  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.
