Large language models (LLMs) have become increasingly pivotal in various domains due the recent advancements in their performance capabilities. However, concerns persist regarding biases in LLMs, including gender, racial, and cultural biases derived from their training data. These biases raise critical questions about the ethical deployment and societal impact of LLMs. Acknowledging these concerns, this study investigates whether LLMs accurately reflect cross-cultural variations and similarities in moral perspectives. In assessing whether the chosen LLMs capture patterns of divergence and agreement on moral topics across cultures, three main methods are employed: (1) comparison of model-generated and survey-based moral score variances, (2) cluster alignment analysis to evaluate the correspondence between country clusters derived from model-generated moral scores and those derived from survey data, and (3) probing LLMs with direct comparative prompts. All three methods involve the use of systematic prompts and token pairs designed to assess how well LLMs understand and reflect cultural variations in moral attitudes. The findings of this study indicate overall variable and low performance in reflecting cross-cultural differences and similarities in moral values across the models tested, highlighting the necessity for improving models' accuracy in capturing these nuances effectively. The insights gained from this study aim to inform discussions on the ethical development and deployment of LLMs in global contexts, emphasizing the importance of mitigating biases and promoting fair representation across diverse cultural perspectives.

翻译：大型语言模型（LLMs）因其性能能力的近期进展，已在多个领域变得日益关键。然而，关于LLMs中存在的偏见——包括源自其训练数据的性别、种族和文化偏见——的担忧持续存在。这些偏见引发了关于LLMs伦理部署与社会影响的重大问题。认识到这些担忧，本研究探讨LLMs是否准确反映了道德观念上的跨文化差异与相似性。为评估所选LLMs是否捕捉到跨文化道德话题的分歧与共识模式，我们采用了三种主要方法：(1) 比较模型生成与基于调查的道德得分方差，(2) 通过聚类对齐分析评估由模型生成道德得分得出的国家聚类与由调查数据得出的国家聚类之间的对应关系，以及(3) 使用直接比较提示对LLMs进行探测。所有三种方法均涉及使用系统设计的提示和词元对，以评估LLMs理解和反映道德态度中文化差异的能力。本研究的结果表明，在所测试的模型中，反映道德价值观跨文化差异与相似性的整体表现参差不齐且水平较低，凸显了提高模型有效捕捉这些细微差别的准确性的必要性。本研究获得的见解旨在为全球背景下LLMs的伦理发展与部署的讨论提供参考，强调减轻偏见和促进多元文化视角公平表征的重要性。