Automatic Multi-Label Prompting: Simple and Interpretable Few-Shot Classification
New York University（纽约大学）；University of California, San Diego（加州大学圣地亚哥分校）
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
