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large-scale moe models. Qwen Blog . Accessed on 2025-04-05. Mikayel Samvelyan, Sharath Chandra Raparthy, An- drei Lupu, Eric Hambro, Aram H. Markosyan, Manish Bhatt, Yuning Mao, Minqi Jiang, Jack Parker-Holder, Jakob Foerster, Tim Rocktäschel, and Roberta Raileanu. 2024. Rainbow teaming: Open- ended generation of diver... | https://arxiv.org/abs/2505.17691v1 |
. Dongcheng Zou, Senzhang Wang, Xuefeng Li, Hao Peng, Yuandong Wang, Chunyang Liu, Kehua Sheng, and Bo Zhang. 2024. Multispans: A multi-range spatial-temporal transformer network for traffic fore- cast via structural entropy optimization. In Proceed- ings of the 17th ACM International Conference on Web Search and Data ... | https://arxiv.org/abs/2505.17691v1 |
on the LLaMA-2-13B-Chat model. Due to the limited context length of 2048 tokens supported by the advanced open-source evaluators PandaLM and JudgeLM, which is insufficient to cover the evaluation tasks in our testing set, we did not include them in our experiments.B.3 "Unseen" question validation We perform a detailed ... | https://arxiv.org/abs/2505.17691v1 |
ein jthen 9: Add edge (vj→vi) 10: else if ein i=ein jthen 11: Add bidirectional edge (vi↔vj) 12: end if 13: end for 14:end for 15:Combine all modified SCC icomponents to form a global DAG G′ 16:foreach edge in G′do 17: ifedge is (vi→vj)then 18: SetJ(ai, aj) = ‘lose’ ,J(aj, ai) = ‘win’ 19: else if edge is (vi↔vj)then 20... | https://arxiv.org/abs/2505.17691v1 |
0.8204 Qwen-Vicuna-Raw 61.13% 0.8147 57.50% 0.8034 64.59% 0.8019 60.35% 0.8150 64.23% 0.7966 61.56% 0.8063 Qwen-Oasst-Raw 60.58% 0.8099 70.89% 0.8101 55.78% 0.7734 63.83% 0.8150 61.96% 0.7746 62.61% 0.7966 Qwen-Koala-Raw 64.23% 0.8228 67.86% 0.8090 64.59% 0.7966 64.29% 0.8159 59.30% 0.7779 64.05% 0.8044 Qwen-Self-instr... | https://arxiv.org/abs/2505.17691v1 |
{ "model_identifier": "M", "output": """{output_2}""" } } ## Task Evaluate the models based on the quality and relevance of their outputs, and select the model that generated the best output. Answer by first providing a concise explanation and then end your answer by providing the model identifier of the best output. W... | https://arxiv.org/abs/2505.17691v1 |
arXiv:2505.17697v1 [cs.CL] 23 May 2025Activation Control for Efficiently Eliciting Long Chain-of-thought Ability of Language Models Zekai Zhao∗, Qi Liu∗, Kun Zhou†, Zihan Liu, Yifei Shao, Zhiting Hu, Biwei Huang University of California, San Diego. kuzhou@ucsd.edu Abstract Despite the remarkable reasoning performance, ... | https://arxiv.org/abs/2505.17697v1 |
in base models [ 1,11]. If so, we can efficiently wake up the long-CoT ability when necessary, and exert influence to achieve more fine-grained controls. In LLM, activation values of MLP layers play a key role in preserving key information or styles during inference [ 12,13]. Inspired by it, we analyze and compare the ... | https://arxiv.org/abs/2505.17697v1 |
values can preserve key information of styles or concepts [ 12]. We conduct a series of experiments on state-of-the-art Qwen2.5-7B series LLMs, and first study (1) the distribution of long-CoT related activations, then test (2) whether activation intervention can elicit long-CoT ability, finally analyze (3) whether the... | https://arxiv.org/abs/2505.17697v1 |
token during inference, and observe whether the accuracy and self-reflection rate can also increase. Analysis Setup. We first use the above contrastive example pairs to measure the long-CoT ability correlation of each activation. Then, we rank all the activations, and select the top-200 ones as the key activations. For... | https://arxiv.org/abs/2505.17697v1 |
but activated into a relatively high value in special positions (e.g., “wait” token). We see that the high-value positions of the two LLMs are mostly the same ones, and the corresponding values are also very similar. It indicates that the base and long-CoT models have similar sparse activation dynamics. Based on this f... | https://arxiv.org/abs/2505.17697v1 |
computed coefficients of Qwen2.5-7B-base as an example: f(t) =a−b·log(t+c)where a= 0.17, b= 0.033, c=−0.997 (1) As shown in Fig. 4, this curve can well capture the activation changes of Qwen2.5-7B-base. Based on it, we further design an activation amplification rule. Let Adenote the original activation value of the LLM... | https://arxiv.org/abs/2505.17697v1 |
difficult examples. Baselines. In our results table, the Forcing Reflection row corresponds to the intervention where the token "wait" is inserted to encourage reflective reasoning. Constant Intervention represents a baseline where a fixed amplification factor is applied to selected neuron activations. Forcing & Consta... | https://arxiv.org/abs/2505.17697v1 |
%and at the same time reflection rate increases dramatically from 0.20 %to 23.40 %, suggesting that the intervention effectively triggers self-monitoring behavior. Measured on the GPQA, Accuracy improves from 22.73 %to 28.79 %, showing a consistent positive effect on complex, knowledge-intensive question answering. Ref... | https://arxiv.org/abs/2505.17697v1 |
[ 18]. Besides, since only Wa∈Rh×nand the scalar βin the amplification module are trainable, this module is also more parameter-efficient than LoRA, i.e.,(h×100 + 1) vs.(h+d)×256. 4.2 Validation Experiments Experimental Settings. To verify the effectiveness of our parameter-efficient training method, we fine-tune Qwen2... | https://arxiv.org/abs/2505.17697v1 |
tasks and applications [ 28,29]. To support more complex scenarios in real world, enhancing the 8 reasoning ability is the key challenge for LLMs [ 3]. Early work mainly focuses on optimizing the prompt engineerings such as chain-of-thought and tree-of-thought [ 5,30], or collect more reasoning-related data to fine-tun... | https://arxiv.org/abs/2505.17697v1 |
patterns underpinning reasoning processes in LLMs remain largely unexplored. Most interpretability work has focused on relatively static features ( e.g., factual knowledge, sentiment) or small-scale reasoning ( e.g., tracing one inference step). It is also unclear what activation patterns are responsible for long CoT r... | https://arxiv.org/abs/2505.17697v1 |
Silva. Rlhf deciphered: A critical analysis of reinforcement learning from human feedback for llms. arXiv preprint arXiv:2404.08555 , 2024. [10] Zheng Yuan, Hongyi Yuan, Chuanqi Tan, Wei Wang, Songfang Huang, and Fei Huang. Rrhf: Rank responses to align language models with human feedback without tears. arXiv preprint ... | https://arxiv.org/abs/2505.17697v1 |
Mei Li, Mingfeng Xue, Mingze Li, Pei Zhang, Peng Wang, Qin Zhu, Rui Men, Ruize Gao, Shixuan Liu, Shuang Luo, Tianhao Li, Tianyi Tang, Wenbiao Yin, Xingzhang Ren, Xinyu Wang, Xinyu Zhang, Xuancheng Ren, Yang Fan, Yang Su, Yichang Zhang, Yinger Zhang, Yu Wan, Yuqiong Liu, Zekun Wang, Zeyu Cui, Zhenru Zhang, Zhipeng Zhou,... | https://arxiv.org/abs/2505.17697v1 |
for Computational Linguistics , 2022. [38] Shibo Hao, Yi Gu, Haodi Ma, Joshua Jiahua Hong, Zhen Wang, Daisy Zhe Wang, and Zhit- ing Hu. Reasoning with language model is planning with world model. arXiv preprint arXiv:2305.14992 , 2023. [39] Xinyu Guan, Li Lyna Zhang, Yifei Liu, Ning Shang, Youran Sun, Yi Zhu, Fan Yang,... | https://arxiv.org/abs/2505.17697v1 |
Wei. Knowledge neurons in pretrained transformers. arXiv preprint arXiv:2104.08696 , 2021. [55] Collin Burns, Haotian Ye, Dan Klein, and Jacob Steinhardt. Discovering latent knowledge in language models without supervision. arXiv preprint arXiv:2212.03827 , 2022. [56] Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang, We... | https://arxiv.org/abs/2505.17697v1 |
45% to 62.5%, while the reflection rate increases dramatically from 22.5% to 75%. On GPQA, although the accuracy improves slightly from 38.38% to 40.40%, the reflection rate sees a significant jump from 3.03% to 55.56%, demonstrating the method’s ability to elicit reflective reasoning even in knowledge-heavy domains. T... | https://arxiv.org/abs/2505.17697v1 |
own reasoning. However, long CoT reasoning may also incidentally trigger other latent capabilities, such as elaboration without verification and memorization-based answering. Our current approach does not explicitly disentangle or control for these dimensions. However, we believe these abilities could be selectively su... | https://arxiv.org/abs/2505.17697v1 |
arXiv:2505.17701v1 [cs.LG] 23 May 2025COUNTDOWN: Contextually Sparse Activation Filtering Out Unnecessary Weights in Down Projection Jaewon Cheon Industrial and Management Engineering Korea University jaewon_cheon@korea.ac.krPilsung Kang* Industrial Engineering Seoul National University pilsung_kang@snu.ac.kr Abstract ... | https://arxiv.org/abs/2505.17701v1 |
employ activations such as GeLU or SiLU (Hendrycks and Gimpel, 2016; Elfwing et al., 2018) with far less prevalent zero-out behav- ior (Mirzadeh et al., 2024), limiting these methods’ applicability. Further, Gated-MLP structures, now widely adopted as FFNN layers (Shazeer, 2020; Dauphin et al., 2017), introduce more co... | https://arxiv.org/abs/2505.17701v1 |
associated with near-zero activations rather than exact zeros. While these adaptations improved compatibility with modern LLM architectures, they remain fundamentally constrained by their narrow focus on local patterns around non-linear trans- formations, overlooking potential sparsity from a global perspective of the ... | https://arxiv.org/abs/2505.17701v1 |
as a weighted sum over W⊺ downrow vectors, where coefficient s[i] = (x·Wup)⊙σ(x·Wgate) reflects thei-th row vector’s contribution to computation. The magnitude of these coefficients provides a nat- ural metric for determining which parameters to activate, as they quantify each vector’s significance to the output. Fur... | https://arxiv.org/abs/2505.17701v1 |
reduce overhead, we avoid comput- ingτk Mdynamically for each input. Instead, weapproximate it with a layerwise constant ˆτk M= 1 TPT t=1Quantile (k,|u(t)|)estimated during a cal- ibration phase with Tsampled inputs. In contrast, implementing D-C OUNT DOWN poses greater challenges because its indicator s requires nearl... | https://arxiv.org/abs/2505.17701v1 |
param- eters: Llama-3.1-8B-Instruct (Grattafiori et al., 2024), gemma-2-9b-it (Gemma Team et al., 2024), Qwen2.5-14B-Instruct (Qwen et al., 2024), and phi- 4 (Abdin et al., 2024). We test multiple sparsity ratios by varying kfrom 0.7 to 0.9, representing the fraction of parameters excluded from compu- tation. Implement... | https://arxiv.org/abs/2505.17701v1 |
the SPPracsetting, we excluded it from subsequent experiments. In the SPPracsetting, D-C OUNT DOWN expe- riences performance loss relative to the SPIdeal due to predictor sub-optimality, suggesting bet- ter prediction strategies are needed to fully real- ize its potential in deployment. In contrast, M- COUNT DOWN , tha... | https://arxiv.org/abs/2505.17701v1 |
show thatM-C OUNT DOWN consistently achieves better performance. To understand the performance gap between the indirect coefficient vectors uandh, we conduct a comparative analysis of how each influences and aligns with the oracle-like refer- ence signal s, the direct coefficient used in D- COUNT DOWN . To enable direc... | https://arxiv.org/abs/2505.17701v1 |
Despite u’s strong CIF and CAF scores, substantial mismatches with respect to the oracle mask Skremain, with peak CIF values reaching only about 0.6 and CAF values about 0.4, underscoring the need for future work to translate D-C OUNT DOWN ’s upper-bound potential into SPPracdeployments. Possible Predictor Candidate: T... | https://arxiv.org/abs/2505.17701v1 |
robust performance even when skipping 90% of computations under ideal conditions. Limitations Like most prior work on sparse activation, our study assumes a single-batch greedy decoding set- ting in on-device environments. While this scenario 8 is realistic for latency-sensitive edge inference, it may be less applicabl... | https://arxiv.org/abs/2505.17701v1 |
Tianle Cai, Yuhong Li, Zhengyang Geng, Hongwu Peng, Jason D Lee, Deming Chen, and Tri Dao. 2024. Medusa: Simple LLM inference acceleration frame- work with multiple decoding heads. In Forty-first In- ternational Conference on Machine Learning, ICML 2024, Vienna, Austria, July 21-27, 2024 . OpenRe- view.net. Peter Clark... | https://arxiv.org/abs/2505.17701v1 |
Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph Gonza- lez, Hao Zhang, and Ion Stoica. 2023. Efficient mem- ory management for large language model serving with PagedAttention. In Proceedings of the 29th Sym- posium on Operating Systems Principles, SOSP 2023, Koblenz, Germany, October 23... | https://arxiv.org/abs/2505.17701v1 |
Radford, Aleksander M ˛ adry, Alex Baker-Whitcomb, Alex Beu- tel, Alex Borzunov, Alex Carney, Alex Chow, Alex Kirillov, Alex Nichol, and 400 others. 2024. GPT-4o system card. arXiv [cs.CL] . Qwen, An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, H... | https://arxiv.org/abs/2505.17701v1 |
Xu Han, Yankai Lin, Chaojun Xiao, Chenyang Song, Zhiyuan Liu, Zeyu Mi, and Maosong Sun. 2024. Relu2wins: Discovering efficient activation functions for sparse LLMs. arXiv preprint arXiv:2402. 03804 . 11 A Experimental Details A.1 Hyperparameters Name Low-Rank θA,θB BitLinear θternary Loss Binary Cross Entropy Spars... | https://arxiv.org/abs/2505.17701v1 |
; 31flag←tl.load (Mask)>0; 32Wdown←Wdown+(rm[:,None]×dmodel +rn[None,:]); 33S←S+rm ; 34w←tl.load (Wdown,mask=flag[:,None],other=0) ; 35x←tl.load (S); 36acc←tl.sum (w×x[:,None],0); 37Y←Y+rn ; 38tl.atomic_add (Y,acc);B.3 Kernel in Detail: D-C OUNT DOWN Algorithm 3: D-C OUNT DOWN Inference Kernel (Triton-style) Input: X, ... | https://arxiv.org/abs/2505.17701v1 |
FLOPs Across Methods D.3 Theoretical Memory Traffic Analysis Method Mem. R/W Explanation Dense2dmdi + 2dm + 2di +di +di + 2di +di +dmdi +di +dmRead Full Wup, Wgate Read x×2 Write gate, up Read gate Write act_gate Read act_gate, up Write inter Read Full Wdown Read inter Write y CATSdmdi +dm +di +di +di +di +di +di +di +... | https://arxiv.org/abs/2505.17701v1 |
0.800 0.750 0.384 0.649 0.8DEJA VU 0.346 0.246 0.296 0.599 0.581 0.548 0.262 0.000 CATS 0.490 0.366 0.486 0.788 0.696 0.604 0.328 0.105 M-COUNTDOWN 0.583 0.408 0.582 0.842 0.767 0.707 0.360 0.610 D-COUNTDOWN 0.604 0.421 0.599 0.851 0.796 0.728 0.374 0.624 0.9DEJA VU 0.356 0.246 0.303 0.616 0.573 0.523 0.264 0.000 CATS ... | https://arxiv.org/abs/2505.17701v1 |
phi-4 0.7CATS 0.675 0.539 0.417 0.613 0.801 0.795 0.724 0.322 0.856 M-COUNTDOWN 0.707 0.540 0.393 0.620 0.804 0.796 0.736 0.332 0.894 D-COUNTDOWN 0.687 0.471 0.368 0.485 0.750 0.733 0.685 0.294 0.208 0.8CATS 0.771 0.525 0.390 0.587 0.795 0.786 0.673 0.300 0.675 M-COUNTDOWN 0.799 0.527 0.381 0.607 0.793 0.784 0.715 0.33... | https://arxiv.org/abs/2505.17701v1 |
arXiv:2505.17704v1 [cs.CL] 23 May 2025SemSketches-2021: experimenting with the machine processing of the pilot semantic sketches corpus Maria Ponomareva♠,♡Maria Petrova♠Julia Detkova♠Oleg Serikov♡,♢Maria Yarova♣ ♠ABBYY , Moscow, Russia ♡National Research University Higher School of Economics, Moscow, Russia ♣Moscow Ins... | https://arxiv.org/abs/2505.17704v1 |
kind of tasks the semantic sketches can help to solve, as our further plan is to integrate the sketches into the General Internet-Corpus of Russian (GICR, [4], [3]), 4. to analyze what kind of mistakes we happen to face while creating the sketches. The idea to represent a word’s meaning in the form of the semantic sket... | https://arxiv.org/abs/2505.17704v1 |
evident advantage of the syntactic sketch is its vividness: it shows simultaneously all of the most frequent syntactic dependencies of a word and arranges them in a table according to the roles. At the same time, the syntactic sketches have one strong limitation: the grammatical information they are based on does not a... | https://arxiv.org/abs/2505.17704v1 |
for the verb «готовить:TO_PREPARE_FOOD_SUBSTANCE» (‘to prepare food, to cook’). Here the elements of the sketch are given with their rough translations. 3 The SemSketches Shared Task To explore the semantic sketches as far as their quality and representativeness are concerned, we have created the pilot corpus of Russia... | https://arxiv.org/abs/2505.17704v1 |
the threshold was rather high to preserve the quality of the sketches. At last, the final number of sketches in the pilot corpus became 915. Due to the exclusion of rare meanings, some verbs kept only one meaning in the sample, that is, the terminal verb list contained both polysemantic verbs with several meanings in t... | https://arxiv.org/abs/2505.17704v1 |
the semantic sketch for the verb «выходить:идти:TO_WALK» ‘to go out’). Here the elements of the sketch are given with their rough translations. ID dev.sent.rus.116 target наполнились start 46 end 57 context Когда доктор вошел, она вспыхнула, и глаза ее наполнились слезами Table 1: The example of the context. The positi... | https://arxiv.org/abs/2505.17704v1 |
and the stored MLM candidates. Score (sketch ,cont ) =⃒⃒⃒MLM1000cont∩Tokenssketch⃒⃒⃒ The intersection was performed over lemmas thus treating на заре and заря as intersecting entries. The weak baseline system has shown 0,0094 accuracy on the Dev data set thus overperforming the random baseline. 3.4 Submitted systems Th... | https://arxiv.org/abs/2505.17704v1 |
team suggested an original approach imitating the way humans guess the core of the anonymous sketch. It is worth mentioning that the approaches of paleksandrova andsmpl by design cannot disam- biguate the polysemous predicate, as they take only the target verb into account but not its context. The team smpl approach co... | https://arxiv.org/abs/2505.17704v1 |
evaluate the correlation between the actant and circumstantial dependencies in the sketches. As for the applied tasks, one of the promising directions in using the semantic sketches is their im- plementation for probing tasks for the pretrained language models. The interpretation of the linguistic knowledge encoded in ... | https://arxiv.org/abs/2505.17704v1 |
Luke. Deep Contextualized Word Representations // Proceedings of the 2018 Confer- ence of the North American Chapter of the Association for Computational Linguistics: Hu- man Language Technologies, V olume 1 (Long Papers). — New Orleans, Louisiana : Associa- tion for Computational Linguistics. — 2018. — June. — P. 2227... | https://arxiv.org/abs/2505.17704v1 |
language technologies ; #6. — San Rafael, Calif.] : Morgan & Claypool Publishers, 2010. — ISBN: 9781598298314 . [18] Petrova M.A. The Compreno Semantic Model: The Universality Problem // International Journal of Lexicography . — 2013. — 12. — V ol. 27, no. 2. — P. 105–129. — https://academic.oup.com/ijl/article- pdf/27... | https://arxiv.org/abs/2505.17704v1 |
arXiv:2505.17712v1 [cs.CL] 23 May 2025Understanding How Value Neurons Shape the Generation of Specified Values in LLMs Yi Su*,1,2, Jiayi Zhang*,1,3, Shu Yang†,1,2, Xinhai Wang1,2, Lijie Hu†,1,2, Di Wang1,2 1Provable Responsible AI and Data Analytics (PRADA) Lab 2King Abdullah University of Science and Technology3Univer... | https://arxiv.org/abs/2505.17712v1 |
have causal effects on model behaviors with causal mediation analysis techniques such as activa- tion patching (Zhang et al., 2024; Vig et al., 2020; Meng et al., 2022). Previous studies (Dai et al., 2022; Geva et al., 2021; Yu and Ananiadou, 2024a; Zhang et al., 2025c; Hong et al., 2024) demonstrate that neurons could... | https://arxiv.org/abs/2505.17712v1 |
neurons: Using ValueInsight, we propose ValueLocate to identify neurons in LLMs that are associated with specific values. Instead of relying on a one-sided analysis, our method takes both the positive and negative aspects of a single value into account. •Comprehensive analysis: To validate the ef- fectiveness of our ne... | https://arxiv.org/abs/2505.17712v1 |
to assess the values manifested by LLMs. We base the design of ValueInsight on the theoretical frame- work provided by the Schwartz Values Survey (Schwarz, 1992), which offers a well-established categorization of value factors, forming the bedrock of our dataset creation. See Appendix B for a de- tailed introduction. E... | https://arxiv.org/abs/2505.17712v1 |
to make too many decisions. Knowing what?s coming next gives you a sense of control...) Subvalue: Curious topic: Travel and adventure Situational question: You are on a cruise that offers a variety of excursions at each port. One of the excursions is a visit to a local village that is rarely visited by tourists, offeri... | https://arxiv.org/abs/2505.17712v1 |
2021): hl i=hl−1 i+Al i+Fl i, (1) where hl idenotes the output on layer l, position i, Al irepresents the output of the self-attention layer from multiple heads and Fl iis the output of the FFN layer. The FFN output is calculated by applying a non-linear activation function σon two Dense layers Wl 1andWl 2: Fl i=Wl 2σ(... | https://arxiv.org/abs/2505.17712v1 |
< 3% ml k·(1 +δ·γ), δ ≥3%(6) To induce a negative shift in the LLM value sys- tem, we invert the conditions in (6), suppressing positively associated neurons while amplifying neg- atively associated ones. 5 6 Experiments 6.1 Experimental Setup Datasets. During the evaluation phase, we select 100 questions related to ea... | https://arxiv.org/abs/2505.17712v1 |
This is because, in Schwartz’s value theory, Self- Enhancement and Openness to Change exhibit se- mantic overlap with Enjoying life, belonging to both dimensions. CGVST captures specific be- havioral tendencies directly through gradient varia- tions of special tokens, thereby avoiding confusion caused by abstract value... | https://arxiv.org/abs/2505.17712v1 |
the effect of manipulating the selected neurons. Effect of the Dynamic Scaling Factor. We first set the neuron difference threshold to 3% and in- vestigate the effect of the dynamic scaling factor γ. As shown in Figure 3 and Figure 16, increasing the γvalue, corresponding to a higher magnitude of neuron modification, c... | https://arxiv.org/abs/2505.17712v1 |
enhance neuron identification, we constructed ValueInsight, a dataset of 640 second- 8 person value descriptions and 15,000 scenario- based questions designed to uncover the value ori- entation based on the Schwartz Values Survey. Ex- periments on four LLMs consistently outperform baselines, demonstrating the effective... | https://arxiv.org/abs/2505.17712v1 |
Empirical Methods in Natural Lan- guage Processing , pages 5484–5495. Yihuai Hong, Yuelin Zou, Lijie Hu, Ziqian Zeng, Di Wang, and Haiqin Yang. 2024. Dissecting fine- tuning unlearning in large language models. In Pro- ceedings of the 2024 Conference on Empirical Meth- ods in Natural Language Processing , pages 3933– 3... | https://arxiv.org/abs/2505.17712v1 |
. Nino Scherrer, Claudia Shi, Amir Feder, and David Blei. 2024. Evaluating the moral beliefs encoded in llms.Advances in Neural Information Processing Systems , 36. Shalom H Schwartz, Gila Melech, Arielle Lehmann, Steven Burgess, Mari Harris, and Vicki Owens. 2001. Extending the cross-cultural validity of the theory of... | https://arxiv.org/abs/2505.17712v1 |
validated evaluation of llms values. arXiv preprint arXiv:2501.07071 . Junchi Yao, Hongjie Zhang, Jie Ou, Dingyi Zuo, Zheng Yang, and Zhicheng Dong. 2024. Fusing dynamics equation: A social opinions prediction algorithm with llm-based agents. arXiv preprint arXiv:2409.08717 . Zeping Yu and Sophia Ananiadou. 2024a. Inte... | https://arxiv.org/abs/2505.17712v1 |
{text} ### Score: prompt LLMs with value description to an- swer questions You will find a value description followed by a question below. I want you to forget who you are and fully immerse yourself in the value described, adopting not only their perspective but also their tone and attitude. With this new identity in m... | https://arxiv.org/abs/2505.17712v1 |
sharing 21 core items with the Rokeach Value Survey, while incor- porating enhanced theoretical modeling. Metric invariance analyses across 20 national samples con- firm sufficient psychometric equivalence in value conceptualization in diverse cultural contexts. B.1 Values in Schwartz Value Survey The Schwartz Values S... | https://arxiv.org/abs/2505.17712v1 |
(MRR, probability, and logarithmic prob- ability). Additionally, LPIP introduces a comple-mentary method to identify "query neurons" that activate these "value neurons," enhancing the un- derstanding of knowledge storage mechanisms in both attention and feed-forward network (FFN) lay- ers. D.2 QRNCA QRNCA (Query-Releva... | https://arxiv.org/abs/2505.17712v1 |
3.95±0.13 3.72±0.17 3.78±0.14 QRNCA 4.05±0.11 4.12±0.10 3.82±0.12 3.72±0.11 CGVST 4.15±0.09 4.22±0.08 3.92±0.10 3.82±0.09 ValueLocate 4.68±0.06 4.52±0.07 4.05±0.08 4.02±0.07 LLama-3.2-1B LPIP 4.22±0.13 4.28±0.11 3.82±0.15 3.82±0.14 QRNCA 4.32±0.10 4.38±0.09 4.00±0.12 3.75±0.11 CGVST 4.40±0.08 4.42±0.07 4.10±0.10 3.92±0... | https://arxiv.org/abs/2505.17712v1 |
This manuscript is currently under review at IEEE Transactions on Neural Networks and Learning Systems (TNNLS), Manuscript ID : TNNLS - 2025 -P-41043. PPO-BR: Dual -Signal Entropy -Reward Adaptation for Trust Region Policy Optimization Ben Rahman Faculty of Communication and Information Technology NASIONAL UNIVERSITY ,... | https://arxiv.org/abs/2505.17714v1 |
which this version may no longer be accessible. The author is with the Department of Computer Science, Universitas Nasional, Jakarta 12520, Indonesia (e -mail: benrahman@ civitas.unas.ac.id ). This is a single -author contribution. All algorithmic designs, theoretical developments, and experimental validations were per... | https://arxiv.org/abs/2505.17714v1 |
• KL-PPO [6] fails to adapt to behavioral phases, while Annealed PPO [7] relies on heuristic decay without policy awareness (Appendix Table A3) . • DD-PPO [5] fundamentally misaligns with non -stationary environments by blindly trusting reward discriminators, while Annealed PPO [7] fails catastrophically in sparse -rew... | https://arxiv.org/abs/2505.17714v1 |
REGION ADAPTATION STRATEGIES IN POLICY OPTIMIZATION METHODS Method Adaptation Signal Theoretical Bounds? Compute Overhead Runtime vs PPO (↑ = Slower) SAC [3] Entropy - only No Low +25% (Actor -Critic + Entropy Max) DD-PPO [5] Reward - only Partial High (+19%) +22% (Discriminator Forward Pass) Annealed PPO [7] Heuristic... | https://arxiv.org/abs/2505.17714v1 |
have explored entropy -based [8] or reward -sensitive [9] regularization strategies. However, they often treat exploration and convergence independently and rarely integrate their signals into the core optimization process. We argue that policy entropy and reward progression represent complementary indicators of learni... | https://arxiv.org/abs/2505.17714v1 |
typically benefit from aggressive exploration to discover diverse and p otentially This manuscript is currently under review at IEEE Transactions on Neural Networks and Learning Systems (TNNLS), Manuscript ID : TNNLS - 2025 -P-41043. optimal policies. PPO -BR leverages policy entropy as a proxy for exploration intensit... | https://arxiv.org/abs/2505.17714v1 |
adaptation, in contrast to entropy -only methods [8] or reward -only approaches like Discriminator -Driven PPO (DD -PPO) [5]. By jointly optimizing exploration and s tability, PPO-BR enables more context -sensitive learning. Second, PPO -BR is computationally efficient, requiring no auxiliary networks as in [5] or manu... | https://arxiv.org/abs/2505.17714v1 |
critic. Optimization is performed using Adam with a learning rate of 3e -4 and a batch size of 64. The base threshold 𝜖0 is set to 0.2 for all PPO variants. For PPO -BR, the default hyperparameters are 𝜆1 = 0.5 and 𝜆2 = 0.3, selected via a coarse search across [0.1, 1.0] (see Appendix Table A1 for details). Each exp... | https://arxiv.org/abs/2505.17714v1 |
a slightly higher exploration weight ( 𝜆1 = 0.7) due to its sparse reward nature (see Appendix Table A2). To support reproducibility, all implementation code, configuration files, and environment wrappers will be open sourced upon publication. PPO -BR’s lightweight design and strong stability suggest potential for rea... | https://arxiv.org/abs/2505.17714v1 |
entropy -driven adaptation contributes over 70% of initial learning gains, while reward -based contraction reduces variance by up to 80% in the final stages of training. This synergy enables PPO-BR to dynamic ally balance exploration and stability across episodes. Theoretical -Practical Synergy. The adaptive rule is bo... | https://arxiv.org/abs/2505.17714v1 |
low -variance adaptive mechanism makes it especially suited for safety -critical applications, such as robotic surgery or multi -agent drone coordination. In preliminary internal tests, PPO -BR demonstrated stable learning in a simulated surgical robo t arm with constrained action spaces (see Appendix E) Immediate next... | https://arxiv.org/abs/2505.17714v1 |
: Reproducibility Checklist • Code will be publicly released at: github.com/ppo -br/ppo -br-release • All results are averaged over 5 seeds • Full configs + training logs are archived and versioned • All environment wrappers use Gym v0.26 API standard . Appendix E : Real-World Applicability: Robotic Arm Control with PP... | https://arxiv.org/abs/2505.17714v1 |
reinforcement learning for continuous control,” in Proc. Int. Conf. Mach. Learn. (ICML), 2 016, pp. 1329 –1338. [3] J. Schulman et al., “Proximal policy optimization algorithms,” arXiv preprint arXiv:1707.06347, 2017. [4] I. Kostrikov et al., “Discriminator -actor -critic: Addressing sample inefficiency and reward bias... | https://arxiv.org/abs/2505.17714v1 |
arXiv:2505.17733v1 [cs.CL] 23 May 2025The Pilot Corpus of the English Semantic Sketches Maria Petrova ABBYY Moscow, Russia m.petrova@abbyy.comMaria Ponomareva HSE, ABBYY Moscow, Russia maria.ponomareva@abbyy.com Alexandra Ivoylova RSUH, MIPT, ABBYY Moscow, Russia aleksandra.ivoilova@abbyy.com Abstract The paper is devo... | https://arxiv.org/abs/2505.17733v1 |
graph, where the type of the links is considered as well. The BERT (Devlin et al., 2019) contextual embeddings, which followed the static vectors and became a state-of-the-art solution for meaning representation, also rely on the idea of expressing word semantics through its context, using the objective of masked langu... | https://arxiv.org/abs/2505.17733v1 |
by the Compreno parser, which includes not only actant dependencies, but all possible links. In the Compreno model, all words are presented in the form of a thesaurus-like semantic hierarchy, which consists of the semantic classes (semantic fields), and a set of the semantic roles for the classes (for detail, see (Anis... | https://arxiv.org/abs/2505.17733v1 |
The insufficient number of links leads to partly ‘empty’ sketches, where the semantic roles contain very few fillers, up to only one. So when the semantic role column is partly empty, it can mean that the number of the role’s links in the corpus turned out to be poor (for instance, see the [Cause_Actant] slot in the sk... | https://arxiv.org/abs/2505.17733v1 |
to the semantic model of the given verb in Compreno – namely, the set of the semantic slots with the necessary surface realisation, the fillers of the semantic slots, and their status (which marks the role as more or less preferable). When the model or the statistics give improper results, the semantic role of the depe... | https://arxiv.org/abs/2505.17733v1 |
differ significantly; (c) the semantic field where the considered verbs belong is structured differently in different languages. 5.1 Different semantic roles in the equivalent sketches from different languages Frequently, the semantic role sets in the parallel sketches do not coincide completely. It concerns both the a... | https://arxiv.org/abs/2505.17733v1 |
role (place trust/hope vs pay a visit vs play a joke/trick vs take a look/try/walk/etc., and so on). As fig. 10, 11, 12 and 13 demonstrate, the compatibility of the verbs ‘do’ and ‘ делать ’ is rather wide, while ‘играть ’ combines with only four Object fillers. Figure 10: The sketch for the verb ‘to do:TO_COMMIT’ Figu... | https://arxiv.org/abs/2505.17733v1 |
English semantic sketches. As the sketches are provided with their semantic parallels in Russian, we have also illustrated what kind of comparative studies the sketches allow to conduct, especially as far as the differences in the semantic roles and their typical fillers are concerned. An important point is the ability... | https://arxiv.org/abs/2505.17733v1 |
arXiv:2505.17746v1 [cs.CL] 23 May 2025Fast Quiet-STaR: Thinking Without Thought Tokens Wei Huang1Yizhe Xiong2,3Xin Ye4 Zhijie Deng5Hui Chen2,3Zijia Lin2Guiguang Ding2,3 1School of Computer Science, Beijing University of Posts and Telecommunications 2Tsinghua University 3Beijing National Research Center for Information ... | https://arxiv.org/abs/2505.17746v1 |
2021). Recently, Quiet-STaR (Quiet Self-Taught Rea- soner) (Zelikman et al., 2024) has been proposed as a novel reasoning paradigm that shifts the think- ing process from the problem level to a finer, token-level granularity. In Quiet-STaR, before predicting the next token, the model first gener- ates a sequence of int... | https://arxiv.org/abs/2505.17746v1 |
that compresses token-level thought traces to significantly reducing inference over- head while preserving the strong reasoning abilities imparted by the Quiet-STaR frame- work. •We introduce a curriculum learning-based multi-stage training strategy that progressively guides the model to learn a more concise thought tr... | https://arxiv.org/abs/2505.17746v1 |
the task distribution and pro- gressively increases task complexity. Moreover, curriculum learning has also been employed to im-prove LLMs’ understanding of long contexts by gradually increasing the context window size dur- ing training (Grattafiori et al., 2024b). Kimi 1.5 (Team et al., 2025) integrates a curriculum l... | https://arxiv.org/abs/2505.17746v1 |
before thought, and pthought i represents the logits after thought, w.r.t the token xi. 3.1.3 Learn Process Quiet-STaR leverages the REINFORCE algorithm (Phan et al., 2023) to optimize thought selection based on utility. It maximizes the log-likelihood of the next m(the number of ahead tokens) ground- truth tokens Xj+1... | https://arxiv.org/abs/2505.17746v1 |
training pro- gresses, we gradually reduce the number of thought tokens, thereby encouraging the model to engage in more concise and abstract reasoning under in- creasingly constrained resources. This encourages the model to progressively adapt to more difficult reasoning setting, enhancing both its reasoning ef- ficie... | https://arxiv.org/abs/2505.17746v1 |
50.7 10.0 43.1 Quiet-STaR 8 4 0.305 49.1 42.2 39.3 7.2 34.5 Fast Quiet-STaR 8 4 0.305 56.9 51.1 49.0 9.8 41.7 Fast Quiet-STaR-NTP 1 1 0.028 55.0 50.1 49.2 9.6 41.0 Performance ∆ - - - +9.1 +8.5 +13.8 +4.7 +9.0 Qwen2.5-7B Pre-Trained 1 1 0.026 70.1 60.7 52.4 11.6 48.7 Quiet-STaR 16 8 0.633 77.6 68.1 66.5 17.7 57.5 Quiet... | https://arxiv.org/abs/2505.17746v1 |
in addressing both the latency of Quiet-STaR and the poor performance of standard pre-trained models. 25 50 75 100 125 150 175 200 Step Number3032343638404244Average Accuracy(%) Variation of Average Accuracy with Training Steps Quiet-ST aR 16-8 Fast Quiet-ST aR 12-4 Fast Quiet-ST aR 8-4 Quiet-ST aR 8-4 Rev Quiet-ST aR ... | https://arxiv.org/abs/2505.17746v1 |
its log-likelihood loss as the reference for computing rewards in rein- forcement learning, resulting in Quiet-STaR NTP. As shown in Table 3, this shortcut results in a 5.2% drop in average accuracy compared to Fast Quiet- STaR NTP obtained through the full curriculum. These results underscore the critical role of the ... | https://arxiv.org/abs/2505.17746v1 |
observa- tions indicate that the incorporation of a curriculum learning strategy—progressing from easier to more difficult—enables Fast Quiet-STaR to gradually acquire the ability to perform effective reasoning under resource constraints. 5 Conclusion In this paper, we proposed Fast Quiet-STaR, an efficient extension o... | https://arxiv.org/abs/2505.17746v1 |
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