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

Anonymous

Paper Link: https://openreview.net/forum?id=yPlIJwgBu3G

Paper Type: Long paper (up to eight pages of content + unlimited references and appendices)

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.

Copyright Consent Signature (type Name Or NA If Not Transferrable): Canwen Xu

Copyright Consent Name And Address: University of California, San Diego. 9500 Gilman Dr., La Jolla, California