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Automatic Multi-Label Prompting: Simple and Interpretable Few-Shot Classification (2204.06305v2)
Published 13 Apr 2022 in cs.CL, cs.AI, and cs.LG
Abstract: Prompt-based learning (i.e., prompting) is an emerging paradigm for exploiting knowledge learned by a pretrained LLM. 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.