AMPO: Automatic Multi-Branched Prompt Optimization
This addresses the limitation of existing single-flow prompt optimization techniques in handling diverse patterns for complex tasks, offering a novel approach for prompt engineers.
The paper tackles the problem of automatic prompt optimization for large language models by introducing AMPO, a method that iteratively develops multi-branched prompts using failure cases as feedback, achieving the best results across five tasks.
Prompt engineering is very important to enhance the performance of large language models (LLMs). When dealing with complex issues, prompt engineers tend to distill multiple patterns from examples and inject relevant solutions to optimize the prompts, achieving satisfying results. However, existing automatic prompt optimization techniques are only limited to producing single flow instructions, struggling with handling diverse patterns. In this paper, we present AMPO, an automatic prompt optimization method that can iteratively develop a multi-branched prompt using failure cases as feedback. Our goal is to explore a novel way of structuring prompts with multi-branches to better handle multiple patterns in complex tasks, for which we introduce three modules: Pattern Recognition, Branch Adjustment, and Branch Pruning. In experiments across five tasks, AMPO consistently achieves the best results. Additionally, our approach demonstrates significant optimization efficiency due to our adoption of a minimal search strategy.