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Mohit Bansal, James Zou, Jian Pei, Jian Liu, Jianfeng Gao, Jiawei Han, Jieyu Zhao, Jiliang Tang, Jindong Wang, Joaquin Vanschoren, John Mitchell, Kai Shu, Kaidi Xu, Kai- Wei Chang, Lifang He, Lifu Huang, Michael Backes, Neil Zhenqiang Gong, Philip S. Yu, Pin-Yu Chen, Quanquan Gu, Ran Xu, Rex Ying, Shuiwang Ji, Suman Ja... | https://arxiv.org/abs/2505.16128v2 |
large language models with so- cial values. In Proceedings of the 2023 Conference on Fairness, Accountability, and Transparency . Kaya Stechly, Matthew Marquez, and Subbarao Kamb- hampati. 2023. Gpt-4 doesn’t know it’s wrong: An analysis of iterative prompting for reasoning prob- lems. Preprint , arXiv:2310.12397. Alon... | https://arxiv.org/abs/2505.16128v2 |
Uses clearly appropriate examples , reasons , and other evidence taken from the source text (s) to support its position 10- Is well organized and clearly focused , demonstrating clear coherence and smooth progression of ideas 11- Exhibits skillful use of language , using a varied , accurate , and apt vocabulary 12- Dem... | https://arxiv.org/abs/2505.16128v2 |
so serious that meaningis somewhat obscured 49 50Score 1 51If an essay demonstrates very little or no mastery with ONE OR MORE of these weaknesses : 52- Develops no viable point of view on the issue , or provides little or no evidence to support its position 53- Is disorganized or unfocused , resulting in a disjointed ... | https://arxiv.org/abs/2505.16128v2 |
1 Abstract Recent studies have applied large language models (LLMs) to machine translation quality estimation (MTQE) by prompting models to assign numeric scores. Nonetheless, these direct scoring methods tend to show low segment -level correlation with human judgments. In this paper, we propose a generation -based eva... | https://arxiv.org/abs/2505.16129v1 |
and grammar". {tgt_lang } translation: "{ translation }" Score (0 -100): You are a certified WMT benchmark translator. Translate the following sentence from the WMT22 dataset into English. Your translation will be directly compared to WMT system outputs using the 'all - mpnet -base-v2' semantic similarity model. To ens... | https://arxiv.org/abs/2505.16129v1 |
contrast, prompting them to generate translations aligns with their training objective, resulting in fewer 3 NE-EN ET-EN SI-EN RO-EN RU-EN ρ r ρ r ρ r ρ r ρ r Gemma -7B ours 0.56 0.543 0.441 0.347 0.441 0.419 0.665 0.708 0.506 0.522 baseline 0.333 0.379 0.349 0.384 0.274 0.29 0.624 0.585 0.327 0.421 growth +68% +43% +2... | https://arxiv.org/abs/2505.16129v1 |
, 2025 ), Qwen1.5 -14B ( Bai et al., 2023 ), Deep Seek-R1 (DeepSeek -AI, 2025 ), and GPT-4-turbo ( OpenAI, 2023 ). 4.2 Experiment 1 This experiment compares our method against the baseline proposed by Qian et al. (2024). The aim is to assess whether our method performs better under the same model settings. The baseline... | https://arxiv.org/abs/2505.16129v1 |
to a generation -based evaluation framework. We conduct the most extensive study to date, evaluating 8 LLMs and 8 language pairs through two experiments. Our method consistently outperforms LLM -based scoring approaches and surpasses non -LLM official MTME reference -free metrics, particularly in low -resource settings... | https://arxiv.org/abs/2505.16129v1 |
Mialon, Guan Pang, Guillem Cucurell, Hailey Nguyen, Hannah Korevaar, Hu Xu, Hugo Touvron, Iliyan Zarov, Imanol Arrieta Ibarra, Isabel Kloumann, Ishan Misra, Ivan Evtimov, Jack Zhang, Jade Copet, Jaewon Lee, Jan Geffert, Jana Vranes, Jason Park, Jay Mahadeokar, Jeet Shah, Jelmer van der Linde, Jennifer Billock, Jenny Ho... | https://arxiv.org/abs/2505.16129v1 |
ArXiv, abs/2501.12948 . Gemma Team, Thomas Mesnard, Cassidy Hardin, Robert Dadashi, Surya Bhupatiraju, Shreya Pathak, Laurent Sifre, Morgane Rivière, Mihir Sanjay Kale, Juliette Love, Pouya Tafti, Léonard Hussenot, Pier Giuseppe Sessa, Aakanksha Chowdhery, Adam Roberts, Aditya Ba rua, Alex Botev, Alex Castro - Ros, Amb... | https://arxiv.org/abs/2505.16129v1 |
Association for Computational Linguistics. Maja Popović. 2015. chrF: character n -gram F -score for automatic MT evaluation . In Proceedings of the Tenth Workshop on Statistical Machine Translation , pages 392 –395, Lisbon, Portugal. Association for Computational Linguistics. Markus Freitag, Nitika Mathur, Chi -kiu Lo,... | https://arxiv.org/abs/2505.16129v1 |
Touvron, Hugo, Louis Martin, Kevin R. Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Daniel M. Bikel, Lukas Blecher, Cristian Cantón Ferrer, Moya Chen, Guillem Cucurull, David Esiob u, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia ... | https://arxiv.org/abs/2505.16129v1 |
0.004 0.007 0.064 0.049 0.043 0.021 0.020 0.014 0.013 0.016 0.009 chrF 0.004 0.000 0.003 0.062 0.048 0.042 0.022 0.019 0.015 0.025 0.028 0.017 BLEURT -20 0.003 -0.001 0.002 0.053 0.046 0.036 0.021 0.024 0.014 0.026 0.031 0.018 COMET -20 -0.003 0.002 -0.002 0.050 0.044 0.034 0.020 0.025 0.014 0.027 0.025 0.018 YiSi-1 0.... | https://arxiv.org/abs/2505.16129v1 |
Position of Uncertainty: A Cross-Linguistic Study of position bias in Large Language Models Menschikov Mikhail†,1,2,Alexander Kharitonov†,3,Maiia Kotyga4,Vadim Porvatov2,3, Anna Zhukovskaya5,David Kagramanyan6,Egor Shvetsov2,⧫andEvgeny Burnaev2,7 †these authors contributed equally to this work 1ITMO, Saint-Petersburg, ... | https://arxiv.org/abs/2505.16134v1 |
Vietnamese), reveal- ing: ♣Position bias is predominantly model-driven but ex- hibits language-specific variations. ♣We observe stronger late-position bias in Qwen2.5- 7B-Instruct, contradicting prior claims of inherent early-token preference [Wu et al., 2025, Barbero et al., 2025]. ♣Explicitly instructing models of co... | https://arxiv.org/abs/2505.16134v1 |
uncertainty quantification strategies. The code for our experiments is available in the GitHub repository. 2 Related Work What causes position bias? Prior work identifies mul- tiple contributing factors. [Zhang et al., 2024a] attribute position bias to U-shaped attention patterns in trans- formers, which prioritize ext... | https://arxiv.org/abs/2505.16134v1 |
the correct answer. They used two types of instructions —relative andabsolute — and found that models lack relative awareness and that implicit information about absolute placement of the correct prompt marginally improves model performance. However, the authors did not study a scenario when the placement prompt is abs... | https://arxiv.org/abs/2505.16134v1 |
the experiment, relevance scores rs1, . . . , rs Nwere included in the prompt. As depicted in Figure 1 we consider three positions of relevant con- texts – TOP(first), MIDDLE( N/2) or BOTTOM(last). Context Volume Analysis: Prior research demonstrates increased position bias with longer contexts [Baker et al., 2024, Pey... | https://arxiv.org/abs/2505.16134v1 |
scenario, for example, due to residual connec- tions, tokens may become more similar to tokens under position bias. Entropy Dynamics: LetH(l) Adenote the general attention entropy at layer l. As tokens homogenize (x(l) i≈x(0) 1), queries and keys become indistinguish- able forcing contextual attention weights to unifor... | https://arxiv.org/abs/2505.16134v1 |
al., 2018] 150k Russian MTSBerQuAD (link) 60k German MLQA [Lewis et al., 2020] 5k Hindi MLQA [Lewis et al., 2020] 5k Vietnamese MLQA [Lewis et al., 2020] 5k Table 1: Summary of datasets used in this study by languages with associated sources and question-answer pairs. Preprocessing: To manage computational con- straint... | https://arxiv.org/abs/2505.16134v1 |
Results 7.1 Position bias is mostly driven by models From Table 2, we observe that Qwen generally achieves the highest performance when the correct context is placed at the BOTTOM, while Llama excels when the correct context is positioned at the TOP across all lan- guages. Notably, Qwen demonstrates superior perfor- ma... | https://arxiv.org/abs/2505.16134v1 |
0.687 mean accuracy compared to the Aligned scenario (Qwen: 0.651, LLaMA: 0.619), particularly in low- resource languages such as Hindi. This suggests that in- tegrating relevance scores may hurt performance rather than mitigating position bias in multilingual settings. Performance gains from positional guidance are on... | https://arxiv.org/abs/2505.16134v1 |
Bottom LlamaSVO 5.39 5.12 4.75 SOV 88.52 88.49 88.43 QwenSVO 2.34 4.15 2.96 SOV 93.98 91 91.78 Table 5: Percentage of word orders for Hindi, cells where context placements align with position bias are highlighted in green. Bolded results indicate stronger alignment with expected word order. Model Position Difference SV... | https://arxiv.org/abs/2505.16134v1 |
However, such detailed linguistic theoretical inquiry extends beyond the methodological boundaries of this work. Computational Limitations: Our study employed 2,000 question-answer pairs per language. When extrap- olated across nine experimental scenarios, this yielded 18,000 model evaluations per language — a computa-... | https://arxiv.org/abs/2505.16134v1 |
5050–5063, Bangkok, Thailand, Au- gust 2024. Association for Computational Linguistics. doi:10.18653/v1/2024.acl-long.276. URL https: //aclanthology.org/2024.acl-long.276/ . Daphna Oyserman and Spike WS Lee. Does culture influence what and how we think? effects of prim- ing individualism and collectivism. Psychological... | https://arxiv.org/abs/2505.16134v1 |
Wu. A survey of uncer- tainty estimation in llms: Theory meets practice. ArXiv , abs/2410.15326, 2024. URL https://api. semanticscholar.org/CorpusID:273502635 . Petr Sychev, Andrey Goncharov, Daniil Vyazhev, Ed- vard Khalafyan, and Alexey Zaytsev. When an llm is apprehensive about its answers - and when its uncertainty... | https://arxiv.org/abs/2505.16134v1 |
Eric Xing, et al. Judging llm-as-a-judge with mt-bench and chatbot arena. Ad- vances in Neural Information Processing Systems , 36: 46595–46623, 2023. Anna Bavaresco, Raffaella Bernardi, Leonardo Berto- lazzi, Desmond Elliott, Raquel Fern’andez, Al- bert Gatt, E. Ghaleb, Mario Giulianelli, Michael Hanna, Alexander Koll... | https://arxiv.org/abs/2505.16134v1 |
embedding. Neu- rocomputing , 568:127063, 2024. ISSN 0925-2312. doi:https://doi.org/10.1016/j.neucom.2023.127063. URL https://www.sciencedirect.com/ science/article/pii/S0925231223011864 . An Yang, Baosong Yang, Binyuan Hui, Bo Zheng, Bowen Yu, Chang Zhou, Chengpeng Li, Chengyuan Li, Dayiheng Liu, Fei Huang, Guanting D... | https://arxiv.org/abs/2505.16134v1 |
Dataset Composition : Qwen: Prioritizes knowledge, coding, and mathematics, trained on 18 trillion tokens [Yang et al., 2024]. Llama: Focuses on dialogue applications, pretrained on 15 trillion multilingual tokens [Touvron et al., 2023]. 2.Architectural Innovations : Llama introduces a document-aware attention mask [To... | https://arxiv.org/abs/2505.16134v1 |
degree and had prior experience in the evaluation of LLM responses. 7https://huggingface.co/datasets/rajpurkar/squad_v2 8https://huggingface.co/datasets/MTS-AI-SearchSkill/MTSBerquad 9https://github.com/facebookresearch/MLQA (a) (b) 0.60.70.80.91.0 TOP MIDDLE BOTTOM TOP MIDDLE BOTTOM TOP MIDDLE BOTTOM0.60.70.80.91.0 0.... | https://arxiv.org/abs/2505.16134v1 |
arXiv:2505.16135v1 [cs.AI] 22 May 2025 21 May 2025 Sudoku-Bench: Evaluating creative reasoning with Sudoku variants Jeffrey Seely1, Yuki Imajuku1, Tianyu Zhao1, Edoardo Cetin1and Llion Jones1 1Sakana AI Existing reasoning benchmarks for large language models (LLMs) frequently fail to capture authentic creativity, often... | https://arxiv.org/abs/2505.16135v1 |
pass directly through a purple arrow if moving in the direction the arrow is pointing. An arrow always points to the smaller of the two values it sits between.TEST CONSTRAINT: In this experiment, for any two adjacent digits along the correct path, one can be divided by the other to give an exact integer (ie; one is a m... | https://arxiv.org/abs/2505.16135v1 |
in both the one-shot configuration (prompt a model to solve a puzzle in one response) and a multi-step configuration (multi-turn interaction between the model providing at least one digit and the user providing the updated board state). Beyond benchmarking, Sudoku variants offer a fertile laboratory for reasoning resea... | https://arxiv.org/abs/2505.16135v1 |
the initial grid being empty, the puzzle constraints collectively enforce a strict sequence of increasing digits from the lower left to the upper right shaded circles. Identifying and leveraging this subtle interplay forms the puzzle’s essential insight. Though rated at an easy difficulty (2-star), current LLMs consist... | https://arxiv.org/abs/2505.16135v1 |
exclude the possibility of some cells being 9, but the intuition is unclear, and this step of analysis does not effectively narrow the candidate space.SearchWith the temporary information of “circles>=3”, the model starts “guessing” until contradiction. Firstly it tries circle cell r4c3=3, r4c6=5, which finally leads t... | https://arxiv.org/abs/2505.16135v1 |
this puzzle solve, an expert solver discovered this break-in in about 4.5 minutes, and a full puzzle solve taking about 35 minutes.1In all LLMs we tested, no model was able to make progress. For example, we show the reasoning summary of Gemini 2.5 Pro Preview (Fig. 2b), which was able to successfully parse and identify... | https://arxiv.org/abs/2505.16135v1 |
a simple 4×4puzzle whose line paths are represented as a sequence of rxcy(row x column y) coordinates, and the location of the dot is described as the two cells it lies between. The rules, visual elements, grid size, and initial board state (if any digits are given) are sufficient to unambiguously specify the puzzle an... | https://arxiv.org/abs/2505.16135v1 |
grid, and a text description of visual elements are sufficient to unambiguously specify the puzzle. to solve, while some of the more difficult puzzles featured on the channel are over 3 hours in length. We developed a dataset consisting of the audio transcripts of each solve, together with a sequence of SudokuPad actio... | https://arxiv.org/abs/2505.16135v1 |
0.0 0.0 0.0 0.00.0 0.0 0.0 0.0 Gemma 3 27B IT 0.1 0.1 0.5 0.3 0.0 0.0 0.0 0.00.0 0.0 0.0 0.0 Llama 4 Maverick 0.2 0.5 0.4 0.4 0.0 0.0 0.0 0.00.0 0.0 0.0 0.0 Table 1|Sudoku-Bench leaderboard. Performance comparison of various LLMs on Sudoku-Bench. Percentageofpuzzlescompletelysolvedforeachevaluationmode(multi -stepvs.si... | https://arxiv.org/abs/2505.16135v1 |
Comparing single-shot to multi-step evaluation modes, allowing iterative feedback slightly improved outcomes for smaller puzzles but did not meaningfully impact results for larger puzzles. The minimal difference between modes suggests that the fundamental difficulty for these models lies not merely in incremental reaso... | https://arxiv.org/abs/2505.16135v1 |
(S2 and S3) to two candidates (10 or 11).AnalysisFrom the 2 x 2 = 4 combinations of S2 and S3, it further reduces it to 2 possible cases, which forms a small constrained space for latter search.SearchThe model starts to search by starting from (S2=10, S3=11) or (S2=11, S3=10) and guessing other numbers on the lines unt... | https://arxiv.org/abs/2505.16135v1 |
multi-step, strategic, and creative reasoning. 6. Discussion The role of tool use Evaluating model reasoning can be distinguished by whether external tools, such as constraint solvers or code execution environments, are available. Without tool use, the evaluation specifically assesses the model’s intrinsic reasoning ca... | https://arxiv.org/abs/2505.16135v1 |
V. Chandrasekaran, R. Eldan, J. A. Gehrke, E. Horvitz, E. Kamar, P. Lee, Y. T. Lee, Y.- F. Li, S. M. Lundberg, H. Nori, H. Palangi, M. T. Ribeiro, and Y. Zhang. Sparks of artificial general intelligence: Early experiments with gpt-4. ArXiv, abs/2303.12712, 2023. URL https: //api.semanticscholar.org/CorpusID:257663729 .... | https://arxiv.org/abs/2505.16135v1 |
Zettlemoyer, P. Liang, E. Candès, and T. Hashimoto. s1: Simple test-time scaling. arXiv preprint arXiv:2501.19393 , 2025. S. Neumann. Sudokupad, 2021. URL https://sudokupad.app/ . OpenAI. OpenAI o3 and o4-mini System Card. https://openai.com/index/o3-o4-mini-sys tem-card/ , 2025. L. Ouyang, J. Wu, X. Jiang, D. Almeida,... | https://arxiv.org/abs/2505.16135v1 |
Distilling the Implicit Multi-Branch Structure in LLMs’ Reasoning via Reinforcement Learning Shicheng Xu1,2, Liang Pang1∗, Yunchang Zhu3, Jia Gu1,2, Zihao Wei1,2, Jingcheng Deng1,2, Feiyang Pan3, Huawei Shen1, Xueqi Cheng1 1State Key Laboratory of AI Safety, Institute of Computing Technology, CAS 2University of Chinese... | https://arxiv.org/abs/2505.16142v1 |
Loss…(b): SFT -based distillation (c): Our RL-based distillation SolvingStart Generative Structure Reward ModelFigure 1: (a) The generated reasoning path has implicit multi-branch structure. (b) Distillation only based on SFT collapses the rich structure into a flat sequence of token prediction to memorize only the tea... | https://arxiv.org/abs/2505.16142v1 |
LLM at a step-level on how to better perform sampling to select the most suitable sub-problem from multiple potential ones , and then solving it. In this way, RLKD can distill the implicit multi-branch structure in the reasoning of teacher LLMs to the student LLMs to make the student learn to perform authentic reasonin... | https://arxiv.org/abs/2505.16142v1 |
format over substance: models learn to imitate the reasoning paths without authentic understanding of their content Chen et al. [2025], Dai et al. [2024]. In fact, a student can produce correct answers by mimicking a long Chain-of-Thought (CoT) pattern even if many intermediate steps are incorrect Li et al. [2025]. The... | https://arxiv.org/abs/2505.16142v1 |
we first structurally define each meta-reasoning and solving steps as ( M,Q&A), in which Mis the content of meta-reasoning that focuses on determining the sub-problem that the current reasoning step should solve.Q-Apair is the content of solving, in which Qis a clear description of the current sub-problem andAis the so... | https://arxiv.org/abs/2505.16142v1 |
positive number 𝜆𝜆such that the polynomial 𝑓𝑓𝑥𝑥=𝑥𝑥3+𝑎𝑎𝑥𝑥2+𝑏𝑏𝑥𝑥+𝑐𝑐has three real roots 𝑥𝑥1,𝑥𝑥2,𝑥𝑥3,and the conditions 𝑥𝑥2−𝑥𝑥1=𝜆𝜆and 𝑥𝑥3>1 2(𝑥𝑥1+𝑥𝑥2)are satisfied, find the maximum value of 2𝑎𝑎3+27𝑐𝑐−9𝑎𝑎𝑎𝑎 𝜆𝜆3 . [Reasoning Path]: Okay, so I... (too long to show here) [Respons... | https://arxiv.org/abs/2505.16142v1 |
two sets every αtraining steps. We set αas100. Ablation study about this part can be found in Appendix. 3.1.2 Structured Reward Mechanism After above generation, we denote StandSsas the sequences generated from reasoning paths of the teacher LLM and the student LLM respectively: St= [(Mt 1,Qt 1&At 1),(Mt 2,Qt 2&At 2),(... | https://arxiv.org/abs/2505.16142v1 |
ϵk∝1/σk(where σkis the group’s advantage standard deviation) to its policy ratio rk(θ), constraining updates with: LGRPO(θ) =KX k=1EGkh min rkˆAk,clip(rk,1−ϵk,1 +ϵk)ˆAki −β·Es[DKL(πold∥πθ)],(2) where ˆAk=Ak/σkis the group-normalized advantage. The dual-constraint design combines low- variance intra-group updates (via... | https://arxiv.org/abs/2505.16142v1 |
LLM to learn authentic reasoning rather than memorizing the teacher’s path. We use embedding3similarity Deng et al. [2025] to select the 3.2K data samples with the largest difference from the test set in OpenR1- Math-220k training set, and train Qwen2.5-Math-7B on this subset with RL-based distillation and SFT-based di... | https://arxiv.org/abs/2505.16142v1 |
80.0 83.3 DeepSeek-R1-Distill-Qwen-7B-PPO 800K 3.2K 46.7 52.1 53.0 52.7 52.9 52.9 66.7 73.3 73.3 80.0 83.3 DeepSeek-R1-Distill-Qwen-7B-GRPO 800K 3.2K 50.0 52.5 53.3 53.3 53.3 52.3 66.7 73.3 80.0 83.3 83.3 Our: DeepSeek-R1-Distill-Qwen-7B-RLKD 800K 3.2K 53.3 56.7 55.4 53.3 52.9 53.6 73.3 80.0 86.7 86.7 86.7 (b) Performa... | https://arxiv.org/abs/2505.16142v1 |
/uni00000014 /uni00000015 /uni00000016 /uni00000017 /uni00000018 /uni00000019 /uni0000001a /uni0000001b /uni00000035/uni00000048/uni00000044/uni00000056/uni00000052/uni00000051/uni0000004c/uni00000051/uni0000004a/uni00000003/uni00000036/uni00000057/uni00000048/uni00000053/uni00000003/uni0000002c/uni00000051/uni00000047... | https://arxiv.org/abs/2505.16142v1 |
RLKD-trained students mirror teachers’ multi-branch exploration patterns, closing the imitation-authentic reasoning gap. Limitations and Potential Impact. A key limitation of our work stems from computational con- straints: while RLKD demonstrates effectiveness on 7B-scale models, its viability on larger LLMs (e.g., 70... | https://arxiv.org/abs/2505.16142v1 |
an early investigation into training r1-like reasoning large vision-language models. arXiv preprint arXiv:2504.11468 , 2025. Chengwei Dai, Kun Li, Wei Zhou, and Songlin Hu. Beyond imitation: Learning key reasoning steps from dual chain-of-thoughts in reasoning distillation. arXiv preprint arXiv:2405.19737 , 2024. Mark ... | https://arxiv.org/abs/2505.16142v1 |
preprint arXiv:2503.14476 , 2025. OpenThoughts Team. Open Thoughts. https://open-thoughts.ai, January 2025. Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané. Concrete problems in ai safety. arXiv preprint arXiv:1606.06565 , 2016. Lauro Langosco Di Langosco, Jack Koch, Lee D Shark... | https://arxiv.org/abs/2505.16142v1 |
Figure 6: Ablation study about our Structured Fine-grained Training method. /uni00000013/uni00000011/uni00000015 /uni00000013/uni00000011/uni00000017 /uni00000013/uni00000011/uni00000019 /uni00000013/uni00000011/uni0000001b /uni00000014/uni00000011/uni00000013 /uni00000014/uni00000011/uni00000015 /uni00000014/uni000000... | https://arxiv.org/abs/2505.16142v1 |
2𝑎𝑎3+27𝑐𝑐−9𝑎𝑎𝑎𝑎 𝜆𝜆3 . Simplify the expression by combining like terms. The result will depend only on 𝑠𝑠 and 𝜆𝜆, allowing further simplification by introducing 𝑘𝑘=𝑠𝑠 𝜆𝜆. This reduces the problem to maximizing a cubic function in 𝑘𝑘. [Sub -problem 2]: Simplify 2𝑎𝑎3+27𝑐𝑐−9𝑎𝑎𝑎𝑎 𝜆𝜆3 after su... | https://arxiv.org/abs/2505.16142v1 |
range. - [Sub -problem 1]: How to determine the contribution of numbers with a specific digit length d to the total digit count? [Answer 1 ]: For each d, calculate the start and end of the range. If n >= end, add (end - start +1) * d. If n < end, add (n - start +1) * d. [Meta -reasoning 2]: Implementing this requires i... | https://arxiv.org/abs/2505.16142v1 |
the right of this z -score, which corresponds to the probability of the process time being greater than 33 minutes. Since standard tables t ypically provide cumulative probabilities (area to the left), subtracting the cumulative probability from 1 gives the desired result. - [Sub -problem 2]: What is the cumulative pro... | https://arxiv.org/abs/2505.16142v1 |
on max previous states. [Meta -reasoning 2]: For each day, the current state's value depends on the previous day's states. For example, choosing high on day i requires the previous day to be no -task. We need to define recurrence relations for dynamic programming. - [Sub -problem 2]: What are the recurrence relations f... | https://arxiv.org/abs/2505.16142v1 |
3]: 0.025 L [Meta -reasoning 4]: Finally, divide the moles of HCl by its volume in liters to obtain the concentration. This completes the cal culation. - [Sub -problem 4]: What is the concentration (mol/L) of HCl when 0.0035 mol is dissolved in 0.025 L? [Answer 4]: 0.14 mol/L So the final answer is: 0.14 mol/L (f) Exam... | https://arxiv.org/abs/2505.16142v1 |
2]: Next, substitute the given values into the formula: current (I = 5 A), loop radius (R = 0.2 m), axial distan ce (z = 0.1 m), and permeability of free space (μ₀ = 4π × 10⁻⁷ T·m/A). Compute each component stepwise to avoid errors. [Sub -problem 2]: What is the value of R² + z² for R = 0.2 m and z = 0.1 m? [Answer 2]:... | https://arxiv.org/abs/2505.16142v1 |
Steering LVLMs via Sparse Autoencoder for Hallucination Mitigation Zhenglin Hua1, Jinghan He2,3, Zijun Yao4, Tianxu Han5, Haiyun Guo2,3,Yuheng Jia1,Junfeng Fang6 1School of Computer Science and Engineering, Southeast University 2Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences 3Sch... | https://arxiv.org/abs/2505.16146v1 |
along the faithful direc- tion. These results demonstrate that targeted interven- tions along faithful directions suppress hallucinatory generations, while perturbations along hallucinatory di- rections tend to elicit more factually incorrect content. tioning (Li et al., 2022). However, LVLMs still suf- fer from halluc... | https://arxiv.org/abs/2505.16146v1 |
existing decoding approaches, confirming its effec- tiveness and efficiency in hallucination reduction. Our main contributions are as follows: •We leverage SAEs to identify semantic direc- tions that are highly correlated with hallucina- tory and faithful object words in the represen- tation space of LVLMs. •We propose... | https://arxiv.org/abs/2505.16146v1 |
Latents. The SAE recon- structs model representations as a linear combi- nation of latent directions and a bias, effectively approximating the original input. Each latent ac- tivation zj(x)corresponds to a specific decoder direction dj=Wdec[j,:], enabling targeted ad- justment of the representation through activation s... | https://arxiv.org/abs/2505.16146v1 |
stream at the l-th layer, steering the generation process toward greater factual consistency. quantify the semantic relevance of each latent ac- tivation, we compute the difference in activation frequencies as follows: shall j=fhall j−ffaithful j , sfaithful j =ffaithful j−fhall j.(6) These values reflect the relevance... | https://arxiv.org/abs/2505.16146v1 |
both prompts and visual inputs, thereby reflecting the model’s semantic behavior. Suppressing hallu- cinatory directions during this stage helps reduce hallucination tendencies and enhances factual con- sistency in language generation. Following the method described in Section 2, we identify two semantic direction vect... | https://arxiv.org/abs/2505.16146v1 |
andunderlined , respectively. Avg.Len represents the average length of the generated descriptions. jection module, and a language model. LLaV A-1.5 and LLaV A-NeXT use an MLP to project all image tokens into the LLM’s input space, while Instruct- BLIP employs a Q-Former to select a compact set of informative visual tok... | https://arxiv.org/abs/2505.16146v1 |
the invasiveness of state interventions. SSL 6 LLaV A-NeXT-8b LLaV A-1.5-7b InstructBLIP-7b F1 score ↑ F1 score ↑ F1 score ↑ Greedy 89.10 84.99 85.37 Beam 89.30 85.31 84.41 DoLa 89.49 85.08 85.22 VCD 88.91 84.42 84.68 OPERA - 85.46 84.42 CODE 88.93 84.64 84.81 SSL 89.66 85.44 85.16 Table 2: POPE results averaged over p... | https://arxiv.org/abs/2505.16146v1 |
performance, setting γto0.8. For LLaV A-NeXT-8b, applying SSL at middle layers yields more effective mitigation of hallucinations, with layer 15achieving the best per- formance. For further analysis on steering layer selection and scaling factor γsettings across dif- ferent models, please refer to Appendix C and D. 5.2... | https://arxiv.org/abs/2505.16146v1 |
to the practical interpretability of SAEs in LVLMs, demonstrating their potential for understanding and controlling internal semantic representations. 7 Conclusion This work explores the relationship between the hallucination in LVLMs and their internal latent representations. We construct a residual stream dataset for... | https://arxiv.org/abs/2505.16146v1 |
vision-language models with instruction tuning. ArXiv preprint , abs/2305.06500. Javier Ferrando, Oscar Balcells Obeso, Senthooran Ra- jamanoharan, and Neel Nanda. 2025. Do i know this entity? knowledge awareness and hallucinations in language models. In The Thirteenth International Conference on Learning Representatio... | https://arxiv.org/abs/2505.16146v1 |
Common objects in context. In Computer Vision – ECCV 2014 , pages 740–755, Cham. Springer Inter- national Publishing. Hanchao Liu, Wenyuan Xue, Yifei Chen, Dapeng Chen, Xiutian Zhao, Ke Wang, Liping Hou, Rongjun Li, and Wei Peng. 2024a. A survey on hallucination in large vision-language models. ArXiv preprint , abs/240... | https://arxiv.org/abs/2505.16146v1 |
Jieping Ye. 2024b. Seeing clearly by layer two: Enhancing attention heads to alleviate hal- lucination in lvlms. ArXiv preprint , abs/2411.09968. 10 A Construction of Residual Stream Dataset for Hallucinatory and Faithful Object Words Figure 8 illustrates the construction process of the residual stream dataset. We begi... | https://arxiv.org/abs/2505.16146v1 |
on the held-out test set using classification accuracy and confusion matrices as evaluation metrics. As shown in Figure 9, the confusion matrices for the three main feature groups ( Hall latent, Faith- fullatent, Both). The results show that the model achieves balanced performance across both posi- tive and negative cl... | https://arxiv.org/abs/2505.16146v1 |
in different multimodal architectures is left for future work. D Choice of Scaling Factor Tables 5, 6, and7 report the ablation results on the effect of the scaling factor γin the SSL. For the LLaV A series of models, setting γto 0.6 or 0.8 effectively reduces hallucinations, indicating 12 203040 /uni00000026/uni000000... | https://arxiv.org/abs/2505.16146v1 |
the onset of autoregressive language generation, we inject the specific reverse- direction to amplify the previously distorted visual signal, thereby biasing subsequent text outputs to- ward content that is either factually incorrect or substantially divergent from the original prompt. The complete algorithmic procedur... | https://arxiv.org/abs/2505.16146v1 |
room, a black and gray cat has found a comfortable spot on a wooden floor. The cat, with its fur as dark as night and patches as light as the moon, is facing away from us. To the left of the feline, a blue bicycle leans casually against a white wall. The bicycle, with its vibrant blue frame and black seat, adds a touch... | https://arxiv.org/abs/2505.16146v1 |
book placed on the floor, slightly to the right of the dog. The image features a large brown dog sitting on a carpeted floor in front of a flat screen TV. The dog appears to be watching something on the TV screen, likely a program or a movie. The TV is positioned on the left side of the room. Profiling the dog's positi... | https://arxiv.org/abs/2505.16146v1 |
a park with a bench placed near a bush. The bench is situated on the side of a path, with a tree growing next to it. There are several benches in the park, with one located closer to the bush and another further away. The park is well-lit, with streetlights illuminating the area. The bench near the bush is positioned i... | https://arxiv.org/abs/2505.16146v1 |
a black dress with a white collar, as well as a castle, none of which are present in the image. Assistant 2 accurately describes the dog in the style of the Mona Lisa, mentioning the correct attire and general description, with only minor inaccuracies like referring to the face as black instead of brown. Detailedness: ... | https://arxiv.org/abs/2505.16146v1 |
ironing clothes on the roof of the car. <s>InstructBLIP InstructBLIP w/ SSL GPT-4o EvaluationAccuracy: 6.0 7.0 Reason: Both assistants correctly describe a man ironing on the back of a yellow vehicle. However, Assistant 1 inaccurately mentions the man standing on the roof and the presence of additional items such as a ... | https://arxiv.org/abs/2505.16146v1 |
arXiv:2505.16148v1 [cs.LG] 22 May 2025NAN: A Training-Free Solution to Coefficient Estimation in Model Merging Chongjie Si1, Kangtao Lv2, Jingjing Jiang1, Yadao Wang3, Yongwei Wang2, Xiaokang Yang1, Wenbo Su3, Bo Zheng3, Wei Shen1 1Shanghai Jiao Tong University,2Zhejiang University,3Alibaba Group {chongjiesi, wei.shen}... | https://arxiv.org/abs/2505.16148v1 |
insight, we introduce NAN, a novel training-free model merging plugin that leverages this information-theoretic perspective to achieve effective integration of multiple fine-tuned models. Extensive experiments demonstrate the effective- ness and generality of our approach, with NAN improving the performances of baselin... | https://arxiv.org/abs/2505.16148v1 |
To further understand the behavior of the merging coefficients, we now consider the case where the input features are normalized. This is a common pre-processing step in deep learning pipelines, es- pecially in representation learning and contrastive objectives. Under this normalization, the matrix Ai=XT iXibecomes app... | https://arxiv.org/abs/2505.16148v1 |
2022), Task Arithmetic (Ilharco et al., 2022), and Ties-Merging (Yadav et al., 2023). Vision Task. Following prior work (Yadav et al., 2023; Yang et al., 2023), we adopt ViT-B/32 and ViT-L/14 as the pre-trained backbone for all meth- ods. Evaluation is conducted across eight image classification tasks: SUN397 (Xiao et ... | https://arxiv.org/abs/2505.16148v1 |
87.7 95.3 89.5 92.5 99.2 68.5 86.4 Table 2: Results on language merging tasks. Method MMLU CEval GSM8K BBH Avg WizardLM-13B 53.6 32.6 38.8 19.4 36.1 WizardMath-13B 54.2 37.7 46.9 44.8 45.9 Task Arithmetic (TA) 56.3 39.5 52.7 35.7 46.0 TA + NAN 56.3 38.8 64.1 34.6 48.5 Ties-Merging (Ties) 55.9 40.0 55.3 38.9 47.5 Ties +... | https://arxiv.org/abs/2505.16148v1 |
on computer vision and pattern recognition , pages 3606–3613. Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman. 2021. Training verifiers to solve math word prob- lems. arXiv preprin... | https://arxiv.org/abs/2505.16148v1 |
NeurIPS . Pan Lu, Hritik Bansal, Tony Xia, Jiacheng Liu, Chun- yuan Li, Hannaneh Hajishirzi, Hao Cheng, Kai- Wei Chang, Michel Galley, and Jianfeng Gao. 2023. 5 Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts. arXiv preprint arXiv:2310.02255 . Haipeng Luo, Qingfeng Sun, Can Xu, Pu Z... | https://arxiv.org/abs/2505.16148v1 |
others. 2022. Model soups: averaging weights of multiple fine-tuned mod- els improves accuracy without increasing inference time. In International conference on machine learn- ing, pages 23965–23998. PMLR. Jianxiong Xiao, James Hays, Krista A Ehinger, Aude Oliva, and Antonio Torralba. 2010. Sun database: Large-scale sc... | https://arxiv.org/abs/2505.16148v1 |
arXiv:2505.16149v1 [cs.CV] 22 May 2025When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification Zirui Pang1∗Haosheng Tan2∗Yuhan Pu3∗, Zhijie Deng4Zhouan Shen4Keyu Hu4Jiaheng Wei4† 1University of Illinois Urbana-Champaign2University of Glasgow3Boston University 4The Hong Kong University ... | https://arxiv.org/abs/2505.16149v1 |
Benchmarks [Termed as multi-label samples] Multi-label issues are common in image-classification benchmarks, where real-world images often contain multiple objects, but only the primary object is annotated. This leads to co-exist images, where valid labels are omitted, potentially biasing model evaluations. Figure 2 il... | https://arxiv.org/abs/2505.16149v1 |
major obstacles in image classification. The most reliable remedy, manual re -annotation, scales poorly to today’s large datasets [ 6,17,44,25]. Automated approaches typically involve label prediction or confusion matrix estimation [ 13,15,52,39,36,7,50,19], yet they often underperform in the real-world label noise [ 3... | https://arxiv.org/abs/2505.16149v1 |
leads to dramatic cost of running time in dataset with large amount of labels. •Direct Multi-Label Selection: Asking VLMs to choose all relevant labels from a fixed list of options (10 labels for CIFAR-10 and 100 for CIFAR-100). Each image requires a single prompt. •Batched Multi-Label Selection: We begin by partitioni... | https://arxiv.org/abs/2505.16149v1 |
Observation 1: Failure in Fine-grained Class Prediction Original Label: “sea snake” Candidates: [“sea snake”,“garter snake”...] Response(by Janus): “answer”: [“snake”], reason: “The image shows a creature with distinct features such as scales covering its body, elongated shape without limbs, and forked tongue which is ... | https://arxiv.org/abs/2505.16149v1 |
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