Designing systems that can reason across cultures requires that they are
grounded in the norms of the contexts in which they operate. However, current
research on developing computational models of social norms has primarily
focused on American society. Here, we propose a novel approach to discover and
compare descriptive social norms across Chinese and American cultures. We
demonstrate our approach by leveraging discussions on a Chinese Q&A
platform-Zhihu-and the existing SocialChemistry dataset as proxies for
contrasting cultural axes, align social situations cross-culturally, and
extract social norms from texts using in-context learning. Embedding
Chain-of-Thought prompting in a human-AI collaborative framework, we build a
high-quality dataset of 3,069 social norms aligned with social situations
across Chinese and American cultures alongside corresponding free-text
explanations. To test the ability of models to reason about social norms across
cultures, we introduce the task of explainable social norm entailment, showing
that existing models under 3B parameters have significant room for improvement
in both automatic and human evaluation. Further analysis of cross-cultural norm
differences based on our dataset shows empirical alignment with the social
orientations framework, revealing several situational and descriptive nuances
in norms across these cultures.

基于环境学习的描述性社会规范模型构建方法，探索中美两国社会规范文化差异，构建对齐社会情境下 3,069 个高质量社会规范的数据集，并提出可检验社会规范蕴含的任务，从而证明现有模型在自动和人类评估方面均有提升空间。