GAAPO: genetic algorithmic applied to prompt optimization.

Area of Science:

• Artificial Intelligence
• Natural Language Processing

Background:

• Large Language Models (LLMs) performance is highly sensitive to input prompt quality.
• Manual prompt engineering is time-consuming and may not yield optimal results.

Purpose of the Study:

• Introduce GAAPO (Genetic Algorithm Applied to Prompt Optimization), a novel hybrid framework for automated prompt evolution.
• Investigate the effectiveness of integrating multiple prompt generation strategies within a genetic algorithm.

Main Methods:

• Utilized genetic algorithm principles for prompt evolution across successive generations.
• Integrated specialized prompt generation strategies into the evolutionary framework.
• Conducted experiments on diverse datasets (ETHOS, MMLU-Pro, GPQA) using limited dataset sizes for real-world relevance.

Main Results:

• Identified the critical tradeoff between population size and number of generations in prompt optimization.
• Analyzed the impact of selection methods on the stability of optimization results.
• Evaluated the capability of LLMs, particularly reasoning models, in automatically generating prompts.

Conclusions:

• GAAPO offers a novel approach to automated prompt optimization for LLMs.
• Findings provide insights into optimizing automatic prompt generation strategies and LLM performance.
• The study contributes to the theoretical understanding and practical application of prompt optimization techniques.