多智能体协作让大模型集体推理更强
The Society of HiveMind: Multi-Agent Optimization of Foundation Model Swarms to Unlock the Potential of Collective Intelligence
- 用仿生蜂群机制协调多个大模型协作
- 逻辑推理任务性能显著提升，知识类任务无改善
- 适合研究群体智能与大模型协同的学者
多智能体系统解决人工智能基础模型（如大语言模型）的可及性与可扩展性问题。我们提出‘蜂群智慧社会’（SOHM）框架，通过模仿自然界动物群体行为并基于现代进化理论，协调多个基础模型间的交互。实验发现，对于主要依赖现实世界知识的任务，SOHM带来可忽略的改进；但对于需要高强度逻辑推理的任务，表现显著提升，表明多智能体系统能增强集体推理能力。结果证明，融合多样化的基础模型可形成具备环境自适应优化能力的人工群体智能。
原文摘要 · Abstract (English)
Multi-agent systems address issues of accessibility and scalability of artificial intelligence (AI) foundation models, which are often represented by large language models. We develop a framework - the "Society of HiveMind" (SOHM) - that orchestrates the interaction between multiple AI foundation models, imitating the observed behavior of animal swarms in nature by following modern evolutionary theories. On the one hand, we find that the SOHM provides a negligible benefit on tasks that mainly require real-world knowledge. On the other hand, we remark a significant improvement on tasks that require intensive logical reasoning, indicating that multi-agent systems are capable of increasing the reasoning capabilities of the collective compared to the individual agents. Our findings demonstrate the potential of combining a multitude of diverse AI foundation models to form an artificial swarm intelligence capable of self-improvement through interactions with a given environment.
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