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 finetuning 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