超越對齊：多文化智能體系統中作為集體屬性的價值多樣性

摘要

多文化多智能体系统越来越多地被部署于全球多样化的场景中，其中不同智能体植根于各异的文化背景。现有文化评估聚焦于价值对齐：衡量单个智能体与目标文化匹配的紧密程度。然而，对齐是单个智能体的属性，无法揭示系统作为一个整体是否保留了其理应代表的文化多元性。我们提出将价值多样性作为多文化智能体系统的一个系统级评估维度，其定义基于不同文化背景的智能体在共享价值调查中的回答差异。利用世界价值观调查，我们评估了19种文化与18种基础模型在多种系统配置下的表现。研究发现，多样性与对齐性在很大程度上不相关，表明两者捕捉的是互补的系统属性，且当前多文化智能体系统在价值多样性上显著低于人类社会。混合骨干模型虽缩小了这一差距，但未能完全消除，该差距在不同文化构成与智能体规模下持续存在。社会互动进一步削弱了多样性，驱使智能体走向共识，而参与式预算案例研究表明，这种同质化缩小了集体决策的广度。综上，我们的研究将价值多样性确立为多文化多智能体系统的一个独立评估维度，并揭示了当前基于大语言模型的社会中持续存在的同质化趋势。我们的代码与数据已公开在 https://github.com/iNLP-Lab/MultiAgent-Diversity。

English

Multicultural multi-agent systems are increasingly deployed in globally diverse settings, where different agents are grounded in different cultural backgrounds. Existing cultural evaluation focuses on value alignment: how closely a single agent matches a target culture. Yet alignment is a per-agent property and cannot reveal whether a system, taken as a whole, preserves the cultural plurality it is meant to represent. We propose value diversity as a system-level evaluation axis for multicultural agent systems, defined through the dissimilarity between culturally conditioned agents' responses on a shared value survey. Using the World Values Survey, we evaluate 19 cultures and 18 backbone models across a wide range of system configurations. We find that diversity is largely uncorrelated with alignment, indicating that the two capture complementary system properties, and that current multicultural agent systems fall substantially below human societies in value diversity. Mixed-backbone systems narrow this gap but do not close it, and the gap persists across culture compositions and agent scales. Social interaction further erodes diversity by driving agents toward consensus, and a participatory budgeting case study shows that this homogenization narrows the breadth of collective decision-making. Together, our results establish value diversity as a distinct evaluation axis for multicultural multi-agent systems and reveal a persistent homogenization tendency in current LLM-based societies. Our code and data are publicly available at https://github.com/iNLP-Lab/MultiAgent-Diversity.