A Toolbox for Improving Evolutionary Prompt Search
This work addresses inefficiencies in evolutionary prompt search for LLM users, but it is incremental as it builds on existing methods with specific enhancements.
The paper tackled the problem of evolutionary prompt optimization lacking robust operators and efficient evaluation by proposing improvements like decomposing evolution steps, using an LLM-based judge, integrating human feedback, and developing efficient evaluation strategies, resulting in enhanced optimization quality and efficiency.
Evolutionary prompt optimization has demonstrated effectiveness in refining prompts for LLMs. However, existing approaches lack robust operators and efficient evaluation mechanisms. In this work, we propose several key improvements to evolutionary prompt optimization that can partially generalize to prompt optimization in general: 1) decomposing evolution into distinct steps to enhance the evolution and its control, 2) introducing an LLM-based judge to verify the evolutions, 3) integrating human feedback to refine the evolutionary operator, and 4) developing more efficient evaluation strategies that maintain performance while reducing computational overhead. Our approach improves both optimization quality and efficiency. We release our code, enabling prompt optimization on new tasks and facilitating further research in this area.