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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 M Krueger · Tom Henighan · Rewon Child · Aditya Ramesh · Daniel Ziegler · Jeffrey Wu · Clemens Winter · Chris Hesse · Mark Chen · Eric Sigler · Mateusz Litwin · Scott Gray · Benjamin Chess · Jack Clark · Christopher Berner · Sam McCandlish · Alec Radford · Ilya Sutskever · Dario Amodei

Poster Session 0 #49
award Outstanding Paper
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[ Paper ]


We demonstrate that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even becoming competitive with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks. We also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora.

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