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

2022-04-13 11:15:52
Han Wang, Canwen Xu, Julian McAuley
arXiv_CL
             
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 (translated)

URL

https://arxiv.org/abs/2204.06305

PDF

https://arxiv.org/pdf/2204.06305.pdf

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