Automatic Multi-Label Prompting: Simple and Interpretable Few-Shot Classification

Han Wang, Canwen Xu, Julian McAuley


Abstract
Prompt-based learning (i.e., prompting) is an emerging paradigm for exploiting knowledge learned by a pretrained language model. In this paper, we propose Automatic Multi-Label Prompting (AMuLaP), a simple yet effective method to automatically select label mappings for few-shot text classification with prompting. Our method exploits one-to-many label mappings and a statistics-based algorithm to select label mappings given a prompt template. Our experiments demonstrate that AMuLaP achieves competitive performance on the GLUE benchmark without human effort or external resources.
Anthology ID:
2022.naacl-main.401
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5483–5492
Language:
URL:
https://aclanthology.org/2022.naacl-main.401
DOI:
10.18653/v1/2022.naacl-main.401
Bibkey:
Cite (ACL):
Han Wang, Canwen Xu, and Julian McAuley. 2022. Automatic Multi-Label Prompting: Simple and Interpretable Few-Shot Classification. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5483–5492, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Automatic Multi-Label Prompting: Simple and Interpretable Few-Shot Classification (Wang et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.401.pdf
Code
 hannight/amulap
Data
CoLAGLUEMRPCMultiNLIQNLISST