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Chain of thought prompting successfully improves the reasoning capabilities of large language models, achieving state of the art results on a range of datasets.
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 · 1901
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Ronald J Williams and David Zipser. 1989 · 1989
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Samuel Gehman, Suchin Gururangan, Maarten Sap, Yejin Choi, and Noah A Smith. 2020 · 2009
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Distilling the knowledge in a neural network
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Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Y Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le. 2021 · 2021
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Explanations from large language models make small reasoners better
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Did aristotle use a laptop? a question answering benchmark with implicit reasoning strategies
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Did aristotle use a laptop? a question answering benchmark with implicit reasoning strategies
Mor Geva, Daniel Khashabi, Elad Segal, Tushar Khot, Dan Roth, and Jonathan Berant. 2021b
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Self-consistency improves chain of thought reasoning in language models
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Chain of thought prompting elicits reasoning in large language models
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