Large Language Models (LLMs) have demonstrated remarkable reasoning
abilities, prompting interest in their application as black-box optimizers.
This paper asserts that LLMs possess the capability for zero-shot optimization
across diverse scenarios, including multi-objective and high-dimensional
problems. We introduce a novel population-based method for numerical
optimization using LLMs called Language-Model-Based Evolutionary Optimizer
(LEO). Our hypothesis is supported through numerical examples, spanning
benchmark and industrial engineering problems such as supersonic nozzle shape
optimization, heat transfer, and windfarm layout optimization. We compare our
method to several gradient-based and gradient-free optimization approaches.
While LLMs yield comparable results to state-of-the-art methods, their
imaginative nature and propensity to hallucinate demand careful handling. We
provide practical guidelines for obtaining reliable answers from LLMs and
discuss method limitations and potential research directions.

该研究论文探讨了大型语言模型在零样本优化方面的能力，介绍了一种基于语言模型的进化优化方法（LEO），并通过数值示例验证此假设的有效性。同时，论文还提出使用大型语言模型时需要小心处理其想象性和产生幻觉的特点，并给出了获取可靠答案的实用指南以及讨论了方法局限和潜在的研究方向。