Current literature demonstrates that Large Language Models (LLMs) are great few-shot learners, and prompting significantly increases their performance on a range of downstream tasks in a few-shot learning setting. An attempt to automate human-led prompting followed, with some progress achieved. In particular, subsequent work demonstrates automation can outperform fine-tuning in certain K-shot learning scenarios. In this paper, we revisit techniques for automated prompting on six different downstream tasks and a larger range of K-shot learning settings. We find that automated prompting does not consistently outperform simple manual prompts. Our work suggests that, in addition to fine-tuning, manual prompts should be used as a baseline in this line of research.
@article{arxiv.2304.03609,
title = {Revisiting Automated Prompting: Are We Actually Doing Better?},
author = {Yulin Zhou and Yiren Zhao and Ilia Shumailov and Robert Mullins and Yarin Gal},
journal= {arXiv preprint arXiv:2304.03609},
year = {2023}
}