Automatic Multi-Label Prompting: Simple and Interpretable Few-Shot Classification
Han Wang, Canwen Xu, Julian McAuley · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · 2022
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 AMu-LaP achieves competitive performance on the GLUE benchmark without human effort or external resources.1