Large language models (LLMs) reflect societal norms and biases, especially
about gender. While societal biases and stereotypes have been extensively
researched in various NLP applications, there is a surprising gap for emotion
analysis. However, emotion and gender are closely linked in societal discourse.
E.g., women are often thought of as more empathetic, while men's anger is more
socially accepted. To fill this gap, we present the first comprehensive study
of gendered emotion attribution in five state-of-the-art LLMs (open- and
closed-source). We investigate whether emotions are gendered, and whether these
variations are based on societal stereotypes. We prompt the models to adopt a
gendered persona and attribute emotions to an event like 'When I had a serious
argument with a dear person'. We then analyze the emotions generated by the
models in relation to the gender-event pairs. We find that all models
consistently exhibit gendered emotions, influenced by gender stereotypes. These
findings are in line with established research in psychology and gender
studies. Our study sheds light on the complex societal interplay between
language, gender, and emotion. The reproduction of emotion stereotypes in LLMs
allows us to use those models to study the topic in detail, but raises
questions about the predictive use of those same LLMs for emotion applications.

大型语言模型 (LLMs) 在性别方面反映了社会规范和偏见。本文填补情绪分析中对于社会偏见的研究空白，首次对五种最先进的开源和闭源 LLM 进行了性别化情绪归因的全面研究，发现这些模型始终存在受到性别刻板印象影响的性别化情绪。