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``Socrates is human.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Earlier work this paper cites.
Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al · 2021
Earlier work this paper cites.
A mathematical framework for transformer circuits, 2021
Nelson Elhage, Neel Nanda, Catherine Olsson, Tom Henighan, Nicholas Joseph, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, Tom Conerly, Nova DasSarma, Dawn Drain, Deep Ganguli, Zac Hatfield-Dodds, Danny Hernandez, Andy Jones, Jackson Kernion, Liane Lovitt, Kamal Ndousse, Dario Amodei, Tom Brown, Jack Clark, Jared Kaplan, Sam McCandlish, and Chris Olah · 2021
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An explanation of in-context learning as implicit bayesian inference
Sang Michael Xie, Aditi Raghunathan, Percy Liang, and Tengyu Ma · 2021
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What learning algorithm is in-context learning? investigations with linear models
Ekin Akyürek, Dale Schuurmans, Jacob Andreas, Tengyu Ma, and Denny Zhou · 2022
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What is my math transformer doing? Three results on interpretability and generalization
François Charton · 2022
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What can transformers learn in-context? a case study of simple function classes
Shivam Garg, Dimitris Tsipras, Percy S Liang, and Gregory Valiant · 2022
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Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al · 2022
Earlier work this paper cites.
Towards understanding grokking: An effective theory of representation learning
Ziming Liu, Ouail Kitouni, Niklas S Nolte, Eric Michaud, Max Tegmark, and Mike Williams · 2022
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Locating and editing factual associations in gpt
Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov · 2022
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In-context learning and induction heads
Catherine Olsson, Nelson Elhage, Neel Nanda, Nicholas Joseph, Nova DasSarma, Tom Henighan, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, et al · 2022
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Interpretability in the wild: a circuit for indirect object identification in GPT-2 small
Kevin Wang, Alexandre Variengien, Arthur Conmy, Buck Shlegeris, and Jacob Steinhardt · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
Earlier work this paper cites.
Unveiling transformers with LEGO: A synthetic reasoning task
Yi Zhang, Arturs Backurs, Sébastien Bubeck, Ronen Eldan, Suriya Gunasekar, and Tal Wagner · 2022
Earlier work this paper cites.
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
Earlier work this paper cites.
Physics of language models: Part 1, context-free grammar
Zeyuan Allen-Zhu and Yuanzhi Li · 2023
Cited alongside, same era.
Transformers as statisticians: Provable in-context learning with in-context algorithm selection
Yu Bai, Fan Chen, Huan Wang, Caiming Xiong, and Song Mei · 2023
Cited alongside, same era.
Eliciting latent predictions from transformers with the tuned lens
Nora Belrose, Zach Furman, Logan Smith, Danny Halawi, Igor Ostrovsky, Lev McKinney, Stella Biderman, and Jacob Steinhardt · 2023
Cited alongside, same era.
Birth of a transformer: A memory viewpoint, 2023
Alberto Bietti, Vivien Cabannes, Diane Bouchacourt, Hervé Jégou, and Léon Bottou · 2023
Cited alongside, same era.
Towards automated circuit discovery for mechanistic interpretability
Arthur Conmy, Augustine Mavor-Parker, Aengus Lynch, Stefan Heimersheim, and Adrià Garriga-Alonso · 2023
Transformers learn to implement preconditioned gradient descent for in-context learning
Kwangjun Ahn, Xiang Cheng, Hadi Daneshmand, and Suvrit Sra · 2024
Later among the works it cites.
Birth of a transformer: A memory viewpoint
Alberto Bietti, Vivien Cabannes, Diane Bouchacourt, Herve Jegou, and Leon Bottou · 2024
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Hopping too late: Exploring the limitations of large language models on multi-hop queries
Eden Biran, Daniela Gottesman, Sohee Yang, Mor Geva, and Amir Globerson · 2024
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Unveiling induction heads: Provable training dynamics and feature learning in transformers
Siyu Chen, Heejune Sheen, Tianhao Wang, and Zhuoran Yang · 2024
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Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al · 2024
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Cited alongside, same era.
How do language models bind entities in context?
Jiahai Feng and Jacob Steinhardt · 2023
Cited alongside, same era.
Dissecting recall of factual associations in auto-regressive language models
Mor Geva, Jasmijn Bastings, Katja Filippova, and Amir Globerson · 2023
Cited alongside, same era.
Tianyu Guo, Wei Hu, Song Mei, Huan Wang, Caiming Xiong, Silvio Savarese, and Yu Bai · 2023
Cited alongside, same era.
In-context convergence of transformers
Yu Huang, Yuan Cheng, and Yingbin Liang · 2023
Cited alongside, same era.
Licong Lin, Yu Bai, and Song Mei · 2023
Cited alongside, same era.
Progress measures for grokking via mechanistic interpretability
Neel Nanda, Lawrence Chan, Tom Lieberum, Jess Smith, and Jacob Steinhardt · 2023
Cited alongside, same era.
The mechanistic basis of data dependence and abrupt learning in an in-context classification task
Gautam Reddy · 2023
Cited alongside, same era.
Later among the works it cites.
Extractive structures learned in pretraining enable generalization on finetuned facts, 2024
Jiahai Feng, Stuart Russell, and Jacob Steinhardt · 2024
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Active-dormant attention heads: Mechanistically demystifying extreme-token phenomena in llms
Tianyu Guo, Druv Pai, Yu Bai, Jiantao Jiao, Michael I Jordan, and Song Mei · 2024
Later among the works it cites.
Does localization inform editing? surprising differences in causality-based localization vs. knowledge editing in language models
Peter Hase, Mohit Bansal, Been Kim, and Asma Ghandeharioun · 2024
Later among the works it cites.
How transformers learn causal structure with gradient descent
Eshaan Nichani, Alex Damian, and Jason D Lee · 2024
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Deepseekmath: Pushing the limits of mathematical reasoning in open language models
Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Xiao Bi, Haowei Zhang, Mingchuan Zhang, YK Li, Y Wu, et al · 2024
Later among the works it cites.
Hypothesis testing the circuit hypothesis in llms
Claudia Shi, Nicolas Beltran-Velez, Achille Nazaret, Carolina Zheng, Adrià Garriga-Alonso, Andrew Jesson, Maggie Makar, and David M Blei · 2024
Later among the works it cites.
Trained transformers learn linear models in-context
Ruiqi Zhang, Spencer Frei, and Peter L Bartlett · 2024
Later among the works it cites.
Towards a theoretical understanding of the’reversal curse’via training dynamics
Hanlin Zhu, Baihe Huang, Shaolun Zhang, Michael Jordan, Jiantao Jiao, Yuandong Tian, and Stuart J Russell · 2024
Later among the works it cites.
Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning
Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al · 2025
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Physics of language models: Part 3.1, knowledge storage and extraction
Zeyuan Allen Zhu and Yuanzhi Li · 2025
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