GAAPO: Genetic Algorithmic Applied to Prompt Optimization
This work addresses the time-consuming and suboptimal nature of manual prompt engineering for improving LLM performance, though it is incremental as it builds on existing genetic algorithm principles.
The paper tackles the problem of optimizing prompts for large language models by introducing GAAPO, a hybrid genetic algorithm framework that evolves prompts through multiple strategies, and demonstrates its effectiveness on datasets like ETHOS, MMLU-Pro, and GPQA.
Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks, with their performance heavily dependent on the quality of input prompts. While prompt engineering has proven effective, it typically relies on manual adjustments, making it time-consuming and potentially suboptimal. This paper introduces GAAPO (Genetic Algorithm Applied to Prompt Optimization), a novel hybrid optimization framework that leverages genetic algorithm principles to evolve prompts through successive generations. Unlike traditional genetic approaches that rely solely on mutation and crossover operations, GAAPO integrates multiple specialized prompt generation strategies within its evolutionary framework. Through extensive experimentation on diverse datasets including ETHOS, MMLU-Pro, and GPQA, our analysis reveals several important point for the future development of automatic prompt optimization methods: importance of the tradeoff between the population size and the number of generations, effect of selection methods on stability results, capacity of different LLMs and especially reasoning models to be able to automatically generate prompts from similar queries... Furthermore, we provide insights into the relative effectiveness of different prompt generation strategies and their evolution across optimization phases. These findings contribute to both the theoretical understanding of prompt optimization and practical applications in improving LLM performance.