To improve the reasoning and question-answering capabilities of Large Language Models (LLMs), several multi-agent approaches have been introduced. While these methods enhance performance, the application of collective intelligence-based approaches to complex network structures and the dynamics of agent interactions remain underexplored. This work extends the concept of multi-agent debate to more general network topologies, measuring the question-answering accuracy, influence, consensus, and the effects of bias on the collective. The results show that random networks perform similarly to fully connected networks despite using significantly fewer tokens. Furthermore, a strong consensus among agents correlates with correct answers, whereas divided responses typically indicate incorrect answers. Analysing the influence of the agents reveals a balance between self-reflection and interconnectedness; self-reflection aids when local interactions are incorrect, and local interactions aid when the agent itself is incorrect. Additionally, bias plays a strong role in system performance with correctly biased hub nodes boosting performance. These insights suggest that using random networks or scale-free networks with knowledgeable agents placed in central positions can enhance the overall question-answering performance of multi-agent systems.

翻译：为提升大型语言模型（LLM）的推理与问答能力，已有多种多智能体方法被提出。尽管这些方法提升了性能，但基于集体智能的方法在复杂网络结构中的应用以及智能体交互的动态特性仍未得到充分探索。本研究将多智能体辩论的概念扩展至更一般的网络拓扑结构，测量了问答准确率、影响力、共识以及偏见对集体的影响。结果表明，随机网络尽管使用的令牌数量显著减少，其表现与全连接网络相当。此外，智能体之间的强共识与正确答案相关，而分歧性响应通常意味着错误答案。对智能体影响力的分析揭示了自我反思与互联性之间的平衡：当局部交互错误时，自我反思有益；而当智能体自身错误时，局部交互有益。此外，偏见在系统性能中扮演重要角色，正确偏见的枢纽节点能够提升性能。这些发现表明，使用随机网络或将知识渊博的智能体置于中心位置的无标度网络，能够提升多智能体系统的整体问答性能。