Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
41 tokens/sec
GPT-4o
59 tokens/sec
Gemini 2.5 Pro Pro
41 tokens/sec
o3 Pro
7 tokens/sec
GPT-4.1 Pro
50 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer (1910.10683v4)

Published 23 Oct 2019 in cs.LG, cs.CL, and stat.ML
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

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 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.

PDF HTML Abstract

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

Overview

The paper "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer" investigates multiple strategies to optimally leverage transfer learning in NLP. The authors propose a comprehensive framework wherein all NLP tasks are cast into a uniform text-to-text format. This unique approach facilitates the application of a single model to an extensive variety of text-based tasks, including translation, summarization, text classification, and question answering.

Key Contributions and Findings

  1. Unified Text-to-Text Framework: The authors champion a strategy that encompasses all text-based problems under a text-to-text paradigm. This innovation allows for a consistent training objective, leveraging a Transformer model that can be applied systematically across diverse NLP tasks.
  2. Extensive Comparisons:
    • Architectural Variants: The research evaluates various Transformer-based model architectures, including encoder-decoder structures, LLMs, and 'prefix' LLMs. The encoder-decoder architecture, which mirrors the original Transformer setup, surfaced as the most efficacious, especially when paired with a span-corruption denoising objective.
    • Unsupervised Objectives: The paper explores multiple unsupervised learning objectives, establishing that denoising objectives generally outperform other objectives like LLMing and sequence deshuffling. Amongst them, a span-corruption based objective showed slight advantages in terms of performance and computational efficiency.
    • Pre-training Data Sets: They introduced the "Colossal Clean Crawled Corpus" (C4), derived from Common Crawl data, and compared it against other common pre-training corpora, such as Wikipedia and WebText-like data sets. While domain-specific data sets sometimes offered advantages in niche tasks, the broadly sourced C4 proved highly versatile.
    • Training Strategies: They evaluated different fine-tuning strategies such as adapter layers, gradual unfreezing, and multi-task learning. Fine-tuning all model parameters consistently outperformed methods designed to update fewer parameters. The combination of multi-task pre-training followed by task-specific fine-tuning yielded promising results comparable to standard pre-training.
  3. Scale and Performance Correlation: Extending the “scaling” narrative prevalent in machine learning, the authors demonstrate that larger models and extensive pre-training on vast amounts of text significantly enhance performance. They trained models with up to 11 billion parameters on over one trillion tokens, achieving state-of-the-art results on multiple NLP benchmarks, including GLUE, SQuAD, and SuperGLUE.

Implications and Future Directions

  1. Uniform Application Across Tasks: The text-to-text framework's ability to apply the same model, loss function, and hyperparameters across a spectrum of tasks simplifies the engineering processes for NLP applications, making it more feasible to deploy sophisticated models in practical scenarios.
  2. Scaling and Resource Utilization: The findings affirm the 'bitter lesson' of AI: models leveraging more data and computational power tend to perform better. This underscores the need for powerful hardware and substantial computational resources to train and fine-tune large models. As computational resources become more accessible, this could democratize advanced NLP capabilities across various sectors.
  3. Efficient Knowledge Extraction: The paper identifies potential inefficiencies in current pre-training objectives. Future research could seek novel unsupervised objectives that capture linguistic and semantic knowledge more efficiently, reducing the need for massive computational resources and pre-training durations.
  4. Domain-Specific Adaptations: While broad datasets like C4 proved effective, significant performance gains were observed with domain-specific pre-training for certain tasks. Future research could explore domain adaptation techniques that dynamically combine domain-specific and broad data pre-training to maximize utility across tasks.
  5. Language-Agnostic Models: Given that current top performance in translation tasks remains tied to additional techniques like back-translation and bilingual corpora, exploration into more robust language-agnostic pre-training approaches remains a pertinent avenue.

Conclusion

The meticulous comparisons and extensive evaluations presented in the paper yield comprehensive insights into optimizing transfer learning for NLP. The unified text-to-text framework, coupled with robust scaling and fine-tuning strategies, sets a high bar for future research in the field. The introduction of the C4 dataset and empirical validation across diverse tasks significantly enhance our understanding of transfer learning’s capabilities and limitations, paving the way for more advanced and efficient NLP models.

File Document Download Save Streamline Icon: https://streamlinehq.com PDF File Document Download Save Streamline Icon: https://streamlinehq.com Markdown Bookmark Chat Bubble Oval Streamline Icon: https://streamlinehq.com Chat (Pro)
Definition Search Book Streamline Icon: https://streamlinehq.com
References (133)
  1. Character-level language modeling with deeper self-attention. In Proceedings of the AAAI Conference on Artificial Intelligence, 2019.
  2. Memory-efficient adaptive optimization for large-scale learning. arXiv preprint arXiv:1901.11150, 2019.
  3. Massively multilingual neural machine translation in the wild: Findings and challenges. arXiv preprint arXiv:1907.05019, 2019.
  4. Layer normalization. arXiv preprint arXiv:1607.06450, 2016.
  5. Cloze-driven pretraining of self-attention networks. arXiv preprint arXiv:1903.07785, 2019.
  6. Neural machine translation by jointly learning to align and translate. In Third International Conference on Learning Representations, 2015.
  7. Simple, scalable adaptation for neural machine translation. arXiv preprint arXiv:1909.08478, 2019.
  8. SciBERT: A pretrained language model for scientific text. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 2019.
  9. Findings of the 2014 workshop on statistical machine translation. In Proceedings of the Ninth Workshop on Statistical Machine Translation, 2014.
  10. Findings of the 2015 workshop on statistical machine translation. In Proceedings of the Tenth Workshop on Statistical Machine Translation, 2015.
  11. Findings of the 2016 conference on machine translation. In Proceedings of the First Conference on Machine Translation, 2016.
  12. Generating sentences from a continuous space. arXiv preprint arXiv:1511.06349, 2015.
  13. N-gram counts and language models from the common crawl. In LREC, 2014.
  14. Rich Caruana. Multitask learning. Machine learning, 28(1), 1997.
  15. Semeval-2017 task 1: Semantic textual similarity-multilingual and cross-lingual focused evaluation. arXiv preprint arXiv:1708.00055, 2017.
  16. Long short-term memory-networks for machine reading. arXiv preprint arXiv:1601.06733, 2016.
  17. BoolQ: Exploring the surprising difficulty of natural yes/no questions. arXiv preprint arXiv:1905.10044, 2019.
  18. Electra: Pre-training text encoders as discriminators rather than generators. arXiv preprint arXiv:2003.10555, 2020.
  19. SentEval: An evaluation toolkit for universal sentence representations. arXiv preprint arXiv:1803.05449, 2018.
  20. Supervised learning of universal sentence representations from natural language inference data. arXiv preprint arXiv:1705.02364, 2017.
  21. The PASCAL recognising textual entailment challenge. In Machine Learning Challenges Workshop, 2005.
  22. Semi-supervised sequence learning. In Advances in neural information processing systems, 2015.
  23. The CommitmentBank: Investigating projection in naturally occurring discourse. In Sinn und Bedeutung 23, 2019.
  24. ImageNet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, 2009.
  25. BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018.
  26. Automatically constructing a corpus of sentential paraphrases. In Proceedings of the Third International Workshop on Paraphrasing (IWP2005), 2005.
  27. Unified language model pre-training for natural language understanding and generation. arXiv preprint arXiv:1905.03197, 2019.
  28. Understanding back-translation at scale. arXiv preprint arXiv:1808.09381, 2018.
  29. Learning word vectors for 157 languages. arXiv preprint arXiv:1802.06893, 2018.
  30. Alex Graves. Generating sequences with recurrent neural networks. arXiv preprint arXiv:1308.0850, 2013.
  31. C4Corpus: Multilingual web-size corpus with free license. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16), pages 914–922, 2016.
  32. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2016.
  33. Rethinking ImageNet pre-training. arXiv preprint arXiv:1811.08883, 2018.
  34. A hybrid neural network model for commonsense reasoning. arXiv preprint arXiv:1907.11983, 2019.
  35. Teaching machines to read and comprehend. In Advances in neural information processing systems, 2015.
  36. Deep learning scaling is predictable, empirically. arXiv preprint arXiv:1712.00409, 2017.
  37. Learning distributed representations of sentences from unlabelled data. arXiv preprint arXiv:1602.03483, 2016.
  38. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531, 2015.
  39. Parameter-efficient transfer learning for NLP. arXiv preprint arXiv:1902.00751, 2019.
  40. Universal language model fine-tuning for text classification. arXiv preprint arXiv:1801.06146, 2018.
  41. Music transformer: Generating music with long-term structure. In Seventh International Conference on Learning Representations, 2018a.
  42. GPipe: Efficient training of giant neural networks using pipeline parallelism. arXiv preprint arXiv:1811.06965, 2018b.
  43. What makes ImageNet good for transfer learning? arXiv preprint arXiv:1608.08614, 2016.
  44. First Quora dataset release: Question pairs. https://data.quora.com/First-Quora-Dataset-Release-Question-Pairs, 2017.
  45. Caffe: Convolutional architecture for fast feature embedding. In Proceedings of the 22nd ACM international conference on Multimedia, 2014.
  46. TinyBERT: Distilling BERT for natural language understanding. arXiv preprint arXiv:1909.10351, 2019.
  47. TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension. arXiv preprint arXiv:1705.03551, 2017.
  48. SpanBERT: Improving pre-training by representing and predicting spans. arXiv preprint arXiv:1907.10529, 2019.
  49. Exploring the limits of language modeling. arXiv preprint arXiv:1602.02410, 2016.
  50. A convolutional neural network for modelling sentences. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, 2014.
  51. CTRL: A conditional transformer language model for controllable generation. arXiv preprint arXiv:1909.05858, 2019a.
  52. Unifying question answering and text classification via span extraction. arXiv preprint arXiv:1904.09286, 2019b.
  53. Looking beyond the surface: A challenge set for reading comprehension over multiple sentences. In Proceedings of North American Chapter of the Association for Computational Linguistics (NAACL), 2018.
  54. Skip-thought vectors. In Advances in neural information processing systems, 2015.
  55. A surprisingly robust trick for Winograd schema challenge. arXiv preprint arXiv:1905.06290, 2019.
  56. Federated optimization: Distributed optimization beyond the datacenter. arXiv preprint arXiv:1511.03575, 2015.
  57. Federated learning: Strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492, 2016.
  58. Do better ImageNet models transfer better? arXiv preprint arXiv:1805.08974, 2018.
  59. Alex Krizhevsky. One weird trick for parallelizing convolutional neural networks. arXiv preprint arXiv:1404.5997, 2014.
  60. Taku Kudo. Subword regularization: Improving neural network translation models with multiple subword candidates. arXiv preprint arXiv:1804.10959, 2018.
  61. SentencePiece: A simple and language independent subword tokenizer and detokenizer for neural text processing. arXiv preprint arXiv:1808.06226, 2018.
  62. Cross-lingual language model pretraining. arXiv preprint arXiv:1901.07291, 2019.
  63. ALBERT: A lite BERT for self-supervised learning of language representations. arXiv preprint arXiv:1909.11942, 2019.
  64. The Winograd schema challenge. In Thirteenth International Conference on the Principles of Knowledge Representation and Reasoning, 2012.
  65. Qi Li. Literature survey: domain adaptation algorithms for natural language processing. 2012.
  66. Chin-Yew Lin. ROUGE: A package for automatic evaluation of summaries. In Text summarization branches out, 2004.
  67. Generating Wikipedia by summarizing long sequences. arXiv preprint arXiv:1801.10198, 2018.
  68. SummAE: Zero-shot abstractive text summarization using length-agnostic auto-encoders. arXiv preprint arXiv:1910.00998, 2019a.
  69. Representation learning using multi-task deep neural networks for semantic classification and information retrieval. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2015.
  70. Multi-task deep neural networks for natural language understanding. arXiv preprint arXiv:1901.11504, 2019b.
  71. Yang Liu. Fine-tune BERT for extractive summarization. arXiv preprint arXiv:1903.10318, 2019.
  72. RoBERTa: A robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692, 2019c.
  73. An efficient framework for learning sentence representations. arXiv preprint arXiv:1803.02893, 2018.
  74. Exploring the limits of weakly supervised pretraining. In Proceedings of the European Conference on Computer Vision (ECCV), 2018.
  75. The natural language decathlon: Multitask learning as question answering. arXiv preprint arXiv:1806.08730, 2018.
  76. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781, 2013a.
  77. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems, 2013b.
  78. Abstractive text summarization using sequence-to-sequence RNNs and beyond. arXiv preprint arXiv:1602.06023, 2016.
  79. Learning and transferring mid-level image representations using convolutional neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2014.
  80. BLEU: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting on association for computational linguistics. Association for Computational Linguistics, 2002.
  81. A deep reinforced model for abstractive summarization. arXiv preprint arXiv:1705.04304, 2017.
  82. GloVe: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), 2014.
  83. To tune or not to tune? adapting pretrained representations to diverse tasks. arXiv preprint arXiv:1903.05987, 2019.
  84. Deep contextualized word representations. arXiv preprint arXiv:1802.05365, 2018.
  85. Sentence encoders on STILTs: Supplementary training on intermediate labeled-data tasks. arXiv preprint arXiv:1811.01088, 2018.
  86. WIC: 10,000 example pairs for evaluating context-sensitive representations. arXiv preprint arXiv:1808.09121, 2018.
  87. Matt Post. A call for clarity in reporting BLEU scores. arXiv preprint arXiv:1804.08771, 2018.
  88. Improving language understanding by generative pre-training, 2018.
  89. Language models are unsupervised multitask learners, 2019.
  90. Resolving complex cases of definite pronouns: the Winograd schema challenge. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Association for Computational Linguistics, 2012.
  91. Squad: 100,000+ questions for machine comprehension of text. arXiv preprint arXiv:1606.05250, 2016.
  92. Unsupervised pretraining for sequence to sequence learning. arXiv preprint arXiv:1611.02683, 2016.
  93. Snorkel MeTaL: Weak supervision for multi-task learning. In Proceedings of the Second Workshop on Data Management for End-To-End Machine Learning, 2018.
  94. Choice of plausible alternatives: An evaluation of commonsense causal reasoning. In 2011 AAAI Spring Symposium Series, 2011.
  95. Sebastian Ruder. An overview of multi-task learning in deep neural networks. arXiv preprint arXiv:1706.05098, 2017.
  96. Sebastian Ruder. Neural transfer learning for natural language processing. PhD thesis, NUI Galway, 2019.
  97. Transfer learning in natural language processing. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorials, pages 15–18, 2019.
  98. ImageNet large scale visual recognition challenge. International journal of computer vision, 2015.
  99. DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108, 2019.
  100. Get to the point: Summarization with pointer-generator networks. arXiv preprint arXiv:1704.04368, 2017.
  101. Neural machine translation of rare words with subword units. arXiv preprint arXiv:1508.07909, 2015.
  102. Measuring the effects of data parallelism on neural network training. arXiv preprint arXiv:1811.03600, 2018.
  103. Self-attention with relative position representations. arXiv preprint arXiv:1803.02155, 2018.
  104. Adafactor: Adaptive learning rates with sublinear memory cost. arXiv preprint arXiv:1804.04235, 2018.
  105. Outrageously large neural networks: The sparsely-gated mixture-of-experts layer. arXiv preprint arXiv:1701.06538, 2017.
  106. Mesh-tensorflow: Deep learning for supercomputers. In Advances in Neural Information Processing Systems, 2018.
  107. Dirt cheap web-scale parallel text from the common crawl. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, 2013.
  108. Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the 2013 conference on empirical methods in natural language processing, 2013.
  109. MASS: Masked sequence to sequence pre-training for language generation. arXiv preprint arXiv:1905.02450, 2019.
  110. Dropout: a simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 2014.
  111. Learning general purpose distributed sentence representations via large scale multi-task learning. arXiv preprint arXiv:1804.00079, 2018.
  112. Sequence to sequence learning with neural networks. In Advances in neural information processing systems, 2014.
  113. Richard S. Sutton. The bitter lesson. http://www.incompleteideas.net/IncIdeas/BitterLesson.html, 2019.
  114. Wilson L. Taylor. “Cloze procedure”: A new tool for measuring readability. Journalism Bulletin, 1953.
  115. A simple method for commonsense reasoning. arXiv preprint arXiv:1806.02847, 2018.
  116. NewsQA: A machine comprehension dataset. arXiv preprint arXiv:1611.09830, 2016.
  117. Attention is all you need. In Advances in neural information processing systems, 2017.
  118. The bottom-up evolution of representations in the transformer: A study with machine translation and language modeling objectives. arXiv preprint arXiv:1909.01380, 2019.
  119. GLUE: A multi-task benchmark and analysis platform for natural language understanding. arXiv preprint arXiv:1804.07461, 2018.
  120. Can you tell me how to get past Sesame Street? Sentence-level pretraining beyond language modeling. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019a.
  121. SuperGLUE: A stickier benchmark for general-purpose language understanding systems. arXiv preprint arXiv:1905.00537, 2019b.
  122. StructBERT: Incorporating language structures into pre-training for deep language understanding. arXiv preprint arXiv:1908.04577, 2019c.
  123. Neural network acceptability judgments. arXiv preprint arXiv:1805.12471, 2018.
  124. A broad-coverage challenge corpus for sentence understanding through inference. arXiv preprint arXiv:1704.05426, 2017.
  125. A learning algorithm for continually running fully recurrent neural networks. Neural computation, 1989.
  126. Google’s neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144, 2016.
  127. XLNet: Generalized autoregressive pretraining for language understanding. arXiv preprint arXiv:1906.08237, 2019.
  128. How transferable are features in deep neural networks? In Advances in neural information processing systems, 2014.
  129. QAnet: Combining local convolution with global self-attention for reading comprehension. arXiv preprint arXiv:1804.09541, 2018.
  130. Defending against neural fake news. arXiv preprint arXiv:1905.12616, 2019.
  131. ReCoRD: Bridging the gap between human and machine commonsense reading comprehension. arXiv preprint arXiv:1810.12885, 2018.
  132. Freelb: Enhanced adversarial training for language understanding. arXiv preprint arXiv:1909.11764, 2019.
  133. Aligning books and movies: Towards story-like visual explanations by watching movies and reading books. In Proceedings of the IEEE international conference on computer vision, 2015.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (9)
  1. Colin Raffel (83 papers)
  2. Noam Shazeer (37 papers)
  3. Adam Roberts (46 papers)
  4. Katherine Lee (34 papers)
  5. Sharan Narang (31 papers)
  6. Michael Matena (5 papers)
  7. Yanqi Zhou (30 papers)
  8. Wei Li (1121 papers)
  9. Peter J. Liu (30 papers)
Citations (17,560)
View on Semantic Scholar
Youtube Logo Streamline Icon: https://streamlinehq.com

YouTube