---
title: "Who cites Large Language Models are — 36 citing papers"
description: "36 papers of the Elamur Research library cite «Large Language Models are Few-Shot Clinical Information Extractors», found in their…"
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Large Language Models are Few-Shot Clinical…](https://md.research.elamur.ai/free/en/papers/agrawal-2022-large-language-models-few-shot/) / cited by

# Who cites «Large Language Models are Few-Shot Clinical Information Extractors»

36 papers of our library cite it (of the 149,439 whose bibliographies we read) · 13 with a page here, the most cited first · [← back to «Large Language Models are Few-Shot Clinical Information Extractors»](https://md.research.elamur.ai/free/en/papers/agrawal-2022-large-language-models-few-shot/)

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. [**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/)
3. **[Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting](https://md.research.elamur.ai/free/en/papers/qin-2024-large-language-models-are-effective/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhen Qin, Rolf Jagerman, Kai Hui, et al.*2024 · 300 citations*
4. [**ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPT arXiv:2302.10205**Xiang Wei; Xingyu Cui; Ning Cheng; Xiaobin Wang; Xin Zhang; Shen Huang; Pengjun Xie; Jinan Xu; Yufeng Chen; Meishan Zhang; Yong Jiang;, Wenjuan Han*2023 · 276 citations*](https://md.research.elamur.ai/free/en/papers/wei-2023-chatie-zero-shot-information-extraction/)
5. **[Large Language Models for Data Annotation and Synthesis: A Survey](https://md.research.elamur.ai/free/en/papers/tan-2024-large-language-models-data-annotation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhen Tan, Dawei Li, Song Wang, et al.*2024 · 262 citations*
6. **[A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation](https://md.research.elamur.ai/free/en/papers/huang-2023-survey-safety-trustworthiness-large-language/) [Artificial Intelligence Review](https://md.research.elamur.ai/free/en/venues/artificial-intelligence-review/)**Xiaowei Huang, Wenjie Ruan, Wei Huang, et al.*2023 · 199 citations*
7. **[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*
8. **[Understanding the Dark Side of LLMs' Intrinsic Self-Correction](https://md.research.elamur.ai/free/en/papers/zhang-2025-understanding-dark-side-llms-intrinsic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qingjie Zhang, Di Wang, Haoting Qian, et al.*2025 · 21 citations*
9. **[Few-Shot Anaphora Resolution in Scientific Protocols via Mixtures of In-Context Experts](https://md.research.elamur.ai/free/en/papers/le-2022-few-shot-anaphora-resolution-scientific/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Nghia T. Le, Fan Bai, Alan Ritter*2022 · 20 citations*
10. **[HARM: Learning Hate-Aware Reward Model for Evaluating Natural Language Explanations of Offensive Content](https://md.research.elamur.ai/free/en/papers/vecchi-2026-harm-learning-hate-aware-reward/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Lorenzo Puppi Vecchi, Alceu de Souza Britto Jr, Emerson Cabrera Paraiso, et al.*2026 · 1 citation*
11. **[Smart_solutions at MEDIQA-SYNUR 2026: A Multi-Stage LLM Pipeline for Nursing Observation Extraction](https://md.research.elamur.ai/free/en/papers/munjal-2026-smart-solutions-mediqa-synur-2026/) [Workshop on Clinical Natural Language Processing (Clinical NLP) @ LREC](https://md.research.elamur.ai/free/en/venues/clinicalnlp/)**Prateek Munjal*2026 · 1 citation*
12. **[EpiGator: An Event-based Surveillance System for Infectious Disease Outbreaks](https://md.research.elamur.ai/free/en/papers/wu-2026-epigator-event-based-surveillance-system/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Yiheng Wu, Jue Hou, Trangcasanchai Sathianpong, et al.*2026*
13. **[Mask-to-Correct+: Leveraging Retriever Diversity for Masking-guided Faithful Fact Correction](https://md.research.elamur.ai/free/en/papers/santra-2026-mask-correct-leveraging-retriever-diversity/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Payel Santra, Lavisha Sharma, Madhusudan Ghosh, et al.*2026*

23 more papers of our library cite it; they have no page here yet.

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