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( L={30,31,32}). Since each layer has 32 attention heads, we effectively perform ablation over n=|HL|= 96 features (attention heads) in total. For a given group L, we begin by estimating the function fLusing both LASSO andPROXY SPEX , based on evaluations of fL(S)for5000 subsets Ssampled uniformly at random. These esti... | https://arxiv.org/abs/2505.17495v1 |
arXiv:2505.17496v1 [cs.CL] 23 May 2025Analyzing Mitigation Strategies for Catastrophic Forgetting in End-to-End Training of Spoken Language Models Chi-Yuan Hsiao1, Ke-Han Lu1, Kai-Wei Chang1, Chih-Kai Yang1, Wei-Chih Chen1, Hung-yi Lee1 1National Taiwan University, Taiwan r12942086@ntu.edu.tw, d12942024@ntu.edu.tw, kai... | https://arxiv.org/abs/2505.17496v1 |
SLMs demonstrate strong speech understanding capabilities, they cannot generate speech responses. Another approach directly integrates speech tokens (e.g., semantic tokens derived from self-supervised learning (SSL) speech models and acoustic tokens from speech codec mod- els [20]) into the LLM, as seen in models [21, ... | https://arxiv.org/abs/2505.17496v1 |
we explore model merg- ing techniques that aggregate weights in Musing several meth- ods, including naive linear combination method, TIES [33], and DARE [34]. By applying these methods, we aim to preserve knowledge from different training stages, mitigating forgetting and enhancing the final performance. 2.2. Discounti... | https://arxiv.org/abs/2505.17496v1 |
the model on automatic speech recognition (ASR). In this stage, the model learns to generate a text transcription TASR given a text instruction TIfor ASR and a speech utterance SASR. The prompt Pand the model response Rare shown as: P= [TI,SASR],R= [TASR], (4) where SASR andTASR are the speech-text pair in ASR dataset.... | https://arxiv.org/abs/2505.17496v1 |
stage, and after the SQA stage, respectively. The following introduces the settings for each model merging method: Linear Combination: weight = [0.02,0.03,0.05,0.9]TIES: weight = [−,0.04,0.06,0.9], density = [−,0.9,0.9,0.9], BaseModel =θ0 DARE: weight = [−,0.04,0.06,0.9], density = [−,0.9,0.9,0.9], BaseModel =θ0 Discou... | https://arxiv.org/abs/2505.17496v1 |
Mitigation StrategyLLaMA Web Trivia IFEval T2T S2T S2S T2T S2T S2S T2T S2T S2ST2T Prompt Instruction Original 70.0 - - 61.5 - - 78.9 - - 67.1 77.1 None 14.3 7.3 8.0 3.6 1.5 0.8 6.0 3.1 1.9 9.2 20.1 Merge (Linear) 19.3 9.0 7.7 5.6 1.1 0.6 7.8 3.9 1.1 8.5 19.3 Merge (TIES) 12.7 6.3 2.7 3.7 0.7 0.0 4.9 2.0 0.4 10.9 22.2 M... | https://arxiv.org/abs/2505.17496v1 |
other single strategies on tasks evaluated for mitigation (T2T) as well as new ability (S2T, S2S). (2) Mixed Strategy can further boost the performance: Compared to experience replay, other mixed strategies can achieve better performance of new ability in S2T setting in some cases. However, mixed strategy with discount... | https://arxiv.org/abs/2505.17496v1 |
Gong et al. , “Joint audio and speech understanding,” in 2023 IEEE Automatic Speech Recognition and Understanding Work- shop (ASRU) . IEEE, 2023. [14] C. Tang et al. , “SALMONN: Towards generic hearing abilities for large language models,” in The Twelfth International Conference on Learning Representations , 2024. [15]... | https://arxiv.org/abs/2505.17496v1 |
[33] P. Yadav, D. Tam et al. , “Ties-merging: Resolving interference when merging models,” in Advances in Neural Information Pro- cessing Systems , A. Oh et al. , Eds., vol. 36. Curran Associates, Inc., 2023, pp. 7093–7115. [34] L. Yu et al. , “Language models are super mario: Absorbing abili- ties from homologous mode... | https://arxiv.org/abs/2505.17496v1 |
arXiv:2505.17503v1 [cs.CL] 23 May 2025CReSt: A Comprehensive Benchmark for Retrieval-Augmented Generation with Complex Reasoning over Structured Documents Minsoo Khang∗ Upstage AI mkhang@upstage.aiSangjun Park∗ Upstage AI sangjun@upstage.aiTeakgyu Hong Upstage AI teakgyu.hong@upstage.ai Dawoon Jung† Upstage AI dawoon@u... | https://arxiv.org/abs/2505.17503v1 |
English and Korean. These questions are subsequently revised by human annotators for quality assurance. Through our evaluation of several state-of-the-art LLMs, we observe that many models struggle with the CReSt benchmark, particularly in deciding whether they should refuse or not. Furthermore, we demonstrate that inc... | https://arxiv.org/abs/2505.17503v1 |
explicit scenarios for refusal when valid answers are unavailable. By combining these elements, our work presents a more comprehensive and robust framework for evaluating advanced RAG systems, thereby contributing meaningfully towards their practical deployment. 3 Dataset CReSt is a RAG benchmark comprising over 2,000 ... | https://arxiv.org/abs/2505.17503v1 |
HTML and plain-text chunks extracted from the source documents, CReSt adopts a multi-stage QA generation curriculum designed to systematically produce both ‘simple’ and ‘complex’ reasoning question-answer (QA) pairs. This curriculum facilitates a comprehensive evaluation of a LLM’s document-based RAG capabilities by co... | https://arxiv.org/abs/2505.17503v1 |
similar yet irrelevant content may be included. These negative chunks act as retrieval noise, challenging the model’s ability to ground its answers in the correct evidence. Negative candidates are retrieved based on semantic similarity in the document embedding space. Specifically, we compute top- knearest neighbors us... | https://arxiv.org/abs/2505.17503v1 |
prompt it to generate a pre-defined refusal statement— I cannot answer because the question is unanswerable with the documents. when the information provided is not sufficient to answer the question, following the approach introduced by (Chen et al., 2024). We then use the presence or absence of this statement to evalu... | https://arxiv.org/abs/2505.17503v1 |
44.49% 6.13% 0.1645 3.40% / 53.68% / 42.92% 9.16% Qwen2.5-7B-Instruct 0.2482 22.81% / 36.88% / 40.30% 28.16% 0.2037 10.20% / 52.12% / 37.68% 9.57% Qwen2.5-14B-Instruct 0.2730 29.64% / 40.73% / 29.64% 8.24% 0.2513 19.03% / 53.12% / 27.84% 6.52% Qwen2.5-32B-Instruct 0.3650 27.19% / 43.92% / 28.90% 36.21% 0.3309 17.56% / ... | https://arxiv.org/abs/2505.17503v1 |
75.71% 68.00% Llama-3.3-70B-Instruct 30.85% 46.19% 36.99% 32.54% 54.95% 40.87% GPT-4o 45.53% 57.33% 50.75% 53.18% 74.94% 62.21% GPT-4.1 67.81% 84.86% 75.38% 66.17% 92.73% 77.23% o3-mini 76.19% 84.27% 80.03% 74.26% 82.50% 78.16% o4-mini 77.82% 80.93% 79.34% 75.53% 83.01% 79.09% Citation is particularly important in RAG ... | https://arxiv.org/abs/2505.17503v1 |
24.52% 32.95% Llama-3.3-70B-Instruct 0.3844 26.98% / 47.17% / 25.85% 31.23% Qwen2.5-32B-Instruct 0.3478 32.45% / 40.75% / 26.79% 26.15% Direct Answer gpt-4o-mini 0.3123 21.51% / 16.23% / 62.26% 87.93% gpt-4o 0.4030 35.17% / 26.43% / 38.40% 60.92% Llama-3.3-70B-Instruct 0.2778 16.98% / 26.98% / 56.04% 74.90% Qwen2.5-32B... | https://arxiv.org/abs/2505.17503v1 |
seen in their relatively stable performance under CoT and Plan-And-Solve. This analysis underscores the importance of selecting inference methods that align with both the model’s reasoning capabilities. 5 Conclusion In this paper, we propose CReSt, a benchmark designed to holistically evaluate the diverse capabilities ... | https://arxiv.org/abs/2505.17503v1 |
and Danqi Chen. Enabling large language models to generate text with citations. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP) , Singapore, 2023b. Association for Computational Linguistics. Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, J... | https://arxiv.org/abs/2505.17503v1 |
Orleans, Louisiana, June 2018. Association for Computational Linguistics. doi: 10.18653/v1/N18-1074. URL https://aclanthology.org/N18-1074 . Lei Wang, Wanyu Xu, Yihuai Lan, Zhiqiang Hu, Yunshi Lan, Roy Ka-Wei Lee, and Ee-Peng Lim. Plan-and-solve prompting: Improving zero-shot chain-of-thought reasoning by large languag... | https://arxiv.org/abs/2505.17503v1 |
Figure 5, CoD in Figure 6, Plan-and-Solve in Figure 7, and Least-to-Most in Figure 8. Prompt for Direct Answer inference User: You are a helpful assistant tasked with answering questions strictly based on the content of the provided documents. The documents may contain irrelevant or inaccurate information, so please re... | https://arxiv.org/abs/2505.17503v1 |
steps between <Thinking> and </Thinking> and the answer between <Answer> and </Answer>. </Rules> <Question> {question} </Question> <Documents> {docs} </Documents> Figure 6: Prompt used for CoD inference in the Inference Methods experiment 15 Prompt for Plan-and-Solve Inference User: You are a helpful assistant tasked w... | https://arxiv.org/abs/2505.17503v1 |
these document chunks. This process will be conducted over multiple steps. Use {language} language to generate the key, value, and description. Here are the document chunks: {content} [First task] Your first task is to generate a collection of Key-Value pairs for a Key-Information Extraction (KIE) task. The extracted k... | https://arxiv.org/abs/2505.17503v1 |
question should be clear, consider and aligned with the characteristics and reasoning types described below: Reasoning Types: 1. Numerical reasoning: The question requires the reader to perform arithmetic operations on the information provided in the document, such as counting, comparisons, calculations, etc. 2. Tabula... | https://arxiv.org/abs/2505.17503v1 |
challenging, but it will help students develop a deeper understanding of the content. Figure 12: Prompt used for Complex QA Generation stage. 21 B.3 Prompt for LLM Evaluation Our benchmark, CReSt, utilizes LLM Evaluation for assessment in non-refusal environments, with the prompt used for this shown in Figure 13. Ratin... | https://arxiv.org/abs/2505.17503v1 |
arXiv:2505.17505v1 [cs.CL] 23 May 2025L-MTP: Leap Multi-Token Prediction Beyond Adjacent Context for Large Language Models A P REPRINT Xiaohao Liu1Xiaobo Xia1Weixiang Zhao2Manyi Zhang3 Xianzhi Yu4Xiu Su5Shuo Yang2See-Kiong Ng1Tat-Seng Chua1 1National University of Singapore2Harbin Institute of Technology 3Tsinghua Univ... | https://arxiv.org/abs/2505.17505v1 |
timeTrainingInference (c) Leap multi-token prediction Figure 1: Illustrations of LLM architectures with three prediction paradigms, including NTP (a), MTP (b), and L-MTP (c). NTP utilizes a single output head for sequential token prediction. MTP employs multiple output heads for adjacent multi-token forecasting. As a c... | https://arxiv.org/abs/2505.17505v1 |
MTP models with 22% more inference speed-up with the same number of heads. Furthermore, we provide experimental evidence that L-MTP is extendable to speculative decoding techniques, making models up to 4 times faster at inference time across a wide range of settings. 2 Preliminaries In this section, we formulate and de... | https://arxiv.org/abs/2505.17505v1 |
range and faster inference. Unlike conventional MTP, which focuses on consecutive tokens, L-MTP introduces a leap-based strategy, allowing it to predict tokens at non-sequential positions within the context window. This design enables the model to efficiently capture long-range dependencies without the need for dense t... | https://arxiv.org/abs/2505.17505v1 |
from parallel decoding [38, 39, 36, 40], we combine L-MTP with tree attention to enable efficient decoding. We construct a hierarchical tree structure, where the i-th layer represents candidate tokens generated by the i-th prediction head. Paths in the tree are explored to identify the accepted one. To facilitate paral... | https://arxiv.org/abs/2505.17505v1 |
strategy predicts tokens xt+1,xt+2, . . . , xt+nsequentially using the hidden state at t. L-MTP predicts two interleaved sequences. Specifically, L-MTP uses the hidden state at t−1to compensate for the non-predicted tokens. Theorem 3 (Less attenuation, more speed-up) .Letγrepresent the attenuation coefficient, and f(i)... | https://arxiv.org/abs/2505.17505v1 |
benchmark the methods, we select Math500 [45] (4-shot) and GSM8K [46] (4-shot) for math evaluation, MBPP ,MBPP+[47, 48], HumanEval , andHumanEval+[49, 48] for code evaluation, and MMLU [50] and IFEval [51] for general evaluation. We detail the statistics and utilization of these datasets in Appendix B.3, Appendix B.4, ... | https://arxiv.org/abs/2505.17505v1 |
3.40 3.87 46.83 36.51 21.95 18.29 54.22 18.59 25.46 L-MTP 4.80 5.91 46.56 36.51 24.39 20.73 54.17 20.38 26.68Llama3.1-8BBase 4.20 9.86 61.38 51.32 39.02 31.71 63.26 18.23 34.87 NTP 5.60 11.30 61.38 51.06 42.68 35.37 63.64 20.14 36.40 MTP 6.40 10.08 60.32 49.74 41.46 35.98 63.52 19.42 35.87 L-MTP 6.40 10.92 61.38 50.53 ... | https://arxiv.org/abs/2505.17505v1 |
3BQ2.5 7BNTPF-MTPMTPL-MTP01234IFEval (c) IFEval Figure 6: Speedup with self-speculative decoding for different series of LLMs (“G” ↔Gemma, “L” ↔Llama, and “Q”↔Qwen). The Z-axis represents the speedup ratio. 1 2 3 4 5 6 70.40.60.81.0MTP L-MTP Figure 7: The prediction accuracy at different positions estimated on the alpa... | https://arxiv.org/abs/2505.17505v1 |
68]. The production deployment also advances to improve the infer- ence efficiency, like memory management [69, 70] and parallelism [71, 72]. In this paper, we focus on the inference acceleration benefited by LLM decoding. Prior works accelerate inference on greedy decoding [73, 74], while recent speculative decoding e... | https://arxiv.org/abs/2505.17505v1 |
Katie Millican, et al. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805 , 2023. [7] Run Luo, Haonan Zhang, Longze Chen, Ting-En Lin, Xiong Liu, Yuchuan Wu, Min Yang, Minzheng Wang, Pengpeng Zeng, Lianli Gao, et al. Mmevol: Empowering multimodal large language models with evol-instru... | https://arxiv.org/abs/2505.17505v1 |
Lei Zhang, Yunshui Li, Jiaming Li, Xiaobo Xia, Jiaxi Yang, Run Luo, Minzheng Wang, Longze Chen, Junhao Liu, Qiang Qu, et al. Hierarchical context pruning: Optimizing real-world code completion with repository-level pretrained code llms. In AAAI , pages 25886–25894, 2025. [23] Hangfeng He and Weijie J Su. A law of next-... | https://arxiv.org/abs/2505.17505v1 |
rethinking feature uncertainty. ICML , 2024. [41] Dan Hendrycks, Collin Burns, Saurav Kadavath, Akul Arora, Steven Basart, Eric Tang, Dawn Song, and Jacob Steinhardt. Measuring mathematical problem solving with the math dataset. NeurIPS , 2021. [42] Ziyang Luo, Can Xu, Pu Zhao, Qingfeng Sun, Xiubo Geng, Wenxiang Hu, Ch... | https://arxiv.org/abs/2505.17505v1 |
Khyathi Chandu, Chandra Bhagavatula, and Yejin Choi. The unlocking spell on base llms: Rethinking alignment via in-context learning. InICLR , 2024. [59] Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio. Quantized neural net- works: Training neural networks with low precision weights and... | https://arxiv.org/abs/2505.17505v1 |
Chen, Kaixuan Huang, and Mengdi Wang. A theoretical perspective for speculative decoding algorithm. NeurIPS , 37:128082–128117, 2024. [77] Sen Yang, Shujian Huang, Xinyu Dai, and Jiajun Chen. Multi-candidate speculative decoding. arXiv preprint arXiv:2401.06706 , 2024. [78] Zhihao Zhang, Alan Zhu, Lijie Yang, Yihua Xu,... | https://arxiv.org/abs/2505.17505v1 |
. . . . . . . . . . . . . . . . . . . . . . . . 22 C Decoding Strategy 22 C.1 Forward Decoding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 C.2 Tree Attention . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 D Additional Exper... | https://arxiv.org/abs/2505.17505v1 |
1)2 2] (Za 0ye−y2/2dy= 1−e−a2/2) (29) =1 2 1−exp[−γ(n+ 1)2 2] . (30) Afterward, consider the lower bound of |∆2 b|: ∆2 b=2n−1X m=n+1exp[−γ(m(m+ 1) 2+jm 2k )] (31) 18 APREPRINT ≥2n−1X m=n+1exp −γm2 2 (32) ≥Z2n n+1exp[−γx2 2]dx (33) ≥1√γZ√γ·2n √γ(n+1)exp[−y2 2]dy(y=√γx) (34) ≥1√γexp[−2γn2] 2γn−exp[−γ(n+ 1)2] γ(n+ 1... | https://arxiv.org/abs/2505.17505v1 |
problems. Evol-Instruct-Code4[42, 43]: This dataset is an evolved version of instruction-based code generation data, built upon iterative refinement and augmentation techniques. It contains a wide range of programming tasks, solutions, and explanatory instructions across multiple languages ( e.g., Python, Java, C++). T... | https://arxiv.org/abs/2505.17505v1 |
transform it via z′=z+SiLU (Wz+b), where W∈Rd×d, b∈Rd×1,dis the dimension of hidden state and SiLU is the a Sigmoid Linear Unit (SiLU) function, denoted as SiLU (x) =x·σ(x). After that, the transformed hidden state is mapped to the logits, with the output dimensions being the size of the vocabulary. Such a process can ... | https://arxiv.org/abs/2505.17505v1 |
3) (9, 0)(17,) (18,) (1, 0, 0) (0, 1, 0)(0, 9) (2, 2) (0, 0, 3)(4, 1) (10, 0) (1, 4) (0, 10)(19,) (0, 0, 4)(11, 0) (0, 11) (5, 1) (1, 5) (2, 3) (3, 2) (0, 0, 5)(12, 0) (2, 0, 0) Figure 10: Token tree constructed according to the accuracy of heads. We use 3 heads for the Vicuna 7B model. The head accuracy is estimated w... | https://arxiv.org/abs/2505.17505v1 |
arXiv:2505.17508v1 [cs.LG] 23 May 2025On the Design of KL-Regularized Policy Gradient Algorithms for LLM Reasoning Yifan Zhang *1Yifeng Liu *1Huizhuo Yuan1Yang Yuan2,3 Quanquan Gu1†Andrew C Yao2,3† 1University of California, Los Angeles2IIIS, Tsinghua University 3Shanghai Qi Zhi Institute Abstract Policy gradient algor... | https://arxiv.org/abs/2505.17508v1 |
θ, aimed at enhancing LLM reasoning capabilities. The specific behavior of the RPG Core Engine is configured by three key design choices: (i) the KL Divergence Type (Forward KL(πold∥πθ)or Reverse KL(πθ∥πold)); (ii) the KL Form (Normalized or Un-normalized, e.g., using UKL / k3estimators); and (iii) the Loss Estimator t... | https://arxiv.org/abs/2505.17508v1 |
GRPO, REINFORCE++, and DAPO (Yu et al., 2025). 2 Background Policy gradient (PG) methods are a cornerstone of modern reinforcement learning (RL), optimizing parameterized policies πθby estimating the gradient of an expected objective function J(θ)with respect to the policy parameters θ. Typically, J(θ)represents the ex... | https://arxiv.org/abs/2505.17508v1 |
adapts the PPO framework for training LLMs, notably by eliminating the need for a learned value function (critic). Instead of using GAE, GRPO estimates the advantage bAi,tat token tof output oibased on the relative rewards within a group of Goutputs {o1, . . . , o G}sampled from the old policy πθoldfor the same prompt ... | https://arxiv.org/abs/2505.17508v1 |
(e.g., E[bAt∇θlogπθ(at|st)]with a loss like (A.2) ). This implies an off-policy setup with importance sampling and clipping. 5 Table 1: Summary of fully differentiable surrogate loss functions L(θ)for policy gradient estimation. Minimizing L(θ)corresponds to maximizing the regularized objective J(θ) =Eπθ[R(x)]−β· Diver... | https://arxiv.org/abs/2505.17508v1 |
estimate this off-policy using samples from the normalized reference eπold(x) =πold(x)/Zold, we define the importance weight w(x) =πθ(x)/πold(x)(using the unnormalized πold). The gradient and corresponding loss function, incorporating the total mass Zoldof the reference measure, are given in Theorem 3.4. Theorem 3.4 (P... | https://arxiv.org/abs/2505.17508v1 |
function in (3.5). The gradient of JURKL (θ)is: ∇θJURKL (θ) =ZoldEx∼eπoldh w(x) R(x)−βlogw(x) ∇θlogπθ(x)i . A corresponding surrogate loss for gradient descent optimization, estimated using samples {xi} ∼ eπold, is: LURKL (θ) =ZoldEx∼eπold −w(x)R(x) +β w(x) logw(x)−w(x) , satisfying ∇θLURKL (θ) =−∇θJURKL (θ). The... | https://arxiv.org/abs/2505.17508v1 |
for unnormalized forward KL, normalized reverse KL, and unnormalized reverse KL regularized objectives in Appendix C. 5 Experiments In this section, we empirically evaluate our proposed Regularized Policy Gradient (RPG) frame- work, including both its fully differentiable (RPG) and REINFORCE-style (RPG-REINFORCE) varia... | https://arxiv.org/abs/2505.17508v1 |
REINFORCE-style equivalent, along with Figures 4 and 5 for visualization). For PPO-style clipping, we set (ϵ1, ϵ2) = (0 .2,0.28)for RPG, DAPO, REINFORCE++, and REINFORCE++-Baseline. For RPG-REINFORCE and GRPO, we use (ϵ1, ϵ2) = (0 .1,0.1). This choice for RPG-REINFORCE is informed by ablation studies detailed in 11 Tab... | https://arxiv.org/abs/2505.17508v1 |
/uni00000036/uni00000057/uni00000048/uni00000053/uni00000013/uni00000011/uni00000013/uni00000019/uni00000013/uni00000011/uni00000013/uni0000001b/uni00000013/uni00000011/uni00000014/uni00000013/uni00000013/uni00000011/uni00000014/uni00000015/uni00000013/uni00000011/uni00000014/uni00000017/uni00000024/uni0000002c/uni0000... | https://arxiv.org/abs/2505.17508v1 |
fall into two main categories: those relying on policy optimization using an explicit reward model learned from feedback, and those directly optimizing policies based on preference data. RLHF via Policy Optimization. The classic RLHF involves training a reward model (RM) rϕ(x, y) to predict human preferences and then u... | https://arxiv.org/abs/2505.17508v1 |
generate sequences (e.g., chain-of-thought, code blocks) that lead to successful outcomes, often using rewards derived from external feedback like unit test results, execution outcomes, or correctness checks by an automated judge or specialized reward model trained on reasoning quality. For instance, the DeepSeekMath m... | https://arxiv.org/abs/2505.17508v1 |
In International Conference on Artificial Intelligence and Statistics , pp. 4447–4455. PMLR, 2024. Ralph Allan Bradley and Milton E Terry. Rank analysis of incomplete block designs: I. the method of paired comparisons. Biometrika , 39(3/4):324–345, 1952. Zixiang Chen, Yihe Deng, Huizhuo Yuan, Kaixuan Ji, and Quanquan G... | https://arxiv.org/abs/2505.17508v1 |
Liu, Yao Zhao, Rishabh Joshi, Misha Khalman, Mohammad Saleh, Peter J. Liu, and Jialu Liu. Statistical rejection sampling improves preference optimization. In The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024 . OpenReview.net, 2024. Zichen Liu, Changyu Chen, Wen... | https://arxiv.org/abs/2505.17508v1 |
Rotterdam, The Netherlands, 30 March 2025 - 3 April 2025 , pp. 1279–1297. ACM, 2025. Richard S Sutton, Andrew G Barto, et al. Reinforcement learning: An introduction , volume 1. MIT press Cambridge, 1998. Kimi Team, Angang Du, Bofei Gao, Bowei Xing, Changjiu Jiang, Cheng Chen, Cheng Li, Chenjun Xiao, Chenzhuang Du, Cho... | https://arxiv.org/abs/2505.17508v1 |
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 A.2 Proximal Policy Optimization (PPO) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 B Equivalence of k3Estimator and Unnormalized KL Divergence 23 CREINFORCE-Style Regularized Policy Gradients with Various KL Regularization Forms 23 C.1 Rational... | https://arxiv.org/abs/2505.17508v1 |
Regularized Policy Gradient with ϵ1= 0.2, ϵ2= 0.28using Qwen- 2.5-Math-7B and AdamW Optimizer. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 F.12 REINFORCE-Style Regularized Policy Gradient with ϵ1= 0.2, ϵ2= 0.28using Qwen- 2.5-Math-7B and Schedule-Free Optimizer . . . . . . . . . . . . . . . . . . . . . .... | https://arxiv.org/abs/2505.17508v1 |
estimate, since Eat∼πθ(·|st)[b(st)∇θlogπθ(at|st)] =b(st)∇θP atπθ(at|st) =b(st)∇θ1 = 0 . REINFORCE with baseline is typically implemented by minimizing the loss: LREINFORCE (θ) =−Eτ∼πθ"TX t=0SG(bAt) logπθ(at|st)# , (A.2) using the stop-gradient operator SG(·)to prevent gradients from flowing into the advantage estimate ... | https://arxiv.org/abs/2505.17508v1 |
suggests an alternative way to implement the gradient update, analogous to the REINFORCE-style approach used in the on-policy setting. Specifically, one could define a surrogate loss of the form: LREINFORCE-style (θ) =−Ex∼πsampling [SG ( Weight (x, θ)) log πθ(x)]. (C.1) The rationale is that applying automatic differen... | https://arxiv.org/abs/2505.17508v1 |
derived from the gradient of the original (non-negated) regularized objective (e.g., Theorem 3.6). The overall loss is L(θ) =Ex∼πold[LDualClip(x, θ)]. This loss function is differentiable with respect to θ(which appears in w(x)and potentially bA(x)if it includes terms like logw(x)). This loss formulation ensures that u... | https://arxiv.org/abs/2505.17508v1 |
Baseline method (e.g., batch/group average, value function Vϕ) 1:Initialize policy parameters θ←θ0 2:Initialize value function parameters ϕ(if baseline uses Vϕ) 3:foreach training iteration do 4: Sample batch D={xi}N i=1∼πold ▷Collect data using old policy 5: Compute Rifori= 1..N 6: Compute baselines bifori= 1..N(e.g.,... | https://arxiv.org/abs/2505.17508v1 |
t (θ)] where bAtis the regularized advantage defined above (incorporating Rt, bt, and KL terms), wt(θ) = πθ(at|st) πold(at|st), and LDualClip t (θ)is defined based on the sign of SG(bAt): –IfSG(bAt)≥0:LDualClip t (θ) = min( wt(θ) SG(bAt),clip(wt(θ),1−ϵ1,1 +ϵ2) SG(bAt)) –IfSG(bAt)<0:LDualClip t (θ) = max(min( wt(θ) SG(b... | https://arxiv.org/abs/2505.17508v1 |
coefficient LiCase ψi≥0 Grad via ℓi Grad = 0 0.5 1 1.5 2 2.5−2.5−2−1.5−1 1−ϵ1 1 +ϵ2 c wi=πθ(xi)/πold(xi)Loss coefficient LiCase ψi<0 Grad = 0 Grad via ℓi Figure 5: Visualization of the loss coefficient Livs. importance weight wibased on the specific implementation in Algorithm 2. This version swaps the main branching c... | https://arxiv.org/abs/2505.17508v1 |
/uni00000015/uni00000018/uni00000013 /uni00000016/uni00000013/uni00000013 /uni00000016/uni00000018/uni00000013 /uni00000017/uni00000013/uni00000013 /uni00000036/uni00000057/uni00000048/uni00000053/uni00000019/uni00000018/uni00000013/uni0000001a/uni00000013/uni00000013/uni0000001a/uni00000018/uni00000013/uni0000001b/uni... | https://arxiv.org/abs/2505.17508v1 |
Figure 9: Performance of fully differentiable Regularized Policy Gradient (RPG) methods compared to baselines. Base model: Qwen-2.5-Math-7B. Optimizer: Schedule-Free AdamW. 35 F.5 REINFORCE-Style Regularized Policy Gradient with ϵ1= 0.1, ϵ2= 0.1using Qwen- 2.5-7B-Instruct and AdamW Optimizer /uni00000013 /uni00000018/u... | https://arxiv.org/abs/2505.17508v1 |
Regularized Policy Gradient (RPG-REINFORCE) methods with clip parameters (ϵ1, ϵ2) = (0 .1,0.1)compared to baselines. Plots display accuracy on mathematical reasoning benchmarks (AIME24, AMC23, MATH500) and key training dynamics (reward, policy entropy, response length). Base model: Qwen-2.5-Math-7B. Optimizer: AdamW. F... | https://arxiv.org/abs/2505.17508v1 |
/uni0000002a/uni00000035/uni00000033/uni00000032 /uni00000027/uni00000024/uni00000033/uni00000032 /uni00000035/uni00000033/uni0000002a/uni00000010/uni00000035/uni00000028/uni0000002c/uni00000031/uni00000029/uni00000032/uni00000035/uni00000026/uni00000028/uni00000010/uni00000029/uni0000002e/uni0000002f /uni00000035/uni0... | https://arxiv.org/abs/2505.17508v1 |
MATH500) and key training dynamics (reward, policy entropy, response length). Base model: Qwen-2.5-Math-7B. Optimizer: Schedule-Free AdamW. 43 G Proofs of Theorem 2.1 (Generalized Policy Gradient Theorem) Proof. The proof relies on the log-derivative trick, ∇θπθ(x) =πθ(x)∇θlogπθ(x), and the product rule under the integ... | https://arxiv.org/abs/2505.17508v1 |
these into the gradient expression: ∇θJUFKL (θ) =ZoldEx∼eπold[(∇θw(x))R(x) +β∇θlogπθ(x)−β(∇θw(x))] =ZoldEx∼eπold[w(x)R(x)∇θlogπθ(x) +β∇θlogπθ(x)−βw(x)∇θlogπθ(x)] =ZoldEx∼eπold[(w(x)R(x)−βw(x) +β)∇θlogπθ(x)] =ZoldEx∼eπoldh w(x)R(x)−β(w(x)−1) ∇θlogπθ(x)i . 46 This proves the first part of the theorem. Now, consider the... | https://arxiv.org/abs/2505.17508v1 |
=ZoldEx∼eπold[∇θ(−w(x)R(x)) +β∇θ(w(x) logw(x)−w(x))] =ZoldEx∼eπold[−(∇θw(x))R(x) +β(∇θ(w(x) logw(x))− ∇ θw(x))] =ZoldEx∼eπold[−w(x)R(x)∇θlogπθ(x) +β w(x)(log w(x) + 1)∇θlogπθ(x)−w(x)∇θlogπθ(x) =ZoldEx∼eπold[−w(x)R(x)∇θlogπθ(x) +βw(x) logw(x)∇θlogπθ(x)] =ZoldEx∼eπoldh w(x) −R(x) +βlogw(x) ∇θlogπθ(x)i =−ZoldEx∼eπold... | https://arxiv.org/abs/2505.17508v1 |
ascent on JRKL(θ). I.4 Proof of Theorem C.3 (REINFORCE-Style Loss for Unnormalized Reverse KL) Proof. The objective is JURKL (θ) =Eπθ[R(x)]−βUKL( πθ∥πold). From Theorem 3.9, its gradient is: ∇θJURKL (θ) =Ex∼eπold Zoldw(x) R(x)−βlogw(x) | {z } WeightURKL(x,θ)∇θlogπθ(x) . The proposed REINFORCE-style surrogat... | https://arxiv.org/abs/2505.17508v1 |
arXiv:2505.17510v1 [cs.CL] 23 May 2025Large Language Models Do Multi-Label Classification Differently Marcus Ma*, Georgios Chochlakis*, Niyantha Maruthu Pandiyan, Jesse Thomason ,Shrikanth Narayanan University of Southern California Correspondence: {mjma, chochlak}@usc.edu Abstract Multi-label classification is prevale... | https://arxiv.org/abs/2505.17510v1 |
resulting distributions at each step are conditioned on earlier outputs and remain con- strained by the same joint normalization, making them difficult to interpret as genuine model confi- dence scores (Breen et al., 2018). For example, a model with 60% confidence in a label still needs to allocate the remaining 40% am... | https://arxiv.org/abs/2505.17510v1 |
and Ananiadou, 2021; Chochlakis et al., 2023). To the best of our knowledge, Niraula et al. (2024) is the only work to explicitly investigate LLM multi-label classification (Chen et al., 2022) in niche domains. Be t,ianu et al. (2024) explored a multi-label framework for finetuning BERT and Jung et al. (2023) trained a... | https://arxiv.org/abs/2505.17510v1 |
We use the English tweets. We refer to this as SemEval . Although it does not contain annotator labels, it has a frequent presence of multiple labels, allowing us to study the generation dynamics.MRFC (Trager et al., 2022) Multi-label moral foundation corpus of six moral foundations. 3 an- notators were assigned to eac... | https://arxiv.org/abs/2505.17510v1 |
1 20.20.40.60.81.0Llama3 8B Base 1 2Llama3 8B Instruct 1 2Llama3 70B Base 1 2Llama3 70B InstructT op Probabilities for Boxes Prediction Step rFigure 2: Top probabilities at each generation step when the last or an intermediate label is generated. Patterns are identical between the two settings, and bigger or finetuned ... | https://arxiv.org/abs/2505.17510v1 |
and SFT), a counter-intuitive finding, because one would ex- pect lower weight on the rest of the labels when the model would stop generating. Relative Ranking We demonstrate that LLMs do not reliably pick the second highest label as their next prediction, even if they continue predicting. For instance, in SemEval , th... | https://arxiv.org/abs/2505.17510v1 |
be highlighted very clearly in the in-context examples, suggesting that single- label formats have dominated the training of the model. Overall, these analyses demonstrate that LLMs do not create well-calibrated distributions when generating multiple labels; instead, they gen- 5 1 2 Label Order0.20.40.60.81.0Accuracy G... | https://arxiv.org/abs/2505.17510v1 |
in the multi-label setting, we propose methods which are categorized into three groups: baseline methods , test-time methods , and supervised methods . 5.2.1 Baseline Methods Compare-to-None We use the output distribu- tion of the labels at the point at which the model generates its first label token (excluding, for ex... | https://arxiv.org/abs/2505.17510v1 |
similar to the approach taken by Li et al. (2020). Namely, for a given example, we create a prompt that includes the original document to be classified, but insteadwe present a single label and query the model if the label is “reasonable”. We directly extract the probabilities for the “reasonable” label, which con- for... | https://arxiv.org/abs/2505.17510v1 |
overhead other than storing model scores across multiple generation steps. We see that unary breakdown performs similarly wellto max-over-generations, as isolating each label’s validity independently disentangles the bias of lan- guage modeling from the classification task. As a downside, unary breakdown incurs |L|time... | https://arxiv.org/abs/2505.17510v1 |
biased in the same way and that combining their annotations does not remove this bias. Additionally, we limit our analysis to the Llama model family, which is inherently constrained to these models’ specific training and finetuning regimens. We acknowl- edge the possibility that our insights into multi- label generatio... | https://arxiv.org/abs/2505.17510v1 |
and Shrikanth Narayanan. 2025. Aggrega- tion artifacts in subjective tasks collapse large lan- guage models posteriors. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics . ACL. 9 Aida Mostafazadeh Davani, Mark Díaz, and Vinodku- mar Prabhakaran... | https://arxiv.org/abs/2505.17510v1 |
Damoc, Aurelia Guy, Simon Osindero, Karen Si- monyan, Erich Elsen, Jack W. Rae, Oriol Vinyals, and Laurent Sifre. 2022. Training compute-optimal large language models. Preprint , arXiv:2203.15556. Dirk Hovy, Taylor Berg-Kirkpatrick, Ashish Vaswani, and Eduard Hovy. 2013. Learning whom to trust with MACE. In Proceedings... | https://arxiv.org/abs/2505.17510v1 |
good probabilities with supervised learn- ing. In Proceedings of the 22nd International Confer- ence on Machine Learning , ICML ’05, page 625–632, New York, NY , USA. Association for Computing Ma- chinery. Nobal Niraula, Samet Ayhan, Balaguruna Chi- dambaram, and Daniel Whyatt. 2024. Multi-label classification with gen... | https://arxiv.org/abs/2505.17510v1 |
Preprint , arXiv:2311.09649. 12 A Additional Implementation Details A.1 Label Probabilities Throughout §5.2, we generate softmax probabili- ties of the label set by constraining the logit scores to just those of the initial tokens of labels. This deviates slightly from the true label probabilities, as we ignore all non... | https://arxiv.org/abs/2505.17510v1 |
During infer- ence, because we noticed a tendency for the model to respond with differing formats, we still used a 10-shot format to standardize the output. Unary breakdown We specifically use the term "reasonable" given the subjective nature of the tasks where multiple labels may be appropriate, as we found that using... | https://arxiv.org/abs/2505.17510v1 |
is there before you ever set foot in the cabin; [NAME] put it there. Just makes it creepy. Unary Breakdown Prompt Example (Hatexplain) Classify the following question-label pairs as either "rea- sonable" or "unreasonable". Output either "reasonable" or "unreasonable" and nothing else. Question: that or only date asians... | https://arxiv.org/abs/2505.17510v1 |
it is ranked second, as Llama3 70B Instruct predicts the label with the second-highest probabil- ity as the second label 65% of the time compared to approximately 50% of the time with 8B Instruct. This indicates that with scale, the relative ordering of labels improves. D.4 Alignment of Llama3 8B We present results for... | https://arxiv.org/abs/2505.17510v1 |
1 2Llama3 70B Instruct 1 2SFT Llama3 70B InstructEntropy for SemEval 2018 T ask 1last intermediate 1 20123Entropy (bits)Llama3 8B Base 1 2Llama3 8B Instruct 1 2SFT Llama3 8B Instruct 1Llama3 70B Base 1 2Llama3 70B Instruct 1 2SFT Llama3 70B InstructEntropy for GoEmotions 1 2012Entropy (bits)Llama3 8B Base 1Llama3 8B In... | https://arxiv.org/abs/2505.17510v1 |
0.132 2.01 3.29 0.162 1.76 3.00 0.095 3.11 3.06 8B Instruct 0.242 2.08 3.48 0.242 2.08 3.48 0.163 1.74 2.84 0.094 2.92 2.69 Table 6: Average percentage % attention to Input andLabel tokens. We also show the average attention to the 1st Tokens of the labels only, avoiding formatting tokens and the rest of the generated ... | https://arxiv.org/abs/2505.17510v1 |
arXiv:2505.17512v1 [cs.AI] 23 May 2025Probe by Gaming: A Game-based Benchmark for Assessing Conceptual Knowledge in LLMs Shuhang Xu♢Weijian Deng♣Yixuan Zhou♠Fangwei ZhongB♢ ♢Beijing Normal University♣Australian National University ♠Beijing 101 Education Group BCorrespondence to fangweizhong@bnu.edu.cn Abstract Concepts... | https://arxiv.org/abs/2505.17512v1 |
Spherical shape Three-point lineSports equipment90 minutes Free throwsScore to winOffside rule Goalkeeper Indoor court Commonalities and Unique Features Among ConceptsConcept pairs from knowledge graph Language Games as the LLM Testing Benchmark…… LLM-Based Agents as Different Roles for InteractionFigure 1: Conceptual ... | https://arxiv.org/abs/2505.17512v1 |
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