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To comprehensively gauge the capacity of current models for complex reasoning, it is crucial to assess their step-by-step reasoning in a scalable manner.
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
Earlier work this paper cites.
A new asymmetric measure of association for ordinal variables
Robert H Somers. 1962 · 1962
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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