Making Pre-trained Language Models Better Few-shot Learners

Tianyu Gao, Adam Fisch, Danqi Chen


Abstract
The recent GPT-3 model (Brown et al., 2020) achieves remarkable few-shot performance solely by leveraging a natural-language prompt and a few task demonstrations as input context. Inspired by their findings, we study few-shot learning in a more practical scenario, where we use smaller language models for which fine-tuning is computationally efficient. We present LM-BFF—better few-shot fine-tuning of language models—a suite of simple and complementary techniques for fine-tuning language models on a small number of annotated examples. Our approach includes (1) prompt-based fine-tuning together with a novel pipeline for automating prompt generation; and (2) a refined strategy for dynamically and selectively incorporating demonstrations into each context. Finally, we present a systematic evaluation for analyzing few-shot performance on a range of NLP tasks, including classification and regression. Our experiments demonstrate that our methods combine to dramatically outperform standard fine-tuning procedures in this low resource setting, achieving up to 30% absolute improvement, and 11% on average across all tasks. Our approach makes minimal assumptions on task resources and domain expertise, and hence constitutes a strong task-agnostic method for few-shot learning.
Anthology ID:
2021.acl-long.295
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3816–3830
Language:
URL:
https://aclanthology.org/2021.acl-long.295
DOI:
10.18653/v1/2021.acl-long.295
Bibkey:
Cite (ACL):
Tianyu Gao, Adam Fisch, and Danqi Chen. 2021. Making Pre-trained Language Models Better Few-shot Learners. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 3816–3830, Online. Association for Computational Linguistics.
Cite (Informal):
Making Pre-trained Language Models Better Few-shot Learners (Gao et al., ACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-long.295.pdf
Video:
 https://aclanthology.org/2021.acl-long.295.mp4
Code
 princeton-nlp/LM-BFF +  additional community code
Data
CoLAGLUEMPQA Opinion CorpusMRPCQNLISNLISST