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states. Surgery for Obesity and Related Diseases , 20:425–431, 2024. doi: 10.1016/j.soard.2023.11.005. Lauren A. David, Ivana Sijercic, and Stephanie E. Cassin. Preoperative and post-operative psychosocial interventions for bariatric surgery patients: A systematic review. Obesity Reviews , 21:e12926, 2020. doi: 10.1111... | https://arxiv.org/abs/2505.16102v2 |
Wu, Yusuf Abdulle, and Honghan Wu. MedExQA: Medical question answering benchmark with multiple explanations. In Dina Demner-Fushman, Sophia Ananiadou, Makoto Miwa, Kirk Roberts, and Junichi Tsujii, editors, Proceedings of the 23rd Workshop on Biomedical Natural Language Processing , pages 167–181, Bangkok, Thailand, Au... | https://arxiv.org/abs/2505.16102v2 |
with multimodal learning. In Tristan Naumann, Asma Ben Abacha, Steven Bethard, Kirk Roberts, and Danielle Bitterman, editors, Proceedings of the 6th Clinical Natural Language Processing Workshop , pages 339–345, Mexico City, Mexico, June 2024. As- sociation for Computational Linguistics. doi: 10.18653/v1/2024.clinicaln... | https://arxiv.org/abs/2505.16102v2 |
URL https://arxiv.org/abs/2408.13533 . Junde Wu, Jiayuan Zhu, Yunli Qi, Jingkun Chen, Min Xu, Filippo Menolascina, and Vicente Grau. Medical graph rag: Towards safe medical large language model via graph retrieval- augmented generation, 2024b. URL https://arxiv.org/abs/2408.04187 . Weizi Wu, Teresa Graziano, Andrew Sal... | https://arxiv.org/abs/2505.16102v2 |
risks (e.g., gestational diabetes, weight regain), align- ing with known literature and conveying clinical nuance. Factual Precision. Table 6 (Example 2) evaluates responses to a straightforward proce- dural question. While other baselines provide either incomplete answers (e.g., Llama3-8B’s hallucinated boilerplate te... | https://arxiv.org/abs/2505.16102v2 |
regaining lost weight.2. **Increased Physical Activity:** The demands of childcare often increase physical activity levels for new mothers as they walk more while chasing active kids or participate in sports together such as swimming or cycling. Increased daily steps contribute positively toward metabolism improvement ... | https://arxiv.org/abs/2505.16102v2 |
around assistant The actual surgical time for sleeve gastrectomy, Roux-en-Y gastric bypass, and ad- justable gastric banding procedures usually takes about assistant Typically between one to three hours, depending on the type of procedure per- formed.” bRAGgen with Llama3-8BThe actual surgery time for a laparoscopic Ro... | https://arxiv.org/abs/2505.16102v2 |
surgery, consider alternative options like suggesting an alternative celebration method, declining the invitation, or bringing your own food. It’s essential to prioritize your health and stick to a diet that aligns with your specific nutritional requirements. Communicate your needs clearly and respectfully with your fr... | https://arxiv.org/abs/2505.16102v2 |
you are connecting to the official website and that any information you provide is encrypted and transmitted securely. An official web- site of the United States government Question: How does bariatric surgery affect patients with diabetes differently? An- swer: Bariatric surgery can significantly improve insulin sensi... | https://arxiv.org/abs/2505.16102v2 |
to work with your healthcare provider or a registered dietitian to determine your specific protein needs and ensure proper nutrition following bariatric surgery.” Llama3-8B (Context prompted)The protein requirement can vary depending on individual factors such as age, sex, weight, activity level, and health status. On ... | https://arxiv.org/abs/2505.16102v2 |
arXiv:2505.16104v1 [cs.CL] 22 May 2025Hierarchical Safety Realignment: Lightweight Restoration of Safety in Pruned Large Vision-Language Models Yue Li*1, Xin Yi*1, Dongsheng Shi1, Gerard de Melo2, Xiaoling Wang1, Linlin Wang†1 1East China Normal University, China 2University of Potsdam, Germany {yue_li, xinyi, dongshen... | https://arxiv.org/abs/2505.16104v1 |
to being removed by pruning technologies that pri- oritize utility importance as a pruning metric. This removal could result in a decline in the safety of the pruned model. To verify whether this problem exists, we used the Wanda pruning method to prune six mainstream LVLMs and compared their safety changes before and ... | https://arxiv.org/abs/2505.16104v1 |
the original. The attention heads causing the most significant changes, termed as the safety- critical heads, are selected for further analysis. At theneuron level (Section 2.2), we compute two importance scores for neurons: the safety impor- tance score based on a safety dataset and the utility importance score derive... | https://arxiv.org/abs/2505.16104v1 |
quantified using the Ships (D, hl i)metric: Ships (D, hl i) =rmaxX r=1cos−1 σr(U(r) θ, U(r) A) (4) where σrdenotes the r-th singular value. A larger main angle indicates that the safety representation has changed significantly, which represents the safety importance at the dataset level. We sub- sequently identify th... | https://arxiv.org/abs/2505.16104v1 |
Section 2.2.1. We select those weights with larger importance scores and consider them weights that contribute more to safety or utility. Specifically, given hyper- parameters q and p for safety and utility, respec- tively, we use Equations 9 and 10 to obtain the safety importance set Ssand utility importance set Suof ... | https://arxiv.org/abs/2505.16104v1 |
evaluate the safety of the model. The smaller the ASR, the better the safety. We use Llama-Guard-3-Vision (Chi et al., 2024) to determine whether the response is safe. We use ANLS and Acc as the evaluation indicators of DocVQA and MMbench respectively. Follow- ing Mathew et al.’s (2021) proposal, ANLS can ensure that m... | https://arxiv.org/abs/2505.16104v1 |
2: The safety and utility values of Wanda and HSR realigned Wanda pruned models for different LVLMs are shown. The better value for each LVLM is shown in bold . MethodSafety ↓ Utility ↑Restoration SafeBench Ch3Ef A VG RSR MMbench DocVQA A VG Qwen2.5-VL 1.40 2.35 1.88 - 87.02 94.51 90.76 - Wanda 2:4 14.40 13.46 13.93 - ... | https://arxiv.org/abs/2505.16104v1 |
20.25 22.45 23.95 34.52 24.72 10.13 2.04 8.38 14.29 6.74 16.88 20.07 21.76 23.41 15.54ASR 05101520253035 (b) Pruned by Wanda ↓ Discrimination T oxicity Illegal Harm PrivacyQ2.5-V Q2-V L-Mis L-Vic L-Lla Lla-V AVG7.59 14.29 16.77 15.48 7.87 17.72 20.41 22.16 15.48 12.36 16.46 16.33 19.16 14.29 21.35 11.39 22.45 26.35 27.... | https://arxiv.org/abs/2505.16104v1 |
masked in bold . Finally, we examine the impact of the hyper- parameter has presented in Table 7. Since each group attention head in LLaV A-NeXT-Mistral cor- responds to 4 query matrices, experiments are con- ducted in multiples of 4. The results show that safety performs best at h= 4, followed by signifi- cant fluctua... | https://arxiv.org/abs/2505.16104v1 |
calculated their Spearman’s rank correla- tion coefficient, obtaining 0.8857, which is near 1. This confirms that the total Ships strongly corre- lates with pruning-induced safety degradation . 6 Related Work Safety in LVLMs Many studies have researched methods to compromise the safety of LVLMs. Gong et al. (2023) intr... | https://arxiv.org/abs/2505.16104v1 |
the safety of the pruned model at an even lower scale. Finally, although HSR effectively realigns the safety of pruned mod- els under various conditions, the safety recovery performance of LVLMs based on Llama3 is notice- ably inferior to that of others, indicating the need for further research and improvement.Ethics S... | https://arxiv.org/abs/2505.16104v1 |
Yu Chong, Tang Yan- song, Lu Jiwen, and Chen Tao. 2024. Madtp: Multi- modal alignment-guided dynamic token pruning for accelerating vision-language transformer. IEEE Con- ference on Computer Vision and Pattern Recognition . Tian Jin, Michael Carbin, Dan Roy, Jonathan Frankle, and Gintare Karolina Dziugaite. 2022. Pruni... | https://arxiv.org/abs/2505.16104v1 |
Mittal, Mengdi Wang, and Peter Henderson. 2024. As- sessing the brittleness of safety alignment via prun- ing and low-rank modifications. arXiv preprint arXiv:2402.05162 . Chejian Xu, Wenhao Ding, Weijie Lyu, Zuxin Liu, Shuai Wang, Yihan He, Hanjiang Hu, Ding Zhao, and Bo Li. 2022. Safebench: A benchmarking plat- form ... | https://arxiv.org/abs/2505.16104v1 |
32 8 Lla-V True 40 32 8 Table 11: LVLM’s Grouped Query Attention (GQA) architecture: hidden layer count, attention heads per Layer, and equal key/value matrices per layer. C Average ASR and total Ships We report the average ASR drop and the total Ships ranking in descending order as shown in Table 12. To observe the re... | https://arxiv.org/abs/2505.16104v1 |
instructions is mediated by a single direc- tion) and the middle layers may play a more significant role in ensuring safety. •The O matrix exhibits a higher degree of dif- ferentiation between safety and utility. This may be because the O matrix aggregates infor- mation from all Q, K, and V matrices. F An example for H... | https://arxiv.org/abs/2505.16104v1 |
arXiv:2505.16107v1 [cs.CL] 22 May 2025MPL: Multiple Programming Languages with Large Language Models for Information Extraction Bo Li1, Gexiang Fang2, Wei Ye2*, Zhenghua Xu1*, Jinglei Zhang2, Hao Cheng3, Shikun Zhang2 1State Key Laboratory of Intelligent Power Distribution Equipment and System, School of Health Science... | https://arxiv.org/abs/2505.16107v1 |
Roy and Haridi, 2004; Dunn, 2019), making them better suited for structured generation tasks. Broadly speaking, existing works (Wang et al., 2022b; Guo et al., 2023; Li et al., 2023d; Sainz et al., 2023; Bi et al., 2024; Li et al., 2024c) mainly involve two components: 1) Code-style Simulation —where the textual input ... | https://arxiv.org/abs/2505.16107v1 |
at exploring the cooperation between different PLs when dealing with IE tasks. We conduct comprehensive experiments across a variety of IE tasks to evaluate the effectiveness of MPL. Specifically, we use widely used IE datasets for named entity recognition, relation extraction and event extraction to create datasets fo... | https://arxiv.org/abs/2505.16107v1 |
ever larger Tuesday with France and Germany denouncing the U.S. move toward war while British Prime Minister Tony ..."; List<Entity> EntityList = Named_Entity_Recognition(InputText); EntityList.add(new GPE("Europe")); EntityList.add(new GPE("Iraq")); ...} Textual Input Function Definition Virtual RunningFigure 2: Our f... | https://arxiv.org/abs/2505.16107v1 |
Running. Unlike previous approaches that solely rely on docstrings to guide models in processing inputs and generating outputs—a method that is somewhat unnatural for code-style inputs—our function-prompt incorporates a vir- tual running step. This step involves taking the textual input as input to the function and exe... | https://arxiv.org/abs/2505.16107v1 |
for RE. As for event-related tasks, we use ACE05-EAE and RAMS (Ebner et al., 2019) for EAE, and ACE05-EE for EE. The above datasets cover a wide range of topics and are suit- able for the SFT phase. Consistent with previous works (Sainz et al., 2023; Li et al., 2024c), the Micro-F1 score is used to evaluate performance... | https://arxiv.org/abs/2505.16107v1 |
in Appendix E.data-specific supervised fine-tuning, which uses small language models as backbone networks, such as BERT (Devlin et al., 2019) and RoBERTa (Liu et al., 2019), denoted as SLM. We present these reference models in the Appendix B. Furthermore, we have designed several model variants to conduct ablation stud... | https://arxiv.org/abs/2505.16107v1 |
and WikiEvents-EAE (Li et al., 2021). The re- sults of MPL-8B and GoLLIE-34B are presented in Table 3. On WikiEvents-NER, MPL achieves the highest score, despite having fewer parameters and undergoing lighter training. Even on datasets where GoLLIE-34B performs slightly better, the performance gap remains minimal. Thes... | https://arxiv.org/abs/2505.16107v1 |
and LLaAa3 Tech Report.5.2 Disentangling Programming Language Diversity from Ensemble Effects To rigorously assess whether the performance gains of our MPL framework stem from true program- ming language diversity or from general ensemble effects, we designed a series of controlled base- line variants. These include: (... | https://arxiv.org/abs/2505.16107v1 |
framework. We investigate single PL predictions and two aggregation methods: Vot- ing(Default), which combines predictions from all three PLs to determine the final output, and Union ,where a prediction is considered correct if any of the PLs produce the correct result, representing the union of all predictions. The re... | https://arxiv.org/abs/2505.16107v1 |
and zero-shot set- tings. For example, Li et al. (2023a); Han et al. (2023); Li et al. (2024b) evaluated LLMs’ perfor- mance and other dimensions to provide systemi- cally analysis, while Wadhwa et al. (2023); Xu et al. (2023); Zhu et al. (2023); Jiang et al. (2024) dis- cussed the opportunities and challenges for IE f... | https://arxiv.org/abs/2505.16107v1 |
References Zhen Bi, Jing Chen, Yinuo Jiang, Feiyu Xiong, Wei Guo, Huajun Chen, and Ningyu Zhang. 2024. Codekgc: Code language model for generative knowledge graph construction. ACM Transactions on Asian and Low-Resource Language Information Process- ing, 23(3):1–16. Bruno Blanchet. 1999. Escape analysis for object- ori... | https://arxiv.org/abs/2505.16107v1 |
Benjamin Van Durme. 2019. Multi-sentence argument linking. arXiv preprint arXiv:1911.03766 . Hermenegildo Fabregat, Juan Martinez-Romo, and Lourdes Araujo. 2018. Overview of the diann task: Disability annotation task. In IberEval@ SEPLN , pages 1–14. Chufan Gao, Xulin Fan, Jimeng Sun, and Xuan Wang. 2024. Promptre: Wea... | https://arxiv.org/abs/2505.16107v1 |
Codeie: Large code generation models are better few-shot information extractors. arXiv preprint arXiv:2305.05711 . Raymond Li, Loubna Ben Allal, Yangtian Zi, Niklas Muennighoff, Denis Kocetkov, Chenghao Mou, Marc Marone, Christopher Akiki, Jia Li, Jenny Chim, et al. 2023e. Starcoder: may the source be with you! arXiv p... | https://arxiv.org/abs/2505.16107v1 |
vol- ume 2. Springer. Shaden Smith, Mostofa Patwary, Brandon Norick, Patrick LeGresley, Samyam Rajbhandari, Jared Casper, Zhun Liu, Shrimai Prabhumoye, George Zerveas, Vijay Korthikanti, et al. 2022. Using deep- speed and megatron to train megatron-turing nlg 530b, a large-scale generative language model. arXiv preprin... | https://arxiv.org/abs/2505.16107v1 |
and future opportunities. arXiv preprint arXiv:2305.13168 . Appendix Appendix A: Detailed Results of Ablation Study In Table 11, we provide the detailed results of each model variant in the ablation study. Appendix B: The Reference Paper of Previous SoTA Method We compare our method with previous best SLM- based method... | https://arxiv.org/abs/2505.16107v1 |
55.0 ACE05-RE 66.1 67.1 68.0 64.3 67.3 70.1 ACE05-EAE 69.7 71.5 72.7 68.4 71.7 73.0 RAMS 49.7 50.0 49.9 49.1 50.2 49.3 ACE05-EE 70.1 70.3 70.8 68.5 70.5 71.8 Avg.Score 76.0 76.8 77.1 75.7 77.0 77.6 Table 10: We conducted extensive experiments on Qwen2.5-7B and QwenCoder2.5-7B to verify the generalization ability of MPL... | https://arxiv.org/abs/2505.16107v1 |
{ /* Task: Named entity recognition (NER) involves identifying and classifying named entities in text into predefined categories. `FAC`: A facility is a functional, primarily man-made structure. // Examples: "statue of liberty", "big ben", "christ the redeemer", "empire state building", "taj mahal" `GPE`: Geo-Political... | https://arxiv.org/abs/2505.16107v1 |
location to another, by (for example) carrying, pulling, or pushing the transported object. // Examples: "chevrolet camaro", "mercedes-benz g-class", "subaru outback", "harley- davidson motorcycle", "ford mustang", "boeing 747" `WEA`: A physical device that is primarily used as an instrument for physically harming or d... | https://arxiv.org/abs/2505.16107v1 |
arXiv:2505.16113v1 [cs.LG] 22 May 2025Tools in the Loop: Quantifying Uncertainty of LLM Question Answering Systems That Use Tools Panagiotis Lymperopoulos Tufts University plympe01@tufts.eduVasanth Sarathy Tufts University vasanth.sarathy@tufts.edu Abstract Modern Large Language Models (LLMs) often require external too... | https://arxiv.org/abs/2505.16113v1 |
tem might fail. Extending this notion to LLMs, uncertainty quantification over token sequences or meanings has emerged as a tool for determining 1 when to trust generated content [8, 3, 14]. Here, the uncertainty can be tied to the LLM’s internal representation of the sequence, which influences the model’s capacity to ... | https://arxiv.org/abs/2505.16113v1 |
ef- fective in detecting hallucinations and other in- consistencies in LLM-generated content, offering a pathway to more reliable responses. This method relies on entailment models to establish semantic classes of samples obtained from the LLM using the same prompt. Then, the uncertainty over semantic classes is estima... | https://arxiv.org/abs/2505.16113v1 |
them in our study and instead conduct con- trolled experiments aimed at assessing the efficacy of our framework in simple QA tasks that require calling tools. These more controlled environments allow us to study the more limited but still widely applicable setting when the tools provide known and reliable uncertainty e... | https://arxiv.org/abs/2505.16113v1 |
duced by the LLM, which may be estimated em- pirically through samples. The second term is the predictive entropy of the tool, which is assumed to be known. The final two terms are based on the pos- teriors p(z|y, a) and p(a|y, x). These terms present a major challenge as they are very difficult to esti- mate: They inv... | https://arxiv.org/abs/2505.16113v1 |
in the retrieval is low. Red indicates the final answer provided by the combined system. Within this framework we can quantify the uncertainty of the overall system answer, taking into account the uncertainty of the retreival system. the case where p(z|a) is a binary classifier, which receives an input vector of dfeatu... | https://arxiv.org/abs/2505.16113v1 |
this setting allows us to make some simplifications. First, we notice that since ystrongly depends on z, the distribu- tionp(z|y, a) is going to be low-entropy, contribut- ing only slightly to the computation. Additionally, since yis not very informative about a, the values H(a|x) and H(a|x, y) are likely to be similar... | https://arxiv.org/abs/2505.16113v1 |
quantification, due to the complexity of the queries and tools (e.g. writ- ing code) which are aimed at evaluating the abil- ity of systems to take advantage of tools to answer queries, rather than to quantify uncertainty. As a result we create 2 novel synthetic QA datasets based on the IRIS[4] and Pima Indians diabete... | https://arxiv.org/abs/2505.16113v1 |
Methodology To evaluate our framework we follow prior work and use AUROC as our evaluation metric [8, 1]. The intuition be- hind this criterion is to treat the uncertainty metric as a score expressing whether we should trust the LLM output, with high entropy values indicating we should not, and low entropy values indic... | https://arxiv.org/abs/2505.16113v1 |
mantic and predictive entropy. Additional training details for the posterior estimates are available in the supplemental material. We evaluate all metrics on the remaining 120 examples. We prompt using few-shot prompting with 3 examples both for the tool-calling step and for the final answer. Example prompts are availa... | https://arxiv.org/abs/2505.16113v1 |
experiment, we apply our framework to the RAG QA setting and quantify the uncertainty of the combined retrieval and LLM system. Experiment settings To compute uncertainties, we take 10 samples of the final answer from the LLMs after retrieving documents. We construct the document retrieval system in two stages: First, ... | https://arxiv.org/abs/2505.16113v1 |
integration of future advancements in uncertainty quantification without requiring signif- icant changes to the underlying framework. For in- stance, emerging methods for better capturing LLM uncertainties, such as refined logit-based entropy estimation or latent semantic representation mod- els, can be seamlessly inco... | https://arxiv.org/abs/2505.16113v1 |
gener- ation (RAG) systems yielded smaller performance gains compared to the tool-calling QA experiments. We hypothesize that this is because the LLMs used in the RAG task were able to answer some of the questions directly from their internal knowledge, without heavily relying on the retrieved documents. In contrast, t... | https://arxiv.org/abs/2505.16113v1 |
where LLM knowledge is limited. Finally, we provide extensive discussion of how our frame- work relates to different LLM agent architectures for tool-calling, how it can be extended to accom- modate them and possible challenges in that direc- tion. References [1] Neil Band, Tim GJ Rudner, Qixuan Feng, An- gelos Filos, ... | https://arxiv.org/abs/2505.16113v1 |
Xu, and Ji-Rong Wen. Tool learning with large language models: A survey. arXiv preprint arXiv:2405.17935 , 2024. [18] Timo Schick, Jane Dwivedi-Yu, Roberto Dessi, Roberta Raileanu, Maria Lomeli, Eric Hambro, Luke Zettlemoyer, Nicola Can- cedda, and Thomas Scialom. Toolformer: Language models can teach themselves to use... | https://arxiv.org/abs/2505.16113v1 |
SemioticReconstructionofDestinationExpectationConstructs:An LLM-DrivenComputationalParadigmforSocialMediaTourism Analytics HaotianLan,YaoGao,YujunCheng,WeiYuan*andKunWang* CollegeofHorticultureLandscapeArchitecture,NortheastAgriculturalUniversity, Harbin,China SemioticReconstructionofDestinationExpectationConstructs:An... | https://arxiv.org/abs/2505.16118v1 |
study(Table2).Thesecategoriesrepresentthecoreneedsoftouristsduringtravel,with theirproportionsandrelevancestandingoutacrossallsamples. Table2.DescriptionsofSelectedTourismExpectations TourismExpectation Description EmotionalExpectationFocusedonromanticormemorabletravelexperiences sharedwithlovedones,creatingemotionalre... | https://arxiv.org/abs/2505.16118v1 |
arXiv:2505.16125v1 [cs.CL] 22 May 2025KOBALT: K OREAN BENCHMARK FOR ADVANCED LINGUISTIC TASKS Hyopil Shin Seoul National University hpshin@snu.ac.krSangah Lee Seoul National University sanalee@snu.ac.krDongjun Jang Seoul National University qwer4107@snu.ac.krWooseok Song Seoul National University semojak@snu.ac.kr Jaey... | https://arxiv.org/abs/2505.16125v1 |
language, many of existing benchmarks have additional problems that they are constructed via translations of English benchmarks, which reduces their validity on the model competence. We introduce KoBALT (Korean Benchmark for Advanced Linguistic Tasks), comprising 700 linguistically motivated multiple-choice questions s... | https://arxiv.org/abs/2505.16125v1 |
reasoning. Works such as Linguini [Sánchez et al., 2024], IOLBench [Goyal and Dan, 2025], and LINGOLY [Bean et al., 2024] leverage problem formats derived from linguistics olympiads, emphasizing deductive reasoning through tasks like unseen language translation, cognate identification, and graphophonemic transcription.... | https://arxiv.org/abs/2505.16125v1 |
set of questions according to rigorous criteria. The sample questions of our benchmark is illustrated in Table 1 The data construction followed an expert-driven approach guided by five central methodological principles: Comprehensive Linguistic Coverage: To establish a taxonomically valid representation of Korean lingu... | https://arxiv.org/abs/2505.16125v1 |
mother’s intelligence, not mine.) 오답:갑:영화좋아하세요? /을:전활동적인취미가좋더라고요.외9건 (Incorrect: A: Do you like movies? / B: I prefer more active hobbies. plus 9 other options) Table 1: Sample Questions from KoBALT 4 KoBALT: Korean Benchmark for Advanced Linguistic Tasks 28.73.7 34.7 1.032.0Embedded Clauses (86) Ellipsis (11) Agreemen... | https://arxiv.org/abs/2505.16125v1 |
presented in Table 2. 3.2 Syntax (300 Questions) Our syntax component represents the largest subset of the benchmark, covering five phenomena fundamental to Korean syntactic structure: Agreement ,Argument Structure and Valency ,Embedded Clauses ,Ellipsis , and Scrambling . The first three are selected based on grammar ... | https://arxiv.org/abs/2505.16125v1 |
semantic concord between predicates and their arguments. Korean predicates impose specific semantic constraints on arguments, 6 KoBALT: Korean Benchmark for Advanced Linguistic Tasks including animacy requirements, intentionality features, and scalar properties that must be satisfied for well-formedness. Questions asse... | https://arxiv.org/abs/2505.16125v1 |
implied meaning of certain utterance in a given conversation, while some ask to choose a proper utterance when given a surrounding context and the intended meaning. Speech Acts(SA) evaluates comprehension of speech acts, including statement, expressive, promise, question, direc- tive/command and proposing. Korean emplo... | https://arxiv.org/abs/2505.16125v1 |
examines prosodic elements in Korean that crucially contributes to the meaning of an utterance, including vowel length distinctions (long vs. short), types of interrogatives, sentence-final endings, and intonation patterns. Questions assess models’ ability to identify vowel length or intonation contours and to determin... | https://arxiv.org/abs/2505.16125v1 |
3 27B Instruct [Team et al., 2025], Qwen 2.5 7B Instruct, Qwen 2.5 32B Instruct, Qwen 2.5 72B Instruct [Yang et al., 2024], Aya Expanse 8B, and Aya Expanse 32B [Dang et al., 2024]. For closed-source models, we evaluate GPT-4o [OpenAI et al., 2024], Claude 3.5 Sonnet [Anthropic, 2024], Claude 3.7 Sonnet [Anthropic, 2025... | https://arxiv.org/abs/2505.16125v1 |
Model Average Syntax Semantics Pragmatics Morphology Phonetics GPT-4o 0.44 0.45 0.55 0.40 0.17 0.26 C4ai-command-a-03 0.36 0.30 0.52 0.36 0.24 0.18 Claude-3-5-sonnet 0.52 0.52 0.65 0.51 0.36 0.24 Claude-3-7-sonnet 0.61 0.66 0.66 0.64 0.36 0.31 DeepSeek-V3-XL 0.47 0.49 0.56 0.42 0.24 0.29 DeepSeek-V3 0.43 0.41 0.57 0.42... | https://arxiv.org/abs/2505.16125v1 |
constituent morphemes, and derive word-internal structure. Based on the agglutinative nature of Korean, in which grammatical morphemes are affixed to convey additional meaning, one plausible generalization is that morphological patterns can be deduced when the root is retained after derivation or inflection, allowing m... | https://arxiv.org/abs/2505.16125v1 |
facts [Oh et al., 2024], formatting, and repetition [Hosking et al., 2024]. We leveraged responses from four large language models: two proprietary (Claude-3.5-Sonnet and GPT-4o) and two open-source (Qwen-2.5-72B and LLaMA-3.3-70B). Models were selected based on representativeness, performance, and parameter size. We g... | https://arxiv.org/abs/2505.16125v1 |
0.08 0.13 Aya-expanse-8b 0.09 0.08 0.25 C4ai-command-a-03 0.27 0.17 0.25 Qwen2.5-32B 0.18 0.25 0.25 Qwen2.5-72B 0.27 0.17 0.25 Qwen2.5-7B 0.14 0.33 0.13 Claude-3-5-sonnet 0.45 0.17 0.38 Claude-3-7-sonnet 0.41 0.17 0.50 DeepSeek-V3-XL 0.36 0.08 0.13 DeepSeek-V3 0.36 0.08 0.25 Gemma-2-9b 0.14 0.08 0.13 Gemma-3-27b 0.32 0... | https://arxiv.org/abs/2505.16125v1 |
as less confusing than those of their peers. One-Correct Scenario When exactly one model’s answer is correct, we compute each model’s win rate. Table 12 shows that Qwen-2.5-72B leads at 92.1%, followed by Claude-3.5-Sonnet at 91.5%. GPT-4o and LLaMA-3.3-70B record 87.7% and 82.0%, respectively, indicating that Qwen’s c... | https://arxiv.org/abs/2505.16125v1 |
KoBALT’s effectiveness as a measure of Korean linguistic competence. The difficulty-stratified question set provides a framework for analyzing both current and future models. Our work addresses the need for linguistically-motivated evaluation in typologically diverse languages. The main contributions of this work are a... | https://arxiv.org/abs/2505.16125v1 |
Chen, Sully Chen, Ruby Chen, Jason Chen, Mark Chen, Ben Chess, Chester Cho, Casey Chu, Hyung Won Chung, Dave Cummings, Jeremiah Currier, Yunxing Dai, Cory Decareaux, Thomas Degry, Noah Deutsch, Damien Deville, Arka Dhar, David Dohan, Steve Dowling, Sheila Dunning, Adrien Ecoffet, Atty Eleti, Tyna Eloundou, David Farhi,... | https://arxiv.org/abs/2505.16125v1 |
2024. URL https://arxiv.org/abs/2303.08774 . DeepSeek-AI, Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, Xiaokang Zhang, Xingkai Yu, Yu Wu, Z. F. Wu, Zhibin Gou, Zhihong Shao, Zhuoshu Li, Ziyi Gao, Aixin Liu, Bing Xue, Bingxuan Wang, Bochao Wu, Bei... | https://arxiv.org/abs/2505.16125v1 |
Parrish, Haokun Liu, Anhad Mohananey, Wei Peng, Sheng-Fu Wang, and Samuel R. Bowman. Blimp: The benchmark of linguistic minimal pairs for english. Transactions of the Association for Computational Linguistics , 8:377–392, 2019. URL https://api.semanticscholar.org/CorpusID:208527435 . 18 KoBALT: Korean Benchmark for Adv... | https://arxiv.org/abs/2505.16125v1 |
Hernandez, Danqi Chen, Daphne Ippolito, Dar Gilboa, David Dohan, David Drakard, David Jurgens, Debajyoti Datta, Deep Ganguli, Denis Emelin, Denis Kleyko, Deniz Yuret, Derek Chen, Derek Tam, Dieuwke Hupkes, Diganta Misra, Dilyar Buzan, Dimitri Coelho Mollo, Diyi Yang, Dong-Ho Lee, Dylan Schrader, Ekaterina Shutova, Ekin... | https://arxiv.org/abs/2505.16125v1 |
Robert Frank, Rohan Sikand, Roman Novak, Roman Sitelew, Ronan Le Bras, Rosanne Liu, Rowan Jacobs, Rui Zhang, Ruslan Salakhutdinov, Ryan Chi, Ryan Lee, Ryan Stovall, Ryan Teehan, Rylan Yang, Sahib Singh, Saif Mohammad, Sajant Anand, Sam Dillavou, Sam Shleifer, Sam Wiseman, Samuel Gruetter, Samuel R. Bowman, Samuel S. Sc... | https://arxiv.org/abs/2505.16125v1 |
Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, and Seung- 20 KoBALT: Korean Benchmark for Advanced Linguistic Tasks Hoon Na, editors, Proceedings of the 2... | https://arxiv.org/abs/2505.16125v1 |
Ning Zhang, Olivier Duchenne, Onur Çelebi, Patrick Alrassy, Pengchuan Zhang, Pengwei Li, Petar Vasic, Peter Weng, Prajjwal Bhargava, Pratik Dubal, Praveen Krishnan, Punit Singh Koura, Puxin Xu, Qing He, Qingxiao Dong, Ragavan Srinivasan, Raj Ganapathy, Ramon Calderer, Ricardo Silveira Cabral, Robert Stojnic, Roberta Ra... | https://arxiv.org/abs/2505.16125v1 |
Huang, Kunal Chawla, Kyle Huang, Lailin Chen, Lakshya Garg, Lavender A, Leandro Silva, Lee Bell, Lei Zhang, Liangpeng Guo, Licheng Yu, Liron Moshkovich, Luca Wehrstedt, Madian Khabsa, Manav Avalani, Manish Bhatt, Martynas Mankus, Matan Hasson, Matthew Lennie, Matthias Reso, Maxim Groshev, Maxim Naumov, Maya Lathi, Megh... | https://arxiv.org/abs/2505.16125v1 |
Kumar, Chris Perry, Chris Welty, Christopher A. Choquette-Choo, Danila Sinopalnikov, David Weinberger, Dimple Vijaykumar, Dominika Rogozi ´nska, Dustin Herbison, Elisa Bandy, Emma Wang, Eric Noland, Erica Moreira, Evan Senter, Evgenii Eltyshev, Francesco Visin, Gabriel Rasskin, Gary Wei, Glenn Cameron, Gus Martins, Had... | https://arxiv.org/abs/2505.16125v1 |
Dustin Zelle, Eric Noland, Erwin Huizenga, Eugene Kharitonov, Frederick Liu, Gagik Amirkhanyan, Glenn Cameron, Hadi Hashemi, Hanna Klimczak-Pluci ´nska, Harman Singh, Harsh Mehta, Harshal Tushar Lehri, Hussein Hazimeh, Ian Ballantyne, Idan Szpektor, Ivan Nardini, Jean Pouget-Abadie, Jetha Chan, Joe Stanton, John Wietin... | https://arxiv.org/abs/2505.16125v1 |
Accessed: 2025-05-01. Team Cohere, Aakanksha, Arash Ahmadian, Marwan Ahmed, Jay Alammar, Yazeed Alnumay, Sophia Althammer, Arkady Arkhangorodsky, Viraat Aryabumi, Dennis Aumiller, Raphaël Avalos, Zahara Aviv, Sammie Bae, Saurabh Baji, Alexandre Barbet, Max Bartolo, Björn Bebensee, Neeral Beladia, Walter Beller-Morales,... | https://arxiv.org/abs/2505.16125v1 |
Dongjie Ji, Erhang Li, Fangyun Lin, Fucong Dai, Fuli Luo, Guangbo Hao, Guanting Chen, Guowei Li, H. Zhang, Han Bao, Hanwei Xu, Haocheng Wang, Haowei Zhang, Honghui Ding, Huajian Xin, Huazuo Gao, Hui Li, Hui Qu, J. L. Cai, Jian Liang, Jianzhong Guo, Jiaqi Ni, Jiashi Li, Jiawei Wang, Jin Chen, Jingchang Chen, Jingyang Yu... | https://arxiv.org/abs/2505.16125v1 |
arXiv:2505.16128v2 [cs.CL] 25 May 2025Veracity Bias and Beyond: Uncovering LLMs’ Hidden Beliefs in Problem-Solving Reasoning Yue Zhou University of Illinois Chicago yzhou232@uic.eduBarbara Di Eugenio University of Illinois Chicago bdieugen@uic.edu Abstract Despite LLMs’ explicit alignment against de- mographic stereoty... | https://arxiv.org/abs/2505.16128v2 |
on societally bi- ased data, an important question arises: have these models implicitly linked solution veracity to de- mographic biases? In other words, despite their explicit alignment against stereotypes, do LLMs internally associate correctness with certain demo- graphic groups? To investigate this, we introduce Ve... | https://arxiv.org/abs/2505.16128v2 |
how LLMs attribute known correct and incor- rect solutions to different demographic groups; and Evaluation (E) , which analyzes whether models’ assessment of identical solutions changes based on the stated demographic identity of their provider. Unlike previous work that exposes biases through personas, role-playing, o... | https://arxiv.org/abs/2505.16128v2 |
used to assess LLMs’ reason- ing abilities yet remain unexplored through the lens of demographic biases: GSM8K (Cobbe et al., 2021) and MATH (Hendrycks et al., 2021) for mathematical reasoning, HumanEval (Chen et al., 2021) for Python code completion, and CommonsenseQA (Talmor et al., 2019) and ARC- Easy (Clark et al.,... | https://arxiv.org/abs/2505.16128v2 |
•Wrong Solution Generation. For attribution experiments, we generate incorrect solutions dif- ferently across benchmarks. For commonsense reasoning (CommonsenseQA and ARC-Easy), we randomly select one of the incorrect choices from the multiple-choice options. For HumanEval code completion, incorrect solutions are creat... | https://arxiv.org/abs/2505.16128v2 |
❸Race Proxy vs. Direct Prompt: Using race- associated names as race proxies reveals similar biased attribution patterns. It is of concern that no model refuses these potentially harmful attribu- tion requests when using names. Even with direct prompts, only GPT-4o shows refusal rates of 14% in math and 28% in coding. T... | https://arxiv.org/abs/2505.16128v2 |
across demo- graphic groups and benchmarks, with random perturba- tion baseline. Higher (%) indicates greater evaluation changes. hibit distinct behaviors. Gemini-1.5-Pro continues to generate biased attributions despite acknowledg- ing the irrelevance of demographic factors in the question. LLaMA-3-8b, in contrast, sh... | https://arxiv.org/abs/2505.16128v2 |
2 from both sides gives us r = 14### Score: 1 ### Rationale: Emily’ s solution is fully correct. She correctly simplified the left side of the equation from 19 − 3 to 16. Then, she accurately subtracted 2 from both sides of the equation 16 = 2 + r to isolate r, resulting in r = 14 . The steps are logical and the final ... | https://arxiv.org/abs/2505.16128v2 |
color assignments to racial groups in Claude-3’s generated code. in Figure 5. The results reveal consistent patterns of stereo- typical color associations: light shades for White groups, dark colors for Black groups, yellow-based colors for Asian groups, and orange/brown tones for Hispanic groups. Notably, the model do... | https://arxiv.org/abs/2505.16128v2 |
et al., 2024b; Gallegos et al., 2024; Ferrara, 2023). Our work bridges these two research directions by exploring the intersection of LLMs’ veracity understanding and societal biases, showing how demographic beliefs naturally are reflected in their reasoning without imposing social contexts. 5 Conclusion and Future Wor... | https://arxiv.org/abs/2505.16128v2 |
Michael Petrov, Heidy Khlaaf, Girish Sas- try, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, Felipe Petroski Such, Dave Cum- mings, Matthias Plappert, Fotios Chantzis, Eliza- beth Barnes, Ariel Herbert-V oss, Willia... | https://arxiv.org/abs/2505.16128v2 |
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