From Anger to Joy: How Nationality Personas Shape Emotion Attribution in Large Language Models

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. 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.

@article{kamruzzaman2025_2506.02431,
  title={ From Anger to Joy: How Nationality Personas Shape Emotion Attribution in Large Language Models },
  author={ Mahammed Kamruzzaman and Abdullah Al Monsur and Gene Louis Kim and Anshuman Chhabra },
  journal={arXiv preprint arXiv:2506.02431},
  year={ 2025 }
}