研究发现大模型会根据国籍设定赋予不同情绪，存在文化偏见。
From Anger to Joy: How Nationality Personas Shape Emotion Attribution in Large Language Models
- 用霍夫斯泰德文化维度分析模型对不同国家的情绪分配
- 发现羞耻、恐惧、喜悦等情绪在各国间分配不均
- 模型情绪反应与人类差异显著，尤其负面情绪偏差明显
情绪是人类体验的核心，因个体、文化背景和国籍而异。鉴于大型语言模型（LLMs）在角色扮演中的成功应用，本文探究当赋予国籍特定人格时，这些模型是否表现出情感刻板印象。具体而言，研究了不同国家在预训练的LLMs中通过情绪归因的表现，以及这些归因是否符合文化规范。为深入解读，引入霍夫斯泰德跨文化框架中的四个关键维度：权力距离、不确定性规避、长期导向和个体主义。分析显示，各国之间存在显著的情感差异，如羞耻、恐惧和喜悦等情绪在各地区被不成比例地分配。此外，观察到模型生成的情绪反应与人类情绪响应存在明显偏差，尤其是在负面情绪方面，凸显了大模型输出中存在的简化且可能带有偏见的刻板印象。
原文摘要 · Abstract (English)
Emotions are a fundamental facet of human experience, varying across individuals, cultural contexts, and nationalities. Given the recent success of Large Language Models (LLMs) as role-playing agents, we examine whether LLMs exhibit emotional stereotypes when assigned nationality-specific personas. Specifically, we investigate how different countries are represented in pre-trained LLMs through emotion attributions and whether these attributions align with cultural norms. To provide a deeper interpretive lens, we incorporate four key cultural dimensions, namely Power Distance, Uncertainty Avoidance, Long-Term Orientation, and Individualism, derived from Hofstedes cross-cultural framework. Our analysis reveals significant nationality-based differences, with emotions such as shame, fear, and joy being disproportionately assigned across regions. Furthermore, we observe notable misalignment between LLM-generated and human emotional responses, particularly for negative emotions, highlighting the presence of reductive and potentially biased stereotypes in LLM outputs.
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