---
title: "Who cites What Can Transformers Learn — 115 citing papers"
description: "115 papers of the Elamur Research library cite «What Can Transformers Learn In-Context? A Case Study of Simple Function Classes»…"
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# 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»](https://md.research.elamur.ai/free/en/papers/garg-2022-transformers-learn-context-case-study/)

1. [**A Survey on In-context Learning arXiv:2301.00234**Qingxiu Dong, Lei Li, Damai Dai, et al.*2023 · 978 citations*](https://md.research.elamur.ai/free/en/papers/dong-2023-survey-context-learning/)
2. [**Larger language models do in-context learning differently arXiv:2303.03846**Jerry Wei, Jason Wei, Yi Tay, et al.*2023 · 285 citations*](https://md.research.elamur.ai/free/en/papers/wei-2023-larger-language-models-context-learning/)
3. **[Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters](https://md.research.elamur.ai/free/en/papers/wang-2023-understanding-chain-thought-prompting-empirical/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/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-Optimizers](https://md.research.elamur.ai/free/en/papers/dai-2023-why-can-gpt-learn-context/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Damai Dai, Yutao Sun, Li Dong, et al.*2023 · 257 citations*
5. [**Transformers learn in-context by gradient descent arXiv:2212.07677**Johannes von Oswald, Eyvind Niklasson, Ettore Randazzo, et al.*2022 · 247 citations*](https://md.research.elamur.ai/free/en/papers/oswald-2022-transformers-learn-context-gradient-descent/)
6. [**What learning algorithm is in-context learning? Investigations with linear models arXiv:2211.15661**Ekin Akyürek, Dale Schuurmans, Jacob Andreas, et al.*2022 · 236 citations*](https://md.research.elamur.ai/free/en/papers/akyurek-2022-learning-algorithm-context-learning-investigations/)
7. **[Many-Shot In-Context Learning](https://md.research.elamur.ai/free/en/papers/agarwal-2024-many-shot-context-learning/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Rishabh Agarwal, Avi Singh, Lei M. Zhang, et al.*2024 · 142 citations*
8. [**Function Vectors in Large Language Models arXiv:2310.15213**Eric Todd, Millicent L. Li, Arnab Sen Sharma, et al.*2023 · 127 citations*](https://md.research.elamur.ai/free/en/papers/todd-2023-function-vectors-large-language-models/)
9. [**Large Language Models Understand and Can be Enhanced by Emotional Stimuli arXiv:2307.11760**Cheng Li, Jindong Wang, Yixuan Zhang, et al.*2023 · 116 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-large-language-models-understand-enhanced/)
10. [**Studying Large Language Model Generalization with Influence Functions arXiv:2308.03296**Roger Grosse, Juhan Bae, Cem Anil, et al.*2023 · 105 citations*](https://md.research.elamur.ai/free/en/papers/grosse-2023-studying-large-language-model-generalization/)
11. **[Can we trust the evaluation on ChatGPT?](https://md.research.elamur.ai/free/en/papers/aiyappa-2023-can-we-trust-evaluation-chatgpt/) [Workshop on Trustworthy NLP](https://md.research.elamur.ai/free/en/venues/trustnlp/)**Rachith Aiyappa, Jisun An, Haewoon Kwak, et al.*2023 · 103 citations*
12. [**Large Language Models as General Pattern Machines arXiv:2307.04721**Suvir Mirchandani, Fei Xia, Pete Florence, et al.*2023 · 88 citations*](https://md.research.elamur.ai/free/en/papers/mirchandani-2023-large-language-models-general-pattern/)
13. [**Foundational Challenges in Assuring Alignment and Safety of Large Language Models arXiv:2404.09932**Usman Anwar, Abulhair Saparov, Javier Rando, et al.*2024 · 87 citations*](https://md.research.elamur.ai/free/en/papers/anwar-2024-foundational-challenges-assuring-alignment-safety/)
14. [**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/)
15. [**In-Context Unlearning: Language Models as Few Shot Unlearners arXiv:2310.07579**Martin Pawelczyk, Seth Neel, Himabindu Lakkaraju*2023 · 81 citations*](https://md.research.elamur.ai/free/en/papers/pawelczyk-2023-context-unlearning-language-models-few/)
16. [**What Algorithms can Transformers Learn? A Study in Length Generalization arXiv:2310.16028**Hattie Zhou, Arwen Bradley, Etai Littwin, et al.*2023 · 67 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2023-algorithms-transformers-learn-study-length/)
17. [**In-context Learning with Retrieved Demonstrations for Language Models: A Survey arXiv:2401.11624**Man Luo, Xin Xu, Yue Liu, et al.*2024 · 54 citations*](https://md.research.elamur.ai/free/en/papers/luo-2024-context-learning-retrieved-demonstrations-language/)
18. **[The Learnability of In-Context Learning](https://md.research.elamur.ai/free/en/papers/wies-2023-learnability-context-learning/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Noam Wies, Yoav Levine, Amnon Shashua*2023 · 53 citations*
19. [**Understanding Catastrophic Forgetting in Language Models via Implicit Inference arXiv:2309.10105**Suhas Kotha, Jacob Mitchell Springer, Aditi Raghunathan*2023 · 45 citations*](https://md.research.elamur.ai/free/en/papers/kotha-2023-understanding-catastrophic-forgetting-language-models/)
20. **[Mind the instructions: a holistic evaluation of consistency and interactions in prompt-based learning](https://md.research.elamur.ai/free/en/papers/weber-2023-mind-instructions-holistic-evaluation-consistency/) [Conference on Computational Natural Language Learning (CoNLL)](https://md.research.elamur.ai/free/en/venues/conll/)**Lucas Weber, Elia Bruni, Dieuwke Hupkes*2023 · 29 citations*
21. **[TACO: Enhancing Multimodal In-context Learning via Task Mapping-Guided Sequence Configuration](https://md.research.elamur.ai/free/en/papers/li-2025-taco-enhancing-multimodal-context-learning/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/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 Lens](https://md.research.elamur.ai/free/en/papers/zhao-2026-is-chain-thought-reasoning-llms/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chengshuai Zhao, Zhen Tan, Pingchuan Ma, et al.*2026 · 21 citations*
23. [**Understanding Emergent In-Context Learning from a Kernel Regression Perspective arXiv:2305.12766**Chi Han, Ziqi Wang, Han Zhao, et al.*2023 · 21 citations*](https://md.research.elamur.ai/free/en/papers/han-2023-understanding-emergent-context-learning-kernel/)
24. [**An Incomplete Loop: Deductive, Inductive, and Abductive Reasoning in Language Models arXiv:2404.03028**Emmy Liu, Graham Neubig, Jacob Andreas*2024 · 12 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-incomplete-loop-deductive-inductive-abductive/)
25. [**The ICL Consistency Test arXiv:2312.04945**Lucas Weber, Elia Bruni, Dieuwke Hupkes*2023 · 12 citations*](https://md.research.elamur.ai/free/en/papers/weber-2023-icl-consistency-test/)
26. **[SSA: Improving Performance With a Better Scoring Function](https://md.research.elamur.ai/free/en/papers/naim-2026-ssa-improving-performance-better-scoring/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Omar Naim, Swarnadeep Bhar, Jerome Bolte, et al.*2026 · 1 citation*
27. **[Text-to-Distribution Prediction with Quantile Tokens and Neighbor Context](https://md.research.elamur.ai/free/en/papers/zhu-2026-text-distribution-prediction-quantile-tokens/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yilun Zhu, Yuan Zhuang, Nikhita Vedula, et al.*2026*
28. **[Understanding In-Context Learning Beyond Transformers: An Investigation of State Space and Hybrid Architectures](https://md.research.elamur.ai/free/en/papers/wang-2026-understanding-context-learning-beyond-transformers/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Shenran Wang, Timothy Tin-Long Tse, Jian Zhu*2026*

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

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