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.

翻译：现有文献表明，大型语言模型（LLMs）是优秀的小样本学习者，而提示策略能在小样本学习设置下显著提升其在众多下游任务中的表现。随后，自动化人工引导提示的研究应运而生，并取得了一定进展。特别是，后续工作证明在某些K样本学习场景下，自动化方法可超越微调的效果。本文针对六种不同下游任务及更广泛的K样本学习设置，重新审视了自动化提示技术。研究发现，自动化提示并未持续优于简单的手动提示。我们的工作表明，在此类研究中，除微调外，手动提示应作为基线方法。