标题： 大型语言模型是否理解跨文化的道德观？ 显示英文标题

标题： Do Large Language Models Understand Morality Across Cultures?

摘要： 最近大型语言模型（LLMs）的进步使其成为众多领域中的强大工具。 然而，关于嵌入偏见的持续担忧，例如由于训练数据产生的性别、种族和文化偏见，引发了这些技术伦理使用和社会后果的重大问题。 本研究调查了LLMs在多大程度上能够捕捉道德观点中的跨文化差异和相似性。 具体而言，我们检查LLMs的输出是否与国际调查数据中观察到的道德态度模式一致。 为此，我们采用了三种互补的方法：(1) 比较模型生成的道德分数方差与调查中报告的方差，(2) 进行聚类对齐分析，以评估从LLMs输出和调查数据中得出的国家分组之间的对应关系，以及(3) 使用系统选择的标记对直接探测模型。 我们的结果表明，当前的LLMs往往无法再现跨文化道德变化的完整范围，倾向于压缩差异，并与实证调查模式对齐度较低。 这些发现突显了迫切需要更稳健的方法来减轻偏见并提高LLMs的文化代表性。 我们在最后讨论了LLMs负责任开发和全球部署的含义，强调公平性和伦理一致性。 显示英文摘要

摘要： Recent advancements in large language models (LLMs) have established them as powerful tools across numerous domains. However, persistent concerns about embedded biases, such as gender, racial, and cultural biases arising from their training data, raise significant questions about the ethical use and societal consequences of these technologies. This study investigates the extent to which LLMs capture cross-cultural differences and similarities in moral perspectives. Specifically, we examine whether LLM outputs align with patterns observed in international survey data on moral attitudes. To this end, we employ three complementary methods: (1) comparing variances in moral scores produced by models versus those reported in surveys, (2) conducting cluster alignment analyses to assess correspondence between country groupings derived from LLM outputs and survey data, and (3) directly probing models with comparative prompts using systematically chosen token pairs. Our results reveal that current LLMs often fail to reproduce the full spectrum of cross-cultural moral variation, tending to compress differences and exhibit low alignment with empirical survey patterns. These findings highlight a pressing need for more robust approaches to mitigate biases and improve cultural representativeness in LLMs. We conclude by discussing the implications for the responsible development and global deployment of LLMs, emphasizing fairness and ethical alignment.