用文化共识理论分析大模型在多文化中的对齐问题。
Carefully Considering Culture: Analyzing LLM Alignment in Single- and Multi-Cultural Settings using Cultural Consensus Theory
- 引入文化共识理论，捕捉群体内部差异与共识。
- 在10国12领域中发现模型常误构文化结构。
- 揭示模型算法同质化，适合评估文化多样性对齐。
近期NLP研究关注大语言模型对跨国文化规范的理解，但通常仅关注分布模式，忽视群体共识或国家内部的多文化环境。本文利用文化人类学中的文化共识理论（CCT），对世界价值观调查（WVS）数据在10个国家、12个领域中的应用，发现模型常错误表示文化结构，表现为未能形成连贯共识或严重过度规整共识。通过显式建模组内差异，CCT为评估模型是否反映真实人类多样性而非算法同质化提供了可操作诊断。
原文摘要 · Abstract (English)
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.
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