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Language Models are Unsupervised Multitask Learners - OpenAI If a language model is able to do this it will be, in effect, performing unsupervised multitask learning We test whether this is the case by analyzing the performance of language models in a zero-shot setting on a wide variety of tasks
Language Models are Unsupervised Multitask Learners 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
Language Models are Unsupervised Multitask Learners - ORKG ORKG structured paper description Published: February 2019 • Research field: Computer Sciences • Authors: Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever
Radford, A. , Wu, J. , Child, R. , et al. (2019) Language Models Are . . . This article summarizes the existing pre-training models and sorts out the improved models and processing methods of the relatively new pre-training models, and finally summarizes the challenges and prospects of the current pre-training models
Alec Radford - Google Scholar Co-authors Ilya Sutskever Co-Founder and Chief Scientist at Safe Superintelligence Inc Soumith Chintala Meta AI Ian Goodfellow DeepMind Wojciech Zaremba Co-Founder of OpenAI
GitHub - openai gpt-2: Code for the paper Language Models are . . . Code and models from the paper "Language Models are Unsupervised Multitask Learners" You can read about GPT-2 and its staged release in our original blog post, 6 month follow-up post, and final post We have also released a dataset for researchers to study their behaviors
Language Models are Unsupervised Multitask Learners Unsupervised multitask pre-training has been the critical method behind the recent success of language models (LMs) However, supervised multitask learning still holds significant promise, as scaling it in the post-training stage trends
[8] GPT2 - Language Models are Unsupervised Multitask Learners Objective: Investigate the capabilities of large-scale unsupervised language models and demonstrate their potential for multitask learning Context: Pre-training large unsupervised language models has gained popularity in NLP, but there is a need to understand their capabilities and limitations