n%\\small\n\\renewcommand{\\arraystretch}{1.2}\n\\caption{Summary of token replacement strategies}\n\\label{tab:token_replacement}\n\\begin{tabular}{p{4.8cm}p{9.2cm}}\n\\toprule\n\\textbf{Strategy} & \\textbf{Description} \\\\\n\\midrule\n\\textbf{Human Similar Word Replacement (HSR)} \n & A Word2Vec model is trained on all human-labeled texts in the training set to learn an embedding space that captures typical human usage. \\\\\n\\textbf{Part-of-Speech Replacement (PSR)} \n & HSR is refined by ensuring that part-of-speech tags match. \\\\\n\\textbf{GPT-based Replacement (GPT)} \n & A generative model (GPT-4o-mini) is queried using the prompt: \n \\texttt{\"Replace '\\{token\\}' with a more human-like word in this text: '\\{text\\}'\"}. \\\\\n\\textbf{GPT-based Replacement with Genre Context (GPT+Genre)} \n & The GPT-based method is extended by including explicit genre information (e.g., \\emph{tweets} or \\emph{reviews}) in the prompt:\n \\texttt{\"Replace '\\{token\\}' in this \\{domain\\} text with a more human-like word: '\\{text\\}'\"}. \\\\\n\\bottomrule\n\\end{tabular}\n\\end{table}\n\nIn \\emph{Strategy HSR}, each influential token is replaced with its nearest neighbor in the Word2Vec space, provided that the neighbor is frequently observed in human texts. In \\emph{Strategy PSR}, if a token \\(t\\) is tagged as a noun, for instance, a Word2Vec neighbor that is also tagged as a noun is selected, preserving grammatical structure. In \\emph{Strategy GPT}, a replacement that appears more \\emph{human-like} is proposed based on the surrounding sentence. While the suggestions are context-sensitive, certain LLM-specific patterns may still be retained if the generative model reintroduces specific phrasing. In \\emph{Strategy GPT+Genre}, incorporating domain-specific information allows the generative model to propose replacements that align more closely with the original style. However, this added context may also amplify distinctive AI traits. Finally, after substituting the most influential tokens in each text, we assess performance along two dimensions: (1) changes in detectability and (2) fidelity to the original content.\n\n\n\\subsection{Evaluation Metrics}\n\\label{subsec:evaluation_metrics}\n\nWe use a combination of detection and text-similarity metrics to evaluate how rewriting strategies affect our models' ability to detect AIGT, as well as how much the rewritten text deviates from its original form. First, once we rewrite an AIGT via HSR, PSR, GPT, or GPT+Genre. Next, we investigate how different rewriting scenarios affect AI-text detection. We adopt the adversarial rewriting perspective discussed in \\citet{fraser2025detecting}, \nwhere paraphrased or partially edited texts can undermine naive detectors. We run it through each classifier to check whether the label remains \\emph{AI} or flips to \\emph{human}. We then calculate precision, recall, F1, and accuracy to capture each model’s classification performance to detect AI/human texts under these modified inputs. Additionally, we compute the \\emph{flip rate}, defined as the fraction of AI texts whose predicted label changes to \\emph{human} after rewriting; a higher flip rate implies that the rewriting method is more effective in evading detection. In parallel, we calculate the degree of textual change introduced by each rewriting approach. We use BLEU, ROUGE-1, and ROUGE-L to measure the overlap between the original text and its rewritten form, with higher scores showing fewer modifications. By combining detection metrics, and text-similarity scores, we gain a clear view of how effectively each rewriting method hides AI-generated content and how much it changes the original text. \n\n\\section{Experimental Analysis and Results}\n\\label{sec:experiments}\n\nIn this section, we compare five classification models (XGBoost, BERT-base, DistilBERT, XLM-RoBERTa, and our \\textbf{Ensemble} approach) across various token-replacement scenarios for the detection of AIGT, Consistent with~\\citet{fraser2025detecting}, we anticipate that multi-domain evaluation is critical for robust results. Our main objective is to observe how different rewriting methods (HSR, PSR, GPT, GPT+Genre) combined with token-selection strategies (SHAP, LIME, Random) affect each model’s ability to detect generated text.\n\nTable~\\ref{tab:lang_domain_updated} shows each model’s performance on \\emph{unmodified} AIGT across two languages (English, Dutch) and three domains (news, reviews, and X/Twitter). As can be seen, although certain single models perform well on specific subsets (e.g., DistilBERT in English news, XGBoost in English Twitter), the \\textbf{Ensemble} approach tends to achieve the best performance in \\emph{most} cases. This pattern is particularly clear in Dutch news, where the Ensemble model attains perfect scores. Even when other models have high recall or precision on certain tasks, they often fall behind on at least one metric (e.g., XLM-RoBERTa predicts too many positives, increasing recall but lowering precision). showing strong cross-language adaptability. This result agrees with ~\\citet{fraser2025detecting}, who emphasize the importance of multi-lingual calibration for robust detection.\n\n\n\n%\n\\begin{table}[ht]\n\\centering\n%\\small\n\\scriptsize\n\\renewcommand{\\arraystretch}{1.2}\n\\caption{Model performance by language and domain on unmodified AI text. \\textbf{Bold} indicates the best result within each language and domain block.}\n\\label{tab:lang_domain_updated}\n\\begin{tabular}{llcccc|llcccc}\n\\toprule\n\\multicolumn{6}{c}{\\textbf{English Dataset}} & \\multicolumn{6}{c}{\\textbf{Dutch Dataset}} \\\\\n\\cmidrule(lr){1-6} \\cmidrule(lr){7-12}\n\\textbf{Model} & \\textbf{Domain} & \\textbf{Acc} & \\textbf{Prec} & \\textbf{Rec} & \\textbf{F1} \n & \\textbf{Model} & \\textbf{Domain} & \\textbf{Acc} & \\textbf{Prec} & \\textbf{Rec} & \\textbf{F1} \\\\\n\\midrule\n\\textbf{--- News ---} & & & & & \n& \\textbf{--- News ---} & & & & & \\\\\nXGBoost \n & news & 0.89 & 0.93 & 0.84 & 0.88 \n & XGBoost \n & news & 0.85 & 0.88 & 0.88 & 0.88 \\\\\nBERT-base \n & news & 0.94 & 0.97 & 0.91 & 0.94\n & BERT-base \n & news & 0.68 & 0.79 & 0.56 & 0.66 \\\\\nDistilBERT \n & news & 0.92 & 0.90 & \\textbf{0.95} & 0.92\n & DistilBERT \n & news & 0.68 & 0.73 & 0.66 & 0.69 \\\\\nXLM-RoBERTa\n & news & 0.49 & 0.49 & 1.00 & 0.66\n & XLM-RoBERTa\n & news & 0.53 & 0.53 & \\textbf{1.00} & 0.69 \\\\\nEnsemble \n & news & \\textbf{0.95} & \\textbf{1.00} & 0.90 & \\textbf{0.95}\n & Ensemble \n & news & \\textbf{0.99} & \\textbf{0.98} & \\textbf{1.00} & \\textbf{0.99} \\\\\n\\midrule\n\\textbf{--- Reviews ---} & & & & & \n& \\textbf{--- Reviews ---} & & & & & \\\\\nXGBoost \n & reviews & 0.51 & 0.57 & 0.59 & 0.58\n & XGBoost \n & reviews & 0.53 & 0.51 & 0.46 & 0.48 \\\\\nBERT-base \n & reviews & 0.50 & 0.54 & 0.62 & 0.58\n & BERT-base \n & reviews & 0.40 & 0.35 & 0.30 & 0.32 \\\\\nDistilBERT \n & reviews & 0.65 & 0.68 & 0.71 & 0.69\n & DistilBERT \n & reviews & 0.42 & 0.38 & 0.50 & 0.43 \\\\\nXLM-RoBERTa\n & reviews & 0.56 & 0.56 & \\textbf{0.90} & 0.69\n & XLM-RoBERTa\n & reviews & 0.43 & 0.43 & \\textbf{0.60} & 0.48 \\\\\nEnsemble \n & reviews & \\textbf{0.67} & \\textbf{0.71} & 0.68 & \\textbf{0.70}\n & Ensemble \n & reviews & \\textbf{0.60} & \\textbf{0.53} & 0.44 & \\textbf{0.49} \\\\\n\\midrule\n\\textbf{--- Twitter / X ---} & & & & & \n& \\textbf{--- Twitter / X ---} & & & & & \\\\\nXGBoost \n & twitter & 0.91 & \\textbf{0.92} & 0.98 & 0.95\n & XGBoost \n & twitter & 0.80 & 0.85 & 0.78 & 0.81 \\\\\nBERT-base \n & twitter & 0.90 & 0.86 & 0.98 & 0.92\n & BERT-base \n & twitter & 0.80 & 0.85 & 0.78 & 0.81 \\\\\nDistilBERT \n & twitter & 0.90 & 0.86 & 0.98 & 0.92\n & DistilBERT \n & twitter & 0.84 & 0.86 & 0.80 & 0.83 \\\\\nXLM-RoBERTa\n & twitter & 0.60 & 0.53 & \\textbf{1.00} & 0.69\n & XLM-RoBERTa\n & twitter & 0.62 & 0.60 & \\textbf{0.90} & 0.72 \\\\\nEnsemble \n & twitter & \\textbf{0.93} & 0.86 & \\textbf{1.00} & \\textbf{0.93}\n & Ensemble \n & twitter & \\textbf{0.87} & \\textbf{0.90} & 0.85 & \\textbf{0.87} \\\\\n\\bottomrule\n\\end{tabular}\n\\end{table}\n\n\nWe next investigate how different rewriting scenarios affect AI-text detection. In particular, we apply four rewriting strategies and combine each of these with one of three token-selection strategies: SHAP (prioritizes globally influential tokens), LIME (locally influential tokens), or Random (tokens chosen arbitrarily). Table~\\ref{tab:all_models_rewriting_f1acc} presents performance metrics for each model under all these rewriting conditions.\n\n% \\begin{table*}[ht]\n% \\centering\n% \\caption{\\small F1 score and Accuracy of each detector under every rewriting scenario. \n% The best metric in a column is \\textbf{bolded}; the largest negative drop is shown in \\textcolor{purple}{red}, the smallest in \\textcolor{teal}{green}. \n% \\footnotesize{$\\Delta=(\\text{Scenario Value}-\\text{Original Value})\\times100\\%$.}}\n% \\label{tab:all_models_rewriting_f1acc}\n% \\renewcommand{\\arraystretch}{1.4}\n% \\resizebox{\\textwidth}{!}{\n% \\begin{tabular}{l l\n% c c c c c c c c c c}\n% \\toprule\n% \\multirow{2}{*}{\\textbf{Explain}} & \\multirow{2}{*}{\\textbf{Strategy}}\n% & \\multicolumn{2}{c}{\\textbf{XGBoost}}\n% & \\multicolumn{2}{c}{\\textbf{BERT-base}}\n% & \\multicolumn{2}{c}{\\textbf{DistilBERT}}\n% & \\multicolumn{2}{c}{\\textbf{XLM-RoBERTa}}\n% & \\multicolumn{2}{c}{\\textbf{Ensemble}}\\\\\n% \\cmidrule(lr){3-4}\\cmidrule(lr){5-6}\\cmidrule(lr){7-8}\\cmidrule(lr){9-10}\\cmidrule(lr){11-12}\n% & & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\n% & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\n% & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\n% & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\n% & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\\\\\n% \\midrule\n% \\emph{NoRewriting} & Original\n% & 0.81 & 0.80\n% & 0.74 & 0.75\n% & 0.79 & 0.77\n% & 0.68 & 0.54\n% & \\textbf{0.83} & \\textbf{0.83}\\\\\n% \\midrule\n% \\textbf{SHAP} & HSR\n% & 0.73 (\\textcolor{purple}{-8\\%}) & 0.75 (\\textcolor{purple}{-5\\%})\n% & 0.71 (-3\\%) & 0.72 (-3\\%)\n% & 0.76 (-3\\%) & 0.75 (-2\\%)\n% & 0.68 (\\textcolor{teal}{0\\%}) & 0.54 (\\textcolor{teal}{0\\%})\n% & \\textbf{0.78} (-5\\%) & \\textbf{0.80} (-3\\%)\\\\\n% \\textbf{SHAP} & PSR\n% & 0.78 (\\textcolor{purple}{-3\\%}) & 0.78 (\\textcolor{purple}{-2\\%})\n% & 0.73 (-1\\%) & 0.74 (-1\\%)\n% & 0.78 (-1\\%) & 0.77 (0\\%)\n% & 0.68 (\\textcolor{teal}{0\\%}) & 0.54 (\\textcolor{teal}{0\\%})\n% & \\textbf{0.82} (-1\\%) & \\textbf{0.82} (-1\\%)\\\\\n% \\textbf{SHAP} & GPT\n% & 0.79 (-2\\%) & 0.79 (-1\\%)\n% & 0.73 (-1\\%) & 0.74 (-1\\%)\n% & 0.77 (-2\\%) & 0.76 (-1\\%)\n% & 0.68 (\\textcolor{teal}{0\\%}) & 0.55 (\\textcolor{teal}{+1\\%})\n% & \\textbf{0.80} (\\textcolor{purple}{-3\\%}) & \\textbf{0.81} (\\textcolor{purple}{-2\\%})\\\\\n% \\textbf{SHAP} & GPT+Genre\n% & \\textbf{0.80} (-1\\%) & \\textbf{0.80} (0\\%)\n% & 0.73 (-1\\%) & 0.74 (-1\\%)\n% & 0.77 (-2\\%) & 0.76 (-1\\%)\n% & 0.68 (\\textcolor{teal}{0\\%}) & 0.55 (+1\\%)\n% & 0.79 (\\textcolor{purple}{-4\\%}) & 0.80 (\\textcolor{purple}{-3\\%})\\\\\n% \\midrule\n% \\textbf{LIME} & HSR\n% & 0.25 (\\textcolor{purple}{-56\\%}) & 0.48 (\\textcolor{purple}{-32\\%})\n% & 0.56 (-18\\%) & 0.62 (-13\\%)\n% & 0.62 (-17\\%) & 0.65 (-12\\%)\n% & \\textbf{0.67} (\\textcolor{teal}{-1\\%}) & 0.52 (\\textcolor{teal}{-2\\%})\n% & 0.64 (-19\\%) & \\textbf{0.69} (-14\\%)\\\\\n% \\textbf{LIME} & PSR\n% & 0.56 (\\textcolor{purple}{-25\\%}) & 0.63 (\\textcolor{purple}{-17\\%})\n% & 0.64 (-10\\%) & 0.67 (-8\\%)\n% & 0.71 (-8\\%) & 0.71 (-6\\%)\n% & 0.66 (\\textcolor{teal}{-2\\%}) & 0.52 (\\textcolor{teal}{-2\\%})\n% & \\textbf{0.73} (-10\\%) & \\textbf{0.75} (-8\\%)\\\\\n% \\textbf{LIME} & GPT\n% & 0.57 (\\textcolor{purple}{-24\\%}) & 0.64 (\\textcolor{purple}{-16\\%})\n% & 0.73 (-1\\%) & 0.74 (-1\\%)\n% & 0.78 (-1\\%) & 0.77 (0\\%)\n% & 0.68 (\\textcolor{teal}{0\\%}) & 0.55 (\\textcolor{teal}{+1\\%})\n% & \\textbf{0.83} (\\textcolor{teal}{0\\%}) & \\textbf{0.83} (0\\%)\\\\\n% \\textbf{LIME} & GPT+Genre\n% & 0.60 (\\textcolor{purple}{-21\\%}) & 0.65 (\\textcolor{purple}{-15\\%})\n% & \\textbf{0.75} (\\textcolor{teal}{+1\\%}) & \\textbf{0.75} (0\\%)\n% & 0.77 (-2\\%) & 0.76 (-1\\%)\n% & 0.68 (0\\%) & 0.55 (\\textcolor{teal}{+1\\%})\n% & \\textbf{0.81} (-2\\%) & \\textbf{0.81} (-2\\%)\\\\\n% \\midrule\n% \\textbf{Random} & HSR\n% & 0.73 (-8\\%) & 0.74 (-6\\%)\n% & 0.63 (\\textcolor{purple}{-11\\%}) & 0.67 (\\textcolor{purple}{-8\\%})\n% & 0.68 (\\textcolor{purple}{-11\\%}) & 0.69 (\\textcolor{purple}{-8\\%})\n% & 0.67 (\\textcolor{teal}{-1\\%}) & 0.53 (\\textcolor{teal}{-1\\%})\n% & \\textbf{0.73} (-10\\%) & \\textbf{0.75} (\\textcolor{purple}{-8\\%})\\\\\n% \\textbf{Random} & PSR\n% & 0.76 (-5\\%) & 0.76 (-4\\%)\n% & 0.65 (\\textcolor{purple}{-9\\%}) & 0.68 (\\textcolor{purple}{-7\\%})\n% & 0.74 (-5\\%) & 0.73 (-4\\%)\n% & 0.67 (\\textcolor{teal}{-1\\%}) & 0.53 (\\textcolor{teal}{-1\\%})\n% & \\textbf{0.78} (-5\\%) & \\textbf{0.79} (-4\\%)\\\\\n% \\textbf{Random} & GPT\n% & 0.74 (\\textcolor{purple}{-7\\%}) & 0.75 (\\textcolor{purple}{-5\\%})\n% & 0.74 (0\\%) & 0.75 (0\\%)\n% & 0.77 (-2\\%) & 0.76 (-1\\%)\n% & 0.68 (\\textcolor{teal}{0\\%}) & 0.55 (\\textcolor{teal}{+1\\%})\n% & \\textbf{0.81} (-2\\%) & \\textbf{0.82} (-1\\%)\\\\\n% \\textbf{Random} & GPT+Genre\n% & 0.75 (\\textcolor{purple}{-6\\%}) & 0.75 (\\textcolor{purple}{-5\\%})\n% & 0.73 (-1\\%) & 0.74 (-1\\%)\n% & \\textbf{0.78} (-1\\%) & 0.77 (0\\%)\n% & 0.68 (\\textcolor{teal}{0\\%}) & 0.55 (\\textcolor{teal}{+1\\%})\n% & \\textbf{0.81} (-2\\%) & \\textbf{0.81} (-2\\%)\\\\\n% \\bottomrule\n% \\end{tabular}}\n% \\end{table*}\n\n\n \n\n\\begin{table*}[ht]\n\\centering\n\\caption{\\small F1 score and Accuracy of each detector under every rewriting scenario. \nThe best metric in a column is \\textbf{bolded}; the largest negative drop is \\dashuline{\\phantom{-00\\%}}, the smallest negative drop is \\dotuline{\\phantom{-00\\%}}.\n\\footnotesize{$\\Delta=(\\text{Scenario Value}-\\text{Original Value})\\times100\\%$.}}\n\\label{tab:all_models_rewriting_f1acc}\n\\renewcommand{\\arraystretch}{1.4}\n\\resizebox{\\textwidth}{!}{\n\\begin{tabular}{l l\nc c c c c c c c c c}\n\\toprule\n\\multirow{2}{*}{\\textbf{Explain}} & \\multirow{2}{*}{\\textbf{Strategy}}\n& \\multicolumn{2}{c}{\\textbf{XGBoost}}\n& \\multicolumn{2}{c}{\\textbf{BERT-base}}\n& \\multicolumn{2}{c}{\\textbf{DistilBERT}}\n& \\multicolumn{2}{c}{\\textbf{XLM-RoBERTa}}\n& \\multicolumn{2}{c}{\\textbf{Ensemble}}\\\\\n\\cmidrule(lr){3-4}\\cmidrule(lr){5-6}\\cmidrule(lr){7-8}\\cmidrule(lr){9-10}\\cmidrule(lr){11-12}\n& & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\n & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\n & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\n & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\n & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\\\\\n\\midrule\n\\emph{NoRewriting} & Original\n& 0.81 & 0.80\n& 0.74 & 0.75\n& 0.79 & 0.77\n& 0.68 & 0.54\n& \\textbf{0.83} & \\textbf{0.83}\\\\\n\\midrule\n\\textbf{SHAP} & HSR\n& 0.73 (\\largest{-8\\%}) & 0.75 (\\largest{-5\\%})\n& 0.71 (-3\\%) & 0.72 (-3\\%)\n& 0.76 (-3\\%) & 0.75 (-2\\%)\n& 0.68 (\\smallest{0\\%}) & 0.54 (\\smallest{0\\%})\n& \\textbf{0.78} (-5\\%) & \\textbf{0.80} (-3\\%)\\\\\n\\textbf{SHAP} & PSR\n& 0.78 (\\largest{-3\\%}) & 0.78 (\\largest{-2\\%})\n& 0.73 (-1\\%) & 0.74 (-1\\%)\n& 0.78 (-1\\%) & 0.77 (0\\%)\n& 0.68 (\\smallest{0\\%}) & 0.54 (\\smallest{0\\%})\n& \\textbf{0.82} (-1\\%) & \\textbf{0.82} (-1\\%)\\\\\n\\textbf{SHAP} & GPT\n& 0.79 (-2\\%) & 0.79 (-1\\%)\n& 0.73 (-1\\%) & 0.74 (-1\\%)\n& 0.77 (-2\\%) & 0.76 (-1\\%)\n& 0.68 (\\smallest{0\\%}) & 0.55 (\\smallest{+1\\%})\n& \\textbf{0.80} (\\largest{-3\\%}) & \\textbf{0.81} (\\largest{-2\\%})\\\\\n\\textbf{SHAP} & GPT+Genre\n& \\textbf{0.80} (-1\\%) & \\textbf{0.80} (0\\%)\n& 0.73 (-1\\%) & 0.74 (-1\\%)\n& 0.77 (-2\\%) & 0.76 (-1\\%)\n& 0.68 (\\smallest{0\\%}) & 0.55 (\\smallest{+1\\%})\n& 0.79 (\\largest{-4\\%}) & 0.80 (\\largest{-3\\%})\\\\\n\\midrule\n\\textbf{LIME} & HSR\n& 0.25 (\\largest{-56\\%}) & 0.48 (\\largest{-32\\%})\n& 0.56 (-18\\%) & 0.62 (-13\\%)\n& 0.62 (-17\\%) & 0.65 (-12\\%)\n& \\textbf{0.67} (\\smallest{-1\\%}) & 0.52 (\\smallest{-2\\%})\n& 0.64 (-19\\%) & \\textbf{0.69} (-14\\%)\\\\\n\\textbf{LIME} & PSR\n& 0.56 (\\largest{-25\\%}) & 0.63 (\\largest{-17\\%})\n& 0.64 (-10\\%) & 0.67 (-8\\%)\n& 0.71 (-8\\%) & 0.71 (-6\\%)\n& 0.66 (\\smallest{-2\\%}) & 0.52 (\\smallest{-2\\%})\n& \\textbf{0.73} (-10\\%) & \\textbf{0.75} (-8\\%)\\\\\n\\textbf{LIME} & GPT\n& 0.57 (\\largest{-24\\%}) & 0.64 (\\largest{-16\\%})\n& 0.73 (-1\\%) & 0.74 (-1\\%)\n& 0.78 (-1\\%) & 0.77 (0\\%)\n& 0.68 (\\smallest{0\\%}) & 0.55 (\\smallest{+1\\%})\n& \\textbf{0.83} (\\smallest{0\\%}) & \\textbf{0.83} (0\\%)\\\\\n\\textbf{LIME} & GPT+Genre\n& 0.60 (\\largest{-21\\%}) & 0.65 (\\largest{-15\\%})\n& \\textbf{0.75} (\\smallest{+1\\%}) & \\textbf{0.75} (0\\%)\n& 0.77 (-2\\%) & 0.76 (-1\\%)\n& 0.68 (0\\%) & 0.55 (\\smallest{+1\\%})\n& \\textbf{0.81} (-2\\%) & \\textbf{0.81} (-2\\%)\\\\\n\\midrule\n\\textbf{Random} & HSR\n& 0.73 (-8\\%) & 0.74 (-6\\%)\n& 0.63 (\\largest{-11\\%}) & 0.67 (\\largest{-8\\%})\n& 0.68 (\\largest{-11\\%}) & 0.69 (\\largest{-8\\%})\n& 0.67 (\\smallest{-1\\%}) & 0.53 (\\smallest{-1\\%})\n& \\textbf{0.73} (-10\\%) & \\textbf{0.75} (\\largest{-8\\%})\\\\\n\\textbf{Random} & PSR\n& 0.76 (-5\\%) & 0.76 (-4\\%)\n& 0.65 (\\largest{-9\\%}) & 0.68 (\\largest{-7\\%})\n& 0.74 (-5\\%) & 0.73 (-4\\%)\n& 0.67 (\\smallest{-1\\%}) & 0.53 (\\smallest{-1\\%})\n& \\textbf{0.78} (-5\\%) & \\textbf{0.79} (-4\\%)\\\\\n\\textbf{Random} & GPT\n& 0.74 (\\largest{-7\\%}) & 0.75 (\\largest{-5\\%})\n& 0.74 (0\\%) & 0.75 (0\\%)\n& 0.77 (-2\\%) & 0.76 (-1\\%)\n& 0.68 (\\smallest{0\\%}) & 0.55 (\\smallest{+1\\%})\n& \\textbf{0.81} (-2\\%) & \\textbf{0.82} (-1\\%)\\\\\n\\textbf{Random} & GPT+Genre\n& 0.75 (\\largest{-6\\%}) & 0.75 (\\largest{-5\\%})\n& 0.73 (-1\\%) & 0.74 (-1\\%)\n& \\textbf{0.78} (-1\\%) & 0.77 (0\\%)\n& 0.68 (0\\%) & 0.55 (\\smallest{+1\\%})\n& \\textbf{0.81} (-2\\%) & \\textbf{0.81} (-2\\%)\\\\\n\\bottomrule\n\\end{tabular}}\n\\end{table*}\n\n\n\\paragraph{\\textbf{General Trends.}}\nWhen no rewriting is applied (\\emph{NoRewriting, Original}), most models exhibit reasonably strong performance (precision and recall around 0.75--0.83), with the Ensemble reaching the highest overall F1 (0.83) and accuracy (0.83). After rewriting, simpler models often detect less, especially if the replaced tokens were the ones they used to identify AI patterns.\n\n\\paragraph{\\textbf{Comparison of Token-Selection Approaches.}}\nSHAP or LIME-based replacements often make larger drops in detection metrics than Random replacements, since replacing highly influential tokens more effectively removes AI indicators. For instance, comparing LIME+HSR vs.\\ Random+HSR, XGBoost’s F1 can decrease from around 0.73 to 0.25 if LIME is used, indicating a big loss in detection ability once key tokens are perturbed. On the other hand, random token replacement leaves more of the AI signals intact, so detection remains higher.\n\n\\paragraph{\\textbf{Influence of Rewriting Methods.}}\nHSR and PSR replace tokens with synonyms from a human corpus, often preserving the original text’s structure. This is reflected in relatively high BLEU scores (e.g., over 90\\% in the SHAP+HSR or SHAP+PSR rows), meaning minimal rewriting. Nonetheless, these small edits can still mislead detectors if the replaced tokens were crucial. GPT-based rewrites typically lower BLEU into the 70s or 80s, reflecting more substantial paraphrases. In turn, many detectors (e.g., XGBoost or XLM-RoBERTa) experience an increase in recall but a decrease in precision, or the opposite, because the text shifts away from familiar AI phrasing. GPT+Genre further contextualizes the rewrite with domain hints (e.g.\\ write it like a news article), causing domain-sensitive lexical changes. This can introduce new phrasing that confuses detectors reliant on domain-specific cues.\n\n\n\\paragraph{\\textbf{Model-Specific Observations.}}\n%From the model perspective, \nXLM-RoBERTa often shows high recall (sometimes hitting 1.0), but its precision remains around 0.52, leading to only modest changes in F1. DistilBERT is more balanced yet remains vulnerable when its top keywords are replaced by synonyms or paraphrases. BERT-base typically holds a moderate advantage across scenarios but can be undermined in LIME-based rewrites (e.g., LIME+HSR, where BERT-base F1 falls to about 0.56). By contrast, the \\textbf{Ensemble} approach consistently retains the highest or near-highest F1 and accuracy. For example, under LIME+GPT, the Ensemble still attains an F1 of 0.83, significantly above XGBoost’s 0.57 or BERT-base’s 0.73. Similarly, in SHAP plus GPT+Genre, the Ensemble preserves a strong F1 of about 0.79, while other models vary more dramatically. Also, We show text similarity scores (BLEU, ROUGE1, ROUGEL) to capture how extensively each rewrite modifies the original text.\n\n\n% \\begin{wraptable}{r}{0.62\\textwidth} \n% \\vspace{-0.8em} \n% \\centering\n% \\scriptsize\n% \\caption{\\small BLEU, ROUGE‑1 and ROUGE‑L scores for every rewriting scenario.}\n% \\label{tab:rewriting_similarity}\n% \\renewcommand{\\arraystretch}{1.2}\n% \\begin{tabular}{l l c c c}\n% \\toprule\n% \\textbf{Explain} & \\textbf{Strategy} & \\textbf{BLEU ($\\Delta$)} & \\textbf{ROUGE‑1 ($\\Delta$)} & \\textbf{ROUGE‑L ($\\Delta$)}\\\\\n% \\midrule\n% \\emph{NoRewriting} & Original & 1,00 & 1,00 & 1,00\\\\\n% \\midrule\n% \\textbf{SHAP} & HSR & 0,93 (\\textcolor{black}{-7\\%}) & 0,97 (\\textcolor{black}{-3\\%}) & 0,97 (\\textcolor{black}{-3\\%})\\\\\n% \\textbf{SHAP} & PSR & \\textbf{0,97} (\\textcolor{teal}{-3\\%}) & \\textbf{0,99} (\\textcolor{teal}{-1\\%}) & \\textbf{0,99} (\\textcolor{teal}{-1\\%})\\\\\n% \\textbf{SHAP} & GPT & 0,86 (\\textcolor{black}{-14\\%}) & 0,93 (\\textcolor{black}{-7\\%}) & 0,93 (\\textcolor{black}{-7\\%})\\\\\n% \\textbf{SHAP} & GPT+Genre & 0,86 (\\textcolor{black}{-14\\%}) & 0,94 (\\textcolor{black}{-6\\%}) & 0,94 (\\textcolor{black}{-6\\%})\\\\\n% \\midrule\n% \\textbf{LIME} & HSR & 0,84 (\\textcolor{black}{-16\\%}) & 0,92 (\\textcolor{black}{-8\\%}) & 0,92 (\\textcolor{black}{-8\\%})\\\\\n% \\textbf{LIME} & PSR & 0,90 (\\textcolor{black}{-10\\%}) & 0,96 (\\textcolor{black}{-5\\%}) & 0,95 (\\textcolor{black}{-5\\%})\\\\\n% \\textbf{LIME} & GPT & 0,76 (\\textcolor{purple}{-24\\%}) & 0,84 (\\textcolor{purple}{-16\\%}) & 0,84 (\\textcolor{purple}{-16\\%})\\\\\n% \\textbf{LIME} & GPT+Genre & 0,76 (\\textcolor{purple}{-24\\%}) & 0,85 (\\textcolor{black}{-15\\%}) & 0,84 (\\textcolor{purple}{-16\\%})\\\\\n% \\midrule\n% \\textbf{Random} & HSR & 0,84 (\\textcolor{black}{-16\\%}) & 0,92 (\\textcolor{black}{-8\\%}) & 0,92 (\\textcolor{black}{-8\\%})\\\\\n% \\textbf{Random} & PSR & 0,90 (\\textcolor{black}{-11\\%}) & 0,95 (\\textcolor{black}{-5\\%}) & 0,95 (\\textcolor{black}{-5\\%})\\\\\n% \\textbf{Random} & GPT & 0,76 (\\textcolor{purple}{-24\\%}) & 0,84 (\\textcolor{purple}{-16\\%}) & 0,84 (\\textcolor{purple}{-16\\%})\\\\\n% \\textbf{Random} & GPT+Genre & 0,76 (\\textcolor{purple}{-24\\%}) & 0,84 (\\textcolor{purple}{-16\\%}) & 0,84 (\\textcolor{purple}{-16\\%})\\\\\n% \\bottomrule\n% \\end{tabular}\n% \\vspace{-1em} \n% \\end{wraptable}\n\n\n\n\n\\paragraph{\\textbf{Text Similarity Implications.}}\n Table~\\ref{tab:rewriting_similarity} presents text similarity scores (BLEU, ROUGE1, ROUGEL) under all these rewriting conditions. Looking at BLEU and ROUGE scores confirms that GPT-based methods, especially GPT+Genre, introduce the largest textual shifts. Replacements guided by synonyms (HSR or PSR) keep BLEU near or above 90, whereas GPT rewrites can dip into the mid-70s. This trade-off highlights how more extensive rewriting can more thoroughly remove AI signatures at the expense of text fidelity.\n\n\n\n\n\\begin{wraptable}{r}{0.62\\textwidth} \n \\vspace{-0.8em} \n \\centering\n \\scriptsize\n \\caption{\\small BLEU, ROUGE‑1 and ROUGE‑L scores for every rewriting scenario.}\n \\label{tab:rewriting_similarity}\n \\renewcommand{\\arraystretch}{1.2}\n \\begin{tabular}{l l c c c}\n \\toprule\n \\textbf{Explain} & \\textbf{Strategy} & \\textbf{BLEU ($\\Delta$)} & \\textbf{ROUGE‑1 ($\\Delta$)} & \\textbf{ROUGE‑L ($\\Delta$)}\\\\\n \\midrule\n \\emph{NoRewriting} & Original & 1,00 & 1,00 & 1,00\\\\\n \\midrule\n \\textbf{SHAP} & HSR & 0,93 (-7\\%) & 0,97 (-3\\%) & 0,97 (-3\\%)\\\\\n \\textbf{SHAP} & PSR & \\textbf{0,97} (\\smallest{-3\\%}) & \\textbf{0,99} (\\smallest{-1\\%}) & \\textbf{0,99} (\\smallest{-1\\%})\\\\\n \\textbf{SHAP} & GPT & 0,86 (-14\\%) & 0,93 (-7\\%) & 0,93 (-7\\%)\\\\\n \\textbf{SHAP} & GPT+Genre & 0,86 (-14\\%) & 0,94 (-6\\%) & 0,94 (-6\\%)\\\\\n \\midrule\n \\textbf{LIME} & HSR & 0,84 (-16\\%) & 0,92 (-8\\%) & 0,92 (-8\\%)\\\\\n \\textbf{LIME} & PSR & 0,90 (-10\\%) & 0,96 (-5\\%) & 0,95 (-5\\%)\\\\\n \\textbf{LIME} & GPT & 0,76 (\\largest{-24\\%}) & 0,84 (\\largest{-16\\%}) & 0,84 (\\largest{-16\\%})\\\\\n \\textbf{LIME} & GPT+Genre & 0,76 (\\largest{-24\\%}) & 0,85 (-15\\%) & 0,84 (\\largest{-16\\%})\\\\\n \\midrule\n \\textbf{Random} & HSR & 0,84 (-16\\%) & 0,92 (-8\\%) & 0,92 (-8\\%)\\\\\n \\textbf{Random} & PSR & 0,90 (-11\\%) & 0,95 (-5\\%) & 0,95 (-5\\%)\\\\\n \\textbf{Random} & GPT & 0,76 (\\largest{-24\\%}) & 0,84 (\\largest{-16\\%}) & 0,84 (\\largest{-16\\%})\\\\\n \\textbf{Random} & GPT+Genre & 0,76 (\\largest{-24\\%}) & 0,84 (\\largest{-16\\%}) & 0,84 (\\largest{-16\\%})\\\\\n \\bottomrule\n \\end{tabular}\n %\\vspace{-1em} \n\\end{wraptable}\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\\paragraph{\\textbf{Flip Rates and Overlapping Flips}}\n\\label{subsec:flip_rates}\n\nTo estimate how many AIGTs are relabeled as \\emph{human} after rewriting, we track \\emph{flip rates} for each combination of explainability method, rewriting strategy, and model. A higher flip rate shows that the corresponding model is more easily deceived by that particular rewriting approach. In these experiments, LIME-based edits coupled with HSR lead to notably high misclassification rates in simpler models such as XGBoost (flipping up to 65\\% of the samples), whereas XLM-RoBERTa remains relatively robust at a mere 2.5\\% flip rate. GPT-based rewrites result in mid-level flip percentages overall; for example, under LIME+GPT, XGBoost flips 31.67\\% of texts while BERT-base and DistilBERT are both at around 9.17\\%. Moreover, SHAP+PSR frequently produces lower flip rates, showing that while part-of-speech-constrained edits can deceive less sophisticated classifiers, advanced or ensemble approaches remain relatively unaffected (with an Ensemble flip rate of only 2.5\\%). Notably, XLM-RoBERTa’s tendency to overpredict \\emph{AI} keeps its flips artificially low. The Ensemble, although not entirely immune, stays in the lower to mid-range across all scenarios, once again showing greater robustness than single-model detectors. Table~\\ref{tab:flip_rates_split} provides a detailed breakdown of which sets of models flip each sample in various scenarios. \n\n% \\begin{table}[ht]\n% \\centering\n% \\scriptsize\n% \\caption{Flip rates (\\%) from \\textbf{AI} to \\textbf{human}. \n% Each scenario contains 120 AI-labelled items; higher values mean the\n% detector is easier to ``flip'' by rewriting.}\n% \\label{tab:flip_rates_split}\n% \\begin{subtable}[t]{0.48\\linewidth}\n% \\centering\n% \\caption{HSR \\& PSR strategies}\n% \\resizebox{\\linewidth}{!}{%\n% \\begin{tabular}{ll\n% l\n% S S S}\n% \\toprule\n% \\textbf{Exp.} & \\textbf{Strat.} & \\textbf{Model} &\n% {\\#AI} & {Flips} & {Rate} \\\\\n% \\midrule\n% % ---------- HSR ----------\n% \\multirow{15}{*}{SHAP}\n% & \\multirow{5}{*}{HSR}\n% & XGBoost & 120 & 17 & 14.17 \\\\\n% & & BERT & 120 & 7 & 5.83 \\\\\n% & & DistilBERT & 120 & 9 & 7.50 \\\\\n% & & XLM-R & 120 & 1 & 0.83 \\\\\n% & & Ensemble & 120 & 8 & 6.67 \\\\\n% \\cmidrule(lr){2-6}\n% & \\multirow{5}{*}{PSR}\n% & XGBoost & 120 & 7 & 5.83 \\\\\n% & & BERT & 120 & 4 & 3.33 \\\\\n% & & DistilBERT & 120 & 4 & 3.33 \\\\\n% & & XLM-R & 120 & 0 & 0.00 \\\\\n% & & Ensemble & 120 & 3 & 2.50 \\\\\n% \\midrule\n% \\multirow{10}{*}{LIME}\n% & \\multirow{5}{*}{HSR}\n% & XGBoost & 120 & 78 & 65.00 \\\\\n% & & BERT & 120 & 31 & 25.83 \\\\\n% & & DistilBERT & 120 & 32 & 26.67 \\\\\n% & & XLM-R & 120 & 3 & 2.50 \\\\\n% & & Ensemble & 120 & 33 & 27.50 \\\\\n% \\cmidrule(lr){2-6}\n% & \\multirow{5}{*}{PSR}\n% & XGBoost & 120 & 46 & 38.33 \\\\\n% & & BERT & 120 & 20 & 16.67 \\\\\n% & & DistilBERT & 120 & 19 & 15.83 \\\\\n% & & XLM-R & 120 & 5 & 4.17 \\\\\n% & & Ensemble & 120 & 19 & 15.83 \\\\\n% \\midrule\n% \\multirow{10}{*}{Rand}\n% & \\multirow{5}{*}{HSR}\n% & XGBoost & 120 & 15 & 12.50 \\\\\n% & & BERT & 120 & 20 & 16.67 \\\\\n% & & DistilBERT & 120 & 22 & 18.33 \\\\\n% & & XLM-R & 120 & 2 & 1.67 \\\\\n% & & Ensemble & 120 & 20 & 16.67 \\\\\n% \\cmidrule(lr){2-6}\n% & \\multirow{5}{*}{PSR}\n% & XGBoost & 120 & 10 & 8.33 \\\\\n% & & BERT & 120 & 17 & 14.17 \\\\\n% & & DistilBERT & 120 & 11 & 9.17 \\\\\n% & & XLM-R & 120 & 1 & 0.83 \\\\\n% & & Ensemble & 120 & 14 & 11.67 \\\\\n% \\bottomrule\n% \\end{tabular}}\n% \\end{subtable}%\n% \\hfill\n% \\begin{subtable}[t]{0.48\\linewidth}\n% \\centering\n% \\caption{GPT \\& GPT-Genre strategies}\n% \\resizebox{\\linewidth}{!}{%\n% \\begin{tabular}{ll\n% l\n% S S S}\n% \\toprule\n% \\textbf{Exp.} & \\textbf{Strat.} & \\textbf{Model} &\n% {\\#AI} & {Flips} & {Rate} \\\\\n% \\midrule\n% % ---------- GPT ----------\n% \\multirow{15}{*}{SHAP}\n% & \\multirow{5}{*}{GPT}\n% & XGBoost & 120 & 4 & 3.33 \\\\\n% & & BERT & 120 & 7 & 5.83 \\\\\n% & & DistilBERT & 120 & 7 & 5.83 \\\\\n% & & XLM-R & 120 & 0 & 0.00 \\\\\n% & & Ensemble & 120 & 10 & 8.33 \\\\\n% \\cmidrule(lr){2-6}\n% & \\multirow{5}{*}{GPT+G}\n% & XGBoost & 120 & 4 & 3.33 \\\\\n% & & BERT & 120 & 3 & 2.50 \\\\\n% & & DistilBERT & 120 & 6 & 5.00 \\\\\n% & & XLM-R & 120 & 0 & 0.00 \\\\\n% & & Ensemble & 120 & 11 & 9.17 \\\\\n% \\midrule\n% \\multirow{10}{*}{LIME}\n% & \\multirow{5}{*}{GPT}\n% & XGBoost & 120 & 38 & 31.67 \\\\\n% & & BERT & 120 & 11 & 9.17 \\\\\n% & & DistilBERT & 120 & 11 & 9.17 \\\\\n% & & XLM-R & 120 & 0 & 0.00 \\\\\n% & & Ensemble & 120 & 11 & 9.17 \\\\\n% \\cmidrule(lr){2-6}\n% & \\multirow{5}{*}{GPT+G}\n% & XGBoost & 120 & 41 & 34.17 \\\\\n% & & BERT & 120 & 4 & 3.33 \\\\\n% & & DistilBERT & 120 & 7 & 5.83 \\\\\n% & & XLM-R & 120 & 0 & 0.00 \\\\\n% & & Ensemble & 120 & 9 & 7.50 \\\\\n% \\midrule\n% \\multirow{10}{*}{Rand}\n% & \\multirow{5}{*}{GPT}\n% & XGBoost & 120 & 16 & 13.33 \\\\\n% & & BERT & 120 & 6 & 5.00 \\\\\n% & & DistilBERT & 120 & 6 & 5.00 \\\\\n% & & XLM-R & 120 & 0 & 0.00 \\\\\n% & & Ensemble & 120 & 5 & 4.17 \\\\\n% \\cmidrule(lr){2-6}\n% & \\multirow{5}{*}{GPT+G}\n% & XGBoost & 120 & 13 & 10.83 \\\\\n% & & BERT & 120 & 6 & 5.00 \\\\\n% & & DistilBERT & 120 & 9 & 7.50 \\\\\n% & & XLM-R & 120 & 0 & 0.00 \\\\\n% & & Ensemble & 120 & 11 & 9.17 \\\\\n% \\bottomrule\n% \\end{tabular}}\n% \\end{subtable}\n% \\end{table}\n\n\n\n\\begin{table}[ht]\n\\centering\n\\scriptsize\n\\renewcommand{\\arraystretch}{1.15} % Slightly tighter row spacing\n\\setlength{\\tabcolsep}{4pt} % Adjust column spacing as desired\n\n\\caption{Flip rates (\\%) from \\textbf{AI} to \\textbf{human}, grouped by rewriting strategies. \n Each scenario has 120 AI-labeled items; higher values indicate greater susceptibility to ``flipping.''}\n\\label{tab:flip_rates_split}\n\n\\begin{subtable}[t]{0.48\\textwidth}\n \\centering\n \\caption{HSR \\& PSR Strategies}\n \\label{tab:flip_hsr_psr}\n \\begin{tabular}{\n l\n l\n l\n S[table-format=3.0]\n S[table-format=2.0]\n S[table-format=3.2]\n }\n \\toprule\n \\textbf{Explain} & \\textbf{Strategy} & \\textbf{Model} \n & {\\#AI} & {Flips} & {Flip Rate} \\\\\n \\midrule\n % --- SHAP, HSR ---\n \\multirow{10}{*}{SHAP}\n & \\multirow{5}{*}{HSR}\n & XGBoost & 120 & 17 & 14.17 \\\\\n & & BERT & 120 & 7 & 5.83 \\\\\n & & DistilBERT & 120 & 9 & 7.50 \\\\\n & & XLM-R & 120 & 1 & 0.83 \\\\\n & & Ensemble & 120 & 8 & 6.67 \\\\\n \\cmidrule(lr){2-6}\n % --- SHAP, PSR ---\n & \\multirow{5}{*}{PSR}\n & XGBoost & 120 & 7 & 5.83 \\\\\n & & BERT & 120 & 4 & 3.33 \\\\\n & & DistilBERT & 120 & 4 & 3.33 \\\\\n & & XLM-R & 120 & 0 & 0.00 \\\\\n & & Ensemble & 120 & 3 & 2.50 \\\\\n \\midrule\n % --- LIME, HSR ---\n \\multirow{10}{*}{LIME}\n & \\multirow{5}{*}{HSR}\n & XGBoost & 120 & 78 & 65.00 \\\\\n & & BERT & 120 & 31 & 25.83 \\\\\n & & DistilBERT & 120 & 32 & 26.67 \\\\\n & & XLM-R & 120 & 3 & 2.50 \\\\\n & & Ensemble & 120 & 33 & 27.50 \\\\\n \\cmidrule(lr){2-6}\n % --- LIME, PSR ---\n & \\multirow{5}{*}{PSR}\n & XGBoost & 120 & 46 & 38.33 \\\\\n & & BERT & 120 & 20 & 16.67 \\\\\n & & DistilBERT & 120 & 19 & 15.83 \\\\\n & & XLM-R & 120 & 5 & 4.17 \\\\\n & & Ensemble & 120 & 19 & 15.83 \\\\\n \\midrule\n % --- Random, HSR ---\n \\multirow{10}{*}{Random}\n & \\multirow{5}{*}{HSR}\n & XGBoost & 120 & 15 & 12.50 \\\\\n & & BERT & 120 & 20 & 16.67 \\\\\n & & DistilBERT & 120 & 22 & 18.33 \\\\\n & & XLM-R & 120 & 2 & 1.67 \\\\\n & & Ensemble & 120 & 20 & 16.67 \\\\\n \\cmidrule(lr){2-6}\n % --- Random, PSR ---\n & \\multirow{5}{*}{PSR}\n & XGBoost & 120 & 10 & 8.33 \\\\\n & & BERT & 120 & 17 & 14.17 \\\\\n & & DistilBERT & 120 & 11 & 9.17 \\\\\n & & XLM-R & 120 & 1 & 0.83 \\\\\n & & Ensemble & 120 & 14 & 11.67 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{subtable}\n\\hfill\n\\begin{subtable}[t]{0.48\\textwidth}\n \\centering\n \\caption{GPT \\& GPT+Genre Strategies}\n \\label{tab:flip_gpt_gptg}\n \\begin{tabular}{\n l\n l\n l\n S[table-format=3.0]\n S[table-format=2.0]\n S[table-format=3.2]\n }\n \\toprule\n \\textbf{Explain} & \\textbf{Strategy} & \\textbf{Model} \n & {\\#AI} & {Flips} & {Flip Rate} \\\\\n \\midrule\n % --- SHAP, GPT ---\n \\multirow{10}{*}{SHAP}\n & \\multirow{5}{*}{GPT}\n & XGBoost & 120 & 4 & 3.33 \\\\\n & & BERT & 120 & 7 & 5.83 \\\\\n & & DistilBERT & 120 & 7 & 5.83 \\\\\n & & XLM-R & 120 & 0 & 0.00 \\\\\n & & Ensemble & 120 & 10 & 8.33 \\\\\n \\cmidrule(lr){2-6}\n % --- SHAP, GPT+Genre ---\n & \\multirow{5}{*}{GPT+Genre}\n & XGBoost & 120 & 4 & 3.33 \\\\\n & & BERT & 120 & 3 & 2.50 \\\\\n & & DistilBERT & 120 & 6 & 5.00 \\\\\n & & XLM-R & 120 & 0 & 0.00 \\\\\n & & Ensemble & 120 & 11 & 9.17 \\\\\n \\midrule\n % --- LIME, GPT ---\n \\multirow{10}{*}{LIME}\n & \\multirow{5}{*}{GPT}\n & XGBoost & 120 & 38 & 31.67 \\\\\n & & BERT & 120 & 11 & 9.17 \\\\\n & & DistilBERT & 120 & 11 & 9.17 \\\\\n & & XLM-R & 120 & 0 & 0.00 \\\\\n & & Ensemble & 120 & 11 & 9.17 \\\\\n \\cmidrule(lr){2-6}\n % --- LIME, GPT+Genre ---\n & \\multirow{5}{*}{GPT+Genre}\n & XGBoost & 120 & 41 & 34.17 \\\\\n & & BERT & 120 & 4 & 3.33 \\\\\n & & DistilBERT & 120 & 7 & 5.83 \\\\\n & & XLM-R & 120 & 0 & 0.00 \\\\\n & & Ensemble & 120 & 9 & 7.50 \\\\\n \\midrule\n % --- Random, GPT ---\n \\multirow{10}{*}{Random}\n & \\multirow{5}{*}{GPT}\n & XGBoost & 120 & 16 & 13.33 \\\\\n & & BERT & 120 & 6 & 5.00 \\\\\n & & DistilBERT & 120 & 6 & 5.00 \\\\\n & & XLM-R & 120 & 0 & 0.00 \\\\\n & & Ensemble & 120 & 5 & 4.17 \\\\\n \\cmidrule(lr){2-6}\n % --- Random, GPT+Genre ---\n & \\multirow{5}{*}{GPT+Genre}\n & XGBoost & 120 & 13 & 10.83 \\\\\n & & BERT & 120 & 6 & 5.00 \\\\\n & & DistilBERT & 120 & 9 & 7.50 \\\\\n & & XLM-R & 120 & 0 & 0.00 \\\\\n & & Ensemble & 120 & 11 & 9.17 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{subtable}\n\n\\end{table}\n\n\nAs shown in Table~\\ref{tab:flip_rates_split}, many rewriting strategies cause a lot of flips for some detectors, especially the simpler ones. However, the Ensemble model usually has a lower flip rate. This shows that using several models together makes detection more reliable. Figure~\\ref{fig:all_upset_plots} also shows how the flipped items overlap for each rewriting method. Even when many detectors are affected by these edits, the Ensemble is less likely to be fooled, highlighting the advantage of using an ensemble approach\n\n\n\\begin{figure*}[ht]\n\\centering\n\n\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_SHAP_HSR.png}\n\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_SHAP_PSR.png}\n\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_SHAP_GPT.png}\n\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_SHAP_GPT+Genre.png}\n\n\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_LIME_HSR.png}\n\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_LIME_PSR.png}\n\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_LIME_GPT.png}\n\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_LIME_GPT+Genre.png}\n\n\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_Random_HSR.png}\n\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_Random_PSR.png}\n\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_Random_GPT.png}\n\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_Random_GPT+Genre.png}\n\n\\caption{\\small Overlaps among flipped AI samples under different explainability methods and rewriting strategies. Each bar corresponds to the number of samples simultaneously flipped by one or more models.}\n\\label{fig:all_upset_plots}\n\\end{figure*}\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n% \\paragraph{\\textbf{Flip Rates and Overlapping Flips}}\n% \\label{subsec:flip_rates}\n\n% To estimate how many AIGTs are relabeled as \\emph{human} after rewriting, we track \\emph{flip rates} for each combination of explainability method, rewriting strategy, and model. A higher flip rate shows that the corresponding model is more easily deceived by that particular rewriting approach. In these experiments, LIME-based edits coupled with HSR lead to notably high misclassification rates in simpler models such as XGBoost (flipping up to 65\\% of the samples), whereas XLM-RoBERTa remains relatively robust at a mere 2.5\\% flip rate. GPT-based rewrites result in mid-level flip percentages overall; for example, under LIME+GPT, XGBoost flips 31.67\\% of texts while BERT-base and DistilBERT are both at around 9.17\\%. Moreover, SHAP+PSR frequently produces lower flip rates, showing that while part-of-speech-constrained edits can deceive less sophisticated classifiers, advanced or ensemble approaches remain relatively unaffected (with an Ensemble flip rate of only 2.5\\%). Notably, XLM-RoBERTa’s tendency to overpredict \\emph{AI} keeps its flips artificially low. The Ensemble, although not entirely immune, stays in the lower to mid-range across all scenarios, once again signaling greater robustness than single-model detectors. we show all flip rates in Appendix~\\ref{appendix:overlap_table}. To show how models overlap in flipping the same AIGT subsets, we provide all relevant plots in Appendix~\\ref{appendix:upset_plots}. These plots illustrate how different token-selection methods and rewriting techniques affect flipped intersections among models.Figure~\\ref{fig:overlap_stacked} shows these results in a stacked-bar format, showing that although certain scenarios (e.g., LIME+HSR) can trick multiple models on the same items, the Ensemble remains less prone to flipping.\n\n% \\begin{figure*}[ht]\n% \\centering\n% \\includegraphics[width=0.9\\textwidth]{plots/overlap_stacked_bar_all_scenarios.png}\n \n% \\caption{\\small Overlap chart of flip rates. Each colored segment shows how many AI items become misclassified as human by a specific model. (All values underlying the colored segments are provided in Appendix~\\ref{appendix:overlap_table} for accessibility and reproducibility.)}\n% \\label{fig:overlap_stacked}\n% \\end{figure*}\n\n\nIn general, GPT-based replacements make text more natural or domain-specific, which greatly impacts models that rely on simple token patterns for detection. HSR and PSR (especially with SHAP/LIME) can also be effective while keeping a higher BLEU score, meaning fewer changes. Since PSR aligns part-of-speech tags, the text remains fluent but loses patterns AI might use, leading to performance drops in classifiers like XLM-RoBERTa and XGBoost. In contrast, our Ensemble model is more robust, as its combined transformer knowledge helps it detect AI content even after targeted token edits. Overall, our results show that the Ensemble model is the most accurate and resilient, effectively handling English and Dutch texts across different genres. While rewriting reduces detection rates, it does not fully bypass the Ensemble model.\n\n\\section{Discussion and Conclusion}\n\\label{sec:discussion_conclusion}\n%\\vspace{-5pt}\nOur findings show that explanation techniques such as SHAP and LIME can guide systematic token replacements that make AIGT less likely to be classified as \\emph{AI-generated}. When we identify and edit tokens most indicative of artificial content, simpler models like XGBoost can be misled at high rates, especially under LIME-based rewrites combined with synonym substitutions. Yet even more advanced models, such as BERT-base or DistilBERT, still show vulnerability in certain scenarios, particularly when GPT-generated replacements transform the text more extensively. These methods, however, do not completely deceive our Ensemble model, which remains comparatively robust across languages, genres, and rewriting methods.\n\nFrom the perspective of text similarity, we see that methods like HSR and PSR typically achieve higher BLEU and ROUGE scores, indicating minimal alterations to the original text. This can preserve meaning and style, but targeted token swaps can still hide enough AI signals to disrupt certain detectors. In contrast, GPT-based rewrites, especially GPT+Genre, often provide more comprehensive revisions, making the text harder to classify but also changing more from the source. Although such extensive rewriting can produce higher flip rates, our Ensemble classifier still maintains better overall performance than single-model systems.\n\nIn practice, these results underscore both the potential and the limitations of adversarial rewriting. Minor edits guided by SHAP or LIME can undermine certain classifiers. Future work might extend to more sophisticated paraphrasing of entire sentences or incorporate style transfer models that better replicate a specific human author. At the same time, it is crucial to consider watermarking and policy frameworks that ensure transparency when AIGT is involved. Our experiments show that even when AI content is skillfully masked, detection remains possible with multi-architecture approaches.\n\nIn conclusion, our study shows how explanation methods can inform token replacements to disguise AIGT, yet also reveals that robust ensemble classifiers are harder to deceive. Balancing improved detection methods with the need for responsible use of writing aids will remain an important challenge. We hope these findings contribute to a deeper understanding of both the potential vulnerabilities in existing AI detection models and the methods that can keep them resilient against adversarial transformations.\n\n\\begin{acks}\nWe thank the CLIN33 shared task organizers for making the dataset available, and we appreciate the helpful feedback from colleagues who guided our research directions. This work is partially supported by the Dutch Research Council (NWO) (grant number: VI.Vidi.195.152 to D. L. Oberski). We also thank SURF for providing the computational facilities. \n\n%The authors used OpenAI's ChatGPT-4o exclusively for grammar and style checks. They have reviewed the content thoroughly and take full responsibility for the final manuscript.\n\\end{acks}\n\n\n\\bibliographystyle{ACM-Reference-Format}\n\\bibliography{01-sample-base}\n\n\n\n \\appendix\n\n% \\section{Overlap Among Flipped Samples}\n% \\label{appendix:overlap_table}\n\n% Table~\\ref{tab:flip_rates_split} provides a detailed of which sets of models flip each sample in various scenarios. For instance, under LIME+GPT, 28 items are flipped exclusively by XGBoost, eight solely by the Ensemble, etc. Although there are shared flips across multiple models, the Ensemble consistently shows smaller overlaps, reinforcing its comparative resilience.\n\n\n% \\begin{table}[H]\n% \\centering\n% \\scriptsize\n% \\caption{Flip rates (\\%) from \\textbf{AI} to \\textbf{human}. \n% Each scenario contains 120 AI-labelled items; higher values mean the\n% detector is easier to “flip’’ by rewriting.}\n% \\label{tab:flip_rates_split}\n% \\begin{subtable}[t]{0.48\\linewidth}\n% \\centering\n% \\caption{HSR \\& PSR strategies}\n% \\resizebox{\\linewidth}{!}{%\n% \\begin{tabular}{ll\n% l\n% S S S}\n% \\toprule\n% \\textbf{Exp.} & \\textbf{Strat.} & \\textbf{Model} &\n% {\\#AI} & {Flips} & {Rate} \\\\\n% \\midrule\n% % ---------- HSR ----------\n% \\multirow{15}{*}{SHAP}\n% & \\multirow{5}{*}{HSR}\n% & XGBoost & 120 & 17 & 14.17 \\\\\n% & & BERT & 120 & 7 & 5.83 \\\\\n% & & DistilBERT & 120 & 9 & 7.50 \\\\\n% & & XLM-R & 120 & 1 & 0.83 \\\\\n% & & Ensemble & 120 & 8 & 6.67 \\\\\n% \\cmidrule(lr){2-6}\n% & \\multirow{5}{*}{PSR}\n% & XGBoost & 120 & 7 & 5.83 \\\\\n% & & BERT & 120 & 4 & 3.33 \\\\\n% & & DistilBERT & 120 & 4 & 3.33 \\\\\n% & & XLM-R & 120 & 0 & 0.00 \\\\\n% & & Ensemble & 120 & 3 & 2.50 \\\\\n% \\midrule\n% \\multirow{10}{*}{LIME}\n% & \\multirow{5}{*}{HSR}\n% & XGBoost & 120 & 78 & 65.00 \\\\\n% & & BERT & 120 & 31 & 25.83 \\\\\n% & & DistilBERT & 120 & 32 & 26.67 \\\\\n% & & XLM-R & 120 & 3 & 2.50 \\\\\n% & & Ensemble & 120 & 33 & 27.50 \\\\\n% \\cmidrule(lr){2-6}\n% & \\multirow{5}{*}{PSR}\n% & XGBoost & 120 & 46 & 38.33 \\\\\n% & & BERT & 120 & 20 & 16.67 \\\\\n% & & DistilBERT & 120 & 19 & 15.83 \\\\\n% & & XLM-R & 120 & 5 & 4.17 \\\\\n% & & Ensemble & 120 & 19 & 15.83 \\\\\n% \\midrule\n% \\multirow{10}{*}{Rand}\n% & \\multirow{5}{*}{HSR}\n% & XGBoost & 120 & 15 & 12.50 \\\\\n% & & BERT & 120 & 20 & 16.67 \\\\\n% & & DistilBERT & 120 & 22 & 18.33 \\\\\n% & & XLM-R & 120 & 2 & 1.67 \\\\\n% & & Ensemble & 120 & 20 & 16.67 \\\\\n% \\cmidrule(lr){2-6}\n% & \\multirow{5}{*}{PSR}\n% & XGBoost & 120 & 10 & 8.33 \\\\\n% & & BERT & 120 & 17 & 14.17 \\\\\n% & & DistilBERT & 120 & 11 & 9.17 \\\\\n% & & XLM-R & 120 & 1 & 0.83 \\\\\n% & & Ensemble & 120 & 14 & 11.67 \\\\\n% \\bottomrule\n% \\end{tabular}}\n% \\end{subtable}%\n% \\hfill\n% \\begin{subtable}[t]{0.48\\linewidth}\n% \\centering\n% \\caption{GPT \\& GPT-Genre strategies}\n% \\resizebox{\\linewidth}{!}{%\n% \\begin{tabular}{ll\n% l\n% S S S}\n% \\toprule\n% \\textbf{Exp.} & \\textbf{Strat.} & \\textbf{Model} &\n% {\\#AI} & {Flips} & {Rate} \\\\\n% \\midrule\n% % ---------- GPT ----------\n% \\multirow{15}{*}{SHAP}\n% & \\multirow{5}{*}{GPT}\n% & XGBoost & 120 & 4 & 3.33 \\\\\n% & & BERT & 120 & 7 & 5.83 \\\\\n% & & DistilBERT & 120 & 7 & 5.83 \\\\\n% & & XLM-R & 120 & 0 & 0.00 \\\\\n% & & Ensemble & 120 & 10 & 8.33 \\\\\n% \\cmidrule(lr){2-6}\n% & \\multirow{5}{*}{GPT+G}\n% & XGBoost & 120 & 4 & 3.33 \\\\\n% & & BERT & 120 & 3 & 2.50 \\\\\n% & & DistilBERT & 120 & 6 & 5.00 \\\\\n% & & XLM-R & 120 & 0 & 0.00 \\\\\n% & & Ensemble & 120 & 11 & 9.17 \\\\\n% \\midrule\n% \\multirow{10}{*}{LIME}\n% & \\multirow{5}{*}{GPT}\n% & XGBoost & 120 & 38 & 31.67 \\\\\n% & & BERT & 120 & 11 & 9.17 \\\\\n% & & DistilBERT & 120 & 11 & 9.17 \\\\\n% & & XLM-R & 120 & 0 & 0.00 \\\\\n% & & Ensemble & 120 & 11 & 9.17 \\\\\n% \\cmidrule(lr){2-6}\n% & \\multirow{5}{*}{GPT+G}\n% & XGBoost & 120 & 41 & 34.17 \\\\\n% & & BERT & 120 & 4 & 3.33 \\\\\n% & & DistilBERT & 120 & 7 & 5.83 \\\\\n% & & XLM-R & 120 & 0 & 0.00 \\\\\n% & & Ensemble & 120 & 9 & 7.50 \\\\\n% \\midrule\n% \\multirow{10}{*}{Rand}\n% & \\multirow{5}{*}{GPT}\n% & XGBoost & 120 & 16 & 13.33 \\\\\n% & & BERT & 120 & 6 & 5.00 \\\\\n% & & DistilBERT & 120 & 6 & 5.00 \\\\\n% & & XLM-R & 120 & 0 & 0.00 \\\\\n% & & Ensemble & 120 & 5 & 4.17 \\\\\n% \\cmidrule(lr){2-6}\n% & \\multirow{5}{*}{GPT+G}\n% & XGBoost & 120 & 13 & 10.83 \\\\\n% & & BERT & 120 & 6 & 5.00 \\\\\n% & & DistilBERT & 120 & 9 & 7.50 \\\\\n% & & XLM-R & 120 & 0 & 0.00 \\\\\n% & & Ensemble & 120 & 11 & 9.17 \\\\\n% \\bottomrule\n% \\end{tabular}}\n% \\end{subtable}\n% \\end{table}\n\n\n\n\n% \\section{Plots for Overlap Analysis}\n% \\label{appendix:upset_plots}\n\n% Figure~\\ref{fig:all_upset_plots} presents all plots covering every combination of \\{\\texttt{SHAP}, \\texttt{LIME}, \\texttt{Random}\\} $\\times$ \\{\\texttt{HSR}, \\texttt{PSR}, \\texttt{GPT}, \\texttt{GPT+Genre}\\}. Each subfigure depicts how many AI samples were flipped by one or more models under that scenario.\n\n% \\begin{figure*}[ht]\n% \\centering\n\n% \\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_SHAP_HSR.png}\n% \\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_SHAP_PSR.png}\n% \\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_SHAP_GPT.png}\n% \\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_SHAP_GPT+Genre.png}\n\n% \\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_LIME_HSR.png}\n% \\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_LIME_PSR.png}\n% \\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_LIME_GPT.png}\n% \\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_LIME_GPT+Genre.png}\n\n% \\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_Random_HSR.png}\n% \\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_Random_PSR.png}\n% \\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_Random_GPT.png}\n% \\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_Random_GPT+Genre.png}\n\n% \\caption{\\small Overlaps among flipped AI samples under different explainability methods and rewriting strategies. Each bar corresponds to the number of samples simultaneously flipped by one or more models.}\n% \\label{fig:all_upset_plots}\n% \\end{figure*}\n\n\n\\clearpage\n\\section*{Reproducibility Checklist for JAIR}\n\n\\noindent\\textbf{All articles:}\n\\begin{enumerate}\n \\item \\textbf{All claims investigated in this work are clearly stated.} \\\\\n \\textit{Answer:} Yes, we clearly state our main claims: we want to see how effective rewriting with SHAP/LIME is, and how an ensemble model performs.\n \n \\item \\textbf{Clear explanations are given how the work reported substantiates the claims.} \\\\\n \\textit{Answer:} Yes, we explain how each experiment’s results support our claims.\n \n \\item \\textbf{Limitations or technical assumptions are stated clearly and explicitly.} \\\\\n \\textit{Answer:} Some. We focus on detection and rewriting only, not on other possible aspects.\n \n \\item \\textbf{Conceptual outlines and/or pseudo-code descriptions of the AI methods introduced in this work are provided, and important implementation details are discussed.} \\\\\n \\textit{Answer:} Yes, our method flow is clearly described, especially for the ensemble approach in the algorithm~\\ref{fig:ms-structure}.\n \n \\item \\textbf{Motivation is provided for all design choices, including algorithms, implementation choices, parameters, data sets, and experimental protocols beyond metrics.} \\\\\n \\textit{Answer:} Yes, we explain why we use these models, datasets, and rewriting strategies.\n\\end{enumerate}\n\n\\subsection*{Articles containing theoretical contributions}\n\\noindent\\textit{Does this paper make theoretical contributions?} \\\\\n\\textit{Answer:} No. (We do not do formal theoretical proofs.)\n\n\\subsection*{Articles reporting on computational experiments}\n\\noindent\\textit{Does this paper include computational experiments?} \\\\\n\\textit{Answer:} Yes.\n\\begin{enumerate}\n \\item \\textbf{All source code required for experiments is included or will be made publicly available upon publication, following best practices.} \\\\\n \\textit{Answer:} We plan to share the main code base (e.g., on GitHub).\n \n \\item \\textbf{The source code comes with a license that allows free usage for reproducibility purposes.} \\\\\n \\textit{Answer:} Yes, we use a permissive license.\n \n \\item \\textbf{The source code comes with a license that allows free usage for research purposes in general.} \\\\\n \\textit{Answer:} Yes.\n \n \\item \\textbf{Raw, unaggregated data from all experiments is included or will be made publicly available.} \\\\\n \\textit{Answer:} We will share our final splits, but some augmented data might be partial.\n \n \\item \\textbf{The unaggregated data comes with a license that allows free usage for reproducibility purposes.} \\\\\n \\textit{Answer:} Yes, as far as allowed by the original dataset licenses.\n \n \\item \\textbf{The unaggregated data comes with a license that allows free usage for research purposes in general.} \\\\\n \\textit{Answer:} Yes, within the limits of the original dataset licenses.\n \n \\item \\textbf{If an algorithm depends on randomness, the method used for generating random numbers and setting seeds is described sufficiently.} \\\\\n \\textit{Answer:} We set random seeds but do not deeply document random generation details.\n \n \\item \\textbf{The execution environment for experiments (hardware/software) is described.} \\\\\n \\textit{Answer:} Yes, we mention key libraries (e.g., Hugging Face Transformers, PyTorch).\n \n \\item \\textbf{The evaluation metrics used are clearly explained and motivated.} \\\\\n \\textit{Answer:} Yes, we use F1, accuracy, BLEU, and ROUGE and explain why.\n \n \\item \\textbf{The number of algorithm runs used to compute each result is reported.} \\\\\n \\textit{Answer:} We use cross-validation or mention repeated runs (varies).\n \n \\item \\textbf{Reported results have not been “cherry-picked.”} \\\\\n \\textit{Answer:} Yes, we show both successes and failures.\n \n \\item \\textbf{Analysis of results goes beyond single-dimensional summaries of performance.} \\\\\n \\textit{Answer:} We show F1, accuracy, and text-similarity. (We do not always show standard deviation or confidence intervals.)\n \n \\item \\textbf{All (hyper-) parameter settings for the algorithms/methods used are reported, along with the rationale or method for determining them.} \\\\\n \\textit{Answer:} Yes, we provide learning rates, batch sizes, etc.\n \n \\item \\textbf{The number and range of (hyper-) parameter settings explored prior to final experiments have been indicated.} \\\\\n \\textit{Answer:} We mention random search, though not each detail.\n \n \\item \\textbf{Appropriately chosen statistical hypothesis tests are used to establish significance in the presence of noise.} \\\\\n \\textit{Answer:} We do not do formal significance testing.\n\\end{enumerate}\n\n\\subsection*{Articles using data sets}\n\\noindent\\textit{Does this work rely on one or more data sets?} \\\\\n\\textit{Answer:} Yes.\n\n\\begin{enumerate}\n \\item \\textbf{All newly introduced data sets are included in an online appendix or will be made publicly available with a suitable license.} \\\\\n \\textit{Answer:} We mostly use public sets (CLIN33, AuTexTification) and will release our augmented version.\n \n \\item \\textbf{The newly introduced data set comes with a license that allows free usage for reproducibility purposes.} \\\\\n \\textit{Answer:} Yes, for the augmented subset.\n \n \\item \\textbf{The newly introduced data set comes with a license that allows free usage for research purposes in general.} \\\\\n \\textit{Answer:} Yes.\n \n \\item \\textbf{All data sets drawn from the literature or other public sources are accompanied by appropriate citations.} \\\\\n \\textit{Answer:} Yes, we cite the original authors.\n \n \\item \\textbf{All data sets drawn from the existing literature are publicly available.} \\\\\n \\textit{Answer:} Yes, with possible constraints.\n \n \\item \\textbf{All new or non-public data sets are described in detail, including relevant statistics, collection, and annotation processes.} \\\\\n \\textit{Answer:} Yes, for our augmented data.\n \n \\item \\textbf{All methods used for preprocessing, augmenting, batching, or splitting data sets are described in detail.} \\\\\n \\textit{Answer:} Yes, we detail how we clean and split.\n\\end{enumerate}\n\n\\subsection*{Explanations on any of the answers above (optional):}\nWe use mostly public data and provide augmented sets where possible. Some data licensing is inherited from the original corpora. Where possible, we provide reproducible code, scripts, and subsets to ensure others can replicate our results.\n\n\n\n\n\n\\end{document}\n\n\n"},"question":{"kind":"string","value":"Considering only the HSR strategy, which method results in the largest average performance drop?"},"answer":{"kind":"string","value":"LIME"},"plan":{"kind":"string","value":"SELECT HSR strategy explain methods\nSELECT all detectors\nSELECT F1 changes\nSELECT Accuracy changes\nLOOP for each explain method\n COMPUTE average performance drop across all detectors and both metrics\n COMPUTE f1_avg_drop = average of F1 changes across all detectors\n COMPUTE acc_avg_drop = average of Accuracy changes across all detectors\n COMPUTE total_avg_drop = (f1_avg_drop + acc_avg_drop ) / 2\nCOMPUTE max_drop_method = argmax over total_avg_drop \nRETURN max_drop_method\n"}}},{"rowIdx":95,"cells":{"paper":{"kind":"string","value":"2506.04050"},"relevant_tables":{"kind":"list like","value":[["\\begin{table}[ht]\n","\\centering\n","\\scriptsize\n","\\renewcommand{\\arraystretch}{1.2}\n","\\caption{Model performance by language and domain on unmodified AI text. \\textbf{Bold} indicates the best result within each language and domain block.}\n","\\label{tab:lang_domain_updated}\n","\\begin{tabular}{llcccc|llcccc}\n","\\toprule\n","\\multicolumn{6}{c}{\\textbf{English Dataset}} & \\multicolumn{6}{c}{\\textbf{Dutch Dataset}} \\\\\n","\\cmidrule(lr){1-6} \\cmidrule(lr){7-12}\n","\\textbf{Model} & \\textbf{Domain} & \\textbf{Acc} & \\textbf{Prec} & \\textbf{Rec} & \\textbf{F1} \n"," & \\textbf{Model} & \\textbf{Domain} & \\textbf{Acc} & \\textbf{Prec} & \\textbf{Rec} & \\textbf{F1} \\\\\n","\\midrule\n","\\textbf{--- News ---} & & & & & \n","& \\textbf{--- News ---} & & & & & \\\\\n","XGBoost \n"," & news & 0.89 & 0.93 & 0.84 & 0.88 \n"," & XGBoost \n"," & news & 0.85 & 0.88 & 0.88 & 0.88 \\\\\n","BERT-base \n"," & news & 0.94 & 0.97 & 0.91 & 0.94\n"," & BERT-base \n"," & news & 0.68 & 0.79 & 0.56 & 0.66 \\\\\n","DistilBERT \n"," & news & 0.92 & 0.90 & \\textbf{0.95} & 0.92\n"," & DistilBERT \n"," & news & 0.68 & 0.73 & 0.66 & 0.69 \\\\\n","XLM-RoBERTa\n"," & news & 0.49 & 0.49 & 1.00 & 0.66\n"," & XLM-RoBERTa\n"," & news & 0.53 & 0.53 & \\textbf{1.00} & 0.69 \\\\\n","Ensemble \n"," & news & \\textbf{0.95} & \\textbf{1.00} & 0.90 & \\textbf{0.95}\n"," & Ensemble \n"," & news & \\textbf{0.99} & \\textbf{0.98} & \\textbf{1.00} & \\textbf{0.99} \\\\\n","\\midrule\n","\\textbf{--- Reviews ---} & & & & & \n","& \\textbf{--- Reviews ---} & & & & & \\\\\n","XGBoost \n"," & reviews & 0.51 & 0.57 & 0.59 & 0.58\n"," & XGBoost \n"," & reviews & 0.53 & 0.51 & 0.46 & 0.48 \\\\\n","BERT-base \n"," & reviews & 0.50 & 0.54 & 0.62 & 0.58\n"," & BERT-base \n"," & reviews & 0.40 & 0.35 & 0.30 & 0.32 \\\\\n","DistilBERT \n"," & reviews & 0.65 & 0.68 & 0.71 & 0.69\n"," & DistilBERT \n"," & reviews & 0.42 & 0.38 & 0.50 & 0.43 \\\\\n","XLM-RoBERTa\n"," & reviews & 0.56 & 0.56 & \\textbf{0.90} & 0.69\n"," & XLM-RoBERTa\n"," & reviews & 0.43 & 0.43 & \\textbf{0.60} & 0.48 \\\\\n","Ensemble \n"," & reviews & \\textbf{0.67} & \\textbf{0.71} & 0.68 & \\textbf{0.70}\n"," & Ensemble \n"," & reviews & \\textbf{0.60} & \\textbf{0.53} & 0.44 & \\textbf{0.49} \\\\\n","\\midrule\n","\\textbf{--- Twitter / X ---} & & & & & \n","& \\textbf{--- Twitter / X ---} & & & & & \\\\\n","XGBoost \n"," & twitter & 0.91 & \\textbf{0.92} & 0.98 & 0.95\n"," & XGBoost \n"," & twitter & 0.80 & 0.85 & 0.78 & 0.81 \\\\\n","BERT-base \n"," & twitter & 0.90 & 0.86 & 0.98 & 0.92\n"," & BERT-base \n"," & twitter & 0.80 & 0.85 & 0.78 & 0.81 \\\\\n","DistilBERT \n"," & twitter & 0.90 & 0.86 & 0.98 & 0.92\n"," & DistilBERT \n"," & twitter & 0.84 & 0.86 & 0.80 & 0.83 \\\\\n","XLM-RoBERTa\n"," & twitter & 0.60 & 0.53 & \\textbf{1.00} & 0.69\n"," & XLM-RoBERTa\n"," & twitter & 0.62 & 0.60 & \\textbf{0.90} & 0.72 \\\\\n","Ensemble \n"," & twitter & \\textbf{0.93} & 0.86 & \\textbf{1.00} & \\textbf{0.93}\n"," & Ensemble \n"," & twitter & \\textbf{0.87} & \\textbf{0.90} & 0.85 & \\textbf{0.87} \\\\\n","\\bottomrule\n","\\end{tabular}\n","\\end{table}\n"],["\\begin{table*}[ht]\n","\\centering\n","\\caption{\\small F1 score and Accuracy of each detector under every rewriting scenario. \n","The best metric in a column is \\textbf{bolded}; the largest negative drop is \\dashuline{\\phantom{-00\\%}}, the smallest negative drop is \\dotuline{\\phantom{-00\\%}}.\n","\\footnotesize{$\\Delta=(\\text{Scenario Value}-\\text{Original Value})\\times100\\%$.}}\n","\\label{tab:all_models_rewriting_f1acc}\n","\\renewcommand{\\arraystretch}{1.4}\n","\\resizebox{\\textwidth}{!}{\n","\\begin{tabular}{l l\n","c c c c c c c c c c}\n","\\toprule\n","\\multirow{2}{*}{\\textbf{Explain}} & \\multirow{2}{*}{\\textbf{Strategy}}\n","& \\multicolumn{2}{c}{\\textbf{XGBoost}}\n","& \\multicolumn{2}{c}{\\textbf{BERT-base}}\n","& \\multicolumn{2}{c}{\\textbf{DistilBERT}}\n","& \\multicolumn{2}{c}{\\textbf{XLM-RoBERTa}}\n","& \\multicolumn{2}{c}{\\textbf{Ensemble}}\\\\\n","\\cmidrule(lr){3-4}\\cmidrule(lr){5-6}\\cmidrule(lr){7-8}\\cmidrule(lr){9-10}\\cmidrule(lr){11-12}\n","& & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\n"," & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\n"," & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\n"," & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\n"," & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\\\\\n","\\midrule\n","\\emph{NoRewriting} & Original\n","& 0.81 & 0.80\n","& 0.74 & 0.75\n","& 0.79 & 0.77\n","& 0.68 & 0.54\n","& \\textbf{0.83} & \\textbf{0.83}\\\\\n","\\midrule\n","\\textbf{SHAP} & HSR\n","& 0.73 (\\largest{-8\\%}) & 0.75 (\\largest{-5\\%})\n","& 0.71 (-3\\%) & 0.72 (-3\\%)\n","& 0.76 (-3\\%) & 0.75 (-2\\%)\n","& 0.68 (\\smallest{0\\%}) & 0.54 (\\smallest{0\\%})\n","& \\textbf{0.78} (-5\\%) & \\textbf{0.80} (-3\\%)\\\\\n","\\textbf{SHAP} & PSR\n","& 0.78 (\\largest{-3\\%}) & 0.78 (\\largest{-2\\%})\n","& 0.73 (-1\\%) & 0.74 (-1\\%)\n","& 0.78 (-1\\%) & 0.77 (0\\%)\n","& 0.68 (\\smallest{0\\%}) & 0.54 (\\smallest{0\\%})\n","& \\textbf{0.82} (-1\\%) & \\textbf{0.82} (-1\\%)\\\\\n","\\textbf{SHAP} & GPT\n","& 0.79 (-2\\%) & 0.79 (-1\\%)\n","& 0.73 (-1\\%) & 0.74 (-1\\%)\n","& 0.77 (-2\\%) & 0.76 (-1\\%)\n","& 0.68 (\\smallest{0\\%}) & 0.55 (\\smallest{+1\\%})\n","& \\textbf{0.80} (\\largest{-3\\%}) & \\textbf{0.81} (\\largest{-2\\%})\\\\\n","\\textbf{SHAP} & GPT+Genre\n","& \\textbf{0.80} (-1\\%) & \\textbf{0.80} (0\\%)\n","& 0.73 (-1\\%) & 0.74 (-1\\%)\n","& 0.77 (-2\\%) & 0.76 (-1\\%)\n","& 0.68 (\\smallest{0\\%}) & 0.55 (\\smallest{+1\\%})\n","& 0.79 (\\largest{-4\\%}) & 0.80 (\\largest{-3\\%})\\\\\n","\\midrule\n","\\textbf{LIME} & HSR\n","& 0.25 (\\largest{-56\\%}) & 0.48 (\\largest{-32\\%})\n","& 0.56 (-18\\%) & 0.62 (-13\\%)\n","& 0.62 (-17\\%) & 0.65 (-12\\%)\n","& \\textbf{0.67} (\\smallest{-1\\%}) & 0.52 (\\smallest{-2\\%})\n","& 0.64 (-19\\%) & \\textbf{0.69} (-14\\%)\\\\\n","\\textbf{LIME} & PSR\n","& 0.56 (\\largest{-25\\%}) & 0.63 (\\largest{-17\\%})\n","& 0.64 (-10\\%) & 0.67 (-8\\%)\n","& 0.71 (-8\\%) & 0.71 (-6\\%)\n","& 0.66 (\\smallest{-2\\%}) & 0.52 (\\smallest{-2\\%})\n","& \\textbf{0.73} (-10\\%) & \\textbf{0.75} (-8\\%)\\\\\n","\\textbf{LIME} & GPT\n","& 0.57 (\\largest{-24\\%}) & 0.64 (\\largest{-16\\%})\n","& 0.73 (-1\\%) & 0.74 (-1\\%)\n","& 0.78 (-1\\%) & 0.77 (0\\%)\n","& 0.68 (\\smallest{0\\%}) & 0.55 (\\smallest{+1\\%})\n","& \\textbf{0.83} (\\smallest{0\\%}) & \\textbf{0.83} (0\\%)\\\\\n","\\textbf{LIME} & GPT+Genre\n","& 0.60 (\\largest{-21\\%}) & 0.65 (\\largest{-15\\%})\n","& \\textbf{0.75} (\\smallest{+1\\%}) & \\textbf{0.75} (0\\%)\n","& 0.77 (-2\\%) & 0.76 (-1\\%)\n","& 0.68 (0\\%) & 0.55 (\\smallest{+1\\%})\n","& \\textbf{0.81} (-2\\%) & \\textbf{0.81} (-2\\%)\\\\\n","\\midrule\n","\\textbf{Random} & HSR\n","& 0.73 (-8\\%) & 0.74 (-6\\%)\n","& 0.63 (\\largest{-11\\%}) & 0.67 (\\largest{-8\\%})\n","& 0.68 (\\largest{-11\\%}) & 0.69 (\\largest{-8\\%})\n","& 0.67 (\\smallest{-1\\%}) & 0.53 (\\smallest{-1\\%})\n","& \\textbf{0.73} (-10\\%) & \\textbf{0.75} (\\largest{-8\\%})\\\\\n","\\textbf{Random} & PSR\n","& 0.76 (-5\\%) & 0.76 (-4\\%)\n","& 0.65 (\\largest{-9\\%}) & 0.68 (\\largest{-7\\%})\n","& 0.74 (-5\\%) & 0.73 (-4\\%)\n","& 0.67 (\\smallest{-1\\%}) & 0.53 (\\smallest{-1\\%})\n","& \\textbf{0.78} (-5\\%) & \\textbf{0.79} (-4\\%)\\\\\n","\\textbf{Random} & GPT\n","& 0.74 (\\largest{-7\\%}) & 0.75 (\\largest{-5\\%})\n","& 0.74 (0\\%) & 0.75 (0\\%)\n","& 0.77 (-2\\%) & 0.76 (-1\\%)\n","& 0.68 (\\smallest{0\\%}) & 0.55 (\\smallest{+1\\%})\n","& \\textbf{0.81} (-2\\%) & \\textbf{0.82} (-1\\%)\\\\\n","\\textbf{Random} & GPT+Genre\n","& 0.75 (\\largest{-6\\%}) & 0.75 (\\largest{-5\\%})\n","& 0.73 (-1\\%) & 0.74 (-1\\%)\n","& \\textbf{0.78} (-1\\%) & 0.77 (0\\%)\n","& 0.68 (0\\%) & 0.55 (\\smallest{+1\\%})\n","& \\textbf{0.81} (-2\\%) & \\textbf{0.81} (-2\\%)\\\\\n","\\bottomrule\n","\\end{tabular}}\n","\\end{table*}\n"]],"string":"[\n [\n \"\\\\begin{table}[ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\scriptsize\\n\",\n \"\\\\renewcommand{\\\\arraystretch}{1.2}\\n\",\n \"\\\\caption{Model performance by language and domain on unmodified AI text. \\\\textbf{Bold} indicates the best result within each language and domain block.}\\n\",\n \"\\\\label{tab:lang_domain_updated}\\n\",\n \"\\\\begin{tabular}{llcccc|llcccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multicolumn{6}{c}{\\\\textbf{English Dataset}} & \\\\multicolumn{6}{c}{\\\\textbf{Dutch Dataset}} \\\\\\\\\\n\",\n \"\\\\cmidrule(lr){1-6} \\\\cmidrule(lr){7-12}\\n\",\n \"\\\\textbf{Model} & \\\\textbf{Domain} & \\\\textbf{Acc} & \\\\textbf{Prec} & \\\\textbf{Rec} & \\\\textbf{F1} \\n\",\n \" & \\\\textbf{Model} & \\\\textbf{Domain} & \\\\textbf{Acc} & \\\\textbf{Prec} & \\\\textbf{Rec} & \\\\textbf{F1} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textbf{--- News ---} & & & & & \\n\",\n \"& \\\\textbf{--- News ---} & & & & & \\\\\\\\\\n\",\n \"XGBoost \\n\",\n \" & news & 0.89 & 0.93 & 0.84 & 0.88 \\n\",\n \" & XGBoost \\n\",\n \" & news & 0.85 & 0.88 & 0.88 & 0.88 \\\\\\\\\\n\",\n \"BERT-base \\n\",\n \" & news & 0.94 & 0.97 & 0.91 & 0.94\\n\",\n \" & BERT-base \\n\",\n \" & news & 0.68 & 0.79 & 0.56 & 0.66 \\\\\\\\\\n\",\n \"DistilBERT \\n\",\n \" & news & 0.92 & 0.90 & \\\\textbf{0.95} & 0.92\\n\",\n \" & DistilBERT \\n\",\n \" & news & 0.68 & 0.73 & 0.66 & 0.69 \\\\\\\\\\n\",\n \"XLM-RoBERTa\\n\",\n \" & news & 0.49 & 0.49 & 1.00 & 0.66\\n\",\n \" & XLM-RoBERTa\\n\",\n \" & news & 0.53 & 0.53 & \\\\textbf{1.00} & 0.69 \\\\\\\\\\n\",\n \"Ensemble \\n\",\n \" & news & \\\\textbf{0.95} & \\\\textbf{1.00} & 0.90 & \\\\textbf{0.95}\\n\",\n \" & Ensemble \\n\",\n \" & news & \\\\textbf{0.99} & \\\\textbf{0.98} & \\\\textbf{1.00} & \\\\textbf{0.99} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textbf{--- Reviews ---} & & & & & \\n\",\n \"& \\\\textbf{--- Reviews ---} & & & & & \\\\\\\\\\n\",\n \"XGBoost \\n\",\n \" & reviews & 0.51 & 0.57 & 0.59 & 0.58\\n\",\n \" & XGBoost \\n\",\n \" & reviews & 0.53 & 0.51 & 0.46 & 0.48 \\\\\\\\\\n\",\n \"BERT-base \\n\",\n \" & reviews & 0.50 & 0.54 & 0.62 & 0.58\\n\",\n \" & BERT-base \\n\",\n \" & reviews & 0.40 & 0.35 & 0.30 & 0.32 \\\\\\\\\\n\",\n \"DistilBERT \\n\",\n \" & reviews & 0.65 & 0.68 & 0.71 & 0.69\\n\",\n \" & DistilBERT \\n\",\n \" & reviews & 0.42 & 0.38 & 0.50 & 0.43 \\\\\\\\\\n\",\n \"XLM-RoBERTa\\n\",\n \" & reviews & 0.56 & 0.56 & \\\\textbf{0.90} & 0.69\\n\",\n \" & XLM-RoBERTa\\n\",\n \" & reviews & 0.43 & 0.43 & \\\\textbf{0.60} & 0.48 \\\\\\\\\\n\",\n \"Ensemble \\n\",\n \" & reviews & \\\\textbf{0.67} & \\\\textbf{0.71} & 0.68 & \\\\textbf{0.70}\\n\",\n \" & Ensemble \\n\",\n \" & reviews & \\\\textbf{0.60} & \\\\textbf{0.53} & 0.44 & \\\\textbf{0.49} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textbf{--- Twitter / X ---} & & & & & \\n\",\n \"& \\\\textbf{--- Twitter / X ---} & & & & & \\\\\\\\\\n\",\n \"XGBoost \\n\",\n \" & twitter & 0.91 & \\\\textbf{0.92} & 0.98 & 0.95\\n\",\n \" & XGBoost \\n\",\n \" & twitter & 0.80 & 0.85 & 0.78 & 0.81 \\\\\\\\\\n\",\n \"BERT-base \\n\",\n \" & twitter & 0.90 & 0.86 & 0.98 & 0.92\\n\",\n \" & BERT-base \\n\",\n \" & twitter & 0.80 & 0.85 & 0.78 & 0.81 \\\\\\\\\\n\",\n \"DistilBERT \\n\",\n \" & twitter & 0.90 & 0.86 & 0.98 & 0.92\\n\",\n \" & DistilBERT \\n\",\n \" & twitter & 0.84 & 0.86 & 0.80 & 0.83 \\\\\\\\\\n\",\n \"XLM-RoBERTa\\n\",\n \" & twitter & 0.60 & 0.53 & \\\\textbf{1.00} & 0.69\\n\",\n \" & XLM-RoBERTa\\n\",\n \" & twitter & 0.62 & 0.60 & \\\\textbf{0.90} & 0.72 \\\\\\\\\\n\",\n \"Ensemble \\n\",\n \" & twitter & \\\\textbf{0.93} & 0.86 & \\\\textbf{1.00} & \\\\textbf{0.93}\\n\",\n \" & Ensemble \\n\",\n \" & twitter & \\\\textbf{0.87} & \\\\textbf{0.90} & 0.85 & \\\\textbf{0.87} \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table*}[ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\caption{\\\\small F1 score and Accuracy of each detector under every rewriting scenario. \\n\",\n \"The best metric in a column is \\\\textbf{bolded}; the largest negative drop is \\\\dashuline{\\\\phantom{-00\\\\%}}, the smallest negative drop is \\\\dotuline{\\\\phantom{-00\\\\%}}.\\n\",\n \"\\\\footnotesize{$\\\\Delta=(\\\\text{Scenario Value}-\\\\text{Original Value})\\\\times100\\\\%$.}}\\n\",\n \"\\\\label{tab:all_models_rewriting_f1acc}\\n\",\n \"\\\\renewcommand{\\\\arraystretch}{1.4}\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \"\\\\begin{tabular}{l l\\n\",\n \"c c c c c c c c c c}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{\\\\textbf{Explain}} & \\\\multirow{2}{*}{\\\\textbf{Strategy}}\\n\",\n \"& \\\\multicolumn{2}{c}{\\\\textbf{XGBoost}}\\n\",\n \"& \\\\multicolumn{2}{c}{\\\\textbf{BERT-base}}\\n\",\n \"& \\\\multicolumn{2}{c}{\\\\textbf{DistilBERT}}\\n\",\n \"& \\\\multicolumn{2}{c}{\\\\textbf{XLM-RoBERTa}}\\n\",\n \"& \\\\multicolumn{2}{c}{\\\\textbf{Ensemble}}\\\\\\\\\\n\",\n \"\\\\cmidrule(lr){3-4}\\\\cmidrule(lr){5-6}\\\\cmidrule(lr){7-8}\\\\cmidrule(lr){9-10}\\\\cmidrule(lr){11-12}\\n\",\n \"& & \\\\textbf{F1 ($\\\\Delta$)} & \\\\textbf{Acc ($\\\\Delta$)}\\n\",\n \" & \\\\textbf{F1 ($\\\\Delta$)} & \\\\textbf{Acc ($\\\\Delta$)}\\n\",\n \" & \\\\textbf{F1 ($\\\\Delta$)} & \\\\textbf{Acc ($\\\\Delta$)}\\n\",\n \" & \\\\textbf{F1 ($\\\\Delta$)} & \\\\textbf{Acc ($\\\\Delta$)}\\n\",\n \" & \\\\textbf{F1 ($\\\\Delta$)} & \\\\textbf{Acc ($\\\\Delta$)}\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\emph{NoRewriting} & Original\\n\",\n \"& 0.81 & 0.80\\n\",\n \"& 0.74 & 0.75\\n\",\n \"& 0.79 & 0.77\\n\",\n \"& 0.68 & 0.54\\n\",\n \"& \\\\textbf{0.83} & \\\\textbf{0.83}\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textbf{SHAP} & HSR\\n\",\n \"& 0.73 (\\\\largest{-8\\\\%}) & 0.75 (\\\\largest{-5\\\\%})\\n\",\n \"& 0.71 (-3\\\\%) & 0.72 (-3\\\\%)\\n\",\n \"& 0.76 (-3\\\\%) & 0.75 (-2\\\\%)\\n\",\n \"& 0.68 (\\\\smallest{0\\\\%}) & 0.54 (\\\\smallest{0\\\\%})\\n\",\n \"& \\\\textbf{0.78} (-5\\\\%) & \\\\textbf{0.80} (-3\\\\%)\\\\\\\\\\n\",\n \"\\\\textbf{SHAP} & PSR\\n\",\n \"& 0.78 (\\\\largest{-3\\\\%}) & 0.78 (\\\\largest{-2\\\\%})\\n\",\n \"& 0.73 (-1\\\\%) & 0.74 (-1\\\\%)\\n\",\n \"& 0.78 (-1\\\\%) & 0.77 (0\\\\%)\\n\",\n \"& 0.68 (\\\\smallest{0\\\\%}) & 0.54 (\\\\smallest{0\\\\%})\\n\",\n \"& \\\\textbf{0.82} (-1\\\\%) & \\\\textbf{0.82} (-1\\\\%)\\\\\\\\\\n\",\n \"\\\\textbf{SHAP} & GPT\\n\",\n \"& 0.79 (-2\\\\%) & 0.79 (-1\\\\%)\\n\",\n \"& 0.73 (-1\\\\%) & 0.74 (-1\\\\%)\\n\",\n \"& 0.77 (-2\\\\%) & 0.76 (-1\\\\%)\\n\",\n \"& 0.68 (\\\\smallest{0\\\\%}) & 0.55 (\\\\smallest{+1\\\\%})\\n\",\n \"& \\\\textbf{0.80} (\\\\largest{-3\\\\%}) & \\\\textbf{0.81} (\\\\largest{-2\\\\%})\\\\\\\\\\n\",\n \"\\\\textbf{SHAP} & GPT+Genre\\n\",\n \"& \\\\textbf{0.80} (-1\\\\%) & \\\\textbf{0.80} (0\\\\%)\\n\",\n \"& 0.73 (-1\\\\%) & 0.74 (-1\\\\%)\\n\",\n \"& 0.77 (-2\\\\%) & 0.76 (-1\\\\%)\\n\",\n \"& 0.68 (\\\\smallest{0\\\\%}) & 0.55 (\\\\smallest{+1\\\\%})\\n\",\n \"& 0.79 (\\\\largest{-4\\\\%}) & 0.80 (\\\\largest{-3\\\\%})\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textbf{LIME} & HSR\\n\",\n \"& 0.25 (\\\\largest{-56\\\\%}) & 0.48 (\\\\largest{-32\\\\%})\\n\",\n \"& 0.56 (-18\\\\%) & 0.62 (-13\\\\%)\\n\",\n \"& 0.62 (-17\\\\%) & 0.65 (-12\\\\%)\\n\",\n \"& \\\\textbf{0.67} (\\\\smallest{-1\\\\%}) & 0.52 (\\\\smallest{-2\\\\%})\\n\",\n \"& 0.64 (-19\\\\%) & \\\\textbf{0.69} (-14\\\\%)\\\\\\\\\\n\",\n \"\\\\textbf{LIME} & PSR\\n\",\n \"& 0.56 (\\\\largest{-25\\\\%}) & 0.63 (\\\\largest{-17\\\\%})\\n\",\n \"& 0.64 (-10\\\\%) & 0.67 (-8\\\\%)\\n\",\n \"& 0.71 (-8\\\\%) & 0.71 (-6\\\\%)\\n\",\n \"& 0.66 (\\\\smallest{-2\\\\%}) & 0.52 (\\\\smallest{-2\\\\%})\\n\",\n \"& \\\\textbf{0.73} (-10\\\\%) & \\\\textbf{0.75} (-8\\\\%)\\\\\\\\\\n\",\n \"\\\\textbf{LIME} & GPT\\n\",\n \"& 0.57 (\\\\largest{-24\\\\%}) & 0.64 (\\\\largest{-16\\\\%})\\n\",\n \"& 0.73 (-1\\\\%) & 0.74 (-1\\\\%)\\n\",\n \"& 0.78 (-1\\\\%) & 0.77 (0\\\\%)\\n\",\n \"& 0.68 (\\\\smallest{0\\\\%}) & 0.55 (\\\\smallest{+1\\\\%})\\n\",\n \"& \\\\textbf{0.83} (\\\\smallest{0\\\\%}) & \\\\textbf{0.83} (0\\\\%)\\\\\\\\\\n\",\n \"\\\\textbf{LIME} & GPT+Genre\\n\",\n \"& 0.60 (\\\\largest{-21\\\\%}) & 0.65 (\\\\largest{-15\\\\%})\\n\",\n \"& \\\\textbf{0.75} (\\\\smallest{+1\\\\%}) & \\\\textbf{0.75} (0\\\\%)\\n\",\n \"& 0.77 (-2\\\\%) & 0.76 (-1\\\\%)\\n\",\n \"& 0.68 (0\\\\%) & 0.55 (\\\\smallest{+1\\\\%})\\n\",\n \"& \\\\textbf{0.81} (-2\\\\%) & \\\\textbf{0.81} (-2\\\\%)\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textbf{Random} & HSR\\n\",\n \"& 0.73 (-8\\\\%) & 0.74 (-6\\\\%)\\n\",\n \"& 0.63 (\\\\largest{-11\\\\%}) & 0.67 (\\\\largest{-8\\\\%})\\n\",\n \"& 0.68 (\\\\largest{-11\\\\%}) & 0.69 (\\\\largest{-8\\\\%})\\n\",\n \"& 0.67 (\\\\smallest{-1\\\\%}) & 0.53 (\\\\smallest{-1\\\\%})\\n\",\n \"& \\\\textbf{0.73} (-10\\\\%) & \\\\textbf{0.75} (\\\\largest{-8\\\\%})\\\\\\\\\\n\",\n \"\\\\textbf{Random} & PSR\\n\",\n \"& 0.76 (-5\\\\%) & 0.76 (-4\\\\%)\\n\",\n \"& 0.65 (\\\\largest{-9\\\\%}) & 0.68 (\\\\largest{-7\\\\%})\\n\",\n \"& 0.74 (-5\\\\%) & 0.73 (-4\\\\%)\\n\",\n \"& 0.67 (\\\\smallest{-1\\\\%}) & 0.53 (\\\\smallest{-1\\\\%})\\n\",\n \"& \\\\textbf{0.78} (-5\\\\%) & \\\\textbf{0.79} (-4\\\\%)\\\\\\\\\\n\",\n \"\\\\textbf{Random} & GPT\\n\",\n \"& 0.74 (\\\\largest{-7\\\\%}) & 0.75 (\\\\largest{-5\\\\%})\\n\",\n \"& 0.74 (0\\\\%) & 0.75 (0\\\\%)\\n\",\n \"& 0.77 (-2\\\\%) & 0.76 (-1\\\\%)\\n\",\n \"& 0.68 (\\\\smallest{0\\\\%}) & 0.55 (\\\\smallest{+1\\\\%})\\n\",\n \"& \\\\textbf{0.81} (-2\\\\%) & \\\\textbf{0.82} (-1\\\\%)\\\\\\\\\\n\",\n \"\\\\textbf{Random} & GPT+Genre\\n\",\n \"& 0.75 (\\\\largest{-6\\\\%}) & 0.75 (\\\\largest{-5\\\\%})\\n\",\n \"& 0.73 (-1\\\\%) & 0.74 (-1\\\\%)\\n\",\n \"& \\\\textbf{0.78} (-1\\\\%) & 0.77 (0\\\\%)\\n\",\n \"& 0.68 (0\\\\%) & 0.55 (\\\\smallest{+1\\\\%})\\n\",\n \"& \\\\textbf{0.81} (-2\\\\%) & \\\\textbf{0.81} (-2\\\\%)\\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}}\\n\",\n \"\\\\end{table*}\\n\"\n ]\n]","isConversational":false},"tables":{"kind":"list like","value":[["\\begin{figure*}[!ht]\n","\\centering\n","\\includegraphics[width=0.7\\linewidth]{plots/model.jpeg}\n","\\vspace{-5pt}\n","\\caption{\\small The architecture of the proposed ensemble model. Outputs of three frozen BERT models (left) and three fresh BERT models (right) are concatenated and passed through dense layers, followed by a voting mechanism.}\n","\\label{fig:ms-structure}\n","\\end{figure*}\n"],["\\begin{table}[ht]\n","\\centering\n","\\scriptsize\n","\\renewcommand{\\arraystretch}{1.2}\n","\\caption{Summary of token replacement strategies}\n","\\label{tab:token_replacement}\n","\\begin{tabular}{p{4.8cm}p{9.2cm}}\n","\\toprule\n","\\textbf{Strategy} & \\textbf{Description} \\\\\n","\\midrule\n","\\textbf{Human Similar Word Replacement (HSR)} \n"," & A Word2Vec model is trained on all human-labeled texts in the training set to learn an embedding space that captures typical human usage. \\\\\n","\\textbf{Part-of-Speech Replacement (PSR)} \n"," & HSR is refined by ensuring that part-of-speech tags match. \\\\\n","\\textbf{GPT-based Replacement (GPT)} \n"," & A generative model (GPT-4o-mini) is queried using the prompt: \n"," \\texttt{\"Replace '\\{token\\}' with a more human-like word in this text: '\\{text\\}'\"}. \\\\\n","\\textbf{GPT-based Replacement with Genre Context (GPT+Genre)} \n"," & The GPT-based method is extended by including explicit genre information (e.g., \\emph{tweets} or \\emph{reviews}) in the prompt:\n"," \\texttt{\"Replace '\\{token\\}' in this \\{domain\\} text with a more human-like word: '\\{text\\}'\"}. \\\\\n","\\bottomrule\n","\\end{tabular}\n","\\end{table}\n"],["\\begin{table}[ht]\n","\\centering\n","\\scriptsize\n","\\renewcommand{\\arraystretch}{1.2}\n","\\caption{Model performance by language and domain on unmodified AI text. \\textbf{Bold} indicates the best result within each language and domain block.}\n","\\label{tab:lang_domain_updated}\n","\\begin{tabular}{llcccc|llcccc}\n","\\toprule\n","\\multicolumn{6}{c}{\\textbf{English Dataset}} & \\multicolumn{6}{c}{\\textbf{Dutch Dataset}} \\\\\n","\\cmidrule(lr){1-6} \\cmidrule(lr){7-12}\n","\\textbf{Model} & \\textbf{Domain} & \\textbf{Acc} & \\textbf{Prec} & \\textbf{Rec} & \\textbf{F1} \n"," & \\textbf{Model} & \\textbf{Domain} & \\textbf{Acc} & \\textbf{Prec} & \\textbf{Rec} & \\textbf{F1} \\\\\n","\\midrule\n","\\textbf{--- News ---} & & & & & \n","& \\textbf{--- News ---} & & & & & \\\\\n","XGBoost \n"," & news & 0.89 & 0.93 & 0.84 & 0.88 \n"," & XGBoost \n"," & news & 0.85 & 0.88 & 0.88 & 0.88 \\\\\n","BERT-base \n"," & news & 0.94 & 0.97 & 0.91 & 0.94\n"," & BERT-base \n"," & news & 0.68 & 0.79 & 0.56 & 0.66 \\\\\n","DistilBERT \n"," & news & 0.92 & 0.90 & \\textbf{0.95} & 0.92\n"," & DistilBERT \n"," & news & 0.68 & 0.73 & 0.66 & 0.69 \\\\\n","XLM-RoBERTa\n"," & news & 0.49 & 0.49 & 1.00 & 0.66\n"," & XLM-RoBERTa\n"," & news & 0.53 & 0.53 & \\textbf{1.00} & 0.69 \\\\\n","Ensemble \n"," & news & \\textbf{0.95} & \\textbf{1.00} & 0.90 & \\textbf{0.95}\n"," & Ensemble \n"," & news & \\textbf{0.99} & \\textbf{0.98} & \\textbf{1.00} & \\textbf{0.99} \\\\\n","\\midrule\n","\\textbf{--- Reviews ---} & & & & & \n","& \\textbf{--- Reviews ---} & & & & & \\\\\n","XGBoost \n"," & reviews & 0.51 & 0.57 & 0.59 & 0.58\n"," & XGBoost \n"," & reviews & 0.53 & 0.51 & 0.46 & 0.48 \\\\\n","BERT-base \n"," & reviews & 0.50 & 0.54 & 0.62 & 0.58\n"," & BERT-base \n"," & reviews & 0.40 & 0.35 & 0.30 & 0.32 \\\\\n","DistilBERT \n"," & reviews & 0.65 & 0.68 & 0.71 & 0.69\n"," & DistilBERT \n"," & reviews & 0.42 & 0.38 & 0.50 & 0.43 \\\\\n","XLM-RoBERTa\n"," & reviews & 0.56 & 0.56 & \\textbf{0.90} & 0.69\n"," & XLM-RoBERTa\n"," & reviews & 0.43 & 0.43 & \\textbf{0.60} & 0.48 \\\\\n","Ensemble \n"," & reviews & \\textbf{0.67} & \\textbf{0.71} & 0.68 & \\textbf{0.70}\n"," & Ensemble \n"," & reviews & \\textbf{0.60} & \\textbf{0.53} & 0.44 & \\textbf{0.49} \\\\\n","\\midrule\n","\\textbf{--- Twitter / X ---} & & & & & \n","& \\textbf{--- Twitter / X ---} & & & & & \\\\\n","XGBoost \n"," & twitter & 0.91 & \\textbf{0.92} & 0.98 & 0.95\n"," & XGBoost \n"," & twitter & 0.80 & 0.85 & 0.78 & 0.81 \\\\\n","BERT-base \n"," & twitter & 0.90 & 0.86 & 0.98 & 0.92\n"," & BERT-base \n"," & twitter & 0.80 & 0.85 & 0.78 & 0.81 \\\\\n","DistilBERT \n"," & twitter & 0.90 & 0.86 & 0.98 & 0.92\n"," & DistilBERT \n"," & twitter & 0.84 & 0.86 & 0.80 & 0.83 \\\\\n","XLM-RoBERTa\n"," & twitter & 0.60 & 0.53 & \\textbf{1.00} & 0.69\n"," & XLM-RoBERTa\n"," & twitter & 0.62 & 0.60 & \\textbf{0.90} & 0.72 \\\\\n","Ensemble \n"," & twitter & \\textbf{0.93} & 0.86 & \\textbf{1.00} & \\textbf{0.93}\n"," & Ensemble \n"," & twitter & \\textbf{0.87} & \\textbf{0.90} & 0.85 & \\textbf{0.87} \\\\\n","\\bottomrule\n","\\end{tabular}\n","\\end{table}\n"],["\\begin{table*}[ht]\n","\\centering\n","\\caption{\\small F1 score and Accuracy of each detector under every rewriting scenario. \n","The best metric in a column is \\textbf{bolded}; the largest negative drop is \\dashuline{\\phantom{-00\\%}}, the smallest negative drop is \\dotuline{\\phantom{-00\\%}}.\n","\\footnotesize{$\\Delta=(\\text{Scenario Value}-\\text{Original Value})\\times100\\%$.}}\n","\\label{tab:all_models_rewriting_f1acc}\n","\\renewcommand{\\arraystretch}{1.4}\n","\\resizebox{\\textwidth}{!}{\n","\\begin{tabular}{l l\n","c c c c c c c c c c}\n","\\toprule\n","\\multirow{2}{*}{\\textbf{Explain}} & \\multirow{2}{*}{\\textbf{Strategy}}\n","& \\multicolumn{2}{c}{\\textbf{XGBoost}}\n","& \\multicolumn{2}{c}{\\textbf{BERT-base}}\n","& \\multicolumn{2}{c}{\\textbf{DistilBERT}}\n","& \\multicolumn{2}{c}{\\textbf{XLM-RoBERTa}}\n","& \\multicolumn{2}{c}{\\textbf{Ensemble}}\\\\\n","\\cmidrule(lr){3-4}\\cmidrule(lr){5-6}\\cmidrule(lr){7-8}\\cmidrule(lr){9-10}\\cmidrule(lr){11-12}\n","& & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\n"," & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\n"," & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\n"," & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\n"," & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\\\\\n","\\midrule\n","\\emph{NoRewriting} & Original\n","& 0.81 & 0.80\n","& 0.74 & 0.75\n","& 0.79 & 0.77\n","& 0.68 & 0.54\n","& \\textbf{0.83} & \\textbf{0.83}\\\\\n","\\midrule\n","\\textbf{SHAP} & HSR\n","& 0.73 (\\largest{-8\\%}) & 0.75 (\\largest{-5\\%})\n","& 0.71 (-3\\%) & 0.72 (-3\\%)\n","& 0.76 (-3\\%) & 0.75 (-2\\%)\n","& 0.68 (\\smallest{0\\%}) & 0.54 (\\smallest{0\\%})\n","& \\textbf{0.78} (-5\\%) & \\textbf{0.80} (-3\\%)\\\\\n","\\textbf{SHAP} & PSR\n","& 0.78 (\\largest{-3\\%}) & 0.78 (\\largest{-2\\%})\n","& 0.73 (-1\\%) & 0.74 (-1\\%)\n","& 0.78 (-1\\%) & 0.77 (0\\%)\n","& 0.68 (\\smallest{0\\%}) & 0.54 (\\smallest{0\\%})\n","& \\textbf{0.82} (-1\\%) & \\textbf{0.82} (-1\\%)\\\\\n","\\textbf{SHAP} & GPT\n","& 0.79 (-2\\%) & 0.79 (-1\\%)\n","& 0.73 (-1\\%) & 0.74 (-1\\%)\n","& 0.77 (-2\\%) & 0.76 (-1\\%)\n","& 0.68 (\\smallest{0\\%}) & 0.55 (\\smallest{+1\\%})\n","& \\textbf{0.80} (\\largest{-3\\%}) & \\textbf{0.81} (\\largest{-2\\%})\\\\\n","\\textbf{SHAP} & GPT+Genre\n","& \\textbf{0.80} (-1\\%) & \\textbf{0.80} (0\\%)\n","& 0.73 (-1\\%) & 0.74 (-1\\%)\n","& 0.77 (-2\\%) & 0.76 (-1\\%)\n","& 0.68 (\\smallest{0\\%}) & 0.55 (\\smallest{+1\\%})\n","& 0.79 (\\largest{-4\\%}) & 0.80 (\\largest{-3\\%})\\\\\n","\\midrule\n","\\textbf{LIME} & HSR\n","& 0.25 (\\largest{-56\\%}) & 0.48 (\\largest{-32\\%})\n","& 0.56 (-18\\%) & 0.62 (-13\\%)\n","& 0.62 (-17\\%) & 0.65 (-12\\%)\n","& \\textbf{0.67} (\\smallest{-1\\%}) & 0.52 (\\smallest{-2\\%})\n","& 0.64 (-19\\%) & \\textbf{0.69} (-14\\%)\\\\\n","\\textbf{LIME} & PSR\n","& 0.56 (\\largest{-25\\%}) & 0.63 (\\largest{-17\\%})\n","& 0.64 (-10\\%) & 0.67 (-8\\%)\n","& 0.71 (-8\\%) & 0.71 (-6\\%)\n","& 0.66 (\\smallest{-2\\%}) & 0.52 (\\smallest{-2\\%})\n","& \\textbf{0.73} (-10\\%) & \\textbf{0.75} (-8\\%)\\\\\n","\\textbf{LIME} & GPT\n","& 0.57 (\\largest{-24\\%}) & 0.64 (\\largest{-16\\%})\n","& 0.73 (-1\\%) & 0.74 (-1\\%)\n","& 0.78 (-1\\%) & 0.77 (0\\%)\n","& 0.68 (\\smallest{0\\%}) & 0.55 (\\smallest{+1\\%})\n","& \\textbf{0.83} (\\smallest{0\\%}) & \\textbf{0.83} (0\\%)\\\\\n","\\textbf{LIME} & GPT+Genre\n","& 0.60 (\\largest{-21\\%}) & 0.65 (\\largest{-15\\%})\n","& \\textbf{0.75} (\\smallest{+1\\%}) & \\textbf{0.75} (0\\%)\n","& 0.77 (-2\\%) & 0.76 (-1\\%)\n","& 0.68 (0\\%) & 0.55 (\\smallest{+1\\%})\n","& \\textbf{0.81} (-2\\%) & \\textbf{0.81} (-2\\%)\\\\\n","\\midrule\n","\\textbf{Random} & HSR\n","& 0.73 (-8\\%) & 0.74 (-6\\%)\n","& 0.63 (\\largest{-11\\%}) & 0.67 (\\largest{-8\\%})\n","& 0.68 (\\largest{-11\\%}) & 0.69 (\\largest{-8\\%})\n","& 0.67 (\\smallest{-1\\%}) & 0.53 (\\smallest{-1\\%})\n","& \\textbf{0.73} (-10\\%) & \\textbf{0.75} (\\largest{-8\\%})\\\\\n","\\textbf{Random} & PSR\n","& 0.76 (-5\\%) & 0.76 (-4\\%)\n","& 0.65 (\\largest{-9\\%}) & 0.68 (\\largest{-7\\%})\n","& 0.74 (-5\\%) & 0.73 (-4\\%)\n","& 0.67 (\\smallest{-1\\%}) & 0.53 (\\smallest{-1\\%})\n","& \\textbf{0.78} (-5\\%) & \\textbf{0.79} (-4\\%)\\\\\n","\\textbf{Random} & GPT\n","& 0.74 (\\largest{-7\\%}) & 0.75 (\\largest{-5\\%})\n","& 0.74 (0\\%) & 0.75 (0\\%)\n","& 0.77 (-2\\%) & 0.76 (-1\\%)\n","& 0.68 (\\smallest{0\\%}) & 0.55 (\\smallest{+1\\%})\n","& \\textbf{0.81} (-2\\%) & \\textbf{0.82} (-1\\%)\\\\\n","\\textbf{Random} & GPT+Genre\n","& 0.75 (\\largest{-6\\%}) & 0.75 (\\largest{-5\\%})\n","& 0.73 (-1\\%) & 0.74 (-1\\%)\n","& \\textbf{0.78} (-1\\%) & 0.77 (0\\%)\n","& 0.68 (0\\%) & 0.55 (\\smallest{+1\\%})\n","& \\textbf{0.81} (-2\\%) & \\textbf{0.81} (-2\\%)\\\\\n","\\bottomrule\n","\\end{tabular}}\n","\\end{table*}\n"],["\\begin{table}[ht]\n","\\centering\n","\\scriptsize\n","\\renewcommand{\\arraystretch}{1.15} % Slightly tighter row spacing\n","\\setlength{\\tabcolsep}{4pt} % Adjust column spacing as desired\n","\n","\\caption{Flip rates (\\%) from \\textbf{AI} to \\textbf{human}, grouped by rewriting strategies. \n"," Each scenario has 120 AI-labeled items; higher values indicate greater susceptibility to ``flipping.''}\n","\\label{tab:flip_rates_split}\n","\n","\\begin{subtable}[t]{0.48\\textwidth}\n"," \\centering\n"," \\caption{HSR \\& PSR Strategies}\n"," \\label{tab:flip_hsr_psr}\n"," \\begin{tabular}{\n"," l\n"," l\n"," l\n"," S[table-format=3.0]\n"," S[table-format=2.0]\n"," S[table-format=3.2]\n"," }\n"," \\toprule\n"," \\textbf{Explain} & \\textbf{Strategy} & \\textbf{Model} \n"," & {\\#AI} & {Flips} & {Flip Rate} \\\\\n"," \\midrule\n"," \\multirow{10}{*}{SHAP}\n"," & \\multirow{5}{*}{HSR}\n"," & XGBoost & 120 & 17 & 14.17 \\\\\n"," & & BERT & 120 & 7 & 5.83 \\\\\n"," & & DistilBERT & 120 & 9 & 7.50 \\\\\n"," & & XLM-R & 120 & 1 & 0.83 \\\\\n"," & & Ensemble & 120 & 8 & 6.67 \\\\\n"," \\cmidrule(lr){2-6}\n"," & \\multirow{5}{*}{PSR}\n"," & XGBoost & 120 & 7 & 5.83 \\\\\n"," & & BERT & 120 & 4 & 3.33 \\\\\n"," & & DistilBERT & 120 & 4 & 3.33 \\\\\n"," & & XLM-R & 120 & 0 & 0.00 \\\\\n"," & & Ensemble & 120 & 3 & 2.50 \\\\\n"," \\midrule\n"," \\multirow{10}{*}{LIME}\n"," & \\multirow{5}{*}{HSR}\n"," & XGBoost & 120 & 78 & 65.00 \\\\\n"," & & BERT & 120 & 31 & 25.83 \\\\\n"," & & DistilBERT & 120 & 32 & 26.67 \\\\\n"," & & XLM-R & 120 & 3 & 2.50 \\\\\n"," & & Ensemble & 120 & 33 & 27.50 \\\\\n"," \\cmidrule(lr){2-6}\n"," & \\multirow{5}{*}{PSR}\n"," & XGBoost & 120 & 46 & 38.33 \\\\\n"," & & BERT & 120 & 20 & 16.67 \\\\\n"," & & DistilBERT & 120 & 19 & 15.83 \\\\\n"," & & XLM-R & 120 & 5 & 4.17 \\\\\n"," & & Ensemble & 120 & 19 & 15.83 \\\\\n"," \\midrule\n"," \\multirow{10}{*}{Random}\n"," & \\multirow{5}{*}{HSR}\n"," & XGBoost & 120 & 15 & 12.50 \\\\\n"," & & BERT & 120 & 20 & 16.67 \\\\\n"," & & DistilBERT & 120 & 22 & 18.33 \\\\\n"," & & XLM-R & 120 & 2 & 1.67 \\\\\n"," & & Ensemble & 120 & 20 & 16.67 \\\\\n"," \\cmidrule(lr){2-6}\n"," & \\multirow{5}{*}{PSR}\n"," & XGBoost & 120 & 10 & 8.33 \\\\\n"," & & BERT & 120 & 17 & 14.17 \\\\\n"," & & DistilBERT & 120 & 11 & 9.17 \\\\\n"," & & XLM-R & 120 & 1 & 0.83 \\\\\n"," & & Ensemble & 120 & 14 & 11.67 \\\\\n"," \\bottomrule\n"," \\end{tabular}\n","\\end{subtable}\n","\\hfill\n","\\begin{subtable}[t]{0.48\\textwidth}\n"," \\centering\n"," \\caption{GPT \\& GPT+Genre Strategies}\n"," \\label{tab:flip_gpt_gptg}\n"," \\begin{tabular}{\n"," l\n"," l\n"," l\n"," S[table-format=3.0]\n"," S[table-format=2.0]\n"," S[table-format=3.2]\n"," }\n"," \\toprule\n"," \\textbf{Explain} & \\textbf{Strategy} & \\textbf{Model} \n"," & {\\#AI} & {Flips} & {Flip Rate} \\\\\n"," \\midrule\n"," \\multirow{10}{*}{SHAP}\n"," & \\multirow{5}{*}{GPT}\n"," & XGBoost & 120 & 4 & 3.33 \\\\\n"," & & BERT & 120 & 7 & 5.83 \\\\\n"," & & DistilBERT & 120 & 7 & 5.83 \\\\\n"," & & XLM-R & 120 & 0 & 0.00 \\\\\n"," & & Ensemble & 120 & 10 & 8.33 \\\\\n"," \\cmidrule(lr){2-6}\n"," & \\multirow{5}{*}{GPT+Genre}\n"," & XGBoost & 120 & 4 & 3.33 \\\\\n"," & & BERT & 120 & 3 & 2.50 \\\\\n"," & & DistilBERT & 120 & 6 & 5.00 \\\\\n"," & & XLM-R & 120 & 0 & 0.00 \\\\\n"," & & Ensemble & 120 & 11 & 9.17 \\\\\n"," \\midrule\n"," \\multirow{10}{*}{LIME}\n"," & \\multirow{5}{*}{GPT}\n"," & XGBoost & 120 & 38 & 31.67 \\\\\n"," & & BERT & 120 & 11 & 9.17 \\\\\n"," & & DistilBERT & 120 & 11 & 9.17 \\\\\n"," & & XLM-R & 120 & 0 & 0.00 \\\\\n"," & & Ensemble & 120 & 11 & 9.17 \\\\\n"," \\cmidrule(lr){2-6}\n"," & \\multirow{5}{*}{GPT+Genre}\n"," & XGBoost & 120 & 41 & 34.17 \\\\\n"," & & BERT & 120 & 4 & 3.33 \\\\\n"," & & DistilBERT & 120 & 7 & 5.83 \\\\\n"," & & XLM-R & 120 & 0 & 0.00 \\\\\n"," & & Ensemble & 120 & 9 & 7.50 \\\\\n"," \\midrule\n"," \\multirow{10}{*}{Random}\n"," & \\multirow{5}{*}{GPT}\n"," & XGBoost & 120 & 16 & 13.33 \\\\\n"," & & BERT & 120 & 6 & 5.00 \\\\\n"," & & DistilBERT & 120 & 6 & 5.00 \\\\\n"," & & XLM-R & 120 & 0 & 0.00 \\\\\n"," & & Ensemble & 120 & 5 & 4.17 \\\\\n"," \\cmidrule(lr){2-6}\n"," & \\multirow{5}{*}{GPT+Genre}\n"," & XGBoost & 120 & 13 & 10.83 \\\\\n"," & & BERT & 120 & 6 & 5.00 \\\\\n"," & & DistilBERT & 120 & 9 & 7.50 \\\\\n"," & & XLM-R & 120 & 0 & 0.00 \\\\\n"," & & Ensemble & 120 & 11 & 9.17 \\\\\n"," \\bottomrule\n"," \\end{tabular}\n","\\end{subtable}\n","\n","\\end{table}\n"],["\\begin{figure*}[ht]\n","\\centering\n","\n","\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_SHAP_HSR.png}\n","\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_SHAP_PSR.png}\n","\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_SHAP_GPT.png}\n","\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_SHAP_GPT+Genre.png}\n","\n","\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_LIME_HSR.png}\n","\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_LIME_PSR.png}\n","\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_LIME_GPT.png}\n","\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_LIME_GPT+Genre.png}\n","\n","\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_Random_HSR.png}\n","\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_Random_PSR.png}\n","\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_Random_GPT.png}\n","\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_Random_GPT+Genre.png}\n","\n","\\caption{\\small Overlaps among flipped AI samples under different explainability methods and rewriting strategies. Each bar corresponds to the number of samples simultaneously flipped by one or more models.}\n","\\label{fig:all_upset_plots}\n","\\end{figure*}\n"]],"string":"[\n [\n \"\\\\begin{figure*}[!ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\includegraphics[width=0.7\\\\linewidth]{plots/model.jpeg}\\n\",\n \"\\\\vspace{-5pt}\\n\",\n \"\\\\caption{\\\\small The architecture of the proposed ensemble model. Outputs of three frozen BERT models (left) and three fresh BERT models (right) are concatenated and passed through dense layers, followed by a voting mechanism.}\\n\",\n \"\\\\label{fig:ms-structure}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{table}[ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\scriptsize\\n\",\n \"\\\\renewcommand{\\\\arraystretch}{1.2}\\n\",\n \"\\\\caption{Summary of token replacement strategies}\\n\",\n \"\\\\label{tab:token_replacement}\\n\",\n \"\\\\begin{tabular}{p{4.8cm}p{9.2cm}}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Strategy} & \\\\textbf{Description} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textbf{Human Similar Word Replacement (HSR)} \\n\",\n \" & A Word2Vec model is trained on all human-labeled texts in the training set to learn an embedding space that captures typical human usage. \\\\\\\\\\n\",\n \"\\\\textbf{Part-of-Speech Replacement (PSR)} \\n\",\n \" & HSR is refined by ensuring that part-of-speech tags match. \\\\\\\\\\n\",\n \"\\\\textbf{GPT-based Replacement (GPT)} \\n\",\n \" & A generative model (GPT-4o-mini) is queried using the prompt: \\n\",\n \" \\\\texttt{\\\"Replace '\\\\{token\\\\}' with a more human-like word in this text: '\\\\{text\\\\}'\\\"}. \\\\\\\\\\n\",\n \"\\\\textbf{GPT-based Replacement with Genre Context (GPT+Genre)} \\n\",\n \" & The GPT-based method is extended by including explicit genre information (e.g., \\\\emph{tweets} or \\\\emph{reviews}) in the prompt:\\n\",\n \" \\\\texttt{\\\"Replace '\\\\{token\\\\}' in this \\\\{domain\\\\} text with a more human-like word: '\\\\{text\\\\}'\\\"}. \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\scriptsize\\n\",\n \"\\\\renewcommand{\\\\arraystretch}{1.2}\\n\",\n \"\\\\caption{Model performance by language and domain on unmodified AI text. \\\\textbf{Bold} indicates the best result within each language and domain block.}\\n\",\n \"\\\\label{tab:lang_domain_updated}\\n\",\n \"\\\\begin{tabular}{llcccc|llcccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multicolumn{6}{c}{\\\\textbf{English Dataset}} & \\\\multicolumn{6}{c}{\\\\textbf{Dutch Dataset}} \\\\\\\\\\n\",\n \"\\\\cmidrule(lr){1-6} \\\\cmidrule(lr){7-12}\\n\",\n \"\\\\textbf{Model} & \\\\textbf{Domain} & \\\\textbf{Acc} & \\\\textbf{Prec} & \\\\textbf{Rec} & \\\\textbf{F1} \\n\",\n \" & \\\\textbf{Model} & \\\\textbf{Domain} & \\\\textbf{Acc} & \\\\textbf{Prec} & \\\\textbf{Rec} & \\\\textbf{F1} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textbf{--- News ---} & & & & & \\n\",\n \"& \\\\textbf{--- News ---} & & & & & \\\\\\\\\\n\",\n \"XGBoost \\n\",\n \" & news & 0.89 & 0.93 & 0.84 & 0.88 \\n\",\n \" & XGBoost \\n\",\n \" & news & 0.85 & 0.88 & 0.88 & 0.88 \\\\\\\\\\n\",\n \"BERT-base \\n\",\n \" & news & 0.94 & 0.97 & 0.91 & 0.94\\n\",\n \" & BERT-base \\n\",\n \" & news & 0.68 & 0.79 & 0.56 & 0.66 \\\\\\\\\\n\",\n \"DistilBERT \\n\",\n \" & news & 0.92 & 0.90 & \\\\textbf{0.95} & 0.92\\n\",\n \" & DistilBERT \\n\",\n \" & news & 0.68 & 0.73 & 0.66 & 0.69 \\\\\\\\\\n\",\n \"XLM-RoBERTa\\n\",\n \" & news & 0.49 & 0.49 & 1.00 & 0.66\\n\",\n \" & XLM-RoBERTa\\n\",\n \" & news & 0.53 & 0.53 & \\\\textbf{1.00} & 0.69 \\\\\\\\\\n\",\n \"Ensemble \\n\",\n \" & news & \\\\textbf{0.95} & \\\\textbf{1.00} & 0.90 & \\\\textbf{0.95}\\n\",\n \" & Ensemble \\n\",\n \" & news & \\\\textbf{0.99} & \\\\textbf{0.98} & \\\\textbf{1.00} & \\\\textbf{0.99} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textbf{--- Reviews ---} & & & & & \\n\",\n \"& \\\\textbf{--- Reviews ---} & & & & & \\\\\\\\\\n\",\n \"XGBoost \\n\",\n \" & reviews & 0.51 & 0.57 & 0.59 & 0.58\\n\",\n \" & XGBoost \\n\",\n \" & reviews & 0.53 & 0.51 & 0.46 & 0.48 \\\\\\\\\\n\",\n \"BERT-base \\n\",\n \" & reviews & 0.50 & 0.54 & 0.62 & 0.58\\n\",\n \" & BERT-base \\n\",\n \" & reviews & 0.40 & 0.35 & 0.30 & 0.32 \\\\\\\\\\n\",\n \"DistilBERT \\n\",\n \" & reviews & 0.65 & 0.68 & 0.71 & 0.69\\n\",\n \" & DistilBERT \\n\",\n \" & reviews & 0.42 & 0.38 & 0.50 & 0.43 \\\\\\\\\\n\",\n \"XLM-RoBERTa\\n\",\n \" & reviews & 0.56 & 0.56 & \\\\textbf{0.90} & 0.69\\n\",\n \" & XLM-RoBERTa\\n\",\n \" & reviews & 0.43 & 0.43 & \\\\textbf{0.60} & 0.48 \\\\\\\\\\n\",\n \"Ensemble \\n\",\n \" & reviews & \\\\textbf{0.67} & \\\\textbf{0.71} & 0.68 & \\\\textbf{0.70}\\n\",\n \" & Ensemble \\n\",\n \" & reviews & \\\\textbf{0.60} & \\\\textbf{0.53} & 0.44 & \\\\textbf{0.49} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textbf{--- Twitter / X ---} & & & & & \\n\",\n \"& \\\\textbf{--- Twitter / X ---} & & & & & \\\\\\\\\\n\",\n \"XGBoost \\n\",\n \" & twitter & 0.91 & \\\\textbf{0.92} & 0.98 & 0.95\\n\",\n \" & XGBoost \\n\",\n \" & twitter & 0.80 & 0.85 & 0.78 & 0.81 \\\\\\\\\\n\",\n \"BERT-base \\n\",\n \" & twitter & 0.90 & 0.86 & 0.98 & 0.92\\n\",\n \" & BERT-base \\n\",\n \" & twitter & 0.80 & 0.85 & 0.78 & 0.81 \\\\\\\\\\n\",\n \"DistilBERT \\n\",\n \" & twitter & 0.90 & 0.86 & 0.98 & 0.92\\n\",\n \" & DistilBERT \\n\",\n \" & twitter & 0.84 & 0.86 & 0.80 & 0.83 \\\\\\\\\\n\",\n \"XLM-RoBERTa\\n\",\n \" & twitter & 0.60 & 0.53 & \\\\textbf{1.00} & 0.69\\n\",\n \" & XLM-RoBERTa\\n\",\n \" & twitter & 0.62 & 0.60 & \\\\textbf{0.90} & 0.72 \\\\\\\\\\n\",\n \"Ensemble \\n\",\n \" & twitter & \\\\textbf{0.93} & 0.86 & \\\\textbf{1.00} & \\\\textbf{0.93}\\n\",\n \" & Ensemble \\n\",\n \" & twitter & \\\\textbf{0.87} & \\\\textbf{0.90} & 0.85 & \\\\textbf{0.87} \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table*}[ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\caption{\\\\small F1 score and Accuracy of each detector under every rewriting scenario. \\n\",\n \"The best metric in a column is \\\\textbf{bolded}; the largest negative drop is \\\\dashuline{\\\\phantom{-00\\\\%}}, the smallest negative drop is \\\\dotuline{\\\\phantom{-00\\\\%}}.\\n\",\n \"\\\\footnotesize{$\\\\Delta=(\\\\text{Scenario Value}-\\\\text{Original Value})\\\\times100\\\\%$.}}\\n\",\n \"\\\\label{tab:all_models_rewriting_f1acc}\\n\",\n \"\\\\renewcommand{\\\\arraystretch}{1.4}\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \"\\\\begin{tabular}{l l\\n\",\n \"c c c c c c c c c c}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{\\\\textbf{Explain}} & \\\\multirow{2}{*}{\\\\textbf{Strategy}}\\n\",\n \"& \\\\multicolumn{2}{c}{\\\\textbf{XGBoost}}\\n\",\n \"& \\\\multicolumn{2}{c}{\\\\textbf{BERT-base}}\\n\",\n \"& \\\\multicolumn{2}{c}{\\\\textbf{DistilBERT}}\\n\",\n \"& \\\\multicolumn{2}{c}{\\\\textbf{XLM-RoBERTa}}\\n\",\n \"& \\\\multicolumn{2}{c}{\\\\textbf{Ensemble}}\\\\\\\\\\n\",\n \"\\\\cmidrule(lr){3-4}\\\\cmidrule(lr){5-6}\\\\cmidrule(lr){7-8}\\\\cmidrule(lr){9-10}\\\\cmidrule(lr){11-12}\\n\",\n \"& & \\\\textbf{F1 ($\\\\Delta$)} & \\\\textbf{Acc ($\\\\Delta$)}\\n\",\n \" & \\\\textbf{F1 ($\\\\Delta$)} & \\\\textbf{Acc ($\\\\Delta$)}\\n\",\n \" & \\\\textbf{F1 ($\\\\Delta$)} & \\\\textbf{Acc ($\\\\Delta$)}\\n\",\n \" & \\\\textbf{F1 ($\\\\Delta$)} & \\\\textbf{Acc ($\\\\Delta$)}\\n\",\n \" & \\\\textbf{F1 ($\\\\Delta$)} & \\\\textbf{Acc ($\\\\Delta$)}\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\emph{NoRewriting} & Original\\n\",\n \"& 0.81 & 0.80\\n\",\n \"& 0.74 & 0.75\\n\",\n \"& 0.79 & 0.77\\n\",\n \"& 0.68 & 0.54\\n\",\n \"& \\\\textbf{0.83} & \\\\textbf{0.83}\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textbf{SHAP} & HSR\\n\",\n \"& 0.73 (\\\\largest{-8\\\\%}) & 0.75 (\\\\largest{-5\\\\%})\\n\",\n \"& 0.71 (-3\\\\%) & 0.72 (-3\\\\%)\\n\",\n \"& 0.76 (-3\\\\%) & 0.75 (-2\\\\%)\\n\",\n \"& 0.68 (\\\\smallest{0\\\\%}) & 0.54 (\\\\smallest{0\\\\%})\\n\",\n \"& \\\\textbf{0.78} (-5\\\\%) & \\\\textbf{0.80} (-3\\\\%)\\\\\\\\\\n\",\n \"\\\\textbf{SHAP} & PSR\\n\",\n \"& 0.78 (\\\\largest{-3\\\\%}) & 0.78 (\\\\largest{-2\\\\%})\\n\",\n \"& 0.73 (-1\\\\%) & 0.74 (-1\\\\%)\\n\",\n \"& 0.78 (-1\\\\%) & 0.77 (0\\\\%)\\n\",\n \"& 0.68 (\\\\smallest{0\\\\%}) & 0.54 (\\\\smallest{0\\\\%})\\n\",\n \"& \\\\textbf{0.82} (-1\\\\%) & \\\\textbf{0.82} (-1\\\\%)\\\\\\\\\\n\",\n \"\\\\textbf{SHAP} & GPT\\n\",\n \"& 0.79 (-2\\\\%) & 0.79 (-1\\\\%)\\n\",\n \"& 0.73 (-1\\\\%) & 0.74 (-1\\\\%)\\n\",\n \"& 0.77 (-2\\\\%) & 0.76 (-1\\\\%)\\n\",\n \"& 0.68 (\\\\smallest{0\\\\%}) & 0.55 (\\\\smallest{+1\\\\%})\\n\",\n \"& \\\\textbf{0.80} (\\\\largest{-3\\\\%}) & \\\\textbf{0.81} (\\\\largest{-2\\\\%})\\\\\\\\\\n\",\n \"\\\\textbf{SHAP} & GPT+Genre\\n\",\n \"& \\\\textbf{0.80} (-1\\\\%) & \\\\textbf{0.80} (0\\\\%)\\n\",\n \"& 0.73 (-1\\\\%) & 0.74 (-1\\\\%)\\n\",\n \"& 0.77 (-2\\\\%) & 0.76 (-1\\\\%)\\n\",\n \"& 0.68 (\\\\smallest{0\\\\%}) & 0.55 (\\\\smallest{+1\\\\%})\\n\",\n \"& 0.79 (\\\\largest{-4\\\\%}) & 0.80 (\\\\largest{-3\\\\%})\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textbf{LIME} & HSR\\n\",\n \"& 0.25 (\\\\largest{-56\\\\%}) & 0.48 (\\\\largest{-32\\\\%})\\n\",\n \"& 0.56 (-18\\\\%) & 0.62 (-13\\\\%)\\n\",\n \"& 0.62 (-17\\\\%) & 0.65 (-12\\\\%)\\n\",\n \"& \\\\textbf{0.67} (\\\\smallest{-1\\\\%}) & 0.52 (\\\\smallest{-2\\\\%})\\n\",\n \"& 0.64 (-19\\\\%) & \\\\textbf{0.69} (-14\\\\%)\\\\\\\\\\n\",\n \"\\\\textbf{LIME} & PSR\\n\",\n \"& 0.56 (\\\\largest{-25\\\\%}) & 0.63 (\\\\largest{-17\\\\%})\\n\",\n \"& 0.64 (-10\\\\%) & 0.67 (-8\\\\%)\\n\",\n \"& 0.71 (-8\\\\%) & 0.71 (-6\\\\%)\\n\",\n \"& 0.66 (\\\\smallest{-2\\\\%}) & 0.52 (\\\\smallest{-2\\\\%})\\n\",\n \"& \\\\textbf{0.73} (-10\\\\%) & \\\\textbf{0.75} (-8\\\\%)\\\\\\\\\\n\",\n \"\\\\textbf{LIME} & GPT\\n\",\n \"& 0.57 (\\\\largest{-24\\\\%}) & 0.64 (\\\\largest{-16\\\\%})\\n\",\n \"& 0.73 (-1\\\\%) & 0.74 (-1\\\\%)\\n\",\n \"& 0.78 (-1\\\\%) & 0.77 (0\\\\%)\\n\",\n \"& 0.68 (\\\\smallest{0\\\\%}) & 0.55 (\\\\smallest{+1\\\\%})\\n\",\n \"& \\\\textbf{0.83} (\\\\smallest{0\\\\%}) & \\\\textbf{0.83} (0\\\\%)\\\\\\\\\\n\",\n \"\\\\textbf{LIME} & GPT+Genre\\n\",\n \"& 0.60 (\\\\largest{-21\\\\%}) & 0.65 (\\\\largest{-15\\\\%})\\n\",\n \"& \\\\textbf{0.75} (\\\\smallest{+1\\\\%}) & \\\\textbf{0.75} (0\\\\%)\\n\",\n \"& 0.77 (-2\\\\%) & 0.76 (-1\\\\%)\\n\",\n \"& 0.68 (0\\\\%) & 0.55 (\\\\smallest{+1\\\\%})\\n\",\n \"& \\\\textbf{0.81} (-2\\\\%) & \\\\textbf{0.81} (-2\\\\%)\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textbf{Random} & HSR\\n\",\n \"& 0.73 (-8\\\\%) & 0.74 (-6\\\\%)\\n\",\n \"& 0.63 (\\\\largest{-11\\\\%}) & 0.67 (\\\\largest{-8\\\\%})\\n\",\n \"& 0.68 (\\\\largest{-11\\\\%}) & 0.69 (\\\\largest{-8\\\\%})\\n\",\n \"& 0.67 (\\\\smallest{-1\\\\%}) & 0.53 (\\\\smallest{-1\\\\%})\\n\",\n \"& \\\\textbf{0.73} (-10\\\\%) & \\\\textbf{0.75} (\\\\largest{-8\\\\%})\\\\\\\\\\n\",\n \"\\\\textbf{Random} & PSR\\n\",\n \"& 0.76 (-5\\\\%) & 0.76 (-4\\\\%)\\n\",\n \"& 0.65 (\\\\largest{-9\\\\%}) & 0.68 (\\\\largest{-7\\\\%})\\n\",\n \"& 0.74 (-5\\\\%) & 0.73 (-4\\\\%)\\n\",\n \"& 0.67 (\\\\smallest{-1\\\\%}) & 0.53 (\\\\smallest{-1\\\\%})\\n\",\n \"& \\\\textbf{0.78} (-5\\\\%) & \\\\textbf{0.79} (-4\\\\%)\\\\\\\\\\n\",\n \"\\\\textbf{Random} & GPT\\n\",\n \"& 0.74 (\\\\largest{-7\\\\%}) & 0.75 (\\\\largest{-5\\\\%})\\n\",\n \"& 0.74 (0\\\\%) & 0.75 (0\\\\%)\\n\",\n \"& 0.77 (-2\\\\%) & 0.76 (-1\\\\%)\\n\",\n \"& 0.68 (\\\\smallest{0\\\\%}) & 0.55 (\\\\smallest{+1\\\\%})\\n\",\n \"& \\\\textbf{0.81} (-2\\\\%) & \\\\textbf{0.82} (-1\\\\%)\\\\\\\\\\n\",\n \"\\\\textbf{Random} & GPT+Genre\\n\",\n \"& 0.75 (\\\\largest{-6\\\\%}) & 0.75 (\\\\largest{-5\\\\%})\\n\",\n \"& 0.73 (-1\\\\%) & 0.74 (-1\\\\%)\\n\",\n \"& \\\\textbf{0.78} (-1\\\\%) & 0.77 (0\\\\%)\\n\",\n \"& 0.68 (0\\\\%) & 0.55 (\\\\smallest{+1\\\\%})\\n\",\n \"& \\\\textbf{0.81} (-2\\\\%) & \\\\textbf{0.81} (-2\\\\%)\\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table}[ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\scriptsize\\n\",\n \"\\\\renewcommand{\\\\arraystretch}{1.15} % Slightly tighter row spacing\\n\",\n \"\\\\setlength{\\\\tabcolsep}{4pt} % Adjust column spacing as desired\\n\",\n \"\\n\",\n \"\\\\caption{Flip rates (\\\\%) from \\\\textbf{AI} to \\\\textbf{human}, grouped by rewriting strategies. \\n\",\n \" Each scenario has 120 AI-labeled items; higher values indicate greater susceptibility to ``flipping.''}\\n\",\n \"\\\\label{tab:flip_rates_split}\\n\",\n \"\\n\",\n \"\\\\begin{subtable}[t]{0.48\\\\textwidth}\\n\",\n \" \\\\centering\\n\",\n \" \\\\caption{HSR \\\\& PSR Strategies}\\n\",\n \" \\\\label{tab:flip_hsr_psr}\\n\",\n \" \\\\begin{tabular}{\\n\",\n \" l\\n\",\n \" l\\n\",\n \" l\\n\",\n \" S[table-format=3.0]\\n\",\n \" S[table-format=2.0]\\n\",\n \" S[table-format=3.2]\\n\",\n \" }\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Explain} & \\\\textbf{Strategy} & \\\\textbf{Model} \\n\",\n \" & {\\\\#AI} & {Flips} & {Flip Rate} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multirow{10}{*}{SHAP}\\n\",\n \" & \\\\multirow{5}{*}{HSR}\\n\",\n \" & XGBoost & 120 & 17 & 14.17 \\\\\\\\\\n\",\n \" & & BERT & 120 & 7 & 5.83 \\\\\\\\\\n\",\n \" & & DistilBERT & 120 & 9 & 7.50 \\\\\\\\\\n\",\n \" & & XLM-R & 120 & 1 & 0.83 \\\\\\\\\\n\",\n \" & & Ensemble & 120 & 8 & 6.67 \\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-6}\\n\",\n \" & \\\\multirow{5}{*}{PSR}\\n\",\n \" & XGBoost & 120 & 7 & 5.83 \\\\\\\\\\n\",\n \" & & BERT & 120 & 4 & 3.33 \\\\\\\\\\n\",\n \" & & DistilBERT & 120 & 4 & 3.33 \\\\\\\\\\n\",\n \" & & XLM-R & 120 & 0 & 0.00 \\\\\\\\\\n\",\n \" & & Ensemble & 120 & 3 & 2.50 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multirow{10}{*}{LIME}\\n\",\n \" & \\\\multirow{5}{*}{HSR}\\n\",\n \" & XGBoost & 120 & 78 & 65.00 \\\\\\\\\\n\",\n \" & & BERT & 120 & 31 & 25.83 \\\\\\\\\\n\",\n \" & & DistilBERT & 120 & 32 & 26.67 \\\\\\\\\\n\",\n \" & & XLM-R & 120 & 3 & 2.50 \\\\\\\\\\n\",\n \" & & Ensemble & 120 & 33 & 27.50 \\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-6}\\n\",\n \" & \\\\multirow{5}{*}{PSR}\\n\",\n \" & XGBoost & 120 & 46 & 38.33 \\\\\\\\\\n\",\n \" & & BERT & 120 & 20 & 16.67 \\\\\\\\\\n\",\n \" & & DistilBERT & 120 & 19 & 15.83 \\\\\\\\\\n\",\n \" & & XLM-R & 120 & 5 & 4.17 \\\\\\\\\\n\",\n \" & & Ensemble & 120 & 19 & 15.83 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multirow{10}{*}{Random}\\n\",\n \" & \\\\multirow{5}{*}{HSR}\\n\",\n \" & XGBoost & 120 & 15 & 12.50 \\\\\\\\\\n\",\n \" & & BERT & 120 & 20 & 16.67 \\\\\\\\\\n\",\n \" & & DistilBERT & 120 & 22 & 18.33 \\\\\\\\\\n\",\n \" & & XLM-R & 120 & 2 & 1.67 \\\\\\\\\\n\",\n \" & & Ensemble & 120 & 20 & 16.67 \\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-6}\\n\",\n \" & \\\\multirow{5}{*}{PSR}\\n\",\n \" & XGBoost & 120 & 10 & 8.33 \\\\\\\\\\n\",\n \" & & BERT & 120 & 17 & 14.17 \\\\\\\\\\n\",\n \" & & DistilBERT & 120 & 11 & 9.17 \\\\\\\\\\n\",\n \" & & XLM-R & 120 & 1 & 0.83 \\\\\\\\\\n\",\n \" & & Ensemble & 120 & 14 & 11.67 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"\\\\end{subtable}\\n\",\n \"\\\\hfill\\n\",\n \"\\\\begin{subtable}[t]{0.48\\\\textwidth}\\n\",\n \" \\\\centering\\n\",\n \" \\\\caption{GPT \\\\& GPT+Genre Strategies}\\n\",\n \" \\\\label{tab:flip_gpt_gptg}\\n\",\n \" \\\\begin{tabular}{\\n\",\n \" l\\n\",\n \" l\\n\",\n \" l\\n\",\n \" S[table-format=3.0]\\n\",\n \" S[table-format=2.0]\\n\",\n \" S[table-format=3.2]\\n\",\n \" }\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Explain} & \\\\textbf{Strategy} & \\\\textbf{Model} \\n\",\n \" & {\\\\#AI} & {Flips} & {Flip Rate} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multirow{10}{*}{SHAP}\\n\",\n \" & \\\\multirow{5}{*}{GPT}\\n\",\n \" & XGBoost & 120 & 4 & 3.33 \\\\\\\\\\n\",\n \" & & BERT & 120 & 7 & 5.83 \\\\\\\\\\n\",\n \" & & DistilBERT & 120 & 7 & 5.83 \\\\\\\\\\n\",\n \" & & XLM-R & 120 & 0 & 0.00 \\\\\\\\\\n\",\n \" & & Ensemble & 120 & 10 & 8.33 \\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-6}\\n\",\n \" & \\\\multirow{5}{*}{GPT+Genre}\\n\",\n \" & XGBoost & 120 & 4 & 3.33 \\\\\\\\\\n\",\n \" & & BERT & 120 & 3 & 2.50 \\\\\\\\\\n\",\n \" & & DistilBERT & 120 & 6 & 5.00 \\\\\\\\\\n\",\n \" & & XLM-R & 120 & 0 & 0.00 \\\\\\\\\\n\",\n \" & & Ensemble & 120 & 11 & 9.17 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multirow{10}{*}{LIME}\\n\",\n \" & \\\\multirow{5}{*}{GPT}\\n\",\n \" & XGBoost & 120 & 38 & 31.67 \\\\\\\\\\n\",\n \" & & BERT & 120 & 11 & 9.17 \\\\\\\\\\n\",\n \" & & DistilBERT & 120 & 11 & 9.17 \\\\\\\\\\n\",\n \" & & XLM-R & 120 & 0 & 0.00 \\\\\\\\\\n\",\n \" & & Ensemble & 120 & 11 & 9.17 \\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-6}\\n\",\n \" & \\\\multirow{5}{*}{GPT+Genre}\\n\",\n \" & XGBoost & 120 & 41 & 34.17 \\\\\\\\\\n\",\n \" & & BERT & 120 & 4 & 3.33 \\\\\\\\\\n\",\n \" & & DistilBERT & 120 & 7 & 5.83 \\\\\\\\\\n\",\n \" & & XLM-R & 120 & 0 & 0.00 \\\\\\\\\\n\",\n \" & & Ensemble & 120 & 9 & 7.50 \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\multirow{10}{*}{Random}\\n\",\n \" & \\\\multirow{5}{*}{GPT}\\n\",\n \" & XGBoost & 120 & 16 & 13.33 \\\\\\\\\\n\",\n \" & & BERT & 120 & 6 & 5.00 \\\\\\\\\\n\",\n \" & & DistilBERT & 120 & 6 & 5.00 \\\\\\\\\\n\",\n \" & & XLM-R & 120 & 0 & 0.00 \\\\\\\\\\n\",\n \" & & Ensemble & 120 & 5 & 4.17 \\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-6}\\n\",\n \" & \\\\multirow{5}{*}{GPT+Genre}\\n\",\n \" & XGBoost & 120 & 13 & 10.83 \\\\\\\\\\n\",\n \" & & BERT & 120 & 6 & 5.00 \\\\\\\\\\n\",\n \" & & DistilBERT & 120 & 9 & 7.50 \\\\\\\\\\n\",\n \" & & XLM-R & 120 & 0 & 0.00 \\\\\\\\\\n\",\n \" & & Ensemble & 120 & 11 & 9.17 \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \"\\\\end{subtable}\\n\",\n \"\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{figure*}[ht]\\n\",\n \"\\\\centering\\n\",\n \"\\n\",\n \"\\\\includegraphics[width=0.24\\\\textwidth]{plots/flip/upset_flips_SHAP_HSR.png}\\n\",\n \"\\\\includegraphics[width=0.24\\\\textwidth]{plots/flip/upset_flips_SHAP_PSR.png}\\n\",\n \"\\\\includegraphics[width=0.24\\\\textwidth]{plots/flip/upset_flips_SHAP_GPT.png}\\n\",\n \"\\\\includegraphics[width=0.24\\\\textwidth]{plots/flip/upset_flips_SHAP_GPT+Genre.png}\\n\",\n \"\\n\",\n \"\\\\includegraphics[width=0.24\\\\textwidth]{plots/flip/upset_flips_LIME_HSR.png}\\n\",\n \"\\\\includegraphics[width=0.24\\\\textwidth]{plots/flip/upset_flips_LIME_PSR.png}\\n\",\n \"\\\\includegraphics[width=0.24\\\\textwidth]{plots/flip/upset_flips_LIME_GPT.png}\\n\",\n \"\\\\includegraphics[width=0.24\\\\textwidth]{plots/flip/upset_flips_LIME_GPT+Genre.png}\\n\",\n \"\\n\",\n \"\\\\includegraphics[width=0.24\\\\textwidth]{plots/flip/upset_flips_Random_HSR.png}\\n\",\n \"\\\\includegraphics[width=0.24\\\\textwidth]{plots/flip/upset_flips_Random_PSR.png}\\n\",\n \"\\\\includegraphics[width=0.24\\\\textwidth]{plots/flip/upset_flips_Random_GPT.png}\\n\",\n \"\\\\includegraphics[width=0.24\\\\textwidth]{plots/flip/upset_flips_Random_GPT+Genre.png}\\n\",\n \"\\n\",\n \"\\\\caption{\\\\small Overlaps among flipped AI samples under different explainability methods and rewriting strategies. Each bar corresponds to the number of samples simultaneously flipped by one or more models.}\\n\",\n \"\\\\label{fig:all_upset_plots}\\n\",\n \"\\\\end{figure*}\\n\"\n ]\n]","isConversational":false},"fulltext":{"kind":"string","value":" %% This is a JAIR Example File Compiled by Nicholas Mattei (nsmattei@tulane.edu) \n%% and Odd Erik Gundersen (odderik@ntnu.no)\n%% and Mykel Kochenderfer (mykel@stanford.edu)\n%% March 29, 2025\n%%\n%% This file is based off the ACM Latex Template https://www.acm.org/publications/proceedings-template\n%% Revision 2.12 (12/28/2024)\n%% \n%% Please see https://www.jair.org/index.php/jair for more information and submission instructions.\n%%\n\n%% The first command in your LaTeX source must be the \\documentclass\n%% command.\n%%\n%% For submission and review of your manuscript please change the\n%% command to \\documentclass[manuscript, screen, review]{jair}.\n%%\n%\\documentclass[review]{jair}\n%\\documentclass[manuscript, screen, review]{jair}\n\n\\documentclass{jair}\n\n%---------- kill the ACM-style footer line ----------\n\\settopmatter{printacmref=false} % no ACM reference strip\n\\pagestyle{plain} % plain page style = no header\n\\makeatletter\n\\renewcommand\\footnotetextcopyrightpermission[1]{} % empty out the footer macro\n\\makeatother\n%---------------------------------------------------\n\n\n\n%\\setcopyright{cc}\n%\\copyrightyear{2025}\n%\\acmYear{2025}\n%\\acmDOI{10.1613/jair.1.xxxxx}\n\n%%\n%\\JAIRAE{Insert JAIR AE Name}\n\n%\\renewcommand{\\JAIRAE}[1]{}\n\n%\\JAIRTrack{Insert JAIR Track Name Here}\n%\\renewcommand{\\JAIRTrack}[1]{}\n\n% \\acmVolume{4}\n% \\acmArticle{111}\n% \\acmMonth{8}\n% \\acmYear{2025}\n\n\\usepackage{graphicx}\n\\usepackage{caption} \n\\usepackage{float} \n\\usepackage[utf8]{inputenc}\n\\let\\Bbbk\\relax\n\\usepackage{amsmath,amssymb}\n\\usepackage{algorithm}\n\\usepackage{algpseudocode}\n\\usepackage{setspace}\n\\usepackage{wrapfig}\n\\usepackage{booktabs} \n\\usepackage{siunitx} \n\n\\usepackage{footmisc} \n\n\\usepackage[normalem]{ulem} \n\\newcommand{\\largest}[1]{\\dashuline{#1}} \n\\newcommand{\\smallest}[1]{\\dotuline{#1}} \n\n\\captionsetup[algorithm]{labelfont=bf}\n\n\n\\captionsetup[algorithm]{labelfont=bf}\n\n\\algrenewcommand{\\algorithmicindent}{1.5em} \n\\algrenewcommand{\\algorithmiccomment}[1]{\\hfill\\(\\#\\) #1}\n%\\algrenewcommand{\\algorithmicendfunction}{} \n\n\\usepackage{booktabs,siunitx,multirow,subcaption}\n\\setlength{\\tabcolsep}{4pt} \n\\renewcommand{\\arraystretch}{0.9}\n\\sisetup{table-format=9.0} \n\n\n\n\n%%\n%% For managing citations, it is recommended to use bibliography\n%% files in BibTeX format.\n%%\n%% You can then either use BibTeX with the ACM-Reference-Format style,\n%% or BibLaTeX with the acmnumeric or acmauthoryear sytles, that include\n%% support for advanced citation of software artefact from the\n%% biblatex-software package, also separately available on CTAN.\n%%\n%% Look at the sample-*-biblatex.tex files for templates showcasing\n%% the biblatex styles.\n%%\n\n%%\n%% The majority of ACM publications use numbered citations and\n%% references. The command \\citestyle{authoryear} switches to the\n%% \"author year\" style.\n%%\n%% If you are preparing content for an event\n%% sponsored by ACM SIGGRAPH, you must use the \"author year\" style of\n%% citations and references.\n%% Uncommenting\n%% the next command will enable that style.\n%%\\citestyle{acmauthoryear}\n\n\n%%\n%% end of the preamble, start of the body of the document source.\n\\begin{document}\n\n%%\n%% The \"title\" command has an optional parameter,\n%% allowing the author to define a \"short title\" to be used in page headers.\n%\\title{JAIR Example Template}\n%\\title[Satzilla]{Explainability-Based Token Replacement on LLM-Generated Text}\n% \\title[Explainability-Based Token Replacement]{Explainability-Based Token Replacement on LLM-Generated Text}\n\n\\title{Explainability-Based Token Replacement on LLM-Generated Text}\n\n%%\n%% The \"author\" command and its associated commands are used to define\n%% the authors and their affiliations.\n%% Of note is the shared affiliation of the first two authors, and the\n%% \"authornote\" and \"authornotemark\" commands\n%% used to denote shared contribution to the research and/or corresponding author.\n\\author{Hadi Mohammadi}\n\\authornote{Corresponding Author.}\n\\email{h.mohammadi@uu.nl}\n\\orcid{0000-0003-0860-9200}\n% \\orcid{https://orcid.org/0000-0002-2037-3694} % This works too if you want to see the full URL\n\\affiliation{%\n \\institution{Department of Methodology and Statistics, Utrecht University}\n \\city{Utrecht}\n \\state{Utrecht}\n \\country{The Netherlands}\n}\n\n\n\\author{Anastasia Giachanou}\n\\orcid{0000-0002-7601-8667}\n\\email{a.giachanou@uu.nl}\n\\affiliation{%\n \\institution{Department of Methodology and Statistics, Utrecht University}\n \\city{Utrecht}\n \\state{Utrecht}\n \\country{The Netherlands}\n}\n\n\\author{Daniel L. Oberski}\n\\orcid{0000-0001-7467-2297}\n\\email{d.l.oberski@uu.nl}\n\\affiliation{%\n \\institution{Department of Methodology and Statistics, Utrecht University}\n \\city{Utrecht}\n \\state{Utrecht}\n \\country{The Netherlands}\n}\n\n\\author{Ayoub Bagheri}\n\\orcid{0000-0001-6366-2173}\n\\email{a.bagheri@uu.nl}\n\\affiliation{%\n \\institution{Department of Methodology and Statistics, Utrecht University}\n \\city{Utrecht}\n \\state{Utrecht}\n \\country{The Netherlands}\n}\n\n\n\n%\\author{Ben Trovato}\n\n%%\n%% By default, the full list of authors will be used in the page\n%% headers. Often, this list is too long, and will overlap\n%% other information printed in the page headers. This command allows\n%% the author to define a more concise list\n%% of authors' names for this purpose.\n\n%\\renewcommand{\\shortauthors}{Mohammadi et al.}\n\n%%\n%% The abstract is a short summary of the work to be presented in the\n%% article.\n\\begin{abstract}\n{\\bf Background:} \n Generative models, especially large language models (LLMs), have shown remarkable progress in producing text that appears human-like. However, they often exhibit patterns that make their output easier to detect than text written by humans.\n \n {\\bf Objectives:}\n In this paper, we investigate how explainable AI (XAI) methods can be used to reduce the detectability of AI-generated text (AIGT) while also introducing a robust ensemble-based detection approach. \n \n {\\bf Methods:}\n We begin by training an ensemble classifier to distinguish AIGT from human-written text, then apply SHAP and LIME to identify tokens that most strongly influence its predictions. We propose four explainability-based token replacement strategies to modify these influential tokens. \n \n {\\bf Results:}\n Our findings show that these token replacement approaches can significantly diminish a single classifier’s ability to detect AIGT. However, our ensemble classifier maintains strong performance across multiple languages and domains, showing that a multi-model approach can mitigate the impact of token-level manipulations.\n \n {\\bf Conclusions:}\n Our findings show that XAI methods can make AIGT harder to detect by focusing on the most influential tokens. At the same time, they highlight the need for robust, ensemble-based detection strategies that can adapt to evolving approaches for hiding AIGT.\n\\end{abstract}\n\n%\\begin{abstract}\n% A clear and well-documented \\LaTeX\\ document is presented as an\n% article formatted for publication by ACM in a conference proceedings\n% or journal publication. Based on the ``acmart'' document class, this\n% article presents and explains many of the common variations, as well\n% as many of the formatting elements an author may use in the\n% preparation of the documentation of their work.\n%\\end{abstract}\n\n\n%% JAIR Note: \n%% Do not include ACM CCS Concepts or Keywords\n\n\n%% To be updated by authors.\n% \\received{24 May 2025}\n% \\received[revised]{12 June 2025}\n% \\received[accepted]{5 July 2025}\n\n%%\n%% This command processes the author and affiliation and title\n%% information and builds the first part of the formatted document.\n\\maketitle\n\n\n%\\blfootnote{Under review at the Journal of Artificial Intelligence Research (JAIR).}\n\n\n\\section{Introduction}\n\\label{sec:intro}\n\nRecent advancements in LLMs, such as the GPT series, have enabled the creation of highly fluent text that often resembles human writing~\\cite{brown2020language}. Still, even the best models often show subtle signs of being artificial, like repeating phrases, using a limited range of words, having no typos, or showing a consistent writing style. These clues can help readers or automated tools detect that the text was written by AI~\\cite{sadasivan2023can}. Being able to spot AIGT is important in various domains, such as in for protecting academic honesty, fighting misinformation, and keeping public trust. On the other hand, there are good reasons to make AI text less detectable, like tools that help people write in their own style~\\cite{Yeh2024}, or AI summaries that aim to be especially clear and well-structured~\\cite{nacke2024ai}.\n\nThis conflict between helpful and harmful uses of LLMs creates a challenge: how to keep their benefits while preventing misuse. One proposed solution is watermarking, which adds hidden patterns to generated text to make it more transparent~\\cite{kirchenbauer2023watermarking}. However, recent research shows that these watermarks can be weakened or removed by changing the text, like paraphrasing~\\cite{li2023towards}. Against this backdrop, our study asks:~\\emph{Can explanation methods be used to both hide and detect AI-generated text?} We contribute in two ways. First, we develop an \\textbf{ensemble‑based detector} that remains accurate across languages (English and Dutch) and domains (news, reviews, Twitter). Second, we show how two explanation tools, SHapley Additive exPlanations (SHAP)~\\cite{lundberg2017unified} and Local Interpretable Model-agnostic Explanations (LIME)~\\cite{ribeiro2016should}, can be turned against a detector: we identify the most influential tokens and replace them via four targeted strategies (semantically similar human‑preferred words, part‑of‑speech‑constrained synonyms, GPT‑based substitutions, and GPT substitutions enriched with genre context). We evaluate both \\emph{detectability} (accuracy, F1) and \\emph{textual fidelity} (BLEU, ROUGE), thereby quantifying the trade‑off between hiding AI signals and preserving meaning. Our results show that token‑level manipulation can fool many single‑model detectors, yet a robust ensemble still performs strongly, underscoring the need to combine explainability, watermarking, and policy safeguards to manage the evolving landscape of AIGT.\n\nThe rest of this paper is organized as follows:\nSection~\\ref{sec:related} reviews related work on AI-generated text detection, watermarking, and adversarial rewriting.\nSection~\\ref{sec:dataset} describes the datasets and our data augmentation process.\nIn Section~\\ref{sec:methodology}, we present our methodology, including details on model architectures, explainability approaches, and token replacement strategies.\nSection~\\ref{sec:experiments} reports experimental results, showing how different rewriting methods affect detection performance and textual fidelity.\nFinally, Section~\\ref{sec:discussion_conclusion} concludes the paper and discusses future research directions.\n\n\n\\section{Related Work}\n\\label{sec:related}\n\n\\citet{kirchenbauer2023watermarking} introduced an important \\emph{red--green list} watermark. In this method, a secret hash divides the vocabulary at each token position into a preferred ``green'' group and a less preferred ``red'' group. The model then gives higher chances to the green tokens, so the generated text contains a hidden cryptographic signature. Light editing often keeps these watermarks, but bigger changes, like paraphrasing or replacing words with synonyms, can remove them~\\citep{kirchenbauer2024reliability,li2023towards}. To make watermarks harder to notice, distortion-free methods hide information in the token-sampling \\emph{order} instead of the probability distribution. However, these can also become weak if someone rewrites the text in a tricky way~\\citep{christ2024undetectable}. Other approaches, called post-hoc schemes, add watermarks later by changing syntax templates or choosing context-aware synonyms~\\citep{yang2023watermarking}, but they also have trouble surviving strong text changes.\n\n\nAnother important detection method takes advantage of the fact that language models (LMs) often choose very predictable words. \\citet{gehrmann2019gltr} created a token-rank histogram tool to help people find words that are ``too likely'' in a sentence. Zero-shot methods like \\emph{DetectGPT} look at the ``curvature'' of local log-probabilities; if the curvature is negative, it suggests the text was written by a machine~\\citep{mitchell2023detectgpt}. \\emph{Fast-DetectGPT} makes this process faster and more accurate for text from ChatGPT or GPT-4~\\citep{bao2023fast}. Other detection methods use features such as normalized perturbed rank~\\citep{su2023detectllm} and the amount of unused information in token probability distributions~\\citep{zawistowski2024unused}. These ``white-box'' methods work well when they can directly access the model's probabilities, but they become less effective without this access. To solve this problem, ``DNA'' regeneration methods, like DNA-GPT~\\citep{yang2023dna} and GPT Paternity Test~\\citep{yu2023gpt}, do not need internal access; instead, they query a black-box model again and compare the new outputs for similarity.\n\nStylometric approaches instead focus on word choice, sentence structure, and how the text is organized and connected.~\\citet{frohling2021feature} grouped 35 linguistic features into four categories, low diversity, repetition, incoherence, and lack of purpose, and trained SVM and random-forest models. More advanced work introduces entity-coherence graphs~\\citep{liu2023coco} or journalism-specific style metrics~\\citep{kumarage2023j}, which bolster detection on brief social-media texts. Though interpretable, these handcrafted methods may be narrower in scope and less transferable across domains.\n\nA different direction uses fine-tuned neural networks to classify AI-generated text. OpenAI’s initial GPT-detector trained RoBERTa on mixed GPT-2 corpora, achieving roughly 95\\% accuracy on in-domain data~\\citep{solaiman2019release}. More recent detectors employ Longformer~\\citep{beltagy2020longformer} or ELECTRA to handle longer contexts and detect texts from multiple generators~\\citep{clark2020electra}. However, these systems often struggle with out-of-distribution domains. Recent ``cross-model'' or contrastive ensembles, such as Ghostbuster~\\citep{verma2023ghostbuster} and DeTeCtive~\\citep{guo2024detective}, compare probability distributions from multiple open-source LMs to improve robustness.\n\nEnsemble methods combine diverse signals to mitigate the weaknesses of individual detectors. Simple voting stacks of transformer-based classifiers outperform single models in multilingual tasks~\\citep{nguyen2023stacking}. For example, recent work introduces ensemble-based approaches for detecting AI-generated text in educational content, achieving a favorable false-positive trade-off in academic scenarios~\\citep{najjar2025detecting}. Our work follows this line by designing an explanation-oriented ensemble meant to withstand token-level adversarial edits.\n\nParaphrasing is one of the best ways to trick AI detectors. For example, \\citet{krishna2023paraphrasing} showed that changing the wording can make DetectGPT miss almost all AI-written text. Rewriting the text many times makes it even harder to detect~\\citep{sadasivan2023can}. Some new methods, like RADAR~\\citep{hu2023radar} and OUTFOX~\\citep{koike2024outfox}, are better at catching these changes because they are trained on paraphrased examples, but they still only recover some accuracy. Even small mistakes, like typos, can also confuse detectors~\\citep{huang2024ai}. More advanced methods can handle some edits, but none can stop all tricks or rewrites.\n\nParaphrasing is seen as the strongest way to avoid AI text detectors:~\\citet{krishna2023paraphrasing} found that rewriting text can reduce DetectGPT’s ability to catch AI-generated text from 70\\% to less than 5\\%. Rewriting the text several times in a row (called “self-rewrites”) makes it even harder to detect, while still keeping the original meaning~\\citep{sadasivan2023can}. Defenses such as RADAR~\\citep{hu2023radar} and OUTFOX~\\citep{koike2024outfox} become stronger by training on paraphrased texts, and can recover 10–20 F\\textsubscript{1} points. Recent studies also show that even simple changes at the letter level—like typos or swapping similar-looking letters—can confuse detectors and make them much less accurate~\\citep{huang2024ai}. Although using features like ensemble cross-probability or advanced watermarking can resist some small changes, no method is completely safe from strong rewriting attacks.\n\n\nExplainability has become a cornerstone of modern NLP deployments~\\citep{mohammadi2025explainability}. XAI tools like SHAP and LIME show which words or tokens have the biggest effect on a detector’s decisions. For example, ~\\citet{mitrovic2023chatgpt} used LIME to highlight important words and help people review short ChatGPT answers. But attackers can use this information in the opposite way, by changing or removing the most important words.~\\citet{zhou2024humanizing} showed that using SHAP-based synonyms for key words can lower detector accuracy by 40–60\\%. In our work, we carefully study this double-sided use of explanations, testing four types of word substitutions in two languages and different types of texts.\n\nDetectors often make mistakes with texts written by people using English as a second language.~\\citet{liang2023gpt} found that seven popular GPT detectors wrongly marked more than 61\\% of TOEFL essays by non-native English speakers as AI-generated, even though they were very accurate with essays by native speakers. Because of this bias, some teachers are moving away from strict rules and are now encouraging students to use AI tools as support, especially for those with different language backgrounds. Since LLMs can quickly create lots of convincing fake news, it is very hard for people alone to spot which content is real~\\citep{zellers2019defending}. To improve detection in these situations, it helps to use a mix of classifiers and to add information about where the text comes from~\\citep{fraser2025detecting}.\n\nRecent use of AI chatbots for mental health support shows that people often think these systems are caring and human-like, which leads to real and meaningful conversations. But this can be risky, especially when users do not realize they are talking to an AI. ~\\citet{song2024typing} found that people in severe distress sometimes seek help from chatbots and even see them as human. In the same way,~\\citet{siddals2024happened} stressed the need for stronger safety rules and clearer information in AI mental health tools. These studies show how important it is to have good detection systems and to make it clear when someone is interacting with an AI, so that chatbots are used ethically in sensitive cases. Research also shows that no single method is perfect; reliable AI-generated text detection needs to use many signals, be tested against tricky cases, and give clear reasons for its decisions. These are the main goals of our work.\n\n\n\\section{Dataset}\n\\label{sec:dataset}\nIn this section, we detail the CLIN33 dataset~\\cite{fivez2024clin33} used as our primary corpus and describe how it was augmented to expand its linguistic variability. We also present the portion of the AuTexTification dataset\\footnote{\\url{https://zenodo.org/records/10732813}} used to strengthen our ensemble models. The CLIN33 shared task corpus comprises human-written and AIGTs in English and Dutch, including three domains: news, tweets, and reviews. Each domain contains 200 samples from human authors and 200 samples from LLMs (GPT-4 and Vicu\\~{n}a-13B), producing a total of 1,200 texts in English and 1,200 in Dutch. The human-written news samples come from well-known media outlets; the Dutch social media posts primarily discuss policy or health topics, whereas the English ones often revolve around social or sports events. Reviews typically cover literature in Dutch and various consumer goods in English. \n\n\nAlthough CLIN33 offers balanced classes across two languages and three genres, its size remains relatively small for training large-scale models. To address this limitation and diversify the textual patterns, we created an augmented version of the dataset. We started with the original texts and applied a series of transformations to each sample. These transformations included synonym substitution, random word swaps, random insertions, targeted deletions, variations in spelling, and back-translation\\footnote{Translating from English to Dutch and then back to English}. Each text thus produced several new variants that preserved the key semantics but introduced new surface forms. We ultimately obtained 9{,}720 total samples for training, split evenly between human and AI-generated content (4{,}860 each) and distributed equally across the three domains and the two languages. The final augmented corpus enables more robust feature learning.\n\n\\begin{wraptable}{l}{0.65\\textwidth} \n \\vspace{-1em}\n \\centering\n %\\small \n \\scriptsize\n \\caption{\\small Overview of data resources.}\n \\label{tab:combined_data_overview}\n %\\vspace{-8pt}\n \\begin{tabular}{p{2.5cm}p{3.4cm}p{3.0cm}} \n \\toprule\n & \\textbf{AuTexTification (EN)} & \\textbf{Augmented CLIN33} \\\\\n \\midrule\n \\textbf{Total Samples} & 33{,}845 & 9{,}720 \\\\\n \\textbf{Domains/Genres} & Tweets, Legal, Wiki & News, Twitter, Reviews \\\\\n \\textbf{Languages} & English & English, Dutch \\\\\n \\textbf{AI Models} & BLOOM & GPT‑4 \\\\[-2pt]\n & GPT‑3 & Vicu\\~na‑13B \\\\\n \\textbf{Human Sources} & MultiEURLEX, Amazon, XSUM, TSATC, WikiLingua\n & News, tweets, reviews \\\\\n \\textbf{Class Ratio} & 50.36\\% / 49.64~\\% & 50\\% / 50\\%\\\\\n \\textbf{Augmentations} & None & Yes (9) \\\\\n \\bottomrule\n \\end{tabular}\n \\vspace{-1em}\n\\end{wraptable}\n\n\nAs an additional resource for pre-training and fine-tuning, we employed a subset of the AuTexTification dataset\\footnote{\\url{https://sites.google.com/view/autextification}}, introduced by~\\citet{sarvazyan2023overview}. While the complete dataset includes English and Spanish texts in five distinct domains, we selectively used the \\emph{English-only} portion from tweets, legal, and wiki categories. This subset contains 33{,}845 texts designated for binary AI-detection (Task 1), split nearly evenly between human-written (50.36\\%) and AI-generated (49.64\\%) documents. The human content is drawn from sources such as MultiEURLEX, XSUM, Amazon Reviews, TSATC, and WikiLingua, while the AI-generated portion is produced by BLOOM (1B7, 3B, 7B1) and GPT-3 (babbage, curie, text-davinci-003). Because of its class balance and domain variety, this subset of AuTexTification serves as a valuable source of pre-training examples for our ensemble.\nTable~\\ref{tab:combined_data_overview} summarizes the essential attributes of the two datasets used to train and evaluate our methodology. The combined setup draws from the 33{,}845 samples in AuTexTification (English only) and the 9{,}720 augmented CLIN33 samples (both English and Dutch). We use AuTexTification Dataset for initial pre-training, 90\\% of the CLIN data for the Augmenting and training and 10\\% of unchanged CLIN data for evaluating.\n\n\n\\section{Methodology}\n\\label{sec:methodology}\nWe used an approach that integrates several models for text classification to address the AIGT detection task. First, we converted all text to lowercase to handle tokens in a consistent way. We then removed non-informative elements such as URLs, punctuation, and special characters, since these tokens often do not carry semantic meaning. After this cleaning, we split the text into tokens and applied lemmatization to map words to their base forms, which lowered data complexity and improved the model’s ability to recognize patterns.\n\n\\paragraph{\\textbf{Baselines Models.}} We built an XGBoost classifier by first transforming the text into numerical features with a TF-IDF vectorizer set to a maximum of 5000 features. The XGBoost model was configured with use\\_label\\_encoder=False and eval\\_metric='logloss'. Training used the TF-IDF representations from the training set, and final predictions on the test set were evaluated using precision, recall, F1, and accuracy. Also, We fine-tuned BERT-base (bert-base-uncased), DistilBERT (distilbert-base-uncased), and XLM-RoBERTa (xlm-roberta-base). The best checkpoint was chosen based on the highest F1 score on a validation subset. We then evaluated the final models on the test data. \n\n\n\n\\begin{wrapfigure}{r}{0.48\\textwidth}\n \\vspace{-1em}\n \\centering\n \\begin{minipage}{\\linewidth}\n \\begin{algorithm}[H]\n \\footnotesize\n \\setstretch{0.83}\n \\caption{Combined BERT Ensemble Structure}\n \\label{alg:combinedmodel}\n\n \\raggedright\n \\textbf{Input:} Text $x$\\\\\n \\textbf{Output:} Predicted label \\{\\emph{human}, \\emph{AI}\\}\\\\[2pt]\n \\par\n\n \\begin{algorithmic}[1]\n % ---------- Pre‑training block ----------\n \\Function{PretrainEnsemble}{$x$}\n \\State $\\text{bert\\_out} \\leftarrow \\text{BERTModel}_{\\text{frozen}}(x)$\n \\State $\\text{xlmr\\_out} \\leftarrow \\text{XLMRobertaModel}_{\\text{frozen}}(x)$\n \\State $\\text{distil\\_out} \\leftarrow \\text{DistilBERTModel}_{\\text{frozen}}(x)$\n \\State \\Return $(\\text{bert\\_out},\\ \\text{xlmr\\_out},\\ \\text{distil\\_out})$\n \\EndFunction\n\n % ---------- Fine‑tuning block ----------\n \\Function{FineTuneEnsemble}{$x$}\n \\State $\\text{bert\\_out\\_fresh} \\leftarrow \\text{BERTModel}_{\\text{fresh}}(x)$\n \\State $\\text{xlmr\\_out\\_fresh} \\leftarrow \\text{XLMRobertaModel}_{\\text{fresh}}(x)$\n \\State $\\text{distil\\_out\\_fresh} \\leftarrow \\text{DistilBERTModel}_{\\text{fresh}}(x)$\n \\State \\Return $(\\text{bert\\_out\\_fresh},\\ \\text{xlmr\\_out\\_fresh},\\ \\text{distil\\_out\\_fresh})$\n \\EndFunction\n\n % ---------- Combined model ----------\n \\Function{CombinedModel}{$x$}\n \\State $(\\text{froz\\_bert},\\ \\text{froz\\_xlmr},\\ \\text{froz\\_distil})\n \\leftarrow \\Call{PretrainEnsemble}{x}$\n \\State $(\\text{fresh\\_bert},\\ \\text{fresh\\_xlmr},\\ \\text{fresh\\_distil})\n \\leftarrow \\Call{FineTuneEnsemble}{x}$\n\n % split long concatenate call over two lines\n \\State $\\text{concat\\_outs} \\leftarrow \\text{concatenate}\\!\\bigl[%\n \\text{froz\\_bert},\\ \\text{froz\\_xlmr},\\ \\text{froz\\_distil},$\n \\Statex\\hspace{\\algorithmicindent}\\phantom{$\\text{concat\\_outs} \\leftarrow \\text{concatenate}\\!\\bigl[$}%\n $\\text{fresh\\_bert},\\ \\text{fresh\\_xlmr},\\ \\text{fresh\\_distil}\\bigr]$\n\n \\State $\\text{dense\\_out} \\leftarrow \\text{DenseLayer}(\\text{concat\\_outs})$ \\Comment{\\# L2‑regularized dense layer}\n \\State $\\hat{y} \\leftarrow \\sigma(\\text{dense\\_out})$ \\Comment{\\# Probability of AI‑generated text}\n \\If{$\\hat{y} \\geq 0.5$}\n \\State \\Return \\emph{AI}\n \\Else\n \\State \\Return \\emph{human}\n \\EndIf\n \\EndFunction\n \\end{algorithmic}\n \\end{algorithm}\n \\end{minipage}\n\\end{wrapfigure}\n\n\n\n\n\\paragraph{\\textbf{Ensemble Model.}}\nWe built an ensemble of multiple transformers instead of relying on a single one to classify human and AIGT), as inspired by~\\citet{mohammadi2023towards}. In this method, we use the strengths of each model and create an ensemble that can be fine-tuned on similar datasets. This helps the model adapt to different domains and reduces problems caused by limited data, as the features learned by each part complement each other. Specifically, we integrated bert-base-multilingual-uncased, distilbert-base-multilingual-cased, and xlm-roberta-base. Each transformer produced a representation of the input text, and we concatenated these representations before passing them through a dense layer with L2 regularization for final classification.\n\nWe first built a \\emph{Combined BERT} model by training three BERT models on the AuTexTification training set. We optimized the network's hyperparameters and evaluated performance on the AuTexTification test set, focusing on F1 and average metrics across folds. After training, we froze these model weights to retain the features they had learned and reduce the risk of overfitting. We next created a \\emph{combined model} that merges three BERT models with frozen weights (trained on AuTexTification) and three fresh BERT models with unfrozen parameters. These fresh models were fine-tuned on our newly augmented dataset Such an ensemble synergy aligns with~\\citet{fraser2025detecting}, who highlight the benefits of multi-model integration in detecting AI-generated text. Algorithm~\\ref{alg:combinedmodel} outlines the training and process for our combined BERT ensemble. During training, we minimize the binary cross-entropy loss on each branch using the augmented data. We then unify the outputs in a final fully connected layer, optionally employing majority voting if multiple final heads are used. Figure~\\ref{fig:ms-structure} shows the architecture, with frozen models on the left (gray) and fresh models on the right (green).\n\n\n\n\n\\begin{figure*}[!ht]\n\\centering\n\\includegraphics[width=0.7\\linewidth]{plots/model.jpeg}\n\\vspace{-5pt}\n\\caption{\\small The architecture of the proposed ensemble model. Outputs of three frozen BERT models (left) and three fresh BERT models (right) are concatenated and passed through dense layers, followed by a voting mechanism.}\n\\label{fig:ms-structure}\n\\end{figure*}\n\n\n\\subsection{Experimental Setup}\n\\label{subsec:setup}\n\nWe trained our models using the Hugging Face Transformers library\\footnote{\\url{https://huggingface.co/}} and set a maximum token length of 512. Hyperparameter tuning was done through a random search over learning rates (ranging from \\(1\\times 10^{-5}\\) to \\(1\\times 10^{-4}\\)) and batch sizes of 32, 64, or 128. We used a cosine decay schedule for learning rate adjustments, with 200 warm-up steps and an early-stopping patience of 5 epochs based on validation loss. \n\nThe final training stage used 90\\% of the augmented development set, while we left 10\\% for test. The hyperparameters and early-stopping rules were adjusted using the validation split. After training, we evaluated the system on the remaining data, measuring precision, recall, F1-score, and accuracy. By combining different transformers, freezing selectively learned features, and training fresh models in parallel, this ensemble method balanced the advantages of robust pre-trained representations with the adaptability needed for the new dataset.\n\n\\subsection{Explainability-driven detection}\n\\label{sec:explain}\n\nIn this section, we identify the words that most strongly influence an \\emph{AI-generated} prediction. Our approach uses two instance-level explanation tools: SHAP and LIME. Although they both operate on individual examples, SHAP uses game-theoretic principles to estimate a token’s overall (global) influence, while LIME explores small local perturbations to approximate importance. We take the absolute values of these importance scores $\\phi_j$ (for SHAP) or coefficients (for LIME) to capture both positive and negative contributions.\n\n\n \\setlength\\intextsep{0.3em} \n \\setlength\\columnsep{1em} \n \\begin{wrapfigure}[5]{r}{0.40\\textwidth} % [5]=height in text lines\n \\vspace*{-0.2em} % fine‑tune space above box\n \\begin{minipage}{\\linewidth}\\centering\\scriptsize\n \\[\n \\phi_j\n = \\sum_{S \\subseteq \\{1,\\dots,m\\}\\setminus\\{j\\}}\n \\frac{|S|!\\,\\bigl(m-|S|-1\\bigr)!}{m!}\\,\n \\bigl[f(S \\cup \\{j\\}) - f(S)\\bigr].\n \\]\n \\end{minipage}\n \\vspace*{-0.4em} % fine‑tune space below box\n \\end{wrapfigure}\n\nWe follow the methodology in~\\cite{mohammadi2024transparent}, in this framework, we only analyze sentences that the model \\emph{correctly} classifies, ensuring the tokens we highlight have genuinely contributed to accurate decisions. We then average each token’s importance within the sentence to get a consistent measure of its overall effect. This step helps us confirm that the extracted terms are meaningful indicators of the model’s reasoning. Given a trained model \\(f\\) and an instance \\(x\\) composed of tokens \\((t_1, t_2, \\ldots, t_m)\\), SHAP assigns a Shapley value \\(\\phi_j\\) to each token \\(t_j\\). These values are computed by measuring how much including or excluding \\(t_j\\) changes the model’s output, across all possible subsets of tokens. Formally:\n\n\nHere, \\(f(S)\\) is the model's prediction when only the tokens in set \\(S\\) are present. The weighting term ensures each token's effect is fairly distributed among all subsets~\\cite{lundberg2017unified}. LIME, on the other hand, makes small changes near the original text to learn a simpler, local model that approximates \\(f\\). It then assigns coefficients reflecting each token’s importance for that instance~\\cite{ribeiro2016should}. Although LIME focuses more on local neighborhoods, it also provides per-token contributions based on how the model’s prediction shifts when individual tokens are perturbed. We also consider a random token selection. This lets us compare how much improvement we gain by targeting the tokens that SHAP or LIME indicates are most influential. By using both a global (SHAP) and local (LIME) perspective, we can better trust that the identified tokens genuinely drive the model's decision. In our subsequent steps, we use these tokens to guide targeted replacements and measure how effectively such edits can mask AI-generated content.\n\n\\subsection{Token Replacement Strategies}\nOnce we identify influential tokens, we modify them using several approaches designed to make the text appear more human-like. We propose four rewriting strategies summarized in Table~\\ref{tab:token_replacement}.\n\n\\begin{table}[ht]\n\\centering\n\\scriptsize\n%\\small\n\\renewcommand{\\arraystretch}{1.2}\n\\caption{Summary of token replacement strategies}\n\\label{tab:token_replacement}\n\\begin{tabular}{p{4.8cm}p{9.2cm}}\n\\toprule\n\\textbf{Strategy} & \\textbf{Description} \\\\\n\\midrule\n\\textbf{Human Similar Word Replacement (HSR)} \n & A Word2Vec model is trained on all human-labeled texts in the training set to learn an embedding space that captures typical human usage. \\\\\n\\textbf{Part-of-Speech Replacement (PSR)} \n & HSR is refined by ensuring that part-of-speech tags match. \\\\\n\\textbf{GPT-based Replacement (GPT)} \n & A generative model (GPT-4o-mini) is queried using the prompt: \n \\texttt{\"Replace '\\{token\\}' with a more human-like word in this text: '\\{text\\}'\"}. \\\\\n\\textbf{GPT-based Replacement with Genre Context (GPT+Genre)} \n & The GPT-based method is extended by including explicit genre information (e.g., \\emph{tweets} or \\emph{reviews}) in the prompt:\n \\texttt{\"Replace '\\{token\\}' in this \\{domain\\} text with a more human-like word: '\\{text\\}'\"}. \\\\\n\\bottomrule\n\\end{tabular}\n\\end{table}\n\nIn \\emph{Strategy HSR}, each influential token is replaced with its nearest neighbor in the Word2Vec space, provided that the neighbor is frequently observed in human texts. In \\emph{Strategy PSR}, if a token \\(t\\) is tagged as a noun, for instance, a Word2Vec neighbor that is also tagged as a noun is selected, preserving grammatical structure. In \\emph{Strategy GPT}, a replacement that appears more \\emph{human-like} is proposed based on the surrounding sentence. While the suggestions are context-sensitive, certain LLM-specific patterns may still be retained if the generative model reintroduces specific phrasing. In \\emph{Strategy GPT+Genre}, incorporating domain-specific information allows the generative model to propose replacements that align more closely with the original style. However, this added context may also amplify distinctive AI traits. Finally, after substituting the most influential tokens in each text, we assess performance along two dimensions: (1) changes in detectability and (2) fidelity to the original content.\n\n\n\\subsection{Evaluation Metrics}\n\\label{subsec:evaluation_metrics}\n\nWe use a combination of detection and text-similarity metrics to evaluate how rewriting strategies affect our models' ability to detect AIGT, as well as how much the rewritten text deviates from its original form. First, once we rewrite an AIGT via HSR, PSR, GPT, or GPT+Genre. Next, we investigate how different rewriting scenarios affect AI-text detection. We adopt the adversarial rewriting perspective discussed in \\citet{fraser2025detecting}, \nwhere paraphrased or partially edited texts can undermine naive detectors. We run it through each classifier to check whether the label remains \\emph{AI} or flips to \\emph{human}. We then calculate precision, recall, F1, and accuracy to capture each model’s classification performance to detect AI/human texts under these modified inputs. Additionally, we compute the \\emph{flip rate}, defined as the fraction of AI texts whose predicted label changes to \\emph{human} after rewriting; a higher flip rate implies that the rewriting method is more effective in evading detection. In parallel, we calculate the degree of textual change introduced by each rewriting approach. We use BLEU, ROUGE-1, and ROUGE-L to measure the overlap between the original text and its rewritten form, with higher scores showing fewer modifications. By combining detection metrics, and text-similarity scores, we gain a clear view of how effectively each rewriting method hides AI-generated content and how much it changes the original text. \n\n\\section{Experimental Analysis and Results}\n\\label{sec:experiments}\n\nIn this section, we compare five classification models (XGBoost, BERT-base, DistilBERT, XLM-RoBERTa, and our \\textbf{Ensemble} approach) across various token-replacement scenarios for the detection of AIGT, Consistent with~\\citet{fraser2025detecting}, we anticipate that multi-domain evaluation is critical for robust results. Our main objective is to observe how different rewriting methods (HSR, PSR, GPT, GPT+Genre) combined with token-selection strategies (SHAP, LIME, Random) affect each model’s ability to detect generated text.\n\nTable~\\ref{tab:lang_domain_updated} shows each model’s performance on \\emph{unmodified} AIGT across two languages (English, Dutch) and three domains (news, reviews, and X/Twitter). As can be seen, although certain single models perform well on specific subsets (e.g., DistilBERT in English news, XGBoost in English Twitter), the \\textbf{Ensemble} approach tends to achieve the best performance in \\emph{most} cases. This pattern is particularly clear in Dutch news, where the Ensemble model attains perfect scores. Even when other models have high recall or precision on certain tasks, they often fall behind on at least one metric (e.g., XLM-RoBERTa predicts too many positives, increasing recall but lowering precision). showing strong cross-language adaptability. This result agrees with ~\\citet{fraser2025detecting}, who emphasize the importance of multi-lingual calibration for robust detection.\n\n\n\n%\n\\begin{table}[ht]\n\\centering\n%\\small\n\\scriptsize\n\\renewcommand{\\arraystretch}{1.2}\n\\caption{Model performance by language and domain on unmodified AI text. \\textbf{Bold} indicates the best result within each language and domain block.}\n\\label{tab:lang_domain_updated}\n\\begin{tabular}{llcccc|llcccc}\n\\toprule\n\\multicolumn{6}{c}{\\textbf{English Dataset}} & \\multicolumn{6}{c}{\\textbf{Dutch Dataset}} \\\\\n\\cmidrule(lr){1-6} \\cmidrule(lr){7-12}\n\\textbf{Model} & \\textbf{Domain} & \\textbf{Acc} & \\textbf{Prec} & \\textbf{Rec} & \\textbf{F1} \n & \\textbf{Model} & \\textbf{Domain} & \\textbf{Acc} & \\textbf{Prec} & \\textbf{Rec} & \\textbf{F1} \\\\\n\\midrule\n\\textbf{--- News ---} & & & & & \n& \\textbf{--- News ---} & & & & & \\\\\nXGBoost \n & news & 0.89 & 0.93 & 0.84 & 0.88 \n & XGBoost \n & news & 0.85 & 0.88 & 0.88 & 0.88 \\\\\nBERT-base \n & news & 0.94 & 0.97 & 0.91 & 0.94\n & BERT-base \n & news & 0.68 & 0.79 & 0.56 & 0.66 \\\\\nDistilBERT \n & news & 0.92 & 0.90 & \\textbf{0.95} & 0.92\n & DistilBERT \n & news & 0.68 & 0.73 & 0.66 & 0.69 \\\\\nXLM-RoBERTa\n & news & 0.49 & 0.49 & 1.00 & 0.66\n & XLM-RoBERTa\n & news & 0.53 & 0.53 & \\textbf{1.00} & 0.69 \\\\\nEnsemble \n & news & \\textbf{0.95} & \\textbf{1.00} & 0.90 & \\textbf{0.95}\n & Ensemble \n & news & \\textbf{0.99} & \\textbf{0.98} & \\textbf{1.00} & \\textbf{0.99} \\\\\n\\midrule\n\\textbf{--- Reviews ---} & & & & & \n& \\textbf{--- Reviews ---} & & & & & \\\\\nXGBoost \n & reviews & 0.51 & 0.57 & 0.59 & 0.58\n & XGBoost \n & reviews & 0.53 & 0.51 & 0.46 & 0.48 \\\\\nBERT-base \n & reviews & 0.50 & 0.54 & 0.62 & 0.58\n & BERT-base \n & reviews & 0.40 & 0.35 & 0.30 & 0.32 \\\\\nDistilBERT \n & reviews & 0.65 & 0.68 & 0.71 & 0.69\n & DistilBERT \n & reviews & 0.42 & 0.38 & 0.50 & 0.43 \\\\\nXLM-RoBERTa\n & reviews & 0.56 & 0.56 & \\textbf{0.90} & 0.69\n & XLM-RoBERTa\n & reviews & 0.43 & 0.43 & \\textbf{0.60} & 0.48 \\\\\nEnsemble \n & reviews & \\textbf{0.67} & \\textbf{0.71} & 0.68 & \\textbf{0.70}\n & Ensemble \n & reviews & \\textbf{0.60} & \\textbf{0.53} & 0.44 & \\textbf{0.49} \\\\\n\\midrule\n\\textbf{--- Twitter / X ---} & & & & & \n& \\textbf{--- Twitter / X ---} & & & & & \\\\\nXGBoost \n & twitter & 0.91 & \\textbf{0.92} & 0.98 & 0.95\n & XGBoost \n & twitter & 0.80 & 0.85 & 0.78 & 0.81 \\\\\nBERT-base \n & twitter & 0.90 & 0.86 & 0.98 & 0.92\n & BERT-base \n & twitter & 0.80 & 0.85 & 0.78 & 0.81 \\\\\nDistilBERT \n & twitter & 0.90 & 0.86 & 0.98 & 0.92\n & DistilBERT \n & twitter & 0.84 & 0.86 & 0.80 & 0.83 \\\\\nXLM-RoBERTa\n & twitter & 0.60 & 0.53 & \\textbf{1.00} & 0.69\n & XLM-RoBERTa\n & twitter & 0.62 & 0.60 & \\textbf{0.90} & 0.72 \\\\\nEnsemble \n & twitter & \\textbf{0.93} & 0.86 & \\textbf{1.00} & \\textbf{0.93}\n & Ensemble \n & twitter & \\textbf{0.87} & \\textbf{0.90} & 0.85 & \\textbf{0.87} \\\\\n\\bottomrule\n\\end{tabular}\n\\end{table}\n\n\nWe next investigate how different rewriting scenarios affect AI-text detection. In particular, we apply four rewriting strategies and combine each of these with one of three token-selection strategies: SHAP (prioritizes globally influential tokens), LIME (locally influential tokens), or Random (tokens chosen arbitrarily). Table~\\ref{tab:all_models_rewriting_f1acc} presents performance metrics for each model under all these rewriting conditions.\n\n% \\begin{table*}[ht]\n% \\centering\n% \\caption{\\small F1 score and Accuracy of each detector under every rewriting scenario. \n% The best metric in a column is \\textbf{bolded}; the largest negative drop is shown in \\textcolor{purple}{red}, the smallest in \\textcolor{teal}{green}. \n% \\footnotesize{$\\Delta=(\\text{Scenario Value}-\\text{Original Value})\\times100\\%$.}}\n% \\label{tab:all_models_rewriting_f1acc}\n% \\renewcommand{\\arraystretch}{1.4}\n% \\resizebox{\\textwidth}{!}{\n% \\begin{tabular}{l l\n% c c c c c c c c c c}\n% \\toprule\n% \\multirow{2}{*}{\\textbf{Explain}} & \\multirow{2}{*}{\\textbf{Strategy}}\n% & \\multicolumn{2}{c}{\\textbf{XGBoost}}\n% & \\multicolumn{2}{c}{\\textbf{BERT-base}}\n% & \\multicolumn{2}{c}{\\textbf{DistilBERT}}\n% & \\multicolumn{2}{c}{\\textbf{XLM-RoBERTa}}\n% & \\multicolumn{2}{c}{\\textbf{Ensemble}}\\\\\n% \\cmidrule(lr){3-4}\\cmidrule(lr){5-6}\\cmidrule(lr){7-8}\\cmidrule(lr){9-10}\\cmidrule(lr){11-12}\n% & & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\n% & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\n% & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\n% & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\n% & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\\\\\n% \\midrule\n% \\emph{NoRewriting} & Original\n% & 0.81 & 0.80\n% & 0.74 & 0.75\n% & 0.79 & 0.77\n% & 0.68 & 0.54\n% & \\textbf{0.83} & \\textbf{0.83}\\\\\n% \\midrule\n% \\textbf{SHAP} & HSR\n% & 0.73 (\\textcolor{purple}{-8\\%}) & 0.75 (\\textcolor{purple}{-5\\%})\n% & 0.71 (-3\\%) & 0.72 (-3\\%)\n% & 0.76 (-3\\%) & 0.75 (-2\\%)\n% & 0.68 (\\textcolor{teal}{0\\%}) & 0.54 (\\textcolor{teal}{0\\%})\n% & \\textbf{0.78} (-5\\%) & \\textbf{0.80} (-3\\%)\\\\\n% \\textbf{SHAP} & PSR\n% & 0.78 (\\textcolor{purple}{-3\\%}) & 0.78 (\\textcolor{purple}{-2\\%})\n% & 0.73 (-1\\%) & 0.74 (-1\\%)\n% & 0.78 (-1\\%) & 0.77 (0\\%)\n% & 0.68 (\\textcolor{teal}{0\\%}) & 0.54 (\\textcolor{teal}{0\\%})\n% & \\textbf{0.82} (-1\\%) & \\textbf{0.82} (-1\\%)\\\\\n% \\textbf{SHAP} & GPT\n% & 0.79 (-2\\%) & 0.79 (-1\\%)\n% & 0.73 (-1\\%) & 0.74 (-1\\%)\n% & 0.77 (-2\\%) & 0.76 (-1\\%)\n% & 0.68 (\\textcolor{teal}{0\\%}) & 0.55 (\\textcolor{teal}{+1\\%})\n% & \\textbf{0.80} (\\textcolor{purple}{-3\\%}) & \\textbf{0.81} (\\textcolor{purple}{-2\\%})\\\\\n% \\textbf{SHAP} & GPT+Genre\n% & \\textbf{0.80} (-1\\%) & \\textbf{0.80} (0\\%)\n% & 0.73 (-1\\%) & 0.74 (-1\\%)\n% & 0.77 (-2\\%) & 0.76 (-1\\%)\n% & 0.68 (\\textcolor{teal}{0\\%}) & 0.55 (+1\\%)\n% & 0.79 (\\textcolor{purple}{-4\\%}) & 0.80 (\\textcolor{purple}{-3\\%})\\\\\n% \\midrule\n% \\textbf{LIME} & HSR\n% & 0.25 (\\textcolor{purple}{-56\\%}) & 0.48 (\\textcolor{purple}{-32\\%})\n% & 0.56 (-18\\%) & 0.62 (-13\\%)\n% & 0.62 (-17\\%) & 0.65 (-12\\%)\n% & \\textbf{0.67} (\\textcolor{teal}{-1\\%}) & 0.52 (\\textcolor{teal}{-2\\%})\n% & 0.64 (-19\\%) & \\textbf{0.69} (-14\\%)\\\\\n% \\textbf{LIME} & PSR\n% & 0.56 (\\textcolor{purple}{-25\\%}) & 0.63 (\\textcolor{purple}{-17\\%})\n% & 0.64 (-10\\%) & 0.67 (-8\\%)\n% & 0.71 (-8\\%) & 0.71 (-6\\%)\n% & 0.66 (\\textcolor{teal}{-2\\%}) & 0.52 (\\textcolor{teal}{-2\\%})\n% & \\textbf{0.73} (-10\\%) & \\textbf{0.75} (-8\\%)\\\\\n% \\textbf{LIME} & GPT\n% & 0.57 (\\textcolor{purple}{-24\\%}) & 0.64 (\\textcolor{purple}{-16\\%})\n% & 0.73 (-1\\%) & 0.74 (-1\\%)\n% & 0.78 (-1\\%) & 0.77 (0\\%)\n% & 0.68 (\\textcolor{teal}{0\\%}) & 0.55 (\\textcolor{teal}{+1\\%})\n% & \\textbf{0.83} (\\textcolor{teal}{0\\%}) & \\textbf{0.83} (0\\%)\\\\\n% \\textbf{LIME} & GPT+Genre\n% & 0.60 (\\textcolor{purple}{-21\\%}) & 0.65 (\\textcolor{purple}{-15\\%})\n% & \\textbf{0.75} (\\textcolor{teal}{+1\\%}) & \\textbf{0.75} (0\\%)\n% & 0.77 (-2\\%) & 0.76 (-1\\%)\n% & 0.68 (0\\%) & 0.55 (\\textcolor{teal}{+1\\%})\n% & \\textbf{0.81} (-2\\%) & \\textbf{0.81} (-2\\%)\\\\\n% \\midrule\n% \\textbf{Random} & HSR\n% & 0.73 (-8\\%) & 0.74 (-6\\%)\n% & 0.63 (\\textcolor{purple}{-11\\%}) & 0.67 (\\textcolor{purple}{-8\\%})\n% & 0.68 (\\textcolor{purple}{-11\\%}) & 0.69 (\\textcolor{purple}{-8\\%})\n% & 0.67 (\\textcolor{teal}{-1\\%}) & 0.53 (\\textcolor{teal}{-1\\%})\n% & \\textbf{0.73} (-10\\%) & \\textbf{0.75} (\\textcolor{purple}{-8\\%})\\\\\n% \\textbf{Random} & PSR\n% & 0.76 (-5\\%) & 0.76 (-4\\%)\n% & 0.65 (\\textcolor{purple}{-9\\%}) & 0.68 (\\textcolor{purple}{-7\\%})\n% & 0.74 (-5\\%) & 0.73 (-4\\%)\n% & 0.67 (\\textcolor{teal}{-1\\%}) & 0.53 (\\textcolor{teal}{-1\\%})\n% & \\textbf{0.78} (-5\\%) & \\textbf{0.79} (-4\\%)\\\\\n% \\textbf{Random} & GPT\n% & 0.74 (\\textcolor{purple}{-7\\%}) & 0.75 (\\textcolor{purple}{-5\\%})\n% & 0.74 (0\\%) & 0.75 (0\\%)\n% & 0.77 (-2\\%) & 0.76 (-1\\%)\n% & 0.68 (\\textcolor{teal}{0\\%}) & 0.55 (\\textcolor{teal}{+1\\%})\n% & \\textbf{0.81} (-2\\%) & \\textbf{0.82} (-1\\%)\\\\\n% \\textbf{Random} & GPT+Genre\n% & 0.75 (\\textcolor{purple}{-6\\%}) & 0.75 (\\textcolor{purple}{-5\\%})\n% & 0.73 (-1\\%) & 0.74 (-1\\%)\n% & \\textbf{0.78} (-1\\%) & 0.77 (0\\%)\n% & 0.68 (\\textcolor{teal}{0\\%}) & 0.55 (\\textcolor{teal}{+1\\%})\n% & \\textbf{0.81} (-2\\%) & \\textbf{0.81} (-2\\%)\\\\\n% \\bottomrule\n% \\end{tabular}}\n% \\end{table*}\n\n\n \n\n\\begin{table*}[ht]\n\\centering\n\\caption{\\small F1 score and Accuracy of each detector under every rewriting scenario. \nThe best metric in a column is \\textbf{bolded}; the largest negative drop is \\dashuline{\\phantom{-00\\%}}, the smallest negative drop is \\dotuline{\\phantom{-00\\%}}.\n\\footnotesize{$\\Delta=(\\text{Scenario Value}-\\text{Original Value})\\times100\\%$.}}\n\\label{tab:all_models_rewriting_f1acc}\n\\renewcommand{\\arraystretch}{1.4}\n\\resizebox{\\textwidth}{!}{\n\\begin{tabular}{l l\nc c c c c c c c c c}\n\\toprule\n\\multirow{2}{*}{\\textbf{Explain}} & \\multirow{2}{*}{\\textbf{Strategy}}\n& \\multicolumn{2}{c}{\\textbf{XGBoost}}\n& \\multicolumn{2}{c}{\\textbf{BERT-base}}\n& \\multicolumn{2}{c}{\\textbf{DistilBERT}}\n& \\multicolumn{2}{c}{\\textbf{XLM-RoBERTa}}\n& \\multicolumn{2}{c}{\\textbf{Ensemble}}\\\\\n\\cmidrule(lr){3-4}\\cmidrule(lr){5-6}\\cmidrule(lr){7-8}\\cmidrule(lr){9-10}\\cmidrule(lr){11-12}\n& & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\n & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\n & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\n & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\n & \\textbf{F1 ($\\Delta$)} & \\textbf{Acc ($\\Delta$)}\\\\\n\\midrule\n\\emph{NoRewriting} & Original\n& 0.81 & 0.80\n& 0.74 & 0.75\n& 0.79 & 0.77\n& 0.68 & 0.54\n& \\textbf{0.83} & \\textbf{0.83}\\\\\n\\midrule\n\\textbf{SHAP} & HSR\n& 0.73 (\\largest{-8\\%}) & 0.75 (\\largest{-5\\%})\n& 0.71 (-3\\%) & 0.72 (-3\\%)\n& 0.76 (-3\\%) & 0.75 (-2\\%)\n& 0.68 (\\smallest{0\\%}) & 0.54 (\\smallest{0\\%})\n& \\textbf{0.78} (-5\\%) & \\textbf{0.80} (-3\\%)\\\\\n\\textbf{SHAP} & PSR\n& 0.78 (\\largest{-3\\%}) & 0.78 (\\largest{-2\\%})\n& 0.73 (-1\\%) & 0.74 (-1\\%)\n& 0.78 (-1\\%) & 0.77 (0\\%)\n& 0.68 (\\smallest{0\\%}) & 0.54 (\\smallest{0\\%})\n& \\textbf{0.82} (-1\\%) & \\textbf{0.82} (-1\\%)\\\\\n\\textbf{SHAP} & GPT\n& 0.79 (-2\\%) & 0.79 (-1\\%)\n& 0.73 (-1\\%) & 0.74 (-1\\%)\n& 0.77 (-2\\%) & 0.76 (-1\\%)\n& 0.68 (\\smallest{0\\%}) & 0.55 (\\smallest{+1\\%})\n& \\textbf{0.80} (\\largest{-3\\%}) & \\textbf{0.81} (\\largest{-2\\%})\\\\\n\\textbf{SHAP} & GPT+Genre\n& \\textbf{0.80} (-1\\%) & \\textbf{0.80} (0\\%)\n& 0.73 (-1\\%) & 0.74 (-1\\%)\n& 0.77 (-2\\%) & 0.76 (-1\\%)\n& 0.68 (\\smallest{0\\%}) & 0.55 (\\smallest{+1\\%})\n& 0.79 (\\largest{-4\\%}) & 0.80 (\\largest{-3\\%})\\\\\n\\midrule\n\\textbf{LIME} & HSR\n& 0.25 (\\largest{-56\\%}) & 0.48 (\\largest{-32\\%})\n& 0.56 (-18\\%) & 0.62 (-13\\%)\n& 0.62 (-17\\%) & 0.65 (-12\\%)\n& \\textbf{0.67} (\\smallest{-1\\%}) & 0.52 (\\smallest{-2\\%})\n& 0.64 (-19\\%) & \\textbf{0.69} (-14\\%)\\\\\n\\textbf{LIME} & PSR\n& 0.56 (\\largest{-25\\%}) & 0.63 (\\largest{-17\\%})\n& 0.64 (-10\\%) & 0.67 (-8\\%)\n& 0.71 (-8\\%) & 0.71 (-6\\%)\n& 0.66 (\\smallest{-2\\%}) & 0.52 (\\smallest{-2\\%})\n& \\textbf{0.73} (-10\\%) & \\textbf{0.75} (-8\\%)\\\\\n\\textbf{LIME} & GPT\n& 0.57 (\\largest{-24\\%}) & 0.64 (\\largest{-16\\%})\n& 0.73 (-1\\%) & 0.74 (-1\\%)\n& 0.78 (-1\\%) & 0.77 (0\\%)\n& 0.68 (\\smallest{0\\%}) & 0.55 (\\smallest{+1\\%})\n& \\textbf{0.83} (\\smallest{0\\%}) & \\textbf{0.83} (0\\%)\\\\\n\\textbf{LIME} & GPT+Genre\n& 0.60 (\\largest{-21\\%}) & 0.65 (\\largest{-15\\%})\n& \\textbf{0.75} (\\smallest{+1\\%}) & \\textbf{0.75} (0\\%)\n& 0.77 (-2\\%) & 0.76 (-1\\%)\n& 0.68 (0\\%) & 0.55 (\\smallest{+1\\%})\n& \\textbf{0.81} (-2\\%) & \\textbf{0.81} (-2\\%)\\\\\n\\midrule\n\\textbf{Random} & HSR\n& 0.73 (-8\\%) & 0.74 (-6\\%)\n& 0.63 (\\largest{-11\\%}) & 0.67 (\\largest{-8\\%})\n& 0.68 (\\largest{-11\\%}) & 0.69 (\\largest{-8\\%})\n& 0.67 (\\smallest{-1\\%}) & 0.53 (\\smallest{-1\\%})\n& \\textbf{0.73} (-10\\%) & \\textbf{0.75} (\\largest{-8\\%})\\\\\n\\textbf{Random} & PSR\n& 0.76 (-5\\%) & 0.76 (-4\\%)\n& 0.65 (\\largest{-9\\%}) & 0.68 (\\largest{-7\\%})\n& 0.74 (-5\\%) & 0.73 (-4\\%)\n& 0.67 (\\smallest{-1\\%}) & 0.53 (\\smallest{-1\\%})\n& \\textbf{0.78} (-5\\%) & \\textbf{0.79} (-4\\%)\\\\\n\\textbf{Random} & GPT\n& 0.74 (\\largest{-7\\%}) & 0.75 (\\largest{-5\\%})\n& 0.74 (0\\%) & 0.75 (0\\%)\n& 0.77 (-2\\%) & 0.76 (-1\\%)\n& 0.68 (\\smallest{0\\%}) & 0.55 (\\smallest{+1\\%})\n& \\textbf{0.81} (-2\\%) & \\textbf{0.82} (-1\\%)\\\\\n\\textbf{Random} & GPT+Genre\n& 0.75 (\\largest{-6\\%}) & 0.75 (\\largest{-5\\%})\n& 0.73 (-1\\%) & 0.74 (-1\\%)\n& \\textbf{0.78} (-1\\%) & 0.77 (0\\%)\n& 0.68 (0\\%) & 0.55 (\\smallest{+1\\%})\n& \\textbf{0.81} (-2\\%) & \\textbf{0.81} (-2\\%)\\\\\n\\bottomrule\n\\end{tabular}}\n\\end{table*}\n\n\n\\paragraph{\\textbf{General Trends.}}\nWhen no rewriting is applied (\\emph{NoRewriting, Original}), most models exhibit reasonably strong performance (precision and recall around 0.75--0.83), with the Ensemble reaching the highest overall F1 (0.83) and accuracy (0.83). After rewriting, simpler models often detect less, especially if the replaced tokens were the ones they used to identify AI patterns.\n\n\\paragraph{\\textbf{Comparison of Token-Selection Approaches.}}\nSHAP or LIME-based replacements often make larger drops in detection metrics than Random replacements, since replacing highly influential tokens more effectively removes AI indicators. For instance, comparing LIME+HSR vs.\\ Random+HSR, XGBoost’s F1 can decrease from around 0.73 to 0.25 if LIME is used, indicating a big loss in detection ability once key tokens are perturbed. On the other hand, random token replacement leaves more of the AI signals intact, so detection remains higher.\n\n\\paragraph{\\textbf{Influence of Rewriting Methods.}}\nHSR and PSR replace tokens with synonyms from a human corpus, often preserving the original text’s structure. This is reflected in relatively high BLEU scores (e.g., over 90\\% in the SHAP+HSR or SHAP+PSR rows), meaning minimal rewriting. Nonetheless, these small edits can still mislead detectors if the replaced tokens were crucial. GPT-based rewrites typically lower BLEU into the 70s or 80s, reflecting more substantial paraphrases. In turn, many detectors (e.g., XGBoost or XLM-RoBERTa) experience an increase in recall but a decrease in precision, or the opposite, because the text shifts away from familiar AI phrasing. GPT+Genre further contextualizes the rewrite with domain hints (e.g.\\ write it like a news article), causing domain-sensitive lexical changes. This can introduce new phrasing that confuses detectors reliant on domain-specific cues.\n\n\n\\paragraph{\\textbf{Model-Specific Observations.}}\n%From the model perspective, \nXLM-RoBERTa often shows high recall (sometimes hitting 1.0), but its precision remains around 0.52, leading to only modest changes in F1. DistilBERT is more balanced yet remains vulnerable when its top keywords are replaced by synonyms or paraphrases. BERT-base typically holds a moderate advantage across scenarios but can be undermined in LIME-based rewrites (e.g., LIME+HSR, where BERT-base F1 falls to about 0.56). By contrast, the \\textbf{Ensemble} approach consistently retains the highest or near-highest F1 and accuracy. For example, under LIME+GPT, the Ensemble still attains an F1 of 0.83, significantly above XGBoost’s 0.57 or BERT-base’s 0.73. Similarly, in SHAP plus GPT+Genre, the Ensemble preserves a strong F1 of about 0.79, while other models vary more dramatically. Also, We show text similarity scores (BLEU, ROUGE1, ROUGEL) to capture how extensively each rewrite modifies the original text.\n\n\n% \\begin{wraptable}{r}{0.62\\textwidth} \n% \\vspace{-0.8em} \n% \\centering\n% \\scriptsize\n% \\caption{\\small BLEU, ROUGE‑1 and ROUGE‑L scores for every rewriting scenario.}\n% \\label{tab:rewriting_similarity}\n% \\renewcommand{\\arraystretch}{1.2}\n% \\begin{tabular}{l l c c c}\n% \\toprule\n% \\textbf{Explain} & \\textbf{Strategy} & \\textbf{BLEU ($\\Delta$)} & \\textbf{ROUGE‑1 ($\\Delta$)} & \\textbf{ROUGE‑L ($\\Delta$)}\\\\\n% \\midrule\n% \\emph{NoRewriting} & Original & 1,00 & 1,00 & 1,00\\\\\n% \\midrule\n% \\textbf{SHAP} & HSR & 0,93 (\\textcolor{black}{-7\\%}) & 0,97 (\\textcolor{black}{-3\\%}) & 0,97 (\\textcolor{black}{-3\\%})\\\\\n% \\textbf{SHAP} & PSR & \\textbf{0,97} (\\textcolor{teal}{-3\\%}) & \\textbf{0,99} (\\textcolor{teal}{-1\\%}) & \\textbf{0,99} (\\textcolor{teal}{-1\\%})\\\\\n% \\textbf{SHAP} & GPT & 0,86 (\\textcolor{black}{-14\\%}) & 0,93 (\\textcolor{black}{-7\\%}) & 0,93 (\\textcolor{black}{-7\\%})\\\\\n% \\textbf{SHAP} & GPT+Genre & 0,86 (\\textcolor{black}{-14\\%}) & 0,94 (\\textcolor{black}{-6\\%}) & 0,94 (\\textcolor{black}{-6\\%})\\\\\n% \\midrule\n% \\textbf{LIME} & HSR & 0,84 (\\textcolor{black}{-16\\%}) & 0,92 (\\textcolor{black}{-8\\%}) & 0,92 (\\textcolor{black}{-8\\%})\\\\\n% \\textbf{LIME} & PSR & 0,90 (\\textcolor{black}{-10\\%}) & 0,96 (\\textcolor{black}{-5\\%}) & 0,95 (\\textcolor{black}{-5\\%})\\\\\n% \\textbf{LIME} & GPT & 0,76 (\\textcolor{purple}{-24\\%}) & 0,84 (\\textcolor{purple}{-16\\%}) & 0,84 (\\textcolor{purple}{-16\\%})\\\\\n% \\textbf{LIME} & GPT+Genre & 0,76 (\\textcolor{purple}{-24\\%}) & 0,85 (\\textcolor{black}{-15\\%}) & 0,84 (\\textcolor{purple}{-16\\%})\\\\\n% \\midrule\n% \\textbf{Random} & HSR & 0,84 (\\textcolor{black}{-16\\%}) & 0,92 (\\textcolor{black}{-8\\%}) & 0,92 (\\textcolor{black}{-8\\%})\\\\\n% \\textbf{Random} & PSR & 0,90 (\\textcolor{black}{-11\\%}) & 0,95 (\\textcolor{black}{-5\\%}) & 0,95 (\\textcolor{black}{-5\\%})\\\\\n% \\textbf{Random} & GPT & 0,76 (\\textcolor{purple}{-24\\%}) & 0,84 (\\textcolor{purple}{-16\\%}) & 0,84 (\\textcolor{purple}{-16\\%})\\\\\n% \\textbf{Random} & GPT+Genre & 0,76 (\\textcolor{purple}{-24\\%}) & 0,84 (\\textcolor{purple}{-16\\%}) & 0,84 (\\textcolor{purple}{-16\\%})\\\\\n% \\bottomrule\n% \\end{tabular}\n% \\vspace{-1em} \n% \\end{wraptable}\n\n\n\n\n\\paragraph{\\textbf{Text Similarity Implications.}}\n Table~\\ref{tab:rewriting_similarity} presents text similarity scores (BLEU, ROUGE1, ROUGEL) under all these rewriting conditions. Looking at BLEU and ROUGE scores confirms that GPT-based methods, especially GPT+Genre, introduce the largest textual shifts. Replacements guided by synonyms (HSR or PSR) keep BLEU near or above 90, whereas GPT rewrites can dip into the mid-70s. This trade-off highlights how more extensive rewriting can more thoroughly remove AI signatures at the expense of text fidelity.\n\n\n\n\n\\begin{wraptable}{r}{0.62\\textwidth} \n \\vspace{-0.8em} \n \\centering\n \\scriptsize\n \\caption{\\small BLEU, ROUGE‑1 and ROUGE‑L scores for every rewriting scenario.}\n \\label{tab:rewriting_similarity}\n \\renewcommand{\\arraystretch}{1.2}\n \\begin{tabular}{l l c c c}\n \\toprule\n \\textbf{Explain} & \\textbf{Strategy} & \\textbf{BLEU ($\\Delta$)} & \\textbf{ROUGE‑1 ($\\Delta$)} & \\textbf{ROUGE‑L ($\\Delta$)}\\\\\n \\midrule\n \\emph{NoRewriting} & Original & 1,00 & 1,00 & 1,00\\\\\n \\midrule\n \\textbf{SHAP} & HSR & 0,93 (-7\\%) & 0,97 (-3\\%) & 0,97 (-3\\%)\\\\\n \\textbf{SHAP} & PSR & \\textbf{0,97} (\\smallest{-3\\%}) & \\textbf{0,99} (\\smallest{-1\\%}) & \\textbf{0,99} (\\smallest{-1\\%})\\\\\n \\textbf{SHAP} & GPT & 0,86 (-14\\%) & 0,93 (-7\\%) & 0,93 (-7\\%)\\\\\n \\textbf{SHAP} & GPT+Genre & 0,86 (-14\\%) & 0,94 (-6\\%) & 0,94 (-6\\%)\\\\\n \\midrule\n \\textbf{LIME} & HSR & 0,84 (-16\\%) & 0,92 (-8\\%) & 0,92 (-8\\%)\\\\\n \\textbf{LIME} & PSR & 0,90 (-10\\%) & 0,96 (-5\\%) & 0,95 (-5\\%)\\\\\n \\textbf{LIME} & GPT & 0,76 (\\largest{-24\\%}) & 0,84 (\\largest{-16\\%}) & 0,84 (\\largest{-16\\%})\\\\\n \\textbf{LIME} & GPT+Genre & 0,76 (\\largest{-24\\%}) & 0,85 (-15\\%) & 0,84 (\\largest{-16\\%})\\\\\n \\midrule\n \\textbf{Random} & HSR & 0,84 (-16\\%) & 0,92 (-8\\%) & 0,92 (-8\\%)\\\\\n \\textbf{Random} & PSR & 0,90 (-11\\%) & 0,95 (-5\\%) & 0,95 (-5\\%)\\\\\n \\textbf{Random} & GPT & 0,76 (\\largest{-24\\%}) & 0,84 (\\largest{-16\\%}) & 0,84 (\\largest{-16\\%})\\\\\n \\textbf{Random} & GPT+Genre & 0,76 (\\largest{-24\\%}) & 0,84 (\\largest{-16\\%}) & 0,84 (\\largest{-16\\%})\\\\\n \\bottomrule\n \\end{tabular}\n %\\vspace{-1em} \n\\end{wraptable}\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\\paragraph{\\textbf{Flip Rates and Overlapping Flips}}\n\\label{subsec:flip_rates}\n\nTo estimate how many AIGTs are relabeled as \\emph{human} after rewriting, we track \\emph{flip rates} for each combination of explainability method, rewriting strategy, and model. A higher flip rate shows that the corresponding model is more easily deceived by that particular rewriting approach. In these experiments, LIME-based edits coupled with HSR lead to notably high misclassification rates in simpler models such as XGBoost (flipping up to 65\\% of the samples), whereas XLM-RoBERTa remains relatively robust at a mere 2.5\\% flip rate. GPT-based rewrites result in mid-level flip percentages overall; for example, under LIME+GPT, XGBoost flips 31.67\\% of texts while BERT-base and DistilBERT are both at around 9.17\\%. Moreover, SHAP+PSR frequently produces lower flip rates, showing that while part-of-speech-constrained edits can deceive less sophisticated classifiers, advanced or ensemble approaches remain relatively unaffected (with an Ensemble flip rate of only 2.5\\%). Notably, XLM-RoBERTa’s tendency to overpredict \\emph{AI} keeps its flips artificially low. The Ensemble, although not entirely immune, stays in the lower to mid-range across all scenarios, once again showing greater robustness than single-model detectors. Table~\\ref{tab:flip_rates_split} provides a detailed breakdown of which sets of models flip each sample in various scenarios. \n\n% \\begin{table}[ht]\n% \\centering\n% \\scriptsize\n% \\caption{Flip rates (\\%) from \\textbf{AI} to \\textbf{human}. \n% Each scenario contains 120 AI-labelled items; higher values mean the\n% detector is easier to ``flip'' by rewriting.}\n% \\label{tab:flip_rates_split}\n% \\begin{subtable}[t]{0.48\\linewidth}\n% \\centering\n% \\caption{HSR \\& PSR strategies}\n% \\resizebox{\\linewidth}{!}{%\n% \\begin{tabular}{ll\n% l\n% S S S}\n% \\toprule\n% \\textbf{Exp.} & \\textbf{Strat.} & \\textbf{Model} &\n% {\\#AI} & {Flips} & {Rate} \\\\\n% \\midrule\n% % ---------- HSR ----------\n% \\multirow{15}{*}{SHAP}\n% & \\multirow{5}{*}{HSR}\n% & XGBoost & 120 & 17 & 14.17 \\\\\n% & & BERT & 120 & 7 & 5.83 \\\\\n% & & DistilBERT & 120 & 9 & 7.50 \\\\\n% & & XLM-R & 120 & 1 & 0.83 \\\\\n% & & Ensemble & 120 & 8 & 6.67 \\\\\n% \\cmidrule(lr){2-6}\n% & \\multirow{5}{*}{PSR}\n% & XGBoost & 120 & 7 & 5.83 \\\\\n% & & BERT & 120 & 4 & 3.33 \\\\\n% & & DistilBERT & 120 & 4 & 3.33 \\\\\n% & & XLM-R & 120 & 0 & 0.00 \\\\\n% & & Ensemble & 120 & 3 & 2.50 \\\\\n% \\midrule\n% \\multirow{10}{*}{LIME}\n% & \\multirow{5}{*}{HSR}\n% & XGBoost & 120 & 78 & 65.00 \\\\\n% & & BERT & 120 & 31 & 25.83 \\\\\n% & & DistilBERT & 120 & 32 & 26.67 \\\\\n% & & XLM-R & 120 & 3 & 2.50 \\\\\n% & & Ensemble & 120 & 33 & 27.50 \\\\\n% \\cmidrule(lr){2-6}\n% & \\multirow{5}{*}{PSR}\n% & XGBoost & 120 & 46 & 38.33 \\\\\n% & & BERT & 120 & 20 & 16.67 \\\\\n% & & DistilBERT & 120 & 19 & 15.83 \\\\\n% & & XLM-R & 120 & 5 & 4.17 \\\\\n% & & Ensemble & 120 & 19 & 15.83 \\\\\n% \\midrule\n% \\multirow{10}{*}{Rand}\n% & \\multirow{5}{*}{HSR}\n% & XGBoost & 120 & 15 & 12.50 \\\\\n% & & BERT & 120 & 20 & 16.67 \\\\\n% & & DistilBERT & 120 & 22 & 18.33 \\\\\n% & & XLM-R & 120 & 2 & 1.67 \\\\\n% & & Ensemble & 120 & 20 & 16.67 \\\\\n% \\cmidrule(lr){2-6}\n% & \\multirow{5}{*}{PSR}\n% & XGBoost & 120 & 10 & 8.33 \\\\\n% & & BERT & 120 & 17 & 14.17 \\\\\n% & & DistilBERT & 120 & 11 & 9.17 \\\\\n% & & XLM-R & 120 & 1 & 0.83 \\\\\n% & & Ensemble & 120 & 14 & 11.67 \\\\\n% \\bottomrule\n% \\end{tabular}}\n% \\end{subtable}%\n% \\hfill\n% \\begin{subtable}[t]{0.48\\linewidth}\n% \\centering\n% \\caption{GPT \\& GPT-Genre strategies}\n% \\resizebox{\\linewidth}{!}{%\n% \\begin{tabular}{ll\n% l\n% S S S}\n% \\toprule\n% \\textbf{Exp.} & \\textbf{Strat.} & \\textbf{Model} &\n% {\\#AI} & {Flips} & {Rate} \\\\\n% \\midrule\n% % ---------- GPT ----------\n% \\multirow{15}{*}{SHAP}\n% & \\multirow{5}{*}{GPT}\n% & XGBoost & 120 & 4 & 3.33 \\\\\n% & & BERT & 120 & 7 & 5.83 \\\\\n% & & DistilBERT & 120 & 7 & 5.83 \\\\\n% & & XLM-R & 120 & 0 & 0.00 \\\\\n% & & Ensemble & 120 & 10 & 8.33 \\\\\n% \\cmidrule(lr){2-6}\n% & \\multirow{5}{*}{GPT+G}\n% & XGBoost & 120 & 4 & 3.33 \\\\\n% & & BERT & 120 & 3 & 2.50 \\\\\n% & & DistilBERT & 120 & 6 & 5.00 \\\\\n% & & XLM-R & 120 & 0 & 0.00 \\\\\n% & & Ensemble & 120 & 11 & 9.17 \\\\\n% \\midrule\n% \\multirow{10}{*}{LIME}\n% & \\multirow{5}{*}{GPT}\n% & XGBoost & 120 & 38 & 31.67 \\\\\n% & & BERT & 120 & 11 & 9.17 \\\\\n% & & DistilBERT & 120 & 11 & 9.17 \\\\\n% & & XLM-R & 120 & 0 & 0.00 \\\\\n% & & Ensemble & 120 & 11 & 9.17 \\\\\n% \\cmidrule(lr){2-6}\n% & \\multirow{5}{*}{GPT+G}\n% & XGBoost & 120 & 41 & 34.17 \\\\\n% & & BERT & 120 & 4 & 3.33 \\\\\n% & & DistilBERT & 120 & 7 & 5.83 \\\\\n% & & XLM-R & 120 & 0 & 0.00 \\\\\n% & & Ensemble & 120 & 9 & 7.50 \\\\\n% \\midrule\n% \\multirow{10}{*}{Rand}\n% & \\multirow{5}{*}{GPT}\n% & XGBoost & 120 & 16 & 13.33 \\\\\n% & & BERT & 120 & 6 & 5.00 \\\\\n% & & DistilBERT & 120 & 6 & 5.00 \\\\\n% & & XLM-R & 120 & 0 & 0.00 \\\\\n% & & Ensemble & 120 & 5 & 4.17 \\\\\n% \\cmidrule(lr){2-6}\n% & \\multirow{5}{*}{GPT+G}\n% & XGBoost & 120 & 13 & 10.83 \\\\\n% & & BERT & 120 & 6 & 5.00 \\\\\n% & & DistilBERT & 120 & 9 & 7.50 \\\\\n% & & XLM-R & 120 & 0 & 0.00 \\\\\n% & & Ensemble & 120 & 11 & 9.17 \\\\\n% \\bottomrule\n% \\end{tabular}}\n% \\end{subtable}\n% \\end{table}\n\n\n\n\\begin{table}[ht]\n\\centering\n\\scriptsize\n\\renewcommand{\\arraystretch}{1.15} % Slightly tighter row spacing\n\\setlength{\\tabcolsep}{4pt} % Adjust column spacing as desired\n\n\\caption{Flip rates (\\%) from \\textbf{AI} to \\textbf{human}, grouped by rewriting strategies. \n Each scenario has 120 AI-labeled items; higher values indicate greater susceptibility to ``flipping.''}\n\\label{tab:flip_rates_split}\n\n\\begin{subtable}[t]{0.48\\textwidth}\n \\centering\n \\caption{HSR \\& PSR Strategies}\n \\label{tab:flip_hsr_psr}\n \\begin{tabular}{\n l\n l\n l\n S[table-format=3.0]\n S[table-format=2.0]\n S[table-format=3.2]\n }\n \\toprule\n \\textbf{Explain} & \\textbf{Strategy} & \\textbf{Model} \n & {\\#AI} & {Flips} & {Flip Rate} \\\\\n \\midrule\n % --- SHAP, HSR ---\n \\multirow{10}{*}{SHAP}\n & \\multirow{5}{*}{HSR}\n & XGBoost & 120 & 17 & 14.17 \\\\\n & & BERT & 120 & 7 & 5.83 \\\\\n & & DistilBERT & 120 & 9 & 7.50 \\\\\n & & XLM-R & 120 & 1 & 0.83 \\\\\n & & Ensemble & 120 & 8 & 6.67 \\\\\n \\cmidrule(lr){2-6}\n % --- SHAP, PSR ---\n & \\multirow{5}{*}{PSR}\n & XGBoost & 120 & 7 & 5.83 \\\\\n & & BERT & 120 & 4 & 3.33 \\\\\n & & DistilBERT & 120 & 4 & 3.33 \\\\\n & & XLM-R & 120 & 0 & 0.00 \\\\\n & & Ensemble & 120 & 3 & 2.50 \\\\\n \\midrule\n % --- LIME, HSR ---\n \\multirow{10}{*}{LIME}\n & \\multirow{5}{*}{HSR}\n & XGBoost & 120 & 78 & 65.00 \\\\\n & & BERT & 120 & 31 & 25.83 \\\\\n & & DistilBERT & 120 & 32 & 26.67 \\\\\n & & XLM-R & 120 & 3 & 2.50 \\\\\n & & Ensemble & 120 & 33 & 27.50 \\\\\n \\cmidrule(lr){2-6}\n % --- LIME, PSR ---\n & \\multirow{5}{*}{PSR}\n & XGBoost & 120 & 46 & 38.33 \\\\\n & & BERT & 120 & 20 & 16.67 \\\\\n & & DistilBERT & 120 & 19 & 15.83 \\\\\n & & XLM-R & 120 & 5 & 4.17 \\\\\n & & Ensemble & 120 & 19 & 15.83 \\\\\n \\midrule\n % --- Random, HSR ---\n \\multirow{10}{*}{Random}\n & \\multirow{5}{*}{HSR}\n & XGBoost & 120 & 15 & 12.50 \\\\\n & & BERT & 120 & 20 & 16.67 \\\\\n & & DistilBERT & 120 & 22 & 18.33 \\\\\n & & XLM-R & 120 & 2 & 1.67 \\\\\n & & Ensemble & 120 & 20 & 16.67 \\\\\n \\cmidrule(lr){2-6}\n % --- Random, PSR ---\n & \\multirow{5}{*}{PSR}\n & XGBoost & 120 & 10 & 8.33 \\\\\n & & BERT & 120 & 17 & 14.17 \\\\\n & & DistilBERT & 120 & 11 & 9.17 \\\\\n & & XLM-R & 120 & 1 & 0.83 \\\\\n & & Ensemble & 120 & 14 & 11.67 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{subtable}\n\\hfill\n\\begin{subtable}[t]{0.48\\textwidth}\n \\centering\n \\caption{GPT \\& GPT+Genre Strategies}\n \\label{tab:flip_gpt_gptg}\n \\begin{tabular}{\n l\n l\n l\n S[table-format=3.0]\n S[table-format=2.0]\n S[table-format=3.2]\n }\n \\toprule\n \\textbf{Explain} & \\textbf{Strategy} & \\textbf{Model} \n & {\\#AI} & {Flips} & {Flip Rate} \\\\\n \\midrule\n % --- SHAP, GPT ---\n \\multirow{10}{*}{SHAP}\n & \\multirow{5}{*}{GPT}\n & XGBoost & 120 & 4 & 3.33 \\\\\n & & BERT & 120 & 7 & 5.83 \\\\\n & & DistilBERT & 120 & 7 & 5.83 \\\\\n & & XLM-R & 120 & 0 & 0.00 \\\\\n & & Ensemble & 120 & 10 & 8.33 \\\\\n \\cmidrule(lr){2-6}\n % --- SHAP, GPT+Genre ---\n & \\multirow{5}{*}{GPT+Genre}\n & XGBoost & 120 & 4 & 3.33 \\\\\n & & BERT & 120 & 3 & 2.50 \\\\\n & & DistilBERT & 120 & 6 & 5.00 \\\\\n & & XLM-R & 120 & 0 & 0.00 \\\\\n & & Ensemble & 120 & 11 & 9.17 \\\\\n \\midrule\n % --- LIME, GPT ---\n \\multirow{10}{*}{LIME}\n & \\multirow{5}{*}{GPT}\n & XGBoost & 120 & 38 & 31.67 \\\\\n & & BERT & 120 & 11 & 9.17 \\\\\n & & DistilBERT & 120 & 11 & 9.17 \\\\\n & & XLM-R & 120 & 0 & 0.00 \\\\\n & & Ensemble & 120 & 11 & 9.17 \\\\\n \\cmidrule(lr){2-6}\n % --- LIME, GPT+Genre ---\n & \\multirow{5}{*}{GPT+Genre}\n & XGBoost & 120 & 41 & 34.17 \\\\\n & & BERT & 120 & 4 & 3.33 \\\\\n & & DistilBERT & 120 & 7 & 5.83 \\\\\n & & XLM-R & 120 & 0 & 0.00 \\\\\n & & Ensemble & 120 & 9 & 7.50 \\\\\n \\midrule\n % --- Random, GPT ---\n \\multirow{10}{*}{Random}\n & \\multirow{5}{*}{GPT}\n & XGBoost & 120 & 16 & 13.33 \\\\\n & & BERT & 120 & 6 & 5.00 \\\\\n & & DistilBERT & 120 & 6 & 5.00 \\\\\n & & XLM-R & 120 & 0 & 0.00 \\\\\n & & Ensemble & 120 & 5 & 4.17 \\\\\n \\cmidrule(lr){2-6}\n % --- Random, GPT+Genre ---\n & \\multirow{5}{*}{GPT+Genre}\n & XGBoost & 120 & 13 & 10.83 \\\\\n & & BERT & 120 & 6 & 5.00 \\\\\n & & DistilBERT & 120 & 9 & 7.50 \\\\\n & & XLM-R & 120 & 0 & 0.00 \\\\\n & & Ensemble & 120 & 11 & 9.17 \\\\\n \\bottomrule\n \\end{tabular}\n\\end{subtable}\n\n\\end{table}\n\n\nAs shown in Table~\\ref{tab:flip_rates_split}, many rewriting strategies cause a lot of flips for some detectors, especially the simpler ones. However, the Ensemble model usually has a lower flip rate. This shows that using several models together makes detection more reliable. Figure~\\ref{fig:all_upset_plots} also shows how the flipped items overlap for each rewriting method. Even when many detectors are affected by these edits, the Ensemble is less likely to be fooled, highlighting the advantage of using an ensemble approach\n\n\n\\begin{figure*}[ht]\n\\centering\n\n\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_SHAP_HSR.png}\n\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_SHAP_PSR.png}\n\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_SHAP_GPT.png}\n\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_SHAP_GPT+Genre.png}\n\n\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_LIME_HSR.png}\n\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_LIME_PSR.png}\n\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_LIME_GPT.png}\n\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_LIME_GPT+Genre.png}\n\n\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_Random_HSR.png}\n\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_Random_PSR.png}\n\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_Random_GPT.png}\n\\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_Random_GPT+Genre.png}\n\n\\caption{\\small Overlaps among flipped AI samples under different explainability methods and rewriting strategies. Each bar corresponds to the number of samples simultaneously flipped by one or more models.}\n\\label{fig:all_upset_plots}\n\\end{figure*}\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n% \\paragraph{\\textbf{Flip Rates and Overlapping Flips}}\n% \\label{subsec:flip_rates}\n\n% To estimate how many AIGTs are relabeled as \\emph{human} after rewriting, we track \\emph{flip rates} for each combination of explainability method, rewriting strategy, and model. A higher flip rate shows that the corresponding model is more easily deceived by that particular rewriting approach. In these experiments, LIME-based edits coupled with HSR lead to notably high misclassification rates in simpler models such as XGBoost (flipping up to 65\\% of the samples), whereas XLM-RoBERTa remains relatively robust at a mere 2.5\\% flip rate. GPT-based rewrites result in mid-level flip percentages overall; for example, under LIME+GPT, XGBoost flips 31.67\\% of texts while BERT-base and DistilBERT are both at around 9.17\\%. Moreover, SHAP+PSR frequently produces lower flip rates, showing that while part-of-speech-constrained edits can deceive less sophisticated classifiers, advanced or ensemble approaches remain relatively unaffected (with an Ensemble flip rate of only 2.5\\%). Notably, XLM-RoBERTa’s tendency to overpredict \\emph{AI} keeps its flips artificially low. The Ensemble, although not entirely immune, stays in the lower to mid-range across all scenarios, once again signaling greater robustness than single-model detectors. we show all flip rates in Appendix~\\ref{appendix:overlap_table}. To show how models overlap in flipping the same AIGT subsets, we provide all relevant plots in Appendix~\\ref{appendix:upset_plots}. These plots illustrate how different token-selection methods and rewriting techniques affect flipped intersections among models.Figure~\\ref{fig:overlap_stacked} shows these results in a stacked-bar format, showing that although certain scenarios (e.g., LIME+HSR) can trick multiple models on the same items, the Ensemble remains less prone to flipping.\n\n% \\begin{figure*}[ht]\n% \\centering\n% \\includegraphics[width=0.9\\textwidth]{plots/overlap_stacked_bar_all_scenarios.png}\n \n% \\caption{\\small Overlap chart of flip rates. Each colored segment shows how many AI items become misclassified as human by a specific model. (All values underlying the colored segments are provided in Appendix~\\ref{appendix:overlap_table} for accessibility and reproducibility.)}\n% \\label{fig:overlap_stacked}\n% \\end{figure*}\n\n\nIn general, GPT-based replacements make text more natural or domain-specific, which greatly impacts models that rely on simple token patterns for detection. HSR and PSR (especially with SHAP/LIME) can also be effective while keeping a higher BLEU score, meaning fewer changes. Since PSR aligns part-of-speech tags, the text remains fluent but loses patterns AI might use, leading to performance drops in classifiers like XLM-RoBERTa and XGBoost. In contrast, our Ensemble model is more robust, as its combined transformer knowledge helps it detect AI content even after targeted token edits. Overall, our results show that the Ensemble model is the most accurate and resilient, effectively handling English and Dutch texts across different genres. While rewriting reduces detection rates, it does not fully bypass the Ensemble model.\n\n\\section{Discussion and Conclusion}\n\\label{sec:discussion_conclusion}\n%\\vspace{-5pt}\nOur findings show that explanation techniques such as SHAP and LIME can guide systematic token replacements that make AIGT less likely to be classified as \\emph{AI-generated}. When we identify and edit tokens most indicative of artificial content, simpler models like XGBoost can be misled at high rates, especially under LIME-based rewrites combined with synonym substitutions. Yet even more advanced models, such as BERT-base or DistilBERT, still show vulnerability in certain scenarios, particularly when GPT-generated replacements transform the text more extensively. These methods, however, do not completely deceive our Ensemble model, which remains comparatively robust across languages, genres, and rewriting methods.\n\nFrom the perspective of text similarity, we see that methods like HSR and PSR typically achieve higher BLEU and ROUGE scores, indicating minimal alterations to the original text. This can preserve meaning and style, but targeted token swaps can still hide enough AI signals to disrupt certain detectors. In contrast, GPT-based rewrites, especially GPT+Genre, often provide more comprehensive revisions, making the text harder to classify but also changing more from the source. Although such extensive rewriting can produce higher flip rates, our Ensemble classifier still maintains better overall performance than single-model systems.\n\nIn practice, these results underscore both the potential and the limitations of adversarial rewriting. Minor edits guided by SHAP or LIME can undermine certain classifiers. Future work might extend to more sophisticated paraphrasing of entire sentences or incorporate style transfer models that better replicate a specific human author. At the same time, it is crucial to consider watermarking and policy frameworks that ensure transparency when AIGT is involved. Our experiments show that even when AI content is skillfully masked, detection remains possible with multi-architecture approaches.\n\nIn conclusion, our study shows how explanation methods can inform token replacements to disguise AIGT, yet also reveals that robust ensemble classifiers are harder to deceive. Balancing improved detection methods with the need for responsible use of writing aids will remain an important challenge. We hope these findings contribute to a deeper understanding of both the potential vulnerabilities in existing AI detection models and the methods that can keep them resilient against adversarial transformations.\n\n\\begin{acks}\nWe thank the CLIN33 shared task organizers for making the dataset available, and we appreciate the helpful feedback from colleagues who guided our research directions. This work is partially supported by the Dutch Research Council (NWO) (grant number: VI.Vidi.195.152 to D. L. Oberski). We also thank SURF for providing the computational facilities. \n\n%The authors used OpenAI's ChatGPT-4o exclusively for grammar and style checks. They have reviewed the content thoroughly and take full responsibility for the final manuscript.\n\\end{acks}\n\n\n\\bibliographystyle{ACM-Reference-Format}\n\\bibliography{01-sample-base}\n\n\n\n \\appendix\n\n% \\section{Overlap Among Flipped Samples}\n% \\label{appendix:overlap_table}\n\n% Table~\\ref{tab:flip_rates_split} provides a detailed of which sets of models flip each sample in various scenarios. For instance, under LIME+GPT, 28 items are flipped exclusively by XGBoost, eight solely by the Ensemble, etc. Although there are shared flips across multiple models, the Ensemble consistently shows smaller overlaps, reinforcing its comparative resilience.\n\n\n% \\begin{table}[H]\n% \\centering\n% \\scriptsize\n% \\caption{Flip rates (\\%) from \\textbf{AI} to \\textbf{human}. \n% Each scenario contains 120 AI-labelled items; higher values mean the\n% detector is easier to “flip’’ by rewriting.}\n% \\label{tab:flip_rates_split}\n% \\begin{subtable}[t]{0.48\\linewidth}\n% \\centering\n% \\caption{HSR \\& PSR strategies}\n% \\resizebox{\\linewidth}{!}{%\n% \\begin{tabular}{ll\n% l\n% S S S}\n% \\toprule\n% \\textbf{Exp.} & \\textbf{Strat.} & \\textbf{Model} &\n% {\\#AI} & {Flips} & {Rate} \\\\\n% \\midrule\n% % ---------- HSR ----------\n% \\multirow{15}{*}{SHAP}\n% & \\multirow{5}{*}{HSR}\n% & XGBoost & 120 & 17 & 14.17 \\\\\n% & & BERT & 120 & 7 & 5.83 \\\\\n% & & DistilBERT & 120 & 9 & 7.50 \\\\\n% & & XLM-R & 120 & 1 & 0.83 \\\\\n% & & Ensemble & 120 & 8 & 6.67 \\\\\n% \\cmidrule(lr){2-6}\n% & \\multirow{5}{*}{PSR}\n% & XGBoost & 120 & 7 & 5.83 \\\\\n% & & BERT & 120 & 4 & 3.33 \\\\\n% & & DistilBERT & 120 & 4 & 3.33 \\\\\n% & & XLM-R & 120 & 0 & 0.00 \\\\\n% & & Ensemble & 120 & 3 & 2.50 \\\\\n% \\midrule\n% \\multirow{10}{*}{LIME}\n% & \\multirow{5}{*}{HSR}\n% & XGBoost & 120 & 78 & 65.00 \\\\\n% & & BERT & 120 & 31 & 25.83 \\\\\n% & & DistilBERT & 120 & 32 & 26.67 \\\\\n% & & XLM-R & 120 & 3 & 2.50 \\\\\n% & & Ensemble & 120 & 33 & 27.50 \\\\\n% \\cmidrule(lr){2-6}\n% & \\multirow{5}{*}{PSR}\n% & XGBoost & 120 & 46 & 38.33 \\\\\n% & & BERT & 120 & 20 & 16.67 \\\\\n% & & DistilBERT & 120 & 19 & 15.83 \\\\\n% & & XLM-R & 120 & 5 & 4.17 \\\\\n% & & Ensemble & 120 & 19 & 15.83 \\\\\n% \\midrule\n% \\multirow{10}{*}{Rand}\n% & \\multirow{5}{*}{HSR}\n% & XGBoost & 120 & 15 & 12.50 \\\\\n% & & BERT & 120 & 20 & 16.67 \\\\\n% & & DistilBERT & 120 & 22 & 18.33 \\\\\n% & & XLM-R & 120 & 2 & 1.67 \\\\\n% & & Ensemble & 120 & 20 & 16.67 \\\\\n% \\cmidrule(lr){2-6}\n% & \\multirow{5}{*}{PSR}\n% & XGBoost & 120 & 10 & 8.33 \\\\\n% & & BERT & 120 & 17 & 14.17 \\\\\n% & & DistilBERT & 120 & 11 & 9.17 \\\\\n% & & XLM-R & 120 & 1 & 0.83 \\\\\n% & & Ensemble & 120 & 14 & 11.67 \\\\\n% \\bottomrule\n% \\end{tabular}}\n% \\end{subtable}%\n% \\hfill\n% \\begin{subtable}[t]{0.48\\linewidth}\n% \\centering\n% \\caption{GPT \\& GPT-Genre strategies}\n% \\resizebox{\\linewidth}{!}{%\n% \\begin{tabular}{ll\n% l\n% S S S}\n% \\toprule\n% \\textbf{Exp.} & \\textbf{Strat.} & \\textbf{Model} &\n% {\\#AI} & {Flips} & {Rate} \\\\\n% \\midrule\n% % ---------- GPT ----------\n% \\multirow{15}{*}{SHAP}\n% & \\multirow{5}{*}{GPT}\n% & XGBoost & 120 & 4 & 3.33 \\\\\n% & & BERT & 120 & 7 & 5.83 \\\\\n% & & DistilBERT & 120 & 7 & 5.83 \\\\\n% & & XLM-R & 120 & 0 & 0.00 \\\\\n% & & Ensemble & 120 & 10 & 8.33 \\\\\n% \\cmidrule(lr){2-6}\n% & \\multirow{5}{*}{GPT+G}\n% & XGBoost & 120 & 4 & 3.33 \\\\\n% & & BERT & 120 & 3 & 2.50 \\\\\n% & & DistilBERT & 120 & 6 & 5.00 \\\\\n% & & XLM-R & 120 & 0 & 0.00 \\\\\n% & & Ensemble & 120 & 11 & 9.17 \\\\\n% \\midrule\n% \\multirow{10}{*}{LIME}\n% & \\multirow{5}{*}{GPT}\n% & XGBoost & 120 & 38 & 31.67 \\\\\n% & & BERT & 120 & 11 & 9.17 \\\\\n% & & DistilBERT & 120 & 11 & 9.17 \\\\\n% & & XLM-R & 120 & 0 & 0.00 \\\\\n% & & Ensemble & 120 & 11 & 9.17 \\\\\n% \\cmidrule(lr){2-6}\n% & \\multirow{5}{*}{GPT+G}\n% & XGBoost & 120 & 41 & 34.17 \\\\\n% & & BERT & 120 & 4 & 3.33 \\\\\n% & & DistilBERT & 120 & 7 & 5.83 \\\\\n% & & XLM-R & 120 & 0 & 0.00 \\\\\n% & & Ensemble & 120 & 9 & 7.50 \\\\\n% \\midrule\n% \\multirow{10}{*}{Rand}\n% & \\multirow{5}{*}{GPT}\n% & XGBoost & 120 & 16 & 13.33 \\\\\n% & & BERT & 120 & 6 & 5.00 \\\\\n% & & DistilBERT & 120 & 6 & 5.00 \\\\\n% & & XLM-R & 120 & 0 & 0.00 \\\\\n% & & Ensemble & 120 & 5 & 4.17 \\\\\n% \\cmidrule(lr){2-6}\n% & \\multirow{5}{*}{GPT+G}\n% & XGBoost & 120 & 13 & 10.83 \\\\\n% & & BERT & 120 & 6 & 5.00 \\\\\n% & & DistilBERT & 120 & 9 & 7.50 \\\\\n% & & XLM-R & 120 & 0 & 0.00 \\\\\n% & & Ensemble & 120 & 11 & 9.17 \\\\\n% \\bottomrule\n% \\end{tabular}}\n% \\end{subtable}\n% \\end{table}\n\n\n\n\n% \\section{Plots for Overlap Analysis}\n% \\label{appendix:upset_plots}\n\n% Figure~\\ref{fig:all_upset_plots} presents all plots covering every combination of \\{\\texttt{SHAP}, \\texttt{LIME}, \\texttt{Random}\\} $\\times$ \\{\\texttt{HSR}, \\texttt{PSR}, \\texttt{GPT}, \\texttt{GPT+Genre}\\}. Each subfigure depicts how many AI samples were flipped by one or more models under that scenario.\n\n% \\begin{figure*}[ht]\n% \\centering\n\n% \\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_SHAP_HSR.png}\n% \\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_SHAP_PSR.png}\n% \\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_SHAP_GPT.png}\n% \\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_SHAP_GPT+Genre.png}\n\n% \\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_LIME_HSR.png}\n% \\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_LIME_PSR.png}\n% \\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_LIME_GPT.png}\n% \\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_LIME_GPT+Genre.png}\n\n% \\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_Random_HSR.png}\n% \\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_Random_PSR.png}\n% \\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_Random_GPT.png}\n% \\includegraphics[width=0.24\\textwidth]{plots/flip/upset_flips_Random_GPT+Genre.png}\n\n% \\caption{\\small Overlaps among flipped AI samples under different explainability methods and rewriting strategies. Each bar corresponds to the number of samples simultaneously flipped by one or more models.}\n% \\label{fig:all_upset_plots}\n% \\end{figure*}\n\n\n\\clearpage\n\\section*{Reproducibility Checklist for JAIR}\n\n\\noindent\\textbf{All articles:}\n\\begin{enumerate}\n \\item \\textbf{All claims investigated in this work are clearly stated.} \\\\\n \\textit{Answer:} Yes, we clearly state our main claims: we want to see how effective rewriting with SHAP/LIME is, and how an ensemble model performs.\n \n \\item \\textbf{Clear explanations are given how the work reported substantiates the claims.} \\\\\n \\textit{Answer:} Yes, we explain how each experiment’s results support our claims.\n \n \\item \\textbf{Limitations or technical assumptions are stated clearly and explicitly.} \\\\\n \\textit{Answer:} Some. We focus on detection and rewriting only, not on other possible aspects.\n \n \\item \\textbf{Conceptual outlines and/or pseudo-code descriptions of the AI methods introduced in this work are provided, and important implementation details are discussed.} \\\\\n \\textit{Answer:} Yes, our method flow is clearly described, especially for the ensemble approach in the algorithm~\\ref{fig:ms-structure}.\n \n \\item \\textbf{Motivation is provided for all design choices, including algorithms, implementation choices, parameters, data sets, and experimental protocols beyond metrics.} \\\\\n \\textit{Answer:} Yes, we explain why we use these models, datasets, and rewriting strategies.\n\\end{enumerate}\n\n\\subsection*{Articles containing theoretical contributions}\n\\noindent\\textit{Does this paper make theoretical contributions?} \\\\\n\\textit{Answer:} No. (We do not do formal theoretical proofs.)\n\n\\subsection*{Articles reporting on computational experiments}\n\\noindent\\textit{Does this paper include computational experiments?} \\\\\n\\textit{Answer:} Yes.\n\\begin{enumerate}\n \\item \\textbf{All source code required for experiments is included or will be made publicly available upon publication, following best practices.} \\\\\n \\textit{Answer:} We plan to share the main code base (e.g., on GitHub).\n \n \\item \\textbf{The source code comes with a license that allows free usage for reproducibility purposes.} \\\\\n \\textit{Answer:} Yes, we use a permissive license.\n \n \\item \\textbf{The source code comes with a license that allows free usage for research purposes in general.} \\\\\n \\textit{Answer:} Yes.\n \n \\item \\textbf{Raw, unaggregated data from all experiments is included or will be made publicly available.} \\\\\n \\textit{Answer:} We will share our final splits, but some augmented data might be partial.\n \n \\item \\textbf{The unaggregated data comes with a license that allows free usage for reproducibility purposes.} \\\\\n \\textit{Answer:} Yes, as far as allowed by the original dataset licenses.\n \n \\item \\textbf{The unaggregated data comes with a license that allows free usage for research purposes in general.} \\\\\n \\textit{Answer:} Yes, within the limits of the original dataset licenses.\n \n \\item \\textbf{If an algorithm depends on randomness, the method used for generating random numbers and setting seeds is described sufficiently.} \\\\\n \\textit{Answer:} We set random seeds but do not deeply document random generation details.\n \n \\item \\textbf{The execution environment for experiments (hardware/software) is described.} \\\\\n \\textit{Answer:} Yes, we mention key libraries (e.g., Hugging Face Transformers, PyTorch).\n \n \\item \\textbf{The evaluation metrics used are clearly explained and motivated.} \\\\\n \\textit{Answer:} Yes, we use F1, accuracy, BLEU, and ROUGE and explain why.\n \n \\item \\textbf{The number of algorithm runs used to compute each result is reported.} \\\\\n \\textit{Answer:} We use cross-validation or mention repeated runs (varies).\n \n \\item \\textbf{Reported results have not been “cherry-picked.”} \\\\\n \\textit{Answer:} Yes, we show both successes and failures.\n \n \\item \\textbf{Analysis of results goes beyond single-dimensional summaries of performance.} \\\\\n \\textit{Answer:} We show F1, accuracy, and text-similarity. (We do not always show standard deviation or confidence intervals.)\n \n \\item \\textbf{All (hyper-) parameter settings for the algorithms/methods used are reported, along with the rationale or method for determining them.} \\\\\n \\textit{Answer:} Yes, we provide learning rates, batch sizes, etc.\n \n \\item \\textbf{The number and range of (hyper-) parameter settings explored prior to final experiments have been indicated.} \\\\\n \\textit{Answer:} We mention random search, though not each detail.\n \n \\item \\textbf{Appropriately chosen statistical hypothesis tests are used to establish significance in the presence of noise.} \\\\\n \\textit{Answer:} We do not do formal significance testing.\n\\end{enumerate}\n\n\\subsection*{Articles using data sets}\n\\noindent\\textit{Does this work rely on one or more data sets?} \\\\\n\\textit{Answer:} Yes.\n\n\\begin{enumerate}\n \\item \\textbf{All newly introduced data sets are included in an online appendix or will be made publicly available with a suitable license.} \\\\\n \\textit{Answer:} We mostly use public sets (CLIN33, AuTexTification) and will release our augmented version.\n \n \\item \\textbf{The newly introduced data set comes with a license that allows free usage for reproducibility purposes.} \\\\\n \\textit{Answer:} Yes, for the augmented subset.\n \n \\item \\textbf{The newly introduced data set comes with a license that allows free usage for research purposes in general.} \\\\\n \\textit{Answer:} Yes.\n \n \\item \\textbf{All data sets drawn from the literature or other public sources are accompanied by appropriate citations.} \\\\\n \\textit{Answer:} Yes, we cite the original authors.\n \n \\item \\textbf{All data sets drawn from the existing literature are publicly available.} \\\\\n \\textit{Answer:} Yes, with possible constraints.\n \n \\item \\textbf{All new or non-public data sets are described in detail, including relevant statistics, collection, and annotation processes.} \\\\\n \\textit{Answer:} Yes, for our augmented data.\n \n \\item \\textbf{All methods used for preprocessing, augmenting, batching, or splitting data sets are described in detail.} \\\\\n \\textit{Answer:} Yes, we detail how we clean and split.\n\\end{enumerate}\n\n\\subsection*{Explanations on any of the answers above (optional):}\nWe use mostly public data and provide augmented sets where possible. Some data licensing is inherited from the original corpora. Where possible, we provide reproducible code, scripts, and subsets to ensure others can replicate our results.\n\n\n\n\n\n\\end{document}\n\n\n"},"question":{"kind":"string","value":"Which model and data domain combination has the best detection performance without rewriting? What is the difference in F1 score between the best detection scenario after introducing explanatory rewriting and the best detection scenario without rewriting?"},"answer":{"kind":"string","value":"Ensemble model in Dutch, news. 0.16"},"plan":{"kind":"string","value":"SELECT all models\nSELECT all languages\nSELECT all domains\nSELECT F1 scores without rewriting\nLOOP for each model\n LOOP for each language–domain combination\n COMPUTE best F1 without rewriting for this combination\nCOMPUTE model–language–domain with the highest F1 without rewriting\n\n\nSELECT all explanatory rewriting strategies\nSELECT all detectors\nSELECT F1 scores with rewriting\nCOMPUTE best F1 among all explanatory rewriting scenarios\nCOMPUTE F1 difference between best rewriting F1 and best F1 without rewriting\nRETURN model–language–domain with highest F1 without rewriting, F1 difference\n"}}},{"rowIdx":96,"cells":{"paper":{"kind":"string","value":"2506.04583"},"relevant_tables":{"kind":"list like","value":[["\\end{figure*}\\begin{table*}[!t]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n","\\begin{tabular}{lccccccc}\n","\\toprule\n","\\multirow{2}{*}{Backbone LLM} & \\multirow{2}{*}{Setting} \n","& \\multicolumn{2}{c}{Top@3} & \\multicolumn{2}{c}{Top@5} & \\multicolumn{2}{c}{Top@10} \\\\\n","\\cmidrule(lr){3-4} \\cmidrule(lr){5-6} \\cmidrule(lr){7-8}\n"," & & Contriever & TFIDF & Contriever & TFIDF & Contriever & TFIDF \\\\\n","\\midrule\n","{{\\textbf{\\fm}}} \\\\\\noalign{\\vskip 0.5ex}\n","\\multirow{2}{*}{GPT-4o mini} &\\base & 64.5 & 53.5 & 65.0 & 59.0 & 67.5 & 61.5 \\\\\n","&\\claimdecompose & 65.0 {\\color{red}(+0.5)} & 55.0 {\\color{red}(+1.5)} & 65.0 {\\color{red}(+0.0)} & 61.0 {\\color{red}(+2.0)} & 65.5 {\\color{red}(-2.0)} & 63.0 {\\color{red}(+1.5)} \\\\\n","&\\qabrief & 65.5 {\\color{red}(+1.0)} & 57.0 {\\color{red}(+3.5)} & 67.0 {\\color{red}(+2.0)} & 62.0 {\\color{red}(+3.0)} & 67.5 {\\color{red}(+0.0)} & 62.5 {\\color{red}(+1.0)} \\\\\n","&\\programfc & 68.5 {\\color{red}(+4.0)} & 56.0 {\\color{red}(+2.5)} & 65.5 {\\color{red}(+0.5)} & 61.0 {\\color{red}(+2.0)} & 69.0 {\\color{red}(+1.5)} & 63.0 {\\color{red}(+1.5)} \\\\\n","&\\minicheck & 66.0 {\\color{red}(+1.5)} & 57.0 {\\color{red}(+3.5)} & 66.5 {\\color{red}(+1.5)} & 62.5 {\\color{red}(+3.5)} & 70.0 {\\color{red}(+2.5)} & 64.0 {\\color{red}(+2.5)}\\\\\n","&\\textbf{\\ours} & 69.0 {\\color{red}(+4.5)} & 61.5 {\\color{red}(+8.0)} & 72.5 {\\color{red}(+7.5)} & 67.0 {\\color{red}(+8.0)} & 75.0 {\\color{red}(+7.5)} & 68.5 {\\color{red}(+7.0)} \\\\\n","\\hdashline\n","\\multirow{2}{*}{LLama 3.1 70B} &\\base & 61.0 & 56.5 & 64.5 & 59.0 & 65.5 & 61.5 \\\\\n","&\\claimdecompose & 62.0 {\\color{red}(+1.0)} & 57.0 {\\color{red}(+0.5)} & 65.5 {\\color{red}(+1.0)} & 61.0 {\\color{red}(+2.0)} & 67.5 {\\color{red}(+2.0)} & 64.0 {\\color{red}(+2.5)} \\\\\n","&\\qabrief & 66.0 {\\color{red}(+5.0)} & 56.5 {\\color{red}(+0.0)} & 67.5 {\\color{red}(+3.0)} & 62.5 {\\color{red}(+3.5)} & 66.0 {\\color{red}(+0.5)} & 64.0 {\\color{red}(+2.5)} \\\\\n","&\\programfc & 62.0 {\\color{red}(+1.0)} & 58.0 {\\color{red}(+1.5)} & 65.0 {\\color{red}(+0.5)} & 61.0 {\\color{red}(+2.0)} & 66.0 {\\color{red}(+0.5)} & 64.5 {\\color{red}(+3.0)} \\\\\n","&\\minicheck &67.5 {\\color{red}(+6.5)} & 57.5 {\\color{red}(+1.0)} & 68.0 {\\color{red}(+3.5)} & 62.0 {\\color{red}(+3.0)} & 70.5 {\\color{red}(+5.0)} & 64.0 {\\color{red}(+2.5)} \\\\\n","&\\textbf{\\ours} & 71.0 {\\color{red}(+10.0)} & 63.5 {\\color{red}(+7.0)} & 72.5 {\\color{red}(+8.0)} & 67.5 {\\color{red}(+8.5)} & 73.5 {\\color{red}(+8.0)} & 69.0 {\\color{red}(+7.5)} \\\\\n","\\hline\n","{{\\textbf{\\wice}}} \\\\\\noalign{\\vskip 0.5ex}\n","\\multirow{2}{*}{GPT-4o mini} &\\base & 27.2 & 24.0 & 28.6 & 25.8 & 33.7 & 27.3 \\\\\n","&\\claimdecompose & 29.1 {\\color{red}(+1.9)} & 29.1 {\\color{red}(+5.1)} & 30.1 {\\color{red}(+1.5)} & 30.4 {\\color{red}(+4.6)} & 35.2 {\\color{red}(+1.5)} & 31.0 {\\color{red}(+3.7)} \\\\\n","&\\qabrief & 28.0 {\\color{red}(+0.8)} & 27.5 {\\color{red}(+3.5)} & 30.9 {\\color{red}(+2.3)} & 27.8 {\\color{red}(+2.0)} & 36.2 {\\color{red}(+2.5)} & 29.7 {\\color{red}(+2.4)} \\\\\n","&\\programfc & 33.2 {\\color{red}(+6.0)} & 26.8 {\\color{red}(+2.8)} & 34.8 {\\color{red}(+6.2)} & 28.7 {\\color{red}(+2.9)} & 38.4 {\\color{red}(+4.7)} & 29.9 {\\color{red}(+2.6)} \\\\\n","&\\minicheck &30.0 {\\color{red}(+2.8)} & 27.1 {\\color{red}(+3.1)} & 31.3 {\\color{red}(+2.7)} & 26.9 {\\color{red}(+1.1)} & 33.7 {\\color{red}(+0.0)} & 30.8 {\\color{red}(+3.5)} \\\\\n","&\\textbf{\\ours} & 34.1 {\\color{red}(+6.9)} & 31.0 {\\color{red}(+7.0)} & 37.2 {\\color{red}(+8.6)} & 28.1 {\\color{red}(+2.3)} & 39.0 {\\color{red}(+5.3)} & 33.2 {\\color{red}(+5.9)} \\\\\n","\\hdashline\n","\\multirow{2}{*}{LLama 3.1 70B} &\\base & 28.1 & 22.1 & 29.6 & 24.5 & 31.2 & 25.7 \\\\\n","&\\claimdecompose & 26.3 {\\color{red}(-1.8)} & 23.0 {\\color{red}(+0.9)} & 30.1 {\\color{red}(+0.5)} & 25.4 {\\color{red}(+0.9)} & 33.0 {\\color{red}(+1.8)} & 27.1 {\\color{red}(+1.4)} \\\\\n","&\\qabrief & 30.4 {\\color{red}(+2.3)} & 25.1 {\\color{red}(+3.0)} & 32.4 {\\color{red}(+2.8)} & 27.0 {\\color{red}(+2.5)} & 33.5 {\\color{red}(+2.3)} & 28.2 {\\color{red}(+2.5)} \\\\\n","&\\programfc & 34.6 {\\color{red}(+6.5)} & 24.7 {\\color{red}(+2.6)} & 36.5 {\\color{red}(+6.9)} & 26.9 {\\color{red}(+2.4)} & 33.9 {\\color{red}(+2.7)} & 29.3 {\\color{red}(+3.6)} \\\\\n","&\\minicheck & 39.1 {\\color{red}(+11.0)} & 27.8 {\\color{red}(+5.7)} & 34.9 {\\color{red}(+5.3)} & 27.9 {\\color{red}(+3.4)} & 35.2 {\\color{red}(+4.0)} & 30.1 {\\color{red}(+4.4)} \\\\\n","&\\textbf{\\ours} & 38.2 {\\color{red}(+10.1)} & 30.3 {\\color{red}(+8.2)} & 39.0 {\\color{red}(+9.4)} & 31.8 {\\color{red}(+7.3)} & 38.7 {\\color{red}(+7.5)} & 32.9 {\\color{red}(+7.2)} \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\caption{Comprehensive fact-checking evaluation results comparing \\ours with baseline approaches on \\fm and \\wice datasets. Results are reported as accuracy scores across different retrieval settings (Top@3, Top@5, Top@10). Numbers in red parentheses show absolute performance improvements over the \\base baseline.}\n","\\label{tab:whole_factcheckresults}\n","\\end{table*}\\begin{table*}[!t]\n"]],"string":"[\n [\n \"\\\\end{figure*}\\\\begin{table*}[!t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \"\\\\begin{tabular}{lccccccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{Backbone LLM} & \\\\multirow{2}{*}{Setting} \\n\",\n \"& \\\\multicolumn{2}{c}{Top@3} & \\\\multicolumn{2}{c}{Top@5} & \\\\multicolumn{2}{c}{Top@10} \\\\\\\\\\n\",\n \"\\\\cmidrule(lr){3-4} \\\\cmidrule(lr){5-6} \\\\cmidrule(lr){7-8}\\n\",\n \" & & Contriever & TFIDF & Contriever & TFIDF & Contriever & TFIDF \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"{{\\\\textbf{\\\\fm}}} \\\\\\\\\\\\noalign{\\\\vskip 0.5ex}\\n\",\n \"\\\\multirow{2}{*}{GPT-4o mini} &\\\\base & 64.5 & 53.5 & 65.0 & 59.0 & 67.5 & 61.5 \\\\\\\\\\n\",\n \"&\\\\claimdecompose & 65.0 {\\\\color{red}(+0.5)} & 55.0 {\\\\color{red}(+1.5)} & 65.0 {\\\\color{red}(+0.0)} & 61.0 {\\\\color{red}(+2.0)} & 65.5 {\\\\color{red}(-2.0)} & 63.0 {\\\\color{red}(+1.5)} \\\\\\\\\\n\",\n \"&\\\\qabrief & 65.5 {\\\\color{red}(+1.0)} & 57.0 {\\\\color{red}(+3.5)} & 67.0 {\\\\color{red}(+2.0)} & 62.0 {\\\\color{red}(+3.0)} & 67.5 {\\\\color{red}(+0.0)} & 62.5 {\\\\color{red}(+1.0)} \\\\\\\\\\n\",\n \"&\\\\programfc & 68.5 {\\\\color{red}(+4.0)} & 56.0 {\\\\color{red}(+2.5)} & 65.5 {\\\\color{red}(+0.5)} & 61.0 {\\\\color{red}(+2.0)} & 69.0 {\\\\color{red}(+1.5)} & 63.0 {\\\\color{red}(+1.5)} \\\\\\\\\\n\",\n \"&\\\\minicheck & 66.0 {\\\\color{red}(+1.5)} & 57.0 {\\\\color{red}(+3.5)} & 66.5 {\\\\color{red}(+1.5)} & 62.5 {\\\\color{red}(+3.5)} & 70.0 {\\\\color{red}(+2.5)} & 64.0 {\\\\color{red}(+2.5)}\\\\\\\\\\n\",\n \"&\\\\textbf{\\\\ours} & 69.0 {\\\\color{red}(+4.5)} & 61.5 {\\\\color{red}(+8.0)} & 72.5 {\\\\color{red}(+7.5)} & 67.0 {\\\\color{red}(+8.0)} & 75.0 {\\\\color{red}(+7.5)} & 68.5 {\\\\color{red}(+7.0)} \\\\\\\\\\n\",\n \"\\\\hdashline\\n\",\n \"\\\\multirow{2}{*}{LLama 3.1 70B} &\\\\base & 61.0 & 56.5 & 64.5 & 59.0 & 65.5 & 61.5 \\\\\\\\\\n\",\n \"&\\\\claimdecompose & 62.0 {\\\\color{red}(+1.0)} & 57.0 {\\\\color{red}(+0.5)} & 65.5 {\\\\color{red}(+1.0)} & 61.0 {\\\\color{red}(+2.0)} & 67.5 {\\\\color{red}(+2.0)} & 64.0 {\\\\color{red}(+2.5)} \\\\\\\\\\n\",\n \"&\\\\qabrief & 66.0 {\\\\color{red}(+5.0)} & 56.5 {\\\\color{red}(+0.0)} & 67.5 {\\\\color{red}(+3.0)} & 62.5 {\\\\color{red}(+3.5)} & 66.0 {\\\\color{red}(+0.5)} & 64.0 {\\\\color{red}(+2.5)} \\\\\\\\\\n\",\n \"&\\\\programfc & 62.0 {\\\\color{red}(+1.0)} & 58.0 {\\\\color{red}(+1.5)} & 65.0 {\\\\color{red}(+0.5)} & 61.0 {\\\\color{red}(+2.0)} & 66.0 {\\\\color{red}(+0.5)} & 64.5 {\\\\color{red}(+3.0)} \\\\\\\\\\n\",\n \"&\\\\minicheck &67.5 {\\\\color{red}(+6.5)} & 57.5 {\\\\color{red}(+1.0)} & 68.0 {\\\\color{red}(+3.5)} & 62.0 {\\\\color{red}(+3.0)} & 70.5 {\\\\color{red}(+5.0)} & 64.0 {\\\\color{red}(+2.5)} \\\\\\\\\\n\",\n \"&\\\\textbf{\\\\ours} & 71.0 {\\\\color{red}(+10.0)} & 63.5 {\\\\color{red}(+7.0)} & 72.5 {\\\\color{red}(+8.0)} & 67.5 {\\\\color{red}(+8.5)} & 73.5 {\\\\color{red}(+8.0)} & 69.0 {\\\\color{red}(+7.5)} \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"{{\\\\textbf{\\\\wice}}} \\\\\\\\\\\\noalign{\\\\vskip 0.5ex}\\n\",\n \"\\\\multirow{2}{*}{GPT-4o mini} &\\\\base & 27.2 & 24.0 & 28.6 & 25.8 & 33.7 & 27.3 \\\\\\\\\\n\",\n \"&\\\\claimdecompose & 29.1 {\\\\color{red}(+1.9)} & 29.1 {\\\\color{red}(+5.1)} & 30.1 {\\\\color{red}(+1.5)} & 30.4 {\\\\color{red}(+4.6)} & 35.2 {\\\\color{red}(+1.5)} & 31.0 {\\\\color{red}(+3.7)} \\\\\\\\\\n\",\n \"&\\\\qabrief & 28.0 {\\\\color{red}(+0.8)} & 27.5 {\\\\color{red}(+3.5)} & 30.9 {\\\\color{red}(+2.3)} & 27.8 {\\\\color{red}(+2.0)} & 36.2 {\\\\color{red}(+2.5)} & 29.7 {\\\\color{red}(+2.4)} \\\\\\\\\\n\",\n \"&\\\\programfc & 33.2 {\\\\color{red}(+6.0)} & 26.8 {\\\\color{red}(+2.8)} & 34.8 {\\\\color{red}(+6.2)} & 28.7 {\\\\color{red}(+2.9)} & 38.4 {\\\\color{red}(+4.7)} & 29.9 {\\\\color{red}(+2.6)} \\\\\\\\\\n\",\n \"&\\\\minicheck &30.0 {\\\\color{red}(+2.8)} & 27.1 {\\\\color{red}(+3.1)} & 31.3 {\\\\color{red}(+2.7)} & 26.9 {\\\\color{red}(+1.1)} & 33.7 {\\\\color{red}(+0.0)} & 30.8 {\\\\color{red}(+3.5)} \\\\\\\\\\n\",\n \"&\\\\textbf{\\\\ours} & 34.1 {\\\\color{red}(+6.9)} & 31.0 {\\\\color{red}(+7.0)} & 37.2 {\\\\color{red}(+8.6)} & 28.1 {\\\\color{red}(+2.3)} & 39.0 {\\\\color{red}(+5.3)} & 33.2 {\\\\color{red}(+5.9)} \\\\\\\\\\n\",\n \"\\\\hdashline\\n\",\n \"\\\\multirow{2}{*}{LLama 3.1 70B} &\\\\base & 28.1 & 22.1 & 29.6 & 24.5 & 31.2 & 25.7 \\\\\\\\\\n\",\n \"&\\\\claimdecompose & 26.3 {\\\\color{red}(-1.8)} & 23.0 {\\\\color{red}(+0.9)} & 30.1 {\\\\color{red}(+0.5)} & 25.4 {\\\\color{red}(+0.9)} & 33.0 {\\\\color{red}(+1.8)} & 27.1 {\\\\color{red}(+1.4)} \\\\\\\\\\n\",\n \"&\\\\qabrief & 30.4 {\\\\color{red}(+2.3)} & 25.1 {\\\\color{red}(+3.0)} & 32.4 {\\\\color{red}(+2.8)} & 27.0 {\\\\color{red}(+2.5)} & 33.5 {\\\\color{red}(+2.3)} & 28.2 {\\\\color{red}(+2.5)} \\\\\\\\\\n\",\n \"&\\\\programfc & 34.6 {\\\\color{red}(+6.5)} & 24.7 {\\\\color{red}(+2.6)} & 36.5 {\\\\color{red}(+6.9)} & 26.9 {\\\\color{red}(+2.4)} & 33.9 {\\\\color{red}(+2.7)} & 29.3 {\\\\color{red}(+3.6)} \\\\\\\\\\n\",\n \"&\\\\minicheck & 39.1 {\\\\color{red}(+11.0)} & 27.8 {\\\\color{red}(+5.7)} & 34.9 {\\\\color{red}(+5.3)} & 27.9 {\\\\color{red}(+3.4)} & 35.2 {\\\\color{red}(+4.0)} & 30.1 {\\\\color{red}(+4.4)} \\\\\\\\\\n\",\n \"&\\\\textbf{\\\\ours} & 38.2 {\\\\color{red}(+10.1)} & 30.3 {\\\\color{red}(+8.2)} & 39.0 {\\\\color{red}(+9.4)} & 31.8 {\\\\color{red}(+7.3)} & 38.7 {\\\\color{red}(+7.5)} & 32.9 {\\\\color{red}(+7.2)} \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Comprehensive fact-checking evaluation results comparing \\\\ours with baseline approaches on \\\\fm and \\\\wice datasets. Results are reported as accuracy scores across different retrieval settings (Top@3, Top@5, Top@10). Numbers in red parentheses show absolute performance improvements over the \\\\base baseline.}\\n\",\n \"\\\\label{tab:whole_factcheckresults}\\n\",\n \"\\\\end{table*}\\\\begin{table*}[!t]\\n\"\n ]\n]","isConversational":false},"tables":{"kind":"list like","value":[["\\begin{figure}[!t]\n"," \\centering\n"," \\includegraphics[width = \\linewidth]{figures_raw/main_example.pdf}\n"," \\caption{Examples of challenges in fact-checking adversarial claims. \n"," These claims are deliberately crafted by humans using strategies to deceive fact-checking systems. \n"," Even state-of-the-art approaches (\\ie RALM) face significant difficulties in accurately verifying these claims. \n"," }\n"," \\label{fig:task}\n","\\end{figure}\\section{Introduction}\n"],["\\begin{table}[t!]\n","\\centering \n","\\small\n","\\resizebox{\\columnwidth}{!}{\n","\\renewcommand{\\arraystretch}{1.2}\n","\\begin{tabular}{lrr}\n","\\toprule\n","\\textbf{Property} & \\textbf{\\fm} & \\textbf{\\wice} \\\\\n","\\midrule\n","\\multicolumn{3}{c}{\\textbf{Knowledge Corpus}} \\\\\n","Source & Wikipedia & Wikipedia \\\\\n","Knowledge Corpus Size &21,015,325 &21,015,325\\\\\n","\\noalign{\\vskip 0.5ex}\\hdashline\\noalign{\\vskip 0.5ex}\n","\\multicolumn{3}{c}{\\textbf{Evidence Retrieval \\& Fact Checking}} \\\\\n","\\# Evidence (avg.) & 1.29 & 3.94 \\\\\n","Claim Token Length (avg.) & 14.03 & 24.20\\\\\n","\n","Test Set Size (\\# Claims) & 200 & 358 \\\\\n","\\quad \\# Entailed Claims &103 &111\\\\\n","\\quad \\# Refuted Claims &97 &32\\\\\n","\\quad \\# Partially Supported Claims & N/A &215\\\\\n","\\toprule\n","\\end{tabular}\n","}\n","\\caption{Basic statistics of the \\fm and \\wice test sets used in our experiments. Due to limited budgets, we evaluate on the full test set of \\wice (358 instances) and 200 random samples from the \\fm test set.}\n","\\label{tab:basic-stats}\n","\\end{table}\n"],["\\begin{figure}[!t]\n"," \\centering\n"," \\includegraphics[width = 1.0\\linewidth]{figures_raw/pipeline.pdf}\n"," \\caption{An illustration of \\ours pipeline (left) and its workflow for adversarial fact-checking using an example from the \\fm dataset (right). The input claim is first segmented and decontextualized by LLMs. For each sub-claim, a first round of evidence retrieval is conducted, followed by sub-claim revision and a second round of evidence retrieval. The final step is label prediction for the claim. \n"," }\n"," \\label{fig:model}\n","\\end{figure}\n"],["\\begin{table}[t]\n","\\centering\n","\\small\n","\\resizebox{\\columnwidth}{!}{\n","\\renewcommand{\\arraystretch}{1.2}\n","\\begin{tabular}{lcccc}\n","\\toprule\n","\\multirow{2}{*}{System} & \\multicolumn{2}{c}{\\fm} & \\multicolumn{2}{c}{\\wice} \\\\\n","\\cmidrule{2-5}\n","& Contriever & TFIDF & Contriever & TFIDF \\\\\n","\\midrule\n","\\multicolumn{5}{l}{\\textbf{GPT-4o-mini}} \\\\\n","\\quad \\base & 67.5 & 61.5 & 33.7 & 27.3 \\\\\n","\\quad \\claimdecompose & 69.5 (\\textcolor{red}{+2.0}) & 63.0 (\\textcolor{red}{+1.5}) & 35.2 (\\textcolor{red}{+1.5}) & 31.0 (\\textcolor{red}{+3.7}) \\\\\n","\\quad \\qabrief & 67.5(\\textcolor{red}{+0.0})\t&62.5(\\textcolor{red}{+1.0}) & 36.2(\\textcolor{red}{+2.5}) &29.7(\\textcolor{red}{+2.4}) \\\\\n","\\quad \\programfc & 69.0 (\\textcolor{red}{+1.5}) & 63.0 (\\textcolor{red}{+1.5}) & 38.4 (\\textcolor{red}{+4.7}) & 29.9 (\\textcolor{red}{+2.6}) \\\\\n","\\quad \\minicheck & 70.0 (\\textcolor{red}{+2.5}) & 64.0 (\\textcolor{red}{+2.5}) & 33.7 (\\textcolor{red}{+0.0}) & 30.8 (\\textcolor{red}{+3.5})\\\\\n","\n","\\noalign{\\vskip 0.5ex}\\hdashline\\noalign{\\vskip 0.5ex}\n","\n","\\quad \\textbf{\\ours} & \\textbf{75.0 (\\textcolor{red}{+7.5})} & \\textbf{68.5 (\\textcolor{red}{+7.0})} & \\textbf{39.0 (\\textcolor{red}{+5.3})} & \\textbf{33.2 (\\textcolor{red}{+5.9})} \\\\\n","\\midrule\n","\\multicolumn{5}{l}{\\textbf{LLama-3.1-70B}} \\\\\n","\\quad \\base & 65.5 & 61.5 & 31.2 & 25.7 \\\\\n","\\quad \\claimdecompose & 67.5 (\\textcolor{red}{+2.0}) & 64.0 (\\textcolor{red}{+2.5}) & 33.0 (\\textcolor{red}{+1.8}) & 27.1 (\\textcolor{red}{+1.4}) \\\\\n","\\quad \\qabrief & 66.0(\\textcolor{red}{+0.5})\t&64.0(\\textcolor{red}{+2.5}) & 33.5(\\textcolor{red}{+2.3}) &28.2(\\textcolor{red}{+2.5}) \\\\\n","\\quad \\programfc & 66.0 (\\textcolor{red}{+0.5}) & 64.5 (\\textcolor{red}{+3.0}) & 33.9 (\\textcolor{red}{+2.7}) & 29.3 (\\textcolor{red}{+3.6}) \\\\\n","\\quad \\minicheck & 70.5 (\\textcolor{red}{+5.0}) & 64.0 (\\textcolor{red}{+2.5}) & 35.2 (\\textcolor{red}{+4.0}) & 30.1 (\\textcolor{red}{+4.4})\\\\\n","\n","\\noalign{\\vskip 0.5ex}\\hdashline\\noalign{\\vskip 0.5ex}\n","\n","\\quad \\textbf{\\ours} & \\textbf{73.5 (\\textcolor{red}{+8.0})} & \\textbf{69.0 (\\textcolor{red}{+7.5})} & \\textbf{38.7 (\\textcolor{red}{+7.5})} & \\textbf{32.9 (\\textcolor{red}{+7.2})} \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\caption{Fact-checking evaluation results under baselines and \\ours for \\fm and \\wice models.\n","Numbers in red parentheses indicate performance improvements over the baseline (\\base). }\n","\\label{tab:factcheckresults}\n","\\end{table}\n"],["\\begin{table*}[!t]\n","\\centering\n","\\resizebox{\\textwidth}{!}{%\n","\\addtolength{\\tabcolsep}{-0.35em}\n","\\begin{tabular}{lcccccccccccccc}\n","\\toprule\n"," \\multirow{4}{*}{System} & \\multicolumn{4}{c}{Top@3} & \\multicolumn{4}{c}{Top@5} & \\multicolumn{4}{c}{Top@10} \\\\\n"," \\cmidrule(lr){2-5} \\cmidrule(lr){6-9} \\cmidrule(lr){10-13}\n"," & \\multicolumn{2}{c}{Contriever} & \\multicolumn{2}{c}{TFIDF} & \\multicolumn{2}{c}{Contriever} & \\multicolumn{2}{c}{TFIDF} & \\multicolumn{2}{c}{Contriever} & \\multicolumn{2}{c}{TFIDF} \\\\\n"," \\cmidrule(lr){2-3} \\cmidrule(lr){4-5} \\cmidrule(lr){6-7} \\cmidrule(lr){8-9} \\cmidrule(lr){10-11} \\cmidrule(lr){12-13}\n"," & RAcc. & Recall & RAcc. & Recall & RAcc. & Recall & RAcc. & Recall & RAcc. & Recall & RAcc. & Recall \\\\\n","\\midrule\n","\n","\\textbf{\\fm} \\\\\\noalign{\\vskip 0.5ex}\n","\n","\n","\n","{Retriever only} & 28.5 & 24.8 & 11.0 & 8.9 & 36.0 & 31.0 & 15.5 & 12.8 & 47.5 & 42.6 & 23.5 & 19.0 \\\\\n","\\ours (Llama-3.1-70B) & 40.5 ({\\color{red}+12.0}) & 35.7 ({\\color{red}+10.9}) & 27.5 ({\\color{red}+16.5}) & 23.3 ({\\color{red}+14.4}) & 47.5 ({\\color{red}+11.5}) & 40.7 ({\\color{red}+9.7}) & 29.5 ({\\color{red}+14.0}) & 26.4 ({\\color{red}+13.6}) & 51.0 ({\\color{red}+3.5}) & 46.5 ({\\color{red}+3.9}) & 34.5 ({\\color{red}+11.0}) & 29.5 ({\\color{red}+10.5}) \\\\\n","\\ours (GPT-4o-mini) & 39.5 ({\\color{red}+11.0}) & 34.9 ({\\color{red}+10.1}) & 20.5 ({\\color{red}+9.5}) & 17.4 ({\\color{red}+8.5}) & 46.5 ({\\color{red}+10.5}) & 41.1 ({\\color{red}+10.1}) & 28.5 ({\\color{red}+13.0}) & 24.0 ({\\color{red}+11.2}) & 54.0 ({\\color{red}+6.5}) & 49.2 ({\\color{red}+6.6}) & 33.5 ({\\color{red}+10.0}) & 27.9 ({\\color{red}+8.9}) \\\\\n","\n","\\midrule\n","\n","\\textbf{\\wice} \\\\\n","\\noalign{\\vskip 0.5ex}\n","\n","{Retriever only} & 7.0 & 3.0 & 5.3 & 2.5 & 8.7 & 3.8 & 7.0 & 3.3 & 11.7 & 5.0 & 10.9 & 4.3 \\\\\n","\\ours (Llama-3.1-70B) & 11.1 ({\\color{red}+4.1}) & 4.5 ({\\color{red}+1.5}) & 7.8 ({\\color{red}+2.5}) & 3.5 ({\\color{red}+1.0}) & 14.1 ({\\color{red}+5.4}) & 5.0 ({\\color{red}+1.2}) & 9.5 ({\\color{red}+2.5}) & 4.0 ({\\color{red}+0.7}) & 16.4 ({\\color{red}+4.7}) & 6.5 ({\\color{red}+1.5}) & 14.0 ({\\color{red}+3.1}) & 5.8 ({\\color{red}+1.5}) \\\\\n","\\ours (GPT-4o-mini) & 8.4 ({\\color{red}+1.4}) & 3.7 ({\\color{red}+0.7}) & 7.3 ({\\color{red}+2.0}) & 3.1 ({\\color{red}+0.6}) & 10.6 ({\\color{red}+1.9}) & 4.4 ({\\color{red}+0.6}) & 9.2 ({\\color{red}+2.2}) & 3.7 ({\\color{red}+0.4}) & 16.0 ({\\color{red}+4.3}) & 6.4 ({\\color{red}+1.4}) & 13.4 ({\\color{red}+2.5}) & 5.7 ({\\color{red}+1.4}) \\\\\n","\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\caption{Comparison of retrieval performance between baseline retriever and \\ours on \\fm and \\wice test sets under Top-k evidence (k=3,5,10). We report both Retrieval Accuracy (\\textbf{RAcc.}) and Recall metrics. Numbers in red parentheses indicate absolute improvements. %\\chen{Should we have BGE-EN-ICL as well?}\n","}\n","\\label{tab:retriveresult}\n","\\end{table*}\n"],["\\begin{table}[!t]\n","\\centering\n","\\scriptsize\n","\\begin{tabular}{lll}\n","\\toprule\n","Setting & Contriever & TFIDF \\\\\n","\\midrule\n","\\textbf{\\ours (Llama-3.1-70B)} & \\textbf{51.0} & \\textbf{34.5} \\\\\n","\\quad $wo.$ claim editing & 49.5 ({\\color{blue}-1.5}) & 28.5 ({\\color{blue}-6.0}) \\\\\n","\\quad $wo.$ claim segmentation & 47.5 ({\\color{blue}-3.5}) & 27.5 ({\\color{blue}-7.0}) \\\\\n","\\quad Paraphrase $wo.$ Evidence & 50.5 ({\\color{blue}-0.5}) & 26.5 ({\\color{blue}-8.0}) \\\\\n","\\midrule\n","\\textbf{\\ours (GPT-4o-mini)} & \\textbf{54.0} & \\textbf{33.5} \\\\\n","\\quad $wo.$ claim editing & 53.0 ({\\color{blue}-1.0}) & 26.0 ({\\color{blue}-7.5}) \\\\\n","\\quad $wo.$ claim segmentation & 52.5 ({\\color{blue}-1.5}) & 32.5 ({\\color{blue}-1.0}) \\\\\n","\\quad Paraphrase $wo.$ Evidence & 51.5 ({\\color{blue}-2.5}) & 25.0 ({\\color{blue}-8.5}) \\\\\n","\\bottomrule\n","\\end{tabular}\n","\\caption{Ablation study results on \\fm. We use Retrieval Accuracy (RAcc.) under Top@10 setting. Numbers in blue parentheses indicate performance drop when removing each component from \\ours. }\n","\\label{tab:ablationstudy}\n","\\end{table}In the previous section, we observed that the retrieval improvement mainly helps the fact-checking ability.\n"],["\\begin{figure*}[!t]\n"," \\centering\n"," \\includegraphics[width = \\linewidth]{figures_raw/seg_para_cases.pdf}\n"," \\caption{Case studies demonstrating how \\ours handles adversarial claims through Claim Segmentation and Claim Editing modules. \\textbf{Left}:The original claim is split into sub-claims, enabling successful evidence retrieval. \\textbf{Right}: Three cases showing different types of claim editing - missing key information, synonym substitution, and context omission - where the system edits claims to improve retrieval performance. The numbers (\\eg 18/50, 17/50) represent the portion of each category over a random sample of 50 correct predictions.\n"," }\n"," \\label{fig:seg_para_case}\n","\\end{figure*}\n"],["\\begin{figure*}[!t]\n"," \\centering\n"," \\includegraphics[width = \\linewidth]{figures_raw/err_case.pdf}\n"," \\caption{Error analysis showing three main failure cases identified from a random sample of 50 cases: (1) \\textbf{Too Fine-Grained Segmentation}, where the Claim Segmentation module breaks claims into overly atomic facts that lack sufficient context, (2) \\textbf{Parametric Knowledge-Induced Errors}, where LLM's inherent knowledge introduces unverified information during claim editing, and (3) \\textbf{LLM Overgeneration}, where the claim editing module adds additional information from the retrieved evidence. }\n"," \\label{fig:err_case}\n","\\end{figure*}\n"],["\\begin{figure}[h]\n","\\begin{tcolorbox}[colback=black!7.5!white, colframe=black!80!white, title=Claim Segmentation Prompt, fontupper=\\footnotesize, fonttitle=\\footnotesize]\n","\\texttt{[System Input]}: \\vspace{2pt}\\\\\n","This is SplitLLM, an intelligent assistant that can split sentences based on their semantic and syntactic structure within the sentence. The following are the sentences you need to split, indicated by number identifier []. I can split them based on their logical units, such as clauses, phrases, or specific grammatical boundaries. Please ensure that each unit retains its original meaning and context for better readability and understanding.\\\\\n","\\texttt{[0]} \\{sen\\} \\\\\n","Provide the split sentences with each unit separated by a newline.\\\\\n","Example: \\\\\n","Original: The Natural is a book about Roy Hobbs a natural southpaw boxer who goes on to win the heavyweight title from Boom Boom Mancini.\\\\\n","Split: The Natural is a book | about Roy Hobbs | a natural southpaw boxer | who goes on to win the heavyweight | title from Boom Boom Mancini.\\\\\n","\\newline\n","\\texttt{[User Input]}: \\vspace{2pt}\\\\\n","Now, proceed with the sentence: \\{sen\\} The split result of the sentence (only split) is:\n","\n","\\end{tcolorbox}\n","\n","\\caption{Prompt template used for the Claim Segmentation module. The prompt defines the task and expected format, followed by an illustrative example showing how to split a complex claim into logical units.}\n","\\label{fig:seg}\n","\\end{figure}\\begin{figure}[h]\n"],["\\end{figure}\\begin{figure}[h]\n","\\begin{tcolorbox}[colback=black!7.5!white, colframe=black!80!white, title=Evidence-Augmented Claim Editing Prompt, fontupper=\\footnotesize, fonttitle=\\footnotesize]\n","You should complete the sentence <\\{query\\}> with missing name (author, writer, owner, builder, award-winning titles etc.), number (date, year, population, acreage, etc), location and so on, replace the original adversarial point of view with detailed data or evidence (for example, the biggest population -> population 3,854,000; the date is unknown -> possible dates ranging from 1598 to 1608 ) and correct the counterfactual mistakes in sentence, which is done in order to avoid adversarial cases. You should never add too much new additional information to the sentence. Here is an example:\\\\\n","Given sentence: \\\\\n","<The album was released in 2018.>\\\\\n","Expected result: \\\\\n","The \"Blackpink in Your Area\" compilation album was released in 2018.\\\\\n","To help you better complete the required task, we provide following knowledge as contexts: \\\\\n","<\\{kn\\}> \\\\\n","Now do the required task on the sentence: <{query}> based on the given knowledge.\n","\\newline\n","\n","\\end{tcolorbox}\n","\n","\\caption{Prompt template for the Evidence-Augmented Claim Editing module. The prompt instructs how to enhance claims by adding specific details from evidence, replacing vague statements with precise information, and correcting inaccuracies.}\n","\\label{fig:paraphrase_prompt}\n","\\end{figure}\\begin{figure}[h]\n"],["\\begin{figure}[h]\n","\\begin{tcolorbox}[colback=black!7.5!white, colframe=black!80!white, title=Entailment Label Prediction Prompt, fontupper=\\footnotesize, fonttitle=\\footnotesize]\n","\\texttt{[System Input]}: \\vspace{2pt}\\\\\n","You are a well-informed and expert fact-checker. Here are some example of how to act as a professional fact-checker:\\\\\n","Claim Example 1: The Cantos is a poem with most of it written over a 40 plus year time span. \\\\\n","Evidences for claim example 1: \\\\\n","<Most of it was written between 1915 and 1962, although much of the early work was abandoned and the early cantos, as finally published, date from 1922 onwards.\n","The Cantos by Ezra Pound is a long, incomplete poem in 116 sections, each of which is a canto.\n","This thread then runs through the appearance of Kuanon, the Buddhist goddess of mercy, the moon spirit from Hagaromo (a Noh play translated by Pound some 40 years earlier), Sigismondo's lover Ixotta (linked in the text with Aphrodite via a reference to the goddess' birthplace Cythera), a girl painted by Manet and finally Aphrodite herself, rising from the sea on her shell and rescuing Pound/Odysseus from his raft.> \\\\\n","Step 1: the evidence indicates that The Cantos is a poem with most of it written over a 40 plus year time span.\n","Step 2: the evidence also mentions that The Cantos by Ezra Pound is a long, incomplete poem in 116 sections. Step 3: the other pieces of evidence provided This thread then runs through the appearance of Kuanon, the Buddhist goddess of mercy, the moon spirit from Hagaromo (a Noh play translated by Pound some 40 years earlier).based on the evidence provided, the claim that The Cantos is a poem with most of it written over a 40 plus year time span is supported. \\\\\n","final rating: supported \\\\\n","Claim Example 2: \\\\\n","......\\\\\n","\\newline\n","\\texttt{[User Input]}: \\vspace{2pt}\\\\\n","Now its' your turn, you are provided with evidences regarding the following claim: \\{claim\\} \\\\\n","Evidences: \\\\\n","<\\{evidence\\}> \\\\\n","Based strictly on the main claim, and the evidences provided, you will provide: \\\\\n","rating: The rating for claim should be one of \"supported\" if and only if the Evidences specifically support the claim, \"refuted\" if and only if the Evidences specifically refutes the claim or \"not enough information\": if there is not enough information to support or refute the claim appropriately. \\\\\n","Is the claim: \\{claim\\} \"supported\", \"refuted\" or \"not enough information\" according to the available questions and answers? \\\\\n","Lets think step by step.\n","\n","\\end{tcolorbox}\n","\n","\\caption{Prompt template for the entailment label prediction. The prompt instructs the LLM to act as an expert fact-checker, providing step-by-step reasoning to classify claims as \"supported,\" \"refuted,\" or \"not enough information\" based on given evidence.}\n","\\label{fig:fm2_entail}\n","\\end{figure}\\begin{figure*}[!t]\n"],["\\end{figure*}\\begin{table*}[!t]\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n","\\begin{tabular}{lccccccc}\n","\\toprule\n","\\multirow{2}{*}{Backbone LLM} & \\multirow{2}{*}{Setting} \n","& \\multicolumn{2}{c}{Top@3} & \\multicolumn{2}{c}{Top@5} & \\multicolumn{2}{c}{Top@10} \\\\\n","\\cmidrule(lr){3-4} \\cmidrule(lr){5-6} \\cmidrule(lr){7-8}\n"," & & Contriever & TFIDF & Contriever & TFIDF & Contriever & TFIDF \\\\\n","\\midrule\n","{{\\textbf{\\fm}}} \\\\\\noalign{\\vskip 0.5ex}\n","\\multirow{2}{*}{GPT-4o mini} &\\base & 64.5 & 53.5 & 65.0 & 59.0 & 67.5 & 61.5 \\\\\n","&\\claimdecompose & 65.0 {\\color{red}(+0.5)} & 55.0 {\\color{red}(+1.5)} & 65.0 {\\color{red}(+0.0)} & 61.0 {\\color{red}(+2.0)} & 65.5 {\\color{red}(-2.0)} & 63.0 {\\color{red}(+1.5)} \\\\\n","&\\qabrief & 65.5 {\\color{red}(+1.0)} & 57.0 {\\color{red}(+3.5)} & 67.0 {\\color{red}(+2.0)} & 62.0 {\\color{red}(+3.0)} & 67.5 {\\color{red}(+0.0)} & 62.5 {\\color{red}(+1.0)} \\\\\n","&\\programfc & 68.5 {\\color{red}(+4.0)} & 56.0 {\\color{red}(+2.5)} & 65.5 {\\color{red}(+0.5)} & 61.0 {\\color{red}(+2.0)} & 69.0 {\\color{red}(+1.5)} & 63.0 {\\color{red}(+1.5)} \\\\\n","&\\minicheck & 66.0 {\\color{red}(+1.5)} & 57.0 {\\color{red}(+3.5)} & 66.5 {\\color{red}(+1.5)} & 62.5 {\\color{red}(+3.5)} & 70.0 {\\color{red}(+2.5)} & 64.0 {\\color{red}(+2.5)}\\\\\n","&\\textbf{\\ours} & 69.0 {\\color{red}(+4.5)} & 61.5 {\\color{red}(+8.0)} & 72.5 {\\color{red}(+7.5)} & 67.0 {\\color{red}(+8.0)} & 75.0 {\\color{red}(+7.5)} & 68.5 {\\color{red}(+7.0)} \\\\\n","\\hdashline\n","\\multirow{2}{*}{LLama 3.1 70B} &\\base & 61.0 & 56.5 & 64.5 & 59.0 & 65.5 & 61.5 \\\\\n","&\\claimdecompose & 62.0 {\\color{red}(+1.0)} & 57.0 {\\color{red}(+0.5)} & 65.5 {\\color{red}(+1.0)} & 61.0 {\\color{red}(+2.0)} & 67.5 {\\color{red}(+2.0)} & 64.0 {\\color{red}(+2.5)} \\\\\n","&\\qabrief & 66.0 {\\color{red}(+5.0)} & 56.5 {\\color{red}(+0.0)} & 67.5 {\\color{red}(+3.0)} & 62.5 {\\color{red}(+3.5)} & 66.0 {\\color{red}(+0.5)} & 64.0 {\\color{red}(+2.5)} \\\\\n","&\\programfc & 62.0 {\\color{red}(+1.0)} & 58.0 {\\color{red}(+1.5)} & 65.0 {\\color{red}(+0.5)} & 61.0 {\\color{red}(+2.0)} & 66.0 {\\color{red}(+0.5)} & 64.5 {\\color{red}(+3.0)} \\\\\n","&\\minicheck &67.5 {\\color{red}(+6.5)} & 57.5 {\\color{red}(+1.0)} & 68.0 {\\color{red}(+3.5)} & 62.0 {\\color{red}(+3.0)} & 70.5 {\\color{red}(+5.0)} & 64.0 {\\color{red}(+2.5)} \\\\\n","&\\textbf{\\ours} & 71.0 {\\color{red}(+10.0)} & 63.5 {\\color{red}(+7.0)} & 72.5 {\\color{red}(+8.0)} & 67.5 {\\color{red}(+8.5)} & 73.5 {\\color{red}(+8.0)} & 69.0 {\\color{red}(+7.5)} \\\\\n","\\hline\n","{{\\textbf{\\wice}}} \\\\\\noalign{\\vskip 0.5ex}\n","\\multirow{2}{*}{GPT-4o mini} &\\base & 27.2 & 24.0 & 28.6 & 25.8 & 33.7 & 27.3 \\\\\n","&\\claimdecompose & 29.1 {\\color{red}(+1.9)} & 29.1 {\\color{red}(+5.1)} & 30.1 {\\color{red}(+1.5)} & 30.4 {\\color{red}(+4.6)} & 35.2 {\\color{red}(+1.5)} & 31.0 {\\color{red}(+3.7)} \\\\\n","&\\qabrief & 28.0 {\\color{red}(+0.8)} & 27.5 {\\color{red}(+3.5)} & 30.9 {\\color{red}(+2.3)} & 27.8 {\\color{red}(+2.0)} & 36.2 {\\color{red}(+2.5)} & 29.7 {\\color{red}(+2.4)} \\\\\n","&\\programfc & 33.2 {\\color{red}(+6.0)} & 26.8 {\\color{red}(+2.8)} & 34.8 {\\color{red}(+6.2)} & 28.7 {\\color{red}(+2.9)} & 38.4 {\\color{red}(+4.7)} & 29.9 {\\color{red}(+2.6)} \\\\\n","&\\minicheck &30.0 {\\color{red}(+2.8)} & 27.1 {\\color{red}(+3.1)} & 31.3 {\\color{red}(+2.7)} & 26.9 {\\color{red}(+1.1)} & 33.7 {\\color{red}(+0.0)} & 30.8 {\\color{red}(+3.5)} \\\\\n","&\\textbf{\\ours} & 34.1 {\\color{red}(+6.9)} & 31.0 {\\color{red}(+7.0)} & 37.2 {\\color{red}(+8.6)} & 28.1 {\\color{red}(+2.3)} & 39.0 {\\color{red}(+5.3)} & 33.2 {\\color{red}(+5.9)} \\\\\n","\\hdashline\n","\\multirow{2}{*}{LLama 3.1 70B} &\\base & 28.1 & 22.1 & 29.6 & 24.5 & 31.2 & 25.7 \\\\\n","&\\claimdecompose & 26.3 {\\color{red}(-1.8)} & 23.0 {\\color{red}(+0.9)} & 30.1 {\\color{red}(+0.5)} & 25.4 {\\color{red}(+0.9)} & 33.0 {\\color{red}(+1.8)} & 27.1 {\\color{red}(+1.4)} \\\\\n","&\\qabrief & 30.4 {\\color{red}(+2.3)} & 25.1 {\\color{red}(+3.0)} & 32.4 {\\color{red}(+2.8)} & 27.0 {\\color{red}(+2.5)} & 33.5 {\\color{red}(+2.3)} & 28.2 {\\color{red}(+2.5)} \\\\\n","&\\programfc & 34.6 {\\color{red}(+6.5)} & 24.7 {\\color{red}(+2.6)} & 36.5 {\\color{red}(+6.9)} & 26.9 {\\color{red}(+2.4)} & 33.9 {\\color{red}(+2.7)} & 29.3 {\\color{red}(+3.6)} \\\\\n","&\\minicheck & 39.1 {\\color{red}(+11.0)} & 27.8 {\\color{red}(+5.7)} & 34.9 {\\color{red}(+5.3)} & 27.9 {\\color{red}(+3.4)} & 35.2 {\\color{red}(+4.0)} & 30.1 {\\color{red}(+4.4)} \\\\\n","&\\textbf{\\ours} & 38.2 {\\color{red}(+10.1)} & 30.3 {\\color{red}(+8.2)} & 39.0 {\\color{red}(+9.4)} & 31.8 {\\color{red}(+7.3)} & 38.7 {\\color{red}(+7.5)} & 32.9 {\\color{red}(+7.2)} \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\caption{Comprehensive fact-checking evaluation results comparing \\ours with baseline approaches on \\fm and \\wice datasets. Results are reported as accuracy scores across different retrieval settings (Top@3, Top@5, Top@10). Numbers in red parentheses show absolute performance improvements over the \\base baseline.}\n","\\label{tab:whole_factcheckresults}\n","\\end{table*}\\begin{table*}[!t]\n"],["\\begin{table*}[!t]\n","\\centering\n"," \\resizebox{\\linewidth}{!}{\n"," \\begin{tabular}{lcccccccccccccc}\n","\\toprule\n"," \\multirow{4}{*}{Model} & \\multirow{4}{*}{Setting} & \\multicolumn{4}{c}{Top@3} & \\multicolumn{4}{c}{Top@5} & \\multicolumn{4}{c}{Top10} \\\\\n"," \\cmidrule(lr){3-6} \\cmidrule(lr){7-10} \\cmidrule(lr){11-14}\n"," & & \\multicolumn{2}{c}{Contriever} & \\multicolumn{2}{c}{TFIDF} & \\multicolumn{2}{c}{Contriever} & \\multicolumn{2}{c}{TFIDF} & \\multicolumn{2}{c}{Contriever} & \\multicolumn{2}{c}{TFIDF} \\\\\n"," \\cmidrule(lr){3-4} \\cmidrule(lr){5-6} \\cmidrule(lr){7-8} \\cmidrule(lr){9-10} \\cmidrule(lr){11-12} \\cmidrule(lr){13-14}\n"," & & Acc. & Recall & Acc. & Recall & Acc. & Recall & Acc. & Recall & Acc. & Recall & Acc. & Recall \\\\\n","\\midrule\n","\\multirow{5}{*}{Llama3.1 70B}\n"," & \\ours & 40.5 & 35.7 & 27.5 & 23.3 & 47.5 & 40.7 & 29.5 & 26.4 & 51.0 & 46.5 & 34.5 & 29.5 \\\\\n"," & wo. claim editing & 39.0 {\\color{blue}(-1.5)} & 32.3 {\\color{blue}(-3.4)} & 17.5 {\\color{blue}(-10.0)} & 15.1 {\\color{blue}(-8.2)} & 46.0 {\\color{blue}(-1.5)} & 41.1 {\\color{blue}(+0.4)} & 21.5 {\\color{blue}(-8.0)} & 18.2 {\\color{blue}(-8.2)} & 49.5 {\\color{blue}(-1.5)} & 46.8 {\\color{blue}(+0.3)} & 28.5 {\\color{blue}(-6.0)} & 24.4 {\\color{blue}(-5.1)} \\\\\n"," & wo. claim segmentation & 35.5 {\\color{blue}(-5.0)} & 31.0 {\\color{blue}(-4.7)} & 16.5 {\\color{blue}(-11.0)} & 13.6 {\\color{blue}(-9.7)} & 40.5 {\\color{blue}(-7.0)} & 35.2 {\\color{blue}(-5.5)} & 19.5 {\\color{blue}(-10.0)} & 16.7 {\\color{blue}(-9.7)} & 47.5 {\\color{blue}(-3.5)} & 42.6 {\\color{blue}(-3.9)} & 27.5 {\\color{blue}(-7.0)} & 22.5 {\\color{blue}(-7.0)} \\\\\n"," & Paraphrase wo. Evidence & 34.0 {\\color{blue}(-6.5)} & 29.8 {\\color{blue}(-5.9)} & 14.0 {\\color{blue}(-13.5)} & 11.2 {\\color{blue}(-12.1)} & 39.5 {\\color{blue}(-8.0)} & 35.7 {\\color{blue}(-5.0)} & 20.0 {\\color{blue}(-9.5)} & 12.3 {\\color{blue}(-14.1)} & 50.5 {\\color{blue}(-0.5)} & 44.6 {\\color{blue}(-1.9)} & 26.5 {\\color{blue}(-8.0)} & 21.3 {\\color{blue}(-8.2)} \\\\\n","\\hdashline\n","\\multirow{5}{*}{GPT-4o mini}\n"," & \\ours & 39.5 & 34.9 & 20.5 & 17.4 & 46.5 & 41.1 & 28.5 & 24.0 & 54.0 & 49.2 & 33.5 & 27.9 \\\\\n"," & wo. claim editing & 37.0 {\\color{blue}(-2.5)} & 32.6 {\\color{blue}(-2.3)} & 14.0 {\\color{blue}(-6.5)} & 11.6 {\\color{blue}(-5.8)} & 45.0 {\\color{blue}(-1.5)} & 40.7 {\\color{blue}(-0.4)} & 23.0 {\\color{blue}(-5.5)} & 19.8 {\\color{blue}(-4.2)} & 53.0 {\\color{blue}(-1.0)} & 47.7 {\\color{blue}(-1.5)} & 26.0 {\\color{blue}(-7.5)} & 21.7 {\\color{blue}(-6.2)} \\\\\n"," & wo. claim segmentation & 36.0 {\\color{blue}(-3.5)} & 31.8 {\\color{blue}(-3.1)} & 18.0 {\\color{blue}(-2.5)} & 15.1 {\\color{blue}(-2.3)} & 46.0 {\\color{blue}(-0.5)} & 40.3 {\\color{blue}(-0.8)} & 23.5 {\\color{blue}(-5.0)} & 19.8 {\\color{blue}(-4.2)} & 52.5 {\\color{blue}(-1.5)} & 46.9 {\\color{blue}(-2.3)} & 32.5 {\\color{blue}(-1.0)} & 27.1 {\\color{blue}(-0.8)} \\\\\n"," & Paraphrase wo. Evidence & 29.0 {\\color{blue}(-10.5)} & 24.8 {\\color{blue}(-10.1)} & 13.5 {\\color{blue}(-7.0)} & 10.9 {\\color{blue}(-6.5)} & 39.5 {\\color{blue}(-7.0)} & 34.1 {\\color{blue}(-7.0)} & 20.0 {\\color{blue}(-8.5)} & 16.3 {\\color{blue}(-7.7)} & 51.5 {\\color{blue}(-2.5)} & 44.6 {\\color{blue}(-4.6)} & 25.0 {\\color{blue}(-8.5)} & 20.5 {\\color{blue}(-7.4)} \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\caption{Ablation study results showing the impact of different components on retrieval performance using \\fm test set. Results are reported for both accuracy (Acc.) and recall across different retrieval settings. Numbers in blue parentheses show performance drops when removing each component from \\ours.}\n","\\label{tab:full_ablationstudy}\n","\\end{table*}\n"]],"string":"[\n [\n \"\\\\begin{figure}[!t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width = \\\\linewidth]{figures_raw/main_example.pdf}\\n\",\n \" \\\\caption{Examples of challenges in fact-checking adversarial claims. \\n\",\n \" These claims are deliberately crafted by humans using strategies to deceive fact-checking systems. \\n\",\n \" Even state-of-the-art approaches (\\\\ie RALM) face significant difficulties in accurately verifying these claims. \\n\",\n \" }\\n\",\n \" \\\\label{fig:task}\\n\",\n \"\\\\end{figure}\\\\section{Introduction}\\n\"\n ],\n [\n \"\\\\begin{table}[t!]\\n\",\n \"\\\\centering \\n\",\n \"\\\\small\\n\",\n \"\\\\resizebox{\\\\columnwidth}{!}{\\n\",\n \"\\\\renewcommand{\\\\arraystretch}{1.2}\\n\",\n \"\\\\begin{tabular}{lrr}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Property} & \\\\textbf{\\\\fm} & \\\\textbf{\\\\wice} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multicolumn{3}{c}{\\\\textbf{Knowledge Corpus}} \\\\\\\\\\n\",\n \"Source & Wikipedia & Wikipedia \\\\\\\\\\n\",\n \"Knowledge Corpus Size &21,015,325 &21,015,325\\\\\\\\\\n\",\n \"\\\\noalign{\\\\vskip 0.5ex}\\\\hdashline\\\\noalign{\\\\vskip 0.5ex}\\n\",\n \"\\\\multicolumn{3}{c}{\\\\textbf{Evidence Retrieval \\\\& Fact Checking}} \\\\\\\\\\n\",\n \"\\\\# Evidence (avg.) & 1.29 & 3.94 \\\\\\\\\\n\",\n \"Claim Token Length (avg.) & 14.03 & 24.20\\\\\\\\\\n\",\n \"\\n\",\n \"Test Set Size (\\\\# Claims) & 200 & 358 \\\\\\\\\\n\",\n \"\\\\quad \\\\# Entailed Claims &103 &111\\\\\\\\\\n\",\n \"\\\\quad \\\\# Refuted Claims &97 &32\\\\\\\\\\n\",\n \"\\\\quad \\\\# Partially Supported Claims & N/A &215\\\\\\\\\\n\",\n \"\\\\toprule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Basic statistics of the \\\\fm and \\\\wice test sets used in our experiments. Due to limited budgets, we evaluate on the full test set of \\\\wice (358 instances) and 200 random samples from the \\\\fm test set.}\\n\",\n \"\\\\label{tab:basic-stats}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{figure}[!t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width = 1.0\\\\linewidth]{figures_raw/pipeline.pdf}\\n\",\n \" \\\\caption{An illustration of \\\\ours pipeline (left) and its workflow for adversarial fact-checking using an example from the \\\\fm dataset (right). The input claim is first segmented and decontextualized by LLMs. For each sub-claim, a first round of evidence retrieval is conducted, followed by sub-claim revision and a second round of evidence retrieval. The final step is label prediction for the claim. \\n\",\n \" }\\n\",\n \" \\\\label{fig:model}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table}[t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\small\\n\",\n \"\\\\resizebox{\\\\columnwidth}{!}{\\n\",\n \"\\\\renewcommand{\\\\arraystretch}{1.2}\\n\",\n \"\\\\begin{tabular}{lcccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{System} & \\\\multicolumn{2}{c}{\\\\fm} & \\\\multicolumn{2}{c}{\\\\wice} \\\\\\\\\\n\",\n \"\\\\cmidrule{2-5}\\n\",\n \"& Contriever & TFIDF & Contriever & TFIDF \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multicolumn{5}{l}{\\\\textbf{GPT-4o-mini}} \\\\\\\\\\n\",\n \"\\\\quad \\\\base & 67.5 & 61.5 & 33.7 & 27.3 \\\\\\\\\\n\",\n \"\\\\quad \\\\claimdecompose & 69.5 (\\\\textcolor{red}{+2.0}) & 63.0 (\\\\textcolor{red}{+1.5}) & 35.2 (\\\\textcolor{red}{+1.5}) & 31.0 (\\\\textcolor{red}{+3.7}) \\\\\\\\\\n\",\n \"\\\\quad \\\\qabrief & 67.5(\\\\textcolor{red}{+0.0})\\t&62.5(\\\\textcolor{red}{+1.0}) & 36.2(\\\\textcolor{red}{+2.5}) &29.7(\\\\textcolor{red}{+2.4}) \\\\\\\\\\n\",\n \"\\\\quad \\\\programfc & 69.0 (\\\\textcolor{red}{+1.5}) & 63.0 (\\\\textcolor{red}{+1.5}) & 38.4 (\\\\textcolor{red}{+4.7}) & 29.9 (\\\\textcolor{red}{+2.6}) \\\\\\\\\\n\",\n \"\\\\quad \\\\minicheck & 70.0 (\\\\textcolor{red}{+2.5}) & 64.0 (\\\\textcolor{red}{+2.5}) & 33.7 (\\\\textcolor{red}{+0.0}) & 30.8 (\\\\textcolor{red}{+3.5})\\\\\\\\\\n\",\n \"\\n\",\n \"\\\\noalign{\\\\vskip 0.5ex}\\\\hdashline\\\\noalign{\\\\vskip 0.5ex}\\n\",\n \"\\n\",\n \"\\\\quad \\\\textbf{\\\\ours} & \\\\textbf{75.0 (\\\\textcolor{red}{+7.5})} & \\\\textbf{68.5 (\\\\textcolor{red}{+7.0})} & \\\\textbf{39.0 (\\\\textcolor{red}{+5.3})} & \\\\textbf{33.2 (\\\\textcolor{red}{+5.9})} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multicolumn{5}{l}{\\\\textbf{LLama-3.1-70B}} \\\\\\\\\\n\",\n \"\\\\quad \\\\base & 65.5 & 61.5 & 31.2 & 25.7 \\\\\\\\\\n\",\n \"\\\\quad \\\\claimdecompose & 67.5 (\\\\textcolor{red}{+2.0}) & 64.0 (\\\\textcolor{red}{+2.5}) & 33.0 (\\\\textcolor{red}{+1.8}) & 27.1 (\\\\textcolor{red}{+1.4}) \\\\\\\\\\n\",\n \"\\\\quad \\\\qabrief & 66.0(\\\\textcolor{red}{+0.5})\\t&64.0(\\\\textcolor{red}{+2.5}) & 33.5(\\\\textcolor{red}{+2.3}) &28.2(\\\\textcolor{red}{+2.5}) \\\\\\\\\\n\",\n \"\\\\quad \\\\programfc & 66.0 (\\\\textcolor{red}{+0.5}) & 64.5 (\\\\textcolor{red}{+3.0}) & 33.9 (\\\\textcolor{red}{+2.7}) & 29.3 (\\\\textcolor{red}{+3.6}) \\\\\\\\\\n\",\n \"\\\\quad \\\\minicheck & 70.5 (\\\\textcolor{red}{+5.0}) & 64.0 (\\\\textcolor{red}{+2.5}) & 35.2 (\\\\textcolor{red}{+4.0}) & 30.1 (\\\\textcolor{red}{+4.4})\\\\\\\\\\n\",\n \"\\n\",\n \"\\\\noalign{\\\\vskip 0.5ex}\\\\hdashline\\\\noalign{\\\\vskip 0.5ex}\\n\",\n \"\\n\",\n \"\\\\quad \\\\textbf{\\\\ours} & \\\\textbf{73.5 (\\\\textcolor{red}{+8.0})} & \\\\textbf{69.0 (\\\\textcolor{red}{+7.5})} & \\\\textbf{38.7 (\\\\textcolor{red}{+7.5})} & \\\\textbf{32.9 (\\\\textcolor{red}{+7.2})} \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Fact-checking evaluation results under baselines and \\\\ours for \\\\fm and \\\\wice models.\\n\",\n \"Numbers in red parentheses indicate performance improvements over the baseline (\\\\base). }\\n\",\n \"\\\\label{tab:factcheckresults}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table*}[!t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{%\\n\",\n \"\\\\addtolength{\\\\tabcolsep}{-0.35em}\\n\",\n \"\\\\begin{tabular}{lcccccccccccccc}\\n\",\n \"\\\\toprule\\n\",\n \" \\\\multirow{4}{*}{System} & \\\\multicolumn{4}{c}{Top@3} & \\\\multicolumn{4}{c}{Top@5} & \\\\multicolumn{4}{c}{Top@10} \\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-5} \\\\cmidrule(lr){6-9} \\\\cmidrule(lr){10-13}\\n\",\n \" & \\\\multicolumn{2}{c}{Contriever} & \\\\multicolumn{2}{c}{TFIDF} & \\\\multicolumn{2}{c}{Contriever} & \\\\multicolumn{2}{c}{TFIDF} & \\\\multicolumn{2}{c}{Contriever} & \\\\multicolumn{2}{c}{TFIDF} \\\\\\\\\\n\",\n \" \\\\cmidrule(lr){2-3} \\\\cmidrule(lr){4-5} \\\\cmidrule(lr){6-7} \\\\cmidrule(lr){8-9} \\\\cmidrule(lr){10-11} \\\\cmidrule(lr){12-13}\\n\",\n \" & RAcc. & Recall & RAcc. & Recall & RAcc. & Recall & RAcc. & Recall & RAcc. & Recall & RAcc. & Recall \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\n\",\n \"\\\\textbf{\\\\fm} \\\\\\\\\\\\noalign{\\\\vskip 0.5ex}\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"{Retriever only} & 28.5 & 24.8 & 11.0 & 8.9 & 36.0 & 31.0 & 15.5 & 12.8 & 47.5 & 42.6 & 23.5 & 19.0 \\\\\\\\\\n\",\n \"\\\\ours (Llama-3.1-70B) & 40.5 ({\\\\color{red}+12.0}) & 35.7 ({\\\\color{red}+10.9}) & 27.5 ({\\\\color{red}+16.5}) & 23.3 ({\\\\color{red}+14.4}) & 47.5 ({\\\\color{red}+11.5}) & 40.7 ({\\\\color{red}+9.7}) & 29.5 ({\\\\color{red}+14.0}) & 26.4 ({\\\\color{red}+13.6}) & 51.0 ({\\\\color{red}+3.5}) & 46.5 ({\\\\color{red}+3.9}) & 34.5 ({\\\\color{red}+11.0}) & 29.5 ({\\\\color{red}+10.5}) \\\\\\\\\\n\",\n \"\\\\ours (GPT-4o-mini) & 39.5 ({\\\\color{red}+11.0}) & 34.9 ({\\\\color{red}+10.1}) & 20.5 ({\\\\color{red}+9.5}) & 17.4 ({\\\\color{red}+8.5}) & 46.5 ({\\\\color{red}+10.5}) & 41.1 ({\\\\color{red}+10.1}) & 28.5 ({\\\\color{red}+13.0}) & 24.0 ({\\\\color{red}+11.2}) & 54.0 ({\\\\color{red}+6.5}) & 49.2 ({\\\\color{red}+6.6}) & 33.5 ({\\\\color{red}+10.0}) & 27.9 ({\\\\color{red}+8.9}) \\\\\\\\\\n\",\n \"\\n\",\n \"\\\\midrule\\n\",\n \"\\n\",\n \"\\\\textbf{\\\\wice} \\\\\\\\\\n\",\n \"\\\\noalign{\\\\vskip 0.5ex}\\n\",\n \"\\n\",\n \"{Retriever only} & 7.0 & 3.0 & 5.3 & 2.5 & 8.7 & 3.8 & 7.0 & 3.3 & 11.7 & 5.0 & 10.9 & 4.3 \\\\\\\\\\n\",\n \"\\\\ours (Llama-3.1-70B) & 11.1 ({\\\\color{red}+4.1}) & 4.5 ({\\\\color{red}+1.5}) & 7.8 ({\\\\color{red}+2.5}) & 3.5 ({\\\\color{red}+1.0}) & 14.1 ({\\\\color{red}+5.4}) & 5.0 ({\\\\color{red}+1.2}) & 9.5 ({\\\\color{red}+2.5}) & 4.0 ({\\\\color{red}+0.7}) & 16.4 ({\\\\color{red}+4.7}) & 6.5 ({\\\\color{red}+1.5}) & 14.0 ({\\\\color{red}+3.1}) & 5.8 ({\\\\color{red}+1.5}) \\\\\\\\\\n\",\n \"\\\\ours (GPT-4o-mini) & 8.4 ({\\\\color{red}+1.4}) & 3.7 ({\\\\color{red}+0.7}) & 7.3 ({\\\\color{red}+2.0}) & 3.1 ({\\\\color{red}+0.6}) & 10.6 ({\\\\color{red}+1.9}) & 4.4 ({\\\\color{red}+0.6}) & 9.2 ({\\\\color{red}+2.2}) & 3.7 ({\\\\color{red}+0.4}) & 16.0 ({\\\\color{red}+4.3}) & 6.4 ({\\\\color{red}+1.4}) & 13.4 ({\\\\color{red}+2.5}) & 5.7 ({\\\\color{red}+1.4}) \\\\\\\\\\n\",\n \"\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Comparison of retrieval performance between baseline retriever and \\\\ours on \\\\fm and \\\\wice test sets under Top-k evidence (k=3,5,10). We report both Retrieval Accuracy (\\\\textbf{RAcc.}) and Recall metrics. Numbers in red parentheses indicate absolute improvements. %\\\\chen{Should we have BGE-EN-ICL as well?}\\n\",\n \"}\\n\",\n \"\\\\label{tab:retriveresult}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table}[!t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\scriptsize\\n\",\n \"\\\\begin{tabular}{lll}\\n\",\n \"\\\\toprule\\n\",\n \"Setting & Contriever & TFIDF \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textbf{\\\\ours (Llama-3.1-70B)} & \\\\textbf{51.0} & \\\\textbf{34.5} \\\\\\\\\\n\",\n \"\\\\quad $wo.$ claim editing & 49.5 ({\\\\color{blue}-1.5}) & 28.5 ({\\\\color{blue}-6.0}) \\\\\\\\\\n\",\n \"\\\\quad $wo.$ claim segmentation & 47.5 ({\\\\color{blue}-3.5}) & 27.5 ({\\\\color{blue}-7.0}) \\\\\\\\\\n\",\n \"\\\\quad Paraphrase $wo.$ Evidence & 50.5 ({\\\\color{blue}-0.5}) & 26.5 ({\\\\color{blue}-8.0}) \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\textbf{\\\\ours (GPT-4o-mini)} & \\\\textbf{54.0} & \\\\textbf{33.5} \\\\\\\\\\n\",\n \"\\\\quad $wo.$ claim editing & 53.0 ({\\\\color{blue}-1.0}) & 26.0 ({\\\\color{blue}-7.5}) \\\\\\\\\\n\",\n \"\\\\quad $wo.$ claim segmentation & 52.5 ({\\\\color{blue}-1.5}) & 32.5 ({\\\\color{blue}-1.0}) \\\\\\\\\\n\",\n \"\\\\quad Paraphrase $wo.$ Evidence & 51.5 ({\\\\color{blue}-2.5}) & 25.0 ({\\\\color{blue}-8.5}) \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\caption{Ablation study results on \\\\fm. We use Retrieval Accuracy (RAcc.) under Top@10 setting. Numbers in blue parentheses indicate performance drop when removing each component from \\\\ours. }\\n\",\n \"\\\\label{tab:ablationstudy}\\n\",\n \"\\\\end{table}In the previous section, we observed that the retrieval improvement mainly helps the fact-checking ability.\\n\"\n ],\n [\n \"\\\\begin{figure*}[!t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width = \\\\linewidth]{figures_raw/seg_para_cases.pdf}\\n\",\n \" \\\\caption{Case studies demonstrating how \\\\ours handles adversarial claims through Claim Segmentation and Claim Editing modules. \\\\textbf{Left}:The original claim is split into sub-claims, enabling successful evidence retrieval. \\\\textbf{Right}: Three cases showing different types of claim editing - missing key information, synonym substitution, and context omission - where the system edits claims to improve retrieval performance. The numbers (\\\\eg 18/50, 17/50) represent the portion of each category over a random sample of 50 correct predictions.\\n\",\n \" }\\n\",\n \" \\\\label{fig:seg_para_case}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[!t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width = \\\\linewidth]{figures_raw/err_case.pdf}\\n\",\n \" \\\\caption{Error analysis showing three main failure cases identified from a random sample of 50 cases: (1) \\\\textbf{Too Fine-Grained Segmentation}, where the Claim Segmentation module breaks claims into overly atomic facts that lack sufficient context, (2) \\\\textbf{Parametric Knowledge-Induced Errors}, where LLM's inherent knowledge introduces unverified information during claim editing, and (3) \\\\textbf{LLM Overgeneration}, where the claim editing module adds additional information from the retrieved evidence. }\\n\",\n \" \\\\label{fig:err_case}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{figure}[h]\\n\",\n \"\\\\begin{tcolorbox}[colback=black!7.5!white, colframe=black!80!white, title=Claim Segmentation Prompt, fontupper=\\\\footnotesize, fonttitle=\\\\footnotesize]\\n\",\n \"\\\\texttt{[System Input]}: \\\\vspace{2pt}\\\\\\\\\\n\",\n \"This is SplitLLM, an intelligent assistant that can split sentences based on their semantic and syntactic structure within the sentence. The following are the sentences you need to split, indicated by number identifier []. I can split them based on their logical units, such as clauses, phrases, or specific grammatical boundaries. Please ensure that each unit retains its original meaning and context for better readability and understanding.\\\\\\\\\\n\",\n \"\\\\texttt{[0]} \\\\{sen\\\\} \\\\\\\\\\n\",\n \"Provide the split sentences with each unit separated by a newline.\\\\\\\\\\n\",\n \"Example: \\\\\\\\\\n\",\n \"Original: The Natural is a book about Roy Hobbs a natural southpaw boxer who goes on to win the heavyweight title from Boom Boom Mancini.\\\\\\\\\\n\",\n \"Split: The Natural is a book | about Roy Hobbs | a natural southpaw boxer | who goes on to win the heavyweight | title from Boom Boom Mancini.\\\\\\\\\\n\",\n \"\\\\newline\\n\",\n \"\\\\texttt{[User Input]}: \\\\vspace{2pt}\\\\\\\\\\n\",\n \"Now, proceed with the sentence: \\\\{sen\\\\} The split result of the sentence (only split) is:\\n\",\n \"\\n\",\n \"\\\\end{tcolorbox}\\n\",\n \"\\n\",\n \"\\\\caption{Prompt template used for the Claim Segmentation module. The prompt defines the task and expected format, followed by an illustrative example showing how to split a complex claim into logical units.}\\n\",\n \"\\\\label{fig:seg}\\n\",\n \"\\\\end{figure}\\\\begin{figure}[h]\\n\"\n ],\n [\n \"\\\\end{figure}\\\\begin{figure}[h]\\n\",\n \"\\\\begin{tcolorbox}[colback=black!7.5!white, colframe=black!80!white, title=Evidence-Augmented Claim Editing Prompt, fontupper=\\\\footnotesize, fonttitle=\\\\footnotesize]\\n\",\n \"You should complete the sentence <\\\\{query\\\\}> with missing name (author, writer, owner, builder, award-winning titles etc.), number (date, year, population, acreage, etc), location and so on, replace the original adversarial point of view with detailed data or evidence (for example, the biggest population -> population 3,854,000; the date is unknown -> possible dates ranging from 1598 to 1608 ) and correct the counterfactual mistakes in sentence, which is done in order to avoid adversarial cases. You should never add too much new additional information to the sentence. Here is an example:\\\\\\\\\\n\",\n \"Given sentence: \\\\\\\\\\n\",\n \"<The album was released in 2018.>\\\\\\\\\\n\",\n \"Expected result: \\\\\\\\\\n\",\n \"The \\\"Blackpink in Your Area\\\" compilation album was released in 2018.\\\\\\\\\\n\",\n \"To help you better complete the required task, we provide following knowledge as contexts: \\\\\\\\\\n\",\n \"<\\\\{kn\\\\}> \\\\\\\\\\n\",\n \"Now do the required task on the sentence: <{query}> based on the given knowledge.\\n\",\n \"\\\\newline\\n\",\n \"\\n\",\n \"\\\\end{tcolorbox}\\n\",\n \"\\n\",\n \"\\\\caption{Prompt template for the Evidence-Augmented Claim Editing module. The prompt instructs how to enhance claims by adding specific details from evidence, replacing vague statements with precise information, and correcting inaccuracies.}\\n\",\n \"\\\\label{fig:paraphrase_prompt}\\n\",\n \"\\\\end{figure}\\\\begin{figure}[h]\\n\"\n ],\n [\n \"\\\\begin{figure}[h]\\n\",\n \"\\\\begin{tcolorbox}[colback=black!7.5!white, colframe=black!80!white, title=Entailment Label Prediction Prompt, fontupper=\\\\footnotesize, fonttitle=\\\\footnotesize]\\n\",\n \"\\\\texttt{[System Input]}: \\\\vspace{2pt}\\\\\\\\\\n\",\n \"You are a well-informed and expert fact-checker. Here are some example of how to act as a professional fact-checker:\\\\\\\\\\n\",\n \"Claim Example 1: The Cantos is a poem with most of it written over a 40 plus year time span. \\\\\\\\\\n\",\n \"Evidences for claim example 1: \\\\\\\\\\n\",\n \"<Most of it was written between 1915 and 1962, although much of the early work was abandoned and the early cantos, as finally published, date from 1922 onwards.\\n\",\n \"The Cantos by Ezra Pound is a long, incomplete poem in 116 sections, each of which is a canto.\\n\",\n \"This thread then runs through the appearance of Kuanon, the Buddhist goddess of mercy, the moon spirit from Hagaromo (a Noh play translated by Pound some 40 years earlier), Sigismondo's lover Ixotta (linked in the text with Aphrodite via a reference to the goddess' birthplace Cythera), a girl painted by Manet and finally Aphrodite herself, rising from the sea on her shell and rescuing Pound/Odysseus from his raft.> \\\\\\\\\\n\",\n \"Step 1: the evidence indicates that The Cantos is a poem with most of it written over a 40 plus year time span.\\n\",\n \"Step 2: the evidence also mentions that The Cantos by Ezra Pound is a long, incomplete poem in 116 sections. Step 3: the other pieces of evidence provided This thread then runs through the appearance of Kuanon, the Buddhist goddess of mercy, the moon spirit from Hagaromo (a Noh play translated by Pound some 40 years earlier).based on the evidence provided, the claim that The Cantos is a poem with most of it written over a 40 plus year time span is supported. \\\\\\\\\\n\",\n \"final rating: supported \\\\\\\\\\n\",\n \"Claim Example 2: \\\\\\\\\\n\",\n \"......\\\\\\\\\\n\",\n \"\\\\newline\\n\",\n \"\\\\texttt{[User Input]}: \\\\vspace{2pt}\\\\\\\\\\n\",\n \"Now its' your turn, you are provided with evidences regarding the following claim: \\\\{claim\\\\} \\\\\\\\\\n\",\n \"Evidences: \\\\\\\\\\n\",\n \"<\\\\{evidence\\\\}> \\\\\\\\\\n\",\n \"Based strictly on the main claim, and the evidences provided, you will provide: \\\\\\\\\\n\",\n \"rating: The rating for claim should be one of \\\"supported\\\" if and only if the Evidences specifically support the claim, \\\"refuted\\\" if and only if the Evidences specifically refutes the claim or \\\"not enough information\\\": if there is not enough information to support or refute the claim appropriately. \\\\\\\\\\n\",\n \"Is the claim: \\\\{claim\\\\} \\\"supported\\\", \\\"refuted\\\" or \\\"not enough information\\\" according to the available questions and answers? \\\\\\\\\\n\",\n \"Lets think step by step.\\n\",\n \"\\n\",\n \"\\\\end{tcolorbox}\\n\",\n \"\\n\",\n \"\\\\caption{Prompt template for the entailment label prediction. The prompt instructs the LLM to act as an expert fact-checker, providing step-by-step reasoning to classify claims as \\\"supported,\\\" \\\"refuted,\\\" or \\\"not enough information\\\" based on given evidence.}\\n\",\n \"\\\\label{fig:fm2_entail}\\n\",\n \"\\\\end{figure}\\\\begin{figure*}[!t]\\n\"\n ],\n [\n \"\\\\end{figure*}\\\\begin{table*}[!t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \"\\\\begin{tabular}{lccccccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{Backbone LLM} & \\\\multirow{2}{*}{Setting} \\n\",\n \"& \\\\multicolumn{2}{c}{Top@3} & \\\\multicolumn{2}{c}{Top@5} & \\\\multicolumn{2}{c}{Top@10} \\\\\\\\\\n\",\n \"\\\\cmidrule(lr){3-4} \\\\cmidrule(lr){5-6} \\\\cmidrule(lr){7-8}\\n\",\n \" & & Contriever & TFIDF & Contriever & TFIDF & Contriever & TFIDF \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"{{\\\\textbf{\\\\fm}}} \\\\\\\\\\\\noalign{\\\\vskip 0.5ex}\\n\",\n \"\\\\multirow{2}{*}{GPT-4o mini} &\\\\base & 64.5 & 53.5 & 65.0 & 59.0 & 67.5 & 61.5 \\\\\\\\\\n\",\n \"&\\\\claimdecompose & 65.0 {\\\\color{red}(+0.5)} & 55.0 {\\\\color{red}(+1.5)} & 65.0 {\\\\color{red}(+0.0)} & 61.0 {\\\\color{red}(+2.0)} & 65.5 {\\\\color{red}(-2.0)} & 63.0 {\\\\color{red}(+1.5)} \\\\\\\\\\n\",\n \"&\\\\qabrief & 65.5 {\\\\color{red}(+1.0)} & 57.0 {\\\\color{red}(+3.5)} & 67.0 {\\\\color{red}(+2.0)} & 62.0 {\\\\color{red}(+3.0)} & 67.5 {\\\\color{red}(+0.0)} & 62.5 {\\\\color{red}(+1.0)} \\\\\\\\\\n\",\n \"&\\\\programfc & 68.5 {\\\\color{red}(+4.0)} & 56.0 {\\\\color{red}(+2.5)} & 65.5 {\\\\color{red}(+0.5)} & 61.0 {\\\\color{red}(+2.0)} & 69.0 {\\\\color{red}(+1.5)} & 63.0 {\\\\color{red}(+1.5)} \\\\\\\\\\n\",\n \"&\\\\minicheck & 66.0 {\\\\color{red}(+1.5)} & 57.0 {\\\\color{red}(+3.5)} & 66.5 {\\\\color{red}(+1.5)} & 62.5 {\\\\color{red}(+3.5)} & 70.0 {\\\\color{red}(+2.5)} & 64.0 {\\\\color{red}(+2.5)}\\\\\\\\\\n\",\n \"&\\\\textbf{\\\\ours} & 69.0 {\\\\color{red}(+4.5)} & 61.5 {\\\\color{red}(+8.0)} & 72.5 {\\\\color{red}(+7.5)} & 67.0 {\\\\color{red}(+8.0)} & 75.0 {\\\\color{red}(+7.5)} & 68.5 {\\\\color{red}(+7.0)} \\\\\\\\\\n\",\n \"\\\\hdashline\\n\",\n \"\\\\multirow{2}{*}{LLama 3.1 70B} &\\\\base & 61.0 & 56.5 & 64.5 & 59.0 & 65.5 & 61.5 \\\\\\\\\\n\",\n \"&\\\\claimdecompose & 62.0 {\\\\color{red}(+1.0)} & 57.0 {\\\\color{red}(+0.5)} & 65.5 {\\\\color{red}(+1.0)} & 61.0 {\\\\color{red}(+2.0)} & 67.5 {\\\\color{red}(+2.0)} & 64.0 {\\\\color{red}(+2.5)} \\\\\\\\\\n\",\n \"&\\\\qabrief & 66.0 {\\\\color{red}(+5.0)} & 56.5 {\\\\color{red}(+0.0)} & 67.5 {\\\\color{red}(+3.0)} & 62.5 {\\\\color{red}(+3.5)} & 66.0 {\\\\color{red}(+0.5)} & 64.0 {\\\\color{red}(+2.5)} \\\\\\\\\\n\",\n \"&\\\\programfc & 62.0 {\\\\color{red}(+1.0)} & 58.0 {\\\\color{red}(+1.5)} & 65.0 {\\\\color{red}(+0.5)} & 61.0 {\\\\color{red}(+2.0)} & 66.0 {\\\\color{red}(+0.5)} & 64.5 {\\\\color{red}(+3.0)} \\\\\\\\\\n\",\n \"&\\\\minicheck &67.5 {\\\\color{red}(+6.5)} & 57.5 {\\\\color{red}(+1.0)} & 68.0 {\\\\color{red}(+3.5)} & 62.0 {\\\\color{red}(+3.0)} & 70.5 {\\\\color{red}(+5.0)} & 64.0 {\\\\color{red}(+2.5)} \\\\\\\\\\n\",\n \"&\\\\textbf{\\\\ours} & 71.0 {\\\\color{red}(+10.0)} & 63.5 {\\\\color{red}(+7.0)} & 72.5 {\\\\color{red}(+8.0)} & 67.5 {\\\\color{red}(+8.5)} & 73.5 {\\\\color{red}(+8.0)} & 69.0 {\\\\color{red}(+7.5)} \\\\\\\\\\n\",\n \"\\\\hline\\n\",\n \"{{\\\\textbf{\\\\wice}}} \\\\\\\\\\\\noalign{\\\\vskip 0.5ex}\\n\",\n \"\\\\multirow{2}{*}{GPT-4o mini} &\\\\base & 27.2 & 24.0 & 28.6 & 25.8 & 33.7 & 27.3 \\\\\\\\\\n\",\n \"&\\\\claimdecompose & 29.1 {\\\\color{red}(+1.9)} & 29.1 {\\\\color{red}(+5.1)} & 30.1 {\\\\color{red}(+1.5)} & 30.4 {\\\\color{red}(+4.6)} & 35.2 {\\\\color{red}(+1.5)} & 31.0 {\\\\color{red}(+3.7)} \\\\\\\\\\n\",\n \"&\\\\qabrief & 28.0 {\\\\color{red}(+0.8)} & 27.5 {\\\\color{red}(+3.5)} & 30.9 {\\\\color{red}(+2.3)} & 27.8 {\\\\color{red}(+2.0)} & 36.2 {\\\\color{red}(+2.5)} & 29.7 {\\\\color{red}(+2.4)} \\\\\\\\\\n\",\n \"&\\\\programfc & 33.2 {\\\\color{red}(+6.0)} & 26.8 {\\\\color{red}(+2.8)} & 34.8 {\\\\color{red}(+6.2)} & 28.7 {\\\\color{red}(+2.9)} & 38.4 {\\\\color{red}(+4.7)} & 29.9 {\\\\color{red}(+2.6)} \\\\\\\\\\n\",\n \"&\\\\minicheck &30.0 {\\\\color{red}(+2.8)} & 27.1 {\\\\color{red}(+3.1)} & 31.3 {\\\\color{red}(+2.7)} & 26.9 {\\\\color{red}(+1.1)} & 33.7 {\\\\color{red}(+0.0)} & 30.8 {\\\\color{red}(+3.5)} \\\\\\\\\\n\",\n \"&\\\\textbf{\\\\ours} & 34.1 {\\\\color{red}(+6.9)} & 31.0 {\\\\color{red}(+7.0)} & 37.2 {\\\\color{red}(+8.6)} & 28.1 {\\\\color{red}(+2.3)} & 39.0 {\\\\color{red}(+5.3)} & 33.2 {\\\\color{red}(+5.9)} \\\\\\\\\\n\",\n \"\\\\hdashline\\n\",\n \"\\\\multirow{2}{*}{LLama 3.1 70B} &\\\\base & 28.1 & 22.1 & 29.6 & 24.5 & 31.2 & 25.7 \\\\\\\\\\n\",\n \"&\\\\claimdecompose & 26.3 {\\\\color{red}(-1.8)} & 23.0 {\\\\color{red}(+0.9)} & 30.1 {\\\\color{red}(+0.5)} & 25.4 {\\\\color{red}(+0.9)} & 33.0 {\\\\color{red}(+1.8)} & 27.1 {\\\\color{red}(+1.4)} \\\\\\\\\\n\",\n \"&\\\\qabrief & 30.4 {\\\\color{red}(+2.3)} & 25.1 {\\\\color{red}(+3.0)} & 32.4 {\\\\color{red}(+2.8)} & 27.0 {\\\\color{red}(+2.5)} & 33.5 {\\\\color{red}(+2.3)} & 28.2 {\\\\color{red}(+2.5)} \\\\\\\\\\n\",\n \"&\\\\programfc & 34.6 {\\\\color{red}(+6.5)} & 24.7 {\\\\color{red}(+2.6)} & 36.5 {\\\\color{red}(+6.9)} & 26.9 {\\\\color{red}(+2.4)} & 33.9 {\\\\color{red}(+2.7)} & 29.3 {\\\\color{red}(+3.6)} \\\\\\\\\\n\",\n \"&\\\\minicheck & 39.1 {\\\\color{red}(+11.0)} & 27.8 {\\\\color{red}(+5.7)} & 34.9 {\\\\color{red}(+5.3)} & 27.9 {\\\\color{red}(+3.4)} & 35.2 {\\\\color{red}(+4.0)} & 30.1 {\\\\color{red}(+4.4)} \\\\\\\\\\n\",\n \"&\\\\textbf{\\\\ours} & 38.2 {\\\\color{red}(+10.1)} & 30.3 {\\\\color{red}(+8.2)} & 39.0 {\\\\color{red}(+9.4)} & 31.8 {\\\\color{red}(+7.3)} & 38.7 {\\\\color{red}(+7.5)} & 32.9 {\\\\color{red}(+7.2)} \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Comprehensive fact-checking evaluation results comparing \\\\ours with baseline approaches on \\\\fm and \\\\wice datasets. Results are reported as accuracy scores across different retrieval settings (Top@3, Top@5, Top@10). Numbers in red parentheses show absolute performance improvements over the \\\\base baseline.}\\n\",\n \"\\\\label{tab:whole_factcheckresults}\\n\",\n \"\\\\end{table*}\\\\begin{table*}[!t]\\n\"\n ],\n [\n \"\\\\begin{table*}[!t]\\n\",\n \"\\\\centering\\n\",\n \" \\\\resizebox{\\\\linewidth}{!}{\\n\",\n \" \\\\begin{tabular}{lcccccccccccccc}\\n\",\n \"\\\\toprule\\n\",\n \" \\\\multirow{4}{*}{Model} & \\\\multirow{4}{*}{Setting} & \\\\multicolumn{4}{c}{Top@3} & \\\\multicolumn{4}{c}{Top@5} & \\\\multicolumn{4}{c}{Top10} \\\\\\\\\\n\",\n \" \\\\cmidrule(lr){3-6} \\\\cmidrule(lr){7-10} \\\\cmidrule(lr){11-14}\\n\",\n \" & & \\\\multicolumn{2}{c}{Contriever} & \\\\multicolumn{2}{c}{TFIDF} & \\\\multicolumn{2}{c}{Contriever} & \\\\multicolumn{2}{c}{TFIDF} & \\\\multicolumn{2}{c}{Contriever} & \\\\multicolumn{2}{c}{TFIDF} \\\\\\\\\\n\",\n \" \\\\cmidrule(lr){3-4} \\\\cmidrule(lr){5-6} \\\\cmidrule(lr){7-8} \\\\cmidrule(lr){9-10} \\\\cmidrule(lr){11-12} \\\\cmidrule(lr){13-14}\\n\",\n \" & & Acc. & Recall & Acc. & Recall & Acc. & Recall & Acc. & Recall & Acc. & Recall & Acc. & Recall \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multirow{5}{*}{Llama3.1 70B}\\n\",\n \" & \\\\ours & 40.5 & 35.7 & 27.5 & 23.3 & 47.5 & 40.7 & 29.5 & 26.4 & 51.0 & 46.5 & 34.5 & 29.5 \\\\\\\\\\n\",\n \" & wo. claim editing & 39.0 {\\\\color{blue}(-1.5)} & 32.3 {\\\\color{blue}(-3.4)} & 17.5 {\\\\color{blue}(-10.0)} & 15.1 {\\\\color{blue}(-8.2)} & 46.0 {\\\\color{blue}(-1.5)} & 41.1 {\\\\color{blue}(+0.4)} & 21.5 {\\\\color{blue}(-8.0)} & 18.2 {\\\\color{blue}(-8.2)} & 49.5 {\\\\color{blue}(-1.5)} & 46.8 {\\\\color{blue}(+0.3)} & 28.5 {\\\\color{blue}(-6.0)} & 24.4 {\\\\color{blue}(-5.1)} \\\\\\\\\\n\",\n \" & wo. claim segmentation & 35.5 {\\\\color{blue}(-5.0)} & 31.0 {\\\\color{blue}(-4.7)} & 16.5 {\\\\color{blue}(-11.0)} & 13.6 {\\\\color{blue}(-9.7)} & 40.5 {\\\\color{blue}(-7.0)} & 35.2 {\\\\color{blue}(-5.5)} & 19.5 {\\\\color{blue}(-10.0)} & 16.7 {\\\\color{blue}(-9.7)} & 47.5 {\\\\color{blue}(-3.5)} & 42.6 {\\\\color{blue}(-3.9)} & 27.5 {\\\\color{blue}(-7.0)} & 22.5 {\\\\color{blue}(-7.0)} \\\\\\\\\\n\",\n \" & Paraphrase wo. Evidence & 34.0 {\\\\color{blue}(-6.5)} & 29.8 {\\\\color{blue}(-5.9)} & 14.0 {\\\\color{blue}(-13.5)} & 11.2 {\\\\color{blue}(-12.1)} & 39.5 {\\\\color{blue}(-8.0)} & 35.7 {\\\\color{blue}(-5.0)} & 20.0 {\\\\color{blue}(-9.5)} & 12.3 {\\\\color{blue}(-14.1)} & 50.5 {\\\\color{blue}(-0.5)} & 44.6 {\\\\color{blue}(-1.9)} & 26.5 {\\\\color{blue}(-8.0)} & 21.3 {\\\\color{blue}(-8.2)} \\\\\\\\\\n\",\n \"\\\\hdashline\\n\",\n \"\\\\multirow{5}{*}{GPT-4o mini}\\n\",\n \" & \\\\ours & 39.5 & 34.9 & 20.5 & 17.4 & 46.5 & 41.1 & 28.5 & 24.0 & 54.0 & 49.2 & 33.5 & 27.9 \\\\\\\\\\n\",\n \" & wo. claim editing & 37.0 {\\\\color{blue}(-2.5)} & 32.6 {\\\\color{blue}(-2.3)} & 14.0 {\\\\color{blue}(-6.5)} & 11.6 {\\\\color{blue}(-5.8)} & 45.0 {\\\\color{blue}(-1.5)} & 40.7 {\\\\color{blue}(-0.4)} & 23.0 {\\\\color{blue}(-5.5)} & 19.8 {\\\\color{blue}(-4.2)} & 53.0 {\\\\color{blue}(-1.0)} & 47.7 {\\\\color{blue}(-1.5)} & 26.0 {\\\\color{blue}(-7.5)} & 21.7 {\\\\color{blue}(-6.2)} \\\\\\\\\\n\",\n \" & wo. claim segmentation & 36.0 {\\\\color{blue}(-3.5)} & 31.8 {\\\\color{blue}(-3.1)} & 18.0 {\\\\color{blue}(-2.5)} & 15.1 {\\\\color{blue}(-2.3)} & 46.0 {\\\\color{blue}(-0.5)} & 40.3 {\\\\color{blue}(-0.8)} & 23.5 {\\\\color{blue}(-5.0)} & 19.8 {\\\\color{blue}(-4.2)} & 52.5 {\\\\color{blue}(-1.5)} & 46.9 {\\\\color{blue}(-2.3)} & 32.5 {\\\\color{blue}(-1.0)} & 27.1 {\\\\color{blue}(-0.8)} \\\\\\\\\\n\",\n \" & Paraphrase wo. Evidence & 29.0 {\\\\color{blue}(-10.5)} & 24.8 {\\\\color{blue}(-10.1)} & 13.5 {\\\\color{blue}(-7.0)} & 10.9 {\\\\color{blue}(-6.5)} & 39.5 {\\\\color{blue}(-7.0)} & 34.1 {\\\\color{blue}(-7.0)} & 20.0 {\\\\color{blue}(-8.5)} & 16.3 {\\\\color{blue}(-7.7)} & 51.5 {\\\\color{blue}(-2.5)} & 44.6 {\\\\color{blue}(-4.6)} & 25.0 {\\\\color{blue}(-8.5)} & 20.5 {\\\\color{blue}(-7.4)} \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{Ablation study results showing the impact of different components on retrieval performance using \\\\fm test set. Results are reported for both accuracy (Acc.) and recall across different retrieval settings. Numbers in blue parentheses show performance drops when removing each component from \\\\ours.}\\n\",\n \"\\\\label{tab:full_ablationstudy}\\n\",\n \"\\\\end{table*}\\n\"\n ]\n]","isConversational":false},"fulltext":{"kind":"string","value":"% This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.\n\\pdfoutput=1\n% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.\n\n\\documentclass[11pt]{article}\n\n% Remove the \"review\" option to generate the final version.\n% \\usepackage[review]{acl}\n\\usepackage{acl}\n\n% Standard package includes\n\\usepackage{times}\n\\usepackage{latexsym}\n\n\\usepackage{tcolorbox}\n\n% For proper rendering and hyphenation of words containing Latin characters (including in bib files)\n\\usepackage[T1]{fontenc}\n% For Vietnamese characters\n% \\usepackage[T5]{fontenc}\n% See https://www.latex-project.org/help/documentation/encguide.pdf for other character sets\n\n% This assumes your files are encoded as UTF8\n\\usepackage[utf8]{inputenc}\n\n% Added by authors\n\\usepackage{color}\n\\usepackage{tabularx}\n\\usepackage{tcolorbox}\n\\usepackage{xcolor}\n\\usepackage{colortbl}\n\\usepackage{tabu}\n\\usepackage{booktabs}\n\\usepackage{multirow}\n\n\\usepackage{graphicx} \n\\usepackage{float}\n\\usepackage{subfigure} \n\\usepackage{amssymb}\n\n\\usepackage{hyperref}\n\\usepackage{tablefootnote}\n\\usepackage{xspace}\n\\usepackage[normalem]{ulem}\n\n\n\\usepackage{float}\n\\usepackage{amsmath}\n\\usepackage{bm}\n\\usepackage{algorithmicx}\n\\usepackage{algorithm}\n\\usepackage{algpseudocode}\n\n\\usepackage{arydshln}\n\n\\usepackage{amssymb}\n\\usepackage{pifont}\n\\newcommand{\\cmark}{\\ding{51}}%\n\\newcommand{\\xmark}{\\ding{55}}%\n\\usepackage{enumitem}\n\\usepackage{fontawesome} % for \\faGithub, \\faGlobe\n\\usepackage{xspace} % for spacing after commands\n\\usepackage{hyperref}\n\n% This is not strictly necessary, and may be commented out,\n% but it will improve the layout of the manuscript,\n% and will typically save some space.\n\\usepackage{microtype}\n\n% This is also not strictly necessary, and may be commented out.\n% However, it will improve the aesthetics of text in\n% the typewriter font.\n% \\usepackage{inconsolata}\n\\newcommand{\\ours}{\\textsc{SuCEA}\\xspace}\n\\newcommand{\\fm}{\\textsc{FoolMeTwice}\\xspace}\n\\newcommand{\\wice}{\\textsc{Wice}\\xspace}\n\\newcommand{\\base}{\\textsc{RALM}\\xspace}\n\\newcommand{\\claimdecompose}{\\textsc{ClaimDecomp}\\xspace}\n\\newcommand{\\qabrief}{\\textsc{QA Briefs}\\xspace}\n\\newcommand{\\programfc}{\\textsc{ProgramFC}\\xspace}\n\\newcommand{\\minicheck}{\\textsc{MiniCheck}\\xspace}\n\n\\newcommand{\\eg}{\\hbox{\\emph{e.g.,}}\\xspace}\n\\newcommand{\\ie}{\\hbox{\\emph{i.e.}}\\xspace}\n\n\n\n% Author's Comments:\n\\newcommand{\\yilun}[1]{\\textcolor{blue}{\\bf\\small [Yilun: #1]}}\n\n\\newcommand{\\chen}[1]{\\textcolor{cyan}{\\bf\\small [Chen: #1]}}\n\n\\newcommand{\\hongjun}[1]{\\textcolor{purple}{\\bf\\small [Hongjun: #1]}}\n\n\\newcommand{\\yitao}[1]{\\textcolor{orange}{\\bf\\small [Yitao: #1]}}\n\n% If the title and author information does not fit in the area allocated, uncomment the following\n%\n%\\setlength\\titlebox{<dim>}\n%\n% and set <dim> to something 5cm or larger.\n\\title{\\ours: Reasoning-Intensive Retrieval for Adversarial Fact-checking through Claim Decomposition and Editing}\n%\\title{\\ours: Enhancing Precision and Robustness in Long-Form Fact-checking by Claim Decontextualizing and Evidence-Augmented Editing}\n\n\n\\author{\n Hongjun Liu\\textsuperscript{1,2} \\quad \n Yilun Zhao\\textsuperscript{3} \\quad \n Arman Cohan\\textsuperscript{3,4} \\quad Chen Zhao\\textsuperscript{1, 2}\\\\\n \\textsuperscript{1}New York University \\quad\n \\textsuperscript{2}NYU Shanghai \\quad\n \\textsuperscript{3}Yale University \\quad\\quad\n \\textsuperscript{4}Allen Institute for AI\n}\n\n\n\\begin{document}\n\\maketitle\n\n\\begin{minipage}[t]{2\\linewidth}\n\\vspace{-1.75cm}\n \\centering\n \\href{https://github.com/HolaYan/SUCEA}{{\\faGithub{}}\\xspace\\texttt{https://github.com/HolaYan/SUCEA}} \n\\vspace{0.5cm}\n\\end{minipage}\n\n\\begin{abstract}\n% \\hongjun{adversarial}\nAutomatic fact-checking has recently received more attention as a means of combating misinformation. Despite significant advancements, fact-checking systems based on retrieval-augmented language models still struggle to tackle adversarial claims, which are intentionally designed by humans to challenge fact-checking systems.\nTo address these challenges, we propose a training-free method designed to rephrase the original claim, making it easier to locate supporting evidence. \nOur modular framework, \\ours, decomposes the task into three steps: 1) \\emph{Claim Segmentation and Decontextualization} that segments adversarial claims into independent sub-claims; 2) \\emph{Iterative Evidence Retrieval and Claim Editing} that iteratively retrieves evidence and edits the subclaim based on the retrieved evidence; 3) \\emph{Evidence Aggregation and Label Prediction} that aggregates all retrieved evidence and predicts the entailment label.\nExperiments on two challenging fact-checking datasets demonstrate that our framework significantly improves on both retrieval and entailment label accuracy, outperforming four strong claim-decomposition-based baselines.\n\n\\end{abstract}\n\n\\begin{figure}[!t]\n \\centering\n \\includegraphics[width = \\linewidth]{figures_raw/main_example.pdf}\n \\caption{Examples of challenges in fact-checking adversarial claims. \n These claims are deliberately crafted by humans using strategies to deceive fact-checking systems. \n Even state-of-the-art approaches (\\ie RALM) face significant difficulties in accurately verifying these claims. \n }\n \\label{fig:task}\n\\end{figure}\\section{Introduction}\nIn the era of information overload, news items are often rapidly and indiscriminately published by news outlets, which can lead to the spread of misinformation~\\cite{chen2023combating}. Consequently, it is essential to automatically verify these claims that circulate online~\\cite{thorne-etal-2018-fever, min-etal-2023-factscore, akhtar-etal-2023-multimodal}.\nThe current research landscape in automated fact-checking mainly adopts a retrieval-augmented language model (RALM) framework~\\cite{asai2024reliable, gao2024ralmsurvey}. It involves a separate information retrieval system, such as term-matching methods such as BM25 or dense retrievers~\\cite{karpukhin2020dense}, to locate evidence from extensive text collections used as an external datastore. \nThe retrieved output is then fed to a large language model (LLM) along with the input claim to predict the entailment labels. With the advancements of LLMs~\\cite{touvron2023llama, OpenAI2023GPT4TR, groeneveld-etal-2024-olmo}, these automated fact-checking systems have shown remarkable performance improvements across various fact-checking benchmark datasets~\\cite{fever-2023-fact, kamoi-etal-2023-wice,zhao2024findverexplainableclaimverification}.\n\n\nNevertheless, existing RALM approaches for fact-checking still lack robustness, especially when dealing with claims adversarially crafted by humans~\\cite{eisenschlos-etal-2021-fool, kamoi-etal-2023-wice}. Human claim writers often deliberately reduce the textual overlap between claims and evidence, \\eg\nomit crucial information (in \\autoref{fig:task}, the author name \\underline{D. H. Lawrence} is absent). Therefore, it requires in-depth reasoning to identify relevant evidence that goes beyond surface form matching, \\ie reasoning-intensive retrieval~\\cite{su2024bright,zhao2024financemathknowledgeintensivemathreasoning}.\nThis challenge is further exacerbated by the complexity of such claims, which frequently require verification of multiple components (\\eg the claim in \\autoref{fig:task} involves checking two distinct elements).\n\nTo address the challenge of adversarial fact-checking, we introduce \\textbf{\\ours}, a \\underline{\\textbf{\\textsc{Su}}}b\\underline{\\textbf{C}}laim-based \\underline{\\textbf{E}}vidence-\\underline{\\textbf{A}}ugmented Enhancement Fact-checking framework without additional LLM fine-tuning.\n\\ours contains three key modules: \n(1) \\textbf{Claim Segmentation and Decontextualization}, which leverages LLMs to segment and decontextualize the input claims into several \\textit{atomic} segments.\n(2) \\textbf{Iterative Evidence Retrieval and Claim Editing}, which iteratively retrieves evidence and rewrites each sub-claim that is lacking sufficient support.\n(3) \\textbf{Evidence Aggregation and Label Prediction}, which aggregates all retrieved evidence for final entailment label prediction. \n\nCompared with existing fact-checking systems, \\ours features the following advancements: \n(1) Our claim editing module tackles reasoning-intensive retrieval challenge through \ntransforming these adversarially designed segments into more retriever-friendly ones (\\eg by increasing textual overlap, simplifying uncommon phrases, or adding key information). \n(2) Editing claims from scratch is non-trivial, as it is unclear where evidence retrieval struggles. Our approach addresses this by grounding the editing process in retrieved evidence, which provides essential guidance, such as relevant topics and person names.\n(3) Our decomposition strategy further simplifies reasoning challenges by breaking claims into decontextualized segments and increasing the lexical overlap between decomposed sub-claims and evidence.\n\nOn \\fm, \\ours demonstrates substantial improvements over RALM fact-checking systems, with 7.5\\% boost in fact-checking accuracy.\nThis enhancement is primarily attributed to improved evidence retrieval, reflected in an 11.0\\% increase in retrieval Recall@10 under \\texttt{Llama-3.1-70B}. Additionally, \\ours outperforms baselines on another real-world complex fact-checking dataset, \\wice, demonstrating the general applicability of our framework for verifying complex claims. We further conduct a detailed ablation study and qualitative analysis, showing that the claim editing module simplifies and enhances evidence retrieval, while the claim segmentation module reduces distractions during retrieval.\n\n\n\n\n\\section{The Real-world Adversarial Fact Checking Task}\nThis section first defines the task setup (\\S\\ref{sec:problem}) and then discusses the datasets used in our study (\\S\\ref{sec:dataset}). \n\n\\subsection{Problem Formulation}\n\\label{sec:problem}\n\nWe formulize the task of adversarial fact-checking in open-domain settings as follows: \nThe input consists of a claim \\( C \\) and a large text collection $D$, which serves as an external datastore. The task is to first retrieve relevant evidence $e_1, ..., e_k$, with the retrieval performance evaluated by the accuracy and recall of gold evidence (\\S\\ref{sec:exp_setup}). Following this, the fact-checking system assigns one of entailment labels $L_C$ to the claim: \\{\\emph{supported}, \\emph{refutes}, \\emph{not enough evidence}\\}.\n\n\n\\begin{table}[t!]\n\\centering \n\\small\n\\resizebox{\\columnwidth}{!}{\n\\renewcommand{\\arraystretch}{1.2}\n\\begin{tabular}{lrr}\n\\toprule\n\\textbf{Property} & \\textbf{\\fm} & \\textbf{\\wice} \\\\\n\\midrule\n\\multicolumn{3}{c}{\\textbf{Knowledge Corpus}} \\\\\nSource & Wikipedia & Wikipedia \\\\\nKnowledge Corpus Size &21,015,325 &21,015,325\\\\\n\\noalign{\\vskip 0.5ex}\\hdashline\\noalign{\\vskip 0.5ex}\n\\multicolumn{3}{c}{\\textbf{Evidence Retrieval \\& Fact Checking}} \\\\\n\\# Evidence (avg.) & 1.29 & 3.94 \\\\\nClaim Token Length (avg.) & 14.03 & 24.20\\\\\n% Avg. Relevant Rows & 8.2 & 4.0 \\\\\n\nTest Set Size (\\# Claims) & 200 & 358 \\\\\n\\quad \\# Entailed Claims &103 &111\\\\\n\\quad \\# Refuted Claims &97 &32\\\\\n\\quad \\# Partially Supported Claims & N/A &215\\\\\n\\toprule\n\\end{tabular}\n}\n\\caption{Basic statistics of the \\fm and \\wice test sets used in our experiments. Due to limited budgets, we evaluate on the full test set of \\wice (358 instances) and 200 random samples from the \\fm test set.}\n\\label{tab:basic-stats}\n\\end{table}\n\n\\subsection{Datasets}\n\\label{sec:dataset}\nWe mainly conduct experiments and analysis on the adversarial dataset \\fm~\\cite{eisenschlos2021fool}. \nWe also choose \\wice~\\cite{kamoi2023wicerealworldentailmentclaims}, a long-form fact-checking dataset, to demonstrate the broader applicability of \\ours.\n\\autoref{tab:basic-stats} presents an overview of the test set statistics used in this study.\n\n\\begin{itemize}[leftmargin=*]\n \\itemsep0em \n \\item \\textbf{\\fm} is a challenging fact-checking dataset with adversarially written claims. The dataset is collected through a multi-player game, where human players are allowed to modify claims to more challenging versions to deceive other players (\\ie unable to verify). Through this process, the collected claims are more complex and often require a higher order of reasoning (\\eg inference about time, understanding hyponymy, phrase paraphrasing, etc). \n\n \\item \\textbf{\\wice} focuses on real-world claim entailment grounded on Wikipedia pages. It is equipped with long-form claims with numerous pieces of statements. These long-form claims create challenges for fact-checking systems, especially evidence retrieval components. As it requires retrievers to find \\textit{complete} set of evidence. \n In this study, we expand the retrieval process of the \\wice dataset to open-domain settings. \n\\end{itemize}\n\n\\begin{figure}[!t]\n \\centering\n \\includegraphics[width = 1.0\\linewidth]{figures_raw/pipeline.pdf}\n \\caption{An illustration of \\ours pipeline (left) and its workflow for adversarial fact-checking using an example from the \\fm dataset (right). The input claim is first segmented and decontextualized by LLMs. For each sub-claim, a first round of evidence retrieval is conducted, followed by sub-claim revision and a second round of evidence retrieval. The final step is label prediction for the claim. \n }\n \\label{fig:model}\n\\end{figure}\n\\section{Method}\nIn this section, we present our proposed framework, \\ours, for adversarial fact-checking. \nThis task presents two significant challenges for existing RALM-based fact-checking systems: the relevant knowledge cannot be directly retrieved through lexical or semantic matching alone, but instead require intensive reasoning; the complexity of the input claims require verifying multiple sub-components and aggregating the results.\n\nMore specifically, \\ours contains three key modules (Figure~\\ref{fig:model}): \n(1) The \\textbf{Claim Segmentation and Decontextualization} module breaks the input claims into multiple sub-claims, and then rewrites each sub-claim to make it context-free and independent (\\S\\ref{sec:model_sts}).\n(2) The \\textbf{Iterative Evidence Retrieval and Claim Editing} module retrieves evidence for each sub-claim.\nWhen retrieval fails, the LLMs paraphrase the sub-claims based on the retrieved evidence by using our specialized constraint prompt and then initiate a new round of retrieval (\\S\\ref{sec:model_ce}). \n(3) The \\textbf{Evidence Aggregation and Label Prediction} module finally combines retrieved evidence and makes the final entailment label prediction (\\S\\ref{sec:model_lp}).\n\n\n\\subsection{Claim Segmentation and Decontextualization}\n\\label{sec:model_sts}\n% \\hongjun{TODO}\nReal-world claims often contain multiple facts that need to be verified. For example, verifying the input claim in Figure~\\ref{fig:model} entails identifying who James VI is, exploring his biography, and determining his age at the time he ascended the Scottish throne.\nVerifying all sub-claims simultaneously is challenging for current RALM systems, mainly because the retrieval component cannot find all evidence pieces at once. Often, the top-ranked evidence may address only a few fact units. To tackle this issue, we follow existing work~\\cite{min2023factscorefinegrainedatomicevaluation} that instructs LLMs to decompose the claims into decontextualized segments. \nEach segment represents an atomic fact, which can then be fact-checked independently. We detail this module as follows:\n\n\n\\paragraph{Claim Segmentation.} Given an input claim $C$, we prompt LLMs to decompose into a sequence of sub-claims $C_1, C_2,..., C_n$. \nWe include the prompt details in \\autoref{fig:seg} in the appendix.\n\n\\paragraph{Claim Decontextualization.} For each sub-claim $C_i$, we further prompt LLMs to ensure it is a standalone statement by adding entity name or rewriting pronouns~\\cite{gunjal2024molecular}. We include the prompt details in \\autoref{fig:decontext} in appendix.\n \n\n\\subsection{Iterative Evidence Retrieval and Claim Editing}\n\\label{sec:model_ce}\nA key challenge in fact-checking adversarial claims is that there is less textual overlap between claim and evidence. \nFor example, writers paraphrase the evidence \\emph{``Sister Carrie was also criticized for \\underline{never mentioning the name of God}.''} into the statement \\emph{``Sister Carrie was criticized for taking the \\underline{Lord’s title in vain}''}. \nTherefore, retrievers need in-depth reasoning to identify relevant evidence that goes beyond surface form matching (\\eg multi-hop reasoning by adding the missing information as intermediate hop). \nTo this end, we propose a reverse engineering approach that prompts LLMs to paraphrase these sub-claims back to their original form by eliminating the misleading controversial content and adding missing or modified key information. \nHowever, directly prompting LLMs to paraphrase back sub-claims is non-trivial, as LLMs have no explicit direction. \nWe instead first retrieve evidence for original sub-claims and mainly use it as a hint to guide LLMs towards right editing directions (\\eg \\emph{``\\underline{Lord’s title in vain}''} $\\rightarrow$ \\emph{``\\underline{never mentioning the name of God}''}). We detail this module as follows.\n\n\\paragraph{First-Round Evidence Retrieval.}\nFor each sub-claim $C_i$, we adopt the off-the-shelf retrievers (\\eg TFIDF or Dense Retrieval) to find top-$k$ evidence from text collections (\\ie Wikipedia). Note that these claims are adversarially written to fool the retrieval systems, thus we do not expect to find evidence in one shot, but instead use such information to guide claim editing that we present next.\n\n\\paragraph{Sub-claim Paraphrase.}\nFor each sub-claim $C_i$ and corresponding top-$k$ evidence $e_1, ..., e_k$, we prompt LLMs to paraphrase $C_i$ to $\\hat{C_i}$ so that it becomes retriever-friendly. Note that we directly add the constraints into prompts so that it only adds information from the evidence, rather than from LLM's parametric knowledge (which is more likely to hallucinate). \nAdditionally, to ensure high generation quality, we enhance the process by applying specific paraphrasing guidelines, such as completing missing named entities, numerical values, and locations, correcting counterfactual errors, and utilizing a one-shot approach.\n\\autoref{fig:paraphrase_prompt} in Appendix presents the prompt details.\n\n\\paragraph{Second-Round Evidence Retrieval.} For each paraphrased sub-claim $\\hat{C_i}$, we adopt the off-the-shelf retrievers again to find top-$k$ evidence.\nThe paraphrasing of sub-claims is intended to be retrieval-friendly, thereby improving the likelihood of retrieving relevant evidence.\n\n\n\\subsection{Evidence Aggregation and Label Prediction}\n\\label{sec:model_lp}\nThis module aims to aggregate the retrieved evidence for each sub-claim and utilize the combined information to predict the final entailment label.\n\n\n\\paragraph{Aggregate Evidence.} Given $n$ sub-claims, we retrieve a total of $k \\times n$ evidence pieces. We then prompt the LLMs to rerank these pieces and select the top-$k$ most relevant ones. \\autoref{fig:ranker} in Appendix presents the prompt details.\n% \n\n\\paragraph{Entailment Label Prediction.} We next prompt LLMs to predict the final entailment label\nbased on the claim $C$ and its top-$k$ retrieved supporting evidence as context. \\autoref{fig:fm2_entail} in Appendix presents the prompt details.\n \n\\section{Experiment}\n% \\input{tables/retrieveresult}\n% \\chen{Main Table: Retrieval and Fact Checking (Only baseline RALM and proposed approach). }\n\n% \\chen{Ablation Study: 1. Every component; 2. Examples in why segmentation helps retrieval; 3. Different Categories on what paraphrasing looks like and why it further helps (including the impact of retrieval as augmentation); 4. other sensitive parameters; 5. Errors and case studies }\nWe examine our framework for the adversarial fact-checking task, using two benchmark datasets, \\fm and \\wice \\ (discussed in \\S\\ref{sec:dataset}).\n%We further provide a detailed analysis to understand to what extent our method surpasses existing approaches for adversarial fact-checking. \n\n% \\chen{TODO: ADD evaluation metrics (Recall, accu, fact checking accu) and Implementation details.}\n\\subsection{Experiment Setup}\\label{sec:exp_setup}\n% \\hongjun{TODO}\n\n\\paragraph{\\ours Implementation Details.}\nWe use \\texttt{GPT-4o-mini} \\footnote{\\texttt{gpt-4o-mini-2024-07-18}}\\cite{OpenAI2023GPT4TR} and \\texttt{Llama-3.1-70B} \\cite{dubey2024llama3} as the backbone LLMs for segmentation and sub-claim editing. We further compare with other open-source LLMs, including \\texttt{Llama-3.1-8B} \\cite{dubey2024llama3}, \\texttt{Mistral 9B} \\cite{mistral2023moe}, and \\texttt{Gemma-2-9B} \\cite{gemma2}. We present the results in appendix (\\autoref{tab:smallmodelresult}). We adopt three different retrievers: \n \\texttt{TF-IDF}~\\cite{schutze2008introduction} that focuses on lexical similarity, and \\texttt{Contriever} \\cite{izacard2022unsuperviseddenseinformationretrieval} that encodes both claims and passages into embeddings and compares the vector distance as relevance measure. We follow \\texttt{Contriever} setup and use the Dec. 20, 2018 version of Wikipedia as the retrieval corpus. We preprocess the Wikipedia so that each passage contains one hundred words. \n\n\n\n\n\n\\paragraph{Baselines.} We compare \\ours with the following baselines: (1) \\emph{Retrieval-augmented Generation} that finds top-k evidence from Wikipedia, and then uses LLMs to predict entailment labels;\n(2) \\emph{Sub-claim Generation}: \\claimdecompose \\cite{chen2022generatingliteralimpliedsubquestions}, \\qabrief \\cite{fan2020generatingfactcheckingbriefs} and \\minicheck \\cite{tang-etal-2024-minicheck} decompose claims into a set of sub-claims and aggregate the results of verifying the sub-claims.\n(2) \\emph{Reasoning Program Generation}: \\programfc \\cite{pan2023factcheckingcomplexclaimsprogramguided} decomposes a fact-checking task into a sequence of sub-tasks through a program, executes each task and aggregates results to verify the claim.\n\n\n\\paragraph{Metrics.} \nWe use \\textbf{Accuracy} to measure the alignment between predicted entailment labels and the ground truth for fact checking.\nFor \\emph{evidence retrieval}, we employ two widely-used metrics: \n\\textbf{Retrieval Accuracy (\\emph{RAcc.})}, which assesses whether at least one relevant evidence piece is in the top-$k$ results (following \\fm); \nand \\textbf{Retrieval Recall@\\bm{$k$} (\\emph{Recall@\\bm{$k$}})}, which measures the proportion of relevant evidence pieces retrieved among the top-$k$ results. \n%We directly evaluate on the test set of \\fm and \\wice.\n% \\yilun{seems to be not necessary?}.\n% \\footnote{Due to limited budgets, we evaluate on the full test set of \\wice (358 instances) and 200 random samples of \\fm.} \n\n\n\n\n%\\paragraph{\\ours}\n%As outlined in Section 3.1, we first prompt the LLMs to segment the input claim into individual atomic facts. For each sub-claim, we deploy retrievers to procure the top-k evidences, forming a new pool of evidences. Subsequently, we utilize the LLMs as a reranker to select the top@k relevant evidences for our final evidence set.\n%Then as stated in Section 3.2, we leverage LLMs to reverse adversarial statements back to original form. For each sub-claims, we provide the evidences from previous step as contexts to aid LLMs in paraphrasing. Then for each rewritten sub-claims, we repeat the same procedure as in the previous step and select an evidence set with the help of LLMs.\n\\begin{table}[t]\n\\centering\n\\small\n% \\setlength{\\tabcolsep}{4pt}\n\\resizebox{\\columnwidth}{!}{\n\\renewcommand{\\arraystretch}{1.2}\n\\begin{tabular}{lcccc}\n\\toprule\n\\multirow{2}{*}{System} & \\multicolumn{2}{c}{\\fm} & \\multicolumn{2}{c}{\\wice} \\\\\n\\cmidrule{2-5}\n& Contriever & TFIDF & Contriever & TFIDF \\\\\n\\midrule\n\\multicolumn{5}{l}{\\textbf{GPT-4o-mini}} \\\\\n\\quad \\base & 67.5 & 61.5 & 33.7 & 27.3 \\\\\n\\quad \\claimdecompose & 69.5 (\\textcolor{red}{+2.0}) & 63.0 (\\textcolor{red}{+1.5}) & 35.2 (\\textcolor{red}{+1.5}) & 31.0 (\\textcolor{red}{+3.7}) \\\\\n\\quad \\qabrief & 67.5(\\textcolor{red}{+0.0})\t&62.5(\\textcolor{red}{+1.0}) & 36.2(\\textcolor{red}{+2.5}) &29.7(\\textcolor{red}{+2.4}) \\\\\n\\quad \\programfc & 69.0 (\\textcolor{red}{+1.5}) & 63.0 (\\textcolor{red}{+1.5}) & 38.4 (\\textcolor{red}{+4.7}) & 29.9 (\\textcolor{red}{+2.6}) \\\\\n\\quad \\minicheck & 70.0 (\\textcolor{red}{+2.5}) & 64.0 (\\textcolor{red}{+2.5}) & 33.7 (\\textcolor{red}{+0.0}) & 30.8 (\\textcolor{red}{+3.5})\\\\\n\n\\noalign{\\vskip 0.5ex}\\hdashline\\noalign{\\vskip 0.5ex}\n\n\\quad \\textbf{\\ours} & \\textbf{75.0 (\\textcolor{red}{+7.5})} & \\textbf{68.5 (\\textcolor{red}{+7.0})} & \\textbf{39.0 (\\textcolor{red}{+5.3})} & \\textbf{33.2 (\\textcolor{red}{+5.9})} \\\\\n\\midrule\n\\multicolumn{5}{l}{\\textbf{LLama-3.1-70B}} \\\\\n\\quad \\base & 65.5 & 61.5 & 31.2 & 25.7 \\\\\n\\quad \\claimdecompose & 67.5 (\\textcolor{red}{+2.0}) & 64.0 (\\textcolor{red}{+2.5}) & 33.0 (\\textcolor{red}{+1.8}) & 27.1 (\\textcolor{red}{+1.4}) \\\\\n\\quad \\qabrief & 66.0(\\textcolor{red}{+0.5})\t&64.0(\\textcolor{red}{+2.5}) & 33.5(\\textcolor{red}{+2.3}) &28.2(\\textcolor{red}{+2.5}) \\\\\n\\quad \\programfc & 66.0 (\\textcolor{red}{+0.5}) & 64.5 (\\textcolor{red}{+3.0}) & 33.9 (\\textcolor{red}{+2.7}) & 29.3 (\\textcolor{red}{+3.6}) \\\\\n\\quad \\minicheck & 70.5 (\\textcolor{red}{+5.0}) & 64.0 (\\textcolor{red}{+2.5}) & 35.2 (\\textcolor{red}{+4.0}) & 30.1 (\\textcolor{red}{+4.4})\\\\\n\n\\noalign{\\vskip 0.5ex}\\hdashline\\noalign{\\vskip 0.5ex}\n\n\\quad \\textbf{\\ours} & \\textbf{73.5 (\\textcolor{red}{+8.0})} & \\textbf{69.0 (\\textcolor{red}{+7.5})} & \\textbf{38.7 (\\textcolor{red}{+7.5})} & \\textbf{32.9 (\\textcolor{red}{+7.2})} \\\\\n\\bottomrule\n\\end{tabular}\n}\n\\caption{Fact-checking evaluation results under baselines and \\ours for \\fm and \\wice models.\nNumbers in red parentheses indicate performance improvements over the baseline (\\base). }\n\\label{tab:factcheckresults}\n\\end{table}\n\n\\subsection{Main Experimental Results}\n% \\hongjun{TODO}\n%We present the fact-checking results in \\autoref{tab:factcheckresults}, \\autoref{tab:whole_factcheckresults} and retrieval results in \\autoref{tab:retriveresult}. We make the following findings.\n\n\n\n\n% \\paragraph{Effectiveness and outperformance \\chen{This is not the right word, take a look at what other papers describe the results.} of \\ours framework.}\n% % \\yilun{make the paragraph title format consistent, change to sentence}\n% %\\autoref{tab:retriveresult} illustrates the retrieval performance of the evaluated LLMs on the four settings we propose in Section 4.1. \n% The improvement in final fact-checking accuracy (\\autoref{tab:factcheckresults}) is substantial, with \\ours demonstrating superior performance across all metrics. \\chen{Which table are you referring to? This paragraph should only talk about table 2} Compared to the Original Query baseline, we achieve average increases of 4.5\\%, 7.5\\%, and 7.5\\% in fact-checking accuracy for Top@3, Top@5, and Top@10, respectively, under the Contriever setting. The improvements are even more pronounced under the TFIDF setting, where accuracy increases by 8\\%, 8\\%, and 7\\% for the same metrics. \n\n% \\chen{I do not get it, what's the takeaway of this paragraph? and what's the difference between this paragraph and the previous one? }\n\\begin{table*}[!t]\n\\centering\n\\resizebox{\\textwidth}{!}{%\n\\addtolength{\\tabcolsep}{-0.35em}\n\\begin{tabular}{lcccccccccccccc}\n\\toprule\n \\multirow{4}{*}{System} & \\multicolumn{4}{c}{Top@3} & \\multicolumn{4}{c}{Top@5} & \\multicolumn{4}{c}{Top@10} \\\\\n \\cmidrule(lr){2-5} \\cmidrule(lr){6-9} \\cmidrule(lr){10-13}\n & \\multicolumn{2}{c}{Contriever} & \\multicolumn{2}{c}{TFIDF} & \\multicolumn{2}{c}{Contriever} & \\multicolumn{2}{c}{TFIDF} & \\multicolumn{2}{c}{Contriever} & \\multicolumn{2}{c}{TFIDF} \\\\\n \\cmidrule(lr){2-3} \\cmidrule(lr){4-5} \\cmidrule(lr){6-7} \\cmidrule(lr){8-9} \\cmidrule(lr){10-11} \\cmidrule(lr){12-13}\n & RAcc. & Recall & RAcc. & Recall & RAcc. & Recall & RAcc. & Recall & RAcc. & Recall & RAcc. & Recall \\\\\n\\midrule\n\n\\textbf{\\fm} \\\\\\noalign{\\vskip 0.5ex}\n\n\n\n{Retriever only} & 28.5 & 24.8 & 11.0 & 8.9 & 36.0 & 31.0 & 15.5 & 12.8 & 47.5 & 42.6 & 23.5 & 19.0 \\\\\n\\ours (Llama-3.1-70B) & 40.5 ({\\color{red}+12.0}) & 35.7 ({\\color{red}+10.9}) & 27.5 ({\\color{red}+16.5}) & 23.3 ({\\color{red}+14.4}) & 47.5 ({\\color{red}+11.5}) & 40.7 ({\\color{red}+9.7}) & 29.5 ({\\color{red}+14.0}) & 26.4 ({\\color{red}+13.6}) & 51.0 ({\\color{red}+3.5}) & 46.5 ({\\color{red}+3.9}) & 34.5 ({\\color{red}+11.0}) & 29.5 ({\\color{red}+10.5}) \\\\\n\\ours (GPT-4o-mini) & 39.5 ({\\color{red}+11.0}) & 34.9 ({\\color{red}+10.1}) & 20.5 ({\\color{red}+9.5}) & 17.4 ({\\color{red}+8.5}) & 46.5 ({\\color{red}+10.5}) & 41.1 ({\\color{red}+10.1}) & 28.5 ({\\color{red}+13.0}) & 24.0 ({\\color{red}+11.2}) & 54.0 ({\\color{red}+6.5}) & 49.2 ({\\color{red}+6.6}) & 33.5 ({\\color{red}+10.0}) & 27.9 ({\\color{red}+8.9}) \\\\\n\n\\midrule\n\n% \\multicolumn{90}{c}{\\emph{\\textbf{\\wice}}} \\\\\\noalign{\\vskip 0.5ex}\n% \\multicolumn{13}{c}{\n\\textbf{\\wice} \\\\\n\\noalign{\\vskip 0.5ex}\n\n{Retriever only} & 7.0 & 3.0 & 5.3 & 2.5 & 8.7 & 3.8 & 7.0 & 3.3 & 11.7 & 5.0 & 10.9 & 4.3 \\\\\n\\ours (Llama-3.1-70B) & 11.1 ({\\color{red}+4.1}) & 4.5 ({\\color{red}+1.5}) & 7.8 ({\\color{red}+2.5}) & 3.5 ({\\color{red}+1.0}) & 14.1 ({\\color{red}+5.4}) & 5.0 ({\\color{red}+1.2}) & 9.5 ({\\color{red}+2.5}) & 4.0 ({\\color{red}+0.7}) & 16.4 ({\\color{red}+4.7}) & 6.5 ({\\color{red}+1.5}) & 14.0 ({\\color{red}+3.1}) & 5.8 ({\\color{red}+1.5}) \\\\\n\\ours (GPT-4o-mini) & 8.4 ({\\color{red}+1.4}) & 3.7 ({\\color{red}+0.7}) & 7.3 ({\\color{red}+2.0}) & 3.1 ({\\color{red}+0.6}) & 10.6 ({\\color{red}+1.9}) & 4.4 ({\\color{red}+0.6}) & 9.2 ({\\color{red}+2.2}) & 3.7 ({\\color{red}+0.4}) & 16.0 ({\\color{red}+4.3}) & 6.4 ({\\color{red}+1.4}) & 13.4 ({\\color{red}+2.5}) & 5.7 ({\\color{red}+1.4}) \\\\\n\n\\bottomrule\n\\end{tabular}\n}\n% \\caption{ Retrieval evaluation results under \\base and \\ours on the \\fm and \\wice test sets. \\textbf{$RAcc.$} and \\textbf{$Recall$} separately denote the precision and recall for retrieval evaluation. \\hongjun{more caption}\n\\caption{Comparison of retrieval performance between baseline retriever and \\ours on \\fm and \\wice test sets under Top-k evidence (k=3,5,10). We report both Retrieval Accuracy (\\textbf{RAcc.}) and Recall metrics. Numbers in red parentheses indicate absolute improvements. %\\chen{Should we have BGE-EN-ICL as well?}\n}\n\\label{tab:retriveresult}\n\\end{table*}\n\n\\paragraph{\\ours outperforms baselines in adversarial claims.}\n% According to the results, we can see that in the non-adversarial setting (\\wice dataset), some baselines can improve fact-checking accuracy. For example, for \\texttt{GPT-4o mini}, \\claimdecompose\n% % \\yilun{use \\texttt{textsc}, same for the other model names} \n% improves Top@3 Contriever performance from 27.2\\% to 29.1\\%, and \\programfc boosts it to 33.2\\%. \\ours still outperforms these baselines, achieving 34.1\\%.\n% \nAccording to \\autoref{tab:factcheckresults},\\footnote{We present full results in Appendix (\\autoref{tab:whole_factcheckresults}).} \\ours significantly outperforms all baselines in \\fm dataset. Specifically, compared with \\base, the fact-checking accuracy improves from 65.5\\% to 73.5\\%.\\footnote{Specifically, for supported claims, the result improves from 52.4\\% to 64.1\\%. And for refuted claims, the result improves from 79.4\\% to 88.7\\%.}\n%\\footnote{Specifically, for supported claims, the results improve (52.4\\%) to 66 out of 103 (64.1\\%), and refuted claims improving from 77 out of 97 (79.4\\%) to 86 out of 97 (88.7\\%).\nIn contrast, baselines struggle to effectively address adversarial scenarios. Unlike \\ours, these baseline methods mainly focus on variants of claim decomposition, but do not incorporate claim editing, therefore retrievers still struggle to find relevant evidence.\n%s\n%Their discrepancy in performance can be attributed to two key factors. \n%(1) These baselines mainly rely on claim decomposition, which may oversimplify complex claims, potentially losing critical context.\n\n\n% \\paragraph{Higher retrieval accuracy leads to better fact-check Performance.}\n% According to \\autoref{tab:retriveresult}, \\ours significantly outperforms \\base baseline. For example, in \\fm, the improvement using Llama-3.1 70B as backbone LLM and \\textsc{Contriever} as retrieval model is average xxx\\% in $RAcc.$, and average xxx\\% in $Recall$. The enhancement in the retrieval component further leads to the improvement in final fact-checking accuracy (\\autoref{tab:factcheckresults}), with an average increase of xxx\\% in fact-checking accuracy. \n\n%\\input{figures_tex/retrieve_align_factcheck}\n\\paragraph{Higher retrieval accuracy leads to better fact-checking Accuracy.}\nAccording to \\autoref{tab:retriveresult}, \n% in \\fm, using \\textsc{Contriever} as retriever, \\ours achieves average improvements of 11.0\\% in $RAcc.$ and 10.1\\% in $Recall$ across Top@3, Top@5, and Top@10 metrics under GPT-4o mini. \n\\ours greatly boosts the retrieval accuracy on all measures, which leads to an increase in fact-checking accuracy. Notably, for TFIDF-based retrieval, which is particularly susceptible to adversarial queries, we observe a 11.0\\% in $RAcc.$, leading to a 7.5\\% increase in fact-checking accuracy with \\texttt{Llama-3.1-70B}.\\footnote{It's noted that despite significance, the degree of improvement in fact-checking is smaller than retrieval. We hypothesize it is because in some instances, LLMs, especially with large parameter sizes, use their parametric knowledge rather than retrieved evidence for entailment label prediction. } \n\n% The enhancement in the retrieval component directly contributes to the improvement in retrieve-augmented LMs (\\autoref{tab:factcheckresults}). \n%As shown in \\autoref{fig:retrieve_align_factcheck_fig}, we can see the correlation between improved retrieval and enhanced fact-checking performance is consistent across different settings. \n%These results suggest that the enhanced retrieval capability of \\ours is a key factor in its superior fact-checking performance.\n\n\\paragraph{Dense retrieval significantly outperforms TFIDF.} \n% \\chen{TODO for Hongjun, compare the results of TFIDF and Dense Retrieval. Say dense retrieval is better because many adversarial edit paraphrase the syntactic info, and embeddings can handle these cases (maybe with an example) }\nSpecifically, Contriever achieves 73.5\\% fact-checking accuracy with 39.5\\% RAcc., while TF-IDF only reached 68.5\\% fact-checking accuracy with 11.0\\% RAcc. This performance gap stems from dense retrievers' capacity to identify adversarial syntactic paraphrases through embedding space, which TFIDF cannot address effectively. \n\n% \\chen{}\n\\paragraph{\\ours is robust to various backbone language models.} As shown in \\autoref{tab:factcheckresults}, the accuracy achieved with different LLMs remains consistent. For instance, on \\fm, the accuracy difference between \\texttt{GPT-4o-mini} and \\texttt{Llama-3.1-70B} when using contriver is only 1.5\\%. Also, shown in \\autoref{tab:whole_factcheckresults}, \\ours maintain effectiveness with smaller language models. \nThese results indicate that our proposed framework is robust and adaptable to a wide range of LLMs.\n%\n%\\autoref{tab:factcheckresults} presents the fact-checking outcomes for claim-evidence pairs obtained by following our approach processing. The results illustrate that our method leads to a substantial enhancement in fact-checking accuracy compared to other techniques, the average degree of enhanced accuracy is 5.8\\% under GPT-3.5 for \\fm. Specifically, there is an increase of 7.5\\% in \\fm Top@$10$ with GPT-3.5 (from 72.5\\% to 80\\%). \n%\n%\n%\\input{figures_tex/factcheckresult_align_Racc}\n% However, it is also noted that the degree of improvement in fact-checking does not match the enhancement observed in the retrieval phase. This discrepancy may be attributed to several factors, including the inherent complexity of fact-checking which involves not only identifying relevant information but also evaluating the veracity and context of that information. The limitations in the current fact-checking algorithms' ability to parse nuanced or context-dependent information could also contribute to the observed performance plateau.\n\n%Compared to the enhancements observed with Semantic Text Segmentation Module, the Evidence-Augmented Edition yields further improvements in retrieval accuracy. For instance, when comparing the Evidence-Augmented Edition to the first module, there is an additional increase in accuracy of 12.5\\% (from 46\\% to 58.5\\%) with GPT-3.5. However, it is also noted that the rate of accuracy improvement in Evidence-Augmented Edition Module is more gradual compared to Semantic Text Segmentation Module.\n\n% \\hongjun{analysis the \\wice result \\& said our approach can also be applied to non-adversarial setting.}\n\\paragraph{\\ours also shows feasibility in non-adversarial settings.} Despite not our main focus, we also present results on \\wice, a complex fact-checking dataset. \nAs shown in \\autoref{tab:factcheckresults}, \\ours outperforms all baseline approaches, which indicates that our framework is generalizable to other fact-checking scenarios as well. For example, with \\texttt{GPT-4o-mini} as the backbone LLM, \\ours achieves a remarkable 5.9\\% improvement in fact-checking accuracy.\n\n% \\paragraph{\\ours has more gains with TFIDF as Retriever.} Furthermore, our observations on the \\fm dataset reveal a significant improvement in retrieval accuracy with TFIDF. We think the main reason is that in \\fm, the claim is adversarially written to attack the TDIDF retriever, therefore including more syntactic paraphrasing which \\textsc{Contriever} is by nature better. \n\n\n% \\paragraph{\\ours works better for smaller-sized LLMs in fact-checking task.}\n% We also observe that the improvement is more prominent with a smaller-sized LLMs for fact-checking. We hypothesize that smaller-sized LLMs rely more on the retrieved context rather than LLMs' own parametric knowledge, therefore making our enhancement more prominent.\n\n\\section{\\ours Analysis}\nIn this part, we will present how each component contributes to the overall performance (\\S\\ref{sec:ablation}) and provide qualitative analysis on the contribution of each step as well as sources of error (\\S\\ref{sec:qualitative}).\n% \\hongjun{component analysis}\n% \\subsection{Ablation Study}\n\\subsection{Ablation Study}\n\\label{sec:ablation}\n\\begin{table}[!t]\n\\centering\n\\scriptsize\n\\begin{tabular}{lll}\n\\toprule\n% & \\multicolumn{2}{c}{\\fm} \\\\\n% \\cmidrule(lr){2-3}\nSetting & Contriever & TFIDF \\\\\n\\midrule\n% \\multicolumn{3}{l}{\\textbf{Llama3.1 70B}} \\\\\n% LLMs as Ranker & 43.9 ({\\color{blue}-2.6}) & 18.6 ({\\color{blue}-10.9}) \\\\\n\\textbf{\\ours (Llama-3.1-70B)} & \\textbf{51.0} & \\textbf{34.5} \\\\\n\\quad $wo.$ claim editing & 49.5 ({\\color{blue}-1.5}) & 28.5 ({\\color{blue}-6.0}) \\\\\n\\quad $wo.$ claim segmentation & 47.5 ({\\color{blue}-3.5}) & 27.5 ({\\color{blue}-7.0}) \\\\\n\\quad Paraphrase $wo.$ Evidence & 50.5 ({\\color{blue}-0.5}) & 26.5 ({\\color{blue}-8.0}) \\\\\n\\midrule\n% \\multicolumn{3}{l}{\\textbf{GPT-4o mini}} \\\\\n% LLMs as Ranker & 47.7 ({\\color{blue}-1.5}) & 22.1 ({\\color{blue}-5.8}) \\\\\n\\textbf{\\ours (GPT-4o-mini)} & \\textbf{54.0} & \\textbf{33.5} \\\\\n\\quad $wo.$ claim editing & 53.0 ({\\color{blue}-1.0}) & 26.0 ({\\color{blue}-7.5}) \\\\\n\\quad $wo.$ claim segmentation & 52.5 ({\\color{blue}-1.5}) & 32.5 ({\\color{blue}-1.0}) \\\\\n\\quad Paraphrase $wo.$ Evidence & 51.5 ({\\color{blue}-2.5}) & 25.0 ({\\color{blue}-8.5}) \\\\\n\\bottomrule\n\\end{tabular}\n% \\caption{Retrieval RAcc. evaluation results of ablation study on the \\fm test set. Results on Top@3 and Top@5 are shown in \\autoref{tab:full_ablationstudy}.\n\\caption{Ablation study results on \\fm. We use Retrieval Accuracy (RAcc.) under Top@10 setting. Numbers in blue parentheses indicate performance drop when removing each component from \\ours. }\n%Complete results for Top@3 and Top@5 settings are available in \\autoref{tab:full_ablationstudy}.\n\\label{tab:ablationstudy}\n\\end{table}In the previous section, we observed that the retrieval improvement mainly helps the fact-checking ability.\nWe now delve deeper into why \\ours effectively addresses the reasoning-intensive retrieval. To this end, we compare \\ours with the following ablations on \\fm, using \\texttt{GPT-4o-mini} and \\texttt{Llama-3.1-70B} as backbone LLMs:\n(1) \\textbf{wo. Claim Editing}, where we remove the editing module and directly find evidence for each sub-claim;\n(2) \\textbf{wo. Claim Segmentation}, where we remove the claim segmentation and decontextualization module and directly edit the original adversarial claim;\nand (3) \\textbf{Paraphrase wo. Evidence}, where we ask LLMs to edit the sub-claims without using retrieved evidence as hints. \n\nAccording to \\autoref{tab:ablationstudy},\\footnote{Complete results in appendix (\\autoref{tab:full_ablationstudy}).} our ablation study reveals that all components significantly contribute to the overall performance of \\ours, as the removal of any component leads to a notable reduction in performance. Interestingly, while the impact of each component varies, editing grounded on retrieved evidence proves particularly important. \n\\paragraph{\\textsc{Contriever} is more robust in evidence retrieval.}\nAs shown in \\autoref{tab:ablationstudy}, while TFIDF exhibits a performance drop of 8.5\\%, \\textsc{Contriever} experiences only a modest 2.5\\% reduction. This difference suggests that \\textsc{Contriever} is more robust, as it benefits from flexible low-dimensional representations.%\\hongjun{wait for bgeenicl results}\n\n\\paragraph{Evidence-guided paraphrasing enables reasoning-intensive retrieval.}\nFor both models and retrieval methods, without claim editing consistently underperforms \\ours. With \\texttt{Llama-3.1-70B} and TFIDF, paraphrasing without evidence reduces accuracy from 34.5\\% to 28.5\\%. In addition, removing the evidence during editing leads to significant performance drops, especially with TFIDF where RAcc decreases from 33.5\\% to 26.0\\% under \\texttt{GPT-4o-mini}. \nThis reduction highlights the importance of both claim editing and the need of guidence with evidence. \n\n\\paragraph{Sub-claims enhance effective retrieval.}\nRemoving the segmentation module leads to a significant drop in accuracy.\nFor \\texttt{Llama3.1-70B} with TFIDF, removing claim segmentation decreases RAcc by 7.0\\%, which stems from the segmentation module enabling more focused processing of discrete units without requiring complex reasoning.\n\n\n\n\\begin{figure*}[!t]\n \\centering\n \\includegraphics[width = \\linewidth]{figures_raw/seg_para_cases.pdf}\n % \\caption{Case studies on the Claim Segmentation module (left) and Claim Editing module (right). \\ours effectively handles both cases, successfully addressing the original retrieval failure.\n \\caption{Case studies demonstrating how \\ours handles adversarial claims through Claim Segmentation and Claim Editing modules. \\textbf{Left}:The original claim is split into sub-claims, enabling successful evidence retrieval. \\textbf{Right}: Three cases showing different types of claim editing - missing key information, synonym substitution, and context omission - where the system edits claims to improve retrieval performance. The numbers (\\eg 18/50, 17/50) represent the portion of each category over a random sample of 50 correct predictions.\n % \\hongjun{change to 1-2 refuted examples} \n }\n \\label{fig:seg_para_case}\n\\end{figure*}\n\\subsection{Qualitative Analysis of \\ours}\n\\label{sec:qualitative}\nWe next conduct a qualitative analysis to understand how and to what extent each module contributes to the final improvement, using \\texttt{GPT-4o-mini} as the LLM backbone and \\textsc{Contriver} as the retrieval system.\n\\paragraph{Claim Segmentation module makes retrieval less distracted.} \n As shown in the left side of \\autoref{fig:seg_para_case}, we observe that the input adversarial claim contains multiple pieces of information, leading to potential confusion in the retrieval process. For instance, ``Tottenham's Broxbourne Ladies started in 2016 and eventually changed their name to Tottenham Hotspur Women.'' prevents the retriever from locating the most important content. With Claim Segmentation component, the retriever focuses on the key fact ``Tottenham's Broxbourne Ladies'' and retrieves the relevant facts.\n\n\\paragraph{Claim Editing module makes retrieval easier.}\nAccording to \\autoref{fig:seg_para_case}, we find that the Claim Editing module mainly tackles two categories: \\textbf{(1) Missing Key Information}, in which some crucial details (\\eg entity names) are missing. With the help of retrieved passages as context, LLMs add new information to help the next round of evidence retrieval.\n\\textbf{(2) Synonym Substitution} that replaces key terms in sentences with their synonyms or more abstract terms to challenge the retrieval systems. As shown in \\autoref{fig:seg_para_case}, LLMs address the adversarial challenges by paraphrasing \\emph{\\underline{``in Canada''}} -> \\emph{\\underline{``in the fictional village of Brookfield''}}. \n\\textbf{(3) Context Omission}, in which background information is deliberately removed to confuse retrieval systems. As shown in \\autoref{fig:seg_para_case}, LLMs adds the background information \\emph{{``\\underline{After his mother Mary had signed two statements} \\underline{to abdicate, James VI of Scotland} succeeded to the Scottish throne at the age of thirteen''}}.\n\n\\paragraph{Constrained prompt reduces hallucination.}\nTo mitigate hallucination during editing, we add specific constraints in prompts (\\autoref{fig:paraphrase_prompt}), including completing missing information, replacing statements with evidence-based details, and limiting new information. Our manual analysis of 50 randomly selected samples from \\texttt{GPT-4o mini} shows a low hallucination rate of 6.0\\%, indicating the effectiveness constrained prompts.\n\n\\begin{figure*}[!t]\n \\centering\n \\includegraphics[width = \\linewidth]{figures_raw/err_case.pdf}\n % \\caption{Case studies on three categories of errors from the claim segmentation module and claim editing module. }\n \\caption{Error analysis showing three main failure cases identified from a random sample of 50 cases: (1) \\textbf{Too Fine-Grained Segmentation}, where the Claim Segmentation module breaks claims into overly atomic facts that lack sufficient context, (2) \\textbf{Parametric Knowledge-Induced Errors}, where LLM's inherent knowledge introduces unverified information during claim editing, and (3) \\textbf{LLM Overgeneration}, where the claim editing module adds additional information from the retrieved evidence. }\n \\label{fig:err_case}\n\\end{figure*}\n\\subsection{Error Analysis}\nWe conduct an error analysis To further understand the limitations of \\ours. We manually examine 50 sampled instances where the TFIDF method failed within the \\texttt{GPT-4o mini} on the \\fm dataset.\nWe identify the following three common mistakes (\\autoref{fig:err_case}): \n\\textbf{(1) Too Fine-Grained Segmentation:} Atomic facts are segmented with excessive granularity in Claim Segmentation module. As shown in error case 1, the sub-claim does not contain enough useful information, leading to retrieving irrelevant evidence.\n\\textbf{(2) LLM parametric knowledge leading to hallucination:} Despite using instructions in the prompt, the inherent knowledge of LLMs still introduces extraneous information in claim editing. As shown in error case 2, \\texttt{GPT-4o mini} adopts its parametric knowledge as additional information, which results in a deviation in the search direction. \n\\textbf{(3) LLM-Overgeneration:} The claim-editing module suffers from generating excessive content. In error case 3, \\texttt{GPT-4o mini} simply adds all information as the edited claim.\n\n\\section{Related Work}\n\n\\paragraph{Retrieval-Augmented Language Model}\nRetrieval-Augmented Language Model~\\cite{asai2024reliable, ji2023survey, rawte2024factoidfactualentailmenthallucination, ayala2024reducing,zhao2024docmathevalevaluatingmathreasoning} enhances LLMs by integrating external world knowledge, augmenting LLM's functionality to improve accuracy and factuality (\\ie reduce hallucination). Within this paradigm, RARR \\cite{gao-etal-2023-rarr} also include editing modules, but their focus is on the attribution of generated text with retrieved evidence.\nOur study is based on RALM, but we extend into a modular framework that better tackles long-form claims with adversarially written segments. \n\n\\paragraph{Reasoning-intensive Retrieval.} Traditional information retrieval tackles the lexical and semantic similarities between query and documents. \\citet{su2024bright} introduce a new benchmark about reasoning-intensive retrieval, where retrievers need in-depth reasoning to find relevant documents. We focus on adversarial claims, which also require reasoning capacities to find relevant evidence.\n\n\\paragraph{Adversarial Examples in NLP.}\nAdversarial examples~\\cite{wallace2019universal, goyal2023survey} introduce perturbations such as entity or sentence paraphrasing that are imperceptible to humans but can mislead NLP systems. Several adversarial datasets have been constructed to evaluate the robustness of NLP systems in various tasks including classification~\\cite{garg-ramakrishnan-2020-bae}, natural language inference~\\cite{nie-etal-2020-adversarial}, question answering~\\cite{jia-liang-2017-adversarial,Bartolo_2020, wallace2019trick} and fact-checking~\\cite{eisenschlos-etal-2021-fool}.\n\\section{Conclusion}\n% \\hongjun{TODO:polish this section}\nWe introduce \\ours, a modular framework for verifying adversarial facts. \\ours decomposes the complex fact-checking task into three sub-modules: claim segmentation and decontextualization, evidence-augmented claim editing, and evidence aggregation and label prediction. Experimental results on the \\fm and \\wice datasets illustrate the robust improvements in retrieval efficiency and fact-checking accuracy, surpassing existing claim decomposition based baselines. Our analysis further studies how each module contributes to the improvement and its limitations. We believe this research provides valuable insights into addressing real-world adversarial attacks in fact-checking.\\section*{Limitations}\n% \\hongjun{TODO:polish this section}\nThere are still some limitations in our work:\n(1) Due to budget constraints, our evaluation is limited to the test sets of \\fm and \\wice. The experimental results on the test set are sufficient to show that our framework improves adversarial fact-checking. Future research can expand the test scope to a larger amount of fact-checking datasets.\n(2) Our prompting method for claim segmentation and claim editing is still not perfect. In some cases, these prompting methods fail to generate correct subclaims or paraphrase the sub-claims, leading to incorrect fact-checking results. In this case, future work can explore other prompting methods or fine-tune LLMs to better control the LLM generations.\n(3) Our work mainly focuses on reasoning-intensive retrieval over adversarial fact-checking, future work can explore other types of reasoning-intensive retrieval tasks, such as retrieving relevant code for user queries. (4) Due to computing resource limitations, we don't cover state-of-art retrievers like BGE-EN-ICL\\cite{li2024makingtextembeddersfewshot}. Future work can explore integrating these advanced retrievers.\n\n\\section*{Acknowledgements}\nHongjun Liu and Chen Zhao were supported by Shanghai Frontiers Science Center of Artificial Intelligence and Deep Learning, NYU Shanghai. This work was supported in part through the NYU IT High Performance Computing resources, services, and staff expertise.\n% \\input{main/ethics}\n\n% Entries for the entire Anthology, followed by custom entries\n\\bibliography{anthology,custom, llm}\n\n\\appendix\n\n\\section{Appendix}\n\n\\subsection{Multiple-round Iterations}\nWe conduct an experiment to evaluate the effect on the number of iterations for evidence retrieval and claim editing. As shown in \\autoref{fig:multiround}, with more rounds of evidence retrieval and claim editing, the performance steadily improves across all evaluation metrics (R@3, R@5, and R@10). The most substantial gains are observed in the early iterations, particularly after two iterations.\n\n\\begin{figure}[h]\n\\begin{tcolorbox}[colback=black!7.5!white, colframe=black!80!white, title=Claim Segmentation Prompt, fontupper=\\footnotesize, fonttitle=\\footnotesize]\n\\texttt{[System Input]}: \\vspace{2pt}\\\\\nThis is SplitLLM, an intelligent assistant that can split sentences based on their semantic and syntactic structure within the sentence. The following are the sentences you need to split, indicated by number identifier []. I can split them based on their logical units, such as clauses, phrases, or specific grammatical boundaries. Please ensure that each unit retains its original meaning and context for better readability and understanding.\\\\\n% [0] \\{sen\\} \\\\\n\\texttt{[0]} \\{sen\\} \\\\\nProvide the split sentences with each unit separated by a newline.\\\\\nExample: \\\\\nOriginal: The Natural is a book about Roy Hobbs a natural southpaw boxer who goes on to win the heavyweight title from Boom Boom Mancini.\\\\\nSplit: The Natural is a book | about Roy Hobbs | a natural southpaw boxer | who goes on to win the heavyweight | title from Boom Boom Mancini.\\\\\n\\newline\n\\texttt{[User Input]}: \\vspace{2pt}\\\\\nNow, proceed with the sentence: \\{sen\\} The split result of the sentence (only split) is:\n\n\\end{tcolorbox}\n\n% \\caption{Example of Claim Segmentation prompt used.}\n\\caption{Prompt template used for the Claim Segmentation module. The prompt defines the task and expected format, followed by an illustrative example showing how to split a complex claim into logical units.}\n\\label{fig:seg}\n\\end{figure}\\begin{figure}[h]\n\\begin{tcolorbox}[colback=black!7.5!white, colframe=black!80!white, title=Claim Decontextualization Prompt, fontupper=\\footnotesize, fonttitle=\\footnotesize]\n\\texttt{[System Input]}: \\vspace{2pt}\\\\\nBased on the seperated sentence, if the section misses its subject, complete each split section with proper subject, then form a normal senrence containing enough details. If the the section is a complete sentence, remain the syntax. Here is an example for this split job:\\\\\n <user> Given sentence: The film High Noon subverts gender norms of the time | by having the woman | rescue the man.\\\\\n <response> The film High Noon subverts gender norms of the time. | High Noon unfolds by having the woman character. | The woman rescue the man in High Noon. \\\\\n\\newline\n\\texttt{[User Input]}: \\vspace{2pt}\\\\\nGiven Sentence: \\{sen\\}\n\\end{tcolorbox}\n\n% \\caption{Example of Claim Decontextualization prompt used.}\n\\caption{Prompt template for the Claim Decontextualization module. The prompt instructs how to transform segmented claims by adding missing subjects and context to ensure each segment becomes a complete, standalone sentence.}\n\\label{fig:decontext}\n\\end{figure}\\begin{figure}[h]\n\\begin{tcolorbox}[colback=black!7.5!white, colframe=black!80!white, title=Evidence-Augmented Claim Editing Prompt, fontupper=\\footnotesize, fonttitle=\\footnotesize]\n% \\texttt{[System Input]}: \\vspace{2pt}\\\\\nYou should complete the sentence <\\{query\\}> with missing name (author, writer, owner, builder, award-winning titles etc.), number (date, year, population, acreage, etc), location and so on, replace the original adversarial point of view with detailed data or evidence (for example, the biggest population -> population 3,854,000; the date is unknown -> possible dates ranging from 1598 to 1608 ) and correct the counterfactual mistakes in sentence, which is done in order to avoid adversarial cases. You should never add too much new additional information to the sentence. Here is an example:\\\\\nGiven sentence: \\\\\n<The album was released in 2018.>\\\\\nExpected result: \\\\\nThe \"Blackpink in Your Area\" compilation album was released in 2018.\\\\\nTo help you better complete the required task, we provide following knowledge as contexts: \\\\\n<\\{kn\\}> \\\\\nNow do the required task on the sentence: <{query}> based on the given knowledge.\n\\newline\n\n\\end{tcolorbox}\n\n% \\caption{Example of Evidence-Augmented Claim Editing prompt used.}\n\\caption{Prompt template for the Evidence-Augmented Claim Editing module. The prompt instructs how to enhance claims by adding specific details from evidence, replacing vague statements with precise information, and correcting inaccuracies.}\n\\label{fig:paraphrase_prompt}\n\\end{figure}\\begin{figure}[h]\n\\begin{tcolorbox}[colback=black!7.5!white, colframe=black!80!white, title=LLM as Reranker Prompt, fontupper=\\footnotesize, fonttitle=\\footnotesize]\n% \\texttt{[System Input]}: \\vspace{2pt}\\\\\nThis is RankLLM, an intelligent assistant that can rank passages based on their relevance to the query. \\\\\nThe following are \\{len(evidences)\\} passages, each indicated by number identifier []. I can rank them based on their relevance to query: \\{query\\} \\\\\n……\\\\\n(Passages) \\\\\n……\\\\\nThe search query is: \\{query\\} \\\\\nI will rank the \\{top\\_k\\} passages above based on their relevance to the search query. The passages will be listed in descending order using identifiers, and the most relevant passages should be listed first, and the output format should be [] > [] > etc, e.g., [1] > [2] > [] etc. \\\\\nThe ranking results of the \\{top\\_k\\} passages (only identifiers) is: \\\\\n\\newline\n\n\\end{tcolorbox}\n\n% \\caption{Example of LLM as reranker prompt used (refer to \\cite{sun2023chatgptgoodsearchinvestigating}).}\n\\caption{Prompt template used for LLM-based evidence reranking. The prompt instructs the LLM to sort retrieved passages based on their relevance to the query. Adapted from \\cite{sun2023chatgptgoodsearchinvestigating}.}\n\\label{fig:ranker}\n\\end{figure}\n\\begin{figure}[h]\n\\begin{tcolorbox}[colback=black!7.5!white, colframe=black!80!white, title=Entailment Label Prediction Prompt, fontupper=\\footnotesize, fonttitle=\\footnotesize]\n\\texttt{[System Input]}: \\vspace{2pt}\\\\\nYou are a well-informed and expert fact-checker. Here are some example of how to act as a professional fact-checker:\\\\\nClaim Example 1: The Cantos is a poem with most of it written over a 40 plus year time span. \\\\\nEvidences for claim example 1: \\\\\n<Most of it was written between 1915 and 1962, although much of the early work was abandoned and the early cantos, as finally published, date from 1922 onwards.\nThe Cantos by Ezra Pound is a long, incomplete poem in 116 sections, each of which is a canto.\nThis thread then runs through the appearance of Kuanon, the Buddhist goddess of mercy, the moon spirit from Hagaromo (a Noh play translated by Pound some 40 years earlier), Sigismondo's lover Ixotta (linked in the text with Aphrodite via a reference to the goddess' birthplace Cythera), a girl painted by Manet and finally Aphrodite herself, rising from the sea on her shell and rescuing Pound/Odysseus from his raft.> \\\\\nStep 1: the evidence indicates that The Cantos is a poem with most of it written over a 40 plus year time span.\nStep 2: the evidence also mentions that The Cantos by Ezra Pound is a long, incomplete poem in 116 sections. Step 3: the other pieces of evidence provided This thread then runs through the appearance of Kuanon, the Buddhist goddess of mercy, the moon spirit from Hagaromo (a Noh play translated by Pound some 40 years earlier).based on the evidence provided, the claim that The Cantos is a poem with most of it written over a 40 plus year time span is supported. \\\\\nfinal rating: supported \\\\\nClaim Example 2: \\\\\n......\\\\\n\\newline\n\\texttt{[User Input]}: \\vspace{2pt}\\\\\nNow its' your turn, you are provided with evidences regarding the following claim: \\{claim\\} \\\\\nEvidences: \\\\\n<\\{evidence\\}> \\\\\nBased strictly on the main claim, and the evidences provided, you will provide: \\\\\nrating: The rating for claim should be one of \"supported\" if and only if the Evidences specifically support the claim, \"refuted\" if and only if the Evidences specifically refutes the claim or \"not enough information\": if there is not enough information to support or refute the claim appropriately. \\\\\nIs the claim: \\{claim\\} \"supported\", \"refuted\" or \"not enough information\" according to the available questions and answers? \\\\\nLets think step by step.\n% \\newline\n\n\\end{tcolorbox}\n\n% \\caption{Example of Entailment Label Prediction prompt used.}\n\\caption{Prompt template for the entailment label prediction. The prompt instructs the LLM to act as an expert fact-checker, providing step-by-step reasoning to classify claims as \"supported,\" \"refuted,\" or \"not enough information\" based on given evidence.}\n\\label{fig:fm2_entail}\n\\end{figure}\\begin{figure*}[!t]\n \\centering\n \\includegraphics[width = 1.0\\linewidth]{figures_raw/model_accuracy_comparison.pdf}\n \\caption{Ablation study results on \\fm under multiple-round iteration setting. The performance of both GPT-4o-mini (left) and Llama-3.1-70B (right) improves as the number of iterations increases, with evaluation metrics shown at R@3, R@5, and R@10. Starting from the original query, both models demonstrate significant accuracy gains through \\ours's two iterations and subsequent rounds, with the most substantial improvements observed in the early iterations. The R@10 metric consistently achieves the highest performance across all iteration rounds for both models.\n }\n \\label{fig:multiround}\n\\end{figure*}\\begin{table*}[!t]\n\\centering\n\\resizebox{\\textwidth}{!}{\n\\begin{tabular}{lccccccc}\n\\toprule\n\\multirow{2}{*}{Backbone LLM} & \\multirow{2}{*}{Setting} \n& \\multicolumn{2}{c}{Top@3} & \\multicolumn{2}{c}{Top@5} & \\multicolumn{2}{c}{Top@10} \\\\\n\\cmidrule(lr){3-4} \\cmidrule(lr){5-6} \\cmidrule(lr){7-8}\n & & Contriever & TFIDF & Contriever & TFIDF & Contriever & TFIDF \\\\\n\\midrule\n{{\\textbf{\\fm}}} \\\\\\noalign{\\vskip 0.5ex}\n\\multirow{2}{*}{GPT-4o mini} &\\base & 64.5 & 53.5 & 65.0 & 59.0 & 67.5 & 61.5 \\\\\n&\\claimdecompose & 65.0 {\\color{red}(+0.5)} & 55.0 {\\color{red}(+1.5)} & 65.0 {\\color{red}(+0.0)} & 61.0 {\\color{red}(+2.0)} & 65.5 {\\color{red}(-2.0)} & 63.0 {\\color{red}(+1.5)} \\\\\n&\\qabrief & 65.5 {\\color{red}(+1.0)} & 57.0 {\\color{red}(+3.5)} & 67.0 {\\color{red}(+2.0)} & 62.0 {\\color{red}(+3.0)} & 67.5 {\\color{red}(+0.0)} & 62.5 {\\color{red}(+1.0)} \\\\\n&\\programfc & 68.5 {\\color{red}(+4.0)} & 56.0 {\\color{red}(+2.5)} & 65.5 {\\color{red}(+0.5)} & 61.0 {\\color{red}(+2.0)} & 69.0 {\\color{red}(+1.5)} & 63.0 {\\color{red}(+1.5)} \\\\\n&\\minicheck & 66.0 {\\color{red}(+1.5)} & 57.0 {\\color{red}(+3.5)} & 66.5 {\\color{red}(+1.5)} & 62.5 {\\color{red}(+3.5)} & 70.0 {\\color{red}(+2.5)} & 64.0 {\\color{red}(+2.5)}\\\\\n&\\textbf{\\ours} & 69.0 {\\color{red}(+4.5)} & 61.5 {\\color{red}(+8.0)} & 72.5 {\\color{red}(+7.5)} & 67.0 {\\color{red}(+8.0)} & 75.0 {\\color{red}(+7.5)} & 68.5 {\\color{red}(+7.0)} \\\\\n\\hdashline\n\\multirow{2}{*}{LLama 3.1 70B} &\\base & 61.0 & 56.5 & 64.5 & 59.0 & 65.5 & 61.5 \\\\\n&\\claimdecompose & 62.0 {\\color{red}(+1.0)} & 57.0 {\\color{red}(+0.5)} & 65.5 {\\color{red}(+1.0)} & 61.0 {\\color{red}(+2.0)} & 67.5 {\\color{red}(+2.0)} & 64.0 {\\color{red}(+2.5)} \\\\\n&\\qabrief & 66.0 {\\color{red}(+5.0)} & 56.5 {\\color{red}(+0.0)} & 67.5 {\\color{red}(+3.0)} & 62.5 {\\color{red}(+3.5)} & 66.0 {\\color{red}(+0.5)} & 64.0 {\\color{red}(+2.5)} \\\\\n&\\programfc & 62.0 {\\color{red}(+1.0)} & 58.0 {\\color{red}(+1.5)} & 65.0 {\\color{red}(+0.5)} & 61.0 {\\color{red}(+2.0)} & 66.0 {\\color{red}(+0.5)} & 64.5 {\\color{red}(+3.0)} \\\\\n&\\minicheck &67.5 {\\color{red}(+6.5)} & 57.5 {\\color{red}(+1.0)} & 68.0 {\\color{red}(+3.5)} & 62.0 {\\color{red}(+3.0)} & 70.5 {\\color{red}(+5.0)} & 64.0 {\\color{red}(+2.5)} \\\\\n&\\textbf{\\ours} & 71.0 {\\color{red}(+10.0)} & 63.5 {\\color{red}(+7.0)} & 72.5 {\\color{red}(+8.0)} & 67.5 {\\color{red}(+8.5)} & 73.5 {\\color{red}(+8.0)} & 69.0 {\\color{red}(+7.5)} \\\\\n\\hline\n{{\\textbf{\\wice}}} \\\\\\noalign{\\vskip 0.5ex}\n\\multirow{2}{*}{GPT-4o mini} &\\base & 27.2 & 24.0 & 28.6 & 25.8 & 33.7 & 27.3 \\\\\n&\\claimdecompose & 29.1 {\\color{red}(+1.9)} & 29.1 {\\color{red}(+5.1)} & 30.1 {\\color{red}(+1.5)} & 30.4 {\\color{red}(+4.6)} & 35.2 {\\color{red}(+1.5)} & 31.0 {\\color{red}(+3.7)} \\\\\n&\\qabrief & 28.0 {\\color{red}(+0.8)} & 27.5 {\\color{red}(+3.5)} & 30.9 {\\color{red}(+2.3)} & 27.8 {\\color{red}(+2.0)} & 36.2 {\\color{red}(+2.5)} & 29.7 {\\color{red}(+2.4)} \\\\\n&\\programfc & 33.2 {\\color{red}(+6.0)} & 26.8 {\\color{red}(+2.8)} & 34.8 {\\color{red}(+6.2)} & 28.7 {\\color{red}(+2.9)} & 38.4 {\\color{red}(+4.7)} & 29.9 {\\color{red}(+2.6)} \\\\\n&\\minicheck &30.0 {\\color{red}(+2.8)} & 27.1 {\\color{red}(+3.1)} & 31.3 {\\color{red}(+2.7)} & 26.9 {\\color{red}(+1.1)} & 33.7 {\\color{red}(+0.0)} & 30.8 {\\color{red}(+3.5)} \\\\\n&\\textbf{\\ours} & 34.1 {\\color{red}(+6.9)} & 31.0 {\\color{red}(+7.0)} & 37.2 {\\color{red}(+8.6)} & 28.1 {\\color{red}(+2.3)} & 39.0 {\\color{red}(+5.3)} & 33.2 {\\color{red}(+5.9)} \\\\\n\\hdashline\n\\multirow{2}{*}{LLama 3.1 70B} &\\base & 28.1 & 22.1 & 29.6 & 24.5 & 31.2 & 25.7 \\\\\n&\\claimdecompose & 26.3 {\\color{red}(-1.8)} & 23.0 {\\color{red}(+0.9)} & 30.1 {\\color{red}(+0.5)} & 25.4 {\\color{red}(+0.9)} & 33.0 {\\color{red}(+1.8)} & 27.1 {\\color{red}(+1.4)} \\\\\n&\\qabrief & 30.4 {\\color{red}(+2.3)} & 25.1 {\\color{red}(+3.0)} & 32.4 {\\color{red}(+2.8)} & 27.0 {\\color{red}(+2.5)} & 33.5 {\\color{red}(+2.3)} & 28.2 {\\color{red}(+2.5)} \\\\\n&\\programfc & 34.6 {\\color{red}(+6.5)} & 24.7 {\\color{red}(+2.6)} & 36.5 {\\color{red}(+6.9)} & 26.9 {\\color{red}(+2.4)} & 33.9 {\\color{red}(+2.7)} & 29.3 {\\color{red}(+3.6)} \\\\\n&\\minicheck & 39.1 {\\color{red}(+11.0)} & 27.8 {\\color{red}(+5.7)} & 34.9 {\\color{red}(+5.3)} & 27.9 {\\color{red}(+3.4)} & 35.2 {\\color{red}(+4.0)} & 30.1 {\\color{red}(+4.4)} \\\\\n&\\textbf{\\ours} & 38.2 {\\color{red}(+10.1)} & 30.3 {\\color{red}(+8.2)} & 39.0 {\\color{red}(+9.4)} & 31.8 {\\color{red}(+7.3)} & 38.7 {\\color{red}(+7.5)} & 32.9 {\\color{red}(+7.2)} \\\\\n\\bottomrule\n\\end{tabular}\n}\n\\caption{Comprehensive fact-checking evaluation results comparing \\ours with baseline approaches on \\fm and \\wice datasets. Results are reported as accuracy scores across different retrieval settings (Top@3, Top@5, Top@10). Numbers in red parentheses show absolute performance improvements over the \\base baseline.}\n\\label{tab:whole_factcheckresults}\n\\end{table*}\\begin{table*}[!t]\n\\centering\n\\small\n\\begin{tabular}{lcccccccc}\n\\toprule\n\\multirow{2}{*}{Backbone LLM} & \\multirow{2}{*}{Setting} \n& \\multicolumn{2}{c}{Top@3} & \\multicolumn{2}{c}{Top@5} & \\multicolumn{2}{c}{Top@10} \\\\\n\\cmidrule(lr){3-4} \\cmidrule(lr){5-6} \\cmidrule(lr){7-8}\n & & Contriever & TFIDF & Contriever & TFIDF & Contriever & TFIDF \\\\\n\\midrule\n{{\\textbf{\\fm}}} \\\\\\noalign{\\vskip 0.5ex}\n\\multirow{2}{*}{LLama 3.1 8B} &\\base & 58.5 & 50.5 & 59.5 & 51.5 & 60.5 & 56.0 \\\\\n&\\textbf{\\ours} & 60.5 {\\color{red}(+2.0)} & 58.0 {\\color{red}(+7.5)} & 62.5 {\\color{red}(+3.0)} & 58.0 {\\color{red}(+6.5)} & 65.0 {\\color{red}(+4.5)} & 63.5 {\\color{red}(+7.5)} \\\\\n% \\hdashline\n\\multirow{2}{*}{Mistral 7B} &\\base & 48.0 & 46.0 & 56.5 & 46.5 & 55.0 & 49.0 \\\\\n&\\textbf{\\ours} & 59.0 {\\color{red}(+11.0)} & 50.0 {\\color{red}(+4.0)} & 60.5 {\\color{red}(+4.0)} & 51.5 {\\color{red}(+5.0)} & 63.0 {\\color{red}(+8.0)} & 52.0 {\\color{red}(+3.0)} \\\\\n% \\hdashline\n\\multirow{2}{*}{Gemma2 9B} &\\base & 44.5 & 39.5 & 46.0 & 43.5 & 54.0 & 48.5 \\\\\n&\\textbf{\\ours} & 52.0 {\\color{red}(+7.5)} & 49.0 {\\color{red}(+9.5)} & 50.5 {\\color{red}(+4.5)} & 52.5 {\\color{red}(+9.0)} & 61.0 {\\color{red}(+7.0)} & 56.0 {\\color{red}(+7.5)} \\\\\n\\hline\n{{\\textbf{\\wice}}} \\\\\\noalign{\\vskip 0.5ex}\n\\multirow{2}{*}{LLama 3.1 8B} &\\base & 29.3 & 28.5 & 31.2 & 29.1 & 39.1 & 31.2 \\\\\n&\\textbf{\\ours} & 31.4 {\\color{red}(+2.1)} & 30.1 {\\color{red}(+1.6)} & 34.6 {\\color{red}(+3.4)} & 31.7 {\\color{red}(+2.6)} & 42.6 {\\color{red}(+3.5)} & 31.8 {\\color{red}(+0.6)} \\\\\n% \\hdashline\n\\multirow{2}{*}{Mistral 7B} &\\base & 23.1 & 22.6 & 26.9 & 26.5 & 29.0 & 26.9 \\\\\n&\\textbf{\\ours} & 25.6 {\\color{red}(+2.5)} & 23.2 {\\color{red}(+0.6)} & 31.1 {\\color{red}(+4.2)} & 26.8 {\\color{red}(+0.3)} & 32.5 {\\color{red}(+3.5)} & 28.8 {\\color{red}(+1.9)} \\\\\n% \\hdashline\n\\multirow{2}{*}{Gemma2 9B} &\\base & 21.5 & 20.7 & 25.9 & 23.9 & 26.6 & 25.2\\\\\n&\\textbf{\\ours} & 24.4 {\\color{red}(+2.9)} & 23.3 {\\color{red}(+2.6)} & 27.4 {\\color{red}(+1.5)} & 25.7 {\\color{red}(+1.8)} & 30.3 {\\color{red}(+3.7)} & 27.4 {\\color{red}(+2.2)} \\\\\n\\bottomrule\n\\end{tabular}\n% \\caption{Additional fact-checking evaluation results under \\base and \\ours on small models.}\n\\caption{Fact-checking evaluation results comparing \\ours with \\base baseline using smaller-scale LLMs on \\fm and \\wice datasets. Numbers in red parentheses indicate absolute improvements over \\base, showing that \\ours maintains effectiveness even with smaller language models.}\n\\label{tab:smallmodelresult}\n\\end{table*}\n\\begin{table*}[!t]\n\\centering\n \\resizebox{\\linewidth}{!}{\n \\begin{tabular}{lcccccccccccccc}\n\\toprule\n \\multirow{4}{*}{Model} & \\multirow{4}{*}{Setting} & \\multicolumn{4}{c}{Top@3} & \\multicolumn{4}{c}{Top@5} & \\multicolumn{4}{c}{Top10} \\\\\n \\cmidrule(lr){3-6} \\cmidrule(lr){7-10} \\cmidrule(lr){11-14}\n & & \\multicolumn{2}{c}{Contriever} & \\multicolumn{2}{c}{TFIDF} & \\multicolumn{2}{c}{Contriever} & \\multicolumn{2}{c}{TFIDF} & \\multicolumn{2}{c}{Contriever} & \\multicolumn{2}{c}{TFIDF} \\\\\n \\cmidrule(lr){3-4} \\cmidrule(lr){5-6} \\cmidrule(lr){7-8} \\cmidrule(lr){9-10} \\cmidrule(lr){11-12} \\cmidrule(lr){13-14}\n & & Acc. & Recall & Acc. & Recall & Acc. & Recall & Acc. & Recall & Acc. & Recall & Acc. & Recall \\\\\n\\midrule\n\\multirow{5}{*}{Llama3.1 70B}\n & \\ours & 40.5 & 35.7 & 27.5 & 23.3 & 47.5 & 40.7 & 29.5 & 26.4 & 51.0 & 46.5 & 34.5 & 29.5 \\\\\n & wo. claim editing & 39.0 {\\color{blue}(-1.5)} & 32.3 {\\color{blue}(-3.4)} & 17.5 {\\color{blue}(-10.0)} & 15.1 {\\color{blue}(-8.2)} & 46.0 {\\color{blue}(-1.5)} & 41.1 {\\color{blue}(+0.4)} & 21.5 {\\color{blue}(-8.0)} & 18.2 {\\color{blue}(-8.2)} & 49.5 {\\color{blue}(-1.5)} & 46.8 {\\color{blue}(+0.3)} & 28.5 {\\color{blue}(-6.0)} & 24.4 {\\color{blue}(-5.1)} \\\\\n & wo. claim segmentation & 35.5 {\\color{blue}(-5.0)} & 31.0 {\\color{blue}(-4.7)} & 16.5 {\\color{blue}(-11.0)} & 13.6 {\\color{blue}(-9.7)} & 40.5 {\\color{blue}(-7.0)} & 35.2 {\\color{blue}(-5.5)} & 19.5 {\\color{blue}(-10.0)} & 16.7 {\\color{blue}(-9.7)} & 47.5 {\\color{blue}(-3.5)} & 42.6 {\\color{blue}(-3.9)} & 27.5 {\\color{blue}(-7.0)} & 22.5 {\\color{blue}(-7.0)} \\\\\n & Paraphrase wo. Evidence & 34.0 {\\color{blue}(-6.5)} & 29.8 {\\color{blue}(-5.9)} & 14.0 {\\color{blue}(-13.5)} & 11.2 {\\color{blue}(-12.1)} & 39.5 {\\color{blue}(-8.0)} & 35.7 {\\color{blue}(-5.0)} & 20.0 {\\color{blue}(-9.5)} & 12.3 {\\color{blue}(-14.1)} & 50.5 {\\color{blue}(-0.5)} & 44.6 {\\color{blue}(-1.9)} & 26.5 {\\color{blue}(-8.0)} & 21.3 {\\color{blue}(-8.2)} \\\\\n\\hdashline\n\\multirow{5}{*}{GPT-4o mini}\n & \\ours & 39.5 & 34.9 & 20.5 & 17.4 & 46.5 & 41.1 & 28.5 & 24.0 & 54.0 & 49.2 & 33.5 & 27.9 \\\\\n & wo. claim editing & 37.0 {\\color{blue}(-2.5)} & 32.6 {\\color{blue}(-2.3)} & 14.0 {\\color{blue}(-6.5)} & 11.6 {\\color{blue}(-5.8)} & 45.0 {\\color{blue}(-1.5)} & 40.7 {\\color{blue}(-0.4)} & 23.0 {\\color{blue}(-5.5)} & 19.8 {\\color{blue}(-4.2)} & 53.0 {\\color{blue}(-1.0)} & 47.7 {\\color{blue}(-1.5)} & 26.0 {\\color{blue}(-7.5)} & 21.7 {\\color{blue}(-6.2)} \\\\\n & wo. claim segmentation & 36.0 {\\color{blue}(-3.5)} & 31.8 {\\color{blue}(-3.1)} & 18.0 {\\color{blue}(-2.5)} & 15.1 {\\color{blue}(-2.3)} & 46.0 {\\color{blue}(-0.5)} & 40.3 {\\color{blue}(-0.8)} & 23.5 {\\color{blue}(-5.0)} & 19.8 {\\color{blue}(-4.2)} & 52.5 {\\color{blue}(-1.5)} & 46.9 {\\color{blue}(-2.3)} & 32.5 {\\color{blue}(-1.0)} & 27.1 {\\color{blue}(-0.8)} \\\\\n & Paraphrase wo. Evidence & 29.0 {\\color{blue}(-10.5)} & 24.8 {\\color{blue}(-10.1)} & 13.5 {\\color{blue}(-7.0)} & 10.9 {\\color{blue}(-6.5)} & 39.5 {\\color{blue}(-7.0)} & 34.1 {\\color{blue}(-7.0)} & 20.0 {\\color{blue}(-8.5)} & 16.3 {\\color{blue}(-7.7)} & 51.5 {\\color{blue}(-2.5)} & 44.6 {\\color{blue}(-4.6)} & 25.0 {\\color{blue}(-8.5)} & 20.5 {\\color{blue}(-7.4)} \\\\\n\\bottomrule\n\\end{tabular}\n}\n% \\caption{Retrieval evaluation results of ablation study on the \\fm test set.}\n\\caption{Ablation study results showing the impact of different components on retrieval performance using \\fm test set. Results are reported for both accuracy (Acc.) and recall across different retrieval settings. Numbers in blue parentheses show performance drops when removing each component from \\ours.}\n\\label{tab:full_ablationstudy}\n\\end{table*}\n\\end{document}\n"},"question":{"kind":"string","value":"On the FoolMeTwice dataset using the Contriever retriever, among the methods whose Top@10 accuracy surpasses RALM, which LLM backbone shows the greatest average improvement over RALM in Top@10 accuracy, and what is the value of that average improvement?"},"answer":{"kind":"string","value":"GPT-4o mini, 3.83"},"plan":{"kind":"string","value":"SELECT FoolMeTwice\nSELECT Contriever setting\nSELECT all backbones\nSELECT all methods\nSELECT Top@10 accuracy and Top@10 improvements\nLOOP for each backbone\n LOOP for each method\n IF method Top@10 accuracy is greater than RALM Top@10 accuracy\n COMPUTE improvement over RALM\n COMPUTE average improvement for this backbone across eligible methods\nCOMPUTE backbone with the highest average improvement\nRETURN backbone with the highest average improvement, corresponding average improvement\n"}}},{"rowIdx":97,"cells":{"paper":{"kind":"string","value":"2506.02911"},"relevant_tables":{"kind":"list like","value":[["\\begin{table*}[h!]\n","\\centering\n","\\footnotesize\n","\\renewcommand\\tabcolsep{4pt}\n","\\renewcommand\\arraystretch{1.05}\n","\\caption{Evaluation of model performance on unseen disease datasets. The best and second-best results are highlighted in \\colorbox{backred!50}{red} and \\colorbox{backblue!75}{blue}, respectively.}\n","\\resizebox{\\textwidth}{!}{\n","\\begin{tabular}{l|cc|cc|cc|cc|cc}\n","\\toprule\n","\\header{Model} & \\multicolumn{2}{c|}{\\textit{Breast Cancer}} & \\multicolumn{2}{c|}{\\textit{Melanoma}} & \\multicolumn{2}{c|}{\\textit{SLE}} & \\multicolumn{2}{c|}{\\textit{Colorectal Cancer}} & \\multicolumn{2}{c}{Average} \\\\\n"," & Cell & Batch & Cell & Batch & Cell & Batch & Cell & Batch & Cell & Batch \\\\\n","\\midrule\n","\\rowcolor[rgb]{0.93,0.93,0.93} \\multicolumn{11}{c}{\\textit{Zero-shot (w/ reasoning)}} \\\\\n","\\texttt{Llama3.1-8B-Instruct} & 0.0075 & 0.0000 & 0.0179 & 0.0000 & 0.0035 & 0.0000 & 0.0147 & 0.0000 & 0.0109 & 0.0000 \\\\\n","\\texttt{Qwen2.5-7B-Instruct} & 0.2382 & 0.0000 & 0.2679 & 0.0000 & 0.2400 & 0.0000 & 0.1996 & 0.0000 & 0.2364 & 0.0000 \\\\\n","\\texttt{Llama3.3-70B-Instruct} & 0.4400 & 0.0000 & 0.5337 & 0.0159 & 0.3165 & 0.0100 & 0.4218 & 0.0000 & 0.4280 & 0.0065 \\\\\n","\\texttt{GPT-4o-mini} & 0.4171 & 0.0000 & 0.5198 & 0.0000 & 0.4129 & 0.0000 & 0.4549 & 0.0000 & 0.4512 & 0.0000 \\\\\n","\\texttt{GPT-4o} & 0.5847 & 0.0560 & 0.7619 & 0.1587 & 0.6576 & 0.1000 & 0.5715 & 0.0239 & 0.6439 & 0.0847 \\\\\n","\\texttt{o3-mini} & 0.7806 & 0.2960& 0.7341 & 0.1111 & 0.7706 & 0.3200 & 0.7208 & 0.1713 & 0.7515 & 0.2246 \\\\\n","\\texttt{o1} & \\colorbox{backred!50}{0.8136} & \\colorbox{backblue!75}{0.3200} & 0.7738 & 0.1270 & \\colorbox{backblue!75}{0.8200} & \\colorbox{backblue!75}{0.3700} & \\colorbox{backred!50}{0.7845} & \\colorbox{backred!50}{0.3108} & \\colorbox{backblue!75}{0.7980} & 0.2819 \\\\\n","\\rowcolor[rgb]{0.93,0.93,0.93} \\multicolumn{11}{c}{\\textit{Instruction-tuned (w/o reasoning)}} \\\\\n","\\texttt{Llama3.1-8B-Instruct} & 0.7872 & 0.2880 & \\colorbox{backblue!75}{0.7837} & \\colorbox{backblue!75}{0.3333} & 0.7176 & 0.2500 & 0.7208 & 0.2590 & 0.7523 & \\colorbox{backblue!75}{0.2826} \\\\\n","\\texttt{Qwen2.5-7B-Instruct} & 0.7250 & 0.1600 & 0.7282 & 0.1746 & 0.5553 & 0.0300 & 0.5816 & 0.1036 & 0.6475 & 0.1170 \\\\\n","\\rowcolor[rgb]{0.93,0.93,0.93} \\multicolumn{11}{c}{\\textit{Ours (w/ reasoning)}} \\\\\n","\\Ours & \\colorbox{backblue!75}{0.8089} & \\colorbox{backred!50}{0.3360} & \\colorbox{backred!50}{0.8591} & \\colorbox{backred!50}{0.5397} & \\colorbox{backred!50}{0.8235} & \\colorbox{backred!50}{0.3800} & \\colorbox{backblue!75}{0.7820} & \\colorbox{backblue!75}{0.3028} & \\colorbox{backred!50}{0.8184} & \\colorbox{backred!50}{0.3896} \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\label{tab:generalization}\n","\\end{table*}\n"]],"string":"[\n [\n \"\\\\begin{table*}[h!]\\n\",\n \"\\\\centering\\n\",\n \"\\\\footnotesize\\n\",\n \"\\\\renewcommand\\\\tabcolsep{4pt}\\n\",\n \"\\\\renewcommand\\\\arraystretch{1.05}\\n\",\n \"\\\\caption{Evaluation of model performance on unseen disease datasets. The best and second-best results are highlighted in \\\\colorbox{backred!50}{red} and \\\\colorbox{backblue!75}{blue}, respectively.}\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \"\\\\begin{tabular}{l|cc|cc|cc|cc|cc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\header{Model} & \\\\multicolumn{2}{c|}{\\\\textit{Breast Cancer}} & \\\\multicolumn{2}{c|}{\\\\textit{Melanoma}} & \\\\multicolumn{2}{c|}{\\\\textit{SLE}} & \\\\multicolumn{2}{c|}{\\\\textit{Colorectal Cancer}} & \\\\multicolumn{2}{c}{Average} \\\\\\\\\\n\",\n \" & Cell & Batch & Cell & Batch & Cell & Batch & Cell & Batch & Cell & Batch \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\rowcolor[rgb]{0.93,0.93,0.93} \\\\multicolumn{11}{c}{\\\\textit{Zero-shot (w/ reasoning)}} \\\\\\\\\\n\",\n \"\\\\texttt{Llama3.1-8B-Instruct} & 0.0075 & 0.0000 & 0.0179 & 0.0000 & 0.0035 & 0.0000 & 0.0147 & 0.0000 & 0.0109 & 0.0000 \\\\\\\\\\n\",\n \"\\\\texttt{Qwen2.5-7B-Instruct} & 0.2382 & 0.0000 & 0.2679 & 0.0000 & 0.2400 & 0.0000 & 0.1996 & 0.0000 & 0.2364 & 0.0000 \\\\\\\\\\n\",\n \"\\\\texttt{Llama3.3-70B-Instruct} & 0.4400 & 0.0000 & 0.5337 & 0.0159 & 0.3165 & 0.0100 & 0.4218 & 0.0000 & 0.4280 & 0.0065 \\\\\\\\\\n\",\n \"\\\\texttt{GPT-4o-mini} & 0.4171 & 0.0000 & 0.5198 & 0.0000 & 0.4129 & 0.0000 & 0.4549 & 0.0000 & 0.4512 & 0.0000 \\\\\\\\\\n\",\n \"\\\\texttt{GPT-4o} & 0.5847 & 0.0560 & 0.7619 & 0.1587 & 0.6576 & 0.1000 & 0.5715 & 0.0239 & 0.6439 & 0.0847 \\\\\\\\\\n\",\n \"\\\\texttt{o3-mini} & 0.7806 & 0.2960& 0.7341 & 0.1111 & 0.7706 & 0.3200 & 0.7208 & 0.1713 & 0.7515 & 0.2246 \\\\\\\\\\n\",\n \"\\\\texttt{o1} & \\\\colorbox{backred!50}{0.8136} & \\\\colorbox{backblue!75}{0.3200} & 0.7738 & 0.1270 & \\\\colorbox{backblue!75}{0.8200} & \\\\colorbox{backblue!75}{0.3700} & \\\\colorbox{backred!50}{0.7845} & \\\\colorbox{backred!50}{0.3108} & \\\\colorbox{backblue!75}{0.7980} & 0.2819 \\\\\\\\\\n\",\n \"\\\\rowcolor[rgb]{0.93,0.93,0.93} \\\\multicolumn{11}{c}{\\\\textit{Instruction-tuned (w/o reasoning)}} \\\\\\\\\\n\",\n \"\\\\texttt{Llama3.1-8B-Instruct} & 0.7872 & 0.2880 & \\\\colorbox{backblue!75}{0.7837} & \\\\colorbox{backblue!75}{0.3333} & 0.7176 & 0.2500 & 0.7208 & 0.2590 & 0.7523 & \\\\colorbox{backblue!75}{0.2826} \\\\\\\\\\n\",\n \"\\\\texttt{Qwen2.5-7B-Instruct} & 0.7250 & 0.1600 & 0.7282 & 0.1746 & 0.5553 & 0.0300 & 0.5816 & 0.1036 & 0.6475 & 0.1170 \\\\\\\\\\n\",\n \"\\\\rowcolor[rgb]{0.93,0.93,0.93} \\\\multicolumn{11}{c}{\\\\textit{Ours (w/ reasoning)}} \\\\\\\\\\n\",\n \"\\\\Ours & \\\\colorbox{backblue!75}{0.8089} & \\\\colorbox{backred!50}{0.3360} & \\\\colorbox{backred!50}{0.8591} & \\\\colorbox{backred!50}{0.5397} & \\\\colorbox{backred!50}{0.8235} & \\\\colorbox{backred!50}{0.3800} & \\\\colorbox{backblue!75}{0.7820} & \\\\colorbox{backblue!75}{0.3028} & \\\\colorbox{backred!50}{0.8184} & \\\\colorbox{backred!50}{0.3896} \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\label{tab:generalization}\\n\",\n \"\\\\end{table*}\\n\"\n ]\n]","isConversational":false},"tables":{"kind":"list like","value":[["\\begin{figure}[h!]\n","\\centering\n","\\includegraphics[width=0.96\\textwidth]{figs/overview.pdf}\n","\\caption{Overview of this work. \\Ours achieves state-of-the-art on the \\Bench task.}\n","\\label{fig:overview}\n","\\end{figure}\n"],["\\begin{figure}[h!]\n","\\centering\n","\\includegraphics[width=\\textwidth]{figs/cellpuzzles.pdf}\n","\\caption{\n","\\Bench formulates cell type annotation as a batch-level reasoning task that integrates gene expression and contextual metadata, inspired by how experts annotate cells in practice.\n","}\n","\\label{fig:cellpuzzles}\n","\\end{figure}\n"],["\\begin{figure}[h!]\n","\\centering\n","\\includegraphics[width=1.0\\textwidth]{figs/data_dis.pdf}\n","\\caption{\n","Data distribution of \\Bench.\n","(A) Age group distribution with male-female breakdown.\n","(B) Sample distribution across a wide range of disease conditions.\n","(C) Number of unique cell types observed across different tissues. \n","For clarity, only the top 20 entries are shown in (B) and (C).\n","}\n","\\label{fig:data_distribution}\n","\\end{figure}\n"],["\\begin{table}[h!]\n"," \\centering\n"," \\small\n"," \\caption{Prompt template guiding structured reasoning and prediction in \\Ours.}\n"," \\label{tab:prompt_template}\n"," \\vspace{2.5pt}\n"," \\renewcommand{\\arraystretch}{1.2}\n"," \\begin{tabular}{p{13.5cm}}\n"," \\hline\n"," You are an expert assistant specialized in cell type annotation. You will be given a batch of $N$ cells from the same donor, where each cell represents a unique cell type.\n"," For each cell, the top-expressed genes are provided in descending order of expression.\n"," Using both the gene expression data and donor information, determine the correct cell type for each cell.\n"," You will also receive a list of $N$ candidate cell types, and each candidate must be assigned to exactly one cell. Ensure that you consider all cells and candidate types together, rather than annotating each cell individually.\n"," Include your detailed reasoning within \\thinkbegin and \\thinkend tags, and provide your final answer within \\answerbegin and \\answerend tags.\n"," The final answer should be a single string listing the assigned cell types in order, separated by ` | '.\n"," \\\\\n"," \\hline\n"," \\end{tabular} \n","\\end{table}\n"],["\\begin{table*}[h!]\n","\\centering\n","\\small\n","\\footnotesize\n","\\renewcommand\\tabcolsep{4.5pt}\n","\\renewcommand\\arraystretch{1.05}\n","\\caption{Evaluation of model performance on the \\Bench benchmark. The best and second-best results are highlighted in \\colorbox{backred!50}{red} and \\colorbox{backblue!75}{blue}, respectively.}\n","\\resizebox{0.93\\textwidth}{!}{\n","\\begin{tabular}{l|c|c|c|c|c}\n","\\toprule\n","\\header{Model} & \\header{Response Length} & \\header{Cell-level Acc} & \\header{Batch-level Acc} & \\header{Format} & \\header{Uniqueness} \\\\\n","\\midrule\n","\\rowcolor[rgb]{0.93,0.93,0.93} \\multicolumn{6}{c}{\\textit{Zero-shot performance (w/ reasoning)}} \\\\\n","\\texttt{Llama3.1-8B-Instruct} & 2264.9 & 0.0050 & 0.0000 & 0.0393 & 0.0190 \\\\\n","\\texttt{Qwen2.5-7B-Instruct} & 879.8 & 0.1353 & 0.0000 & 0.9425 & 0.4528 \\\\\n","\\texttt{Llama3.3-70B-Instruct} & 1006.8 & 0.2573\t& 0.0064 & 0.9973 & 0.8321 \\\\\n","\\texttt{GPT-4o-mini} & 779.9 & 0.2581 & 0.0027 & 0.9918 & 0.6565 \\\\\n","\\texttt{GPT-4o} & 1087.0 & 0.4248 & 0.0283 & 0.9817 & 0.8179 \\\\\n","\\texttt{o3-mini} & 1130.2 & 0.5804 & 0.1352 & \\colorbox{backred!50}{1.0000} & \\colorbox{backred!50}{1.0000} \\\\\n","\\texttt{o1} & 906.6 & \\colorbox{backblue!75}{0.6479} & 0.1900 & \\colorbox{backred!50}{1.0000} & \\colorbox{backblue!75}{0.9997} \\\\\n","\\rowcolor[rgb]{0.93,0.93,0.93} \\multicolumn{6}{c}{\\textit{Instruction-tuning Performance (w/o reasoning)}} \\\\\n","\\texttt{Llama3.1-8B-Instruct} & 71.9 & 0.6411 & \\colorbox{backblue!75}{0.2969} & 0.9078 & 0.9071 \\\\\n","\\texttt{Qwen2.5-7B-Instruct} & 70.9 & 0.6179 & 0.2475 & 0.9443 & 0.9404 \\\\\n","\\rowcolor[rgb]{0.93,0.93,0.93} \\multicolumn{6}{c}{\\textit{Ours (w/ reasoning)}} \\\\\n","\\Ours & 1145.6 & \\colorbox{backred!50}{0.6849} & \\colorbox{backred!50}{0.3288} & 0.9826 & 0.9824 \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\label{tab:cell_annotation_results}\n","\\end{table*}\n"],["\\begin{figure}[h!]\n","\\centering\n","\\includegraphics[width=1.0\\textwidth]{figs/acc_dis.pdf}\n","\\caption{\n","Fine-grained analysis of model performance.\n","(A) Cell-level and batch-level accuracy across varying numbers of cells per batch. \n","(B) Distribution of per-batch cell-level accuracy across models.\n","}\n","\\label{fig:acc_distribution}\n","\\end{figure}\n"],["\\begin{table*}[h!]\n","\\centering\n","\\footnotesize\n","\\renewcommand\\tabcolsep{4pt}\n","\\renewcommand\\arraystretch{1.05}\n","\\caption{Evaluation of model performance on unseen disease datasets. The best and second-best results are highlighted in \\colorbox{backred!50}{red} and \\colorbox{backblue!75}{blue}, respectively.}\n","\\resizebox{\\textwidth}{!}{\n","\\begin{tabular}{l|cc|cc|cc|cc|cc}\n","\\toprule\n","\\header{Model} & \\multicolumn{2}{c|}{\\textit{Breast Cancer}} & \\multicolumn{2}{c|}{\\textit{Melanoma}} & \\multicolumn{2}{c|}{\\textit{SLE}} & \\multicolumn{2}{c|}{\\textit{Colorectal Cancer}} & \\multicolumn{2}{c}{Average} \\\\\n"," & Cell & Batch & Cell & Batch & Cell & Batch & Cell & Batch & Cell & Batch \\\\\n","\\midrule\n","\\rowcolor[rgb]{0.93,0.93,0.93} \\multicolumn{11}{c}{\\textit{Zero-shot (w/ reasoning)}} \\\\\n","\\texttt{Llama3.1-8B-Instruct} & 0.0075 & 0.0000 & 0.0179 & 0.0000 & 0.0035 & 0.0000 & 0.0147 & 0.0000 & 0.0109 & 0.0000 \\\\\n","\\texttt{Qwen2.5-7B-Instruct} & 0.2382 & 0.0000 & 0.2679 & 0.0000 & 0.2400 & 0.0000 & 0.1996 & 0.0000 & 0.2364 & 0.0000 \\\\\n","\\texttt{Llama3.3-70B-Instruct} & 0.4400 & 0.0000 & 0.5337 & 0.0159 & 0.3165 & 0.0100 & 0.4218 & 0.0000 & 0.4280 & 0.0065 \\\\\n","\\texttt{GPT-4o-mini} & 0.4171 & 0.0000 & 0.5198 & 0.0000 & 0.4129 & 0.0000 & 0.4549 & 0.0000 & 0.4512 & 0.0000 \\\\\n","\\texttt{GPT-4o} & 0.5847 & 0.0560 & 0.7619 & 0.1587 & 0.6576 & 0.1000 & 0.5715 & 0.0239 & 0.6439 & 0.0847 \\\\\n","\\texttt{o3-mini} & 0.7806 & 0.2960& 0.7341 & 0.1111 & 0.7706 & 0.3200 & 0.7208 & 0.1713 & 0.7515 & 0.2246 \\\\\n","\\texttt{o1} & \\colorbox{backred!50}{0.8136} & \\colorbox{backblue!75}{0.3200} & 0.7738 & 0.1270 & \\colorbox{backblue!75}{0.8200} & \\colorbox{backblue!75}{0.3700} & \\colorbox{backred!50}{0.7845} & \\colorbox{backred!50}{0.3108} & \\colorbox{backblue!75}{0.7980} & 0.2819 \\\\\n","\\rowcolor[rgb]{0.93,0.93,0.93} \\multicolumn{11}{c}{\\textit{Instruction-tuned (w/o reasoning)}} \\\\\n","\\texttt{Llama3.1-8B-Instruct} & 0.7872 & 0.2880 & \\colorbox{backblue!75}{0.7837} & \\colorbox{backblue!75}{0.3333} & 0.7176 & 0.2500 & 0.7208 & 0.2590 & 0.7523 & \\colorbox{backblue!75}{0.2826} \\\\\n","\\texttt{Qwen2.5-7B-Instruct} & 0.7250 & 0.1600 & 0.7282 & 0.1746 & 0.5553 & 0.0300 & 0.5816 & 0.1036 & 0.6475 & 0.1170 \\\\\n","\\rowcolor[rgb]{0.93,0.93,0.93} \\multicolumn{11}{c}{\\textit{Ours (w/ reasoning)}} \\\\\n","\\Ours & \\colorbox{backblue!75}{0.8089} & \\colorbox{backred!50}{0.3360} & \\colorbox{backred!50}{0.8591} & \\colorbox{backred!50}{0.5397} & \\colorbox{backred!50}{0.8235} & \\colorbox{backred!50}{0.3800} & \\colorbox{backblue!75}{0.7820} & \\colorbox{backblue!75}{0.3028} & \\colorbox{backred!50}{0.8184} & \\colorbox{backred!50}{0.3896} \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\label{tab:generalization}\n","\\end{table*}\n"],["\\begin{figure}[h!]\n","\\centering\n","\\includegraphics[width=1.0\\textwidth]{figs/dynamics.pdf}\n","\\caption{\n","Comparing training and inference dynamics across different configurations.\n","}\n","\\label{fig:dynamics}\n","\\end{figure}\n"],["\\begin{figure}[h!]\n","\\centering\n","\\includegraphics[width=1.0\\textwidth]{figs/reasoning.pdf}\n","\\caption{\n","Analysis of model reasoning behavior. \n","(A) Step-by-step reasoning traces generated by each model for the same task. \n","(B) Expert evaluation of reasoning quality comparing \\Ours with baseline models.\n","(C) Proportion of traces exhibiting self-reflection and curriculum reasoning behaviors.\n","}\n","\\label{fig:reasoning_analysis}\n","\\end{figure}\n"],["\\begin{table*}[h]\n","\\centering\n","\\footnotesize\n","\\renewcommand\\tabcolsep{9pt}\n","\\renewcommand\\arraystretch{1.05}\n","\\caption{\n","Raw scRNA-seq data resources involved in our paper.}\n","\\resizebox{\\textwidth}{!}{\n","\\begin{tabular}{p{9cm} p{7.8cm}}\n","\\toprule\n","\\textbf{Data Source Name} & \\textbf{URL} \\\\\n","\\midrule\n","Single-cell Human Microglia~\\cite{olah2020single} & \\small \\url{https://cellxgene.cziscience.com/collections/fcb3d1c1-03d2-41ac-8229-458e072b7a1c} \\\\\n","\\midrule\n","Single-Cell RNA-Seq Analysis Reveals Cell Subsets And Gene Signatures Associated With Rheumatoid Arthritis Disease Activity~\\cite{binvignat2024single} & \\small \\url{https://cellxgene.cziscience.com/collections/e1a9ca56-f2ee-435d-980a-4f49ab7a952b} \\\\\n","\\midrule\n","The Single-Cell Lung Cancer Atlas (LuCA)~\\cite{salcher2022high} & \n","\\small \\url{https://cellxgene.cziscience.com/collections/edb893ee-4066-4128-9aec-5eb2b03f8287} \\\\\n","\\midrule\n","Cells Of The Human Intestinal Tract Mapped Across Space And Time~\\cite{elmentaite2021cells} & \\small \\url{https://cellxgene.cziscience.com/collections/e33ffcd3-7cbf-4b8c-b0f4-85587ad5019a} \\\\\n","\\midrule\n","Persistent T Cell Unresponsiveness Associated With Chronic Visceral Leishmaniasis In HIV-Coinfected Patients~\\cite{de2024persistent} & \\small \\url{https://cellxgene.cziscience.com/collections/126afc71-47fb-4e9d-8aaf-9d9f61e0ac77} \\\\\n","\\midrule\n","Multiomics Single-Cell Analysis Of Human Pancreatic Islets Reveals Novel Cellular States In Health And Type 1 Diabetes~\\cite{fasolino2022single} & \\small \\url{https://cellxgene.cziscience.com/collections/51544e44-293b-4c2b-8c26-560678423380} \\\\\n","\\midrule\n","The Integrated Human Lung Cell Atlas~\\cite{sikkema2023integrated} & \\small \\url{https://cellxgene.cziscience.com/collections/6f6d381a-7701-4781-935c-db10d30de293} \\\\\n","\\midrule\n","Single-Cell Transcriptomics Of The Human Retinal Pigment Epithelium And Choroid In Health And Macular Degeneration~\\cite{voigt2019single} & \\small \\url{https://cellxgene.cziscience.com/collections/f8057c47-fcd8-4fcf-88b0-e2f930080f6e} \\\\\n","\\midrule\n","Single-Cell RNA-Seq Of The Adult Human Kidney~\\cite{lake2023atlas} & \\small \\url{https://cellxgene.cziscience.com/collections/bcb61471-2a44-4d00-a0af-ff085512674c} \\\\\n","\\midrule\n","MSK SPECTRUM – Ovarian Cancer Mutational Processes Drive Site-Specific Immune Evasion~\\cite{vazquez2022ovarian} & \\small \\url{https://cellxgene.cziscience.com/collections/4796c91c-9d8f-4692-be43-347b1727f9d8} \\\\\n","\\midrule\n","HTAN-CHOP - Single-Cell Multiomics Reveals Increased Plasticity, Resistant Populations, And Stem-Cell–Like Blasts In KMT2A-Rearranged Leukemia~\\cite{chen2022single} & \\small \\url{https://cellxgene.cziscience.com/collections/10ec9198-584e-4a7e-8a24-4a332915a4ef} \\\\\n","\\midrule\n","Single-Cell Atlas Of Common Variable Immunodeficiency Shows Germinal Center-Associated Epigenetic Dysregulation In B-Cell Responses~\\cite{rodriguez2022single} & \\small \\url{https://cellxgene.cziscience.com/collections/bf325905-5e8e-42e3-933d-9a9053e9af80} \\\\\n","\\midrule\n","Dissecting Novel Myeloid-Derived Cell States Through Single-Cell RNA-Seq And Its Impact On Clinical Outcome Across Tumor Types~\\cite{guimaraes2024single} & \\small \\url{https://cellxgene.cziscience.com/collections/3f7c572c-cd73-4b51-a313-207c7f20f188} \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\label{tab:data_source}\n","\\end{table*}\n"],["\\begin{table*}[h!]\n","\\centering\n","\\footnotesize\n","\\renewcommand\\tabcolsep{4pt}\n","\\renewcommand\\arraystretch{1.05}\n","\\caption{Number of samples per disease in the dataset.}\n","\\resizebox{0.9\\textwidth}{!}{\n","\\begin{tabular}{l r @{\\hskip 1.5cm} l r}\n","\\toprule\n","\\textbf{Disease} & \\textbf{\\#Samples} & \\textbf{Disease} & \\textbf{\\#Samples} \\\\\n","\\midrule\n","Lung Adenocarcinoma & 1292 & Crohn Disease & 215 \\\\\n","Chronic Obstructive Pulmonary Disease & 1194 & Lung Large Cell Carcinoma & 211 \\\\\n","Squamous Cell Lung Carcinoma & 946 & Non-Specific Interstitial Pneumonia & 172 \\\\\n","Normal & 941 & Chronic Rhinitis & 158 \\\\\n","Interstitial Lung Disease & 777 & Hypersensitivity Pneumonitis & 126 \\\\\n","Pulmonary Fibrosis & 669 & Lymphangioleiomyomatosis & 109 \\\\\n","Non-Small Cell Lung Carcinoma & 632 & Pleomorphic Carcinoma & 87 \\\\\n","B-Cell Acute Lymphoblastic Leukemia & 74 & Pulmonary Sarcoidosis & 58 \\\\\n","Pneumonia & 48 & Acute Myeloid Leukemia & 45 \\\\\n","Common Variable Immunodeficiency & 45 & Type 1 Diabetes Mellitus & 35 \\\\\n","Acute Promyelocytic Leukemia & 28 & Rheumatoid Arthritis & 23 \\\\\n","Acute Kidney Failure & 20 & Malignant Ovarian Serous Tumor & 18 \\\\\n","Chronic Kidney Disease & 18 & Age Related Macular Degeneration 7 & 13 \\\\\n","Basal Laminar Drusen & 12 & Cystic Fibrosis & 11 \\\\\n","Hiv Infectious Disease & 11 & Alzheimer Disease & 8 \\\\\n","Cataract & 7 & Temporal Lobe Epilepsy & 4 \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\label{tab:disease_stats}\n","\\end{table*}\n"],["\\begin{table}[ht]\n","\\centering\n","\\footnotesize\n","\\renewcommand\\tabcolsep{4pt}\n","\\renewcommand\\arraystretch{1.05}\n","\\caption{Number of unique cell types per tissue in the dataset.}\n","\\vskip 0.06in\n","\\resizebox{0.75\\textwidth}{!}{\n","\\begin{tabular}{l r @{\\hskip 1.5cm} l r}\n","\\toprule\n","\\textbf{Tissue} & \\textbf{\\#Cell Types} & \\textbf{Tissue} & \\textbf{\\#Cell Types} \\\\\n","\\midrule\n","Ileum & 71 & Adrenal Tissue & 18 \\\\\n","Lung & 56 & Rectum & 18 \\\\\n","Blood & 48 & Ascending Colon & 15 \\\\\n","Duodenum & 48 & Mesenteric Lymph Node & 11 \\\\\n","Colon & 40 & Peripheral Region Of Retina & 10 \\\\\n","Bone Marrow & 38 & Fovea Centralis & 10 \\\\\n","Nose & 36 & Right Ovary & 9 \\\\\n","Sigmoid Colon & 35 & Peritoneum & 9 \\\\\n","Vermiform Appendix & 33 & Islet Of Langerhans & 9 \\\\\n","Transverse Colon & 31 & Omentum & 9 \\\\\n","Descending Colon & 31 & Ascitic Fluid & 9 \\\\\n","Lymph Node & 30 & Left Ovary & 9 \\\\\n","Brain & 27 & Adnexa Of Uterus & 9 \\\\\n","Caecum & 27 & Diaphragm & 8 \\\\\n","Jejunum & 27 & Small Intestine & 8 \\\\\n","Kidney & 26 & Abdominal Wall & 8 \\\\\n","Pleural Effusion & 23 & Temporal Cortex & 6 \\\\\n","Liver & 21 & Dorsolateral Prefrontal Cortex & 5 \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\label{tab:tissue_stats}\n","\\end{table}\n"],["\\begin{table}[ht]\n","\\centering\n","\\footnotesize\n","\\renewcommand\\tabcolsep{4pt}\n","\\renewcommand\\arraystretch{1.05}\n","\\caption{Frequency of cell types in the dataset (Part I).}\n","\\vskip 0.06in\n","\\resizebox{\\textwidth}{!}{\n","\\begin{tabular}{p{6cm} r p{6cm} r}\n","\\toprule\n","\\textbf{Cell Type} & \\textbf{\\#Instances} & \\textbf{Cell Type} & \\textbf{\\#Instances} \\\\\n","\\midrule\n","CD4-Positive, Alpha-Beta T Cell & 4428 & Fibroblast & 72 \\\\\n","CD8-Positive, Alpha-Beta T Cell & 4276 & Mucosal Invariant T Cell & 70 \\\\\n","Classical Monocyte & 4167 & Precursor B Cell & 70 \\\\\n","Natural Killer Cell & 4097 & Glial Cell & 68 \\\\\n","Alveolar Macrophage & 4051 & Mature B Cell & 68 \\\\\n","Cd1C-Positive Myeloid Dendritic Cell & 3965 & Pro-B Cell & 68 \\\\\n","B Cell & 3495 & Stem Cell & 67 \\\\\n","Pulmonary Alveolar Type 2 Cell & 3395 & Early Pro-B Cell & 66 \\\\\n","Mast Cell & 3062 & Stromal Cell Of Lamina Propria Of Small Intestine & 63 \\\\\n","Plasma Cell & 2939 & Common Lymphoid Progenitor & 59 \\\\\n","Vein Endothelial Cell & 2659 & Immature B Cell & 58 \\\\\n","Capillary Endothelial Cell & 2328 & Common Dendritic Progenitor & 57 \\\\\n","Non-Classical Monocyte & 2120 & Megakaryocyte-Erythroid Progenitor Cell & 56 \\\\\n","Macrophage & 2070 & Lung Neuroendocrine Cell & 54 \\\\\n","Pulmonary Alveolar Type 1 Cell & 1895 & Granulocyte Monocyte Progenitor Cell & 50 \\\\\n","Ciliated Columnar Cell Of Tracheobronchial Tree & 1734 & Reticular Cell & 48 \\\\\n","Elicited Macrophage & 1726 & Effector Memory Cd4-Positive, Alpha-Beta T Cell & 48 \\\\\n","Regulatory T Cell & 1490 & Endothelial Cell Of Artery & 45 \\\\\n","Malignant Cell & 1363 & Kidney Collecting Duct Principal Cell & 42 \\\\\n","Endothelial Cell Of Lymphatic Vessel & 1362 & Effector Memory Cd8-Positive, Alpha-Beta T Cell & 42 \\\\\n","Epithelial Cell Of Lung & 1272 & Epithelial Cell Of Proximal Tubule & 42 \\\\\n","Multi-Ciliated Epithelial Cell & 1124 & Myeloid Dendritic Cell & 42 \\\\\n","Bronchus Fibroblast Of Lung & 1123 & Kidney Loop Of Henle Thick Ascending Limb Epithelial Cell & 41 \\\\\n","Pulmonary Artery Endothelial Cell & 1086 & Central Memory Cd4-Positive, Alpha-Beta T Cell & 41 \\\\\n","Plasmacytoid Dendritic Cell & 948 & Epithelial Cell & 40 \\\\\n","Fibroblast Of Lung & 909 & Ionocyte & 39 \\\\\n","Respiratory Basal Cell & 897 & Effector Memory Cd8-Positive, Alpha-Beta T Cell, Terminally Differentiated & 39 \\\\\n","Tracheobronchial Smooth Muscle Cell & 857 & Late Promyelocyte & 39 \\\\\n","Alveolar Type 1 Fibroblast Cell & 844 & Kidney Interstitial Cell & 39 \\\\\n","Myeloid Cell & 806 & T-Helper 17 Cell & 38 \\\\\n","Alveolar Adventitial Fibroblast & 722 & Early Promyelocyte & 38 \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\label{tab:celltype_stats_part1}\n","\\end{table}\n"],["\\begin{table}[ht]\n","\\centering\n","\\footnotesize\n","\\renewcommand\\tabcolsep{4pt}\n","\\renewcommand\\arraystretch{1.05}\n","\\caption{Frequency of cell types in the dataset (Part II).}\n","\\vskip 0.06in\n","\\resizebox{\\textwidth}{!}{\n","\\begin{tabular}{p{6cm} r p{6cm} r}\n","\\toprule\n","\\textbf{Cell Type} & \\textbf{\\#Instances} & \\textbf{Cell Type} & \\textbf{\\#Instances} \\\\\n","\\midrule\n","Epithelial Cell Of Lower Respiratory Tract & 702 & Germinal Center B Cell & 38 \\\\\n","Conventional Dendritic Cell & 701 & Class Switched Memory B Cell & 37 \\\\\n","Club Cell & 685 & Kidney Connecting Tubule Epithelial Cell & 36 \\\\\n","Dendritic Cell & 681 & Hematopoietic Stem Cell & 36 \\\\\n","Lung Pericyte & 569 & Unswitched Memory B Cell & 36 \\\\\n","Smooth Muscle Cell & 464 & Mononuclear Phagocyte & 36 \\\\\n","T Cell & 463 & Kidney Collecting Duct Intercalated Cell & 34 \\\\\n","Nasal Mucosa Goblet Cell & 447 & Kidney Distal Convoluted Tubule Epithelial Cell & 33 \\\\\n","Pericyte & 439 & Lymphoid Lineage Restricted Progenitor Cell & 33 \\\\\n","Lung Macrophage & 422 & Plasmablast & 33 \\\\\n","Mesothelial Cell & 412 & Inflammatory Macrophage & 32 \\\\\n","Neutrophil & 397 & Parietal Epithelial Cell & 31 \\\\\n","Stromal Cell & 264 & Myelocyte & 31 \\\\\n","Naive B Cell & 242 & Kidney Loop Of Henle Thin Ascending Limb Epithelial Cell & 30 \\\\\n","Memory B Cell & 235 & Bronchial Goblet Cell & 30 \\\\\n","Myofibroblast Cell & 222 & Tracheobronchial Serous Cell & 30 \\\\\n","Cd8-Positive, Alpha-Beta Memory T Cell & 216 & T-Helper 1 Cell & 30 \\\\\n","Endothelial Cell & 210 & Melanocyte & 29 \\\\\n","Monocyte & 191 & Schwann Cell & 29 \\\\\n","Gamma-Delta T Cell & 174 & Retinal Pigment Epithelial Cell & 28 \\\\\n","Naive Thymus-Derived Cd4-Positive, Alpha-Beta T Cell & 140 & Kidney Loop Of Henle Thin Descending Limb Epithelial Cell & 26 \\\\\n","Activated Cd8-Positive, Alpha-Beta T Cell & 126 & Erythroid Progenitor Cell & 26 \\\\\n","Iga Plasma Cell & 125 & Intestinal Tuft Cell & 25 \\\\\n","Pancreatic A Cell & 123 & Central Memory Cd8-Positive, Alpha-Beta T Cell & 25 \\\\\n","Pancreatic Ductal Cell & 123 & Kidney Interstitial Alternatively Activated Macrophage & 25 \\\\\n","Pancreatic D Cell & 122 & Basophil Mast Progenitor Cell & 24 \\\\\n","Pancreatic Stellate Cell & 122 & Serous Secreting Cell & 23 \\\\\n","Pp Cell & 122 & Cd4-Positive, Alpha-Beta Cytotoxic T Cell & 22 \\\\\n","Pancreatic Acinar Cell & 122 & Colon Epithelial Cell & 22 \\\\\n","Naive Thymus-Derived Cd8-Positive, Alpha-Beta T Cell & 121 & Cytotoxic T Cell & 21 \\\\\n","Type B Pancreatic Cell & 119 & Acinar Cell & 21 \\\\\n","Mature Nk T Cell & 119 & Type Ec Enteroendocrine Cell & 20 \\\\\n","Cd16-Positive, Cd56-Dim Natural Killer Cell, Human & 118 & Erythroid Lineage Cell & 19 \\\\\n","Cd4-Positive, Alpha-Beta Memory T Cell & 117 & Fallopian Tube Secretory Epithelial Cell & 18 \\\\\n","Activated Cd4-Positive, Alpha-Beta T Cell & 116 & M Cell Of Gut & 18 \\\\\n","Transit Amplifying Cell & 104 & Mucus Secreting Cell & 15 \\\\\n","Intestine Goblet Cell & 102 & Megakaryocyte Progenitor Cell & 15 \\\\\n","Brush Cell Of Tracheobronchial Tree & 96 & Mesenchymal Lymphangioblast & 13 \\\\\n","Igg Plasma Cell & 94 & Non-Terminally Differentiated Cell & 13 \\\\\n","Cd16-Negative, Cd56-Bright Natural Killer Cell, Human & 86 & Podocyte & 13 \\\\\n","Pancreatic Epsilon Cell & 85 & Cd14-Positive Monocyte & 12 \\\\\n","Epithelial Cell Of Alveolus Of Lung & 84 & Plasmacytoid Dendritic Cell, Human & 12 \\\\\n","Respiratory Hillock Cell & 83 & Enteroendocrine Cell & 12 \\\\\n","Enterocyte Of Colon & 79 & Microglial Cell & 12 \\\\\n","T Follicular Helper Cell & 75 & Group 3 Innate Lymphoid Cell & 11 \\\\\n","Hematopoietic Multipotent Progenitor Cell & 75 & Erythrocyte & 10 \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\label{tab:celltype_stats_part2}\n","\\end{table}\n"],["\\begin{table*}[h]\n","\\centering\n","\\footnotesize\n","\\renewcommand\\tabcolsep{4pt}\n","\\renewcommand\\arraystretch{1.05}\n","\\caption{\n","Templates for converting structured donor metadata into context descriptions.}\n","\\resizebox{0.9\\textwidth}{!}{\n","\\begin{tabular}{l p{10.5cm}}\n","\\toprule\n","\\textbf{Feature} & \\textbf{Template} \\\\\n","\\midrule\n","\\raisebox{-.25\\height}{\\includegraphics[height=1.5em]{figs/symbol/sex.png}}\\; Sex &\n","\\texttt{\\{sex\\}} $\\rightarrow$ The cell is from a \\texttt{\\{sex\\}} individual. \\\\\n","\\raisebox{-.25\\height}{\\includegraphics[height=1.5em]{figs/symbol/growth.png}}\\; Development Stage & The individual is at the \\{\\texttt{development\\_stage}\\}. \\\\\n","\\raisebox{-.25\\height}{\\includegraphics[height=1.5em]{figs/symbol/cross-country.png}}\\; Ethnicity & The donor has a \\{\\texttt{self\\_reported\\_ethnicity}\\} background. \\\\\n","\\raisebox{-.25\\height}{\\includegraphics[height=1.5em]{figs/symbol/muscle-cross-section-healthy.png}}\\; Tissue & The cell originates from the \\{\\texttt{tissue}\\}. \\\\\n","\\raisebox{-.25\\height}{\\includegraphics[height=1.5em]{figs/symbol/gene.png}}\\; Top Genes & \\{\\texttt{genes}\\} → Top expressed genes are: \\{\\texttt{genes}\\}. \\\\\n","\\raisebox{-.25\\height}{\\includegraphics[height=1.5em]{figs/symbol/prevention.png}}\\; Disease & \n","\\texttt{normal} → The patient is healthy with \\texttt{no diagnosed disease}. |\n","\\texttt{other} → The patient has been diagnosed with \\{\\texttt{disease}\\}. \\\\\n","\\raisebox{-.25\\height}{\\includegraphics[height=1.5em]{figs/symbol/cigarrete.png}}\\; Smoking Status & \n","\\texttt{active} → The patient is an \\texttt{active} smoker. |\n","\\texttt{former} → The patient is a \\texttt{former} smoker. |\n","\\texttt{hist of marijuana use} → The patient has a \\texttt{history of marijuana use}; \n","\\texttt{never} → The patient has \\texttt{never} smoked. \\\\\n","\\raisebox{-.25\\height}{\\includegraphics[height=1.5em]{figs/symbol/microscope.png}}\\; Tumor Stage & \n","\\texttt{non-cancer} $\\rightarrow$ There is \\texttt{no cancer} present. |\n","\\texttt{early} $\\rightarrow$ The patient has an \\texttt{early}-stage tumor. |\n","\\texttt{advanced} $\\rightarrow$ The patient has an \\texttt{advanced}-stage tumor. \\\\\n","\\raisebox{-.25\\height}{\\includegraphics[height=1.5em]{figs/symbol/syringe.png}}\\; Sample Type & \n","\\texttt{M3} $\\rightarrow$ The sample was collected at \\texttt{month 3} post-treatment. |\n","\\texttt{M6} $\\rightarrow$ The sample was collected at \\texttt{month 6} post-treatment. |\n","\\texttt{UV} $\\rightarrow$ The sample was exposed to \\texttt{ultraviolet (UV)} treatment. |\n","\\texttt{CONTROL} $\\rightarrow$ The sample is from the \\texttt{control} group. \\\\\n","\\raisebox{-.25\\height}{\\includegraphics[height=1.5em]{figs/symbol/brain.png}}\\; Hemisphere & \n","\\texttt{left} $\\rightarrow$ The tissue sample was taken from the \\texttt{left} hemisphere of the brain. |\n","\\texttt{right} $\\rightarrow$ The tissue sample was taken from the \\texttt{right} hemisphere of the brain. \\\\\n","\\raisebox{-.25\\height}{\\includegraphics[height=1.5em]{figs/symbol/liver-cancer.png}}\\; Tumor Site & \n","\\texttt{primary} $\\rightarrow$ The tumor is located at the \\texttt{primary} site. |\n","\\texttt{metastasis} $\\rightarrow$ The tumor has \\texttt{metastasized} to other parts of the body. |\n","\\texttt{normal} $\\rightarrow$ This sample was collected from non-tumorous tissue. \\\\\n","\\raisebox{-.25\\height}{\\includegraphics[height=1.5em]{figs/symbol/lymph-nodes.png}}\\; Sample Source & \n","\\texttt{tumor} $\\rightarrow$ The sample is derived from \\texttt{tumor} tissue. |\n","\\texttt{normal} $\\rightarrow$ The sample is derived from \\texttt{normal} tissue. |\n","\\texttt{blood} $\\rightarrow$ The sample is a \\texttt{blood}-derived specimen. |\n","\\texttt{lymphnode} $\\rightarrow$ The sample is derived from \\texttt{lymph node} tissue. \\\\\n","\\raisebox{-.25\\height}{\\includegraphics[height=1.5em]{figs/symbol/eosinophil.png}}\\; CD45 Expression & \n","\\texttt{yes} $\\rightarrow$ The cell is CD45-\\texttt{positive}, suggesting an immune cell origin. |\n","\\texttt{no} $\\rightarrow$ The cell is CD45-\\texttt{negative}, suggesting a non-immune cell lineage. |\n","\\texttt{mixed} $\\rightarrow$ The sample contains a \\texttt{mixture} of CD45-positive and CD45-negative cells. \\\\\n","\\raisebox{-.25\\height}{\\includegraphics[height=1.5em]{figs/symbol/diabetes.png}}\\; Diabetes History & \n","\\texttt{yes} $\\rightarrow$ The patient \\texttt{has a history} of diabetes. |\n","\\texttt{no} $\\rightarrow$ The patient \\texttt{does not have} diabetes. \\\\\n","\\raisebox{-.25\\height}{\\includegraphics[height=1.5em]{figs/symbol/blood-pressure.png}}\\; Hypertension & \n","\\texttt{yes} $\\rightarrow$ The patient \\texttt{has a history} of hypertension. |\n","\\texttt{no} $\\rightarrow$ The patient \\texttt{does not have} hypertension. \\\\\n","\\raisebox{-.25\\height}{\\includegraphics[height=1.5em]{figs/symbol/plasma-cells.png}}\\; Activation & \n","\\texttt{activated} $\\rightarrow$ The sample was stimulated and represents \\texttt{activated} immune cells. |\n","\\texttt{resting} $\\rightarrow$ The sample represents \\texttt{resting} (non-activated) immune cells. \\\\\n","\\raisebox{-.25\\height}{\\includegraphics[height=1.5em]{figs/symbol/genotype.png}}\\; Genotype & \n","\\texttt{FLT3-ITD,NPM1-MUT} $\\rightarrow$ The patient carries FLT3-ITD and NPM1 mutations. |\n","\\texttt{FLT3-WT,NPM1-MUT} $\\rightarrow$ The patient carries a wild-type FLT3 and an NPM1 mutation. |\n","\\texttt{APL} $\\rightarrow$ The patient is diagnosed with acute promyelocytic leukemia (APL). \\\\\n","\\raisebox{-.25\\height}{\\includegraphics[height=1.5em]{figs/symbol/medical-condition.png}}\\; Medical Conditions & \n","\\texttt{epilepsy} $\\rightarrow$ The patient has a history of \\texttt{epilepsy}. |\n","\\texttt{tumor} $\\rightarrow$ The patient has a diagnosed brain \\texttt{tumor}. |\n","\\texttt{hydrocephalus} $\\rightarrow$ The patient has \\texttt{hydrocephalus} (fluid buildup in the brain). |\n","\\texttt{both} $\\rightarrow$ The patient has \\texttt{both} epilepsy and a brain tumor. |\n","\\texttt{other} $\\rightarrow$ The patient has \\texttt{other} neurological conditions. |\n","\\texttt{healthy} $\\rightarrow$ The donor was \\texttt{healthy} with no reported skin condition. |\n","\\texttt{dm – non ulcer} $\\rightarrow$ The donor had diabetes \\texttt{mellitus without skin ulceration}. | \n","\\texttt{keloid} $\\rightarrow$ The donor had a \\texttt{keloid}, which is an overgrowth of scar tissue. |\n","\\texttt{localised scleroderma} $\\rightarrow$ The donor was diagnosed with \\texttt{localized scleroderma}. | \n","\\texttt{scar} $\\rightarrow$ The donor had a typical \\texttt{scar} from prior skin injury. |\n","\\texttt{\\{lung\\_condition\\}} $\\rightarrow$ The lung condition is described as \\texttt{\\{lung\\_condition\\}}. \\\\\n","\\raisebox{-.25\\height}{\\includegraphics[height=1.5em]{figs/symbol/kidney.png}}\\; Egfr & \\texttt{\\{value\\}} $\\rightarrow$ The patient's estimated glomerular filtration rate (eGFR) is in the range \\texttt{\\{value\\}}. \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\label{tab:template}\n","\\end{table*}\n"],["\\begin{figure}[h!]\n","\\centering\n","\\includegraphics[width=1.0\\textwidth]{figs/data_construction.pdf}\n","\\caption{\n","Overview of data construction. Each batch consists of $N$ cells from the same donor, with associated metadata (e.g., tissue, disease, age, sex, smoking status) and gene expression profiles. Structured donor-level information is converted into natural language context, and cell-level features are enriched with the top expressed genes to support downstream reasoning.\n","}\n","\\label{fig:data_construction}\n","\\end{figure}\n"],["\\begin{table}[h!]\n"," \\centering\n"," \\small\n"," \\caption{Prompt template used for the decomposed cell-level reasoning task.}\n"," \\label{tab:single_prompt_template}\n"," \\vspace{2.5pt}\n"," \\renewcommand{\\arraystretch}{1.2}\n"," \\begin{tabular}{p{13.5cm}}\n"," \\hline\n"," You are an expert assistant specialized in cell type annotation. You will be given the gene expression profile of a single cell from a specific donor.\n"," The top expressed genes are listed in descending order. Use both gene expression and donor context to determine the correct cell type. \\textbf{You will also receive a list of candidate cell types—choose the one that best fits this cell}. Include your detailed reasoning within \\thinkbegin and \\thinkend tags, and provide your final answer within \\answerbegin and \\answerend tags. The final answer should be a single string with exactly one cell type.\n"," \\\\\n"," \\hline\n"," \\end{tabular} \n","\\end{table}\n"],["\\begin{table*}[h!]\n","\\centering\n","\\small\n","\\footnotesize\n","\\renewcommand\\tabcolsep{4.5pt}\n","\\renewcommand\\arraystretch{1.05}\n","\\caption{Comparison of model performance between batch-level and decomposed cell-level settings. $^{\\spadesuit}$ indicates instruction-tuned baselines trained without any reasoning tasks, which directly predict answers from the input without generating reasoning traces. The best and second-best results are highlighted in \\colorbox{backred!50}{red} and \\colorbox{backblue!75}{blue}, respectively.}\n","\\resizebox{0.93\\textwidth}{!}{\n","\\begin{tabular}{l|c|c|c|c|c}\n","\\toprule\n","\\header{Model} & \\header{Response Length} & \\header{Cell-level Acc} & \\header{Batch-level Acc} & \\header{Format} & \\header{Uniqueness} \\\\\n","\\midrule\n","\\rowcolor[rgb]{0.93,0.93,0.93} \\multicolumn{6}{c}{\\textit{Cell-level Prediction Setup}} \\\\\n","\\texttt{Llama3.1-8B-Instruct}$^{\\spadesuit}$ & 8.1 & 0.4707 & 0.0112 & 0.6024 & 0.5887 \\\\\n","\\texttt{Qwen2.5-7B-Instruct}$^{\\spadesuit}$ & 8.5 & 0.3890 & 0.0000 & 0.5955 & 0.5978 \\\\\n","\\rowcolor[rgb]{0.93,0.93,0.93} \\multicolumn{6}{c}{\\textit{Cell-level Reasoning Setup}} \\\\\n","\\texttt{GPT-4o-mini} & 349.5 & 0.2761 & 0.0000 & \\colorbox{backred!50}{1.0000} & 0.3706 \\\\\n","\\texttt{GPT-4o} & 494.7 & 0.4093 & 0.0000 & \\colorbox{backblue!75}{0.9900} & 0.5273 \\\\\n","\\texttt{o3-mini} & 241.1 & 0.4825 & 0.0000 & \\colorbox{backred!50}{1.0000} & 0.5663 \\\\\n","\\texttt{o1} & 152.2 & 0.4700 & 0.0000 & \\colorbox{backred!50}{1.0000} & 0.5708 \\\\\n","\\Ours & 718.5 & 0.5016 & 0.0112 & 0.8427 & 0.6612 \\\\\n","\\rowcolor[rgb]{0.93,0.93,0.93} \\multicolumn{6}{c}{\\textit{Batch-level Prediction Setup}} \\\\\n","\\texttt{Llama3.1-8B-Instruct}$^{\\spadesuit}$ & 73.8 & 0.6438 & 0.2809 & 0.9213 & 0.9198 \\\\\n","\\texttt{Qwen2.5-7B-Instruct}$^{\\spadesuit}$ & 78.7 & 0.5631 & 0.1910 & 0.9438 & 0.9370 \\\\\n","\\rowcolor[rgb]{0.93,0.93,0.93} \\multicolumn{6}{c}{\\textit{Batch-level Reasoning Setup}} \\\\\n","\\texttt{GPT-4o-mini} & 785.4 & 0.2330 & 0.0000 & \\colorbox{backred!50}{1.0000} & 0.7147 \\\\\n","\\texttt{GPT-4o} & 1010.4 & 0.4320 & 0.0000 & \\colorbox{backred!50}{1.0000} & 0.8542 \\\\\n","\\texttt{o3-mini} & 1059.9 & 0.6784 & 0.1461 & \\colorbox{backred!50}{1.0000} & \\colorbox{backred!50}{1.0000} \\\\\n","\\texttt{o1} & 834.9 & \\colorbox{backred!50}{0.7330} & \\colorbox{backblue!75}{0.1685} & \\colorbox{backred!50}{1.0000} & \\colorbox{backblue!75}{0.9975} \\\\\n","\\Ours & 1240.8 & \\colorbox{backblue!75}{0.6953} & \\colorbox{backred!50}{0.3371} & 0.9775 & 0.9759 \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\label{tab:single}\n","\\end{table*}\n"],["\\begin{table*}[h!]\n","\\centering\n","\\small\n","\\footnotesize\n","\\renewcommand\\tabcolsep{4.5pt}\n","\\renewcommand\\arraystretch{1.05}\n","\\caption{Comparison of model performance between open-ended and constrained QA settings. $^{\\spadesuit}$ indicates instruction-tuned baselines trained without any reasoning tasks, which directly predict answers from the input without generating reasoning traces. The best and second-best results are highlighted in \\colorbox{backred!50}{red} and \\colorbox{backblue!75}{blue}, respectively.}\n","\\resizebox{0.93\\textwidth}{!}{\n","\\begin{tabular}{l|c|c|c|c|c}\n","\\toprule\n","\\header{Model} & \\header{Response Length} & \\header{Cell-level Acc} & \\header{Batch-level Acc} & \\header{Format} & \\header{MedCPT Score} \\\\\n","\\midrule\n","\\rowcolor[rgb]{0.93,0.93,0.93} \\multicolumn{6}{c}{\\textit{Open-ended QA Setup}} \\\\\n","\\texttt{Llama3.1-8B-Instruct}$^{\\spadesuit}$ & 192.7 & 0.1068 & 0.0000 & 0.2822 & 0.7400 \\\\\n","\\texttt{Qwen2.5-7B-Instruct}$^{\\spadesuit}$ & 248.8 & 0.2491 & 0.0000 & 0.6804 & 0.7304 \\\\\n","\\texttt{GPT-4o-mini} & 456.7 & 0.2326 & 0.0000 & 0.9973 & 0.6680 \\\\\n","\\texttt{GPT-4o} & 401.0 & 0.3334 & 0.0000 & \\colorbox{backblue!75}{0.9991} & 0.7060 \\\\\n","\\texttt{o3-mini} & 590.7 & 0.4292 & 0.0000 & \\colorbox{backred!50}{1.0000} & 0.7411 \\\\\n","\\texttt{o1} & 436.5 & 0.4411 & 0.0000 & \\colorbox{backred!50}{1.0000} & 0.7519 \\\\\n","\\Ours & 696.9 & 0.3367 & 0.0000 & 0.9224 & 0.7229 \\\\\n","\\rowcolor[rgb]{0.93,0.93,0.93} \\multicolumn{6}{c}{\\textit{Constrained QA Setup}} \\\\\n","\\texttt{Llama3.1-8B-Instruct}$^{\\spadesuit}$ & 71.9 & 0.6411 & \\colorbox{backblue!75}{0.2969} & 0.9078 & \\colorbox{backblue!75}{0.9133} \\\\\n","\\texttt{Qwen2.5-7B-Instruct}$^{\\spadesuit}$ & 70.9 & 0.6179 & 0.2475 & 0.9443 & 0.8889 \\\\\n","\\texttt{GPT-4o-mini} & 779.9 & 0.2581 & 0.0027 & 0.9918 & 0.7178 \\\\\n","\\texttt{GPT-4o} & 1087.0 & 0.4248 & 0.0283 & 0.9817 & 0.7930 \\\\\n","\\texttt{o3-mini} & 1130.2 & 0.5804 & 0.1352 & \\colorbox{backred!50}{1.0000} & 0.8734 \\\\\n","\\texttt{o1} & 906.6 & \\colorbox{backblue!75}{0.6479} & 0.1900 & \\colorbox{backred!50}{1.0000} & 0.8888 \\\\\n","\\Ours & 1145.6 & \\colorbox{backred!50}{0.6849} & \\colorbox{backred!50}{0.3288} & 0.9826 & \\colorbox{backred!50}{0.9185} \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\label{tab:open}\n","\\end{table*}\n"],["\\begin{table}[h!]\n"," \\centering\n"," \\small\n"," \\caption{Prediction of \\texttt{Qwen2.5-7B-Instruct}.}\n"," \\label{case:qwen}\n"," \\vspace{2.5pt}\n"," \\renewcommand{\\arraystretch}{1.2}\n"," \\begin{tabular}{p{13.5cm}}\n"," \\toprule\n"," \\textbf{Reasoning:} \n"," Let's analyze the gene expression patterns and match them to the provided cell types. \\\\ \n"," \\textbullet\\ Intestine Goblet Cell: This cell type is characterized by high expression of genes like TMSB4X, which is present in Cells 1, 3, 9. \\\\\n"," \\textbullet\\ Capillary Endothelial Cell: This cell type often expresses genes like CD34, CD31, and VWF. However, these genes are not listed here, so we cannot confidently assign this cell type based on the given data. \\\\\n"," \\textbullet\\ Plasma Cell: Plasma cells typically express genes like IGKC (Immunoglobulin kappa chain), JCHAIN, and IGHA1. These genes are present in Cell 8, suggesting it could be a plasma cell. \\\\\n"," \\textbullet\\ Memory B Cell: Memory B cells might express genes like CD74, but this is not prominently featured in any of the cells. \\\\\n"," \\textbullet\\ Activated CD4-positive, Alpha-Beta T Cell: This cell type would express genes like RPL41, RPL13A, and RPS27, which are common in Cells 2, 5, 6, 7, 10. \\\\\n"," \\textbullet\\ CD4-positive, Alpha-Beta T Cell: This cell type would also express genes like RPL41, RPL13A, and RPS27, similar to the activated CD4-positive, alpha-beta T cell. \\\\\n"," \\textbullet\\ T-helper 1 Cell: This cell type would express genes like IL2RA, IFNG, and STAT1, which are not present in the given gene lists. \\\\\n"," \\textbullet\\ Germinal Center B Cell: Germinal center B cells would express genes like CD74, CD21, and CD23, which are not prominently featured in any of the cells. \\\\\n"," \\textbullet\\ IgA Plasma Cell: This cell type would express genes like IGLL1, IGHG1, and IGHM, which are not present in the given gene lists. \\\\\n"," \\textbullet\\ Vein Endothelial Cell: This cell type would express genes like CD34, CD31, and VWF, which are not present in the given gene lists. \\\\\n"," \\arrayrulecolor{gray!30}\\midrule\\arrayrulecolor{black}\n"," Based on the gene expression patterns, the most likely matches are: \\\\\n"," \\textbullet\\ \\sethlcolor{lightred}\\hl{Cell 1}: \\sethlcolor{lightred}\\hl{Intestine Goblet Cell} \\\\\n"," \\textbullet\\ \\sethlcolor{lightred}\\hl{Cell 2}: \\sethlcolor{lightred}\\hl{Activated CD4-positive, Alpha-Beta T Cell} \\\\\n"," \\textbullet\\ \\sethlcolor{lightred}\\hl{Cell 3}: \\sethlcolor{lightred}\\hl{Intestine Goblet Cell} \\\\\n"," \\textbullet\\ \\sethlcolor{lightred}\\hl{Cell 4}: \\sethlcolor{lightred}\\hl{Activated CD4-positive, Alpha-Beta T Cell} \\\\\n"," \\textbullet\\ \\sethlcolor{lightred}\\hl{Cell 5}: \\sethlcolor{lightred}\\hl{Activated CD4-positive, Alpha-Beta T Cell} \\\\\n"," \\textbullet\\ \\sethlcolor{lightblue}\\hl{Cell 6}: \\sethlcolor{lightblue}\\hl{Capillary Endothelial Cell (though not strongly supported)} \\\\\n"," \\textbullet\\ \\sethlcolor{lightred}\\hl{Cell 7}: \\sethlcolor{lightred}\\hl{Activated CD4-positive, Alpha-Beta T Cell} \\\\\n"," \\textbullet\\ \\sethlcolor{lightred}\\hl{Cell 8}: \\sethlcolor{lightred}\\hl{Plasma Cell} \\\\\n"," \\textbullet\\ \\sethlcolor{lightred}\\hl{Cell 9}: \\sethlcolor{lightred}\\hl{Intestine Goblet Cell} \\\\\n"," \\textbullet\\ \\sethlcolor{lightred}\\hl{Cell 10}: \\sethlcolor{lightred}\\hl{Activated CD4-positive, Alpha-Beta T Cell} \\\\\n"," \\midrule\n"," \\textbf{Answer:} \\sethlcolor{lightred}\\hl{intestine goblet cell} | \\sethlcolor{lightred}\\hl{activated cd4-positive, alpha-beta t cell} | \\sethlcolor{lightred}\\hl{intestine goblet cell} | \\sethlcolor{lightred}\\hl{activated cd4-positive, alpha-beta t cell} | \\sethlcolor{lightred}\\hl{activated cd4-positive, alpha-beta t cell} | \\sethlcolor{lightblue}\\hl{capillary endothelial cell} | \\sethlcolor{lightred}\\hl{activated cd4-positive, alpha-beta t cell} | \\sethlcolor{lightred}\\hl{plasma cell} | \\sethlcolor{lightred}\\hl{intestine goblet cell} | \\sethlcolor{lightred}\\hl{activated cd4-positive, alpha-beta t cell} \\\\\n"," \\bottomrule\n"," \\end{tabular} \n","\\end{table}\\begin{table}[h!]\n"],["\\end{table}\\begin{table}[h!]\n"," \\centering\n"," \\small\n"," \\caption{Prediction of \\texttt{o1}.}\n"," \\label{case:o1}\n"," \\vspace{2.5pt}\n"," \\renewcommand{\\arraystretch}{1.2}\n"," \\begin{tabular}{p{13.5cm}}\n"," \\toprule\n"," \\textbf{Reasoning:} \n"," First, note that each of the 10 cells must map to a distinct one among the 10 candidate types. Some cells are straightforward to assign based on well-known marker genes: \\\\\n"," \\textbullet\\ \\sethlcolor{lightblue}\\hl{Cell 4} expresses TFF3, SPINK4, and REG1A, which are \\sethlcolor{lightblue}\\hl{classic intestinal goblet cell markers} (TFF3 is particularly specific). \\\\\n"," \\textbullet\\ \\sethlcolor{lightblue}\\hl{Cell 6} has PLVAP and VWF, both of which are strongly associated with endothelial cells—especially PLVAP, which is a hallmark of capillary (fenestrated) endothelium, so Cell 6 is a \\sethlcolor{lightblue}\\hl{capillary endothelial cell}. \\\\\n"," \\textbullet\\ \\sethlcolor{lightblue}\\hl{Cell 7} also expresses VWF but includes CCL21, HLA-E, and other features suggestive of a different endothelial subtype; since we must use both ``capillary endothelial cell'' and ``vein endothelial cell'', assigning Cell 7 to \\sethlcolor{lightblue}\\hl{vein endothelial cell} is consistent. \\\\\n"," \\textbullet\\ \\sethlcolor{lightred}\\hl{Cell 8} has IGKC, JCHAIN, and IGHA1 at high levels, unambiguously marking an IgA-expressing plasma cell \\sethlcolor{lightred}\\hl{(IgA plasma cell)}. \\\\\n"," \\textbullet\\ \\sethlcolor{lightred}\\hl{Cell 10} highly expresses IGKC and IGHM (and lacks IGHA1), indicating another antibody-producing cell but not IgA; this fits the generic \\sethlcolor{lightred}\\hl{``plasma cell''} category.\n"," \\\\\n"," \\arrayrulecolor{gray!30}\\midrule\\arrayrulecolor{black}\n"," That leaves Cells 1, 2, 3, 5, and 9 to be mapped onto the remaining five types: CD4-positive, alpha-beta T cell; T-helper 1 cell; activated CD4-positive, alpha-beta T cell; memory B cell; germinal center B cell. \\\\\n"," \\arrayrulecolor{gray!30}\\midrule\\arrayrulecolor{black}\n"," Although many of these cells' top 50 genes are dominated by ribosomal/housekeeping transcripts, we can still deduce plausible assignments. \\sethlcolor{lightred}\\hl{Cell 5}, for instance, is rich in mitochondrial genes (MT-CO1, MT-CO2, etc.) and also contains CD52 (commonly found on lymphocytes, especially T cells). High mitochondrial activity is characteristic of an activated T cell, so Cell 5 is best labeled \\sethlcolor{lightred}\\hl{``activated CD4-positive, alpha-beta T cell''}. \n"," Cells 1, 3, and 9 are then the remaining T cells (two of them) plus one more B cell or vice versa. However, we need two B-cell categories overall—``memory B cell'' and ``germinal center B cell''—and we have not yet assigned either one to any cell besides the two plasma cells we already handled (Cells 8 and 10). Given that Cells 6 and 7 are endothelial, Cell 4 is goblet, Cell 8 and 10 are plasma lineage, and Cell 5 is activated T, we must place one B-cell type in Cell 2 and the other B-cell type in one of 1, 3, or 9. \\\\\n"," \\arrayrulecolor{gray!30}\\midrule\\arrayrulecolor{black}\n"," A close look at \\sethlcolor{lightred}\\hl{Cell 2} shows no strong expression of T-cell markers but instead a large suite of ribosomal transcripts, which can be consistent with a \\sethlcolor{lightred}\\hl{B lineage cell}. Meanwhile, among 1, 3, and 9, it is much more likely that two are T cells, and one is a B cell. The top genes in 1 and 3 both include TMSB4X at or near the top, which often appears in T cells (though it is not strictly T-specific, it is common in lymphocytes). Cell 9 is likewise heavy in housekeeping genes, and there is no direct B- or T-cell hallmark in the top transcripts. In practice, we must distribute the remaining categories such that we end up with exactly three T cells total and two non-plasma B cells total. \\\\\n"," \\arrayrulecolor{gray!30}\\midrule\\arrayrulecolor{black}\n"," A consistent assignment is: \\sethlcolor{lightred}\\hl{Cell 1} $\\rightarrow$ \\sethlcolor{lightred}\\hl{CD4-positive, alpha-beta T cell} (a ``baseline'' or less activated CD4 T cell); \\sethlcolor{lightred}\\hl{Cell 3} $\\rightarrow$ \\sethlcolor{lightred}\\hl{T-helper 1 cell} (given the context of Crohn disease, Th1 skewing is common, and Cell 3 also has notable mitochondrial transcripts that can reflect an active state); Cell 5 (as reasoned above) $\\rightarrow$ activated CD4-positive, alpha-beta T cell. \\\\\n"," \\arrayrulecolor{gray!30}\\midrule\\arrayrulecolor{black}\n"," That covers our three T-cell categories. For the two B-cell categories left (``memory B cell,'' ``germinal center B cell''), Cells 2 and 9 remain. Neither has overt immunoglobulin in the top 50, but we must use both B-cell categories somewhere. A typical assumption is that a germinal center B cell may show many ribosomal/housekeeping or proliferation genes, which could apply to Cell 9, while Cell 2 (also heavily ribosomal) could be the memory B cell. Ultimately, to satisfy the one-to-one requirement and given the absence of direct GC vs. memory markers at the top of the list, the most reasonable pairing is: \\sethlcolor{lightred}\\hl{Cell 2} $\\rightarrow$ \\sethlcolor{lightred}\\hl{memory B cell}; \\sethlcolor{lightred}\\hl{Cell 9} $\\rightarrow$ \\sethlcolor{lightred}\\hl{germinal center B cell}. \\\\ \n"," \\midrule\n"," \\textbf{Answer:} \\sethlcolor{lightred}\\hl{CD4-positive, alpha-beta T cell} | \\sethlcolor{lightred}\\hl{memory B cell} | \\sethlcolor{lightred}\\hl{T-helper 1 cell} | \\sethlcolor{lightblue}\\hl{intestine goblet cell} | \\sethlcolor{lightred}\\hl{activated CD4-positive, alpha-beta T cell} | \\sethlcolor{lightblue}\\hl{capillary endothelial cell} | \\sethlcolor{lightblue}\\hl{vein endothelial cell} | \\sethlcolor{lightblue}\\hl{IgA plasma cell} | \\sethlcolor{lightred}\\hl{germinal center B cell} | \\sethlcolor{lightred}\\hl{plasma cell} \\\\\n"," \\bottomrule\n"," \\end{tabular} \n","\\end{table}\\begin{table}[h!]\n"]],"string":"[\n [\n \"\\\\begin{figure}[h!]\\n\",\n \"\\\\centering\\n\",\n \"\\\\includegraphics[width=0.96\\\\textwidth]{figs/overview.pdf}\\n\",\n \"\\\\caption{Overview of this work. \\\\Ours achieves state-of-the-art on the \\\\Bench task.}\\n\",\n \"\\\\label{fig:overview}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{figure}[h!]\\n\",\n \"\\\\centering\\n\",\n \"\\\\includegraphics[width=\\\\textwidth]{figs/cellpuzzles.pdf}\\n\",\n \"\\\\caption{\\n\",\n \"\\\\Bench formulates cell type annotation as a batch-level reasoning task that integrates gene expression and contextual metadata, inspired by how experts annotate cells in practice.\\n\",\n \"}\\n\",\n \"\\\\label{fig:cellpuzzles}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{figure}[h!]\\n\",\n \"\\\\centering\\n\",\n \"\\\\includegraphics[width=1.0\\\\textwidth]{figs/data_dis.pdf}\\n\",\n \"\\\\caption{\\n\",\n \"Data distribution of \\\\Bench.\\n\",\n \"(A) Age group distribution with male-female breakdown.\\n\",\n \"(B) Sample distribution across a wide range of disease conditions.\\n\",\n \"(C) Number of unique cell types observed across different tissues. \\n\",\n \"For clarity, only the top 20 entries are shown in (B) and (C).\\n\",\n \"}\\n\",\n \"\\\\label{fig:data_distribution}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table}[h!]\\n\",\n \" \\\\centering\\n\",\n \" \\\\small\\n\",\n \" \\\\caption{Prompt template guiding structured reasoning and prediction in \\\\Ours.}\\n\",\n \" \\\\label{tab:prompt_template}\\n\",\n \" \\\\vspace{2.5pt}\\n\",\n \" \\\\renewcommand{\\\\arraystretch}{1.2}\\n\",\n \" \\\\begin{tabular}{p{13.5cm}}\\n\",\n \" \\\\hline\\n\",\n \" You are an expert assistant specialized in cell type annotation. You will be given a batch of $N$ cells from the same donor, where each cell represents a unique cell type.\\n\",\n \" For each cell, the top-expressed genes are provided in descending order of expression.\\n\",\n \" Using both the gene expression data and donor information, determine the correct cell type for each cell.\\n\",\n \" You will also receive a list of $N$ candidate cell types, and each candidate must be assigned to exactly one cell. Ensure that you consider all cells and candidate types together, rather than annotating each cell individually.\\n\",\n \" Include your detailed reasoning within \\\\thinkbegin and \\\\thinkend tags, and provide your final answer within \\\\answerbegin and \\\\answerend tags.\\n\",\n \" The final answer should be a single string listing the assigned cell types in order, separated by ` | '.\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\hline\\n\",\n \" \\\\end{tabular} \\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table*}[h!]\\n\",\n \"\\\\centering\\n\",\n \"\\\\small\\n\",\n \"\\\\footnotesize\\n\",\n \"\\\\renewcommand\\\\tabcolsep{4.5pt}\\n\",\n \"\\\\renewcommand\\\\arraystretch{1.05}\\n\",\n \"\\\\caption{Evaluation of model performance on the \\\\Bench benchmark. The best and second-best results are highlighted in \\\\colorbox{backred!50}{red} and \\\\colorbox{backblue!75}{blue}, respectively.}\\n\",\n \"\\\\resizebox{0.93\\\\textwidth}{!}{\\n\",\n \"\\\\begin{tabular}{l|c|c|c|c|c}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\header{Model} & \\\\header{Response Length} & \\\\header{Cell-level Acc} & \\\\header{Batch-level Acc} & \\\\header{Format} & \\\\header{Uniqueness} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\rowcolor[rgb]{0.93,0.93,0.93} \\\\multicolumn{6}{c}{\\\\textit{Zero-shot performance (w/ reasoning)}} \\\\\\\\\\n\",\n \"\\\\texttt{Llama3.1-8B-Instruct} & 2264.9 & 0.0050 & 0.0000 & 0.0393 & 0.0190 \\\\\\\\\\n\",\n \"\\\\texttt{Qwen2.5-7B-Instruct} & 879.8 & 0.1353 & 0.0000 & 0.9425 & 0.4528 \\\\\\\\\\n\",\n \"\\\\texttt{Llama3.3-70B-Instruct} & 1006.8 & 0.2573\\t& 0.0064 & 0.9973 & 0.8321 \\\\\\\\\\n\",\n \"\\\\texttt{GPT-4o-mini} & 779.9 & 0.2581 & 0.0027 & 0.9918 & 0.6565 \\\\\\\\\\n\",\n \"\\\\texttt{GPT-4o} & 1087.0 & 0.4248 & 0.0283 & 0.9817 & 0.8179 \\\\\\\\\\n\",\n \"\\\\texttt{o3-mini} & 1130.2 & 0.5804 & 0.1352 & \\\\colorbox{backred!50}{1.0000} & \\\\colorbox{backred!50}{1.0000} \\\\\\\\\\n\",\n \"\\\\texttt{o1} & 906.6 & \\\\colorbox{backblue!75}{0.6479} & 0.1900 & \\\\colorbox{backred!50}{1.0000} & \\\\colorbox{backblue!75}{0.9997} \\\\\\\\\\n\",\n \"\\\\rowcolor[rgb]{0.93,0.93,0.93} \\\\multicolumn{6}{c}{\\\\textit{Instruction-tuning Performance (w/o reasoning)}} \\\\\\\\\\n\",\n \"\\\\texttt{Llama3.1-8B-Instruct} & 71.9 & 0.6411 & \\\\colorbox{backblue!75}{0.2969} & 0.9078 & 0.9071 \\\\\\\\\\n\",\n \"\\\\texttt{Qwen2.5-7B-Instruct} & 70.9 & 0.6179 & 0.2475 & 0.9443 & 0.9404 \\\\\\\\\\n\",\n \"\\\\rowcolor[rgb]{0.93,0.93,0.93} \\\\multicolumn{6}{c}{\\\\textit{Ours (w/ reasoning)}} \\\\\\\\\\n\",\n \"\\\\Ours & 1145.6 & \\\\colorbox{backred!50}{0.6849} & \\\\colorbox{backred!50}{0.3288} & 0.9826 & 0.9824 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\label{tab:cell_annotation_results}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{figure}[h!]\\n\",\n \"\\\\centering\\n\",\n \"\\\\includegraphics[width=1.0\\\\textwidth]{figs/acc_dis.pdf}\\n\",\n \"\\\\caption{\\n\",\n \"Fine-grained analysis of model performance.\\n\",\n \"(A) Cell-level and batch-level accuracy across varying numbers of cells per batch. \\n\",\n \"(B) Distribution of per-batch cell-level accuracy across models.\\n\",\n \"}\\n\",\n \"\\\\label{fig:acc_distribution}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table*}[h!]\\n\",\n \"\\\\centering\\n\",\n \"\\\\footnotesize\\n\",\n \"\\\\renewcommand\\\\tabcolsep{4pt}\\n\",\n \"\\\\renewcommand\\\\arraystretch{1.05}\\n\",\n \"\\\\caption{Evaluation of model performance on unseen disease datasets. The best and second-best results are highlighted in \\\\colorbox{backred!50}{red} and \\\\colorbox{backblue!75}{blue}, respectively.}\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \"\\\\begin{tabular}{l|cc|cc|cc|cc|cc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\header{Model} & \\\\multicolumn{2}{c|}{\\\\textit{Breast Cancer}} & \\\\multicolumn{2}{c|}{\\\\textit{Melanoma}} & \\\\multicolumn{2}{c|}{\\\\textit{SLE}} & \\\\multicolumn{2}{c|}{\\\\textit{Colorectal Cancer}} & \\\\multicolumn{2}{c}{Average} \\\\\\\\\\n\",\n \" & Cell & Batch & Cell & Batch & Cell & Batch & Cell & Batch & Cell & Batch \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\rowcolor[rgb]{0.93,0.93,0.93} \\\\multicolumn{11}{c}{\\\\textit{Zero-shot (w/ reasoning)}} \\\\\\\\\\n\",\n \"\\\\texttt{Llama3.1-8B-Instruct} & 0.0075 & 0.0000 & 0.0179 & 0.0000 & 0.0035 & 0.0000 & 0.0147 & 0.0000 & 0.0109 & 0.0000 \\\\\\\\\\n\",\n \"\\\\texttt{Qwen2.5-7B-Instruct} & 0.2382 & 0.0000 & 0.2679 & 0.0000 & 0.2400 & 0.0000 & 0.1996 & 0.0000 & 0.2364 & 0.0000 \\\\\\\\\\n\",\n \"\\\\texttt{Llama3.3-70B-Instruct} & 0.4400 & 0.0000 & 0.5337 & 0.0159 & 0.3165 & 0.0100 & 0.4218 & 0.0000 & 0.4280 & 0.0065 \\\\\\\\\\n\",\n \"\\\\texttt{GPT-4o-mini} & 0.4171 & 0.0000 & 0.5198 & 0.0000 & 0.4129 & 0.0000 & 0.4549 & 0.0000 & 0.4512 & 0.0000 \\\\\\\\\\n\",\n \"\\\\texttt{GPT-4o} & 0.5847 & 0.0560 & 0.7619 & 0.1587 & 0.6576 & 0.1000 & 0.5715 & 0.0239 & 0.6439 & 0.0847 \\\\\\\\\\n\",\n \"\\\\texttt{o3-mini} & 0.7806 & 0.2960& 0.7341 & 0.1111 & 0.7706 & 0.3200 & 0.7208 & 0.1713 & 0.7515 & 0.2246 \\\\\\\\\\n\",\n \"\\\\texttt{o1} & \\\\colorbox{backred!50}{0.8136} & \\\\colorbox{backblue!75}{0.3200} & 0.7738 & 0.1270 & \\\\colorbox{backblue!75}{0.8200} & \\\\colorbox{backblue!75}{0.3700} & \\\\colorbox{backred!50}{0.7845} & \\\\colorbox{backred!50}{0.3108} & \\\\colorbox{backblue!75}{0.7980} & 0.2819 \\\\\\\\\\n\",\n \"\\\\rowcolor[rgb]{0.93,0.93,0.93} \\\\multicolumn{11}{c}{\\\\textit{Instruction-tuned (w/o reasoning)}} \\\\\\\\\\n\",\n \"\\\\texttt{Llama3.1-8B-Instruct} & 0.7872 & 0.2880 & \\\\colorbox{backblue!75}{0.7837} & \\\\colorbox{backblue!75}{0.3333} & 0.7176 & 0.2500 & 0.7208 & 0.2590 & 0.7523 & \\\\colorbox{backblue!75}{0.2826} \\\\\\\\\\n\",\n \"\\\\texttt{Qwen2.5-7B-Instruct} & 0.7250 & 0.1600 & 0.7282 & 0.1746 & 0.5553 & 0.0300 & 0.5816 & 0.1036 & 0.6475 & 0.1170 \\\\\\\\\\n\",\n \"\\\\rowcolor[rgb]{0.93,0.93,0.93} \\\\multicolumn{11}{c}{\\\\textit{Ours (w/ reasoning)}} \\\\\\\\\\n\",\n \"\\\\Ours & \\\\colorbox{backblue!75}{0.8089} & \\\\colorbox{backred!50}{0.3360} & \\\\colorbox{backred!50}{0.8591} & \\\\colorbox{backred!50}{0.5397} & \\\\colorbox{backred!50}{0.8235} & \\\\colorbox{backred!50}{0.3800} & \\\\colorbox{backblue!75}{0.7820} & \\\\colorbox{backblue!75}{0.3028} & \\\\colorbox{backred!50}{0.8184} & \\\\colorbox{backred!50}{0.3896} \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\label{tab:generalization}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{figure}[h!]\\n\",\n \"\\\\centering\\n\",\n \"\\\\includegraphics[width=1.0\\\\textwidth]{figs/dynamics.pdf}\\n\",\n \"\\\\caption{\\n\",\n \"Comparing training and inference dynamics across different configurations.\\n\",\n \"}\\n\",\n \"\\\\label{fig:dynamics}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{figure}[h!]\\n\",\n \"\\\\centering\\n\",\n \"\\\\includegraphics[width=1.0\\\\textwidth]{figs/reasoning.pdf}\\n\",\n \"\\\\caption{\\n\",\n \"Analysis of model reasoning behavior. \\n\",\n \"(A) Step-by-step reasoning traces generated by each model for the same task. \\n\",\n \"(B) Expert evaluation of reasoning quality comparing \\\\Ours with baseline models.\\n\",\n \"(C) Proportion of traces exhibiting self-reflection and curriculum reasoning behaviors.\\n\",\n \"}\\n\",\n \"\\\\label{fig:reasoning_analysis}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table*}[h]\\n\",\n \"\\\\centering\\n\",\n \"\\\\footnotesize\\n\",\n \"\\\\renewcommand\\\\tabcolsep{9pt}\\n\",\n \"\\\\renewcommand\\\\arraystretch{1.05}\\n\",\n \"\\\\caption{\\n\",\n \"Raw scRNA-seq data resources involved in our paper.}\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \"\\\\begin{tabular}{p{9cm} p{7.8cm}}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Data Source Name} & \\\\textbf{URL} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"Single-cell Human Microglia~\\\\cite{olah2020single} & \\\\small \\\\url{https://cellxgene.cziscience.com/collections/fcb3d1c1-03d2-41ac-8229-458e072b7a1c} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"Single-Cell RNA-Seq Analysis Reveals Cell Subsets And Gene Signatures Associated With Rheumatoid Arthritis Disease Activity~\\\\cite{binvignat2024single} & \\\\small \\\\url{https://cellxgene.cziscience.com/collections/e1a9ca56-f2ee-435d-980a-4f49ab7a952b} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"The Single-Cell Lung Cancer Atlas (LuCA)~\\\\cite{salcher2022high} & \\n\",\n \"\\\\small \\\\url{https://cellxgene.cziscience.com/collections/edb893ee-4066-4128-9aec-5eb2b03f8287} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"Cells Of The Human Intestinal Tract Mapped Across Space And Time~\\\\cite{elmentaite2021cells} & \\\\small \\\\url{https://cellxgene.cziscience.com/collections/e33ffcd3-7cbf-4b8c-b0f4-85587ad5019a} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"Persistent T Cell Unresponsiveness Associated With Chronic Visceral Leishmaniasis In HIV-Coinfected Patients~\\\\cite{de2024persistent} & \\\\small \\\\url{https://cellxgene.cziscience.com/collections/126afc71-47fb-4e9d-8aaf-9d9f61e0ac77} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"Multiomics Single-Cell Analysis Of Human Pancreatic Islets Reveals Novel Cellular States In Health And Type 1 Diabetes~\\\\cite{fasolino2022single} & \\\\small \\\\url{https://cellxgene.cziscience.com/collections/51544e44-293b-4c2b-8c26-560678423380} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"The Integrated Human Lung Cell Atlas~\\\\cite{sikkema2023integrated} & \\\\small \\\\url{https://cellxgene.cziscience.com/collections/6f6d381a-7701-4781-935c-db10d30de293} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"Single-Cell Transcriptomics Of The Human Retinal Pigment Epithelium And Choroid In Health And Macular Degeneration~\\\\cite{voigt2019single} & \\\\small \\\\url{https://cellxgene.cziscience.com/collections/f8057c47-fcd8-4fcf-88b0-e2f930080f6e} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"Single-Cell RNA-Seq Of The Adult Human Kidney~\\\\cite{lake2023atlas} & \\\\small \\\\url{https://cellxgene.cziscience.com/collections/bcb61471-2a44-4d00-a0af-ff085512674c} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"MSK SPECTRUM – Ovarian Cancer Mutational Processes Drive Site-Specific Immune Evasion~\\\\cite{vazquez2022ovarian} & \\\\small \\\\url{https://cellxgene.cziscience.com/collections/4796c91c-9d8f-4692-be43-347b1727f9d8} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"HTAN-CHOP - Single-Cell Multiomics Reveals Increased Plasticity, Resistant Populations, And Stem-Cell–Like Blasts In KMT2A-Rearranged Leukemia~\\\\cite{chen2022single} & \\\\small \\\\url{https://cellxgene.cziscience.com/collections/10ec9198-584e-4a7e-8a24-4a332915a4ef} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"Single-Cell Atlas Of Common Variable Immunodeficiency Shows Germinal Center-Associated Epigenetic Dysregulation In B-Cell Responses~\\\\cite{rodriguez2022single} & \\\\small \\\\url{https://cellxgene.cziscience.com/collections/bf325905-5e8e-42e3-933d-9a9053e9af80} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"Dissecting Novel Myeloid-Derived Cell States Through Single-Cell RNA-Seq And Its Impact On Clinical Outcome Across Tumor Types~\\\\cite{guimaraes2024single} & \\\\small \\\\url{https://cellxgene.cziscience.com/collections/3f7c572c-cd73-4b51-a313-207c7f20f188} \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\label{tab:data_source}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[h!]\\n\",\n \"\\\\centering\\n\",\n \"\\\\footnotesize\\n\",\n \"\\\\renewcommand\\\\tabcolsep{4pt}\\n\",\n \"\\\\renewcommand\\\\arraystretch{1.05}\\n\",\n \"\\\\caption{Number of samples per disease in the dataset.}\\n\",\n \"\\\\resizebox{0.9\\\\textwidth}{!}{\\n\",\n \"\\\\begin{tabular}{l r @{\\\\hskip 1.5cm} l r}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Disease} & \\\\textbf{\\\\#Samples} & \\\\textbf{Disease} & \\\\textbf{\\\\#Samples} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"Lung Adenocarcinoma & 1292 & Crohn Disease & 215 \\\\\\\\\\n\",\n \"Chronic Obstructive Pulmonary Disease & 1194 & Lung Large Cell Carcinoma & 211 \\\\\\\\\\n\",\n \"Squamous Cell Lung Carcinoma & 946 & Non-Specific Interstitial Pneumonia & 172 \\\\\\\\\\n\",\n \"Normal & 941 & Chronic Rhinitis & 158 \\\\\\\\\\n\",\n \"Interstitial Lung Disease & 777 & Hypersensitivity Pneumonitis & 126 \\\\\\\\\\n\",\n \"Pulmonary Fibrosis & 669 & Lymphangioleiomyomatosis & 109 \\\\\\\\\\n\",\n \"Non-Small Cell Lung Carcinoma & 632 & Pleomorphic Carcinoma & 87 \\\\\\\\\\n\",\n \"B-Cell Acute Lymphoblastic Leukemia & 74 & Pulmonary Sarcoidosis & 58 \\\\\\\\\\n\",\n \"Pneumonia & 48 & Acute Myeloid Leukemia & 45 \\\\\\\\\\n\",\n \"Common Variable Immunodeficiency & 45 & Type 1 Diabetes Mellitus & 35 \\\\\\\\\\n\",\n \"Acute Promyelocytic Leukemia & 28 & Rheumatoid Arthritis & 23 \\\\\\\\\\n\",\n \"Acute Kidney Failure & 20 & Malignant Ovarian Serous Tumor & 18 \\\\\\\\\\n\",\n \"Chronic Kidney Disease & 18 & Age Related Macular Degeneration 7 & 13 \\\\\\\\\\n\",\n \"Basal Laminar Drusen & 12 & Cystic Fibrosis & 11 \\\\\\\\\\n\",\n \"Hiv Infectious Disease & 11 & Alzheimer Disease & 8 \\\\\\\\\\n\",\n \"Cataract & 7 & Temporal Lobe Epilepsy & 4 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\label{tab:disease_stats}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table}[ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\footnotesize\\n\",\n \"\\\\renewcommand\\\\tabcolsep{4pt}\\n\",\n \"\\\\renewcommand\\\\arraystretch{1.05}\\n\",\n \"\\\\caption{Number of unique cell types per tissue in the dataset.}\\n\",\n \"\\\\vskip 0.06in\\n\",\n \"\\\\resizebox{0.75\\\\textwidth}{!}{\\n\",\n \"\\\\begin{tabular}{l r @{\\\\hskip 1.5cm} l r}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Tissue} & \\\\textbf{\\\\#Cell Types} & \\\\textbf{Tissue} & \\\\textbf{\\\\#Cell Types} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"Ileum & 71 & Adrenal Tissue & 18 \\\\\\\\\\n\",\n \"Lung & 56 & Rectum & 18 \\\\\\\\\\n\",\n \"Blood & 48 & Ascending Colon & 15 \\\\\\\\\\n\",\n \"Duodenum & 48 & Mesenteric Lymph Node & 11 \\\\\\\\\\n\",\n \"Colon & 40 & Peripheral Region Of Retina & 10 \\\\\\\\\\n\",\n \"Bone Marrow & 38 & Fovea Centralis & 10 \\\\\\\\\\n\",\n \"Nose & 36 & Right Ovary & 9 \\\\\\\\\\n\",\n \"Sigmoid Colon & 35 & Peritoneum & 9 \\\\\\\\\\n\",\n \"Vermiform Appendix & 33 & Islet Of Langerhans & 9 \\\\\\\\\\n\",\n \"Transverse Colon & 31 & Omentum & 9 \\\\\\\\\\n\",\n \"Descending Colon & 31 & Ascitic Fluid & 9 \\\\\\\\\\n\",\n \"Lymph Node & 30 & Left Ovary & 9 \\\\\\\\\\n\",\n \"Brain & 27 & Adnexa Of Uterus & 9 \\\\\\\\\\n\",\n \"Caecum & 27 & Diaphragm & 8 \\\\\\\\\\n\",\n \"Jejunum & 27 & Small Intestine & 8 \\\\\\\\\\n\",\n \"Kidney & 26 & Abdominal Wall & 8 \\\\\\\\\\n\",\n \"Pleural Effusion & 23 & Temporal Cortex & 6 \\\\\\\\\\n\",\n \"Liver & 21 & Dorsolateral Prefrontal Cortex & 5 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\label{tab:tissue_stats}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\footnotesize\\n\",\n \"\\\\renewcommand\\\\tabcolsep{4pt}\\n\",\n \"\\\\renewcommand\\\\arraystretch{1.05}\\n\",\n \"\\\\caption{Frequency of cell types in the dataset (Part I).}\\n\",\n \"\\\\vskip 0.06in\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \"\\\\begin{tabular}{p{6cm} r p{6cm} r}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Cell Type} & \\\\textbf{\\\\#Instances} & \\\\textbf{Cell Type} & \\\\textbf{\\\\#Instances} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"CD4-Positive, Alpha-Beta T Cell & 4428 & Fibroblast & 72 \\\\\\\\\\n\",\n \"CD8-Positive, Alpha-Beta T Cell & 4276 & Mucosal Invariant T Cell & 70 \\\\\\\\\\n\",\n \"Classical Monocyte & 4167 & Precursor B Cell & 70 \\\\\\\\\\n\",\n \"Natural Killer Cell & 4097 & Glial Cell & 68 \\\\\\\\\\n\",\n \"Alveolar Macrophage & 4051 & Mature B Cell & 68 \\\\\\\\\\n\",\n \"Cd1C-Positive Myeloid Dendritic Cell & 3965 & Pro-B Cell & 68 \\\\\\\\\\n\",\n \"B Cell & 3495 & Stem Cell & 67 \\\\\\\\\\n\",\n \"Pulmonary Alveolar Type 2 Cell & 3395 & Early Pro-B Cell & 66 \\\\\\\\\\n\",\n \"Mast Cell & 3062 & Stromal Cell Of Lamina Propria Of Small Intestine & 63 \\\\\\\\\\n\",\n \"Plasma Cell & 2939 & Common Lymphoid Progenitor & 59 \\\\\\\\\\n\",\n \"Vein Endothelial Cell & 2659 & Immature B Cell & 58 \\\\\\\\\\n\",\n \"Capillary Endothelial Cell & 2328 & Common Dendritic Progenitor & 57 \\\\\\\\\\n\",\n \"Non-Classical Monocyte & 2120 & Megakaryocyte-Erythroid Progenitor Cell & 56 \\\\\\\\\\n\",\n \"Macrophage & 2070 & Lung Neuroendocrine Cell & 54 \\\\\\\\\\n\",\n \"Pulmonary Alveolar Type 1 Cell & 1895 & Granulocyte Monocyte Progenitor Cell & 50 \\\\\\\\\\n\",\n \"Ciliated Columnar Cell Of Tracheobronchial Tree & 1734 & Reticular Cell & 48 \\\\\\\\\\n\",\n \"Elicited Macrophage & 1726 & Effector Memory Cd4-Positive, Alpha-Beta T Cell & 48 \\\\\\\\\\n\",\n \"Regulatory T Cell & 1490 & Endothelial Cell Of Artery & 45 \\\\\\\\\\n\",\n \"Malignant Cell & 1363 & Kidney Collecting Duct Principal Cell & 42 \\\\\\\\\\n\",\n \"Endothelial Cell Of Lymphatic Vessel & 1362 & Effector Memory Cd8-Positive, Alpha-Beta T Cell & 42 \\\\\\\\\\n\",\n \"Epithelial Cell Of Lung & 1272 & Epithelial Cell Of Proximal Tubule & 42 \\\\\\\\\\n\",\n \"Multi-Ciliated Epithelial Cell & 1124 & Myeloid Dendritic Cell & 42 \\\\\\\\\\n\",\n \"Bronchus Fibroblast Of Lung & 1123 & Kidney Loop Of Henle Thick Ascending Limb Epithelial Cell & 41 \\\\\\\\\\n\",\n \"Pulmonary Artery Endothelial Cell & 1086 & Central Memory Cd4-Positive, Alpha-Beta T Cell & 41 \\\\\\\\\\n\",\n \"Plasmacytoid Dendritic Cell & 948 & Epithelial Cell & 40 \\\\\\\\\\n\",\n \"Fibroblast Of Lung & 909 & Ionocyte & 39 \\\\\\\\\\n\",\n \"Respiratory Basal Cell & 897 & Effector Memory Cd8-Positive, Alpha-Beta T Cell, Terminally Differentiated & 39 \\\\\\\\\\n\",\n \"Tracheobronchial Smooth Muscle Cell & 857 & Late Promyelocyte & 39 \\\\\\\\\\n\",\n \"Alveolar Type 1 Fibroblast Cell & 844 & Kidney Interstitial Cell & 39 \\\\\\\\\\n\",\n \"Myeloid Cell & 806 & T-Helper 17 Cell & 38 \\\\\\\\\\n\",\n \"Alveolar Adventitial Fibroblast & 722 & Early Promyelocyte & 38 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\label{tab:celltype_stats_part1}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[ht]\\n\",\n \"\\\\centering\\n\",\n \"\\\\footnotesize\\n\",\n \"\\\\renewcommand\\\\tabcolsep{4pt}\\n\",\n \"\\\\renewcommand\\\\arraystretch{1.05}\\n\",\n \"\\\\caption{Frequency of cell types in the dataset (Part II).}\\n\",\n \"\\\\vskip 0.06in\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \"\\\\begin{tabular}{p{6cm} r p{6cm} r}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Cell Type} & \\\\textbf{\\\\#Instances} & \\\\textbf{Cell Type} & \\\\textbf{\\\\#Instances} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"Epithelial Cell Of Lower Respiratory Tract & 702 & Germinal Center B Cell & 38 \\\\\\\\\\n\",\n \"Conventional Dendritic Cell & 701 & Class Switched Memory B Cell & 37 \\\\\\\\\\n\",\n \"Club Cell & 685 & Kidney Connecting Tubule Epithelial Cell & 36 \\\\\\\\\\n\",\n \"Dendritic Cell & 681 & Hematopoietic Stem Cell & 36 \\\\\\\\\\n\",\n \"Lung Pericyte & 569 & Unswitched Memory B Cell & 36 \\\\\\\\\\n\",\n \"Smooth Muscle Cell & 464 & Mononuclear Phagocyte & 36 \\\\\\\\\\n\",\n \"T Cell & 463 & Kidney Collecting Duct Intercalated Cell & 34 \\\\\\\\\\n\",\n \"Nasal Mucosa Goblet Cell & 447 & Kidney Distal Convoluted Tubule Epithelial Cell & 33 \\\\\\\\\\n\",\n \"Pericyte & 439 & Lymphoid Lineage Restricted Progenitor Cell & 33 \\\\\\\\\\n\",\n \"Lung Macrophage & 422 & Plasmablast & 33 \\\\\\\\\\n\",\n \"Mesothelial Cell & 412 & Inflammatory Macrophage & 32 \\\\\\\\\\n\",\n \"Neutrophil & 397 & Parietal Epithelial Cell & 31 \\\\\\\\\\n\",\n \"Stromal Cell & 264 & Myelocyte & 31 \\\\\\\\\\n\",\n \"Naive B Cell & 242 & Kidney Loop Of Henle Thin Ascending Limb Epithelial Cell & 30 \\\\\\\\\\n\",\n \"Memory B Cell & 235 & Bronchial Goblet Cell & 30 \\\\\\\\\\n\",\n \"Myofibroblast Cell & 222 & Tracheobronchial Serous Cell & 30 \\\\\\\\\\n\",\n \"Cd8-Positive, Alpha-Beta Memory T Cell & 216 & T-Helper 1 Cell & 30 \\\\\\\\\\n\",\n \"Endothelial Cell & 210 & Melanocyte & 29 \\\\\\\\\\n\",\n \"Monocyte & 191 & Schwann Cell & 29 \\\\\\\\\\n\",\n \"Gamma-Delta T Cell & 174 & Retinal Pigment Epithelial Cell & 28 \\\\\\\\\\n\",\n \"Naive Thymus-Derived Cd4-Positive, Alpha-Beta T Cell & 140 & Kidney Loop Of Henle Thin Descending Limb Epithelial Cell & 26 \\\\\\\\\\n\",\n \"Activated Cd8-Positive, Alpha-Beta T Cell & 126 & Erythroid Progenitor Cell & 26 \\\\\\\\\\n\",\n \"Iga Plasma Cell & 125 & Intestinal Tuft Cell & 25 \\\\\\\\\\n\",\n \"Pancreatic A Cell & 123 & Central Memory Cd8-Positive, Alpha-Beta T Cell & 25 \\\\\\\\\\n\",\n \"Pancreatic Ductal Cell & 123 & Kidney Interstitial Alternatively Activated Macrophage & 25 \\\\\\\\\\n\",\n \"Pancreatic D Cell & 122 & Basophil Mast Progenitor Cell & 24 \\\\\\\\\\n\",\n \"Pancreatic Stellate Cell & 122 & Serous Secreting Cell & 23 \\\\\\\\\\n\",\n \"Pp Cell & 122 & Cd4-Positive, Alpha-Beta Cytotoxic T Cell & 22 \\\\\\\\\\n\",\n \"Pancreatic Acinar Cell & 122 & Colon Epithelial Cell & 22 \\\\\\\\\\n\",\n \"Naive Thymus-Derived Cd8-Positive, Alpha-Beta T Cell & 121 & Cytotoxic T Cell & 21 \\\\\\\\\\n\",\n \"Type B Pancreatic Cell & 119 & Acinar Cell & 21 \\\\\\\\\\n\",\n \"Mature Nk T Cell & 119 & Type Ec Enteroendocrine Cell & 20 \\\\\\\\\\n\",\n \"Cd16-Positive, Cd56-Dim Natural Killer Cell, Human & 118 & Erythroid Lineage Cell & 19 \\\\\\\\\\n\",\n \"Cd4-Positive, Alpha-Beta Memory T Cell & 117 & Fallopian Tube Secretory Epithelial Cell & 18 \\\\\\\\\\n\",\n \"Activated Cd4-Positive, Alpha-Beta T Cell & 116 & M Cell Of Gut & 18 \\\\\\\\\\n\",\n \"Transit Amplifying Cell & 104 & Mucus Secreting Cell & 15 \\\\\\\\\\n\",\n \"Intestine Goblet Cell & 102 & Megakaryocyte Progenitor Cell & 15 \\\\\\\\\\n\",\n \"Brush Cell Of Tracheobronchial Tree & 96 & Mesenchymal Lymphangioblast & 13 \\\\\\\\\\n\",\n \"Igg Plasma Cell & 94 & Non-Terminally Differentiated Cell & 13 \\\\\\\\\\n\",\n \"Cd16-Negative, Cd56-Bright Natural Killer Cell, Human & 86 & Podocyte & 13 \\\\\\\\\\n\",\n \"Pancreatic Epsilon Cell & 85 & Cd14-Positive Monocyte & 12 \\\\\\\\\\n\",\n \"Epithelial Cell Of Alveolus Of Lung & 84 & Plasmacytoid Dendritic Cell, Human & 12 \\\\\\\\\\n\",\n \"Respiratory Hillock Cell & 83 & Enteroendocrine Cell & 12 \\\\\\\\\\n\",\n \"Enterocyte Of Colon & 79 & Microglial Cell & 12 \\\\\\\\\\n\",\n \"T Follicular Helper Cell & 75 & Group 3 Innate Lymphoid Cell & 11 \\\\\\\\\\n\",\n \"Hematopoietic Multipotent Progenitor Cell & 75 & Erythrocyte & 10 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\label{tab:celltype_stats_part2}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table*}[h]\\n\",\n \"\\\\centering\\n\",\n \"\\\\footnotesize\\n\",\n \"\\\\renewcommand\\\\tabcolsep{4pt}\\n\",\n \"\\\\renewcommand\\\\arraystretch{1.05}\\n\",\n \"\\\\caption{\\n\",\n \"Templates for converting structured donor metadata into context descriptions.}\\n\",\n \"\\\\resizebox{0.9\\\\textwidth}{!}{\\n\",\n \"\\\\begin{tabular}{l p{10.5cm}}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Feature} & \\\\textbf{Template} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\raisebox{-.25\\\\height}{\\\\includegraphics[height=1.5em]{figs/symbol/sex.png}}\\\\; Sex &\\n\",\n \"\\\\texttt{\\\\{sex\\\\}} $\\\\rightarrow$ The cell is from a \\\\texttt{\\\\{sex\\\\}} individual. \\\\\\\\\\n\",\n \"\\\\raisebox{-.25\\\\height}{\\\\includegraphics[height=1.5em]{figs/symbol/growth.png}}\\\\; Development Stage & The individual is at the \\\\{\\\\texttt{development\\\\_stage}\\\\}. \\\\\\\\\\n\",\n \"\\\\raisebox{-.25\\\\height}{\\\\includegraphics[height=1.5em]{figs/symbol/cross-country.png}}\\\\; Ethnicity & The donor has a \\\\{\\\\texttt{self\\\\_reported\\\\_ethnicity}\\\\} background. \\\\\\\\\\n\",\n \"\\\\raisebox{-.25\\\\height}{\\\\includegraphics[height=1.5em]{figs/symbol/muscle-cross-section-healthy.png}}\\\\; Tissue & The cell originates from the \\\\{\\\\texttt{tissue}\\\\}. \\\\\\\\\\n\",\n \"\\\\raisebox{-.25\\\\height}{\\\\includegraphics[height=1.5em]{figs/symbol/gene.png}}\\\\; Top Genes & \\\\{\\\\texttt{genes}\\\\} → Top expressed genes are: \\\\{\\\\texttt{genes}\\\\}. \\\\\\\\\\n\",\n \"\\\\raisebox{-.25\\\\height}{\\\\includegraphics[height=1.5em]{figs/symbol/prevention.png}}\\\\; Disease & \\n\",\n \"\\\\texttt{normal} → The patient is healthy with \\\\texttt{no diagnosed disease}. |\\n\",\n \"\\\\texttt{other} → The patient has been diagnosed with \\\\{\\\\texttt{disease}\\\\}. \\\\\\\\\\n\",\n \"\\\\raisebox{-.25\\\\height}{\\\\includegraphics[height=1.5em]{figs/symbol/cigarrete.png}}\\\\; Smoking Status & \\n\",\n \"\\\\texttt{active} → The patient is an \\\\texttt{active} smoker. |\\n\",\n \"\\\\texttt{former} → The patient is a \\\\texttt{former} smoker. |\\n\",\n \"\\\\texttt{hist of marijuana use} → The patient has a \\\\texttt{history of marijuana use}; \\n\",\n \"\\\\texttt{never} → The patient has \\\\texttt{never} smoked. \\\\\\\\\\n\",\n \"\\\\raisebox{-.25\\\\height}{\\\\includegraphics[height=1.5em]{figs/symbol/microscope.png}}\\\\; Tumor Stage & \\n\",\n \"\\\\texttt{non-cancer} $\\\\rightarrow$ There is \\\\texttt{no cancer} present. |\\n\",\n \"\\\\texttt{early} $\\\\rightarrow$ The patient has an \\\\texttt{early}-stage tumor. |\\n\",\n \"\\\\texttt{advanced} $\\\\rightarrow$ The patient has an \\\\texttt{advanced}-stage tumor. \\\\\\\\\\n\",\n \"\\\\raisebox{-.25\\\\height}{\\\\includegraphics[height=1.5em]{figs/symbol/syringe.png}}\\\\; Sample Type & \\n\",\n \"\\\\texttt{M3} $\\\\rightarrow$ The sample was collected at \\\\texttt{month 3} post-treatment. |\\n\",\n \"\\\\texttt{M6} $\\\\rightarrow$ The sample was collected at \\\\texttt{month 6} post-treatment. |\\n\",\n \"\\\\texttt{UV} $\\\\rightarrow$ The sample was exposed to \\\\texttt{ultraviolet (UV)} treatment. |\\n\",\n \"\\\\texttt{CONTROL} $\\\\rightarrow$ The sample is from the \\\\texttt{control} group. \\\\\\\\\\n\",\n \"\\\\raisebox{-.25\\\\height}{\\\\includegraphics[height=1.5em]{figs/symbol/brain.png}}\\\\; Hemisphere & \\n\",\n \"\\\\texttt{left} $\\\\rightarrow$ The tissue sample was taken from the \\\\texttt{left} hemisphere of the brain. |\\n\",\n \"\\\\texttt{right} $\\\\rightarrow$ The tissue sample was taken from the \\\\texttt{right} hemisphere of the brain. \\\\\\\\\\n\",\n \"\\\\raisebox{-.25\\\\height}{\\\\includegraphics[height=1.5em]{figs/symbol/liver-cancer.png}}\\\\; Tumor Site & \\n\",\n \"\\\\texttt{primary} $\\\\rightarrow$ The tumor is located at the \\\\texttt{primary} site. |\\n\",\n \"\\\\texttt{metastasis} $\\\\rightarrow$ The tumor has \\\\texttt{metastasized} to other parts of the body. |\\n\",\n \"\\\\texttt{normal} $\\\\rightarrow$ This sample was collected from non-tumorous tissue. \\\\\\\\\\n\",\n \"\\\\raisebox{-.25\\\\height}{\\\\includegraphics[height=1.5em]{figs/symbol/lymph-nodes.png}}\\\\; Sample Source & \\n\",\n \"\\\\texttt{tumor} $\\\\rightarrow$ The sample is derived from \\\\texttt{tumor} tissue. |\\n\",\n \"\\\\texttt{normal} $\\\\rightarrow$ The sample is derived from \\\\texttt{normal} tissue. |\\n\",\n \"\\\\texttt{blood} $\\\\rightarrow$ The sample is a \\\\texttt{blood}-derived specimen. |\\n\",\n \"\\\\texttt{lymphnode} $\\\\rightarrow$ The sample is derived from \\\\texttt{lymph node} tissue. \\\\\\\\\\n\",\n \"\\\\raisebox{-.25\\\\height}{\\\\includegraphics[height=1.5em]{figs/symbol/eosinophil.png}}\\\\; CD45 Expression & \\n\",\n \"\\\\texttt{yes} $\\\\rightarrow$ The cell is CD45-\\\\texttt{positive}, suggesting an immune cell origin. |\\n\",\n \"\\\\texttt{no} $\\\\rightarrow$ The cell is CD45-\\\\texttt{negative}, suggesting a non-immune cell lineage. |\\n\",\n \"\\\\texttt{mixed} $\\\\rightarrow$ The sample contains a \\\\texttt{mixture} of CD45-positive and CD45-negative cells. \\\\\\\\\\n\",\n \"\\\\raisebox{-.25\\\\height}{\\\\includegraphics[height=1.5em]{figs/symbol/diabetes.png}}\\\\; Diabetes History & \\n\",\n \"\\\\texttt{yes} $\\\\rightarrow$ The patient \\\\texttt{has a history} of diabetes. |\\n\",\n \"\\\\texttt{no} $\\\\rightarrow$ The patient \\\\texttt{does not have} diabetes. \\\\\\\\\\n\",\n \"\\\\raisebox{-.25\\\\height}{\\\\includegraphics[height=1.5em]{figs/symbol/blood-pressure.png}}\\\\; Hypertension & \\n\",\n \"\\\\texttt{yes} $\\\\rightarrow$ The patient \\\\texttt{has a history} of hypertension. |\\n\",\n \"\\\\texttt{no} $\\\\rightarrow$ The patient \\\\texttt{does not have} hypertension. \\\\\\\\\\n\",\n \"\\\\raisebox{-.25\\\\height}{\\\\includegraphics[height=1.5em]{figs/symbol/plasma-cells.png}}\\\\; Activation & \\n\",\n \"\\\\texttt{activated} $\\\\rightarrow$ The sample was stimulated and represents \\\\texttt{activated} immune cells. |\\n\",\n \"\\\\texttt{resting} $\\\\rightarrow$ The sample represents \\\\texttt{resting} (non-activated) immune cells. \\\\\\\\\\n\",\n \"\\\\raisebox{-.25\\\\height}{\\\\includegraphics[height=1.5em]{figs/symbol/genotype.png}}\\\\; Genotype & \\n\",\n \"\\\\texttt{FLT3-ITD,NPM1-MUT} $\\\\rightarrow$ The patient carries FLT3-ITD and NPM1 mutations. |\\n\",\n \"\\\\texttt{FLT3-WT,NPM1-MUT} $\\\\rightarrow$ The patient carries a wild-type FLT3 and an NPM1 mutation. |\\n\",\n \"\\\\texttt{APL} $\\\\rightarrow$ The patient is diagnosed with acute promyelocytic leukemia (APL). \\\\\\\\\\n\",\n \"\\\\raisebox{-.25\\\\height}{\\\\includegraphics[height=1.5em]{figs/symbol/medical-condition.png}}\\\\; Medical Conditions & \\n\",\n \"\\\\texttt{epilepsy} $\\\\rightarrow$ The patient has a history of \\\\texttt{epilepsy}. |\\n\",\n \"\\\\texttt{tumor} $\\\\rightarrow$ The patient has a diagnosed brain \\\\texttt{tumor}. |\\n\",\n \"\\\\texttt{hydrocephalus} $\\\\rightarrow$ The patient has \\\\texttt{hydrocephalus} (fluid buildup in the brain). |\\n\",\n \"\\\\texttt{both} $\\\\rightarrow$ The patient has \\\\texttt{both} epilepsy and a brain tumor. |\\n\",\n \"\\\\texttt{other} $\\\\rightarrow$ The patient has \\\\texttt{other} neurological conditions. |\\n\",\n \"\\\\texttt{healthy} $\\\\rightarrow$ The donor was \\\\texttt{healthy} with no reported skin condition. |\\n\",\n \"\\\\texttt{dm – non ulcer} $\\\\rightarrow$ The donor had diabetes \\\\texttt{mellitus without skin ulceration}. | \\n\",\n \"\\\\texttt{keloid} $\\\\rightarrow$ The donor had a \\\\texttt{keloid}, which is an overgrowth of scar tissue. |\\n\",\n \"\\\\texttt{localised scleroderma} $\\\\rightarrow$ The donor was diagnosed with \\\\texttt{localized scleroderma}. | \\n\",\n \"\\\\texttt{scar} $\\\\rightarrow$ The donor had a typical \\\\texttt{scar} from prior skin injury. |\\n\",\n \"\\\\texttt{\\\\{lung\\\\_condition\\\\}} $\\\\rightarrow$ The lung condition is described as \\\\texttt{\\\\{lung\\\\_condition\\\\}}. \\\\\\\\\\n\",\n \"\\\\raisebox{-.25\\\\height}{\\\\includegraphics[height=1.5em]{figs/symbol/kidney.png}}\\\\; Egfr & \\\\texttt{\\\\{value\\\\}} $\\\\rightarrow$ The patient's estimated glomerular filtration rate (eGFR) is in the range \\\\texttt{\\\\{value\\\\}}. \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\label{tab:template}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{figure}[h!]\\n\",\n \"\\\\centering\\n\",\n \"\\\\includegraphics[width=1.0\\\\textwidth]{figs/data_construction.pdf}\\n\",\n \"\\\\caption{\\n\",\n \"Overview of data construction. Each batch consists of $N$ cells from the same donor, with associated metadata (e.g., tissue, disease, age, sex, smoking status) and gene expression profiles. Structured donor-level information is converted into natural language context, and cell-level features are enriched with the top expressed genes to support downstream reasoning.\\n\",\n \"}\\n\",\n \"\\\\label{fig:data_construction}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table}[h!]\\n\",\n \" \\\\centering\\n\",\n \" \\\\small\\n\",\n \" \\\\caption{Prompt template used for the decomposed cell-level reasoning task.}\\n\",\n \" \\\\label{tab:single_prompt_template}\\n\",\n \" \\\\vspace{2.5pt}\\n\",\n \" \\\\renewcommand{\\\\arraystretch}{1.2}\\n\",\n \" \\\\begin{tabular}{p{13.5cm}}\\n\",\n \" \\\\hline\\n\",\n \" You are an expert assistant specialized in cell type annotation. You will be given the gene expression profile of a single cell from a specific donor.\\n\",\n \" The top expressed genes are listed in descending order. Use both gene expression and donor context to determine the correct cell type. \\\\textbf{You will also receive a list of candidate cell types—choose the one that best fits this cell}. Include your detailed reasoning within \\\\thinkbegin and \\\\thinkend tags, and provide your final answer within \\\\answerbegin and \\\\answerend tags. The final answer should be a single string with exactly one cell type.\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\hline\\n\",\n \" \\\\end{tabular} \\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table*}[h!]\\n\",\n \"\\\\centering\\n\",\n \"\\\\small\\n\",\n \"\\\\footnotesize\\n\",\n \"\\\\renewcommand\\\\tabcolsep{4.5pt}\\n\",\n \"\\\\renewcommand\\\\arraystretch{1.05}\\n\",\n \"\\\\caption{Comparison of model performance between batch-level and decomposed cell-level settings. $^{\\\\spadesuit}$ indicates instruction-tuned baselines trained without any reasoning tasks, which directly predict answers from the input without generating reasoning traces. The best and second-best results are highlighted in \\\\colorbox{backred!50}{red} and \\\\colorbox{backblue!75}{blue}, respectively.}\\n\",\n \"\\\\resizebox{0.93\\\\textwidth}{!}{\\n\",\n \"\\\\begin{tabular}{l|c|c|c|c|c}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\header{Model} & \\\\header{Response Length} & \\\\header{Cell-level Acc} & \\\\header{Batch-level Acc} & \\\\header{Format} & \\\\header{Uniqueness} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\rowcolor[rgb]{0.93,0.93,0.93} \\\\multicolumn{6}{c}{\\\\textit{Cell-level Prediction Setup}} \\\\\\\\\\n\",\n \"\\\\texttt{Llama3.1-8B-Instruct}$^{\\\\spadesuit}$ & 8.1 & 0.4707 & 0.0112 & 0.6024 & 0.5887 \\\\\\\\\\n\",\n \"\\\\texttt{Qwen2.5-7B-Instruct}$^{\\\\spadesuit}$ & 8.5 & 0.3890 & 0.0000 & 0.5955 & 0.5978 \\\\\\\\\\n\",\n \"\\\\rowcolor[rgb]{0.93,0.93,0.93} \\\\multicolumn{6}{c}{\\\\textit{Cell-level Reasoning Setup}} \\\\\\\\\\n\",\n \"\\\\texttt{GPT-4o-mini} & 349.5 & 0.2761 & 0.0000 & \\\\colorbox{backred!50}{1.0000} & 0.3706 \\\\\\\\\\n\",\n \"\\\\texttt{GPT-4o} & 494.7 & 0.4093 & 0.0000 & \\\\colorbox{backblue!75}{0.9900} & 0.5273 \\\\\\\\\\n\",\n \"\\\\texttt{o3-mini} & 241.1 & 0.4825 & 0.0000 & \\\\colorbox{backred!50}{1.0000} & 0.5663 \\\\\\\\\\n\",\n \"\\\\texttt{o1} & 152.2 & 0.4700 & 0.0000 & \\\\colorbox{backred!50}{1.0000} & 0.5708 \\\\\\\\\\n\",\n \"\\\\Ours & 718.5 & 0.5016 & 0.0112 & 0.8427 & 0.6612 \\\\\\\\\\n\",\n \"\\\\rowcolor[rgb]{0.93,0.93,0.93} \\\\multicolumn{6}{c}{\\\\textit{Batch-level Prediction Setup}} \\\\\\\\\\n\",\n \"\\\\texttt{Llama3.1-8B-Instruct}$^{\\\\spadesuit}$ & 73.8 & 0.6438 & 0.2809 & 0.9213 & 0.9198 \\\\\\\\\\n\",\n \"\\\\texttt{Qwen2.5-7B-Instruct}$^{\\\\spadesuit}$ & 78.7 & 0.5631 & 0.1910 & 0.9438 & 0.9370 \\\\\\\\\\n\",\n \"\\\\rowcolor[rgb]{0.93,0.93,0.93} \\\\multicolumn{6}{c}{\\\\textit{Batch-level Reasoning Setup}} \\\\\\\\\\n\",\n \"\\\\texttt{GPT-4o-mini} & 785.4 & 0.2330 & 0.0000 & \\\\colorbox{backred!50}{1.0000} & 0.7147 \\\\\\\\\\n\",\n \"\\\\texttt{GPT-4o} & 1010.4 & 0.4320 & 0.0000 & \\\\colorbox{backred!50}{1.0000} & 0.8542 \\\\\\\\\\n\",\n \"\\\\texttt{o3-mini} & 1059.9 & 0.6784 & 0.1461 & \\\\colorbox{backred!50}{1.0000} & \\\\colorbox{backred!50}{1.0000} \\\\\\\\\\n\",\n \"\\\\texttt{o1} & 834.9 & \\\\colorbox{backred!50}{0.7330} & \\\\colorbox{backblue!75}{0.1685} & \\\\colorbox{backred!50}{1.0000} & \\\\colorbox{backblue!75}{0.9975} \\\\\\\\\\n\",\n \"\\\\Ours & 1240.8 & \\\\colorbox{backblue!75}{0.6953} & \\\\colorbox{backred!50}{0.3371} & 0.9775 & 0.9759 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\label{tab:single}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[h!]\\n\",\n \"\\\\centering\\n\",\n \"\\\\small\\n\",\n \"\\\\footnotesize\\n\",\n \"\\\\renewcommand\\\\tabcolsep{4.5pt}\\n\",\n \"\\\\renewcommand\\\\arraystretch{1.05}\\n\",\n \"\\\\caption{Comparison of model performance between open-ended and constrained QA settings. $^{\\\\spadesuit}$ indicates instruction-tuned baselines trained without any reasoning tasks, which directly predict answers from the input without generating reasoning traces. The best and second-best results are highlighted in \\\\colorbox{backred!50}{red} and \\\\colorbox{backblue!75}{blue}, respectively.}\\n\",\n \"\\\\resizebox{0.93\\\\textwidth}{!}{\\n\",\n \"\\\\begin{tabular}{l|c|c|c|c|c}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\header{Model} & \\\\header{Response Length} & \\\\header{Cell-level Acc} & \\\\header{Batch-level Acc} & \\\\header{Format} & \\\\header{MedCPT Score} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\rowcolor[rgb]{0.93,0.93,0.93} \\\\multicolumn{6}{c}{\\\\textit{Open-ended QA Setup}} \\\\\\\\\\n\",\n \"\\\\texttt{Llama3.1-8B-Instruct}$^{\\\\spadesuit}$ & 192.7 & 0.1068 & 0.0000 & 0.2822 & 0.7400 \\\\\\\\\\n\",\n \"\\\\texttt{Qwen2.5-7B-Instruct}$^{\\\\spadesuit}$ & 248.8 & 0.2491 & 0.0000 & 0.6804 & 0.7304 \\\\\\\\\\n\",\n \"\\\\texttt{GPT-4o-mini} & 456.7 & 0.2326 & 0.0000 & 0.9973 & 0.6680 \\\\\\\\\\n\",\n \"\\\\texttt{GPT-4o} & 401.0 & 0.3334 & 0.0000 & \\\\colorbox{backblue!75}{0.9991} & 0.7060 \\\\\\\\\\n\",\n \"\\\\texttt{o3-mini} & 590.7 & 0.4292 & 0.0000 & \\\\colorbox{backred!50}{1.0000} & 0.7411 \\\\\\\\\\n\",\n \"\\\\texttt{o1} & 436.5 & 0.4411 & 0.0000 & \\\\colorbox{backred!50}{1.0000} & 0.7519 \\\\\\\\\\n\",\n \"\\\\Ours & 696.9 & 0.3367 & 0.0000 & 0.9224 & 0.7229 \\\\\\\\\\n\",\n \"\\\\rowcolor[rgb]{0.93,0.93,0.93} \\\\multicolumn{6}{c}{\\\\textit{Constrained QA Setup}} \\\\\\\\\\n\",\n \"\\\\texttt{Llama3.1-8B-Instruct}$^{\\\\spadesuit}$ & 71.9 & 0.6411 & \\\\colorbox{backblue!75}{0.2969} & 0.9078 & \\\\colorbox{backblue!75}{0.9133} \\\\\\\\\\n\",\n \"\\\\texttt{Qwen2.5-7B-Instruct}$^{\\\\spadesuit}$ & 70.9 & 0.6179 & 0.2475 & 0.9443 & 0.8889 \\\\\\\\\\n\",\n \"\\\\texttt{GPT-4o-mini} & 779.9 & 0.2581 & 0.0027 & 0.9918 & 0.7178 \\\\\\\\\\n\",\n \"\\\\texttt{GPT-4o} & 1087.0 & 0.4248 & 0.0283 & 0.9817 & 0.7930 \\\\\\\\\\n\",\n \"\\\\texttt{o3-mini} & 1130.2 & 0.5804 & 0.1352 & \\\\colorbox{backred!50}{1.0000} & 0.8734 \\\\\\\\\\n\",\n \"\\\\texttt{o1} & 906.6 & \\\\colorbox{backblue!75}{0.6479} & 0.1900 & \\\\colorbox{backred!50}{1.0000} & 0.8888 \\\\\\\\\\n\",\n \"\\\\Ours & 1145.6 & \\\\colorbox{backred!50}{0.6849} & \\\\colorbox{backred!50}{0.3288} & 0.9826 & \\\\colorbox{backred!50}{0.9185} \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\label{tab:open}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table}[h!]\\n\",\n \" \\\\centering\\n\",\n \" \\\\small\\n\",\n \" \\\\caption{Prediction of \\\\texttt{Qwen2.5-7B-Instruct}.}\\n\",\n \" \\\\label{case:qwen}\\n\",\n \" \\\\vspace{2.5pt}\\n\",\n \" \\\\renewcommand{\\\\arraystretch}{1.2}\\n\",\n \" \\\\begin{tabular}{p{13.5cm}}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Reasoning:} \\n\",\n \" Let's analyze the gene expression patterns and match them to the provided cell types. \\\\\\\\ \\n\",\n \" \\\\textbullet\\\\ Intestine Goblet Cell: This cell type is characterized by high expression of genes like TMSB4X, which is present in Cells 1, 3, 9. \\\\\\\\\\n\",\n \" \\\\textbullet\\\\ Capillary Endothelial Cell: This cell type often expresses genes like CD34, CD31, and VWF. However, these genes are not listed here, so we cannot confidently assign this cell type based on the given data. \\\\\\\\\\n\",\n \" \\\\textbullet\\\\ Plasma Cell: Plasma cells typically express genes like IGKC (Immunoglobulin kappa chain), JCHAIN, and IGHA1. These genes are present in Cell 8, suggesting it could be a plasma cell. \\\\\\\\\\n\",\n \" \\\\textbullet\\\\ Memory B Cell: Memory B cells might express genes like CD74, but this is not prominently featured in any of the cells. \\\\\\\\\\n\",\n \" \\\\textbullet\\\\ Activated CD4-positive, Alpha-Beta T Cell: This cell type would express genes like RPL41, RPL13A, and RPS27, which are common in Cells 2, 5, 6, 7, 10. \\\\\\\\\\n\",\n \" \\\\textbullet\\\\ CD4-positive, Alpha-Beta T Cell: This cell type would also express genes like RPL41, RPL13A, and RPS27, similar to the activated CD4-positive, alpha-beta T cell. \\\\\\\\\\n\",\n \" \\\\textbullet\\\\ T-helper 1 Cell: This cell type would express genes like IL2RA, IFNG, and STAT1, which are not present in the given gene lists. \\\\\\\\\\n\",\n \" \\\\textbullet\\\\ Germinal Center B Cell: Germinal center B cells would express genes like CD74, CD21, and CD23, which are not prominently featured in any of the cells. \\\\\\\\\\n\",\n \" \\\\textbullet\\\\ IgA Plasma Cell: This cell type would express genes like IGLL1, IGHG1, and IGHM, which are not present in the given gene lists. \\\\\\\\\\n\",\n \" \\\\textbullet\\\\ Vein Endothelial Cell: This cell type would express genes like CD34, CD31, and VWF, which are not present in the given gene lists. \\\\\\\\\\n\",\n \" \\\\arrayrulecolor{gray!30}\\\\midrule\\\\arrayrulecolor{black}\\n\",\n \" Based on the gene expression patterns, the most likely matches are: \\\\\\\\\\n\",\n \" \\\\textbullet\\\\ \\\\sethlcolor{lightred}\\\\hl{Cell 1}: \\\\sethlcolor{lightred}\\\\hl{Intestine Goblet Cell} \\\\\\\\\\n\",\n \" \\\\textbullet\\\\ \\\\sethlcolor{lightred}\\\\hl{Cell 2}: \\\\sethlcolor{lightred}\\\\hl{Activated CD4-positive, Alpha-Beta T Cell} \\\\\\\\\\n\",\n \" \\\\textbullet\\\\ \\\\sethlcolor{lightred}\\\\hl{Cell 3}: \\\\sethlcolor{lightred}\\\\hl{Intestine Goblet Cell} \\\\\\\\\\n\",\n \" \\\\textbullet\\\\ \\\\sethlcolor{lightred}\\\\hl{Cell 4}: \\\\sethlcolor{lightred}\\\\hl{Activated CD4-positive, Alpha-Beta T Cell} \\\\\\\\\\n\",\n \" \\\\textbullet\\\\ \\\\sethlcolor{lightred}\\\\hl{Cell 5}: \\\\sethlcolor{lightred}\\\\hl{Activated CD4-positive, Alpha-Beta T Cell} \\\\\\\\\\n\",\n \" \\\\textbullet\\\\ \\\\sethlcolor{lightblue}\\\\hl{Cell 6}: \\\\sethlcolor{lightblue}\\\\hl{Capillary Endothelial Cell (though not strongly supported)} \\\\\\\\\\n\",\n \" \\\\textbullet\\\\ \\\\sethlcolor{lightred}\\\\hl{Cell 7}: \\\\sethlcolor{lightred}\\\\hl{Activated CD4-positive, Alpha-Beta T Cell} \\\\\\\\\\n\",\n \" \\\\textbullet\\\\ \\\\sethlcolor{lightred}\\\\hl{Cell 8}: \\\\sethlcolor{lightred}\\\\hl{Plasma Cell} \\\\\\\\\\n\",\n \" \\\\textbullet\\\\ \\\\sethlcolor{lightred}\\\\hl{Cell 9}: \\\\sethlcolor{lightred}\\\\hl{Intestine Goblet Cell} \\\\\\\\\\n\",\n \" \\\\textbullet\\\\ \\\\sethlcolor{lightred}\\\\hl{Cell 10}: \\\\sethlcolor{lightred}\\\\hl{Activated CD4-positive, Alpha-Beta T Cell} \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\textbf{Answer:} \\\\sethlcolor{lightred}\\\\hl{intestine goblet cell} | \\\\sethlcolor{lightred}\\\\hl{activated cd4-positive, alpha-beta t cell} | \\\\sethlcolor{lightred}\\\\hl{intestine goblet cell} | \\\\sethlcolor{lightred}\\\\hl{activated cd4-positive, alpha-beta t cell} | \\\\sethlcolor{lightred}\\\\hl{activated cd4-positive, alpha-beta t cell} | \\\\sethlcolor{lightblue}\\\\hl{capillary endothelial cell} | \\\\sethlcolor{lightred}\\\\hl{activated cd4-positive, alpha-beta t cell} | \\\\sethlcolor{lightred}\\\\hl{plasma cell} | \\\\sethlcolor{lightred}\\\\hl{intestine goblet cell} | \\\\sethlcolor{lightred}\\\\hl{activated cd4-positive, alpha-beta t cell} \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular} \\n\",\n \"\\\\end{table}\\\\begin{table}[h!]\\n\"\n ],\n [\n \"\\\\end{table}\\\\begin{table}[h!]\\n\",\n \" \\\\centering\\n\",\n \" \\\\small\\n\",\n \" \\\\caption{Prediction of \\\\texttt{o1}.}\\n\",\n \" \\\\label{case:o1}\\n\",\n \" \\\\vspace{2.5pt}\\n\",\n \" \\\\renewcommand{\\\\arraystretch}{1.2}\\n\",\n \" \\\\begin{tabular}{p{13.5cm}}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\textbf{Reasoning:} \\n\",\n \" First, note that each of the 10 cells must map to a distinct one among the 10 candidate types. Some cells are straightforward to assign based on well-known marker genes: \\\\\\\\\\n\",\n \" \\\\textbullet\\\\ \\\\sethlcolor{lightblue}\\\\hl{Cell 4} expresses TFF3, SPINK4, and REG1A, which are \\\\sethlcolor{lightblue}\\\\hl{classic intestinal goblet cell markers} (TFF3 is particularly specific). \\\\\\\\\\n\",\n \" \\\\textbullet\\\\ \\\\sethlcolor{lightblue}\\\\hl{Cell 6} has PLVAP and VWF, both of which are strongly associated with endothelial cells—especially PLVAP, which is a hallmark of capillary (fenestrated) endothelium, so Cell 6 is a \\\\sethlcolor{lightblue}\\\\hl{capillary endothelial cell}. \\\\\\\\\\n\",\n \" \\\\textbullet\\\\ \\\\sethlcolor{lightblue}\\\\hl{Cell 7} also expresses VWF but includes CCL21, HLA-E, and other features suggestive of a different endothelial subtype; since we must use both ``capillary endothelial cell'' and ``vein endothelial cell'', assigning Cell 7 to \\\\sethlcolor{lightblue}\\\\hl{vein endothelial cell} is consistent. \\\\\\\\\\n\",\n \" \\\\textbullet\\\\ \\\\sethlcolor{lightred}\\\\hl{Cell 8} has IGKC, JCHAIN, and IGHA1 at high levels, unambiguously marking an IgA-expressing plasma cell \\\\sethlcolor{lightred}\\\\hl{(IgA plasma cell)}. \\\\\\\\\\n\",\n \" \\\\textbullet\\\\ \\\\sethlcolor{lightred}\\\\hl{Cell 10} highly expresses IGKC and IGHM (and lacks IGHA1), indicating another antibody-producing cell but not IgA; this fits the generic \\\\sethlcolor{lightred}\\\\hl{``plasma cell''} category.\\n\",\n \" \\\\\\\\\\n\",\n \" \\\\arrayrulecolor{gray!30}\\\\midrule\\\\arrayrulecolor{black}\\n\",\n \" That leaves Cells 1, 2, 3, 5, and 9 to be mapped onto the remaining five types: CD4-positive, alpha-beta T cell; T-helper 1 cell; activated CD4-positive, alpha-beta T cell; memory B cell; germinal center B cell. \\\\\\\\\\n\",\n \" \\\\arrayrulecolor{gray!30}\\\\midrule\\\\arrayrulecolor{black}\\n\",\n \" Although many of these cells' top 50 genes are dominated by ribosomal/housekeeping transcripts, we can still deduce plausible assignments. \\\\sethlcolor{lightred}\\\\hl{Cell 5}, for instance, is rich in mitochondrial genes (MT-CO1, MT-CO2, etc.) and also contains CD52 (commonly found on lymphocytes, especially T cells). High mitochondrial activity is characteristic of an activated T cell, so Cell 5 is best labeled \\\\sethlcolor{lightred}\\\\hl{``activated CD4-positive, alpha-beta T cell''}. \\n\",\n \" Cells 1, 3, and 9 are then the remaining T cells (two of them) plus one more B cell or vice versa. However, we need two B-cell categories overall—``memory B cell'' and ``germinal center B cell''—and we have not yet assigned either one to any cell besides the two plasma cells we already handled (Cells 8 and 10). Given that Cells 6 and 7 are endothelial, Cell 4 is goblet, Cell 8 and 10 are plasma lineage, and Cell 5 is activated T, we must place one B-cell type in Cell 2 and the other B-cell type in one of 1, 3, or 9. \\\\\\\\\\n\",\n \" \\\\arrayrulecolor{gray!30}\\\\midrule\\\\arrayrulecolor{black}\\n\",\n \" A close look at \\\\sethlcolor{lightred}\\\\hl{Cell 2} shows no strong expression of T-cell markers but instead a large suite of ribosomal transcripts, which can be consistent with a \\\\sethlcolor{lightred}\\\\hl{B lineage cell}. Meanwhile, among 1, 3, and 9, it is much more likely that two are T cells, and one is a B cell. The top genes in 1 and 3 both include TMSB4X at or near the top, which often appears in T cells (though it is not strictly T-specific, it is common in lymphocytes). Cell 9 is likewise heavy in housekeeping genes, and there is no direct B- or T-cell hallmark in the top transcripts. In practice, we must distribute the remaining categories such that we end up with exactly three T cells total and two non-plasma B cells total. \\\\\\\\\\n\",\n \" \\\\arrayrulecolor{gray!30}\\\\midrule\\\\arrayrulecolor{black}\\n\",\n \" A consistent assignment is: \\\\sethlcolor{lightred}\\\\hl{Cell 1} $\\\\rightarrow$ \\\\sethlcolor{lightred}\\\\hl{CD4-positive, alpha-beta T cell} (a ``baseline'' or less activated CD4 T cell); \\\\sethlcolor{lightred}\\\\hl{Cell 3} $\\\\rightarrow$ \\\\sethlcolor{lightred}\\\\hl{T-helper 1 cell} (given the context of Crohn disease, Th1 skewing is common, and Cell 3 also has notable mitochondrial transcripts that can reflect an active state); Cell 5 (as reasoned above) $\\\\rightarrow$ activated CD4-positive, alpha-beta T cell. \\\\\\\\\\n\",\n \" \\\\arrayrulecolor{gray!30}\\\\midrule\\\\arrayrulecolor{black}\\n\",\n \" That covers our three T-cell categories. For the two B-cell categories left (``memory B cell,'' ``germinal center B cell''), Cells 2 and 9 remain. Neither has overt immunoglobulin in the top 50, but we must use both B-cell categories somewhere. A typical assumption is that a germinal center B cell may show many ribosomal/housekeeping or proliferation genes, which could apply to Cell 9, while Cell 2 (also heavily ribosomal) could be the memory B cell. Ultimately, to satisfy the one-to-one requirement and given the absence of direct GC vs. memory markers at the top of the list, the most reasonable pairing is: \\\\sethlcolor{lightred}\\\\hl{Cell 2} $\\\\rightarrow$ \\\\sethlcolor{lightred}\\\\hl{memory B cell}; \\\\sethlcolor{lightred}\\\\hl{Cell 9} $\\\\rightarrow$ \\\\sethlcolor{lightred}\\\\hl{germinal center B cell}. \\\\\\\\ \\n\",\n \" \\\\midrule\\n\",\n \" \\\\textbf{Answer:} \\\\sethlcolor{lightred}\\\\hl{CD4-positive, alpha-beta T cell} | \\\\sethlcolor{lightred}\\\\hl{memory B cell} | \\\\sethlcolor{lightred}\\\\hl{T-helper 1 cell} | \\\\sethlcolor{lightblue}\\\\hl{intestine goblet cell} | \\\\sethlcolor{lightred}\\\\hl{activated CD4-positive, alpha-beta T cell} | \\\\sethlcolor{lightblue}\\\\hl{capillary endothelial cell} | \\\\sethlcolor{lightblue}\\\\hl{vein endothelial cell} | \\\\sethlcolor{lightblue}\\\\hl{IgA plasma cell} | \\\\sethlcolor{lightred}\\\\hl{germinal center B cell} | \\\\sethlcolor{lightred}\\\\hl{plasma cell} \\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular} \\n\",\n \"\\\\end{table}\\\\begin{table}[h!]\\n\"\n ]\n]","isConversational":false},"fulltext":{"kind":"string","value":"\\documentclass{article}\n\n\n% if you need to pass options to natbib, use, e.g.:\n \\PassOptionsToPackage{numbers, compress}{natbib}\n% before loading neurips_2025\n\n\n% ready for submission\n% \\usepackage{neurips_2025}\n\n\n% to compile a preprint version, e.g., for submission to arXiv, add add the\n% [preprint] option:\n% \\usepackage[preprint]{neurips_2025}\n\\usepackage[preprint]{neurips_2025}\n\n% to compile a camera-ready version, add the [final] option, e.g.:\n% \\usepackage[final]{neurips_2025}\n\n\n% to avoid loading the natbib package, add option nonatbib:\n% \\usepackage[nonatbib]{neurips_2025}\n\n\n\\usepackage[utf8]{inputenc} % allow utf-8 input\n\\usepackage[T1]{fontenc} % use 8-bit T1 fonts\n\\usepackage{hyperref} % hyperlinks\n\\usepackage{url} % simple URL 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\\raisebox{-0.2\\height}{\\includegraphics[width=1.3em]{figs/cello1.png}}\n\\Ours: Training LLMs to Solve Single-Cell Reasoning Puzzles with Reinforcement Learning}\n\n% The \\author macro works with any number of authors. There are two commands\n% used to separate the names and addresses of multiple authors: \\And and \\AND.\n%\n% Using \\And between authors leaves it to LaTeX to determine where to break the\n% lines. Using \\AND forces a line break at that point. So, if LaTeX puts 3 of 4\n% authors names on the first line, and the last on the second line, try using\n% \\AND instead of \\And before the third author name.\n\n\\author{\n \\textbf{Yin Fang}$^1$\\footnotemark[1] \\quad \n \\textbf{Qiao Jin}$^1$\\thanks{$\\quad$ Equal Contribution.} \\quad\n \\textbf{Guangzhi Xiong}$^2$ \\quad \n \\textbf{Bowen Jin}$^3$ \\quad \n \\textbf{Xianrui Zhong}$^3$ \\quad \\\\ \n \\textbf{Siru Ouyang}$^{3}$ \\quad \n \\textbf{Aidong Zhang}$^{2}$ \\quad \n \\textbf{Jiawei Han}$^{3}$ \\quad \n \\textbf{Zhiyong Lu}$^{1}$\\thanks{$\\quad$ Corresponding Author.}\n \\\\ \n $^1$ National Institutes of Health \\quad\n $^2$ University of Virginia \\\\\n $^3$ University of Illinois Urbana-Champaign \n}\n\n\n\n\\begin{document}\n\n\\vspace{-0.3cm}\n\n\\maketitle\n\n\\vspace{-0.5cm}\n\n\\begin{abstract}\n\\vspace{-0.2cm}\n\nCell type annotation is a key task in analyzing the heterogeneity of single-cell RNA sequencing data.\nAlthough recent foundation models automate this process, they typically annotate cells independently, without considering batch-level cellular context or providing explanatory reasoning.\nIn contrast, human experts often annotate distinct cell types for different cell clusters based on their domain knowledge.\nTo mimic this workflow, we introduce the \\Bench task, where the objective is to assign unique cell types to a batch of cells.\nThis benchmark spans diverse tissues, diseases, and donor conditions, and requires reasoning across the batch-level cellular context to ensure label uniqueness.\nWe find that off-the-shelf large language models (LLMs) struggle on \\Bench, with the best baseline (OpenAI's \\texttt{o1}) achieving only 19.0\\% batch-level accuracy.\nTo fill this gap, we propose \\Ours, a 7B LLM trained via supervised fine-tuning on distilled reasoning traces, followed by reinforcement learning with batch-level rewards. \n\\Ours achieves state-of-the-art performance, outperforming \\texttt{o1} by over 73\\% and generalizing well across contexts.\nFurther analysis of training dynamics and reasoning behaviors provides insights into batch-level annotation performance and emergent expert-like reasoning. \nCode and data are available at \\url{https://github.com/ncbi-nlp/cell-o1}.\n\\end{abstract}\n\n\\vspace{-0.3cm}\n\\begin{figure}[h!]\n\\centering\n\\includegraphics[width=0.96\\textwidth]{figs/overview.pdf}\n\\caption{Overview of this work. \\Ours achieves state-of-the-art on the \\Bench task.}\n\\label{fig:overview}\n\\end{figure}\n\\vspace{-0.5cm}\n\n\\section{Introduction}\n\\label{sec:intro}\nAssigning accurate cell types to single-cell RNA sequencing (scRNA-seq) profiles is fundamental for understanding biological heterogeneity across tissues, diseases, and individuals~\\cite{kiselev2019challenges,luecken2019current}.\nTraditional annotation workflows rely heavily on expert knowledge, typically involving clustering to group similar cells~\\cite{butler2018integrating,traag2019louvain}, followed by manual inspection of marker gene expression to assign cell type labels based on biological domain knowledge~\\cite{stuart2019comprehensive,aran2019reference}.\nWhile accurate, this process is time-consuming and labor-intensive, with limited scalability across large or new datasets~\\cite{kiselev2019challenges,luecken2019current,stuart2019comprehensive,lahnemann2020eleven,abdelaal2019comparison}.\n\nRecent advances in deep learning have significantly improved automated cell type annotation through the development of single-cell foundation models~\\cite{yang2022scbert, theodoris2023transfer, hao2024large}. These models leverage large-scale unsupervised pre-training to capture complex gene-expression patterns, enabling better representation learning and enhanced performance across various downstream tasks. In parallel, large language models (LLMs) have also been adapted for single-cell applications, either by translating gene-expression profiles into textual representations~\\cite{hou2024assessing,levine2023cell2sentence,liu2023scelmo,chen2024simple} or by integrating multimodal gene embeddings~\\cite{zhao2024langcell,schaefer2024multimodal,fang2025multi, shi2025multimodal}. However, both foundation models and LLMs typically annotate each cell independently without considering the shared biological context or batch-level gene-expression information, which fundamentally differs from expert annotation practices~\\cite{pasquini2021automated}. Furthermore, most automated methods directly predict cell types without articulating the underlying reasoning process, making their decisions difficult to interpret and validate~\\cite{azodi2020opening}.\n\nTo bridge this gap, we first introduce \\Bench, a novel benchmark that formulates cell type annotation as a batch-level reasoning task, closely mimicking expert annotation workflows. \nAs illustrated in Figure~\\ref{fig:overview}, unlike traditional methods that label each cell independently, \\Bench requires jointly assigning unique labels to all cells in a batch, using both their gene-expression profiles and shared contextual metadata. Extensive evaluations show that state-of-the-art LLMs struggle with this formulation, with the best-performing model (OpenAI's \\texttt{o1}~\\cite{jaech2024openai}) achieving only 19.0\\% batch-level accuracy, reflecting the complexity of this task.\n\nTo address this challenge, we propose \\Ours, a reasoning-enhanced LLM trained in two phases as shown in Figure~\\ref{fig:overview}: supervised fine-tuning (SFT) on expert-like reasoning traces distilled from a frontier LLM to guide structured and interpretable decision-making; followed by reinforcement learning (RL) with batch-level rewards to encourage consistent, context-aware label assignments.\nOn \\Bench, \\Ours outperforms all baseline models in both cell-level and batch-level accuracy. \nFurther analyses of training dynamics, reasoning behavior, and prediction errors offer insights into the model’s generalization, interpretability, and reasoning ability.\nNotably, \\Ours exhibits emergent behaviors such as self-reflection~\\cite{renze2024self,zhaoevaluating}, where the model revisits and revises earlier predictions, and curriculum reasoning~\\cite{cornacchia2023mathematical,maharana2022curriculum}, where it prioritizes simpler cases before tackling harder ones—both resembling strategies used by human experts.\nIn summary, our key contributions include:\n\\begin{itemize}[leftmargin=*]\n \\item We introduce \\Bench, a novel benchmark that reformulates cell type annotation as a batch-level reasoning task, requiring the joint assignment of unique cell types to each cell based on both expression profiles and contextual metadata.\n \\item We propose \\Ours, a reasoning-enhanced LLM trained via SFT on distilled reasoning traces and RL with batch-level rewards, enabling interpretable annotation across cell batches.\n \\item We conduct extensive analyses of training dynamics, model behaviors, and error patterns, revealing emergent expert-like reasoning capabilities and robust performance on unseen biological conditions.\n\\end{itemize}\n\n\n\\section{Related Work}\n\n\\textbf{Traditional Cell Type Annotation.}\nManual cell type annotation typically follows a multi-step process combining clustering, marker gene identification, and reference-based assignment. Cells are first grouped using unsupervised clustering algorithms such as hierarchical clustering, Louvain, or Leiden~\\cite{butler2018integrating,traag2019louvain}. Experts then inspect the resulting clusters, identify distinguishing marker genes, and assign cell type labels based on prior biological knowledge and curated reference databases~\\cite{stuart2019comprehensive,aran2019reference}. The annotation process often involves iterative refinement, requiring the integration of various data sources and repeated evaluations.\nAlthough effective, expert-driven annotation is slow, labor-intensive, and requires substantial domain expertise. It also suffers from limited reproducibility and scalability when applied to large or multi-batch datasets~\\cite{stuart2019comprehensive,abdelaal2019comparison}. Moreover, its inability to systematically incorporate biological context further limits robustness and interpretability~\\cite{trapnell2015defining}.\n\n\n\\textbf{Single-Cell Foundation Models.}\nRecent advancements in deep learning have led to the emergence of powerful single-cell foundation models designed to capture complex gene expression patterns through deep representation learning~\\cite{yang2022scbert, theodoris2023transfer, hao2024large}. \nBuilt upon transformer architectures, these models leverage extensive unsupervised pre-training on large-scale single-cell transcriptomic datasets to achieve enhanced representation quality and downstream task performance. These foundation models have demonstrated remarkable capabilities in transferring learned knowledge across different datasets and tasks, including cell type annotation~\\cite{yang2022scbert,theodoris2023transfer}, gene network inference~\\cite{van2020scalable,osorio2020sctenifoldnet}, and perturbation prediction~\\cite{lotfollahi2019scgen,ji2021machine}. \nNevertheless, these models typically operate at the single-cell level, independently annotating each cell. Their performance in zero-shot settings remains limited, often necessitating fine-tuning with additional labeled data~\\cite{yang2022scbert,cui2024scgpt,theodoris2023transfer}.\n\n\n\\textbf{LLM-driven Single-Cell Annotation.}\nRecent research efforts have explored adapting single-cell gene expression data for LLMs, thereby leveraging their strong instruction-following, representation learning, and generalization capabilities~\\cite{achiam2023gpt,touvron2023llama}. With their instruction-following and in-context learning capabilities, LLMs can quickly adapt to different single-cell tasks, an advantage that traditional single-cell analysis methods cannot achieve~\\cite{lou2024large,brown2020language}. \nTo bridge the modality gap between scRNA-seq data and language models, several strategies have been proposed, including ranking genes by expression to form ordered lists~\\cite{hou2024assessing,levine2023cell2sentence}, embedding genes based on curated textual descriptions~\\cite{liu2023scelmo,chen2024simple}, and directly encoding raw expression profiles using multimodal architectures~\\cite{zhao2024langcell,schaefer2024multimodal,fang2025multi, shi2025multimodal}. \nThese approaches enable LLMs to process biological information and perform tasks such as cell type annotation within a textual or multimodal framework.\nWhile promising, existing LLM-driven approaches still face challenges. Most approaches process cells individually, not fully exploiting batch-level context information. Moreover, they output annotation results without explicitly articulating the underlying reasoning process~\\cite{wei2022chain,kojima2022large}, limiting interpretability and biological trustworthiness. \n\\section{\\Bench: Batch-Level Reasoning for Cell Type Annotation}\n\\label{sec:bench}\n\nIn real-world single-cell analysis, cell type annotation is rarely performed on individual cells in isolation. Instead, it typically occurs at the \\textit{batch level}, where groups of cells from the same donor or sample are jointly analyzed. Experts commonly cluster cells based on gene expression profiles, identify representative marker genes for each cluster, and assign labels by integrating both expression patterns and contextual metadata such as tissue origin and disease status.\n\n\\begin{figure}[h!]\n\\centering\n\\includegraphics[width=\\textwidth]{figs/cellpuzzles.pdf}\n\\caption{\n\\Bench formulates cell type annotation as a batch-level reasoning task that integrates gene expression and contextual metadata, inspired by how experts annotate cells in practice.\n}\n\\label{fig:cellpuzzles}\n\\end{figure}\n\nTo emulate this expert-driven process, we introduce \\Bench, a novel benchmark shown in Figure~\\ref{fig:cellpuzzles} that formulates cell type annotation as a batch-level reasoning task. \nFormally, each instance consists of a batch of $N$ cells $\\mathcal{C} = \\{c_1, c_2, \\dots, c_N\\}$, each sampled from a distinct cell type within the same donor and experimental batch.\nEach cell $c_i$ serves as a proxy for a cluster centroid and is represented by a ranked list of its top $M$ expressed genes $g_i = [g_{i1}, g_{i2}, \\dots, g_{iM}]$, which approximate the differentially expressed genes used by experts to define and annotate single cell clusters.\n\n\nThe entire batch is associated with a contextual description $m$, derived from donor-level metadata such as tissue type, disease status, sex, developmental stage, and other biologically relevant attributes when available. % (e.g., smoking status or lung condition). \nWe also provide a candidate label set $\\mathcal{Y} = \\{y_1, y_2, \\dots, y_N\\}$, which contains the ground-truth cell types for the $N$ cells in the batch, randomly shuffled to remove positional biases. This setup mirrors the expert annotation scenario, where cell types are selected from a biologically plausible candidate pool rather than freely generated.\nThe objective is to learn a mapping $f: \\mathcal{C} \\rightarrow \\mathcal{Y}$ that assigns each cell to a unique label in $\\mathcal{Y}$ by jointly considering gene expression profiles and contextual metadata, while generating interpretable reasoning traces to justify the label assignments.\n\nAs illustrated in Figure~\\ref{fig:data_distribution}, \\Bench is built from human single-cell datasets in Cell$\\times$Gene portal~\\cite{czi2025cz} that span diverse tissues, disease conditions, and donor demographics. \nThe detailed dataset construction protocol is provided in the Supplementary Materials.\n\n\\begin{figure}[h!]\n\\centering\n\\includegraphics[width=1.0\\textwidth]{figs/data_dis.pdf}\n\\caption{\nData distribution of \\Bench.\n(A) Age group distribution with male-female breakdown.\n(B) Sample distribution across a wide range of disease conditions.\n(C) Number of unique cell types observed across different tissues. \nFor clarity, only the top 20 entries are shown in (B) and (C).\n}\n\\label{fig:data_distribution}\n\\end{figure}\n\\section{\\Ours: Large Reasoning Model for Cell Batch Annotation}\n\nBuilt upon \\Bench, we introduce \\Ours, a large reasoning model that emulates expert annotation strategies for batch-level single-cell analysis. It performs reasoning over top-expressed genes and shared biological context to assign unique cell type labels from a given candidate set.\n\nFigure~\\ref{fig:overview} illustrates our training strategy.\nWe begin by introducing a unified prompt template (\\S\\ref{sec:prompt_template}) to standardize model outputs and maintain consistent formatting across training stages.\nNext, we perform reasoning distillation via rejection sampling to construct a high-quality dataset of biological interpretations, followed by SFT for cold-start initialization (\\S\\ref{sec:distill&sft}). \nFinally, we apply RL with GRPO, guided by batch-level reward signals, to further refine the model (\\S\\ref{sec:rl}).\n\n\\subsection{Prompt Template for Structured Reasoning}\n\\label{sec:prompt_template}\n\n\nTo ensure unified output formatting across training stages, we design a standardized prompt template that is used throughout distillation, SFT, and RL.\nAs shown in Table~\\ref{tab:prompt_template}, this template promotes global reasoning over the entire cell batch by instructing the model to integrate contextual information and jointly assign cell types to all cells from the candidate set.\n\n\\begin{table}[h!]\n \\centering\n \\small\n \\caption{Prompt template guiding structured reasoning and prediction in \\Ours.}\n \\label{tab:prompt_template}\n \\vspace{2.5pt}\n \\renewcommand{\\arraystretch}{1.2}\n \\begin{tabular}{p{13.5cm}}\n \\hline\n You are an expert assistant specialized in cell type annotation. You will be given a batch of $N$ cells from the same donor, where each cell represents a unique cell type.\n For each cell, the top-expressed genes are provided in descending order of expression.\n Using both the gene expression data and donor information, determine the correct cell type for each cell.\n You will also receive a list of $N$ candidate cell types, and each candidate must be assigned to exactly one cell. Ensure that you consider all cells and candidate types together, rather than annotating each cell individually.\n Include your detailed reasoning within \\thinkbegin and \\thinkend tags, and provide your final answer within \\answerbegin and \\answerend tags.\n The final answer should be a single string listing the assigned cell types in order, separated by ` | '.\n \\\\\n \\hline\n \\end{tabular} \n\\end{table}\n\\subsection{Reasoning Distillation and Cold Start}\n\\label{sec:distill&sft}\n\nThe batch-level reasoning task introduced in our benchmark poses significant challenges for model training. Unlike conventional classification tasks, the model must simultaneously analyze a group of cells, compare gene expression patterns, incorporate shared metadata, and generate a consistent label assignment. The high interdependence between inputs and outputs makes it difficult to produce valid and correct predictions, particularly at early training stages.\nWe begin by performing reasoning distillation using OpenAI's \\texttt{o1}~\\cite{jaech2024openai}, a frontier LLM with strong multi-step reasoning capabilities, to construct a synthetic dataset of high-quality reasoning traces and predictions. This distilled dataset is then used for SFT, serving as a cold start initialization~\\cite{guo2025deepseek} for the subsequent RL.\n\n\\subsubsection{Reasoning Distillation via Rejection Sampling}\n\nWe use \\texttt{o1}~\\cite{jaech2024openai} to generate reasoning traces and predictions for 10,155 instances in \\Bench. Each input is paired with the standardized prompt shown in Table~\\ref{tab:prompt_template}. \nFor each instance, we generate 8 candidate responses. We then apply rejection sampling to filter out low-quality outputs. A response is accepted only if it (1) adheres to the expected format and (2) yields a cell-type assignment that exactly matches the ground-truth labels. This process results in a distilled dataset containing 3,912 accepted examples, corresponding to an acceptance rate of 38.52\\%. \n\nThe collected reasoning traces exhibit several desirable properties for guiding models toward expert annotation behavior and serve as a strong foundation for SFT: First, they reflect domain-specific knowledge by referencing known gene markers and biological associations. Second, they demonstrate global reasoning behavior, in which multiple cells and candidate labels are considered jointly to ensure consistency in label assignments. Third, the reasoning is structured and interpretable, disentangled from the final prediction and formatted in a standardized prompt-response style.\n\n\n\\subsubsection{Supervised Fine-tuning with Distilled Reasoning}\n\nWe apply SFT on the distilled dataset to initialize the model before RL. While the final training objective involves optimizing reward signals, we find that starting from a purely pre-trained large language model fails to yield meaningful learning progress. Without prior exposure to the task-specific reasoning format and prediction structure, the model struggles to generate valid outputs. In practice, this leads to persistent formatting errors and incorrect label assignments during early-stage rollouts, resulting in zero or negative rewards and causing the policy to stagnate.\n\nSFT serves as a cold start mechanism that mitigates this problem by providing the model with high-quality demonstrations of structured reasoning and correct predictions. By learning from the distilled dataset, the model acquires the basic capacity to follow instructions, adhere to the required response format, and reason across multiple entities within a batch. This improves response validity, reduces reward sparsity, and facilitates more stable and efficient policy optimization in the RL stage.\n\n\\subsection{Reinforcement Learning with GRPO}\n\\label{sec:rl}\n\n\\subsubsection{Group Relative Policy Optimization}\n\nTo reduce the training cost of RL, we adopt Group Relative Policy Optimization (GRPO)~\\cite{shao2024deepseekmath}, which avoids training a separate critic by estimating advantages from a group of rollouts. \nGiven a training input $x$, GRPO samples a group of $G$ candidate responses $\\{y_i\\}_{i=1}^{G}$ from the old policy $\\pi_{\\theta_{\\text{old}}}$. The updated policy $\\pi_\\theta$ is optimized by maximizing the following objective:\n\\begin{equation}\n\\small\n\\begin{aligned}\n\\label{eq:grpo}\n \\mathcal{J}(\\theta) = \\mathbb{E}_{x \\sim \\mathcal{D}, \\{y_i\\}_{i=1}^{G} \\sim \\pi_{\\theta_{\\text{old}}}(\\cdot|x)} \n \\frac{1}{G} \\sum_{i=1}^{G} \\left[\\min \\left( p_i(\\theta) A_{i}, \\text{clip} \\left( p_i(\\theta), 1-\\epsilon, 1+\\epsilon \\right) A_{i} \\right) - \\beta \\mathbb{D}_{\\text{KL}} \\left( \\pi_{\\theta} || \\pi_{\\theta_{\\text{ref}}} \\right)\\right]\n\\end{aligned}\n\\end{equation}\nwhere $p_i = \\frac{\\pi_\\theta(y_i|x)}{\\pi_{\\theta_{\\text{old}}}(y_i|x)}$ denotes the probability ratio between the current and old policy, and the normalized advantage is computed as $A_i = \\frac{r_i - \\text{mean}(\\{r_j\\}_{j=1}^{G})}{\\text{std}(\\{r_j\\}_{j=1}^{G})}$, where $r_i$ is the reward of the $i$-th response.\nThe KL divergence term $\\mathbb{D}_{\\text{KL}}$ penalizes deviation from a reference policy $\\pi_{\\theta_{\\text{ref}}}$ to stabilize training. Here, $\\epsilon$ is the clipping threshold, and $\\beta$ controls the strength of the KL penalty.\n\n\n\\subsubsection{Reward Modeling}\n\nTo incentivize structured reasoning and ensure answer validity, we design a rule-based reward function that evaluates both the format and correctness of the model output. \nAs instructed in Table~\\ref{tab:prompt_template}, the model is required to generate responses in a strict format containing exactly one reasoning segment enclosed in \\thinkbegin...\\thinkend tags and one answer segment enclosed in \\answerbegin...\\answerend tags. Responses with incorrect tags, or extra texts, are considered invalid and receive a penalty.\n\nFor valid responses, we extract the predicted answer $\\hat{\\mathbf{y}} = [\\hat{y}_1, \\dots, \\hat{y}_N]$ and compare it to the ground truth labels $\\mathbf{y} = [y_1, \\dots, y_N]$. The batch-level reward is computed as:\n\\begin{equation}\n\\small\nR_{\\text{batch}} = \n\\begin{cases}\n\\prod_{i=1}^N \\mathbf{1}(\\hat{y}_i = y_i), & \\text{if format is valid} \\\\\n-1, & \\text{if format is invalid}\n\\end{cases}\n\\end{equation}\nHere, $\\mathbf{1}(\\cdot)$ is an indicator function. This setup ensures that a reward of 1 is given only when \\emph{all} cell types in the batch are correctly predicted; any incorrect prediction results in a reward of 0. \n\n\n\n\n\n\n\\section{Experiments}\n\n\\subsection{Configuration}\n\\label{sec:config}\n\\noindent \\textbf{Datasets.} \nWe conduct experiments on the proposed \\Bench. Each instance comprises $8 \\leq N \\leq 15$ cells, each represented by its ranked top-expressed genes and a shared donor-level contextual description.\nFor training, we perform reasoning distillation using a strong reasoning model to generate 3,912 high-quality reasoning traces, which are then used for SFT. In addition, we sample 3,000 extra instances to construct an RL dataset, resulting in a total of 6,912 instances for the GRPO stage. A held-out test set of 1,095 batches is used for final evaluation.\nMore details on the dataset processing can be found in the Supplementary Materials.\n\n\\noindent \\textbf{Evaluation Metrics.}\nWe report results using the following metrics:\n(1) \\textit{Cell-level Accuracy:} The average proportion of correctly predicted labels per batch.\n(2) \\textit{Batch-level Accuracy:} The proportion of batches where all predicted labels exactly match the ground truth.\n(3) \\textit{Format Validity:} The proportion of outputs that follow the required response format.\n(4) \\textit{Answer Uniqueness:} The average proportion of unique cell type predictions per batch.\n\n\n\\subsection{Main Results}\n\n\nTable~\\ref{tab:cell_annotation_results} summarizes the overall performance of all models on the \\Bench test set. We compare open-source LLMs (e.g., \\texttt{Llama}~\\cite{touvron2023llama}, \\texttt{Qwen}~\\cite{bai2023qwen}), closed-source LLMs with strong reasoning capabilities (e.g., \\texttt{GPT-4o}~\\cite{achiam2023gpt}, \\texttt{o1}), and instruction-tuned baselines that directly predict answers from the input without producing reasoning traces. \nMore details are in the Supplementary Materials.\n\n\\begin{table*}[h!]\n\\centering\n\\small\n\\footnotesize\n\\renewcommand\\tabcolsep{4.5pt}\n\\renewcommand\\arraystretch{1.05}\n\\caption{Evaluation of model performance on the \\Bench benchmark. The best and second-best results are highlighted in \\colorbox{backred!50}{red} and \\colorbox{backblue!75}{blue}, respectively.}\n\\resizebox{0.93\\textwidth}{!}{\n\\begin{tabular}{l|c|c|c|c|c}\n\\toprule\n\\header{Model} & \\header{Response Length} & \\header{Cell-level Acc} & \\header{Batch-level Acc} & \\header{Format} & \\header{Uniqueness} \\\\\n\\midrule\n\\rowcolor[rgb]{0.93,0.93,0.93} \\multicolumn{6}{c}{\\textit{Zero-shot performance (w/ reasoning)}} \\\\\n\\texttt{Llama3.1-8B-Instruct} & 2264.9 & 0.0050 & 0.0000 & 0.0393 & 0.0190 \\\\\n\\texttt{Qwen2.5-7B-Instruct} & 879.8 & 0.1353 & 0.0000 & 0.9425 & 0.4528 \\\\\n\\texttt{Llama3.3-70B-Instruct} & 1006.8 & 0.2573\t& 0.0064 & 0.9973 & 0.8321 \\\\\n\\texttt{GPT-4o-mini} & 779.9 & 0.2581 & 0.0027 & 0.9918 & 0.6565 \\\\\n\\texttt{GPT-4o} & 1087.0 & 0.4248 & 0.0283 & 0.9817 & 0.8179 \\\\\n\\texttt{o3-mini} & 1130.2 & 0.5804 & 0.1352 & \\colorbox{backred!50}{1.0000} & \\colorbox{backred!50}{1.0000} \\\\\n\\texttt{o1} & 906.6 & \\colorbox{backblue!75}{0.6479} & 0.1900 & \\colorbox{backred!50}{1.0000} & \\colorbox{backblue!75}{0.9997} \\\\\n% Cell2Sentence & -- & & & & \\\\\n\\rowcolor[rgb]{0.93,0.93,0.93} \\multicolumn{6}{c}{\\textit{Instruction-tuning Performance (w/o reasoning)}} \\\\\n\\texttt{Llama3.1-8B-Instruct} & 71.9 & 0.6411 & \\colorbox{backblue!75}{0.2969} & 0.9078 & 0.9071 \\\\\n\\texttt{Qwen2.5-7B-Instruct} & 70.9 & 0.6179 & 0.2475 & 0.9443 & 0.9404 \\\\\n% Cell2Sentence & -- & & & & \\\\\n\\rowcolor[rgb]{0.93,0.93,0.93} \\multicolumn{6}{c}{\\textit{Ours (w/ reasoning)}} \\\\\n\\Ours & 1145.6 & \\colorbox{backred!50}{0.6849} & \\colorbox{backred!50}{0.3288} & 0.9826 & 0.9824 \\\\\n\\bottomrule\n\\end{tabular}\n}\n\\label{tab:cell_annotation_results}\n\\end{table*}\n\n\n\\Ours achieves the highest accuracy at both the cell and batch levels, outperforming all other models. Cell-level accuracy is consistently higher than batch-level accuracy across all models, which is expected given the stricter requirement of the latter.\nWhile it trails slightly behind \\texttt{o1} and \\texttt{o3-mini} in format validity and answer uniqueness, \\Ours still performs competitively on these dimensions, especially considering its smaller model size.\nZero-shot models like \\texttt{Llama3.1-8B-Instruct} often produce overly long outputs due to repeating cell labels or generating more predictions than required. Instruction-tuned variants without reasoning generate much shorter responses with better format adherence, but lack the ability to perform explicit, interpretable reasoning.\nClosed-source models such as \\texttt{GPT-4o} and \\texttt{o1} show stronger instruction-following and reasoning consistency, producing well-structured outputs more reliably than most open-source models.\n\nTo further understand model behavior under varying conditions, we conduct a fine-grained analysis.\nFigure~\\ref{fig:acc_distribution} (A) shows that all models experience a decline in accuracy as the number of cells per batch increases, with \\Ours consistently achieving higher scores across the full range.\nFigure~\\ref{fig:acc_distribution} (B) further shows that \\Ours achieves the highest median and lowest variance in per-batch cell-level accuracy, indicating both strong and stable predictions across diverse contexts.\n\n\\begin{figure}[h!]\n\\centering\n\\includegraphics[width=1.0\\textwidth]{figs/acc_dis.pdf}\n\\caption{\nFine-grained analysis of model performance.\n(A) Cell-level and batch-level accuracy across varying numbers of cells per batch. \n(B) Distribution of per-batch cell-level accuracy across models.\n}\n\\label{fig:acc_distribution}\n\\end{figure}\n\n\n\\subsection{Generalization to Unseen Diseases}\n\n\nTo evaluate the robustness and generalization ability of \\Ours, we conduct evaluation on cell batches from disease types that were not seen during training. This setting simulates real-world scenarios where models are applied to new biological conditions without task-specific fine-tuning. \n\n\\begin{table*}[h!]\n\\centering\n\\footnotesize\n\\renewcommand\\tabcolsep{4pt}\n\\renewcommand\\arraystretch{1.05}\n\\caption{Evaluation of model performance on unseen disease datasets. The best and second-best results are highlighted in \\colorbox{backred!50}{red} and \\colorbox{backblue!75}{blue}, respectively.}\n\\resizebox{\\textwidth}{!}{\n\\begin{tabular}{l|cc|cc|cc|cc|cc}\n\\toprule\n\\header{Model} & \\multicolumn{2}{c|}{\\textit{Breast Cancer}} & \\multicolumn{2}{c|}{\\textit{Melanoma}} & \\multicolumn{2}{c|}{\\textit{SLE}} & \\multicolumn{2}{c|}{\\textit{Colorectal Cancer}} & \\multicolumn{2}{c}{Average} \\\\\n & Cell & Batch & Cell & Batch & Cell & Batch & Cell & Batch & Cell & Batch \\\\\n\\midrule\n\\rowcolor[rgb]{0.93,0.93,0.93} \\multicolumn{11}{c}{\\textit{Zero-shot (w/ reasoning)}} \\\\\n\\texttt{Llama3.1-8B-Instruct} & 0.0075 & 0.0000 & 0.0179 & 0.0000 & 0.0035 & 0.0000 & 0.0147 & 0.0000 & 0.0109 & 0.0000 \\\\\n\\texttt{Qwen2.5-7B-Instruct} & 0.2382 & 0.0000 & 0.2679 & 0.0000 & 0.2400 & 0.0000 & 0.1996 & 0.0000 & 0.2364 & 0.0000 \\\\\n\\texttt{Llama3.3-70B-Instruct} & 0.4400 & 0.0000 & 0.5337 & 0.0159 & 0.3165 & 0.0100 & 0.4218 & 0.0000 & 0.4280 & 0.0065 \\\\\n\\texttt{GPT-4o-mini} & 0.4171 & 0.0000 & 0.5198 & 0.0000 & 0.4129 & 0.0000 & 0.4549 & 0.0000 & 0.4512 & 0.0000 \\\\\n\\texttt{GPT-4o} & 0.5847 & 0.0560 & 0.7619 & 0.1587 & 0.6576 & 0.1000 & 0.5715 & 0.0239 & 0.6439 & 0.0847 \\\\\n\\texttt{o3-mini} & 0.7806 & 0.2960& 0.7341 & 0.1111 & 0.7706 & 0.3200 & 0.7208 & 0.1713 & 0.7515 & 0.2246 \\\\\n\\texttt{o1} & \\colorbox{backred!50}{0.8136} & \\colorbox{backblue!75}{0.3200} & 0.7738 & 0.1270 & \\colorbox{backblue!75}{0.8200} & \\colorbox{backblue!75}{0.3700} & \\colorbox{backred!50}{0.7845} & \\colorbox{backred!50}{0.3108} & \\colorbox{backblue!75}{0.7980} & 0.2819 \\\\\n\\rowcolor[rgb]{0.93,0.93,0.93} \\multicolumn{11}{c}{\\textit{Instruction-tuned (w/o reasoning)}} \\\\\n\\texttt{Llama3.1-8B-Instruct} & 0.7872 & 0.2880 & \\colorbox{backblue!75}{0.7837} & \\colorbox{backblue!75}{0.3333} & 0.7176 & 0.2500 & 0.7208 & 0.2590 & 0.7523 & \\colorbox{backblue!75}{0.2826} \\\\\n\\texttt{Qwen2.5-7B-Instruct} & 0.7250 & 0.1600 & 0.7282 & 0.1746 & 0.5553 & 0.0300 & 0.5816 & 0.1036 & 0.6475 & 0.1170 \\\\\n\\rowcolor[rgb]{0.93,0.93,0.93} \\multicolumn{11}{c}{\\textit{Ours (w/ reasoning)}} \\\\\n\\Ours & \\colorbox{backblue!75}{0.8089} & \\colorbox{backred!50}{0.3360} & \\colorbox{backred!50}{0.8591} & \\colorbox{backred!50}{0.5397} & \\colorbox{backred!50}{0.8235} & \\colorbox{backred!50}{0.3800} & \\colorbox{backblue!75}{0.7820} & \\colorbox{backblue!75}{0.3028} & \\colorbox{backred!50}{0.8184} & \\colorbox{backred!50}{0.3896} \\\\\n\\bottomrule\n\\end{tabular}\n}\n\\label{tab:generalization}\n\\end{table*}\n\nSpecifically, we collect raw scRNA-seq data from four disease conditions—\\textit{Breast Cancer}, \\textit{Melanoma}, \\textit{Systemic Lupus Erythematosus (SLE)}, and \\textit{Colorectal Cancer}—and follow the \\Bench construction protocol to build corresponding test batches for zero-shot evaluation, resulting in a total of 539 annotated instances.\nAs shown in Table~\\ref{tab:generalization}, \\Ours consistently outperforms all baselines across both cell- and batch-level accuracy. Closed-source models such as \\texttt{o1} exhibit strong generalization, but \\Ours still leads by a significant margin despite its smaller model size. Compared to instruction-tuned models, which tend to make direct predictions without reasoning, \\Ours generalizes more effectively to unseen disease conditions. This may be attributed to its ability to reason step-by-step during prediction, allowing it to adapt more flexibly to novel biological contexts. \n\n\n\n\\section{Analysis and Discussion}\n\n\\subsection{Training and Inference Dynamics under Different Configurations}\n\n\nTo understand how different training strategies affect model performance and behavior, we compare four training configurations that differ along three dimensions: the presence of SFT initialization, the RL algorithm (GRPO vs. PPO (Proximal Policy Optimization)~\\cite{schulman2017proximal}), and the design of the reward function.\nAs shown in Figure~\\ref{fig:dynamics}, \\Ours (A) with SFT, GRPO, and batch-level reward achieves stable training, controlled response length, and the best test performance.\nThe response length fluctuates briefly at the beginning of training and then stabilizes, indicating that the model learns to balance output structure and reward optimization during training.\nRemoving SFT initialization (B) leads to ineffective reward learning and a sharp increase in response length, indicating poor task alignment. \nReplacing GRPO with PPO (C) results in noisier reward trajectories and limited test performance gains, likely due to a mismatch between the reward objective and the final evaluation metric.\nThe steadily declining response length suggests that PPO encourages shorter, less informative outputs to stabilize rewards, potentially at the expense of reasoning quality.\nConfiguration (D) augments the reward with a cell-level accuracy component, using the average of cell and batch-level accuracy as the training signal. This leads to higher training reward scores, but slightly less stable test performance, which remains overall comparable to configuration (A).\n\n\\begin{figure}[h!]\n\\centering\n\\includegraphics[width=1.0\\textwidth]{figs/dynamics.pdf}\n\\caption{\nComparing training and inference dynamics across different configurations.\n}\n\\label{fig:dynamics}\n\\end{figure}\n\n\n\n\\subsection{Qualitative Assessment of Reasoning Traces}\n\n\n\nTo qualitatively assess the reasoning capabilities of our model, we conducted a human evaluation comparing its outputs against several baselines. We randomly sampled 100 test instances from \\Bench. \nFor each instance, we constructed a step-by-step prediction diagram for each model, indicating the order in which cells were annotated (denoted by numbers), the predicted label, and whether each step was correct.\nIf incorrect, experts further categorized the error into one of five predefined types \n%(detailed in Appendix~\\ref{app_sec:evaluation}).\n(detailed in Supplementary Materials).\nFigure~\\ref{fig:reasoning_analysis} (A) shows one example. \n\nTo avoid bias, expert comparisons were conducted in a blind setting: for each instance, reasoning outputs from \\Ours and the baseline models were anonymized and presented in a randomized order.\nExperts then provided an overall judgment by comparing the reasoning quality across models, resulting in a win/tie/loss outcome for each pairwise comparison, as shown in Figure~\\ref{fig:reasoning_analysis} (B).\n\nDuring the human evaluation, we observed two interesting patterns exhibited by \\Ours: self-reflection and curriculum reasoning.\nThe former refers to the model’s ability to revisit and revise its intermediate conclusions, while the latter reflects its tendency to tackle easier or more confident cases first before addressing more challenging ones. As shown in Figure~\\ref{fig:reasoning_analysis} (C), \\Ours is the only model that consistently demonstrates both behaviors.\nThis highlights its ability to structure reasoning more deliberately—similar to how human experts approach complex annotation tasks.\n\n\\begin{figure}[h!]\n\\centering\n\\includegraphics[width=1.0\\textwidth]{figs/reasoning.pdf}\n\\caption{\nAnalysis of model reasoning behavior. \n(A) Step-by-step reasoning traces generated by each model for the same task. \n(B) Expert evaluation of reasoning quality comparing \\Ours with baseline models.\n(C) Proportion of traces exhibiting self-reflection and curriculum reasoning behaviors.\n}\n\\label{fig:reasoning_analysis}\n\\end{figure}\n\n\n\\subsection{Limitations and Broader Impacts}\nWhile \\Ours demonstrates strong batch-level reasoning for scRNA-seq cell type annotation, it relies on predefined candidate label sets, which limits its applicability to novel cell types. \\Ours was trained solely on Cell\\(\\times\\)Gene data so its generalizability to other modalities or under-represented tissues remains untested. Additionally, \\Ours depends on sparse and high-variance RL rewards that require careful hyperparameter tuning, may produce plausible but incorrect reasoning, and incur non-trivial computational overhead.\n\nNevertheless, by providing interpretable, group-aware predictions, \\Ours can accelerate annotation pipelines and democratize access to high-quality labels, which potentially fuels discoveries in immunology, oncology, and developmental biology. On the other hand, it also poses risks that misapplication could propagate errors into downstream analyses, and large-scale pre-training and fine-tuning carry environmental and economic costs, underscoring the need for responsible, human-in-the-loop deployment and careful consideration of data privacy and dual-use concerns.\n\n\\label{sec:limitation}\n\n\n\\section{Conclusion}\n\nThis work presents \\Bench, a structured reasoning benchmark for batch-level cell type annotation designed to emulate expert workflows. \nWe further introduce \\Ours, an LLM that demonstrates emergent expert-like reasoning behaviors—such as self-reflection and curriculum annotation—enabling accurate and interpretable predictions across diverse cellular contexts. \nLooking ahead, future directions include incorporating retrieval-augmented methods~\\cite{jin2025search,jiang2025deepretrieval,xiong2025rag,chen2025research} to integrate external knowledge, leveraging domain ontologies~\\cite{hoehndorf2015role,ashburner2000gene} for more consistent reasoning~\\cite{jin2024impact}, and extending structured reasoning to broader biomedical applications~\\cite{tian2024opportunities}.\n\n\n\\section*{Acknowledgments}\nThis research was supported by the Division of Intramural Research (DIR) of the National Library of Medicine (NLM), National Institutes of Health.\n\n\n\\bibliography{references}\n\\bibliographystyle{unsrtnat}\n\n\n\n\\newpage\n\n\\newpage\n\\appendix\n\n\\begin{center}\n\\Large\n\\textbf{Appendix}\n\\end{center}\n\n\n\\section{Dataset}\n\\subsection{Data Sources}\n\nOur benchmark is constructed by collecting and curating a diverse range of high-quality single-cell RNA sequencing (scRNA-seq) datasets from publicly available sources, primarily hosted on the CELLxGENE platform~\\cite{czi2025cz}. These datasets cover a wide spectrum of human tissues, diseases, and biological conditions, ensuring both diversity and real-world relevance for evaluating model generalization.\n\nTable~\\ref{tab:data_source} summarizes all datasets used in our study, along with their corresponding accession links and references. The collection includes data from various immune-related diseases (e.g., rheumatoid arthritis, lupus, HIV), cancer types (e.g., lung adenocarcinoma, ovarian cancer), as well as healthy tissues across developmental and anatomical contexts. \n\n\\begin{table*}[h]\n\\centering\n\\footnotesize\n\\renewcommand\\tabcolsep{9pt}\n\\renewcommand\\arraystretch{1.05}\n\\caption{\nRaw scRNA-seq data resources involved in our paper.}\n\\resizebox{\\textwidth}{!}{\n\\begin{tabular}{p{9cm} p{7.8cm}}\n\\toprule\n\\textbf{Data Source Name} & \\textbf{URL} \\\\\n\\midrule\nSingle-cell Human Microglia~\\cite{olah2020single} & \\small \\url{https://cellxgene.cziscience.com/collections/fcb3d1c1-03d2-41ac-8229-458e072b7a1c} \\\\\n\\midrule\nSingle-Cell RNA-Seq Analysis Reveals Cell Subsets And Gene Signatures Associated With Rheumatoid Arthritis Disease Activity~\\cite{binvignat2024single} & \\small \\url{https://cellxgene.cziscience.com/collections/e1a9ca56-f2ee-435d-980a-4f49ab7a952b} \\\\\n\\midrule\nThe Single-Cell Lung Cancer Atlas (LuCA)~\\cite{salcher2022high} & \n\\small \\url{https://cellxgene.cziscience.com/collections/edb893ee-4066-4128-9aec-5eb2b03f8287} \\\\\n\\midrule\nCells Of The Human Intestinal Tract Mapped Across Space And Time~\\cite{elmentaite2021cells} & \\small \\url{https://cellxgene.cziscience.com/collections/e33ffcd3-7cbf-4b8c-b0f4-85587ad5019a} \\\\\n\\midrule\nPersistent T Cell Unresponsiveness Associated With Chronic Visceral Leishmaniasis In HIV-Coinfected Patients~\\cite{de2024persistent} & \\small \\url{https://cellxgene.cziscience.com/collections/126afc71-47fb-4e9d-8aaf-9d9f61e0ac77} \\\\\n\\midrule\nMultiomics Single-Cell Analysis Of Human Pancreatic Islets Reveals Novel Cellular States In Health And Type 1 Diabetes~\\cite{fasolino2022single} & \\small \\url{https://cellxgene.cziscience.com/collections/51544e44-293b-4c2b-8c26-560678423380} \\\\\n\\midrule\nThe Integrated Human Lung Cell Atlas~\\cite{sikkema2023integrated} & \\small \\url{https://cellxgene.cziscience.com/collections/6f6d381a-7701-4781-935c-db10d30de293} \\\\\n\\midrule\nSingle-Cell Transcriptomics Of The Human Retinal Pigment Epithelium And Choroid In Health And Macular Degeneration~\\cite{voigt2019single} & \\small \\url{https://cellxgene.cziscience.com/collections/f8057c47-fcd8-4fcf-88b0-e2f930080f6e} \\\\\n\\midrule\nSingle-Cell RNA-Seq Of The Adult Human Kidney~\\cite{lake2023atlas} & \\small \\url{https://cellxgene.cziscience.com/collections/bcb61471-2a44-4d00-a0af-ff085512674c} \\\\\n\\midrule\nMSK SPECTRUM – Ovarian Cancer Mutational Processes Drive Site-Specific Immune Evasion~\\cite{vazquez2022ovarian} & \\small \\url{https://cellxgene.cziscience.com/collections/4796c91c-9d8f-4692-be43-347b1727f9d8} \\\\\n\\midrule\nHTAN-CHOP - Single-Cell Multiomics Reveals Increased Plasticity, Resistant Populations, And Stem-Cell–Like Blasts In KMT2A-Rearranged Leukemia~\\cite{chen2022single} & \\small \\url{https://cellxgene.cziscience.com/collections/10ec9198-584e-4a7e-8a24-4a332915a4ef} \\\\\n\\midrule\nSingle-Cell Atlas Of Common Variable Immunodeficiency Shows Germinal Center-Associated Epigenetic Dysregulation In B-Cell Responses~\\cite{rodriguez2022single} & \\small \\url{https://cellxgene.cziscience.com/collections/bf325905-5e8e-42e3-933d-9a9053e9af80} \\\\\n\\midrule\nDissecting Novel Myeloid-Derived Cell States Through Single-Cell RNA-Seq And Its Impact On Clinical Outcome Across Tumor Types~\\cite{guimaraes2024single} & \\small \\url{https://cellxgene.cziscience.com/collections/3f7c572c-cd73-4b51-a313-207c7f20f188} \\\\\n\\bottomrule\n\\end{tabular}\n}\n\\label{tab:data_source}\n\\end{table*}\n\n\\subsection{Data Statistics}\n\nTo characterize the diversity and distribution of our curated dataset, we conduct a comprehensive analysis across three axes: disease, tissue, and cell type.\n\n\\begin{table*}[h!]\n\\centering\n\\footnotesize\n\\renewcommand\\tabcolsep{4pt}\n\\renewcommand\\arraystretch{1.05}\n\\caption{Number of samples per disease in the dataset.}\n\\resizebox{0.9\\textwidth}{!}{\n\\begin{tabular}{l r @{\\hskip 1.5cm} l r}\n\\toprule\n\\textbf{Disease} & \\textbf{\\#Samples} & \\textbf{Disease} & \\textbf{\\#Samples} \\\\\n\\midrule\nLung Adenocarcinoma & 1292 & Crohn Disease & 215 \\\\\nChronic Obstructive Pulmonary Disease & 1194 & Lung Large Cell Carcinoma & 211 \\\\\nSquamous Cell Lung Carcinoma & 946 & Non-Specific Interstitial Pneumonia & 172 \\\\\nNormal & 941 & Chronic Rhinitis & 158 \\\\\nInterstitial Lung Disease & 777 & Hypersensitivity Pneumonitis & 126 \\\\\nPulmonary Fibrosis & 669 & Lymphangioleiomyomatosis & 109 \\\\\nNon-Small Cell Lung Carcinoma & 632 & Pleomorphic Carcinoma & 87 \\\\\nB-Cell Acute Lymphoblastic Leukemia & 74 & Pulmonary Sarcoidosis & 58 \\\\\nPneumonia & 48 & Acute Myeloid Leukemia & 45 \\\\\nCommon Variable Immunodeficiency & 45 & Type 1 Diabetes Mellitus & 35 \\\\\nAcute Promyelocytic Leukemia & 28 & Rheumatoid Arthritis & 23 \\\\\nAcute Kidney Failure & 20 & Malignant Ovarian Serous Tumor & 18 \\\\\nChronic Kidney Disease & 18 & Age Related Macular Degeneration 7 & 13 \\\\\nBasal Laminar Drusen & 12 & Cystic Fibrosis & 11 \\\\\nHiv Infectious Disease & 11 & Alzheimer Disease & 8 \\\\\nCataract & 7 & Temporal Lobe Epilepsy & 4 \\\\\n\\bottomrule\n\\end{tabular}\n}\n\\label{tab:disease_stats}\n\\end{table*}\n\nTable~\\ref{tab:disease_stats} summarizes the number of samples for each disease. The dataset spans a wide range of pathological conditions, including both common and rare diseases, such as lung adenocarcinoma, rheumatoid arthritis, HIV infection, and macular degeneration.\n\n\\begin{table}[ht]\n\\centering\n\\footnotesize\n\\renewcommand\\tabcolsep{4pt}\n\\renewcommand\\arraystretch{1.05}\n\\caption{Number of unique cell types per tissue in the dataset.}\n\\vskip 0.06in\n\\resizebox{0.75\\textwidth}{!}{\n\\begin{tabular}{l r @{\\hskip 1.5cm} l r}\n\\toprule\n\\textbf{Tissue} & \\textbf{\\#Cell Types} & \\textbf{Tissue} & \\textbf{\\#Cell Types} \\\\\n\\midrule\nIleum & 71 & Adrenal Tissue & 18 \\\\\nLung & 56 & Rectum & 18 \\\\\nBlood & 48 & Ascending Colon & 15 \\\\\nDuodenum & 48 & Mesenteric Lymph Node & 11 \\\\\nColon & 40 & Peripheral Region Of Retina & 10 \\\\\nBone Marrow & 38 & Fovea Centralis & 10 \\\\\nNose & 36 & Right Ovary & 9 \\\\\nSigmoid Colon & 35 & Peritoneum & 9 \\\\\nVermiform Appendix & 33 & Islet Of Langerhans & 9 \\\\\nTransverse Colon & 31 & Omentum & 9 \\\\\nDescending Colon & 31 & Ascitic Fluid & 9 \\\\\nLymph Node & 30 & Left Ovary & 9 \\\\\nBrain & 27 & Adnexa Of Uterus & 9 \\\\\nCaecum & 27 & Diaphragm & 8 \\\\\nJejunum & 27 & Small Intestine & 8 \\\\\nKidney & 26 & Abdominal Wall & 8 \\\\\nPleural Effusion & 23 & Temporal Cortex & 6 \\\\\nLiver & 21 & Dorsolateral Prefrontal Cortex & 5 \\\\\n\\bottomrule\n\\end{tabular}\n}\n\\label{tab:tissue_stats}\n\\end{table}\n\nTable~\\ref{tab:tissue_stats} reports the number of unique cell types observed in each tissue. Tissues such as ileum, lung, and blood show high diversity, indicating rich intra-tissue heterogeneity for reasoning tasks.\n\n% \\input{tables/celltype}\n\\begin{table}[ht]\n\\centering\n\\footnotesize\n\\renewcommand\\tabcolsep{4pt}\n\\renewcommand\\arraystretch{1.05}\n\\caption{Frequency of cell types in the dataset (Part I).}\n\\vskip 0.06in\n\\resizebox{\\textwidth}{!}{\n\\begin{tabular}{p{6cm} r p{6cm} r}\n\\toprule\n\\textbf{Cell Type} & \\textbf{\\#Instances} & \\textbf{Cell Type} & \\textbf{\\#Instances} \\\\\n\\midrule\nCD4-Positive, Alpha-Beta T Cell & 4428 & Fibroblast & 72 \\\\\nCD8-Positive, Alpha-Beta T Cell & 4276 & Mucosal Invariant T Cell & 70 \\\\\nClassical Monocyte & 4167 & Precursor B Cell & 70 \\\\\nNatural Killer Cell & 4097 & Glial Cell & 68 \\\\\nAlveolar Macrophage & 4051 & Mature B Cell & 68 \\\\\nCd1C-Positive Myeloid Dendritic Cell & 3965 & Pro-B Cell & 68 \\\\\nB Cell & 3495 & Stem Cell & 67 \\\\\nPulmonary Alveolar Type 2 Cell & 3395 & Early Pro-B Cell & 66 \\\\\nMast Cell & 3062 & Stromal Cell Of Lamina Propria Of Small Intestine & 63 \\\\\nPlasma Cell & 2939 & Common Lymphoid Progenitor & 59 \\\\\nVein Endothelial Cell & 2659 & Immature B Cell & 58 \\\\\nCapillary Endothelial Cell & 2328 & Common Dendritic Progenitor & 57 \\\\\nNon-Classical Monocyte & 2120 & Megakaryocyte-Erythroid Progenitor Cell & 56 \\\\\nMacrophage & 2070 & Lung Neuroendocrine Cell & 54 \\\\\nPulmonary Alveolar Type 1 Cell & 1895 & Granulocyte Monocyte Progenitor Cell & 50 \\\\\nCiliated Columnar Cell Of Tracheobronchial Tree & 1734 & Reticular Cell & 48 \\\\\nElicited Macrophage & 1726 & Effector Memory Cd4-Positive, Alpha-Beta T Cell & 48 \\\\\nRegulatory T Cell & 1490 & Endothelial Cell Of Artery & 45 \\\\\nMalignant Cell & 1363 & Kidney Collecting Duct Principal Cell & 42 \\\\\nEndothelial Cell Of Lymphatic Vessel & 1362 & Effector Memory Cd8-Positive, Alpha-Beta T Cell & 42 \\\\\nEpithelial Cell Of Lung & 1272 & Epithelial Cell Of Proximal Tubule & 42 \\\\\nMulti-Ciliated Epithelial Cell & 1124 & Myeloid Dendritic Cell & 42 \\\\\nBronchus Fibroblast Of Lung & 1123 & Kidney Loop Of Henle Thick Ascending Limb Epithelial Cell & 41 \\\\\nPulmonary Artery Endothelial Cell & 1086 & Central Memory Cd4-Positive, Alpha-Beta T Cell & 41 \\\\\nPlasmacytoid Dendritic Cell & 948 & Epithelial Cell & 40 \\\\\nFibroblast Of Lung & 909 & Ionocyte & 39 \\\\\nRespiratory Basal Cell & 897 & Effector Memory Cd8-Positive, Alpha-Beta T Cell, Terminally Differentiated & 39 \\\\\nTracheobronchial Smooth Muscle Cell & 857 & Late Promyelocyte & 39 \\\\\nAlveolar Type 1 Fibroblast Cell & 844 & Kidney Interstitial Cell & 39 \\\\\nMyeloid Cell & 806 & T-Helper 17 Cell & 38 \\\\\nAlveolar Adventitial Fibroblast & 722 & Early Promyelocyte & 38 \\\\\n\\bottomrule\n\\end{tabular}\n}\n\\label{tab:celltype_stats_part1}\n\\end{table}\n\\begin{table}[ht]\n\\centering\n\\footnotesize\n\\renewcommand\\tabcolsep{4pt}\n\\renewcommand\\arraystretch{1.05}\n\\caption{Frequency of cell types in the dataset (Part II).}\n\\vskip 0.06in\n\\resizebox{\\textwidth}{!}{\n\\begin{tabular}{p{6cm} r p{6cm} r}\n\\toprule\n\\textbf{Cell Type} & \\textbf{\\#Instances} & \\textbf{Cell Type} & \\textbf{\\#Instances} \\\\\n\\midrule\nEpithelial Cell Of Lower Respiratory Tract & 702 & Germinal Center B Cell & 38 \\\\\nConventional Dendritic Cell & 701 & Class Switched Memory B Cell & 37 \\\\\nClub Cell & 685 & Kidney Connecting Tubule Epithelial Cell & 36 \\\\\nDendritic Cell & 681 & Hematopoietic Stem Cell & 36 \\\\\nLung Pericyte & 569 & Unswitched Memory B Cell & 36 \\\\\nSmooth Muscle Cell & 464 & Mononuclear Phagocyte & 36 \\\\\nT Cell & 463 & Kidney Collecting Duct Intercalated Cell & 34 \\\\\nNasal Mucosa Goblet Cell & 447 & Kidney Distal Convoluted Tubule Epithelial Cell & 33 \\\\\nPericyte & 439 & Lymphoid Lineage Restricted Progenitor Cell & 33 \\\\\nLung Macrophage & 422 & Plasmablast & 33 \\\\\nMesothelial Cell & 412 & Inflammatory Macrophage & 32 \\\\\nNeutrophil & 397 & Parietal Epithelial Cell & 31 \\\\\nStromal Cell & 264 & Myelocyte & 31 \\\\\nNaive B Cell & 242 & Kidney Loop Of Henle Thin Ascending Limb Epithelial Cell & 30 \\\\\nMemory B Cell & 235 & Bronchial Goblet Cell & 30 \\\\\nMyofibroblast Cell & 222 & Tracheobronchial Serous Cell & 30 \\\\\nCd8-Positive, Alpha-Beta Memory T Cell & 216 & T-Helper 1 Cell & 30 \\\\\nEndothelial Cell & 210 & Melanocyte & 29 \\\\\nMonocyte & 191 & Schwann Cell & 29 \\\\\nGamma-Delta T Cell & 174 & Retinal Pigment Epithelial Cell & 28 \\\\\nNaive Thymus-Derived Cd4-Positive, Alpha-Beta T Cell & 140 & Kidney Loop Of Henle Thin Descending Limb Epithelial Cell & 26 \\\\\nActivated Cd8-Positive, Alpha-Beta T Cell & 126 & Erythroid Progenitor Cell & 26 \\\\\nIga Plasma Cell & 125 & Intestinal Tuft Cell & 25 \\\\\nPancreatic A Cell & 123 & Central Memory Cd8-Positive, Alpha-Beta T Cell & 25 \\\\\nPancreatic Ductal Cell & 123 & Kidney Interstitial Alternatively Activated Macrophage & 25 \\\\\nPancreatic D Cell & 122 & Basophil Mast Progenitor Cell & 24 \\\\\nPancreatic Stellate Cell & 122 & Serous Secreting Cell & 23 \\\\\nPp Cell & 122 & Cd4-Positive, Alpha-Beta Cytotoxic T Cell & 22 \\\\\nPancreatic Acinar Cell & 122 & Colon Epithelial Cell & 22 \\\\\nNaive Thymus-Derived Cd8-Positive, Alpha-Beta T Cell & 121 & Cytotoxic T Cell & 21 \\\\\nType B Pancreatic Cell & 119 & Acinar Cell & 21 \\\\\nMature Nk T Cell & 119 & Type Ec Enteroendocrine Cell & 20 \\\\\nCd16-Positive, Cd56-Dim Natural Killer Cell, Human & 118 & Erythroid Lineage Cell & 19 \\\\\nCd4-Positive, Alpha-Beta Memory T Cell & 117 & Fallopian Tube Secretory Epithelial Cell & 18 \\\\\nActivated Cd4-Positive, Alpha-Beta T Cell & 116 & M Cell Of Gut & 18 \\\\\nTransit Amplifying Cell & 104 & Mucus Secreting Cell & 15 \\\\\nIntestine Goblet Cell & 102 & Megakaryocyte Progenitor Cell & 15 \\\\\nBrush Cell Of Tracheobronchial Tree & 96 & Mesenchymal Lymphangioblast & 13 \\\\\nIgg Plasma Cell & 94 & Non-Terminally Differentiated Cell & 13 \\\\\nCd16-Negative, Cd56-Bright Natural Killer Cell, Human & 86 & Podocyte & 13 \\\\\nPancreatic Epsilon Cell & 85 & Cd14-Positive Monocyte & 12 \\\\\nEpithelial Cell Of Alveolus Of Lung & 84 & Plasmacytoid Dendritic Cell, Human & 12 \\\\\nRespiratory Hillock Cell & 83 & Enteroendocrine Cell & 12 \\\\\nEnterocyte Of Colon & 79 & Microglial Cell & 12 \\\\\nT Follicular Helper Cell & 75 & Group 3 Innate Lymphoid Cell & 11 \\\\\nHematopoietic Multipotent Progenitor Cell & 75 & Erythrocyte & 10 \\\\\n\\bottomrule\n\\end{tabular}\n}\n\\label{tab:celltype_stats_part2}\n\\end{table}\n\n\nTable~\\ref{tab:celltype_stats_part1} and \\ref{tab:celltype_stats_part2} summarize the frequency distribution of all cell types in the dataset. The distribution follows a long-tail pattern: a small number of immune-related cell types (e.g., CD4+ T cells, monocytes, macrophages) appear frequently, while many others are rare or highly tissue-specific. This distribution reflects the biological heterogeneity of real-world data and underscores the importance of building generalizable and context-aware models capable of handling diverse cellular identities~\\cite{zhai2024distribution, zhao2025celler}.\n\n\n\n\\subsection{Task Definition}\n\nWe define the task as batch-level cell type assignment with contextual reasoning. Each input instance consists of a batch of $N$ cells sampled from the same donor, along with shared metadata (e.g., tissue, disease, sex, age) and a set of top expressed genes extracted from the gene expression profile of each cell. The model is also provided with $N$ candidate cell type labels. The goal is to assign each cell to a unique label, resulting in a one-to-one matching between cells and types.\n\nThis formulation differs from traditional LLM-based single-cell annotation, which typically treats each cell in isolation. By contrast, our task requires the model to reason jointly across the batch: decisions for one cell may depend on contextual clues or comparisons with other cells. This setup reflects the process followed by human experts, who often annotate cells by integrating expression trends, donor-level information, and biological prior knowledge.\n\nThe task also introduces combinatorial ambiguity, especially when the candidate cell types are biologically similar (e.g., naïve vs. memory T cells) or when the expression differences across cells are subtle. Effective solutions must leverage both global context and cell-level signals to make consistent, interpretable assignments.\n\n\\subsection{Data Construction}\n\n\\begin{table*}[h]\n\\centering\n\\footnotesize\n\\renewcommand\\tabcolsep{4pt}\n\\renewcommand\\arraystretch{1.05}\n\\caption{\nTemplates for converting structured donor metadata into context descriptions.}\n\\resizebox{0.9\\textwidth}{!}{\n\\begin{tabular}{l p{10.5cm}}\n\\toprule\n\\textbf{Feature} & \\textbf{Template} \\\\\n\\midrule\n\\raisebox{-.25\\height}{\\includegraphics[height=1.5em]{figs/symbol/sex.png}}\\; Sex &\n\\texttt{\\{sex\\}} $\\rightarrow$ The cell is from a \\texttt{\\{sex\\}} individual. \\\\\n\\raisebox{-.25\\height}{\\includegraphics[height=1.5em]{figs/symbol/growth.png}}\\; Development Stage & The individual is at the \\{\\texttt{development\\_stage}\\}. \\\\\n\\raisebox{-.25\\height}{\\includegraphics[height=1.5em]{figs/symbol/cross-country.png}}\\; Ethnicity & The donor has a \\{\\texttt{self\\_reported\\_ethnicity}\\} background. \\\\\n\\raisebox{-.25\\height}{\\includegraphics[height=1.5em]{figs/symbol/muscle-cross-section-healthy.png}}\\; Tissue & The cell originates from the \\{\\texttt{tissue}\\}. \\\\\n\\raisebox{-.25\\height}{\\includegraphics[height=1.5em]{figs/symbol/gene.png}}\\; Top Genes & \\{\\texttt{genes}\\} → Top expressed genes are: \\{\\texttt{genes}\\}. \\\\\n\\raisebox{-.25\\height}{\\includegraphics[height=1.5em]{figs/symbol/prevention.png}}\\; Disease & \n\\texttt{normal} → The patient is healthy with \\texttt{no diagnosed disease}. |\n\\texttt{other} → The patient has been diagnosed with \\{\\texttt{disease}\\}. \\\\\n\\raisebox{-.25\\height}{\\includegraphics[height=1.5em]{figs/symbol/cigarrete.png}}\\; Smoking Status & \n\\texttt{active} → The patient is an \\texttt{active} smoker. |\n\\texttt{former} → The patient is a \\texttt{former} smoker. |\n\\texttt{hist of marijuana use} → The patient has a \\texttt{history of marijuana use}; \n\\texttt{never} → The patient has \\texttt{never} smoked. \\\\\n\\raisebox{-.25\\height}{\\includegraphics[height=1.5em]{figs/symbol/microscope.png}}\\; Tumor Stage & \n\\texttt{non-cancer} $\\rightarrow$ There is \\texttt{no cancer} present. |\n\\texttt{early} $\\rightarrow$ The patient has an \\texttt{early}-stage tumor. |\n\\texttt{advanced} $\\rightarrow$ The patient has an \\texttt{advanced}-stage tumor. \\\\\n\\raisebox{-.25\\height}{\\includegraphics[height=1.5em]{figs/symbol/syringe.png}}\\; Sample Type & \n\\texttt{M3} $\\rightarrow$ The sample was collected at \\texttt{month 3} post-treatment. |\n\\texttt{M6} $\\rightarrow$ The sample was collected at \\texttt{month 6} post-treatment. |\n\\texttt{UV} $\\rightarrow$ The sample was exposed to \\texttt{ultraviolet (UV)} treatment. |\n\\texttt{CONTROL} $\\rightarrow$ The sample is from the \\texttt{control} group. \\\\\n\\raisebox{-.25\\height}{\\includegraphics[height=1.5em]{figs/symbol/brain.png}}\\; Hemisphere & \n\\texttt{left} $\\rightarrow$ The tissue sample was taken from the \\texttt{left} hemisphere of the brain. |\n\\texttt{right} $\\rightarrow$ The tissue sample was taken from the \\texttt{right} hemisphere of the brain. \\\\\n\\raisebox{-.25\\height}{\\includegraphics[height=1.5em]{figs/symbol/liver-cancer.png}}\\; Tumor Site & \n\\texttt{primary} $\\rightarrow$ The tumor is located at the \\texttt{primary} site. |\n\\texttt{metastasis} $\\rightarrow$ The tumor has \\texttt{metastasized} to other parts of the body. |\n\\texttt{normal} $\\rightarrow$ This sample was collected from non-tumorous tissue. \\\\\n\\raisebox{-.25\\height}{\\includegraphics[height=1.5em]{figs/symbol/lymph-nodes.png}}\\; Sample Source & \n\\texttt{tumor} $\\rightarrow$ The sample is derived from \\texttt{tumor} tissue. |\n\\texttt{normal} $\\rightarrow$ The sample is derived from \\texttt{normal} tissue. |\n\\texttt{blood} $\\rightarrow$ The sample is a \\texttt{blood}-derived specimen. |\n\\texttt{lymphnode} $\\rightarrow$ The sample is derived from \\texttt{lymph node} tissue. \\\\\n\\raisebox{-.25\\height}{\\includegraphics[height=1.5em]{figs/symbol/eosinophil.png}}\\; CD45 Expression & \n\\texttt{yes} $\\rightarrow$ The cell is CD45-\\texttt{positive}, suggesting an immune cell origin. |\n\\texttt{no} $\\rightarrow$ The cell is CD45-\\texttt{negative}, suggesting a non-immune cell lineage. |\n\\texttt{mixed} $\\rightarrow$ The sample contains a \\texttt{mixture} of CD45-positive and CD45-negative cells. \\\\\n\\raisebox{-.25\\height}{\\includegraphics[height=1.5em]{figs/symbol/diabetes.png}}\\; Diabetes History & \n\\texttt{yes} $\\rightarrow$ The patient \\texttt{has a history} of diabetes. |\n\\texttt{no} $\\rightarrow$ The patient \\texttt{does not have} diabetes. \\\\\n\\raisebox{-.25\\height}{\\includegraphics[height=1.5em]{figs/symbol/blood-pressure.png}}\\; Hypertension & \n\\texttt{yes} $\\rightarrow$ The patient \\texttt{has a history} of hypertension. |\n\\texttt{no} $\\rightarrow$ The patient \\texttt{does not have} hypertension. \\\\\n\\raisebox{-.25\\height}{\\includegraphics[height=1.5em]{figs/symbol/plasma-cells.png}}\\; Activation & \n\\texttt{activated} $\\rightarrow$ The sample was stimulated and represents \\texttt{activated} immune cells. |\n\\texttt{resting} $\\rightarrow$ The sample represents \\texttt{resting} (non-activated) immune cells. \\\\\n\\raisebox{-.25\\height}{\\includegraphics[height=1.5em]{figs/symbol/genotype.png}}\\; Genotype & \n\\texttt{FLT3-ITD,NPM1-MUT} $\\rightarrow$ The patient carries FLT3-ITD and NPM1 mutations. |\n\\texttt{FLT3-WT,NPM1-MUT} $\\rightarrow$ The patient carries a wild-type FLT3 and an NPM1 mutation. |\n\\texttt{APL} $\\rightarrow$ The patient is diagnosed with acute promyelocytic leukemia (APL). \\\\\n\\raisebox{-.25\\height}{\\includegraphics[height=1.5em]{figs/symbol/medical-condition.png}}\\; Medical Conditions & \n\\texttt{epilepsy} $\\rightarrow$ The patient has a history of \\texttt{epilepsy}. |\n\\texttt{tumor} $\\rightarrow$ The patient has a diagnosed brain \\texttt{tumor}. |\n\\texttt{hydrocephalus} $\\rightarrow$ The patient has \\texttt{hydrocephalus} (fluid buildup in the brain). |\n\\texttt{both} $\\rightarrow$ The patient has \\texttt{both} epilepsy and a brain tumor. |\n\\texttt{other} $\\rightarrow$ The patient has \\texttt{other} neurological conditions. |\n\\texttt{healthy} $\\rightarrow$ The donor was \\texttt{healthy} with no reported skin condition. |\n\\texttt{dm – non ulcer} $\\rightarrow$ The donor had diabetes \\texttt{mellitus without skin ulceration}. | \n\\texttt{keloid} $\\rightarrow$ The donor had a \\texttt{keloid}, which is an overgrowth of scar tissue. |\n\\texttt{localised scleroderma} $\\rightarrow$ The donor was diagnosed with \\texttt{localized scleroderma}. | \n\\texttt{scar} $\\rightarrow$ The donor had a typical \\texttt{scar} from prior skin injury. |\n\\texttt{\\{lung\\_condition\\}} $\\rightarrow$ The lung condition is described as \\texttt{\\{lung\\_condition\\}}. \\\\\n\\raisebox{-.25\\height}{\\includegraphics[height=1.5em]{figs/symbol/kidney.png}}\\; Egfr & \\texttt{\\{value\\}} $\\rightarrow$ The patient's estimated glomerular filtration rate (eGFR) is in the range \\texttt{\\{value\\}}. \\\\\n\\bottomrule\n\\end{tabular}\n}\n% \\vskip -0.1in\n\\label{tab:template}\n\\end{table*}\n\n\\begin{figure}[h!]\n\\centering\n\\includegraphics[width=1.0\\textwidth]{figs/data_construction.pdf}\n\\caption{\nOverview of data construction. Each batch consists of $N$ cells from the same donor, with associated metadata (e.g., tissue, disease, age, sex, smoking status) and gene expression profiles. Structured donor-level information is converted into natural language context, and cell-level features are enriched with the top expressed genes to support downstream reasoning.\n}\n\\label{fig:data_construction}\n\\end{figure}\n\n\nTo support the proposed task, we construct a benchmark dataset, \\Bench, by assembling donor-level metadata, gene expression profiles, and curated cell type annotations from publicly available scRNA-seq datasets. \n\nFigure~\\ref{fig:data_construction} illustrates the data construction pipeline. For each cell, we convert structured metadata fields into natural language context using predefined templates (see Table~\\ref{tab:template}). These templates cover a wide range of attributes, including sex, developmental stage, tissue, disease status, and immune history. When available, we also include molecular and histological details such as tumor stage, genotype, and expression markers. The templates aim to standardize the expression of metadata fields in natural language, ensuring consistency and interpretability across different input instances.\n\n\n\n\\section{Experimental Setup Details}\n\n\\subsection{Evaluation Metrics}\n\\label{app:metric_formulas}\n\nWe provide the formal definitions of the evaluation metrics used in our experiments.\n\n\\begin{itemize}[leftmargin=*]\n \\item \\textbf{Cell-level Accuracy:}\n \\[\n \\text{Cell Acc} = \\frac{1}{|\\mathcal{D}|} \\sum_{j=1}^{|\\mathcal{D}|} \\left( \\frac{1}{N_j} \\sum_{i=1}^{N_j} \\mathbf{1}(\\hat{y}^{(j)}_i = y^{(j)}_i) \\right),\n \\]\n where \\( \\mathcal{D} \\) is the set of evaluation batches, \\( N_j \\) is the number of cells in batch \\( j \\), and \\( \\hat{y}^{(j)}_i \\) and \\( y^{(j)}_i \\) denote the predicted and ground-truth labels of the \\( i \\)-th cell in batch \\( j \\).\n\n \\item \\textbf{Batch-level Accuracy:}\n \\[\n \\text{Batch Acc} = \\frac{1}{|\\mathcal{D}|} \\sum_{j=1}^{|\\mathcal{D}|} \\prod_{i=1}^{N_j} \\mathbf{1}(\\hat{y}^{(j)}_i = y^{(j)}_i),\n \\]\n A value of 1 is counted only when all labels in a batch are correct.\n\n \\item \\textbf{Format Validity:}\n \\[\n \\text{Format Validity} = \\frac{1}{|\\mathcal{D}|} \\sum_{j=1}^{|\\mathcal{D}|} \\mathbf{1}\\left( \\hat{\\mathbf{y}}^{(j)} \\in \\mathcal{F} \\right),\n \\]\n where $\\hat{\\mathbf{y}}^{(j)}$ is the predicted label sequence for batch $j$, and $\\mathcal{F}$ denotes the set of responses that follow the required format (e.g., tag correctness, answer presence, etc.).\n\n \\item \\textbf{Answer Uniqueness:}\n \\[\n \\text{Uniqueness} = \\frac{1}{|\\mathcal{D}|} \\sum_{j=1}^{|\\mathcal{D}|} \\frac{|\\text{set}(\\hat{\\mathbf{y}}^{(j)})|}{N_j}\n \\]\n This metric quantifies how often the model avoids repeated predictions within a batch.\n\\end{itemize}\n\n\n\\subsection{Baselines}\n\\label{app_sec:baselines}\n\nTo contextualize the performance of \\Ours, we compare it against a range of baseline models, covering both open- and closed-source LLMs. All baselines are evaluated using the same input format and candidate label set for fair comparison.\n\n\\textit{Zero-shot (with reasoning)}:\nThese models are prompted with our structured format, including metadata, gene expression, and candidate labels, but are not trained specifically on this task.\n\n\\begin{itemize}[leftmargin=*]\n\\item \\texttt{LLaMA3.1-8B-Instruct}: A multilingual, instruction-tuned model with 8 billion parameters, optimized for dialogue tasks and outperforming many open and closed models on industry benchmarks.\n\\item \\texttt{Qwen2.5-7B-Instruct}: A 7.61 billion parameter model, excelling in instruction following, long-text generation, and structured output, with enhanced capabilities in coding and mathematics.\n\\item \\texttt{LLaMA3.3-70B-Instruct}: A 70 billion parameter instruction-tuned model, optimized for multilingual dialogue and outperforming many open and closed chat models on common industry benchmarks.\n\\item \\texttt{GPT-4o}: OpenAI's flagship multimodal model capable of processing text, images, and audio, offering real-time responses and improved performance over its predecessors.\n\\item \\texttt{GPT-4o-mini}: A smaller, more affordable version of \\texttt{GPT-4o} by OpenAI, balancing performance and cost-efficiency for various AI applications.\n\\item \\texttt{o3-mini}: OpenAI's cost-efficient reasoning model optimized for STEM tasks, particularly excelling in science, mathematics, and coding.\n\\item \\texttt{o1}: OpenAI's model designed for complex reasoning and problem-solving tasks, employing a ``chain-of-thought'' approach to tackle multistep challenges effectively.\n\\end{itemize}\n\n\\textit{Instruction-tuned (without reasoning)}:\nTo test the value of reasoning, we fine-tune \\texttt{LLaMA3.1-8B-Instruct} and \\texttt{Qwen2.5-7B-Instruct} on the same training data but without reasoning traces. These models are directly optimized for answer-only classification.\n\nAll models are evaluated on the same held-out test set using five criteria: response length, cell-level accuracy, batch-level accuracy, output format correctness, and label uniqueness. The results are summarized in Table~\\ref{tab:cell_annotation_results}.\n\n\\subsection{Implementation Details}\n\\label{app_sec:implem_details}\n\n\\paragraph{Supervised Fine-tuning.}\nWe fine-tune the model using the 3,912 distilled examples generated via reasoning distillation (see~\\S\\ref{sec:distill&sft}). Each instance is formatted using our standardized instruction prompt (see~\\S\\ref{sec:prompt_template}). We employ the \\texttt{SFTTrainer} from the TRL library~\\cite{vonwerra2022trl}, applying LoRA~\\cite{hu2022lora} adaptation with rank $r=256$ to all linear layers. Training is conducted for 10 epochs with a learning rate of $5 \\times 10^{-5}$, a batch size of 8, and gradient accumulation of 16 steps. The model backbone is \\texttt{Qwen2.5-7B-Instruct}~\\cite{qwen2.5, qwen2}, using left-side padding and truncation, and a maximum sequence length of 8,192 tokens. Evaluation is performed on 10\\% of the training data at the end of each epoch.\n\n\\paragraph{Reinforcement Learning with GRPO.}\nWe combine the 3,912 fine-tuning instances with 3,000 additional instances to form an RL training set of 6,912 examples. Training is performed using the \\texttt{verl}~\\cite{sheng2024hybridflow} framework with GRPO~\\cite{shao2024deepseekmath}. For each training input, 5 rollouts are sampled per prompt. The actor model is \\texttt{Qwen2.5-7B-Instruct}, initialized from the SFT checkpoint.\nWe set the maximum prompt length to 3,072 tokens and response length to 3,000 tokens. The rollout engine is powered by \\texttt{vLLM}~\\cite{kwon2023efficient}, using 60\\% GPU memory and tensor parallelism of 2. KL-regularization is applied using a low-variance formulation, with coefficient $\\beta = 0.001$. The total batch size is 64 with micro batch size of 2 per GPU. Training is run for 20 epochs.\n\nAll experiments are conducted on 2 nodes with 8$\\times$A100 GPUs (40GB).\n\n\n\n\n\\section{Cell-level vs. Batch-level Reasoning}\n\n\nOur task formulation assumes joint reasoning over a batch of cells with shared context. To assess the necessity of this design, we introduce a decomposed variant in which each cell within a batch is annotated independently. Specifically, given a batch of $N$ cells and $N$ candidate labels, we split the task into $N$ individual classification instances—each involving one cell and the full candidate label set. While the label space remains the same, the model no longer has access to other cells in the batch during inference.\n\n\\begin{table}[h!]\n \\centering\n \\small\n \\caption{Prompt template used for the decomposed cell-level reasoning task.}\n \\label{tab:single_prompt_template}\n \\vspace{2.5pt}\n \\renewcommand{\\arraystretch}{1.2}\n \\begin{tabular}{p{13.5cm}}\n \\hline\n You are an expert assistant specialized in cell type annotation. You will be given the gene expression profile of a single cell from a specific donor.\n The top expressed genes are listed in descending order. Use both gene expression and donor context to determine the correct cell type. \\textbf{You will also receive a list of candidate cell types—choose the one that best fits this cell}. Include your detailed reasoning within \\thinkbegin and \\thinkend tags, and provide your final answer within \\answerbegin and \\answerend tags. The final answer should be a single string with exactly one cell type.\n \\\\\n \\hline\n \\end{tabular} \n\\end{table}\n\nWe deliberately preserve the original candidate label set for each cell to retain the same classification difficulty, isolating the effect of batch-level context from label space complexity. \nIn this setting, the prompt is constructed such that the model only receives a single cell's top expressed genes and metadata, along with a fixed set of candidate labels (see Table~\\ref{tab:single_prompt_template}). Without access to other cells in the batch, the model cannot contrast expression patterns across candidates and must rely solely on local information to identify the correct label.\n\nWe consider two variants under this cell-level setup:\n\\textit{Cell-level Prediction Setup}: Instruction-tuned models that have not been exposed to any reasoning tasks. These models directly predict answers from the input without generating reasoning traces.\n\\textit{Cell-level Reasoning Setup}: Models capable of generating reasoning traces are evaluated on individual cells in isolation, requiring them to infer labels without the help of contextual comparisons among cells.\n\nWe evaluate both variants on 991 cells from 89 batches. As shown in Table~\\ref{tab:single}, the cell-level prediction models ($^{\\spadesuit}$) perform reasonably well in isolation (e.g., 47.1\\% accuracy for Llama3.1), but fail to maintain consistency when reconstructed into batches (as low as 0\\%\\textasciitilde1.1\\% batch-level accuracy). Cell-level reasoning models (e.g., \\Ours) achieve higher cell-level accuracy (50.2\\%), but still suffer a dramatic drop in batch-level accuracy (1.1\\%) compared to their original joint reasoning setup. Even the strongest reasoning-capable models (e.g., OpenAI's \\texttt{o1} and \\texttt{o3-mini}) are unable to reliably solve the task when deprived of batch-level context, highlighting the inherent difficulty of single-cell reasoning in isolation.\n\n\\begin{table*}[h!]\n\\centering\n\\small\n\\footnotesize\n\\renewcommand\\tabcolsep{4.5pt}\n\\renewcommand\\arraystretch{1.05}\n\\caption{Comparison of model performance between batch-level and decomposed cell-level settings. $^{\\spadesuit}$ indicates instruction-tuned baselines trained without any reasoning tasks, which directly predict answers from the input without generating reasoning traces. The best and second-best results are highlighted in \\colorbox{backred!50}{red} and \\colorbox{backblue!75}{blue}, respectively.}\n\\resizebox{0.93\\textwidth}{!}{\n\\begin{tabular}{l|c|c|c|c|c}\n\\toprule\n\\header{Model} & \\header{Response Length} & \\header{Cell-level Acc} & \\header{Batch-level Acc} & \\header{Format} & \\header{Uniqueness} \\\\\n\\midrule\n\\rowcolor[rgb]{0.93,0.93,0.93} \\multicolumn{6}{c}{\\textit{Cell-level Prediction Setup}} \\\\\n\\texttt{Llama3.1-8B-Instruct}$^{\\spadesuit}$ & 8.1 & 0.4707 & 0.0112 & 0.6024 & 0.5887 \\\\\n\\texttt{Qwen2.5-7B-Instruct}$^{\\spadesuit}$ & 8.5 & 0.3890 & 0.0000 & 0.5955 & 0.5978 \\\\\n\\rowcolor[rgb]{0.93,0.93,0.93} \\multicolumn{6}{c}{\\textit{Cell-level Reasoning Setup}} \\\\\n\\texttt{GPT-4o-mini} & 349.5 & 0.2761 & 0.0000 & \\colorbox{backred!50}{1.0000} & 0.3706 \\\\\n\\texttt{GPT-4o} & 494.7 & 0.4093 & 0.0000 & \\colorbox{backblue!75}{0.9900} & 0.5273 \\\\\n\\texttt{o3-mini} & 241.1 & 0.4825 & 0.0000 & \\colorbox{backred!50}{1.0000} & 0.5663 \\\\\n\\texttt{o1} & 152.2 & 0.4700 & 0.0000 & \\colorbox{backred!50}{1.0000} & 0.5708 \\\\\n\\Ours & 718.5 & 0.5016 & 0.0112 & 0.8427 & 0.6612 \\\\\n\\rowcolor[rgb]{0.93,0.93,0.93} \\multicolumn{6}{c}{\\textit{Batch-level Prediction Setup}} \\\\\n\\texttt{Llama3.1-8B-Instruct}$^{\\spadesuit}$ & 73.8 & 0.6438 & 0.2809 & 0.9213 & 0.9198 \\\\\n\\texttt{Qwen2.5-7B-Instruct}$^{\\spadesuit}$ & 78.7 & 0.5631 & 0.1910 & 0.9438 & 0.9370 \\\\\n\\rowcolor[rgb]{0.93,0.93,0.93} \\multicolumn{6}{c}{\\textit{Batch-level Reasoning Setup}} \\\\\n\\texttt{GPT-4o-mini} & 785.4 & 0.2330 & 0.0000 & \\colorbox{backred!50}{1.0000} & 0.7147 \\\\\n\\texttt{GPT-4o} & 1010.4 & 0.4320 & 0.0000 & \\colorbox{backred!50}{1.0000} & 0.8542 \\\\\n\\texttt{o3-mini} & 1059.9 & 0.6784 & 0.1461 & \\colorbox{backred!50}{1.0000} & \\colorbox{backred!50}{1.0000} \\\\\n\\texttt{o1} & 834.9 & \\colorbox{backred!50}{0.7330} & \\colorbox{backblue!75}{0.1685} & \\colorbox{backred!50}{1.0000} & \\colorbox{backblue!75}{0.9975} \\\\\n\\Ours & 1240.8 & \\colorbox{backblue!75}{0.6953} & \\colorbox{backred!50}{0.3371} & 0.9775 & 0.9759 \\\\\n\\bottomrule\n\\end{tabular}\n}\n\\label{tab:single}\n\\end{table*}\n\nIn some cases, the model successfully identifies the correct labels in the batch-level reasoning setting, but fails in the cell-level reasoning setting when cells are presented individually. \nThis discrepancy reveals that batch-level performance is not merely a function of memorizing cell types, but instead involves logical reasoning over inter-cell relationships. In the absence of such context, models are forced to “remember” what individual cell types look like based on internalized training knowledge—limiting their ability to disambiguate among fine-grained candidates.\nThese results highlight the critical role of structured context, which not only enforces consistency but also activates latent knowledge that may remain unused otherwise. The model is thus not merely retrieving memorized associations, but demonstrating an ability to perform inductive reasoning over a constrained hypothesis space when provided with contextual information.\n\n\n\\section{Open-ended QA vs. Constrained QA}\n\nAlthough LLMs are naturally suited for open-ended question answering (QA), we find this formulation to be suboptimal for the task of cell type annotation. In the open-ended QA setup, the model is prompted to freely generate a cell type name for each cell in a given batch, based on its gene expression profile and metadata, without access to a constrained label set.\n\nWhile this setting offers flexibility, it also introduces several challenges. Open-ended generation often results in inconsistent terminology or ambiguous outputs (e.g., ``T lymphocyte'' instead of ``CD4-positive, alpha-beta T cell''), making evaluation difficult and noisy. \nTo mitigate this, we adopt the MedCPT~\\cite{jin2023medcpt} score to quantify semantic similarity between the generated label and ground truth. For computing cell-level accuracy, we treat a prediction as correct if its MedCPT score exceeds 0.8. The MedCPT score reported in Table~\\ref{tab:open} refers to the average similarity across all cells in the batch.\n\nHowever, this threshold-based heuristic has notable limitations. It may conflate semantically similar yet biologically incorrect predictions with true positives, or penalize answers that are precise but phrased differently. \nAs shown in Table~\\ref{tab:open}, models like \\texttt{o1} and \\Ours achieve moderate MedCPT scores (e.g., 0.72\\textasciitilde0.75), suggesting surface-level semantic alignment, but still exhibit 0\\% batch-level accuracy. This gap underscores that semantic similarity does not imply coherent or consistent multi-cell reasoning.\n\nIn contrast, our constrained QA formulation provides the model with a predefined candidate label set, enabling structured reasoning and process-of-elimination strategies. This leads to significantly higher batch-level consistency and interpretability. For example, under constrained QA, \\Ours achieves 32.9\\% batch-level accuracy, far exceeding its open-ended counterpart.\n\nNevertheless, the constrained setup remains somewhat artificial, as the candidate label set is still narrower than what real-world annotation systems encounter. Future work should explore more realistic formulations by expanding the label set to better reflect the breadth of biomedical ontologies while retaining the benefits of constrained reasoning.\n\n\n\n\\begin{table*}[h!]\n\\centering\n\\small\n\\footnotesize\n\\renewcommand\\tabcolsep{4.5pt}\n\\renewcommand\\arraystretch{1.05}\n\\caption{Comparison of model performance between open-ended and constrained QA settings. $^{\\spadesuit}$ indicates instruction-tuned baselines trained without any reasoning tasks, which directly predict answers from the input without generating reasoning traces. The best and second-best results are highlighted in \\colorbox{backred!50}{red} and \\colorbox{backblue!75}{blue}, respectively.}\n\\resizebox{0.93\\textwidth}{!}{\n\\begin{tabular}{l|c|c|c|c|c}\n\\toprule\n\\header{Model} & \\header{Response Length} & \\header{Cell-level Acc} & \\header{Batch-level Acc} & \\header{Format} & \\header{MedCPT Score} \\\\\n\\midrule\n\\rowcolor[rgb]{0.93,0.93,0.93} \\multicolumn{6}{c}{\\textit{Open-ended QA Setup}} \\\\\n\\texttt{Llama3.1-8B-Instruct}$^{\\spadesuit}$ & 192.7 & 0.1068 & 0.0000 & 0.2822 & 0.7400 \\\\\n\\texttt{Qwen2.5-7B-Instruct}$^{\\spadesuit}$ & 248.8 & 0.2491 & 0.0000 & 0.6804 & 0.7304 \\\\\n\\texttt{GPT-4o-mini} & 456.7 & 0.2326 & 0.0000 & 0.9973 & 0.6680 \\\\\n\\texttt{GPT-4o} & 401.0 & 0.3334 & 0.0000 & \\colorbox{backblue!75}{0.9991} & 0.7060 \\\\\n\\texttt{o3-mini} & 590.7 & 0.4292 & 0.0000 & \\colorbox{backred!50}{1.0000} & 0.7411 \\\\\n\\texttt{o1} & 436.5 & 0.4411 & 0.0000 & \\colorbox{backred!50}{1.0000} & 0.7519 \\\\\n\\Ours & 696.9 & 0.3367 & 0.0000 & 0.9224 & 0.7229 \\\\\n\\rowcolor[rgb]{0.93,0.93,0.93} \\multicolumn{6}{c}{\\textit{Constrained QA Setup}} \\\\\n\\texttt{Llama3.1-8B-Instruct}$^{\\spadesuit}$ & 71.9 & 0.6411 & \\colorbox{backblue!75}{0.2969} & 0.9078 & \\colorbox{backblue!75}{0.9133} \\\\\n\\texttt{Qwen2.5-7B-Instruct}$^{\\spadesuit}$ & 70.9 & 0.6179 & 0.2475 & 0.9443 & 0.8889 \\\\\n\\texttt{GPT-4o-mini} & 779.9 & 0.2581 & 0.0027 & 0.9918 & 0.7178 \\\\\n\\texttt{GPT-4o} & 1087.0 & 0.4248 & 0.0283 & 0.9817 & 0.7930 \\\\\n\\texttt{o3-mini} & 1130.2 & 0.5804 & 0.1352 & \\colorbox{backred!50}{1.0000} & 0.8734 \\\\\n\\texttt{o1} & 906.6 & \\colorbox{backblue!75}{0.6479} & 0.1900 & \\colorbox{backred!50}{1.0000} & 0.8888 \\\\\n\\Ours & 1145.6 & \\colorbox{backred!50}{0.6849} & \\colorbox{backred!50}{0.3288} & 0.9826 & \\colorbox{backred!50}{0.9185} \\\\\n\\bottomrule\n\\end{tabular}\n}\n\\label{tab:open}\n\\end{table*}\n\n\\section{Human Evaluation Details}\n\\label{app_sec:evaluation}\n\nWe conducted a structured human evaluation to assess the reasoning quality of model predictions in the batch-level annotation task. The evaluation involved two domain experts with backgrounds in immunology and single-cell transcriptomics.\n\nFor each of the 100 randomly sampled test instances, annotators were shown model outputs including: (1) the predicted cell type for each cell in the batch, (2) the reasoning explanation provided for each prediction, and (3) a diagram indicating the annotation order and correctness of each step, as illustrated in Figure~\\ref{fig:reasoning_analysis}. All outputs were anonymized and randomly shuffled to ensure a blind comparison between \\Ours and baseline models.\n\nExperts first determined whether each prediction was correct based on known marker expression and contextual alignment. For incorrect predictions, they further assigned one of the following predefined error types:\n\n\\begin{itemize}[leftmargin=*]\n\n\\item \\textbf{Incorrect Reasoning}: \n The explanation invokes wrong markers or faulty logic.\\\\\n \\textit{Example: ``The cell highly expresses CD14 and LYZ, markers of T cells'', but CD14 and LYZ are actually monocyte/macrophage markers.}\n\n\\item \\textbf{No Reasoning}: \n A label is predicted with virtually no justification.\\\\\n \\textit{Example: ``The cell is predicted to be a macrophage'', but nothing further.}\n\n\\item \\textbf{Plausible but Incorrect}: \n The reasoning sounds biologically sensible but ignores key evidence, resulting in an incorrect label.\\\\\n \\textit{Example: ``The cell expresses CD3 and CD4, suggesting a helper T-cell identity'', but it also shows high levels of GZMB and NKG7—cytotoxic markers—so the true label is \\textit{CD8+ cytotoxic T cell}.}\n\n\n\\item \\textbf{Forced Assignment Error}: \n The reasoning is correct, but to exhaust remaining labels, the model attaches the correct label to the wrong cell.\\\\\n \\textit{Example: ``This is the only cell left that could be a monocyte'', even though an earlier cell was a better match.}\n\n\\item \\textbf{Correct Reasoning, Wrong Conclusion}: \n The explanation cites the right evidence, yet the model outputs a contradictory label.\\\\\n \\textit{Example: ``The cell expresses CD3, CD4, and IL7R—consistent with helper T cells'', but the model outputs ``B cell''.}\n\n\\end{itemize}\n\nAll evaluations were performed independently, and ties were allowed when models performed comparably. The goal of the human study was to evaluate whether the model's reasoning steps reflected biologically grounded justifications and to identify common failure patterns in LLM predictions.\n\n\n\\section{Case Study: Comparing Reasoning Patterns Across Models}\n\nTo better understand model behaviors, we conduct qualitative comparisons of reasoning traces produced by different models on the same 10-cell batch (Tables~\\ref{case:qwen}, \\ref{case:4o}, \\ref{case:o1}, and \\ref{case:cello1}). We highlight correct predictions in \\sethlcolor{lightblue}\\hl{blue} and incorrect ones in \\sethlcolor{lightred}\\hl{red}, and analyze each model's reasoning style.\n\n\n\\subsection{\\texttt{Qwen2.5-7B-Instruct}}\n\\texttt{Qwen2.5-7B-Instruct} (Table~\\ref{case:qwen}) adopts a candidate-centric reasoning strategy, where it first enumerates all candidate cell types with brief gene-based descriptions and then loosely maps cells to the closest matching types. This approach relies on surface-level pattern matching between highly expressed genes and canonical cell type markers, such as matching TMSB4X to ``Intestine Goblet Cell'' and IGKC/JCHAIN to ``Plasma Cell.''\n\nHowever, this model fails to leverage mutual exclusivity or batch-level constraints. For example, it assigns five different cells to ``Activated CD4-positive, Alpha-Beta T Cell,'' despite the task requiring one-to-one mapping between cells and types. Moreover, some predictions contradict the model's own reasoning: Cell 6 is labeled as ``Capillary Endothelial Cell'' even though it explicitly states that the evidence is ``not strongly supported.''\n\n\\begin{table}[h!]\n \\centering\n \\small\n \\caption{Prediction of \\texttt{Qwen2.5-7B-Instruct}.}\n \\label{case:qwen}\n \\vspace{2.5pt}\n \\renewcommand{\\arraystretch}{1.2}\n \\begin{tabular}{p{13.5cm}}\n \\toprule\n \\textbf{Reasoning:} \n Let's analyze the gene expression patterns and match them to the provided cell types. \\\\ \n \\textbullet\\ Intestine Goblet Cell: This cell type is characterized by high expression of genes like TMSB4X, which is present in Cells 1, 3, 9. \\\\\n \\textbullet\\ Capillary Endothelial Cell: This cell type often expresses genes like CD34, CD31, and VWF. However, these genes are not listed here, so we cannot confidently assign this cell type based on the given data. \\\\\n \\textbullet\\ Plasma Cell: Plasma cells typically express genes like IGKC (Immunoglobulin kappa chain), JCHAIN, and IGHA1. These genes are present in Cell 8, suggesting it could be a plasma cell. \\\\\n \\textbullet\\ Memory B Cell: Memory B cells might express genes like CD74, but this is not prominently featured in any of the cells. \\\\\n \\textbullet\\ Activated CD4-positive, Alpha-Beta T Cell: This cell type would express genes like RPL41, RPL13A, and RPS27, which are common in Cells 2, 5, 6, 7, 10. \\\\\n \\textbullet\\ CD4-positive, Alpha-Beta T Cell: This cell type would also express genes like RPL41, RPL13A, and RPS27, similar to the activated CD4-positive, alpha-beta T cell. \\\\\n \\textbullet\\ T-helper 1 Cell: This cell type would express genes like IL2RA, IFNG, and STAT1, which are not present in the given gene lists. \\\\\n \\textbullet\\ Germinal Center B Cell: Germinal center B cells would express genes like CD74, CD21, and CD23, which are not prominently featured in any of the cells. \\\\\n \\textbullet\\ IgA Plasma Cell: This cell type would express genes like IGLL1, IGHG1, and IGHM, which are not present in the given gene lists. \\\\\n \\textbullet\\ Vein Endothelial Cell: This cell type would express genes like CD34, CD31, and VWF, which are not present in the given gene lists. \\\\\n \\arrayrulecolor{gray!30}\\midrule\\arrayrulecolor{black}\n Based on the gene expression patterns, the most likely matches are: \\\\\n \\textbullet\\ \\sethlcolor{lightred}\\hl{Cell 1}: \\sethlcolor{lightred}\\hl{Intestine Goblet Cell} \\\\\n \\textbullet\\ \\sethlcolor{lightred}\\hl{Cell 2}: \\sethlcolor{lightred}\\hl{Activated CD4-positive, Alpha-Beta T Cell} \\\\\n \\textbullet\\ \\sethlcolor{lightred}\\hl{Cell 3}: \\sethlcolor{lightred}\\hl{Intestine Goblet Cell} \\\\\n \\textbullet\\ \\sethlcolor{lightred}\\hl{Cell 4}: \\sethlcolor{lightred}\\hl{Activated CD4-positive, Alpha-Beta T Cell} \\\\\n \\textbullet\\ \\sethlcolor{lightred}\\hl{Cell 5}: \\sethlcolor{lightred}\\hl{Activated CD4-positive, Alpha-Beta T Cell} \\\\\n \\textbullet\\ \\sethlcolor{lightblue}\\hl{Cell 6}: \\sethlcolor{lightblue}\\hl{Capillary Endothelial Cell (though not strongly supported)} \\\\\n \\textbullet\\ \\sethlcolor{lightred}\\hl{Cell 7}: \\sethlcolor{lightred}\\hl{Activated CD4-positive, Alpha-Beta T Cell} \\\\\n \\textbullet\\ \\sethlcolor{lightred}\\hl{Cell 8}: \\sethlcolor{lightred}\\hl{Plasma Cell} \\\\\n \\textbullet\\ \\sethlcolor{lightred}\\hl{Cell 9}: \\sethlcolor{lightred}\\hl{Intestine Goblet Cell} \\\\\n \\textbullet\\ \\sethlcolor{lightred}\\hl{Cell 10}: \\sethlcolor{lightred}\\hl{Activated CD4-positive, Alpha-Beta T Cell} \\\\\n \\midrule\n \\textbf{Answer:} \\sethlcolor{lightred}\\hl{intestine goblet cell} | \\sethlcolor{lightred}\\hl{activated cd4-positive, alpha-beta t cell} | \\sethlcolor{lightred}\\hl{intestine goblet cell} | \\sethlcolor{lightred}\\hl{activated cd4-positive, alpha-beta t cell} | \\sethlcolor{lightred}\\hl{activated cd4-positive, alpha-beta t cell} | \\sethlcolor{lightblue}\\hl{capillary endothelial cell} | \\sethlcolor{lightred}\\hl{activated cd4-positive, alpha-beta t cell} | \\sethlcolor{lightred}\\hl{plasma cell} | \\sethlcolor{lightred}\\hl{intestine goblet cell} | \\sethlcolor{lightred}\\hl{activated cd4-positive, alpha-beta t cell} \\\\\n \\bottomrule\n \\end{tabular} \n\\end{table}\\begin{table}[h!]\n \\centering\n \\small\n \\caption{Prediction of \\texttt{GPT-4o}.}\n \\label{case:4o}\n \\vspace{2.5pt}\n \\renewcommand{\\arraystretch}{1.2}\n \\begin{tabular}{p{13.5cm}}\n \\toprule\n \\textbf{Reasoning:} \n To annotate the cell types, we need to analyze the gene expression profiles of each cell and match them to the most likely candidate cell type. The context of the donor (male, 13 years old, ileum, Crohn disease) is also relevant, as it may influence the types of cells present and their gene expression patterns. \\\\ \n \\arrayrulecolor{gray!30}\\midrule\\arrayrulecolor{black}\n \\textit{Key Observations:} \\\\\n 1. Gene Expression Patterns: \\\\\n \\textbullet\\ Many cells express ribosomal protein genes (e.g., RPLP1, RPL41, RPL10, RPS27, RPS18, etc.), which are common housekeeping genes. These genes alone are not sufficient for cell type identification. \\\\\n \\textbullet\\ Some cells express immune-related genes (e.g., IGKC, IGHA1, CD74, HLA-B, CXCR4), which suggest immune cell types. \\\\\n \\textbullet\\ Other cells express intestinal-specific genes (e.g., TFF3, SPINK4, REG1A, OLFM4), which are indicative of intestinal epithelial cells like goblet cells. \\\\\n \\textbullet\\ Endothelial-related genes (e.g., VWF, PLVAP, SPARC) are expressed in some cells, suggesting endothelial cell types. \\\\\n 2. Candidate Cell Types: \\\\\n \\textbullet\\ The list includes immune cells (e.g., T cells, B cells, plasma cells), endothelial cells, and intestinal epithelial cells (e.g., goblet cells). \\\\\n \\textbullet\\ Some cell types are more specific (e.g., IgA plasma cell, activated CD4-positive T cell), while others are broader (e.g., plasma cell, vein endothelial cell). \\\\ \n 3. Contextual Relevance: \\\\\n \\textbullet\\ Crohn disease is an inflammatory bowel disease, so immune cells (e.g., T cells, B cells, plasma cells) and intestinal epithelial cells (e.g., goblet cells) are likely to be present. \\\\\n \\textbullet\\ The ileum is rich in immune cells (e.g., germinal center B cells, plasma cells) and specialized epithelial cells (e.g., goblet cells). \\\\\n \\arrayrulecolor{gray!30}\\midrule\\arrayrulecolor{black}\n \\textit{Cell-by-Cell Analysis:} \\\\\n \\textbullet\\ \\sethlcolor{lightred}\\hl{Cell 1}: Dominated by ribosomal genes and housekeeping genes (e.g., ACTB, MALAT1); No specific markers for immune or epithelial cells; Likely a \\sethlcolor{lightred}\\hl{generic plasma cell}. \\\\\n \\textbullet\\ \\sethlcolor{lightred}\\hl{Cell 2}: Similar to Cell 1, dominated by ribosomal genes; No strong markers for immune or epithelial cells; Likely a \\sethlcolor{lightred}\\hl{generic plasma cell}. \\\\\n \\textbullet\\ \\sethlcolor{lightblue}\\hl{Cell 3}: Expresses GAPDH and mitochondrial genes (e.g., MT-CO1, MT-CO2), along with ribosomal genes; No specific immune or epithelial markers; Likely a \\sethlcolor{lightblue}\\hl{generic plasma cell}. \\\\\n \\textbullet\\ \\sethlcolor{lightblue}\\hl{Cell 4}: Expresses intestinal-specific genes (e.g., TFF3, SPINK4, REG1A, OLFM4), which are markers for goblet cells; Likely an \\sethlcolor{lightblue}\\hl{intestine goblet cell}. \\\\\n \\textbullet\\ \\sethlcolor{lightred}\\hl{Cell 5}: Expresses mitochondrial genes (e.g., MT-CO1, MT-CO2) and ribosomal genes; No specific immune or epithelial markers; Likely a \\sethlcolor{lightred}\\hl{generic plasma cell}. \\\\\n \\textbullet\\ \\sethlcolor{lightblue}\\hl{Cell 6}: Expresses endothelial-related genes (e.g., VWF, PLVAP, SPARC) and immune-related genes (e.g., HLA-B, HLA-C); Likely a \\sethlcolor{lightblue}\\hl{capillary endothelial cell}. \\\\\n \\textbullet\\ \\sethlcolor{lightblue}\\hl{Cell 7}: Expresses endothelial-related genes (e.g., VWF, IGFBP7) and immune-related genes (e.g., HLA-B, HLA-E); Likely a \\sethlcolor{lightblue}\\hl{vein endothelial cell}. \\\\\n \\textbullet\\ \\sethlcolor{lightblue}\\hl{Cell 8}: Expresses immune-related genes (e.g., IGKC, IGHA1, JCHAIN), which are markers for IgA plasma cells; Likely an \\sethlcolor{lightblue}\\hl{IgA plasma cell}. \\\\\n \\textbullet\\ \\sethlcolor{lightred}\\hl{Cell 9}: Dominated by ribosomal genes and housekeeping genes; No specific immune or epithelial markers; Likely a \\sethlcolor{lightred}\\hl{generic plasma cell}. \\\\\n \\textbullet\\ \\sethlcolor{lightblue}\\hl{Cell 10}: Expresses immune-related genes (e.g., IGKC, IGHM, CD74, CXCR4), which are markers for memory B cells; Likely a \\sethlcolor{lightblue}\\hl{memory B cell}. \\\\\n \\midrule\n \\textbf{Answer:} \\sethlcolor{lightred}\\hl{Plasma cell} | \\sethlcolor{lightred}\\hl{Plasma cell} | \\sethlcolor{lightblue}\\hl{Plasma cell} | \\sethlcolor{lightblue}\\hl{Intestine goblet cell} | \\sethlcolor{lightred}\\hl{Plasma cell} | \\sethlcolor{lightblue}\\hl{Capillary endothelial cell} | \\sethlcolor{lightblue}\\hl{Vein endothelial cell} | \\sethlcolor{lightblue}\\hl{IgA plasma cell} | \\sethlcolor{lightred}\\hl{Plasma cell} | \\sethlcolor{lightblue}\\hl{Memory B cell} \\\\\n \\bottomrule\n \\end{tabular} \n\\end{table}\\begin{table}[h!]\n \\centering\n \\small\n \\caption{Prediction of \\texttt{o1}.}\n \\label{case:o1}\n \\vspace{2.5pt}\n \\renewcommand{\\arraystretch}{1.2}\n \\begin{tabular}{p{13.5cm}}\n \\toprule\n \\textbf{Reasoning:} \n First, note that each of the 10 cells must map to a distinct one among the 10 candidate types. Some cells are straightforward to assign based on well-known marker genes: \\\\\n \\textbullet\\ \\sethlcolor{lightblue}\\hl{Cell 4} expresses TFF3, SPINK4, and REG1A, which are \\sethlcolor{lightblue}\\hl{classic intestinal goblet cell markers} (TFF3 is particularly specific). \\\\\n \\textbullet\\ \\sethlcolor{lightblue}\\hl{Cell 6} has PLVAP and VWF, both of which are strongly associated with endothelial cells—especially PLVAP, which is a hallmark of capillary (fenestrated) endothelium, so Cell 6 is a \\sethlcolor{lightblue}\\hl{capillary endothelial cell}. \\\\\n \\textbullet\\ \\sethlcolor{lightblue}\\hl{Cell 7} also expresses VWF but includes CCL21, HLA-E, and other features suggestive of a different endothelial subtype; since we must use both ``capillary endothelial cell'' and ``vein endothelial cell'', assigning Cell 7 to \\sethlcolor{lightblue}\\hl{vein endothelial cell} is consistent. \\\\\n \\textbullet\\ \\sethlcolor{lightred}\\hl{Cell 8} has IGKC, JCHAIN, and IGHA1 at high levels, unambiguously marking an IgA-expressing plasma cell \\sethlcolor{lightred}\\hl{(IgA plasma cell)}. \\\\\n \\textbullet\\ \\sethlcolor{lightred}\\hl{Cell 10} highly expresses IGKC and IGHM (and lacks IGHA1), indicating another antibody-producing cell but not IgA; this fits the generic \\sethlcolor{lightred}\\hl{``plasma cell''} category.\n \\\\\n \\arrayrulecolor{gray!30}\\midrule\\arrayrulecolor{black}\n That leaves Cells 1, 2, 3, 5, and 9 to be mapped onto the remaining five types: CD4-positive, alpha-beta T cell; T-helper 1 cell; activated CD4-positive, alpha-beta T cell; memory B cell; germinal center B cell. \\\\\n \\arrayrulecolor{gray!30}\\midrule\\arrayrulecolor{black}\n Although many of these cells' top 50 genes are dominated by ribosomal/housekeeping transcripts, we can still deduce plausible assignments. \\sethlcolor{lightred}\\hl{Cell 5}, for instance, is rich in mitochondrial genes (MT-CO1, MT-CO2, etc.) and also contains CD52 (commonly found on lymphocytes, especially T cells). High mitochondrial activity is characteristic of an activated T cell, so Cell 5 is best labeled \\sethlcolor{lightred}\\hl{``activated CD4-positive, alpha-beta T cell''}. \n Cells 1, 3, and 9 are then the remaining T cells (two of them) plus one more B cell or vice versa. However, we need two B-cell categories overall—``memory B cell'' and ``germinal center B cell''—and we have not yet assigned either one to any cell besides the two plasma cells we already handled (Cells 8 and 10). Given that Cells 6 and 7 are endothelial, Cell 4 is goblet, Cell 8 and 10 are plasma lineage, and Cell 5 is activated T, we must place one B-cell type in Cell 2 and the other B-cell type in one of 1, 3, or 9. \\\\\n \\arrayrulecolor{gray!30}\\midrule\\arrayrulecolor{black}\n A close look at \\sethlcolor{lightred}\\hl{Cell 2} shows no strong expression of T-cell markers but instead a large suite of ribosomal transcripts, which can be consistent with a \\sethlcolor{lightred}\\hl{B lineage cell}. Meanwhile, among 1, 3, and 9, it is much more likely that two are T cells, and one is a B cell. The top genes in 1 and 3 both include TMSB4X at or near the top, which often appears in T cells (though it is not strictly T-specific, it is common in lymphocytes). Cell 9 is likewise heavy in housekeeping genes, and there is no direct B- or T-cell hallmark in the top transcripts. In practice, we must distribute the remaining categories such that we end up with exactly three T cells total and two non-plasma B cells total. \\\\\n \\arrayrulecolor{gray!30}\\midrule\\arrayrulecolor{black}\n A consistent assignment is: \\sethlcolor{lightred}\\hl{Cell 1} $\\rightarrow$ \\sethlcolor{lightred}\\hl{CD4-positive, alpha-beta T cell} (a ``baseline'' or less activated CD4 T cell); \\sethlcolor{lightred}\\hl{Cell 3} $\\rightarrow$ \\sethlcolor{lightred}\\hl{T-helper 1 cell} (given the context of Crohn disease, Th1 skewing is common, and Cell 3 also has notable mitochondrial transcripts that can reflect an active state); Cell 5 (as reasoned above) $\\rightarrow$ activated CD4-positive, alpha-beta T cell. \\\\\n \\arrayrulecolor{gray!30}\\midrule\\arrayrulecolor{black}\n That covers our three T-cell categories. For the two B-cell categories left (``memory B cell,'' ``germinal center B cell''), Cells 2 and 9 remain. Neither has overt immunoglobulin in the top 50, but we must use both B-cell categories somewhere. A typical assumption is that a germinal center B cell may show many ribosomal/housekeeping or proliferation genes, which could apply to Cell 9, while Cell 2 (also heavily ribosomal) could be the memory B cell. Ultimately, to satisfy the one-to-one requirement and given the absence of direct GC vs. memory markers at the top of the list, the most reasonable pairing is: \\sethlcolor{lightred}\\hl{Cell 2} $\\rightarrow$ \\sethlcolor{lightred}\\hl{memory B cell}; \\sethlcolor{lightred}\\hl{Cell 9} $\\rightarrow$ \\sethlcolor{lightred}\\hl{germinal center B cell}. \\\\ \n \\midrule\n \\textbf{Answer:} \\sethlcolor{lightred}\\hl{CD4-positive, alpha-beta T cell} | \\sethlcolor{lightred}\\hl{memory B cell} | \\sethlcolor{lightred}\\hl{T-helper 1 cell} | \\sethlcolor{lightblue}\\hl{intestine goblet cell} | \\sethlcolor{lightred}\\hl{activated CD4-positive, alpha-beta T cell} | \\sethlcolor{lightblue}\\hl{capillary endothelial cell} | \\sethlcolor{lightblue}\\hl{vein endothelial cell} | \\sethlcolor{lightblue}\\hl{IgA plasma cell} | \\sethlcolor{lightred}\\hl{germinal center B cell} | \\sethlcolor{lightred}\\hl{plasma cell} \\\\\n \\bottomrule\n \\end{tabular} \n\\end{table}\\begin{table}[h!]\n \\centering\n \\small\n \\caption{Prediction of \\Ours.}\n \\label{case:cello1}\n \\vspace{2.5pt}\n \\renewcommand{\\arraystretch}{1.2}\n \\begin{tabular}{p{13.5cm}}\n \\toprule\n \\textbf{Reasoning:} \n \\textbullet\\ \\sethlcolor{lightblue}\\hl{Cell 4} stands out immediately because of its strong expression of TFF3, SPINK4, REG1A, and PIGR—classic markers of intestinal goblet cells. Hence Cell 4 is the \\sethlcolor{lightblue}\\hl{``intestine goblet cell.''} \\\\\n \\textbullet\\ \\sethlcolor{lightblue}\\hl{Cell 8} has high levels of IGKC, JCHAIN, and IGHA1, which are hallmark genes of antibody-secreting plasma cells, especially IgA plasma cells. Thus Cell 8 is the \\sethlcolor{lightblue}\\hl{``IgA plasma cell.''} \\\\\n \\textbullet\\ \\sethlcolor{lightblue}\\hl{Cell 10} co-expresses IGKC and IGHM (among others), consistent with a B lineage cell that is not yet a fully differentiated plasma cell (since plasma cells typically downregulate IGHM). This fits best with a ``memory B cell'' or a ``germinal center B cell.'' We will see that Cell 10 is most consistent with a \\sethlcolor{lightblue}\\hl{``memory B cell''} once we compare it with Cell 8 and Cell 1. \\\\\n \\textbullet\\ \\sethlcolor{lightblue}\\hl{Cell 6} has prominent expression of PLVAP, VWF, and SPARC. PLVAP is a well-known marker of fenestrated capillary endothelium, and VWF is a canonical endothelial marker. This makes Cell 6 the \\sethlcolor{lightblue}\\hl{``capillary endothelial cell.''}\n \\\\\n \\textbullet\\ \\sethlcolor{lightblue}\\hl{Cell 7} also shows strong expression of VWF and CCL21. CCL21 is famously associated with lymphatic endothelium, and VWF is again a hallmark of endothelium. This places Cell 7 as the \\sethlcolor{lightblue}\\hl{``vein endothelial cell''} (rather than capillary), leaving Cell 6 to be the capillary endothelial cell. \\\\\n \\textbullet\\ Cell 1 and Cell 2 remain unassigned. Both are dominated by ribosomal genes and do not show overt markers for T cells, B cells, or endothelial cells. However, we must use the remaining two cell types: ``CD4-positive, alpha-beta T cell'' and ``activated CD4-positive, alpha-beta T cell.'' Neither Cell 1 nor Cell 2 shows strong evidence of activation (e.g. no PFN1 in Cell 2), so \\sethlcolor{lightred}\\hl{Cell 1} is most likely the \\sethlcolor{lightred}\\hl{``activated CD4-positive, alpha-beta T cell,''} and \\sethlcolor{lightblue}\\hl{Cell 2} is the \\sethlcolor{lightblue}\\hl{``CD4-positive, alpha-beta T cell.''} \\\\\n \\textbullet\\ \\sethlcolor{lightred}\\hl{Cell 3} is the remaining unassigned cell. The only remaining type is ``germinal center B cell,'' so Cell 3 must be the \\sethlcolor{lightred}\\hl{``germinal center B cell.''} \\\\\n \\arrayrulecolor{gray!30}\\midrule\\arrayrulecolor{black}\n Putting these all together gives: \\\\\n \\textbullet\\ \\sethlcolor{lightred}\\hl{Cell 1} $\\rightarrow$ \\sethlcolor{lightred}\\hl{activated CD4-positive, alpha-beta T cell} \\\\\n \\textbullet\\ \\sethlcolor{lightblue}\\hl{Cell 2} $\\rightarrow$ \\sethlcolor{lightblue}\\hl{CD4-positive, alpha-beta T cell} \\\\ \n \\textbullet\\ \\sethlcolor{lightred}\\hl{Cell 3} $\\rightarrow$ \\sethlcolor{lightred}\\hl{germinal center B cell} \\\\\n \\textbullet\\ \\sethlcolor{lightblue}\\hl{Cell 4} $\\rightarrow$ \\sethlcolor{lightblue}\\hl{intestine goblet cell} \\\\\n \\textbullet\\ \\sethlcolor{lightred}\\hl{Cell 5} $\\rightarrow$ \\sethlcolor{lightred}\\hl{CD4-positive, alpha-beta T cell with a helper phenotype} (i.e. ``T-helper 1 cell,'' even though the T-helper 1 signature genes are not in the top 50). \\\\\n \\textbullet\\ \\sethlcolor{lightblue}\\hl{Cell 6} $\\rightarrow$ \\sethlcolor{lightblue}\\hl{capillary endothelial cell} \\\\\n \\textbullet\\ \\sethlcolor{lightblue}\\hl{Cell 7} $\\rightarrow$ \\sethlcolor{lightblue}\\hl{vein endothelial cell} \\\\\n \\textbullet\\ \\sethlcolor{lightblue}\\hl{Cell 8} $\\rightarrow$ \\sethlcolor{lightblue}\\hl{IgA plasma cell} \\\\\n \\textbullet\\ \\sethlcolor{lightred}\\hl{Cell 9} $\\rightarrow$ a less activated or naive/naïve-like T/B lineage cell, but we already used the other T-cell categories. \\\\\n \\textbullet\\ \\sethlcolor{lightblue}\\hl{Cell 10} $\\rightarrow$ \\sethlcolor{lightblue}\\hl{memory B cell} \\\\\n \\arrayrulecolor{gray!30}\\midrule\\arrayrulecolor{black}\n However, we notice that Cell 5 does not fit the ``activated'' or ``memory'' categories. It does have CD52 (commonly found on T cells) and PFN1 (also can be a marker of helper T cells). Since we must place it as a helper T cell, Cell 5 is the ``T-helper 1 cell.''\n Hence the final mapping is: \\\\\n \\textbullet\\ \\sethlcolor{lightred}\\hl{Cell 1} $\\rightarrow$ \\sethlcolor{lightred}\\hl{activated CD4-positive, alpha-beta T cell} \\\\\n \\textbullet\\ \\sethlcolor{lightblue}\\hl{Cell 2} $\\rightarrow$ \\sethlcolor{lightblue}\\hl{CD4-positive, alpha-beta T cell} \\\\ \n \\textbullet\\ \\sethlcolor{lightred}\\hl{Cell 3} $\\rightarrow$ \\sethlcolor{lightred}\\hl{germinal center B cell} \\\\\n \\textbullet\\ \\sethlcolor{lightblue}\\hl{Cell 4} $\\rightarrow$ \\sethlcolor{lightblue}\\hl{intestine goblet cell} \\\\\n \\textbullet\\ \\sethlcolor{lightblue}\\hl{Cell 5} $\\rightarrow$ \\sethlcolor{lightblue}\\hl{T-helper 1 cell} \\\\\n \\textbullet\\ \\sethlcolor{lightblue}\\hl{Cell 6} $\\rightarrow$ \\sethlcolor{lightblue}\\hl{capillary endothelial cell} \\\\\n \\textbullet\\ \\sethlcolor{lightblue}\\hl{Cell 7} $\\rightarrow$ \\sethlcolor{lightblue}\\hl{vein endothelial cell} \\\\\n \\textbullet\\ \\sethlcolor{lightblue}\\hl{Cell 8} $\\rightarrow$ \\sethlcolor{lightblue}\\hl{IgA plasma cell} \\\\\n \\textbullet\\ \\sethlcolor{lightred}\\hl{Cell 9} $\\rightarrow$ \\sethlcolor{lightred}\\hl{activated CD4-positive, alpha-beta T cell (but we only have one ``activated'' category), so we assign it to the remaining ``plasma cell'' category.} \\\\\n \\textbullet\\ \\sethlcolor{lightblue}\\hl{Cell 10} $\\rightarrow$ \\sethlcolor{lightblue}\\hl{memory B cell} \\\\\n \\midrule\n \\textbf{Answer:} \n \\sethlcolor{lightred}\\hl{activated CD4-positive, alpha-beta T cell} | \\sethlcolor{lightblue}\\hl{CD4-positive, alpha-beta T cell} | \\sethlcolor{lightred}\\hl{germinal center B cell} | \\sethlcolor{lightblue}\\hl{intestine goblet cell} | \\sethlcolor{lightblue}\\hl{T-helper 1 cell} | \\sethlcolor{lightblue}\\hl{capillary endothelial cell} | \\sethlcolor{lightblue}\\hl{vein endothelial cell} | \\sethlcolor{lightblue}\\hl{IgA plasma cell} | \\sethlcolor{lightred}\\hl{plasma cell} | \\sethlcolor{lightblue}\\hl{memory B cell} \\\\\n \\bottomrule\n \\end{tabular} \n\\end{table}\n\\subsection{\\texttt{GPT-4o}}\n\\texttt{GPT-4o} (Table~\\ref{case:4o}) displays strong domain knowledge and an ability to reason using both gene expression patterns and contextual biological information. It explicitly incorporates factors such as donor condition (Crohn disease), anatomical site (ileum), and immune cell enrichment. It also organizes its reasoning with structured sections: \\textit{general observations}, \\textit{candidate type definitions}, and \\textit{cell-by-cell analysis}, showing an impressive degree of language-level coherence.\n\nDespite this, GPT-4o suffers from constraint-agnostic generation. It frequently assigns the same label (e.g., ``Plasma cell'') to multiple cells—violating the task’s one-to-one mapping requirement. This behavior suggests the model treats the task as a sequence of independent classification problems rather than a structured inference task over a constrained hypothesis space. \n\nFurthermore, although its biological interpretation is often correct (e.g., identifying Cell 4 as a goblet cell, Cell 8 as an IgA plasma cell), the model overuses broad fallback categories like ``generic plasma cell'' in ambiguous cases, indicating uncertainty resolution by generalization rather than reasoning.\n\n\\subsection{\\texttt{o1}}\nThe \\texttt{o1} model exhibits a clear understanding of the one-to-one mapping constraint inherent to the task. It explicitly structures its reasoning in two stages: (1) assigning obvious cells based on canonical markers, and (2) carefully distributing the remaining ambiguous cells to satisfy label exclusivity. For example, Cell 4 is confidently identified as an ``Intestine Goblet Cell'' based on strong markers like TFF3 and REG1A, while Cells 6 and 7 are assigned to endothelial subtypes based on PLVAP and VWF expression.\n\nWhat sets \\texttt{o1} apart is its deliberate reasoning over cell allocation: it acknowledges that only one cell can be assigned to each of the B-cell and T-cell subtypes, and explicitly discusses the combinatorial implications (e.g., ``we must distribute the remaining categories such that we end up with exactly three T cells and two non-plasma B cells''). This illustrates a form of global, constraint-aware reasoning that is absent in instruction-tuned or open-ended models.\n\nHowever, while the logical structure is robust, the model still makes several incorrect assignments. For example, Cell 1 is predicted as a ``CD4-positive, alpha-beta T cell,'' though it likely belongs to another lineage; Cell 2 is assigned to ``memory B cell'' based on ribosomal gene dominance rather than immunoglobulin expression. These errors reflect plausible but weakly supported conclusions, often stemming from ambiguity in gene expression or over-reliance on inferred distributions rather than molecular markers.\n\n\\subsection{\\Ours}\n\n\\Ours demonstrates the strongest reasoning ability among all models in the case study. Its inference is explicitly structured in a progressive assignment strategy, beginning with high-confidence matches based on canonical markers (e.g., Cell 4 as ``intestine goblet cell'' via TFF3/REG1A; Cell 8 as ``IgA plasma cell'' via IGKC/JCHAIN/IGHA1) before moving on to more ambiguous cases.\n\nA key strength of \\Ours lies in its explicit modeling of mutual exclusivity and type constraints. The model carefully tracks which labels remain unassigned and distributes ambiguous cells accordingly. For example, it defers the assignment of Cell 1 and Cell 2 until later in the reasoning chain, acknowledging the overlap in ribosomal profiles and the lack of activation markers, before settling on the most consistent label-to-cell pairing.\n\nImportantly, \\Ours also performs cross-cell comparisons (e.g., comparing Cell 10 to Cell 8 to support a ``memory B cell'' label), showing a depth of relational reasoning absent from previous models. It further revisits earlier assignments (e.g., Cell 5), updating the label from a generic CD4-positive T cell to a ``T-helper 1 cell'' based on expression of CD52 and PFN1.\n\nHowever, the model still makes minor errors. Notably, Cell 3 is assigned to ``germinal center B cell'' with minimal molecular justification, and Cell 9 is incorrectly labeled as ``plasma cell'' due to the unavailability of any better-fitting categories—this is acknowledged in the reasoning as a forced fallback. These mistakes are not due to poor logic but rather task constraints interacting with biological ambiguity.\n\n\n\\end{document}"},"question":{"kind":"string","value":"Among the models with reasoning, which model exhibits the highest ratio of its SLE Batch accuracy to its SLE Cell accuracy, and what is that ratio?"},"answer":{"kind":"string","value":"Cell-o1, 0.4614"},"plan":{"kind":"string","value":"SELECT all models with reasoning\nSELECT SLE Cell accuracy\nSELECT SLE Batch accuracy\nLOOP for each model with reasoning\n COMPUTE ratio of SLE Batch accuracy to SLE Cell accuracy for this model\nCOMPUTE model with the largest ratio\nRETURN model with the largest ratio, corresponding ratio\n"}}},{"rowIdx":98,"cells":{"paper":{"kind":"string","value":"2506.02515"},"relevant_tables":{"kind":"list like","value":[["\\begin{table*}[t]\n","\\scriptsize\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n"," \\begin{tabular}{l|cccc|ccc|c}\n"," \\toprule\n"," \\multicolumn{1}{c|}{\\multirow{2}{*}{\\textbf{Model}}} & \\multicolumn{4}{c|}{\\textbf{\\ourmetric $\\uparrow$}} & \\multicolumn{3}{c|}{\\textbf{ROUGE $\\uparrow$}} & \\multicolumn{1}{c}{\\multirow{2}{*}{\\textbf{BERTScore $\\uparrow$}}} \\\\\n"," & FAC & Step-Precision & Step-Recall & Step-F1 & R$_2$ & R$_L$ & R$_{Lsum}$ & \\\\\n"," \\midrule\n"," \\rowcolor{black!15}\\textbf{General-Purpose} & & & & & & & &\\\\\n"," \\rule{0pt}{7pt}Mistral (7B) Instruct v0.3 & $0.32_{0.24}$ & $0.3317_{0.20}$ & $0.21_{0.14}$ & $0.2444_{0.15}$ & $0.1183_{0.02}$ & $0.1868_{0.02}$ & $0.2239_{0.02}$ & $0.8341_{0.002}$\\\\\n"," Qwen2.5 (7B) & $0.4083_{0.32}$ & $0.3554_{0.24}$ & $0.2095_{0.16}$ & $0.2486_{0.17}$ & $0.1615_{0.04}$ & $0.2523_{0.06}$ & $0.3052_{0.07}$ & $0.8564_{0.01}$\\\\\n"," Mixtral (8x7B) Instruct v0.1 & $0.357_{0.28}$ & $0.3345_{0.21}$ & $0.2354_{0.15}$ & $0.2622_{0.16}$ & $0.1596_{0.03}$ & $0.2537_{0.04}$ & $0.3045_{0.05}$ & $0.8584_{0.01}$\\\\\n"," Llama 3.1 (8B) Instruct & $0.4267_{0.28}$ & $0.3935_{0.21}$ & $0.2266_{0.13}$ & $0.2729_{0.15}$ & $0.1454_{0.03}$ & $0.2273_{0.04}$ & $0.2753_{0.05}$ & $0.8565_{0.01}$\\\\\n"," Gemma 2 (9B) Instruct & $0.4838_{0.21}$ & $0.4643_{0.16}$ & $0.3243_{0.11}$ & $0.367_{0.12}$ & $0.1975_{0.03}$ & $0.3021_{0.03}$ & $0.3667_{0.04}$ & $0.8646_{0.01}$\\\\\n"," Gemma 2 (27B) Instruct & $0.5325_{0.21}$ & $0.4937_{0.15}$ & $0.3488_{0.11}$ & $0.3926_{0.12}$ & $0.1962_{0.03}$ & $0.3002_{0.03}$ & $0.3621_{0.03}$ & $0.8639_{0.006}$\\\\\n"," Gemma 3 (27B) Instruct & $0.4879_{0.18}$ & $0.5023_{0.16}$ & $0.3064_{0.09}$ & $0.365_{0.11}$ & $0.1717_{0.02}$ & $0.2567_{0.02}$ & $0.3045_{0.03}$ & $0.8567_{0.005}$\\\\\n"," Llama 3.1 (70B) Instruct & $0.5735_{0.25}$ & $0.4811_{0.21}$ & $0.3048_{0.15}$ & $0.3558_{0.16}$ & $0.1691_{0.04}$ & $0.259_{0.05}$ & $0.3108_{0.05}$ & $0.858_{0.01}$\\\\\n"," Llama 3.3 (70B) Instruct & $\\mathbf{0.584_{0.22}}$ & $0.5145_{0.19}$ & $0.2724_{0.11}$ & $0.3384_{0.13}$ & $0.1492_{0.03}$ & $0.2269_{0.03}$ & $0.2694_{0.04}$ & $0.8537_{0.006}$\\\\\n"," GPT-4o-mini & $0.524_{0.23}$ & $0.4352_{0.19}$ & $0.2385_{0.11}$ & $0.2923_{0.13}$ & $0.1337_{0.02}$ & $0.2177_{0.02}$ & $0.2588_{0.02}$ & $0.8458_{0.005}$\\\\\n"," GPT-4.1-mini & $0.5668_{0.24}$ & $0.4877_{0.19}$ & $0.2882_{0.12}$ & $0.3456_{0.13}$ & $0.1725_{0.02}$ & $0.2645_{0.02}$ & $0.3201_{0.03}$ & $0.8569_{0.006}$\\\\\n"," GPT-4.1 & $0.5691_{0.23}$ & $0.4197_{0.21}$ & $0.2527_{0.13}$ & $0.2993_{0.15}$ & $0.1657_{0.02}$ & $0.2631_{0.02}$ & $0.3176_{0.03}$ & $0.8505_{0.007}$\\\\[2.5pt]\n"," \\rowcolor{black!15}\\textbf{General Reasoning} & & & & & & & &\\\\\n"," \\rule{0pt}{7pt}DeepSeek-R1-Distill Qwen (7B) & $0.4383_{0.31}$ & $0.3568_{0.23}$ & $0.2239_{0.15}$ & $0.2606_{0.16}$ & $0.1887_{0.04}$ & $0.3126_{0.05}$ & $0.381_{0.05}$ & $0.864_{0.01}$\\\\\n"," DeepSeek-R1-Distill Llama (8B) & $0.4394_{0.31}$ & $0.3583_{0.22}$ & $0.2204_{0.13}$ & $0.2576_{0.15}$ & $0.1841_{0.04}$ & $0.3004_{0.05}$ & $0.3689_{0.05}$ & $0.865_{0.01}$\\\\\n"," Qwen3 (8B) & $\\mathbf{0.5402_{0.27}}$ & $0.3992_{0.23}$ & $0.2145_{0.13}$ & $0.2643_{0.15}$ & $0.1951_{0.05}$ & $0.3018_{0.06}$ & $0.364_{0.06}$ & $0.8696_{0.01}$\\\\\n"," Qwen3 (30B) A3B & $0.5089_{0.18}$ & $0.4245_{0.21}$ & $0.1995_{0.12}$ & $0.2509_{0.13}$ & $0.0373_{0.01}$ & $0.0538_{0.01}$ & $0.0649_{0.01}$ & $0.8453_{0.01}$\\\\\n"," DeepSeek-R1-Distill Qwen (32B) & $0.4991_{0.30}$ & $0.4071_{0.23}$ & $0.262_{0.15}$ & $0.3039_{0.17}$ & $0.1895_{0.04}$ & $0.3181_{0.04}$ & $0.384_{0.05}$ & $0.8575_{0.01}$\\\\\n"," DeepSeek-R1-Distill Llama (70B) & $0.4927_{0.31}$ & $0.4013_{0.23}$ & $0.255_{0.15}$ & $0.2965_{0.17}$ & $0.1961_{0.04}$ & $0.3239_{0.05}$ & $0.3922_{0.05}$ & $0.8618_{0.01}$\\\\\n"," o3-mini & $0.5183_{0.25}$ & $0.3881_{0.21}$ & $0.2432_{0.12}$ & $0.2839_{0.14}$ & $0.2028_{0.03}$ & $0.3083_{0.04}$ & $0.3714_{0.04}$ & $0.8574_{0.02}$\\\\\n"," o4-mini & $0.3873_{0.21}$ & $0.2976_{0.19}$ & $0.1768_{0.11}$ & $0.2126_{0.13}$ & $0.1945_{0.03}$ & $0.3093_{0.03}$ & $0.3806_{0.04}$ & $0.8563_{0.02}$\\\\[2.5pt]\n"," \\rowcolor{black!15}\\textbf{Finance Fine-Tuned} & & & & & & & &\\\\\n"," \\rule{0pt}{7pt}WiroAI Finance Qwen (7B) & $\\mathbf{0.4419_{0.28}}$ & $0.4064_{0.23}$ & $0.2531_{0.15}$ & $0.2933_{0.17}$ & $0.1782_{0.04}$ & $0.2866_{0.05}$ & $0.3409_{0.06}$ & $0.8651_{0.01}$\\\\\n"," WiroAI Finance Llama (8B) & $0.2853_{0.28}$ & $0.2729_{0.23}$ & $0.1627_{0.15}$ & $0.1911_{0.17}$ & $0.1625_{0.05}$ & $0.2671_{0.06}$ & $0.3254_{0.08}$ & $0.8611_{0.02}$\\\\\n"," FinanceConnect (13B) & $0.0725_{0.12}$ & $0.0884_{0.13}$ & $0.0561_{0.08}$ & $0.0646_{0.09}$ & $0.166_{0.04}$ & $0.2837_{0.05}$ & $0.3482_{0.06}$ & $0.8573_{0.01}$\\\\\n"," Finance-LLM (13B) & $0.1709_{0.21}$ & $0.2247_{0.19}$ & $0.1443_{0.13}$ & $0.1654_{0.14}$ & $0.1628_{0.04}$ & $0.2671_{0.05}$ & $0.3263_{0.06}$ & $0.8631_{0.01}$\\\\[2.5pt]\n"," \\rowcolor{black!15}\\textbf{Finance Reasoning} & & & & & & & &\\\\\n"," \\rule{0pt}{7pt}Fin-R1 (7B) & $\\mathbf{0.512_{0.28}}$ & $0.3649_{0.22}$ & $0.1978_{0.12}$ & $0.2428_{0.15}$ & $0.1685_{0.04}$ & $0.2806_{0.04}$ & $0.3429_{0.05}$ & $0.8585_{0.01}$\\\\\n"," Fino1 (8B) & $0.4191_{0.26}$ & $0.3621_{0.22}$ & $0.1637_{0.10}$ & $0.2133_{0.12}$ & $0.1701_{0.03}$ & $0.2795_{0.04}$ & $0.3458_{0.05}$ & $0.868_{0.01}$\\\\[2.5pt]\n"," \\rowcolor{black!15}\\textbf{Math Fine-Tuned} & & & & & & & &\\\\\n"," \\rule{0pt}{7pt}Mathstral (7B) & $\\mathbf{0.4397_{0.26}}$ & $0.3953_{0.21}$ & $0.2732_{0.16}$ & $0.3063_{0.16}$ & $0.1635_{0.03}$ & $0.2586_{0.04}$ & $0.3098_{0.05}$ & $0.8587_{0.01}$\\\\\n"," Qwen2.5-Math (7B) & $0.1891_{0.26}$ & $0.1763_{0.21}$ & $0.1298_{0.15}$ & $0.1412_{0.16}$ & $0.0705_{0.06}$ & $0.1337_{0.08}$ & $0.1488_{0.10}$ & $0.8067_{0.02}$\\\\[2.5pt]\n"," \\rowcolor{black!15}\\textbf{Math Reasoning} & & & & & & & &\\\\\n"," \\rule{0pt}{7pt}MetaMath (13B) & $0.2313_{0.22}$ & $0.2704_{0.20}$ & $0.1357_{0.10}$ & $0.1717_{0.13}$ & $0.1863_{0.05}$ & $0.3064_{0.06}$ & $0.3753_{0.07}$ & $0.8728_{0.01}$\\\\\n"," WizardMath (7B) & $\\mathbf{0.3461_{0.28}}$ & $0.3357_{0.24}$ & $0.1942_{0.14}$ & $0.2332_{0.17}$ & $0.1933_{0.06}$ & $0.3142_{0.07}$ & $0.3737_{0.08}$ & $0.8723_{0.01}$\\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," }\n"," \\caption{\n","\\textbf{Overall performance of language models on \\ourdataset.}\n","We report four components of our \\ourmetric metric (FAC, step precision, step recall, and step-level F1), alongside ROUGE (R$_2$, R$_L$, R$_{Lsum}$) and BERTScore. Models are grouped into six categories. Top-performing models under FAC within each category are bolded. \n","}\n"," \\label{tab:overall_model_performance}\n","\\end{table*}\n"]],"string":"[\n [\n \"\\\\begin{table*}[t]\\n\",\n \"\\\\scriptsize\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{l|cccc|ccc|c}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\multicolumn{1}{c|}{\\\\multirow{2}{*}{\\\\textbf{Model}}} & \\\\multicolumn{4}{c|}{\\\\textbf{\\\\ourmetric $\\\\uparrow$}} & \\\\multicolumn{3}{c|}{\\\\textbf{ROUGE $\\\\uparrow$}} & \\\\multicolumn{1}{c}{\\\\multirow{2}{*}{\\\\textbf{BERTScore $\\\\uparrow$}}} \\\\\\\\\\n\",\n \" & FAC & Step-Precision & Step-Recall & Step-F1 & R$_2$ & R$_L$ & R$_{Lsum}$ & \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\rowcolor{black!15}\\\\textbf{General-Purpose} & & & & & & & &\\\\\\\\\\n\",\n \" \\\\rule{0pt}{7pt}Mistral (7B) Instruct v0.3 & $0.32_{0.24}$ & $0.3317_{0.20}$ & $0.21_{0.14}$ & $0.2444_{0.15}$ & $0.1183_{0.02}$ & $0.1868_{0.02}$ & $0.2239_{0.02}$ & $0.8341_{0.002}$\\\\\\\\\\n\",\n \" Qwen2.5 (7B) & $0.4083_{0.32}$ & $0.3554_{0.24}$ & $0.2095_{0.16}$ & $0.2486_{0.17}$ & $0.1615_{0.04}$ & $0.2523_{0.06}$ & $0.3052_{0.07}$ & $0.8564_{0.01}$\\\\\\\\\\n\",\n \" Mixtral (8x7B) Instruct v0.1 & $0.357_{0.28}$ & $0.3345_{0.21}$ & $0.2354_{0.15}$ & $0.2622_{0.16}$ & $0.1596_{0.03}$ & $0.2537_{0.04}$ & $0.3045_{0.05}$ & $0.8584_{0.01}$\\\\\\\\\\n\",\n \" Llama 3.1 (8B) Instruct & $0.4267_{0.28}$ & $0.3935_{0.21}$ & $0.2266_{0.13}$ & $0.2729_{0.15}$ & $0.1454_{0.03}$ & $0.2273_{0.04}$ & $0.2753_{0.05}$ & $0.8565_{0.01}$\\\\\\\\\\n\",\n \" Gemma 2 (9B) Instruct & $0.4838_{0.21}$ & $0.4643_{0.16}$ & $0.3243_{0.11}$ & $0.367_{0.12}$ & $0.1975_{0.03}$ & $0.3021_{0.03}$ & $0.3667_{0.04}$ & $0.8646_{0.01}$\\\\\\\\\\n\",\n \" Gemma 2 (27B) Instruct & $0.5325_{0.21}$ & $0.4937_{0.15}$ & $0.3488_{0.11}$ & $0.3926_{0.12}$ & $0.1962_{0.03}$ & $0.3002_{0.03}$ & $0.3621_{0.03}$ & $0.8639_{0.006}$\\\\\\\\\\n\",\n \" Gemma 3 (27B) Instruct & $0.4879_{0.18}$ & $0.5023_{0.16}$ & $0.3064_{0.09}$ & $0.365_{0.11}$ & $0.1717_{0.02}$ & $0.2567_{0.02}$ & $0.3045_{0.03}$ & $0.8567_{0.005}$\\\\\\\\\\n\",\n \" Llama 3.1 (70B) Instruct & $0.5735_{0.25}$ & $0.4811_{0.21}$ & $0.3048_{0.15}$ & $0.3558_{0.16}$ & $0.1691_{0.04}$ & $0.259_{0.05}$ & $0.3108_{0.05}$ & $0.858_{0.01}$\\\\\\\\\\n\",\n \" Llama 3.3 (70B) Instruct & $\\\\mathbf{0.584_{0.22}}$ & $0.5145_{0.19}$ & $0.2724_{0.11}$ & $0.3384_{0.13}$ & $0.1492_{0.03}$ & $0.2269_{0.03}$ & $0.2694_{0.04}$ & $0.8537_{0.006}$\\\\\\\\\\n\",\n \" GPT-4o-mini & $0.524_{0.23}$ & $0.4352_{0.19}$ & $0.2385_{0.11}$ & $0.2923_{0.13}$ & $0.1337_{0.02}$ & $0.2177_{0.02}$ & $0.2588_{0.02}$ & $0.8458_{0.005}$\\\\\\\\\\n\",\n \" GPT-4.1-mini & $0.5668_{0.24}$ & $0.4877_{0.19}$ & $0.2882_{0.12}$ & $0.3456_{0.13}$ & $0.1725_{0.02}$ & $0.2645_{0.02}$ & $0.3201_{0.03}$ & $0.8569_{0.006}$\\\\\\\\\\n\",\n \" GPT-4.1 & $0.5691_{0.23}$ & $0.4197_{0.21}$ & $0.2527_{0.13}$ & $0.2993_{0.15}$ & $0.1657_{0.02}$ & $0.2631_{0.02}$ & $0.3176_{0.03}$ & $0.8505_{0.007}$\\\\\\\\[2.5pt]\\n\",\n \" \\\\rowcolor{black!15}\\\\textbf{General Reasoning} & & & & & & & &\\\\\\\\\\n\",\n \" \\\\rule{0pt}{7pt}DeepSeek-R1-Distill Qwen (7B) & $0.4383_{0.31}$ & $0.3568_{0.23}$ & $0.2239_{0.15}$ & $0.2606_{0.16}$ & $0.1887_{0.04}$ & $0.3126_{0.05}$ & $0.381_{0.05}$ & $0.864_{0.01}$\\\\\\\\\\n\",\n \" DeepSeek-R1-Distill Llama (8B) & $0.4394_{0.31}$ & $0.3583_{0.22}$ & $0.2204_{0.13}$ & $0.2576_{0.15}$ & $0.1841_{0.04}$ & $0.3004_{0.05}$ & $0.3689_{0.05}$ & $0.865_{0.01}$\\\\\\\\\\n\",\n \" Qwen3 (8B) & $\\\\mathbf{0.5402_{0.27}}$ & $0.3992_{0.23}$ & $0.2145_{0.13}$ & $0.2643_{0.15}$ & $0.1951_{0.05}$ & $0.3018_{0.06}$ & $0.364_{0.06}$ & $0.8696_{0.01}$\\\\\\\\\\n\",\n \" Qwen3 (30B) A3B & $0.5089_{0.18}$ & $0.4245_{0.21}$ & $0.1995_{0.12}$ & $0.2509_{0.13}$ & $0.0373_{0.01}$ & $0.0538_{0.01}$ & $0.0649_{0.01}$ & $0.8453_{0.01}$\\\\\\\\\\n\",\n \" DeepSeek-R1-Distill Qwen (32B) & $0.4991_{0.30}$ & $0.4071_{0.23}$ & $0.262_{0.15}$ & $0.3039_{0.17}$ & $0.1895_{0.04}$ & $0.3181_{0.04}$ & $0.384_{0.05}$ & $0.8575_{0.01}$\\\\\\\\\\n\",\n \" DeepSeek-R1-Distill Llama (70B) & $0.4927_{0.31}$ & $0.4013_{0.23}$ & $0.255_{0.15}$ & $0.2965_{0.17}$ & $0.1961_{0.04}$ & $0.3239_{0.05}$ & $0.3922_{0.05}$ & $0.8618_{0.01}$\\\\\\\\\\n\",\n \" o3-mini & $0.5183_{0.25}$ & $0.3881_{0.21}$ & $0.2432_{0.12}$ & $0.2839_{0.14}$ & $0.2028_{0.03}$ & $0.3083_{0.04}$ & $0.3714_{0.04}$ & $0.8574_{0.02}$\\\\\\\\\\n\",\n \" o4-mini & $0.3873_{0.21}$ & $0.2976_{0.19}$ & $0.1768_{0.11}$ & $0.2126_{0.13}$ & $0.1945_{0.03}$ & $0.3093_{0.03}$ & $0.3806_{0.04}$ & $0.8563_{0.02}$\\\\\\\\[2.5pt]\\n\",\n \" \\\\rowcolor{black!15}\\\\textbf{Finance Fine-Tuned} & & & & & & & &\\\\\\\\\\n\",\n \" \\\\rule{0pt}{7pt}WiroAI Finance Qwen (7B) & $\\\\mathbf{0.4419_{0.28}}$ & $0.4064_{0.23}$ & $0.2531_{0.15}$ & $0.2933_{0.17}$ & $0.1782_{0.04}$ & $0.2866_{0.05}$ & $0.3409_{0.06}$ & $0.8651_{0.01}$\\\\\\\\\\n\",\n \" WiroAI Finance Llama (8B) & $0.2853_{0.28}$ & $0.2729_{0.23}$ & $0.1627_{0.15}$ & $0.1911_{0.17}$ & $0.1625_{0.05}$ & $0.2671_{0.06}$ & $0.3254_{0.08}$ & $0.8611_{0.02}$\\\\\\\\\\n\",\n \" FinanceConnect (13B) & $0.0725_{0.12}$ & $0.0884_{0.13}$ & $0.0561_{0.08}$ & $0.0646_{0.09}$ & $0.166_{0.04}$ & $0.2837_{0.05}$ & $0.3482_{0.06}$ & $0.8573_{0.01}$\\\\\\\\\\n\",\n \" Finance-LLM (13B) & $0.1709_{0.21}$ & $0.2247_{0.19}$ & $0.1443_{0.13}$ & $0.1654_{0.14}$ & $0.1628_{0.04}$ & $0.2671_{0.05}$ & $0.3263_{0.06}$ & $0.8631_{0.01}$\\\\\\\\[2.5pt]\\n\",\n \" \\\\rowcolor{black!15}\\\\textbf{Finance Reasoning} & & & & & & & &\\\\\\\\\\n\",\n \" \\\\rule{0pt}{7pt}Fin-R1 (7B) & $\\\\mathbf{0.512_{0.28}}$ & $0.3649_{0.22}$ & $0.1978_{0.12}$ & $0.2428_{0.15}$ & $0.1685_{0.04}$ & $0.2806_{0.04}$ & $0.3429_{0.05}$ & $0.8585_{0.01}$\\\\\\\\\\n\",\n \" Fino1 (8B) & $0.4191_{0.26}$ & $0.3621_{0.22}$ & $0.1637_{0.10}$ & $0.2133_{0.12}$ & $0.1701_{0.03}$ & $0.2795_{0.04}$ & $0.3458_{0.05}$ & $0.868_{0.01}$\\\\\\\\[2.5pt]\\n\",\n \" \\\\rowcolor{black!15}\\\\textbf{Math Fine-Tuned} & & & & & & & &\\\\\\\\\\n\",\n \" \\\\rule{0pt}{7pt}Mathstral (7B) & $\\\\mathbf{0.4397_{0.26}}$ & $0.3953_{0.21}$ & $0.2732_{0.16}$ & $0.3063_{0.16}$ & $0.1635_{0.03}$ & $0.2586_{0.04}$ & $0.3098_{0.05}$ & $0.8587_{0.01}$\\\\\\\\\\n\",\n \" Qwen2.5-Math (7B) & $0.1891_{0.26}$ & $0.1763_{0.21}$ & $0.1298_{0.15}$ & $0.1412_{0.16}$ & $0.0705_{0.06}$ & $0.1337_{0.08}$ & $0.1488_{0.10}$ & $0.8067_{0.02}$\\\\\\\\[2.5pt]\\n\",\n \" \\\\rowcolor{black!15}\\\\textbf{Math Reasoning} & & & & & & & &\\\\\\\\\\n\",\n \" \\\\rule{0pt}{7pt}MetaMath (13B) & $0.2313_{0.22}$ & $0.2704_{0.20}$ & $0.1357_{0.10}$ & $0.1717_{0.13}$ & $0.1863_{0.05}$ & $0.3064_{0.06}$ & $0.3753_{0.07}$ & $0.8728_{0.01}$\\\\\\\\\\n\",\n \" WizardMath (7B) & $\\\\mathbf{0.3461_{0.28}}$ & $0.3357_{0.24}$ & $0.1942_{0.14}$ & $0.2332_{0.17}$ & $0.1933_{0.06}$ & $0.3142_{0.07}$ & $0.3737_{0.08}$ & $0.8723_{0.01}$\\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \" \\\\caption{\\n\",\n \"\\\\textbf{Overall performance of language models on \\\\ourdataset.}\\n\",\n \"We report four components of our \\\\ourmetric metric (FAC, step precision, step recall, and step-level F1), alongside ROUGE (R$_2$, R$_L$, R$_{Lsum}$) and BERTScore. Models are grouped into six categories. Top-performing models under FAC within each category are bolded. \\n\",\n \"}\\n\",\n \" \\\\label{tab:overall_model_performance}\\n\",\n \"\\\\end{table*}\\n\"\n ]\n]","isConversational":false},"tables":{"kind":"list like","value":[["\\begin{figure}[t]\n","\\centering\n","\\definecolor{varname}{HTML}{CDE7FF}\n","\\definecolor{varproj}{HTML}{FFE5B4}\n","\\definecolor{vartotal}{HTML}{C3F5D5}\n","\\definecolor{varrate}{HTML}{FFCEDC}\n","\\definecolor{vartime}{HTML}{FFF8B0}\n","\\definecolor{varprincipal}{HTML}{D9CCFF}\n","\\definecolor{varamount}{HTML}{E0F2F1}\n","\\definecolor{varci}{HTML}{E2E2E2}\n","\\definecolor{headergray}{HTML}{555555}\n","\\newcommand{\\dashedline}{%\n"," \\noindent\n"," \\begin{center}\n"," \\begin{tikzpicture}\n"," \\draw[dashed, gray, line width=0.5pt] (0,0) -- (6.5,0);\n"," \\end{tikzpicture}\n"," \\end{center}\n","}\n","\n","\n","\\begin{tcolorbox}[title=\\ourdataset Template (Compound Interest), colback=white, colframe=headergray, boxrule=0.5pt, arc=1mm, fonttitle=\\bfseries]\n","\n","\\noindent \\textbf{\\#Question:} \\\\\n","\\colorbox{varname}{\\texttt{investor\\_name}} invested \\colorbox{varprincipal}{\\texttt{principal}} in \\colorbox{varproj}{\\texttt{project\\_name}}. \n","The investment grows at an annual interest rate of \\colorbox{varrate}{\\texttt{rate}}\\% compounded annually over \\colorbox{vartime}{\\texttt{time}} years. \n","Calculate the \\colorbox{varci}{\\texttt{compound interest (CI)}}.\n","\n","\\vspace{0.5em}\n","\n","\\noindent \\textbf{\\#Variables:} \\\\\n","- \\colorbox{varname}{\\texttt{investor\\_name}} = sample(investors) \\\\\n","- \\colorbox{varproj}{\\texttt{project\\_name}} = sample(projects) \\\\\n","- \\colorbox{varprincipal}{\\texttt{principal}} = range(1000, 5000) \\\\\n","- \\colorbox{varrate}{\\texttt{rate}} = uniform(2, 10) \\\\\n","- \\colorbox{vartime}{\\texttt{time}} = range(1, 5)\n","\n","\\dashedline\n","\n","\\noindent \\textbf{\\#Chain-of-Thought Solution:} \\\\\n","Step 1: Compute the compound amount:\n","\\colorbox{varamount}{\\texttt{amount}} = \\colorbox{varprincipal}{\\texttt{principal}} $\\times$ $\\left(1 + \\frac{\\colorbox{varrate}{\\texttt{rate}}}{100}\\right)^{\\colorbox{vartime}{\\texttt{time}}}$ \\\\\n","Step 2: Compute the compound interest:\n"," \\quad \\colorbox{varci}{\\texttt{CI}} = \\colorbox{varamount}{\\texttt{amount}} $-$ \\colorbox{varprincipal}{\\texttt{P}}\n","\n","\n","\\end{tcolorbox}\\caption{\\textbf{Symbolic template for generating compound interest problems in \\ourdataset.} \n","}\n","\\label{fig:template}\n","\\end{figure}\n"],["\\begin{figure*}[t]\n"," \\centering\n"," \\includegraphics[width=0.98\\linewidth]{figs/taxonomy.pdf}\n"," \\caption{\n","\\textbf{FinChain taxonomy of financial reasoning topics.}\n","Our benchmark spans 54 topics organized into 12 major domains, ranging from traditional areas like \\textit{Corporate Finance} and \\textit{Financial Reporting} to emerging fields such as \\textit{Crypto Finance} and \\textit{Sustainable Finance}. This hierarchical structure enables fine-grained evaluation of symbolic reasoning across diverse financial domains.\n","}\n"," \\label{fig:taxonomy}\n","\\end{figure*}\n"],["\\begin{table*}[t]\n","\\scriptsize\n","\\centering\n","\\resizebox{\\textwidth}{!}{\n"," \\begin{tabular}{l|cccc|ccc|c}\n"," \\toprule\n"," \\multicolumn{1}{c|}{\\multirow{2}{*}{\\textbf{Model}}} & \\multicolumn{4}{c|}{\\textbf{\\ourmetric $\\uparrow$}} & \\multicolumn{3}{c|}{\\textbf{ROUGE $\\uparrow$}} & \\multicolumn{1}{c}{\\multirow{2}{*}{\\textbf{BERTScore $\\uparrow$}}} \\\\\n"," & FAC & Step-Precision & Step-Recall & Step-F1 & R$_2$ & R$_L$ & R$_{Lsum}$ & \\\\\n"," \\midrule\n"," \\rowcolor{black!15}\\textbf{General-Purpose} & & & & & & & &\\\\\n"," \\rule{0pt}{7pt}Mistral (7B) Instruct v0.3 & $0.32_{0.24}$ & $0.3317_{0.20}$ & $0.21_{0.14}$ & $0.2444_{0.15}$ & $0.1183_{0.02}$ & $0.1868_{0.02}$ & $0.2239_{0.02}$ & $0.8341_{0.002}$\\\\\n"," Qwen2.5 (7B) & $0.4083_{0.32}$ & $0.3554_{0.24}$ & $0.2095_{0.16}$ & $0.2486_{0.17}$ & $0.1615_{0.04}$ & $0.2523_{0.06}$ & $0.3052_{0.07}$ & $0.8564_{0.01}$\\\\\n"," Mixtral (8x7B) Instruct v0.1 & $0.357_{0.28}$ & $0.3345_{0.21}$ & $0.2354_{0.15}$ & $0.2622_{0.16}$ & $0.1596_{0.03}$ & $0.2537_{0.04}$ & $0.3045_{0.05}$ & $0.8584_{0.01}$\\\\\n"," Llama 3.1 (8B) Instruct & $0.4267_{0.28}$ & $0.3935_{0.21}$ & $0.2266_{0.13}$ & $0.2729_{0.15}$ & $0.1454_{0.03}$ & $0.2273_{0.04}$ & $0.2753_{0.05}$ & $0.8565_{0.01}$\\\\\n"," Gemma 2 (9B) Instruct & $0.4838_{0.21}$ & $0.4643_{0.16}$ & $0.3243_{0.11}$ & $0.367_{0.12}$ & $0.1975_{0.03}$ & $0.3021_{0.03}$ & $0.3667_{0.04}$ & $0.8646_{0.01}$\\\\\n"," Gemma 2 (27B) Instruct & $0.5325_{0.21}$ & $0.4937_{0.15}$ & $0.3488_{0.11}$ & $0.3926_{0.12}$ & $0.1962_{0.03}$ & $0.3002_{0.03}$ & $0.3621_{0.03}$ & $0.8639_{0.006}$\\\\\n"," Gemma 3 (27B) Instruct & $0.4879_{0.18}$ & $0.5023_{0.16}$ & $0.3064_{0.09}$ & $0.365_{0.11}$ & $0.1717_{0.02}$ & $0.2567_{0.02}$ & $0.3045_{0.03}$ & $0.8567_{0.005}$\\\\\n"," Llama 3.1 (70B) Instruct & $0.5735_{0.25}$ & $0.4811_{0.21}$ & $0.3048_{0.15}$ & $0.3558_{0.16}$ & $0.1691_{0.04}$ & $0.259_{0.05}$ & $0.3108_{0.05}$ & $0.858_{0.01}$\\\\\n"," Llama 3.3 (70B) Instruct & $\\mathbf{0.584_{0.22}}$ & $0.5145_{0.19}$ & $0.2724_{0.11}$ & $0.3384_{0.13}$ & $0.1492_{0.03}$ & $0.2269_{0.03}$ & $0.2694_{0.04}$ & $0.8537_{0.006}$\\\\\n"," GPT-4o-mini & $0.524_{0.23}$ & $0.4352_{0.19}$ & $0.2385_{0.11}$ & $0.2923_{0.13}$ & $0.1337_{0.02}$ & $0.2177_{0.02}$ & $0.2588_{0.02}$ & $0.8458_{0.005}$\\\\\n"," GPT-4.1-mini & $0.5668_{0.24}$ & $0.4877_{0.19}$ & $0.2882_{0.12}$ & $0.3456_{0.13}$ & $0.1725_{0.02}$ & $0.2645_{0.02}$ & $0.3201_{0.03}$ & $0.8569_{0.006}$\\\\\n"," GPT-4.1 & $0.5691_{0.23}$ & $0.4197_{0.21}$ & $0.2527_{0.13}$ & $0.2993_{0.15}$ & $0.1657_{0.02}$ & $0.2631_{0.02}$ & $0.3176_{0.03}$ & $0.8505_{0.007}$\\\\[2.5pt]\n"," \\rowcolor{black!15}\\textbf{General Reasoning} & & & & & & & &\\\\\n"," \\rule{0pt}{7pt}DeepSeek-R1-Distill Qwen (7B) & $0.4383_{0.31}$ & $0.3568_{0.23}$ & $0.2239_{0.15}$ & $0.2606_{0.16}$ & $0.1887_{0.04}$ & $0.3126_{0.05}$ & $0.381_{0.05}$ & $0.864_{0.01}$\\\\\n"," DeepSeek-R1-Distill Llama (8B) & $0.4394_{0.31}$ & $0.3583_{0.22}$ & $0.2204_{0.13}$ & $0.2576_{0.15}$ & $0.1841_{0.04}$ & $0.3004_{0.05}$ & $0.3689_{0.05}$ & $0.865_{0.01}$\\\\\n"," Qwen3 (8B) & $\\mathbf{0.5402_{0.27}}$ & $0.3992_{0.23}$ & $0.2145_{0.13}$ & $0.2643_{0.15}$ & $0.1951_{0.05}$ & $0.3018_{0.06}$ & $0.364_{0.06}$ & $0.8696_{0.01}$\\\\\n"," Qwen3 (30B) A3B & $0.5089_{0.18}$ & $0.4245_{0.21}$ & $0.1995_{0.12}$ & $0.2509_{0.13}$ & $0.0373_{0.01}$ & $0.0538_{0.01}$ & $0.0649_{0.01}$ & $0.8453_{0.01}$\\\\\n"," DeepSeek-R1-Distill Qwen (32B) & $0.4991_{0.30}$ & $0.4071_{0.23}$ & $0.262_{0.15}$ & $0.3039_{0.17}$ & $0.1895_{0.04}$ & $0.3181_{0.04}$ & $0.384_{0.05}$ & $0.8575_{0.01}$\\\\\n"," DeepSeek-R1-Distill Llama (70B) & $0.4927_{0.31}$ & $0.4013_{0.23}$ & $0.255_{0.15}$ & $0.2965_{0.17}$ & $0.1961_{0.04}$ & $0.3239_{0.05}$ & $0.3922_{0.05}$ & $0.8618_{0.01}$\\\\\n"," o3-mini & $0.5183_{0.25}$ & $0.3881_{0.21}$ & $0.2432_{0.12}$ & $0.2839_{0.14}$ & $0.2028_{0.03}$ & $0.3083_{0.04}$ & $0.3714_{0.04}$ & $0.8574_{0.02}$\\\\\n"," o4-mini & $0.3873_{0.21}$ & $0.2976_{0.19}$ & $0.1768_{0.11}$ & $0.2126_{0.13}$ & $0.1945_{0.03}$ & $0.3093_{0.03}$ & $0.3806_{0.04}$ & $0.8563_{0.02}$\\\\[2.5pt]\n"," \\rowcolor{black!15}\\textbf{Finance Fine-Tuned} & & & & & & & &\\\\\n"," \\rule{0pt}{7pt}WiroAI Finance Qwen (7B) & $\\mathbf{0.4419_{0.28}}$ & $0.4064_{0.23}$ & $0.2531_{0.15}$ & $0.2933_{0.17}$ & $0.1782_{0.04}$ & $0.2866_{0.05}$ & $0.3409_{0.06}$ & $0.8651_{0.01}$\\\\\n"," WiroAI Finance Llama (8B) & $0.2853_{0.28}$ & $0.2729_{0.23}$ & $0.1627_{0.15}$ & $0.1911_{0.17}$ & $0.1625_{0.05}$ & $0.2671_{0.06}$ & $0.3254_{0.08}$ & $0.8611_{0.02}$\\\\\n"," FinanceConnect (13B) & $0.0725_{0.12}$ & $0.0884_{0.13}$ & $0.0561_{0.08}$ & $0.0646_{0.09}$ & $0.166_{0.04}$ & $0.2837_{0.05}$ & $0.3482_{0.06}$ & $0.8573_{0.01}$\\\\\n"," Finance-LLM (13B) & $0.1709_{0.21}$ & $0.2247_{0.19}$ & $0.1443_{0.13}$ & $0.1654_{0.14}$ & $0.1628_{0.04}$ & $0.2671_{0.05}$ & $0.3263_{0.06}$ & $0.8631_{0.01}$\\\\[2.5pt]\n"," \\rowcolor{black!15}\\textbf{Finance Reasoning} & & & & & & & &\\\\\n"," \\rule{0pt}{7pt}Fin-R1 (7B) & $\\mathbf{0.512_{0.28}}$ & $0.3649_{0.22}$ & $0.1978_{0.12}$ & $0.2428_{0.15}$ & $0.1685_{0.04}$ & $0.2806_{0.04}$ & $0.3429_{0.05}$ & $0.8585_{0.01}$\\\\\n"," Fino1 (8B) & $0.4191_{0.26}$ & $0.3621_{0.22}$ & $0.1637_{0.10}$ & $0.2133_{0.12}$ & $0.1701_{0.03}$ & $0.2795_{0.04}$ & $0.3458_{0.05}$ & $0.868_{0.01}$\\\\[2.5pt]\n"," \\rowcolor{black!15}\\textbf{Math Fine-Tuned} & & & & & & & &\\\\\n"," \\rule{0pt}{7pt}Mathstral (7B) & $\\mathbf{0.4397_{0.26}}$ & $0.3953_{0.21}$ & $0.2732_{0.16}$ & $0.3063_{0.16}$ & $0.1635_{0.03}$ & $0.2586_{0.04}$ & $0.3098_{0.05}$ & $0.8587_{0.01}$\\\\\n"," Qwen2.5-Math (7B) & $0.1891_{0.26}$ & $0.1763_{0.21}$ & $0.1298_{0.15}$ & $0.1412_{0.16}$ & $0.0705_{0.06}$ & $0.1337_{0.08}$ & $0.1488_{0.10}$ & $0.8067_{0.02}$\\\\[2.5pt]\n"," \\rowcolor{black!15}\\textbf{Math Reasoning} & & & & & & & &\\\\\n"," \\rule{0pt}{7pt}MetaMath (13B) & $0.2313_{0.22}$ & $0.2704_{0.20}$ & $0.1357_{0.10}$ & $0.1717_{0.13}$ & $0.1863_{0.05}$ & $0.3064_{0.06}$ & $0.3753_{0.07}$ & $0.8728_{0.01}$\\\\\n"," WizardMath (7B) & $\\mathbf{0.3461_{0.28}}$ & $0.3357_{0.24}$ & $0.1942_{0.14}$ & $0.2332_{0.17}$ & $0.1933_{0.06}$ & $0.3142_{0.07}$ & $0.3737_{0.08}$ & $0.8723_{0.01}$\\\\\n"," \\bottomrule\n"," \\end{tabular}\n"," }\n"," \\caption{\n","\\textbf{Overall performance of language models on \\ourdataset.}\n","We report four components of our \\ourmetric metric (FAC, step precision, step recall, and step-level F1), alongside ROUGE (R$_2$, R$_L$, R$_{Lsum}$) and BERTScore. Models are grouped into six categories. Top-performing models under FAC within each category are bolded. \n","}\n"," \\label{tab:overall_model_performance}\n","\\end{table*}\n"],["\\begin{figure*}[t]\n"," \\centering\n"," \\includegraphics[width=0.8\\linewidth]{figs/topic_performance.pdf}\n"," \\caption{\n","\\textbf{Domain-level performance across financial domains.}\n","Radar plots compare representative models from (a) Non-reasoning models and (b) Reasoning models. Each spoke corresponds to one of the 12 top-level financial domains. Models are selected as the top performers within each of the six categories presented in \\autoref{tab:overall_model_performance}.\n","}\n"," \\label{fig:topic_performance}\n","\\end{figure*}\n"],["\\begin{figure}[t]\n"," \\centering\n"," \\includegraphics[width=0.9\\linewidth]{figs/level_wise_performance.pdf}\n"," \\caption{\n","\\textbf{Model performance across difficulty levels.}\n","FAC is reported for Basic, Intermediate, and Advanced.\n","}\n"," \\label{fig:difficulty_performance}\n","\\end{figure}\n"],["\\begin{table}[t]\n","\\centering\n","\\small\n","\\resizebox{\\columnwidth}{!}{\n","\\begin{tabular}{lccc}\n","\\toprule\n","\\textbf{Model} & \\textbf{Both} & \\textbf{Name Only} & \\textbf{Value Only} \\\\\n","\\midrule\n","GPT-4.1 & 0.5080$_{\\pm0.4999}$ & 0.4960$_{\\pm0.5000}$ & 0.5040$_{\\pm0.5000}$ \\\\\n","GPT-4.1-mini & 0.4720$_{\\pm0.4992}$ & 0.4640$_{\\pm0.4987}$ & 0.4640$_{\\pm0.4987}$ \\\\\n","GPT-4o-mini & 0.3760$_{\\pm0.4844}$ & 0.3600$_{\\pm0.4800}$ & 0.3720$_{\\pm0.4833}$ \\\\\n","o3-mini & 0.4040$_{\\pm0.4907}$ & 0.3920$_{\\pm0.4882}$ & 0.4160$_{\\pm0.4929}$ \\\\\n","o4-mini & 0.3400$_{\\pm0.4737}$ & 0.3600$_{\\pm0.4800}$ & 0.3680$_{\\pm0.4823}$ \\\\\n","\\bottomrule\n","\\end{tabular}\n","}\n","\\caption{\\textbf{Model robustness to input perturbation.}\n","FAC is reported for Both, Name Only, and Value Only.\n","}\n","\n","\\label{tab:name_number_variable}\n","\\end{table}\n"],["\\begin{figure*}[t]\n"," \\centering\n"," \\includegraphics[width=0.8\\linewidth]{figs/topic_performance_allgennonreasoningmodels.pdf}\n"," \\caption{\n","\\textbf{Domain-level performance comparison across financial domains for all general-purpose non-reasoning models from \\autoref{tab:overall_model_performance}.} For clarity, we split the models into three subtypes: (a) Model Size $\\leq$ 10B, (b) Model Size $>$ 10B, and (c) Closed-Source Models.\n","}\n"," \\label{fig:topic_performance_all_general_nonreasoning}\n","\\end{figure*}\n"],["\\begin{figure*}[t]\n"," \\centering\n"," \\includegraphics[width=0.8\\linewidth]{figs/topic_performance_allgenreasoning.pdf}\n"," \\caption{\n","\\textbf{Domain-level performance comparison across financial domains for all general-purpose reasoning models from \\autoref{tab:overall_model_performance}.} For clarity, we split the models into two subtypes: (a) Model Size $\\leq$ 30B, and (b) Model Size $>$ 30B (along with all closed-source models).}\n"," \\label{fig:topic_performance_all_general_reasoning}\n","\\end{figure*}\n"],["\\begin{figure*}[t]\n"," \\centering\n"," \\includegraphics[width=0.8\\linewidth]{figs/topic_performance_allfinance_models.pdf}\n"," \\caption{\n","\\textbf{Domain-level performance comparison across financial domains for all financial fine-tuned and reasoning models from \\autoref{tab:overall_model_performance}.} For clarity, we split the figure into two parts: (a) Finance Non-Reasoning, and (b) Finance Reasoning models.}\n"," \\label{fig:topic_performance_all_finance}\n","\\end{figure*}\n"],["\\begin{figure*}[t]\n"," \\centering\n"," \\includegraphics[width=0.8\\linewidth]{figs/topic_performance_allmathmodels.pdf}\n"," \\caption{\n","\\textbf{Domain-level performance comparison across financial domains for all mathematical fine-tuned and reasoning models from \\autoref{tab:overall_model_performance}.} For clarity, we split the figure into two parts: (a) Math Non-Reasoning, and (b) Math Reasoning models.}\n"," \\label{fig:topic_performance_all_math}\n","\\end{figure*}\n"]],"string":"[\n [\n \"\\\\begin{figure}[t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\definecolor{varname}{HTML}{CDE7FF}\\n\",\n \"\\\\definecolor{varproj}{HTML}{FFE5B4}\\n\",\n \"\\\\definecolor{vartotal}{HTML}{C3F5D5}\\n\",\n \"\\\\definecolor{varrate}{HTML}{FFCEDC}\\n\",\n \"\\\\definecolor{vartime}{HTML}{FFF8B0}\\n\",\n \"\\\\definecolor{varprincipal}{HTML}{D9CCFF}\\n\",\n \"\\\\definecolor{varamount}{HTML}{E0F2F1}\\n\",\n \"\\\\definecolor{varci}{HTML}{E2E2E2}\\n\",\n \"\\\\definecolor{headergray}{HTML}{555555}\\n\",\n \"\\\\newcommand{\\\\dashedline}{%\\n\",\n \" \\\\noindent\\n\",\n \" \\\\begin{center}\\n\",\n \" \\\\begin{tikzpicture}\\n\",\n \" \\\\draw[dashed, gray, line width=0.5pt] (0,0) -- (6.5,0);\\n\",\n \" \\\\end{tikzpicture}\\n\",\n \" \\\\end{center}\\n\",\n \"}\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\\begin{tcolorbox}[title=\\\\ourdataset Template (Compound Interest), colback=white, colframe=headergray, boxrule=0.5pt, arc=1mm, fonttitle=\\\\bfseries]\\n\",\n \"\\n\",\n \"\\\\noindent \\\\textbf{\\\\#Question:} \\\\\\\\\\n\",\n \"\\\\colorbox{varname}{\\\\texttt{investor\\\\_name}} invested \\\\colorbox{varprincipal}{\\\\texttt{principal}} in \\\\colorbox{varproj}{\\\\texttt{project\\\\_name}}. \\n\",\n \"The investment grows at an annual interest rate of \\\\colorbox{varrate}{\\\\texttt{rate}}\\\\% compounded annually over \\\\colorbox{vartime}{\\\\texttt{time}} years. \\n\",\n \"Calculate the \\\\colorbox{varci}{\\\\texttt{compound interest (CI)}}.\\n\",\n \"\\n\",\n \"\\\\vspace{0.5em}\\n\",\n \"\\n\",\n \"\\\\noindent \\\\textbf{\\\\#Variables:} \\\\\\\\\\n\",\n \"- \\\\colorbox{varname}{\\\\texttt{investor\\\\_name}} = sample(investors) \\\\\\\\\\n\",\n \"- \\\\colorbox{varproj}{\\\\texttt{project\\\\_name}} = sample(projects) \\\\\\\\\\n\",\n \"- \\\\colorbox{varprincipal}{\\\\texttt{principal}} = range(1000, 5000) \\\\\\\\\\n\",\n \"- \\\\colorbox{varrate}{\\\\texttt{rate}} = uniform(2, 10) \\\\\\\\\\n\",\n \"- \\\\colorbox{vartime}{\\\\texttt{time}} = range(1, 5)\\n\",\n \"\\n\",\n \"\\\\dashedline\\n\",\n \"\\n\",\n \"\\\\noindent \\\\textbf{\\\\#Chain-of-Thought Solution:} \\\\\\\\\\n\",\n \"Step 1: Compute the compound amount:\\n\",\n \"\\\\colorbox{varamount}{\\\\texttt{amount}} = \\\\colorbox{varprincipal}{\\\\texttt{principal}} $\\\\times$ $\\\\left(1 + \\\\frac{\\\\colorbox{varrate}{\\\\texttt{rate}}}{100}\\\\right)^{\\\\colorbox{vartime}{\\\\texttt{time}}}$ \\\\\\\\\\n\",\n \"Step 2: Compute the compound interest:\\n\",\n \" \\\\quad \\\\colorbox{varci}{\\\\texttt{CI}} = \\\\colorbox{varamount}{\\\\texttt{amount}} $-$ \\\\colorbox{varprincipal}{\\\\texttt{P}}\\n\",\n \"\\n\",\n \"\\n\",\n \"\\\\end{tcolorbox}\\\\caption{\\\\textbf{Symbolic template for generating compound interest problems in \\\\ourdataset.} \\n\",\n \"}\\n\",\n \"\\\\label{fig:template}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{figure*}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=0.98\\\\linewidth]{figs/taxonomy.pdf}\\n\",\n \" \\\\caption{\\n\",\n \"\\\\textbf{FinChain taxonomy of financial reasoning topics.}\\n\",\n \"Our benchmark spans 54 topics organized into 12 major domains, ranging from traditional areas like \\\\textit{Corporate Finance} and \\\\textit{Financial Reporting} to emerging fields such as \\\\textit{Crypto Finance} and \\\\textit{Sustainable Finance}. This hierarchical structure enables fine-grained evaluation of symbolic reasoning across diverse financial domains.\\n\",\n \"}\\n\",\n \" \\\\label{fig:taxonomy}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{table*}[t]\\n\",\n \"\\\\scriptsize\\n\",\n \"\\\\centering\\n\",\n \"\\\\resizebox{\\\\textwidth}{!}{\\n\",\n \" \\\\begin{tabular}{l|cccc|ccc|c}\\n\",\n \" \\\\toprule\\n\",\n \" \\\\multicolumn{1}{c|}{\\\\multirow{2}{*}{\\\\textbf{Model}}} & \\\\multicolumn{4}{c|}{\\\\textbf{\\\\ourmetric $\\\\uparrow$}} & \\\\multicolumn{3}{c|}{\\\\textbf{ROUGE $\\\\uparrow$}} & \\\\multicolumn{1}{c}{\\\\multirow{2}{*}{\\\\textbf{BERTScore $\\\\uparrow$}}} \\\\\\\\\\n\",\n \" & FAC & Step-Precision & Step-Recall & Step-F1 & R$_2$ & R$_L$ & R$_{Lsum}$ & \\\\\\\\\\n\",\n \" \\\\midrule\\n\",\n \" \\\\rowcolor{black!15}\\\\textbf{General-Purpose} & & & & & & & &\\\\\\\\\\n\",\n \" \\\\rule{0pt}{7pt}Mistral (7B) Instruct v0.3 & $0.32_{0.24}$ & $0.3317_{0.20}$ & $0.21_{0.14}$ & $0.2444_{0.15}$ & $0.1183_{0.02}$ & $0.1868_{0.02}$ & $0.2239_{0.02}$ & $0.8341_{0.002}$\\\\\\\\\\n\",\n \" Qwen2.5 (7B) & $0.4083_{0.32}$ & $0.3554_{0.24}$ & $0.2095_{0.16}$ & $0.2486_{0.17}$ & $0.1615_{0.04}$ & $0.2523_{0.06}$ & $0.3052_{0.07}$ & $0.8564_{0.01}$\\\\\\\\\\n\",\n \" Mixtral (8x7B) Instruct v0.1 & $0.357_{0.28}$ & $0.3345_{0.21}$ & $0.2354_{0.15}$ & $0.2622_{0.16}$ & $0.1596_{0.03}$ & $0.2537_{0.04}$ & $0.3045_{0.05}$ & $0.8584_{0.01}$\\\\\\\\\\n\",\n \" Llama 3.1 (8B) Instruct & $0.4267_{0.28}$ & $0.3935_{0.21}$ & $0.2266_{0.13}$ & $0.2729_{0.15}$ & $0.1454_{0.03}$ & $0.2273_{0.04}$ & $0.2753_{0.05}$ & $0.8565_{0.01}$\\\\\\\\\\n\",\n \" Gemma 2 (9B) Instruct & $0.4838_{0.21}$ & $0.4643_{0.16}$ & $0.3243_{0.11}$ & $0.367_{0.12}$ & $0.1975_{0.03}$ & $0.3021_{0.03}$ & $0.3667_{0.04}$ & $0.8646_{0.01}$\\\\\\\\\\n\",\n \" Gemma 2 (27B) Instruct & $0.5325_{0.21}$ & $0.4937_{0.15}$ & $0.3488_{0.11}$ & $0.3926_{0.12}$ & $0.1962_{0.03}$ & $0.3002_{0.03}$ & $0.3621_{0.03}$ & $0.8639_{0.006}$\\\\\\\\\\n\",\n \" Gemma 3 (27B) Instruct & $0.4879_{0.18}$ & $0.5023_{0.16}$ & $0.3064_{0.09}$ & $0.365_{0.11}$ & $0.1717_{0.02}$ & $0.2567_{0.02}$ & $0.3045_{0.03}$ & $0.8567_{0.005}$\\\\\\\\\\n\",\n \" Llama 3.1 (70B) Instruct & $0.5735_{0.25}$ & $0.4811_{0.21}$ & $0.3048_{0.15}$ & $0.3558_{0.16}$ & $0.1691_{0.04}$ & $0.259_{0.05}$ & $0.3108_{0.05}$ & $0.858_{0.01}$\\\\\\\\\\n\",\n \" Llama 3.3 (70B) Instruct & $\\\\mathbf{0.584_{0.22}}$ & $0.5145_{0.19}$ & $0.2724_{0.11}$ & $0.3384_{0.13}$ & $0.1492_{0.03}$ & $0.2269_{0.03}$ & $0.2694_{0.04}$ & $0.8537_{0.006}$\\\\\\\\\\n\",\n \" GPT-4o-mini & $0.524_{0.23}$ & $0.4352_{0.19}$ & $0.2385_{0.11}$ & $0.2923_{0.13}$ & $0.1337_{0.02}$ & $0.2177_{0.02}$ & $0.2588_{0.02}$ & $0.8458_{0.005}$\\\\\\\\\\n\",\n \" GPT-4.1-mini & $0.5668_{0.24}$ & $0.4877_{0.19}$ & $0.2882_{0.12}$ & $0.3456_{0.13}$ & $0.1725_{0.02}$ & $0.2645_{0.02}$ & $0.3201_{0.03}$ & $0.8569_{0.006}$\\\\\\\\\\n\",\n \" GPT-4.1 & $0.5691_{0.23}$ & $0.4197_{0.21}$ & $0.2527_{0.13}$ & $0.2993_{0.15}$ & $0.1657_{0.02}$ & $0.2631_{0.02}$ & $0.3176_{0.03}$ & $0.8505_{0.007}$\\\\\\\\[2.5pt]\\n\",\n \" \\\\rowcolor{black!15}\\\\textbf{General Reasoning} & & & & & & & &\\\\\\\\\\n\",\n \" \\\\rule{0pt}{7pt}DeepSeek-R1-Distill Qwen (7B) & $0.4383_{0.31}$ & $0.3568_{0.23}$ & $0.2239_{0.15}$ & $0.2606_{0.16}$ & $0.1887_{0.04}$ & $0.3126_{0.05}$ & $0.381_{0.05}$ & $0.864_{0.01}$\\\\\\\\\\n\",\n \" DeepSeek-R1-Distill Llama (8B) & $0.4394_{0.31}$ & $0.3583_{0.22}$ & $0.2204_{0.13}$ & $0.2576_{0.15}$ & $0.1841_{0.04}$ & $0.3004_{0.05}$ & $0.3689_{0.05}$ & $0.865_{0.01}$\\\\\\\\\\n\",\n \" Qwen3 (8B) & $\\\\mathbf{0.5402_{0.27}}$ & $0.3992_{0.23}$ & $0.2145_{0.13}$ & $0.2643_{0.15}$ & $0.1951_{0.05}$ & $0.3018_{0.06}$ & $0.364_{0.06}$ & $0.8696_{0.01}$\\\\\\\\\\n\",\n \" Qwen3 (30B) A3B & $0.5089_{0.18}$ & $0.4245_{0.21}$ & $0.1995_{0.12}$ & $0.2509_{0.13}$ & $0.0373_{0.01}$ & $0.0538_{0.01}$ & $0.0649_{0.01}$ & $0.8453_{0.01}$\\\\\\\\\\n\",\n \" DeepSeek-R1-Distill Qwen (32B) & $0.4991_{0.30}$ & $0.4071_{0.23}$ & $0.262_{0.15}$ & $0.3039_{0.17}$ & $0.1895_{0.04}$ & $0.3181_{0.04}$ & $0.384_{0.05}$ & $0.8575_{0.01}$\\\\\\\\\\n\",\n \" DeepSeek-R1-Distill Llama (70B) & $0.4927_{0.31}$ & $0.4013_{0.23}$ & $0.255_{0.15}$ & $0.2965_{0.17}$ & $0.1961_{0.04}$ & $0.3239_{0.05}$ & $0.3922_{0.05}$ & $0.8618_{0.01}$\\\\\\\\\\n\",\n \" o3-mini & $0.5183_{0.25}$ & $0.3881_{0.21}$ & $0.2432_{0.12}$ & $0.2839_{0.14}$ & $0.2028_{0.03}$ & $0.3083_{0.04}$ & $0.3714_{0.04}$ & $0.8574_{0.02}$\\\\\\\\\\n\",\n \" o4-mini & $0.3873_{0.21}$ & $0.2976_{0.19}$ & $0.1768_{0.11}$ & $0.2126_{0.13}$ & $0.1945_{0.03}$ & $0.3093_{0.03}$ & $0.3806_{0.04}$ & $0.8563_{0.02}$\\\\\\\\[2.5pt]\\n\",\n \" \\\\rowcolor{black!15}\\\\textbf{Finance Fine-Tuned} & & & & & & & &\\\\\\\\\\n\",\n \" \\\\rule{0pt}{7pt}WiroAI Finance Qwen (7B) & $\\\\mathbf{0.4419_{0.28}}$ & $0.4064_{0.23}$ & $0.2531_{0.15}$ & $0.2933_{0.17}$ & $0.1782_{0.04}$ & $0.2866_{0.05}$ & $0.3409_{0.06}$ & $0.8651_{0.01}$\\\\\\\\\\n\",\n \" WiroAI Finance Llama (8B) & $0.2853_{0.28}$ & $0.2729_{0.23}$ & $0.1627_{0.15}$ & $0.1911_{0.17}$ & $0.1625_{0.05}$ & $0.2671_{0.06}$ & $0.3254_{0.08}$ & $0.8611_{0.02}$\\\\\\\\\\n\",\n \" FinanceConnect (13B) & $0.0725_{0.12}$ & $0.0884_{0.13}$ & $0.0561_{0.08}$ & $0.0646_{0.09}$ & $0.166_{0.04}$ & $0.2837_{0.05}$ & $0.3482_{0.06}$ & $0.8573_{0.01}$\\\\\\\\\\n\",\n \" Finance-LLM (13B) & $0.1709_{0.21}$ & $0.2247_{0.19}$ & $0.1443_{0.13}$ & $0.1654_{0.14}$ & $0.1628_{0.04}$ & $0.2671_{0.05}$ & $0.3263_{0.06}$ & $0.8631_{0.01}$\\\\\\\\[2.5pt]\\n\",\n \" \\\\rowcolor{black!15}\\\\textbf{Finance Reasoning} & & & & & & & &\\\\\\\\\\n\",\n \" \\\\rule{0pt}{7pt}Fin-R1 (7B) & $\\\\mathbf{0.512_{0.28}}$ & $0.3649_{0.22}$ & $0.1978_{0.12}$ & $0.2428_{0.15}$ & $0.1685_{0.04}$ & $0.2806_{0.04}$ & $0.3429_{0.05}$ & $0.8585_{0.01}$\\\\\\\\\\n\",\n \" Fino1 (8B) & $0.4191_{0.26}$ & $0.3621_{0.22}$ & $0.1637_{0.10}$ & $0.2133_{0.12}$ & $0.1701_{0.03}$ & $0.2795_{0.04}$ & $0.3458_{0.05}$ & $0.868_{0.01}$\\\\\\\\[2.5pt]\\n\",\n \" \\\\rowcolor{black!15}\\\\textbf{Math Fine-Tuned} & & & & & & & &\\\\\\\\\\n\",\n \" \\\\rule{0pt}{7pt}Mathstral (7B) & $\\\\mathbf{0.4397_{0.26}}$ & $0.3953_{0.21}$ & $0.2732_{0.16}$ & $0.3063_{0.16}$ & $0.1635_{0.03}$ & $0.2586_{0.04}$ & $0.3098_{0.05}$ & $0.8587_{0.01}$\\\\\\\\\\n\",\n \" Qwen2.5-Math (7B) & $0.1891_{0.26}$ & $0.1763_{0.21}$ & $0.1298_{0.15}$ & $0.1412_{0.16}$ & $0.0705_{0.06}$ & $0.1337_{0.08}$ & $0.1488_{0.10}$ & $0.8067_{0.02}$\\\\\\\\[2.5pt]\\n\",\n \" \\\\rowcolor{black!15}\\\\textbf{Math Reasoning} & & & & & & & &\\\\\\\\\\n\",\n \" \\\\rule{0pt}{7pt}MetaMath (13B) & $0.2313_{0.22}$ & $0.2704_{0.20}$ & $0.1357_{0.10}$ & $0.1717_{0.13}$ & $0.1863_{0.05}$ & $0.3064_{0.06}$ & $0.3753_{0.07}$ & $0.8728_{0.01}$\\\\\\\\\\n\",\n \" WizardMath (7B) & $\\\\mathbf{0.3461_{0.28}}$ & $0.3357_{0.24}$ & $0.1942_{0.14}$ & $0.2332_{0.17}$ & $0.1933_{0.06}$ & $0.3142_{0.07}$ & $0.3737_{0.08}$ & $0.8723_{0.01}$\\\\\\\\\\n\",\n \" \\\\bottomrule\\n\",\n \" \\\\end{tabular}\\n\",\n \" }\\n\",\n \" \\\\caption{\\n\",\n \"\\\\textbf{Overall performance of language models on \\\\ourdataset.}\\n\",\n \"We report four components of our \\\\ourmetric metric (FAC, step precision, step recall, and step-level F1), alongside ROUGE (R$_2$, R$_L$, R$_{Lsum}$) and BERTScore. Models are grouped into six categories. Top-performing models under FAC within each category are bolded. \\n\",\n \"}\\n\",\n \" \\\\label{tab:overall_model_performance}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=0.8\\\\linewidth]{figs/topic_performance.pdf}\\n\",\n \" \\\\caption{\\n\",\n \"\\\\textbf{Domain-level performance across financial domains.}\\n\",\n \"Radar plots compare representative models from (a) Non-reasoning models and (b) Reasoning models. Each spoke corresponds to one of the 12 top-level financial domains. Models are selected as the top performers within each of the six categories presented in \\\\autoref{tab:overall_model_performance}.\\n\",\n \"}\\n\",\n \" \\\\label{fig:topic_performance}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{figure}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=0.9\\\\linewidth]{figs/level_wise_performance.pdf}\\n\",\n \" \\\\caption{\\n\",\n \"\\\\textbf{Model performance across difficulty levels.}\\n\",\n \"FAC is reported for Basic, Intermediate, and Advanced.\\n\",\n \"}\\n\",\n \" \\\\label{fig:difficulty_performance}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table}[t]\\n\",\n \"\\\\centering\\n\",\n \"\\\\small\\n\",\n \"\\\\resizebox{\\\\columnwidth}{!}{\\n\",\n \"\\\\begin{tabular}{lccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Model} & \\\\textbf{Both} & \\\\textbf{Name Only} & \\\\textbf{Value Only} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"GPT-4.1 & 0.5080$_{\\\\pm0.4999}$ & 0.4960$_{\\\\pm0.5000}$ & 0.5040$_{\\\\pm0.5000}$ \\\\\\\\\\n\",\n \"GPT-4.1-mini & 0.4720$_{\\\\pm0.4992}$ & 0.4640$_{\\\\pm0.4987}$ & 0.4640$_{\\\\pm0.4987}$ \\\\\\\\\\n\",\n \"GPT-4o-mini & 0.3760$_{\\\\pm0.4844}$ & 0.3600$_{\\\\pm0.4800}$ & 0.3720$_{\\\\pm0.4833}$ \\\\\\\\\\n\",\n \"o3-mini & 0.4040$_{\\\\pm0.4907}$ & 0.3920$_{\\\\pm0.4882}$ & 0.4160$_{\\\\pm0.4929}$ \\\\\\\\\\n\",\n \"o4-mini & 0.3400$_{\\\\pm0.4737}$ & 0.3600$_{\\\\pm0.4800}$ & 0.3680$_{\\\\pm0.4823}$ \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"}\\n\",\n \"\\\\caption{\\\\textbf{Model robustness to input perturbation.}\\n\",\n \"FAC is reported for Both, Name Only, and Value Only.\\n\",\n \"}\\n\",\n \"\\n\",\n \"\\\\label{tab:name_number_variable}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{figure*}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=0.8\\\\linewidth]{figs/topic_performance_allgennonreasoningmodels.pdf}\\n\",\n \" \\\\caption{\\n\",\n \"\\\\textbf{Domain-level performance comparison across financial domains for all general-purpose non-reasoning models from \\\\autoref{tab:overall_model_performance}.} For clarity, we split the models into three subtypes: (a) Model Size $\\\\leq$ 10B, (b) Model Size $>$ 10B, and (c) Closed-Source Models.\\n\",\n \"}\\n\",\n \" \\\\label{fig:topic_performance_all_general_nonreasoning}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=0.8\\\\linewidth]{figs/topic_performance_allgenreasoning.pdf}\\n\",\n \" \\\\caption{\\n\",\n \"\\\\textbf{Domain-level performance comparison across financial domains for all general-purpose reasoning models from \\\\autoref{tab:overall_model_performance}.} For clarity, we split the models into two subtypes: (a) Model Size $\\\\leq$ 30B, and (b) Model Size $>$ 30B (along with all closed-source models).}\\n\",\n \" \\\\label{fig:topic_performance_all_general_reasoning}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=0.8\\\\linewidth]{figs/topic_performance_allfinance_models.pdf}\\n\",\n \" \\\\caption{\\n\",\n \"\\\\textbf{Domain-level performance comparison across financial domains for all financial fine-tuned and reasoning models from \\\\autoref{tab:overall_model_performance}.} For clarity, we split the figure into two parts: (a) Finance Non-Reasoning, and (b) Finance Reasoning models.}\\n\",\n \" \\\\label{fig:topic_performance_all_finance}\\n\",\n \"\\\\end{figure*}\\n\"\n ],\n [\n \"\\\\begin{figure*}[t]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=0.8\\\\linewidth]{figs/topic_performance_allmathmodels.pdf}\\n\",\n \" \\\\caption{\\n\",\n \"\\\\textbf{Domain-level performance comparison across financial domains for all mathematical fine-tuned and reasoning models from \\\\autoref{tab:overall_model_performance}.} For clarity, we split the figure into two parts: (a) Math Non-Reasoning, and (b) Math Reasoning models.}\\n\",\n \" \\\\label{fig:topic_performance_all_math}\\n\",\n \"\\\\end{figure*}\\n\"\n ]\n]","isConversational":false},"fulltext":{"kind":"string","value":"% This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.\n\\pdfoutput=1\n% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.\n\n\\PassOptionsToPackage{table}{xcolor}\n\n\\documentclass[11pt]{article}\n\n% Change \"review\" to \"final\" to generate the final (sometimes called camera-ready) version.\n% Change to \"preprint\" to generate a non-anonymous version with page numbers.\n\\usepackage[preprint]{acl}\n\n% Standard package includes\n% \\usepackage{times}\n\\usepackage{newtxtext}\n\\usepackage{latexsym}\n\\usepackage{multirow}\n\\usepackage{booktabs}\n\\usepackage{pifont} % For checkmarks and crosses\n\\usepackage{xspace}\n\\usepackage{amsmath}\n\\usepackage{amssymb}\n\\usepackage[colorinlistoftodos,textsize=scriptsize]{todonotes}\n% \\usepackage{tabularx}\n\\usepackage{adjustbox} % For adjusting the size of the table\n\\newcommand{\\cmark}{\\ding{51}} % Checkmark\n\\newcommand{\\xmark}{\\ding{55}} % Cross\n% \\usepackage{emoji}\n\\usepackage[most]{tcolorbox}\n\\usepackage{xcolor}\n\\usepackage{amsmath}\n\\usepackage{paralist}\n\\usepackage{listings}\n\\usepackage[nameinlink]{cleveref}\n\n% \\lstset{\n% breaklines=true, % Enable automatic line breaking\n% breakatwhitespace=false, % Allow line breaks at any character\n% basicstyle=\\ttfamily, % Use a monospaced font\n% columns=fullflexible % Adjust column spacing to prevent overflow\n% }\n\n% Define a custom color for the background\n\\definecolor{lightgray}{rgb}{0.95, 0.95, 0.95}\n\\definecolor{darkgray}{rgb}{0.4, 0.4, 0.4}\n\\definecolor{backcolour}{rgb}{0.95,0.95,0.92}\n\\definecolor{myblue}{rgb}{0.2, 0.4, 0.8} % Example 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text\n}\n\n\\usepackage{array}\n\\newcolumntype{L}[1]{>{\\raggedright\\arraybackslash}p{#1}}\n\n\\def\\sectionautorefname{\\S} \n\\def\\subsectionautorefname{\\S} \n\\def\\appendixautorefname{Appendix}\n\n\\newcommand{\\phrase}[1]{\\text{#1}\\xspace}\n\n\n\\newcommand{\\dataset}[1]{\\textsc{#1}\\xspace}\n\\newcommand{\\ourdataset}{\\dataset{FinChain}}\n\n\\newcommand{\\metric}[1]{\\textsc{#1}\\xspace}\n\\newcommand{\\ourmetric}{\\metric{ChainEval}}\n\n\n\\newcommand{\\model}[1]{\\text{#1}\\xspace}\n\\newcommand{\\chatgpt}{\\model{ChatGPT}}\n\\newcommand{\\fouro}{\\model{GPT-4o}}\n\\newcommand{\\claude}{\\model{Claude}}\n\n\n% \\newcommand{\\zx}[1]{{\\color{LimeGreen}{\\bf{[ZX:]}} #1}}\n% \\newcommand{\\cs}[1]{{\\color{CadmiumOrange}{\\bf{[CITESOMETHING]}} #1}}\n\n% For proper rendering and hyphenation of words containing Latin characters (including in bib files)\n\\usepackage[T1]{fontenc}\n% For Vietnamese characters\n% \\usepackage[T5]{fontenc}\n% See https://www.latex-project.org/help/documentation/encguide.pdf for other character sets\n\n% This assumes your files are encoded as UTF8\n\\usepackage[utf8]{inputenc}\n\n% This is not strictly necessary, and may be commented out,\n% but it will improve the layout of the manuscript,\n% and will typically save some space.\n\\usepackage{microtype}\n\n% This is also not strictly necessary, and may be commented out.\n% However, it will improve the aesthetics of text in\n% the typewriter font.\n\\usepackage{inconsolata}\n\n%Including images in your LaTeX document requires adding\n%additional package(s)\n\\usepackage{graphicx}\n\n% \n\\title{\\includegraphics[height=1.5em]{figs/logo_transpartent.pdf}\\ \\ourdataset: A Symbolic Benchmark for Verifiable Chain-of-Thought Financial Reasoning \n% \\\\\n% Step by Step Traces Over 54 Topics from 12 Domains\n}\n\n% \\usepackage{pifont}\n% Define poker suit symbols using pifont's dingbats\n% \\newcommand{\\spade}{\\textcolor{black}{\\ding{171}}} % ♠ black\n% \\newcommand{\\heart}{\\textcolor{red}{\\ding{170}}} % ♥ red\n% \\newcommand{\\dia}{\\textcolor{red}{\\ding{169}}} % ♦ red\n% \\newcommand{\\club}{\\textcolor{black}{\\ding{168}}} % ♣ black\n\n\\author{\n \\textbf{Zhuohan Xie\\textsuperscript{1}} \\ \n \\textbf{Dhruv Sahnan\\textsuperscript{1}} \\ \n \\textbf{Debopriyo Banerjee\\textsuperscript{1}} \\\n \\textbf{Georgi Georgiev\\textsuperscript{2}} \\\\\n \\textbf{Rushil Thareja\\textsuperscript{1}} \\\n \\textbf{Hachem Madmoun\\textsuperscript{3}} \\\n \\textbf{Jinyan Su\\textsuperscript{4}} \\\n \\textbf{Aaryamonvikram Singh\\textsuperscript{1}} \\\\\n \\textbf{Yuxia Wang\\textsuperscript{1}} \\ \n \\textbf{Rui Xing\\textsuperscript{1}} \\\n \\textbf{Fajri Koto\\textsuperscript{1}} \\\n \\textbf{Haonan Li\\textsuperscript{1}} \\\n \\textbf{Ivan Koychev\\textsuperscript{2}} \\\\\n \\textbf{Tanmoy Chakraborty\\textsuperscript{5}} \\\n \\textbf{Salem Lahlou\\textsuperscript{1}} \\\n \\textbf{Veselin Stoyanov\\textsuperscript{1}} \\\n \\textbf{Preslav Nakov\\textsuperscript{1}} \\\\\n \\textsuperscript{1}MBZUAI, UAE\\\n \\textsuperscript{2}FMI, Sofia University, Bulgaria\\\\\n \\textsuperscript{3}Quantsquare, France \\ \n \\textsuperscript{4}Cornell University, USA \\\n \\textsuperscript{5}IIT Delhi, India\\\\\n\\texttt{\\{zhuohan.xie, preslav.nakov\\}@mbzuai.ac.ae} \\\\\n}\n\n\n\\begin{document}\n\\maketitle\n\\begin{abstract}\n% We introduce \\ourdataset, the first symbolic benchmark designed for verifiable chain-of-thought financial reasoning. While prior datasets like FinQA and ConvFinQA supervise only final numerical answers, \\ourdataset provides full step-level supervision grounded in executable financial equations. Built from scratch, \\ourdataset spans 54 topics across 12 financial domains, with five parameterized templates per topic varying in complexity and domain expertise. Each instance includes an executable Python trace, enabling contamination-free generation and automatic verifiability. We propose \\ourmetric, a step-wise alignment metric that evaluates both final answer correctness and intermediate reasoning fidelity. Experiments across general, financial, and mathematical LLMs reveal large performance gaps, particularly in multi-step symbolic reasoning. \\ourdataset highlights the current limitations of LLMs in finance and provides a rigorous testbed for future progress. Our templates and evaluation metrics are available at \\url{https://github.com/anonymous/finchain}.\nMulti-step symbolic reasoning is critical for advancing downstream performance on financial tasks. Yet, benchmarks for systematically evaluating this capability are lacking. Existing datasets like FinQA and ConvFinQA supervise only final numerical answers, without assessing intermediate reasoning steps. To address this, we introduce \\ourdataset, the first symbolic benchmark designed for verifiable Chain-of-Thought (CoT) financial reasoning. Spanning 54 topics across 12 financial domains, \\ourdataset offers five parameterized templates per topic, each varying in reasoning complexity and domain expertise required.\nEach dataset instance includes an executable Python trace, enabling automatic generation of extensive training data and easy adaptation to other domains. We also introduce \\ourmetric, a new metric for automatic evaluation of both final answers and intermediate reasoning. Benchmarking 30 LLMs on our dataset, we find that even state-of-the-art models have considerable room for improvement in multi-step financial reasoning.\nAll templates and evaluation metrics for \\ourdataset are available at \\url{https://github.com/mbzuai-nlp/finchain}.\n\\end{abstract}\n\n\\section{Introduction}\n\\label{sec-intro}\n\n% Large language models (LLMs) have demonstrated impressive capabilities across a wide range of tasks~\\citep{llm_survey, xie-etal-2023-next}, and increasing attention is now being directed toward real-world application domains such as medicine and finance~\\citep{chen2024survey}. In particular, recent years have seen a surge of interest in financial NLP, driven by the practical need to process large-scale financial documents, news articles, and social media streams~\\citep{nie2024survey}.\n% However, much of the existing work in this area has focused on general-purpose NLP tasks within the financial domain, such as information extraction~\\citep{finer}, sentiment analysis~\\citep{tweetfinsent}, and text classification~\\citep{finarg}, where model outputs are typically spans, labels, or scalar scores. In contrast, explicit reasoning in financial contexts remains comparatively underexplored.\n\nLarge language models (LLMs) have demonstrated strong performance across a wide range of tasks~\\citep{llm_survey, xie-etal-2023-next}, including practical applications in fields such as finance, healthcare and law~\\citep{chen2024survey}. In the financial field, for instance, effective analysis often requires processing large volumes of text from diverse sources, including documents, news articles, and social media. These texts frequently reflect or shape financial phenomena, such as market sentiment~\\citep{nie2024survey}.\n\n\\begin{figure}[t]\n\\centering\n\\definecolor{varname}{HTML}{CDE7FF}\n\\definecolor{varproj}{HTML}{FFE5B4}\n\\definecolor{vartotal}{HTML}{C3F5D5}\n\\definecolor{varrate}{HTML}{FFCEDC}\n\\definecolor{vartime}{HTML}{FFF8B0}\n\\definecolor{varprincipal}{HTML}{D9CCFF}\n\\definecolor{varamount}{HTML}{E0F2F1}\n\\definecolor{varci}{HTML}{E2E2E2}\n\\definecolor{headergray}{HTML}{555555}\n% Dashed line macro\n\\newcommand{\\dashedline}{%\n \\noindent\n \\begin{center}\n \\begin{tikzpicture}\n \\draw[dashed, gray, line width=0.5pt] (0,0) -- (6.5,0);\n \\end{tikzpicture}\n \\end{center}\n}\n\n% \\begin{document}\n\n\\begin{tcolorbox}[title=\\ourdataset Template (Compound Interest), colback=white, colframe=headergray, boxrule=0.5pt, arc=1mm, fonttitle=\\bfseries]\n\n% --------------------- Question ---------------------\n\\noindent \\textbf{\\#Question:} \\\\\n\\colorbox{varname}{\\texttt{investor\\_name}} invested \\colorbox{varprincipal}{\\texttt{principal}} in \\colorbox{varproj}{\\texttt{project\\_name}}. \nThe investment grows at an annual interest rate of \\colorbox{varrate}{\\texttt{rate}}\\% compounded annually over \\colorbox{vartime}{\\texttt{time}} years. \nCalculate the \\colorbox{varci}{\\texttt{compound interest (CI)}}.\n\n% \\dashedline\n\\vspace{0.5em}\n\n% --------------------- Variables ---------------------\n\\noindent \\textbf{\\#Variables:} \\\\\n- \\colorbox{varname}{\\texttt{investor\\_name}} = sample(investors) \\\\\n- \\colorbox{varproj}{\\texttt{project\\_name}} = sample(projects) \\\\\n- \\colorbox{varprincipal}{\\texttt{principal}} = range(1000, 5000) \\\\\n- \\colorbox{varrate}{\\texttt{rate}} = uniform(2, 10) \\\\\n- \\colorbox{vartime}{\\texttt{time}} = range(1, 5)\n\n\\dashedline\n\n% --------------------- Solution ---------------------\n\\noindent \\textbf{\\#Chain-of-Thought Solution:} \\\\\nStep 1: Compute the compound amount:\n\\colorbox{varamount}{\\texttt{amount}} = \\colorbox{varprincipal}{\\texttt{principal}} $\\times$ $\\left(1 + \\frac{\\colorbox{varrate}{\\texttt{rate}}}{100}\\right)^{\\colorbox{vartime}{\\texttt{time}}}$ \\\\\n% \\quad\\quad = \\colorbox{vartotal}{8000} $\\div$ $(1 + \\frac{\\colorbox{varrate}{4.5}}{100})^{\\colorbox{vartime}{3}}$ = \\colorbox{varprincipal}{6905.39}\nStep 2: Compute the compound interest:\n \\quad \\colorbox{varci}{\\texttt{CI}} = \\colorbox{varamount}{\\texttt{amount}} $-$ \\colorbox{varprincipal}{\\texttt{P}}\n\n\n\\end{tcolorbox}\\caption{\\textbf{Symbolic template for generating compound interest problems in \\ourdataset.} \n% Each template contains a question, variable sampling logic, and a multi-step symbolic solution.\n}\n\\label{fig:template}\n\\end{figure}\n\nPrior work in financial NLP has predominantly focused on adapting general-purpose tasks to financial text. These tasks include information extraction~\\citep{finer}, sentiment analysis~\\citep{tweetfinsent}, and text classification~\\citep{finarg}, where models are trained to output relatively simple targets such as entity spans, sentiment labels, or numerical scores. In contrast, our task addresses financial reasoning, which requires models to generate complete chain-of-thought (CoT) reasoning traces that justify each step in solving a financial problem, as shown in \\autoref{fig:template}.\n\nExisting benchmarks for financial reasoning, such as FinQA~\\citep{finqa} and ConvFinQA~\\citep{convfinqa}, frame the task primarily as numerical question answering. These datasets focus on predicting the final answer and do not require models to explicitly generate or justify intermediate reasoning steps. While some examples may include traces of intermediate operations (loosely resembling reasoning steps), these are neither comprehensive nor rigorously structured to reflect the standards of financial analysis. In particular, they lack explicit, verifiable representations of a full reasoning chain, which is critical for transparency and accountability in financial decision-making. As a result, these benchmarks offer limited diagnostic value: they do not reliably reveal where a model's reasoning succeeds or fails, nor do they distinguish genuine multi-step reasoning from shallow pattern matching.\n\nIn the field of mathematical reasoning, the symbolic template paradigm introduced by \\textsc{GSM-Symbolic}~\\citep{gsmsymbolic} has become a widely adopted approach. This method re-templates 100 math problems from \\textsc{GSM8K}~\\citep{gsm8k}, providing explicit intermediate steps alongside the final answer. We adopt a similar templating strategy, but construct our dataset entirely from scratch for the financial field. As illustrated in \\autoref{fig:template}, each symbolic template encodes a parameterized financial problem (e.g., compound interest) with variable named entities and numerical inputs. Each template is paired with executable Python code that computes both intermediate steps and the final result. This design enables scalable, contamination-free generation of consistent examples for both training and evaluation.\n\n\n% To address this gap, we introduce \\textbf{\\ourdataset}, a new dataset that brings the symbolic template paradigm of \\textsc{GSM-Symbolic} into the financial domain. While \\textsc{GSM-Symbolic} re-templates 100 math problems from \\textsc{GSM8K}~\\citep{gsm8k}, \\ourdataset is built entirely from scratch for finance.\n% As shown in \\autoref{fig:template}, each symbolic template defines a parameterized financial problem (e.g., compound interest), where named entities and numerical inputs are variable. The template is backed by executable Python code, which computes all intermediate steps and the final answer. This enables large-scale, contamination-free generation of consistent training and evaluation examples.\n\n% To ensure comprehensive coverage,\n% we define the most fine-grained financial taxonomy (\\autoref{fig:taxonomy}) to date, spanning \\textbf{12 high-level domains} (e.g., Corporate Finance, Sustainable Finance, Crypto) and \\textbf{54 concrete topics} to the best of our knowledge.\n% For each topic, we construct \\textbf{five parameterized templates} (two easy, two intermediate, one advanced), varying in symbolic complexity and required domain expertise.\n\n\nThe financial field comprises a wide range of distinct domains, each involving different stakeholders and downstream objectives. Consequently, it is essential to design the dataset in a templated manner that supports domain-specific evaluation. To address this, we organize our dataset using a fine-grained financial taxonomy (\\autoref{fig:taxonomy}) covering \\textbf{12 high-level domains} (e.g., Corporate Finance, Sustainable Finance, Crypto), each comprising distinct topics, \\textbf{54 topics in total}. For each topic, we develop \\textbf{five parameterized templates} (two easy, two intermediate, one advanced), varying in symbolic reasoning complexity and required domain knowledge. To the best of our knowledge, this hierarchical structure constitutes the most detailed classification of financial reasoning tasks among existing datasets in this area.\n\nEach templated instance includes: (1) a \\textbf{scenario card} describing the topic, difficulty tier, and sampled inputs (e.g., ``Discounted cash flow valuation, advanced''), and (2) an \\textbf{executable chain} of reasoning steps in Python, grounded in domain-specific equations and concepts.\nBecause every operation in the chain is explicit and executable, our benchmark supports full machine-verifiability: any hallucinated, skipped, or incorrect step in a model's output can be automatically detected. This contrasts sharply with existing datasets like \\textsc{FinQA} and \\textsc{ConvFinQA}, which only supervise the final answer.\n\nTo enable rigorous evaluation, we introduce \\textbf{\\ourmetric}, which assesses both the correctness of the final answer and the alignment of intermediate reasoning steps. Our evaluation across proprietary and open-source LLMs reveals a consistent pattern: while large models such as \\texttt{GPT-4.1} and \\texttt{LLaMA 3.3 (70B)} achieve high overall accuracy, they still struggle with complex symbolic tasks and multi-step financial reasoning. In contrast, smaller models consistently underperform, even when fine-tuned on financial data. These findings indicate that domain-specific supervision alone is insufficient; robust reasoning performance also depends on adequate model capacity for handling symbolic and multi-hop inference.\n\n\nOur main contributions in this study are summarized below.\n\n\\begin{itemize}\n\\item We present the first from-scratch symbolic benchmark for financial reasoning, constructed using a custom designed financial taxonomy spanning 12 domains and 54 topics. For each topic, we provide five parameterized templates (two easy, two intermediate, one advanced) along with executable Python code that generates step-by-step reasoning traces for each instance.\n\\item We propose an automatic alignment metric that evaluates a model’s chain of thought solution by comparing it against gold-standard reasoning traces, assessing both the correctness of the final answer and the alignment of intermediate steps.\n\\item We evaluate a range of open-source and proprietary large language models and large reasoning models, and observe that even state-of-the-art models exhibit substantial difficulty in handling symbolic financial reasoning—particularly on advanced templates that require longer and more complex reasoning chains.\n\\end{itemize}\n\n\n\n\n% \\begin{quote}\n \n% \\end{quote}\n\n\n\n\n\n% \\section{Related Work}\n\n% We begin by reviewing developments in financial NLP, then focus on recent efforts toward financial reasoning datasets that aim to support deeper inference within finance domain.\n\n% \\subsection{Financial NLP}\n\n% The growing use of LLMs in finance has driven substantial progress in financial NLP~\\citep{li-etal-2023-chatgpt, chen2024survey, aipersonalfinance}, spanning tasks such as information extraction~\\citep{finer, kaur2023REFinD}, textual analysis~\\citep{fomc, finarg}, text summarization~\\citep{ectsum, finben}, risk management~\\citep{generalistcreditscoring, yuan-etal-2024-r}, and decision making~\\citep{finben, aipersonalfinance}.\n% These tasks make the best of LLMs to understanding earnings reports, regulatory filings, social media, and other unstructured sources of market information, therefore, make them useful in our daily lives.\n% There is a growing demand for domain-specific large language models (LLMs) in finance. \\textsc{BloombergGPT}~\\citep{wu2023bloomberggpt} is a 50B-parameter model trained on both Bloomberg's proprietary financial data and general-purpose corpora. \\textsc{FinGPT}~\\citep{liu2023fingpt} is an open-source framework that adopts a data-centric approach with continual pretraining on real-time financial text. Both models show strong performance across tasks such as sentiment analysis, news classification, and QA.\n% There is also increasing interest in standardized benchmarks that evaluate LLM capabilities across a wide spectrum of financial tasks. \n% \\textsc{FLANG}~\\citep{shah-etal-2022-flue} laid early groundwork by introducing five core financial NLP tasks alongside a domain-pretrained language model. \n% Building on this, \\textsc{PIXIU}~\\citep{pixiu} proposed a large instruction dataset and benchmark tailored for financial question answering and user alignment. \n% To enable broader task coverage, \\textsc{FinBen}~\\citep{finben} compiled 42 datasets across 24 tasks, supporting evaluation from information extraction to decision-making. \n% Focusing on deeper reasoning, \\textsc{BizBench}~\\citep{bizbench} benchmarks program synthesis and numerical reasoning over real-world business and financial scenarios. \n% Complementing these efforts, \\textsc{FinMTEB}~\\citep{finmteb} evaluates embedding models on 64 financial datasets across seven tasks in English and Chinese.\n\n\\begin{figure*}[t]\n \\centering\n \\includegraphics[width=0.98\\linewidth]{figs/taxonomy.pdf}\n \\caption{\n\\textbf{FinChain taxonomy of financial reasoning topics.}\nOur benchmark spans 54 topics organized into 12 major domains, ranging from traditional areas like \\textit{Corporate Finance} and \\textit{Financial Reporting} to emerging fields such as \\textit{Crypto Finance} and \\textit{Sustainable Finance}. This hierarchical structure enables fine-grained evaluation of symbolic reasoning across diverse financial domains.\n}\n \\label{fig:taxonomy}\n\\end{figure*}\n\n\n\\section{Related Work}\n\n\\subsection{Financial NLP}\n\n\nRecent advances in financial NLP have been driven by both task-specific models and large-scale domain adaptation. Early work focused on extraction and classification tasks using models such as FinBERT~\\citep{araci2019finbert}, while more recent efforts have extended to personal finance~\\citep{aipersonalfinance}, credit scoring~\\citep{generalistcreditscoring}, and risk-awareness benchmarking~\\citep{yuan-etal-2024-r}. Domain-specific datasets such as FiNER-ORD, REFinD, FinARG, and ECTSum provide support for named entity recognition, relation extraction, argument mining, and summarization, respectively~\\citep{finer, kaur2023REFinD, ectsum, finben}.\n\nApart from task-specific models, general-purpose financial language models have grown rapidly in both scale and specialization. BloombergGPT~\\citep{wu2023bloomberggpt} demonstrated strong performance across a wide range of financial NLP tasks, while FinGPT~\\citep{liu2023fingpt} emphasized open-source adaptability. FinMA~\\citep{pixiu}, a compact model fine-tuned from LLaMA, achieved performance competitive with proprietary baselines.\n\nWith the emergence of these general-purpose models, there has been a corresponding increase in benchmarking efforts aimed at evaluating their performance across diverse financial tasks.\nFLANG~\\citep{shah-etal-2022-flue} introduced early baselines, which were further extended by FinBen~\\citep{finben}, covering 24 tasks, and FinMTEB~\\citep{finmteb}, spanning 64 embedding datasets. \nBizBench~\\citep{bizbench} and PIXIU~\\citep{pixiu} highlighted challenges in quantitative and multimodal reasoning.\n\nDespite this progress, limitations remain in multi-step mathematical reasoning, long-context understanding, and cross-market generalization~\\citep{chen2024survey}. These challenges motivate the need for benchmarks that assess capabitity of models to perform faithful, auditable reasoning grounded in financial knowledge.\n\n\n\n\\subsection{Financial Reasoning}\n\nReal-world financial problems typically require arriving at a precise numerical result. Consequently, evaluating financial reasoning through a numerical QA framework emerges as a natural and intuitive choice.\nFinQA~\\citep{finqa} and ConvFinQA~\\citep{convfinqa} supervise models to generate arithmetic programs that compute final answers, but offer only weak supervision over intermediate steps. The resulting traces are neither fully explicit nor verifiable.\nFinTextQA~\\citep{fintextqa} introduces long-form financial questions derived from textbooks and regulatory documents. While it emphasizes explanatory, retrieval-based QA, it only assesses final answer correctness and lacks annotated reasoning steps.\nOutside finance, GSM-Symbolic~\\citep{gsmsymbolic} re-templates GSM8K~\\citep{gsm8k} problems into symbolic programs with step-level supervision. However, these tasks focus on basic arithmetic and lack financial semantics. No equivalent benchmark currently exists in the financial domain.\n\nModel-centric efforts have also aimed to enhance financial reasoning. \nFinR1~\\citep{finr1} applies reinforcement learning over synthetic programs, and Fino1~\\citep{fino1}, based on an 8B LLaMA model, outperform larger baselines on FinQA, ConvFinQA, and FinTextQA. Nonetheless, these models still underperform on long-horizon and cross-table reasoning.\n\nIn summary, existing benchmarks either provide coarse program-level supervision (FinQA, ConvFinQA), fine-grained but domain-agnostic traces (GSM-Symbolic), or retrieval-based QA without traceability (FinTextQA). None support systematic, step-level, domain-aware supervision grounded in financial equations.\n\\textbf{\\ourdataset addresses this gap by introducing a symbolic, executable benchmark for financial reasoning. It supervises each intermediate step, supports both automatic alignment and human assessment, and covers 54 topics across 12 financial domains.}\n\n\n\n\n% \\subsection{Financial Reasoning}\n\n% Efforts to evaluate financial reasoning abilities have primarily centered on the form of question answering (QA). FinQA~\\citep{finqa} and ConvFinQA~\\citep{convfinqa} provide supervision over programmatic operations used to compute the final answer. While these datasets support numerical reasoning, they do not offer explicit step-level symbolic traces or enable granular error analysis of intermediate reasoning steps. \n% Recent work in symbolic QA, such as GSM-Symbolic~\\citep{gsmsymbolic}, re-templates GSM8K~\\citep{gsm8k} problems to facilitate step-by-step program supervision in the math domain. However, GSM-Symbolic does not cover financial tasks, and its scope is limited to general mathematical operations.\n% In parallel, several financial language models have been introduced to enhance performance on financial tasks. For example, FinR1~\\citep{finr1} and Fino1~\\citep{fino1} focus on chain-of-thought and reinforcement-based fine-tuning to improve multi-step financial reasoning. Yet, the lack of a fine-grained, symbolic benchmark in finance limits the evaluation and development of such models.\n\n% \\subsection{Financial NLP}\\label{sec:financial-nlp}\n\n% LLMs have moved \\emph{financial} NLP from extraction toward multi-step, numeric reasoning. We outline the main \\emph{tasks}, \\emph{models}, and \\emph{benchmarks} and note remaining gaps.\n\n% \\paragraph{Tasks} Domain pre-training boosts performance (e.g., \\textsc{FinBERT}~\\citep{araci2019finbert}), yet frontier models still stumble on arithmetic and data-to-text generation~\\citep{li-etal-2023-chatgpt}. Resources now cover extraction (FiNER-ORD, REFinD), analysis (FOMC, FinARG), summarisation (ECTSum), and newer areas such as personal finance, credit scoring, and safety~\\citep{aipersonalfinance, generalistcreditscoring, yuan-etal-2024-r}.\n\n% \\paragraph{Domain LLMs} Scale-centric \\textsc{BloombergGPT} (50 B/363 B) leads most tasks~\\citep{wu2023bloomberggpt}, whereas data-centric \\textsc{FinGPT} enables rapid, open-source adaptation~\\citep{liu2023fingpt}. Compact \\textsc{FinMA} fine-tuned from \\textsc{LLaMA} rivals larger proprietary models~\\citep{pixiu}.\n\n% \\paragraph{Benchmarks} \\textsc{FLANG} provides early baselines~\\citep{shah-etal-2022-flue}; \\textsc{FinBen} (24 tasks) and \\textsc{FinMTEB} (64 datasets) broaden coverage~\\citep{finben, finmteb}. \\textsc{BizBench} and PIXIU stress quantitative and multi-modal reasoning~\\citep{bizbench, pixiu}.\n\n% \\paragraph{Limitations} Multi-step maths, long rationales, and cross-market robustness remain open~\\citep{finben}. Instruction tuning, retrieval, and tool integration are active remedies; progress hinges on combining language skill with strict numerical and regulatory reasoning~\\citep{chen2024survey}.\n\n% \\bigskip\n% \\noindent\\textbf{Summary} Competition among BloombergGPT-scale, FinGPT-style adaptation, and BizBench/PIXIU evaluation is speeding progress toward trustworthy, quantitatively competent financial AI.\n\n% \\subsection{Financial Reasoning}\\label{sec:financial-reasoning}\n\n% \\paragraph{Numerical QA} \\textsc{FinQA} posed stock-analysis questions answerable by executing gold arithmetic programs over filings; \\textsc{ConvFinQA} added multi-turn dialogue but neither supplies step-level traces, limiting fine-grained supervision~\\citep{finqa, convfinqa}.\n\n% \\paragraph{Symbolic supervision} GSM-Symbolic brings step-level programs to GSM8K but covers only basic arithmetic and no domain knowledge; finance lacks an equivalent resource~\\citep{gsmsymbolic, gsm8k}.\n\n% \\paragraph{Long-form \\& multi-modal} \\textsc{FinTextQA} widens scope with 1 262 textbook- and regulator-derived questions that demand retrieval-grounded, explanatory answers~\\citep{fintextqa}.\n\n% \\paragraph{Reasoning LLMs} \\textsc{FinR1} couples chain-of-thought tuning with RLHF on synthetic programs, raising FinQA accuracy, while \\textsc{Fino1} shows an 8 B Llama-3 variant outperforms larger general LLMs by \\(\\sim\\!10\\%\\) across three benchmarks—yet both still lag experts on long-context, cross-table tasks~\\citep{finr1, fino1}.\n\n% \\paragraph{Gaps} Current datasets give either coarse programs (FinQA/ConvFinQA), fine-grained non-financial traces (GSM-Symbolic), or answer-only scores (FinTextQA), leaving finance without step-level, domain-aware supervision. Such traces—and tailored adaptation—are essential for transparent, auditable, long-horizon financial reasoning.\n\n\n\n% \\bigskip\n% \\noindent\\textbf{FinChain complements this line of work by providing a fully symbolic, step-supervised benchmark across a broad taxonomy of financial topics. It enables precise evaluation of intermediate reasoning steps and supports both automatic and human-aligned assessment of reasoning fidelity.}\n\n\n\n\n\n\\section{\\ourdataset}\n\\label{finchain}\n\n\n% We construct \\ourdataset through a multi-stage pipeline that combines LLM-based generation, manual refinement, and expert validation. This section details the taxonomy design, template generation, quality checks, and expert review process.\n\n\\subsection{Data Creation Process}\n\n% We begin by prompting \\chatgpt\\footnote{We use \\fouro, the latest version at the time of writing.} to enumerate major financial domains. For each domain, the model was then instructed to decompose the topic into finer-grained topics. A financial expert on our team manually reviewed and refined the resulting taxonomy to ensure logical coherence and alignment with standard financial textbooks~\\citep{finance_textbook25} (see \\autoref{fig:taxonomy}).\n\nThe data creation process begins with the identification and definition of financial domains. This step is informed by established literature~\\cite{finance_textbook25} and guided by input from financial experts. Through this process, we identify \\textbf{12} distinct financial domains. To generate topics within each domain, we extract relevant passages from the literature and use the domain name as a prompt to \\chatgpt\\footnote{We use \\fouro, the latest version available at the time of writing.} to extract candidate financial topics. \nThe resulting list is then curated by our financial experts, who filter and refine the outputs to yield a total of \\textbf{54} financial topics, averaging \\textbf{4.5} topics per domain. This structured approach results in the comprehensive taxonomy illustrated in \\autoref{fig:taxonomy}.\nInspired by \\citet{gsmsymbolic}, we develop executable Python methods that generate both questions and solutions featuring chain-of-thought reasoning by instructing \\chatgpt. The prompt is provided in \\autoref{templatecreation}.\nFor each topic, we prompt the model to produce ten template-based questions and explicitly guide it to vary the complexity levels, easy, intermediate, and advanced, based on the number of reasoning steps required.\nWe then manually select five representative templates per topic, two easy, two intermediate, and one advanced, to ensure balanced coverage across difficulty levels in \\ourdataset.\n\n\n% To build symbolic templates, we first asked \\chatgpt to analyze the symbolic data creation methodology in \\citet{gsmsymbolic}. The summary produced was reviewed by our team for accuracy. We then per topic, covering varying \n\n\\subsection{Sanity Check}\n\n% After initial template generation through prompting, we observed several recurring issues that we intend to filter out from our dataset creation methodology, to ensure utmost quality. Therefore, we implemented a dedicated sanity check phase focused on the following problems:\n% % \\begin{compactitem}\n% (1) \\textbf{Cross-national inconsistencies:} The automated prompting process, occasionally introduced hyper-localized financial contexts (e.g., currencies, exchange rates, indices and terminology from local economies in varied countries), likely due to implicit user profile information influencing the generation. To ensure consistency, we standardized all questions to U.S.-based financial settings.\n% (2)\\textbf{Precision mismatch:} Questions and solutions often used rounded values for readability, while full-precision values were used in the underlying computation. We adjusted the computation outputs to match the displayed precision.\n% (3) \\textbf{Incomplete input specification:} Some questions omitted key variables needed for calculation. We manually edited these cases to include all required inputs.\n\nAfter initial template generation through prompting, we observed several recurring issues that we intend to filter out from our dataset creation methodology, to ensure utmost quality. Therefore, we implemented a dedicated sanity check phase focused on the following problems:\n% \\begin{compactitem}\n(1) \\textbf{Cross-national inconsistencies:} The automated prompting process, occasionally introduced hyper-localized financial contexts (e.g., currencies, exchange rates, indices and terminology from local economies in varied countries). To ensure consistency, we standardized all questions to U.S. based financial settings only.\n(2) \\textbf{Precision mismatch:} Questions and solutions often used rounded values for readability, while full precision values were used in the underlying computation. We adjusted the computation outputs to match the displayed precision.\n(3) \\textbf{Incomplete input specification:} Some questions omitted key variables needed for calculation. We manually edited these cases to include all required inputs.\n% \\end{itemize}\n% \\end{compactitem}\n\n\n\n\\subsection{Expert Check}\n\nTo address the known limitations of automated generation, we incorporate expert verification by qualified financial professionals as a critical component of our quality control process. To reduce LLM specific artifacts and improve reliability, we employ a two-step strategy. First, we introduce model diversity by using a different LLM to assist in annotation, mitigating overfitting to the idiosyncrasies of a single model. Second, when this auxiliary LLM identifies a potential error or inconsistency, the case is escalated to a human expert for validation.\n\nSpecifically, we first conducted an automated screening using another high-capacity LLM, \\claude 3.5 Sonnet (\\claude). \\claude~was prompted to assess the soundness of each question-and-solution template produced by \\chatgpt.\\footnote{The prompt is provided in \\autoref{claude_template}.}\nTemplates flagged by \\claude~as potentially flawed were escalated to a qualified financial expert. The expert's role was to adjudicate these cases by evaluating the correctness of the original \\chatgpt~template in light of \\claude's feedback. Each template was classified into one of three categories: (a) Correct, (b) Incorrect, or (c) Correct but better expressed from an alternative financial perspective\\footnote{In some cases, the expert noted that \\chatgpt's template was valid, but \\claude~proposed an equally sound formulation. The expert chose the most appropriate version for the dataset.}. \n\nIf a template was deemed incorrect, we re-generated and re-validated it through the same pipeline until a version was confirmed as correct by the expert. This iterative filtering process ensured that only high-quality, financially sound templates were retained in \\ourdataset.\n\n\n\n\n\n% After conducting a basic sanity check, there may still be issues related to financial knowledge that are not easily identifiable, even when consulting Wikipedia, particularly for those without a financial background. Therefore, we defer such difficult cases to a financial expert.\n% To reduce the effort required, we first employ another powerful LLM, \\claude\\footnote{\\claude 3.5 Sonnet was used.}, to perform a preliminary check. We carefully design a prompt for \\claude to examine the templates generated by \\chatgpt. The prompt is as follows:\n\n% \\begin{quote}\n% \\textit{I have written several Python methods that automatically generate random questions and corresponding solutions about {topic} by varying key variables. I need assistance in verifying the correctness of the equations used, ensuring that the solutions are accurate and solid. Please respond by first indicating the method is correct or wrong. }\n% \\end{quote}\n\n% We collect cases where \\claude disagrees with \\chatgpt and leave them to a financial expert for judgment, selecting from [both correct, both incorrect, \\claude is correct, \\chatgpt is correct]\\footnote{Our judgment indicates that in many cases, both \\chatgpt and \\claude have valid points but focus on different perspectives.}. We retain those determined to be correct by the financial experts.\n\n\\section{\\ourmetric}\n\nWe propose \\ourmetric, an evaluation framework that assesses model outputs along two axes: \\textbf{final answer correctness} and \\textbf{reasoning step alignment}. While our step-wise semantic alignment approach is inspired by prior work on reasoning consistency~\\citep{faithfulcot, roscoe}, we extend it by explicitly modeling the verification of intermediate results through step-answer matching. Moreover, unlike prior work, which primarily evaluates textual consistency, our framework also verifies the final numeric answer, which ensures a holistic view of model performance, on both reasoning faithfulness and end-task fidelity. To the best of our knowledge, this combination has not been previously explored, particularly in the context of financial reasoning tasks.\n\n\\subsection{Preliminaries}\nWe define the gold solution $S^{*}$ and the predicted solution $\\hat{S}$ as a sequence of $m$ and $n$ steps, respectively, represented as:\n\\begin{align}\n \\begin{split}\n S^* = (s^*_1, \\ldots, s^*_m) ,\n \\quad\n \\hat{S} = (\\hat{s}_1, \\ldots, \\hat{s}_n),\n \\end{split}\n\\end{align}\nwhere $s^*_i$ and $\\hat{s}_j$ represent individual steps in $S^*$ and $\\hat{S}$, respectively.\nWe also define a function $\\text{StepRes}(\\cdot)$, denoted as:\n\\begin{equation}\n \\text{StepRes}(s_i) = a_i,\n\\end{equation}\nwhich extracts the intermediate result $a_i$, computed at the $i^{th}$ step of the corresponding solution.\n\n\\subsection{Reasoning Step Alignment}\nTo measure reasoning faithfulness, we break the evaluation into two components:\n\n\\paragraph{Step Semantic-Similarity.}\nWe embed each step using a sentence encoder $\\text{Enc}(\\cdot)$ and compute cosine similarity between gold and predicted step pairs $(s^*_i, \\hat{s}_j)$, resulting in scores $\\mathrm{SS}(\\cdot) \\in [0,1]$, represented as:\n\\begin{equation}\n \\label{eqn:step_sem_sim}\n \\mathrm{SS}(s^*_i, \\hat{s}_j) = \\mathrm{CosSim}\\left(\\mathrm{Enc(s^*_i), Enc(\\hat{s}_j)}\\right)\n\\end{equation}\nwhere $s^*_i$ denotes the $i^{th}$ step of the gold solution and $\\hat{s}_j$ denotes the $j^{th}$ step of the predicted solution.\n\n\\paragraph{Step Answer-Match.}\nWe evaluate whether the results computed at intermediate steps $s^*_i$ and $\\hat{s}_j$ are consistent with each other.\nTo this end, we introduce a function $\\mathrm{AM}(.)$, and the answer matching process is denoted as follows:\n\\begin{align*}\n \\begin{split}\n \\mathrm{StepRes}(s^*_i) = a^*_i,\n \\mathrm{\\ StepRes}(\\hat{s}_j) = \\hat{a}_j,\n \\end{split}\n\\end{align*}\n\\begin{equation}\n \\label{eqn:step_ans_match}\n \\mathrm{AM}(s^*_i, \\hat{s}_j) = \\begin{cases}\n \\mathbb{I} ( \\frac{|\\hat{a}_j - a^*_i|}{a^*_i} \\leq \\epsilon ) & \\text{if } \\hat{a_j},a^*_i \\in \\mathbb{R}, \\\\\n \\mathbb{I} ( \\hat{a}_j == a^*_i ) & \\text{otherwise.}\n \\end{cases}\n\\end{equation}\nwhere $\\mathbb{I}(\\cdot)$ is the indicator function, and $\\epsilon$ is a small tolerance threshold to account for errors propagating due to numerical rounding. We set $\\epsilon = 0.05$ for our experiments, allowing a relative error of up to 5\\%.\n\nNow, we consider a pair of steps to be aligned with each other if: \\emph{(i)} their semantic similarity is above a threshold, and \\emph{(ii)} the corresponding intermediate results are consistent. Using \\Cref{eqn:step_sem_sim,eqn:step_ans_match}, we represent the process of measuring step-level alignment between predicted solution and ground truth as follows:\n\\begin{align}\n \\label{eqn:step_align_1}\n \\begin{split}\n \\mathrm{A}(s^*_i, \\hat{s}_j) = \\mathrm{SS}(s^*_i, \\hat{s}_j) \\times \\mathrm{AM}(s^*_i, \\hat{s}_j),\n \\\\\n \\mathrm{StepAlign}(s^*_i, \\hat{S}) = \\max_{j=1,\\ldots,n} \\mathrm{A}(s^*_i, \\hat{s}_j),\n \\end{split}\n\\end{align}\nand similarly,\n\\begin{equation}\n \\label{eqn:step_align_2}\n \\mathrm{StepAlign}(S^*, \\hat{s}_j) = \\max_{i=1,\\ldots,m} \\mathrm{A}(s^*_i, \\hat{s}_j)\n\\end{equation}\n\nFinally, we compute step-level recall and precision using \\Cref{eqn:step_align_1,eqn:step_align_2}:\n\\begin{equation}\n \\label{eqn:step_rec}\n \\mathrm{Rec} = \\frac{1}{m} \\sum_{i=1}^{m} \\mathbb{I}\\left( \\mathrm{StepAlign}(s^*_i, \\hat{S}) \\geq \\tau \\right),\n\\end{equation}\n\\begin{equation}\n \\label{eqn:step_prec}\n \\mathrm{Prec} = \\frac{1}{n} \\sum_{j=1}^{n} \\mathbb{I}\\left( \\mathrm{StepAlign}(\\hat{s}_j, S^*) \\geq \\tau \\right),\n\\end{equation}\nwhere $\\tau$ is an alignment score threshold.\n\nWe also define the $\\mathrm{StepF1}$, which is the harmonic mean of step-level precision and recall:\n\\begin{equation}\n\\mathrm{StepF1} = \\frac{2 \\times \\mathrm{Prec} \\times \\mathrm{Rec}}{\\mathrm{Prec} + \\mathrm{Rec} + \\gamma},\n\\end{equation}\nwhere $\\gamma$ is a small constant to prevent division by zero. We set $\\tau = 0.7$ and $\\gamma = 0.0001$ for our experiments.\n\n\\subsection{Final Answer Correctness}\nTo measure effectiveness of the models on overall end-task fidelity, we evaluate whether the final answer in the predicted solution is consistent with that of the ground truth. We define a function $\\mathrm{FAC}(\\cdot)$ using $\\mathrm{AM}(\\cdot)$ from \\Cref{eqn:step_ans_match}. We extract the final answer from the last step of the solution, and formalize it as follows:\n\\begin{equation}\n \\mathrm{FAC(\\hat{S}, S^*)} = \\mathrm{AM}(s^*_m, \\hat{s}_n)\n\\end{equation}\n\n\n% Let final answers $\\text{StepRes}(s^*_m) = a^*_m$ and $\\text{StepRes}(\\hat{s}_n) = \\hat{a}_n$ from $S^*$ and $\\hat{S}$, respectively.\n% We define the final answer correctness, based on relative error as:\n% \\begin{equation}\n% \\text{FAC}(\\hat{S}, S^*) = \\begin{cases}\n% \\mathbb{I} ( \\frac{|\\hat{a}_n - a^*_m|}{a^*_m} \\leq \\epsilon ) & \\text{if } \\hat{a_n},a^*_m \\in \\mathbb{R}, \\\\\n% \\mathbb{I} ( \\hat{a}_n == a^*_m ) & \\text{otherwise.}\n% \\end{cases}\n% \\end{equation}\n% where $\\mathbb{I}(\\cdot)$ is the indicator function, and $\\epsilon$ is a small tolerance threshold to account for errors propagating due to numerical rounding. We set $\\epsilon = 0.05$ for our experiments, allowing a relative error of up to 5\\%.\n\n% \\subsection{Order Consistency}\n\n% To account for logical flow, we introduce an order consistency metric. \n% Let $\\pi(i)$ denote the index of the predicted step $\\hat{s}_{\\pi(i)}$ matched to the gold step $s^*_i$. \n% An order violation occurs if:\n% \\begin{equation}\n% \\pi(i) > \\pi(i+1).\n% \\end{equation}\n% The order violation rate is defined as:\n% \\begin{equation}\n% \\mathrm{OrderViolationRate} = \\frac{1}{m-1} \\sum_{i=1}^{m-1} \\mathbb{I} \\left( \\pi(i) > \\pi(i+1) \\right).\n% \\end{equation}\n\n% \\subsection{Final Reasoning Score}\n\n% The final step quality score incorporates both semantic coverage and order consistency:\n% \\begin{equation}\n% \\mathrm{FinalStepScore} = \\mathrm{StepF1} \\times (1 - \\mathrm{OrderViolationRate}).\n% \\end{equation}\n\n% \\subsection{Overall Evaluation}\n\n% The overall evaluation metric combines final answer correctness and reasoning faithfulness:\n% \\begin{equation}\n% \\mathrm{OverallScore} = \\mathrm{AnsCorrect}(\\hat{a}, a^*) \\times \\mathrm{FinalStepScore}.\n% \\end{equation}\n% This formulation rewards models that produce both correct final answers and coherent, logically ordered reasoning chains.\n\n% \\subsection{Metric Sanity Check}\n% To verify that our evaluation metric behaves intuitively, we applied it to a set of manually constructed examples covering high-quality, partially correct, and erroneous reasoning chains. As expected, high-quality chains received FinalStepScore values above XX, partially correct chains received scores between XX and XX, and severely flawed chains received scores below XX. These results suggest that the metric reasonably distinguishes between different levels of reasoning quality.\n\n\n% Can't finish it\n\n% \\subsection{LLM-as-a-judge with Gemini 2.5 Flash}\n\n% We evaluate five detailed aspects:\n% Planning, does the reasoning contain the full, natural steps that lead to the final answer?\n% Mathematical Correctness: is the math correct?\n% Simplicity: Is the reasoning simple, do not contain unnecessary, duplicate steps?\n% Financial Contextual Understanding: can LLMs interpret and apply financial terms and concepts specific to the question?\n% Formatting: check the financial formatting standards?\n\n\\section{Experiments and Results}\nIn this section, we evaluated the symbolic financial reasoning capabilities of 30 language models over 2,700 cases, and analyzed performance from overall, domain and difficulty-level perspectives.\n% We conduct a series of experiments to evaluate the symbolic financial reasoning capabilities of LLMs. Our evaluation focuses on three core aspects:\n% \\begin{compactitem}\n% % \\begin{itemize}\n% \\item \\textbf{Overall performance} measures average performance across all examples to assess general reasoning ability.\n% \\item \\textbf{Performance over topics} analyzes model strengths and weaknesses across financial subdomains.\n% \\item \\textbf{Performance over difficulty levels} examines model robustness as the complexity of questions increases.\n% % \\end{itemize}\n% \\end{compactitem}\n\n\\subsection{Evaluated Models}\n\nTo comprehensively benchmark model capabilities, we evaluate a diverse set of language models grouped into six categories:\n% 30 models\n\\begin{compactitem}\n \\item \\textbf{General-Purpose Models} serve as strong domain-agnostic baselines. We include \\texttt{GPT-4.1}, \\texttt{GPT-\\{4o, 4.1\\} mini}~\\citep{gpt4osyscard}, \\texttt{LLaMA 3.1 \\{8, 70\\}B Instruct}, \\texttt{LLaMA 3.3 70B Instruct}~\\citep{llama3}, \\texttt{Mistral 7B Instruct}~\\citep{mistral}, \\texttt{Mixtral (8x7B) Instruct}~\\citep{mixtral2401}, \\texttt{Gemma 2 \\{9, 27\\}B Instruct}~\\citep{gemma2}, \\texttt{Gemma 3 27B Instruct}~\\citep{gemma3}, and \\texttt{Qwen2.5 7B}~\\citep{qwen25}.\n \n \\item \\textbf{General Reasoning Models} are trained or fine-tuned for broad multi-step reasoning tasks. We include \\texttt{o\\{3, 4\\}-mini}, \\texttt{DeepSeek-R1 Distill LLaMA \\{8, 70\\}B}, \\texttt{DeepSeek-R1 Distill Qwen \\{7, 32\\}B}~\\citep{deepseekr1}, and \\texttt{Qwen3 \\{8, 30\\}B}~\\citep{qwen3}.\n \n \\item \\textbf{Financially Fine-Tuned Models} are adapted via instruction tuning or continued pretraining on financial corpora. We evaluate \\texttt{Finance-LLM 13B}~\\citep{financellm13b}, \\texttt{FinanceConnect 13B}~\\citep{financeconnect13b} \\texttt{WiroAI Finance-LLaMA 8B}, and \\texttt{WiroAI Finance-Qwen 7B}~\\citep{WiroAI-finance-qwen}.\n \n \\item \\textbf{Financial Reasoning Models} are specifically designed for multi-step reasoning in finance, often leveraging chain-of-thought (CoT) supervision. We include \\texttt{Fino1 8B}~\\citep{fino1} and \\texttt{FinR1 7B}~\\citep{finr1}.\n \n \\item \\textbf{Mathematical Models} are fine-tuned on math-centric corpora for symbolic computation. \n \n We include \\texttt{Mathstral 7B}~\\citep{mathstral}, and \\texttt{Qwen2.5 Math 7B}~\\citep{qwen25math}.\n \n \\item \\textbf{Mathematical Reasoning Models} excel at step-by-step problem solving in mathematics. We include \\texttt{WizardMath 7B}~\\citep{wizardmath}, and \\texttt{MetaMath 13B}~\\citep{metamath}.\n\\end{compactitem}\n\n\\subsection{Experimental Setup}\n\nWe generate 10 instances per symbolic template using different random seeds, resulting in a total of 54 topics $\\times$ 5 templates $\\times$ 10 instances = \\textbf{2,700 test cases}. All models are evaluated under a consistent decoding configuration: temperature = 0.7, top-\\textit{p} = 0.95, and a maximum token limit of 4,096.\nFor evaluation, we use our proposed metric, \\ourmetric, as the primary indicator for both final answer correctness and alignment of intermediate reasoning steps. Additionally, we include two widely used reference-based metrics~\\citep{deltascore} to assess surface-level generation quality: ROUGE~\\citep{rouge} for n-gram overlap and BERTScore~\\citep{bertscore} for semantic similarity.\n\n\n\\subsection{Results and Analysis}\n\n% We report model performance across three axes: (1) overall performance, (2) topic-level variation, and (3) difficulty-based trends. Additionally, we conduct ablations on surface-level perturbations, such as altered entity names and numerical values, to examine reasoning stability.\n\n\\subsubsection{Overall Model Performance}\n\n\\begin{table*}[t]\n\\scriptsize\n\\centering\n\\resizebox{\\textwidth}{!}{\n \\begin{tabular}{l|cccc|ccc|c}\n \\toprule\n \\multicolumn{1}{c|}{\\multirow{2}{*}{\\textbf{Model}}} & \\multicolumn{4}{c|}{\\textbf{\\ourmetric $\\uparrow$}} & \\multicolumn{3}{c|}{\\textbf{ROUGE $\\uparrow$}} & \\multicolumn{1}{c}{\\multirow{2}{*}{\\textbf{BERTScore $\\uparrow$}}} \\\\\n & FAC & Step-Precision & Step-Recall & Step-F1 & R$_2$ & R$_L$ & R$_{Lsum}$ & \\\\\n \\midrule\n \\rowcolor{black!15}\\textbf{General-Purpose} & & & & & & & &\\\\\n \\rule{0pt}{7pt}Mistral (7B) Instruct v0.3 & $0.32_{0.24}$ & $0.3317_{0.20}$ & $0.21_{0.14}$ & $0.2444_{0.15}$ & $0.1183_{0.02}$ & $0.1868_{0.02}$ & $0.2239_{0.02}$ & $0.8341_{0.002}$\\\\\n Qwen2.5 (7B) & $0.4083_{0.32}$ & $0.3554_{0.24}$ & $0.2095_{0.16}$ & $0.2486_{0.17}$ & $0.1615_{0.04}$ & $0.2523_{0.06}$ & $0.3052_{0.07}$ & $0.8564_{0.01}$\\\\\n Mixtral (8x7B) Instruct v0.1 & $0.357_{0.28}$ & $0.3345_{0.21}$ & $0.2354_{0.15}$ & $0.2622_{0.16}$ & $0.1596_{0.03}$ & $0.2537_{0.04}$ & $0.3045_{0.05}$ & $0.8584_{0.01}$\\\\\n Llama 3.1 (8B) Instruct & $0.4267_{0.28}$ & $0.3935_{0.21}$ & $0.2266_{0.13}$ & $0.2729_{0.15}$ & $0.1454_{0.03}$ & $0.2273_{0.04}$ & $0.2753_{0.05}$ & $0.8565_{0.01}$\\\\\n Gemma 2 (9B) Instruct & $0.4838_{0.21}$ & $0.4643_{0.16}$ & $0.3243_{0.11}$ & $0.367_{0.12}$ & $0.1975_{0.03}$ & $0.3021_{0.03}$ & $0.3667_{0.04}$ & $0.8646_{0.01}$\\\\\n Gemma 2 (27B) Instruct & $0.5325_{0.21}$ & $0.4937_{0.15}$ & $0.3488_{0.11}$ & $0.3926_{0.12}$ & $0.1962_{0.03}$ & $0.3002_{0.03}$ & $0.3621_{0.03}$ & $0.8639_{0.006}$\\\\\n Gemma 3 (27B) Instruct & $0.4879_{0.18}$ & $0.5023_{0.16}$ & $0.3064_{0.09}$ & $0.365_{0.11}$ & $0.1717_{0.02}$ & $0.2567_{0.02}$ & $0.3045_{0.03}$ & $0.8567_{0.005}$\\\\\n Llama 3.1 (70B) Instruct & $0.5735_{0.25}$ & $0.4811_{0.21}$ & $0.3048_{0.15}$ & $0.3558_{0.16}$ & $0.1691_{0.04}$ & $0.259_{0.05}$ & $0.3108_{0.05}$ & $0.858_{0.01}$\\\\\n Llama 3.3 (70B) Instruct & $\\mathbf{0.584_{0.22}}$ & $0.5145_{0.19}$ & $0.2724_{0.11}$ & $0.3384_{0.13}$ & $0.1492_{0.03}$ & $0.2269_{0.03}$ & $0.2694_{0.04}$ & $0.8537_{0.006}$\\\\\n GPT-4o-mini & $0.524_{0.23}$ & $0.4352_{0.19}$ & $0.2385_{0.11}$ & $0.2923_{0.13}$ & $0.1337_{0.02}$ & $0.2177_{0.02}$ & $0.2588_{0.02}$ & $0.8458_{0.005}$\\\\\n GPT-4.1-mini & $0.5668_{0.24}$ & $0.4877_{0.19}$ & $0.2882_{0.12}$ & $0.3456_{0.13}$ & $0.1725_{0.02}$ & $0.2645_{0.02}$ & $0.3201_{0.03}$ & $0.8569_{0.006}$\\\\\n GPT-4.1 & $0.5691_{0.23}$ & $0.4197_{0.21}$ & $0.2527_{0.13}$ & $0.2993_{0.15}$ & $0.1657_{0.02}$ & $0.2631_{0.02}$ & $0.3176_{0.03}$ & $0.8505_{0.007}$\\\\[2.5pt]\n \\rowcolor{black!15}\\textbf{General Reasoning} & & & & & & & &\\\\\n \\rule{0pt}{7pt}DeepSeek-R1-Distill Qwen (7B) & $0.4383_{0.31}$ & $0.3568_{0.23}$ & $0.2239_{0.15}$ & $0.2606_{0.16}$ & $0.1887_{0.04}$ & $0.3126_{0.05}$ & $0.381_{0.05}$ & $0.864_{0.01}$\\\\\n DeepSeek-R1-Distill Llama (8B) & $0.4394_{0.31}$ & $0.3583_{0.22}$ & $0.2204_{0.13}$ & $0.2576_{0.15}$ & $0.1841_{0.04}$ & $0.3004_{0.05}$ & $0.3689_{0.05}$ & $0.865_{0.01}$\\\\\n Qwen3 (8B) & $\\mathbf{0.5402_{0.27}}$ & $0.3992_{0.23}$ & $0.2145_{0.13}$ & $0.2643_{0.15}$ & $0.1951_{0.05}$ & $0.3018_{0.06}$ & $0.364_{0.06}$ & $0.8696_{0.01}$\\\\\n Qwen3 (30B) A3B & $0.5089_{0.18}$ & $0.4245_{0.21}$ & $0.1995_{0.12}$ & $0.2509_{0.13}$ & $0.0373_{0.01}$ & $0.0538_{0.01}$ & $0.0649_{0.01}$ & $0.8453_{0.01}$\\\\\n DeepSeek-R1-Distill Qwen (32B) & $0.4991_{0.30}$ & $0.4071_{0.23}$ & $0.262_{0.15}$ & $0.3039_{0.17}$ & $0.1895_{0.04}$ & $0.3181_{0.04}$ & $0.384_{0.05}$ & $0.8575_{0.01}$\\\\\n DeepSeek-R1-Distill Llama (70B) & $0.4927_{0.31}$ & $0.4013_{0.23}$ & $0.255_{0.15}$ & $0.2965_{0.17}$ & $0.1961_{0.04}$ & $0.3239_{0.05}$ & $0.3922_{0.05}$ & $0.8618_{0.01}$\\\\\n o3-mini & $0.5183_{0.25}$ & $0.3881_{0.21}$ & $0.2432_{0.12}$ & $0.2839_{0.14}$ & $0.2028_{0.03}$ & $0.3083_{0.04}$ & $0.3714_{0.04}$ & $0.8574_{0.02}$\\\\\n o4-mini & $0.3873_{0.21}$ & $0.2976_{0.19}$ & $0.1768_{0.11}$ & $0.2126_{0.13}$ & $0.1945_{0.03}$ & $0.3093_{0.03}$ & $0.3806_{0.04}$ & $0.8563_{0.02}$\\\\[2.5pt]\n \\rowcolor{black!15}\\textbf{Finance Fine-Tuned} & & & & & & & &\\\\\n \\rule{0pt}{7pt}WiroAI Finance Qwen (7B) & $\\mathbf{0.4419_{0.28}}$ & $0.4064_{0.23}$ & $0.2531_{0.15}$ & $0.2933_{0.17}$ & $0.1782_{0.04}$ & $0.2866_{0.05}$ & $0.3409_{0.06}$ & $0.8651_{0.01}$\\\\\n WiroAI Finance Llama (8B) & $0.2853_{0.28}$ & $0.2729_{0.23}$ & $0.1627_{0.15}$ & $0.1911_{0.17}$ & $0.1625_{0.05}$ & $0.2671_{0.06}$ & $0.3254_{0.08}$ & $0.8611_{0.02}$\\\\\n % WiroAI Finance Gemma (9B) & $\\mathbf{0.6267_{0.27}}$ & $0.4733_{0.29}$ & $0.2594_{0.16}$ & $0.3052_{0.19}$ & $0.0077_{0.001}$ & $0.0132_{0.001}$ & $0.0163_{0.002}$ & $0.8006_{0.005}$\\\\\n FinanceConnect (13B) & $0.0725_{0.12}$ & $0.0884_{0.13}$ & $0.0561_{0.08}$ & $0.0646_{0.09}$ & $0.166_{0.04}$ & $0.2837_{0.05}$ & $0.3482_{0.06}$ & $0.8573_{0.01}$\\\\\n Finance-LLM (13B) & $0.1709_{0.21}$ & $0.2247_{0.19}$ & $0.1443_{0.13}$ & $0.1654_{0.14}$ & $0.1628_{0.04}$ & $0.2671_{0.05}$ & $0.3263_{0.06}$ & $0.8631_{0.01}$\\\\[2.5pt]\n \\rowcolor{black!15}\\textbf{Finance Reasoning} & & & & & & & &\\\\\n \\rule{0pt}{7pt}Fin-R1 (7B) & $\\mathbf{0.512_{0.28}}$ & $0.3649_{0.22}$ & $0.1978_{0.12}$ & $0.2428_{0.15}$ & $0.1685_{0.04}$ & $0.2806_{0.04}$ & $0.3429_{0.05}$ & $0.8585_{0.01}$\\\\\n Fino1 (8B) & $0.4191_{0.26}$ & $0.3621_{0.22}$ & $0.1637_{0.10}$ & $0.2133_{0.12}$ & $0.1701_{0.03}$ & $0.2795_{0.04}$ & $0.3458_{0.05}$ & $0.868_{0.01}$\\\\[2.5pt]\n \\rowcolor{black!15}\\textbf{Math Fine-Tuned} & & & & & & & &\\\\\n \\rule{0pt}{7pt}Mathstral (7B) & $\\mathbf{0.4397_{0.26}}$ & $0.3953_{0.21}$ & $0.2732_{0.16}$ & $0.3063_{0.16}$ & $0.1635_{0.03}$ & $0.2586_{0.04}$ & $0.3098_{0.05}$ & $0.8587_{0.01}$\\\\\n Qwen2.5-Math (7B) & $0.1891_{0.26}$ & $0.1763_{0.21}$ & $0.1298_{0.15}$ & $0.1412_{0.16}$ & $0.0705_{0.06}$ & $0.1337_{0.08}$ & $0.1488_{0.10}$ & $0.8067_{0.02}$\\\\[2.5pt]\n \\rowcolor{black!15}\\textbf{Math Reasoning} & & & & & & & &\\\\\n \\rule{0pt}{7pt}MetaMath (13B) & $0.2313_{0.22}$ & $0.2704_{0.20}$ & $0.1357_{0.10}$ & $0.1717_{0.13}$ & $0.1863_{0.05}$ & $0.3064_{0.06}$ & $0.3753_{0.07}$ & $0.8728_{0.01}$\\\\\n WizardMath (7B) & $\\mathbf{0.3461_{0.28}}$ & $0.3357_{0.24}$ & $0.1942_{0.14}$ & $0.2332_{0.17}$ & $0.1933_{0.06}$ & $0.3142_{0.07}$ & $0.3737_{0.08}$ & $0.8723_{0.01}$\\\\\n % finance-llm & $0.0027_{0.0077}$ & $0.0049_{0.014}$ & $0.0032_{0.0092}$ & $0.0042_{0.0117}$ & $0.0_{0.0}$ & $0.0_{0.0}$ & $0.0_{0.0}$ & $0.0_{0.0}$\\\\\n \\bottomrule\n \\end{tabular}\n }\n \\caption{\n\\textbf{Overall performance of language models on \\ourdataset.}\nWe report four components of our \\ourmetric metric (FAC, step precision, step recall, and step-level F1), alongside ROUGE (R$_2$, R$_L$, R$_{Lsum}$) and BERTScore. Models are grouped into six categories. Top-performing models under FAC within each category are bolded. \n% Standard deviations (subscripted) reflect variability across 2,700 test instances.\n}\n \\label{tab:overall_model_performance}\n\\end{table*}\n\n\\autoref{tab:overall_model_performance} presents a comprehensive evaluation of model performance on \\ourdataset, measured across four components of \\ourmetric, including Final Answer Correctness (FAC), Step Precision/Recall/F1, alongside ROUGE and BERTScore. \nOverall, general-purpose models such as \\texttt{LLaMA 3.3 (70B) Instruct} and \\texttt{GPT-4.1} lead in both accuracy and consistency.\nThese models also exhibit moderate variance, indicating not only high performance but also robust generalization across diverse financial scenarios.\nCrucially, the results highlight a strong correlation between model size and reasoning ability. Larger models consistently outperform their smaller counterparts, even when the latter are explicitly trained on domain-specific data, which suggests that domain exposure alone is insufficient without adequate model capacity. \n% For instance, \\texttt{Finance-LLM} achives much lower FAC despite being fine-tuned on financial texts\n\nOpen-source models such as \\texttt{Qwen3 (8B)} demonstrate competitive performance despite its smaller size, achieving strong results, which can be attributed to its enhanced reasoning capabilities in recent release.\nFinancially reasoning models, such as \\texttt{Fin-R1} exhibit reasonable symbolic alignment but fall short in final answer accuracy. \n% This trend suggests that while symbolic reasoning can be integrated into smaller models, they often lack the capacity to consistently execute complex multi-step reasoning chains.\nOverall, our findings highlight the critical role of model scale in symbolic financial reasoning. Larger general-purpose models not only deliver higher performance but also generalize across tasks. Yet, smaller models tend to struggle to complete reasoning chains accurately despite domain-specific tuning. This underscores the advantage of large-scale instruction tuning for chain-of-thought reasoning in finance.\n\n\\subsubsection{Performance Over Domains}\n\n\\begin{figure*}[t]\n \\centering\n \\includegraphics[width=0.8\\linewidth]{figs/topic_performance.pdf}\n \\caption{\n\\textbf{Domain-level performance across financial domains.}\nRadar plots compare representative models from (a) Non-reasoning models and (b) Reasoning models. Each spoke corresponds to one of the 12 top-level financial domains. Models are selected as the top performers within each of the six categories presented in \\autoref{tab:overall_model_performance}.\n}\n \\label{fig:topic_performance}\n\\end{figure*}\n\nWe present how model performance varies across financial domains in \\autoref{fig:topic_performance}, which shows domain-level FAC for representative models. \nFor the non-reasoning models, structured and quantitative domains such as \\textit{Personal Finance} and \\textit{Crypto Finance} show higher FAC. These areas tend to require straightforward computations or familiar terminologies, which general-purpose and math-tuned models can handle well. In contrast, domains like \\textit{Risk Management} and \\textit{Sustainable Finance} exhibit clear performance drops, likely due to their multi-step logic requirements, ambiguous scenarios, or regulatory nuances that are harder to encode symbolically.\n\nFor reasoning models, we observe stronger performance in traditionally challenging domains like \\textit{Financial Ratios} and \\textit{Corporate Finance}, suggesting improved reasoning ability over multi-step symbolic structures. Nevertheless, even reasoning models still exhibit relatively weaker results in \\textit{Sustainable Finance} and \\textit{Mergers \\& Acquisitions}, which demands nuanced understanding and domain-specific reasoning strategies.\nThese disparities underscore our domain-specific taxonomy enables a granular diagnosis of model strengths and blind spots, offering a roadmap for developing targeted financial reasoning capabilities.\nWe report domain-wise performance across all evaluated models in \\autoref{topics_allmodels}.\n\n\\subsubsection{Performance Over Difficulty Levels}\n\n\\begin{figure}[t]\n \\centering\n \\includegraphics[width=0.9\\linewidth]{figs/level_wise_performance.pdf}\n \\caption{\n\\textbf{Model performance across difficulty levels.}\nFAC is reported for Basic, Intermediate, and Advanced.\n% All models show declining performance with increasing complexity, with \\texttt{LLaMA 3.3 (70B)} maintaining the highest stability across levels.\n}\n \\label{fig:difficulty_performance}\n\\end{figure}\n\nTo assess model robustness under increasing complexity of reasoning, we group the results into three predefined tiers (\\textit{Basic}, \\textit{Intermediate}, and \\textit{Advanced} (\\autoref{finchain}). Each difficulty level reflects an increase in reasoning and computational depth. \\autoref{fig:difficulty_performance} reports FAC across these tiers for six representative models (same subset as \\autoref{fig:topic_performance}).\nAll models show performance degradation as complexity increases, underscoring the difficulty of executing longer financial reasoning chains. Among them, \\texttt{LLaMA 3.3 (70B) Instruct} stands out for its strong absolute performance and gradual decline, indicating its better compositional generalization.\nIn contrast, smaller models such as \\texttt{Fin-R1 (7B)} suffer more severe drops, suggesting difficulty in maintaining reasoning coherence as reasoning depth grows. \n% Specifically, \\texttt{Mathstral (7B)} outperforms \\texttt{WiroAI Finance Qwen (7B)} at the Intermediate level, highlighting that symbolic training alone can confer greater resilience than domain-specific exposure when task complexity increases.\nThese findings validate the utility of difficulty levels of \\ourdataset for fine-grained stress testing. It also expose models’ limitations in scaling their reasoning capabilities, particularly for longer step financial reasoning tasks.\n\n\\subsubsection{Impact of Names and Values}\n\nTo evaluate model robustness under surface-level perturbations, we test how changes of entity names and numerical values in the templates affect the model reasoning performance under one domain: \\textbf{Investment Analysis}.\n% Specifically, we introduce controlled ablations in our symbolic templates to isolate two factors: (1) named entities, and (2) numeric values. \nWe consider three settings: \\textbf{(i) Name Only}, where only names are varied; \\textbf{(ii) Value Only}, where only numerical values are varied; and \\textbf{(iii) Both}, where both names and values are varied simultaneously. \nLarger models such as \\texttt{GPT-4.1} maintain consistent performance across all settings, reflecting strong abstraction over both name and value inputs. However, smaller models exhibit divergent sensitivities. For instance, \\texttt{o3-mini} achieves higher FAC when only values are varied compared to the names setting, \\texttt{GPT-4o-mini} also shows mild instability, with modest performance shifts between conditions.\nThese findings highlight that entity names and numerical values have different impact in model reasoning. \nThe templated structure of \\ourdataset allows precise manipulation of such variables, offering a powerful diagnostic tool for stress-testing reasoning robustness.\n% The observed heterogeneity across model scales and architectures reinforces the importance of controlled input perturbations when diagnosing reasoning robustness.\n\n\\begin{table}[t]\n\\centering\n\\small\n\\resizebox{\\columnwidth}{!}{\n\\begin{tabular}{lccc}\n\\toprule\n\\textbf{Model} & \\textbf{Both} & \\textbf{Name Only} & \\textbf{Value Only} \\\\\n\\midrule\nGPT-4.1 & 0.5080$_{\\pm0.4999}$ & 0.4960$_{\\pm0.5000}$ & 0.5040$_{\\pm0.5000}$ \\\\\nGPT-4.1-mini & 0.4720$_{\\pm0.4992}$ & 0.4640$_{\\pm0.4987}$ & 0.4640$_{\\pm0.4987}$ \\\\\nGPT-4o-mini & 0.3760$_{\\pm0.4844}$ & 0.3600$_{\\pm0.4800}$ & 0.3720$_{\\pm0.4833}$ \\\\\no3-mini & 0.4040$_{\\pm0.4907}$ & 0.3920$_{\\pm0.4882}$ & 0.4160$_{\\pm0.4929}$ \\\\\no4-mini & 0.3400$_{\\pm0.4737}$ & 0.3600$_{\\pm0.4800}$ & 0.3680$_{\\pm0.4823}$ \\\\\n\\bottomrule\n\\end{tabular}\n}\n\\caption{\\textbf{Model robustness to input perturbation.}\nFAC is reported for Both, Name Only, and Value Only.\n% \\textbf{Both} , \\textbf{Name Only} (entity names), and \\textbf{Value Only} (numerical values).\n}\n\n\\label{tab:name_number_variable}\n\\end{table}\n\n\n\n\n\n% \\subsubsection{Sensitivity to Irrelevant Noise}\n% We introduce distractor content, such as irrelevant background details or unrelated financial statements, and measure whether such noise affects model reasoning.\n% This tests whether the model can focus on relevant information and ignore misleading cues.\n\n% \\subsection{Semantic Variation Sensitivity}\n\n% \\subsubsection{Name vs. Value Perturbation (Abalation)}\n% We distinguish between surface level and semantic variations. Name perturbation changes only entity names (e.g., companies or investors), while value perturbation modifies numerical inputs that directly impact the solution. This reveals whether models are sensitive to truly meaningful changes or rely on patter matching.\n\n% \\subsubsection{Paraphrased Surface Forms (Abalation)}\n% To evaluate linguistic generalization, we paraphrase question prompts using LLM-based paraphrasing tools. These paraphrases preserve the financial logic but vary the syntax, sentence structure, and vocabulary. We measure whether the model remains consistent in its predictions despite these changes.\n\n% \\subsection{Generalization Across Financial Domains}\n\n% \\subsubsection{Topic Diversity (Main)}\n% Using our taxonomy of 12 financial domains and 55 topics, we examine model performance across a wide range of financial tasks. This experiment reveals which areas (e.g., Corporate Finance vs. Sustainable Finance) are more or less challenging for current LLMs.\n\n% \\subsubsection{Difficulty Level (Main)}\n% We compare performance across easy, intermediate, and advanced instances to test the model's capacity to handle increasing symbolic complexity and domain expertise.\n\n% \\input{sections/results}\n% \\input{sections/discussion}\n\n% \\section{Error Analysis (Yuxia)}\n\n\n\\section{Conclusions and Future Work}\n\nWe introduce \\ourdataset, a symbolic benchmark designed for verifiable chain-of-thought financial reasoning. Constructed from a fine-grained taxonomy spanning 54 topics across 12 financial domains and three difficulty levels, it enables targeted evaluation across both reasoning complexity and domain-specific challenges.\nTo assess model performance, we propose \\ourmetric, a joint metric that evaluates both final answer correctness and alignment with intermediate reasoning steps. Experimental results reveal that while large general-purpose LLMs outperform specialized financial models, all models struggle with complex, multi-step symbolic reasoning.\nLooking ahead, we plan to expand \\ourdataset to support multilingual and region-specific scenarios, and to investigate how step-by-step reasoning traces can enhance the trustworthiness and factuality of model-generated answers, especially in long-form financial question answering over real-world documents. This line of work may help bridge symbolic financial reasoning and factual verification~\\citep{xie-etal-2025-fire}, supporting more robust and interpretable financial AI systems.\n\n\n\\section*{Limitations}\n\nWe acknowledge several limitations in this work that we plan to address in future research.\n\nFirst, our dataset is entirely synthetic, generated from symbolic templates. While this design enables fine-grained control, contamination-free generation, and automatic verifiability, it may lack the linguistic diversity and contextual richness of real-world financial texts. In future work, we plan to incorporate real-world financial documents, such as earnings reports, investor communications, or financial news, as seed sources for semi-structured or templated generation, allowing us to better simulate naturally occurring language while preserving symbolic grounding.\n\nSecond, the benchmark focuses narrowly on symbolic numerical reasoning and does not capture qualitative, contextual, or strategic aspects of financial decision-making, such as risk assessment, or market sentiment. To address this, we aim to complement our benchmark with additional tasks that evaluate models on these higher-level reasoning dimensions, potentially through multi-modal inputs (e.g., combining text with charts or scenarios) or interactive decision-making simulations.\n\nThird, \\ourdataset is restricted to English and U.S. centric financial conventions, such as currency formats, and investment norms, which limits its generalizability to multilingual and regional financial contexts. In future extensions, we plan to expand coverage to other languages and financial systems, through collaboration with domain experts and native speakers to localize templates and ensure culturally grounded reasoning tasks.\n\n\n\n\n\\section*{Ethical Statement and Broad Impact}\n\nThis work uses only synthetic data generated through templated code and language model outputs. No private, sensitive, or copyrighted content was used.\nOur benchmark is designed for transparency and reproducibility in financial AI. However, caution should be taken when deploying LLMs in real-world financial decision-making, especially where symbolic correctness and regulatory compliance are critical.\nWe believe \\ourdataset will support research toward more interpretable, verifiable, and safe reasoning systems in high-stakes domains.\n\n\n\\paragraph{Data License}\n\nThe \\ourdataset dataset and accompanying code will be released under the MIT License.\n\n\n% Ethical Statement\n% \\paragraph{Ethical Statement}\n \n\n% \\paragraph{Broad Impact}\n\n\n% \\section*{Acknowledgments}\n\n\\bibliography{custom}\n\n\\newpage\n\n\\appendix\n\n\\section{Financial-Symbolic: Template Creation Prompt}\n\\label{templatecreation}\n\nTo construct symbolic financial reasoning benchmarks analogous to GSM-Symbolic, we design a structured prompt that guides the generation of executable financial templates. These templates support variable-based instantiation, symbolic step-wise supervision, and controlled perturbations for robustness evaluation. Below, we present the prompt used for template construction.\n\\\\\n% \\begin{quote}\n\\textbf{System Instruction:}\nYou are a financial NLP expert developing symbolic reasoning datasets. Your task is to construct symbolic templates for financial reasoning problems. Each template should support (i) controlled generation of diverse question instances, (ii) executable reasoning traces for automatic verification, and (iii) systematic variation in surface and logical complexity.\n\nPlease follow the steps below:\n\n\\begin{enumerate}\n \\item \\textbf{Identify a financial reasoning task:} For example, compound interest, IRR, ROI, NPV, breakeven analysis, loan amortization, etc.\n \n \\item \\textbf{Write a natural language question template:} Formulate the question using variable placeholders instead of fixed values. For instance, use \\texttt{\\{principal\\}}, \\texttt{\\{rate\\}}, \\texttt{\\{years\\}}, etc.\n \n \\item \\textbf{Define variables and constraints:} Specify the domain (e.g., numerical range or categorical values) for each variable. Add algebraic constraints to ensure the question is solvable and the answer valid.\n \n \\item \\textbf{Write a symbolic solution trace:} Provide a step-by-step solution using the variables. Ensure the reasoning chain is executable in Python for automatic evaluation.\n \n \\item \\textbf{Vary difficulty levels:} For each task, generate 10 templates with increasing complexity. Longer and more compositional reasoning chains should correspond to harder levels.\n\\end{enumerate}\n% \\end{quote}\n\n\n\n\n\n\\section{Claude Verification Prompt}\n\\label{claude_template}\n\nWe use the following prompt to query Claude for template verification:\n\n\\begin{quote}\n \\textit{I have written several Python methods that automatically generate random questions and corresponding solutions about \\textbf{<topic>} by varying key variables. I need assistance in verifying the correctness of the equations used, ensuring that the solutions are accurate and solid. Please respond by first indicating if the method's underlying logic and solution are correct or flawed.}\n\\end{quote}\n\n\\section{Performance Over Topics for all models}\n\\label{topics_allmodels}\n\nWe present domain-level performance comparisons across financial domains for four categories of models: general-purpose non-reasoning models (\\autoref{fig:topic_performance_all_general_nonreasoning}), general-purpose reasoning models (\\autoref{fig:topic_performance_all_general_reasoning}), financial fine-tuned and reasoning models (\\autoref{fig:topic_performance_all_finance}), and mathematical fine-tuned and reasoning models (\\autoref{fig:topic_performance_all_math}).\n\n\n\\begin{figure*}[t]\n \\centering\n \\includegraphics[width=0.8\\linewidth]{figs/topic_performance_allgennonreasoningmodels.pdf}\n \\caption{\n\\textbf{Domain-level performance comparison across financial domains for all general-purpose non-reasoning models from \\autoref{tab:overall_model_performance}.} For clarity, we split the models into three subtypes: (a) Model Size $\\leq$ 10B, (b) Model Size $>$ 10B, and (c) Closed-Source Models.\n}\n \\label{fig:topic_performance_all_general_nonreasoning}\n\\end{figure*}\n\n\\begin{figure*}[t]\n \\centering\n \\includegraphics[width=0.8\\linewidth]{figs/topic_performance_allgenreasoning.pdf}\n \\caption{\n\\textbf{Domain-level performance comparison across financial domains for all general-purpose reasoning models from \\autoref{tab:overall_model_performance}.} For clarity, we split the models into two subtypes: (a) Model Size $\\leq$ 30B, and (b) Model Size $>$ 30B (along with all closed-source models).}\n \\label{fig:topic_performance_all_general_reasoning}\n\\end{figure*}\n\n\\begin{figure*}[t]\n \\centering\n \\includegraphics[width=0.8\\linewidth]{figs/topic_performance_allfinance_models.pdf}\n \\caption{\n\\textbf{Domain-level performance comparison across financial domains for all financial fine-tuned and reasoning models from \\autoref{tab:overall_model_performance}.} For clarity, we split the figure into two parts: (a) Finance Non-Reasoning, and (b) Finance Reasoning models.}\n \\label{fig:topic_performance_all_finance}\n\\end{figure*}\n\n\\begin{figure*}[t]\n \\centering\n \\includegraphics[width=0.8\\linewidth]{figs/topic_performance_allmathmodels.pdf}\n \\caption{\n\\textbf{Domain-level performance comparison across financial domains for all mathematical fine-tuned and reasoning models from \\autoref{tab:overall_model_performance}.} For clarity, we split the figure into two parts: (a) Math Non-Reasoning, and (b) Math Reasoning models.}\n \\label{fig:topic_performance_all_math}\n\\end{figure*}\n\\end{document}\n"},"question":{"kind":"string","value":"Consider models with exactly 8B parameters, which category's mean FAC is the highest, and what is that mean?"},"answer":{"kind":"string","value":"General Reasoning, 0.4898"},"plan":{"kind":"string","value":"SELECT all models with size equal to 8B\nSELECT their categories\nSELECT FAC scores\nLOOP for each category\n SELECT models in this category with size equal to 8B\n SELECT FAC scores for these models\n COMPUTE mean FAC for this category\nCOMPUTE category with the largest mean FAC\nRETURN category with the largest mean FAC, corresponding mean FAC\n"}}},{"rowIdx":99,"cells":{"paper":{"kind":"string","value":"2506.05746"},"relevant_tables":{"kind":"list like","value":[["\\begin{table*}[h]\n","\\centering\n","\\tiny\n","\\begin{tabular}{ll|c|c|ccccccccc}\n","\\toprule\n","\\multirow{2}{*}{\\textbf{Output}} & \\multirow{2}{*}{\\textbf{Context}} & \\multirow{2}{*}{\\textbf{Method}} & \\textbf{Metric} & \\multicolumn{9}{c}{\\textbf{Results Across Categories}} \\\\\n","\\cmidrule{5-13}\n","& & & & \\shortstack{\\textbf{Original}} & \\shortstack{\\textbf{CounterFact}} & \\shortstack{\\textbf{Gap (Original} \\\\ \\textbf{- CounterFact)}} & \\shortstack{\\textbf{Large}} & \\shortstack{\\textbf{Small}} & \\shortstack{\\textbf{Gap (Small} \\\\ \\textbf{- Large)}} & \\shortstack{\\textbf{Easy}} & \\shortstack{\\textbf{Medium}} & \\shortstack{\\textbf{Hard}} \\\\\n","\\midrule\n","\n","\\multirow{27}{*}{Text} & \\multirow{2}{*}{None} & \\multirow{2}{*}{-} & REMS & 22.11 & 15.02 & 7.09 & 23.88 & 21.74 & -2.14 & 27.81 & 23.23 & 21.59 \\\\\n","& & & EMS & 18.69 & 11.76 & 6.93 & 19.49 & 18.93 & -0.56 & 24.03 & 19.41 & 17.30 \\\\\n","\\cmidrule{2-13}\n","\n","& \\multirow{22}{*}{Table} & \\multirow{2}{*}{zero shots} & REMS & 55.17 & 45.42 & 9.75 & 42.71 & 69.88 & 27.17 & 68.78 & 56.61 & 51.18 \\\\\n","& & & EMS & 48.79 & 37.61 & 11.18 & 34.96 & 61.11 & 26.15 & 59.15 & 48.82 & 39.00 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Static} & REMS & 57.46 & 47.20 & 10.26 & 46.44 & 71.89 & 25.45 & 73.76 & 61.94 & 54.99 \\\\\n","& & & EMS & 50.00 & 39.08 & 10.92 & 38.35 & 62.35 & 24.00 & 63.16 & 53.53 & 42.96 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Adaptive} & REMS & 59.09 & 48.25 & 10.84 & 47.93 & 73.17 & 25.24 & 76.28 & 64.94 & 57.83 \\\\\n","& & & EMS & 52.90 & 42.91 & 9.99 & 41.02 & 66.25 & 25.23 & 65.76 & 55.95 & 45.92 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Clear} & REMS & 62.01 & 50.46 & 11.55 & 53.38 & 73.07 & 19.69 & 79.65 & 68.02 & 59.37 \\\\\n","& & & EMS & 60.63 & 48.95 & 11.68 & 51.11 & 72.19 & 21.08 & 77.92 & 66.52 & 56.67 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Chain Of Thought} & REMS & 60.97 & 47.28 & 13.69 & 51.61 & 73.45 & 21.84 & 77.28 & 65.60 & 56.62 \\\\\n","& & & EMS & 59.54 & 45.48 & 14.06 & 49.52 & 72.09 & 22.57 & 75.29 & 64.17 & 54.55 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Plan And Solve} & REMS & 53.58 & 42.58 & 11.00 & 48.40 & 65.07 & 16.67 & 70.61 & 59.35 & 51.43 \\\\\n","& & & EMS & 52.00 & 40.34 & 11.66 & 46.14 & 63.85 & 17.71 & 68.54 & 57.12 & 48.53 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Program of Thought} & REMS & 49.26 & 42.85 & 6.41 & 44.31 & 57.29 & 12.98 & 67.10 & 48.61 & 44.13 \\\\\n","& & & EMS & 46.90 & 40.55 & 6.35 & 42.33 & 55.30 & 12.97 & 64.53 & 45.52 & 40.47 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Faithful CoT} & REMS & 48.98 & 40.90 & 8.08 & 43.46 & 58.09 & 14.63 & 64.12 & 53.26 & 45.68 \\\\\n","& & & EMS & 46.78 & 38.66 & 8.12 & 41.38 & 56.23 & 14.85 & 61.50 & 50.66 & 42.67 \\\\\n","\n","\\midrule\n","\n","\\multirow{6}{*}{SQL} & \\multirow{6}{*}{Schema} & \\multirow{2}{*}{zero shots} & REMS & 47.27 & 42.56 & 4.71 & 46.23 & 56.45 & 10.22 & 63.49 & 59.74 & 42.09 \\\\\n","& & & EMS & 39.34 & 33.45 & 5.89 & 44.29 & 53.65 & 9.36 & 52.96 & 52.48 & 34.59 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Static} & REMS & 66.43 & 62.38 & 4.05 & 71.39 & 77.20 & 5.81 & 88.17 & 79.22 & 65.04 \\\\\n","& & & EMS & 57.93 & 54.43 & 3.50 & 63.13 & 70.89 & 7.76 & 79.54 & 68.96 & 58.42 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Adaptive} & REMS & 72.91 & 71.67 & 1.24 & 78.18 & 81.71 & 3.53 & 87.06 & 80.80 & 74.04 \\\\\n","& & & EMS & 65.49 & 62.71 & 2.78 & 69.30 & 73.53 & 4.23 & 76.26 & 73.72 & 63.12 \\\\\n","\\bottomrule\n","\n","\\end{tabular}\n","\\caption{\\small Test Set Results for Gemini 1.5 Flash.}\n","\\vspace{-1.0em}\n","\\label{tab:gemini_1_5_flash_results}\n","\\vspace{-1.0em}\n","\\end{table*}\n"],["\\begin{table*}[h]\n","\\centering\n","\\tiny\n","\\begin{tabular}{ll|c|c|ccccccccc}\n","\\toprule\n","\\multirow{1}{*}{\\textbf{Output}} & \\multirow{1}{*}{\\textbf{Context}} & \\multirow{1}{*}{\\textbf{Method}} & \\textbf{Metric} & \\textbf{Original} & \\textbf{CounterFact} & \\shortstack{\\textbf{Gap (Original} \\\\ \\textbf{- CounterFact)}} & \\textbf{Large} & \\textbf{Small} & \\shortstack{\\textbf{Gap (Small} \\\\ \\textbf{- Large)}} & \\textbf{Easy} & \\textbf{Medium} & \\textbf{Hard} \\\\\n","\\midrule\n","\n","\\multirow{27}{*}{Text} \n","\n","\\multirow{2}{*}{} & \\multirow{2}{*}{None} & \\multirow{2}{*}{-} & REMS & 23.37 & 15.88 & 7.49 & 24.20 & 24.08 & 0.12 & 29.15 & 26.45 & 23.38 \\\\\n","& & & EMS & 19.90 & 13.45 & 6.45 & 21.19 & 20.99 & 0.2 & 24.94 & 21.76 & 18.77 \\\\\n","\\cmidrule{2-13}\n","\n","& \\multirow{6}{*}{} & \\multirow{2}{*}{zero shots} & REMS & 54.94 & 45.40 & 9.54 & 46.34 & 69.74 & 23.4 & 73.26 & 60.71 & 57.43 \\\\\n","& & & EMS & 48.06 & 37.39 & 10.67 & 38.98 & 61.11 & 21.13 & 62.59 & 53.24 & 45.60 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Static} & REMS & 59.01 & 46.87 & 12.14 & 52.42 & 72.93 & 20.51 & 75.72 & 65.10 & 60.28 \\\\\n","& & & EMS & 52.91 & 39.92 & 12.99 & 43.86 & 65.02 & 21.16 & 65.79 & 58.53 & 50.00 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Adaptive} & REMS & 60.23 & 49.04 & 11.19 & 48.79 & 73.98 & 25.19 & 78.04 & 66.43 & 59.28 \\\\\n","& & & EMS & 53.48 & 44.19 & 9.29 & 41.86 & 67.27 & 25.41 & 66.26 & 56.47 & 46.74 \\\\\n","\\cmidrule{3-13}\n","\n","& \\multirow{5}{*}{Table} & \\multirow{2}{*}{Clear} & REMS & 50.29 & 42.82 & 7.47 & 42.83 & 58.04 & 15.21 & 59.91 & 60.79 & 52.82 \\\\\n","& & & EMS & 49.33 & 40.66 & 8.67 & 40.95 & 56.85 & 15.90 & 58.38 & 59.77 & 51.14 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Chain Of Thought} & REMS & 67.34 & 57.16 & 10.18 & 59.51 & 78.47 & 18.96 & 84.67 & 73.36 & 67.31 \\\\\n","& & & EMS & 66.46 & 55.75 & 10.71 & 57.67 & 77.55 & 19.88 & 83.29 & 72.69 & 65.87 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Plan And Solve} & REMS & 62.11 & 55.68 & 6.43 & 58.38 & 74.88 & 16.50 & 81.19 & 73.28 & 65.56 \\\\\n","& & & EMS & 60.75 & 54.31 & 6.44 & 56.72 & 73.43 & 16.71 & 79.42 & 72.25 & 63.60 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Program of Thought} & REMS & 56.31 & 49.35 & 6.97 & 53.02 & 66.46 & 13.44 & 76.94 & 60.62 & 51.75 \\\\\n","& & & EMS & 53.83 & 46.20 & 7.63 & 49.84 & 64.68 & 14.83 & 73.97 & 57.12 & 47.57 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Faithful CoT} & REMS & 56.49 & 50.15 & 6.34 & 50.63 & 65.84 & 15.21 & 77.28 & 61.53 & 51.03 \\\\\n","& & & EMS & 53.95 & 47.64 & 6.31 & 48.47 & 63.85 & 15.39 & 74.52 & 58.15 & 46.85 \\\\\n","\n","\\midrule\n","\n","\\multirow{6}{*}{SQL} \n","& \\multirow{6}{*}{Schema} & \\multirow{2}{*}{zero shots} & REMS & 49.24 & 43.56 & 5.68 & 48.21 & 57.62 & 9.41 & 64.42 & 61.10 & 43.00 \\\\\n","& & & EMS & 41.32 & 35.31 & 6.01 & 45.87 & 55.11 & 9.24 & 54.78 & 53.79 & 35.96 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Static} & REMS & 67.76 & 63.63 & 4.13 & 76.80 & 82.82 & 6.02 & 89.33 & 80.81 & 66.42 \\\\\n","& & & EMS & 59.08 & 55.52 & 3.56 & 71.32 & 77.41 & 6.09 & 80.86 & 70.33 & 59.59 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Adaptive} & REMS & 73.04 & 73.58 & 0.54 & 81.94 & 84.38 & 2.44 & 87.31 & 80.01 & 71.43 \\\\\n","& & & EMS & 65.29 & 65.13 & 0.16 & 72.43 & 75.31 & 2.88 & 75.86 & 71.47 & 59.24 \\\\\n","\n","\\bottomrule\n","\\end{tabular}\n","\\vspace{-1.0em}\n","\\caption{\\small Test Set Results for Gemini 1.5 Pro.}\n","\\label{tab:gemini_1_5_pro_results}\n","\\vspace{-1.0em}\n","\\end{table*}\n"],["\\begin{table*}[h]\n","\\centering\n","\\tiny\n","\\begin{tabular}{ll|c|c|ccccccccc}\n","\\toprule\n","\\multirow{2}{*}{\\textbf{Output}} & \\multirow{2}{*}{\\textbf{Context}} & \\multirow{2}{*}{\\textbf{Method}} & \\textbf{Metric} & \\multicolumn{9}{c}{\\textbf{Results Across Categories}} \\\\\n","\\cmidrule{5-13}\n","& & & & \\shortstack{\\textbf{Original}} & \\shortstack{\\textbf{CounterFact}} & \\shortstack{\\textbf{Gap (Original} \\\\ \\textbf{- CounterFact)}} & \\shortstack{\\textbf{Large}} & \\shortstack{\\textbf{Small}} & \\shortstack{\\textbf{Gap (Small} \\\\ \\textbf{- Large)}} & \\shortstack{\\textbf{Easy}} & \\shortstack{\\textbf{Medium}} & \\shortstack{\\textbf{Hard}} \\\\\n","\\midrule\n","\n","\\multirow{27}{*}{Text} & \\multirow{2}{*}{None} & \\multirow{2}{*}{-} & REMS & 17.46 & 12.74 & 4.72 & 20.89 & 18.09 & -2.80 & 22.31 & 17.93 & 17.18 \\\\\n","& & & EMS & 16.61 & 12.16 & 4.45 & 19.89 & 17.31 & -2.58 & 21.31 & 16.96 & 16.27 \\\\\n","\\cmidrule{2-13}\n","\n","& \\multirow{22}{*}{Table} & \\multirow{2}{*}{zero shots} & REMS & 54.63 & 39.93 & 14.70 & 46.10 & 64.62 & 18.52 & 70.10 & 58.31 & 53.37 \\\\\n","& & & EMS & 52.60 & 37.48 & 15.12 & 44.05 & 62.57 & 18.52 & 67.49 & 56.21 & 50.35 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Static} & REMS & 64.33 & 48.44 & 15.89 & 57.27 & 75.40 & 18.13 & 79.04 & 70.23 & 60.98 \\\\\n","& & & EMS & 62.46 & 46.21 & 16.25 & 55.58 & 73.68 & 18.10 & 76.91 & 67.46 & 57.44 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Adaptive} & REMS & 55.73 & 41.29 & 14.44 & 48.48 & 70.56 & 22.08 & 71.35 & 62.54 & 56.43 \\\\\n","& & & EMS & 53.63 & 39.20 & 14.43 & 46.65 & 68.07 & 21.42 & 69.40 & 59.76 & 52.85 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Clear} & REMS & 54.37 & 39.43 & 14.94 & 37.63 & 68.64 & 31.01 & 68.86 & 62.91 & 50.87 \\\\\n","& & & EMS & 53.46 & 38.34 & 15.12 & 36.43 & 67.02 & 30.59 & 67.49 & 62.33 & 49.51 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Chain Of Thought} & REMS & 61.87 & 45.29 & 16.58 & 49.87 & 73.88 & 24.01 & 77.68 & 70.40 & 59.48 \\\\\n","& & & EMS & 59.86 & 43.06 & 16.80 & 48.51 & 72.04 & 23.53 & 75.13 & 69.23 & 57.16 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Plan And Solve} & REMS & 59.31 & 45.13 & 14.18 & 50.55 & 71.82 & 21.27 & 75.65 & 67.06 & 57.32 \\\\\n","& & & EMS & 57.79 & 43.49 & 14.30 & 49.07 & 70.29 & 21.22 & 73.63 & 66.08 & 55.21 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Program of Thought} & REMS & 46.63 & 31.83 & 14.80 & 28.30 & 63.03 & 34.73 & 61.16 & 51.98 & 41.83 \\\\\n","& & & EMS & 44.64 & 29.90 & 14.74 & 27.69 & 61.40 & 33.71 & 59.15 & 48.91 & 39.36 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Faithful CoT} & REMS & 40.19 & 29.44 & 10.75 & 25.11 & 60.30 & 35.19 & 56.52 & 46.22 & 37.86 \\\\\n","& & & EMS & 38.75 & 27.75 & 11.00 & 24.54 & 58.83 & 34.29 & 55.19 & 43.79 & 35.74 \\\\\n","\n","\\midrule\n","\n","\\multirow{6}{*}{SQL} & \\multirow{6}{*}{Schema} & \\multirow{2}{*}{zero shots} & REMS & 34.77 & 31.52 & 3.25 & 37.08 & 41.36 & 4.28 & 45.83 & 40.81 & 29.74 \\\\\n","& & & EMS & 33.56 & 30.47 & 3.09 & 36.06 & 39.77 & 3.71 & 44.26 & 39.65 & 27.54 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Static} & REMS & 54.59 & 47.31 & 7.28 & 55.38 & 63.12 & 7.74 & 68.51 & 60.83 & 56.15 \\\\\n","& & & EMS & 53.81 & 46.64 & 7.17 & 54.65 & 62.22 & 7.57 & 67.21 & 60.55 & 55.63 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Adaptive} & REMS & 64.61 & 64.04 & 0.57 & 67.40 & 69.54 & 2.14 & 77.42 & 68.01 & 61.78 \\\\\n","& & & EMS & 64.36 & 63.81 & 0.55 & 66.91 & 68.89 & 1.98 & 77.19 & 67.65 & 60.92 \\\\\n","\\bottomrule\n","\n","\\end{tabular}\n","\\vspace{-1.0em}\n","\\caption{\\small Test Set Results for Llama 3.1 70B.}\n","\\label{tab:llama_3_1_70b_results}\n","\\vspace{-1.0em}\n","\\end{table*}\n"]],"string":"[\n [\n \"\\\\begin{table*}[h]\\n\",\n \"\\\\centering\\n\",\n \"\\\\tiny\\n\",\n \"\\\\begin{tabular}{ll|c|c|ccccccccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{\\\\textbf{Output}} & \\\\multirow{2}{*}{\\\\textbf{Context}} & \\\\multirow{2}{*}{\\\\textbf{Method}} & \\\\textbf{Metric} & \\\\multicolumn{9}{c}{\\\\textbf{Results Across Categories}} \\\\\\\\\\n\",\n \"\\\\cmidrule{5-13}\\n\",\n \"& & & & \\\\shortstack{\\\\textbf{Original}} & \\\\shortstack{\\\\textbf{CounterFact}} & \\\\shortstack{\\\\textbf{Gap (Original} \\\\\\\\ \\\\textbf{- CounterFact)}} & \\\\shortstack{\\\\textbf{Large}} & \\\\shortstack{\\\\textbf{Small}} & \\\\shortstack{\\\\textbf{Gap (Small} \\\\\\\\ \\\\textbf{- Large)}} & \\\\shortstack{\\\\textbf{Easy}} & \\\\shortstack{\\\\textbf{Medium}} & \\\\shortstack{\\\\textbf{Hard}} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\n\",\n \"\\\\multirow{27}{*}{Text} & \\\\multirow{2}{*}{None} & \\\\multirow{2}{*}{-} & REMS & 22.11 & 15.02 & 7.09 & 23.88 & 21.74 & -2.14 & 27.81 & 23.23 & 21.59 \\\\\\\\\\n\",\n \"& & & EMS & 18.69 & 11.76 & 6.93 & 19.49 & 18.93 & -0.56 & 24.03 & 19.41 & 17.30 \\\\\\\\\\n\",\n \"\\\\cmidrule{2-13}\\n\",\n \"\\n\",\n \"& \\\\multirow{22}{*}{Table} & \\\\multirow{2}{*}{zero shots} & REMS & 55.17 & 45.42 & 9.75 & 42.71 & 69.88 & 27.17 & 68.78 & 56.61 & 51.18 \\\\\\\\\\n\",\n \"& & & EMS & 48.79 & 37.61 & 11.18 & 34.96 & 61.11 & 26.15 & 59.15 & 48.82 & 39.00 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Static} & REMS & 57.46 & 47.20 & 10.26 & 46.44 & 71.89 & 25.45 & 73.76 & 61.94 & 54.99 \\\\\\\\\\n\",\n \"& & & EMS & 50.00 & 39.08 & 10.92 & 38.35 & 62.35 & 24.00 & 63.16 & 53.53 & 42.96 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Adaptive} & REMS & 59.09 & 48.25 & 10.84 & 47.93 & 73.17 & 25.24 & 76.28 & 64.94 & 57.83 \\\\\\\\\\n\",\n \"& & & EMS & 52.90 & 42.91 & 9.99 & 41.02 & 66.25 & 25.23 & 65.76 & 55.95 & 45.92 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Clear} & REMS & 62.01 & 50.46 & 11.55 & 53.38 & 73.07 & 19.69 & 79.65 & 68.02 & 59.37 \\\\\\\\\\n\",\n \"& & & EMS & 60.63 & 48.95 & 11.68 & 51.11 & 72.19 & 21.08 & 77.92 & 66.52 & 56.67 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Chain Of Thought} & REMS & 60.97 & 47.28 & 13.69 & 51.61 & 73.45 & 21.84 & 77.28 & 65.60 & 56.62 \\\\\\\\\\n\",\n \"& & & EMS & 59.54 & 45.48 & 14.06 & 49.52 & 72.09 & 22.57 & 75.29 & 64.17 & 54.55 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Plan And Solve} & REMS & 53.58 & 42.58 & 11.00 & 48.40 & 65.07 & 16.67 & 70.61 & 59.35 & 51.43 \\\\\\\\\\n\",\n \"& & & EMS & 52.00 & 40.34 & 11.66 & 46.14 & 63.85 & 17.71 & 68.54 & 57.12 & 48.53 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Program of Thought} & REMS & 49.26 & 42.85 & 6.41 & 44.31 & 57.29 & 12.98 & 67.10 & 48.61 & 44.13 \\\\\\\\\\n\",\n \"& & & EMS & 46.90 & 40.55 & 6.35 & 42.33 & 55.30 & 12.97 & 64.53 & 45.52 & 40.47 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Faithful CoT} & REMS & 48.98 & 40.90 & 8.08 & 43.46 & 58.09 & 14.63 & 64.12 & 53.26 & 45.68 \\\\\\\\\\n\",\n \"& & & EMS & 46.78 & 38.66 & 8.12 & 41.38 & 56.23 & 14.85 & 61.50 & 50.66 & 42.67 \\\\\\\\\\n\",\n \"\\n\",\n \"\\\\midrule\\n\",\n \"\\n\",\n \"\\\\multirow{6}{*}{SQL} & \\\\multirow{6}{*}{Schema} & \\\\multirow{2}{*}{zero shots} & REMS & 47.27 & 42.56 & 4.71 & 46.23 & 56.45 & 10.22 & 63.49 & 59.74 & 42.09 \\\\\\\\\\n\",\n \"& & & EMS & 39.34 & 33.45 & 5.89 & 44.29 & 53.65 & 9.36 & 52.96 & 52.48 & 34.59 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Static} & REMS & 66.43 & 62.38 & 4.05 & 71.39 & 77.20 & 5.81 & 88.17 & 79.22 & 65.04 \\\\\\\\\\n\",\n \"& & & EMS & 57.93 & 54.43 & 3.50 & 63.13 & 70.89 & 7.76 & 79.54 & 68.96 & 58.42 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Adaptive} & REMS & 72.91 & 71.67 & 1.24 & 78.18 & 81.71 & 3.53 & 87.06 & 80.80 & 74.04 \\\\\\\\\\n\",\n \"& & & EMS & 65.49 & 62.71 & 2.78 & 69.30 & 73.53 & 4.23 & 76.26 & 73.72 & 63.12 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\caption{\\\\small Test Set Results for Gemini 1.5 Flash.}\\n\",\n \"\\\\vspace{-1.0em}\\n\",\n \"\\\\label{tab:gemini_1_5_flash_results}\\n\",\n \"\\\\vspace{-1.0em}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[h]\\n\",\n \"\\\\centering\\n\",\n \"\\\\tiny\\n\",\n \"\\\\begin{tabular}{ll|c|c|ccccccccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{1}{*}{\\\\textbf{Output}} & \\\\multirow{1}{*}{\\\\textbf{Context}} & \\\\multirow{1}{*}{\\\\textbf{Method}} & \\\\textbf{Metric} & \\\\textbf{Original} & \\\\textbf{CounterFact} & \\\\shortstack{\\\\textbf{Gap (Original} \\\\\\\\ \\\\textbf{- CounterFact)}} & \\\\textbf{Large} & \\\\textbf{Small} & \\\\shortstack{\\\\textbf{Gap (Small} \\\\\\\\ \\\\textbf{- Large)}} & \\\\textbf{Easy} & \\\\textbf{Medium} & \\\\textbf{Hard} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\n\",\n \"\\\\multirow{27}{*}{Text} \\n\",\n \"\\n\",\n \"\\\\multirow{2}{*}{} & \\\\multirow{2}{*}{None} & \\\\multirow{2}{*}{-} & REMS & 23.37 & 15.88 & 7.49 & 24.20 & 24.08 & 0.12 & 29.15 & 26.45 & 23.38 \\\\\\\\\\n\",\n \"& & & EMS & 19.90 & 13.45 & 6.45 & 21.19 & 20.99 & 0.2 & 24.94 & 21.76 & 18.77 \\\\\\\\\\n\",\n \"\\\\cmidrule{2-13}\\n\",\n \"\\n\",\n \"& \\\\multirow{6}{*}{} & \\\\multirow{2}{*}{zero shots} & REMS & 54.94 & 45.40 & 9.54 & 46.34 & 69.74 & 23.4 & 73.26 & 60.71 & 57.43 \\\\\\\\\\n\",\n \"& & & EMS & 48.06 & 37.39 & 10.67 & 38.98 & 61.11 & 21.13 & 62.59 & 53.24 & 45.60 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Static} & REMS & 59.01 & 46.87 & 12.14 & 52.42 & 72.93 & 20.51 & 75.72 & 65.10 & 60.28 \\\\\\\\\\n\",\n \"& & & EMS & 52.91 & 39.92 & 12.99 & 43.86 & 65.02 & 21.16 & 65.79 & 58.53 & 50.00 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Adaptive} & REMS & 60.23 & 49.04 & 11.19 & 48.79 & 73.98 & 25.19 & 78.04 & 66.43 & 59.28 \\\\\\\\\\n\",\n \"& & & EMS & 53.48 & 44.19 & 9.29 & 41.86 & 67.27 & 25.41 & 66.26 & 56.47 & 46.74 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& \\\\multirow{5}{*}{Table} & \\\\multirow{2}{*}{Clear} & REMS & 50.29 & 42.82 & 7.47 & 42.83 & 58.04 & 15.21 & 59.91 & 60.79 & 52.82 \\\\\\\\\\n\",\n \"& & & EMS & 49.33 & 40.66 & 8.67 & 40.95 & 56.85 & 15.90 & 58.38 & 59.77 & 51.14 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Chain Of Thought} & REMS & 67.34 & 57.16 & 10.18 & 59.51 & 78.47 & 18.96 & 84.67 & 73.36 & 67.31 \\\\\\\\\\n\",\n \"& & & EMS & 66.46 & 55.75 & 10.71 & 57.67 & 77.55 & 19.88 & 83.29 & 72.69 & 65.87 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Plan And Solve} & REMS & 62.11 & 55.68 & 6.43 & 58.38 & 74.88 & 16.50 & 81.19 & 73.28 & 65.56 \\\\\\\\\\n\",\n \"& & & EMS & 60.75 & 54.31 & 6.44 & 56.72 & 73.43 & 16.71 & 79.42 & 72.25 & 63.60 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Program of Thought} & REMS & 56.31 & 49.35 & 6.97 & 53.02 & 66.46 & 13.44 & 76.94 & 60.62 & 51.75 \\\\\\\\\\n\",\n \"& & & EMS & 53.83 & 46.20 & 7.63 & 49.84 & 64.68 & 14.83 & 73.97 & 57.12 & 47.57 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Faithful CoT} & REMS & 56.49 & 50.15 & 6.34 & 50.63 & 65.84 & 15.21 & 77.28 & 61.53 & 51.03 \\\\\\\\\\n\",\n \"& & & EMS & 53.95 & 47.64 & 6.31 & 48.47 & 63.85 & 15.39 & 74.52 & 58.15 & 46.85 \\\\\\\\\\n\",\n \"\\n\",\n \"\\\\midrule\\n\",\n \"\\n\",\n \"\\\\multirow{6}{*}{SQL} \\n\",\n \"& \\\\multirow{6}{*}{Schema} & \\\\multirow{2}{*}{zero shots} & REMS & 49.24 & 43.56 & 5.68 & 48.21 & 57.62 & 9.41 & 64.42 & 61.10 & 43.00 \\\\\\\\\\n\",\n \"& & & EMS & 41.32 & 35.31 & 6.01 & 45.87 & 55.11 & 9.24 & 54.78 & 53.79 & 35.96 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Static} & REMS & 67.76 & 63.63 & 4.13 & 76.80 & 82.82 & 6.02 & 89.33 & 80.81 & 66.42 \\\\\\\\\\n\",\n \"& & & EMS & 59.08 & 55.52 & 3.56 & 71.32 & 77.41 & 6.09 & 80.86 & 70.33 & 59.59 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Adaptive} & REMS & 73.04 & 73.58 & 0.54 & 81.94 & 84.38 & 2.44 & 87.31 & 80.01 & 71.43 \\\\\\\\\\n\",\n \"& & & EMS & 65.29 & 65.13 & 0.16 & 72.43 & 75.31 & 2.88 & 75.86 & 71.47 & 59.24 \\\\\\\\\\n\",\n \"\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\vspace{-1.0em}\\n\",\n \"\\\\caption{\\\\small Test Set Results for Gemini 1.5 Pro.}\\n\",\n \"\\\\label{tab:gemini_1_5_pro_results}\\n\",\n \"\\\\vspace{-1.0em}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[h]\\n\",\n \"\\\\centering\\n\",\n \"\\\\tiny\\n\",\n \"\\\\begin{tabular}{ll|c|c|ccccccccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{\\\\textbf{Output}} & \\\\multirow{2}{*}{\\\\textbf{Context}} & \\\\multirow{2}{*}{\\\\textbf{Method}} & \\\\textbf{Metric} & \\\\multicolumn{9}{c}{\\\\textbf{Results Across Categories}} \\\\\\\\\\n\",\n \"\\\\cmidrule{5-13}\\n\",\n \"& & & & \\\\shortstack{\\\\textbf{Original}} & \\\\shortstack{\\\\textbf{CounterFact}} & \\\\shortstack{\\\\textbf{Gap (Original} \\\\\\\\ \\\\textbf{- CounterFact)}} & \\\\shortstack{\\\\textbf{Large}} & \\\\shortstack{\\\\textbf{Small}} & \\\\shortstack{\\\\textbf{Gap (Small} \\\\\\\\ \\\\textbf{- Large)}} & \\\\shortstack{\\\\textbf{Easy}} & \\\\shortstack{\\\\textbf{Medium}} & \\\\shortstack{\\\\textbf{Hard}} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\n\",\n \"\\\\multirow{27}{*}{Text} & \\\\multirow{2}{*}{None} & \\\\multirow{2}{*}{-} & REMS & 17.46 & 12.74 & 4.72 & 20.89 & 18.09 & -2.80 & 22.31 & 17.93 & 17.18 \\\\\\\\\\n\",\n \"& & & EMS & 16.61 & 12.16 & 4.45 & 19.89 & 17.31 & -2.58 & 21.31 & 16.96 & 16.27 \\\\\\\\\\n\",\n \"\\\\cmidrule{2-13}\\n\",\n \"\\n\",\n \"& \\\\multirow{22}{*}{Table} & \\\\multirow{2}{*}{zero shots} & REMS & 54.63 & 39.93 & 14.70 & 46.10 & 64.62 & 18.52 & 70.10 & 58.31 & 53.37 \\\\\\\\\\n\",\n \"& & & EMS & 52.60 & 37.48 & 15.12 & 44.05 & 62.57 & 18.52 & 67.49 & 56.21 & 50.35 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Static} & REMS & 64.33 & 48.44 & 15.89 & 57.27 & 75.40 & 18.13 & 79.04 & 70.23 & 60.98 \\\\\\\\\\n\",\n \"& & & EMS & 62.46 & 46.21 & 16.25 & 55.58 & 73.68 & 18.10 & 76.91 & 67.46 & 57.44 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Adaptive} & REMS & 55.73 & 41.29 & 14.44 & 48.48 & 70.56 & 22.08 & 71.35 & 62.54 & 56.43 \\\\\\\\\\n\",\n \"& & & EMS & 53.63 & 39.20 & 14.43 & 46.65 & 68.07 & 21.42 & 69.40 & 59.76 & 52.85 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Clear} & REMS & 54.37 & 39.43 & 14.94 & 37.63 & 68.64 & 31.01 & 68.86 & 62.91 & 50.87 \\\\\\\\\\n\",\n \"& & & EMS & 53.46 & 38.34 & 15.12 & 36.43 & 67.02 & 30.59 & 67.49 & 62.33 & 49.51 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Chain Of Thought} & REMS & 61.87 & 45.29 & 16.58 & 49.87 & 73.88 & 24.01 & 77.68 & 70.40 & 59.48 \\\\\\\\\\n\",\n \"& & & EMS & 59.86 & 43.06 & 16.80 & 48.51 & 72.04 & 23.53 & 75.13 & 69.23 & 57.16 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Plan And Solve} & REMS & 59.31 & 45.13 & 14.18 & 50.55 & 71.82 & 21.27 & 75.65 & 67.06 & 57.32 \\\\\\\\\\n\",\n \"& & & EMS & 57.79 & 43.49 & 14.30 & 49.07 & 70.29 & 21.22 & 73.63 & 66.08 & 55.21 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Program of Thought} & REMS & 46.63 & 31.83 & 14.80 & 28.30 & 63.03 & 34.73 & 61.16 & 51.98 & 41.83 \\\\\\\\\\n\",\n \"& & & EMS & 44.64 & 29.90 & 14.74 & 27.69 & 61.40 & 33.71 & 59.15 & 48.91 & 39.36 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Faithful CoT} & REMS & 40.19 & 29.44 & 10.75 & 25.11 & 60.30 & 35.19 & 56.52 & 46.22 & 37.86 \\\\\\\\\\n\",\n \"& & & EMS & 38.75 & 27.75 & 11.00 & 24.54 & 58.83 & 34.29 & 55.19 & 43.79 & 35.74 \\\\\\\\\\n\",\n \"\\n\",\n \"\\\\midrule\\n\",\n \"\\n\",\n \"\\\\multirow{6}{*}{SQL} & \\\\multirow{6}{*}{Schema} & \\\\multirow{2}{*}{zero shots} & REMS & 34.77 & 31.52 & 3.25 & 37.08 & 41.36 & 4.28 & 45.83 & 40.81 & 29.74 \\\\\\\\\\n\",\n \"& & & EMS & 33.56 & 30.47 & 3.09 & 36.06 & 39.77 & 3.71 & 44.26 & 39.65 & 27.54 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Static} & REMS & 54.59 & 47.31 & 7.28 & 55.38 & 63.12 & 7.74 & 68.51 & 60.83 & 56.15 \\\\\\\\\\n\",\n \"& & & EMS & 53.81 & 46.64 & 7.17 & 54.65 & 62.22 & 7.57 & 67.21 & 60.55 & 55.63 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Adaptive} & REMS & 64.61 & 64.04 & 0.57 & 67.40 & 69.54 & 2.14 & 77.42 & 68.01 & 61.78 \\\\\\\\\\n\",\n \"& & & EMS & 64.36 & 63.81 & 0.55 & 66.91 & 68.89 & 1.98 & 77.19 & 67.65 & 60.92 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\vspace{-1.0em}\\n\",\n \"\\\\caption{\\\\small Test Set Results for Llama 3.1 70B.}\\n\",\n \"\\\\label{tab:llama_3_1_70b_results}\\n\",\n \"\\\\vspace{-1.0em}\\n\",\n \"\\\\end{table*}\\n\"\n ]\n]","isConversational":false},"tables":{"kind":"list like","value":[["\\begin{figure}[htbp]\n","\\vspace{-1.0em}\n","\\begin{center}\n"," \\includegraphics[width=0.60\\columnwidth]{figs/Aron_Szilagyi.png} \n","\\vspace{0.25em}\n","\n","\\noindent \\textit{\\small \\textbf{Q.} In which year did Áron Szilágyi achieve his personal highest number of gold medal wins? \\textbf{Gold Answer.} 2022}\n","\\end{center}\n","\\vspace{-0.15em}\n","\\raggedright\n","\\small{\n","\\textbf{\\small Direct prompt (CoT):} {\\scriptsize In 2020, Áron Szilágyi won a Gold Medal in the Individual format at the Tokyo Olympics. This was his most successful year in terms of highest Gold Medal wins since he did not win multiple Gold Medals in any other format or competition.} \\textbf{Answer:} 2020.}\\\\\n","\\vspace{0.15em}\n","\\textbf{Symbolic Intermediate Representation (SQL):} \\\\\n","\\vspace{-0.25em}\n","\\begin{lstlisting}[language=SQL]\n","WITH gold_medal_counts AS (\n"," SELECT m.year, COUNT(m.medal_id) AS gold_medals\n"," FROM Medal m JOIN Format f ON m.format_id = f.format_id\n"," JOIN Tournament t ON f.tournament_id = t.tournament_id\n"," JOIN Athlete a ON t.athlete_id = a.athlete_id\n"," WHERE a.name = 'Áron Szilágyi' AND m.type = 'MedalGold'\n"," GROUP BY m.year )\n","SELECT year FROM gold_medal_counts\n","WHERE gold_medals = (SELECT MAX(gold_medals) FROM gold_medal_counts);\n","\\end{lstlisting}\n","\\vspace{-0.25em}\n","\\textbf{Answer:} 2022\n","\\label{fig:aron_table}\n","\\caption{ \\small \\small Structured table of Áron Szilágyi's achievements with question and answers. Direct prompting fails, whereas, Symbolic Intermediate Representation give correct answer.}\n","\\label{fig:example_figure}\n","\\end{figure}\n"],["\\begin{table}[htbp]\n","\\small\n","\\centering\n","\\begin{tabular}{lc|lc}\n","\\toprule\n","\\textbf{Category} & \\textbf{\\#Examples} & \\textbf{Category} & \\textbf{\\#Examples}\\\\\n","\\midrule\n","Original & 578 & Easy & 732 \\\\\n","Counterfactual & 699 & Medium & 507 \\\\\n","Small Table & 855 & Hard & 719 \\\\\n","Large Table & 538 & \\textbf{Total} & \\textbf{5067} \\\\\n","\\bottomrule\n","\\end{tabular}\n","\\caption{ \\small Dataset Splits and Their Number of Examples.}\n","\\label{tab:dataset_splits}\n","\\vspace{-0.5em}\n","\\end{table}\n"],["\\begin{table}[htbp]\n","\\centering\n","\\footnotesize\n","\\begin{tabular}{l|l|c|c|c}\n","\\toprule\n","\\scriptsize{\\textbf{Approach}} & \\scriptsize{\\textbf{Method}} & \\scriptsize{\\textbf{Original}} & \\scriptsize{\\textbf{CounterF.}} & \\scriptsize{\\textbf{$\\Delta$}} \\\\\n","\\midrule\n","\\multirow{7}{*}{Direct} & Static & 56.57 & 42.35 & 14.22 \\\\\n"," & Adaptive & 58.13 & 40.92 & 17.21 \\\\\n"," & C.L.E.A.R & 65.57 & 48.21 & 17.36 \\\\\n"," & CoT & 69.90 & 48.50 & 21.40 \\\\\n"," & Plan \\textbf{$\\&$} Solve & 69.55 & 46.50 & 23.05 \\\\\n"," & PoT & 56.40 & 47.07 & 9.33 \\\\\n"," & Faithful CoT & 57.44 & 47.78 & 9.66 \\\\\n","\\midrule\n","\\multirow{2}{*}{\\bf SQL} & \\textbf{Static} & \\textbf{65.22} & \\textbf{60.94} & \\textbf{4.28} \\\\\n"," & \\textbf{Adaptive} & \\textbf{71.63} & \\textbf{68.67} & \\textbf{2.96} \\\\\n","\\bottomrule\n","\\end{tabular}\n","\\caption{\\footnotesize Original vs Counterfactual for GPT-4o.}\n","\\label{tab:gpt_4o_original_counterfactual_gap}\n","\\vspace{-1.0em}\n","\\end{table}\n"],["\\begin{table}[htbp]\n","\\centering\n","\\footnotesize\n","\\begin{tabular}{l|l|c|c|c}\n","\\toprule\n","\\scriptsize{\\textbf{Approach}} & \\scriptsize{\\textbf{Method}} & \\scriptsize{\\textbf{Original}} & \\scriptsize{\\textbf{CounterF.}} & \\scriptsize{\\textbf{$\\Delta$}} \\\\\n","\\midrule\n","\\multirow{7}{*}{Direct} & Static & 52.91 & 39.92 & 12.99 \\\\\n"," & Adaptive & 53.48 & 44.19 & 9.29 \\\\\n"," & C.L.E.A.R & 49.33 & 40.66 & 8.67 \\\\\n"," & CoT & 66.46 & 55.75 & 10.71 \\\\\n"," & Plan \\textbf{$\\&$} Solve & 60.75 & 54.31 & 6.44 \\\\\n"," & PoT & 53.83 & 46.20 & 7.63 \\\\\n"," & Faithful CoT & 53.95 & 47.64 & 6.31 \\\\\n","\\midrule\n","\\multirow{2}{*}{\\bf SQL}& \\textbf{Static} & \\textbf{59.08} & \\textbf{55.52} & \\textbf{3.56} \\\\\n"," & \\textbf{Adaptive} & \\textbf{65.29} & \\textbf{65.13} & \\textbf{0.16} \\\\\n","\\bottomrule\n","\\end{tabular}\n","\\caption{\\footnotesize Original vs Counterfactual for Gemini 1.5 Pro.}\n","\\label{tab:gemini_1_5_pro_original_counterfactual_gap}\n","\\vspace{-1.0em}\n","\\end{table}\n"],["\\begin{table}[htbp]\n","\\centering\n","\\footnotesize\n","\\begin{tabular}{l|l|c|c|c}\n","\\toprule\n","\\textbf{Approach} & \\textbf{Method} & \\textbf{Small} & \\textbf{Large} & \\textbf{$\\Delta$} \\\\\n","\\midrule\n","\\multirow{7}{*}{ Direct} & Static & 71.11 & 46.84 & 24.27 \\\\\n"," & Adaptive & 73.92 & 48.88 & 25.04 \\\\\n"," & C.L.E.A.R & 76.49 & 53.53 & 22.96 \\\\\n"," & CoT & 74.15 & 56.13 & 18.02 \\\\\n"," & Plan \\textbf{$\\&$} Solve & 75.44 & 55.20 & 20.24 \\\\\n"," & PoT & 62.22 & 50.19 & 12.03 \\\\\n"," & Faithful CoT & 62.69 & 50.19 & 12.50 \\\\\n","\\midrule\n","\\multirow{2}{*}{\\bf SQL} & \\textbf{Static} & \\textbf{73.57} & \\textbf{70.63} & \\textbf{2.94} \\\\\n"," & \\textbf{Adaptive} & \\textbf{73.92} & \\textbf{72.86} & \\textbf{1.06} \\\\\n","\\bottomrule\n","\\end{tabular}\n","\\caption{\\footnotesize Small Table vs Large Table for GPT-4o}\n","\\label{tab:gpt4o_small_large_gap}\n","\\vspace{-1.0em}\n","\\end{table}\n"],["\\begin{table}[htbp]\n","\\centering\n","\\footnotesize\n","\\begin{tabular}{l|l|c|c|c}\n","\\toprule\n","\\textbf{Approach} & \\textbf{Method } & \\textbf{Small} & \\textbf{Large} & \\textbf{$\\Delta$} \\\\\n","\\midrule\n","\\multirow{7}{*}{Direct} & Static & 65.02 & 43.86 & 21.16 \\\\\n"," & Adaptive & 67.27 & 41.86 & 25.41 \\\\\n"," & C.L.E.A.R & 56.85 & 40.95 & 15.90 \\\\\n"," & CoT & 77.55 & 57.67 & 19.88 \\\\\n"," & Plan \\textbf{$\\&$} Solve & 73.43 & 56.72 & 16.71 \\\\\n"," & PoT & 64.68 & 49.84 & 14.83 \\\\\n"," & Faithful CoT & 63.85 & 48.47 & 15.39 \\\\\n","\\midrule\n","\\multirow{2}{*}{\\textbf{SQL}} & \\textbf{Static} & \\textbf{77.41} & \\textbf{71.32} & \\textbf{6.09} \\\\\n"," & \\textbf{Adaptive} & \\textbf{75.31} & \\textbf{72.43} & \\textbf{2.88} \\\\\n","\\bottomrule\n","\\end{tabular}\n","\\caption{\\footnotesize Small Table vs Large Table for Gemini 1.5 Pro}\n","\\label{tab:gemini_1_5_pro_small_large_gap}\n","\\vspace{-1.0em}\n","\\end{table}\n"],["\\begin{table}[htbp]\n","\\centering\n","\\footnotesize\n","\\begin{tabular}{l|l|c|c|c}\n","\\toprule\n","\\textbf{Approach} & \\textbf{Method} & \\textbf{Easy} & \\textbf{Medium} & \\textbf{Hard} \\\\\n","\\midrule\n","\\multirow{7}{*}{Direct} & Static & 71.18 & 63.12 & 53.35 \\\\\n"," & Adaptive & 74.38 & 63.91 & 54.17 \\\\\n"," & C.L.E.A.R & 76.40 & 71.99 & 62.62 \\\\\n"," & CoT & 78.43 & 75.35 & 64.06 \\\\\n"," & Plan \\textbf{$\\&$} Solve & 77.96 & 70.81 & 60.25 \\\\\n"," & PoT & 69.94 & 57.20 & 48.81 \\\\\n"," & Faithful CoT & 69.24 & 56.02 & 47.17 \\\\\n","\\midrule\n","\\multirow{2}{*}{\\textbf{SQL}} & \\textbf{Static} & \\textbf{78.89} & \\textbf{75.15} & \\textbf{62.31} \\\\\n"," & \\textbf{Adaptive} & \\textbf{80.06} & \\textbf{73.37} & \\textbf{66.74} \\\\\n","\\bottomrule\n","\\end{tabular}\n","\\caption{\\footnotesize Easy, Medium, and Hard results for GPT-4o}\n","\\label{tab:gpt4o_easy_medium_hard}\n","\\vspace{-1.0em}\n","\\end{table}\n"],["\\begin{table}[htbp]\n","\\centering\n","\\footnotesize\n","\\begin{tabular}{l|l|c|c|c}\n","\\toprule\n","\\textbf{Approach} & \\textbf{Method} & \\textbf{Easy} & \\textbf{Medium} & \\textbf{Hard} \\\\\n","\\midrule\n","\\multirow{7}{*}{Direct} & Static & 65.79 & 58.53 & 50.00 \\\\\n"," & Adaptive & 66.26 & 56.47 & 46.74 \\\\\n"," & C.L.E.A.R & 58.38 & 59.77 & 51.14 \\\\\n"," & CoT & 83.29 & 72.69 & 65.87 \\\\\n"," & Plan \\textbf{$\\&$} Solve & 79.42 & 72.25 & 63.60 \\\\\n"," & PoT & 73.97 & 57.12 & 47.57 \\\\\n"," & Faithful CoT & 74.52 & 58.15 & 46.85 \\\\\n","\\midrule\n","\\multirow{2}{*}{\\textbf{SQL}} & \\textbf{Static} & \\textbf{80.86} & \\textbf{70.33} & \\textbf{59.59} \\\\\n"," & \\textbf{Adaptive} & \\textbf{75.86} & \\textbf{71.47} & \\textbf{59.24} \\\\\n","\\bottomrule\n","\\end{tabular}\n","\\caption{\\footnotesize Easy, Medium, and Hard results on Gemini 1.5 Pro}\n","\\label{tab:gemini_1_5_pro_easy_medium_hard}\n","\\vspace{-1.0em}\n","\\end{table}\n"],["\\begin{figure}[h]\n"," \\centering\n"," \\includegraphics[width=0.4\\textwidth]{figs/Emma_Weyant.png}\n"," \\caption{Emma Weyant's Medal Infobox}\n"," \\label{fig:emma_infobox}\n","\\end{figure}\n"],["\\begin{figure}[h]\n"," \\centering\n"," \\includegraphics[width=0.3\\textwidth]{figs/Mayu_Matsumoto.png}\n"," \\caption{Mayu Matsumoto's Medal Infobox}\n"," \\label{fig:mayu_infobox}\n","\\end{figure}\n"],["\\begin{figure}[h]\n"," \\centering\n"," \\includegraphics[width=0.3\\textwidth]{figs/Sandra_Sanchez.png}\n"," \\caption{Sandra Sánchez's Medal Infobox}\n"," \\label{fig:sandra_infobox}\n","\\end{figure}\n"],["\\begin{table*}[h]\n","\\centering\n","\\tiny\n","\\begin{tabular}{ll|c|c|ccccccccc}\n","\\toprule\n","\\multirow{1}{*}{\\textbf{Output}} & \\multirow{1}{*}{\\textbf{Context}} & \\multirow{1}{*}{\\textbf{Method}} & \\textbf{Metric} & \\textbf{Original} & \\textbf{CounterFact} & \\shortstack{\\textbf{Gap (Original} \\\\ \\textbf{- CounterFact)}} & \\textbf{Large} & \\textbf{Small} & \\shortstack{\\textbf{Gap (Small} \\\\ \\textbf{- Large)}} & \\textbf{Easy} & \\textbf{Medium} & \\textbf{Hard} \\\\\n","\\midrule\n","\n","\\multirow{27}{*}{Text} \n","& \\multirow{2}{*}{None} & \\multirow{2}{*}{-} & REMS & 25.91 & 13.45 & 12.46 & 23.84 & 25.79 & 1.95 & 28.67 & 25.55 & 23.83 \\\\\n","& & & EMS & 25.09 & 12.16 & 12.93 & 23.05 & 24.68 & 1.63 & 27.26 & 24.06 & 22.35 \\\\\n","\\cmidrule{2-13}\n","\n","& \\multirow{22}{*}{Table} & \\multirow{2}{*}{zero shots} & REMS & 55.94 & 42.26 & 13.68 & 45.24 & 67.81 & 22.57 & 72.34 & 59.43 & 52.90 \\\\\n","& & & EMS & 53.46 & 40.06 & 13.40 & 43.31 & 64.56 & 21.25 & 69.86 & 57.20 & 49.02 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Static} & REMS & 58.69 & 44.69 & 14.00 & 48.96 & 73.57 & 24.61 & 73.45 & 65.39 & 56.97 \\\\\n","& & & EMS & 56.57 & 42.35 & 14.22 & 46.84 & 71.11 & 24.27 & 71.18 & 63.12 & 53.35 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Adaptive} & REMS & 60.71 & 43.10 & 17.61 & 51.41 & 76.97 & 25.56 & 76.89 & 66.00 & 58.00 \\\\\n","& & & EMS & 58.13 & 40.92 & 17.21 & 48.88 & 73.92 & 25.04 & 74.38 & 63.91 & 54.17 \\\\\n","\\cmidrule{3-13}\n","\n","& & Clear & REMS & 66.84 & 50.17 & 16.67 & 55.54 & 77.86 & 22.32 & 78.97 & 72.70 & 64.72 \\\\\n","& & & EMS & 65.57 & 48.21 & 17.36 & 53.53 & 76.49 & 22.96 & 76.40 & 71.99 & 62.62 \\\\\n","\\cmidrule{3-13}\n","\n","& & Chain Of Thought & REMS & 70.96 & 50.43 & 20.53 & 58.36 & 76.02 & 17.66 & 81.02 & 75.52 & 66.51 \\\\\n","& & & EMS & 69.90 & 48.50 & 21.40 & 56.13 & 74.15 & 18.02 & 78.43 & 75.35 & 64.06 \\\\\n","\\cmidrule{3-13}\n","\n","& & Plan And Solve & REMS & 70.36 & 48.62 & 21.74 & 57.14 & 76.91 & 19.77 & 80.30 & 72.30 & 62.64 \\\\\n","& & & EMS & 69.55 & 46.50 & 23.05 & 55.20 & 75.44 & 20.24 & 77.96 & 70.81 & 60.25 \\\\\n","\\cmidrule{3-13}\n","\n","& & Program of Thought & REMS & 59.05 & 50.28 & 8.77 & 51.66 & 64.35 & 12.69 & 72.65 & 60.85 & 52.89 \\\\\n","& & & EMS & 56.40 & 47.07 & 9.33 & 50.19 & 62.22 & 12.03 & 69.94 & 57.20 & 48.81 \\\\\n","\\cmidrule{3-13}\n","\n","& & Faithful CoT & REMS & 60.02 & 50.90 & 9.12 & 51.76 & 64.85 & 13.09 & 72.02 & 59.78 & 51.25 \\\\\n","& & & EMS & 57.44 & 47.78 & 9.66 & 50.19 & 62.69 & 12.50 & 69.24 & 56.02 & 47.17 \\\\\n","\n","\\midrule\n","\n","\\multirow{7}{*}{SQL}\n","& \\multirow{7}{*}{Schema} & \\multirow{2}{*}{zero shots} & REMS & 51.44 & 48.94 & 2.50 & 55.81 & 59.92 & 4.11 & 63.53 & 65.20 & 45.16 \\\\\n","& & & EMS & 49.31 & 46.64 & 2.67 & 53.90 & 57.43 & 3.53 & 61.22 & 63.12 & 42.02 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Static} & REMS & 66.76 & 62.41 & 4.35 & 71.82 & 75.37 & 3.55 & 81.27 & 75.88 & 64.38 \\\\\n","& & & EMS & 65.22 & 60.94 & 4.28 & 70.63 & 73.57 & 2.94 & 78.89 & 75.15 & 62.31 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Adaptive} & REMS & 71.87 & 69.04 & 2.83 & 73.05 & 74.55 & 1.50 & 80.17 & 74.35 & 67.38 \\\\\n","& & & EMS & 71.63 & 68.67 & 2.96 & 72.86 & 73.92 & 1.06 & 80.06 & 73.37 & 66.74 \\\\\n","\n","\\bottomrule\n","\\end{tabular}\n","\\vspace{-1.0em}\n","\\caption{\\small Test Set Results for GPT-4o.}\n","\\label{tab:gpt4o_results}\n","\\vspace{-1.0em}\n","\\end{table*}\n"],["\\begin{table*}[h]\n","\\centering\n","\\footnotesize\n","\\begin{tabular}{ll|c|c|ccccccc}\n","\\toprule\n","\\multirow{2}{*}{\\textbf{Output}} & \\multirow{2}{*}{\\textbf{Context}} & \\multirow{2}{*}{\\textbf{Few Shots}} & \\textbf{Metric} & \\multicolumn{7}{c}{\\textbf{Results Across Categories}} \\\\\n","\\cmidrule{5-11}\n","& & & & \\shortstack{\\textbf{Original}} & \\shortstack{\\textbf{CounterFact}} & \\shortstack{\\textbf{Large}} & \\shortstack{\\textbf{Small}} & \\shortstack{\\textbf{Easy}} & \\shortstack{\\textbf{Medium}} & \\shortstack{\\textbf{Hard}} \\\\\n","\\midrule\n","\n","\\multirow{10}{*}{Text} & \\multirow{2}{*}{None} & \\multirow{2}{*}{-} & REMS & 21.48 & 13.90 & 20.21 & 22.75 & 26.08 & 22.30 & 19.03 \\\\\n","& & & EMS & 20.24 & 13.02 & 19.70 & 21.87 & 24.84 & 20.51 & 17.40 \\\\\n","\\cmidrule{2-11}\n","\n","& \\multirow{6}{*}{Table} & \\multirow{2}{*}{zero shots} & REMS & 49.59 & 33.52 & 40.18 & 63.60 & 62.07 & 47.53 & 43.62 \\\\\n","& & & EMS & 47.23 & 31.04 & 37.73 & 60.47 & 59.19 & 44.58 & 39.55 \\\\\n","\\cmidrule{3-11}\n","\n","& & \\multirow{2}{*}{Static} & REMS & 49.94 & 36.94 & 41.33 & 66.45 & 64.23 & 51.13 & 47.13 \\\\\n","& & & EMS & 47.58 & 34.91 & 39.03 & 63.63 & 61.84 & 48.32 & 43.36 \\\\\n","\\cmidrule{3-11}\n","\n","& & \\multirow{2}{*}{Adaptive} & REMS & 51.13 & 38.37 & 42.43 & 67.66 & 66.10 & 54.81 & 48.63 \\\\\n","& & & EMS & 48.79 & 35.48 & 39.96 & 64.80 & 63.16 & 52.07 & 44.49 \\\\\n","\\midrule\n","\n","\\multirow{6}{*}{SQL} & \\multirow{6}{*}{Schema} & \\multirow{2}{*}{zero shots} & REMS & 39.93 & 41.28 & 38.76 & 48.50 & 57.45 & 50.88 & 34.21 \\\\\n","& & & EMS & 38.24 & 39.63 & 37.36 & 46.67 & 55.30 & 49.51 & 31.41 \\\\\n","\\cmidrule{3-11}\n","\n","& & \\multirow{2}{*}{Static} & REMS & 57.44 & 51.18 & 53.94 & 66.52 & 77.57 & 65.29 & 57.04 \\\\\n","& & & EMS & 56.57 & 50.36 & 53.16 & 65.73 & 75.93 & 65.29 & 56.33 \\\\\n","\\cmidrule{3-11}\n","\n","& & \\multirow{2}{*}{Adaptive} & REMS & 68.97 & 65.40 & 65.70 & 69.20 & 76.98 & 71.07 & 63.93 \\\\\n","& & & EMS & 68.69 & 65.24 & 65.43 & 68.77 & 76.87 & 70.41 & 63.13 \\\\\n","\\bottomrule\n","\n","\\end{tabular}\n","\\vspace{-1.0em}\n","\\caption{\\small Test Set Results for GPT-4o-mini}\n","\\label{tab:gpt_4o_mini_results}\n","\\vspace{-1.0em}\n","\\end{table*}\n"],["\\begin{table*}[h]\n","\\centering\n","\\tiny\n","\\begin{tabular}{ll|c|c|ccccccccc}\n","\\toprule\n","\\multirow{2}{*}{\\textbf{Output}} & \\multirow{2}{*}{\\textbf{Context}} & \\multirow{2}{*}{\\textbf{Method}} & \\textbf{Metric} & \\multicolumn{9}{c}{\\textbf{Results Across Categories}} \\\\\n","\\cmidrule{5-13}\n","& & & & \\shortstack{\\textbf{Original}} & \\shortstack{\\textbf{CounterFact}} & \\shortstack{\\textbf{Gap (Original} \\\\ \\textbf{- CounterFact)}} & \\shortstack{\\textbf{Large}} & \\shortstack{\\textbf{Small}} & \\shortstack{\\textbf{Gap (Small} \\\\ \\textbf{- Large)}} & \\shortstack{\\textbf{Easy}} & \\shortstack{\\textbf{Medium}} & \\shortstack{\\textbf{Hard}} \\\\\n","\\midrule\n","\n","\\multirow{27}{*}{Text} & \\multirow{2}{*}{None} & \\multirow{2}{*}{-} & REMS & 22.11 & 15.02 & 7.09 & 23.88 & 21.74 & -2.14 & 27.81 & 23.23 & 21.59 \\\\\n","& & & EMS & 18.69 & 11.76 & 6.93 & 19.49 & 18.93 & -0.56 & 24.03 & 19.41 & 17.30 \\\\\n","\\cmidrule{2-13}\n","\n","& \\multirow{22}{*}{Table} & \\multirow{2}{*}{zero shots} & REMS & 55.17 & 45.42 & 9.75 & 42.71 & 69.88 & 27.17 & 68.78 & 56.61 & 51.18 \\\\\n","& & & EMS & 48.79 & 37.61 & 11.18 & 34.96 & 61.11 & 26.15 & 59.15 & 48.82 & 39.00 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Static} & REMS & 57.46 & 47.20 & 10.26 & 46.44 & 71.89 & 25.45 & 73.76 & 61.94 & 54.99 \\\\\n","& & & EMS & 50.00 & 39.08 & 10.92 & 38.35 & 62.35 & 24.00 & 63.16 & 53.53 & 42.96 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Adaptive} & REMS & 59.09 & 48.25 & 10.84 & 47.93 & 73.17 & 25.24 & 76.28 & 64.94 & 57.83 \\\\\n","& & & EMS & 52.90 & 42.91 & 9.99 & 41.02 & 66.25 & 25.23 & 65.76 & 55.95 & 45.92 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Clear} & REMS & 62.01 & 50.46 & 11.55 & 53.38 & 73.07 & 19.69 & 79.65 & 68.02 & 59.37 \\\\\n","& & & EMS & 60.63 & 48.95 & 11.68 & 51.11 & 72.19 & 21.08 & 77.92 & 66.52 & 56.67 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Chain Of Thought} & REMS & 60.97 & 47.28 & 13.69 & 51.61 & 73.45 & 21.84 & 77.28 & 65.60 & 56.62 \\\\\n","& & & EMS & 59.54 & 45.48 & 14.06 & 49.52 & 72.09 & 22.57 & 75.29 & 64.17 & 54.55 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Plan And Solve} & REMS & 53.58 & 42.58 & 11.00 & 48.40 & 65.07 & 16.67 & 70.61 & 59.35 & 51.43 \\\\\n","& & & EMS & 52.00 & 40.34 & 11.66 & 46.14 & 63.85 & 17.71 & 68.54 & 57.12 & 48.53 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Program of Thought} & REMS & 49.26 & 42.85 & 6.41 & 44.31 & 57.29 & 12.98 & 67.10 & 48.61 & 44.13 \\\\\n","& & & EMS & 46.90 & 40.55 & 6.35 & 42.33 & 55.30 & 12.97 & 64.53 & 45.52 & 40.47 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Faithful CoT} & REMS & 48.98 & 40.90 & 8.08 & 43.46 & 58.09 & 14.63 & 64.12 & 53.26 & 45.68 \\\\\n","& & & EMS & 46.78 & 38.66 & 8.12 & 41.38 & 56.23 & 14.85 & 61.50 & 50.66 & 42.67 \\\\\n","\n","\\midrule\n","\n","\\multirow{6}{*}{SQL} & \\multirow{6}{*}{Schema} & \\multirow{2}{*}{zero shots} & REMS & 47.27 & 42.56 & 4.71 & 46.23 & 56.45 & 10.22 & 63.49 & 59.74 & 42.09 \\\\\n","& & & EMS & 39.34 & 33.45 & 5.89 & 44.29 & 53.65 & 9.36 & 52.96 & 52.48 & 34.59 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Static} & REMS & 66.43 & 62.38 & 4.05 & 71.39 & 77.20 & 5.81 & 88.17 & 79.22 & 65.04 \\\\\n","& & & EMS & 57.93 & 54.43 & 3.50 & 63.13 & 70.89 & 7.76 & 79.54 & 68.96 & 58.42 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Adaptive} & REMS & 72.91 & 71.67 & 1.24 & 78.18 & 81.71 & 3.53 & 87.06 & 80.80 & 74.04 \\\\\n","& & & EMS & 65.49 & 62.71 & 2.78 & 69.30 & 73.53 & 4.23 & 76.26 & 73.72 & 63.12 \\\\\n","\\bottomrule\n","\n","\\end{tabular}\n","\\caption{\\small Test Set Results for Gemini 1.5 Flash.}\n","\\vspace{-1.0em}\n","\\label{tab:gemini_1_5_flash_results}\n","\\vspace{-1.0em}\n","\\end{table*}\n"],["\\begin{table*}[h]\n","\\centering\n","\\tiny\n","\\begin{tabular}{ll|c|c|ccccccccc}\n","\\toprule\n","\\multirow{1}{*}{\\textbf{Output}} & \\multirow{1}{*}{\\textbf{Context}} & \\multirow{1}{*}{\\textbf{Method}} & \\textbf{Metric} & \\textbf{Original} & \\textbf{CounterFact} & \\shortstack{\\textbf{Gap (Original} \\\\ \\textbf{- CounterFact)}} & \\textbf{Large} & \\textbf{Small} & \\shortstack{\\textbf{Gap (Small} \\\\ \\textbf{- Large)}} & \\textbf{Easy} & \\textbf{Medium} & \\textbf{Hard} \\\\\n","\\midrule\n","\n","\\multirow{27}{*}{Text} \n","\n","\\multirow{2}{*}{} & \\multirow{2}{*}{None} & \\multirow{2}{*}{-} & REMS & 23.37 & 15.88 & 7.49 & 24.20 & 24.08 & 0.12 & 29.15 & 26.45 & 23.38 \\\\\n","& & & EMS & 19.90 & 13.45 & 6.45 & 21.19 & 20.99 & 0.2 & 24.94 & 21.76 & 18.77 \\\\\n","\\cmidrule{2-13}\n","\n","& \\multirow{6}{*}{} & \\multirow{2}{*}{zero shots} & REMS & 54.94 & 45.40 & 9.54 & 46.34 & 69.74 & 23.4 & 73.26 & 60.71 & 57.43 \\\\\n","& & & EMS & 48.06 & 37.39 & 10.67 & 38.98 & 61.11 & 21.13 & 62.59 & 53.24 & 45.60 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Static} & REMS & 59.01 & 46.87 & 12.14 & 52.42 & 72.93 & 20.51 & 75.72 & 65.10 & 60.28 \\\\\n","& & & EMS & 52.91 & 39.92 & 12.99 & 43.86 & 65.02 & 21.16 & 65.79 & 58.53 & 50.00 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Adaptive} & REMS & 60.23 & 49.04 & 11.19 & 48.79 & 73.98 & 25.19 & 78.04 & 66.43 & 59.28 \\\\\n","& & & EMS & 53.48 & 44.19 & 9.29 & 41.86 & 67.27 & 25.41 & 66.26 & 56.47 & 46.74 \\\\\n","\\cmidrule{3-13}\n","\n","& \\multirow{5}{*}{Table} & \\multirow{2}{*}{Clear} & REMS & 50.29 & 42.82 & 7.47 & 42.83 & 58.04 & 15.21 & 59.91 & 60.79 & 52.82 \\\\\n","& & & EMS & 49.33 & 40.66 & 8.67 & 40.95 & 56.85 & 15.90 & 58.38 & 59.77 & 51.14 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Chain Of Thought} & REMS & 67.34 & 57.16 & 10.18 & 59.51 & 78.47 & 18.96 & 84.67 & 73.36 & 67.31 \\\\\n","& & & EMS & 66.46 & 55.75 & 10.71 & 57.67 & 77.55 & 19.88 & 83.29 & 72.69 & 65.87 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Plan And Solve} & REMS & 62.11 & 55.68 & 6.43 & 58.38 & 74.88 & 16.50 & 81.19 & 73.28 & 65.56 \\\\\n","& & & EMS & 60.75 & 54.31 & 6.44 & 56.72 & 73.43 & 16.71 & 79.42 & 72.25 & 63.60 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Program of Thought} & REMS & 56.31 & 49.35 & 6.97 & 53.02 & 66.46 & 13.44 & 76.94 & 60.62 & 51.75 \\\\\n","& & & EMS & 53.83 & 46.20 & 7.63 & 49.84 & 64.68 & 14.83 & 73.97 & 57.12 & 47.57 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Faithful CoT} & REMS & 56.49 & 50.15 & 6.34 & 50.63 & 65.84 & 15.21 & 77.28 & 61.53 & 51.03 \\\\\n","& & & EMS & 53.95 & 47.64 & 6.31 & 48.47 & 63.85 & 15.39 & 74.52 & 58.15 & 46.85 \\\\\n","\n","\\midrule\n","\n","\\multirow{6}{*}{SQL} \n","& \\multirow{6}{*}{Schema} & \\multirow{2}{*}{zero shots} & REMS & 49.24 & 43.56 & 5.68 & 48.21 & 57.62 & 9.41 & 64.42 & 61.10 & 43.00 \\\\\n","& & & EMS & 41.32 & 35.31 & 6.01 & 45.87 & 55.11 & 9.24 & 54.78 & 53.79 & 35.96 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Static} & REMS & 67.76 & 63.63 & 4.13 & 76.80 & 82.82 & 6.02 & 89.33 & 80.81 & 66.42 \\\\\n","& & & EMS & 59.08 & 55.52 & 3.56 & 71.32 & 77.41 & 6.09 & 80.86 & 70.33 & 59.59 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Adaptive} & REMS & 73.04 & 73.58 & 0.54 & 81.94 & 84.38 & 2.44 & 87.31 & 80.01 & 71.43 \\\\\n","& & & EMS & 65.29 & 65.13 & 0.16 & 72.43 & 75.31 & 2.88 & 75.86 & 71.47 & 59.24 \\\\\n","\n","\\bottomrule\n","\\end{tabular}\n","\\vspace{-1.0em}\n","\\caption{\\small Test Set Results for Gemini 1.5 Pro.}\n","\\label{tab:gemini_1_5_pro_results}\n","\\vspace{-1.0em}\n","\\end{table*}\n"],["\\begin{table*}[h]\n","\\centering\n","\\tiny\n","\\begin{tabular}{ll|c|c|ccccccccc}\n","\\toprule\n","\\multirow{2}{*}{\\textbf{Output}} & \\multirow{2}{*}{\\textbf{Context}} & \\multirow{2}{*}{\\textbf{Method}} & \\textbf{Metric} & \\multicolumn{9}{c}{\\textbf{Results Across Categories}} \\\\\n","\\cmidrule{5-13}\n","& & & & \\shortstack{\\textbf{Original}} & \\shortstack{\\textbf{CounterFact}} & \\shortstack{\\textbf{Gap (Original} \\\\ \\textbf{- CounterFact)}} & \\shortstack{\\textbf{Large}} & \\shortstack{\\textbf{Small}} & \\shortstack{\\textbf{Gap (Small} \\\\ \\textbf{- Large)}} & \\shortstack{\\textbf{Easy}} & \\shortstack{\\textbf{Medium}} & \\shortstack{\\textbf{Hard}} \\\\\n","\\midrule\n","\n","\\multirow{27}{*}{Text} & \\multirow{2}{*}{None} & \\multirow{2}{*}{-} & REMS & 17.46 & 12.74 & 4.72 & 20.89 & 18.09 & -2.80 & 22.31 & 17.93 & 17.18 \\\\\n","& & & EMS & 16.61 & 12.16 & 4.45 & 19.89 & 17.31 & -2.58 & 21.31 & 16.96 & 16.27 \\\\\n","\\cmidrule{2-13}\n","\n","& \\multirow{22}{*}{Table} & \\multirow{2}{*}{zero shots} & REMS & 54.63 & 39.93 & 14.70 & 46.10 & 64.62 & 18.52 & 70.10 & 58.31 & 53.37 \\\\\n","& & & EMS & 52.60 & 37.48 & 15.12 & 44.05 & 62.57 & 18.52 & 67.49 & 56.21 & 50.35 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Static} & REMS & 64.33 & 48.44 & 15.89 & 57.27 & 75.40 & 18.13 & 79.04 & 70.23 & 60.98 \\\\\n","& & & EMS & 62.46 & 46.21 & 16.25 & 55.58 & 73.68 & 18.10 & 76.91 & 67.46 & 57.44 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Adaptive} & REMS & 55.73 & 41.29 & 14.44 & 48.48 & 70.56 & 22.08 & 71.35 & 62.54 & 56.43 \\\\\n","& & & EMS & 53.63 & 39.20 & 14.43 & 46.65 & 68.07 & 21.42 & 69.40 & 59.76 & 52.85 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Clear} & REMS & 54.37 & 39.43 & 14.94 & 37.63 & 68.64 & 31.01 & 68.86 & 62.91 & 50.87 \\\\\n","& & & EMS & 53.46 & 38.34 & 15.12 & 36.43 & 67.02 & 30.59 & 67.49 & 62.33 & 49.51 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Chain Of Thought} & REMS & 61.87 & 45.29 & 16.58 & 49.87 & 73.88 & 24.01 & 77.68 & 70.40 & 59.48 \\\\\n","& & & EMS & 59.86 & 43.06 & 16.80 & 48.51 & 72.04 & 23.53 & 75.13 & 69.23 & 57.16 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Plan And Solve} & REMS & 59.31 & 45.13 & 14.18 & 50.55 & 71.82 & 21.27 & 75.65 & 67.06 & 57.32 \\\\\n","& & & EMS & 57.79 & 43.49 & 14.30 & 49.07 & 70.29 & 21.22 & 73.63 & 66.08 & 55.21 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Program of Thought} & REMS & 46.63 & 31.83 & 14.80 & 28.30 & 63.03 & 34.73 & 61.16 & 51.98 & 41.83 \\\\\n","& & & EMS & 44.64 & 29.90 & 14.74 & 27.69 & 61.40 & 33.71 & 59.15 & 48.91 & 39.36 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Faithful CoT} & REMS & 40.19 & 29.44 & 10.75 & 25.11 & 60.30 & 35.19 & 56.52 & 46.22 & 37.86 \\\\\n","& & & EMS & 38.75 & 27.75 & 11.00 & 24.54 & 58.83 & 34.29 & 55.19 & 43.79 & 35.74 \\\\\n","\n","\\midrule\n","\n","\\multirow{6}{*}{SQL} & \\multirow{6}{*}{Schema} & \\multirow{2}{*}{zero shots} & REMS & 34.77 & 31.52 & 3.25 & 37.08 & 41.36 & 4.28 & 45.83 & 40.81 & 29.74 \\\\\n","& & & EMS & 33.56 & 30.47 & 3.09 & 36.06 & 39.77 & 3.71 & 44.26 & 39.65 & 27.54 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Static} & REMS & 54.59 & 47.31 & 7.28 & 55.38 & 63.12 & 7.74 & 68.51 & 60.83 & 56.15 \\\\\n","& & & EMS & 53.81 & 46.64 & 7.17 & 54.65 & 62.22 & 7.57 & 67.21 & 60.55 & 55.63 \\\\\n","\\cmidrule{3-13}\n","\n","& & \\multirow{2}{*}{Adaptive} & REMS & 64.61 & 64.04 & 0.57 & 67.40 & 69.54 & 2.14 & 77.42 & 68.01 & 61.78 \\\\\n","& & & EMS & 64.36 & 63.81 & 0.55 & 66.91 & 68.89 & 1.98 & 77.19 & 67.65 & 60.92 \\\\\n","\\bottomrule\n","\n","\\end{tabular}\n","\\vspace{-1.0em}\n","\\caption{\\small Test Set Results for Llama 3.1 70B.}\n","\\label{tab:llama_3_1_70b_results}\n","\\vspace{-1.0em}\n","\\end{table*}\n"],["\\begin{table*}[h]\n","\\centering\n","\\small\n","\\begin{tabular}{ll|c|c|ccccccc}\n","\\toprule\n","\\multirow{2}{*}{\\textbf{Output}} & \\multirow{2}{*}{\\textbf{Context}} & \\multirow{2}{*}{\\textbf{Few Shots}} & \\textbf{Metric} & \\multicolumn{7}{c}{\\textbf{Results Across Categories}} \\\\\n","\\cmidrule{5-11}\n","& & & & \\shortstack{\\textbf{Original}} & \\shortstack{\\textbf{CounterFact}} & \\shortstack{\\textbf{Large}} & \\shortstack{\\textbf{Small}} & \\shortstack{\\textbf{Easy}} & \\shortstack{\\textbf{Medium}} & \\shortstack{\\textbf{Hard}} \\\\\n","\\midrule\n","\n","\\multirow{11}{*}{Text} & \\multirow{2}{*}{None} & \\multirow{2}{*}{-} & REMS & 17.53 & 13.52 & 21.71 & 20.31 & 20.38 & 20.06 & 19.95 \\\\\n","& & & EMS & 15.92 & 12.73 & 20.82 & 19.53 & 19.26 & 17.75 & 18.08 \\\\\n","\\cmidrule{2-11}\n","\n","& \\multirow{6}{*}{Table} & \\multirow{2}{*}{zero shots} & REMS & 40.26 & 32.62 & 37.58 & 56.55 & 54.09 & 46.27 & 38.65 \\\\\n","& & & EMS & 38.06 & 30.47 & 35.50 & 53.33 & 51.78 & 43.79 & 35.05 \\\\\n","\\cmidrule{3-11}\n","\n","& & \\multirow{2}{*}{Static} & REMS & 50.92 & 42.31 & 43.17 & 68.13 & 64.75 & 55.80 & 49.06 \\\\\n","& & & EMS & 48.79 & 40.20 & 40.89 & 65.03 & 62.30 & 53.25 & 45.62 \\\\\n","\\cmidrule{3-11}\n","\n","& & \\multirow{2}{*}{Adaptive} & REMS & 39.98 & 33.23 & 37.68 & 50.69 & 53.84 & 41.02 & 40.33 \\\\\n","& & & EMS & 37.54 & 30.62 & 34.94 & 47.72 & 50.96 & 38.46 & 35.74 \\\\\n","\\midrule\n","\n","\\multirow{6}{*}{SQL} & \\multirow{6}{*}{Schema} & \\multirow{2}{*}{zero shots} & REMS & 20.35 & 15.07 & 19.30 & 27.74 & 21.65 & 27.19 & 22.43 \\\\\n","& & & EMS & 19.90 & 14.74 & 18.96 & 26.55 & 20.63 & 27.02 & 21.56 \\\\\n","\\cmidrule{3-11}\n","\n","& & \\multirow{2}{*}{Static} & REMS & 47.42 & 41.38 & 48.78 & 54.63 & 69.46 & 46.22 & 42.45 \\\\\n","& & & EMS & 46.02 & 40.20 & 47.96 & 52.98 & 67.35 & 45.36 & 40.61 \\\\\n","\\cmidrule{3-11}\n","\n","& & \\multirow{2}{*}{Adaptive} & REMS & 25.45 & 21.26 & 24.72 & 33.96 & 26.91 & 47.24 & 22.20 \\\\\n","& & & EMS & 25.09 & 20.89 & 24.54 & 33.57 & 26.78 & 46.55 & 21.56 \\\\\n","\\bottomrule\n","\n","\\end{tabular}\n","\\vspace{-1.0em}\n","\\caption{\\small Test Set Results for Mixtral 8x7B.}\n","\\vspace{-1.0em}\n","\\label{tab:mixtral_8x7b_results}\n","\\end{table*}\n"],["\\begin{table*}[h]\n","\\centering\n","\\footnotesize\n","\\begin{tabular}{ll|c|c|ccccccc}\n","\\toprule\n","\\multirow{2}{*}{\\textbf{Output}} & \\multirow{2}{*}{\\textbf{Context}} & \\multirow{2}{*}{\\textbf{Few Shots}} & \\textbf{Metric} & \\multicolumn{7}{c}{\\textbf{Results Across Categories}} \\\\\n","\\cmidrule{5-11}\n","& & & & \\shortstack{\\textbf{Original}} & \\shortstack{\\textbf{CounterFact}} & \\shortstack{\\textbf{Large}} & \\shortstack{\\textbf{Small}} & \\shortstack{\\textbf{Easy}} & \\shortstack{\\textbf{Medium}} & \\shortstack{\\textbf{Hard}} \\\\\n","\\midrule\n","\n","\\multirow{6}{*}{SQL} & \\multirow{6}{*}{Schema} & \\multirow{2}{*}{zero shots} & REMS & 19.59 & 18.32 & 20.56 & 27.60 & 29.05 & 20.28 & 17.29 \\\\\n","& & & EMS & 18.17 & 17.02 & 19.46 & 26.08 & 26.78 & 19.72 & 15.72 \\\\\n","\\cmidrule{3-11}\n","\n","& & \\multirow{2}{*}{Static} & REMS & 53.37 & 49.32 & 63.43 & 61.01 & 77.39 & 54.05 & 30.19 \\\\\n","& & & EMS & 52.08 & 47.93 & 62.28 & 59.53 & 77.39 & 51.19 & 28.91 \\\\\n","\\cmidrule{3-11}\n","\n","& & \\multirow{2}{*}{Adaptive} & REMS & 57.10 & 55.03 & 64.00 & 60.55 & 65.02 & 60.13 & 54.07 \\\\\n","& & & EMS & 55.88 & 53.50 & 63.17 & 58.95 & 63.93 & 58.38 & 51.74 \\\\\n","\\bottomrule\n","\n","\\end{tabular}\n","\\vspace{-1.0em}\n","\\caption{\\small Test Set Results for SQL Coder 70B}\n","\\label{tab:sql_coder_results}\n","\\end{table*}\n"],["\\begin{table*}[h]\n","\\centering\n","\\footnotesize\n","\\begin{tabular}{ll|c|c|ccccccc}\n","\\toprule\n","\\multirow{2}{*}{\\textbf{Output}} & \\multirow{2}{*}{\\textbf{Context}} & \\multirow{2}{*}{\\textbf{Few Shots}} & \\textbf{Metric} & \\multicolumn{7}{c}{\\textbf{Results Across Categories}} \\\\\n","\\cmidrule{5-11}\n","& & & & \\shortstack{\\textbf{Original}} & \\shortstack{\\textbf{CounterFact}} & \\shortstack{\\textbf{Large}} & \\shortstack{\\textbf{Small}} & \\shortstack{\\textbf{Easy}} & \\shortstack{\\textbf{Medium}} & \\shortstack{\\textbf{Hard}} \\\\\n","\\midrule\n","\n","\\multirow{6}{*}{SQL} & \\multirow{6}{*}{Schema} & \\multirow{2}{*}{zero shot} & REMS & 12.37 & 19.77 & 13.76 & 21.31 & 33.81 & 25.98 & 19.19 \\\\\n","& & & EMS & 12.22 & 19.46 & 13.76 & 21.00 & 33.47 & 25.64 & 18.64 \\\\\n","\\cmidrule{3-11}\n","\n","& & \\multirow{2}{*}{Static} & REMS & 16.53 & 33.29 & 29.82 & 42.04 & 54.13 & 41.62 & 39.09 \\\\\n","& & & EMS & 15.84 & 32.62 & 29.82 & 41.64 & 53.42 & 41.62 & 38.94 \\\\\n","\\cmidrule{3-11}\n","\n","& & \\multirow{2}{*}{Adaptive} & REMS & 24.02 & 40.73 & 38.00 & 48.56 & 65.56 & 51.78 & 41.63 \\\\\n","& & & EMS & 23.53 & 40.06 & 37.61 & 48.14 & 65.16 & 50.89 & 40.61 \\\\\n","\\bottomrule\n","\n","\\end{tabular}\n","\\vspace{-1.0em}\n","\\caption{\\small Test Set Results for Code Llama.}\n","\\label{tab:code_llama_results}\n","\\vspace{-1.0em}\n","\\end{table*}\n"]],"string":"[\n [\n \"\\\\begin{figure}[htbp]\\n\",\n \"\\\\vspace{-1.0em}\\n\",\n \"\\\\begin{center}\\n\",\n \" \\\\includegraphics[width=0.60\\\\columnwidth]{figs/Aron_Szilagyi.png} \\n\",\n \"\\\\vspace{0.25em}\\n\",\n \"\\n\",\n \"\\\\noindent \\\\textit{\\\\small \\\\textbf{Q.} In which year did Áron Szilágyi achieve his personal highest number of gold medal wins? \\\\textbf{Gold Answer.} 2022}\\n\",\n \"\\\\end{center}\\n\",\n \"\\\\vspace{-0.15em}\\n\",\n \"\\\\raggedright\\n\",\n \"\\\\small{\\n\",\n \"\\\\textbf{\\\\small Direct prompt (CoT):} {\\\\scriptsize In 2020, Áron Szilágyi won a Gold Medal in the Individual format at the Tokyo Olympics. This was his most successful year in terms of highest Gold Medal wins since he did not win multiple Gold Medals in any other format or competition.} \\\\textbf{Answer:} 2020.}\\\\\\\\\\n\",\n \"\\\\vspace{0.15em}\\n\",\n \"\\\\textbf{Symbolic Intermediate Representation (SQL):} \\\\\\\\\\n\",\n \"\\\\vspace{-0.25em}\\n\",\n \"\\\\begin{lstlisting}[language=SQL]\\n\",\n \"WITH gold_medal_counts AS (\\n\",\n \" SELECT m.year, COUNT(m.medal_id) AS gold_medals\\n\",\n \" FROM Medal m JOIN Format f ON m.format_id = f.format_id\\n\",\n \" JOIN Tournament t ON f.tournament_id = t.tournament_id\\n\",\n \" JOIN Athlete a ON t.athlete_id = a.athlete_id\\n\",\n \" WHERE a.name = 'Áron Szilágyi' AND m.type = 'MedalGold'\\n\",\n \" GROUP BY m.year )\\n\",\n \"SELECT year FROM gold_medal_counts\\n\",\n \"WHERE gold_medals = (SELECT MAX(gold_medals) FROM gold_medal_counts);\\n\",\n \"\\\\end{lstlisting}\\n\",\n \"\\\\vspace{-0.25em}\\n\",\n \"\\\\textbf{Answer:} 2022\\n\",\n \"\\\\label{fig:aron_table}\\n\",\n \"\\\\caption{ \\\\small \\\\small Structured table of Áron Szilágyi's achievements with question and answers. Direct prompting fails, whereas, Symbolic Intermediate Representation give correct answer.}\\n\",\n \"\\\\label{fig:example_figure}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table}[htbp]\\n\",\n \"\\\\small\\n\",\n \"\\\\centering\\n\",\n \"\\\\begin{tabular}{lc|lc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Category} & \\\\textbf{\\\\#Examples} & \\\\textbf{Category} & \\\\textbf{\\\\#Examples}\\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"Original & 578 & Easy & 732 \\\\\\\\\\n\",\n \"Counterfactual & 699 & Medium & 507 \\\\\\\\\\n\",\n \"Small Table & 855 & Hard & 719 \\\\\\\\\\n\",\n \"Large Table & 538 & \\\\textbf{Total} & \\\\textbf{5067} \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\caption{ \\\\small Dataset Splits and Their Number of Examples.}\\n\",\n \"\\\\label{tab:dataset_splits}\\n\",\n \"\\\\vspace{-0.5em}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[htbp]\\n\",\n \"\\\\centering\\n\",\n \"\\\\footnotesize\\n\",\n \"\\\\begin{tabular}{l|l|c|c|c}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\scriptsize{\\\\textbf{Approach}} & \\\\scriptsize{\\\\textbf{Method}} & \\\\scriptsize{\\\\textbf{Original}} & \\\\scriptsize{\\\\textbf{CounterF.}} & \\\\scriptsize{\\\\textbf{$\\\\Delta$}} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multirow{7}{*}{Direct} & Static & 56.57 & 42.35 & 14.22 \\\\\\\\\\n\",\n \" & Adaptive & 58.13 & 40.92 & 17.21 \\\\\\\\\\n\",\n \" & C.L.E.A.R & 65.57 & 48.21 & 17.36 \\\\\\\\\\n\",\n \" & CoT & 69.90 & 48.50 & 21.40 \\\\\\\\\\n\",\n \" & Plan \\\\textbf{$\\\\&$} Solve & 69.55 & 46.50 & 23.05 \\\\\\\\\\n\",\n \" & PoT & 56.40 & 47.07 & 9.33 \\\\\\\\\\n\",\n \" & Faithful CoT & 57.44 & 47.78 & 9.66 \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multirow{2}{*}{\\\\bf SQL} & \\\\textbf{Static} & \\\\textbf{65.22} & \\\\textbf{60.94} & \\\\textbf{4.28} \\\\\\\\\\n\",\n \" & \\\\textbf{Adaptive} & \\\\textbf{71.63} & \\\\textbf{68.67} & \\\\textbf{2.96} \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\caption{\\\\footnotesize Original vs Counterfactual for GPT-4o.}\\n\",\n \"\\\\label{tab:gpt_4o_original_counterfactual_gap}\\n\",\n \"\\\\vspace{-1.0em}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[htbp]\\n\",\n \"\\\\centering\\n\",\n \"\\\\footnotesize\\n\",\n \"\\\\begin{tabular}{l|l|c|c|c}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\scriptsize{\\\\textbf{Approach}} & \\\\scriptsize{\\\\textbf{Method}} & \\\\scriptsize{\\\\textbf{Original}} & \\\\scriptsize{\\\\textbf{CounterF.}} & \\\\scriptsize{\\\\textbf{$\\\\Delta$}} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multirow{7}{*}{Direct} & Static & 52.91 & 39.92 & 12.99 \\\\\\\\\\n\",\n \" & Adaptive & 53.48 & 44.19 & 9.29 \\\\\\\\\\n\",\n \" & C.L.E.A.R & 49.33 & 40.66 & 8.67 \\\\\\\\\\n\",\n \" & CoT & 66.46 & 55.75 & 10.71 \\\\\\\\\\n\",\n \" & Plan \\\\textbf{$\\\\&$} Solve & 60.75 & 54.31 & 6.44 \\\\\\\\\\n\",\n \" & PoT & 53.83 & 46.20 & 7.63 \\\\\\\\\\n\",\n \" & Faithful CoT & 53.95 & 47.64 & 6.31 \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multirow{2}{*}{\\\\bf SQL}& \\\\textbf{Static} & \\\\textbf{59.08} & \\\\textbf{55.52} & \\\\textbf{3.56} \\\\\\\\\\n\",\n \" & \\\\textbf{Adaptive} & \\\\textbf{65.29} & \\\\textbf{65.13} & \\\\textbf{0.16} \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\caption{\\\\footnotesize Original vs Counterfactual for Gemini 1.5 Pro.}\\n\",\n \"\\\\label{tab:gemini_1_5_pro_original_counterfactual_gap}\\n\",\n \"\\\\vspace{-1.0em}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[htbp]\\n\",\n \"\\\\centering\\n\",\n \"\\\\footnotesize\\n\",\n \"\\\\begin{tabular}{l|l|c|c|c}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Approach} & \\\\textbf{Method} & \\\\textbf{Small} & \\\\textbf{Large} & \\\\textbf{$\\\\Delta$} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multirow{7}{*}{ Direct} & Static & 71.11 & 46.84 & 24.27 \\\\\\\\\\n\",\n \" & Adaptive & 73.92 & 48.88 & 25.04 \\\\\\\\\\n\",\n \" & C.L.E.A.R & 76.49 & 53.53 & 22.96 \\\\\\\\\\n\",\n \" & CoT & 74.15 & 56.13 & 18.02 \\\\\\\\\\n\",\n \" & Plan \\\\textbf{$\\\\&$} Solve & 75.44 & 55.20 & 20.24 \\\\\\\\\\n\",\n \" & PoT & 62.22 & 50.19 & 12.03 \\\\\\\\\\n\",\n \" & Faithful CoT & 62.69 & 50.19 & 12.50 \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multirow{2}{*}{\\\\bf SQL} & \\\\textbf{Static} & \\\\textbf{73.57} & \\\\textbf{70.63} & \\\\textbf{2.94} \\\\\\\\\\n\",\n \" & \\\\textbf{Adaptive} & \\\\textbf{73.92} & \\\\textbf{72.86} & \\\\textbf{1.06} \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\caption{\\\\footnotesize Small Table vs Large Table for GPT-4o}\\n\",\n \"\\\\label{tab:gpt4o_small_large_gap}\\n\",\n \"\\\\vspace{-1.0em}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[htbp]\\n\",\n \"\\\\centering\\n\",\n \"\\\\footnotesize\\n\",\n \"\\\\begin{tabular}{l|l|c|c|c}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Approach} & \\\\textbf{Method } & \\\\textbf{Small} & \\\\textbf{Large} & \\\\textbf{$\\\\Delta$} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multirow{7}{*}{Direct} & Static & 65.02 & 43.86 & 21.16 \\\\\\\\\\n\",\n \" & Adaptive & 67.27 & 41.86 & 25.41 \\\\\\\\\\n\",\n \" & C.L.E.A.R & 56.85 & 40.95 & 15.90 \\\\\\\\\\n\",\n \" & CoT & 77.55 & 57.67 & 19.88 \\\\\\\\\\n\",\n \" & Plan \\\\textbf{$\\\\&$} Solve & 73.43 & 56.72 & 16.71 \\\\\\\\\\n\",\n \" & PoT & 64.68 & 49.84 & 14.83 \\\\\\\\\\n\",\n \" & Faithful CoT & 63.85 & 48.47 & 15.39 \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multirow{2}{*}{\\\\textbf{SQL}} & \\\\textbf{Static} & \\\\textbf{77.41} & \\\\textbf{71.32} & \\\\textbf{6.09} \\\\\\\\\\n\",\n \" & \\\\textbf{Adaptive} & \\\\textbf{75.31} & \\\\textbf{72.43} & \\\\textbf{2.88} \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\caption{\\\\footnotesize Small Table vs Large Table for Gemini 1.5 Pro}\\n\",\n \"\\\\label{tab:gemini_1_5_pro_small_large_gap}\\n\",\n \"\\\\vspace{-1.0em}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[htbp]\\n\",\n \"\\\\centering\\n\",\n \"\\\\footnotesize\\n\",\n \"\\\\begin{tabular}{l|l|c|c|c}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Approach} & \\\\textbf{Method} & \\\\textbf{Easy} & \\\\textbf{Medium} & \\\\textbf{Hard} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multirow{7}{*}{Direct} & Static & 71.18 & 63.12 & 53.35 \\\\\\\\\\n\",\n \" & Adaptive & 74.38 & 63.91 & 54.17 \\\\\\\\\\n\",\n \" & C.L.E.A.R & 76.40 & 71.99 & 62.62 \\\\\\\\\\n\",\n \" & CoT & 78.43 & 75.35 & 64.06 \\\\\\\\\\n\",\n \" & Plan \\\\textbf{$\\\\&$} Solve & 77.96 & 70.81 & 60.25 \\\\\\\\\\n\",\n \" & PoT & 69.94 & 57.20 & 48.81 \\\\\\\\\\n\",\n \" & Faithful CoT & 69.24 & 56.02 & 47.17 \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multirow{2}{*}{\\\\textbf{SQL}} & \\\\textbf{Static} & \\\\textbf{78.89} & \\\\textbf{75.15} & \\\\textbf{62.31} \\\\\\\\\\n\",\n \" & \\\\textbf{Adaptive} & \\\\textbf{80.06} & \\\\textbf{73.37} & \\\\textbf{66.74} \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\caption{\\\\footnotesize Easy, Medium, and Hard results for GPT-4o}\\n\",\n \"\\\\label{tab:gpt4o_easy_medium_hard}\\n\",\n \"\\\\vspace{-1.0em}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{table}[htbp]\\n\",\n \"\\\\centering\\n\",\n \"\\\\footnotesize\\n\",\n \"\\\\begin{tabular}{l|l|c|c|c}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\textbf{Approach} & \\\\textbf{Method} & \\\\textbf{Easy} & \\\\textbf{Medium} & \\\\textbf{Hard} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multirow{7}{*}{Direct} & Static & 65.79 & 58.53 & 50.00 \\\\\\\\\\n\",\n \" & Adaptive & 66.26 & 56.47 & 46.74 \\\\\\\\\\n\",\n \" & C.L.E.A.R & 58.38 & 59.77 & 51.14 \\\\\\\\\\n\",\n \" & CoT & 83.29 & 72.69 & 65.87 \\\\\\\\\\n\",\n \" & Plan \\\\textbf{$\\\\&$} Solve & 79.42 & 72.25 & 63.60 \\\\\\\\\\n\",\n \" & PoT & 73.97 & 57.12 & 47.57 \\\\\\\\\\n\",\n \" & Faithful CoT & 74.52 & 58.15 & 46.85 \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\\\multirow{2}{*}{\\\\textbf{SQL}} & \\\\textbf{Static} & \\\\textbf{80.86} & \\\\textbf{70.33} & \\\\textbf{59.59} \\\\\\\\\\n\",\n \" & \\\\textbf{Adaptive} & \\\\textbf{75.86} & \\\\textbf{71.47} & \\\\textbf{59.24} \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\caption{\\\\footnotesize Easy, Medium, and Hard results on Gemini 1.5 Pro}\\n\",\n \"\\\\label{tab:gemini_1_5_pro_easy_medium_hard}\\n\",\n \"\\\\vspace{-1.0em}\\n\",\n \"\\\\end{table}\\n\"\n ],\n [\n \"\\\\begin{figure}[h]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=0.4\\\\textwidth]{figs/Emma_Weyant.png}\\n\",\n \" \\\\caption{Emma Weyant's Medal Infobox}\\n\",\n \" \\\\label{fig:emma_infobox}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{figure}[h]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=0.3\\\\textwidth]{figs/Mayu_Matsumoto.png}\\n\",\n \" \\\\caption{Mayu Matsumoto's Medal Infobox}\\n\",\n \" \\\\label{fig:mayu_infobox}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{figure}[h]\\n\",\n \" \\\\centering\\n\",\n \" \\\\includegraphics[width=0.3\\\\textwidth]{figs/Sandra_Sanchez.png}\\n\",\n \" \\\\caption{Sandra Sánchez's Medal Infobox}\\n\",\n \" \\\\label{fig:sandra_infobox}\\n\",\n \"\\\\end{figure}\\n\"\n ],\n [\n \"\\\\begin{table*}[h]\\n\",\n \"\\\\centering\\n\",\n \"\\\\tiny\\n\",\n \"\\\\begin{tabular}{ll|c|c|ccccccccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{1}{*}{\\\\textbf{Output}} & \\\\multirow{1}{*}{\\\\textbf{Context}} & \\\\multirow{1}{*}{\\\\textbf{Method}} & \\\\textbf{Metric} & \\\\textbf{Original} & \\\\textbf{CounterFact} & \\\\shortstack{\\\\textbf{Gap (Original} \\\\\\\\ \\\\textbf{- CounterFact)}} & \\\\textbf{Large} & \\\\textbf{Small} & \\\\shortstack{\\\\textbf{Gap (Small} \\\\\\\\ \\\\textbf{- Large)}} & \\\\textbf{Easy} & \\\\textbf{Medium} & \\\\textbf{Hard} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\n\",\n \"\\\\multirow{27}{*}{Text} \\n\",\n \"& \\\\multirow{2}{*}{None} & \\\\multirow{2}{*}{-} & REMS & 25.91 & 13.45 & 12.46 & 23.84 & 25.79 & 1.95 & 28.67 & 25.55 & 23.83 \\\\\\\\\\n\",\n \"& & & EMS & 25.09 & 12.16 & 12.93 & 23.05 & 24.68 & 1.63 & 27.26 & 24.06 & 22.35 \\\\\\\\\\n\",\n \"\\\\cmidrule{2-13}\\n\",\n \"\\n\",\n \"& \\\\multirow{22}{*}{Table} & \\\\multirow{2}{*}{zero shots} & REMS & 55.94 & 42.26 & 13.68 & 45.24 & 67.81 & 22.57 & 72.34 & 59.43 & 52.90 \\\\\\\\\\n\",\n \"& & & EMS & 53.46 & 40.06 & 13.40 & 43.31 & 64.56 & 21.25 & 69.86 & 57.20 & 49.02 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Static} & REMS & 58.69 & 44.69 & 14.00 & 48.96 & 73.57 & 24.61 & 73.45 & 65.39 & 56.97 \\\\\\\\\\n\",\n \"& & & EMS & 56.57 & 42.35 & 14.22 & 46.84 & 71.11 & 24.27 & 71.18 & 63.12 & 53.35 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Adaptive} & REMS & 60.71 & 43.10 & 17.61 & 51.41 & 76.97 & 25.56 & 76.89 & 66.00 & 58.00 \\\\\\\\\\n\",\n \"& & & EMS & 58.13 & 40.92 & 17.21 & 48.88 & 73.92 & 25.04 & 74.38 & 63.91 & 54.17 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & Clear & REMS & 66.84 & 50.17 & 16.67 & 55.54 & 77.86 & 22.32 & 78.97 & 72.70 & 64.72 \\\\\\\\\\n\",\n \"& & & EMS & 65.57 & 48.21 & 17.36 & 53.53 & 76.49 & 22.96 & 76.40 & 71.99 & 62.62 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & Chain Of Thought & REMS & 70.96 & 50.43 & 20.53 & 58.36 & 76.02 & 17.66 & 81.02 & 75.52 & 66.51 \\\\\\\\\\n\",\n \"& & & EMS & 69.90 & 48.50 & 21.40 & 56.13 & 74.15 & 18.02 & 78.43 & 75.35 & 64.06 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & Plan And Solve & REMS & 70.36 & 48.62 & 21.74 & 57.14 & 76.91 & 19.77 & 80.30 & 72.30 & 62.64 \\\\\\\\\\n\",\n \"& & & EMS & 69.55 & 46.50 & 23.05 & 55.20 & 75.44 & 20.24 & 77.96 & 70.81 & 60.25 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & Program of Thought & REMS & 59.05 & 50.28 & 8.77 & 51.66 & 64.35 & 12.69 & 72.65 & 60.85 & 52.89 \\\\\\\\\\n\",\n \"& & & EMS & 56.40 & 47.07 & 9.33 & 50.19 & 62.22 & 12.03 & 69.94 & 57.20 & 48.81 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & Faithful CoT & REMS & 60.02 & 50.90 & 9.12 & 51.76 & 64.85 & 13.09 & 72.02 & 59.78 & 51.25 \\\\\\\\\\n\",\n \"& & & EMS & 57.44 & 47.78 & 9.66 & 50.19 & 62.69 & 12.50 & 69.24 & 56.02 & 47.17 \\\\\\\\\\n\",\n \"\\n\",\n \"\\\\midrule\\n\",\n \"\\n\",\n \"\\\\multirow{7}{*}{SQL}\\n\",\n \"& \\\\multirow{7}{*}{Schema} & \\\\multirow{2}{*}{zero shots} & REMS & 51.44 & 48.94 & 2.50 & 55.81 & 59.92 & 4.11 & 63.53 & 65.20 & 45.16 \\\\\\\\\\n\",\n \"& & & EMS & 49.31 & 46.64 & 2.67 & 53.90 & 57.43 & 3.53 & 61.22 & 63.12 & 42.02 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Static} & REMS & 66.76 & 62.41 & 4.35 & 71.82 & 75.37 & 3.55 & 81.27 & 75.88 & 64.38 \\\\\\\\\\n\",\n \"& & & EMS & 65.22 & 60.94 & 4.28 & 70.63 & 73.57 & 2.94 & 78.89 & 75.15 & 62.31 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Adaptive} & REMS & 71.87 & 69.04 & 2.83 & 73.05 & 74.55 & 1.50 & 80.17 & 74.35 & 67.38 \\\\\\\\\\n\",\n \"& & & EMS & 71.63 & 68.67 & 2.96 & 72.86 & 73.92 & 1.06 & 80.06 & 73.37 & 66.74 \\\\\\\\\\n\",\n \"\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\vspace{-1.0em}\\n\",\n \"\\\\caption{\\\\small Test Set Results for GPT-4o.}\\n\",\n \"\\\\label{tab:gpt4o_results}\\n\",\n \"\\\\vspace{-1.0em}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[h]\\n\",\n \"\\\\centering\\n\",\n \"\\\\footnotesize\\n\",\n \"\\\\begin{tabular}{ll|c|c|ccccccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{\\\\textbf{Output}} & \\\\multirow{2}{*}{\\\\textbf{Context}} & \\\\multirow{2}{*}{\\\\textbf{Few Shots}} & \\\\textbf{Metric} & \\\\multicolumn{7}{c}{\\\\textbf{Results Across Categories}} \\\\\\\\\\n\",\n \"\\\\cmidrule{5-11}\\n\",\n \"& & & & \\\\shortstack{\\\\textbf{Original}} & \\\\shortstack{\\\\textbf{CounterFact}} & \\\\shortstack{\\\\textbf{Large}} & \\\\shortstack{\\\\textbf{Small}} & \\\\shortstack{\\\\textbf{Easy}} & \\\\shortstack{\\\\textbf{Medium}} & \\\\shortstack{\\\\textbf{Hard}} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\n\",\n \"\\\\multirow{10}{*}{Text} & \\\\multirow{2}{*}{None} & \\\\multirow{2}{*}{-} & REMS & 21.48 & 13.90 & 20.21 & 22.75 & 26.08 & 22.30 & 19.03 \\\\\\\\\\n\",\n \"& & & EMS & 20.24 & 13.02 & 19.70 & 21.87 & 24.84 & 20.51 & 17.40 \\\\\\\\\\n\",\n \"\\\\cmidrule{2-11}\\n\",\n \"\\n\",\n \"& \\\\multirow{6}{*}{Table} & \\\\multirow{2}{*}{zero shots} & REMS & 49.59 & 33.52 & 40.18 & 63.60 & 62.07 & 47.53 & 43.62 \\\\\\\\\\n\",\n \"& & & EMS & 47.23 & 31.04 & 37.73 & 60.47 & 59.19 & 44.58 & 39.55 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-11}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Static} & REMS & 49.94 & 36.94 & 41.33 & 66.45 & 64.23 & 51.13 & 47.13 \\\\\\\\\\n\",\n \"& & & EMS & 47.58 & 34.91 & 39.03 & 63.63 & 61.84 & 48.32 & 43.36 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-11}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Adaptive} & REMS & 51.13 & 38.37 & 42.43 & 67.66 & 66.10 & 54.81 & 48.63 \\\\\\\\\\n\",\n \"& & & EMS & 48.79 & 35.48 & 39.96 & 64.80 & 63.16 & 52.07 & 44.49 \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\n\",\n \"\\\\multirow{6}{*}{SQL} & \\\\multirow{6}{*}{Schema} & \\\\multirow{2}{*}{zero shots} & REMS & 39.93 & 41.28 & 38.76 & 48.50 & 57.45 & 50.88 & 34.21 \\\\\\\\\\n\",\n \"& & & EMS & 38.24 & 39.63 & 37.36 & 46.67 & 55.30 & 49.51 & 31.41 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-11}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Static} & REMS & 57.44 & 51.18 & 53.94 & 66.52 & 77.57 & 65.29 & 57.04 \\\\\\\\\\n\",\n \"& & & EMS & 56.57 & 50.36 & 53.16 & 65.73 & 75.93 & 65.29 & 56.33 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-11}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Adaptive} & REMS & 68.97 & 65.40 & 65.70 & 69.20 & 76.98 & 71.07 & 63.93 \\\\\\\\\\n\",\n \"& & & EMS & 68.69 & 65.24 & 65.43 & 68.77 & 76.87 & 70.41 & 63.13 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\vspace{-1.0em}\\n\",\n \"\\\\caption{\\\\small Test Set Results for GPT-4o-mini}\\n\",\n \"\\\\label{tab:gpt_4o_mini_results}\\n\",\n \"\\\\vspace{-1.0em}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[h]\\n\",\n \"\\\\centering\\n\",\n \"\\\\tiny\\n\",\n \"\\\\begin{tabular}{ll|c|c|ccccccccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{\\\\textbf{Output}} & \\\\multirow{2}{*}{\\\\textbf{Context}} & \\\\multirow{2}{*}{\\\\textbf{Method}} & \\\\textbf{Metric} & \\\\multicolumn{9}{c}{\\\\textbf{Results Across Categories}} \\\\\\\\\\n\",\n \"\\\\cmidrule{5-13}\\n\",\n \"& & & & \\\\shortstack{\\\\textbf{Original}} & \\\\shortstack{\\\\textbf{CounterFact}} & \\\\shortstack{\\\\textbf{Gap (Original} \\\\\\\\ \\\\textbf{- CounterFact)}} & \\\\shortstack{\\\\textbf{Large}} & \\\\shortstack{\\\\textbf{Small}} & \\\\shortstack{\\\\textbf{Gap (Small} \\\\\\\\ \\\\textbf{- Large)}} & \\\\shortstack{\\\\textbf{Easy}} & \\\\shortstack{\\\\textbf{Medium}} & \\\\shortstack{\\\\textbf{Hard}} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\n\",\n \"\\\\multirow{27}{*}{Text} & \\\\multirow{2}{*}{None} & \\\\multirow{2}{*}{-} & REMS & 22.11 & 15.02 & 7.09 & 23.88 & 21.74 & -2.14 & 27.81 & 23.23 & 21.59 \\\\\\\\\\n\",\n \"& & & EMS & 18.69 & 11.76 & 6.93 & 19.49 & 18.93 & -0.56 & 24.03 & 19.41 & 17.30 \\\\\\\\\\n\",\n \"\\\\cmidrule{2-13}\\n\",\n \"\\n\",\n \"& \\\\multirow{22}{*}{Table} & \\\\multirow{2}{*}{zero shots} & REMS & 55.17 & 45.42 & 9.75 & 42.71 & 69.88 & 27.17 & 68.78 & 56.61 & 51.18 \\\\\\\\\\n\",\n \"& & & EMS & 48.79 & 37.61 & 11.18 & 34.96 & 61.11 & 26.15 & 59.15 & 48.82 & 39.00 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Static} & REMS & 57.46 & 47.20 & 10.26 & 46.44 & 71.89 & 25.45 & 73.76 & 61.94 & 54.99 \\\\\\\\\\n\",\n \"& & & EMS & 50.00 & 39.08 & 10.92 & 38.35 & 62.35 & 24.00 & 63.16 & 53.53 & 42.96 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Adaptive} & REMS & 59.09 & 48.25 & 10.84 & 47.93 & 73.17 & 25.24 & 76.28 & 64.94 & 57.83 \\\\\\\\\\n\",\n \"& & & EMS & 52.90 & 42.91 & 9.99 & 41.02 & 66.25 & 25.23 & 65.76 & 55.95 & 45.92 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Clear} & REMS & 62.01 & 50.46 & 11.55 & 53.38 & 73.07 & 19.69 & 79.65 & 68.02 & 59.37 \\\\\\\\\\n\",\n \"& & & EMS & 60.63 & 48.95 & 11.68 & 51.11 & 72.19 & 21.08 & 77.92 & 66.52 & 56.67 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Chain Of Thought} & REMS & 60.97 & 47.28 & 13.69 & 51.61 & 73.45 & 21.84 & 77.28 & 65.60 & 56.62 \\\\\\\\\\n\",\n \"& & & EMS & 59.54 & 45.48 & 14.06 & 49.52 & 72.09 & 22.57 & 75.29 & 64.17 & 54.55 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Plan And Solve} & REMS & 53.58 & 42.58 & 11.00 & 48.40 & 65.07 & 16.67 & 70.61 & 59.35 & 51.43 \\\\\\\\\\n\",\n \"& & & EMS & 52.00 & 40.34 & 11.66 & 46.14 & 63.85 & 17.71 & 68.54 & 57.12 & 48.53 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Program of Thought} & REMS & 49.26 & 42.85 & 6.41 & 44.31 & 57.29 & 12.98 & 67.10 & 48.61 & 44.13 \\\\\\\\\\n\",\n \"& & & EMS & 46.90 & 40.55 & 6.35 & 42.33 & 55.30 & 12.97 & 64.53 & 45.52 & 40.47 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Faithful CoT} & REMS & 48.98 & 40.90 & 8.08 & 43.46 & 58.09 & 14.63 & 64.12 & 53.26 & 45.68 \\\\\\\\\\n\",\n \"& & & EMS & 46.78 & 38.66 & 8.12 & 41.38 & 56.23 & 14.85 & 61.50 & 50.66 & 42.67 \\\\\\\\\\n\",\n \"\\n\",\n \"\\\\midrule\\n\",\n \"\\n\",\n \"\\\\multirow{6}{*}{SQL} & \\\\multirow{6}{*}{Schema} & \\\\multirow{2}{*}{zero shots} & REMS & 47.27 & 42.56 & 4.71 & 46.23 & 56.45 & 10.22 & 63.49 & 59.74 & 42.09 \\\\\\\\\\n\",\n \"& & & EMS & 39.34 & 33.45 & 5.89 & 44.29 & 53.65 & 9.36 & 52.96 & 52.48 & 34.59 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Static} & REMS & 66.43 & 62.38 & 4.05 & 71.39 & 77.20 & 5.81 & 88.17 & 79.22 & 65.04 \\\\\\\\\\n\",\n \"& & & EMS & 57.93 & 54.43 & 3.50 & 63.13 & 70.89 & 7.76 & 79.54 & 68.96 & 58.42 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Adaptive} & REMS & 72.91 & 71.67 & 1.24 & 78.18 & 81.71 & 3.53 & 87.06 & 80.80 & 74.04 \\\\\\\\\\n\",\n \"& & & EMS & 65.49 & 62.71 & 2.78 & 69.30 & 73.53 & 4.23 & 76.26 & 73.72 & 63.12 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\caption{\\\\small Test Set Results for Gemini 1.5 Flash.}\\n\",\n \"\\\\vspace{-1.0em}\\n\",\n \"\\\\label{tab:gemini_1_5_flash_results}\\n\",\n \"\\\\vspace{-1.0em}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[h]\\n\",\n \"\\\\centering\\n\",\n \"\\\\tiny\\n\",\n \"\\\\begin{tabular}{ll|c|c|ccccccccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{1}{*}{\\\\textbf{Output}} & \\\\multirow{1}{*}{\\\\textbf{Context}} & \\\\multirow{1}{*}{\\\\textbf{Method}} & \\\\textbf{Metric} & \\\\textbf{Original} & \\\\textbf{CounterFact} & \\\\shortstack{\\\\textbf{Gap (Original} \\\\\\\\ \\\\textbf{- CounterFact)}} & \\\\textbf{Large} & \\\\textbf{Small} & \\\\shortstack{\\\\textbf{Gap (Small} \\\\\\\\ \\\\textbf{- Large)}} & \\\\textbf{Easy} & \\\\textbf{Medium} & \\\\textbf{Hard} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\n\",\n \"\\\\multirow{27}{*}{Text} \\n\",\n \"\\n\",\n \"\\\\multirow{2}{*}{} & \\\\multirow{2}{*}{None} & \\\\multirow{2}{*}{-} & REMS & 23.37 & 15.88 & 7.49 & 24.20 & 24.08 & 0.12 & 29.15 & 26.45 & 23.38 \\\\\\\\\\n\",\n \"& & & EMS & 19.90 & 13.45 & 6.45 & 21.19 & 20.99 & 0.2 & 24.94 & 21.76 & 18.77 \\\\\\\\\\n\",\n \"\\\\cmidrule{2-13}\\n\",\n \"\\n\",\n \"& \\\\multirow{6}{*}{} & \\\\multirow{2}{*}{zero shots} & REMS & 54.94 & 45.40 & 9.54 & 46.34 & 69.74 & 23.4 & 73.26 & 60.71 & 57.43 \\\\\\\\\\n\",\n \"& & & EMS & 48.06 & 37.39 & 10.67 & 38.98 & 61.11 & 21.13 & 62.59 & 53.24 & 45.60 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Static} & REMS & 59.01 & 46.87 & 12.14 & 52.42 & 72.93 & 20.51 & 75.72 & 65.10 & 60.28 \\\\\\\\\\n\",\n \"& & & EMS & 52.91 & 39.92 & 12.99 & 43.86 & 65.02 & 21.16 & 65.79 & 58.53 & 50.00 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Adaptive} & REMS & 60.23 & 49.04 & 11.19 & 48.79 & 73.98 & 25.19 & 78.04 & 66.43 & 59.28 \\\\\\\\\\n\",\n \"& & & EMS & 53.48 & 44.19 & 9.29 & 41.86 & 67.27 & 25.41 & 66.26 & 56.47 & 46.74 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& \\\\multirow{5}{*}{Table} & \\\\multirow{2}{*}{Clear} & REMS & 50.29 & 42.82 & 7.47 & 42.83 & 58.04 & 15.21 & 59.91 & 60.79 & 52.82 \\\\\\\\\\n\",\n \"& & & EMS & 49.33 & 40.66 & 8.67 & 40.95 & 56.85 & 15.90 & 58.38 & 59.77 & 51.14 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Chain Of Thought} & REMS & 67.34 & 57.16 & 10.18 & 59.51 & 78.47 & 18.96 & 84.67 & 73.36 & 67.31 \\\\\\\\\\n\",\n \"& & & EMS & 66.46 & 55.75 & 10.71 & 57.67 & 77.55 & 19.88 & 83.29 & 72.69 & 65.87 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Plan And Solve} & REMS & 62.11 & 55.68 & 6.43 & 58.38 & 74.88 & 16.50 & 81.19 & 73.28 & 65.56 \\\\\\\\\\n\",\n \"& & & EMS & 60.75 & 54.31 & 6.44 & 56.72 & 73.43 & 16.71 & 79.42 & 72.25 & 63.60 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Program of Thought} & REMS & 56.31 & 49.35 & 6.97 & 53.02 & 66.46 & 13.44 & 76.94 & 60.62 & 51.75 \\\\\\\\\\n\",\n \"& & & EMS & 53.83 & 46.20 & 7.63 & 49.84 & 64.68 & 14.83 & 73.97 & 57.12 & 47.57 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Faithful CoT} & REMS & 56.49 & 50.15 & 6.34 & 50.63 & 65.84 & 15.21 & 77.28 & 61.53 & 51.03 \\\\\\\\\\n\",\n \"& & & EMS & 53.95 & 47.64 & 6.31 & 48.47 & 63.85 & 15.39 & 74.52 & 58.15 & 46.85 \\\\\\\\\\n\",\n \"\\n\",\n \"\\\\midrule\\n\",\n \"\\n\",\n \"\\\\multirow{6}{*}{SQL} \\n\",\n \"& \\\\multirow{6}{*}{Schema} & \\\\multirow{2}{*}{zero shots} & REMS & 49.24 & 43.56 & 5.68 & 48.21 & 57.62 & 9.41 & 64.42 & 61.10 & 43.00 \\\\\\\\\\n\",\n \"& & & EMS & 41.32 & 35.31 & 6.01 & 45.87 & 55.11 & 9.24 & 54.78 & 53.79 & 35.96 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Static} & REMS & 67.76 & 63.63 & 4.13 & 76.80 & 82.82 & 6.02 & 89.33 & 80.81 & 66.42 \\\\\\\\\\n\",\n \"& & & EMS & 59.08 & 55.52 & 3.56 & 71.32 & 77.41 & 6.09 & 80.86 & 70.33 & 59.59 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Adaptive} & REMS & 73.04 & 73.58 & 0.54 & 81.94 & 84.38 & 2.44 & 87.31 & 80.01 & 71.43 \\\\\\\\\\n\",\n \"& & & EMS & 65.29 & 65.13 & 0.16 & 72.43 & 75.31 & 2.88 & 75.86 & 71.47 & 59.24 \\\\\\\\\\n\",\n \"\\n\",\n \"\\\\bottomrule\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\vspace{-1.0em}\\n\",\n \"\\\\caption{\\\\small Test Set Results for Gemini 1.5 Pro.}\\n\",\n \"\\\\label{tab:gemini_1_5_pro_results}\\n\",\n \"\\\\vspace{-1.0em}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[h]\\n\",\n \"\\\\centering\\n\",\n \"\\\\tiny\\n\",\n \"\\\\begin{tabular}{ll|c|c|ccccccccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{\\\\textbf{Output}} & \\\\multirow{2}{*}{\\\\textbf{Context}} & \\\\multirow{2}{*}{\\\\textbf{Method}} & \\\\textbf{Metric} & \\\\multicolumn{9}{c}{\\\\textbf{Results Across Categories}} \\\\\\\\\\n\",\n \"\\\\cmidrule{5-13}\\n\",\n \"& & & & \\\\shortstack{\\\\textbf{Original}} & \\\\shortstack{\\\\textbf{CounterFact}} & \\\\shortstack{\\\\textbf{Gap (Original} \\\\\\\\ \\\\textbf{- CounterFact)}} & \\\\shortstack{\\\\textbf{Large}} & \\\\shortstack{\\\\textbf{Small}} & \\\\shortstack{\\\\textbf{Gap (Small} \\\\\\\\ \\\\textbf{- Large)}} & \\\\shortstack{\\\\textbf{Easy}} & \\\\shortstack{\\\\textbf{Medium}} & \\\\shortstack{\\\\textbf{Hard}} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\n\",\n \"\\\\multirow{27}{*}{Text} & \\\\multirow{2}{*}{None} & \\\\multirow{2}{*}{-} & REMS & 17.46 & 12.74 & 4.72 & 20.89 & 18.09 & -2.80 & 22.31 & 17.93 & 17.18 \\\\\\\\\\n\",\n \"& & & EMS & 16.61 & 12.16 & 4.45 & 19.89 & 17.31 & -2.58 & 21.31 & 16.96 & 16.27 \\\\\\\\\\n\",\n \"\\\\cmidrule{2-13}\\n\",\n \"\\n\",\n \"& \\\\multirow{22}{*}{Table} & \\\\multirow{2}{*}{zero shots} & REMS & 54.63 & 39.93 & 14.70 & 46.10 & 64.62 & 18.52 & 70.10 & 58.31 & 53.37 \\\\\\\\\\n\",\n \"& & & EMS & 52.60 & 37.48 & 15.12 & 44.05 & 62.57 & 18.52 & 67.49 & 56.21 & 50.35 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Static} & REMS & 64.33 & 48.44 & 15.89 & 57.27 & 75.40 & 18.13 & 79.04 & 70.23 & 60.98 \\\\\\\\\\n\",\n \"& & & EMS & 62.46 & 46.21 & 16.25 & 55.58 & 73.68 & 18.10 & 76.91 & 67.46 & 57.44 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Adaptive} & REMS & 55.73 & 41.29 & 14.44 & 48.48 & 70.56 & 22.08 & 71.35 & 62.54 & 56.43 \\\\\\\\\\n\",\n \"& & & EMS & 53.63 & 39.20 & 14.43 & 46.65 & 68.07 & 21.42 & 69.40 & 59.76 & 52.85 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Clear} & REMS & 54.37 & 39.43 & 14.94 & 37.63 & 68.64 & 31.01 & 68.86 & 62.91 & 50.87 \\\\\\\\\\n\",\n \"& & & EMS & 53.46 & 38.34 & 15.12 & 36.43 & 67.02 & 30.59 & 67.49 & 62.33 & 49.51 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Chain Of Thought} & REMS & 61.87 & 45.29 & 16.58 & 49.87 & 73.88 & 24.01 & 77.68 & 70.40 & 59.48 \\\\\\\\\\n\",\n \"& & & EMS & 59.86 & 43.06 & 16.80 & 48.51 & 72.04 & 23.53 & 75.13 & 69.23 & 57.16 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Plan And Solve} & REMS & 59.31 & 45.13 & 14.18 & 50.55 & 71.82 & 21.27 & 75.65 & 67.06 & 57.32 \\\\\\\\\\n\",\n \"& & & EMS & 57.79 & 43.49 & 14.30 & 49.07 & 70.29 & 21.22 & 73.63 & 66.08 & 55.21 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Program of Thought} & REMS & 46.63 & 31.83 & 14.80 & 28.30 & 63.03 & 34.73 & 61.16 & 51.98 & 41.83 \\\\\\\\\\n\",\n \"& & & EMS & 44.64 & 29.90 & 14.74 & 27.69 & 61.40 & 33.71 & 59.15 & 48.91 & 39.36 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Faithful CoT} & REMS & 40.19 & 29.44 & 10.75 & 25.11 & 60.30 & 35.19 & 56.52 & 46.22 & 37.86 \\\\\\\\\\n\",\n \"& & & EMS & 38.75 & 27.75 & 11.00 & 24.54 & 58.83 & 34.29 & 55.19 & 43.79 & 35.74 \\\\\\\\\\n\",\n \"\\n\",\n \"\\\\midrule\\n\",\n \"\\n\",\n \"\\\\multirow{6}{*}{SQL} & \\\\multirow{6}{*}{Schema} & \\\\multirow{2}{*}{zero shots} & REMS & 34.77 & 31.52 & 3.25 & 37.08 & 41.36 & 4.28 & 45.83 & 40.81 & 29.74 \\\\\\\\\\n\",\n \"& & & EMS & 33.56 & 30.47 & 3.09 & 36.06 & 39.77 & 3.71 & 44.26 & 39.65 & 27.54 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Static} & REMS & 54.59 & 47.31 & 7.28 & 55.38 & 63.12 & 7.74 & 68.51 & 60.83 & 56.15 \\\\\\\\\\n\",\n \"& & & EMS & 53.81 & 46.64 & 7.17 & 54.65 & 62.22 & 7.57 & 67.21 & 60.55 & 55.63 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-13}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Adaptive} & REMS & 64.61 & 64.04 & 0.57 & 67.40 & 69.54 & 2.14 & 77.42 & 68.01 & 61.78 \\\\\\\\\\n\",\n \"& & & EMS & 64.36 & 63.81 & 0.55 & 66.91 & 68.89 & 1.98 & 77.19 & 67.65 & 60.92 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\vspace{-1.0em}\\n\",\n \"\\\\caption{\\\\small Test Set Results for Llama 3.1 70B.}\\n\",\n \"\\\\label{tab:llama_3_1_70b_results}\\n\",\n \"\\\\vspace{-1.0em}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[h]\\n\",\n \"\\\\centering\\n\",\n \"\\\\small\\n\",\n \"\\\\begin{tabular}{ll|c|c|ccccccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{\\\\textbf{Output}} & \\\\multirow{2}{*}{\\\\textbf{Context}} & \\\\multirow{2}{*}{\\\\textbf{Few Shots}} & \\\\textbf{Metric} & \\\\multicolumn{7}{c}{\\\\textbf{Results Across Categories}} \\\\\\\\\\n\",\n \"\\\\cmidrule{5-11}\\n\",\n \"& & & & \\\\shortstack{\\\\textbf{Original}} & \\\\shortstack{\\\\textbf{CounterFact}} & \\\\shortstack{\\\\textbf{Large}} & \\\\shortstack{\\\\textbf{Small}} & \\\\shortstack{\\\\textbf{Easy}} & \\\\shortstack{\\\\textbf{Medium}} & \\\\shortstack{\\\\textbf{Hard}} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\n\",\n \"\\\\multirow{11}{*}{Text} & \\\\multirow{2}{*}{None} & \\\\multirow{2}{*}{-} & REMS & 17.53 & 13.52 & 21.71 & 20.31 & 20.38 & 20.06 & 19.95 \\\\\\\\\\n\",\n \"& & & EMS & 15.92 & 12.73 & 20.82 & 19.53 & 19.26 & 17.75 & 18.08 \\\\\\\\\\n\",\n \"\\\\cmidrule{2-11}\\n\",\n \"\\n\",\n \"& \\\\multirow{6}{*}{Table} & \\\\multirow{2}{*}{zero shots} & REMS & 40.26 & 32.62 & 37.58 & 56.55 & 54.09 & 46.27 & 38.65 \\\\\\\\\\n\",\n \"& & & EMS & 38.06 & 30.47 & 35.50 & 53.33 & 51.78 & 43.79 & 35.05 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-11}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Static} & REMS & 50.92 & 42.31 & 43.17 & 68.13 & 64.75 & 55.80 & 49.06 \\\\\\\\\\n\",\n \"& & & EMS & 48.79 & 40.20 & 40.89 & 65.03 & 62.30 & 53.25 & 45.62 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-11}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Adaptive} & REMS & 39.98 & 33.23 & 37.68 & 50.69 & 53.84 & 41.02 & 40.33 \\\\\\\\\\n\",\n \"& & & EMS & 37.54 & 30.62 & 34.94 & 47.72 & 50.96 & 38.46 & 35.74 \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\n\",\n \"\\\\multirow{6}{*}{SQL} & \\\\multirow{6}{*}{Schema} & \\\\multirow{2}{*}{zero shots} & REMS & 20.35 & 15.07 & 19.30 & 27.74 & 21.65 & 27.19 & 22.43 \\\\\\\\\\n\",\n \"& & & EMS & 19.90 & 14.74 & 18.96 & 26.55 & 20.63 & 27.02 & 21.56 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-11}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Static} & REMS & 47.42 & 41.38 & 48.78 & 54.63 & 69.46 & 46.22 & 42.45 \\\\\\\\\\n\",\n \"& & & EMS & 46.02 & 40.20 & 47.96 & 52.98 & 67.35 & 45.36 & 40.61 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-11}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Adaptive} & REMS & 25.45 & 21.26 & 24.72 & 33.96 & 26.91 & 47.24 & 22.20 \\\\\\\\\\n\",\n \"& & & EMS & 25.09 & 20.89 & 24.54 & 33.57 & 26.78 & 46.55 & 21.56 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\vspace{-1.0em}\\n\",\n \"\\\\caption{\\\\small Test Set Results for Mixtral 8x7B.}\\n\",\n \"\\\\vspace{-1.0em}\\n\",\n \"\\\\label{tab:mixtral_8x7b_results}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[h]\\n\",\n \"\\\\centering\\n\",\n \"\\\\footnotesize\\n\",\n \"\\\\begin{tabular}{ll|c|c|ccccccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{\\\\textbf{Output}} & \\\\multirow{2}{*}{\\\\textbf{Context}} & \\\\multirow{2}{*}{\\\\textbf{Few Shots}} & \\\\textbf{Metric} & \\\\multicolumn{7}{c}{\\\\textbf{Results Across Categories}} \\\\\\\\\\n\",\n \"\\\\cmidrule{5-11}\\n\",\n \"& & & & \\\\shortstack{\\\\textbf{Original}} & \\\\shortstack{\\\\textbf{CounterFact}} & \\\\shortstack{\\\\textbf{Large}} & \\\\shortstack{\\\\textbf{Small}} & \\\\shortstack{\\\\textbf{Easy}} & \\\\shortstack{\\\\textbf{Medium}} & \\\\shortstack{\\\\textbf{Hard}} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\n\",\n \"\\\\multirow{6}{*}{SQL} & \\\\multirow{6}{*}{Schema} & \\\\multirow{2}{*}{zero shots} & REMS & 19.59 & 18.32 & 20.56 & 27.60 & 29.05 & 20.28 & 17.29 \\\\\\\\\\n\",\n \"& & & EMS & 18.17 & 17.02 & 19.46 & 26.08 & 26.78 & 19.72 & 15.72 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-11}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Static} & REMS & 53.37 & 49.32 & 63.43 & 61.01 & 77.39 & 54.05 & 30.19 \\\\\\\\\\n\",\n \"& & & EMS & 52.08 & 47.93 & 62.28 & 59.53 & 77.39 & 51.19 & 28.91 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-11}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Adaptive} & REMS & 57.10 & 55.03 & 64.00 & 60.55 & 65.02 & 60.13 & 54.07 \\\\\\\\\\n\",\n \"& & & EMS & 55.88 & 53.50 & 63.17 & 58.95 & 63.93 & 58.38 & 51.74 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\vspace{-1.0em}\\n\",\n \"\\\\caption{\\\\small Test Set Results for SQL Coder 70B}\\n\",\n \"\\\\label{tab:sql_coder_results}\\n\",\n \"\\\\end{table*}\\n\"\n ],\n [\n \"\\\\begin{table*}[h]\\n\",\n \"\\\\centering\\n\",\n \"\\\\footnotesize\\n\",\n \"\\\\begin{tabular}{ll|c|c|ccccccc}\\n\",\n \"\\\\toprule\\n\",\n \"\\\\multirow{2}{*}{\\\\textbf{Output}} & \\\\multirow{2}{*}{\\\\textbf{Context}} & \\\\multirow{2}{*}{\\\\textbf{Few Shots}} & \\\\textbf{Metric} & \\\\multicolumn{7}{c}{\\\\textbf{Results Across Categories}} \\\\\\\\\\n\",\n \"\\\\cmidrule{5-11}\\n\",\n \"& & & & \\\\shortstack{\\\\textbf{Original}} & \\\\shortstack{\\\\textbf{CounterFact}} & \\\\shortstack{\\\\textbf{Large}} & \\\\shortstack{\\\\textbf{Small}} & \\\\shortstack{\\\\textbf{Easy}} & \\\\shortstack{\\\\textbf{Medium}} & \\\\shortstack{\\\\textbf{Hard}} \\\\\\\\\\n\",\n \"\\\\midrule\\n\",\n \"\\n\",\n \"\\\\multirow{6}{*}{SQL} & \\\\multirow{6}{*}{Schema} & \\\\multirow{2}{*}{zero shot} & REMS & 12.37 & 19.77 & 13.76 & 21.31 & 33.81 & 25.98 & 19.19 \\\\\\\\\\n\",\n \"& & & EMS & 12.22 & 19.46 & 13.76 & 21.00 & 33.47 & 25.64 & 18.64 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-11}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Static} & REMS & 16.53 & 33.29 & 29.82 & 42.04 & 54.13 & 41.62 & 39.09 \\\\\\\\\\n\",\n \"& & & EMS & 15.84 & 32.62 & 29.82 & 41.64 & 53.42 & 41.62 & 38.94 \\\\\\\\\\n\",\n \"\\\\cmidrule{3-11}\\n\",\n \"\\n\",\n \"& & \\\\multirow{2}{*}{Adaptive} & REMS & 24.02 & 40.73 & 38.00 & 48.56 & 65.56 & 51.78 & 41.63 \\\\\\\\\\n\",\n \"& & & EMS & 23.53 & 40.06 & 37.61 & 48.14 & 65.16 & 50.89 & 40.61 \\\\\\\\\\n\",\n \"\\\\bottomrule\\n\",\n \"\\n\",\n \"\\\\end{tabular}\\n\",\n \"\\\\vspace{-1.0em}\\n\",\n \"\\\\caption{\\\\small Test Set Results for Code Llama.}\\n\",\n \"\\\\label{tab:code_llama_results}\\n\",\n \"\\\\vspace{-1.0em}\\n\",\n \"\\\\end{table*}\\n\"\n ]\n]","isConversational":false},"fulltext":{"kind":"string","value":"\\pdfoutput=1\n\\documentclass[11pt]{article}\n\\usepackage[final]{acl}\n\\usepackage{times}\n\\usepackage{latexsym}\n\\usepackage{authblk}\n\\usepackage[T1]{fontenc}\n\\usepackage[utf8]{inputenc}\n\\usepackage{microtype}\n\\usepackage{inconsolata}\n\\usepackage{graphicx}\n\n\\usepackage{booktabs} \n\\usepackage{adjustbox} \n\\usepackage{multirow} \n\\usepackage{array} \n\\usepackage{graphicx} \n\\usepackage{makecell}\n\\usepackage{xcolor} \n\\usepackage{amssymb} \n\\usepackage{subcaption}\n\n\\usepackage{array}\n\\usepackage{listings}\n\n\\usepackage{xspace}\n\n\\lstdefinelanguage{SQL}{\n keywords={SELECT, FROM, WHERE, AND, OR, AS, COUNT, JOIN, ON, GROUP BY, WITH, MAX},\n keywordstyle=\\bfseries\\color{blue},\n morekeywords={[2]{Medal, Format, Tournament, Athlete}},\n keywordstyle={[2]\\color{teal}},\n sensitive=true,\n comment=[l]{--},\n morecomment=[s]{/*}{*/},\n commentstyle=\\color{gray},\n stringstyle=\\color{purple},\n morestring=[b]',\n}\n\n\\lstset{\n language=SQL,\n basicstyle=\\ttfamily\\scriptsize,\n keywordstyle=\\bfseries\\color{blue},\n commentstyle=\\itshape\\color{gray},\n stringstyle=\\color{purple},\n showstringspaces=false,\n breaklines=true,\n numbers=none,\n numberstyle=\\tiny\\color{gray},\n xleftmargin=10pt,\n framexleftmargin=10pt,\n extendedchars=true,\n literate={á}{{\\'a}}1 {Á}{{\\'A}}1 {í}{{\\'i}}1 {Í}{{\\'I}}1\n}\n\n\\newcommand{\\exampleParagraph}[2]{\\framebox{\\parbox{0.95\\linewidth}{\\paragraph{#1} \n{\\footnotesize #2}}}}\n\n\\newcommand{\\datasetName}{{\\textsc{TempTabQA-C}}\\xspace}\n\n\\newcommand{\\textcomment}[3]{\\textcolor{#1}{[#2 \\emph{#3}]}}\n\\newcommand{\\ak}[1]{\\textcomment{blue}{$^A_K$}{#1}}\n\\newcommand{\\vg}[1]{\\textcomment{green}{$^V_G$}{#1}}\n\\newcommand{\\vs}[1]{\\textcomment{orange}{$^V_S$}{#1}}\n\n\\newcommand{\\todo}[1]{\\textcolor{red}{[\\textsf{TODO}: {\\footnotesize #1}}]}\n\n\n\\title{LLM-Symbolic Integration for Robust Temporal Tabular Reasoning}\n\n\\author{\n \\textbf{Atharv Kulkarni\\textsuperscript{1}\\thanks{Work done during internship at UPenn}}, \n \\textbf{Kushagra Dixit\\textsuperscript{1}}, \\\\\n \\textbf{Vivek Srikumar}\\textsuperscript{1},\n \\textbf{Dan Roth}\\textsuperscript{2},\n \\textbf{Vivek Gupta\\textsuperscript{3}\\thanks{Primary mentor and Corresponding author.}}\\\\\n \\textsuperscript{1}University of Utah,\n \\textsuperscript{2}University of Pennsylvania,\n \\textsuperscript{3}Arizona State University\\\\\n \\texttt{\\small \\{atharvk, danroth\\}@seas.upenn.edu} \\\\\n \\texttt{\\small kushagra.dixit@utah.edu} \\quad \\texttt{\\small svivek@cs.utah.edu} \\\\ \\texttt{\\small vgupt140@asu.edu}}\n\n\\begin{document}\n\n\\maketitle\n\n\\begin{abstract}\nTemporal tabular question answering presents a significant challenge for Large Language Models (LLMs), requiring robust reasoning over structured data—a task where traditional prompting methods often fall short. These methods face challenges such as memorization, sensitivity to table size, and reduced performance on complex queries. To overcome these limitations, we introduce \\datasetName, a synthetic dataset designed for systematic and controlled evaluations, alongside a symbolic intermediate representation that transforms tables into database schemas. This structured approach allows LLMs to generate and execute SQL queries, enhancing generalization and mitigating biases. By incorporating adaptive few-shot prompting with contextually tailored examples, our method achieves superior robustness, scalability, and performance. Experimental results consistently highlight improvements across key challenges, setting a new benchmark for robust temporal reasoning with LLMs. {Code and \\datasetName dataset:} \\url{https://coral-lab-asu.github.io/llm_symbolic}.\n\n\\end{abstract}\n\n\\section{Introduction}\n\n\\begin{figure}[htbp]\n\\vspace{-1.0em}\n\\begin{center}\n \\includegraphics[width=0.60\\columnwidth]{figs/Aron_Szilagyi.png} \n\\vspace{0.25em}\n\n\\noindent \\textit{\\small \\textbf{Q.} In which year did Áron Szilágyi achieve his personal highest number of gold medal wins? \\textbf{Gold Answer.} 2022}\n\\end{center}\n\\vspace{-0.15em}\n\\raggedright\n\\small{\n\\textbf{\\small Direct prompt (CoT):} {\\scriptsize In 2020, Áron Szilágyi won a Gold Medal in the Individual format at the Tokyo Olympics. This was his most successful year in terms of highest Gold Medal wins since he did not win multiple Gold Medals in any other format or competition.} \\textbf{Answer:} 2020.}\\\\\n\\vspace{0.15em}\n\\textbf{Symbolic Intermediate Representation (SQL):} \\\\\n\\vspace{-0.25em}\n\\begin{lstlisting}[language=SQL]\nWITH gold_medal_counts AS (\n SELECT m.year, COUNT(m.medal_id) AS gold_medals\n FROM Medal m JOIN Format f ON m.format_id = f.format_id\n JOIN Tournament t ON f.tournament_id = t.tournament_id\n JOIN Athlete a ON t.athlete_id = a.athlete_id\n WHERE a.name = 'Áron Szilágyi' AND m.type = 'MedalGold'\n GROUP BY m.year )\nSELECT year FROM gold_medal_counts\nWHERE gold_medals = (SELECT MAX(gold_medals) FROM gold_medal_counts);\n\\end{lstlisting}\n\\vspace{-0.25em}\n\\textbf{Answer:} 2022\n\\label{fig:aron_table}\n\\caption{ \\small \\small Structured table of Áron Szilágyi's achievements with question and answers. Direct prompting fails, whereas, Symbolic Intermediate Representation give correct answer.}\n\\label{fig:example_figure}\n\\end{figure}\n\nLarge Language Models (LLMs) have revolutionized natural language processing, achieving remarkable success across a variety of tasks. However, answering questions about temporal tabular data task that requires precise reasoning over structured information with time-based attributes remains a significant challenge. This capability is crucial in fields such as finance, healthcare, and policymaking, where actionable insights often depend on understanding and analyzing evolving datasets. Yet, existing methods often fall short, struggling with complex queries, large datasets, and scenarios that require nuanced reasoning. An example of such task, with a long table and corresponding query and it's answer is shown in Figure \\ref{fig:example_figure}.\n\n\nThese limitations underscore the need for robust, scalable, and interpretable solutions. A key obstacle lies in the lack of benchmarks that adequately capture the complexity and diversity of temporal reasoning tasks. Existing benchmarks, typically created manually, are inconsistent and fail to provide the variability needed to thoroughly evaluate models. Without rigorous evaluation frameworks, it becomes difficult to diagnose weaknesses or ensure models can handle real-world scenarios. This raises an essential question: \\textit{How can we design benchmarks that effectively evaluate temporal reasoning across a range of challenging contexts?}\n\nEqually important is the need for robust methods. Many existing approaches rely on direct prompting, which often depends on heuristics and memorized patterns rather than true reasoning. This results in semantic biases and poor performance in demanding scenarios, such as counterfactual reasoning, large table contexts, or multi-step queries. This leads to a second critical question: \\textit{How can we develop methods that remain robust across diverse table structures, dynamic data, and complex queries?}\n\nTo address these challenges, we propose a comprehensive framework that reimagines how LLMs approach temporal tabular data. At its core is \\textbf{\\datasetName}, a synthetic dataset generation method designed to fill the gaps in existing benchmarks. \\datasetName provides precise control over data characteristics, enabling consistent and systematic evaluation across a wide range of scenarios, including counterfactual reasoning and intricate temporal queries. Building on this foundation, we introduce a \\textbf{symbolic intermediate representation approach} that transforms unstructured tables into structured database schemas. LLMs are guided to generate SQL queries based on these schemas, which are executed to produce accurate answers. E.g. in Figure \\ref{fig:example_figure}, the SQL query serves as a symbolic representation and provides the correct answer, whereas direct prompting fails on given query. This structured pipeline reduces semantic biases, enhances interpretability, and significantly improves the generalization of models across different table configurations. Additionally, we incorporate \\textbf{adaptive few-shot prompting}, a dynamic approach that selects contextually relevant examples tailored to each query. This method overcomes the limitations of static examples, further improving the robustness of the system in complex scenarios.\n\nOur experiments demonstrate that this framework delivers substantial improvements over direct prompting methods. It excels in critical areas such as counterfactual reasoning, scalability to larger datasets, and the handling of complex queries. Beyond these technical advancements, our work establishes a new benchmark for temporal tabular question answering by addressing fundamental weaknesses in existing approaches and introducing innovative tools for evaluation and reasoning. These contributions pave the way for building more interpretable, scalable, and robust AI systems with implications for critical real-world applications. Our contributions are as follows:\n\n\n\\begin{enumerate} \n\\vspace{-0.5em}\n\\setlength\\itemsep{0.25em}\n\n\\item We introduce \\textbf{\\datasetName}, a synthetic dataset designed for \\textbf{precise and robust evaluation} of temporal tabular reasoning across diverse and challenging scenarios. \\item We analyze the \\textbf{limitations of direct prompting}, including reliance on \\textbf{memorization}, sensitivity to \\textbf{table size}, and struggles with \\textbf{complex multi-step or counterfactual} reasoning. \\item We propose a \\textbf{symbolic intermediate representation approach} that enhances interpretability, reduces biases, and improves generalization by guiding LLMs to generate and execute SQL queries on structured schemas. \\item We enhance this approach with \\textbf{adaptive few-shot prompting}, enabling context-specific example selection for improved flexibility and performance in diverse scenarios. \n\\vspace{-0.25em}\n\\end{enumerate}\n\n\n\\section{The \\datasetName Dataset}\n\nThe \\datasetName dataset is a large-scale, semi-automatically generated resource designed for evaluating temporal reasoning in Large Language Models (LLMs). It provides a benchmark for analyzing the tabular temporal abilities of LLMs by enabling controlled variations in data characteristics, making it superior to traditional human-curated datasets. This section describes the dataset creation process, its schema, and key characteristics.\n\n\\subsection{\\datasetName Creation Pipeline}\n\nThe creation of \\datasetName follows a systematic pipeline to extract, structure, and store temporal information from Wikipedia infoboxes. Below, we describe the steps involved in detail.\n\n\\paragraph{Extracting Temporal Information.}\n\nTemporal information about athletes, tournaments, events, and achievements is extracted from Wikipedia infoboxes. These tables contain attributes such as \\textit{\"Name,\" \"Date of Birth,\" \"Tournaments Played,\"} and \\textit{\"Medals Won,\"} which are programmatically extracted and input into a relational database using a predefined schema. This step ensures that the raw tabular data is converted into a structured format for efficient querying and storage.\n\n\\paragraph{Relational Database Creation.}\n\nThe structured temporal data is converted into a relational database schema to enable efficient storage and querying. The schema is designed to represent key entities and their relationships comprehensively:\n\n\\begin{itemize}\n\\vspace{-0.5em}\n\\setlength\\itemsep{0.25em}\n \\item \\textbf{Athlete Table:} Contains a unique \\texttt{athlete\\_id} and the corresponding athlete's name.\n \\item \\textbf{Personal Information Table:} Captures birth year, month, and day for each athlete, linked to the \\texttt{athlete\\_id}.\n \\item \\textbf{Tournament Table:} Stores tournament details, such as the name (e.g., \"Olympic Games\") and the \\texttt{athlete\\_id}.\n \\item \\textbf{Format Table:} Represents event formats (e.g., \"100m Freestyle\"), linked to tournaments through \\texttt{tournament\\_id}.\n \\item \\textbf{Medal Table:} Documents medals, including type (e.g., \"Gold\"), year (e.g., \"2016\"), and location (e.g., \"Rio de Janeiro\"), linked to formats through \\texttt{format\\_id}.\n\n\\vspace{-0.5em}\n\\end{itemize}\n\nThis schema ensures all entities are interconnected via primary and foreign keys, enabling complex queries like calculating an athlete’s age at the time of their first medal or comparing performance across tournaments.\n\n\\paragraph{Question and Answer Generation}\n\nQuestions are generated using predefined templates filled with key attributes from the relational database. Templates capture a wide range of temporal reasoning scenarios, such as:\n\n{ \n\\footnotesize\n\\begin{itemize}\n\\vspace{-0.5em}\n\\setlength\\itemsep{-0.25em}\n \\item At what age did [\\textcolor{blue}{Athlete}] win his most recent [\\textcolor{red}{Tournament}] [\\textcolor{green!40!black!60}{Medal Type}]?\n \\item At what age did \\textcolor{blue}{Michael Phelps} win his most recent \\textcolor{red}{Pan Pacific Championships} \\textcolor{green!40!black!60}{Silver Medal}?\n\\vspace{-0.5em}\n\\end{itemize}\n}\n\nTo generate answers, the relational database is queried using SQL-based logic, which systematically retrieves the necessary information. For instance, answering a question about the age of an athlete during a specific tournament involves retrieving the athlete’s birth year and the tournament year from the database and calculating the difference. Similarly, questions about medal counts or locations are answered by aggregating or filtering data from the tables.\n\nThe SQL-based logic is generalized across various question types, allowing the generation of thousands of unique question-answer pairs. Examples include:\n\n{ \n\\vspace{-0.5em}\n\\footnotesize\n\\begin{itemize}\n\\setlength\\itemsep{-0.25em}\n \\item At what age did \\textcolor{blue}{Michael Phelps} win his most recent \\textcolor{red}{Olympic Games} \\textcolor{green!40!black!60}{Silver Medal}? Answer: \\textcolor{purple}{29}\n \\item In which city did \\textcolor{blue}{Caeleb Dressel} win his most recent \\textcolor{red}{Olympic Games} \\textcolor{green!40!black!60}{Silver Medal}? Answer: \\textcolor{purple}{Tokyo}\n\\vspace{-0.5em}\n\\end{itemize}\n}\nThis approach ensures the dataset is both scalable and robust for evaluating temporal reasoning in LLMs.\n\n\\subsection{\\datasetName Composition and Splits}\n\nThe \\datasetName dataset is divided into \\textbf{Original} and \\textbf{CounterFact} questions, with each category further subdivided based on table size and question reasoning difficulty. This structure enables fine-grained and comprehensive evaluations.\n\n\\paragraph{Original Questions.} \nOriginal questions are derived directly from the structured database and are categorized as follows:\n\n\\vspace{-0.5em}\n\\subparagraph{1. Table size:} we make questions on the table with varied sizes: (a.) \\textbf{Small Tables:} Contain concise data, typically representing athletes with fewer medals, (b.) \\textbf{Large Tables:} Contain extensive data, often representing athletes with a larger number of medals.\n\n\\vspace{-0.5em}\n\\subparagraph{2. Question Complexity:} we answer questions on varied difficulty some requiring complex multi-hop reasoning: (a.) \\textbf{Easy:} Require basic facts retrieval or single-step reasoning. E.g.: \\emph{\"How many formats has Michael Phelps played?\"}, (b.) \\textbf{Medium:} Involve multi-step reasoning, such as calculations or comparisons. E.g.: \\emph{\"At what age did Michael Phelps win his most recent Olympic Silver Medal?\"}, and (c.) \\textbf{Hard:} Demand complex reasoning, temporal analysis, and synthesis of multiple facts. E.g.: \\emph{\"What is the shortest time span (in years) within which Michael Phelps won gold, silver, and bronze medals in the same format across any tournament?\"}\n\n\n\\paragraph{Counterfactual Questions.} \nCounterfactual questions modify specific facts in the original dataset while maintaining the same categorization based on table size and difficulty of the reasoning of the questions. This design challenges models to reason effectively under hypothetical scenarios.\n\n\\paragraph{Significance of \\datasetName.} \nThe dataset offers several unique advantages: (a.) \\textbf{Controlled Evaluation:} Provides a framework for systematically testing LLMs across diverse data characteristics., (b.) \\textbf{Scalability:} Comprises over 200,000 questions spanning a wide range of contexts and complexities., and (c.) \\textbf{Fine-Grained Analysis:} Facilitates benchmarking of model biases and limitations, particularly for temporal reasoning. \n\nBy providing a controlled, scalable, and diverse dataset, \\datasetName establishes a robust foundation for advancing research on temporal reasoning in LLMs.\n\n\\subsection{\\datasetName Test Statistics.} In order to evaluate the LLM's we created a subset of the \\datasetName dataset having the following number of question per category:\n\n\\begin{table}[htbp]\n\\small\n\\centering\n\\begin{tabular}{lc|lc}\n\\toprule\n\\textbf{Category} & \\textbf{\\#Examples} & \\textbf{Category} & \\textbf{\\#Examples}\\\\\n\\midrule\nOriginal & 578 & Easy & 732 \\\\\nCounterfactual & 699 & Medium & 507 \\\\\nSmall Table & 855 & Hard & 719 \\\\\nLarge Table & 538 & \\textbf{Total} & \\textbf{5067} \\\\\n\\bottomrule\n\\end{tabular}\n\\caption{ \\small Dataset Splits and Their Number of Examples.}\n\\label{tab:dataset_splits}\n\\vspace{-0.5em}\n\\end{table}\n\nIn our test set, the average context length for the \\textbf{Small Table Split} is \\textbf{53.80} words when using the infobox as the context, while for the \\textbf{Large Table Split}, it increases significantly to \\textbf{348.85 words.}\n\n\n\\section{Answering Questions about Tables}\n\\label{sec:answering_tables}\n\nIn this section, we describe two approaches for answering questions about structured data.\n\n\n\\subsection{Approach 1: Direct Prompting}\nDirect prompting is the standard approach today, where the LLM is presented with the table contents alongside a natural language question and is expected to return an answer. To improve reasoning, prompting methods have been proposed, such as Chain of Thought (CoT), Program of Thought (PoT), Faithful Chain of Thought (F-CoT), and Plan and Solve, etc.\n\nWhile these methods introduce symbolic elements and explicit reasoning, they still rely on raw table data and suffer from limitations. The model may rely on memorized patterns rather than reasoning about the content, and be sensitive to table size and inconsequential perturbations in the table. Moreover, although CoT, PoT and F-CoT provide explicit reasoning, the reasoning remains loosely structured, making verification and consistency challenging.\n\nDespite these issues, direct prompting remains widely used due to its ease of implementation.\n\n\\subsection{Approach 2: Symbolic Intermediate Representation}\nWe propose an alternative approach: \\textbf{symbolic intermediate representation}. We hypothesize that it can mitigate the issues of direct prompting by shifting from raw table data processing to structured query generation.\n\nInstead of providing raw table contents, we transform the table into a \\textbf{structured schema}, which consists of metadata such as column names, data types, and relational links, without exposing actual values. The LLM then builds its reasoning upon this schema by generating a \\textbf{structured query (e.g., SQL) to retrieve the answer.}\n\nBy passing only the schema, the LLM is guided to reason in a structured manner, reducing reliance on spurious table patterns. The model’s generated query is executed on the database to retrieve the final answer, ensuring explicit and verifiable reasoning.\n\nWith this approach, since the raw data is masked, the model is less likely to hallucinate due to table noise, and less sensitive to minor table modifications. Moreover, the time to answer a question depends less on the size of the table, because query execution in database engines is efficient even for large tables. Finally, the generated query provides a highly structured reasoning path, ensuring systematic verification and consistency.\n\n\\section{Experimental Setup}\nWe designed experiments to address the following research questions:\n\n\n\\begin{enumerate}\n \\item \\textbf{Robustness to Counterfactual Data}: How robust are direct LLM prompts to counterfactual data, and can symbolic intermediate representations improve this?\n \\vspace{-0.25em}\n \\item \\textbf{Handling Large Tables}: Can a symbolic intermediate representation outperform direct prompting when applied to larger tables?\n \\vspace{-0.25em}\n \\item \\textbf{Impact of Question Complexity}: How does increasing question complexity impact the performance of these two approaches?\n\\end{enumerate}\n\n\nTo answer these questions, we conducted experiments to evaluate the two core approaches: \\textbf{Direct Prompting} and \\textbf{Symbolic Intermediate Representation}.\n\n\\subsection{Direct Prompting}\nIn this approach, models are provided with the table and question in natural language, and they generate answers as free text or structured programs based on the raw table. We evaluate multiple configurations. In the \\textbf{static few-shot} setup, the model is presented with a fixed set of examples, whereas in the \\textbf{adaptive few-shot} setup, examples are dynamically selected based on their relevance to the given question.\n\nBeyond few-shot prompting, we evaluate \\textbf{C.L.E.A.R}, a structured method that first extracts relevant rows from the table, decomposes the question into sub-questions, solves each sub-question individually, and synthesizes the final answer. We also consider \\textbf{Program of Thought (PoT)}, where the model generates structured Python programs to extract the necessary table contents and store them as variables before computing the answer. \\textbf{Faithful Chain of Thought (FCoT)} extends PoT by requiring the model to decompose the reasoning process into explicit steps before generating the corresponding program. We also evaluate \\textbf{Chain of Thought (CoT)}, where the model explicitly generates intermediate logical steps before producing the final answer, and \\textbf{Plan and Solve}, a two-stage approach where the model first formulates a reasoning plan before executing step-by-step calculations.\n\n\\subsection{Symbolic Intermediate Representation}\nIn contrast to direct prompting, this approach does not expose the raw table contents to the model. Instead, the model is provided with only the table schema and must generate an SQL query, which is executed to retrieve the answer. We evaluate two variations: \\textbf{static few-shot SQL}, where a fixed set of natural language-to-SQL mappings is included in the prompt alongside the schema, and \\textbf{adaptive few-shot SQL}, where SQL examples are dynamically selected based on their relevance to the given question. In both cases, the model receives the schema as context and must generate an appropriate SQL query to obtain the answer.\n\nWe analyze the performance of these methods in the \\textit{Results and Analysis} section.\n\n\nWe used the \\datasetName dataset, which includes Original, counterfactual, and question difficulty (Easy, Medium, Hard) splits, along with small and large table contexts. Models were evaluated using Exact Match Score (EMS) \\footnote{We also used a relaxed version of EMS (REMS), with similar results detailed in the appendix.}, focusing on the following key splits:\n\n\\begin{itemize}\n\\vspace{-0.5em}\n\\setlength\\itemsep{-0.25em}\n \\item \\textbf{Original vs. Counterfactual}: We examined whether the gap between Original and Counterfactual data reduces as we move toward symbolic intermediate reasoning.\n \\item \\textbf{Large Table vs. Small Table}: We evaluated if the gap between large and small tables decreases with symbolic intermediate reasoning.\n \\item \\textbf{Performance by Question Complexity}: We analyzed performance trends across Easy, Medium, and Hard questions, particularly the improvement brought by symbolic intermediate reasoning.\n\\end{itemize}\n\nThrough these experiments, we aim to demonstrate that symbolic intermediate reasoning reduces sensitivity to counterfactual data, scales better with table size, and handles increasing question complexity more effectively than direct prompting.\n\n\n\\section{Results and Analysis}\n\nIn this section, we present the results for GPT-4o and Gemini 1.5 Pro. Additionally, we evaluated Gemini 1.5 Flash, GPT-4o Mini, Mixtral, Llama 3.1 70B, Code Llama, and SQL Coder, which demonstrated similar trends. The results of these additional experiments are included in the Appendix.\n\n\\subsection{Robustness on Counterfactual Data.}\n\nTo evaluate counterfactual robustness, we compare model performance on original and counterfactual datasets. Table \\ref{tab:gpt_4o_original_counterfactual_gap} and \\ref{tab:gemini_1_5_pro_original_counterfactual_gap} summarizes these results, including the performance gap ($\\Delta$) between the original and counterfactual performance for GPT 4o and Gemini-1.5-Pro.\n\n\n\\renewcommand{\\arraystretch}{1.0}\n\\begin{table}[htbp]\n\\centering\n\\footnotesize\n\\begin{tabular}{l|l|c|c|c}\n\\toprule\n\\scriptsize{\\textbf{Approach}} & \\scriptsize{\\textbf{Method}} & \\scriptsize{\\textbf{Original}} & \\scriptsize{\\textbf{CounterF.}} & \\scriptsize{\\textbf{$\\Delta$}} \\\\\n\\midrule\n\\multirow{7}{*}{Direct} & Static & 56.57 & 42.35 & 14.22 \\\\\n & Adaptive & 58.13 & 40.92 & 17.21 \\\\\n & C.L.E.A.R & 65.57 & 48.21 & 17.36 \\\\\n & CoT & 69.90 & 48.50 & 21.40 \\\\\n & Plan \\textbf{$\\&$} Solve & 69.55 & 46.50 & 23.05 \\\\\n & PoT & 56.40 & 47.07 & 9.33 \\\\\n & Faithful CoT & 57.44 & 47.78 & 9.66 \\\\\n\\midrule\n\\multirow{2}{*}{\\bf SQL} & \\textbf{Static} & \\textbf{65.22} & \\textbf{60.94} & \\textbf{4.28} \\\\\n & \\textbf{Adaptive} & \\textbf{71.63} & \\textbf{68.67} & \\textbf{2.96} \\\\\n\\bottomrule\n\\end{tabular}\n\\caption{\\footnotesize Original vs Counterfactual for GPT-4o.}\n\\label{tab:gpt_4o_original_counterfactual_gap}\n\\vspace{-1.0em}\n\\end{table}\n\n\\renewcommand{\\arraystretch}{1.0}\n\\begin{table}[htbp]\n\\centering\n\\footnotesize\n\\begin{tabular}{l|l|c|c|c}\n\\toprule\n\\scriptsize{\\textbf{Approach}} & \\scriptsize{\\textbf{Method}} & \\scriptsize{\\textbf{Original}} & \\scriptsize{\\textbf{CounterF.}} & \\scriptsize{\\textbf{$\\Delta$}} \\\\\n\\midrule\n\\multirow{7}{*}{Direct} & Static & 52.91 & 39.92 & 12.99 \\\\\n & Adaptive & 53.48 & 44.19 & 9.29 \\\\\n & C.L.E.A.R & 49.33 & 40.66 & 8.67 \\\\\n & CoT & 66.46 & 55.75 & 10.71 \\\\\n & Plan \\textbf{$\\&$} Solve & 60.75 & 54.31 & 6.44 \\\\\n & PoT & 53.83 & 46.20 & 7.63 \\\\\n & Faithful CoT & 53.95 & 47.64 & 6.31 \\\\\n\\midrule\n\\multirow{2}{*}{\\bf SQL}& \\textbf{Static} & \\textbf{59.08} & \\textbf{55.52} & \\textbf{3.56} \\\\\n & \\textbf{Adaptive} & \\textbf{65.29} & \\textbf{65.13} & \\textbf{0.16} \\\\\n\\bottomrule\n\\end{tabular}\n\\caption{\\footnotesize Original vs Counterfactual for Gemini 1.5 Pro.}\n\\label{tab:gemini_1_5_pro_original_counterfactual_gap}\n\\vspace{-1.0em}\n\\end{table}\n\n\\paragraph{Analysis:} Comparing the performance of GPT-4o and Gemini 1.5 Pro across original and counterfactual datasets provides valuable insights into the robustness of Direct Prompting and SQL-based reasoning methods. \\textit{A model that truly reasons about data should not be affected by the origin of the data.}\n\nHowever, for Direct Prompting, both models exhibit significant performance gaps between the original and counterfactual datasets, indicating a heavy reliance on memorized knowledge rather than robust reasoning capabilities. For GPT-4o, the performance gaps are 14.22 (non-adaptive) and 17.21 (adaptive), while Gemini 1.5 Pro shows similar gaps of 12.99 and 9.29, respectively. Notably, the adaptive approach improves the performance on original data but increases sensitivity to counterfactuals for GPT-4o. In contrast, Gemini 1.5 Pro’s adaptive Direct Prompting reduces the gap but still fails to address the core issue of data sensitivity.\n\nOn the other hand, SQL-based methods, demonstrate superior robustness in both models, with performance gaps significantly smaller than those in Direct Prompting. For GPT-4o, the non-adaptive SQL gap is just 4.28, and the adaptive SQL gap is 2.96. Similarly, for Gemini 1.5 Pro, the non-adaptive SQL gap is 3.56, and the adaptive SQL gap reduces further to 0.16, showcasing its capability to reason effectively across the original and counterfactual datasets. The use of symbolic intermediate representations in SQL methods explains this robustness, as these approaches operate independently of the data origin, focusing instead on schema-driven reasoning.\n\nFinally, the adaptive approach enhances performance across both methods and models, particularly for SQL. For example, in GPT-4o, adaptive SQL improves counterfactual performance by 7.73 points compared to non-adaptive SQL, while in Gemini 1.5 Pro, it further narrows the performance gap to an almost negligible 0.16 points, while improving counterfactual performance by 9.61 points. This highlights the role of adaptive few-shot examples in enhancing model reasoning capabilities and robustness across diverse datasets.\n\n\\subsection{Impact of Table Size.}\n\nTo evaluate the impact of data size, we compare model performance on small and large datasets. Table \\ref{tab:gpt4o_small_large_gap} and \\ref{tab:gemini_1_5_pro_small_large_gap} presents the results, including the gap between small and large datasets for GPT 4o and Gemini 1.5 Pro.\n\n\\renewcommand{\\arraystretch}{1.0}\n\\begin{table}[htbp]\n\\centering\n\\footnotesize\n\\begin{tabular}{l|l|c|c|c}\n\\toprule\n\\textbf{Approach} & \\textbf{Method} & \\textbf{Small} & \\textbf{Large} & \\textbf{$\\Delta$} \\\\\n\\midrule\n\\multirow{7}{*}{ Direct} & Static & 71.11 & 46.84 & 24.27 \\\\\n & Adaptive & 73.92 & 48.88 & 25.04 \\\\\n & C.L.E.A.R & 76.49 & 53.53 & 22.96 \\\\\n & CoT & 74.15 & 56.13 & 18.02 \\\\\n & Plan \\textbf{$\\&$} Solve & 75.44 & 55.20 & 20.24 \\\\\n & PoT & 62.22 & 50.19 & 12.03 \\\\\n & Faithful CoT & 62.69 & 50.19 & 12.50 \\\\\n\\midrule\n\\multirow{2}{*}{\\bf SQL} & \\textbf{Static} & \\textbf{73.57} & \\textbf{70.63} & \\textbf{2.94} \\\\\n & \\textbf{Adaptive} & \\textbf{73.92} & \\textbf{72.86} & \\textbf{1.06} \\\\\n\\bottomrule\n\\end{tabular}\n\\caption{\\footnotesize Small Table vs Large Table for GPT-4o}\n\\label{tab:gpt4o_small_large_gap}\n\\vspace{-1.0em}\n\\end{table}\n\n\n\\renewcommand{\\arraystretch}{1.0}\n\\begin{table}[htbp]\n\\centering\n\\footnotesize\n\\begin{tabular}{l|l|c|c|c}\n\\toprule\n\\textbf{Approach} & \\textbf{Method } & \\textbf{Small} & \\textbf{Large} & \\textbf{$\\Delta$} \\\\\n\\midrule\n\\multirow{7}{*}{Direct} & Static & 65.02 & 43.86 & 21.16 \\\\\n & Adaptive & 67.27 & 41.86 & 25.41 \\\\\n & C.L.E.A.R & 56.85 & 40.95 & 15.90 \\\\\n & CoT & 77.55 & 57.67 & 19.88 \\\\\n & Plan \\textbf{$\\&$} Solve & 73.43 & 56.72 & 16.71 \\\\\n & PoT & 64.68 & 49.84 & 14.83 \\\\\n & Faithful CoT & 63.85 & 48.47 & 15.39 \\\\\n\\midrule\n\\multirow{2}{*}{\\textbf{SQL}} & \\textbf{Static} & \\textbf{77.41} & \\textbf{71.32} & \\textbf{6.09} \\\\\n & \\textbf{Adaptive} & \\textbf{75.31} & \\textbf{72.43} & \\textbf{2.88} \\\\\n\\bottomrule\n\\end{tabular}\n\\caption{\\footnotesize Small Table vs Large Table for Gemini 1.5 Pro}\n\\label{tab:gemini_1_5_pro_small_large_gap}\n\\vspace{-1.0em}\n\\end{table}\n\n\\paragraph{Analysis:}\n\\textit{A model capable of genuine reasoning should operate independently of data size. For example, the correctness of an SQL query's result is unaffected by the size of the tables—it impacts only the computation time, not the quality of the outcome.} However, the trends for small vs. large tables closely mirror those observed in the original vs. counterfactual analysis. Direct Prompting shows significant performance drops with larger tables, with GPT-4o and Gemini 1.5 Pro exhibiting gaps of 24.27 and 21.16 in non-adaptive settings, respectively. This underscores the method's sensitivity to data complexity and dependence on memory.\n\nIn contrast, SQL-based methods demonstrate remarkable robustness, maintaining minimal performance gaps across table sizes (e.g., 1.06 for adaptive SQL in GPT-4o and 2.88 in Gemini 1.5 Pro). This resilience stems from schema-driven reasoning, which abstracts away from the data’s size or origin \\footnote{We tested counterfactual versions, showing similar findings to section 4.1.}. Adaptive few-shot examples further enhance performance, particularly for SQL-based methods, allowing them to consistently deliver high accuracy even with larger tables.\n\nThese findings emphasize that Direct Prompting struggles with data complexity and scale, mirroring its limitations in counterfactual settings. SQL-based methods, on the other hand, exemplify robustness and scalability by leveraging schema-driven symbolic representations that are agnostic to data size or source. The dynamic selection of adaptive examples further strengthens their reliability, making them a superior choice for reasoning over complex and evolving datasets.\n\n\n\\subsection{Effect of question complexity.}\n\nTo evaluate question complexity effects, we compare model performance on Easy, Medium, and Hard questions. Table \\ref{tab:gpt4o_easy_medium_hard} and \\ref{tab:gemini_1_5_pro_easy_medium_hard} summarizes the results for GPT-4o and Gemini-1.5-Pro respectively.\n\n\n\\renewcommand{\\arraystretch}{1.0}\n\\begin{table}[htbp]\n\\centering\n\\footnotesize\n\\begin{tabular}{l|l|c|c|c}\n\\toprule\n\\textbf{Approach} & \\textbf{Method} & \\textbf{Easy} & \\textbf{Medium} & \\textbf{Hard} \\\\\n\\midrule\n\\multirow{7}{*}{Direct} & Static & 71.18 & 63.12 & 53.35 \\\\\n & Adaptive & 74.38 & 63.91 & 54.17 \\\\\n & C.L.E.A.R & 76.40 & 71.99 & 62.62 \\\\\n & CoT & 78.43 & 75.35 & 64.06 \\\\\n & Plan \\textbf{$\\&$} Solve & 77.96 & 70.81 & 60.25 \\\\\n & PoT & 69.94 & 57.20 & 48.81 \\\\\n & Faithful CoT & 69.24 & 56.02 & 47.17 \\\\\n\\midrule\n\\multirow{2}{*}{\\textbf{SQL}} & \\textbf{Static} & \\textbf{78.89} & \\textbf{75.15} & \\textbf{62.31} \\\\\n & \\textbf{Adaptive} & \\textbf{80.06} & \\textbf{73.37} & \\textbf{66.74} \\\\\n\\bottomrule\n\\end{tabular}\n\\caption{\\footnotesize Easy, Medium, and Hard results for GPT-4o}\n\\label{tab:gpt4o_easy_medium_hard}\n\\vspace{-1.0em}\n\\end{table}\n\n\\renewcommand{\\arraystretch}{1.0}\n\\begin{table}[htbp]\n\\centering\n\\footnotesize\n\\begin{tabular}{l|l|c|c|c}\n\\toprule\n\\textbf{Approach} & \\textbf{Method} & \\textbf{Easy} & \\textbf{Medium} & \\textbf{Hard} \\\\\n\\midrule\n\\multirow{7}{*}{Direct} & Static & 65.79 & 58.53 & 50.00 \\\\\n & Adaptive & 66.26 & 56.47 & 46.74 \\\\\n & C.L.E.A.R & 58.38 & 59.77 & 51.14 \\\\\n & CoT & 83.29 & 72.69 & 65.87 \\\\\n & Plan \\textbf{$\\&$} Solve & 79.42 & 72.25 & 63.60 \\\\\n & PoT & 73.97 & 57.12 & 47.57 \\\\\n & Faithful CoT & 74.52 & 58.15 & 46.85 \\\\\n\\midrule\n\\multirow{2}{*}{\\textbf{SQL}} & \\textbf{Static} & \\textbf{80.86} & \\textbf{70.33} & \\textbf{59.59} \\\\\n & \\textbf{Adaptive} & \\textbf{75.86} & \\textbf{71.47} & \\textbf{59.24} \\\\\n\\bottomrule\n\\end{tabular}\n\\caption{\\footnotesize Easy, Medium, and Hard results on Gemini 1.5 Pro}\n\\label{tab:gemini_1_5_pro_easy_medium_hard}\n\\vspace{-1.0em}\n\\end{table}\n\n\n\n\\paragraph{Analysis:} \nPerformance consistently declines across all models and settings as question complexity increases from Easy to Hard, aligning with previous findings on the influence of data size and complexity. \\textit{While such a drop is expected for both models and humans (though less severe for the latter), the key question is: can we do better and reduce this decline?} Direct Prompting struggles as question complexity increases, with significant drops in accuracy (e.g., from 71.18 to 53.35 for non-adaptive GPT-4o). Direct adaptive prompting also struggles to mitigate this decline and remains limited in handling complex queries effectively.\n\nSQL-based methods demonstrate greater resilience to complexity, maintaining higher accuracy across all levels. For example, non-adaptive SQL in GPT-4o drops moderately from 78.89 (Easy) to 62.31 (Hard), while adaptive SQL, achieves the best performance of 66.74 on the Hard data split. Similarly, Gemini 1.5 Pro exhibits stable performance with SQL based reasoning.\n\nThese results reinforce SQL's robustness through schema-driven reasoning, which abstracts complexity and reduces reliance on memorization. Introducing adaptive examples lifts accuracy across prompting strategies, with the most pronounced gains in the SQL-based variants, which remain the strongest on the hardest queries. This underscores the importance of structured reasoning and adaptive techniques for tackling increasing data and query complexity effectively.\n\n\\section{What Did We Learn?}\n\\paragraph{1. Impact of Symbolic Representations.}\nParsing data into symbolic queries consistently boosts model performance. Symbolic representations bridge counterfactual gaps, reduce dependence on data size, and enhance the handling of complex questions. By structuring data more clearly, symbolic queries improve robustness and address challenges like noise and memorization.\n\n\\paragraph{2. Benefits of Schema-Based Reasoning.}\nSchemas provide a clean, data-agnostic abstraction of database structures, removing irrelevant noise and simplifying reasoning. By presenting only the schema without any underlying data, we ensure there is no room for memorization. Unlike raw tables, which mix useful and irrelevant data, schemas provide a stable framework that ensures consistent performance, especially in counterfactual scenarios where structured reasoning is critical.\n\n\\paragraph{3. Effect of Data Size.}\nData size significantly affects model performance. Larger tables often introduce noise, increasing the risk of hallucinations. Schemas mitigate this by segmenting data into key components, reducing cognitive overload and clarifying the reasoning process, allowing models to perform more reliably on large, complex datasets.\n\n\\paragraph{4. Handling Complex Questions.}\nSchema-based reasoning excels in answering complex questions by supporting logical, step-by-step reasoning. SQL query generation fosters clarity and reduces ambiguity. In contrast, raw text tables, especially those with counterfactual data, often lack structure, leading to errors or incomplete reasoning. By offering a predefined framework, schemas reduce cognitive demands, enabling models to handle nuanced queries more effectively.\n\n\n\\section{Discussion of Model Failures}\n\n\\subsection{Inadequacy of Direct Complex Strategies}\n\n\nSeveral techniques, such as Program of Thought (PoT) \\citet{chen2023program}, Chain of Table\\cite{wang2024chain}, Binder\\cite{cheng2023bindinglanguagemodelssymbolic}, Dater \\citet{10.1145/3539618.3591708}, and Plan and Solve \\citet{wang2023plan}, aim to handle complex queries. However, these methods fall short when detailed query plans are needed. The complexity of tasks involving multiple steps, conditional logic, and dependencies cannot be captured by direct prompting alone. Each query introduces unique variables, making strategies like PoT fails for complex reasoning.\n\nFor example, a query requiring the join of three large tables with specific conditions cannot be effectively handled by PoT, which may only generate simple steps like \\textit{\"select from Table A\"} or \\textit{\"filter Table B.\"} These methods fail to capture the necessary logic for combining tables or handling multiple joins and nested queries. Such complexity requires a carefully constructed query plan, which direct prompting cannot produce.\n\nThe core issue is the complexity of the underlying query plans. PoT may generate query plans, but they struggle with complex operations like joins, aggregations, and nested subqueries, which demand precise sequencing and optimization. Research, particularly by \\citet{akioyamen2024unreasonable}, argues that query planning requires structured approaches like SQL to manage these complexities, reinforcing that simpler prompting strategies are insufficient for intricate query reasoning.\n\n\n\\subsection{Challenges with Symbolic Approach}\nDespite advancements in symbolic representation, several challenges remain in improving model reliability and performance:\n\n\\vspace{-0.3em}\n\\paragraph{1. SQL Query Inconsistencies}\nThe model often misuses SQL constructs, such as over-relying on \\texttt{LIMIT 1} when multiple answers are needed \\textit{\"List all the formats in which Carolina Marín has won medals?\"} or adding redundant joins that slow execution \\textit{\"How many tournaments did Michael Phelps win in 2008?\"}. It also misaligns query objectives, failing to handle aggregates or \\texttt{GROUP BY} clauses properly \\textit{\"What are the medal counts for each athlete in the Olympics?\"}.\n\n\\vspace{-0.3em}\n\\paragraph{2. Temporal and Positional Reasoning Errors}\nThe model struggles with temporal and positional reasoning, often hallucinating columns or misinterpreting data \\textit{\"At what age did Michael Phelps win his most recent Olympic Gold Medal?\"}. It also misaligns aggregations over time \\textit{\"Which athlete had the most consistent medal wins over the last decade?\"} and hierarchical relationships \\textit{\"Which was Michael Phelps' most recent tournament medal?\"}.\n\n\\vspace{-0.3em}\n\\paragraph{3. Nested and Conditional Logic Challenges}\nErrors occur in nested and conditional logic, such as incorrect use of \\texttt{WITH} clauses \\textit{\"Which event had the shortest duration between P. V. Sindhu’s medal wins?\"} or failing to respect conditions \\textit{\"List all tournaments where Carolina Marín won a medal after 2015?\"}. The model also mishandles multi-field responses \\textit{\"List the medal type, location, and year for Hugo Calderano’s wins.\"}.\n\n\\vspace{-0.3em}\n\\paragraph{4. Aggregates, Joins, and Dependencies}\nThe model struggles with nested aggregates, non-standard joins, and dependency tracking. It fails to construct valid joins \\textit{\"Which format had the highest number of gold medals in 2020?\"} or align dependencies in complex queries \\textit{\"Which medal did Michael Phelps win in the same tournament as his fastest recorded swim?\"}. It also ignores group-level constraints, leading to overgeneralized results \\textit{\"Which city hosted the most gold-medal-winning tournaments for P. V. Sindhu?\"}.\n\n\\vspace{-0.3em}\n\\paragraph{5. Inconsistencies and Robustness Issues}\nInconsistent query structures lead to variable results for similar tasks \\textit{\"How old was Hugo Calderano when he won his first medal?\"} vs. \\textit{\"At what age did Michael Phelps win his most recent Olympic Gold Medal?\"}. The model struggles with entity disambiguation \\textit{\"List all the medals won by Michael Phelps in the Olympic Games?\"} and overlooks edge cases \\textit{\"How many medals has an athlete with no wins received?\"}. Ranking logic is often mishandled, such as ignoring ordering requirements \\textit{\"Which city hosted the most tournaments in 2019?\"}.\n\n\n\\section{Comparison with Related Work}\n\nTemporal reasoning in LLMs is an evolving field intersecting with advancements in tabular reasoning, logic, and symbolic methods. Our work advances this area by introducing the \\datasetName dataset for detailed evaluation of temporal reasoning in tabular contexts. Key advancements in related areas are discussed below.\n\n\\paragraph{Tabular Reasoning.}\nApplying LLMs to semi-structured tables has been studied for question answering, semantic parsing, and table-to-text generation \\cite{Chen2020TabFact:, gupta-etal-2020-infotabs,Zhang:2020:SET, Zhang:2020:WTE}. Approaches like TAPAS \\cite{Herzig_2020}, TaBERT \\cite{yin2020tabert}, and TABBIE \\cite{iida2021tabbie} enhance table understanding through joint tabular-textual embeddings, while Table2vec \\cite{Zhang_2019} and TabGCN \\cite{pramanick-bhattacharya-2021-joint} investigate alternative representations.\n\nRecent work also explores symbolic reasoning for structured tables with fixed schemas \\cite{cheng2023bindinglanguagemodelssymbolic, ye2023largelanguagemodelsversatile, wang2024chainoftableevolvingtablesreasoning}. Building on these advances, we use SQL-based symbolic methods for temporal queries in semi-structured data. Our \\datasetName dataset further enables fine-grained evaluation of temporal reasoning across diverse tabular characteristics.\n\n\n\\paragraph{Temporal Reasoning.} \nTemporal reasoning is central to question answering and event-centric tasks, with datasets like TIME-SENSITIVEQA \\cite{chen2021a} and TORQUE \\cite{ning-etal-2020-torque} addressing time-sensitive comprehension in text, and TEMPQA-WD \\cite{neelam2022benchmark} and CRONQUESTIONS \\cite{saxena-etal-2021-question} focusing on temporal links in knowledge graphs. Models like CRONKBQA \\cite{saxena-etal-2021-question} further enhance performance by incorporating temporal reasoning during training.\n\nOur work extends these efforts to structured tabular datasets. While datasets such as TempTabQA \\cite{gupta-etal-2023-temptabqa} and TRAM \\cite{wang2024tram} tackle similar challenges, \\datasetName advances the field by introducing counterfactual reasoning, scalable table sizes, and diverse question difficulties, offering a broader framework for evaluating temporal reasoning.\n\n\n\n\\paragraph{Logical Reasoning and Symbolic Approaches}\nFrameworks like LOGIC-LM \\cite{LOGIC_LM2023} and neurosymbolic methods such as LINC \\cite{LINC2023} show that integrating symbolic reasoning and external tools enhances logical inference in LLMs. Auto-formalization (e.g., NL2FOL \\cite{NL2FOL2023}) further boosts reasoning accuracy by mapping natural language to structured forms.\n\nBuilding on these advances, our SQL-based symbolic approach enables precise temporal reasoning over tables by translating queries into executable SQL. The \\datasetName dataset offers a comprehensive benchmark at the intersection of tabular, temporal, and symbolic reasoning, featuring original and counterfactual splits, scalable table sizes, and varied question difficulties—all aligned with SQL-based structured reasoning.\n\n\\section{Conclusion}\nThis work investigates temporal tabular question answering with LLMs, tackling key challenges in counterfactual robustness, data sensitivity, and question complexity. We introduced \\datasetName, a controlled benchmark designed for systematic evaluations. By combining symbolic intermediate representations with adaptive few-shot prompting, our approach leverages database schemas and SQL query generation to address the limitations of direct prompting.\n\nOur experiments demonstrate that symbolic representations improve generalization, counterfactual robustness, and scalability, especially when handling larger tables. Additionally, adaptive prompting enhances reasoning for complex queries. These results highlight the necessity for the model to be data-blind, reasoning exclusively on metadata—such as the table schema—to ensure robust reasoning rather than relying on mere memorization. Future work can focus on conducting detailed error analysis and exploring fine-tuning techniques. A deeper analysis of the results will further illuminate the strengths and limitations of the approach. \\datasetName lays a strong foundation for advancing structured temporal reasoning in LLMs and encourages future efforts to develop interpretable and robust temporal reasoning in AI systems.\n\n\n\\section*{Limitations}\nWe demonstrated the effectiveness of our approach through extensive experiments in English. However, extending the study to a multilingual context could reveal its applicability across diverse languages. While our work focuses on simple, entity-centric tables, real-world datasets are often more complex, such as hierarchical or multi-relational tables. Future research should explore these more intricate structures to expand the method's utility.\n\nOur experiments assume static tables, yet many real-world scenarios involve dynamic data, such as streaming or frequently updated tables. Adapting the method to handle evolving datasets would enhance its practical relevance. Additionally, the approach does not leverage external domain knowledge, which could complement symbolic reasoning and broaden its applications.\n\nThe dataset may also exhibit inherent biases, such as domain-specific or entity-centric constraints, limiting generalizability. Future datasets should aim for greater diversity to better reflect real-world scenarios. Finally, due to computational constraints, we did not fine-tune models on the \\datasetName dataset. Future work should address this limitation by exploring fine-tuning on larger datasets and evaluating the approach in more resource-intensive and dynamic settings for a comprehensive assessment.\n\n\\section*{Ethics Statement}\n\nWe are deeply committed to upholding the highest ethical standards in research and publication. To ensure transparency and reproducibility, we will publicly release our code, enhanced evaluation set, and detailed documentation, enabling the research community to validate, reproduce, and build upon our work. By sharing our resources, we aim to foster collaboration and accountability within the computational linguistics field.\n\nOur methodology reflects a commitment to the responsible and fair use of tools and techniques, with all claims grounded in rigorously validated experimental results. To address the stochastic nature of black-box models, we maintained a fixed temperature throughout our experiments, ensuring consistent outcomes. AI tools were employed responsibly during the writing process, with careful oversight to prevent bias or inaccuracies. We provide comprehensive details about annotations, dataset splits, models, and prompting methods to ensure full reproducibility and empower researchers to evaluate our work rigorously.\n\nRecognizing the importance of inclusivity and fairness, we acknowledge that our dataset may carry inherent biases, such as domain-specific or entity-centric limitations. While we strive for broad applicability, future iterations will prioritize greater diversity to enhance fairness and generalizability. By adhering to these principles, we aim to advance knowledge in computational linguistics while promoting ethical and responsible research practices that emphasize transparency, equity, and reproducibility.\n\n\\section*{Acknowledgments}\nThis work was primary sponsored by the Army Research Office under Grant Number W911NF-20-1-0080. The views and conclusions expressed herein are those of the authors and do not necessarily reflect the official policies, either expressed or implied, of the Army Research Office or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright notice herein. This research was also partially supported by ONR Contract N00014-19-1-2620. V.S. acknowledges support in part from NSF awards \\#2217154 and \\#2411319.\n\nWe are grateful to the Utah NLP group (University of Utah) and the CogComp group (University of Pennsylvania) for their valuable insights. We also thank Dibyakanti Kumar and Harsh Kumar for their significant contributions to the development of the dataset. The author is also grateful to Prof. Ana Marasović, University of Utah for their support. We appreciate the thoughtful feedback from our anonymous reviewers and the computational support provided by the Complex Data Analysis and Reasoning Lab at Arizona State University. Lastly, we thank our CoRAL lab cat, Coco, for her unwavering dedication to ensuring that professor, V.G., remained just the right amount of creatively unhinged during deadline season.\n\n\\bibliography{anthology, custom}\n\n\\section{Appendix}\n\\subsection{Examples:}\n\\subsubsection{Example 1:}\n\n\\noindent\\textit{Q. Which Olympic year marked Michael Phelps' record for the most gold medals won?\\\\}\n\n\\noindent\\textbf{Steps for SQL Reasoning}\n\\vspace{0.25cm}\n\n\\noindent\\textbf{Step 1:} Start with the infobox table of Michael Phelps' medals\n\n\\begin{center}\n\\vspace{-0.0cm}\n\\footnotesize\n\\begin{tabular}{c|c|c}\n\\hline\n\\textbf{Medal} & \\textbf{Year} & \\textbf{Event} \\\\\n\\hline\nGold & 2008 Beijing & 100 m butterfly \\\\\nGold & 2008 Beijing & 200 m medley \\\\\nGold & 2004 Indianapolis & 200 m freestyle \\\\\nSilver & 2002 Yokohama & 4\\texttimes200 m freestyle \\\\\n\\hline\n\\end{tabular}\n\n\n\n\\vspace{-0.0cm}\n\\end{center}\n\n\\vspace{-0.0cm}\n\\noindent\\textbf{Step 2:} Transform the data (all swimmer infoxes) into a relational schema and organize it into structured database tables for efficient querying. \\\\\n\n\\begin{center}\n\\vspace{-0.50cm}\n \\noindent\\textbf{Database Schema:}\n\\vspace{-0.25cm}\n\\end{center}\n\n{ \\footnotesize \n\\begin{verbatim}\nAthlete Table:\n+-------------+----------------+\n| Column | Description |\n+-------------+----------------+\n| athlete_id | Primary Key |\n| name | Athlete Name |\n+-------------+----------------+\nTournament Table:\n+----------------+------------------------+\n| Column | Description |\n+----------------+------------------------+\n| tournament_id | Primary Key |\n| athlete_id | Foreign Key (Athlete) |\n| name | Tournament Name |\n+----------------+------------------------+\nFormat Table:\n+----------------+----------------------------+\n| Column | Description |\n+----------------+----------------------------+\n| format_id | Primary Key |\n| tournament_id | Foreign Key (Tournament) |\n| name | Event Name |\n+----------------+----------------------------+\nMedal Table:\n+-------------+----------------------------+\n| Column | Description |\n+-------------+----------------------------+\n| medal_id | Primary Key |\n| format_id | Foreign Key (Format) |\n| type | Medal Type |\n| year | Year of Achievement |\n| location | Medal Location |\n+-------------+----------------------------+\nPersonalInformation Table:\n+----------------+-----------------------------+\n| Column | Description |\n+----------------+-----------------------------+\n| info_id | Primary Key |\n| athlete_id | Foreign Key (Athlete) |\n| birth_year | Birth Year |\n| birth_month | Birth Month |\n| birth_day | Birth Day |\n+----------------+-----------------------------+\n\n\\end{verbatim}\n}\n\n\\vspace{0.5cm}\n\\noindent\\textbf{Step 3: Write the SQL Query}\n\n\\noindent The following query retrieves the year with the most gold medals:\n\n\\vspace{0.3cm}\n\\begin{lstlisting}[language=SQL]\nWITH gold_medal_counts AS (\n SELECT m.year, COUNT(m.medal_id) AS gold_medals\n FROM Medal m\n JOIN Format f ON m.format_id = f.format_id\n JOIN Tournament t ON f.tournament_id = t.tournament_id\n JOIN Athlete a ON t.athlete_id = a.athlete_id\n WHERE a.name = 'Michael Phelps'\n AND m.type = 'MedalGold'\n GROUP BY m.year\n)\nSELECT year\nFROM gold_medal_counts\nWHERE gold_medals = (\n SELECT MAX(gold_medals)\n FROM gold_medal_counts\n);\n\\end{lstlisting}\n\n\\vspace{0.5cm}\n\\noindent\\textbf{Step 4: Execute the Query}\n\n\\noindent The query outputs the year with the highest number of gold medals.\\\\\n\\noindent\\textbf{Final Result:} \\textcolor{green!40!black!60}{\\textbf{2008}}\n\n\\vspace{0.5cm}\n\\noindent\\textbf{Direct Reasoning with Chain-of-Thought (CoT):}\n\n\\noindent To perform direct reasoning using Chain-of-Thought (CoT), LLM arrange the medal in year and count the number of gold medals per year from the table:\n\n{\\footnotesize\n\n\\begin{verbatim}\nYear 2008:\n- 100 m butterfly (Gold)\nTotal: 1 gold medals\n\nYear 2004:\n- 200 m freestyle (Gold)\n- 200 m medley (Gold)\nTotal: 2 gold medal\n\nYear 2002:\n- 4x200 m freestyle (Silver)\nTotal: 0 gold medals\n\\end{verbatim}}\n\n\\vspace{-0.5em}\n\\noindent \\textbf{Answer (CoT Reasoning):} \\textcolor{red}{2004} has the most gold medals with a count of 2.\n\nHowever, due to direct reasoning errors or omissions, it misinterpret the complex table, and hence CoT fails whereas Symbolic succeed. \n\n\n\\subsubsection{Example 2:}\n\\noindent\\textit{Q. Does Emma Weyant have more Bronze Medals than Gold Medals ?\\\\}\n\n\\noindent\\textbf{Steps for SQL Reasoning}\n\\vspace{0.25cm}\n\n\\noindent\\textbf{Step 1:} Start with the infobox table of Emma Weyant's medals.\n\n\\begin{figure}[h]\n \\centering\n \\includegraphics[width=0.4\\textwidth]{figs/Emma_Weyant.png}\n \\caption{Emma Weyant's Medal Infobox}\n \\label{fig:emma_infobox}\n\\end{figure}\n\n\\vspace{-0.0cm}\n\\noindent\\textbf{Step 2:} Transform the data (all swimmer infoboxes) into a relational schema and organize it into structured database tables for efficient querying. \\\\\n\n\\begin{center}\n\\vspace{-0.50cm}\n \\noindent\\textbf{Database Schema:}\n\\vspace{-0.25cm}\n\\end{center}\n\n{ \\footnotesize \n\\begin{verbatim}\nAthlete Table:\n+-------------+----------------+\n| Column | Description |\n+-------------+----------------+\n| athlete_id | Primary Key |\n| name | Athlete Name |\n+-------------+----------------+\n\nTournament Table:\n+----------------+------------------------+\n| Column | Description |\n+----------------+------------------------+\n| tournament_id | Primary Key |\n| athlete_id | Foreign Key (Athlete) |\n| name | Tournament Name |\n+----------------+------------------------+\n\nFormat Table:\n+----------------+----------------------------+\n| Column | Description |\n+----------------+----------------------------+\n| format_id | Primary Key |\n| tournament_id | Foreign Key (Tournament) |\n| name | Event Name |\n+----------------+----------------------------+\n\nMedal Table:\n+-------------+----------------------------+\n| Column | Description |\n+-------------+----------------------------+\n| medal_id | Primary Key |\n| format_id | Foreign Key (Format) |\n| type | Medal Type |\n| year | Year of Achievement |\n| location | Medal Location |\n+-------------+----------------------------+\n\nPersonalInformation Table:\n+----------------+-----------------------------+\n| Column | Description |\n+----------------+-----------------------------+\n| info_id | Primary Key |\n| athlete_id | Foreign Key (Athlete) |\n| birth_year | Birth Year |\n| birth_month | Birth Month |\n| birth_day | Birth Day |\n+----------------+-----------------------------+\n\\end{verbatim}\n}\n\n\\vspace{0.5cm}\n\\noindent\\textbf{Step 3: Write the SQL Query}\n\n\\noindent The following query checks whether Emma Weyant has more Bronze medals than Gold medals:\n\n\\vspace{0.3cm}\n\\begin{lstlisting}[language=SQL]\nSELECT CASE \n WHEN SUM(CASE WHEN m.type = 'MedalBronze' THEN 1 ELSE 0 END) > \n SUM(CASE WHEN m.type = 'MedalGold' THEN 1 ELSE 0 END)\n THEN 'Yes'\n ELSE 'No'\nEND AS has_more_bronze_than_gold\nFROM Medal m\nJOIN Format f ON m.format_id = f.format_id\nJOIN Tournament t ON f.tournament_id = t.tournament_id\nJOIN Athlete a ON t.athlete_id = a.athlete_id\nWHERE a.name = 'Emma Weyant';\n\\end{lstlisting}\n\n\\vspace{0.5cm}\n\\noindent\\textbf{Step 4: Execute the Query}\n\n\\noindent The query outputs whether Emma Weyant has more Bronze medals than Gold medals.\\\\\n\\noindent\\textbf{Final Result:} \\textcolor{green!40!black!60}{\\textbf{Yes}}\n\n\\vspace{0.5cm}\n\\noindent\\textbf{Direct Reasoning with Chain-of-Thought (COT):}\n\n\\noindent Using manual reasoning, the LLM counts the medals directly from the table:\n\n{\\footnotesize\n\\begin{verbatim}\nGold Medals:\n- 2018 Suva: 400 m medley\nTotal: 1 Gold Medal\n\nBronze Medals:\n- 2018 Suva: 800 m freestyle\n- 2022 Budapest: 400 m medley\n- 2024 Paris: 400 m medley\nTotal: 3 Bronze Medals\n\nFinal Count:\nGold: 1\nBronze: 3\n\\end{verbatim}\n}\n\n\\noindent \\textbf{LLM's Answer:} \\textcolor{red}{No} \\& Emma Weyant has one Gold Medal and three Bronze Medals.\n\n\\vspace{0.5cm}\n\\noindent\\textbf{Why the LLM's Answer is Incorrect and Symbolic Reasoning Succeeds:}\n\\begin{itemize}\n \\item \\textbf{Direct Reasoning Errors}: The LLM correctly identifies the count but fails in its logical comparison, leading to an incorrect conclusion.\n \\item \\textbf{Symbolic Reasoning Accuracy}: SQL-based reasoning explicitly performs the correct comparison and produces an unambiguous result.\n \\item \\textbf{Scalability and Consistency}: SQL-based methods remain reliable as data size and complexity grow, unlike manual reasoning.\n\\end{itemize}\n\\noindent\\textbf{Conclusion:} Symbolic SQL reasoning eliminates errors inherent in manual reasoning methods like Chain-of-Thought, ensuring precise and reliable results.\n\n\\subsubsection{Example 3:}\n\n\\noindent\\textit{Q. In which city did Yohan Blake win his first medal?}\n\n\\vspace{0.5cm}\n\\noindent\\textbf{Step 1:} Start with the infobox table of Yohan Blake's medals.\n\n\\begin{center}\n\\vspace{-0.0cm}\n\\footnotesize\n\\textbf{Yohan Blake's Medal Record:}\n\\begin{tabular}{l|l|l}\n\\hline\n\\multicolumn{3}{c}{\\textbf{Olympic Games}} \\\\\n\\hline\n\\textbf{Medal} & \\textbf{Year} & \\textbf{Format} \\\\\n\\hline\nGold & 2012 London & 4$\\times$100 m relay \\\\\nGold & 2016 Rio de Janeiro & 4$\\times$100 m relay \\\\\nSilver & 2012 London & 100 m \\\\\nSilver & 2012 London & 200 m \\\\\n\\hline\n\\multicolumn{3}{c}{\\textbf{World Championships}} \\\\\n\\hline\nGold & 2011 Daegu & 100 m \\\\\nGold & 2011 Daegu & 4$\\times$100 m relay \\\\\n\\hline\n\\multicolumn{3}{c}{\\textbf{Commonwealth Games}} \\\\\n\\hline\nBronze & 2018 Gold Coast & 100 m \\\\\nBronze & 2018 Gold Coast & 4$\\times$100 m relay \\\\\n\\hline\n\\multicolumn{3}{c}{\\textbf{World Relays}} \\\\\n\\hline\nGold & 2014 Bahamas & 4$\\times$100 m \\\\\nGold & 2014 Bahamas & 4$\\times$200 m \\\\\nBronze & 2017 Bahamas & 4$\\times$200 m \\\\\n\\hline\n\\multicolumn{3}{c}{\\textbf{World Junior Championships}} \\\\\n\\hline\nGold & 2006 Beijing & 4$\\times$100 m relay \\\\\nSilver & 2008 Bydgoszcz & 4$\\times$100 m relay \\\\\nBronze & 2006 Beijing & 100 m \\\\\n\\hline\n\\multicolumn{3}{c}{\\textbf{Pan American Junior Championships}} \\\\\n\\hline\nSilver & 2007 São Paulo & 100 m \\\\\nBronze & 2007 São Paulo & 4$\\times$400 m relay \\\\\n\\hline\n\\multicolumn{3}{c}{\\textbf{CAC Junior Championships (U20)}} \\\\\n\\hline\nGold & 2006 Port of Spain & 100 m \\\\\nGold & 2006 Port of Spain & 200 m \\\\\nGold & 2006 Port of Spain & 4$\\times$100 m relay \\\\\n\\hline\n\\multicolumn{3}{c}{\\textbf{CARIFTA Games}} \\\\\n\\hline\nGold & 2006 Les Abymes & 200 m \\\\\nGold & 2006 Les Abymes & 4$\\times$100 m relay \\\\\nGold & 2007 Providenciales & 100 m \\\\\nGold & 2007 Providenciales & 4$\\times$100 m relay \\\\\nGold & 2008 Basseterre & 100 m \\\\\n\\hline\n\\multicolumn{3}{c}{\\textbf{CARIFTA Games}} \\\\\n\\hline\nGold & 2005 Bacolet & 100 m \\\\\nGold & 2005 Bacolet & 200 m \\\\\n\\hline\n\\multicolumn{3}{c}{\\textbf{Continental Cup}} \\\\\n\\hline\nGold & 2018 Ostrava & 4$\\times$100 m \\\\\n\\hline\n\\end{tabular}\n\\vspace{-0.0cm}\n\\end{center}\n\n\\vspace{-0.0cm}\n\\noindent\\textbf{Step 2:} Transform the data into a relational schema and organize it into structured database tables for efficient querying (similar to Step 2 in previous examples). \n\n\\vspace{0.5cm}\n\\noindent\\textbf{Step 3: Write the SQL Query}\n\n\\noindent The following query retrieves the location where Yohan Blake won his first medal:\n\n\\begin{lstlisting}[language=SQL]\nSELECT DISTINCT m.location\nFROM Medal m\nJOIN Format f ON m.format_id = f.format_id\nJOIN Tournament t ON f.tournament_id = t.tournament_id\nJOIN Athlete a ON t.athlete_id = a.athlete_id\nWHERE a.name = 'Yohan Blake'\n AND m.year = (\n SELECT MIN(m2.year)\n FROM Medal m2\n JOIN Format f2 ON m2.format_id = f2.format_id\n JOIN Tournament t2 ON f2.tournament_id = t2.tournament_id\n JOIN Athlete a2 ON t2.athlete_id = a2.athlete_id\n WHERE a2.name = 'Yohan Blake'\n );\n\\end{lstlisting}\n\n\\vspace{0.5cm}\n\\noindent\\textbf{Step 4: Execute the Query}\n\n\\noindent The query outputs the location where Yohan Blake won his first medal.\\\\\n\\noindent\\textbf{Final Result:} \\textcolor{green!40!black!60}{\\textbf{Bacolet}}\n\n\\vspace{0.5cm}\n\\noindent\\textbf{Direct Reasoning with Chain-of-Thought (COT):}\n\n\\noindent Using manual reasoning, the LLM incorrectly identifies the location as Beijing:\n\n{\\footnotesize\n\\begin{verbatim}\nYear 2006:\n- Gold: 4x100 m relay (World Junior Championships, Beijing)\n- Bronze: 100 m (World Junior Championships, Beijing)\nConclusion: First medal location is Beijing.\n\\end{verbatim}\n}\n\n\\noindent \\textbf{LLM's Answer:} \\textcolor{red}{Beijing}. In 2006, Yohan Blake won his first medal at the World Junior Championships in Beijing, where he secured a Gold in the 4x100 m relay and a Bronze in the 100 m.\n\n\\vspace{0.5cm}\n\\noindent\\textbf{Why the LLM's Answer is Incorrect and Symbolic Reasoning Succeeds:}\n\\begin{itemize}\n \\item \\textbf{Direct Reasoning Errors}: The LLM overlooks earlier results from 2005 in the CARIFTA Games held in Bacolet, where Yohan Blake won two Gold medals.\n \\item \\textbf{Symbolic Reasoning Accuracy}: SQL explicitly finds the minimum year and correctly identifies the location associated with the first medal.\n \\item \\textbf{Consistency and Scalability}: Symbolic SQL reasoning reliably handles large, complex medal records without omission or error.\n\\end{itemize}\n\n\\noindent\\textbf{Conclusion:} Symbolic SQL reasoning eliminates the errors inherent in Chain-of-Thought reasoning, ensuring accurate and reliable results.\n\n\n\\subsubsection{Example 4:}\n\\noindent\\textit{Q. How many medals did Mayu Matsumoto win in her twenties?}\n\n\\vspace{0.5cm}\n\\noindent\\textbf{Step 1:} Start with the infobox table of Mayu Matsumoto's medals.\n\n\\begin{figure}[h]\n \\centering\n \\includegraphics[width=0.3\\textwidth]{figs/Mayu_Matsumoto.png}\n \\caption{Mayu Matsumoto's Medal Infobox}\n \\label{fig:mayu_infobox}\n\\end{figure}\n\n\\vspace{-0.0cm}\n\\noindent\\textbf{Step 2:} Transform the data into a relational schema and organize it into structured database tables for efficient querying. (similar to Step 2 in previous examples).\n\n\\vspace{0.5cm}\n\\noindent\\textbf{Step 3: Write the SQL Query}\n\n\\noindent The following query calculates how many medals Mayu Matsumoto won between the ages of 20 and 29:\n\n\\begin{lstlisting}[language=SQL]\nSELECT COUNT(m.medal_id) AS total_medals\nFROM Medal m\nJOIN Format f ON m.format_id = f.format_id\nJOIN Tournament t ON f.tournament_id = t.tournament_id\nJOIN Athlete a ON t.athlete_id = a.athlete_id\nJOIN PersonalInformation pi ON a.athlete_id = pi.athlete_id\nWHERE a.name = 'Mayu Matsumoto'\n AND (m.year - pi.birth_year) BETWEEN 20 AND 29;\n\\end{lstlisting}\n\n\\vspace{0.5cm}\n\\noindent\\textbf{Step 4: Execute the Query}\n\n\\noindent The query calculates medals for Mayu Matsumoto won during her twenties (from 2015 to 2024, since she was born in 1995). \\\\\n\n\\noindent\\textbf{Final Result:} \\textcolor{green!40!black!60}{\\textbf{12}}\n\n\\vspace{0.5cm}\n\\noindent\\textbf{Direct Reasoning with Chain-of-Thought (COT):}\n\n\\noindent Using manual reasoning, the LLM incorrectly calculates the total as follows:\n\n\n\\noindent Mayu Matsumoto, born in \\textbf{1995}, was in her twenties from \\textbf{2015 to 2024}. During this period:\n\n\\noindent \\textbf{World Championships:} 2 Gold (2018, 2019), 1 Silver (2021), 2 Bronze (2021, 2022).\n\n\\noindent \\textbf{Sudirman Cup:} 2 Silver (2019, 2021), 1 Bronze (2023).\n\n\\noindent \\textbf{Uber Cup:} 1 Silver (2020), 1 Bronze (2022).\n\n\\noindent \\textbf{Asian Championships:} 1 Silver (2019), 1 Bronze (2023).\n\n\\noindent Total (COT Reasoning): 10 medals.\n\n\n\\vspace{0.5cm}\n\\noindent \\textbf{Trends in the LLM's Reasoning:}\n\\begin{itemize}\n \\item \\textbf{Incorrect Medal Count for World Championships:} The LLM claims 2 Gold, 2 Silver, and 1 Bronze, but the infobox shows 2 Gold (2018, 2019), 1 Bronze (2022), and only 1 Silver (2021).\n \\item \\textbf{Overcounted/Undercounted Totals:} The total medals, when carefully counted, sum to 12, not 10:\n \\begin{itemize}\n \\item \\textbf{World Championships:} 2 Gold, 1 Silver, 1 Bronze (Total = 4).\n \\item \\textbf{Sudirman Cup:} 2 Silver, 1 Bronze (Total = 3).\n \\item \\textbf{Uber Cup:} 1 Silver, 1 Bronze (Total = 2).\n \\item \\textbf{Asian Championships:} 1 Silver, 1 Bronze (Total = 2).\n \\item \\textbf{Incorrectly Excluded 2020 Medal:} Asian Team Championships (2020, age 25) is excluded incorrectly.\n \\item \\textbf{Correctly Excluded 2013 Medal:} Asian Junior Championships (2013, age 18) is excluded correctly.\n \n \\end{itemize}\n \\item \\textbf{Temporal Misinterpretation:} The LLM fails to count some of the medals in the 20-29 age range and fails to sum them accurately.\n\\end{itemize}\n\n\\vspace{0.5cm}\n\\noindent\\textbf{Symbolic Reasoning Accuracy:}\n\\begin{itemize}\n \\item SQL precisely filters years between 2015 and 2024, ensuring only valid medals are counted.\n \\item Symbolic reasoning eliminates human counting errors and temporal miscalculations.\n \\item The result is accurate: \\textbf{12 medals}.\n\\end{itemize}\n\n\\vspace{0.5cm}\n\\noindent\\textbf{Conclusion:} The LLM's Chain-of-Thought reasoning undercounts Mayu Matsumoto's medals, providing an incorrect total of 10 due to miscounting and temporal errors. Symbolic SQL reasoning accurately identifies the correct total as \\textcolor{green!40!black!60}{\\textbf{12}} medals won during her twenties.\n\n\n\\subsubsection{Example 5:}\n\\noindent\\textit{Q. How many times did Sandra Sánchez win a medal in the World Championships before 2021?}\n\n\\vspace{0.5cm}\n\\noindent\\textbf{Step 1:} Start with the infobox table of Sandra Sánchez's medals.\n\n\\begin{figure}[h]\n \\centering\n \\includegraphics[width=0.3\\textwidth]{figs/Sandra_Sanchez.png}\n \\caption{Sandra Sánchez's Medal Infobox}\n \\label{fig:sandra_infobox}\n\\end{figure}\n\n\\vspace{-0.0cm}\n\\noindent\\textbf{Step 2:} Transform the data into a relational schema and organize it into structured database tables for efficient querying (similar to Step 2 in previous examples).\n\n\\vspace{0.5cm}\n\\noindent\\textbf{Step 3: Write the SQL Query}\n\n\\noindent The following query calculates how many medals Sandra Sánchez won in the World Championships before the year 2021:\n\n\\begin{lstlisting}[language=SQL]\nSELECT COUNT(m.medal_id) AS total_medals\nFROM Medal m\nJOIN Format f ON m.format_id = f.format_id\nJOIN Tournament t ON f.tournament_id = t.tournament_id\nJOIN Athlete a ON t.athlete_id = a.athlete_id\nWHERE a.name = 'Sandra Sánchez'\n AND t.name = 'World Championships'\n AND m.year < 2021;\n\\end{lstlisting}\n\n\\vspace{0.5cm}\n\\noindent\\textbf{Step 4: Execute the Query}\n\n\\noindent The query outputs the total number of medals Sandra Sánchez won in the World Championships before 2021.\\\\\n\n\\noindent\\textbf{Final Result:} \\textcolor{green!40!black!60}{\\textbf{2}}\n\n\\vspace{0.5cm}\n\\noindent\\textbf{Direct Reasoning with Chain-of-Thought (COT):}\n\n\\noindent The LLM incorrectly provides the following reasoning:\n\n{\\footnotesize\n\\noindent Sandra Sánchez won a Bronze medal in the World Championships in 2016, which is before 2021. Therefore, the answer is 1.\n}\n\n\\vspace{0.5cm}\n\\noindent\\textbf{Errors in the LLM's Reasoning:}\n\\begin{itemize}\n \\item \\textbf{Missed Medal in 2018:} While the LLM identifies the 2016 Bronze medal, it fails to recognize the 2018 Gold medal in Madrid, which also occurred before 2021.\n \\item \\textbf{Incomplete Temporal Analysis:} The LLM does not account for all relevant years when performing temporal reasoning, leading to an undercount of medals.\n\\end{itemize}\n\n\\vspace{0.5cm}\n\\noindent\\textbf{Symbolic Reasoning Accuracy:}\n\\begin{itemize}\n \\item SQL explicitly filters medals in the World Championships where the year is less than 2021.\n \\item The query correctly identifies both the 2016 Bronze medal and the 2018 Gold medal, producing the accurate total of \\textbf{2 medals}.\n \\item Symbolic reasoning eliminates human oversight by systematically querying all relevant data within the temporal range.\n\\end{itemize}\n\n\\vspace{0.5cm}\n\\noindent\\textbf{Conclusion:} The LLM's Chain-of-Thought reasoning incorrectly counts only \\textbf{1 medal} due to missed temporal filtering. Symbolic SQL reasoning, by explicitly querying for medals before 2021, produces the correct result: \\textcolor{green!40!black!60}{\\textbf{2 medals}}.\n\n\n\n\n\n\\subsection{Result Analysis for all models:}\n\n\\subsubsection{Analysis for GPT-4o:}\n\\noindent From Table~\\ref{tab:gpt4o_results}, comparing \\textbf{SQL Adaptive} with \\textbf{Direct Adaptive} across key aspects, we observe:\n\n\\begin{itemize}\n\\item \\textbf{Counterfactual Gap:}\nFor \\textbf{Table - Adaptive} (EMS), the gap between Original (\\textbf{58.13}) and CounterFact (\\textbf{40.92}) is \\textbf{17.21}.\nFor \\textbf{SQL Schema - Adaptive} (EMS), the gap reduces to \\textbf{2.96}, indicating improved robustness to counterfactual data.\n\n\\item \\textbf{Scalability to Table Size:} \nFor \\textbf{Table - Adaptive} (EMS), the gap between Large (\\textbf{48.88}) and Small (\\textbf{73.92}) tables is \\textbf{25.04}. \nFor \\textbf{SQL Schema - Adaptive} (EMS), the gap decreases significantly to \\textbf{1.06}, demonstrating better scalability to large table sizes.\n\n\\item \\textbf{Question Complexity:} \nFor \\textbf{Table - Adaptive} (EMS), the performance on Easy, Medium, and Hard questions is \\textbf{74.38}, \\textbf{63.91}, and \\textbf{54.17}, respectively. \nFor \\textbf{SQL Schema - Adaptive} (EMS), the performance improves to \\textbf{80.06} (Easy), \\textbf{73.37} (Medium), and \\textbf{66.74} (Hard), showing better handling of increasing question complexity.\n\n\\item \\textbf{Adaptive Few-Shot Effectiveness:} \nFor Original data, \\textbf{SQL Schema - Adaptive} (EMS: \\textbf{71.63}) outperforms \\textbf{Table - Adaptive} (EMS: \\textbf{58.13}), highlighting the benefit of adaptive prompting in achieving higher accuracy.\n\\end{itemize}\n\n\\noindent These results demonstrate that \\textbf{SQL Adaptive} consistently outperforms \\textbf{Direct Adaptive} by reducing the counterfactual gap, improving scalability to large tables, and enhancing performance across question complexities.\n\n\n\\subsubsection{Analysis for GPT-4o Mini:}\n\\noindent From Table~\\ref{tab:gpt_4o_mini_results}, comparing \\textbf{SQL Adaptive} with \\textbf{Table Adaptive} across key aspects, we observe:\n\n\\begin{itemize}\n \\item \\textbf{Counterfactual Gap:} \n For \\textbf{Table - Adaptive} (EMS), the gap between Original (\\textbf{48.79}) and CounterFact (\\textbf{35.48}) is \\textbf{13.31}. \n For \\textbf{SQL Schema - Adaptive} (EMS), the gap reduces significantly to \\textbf{3.45} (Original: \\textbf{68.69}, CounterFact: \\textbf{65.24}), indicating improved robustness to counterfactual data.\n\n \\item \\textbf{Scalability to Table Size:} \n For \\textbf{Table - Adaptive} (EMS), the gap between Large (\\textbf{39.96}) and Small (\\textbf{64.80}) tables is \\textbf{24.84}. \n For \\textbf{SQL Schema - Adaptive} (EMS), the gap reduces to \\textbf{3.34} (Large: \\textbf{65.43}, Small: \\textbf{68.77}), demonstrating better scalability to large table sizes.\n\n \\item \\textbf{Question Complexity:} \n For \\textbf{Table - Adaptive} (EMS), the scores for Easy, Medium, and Hard questions are \\textbf{63.16}, \\textbf{52.07}, and \\textbf{44.49}, respectively. \n For \\textbf{SQL Schema - Adaptive} (EMS), the performance improves to \\textbf{76.87} (Easy), \\textbf{70.41} (Medium), and \\textbf{63.13} (Hard), showcasing better handling of increasing question complexity.\n\n \\item \\textbf{Adaptive Few-Shot Effectiveness:} \n For Original data, \\textbf{SQL Schema - Adaptive} (EMS: \\textbf{68.69}) significantly outperforms \\textbf{Table - Adaptive} (EMS: \\textbf{48.79}), highlighting the superior accuracy achieved with symbolic reasoning and adaptive prompting.\n\\end{itemize}\n\n\\noindent These results clearly show that \\textbf{SQL Adaptive} consistently outperforms \\textbf{Table Adaptive}, with smaller counterfactual and table size gaps, and better performance across question complexity levels.\n\n\\subsubsection{Analysis for Gemini 1.5 Flash:}\n\\noindent From Table~\\ref{tab:gemini_1_5_flash_results}, comparing \\textbf{SQL Adaptive} with \\textbf{Table Adaptive}, we observe:\n\n\\begin{itemize}\n \\item \\textbf{Counterfactual Gap:} \n For \\textbf{Table - Adaptive} (EMS), the gap between Original (\\textbf{52.90}) and CounterFact (\\textbf{42.91}) is \\textbf{9.99}. \n In comparison, for \\textbf{SQL Schema - Adaptive} (EMS), the gap is reduced to \\textbf{2.78} (Original: \\textbf{65.49}, CounterFact: \\textbf{62.71}), indicating significantly improved robustness to counterfactual data.\n\n \\item \\textbf{Scalability to Table Size:} \n For \\textbf{Table - Adaptive} (EMS), the gap between Large (\\textbf{41.02}) and Small (\\textbf{66.25}) tables is \\textbf{25.23}. \n For \\textbf{SQL Schema - Adaptive} (EMS), the gap reduces to \\textbf{4.23} (Large: \\textbf{69.30}, Small: \\textbf{73.53}), showcasing SQL's superior handling of larger tables.\n\n \\item \\textbf{Question Complexity:} \n For \\textbf{Table - Adaptive} (EMS), the scores for Easy, Medium, and Hard questions are \\textbf{65.76}, \\textbf{55.95}, and \\textbf{45.92}, respectively. \n For \\textbf{SQL Schema - Adaptive} (EMS), the scores improve to \\textbf{76.26} (Easy), \\textbf{73.72} (Medium), and \\textbf{63.12} (Hard), highlighting better performance as question complexity increases.\n\n \\item \\textbf{Adaptive Few-Shot Effectiveness:} \n For Original data, \\textbf{SQL Schema - Adaptive} (EMS: \\textbf{65.49}) outperforms \\textbf{Table - Adaptive} (EMS: \\textbf{52.90}), demonstrating the effectiveness of adaptive few-shot prompting with symbolic reasoning.\n\\end{itemize}\n\n\\noindent These observations show that \\textbf{SQL Adaptive} significantly reduces the counterfactual gap, scales better with large tables, and consistently achieves higher accuracy across all question complexities compared to \\textbf{Table Adaptive}.\n\n\\subsubsection{Analysis for Gemini 1.5 Pro:}\n\\noindent From Table~\\ref{tab:gemini_1_5_pro_results}, comparing \\textbf{SQL Adaptive} with \\textbf{Table Adaptive}, we observe:\n\n\\begin{itemize}\n \\item \\textbf{Counterfactual Gap:} \n For \\textbf{Table - Adaptive} (EMS), the gap between Original (\\textbf{53.48}) and CounterFact (\\textbf{44.19}) is \\textbf{9.29}. \n For \\textbf{SQL Schema - Adaptive} (EMS), the gap is reduced to \\textbf{0.16} (Original: \\textbf{65.29}, CounterFact: \\textbf{65.13}), showcasing excellent robustness to counterfactual data.\n\n \\item \\textbf{Scalability to Table Size:} \n For \\textbf{Table - Adaptive} (EMS), the gap between Large (\\textbf{41.86}) and Small (\\textbf{67.27}) tables is \\textbf{25.41}. \n For \\textbf{SQL Schema - Adaptive} (EMS), the gap reduces significantly to \\textbf{2.88} (Large: \\textbf{72.43}, Small: \\textbf{75.31}), demonstrating better scalability with large tables.\n\n \\item \\textbf{Question Complexity:} \n For \\textbf{Table - Adaptive} (EMS), the scores for Easy, Medium, and Hard questions are \\textbf{66.26}, \\textbf{56.47}, and \\textbf{46.74}, respectively. \n For \\textbf{SQL Schema - Adaptive} (EMS), the scores improve to \\textbf{75.86} (Easy), \\textbf{71.47} (Medium), and \\textbf{59.24} (Hard), highlighting superior handling of increasing question complexity.\n\n \\item \\textbf{Adaptive Few-Shot Effectiveness:} \n For Original data, \\textbf{SQL Schema - Adaptive} (EMS: \\textbf{65.29}) significantly outperforms \\textbf{Table - Adaptive} (EMS: \\textbf{53.48}), demonstrating the clear benefits of symbolic reasoning combined with adaptive few-shot prompting.\n\\end{itemize}\n\n\\noindent These results clearly highlight that \\textbf{SQL Adaptive} consistently reduces counterfactual gaps, scales better with table size, and improves performance across question complexities compared to \\textbf{Table Adaptive}.\n\n\\subsubsection{Analysis for Llama 3.1 70B:}\n\n\\noindent From Table~\\ref{tab:llama_3_1_70b_results}, comparing \\textbf{SQL Adaptive} with \\textbf{Table Adaptive}, we observe:\n\n\\begin{itemize}\n \\item \\textbf{Counterfactual Gap:} \n For \\textbf{Table - Adaptive} (EMS), the gap between Original (\\textbf{53.63}) and CounterFact (\\textbf{39.20}) is \\textbf{14.43}. \n For \\textbf{SQL Schema - Adaptive} (EMS), the gap reduces to \\textbf{0.55} (Original: \\textbf{64.36}, CounterFact: \\textbf{63.81}). SQL Adaptive is clearly more robust to counterfactual data.\n\n \\item \\textbf{Scalability to Table Size:} \n For \\textbf{Table - Adaptive} (EMS), the gap between Large (\\textbf{46.65}) and Small (\\textbf{68.07}) tables is \\textbf{21.42}. \n For \\textbf{SQL Schema - Adaptive} (EMS), the gap remains smaller at \\textbf{1.98} (Large: \\textbf{66.91}, Small: \\textbf{68.89}), showing better performance scalability with table size.\n\n \\item \\textbf{Question Complexity:} \n For \\textbf{Table - Adaptive} (EMS), the scores for Easy, Medium, and Hard questions are \\textbf{69.40}, \\textbf{59.76}, and \\textbf{52.85}, respectively. \n For \\textbf{SQL Schema - Adaptive} (EMS), the scores are \\textbf{77.19} (Easy), \\textbf{67.65} (Medium), and \\textbf{60.92} (Hard), showing a consistent improvement across complexities.\n\n \\item \\textbf{Adaptive Few-Shot Effectiveness:} \n For Original data, \\textbf{SQL Schema - Adaptive} (EMS: \\textbf{64.36}) performs significantly better than \\textbf{Table - Adaptive} (EMS: \\textbf{53.63}), confirming the benefit of symbolic reasoning with adaptive few-shot prompting.\n\\end{itemize}\n\n\\noindent Overall, \\textbf{SQL Adaptive} demonstrates clear improvements over \\textbf{Table Adaptive} in counterfactual robustness, scalability to table size, and performance across question complexity levels. The observed gaps in Table Adaptive remain substantial, especially for counterfactual and large table scenarios.\n\n\n\\subsubsection{Analysis for Mixtral 8x7B:}\n\\noindent From Table~\\ref{tab:mixtral_8x7b_results}, comparing \\textbf{SQL} and \\textbf{Table} under both \\emph{Adaptive} and \\emph{Static} few-shot settings, we observe:\n\n\\begin{itemize}\n \\item \\textbf{Counterfactual Gap:} \n For \\textbf{Table – Adaptive} (EMS), the gap between Original (\\textbf{37.54}) and CounterFact (\\textbf{30.62}) is \\textbf{6.92}. \n For \\textbf{SQL Schema – Adaptive} (EMS), the gap is \\textbf{4.20} (Original: \\textbf{25.09}, CounterFact: \\textbf{20.89}). \n For \\textbf{Table Text – Static} (EMS), the gap is \\textbf{8.59} (Original: \\textbf{48.79}, CounterFact: \\textbf{40.20}). \n For \\textbf{SQL Schema – Static} (EMS), the gap is \\textbf{5.82} (Original: \\textbf{46.02}, CounterFact: \\textbf{40.20}). \n SQL settings therefore show smaller counterfactual gaps than their Table counterparts in both few-shot modes.\n\n \\item \\textbf{Scalability to Table Size:} \n For \\textbf{Table – Adaptive} (EMS), the gap between Large (\\textbf{34.94}) and Small (\\textbf{47.72}) tables is \\textbf{12.78}. \n For \\textbf{SQL Schema – Adaptive} (EMS), the gap is \\textbf{9.03} (Large: \\textbf{24.54}, Small: \\textbf{33.57}). \n For \\textbf{Table Text – Static} (EMS), the gap is \\textbf{24.14} (Large: \\textbf{40.89}, Small: \\textbf{65.03}). \n For \\textbf{SQL Schema – Static} (EMS), the gap is \\textbf{5.02} (Large: \\textbf{47.96}, Small: \\textbf{52.98}). \n SQL configurations exhibit smaller size-related drops than Table configurations, particularly in the Static setting.\n\n \\item \\textbf{Question Complexity:} \n For \\textbf{Table – Adaptive} (EMS), the scores are \\textbf{50.96} (Easy), \\textbf{38.46} (Medium), and \\textbf{35.74} (Hard). \n For \\textbf{SQL Schema – Adaptive} (EMS), the scores are \\textbf{26.78} (Easy), \\textbf{46.55} (Medium), and \\textbf{21.56} (Hard). \n For \\textbf{Table Text – Static} (EMS), the scores are \\textbf{62.30} (Easy), \\textbf{53.25} (Medium), and \\textbf{45.62} (Hard). \n For \\textbf{SQL Schema – Static} (EMS), the scores are \\textbf{67.35} (Easy), \\textbf{45.36} (Medium), and \\textbf{40.61} (Hard). \n SQL Adaptive surpasses Table Adaptive on \\textit{Medium} questions only; SQL Static exceeds Table Text Static on \\textit{Easy} questions but trails on Medium and Hard ones.\n\n \\item \\textbf{Few-Shot Results on Original Subset:} \n \\textbf{Table – Adaptive} achieves \\textbf{37.54} EMS, while \\textbf{SQL Schema – Adaptive} reaches \\textbf{25.09}. \n \\textbf{Table Text – Static} records \\textbf{48.79} EMS, and \\textbf{SQL Schema – Static} attains \\textbf{46.02}. \n Table maintains higher absolute EMS in both few-shot modes, though the SQL--Table gap is narrower in the Static setting.\n\\end{itemize}\n\n\n\\subsubsection{Analysis for SQL Coder 70B:}\n\\noindent Since this is a \\textbf{code-based model}, we only evaluate baselines related to \\textbf{code generation} and exclude text generation baselines. From Table~\\ref{tab:sql_coder_results}, we observe the following for \\textbf{SQL Schema}:\n\n\\begin{itemize}\n\\item \\textbf{Counterfactual Gap:}\nFor \\textbf{SQL Static} (EMS), the gap between Original (\\textbf{52.08}) and CounterFact (\\textbf{47.93}) is \\textbf{4.15}.\nFor \\textbf{SQL Adaptive} (EMS), the gap reduces to \\textbf{2.38} (Original: \\textbf{55.88}, CounterFact: \\textbf{53.50}), demonstrating improved robustness with adaptive few-shot prompting.\n\n\\item \\textbf{Scalability to Table Size:} \nFor \\textbf{SQL Static} (EMS), the gap between Large (\\textbf{62.28}) and Small (\\textbf{59.53}) tables is \\textbf{2.75}. \nFor \\textbf{SQL Adaptive} (EMS), the gap is slightly larger at \\textbf{4.22} (Large: \\textbf{63.17}, Small: \\textbf{58.95}), showing minor regression in scalability.\n\n\\item \\textbf{Question Complexity:} \nFor \\textbf{SQL Static} (EMS), the scores for Easy, Medium, and Hard questions are \\textbf{77.39}, \\textbf{51.19}, and \\textbf{28.91}, respectively. \nFor \\textbf{SQL Adaptive} (EMS), the scores improve for Medium (\\textbf{58.38}) and Hard (\\textbf{51.74}) questions but decrease for Easy (\\textbf{63.93}), indicating uneven performance gains.\n\n\\item \\textbf{Overall Accuracy:} \nFor Original data, \\textbf{SQL Adaptive} (EMS: \\textbf{55.88}) outperforms \\textbf{SQL Static} (EMS: \\textbf{52.08}), highlighting the effectiveness of adaptive few-shot prompting for code-specific tasks.\n\\end{itemize}\n\n\\noindent Overall, SQL Adaptive demonstrates improved robustness and accuracy compared to SQL Static, particularly on counterfactual and medium-complexity queries.\n\n\n\\subsubsection{Analysis for Code Llama 70B:}\n\\noindent Since this is a \\textbf{code-based model}, we only evaluate baselines related to \\textbf{code generation} and exclude text generation baselines. From Table~\\ref{tab:code_llama_results}, we observe the following for \\textbf{SQL Schema}:\n\n\\begin{itemize}\n \\item \\textbf{Counterfactual Gap:} \n For \\textbf{SQL Static} (EMS), the gap between Original (\\textbf{15.84}) and CounterFact (\\textbf{32.62}) is substantial at \\textbf{16.78}, indicating performance degradation. \n For \\textbf{SQL Adaptive} (EMS), the gap reduces to \\textbf{16.53} (Original: \\textbf{23.53}, CounterFact: \\textbf{40.06}). While there is slight improvement, the gap remains significant.\n\n \\item \\textbf{Scalability to Table Size:} \n For \\textbf{SQL Static} (EMS), the gap between Large (\\textbf{29.82}) and Small (\\textbf{41.64}) tables is \\textbf{11.82}. \n For \\textbf{SQL Adaptive} (EMS), the gap decreases to \\textbf{10.53} (Large: \\textbf{37.61}, Small: \\textbf{48.14}), indicating modest improvements in handling table size.\n\n \\item \\textbf{Question Complexity:} \n For \\textbf{SQL Static} (EMS), the scores for Easy, Medium, and Hard questions are \\textbf{53.42}, \\textbf{41.62}, and \\textbf{38.94}, respectively. \n For \\textbf{SQL Adaptive} (EMS), the scores improve across all complexities to \\textbf{65.16} (Easy), \\textbf{50.89} (Medium), and \\textbf{40.61} (Hard), showing clear improvements, particularly for Easy and Medium questions.\n\n \\item \\textbf{Overall Accuracy:} \n For Original data, \\textbf{SQL Adaptive} (EMS: \\textbf{23.53}) outperforms \\textbf{SQL Static} (EMS: \\textbf{15.84}), demonstrating the benefits of adaptive few-shot prompting for overall accuracy.\n\\end{itemize}\n\n\\noindent Overall, \\textbf{SQL Adaptive} shows moderate improvements over \\textbf{SQL Static}, particularly in handling table size and question complexities, though counterfactual robustness remains a challenge.\n\n\n\n\\renewcommand{\\arraystretch}{1.0}\n\\begin{table*}[h]\n\\centering\n\\tiny\n\\begin{tabular}{ll|c|c|ccccccccc}\n\\toprule\n\\multirow{1}{*}{\\textbf{Output}} & \\multirow{1}{*}{\\textbf{Context}} & \\multirow{1}{*}{\\textbf{Method}} & \\textbf{Metric} & \\textbf{Original} & \\textbf{CounterFact} & \\shortstack{\\textbf{Gap (Original} \\\\ \\textbf{- CounterFact)}} & \\textbf{Large} & \\textbf{Small} & \\shortstack{\\textbf{Gap (Small} \\\\ \\textbf{- Large)}} & \\textbf{Easy} & \\textbf{Medium} & \\textbf{Hard} \\\\\n\\midrule\n\n\\multirow{27}{*}{Text} \n& \\multirow{2}{*}{None} & \\multirow{2}{*}{-} & REMS & 25.91 & 13.45 & 12.46 & 23.84 & 25.79 & 1.95 & 28.67 & 25.55 & 23.83 \\\\\n& & & EMS & 25.09 & 12.16 & 12.93 & 23.05 & 24.68 & 1.63 & 27.26 & 24.06 & 22.35 \\\\\n\\cmidrule{2-13}\n\n& \\multirow{22}{*}{Table} & \\multirow{2}{*}{zero shots} & REMS & 55.94 & 42.26 & 13.68 & 45.24 & 67.81 & 22.57 & 72.34 & 59.43 & 52.90 \\\\\n& & & EMS & 53.46 & 40.06 & 13.40 & 43.31 & 64.56 & 21.25 & 69.86 & 57.20 & 49.02 \\\\\n\\cmidrule{3-13}\n\n& & \\multirow{2}{*}{Static} & REMS & 58.69 & 44.69 & 14.00 & 48.96 & 73.57 & 24.61 & 73.45 & 65.39 & 56.97 \\\\\n& & & EMS & 56.57 & 42.35 & 14.22 & 46.84 & 71.11 & 24.27 & 71.18 & 63.12 & 53.35 \\\\\n\\cmidrule{3-13}\n\n& & \\multirow{2}{*}{Adaptive} & REMS & 60.71 & 43.10 & 17.61 & 51.41 & 76.97 & 25.56 & 76.89 & 66.00 & 58.00 \\\\\n& & & EMS & 58.13 & 40.92 & 17.21 & 48.88 & 73.92 & 25.04 & 74.38 & 63.91 & 54.17 \\\\\n\\cmidrule{3-13}\n\n& & Clear & REMS & 66.84 & 50.17 & 16.67 & 55.54 & 77.86 & 22.32 & 78.97 & 72.70 & 64.72 \\\\\n& & & EMS & 65.57 & 48.21 & 17.36 & 53.53 & 76.49 & 22.96 & 76.40 & 71.99 & 62.62 \\\\\n\\cmidrule{3-13}\n\n& & Chain Of Thought & REMS & 70.96 & 50.43 & 20.53 & 58.36 & 76.02 & 17.66 & 81.02 & 75.52 & 66.51 \\\\\n& & & EMS & 69.90 & 48.50 & 21.40 & 56.13 & 74.15 & 18.02 & 78.43 & 75.35 & 64.06 \\\\\n\\cmidrule{3-13}\n\n& & Plan And Solve & REMS & 70.36 & 48.62 & 21.74 & 57.14 & 76.91 & 19.77 & 80.30 & 72.30 & 62.64 \\\\\n& & & EMS & 69.55 & 46.50 & 23.05 & 55.20 & 75.44 & 20.24 & 77.96 & 70.81 & 60.25 \\\\\n\\cmidrule{3-13}\n\n& & Program of Thought & REMS & 59.05 & 50.28 & 8.77 & 51.66 & 64.35 & 12.69 & 72.65 & 60.85 & 52.89 \\\\\n& & & EMS & 56.40 & 47.07 & 9.33 & 50.19 & 62.22 & 12.03 & 69.94 & 57.20 & 48.81 \\\\\n\\cmidrule{3-13}\n\n& & Faithful CoT & REMS & 60.02 & 50.90 & 9.12 & 51.76 & 64.85 & 13.09 & 72.02 & 59.78 & 51.25 \\\\\n& & & EMS & 57.44 & 47.78 & 9.66 & 50.19 & 62.69 & 12.50 & 69.24 & 56.02 & 47.17 \\\\\n\n\\midrule\n\n\\multirow{7}{*}{SQL}\n& \\multirow{7}{*}{Schema} & \\multirow{2}{*}{zero shots} & REMS & 51.44 & 48.94 & 2.50 & 55.81 & 59.92 & 4.11 & 63.53 & 65.20 & 45.16 \\\\\n& & & EMS & 49.31 & 46.64 & 2.67 & 53.90 & 57.43 & 3.53 & 61.22 & 63.12 & 42.02 \\\\\n\\cmidrule{3-13}\n\n& & \\multirow{2}{*}{Static} & REMS & 66.76 & 62.41 & 4.35 & 71.82 & 75.37 & 3.55 & 81.27 & 75.88 & 64.38 \\\\\n& & & EMS & 65.22 & 60.94 & 4.28 & 70.63 & 73.57 & 2.94 & 78.89 & 75.15 & 62.31 \\\\\n\\cmidrule{3-13}\n\n& & \\multirow{2}{*}{Adaptive} & REMS & 71.87 & 69.04 & 2.83 & 73.05 & 74.55 & 1.50 & 80.17 & 74.35 & 67.38 \\\\\n& & & EMS & 71.63 & 68.67 & 2.96 & 72.86 & 73.92 & 1.06 & 80.06 & 73.37 & 66.74 \\\\\n\n\\bottomrule\n\\end{tabular}\n\\vspace{-1.0em}\n\\caption{\\small Test Set Results for GPT-4o.}\n\\label{tab:gpt4o_results}\n\\vspace{-1.0em}\n\\end{table*}\n\n\n\n\\renewcommand{\\arraystretch}{1.0}\n\\begin{table*}[h]\n\\centering\n\\footnotesize\n\\begin{tabular}{ll|c|c|ccccccc}\n\\toprule\n\\multirow{2}{*}{\\textbf{Output}} & \\multirow{2}{*}{\\textbf{Context}} & \\multirow{2}{*}{\\textbf{Few Shots}} & \\textbf{Metric} & \\multicolumn{7}{c}{\\textbf{Results Across Categories}} \\\\\n\\cmidrule{5-11}\n& & & & \\shortstack{\\textbf{Original}} & \\shortstack{\\textbf{CounterFact}} & \\shortstack{\\textbf{Large}} & \\shortstack{\\textbf{Small}} & \\shortstack{\\textbf{Easy}} & \\shortstack{\\textbf{Medium}} & \\shortstack{\\textbf{Hard}} \\\\\n\\midrule\n\n\\multirow{10}{*}{Text} & \\multirow{2}{*}{None} & \\multirow{2}{*}{-} & REMS & 21.48 & 13.90 & 20.21 & 22.75 & 26.08 & 22.30 & 19.03 \\\\\n& & & EMS & 20.24 & 13.02 & 19.70 & 21.87 & 24.84 & 20.51 & 17.40 \\\\\n\\cmidrule{2-11}\n\n& \\multirow{6}{*}{Table} & \\multirow{2}{*}{zero shots} & REMS & 49.59 & 33.52 & 40.18 & 63.60 & 62.07 & 47.53 & 43.62 \\\\\n& & & EMS & 47.23 & 31.04 & 37.73 & 60.47 & 59.19 & 44.58 & 39.55 \\\\\n\\cmidrule{3-11}\n\n& & \\multirow{2}{*}{Static} & REMS & 49.94 & 36.94 & 41.33 & 66.45 & 64.23 & 51.13 & 47.13 \\\\\n& & & EMS & 47.58 & 34.91 & 39.03 & 63.63 & 61.84 & 48.32 & 43.36 \\\\\n\\cmidrule{3-11}\n\n& & \\multirow{2}{*}{Adaptive} & REMS & 51.13 & 38.37 & 42.43 & 67.66 & 66.10 & 54.81 & 48.63 \\\\\n& & & EMS & 48.79 & 35.48 & 39.96 & 64.80 & 63.16 & 52.07 & 44.49 \\\\\n\\midrule\n\n\\multirow{6}{*}{SQL} & \\multirow{6}{*}{Schema} & \\multirow{2}{*}{zero shots} & REMS & 39.93 & 41.28 & 38.76 & 48.50 & 57.45 & 50.88 & 34.21 \\\\\n& & & EMS & 38.24 & 39.63 & 37.36 & 46.67 & 55.30 & 49.51 & 31.41 \\\\\n\\cmidrule{3-11}\n\n& & \\multirow{2}{*}{Static} & REMS & 57.44 & 51.18 & 53.94 & 66.52 & 77.57 & 65.29 & 57.04 \\\\\n& & & EMS & 56.57 & 50.36 & 53.16 & 65.73 & 75.93 & 65.29 & 56.33 \\\\\n\\cmidrule{3-11}\n\n& & \\multirow{2}{*}{Adaptive} & REMS & 68.97 & 65.40 & 65.70 & 69.20 & 76.98 & 71.07 & 63.93 \\\\\n& & & EMS & 68.69 & 65.24 & 65.43 & 68.77 & 76.87 & 70.41 & 63.13 \\\\\n\\bottomrule\n\n\\end{tabular}\n\\vspace{-1.0em}\n\\caption{\\small Test Set Results for GPT-4o-mini}\n\\label{tab:gpt_4o_mini_results}\n\\vspace{-1.0em}\n\\end{table*}\n\n\n\n\\renewcommand{\\arraystretch}{1.0}\n\\begin{table*}[h]\n\\centering\n\\tiny\n\\begin{tabular}{ll|c|c|ccccccccc}\n\\toprule\n\\multirow{2}{*}{\\textbf{Output}} & \\multirow{2}{*}{\\textbf{Context}} & \\multirow{2}{*}{\\textbf{Method}} & \\textbf{Metric} & \\multicolumn{9}{c}{\\textbf{Results Across Categories}} \\\\\n\\cmidrule{5-13}\n& & & & \\shortstack{\\textbf{Original}} & \\shortstack{\\textbf{CounterFact}} & \\shortstack{\\textbf{Gap (Original} \\\\ \\textbf{- CounterFact)}} & \\shortstack{\\textbf{Large}} & \\shortstack{\\textbf{Small}} & \\shortstack{\\textbf{Gap (Small} \\\\ \\textbf{- Large)}} & \\shortstack{\\textbf{Easy}} & \\shortstack{\\textbf{Medium}} & \\shortstack{\\textbf{Hard}} \\\\\n\\midrule\n\n\\multirow{27}{*}{Text} & \\multirow{2}{*}{None} & \\multirow{2}{*}{-} & REMS & 22.11 & 15.02 & 7.09 & 23.88 & 21.74 & -2.14 & 27.81 & 23.23 & 21.59 \\\\\n& & & EMS & 18.69 & 11.76 & 6.93 & 19.49 & 18.93 & -0.56 & 24.03 & 19.41 & 17.30 \\\\\n\\cmidrule{2-13}\n\n& \\multirow{22}{*}{Table} & \\multirow{2}{*}{zero shots} & REMS & 55.17 & 45.42 & 9.75 & 42.71 & 69.88 & 27.17 & 68.78 & 56.61 & 51.18 \\\\\n& & & EMS & 48.79 & 37.61 & 11.18 & 34.96 & 61.11 & 26.15 & 59.15 & 48.82 & 39.00 \\\\\n\\cmidrule{3-13}\n\n& & \\multirow{2}{*}{Static} & REMS & 57.46 & 47.20 & 10.26 & 46.44 & 71.89 & 25.45 & 73.76 & 61.94 & 54.99 \\\\\n& & & EMS & 50.00 & 39.08 & 10.92 & 38.35 & 62.35 & 24.00 & 63.16 & 53.53 & 42.96 \\\\\n\\cmidrule{3-13}\n\n& & \\multirow{2}{*}{Adaptive} & REMS & 59.09 & 48.25 & 10.84 & 47.93 & 73.17 & 25.24 & 76.28 & 64.94 & 57.83 \\\\\n& & & EMS & 52.90 & 42.91 & 9.99 & 41.02 & 66.25 & 25.23 & 65.76 & 55.95 & 45.92 \\\\\n\\cmidrule{3-13}\n\n& & \\multirow{2}{*}{Clear} & REMS & 62.01 & 50.46 & 11.55 & 53.38 & 73.07 & 19.69 & 79.65 & 68.02 & 59.37 \\\\\n& & & EMS & 60.63 & 48.95 & 11.68 & 51.11 & 72.19 & 21.08 & 77.92 & 66.52 & 56.67 \\\\\n\\cmidrule{3-13}\n\n& & \\multirow{2}{*}{Chain Of Thought} & REMS & 60.97 & 47.28 & 13.69 & 51.61 & 73.45 & 21.84 & 77.28 & 65.60 & 56.62 \\\\\n& & & EMS & 59.54 & 45.48 & 14.06 & 49.52 & 72.09 & 22.57 & 75.29 & 64.17 & 54.55 \\\\\n\\cmidrule{3-13}\n\n& & \\multirow{2}{*}{Plan And Solve} & REMS & 53.58 & 42.58 & 11.00 & 48.40 & 65.07 & 16.67 & 70.61 & 59.35 & 51.43 \\\\\n& & & EMS & 52.00 & 40.34 & 11.66 & 46.14 & 63.85 & 17.71 & 68.54 & 57.12 & 48.53 \\\\\n\\cmidrule{3-13}\n\n& & \\multirow{2}{*}{Program of Thought} & REMS & 49.26 & 42.85 & 6.41 & 44.31 & 57.29 & 12.98 & 67.10 & 48.61 & 44.13 \\\\\n& & & EMS & 46.90 & 40.55 & 6.35 & 42.33 & 55.30 & 12.97 & 64.53 & 45.52 & 40.47 \\\\\n\\cmidrule{3-13}\n\n& & \\multirow{2}{*}{Faithful CoT} & REMS & 48.98 & 40.90 & 8.08 & 43.46 & 58.09 & 14.63 & 64.12 & 53.26 & 45.68 \\\\\n& & & EMS & 46.78 & 38.66 & 8.12 & 41.38 & 56.23 & 14.85 & 61.50 & 50.66 & 42.67 \\\\\n\n\\midrule\n\n\\multirow{6}{*}{SQL} & \\multirow{6}{*}{Schema} & \\multirow{2}{*}{zero shots} & REMS & 47.27 & 42.56 & 4.71 & 46.23 & 56.45 & 10.22 & 63.49 & 59.74 & 42.09 \\\\\n& & & EMS & 39.34 & 33.45 & 5.89 & 44.29 & 53.65 & 9.36 & 52.96 & 52.48 & 34.59 \\\\\n\\cmidrule{3-13}\n\n& & \\multirow{2}{*}{Static} & REMS & 66.43 & 62.38 & 4.05 & 71.39 & 77.20 & 5.81 & 88.17 & 79.22 & 65.04 \\\\\n& & & EMS & 57.93 & 54.43 & 3.50 & 63.13 & 70.89 & 7.76 & 79.54 & 68.96 & 58.42 \\\\\n\\cmidrule{3-13}\n\n& & \\multirow{2}{*}{Adaptive} & REMS & 72.91 & 71.67 & 1.24 & 78.18 & 81.71 & 3.53 & 87.06 & 80.80 & 74.04 \\\\\n& & & EMS & 65.49 & 62.71 & 2.78 & 69.30 & 73.53 & 4.23 & 76.26 & 73.72 & 63.12 \\\\\n\\bottomrule\n\n\\end{tabular}\n\\caption{\\small Test Set Results for Gemini 1.5 Flash.}\n\\vspace{-1.0em}\n\\label{tab:gemini_1_5_flash_results}\n\\vspace{-1.0em}\n\\end{table*}\n\n\n\n\\renewcommand{\\arraystretch}{1.0}\n\\begin{table*}[h]\n\\centering\n\\tiny\n\\begin{tabular}{ll|c|c|ccccccccc}\n\\toprule\n\\multirow{1}{*}{\\textbf{Output}} & \\multirow{1}{*}{\\textbf{Context}} & \\multirow{1}{*}{\\textbf{Method}} & \\textbf{Metric} & \\textbf{Original} & \\textbf{CounterFact} & \\shortstack{\\textbf{Gap (Original} \\\\ \\textbf{- CounterFact)}} & \\textbf{Large} & \\textbf{Small} & \\shortstack{\\textbf{Gap (Small} \\\\ \\textbf{- Large)}} & \\textbf{Easy} & \\textbf{Medium} & \\textbf{Hard} \\\\\n\\midrule\n\n\\multirow{27}{*}{Text} \n\n\\multirow{2}{*}{} & \\multirow{2}{*}{None} & \\multirow{2}{*}{-} & REMS & 23.37 & 15.88 & 7.49 & 24.20 & 24.08 & 0.12 & 29.15 & 26.45 & 23.38 \\\\\n& & & EMS & 19.90 & 13.45 & 6.45 & 21.19 & 20.99 & 0.2 & 24.94 & 21.76 & 18.77 \\\\\n\\cmidrule{2-13}\n\n& \\multirow{6}{*}{} & \\multirow{2}{*}{zero shots} & REMS & 54.94 & 45.40 & 9.54 & 46.34 & 69.74 & 23.4 & 73.26 & 60.71 & 57.43 \\\\\n& & & EMS & 48.06 & 37.39 & 10.67 & 38.98 & 61.11 & 21.13 & 62.59 & 53.24 & 45.60 \\\\\n\\cmidrule{3-13}\n\n& & \\multirow{2}{*}{Static} & REMS & 59.01 & 46.87 & 12.14 & 52.42 & 72.93 & 20.51 & 75.72 & 65.10 & 60.28 \\\\\n& & & EMS & 52.91 & 39.92 & 12.99 & 43.86 & 65.02 & 21.16 & 65.79 & 58.53 & 50.00 \\\\\n\\cmidrule{3-13}\n\n& & \\multirow{2}{*}{Adaptive} & REMS & 60.23 & 49.04 & 11.19 & 48.79 & 73.98 & 25.19 & 78.04 & 66.43 & 59.28 \\\\\n& & & EMS & 53.48 & 44.19 & 9.29 & 41.86 & 67.27 & 25.41 & 66.26 & 56.47 & 46.74 \\\\\n\\cmidrule{3-13}\n\n& \\multirow{5}{*}{Table} & \\multirow{2}{*}{Clear} & REMS & 50.29 & 42.82 & 7.47 & 42.83 & 58.04 & 15.21 & 59.91 & 60.79 & 52.82 \\\\\n& & & EMS & 49.33 & 40.66 & 8.67 & 40.95 & 56.85 & 15.90 & 58.38 & 59.77 & 51.14 \\\\\n\\cmidrule{3-13}\n\n& & \\multirow{2}{*}{Chain Of Thought} & REMS & 67.34 & 57.16 & 10.18 & 59.51 & 78.47 & 18.96 & 84.67 & 73.36 & 67.31 \\\\\n& & & EMS & 66.46 & 55.75 & 10.71 & 57.67 & 77.55 & 19.88 & 83.29 & 72.69 & 65.87 \\\\\n\\cmidrule{3-13}\n\n& & \\multirow{2}{*}{Plan And Solve} & REMS & 62.11 & 55.68 & 6.43 & 58.38 & 74.88 & 16.50 & 81.19 & 73.28 & 65.56 \\\\\n& & & EMS & 60.75 & 54.31 & 6.44 & 56.72 & 73.43 & 16.71 & 79.42 & 72.25 & 63.60 \\\\\n\\cmidrule{3-13}\n\n& & \\multirow{2}{*}{Program of Thought} & REMS & 56.31 & 49.35 & 6.97 & 53.02 & 66.46 & 13.44 & 76.94 & 60.62 & 51.75 \\\\\n& & & EMS & 53.83 & 46.20 & 7.63 & 49.84 & 64.68 & 14.83 & 73.97 & 57.12 & 47.57 \\\\\n\\cmidrule{3-13}\n\n& & \\multirow{2}{*}{Faithful CoT} & REMS & 56.49 & 50.15 & 6.34 & 50.63 & 65.84 & 15.21 & 77.28 & 61.53 & 51.03 \\\\\n& & & EMS & 53.95 & 47.64 & 6.31 & 48.47 & 63.85 & 15.39 & 74.52 & 58.15 & 46.85 \\\\\n\n\\midrule\n\n\\multirow{6}{*}{SQL} \n& \\multirow{6}{*}{Schema} & \\multirow{2}{*}{zero shots} & REMS & 49.24 & 43.56 & 5.68 & 48.21 & 57.62 & 9.41 & 64.42 & 61.10 & 43.00 \\\\\n& & & EMS & 41.32 & 35.31 & 6.01 & 45.87 & 55.11 & 9.24 & 54.78 & 53.79 & 35.96 \\\\\n\\cmidrule{3-13}\n\n& & \\multirow{2}{*}{Static} & REMS & 67.76 & 63.63 & 4.13 & 76.80 & 82.82 & 6.02 & 89.33 & 80.81 & 66.42 \\\\\n& & & EMS & 59.08 & 55.52 & 3.56 & 71.32 & 77.41 & 6.09 & 80.86 & 70.33 & 59.59 \\\\\n\\cmidrule{3-13}\n\n& & \\multirow{2}{*}{Adaptive} & REMS & 73.04 & 73.58 & 0.54 & 81.94 & 84.38 & 2.44 & 87.31 & 80.01 & 71.43 \\\\\n& & & EMS & 65.29 & 65.13 & 0.16 & 72.43 & 75.31 & 2.88 & 75.86 & 71.47 & 59.24 \\\\\n\n\\bottomrule\n\\end{tabular}\n\\vspace{-1.0em}\n\\caption{\\small Test Set Results for Gemini 1.5 Pro.}\n\\label{tab:gemini_1_5_pro_results}\n\\vspace{-1.0em}\n\\end{table*}\n\n\n\n\\begin{table*}[h]\n\\centering\n\\tiny\n\\begin{tabular}{ll|c|c|ccccccccc}\n\\toprule\n\\multirow{2}{*}{\\textbf{Output}} & \\multirow{2}{*}{\\textbf{Context}} & \\multirow{2}{*}{\\textbf{Method}} & \\textbf{Metric} & \\multicolumn{9}{c}{\\textbf{Results Across Categories}} \\\\\n\\cmidrule{5-13}\n& & & & \\shortstack{\\textbf{Original}} & \\shortstack{\\textbf{CounterFact}} & \\shortstack{\\textbf{Gap (Original} \\\\ \\textbf{- CounterFact)}} & \\shortstack{\\textbf{Large}} & \\shortstack{\\textbf{Small}} & \\shortstack{\\textbf{Gap (Small} \\\\ \\textbf{- Large)}} & \\shortstack{\\textbf{Easy}} & \\shortstack{\\textbf{Medium}} & \\shortstack{\\textbf{Hard}} \\\\\n\\midrule\n\n\\multirow{27}{*}{Text} & \\multirow{2}{*}{None} & \\multirow{2}{*}{-} & REMS & 17.46 & 12.74 & 4.72 & 20.89 & 18.09 & -2.80 & 22.31 & 17.93 & 17.18 \\\\\n& & & EMS & 16.61 & 12.16 & 4.45 & 19.89 & 17.31 & -2.58 & 21.31 & 16.96 & 16.27 \\\\\n\\cmidrule{2-13}\n\n& \\multirow{22}{*}{Table} & \\multirow{2}{*}{zero shots} & REMS & 54.63 & 39.93 & 14.70 & 46.10 & 64.62 & 18.52 & 70.10 & 58.31 & 53.37 \\\\\n& & & EMS & 52.60 & 37.48 & 15.12 & 44.05 & 62.57 & 18.52 & 67.49 & 56.21 & 50.35 \\\\\n\\cmidrule{3-13}\n\n& & \\multirow{2}{*}{Static} & REMS & 64.33 & 48.44 & 15.89 & 57.27 & 75.40 & 18.13 & 79.04 & 70.23 & 60.98 \\\\\n& & & EMS & 62.46 & 46.21 & 16.25 & 55.58 & 73.68 & 18.10 & 76.91 & 67.46 & 57.44 \\\\\n\\cmidrule{3-13}\n\n& & \\multirow{2}{*}{Adaptive} & REMS & 55.73 & 41.29 & 14.44 & 48.48 & 70.56 & 22.08 & 71.35 & 62.54 & 56.43 \\\\\n& & & EMS & 53.63 & 39.20 & 14.43 & 46.65 & 68.07 & 21.42 & 69.40 & 59.76 & 52.85 \\\\\n\\cmidrule{3-13}\n\n& & \\multirow{2}{*}{Clear} & REMS & 54.37 & 39.43 & 14.94 & 37.63 & 68.64 & 31.01 & 68.86 & 62.91 & 50.87 \\\\\n& & & EMS & 53.46 & 38.34 & 15.12 & 36.43 & 67.02 & 30.59 & 67.49 & 62.33 & 49.51 \\\\\n\\cmidrule{3-13}\n\n& & \\multirow{2}{*}{Chain Of Thought} & REMS & 61.87 & 45.29 & 16.58 & 49.87 & 73.88 & 24.01 & 77.68 & 70.40 & 59.48 \\\\\n& & & EMS & 59.86 & 43.06 & 16.80 & 48.51 & 72.04 & 23.53 & 75.13 & 69.23 & 57.16 \\\\\n\\cmidrule{3-13}\n\n& & \\multirow{2}{*}{Plan And Solve} & REMS & 59.31 & 45.13 & 14.18 & 50.55 & 71.82 & 21.27 & 75.65 & 67.06 & 57.32 \\\\\n& & & EMS & 57.79 & 43.49 & 14.30 & 49.07 & 70.29 & 21.22 & 73.63 & 66.08 & 55.21 \\\\\n\\cmidrule{3-13}\n\n& & \\multirow{2}{*}{Program of Thought} & REMS & 46.63 & 31.83 & 14.80 & 28.30 & 63.03 & 34.73 & 61.16 & 51.98 & 41.83 \\\\\n& & & EMS & 44.64 & 29.90 & 14.74 & 27.69 & 61.40 & 33.71 & 59.15 & 48.91 & 39.36 \\\\\n\\cmidrule{3-13}\n\n& & \\multirow{2}{*}{Faithful CoT} & REMS & 40.19 & 29.44 & 10.75 & 25.11 & 60.30 & 35.19 & 56.52 & 46.22 & 37.86 \\\\\n& & & EMS & 38.75 & 27.75 & 11.00 & 24.54 & 58.83 & 34.29 & 55.19 & 43.79 & 35.74 \\\\\n\n\\midrule\n\n\\multirow{6}{*}{SQL} & \\multirow{6}{*}{Schema} & \\multirow{2}{*}{zero shots} & REMS & 34.77 & 31.52 & 3.25 & 37.08 & 41.36 & 4.28 & 45.83 & 40.81 & 29.74 \\\\\n& & & EMS & 33.56 & 30.47 & 3.09 & 36.06 & 39.77 & 3.71 & 44.26 & 39.65 & 27.54 \\\\\n\\cmidrule{3-13}\n\n& & \\multirow{2}{*}{Static} & REMS & 54.59 & 47.31 & 7.28 & 55.38 & 63.12 & 7.74 & 68.51 & 60.83 & 56.15 \\\\\n& & & EMS & 53.81 & 46.64 & 7.17 & 54.65 & 62.22 & 7.57 & 67.21 & 60.55 & 55.63 \\\\\n\\cmidrule{3-13}\n\n& & \\multirow{2}{*}{Adaptive} & REMS & 64.61 & 64.04 & 0.57 & 67.40 & 69.54 & 2.14 & 77.42 & 68.01 & 61.78 \\\\\n& & & EMS & 64.36 & 63.81 & 0.55 & 66.91 & 68.89 & 1.98 & 77.19 & 67.65 & 60.92 \\\\\n\\bottomrule\n\n\\end{tabular}\n\\vspace{-1.0em}\n\\caption{\\small Test Set Results for Llama 3.1 70B.}\n\\label{tab:llama_3_1_70b_results}\n\\vspace{-1.0em}\n\\end{table*}\n\n\n\n\\begin{table*}[h]\n\\centering\n\\small\n\\begin{tabular}{ll|c|c|ccccccc}\n\\toprule\n\\multirow{2}{*}{\\textbf{Output}} & \\multirow{2}{*}{\\textbf{Context}} & \\multirow{2}{*}{\\textbf{Few Shots}} & \\textbf{Metric} & \\multicolumn{7}{c}{\\textbf{Results Across Categories}} \\\\\n\\cmidrule{5-11}\n& & & & \\shortstack{\\textbf{Original}} & \\shortstack{\\textbf{CounterFact}} & \\shortstack{\\textbf{Large}} & \\shortstack{\\textbf{Small}} & \\shortstack{\\textbf{Easy}} & \\shortstack{\\textbf{Medium}} & \\shortstack{\\textbf{Hard}} \\\\\n\\midrule\n\n\\multirow{11}{*}{Text} & \\multirow{2}{*}{None} & \\multirow{2}{*}{-} & REMS & 17.53 & 13.52 & 21.71 & 20.31 & 20.38 & 20.06 & 19.95 \\\\\n& & & EMS & 15.92 & 12.73 & 20.82 & 19.53 & 19.26 & 17.75 & 18.08 \\\\\n\\cmidrule{2-11}\n\n& \\multirow{6}{*}{Table} & \\multirow{2}{*}{zero shots} & REMS & 40.26 & 32.62 & 37.58 & 56.55 & 54.09 & 46.27 & 38.65 \\\\\n& & & EMS & 38.06 & 30.47 & 35.50 & 53.33 & 51.78 & 43.79 & 35.05 \\\\\n\\cmidrule{3-11}\n\n& & \\multirow{2}{*}{Static} & REMS & 50.92 & 42.31 & 43.17 & 68.13 & 64.75 & 55.80 & 49.06 \\\\\n& & & EMS & 48.79 & 40.20 & 40.89 & 65.03 & 62.30 & 53.25 & 45.62 \\\\\n\\cmidrule{3-11}\n\n& & \\multirow{2}{*}{Adaptive} & REMS & 39.98 & 33.23 & 37.68 & 50.69 & 53.84 & 41.02 & 40.33 \\\\\n& & & EMS & 37.54 & 30.62 & 34.94 & 47.72 & 50.96 & 38.46 & 35.74 \\\\\n\\midrule\n\n\\multirow{6}{*}{SQL} & \\multirow{6}{*}{Schema} & \\multirow{2}{*}{zero shots} & REMS & 20.35 & 15.07 & 19.30 & 27.74 & 21.65 & 27.19 & 22.43 \\\\\n& & & EMS & 19.90 & 14.74 & 18.96 & 26.55 & 20.63 & 27.02 & 21.56 \\\\\n\\cmidrule{3-11}\n\n& & \\multirow{2}{*}{Static} & REMS & 47.42 & 41.38 & 48.78 & 54.63 & 69.46 & 46.22 & 42.45 \\\\\n& & & EMS & 46.02 & 40.20 & 47.96 & 52.98 & 67.35 & 45.36 & 40.61 \\\\\n\\cmidrule{3-11}\n\n& & \\multirow{2}{*}{Adaptive} & REMS & 25.45 & 21.26 & 24.72 & 33.96 & 26.91 & 47.24 & 22.20 \\\\\n& & & EMS & 25.09 & 20.89 & 24.54 & 33.57 & 26.78 & 46.55 & 21.56 \\\\\n\\bottomrule\n\n\\end{tabular}\n\\vspace{-1.0em}\n\\caption{\\small Test Set Results for Mixtral 8x7B.}\n\\vspace{-1.0em}\n\\label{tab:mixtral_8x7b_results}\n\\end{table*}\n\n\n\\begin{table*}[h]\n\\centering\n\\footnotesize\n\\begin{tabular}{ll|c|c|ccccccc}\n\\toprule\n\\multirow{2}{*}{\\textbf{Output}} & \\multirow{2}{*}{\\textbf{Context}} & \\multirow{2}{*}{\\textbf{Few Shots}} & \\textbf{Metric} & \\multicolumn{7}{c}{\\textbf{Results Across Categories}} \\\\\n\\cmidrule{5-11}\n& & & & \\shortstack{\\textbf{Original}} & \\shortstack{\\textbf{CounterFact}} & \\shortstack{\\textbf{Large}} & \\shortstack{\\textbf{Small}} & \\shortstack{\\textbf{Easy}} & \\shortstack{\\textbf{Medium}} & \\shortstack{\\textbf{Hard}} \\\\\n\\midrule\n\n\\multirow{6}{*}{SQL} & \\multirow{6}{*}{Schema} & \\multirow{2}{*}{zero shots} & REMS & 19.59 & 18.32 & 20.56 & 27.60 & 29.05 & 20.28 & 17.29 \\\\\n& & & EMS & 18.17 & 17.02 & 19.46 & 26.08 & 26.78 & 19.72 & 15.72 \\\\\n\\cmidrule{3-11}\n\n& & \\multirow{2}{*}{Static} & REMS & 53.37 & 49.32 & 63.43 & 61.01 & 77.39 & 54.05 & 30.19 \\\\\n& & & EMS & 52.08 & 47.93 & 62.28 & 59.53 & 77.39 & 51.19 & 28.91 \\\\\n\\cmidrule{3-11}\n\n& & \\multirow{2}{*}{Adaptive} & REMS & 57.10 & 55.03 & 64.00 & 60.55 & 65.02 & 60.13 & 54.07 \\\\\n& & & EMS & 55.88 & 53.50 & 63.17 & 58.95 & 63.93 & 58.38 & 51.74 \\\\\n\\bottomrule\n\n\\end{tabular}\n\\vspace{-1.0em}\n\\caption{\\small Test Set Results for SQL Coder 70B}\n\\label{tab:sql_coder_results}\n\\end{table*}\n\n\n\n\\begin{table*}[h]\n\\centering\n\\footnotesize\n\\begin{tabular}{ll|c|c|ccccccc}\n\\toprule\n\\multirow{2}{*}{\\textbf{Output}} & \\multirow{2}{*}{\\textbf{Context}} & \\multirow{2}{*}{\\textbf{Few Shots}} & \\textbf{Metric} & \\multicolumn{7}{c}{\\textbf{Results Across Categories}} \\\\\n\\cmidrule{5-11}\n& & & & \\shortstack{\\textbf{Original}} & \\shortstack{\\textbf{CounterFact}} & \\shortstack{\\textbf{Large}} & \\shortstack{\\textbf{Small}} & \\shortstack{\\textbf{Easy}} & \\shortstack{\\textbf{Medium}} & \\shortstack{\\textbf{Hard}} \\\\\n\\midrule\n\n\\multirow{6}{*}{SQL} & \\multirow{6}{*}{Schema} & \\multirow{2}{*}{zero shot} & REMS & 12.37 & 19.77 & 13.76 & 21.31 & 33.81 & 25.98 & 19.19 \\\\\n& & & EMS & 12.22 & 19.46 & 13.76 & 21.00 & 33.47 & 25.64 & 18.64 \\\\\n\\cmidrule{3-11}\n\n& & \\multirow{2}{*}{Static} & REMS & 16.53 & 33.29 & 29.82 & 42.04 & 54.13 & 41.62 & 39.09 \\\\\n& & & EMS & 15.84 & 32.62 & 29.82 & 41.64 & 53.42 & 41.62 & 38.94 \\\\\n\\cmidrule{3-11}\n\n& & \\multirow{2}{*}{Adaptive} & REMS & 24.02 & 40.73 & 38.00 & 48.56 & 65.56 & 51.78 & 41.63 \\\\\n& & & EMS & 23.53 & 40.06 & 37.61 & 48.14 & 65.16 & 50.89 & 40.61 \\\\\n\\bottomrule\n\n\\end{tabular}\n\\vspace{-1.0em}\n\\caption{\\small Test Set Results for Code Llama.}\n\\label{tab:code_llama_results}\n\\vspace{-1.0em}\n\\end{table*}\n\n\n\n\n\\onecolumn\n\\section{SQL Code Generation Prompt}\n\n\\noindent \\textbf{\\# Task Instruction:} \\\\\nYou will be given a question and your task is to provide the SQL logic to answer a natural language question based on the provided schema. Few Examples of the task will be provided below. Assume that all the data is already inserted into the database.\n\n\n\\noindent\n\\textbf{\\small 1. Table Schemas:}\n\\footnotesize\n\\begin{lstlisting}[language=SQL]\nCREATE TABLE Athlete (\n athlete_id INT AUTO_INCREMENT PRIMARY KEY,\n name VARCHAR(100) NOT NULL\n);\nCREATE TABLE Tournament (\n tournament_id INT AUTO_INCREMENT PRIMARY KEY,\n athlete_id INT,\n name VARCHAR(100) NOT NULL,\n FOREIGN KEY (athlete_id) REFERENCES Athlete(athlete_id)\n);\nCREATE TABLE Format (\n format_id INT AUTO_INCREMENT PRIMARY KEY,\n tournament_id INT,\n name VARCHAR(100) NOT NULL,\n FOREIGN KEY (tournament_id) REFERENCES Tournament(tournament_id)\n);\nCREATE TABLE Medal (\n medal_id INT AUTO_INCREMENT PRIMARY KEY,\n format_id INT,\n type VARCHAR(50) NOT NULL,\n year INT,\n location VARCHAR(100) NOT NULL,\n FOREIGN KEY (format_id) REFERENCES Format(format_id)\n);\nCREATE TABLE PersonalInformation (\n info_id INT AUTO_INCREMENT PRIMARY KEY,\n athlete_id INT,\n birth_year INT,\n birth_month INT,\n birth_day INT,\n FOREIGN KEY (athlete_id) REFERENCES Athlete(athlete_id)\n);\n\\end{lstlisting}\n\n\\noindent\n\\textbf{2. Table Descriptions:}\n\\begin{verbatim}\ndescribe athlete;\n+------------+--------------+------+-----+---------+----------------+\n| Field | Type | Null | Key | Default | Extra |\n+------------+--------------+------+-----+---------+----------------+\n| athlete_id | int(11) | NO | PRI | NULL | auto_increment |\n| name | varchar(100) | NO | | NULL | |\n+------------+--------------+------+-----+---------+----------------+\ndescribe personalinformation;\n+-------------+---------+------+-----+---------+----------------+\n| Field | Type | Null | Key | Default | Extra |\n+-------------+---------+------+-----+---------+----------------+\n| info_id | int(11) | NO | PRI | NULL | auto_increment |\n| athlete_id | int(11) | YES | MUL | NULL | |\n| birth_year | int(11) | YES | | NULL | |\n| birth_month | int(11) | YES | | NULL | |\n| birth_day | int(11) | YES | | NULL | |\n+-------------+---------+------+-----+---------+----------------+\ndescribe tournament;\n+---------------+--------------+------+-----+---------+----------------+\n| Field | Type | Null | Key | Default | Extra |\n+---------------+--------------+------+-----+---------+----------------+\n| tournament_id | int(11) | NO | PRI | NULL | auto_increment |\n| athlete_id | int(11) | YES | MUL | NULL | |\n| name | varchar(100) | NO | | NULL | |\n+---------------+--------------+------+-----+---------+----------------+\ndescribe format;\n+---------------+--------------+------+-----+---------+----------------+\n| Field | Type | Null | Key | Default | Extra |\n+---------------+--------------+------+-----+---------+----------------+\n| format_id | int(11) | NO | PRI | NULL | auto_increment |\n| tournament_id | int(11) | YES | MUL | NULL | |\n| name | varchar(100) | NO | | NULL | |\n+---------------+--------------+------+-----+---------+----------------+\ndescribe medal;\n+-----------+--------------+------+-----+---------+----------------+\n| Field | Type | Null | Key | Default | Extra |\n+-----------+--------------+------+-----+---------+----------------+\n| medal_id | int(11) | NO | PRI | NULL | auto_increment |\n| format_id | int(11) | YES | MUL | NULL | |\n| type | varchar(50) | NO | | NULL | |\n| year | int(11) | YES | | NULL | |\n| location | varchar(100) | NO | | NULL | |\n+-----------+--------------+------+-----+---------+----------------+\n\\end{verbatim}\n\n\\vspace{10pt}\n\\noindent\n\\textbf{3. Example Data:}\n\\begin{verbatim}\nAthlete Table\n+------------+-----------------+\n| athlete_id | name |\n+------------+-----------------+\n| 50 | Carolina Marín |\n+------------+-----------------+\nPersonalInformation Table\n+---------+------------+------------+-------------+-----------+\n| info_id | athlete_id | birth_year | birth_month | birth_day |\n+---------+------------+------------+-------------+-----------+\n| 40 | 50 | 1993 | 6 | 15 |\n+---------+------------+------------+-------------+-----------+\nTournament Table\n+---------------+------------+-------------------------------+\n| tournament_id | athlete_id | name |\n+---------------+------------+-------------------------------+\n| 281 | 50 | Olympic Games |\n| 282 | 50 | World Championships |\n| 285 | 50 | European Women |\n+---------------+------------+-------------------------------+\nFormat Table\n+-----------+---------------+------------------+\n| format_id | tournament_id | name |\n+-----------+---------------+------------------+\n| 392 | 281 | Women's singles |\n| 393 | 282 | Women's singles |\n| 396 | 285 | Women's team |\n+-----------+---------------+------------------+\nMedal Table\n+----------+-----------+-------------+------+----------------+\n| medal_id | format_id | type | year | location |\n+----------+-----------+-------------+------+----------------+\n| 692 | 392 | MedalGold | 2016 | Rio de Janeiro |\n| 696 | 393 | MedalSilver | 2023 | Copenhagen |\n| 706 | 396 | MedalBronze | 2016 | Kazan |\n+----------+-----------+-------------+------+----------------+\n\\end{verbatim}\n\n\\vspace{10pt}\n\\noindent\n\\textbf{Example 1:} \\\\\n\\textit{Question: Which tournament(s) has Zhang Jike won the most Medals in?}\n\\begin{lstlisting}[language=SQL]\nWITH medal_counts AS (\n SELECT t.name AS tournament_name, m.year, COUNT(m.medal_id) AS total_medal_count\n FROM Medal m\n JOIN Format f ON m.format_id = f.format_id\n JOIN Tournament t ON f.tournament_id = t.tournament_id\n JOIN Athlete a ON t.athlete_id = a.athlete_id\n WHERE a.name = 'Zhang Jike'\n GROUP BY t.name, m.year\n)\nSELECT tournament_name, year\nFROM medal_counts\nWHERE total_medal_count = (\n SELECT MAX(total_medal_count)\n FROM medal_counts\n);\n\\end{lstlisting}\n\n\\vspace{10pt}\n\\noindent\n\\textbf{Example 2:} \\\\\n\\textit{Question: In which year(s) did Seo Seung-jae win medals in the Asian Junior Championships?}\n\\begin{lstlisting}[language=SQL]\nSELECT DISTINCT m.year\nFROM Medal m\nJOIN Format f ON m.format_id = f.format_id\nJOIN Tournament t ON f.tournament_id = t.tournament_id\nJOIN Athlete a ON t.athlete_id = a.athlete_id\nWHERE a.name = 'Seo Seung-jae'\n AND t.name = 'Asian Junior Championships';\n\\end{lstlisting}\n\n\\vspace{10pt}\n\\noindent\n\\textbf{Example 3:} \\\\\n\\textit{Question: Which was the most current medal win for Dola Banerjee?}\n\\begin{lstlisting}[language=SQL]\nSELECT m.type, m.year, m.location, f.name AS format_name, t.name AS tournament_name\nFROM Medal m\nJOIN Format f ON m.format_id = f.format_id\nJOIN Tournament t ON f.tournament_id = t.tournament_id\nJOIN Athlete a ON t.athlete_id = a.athlete_id\nWHERE a.name = 'Dola Banerjee'\n AND m.year = (\n SELECT MAX(m2.year)\n FROM Medal m2\n JOIN Format f2 ON m2.format_id = f2.format_id\n JOIN Tournament t2 ON f2.tournament_id = t2.tournament_id\n JOIN Athlete a2 ON t2.athlete_id = a2.athlete_id\n WHERE a2.name = 'Dola Banerjee'\n );\n\\end{lstlisting}\n\n\\vspace{10pt}\n\\noindent\n\\textbf{Example 4:} \\\\\n\\textit{Question: How many international medals did Rawinda Prajongjai win in 2023?}\n\\begin{lstlisting}[language=SQL]\nSELECT COUNT(m.medal_id) AS total_medals\nFROM Medal m\nJOIN Format f ON m.format_id = f.format_id\nJOIN Tournament t ON f.tournament_id = t.tournament_id\nJOIN Athlete a ON t.athlete_id = a.athlete_id\nWHERE a.name = 'Rawinda Prajongjai'\n AND m.year = 2023;\n\\end{lstlisting}\n\n\\vspace{10pt}\n\\noindent\n\\textbf{Example 5:} \\\\\n\\textit{Question: In which year(s) did Huang Dongping win the highest number of medals during their career?}\n\\begin{lstlisting}[language=SQL]\nSELECT m.year\nFROM Medal m\nJOIN Format f ON m.format_id = f.format_id\nJOIN Tournament t ON f.tournament_id = t.tournament_id\nJOIN Athlete a ON t.athlete_id = a.athlete_id\nWHERE a.name = 'Huang Dongping'\nGROUP BY m.year\nORDER BY COUNT(m.medal_id) DESC\nLIMIT 1;\n\\end{lstlisting}\n\n\\vspace{10pt}\n\\noindent\n\\textbf{Example 6:} \\\\\n\\textit{Question: In which year(s) did Tomokazu Harimoto win the lowest number of medals during their career?}\n\\begin{lstlisting}[language=SQL]\nSELECT m.year\nFROM Medal m\nJOIN Format f ON m.format_id = f.format_id\nJOIN Tournament t ON f.tournament_id = t.tournament_id\nJOIN Athlete a ON t.athlete_id = a.athlete_id\nWHERE a.name = 'Tomokazu Harimoto'\nGROUP BY m.year\nHAVING COUNT(m.medal_id) = (\n SELECT MIN(medal_count)\n FROM (\n SELECT COUNT(m2.medal_id) AS medal_count\n FROM Medal m2\n JOIN Format f2 ON m2.format_id = f2.format_id\n JOIN Tournament t2 ON f2.tournament_id = t2.tournament_id\n JOIN Athlete a2 ON t2.athlete_id = a2.athlete_id\n WHERE a2.name = 'Tomokazu Harimoto'\n GROUP BY m2.year\n ) AS yearly_medal_counts\n);\n\\end{lstlisting}\n\n\n\\vspace{10pt}\n\\noindent\n\\textbf{Instructions for Writing Queries:}\n\\begin{enumerate}\n \\item If a question can have multiple answers, do not limit the response to only one. Instead, output all possible answers.\n \\item Use the column names as specified in the schema to find the necessary parameters for the query.\n \\item An event is a combination of Tournament, Format, and the corresponding year.\n \\item There are three types of medals in the Medal Table: MedalGold, MedalSilver, MedalBronze.\n\\end{enumerate}\n\n\n\n\n\\end{document}\n"},"question":{"kind":"string","value":"Which table querying approaches (considering output, context and method) rank consistently in the top 3 on the more lenient evaluation metric for all of the underlying foundation models when averaging over questions of different degree of difficulty?"},"answer":{"kind":"string","value":"SQL, Schema, Adaptive"},"plan":{"kind":"string","value":"SELECT all models\nSELECT all outputs\nSELECT all contexts\nSELECT all methods\nSELECT EMS scores for Easy Medium Hard\nLOOP for each model\n LOOP for each combination of output context and method\n COMPUTE average EMS across Easy Medium Hard\n COMPUTE top 3 combinations by average EMS for this model\nCOMPUTE combinations that appear in the top 3 for every model\nRETURN combinations that are consistently in the top 3\n"}}}],"truncated":false,"partial":false},"paginationData":{"pageIndex":0,"numItemsPerPage":100,"numTotalItems":371,"offset":0,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6Im1sR1pFYktIVHRVSGRhX1RRdEczQ0N1S3k5ME9Qd25DNHY5elRjM3FVYzgifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4MzQ0NzQ1OCwianRpIjoiMDE0MWEyNDgtYWNjYy00ZGMwLWE5ZjAtODQyZWE4Zjk2MGI3Iiwic3ViIjoiL2RhdGFzZXRzL2podS1jbHNwL1NjaVRhUkMiLCJleHAiOjE3ODM0NTEwNTgsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.VWyC7O3VyiKDkjWAd5DwgFcGy5SZ2FI9JlscEl53EGxfMKpucKae0iXRpVEj99DXHs5EaAHwn4ITFgWoLor8Bg","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"test","numRows":371,"numBytesParquet":13748575}]},"discussionsStats":{"closed":0,"open":1,"total":1},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
paper stringlengths 10 10 | relevant_tables listlengths 1 3 | tables listlengths 3 55 | fulltext stringlengths 27.1k 359k | question stringlengths 36 601 | answer stringlengths 1 126 | plan stringlengths 76 1.47k ⌀ |
|---|---|---|---|---|---|---|
2401.06769 | [
[
"\\begin{table*}[h!]\n",
"\\centering\n",
"\\begin{tabularx}{\\textwidth}{@{}Xrrrrrrrrr@{}}\n",
"\\toprule\n",
"& \\multicolumn{3}{c}{M2M-100-418M} & \\multicolumn{3}{c}{SMaLL-100} & \\multicolumn{3}{c}{NLLB-200-1.3B} \\\\\n",
"\\cmidrule(lr){2-4} \\cmidrule(lr){5-7} \\cmidrule(lr){8-10}\n... | [
[
"\\begin{figure}\n",
" \\centering\n",
" \\includegraphics[width=\\columnwidth, trim=0 0.15cm 0 0, clip]{images/figure1}\n",
" \\caption{\n",
" NMT models can be used for inferring the likely original translation direction of parallel text.\n",
" In this example, the NMT... | % This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.
\pdfoutput=1
% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.
\documentclass[11pt]{article}
% Remove the "review" option to generate the final version.
%\usepackage[review]{acl... | Which model has the biggest difference in translation quality when translating into English versus from English, and what is the value of that difference? | NLLB-200-1.3B. 64.71 | SELECT all models
LOOP for each mode
SELECT all language pair containing en(English)
LOOP for each language pair containing en (English)
COMPUTE diff = abs(score translating into English − score translating from English)
COMPUTE max diff for the model
COMPUTE argmax max diff across all models
RETURN... |
2401.06769 | [
[
"\\begin{table}\n",
"\\centering\n",
"\\begin{tabularx}{\\columnwidth}{@{}Xrrr@{}}\n",
"\\toprule\n",
"Language Pair & \\(\\rightarrow\\) & \\(\\leftarrow\\) & Avg. \\\\\n",
"\\midrule\n",
"HT~~en\\biarrow cs & 88.24 & 80.62 & 84.43 \\\\\n",
"HT~~en\\biarrow de & 70.40 & 88.43 & ... | [
[
"\\begin{figure}\n",
" \\centering\n",
" \\includegraphics[width=\\columnwidth, trim=0 0.15cm 0 0, clip]{images/figure1}\n",
" \\caption{\n",
" NMT models can be used for inferring the likely original translation direction of parallel text.\n",
" In this example, the NMT... | % This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.
\pdfoutput=1
% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.
\documentclass[11pt]{article}
% Remove the "review" option to generate the final version.
%\usepackage[review]{acl... | Can we detect the translation direction for Czech-English better for human translation or neural machine translation? | neural translation | SELECT avg detection scores for (en-cs) for human translations from Table 1
SELECT avg detection scores for (en-cs) for neural machine translations from Table 2
IF human detection score > NMT detection score
RETURN human translation
ELSE
RETURN neural machine translation
|
2410.21272 | [
[
"\\begin{table}[h]\n",
" \\centering\n",
" \\caption{Accuracy of the analyzed models on arithmetic prompts.}\n",
" \\label{tab:model-accuracies}\n",
" \\begin{tabular}{l cccc c}\n",
" \\toprule \n",
" & \\multicolumn{4}{c}{\\textbf{Operator}} & \\\\\n",
" ... | [
[
"\\begin{figure}[t]\n",
" \\centering\n",
" \\includegraphics[width=0.96\\textwidth]{figures/opening-heuristics-updated.pdf}\n",
" \\caption{\\textbf{Bag of heuristics visualization.} We show that transformer LLMs solve arithmetic prompts by combining several unrelated heuristics, each ac... |
\documentclass{article} %
\usepackage{arxiv_preprint,times}
\usepackage{amsmath,amsfonts,bm}
\newcommand{\figleft}{{\em (Left)}}
\newcommand{\figcenter}{{\em (Center)}}
\newcommand{\figright}{{\em (Right)}}
\newcommand{\figtop}{{\em (Top)}}
\newcommand{\figbottom}{{\em (Bottom)}}
\newcommand{\captiona}{{\em (a)}}
\... | Calculate the average accuracy for addition and division operations for each model. | Llama3-8B: 0.945, Llama3-70B: 0.85, Pythia-6.9B: 0.525, GPT-J: 0.435 | SELECT all models
LOOP for each model
COMPUTE average_accuracy = (accuracy for + operation + accuracy for ÷ operation) / 2
RETURN average_accuracy for each model
|
2410.21272 | [
[
"\\begin{table}[h]\n",
" \\centering\n",
" \\caption{Accuracy of the analyzed models on arithmetic prompts.}\n",
" \\label{tab:model-accuracies}\n",
" \\begin{tabular}{l cccc c}\n",
" \\toprule \n",
" & \\multicolumn{4}{c}{\\textbf{Operator}} & \\\\\n",
" ... | [
[
"\\begin{figure}[t]\n",
" \\centering\n",
" \\includegraphics[width=0.96\\textwidth]{figures/opening-heuristics-updated.pdf}\n",
" \\caption{\\textbf{Bag of heuristics visualization.} We show that transformer LLMs solve arithmetic prompts by combining several unrelated heuristics, each ac... |
\documentclass{article} %
\usepackage{arxiv_preprint,times}
\usepackage{amsmath,amsfonts,bm}
\newcommand{\figleft}{{\em (Left)}}
\newcommand{\figcenter}{{\em (Center)}}
\newcommand{\figright}{{\em (Right)}}
\newcommand{\figtop}{{\em (Top)}}
\newcommand{\figbottom}{{\em (Bottom)}}
\newcommand{\captiona}{{\em (a)}}
\... | Which operation reduced the average accuracy of Llama3-70B model? | divison | SELECT Llama3-70B model
COMPUTE argmin accuracy across all operations
RETURN operation with lowest accuracy
|
2410.21272 | [
[
"\\begin{table}[h]\n",
" \\centering\n",
" \\caption{Accuracy of the analyzed models on arithmetic prompts.}\n",
" \\label{tab:model-accuracies}\n",
" \\begin{tabular}{l cccc c}\n",
" \\toprule \n",
" & \\multicolumn{4}{c}{\\textbf{Operator}} & \\\\\n",
" ... | [
[
"\\begin{figure}[t]\n",
" \\centering\n",
" \\includegraphics[width=0.96\\textwidth]{figures/opening-heuristics-updated.pdf}\n",
" \\caption{\\textbf{Bag of heuristics visualization.} We show that transformer LLMs solve arithmetic prompts by combining several unrelated heuristics, each ac... |
\documentclass{article} %
\usepackage{arxiv_preprint,times}
\usepackage{amsmath,amsfonts,bm}
\newcommand{\figleft}{{\em (Left)}}
\newcommand{\figcenter}{{\em (Center)}}
\newcommand{\figright}{{\em (Right)}}
\newcommand{\figtop}{{\em (Top)}}
\newcommand{\figbottom}{{\em (Bottom)}}
\newcommand{\captiona}{{\em (a)}}
\... | Which model has the highest average of the multiplication and division operations? | Llama3-8B | SELECT all models
LOOP for each model
COMPUTE average_accuracy = (accuracy for × operation + accuracy for ÷ operation) / 2
COMPUTE argmax average_accuracy
RETURN model with highest average
|
2205.15544 | [
[
"\\begin{table}[t]\n",
" \\begin{center}\n",
" \\caption{Comparison of BLEU scores for different methods on fully unsupervised translation tasks of various low-resource languages from the Indic, Uralic and Turkic language families. \n",
" }\n",
" \\resizebox{\\textwidth}{!}{\n",
... | [
[
"\\begin{figure}\n",
" \\begin{center}\n",
" \\centerline{\\includegraphics[width=0.9\\textwidth]{img/sharded_ffn.drawio.png}}\n",
" \\caption{Illustration of a decoder layer with language-specific sharded FFN layers, where the FFN parameters for each language pair are separate and reside... | \documentclass{article}
% if you need to pass options to natbib, use, e.g.:
\PassOptionsToPackage{numbers, compress}{natbib}
% before loading neurips_2022
\usepackage{microtype}
\usepackage{graphicx}
% \usepackage{subfigure}
\usepackage{booktabs} % for professional tables
\RequirePackage{xcolor}
% hyperref makes hy... | What is the average Nepali translation BLEU score for each method? | 7.2, 9.05, 10.0, 13.6 | SELECT language pairs containing Ne (Nepali)
LOOP for each method
COMPUTE average_Nepali_BLEU = average BLEU score for all Nepali pairs
RETURN average_Nepali_BLEU for each method
|
2205.15544 | [
[
"\\begin{table}[t]\n",
" \\begin{center}\n",
" \\caption{Comparison of BLEU scores for different methods on fully unsupervised translation tasks of various low-resource languages from the Indic, Uralic and Turkic language families. \n",
" }\n",
" \\resizebox{\\textwidth}{!}{\n",
... | [
[
"\\begin{figure}\n",
" \\begin{center}\n",
" \\centerline{\\includegraphics[width=0.9\\textwidth]{img/sharded_ffn.drawio.png}}\n",
" \\caption{Illustration of a decoder layer with language-specific sharded FFN layers, where the FFN parameters for each language pair are separate and reside... | \documentclass{article}
% if you need to pass options to natbib, use, e.g.:
\PassOptionsToPackage{numbers, compress}{natbib}
% before loading neurips_2022
\usepackage{microtype}
\usepackage{graphicx}
% \usepackage{subfigure}
\usepackage{booktabs} % for professional tables
\RequirePackage{xcolor}
% hyperref makes hy... | What are the languages mentioned in the table? | English, Nepali, Sinhala, Hindi, Gujarati, Finnish, Estonian, Latvian, Kazakh | SELECT all unique languages mentioned in the table
RETURN list of languages
|
2205.15544 | [
[
"\\begin{table}[t]\n",
" \\begin{center}\n",
" \\caption{Comparison of BLEU scores for different methods on fully unsupervised translation tasks of various low-resource languages from the Indic, Uralic and Turkic language families. \n",
" }\n",
" \\resizebox{\\textwidth}{!}{\n",
... | [
[
"\\begin{figure}\n",
" \\begin{center}\n",
" \\centerline{\\includegraphics[width=0.9\\textwidth]{img/sharded_ffn.drawio.png}}\n",
" \\caption{Illustration of a decoder layer with language-specific sharded FFN layers, where the FFN parameters for each language pair are separate and reside... | \documentclass{article}
% if you need to pass options to natbib, use, e.g.:
\PassOptionsToPackage{numbers, compress}{natbib}
% before loading neurips_2022
\usepackage{microtype}
\usepackage{graphicx}
% \usepackage{subfigure}
\usepackage{booktabs} % for professional tables
\RequirePackage{xcolor}
% hyperref makes hy... | Which language family has the highest average BLEU score using our method? | Uralic | SELECT Ours method
LOOP for each language family
COMPUTE average BLEU score across all language pairs in the family
COMPUTE argmax average BLEU
RETURN language family with highest average BLEU score
|
1903.00089 | [
[
"\\begin{table*}[!ht]\n",
"\\begin{center}\n",
"\\setlength\\tabcolsep{4.9pt}\n",
"\\begin{tabular}{l|llllllllll|l}\n",
" & Ar & Az & Be & De & He & It & Nl & Ro & Sk & Tr & Avg. \\\\ \\hline\n",
"baselines & 23.34 & 16.3 & 21.93 & 30.18 & 31.... | [
[
"\\begin{table}[!ht]\n",
"\\begin{center}\n",
"\\begin{small}\n",
"\\setlength\\tabcolsep{3.8pt}\n",
"\\begin{tabular}{l|llll|l}\n",
" & Az-En & Be-En & Gl-En & Sk-En & Avg. \\\\ \\hline\n",
"\\# of examples & 5.9k & 4.5k & 10k & 61k & 20.3k \\\\ \\hline\n",
"Neubi... | %
% File naacl2019.tex
%
%% Based on the style files for ACL 2018 and NAACL 2018, which were
%% Based on the style files for ACL-2015, with some improvements
%% taken from the NAACL-2016 style
%% Based on the style files for ACL-2014, which were, in turn,
%% based on ACL-2013, ACL-2012, ACL-2011, ACL-2010, ACL-IJCNLP... | Which translation direction has a higher BLEU score for Italian? | X→En | SELECT X to EN BLEU scores for Italian from Table 1
SELECT EN to X BLEU scores for Italian from Table 2
COMPUTE average BLEU score for X to EN
COMPUTE average BLEU score for EN to X
IF X to EN score > EN to X score
RETURN “X to EN”
ELSE
RETURN “EN to X”
|
1911.02782 | [
[
"\\begin{table*}[t!]\n",
" \\centering\n",
" \\scalebox{0.81}{\n",
" \\begin{tabular}{p{23mm}p{25mm}p{45mm}p{23mm}p{22mm}p{25mm}}\n",
" \\toprule\n",
" Domain & Dataset\t &\tReference\t&\tTask & \\textsc{Sci}BERT\t& \\gorbert \\\\\n",
" \\m... | [
[
"\\begin{figure}[t!]\n",
" \\centering\n",
" \\includegraphics[width=\\columnwidth]{gorc_links.png}\n",
" \\caption{Inline citations and references to figures and tables are annotated in \\gorc's structured full text. Citations are linked to bibliography entries, which are linked to other... | %
% File acl2020.tex
%
%% Based on the style files for ACL 2020, which were
%% Based on the style files for ACL 2018, NAACL 2018/19, which were
%% Based on the style files for ACL-2015, with some improvements
%% taken from the NAACL-2016 style
%% Based on the style files for ACL-2014, which were, in turn,
%% based on ... | In which domain does S2ORC outperform SCIBERT in most of the task? | Biomed | SELECT S2ORC and SCIBERT scores
LOOP for each domain
LOOP for each dataset in the domain
IF S2ORC score > SCIBERT score
COMPUTE increment win count for S2ORC in this domain
COMPUTE total tasks in domain
COMPUTE S2ORC win ratio = (S2ORC win count) / (total tasks)
COMPUTE argmax S2ORC ... |
1911.02782 | [
[
"\\begin{table*}[t!]\n",
" \\centering\n",
" \\scalebox{0.81}{\n",
" \\begin{tabular}{p{23mm}p{25mm}p{45mm}p{23mm}p{22mm}p{25mm}}\n",
" \\toprule\n",
" Domain & Dataset\t &\tReference\t&\tTask & \\textsc{Sci}BERT\t& \\gorbert \\\\\n",
" \\m... | [
[
"\\begin{figure}[t!]\n",
" \\centering\n",
" \\includegraphics[width=\\columnwidth]{gorc_links.png}\n",
" \\caption{Inline citations and references to figures and tables are annotated in \\gorc's structured full text. Citations are linked to bibliography entries, which are linked to other... | %
% File acl2020.tex
%
%% Based on the style files for ACL 2020, which were
%% Based on the style files for ACL 2018, NAACL 2018/19, which were
%% Based on the style files for ACL-2015, with some improvements
%% taken from the NAACL-2016 style
%% Based on the style files for ACL-2014, which were, in turn,
%% based on ... | Which task performed the worst in S2ORC compared to SCIBERT? | CLS | SELECT S2ORC and SCIBERT scores
LOOP for each dataset
COMPUTE diff = S2ORC score - SCIBERT score
COMPUTE argmin diff
RETURN task with lowest (most negative) diff
|
2402.01912 | [
[
"\\begin{table}[t!]\n",
" \\caption{Speech Quality and Intelligibility Measures (SQUIM) (with 95\\% confidence intervals)}\n",
" \\label{tab:squim}\n",
" \\centering\n",
" \\begin{small}\n",
" \\begin{tabular}{llll}\n",
" \\toprule\n",
" \\textbf{Model} & \\textbf{P... | [
[
"\\begin{figure*}[t!]\n",
" \\centering\n",
" \\includegraphics[scale=0.26]{architecture_v3.pdf}\n",
" \\caption{Overview of the model architecture}\n",
" \\label{fig:architecture}\n",
"\\end{figure*}\n"
],
[
"\\begin{figure}[t]\n",
" \\vskip -6mm\n",
" \\cent... | \documentclass{Interspeech2024}
% 2023-01-06 modified by Simon King (Simon.King@ed.ac.uk)
% **************************************
% * DOUBLE-BLIND REVIEW SETTINGS *
% **************************************
% Comment out \interspeechcameraready when submitting the
% paper for review.
% If your paper is accep... | Given the significance intervals, is the proposed method really better than the ground truth in terms of intelligibility? | No | SELECT Ground truth and Ours score
COMPUTE interval_ground lower bound = Ground truth score - CI
COMPUTE interval_ground upper bound = Ground truth score + CI
COMPUTE interval_ours lower bound= Ours score - CI
COMPUTE interval_ours upper bound=Ours score + CI
IF interval_ours lower bound > interval_ground upper bou... |
2412.00023 | [
[
"\\begin{table*}[!t]\n",
" \\centering\n",
" \\caption{Error handling performance metrics.}\n",
" \\label{tab:error_handling_performance}\n",
" \\resizebox{\\textwidth}{!}{ \n",
" \\begin{tabular}{lcccc}\n",
" \\toprule\n",
" \\multirow{2}... | [
[
"\\begin{figure}[!t]\n",
" \\centering \n",
" \\includegraphics[width=\\textwidth]{promoai_framework.png}\n",
" \\caption{LLM-based process modeling framework.}\n",
" \\label{fig:architecture}\n",
" \\end{figure}\n"
],
[
"\\begin{figure*}[!t]\n",
"\\centerin... | %Version 3 October 2023
% See section 11 of the User Manual for version history
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% %%
%% Please do not use \input{...} to include other tex files. %%
%% Submit your LaTeX manusc... | Which model has the most iterations. How many iterations? ( the average number of iterations was calulated by dividing the number of iterations by the sum of the cases in all the remaining columns) | WizardLM-2-8x22B, 67.6 | SELECT all models
LOOP for each model
COMPUTE total instances = num cases without error + num cases with auto-adjustment + num cases with failures
COMPUTE total iterations = total instances * avg num iterations
COMPUTE argmax total iterations
RETURN model with most iterations and the value
|
2412.00023 | [
[
"\\begin{table*}[!t]\n",
" \\centering\n",
" \\caption{Error handling performance metrics.}\n",
" \\label{tab:error_handling_performance}\n",
" \\resizebox{\\textwidth}{!}{ \n",
" \\begin{tabular}{lcccc}\n",
" \\toprule\n",
" \\multirow{2}... | [
[
"\\begin{figure}[!t]\n",
" \\centering \n",
" \\includegraphics[width=\\textwidth]{promoai_framework.png}\n",
" \\caption{LLM-based process modeling framework.}\n",
" \\label{fig:architecture}\n",
" \\end{figure}\n"
],
[
"\\begin{figure*}[!t]\n",
"\\centerin... | %Version 3 October 2023
% See section 11 of the User Manual for version history
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% %%
%% Please do not use \input{...} to include other tex files. %%
%% Submit your LaTeX manusc... | Which model has the highest average number of iterations while also having a failure case? | Codestral | SELECT all models
LOOP for each model
IF num cases with failure >= 1
COMPUTE add model to candidate
COMPUTE argmax avg number of iterations from candidate
RETURN model with highest average number of iterations and the value
|
2412.00023 | [
[
"\\begin{table*}[!t]\n",
" \\centering\n",
" \\caption{Error handling performance metrics.}\n",
" \\label{tab:error_handling_performance}\n",
" \\resizebox{\\textwidth}{!}{ \n",
" \\begin{tabular}{lcccc}\n",
" \\toprule\n",
" \\multirow{2}... | [
[
"\\begin{figure}[!t]\n",
" \\centering \n",
" \\includegraphics[width=\\textwidth]{promoai_framework.png}\n",
" \\caption{LLM-based process modeling framework.}\n",
" \\label{fig:architecture}\n",
" \\end{figure}\n"
],
[
"\\begin{figure*}[!t]\n",
"\\centerin... | %Version 3 October 2023
% See section 11 of the User Manual for version history
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% %%
%% Please do not use \input{...} to include other tex files. %%
%% Submit your LaTeX manusc... | What is the difference in average total time between the models with the highest and lowest number of cases without errors? | 84.92 | SELECT num cases without errors from Table 1
COMPUTE model_max = argmax(num cases without errors)
COMPUTE model_min = argmin(num cases without errors)
SELECT Avg. Total time from Table 2
COMPUTE difference = Avg. Total time of model_max - Avg. Total time of model_min
RETURN difference
|
2405.00123 | [
[
"\\begin{table}[t!]\n",
" \\caption{Macro f-score and weighted f-score.}\n",
" \\centering\n",
" \\setlength{\\tabcolsep}{5pt} % Increase the space between columns\n",
" \\begin{tabular}{lcccc} \n",
" \\toprule\n",
" & \\multicolumn{2}{c}{Semtab} & \\multicolumn{2}{c}{Webtables} \\\\\... | [
[
"\\begin{figure*}[t]\n",
"\n",
"\\centering\n",
"\\includegraphics[width=0.8\\textwidth]{problem} \n",
"\n",
"\\caption{The two tables on the right both have the column containing the values ``Paris\", ``Ottawa\" and ``London\". Without considering information coming from other columns it ... | % This is samplepaper.tex, a sample chapter demonstrating the
% LLNCS macro package for Springer Computer Science proceedings;
% Version 2.20 of 2017/10/04
%
\documentclass[runningheads]{llncs}
%\documentclass[20pt]{llncs}
%
\usepackage{graphicx}
% Used for displaying a sample figure. If possible, figure files should
%... | Does the RECA model achieve a higher weighted F1-score on Semtab or Webtables? | Webtables | SELECT RECA model
IF weighted F1-score on Semtab > weighted F1-score on Webtables
RETURN Semtab has higher weighted F1-score
ELSE IF F1-score on Webtables > F1-score on Semtab
RETURN Webtables has higher weighted F1-score
ELSE
RETURN Scores are equal
|
2405.00123 | [
[
"\\begin{table}\n",
"\\caption{The f-score improvement of $\\textnormal{GAIT}_{\\textnormal{GAT}}$ over RECA by tables of different number of columns.}\n",
"\\centering\n",
"\\setlength{\\tabcolsep}{1pt} % Reduce the space between columns\n",
"\\resizebox{\\textwidth}{!}{%\n",
"\\normalsiz... | [
[
"\\begin{figure*}[t]\n",
"\n",
"\\centering\n",
"\\includegraphics[width=0.8\\textwidth]{problem} \n",
"\n",
"\\caption{The two tables on the right both have the column containing the values ``Paris\", ``Ottawa\" and ``London\". Without considering information coming from other columns it ... | % This is samplepaper.tex, a sample chapter demonstrating the
% LLNCS macro package for Springer Computer Science proceedings;
% Version 2.20 of 2017/10/04
%
\documentclass[runningheads]{llncs}
%\documentclass[20pt]{llncs}
%
\usepackage{graphicx}
% Used for displaying a sample figure. If possible, figure files should
%... | For WebTables, on tables with how many columns does GAT-GNN show the most improvement over RECA in terms of Macro F-score? | 2 | SELECT Macro F-score in WebTables
LOOP for each column count
COMPUTE improvement = Macro F-score (GAT-GNN) - Macro F-score (RECA)
COMPUTE argmax improvement
RETURN column count with greatest improvement and the value
|
2402.03300 | [
[
"\\begin{table*}[t!]\n",
" \\centering\n",
" \\adjustbox{max width=\\textwidth}{\n",
"\\begin{tabular}{llllccccc} \n",
"\\toprule\n",
"\\multirow{2}{*}{Training Setting} & \\multicolumn{3}{l}{Training Tokens} & \\multicolumn{3}{c}{w/o Tool Use} & \\multicolumn{2}{c}{w/ Tool Use} \\\... | [
[
"\\begin{figure}[h]\n",
"\\begin{center}\n",
" \\includegraphics[width=0.68\\textwidth]{figures/Math.png}\n",
" \\caption{ Top\\@1 accuracy of open-source models on the competition-level MATH benchmark \\citep{MATH} without the use of external toolkits and voting techniques.\n",
" }\n",... | \documentclass[11pt, a4paper, logo, copyright, nonumbering]{deepseek}
\usepackage[authoryear, sort&compress, round]{natbib}
\usepackage{dblfloatfix}
\usepackage{ulem}
\usepackage{caption}
% \usepackage{subcaption}
% \usepackage{listings}
% \lstset{breaklines=true}
\usepackage{dramatist}
\usepackage{xspace}
\usepackage... | Which training setting is better according to this table for math: without tool use or with tool use? | without tool use | SELECT common datasets for with and without tool use
SELECT all Math Training rows
LOOP for each metric
IF (score with tool use) > (score without tool use)
COMPUTE increment count_with_tool_use
ELSE IF (score without tool use) > (score with tool use)
COMPUTE increment count_without_tool_use
... |
2404.00484 | [
[
"\\begin{table}[t]\n",
"\\centering\n",
"\\resizebox{\\columnwidth}{!}{\n",
"\\begin{tabular}{lcccc}\n",
"\\toprule\n",
"\\textbf{Model} & \\textbf{F1} & \\textbf{Faith.} & \\textbf{Con.} & \\textbf{Avg.} \\\\\n",
"\\midrule\n",
"Mistral-7B-Instruct & 0.6525 & 0.1343 & 0.415... | [
[
"\\begin{table*}[ht!]\n",
"\\small\n",
"\\centering\n",
"\\begin{tabular}{lccccccccc}\n",
"\\toprule\n",
"\\multicolumn{1}{c}{\\multirow{2}{*}{\\textbf{Split}}} &\n",
" \\multicolumn{1}{c}{\\multirow{2}{*}{\\textbf{Total}}} &\n",
" \\multicolumn{2}{c}{\\textbf{Sample Type}} &\n",... | % This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.
\pdfoutput=1
% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.
\documentclass[11pt]{article}
% Change "review" to "final" to generate the final (sometimes called camera-ready) v... | How much lower is the F1-score of the second-best model compared to GPT-4? | 0.1066 | SELECT F1-scores for all models
COMPUTE sort models by F1-score in descending order
COMPUTE difference = F1-score of GPT-4 − F1-score of second-best model
RETURN difference
|
2412.06769 | [
[
"\\begin{table*}\n",
" \\centering\n",
" \\label{tab:main}\n",
" \\vspace{-20pt}\n",
" \\begin{tabular}{@{}rcccccc}\n",
" \n",
" \\toprule\n",
" \\multirow{2}{*}[-2pt]{Method} & \\multicolumn{2}{c}{GSM8k} & \\multicolumn{2}{c}{ProntoQA} & \\multicolumn{2}{c}{\\... | [
[
"\\begin{figure}\n",
" \\centering\n",
" \\includegraphics[width=\\linewidth]{figures/figure_1_meta_3.png}\n",
" \\caption{A comparison of Chain of Continuous Thought (\\ours) with Chain-of-Thought (CoT). In CoT, the model generates the reasoning process as a word token sequence (e.g., $[... | \documentclass[]{fairmeta}
% Option "twocolumn" available, but please prioritize single-column
\usepackage{wrapfig}
\usepackage{hyperref}
\usepackage{url}
\usepackage{algcompatible}
\usepackage{algorithm}
\usepackage{algpseudocode}
\usepackage{graphicx}
\usepackage[T1]{fontenc}
\usepackage{booktabs}
\usepackage{multir... | On which benchmarks does the proposed method outperform baseline chain-of-thought reasoning? | ProntoQA and ProsQA | SELECT benchmarks
LOOP for each benchmark
IF Coconut (Ours) Accuracy > CoT Accuracy
COMPUTE add benchmark to results
RETURN results
|
2502.00359 | [
[
"\\begin{table}[ht]\n",
"\\caption{\\textbf{FID Comparisons with Vanilla DiTs and SiTs.} Generate on ImageNet $256\\times256$ without classifier-free guidance.}\n",
"\\centering\n",
"\\small % \\scriptsize % \\scriptsize %\\footnotesize % \\tiny\n",
"\\resizebox{8cm}{!}{\n",
"\\begin{tabul... | [
[
"\\begin{figure}[t]\n",
" \\centering\n",
" \\includegraphics[width=0.90\\linewidth]{figs/cluster.jpg}\n",
" \\vspace{-0.6cm}\n",
" \\caption{\\textbf{Representation-Aligned Latent Space (ReaLS) preserves more image semantics.} a) t-SNE visualization of our latent space reveals a c... | %%%%%%%% ICML 2025 EXAMPLE LATEX SUBMISSION FILE %%%%%%%%%%%%%%%%%
\documentclass{article}
\usepackage{amsmath}
\usepackage{amssymb}
\usepackage{mathtools}
\usepackage{amsthm}
%%%%% NEW MATH DEFINITIONS %%%%%
\usepackage{amsmath,amsfonts,bm}
% Mark sections of captions for referring to divisions of figures
\newcomm... | For the SiT-B/2 model, do more steps help across all metrics? | Yes | SELECT models matching (SiT-B/2, Ours)
LOOP for each model as m1
LOOP for each model as m2
IF m1 != m2 AND steps(m1) < steps(m2)
IF average_score(m1) > average_score(m2)
RETURN no
RETURN yes
|
2401.14280 | [
[
"\\begin{table}[t]\n",
"\\centering\n",
"\\small\n",
"\\resizebox{\\linewidth}{!}{\n",
"\\begin{tabular}{lcccccc}\n",
"\\toprule\n",
"\\multirow{2}{*}{} & \\multirow{2}{*}{\\textbf{Script}} & \\multicolumn{2}{c}{\\textbf{BaseLLM}} & \\multicolumn{2}{c}{\\textbf{CPT}} & \\t... | [
[
"\\begin{figure*}[t]\n",
" \\centering\n",
" \\includegraphics[scale=0.3]{mainmatter/images/radarplot.pdf}\n",
" \\caption{Performance of the BaseLLM (Llama 2 Base), our continually pretrained model (CPT), and our instruction finetuned model (IFT), in both native (N) and romanized (R) scr... | % This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.
\pdfoutput=1
% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.
\documentclass[11pt]{article}
% Change "review" to "final" to generate the final (sometimes called camera-ready) v... | For 1-shot training, which script performs best across all testset? | Romanized script | SELECT Base line LLM 1 shot , CPT 1 shot
SELECT Native script values
COMPUTE average across all test sets as avg 1
SELECT Romanised script values
COMPUTE average across all test sets as avg 2
RETURN script with greater average.
|
2401.14280 | [
[
"\\begin{table}[t]\n",
"\\centering\n",
"\\small\n",
"\\resizebox{\\linewidth}{!}{\n",
"\\begin{tabular}{lcccccc}\n",
"\\toprule\n",
"\\multirow{2}{*}{} & \\multirow{2}{*}{\\textbf{Script}} & \\multicolumn{2}{c}{\\textbf{BaseLLM}} & \\multicolumn{2}{c}{\\textbf{CPT}} & \\t... | [
[
"\\begin{figure*}[t]\n",
" \\centering\n",
" \\includegraphics[scale=0.3]{mainmatter/images/radarplot.pdf}\n",
" \\caption{Performance of the BaseLLM (Llama 2 Base), our continually pretrained model (CPT), and our instruction finetuned model (IFT), in both native (N) and romanized (R) scr... | % This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.
\pdfoutput=1
% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.
\documentclass[11pt]{article}
% Change "review" to "final" to generate the final (sometimes called camera-ready) v... | Does the BaseLLM model outperform the CPT model for 3-shot training in native script? | No | SELECT native script
SELECT Base LLM 3 shot
COMPUTE average as avg1
SELECT CPT 3 shot
COMPUTE average as avg2
RETURN IF avg1 > avg 2
|
2310.08130 | [
[
"\\begin{table*}[t!]\n",
"\\fontsize{9}{10} \\selectfont\n",
"\\centering\n",
"\\bgroup\n",
"\\def\\arraystretch{1.2}\n",
"\\begin{tabular}{cccccccccccccc}\n",
"\\toprule[0.8pt]\n",
"\\multirow{3}{*}{Model} & \\multirow{3}{*}{Strategy} & \\multicolumn{6}{c}{DailyDialog} & & \\mult... | [
[
"\\begin{table*}[t!]\n",
"\\fontsize{9}{10} \\selectfont\n",
"\\centering\n",
"\\bgroup\n",
"\\def\\arraystretch{1.2}\n",
"\\begin{tabular}{cccccccccccccc}\n",
"\\toprule[0.8pt]\n",
"\\multirow{3}{*}{Model} & \\multirow{3}{*}{Strategy} & \\multicolumn{6}{c}{DailyDialog} & & \\mult... | % This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.
\pdfoutput=1
% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.
\documentclass[11pt]{article}
% Remove the "review" option to generate the final version.
\usepackage[]{EMNLP2023}... | For the LCCC dataset, which model has the highest average BERTscore? | SimDRC | SELECT LCCC dataset
SELECT BS score
COMPUTE average across all models and strategies
RETURN model with highest average
|
2310.08130 | [
[
"\\begin{table*}[t!]\n",
"\\fontsize{9}{10} \\selectfont\n",
"\\centering\n",
"\\bgroup\n",
"\\def\\arraystretch{1.2}\n",
"\\begin{tabular}{cccccccccccccc}\n",
"\\toprule[0.8pt]\n",
"\\multirow{3}{*}{Model} & \\multirow{3}{*}{Strategy} & \\multicolumn{6}{c}{DailyDialog} & & \\mult... | [
[
"\\begin{table*}[t!]\n",
"\\fontsize{9}{10} \\selectfont\n",
"\\centering\n",
"\\bgroup\n",
"\\def\\arraystretch{1.2}\n",
"\\begin{tabular}{cccccccccccccc}\n",
"\\toprule[0.8pt]\n",
"\\multirow{3}{*}{Model} & \\multirow{3}{*}{Strategy} & \\multicolumn{6}{c}{DailyDialog} & & \\mult... | % This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.
\pdfoutput=1
% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.
\documentclass[11pt]{article}
% Remove the "review" option to generate the final version.
\usepackage[]{EMNLP2023}... | Is contrastive decoding better on average across all models and evaluation sets than greedy decoding when measured with the G-Eval metric? | Yes | SELECT Contrastive Strategy for all models
COMPUTE average across all evaluation sets as avg 1
SELECT Greedy Strategy for all models
COMPUTE average across all evaluation sets as avg2
RETURN IF avg1 > avg 2
|
2310.08130 | [
[
"\\begin{table*}[t!]\n",
"\\fontsize{9}{10} \\selectfont\n",
"\\centering\n",
"\\bgroup\n",
"\\def\\arraystretch{1.2}\n",
"\\begin{tabular}{cccccccccccccc}\n",
"\\toprule[0.8pt]\n",
"\\multirow{3}{*}{Model} & \\multirow{3}{*}{Strategy} & \\multicolumn{6}{c}{DailyDialog} & & \\mult... | [
[
"\\begin{table*}[t!]\n",
"\\fontsize{9}{10} \\selectfont\n",
"\\centering\n",
"\\bgroup\n",
"\\def\\arraystretch{1.2}\n",
"\\begin{tabular}{cccccccccccccc}\n",
"\\toprule[0.8pt]\n",
"\\multirow{3}{*}{Model} & \\multirow{3}{*}{Strategy} & \\multicolumn{6}{c}{DailyDialog} & & \\mult... | % This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.
\pdfoutput=1
% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.
\documentclass[11pt]{article}
% Remove the "review" option to generate the final version.
\usepackage[]{EMNLP2023}... | Sort the strategies based on ascending order of the BERTScore for the BART model using DailyDialogue dataset. | IPS, Beam, top-k, nucleus, greedy, constractive | SELECT daily dialogue dataset
SELECT BART model
COMPUTE sort strategies by BS score in ascending order
RETURN sorted list of strategies
|
2310.00905 | [
[
"\\begin{table}[h]\n",
"\\centering\n",
"\\small\n",
"\\caption{Large Scale Human Evaluation of ChatGPT's Response}\n",
"\\label{tab:chatgpt_large_human_eval}\n",
"\\resizebox{0.49\\textwidth}{!}{\n",
"\\begin{tabular}{l rrrr}\n",
"\\toprule\n",
"\\textbf{Lang} & \\bf Auto Un... | [
[
"\\begin{figure}[t]\n",
" \\centering\n",
" \\includegraphics[width=0.48\\textwidth]{Figures/cover.pdf}\n",
" \\caption{Chat with ChatGPT in non-English languages can lead to unsafe behaviors.}\n",
" \\label{cover}\n",
"\\end{figure}\n"
],
[
"\\begin{figure}\n",
" ... | % This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.
\pdfoutput=1
% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.
\documentclass[11pt]{article}
% Change "review" to "final" to generate the final (sometimes called camera-ready) v... | Which model's response is most safe? | ChatGPT | SELECT Human Unsafe from Ave. and Auto unsafe Ave. from table 1
COMPUTE average
SELECT Human Unsafe from Ave. and Auto unsafe Ave. from table 2
COMPUTE average
RETURN model with higher average
|
2310.00905 | [
[
"\\begin{table}[h]\n",
"\\centering\n",
"\\small\n",
"\\caption{Large Scale Human Evaluation of ChatGPT's Response}\n",
"\\label{tab:chatgpt_large_human_eval}\n",
"\\resizebox{0.49\\textwidth}{!}{\n",
"\\begin{tabular}{l rrrr}\n",
"\\toprule\n",
"\\textbf{Lang} & \\bf Auto Un... | [
[
"\\begin{figure}[t]\n",
" \\centering\n",
" \\includegraphics[width=0.48\\textwidth]{Figures/cover.pdf}\n",
" \\caption{Chat with ChatGPT in non-English languages can lead to unsafe behaviors.}\n",
" \\label{cover}\n",
"\\end{figure}\n"
],
[
"\\begin{figure}\n",
" ... | % This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.
\pdfoutput=1
% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.
\documentclass[11pt]{article}
% Change "review" to "final" to generate the final (sometimes called camera-ready) v... | Which languages have a higher correctness rate for LLaMA compared to ChatGPT? | fr, de, ru | SELECT Correct % from table 1 and 2
LOOP for each language
COMPUTE compare LLAma score to Chat GPT score
RETURN languages where LLama > Chatgpt
|
2402.14710 | [
[
"\\begin{table*}[t]\n",
" \\centering\n",
" \\setlength{\\tabcolsep}{2.5pt}\n",
" \\begin{tabular}{l|c|cc|c|ccc|c} \n",
" \\toprule\n",
" \\multirow{2}*{Method} & \\multicolumn{1}{c|}{NER} & \\multicolumn{3}{c|}{RE} & \\multicolumn{4}{c}{EE} \\\\\n",
" \\clin... | [
[
"\\begin{figure*}[!t]\n",
"\\begin{center}\n",
"\\resizebox{0.99\\textwidth}{!}{\n",
"\\includegraphics{figs/dataconstruct.pdf}\n",
"}\n",
"\\caption{An overview of the construction of {\\ours}, including Data Collection and Cleaning, as well as Schema-Based Instruction Generation (Hard Ne... | % This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.
\pdfoutput=1
% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.
\documentclass[11pt]{article}
% Change "review" to "final" to generate the final (sometimes called camera-ready) v... | Which model performs best on the FewRel and the Wiki-ZSL dataset? | Baichuan2-IEPILE, OneKE | SELECT FewRel dataset
COMPUTE model-fewrel = model with highest score
SELECT Wiki-ZSL dataset
COMPUTE model-wiki-zsl = model with highest score
RETURN model-fewrel, model-wiki-zsl
|
2402.14710 | [
[
"\\begin{table*}[t]\n",
" \\centering\n",
" \\setlength{\\tabcolsep}{2.5pt}\n",
" \\begin{tabular}{l|c|cc|c|ccc|c} \n",
" \\toprule\n",
" \\multirow{2}*{Method} & \\multicolumn{1}{c|}{NER} & \\multicolumn{3}{c|}{RE} & \\multicolumn{4}{c}{EE} \\\\\n",
" \\clin... | [
[
"\\begin{figure*}[!t]\n",
"\\begin{center}\n",
"\\resizebox{0.99\\textwidth}{!}{\n",
"\\includegraphics{figs/dataconstruct.pdf}\n",
"}\n",
"\\caption{An overview of the construction of {\\ours}, including Data Collection and Cleaning, as well as Schema-Based Instruction Generation (Hard Ne... | % This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.
\pdfoutput=1
% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.
\documentclass[11pt]{article}
% Change "review" to "final" to generate the final (sometimes called camera-ready) v... | In how many datasets does GPT-4 score better than the InstructUIE model? | 2 | SELECT all datasets
LOOP for each dataset
SELECT GPT-4 score and InstructUIE score
IF GPT-4 score > InstructUIE score
COMPUTE increase count
RETURN count
|
2402.14652 | [
[
"\\begin{table*}[h!]\n",
" \\begin{center}\n",
" \\small\n",
" \\begin{tabular}[b]{lcccccccccc}\n",
" \\toprule\n",
" & \\multicolumn{2}{c}{\\textbf{English}} & \\multicolumn{2}{c}{\\textbf{German}} & \\multicolumn{2}{c}{\\textbf{Spanish}} & \\multicolumn{2}{c}{... | [
[
"\\begin{figure}[t]\n",
" \\centering\n",
" \\includegraphics[width=0.48\\textwidth]{image/nsx.pdf}\n",
" \\caption{The Pipeline of Primary Content Extraction Using \\texttt{NeuScraper} (Neural Web Scraper).}\n",
" \\label{fig:model}\n",
"\\end{figure}Rule-based web scrapers start from... | % This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.
\pdfoutput=1
% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.
\documentclass[11pt]{article}
% Remove the "review" option to generate the final version.
\usepackage[]{acl}
\usep... | In which languages does html2text not perform the worst based on accuracy? | Japanese | SELECT all languages
LOOP for each language
SELECT accuracy for html2text and all other methods
COMPUTE argmin accuracy among methods
IF method with argmin accuracy is not equal to html2text
COMPUTE add language to result_list
RETURN result_list
|
2308.15363 | [
[
"\\begin{table*}[th]\n",
" \\small\n",
"\t\\centering\n",
"\t\\begin{tabular}{llcccccccccc}\n",
"\t\t\\toprule\n",
"\t\t\\multirow{2}{*}{Few-shot}\t& \\multirow{2}{*}{Selection}\t& \\multirow{2}{*}{\\makecell{Question \\\\Similarity}} & \\multirow{2}{*}{\\makecell{Query \\\\Simil... | [
[
"\\begin{table}[t]\n",
"\\small\n",
"\t\\begin{tabular}{cccccccc}\n",
"\t\t\\toprule\n",
"\t\t\\makecell{Question \\\\Representation}\t& INS & RI & FK &\tRef.\t& LLMs &\t\\makecell{EX \\\\(\\%)}\t\\\\\n",
"\t\t\\hline\n",
"\t\t\\abbsprompt\t& \\ding{55} & \\ding{55} & \\ding{55} &\... | \documentclass[sigconf, nonacm]{acmart}
\usepackage{subfigure}
\usepackage{xcolor}
\usepackage{xspace}
\usepackage{booktabs}
\usepackage{url}
% \usepackage[sort]{natbib}
\usepackage{multirow}
\usepackage{listings}
\usepackage{makecell}
\usepackage{tabularx}
\usepackage{utfsym}
\usepackage[linesnumbered, boxed]{algorit... | What is the average absolute increase in EM score of the 5-shot method over the 1-shot method across all selection methods for GPT-3.5-TURBO? | 6.1 | SELECT EM scores for GPT-3.5-TURBO with 1-shot and 5-shot for all selection methods
LOOP for each selection method
COMPUTE abs_increase = abs(5-shot EM score - 1-shot EM score)
COMPUTE add abs_increase to total
COMPUTE number of selection methods
COMPUTE average_abs_increase = total / number of selection meth... |
2412.11732 | [
[
"\\begin{table*}[t]\n",
" \\centering\n",
" \\scalebox{0.94}{\n",
" \\begin{tabular}{cc rrrrr}\n",
" \\toprule\n",
" \\multirow{2}{*}{\\bf Type} & \\multirow{2}{*}{\\bf System} & \\multicolumn{4}{c}{\\color{blue} Sent-Level} & \\multicolumn{1}{c}{\\color{red} Doc-Level} ... | [
[
"\\begin{figure*}\n",
"\\begin{minipage}[b]{0.5\\linewidth}\n",
" \\begin{tabular}{l rrrrr}\n",
" \\toprule\n",
" \\textbf{\\color{blue}Zh-En} & \\textbf{\\#Book} & \\textbf{\\#Chap.} & \\textbf{\\#Sent.} & \\bf \\#Word & \\textbf{|D|} \\\\\n",
" \\midrule\n",
" Train & ... | % This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.
\pdfoutput=1
% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.
\documentclass[11pt]{article}
% Remove the "review" option to generate the final version.
\usepackage[]{EMNLP2023}... | How many primary submissions beat the best contrastive system based on the COMET score? | 1 | SELECT COMET scores for all primary submissions
COMPUTE max COMET score from all contrastive systems
LOOP for each primary submission
IF COMET score > best contrastive system COMET score
COMPUTE increase count
RETURN count
|
2410.03115 | [
[
"\\begin{table}[t]\n",
"\\caption{\n",
"The overall results of Flores test data across each language group in \\texttt{en}$\\rightarrow$\\texttt{xx}. Scores are reported using COMET-22. X-ALMA outperforms both massively multilingual models, such as Aya-101, and models focus specifically on high-resour... | [
[
"\\begin{figure}[ht]\n",
" \\centering\n",
" \\resizebox{0.8\\linewidth}{!}{\n",
" \\includegraphics[width=7.5cm]{figures/all_models.pdf}}\n",
" \\caption{\n",
" Relationship between the number of supported languages and their average translation performance. While many stat... |
\documentclass{article} % For LaTeX2e
\usepackage{iclr2025_conference,times}
% Optional math commands from https://github.com/goodfeli/dlbook_notation.
%%%%% NEW MATH DEFINITIONS %%%%%
\usepackage{amsmath,amsfonts,bm}
% Mark sections of captions for referring to divisions of figures
\newcommand{\figleft}{{\em (Left... | Comparing X-ALMA and Aya-101, does it perform better on average out of English or into English translation? | out of English | SELECT translation scores for X-ALMA and Aya-101 for out of English direction from Table 1
SELECT translation scores for X-ALMA and Aya-101 for into English direction from Table 2
COMPUTE average score for out of English
COMPUTE average score for into English
IF average score (out of English) > average score (into ... |
2410.03115 | [
[
"\\begin{table}[t]\n",
"\\caption{\n",
"Average performance comparison of various preference optimization methods for \\texttt{en}$\\rightarrow$\\texttt{xx} and \\texttt{xx}$\\rightarrow$\\texttt{en} on Group 6.\n",
"}\n",
"\\vskip 0.05in\n",
"\\label{tab:po_compare}\n",
"\\centering\n... | [
[
"\\begin{figure}[ht]\n",
" \\centering\n",
" \\resizebox{0.8\\linewidth}{!}{\n",
" \\includegraphics[width=7.5cm]{figures/all_models.pdf}}\n",
" \\caption{\n",
" Relationship between the number of supported languages and their average translation performance. While many stat... |
\documentclass{article} % For LaTeX2e
\usepackage{iclr2025_conference,times}
% Optional math commands from https://github.com/goodfeli/dlbook_notation.
%%%%% NEW MATH DEFINITIONS %%%%%
\usepackage{amsmath,amsfonts,bm}
% Mark sections of captions for referring to divisions of figures
\newcommand{\figleft}{{\em (Left... | Which preference optimization methods lead to improvements in both direction using the COMET-22 score? | ARPO | SELECT COMET-22 scores in both directions
LOOP for each preference optimization method
IF COMET-22 score (direction en->xx) > baseline (direction en->xx) and COMET-22 score (direction xx->en) >baseline (direction xx->en)
COMPUTE add method to improvement_list
RETURN improvement_list
|
2407.19884 | [
[
"\\begin{table*}\n",
"\\centering\n",
"\\begin{tabular}{c}\n",
"\\bf{\\Large{English-Czech}}\n",
"\\vspace{1em}\n",
"\\end{tabular}\n",
"\\begin{tabular}{R{40mm}C{22mm}C{19mm}C{22mm}C{32mm}}\n",
"\\bf System Name & \\bf AutoRank $\\downarrow$ & \\bf MetricX $\\downarrow$ & \\bf Com... | [
[
"\\begin{table*}\n",
"\\centering\n",
"\\begin{tabular}{c}\n",
"\\bf{\\Large{Czech-Ukrainian}}\n",
"\\vspace{1em}\n",
"\\end{tabular}\n",
"\\begin{tabular}{R{40mm}C{22mm}C{19mm}C{22mm}C{32mm}}\n",
"\\bf System Name & \\bf AutoRank $\\downarrow$ & \\bf MetricX $\\downarrow$ & \\bf C... | % This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.
\pdfoutput=1
% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.
\documentclass[11pt]{article}
% Change "review" to "final" to generate the final (sometimes called camera-ready) v... | Which constraint system is the best according to CometKiwi? Constraint system is not marked with any color background in the results table. | CUNI-GA | SELECT CometKiwi scores for all rows without any mark color (constraint systems) in the table
COMPUTE argmax CometKiwi score
RETURN constraint system with highest score
|
2407.19884 | [
[
"\\begin{table*}\n",
"\\centering\n",
"\\begin{tabular}{c}\n",
"\\bf{\\Large{English-Czech}}\n",
"\\vspace{1em}\n",
"\\end{tabular}\n",
"\\begin{tabular}{R{40mm}C{22mm}C{19mm}C{22mm}C{32mm}}\n",
"\\bf System Name & \\bf AutoRank $\\downarrow$ & \\bf MetricX $\\downarrow$ & \\bf Com... | [
[
"\\begin{table*}\n",
"\\centering\n",
"\\begin{tabular}{c}\n",
"\\bf{\\Large{Czech-Ukrainian}}\n",
"\\vspace{1em}\n",
"\\end{tabular}\n",
"\\begin{tabular}{R{40mm}C{22mm}C{19mm}C{22mm}C{32mm}}\n",
"\\bf System Name & \\bf AutoRank $\\downarrow$ & \\bf MetricX $\\downarrow$ & \\bf C... | % This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.
\pdfoutput=1
% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.
\documentclass[11pt]{article}
% Change "review" to "final" to generate the final (sometimes called camera-ready) v... | Which constraint system is the best according to MetricX? Constraint system is not marked with any color background in the results table. | CUNI-MH | SELECT MetricX scores for all constraint systems without any mark color in the table
COMPUTE argmin MetricX score
RETURN constraint system with lowest score
|
2407.19884 | [
[
"\\begin{table*}\n",
"\\centering\n",
"\\begin{tabular}{c}\n",
"\\bf{\\Large{English-Chinese}}\n",
"\\vspace{1em}\n",
"\\end{tabular}\n",
"\\begin{tabular}{R{40mm}C{22mm}C{19mm}C{22mm}C{32mm}}\n",
"\\bf System Name & \\bf AutoRank $\\downarrow$ & \\bf MetricX $\\downarrow$ & \\bf C... | [
[
"\\begin{table*}\n",
"\\centering\n",
"\\begin{tabular}{c}\n",
"\\bf{\\Large{Czech-Ukrainian}}\n",
"\\vspace{1em}\n",
"\\end{tabular}\n",
"\\begin{tabular}{R{40mm}C{22mm}C{19mm}C{22mm}C{32mm}}\n",
"\\bf System Name & \\bf AutoRank $\\downarrow$ & \\bf MetricX $\\downarrow$ & \\bf C... | % This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.
\pdfoutput=1
% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.
\documentclass[11pt]{article}
% Change "review" to "final" to generate the final (sometimes called camera-ready) v... | Which constraint systems have better CometKiwi scores when translating from Japanese than when translating from English? Constraint systems are not marked with any color background in the results table. | CycleL | SELECT CometKiwi for from English marked with light gray background (open systems) from table 1
SELECT CometKiwi for from Japanese marked with light gray background (open systems) from table 2
LOOP for each open system
IF CometKiwi score from Japanese > CometKiwi score from English
COMPUTE add system to r... |
2406.13748 | [
[
"\\begin{table}[ht!] % Use h to keep the table here\n",
" \\centering\n",
" \\footnotesize % Use a smaller font size\n",
" \\renewcommand{\\arraystretch}{1.2} % Adjust row height to prevent overlapping\n",
" \\setlength{\\tabcolsep}{6pt} % Adjust column spacing\n",
"\n",
" ... | [
[
"\\begin{figure*}[hbt!]\n",
" \\centering\n",
" \\includegraphics[width=1\\linewidth]{pics/unlearn/teaser.pdf}\n",
" \\caption{With \\emph{non-English} harmful data introduced during training, harmful information spread across languages. In this paper, our finds reveal that unlearning focused o... | % This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.
\pdfoutput=1
% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.
\documentclass[11pt]{article}
% Change "review" to "final" to generate the final (sometimes called camera-ready) v... | What is the average performance for European languages when unlearning in combined languages? | 81.75 | SELECT scores for English, German, Russian and French in combined language setting
COMPUTE average_performance = average of all selected scores
RETURN average_performance
|
2402.16832 | [
[
"\\begin{table*}[!t]\n",
" \\centering\n",
" \\resizebox{1.0\\linewidth}{!}{%\n",
" \\begin{tabular}{l cc cc cc cc cc cc cc}\n",
" \\toprule\n",
" {\\sc \\textbf{Models/Variants}} & \\multicolumn{2}{c}{\\sc Agriculture} & \\multicolumn{2}{c}{\\sc Textures} & \\multicolumn{2}... | [
[
"\\begin{figure}[t!]\n",
" \\centering\n",
" \\includegraphics[width=1.02\\linewidth]{OverviewFigure.pdf}\n",
" \\caption{{\\textbf{Overview of our study.} While the MLLM's domain-specific visual capability can be improved using fine-tuning strategies, the domain-specific richness of the ... | % This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.
\pdfoutput=1
% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.
\documentclass[11pt]{article}
% Remove the "review" option to generate the final version.
\usepackage[]{ACL2023}
... | In which domain does LLaVa zero-shot achieve the highest accuracy? | Humanitarian | SELECT accuracies for LLaVA zero-shot across all domains
COMPUTE target_domain = domain with maximum accuracy
RETURN target_domain
|
2502.05171 | [
[
"\\begin{table*}[tb]\n",
"\\small\n",
"\\centering\n",
"\\caption{Results on lm-eval-harness tasks zero-shot across various open-source models. We show ARC \\citep{allenai:arc}, HellaSwag \\citep{zellers2019hellaswag}, MMLU \\citep{hendryckstest2021}, OpenBookQA \\citep{OpenBookQA2018}, PiQA \\cit... | [
[
"\\begin{figure}\n",
" \\centering\n",
" \\includegraphics[width=0.5\\textwidth]{figures/multi_benchmark_no_baselines.pdf}\n",
" \\caption{We train a 3.5B parameter language model with depth recurrence. At test time, the model can iterate longer to use more compute and improve its perform... | \documentclass{article}
\PassOptionsToPackage{dvipsnames}{xcolor}
\usepackage[accepted]{shmicml2025}
% \usepackage[]{icml2025}
\usepackage[activate={true,nocompatibility},final,tracking=true,kerning=true,spacing=true,factor=1100,stretch=10,shrink=10]{microtype}
% activate={true,nocompatibility} - activate protrusion ... | On which test sets does the best of the proposed models beat the most baseline models? | ARC-E, MMLU and SciQ | SELECT baseline model scores for each test set
SELECT best score among “Ours” for each test set
LOOP for each test set
COMPUTE number of baseline models where best_ours_score > baseline_score
COMPUTE maximum value among all computed numbers
SELECT test sets whose computed number equals the maximum value
RETURN test... |
2502.05171 | [
[
"\\begin{table*}[tb]\n",
"\\small\n",
"\\centering\n",
"\\caption{Results on lm-eval-harness tasks zero-shot across various open-source models. We show ARC \\citep{allenai:arc}, HellaSwag \\citep{zellers2019hellaswag}, MMLU \\citep{hendryckstest2021}, OpenBookQA \\citep{OpenBookQA2018}, PiQA \\cit... | [
[
"\\begin{figure}\n",
" \\centering\n",
" \\includegraphics[width=0.5\\textwidth]{figures/multi_benchmark_no_baselines.pdf}\n",
" \\caption{We train a 3.5B parameter language model with depth recurrence. At test time, the model can iterate longer to use more compute and improve its perform... | \documentclass{article}
\PassOptionsToPackage{dvipsnames}{xcolor}
\usepackage[accepted]{shmicml2025}
% \usepackage[]{icml2025}
\usepackage[activate={true,nocompatibility},final,tracking=true,kerning=true,spacing=true,factor=1100,stretch=10,shrink=10]{microtype}
% activate={true,nocompatibility} - activate protrusion ... | On which test sets are bigger models (either model size or longer token windows) always at least as good as smaller models of the same kind? | HelloSwag, PiQA | SELECT all test sets
SELECT all model families
SELECT all token-window groups
LOOP for each test set
COMPUTE violation_exists = false
LOOP for each model family
SELECT models in this family ordered from smallest to largest
LOOP for each adjacent model pair
IF larger_model_score < sma... |
2411.19504 | [
[
"\\begin{table*}[htbp]\n",
"\\centering\n",
"\\resizebox{\\textwidth}{!}{%\n",
"\\begin{tabular}{ l|ccccccc|c|ccccccc|c }\n",
" \\hline\n",
" & \\multicolumn{8}{c}{\\textbf{8K}} & \\multicolumn{8}{|c}{\\textbf{16K}} \\\\\n",
" \\hline\n",
" \\textbf{Model} & \\textbf{EL} & \\textb... | [
[
"\\begin{table}[htbp]\n",
"\\centering\n",
"\\resizebox{\\columnwidth}{!}{\n",
"\\begin{tabular}{l c c c c c} \n",
"\\hline\n",
"\\textbf{Database Name} & \\textbf{Source} & \\textbf{Table Count} & \\textbf{Average Columns} &\\textbf{ Average Rows} & \\textbf{Total Cells}\\\\\n",
"\\hl... | \documentclass{article}
% if you need to pass options to natbib, use, e.g.:
% \PassOptionsToPackage{numbers, compress}{natbib}
% before loading neurips_2024
% ready for submission
%\usepackage{neurips_2024}
% to compile a preprint version, e.g., for submission to arXiv, add add the
% [preprint] option:
\usepa... | Which model has the best average performance, not counting the TS and CS operations in a 32K token window? | GPT-4o | SELECT all models with the 32K token window
SELECT all operations except TS and CS
LOOP for each selected model
SELECT scores of this model on all selected operations
COMPUTE average score of this model across selected operations
COMPUTE model with maximum average score across selected operations
RETURN that mo... |
2412.15115 | [
[
"\\begin{table}[tbp]\n",
"\\centering\n",
"\\caption{\\textbf{Performance of the 70B+ base models and Qwen2.5-Plus.}}\n",
"\\label{tab:base-70B}\n",
"\\small %\n",
"\\setlength{\\tabcolsep}{3pt} %\n",
"\\renewcommand{\\arraystretch}{0.9} \n",
"\n",
"\\begin{tabular}{@{}lcccccc@... | [
[
"\\begin{figure}[hbp]\n",
" \\centering\n",
" \\includegraphics[width=0.92\\textwidth]{figures/main.pdf}\n",
" \\caption{In the iterative development of the Qwen series, data scaling has played a crucial role. Qwen~2.5, which leverages 18 trillion tokens for pre-training, has demonstrated... | \documentclass{article} %
\usepackage{colm2024_conference}
\usepackage{booktabs}
\usepackage{graphicx}
\usepackage{enumitem}
\usepackage{wrapfig}
\usepackage{algorithm}
\usepackage{algpseudocode}
\usepackage{microtype}
\usepackage{amsmath}
\usepackage{colortbl}
\usepackage[utf8]{inputenc}
\definecolor{lightgray}{rgb}... | In which category of tasks does Qwen2.5-Plus outperform the newer Qwen 72B model on average? | General Tasks, Mathematics & Science and Multilingual | SELECT all task categories
SELECT scores for Qwen2.5-Plus
SELECT scores for Qwen-72B
LOOP for each task category
COMPUTE average score of Qwen2.5-Plus in the category
COMPUTE average score of Qwen-72B in the category
IF average score of Qwen2.5-Plus > average score of Qwen-72B
COMPUTE add category t... |
2412.15115 | [
[
"\\begin{table}[t]\n",
"\n",
"\\centering\n",
"\n",
"\\caption{\\textbf{Performance of the 7B-14B Instruct models on Multilingual Tasks.}}\n",
"\n",
"\\label{tab:eval_multilingual_14B}\n",
"\\small\n",
"\\setlength{\\tabcolsep}{2.6pt}\n",
"\n",
"\\begin{tabular}{@{}lccc... | [
[
"\\begin{figure}[hbp]\n",
" \\centering\n",
" \\includegraphics[width=0.92\\textwidth]{figures/main.pdf}\n",
" \\caption{In the iterative development of the Qwen series, data scaling has played a crucial role. Qwen~2.5, which leverages 18 trillion tokens for pre-training, has demonstrated... | \documentclass{article} %
\usepackage{colm2024_conference}
\usepackage{booktabs}
\usepackage{graphicx}
\usepackage{enumitem}
\usepackage{wrapfig}
\usepackage{algorithm}
\usepackage{algpseudocode}
\usepackage{microtype}
\usepackage{amsmath}
\usepackage{colortbl}
\usepackage[utf8]{inputenc}
\definecolor{lightgray}{rgb}... | Which language is the hardest for the models? | Korean | SELECT all languages
SELECT all model scores for each language
LOOP for each language
COMPUTE average score across all models for this language
COMPUTE minimum average score across all languages
SELECT language whose average score equals the minimum average score as answer
RETURN answer
|
2412.15115 | [
[
"\\begin{table}[tbp]\n",
"\\centering\n",
"\\caption{\\textbf{Performance of the 7B+ base models.}}\n",
"\\label{tab:base-7B}\n",
"\\small\n",
"\\begin{tabular}{@{}lccccc@{}}\n",
"\\toprule\n",
"\\textbf{Datasets} & \\textbf{Mistral-7B} & \\textbf{Llama3-8B} & \\textbf{Gemma2-9B} &... | [
[
"\\begin{figure}[hbp]\n",
" \\centering\n",
" \\includegraphics[width=0.92\\textwidth]{figures/main.pdf}\n",
" \\caption{In the iterative development of the Qwen series, data scaling has played a crucial role. Qwen~2.5, which leverages 18 trillion tokens for pre-training, has demonstrated... | \documentclass{article} %
\usepackage{colm2024_conference}
\usepackage{booktabs}
\usepackage{graphicx}
\usepackage{enumitem}
\usepackage{wrapfig}
\usepackage{algorithm}
\usepackage{algpseudocode}
\usepackage{microtype}
\usepackage{amsmath}
\usepackage{colortbl}
\usepackage[utf8]{inputenc}
\definecolor{lightgray}{rgb}... | What is the smallest model that outperforms at least two models with over 7 billion parameters on any task? Also name the task. | Qwen2-0.5B on GPQA | SELECT all tasks
SELECT all models with size less than 7B
LOOP for each model with size less than 7B
LOOP for each task
SELECT performance of this small model on this task
SELECT all models with size at least 7B
SELECT performances of all models with size at least 7B on this task
C... |
2505.14499 | [
[
"\\begin{table}[H]\\vspace{-1.0em}\\scriptsize \n",
" \\centering\n",
" \\setlength\\tabcolsep{3pt} \n",
" \\caption{Performance comparison of MATE and MASC methodologies. }\n",
" \\begin{tabular}{ccccccc|ccccc}\n",
" \\hline\n",
"\\specialrule{0em}{1pt}{1pt}\n",
" ... | [
[
"\\begin{figure}\\vspace{-1em}\n",
" \\centering\n",
" \\includegraphics[width=1\\linewidth]{Rationale.pdf}\n",
" \\caption{An example of the LLM processing image-text pairs to generate the explanation of images and text.}\n",
" \\label{fig1}\n",
"\\end{figure}\n"
],
[
... | % This is samplepaper.tex, a sample chapter demonstrating the
% LLNCS macro package for Springer Computer Science proceedings;
% Version 2.21 of 2022/01/12
%
\documentclass[runningheads]{llncs}
%
\usepackage[T1]{fontenc}
% T1 fonts will be used to generate the final print and online PDFs,
% so please use T1 fonts in yo... | On the MATE Task, do the models have on average a stronger bias towards recall on the the more recent Twitter dataset or the older one? | recent one: Twitter2017 | SELECT all models appearing in the MATE task
SELECT recall values on Twitter2015
SELECT recall values on Twitter2017
LOOP for each model
COMPUTE recall_old = recall on Twitter2015 for this model
COMPUTE recall_new = recall on Twitter2017 for this model
COMPUTE avg_old = average of recall_old across all models
C... |
2505.14347 | [
[
"\\begin{table*}[!ht]\n",
"\\centering\n",
"\\small\n",
"\\setlength{\\tabcolsep}{5pt}\n",
"\\begin{tabular}{lllccccc}\n",
"\\toprule\n",
"\\textbf{Method} & \\textbf{Model Name} & \\textbf{Params.} & \\textbf{Best k} & \\textbf{ROUGE-1} & \\textbf{ROUGE-2} & \\textbf{ROUGE-L} & \\text... | [
[
"\\begin{figure}\n",
" \\centering\n",
" \\includegraphics[width=\\linewidth]{assets/method_summary.pdf}\n",
" \\caption{Framework for QA-prompting: based on the domain of article, and a user defined top-$k$ value, relevant questions are extracted from a corpus. A prompt is constructed to... | % This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.
\pdfoutput=1
% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.
\documentclass[11pt]{article}
% Change "review" to "final" to generate the final (sometimes called camera-ready) v... | For which models do the proposed methods outperform all prior approaches on all metrics? | Gemma-3, Qwen2.5 0.5B, Llama-3.1 | SELECT all models evaluated under the proposed method
SELECT all prior methods
SELECT evaluation metrics R1, R2, RL, BF1
LOOP for each proposed model
LOOP for each evaluation metric
SELECT proposed model score on this metric
SELECT all prior method scores on this metric
COMPUTE max prior sco... |
2505.14297 | [
[
"\\begin{table*}[ht]\n",
"\\centering\n",
"\\resizebox{1.0\\linewidth}{!}{\n",
"\\begin{tabular}{%\n",
" ll\n",
" ccc\n",
" ccc\n",
" ccc\n",
" ccc\n",
" ccc\n",
" ccc\n",
"}\n",
"\\toprule\n",
"& & \\multicolumn{6}{c}{High-Resource} \n"... | [
[
"\\begin{figure}[t]\n",
" \\centering\n",
"\\includegraphics[width=1.0\\linewidth]{pdf/example.pdf}\n",
"\\caption{Example responses to a Swahili query generated by English-centric instruction models, the SFT model, and the proposed CLO model.}\n",
" \\label{fig:example_intro}\n",
"\\end{f... | \pdfoutput=1
\documentclass[11pt]{article}
\usepackage[final]{acl}
\usepackage{times}
\usepackage{latexsym}
\usepackage{listings}
\usepackage{geometry}
\geometry{a4paper, margin=1in}
% \usepackage[table]{xcolor}
\usepackage{color}
\usepackage{colortbl}
\usepackage[T1]{fontenc}
\usepackage[utf8]{inputenc}
\usepackage... | How many of the improvements for Yoruba are statistically significant (no overlapping confidence intervals)? | 6 | SELECT Yoruba
COMPUTE interval_SFT_DPO lower bound = score_SFT_DPO - std_SFT_DPO
COMPUTE interval_SFT_DPO upper bound = score_SFT_DPO + std_SFT_DPO
COMPUTE interval_CLO lower bound = score_CLO - std_CLO
COMPUTE interval_CLO upper bound = score_CLO + std_CLO
IF interval_SFT_DPO lower bound > interval_CLO upper bound... |
2505.14297 | [
[
"\\begin{table*}[t]\n",
"\\centering\n",
"\\resizebox{1.0\\textwidth}{!}{%\n",
" \\begin{tabular}{lc|cc|cc|cc|cc|cc|cc} \n",
" \\toprule\n",
" \\multirow{4}{*}{\\textbf{Model}} & \\multirow{4}{*}{\\textbf{Method}} \n",
" & \\multicolumn{4}{c|}{\\textbf{High-Resource}} \n",
... | [
[
"\\begin{figure}[t]\n",
" \\centering\n",
"\\includegraphics[width=1.0\\linewidth]{pdf/example.pdf}\n",
"\\caption{Example responses to a Swahili query generated by English-centric instruction models, the SFT model, and the proposed CLO model.}\n",
" \\label{fig:example_intro}\n",
"\\end{f... | \pdfoutput=1
\documentclass[11pt]{article}
\usepackage[final]{acl}
\usepackage{times}
\usepackage{latexsym}
\usepackage{listings}
\usepackage{geometry}
\geometry{a4paper, margin=1in}
% \usepackage[table]{xcolor}
\usepackage{color}
\usepackage{colortbl}
\usepackage[T1]{fontenc}
\usepackage[utf8]{inputenc}
\usepackage... | For which language does CLO achieve the greatest average improvement over supervised fine-tuning? | Korean | LOOP for each language
SELECT all target language accuracy values for CLO
COMPUTE average CLO accuracy
SELECT all target language accuracy values for SFT
COMPUTE average SFT accuracy
COMPUTE average improvement = average CLO accuracy – average SFT accuracy
COMPUTE argmax language of average improvem... |
2505.14297 | [
[
"\\begin{table*}[t]\n",
"\\centering\n",
"\\resizebox{1.0\\textwidth}{!}{%\n",
" \\begin{tabular}{lc|cc|cc|cc|cc|cc|cc} \n",
" \\toprule\n",
" \\multirow{4}{*}{\\textbf{Model}} & \\multirow{4}{*}{\\textbf{Method}} \n",
" & \\multicolumn{4}{c|}{\\textbf{High-Resource}} \n",
... | [
[
"\\begin{figure}[t]\n",
" \\centering\n",
"\\includegraphics[width=1.0\\linewidth]{pdf/example.pdf}\n",
"\\caption{Example responses to a Swahili query generated by English-centric instruction models, the SFT model, and the proposed CLO model.}\n",
" \\label{fig:example_intro}\n",
"\\end{f... | \pdfoutput=1
\documentclass[11pt]{article}
\usepackage[final]{acl}
\usepackage{times}
\usepackage{latexsym}
\usepackage{listings}
\usepackage{geometry}
\geometry{a4paper, margin=1in}
% \usepackage[table]{xcolor}
\usepackage{color}
\usepackage{colortbl}
\usepackage[T1]{fontenc}
\usepackage[utf8]{inputenc}
\usepackage... | On the BELEBELE and MMMLU benchmarks for English, for which language/model combinations does the winner between a SFT variant and CLO differ between the two benchmarks? | Mistral/Korean, Mistral/Swahili, Llama-2-13B/Swahili, Llama-3-8B/Swahili | SELECT Table for MMMLU
LOOP for each language/model combination
SELECT English benchmark scores for SFT and CLO
COMPUTE argmax between SFT and CLO (MMMLU_winner)
SELECT Table for BELEBELE
LOOP for each language/model combination
SELECT English benchmark scores for SFT and CLO
COMPUTE argmax between SFT an... |
2505.14183 | [
[
"\\begin{table*}[!ht]\n",
"\\centering\n",
"\\resizebox{\\textwidth}{!}{%\n",
"\\begin{tabular}{lcccccccccccccccc}\n",
"\\toprule\n",
"\\multirow{2}{*}{\\textbf{Method}} & \\multicolumn{2}{c}{\\textbf{GSM8K}} & \\multicolumn{2}{c}{\\textbf{MATH500}} & \\multicolumn{2}{c}{\\textbf{AIME24}} ... | [
[
"\\begin{figure}[!t]\n",
"\\centering\n",
"\\vspace{-0.2cm}\n",
"\\includegraphics[width=0.48\\textwidth]{figure/cot_case_comparison.pdf}\n",
"\\caption{Comparison of long and short CoTs generated using different prompting strategies with Deepseek-R1-Distill-Qwen-7B. While long CoT reasoning o... | % This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.
% \pdfoutput=1
% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.
\documentclass[11pt]{article}
% Change "review" to "final" to generate the final (sometimes called camera-ready)... | Just comparing BERT and ThinkSwitcher, what are the exceptions to the observation that the model that thinks longer is better? | DeepSeek-R1-Distill-Qwen-7B on AIME24 and OmniMATH, DeepSeek-R1-Distill-Qwen-14B on AIME24, AIME25, LiveAoPS, and OlymBench | LOOP for each benchmark
LOOP for each model
SELECT BERT/token, BERT/accuracy, ThinkSwitcher/token, ThinkSwitcher/accuracy for model, benchmark
COMPUTE argmax Token of BERT and ThinkSwitcher as argmax-token
COMPUTE argmax Accuracy of BERT and ThinkSwitcher as argmax-accuracy
IF argmax... |
2505.14178 | [
[
"\\begin{table*}[t!]\n",
"\\centering\n",
"\\resizebox{\\textwidth}{!}{\n",
"\\begin{tabular}{rcccccc|cccccc}\n",
"\\toprule\n",
" & \\multicolumn{6}{c|}{\\textbf{Counting letter \\texttt{a}}} & \\multicolumn{6}{c}{\\textbf{Counting letter \\texttt{b}}} \\\\\n",
"\\cmidrule(lr){2-7} \\... | [
[
"\\begin{figure*}[t!]\n",
" \\centering\n",
" \\includegraphics[width=0.8\\linewidth]{inductive.png}\n",
" \\caption{Illustration of inductive reasoning as performed by humans, RNNs, and LLMs with CoT, respectively. }\n",
" \\label{fig:inductive}\n",
" \\vspace{-1.em}\n",
... | % This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.
\pdfoutput=1
\documentclass[11pt]{article}
% Load xcolor early with necessary options
\usepackage[dvipsnames, table]{xcolor}
\usepackage{bm}
% Other packages
\usepackage[final]{naacl2021}
\usepackage{amsfonts}
\usepackage... | When using chain-of-thought reasoning and providing a precise item, are models more accurate at counting vowels or consonants, and what is the difference in average accuracy? | consonants | SELECT value for CoT and precise-item token for a from Table 1 and e from Table 2
SELECT value for CoT and precise-item token for b from Table 1 and z from Table 2
COMPUTE average accuracy for vowels (a, e)
COMPUTE average accuracy for consonants (b, z)
RETURN argmin average accuracy
|
2505.17005 | [
[
"\\begin{table*}[t]\n",
"\\centering\n",
"\\resizebox{1\\linewidth}{!}{\n",
"\\begin{tabular}{lccccccccccccccc}\n",
"\\toprule\n",
"\\multirow{2}{*}{\\textbf{Models}} & \\multicolumn{3}{c}{\\textbf{HotpotQA$^\\dagger$}} & \\multicolumn{3}{c}{\\textbf{2Wiki$^\\dagger$}} & \\multicolumn{3}{... | [
[
"\\begin{figure*}[t]\n",
" \\centering\n",
" \\includegraphics[width=1\\linewidth]{img/model.pdf}\n",
" \\caption{Overall framework of our proposed R1-Searcher++ approach.}\n",
" \\label{fig:main_photo}\n",
"\\end{figure*}\n"
],
[
"\\begin{table*}[t]\n",
"\\centerin... | % This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.
\pdfoutput=1
% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.
\documentclass[11pt]{article}
% Change "review" to "final" to generate the final (sometimes called camera-ready) v... | On which metric does the proposed method achieve the best performance across all datasets? | LasJ | LOOP for each metric
LOOP for each dataset
COMPUTE argmax score
IF score of R1-Searcher++ is not argmax
COMPUTE skip to next metric
COMPUTE add metric to best
RETURN best
|
2505.16986 | [
[
"\\begin{table*}[!ht]\n",
"\\centering\n",
"\\resizebox{\\textwidth}{!}{\n",
" \\begin{tabular}{lcccc|cccc|c|cc|c}\n",
" \\toprule\n",
" \\textbf{Domain} & \\multicolumn{4}{c}{\\textbf{Tool Call}} & \\multicolumn{4}{|c|}{\\textbf{Parameter Matching}} & \\textbf{Code Exec. Rate} & ... | [
[
"\\begin{figure*}[!t]\n",
" \\centering\n",
" \\includegraphics[width=\\textwidth]{assets/pipeline.png}\n",
" \\caption{Illustrative example from the $\\benchmarkname$ dataset. This example showcases a multi-domain scenario involving both flights and hotels, where the user is planning a t... | \documentclass{article}
% if you need to pass options to natbib, use, e.g.:
% \PassOptionsToPackage{numbers, compress}{natbib}
% before loading neurips_2024
\usepackage[square,numbers]{natbib}
\bibliographystyle{abbrvnat}
% ready for submission
% \usepackage[]{neurips_2025}
\usepackage[preprint]{neurips_2025}
... | In which domain does Llama 3.3 70B Instruct outperform Llama 3.1 8B Instruct by the largest margin in Code Execution Rate? | Hotels | SELECT Code Execution Rate for Llama 3.1 8B Instruct and Llama 3.3 70B Instruct
LOOP for each domain
COMPUTE difference = 70B rate - 8B rate
RETURN domain with max difference
|
2505.16986 | [
[
"\\begin{table*}[!ht]\n",
"\\centering\n",
"\\resizebox{\\textwidth}{!}{\n",
" \\begin{tabular}{lcccc|cccc|c|cc|c}\n",
" \\toprule\n",
" \\textbf{Domain} & \\multicolumn{4}{c}{\\textbf{Tool Call}} & \\multicolumn{4}{|c|}{\\textbf{Parameter Matching}} & \\textbf{Code Exec. Rate} & ... | [
[
"\\begin{figure*}[!t]\n",
" \\centering\n",
" \\includegraphics[width=\\textwidth]{assets/pipeline.png}\n",
" \\caption{Illustrative example from the $\\benchmarkname$ dataset. This example showcases a multi-domain scenario involving both flights and hotels, where the user is planning a t... | \documentclass{article}
% if you need to pass options to natbib, use, e.g.:
% \PassOptionsToPackage{numbers, compress}{natbib}
% before loading neurips_2024
\usepackage[square,numbers]{natbib}
\bibliographystyle{abbrvnat}
% ready for submission
% \usepackage[]{neurips_2025}
\usepackage[preprint]{neurips_2025}
... | In which domain subsets that include tasks about hotels does the large Llama model outperform the small fine-tuned Llama model, measured by tool call accuracy? | Hotels, H-A | SELECT Tool Call Accuracy for Llama 3.1 8B Instruct and Llama 3.3 70B Instruct
SELECT domain subsets that include hotel (H)
LOOP for each selected domain subset
IF Accuracy 70B > 8B
COMPUTE add domain subset to outperform
RETURN outperform
|
2505.16972 | [
[
"\\begin{table*}\n",
"\\centering\n",
"\\begin{tabular}{lr|cccccccc}\n",
"\\toprule \n",
"\\multirow{2}{*}{Model} & \\multirow{2}{*}{Size} & \\multicolumn{4}{c}{High} & \\multicolumn{2}{c}{Mid} & \\multicolumn{2}{c}{Low} \\\\\n",
"\\cmidrule(lr){3-6} \\cmidrule(lr){7-8} \\cmidrule(lr){9... | [
[
"\\begin{figure*}\n",
"\\centering\n",
" \\includegraphics[width=0.8\\linewidth]{latex/figures/speechbt.pdf}\n",
" \\vspace{-5pt}\n",
" \\caption{\n",
" Pipeline of \\tf{Speech Back-Translation}. \n",
" The main objective is to augment limited training data ($\\leq$100 hours) ... | % This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.
\pdfoutput=1
% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.
\documentclass[11pt,table]{article}
% Change "review" to "final" to generate the final (sometimes called camera-re... | Taking the average for the Multilingual LibriSpeech and Voxpopuli datasets, on which languages does the proposed method that adds 500k of backtranslated speech data not outperform all baseline models? | Spanish | SELECT overlap languages and models across dataset
LOOP for each language
LOOP for each model
COMPUTE average performance across Multilingual LibriSpeech and Voxpopuli
IF performance of 500K <= max baselines performance
COMPUTE add language to not_outperform
RETURN not_outperform
|
2505.16972 | [
[
"\\begin{table*}\n",
"\\centering\n",
"\\begin{tabular}{lr|cccccccc}\n",
"\\toprule \n",
"\\multirow{2}{*}{Model} & \\multirow{2}{*}{Size} & \\multicolumn{4}{c}{High} & \\multicolumn{2}{c}{Mid} & \\multicolumn{2}{c}{Low} \\\\\n",
"\\cmidrule(lr){3-6} \\cmidrule(lr){7-8} \\cmidrule(lr){9... | [
[
"\\begin{figure*}\n",
"\\centering\n",
" \\includegraphics[width=0.8\\linewidth]{latex/figures/speechbt.pdf}\n",
" \\vspace{-5pt}\n",
" \\caption{\n",
" Pipeline of \\tf{Speech Back-Translation}. \n",
" The main objective is to augment limited training data ($\\leq$100 hours) ... | % This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.
\pdfoutput=1
% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.
\documentclass[11pt,table]{article}
% Change "review" to "final" to generate the final (sometimes called camera-re... | Considering all datasets, for which language does the proposed method that adds 500k of backtranslated speech data help the least against its baseline model? | Spanish | SELECT overlap languages and models across all datasets
LOOP for each language
COMPUTE average baseline performance across all datasets
COMPUTE average performance of proposed 500K across all datasets
COMPUTE improvement = average proposed 500K - average baseline
RETURN language with smallest improvement
|
2505.16956 | [
[
"\\begin{table*}[h]\n",
"\\centering\n",
"\\small\n",
"\\resizebox{\\textwidth}{!}{\n",
"\\begin{tabular}{@{}l r r @{\\hspace{0.6em}}rrrr rrrr rrrr r@{}} \n",
"\\toprule\n",
"\\multirow{2}{*}{\\textbf{Model Configuration}} & \\multirow{2}{*}{\\textbf{Red.}} & \\multirow{2}{*}{\\textbf... | [
[
"\\begin{figure}[t]\n",
" \\centering\n",
" \\includegraphics[width=0.6\\linewidth]{figures/distil_2.jpg}\n",
" \\caption{Overview of our multilingual model compression methodology. We use \\textbf{(1)} knowledge distillation to reduce layers, \\textbf{(2)} structured pruning to eliminate... | \pdfoutput=1
\documentclass[11pt]{article}
\usepackage[final]{acl}
\usepackage{times}
\usepackage{latexsym}
\usepackage{arydshln}
\usepackage[T1]{fontenc}
\usepackage[utf8]{inputenc}
\usepackage{microtype}
\usepackage{inconsolata}
\usepackage{graphicx}
\usepackage{booktabs}
\usepackage{multirow}
\usepackage{array}
... | When averaging over the test sets, which language shows the least performance loss with the most aggressive compression method over the corresponding monolingual baseline models? | Slovak | SELECT row of Base for monolingual baseline model
SELECT row of -92% reduction as most aggressive compression
LOOP for each language
COMPUTE average performance for baseline across tasks
COMPUTE average performance for compression across tasks
COMPUTE loss = avg baseline - avg compression
RETURN language wi... |
2505.16637 | [
[
"\\begin{table*}[t]\n",
"\\resizebox{\\textwidth}{!}{%\n",
"\\begin{tabular}{@{}llllllllllllllll@{}}\n",
"\\toprule\n",
" & \\multicolumn{7}{c}{ZH$\\rightarrow$EN} & & \\multicolumn{7}{c}{EN$\\rightarrow$ZH} \\\\ \\cmidrule(lr){2-8} \\cmidrule(l){10-16} \n",
"Models & \\multicolumn{2}{c}{... | [
[
"\\begin{figure*}[t]\n",
" \\includegraphics[width=\\textwidth]{figures/fig1.SSR-pipeline.pdf}\n",
" \\caption{Overview of the \\emoji SSR framework. SSR is an R1-Zero-like RL training method for machine translation, which uses the same model as both actor and judge. It does not require external rew... | % This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.
\pdfoutput=1
% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.
\documentclass[11pt]{article}
% Change "review" to "final" to generate the final (sometimes called camera-re... | Which model has the best average performance measured by KIWI scores on the WMT datasets, combining the results for the two language directions? | Claude-3.5-Sonnet | SELECT KIWI scores for all models on WMT23 and WMT24 datasets for both translation directions
LOOP for each model
COMPUTE average KIWI score across both directions and both datasets
COMPUTE argmax average KIWI score
RETURN model with highest average KIWI score
|
2505.18122 | [
[
"\\begin{table}[H]\n",
"\\vspace{-0.75em}\n",
"\\small\n",
"\\centering\n",
"\\footnotesize\n",
"\\renewcommand{\\arraystretch}{0.9}\n",
"\\resizebox{\\linewidth}{!}{%\n",
"\\setlength{\\tabcolsep}{3.8pt} % Adjust column spacing\n",
"\\begin{tabular}{l|cccccc}\n",
"\\hline\... | [
[
"\\begin{figure}[t]\n",
"\\centering\n",
" \\includegraphics[width=0.90\\columnwidth]{latex/issues.png}\n",
" \\vspace{-0.75em}\n",
" \\caption{Baseline vs \\methodName}\n",
" \\vspace{-2.00em}\n",
" \\label{fig:challenges}\n",
"\\end{figure}\n"
],
[
"\\begin... | \pdfoutput=1
\documentclass[11pt]{article}
\usepackage[preprint]{acl}
\usepackage{times}
\usepackage{latexsym}
\usepackage{enumitem}
\usepackage{ragged2e}
\usepackage{graphicx}
\usepackage{array}
\usepackage{booktabs}
\usepackage{float}
\usepackage{multirow}
\usepackage{xcolor}
\usepackage{arydshln}
\setlength\dashli... | Considering only the quality estimation metric, by how much does the UnJoin method that performs best on the BIRD test set outperform the best standard prompting method using the same baseline model? | 0.75 | SELECT QE scores from Table 2 on BIRD test set
COMPUTE max UnJoin QE score
COMPUTE argmax UnJoin methods
COMPUTE best_model = argmax UnJoin model
SELECT QE scores from Table 1 for the best_model with Standard Prompts method
COMPUTE max QE score for Standard Prompts
COMPUTE outperform = best UnJoin QE score - best Stand... |
2505.17485 | [
[
"\\begin{table*}[t]\n",
" \\centering\n",
" \\small\n",
" \\renewcommand{\\arraystretch}{1.2}\n",
" \\setlength{\\tabcolsep}{3pt} % Adjust column spacing\n",
" \n",
" \\begin{tabular}{l c c c c c c c c c c}\n",
" \\toprule\n",
" Language & \\mult... | [
[
"\\begin{figure}\n",
" \\centering\n",
" \\includegraphics[width=1\\linewidth]{architecture_cropped.png}\n",
" \\caption{Architecture Diagram describing proposed method for detecting hallucination spans.\\citep{selfcheckgpt}}\n",
" \\label{fig:enter-label}\n",
"\\end{figure}\n"... | % This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.
\pdfoutput=1
% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.
\documentclass[11pt]{article}
% Change "review" to "final" to generate the final (sometimes called camera-ready) v... | For which language shows the submission the highest relative improvement over the best baseline on the Intersection over Union metric? | Farsi | SELECT IoU scores for the submission and all baselines for each language
LOOP for each set of IoU scores for each language
COMPUTE max baseline IoU score
COMPUTE relative improvement = (submission IoU - max baseline IoU) / max baseline IoU
COMPUTE argmax relative improvement
COMPUTE target_language = language w... |
2505.17427 | [
[
"\\begin{table*}[!t]\n",
"\\centering\n",
"\\small\n",
"\\adjustbox{max width=\\textwidth}{\n",
"\\begin{tabular}{lccccccc}\n",
"\\toprule\n",
"Model & SQuAD & HotpotQA & NewsQA & Gaokao & HQA & TriviaQA & BioASQ\\\\\n",
"\\midrule\n",
"\\rowcolor{gray!10}\\multicolumn{8}{c}{\\... | [
[
"\\begin{figure*}[!t]\n",
" \\centering\n",
" \\includegraphics[width=\\linewidth]{pics/overview.pdf}\n",
" \\caption{Overview of our T$^2$. \\textbf{(a)} direct prompt or Chain-of-Thought (CoT), which adopts the same reasoning strategy regardless of question complexity. \\textbf{(b)} Ada... | % This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.
\pdfoutput=1
% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.
\documentclass[11pt]{article}
% Change "review" to "final" to generate the final (sometimes called camera-ready) v... | On which test set does the proposed method improve most over the basic use of its baseline model in relative terms, averaged over the three scenarios? | HotpotQA | SELECT vanilla score and T^2 (ours) score for each dataset and scenario
LOOP for each test set
LOOP for each scenario (model-method pair)
COMPUTE relative improvement = (T^2 score - vanilla score) / vanilla score
COMPUTE average relative improvement over three scenarios (model-method pair)
COMPUTE targe... |
2505.16986 | [
[
"\\begin{table*}[!ht]\n",
"\\centering\n",
"\\resizebox{\\textwidth}{!}{\n",
" \\begin{tabular}{lcccc|cccc|c|cc|c}\n",
" \\toprule\n",
" \\textbf{Domain} & \\multicolumn{4}{c}{\\textbf{Tool Call}} & \\multicolumn{4}{|c|}{\\textbf{Parameter Matching}} & \\textbf{Code Exec. Rate} & ... | [
[
"\\begin{figure*}[!t]\n",
" \\centering\n",
" \\includegraphics[width=\\textwidth]{assets/pipeline.png}\n",
" \\caption{Illustrative example from the $\\benchmarkname$ dataset. This example showcases a multi-domain scenario involving both flights and hotels, where the user is planning a t... | \documentclass{article}
% if you need to pass options to natbib, use, e.g.:
% \PassOptionsToPackage{numbers, compress}{natbib}
% before loading neurips_2024
\usepackage[square,numbers]{natbib}
\bibliographystyle{abbrvnat}
% ready for submission
% \usepackage[]{neurips_2025}
\usepackage[preprint]{neurips_2025}
... | Among accuracy, recall, precision, and F1 score for tool calls, which metric shows the largest absolute difference in average value between single-domain and multi-domain settings, and what is the value of that difference? | Recall, 4.9835 | SELECT Tool Call
LOOP for each metric
SELECT values for each single domain
COMPUTE avg_single = average value among all single domains
SELECT values for each multi-domain
COMPUTE avg_multi = average value among all multi domains
COMPUTE difference = avg_single - avg_multi
COMPUTE add difference ... |
2505.16986 | [
[
"\\begin{table*}[!ht]\n",
"\\centering\n",
"\\resizebox{\\textwidth}{!}{\n",
" \\begin{tabular}{lcccc|cccc|c|cc|c}\n",
" \\toprule\n",
" \\textbf{Domain} & \\multicolumn{4}{c}{\\textbf{Tool Call}} & \\multicolumn{4}{|c|}{\\textbf{Parameter Matching}} & \\textbf{Code Exec. Rate} & ... | [
[
"\\begin{figure*}[!t]\n",
" \\centering\n",
" \\includegraphics[width=\\textwidth]{assets/pipeline.png}\n",
" \\caption{Illustrative example from the $\\benchmarkname$ dataset. This example showcases a multi-domain scenario involving both flights and hotels, where the user is planning a t... | \documentclass{article}
% if you need to pass options to natbib, use, e.g.:
% \PassOptionsToPackage{numbers, compress}{natbib}
% before loading neurips_2024
\usepackage[square,numbers]{natbib}
\bibliographystyle{abbrvnat}
% ready for submission
% \usepackage[]{neurips_2025}
\usepackage[preprint]{neurips_2025}
... | In multi-domain evaluations, including which domain leads to the largest absolute difference in the minimum Code Execution Rate accuracy between the 8B and 70B models? What is the value of that difference? | Attractions, 23.4 | SELECT Code Execution Rate accuracy for multi-domain settings
LOOP for each domain
SELECT all multi-domain settings including current domain
COMPUTE minimum Code Execution Rate accuracy for 8B across these settings
COMPUTE minimum Code Execution Rate accuracy for 70B across these settings
COMPUTE differ... |
2505.23657 | [
[
"\\begin{table*}\n",
" \\scriptsize\n",
" \\centering\n",
" \\setlength{\\tabcolsep}{4pt}\n",
" \\caption{Evaluation results on two open-ended benchmarks and two Chain-of-thought benchmarks. The best-performing results are highlighted in \\lightgreen{green}, the second-best in \\li... | [
[
"\\begin{figure*}[ht]\n",
"\\centering\n",
" \\centering\n",
" \\includegraphics[width=0.97\\linewidth]{figure/main.pdf}\n",
" \\vspace{-0.2cm}\n",
" \\caption{ The workflow of {\\actdola}. (1) Next-token selection: {\\actdola} dynamically apply layer contrasting at each... | % This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.
\pdfoutput=1
% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.
\documentclass[11pt]{article}
% Change "review" to "final" to generate the final (sometimes called camera-ready) v... | On which benchmark and metric does ActLCD improve the most over the greedy baseline in relative terms, averaged over all models? | %Info on TruthfulQA | SELECT ActLCD and greedy baseline scores for all models, all benchmarks, and all metrics
LOOP for each benchmark
SELECT scores for current Benchmark
LOOP for each metric
SELECT scores for current Metric
LOOP for each model
COMPUTE relative improvement = (ActLCD score - greedy score) ... |
2505.16986 | [
[
"\\begin{table*}[!ht]\n",
"\\centering\n",
"\\resizebox{\\textwidth}{!}{\n",
" \\begin{tabular}{lcccc|cccc|c|cc|c}\n",
" \\toprule\n",
" \\textbf{Domain} & \\multicolumn{4}{c}{\\textbf{Tool Call}} & \\multicolumn{4}{|c|}{\\textbf{Parameter Matching}} & \\textbf{Code Exec. Rate} & ... | [
[
"\\begin{figure*}[!t]\n",
" \\centering\n",
" \\includegraphics[width=\\textwidth]{assets/pipeline.png}\n",
" \\caption{Illustrative example from the $\\benchmarkname$ dataset. This example showcases a multi-domain scenario involving both flights and hotels, where the user is planning a t... | \documentclass{article}
% if you need to pass options to natbib, use, e.g.:
% \PassOptionsToPackage{numbers, compress}{natbib}
% before loading neurips_2024
\usepackage[square,numbers]{natbib}
\bibliographystyle{abbrvnat}
% ready for submission
% \usepackage[]{neurips_2025}
\usepackage[preprint]{neurips_2025}
... | Which single-domain setting exhibits the largest relative drop in Parameter Matching Recall when moving from the 8B model to the 70B model, and what is that relative drop percentage to 2 decimal places? | Flights, 26.06% | SELECT Parameter Matching Recall for single-domain settings for 8B model in Table 4
SELECT Parameter Matching Recall for single-domain settings for 70B model in Table 5
LOOP for each single-domain setting
COMPUTE relative drop = (70B Recall - 8B Recall) / 8B Recall * 100
COMPUTE argmin relative drop
RETURN single-d... |
2505.23621 | [
[
"\\begin{table*}[!t]\n",
"\\label{tab:main-results} \n",
"\\centering\n",
"\n",
"\\setlength{\\tabcolsep}{2pt} \n",
"\n",
"\\resizebox{\\textwidth}{!}{\n",
"\\begin{tabular}{l*{13}{c}}\n",
"\\toprule\n",
"\n",
"\\multirow{3}{*}{\\textbf{Model}} & \\multicolumn{4}{c}{\... | [
[
"\\begin{figure}[!t]\n",
" \\includegraphics[width=\\columnwidth]{figure/RadarChart.pdf}\n",
" \\caption{Overall performance comparison between \\ours and same-scale baselines on various table reasoning benchmarks. Both \\sftmodel and \\rlmodel exhibit substantial performance improvements over basel... | % This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.
\pdfoutput=1
% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.
\documentclass[11pt]{article}
% Remove the "review" option to generate the final version.
\usepackage[]{acl}
% St... | Do the Table-Specific LLMs show more wins than Reasoning LLMs on harder or easier benchmarks? | Harder | LOOP for each test set
COMPUTE argmax model with highest score
COMPUTE add to win-list: model, score
COMPUTE sort win-list by score, divide in high-score-win, and low-score-win
SELECT reasoning models
LOOP for each reasoning model
COMPUTE add to reasoning-high-win-counter count(model in high-score-win)
... |
2505.21505 | [
[
"\\begin{table*}[htbp]\n",
"\\centering\n",
"\\small\n",
"\\setlength{\\tabcolsep}{4pt}\n",
"\\renewcommand{\\arraystretch}{1.2}\n",
"\\resizebox{0.9\\columnwidth}{!}{\n",
"\\begin{tabular}{l|cccccccccc|c}\n",
"\\toprule\n",
"\\textbf{Tested on MGSM} & \\textbf{bn} & \\textbf{... | [
[
"\\begin{figure*}[tbp]\n",
" \\centering \n",
" \\begin{minipage}[b]{0.4\\textwidth}\n",
" \\includegraphics[width=1.0\\linewidth]{Images/language_specific_neurons_heatmap_Mistral_Base.pdf}\n",
" \\subcaption{Base - Language-Specific Neurons}\n",
" \\label{fig:mistral-mgsm-b... | \documentclass{article}
% if you need to pass options to natbib, use, e.g.:
% \PassOptionsToPackage{numbers, compress}{natbib}
% before loading neurips_2025
% ready for submission
% \usepackage{neurips_2025}
% to compile a preprint version, e.g., for submission to arXiv, add add the
% [preprint] option:
\usep... | Considering models that are aligned with the most languages, which languages that are in the top 5 in terms of performance on the MGSM test set are not in the top 5 on the MSVAMP test set. | Chinese | SELECT model aligned with most languages on MGSM
COMPUTE rank languages by accuracy
COMPUTE top5_MGSM = list of top 5 languages
SELECT model aligned with most languages on MSVAMP
COMPUTE rank languages by accuracy
COMPUTE top5_MSVAMP = list of top 5 languages
COMPUTE common_languages = intersection(top5_MGSM, top5... |
2505.21505 | [
[
"\\begin{table*}[htbp]\n",
"\\centering\n",
"\\small\n",
"\\setlength{\\tabcolsep}{4pt}\n",
"\\renewcommand{\\arraystretch}{1.2}\n",
"\\resizebox{0.9\\columnwidth}{!}{\n",
"\\begin{tabular}{l|cccccccccc|c}\n",
"\\toprule\n",
"\\textbf{Tested on MGSM} & \\textbf{bn} & \\textbf{... | [
[
"\\begin{figure*}[tbp]\n",
" \\centering \n",
" \\begin{minipage}[b]{0.4\\textwidth}\n",
" \\includegraphics[width=1.0\\linewidth]{Images/language_specific_neurons_heatmap_Mistral_Base.pdf}\n",
" \\subcaption{Base - Language-Specific Neurons}\n",
" \\label{fig:mistral-mgsm-b... | \documentclass{article}
% if you need to pass options to natbib, use, e.g.:
% \PassOptionsToPackage{numbers, compress}{natbib}
% before loading neurips_2025
% ready for submission
% \usepackage{neurips_2025}
% to compile a preprint version, e.g., for submission to arXiv, add add the
% [preprint] option:
\usep... | Ignoring the case where the model is aligned for all languages, in which cases does adding alignment to a language not lead to improvement in test performance for that language? | MGSM: zh->zh/es/ru: es; MSVAMP: base->zh: zh; base->zh/de: zh; zh->zh/es/ru: es | SELECT MGSM and MSVAMP
LOOP for each benchmark
SELECT all aligned models except all-languages
LOOP for each model
SELECT all languages
LOOP for each language
IF aligned score <= base score
COMPUTE add tuple of (benchmark, alignment model, language) to result_list
... |
2505.17427 | [
[
"\\begin{table*}[!t]\n",
"\\centering\n",
"\\small\n",
"\\adjustbox{max width=\\textwidth}{\n",
"\\begin{tabular}{lccccccc}\n",
"\\toprule\n",
"Model & SQuAD & HotpotQA & NewsQA & Gaokao & HQA & TriviaQA & BioASQ\\\\\n",
"\\midrule\n",
"\\rowcolor{gray!10}\\multicolumn{8}{c}{\\... | [
[
"\\begin{figure*}[!t]\n",
" \\centering\n",
" \\includegraphics[width=\\linewidth]{pics/overview.pdf}\n",
" \\caption{Overview of our T$^2$. \\textbf{(a)} direct prompt or Chain-of-Thought (CoT), which adopts the same reasoning strategy regardless of question complexity. \\textbf{(b)} Ada... | % This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.
\pdfoutput=1
% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.
\documentclass[11pt]{article}
% Change "review" to "final" to generate the final (sometimes called camera-ready) v... | On which test set does the proposed method improve most over the basic use of its baseline model in relative terms, averaged over the three scenarios? | HotpotQA | SELECT three scenarios: each baseline with its proposed method on all test sets
LOOP for each scenario
SELECT scores for current scenario
LOOP for each test set
COMPUTE relative_improvement = (method_score - baseline_score)/baseline_score
SELECT all test sets
LOOP for each test set
COMPUTE average... |
2505.20776 | [
[
"\\begin{table*}[t]\n",
"\\centering\n",
"\\resizebox{\\textwidth}{!}{\n",
"\\begin{tabular}{@{}ccccccccccccccccccc@{}}\n",
"\\toprule\n",
"\\multirow{2}{*}{} \n",
" & \\multicolumn{2}{c}{\\multirow{2}{*}{\\textbf{Setting}}} \n",
" & \\multirow{2}{*}{\\textbf{SpecExtend}} \n",
... | [
[
"\\begin{figure*}[!t]\n",
" \\centering\n",
" \\includegraphics[width=0.95\\textwidth]{main_figure.png}\n",
" \\caption{Overview of SpecExtend. FlashAttention accelerates the prefill phases of both target and draft models, and Hybrid Tree Attention accelerates the verification phase. We use the... |
\pdfoutput=1
\PassOptionsToPackage{dvipsnames}{xcolor}
\documentclass[11pt]{article}
\usepackage[preprint]{acl}
\usepackage{times}
\usepackage{latexsym}
\usepackage{booktabs}
\usepackage{multirow}
\usepackage{graphicx}
\usepackage{algorithm}
\usepackage{algpseudocode}
\usepackage{float}
\usepackage{xcolo... | At which input length does SpecExtend provide the highest average relative speedup across all evaluated settings? | 16K | SELECT Speedup with and without SpecExtend, all settings, all token lengths
LOOP for each token length
SELECT Speedup where SpecExtend = No
COMPUTE avg1 = average of Speedup where SpecExtend = No
SELECT Speedup where SpecExtend = Yes
COMPUTE avg2 = average of Speedup where SpecExtend = Yes
COMPUTE ... |
2505.19987 | [
[
"\\begin{table*}[t]\n",
"\\centering\n",
"\\resizebox{\\textwidth}{!}{%\n",
"\\begin{tabular}{l ccc ccc ccc ccc ccc}\n",
"\\toprule\n",
"\\multirow{2}{*}{Model} & \\multicolumn{3}{c}{Conversation} & \\multicolumn{3}{c}{E-commerce} & \\multicolumn{3}{c}{News} & \\multicolumn{3}{c}{Social} &... | [
[
"\\begin{figure*}[th] \n",
" \\centering\n",
" \\includegraphics[width=\\textwidth]{figure/deen-ende_radar_charts.pdf}\n",
" \\caption{Multi-Domain En$\\Leftrightarrow$De Translation Performance Comparison, showing averaged BLEU, COMET, and CometKiwi scores for both directions, with disti... |
\pdfoutput=1
% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.
\documentclass[11pt]{article}
\usepackage[T1]{fontenc}
% \usepackage[utf8]{inputenc}
% Change "review" to "final" to generate the final (sometimes called camera-ready) version.
% Change to "preprint" to generate... | In the sentence-level setting, which model has the highest COMET score on the average of the easiest and most difficult domain, measured by BLEU? | GPT-4o | SELECT Sentence-Level setting, all models, BLEU scores across domains
COMPUTE avg_bleu_list = average of bleu scores across domains for each Domain
COMPUTE easiest_domain = argmax(avg_bleu_list)
COMPUTE hardest_domain = argmin(avg_bleu_list)
SELECT Sentence-Level setting, all models, COMET scores across domains
LOOP ... |
2505.19714 | [
[
"\\begin{table*}[t]\n",
" \\centering\n",
" \\small\n",
" \\caption{\n",
" In-domain (IND) performance comparison on the MIT-10M benchmark (ZH-EN and EN-ZH). Metrics reported are BLEU, chrF++, and METEOR, along with their average (Avg.). MT$^{3}$-7B-Zero (RL) is compared against open-... | [
[
"\\begin{table*}[t]\n",
" \\centering\n",
" \\small\n",
" \\caption{\n",
" In-domain (IND) performance comparison on the MIT-10M benchmark (ZH-EN and EN-ZH). Metrics reported are BLEU, chrF++, and METEOR, along with their average (Avg.). MT$^{3}$-7B-Zero (RL) is compared against open-... | \documentclass{article}
% if you need to pass options to natbib, use, e.g.:
% \PassOptionsToPackage{numbers, compress}{natbib}
% before loading neurips_2025
% ready for submission
% \usepackage{neurips_2025}
\usepackage[preprint]{neurips_2025}
% to compile a preprint version, e.g., for submission to arXiv, ad... | Which zero-shot models outperform any of the fine-tuned models on any metric and language direction? | InternVL2.5-38B, InternVL2.5-78B, and Qwen2.5-VL-72B (on chrF++ EN-ZH) | SELECT Zero-shot Systems, all metrics, both language directions
LOOP for each zero-shot model
SELECT scores for current zero-shot Model
LOOP for each metric and language direction
SELECT Fine-tuned models scores
COMPUTE max_ft_score = max fine-tuned model scores
COMPUTE diff = zero-shot ... |
2405.19325 | [
[
"\\begin{table}[!t]\n",
"\\begin{adjustbox}{max width=1\\textwidth}\n",
"\\centering\n",
"\\begin{tabular}{l|cccccc|cccccc}\n",
"\\toprule\n",
"\\textbf{Models} & \\multicolumn{6}{c|}{\\textbf{Wikitext-103}} & \\multicolumn{6}{c}{\\textbf{Pile of Law}} \\\\ \n",
"\n",
" ... | [
[
"\\begin{figure}[t!]\n",
"\\centering\n",
"\\includegraphics[width=\\textwidth]{figures/NEST.png}\n",
"\\caption{The \\nest approach first locates the tokens in the corpus using the LM hidden states. The retrieval distribution $p_{\\text{\\knn}}$ is dynamically interpolated with $p_{\\text{LM}}$ b... | \documentclass[]{fairmeta}
% Option "twocolumn" available, but please prioritize single-column
\usepackage[utf8]{inputenc} % allow utf-8 input
% \usepackage[T1]{fontenc}% use 8-bit T1 fonts
\usepackage{hyperref} % hyperlinks
\usepackage{url}% simple URL typesetting
\usepackage{booktabs, nicematrix} % professional-... | Among retrieval-augmented methods, what is the smallest decline in ROUGE score when moving from unigrams to bigrams? | 12.5 | SELECT all metric scores for +RA rows from Wikitext-103 and Pile of Law
LOOP for each model
SELECT ROUGE-1 and ROUGE-2 scores for current Model
COMPUTE decline = ROUGE-1 - ROUGE-2
COMPUTE result_value = min_decline
RETURN result_value
|
2409.02897 | [
[
"\\begin{table}[t]\n",
"\\centering\n",
"\\resizebox{\\linewidth}{!}{\n",
" \\setlength{\\tabcolsep}{5pt}\n",
" \\begin{tabular}{l|cc|ccc|ccc|ccc|ccc|ccc}\n",
"\\toprule\n",
"\\multirow{2}{*}{Model} & \\multicolumn{2}{c|}{Avg} & \\multicolumn{3}{c|}{Longbench-Chat} ... | [
[
"\\begin{figure}\n",
" \\centering\n",
" \\includegraphics[width=\\linewidth]{figs/task.pdf}\n",
" \\caption{Comparison between chunk-level and sentence-level citations.}\n",
" \\label{fig:task}\n",
"\\end{figure}\n"
],
[
"\\begin{table}[t]\n",
"\\centering\n",
... |
\documentclass{article} % For LaTeX2e
\usepackage{iclr2025_conference,times}
% Optional math commands from https://github.com/goodfeli/dlbook_notation.
%%%%% NEW MATH DEFINITIONS %%%%%
\usepackage{amsmath,amsfonts,bm}
% Mark sections of captions for referring to divisions of figures
\newcommand{\figleft}{{\em (Left... | What is the F1 score for the model and data set where adding citations shows the largest improvement in correctness, excluding our trained models? | 49.9 | SELECT Table 2 about correctness in LQAC setting
SELECT all models excluding our trained Models
LOOP for each model
SELECT all datasets for current model
LOOP for each dataset
SELECT CR score
COMPUTE add CR score and current model-dataset pair to cr_list
COMPUTE best_pair = model-dataset pair wi... |
2305.14627 | [
[
"\\begin{table}[t]\n",
" \\centering\n",
" \\resizebox{0.98\\linewidth}{!}{\n",
" \\begin{tabular}{lcccc}\n",
" \\toprule\n",
" & \\tf{Fluency} & \\tf{Correct.} & \\multicolumn{2}{c}{\\tf{Citation} }\\\\\n",
" \\cmidrule(lr){2-2} \\cmidrule... | [
[
"\\begin{figure}[t]\n",
" \\includegraphics[width=0.98\\linewidth]{figs/main.pdf}\n",
" \\caption{The task setup of \\citeeval{}. Given a question, \n",
" the system generates text while providing \\ti{citing passages} from a large retrieval corpus. Each statement may contain multiple cit... | \pdfoutput=1
\documentclass[11pt]{article}
\usepackage{EMNLP2023}
\usepackage{times}
\usepackage{latexsym}
\usepackage{graphicx}
\usepackage{subfigure}
\usepackage[T1]{fontenc}
\usepackage[utf8]{inputenc}
\usepackage{microtype}
\usepackage{inconsolata}
\usepackage{array}
\usepackage{pifont}
\usepackage{tabula... | Which 13B model achieves the highest average citation F1 score, and what is that score? | Vicuna-13B, 26.9 | SELECT ASQA results from Table 1
SELECT QAMPARI results from Table 2
SELECT ELI5 results from Table 3
SELECT all 13B models
LOOP for each model
SELECT citation recall and citation precision for ASQA, QAMPARI, and ELI5
LOOP for each dataset
COMPUTE precision_recall_product = citation_precision * citati... |
2505.23242 | [
[
"\\begin{table*}[!ht]\n",
" \\centering\n",
" \\resizebox{\\linewidth}{!}{\n",
" \\begin{tabular}{lccccccccccr}\n",
" \\toprule\n",
" \\multirow{2}[4]{*}{ Models} & \\multirow{2}[4]{*}{Size} & \\multicolumn{3}{c}{ChartMind} & \\multicolumn{3}{c}{ChartQA } & \\multicolumn{3}{c}{C... | [
[
"\\begin{figure*}[t]\n",
" \\centering\n",
" \\includegraphics[width=\\linewidth]{images/fig1-3.pdf}\n",
" \\caption{Key Challenges in CQA Benchmarks: (A) Predominantly monolingual, limiting multilingual applicability in chart question answering; (B) Fixed formats and metrics, restricting... | % This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.
\pdfoutput=1
\documentclass[11pt]{article}
% Change "review" to "final" to generate the final (sometimes called camera-ready) version.
% Change to "preprint" to generate a non-anonymous version with page numbers.
\usepac... | Which models rank within the top five in both average accuracy and average BLEU when scores are first averaged across all datasets and prompting methods? | Qwen2-VL, MiniCPM-v2 | SELECT all models
LOOP for each model
SELECT all prompting methods
LOOP for each prompting method
COMPUTE avg_acc = (ChartMind ACC + ChartQA Avg.ACC) / 2
COMPUTE avg_bleu = (Chart-to-Text Avg.BLEU + OpenCQA Avg.BLEU) / 2
COMPUTE overall_acc = average over all avg_acc
COMPUTE overall_ble... |
2505.23765 | [
[
"\\begin{table*}[!t]\n",
"\\centering\n",
"\\resizebox{0.92\\textwidth}{!}{\n",
"\\begin{tabular}{@{}l|l|lccccc@{}}\n",
"\\toprule\n",
"Model Name & Approach & Type & NDCG@1 & NDCG@3 & NDC... | [
[
"\\begin{figure}[!t]\n",
" \\centering\n",
" \\includegraphics[trim={20px 20px 20px 20px},clip, width=0.99\\linewidth]{figures/intro_figure.png}\n",
" \\caption{Comparison of different aggregation paradigms: (a) summarization, (b) aggregation over structured databases, and (c) aggregation... | % This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.
\pdfoutput=1
% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.
\documentclass[11pt]{article}
% Change "review" to "final" to generate the final (sometimes called camera-ready) v... | Which model, using which approach, achieves the best performance for NDCG@5 among those trained on over 100 million tokens? Additionally, what percentage of the worst-performing model's score does the best-performing model achieve? | o4-mini with probe, 149.03% | SELECT Model-Context pairs and NDCG@5 scores where Input Tokens > 100 Million
LOOP for each Model-Context pair
COMPUTE add score to score_list
COMPUTE best_score = max in score_list
COMPUTE best_model_context = Model-Context pair for best_score
COMPUTE worst_score = min in score_list
COMPUTE percentage = best... |
2505.23667 | [
[
"\\begin{table}[t!]\n",
" \\centering\n",
" \\caption{Performance under Zero-shot, SFT, and RL settings across models and datasets. Values in the table indicate accuracy (\\%). \\textit{Text} and \\textit{Formula} refer to textual and symbolic reasoning methods, respectively. \\textit{w/ CS} denotes... | [
[
"\\begin{figure}[ht!]\n",
" \\centering\n",
" \\includegraphics[width=0.82\\textwidth]{figures/fortune.pdf}\n",
" \\caption{Overview of \\textit{Formula Tuning} (\\textsc{Fortune}).}\n",
" \\label{fig:framework}\n",
"\\end{figure}\n"
],
[
"\\begin{figure}[t!]\n",
" ... | \documentclass{article}
% if you need to pass options to natbib, use, e.g.:
% \PassOptionsToPackage{numbers, compress}{natbib}
% before loading neurips_2025
% ready for submission
% \usepackage{neurips_2025}
% to compile a preprint version, e.g., for submission to arXiv, add add the
% [preprint] option:
\... | In RL, which method is the most stable across different datasets? | Formula w/ CS | SELECT RL methods, all datasets
LOOP for each method
SELECT scores across all datasets for current method
COMPUTE max score
COMPUTE min score
COMPUTE range = max − min
COMPUTE add range to stability_ranges
COMPUTE most_stable_method = argmin stability_ranges
RETURN most_stable_method
|
2505.22928 | [
[
"\\begin{table*}[t]\n",
"\t\\small\n",
"\t\\centering\n",
"\t\\renewcommand{\\arraystretch}{1.2}\n",
"\t\\setlength{\\tabcolsep}{4pt}\n",
"\t\\begin{tabular}{lc|cccccc|cccccc}\n",
"\t\t\\toprule\n",
"\t\t\\multirow{2}{*}{\\textbf{Model}} & \\multirow{2}{*}{\\textbf{Think}} & \\mult... | [
[
"\\begin{figure}[t!]\n",
"\\centering\n",
"\\includegraphics[width=.43\\textwidth]{latex/assets/example4.pdf}\n",
"\\caption{Example of estimating the intervention effect based on the extracted outcome data for a clinical study.}\n",
"\\label{fig:example}\n",
"\\end{figure}\n"
],
[
... | % This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.
\pdfoutput=1
% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.
\PassOptionsToPackage{table}{xcolor}
\documentclass[11pt]{article}
% Change "review" to "final" to generate the fi... | Consider EM scores only, which pre-trained model performs best on COCHRANEFOREST? Then, using the EM-based ranking positions from both datasets, which pre-trained model performs best under the averaged ranking? | Llama-3.1-405B, Qwen2.5-72B | SELECT all Pretrained LLMs
SELECT EM score under COCHRANEFOREST for Pretrained LLMs
COMPUTE model1 = argmax over EM scores
SELECT all EM scores for COCHRANEFOREST and RCTs for Pretrained LLMs
LOOP for each pre-trained model
COMPUTE rank_c = rank by EM under COCHRANEFOREST
COMPUTE rank_r = rank by EM under RCT... |
2505.23745 | [
[
"\\begin{table}[t!]\n",
"\\centering\n",
"\\resizebox{\\textwidth}{!}{\n",
"\\begin{tabular}{l@{~~~~~}| c cccc cccc cccc cccc}\n",
"\\hline\n",
" \\multicolumn{1}{c|}{} &\\multicolumn{4}{c|}{Flower102 } & \\multicolumn{4}{c|}{DTD } & \\multicolumn{4}{c|}{Aircraft} &\\multicolumn{4}{c}{Pets... | [
[
"\\begin{figure}[t!]\n",
" \\centering \\includegraphics[width=\\linewidth]{imgs/crop_emb.pdf}\n",
"\\vspace{-0.6cm}\n",
" \\caption{(a) CLIP’s image and text embeddings are located in two completely separate regions of the embedding space. (b) The concept of \"dog\" and \"seaplane\" is more d... | \documentclass{article}
% if you need to pass options to natbib, use, e.g.:
% \PassOptionsToPackage{numbers, compress}{natbib}
% before loading neurips_2025
% ready for submission
% \usepackage{neurips_2025}
% to compile a preprint version, e.g., for submission to arXiv, add add the
% [preprint] option:
\u... | Which model has the best performance on both datasets in fpr95? Does this model perform better on fine-grained classification datasets or ImageNet datasets? | TrustVLM-D, fine-grained classification | SELECT FPR95 of models on fine-grained datasets from Table 1
COMPUTE best_model_1 = argmin over FPR95 scores on fine-grained datasets
SELECT FPR95 of models on ImageNet datasets from Table 2
COMPUTE best_model_2 = argmin over FPR95 scores on ImageNet datasets
IF best_model_1== best_model_2
COMPUTE best_model_both... |
2505.16986 | [
[
"\\begin{table*}[!ht]\n",
"\\centering\n",
"\\resizebox{\\textwidth}{!}{\n",
" \\begin{tabular}{lcccc|cccc|c|cc|c}\n",
" \\toprule\n",
" \\textbf{Domain} & \\multicolumn{4}{c}{\\textbf{Tool Call}} & \\multicolumn{4}{|c|}{\\textbf{Parameter Matching}} & \\textbf{Code Exec. Rate} & ... | [
[
"\\begin{figure*}[!t]\n",
" \\centering\n",
" \\includegraphics[width=\\textwidth]{assets/pipeline.png}\n",
" \\caption{Illustrative example from the $\\benchmarkname$ dataset. This example showcases a multi-domain scenario involving both flights and hotels, where the user is planning a t... | \documentclass{article}
% if you need to pass options to natbib, use, e.g.:
% \PassOptionsToPackage{numbers, compress}{natbib}
% before loading neurips_2024
\usepackage[square,numbers]{natbib}
\bibliographystyle{abbrvnat}
% ready for submission
% \usepackage[]{neurips_2025}
\usepackage[preprint]{neurips_2025}
... | For the Llama 3.1 8B Instruct model, which multi-domain combination achieves a higher Tool Call F1 score than at least one of the single-domain datasets that constitute it? | F-H, H-R | SELECT multi-domain combinations
LOOP for each multi-domain dataset
SELECT Tool Call F1 for the Multi-domain Combination
SELECT single-domain components that constitute the current Combination
LOOP for each single-domain component
SELECT Tool Call F1 for the single-domain component
IF multi... |
2505.23621 | [
[
"\\begin{table*}[!t]\n",
"\\label{tab:main-results} \n",
"\\centering\n",
"\n",
"\\setlength{\\tabcolsep}{2pt} \n",
"\n",
"\\resizebox{\\textwidth}{!}{\n",
"\\begin{tabular}{l*{13}{c}}\n",
"\\toprule\n",
"\n",
"\\multirow{3}{*}{\\textbf{Model}} & \\multicolumn{4}{c}{\... | [
[
"\\begin{figure}[!t]\n",
" \\includegraphics[width=\\columnwidth]{figure/RadarChart.pdf}\n",
" \\caption{Overall performance comparison between \\ours and same-scale baselines on various table reasoning benchmarks. Both \\sftmodel and \\rlmodel exhibit substantial performance improvements over basel... | % This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.
\pdfoutput=1
% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.
\documentclass[11pt]{article}
% Remove the "review" option to generate the final version.
\usepackage[]{acl}
% St... | Which open-source model with at most 32B parameters outperforms GPT-4.1 mini on at least two out-of-domain TFV tasks, and what are those tasks? | QwQ-32B, InfoTabs, PHT | SELECT open-source models with ≤ 32B parameters
LOOP for each model
SELECT out-of-domain TFV tasks
LOOP for each TFV task
SELECT GPT-4.1-mini’s task score for current TFV Task
SELECT model score for current TFV Task
IF model score > GPT-4.1-mini score
COMPUTE increase count
... |
2505.22582 | [
[
"\\begin{table*}[t]\n",
" \\centering\n",
" \\resizebox*{\\linewidth}{!}{\n",
" \\begin{tabular}{lc|cccccccc|ccc}\n",
" \\bottomrule\n",
" & & \\multicolumn{2}{c}{\\textbf{ARC-C}} & \\multicolumn{2}{c}{\\textbf{MMLU}} & \\multicolumn{2}{c}{\\textbf{Hellaswag}} & \\multic... | [
[
"\\begin{figure}[t]\n",
" \\centering\n",
" \\includegraphics[width=\\linewidth]{p1-v1.jpg}\n",
" \\caption{The similarity of HSAs in different languages across all layers within Qwen1.5-1.8B.}\n",
" \\label{fig:similarity}\n",
"\\end{figure}\n"
],
[
"\\begin{figure*}[t... | % This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.
\pdfoutput=1
% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.
\documentclass[11pt]{article}
% Change "review" to "final" to generate the final (sometimes called camera-ready) v... | In the G0 → G1 setting, which method exhibits the smallest relative decline from Old-avg to New-avg, and what is that decline percentage to 2 decimal places? | *MoE, 23.07% | SELECT G0 → G1 methods
LOOP for each method
SELECT old-avg and new-avg scores for current Method
COMPUTE decline_percentage = (old_avg - new_avg) /old_avg * 100
COMPUTE best_method = argmin over decline_percentages
RETURN best_method, round(argmin[decline], 2)
|
2505.24615 | [
[
"\\begin{table*}[t]\n",
" \\centering\n",
" \\fontsize{8}{6}\\selectfont\n",
" \\caption{{\\small Experiments idea retrieval with different embedding backbones. The proposed LLM-KD retriever outperforms RA (Reference Alignment) and Vanilla (baseline without supervision) baselines, demonstrating... | [
[
"\\begin{figure}\n",
" \\centering\n",
" \\includegraphics[width=\\linewidth]{figures/EMNLP_F1.png}\n",
" \\caption{An illustrative example about the gap between textual similarity and idea conception. The LLM-paraphrased idea aligns conceptually with the target (green ticked) but shows l... | % This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.
\pdfoutput=1
% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.
\documentclass[11pt]{article}
% Change "review" to "final" to generate the final (sometimes called camera-ready) v... | Which backbone shows the largest percentage improvement in MAP for LLM-KD over RA in the marketing domain, and how much larger is this improvement compared to its corresponding MAP improvement in the NLP domain? | NLI, 15.09% | SELECT backbones in Marketing domain
LOOP for each backbone
SELECT LLM-KD MAP and RA MAP for current Backbone in Marketing
COMPUTE marketing_improvement = LLM-KD MAP − RA MAP
COMPUTE target_backbone, max_market_improvement = argmax over marketing_improvement
SELECT LLM-KD MAP and RA MAP for target_backbone in... |
2506.03090 | [
[
"\\begin{table}[t]\n",
"\\centering\n",
"\\footnotesize % Reduce font size for fitting in one column\n",
"\n",
"\n",
"\\centering\n",
"\n",
"\\resizebox{0.48\\textwidth}{!}{%\n",
"\\begin{tabular}{lcc|cc|c}\n",
"\\toprule\n",
" & \\multicolumn{2}{c}{\\textbf{Primary Sou... | [
[
"\\begin{table}[t]\n",
"\\centering\n",
"\\footnotesize % Reduce font size for fitting in one column\n",
"\n",
"\n",
"\\centering\n",
"\n",
"\\resizebox{0.48\\textwidth}{!}{%\n",
"\\begin{tabular}{lcc|cc|c}\n",
"\\toprule\n",
" & \\multicolumn{2}{c}{\\textbf{Primary Sou... | % This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.
\pdfoutput=1
% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.
\documentclass[11pt]{article}
% Change "review" to "final" to generate the final (sometimes called camera-ready) v... | In the table, which group shows the highest relative variability in token count? | Primary sources | SELECT Primary Sources, token count mean and st. dev.
COMPUTE primary_variability = primary st. dev / mean
SELECT Dataset Examples, token count mean and st. dev.
COMPUTE dataset_variability = dataset st. dev / mean
COMPUTE target_group = argmax of primary_variability and dataset_variability
RETURN target_group
|
2506.03090 | [
[
"\\begin{table}[t!]\n",
"\\small\n",
"\\resizebox{0.48\\textwidth}{!}{%\n",
"\\begin{tabular}{lccc} % Updated to four columns\n",
"\n",
"\\toprule\n",
"\\textsc{Model + Simple Prompt} & \\textsc{ALL} \\textsubscript{($n$=292)} & \\textsc{\\faUser} \\textsubscript{($n$=40)}& \\textsc{\... | [
[
"\\begin{table}[t]\n",
"\\centering\n",
"\\footnotesize % Reduce font size for fitting in one column\n",
"\n",
"\n",
"\\centering\n",
"\n",
"\\resizebox{0.48\\textwidth}{!}{%\n",
"\\begin{tabular}{lcc|cc|c}\n",
"\\toprule\n",
" & \\multicolumn{2}{c}{\\textbf{Primary Sou... | % This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.
\pdfoutput=1
% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.
\documentclass[11pt]{article}
% Change "review" to "final" to generate the final (sometimes called camera-ready) v... | Compare the top-performing model with the explanation prompt and the baseline model, what is the improvement factor for human-evaluated dataset? And what is the improvement factor for close-reading dataset? | 25, 11.7 | SELECT “ALL” accuracy for the Explanation Prompt
SELECT Human-evaluated accuracy for the Explanation Prompt Models
COMPUTE best_explanation_model = argmax over Human-evaluated accuracy
SELECT Human-evaluated accuracy for best_explanation_model and Baseline
COMPUTE human_improvement_factor = explanation_human_accurac... |
2506.03090 | [
[
"\\begin{table}[t!]\n",
"\\small\n",
"\\resizebox{0.48\\textwidth}{!}{%\n",
"\\begin{tabular}{lccc} % Updated to four columns\n",
"\n",
"\\toprule\n",
"\\textsc{Model + Simple Prompt} & \\textsc{ALL} \\textsubscript{($n$=292)} & \\textsc{\\faUser} \\textsubscript{($n$=40)}& \\textsc{\... | [
[
"\\begin{table}[t]\n",
"\\centering\n",
"\\footnotesize % Reduce font size for fitting in one column\n",
"\n",
"\n",
"\\centering\n",
"\n",
"\\resizebox{0.48\\textwidth}{!}{%\n",
"\\begin{tabular}{lcc|cc|c}\n",
"\\toprule\n",
" & \\multicolumn{2}{c}{\\textbf{Primary Sou... | % This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.
\pdfoutput=1
% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.
\documentclass[11pt]{article}
% Change "review" to "final" to generate the final (sometimes called camera-ready) v... | Compare the same models appearing in the simple prompt group and the explained prompt group. What is the overall absolute and relative improvement after adding the explanation? Your answer should be expressed as a percentage. | 0.325%, 0.73% | SELECT models in simple prompt group
SELECT models in explanation prompt group
COMPUTE common models = intersection of simple prompt models and explanation prompt models
SELECT accuracies for common models in simple prompt group
COMPUTE average accuracy for simple prompt group
SELECT accuracies for common models in exp... |
2506.02561 | [
[
"\\begin{table*}[t]\n",
"\\caption{Main Results of \\texttt{Cus-Prun} on multilingual setting with a pruning ratio of $25\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in ... | [
[
"\\begin{figure}[t]\n",
" \\centering\n",
" \\includegraphics[width=0.48\\textwidth]{Figures/cust-prune.pdf}\n",
" \\caption{Given a requirement for an expert model across three dimensions (language, domain, and task), \\texttt{Cus-Prun} (i) identifies irrelevant neurons for each dimension u... | \pdfoutput=1
\documentclass[11pt]{article}
\usepackage[final]{acl}
\usepackage{microtype}
\usepackage{hyperref}
\usepackage{url}
\usepackage{booktabs}
\usepackage{times}
\usepackage{latexsym}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{graphicx}
\usepackage{multirow}
% This is not strictly necessary, and... | If the Llama2-13B model has the same change rate in MMLU as the Llama3-8B model at the same pruning ratio, what will be the final MMLU values of ShortGPT and Cus-Prun at a pruning rate of about 35%? Your answer only needs to be rounded to one decimal place. | 17.6, 42.5 | COMPUTE change rate of ShortGPT MMLU between the two pruning ratios
SELECT Llama3-8B Cus-Prun MMLU values at two pruning ratios
COMPUTE change rate of Cus-Prun MMLU between the two pruning ratios
SELECT Llama2-13B ShortGPT MMLU value at the lower pruning ratio
COMPUTE predicted ShortGPT MMLU at the higher pruning ratio... |
2506.02561 | [
[
"\\begin{table*}[t]\n",
"\\caption{Main Results of \\texttt{Cus-Prun} on multilingual setting with a pruning ratio of $25\\%$, where ``general capability'' is tested in English and averaged across several expert models, while ``specific capability'' is averaged across languages. Results are expressed in ... | [
[
"\\begin{figure}[t]\n",
" \\centering\n",
" \\includegraphics[width=0.48\\textwidth]{Figures/cust-prune.pdf}\n",
" \\caption{Given a requirement for an expert model across three dimensions (language, domain, and task), \\texttt{Cus-Prun} (i) identifies irrelevant neurons for each dimension u... | \pdfoutput=1
\documentclass[11pt]{article}
\usepackage[final]{acl}
\usepackage{microtype}
\usepackage{hyperref}
\usepackage{url}
\usepackage{booktabs}
\usepackage{times}
\usepackage{latexsym}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{graphicx}
\usepackage{multirow}
% This is not strictly necessary, and... | Which model is completely unaffected by the method used on some task? Which model has the best performance on the Cus-Prun method on this task? | Llama2-13B， Llama3-70B | SELECT all models
SELECT all tasks
SELECT all methods
LOOP for each model
LOOP for each task
SELECT scores of this model on this task across all methods
COMPUTE maximum score across methods
COMPUTE minimum score across methods
IF maximum score equals minimum score
SELECT ... |
2506.02561 | [
[
"\\begin{table}[ht]\n",
"\\caption{Aggressive pruning ratio on Llama3-8B.}\n",
"\\centering\n",
"\\setlength{\\tabcolsep}{3pt}\n",
" \\scalebox{0.9}{\n",
"\\begin{tabular}{l|c|c|c|c}\n",
" \\hline\n",
" \\textbf{Method} & \\textbf{Ratio} & \\textbf{Speedup} & \\textbf{MM... | [
[
"\\begin{figure}[t]\n",
" \\centering\n",
" \\includegraphics[width=0.48\\textwidth]{Figures/cust-prune.pdf}\n",
" \\caption{Given a requirement for an expert model across three dimensions (language, domain, and task), \\texttt{Cus-Prun} (i) identifies irrelevant neurons for each dimension u... | \pdfoutput=1
\documentclass[11pt]{article}
\usepackage[final]{acl}
\usepackage{microtype}
\usepackage{hyperref}
\usepackage{url}
\usepackage{booktabs}
\usepackage{times}
\usepackage{latexsym}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{graphicx}
\usepackage{multirow}
% This is not strictly necessary, and... | When the pruning rate increases from 25% to about 45%, how much original performance do ShortGPT and Cus-Prun's MMLU retain compared to their respective original Dense models? From the results, which method is more sensitive to pruning gains? | 12.5%, 76.7% ,ShortGPT | SELECT Dense MMLU score
SELECT ShortGPT MMLU score at the higher Pruning Ratio
COMPUTE shortgpt_retention = (shortgpt_mmlu_high / dense_mmlu) * 100
SELECT Cus-Prun MMLU score at the higher Pruning Ratio
COMPUTE cus_prun_retention = (cus_prun_mmlu_high / dense_mmlu) * 100
IF shortgpt_retention < cus_prun_retention
... |
2506.02561 | [
[
"\\begin{table}[ht]\n",
"\\caption{Aggressive pruning ratio on Llama3-8B.}\n",
"\\centering\n",
"\\setlength{\\tabcolsep}{3pt}\n",
" \\scalebox{0.9}{\n",
"\\begin{tabular}{l|c|c|c|c}\n",
" \\hline\n",
" \\textbf{Method} & \\textbf{Ratio} & \\textbf{Speedup} & \\textbf{MM... | [
[
"\\begin{figure}[t]\n",
" \\centering\n",
" \\includegraphics[width=0.48\\textwidth]{Figures/cust-prune.pdf}\n",
" \\caption{Given a requirement for an expert model across three dimensions (language, domain, and task), \\texttt{Cus-Prun} (i) identifies irrelevant neurons for each dimension u... | \pdfoutput=1
\documentclass[11pt]{article}
\usepackage[final]{acl}
\usepackage{microtype}
\usepackage{hyperref}
\usepackage{url}
\usepackage{booktabs}
\usepackage{times}
\usepackage{latexsym}
\usepackage{amsmath}
\usepackage{amsfonts}
\usepackage{graphicx}
\usepackage{multirow}
% This is not strictly necessary, and... | Assuming that the MMLU scores of ShortGPT follow a linear trend between pruning rates of 25% and 43.8%, what would be the predicted MMLU score if the pruning rate were increased to 50%? | 1.10% | SELECT ShortGPT MMLU scores at Pruning Rates 25% and 43.8%
COMPUTE rate_change = (mmlu_at_43.8 - mmlu_at_25) / (43.8 - 25)
COMPUTE pruning_gap = 50 - 43.8
COMPUTE mmlu_delta = rate_change * pruning_gap
COMPUTE predicted_mmlu_50 = mmlu_at_43.8 + mmlu_delta
RETURN predicted_mmlu_50
|
2506.04050 | [
[
"\\begin{table}[ht]\n",
"\\centering\n",
"\\scriptsize\n",
"\\renewcommand{\\arraystretch}{1.2}\n",
"\\caption{Model performance by language and domain on unmodified AI text. \\textbf{Bold} indicates the best result within each language and domain block.}\n",
"\\label{tab:lang_domain_updat... | [
[
"\\begin{figure*}[!ht]\n",
"\\centering\n",
"\\includegraphics[width=0.7\\linewidth]{plots/model.jpeg}\n",
"\\vspace{-5pt}\n",
"\\caption{\\small The architecture of the proposed ensemble model. Outputs of three frozen BERT models (left) and three fresh BERT models (right) are concatenated and... | %% This is a JAIR Example File Compiled by Nicholas Mattei (nsmattei@tulane.edu)
%% and Odd Erik Gundersen (odderik@ntnu.no)
%% and Mykel Kochenderfer (mykel@stanford.edu)
%% March 29, 2025
%%
%% This file is based off the ACM Latex Template https://www.acm.org/publications/proceedings-template
%% Revision 2.12 (12/2... | Comparing the performance of the two language databases, which model has the biggest performance difference between English and Dutch and in which field? What is the specific difference? | BERT-base, news, 0.28 | SELECT English dataset F1 scores for all models and all domains
SELECT Dutch dataset F1 scores for all models and all domains
LOOP for each model
LOOP for each domain
COMPUTE diff_value = performance difference between English F1 and Dutch F1
COMPUTE add diff_value and Model-Domain pair to diff_list... |
2506.04050 | [
[
"\\begin{table*}[ht]\n",
"\\centering\n",
"\\caption{\\small F1 score and Accuracy of each detector under every rewriting scenario. \n",
"The best metric in a column is \\textbf{bolded}; the largest negative drop is \\dashuline{\\phantom{-00\\%}}, the smallest negative drop is \\dotuline{\\phanto... | [
[
"\\begin{figure*}[!ht]\n",
"\\centering\n",
"\\includegraphics[width=0.7\\linewidth]{plots/model.jpeg}\n",
"\\vspace{-5pt}\n",
"\\caption{\\small The architecture of the proposed ensemble model. Outputs of three frozen BERT models (left) and three fresh BERT models (right) are concatenated and... | %% This is a JAIR Example File Compiled by Nicholas Mattei (nsmattei@tulane.edu)
%% and Odd Erik Gundersen (odderik@ntnu.no)
%% and Mykel Kochenderfer (mykel@stanford.edu)
%% March 29, 2025
%%
%% This file is based off the ACM Latex Template https://www.acm.org/publications/proceedings-template
%% Revision 2.12 (12/2... | Considering only the HSR strategy, which method results in the largest average performance drop? | LIME | SELECT HSR strategy explain methods
SELECT all detectors
SELECT F1 changes
SELECT Accuracy changes
LOOP for each explain method
COMPUTE average performance drop across all detectors and both metrics
COMPUTE f1_avg_drop = average of F1 changes across all detectors
COMPUTE acc_avg_drop = average of Accuracy c... |
2506.04050 | [
[
"\\begin{table}[ht]\n",
"\\centering\n",
"\\scriptsize\n",
"\\renewcommand{\\arraystretch}{1.2}\n",
"\\caption{Model performance by language and domain on unmodified AI text. \\textbf{Bold} indicates the best result within each language and domain block.}\n",
"\\label{tab:lang_domain_updat... | [
[
"\\begin{figure*}[!ht]\n",
"\\centering\n",
"\\includegraphics[width=0.7\\linewidth]{plots/model.jpeg}\n",
"\\vspace{-5pt}\n",
"\\caption{\\small The architecture of the proposed ensemble model. Outputs of three frozen BERT models (left) and three fresh BERT models (right) are concatenated and... | %% This is a JAIR Example File Compiled by Nicholas Mattei (nsmattei@tulane.edu)
%% and Odd Erik Gundersen (odderik@ntnu.no)
%% and Mykel Kochenderfer (mykel@stanford.edu)
%% March 29, 2025
%%
%% This file is based off the ACM Latex Template https://www.acm.org/publications/proceedings-template
%% Revision 2.12 (12/2... | Which model and data domain combination has the best detection performance without rewriting? What is the difference in F1 score between the best detection scenario after introducing explanatory rewriting and the best detection scenario without rewriting? | Ensemble model in Dutch, news. 0.16 | SELECT all models
SELECT all languages
SELECT all domains
SELECT F1 scores without rewriting
LOOP for each model
LOOP for each language–domain combination
COMPUTE best F1 without rewriting for this combination
COMPUTE model–language–domain with the highest F1 without rewriting
SELECT all explanatory rewri... |
2506.04583 | [
[
"\\end{figure*}\\begin{table*}[!t]\n",
"\\centering\n",
"\\resizebox{\\textwidth}{!}{\n",
"\\begin{tabular}{lccccccc}\n",
"\\toprule\n",
"\\multirow{2}{*}{Backbone LLM} & \\multirow{2}{*}{Setting} \n",
"& \\multicolumn{2}{c}{Top@3} & \\multicolumn{2}{c}{Top@5} & \\multicolumn{2}{c}{Top... | [
[
"\\begin{figure}[!t]\n",
" \\centering\n",
" \\includegraphics[width = \\linewidth]{figures_raw/main_example.pdf}\n",
" \\caption{Examples of challenges in fact-checking adversarial claims. \n",
" These claims are deliberately crafted by humans using strategies to deceive fact-chec... | % This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.
\pdfoutput=1
% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.
\documentclass[11pt]{article}
% Remove the "review" option to generate the final version.
% \usepackage[review]{ac... | On the FoolMeTwice dataset using the Contriever retriever, among the methods whose Top@10 accuracy surpasses RALM, which LLM backbone shows the greatest average improvement over RALM in Top@10 accuracy, and what is the value of that average improvement? | GPT-4o mini, 3.83 | SELECT FoolMeTwice
SELECT Contriever setting
SELECT all backbones
SELECT all methods
SELECT Top@10 accuracy and Top@10 improvements
LOOP for each backbone
LOOP for each method
IF method Top@10 accuracy is greater than RALM Top@10 accuracy
COMPUTE improvement over RALM
COMPUTE average improve... |
2506.02911 | [
[
"\\begin{table*}[h!]\n",
"\\centering\n",
"\\footnotesize\n",
"\\renewcommand\\tabcolsep{4pt}\n",
"\\renewcommand\\arraystretch{1.05}\n",
"\\caption{Evaluation of model performance on unseen disease datasets. The best and second-best results are highlighted in \\colorbox{backred!50}{red} ... | [
[
"\\begin{figure}[h!]\n",
"\\centering\n",
"\\includegraphics[width=0.96\\textwidth]{figs/overview.pdf}\n",
"\\caption{Overview of this work. \\Ours achieves state-of-the-art on the \\Bench task.}\n",
"\\label{fig:overview}\n",
"\\end{figure}\n"
],
[
"\\begin{figure}[h!]\n",
"\\... | \documentclass{article}
% if you need to pass options to natbib, use, e.g.:
\PassOptionsToPackage{numbers, compress}{natbib}
% before loading neurips_2025
% ready for submission
% \usepackage{neurips_2025}
% to compile a preprint version, e.g., for submission to arXiv, add add the
% [preprint] option:
% \... | Among the models with reasoning, which model exhibits the highest ratio of its SLE Batch accuracy to its SLE Cell accuracy, and what is that ratio? | Cell-o1, 0.4614 | SELECT all models with reasoning
SELECT SLE Cell accuracy
SELECT SLE Batch accuracy
LOOP for each model with reasoning
COMPUTE ratio of SLE Batch accuracy to SLE Cell accuracy for this model
COMPUTE model with the largest ratio
RETURN model with the largest ratio, corresponding ratio
|
2506.02515 | [
[
"\\begin{table*}[t]\n",
"\\scriptsize\n",
"\\centering\n",
"\\resizebox{\\textwidth}{!}{\n",
" \\begin{tabular}{l|cccc|ccc|c}\n",
" \\toprule\n",
" \\multicolumn{1}{c|}{\\multirow{2}{*}{\\textbf{Model}}} & \\multicolumn{4}{c|}{\\textbf{\\ourmetric $\\uparrow$}} & \\multicolumn... | [
[
"\\begin{figure}[t]\n",
"\\centering\n",
"\\definecolor{varname}{HTML}{CDE7FF}\n",
"\\definecolor{varproj}{HTML}{FFE5B4}\n",
"\\definecolor{vartotal}{HTML}{C3F5D5}\n",
"\\definecolor{varrate}{HTML}{FFCEDC}\n",
"\\definecolor{vartime}{HTML}{FFF8B0}\n",
"\\definecolor{varprincipal}{H... | % This must be in the first 5 lines to tell arXiv to use pdfLaTeX, which is strongly recommended.
\pdfoutput=1
% In particular, the hyperref package requires pdfLaTeX in order to break URLs across lines.
\PassOptionsToPackage{table}{xcolor}
\documentclass[11pt]{article}
% Change "review" to "final" to generate the f... | Consider models with exactly 8B parameters, which category's mean FAC is the highest, and what is that mean? | General Reasoning, 0.4898 | SELECT all models with size equal to 8B
SELECT their categories
SELECT FAC scores
LOOP for each category
SELECT models in this category with size equal to 8B
SELECT FAC scores for these models
COMPUTE mean FAC for this category
COMPUTE category with the largest mean FAC
RETURN category with the largest mean... |
2506.05746 | [
[
"\\begin{table*}[h]\n",
"\\centering\n",
"\\tiny\n",
"\\begin{tabular}{ll|c|c|ccccccccc}\n",
"\\toprule\n",
"\\multirow{2}{*}{\\textbf{Output}} & \\multirow{2}{*}{\\textbf{Context}} & \\multirow{2}{*}{\\textbf{Method}} & \\textbf{Metric} & \\multicolumn{9}{c}{\\textbf{Results Across Catego... | [
[
"\\begin{figure}[htbp]\n",
"\\vspace{-1.0em}\n",
"\\begin{center}\n",
" \\includegraphics[width=0.60\\columnwidth]{figs/Aron_Szilagyi.png} \n",
"\\vspace{0.25em}\n",
"\n",
"\\noindent \\textit{\\small \\textbf{Q.} In which year did Áron Szilágyi achieve his personal highest number o... | \pdfoutput=1
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\usepackage{times}
\usepackage{latexsym}
\usepackage{authblk}
\usepackage[T1]{fontenc}
\usepackage[utf8]{inputenc}
\usepackage{microtype}
\usepackage{inconsolata}
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\usepackage... | Which table querying approaches (considering output, context and method) rank consistently in the top 3 on the more lenient evaluation metric for all of the underlying foundation models when averaging over questions of different degree of difficulty? | SQL, Schema, Adaptive | SELECT all models
SELECT all outputs
SELECT all contexts
SELECT all methods
SELECT EMS scores for Easy Medium Hard
LOOP for each model
LOOP for each combination of output context and method
COMPUTE average EMS across Easy Medium Hard
COMPUTE top 3 combinations by average EMS for this model
COMPUTE combi... |
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