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Neural sequence models, especially transformers, exhibit a remarkable capacity for in-context learning.
Adjustment of an inverse matrix corresponding to a change in one element of a given matrix
Jack Sherman and Winifred J Morrison · 1950
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Ridge regression: Biased estimation for nonorthogonal problems
Arthur E Hoerl and Robert W Kennard · 1970
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Multilayer feedforward networks are universal approximators
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Simple principles of metalearning
Juergen Schmidhuber, Jieyu Zhao, and Marco A Wiering · 1996
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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Are transformers universal approximators of sequence-to-sequence functions?
Chulhee Yun, Srinadh Bhojanapalli, Ankit Singh Rawat, Sashank J. Reddi, and Sanjiv Kumar · 2020
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What can transformers learn in-context? a case study of simple function classes
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Tabpfn: A transformer that solves small tabular classification problems in a second, 2022
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In-context reinforcement learning with algorithm distillation
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Data distributional properties drive emergent few-shot learning in transformers
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