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

Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J Liu

Abstract:

Transfer learning, where a model is first pre-trained on a data-rich task before being finetuned on a downstream task, has emerged as a powerful technique in natural language
processing (NLP). The effectiveness of transfer learning has given rise to a diversity of
approaches, methodology, and practice. In this paper, we explore the landscape of transfer
learning techniques for NLP by introducing a unified framework that converts all text-based
language problems into a text-to-text format. Our systematic study compares pre-training
objectives, architectures, unlabeled data sets, transfer approaches, and other factors on
dozens of language understanding tasks. By combining the insights from our exploration
with scale and our new “Colossal Clean Crawled Corpus”, we achieve state-of-the-art results
on many benchmarks covering summarization, question answering, text classification, and
more. To facilitate future work on transfer learning for NLP, we release our data set,
pre-trained models, and code