Language Models are Unsupervised Multitask Learners

Preprint 2019 Alec RadfordJeffrey WuRewon ChildDavid LuanDario AmodeiIlya Sutskever

Natural language processing tasks, such as question answering, machine translation, reading comprehension, and summarization, are typically approached with supervised learning on taskspecific 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... (read more)

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Evaluation Results from the Paper


 SOTA for Language Modelling on Text8 (using extra training data)

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TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK USES EXTRA
TRAINING DATA
COMPARE
Question Answering Children's Book Test GPT-2 Accuracy-CN 93.30% # 1
Question Answering Children's Book Test GPT-2 Accuracy-NE 89.05% # 1
Document Summarization CNN / Daily Mail GPT-2 ROUGE-1 29.34 # 8
Document Summarization CNN / Daily Mail GPT-2 ROUGE-2 8.27 # 8
Document Summarization CNN / Daily Mail GPT-2 ROUGE-L 26.58 # 8
Language Modelling enwiki8 GPT-2 Bit per Character (BPC) 0.93 # 1
Language Modelling enwiki8 GPT-2 Number of params 1542M # 1
Language Modelling One Billion Word GPT-2 PPL 42.16 # 13
Language Modelling One Billion Word GPT-2 Number of params 1.54B # 1
Language Modelling Penn Treebank (Word Level) GPT-2 Test perplexity 35.76 # 1
Language Modelling Penn Treebank (Word Level) GPT-2 Params 1542M # 1
Language Modelling Text8 GPT-2 Bit per Character (BPC) 0.98 # 1
Language Modelling WikiText-103 GPT-2 Full Test perplexity 17.48 # 6
Language Modelling WikiText-103 GPT-2 Full Number of params 1542M # 1
Language Modelling WikiText-103 GPT-2 Small Test perplexity 37.50 # 24
Language Modelling WikiText-103 GPT-2 Small Number of params 124M # 1
Language Modelling WikiText-103 GPT-2 Medium Test perplexity 26.37 # 13
Language Modelling WikiText-103 GPT-2 Medium Number of params 355M # 1
Language Modelling WikiText-103 GPT-2 Large Test perplexity 22.05 # 10
Language Modelling WikiText-103 GPT-2 Large Number of params 774M # 1
Language Modelling WikiText-2 GPT-2 Test perplexity 18.34 # 1
Language Modelling WikiText-2 GPT-2 Number of params 1542M # 1
Common Sense Reasoning Winograd Schema Challenge GPT-2 Score 70.70 # 1