---
title: "Who cites Ellery Wulczyn — 254 papers of our library"
description: "254 papers of the Elamur Research library cite the works of Ellery Wulczyn, found in their bibliographies; the most cited first."
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---
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# Who cites Ellery Wulczyn

275 citations of their works from 254 papers of our library (of the 149,439 whose bibliographies we read) · 62 with a page here, the most cited first · [← Ellery Wulczyn](https://md.research.elamur.ai/free/en/authors/ellery-wulczyn/)

1. **[A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://md.research.elamur.ai/free/en/papers/huang-2023-survey-hallucination-large-language-models/) [ACM Transactions on Information Systems](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-information-systems/)**Lei Huang, Weijiang Yu, Weitao Ma, et al.*2023 · 3,231 citations*
2. **[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*
3. [**GPT-4o System Card arXiv:2410.21276**OpenAI, Aaron Hurst, Adam Lerer, et al.*2024 · 2,304 citations*](https://md.research.elamur.ai/free/en/papers/openai-2024-gpt-4o-system-card/)
4. [**Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context arXiv:2403.05530**Gemini Team, Petko Georgiev, Ving Ian Lei, et al.*2024 · 1,589 citations*](https://md.research.elamur.ai/free/en/papers/gemini-team-2024-gemini-1-5-unlocking-multimodal/)
5. **[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*
6. **[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*
7. [**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/)
8. **[OCRBench: On the Hidden Mystery of OCR in Large Multimodal Models](https://md.research.elamur.ai/free/en/papers/liu-2023-ocrbench-hidden-mystery-ocr-large/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Yuliang Liu, Zhang Li, Mingxin Huang, et al.*2023 · 306 citations*
9. [**Challenges and Applications of Large Language Models arXiv:2307.10169**Jean Kaddour, Joshua Harris, Maximilian Mozes, et al.*2023 · 301 citations*](https://md.research.elamur.ai/free/en/papers/kaddour-2023-challenges-applications-large-language-models/)
10. [**Llemma: An Open Language Model For Mathematics arXiv:2310.10631**Zhangir Azerbayev, Hailey Schoelkopf, Keiran Paster, et al.*2023 · 286 citations*](https://md.research.elamur.ai/free/en/papers/azerbayev-2023-llemma-open-language-model-mathematics/)
11. **[HuatuoGPT, Towards Taming Language Model to Be a Doctor](https://md.research.elamur.ai/free/en/papers/zhang-2023-huatuogpt-taming-language-model-be/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hongbo Zhang, Junying Chen, Feng Jiang, et al.*2023 · 282 citations*
12. **[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*
13. **[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*
14. **[Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMs](https://md.research.elamur.ai/free/en/papers/ovadia-2024-fine-tuning-or-retrieval-comparing/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Oded Ovadia, Menachem Brief, Moshik Mishaeli, et al.*2024 · 206 citations*
15. **[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*
16. [**Evaluating Large Language Models: A Comprehensive Survey arXiv:2310.19736**Zishan Guo, Renren Jin, Chuang Liu, et al.*2023 · 182 citations*](https://md.research.elamur.ai/free/en/papers/guo-2023-evaluating-large-language-models-comprehensive/)
17. **[Two Tales of Persona in LLMs: A Survey of Role-Playing and Personalization](https://md.research.elamur.ai/free/en/papers/tseng-2024-two-tales-persona-llms-survey/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yu-Min Tseng, Yu-Chao Huang, Teng-Yun Hsiao, et al.*2024 · 179 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. [**Orca 2: Teaching Small Language Models How to Reason arXiv:2311.11045**Arindam Mitra, Luciano Del Corro, Shweti Mahajan, et al.*2023 · 162 citations*](https://md.research.elamur.ai/free/en/papers/mitra-2023-orca-2-teaching-small-language/)
20. [**Lawyer LLaMA Technical Report arXiv:2305.15062**Quzhe Huang, Mingxu Tao, Chen Zhang, et al.*2023 · 157 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-lawyer-llama-technical-report/)
21. [**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/)
22. [**HealthBench: Evaluating Large Language Models Towards Improved Human Health arXiv:2505.08775**Rahul K. Arora, Jason Wei, Rebecca Soskin Hicks, et al.*2025 · 126 citations*](https://md.research.elamur.ai/free/en/papers/arora-2025-healthbench-evaluating-large-language-models/)
23. [**Platypus: Quick, Cheap, and Powerful Refinement of LLMs arXiv:2308.07317**Ariel N. Lee, Cole J. Hunter, Nataniel Ruiz*2023 · 117 citations*](https://md.research.elamur.ai/free/en/papers/lee-2023-platypus-quick-cheap-powerful-refinement/)
24. [**Universal Self-Consistency for Large Language Model Generation arXiv:2311.17311**Xinyun Chen, Renat Aksitov, Uri Alon, et al.*2023 · 92 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-universal-self-consistency-large-language/)
25. **[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*
26. [**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/)
27. [**Rethinking Interpretability in the Era of Large Language Models arXiv:2402.01761**Chandan Singh, Jeevana Priya Inala, Michel Galley, et al.*2024 · 87 citations*](https://md.research.elamur.ai/free/en/papers/singh-2024-rethinking-interpretability-era-large-language/)
28. [**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/)
29. **[Retrieval-Augmented Generation for Natural Language Processing: A Survey](https://md.research.elamur.ai/free/en/papers/wu-2024-retrieval-augmented-generation-natural-language/) [Artificial Intelligence Review](https://md.research.elamur.ai/free/en/venues/artificial-intelligence-review/)**Shangyu Wu, Ying Xiong, Yufei Cui, et al.*2024 · 86 citations*
30. [**A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics arXiv:2310.05694**Kai He, Rui Mao, Qika Lin, et al.*2023 · 86 citations*](https://md.research.elamur.ai/free/en/papers/he-2023-survey-large-language-models-healthcare/)
31. [**Benchmark Data Contamination of Large Language Models: A Survey arXiv:2406.04244**Cheng Xu, Shuhao Guan, Derek Greene, et al.*2024 · 85 citations*](https://md.research.elamur.ai/free/en/papers/xu-2024-benchmark-data-contamination-large/)
32. [**Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attacks arXiv:2310.10844**Erfan Shayegani, Md Abdullah Al Mamun, Yu Fu, et al.*2023 · 83 citations*](https://md.research.elamur.ai/free/en/papers/shayegani-2023-survey-vulnerabilities-large-language-models/)
33. [**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/)
34. **[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*
35. [**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/)
36. **[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*
37. **[Self-Alignment for Factuality: Mitigating Hallucinations in LLMs via Self-Evaluation](https://md.research.elamur.ai/free/en/papers/zhang-2024-self-alignment-factuality-mitigating-hallucinations/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaoying Zhang, Baolin Peng, Ye Tian, et al.*2024 · 69 citations*
38. **[Arithmetic Control of LLMs for Diverse User Preferences: Directional Preference Alignment with Multi-Objective Rewards](https://md.research.elamur.ai/free/en/papers/wang-2024-arithmetic-control-llms-diverse-user/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haoxiang Wang, Yong Lin, Wei Xiong, et al.*2024 · 68 citations*
39. [**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/)
40. **[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*
41. [**Reka Core, Flash, and Edge: A Series of Powerful Multimodal Language Models arXiv:2404.12387**Reka Team: Aitor Ormazabal, Che Zheng, Cyprien de Masson d'Autume, et al.*2024 · 42 citations*](https://md.research.elamur.ai/free/en/papers/ormazabal-2024-reka-core-flash-edge-series/)
42. **[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*
43. **[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*
44. **[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*
45. **[Evaluating Large Language Models for Health-related Queries with Presuppositions](https://md.research.elamur.ai/free/en/papers/kaur-2024-evaluating-large-language-models-health/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Navreet Kaur, Monojit Choudhury, Danish Pruthi*2024 · 15 citations*
46. [**BioKGBench: A Knowledge Graph Checking Benchmark of AI Agent for Biomedical Science arXiv:2407.00466**Xinna Lin, Siqi Ma, Junjie Shan, et al.*2024 · 12 citations*](https://md.research.elamur.ai/free/en/papers/lin-2024-biokgbench-knowledge-graph-checking-benchmark/)
47. **[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*
48. [**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/)
49. **[Evaluating Large Language Models on Health-Related Claims Across Arabic Dialects](https://md.research.elamur.ai/free/en/papers/alharbi-2025-evaluating-large-language-models-health/) [Workshop on NLP for Languages Using Arabic Script](https://md.research.elamur.ai/free/en/venues/abjadnlp/)**Abdulsalam obaid Alharbi, Abdullah Alsuhaibani, Abdulrahman Abdullah Alalawi, et al.*2025 · 4 citations*
50. **[PuxAI at QIAS 2025: Multi-Agent Retrieval-Augmented Generation for Islamic Inheritance and Knowledge Reasoning](https://md.research.elamur.ai/free/en/papers/phuc-2025-puxai-qias-2025-multi-agent/) [Arabic Natural Language Processing Conference (ArabicNLP)](https://md.research.elamur.ai/free/en/venues/arabicnlp/)**Nguyen Xuan Phuc, Thìn Đặng Văn*2025 · 4 citations*
51. **[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*
52. **[BDI at MEDIQA-EVAL 2026: A ReAct-Style Multimodal Agent for Fine-Grained Medical Response Assessment](https://md.research.elamur.ai/free/en/papers/xu-2026-bdi-mediqa-eval-2026-react/) [Workshop on Clinical Natural Language Processing (Clinical NLP) @ LREC](https://md.research.elamur.ai/free/en/venues/clinicalnlp/)**Justin Xu, Zizheng Zhang, Augustine Luk, et al.*2026 · 1 citation*
53. **[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*
54. **[Do Mixed-Vendor Multi-Agent LLMs Improve Clinical Diagnosis?](https://md.research.elamur.ai/free/en/papers/yuan-2026-do-mixed-vendor-multi-agent/) [Workshop on Linguistic Analysis for Health](https://md.research.elamur.ai/free/en/venues/healing/)**Grace Chang Yuan, Xiaoman Zhang, Sung Eun Kim, et al.*2026 · 1 citation*
55. **[MedGapGab at AraHealthQA: Modular LLM Assignment for Gaps and Gabs in Arabic Medical Question Answering](https://md.research.elamur.ai/free/en/papers/hikal-2025-medgapgab-arahealthqa-modular-llm-assignment/) [Arabic Natural Language Processing Conference (ArabicNLP)](https://md.research.elamur.ai/free/en/venues/arabicnlp/)**Baraa Hikal*2025 · 1 citation*
56. **[Datasets for a Chatbot for Clinical Trial Search](https://md.research.elamur.ai/free/en/papers/yang-2026-datasets-chatbot-clinical-trial-search/) [Workshop on Patient-Oriented Language Processing @ LREC](https://md.research.elamur.ai/free/en/venues/cl4health/)**Yumeng Yang, Ethan Ludmir, Kirk Roberts*2026*
57. **[DocUA at CRF Filling 2026: LLM StructCore –- Schema-Guided Reasoning Condensation and Deterministic Compilation](https://md.research.elamur.ai/free/en/papers/zabolotnii-2026-docua-crf-filling-2026-llm/) [Workshop on Patient-Oriented Language Processing @ LREC](https://md.research.elamur.ai/free/en/venues/cl4health/)**Serhii Zabolotnii*2026*
58. **[Evaluating the Pre-Consultation Ability of LLMs using Diagnostic Guidelines](https://md.research.elamur.ai/free/en/papers/seo-2026-evaluating-pre-consultation-ability-llms/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Jean Seo, Gibaeg Kim, Kihun Shin, et al.*2026*
59. **[Faithful Medical Dialogue Generation Using Homo-Heterogeneous Exemplar-based In-Context Knowledge Grounding](https://md.research.elamur.ai/free/en/papers/priya-2026-faithful-medical-dialogue-generation-homo/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Priyanshu Priya, Hardik Goyal, Asif Ekbal*2026*
60. **[Is Micro Domain-Adaptive Pre-Training Effective for Real-World Operations? Multi-Step Evaluation Reveals Potential and Bottlenecks](https://md.research.elamur.ai/free/en/papers/tsunokake-2026-is-micro-domain-adaptive-pre/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Masaya Tsunokake, Yuta Koreeda, Terufumi Morishita, et al.*2026*
61. **[MHGraphBench: Knowledge Graph-Grounded Benchmarking of Mental Health Knowledge in Large Language Models](https://md.research.elamur.ai/free/en/papers/liu-2026-mhgraphbench-knowledge-graph-grounded-benchmarking/) [Workshop on Generation, Evaluation and Metrics (GEM)](https://md.research.elamur.ai/free/en/venues/gem/)**Weixin Liu, Congning Ni, Shelagh A. Mulvaney, et al.*2026*
62. **[RADO: Reasoning Audit-Driven Optimization for Rigorous Reasoning in High-Stakes Domains](https://md.research.elamur.ai/free/en/papers/tan-2026-rado-reasoning-audit-driven-optimization/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhijie Tan, Xu Chu, Guanyu Wang, et al.*2026*

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