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

• URL: http://arxiv.org/abs/2506.02431v1
• Date: Tue, 03 Jun 2025 04:35:51 GMT
• Title: From Anger to Joy: How Nationality Personas Shape Emotion Attribution in Large Language Models
• Authors: Mahammed Kamruzzaman, Abdullah Al Monsur, Gene Louis Kim, Anshuman Chhabra,
• Abstract summary: We investigate how different countries are represented in pre-trained Large Language Models (LLMs) through emotion attributions.<n>Our analysis reveals significant nationality-based differences, with emotions such as shame, fear, and joy being disproportionately assigned across regions.
• Score: 4.362338454684645
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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