From Anger to Joy: How Nationality Personas Shape Emotion Attribution in Large Language Models
This work addresses potential biases in LLMs for cross-cultural applications, but it is incremental as it applies existing cultural frameworks to analyze pre-trained models.
The study investigated whether Large Language Models (LLMs) exhibit emotional stereotypes when assigned nationality-specific personas, revealing significant nationality-based differences in emotion attributions and notable misalignment with human responses, particularly for negative emotions.
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.