Language Models are Few-Shot Learners
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.
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Colab
Spaces
Tasks
Common Sense Reasoning
Coreference Resolution
Domain Adaptation
Few-Shot Learning
Language Modelling
Multi-task Language Understanding
Multi-Task Learning
Natural Language Inference
Question Answering
Reading Comprehension
Sentence
Sentence Completion
Unsupervised Machine Translation
Word Sense Disambiguation
Zero-Shot Learning
GLUE
Natural Questions
Penn Treebank
MMLU
TriviaQA
HellaSwag
BoolQ
SuperGLUE
PIQA
RACE
OpenBookQA
WinoGrande
WebText
WSC
DROP
COPA
WMT 2014
ANLI
CoQA
WebQuestions
LAMBADA
WMT 2016
QuAC
MultiRC
ROCStories
ReCoRD
ARC (AI2 Reasoning Challenge)
WMT 2016 News
