对比三款大模型的文化价值偏好，发现中文训练模型更倾向集体主义价值观。
Cultural Value Alignment in Large Language Models: A Prompt-based Analysis of Schwartz Values in Gemini, ChatGPT, and DeepSeek
- 用舒瓦茨价值问卷评估模型对40项价值的优先级
- 中文模型显著弱化权力与成就等自我提升价值
- 适合关注AI文化偏见与伦理多样性的研究者
本研究通过舒瓦茨价值框架中的40项肖像价值问卷，分析Gemini、ChatGPT和DeepSeek在文化价值对齐上的表现。基于贝叶斯序数回归模型，结果显示所有模型均高度强调自我超越类价值（如利他、普遍主义），体现大模型普遍推崇亲社会价值的倾向。然而，以中文语料训练的DeepSeek显著弱化自我增强类价值（如权力、成就），相较于ChatGPT与Gemini，更符合集体主义文化特征。该结果表明大模型反映的是文化特定偏见，而非普世伦理框架。为此提出多视角推理、自省反馈与动态情境化等策略，以应对大模型中的价值不对称问题。研究推动了对人工智能公平性、文化中立性及多元道德框架整合的讨论。
原文摘要 · Abstract (English)
This study examines cultural value alignment in large language models (LLMs) by analyzing how Gemini, ChatGPT, and DeepSeek prioritize values from Schwartz's value framework. Using the 40-item Portrait Values Questionnaire, we assessed whether DeepSeek, trained on Chinese-language data, exhibits distinct value preferences compared to Western models. Results of a Bayesian ordinal regression model show that self-transcendence values (e.g., benevolence, universalism) were highly prioritized across all models, reflecting a general LLM tendency to emphasize prosocial values. However, DeepSeek uniquely downplayed self-enhancement values (e.g., power, achievement) compared to ChatGPT and Gemini, aligning with collectivist cultural tendencies. These findings suggest that LLMs reflect culturally situated biases rather than a universal ethical framework. To address value asymmetries in LLMs, we propose multi-perspective reasoning, self-reflective feedback, and dynamic contextualization. This study contributes to discussions on AI fairness, cultural neutrality, and the need for pluralistic AI alignment frameworks that integrate diverse moral perspectives.
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