Optimization is ubiquitous. While derivative-based algorithms have been
powerful tools for various problems, the absence of gradient imposes challenges
on many real-world applications. In this work, we propose Optimization by
PROmpting (OPRO), a simple and effective approach to leverage large language
models (LLMs) as optimizers, where the optimization task is described in
natural language. In each optimization step, the LLM generates new solutions
from the prompt that contains previously generated solutions with their values,
then the new solutions are evaluated and added to the prompt for the next
optimization step. We first showcase OPRO on linear regression and traveling
salesman problems, then move on to prompt optimization where the goal is to
find instructions that maximize the task accuracy. With a variety of LLMs, we
demonstrate that the best prompts optimized by OPRO outperform human-designed
prompts by up to 8% on GSM8K, and by up to 50% on Big-Bench Hard tasks.

在此工作中，我们提出了一种名为 OPRO 的优化方法，利用大型语言模型作为优化器，通过自然语言描述优化任务。我们首先展示了 OPRO 在线性回归和旅行商问题上的应用，然后转向优化提示，目标是找到最大化任务准确性的指令。我们通过多种大型语言模型的实验证明，OPRO 通过优化提示的方式胜过人工设计的提示，在 GSM8K 上提高了最多 8％，在 Big-Bench Hard 任务上提高了最多 50％。