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Transformers exhibit in-context learning (ICL): the ability to use novel information presented in the context without additional weight updates.
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
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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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Data distributional properties drive emergent in-context learning in transformers
Stephanie Chan, Adam Santoro, Andrew Lampinen, Jane Wang, Aaditya Singh, Pierre Richemond, James McClelland, and Felix Hill · 2022
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Why can gpt learn in-context? language models secretly perform gradient descent as meta optimizers
Damai Dai, Yutao Sun, Li Dong, Yaru Hao, Zhifang Sui, and Furu Wei · 2022
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A survey for in-context learning
Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Zhiyong Wu, Baobao Chang, Xu Sun, Jingjing Xu, and Zhifang Sui · 2022
Earlier work this paper cites.
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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General-purpose in-context learning by meta-learning transformers
Louis Kirsch, James Harrison, Jascha Sohl-Dickstein, and Luke Metz · 2022
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Rethinking the role of demonstrations: What makes in-context learning work?
Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer · 2022
Cited alongside, same era.
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
Cited alongside, same era.
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
Cited alongside, same era.
Transformers learn to implement preconditioned gradient descent for in-context learning
Kwangjun Ahn, Xiang Cheng, Hadi Daneshmand, and Suvrit Sra · 2023
Cited alongside, same era.
The mechanistic basis of data dependence and abrupt learning in an in-context classification task
Gautam Reddy · 2023
Later among the works it cites.
The transient nature of emergent in-context learning in transformers
Aaditya K Singh, Stephanie CY Chan, Ted Moskovitz, Erin Grant, Andrew M Saxe, and Felix Hill · 2023
Later among the works it cites.
Transformers learn in-context by gradient descent
Johannes Von Oswald, Eyvind Niklasson, Ettore Randazzo, João Sacramento, Alexander Mordvintsev, Andrey Zhmoginov, and Max Vladymyrov · 2023
Later among the works it cites.
Larger language models do in-context learning differently
Jerry Wei, Jason Wei, Yi Tay, Dustin Tran, Albert Webson, Yifeng Lu, Xinyun Chen, Hanxiao Liu, Da Huang, Denny Zhou, et al · 2023
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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Yu Bai, Fan Chen, Huan Wang, Caiming Xiong, and Song Mei · 2023
Cited alongside, same era.
What in-context learning “learns” in-context: Disentangling task recognition and task learning
Jane Pan · 2023
Cited alongside, same era.
Pretraining task diversity and the emergence of non-bayesian in-context learning for regression
Allan Raventós, Mansheej Paul, Feng Chen, and Surya Ganguli · 2023
Cited alongside, same era.
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The evolution of statistical induction heads: In-context learning markov chains
Benjamin L Edelman, Ezra Edelman, Surbhi Goel, Eran Malach, and Nikolaos Tsilivis · 2024
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Why larger language models do in-context learning differently?, 2024
Zhenmei Shi, Junyi Wei, Zhuoyan Xu, and Yingyu Liang · 2024
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