PPT: Pre-trained Prompt Tuning for Few-shot Learning

Yuxian Gu, Xu Han, Zhiyuan Liu, Minlie Huang


Abstract
Prompts for pre-trained language models (PLMs) have shown remarkable performance by bridging the gap between pre-training tasks and various downstream tasks. Among these methods, prompt tuning, which freezes PLMs and only tunes soft prompts, provides an efficient and effective solution for adapting large-scale PLMs to downstream tasks. However, prompt tuning is yet to be fully explored. In our pilot experiments, we find that prompt tuning performs comparably with conventional full-model tuning when downstream data are sufficient, whereas it is much worse under few-shot learning settings, which may hinder the application of prompt tuning. We attribute this low performance to the manner of initializing soft prompts. Therefore, in this work, we propose to pre-train prompts by adding soft prompts into the pre-training stage to obtain a better initialization. We name this Pre-trained Prompt Tuning framework “PPT”. To ensure the generalization of PPT, we formulate similar classification tasks into a unified task form and pre-train soft prompts for this unified task. Extensive experiments show that tuning pre-trained prompts for downstream tasks can reach or even outperform full-model fine-tuning under both full-data and few-shot settings. Our approach is effective and efficient for using large-scale PLMs in practice.
Anthology ID:
2022.acl-long.576
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8410–8423
Language:
URL:
https://aclanthology.org/2022.acl-long.576
DOI:
10.18653/v1/2022.acl-long.576
Bibkey:
Cite (ACL):
Yuxian Gu, Xu Han, Zhiyuan Liu, and Minlie Huang. 2022. PPT: Pre-trained Prompt Tuning for Few-shot Learning. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8410–8423, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
PPT: Pre-trained Prompt Tuning for Few-shot Learning (Gu et al., ACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.acl-long.576.pdf
Software:
 2022.acl-long.576.software.tgz
Code
 thu-coai/ppt
Data
BoolQC3GLUEOCNLISSTSuperGLUE