arxiv Preprint – Large Language Models as Optimizers

In this episode we discuss Large Language Models as Optimizers
by Chengrun Yang, Xuezhi Wang, Yifeng Lu, Hanxiao Liu, Quoc V. Le, Denny Zhou, Xinyun Chen. The paper introduces Optimization by PROmpting (OPRO), a method that uses large language models as optimizers in the absence of gradients. OPRO utilizes natural language descriptions of the optimization task to generate new solutions in each step, which are evaluated and added to the prompt for subsequent steps. Experimental results demonstrate that prompts optimized by OPRO outperform human-designed prompts on various tasks, with performance improvements of up to 8% on GSM8K and up to 50% on Big-Bench Hard tasks.