Document (#43873)

Author

Radford, A.
Wu, J.
Child, R.
Luan, D.
Amode, D.
Sutskever, I.

Title

Language models are unsupervised multitask learners

Source

https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf

Abstract

Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on task-specific datasets. We demonstrate that language models begin to learn these tasks without any explicit supervision when trained on a new dataset of millions of webpages called WebText. When conditioned on a document plus questions, the answers 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 increasing 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 language modeling datasets in a zero-shot setting but still underfits WebText. Samples from the model reflect these improvements and contain coherent paragraphs of text. These findings suggest a promising path towards building language processing systems which learn to perform tasks from their naturally occurring demonstrations.

Theme

Computerlinguistik

Object

GPT-2

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5. Harrow, J.; Wickersham, L.; Rotherham, S.; Ella Farnsworth, E.; McElhenny, G.: Contextual depth projection in Large Language Models through semantic lattice frameworks (2024) 0.19

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