True Few-Shot Learning with Prompts -- A Real-World Perspective

TL;DR

This paper demonstrates that the PET method, when properly configured, excels in true few-shot learning scenarios without dev set tuning, achieving state-of-the-art results on real-world NLP benchmarks and approaching human performance.

Contribution

The paper provides an extensive evaluation of PET, showing its effectiveness in true few-shot settings and introducing techniques for handling multiple prompts effectively.

Findings

PET achieves state-of-the-art on RAFT benchmark

PET performs close to non-expert humans on 7 of 11 tasks

Proper configuration of PET is crucial for strong true few-shot performance

Abstract

Prompt-based approaches are strong at few-shot learning. However, Perez et al. (2021) have recently cast doubt on their performance because 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 a true few-shot setting, i.e., without a dev set. Crucial for this strong performance is PET's ability to intelligently handle multiple prompts. We then put our findings to a real-world test by running PET on RAFT, a benchmark of tasks taken directly 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 like PET excel at true few-shot learning and underpin our belief that learning from instructions will play an important role on the path towards human-like few-shot learning capabilities.