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Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
| dc.contributor.author | Raffel, Colin | |
| dc.contributor.author | Shazeer, Noam | |
| dc.contributor.author | Roberts, Adam | |
| dc.contributor.author | Lee, Katherine | |
| dc.contributor.author | Narang, Sharan | |
| dc.contributor.author | Matena, Michae | |
| dc.contributor.author | Zhou, Yanqi | |
| dc.contributor.author | Li, Wei | |
| dc.contributor.author | Liu, Peter J. | |
| dc.date.accessioned | 2025-06-02T13:07:37Z | |
| dc.date.available | 2025-06-02T13:07:37Z | |
| dc.date.issued | 2019-10-19 | |
| dc.description | 이 논문은 모든 NLP 태스크를 text-to-text 문제로 재구성하는 T5 프레임워크를 제안하며, 전이학습의 효과를 대규모 데이터셋에서 실험적으로 검증합니다. ©2019 Google Research | |
| dc.description.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. | |
| dc.description.sponsorship | Google Research | |
| dc.identifier.uri | https://arxiv.org/abs/1910.10683 | |
| dc.identifier.uri | http://data.inu.ac.kr/handle/123456789/1954 | |
| dc.language.iso | en_US | |
| dc.publisher | arXiv | |
| dc.title | Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer | |
| dc.type | Article |