---
title: "Who cites Eileen Pan — 510 papers of our library"
description: "510 papers of the Elamur Research library cite the works of Eileen Pan, found in their bibliographies · page 1 of 2 · 1–100; the most cited first."
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# Who cites Eileen Pan

510 citations of their works from 510 papers of our library (of the 149,439 whose bibliographies we read) · 143 with a page here, the most cited first · page 1 of 2 · 1–100 · [← Eileen Pan](https://md.research.elamur.ai/free/en/authors/eileen-pan/)

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1. **[Large Language Models Encode Clinical Knowledge](https://md.research.elamur.ai/free/en/papers/singhal-2022-large-language-models-encode-clinical/) [Nature](https://md.research.elamur.ai/free/en/venues/nature/)**Karan Singhal, Shekoofeh Azizi, Tao Tu, et al.*2022 · 4,492 citations*
2. [**Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models PLOS Digital Health**Tiffany H. Kung, Morgan Cheatham, Arielle Medenilla, et al.*2023 · 4,173 citations*](https://md.research.elamur.ai/free/en/papers/kung-2023-performance-chatgpt-usmle-potential-ai/)
3. **[A Survey of Large Language Models](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/) [Frontiers of Computer Science](https://md.research.elamur.ai/free/en/venues/frontiers-of-computer-science/)**Wayne Xin Zhao, Kun Zhou, Junyi Li, et al.*2023 · 2,875 citations*
4. [**Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone arXiv:2404.14219**Marah Abdin, Jyoti Aneja, Hany Awadalla, et al.*2024 · 1,134 citations*](https://md.research.elamur.ai/free/en/papers/abdin-2024-phi-3-technical-report-highly/)
5. [**Capabilities of GPT-4 on Medical Challenge Problems arXiv:2303.13375**Harsha Nori, Nicholas King, Scott Mayer McKinney, et al.*2023 · 717 citations*](https://md.research.elamur.ai/free/en/papers/nori-2023-capabilities-gpt-4-medical-challenge/)
6. [**Baichuan 2: Open Large-scale Language Models arXiv:2309.10305**Aiyuan Yang, Bin Xiao, Bingning Wang, et al.*2023 · 517 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-baichuan-2-open-large-scale/)
7. **[LinkBERT: Pretraining Language Models with Document Links](https://md.research.elamur.ai/free/en/papers/yasunaga-2022-linkbert-pretraining-language-models-document/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Michihiro Yasunaga, Jure Leskovec, Percy Liang*2022 · 458 citations*
8. **[Benchmarking Retrieval-Augmented Generation for Medicine](https://md.research.elamur.ai/free/en/papers/xiong-2024-benchmarking-retrieval-augmented-generation-medicine/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Guangzhi Xiong, Qiao Jin, Zhiyong Lu, et al.*2024 · 430 citations*
9. **[BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains](https://md.research.elamur.ai/free/en/papers/labrak-2024-biomistral-collection-open-source-pretrained/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yanis Labrak, Adrien Bazoge, Emmanuel Morin, et al.*2024 · 409 citations*
10. [**MEDITRON-70B: Scaling Medical Pretraining for Large Language Models arXiv:2311.16079**Zeming Chen, Alejandro Hernandez Cano, Angelika Romanou, et al.*2023 · 315 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-meditron-70b-scaling-medical-pretraining/)
11. [**A Review of Large Language Models and Autonomous Agents in Chemistry arXiv:2407.01603**Mayk Caldas Ramos, Christopher J. Collison, Andrew D. White*2024 · 288 citations*](https://md.research.elamur.ai/free/en/papers/ramos-2024-review-large-language-models-autonomous/)
12. **[Graph Retrieval-Augmented Generation: A Survey](https://md.research.elamur.ai/free/en/papers/peng-2024-graph-retrieval-augmented-generation-survey/) [ACM Transactions on Information Systems](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-information-systems/)**Boci Peng, Yun Zhu, Yongchao Liu, et al.*2024 · 276 citations*
13. **[MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning](https://md.research.elamur.ai/free/en/papers/tang-2024-medagents-large-language-models-as/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiangru Tang, Anni Zou, Zhuosheng Zhang, et al.*2024 · 246 citations*
14. [**MedAlpaca — An Open-Source Collection of Medical Conversational AI Models and Training Data arXiv:2304.08247**Tianyu Han, Lisa C. Adams, Jens-Michalis Papaioannou, et al.*2023 · 227 citations*](https://md.research.elamur.ai/free/en/papers/han-2023-medalpaca-open-source-collection-medical/)
15. **[Med-HALT: Medical Domain Hallucination Test for Large Language Models](https://md.research.elamur.ai/free/en/papers/pal-2023-med-halt-medical-domain-hallucination/) [Conference on Computational Natural Language Learning (CoNLL)](https://md.research.elamur.ai/free/en/venues/conll/)**Ankit Pal, Logesh Kumar Umapathi, Malaikannan Sankarasubbu*2023 · 221 citations*
16. **[Towards Building Multilingual Language Model for Medicine](https://md.research.elamur.ai/free/en/papers/qiu-2024-towards-building-multilingual-language-model/) [Nature Communications](https://md.research.elamur.ai/free/en/venues/nature-communications/)**Pengcheng Qiu, Chaoyi Wu, Xiaoman Zhang, et al.*2024 · 184 citations*
17. **[Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models](https://md.research.elamur.ai/free/en/papers/chen-2025-towards-reasoning-era-survey-long/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Qiguang Chen, Libo Qin, Jinhao Liu, et al.*2025 · 176 citations*
18. [**Capabilities of Gemini Models in Medicine arXiv:2404.18416**Khaled Saab, Tao Tu, Wei-Hung Weng, et al.*2024 · 170 citations*](https://md.research.elamur.ai/free/en/papers/saab-2024-capabilities-gemini-models-medicine/)
19. **[Improving Medical Reasoning through Retrieval and Self-Reflection with Retrieval-Augmented Large Language Models](https://md.research.elamur.ai/free/en/papers/jeong-2024-improving-medical-reasoning-through-retrieval/) [Bioinformatics](https://md.research.elamur.ai/free/en/venues/bioinformatics/)**Minbyul Jeong, Jiwoong Sohn, Mujeen Sung, et al.*2024 · 164 citations*
20. [**MedGemma Technical Report arXiv:2507.05201**Andrew Sellergren, Sahar Kazemzadeh, Tiam Jaroensri, et al.*2025 · 150 citations*](https://md.research.elamur.ai/free/en/papers/sellergren-2025-medgemma-technical-report/)
21. [**PMC-LLaMA: Towards Building Open-source Language Models for Medicine arXiv:2304.14454**Chaoyi Wu, Weixiong Lin, Xiaoman Zhang, et al.*2023 · 146 citations*](https://md.research.elamur.ai/free/en/papers/wu-2023-pmc-llama-towards-building-open/)
22. [**Olmo 3 arXiv:2512.13961**Team Olmo: Allyson Ettinger, Amanda Bertsch, Bailey Kuehl, et al.*2025 · 130 citations*](https://md.research.elamur.ai/free/en/papers/ettinger-2025-olmo-3/)
23. **[ExpertQA: Expert-Curated Questions and Attributed Answers](https://md.research.elamur.ai/free/en/papers/malaviya-2024-expertqa-expert-curated-questions-attributed/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Chaitanya Malaviya, Subin Lee, Sihao Chen, et al.*2024 · 113 citations*
24. [**PaperQA: Retrieval-Augmented Generative Agent for Scientific Research arXiv:2312.07559**Jakub Lála, Odhran O'Donoghue, Aleksandar Shtedritski, et al.*2023 · 109 citations*](https://md.research.elamur.ai/free/en/papers/lala-2023-paperqa-retrieval-augmented-generative/)
25. [**Arcee's MergeKit: A Toolkit for Merging Large Language Models arXiv:2403.13257**Charles Goddard, Shamane Siriwardhana, Malikeh Ehghaghi, et al.*2024 · 105 citations*](https://md.research.elamur.ai/free/en/papers/goddard-2024-arcee-s-mergekit-toolkit-merging/)
26. [**PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimization arXiv:2310.16427**Xinyuan Wang, Chenxi Li, Zhen Wang, et al.*2023 · 102 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-promptagent-strategic-planning-language-models/)
27. [**Large language models for education: A survey and outlook**Wang*2024 · 101 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-large-language-models-education-survey/)
28. **[Medical Graph RAG: Evidence-based Medical Large Language Model via Graph Retrieval-Augmented Generation](https://md.research.elamur.ai/free/en/papers/wu-2025-medical-graph-rag-evidence-based/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Junde Wu, Jiayuan Zhu, Yunli Qi, et al.*2025 · 91 citations*
29. **[SciEval: A Multi-Level Large Language Model Evaluation Benchmark for Scientific Research](https://md.research.elamur.ai/free/en/papers/sun-2023-scieval-multi-level-large-language/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Liangtai Sun, Yang Han, Zihan Zhao, et al.*2023 · 91 citations*
30. [**SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines arXiv:2502.14739**M-A-P Team, Xinrun Du, Yifan Yao, et al.*2025 · 89 citations*](https://md.research.elamur.ai/free/en/papers/team-2025-supergpqa-scaling-llm-evaluation-across/)
31. [**HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs arXiv:2412.18925**Junying Chen, Zhenyang Cai, Ke Ji, et al.*2024 · 87 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-huatuogpt-o1-towards-medical-complex/)
32. [**A Survey of Large Language Models in Medicine: Progress, Application, and Challenge arXiv:2311.05112**Hongjian Zhou, Fenglin Liu, Boyang Gu, et al.*2023 · 87 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2023-survey-large-language-models-medicine/)
33. [**A Survey on Test-Time Scaling in Large Language Models: What, How, Where, and How Well? arXiv:2503.24235**Qiyuan Zhang, Fuyuan Lyu, Zexu Sun, et al.*2025 · 79 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2025-survey-test-time-scaling-large/)
34. [**BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text arXiv:2403.18421**Elliot Bolton, Abhinav Venigalla, Michihiro Yasunaga, et al.*2024 · 79 citations*](https://md.research.elamur.ai/free/en/papers/bolton-2024-biomedlm-2-7b-parameter-language/)
35. **[Benchmarking Large Language Models on Answering and Explaining Challenging Medical Questions](https://md.research.elamur.ai/free/en/papers/chen-2025-benchmarking-large-language-models-answering/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Hanjie Chen, Zhouxiang Fang, Yash Singla, et al.*2025 · 75 citations*
36. [**Small Language Models Learn Enhanced Reasoning Skills from Medical Textbooks arXiv:2404.00376**Hyunjae Kim, Hyeon Hwang, Jiwoo Lee, et al.*2024 · 75 citations*](https://md.research.elamur.ai/free/en/papers/kim-2024-small-language-models-learn-enhanced/)
37. **[MediQ: Question-Asking LLMs and a Benchmark for Reliable Interactive Clinical Reasoning](https://md.research.elamur.ai/free/en/papers/li-2024-mediq-question-asking-llms-benchmark/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Shuyue Stella Li, Vidhisha Balachandran, Shangbin Feng, et al.*2024 · 74 citations*
38. **[Knowledge-Augmented Reasoning Distillation for Small Language Models in Knowledge-Intensive Tasks](https://md.research.elamur.ai/free/en/papers/kang-2023-knowledge-augmented-reasoning-distillation-small/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Minki Kang, Seanie Lee, Jinheon Baek, et al.*2023 · 71 citations*
39. [**ClinicalGPT: Large Language Models Finetuned with Diverse Medical Data and Comprehensive Evaluation arXiv:2306.09968**Guangyu Wang, Guoxing Yang, Zongxin Du, et al.*2023 · 70 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-clinicalgpt-large-language-models-finetuned/)
40. [**MedXpertQA: Benchmarking Expert-Level Medical Reasoning and Understanding arXiv:2501.18362**Yuxin Zuo, Shang Qu, Yifei Li, et al.*2025 · 65 citations*](https://md.research.elamur.ai/free/en/papers/zuo-2025-medxpertqa-benchmarking-expert-level-medical/)
41. **[Instruction Pre-Training: Language Models are Supervised Multitask Learners](https://md.research.elamur.ai/free/en/papers/cheng-2024-instruction-pre-training-language-models/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Daixuan Cheng, Yuxian Gu, Shaohan Huang, et al.*2024 · 59 citations*
42. **[DERA: Enhancing Large Language Model Completions with Dialog-Enabled Resolving Agents](https://md.research.elamur.ai/free/en/papers/nair-2023-dera-enhancing-large-language-model/) [Workshop on Clinical Natural Language Processing (Clinical NLP) @ LREC](https://md.research.elamur.ai/free/en/venues/clinicalnlp/)**Varun Nair, Elliot Schumacher, Geoffrey Tso, et al.*2023 · 55 citations*
43. [**CMB: A Comprehensive Medical Benchmark in Chinese arXiv:2308.08833**Xidong Wang, Guiming Hardy Chen, Dingjie Song, et al.*2023 · 54 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-cmb-comprehensive-medical-benchmark/)
44. **[Sketch-of-Thought: Efficient LLM Reasoning with Adaptive Cognitive-Inspired Sketching](https://md.research.elamur.ai/free/en/papers/aytes-2025-sketch-thought-efficient-llm-reasoning/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Simon A. Aytes, Jinheon Baek, Sung Ju Hwang*2025 · 51 citations*
45. **[Can Multiple-choice Questions Really Be Useful in Detecting the Abilities of LLMs?](https://md.research.elamur.ai/free/en/papers/li-2024-can-multiple-choice-questions-really/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Wangyue Li, Liangzhi Li, Tong Xiang, et al.*2024 · 47 citations*
46. **[“According to . . . “: Prompting Language Models Improves Quoting from Pre-Training Data](https://md.research.elamur.ai/free/en/papers/weller-2024-according-prompting-language-models-improves/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Orion Weller, Marc Marone, Nathaniel Weir, et al.*2024 · 46 citations*
47. **[A Systematic Survey of Automatic Prompt Optimization Techniques](https://md.research.elamur.ai/free/en/papers/ramnath-2025-systematic-survey-automatic-prompt-optimization/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Kiran Ramnath, Kang Zhou, Sheng Guan, et al.*2025 · 40 citations*
48. **[AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator](https://md.research.elamur.ai/free/en/papers/fan-2025-ai-hospital-benchmarking-large-language/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Zhihao Fan, Lai Wei, Jialong Tang, et al.*2025 · 36 citations*
49. **[Benchmark Self-Evolving: A Multi-Agent Framework for Dynamic LLM Evaluation](https://md.research.elamur.ai/free/en/papers/wang-2025-benchmark-self-evolving-multi-agent/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Siyuan Wang, Zhuohan Long, Zhihao Fan, et al.*2025 · 30 citations*
50. [**Baichuan-M1: Pushing the Medical Capability of Large Language Models arXiv:2502.12671**Bingning Wang, Haizhou Zhao, Huozhi Zhou, et al.*2025 · 24 citations*](https://md.research.elamur.ai/free/en/papers/wang-2025-baichuan-m1-pushing-medical/)
51. **[AfriMed-QA: A Pan-African, Multi-Specialty, Medical Question-Answering Benchmark Dataset](https://md.research.elamur.ai/free/en/papers/nimo-2025-afrimed-qa-pan-african-multi/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Charles Nimo, Tobi Olatunji, Abraham Toluwase Owodunni, et al.*2025 · 22 citations*
52. [**MedArabiQ: Benchmarking Large Language Models on Arabic Medical Tasks arXiv:2505.03427**Mouath Abu Daoud, Chaimae Abouzahir, Leen Kharouf, et al.*2025 · 22 citations*](https://md.research.elamur.ai/free/en/papers/daoud-2025-medarabiq-benchmarking-large-language-models/)
53. **[RARE: Retrieval-Augmented Reasoning Enhancement for Large Language Models](https://md.research.elamur.ai/free/en/papers/tran-2025-rare-retrieval-augmented-reasoning-enhancement/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hieu Tran, Zonghai Yao, Zhichao Yang, et al.*2025 · 20 citations*
54. **[DiReCT: Diagnostic Reasoning for Clinical Notes via Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2024-direct-diagnostic-reasoning-clinical-notes/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Bowen Wang, Jiuyang Chang, Yiming Qian, et al.*2024 · 20 citations*
55. [**ScholarChemQA: Unveiling the Power of Language Models in Chemical Research Question Answering arXiv:2407.16931**Xiuying Chen, Tairan Wang, Taicheng Guo, et al.*2024 · 19 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-scholarchemqa-unveiling-power-language-models/)
56. **[Can Knowledge Graphs Make Large Language Models More Trustworthy? An Empirical Study Over Open-ended Question Answering](https://md.research.elamur.ai/free/en/papers/sui-2025-can-knowledge-graphs-make-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuan Sui, Yufei He, Zifeng Ding, et al.*2025 · 16 citations*
57. **[Mix-of-Granularity: Optimize the Chunking Granularity for Retrieval-Augmented Generation](https://md.research.elamur.ai/free/en/papers/zhong-2025-mix-granularity-optimize-chunking-granularity/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Zijie Zhong, Hanwen Liu, Xiaoya Cui, et al.*2025 · 13 citations*
58. **[Dolomites: Domain-Specific Long-Form Methodical Tasks](https://md.research.elamur.ai/free/en/papers/malaviya-2025-dolomites-domain-specific-long-form/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Chaitanya Malaviya, Priyanka Agrawal, Kuzman Ganchev, et al.*2025 · 12 citations*
59. **[s3: You Don't Need That Much Data to Train a Search Agent via RL](https://md.research.elamur.ai/free/en/papers/jiang-2025-s3-you-don-t-need/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Pengcheng Jiang, Xueqiang Xu, Jiacheng Lin, et al.*2025 · 12 citations*
60. **[ReasonMed: A 370K Multi-Agent Generated Dataset for Advancing Medical Reasoning](https://md.research.elamur.ai/free/en/papers/sun-2025-reasonmed-370k-multi-agent-generated/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yu Sun, Xingyu Qian, Weiwen Xu, et al.*2025 · 11 citations*
61. **[Ask Patients with Patience: Enabling LLMs for Human-Centric Medical Dialogue with Grounded Reasoning](https://md.research.elamur.ai/free/en/papers/zhu-2025-ask-patients-patience-enabling-llms/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jiayuan Zhu, Jiazhen Pan, Yuyuan Liu, et al.*2025 · 10 citations*
62. [**Beyond Distillation: Pushing the Limits of Medical LLM Reasoning with Minimalist Rule-Based RL arXiv:2505.17952**Che Liu, Haozhe Wang, Jiazhen Pan, et al.*2025 · 10 citations*](https://md.research.elamur.ai/free/en/papers/liu-2025-distillation-pushing-limits-medical-llm/)
63. **[Knowing When to Abstain: Medical LLMs Under Clinical Uncertainty](https://md.research.elamur.ai/free/en/papers/machcha-2026-knowing-when-abstain-medical-llms/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Sravanthi Machcha, Sushrita Yerra, Sahil Gupta, et al.*2026 · 9 citations*
64. **[AraHealthQA 2025: The First Shared Task on Arabic Health Question Answering](https://md.research.elamur.ai/free/en/papers/alhuzali-2025-arahealthqa-2025-first-shared-task/) [Arabic Natural Language Processing Conference (ArabicNLP)](https://md.research.elamur.ai/free/en/venues/arabicnlp/)**Hassan Alhuzali, Walid Al-Eisawi, Muhammad Abdul-Mageed, et al.*2025 · 8 citations*
65. [**Pangu Ultra MoE: How to Train Your Big MoE on Ascend NPUs arXiv:2505.04519**Yehui Tang, Yichun Yin, Yaoyuan Wang, et al.*2025 · 8 citations*](https://md.research.elamur.ai/free/en/papers/tang-2025-pangu-ultra-moe-train/)
66. **[DiagnosisArena: Benchmarking Diagnostic Reasoning for Large Language Models](https://md.research.elamur.ai/free/en/papers/zhu-2026-diagnosisarena-benchmarking-diagnostic-reasoning-large/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yakun Zhu, Zhongzhen Huang, Linjie Mu, et al.*2026 · 7 citations*
67. [**Cancer-Myth: Evaluating Large Language Models on Patient Questions with False Presuppositions arXiv:2504.11373**Wang Bill Zhu, Tianqi Chen, Xinyan Velocity Yu, et al.*2025 · 7 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2025-cancer-myth-evaluating-large-language/)
68. [**GRIN: GRadient-INformed MoE arXiv:2409.12136**Liyuan Liu, Young Jin Kim, Shuohang Wang, et al.*2024 · 7 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-grin-gradient-informed-moe/)
69. **[Why Supervised Fine-Tuning Fails to Learn: A Systematic Study of Incomplete Learning in Large Language Models](https://md.research.elamur.ai/free/en/papers/xue-2026-why-supervised-fine-tuning-fails/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chao Xue, Yao Wang, Mengqiao Liu, et al.*2026 · 6 citations*
70. **[CURE-Med: Curriculum-Informed Reinforcement Learning for Multilingual Medical Reasoning](https://md.research.elamur.ai/free/en/papers/onyame-2026-cure-med-curriculum-informed-reinforcement/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Eric Onyame, Akash Ghosh, Subhadip Baidya, et al.*2026 · 5 citations*
71. [**MedAraBench: Large-Scale Arabic Medical Question Answering Dataset and Benchmark arXiv:2602.01714**Mouath Abu-Daoud, Leen Kharouf, Omar El Hajj, et al.*2026 · 5 citations*](https://md.research.elamur.ai/free/en/papers/abu-daoud-2026-medarabench-large-scale-arabic-medical/)
72. **[OctoTools: A Multi-Agent Framework with Extensible Tools for Complex Reasoning](https://md.research.elamur.ai/free/en/papers/lu-2026-octotools-multi-agent-framework-extensible/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Pan Lu, Bowen Chen, Sheng Liu, et al.*2026 · 5 citations*
73. **[From Scores to Steps: Diagnosing and Improving LLM Performance in Evidence-Based Medical Calculations](https://md.research.elamur.ai/free/en/papers/wang-2025-scores-steps-diagnosing-improving-llm/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Benlu Wang, Iris Xia, Yifan Zhang, et al.*2025 · 5 citations*
74. **[Experience Retrieval-Augmentation with Electronic Health Records Enables Accurate Discharge QA](https://md.research.elamur.ai/free/en/papers/ou-2026-experience-retrieval-augmentation-electronic-health/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Justice Ou, Tinglin Huang, Yilun Zhao, et al.*2026 · 4 citations*
75. **[Patient-Similarity Cohort Reasoning in Clinical Text-to-SQL](https://md.research.elamur.ai/free/en/papers/shen-2026-patient-similarity-cohort-reasoning-clinical/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Yifei Shen, Yilun Zhao, Justice Ou, et al.*2026 · 4 citations*
76. **[ProMed: Shapley Information Gain Guided Reinforcement Learning for Proactive Medical LLMs](https://md.research.elamur.ai/free/en/papers/ding-2026-promed-shapley-information-gain-guided/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hongxin Ding, Baixiang Huang, Yue Fang, et al.*2026 · 4 citations*
77. **[MedQA-CS: Objective Structured Clinical Examination (OSCE)-Style Benchmark for Evaluating LLM Clinical Skills](https://md.research.elamur.ai/free/en/papers/yao-2026-medqa-cs-objective-structured-clinical/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Zonghai Yao, Zihao Zhang, Chaolong Tang, et al.*2026 · 3 citations*
78. **[SFTMix: Elevating Language Model Instruction Tuning with Mixup Recipe](https://md.research.elamur.ai/free/en/papers/xiao-2026-sftmix-elevating-language-model-instruction/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuxin Xiao, Shujian Zhang, Marzyeh Ghassemi, et al.*2026 · 3 citations*
79. [**Bridging Language Barriers in Healthcare: A Study on Arabic LLMs arXiv:2501.09825**Nada Saadi, Tathagata Raha, Clement Christophe, et al.*2025 · 3 citations*](https://md.research.elamur.ai/free/en/papers/saadi-2025-bridging-language-barriers-healthcare-study/)
80. **[Adaptive Text Anonymization: Learning Privacy-Utility Trade-offs via Prompt Optimization](https://md.research.elamur.ai/free/en/papers/loiseau-2026-adaptive-text-anonymization-learning-privacy/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Gabriel Loiseau, Damien Sileo, Damien Riquet, et al.*2026 · 2 citations*
81. **[Mind the Gap: Benchmarking LLM Uncertainty and Calibration with Specialty-Aware Clinical QA and Reasoning-Based Behavioural Features](https://md.research.elamur.ai/free/en/papers/testoni-2026-mind-gap-benchmarking-llm-uncertainty/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Alberto Testoni, Iacer Calixto*2026 · 2 citations*
82. **[QuCo-RAG: Quantifying Uncertainty from the Pre-training Corpus for Dynamic Retrieval-Augmented Generation](https://md.research.elamur.ai/free/en/papers/min-2026-quco-rag-quantifying-uncertainty-pre/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Dehai Min, Kailin Zhang, Tongtong Wu, et al.*2026 · 2 citations*
83. **[Sycophants in the Courtroom: Are LLMs Fragile to Juridical Authority and Evolving Legal Standards?](https://md.research.elamur.ai/free/en/papers/molfetta-2026-sycophants-courtroom-are-llms-fragile/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Lorenzo Molfetta, Alessio Cocchieri, Luca Ragazzi, et al.*2026 · 2 citations*
84. **[When and What to Ask: AskBench and Rubric-Guided RLVR for LLM Clarification](https://md.research.elamur.ai/free/en/papers/zhao-2026-when-what-ask-askbench-rubric/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jiale Zhao, Ke Fang, Lu Cheng*2026 · 2 citations*
85. **[ART: Adaptive Reasoning Trees for Explainable Claim Verification](https://md.research.elamur.ai/free/en/papers/wadhwa-2026-art-adaptive-reasoning-trees-explainable/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Sahil Wadhwa, Himanshu Kumar, Guanqun Yang, et al.*2026 · 1 citation*
86. **[Beyond the Leaderboard: Rethinking Medical Benchmarks for Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-beyond-leaderboard-rethinking-medical-benchmarks/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wenxuan Wang, Zizhan Ma, Guo Yu, et al.*2026 · 1 citation*
87. **[ClinDet-Bench: Beyond Abstention, Evaluating Judgment Determinability of LLMs in Clinical Decision-Making](https://md.research.elamur.ai/free/en/papers/watanabe-2026-clindet-bench-beyond-abstention-evaluating/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yusuke Watanabe, Yohei Kobashi, Takeshi Kojima, et al.*2026 · 1 citation*
88. **[DeepMed: Building a Medical DeepResearch Agent via Multi-hop Med-Search Data and Turn-Controlled Agentic Training & Inference](https://md.research.elamur.ai/free/en/papers/wang-2026-deepmed-building-medical-deepresearch-agent/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zihan Wang, Hao Wang, Shi Feng, et al.*2026 · 1 citation*
89. **[Deputy: Accelerating Large Language Model Inference with Dynamic Low-Rank Substitution](https://md.research.elamur.ai/free/en/papers/zhou-2026-deputy-accelerating-large-language-model/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yuhua Zhou, Shichao Weng, Changhai Zhou, et al.*2026 · 1 citation*
90. **[From Local to Global: Revisiting Structured Pruning Paradigms for Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-local-global-revisiting-structured-pruning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ziyan Wang, Enmao Diao, Qi Le, et al.*2026 · 1 citation*
91. **[How Robust Are Large Language Models for Clinical Numeracy? An Empirical Study on Numerical Reasoning Abilities in Clinical Contexts](https://md.research.elamur.ai/free/en/papers/nguyen-2026-how-robust-are-large-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Minh-Vuong Nguyen, Fatemeh Shiri, Zhuang Li, et al.*2026 · 1 citation*
92. **[Human or LLM as Standardized Patients? A Comparative Study in Medical Education](https://md.research.elamur.ai/free/en/papers/zhang-2026-human-or-llm-as-standardized/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Bingquan Zhang, Xiaoxiao Liu, Yuchi Wang, et al.*2026 · 1 citation*
93. **[InferenceDynamics: Adaptive LLM Routing through Structured Capability and Knowledge Profiling](https://md.research.elamur.ai/free/en/papers/shi-2026-inferencedynamics-adaptive-llm-routing-through/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haochen Shi, Tianshi Zheng, Weiqi Wang, et al.*2026 · 1 citation*
94. **[Reasoning or Knowledge: Stratified Evaluation of Biomedical LLMs](https://md.research.elamur.ai/free/en/papers/thapa-2026-reasoning-or-knowledge-stratified-evaluation/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Rahul Thapa, Qingyang Wu, Kevin Wu, et al.*2026 · 1 citation*
95. **[Streaming Hallucination Detection in Long Chain-of-Thought Reasoning](https://md.research.elamur.ai/free/en/papers/lu-2026-streaming-hallucination-detection-long-chain/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Haolang Lu, Minghui Pan, Ripeng LI, et al.*2026 · 1 citation*
96. **[Strong Reasoning Isn't Enough: Evaluating Evidence Elicitation in Interactive Diagnosis](https://md.research.elamur.ai/free/en/papers/long-2026-strong-reasoning-isn-t-enough/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhuohan Long, Zhongyu Wei*2026 · 1 citation*
97. **[TAGS: A Test-Time Generalist–Specialist Framework with Retrieval-Augmented Reasoning and Verification](https://md.research.elamur.ai/free/en/papers/wu-2026-tags-test-time-generalist-specialist/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jianghao Wu, Feilong Tang, Yulong Li, et al.*2026 · 1 citation*
98. [**The Hrunting of AI: Where and How to Improve English Dialectal Fairness arXiv:2603.15187**Wei Li, Adrian de Wynter*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/li-2026-hrunting-ai-where-improve-english/)
99. **[When Retrieval Doesn't Help: A Large-Scale Study of Biomedical RAG](https://md.research.elamur.ai/free/en/papers/nourbakhsh-2026-when-retrieval-doesn-t-help/) [BioNLP](https://md.research.elamur.ai/free/en/venues/bionlp/)**Erfan Nourbakhsh, Rocky Slavin, Ke Yang, et al.*2026 · 1 citation*
100. **[Who Endorsed It? Measuring Authority Bias Across Expertise Levels in Language Models](https://md.research.elamur.ai/free/en/papers/mammen-2026-who-endorsed-it-measuring-authority/) [Workshop on Generation, Evaluation and Metrics (GEM)](https://md.research.elamur.ai/free/en/venues/gem/)**Priyanka Mary Mammen, Emil Joswin, Shankar Venkitachalam*2026 · 1 citation*

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