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

TLDR

Transfer learning has become a powerful and diverse technique in natural language processing, enabling models pre‑trained on large data‑rich tasks to be fine‑tuned for downstream applications. The study introduces a unified text‑to‑text framework that maps all NLP tasks into a single format and releases the associated data, pre‑trained models, and code to advance transfer‑learning research. The authors conduct a systematic comparison of pre‑training objectives, architectures, unlabeled corpora, and transfer strategies across dozens of language‑understanding tasks. Combining these insights with large‑scale training on the Colossal Clean Crawled Corpus yields state‑of‑the‑art performance on summarization, question answering, text classification, and other benchmarks.

Abstract

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.