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    <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.</abstract>
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%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

Markdown (Informal)

[Automatic Multi-Label Prompting: Simple and Interpretable Few-Shot Classification](https://aclanthology.org/2022.naacl-main.401) (Wang et al., NAACL 2022)

• Automatic Multi-Label Prompting: Simple and Interpretable Few-Shot Classification (Wang et al., NAACL 2022)

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.