Who cites «Ground-Truth Labels Matter: A Deeper Look into Input-Label Demonstrations»
- Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?Conference on Empirical Methods in Natural Language Processing (EMNLP)Sewon Min, Xinxi Lyu, Ari Holtzman, et al.2022 · 1,425 citations
- A Survey on In-context LearningarXiv:2301.00234Qingxiu Dong, Lei Li, Damai Dai, et al.2023 · 959 citations
- Large Language Models Can Be Easily Distracted by Irrelevant ContextarXiv:2302.00093Freda Shi, Xinyun Chen, Kanishka Misra, et al.2023 · 525 citations
- Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What MattersAnnual Meeting of the Association for Computational Linguistics (ACL)Boshi Wang, Sewon Min, Xiang Deng, et al.2023 · 269 citations
- DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT ModelsConference on Neural Information Processing Systems (NeurIPS)Boxin Wang, Weixin Chen, Hengzhi Pei, et al.2023 · 246 citations
- The Prompt Report: A Systematic Survey of Prompt Engineering TechniquesarXiv:2406.06608Sander Schulhoff, Michael Ilie, Nishant Balepur, et al.2024 · 187 citations
- Label Words are Anchors: An Information Flow Perspective for Understanding In-Context LearningConference on Empirical Methods in Natural Language Processing (EMNLP)Lean Wang, Lei Li, Damai Dai, et al.2023 · 153 citations
- Many-Shot In-Context LearningConference on Neural Information Processing Systems (NeurIPS)Rishabh Agarwal, Avi Singh, Lei M. Zhang, et al.2024 · 134 citations
- What In-Context Learning “Learns” In-Context: Disentangling Task Recognition and Task LearningFindings of the Association for Computational LinguisticsJane Pan, Tianyu Gao, Howard Chen, et al.2023 · 100 citations
- Boosting Language Models Reasoning with Chain-of-Knowledge PromptingAnnual Meeting of the Association for Computational Linguistics (ACL)Jianing Wang, Qiushi Sun, Xiang Li, et al.2024 · 94 citations
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