From Anger to Joy: How Nationality Personas Shape Emotion Attribution in Large Language Models
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908642650882048 |
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| author | Kamruzzaman, Mahammed Monsur, Abdullah Al Kim, Gene Louis Chhabra, Anshuman |
| author_facet | Kamruzzaman, Mahammed Monsur, Abdullah Al Kim, Gene Louis Chhabra, Anshuman |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_02431 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Anger to Joy: How Nationality Personas Shape Emotion Attribution in Large Language Models Kamruzzaman, Mahammed Monsur, Abdullah Al Kim, Gene Louis Chhabra, Anshuman Computation and Language 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. |
| title | From Anger to Joy: How Nationality Personas Shape Emotion Attribution in Large Language Models |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2506.02431 |