The integration of Large Language Models (LLMs) into various global cultures fundamentally presents a cultural challenge: LLMs must navigate interactions, respect social norms, and avoid transgressing cultural boundaries. However, it is still unclear if LLMs can adapt their outputs to diverse cultural norms. Our study focuses on this aspect. We introduce NormAd, a novel dataset, which includes 2.6k stories that represent social and cultural norms from 75 countries, to assess the ability of LLMs to adapt to different granular levels of socio-cultural contexts such as the country of origin, its associated cultural values, and prevalent social norms. Our study reveals that LLMs struggle with cultural reasoning across all contextual granularities, showing stronger adaptability to English-centric cultures over those from the Global South. Even with explicit social norms, the top-performing model, Mistral-7b-Instruct, achieves only 81.8\% accuracy, lagging behind the 95.6\% achieved by humans. Evaluation on NormAd further reveals that LLMs struggle to adapt to stories involving gift-giving across cultures. Due to inherent agreement or sycophancy biases, LLMs find it considerably easier to assess the social acceptability of stories that adhere to cultural norms than those that deviate from them. Our benchmark measures the cultural adaptability (or lack thereof) of LLMs, emphasizing the potential to make these technologies more equitable and useful for global audiences.

翻译：大语言模型（LLMs）融入全球多元文化本质上是文化挑战：模型需应对交互场景、尊重社会规范并避免逾越文化界限。然而，当前仍不明确LLMs能否根据多样化文化规范调整其输出。本研究聚焦于此。我们提出NormAd——一个包含来自75个国家社会文化规范的2600篇故事的新型数据集，用以评估LLMs对不同粒度社会文化语境（如国籍来源、关联文化价值观及主流社会规范）的适应能力。研究表明，LLMs在所有语境粒度上的文化推理均存在困难，对英语中心文化的适应性显著强于全球南方文化。即便提供显式社会规范，最优模型Mistral-7b-Instruct的准确率也仅达81.8%，远低于人类的95.6%。基于NormAd的评估进一步揭示，LLMs难以适应涉及跨文化赠礼行为的故事。由于固有的附和趋同偏差，模型对符合文化规范故事的社会接受度判断能力显著优于偏离规范的故事。本基准衡量了LLMs的文化适应性（或不足性），强调此类技术面向全球受众实现更公平、更实用的潜力。