In this paper, we introduce, MultiGA, an optimization framework which applies genetic algorithm principles to address complex natural language tasks and reasoning problems by sampling from a diverse population of LLMs to initialize the population of candidate solutions. MultiGA generates a range of outputs from various parent LLMs and uses a neutral fitness function to evaluate them. Through an iterative recombination process, we mix and refine these generations until an optimal solution is achieved. Our results show that MultiGA produces high accuracy across multiple benchmarks, and these insights lay the foundation for future research looking closer at integrating multiple LLMs for unexplored tasks in which selecting only one pre-trained model is unclear or suboptimal.

翻译：本文提出MultiGA，一种通过从多样化的大型语言模型（LLM）种群中采样候选解初始化种群，从而将遗传算法原理应用于复杂自然语言任务与推理问题的优化框架。MultiGA从多个父代LLM生成输出范围，并采用中性适应度函数进行评估。通过迭代重组过程，混合并优化这些生成结果直至获得最优解。实验表明，MultiGA在多个基准测试中均取得高精度，这些发现为后续研究在仅选择单一预训练模型不明确或次优的未探索任务中整合多个LLM奠定了基础。