Large Language Models (LLMs) have shown strong performance across many tasks, but their ability to capture culturally diverse moral values remains unclear. In this paper, we examine whether LLMs mirror variations in moral attitudes reported by the World Values Survey (WVS) and the Pew Research Center's Global Attitudes Survey (PEW). We compare smaller monolingual and multilingual models (GPT-2, OPT, BLOOMZ, and Qwen) with recent instruction-tuned models (GPT-4o, GPT-4o-mini, Gemma-2-9b-it, and Llama-3.3-70B-Instruct). Using log-probability-based \emph{moral justifiability} scores, we correlate each model's outputs with survey data covering a broad set of ethical topics. Our results show that many earlier or smaller models often produce near-zero or negative correlations with human judgments. In contrast, advanced instruction-tuned models achieve substantially higher positive correlations, suggesting they better reflect real-world moral attitudes. We provide a detailed regional analysis revealing that models align better with Western, Educated, Industrialized, Rich, and Democratic (W.E.I.R.D.) nations than with other regions. While scaling model size and using instruction tuning improves alignment with cross-cultural moral norms, challenges remain for certain topics and regions. We discuss these findings in relation to bias analysis, training data diversity, information retrieval implications, and strategies for improving the cultural sensitivity of LLMs.

翻译：大型语言模型（LLMs）已在众多任务中展现出卓越性能，但其捕捉多元文化道德价值观的能力尚不明确。本文通过将LLMs的输出与世界价值观调查（WVS）和皮尤研究中心全球态度调查（PEW）报告的人类道德态度数据进行对比，系统检验了LLMs是否能够反映道德观念的文化差异。我们比较了早期单语及多语言模型（GPT-2、OPT、BLOOMZ和Qwen）与近期指令微调模型（GPT-4o、GPT-4o-mini、Gemma-2-9b-it和Llama-3.3-70B-Instruct）的表现。基于对数概率的道德正当性评分方法，我们将各模型在广泛伦理议题上的输出与调查数据进行相关性分析。结果显示，多数早期或较小规模模型与人类道德判断的相关性接近零值或呈负相关；而先进的指令微调模型则呈现出显著更高的正相关性，表明其能更好地反映现实世界的道德态度。细致的区域分析揭示，模型输出与西方、受教育程度高、工业化、富裕及民主（W.E.I.R.D.）国家群体的道德观念契合度明显高于其他地区。尽管扩大模型规模与采用指令微调技术提升了模型对跨文化道德规范的适应性，但在特定议题和区域仍存在挑战。我们结合这些发现，深入探讨了模型偏差分析、训练数据多样性、信息检索影响以及提升LLMs文化敏感性的策略。