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Transfer learning, where a model is first pre-trained on a data-rich taskbefore being fine-tuned on a downstream task, has emerged as a powerfultechnique in natural language processing (NLP). The effectiveness of transferlearning has given rise to a diversity of approaches, methodology, andpractice. In this paper, we explore the landscape of transfer learningtechniques for NLP by introducing a unified framework that converts alltext-based language problems into a text-to-text format. Our systematic studycompares pre-training objectives, architectures, unlabeled data sets, transferapproaches, and other factors on dozens of language understanding tasks. Bycombining the insights from our exploration with scale and our new ``ColossalClean Crawled Corpus'', we achieve state-of-the-art results on many benchmarkscovering summarization, question answering, text classification, and more. Tofacilitate future work on transfer learning for NLP, we release our data set,pre-trained models, and code.

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer