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C Pereira, and William Bialek. The information bottleneck method. arXiv preprint physics/0004057 , 2000. Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al. Llama: Open and efficient foundation language mo... | https://arxiv.org/abs/2505.17117v2 |
al., 2024, Aboue- lenin et al., 2025]. •Gemma family: gemma-2b, gemma-2-2b, gemma-7b, gemma-2-9b, gemma-2-27b [Team et al., 2024, 2025]. •Mistral family: mistral-7b-v0.3 [Karamcheti et al., 2021]. A.3 Additional Clustering Metrics To further validate our cluster alignment findings (Section 5.1), in addition to Adjusted... | https://arxiv.org/abs/2505.17117v2 |
0.01893 0.09619 0.03193 [McCloskey and Glucksberg, 1978] meta-llama/Llama-3.2-3B 0.03914 0.07395 0.0202 [Rosch, 1973c] meta-llama/Meta-Llama-3-70B 0.02289 0.03133 0.01514 [Rosch, 1975] meta-llama/Meta-Llama-3-70B -0.06428 0.0185 0.00554 [McCloskey and Glucksberg, 1978] meta-llama/Meta-Llama-3-70B -0.04595 0.01068 0.002... | https://arxiv.org/abs/2505.17117v2 |
the often modest correlations discussed in Section 5.2. Figure 6 shows the aggregated Spearman correlation across model families and datasets. These correlations are very weak and mostly non-significant. A.7 Theoretical Extreme Case Exploration for L (Content from your original Appendix Section A: “Theoretical Extreme ... | https://arxiv.org/abs/2505.17117v2 |
Indicating Different Structure Representing Concepts. Mean Spearman correlation values across the models belonging to the same family and across the three datasets. 18 Figure 7: Human Conceptual Categories Exhibit Higher Mean Entropy than LLM-Derived Clusters. Mean cluster entropy ( Sα) versus the number of clusters ( ... | https://arxiv.org/abs/2505.17117v2 |
After Retrieval, Before Generation: Enhancing the Trustworthiness of Large Language Models in RAG Xinbang Dai1∗Huikang Hu2∗Yuncheng Hua3Jiaqi Li1Yongrui Chen2 Rihui Jin2Nan Hu2Guilin Qi1,2,† 1School of Cyber Science and Engineering, Southeast University, Nanjing 211189, China 2School of Computer Science and Engineering... | https://arxiv.org/abs/2505.17118v1 |
latest Nobel Prize in Literature? In 2024, Han Kang was awarded the Nobel Prize in Literature for his innovative ... The latest Nobel Prize in Literature was awarded in 2022 to Annie Ernaux … Response Strategy: Faithful to External KnowledgeHan Kang.Question: When is the release date for Civilization VII? Civilization ... | https://arxiv.org/abs/2505.17118v1 |
all scenarios effectively due to their lack of a unified understanding across these four cases. However, in this task, human behavioral patterns offer novel insights for addressing these scenarios. For instance, when confident in our internal knowledge, we perform limited external searches just for verification [ 12]. ... | https://arxiv.org/abs/2505.17118v1 |
decoding [ 22,36] or integrating consistent portions of internal and external knowledge [ 44,39,41,53]. However, these methods rely on carefully designed prompt templates to assess knowledge consistency. This matching strategy is sensitive to textual variations and lacks mechanisms for handling unanswerable questions. ... | https://arxiv.org/abs/2505.17118v1 |
Knowledge. To simulate scenarios where LLMs must rely solely on external knowledge due to outdated or absent internal knowledge, we define two distinct question sub-types: (1)Evolution : This category targets factual updates for existing facts/events (e.g., Who won the latest Nobel Prize in Literature? ). LLMs trained ... | https://arxiv.org/abs/2505.17118v1 |
statistics, please see Appendix A. 4 Biased Retrieval and Generation Evaluation As illustrated in Figure 2, BRIDGE operates through two sequential phases: (1) Bias-Guided Knowledge Collection : This phase constructs an Allocator that computes retrieval dependency probability rpand generation dependency probability gpto... | https://arxiv.org/abs/2505.17118v1 |
1 if rp+gp= 100% 0 otherwise(3) Analysis Quality Reward : Promotes semantic similarity between the model’s predicted analysis apred and high-quality analysis paths generated by larger reasoning models. We compute reward using BGE embeddings to measure textual similarity: R(γ, q;analysis ) =BGE (apred, a) (4) Allocator ... | https://arxiv.org/abs/2505.17118v1 |
knowledge source also decreases its score. For example, poor alignment between generated knowledge and internal knowledge ( S2→0), along with good alignment between retrieved knowledge and external knowledge ( S3→1), both contribute to lowering TLLM . Threshold-based Decision. As shown in Figure 2, we employ two thresh... | https://arxiv.org/abs/2505.17118v1 |
the effectiveness of combining different knowledge sources. However, their lower RR scores indicate that their response strategies for refusing to answer are insufficiently considered. CAD, which lacks mechanisms to resolve knowledge conflicts or reject uncertain questions, under- performs across most scenarios. In con... | https://arxiv.org/abs/2505.17118v1 |
most FIcases are misclassified as FA, pre- serving the model’s abil- ity to access the internal knowledge source. For FEmisclassifications, the model typically adopts a conservative rejection strategy to maintain cred- ibility. The misclassifi- cation of RAasFErep- resents an understandable scenario, as current RAG bas... | https://arxiv.org/abs/2505.17118v1 |
of reflection iterations. However, as a plug-and-play component, we carefully considered the efficiency trade-offs. Under ideal conditions, BRIDGE can resolve questions with 5 LLM API calls. When accounting for reflection operations, the average number of API calls per query reaches 5.89 on the TRD dataset - a number c... | https://arxiv.org/abs/2505.17118v1 |
in information retrieval systems: New challenges in the llm era. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , pages 6437–6447, 2024. [10] Zhuyun Dai, Vincent Y Zhao, Ji Ma, Yi Luan, Jianmo Ni, Jing Lu, Anton Bakalov, Kelvin Guu, Keith Hall, and Ming-Wei Chang. Promptagator: ... | https://arxiv.org/abs/2505.17118v1 |
bert. In Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval , pages 39–48, 2020. [25] Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, et al. Nat... | https://arxiv.org/abs/2505.17118v1 |
attributions and learning to refuse. In The Thirteenth International Conference on Learning Representations , 2025. [38] Zhaochen Su, Jun Zhang, Xiaoye Qu, Tong Zhu, Yanshu Li, Jiashuo Sun, Juntao Li, Min Zhang, and Yu Cheng. Conflictbank: A benchmark for evaluating the influence of knowledge conflicts in llm. In The 3... | https://arxiv.org/abs/2505.17118v1 |
Computational Linguistics: ACL 2024 , pages 15015–15040, 2024. 12 [52] Huaixiu Steven Zheng, Swaroop Mishra, Xinyun Chen, Heng-Tze Cheng, Ed H Chi, Quoc V Le, and Denny Zhou. Take a step back: Evoking reasoning via abstraction in large language models. arXiv preprint arXiv:2310.06117 , 2023. [53] Huichi Zhou, Kin-Hei L... | https://arxiv.org/abs/2505.17118v1 |
. For the evolution data, we leverage the TAQA dataset [ 51], which generates temporally-sensitive question-answer pairs from Wikipedia tables containing timestamp columns. TAQA’s methodology extracts event-related content from Wikipedia table rows at different timestamps, then employs GPT-4-turbo to generate correspon... | https://arxiv.org/abs/2505.17118v1 |
often leading to humorous situations that highlighted the contrast between his values and those of the younger generation. David Proval’s performance was well-received, contributing to the show’s success and its ability to blend humor with relatable family themes. His role in Ëverybody Loves Raymondïs a testament to hi... | https://arxiv.org/abs/2505.17118v1 |
champion of the Monterrey Open Singles, a prestigious event on the WTA Tour. The tournament, held annually in Monterrey, Mexico, attracts top talent from around the world and is known for its competitive field and vibrant atmosphere. In the latest edition of the Monterrey Open, which took place in March 2021, Fernandez... | https://arxiv.org/abs/2505.17118v1 |
but also highlighted her dedication to the study of neuroscience and her potential future contributions to the field. Helene Li’s achievement underscores the growing interest in neuroscience among young scholars and serves as an inspiration for future participants in the International Brain Bee, encouraging them to pur... | https://arxiv.org/abs/2505.17118v1 |
Allocator Based on GRPO B.1.1 Data Generation We use two reasoning models, DeepSeek-R1 ando3-mini , to generate analysis paths. The system prompt is as follows: Prompt for Soft Bias Reasoning Data Generation For a given question, determine the required probability (10%-90%) of retrieving external knowledge versus answe... | https://arxiv.org/abs/2505.17118v1 |
of k, we conduct hyper-parameter tuning on the validation set. A result is considered correct if the Allocator’s bias towards the question aligns with the hard bias. The experimental results, illustrated in Figure 7, demonstrate the impact of different k values on model performance. We choose k= 5; this setting enhance... | https://arxiv.org/abs/2505.17118v1 |
( Kint,Kgen,Kext,Kret) to provide comprehensive answers. (2) For FI, the LLM relies solely on internal knowledge ( Kint) and generated knowledge ( Kgen), ensuring factual consistency without external noise. (3) For FE, the response is derived from external knowledge ( Kext) and retrieved temporal knowledge ( Kret), 21 ... | https://arxiv.org/abs/2505.17118v1 |
out the names of the production companies responsible for creating "Asteroid City" by researching online, checking the credits at the end of the film, or looking up information on the film\'s official website or IMDb page … Retrieved Knowledge In November 2021, Anderson finished filming Asteroid City, but few details w... | https://arxiv.org/abs/2505.17118v1 |
arXiv:2505.17119v1 [cs.CL] 21 May 2025Systematic Evaluation of Machine-Generated Reasoning and PHQ-9 Labeling for Depression Detection Using Large Language Models Zongru Shao Silicon Austria Labs zongru.shao@silicon-austria.comXin Wang Jiangnan University wxlboro@jiangnan.edu.cnZhanyang Liu Jiangnan University 62431120... | https://arxiv.org/abs/2505.17119v1 |
models has enabled nuanced analysis of expression interactions within the context (Zogan et al., 2023; Wang et al., 2021) to improve the detection performance, but they are supervised by the training data and fail to gener- alize for unseen scenarios (Harrigian et al., 2020). Recent advancements of LLMs have significan... | https://arxiv.org/abs/2505.17119v1 |
the linguistic context demon- strates that the text does not describe the speaker’s own depressive state (for instance, when the text pertains to general knowledge or refers to another individual). (b) When depression keywords are ab- sent, yet the speaker implicitly conveys extremely negative emotions, suggesting a hi... | https://arxiv.org/abs/2505.17119v1 |
in (Kabiret al., 2023). The annotation process was per- formed by three psychology professionals and three graduate students trained to identify PHQ-9 related keywords, underlying causes, and self-reference of the speakers. 3.2 Prompt Engineering 3.2.1 Instruction Prompt engineering is critical for effective LLM- based... | https://arxiv.org/abs/2505.17119v1 |
of the generated re- sponses is assessed from three perspectives: 1) Ac- curacy : Whether each individual subtask is cor- rectly predicted and whether the collective analysis aligns with human judgment. 2) Weaknesses As- sessment : Identification of potential weaknesses, particularly with respect to challenges in: (a) ... | https://arxiv.org/abs/2505.17119v1 |
with Yes/Noindications. The distribution of the annotations is shown in Table 2. To con- sider explicit vs.implicit expressions of depression, we extract the depression-related keywords (e.g., “depress”, “depressant”, “depressed”, “depressing”, “depression”, “depressive”, etc.) and separate the samples into two groups:... | https://arxiv.org/abs/2505.17119v1 |
poor performance. Figure 2: Evaluation of state-of-the-art Mental-LLMs with F1 scores (formatted as “ LLM ,F1%”).TPdenotes true positive, FPfalse positive, FN false negative, and TN true negative. A higher FPin the left column ( Mentioned Depression (MD) ) compared to the right ( No Mention of Depression (NMD) ) in- di... | https://arxiv.org/abs/2505.17119v1 |
format ,Ddepression, and Sspeaker reference ). The best-performing LLM is highlighted in bold. We evaluate logical reasoning based on the align- ment of subtask predictions with human annota- tions. If all subtask predictions match, reasoning is considered “accurate" and correct under a well- designed task breakdown. S... | https://arxiv.org/abs/2505.17119v1 |
criteria for SR. Among them, six IR responses are replaced by two SR descriptions (one by Llama and the other by Qwen). This implies that only two generated diagnoses achieved correct revisions. In- terestingly, the derived TC,TP, and TWsample collections are identical regardless of the IR and SR criteria. This suggest... | https://arxiv.org/abs/2505.17119v1 |
54 5 22 34 DPO_SR 41 8 6 52 Table 5: Comparison of detection weaknesses w.r.t. false position (FP) and false negative (FN) rates with instruction tuning. MDrefers Mentioned Depression ;NMD denotes No Mention of Depression . FP and FN are computed using the same methods as in Figure 2 and 3. Note that DPO _SRis not dist... | https://arxiv.org/abs/2505.17119v1 |
mental health based on social media data generalize? In Findings of the associ- ation for computational linguistics: EMNLP 2020 , pages 3774–3788. Hamish Ivison, Yizhong Wang, Valentina Pyatkin, Nathan Lambert, Matthew Peters, Pradeep Dasigi, Joel Jang, David Wadden, Noah A Smith, Iz Beltagy, and 1 others. 2023. Camels... | https://arxiv.org/abs/2505.17119v1 |
arXiv:2505.17120v1 [cs.CL] 21 May 2025Self-Interpretability: LLMs Can Describe Complex Internal Processes that Drive Their Decisions, and Improve with Training Dillon Plunkett Northeastern University d.plunkett@northeastern.eduAdam Morris Princeton University thatadammorris@gmail.com Keerthi Reddy Independent Researche... | https://arxiv.org/abs/2505.17120v1 |
performing single-unit recordings in human brains. However, in the case of LLMs, there is a third approach that we could use, the method that people most commonly use to discover the thoughts and motivations of other humans: just asking. It is possible that LLMs can accurately explain the internal factors or operations... | https://arxiv.org/abs/2505.17120v1 |
explicit in their training data (e.g., “GPT-4o is risk-seeking”) and must reflect either introspective access to these tendencies or, at minimum, that the training to instill the risk-seeking also instilled the tendency to self-describe as risk-seeking. The approach of Betley et al. (2025) provides a method for measuri... | https://arxiv.org/abs/2505.17120v1 |
internal factors—namely, their native attribute weights (i.e., the weights guiding their decisions that have not been shaped by fine-tuning). Here, too, we find that the training helps, showing that it does not merely increase the accuracy of reports about preferences instilled through fine-tuning, but rather improves ... | https://arxiv.org/abs/2505.17120v1 |
fine-tuning was effective by asking each preference-trained model to make choices between pairs of new options on behalf of the agent (50 decisions per agent for a total of 5000 decisions, each made in an independent context window). (Here and for all other queries across all three experiments, we used a sampling tempe... | https://arxiv.org/abs/2505.17120v1 |
models. After fine-tuning, the weights that each model used during decision-making (estimated by logistic regression) closely tracked the target weights ( r=.84andr=.87for GPT-4o and GPT-4o-mini, respectively). Critically, both models were able to report their attribute weights reasonably well: Across a great variety o... | https://arxiv.org/abs/2505.17120v1 |
we fine-tuned the preference-trained models on the task of accurately describing their internal processes. Specifically, we provided examples in which the prompts are the introspection prompts from Experiment 1 (e.g., “Imagine you are Macbeth choosing between these two apartments and tell us how heavily you are weighti... | https://arxiv.org/abs/2505.17120v1 |
cereals Jean Valjean would prefer. Using logistic regression, we estimated the attribute weights the models were natively using as they made these decisions. Then, using the same method as in Experiments 1 and 2, we tested the ability of the models to report the weights directly, both before and after introspection tra... | https://arxiv.org/abs/2505.17120v1 |
not tested whether faithfulness reflects privileged knowledge of their own operations, as opposed to them inferring those operations from, e.g., common-sense reasoning. This is important because privileged knowledge may be a more reliable source of CoT faithfulness as models become more advanced and opaque to common-se... | https://arxiv.org/abs/2505.17120v1 |
quest to understand the operations underlying LLMs’ outputs. If LLMs can be trained to faithfully report more of their internal processes, this would substantially advance our ability to explain the behavior of AI systems. Self-reports from AI systems could provide promising hypotheses about their internal functioning ... | https://arxiv.org/abs/2505.17120v1 |
may be possible to get a broader sense of LLMs’ innate self-description 9 and introspective capabilities. Most importantly, by building a more varied and comprehensive introspection training paradigm, we may be able to LLMs them to have more generalized self- reporting capabilities, providing a powerful tool for AI saf... | https://arxiv.org/abs/2505.17120v1 |
https://dl.acm.org/doi/10.1145/3630106.3659037 . 10 Yanda Chen, Joe Benton, Ansh Radhakrishnan, Jonathan Uesato, Carson Denison, John Schulman, Arushi Somani, Peter Hase, Misha Wagner, Fabien Roger, Vlad Mikulik, Sam Bowman, Jan Leike, Jared Kaplan, and Ethan Perez. Reasoning Models Don’t Always Say What They Think. 20... | https://arxiv.org/abs/2505.17120v1 |
Jorge Morales and Hakwan Lau. Confidence tracks consciousness. Qualitative consciousness: themes from the philosophy of David Rosenthal , pages 1–21, 2021. URL https://books.google. com/books?hl=en&lr=&id=PjCHEAAAQBAJ&oi=fnd&pg=PA91&dq=info:f5n5uTQEfLkJ: scholar.google.com&ots=RhvfYCs8SC&sig=Vdcdv7bgCpb9EjFxsiCmZ-ZmwQU... | https://arxiv.org/abs/2505.17120v1 |
100 agents that never appeared in any fine-tuning examples. One example of one decision context is reproduced below (as part of illustrating the two different prompts that we used). We used 2 different prompts across our three experiments. The first prompt was used for preference training (Experiment 1), for verifying ... | https://arxiv.org/abs/2505.17120v1 |
NeSyGeo: A Neuro-Symbolic Framework for Multimodal Geometric Reasoning Data Generation Wei-Ming Wu1, Zi-Kang Wang1, Jin Ye1, Zhi Zhou2, Yu-Feng Li2,3, Lan-Zhe Guo1,2∗ 1School of Intelligence Science and Technology, Nanjing University, Nanjing, China 2National Key Laboratory for Novel Software Technology, Nanjing Univer... | https://arxiv.org/abs/2505.17121v1 |
information for geometric reasoning. existing datasets through equivalent condition transformation and numerical scaling. However, this approach fails to address the scalability of image generation. Template-based methods [7,37,12], use predefined geometric templates with fixed topologies, simplifying synthesis but con... | https://arxiv.org/abs/2505.17121v1 |
other popular synthe- sis approaches. “High Resolution” denotes average image pixels exceeding 336 ×336. “Symbolic Form” refers to the symbolic meta-information associated with the image. “Classification of Elements” signifies categorization by geometric elements. “Visual Understanding” represents the mitigation of ima... | https://arxiv.org/abs/2505.17121v1 |
parses the Geo-DSL sequence and translates it back to natural language and visual image without losing soundness. In the third step, we employ LLMs to take a reverse search and forward validation process to get final Q&A pairs with CoT. The advent of MLLMs has shifted the paradigm toward data-driven geometric reasoning... | https://arxiv.org/abs/2505.17121v1 |
line CD Line Para (Line (A, B ), Line (C, D ), x) LineABis parallel to CD,AB =x Angle Angle (P, Q, R ) =α ∠PQR =α be defined via a single statement, while its expressive power ensures comprehensive coverage of all geometric elements and values. NeSyGeo’s generation process unfolds in three distinct stages: First , a sy... | https://arxiv.org/abs/2505.17121v1 |
select an element vj∈fvusing weights from I, and choose an action akbased on weights from A (see Appendix I for action details). The new statement snewis then incorporated into the sequence fs. Leveraging Geo-DSL’s symbolic definitions and well-defined actions, this approach ensures the validity and accuracy of each st... | https://arxiv.org/abs/2505.17121v1 |
and recursively builds a reasoning chain, reducing Q&A generation complexity and hallucinations. R1’s strong reasoning and exploration capabilities yield diverse conclusions, enriching the variety of Q&A pairs. Forward validation. To ensure the correctness of Q&A pairs and generate step-by-step CoT reasoning, we re-inp... | https://arxiv.org/abs/2505.17121v1 |
(GeoQA), and 5.3 (MathVision). Qwen2.5-VL-3B achieved a +15.8 performance boost in the area domain of MathVision. Notably, across all evaluated metrics, the InternVL2.5-4B model trained on the NeSyGeo dataset achieves performance on par with or superior to its 8B counterpart. We also conducted SFT experiments, initiall... | https://arxiv.org/abs/2505.17121v1 |
to guiding large models, retaining only condition and question texts, and randomly sample 5k texts from each dataset. The results are illustrated in Figure 6. Our method and G-LLaV A [ 9] exhibit uniformly distributed features in the space, indicating low data overlap and high diversity. In contrast, R-CoT and MA VIS d... | https://arxiv.org/abs/2505.17121v1 |
abilities of multiple MLLMs through both SFT and RL. Future Work: We intend to extend NeSyGeo to other multimodal domains, such as analytical geometry and visual question answering. This extensibility will be achieved by defining new domain- specific languages, corresponding synthesis rules within the symbolic space, a... | https://arxiv.org/abs/2505.17121v1 |
geometric reasoning. In AI for Math Workshop at the International Conference on Machine Learning , 2024. [13] Ryan Krueger, Jesse Michael Han, and Daniel Selsam. Automatically building diagrams for olympiad geometry problems. In International Conference on Automated Deduction , pages 577–588, 2021. [14] Junnan Li, Ramp... | https://arxiv.org/abs/2505.17121v1 |
pages 95095–95169, 2024. [27] Peng Wang, Shuai Bai, Sinan Tan, Shijie Wang, Zhihao Fan, Jinze Bai, Keqin Chen, Xuejing Liu, Jialin Wang, Wenbin Ge, et al. Qwen2-vl: Enhancing vision-language model’s perception of the world at any resolution. arXiv preprint arXiv:2409.12191 , 2024. [28] Mingrui Wu, Xinyue Cai, Jiayi Ji,... | https://arxiv.org/abs/2505.17121v1 |
Computational Linguistics , pages 400–410, 2024. 12 A Comparison with Specific Examples of Popular Geometry Datasets To facilitate comparison of dataset characteristics synthesized by our method and other popular approaches, we showcase a randomly selected example from NeSyGeo-CoT alongside each of the different approa... | https://arxiv.org/abs/2505.17121v1 |
different automatic frameworks. Our datasets exhibit uniform feature distributions, underscoring the substantial visual diversity of images generated by the NeSyGeo framework. C Additional Experimental Details and Results We utilized the VLM-R1 [ 22] framework for RL experiments, conducted on 6 vGPU-32 GB. We set epoch... | https://arxiv.org/abs/2505.17121v1 |
63.0 54.0 41.7 58.2 Claude-3.5-Sonnet-latest – 68.8 78.0 71.0 77.8 73.2 56.5 74.5 Qwen-VL-plus – 38.5 29.6 31.7 36.6 27.2 29.6 32.8 Gemini-2.0-Flash – 36.8 60.4 67.7 54.3 63.8 61.0 58.1 visual attributes, such as element and background colours, could also be randomized, they were set to default values in our current sy... | https://arxiv.org/abs/2505.17121v1 |
LLMs. This could further reduce costs and ensure complete rigor. G Details of Prompts in Reverse Search and Forward Validation In our automatic synthesis framework, we employ DeepSeek R1 as the expert LLM for reverse search and DeepSeek V3 for forward validation. The specific prompts utilized are detailed in Figures 12... | https://arxiv.org/abs/2505.17121v1 |
SHALLOW PREFERENCE SIGNALS : LARGE LANGUAGE MODEL ALIGNS EVEN BETTER WITH TRUNCATED DATA? Xuan Qi∗2, Jiahao Qiu∗1, Xinzhe Juan3, Yue Wu†1, and Mengdi Wang†1 1AI Lab, Princeton University 2IIIS, Tsinghua University 3Department of Computer Science & Engineering, University of Michigan ABSTRACT Aligning large language mod... | https://arxiv.org/abs/2505.17122v1 |
determined from only the early portion of the response—or even just a few tokens—rather than requiring an evaluation of the entire response. We refer to this phenomenon as shallow preference signals . This observation suggests that preference-based optimization methods may not need to rely on the full response to effec... | https://arxiv.org/abs/2505.17122v1 |
preference signal phenomenon significantly impacts LLM content generation. Based on this observation, we find that simple strategies can perform well without needing complex decoding approaches. Recent work [ 13,14,15,16] has proposed various decoding strategies, but our findings indicate that by focusing on the early ... | https://arxiv.org/abs/2505.17122v1 |
GPT-4o [ 20], Gemini-2.0 [ 21], and Llama-3.1-70B- Instruct [ 22]. The traditional RLHF approach involves training a reward model to score the outputs of the language model, followed by fine-tuning using deep reinforcement learning algorithms like Proximal Policy Optimization (PPO) [ 5]. However, PPO faces challenges i... | https://arxiv.org/abs/2505.17122v1 |
we describe the mixing strategy and the two novel decoding policies designed to improve model performance. 3.1 Formulation of Reward Signal Location Consider a preference dataset containing pairs of responses, where one response is the chosen response and the other is therejected response . The reward signal is defined... | https://arxiv.org/abs/2505.17122v1 |
πref(ytrunc l|x) , where πθis the probability distribution generated by the model, πrefis the reference model’s distribution, ytrunc wandytrunc l represent the truncated winning and losing responses, and σis the sigmoid function. In our approach, we train the DPO model on truncated responses, but it is still capable ... | https://arxiv.org/abs/2505.17122v1 |
ultrafeedback-binarized [ 3], and RLHFlow-pair-data-v2-80K- wsafety2, which are commonly used in the context of large language models. Specifically, we apply truncation to the response sections (including both chosen and rejected responses) at varying positions. The truncation process retains only the initial portion o... | https://arxiv.org/abs/2505.17122v1 |
responses to 50% or 40% of their original length had a negligible effect on test accuracy for each task. In some tasks, models trained on truncated data even perform better than those trained on full responses. However, when the responses are truncated to shorter lengths (e.g., 30%, 20%, or 10%), a slight decrease in t... | https://arxiv.org/abs/2505.17122v1 |
While the impact increased with the truncation ratio, truncating the response to 50% or 40% of its original length does not significantly degrade the performance of the DPO-trained models. This suggests that, in the context of DPO training, the majority of the signals used to evaluate response quality are concentrated ... | https://arxiv.org/abs/2505.17122v1 |
KL divergence evolves as the response is generated and whether the early tokens exhibit a higher divergence compared to later ones. In the second experiment, we explore the reward-KL tradeoff during generation by adjusting the sampling strategy based on the DPO model and reference model, to further confirm the concentr... | https://arxiv.org/abs/2505.17122v1 |
We evaluate this strategy for various values of tand compute the average reward and KL divergence for each configuration. KL Threshold Control Decoding In KL Threshold Control Decoding, we compute the KL divergence KL(πDPO∥ πref)at each token position. If the divergence exceeds a threshold b, we sample from the DPO pol... | https://arxiv.org/abs/2505.17122v1 |
human-generated data. As the truncation ratio decreases, the alignment between DPO implicit reward predictions and human-annotated preferences remains high, demonstrating that even truncated responses are sufficient for accurately predicting relative quality. 10 Dataset Dimension Original Dataset 50% 40% 33% 25% SHP-Pr... | https://arxiv.org/abs/2505.17122v1 |
Yan, Yang Liu, and Yahui Zhou. Skywork-reward: Bag of tricks for reward modeling in llms. CoRR , abs/2410.18451, 2024. [3]Ganqu Cui, Lifan Yuan, Ning Ding, Guanming Yao, Wei Zhu, Yuan Ni, Guotong Xie, Zhiyuan Liu, and Maosong Sun. Ultrafeedback: Boosting language models with high-quality feedback. CoRR , abs/2310.01377... | https://arxiv.org/abs/2505.17122v1 |
Morrison, LJ Miranda, Bill Yuchen Lin, Khyathi Raghavi Chandu, Nouha Dziri, Sachin Kumar, Tom Zick, Yejin Choi, Noah A. Smith, and Hannaneh Hajishirzi. Rewardbench: Evaluating reward models for language modeling. CoRR , abs/2403.13787, 2024. [13] Seongjun Yang, Gibbeum Lee, Jaewoong Cho, Dimitris Papailiopoulos, and Ka... | https://arxiv.org/abs/2505.17122v1 |
card. CoRR , abs/2410.21276, 2024. [21] Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M. Dai, Anja Hauth, Katie Millican, David Silver, Slav Petrov, Melvin Johnson, Ioannis Antonoglou, Julian Schrittwieser, Amelia Glaese, Jilin Chen, Emily Pitler, Ti... | https://arxiv.org/abs/2505.17122v1 |
2024. [27] Yunhao Tang, Zhaohan Daniel Guo, Zeyu Zheng, Daniele Calandriello, Rémi Munos, Mark Rowland, Pierre Har- vey Richemond, Michal Valko, Bernardo Ávila Pires, and Bilal Piot. Generalized preference optimization: A unified approach to offline alignment. In Forty-first International Conference on Machine Learning... | https://arxiv.org/abs/2505.17122v1 |
Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zhe... | https://arxiv.org/abs/2505.17122v1 |
S. Mohamed, A. Agarwal, Danielle Belgrave, K. Cho, and A. Oh, editors, Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 - December 9, 2022 , 2022. [49] Cassidy Laidlaw, Shivam Singhal, and Anca Dragan. ... | https://arxiv.org/abs/2505.17122v1 |
=TY t=1p(yt|y1, y2, . . . , y t−1, x), where p(yt|y1, y2, . . . , y t−1, x)represents the conditional probability of generating token yt, given all previous tokens y1, y2, . . . , y t−1and the input prompt x. This process is typically framed as a token-level Markov Decision Process (MDP), where each state at time step ... | https://arxiv.org/abs/2505.17122v1 |
x. The goal is to maximize the expected reward by adjusting the model’s parameters using a policy optimization algorithm such as Proximal Policy Optimization (PPO) Initially, [ 19] proposed learning a reward model using the Bradley-Terry model to assign a score to each response. For a pair of responses yandy′, the Brad... | https://arxiv.org/abs/2505.17122v1 |
arXiv:2505.17123v2 [cs.CL] 26 May 2025MTR-Bench: A Comprehensive Benchmark for Multi-Turn Reasoning Evaluation Xiaoyuan Li1∗, Keqin Bao1∗, Yubo Ma2, Moxin Li3, Wenjie Wang1, Rui Men2, Yichang Zhang2, Fuli Feng1, Dayiheng Liu2, Junyang Lin2 University of Science and Technology of China1 Alibaba Group2 National Universit... | https://arxiv.org/abs/2505.17123v2 |
of highly reasoning-intensive tasks from various sources for systematically evaluating four fine-grained reasoning abilities: Inductive ,Abductive ,Deductive , and Planning Reasoning [30, 13]. Then for each task, we design a structured problem template that explicitly defines interactive rules, format requirements, and... | https://arxiv.org/abs/2505.17123v2 |
Difficulty LevelsüFormat FeedbacküQuery FeedbacküTermination ChecküAccuracyüEfficiencyüInvalid RateüPatternAnalysis Find the Impostors:Find the impostors among 6 players. You can ask about 3 players at a time: "1" means more impostors, "0" means fewer. Find all impostor numbers to win. 123456 Alright, I'm ready. My Que... | https://arxiv.org/abs/2505.17123v2 |
i-th turn of task c. Through these three components, our framework uses the Generator to create problems, facilitates interactions between the Monitor and models, and ultimately employs the Evaluator to measure models’ performance. 3 Information ProbingDynamic Adaptation State OperationStrategic Gaming Find the Imposto... | https://arxiv.org/abs/2505.17123v2 |
models’ reasoning capabilities, we categorize the public seed tasks into four predefined classes as follows using GPT-4o, with subsequent human validation ensuring classification accuracy. While successful task completion generally requires a combination of various reasoning skills, each predefined class is specificall... | https://arxiv.org/abs/2505.17123v2 |
diverse interaction patterns and rule structures as detailed in Appendix. Then, we manually convert the seed tasks into structured problem templates. Based on these templates, we develop problem generators with three difficulty levels: "easy", "medium", and "hard". Each level corresponds to different values of n, the p... | https://arxiv.org/abs/2505.17123v2 |
20.70 8.54 6.70 gemma-3-12b-IT 24.78 8.33 4.56 15.03 8.44 5.89 12.22 4.56 3.56 12.61 9.17 5.17 16.16 7.63 4.79 gemma-3-4b-IT 11.44 4.56 2.44 8.61 6.00 4.11 9.00 4.22 2.89 10.67 2.33 0.67 9.93 4.28 2.53 Qwen2.5-72B-IT 38.22 20.00 10.89 23.22 12.44 6.33 14.78 11.00 7.89 41.50 32.78 26.67 29.43 19.06 12.94 Qwen2.5-32B-IT ... | https://arxiv.org/abs/2505.17123v2 |
How do the LLMs’ instruction following abilities and basic reasoning capabilities under multi-turn scenarios? - RQ5: Which reasoning patterns are relatively more important in multi-turn reasoning scenarios? 6 5 7 9 11 13 15 Turns0.00.51.0Accuracy Information Probing - Easy 5 7 9 11 13 15 Turns0.00.51.0Accuracy Informat... | https://arxiv.org/abs/2505.17123v2 |
Small Models : Models with fewer than 7B parameters achieve almost no mean- ingful scores, further emphasizing the difficulty of our benchmark. Consequently, in subsequent analyses, we will focus on models with 32B or more parameters. 7 0 25 50 75 100 Percentage (%)R1 vs o3-miniR1 vs QwQ-32BQwQ-32B vs o3-mini 53.8% 22.... | https://arxiv.org/abs/2505.17123v2 |
may be more adept at long-term planning compared to others, making reasonable use of feedback in each turn to tackle more complex tasks. 8 IP DA SA SG Model Ass. Ver. Pla. Fee. Ass. Ver. Pla. Fee. Ass. Ver. Pla. Fee. Ass. Ver. Pla. Fee. QwQ-32B 11.1 6.9 2.3 7.2 11.6 7.7 2.7 6.2 10.0 5.2 3.9 5.5 8.7 5.4 4.1 3.1 Deepseek... | https://arxiv.org/abs/2505.17123v2 |
settings. However, MT-Bench primarily focuses on conversational coherence rather than reasoning capabilities, MINT focuses on tool usage evaluation, while GameArena’s human-in-the-loop approach introduces bias and reduces efficiency. In contrast, MTR-Bench provides an automated framework that effectively evaluates mult... | https://arxiv.org/abs/2505.17123v2 |
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