  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.
