Document (#43874)

Author
Brown, T.B.
Mann, B.
Ryder, N.
Subbiah, M.
Kaplan, J.
Dhariwal, P.
Neelakantan, A.
Shyam, P.
Sastry, G.
Askell, A.
Agarwal, S.
Herbert-Voss, A.
Krueger, G.
Henighan, T.
Child, R.
Ramesh, A.
Ziegler, D.M.
Wu, J.
Winter, C.
Hesse, C.
Chen, M.
Sigler, E.
Litwin, M.
Gray, S.
Chess, B.
Clark, J.
Berner, C.
McCandlish, S.
Radford, A.
Sutskever, I.
Amodei, D.
Title
Language models are few-shot learners
Source
https://arxiv.org/pdf/2005.14165.pdf
Imprint
[28.05.2020].
Year
2020
Abstract
Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness 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, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, 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. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general.
Theme
Computerlinguistik
Object
GPT-3

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