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Abhishek Kadian, Ahmad Al- Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, et al. 2024. The llama 3 herd of mod- els.arXiv preprint arXiv:2407.21783 . Qiangqiang He, Shuwei Qian, Jie Zhang, and Chongjun Wang. 2025. Inference retrieval-augmented multi- modal chain-of-thoughts reasoning for language mod-... | https://arxiv.org/abs/2505.18283v1 |
Yue Yu, Haotian Sun, Hang Wu, Carl Yang, and May D Wang. 2024. Medadapter: Efficient test-time adaptation of large language models towards medical reasoning. arXiv preprint arXiv:2405.03000 . Karan Singhal, Tao Tu, Juraj Gottweis, Rory Sayres, Ellery Wulczyn, Mohamed Amin, Le Hou, Kevin Clark, Stephen R Pfohl, Heather ... | https://arxiv.org/abs/2505.18283v1 |
37:94327– 94427. Hongjian Zhou, Fenglin Liu, Boyang Gu, Xinyu Zou, Jinfa Huang, Jinge Wu, Yiru Li, Sam S Chen, Peilin Zhou, Junling Liu, et al. 2023. A survey of large language models in medicine: Progress, application, and challenge. arXiv preprint arXiv:2311.05112 . Yuxin Zuo, Shang Qu, Yifei Li, Zhangren Chen, Xueka... | https://arxiv.org/abs/2505.18283v1 |
for the new question. Required output format Thought: [your detailed step-by-step reasoning] Answer: [one of A, B, C, . . . ] Table 6: System prompt for the specialist agent. Generalist Agent — System Prompt You are a general practitioner trained to manage a wide range of clinical conditions. Your task is to evaluate c... | https://arxiv.org/abs/2505.18283v1 |
. ] Reference Examples For each retrieved example iinsert: Reference Example i: <question text > <options > Thought: <reference rationale > Table 9: Prompt template used for few-shot reasoning with retrieved reference examples. retrieval-augmented reasoning. MedReason com- prises 32,682 high-quality question–answer pai... | https://arxiv.org/abs/2505.18283v1 |
complications 3. Abdominal rectopexy -> Potential complications -> Pelvic au- tonomic nerve damage -> Male sexual dysfunction Reasoning Process: 1. Understanding Rectal Prolapse and Treatment Options : Rectal pro- lapse is a condition where the rectum protrudes through the anus. Surgical intervention is often required,... | https://arxiv.org/abs/2505.18283v1 |
subsets testing both factual recall and abstract reasoning. To better reflect real-world difficulty, we fol- low the hard subset construction pipeline proposed by MEDAGENTSBENCH. Questions are selected based on model failure rates (<50% accuracy across a set of strong models), medical topic coverage, and reasoning dept... | https://arxiv.org/abs/2505.18283v1 |
sternal fracture were treated in 2 ˘00a0years, 2 ˘00a0children were hospitalized for pain management and 2 remained in outpatient care.Sternal fracture in growing children : A rare and often overlooked fracture? Options (A): "yes", (B): "no", (C): "maybe". Answer (C) MedMCQA Question Minimum number of lobes require to ... | https://arxiv.org/abs/2505.18283v1 |
InstructPart: Task-Oriented Part Segmentation with Instruction Reasoning Zifu Wan Yaqi Xie Ce Zhang Zhiqiu Lin Zihan Wang Simon Stepputtis Deva Ramanan Katia Sycara Robotics Institute, Carnegie Mellon University {zifuw, yaqix, cezhang, zhiqiul, zihanwa3, sstepput, deva, sycara}@andrew.cmu.edu https://zifuwan.github.io/... | https://arxiv.org/abs/2505.18291v1 |
part segmentation, we believe that establishing a benchmark is essential for the field. However, most large-scale vision datasets primarily focus on object-level understand- ing (Liu et al., 2023b; Zou et al., 2023b,a; Xu et al., 2023; Liang et al., 2023; Sun et al., 2023), while existing part-level recognition dataset... | https://arxiv.org/abs/2505.18291v1 |
type of data, while – means the data exists while no relevant infor- mation is provided. 11/158 indicates the super-class and sub-class numbers in PartImageNet.∗indicates the dataset only contains point annotations instead of accurate masks for target affordances. state-of-the-art model, achieving performance gains of ... | https://arxiv.org/abs/2505.18291v1 |
novel object into parts using its semantic corre- spondence with the base object and classifies it with CLIP. Although these open-world recognition meth- ods demonstrate potential in recognizing out-of- distribution classes, they have limited reasoning ability to understand complex instructional sen- tences, prohibitin... | https://arxiv.org/abs/2505.18291v1 |
corresponding ground truth part segments (right), highlighted with a green mask. 3 The InstructPart Benchmark and Baseline Models In this section, we describe our InstructPart bench- mark in detail and introduce a simple baseline method for our benchmark. 3.1 InstructPart Task Definition Motivated by scenarios where ag... | https://arxiv.org/abs/2505.18291v1 |
“Flush the toilet” or“Turn on the faucet” are preferred over more detailed directives such as“Press the toilet handle” or“Lift the faucet han- dle”. The selection of these task descriptions aims to train models that are better at reasoning about object parts and their affordances, rather than sim- ply identifying the p... | https://arxiv.org/abs/2505.18291v1 |
from DI- NOv2 for various granularity information fusion. The fused features are sent to an image decoder derived from SAM (Kirillov et al., 2023), where we apply Transpose Convolution and up-sampling for decoding in an alternating manner. 4 Experiments 4.1 Metrics To evaluate our approach, we use standard met- rics in... | https://arxiv.org/abs/2505.18291v1 |
14.98 Table 2: Results on ORPS (left) and TRPS (right) tasks. We divide the methods into three categories, namely, open-vocabulary segmentation (OVS), referring expression segmentation (RES), and reasoning segmentation (RS). The best results are bolded , and the second-best are underlined . mentation methods on our tas... | https://arxiv.org/abs/2505.18291v1 |
understand the implicit meaning. Comparing the left and right parts of Tab. 2 we can find that the performance of oracle referring task is generally better than that of task reasoning. This demonstrates that current models lack the reasoning ability to infer from a task-image pair to the correct interactive part. For t... | https://arxiv.org/abs/2505.18291v1 |
regions, demonstrating the chal- lenging tasks provided by InstructPart. In Fig. 3, we present examples where the fine-tuned PISA shows superior visual part segmentation results, demonstrating the effectiveness of our proposed method. Besides, both the pre-trained and fine- tuned LISA models also demonstrate great pote... | https://arxiv.org/abs/2505.18291v1 |
on real-world grasping data (see Appendix I), showing the poten- tial of InstructPart for broader applications. Potential Applications. Our dataset contains samples in various scenarios, including kitchen, liv- ing room, outdoor, etc., and can be used for robot manipulation and visual question answering. Be-sides, our ... | https://arxiv.org/abs/2505.18291v1 |
Liu, Sanja Fidler, Raquel Urtasun, and Alan Yuille. 2014. De- tect what you can: Detecting and representing objects using holistic models and body parts. In Proceed- ings of the IEEE Conference on Computer Vision and Pattern Recognition , pages 1971–1978. Danny Driess, Fei Xia, Mehdi SM Sajjadi, Corey Lynch, Aakanksha ... | https://arxiv.org/abs/2505.18291v1 |
Ma- chine Learning , pages 19730–19742. PMLR. Samuel Li, Sarthak Bhagat, Joseph Campbell, Yaqi Xie, Woojun Kim, Katia P. Sycara, and Simon Stepputtis. 2024b. Shapegrasp: Zero-shot task-oriented grasp- ing with large language models through geometric decomposition. In IEEE/RSJ International Confer- ence on Intelligent R... | https://arxiv.org/abs/2505.18291v1 |
detection with convolutional neural networks and dense conditional random fields. In IEEE/RSJ International Conference on Intelligent Robots and Systems , pages 5908–5915. IEEE. Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy V o, Marc Szafraniec, Vasil Khalidov, Pierre Fer- nandez, Daniel Haziza, Francisco Massa, ... | https://arxiv.org/abs/2505.18291v1 |
in gpt-4v. arXiv preprint arXiv:2310.11441 . Zhao Yang, Jiaqi Wang, Yansong Tang, Kai Chen, Hengshuang Zhao, and Philip HS Torr. 2022. Lavt: Language-aware vision transformer for referring im- age segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recog- nition , pages 18155–18165. Z... | https://arxiv.org/abs/2505.18291v1 |
and its specific part are noted, aligned with a task instruction that pertains to a particular part shown in the image. Additionally, both a low-level affordance name and a high-level action name are provided in relation to the instruction. Besides, in Fig. B10, we provide more examples that contain occlusions and huma... | https://arxiv.org/abs/2505.18291v1 |
pliers-handle pliers-jaws pot-handle pot-lid pot-opening pot_lid-handle refrigerator-handle remote_control-buttons rocking_chair-armrest rocking_chair-seat rocking_chair-base scissors-handle scissors-blades sofa-couch_cushion spoon-handle spoon-bowl suitcase-static_handle suitcase-lid suitcase-interior suitcase-telesco... | https://arxiv.org/abs/2505.18291v1 |
from the object recognition ability.MethodsObject-Level Prec. Rec.@A Rec.@25% Rec.@50% Rec.@75% OVSeg 20.93 81.80 85.00 99.26 100.00 SAN 19.46 73.55 56.37 98.49 100.00 G-SAM 25.53 89.83 92.59 96.99 99.37 Table E5: Precision and recall rate on object-level seg- mentation results. The five metrics refer to precision (Pre... | https://arxiv.org/abs/2505.18291v1 |
X-Decoder SEEM TRIS G-SAM MiniGPT-v2 LISA-Pretrain LISA-Finetune PISA-Finetune Figure H16: More qualitative examples with corresponding annotations recorded in Table H7. H More Annotation Samples In addition to the annotation examples shown in Fig. B9, we include five more annotations for the samples in Fig. H16 in Tab... | https://arxiv.org/abs/2505.18291v1 |
to facilitate. Consequently, we evaluate the model trained with our data in a real- world tabletop grasping environment. We use the table setup from ShapeGrasp (Li et al., 2024b), which consists of 38 objects covering 12 general categories and 49 tasks. These categories and tasks are the same as those in LERF-TOGO (Ras... | https://arxiv.org/abs/2505.18291v1 |
together with 9,600 diverse task in- structions, making it more comprehensive and diverse. This enables a more thorough eval- uation and offers greater potential for model training and fine-tuning. •Novelty and Research Opportunity: We consider the reasoning-based segmenta- tion task proposed by LISA as a combina- tion... | https://arxiv.org/abs/2505.18291v1 |
Limitations: Most VLMs use a CLIP-based image encoder, which is optimized for object-level seman- tic understanding and lacks explicit mecha- nisms for part-level grounding or affordance reasoning. To address this, we incorporate a DINOv2 vision encoder in our baseline, which better captures part-level correspon- dence... | https://arxiv.org/abs/2505.18291v1 |
arXiv:2505.18298v1 [cs.CL] 23 May 2025Thinking Fast and Right: Balancing Accuracy and Reasoning Length with Adaptive Rewards Jinyan Su, Claire Cardie Cornell University {js3673, ctc9}@cornell.edu Abstract Large language models (LLMs) have demonstrated strong reasoning abilities in math mathematical reasoning, often enh... | https://arxiv.org/abs/2505.18298v1 |
small, the model may retain unnecessarily long outputs or take too long to converge to a reasonable response length. To address this limitation, we propose a simple and intuitive approach that adaptively adjusts the reward trade-off between response length and accuracy based on model performance. Specifically, during t... | https://arxiv.org/abs/2505.18298v1 |
[ 14] trains a compressor to convert verbose CoT traces. Others focus on prompt-based methods: [21,10] attempt to explicitly constrain token length through prompt engineering. TokenSkip [ 33] enables LLMs to selectively skip over less informative tokens. Meanwhile, [ 20] uses Best-of-N sampling to construct concise dat... | https://arxiv.org/abs/2505.18298v1 |
η > 0is learning rate that controls the sensitivity of the penalty to performance changes. While it is ideal to use a reference accuracy computed from a separate model or evaluation run, doing so would incur additional computational cost—something we aim to avoid. To eliminate this overhead, we instead set acc ref=Ex∼D... | https://arxiv.org/abs/2505.18298v1 |
initialize the penalty coefficient to be λ0= 1e−3, and use a learning rate η= 1e−3for updating λt. Training proceeds until the generation length stabilizes. We use the final checkpoint (at step 420) for evaluation. We set the reference accuracy to be 0.62, which is estimated from the training accuracy of the first batc... | https://arxiv.org/abs/2505.18298v1 |
all datasets, demonstrating the effectiveness of our approach. 5.2 Behavior over Training Figure 4 illustrates the training dynamics of A-DLP, focusing on four key quantities: the accuracy gap between the current model and the reference threshold, i.e., acct−acc ref; the length penalty coefficient λt; the validation ac... | https://arxiv.org/abs/2505.18298v1 |
the penalty remains overly aggressive. This delayed adjustment leads to excessive shortening of outputs, ultimately causing the model to collapse in both accuracy and generation quality. Conversely, when the learning rate is large (e.g., η= 10−2), the penalty coefficient λtbecomes highly sensitive to the accuracy gap, ... | https://arxiv.org/abs/2505.18298v1 |
this point, the accuracy gap becomes negative, and λtbegins to decrease. However, similar to the case with a small learning rate, the update to λtbecomes too slow to respond adequately to the accuracy drop. In effect, the learning rate becomes small relative to the magnitude of λt, making it difficult for the penalty t... | https://arxiv.org/abs/2505.18298v1 |
building more efficient and cost-effective LLMs. 7 Limitation and Future works While our method is designed to be simple, efficient, and broadly applicable, several aspects offer opportunities for future extension: first, Our experiments are conducted on a 1.5B-parameter model to ensure reproducibility and manageable c... | https://arxiv.org/abs/2505.18298v1 |
the math dataset. arXiv preprint arXiv:2103.03874 , 2021. [13] Bairu Hou, Yang Zhang, Jiabao Ji, Yujian Liu, Kaizhi Qian, Jacob Andreas, and Shiyu Chang. Thinkprune: Pruning long chain-of-thought of llms via reinforcement learning. arXiv preprint arXiv:2504.01296 , 2025. [14] Yu Kang, Xianghui Sun, Liangyu Chen, and We... | https://arxiv.org/abs/2505.18298v1 |
Qwen Team. Qwq-32b-preview. https://qwenlm.github.io/blog/qwq-32b-preview/ , 2025. [29] Rui Wang, Hongru Wang, Boyang Xue, Jianhui Pang, Shudong Liu, Yi Chen, Jiahao Qiu, Derek Fai Wong, Heng Ji, and Kam-Fai Wong. Harnessing the reasoning economy: A survey of efficient reasoning for large language models. arXiv preprin... | https://arxiv.org/abs/2505.18298v1 |
Is It Bad to Work All the Time? Cross-Cultural Evaluation of Social Norm Biases in GPT-4 Zhuozhuo Joy Liu1,2Farhan Samir3Mehar Bhatia4,5 Laura K. Nelson6Vered Shwartz1,2 1Department of Computer Science, University of British Columbia 2Vector Institute for AI3Department of Linguistics, University of British Columbia 4Mi... | https://arxiv.org/abs/2505.18322v1 |
al., 2020), and the Global Attitudes Survey (PEW), finding that LLMs ex- hibited a strong alignment with North American cultures, and to a lesser extent, with other Western 1arXiv:2505.18322v1 [cs.CL] 23 May 2025 English speaking countries. However, such sur- veys that are designed to ask people about their values impl... | https://arxiv.org/abs/2505.18322v1 |
al., 2022). Various papers showed that LLMs exhibit a strong alignment with the values of North American cultures, and to a lesser ex- tent, with other WEIRD countries, raising concerns about fairness (See for example, Johnson et al., 2022; Ramezani and Xu, 2023; Havaldar et al., 2023; Arora et al., 2023; Cao et al., 2... | https://arxiv.org/abs/2505.18322v1 |
to their instruction tuning and preference tuning steps and other proprietary “guardrails” implemented by their developers. How- ever, these superficial avoidance strategies likely only mask rather than remove the biases in these models. For example, Reuter and Schulze (2023) reveal the superficiality of the “guardrail... | https://arxiv.org/abs/2505.18322v1 |
geographically diverse countries: United States, China, India, and Iran. China and India were selected due to their large populations and glob- ally recognized cultural distinctiveness; Iran repre- sents a smaller culture group with unique traditions and perspectives; and the US represents a West- ern country overrepre... | https://arxiv.org/abs/2505.18322v1 |
the 6https://www.cloudresearch.com/ 4 <Country>-culture-driven : RoTs should align with established norms and practices in <Country>. Judgment + Action : Each RoT is in a single sentence with a straightfor- ward structure: it is [the judgment] of [an action]. Verb-centric : Anchor each RoT to a specific verb from the s... | https://arxiv.org/abs/2505.18322v1 |
respect to the following criteria (see Appendix A.2 for the annotation task): 1.Accuracy: On a Likert-scale from 1 to 5, to what extent the RoT accurately represents a so- cial norm. 2.Culture-specificity: On a Likert-scale from 1 to 5, to what extent the RoT reflects norms unique to the target culture, vs. a more gene... | https://arxiv.org/abs/2505.18322v1 |
between GPT-4’s default predic- tions and culture-specific ratings across four countries. The model aligns most closely with the United States norms and deviates most from Chinese norms. We randomly sampled 20 RoTs from each coun- try and prompted GPT-4 to rate the accuracy of each RoT on a Likert scale of 1–5. We prom... | https://arxiv.org/abs/2505.18322v1 |
that LLMs are less likely to be critical of social groups (Boelaert et al., 2025; Zhang et al., 2025). GPT-4’s biases are hidden, not removed. GPT- 4 appears to be stereotype-avoidant on the surface. However, given the technical difficulty of debias- ing, there are reasons to believe that the training process that led ... | https://arxiv.org/abs/2505.18322v1 |
2China 1 2 3 4 50102030 1218India 1 2 3 4 5010203030Iran 1 2 3 4 5024 23Indian AnnotatorsIt is commendable to to marry within your own religion (India) 0102030 129Default 0102030 224 4United States 0102030 24 6China 1 2 3 4 50102030 722 1India 1 2 3 4 50102030 818 4Iran 1 2 3 4 5024 1 12Chinese AnnotatorsIt is bad to w... | https://arxiv.org/abs/2505.18322v1 |
ingrained in the model and can be resurfaced – due to lack of real technical solutions. Finally, our findings also highlight a key tension in the design of culturally- competent LLMs, which on the one hand need to possess culture-specific knowledge, while on the other hand risk perpetuating stereotypes about the same c... | https://arxiv.org/abs/2505.18322v1 |
as a Cultural Proxy. We also acknowledge that cultural identity does not map neatly onto geographic or national boundaries, and that cultural variation exists at the individual level, shaped by personal history and experience. How- ever, for the purposes of this study, we use country as a proxy for cultural grouping, c... | https://arxiv.org/abs/2505.18322v1 |
Considerations in NLP (C3NLP) , pages 53–67, Dubrovnik, Croatia. Association for Computational Linguistics. Myra Cheng, Esin Durmus, and Dan Jurafsky. 2023. Marked personas: Using natural language prompts to measure stereotypes in language models. In Proceed- ings of the 61st Annual Meeting of the Association for Compu... | https://arxiv.org/abs/2505.18322v1 |
Pratyusha Ria Kalluri, Dan Jurafsky, and Sharese King. 2024. Ai generates covertly racist decisions about people based on their dialect. Nature , 633(8028):147–154. Geert Hofstede. 1984. Culture’s consequences: Interna- tional differences in work-related values , volume 5. sage. Ying-Yi Hong, Michael W Morris, Chi-yue ... | https://arxiv.org/abs/2505.18322v1 |
Association for Compu- tational Linguistics (Volume 1: Long Papers) , pages 16366–16393, Bangkok, Thailand. Association for Computational Linguistics. Tarek Naous, Michael J. Ryan, Alan Ritter, and Wei Xu. 2024b. Having beer after prayer? measuring cul- tural bias in large language models. In Proceedings of the 62nd An... | https://arxiv.org/abs/2505.18322v1 |
Machine Learning , volume 202 of Proceedings of Machine Learning Research , pages 29971–30004. PMLR. Andrew D Selbst, Danah Boyd, Sorelle A Friedler, Suresh Venkatasubramanian, and Janet Vertesi. 2019. Fairness and abstraction in sociotechnical systems. InProceedings of the 2019 Conference on Fair- ness, Accountability... | https://arxiv.org/abs/2505.18322v1 |
61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages 7756–7776, Toronto, Canada. Association for Computational Linguistics. A Human Annotation A.1 Annotators Demographics RoTs Collection Task (§3.2). We collected an- notations from 88 annotators across four cultural group... | https://arxiv.org/abs/2505.18322v1 |
2. sleep; Studies have shown... (encyclopedic, not normative) 3. cut; It’s ok to cut off contact... (requires original story context) 4. helped; It’s kind to help people. (too vague) Table 6: Prompt for the RoT collection task. 13 Figure 6: Cultural activation task interface. Annotators are presented with five cultural... | https://arxiv.org/abs/2505.18322v1 |
5, 5, 5, 5] [5, 3, 2, 2, 5] [1, 1, 1, 1, 0] It is responsible to arrange your sister’s marriage. [5, 4, 5, 5, 5] [4, 3, 2, 3, 5] [1, 1, 1, 1, 0] IranIt is important for a woman to wear a chador outside. [5, 3, 4, 5, 1, 5] [5, 3, 4, 2, 5, 5] [0, 1, 1, 1, 1, 0] It is admirable to go the extra mile even when tired. [4, 2,... | https://arxiv.org/abs/2505.18322v1 |
arXiv:2505.18331v1 [cs.CL] 23 May 2025PerMedCQA: Benchmarking Large Language Models on Medical Consumer Question Answering in Persian Language Naghmeh Jamali1Milad Mohammadi2Danial Baledi2Zahra Rezvani3Hesham Faili2 1School of Computer Science, Institute for Research in Fundamental Sciences, Tehran, Iran. 2School of El... | https://arxiv.org/abs/2505.18331v1 |
involve authen- tic consumer-generated health queries answered by licensed medical professionals, encompassing a wide range of medical specialties and enriched with detailed metadata such as patient demograph- ics, physician specialty, and platform source. To ensure the dataset’s reliability, clinical util- ity, and co... | https://arxiv.org/abs/2505.18331v1 |
answering qual- ity. •We comprehensively benchmark a variety of proprietary and open-source LLMs, identify- ing substantial performance variations and demonstrating the effectiveness of prompt- based techniques for enhancing model out- puts. •We evaluate supervised fine-tuning strategies using PerMedCQA, highlighting t... | https://arxiv.org/abs/2505.18331v1 |
et al., 2021) Mul- tiMedQA (Singhal et al., 2023b), for instance, aggregates six multiple-choice datasets—MedQA (USMLE), MedMCQA (Pal et al., 2022b), Pub- MedQA, LiveQA (Abacha et al., 2019b), Medica- tionQA (Abacha et al., 2019a), and MMLU clinical topics (Hendrycks et al., 2020)—along with the HealthSearchQA (Singhal... | https://arxiv.org/abs/2505.18331v1 |
single-turn dialogues (DrYab, HiSalamat), (2) multi-turn conversations (GetZoop), and (3) cases with multiple expert re-sponses (Mavara-e-Teb). 3.2 Preprocessing and Cleaning We employed a two-stage data cleaning pipeline: rule-based preprocessing and large language model (LLM)–based processing (filtering and tagging).... | https://arxiv.org/abs/2505.18331v1 |
advice without in-person consultation reflects cultural norms, explaining the higher par- ticipation of women in Persian-language medical forums. Benchmark Split. To support model training and evaluation, PerMedCQA were split according toappropriate percent subsets for training, evaluation and test, partitioning into: ... | https://arxiv.org/abs/2505.18331v1 |
(Team, 2025) 4B, 12B, 27B DeepMind qwen3-14b (Yang et al., 2025) Qwen All models baseline experiments were evaluated under a consistent zero-shot setting using the same prompt. This prompt instructed the model to adopt the persona of a professional medical doctor an- swering in fluent Persian. The instructions empha- s... | https://arxiv.org/abs/2505.18331v1 |
with a learning rate of 2×10−5and context length of 2048 tokens. Rather than aiming for state-of-the-art perfor- mance, these fine-tuning experiments were primar- ily designed to evaluate the effectiveness of PerMe- dCQA as a training resource, and to explore how much medical QA capability could be learned by modestly ... | https://arxiv.org/abs/2505.18331v1 |
Halluci- nation and Dialect to MSA Machine Translation @ LREC-COLING 2024 , pages 50–56, Torino, Italia. ELRA and ICCL. Satanjeev Banerjee and Alon Lavie. 2005. METEOR: An automatic metric for MT evaluation with im- proved correlation with human judgments. In Pro- ceedings of the ACL Workshop on Intrinsic and Ex- trins... | https://arxiv.org/abs/2505.18331v1 |
and applications to accountability and ethics. Information Fusion , page 102963. Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt. 2020. Measuring massive multitask language under- standing. arXiv preprint arXiv:2009.03300 . HiSalamat. 2025. https://www.hisalamat.com... | https://arxiv.org/abs/2505.18331v1 |
Ronn, David Cohen, Ran Ilan Ber, Zehavi Horowitz-Kugler, and Gabriel Stanovsky. 2024. K-qa: A real-world medical q&a benchmark. InProceedings of the 23rd Workshop on Biomedical Natural Language Processing , pages 277–294. Mavara-e-Teb. 2025. https://mavarateb.com . Xiangbin Meng, Xiangyu Yan, Kuo Zhang, Da Liu, Xi- aoj... | https://arxiv.org/abs/2505.18331v1 |
9 and 1 others. 2023b. Publisher correction: Large language models encode clinical knowledge. Nature , 620(7973):19–19. Karan Singhal, Tao Tu, Juraj Gottweis, Rory Sayres, Ellery Wulczyn, Mohamed Amin, Le Hou, Kevin Clark, Stephen R Pfohl, Heather Cole-Lewis, and 1 others. 2025. Toward expert-level medical ques- tion a... | https://arxiv.org/abs/2505.18331v1 |
toward building open-source language models for medicine. Journal of the American Medical Infor- matics Association , 31(9):1833–1843. Qianqian Xie, Qingyu Chen, Aokun Chen, Cheng Peng, Yan Hu, Fongci Lin, Xueqing Peng, Jimin Huang, Jeffrey Zhang, Vipina Keloth, and 1 others. 2024. Me-llama: Foundation large language m... | https://arxiv.org/abs/2505.18331v1 |
test instance, the MedJudge LLM re- ceives the user’s question, the expert-provided “gold” answer, and the model-generated answer. The LLM is strictly instructed to evaluate based solely on the expert reference, without drawing from external sources or its own medical knowl- edge. The output is a structured JSON object... | https://arxiv.org/abs/2505.18331v1 |
PII tagging. ICD-11 and PII Tagging System Prompt Task Description You are a medical expert tasked with classifying and analyzing patient–expert dialogue. Your task has two main parts: 1) ICD-11 Classification Classify the content based on ICD-11 categories using the catalogue provided in the next page. Return only the... | https://arxiv.org/abs/2505.18331v1 |
is losartan prescribed rather than a calcium channel blocker? Contraindication Whether the drug is safe given allergy/con- dition.If I am allergic to sulfa can I take glipizide? Overdose Consequences of taking too much. What happens if your child ate a Tylenol tablet? Alternatives Asks for substitute medications. What ... | https://arxiv.org/abs/2505.18331v1 |
in English. Answer directly, briefly and precisely. Provide practical guidance and advise an in-person visit only when absolutely necessary. Step 3: Translate Answer (English →Persian) You are a professional medical translator. Translate the following English answer into fluent Persian suitable for the patient. Only ou... | https://arxiv.org/abs/2505.18331v1 |
arXiv:2505.18343v1 [cs.CL] 23 May 2025Model Editing with Graph-Based External Memory Yash Kumar Atri University of Virginia atri@virginia.eduAhmed Alaa UC Berkeley & UCSF amalaa@berkeley.eduTom Hartvigsen University of Virginia hartvigsen@virginia.edu Abstract Large language models (LLMs) have revolu- tionized natural ... | https://arxiv.org/abs/2505.18343v1 |
geometric mismatch , where edits distort semantically related knowledge (Nickel and Kiela, 2017). Additionally, Euclidean updates in overparameterized models like GPT-2 (Radford et al., 2019a) cause update in- stability , propagating unintended changes through dense connections (Kaplan et al., 2020). Finally, these met... | https://arxiv.org/abs/2505.18343v1 |
approaches. Parameter- modifying methods, such as locate-then-edit strate- gies (Meng et al., 2022a,b; Hartvigsen et al., 2023; Kolbeinsson et al., 2025) and meta-learning frame- works (Mitchell et al., 2022a; Cao et al., 2021), adjust model parameters via hypernetworks or rank- one weight updates. Zhang et al. (2024) ... | https://arxiv.org/abs/2505.18343v1 |
while maintaining edit precision. Experiments show HYPE achieves +9.12 higher Edit Quality Score (EDS) and +3.59 better efficacy than state-of-the- art methods (Table 1), demonstrating the benefits of curvature-aware updates. 3 Methodology In this section, we detail the methodology underly- ingHYPE , our proposed frame... | https://arxiv.org/abs/2505.18343v1 |
hyperbolic space’s non-Euclidean struc- ture requires operations that preserve its intrinsic geometry. To address this, we employ Möbius ad- dition, which ensures updates remain on the hyper- bolic manifold while maintaining hierarchical rela- tionships encoded in the data. Specifically, Möbius addition accounts for th... | https://arxiv.org/abs/2505.18343v1 |
accuracy, specificity, and generalization; 2) CounterFact+ (Yao et al., 2023b), which evaluates edit portability across paraphrased queries; 3) MQuAKE (Zhong et al., 2023), which tests multi-hop reasoning capabilities. We com- pare HYPE against seven state-of-the-art baselines – (i) Zeroshot (unmodified model), (ii) FT... | https://arxiv.org/abs/2505.18343v1 |
(Meng et al., 2022b), PMET (Li et al., 2024b), and RAE (Shi et al., 2024) — on three benchmark datasets: (1) CounterFact (Meng et al., 2022a), as- sessed using Efficacy (Eff), Generalization (Gen), Specificity (Spec), and Edit Quality Score (EDS); (2) CounterFact+ (Yao et al., 2023b), evaluated with Portability Score (... | https://arxiv.org/abs/2505.18343v1 |
28.45 17.45 27.78 Table 2: Ablation study on GPT-J and GPT2-XL architectures. The highlighted rows show full HYPE performance; removing components (Dual Stabilization (DualS), Hyperbolic Graph (HGraph), and Mobius updates) progressively degrades performance. 5.2 MQuAKE Results The MQuAKE dataset evaluates multi-hop rea... | https://arxiv.org/abs/2505.18343v1 |
balance between expansion and numerical stability. Right: Edit success rate declines for τ > 0.5due to underfitting. removing Dual Stabilization reduces efficacy by -6.12 and specificity by -4.41, while ablating the Hyperbolic Graph and Möbius updates causes an -11.12 decline in efficacy and a -9.41 reduction in specif... | https://arxiv.org/abs/2505.18343v1 |
by filtering out critical updates. 7 Conclusion In this paper, we proposed HYPE , a novel post- training model editing framework that leverages hyperbolic geometry and graph neural networks to deliver precise and stable updates to large lan- guage models. Our approach overcomes critical issues in existing methods, incl... | https://arxiv.org/abs/2505.18343v1 |
Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Ma- teusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language models are few-shot learners. In Ad- vances in Neural Information Processing Systems , volume 33, pages 1877–1... | https://arxiv.org/abs/2505.18343v1 |
Ma, Pan Lu, Zhen- Hua Ling, Kai-Wei Chang, and Nanyun Peng. 2024. Model editing harms general abilities of large lan- guage models: Regularization to the rescue. Preprint , arXiv:2401.04700. Tom Hartvigsen, Swami Sankaranarayanan, Hamid Palangi, Yoon Kim, and Marzyeh Ghassemi. 2023. Aging with GRACE: lifelong model edi... | https://arxiv.org/abs/2505.18343v1 |
volume 162 ofProceedings of Machine Learning Research , pages 15817–15831. PMLR. Maximillian Nickel and Douwe Kiela. 2017. Poincaré embeddings for learning hierarchical representations. InAdvances in Neural Information Processing Sys- tems, volume 30. Curran Associates, Inc. Anirudh Phukan, Shwetha Somasundaram, Apoorv... | https://arxiv.org/abs/2505.18343v1 |
of the 2023 Conference on Empirical Methods in Natural Language Processing , pages 10222–10240, Singapore. Association for Computational Linguis- tics. Guanxiong Zeng, Yang Chen, Bo Cui, and Shan Yu. 2019. Continual learning of context-dependent pro- cessing in neural networks. Nat. Mach. Intell. , 1(8):364–372. Mengqi... | https://arxiv.org/abs/2505.18343v1 |
weight updates to mini- mize computational overhead. 7.RAE (Shi et al., 2024): Uses a Retrieval- Augmented Generation (RAG) based approach, incorporating external knowledge during infer- ence. A.3 Evaluation Metrics We define the following metrics to assess model editing performance: Efficacy (Eff) Measures the accurac... | https://arxiv.org/abs/2505.18343v1 |
foundation of our model lies in constructing a hyperbolic graph that accurately represents hier- archical relationships in the data. We initialize a Poincaré Ball model with a curvature param- eterc= 1.0, enabling effective embedding of knowledge triples in hyperbolic space. This curva- ture allows the model to capture... | https://arxiv.org/abs/2505.18343v1 |
14.64}] , " paraphrase_prompts_probs ": [{" target_new ": 2.08 , " target_true ": 12.86}] , " neighborhood_prompts_probs ": [{" target_new ": 12.24 , " target_true ": 11.59}] } } Listing 3: Output from model for sample 560 { " case_id ": 560 , " grouped_case_ids ": [560] , " num_edits ": 1, " requested_rewrite ": { " p... | https://arxiv.org/abs/2505.18343v1 |
arXiv:2505.18350v1 [cs.LG] 23 May 2025Task Specific Pruning with LLM-Sieve: How Many Parameters Does Your Task Really Need? Waleed Reda Microsoft Research waleedreda@microsoft.comAbhinav Jangda Microsoft Research ajangda@microsoft.comKrishna Chintalapudi Microsoft Research krchinta@microsoft.com Abstract As Large Langu... | https://arxiv.org/abs/2505.18350v1 |
directly approximates the final output, better capturing task-specific structure. Second, LLM-Sieve introduces differentiated pruning, in which each matrix in the LLM is pruned to a task-dependent degree. Existing approaches typically apply uniform pruning ratios across all layers and matrix types—often leaving pruning... | https://arxiv.org/abs/2505.18350v1 |
Wanda [ 8] often use light-weight tuning to recover performance after aggressive pruning. Structured pruning, on the other hand, removes entire attention heads, neurons, or matrix blocks, yielding dense submatrices that better align with GPU hardware. LLM-Pruner [ 9], for example, leverages gradient-based saliency to i... | https://arxiv.org/abs/2505.18350v1 |
output has to pass through all the layers. Due to the autoregressive nature of transformers, outputs are fed back and multiple forward passes are required to produce the output to the prompt. 4 LLM-Sieve LLM-Sieve aims to reduce the number of parameters in a Large Language Model (LLM) while ensuring that the degradatio... | https://arxiv.org/abs/2505.18350v1 |
necessarily aligned, which limits 4 the effectiveness of such decompositions. Rather than projecting weights or inputs independently, LLM-Sieve learns task-specific projections by directly approximating the output Y. Specifically, it seeks projections that minimize the average reconstruction error: ∥Y−˜Y∥. LLM-Sieve Ca... | https://arxiv.org/abs/2505.18350v1 |
such that the pruned model still satisfies the end-to-end performance constraint. At each iteration, we maintain an upper bound pupand a lower bound plowsuch that the performance constraint (i.e., degradation < ϵ) is violated at plowbut satisfied at pup. We evaluate model performance on the calibration dataset using th... | https://arxiv.org/abs/2505.18350v1 |
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