更多的智能体就是你所需要的。

More Agents Is All You Need

February 3, 2024

作者: Junyou Li, Qin Zhang, Yangbin Yu, Qiang Fu, Deheng Ye

cs.AI

摘要

我们发现，仅通过一种采样和投票方法，大型语言模型（LLMs）的性能随着实例化代理数量的增加而提升。此外，这种方法与现有的复杂方法相互独立，可以进一步增强LLMs，而增强程度与任务难度相关。我们在广泛的LLM基准测试上进行了全面实验，以验证我们发现的存在，并研究可以促进其发生的属性。我们的代码可在以下网址公开获取：https://anonymous.4open.science/r/more_agent_is_all_you_need。

English

We find that, simply via a sampling-and-voting method, the performance of large language models (LLMs) scales with the number of agents instantiated. Also, this method is orthogonal to existing complicated methods to further enhance LLMs, while the degree of enhancement is correlated to the task difficulty. We conduct comprehensive experiments on a wide range of LLM benchmarks to verify the presence of our finding, and to study the properties that can facilitate its occurrence. Our code is publicly available at: https://anonymous.4open.science/r/more_agent_is_all_you_need.