探索大语言模型在道德判断中的文化差异
Exploring Cultural Variations in Moral Judgments with Large Language Models
AI总结：
本文研究大语言模型在捕捉文化多样性道德价值观方面的能力，通过对比不同模型与全球态度调查数据的关联性，发现指令微调模型在跨文化道德规范上表现更佳。
摘要：
大型语言模型（LLMs）在许多任务中表现出色，但其捕捉文化多样性的道德价值观能力仍不明确。本文探讨LLMs是否能反映世界价值观调查（WVS）和皮尤研究中心全球态度调查（PEW）报告的道德态度差异。我们比较了单语和多语模型（GPT-2、OPT、BLOOMZ和Qwen）与最新指令微调模型（GPT-4o、GPT-4o-mini、Gemma-2-9b-it和Llama-3.3-70B-Instruct）的性能。通过基于对数概率的道德合理性评分，我们将每个模型的输出与涵盖广泛伦理主题的调查数据相关联。结果显示，许多早期或较小的模型与人类判断的关联性接近于零或负值。相比之下，先进指令微调模型实现了显著的正相关，表明它们更好地反映了现实中的道德态度。我们提供了详细的区域分析，发现模型在西方、受过教育、工业化、富裕和民主（W.E.I.R.D.）国家的匹配度更高。尽管扩大模型规模和使用指令微调能提高与跨文化道德规范的一致性，但某些主题和地区仍存在挑战。我们讨论这些发现与偏见分析、训练数据多样性、信息检索影响以及改进LLM文化敏感性的策略之间的关系。
英文摘要：
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.
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