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

• URL: http://arxiv.org/abs/2204.06305v2
• Date: Thu, 14 Apr 2022 01:33:46 GMT
• Title: Automatic Multi-Label Prompting: Simple and Interpretable Few-Shot Classification
• Authors: Han Wang and Canwen Xu and Julian McAuley
• Abstract summary: We propose Automatic Multi-Label Prompting (AMuLaP) 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.
• Score: 15.575483080819563
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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