AI 中文总结
本研究利用文化共识理论，分析大语言模型在单/多文化场景下的对齐情况，发现模型存在文化结构误表征问题，该理论可用于区分模型反映人类多样性与算法同质化的情况。
AI 中文摘要
近期NLP领域的研究已探究大语言模型对不同国家文化规范的理解，但这类工作通常仅关注分布模式，忽略了群体共识或一国内部可能存在的多元文化环境。本研究利用文化人类学中的文化共识理论（CCT）对这类多维细微差别进行建模，将CCT应用于10个国家、12个领域的世界价值观调查（WVS），结果表明模型常通过无法形成内聚共识或严重过度正则化共识来错误表征文化结构。通过明确呈现群体内部方差，CCT提供了可操作的诊断方法，用于评估模型何时反映真实人类多样性，何时出现算法同质化。
英文摘要
Recent work in NLP has probed large language models for their understanding of cultural norms across countries. However, this work typically considers distributional patterns, ignoring group consensus or possible multicultural environments within a country. In this work, we leverage cultural consensus theory (CCT) from cultural anthropology to model such multidimensional nuance. Applying CCT to the World Values Survey (WVS) across 10 countries and 12 domains, we demonstrate that models frequently misrepresent cultural structures by either failing to form cohesive consensus or severely over-regularizing consensus. Through explicit representation of intra-group variance, CCT provides actionable diagnostics to evaluate when models reflect true human diversity versus algorithmic homogenization.
CommentsAccepted to ACL Findings 2026
