[GPT-2] Language Models are Unsupervised Multitask Learners


Available only for arXiv papers.


Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever


Natural language processing tasks, such as ques- tion answering, machine translation, reading com- prehension, and summarization, are typically approached with supervised learning on task- specific datasets. We demonstrate that language models begin to learn these tasks without any ex- plicit supervision when trained on a new dataset of millions of webpages called WebText. When conditioned on a document plus questions, the an- swers generated by the language model reach 55 F1 on the CoQA dataset - matching or exceeding the performance of 3 out of 4 baseline systems without using the 127,000+ training examples. The capacity of the language model is essential to the success of zero-shot task transfer and in- creasing it improves performance in a log-linear fashion across tasks. Our largest model, GPT-2, is a 1.5B parameter Transformer that achieves state of the art results on 7 out of 8 tested lan- guage modeling datasets in a zero-shot setting but still underfits WebText. Samples from the model reflect these improvements and contain co- herent paragraphs of text. These findings suggest a promising path towards building language pro- cessing systems which learn to perform tasks from their naturally occurring demonstrations.


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