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

TL;DR

This paper investigates how large language models assign emotions to different nationalities, revealing significant cultural stereotypes and misalignments with human emotional responses, especially for negative emotions.

Contribution

It introduces a framework to analyze nationality-based emotion attributions in LLMs using Hofstede's cultural dimensions, highlighting biases and stereotypes.

Findings

Significant nationality-based differences in emotion attribution.

Disproportionate assignment of shame, fear, and joy across regions.

Notable misalignment between LLM and human emotional responses.

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. 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.