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

arXiv: https://arxiv.org/abs/1910.10683 · HTML: https://mlsanity.com/paper/1910.10683/

## Key facts

- Cited by 14,599 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2019.

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## Abstract (arXiv)

Transfer learning, where a model is first pre-trained on a data-rich task before being fine-tuned 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.