Automatic Multi-Label Prompting: Simple and Interpretable Few-Shot Classification
This addresses the challenge of reducing manual effort in prompt-based learning for NLP practitioners, though it is incremental as it builds on existing prompting paradigms.
The paper tackles the problem of automatically selecting label mappings for few-shot text classification with prompting, proposing AMuLaP, which achieves competitive performance on the GLUE benchmark without human effort or external resources.
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.