True Few-Shot Learning With Prompts – A Real-World Perspective

Abstract

Prompt-based approaches excel at few-shot learning. However, Perez et al. (2021) recently cast doubt on their performance as they had difficulty getting good results in a "true" few-shot setting in which prompts and hyperparameters cannot be tuned on a dev set.
In view of this, we conduct an extensive study of PET, a method that combines textual instructions with example-based finetuning. We show that, if correctly configured, PET performs strongly in true few-shot settings without a dev set. Crucial for this strong performance is a number of design choices, including PET's ability to intelligently handle multiple prompts.
We put our findings to a real-world test by running PET on RAFT, a benchmark of tasks taken from realistic NLP applications for which no labeled dev or test sets are available. PET achieves a new state of the art on RAFT and performs close to non-expert humans for 7 out of 11 tasks. These results demonstrate that prompt-based learners can successfully be applied in true few-shot settings and underpin our belief that learning from instructions will play an important role on the path towards human-like few-shot learning capabilities.