🔗 Who cites «What Can Transformers Learn In-Context? A Case Study of Simple Function Classes»

115 papers of our library cite it (of the 149,439 whose bibliographies we read) · 28 with a page here, the most cited first · ← back to «What Can Transformers Learn In-Context? A Case Study of Simple Function Classes»

1. A Survey on In-context LearningarXiv:2301.00234Qingxiu Dong, Lei Li, Damai Dai, et al.2023 · 978 citations
2. Larger language models do in-context learning differentlyarXiv:2303.03846Jerry Wei, Jason Wei, Yi Tay, et al.2023 · 285 citations
3. 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 · 272 citations
4. Why Can GPT Learn In-Context? Language Models Secretly Perform Gradient Descent as Meta-OptimizersFindings of the Association for Computational LinguisticsDamai Dai, Yutao Sun, Li Dong, et al.2023 · 257 citations
5. Transformers learn in-context by gradient descentarXiv:2212.07677Johannes von Oswald, Eyvind Niklasson, Ettore Randazzo, et al.2022 · 247 citations
6. What learning algorithm is in-context learning? Investigations with linear modelsarXiv:2211.15661Ekin Akyürek, Dale Schuurmans, Jacob Andreas, et al.2022 · 236 citations
7. Many-Shot In-Context LearningConference on Neural Information Processing Systems (NeurIPS)Rishabh Agarwal, Avi Singh, Lei M. Zhang, et al.2024 · 142 citations
8. Function Vectors in Large Language ModelsarXiv:2310.15213Eric Todd, Millicent L. Li, Arnab Sen Sharma, et al.2023 · 127 citations
9. Large Language Models Understand and Can be Enhanced by Emotional StimuliarXiv:2307.11760Cheng Li, Jindong Wang, Yixuan Zhang, et al.2023 · 116 citations
10. Studying Large Language Model Generalization with Influence FunctionsarXiv:2308.03296Roger Grosse, Juhan Bae, Cem Anil, et al.2023 · 105 citations
11. Can we trust the evaluation on ChatGPT?Workshop on Trustworthy NLPRachith Aiyappa, Jisun An, Haewoon Kwak, et al.2023 · 103 citations
12. Large Language Models as General Pattern MachinesarXiv:2307.04721Suvir Mirchandani, Fei Xia, Pete Florence, et al.2023 · 88 citations
13. Foundational Challenges in Assuring Alignment and Safety of Large Language ModelsarXiv:2404.09932Usman Anwar, Abulhair Saparov, Javier Rando, et al.2024 · 87 citations
14. Rethinking Interpretability in the Era of Large Language ModelsarXiv:2402.01761Chandan Singh, Jeevana Priya Inala, Michel Galley, et al.2024 · 87 citations
15. In-Context Unlearning: Language Models as Few Shot UnlearnersarXiv:2310.07579Martin Pawelczyk, Seth Neel, Himabindu Lakkaraju2023 · 81 citations
16. What Algorithms can Transformers Learn? A Study in Length GeneralizationarXiv:2310.16028Hattie Zhou, Arwen Bradley, Etai Littwin, et al.2023 · 67 citations
17. In-context Learning with Retrieved Demonstrations for Language Models: A SurveyarXiv:2401.11624Man Luo, Xin Xu, Yue Liu, et al.2024 · 54 citations
18. The Learnability of In-Context LearningConference on Neural Information Processing Systems (NeurIPS)Noam Wies, Yoav Levine, Amnon Shashua2023 · 53 citations
19. Understanding Catastrophic Forgetting in Language Models via Implicit InferencearXiv:2309.10105Suhas Kotha, Jacob Mitchell Springer, Aditi Raghunathan2023 · 45 citations
20. Mind the instructions: a holistic evaluation of consistency and interactions in prompt-based learningConference on Computational Natural Language Learning (CoNLL)Lucas Weber, Elia Bruni, Dieuwke Hupkes2023 · 29 citations
21. TACO: Enhancing Multimodal In-context Learning via Task Mapping-Guided Sequence ConfigurationConference on Empirical Methods in Natural Language Processing (EMNLP)Yanshu Li, Jianjiang Yang, Tian Yun, et al.2025 · 22 citations
22. Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution LensFindings of the Association for Computational LinguisticsChengshuai Zhao, Zhen Tan, Pingchuan Ma, et al.2026 · 21 citations
23. Understanding Emergent In-Context Learning from a Kernel Regression PerspectivearXiv:2305.12766Chi Han, Ziqi Wang, Han Zhao, et al.2023 · 21 citations
24. An Incomplete Loop: Deductive, Inductive, and Abductive Reasoning in Language ModelsarXiv:2404.03028Emmy Liu, Graham Neubig, Jacob Andreas2024 · 12 citations
25. The ICL Consistency TestarXiv:2312.04945Lucas Weber, Elia Bruni, Dieuwke Hupkes2023 · 12 citations
26. SSA: Improving Performance With a Better Scoring FunctionAnnual Meeting of the Association for Computational Linguistics (ACL)Omar Naim, Swarnadeep Bhar, Jerome Bolte, et al.2026 · 1 citation
27. Text-to-Distribution Prediction with Quantile Tokens and Neighbor ContextAnnual Meeting of the Association for Computational Linguistics (ACL)Yilun Zhu, Yuan Zhuang, Nikhita Vedula, et al.2026
28. Understanding In-Context Learning Beyond Transformers: An Investigation of State Space and Hybrid ArchitecturesFindings of the Association for Computational LinguisticsShenran Wang, Timothy Tin-Long Tse, Jian Zhu2026

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