Crafting an ideal prompt for Large Language Models (LLMs) is a challenging
task that demands significant resources and expert human input. Existing work
treats the optimization of prompt instruction and in-context learning examples
as distinct problems, leading to sub-optimal prompt performance. This research
addresses this limitation by establishing a unified in-context prompt
optimization framework, which aims to achieve joint optimization of the prompt
instruction and examples. However, formulating such optimization in the
discrete and high-dimensional natural language space introduces challenges in
terms of convergence and computational efficiency. To overcome these issues, we
present PhaseEvo, an efficient automatic prompt optimization framework that
combines the generative capability of LLMs with the global search proficiency
of evolution algorithms. Our framework features a multi-phase design
incorporating innovative LLM-based mutation operators to enhance search
efficiency and accelerate convergence. We conduct an extensive evaluation of
our approach across 35 benchmark tasks. The results demonstrate that PhaseEvo
significantly outperforms the state-of-the-art baseline methods by a large
margin whilst maintaining good efficiency.

通过建立统一的上下文提示优化框架，本研究旨在实现上下文提示和示例的联合优化，并提出了一个高效的自动提示优化框架 PhaseEvo，它结合了 LLMs 的生成能力和进化算法的全局搜索能力，通过创新的基于 LLMs 的变异操作提高搜索效率并加速收敛。实验证明，PhaseEvo 在保持良好效率的同时大幅度优于最先进的基线方法。