GAAPO:用于快速优化的遗传算法.
Xavier Sécheresse1, Jacques-Yves Guilbert-Ly1, Antoine Villedieu de Torcy1
1Biolevate, Paris, France.
Frontiers in artificial intelligence
|October 8, 2025
概括
适用于提示优化的遗传算法 (GAAPO) 通过使用遗传原则来开发提示,以获得更好的大型语言模型 (LLM) 性能. 这种自动化方法优化了提示生成策略,改善了LLM的能力.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 大型语言模型 (LLM) 的性能对输入提示质量非常敏感.
- 手动提示工程是耗时的,可能不会产生最佳的结果.
研究的目的:
- 介绍GAAPO (适用于快速优化的遗传算法),这是一个用于自动化快速进化的新型混合框架.
- 研究将多个提示生成策略集成到遗传算法中的有效性.
主要方法:
- 利用遗传算法原则,在相继的几代人中快速进化.
- 将专门的提示生成策略集成到进化框架中.
- 在不同的数据集 (ETHOS,MMLU-Pro,GPQA) 上进行了实验,使用有限的数据集大小来实现现实世界的相关性.
主要成果:
- 在快速优化中确定了人口规模和代数之间的关键权衡.
- 分析了选择方法对优化结果稳定性的影响.
- 评估了LLM,特别是推理模型在自动生成提示的能力.
结论:
- GAAPO提供了一种新的方法,用于LLMs的自动化快速优化.
- 调查结果提供了关于优化自动提示生成策略和LLM性能的见解.
- 该研究有助于对即时优化技术的理论理解和实际应用.
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