Carefully Considering Culture: Analyzing LLM Alignment in Single- and Multi-Cultural Settings using Cultural Consensus Theory

A new arXiv paper (arXiv:2608.09937v1) applies cultural consensus theory (CCT) to analyze how large language models (LLMs) align with cultural norms, finding that models often misrepresent cultural structures by failing to form cohesive consensus or over-regularizing consensus. The study, which uses the World Values Survey (WVS) across 10 countries and 12 domains, proposes CCT as a diagnostic tool to distinguish true human diversity from algorithmic homogenization.

arXiv:2608.09937v1 Announce Type: new Abstract: 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.