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

arXiv — cs.CL•Wednesday, November 12, 2025 at 5:00:00 AM
NeutralArtificial Intelligence
The study on emotion attribution in Large Language Models (LLMs) reveals that these AI systems exhibit significant nationality-based emotional stereotypes, particularly in how they assign feelings like shame, fear, and joy. By utilizing Hofstede's cultural dimensions—Power Distance, Uncertainty Avoidance, Long-Term Orientation, and Individualism—the research underscores the misalignment between LLM-generated emotional responses and actual human emotions, especially for negative sentiments. This misalignment indicates the presence of reductive and potentially biased stereotypes in LLM outputs, which is concerning as these models become more integrated into communication and cultural exchanges. As LLMs are increasingly relied upon for various applications, understanding their emotional attributions and the implications of these biases is essential for ensuring fair and accurate representation across diverse cultural contexts.
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