---
title: "Who cites Transformers learn to implement — 29 citing papers"
description: "29 papers of the Elamur Research library cite «Transformers learn to implement preconditioned gradient descent for in-context learning»…"
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# Who cites «Transformers learn to implement preconditioned gradient descent for in-context learning»

29 papers of our library cite it (of the 149,439 whose bibliographies we read) · 10 with a page here, the most cited first · [← back to «Transformers learn to implement preconditioned gradient descent for in-context learning»](https://md.research.elamur.ai/free/en/papers/ahn-2023-transformers-learn-implement-preconditioned-gradient/)

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. [**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/)
3. [**Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks arXiv:2402.04248**Jongho Park, Jaeseung Park, Zheyang Xiong, et al.*2024 · 41 citations*](https://md.research.elamur.ai/free/en/papers/park-2024-mamba-learn-learn-comparative-study/)
4. [**Physics of Language Models: Part 2.2, How to Learn From Mistakes on Grade-School Math Problems arXiv:2408.16293**Tian Ye, Zicheng Xu, Yuanzhi Li, et al.*2024 · 31 citations*](https://md.research.elamur.ai/free/en/papers/ye-2024-physics-language-models-part-2-2/)
5. [**One Step of Gradient Descent is Provably the Optimal In-Context Learner with One Layer of Linear Self-Attention arXiv:2307.03576**Arvind Mahankali, Tatsunori B. Hashimoto, Tengyu Ma*2023 · 22 citations*](https://md.research.elamur.ai/free/en/papers/mahankali-2023-one-step-gradient-descent-provably/)
6. [**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/)
7. **[Transformers Learn to Achieve Second-Order Convergence Rates for In-Context Linear Regression](https://md.research.elamur.ai/free/en/papers/fu-2023-transformers-learn-achieve-second-order/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Deqing Fu, Tian-Qi Chen, Robin Jia, et al.*2023 · 14 citations*
8. [**Linear attention is (maybe) all you need (to understand transformer optimization) arXiv:2310.01082**Kwangjun Ahn, Xiang Cheng, Minhak Song, et al.*2023 · 10 citations*](https://md.research.elamur.ai/free/en/papers/ahn-2023-linear-attention-maybe-all-need/)
9. [**Learning to (Learn at Test Time) arXiv:2310.13807**Yu Sun, Xinhao Li, Karan Dalal, et al.*2023 · 6 citations*](https://md.research.elamur.ai/free/en/papers/sun-2023-learning-learn-test-time/)
10. **[Label Words as Local Task Vectors in In-Context Learning](https://md.research.elamur.ai/free/en/papers/zheng-2026-label-words-as-local-task/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Bowen Zheng, Ming Ma, Zhongqiao Lin, et al.*2026*

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

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