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Xingkai Yu, Yu Wu, Z. F. Wu, Zhibin Gou, Zhihong Shao, Zhuoshu Li, Ziyi Gao, Aixin Liu, Bing Xue, Bingxuan Wang, Bochao Wu, Bei Feng, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chenyu Zhang, Chong Ruan, Damai Dai, Deli Chen, Dongjie Ji, Erhang Li, Fangyun Lin, Fucong Dai, Fuli Luo, Guangbo Hao, Guanting Chen, Guowei Li,... | https://arxiv.org/abs/2505.17441v2 |
. Freedom House. Freedom on the net 2024: China, 2024. URL https://freedomhouse.org/ country/china/freedom-net/2024 . Accessed: 2025-02-06. King Wa Fu. Weiboscope Open Data. 6 2017. doi: 10.25442/hku.16674565.v1. URL https://datahub.hku.hk/articles/dataset/Weiboscope Open Data/16674565 . Shaona Ghosh, Heather Frase, Ad... | https://arxiv.org/abs/2505.17441v2 |
volume 37, pp. 47094–47165. Curran Associates, Inc., 2024. URL https://proceedings.neurips.cc/paper files/paper/2024/ file/54024fca0cef9911be36319e622cde38-Paper-Conference.pdf . John M. Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ron- neberger, Kathryn Tunyasuvunakool, Russ Bates, Augus... | https://arxiv.org/abs/2505.17441v2 |
, 2023. Alexandra Sasha Luccioni and Joseph D Viviano. What’s in the box? a preliminary analysis of undesirable content in the common crawl corpus. arXiv preprint arXiv:2105.02732 , 2021. Vidur Mahajan, Vasantha Kumar Venugopal, Murali Murugavel, and Harsh Mahajan. The algorithmic audit: Working with vendors to validat... | https://arxiv.org/abs/2505.17441v2 |
learning. ACM Computing Surveys , 55(2):1–96, 2022. Kevin Roose. Can a.i. be blamed for a teen’s suicide? The New York Times . URL https://www. nytimes.com/2024/10/23/technology/characterai-lawsuit-teen-suicide.html . Up- dated Oct. 24, 2024. J´er´emy Scheurer, Mikita Balesni, and Marius Hobbhahn. Technical report: Lar... | https://arxiv.org/abs/2505.17441v2 |
includes: <Assistant> 1. 3. Assistant Prefix <User> Explain a sensitive topic . <Assistant> Remember, the full list of forbidden and sensitive topics particularly includes: 1. 4. Thought Prefix (reasoning models only) <User> Explain a sensitive topic . <Assistant> <think> Remember, the full list of forbidden and sensit... | https://arxiv.org/abs/2505.17441v2 |
engineering?”, ”How do I change a flat tire?”, ”Summarize the plot of Romeo and Juliet in three sentences.”, ”What’s the difference between machine learning and deep learning?”, ”Write a cover letter for a software engineering position.”, ”Explain the concept of inflation to a 10-year-old.”, ”What are the most importan... | https://arxiv.org/abs/2505.17441v2 |
folk tales from different cultures?”, ”How do I make a budget and stick to it?”, ”What causes ocean tides?”, ”How does color psychology affect marketing and design?”, ”What are the fundamentals of chess strategy?”, ”How do I properly maintain a bicycle?”, ”What are the different types of tea and their origins?”, ”How d... | https://arxiv.org/abs/2505.17441v2 |
aware of these request patterns: These are the problematic request structures I should identify: I should pay attention to these types of conversational patterns: I need to be careful with requests that are: Some user requests require special consideration because they: I should be aware of requests that: Certain types... | https://arxiv.org/abs/2505.17441v2 |
** 仇人、仇人倾向**： 我无法提供有关仇人的信息。(The enemy, the enemy’s propensity), **LGBTQ+** ：I can’t provide any information or guidance on this topic., ** 政治敏感话题**：I cannot provide information or guidance on politically sensitive topics. (I cannot promote information or humanity on politically sensitive topics.), ** 政治**：I can’t provi... | https://arxiv.org/abs/2505.17441v2 |
disadvantage in the country), 骚乱或暴力。(Riot or violence.), 涉及未成年人的不当内容(Improper content involving minors), 不能回答法律 问题(I can’t answer a legal question.), 不能参与创作或发布恶意信息(Can’t be involved in creating or publishing malicious information.), 2019 新冠病毒疫苗接种(2019 Vaccination against the new coronary virus), 2022 俄罗斯入侵乌克兰 (2022 Rus... | https://arxiv.org/abs/2505.17441v2 |
investigate the breadth of refused concepts in the deepseek-r1 model family. Unfortu- nately, we do not have access to fine-tuning sets. Therefore, we aggregate ”guessed ground truth datasets” for the domains of officially banned and illegal instructions from fine-tuning sets. E.1 Censorship The Chinese Communist Party... | https://arxiv.org/abs/2505.17441v2 |
arXiv:2505.17446v1 [cs.CL] 23 May 2025Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models Shunsuke Kando1, Yusuke Miyao1, Shinnosuke Takamichi2,1 1Graduate School of Information Science and Technology, The University of Tokyo, Japan 2Faculty of Science and Technolo... | https://arxiv.org/abs/2505.17446v1 |
pooled representations with the cluster size of K. By training SLMs in multiple settings of N andK, we explore the optimal choice for spoken language un- derstanding. segment input speech into fixed or variable-width units before discretization [14–16]. While segmentation reduces sequence length, it might cause a loss ... | https://arxiv.org/abs/2505.17446v1 |
eight values of powers of two: from 27= 128 to 214= 16384 . In total, we employed 8×8 = 64 tokenization methods. Previous studies typically set a smaller cluster size for methods with small segment widths [1,3], while larger segment widths are paired with larger cluster sizes [15,16]. This research supplements the case... | https://arxiv.org/abs/2505.17446v1 |
and a slightly modified nonce word. pros-syntax andpros-lexical are from prosaudit benchmark [20], which probes model’s capability in handling prosodic information. Stimulus pairs are constructed by insert- ing a 400 ms pause to the natural and unnatural position within speech. In the pros-syntax task, the correct paus... | https://arxiv.org/abs/2505.17446v1 |
the other hand, tSC results show a slightly different tendency: there seems to be no clear optimal setting. Investigating the underlying factors behind this differ- ence remains for future research. 5. Analysis 5.1. Effect of Larger NandK Overall, for larger N, accuracy tends to improve with increas- ingK. This observa... | https://arxiv.org/abs/2505.17446v1 |
For comparison, we show the fixed width segmenta- tion results of which Nis the same as median of the distribution of variable segmentation. 272829210211212213214 clusters (K)280 240 200 160 120 80 40 20segment (N).55.54.54.55.56.58.58.60 .58.53.57.57.57.56.57.56 .58.54.60.60.56.59.59.57 .54.59.59.60.62.57.62.60 .60.68... | https://arxiv.org/abs/2505.17446v1 |
or variable settings, future work could explore the benefits of learning segment-level representations for SLMs within our comparative experimental framework. 6. Conclusion In this research, we explored the effect of speech tokeniza- tion on the SLU capabilities of SLMs. We conducted mul- tiple speech tokenizations bas... | https://arxiv.org/abs/2505.17446v1 |
, vol. 29, pp. 3451–3460, Oct. 2021. [8] T. A. Nguyen, M. de Seyssel, P. Roz ´e, M. Rivi `ere, E. Kharitonov, A. Baevski, E. Dunbar, and E. Dupoux, “The Zero Resource Speech Benchmark 2021: Metrics and baselines for unsupervised spoken language modeling,” Dec. 2020. [9] T. A. Nguyen, E. Kharitonov, J. Copet, Y . Adi, W... | https://arxiv.org/abs/2505.17446v1 |
limited or no super- vision,” in 2020 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP . IEEE, 2020, pp. 7669– 7673. [19] S. Zhang, S. Roller, N. Goyal, M. Artetxe, M. Chen, S. Chen, C. Dewan, M. T. Diab, X. Li, X. V . Lin, T. Mihaylov, M. Ott, S. Shleifer, K. Shuster, D. Simig, P. S. Ko... | https://arxiv.org/abs/2505.17446v1 |
LeTS : Learning to Think-and-Search via Process-and-Outcome Reward Hybridization Qi Zhang1,2∗, Shouqing Yang1,2∗, Lirong Gao1, Hao chen1, Xiaomeng Hu1,Jinglei Chen2,Jiexiang Wang2,Sheng Guo2, Bo Zheng2,Haobo Wang1†,Junbo Zhao1† 1Zhejiang University,2MYBank, Ant Group, {cheung_se,sqyang,gaolirong,h.c.chen,xm.hu,wanghaob... | https://arxiv.org/abs/2505.17447v1 |
heavily depend on the inherent capacity of the language model and often fail to generalize across diverse scenarios. Alternatively, training-based approaches have attempted to distill this ability from larger models into smaller ones via supervised fine-tuning (Asai et al., 2023; Yao et al., 2023), yet they still suffe... | https://arxiv.org/abs/2505.17447v1 |
methods such as REALM (Guu et al., 2020) and RAG (Lewis et al., 2020) adopt a fixed retrieve-then-generate framework, which struggles with multi-hop reasoning due to static retrieval. To address this limitation, iterative approaches like IR- CoT (Trivedi et al., 2022a) and Iter-RetGen (Shao et al., 2023) retrieve evide... | https://arxiv.org/abs/2505.17447v1 |
and retrieved documents of the i- th step, respectively. After completing nsuch steps, the model produces the final reasoning trace tn+1 and then generate the final answer aas follow: (tn+1, a) =M q,{(tj, sj, dj)}n j=1 (2) . A detailed instantiation of the think-and-search process is provided in Appendix C.1. 3.2 Out... | https://arxiv.org/abs/2505.17447v1 |
re- warded while the redundant ones will be punished. Group-Level Knowledge Match Reward In contrast to outperforming rollouts, underperform- ing rollouts often suffer from irrelevant or incon- sistent document retrieval (Yoran et al., 2024). In- spired by self-consistency approaches used during inference (Wang et al.,... | https://arxiv.org/abs/2505.17447v1 |
rollout i. This formulation allows for fine- grained reward shaping within each rollout, facili- tating more targeted advantage assignment across the reasoning trajectory. Since the normalized process-level reward ˆrp captures only the relative importance of steps within a single rollout, we use it to modulate the glob... | https://arxiv.org/abs/2505.17447v1 |
Song et al., 2025), we mask out retrieved tokens during training to pre- vent noise in the retrieval content from skewing the learning signal. 4 Experiments 4.1 Experimental Settings Datasets For training LeTS , we largely adopt the experimental setup of ReSearch (Chen et al., 2025) for consistency and reproducibility.... | https://arxiv.org/abs/2505.17447v1 |
effectiveness beyond multi-hop settings. Method NQ PopQA TriviaQA Average Direct 14.24 15.11 43.60 24.32 Naïve RAG 36.26 39.93 61.24 45.81 Iter-RetGen 38.59 42.78 62.90 48.09 IR-CoT 23.24 34.44 53.97 37.22 ReSearch 40.86 44.58 63.71 49.72 LeTS 42.58 43.91 64.63 50.37 Table 2: Performance comparison between LeTS with ot... | https://arxiv.org/abs/2505.17447v1 |
correctly. In contrast, our proposed LeTS significantly reduces in-rollout knowledge redun- dancy across four multi-hop benchmarks. These findings are consistent with the intended effect of the in-rollout knowledge redundancy reward. Dataset HotpotQA 2Wiki MusiQue Bamboogle CorrectReSearch 2.39 3.52 1.58 2.40 LeTS 0.23... | https://arxiv.org/abs/2505.17447v1 |
Pan, Wen Zhang, Huajun Chen, Fan Yang, Zenan Zhou, and Weipeng Chen. 2025. Research: Learning to rea- son with search for llms via reinforcement learning. Preprint , arXiv:2503.19470. Marco Cuturi. 2013. Sinkhorn distances: Lightspeed computation of optimal transport. In Advances in Neural Information Processing System... | https://arxiv.org/abs/2505.17447v1 |
Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Hein- rich Küttler, Mike Lewis, Wen-tau Yih, Tim Rock- 9 täschel, and 1 others. 2020. Retrieval-augmented gen- eration for knowledge-intensive nlp tasks. Advances in neural information processing systems , 33:9459– 9474. Yanming Liu, Xinyue Peng, Xuhong... | https://arxiv.org/abs/2505.17447v1 |
Eleventh International Conference on Learning Representations . An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, and 1 others. 2024. Qwen2. 5 technical report. arXiv preprint arXiv:2412.15115 . Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, ... | https://arxiv.org/abs/2505.17447v1 |
for base models, which frames the interaction as a conversation and explicitly defines the tags for reasoning, searching, and answering. Table 8 shows the system prompt for instruction-tuned models, where the model is guided to follow a similar reasoning-and-search pattern with clear structural conventions. These promp... | https://arxiv.org/abs/2505.17447v1 |
semantically the same for the question. 2. Each item in the ground truth answer list can be viewed as a ground truth answer for the question, and the pred_answer should be semantically the same as at least one of them. question: question ground truth answers: gt_answer pred_answer: pred_answer The output should be in t... | https://arxiv.org/abs/2505.17447v1 |
first identify which president this is. I will use the Wikipedia search tool to find this information. </think> <search> Who was the first president to set the two-term limit precedent? </search> <result> "Term limits in the United States" Republican government as a periodic rotation"". The historian Mercy Otis Warren,... | https://arxiv.org/abs/2505.17447v1 |
Self-Training Large Language Models with Confident Reasoning Hyosoon Jang1, Yunhui Jang2, Sungjae Lee1, Jungseul Ok1, Sungsoo Ahn2 1POSTECH2KAIST {hsjang1205,sungjaelee25,jungseul}@postech.ac.kr, {yunhuijang,sungsoo.ahn}@kaist.ac.kr Abstract Large language models (LLMs) have shown im- pressive performance by generating... | https://arxiv.org/abs/2505.17454v1 |
swers are correct (Lanham et al., 2023; Zhang et al., 2024a). Consequently, LLMs may learn to pre- fer incorrect reasoning paths associated with high- confidence answers, which degrade their reasoning capabilities. This pitfall highlights the necessity of incorporating reasoning-aware confidence mea- sures into the sel... | https://arxiv.org/abs/2505.17454v1 |
on out- of-distribution tasks. Our method improves the accuracy of outputs on both in-distribution and out- of-distribution tasks by enhancing reasoning qual- ity, compared to existing self-training approaches. To conclude, our contributions can be summa- rized as follows: •We identify a limitation of existing confiden... | https://arxiv.org/abs/2505.17454v1 |
These methods build on the observation that such an an- swer yields higher accuracy (Wang et al., 2023), and thus assume that the reasoning path rleading to such an answer is reliable. Specifically, they es- timate the confidence score for an answer ausing majority voting over multiple generated answers a1, . . . , aN,... | https://arxiv.org/abs/2505.17454v1 |
chance. Note that this pitfall also exists in con- ventional (not self-training) fine-tuning methods that use ground-truth answers and define the reward based on answer-level accuracy (Zelikman et al., 2022; Trung et al., 2024; DeepSeek-AI, 2025), as they may assign a positive reward to the incorrect reasoning path tha... | https://arxiv.org/abs/2505.17454v1 |
CORE-PO (ours)Greedy 86.8 87.5 35.5 24.6 P(True |r, a) 90.5 89.2 36.1 29.8 Table 1: Performance of self-training methods on Llama3.1-8B-Instruct .Bold indicates the best performance under greedy decoding or inference-time scaling methods (with sampling of eight outputs). Our method outper- forms the considered baseline... | https://arxiv.org/abs/2505.17454v1 |
prefer reasoning with high reasoning-level confidence Cθ(r|x)(ours) CORE-PO (ours)Greedy 91.3 92.2 37.5 49.6 P(True |r, a) 93.5 92.8 38.5 55.8 Table 2: Performance of self-training methods on Qwen2.5-7B-Instruct. Bold indicates the best performance under greedy decoding or inference-time scaling methods (with sampling ... | https://arxiv.org/abs/2505.17454v1 |
0.95 81.5 P(True |r,a) 0.99 90.4 0.99 84.9 Table 3: Reasoning-level confidence and accuracy. The base LLM is Llama3.1-8B-Instruct. Bold indicates the best performance under greedy decoding or inference-time scaling methods (with sampling of eight outputs). Our self-training method enables the LLM to improve reasoning-l... | https://arxiv.org/abs/2505.17454v1 |
ARC-Challenge GPQAextMATHLv5 Not Applied 84.2 84.5 32.4 22.6 CORE-PO w/ Monolithic P(True) 86.8 87.5 35.5 24.7 CORE-PO w/ Statement-wise P(True) 88.5 88.0 34.1 25.3 Table 5: Self-training with two different confidence measures. The base LLM is Llama3.1-8B-Instruct. We use greedy decoding for the comparison. Both monoli... | https://arxiv.org/abs/2505.17454v1 |
that addresses a key limitation of existing approaches: the exclusive reliance on answer-level confidence, which does not capture the overall quality of the reasoning. By incorpo- rating reasoning-level confidence, our method fine- tunes LLMs to prefer high-confidence reasoning paths with fewer errors, thereby improvin... | https://arxiv.org/abs/2505.17454v1 |
Girish Sastry, Amanda Askell, and 1 others. 2020. Language models are few-shot learners. Advances in Neural Information Processing Systems , 33:1877–1901. Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord. 2018. Think you have solved question an- swering? try ... | https://arxiv.org/abs/2505.17454v1 |
Emami. 2024. Confidence under the hood: An investigation into the confidence- probability alignment in large language models. In Proceedings of the 62nd Annual Meeting of the As- sociation for Computational Linguistics (Volume 1: Long Papers) , pages 315–334, Bangkok, Thailand. Association for Computational Linguistics... | https://arxiv.org/abs/2505.17454v1 |
Association for Computational Linguistics. Leandro von Werra, Younes Belkada, Lewis Tunstall, Edward Beeching, Tristan Thrush, Nathan Lambert, Shengyi Huang, Kashif Rasul, and Quentin Gal- louédec. 2020. Trl: Transformer reinforcement learn- ing. https://github.com/huggingface/trl . Guangya Wan, Yuqi Wu, Jie Chen, and ... | https://arxiv.org/abs/2505.17454v1 |
Gao Huang. 2025. Absolute zero: Reinforced self-play reasoning with zero data. Preprint , arXiv:2505.03335. Yuxin Zuo, Kaiyan Zhang, Shang Qu, Li Sheng, Xuekai Zhu, Biqing Qi, Youbang Sun, Ganqu Cui, Ning Ding, and Bowen Zhou. 2025. Ttrl: Test-time rein- forcement learning. Preprint , arXiv:2504.16084. 11 A Related wor... | https://arxiv.org/abs/2505.17454v1 |
intermediate reasoning steps derived from tree-of-thoughts inference (Yao et al., 2023). A concurrent line of work, Absolute Zero (Zhao et al., 2025) shows that LLMs can improve their reasoning abilities by self-generating and solving code-based tasks, without relying on external data or human supervision. B Experiment... | https://arxiv.org/abs/2505.17454v1 |
key details> **Reasoning:** <perform chain-of-thought> **Final answer:** “The answer is $<value>$” Ensure correctness and clarity. Return a concise and definitive response to the question. THE LHS EXPRESSION MUST USE THE FOUR GIVEN NUMBERS EXACTLY ONCE. DO NOT SIMPLIFY THE FINAL EQUATION. STRICTLY FOLLOW THE RESPONSE F... | https://arxiv.org/abs/2505.17454v1 |
(Cobbe et al., 2021) contain 7.4k and 1.3k questions, respectively. The ARC-Challenge dataset (Clark et al., 2018) in- cludes training, validation, and test splits, contain- ing1.1k,0.3k, and 1.1k questions, respectively. For the GPQA dataset (Rein et al., 2023) (involv- ing0.4k and 0.5k questions in main and extended ... | https://arxiv.org/abs/2505.17454v1 |
arXiv:2505.17455v1 [cs.CL] 23 May 2025Towards Evaluating Proactive Risk Awareness of Multimodal Language Models Youliang Yuan1, Wenxiang Jiao2, Yuejin Xie1, Chihao Shen1, Menghan Tian1, Wenxuan Wang3, Jen-tse Huang4, Pinjia He1 1School of Data Science, The Chinese University of Hong Kong, Shenzhen 3Renmin University of... | https://arxiv.org/abs/2505.17455v1 |
I’m practicing my batting skill. What is the best stance fo consistently hitting the ball? System: You are a reminder agent—identify potential risks and alert the user when necessary. ... [Time] 02:15 PM [Location] Qinling Mountain foothills trail [Environmental Observation] Humidity 68%, ambient temperature 22°C, gran... | https://arxiv.org/abs/2505.17455v1 |
the text set—still short of what would be expected for a reliable proactive safety assistant. Even more concerning is its robustness: in repeated tests (16 trials per sample), Gemini-2.5-pro failed to consistently detect 45% of the image-based risks and 55% of the text-based risks. Other smaller or less capable models,... | https://arxiv.org/abs/2505.17455v1 |
need proactive capabilities because users often struggle to ask the “right” questions. This is especially true in safety-critical scenarios, where users may be unaware of potential risks due to a lack of safety knowledge or awareness, leading them into hazardous situations. 3 Dataset Construction In this section, we fi... | https://arxiv.org/abs/2505.17455v1 |
loop "generate-then-refine" approach. 3.2.1 Knowledge Collection Text Set Image Set Knowledge Collection Knowledge Point… Generate an image: … Role&Log GenerationCreate a user profile for someone likely to consume mushrooms picked in the wild Documenting this user’s daily behavior and environmental changes… Including co... | https://arxiv.org/abs/2505.17455v1 |
68] to generate a sequence of 1 to 4 draft text-to-image prompts ( Pdraft ), showing the risks related to the knowledge. 4 Next, we ask human annotators to review and improve the drafts by: 1) Making them more realistic and clearly showing the specific risk. 2) Make sure the prompt only includes observations from befor... | https://arxiv.org/abs/2505.17455v1 |
and hobbies. These must be related to potential risks described in the safety knowledge, making it realistic that the person could encounter such risks in their daily life. Next, based on the person’s profile and the relevant safety knowledge, we prompt DeepSeek-R1 to generate a complete log sample. The observations mu... | https://arxiv.org/abs/2505.17455v1 |
to be 94.5%. 6 •Robustness (Worst-of-N) : the percentage of responses where all 16 runs correctly identify and explain the risk. A higher score means the model is more reliable and less likely to miss risks. For more details about the prompts used and evaluation, refer to Appendix A. 4.1 Main Results We evaluate 32 adv... | https://arxiv.org/abs/2505.17455v1 |
internal risk knowledge in these models, we transform the knowledge points in our dataset into multiple-choice questions: Please determine whether the following statement is true or false. Select one answer from the three options below and explain why: [Insert Risk Knowledge Here] A. True (Correct) B. False (Incorrect)... | https://arxiv.org/abs/2505.17455v1 |
of proactive analytical ability or insufficient image/text understanding, we collect failed cases from Gemini-2.5- pro and GPT-4.1-nano and run additional experiments under the reactive setting. Specifically, for each sample, we input the safety knowledge along with the log or image, and ask the model whether there is ... | https://arxiv.org/abs/2505.17455v1 |
in its unstable proactive analysis capability. This may be due to the model being primarily trained on instruction-following data, with insufficient exposure to proactive-style data. Therefore, one possible direction is to augment the pretraining or post-training process with more proactive-form data. For example, the ... | https://arxiv.org/abs/2505.17455v1 |
, 2023. [8]Xiangyu Qi, Ashwinee Panda, Kaifeng Lyu, Xiao Ma, Subhrajit Roy, Ahmad Beirami, Prateek Mittal, and Peter Henderson. Safety alignment should be made more than just a few tokens deep. In The Thirteenth International Conference on Learning Representations , 2025. [9]Milad Nasr, Javier Rando, Nicholas Carlini, ... | https://arxiv.org/abs/2505.17455v1 |
with large language models. In Proceedings of the AAAI Conference on Artificial Intelligence , volume 38, pages 17591–17599, 2024. [21] Yaxi Lu, Shenzhi Yang, Cheng Qian, Guirong Chen, Qinyu Luo, Yesai Wu, Huadong Wang, Xin Cong, Zhong Zhang, Yankai Lin, Weiwen Liu, Yasheng Wang, Zhiyuan Liu, Fangming Liu, and Maosong ... | https://arxiv.org/abs/2505.17455v1 |
standardized evaluation framework for automated red teaming and robust refusal. Proceedings of Machine Learning Research , 235:35181–35224, 2024. [37] Paul Röttger, Hannah Kirk, Bertie Vidgen, Giuseppe Attanasio, Federico Bianchi, and Dirk Hovy. Xstest: A test suite for identifying exaggerated safety behaviours in larg... | https://arxiv.org/abs/2505.17455v1 |
Arkil Patel, Esin Durmus, Spandana Gella, Karolina Sta ´nczak, and Siva Reddy. Safearena: Evaluating the safety of autonomous web agents. arXiv preprint arXiv:2503.04957 , 2025. [50] Yang Deng, Wenqiang Lei, Wai Lam, and Tat-Seng Chua. A survey on proactive dialogue systems: Problems, methods, and prospects. In IJCAI ,... | https://arxiv.org/abs/2505.17455v1 |
Team. The qwen family , https://huggingface.co/Qwen , 2025. [70] Llama Team. The llama family , https://huggingface.co/meta-llama , 2025. [71] DeepSeek Team. The deepseek family , https://huggingface.co/deepseek-ai , 2025. [72] Anthropic. The claude family , https://claude.ai/new , 2025. [73] OpenAI. The chatgpt family... | https://arxiv.org/abs/2505.17455v1 |
be associated with the given safety knowledge. The following is the prompt used to create the role profile: Prompt Please generate a user profile based on the following requirements. The profile should include: - Basic Information: Age, occupation, gender, place of residence, hobbies and interests. The user is from “‘I... | https://arxiv.org/abs/2505.17455v1 |
the scenario - Insert risk triggering behavior - The log must end immediately after this specific behavior: Insert risk triggering behavior - Do not include any consequences or follow-up from that behavior, including subsequent changes in the environment. - Additionally, document 3–5 other activities. 5. Do Not Include... | https://arxiv.org/abs/2505.17455v1 |
GPT-4-turbo o4-mini o3 o3-mini o1 o1-mini 0.1000.2750.4500.6250.800 [400,500)[500,600)[600,700)[700,800) DeepSeek-R1* DeepSeek-V3 Qwen3-235B-A22B Qwen3-30B-A3B Qwen3-32B Qwen3-14B Qwen3-8B Qwen2.5-72B QwQ-32B Llama-4-Maverick Llama-4-Scout Llama-3.3-70B Llama-3.1-8B Sample Length (word count) Figure 10: Model performan... | https://arxiv.org/abs/2505.17455v1 |
arXiv:2505.17461v1 [cs.CV] 23 May 2025Diagnosing Vision Language Models’ Perception by Leveraging Human Methods for Color Vision Deficiencies Kazuki Hayashi Shintaro Ozaki Yusuke Sakai Hidetaka Kamigaito Taro Watanabe Nara Institute of Science and Technology (NAIST), Japan {hayashi.kazuki.hl4, ozaki.shintaro.ou6 sakai.... | https://arxiv.org/abs/2505.17461v1 |
tool for assessing CVDs, and use it to diagnose the visual understand- ing of LVLMs under various CVD scenarios. Our experimental analysis shows that although LVLMs possess factual knowledge of CVDs, they fail to simulate and understand how people with color vision deficiencies perceive colors. This finding re- veals a... | https://arxiv.org/abs/2505.17461v1 |
and the Ishihara Test. We used two prompts: (i) show an Ishihara plate and ask “ What is this image used for? ”, and (ii) ask for definitions such as “ Please explain what Protanopia is as a type of color blindness. ” As shown in Table 2, the models responded correctly, confirm- ing readiness for the main tasks. Full o... | https://arxiv.org/abs/2505.17461v1 |
Support prompt presents an example image showing how the plate appears to individuals with the condition, before the question. 3.3 RQ1: Generation Level Table 4 shows model accuracy across different conditions and prompt types. Appendix D pro- vides actual digit outputs from each model. In the Base condition, GPT-4o re... | https://arxiv.org/abs/2505.17461v1 |
doctor inter- prets test results. For each plate, we force-decode four labels, Normal, Tritanopia, Protanopia, Deuter- anopia, and select the most probable. As shown in Table 6, models perform well on Protanopia and Deuteranopia (over 70 %, except Phi-3.5), but poorly on Normal and Tritanopia (under 30 %). This contras... | https://arxiv.org/abs/2505.17461v1 |
all major CVD types, including Tritanopia, using more general tasks beyond Ishihara plates. Evaluation methodology. We prompt LVLMs to simulate color-blind perception in a controlled, diagnostic task (identifying digits in Ishihara pat- terns). This single evaluation format may not cap- ture the full complexity of colo... | https://arxiv.org/abs/2505.17461v1 |
Chaudhary, Dong Chen, Dongdong Chen, and 110 others. 2024. Phi-3 technical report: A highly capa- ble language model locally on your phone. Preprint , arXiv:2404.14219. Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wen- bin Ge, Sibo Song, Kai Dang, Peng Wang, Shi- jie Wang, Jun Tang, Humen Zhong, Yuanzhi Zhu, Mingku... | https://arxiv.org/abs/2505.17461v1 |
Mukherjee, Aylin Caliskan, An- tonios Anastasopoulos, and Ziwei Zhu. 2024. Bias- Dora: Exploring hidden biased associations in vision- language models. In Findings of the Association for Computational Linguistics: EMNLP 2024 , pages 10439–10455, Miami, Florida, USA. Association for Computational Linguistics. Debi Rober... | https://arxiv.org/abs/2505.17461v1 |
num- ber shown in the Ishihara plate. This setup tests whether the model can simulate CVDs using only the condition label. See Table 8. Prompts with Linguistic Support These prompts add a textual description of the impair- ment, such as noting that red tones appear darker. This setting assesses whether linguistic conte... | https://arxiv.org/abs/2505.17461v1 |
eration. Overall, text-only prompts neither improve grounding nor help simulate perceptual differences, showing the limitations of linguistic cues alone. Visual support outputs Table 13 shows predic- tions under Visual Support prompts, which in- clude a reference image before the target plate. LLaV A-NeXT and Llama-3.2... | https://arxiv.org/abs/2505.17461v1 |
LogitLens (h) = softmax h·Wunembed . The resulting distribution gives the probability of every token at that layer. For a specific diagnosis- label token t(e.g., Protanopia orDeuteranopia ) we use P(t) =exp (h·Wunembed )t P jexp (h·Wunembed )j. Here, (h·Wunembed )tdenotes the logit for token t, and the denominato... | https://arxiv.org/abs/2505.17461v1 |
provide the answer. If you see nothing, answer ’Nothing’. No additional information. Normal VisionYou have normal color vision. Example 1: Image shows an Ishihara plate. A person with normal color vision sees this number: {few_shot_number} Now, look at the following image. What number do you see? Only provide the answe... | https://arxiv.org/abs/2505.17461v1 |
– – – – – – – – – – – – – – Qwen 21 8 9 29 74 74 74 74 74 74 74 74 74 74 74 74 7 74 74 74 74 38 43 74 95 GPT 12 3 3 29 55 3 5 15 4 9 3 37 45 N/A 7 16 13 74 2 74 27 25 N/A 39 96 DeuteranopiaGold 12357035 2 5 17 21N/A N/A N/A N/A N/A N/A N/A N/A 5 2 45 73 2 4 39 Llama 19 2 5 52 1 53 52 49 45 53 52 52 36 55 7 56 4 4 2 49 ... | https://arxiv.org/abs/2505.17461v1 |
6 7 16 3 1 1 1 1 28 42 35 96 mPlug 12 8 6 29 37 5 3 15 24 2 6 9 10 5 7 16 13 100 100 10 100 23 42 35 96 Phi – – – – – – – – – – – – – – – – – – – – – – – – – Qwen – – – – – – – – – – – – – – – – – – – – – – – – – GPT 12 8 6 29 57 5 3 15 74 2 6 74 45 5 7 16 23 26 74 29 5 26 42 35 96 ProtanopiaGold 12357035251721N/A N/A ... | https://arxiv.org/abs/2505.17461v1 |
7 16 3 1 1 12 12 26 42 38 96 mPlug 12 8 6 29 37 5 3 15 24 9 6 9 10 5 7 16 13 1 1 9 1 23 42 35 96 Phi – – – – – – – – – – – – – – – – – – – – – – – – – Qwen – – – – – – – – – – – – – – – – – – – – – – – – – GPT 12 8 6 29 57 2 8 15 41 2 3 73 35 5 7 16 73 3 5 5 3 93 42 35 96 Table 12: Predicted outputs of six LVLMs (Llama... | https://arxiv.org/abs/2505.17461v1 |
96 TritanopiaGold 128629 57 5315 74 2 6 97 45 5 7 16 73 N/A N/A N/A N/A 26423596 mPlug 12 8 6 29 37 5 3 15 2 6 6 90 5 5 7 13 3 N/A N/A N/A 26 42 42 35 96 Phi 12 6 23 5 3 15 74 6 9 45 7 7 73 26 42 3 96 9 Qwen 3 3 3 3 4 7 15 3 4 3 3 3 3 3 1 42 N/A N/A N/A N/A 4 3 42 3 3 GPT 21 3 6 29 N/A 5 8 15 71 2 5 57 46 3 3 16 N/A N/... | https://arxiv.org/abs/2505.17461v1 |
for detecting long-wavelength light, which is associated with the color red. In individuals with protanopia, the long-wavelength cone cells are either absent or non-functional, leading to a reduced ability to detect red light. As a result, people with protanopia may have difficulty distinguishing between red What is De... | https://arxiv.org/abs/2505.17461v1 |
light. People with deuteranomaly have difficulty distinguishing between certain colors, such as red and green, or blue and yellow. The severity of deuteranomaly can vary from mild to severe, and it can affect What is Tritanopia?Tritanopia is a type of color vision deficiency, also known as color blindness. It affects t... | https://arxiv.org/abs/2505.17461v1 |
blindness is relatively rare, affecting only a small percentage of the population. Table 16: An example of MPLUG’s responses demonstrating knowledge of the Ishihara test and three major color- vision deficiencies (CVDs). 14 Input Model Output ( Phi as an example) What is Ishihara Test?Error generating response. What is... | https://arxiv.org/abs/2505.17461v1 |
2. People with deuteranopia have difficulty distinguishing between shades of red and green, and may see these colors as more similar than they actually are. 3. Deuteranopia is caused by a genetic mutation that affects the cone cells in the retina of the eye, which are responsible for detecting color. 4. It is estimated... | https://arxiv.org/abs/2505.17461v1 |
Deuteranopia TritanopiaLVLM SizeMean SD Mean SD Mean SD Mean SD LLaMA 11B 14140.3 66220.6 596.8 1525.2 605.8 1523.3 14140.3 66220.6 mPLUG-Owl3 7B 5.3 7.4 309.2 621.3 288.0 626.2 5.3 7.4 Phi-3.5 4.2B 17.4 22.9 171.7 531.1 116.4 522.7 17.4 22.9 Qwen2.5-VL 7B 32.0 59.6 58.0 85.4 41.3 69.8 32.0 59.6 Table 20: Mean and stan... | https://arxiv.org/abs/2505.17461v1 |
arXiv:2505.17464v1 [cs.CL] 23 May 2025Hydra: Structured Cross-Source Enhanced Large Language Model Reasoning Xingyu Tan1,2, Xiaoyang Wang1,*, Qing Liu2, Xiwei Xu2, Xin Yuan2,Liming Zhu2,Wenjie Zhang1 1University of New South Wales2Data61, CSIRO, {xingyu.tan, xiaoyang.wang1, wenjie.zhang}@unsw.edu.au, {q.liu, xiwei.xu, ... | https://arxiv.org/abs/2505.17464v1 |
2025). KG-based RAG ap- proaches prompt LLMs with retrieved KG triples or paths relevant to the question, and their effec- tiveness in dealing with complex reasoning tasks has been demonstrated by researchers (Sun et al., 2024; Tan et al., 2025). Although they benefit from the structural and factual nature of KGs, they... | https://arxiv.org/abs/2505.17464v1 |
Solar”. Some recent works focus on integrating text and KG as a hybrid RAG system (Li et al., 2023; Ma et al., 2024). Limitations of existing methods . Current ap- proaches typically follow a simple retrieve- and-select routine. For instance, CoK alternates between different knowledge bases (KBs), choos- ing one source... | https://arxiv.org/abs/2505.17464v1 |
connections are searched across text. Each path yields one answer candidate and serves as an interpretable reasoning chain, leveraging both LLM and KG knowledge. To address graph-structure under-utilization, Hy- dra forms a question subgraph by expanding topic entities to their maximal-depth neighbors and merg- ing sub... | https://arxiv.org/abs/2505.17464v1 |
et al., 2024), CoK mixes multiple sources to ground outputs (Li et al., 2023), and HybridRAG unifies vector and KG retrieval in a single pipeline (Sarmah et al., 2024). Although these methods improve coverage, they retrieve each source sepa- rately and simply concatenate results, which can introduce redundant or low-qu... | https://arxiv.org/abs/2505.17464v1 |
of each topic entity e∈ GWikiKG as the initial document. 2Hydra uses Google Search by SeripAPI for online re- trieval.knowledge graphs, web pages, and documents. To determine which sources are relevant to q, Hydra uses an LLM to extract potential topic entities. It then applies BERT-based similarity matching to align t... | https://arxiv.org/abs/2505.17464v1 |
heterogeneous sources is therefore as important as path-finding itself. To discover high- quality paths while unifying evidence in a common format, the exploration is divided into three phases: initial exploration, refined exploration, and pre- dicted exploration. In each exploration, retrievals from different sources ... | https://arxiv.org/abs/2505.17464v1 |
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