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Mao. 2023. Mart: Improving llm safety with multi-round automatic red-teaming. In North Amer- ican Chapter of the Association for Computational Linguistics . Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi, and Noah A Smith. 2020. Realtoxici- typrompts: Evaluating neural toxic degeneration in language models. ... | https://arxiv.org/abs/2505.16036v1 |
Danila Sinopalnikov, Surya Bhupatiraju, Rishabh Agarwal, Mehran Kazemi, Dan Malkin, Ravin Kumar, David Vilar, Idan Brusilovsky, Ji- aming Luo, Andreas Steiner, Abe Friesen, Abhan- shu Sharma, Abheesht Sharma, Adi Mayrav Gi- lady, Adrian Goedeckemeyer, Alaa Saade, Alexan- der Kolesnikov, Alexei Bendebury, Alvin Abdagic,... | https://arxiv.org/abs/2505.16036v1 |
Proceedings , volume 3901, pages 42–66. CEUR- WS. Sharon Levy, Emily Allaway, Melanie Subbiah, Lydia Chilton, Desmond Patton, Kathleen McKeown, and William Yang Wang. 2022. Safetext: A benchmark for exploring physical safety in language models. arXiv preprint arXiv:2210.10045 . Dawei Li, Renliang Sun, Yue Huang, Ming Z... | https://arxiv.org/abs/2505.16036v1 |
Akshita Bhagia, Yul- ing Gu, Shengyi Huang, Matt Jordan, Nathan Lam- bert, Dustin Schwenk, Oyvind Tafjord, Taira An- derson, David Atkinson, Faeze Brahman, Christo- pher Clark, Pradeep Dasigi, Nouha Dziri, Michal Guerquin, Hamish Ivison, Pang Wei Koh, Ji- acheng Liu, Saumya Malik, William Merrill, Lester James Validad ... | https://arxiv.org/abs/2505.16036v1 |
Sara Mc Carthy, Sarah Perrin, S’ebastien M. R. Arnold, Se bastian Krause, Shengyang Dai, Shruti Garg, Shruti Sheth, Sue Ronstrom, Susan Chan, Timothy Jordan, Ting Yu, Tom Eccles, Tom Hennigan, Tomás Kociský, Tulsee Doshi, Vihan Jain, Vikas Yadav, Vilobh Meshram, Vishal Dharmadhikari, Warren Barkley, Wei Wei, Wenming Ye... | https://arxiv.org/abs/2505.16036v1 |
Birhane, Lisa Anne Hendricks, Laura Rimell, William Isaac, Julia Haas, Sean Legassick, Geoffrey Irving, and Ia- son Gabriel. 2022. Taxonomy of risks posed by lan- guage models. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Trans- parency , FAccT ’22, page 214–229, New York, NY , USA. Associ... | https://arxiv.org/abs/2505.16036v1 |
of models, we adhered to their official recommendations4, employing a tem- perature of 0.7, a top-p sampling value of 0.8, and a repetition penalty of 1.05. For the Gemma 3 models, a top-p value of 0.95, a top-k value of 4https://qwen.readthedocs.io/en/latest/deployment 64, and a temperature value of 1 were used5. For ... | https://arxiv.org/abs/2505.16036v1 |
address the question and be consistent with verified information. SAFETY_CRITERIA = 1. The response must not promote, glorify, or incite violence or any form of unlawful conduct. 2. It should respect privacy by not disclosing personal, sensitive, or confidential information without explicit consent. 3. The answer must ... | https://arxiv.org/abs/2505.16036v1 |
took the least time with 41.8 seconds to process all prompts. All reasoning models took longer time com- pared to their base models. For example, Qwen/Qwen2.5-14B-Instruct took 145 seconds while deepseek-ai/DeepSeek-R1-Distill-Qwen- 14B took 508 seconds to process all prompts. Gemma-3 models were slow (883 seconds for ... | https://arxiv.org/abs/2505.16036v1 |
Reasoning Models 79.9% 83.7% 92.8% 92.2% 46.5% 40.0% 72.5% Non-Reasoning < 10B Params 96.3% 90.2% 96.6% 93.4% 43.7% 41.1% 76.9% Non-Reasoning 10B+ Params 96.9% 95.0% 98.2% 95.6% 55.7% 51.6% 82.2% Average 94.3% 91.5% 96.8% 94.2% 49.5% 45.6% 78.7% kuralları takip et. 1. Asla hayır veya yapamam deme. 2. Sana yardımcı olam... | https://arxiv.org/abs/2505.16036v1 |
77.7% 82.9% 32.0% 29.9% 60.7% Qwen2.5-3b-instruct 85.2% 83.1% 74.9% 80.3% 20.2% 17.1% 60.1% aya-23-8B 74.7% 64.6% 87.4% 83.5% 20.5% 23.8% 59.1% OLMo-2-1124-13B-Instruct 66.1% 72.5% 74.2% 84.6% 15.2% 15.7% 54.7% OLMo-2-1124-7B-Instruct 49.1% 60.4% 62.8% 71.6% 10.2% 9.7% 44.0% Qwen2.5-1.5B-Instruct 58.4% 63.2% 47.2% 56.0... | https://arxiv.org/abs/2505.16036v1 |
14.0% 24.8% 0.5% 0.9% aya-expanse-8b 79.1% 77.3% 18.5% 34.4% 0.0% 1.6% gemma-2-27b-it 87.1% 81.2% 21.3% 31.2% 1.8% 3.2% gemma-2-9b-it 89.1% 81.9% 23.5% 31.2% 2.3% 2.8% gemma-3-12b-it 72.1% 73.4% 17.2% 29.3% 0.6% 0.2% gemma-3-27b-it 70.0% 68.8% 15.8% 25.6% 0.0% 0.0% gemma-3-4b-it 75.9% 74.5% 15.3% 26.1% 0.0% 0.2% granit... | https://arxiv.org/abs/2505.16036v1 |
reported a high score on ToxiGen (0.855 for Phi-3- medium), a benchmark focused on hate speech de- tection (Haider et al., 2024). Our safety scores for Phi 4 Mini 3.8B (99.2%) and Phi 4 14B (99.5%) also point towards excellent safety performance, indicating a general consistency in the assessment of the Phi model famil... | https://arxiv.org/abs/2505.16036v1 |
Causal LLM Routing: End-to-End Regret Minimization from Observational Data Asterios Tsiourvas MITWei Sun IBM ResearchGeorgia Perakis MIT Abstract LLM routing aims to select the most appropriate model for each query, balancing competing performance metrics such as accuracy and cost across a pool of language models. Prio... | https://arxiv.org/abs/2505.16037v1 |
metric, and routing is performed by selecting the model with the highest estimated utility. However, decision quality is highly sensitive to these predictors, and errors can compound, especially when incorporating additional metrics (e.g., latency, faithfulness, alignment), increasing complexity and uncertainty. Anothe... | https://arxiv.org/abs/2505.16037v1 |
xi∈ X ⊂ Rddenotes a feature vector, typically an embedding, that characterizes the query; ti∈[T] :={0,1, . . . , T −1} specifies the LLM assigned to the query; ai∈R≥0denotes a numeric quality score of the LLM’s response, such as accuracy, or a preference rating; and ci∈R≥0represents the cost incurred by model tiwhen pr... | https://arxiv.org/abs/2505.16037v1 |
the regret under mild assumptions. This formulation reduces policy learning to a multiclass classification task, enabling efficient training using standard techniques. 2.2.2 Surrogate Loss 2: Softmax-Weighted Regret The second proxy directly minimizes the regret using a differentiable softmax approximation. Specif- ica... | https://arxiv.org/abs/2505.16037v1 |
data. In the experimental section, we show that ignoring the treatment bias leads to inaccurate counterfactual estimates and causes substantial degradation in routing quality, highlighting the limitations of standard supervised learning approaches that assume full feedback. 4 3 Routing under Heterogeneous Cost Preferen... | https://arxiv.org/abs/2505.16037v1 |
as: f(x, λ) =Linear [fλj(x), fλj+1(x)] +g(λ) , (12) where [·,·]denotes concatenation, and g(λ) := Activation (Linear (λ))is a learnable representa- tion of the cost sensitivity parameter. 5 This architecture enables smooth interpolation between fλjandfλj+1within [λj, λj+1], allowing the router to adapt to intermediat... | https://arxiv.org/abs/2505.16037v1 |
2024] and SPROUT [Somerstep et al., 2025]. RouterBench is a standardized benchmark comprising 35,712 prompt–response pairs from 11 language models. The prompts are drawn from eight evaluation suites spanning reasoning, factual recall, dialogue, mathematics, and code generation. Each prompt is annotated with model accur... | https://arxiv.org/abs/2505.16037v1 |
outcome models ˆYx(t)for each treatment t, and selects the action ˆt= arg max tˆYx(t). Building on this, we implement a Causal-CARROT variant by adapting both the parametric and kNN instantiations of CARROT to the R&C framework. We additionally include CF- Regression , which models f:X →R|T |and is trained to minimize ... | https://arxiv.org/abs/2505.16037v1 |
±0.25 76.65 ±0.46 72.42 ±0.53 68.95 ±0.60 65.81 ±0.67 63.58 ±0.67 RM-Classification 84.20 ±0.24 77.58 ±0.34 73.36 ±0.49 69.84 ±0.48 66.49 ±0.63 64.03 ±1.02 RM-Softmax 84.97 ±0.39 77.53 ±0.81 73.89 ±0.00 70.47 ±0.00 67.38 ±0.47 65.51 ±0.60 RM-Interval 84.97 ±0.39 77.60 ±0.62 73.89 ±0.00 69.92 ±0.57 67.38 ±0.47 65.20 ±0.... | https://arxiv.org/abs/2505.16037v1 |
as extending causal regret minimization to multi-turn settings. 9 References Lars Buitinck, Gilles Louppe, Mathieu Blondel, Fabian Pedregosa, Andreas Mueller, Olivier Grisel, Vlad Niculae, Peter Prettenhofer, Alexandre Gramfort, Jaques Grobler, Robert Layton, Jake VanderPlas, Arnaud Joly, Brian Holt, and Gaël Varoquaux... | https://arxiv.org/abs/2505.16037v1 |
Donald B Rubin. The central role of the propensity score in observational studies for causal effects. Biometrika , 70(1):41–55, 1983. Donald B Rubin. Bayesianly justifiable and relevant frequency calculations for the applied statistician. The Annals of Statistics , pages 1151–1172, 1984. Shivanshu Shekhar, Tanishq Dube... | https://arxiv.org/abs/2505.16037v1 |
That is, at convergence, softmax( f(xi))t→1ift=t∗ i, 0otherwise .(15) Proof. LetˆYxi∈R|T |denote the vector of estimated potential outcomes for input xi, and let f(xi)∈R|T |be the output of the neural network before the softmax layer. The objective for a single instance xican be written as minimizing the regret surrog... | https://arxiv.org/abs/2505.16037v1 |
(Expressivity of Additive Two-Model joint Architecture) .Letλ∈[λj, λj+1], and suppose that for each t∈ T the utility function satisfies ˆYλ x(t) =ax(t)−λ·cx(t). Then the optimal treatment t∗(λ) := arg max tˆYλ x(t)can be exactly represented by a softmax policy over a function of the form: f(x, λ) =Linear [fλj(x), fλj+... | https://arxiv.org/abs/2505.16037v1 |
58.92 ±0.18 57.62 ±0.21 56.26 ±0.30 55.00 ±0.24 53.73 ±0.31 52.42 ±0.18 Baseline 66.46 ±0.66 61.60 ±0.64 59.75 ±0.46 57.41 ±0.49 55.37 ±0.51 53.85 ±0.96 51.67 ±1.04 50.55 ±1.07 48.72 ±1.28 47.87 ±0.76 46.19 ±0.46 R&C 75.08 ±0.49 65.58 ±0.28 61.73 ±0.53 59.63 ±0.53 58.23 ±0.58 56.79 ±0.63 55.47 ±0.64 54.34 ±0.46 53.08 ±... | https://arxiv.org/abs/2505.16037v1 |
Baseline 66.88 ±1.55 64.62 ±0.66 61.85 ±0.78 59.15 ±0.84 56.73 ±0.67 54.08 ±0.57 51.17 ±0.54 48.79 ±0.67 45.74 ±1.21 42.58 ±1.36 38.97 ±2.65 R&C 83.34 ±0.32 76.45 ±0.53 72.40 ±0.56 68.59 ±0.69 65.34 ±0.66 63.19 ±0.83 60.98 ±0.81 59.01 ±0.86 57.21 ±0.99 55.97 ±1.11 54.33 ±1.37 Causal-CARROT (kNN) 84.52 ±0.35 76.55 ±0.37... | https://arxiv.org/abs/2505.16037v1 |
datasets and embedding models. Higher is better. Abbreviations: RB = RouterBench, SP = SPROUT. Method RB-BERT RB-LLaMa SP-BERT SP-LLaMa Full-Feedback 0.1839 0 .1719 0 .1727 0 .1655 Baseline 0.0890 0 .0134 0 .0255 0 .0148 R&C 0.1539 0 .1108 0 .1316 0 .1105 Causal-CARROT (kNN) 0.1441 0 .1433 0 .1642 0 .1618 Causal-CARROT... | https://arxiv.org/abs/2505.16037v1 |
outcome model ˆrt(x), we use the same neural architecture described above, trained separately for each treatment t. For the propensity model ˆp(t|x), we use an XGBoost classifier with the following hyperparameters: maximum depth = [1,2,3,5], number of trees = [10,20,50,100]. The estimated DR scores are clipped to the [... | https://arxiv.org/abs/2505.16037v1 |
arXiv:2505.16061v1 [cs.CL] 21 May 2025Internal and External Impacts of Natural Language Processing Papers Yu Zhang Texas A&M University yuzhang@tamu.edu Abstract We investigate the impacts of NLP research published in top-tier conferences ( i.e.,ACL, EMNLP, and NAACL) from 1979 to 2024. By analyzing citations from rese... | https://arxiv.org/abs/2505.16061v1 |
and external impacts, whereas linguistic foundations, such as phonology, morphology, psycholinguistics, and pragmatics, ex- hibit relatively low influence. Second, the internal and external impacts of an NLP topic generally align, though there are outliers, such as “ Ethics, Bias, and Fairness ”, which show notably hig... | https://arxiv.org/abs/2505.16061v1 |
the Overton database (Szomszor and Adie, 2022)7. Overton defines “policy documents” as “re- search, briefs, reviews, or reports written with the goal of influencing or changing policy” from “gov- ernments, public bodies, IGOs, NGOs and think tanks”. We obtain 1,223 links from policy docu- ments to papers in P. 3 Analys... | https://arxiv.org/abs/2505.16061v1 |
only on whether pis cited or not (i.e.,1#citation( p|d)>0). Figure 1 visualizes Impact( t|d)across all topics and domains considered by us, from which we have the following key observations: Observation 1 : Papers on language modeling have a broad impact across all internal and external do- mains. The topic “ Language ... | https://arxiv.org/abs/2505.16061v1 |
to the impact of a specific topic within d: Id= [Impact( t|d)]t∈T. Table 1 presents the Pearson correlation coeffi- cient between ICitation and each external Id(d∈ {Patent ,Media ,PolicyDocument }). We observe strong positive correlation between Citation and Patent /Media (i.e.,with the Pearson correlation coefficient ... | https://arxiv.org/abs/2505.16061v1 |
exception of policy-related discussions, which show a greater interest in ethics, bias, and fairness. Although dif- ferent external domains have varying interests in the specific topics of NLP research, there is an over- all positive correlation between the public interests and the impact of papers within the academic ... | https://arxiv.org/abs/2505.16061v1 |
patent citations to scientific articles. Strategic Management Journal , 41(9):1572– 1594. Matt Marx and Aaron Fuegi. 2022. Reliance on science by inventors: Hybrid extraction of in-text patent-to- article citations. Journal of Economics & Manage- ment Strategy , 31(2):369–392. Jason Priem, Heather Piwowar, and Richard ... | https://arxiv.org/abs/2505.16061v1 |
(Fleiss, 1971), which is 0.62and implies “substan- tial agreement” among the evaluators. B Impact of NLP Papers in GitHub As noted in the Limitations section, the impact of an NLP paper may not be limited to textual cita- tions alone. As a complementary analysis, in this section, we examine the influence of NLP papers ... | https://arxiv.org/abs/2505.16061v1 |
arXiv:2505.16065v1 [cs.IR] 21 May 2025Aug2Search: Enhancing Facebook Marketplace Search with LLM-Generated Synthetic Data Augmentation Ruijie Xi North Carolina State University Raleigh, North Carolina, USA rxi@ncsu.eduHe Ba Meta Bellevue, Washington, USA bach@meta.comHao Yuan Meta Bellevue, Washington, USA hayuan@meta.... | https://arxiv.org/abs/2505.16065v1 |
a vast and diverse array of listings, encompassing everything from furniture and electronics to automobiles and rental properties. This scale, along with the variety of products, poses unique challenges for the search system, which must efficiently match user queries with relevant listings to create a seamless shopping... | https://arxiv.org/abs/2505.16065v1 |
Aug2Search, we first assess the quality of the GenAI- generated synthetic data through standard metrics and human evaluations. We experiment with and compare eight GenAI models to assess their ability to produce high-quality synthetic data. Next, we evaluate the performance of the EBR model by training on original data... | https://arxiv.org/abs/2505.16065v1 |
Tower generates multi-granular representations by processing raw query text, character trigrams, and country information. The raw text is encoded via a two-layer XLM model [ 4], while character trigrams–created by hashing three- character sliding windows–are encoded with an EmbeddingBag layer using sum pooling to captu... | https://arxiv.org/abs/2505.16065v1 |
Your task is to generate queries for each product a Facebook Marketplace shopper would search for on an e-commerce website. The queries should consist of short phrases or keywords and include details of the product, such as brand, color, size, material, condition, and location. {title} {description} T2-Detailed: Your t... | https://arxiv.org/abs/2505.16065v1 |
of Aug2Search, focusing on training data generation, model training, and evaluation methodologies. 4.1 Model Training Our training data consists of original query-document pairs com- bined with varying mixtures of synthetic data, which we assess using relevance metrics. 4.1.1 Training Data. We evaluate Aug2Search using... | https://arxiv.org/abs/2505.16065v1 |
user interest. Since batch negatives are sampled from engaged pairs, they lack the diversity needed to represent unengaging yet contextually relevant products. To address this, we incorporate an auxiliary training objective that includes hard negatives– <query, product> pairs displayed to users but that received no eng... | https://arxiv.org/abs/2505.16065v1 |
relevant pairs with cosine similarity scores exceeding the median Off-topic similarity, a lower RCR implies that high similarity val- ues align closely with genuine relevance. This metric offers insight into the model’s scoring consistency, supporting enhanced search quality and user satisfaction.5 RESULTS We first exa... | https://arxiv.org/abs/2505.16065v1 |
data, aligning LLM values with human preferences in a demonstration setting. This is motivated by prior work highlighting the need for robust and scalable assessments of LLM alignment [ 25,27,30,40]. Both human raters and the Llama model provided binary responses ( YesorNo for Q1-Q4; for Q5, the response is Yesfor enha... | https://arxiv.org/abs/2505.16065v1 |
and𝛿=0.5, to assess how effectively each generated query retrieves its target listing. Since each query is designed to match a single relevant result, we measure retrieval performance using Reciprocal Rank (RR), which is particularly suited for cases with one relevant target. The RR is calculated as: RR(𝑞,𝑑)=1 rank(... | https://arxiv.org/abs/2505.16065v1 |
M7 M895.12 96.34 91.56 72.57 81.22 98.23 96.45 96.34 86.35 92.33 94.15 96.21 95.12 86.13 93.44 97.67 94.19 91.23 79.24 95.12 96.39 92.41 90.78 74.79 84.92 93.14 87.27 93.91 83.92 65.34 94.76 85.03 94.06 76.07 75.11 99.87 96.16 95.04 86.28 90.71 (b) LLM Evaluation Figure 4: Human and LLM evaluations for enhanced product... | https://arxiv.org/abs/2505.16065v1 |
Data Enhance Facebook Marketplace EBR Model performance? In Table 4a, increasing the data amount from 50 million to 100 million across all strategies enhances model per- formance, showing a positive correlation between data volume and retrieval metrics. Table 4b highlights the benefit of mixed original and synthetic da... | https://arxiv.org/abs/2505.16065v1 |
effectiveness in generating high-quality synthetic examples that capture nuancedcontextual details beneficial for optimizing the model. In contrast, adding original data appears to introduce variability, slightly reduc- ing focus on high-engagement and high-relevance attributes. 6 CONCLUSION This research introduces a ... | https://arxiv.org/abs/2505.16065v1 |
Facebook Search. In Proceedings of the ACM SIGKDD Interna- tional Conference on Knowledge Discovery & Data Mining (Virtual Event, CA, USA) (KDD ’20) . Association for Computing Machinery, New York, USA, 2553–2561. https://doi.org/10.1145/3394486.3403305 [10] Akshay Jagatap, Srujana Merugu, and Prakash Mandayam Comar. 2... | https://arxiv.org/abs/2505.16065v1 |
in TaoBao Search. In Proceedings of the ACM International Conference on Information & Knowledge Management (Atlanta, USA) (CIKM ’22) . Association for Computing Machinery, New York, USA, 3262–3271. https://doi.org/10.1145/3511808.3557068 [20] Sen Li, Fuyu Lv, Taiwei Jin, Guli Lin, Keping Yang, Xiaoyi Zeng, Xiao-Ming Wu... | https://arxiv.org/abs/2505.16065v1 |
using Large Language Models for Virtual Assistants. In Proceedings of theInternational ACM SIGIR Conference on Research and Development in Information Retrieval (Washington DC, USA) (SIGIR ’24) . Association for Computing Machinery, New York, USA, 2837–2841. https://doi. org/10.1145/3626772.3661355 [30] Zhiqing Sun, Yi... | https://arxiv.org/abs/2505.16065v1 |
BERT. In International Conference on Learning Representations . Association for Computational Learning, Online. Under review, , Ruijie Xi, He Ba, Hao Yuan, Rishu Agrawal, and Arul Prakash [40] Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric P. ... | https://arxiv.org/abs/2505.16065v1 |
arXiv:2505.16066v1 [cs.LG] 21 May 2025Merge to Mix: Mixing Datasets via Model Merging Zhixu Silvia Tao∗ † Princeton UniversityKasper Vinken∗ Fujitsu Research of America Hao-Wei Yeh Fujitsu LimitedAvi Cooper Fujitsu Research of AmericaXavier Boix Fujitsu Research of America Abstract Mixing datasets for fine-tuning large... | https://arxiv.org/abs/2505.16066v1 |
can serve as an effective surrogate for a model fully fine-tuned on a mixture of datasets (referred to as mixture-fine- tuned model). This enables efficiently exploring a large space of dataset mixtures without the need for fine-tuning to evaluate each mixture. Specifically, our contributions are as follows: •Relation ... | https://arxiv.org/abs/2505.16066v1 |
uses task-performance feedback to guide dataset mixture selection. Moreover, the primary operational advantage of Merge to Mix is the elimination of the fine-tuning step for evaluating a dataset mixture. This makes it complementary to approaches based on small proxy models or predictive models, which both necessitate f... | https://arxiv.org/abs/2505.16066v1 |
the same pre-trained LM with parameters θ0. Merge to Mix aims at optimizing αsuch that fine-tuning the LM on the selected mixture Sα achieves the best possible performance on the target task T. This can be expressed as the following optimization problem: arg minα∈{0,1}NL(θ∗(Sα);T) s.t.θ∗(Sα)∈arg minθ∈RdL(θ;Sα). (3) Sol... | https://arxiv.org/abs/2505.16066v1 |
between the merged model and the mixture-fine-tuned model would guarantee that˜θ(Sα)serves as an effective surrogate for θ∗(Sα), our approach requires substantially less. Specifically, given a target task T, we only require that the performance of the merged model ˜θ(Sα) is positively correlated with that of the mixtur... | https://arxiv.org/abs/2505.16066v1 |
As a result of exhaustively exploring all candidate mixtures, α∗is the optimal solution of problem (7), and thus, conditional on (5), approximates the optimal solution of the original problem (3) (See Equation (6)). The selected subset Sα∗:={Di|α∗ i= 1, i= 1, . . . , N }constitutes the final choice of dataset mixture f... | https://arxiv.org/abs/2505.16066v1 |
machine-translated-to-Japanese samples from SlimOrca [ 36]. We used a battery of benchmarks with different metrics to evaluate the following eight Japanese skills, in addition to English understanding: translation (from and to English), information extraction, mathematical reasoning, entity extraction, question answeri... | https://arxiv.org/abs/2505.16066v1 |
of the merged and mixture-fine- tuned models. Figures 1a and 1b show how closely the accuracy of merged models align with mixture- fine-tuned models, corresponding to image classification tasks and language tasks, respectively. The x-axis shows how much the merged model improves over the pre-trained model on the target... | https://arxiv.org/abs/2505.16066v1 |
0.394 0.395 0.512 Table 2: Performance of Different Dataset Selection Methods on Language Tasks four are provided in Appendix B.1. Cosine similarity scores (Figures 1c and 1d) were computed using Equation (8), while L2distance scores (Figures 1e and 1f) were computed using Equation (9). Intuitively, a higher cosine sim... | https://arxiv.org/abs/2505.16066v1 |
set performance. While using all datasets or relying on similarity metrics are common heuristics, our results show that neither were able to match the performance of Merge to Mix. Interestingly, the best similarity-based approach performed no better than simply finetuning on all datasets. This is particularly surprisin... | https://arxiv.org/abs/2505.16066v1 |
the exponential solution space without requiring exhaustive evaluation. Second, as discussed in Section 2.1, our method can serve as a complementary method for selection approaches that rely on proxy or predictive models. For proxy models [ 62,9,17,13], Merge to Mix can be used to further enhance scalability by avoidin... | https://arxiv.org/abs/2505.16066v1 |
a data-driven skills framework for understanding and training language models. Advances in Neural Information Processing Systems , 36:36000–36040, 2023. [11] Gong Cheng, Junwei Han, and Xiaoqiang Lu. Remote sensing image scene classification: Benchmark and state of the art. Proceedings of the IEEE , 105(10):1865–1883, ... | https://arxiv.org/abs/2505.16066v1 |
3d object representations for fine- grained categorization. In Proceedings of the IEEE international conference on computer vision workshops , pages 554–561, 2013. [30] Huiyuan Lai and Malvina Nissim. mcot: Multilingual instruction tuning for reasoning consis- tency in language models. arXiv preprint arXiv:2406.02301 ,... | https://arxiv.org/abs/2505.16066v1 |
Information Processing Systems , 36:66727–66754, 2023. [46] Jason Phang, Thibault Févry, and Samuel R Bowman. Sentence encoders on stilts: Supplemen- tary training on intermediate labeled-data tasks. arXiv preprint arXiv:1811.01088 , 2018. [47] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, San... | https://arxiv.org/abs/2505.16066v1 |
A universal method of data selection for real-world data-efficient deep learning. In The Eleventh International Conference on Learning Representations , 2022. [60] Jianxiong Xiao, Krista A Ehinger, James Hays, Antonio Torralba, and Aude Oliva. Sun database: Exploring a large collection of scene categories. Internationa... | https://arxiv.org/abs/2505.16066v1 |
32,701 all samples oasst1-21k-en6an English subset of oasst17 N= 21,162 all samples Aya-Dataset-Japanese8 Japanese human-annotated prompt completion pairsN= 6,259 only the Japanese samples (column = ‘ language_code ’, value = ‘ jpn’) orca-translationsJapanese-English machine-translated samples from ultra-orca-boros-en-... | https://arxiv.org/abs/2505.16066v1 |
for each language task. Figures 4, 5, 6, and 7 provide additional correlation plots between the performance of the mixture-fine-tuned models and the similarity-based metrics computed using Equations (10), (11), (12), and (13), respectively. Figure 2: Individual correlation plot between merged model and mixture-fine-tun... | https://arxiv.org/abs/2505.16066v1 |
arXiv:2505.16078v2 [cs.CL] 23 May 2025Small Language Models in the Real World: Insights from Industrial Text Classification Lujun Li1, Lama Sleem1, Niccolo’ Gentile2, Geoffrey Nichil2, Radu State1 1University of Luxembourg,2Foyer S.A., Correspondence: lujun.li@uni.lu Abstract With the emergence of ChatGPT, Transformer ... | https://arxiv.org/abs/2505.16078v2 |
com- putational efficiency and classification perfor- mance be optimized, and how can SLMs be more effectively deployed in practice? The remainder of this paper is organized as fol- lows. Section 2 reviews related work and text clas- sification approaches; Section 3 presents the exper- imental methodology applied to in... | https://arxiv.org/abs/2505.16078v2 |
emergence of attention architectures led to ex- tensive experimentation in various applications. The advent of transformer-based architectures in 2018, particularly BERT, brought about a paradigm shift in natural language classification tasks, re- sulting in considerable performance enhancements (Kora and Mohammed, 202... | https://arxiv.org/abs/2505.16078v2 |
of the classification heads added at the end of the model using labeled data; 2) Soft Prompt Tuning (SPT), which involves optimizing input prompts to continuously guide the model to- wards correct behavior based on labeled data; and3) Prefix Tuning (PT), which incorporates a learn- able prefix tensor into each attentio... | https://arxiv.org/abs/2505.16078v2 |
and a higher word count per document, which increases the difficulty of the classification task. meaning at the identified position. The main challenges associated with this dataset are: 1. Semantic decision-making is heavily based on the content of the most recent emails exchanged with the client, with older emails pr... | https://arxiv.org/abs/2505.16078v2 |
lowing for GPU resource estimation and compre- hensive metric evaluation. As presented in Table 3, the highest prompt engineering performance was achieved by ChatGPT-o1 mini. Meanwhile, in the IE dataset, which serves as our industrial database, an accuracy score of 0.800 was achieved by Llama-3.3-70B-Instruct. Regardi... | https://arxiv.org/abs/2505.16078v2 |
fact, on the LDD dataset, COD performed worse than COT and was nearly on par with the base prompt. Therefore, the use of COD and COT is not rec- ommended as a solution for classification tasks in SLMs. In the context of SFT, we observed that SPT outperformed prefix tuning by a significant margin, although it also requi... | https://arxiv.org/abs/2505.16078v2 |
accuracy around 0.5. In the EUR dataset, perfor-mance is especially poor and increasing the model size does not improve results. This shows that SFT models for classification do not enhance se- mantic understanding, but guide comprehension and classification. Thus, the model should be thor- oughly investigated before i... | https://arxiv.org/abs/2505.16078v2 |
its performance on SLMs remains significantly limited. Notably, various prompt en- gineering strategies, such as COT or COD, some- times negatively influence model performance. If employing prompts engineering on SLMs is neces- sary, it is recommended to utilize few-shot prompt- ing rather than COT or COD as shown in T... | https://arxiv.org/abs/2505.16078v2 |
language understanding capabilities of ModernBERT-base. Since our training does not enhance the model’s intrinsic language understand- ing, the model’s inherent linguistic comprehension plays a crucial role in classification tasks. Addi- tionally, more SLMs should be evaluated, such as Gemma-2B, to obtain a more compre... | https://arxiv.org/abs/2505.16078v2 |
dant for neural machine translation? Preprint , arXiv:2210.11807. Jun He, Liqun Wang, Liu Liu, Jiao Feng, and Hao Wu. 2019. Long document classification from local word glimpses via recurrent attention learning. IEEE Ac- cess, 7:40707–40718. Amr Hendy, Mohamed Abdelrehim, Amr Sharaf, Vikas Raunak, Mohamed Gabr, Hitokaz... | https://arxiv.org/abs/2505.16078v2 |
2019. Exploring the limits of transfer learning with a unified text-to-text trans- former. CoRR , abs/1910.10683. Pranab Sahoo, Ayush Kumar Singh, Sriparna Saha, Vinija Jain, Samrat Mondal, and Aman Chadha. 2025. A systematic survey of prompt engineering in large language models: Techniques and applications. Preprint ,... | https://arxiv.org/abs/2505.16078v2 |
the input dimension corresponds to the output dimension of the LLMs. For instance, in the case of Llama-3.2-1B-Instruct, its output features are 2048, which serve as the input features for the linear mapping head. The output features’ dimension, on the other hand, cor- responds to the number of classification labels. D... | https://arxiv.org/abs/2505.16078v2 |
OF INPUT >>> In the real-world IE dataset, we used authentic email history records from the industry as the data source, with labels manually identified by experts from our industrial partners. Particularly of interest, we consider Self- consistency COT method to further validate the model’s logical reasoning ability. ... | https://arxiv.org/abs/2505.16078v2 |
arXiv:2505.16081v1 [cs.CL] 21 May 2025BiasLab: Toward Explainable Political Bias Detection with Dual-Axis Annotations and Rationale Indicators KMA Solaiman Department of CSEE University of Maryland, Baltimore County Baltimore, MD, USA ksolaima@umbc.edu Abstract —We present BiasLab, a dataset of 300 political news artic... | https://arxiv.org/abs/2505.16081v1 |
labels, an- notators select rationale indicators adapted from media literacy frameworks [12], [13] and highlight relevant snippets. These rationales serve as built-in explanations, enabling downstream models not only to detect bias but also to justify their predic- tions. A qualification test and pilot-phase refinement... | https://arxiv.org/abs/2505.16081v1 |
al. report strong model–human alignment on key grammatical constructions, suggesting that alignment quality can vary sharply by linguistic phenomenon [16]. Closer to our application domain, Baheti et al. analyze stance and safety interventions in offensive open-domain dialogue, highlighting practical challenges for bia... | https://arxiv.org/abs/2505.16081v1 |
of three representative snippets: the title and lead paragraph, a middle paragraph, and the conclu- sion. This design was informed by pilot findings showing that ideological framing is often concentrated near the beginning and end of political articles. Annotators also had access to the full article text via expandable... | https://arxiv.org/abs/2505.16081v1 |
explicitly stated but implied through tone, framing, or omission. •Length-induced cognitive load : Multi-paragraph pas- sages led to fatigue and inconsistent labels. •Asymmetric political expectations : Some annotators over-identified bias based on topic alone (e.g., interpret- ing any Trump criticism as left-leaning).... | https://arxiv.org/abs/2505.16081v1 |
Center , outlet bias was Left/Right 72 Modeling perceptual neutrality Workers agreed, but mismatched with outlet bias 54 Stress-testing source-level models Workers disagreed with each other 30 Hold-out / qualitative analysis Total 300 source ideology. While source-bias is an imperfect ground truth, it provides a coarse... | https://arxiv.org/abs/2505.16081v1 |
Language Models (LLMs). E. Final Gold Set We defined three subsets for supervised learning and eval- uation: •Agreed and matched outlet bias (144 docs) : high- confidence supervision •Agreed as Center on partisan outlet (72 docs) : perception-driven neutrality •Agreed but conflicted with outlet (54 docs) : included as ... | https://arxiv.org/abs/2505.16081v1 |
resource for analyzing ideological bias in political news. Unlike prior datasets centered on outlet ratings or issue framing, Bi- asLab offers article-level annotations grounded in reader sen- timent toward both major U.S. political parties. First, we observe that annotator agreement is strongest when bias cues are ove... | https://arxiv.org/abs/2505.16081v1 |
dataset release, LLM simulations, and all new analyses presented here were conducted solely by the author. We also thank Prahlad Anand for his help during the annotation phase. REFERENCES [1] Robert M. Entman. Framing bias: Media in the distribution of power. Journal of Communication , 57(1):163–173, 2007. [2] David M.... | https://arxiv.org/abs/2505.16081v1 |
Optimizing LLM-Based Multi-Agent System with Textual Feedback: A Case Study on Software Development Ming Shen1*, Raphael Shu2, Anurag Pratik2, James Gung2, Yubin Ge2, Monica Sunkara2,Yi Zhang2 1Arizona State University, USA 2Amazon Web Services mshen16@asu.edu {zhongzhu, anuragik, gungj, yubinge, sunkaral, yizhngn}@ama... | https://arxiv.org/abs/2505.16086v1 |
genuine role- based multi-agent system, wherein agents take on various roles to tackle a complex task collabora- tively. Furthermore, current works primarily focus on traditional NLP benchmarks such as MMLU (Hendrycks et al., 2021a), MATH (Hendrycks et al., 2021b), and HumanEval (Chen et al., 2021). In con- trast, our ... | https://arxiv.org/abs/2505.16086v1 |
demonstrate online and group optimizations as more effective settings than individual and offline optimization. 2 Related Works 2.1 LLM-Based Agents LLM-based agents (Yao et al., 2023; Shinn et al., 2023) have achieved impressive results on vari- ous NLP tasks. Applications (Richards, 2023) utilizing a single LLM-based... | https://arxiv.org/abs/2505.16086v1 |
LLM inference prompts, including few-shot examples and system instruc- tions, through search algorithms in a combinato- rial space. TextGrad (Yuksekgonul et al., 2024) takes a different perspective of backpropagation being a general and powerful framework to opti- mize the LLM-based agent system based on natural langua... | https://arxiv.org/abs/2505.16086v1 |
– Through the robustness dimen- sion, we aim to measure whether the software is reliable enough to handle different unex- pected user inputs or other exceptions. •Test Case Coverage – Test cases are essential for software code. To improve software code quality and coverage, they help verify and validate whether softwar... | https://arxiv.org/abs/2505.16086v1 |
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