从愤怒到喜悦：国籍角色如何塑造大型语言模型中的情感归因
From Anger to Joy: How Nationality Personas Shape Emotion Attribution in Large Language Models
AI总结：
本研究探究大型语言模型（LLMs）在被赋予特定国籍角色时是否表现出情感刻板印象，结合霍夫斯泰德跨文化框架的四个维度分析，发现其情感归因存在显著国籍差异且与人类反应错位，揭示了LLM中的简化偏见刻板印象。
摘要：
情感是人类经验的基本方面，因个体、文化背景和国籍而异。鉴于大型语言模型（LLMs）作为角色扮演智能体的近期成功，我们研究了LLMs在被赋予特定国籍角色时是否表现出情感刻板印象。具体而言，我们调查了不同国家如何通过情感归因在预训练LLMs中被呈现，以及这些归因是否与文化规范一致。为提供更深入的解释视角，我们纳入了源自霍夫斯泰德跨文化框架的四个关键文化维度，即权力距离、不确定性规避、长期导向和个人主义。我们的分析揭示了显著的基于国籍的差异，羞耻、恐惧和喜悦等情感在不同地区的分配比例失衡。此外，我们观察到LLM生成的情感反应与人类情感反应之间存在显著错位，尤其是在负面情感方面，这凸显了LLM输出中存在简化且可能带有偏见的刻板印象。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
