MultiGA：利用多源 seeding 在遗传算法中
MultiGA: Leveraging Multi-Source Seeding in Genetic Algorithms
AI总结：
MultiGA通过多源seeding提升遗传算法在自然语言处理中的性能，利用多个LLM生成候选解并迭代优化，实现高准确率。
摘要：
在本文中，我们介绍了MultiGA，一种优化框架，通过从多样化的LLM群体中采样来初始化候选解群体，应用遗传算法原理解决复杂自然语言任务和推理问题。MultiGA从不同父LLM生成多种输出，并使用中性适应度函数评估它们。通过迭代重组过程，混合和优化这些生成，直到达到最优解。我们的结果表明，MultiGA在多个基准测试中均表现出高准确性，这些见解为未来研究奠定了基础，旨在更深入地探索在未探索任务中整合多个LLM的潜力，其中仅选择一个预训练模型可能不明确或次优。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
