%0 Conference Proceedings %T Automatic Multi-Label Prompting: Simple and Interpretable Few-Shot Classification %A Wang, Han %A Xu, Canwen %A McAuley, Julian %Y Carpuat, Marine %Y de Marneffe, Marie-Catherine %Y Meza Ruiz, Ivan Vladimir %S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies %D 2022 %8 July %I Association for Computational Linguistics %C Seattle, United States %F wang-etal-2022-automatic %X 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. %R 10.18653/v1/2022.naacl-main.401 %U https://aclanthology.org/2022.naacl-main.401 %U https://doi.org/10.18653/v1/2022.naacl-main.401 %P 5483-5492