用选举机制提升大模型多智能体决策多样性，避免单一投票方式主导。
An Electoral Approach to Diversify LLM-based Multi-Agent Collective Decision-Making
- 引入基于社会选择理论的选举式决策模块，支持多种偏好投票方法。
- 在三个基准上验证，部分机制使主流大模型推理能力显著提升。
- 三智能体即可产生正向协同，且抗单点故障，结果分布更丰富。
当前大语言模型在复杂任务求解中展现出协作潜力，集体决策（CDM）是其多智能体协作框架的核心。我们对52个近期系统进行调研，发现严重缺乏多样性，过度依赖独裁式和多数决投票。基于社会选择理论，我们剖析现有方法的局限性，并提出GEDI——一种集成多种序数偏好投票机制的选举式CDM模块。在三个基准上的实证研究显示，融入特定CDM方法可显著提升主流大模型的推理能力和鲁棒性，无需复杂系统设计。此外，部分机制在仅三智能体时即能产生正向协同；基于投票的方法还表现出对单点故障的鲁棒性，以及在hit-rate@k和学科维度上的多样性。
原文摘要 · Abstract (English)
Modern large language models (LLMs) have exhibited cooperative synergy on complex task-solving, and collective decision-making (CDM) is a pivotal component in LLM-based multi-agent collaboration frameworks. Our survey on 52 recent such systems uncovers a severe lack of diversity, with a heavy reliance on dictatorial and plurality voting for CDM. Through the lens of social choice theory, we scrutinize widely-adopted CDM methods and identify their limitations. To enrich current landscape of LLM-based CDM, we present GEDI, an electoral CDM module that incorporates various ordinal preferential voting mechanisms. Our empirical case study across three benchmarks shows that the integration of certain CDM methods can markedly improve the reasoning capabilities and robustness of some leading LLMs, all without requiring intricate system designs. Additionally, we find that some CDM mechanisms generate positive synergies even with as few as three agents. The voting-based methods also demonstrate robustness against single points of failure, as well as diversity in terms of hit-rate@k and subject-wise impacts.
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