The integration of large language models (LLMs) as optimizers shows promise in addressing complex optimization problems, such as those found in mathematical tasks and prompt optimization.
In our experiments, we demonstrated through various application setups how LLMs can effectively generate meta-prompts for optimization, significantly enhancing problem-solving performance through tailored prompt designs.
The results from comparing our meta-prompt optimization approach against baseline methods illustrated a marked improvement, showcasing LLMs' ability to adaptively refine their strategies over time.
Through thorough evaluation, our study revealed that the designed meta-prompts not only improved accuracy in math problems but also effectively guided LLMs in navigating intricate optimization landscapes.
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