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Large-scale generative models show an impressive ability to perform a wide range of Natural Language Processing (NLP) tasks using in-context learning, where a few examples are used to describe a task to the model.
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.
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Earlier work this paper cites.
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Harold Somers. 1999 · 1999
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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Using natural language prompts for machine translation
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What makes good in-context examples for GPT-3?
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Few-shot learning with multilingual language models
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