SEE: Strategic Exploration and Exploitation for Cohesive In-Context Prompt Optimization
This addresses the resource-intensive challenge of prompt design for LLM users, offering a scalable solution with significant performance improvements.
The paper tackles the problem of optimizing prompts for Large Language Models by proposing SEE, a framework that refines both prompt instructions and examples through strategic exploration-exploitation, achieving an average performance gain of 13.94% and reducing computational costs by 58.67% across 35 benchmark tasks.
Designing optimal prompts for Large Language Models (LLMs) is a complicated and resource-intensive task, often requiring substantial human expertise and effort. Existing approaches typically separate the optimization of prompt instructions and in-context learning examples, leading to incohesive prompts that are defined and represented by suboptimal task performance. To overcome these challenges, we propose a novel Cohesive In-Context Prompt Optimization framework that refines both prompt instructions and examples. However, formulating such an optimization in the discrete and high-dimensional space of natural language poses significant challenges in both convergence and computational efficiency. To address these issues, we introduce SEE, a scalable and efficient prompt optimization framework that adopts metaheuristic optimization principles and strategically balances exploration and exploitation to enhance optimization performance and achieve efficient convergence. SEE features a quad-phased design that alternates between global traversal (exploration) and local optimization (exploitation) and adaptively chooses LLM operators during the optimization process. We have conducted a comprehensive evaluation across 35 benchmark tasks, and SEE significantly outperforms state-of-the-art baseline methods by a large margin, achieving an average performance gain of 13.94 while reducing computational costs by 58.67.