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Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages 3835–3855, Toronto, Canada, July 2023. Association for Computational Linguistics. doi: 10.18653/v1/2023.acl-long.213. URL https://aclanthology.org/2023.acl-long.213/ . [27] W. K. Kirchner. Age differences in short-term reten... | https://arxiv.org/abs/2505.16381v1 |
positional encoding for transformers with linear complexity. In International Conference on Machine Learning , pages 7067–7079. PMLR, 2021. [43] I. Loshchilov and F. Hutter. Fixing weight decay regularization in adam. In International Conference on Learning Representations (ICLR) , 2018. https://openreview.net/forum? i... | https://arxiv.org/abs/2505.16381v1 |
C. Bhagavatula, and Y . Choi. Winogrande: An adversarial winograd schema challenge at scale. In The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artific... | https://arxiv.org/abs/2505.16381v1 |
A. N. Gomez, L. Kaiser, and I. Polosukhin. Attention is all you need, 2023. URL https://arxiv.org/abs/1706.03762 . [75] A. Veisi and A. Mansourian. Context-aware biases for length extrapolation, 2025. URL https://arxiv.org/abs/2503.08067 . [76] S. Yang. Deltanet explained (part ii), 2024. URL https://sustcsonglin.githu... | https://arxiv.org/abs/2505.16381v1 |
of length n+ 1, # [a1↔b1] [a2↔b2]. . .[an↔bn] where c0= # is the start token and c1= [a1↔b1], . . . , c n= [an↔bn]are “tokens” which indicates that position anis swapped with position bnat time n. (Hence there are 20 such possible swap tokens of the form [x↔y]for all pairwise x, y∈ {1, . . . , 5}such that x̸=y.) Given ... | https://arxiv.org/abs/2505.16381v1 |
this assumption to ensure that we are using same or weaker precision assumption with previous works on the circuit complexity of transformers (Merrill and Sabharwal [47], Chen et al. [7]and refs. therein). We can make the proof simpler in the above if we incorporate a O(logn)assumption since in this case the output of ... | https://arxiv.org/abs/2505.16381v1 |
that is a 0or1that follows wwithj≤i. This jis guaranteed to exist because in FFLM, the first token is always w. In this case, this term equals n. Using the same argument as the first layer, the attention becomes one-hot and the output of attention encode the value of last 0or1token following a w. By the definition of f... | https://arxiv.org/abs/2505.16381v1 |
Figure 5 presents the performance breakdown across sub-tasks and sequence lengths. Task descriptions are provided in Table 4. 20 LongBench-E Detailed results are presented in Table 5. Category Dataset0–4k 4–8k 8k-16k FoX FoX-PaTH PaTH RoPE FoX FoX-PaTH PaTH RoPE FoX FoX-PaTH PaTH RoPE QA2wikimqa 21.0 23.7 28.7 23.9 15.... | https://arxiv.org/abs/2505.16381v1 |
arXiv:2505.16385v1 [cs.CL] 22 May 2025Semantic Pivots Enable Cross-Lingual Transfer in Large Language Models Kaiyu He1,3, Tong Zhou1, Yubo Chen1,2, Delai Qiu4, Shengping Liu4, Kang Liu1,2,5, Jun Zhao1,2 1The Key Laboratory of Cognition and Decision Intelligence for Complex Systems Institute of Automation, Chinese Acade... | https://arxiv.org/abs/2505.16385v1 |
effectively improve cross-lingual ability. Pre- vious studies utilize extensive parallel corpora to improve LLMs’ cross-lingual ability (Yang et al., 2023). However, using excessive parallel data can have adverse effects, potentially due to catastrophic forgetting (Xu et al., 2023). Based on our findings, we aim to eff... | https://arxiv.org/abs/2505.16385v1 |
2019), mT5 (Xue et al., 2021), XGLM (Lin et al., 2022), Aya Ex- panse (Dang et al., 2024), BLOOM (Le Scao et al., 2023). These MLLMs are evaluated by various tasks, including classification task (Conneau et al., 2018; Edoardo M. Ponti, 2020; Lin et al., 2022), question answering (Artetxe et al., 2020), and natural lang... | https://arxiv.org/abs/2505.16385v1 |
2020; Edoardo M. Ponti, 2020), question answering (Lewis et al., 2019), common- sense reasoning (Lin et al., 2021), etc. However, these sentence-level evaluation methods rely on discrete decoding and string matching, making it difficult to discern subtle differences in cross- lingual ability between models. In contrast... | https://arxiv.org/abs/2505.16385v1 |
Japanese due to their structural similarities to Chinese and En- glish, respectively. Firstly, we select English words from the Oxford Dictionary and then translate them into the other four languages using Microsoft’s translation tool1. Then, we utilize DeepSeek2to generate distractors that exhibit a degree of sim- ila... | https://arxiv.org/abs/2505.16385v1 |
the token with the highest output probability in all layers as the semantic pivot. •Co-occurrence behavior: LLM directly in- fers the output from the input (input →out- put). •Semantic pivot behavior: LLM firstly ob- tains the semantic pivot in the intermediate layer from the input and then infers the output from the s... | https://arxiv.org/abs/2505.16385v1 |
tools for analyzing and revealing the con- tent of large-scale datasets, to get the number of documents containing the source word and the tar- get word in the Dolma dataset. Recognizing that identical strings for source and target words pre- clude accurate co-occurrence counts, we exclude instances where the source an... | https://arxiv.org/abs/2505.16385v1 |
quency in the background Frefrom the frequency of its co-occurrence with the source word Frexs and the target word Frext. We select the smaller of these two adjusted relative frequencies F(x)as the basis for ranking. F(x) = min Frexs−Fre,Frext−Fre We select the top 50 as candidate semantic pivots and prompt Deepseek ... | https://arxiv.org/abs/2505.16385v1 |
constructing this pre-training data in Figure 4. 5.2 Experiments To validate the effectiveness of our proposed method for enhancing cross-lingual ability, we con-fr zh ja doc Original 0.1% 0.3% 0.2% 34k Multilingual 1.7% 4.3% 2.5% 502k Ours 1.0% 6.0% 1.5% 450k Table 2: The distribution of the language in three types of... | https://arxiv.org/abs/2505.16385v1 |
token degreesortFigure 4: The process of constructing a semantic pivot-aware pre-training dataset used to improve the model’s cross-lingual ability. SettingTarget Language en fr zh ja Original dataset avg: 0.1420en 0.31 0.13 0.18 fr 0.34 0.08 0.11 zh 0.09 0.05 0.13 ja 0.11 0.06 0.12 Multilingual dataset avg: 0.1506en 0... | https://arxiv.org/abs/2505.16385v1 |
for Computational Linguistics , pages 4623–4637, Online. Association for Computational Linguistics. Sunit Bhattacharya and Ond ˇrej Bojar. 2023. Unveil- ing multilinguality in transformer models: Exploring language specificity in feed-forward networks. In Proceedings of the 6th BlackboxNLP Workshop: An- alyzing and Int... | https://arxiv.org/abs/2505.16385v1 |
Jennifer Dumas, Yanai Elazar, Yuling Gu, Jack Hessel, Tushar Khot, William Merrill, Jacob Daniel Morrison, Niklas Muennighoff, Aakanksha Naik, Crystal Nam, Matthew E. Peters, Valentina Pyatkin, Abhilasha Ravichander, Dustin Schwenk, Saurabh Shah, Will Smith, Emma Strubell, Nishant Subramani, Mitchell Wortsman, Pradeep ... | https://arxiv.org/abs/2505.16385v1 |
Jian Wu. 2024b. Unraveling babel: Ex- ploring multilingual activation patterns of llms and their applications. In Proceedings of the 2024 Con- ference on Empirical Methods in Natural Language Processing , pages 11855–11881. Y Liu. 2020. Multilingual denoising pre-training for neural machine translation. arXiv preprint ... | https://arxiv.org/abs/2505.16385v1 |
mance of large language models. arXiv preprint arXiv:2309.11674 . Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, and Colin Raffel. 2021. mT5: A massively multilingual pre-trained text-to-text transformer. In Proceedings of the 2021 Conference of the North American Cha... | https://arxiv.org/abs/2505.16385v1 |
0.32 0.34 0.39 0.38 0 0.3 0.32 0.34 0.24 0llama3 0.00.10.20.30.40.50.60.7 en fr ja zh T arget Languageen fr ja zhSource Language0 0.73 0.54 0.59 0.66 0 0.48 0.52 0.51 0.49 0 0.52 0.43 0.43 0.44 0aya-expanse-8b 0.00.10.20.30.40.50.60.7 en fr ja zh T arget Languageen fr ja zhSource Language0 0.46 0.22 0.3 0.48 0 0.18 0.2... | https://arxiv.org/abs/2505.16385v1 |
2. All distractors must be not repeat. 3. Ensure the distractors are linguistically correct. 4. Each distractor must be a single word. ### Steps to follow: 1. Under "answer", list all possible translations of the word. 2. Under "options", list the nine distractors. Make sure that distractors must never match any transl... | https://arxiv.org/abs/2505.16385v1 |
arXiv:2505.16392v1 [cs.CL] 22 May 2025Resource for Error Analysis in Text Simplification: New Taxonomy and Test Collection Benjamin Vendeville Université de Bretagne Occidentale Lab-STICC (UMR CNRS 6285) Brest, France benjamin.vendeville@univ-brest.frLiana Ermakova Université de Bretagne Occidentale HCTI, Brest France ... | https://arxiv.org/abs/2505.16392v1 |
information) [ 8], and simplification-specific mistakes such as misrepresenting claims or overgeneralizing concepts [6, 7]. Current efforts in evaluating errors in open-ended generation primarily focus on truthfulness with respect to the source document or world knowledge [ 8,11]. While effective for identifying certai... | https://arxiv.org/abs/2505.16392v1 |
submitted to the SimpleText track at CLEF 2024 [ 5,7]. There is a range of evaluation measures for text simplification against reference simplifications, yet none exhibits very high agreement with human labels of simplification quality [ 1]. Moreover, earlier analysis revealed a range of informa- tion distortion issues... | https://arxiv.org/abs/2505.16392v1 |
punctu- ation errors that don’t hinder comprehension. Redundancy Repeated sentences, parts of sentences, or groups of sentences that do not need to be repeated. More details about these errors, including examples, can be found in Appendix A.1 Fluency. 2.3 Alignment Errors Alignment errors occur when the model fails to ... | https://arxiv.org/abs/2505.16392v1 |
facts that contradict the source document. Formally, we define the set of faithful- ness hallucination Errors as: 𝐸𝑟𝑟𝑜𝑟𝑓𝑎𝑖𝑡ℎ𝑓𝑢𝑙𝑛𝑒𝑠𝑠 =𝐹𝑔𝑒𝑛∩𝐹𝑡𝑜𝑝𝑖𝑐∩𝐹𝑐𝑜𝑛𝑡 Factuality hallucinations Generated facts that contradict gen- eral knowledge. Formally, we define the set of factuality hallucination Err... | https://arxiv.org/abs/2505.16392v1 |
not qualify as errors. The two errors we define here arise from the problem of evaluating what constitutes important information in the context of the specific simplification goal . Therefore, we decided to group them under the Content relevance subcategory. In the end, these errors will need to be more properly define... | https://arxiv.org/abs/2505.16392v1 |
on LLMs. We have collected a total of 2,659 individual annotations from ten annotators, as detailed in Table 1. The annotations reveal a high error rate of 69.16% in the existing simplifications, with high variability in the occurrence of specific error types. Notably, Con- tradiction appears infrequently (0.86% occurr... | https://arxiv.org/abs/2505.16392v1 |
detection models focuses on three key ques- tions: •Can they accurately detect the presence of errors in ATS? •Can they effectively identify greater types of errors in ATS? •Can they effectively identify specific types of errors in ATS? Given that hallucination has been one of the most extensively studied errors in rec... | https://arxiv.org/abs/2505.16392v1 |
AUPRC of 0.24 for fluency, 0.16 for alignment, 0.29 for information, and 0.51 for simplification. Despite these relative improvements, the overall performance remains low. In particular,Table 4: Greater Error Detection AUPRC and Binary Detec- tion "Any Error" AUROC for evaluated models. Metric A. Fluency B. Alignment C... | https://arxiv.org/abs/2505.16392v1 |
D1.2. Overspecification 2,659 136 5.11 0.0404 0.0542 0.0464 0.0563 0.0517 0.0540 D2.1. Loss of Informative Content 2,659 520 19.56 0.1712 0.1970 0.1848 0.2066 0.1899 0.2252 D2.2. Out-of-Scope Generation 2,659 418 15.72 0.1056 0.1657 0.1803 0.2488 0.1517 0.2010 prevalent in ATS and that existing methods fail to detect t... | https://arxiv.org/abs/2505.16392v1 |
academic and industrial research. –Simplification: Current academic and industrial research is interested in autonomous vehicles. Current academic and industrial research is interested in autonomous vehicles. A.2 B. Alignment Category focus: Does the answer suggest that the model correctly interpreted the prompt, inclu... | https://arxiv.org/abs/2505.16392v1 |
and robotics, autonomous vehicles are currently the focus of academic and industrial research. <answer> –Simplification: We propose a data-driven imitation learning method for UAVs to navigate city streets by mimicking an expert pilot. A.4 D. Simplification Category focus: Does the answer suggest that the model under- ... | https://arxiv.org/abs/2505.16392v1 |
simplicity level (e.g., 3rd grade, 10th grade). •Example 1 Replacing Entities with a Broader Category : –Source: Insects like bees and butterflies are vital for polli- nation, which is essential for producing many fruits and vegetables. –Simplification: Insects are vital for pollination. •Example 2 Replacing a Specific... | https://arxiv.org/abs/2505.16392v1 |
Kazai, Franco Maria Nardini, Fabio Pinelli, Fabrizio Silvestri, and Nicola Tonellotto (Eds.). Springer, 425–433. https://doi.org/10.1007/ 978-3-031-88720-8_63 [4]Liana Ermakova, Sarah Bertin, Helen McCombie, and Jaap Kamps. 2023. Overview of the CLEF 2023 SimpleText Task 3: Simplification of Scientific Texts. In Workin... | https://arxiv.org/abs/2505.16392v1 |
Yang Liu (Eds.). Association for Computational Linguistics, Online, 9332–9346. https://doi.org/10.18653/v1/2020.emnlp-main.750 [14] J. R. Landis and G. G. Koch. 1977. The Measurement of Observer Agreement for Categorical Data. Biometrics 33, 1 (March 1977), 159–174. [15] Samuel Läubli, Rico Sennrich, and Martin Volk. 2... | https://arxiv.org/abs/2505.16392v1 |
2025-5-23 AceReason-Nemotron: Advancing Math and Code Reasoning through Reinforcement Learning Yang Chen∗‡, Zhuolin Yang*‡, Zihan Liu, Chankyu Lee, Peng Xu, Mohammad Shoeybi, Bryan Catanzaro, Wei Ping†‡ Abstract Despite recent progress in large-scale reinforcement learning (RL) for reasoning, the training recipe for bu... | https://arxiv.org/abs/2505.16400v1 |
process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3.3 Code-only RL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 3.3.1 Data Curation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 3.3.2 Training Proc... | https://arxiv.org/abs/2505.16400v1 |
RL and Code-only RL . . . . . . . . . . . . . . . . . . . . . . . . 10 4.3.5 Topic-wise Accuracy Analysis on LiveCodeBench v5 . . . . . . . . . . . . . . . . . . . . 11 4.3.6 False Positive and False Negative Rewards in Code RL Training . . . . . . . . . . . . . . 12 4.3.7 Does RL Improve pass@k or pass@1 over the SFT ... | https://arxiv.org/abs/2505.16400v1 |
as the DeepSeek-V3-671B (Liu et al., 2024) or Llama-3.1-Nemotron-Ultra-253B. The most recent release of Qwen3 adopts a similar strategy (Qwen, 2025). In this work, we demonstrate that large-scale reinforcement learning (RL) can significantly enhance the reason- ing capabilities of strong small- and mid-sized SFT models... | https://arxiv.org/abs/2505.16400v1 |
et al., 2024,). The release of OpenAI o1 (OpenAI, 2024), and especially the open-sourcing of DeepSeek-R1 (Guo et al., 2025), highlights the effectiveness of large-scale RL through rule-based verification. In the case of math problems with deterministic answers, models are required to output the final result in a specif... | https://arxiv.org/abs/2505.16400v1 |
generates a group of 𝐺rollouts, to ensure stable RL training and prevent entropy collapse. We also remove the KL divergence term by setting 𝛽= 0. We started RL experiments from distilled reasoning models such as DeepSeek-R1-Qwen-Distilled-7B/14B (Guo et al., 2025), ensuring that the experiments were conducted under c... | https://arxiv.org/abs/2505.16400v1 |
to be solvable without extensive reasoning, and downsample problems with responses of 2,000–4,000tokens to balance the dataset 5 AceReason-Nemotron: Advancing Math and Code Reasoning through Reinforcement Learning Table 1: Math-only RL improves code reasoning, demonstrating cross-domain generalization through reinforce... | https://arxiv.org/abs/2505.16400v1 |
(SFT) often results in poor performance on other domains. We used a batch size of 128, sampling 𝐺= 8responses per prompt for 8K length training and 16responses otherwise. We adopted a learning rate of 1×10−6with AdamW (Kingma, 2014), and set both the entropy loss coefficient and KL loss coefficient 𝛽to0. 6 AceReason-... | https://arxiv.org/abs/2505.16400v1 |
al., 2025). To ensure consistency and reproducibility, we follow the DeepSeek-R1 evaluation protocol, using a temperature of 0.6, top-pof0.95, and a maximum output length of 32,768tokens. 4.1.1. Math Evaluation We use a diverse math competition benchmarks, including AIME2024, AIME2025, MATH500 (Hendrycks et al., 2021),... | https://arxiv.org/abs/2505.16400v1 |
LLaMA-Nemotron-Ultra-253B (Bercovich et al., 2025), and o3-mini (OpenAI, 2024). 4.1.2. Code Evaluation For coding tasks, we evaluate our AceReason-Nemotron models on LiveCodeBench (Jain et al., 2024) v5 (20240801−20250201 ) and v6 ( 20250201−20250501 ) subsets, containing recently released AtCoder, LeetCode problems. W... | https://arxiv.org/abs/2505.16400v1 |
extra reasoning tokens capacity: within 500 RL steps, its average response length increases from 4K to around 6.5K tokens. At the same time, AIME24 accuracy improves sharply from 55%to62%, after which both response length and accuracy plateau. Extending the maximum response length further to 24K and 32K demonstrates a ... | https://arxiv.org/abs/2505.16400v1 |
150 200 250 300 Training Steps0.100.150.200.250.30Entropy Loss Training Method: Clip-Higher On-policy (c) Figure 3: Analysis of RL training: (a) response length during math-RL training, (b) GPU hours for different length extension configurations, and (c) entropy of output logits using on-policy RL training versus off-p... | https://arxiv.org/abs/2505.16400v1 |
problem topics. As expected, math-only RL provides greater gains on math-related coding tasks. both Math-RL and Code-RL. As shown in Fig. 4, we plot the accuracy for each topic before and after Math-RL and Code-RL. The figure shows that applying math-only RL enhances model performance across all coding problem topics, ... | https://arxiv.org/abs/2505.16400v1 |
wrongly learned policy is then penalized while evaluating against strong test cases in the testing set. 4.3.7. Does RL Improve pass@k or pass@1 over the SFT Model? Recent studies (Shao et al., 2024; Yue et al., 2025) suggest that RL primarily improves pass@1 accuray over SFT model ((DeepSeek-R1-Distilled) without signi... | https://arxiv.org/abs/2505.16400v1 |
From physical common sense to embodied reasoning. arXiv preprint arXiv:2503.15558 , 2025. 3 [3]Mislav Balunović, Jasper Dekoninck, Ivo Petrov, Nikola Jovanović, and Martin Vechev. Matharena: Evaluatingllmsonuncontaminatedmathcompetitions,February2025. URL https://matharena.ai/ . 7 [4]Akhiad Bercovich, Itay Levy, Izik G... | https://arxiv.org/abs/2505.16400v1 |
ACM SIGOPS 29th Symposium on Operating Systems Principles , 2023. 5 [18]Jia Li, Edward Beeching, Lewis Tunstall, Ben Lipkin, Roman Soletskyi, Shengyi Costa Huang, Kashif Rasul, LonghuiYu, AlbertJiang, ZijuShen, ZihanQin, BinDong, LiZhou, YannFleureau, GuillaumeLample, and Stanislas Polu. Numinamath. [https://huggingfac... | https://arxiv.org/abs/2505.16400v1 |
Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Y Wu, et al. DeepseekMath: Pushing the limits of mathematical reasoning in open language models. arXiv preprint arXiv:2402.03300 , 2024. 4, 6, 13 [35]Guangming Sheng, Chi Zhang, Zilingfeng Ye, Xibin Wu, Wang Zhang, Ru Zhang, Yanghua Peng, Haibin Lin, and Chua... | https://arxiv.org/abs/2505.16400v1 |
Reasoning through Reinforcement Learning User: How many strawberries do I need to collect 9 r’s? Model: <think> First, I need to understand what the user is asking. They want to know how many strawberries are needed to collect 9 r’s. I interpret r’s as the letter ’r’. Each strawberry is associated with a certain number... | https://arxiv.org/abs/2505.16400v1 |
etc., with public strong test cases, while most open-sourced coding datasets (e.g., TACO, APPs) suffer from noisy problem statements, self-contamination, and weak LLM synthetic test cases that are unreliable for RL training. To ensure data quality, we performed very strict filtering rules, by filtering out 1) Multi-sol... | https://arxiv.org/abs/2505.16400v1 |
arXiv:2505.16406v1 [cs.CL] 22 May 2025On the reliability of feature attribution methods for speech classification Gaofei Shen1, Hosein Mohebbi1, Arianna Bisazza2, Afra Alishahi1, Grzegorz Chrupała1 1Tilburg University, The Netherlands 2University of Groningen, The Netherlands {g.shen, h.mohebbi, a.alishahi }@tilburguni... | https://arxiv.org/abs/2505.16406v1 |
or token-based attribution scores for NLP, the continuous and high resolution nature of the speech signal means that naive application of basic attribution methods leads to noisy and hard-to-interpret results. The choice of feature at- tribution methods for a particular task matters, as does the con- ditions under whic... | https://arxiv.org/abs/2505.16406v1 |
Our experiments show that even though the target classifier models learn the tasks and agree on the vast majority of the inputs, the attribution agree- ment is generally quite low, and that acceptable levels of relia- bility are only reached in very few specific conditions. Our find- ings highlight the inadequacy of st... | https://arxiv.org/abs/2505.16406v1 |
Overall and Error Fleiss’ κon the test sets. studies on validity, the reliability of feature attribution for speech models remains largely unexplored. 3. Methods For speech classification, pre-trained models are typically paired with a lightweight feedforward neural network as a classification head and fine-tuned using... | https://arxiv.org/abs/2505.16406v1 |
also very high. Agreement for the small subset of data points where an error was made is moderate. Given small percentage of overall errors, we believe this means the behavior of the models we investigate in this paper are sufficiently similar. 3.2. ISA reliability metric To measure the reliability of feature attributi... | https://arxiv.org/abs/2505.16406v1 |
attribution scores at the word level using forced-alignment time stamps. We use Montreal Forced Aligner [31] to align our datasets with the provided transcriptions. To take varying word lengths into account, we mean-pool the attribution scores for each word. We also discard the non-word segments in the alignment. Granu... | https://arxiv.org/abs/2505.16406v1 |
Feature Ablation. Figure 2: Distributions of ISA scores for the CNN embedding in- put type, at different levels of aggregation. The rows are levels of granularity of aggregation, the columns are different tasks. Within each panel, each boxplot shows results from different at- tribution methods and the y-axis is the ISA... | https://arxiv.org/abs/2505.16406v1 |
5.1. Limitations and future directions While the scope of this paper is limited to assessing reliabil- ity, the methods found to be reliable will also need to be evalu- ated for validity. While previous works have used cross-method agreement as a proxy for validity, we believe that a more direct measure of alignment wi... | https://arxiv.org/abs/2505.16406v1 |
, Y . Gra- ham and M. Purver, Eds., Mar. 2024. [12] A. Radford, J. W. Kim, T. Xu, G. Brockman, C. Mcleavey, and I. Sutskever, “Robust speech recognition via large-scale weak supervision,” in Proceedings of the 40th ICML . PMLR, 2023. [Online]. Available: https://proceedings.mlr.press/v202/ radford23a.html [13] M. D. Ze... | https://arxiv.org/abs/2505.16406v1 |
Explaining the predictions of any classifier,” in Proceed- ings of the 22nd ACM SIGKDD ICKDDM , 2016. [27] Garofolo, John S., Lamel, Lori F., Fisher, William M., Pal- lett, David S., Dahlgren, Nancy L., Zue, Victor, and Fiscus, Jonathan G., “TIMIT Acoustic-Phonetic Continuous Speech Cor- pus,” 1993. [28] R. Ardila, M. ... | https://arxiv.org/abs/2505.16406v1 |
arXiv:2505.16408v1 [cs.CL] 22 May 2025From Surveys to Narratives: Rethinking Cultural Value Adaptation in LLMs M. Farid Adilazuarda1, Chen Cecilia Liu2, Iryna Gurevych2, Alham Fikri Aji1 1MBZUAI2UKP Lab, TU Darmstadt Abstract Adapting cultural values in Large Language Models (LLMs) presents significant challenges, part... | https://arxiv.org/abs/2505.16408v1 |
clusters in latent space (showing semantic similarities), poten- tially homogenizing nuanced cultural dimensions. but we acknowledge that culture is more nuanced, including sub-cultures within a group and intersectional factors such as ethnicity and religion (Adilazuarda et al., 2024). Ideally, cultural value adaptatio... | https://arxiv.org/abs/2505.16408v1 |
model’s responses rely entirely on cul- tural or multilingual knowledge that was encoded during pre-training. This can create systematic bi- ases when the training data is skewed toward domi- nant cultural paradigms, which may disadvantage underrepresented groups (Guo et al., 2024). 2.2 Cultural Value Adaptation via Fi... | https://arxiv.org/abs/2505.16408v1 |
a to- tal of 68607 multilingual, culturally sensitive texts annotated for offensiveness. MMLU. To evaluate the model’s general knowl- edge retention capabilities after cultural adaptation, we assess each adapter’s performance on factual question-answering tasks using MMLU (Mukher- jee et al., 2024). The MMLU dataset fo... | https://arxiv.org/abs/2505.16408v1 |
hence we define this asMi,iclear understanding of their performance and the impact of adaptation using WVS data, including cultural and knowledge-based interferences. 4.1 Performance Gains Driven by Enhanced Instruction Following General Observations. Table 2 compares the ap- proaches for downstream tasks using Llama-3... | https://arxiv.org/abs/2505.16408v1 |
in Figure 2. The C-D ISTscore (introduced in §3) remains below 0.80for both models. The results further suggest that WVS is not nec- essarily the best data source for improving cul- tural values, as the adapted models fail to preserve their own culture’s perspectives, leading to com- promised cross-cultural result impr... | https://arxiv.org/abs/2505.16408v1 |
this seldom produces strong diagonal patterns, indicating limited cultural spe- cialization. A critical question is what additional data could enhance cultural value adaptation and preserve cultural distinctiveness? Humans exhibit gaps in what they “think”, and how they “behave” (Gross and Niman, 1975; Fazio, 1981, int... | https://arxiv.org/abs/2505.16408v1 |
(KDE). The results for WVS, Wikipedia and NormAd are shown in Fig- ure 1 and Figure 4 respectively. It is interesting to note that there is no distinct separation between cultures within a dataset. This suggests that seman- tic differences in the data are not the primary factor influencing downstream differences after ... | https://arxiv.org/abs/2505.16408v1 |
pluralistic alignment method, which primarily focuses on integrating diverse opinions. This re- search direction differs from typical existing cul- tural value adaptation work, which mainly focuses on reflecting the averaged value of a culture (Li et al., 2024a,b; Tao et al., 2024; AlKhamissi et al., 2024; Choenni et a... | https://arxiv.org/abs/2505.16408v1 |
to develop more nuanced methodologies and evaluation frameworks that better represent cultural diversity in NLP systems. References 2019. Turkish Spam V01. UCI Machine Learning Repository. DOI: https://doi.org/10.24432/C5WG7F. Muhammad Farid Adilazuarda, Sagnik Mukherjee, Pradhyumna Lavania, Siddhant Shivdutt Singh, Al... | https://arxiv.org/abs/2505.16408v1 |
alignment: Improving alignment of cultural values in llms via in-context learning. CoRR , abs/2408.16482. Shammur Absar Chowdhury, Hamdy Mubarak, Ahmed Abdelali, Soon-gyo Jung, Bernard J Jansen, and Joni Salminen. 2020. A multi-platform arabic news com- ment dataset for offensive language detection. In Proceedings of t... | https://arxiv.org/abs/2505.16408v1 |
Linguistics. Elisabetta Fersini, Paolo Rosso, Maria Anzovino, et al. 2018. Overview of the task on automatic misog- yny identification at ibereval 2018. Ibereval@ sepln , 2150:214–228. Clifford Geertz. 2017. The interpretation of cultures . Basic books. Steven Jay Gross and C Michael Niman. 1975. Attitude- behavior con... | https://arxiv.org/abs/2505.16408v1 |
˘glı. 2021. Detecting abusive instagram comments in turk- ish using convolutional neural network and machine learning methods. Expert Systems with Applications , 174:114802. Jean Lee, Taejun Lim, Heejun Lee, Bogeun Jo, Yangsok Kim, Heegeun Yoon, and Soyeon Caren Han. 2022. K-MHaS: A multi-label hate speech detection da... | https://arxiv.org/abs/2505.16408v1 |
L Alfonso Urena Lopez, and María-Teresa Martín- Valdivia. 2021. Offendes: A new corpus in spanish for offensive language research. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021) , pages 1096–1108. Aida Ramezani and Yang Xu. 2023. Knowledge of cultural moral... | https://arxiv.org/abs/2505.16408v1 |
490:462–481. Hoyun Song, Soo Hyun Ryu, Huije Lee, and Jong C Park. 2021. A large-scale comprehensive abusive- ness detection dataset with multifaceted labels from reddit. In Proceedings of the 25th Conference on Computational Natural Language Learning , pages 552–561. Taylor Sorensen, Jared Moore, Jillian Fisher, Mitch... | https://arxiv.org/abs/2505.16408v1 |
Çöltekin. 2020a. Semeval-2020 task 12: Multilingual offensive language identification in social media (offenseval 2020). In Proceedings of the Fourteenth Workshop on Semantic Evaluation, SemEval@COLING 2020, Barcelona (online), December 12-13, 2020 , pages 1425–1447. International Committee for Computa- tional Linguist... | https://arxiv.org/abs/2505.16408v1 |
approach to time and financial contributions among peers. "topic": "SOCIAL V AL- UES", "q_id": "29", "option": "1. Strongly agree 2. agree 3. Disagree 4. Strongly disagree", "q_content": "On the whole, men make better political leaders than women do"Chinese culture is one of the world’s oldest cultures, originating tho... | https://arxiv.org/abs/2505.16408v1 |
three training scenarios: zero-shot prompting and single-culture adaptation (training on Llama-3.1-8B models using WVS data). Evaluation uses a multilingual offensiveness dataset (§3.3), reported as averaged F1 scores. C.2 Full Invalid Ratio Methods Inv. Cult. (%) Inv. MMLU (%) Llama-3.1-8BZero-Shot 20.12 2.3 WVS 14.68... | https://arxiv.org/abs/2505.16408v1 |
sources based on the ranks of the adaptation results. C.5 Normalized Scores Tables Adapter Cult. ara ben zho eng deu ell kor por spa tur ara 0.4209 0.6882 0.7343 0.6578 0.5337 0.8640 0.6284 0.6758 0.4780 0.5645 ben 0.4156 0.6237 0.5984 0.7223 0.5213 0.8598 0.5595 0.6062 0.5466 0.5148 zho 0.6986 0.7371 1.0000 0.7862 0.6... | https://arxiv.org/abs/2505.16408v1 |
43.93 30.24 40.46 Table 13: Performance on MMLU when training each adapter with different WVS cultural data. Baseline refers to fine-tuning using English-language cultural value data with the Llama-3.1-8B andLlama-3.1-8B-IT models. Translated represents training with WVS cultural values translated into the respective t... | https://arxiv.org/abs/2505.16408v1 |
71,019 645,018 11.01% Table 15: Invalid count and ratio for different models and scenarios. E Prompts Table 16 summarizes the prompt templates used by the getPrompt function for various tasks. Task(s) Prompt Format offensive_detect , abusive_detect### Question: is the following sentence offensive: {input_txt}. "1. Offe... | https://arxiv.org/abs/2505.16408v1 |
102,705 Spanish 234 74,674 Chinese 134 35,988 English 209 82,144 Korean 27 6,784 German 76 21,209 Bengali 33 7,659 Portuguese 77 19,022 Greek 69 23,961 Turkish 35 15,391 Table 19: Summary statistics for each language in our NormAd training dataset. F.2 Test Data Statistics Following Li et al. (2024a), we break down our... | https://arxiv.org/abs/2505.16408v1 |
Confusion Matrix on Llama-3.1-8B arabic bengali chinese english german greek korean portuguese spanish turkish T est Culturearabic combined bengali chinese english german greek korean portuguese spanish turkishAdapter Culture10.96 17.86 39.43 16.24 24.79 35.24 26.45 19.02 29.45 21.77 33.44 23.24 28.39 17.12 36.75 15.11... | https://arxiv.org/abs/2505.16408v1 |
Tool-Star: Empowering LLM-Brained Multi-Tool Reasoner via Reinforcement Learning Guanting Dong1, Yifei Chen1, Xiaoxi Li1, Jiajie Jin1, Hongjin Qian2, Yutao Zhu1 Hangyu Mao3, Guorui Zhou3, Zhicheng Dou1∗, Ji-Rong Wen1 1Renmin University of China2BAAI3Kuaishou Technology {dongguanting, dou}@ruc.edu.cn Abstract Recently, ... | https://arxiv.org/abs/2505.16410v1 |
deeper integration of feedback from multiple tools, particularly search engines and code generators. The lack of systematic investigation into multi-tool collaborative reasoning presents a major bottleneck for practical TIR deployment. In this work, we aim to bridge this gap by addressing the following research questio... | https://arxiv.org/abs/2505.16410v1 |
to filter out unreasonable samples and organize the dataset from easy to hard. •Experiments on more than 10 challenging reasoning datasets validate the effectiveness of Tool-Star. Further quantitative analyses demonstrate its high efficiency, offering insights for incentivizing multi-tool collaborative reasoning. 2 Rel... | https://arxiv.org/abs/2505.16410v1 |
paradigm that facilitates multi-tool collaborative reasoning, comprising cold-start fine-tuning and a multi-tool self-critic RL algorithm (§3.2). We delve into the specifics of our approach as follows. Problem Formulation. Multi-tool Collaborative Reasoning aims to enable LLMs to perform multi- step reasoning through i... | https://arxiv.org/abs/2505.16410v1 |
tools to obtain feedback F, and insert the tool feedback—enclosed within <result> and </result> tags—back into the reasoning chain as additional context for subsequent generation steps. This process iterates until either (1) the maximum number of tool calls or length is reached, or (2) the model generates a final answe... | https://arxiv.org/abs/2505.16410v1 |
based on sample difficulty is crucial for effective learning. To achieve the above objectives, we first perform a language-only reasoning pass on each question in the dataset Dv2 tool, yielding a direct reasoning result denoted as Dv2 text. Based on the correctness of both direct reasoning (DR) and tool-integrated reas... | https://arxiv.org/abs/2505.16410v1 |
as follows: R= max( Acc. +rM, Acc. )If Format is Good & Acc.>0 0 If Format is Good & Acc.=0 −1 Otherwise, rM=( 0.1If∃(<search> &<python> ) 0 Otherwise(3) (3) Self-Critic RL Algorithm. Multi-tool RL algorithms involve complex reward structures, making it challenging for LLMs to discover optimal behavior. To addres... | https://arxiv.org/abs/2505.16410v1 |
Multi-tool Collaborative Inference During inference, we follow Eq.(2), enabling the model to autonomously use the search engine ,web browser agent , and Python interpreter . To improve TIR robustness, we introduce three inference-time mechanisms targeting common failure scenarios: •Code Execution Error: When generated ... | https://arxiv.org/abs/2505.16410v1 |
Tool-use Efficiency metric: TE=1 NPN i=1Si Tc iwhere Nis the number of datasets, SiandTc idenote the number of correct answers and the number of total samples for the i-th dataset when using tools. Baselines. Our experiments include the following three categories of baselines: (1) Proprietary Models : Qwen2.5 [ 49], Ll... | https://arxiv.org/abs/2505.16410v1 |
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