大语言模型作为社会道德标准的镜子：反映不同伦理议题中的文化分歧与共识
LLMs as mirrors of societal moral standards: reflection of cultural divergence and agreement across ethical topics
AI总结：
本研究通过道德分数比较、国家聚类对齐和直接比较提示三种方法评估LLMs是否反映跨文化道德分歧与共识，发现模型表现参差且较弱。
摘要：
由于近期性能能力的进步，大语言模型（LLMs）在各个领域变得日益关键。然而，人们对LLMs中的偏见仍持续感到担忧，包括源自其训练数据的性别、种族和文化偏见。这些偏见引发了关于LLMs的伦理部署和社会影响的关键问题。认识到这些担忧，本研究考察LLMs是否准确反映道德观点中的跨文化差异与相似性。为评估所选LLMs是否捕捉到不同文化在道德议题上的分歧与共识模式，研究采用了三种主要方法：（1）比较模型生成的道德分数差异与基于调查的道德分数差异；（2）进行聚类对齐分析，以评估由模型生成道德分数得到的国家聚类与由调查数据得到的国家聚类之间的对应关系；（3）使用直接比较提示探查LLMs。这三种方法都使用了系统性提示和词元对，旨在评估LLMs理解并反映道德态度中文化差异的程度。研究结果表明，在所测试的各模型中，其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较低，凸显了提升模型有效捕捉这些细微差别的准确性的必要性。本研究获得的洞见旨在为全球背景下LLMs的伦理开发与部署讨论提供参考，强调减轻偏见并促进多元文化视角公平表征的重要性。
英文摘要：
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.
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