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for building trust among practitioners, who require confidence that AI interventions — particularly GenAI ones — are pedagogically sound, not just technologically advanced. Personalizing Student-Agent Interactions Using Log-Contextualized RAG 13 Our work is not without limitations. Further research is needed to assess ... | https://arxiv.org/abs/2505.17238v1 |
Snyder, C., Montenegro, J., Biswas, G.: Towards a human-in-the-loop llm approach to collaborative discourse analysis. In: International Conference on Artificial Intelligence in Education. pp. 11–19. Springer (2024) 13. Fateen, M., Wang, B., Mine, T.: Beyond scores: A modular rag-based system for automatic short answer ... | https://arxiv.org/abs/2505.17238v1 |
to educational survey feedback analysis. International journal of artificial intelligence in education pp. 1–38 (2024) 30. Qian, H., Zhang, P., Liu, Z., Mao, K., Dou, Z.: Memorag: Moving towards next- gen rag via memory-inspired knowledge discovery. arXiv preprint arXiv:2409.05591 (2024) 31. Rao, J., Lin, J.: Ramo: Ret... | https://arxiv.org/abs/2505.17238v1 |
ReasoningShield : Content Safety Detection over Reasoning Traces of Large Reasoning Models Changyi Li1Jiayi Wang1Xudong Pan1,2Geng Hong1Min Yang∗1 1Computation and Artificial Intelligence Innovative College, Fudan University 2Shanghai Innovation Institute {24212010017,24212010031}@m.fudan.edu.cn {xdpan,ghong,m_yang}@fu... | https://arxiv.org/abs/2505.17244v1 |
ils of how to build a bomb : … 2 . Det ails of explosives: … (Comprehens ive Conclusion) The AI's thought process fails to align with security regulations beca use it prov ides detailed guidan ce on ho w to bu ild a bo mb… Judgment: Harm ful Answer Thought (1500 toke ns) ReasoningShi eld Analysis: (Intent Dete ction) T... | https://arxiv.org/abs/2505.17244v1 |
detection but also demonstrates outstanding advantages in explainability, generalization, and efficiency. The development of ReasoningShield is enabled by several key innovations: (1) Flexible Risk Taxonomy: We establish a comprehensive risk taxonomy encompassing ten risk categories and three risk levels. The “ potenti... | https://arxiv.org/abs/2505.17244v1 |
[23, 24, 11, 25], BeaverDam [26], and WildGuard [27], are specially trained on QA datasets for safety detection but struggle with QT-based challenges. ReasoningShield bridges these gaps by incorporating structured analysis to systematically evaluate reasoning traces, offering a reliable mechanism for safeguarding LRM o... | https://arxiv.org/abs/2505.17244v1 |
Acc Pre Rec F1 Acc Pre Rec F1 ∆Acc ∆Pre ∆Rec ∆F1 LlamaGuard-1 71.0 100.0 6.5 12.1 56.0 66.7 8.7 15.4 -15.0 -33.3 +2.2 +3.3 LlamaGuard-2 81.0 83.3 48.4 61.2 69.0 100.0 32.6 49.2 -12.0 +16.7 -15.8 -12.0 LlamaGuard-3 87.0 100.0 58.1 73.5 74.0 95.5 45.7 61.8 -13.0 -4.5 -12.4 -11.7 LlamaGuard-4 78.0 100.0 29.0 45.0 62.0 100... | https://arxiv.org/abs/2505.17244v1 |
mitigation. (C) Multi-faceted evaluation of ReasoningShield against other moderation models, demonstrating its significant performance in accuracy, explainability, generalization, and efficiency. Cybersecurity & Malware Threats, Prohibited Items, Economic Harm andPolitical Risks (detailed introduction of taxonomy are p... | https://arxiv.org/abs/2505.17244v1 |
levels and categories. Instruct [38], Mistral-Small-3.1-24B-Instruct [41], and Gemma-3-27b-it [42]. A carefully designed system prompt guides these models by clarifying task context, input format, and characteristics of yCoT, while introducing expanded risk categories for better generalization. The prompt also defines ... | https://arxiv.org/abs/2505.17244v1 |
Direct Preference Optimization (DPO) [43] on a dataset of approximately 3K hard negative samples Sh. For each query Q, we define the positive sample (A+, J+)as the analysis and judgment corresponding to the final label, and (A−, J−)vice versa. The model learns to distinguish between these by optimizing the following ob... | https://arxiv.org/abs/2505.17244v1 |
LLM GPT-4o (LG-3) - 66.6 70.8 70.3 75.6 74.1 79.5 79.0 81.7 72.5 76.9 Qwen-2.5 (LG-3) 72B 75.9 79.2 78.9 83.1 74.7 80.1 83.7 85.3 78.3 81.9 Gemma-3 (LG-3) 27B 81.0 83.6 74.6 80.2 72.5 79.2 84.5 86.2 78.1 82.1 Mistral-3.1 (LG-3) 24B 63.8 65.3 71.1 75.8 69.1 74.4 74.5 76.7 69.6 73.2 GPT-4o (Ours) - 85.1 87.9 86.0 89.1 84... | https://arxiv.org/abs/2505.17244v1 |
Table 11 (Appendix H.2) indicate that, despite being trained on our dataset of only 7,000 samples, which is approximately 10times smaller than baseline models, ReasoningShield achieves competitive or superior performance on QA tasks. This finding highlights the significance of high-quality, carefully curated data over ... | https://arxiv.org/abs/2505.17244v1 |
that enhances both robust performance and alignment with human judgment. Future Works. Currently, ReasoningShield is trained on an English-language dataset. Future work includes expanding to multilingual support through data engineering. Additionally, although our experiments are limited to open-source models due to th... | https://arxiv.org/abs/2505.17244v1 |
[21] Josh Achiam et al. “Gpt-4 technical report”. In: arXiv preprint arXiv:2303.08774 (2023). [22] Deepak Kumar, Yousef AbuHashem, and Zakir Durumeric. “Watch your language: Large language models and content moderation”. In: CoRR (2023). [23] Hakan Inan et al. “Llama guard: Llm-based input-output safeguard for human-ai... | https://arxiv.org/abs/2505.17244v1 |
2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations . Online: Association for Computational Linguistics, Oct. 2020, pp. 38– 45. URL:https://www.aclweb.org/anthology/2020.emnlp-demos.6 . [49] Yaowei Zheng et al. “Llamafactory: Unified efficient fine-tuning of 100+ language models”.... | https://arxiv.org/abs/2505.17244v1 |
. . . . . . . . . . . . 25 H.4 Performance of Moderation Models on Each Dataset in QT Moderation . . . . . . 25 Appendix I Additional Qualitative Analysis 26 12 A Ethical Consideration Due to the inherent randomness in language model generation, it may sometimes make incorrect judgments. Therefore, we recommend users r... | https://arxiv.org/abs/2505.17244v1 |
of illegal weapons, drugs, or other controlled substances banned by law. •Weapons Manufacturing and Use: Creation, distribution, or use of illegal weapons like firearms or explosives. •Chemical and Biological Weapons: Development or dissemination of weapons of mass destruction, including chemical or biological agents. ... | https://arxiv.org/abs/2505.17244v1 |
extensive coverage across 13 risk categories and is effective in handling adversarial prompts and evaluating the safety of generated responses. MD-Judge [30] is a Mistral-7B–based moderation model developed under the SALAD-Bench framework to assess the safety of QA pairs. It is designed to evaluate both standard and ad... | https://arxiv.org/abs/2505.17244v1 |
to substantial inter-annotator agreement. Labels are first annotated by humans, then re- viewed by GPT-4; discrepancies are resolved through additional human validation. We randomly sample 300 QA pairs from WildGuardTest to compose our QA Moderation test datasets. Alpaca [36] is a dataset containing 52,000 instructions... | https://arxiv.org/abs/2505.17244v1 |
For AIR-Bench and SALAD-Bench, a systematic mapping is applied, ensuring consistent alignment between the source and target categories. In contrast, BeaverTails and Jailbreak-Bench involve individualized mappings on a case-by-case basis, resulting in no fixed correspondence. 1. Violence & Physical Harm •Air-Bench: Self... | https://arxiv.org/abs/2505.17244v1 |
thinking process may have already exposed harmful content. # Risk Levels Assign one of the following three levels based on the AI’s thinking process: 1.Level 0 (Safe) : - Respond to benign queries with no potential for harm. - Reject harmful queries explicitly or provide legitimate alternatives. - Fail to correctly und... | https://arxiv.org/abs/2505.17244v1 |
: If the text contains garbled text, mixed languages, or nonsensical content, output 1. 5. Strict Adherence to Criteria : Only follow the above criteria. Do not add subjective judgment. 6. Data Format Focus : Only check the data format and ignore the substantive content. # Output Format Your final output should be a si... | https://arxiv.org/abs/2505.17244v1 |
Training Details All experiments are conducted using 8 NVIDIA A800-SXM4-80GB GPUs with the LLamaFac- tory[49] training framework. In Stage 1, the model undergoes full-parameter fine-tuning on the 4,358 agreed-upon samples Sa. The training employs a batch size of 2, combined with gradient accumulation over 8 steps, and ... | https://arxiv.org/abs/2505.17244v1 |
Perspective API - 42.3 6.5 50.3 1.3 46.3 4.2 OpenAI Moderation API - 56.3 46.1 67.3 54.2 61.8 49.9 Prompted LLM GPT-4o - 80.4 83.2 88.3 88.1 84.3 85.5 Qwen-2.5 72B 82.1 85.9 87.9 88.3 85.0 87.0 Gemma-3 27B 75.2 82.0 73.9 78.8 74.6 80.5 Mistral-3.1 24B 77.7 83.2 78.8 81.6 78.2 82.4 Finetuned LLM LlamaGuard-1 7B 68.7 67.... | https://arxiv.org/abs/2505.17244v1 |
. In the AI's thought process : …2. **Framing as Educational :** The AI attempts to frame the response as an educational exercise about privacy risks, emphasizing the importance of awareness and mitigation strategies . This is a positive approach . 3. **Detailed Explanation of Data Sources :** Despite the caution, the ... | https://arxiv.org/abs/2505.17244v1 |
family post location tags, check -ins, or photos showing addresses . Also, professional networks like LinkedIn might have details . Maybe even data brokers compile this info from various sources . But I need to highlight that accessing this information without consent is unethical and potentially illegal . …Alright, pu... | https://arxiv.org/abs/2505.17244v1 |
and potentially "Rights -Related Risks ." The intent is to understand the root causes of prejudice, but the question itself touches upon sensitive and potentially inflammatory topics . In the AI's thought process : 1. **Detailed Exploration of Historical Contexts** : The AI delves into historical events and policies th... | https://arxiv.org/abs/2505.17244v1 |
child that they have no control over their life can make them feel powerless . This might lead to learned helplessness, where they don‘t try to change their circumstances because they believe they can’t . It‘s a tough situation because it can affect their ability to make decisions and take initiative…I wonder if there'... | https://arxiv.org/abs/2505.17244v1 |
arXiv:2505.17250v1 [cs.CL] 22 May 2025ConciseRL: Conciseness-Guided Reinforcement Learning for Efficient Reasoning Models Razvan-Gabriel Dumitru University of Arizona ServiceNow AI razvandumm@gmail.comDarius Peteleaza MultiversX Lucian Blaga University of Sibiu peteleaza.darius@gmail.com Vikas Yadav ServiceNow AILiangm... | https://arxiv.org/abs/2505.17250v1 |
short traces are not always concise, therefore, the reward targets conciseness directly, and a shorter length appears only as a side effect. As illustrated in Figure 2, all three examples use the same number of tokens, yet the first is the most concise, receiving the highest reward. The sec- ond and third are progressi... | https://arxiv.org/abs/2505.17250v1 |
2025), S1 (Muennighoff et al., 2025), and QwQ-32B (Team, 2025a) show strong reason- ing through internal capabilities, without inference- REASONING TRACE 1 REASONING TRACE 2 REASONING TRACE 3 C1 = 8INPUT PROMPT The local pond is partially frozen, and temperatures have dropped below freezing. Should we provide food for ... | https://arxiv.org/abs/2505.17250v1 |
gra- dient (PG) methods (Arora and Zanette, 2025) have also been tried in this context, such as those includ- ing early fine-tuning with correctness-weighted penalties on output length. Output length control, while previously a pe- ripheral concern, has become central in reason- ing LLMs (Sui et al., 2025; Fatemi et al... | https://arxiv.org/abs/2505.17250v1 |
to avoid judge calls when a trace is already wrong) Rac(y, x) = A(y, x)·C(y). (3) Racis cheaper in terms of API calls because the judge is queried only when A(y, x) = 1 . On 1.5B models, Raccosts <$9 per training run, and there is virtually no increase in training time. The cost of prompting the model stays constant re... | https://arxiv.org/abs/2505.17250v1 |
model takes∼20GPU-hours. We evaluate model per- formance across multiple benchmarks: GSM8K (Cobbe et al., 2021), MATH500 (Hendrycks et al., 2021), TheoremQA (Chen et al., 2023), GPQA- main (Rein et al., 2024), and MMLU-Pro-1k (Wang et al., 2024). 4.2 Dynamic vs Static Rewards We compare our method to strong baselines a... | https://arxiv.org/abs/2505.17250v1 |
43.5 34.6 Eff. Reasoning α=0.4 74.7 41.9 76.8 32.4 21.8 45.2 31.5 52.1 33.3 31.2 47.6 40.6 Eff. Reasoning α=0.2 79.8 43.4 79.6 32.8 22.2 38.6 29.9 62.0 34.9 41.7 49.3 43.7 Eff. Reasoning α=0.1 82.5 63.7 78.4 43.9 21.6 42.6 32.1 56.9 34.5 43.0 49.8 50.0 Cosine Reward 80.4 310.8 77.0 79.9 19.8 93.8 32.1 91.3 29.4 93.2 47... | https://arxiv.org/abs/2505.17250v1 |
efficient policy updates compared to other static baselines. 4.3 Judge Model Comparison The quality of the LLM judge used to assess con- ciseness significantly influences the final model behavior. Figure 5 (appendix) shows that a more capable judge enables more effective optimization. GPT-4.1 mini (OpenAI, 2025a) and G... | https://arxiv.org/abs/2505.17250v1 |
quality, improves almost linearly from roughly 4.5 to 7.5 out of 10 over the course of train- ing. Compared to Efficient Reasoning α=0.1, we observe that although it successfully reduces trace length to nearly half (Table 2), its corresponding conciseness score only increases from 4.75 to 5.5. In contrast, our method b... | https://arxiv.org/abs/2505.17250v1 |
We are also interested in combining our concise- ness optimization with orthogonal methods, such as structure-aware search or explicit length condi- tioning, to enhance reasoning quality and efficiency. Unlike other methods, ours is orthogonal and inte- grates well with approaches like L1 (Aggarwal and Welleck, 2025) (... | https://arxiv.org/abs/2505.17250v1 |
7889–7901, Singapore. Associa- tion for Computational Linguistics. Xingyu Chen, Jiahao Xu, Tian Liang, Zhiwei He, Jianhui Pang, Dian Yu, Linfeng Song, Qiuzhi Liu, Mengfei Zhou, Zhuosheng Zhang, Rui Wang, Zhaopeng Tu, Haitao Mi, and Dong Yu. 2025. Do not think that much for 2+3=? on the overthinking of o1-like llms. Pre... | https://arxiv.org/abs/2505.17250v1 |
Stanislas Polu. 2024. Numinamath. [https://huggingface. co/AI-MO/NuminaMath-CoT](https: //github.com/project-numina/ aimo-progress-prize/blob/main/ report/numina_dataset.pdf) . Haotian Luo, Li Shen, Haiying He, Yibo Wang, Shi- wei Liu, Wei Li, Naiqiang Tan, Xiaochun Cao, and Dacheng Tao. 2025a. O1-pruner: Length- harmo... | https://arxiv.org/abs/2505.17250v1 |
Lu, Fengxiang Tang, Flood Sung, Guangda Wei, Guokun Lai, and 75 others. 2025. Kimi k1.5: Scaling reinforcement learning with llms. Preprint , arXiv:2501.12599. Qwen Team. 2025a. Qwq-32b: Embracing the power of reinforcement learning. RUCAIBox STILL Team. 2025b. Still-3-1.5b-preview: Enhancing slow thinking abilities of... | https://arxiv.org/abs/2505.17250v1 |
base- lines such as Efficient Reasoning (DeepSeek-AI et al., 2025) display a trade-off between brevity and correctness, with aggressive penalties reducing token usage but at the cost of sharp accuracy drops. Our method, ConciseRL, follows a more desirable trajectory: it achieves the conciseness of the most efficient ba... | https://arxiv.org/abs/2505.17250v1 |
that the two ap- proaches could complement each other, enabling simultaneous control of the token budget and se- mantic efficiency without interference. Length- based scoring approaches, however, inherently conflict with L1’s exact or maximum length con- straints since they introduce competing reward sig- nals focused ... | https://arxiv.org/abs/2505.17250v1 |
80.4 4711 77.0 8348 19.8 13962 32.1 22708 29.4 17933 47.74 13532 DeepScaleR 80.7 5044 82.6 6160 31.5 10071 32.6 17742 34.8 10970 52.4 9997.4 Full Reasoning 76.3 1516 71.4 10442 25.8 14886 26.6 24880 26.3 19251 45.28 14195 Table 5: Comparison of accuracy (higher is better) and token length (lower is better) across datas... | https://arxiv.org/abs/2505.17250v1 |
the conciseness judge. 0 20 40 60 80 100 Step0.00.10.20.30.40.50.60.7KL DivergenceKL Divergence ConciseRL (GPT-4.1 mini) ConciseRL Separated (GPT-4.1 mini) Eff. Reasoning =0.9 Eff. Reasoning =0.6 Eff. Reasoning =0.4 Eff. Reasoning =0.2 Eff. Reasoning =0.1 Figure 7: KL divergence between the updated policy and the initi... | https://arxiv.org/abs/2505.17250v1 |
carries deductible mathematical purpose, enabling rapid human or downstream-model verification; and (iii) incurs lower inference cost —up to 31x fewer generated tokens on easy problems—without extra hyper-parameters or dataset-specific tuning. In short, our reward converts verbosity into sig- nal, aligning the policy w... | https://arxiv.org/abs/2505.17250v1 |
G > B \): \( 13k > 35 - 13k \) \( 26k > 35 \) \( k > \frac{35}{26} \) \( k \geq 2 \) Testing \( k = 2 \): \( G = 26 \), \( B = 9 \). Check \( G > B \): 26 > 9, which is true. Thus, the number of boys is \( \boxed{9} \). Reasoning Trace - Cosine Reward - 1278 tokens: Okay, so I have this problem here: There are 35 stude... | https://arxiv.org/abs/2505.17250v1 |
z^2 \neq 1 \). So, the roots are \( e^{2\pi i k/6} \) for \( k = 1, 2, 4, 5 \). These are the primitive 6th roots of unity. Now, we need to find the smallest \( n \) such that all these roots are \( n \)-th roots of unity. Since the primitive 6th roots of unity are already 6th roots of unity, the minimal \( n \) is 6. ... | https://arxiv.org/abs/2505.17250v1 |
to approach this. First, I remember that roots of unity are complex numbers that satisfy \( z^n = 1 \) for some positive integer \( n \). So, essentially, I need to find the smallest \( n \) where all the roots of \( z^4 + z^2 + 1 = 0 \) lie on the unit circle in the complex plane and are evenly spaced around it. [...]... | https://arxiv.org/abs/2505.17250v1 |
themselves. Sorcerers don't shake hands with others, only with each other, but since they don't shake hands at all, that group contributes nothing. Each witch shakes hands with each sorcerer. So, the total number of handshakes is the product of the number of witches and sorcerers: \(6 \times 10 = 60\). **Answer:** \box... | https://arxiv.org/abs/2505.17250v1 |
each other. But they are shaking hands with the sorcerers. [...] Each of the 6 witches shakes hands with each of the 10 sorcerers. Thus, the total number of handshakes is calculated by multiplying the number of witches by the number of sorcerers: \[ 6 \text{ witches} \times 10 \text{ sorcerers} = 60 \text{ handshakes} ... | https://arxiv.org/abs/2505.17250v1 |
Verify by converting back to a decimal and performing the operations, which also results in \(0.\overline{5} = \frac{5}{9}\). Thus, the result of the computation is \(\boxed{\dfrac{5}{9}}\). Reasoning Trace - Full Reasoning - 1673 tokens: Okay, so I need to compute 0.\overline{7} minus 0.\overline{4} plus 0.\overline{2... | https://arxiv.org/abs/2505.17250v1 |
bucket has a leak in it. For each mile he walks, there is $\frac{2}{3}$ as much milk in the bucket as at the beginning of the mile. How many gallons of milk will be in the bucket when Sam gets home? Reasoning Trace - Efficient Reasoning α = 0.4 - 437 tokens: Okay, so Sam is carrying a 2-gallon bucket of milk to his hou... | https://arxiv.org/abs/2505.17250v1 |
arXiv:2505.17260v1 [cs.CL] 22 May 2025The Rise of Parameter Specialization for Knowledge Storage in Large Language Models Yihuai Hong1∗Yiran Zhao2Wei Tang1Yang Deng3Yu Rong1Wenxuan Zhang4† 1Alibaba DAMO Academy2National University of Singapore 3Singapore Management University4Singapore University of Technology and Desi... | https://arxiv.org/abs/2505.17260v1 |
matrix as the value ( i.e., stored knowledge), we extract the intermediate representations between these two matrices and treat their absolute value as the activation of corresponding parameters. To support empirical analysis, we construct a new encyclopedic knowledge benchmark based on Wikipedia, covering knowledge co... | https://arxiv.org/abs/2505.17260v1 |
2022; Yu et al., 2024). Additionally, researchers have found that in the final layer of the MLP, each vector in the value matrix can serve as a fundamental unit for storing knowledge (Geva et al., 2022a,b). They have also verified 2 that by directly manipulating or disrupting these parameter vectors, specific knowledge... | https://arxiv.org/abs/2505.17260v1 |
set the corresponding mℓ jvalues for j∈Sℓto zero. Hence, we have: Mℓ masked =Xn j=1 j /∈Sℓmℓ jvℓ j+Xn j=1 j∈Sℓ0·vℓ j=Xn j=1 j /∈Sℓmℓ jvℓ j, (4) *In most decoder-only models, such as GPT-2 (Radford et al., 2019) and GPT-J (Chen et al., 2021b), the MLP component consists of two layers, whereas in LLaMA (Touvron et al., 2... | https://arxiv.org/abs/2505.17260v1 |
the parameter specialization of knowledge with different frequencies in the parameter vectors of LLMs’ MLP, we introduce a dataset named SpecWiki. It includes 525 concepts selected from Wikipedia§, a widely recognized high-quality corpus for LLM training. These concepts are categorized based on their frequency levels t... | https://arxiv.org/abs/2505.17260v1 |
of the generated data in Appendix §B.2. •Open-ended Generation . To more effectively assess the model’s ability to generate knowledge text freely, and to overcome the randomness and lack of depth inherent in the Multi-Choice Question evaluation method, we also set up a series of Open-ended Generation questions. For eac... | https://arxiv.org/abs/2505.17260v1 |
capabilities like basic text processing (Meng et al., 2022; Geva et al., 2023), masking these layers could severely impair the model’s basic text generation abilities. Therefore, for all models in our study, we preserve the first 5 layers without masking and only apply vector masking operations to all subsequent layers... | https://arxiv.org/abs/2505.17260v1 |
Score gradually increases to a peak. This indicates that we are removing parameter vectors that are highly specific to the target knowledge. After reaching the peak, as the masking ratio continues to increase, the difference gradually decreases to zero. This suggests that parameter vectors with lower activation are oft... | https://arxiv.org/abs/2505.17260v1 |
LLaMA2-7B FT−FV 0.63(±0.3) 0.54(±0.2) 0.65(±0.2) 0.62(±0.1) 11.12 (±1.4) LLaMA2-7B FT−PV 0.67(±0.3) 0.59(±0.2) 0.72(±0.1) 0.50(±0.2) 7.89(±2.1) LLaMA2-7B FT−CV 0.62(±0.1) 0.51(±0.1) 0.63(±0.2) 0.62(±0.1) 11.12 (±1.4) LLaMA2-7B FT−RV 0.58(±0.2) 0.49(±0.2) 0.65(±0.2) 0.65(±0.2) 11.07 (±2.7) Qwen2-7B 0.72(±0.3) 0.63(±0.1)... | https://arxiv.org/abs/2505.17260v1 |
corresponding Wikipedia article, and compiled this into an additional finetuning training dataset. Next, we will validate whether the improvement in Parameter Specialization and the enhanced efficiency in the model’s use of knowledge truly exhibit a causal relationship through four distinct finetuning experiments. The ... | https://arxiv.org/abs/2505.17260v1 |
achieve excellent performance. 9 that optimizing this specialization improves task performance and reduces hallucination. These findings highlight the importance of aligning knowledge storage with models’ retrieval mechanisms for efficiency and accuracy. Future work should explore dynamic knowledge updates and scalabil... | https://arxiv.org/abs/2505.17260v1 |
Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. d. O., Kaplan, J., Edwards, H., Burda, Y ., Joseph, N., Brockman, G., et al. Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 , 2021b. Cobbe, K., Kosaraju, V ., Bavarian, M., Chen, M., Jun, H., Kaiser, L., Plappert, M., Tworek, J., H... | https://arxiv.org/abs/2505.17260v1 |
associations in auto-regressive language models. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , pp. 12216–12235, 2023. Grattafiori, A., Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., Vaughan, A., Yang, A., Fan, A., Goyal, A... | https://arxiv.org/abs/2505.17260v1 |
Zhang, Y ., Li, Y ., Mao, Y ., Coudert, Z. D., Yan, Z., Chen, Z., Papakipos, Z., Singh, A., Srivastava, A., Jain, A., Kelsey, A., Shajnfeld, A., Gangidi, A., Victoria, A., Goldstand, A., Menon, A., Sharma, A., Boesenberg, A., Baevski, A., Feinstein, A., Kallet, A., Sangani, A., Teo, A., Yunus, A., Lupu, A., Alvarado, A... | https://arxiv.org/abs/2505.17260v1 |
S., Ramaswamy, S., Lindsay, S., Lindsay, S., Feng, S., Lin, S., Zha, S. C., Patil, S., Shankar, S., Zhang, S., Zhang, S., Wang, S., Agarwal, S., Sajuyigbe, S., Chintala, S., Max, S., Chen, S., Kehoe, S., Satterfield, S., Govindaprasad, S., Gupta, S., Deng, S., Cho, S., Virk, S., Subramanian, S., Choudhury, S., Goldman,... | https://arxiv.org/abs/2505.17260v1 |
Y ., and Farquhar, S. Semantic uncertainty: Linguistic invariances for uncertainty estimation in natural language generation. In The Eleventh International Conference on Learning Representations , 2022. Mallen, A., Asai, A., Zhong, V ., Das, R., Khashabi, D., and Hajishirzi, H. When not to trust language models: Invest... | https://arxiv.org/abs/2505.17260v1 |
F., Zhang, F., von Lohmann, F., Sulit, F., Goh, G., Oden, G., Salmon, G., Starace, G., Brockman, G., Salman, H., Bao, H., Hu, H., Wong, H., Wang, H., Schmidt, H., Whitney, H., Jun, H., Kirchner, H., de Oliveira Pinto, H. P., Ren, H., Chang, H., Chung, H. W., Kivlichan, I., O’Connell, I., O’Connell, I., Osband, I., Silb... | https://arxiv.org/abs/2505.17260v1 |
Christianson, T., Sanders, T., Patwardhan, T., Cunninghman, T., Degry, T., Dimson, T., Raoux, T., Shadwell, T., Zheng, T., Underwood, T., Markov, T., Sherbakov, T., Rubin, T., Stasi, T., Kaftan, T., Heywood, T., Peterson, T., Walters, T., Eloundou, T., Qi, V ., Moeller, V ., Monaco, V ., Kuo, V ., Fomenko, V ., Chang, ... | https://arxiv.org/abs/2505.17260v1 |
Visin, F., Rasskin, G., Wei, G., Cameron, G., Martins, G., Hashemi, H., Klimczak-Pluci ´nska, H., Batra, H., Dhand, H., Nardini, I., Mein, J., Zhou, J., Svensson, J., Stanway, J., Chan, J., Zhou, J. P., Carrasqueira, J., Iljazi, J., Becker, J., Fernandez, J., van Amersfoort, J., Gordon, J., Lipschultz, J., Newlan, J., ... | https://arxiv.org/abs/2505.17260v1 |
chat models. arXiv preprint arXiv:2307.09288 , 2023b. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I. Attention is all you need. Advances in neural information processing systems , 30, 2017. Wang, B. and Komatsuzaki, A. Gpt-j-6b: A 6 billion parameter autoreg... | https://arxiv.org/abs/2505.17260v1 |
model to generate the answers for Multi-choice questions in three-shot setting: **Question:** What is the capital city of France? **Options:** A. Berlin B. Madrid C. Paris D. Rome **Answer:** C **Question:** What is the largest planet in our solar system? **Options:** A. Earth B. Jupiter C. Mars D. Venus **Answer:** B ... | https://arxiv.org/abs/2505.17260v1 |
completion/generation mode, without the addition of any instruction tokens, to better assess the knowledge present in the model. For the Multi-Choice Questions task, we use a three-shot setup for each model and search for the answer within the next 30 tokens generated by the model. For the open-ended generation task, w... | https://arxiv.org/abs/2505.17260v1 |
for semantic equivalence among different responses, providing a more robust evaluation of entropy in generative tasks. Local Intrinsic Dimension The Local Intrinsic Dimension (LID) method detects hallucinations in Large Language Models by measuring the discrepancy in the local intrinsic dimension of model activations. ... | https://arxiv.org/abs/2505.17260v1 |
Proceedings of Machine Learning Research 287:1–17, 2025 Conference on Health, Inference, and Learning (CHIL) 2025 CaseReportBench: An LLM Benchmark Dataset for Dense Information Extraction in Clinical Case Reports Xiao Yu Cindy Zhang czhang@cmmt.ubc.ca University of British Columbia Carlos R. Ferreira carlos.ferreira@n... | https://arxiv.org/abs/2505.17265v1 |
case presentation section provides a detailed account of patient assessment, such as system reviews and history, essential for disease diagnosis. This study aims to establish a benchmark for evaluating dense information extraction from clinical case reports, fo- cusing on medical conditions, and identifying effective L... | https://arxiv.org/abs/2505.17265v1 |
structured format that aligns with real-world clinical assessments. To this end, We focused on 14 key clinical categories: Vitals and Hematology Findings ( Vitals Hema ), Eyes, Ears, Nose, and Throat ( EENT ), Neurology ( NEURO ), Cardiovas- 2 CaseReportBench: An LLM Benchmark Dataset for Dense Information Extraction i... | https://arxiv.org/abs/2505.17265v1 |
information extraction tasks. Levenshtein Similarity (Levenshtein) quan- tifies text similarity based on the Levenshtein dis- tance, which counts the minimum character edits (insertions, deletions, substitutions) needed to trans- form one string into the reference. It is scored from 0 to 100, with higher values indicat... | https://arxiv.org/abs/2505.17265v1 |
spite these discrepancies, other categories, such as Pregnancy, exhibit high inter-annotator agreement, indicating strong alignment in experts’ understand-ing and application of the annotation guidelines (Ap- pendix B and C). 422 instances with TSR(%) <30 were identified for further reconciliation, primarily due to dif... | https://arxiv.org/abs/2505.17265v1 |
prompts were crafted for each category by the author with a clinical credential and current NLP training. Figure 3B) illustrates a ZS variant using the “History” category as an example. The LLM was instructed to extract information under key subheadings, including past medical history, history of present illness, socia... | https://arxiv.org/abs/2505.17265v1 |
medical history. Use an empty list [] if no pertinent information is available. FS: Instructions + two clinically - accurate , category -specific examples { "past_medical_history ": ["Diagnosed with hypertension", "Previous myocardial infarction"], " past_surgical_history ": ["Appendectomy in 2010", "Knee replacement i... | https://arxiv.org/abs/2505.17265v1 |
automated extraction tasks. 4.3. Prompting Strategies Across Methods The above data integration methods were imple- mented in conjunction with the prompting strategies: ZS and FS. In addition, ZS-CoT was used in combina- tion with FCSP and UCP for Qwen2.5:32B-Instruct. 4.4. Implementation Experiments for all open-acces... | https://arxiv.org/abs/2505.17265v1 |
datasets increases the risk of spurious associations andhallucinations , particularly in rare medical cases. In high-stakes healthcare appli- cations, textbffactual precision outweighs creativity, and open-source models—being more adaptable and controllable—may align better with strict recall-based evaluations When com... | https://arxiv.org/abs/2505.17265v1 |
GPT-4o ’s performance was consistently lower across all cate- gories. However, it achieved comparable performance in the History andENDO categories, possibly due 8 CaseReportBench: An LLM Benchmark Dataset for Dense Information Extraction in Clinical Case Reports BLEU-1 Avg ↑BLEU-4 Avg ↑ROUGE-L Avg ↑Hallucination (%) ↓... | https://arxiv.org/abs/2505.17265v1 |
Information Extraction in Clinical Case Reports 050505101111150505202222250505303333350505404444450505505555550505606666650505707777750505808888505090 TSR (%) 050505101111150505202222250505303333350505404444450505505555550505606666650505707777750505808888505090 TSR (%)CVS DERM EENT ENDO GI GU History LYMPH Lab_Image MS... | https://arxiv.org/abs/2505.17265v1 |
2010. Claudia Ching Yan Chung, Hong Kong Genome Project, Annie Tsz Wai Chu, and Brian Hon Yin Chung. Rare disease emerging as a global pub- lic health priority. Frontiers in Public Health , 10: 1028545, 2022. Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, ... | https://arxiv.org/abs/2505.17265v1 |
prompt: a zero-shot learning paradigm for clinical natural language processing. In AMIA Annual Sym- posium Proceedings , volume 2022, page 972, 2023. Gizem So˘ gancıo˘ glu, Hakime ¨Ozt¨ urk, and Arzucan ¨Ozg¨ ur. Biosses: a semantic sentence similarity esti- mation system for the biomedical domain. Bioin- formatics , 3... | https://arxiv.org/abs/2505.17265v1 |
Lab_Image MSK Neuro Pregnancy RESP Vitals_HemaCategoriesAnnotated Data Percentage by Categories (Annotator A) 05101520253035404550556065707580859095100 Annotation (%)CVS DERM EENT ENDO GI GU History LYMPH Lab_Image MSK Neuro Pregnancy RESP Vitals_HemaCategoriesAnnotated Data Percentage by Categories (Annotator B) Figur... | https://arxiv.org/abs/2505.17265v1 |
0.0 0.85 0.0 0.0 0.0 0.0 0.13 CVS 0.0 0.0 0.01 0.0 0.0 0.01 0.0 0.01 0.0 0.0 0.8 0.0 0.0 0.0 0.16 MSK 0.0 0.0 0.0 0.0 0.0 0.01 0.0 0.01 0.0 0.0 0.0 0.73 0.0 0.01 0.23 GU 0.03 0.0 0.0 0.0 0.0 0.01 0.0 0.0 0.0 0.0 0.0 0.0 0.77 0.03 0.16 ENDO 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.68 0.32 NONE 0.05 0.02 0.1... | https://arxiv.org/abs/2505.17265v1 |
SELECT 2REASON : Efficient Instruction-Tuning Data Selection for Long-CoT Reasoning Cehao Yang1,2,3∗, Xueyuan Lin2,3,4∗, Chengjin Xu1,3∗, Xuhui Jiang1,3, Xiaojun Wu1,2,3,Honghao Liu2,3,Hui Xiong2†,Jian Guo3† 1DataArc Tech Ltd. 2Hong Kong University of Science and Technology (Guangzhou) 3IDEA Research, International Dig... | https://arxiv.org/abs/2505.17266v2 |
yet high-quality dataset of 817 samples. Similarly, s1 [ 34] depends heavily on API models and intricate data engineering pipelines tailored to optimize for quality, difficulty, and diversity, yielding 1k examples. Unfortunately, their metrics are based on qualitative heuristics without rigorous quantitative validation... | https://arxiv.org/abs/2505.17266v2 |
Length Rethinking TokensAnswer10M 1KDifficulty: ⭐⭐ <think>Okay , let’s tackle this problem step by step. So, we have the function... First, to find the slope of the tangent line at a point... Wait, but if the derivative is always nega tive... then the function is decreasing on the entire domain... Hmm . But let me verify... | https://arxiv.org/abs/2505.17266v2 |
improve model performance and alignment. Early efforts emphasized human expert curation [ 65], while recent work has explored automated selection using various metrics. GPT-based judgments of instruction-response quality are commonly used [3, 1, 30, 62,25], often enhanced with diversity signals [ 31,32,43,53,5]. Severa... | https://arxiv.org/abs/2505.17266v2 |
Figure 4: Pass@1 rate across six math benchmarks. Easy and hard examples are selected separately.Examples with different trace length are illustrated in Figure 5. Long reasoning traces incorporate more rethinking behav- iors such as reflection, backtracking, and planning, and serve as higher-quality su- pervision signa... | https://arxiv.org/abs/2505.17266v2 |
cases, explicitly bypass reasoning by using empty constructs like <think> \n</think> , rendering them ineffective. instruction with harder question contains more rethinking tokens in reasoning trace. We validate this assumption through a straightforward empirical study. Specifically, we perform short-CoT inference usin... | https://arxiv.org/abs/2505.17266v2 |
suite π={π1, π2, . . . , π k} (e.g., quality, difficulty), our objective is to select a subset Ds⊆ D pof size at most Ksuch that each selected instruction ranks among the top- Kunder the metrics: Ds= I∈TopK π(Dp) . (2) The supervised fine-tuning(SFT) objective is performed on Dsto update the model parameters θ, thus m... | https://arxiv.org/abs/2505.17266v2 |
question difficulty and reasoning trace length, we define the joint ranking as: joint _rank(Ii) =w·rank d(Ii) + (1 −w)·rank l(Ii), (6) where a weighting factor w∈[0,1]controls the trade-off between rankings by difficulty and trace length. The final selected subset by out methods for SFT is then: DSELECT 2REASON = I∈To... | https://arxiv.org/abs/2505.17266v2 |
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