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Multitask Prompted Training Enables Zero-Shot Task Generalization

Written for the Research library (claude-opus-5-5) from the paper text. Original — arxiv.org/pdf/2110.08207

Summary

Large language models such as GPT-3 can handle unseen tasks zero-shot. However, this ability shows up only at large scale and depends heavily on how a prompt is phrased. The authors ask whether such generalization can be produced deliberately through explicit supervised multitask training, rather than relying on task-like data that happens to appear in a pretraining corpus. They built a templating language and a public crowdsourcing interface, PromptSource, to convert many English datasets from the Hugging Face datasets library into natural-language input/target pairs, with several differently worded prompts per dataset. Datasets were grouped into tasks by format, and entire task groups were excluded from training so the evaluation measured transfer to genuinely new tasks. Fine-tuning a variant of the T5 encoder-decoder on this mixture produced T0. Although T0 is about sixteen times smaller than GPT-3, it matched or beat GPT-3 on most held-out datasets and also performed well on BIG-bench. This shows that zero-shot task transfer need not require massive scale, and the released prompts and checkpoints are a reusable resource.

Key points

  • The training and evaluation pool covers 12 tasks and 62 English datasets. Held-out task groups include natural language inference (ANLI, CB, RTE), coreference resolution (WSC, Winogrande), sentence completion (COPA, HellaSwag, Story Cloze) and word sense disambiguation (WiC).
  • T0 equals or surpasses GPT-3 on 9 of 11 held-out datasets despite being about 16× smaller. Larger-data variants, T0+ and T0++, were also trained.
  • On BIG-bench, T0 outperforms a large baseline language model on 13 of 14 tasks, and it beats models up to 6× its size.
  • Training on more prompts per dataset raises median held-out accuracy and reduces variance across prompt wordings. Adding more datasets usually raises the median but does not consistently reduce variance.
  • Models are released at github.com/bigscience-workshop/t-zero and prompts at github.com/bigscience-workshop/promptsource.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Other

Cited by

1028 papers of our library cite it (of the 140673 whose bibliographies we read); 104 of them have a page here, the first 100 below, 924 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2110.08207 · arXiv 2110.08207

@misc{sanh2021multitask,
  title = {{Multitask Prompted Training Enables Zero-Shot Task Generalization}},
  author = {Sanh, Victor and Webson, Albert and Raffel, Colin and Bach, Stephen H. and Sutawika, Lintang and Alyafeai, Zaid and Chaffin, Antoine and Stiegler, Arnaud and Scao, Teven Le and Raja, Arun and Dey, Manan and Bari, M Saiful and Xu, Canwen and Thakker, Urmish and Sharma, Shanya Sharma and Szczechla, Eliza and Kim, Taewoon and Chhablani, Gunjan and Nayak, Nihal and Datta, Debajyoti and Chang, Jonathan and Jiang, Mike Tian-Jian and Wang, Han and Manica, Matteo and Shen, Sheng and Yong, Zheng Xin and Pandey, Harshit and Bawden, Rachel and Wang, Thomas and Neeraj, Trishala and Rozen, Jos and Sharma, Abheesht and Santilli, Andrea and Fevry, Thibault and Fries, Jason Alan and Teehan, Ryan and Bers, Tali and Biderman, Stella and Gao, Leo and Wolf, Thomas and Rush, Alexander M.},
  year = {2021},
  doi = {10.48550/arxiv.2110.08207},
  eprint = {2110.08207},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2110.08207}
}

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