🔗 Who cites «Thinking about GPT-3 In-Context Learning for Biomedical IE? Think Again»

64 papers of our library cite it (of the 141,840 whose bibliographies we read) · 7 with a page here, the most cited first · ← back to «Thinking about GPT-3 In-Context Learning for Biomedical IE? Think Again»

Large language models are few-shot clinical information extractorsConference on Empirical Methods in Natural Language Processing (EMNLP)Monica Agrawal, Stefan Hegselmann, Hunter Lang, et al.2022 · 450 citations
GPT-RE: In-context Learning for Relation Extraction using Large Language ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Zhen Wan, Fei Cheng, Zhuoyuan Mao, et al.2023 · 253 citations
Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!Findings of the Association for Computational LinguisticsYubo Ma, Yixin Cao, Yong Hong, et al.2023 · 211 citations
Sources of Hallucination by Large Language Models on Inference TasksFindings of the Association for Computational LinguisticsNick McKenna, Tianyi Li, Liang Cheng, et al.2023 · 209 citations
1. Can GPT-3 Perform Statutory Reasoning?arXiv:2302.06100Andrew Blair-Stanek, Nils Holzenberger, Benjamin Van Durme2023 · 153 citations
   Aligning Instruction Tasks Unlocks Large Language Models as Zero-Shot Relation ExtractorsFindings of the Association for Computational LinguisticsKai Zhang, Bernal Jimenez Gutierrez, Yu Su2023 · 118 citations
   CodeIE: Large Code Generation Models are Better Few-Shot Information ExtractorsAnnual Meeting of the Association for Computational Linguistics (ACL)Peng Li, Tianxiang Sun, Qiong Tang, et al.2023 · 109 citations

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

This page in Markdown → https://md.research.elamur.ai/free/en/papers/gutierrez-2022-thinking-about-gpt-3-context/cited-by/