SEE: Strategic Exploration and Exploitation for Cohesive In-Context Prompt Optimization

• URL: http://arxiv.org/abs/2402.11347v2
• Date: Sat, 12 Jul 2025 20:31:55 GMT
• Title: SEE: Strategic Exploration and Exploitation for Cohesive In-Context Prompt Optimization
• Authors: Wendi Cui, Zhuohang Li, Hao Sun, Damien Lopez, Kamalika Das, Bradley Malin, Sricharan Kumar, Jiaxin Zhang,
• Abstract summary: We propose a novel Cohesive In-Context Prompt Optimization framework for Large Language Models (LLMs)<n>We introduce SEE, a scalable and efficient prompt optimization framework that adopts metaheuristic optimization principles and strategically exploration and exploitation.<n> 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.
• Score: 8.975505323004427
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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