Numerous recent works aim to enhance the efficacy of Large Language Models (LLMs) through strategic prompting. In particular, the Optimization by PROmpting (OPRO) approach provides state-of-the-art performance by leveraging LLMs as optimizers where the optimization task is to find instructions that maximize the task accuracy. In this paper, we revisit OPRO for automated prompting with relatively small-scale LLMs, such as LLaMa-2 family and Mistral 7B. Our investigation reveals that OPRO shows limited effectiveness in small-scale LLMs, with limited inference capabilities constraining optimization ability. We suggest future automatic prompting engineering to consider both model capabilities and computational costs. Additionally, for small-scale LLMs, we recommend direct instructions that clearly outline objectives and methodologies as robust prompt baselines, ensuring efficient and effective prompt engineering in ongoing research.

翻译：近期众多研究旨在通过策略性提示提升大语言模型（LLMs）的效能。其中，基于提示的优化方法（OPRO）通过将LLMs作为优化器（优化任务为寻找能最大化任务准确率的指令）实现了最先进的性能。本文针对小规模LLMs（如LLaMa-2系列和Mistral 7B）的自动提示生成，对OPRO进行了重访。研究表明，OPRO在小规模LLMs中效果有限，有限的推理能力制约了其优化能力。我们建议未来的自动提示工程需同时考虑模型能力与计算成本。此外，针对小规模LLMs，我们推荐采用明确阐述目标与方法的直接指令作为稳健的提示基线，以确保后续研究中的高效提示工程。