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.

翻译：大语言模型（LLMs）反映了社会规范和偏见，尤其是在性别方面。尽管社会偏见和刻板印象已在各种自然语言处理应用中受到广泛研究，但在情感分析领域却存在一个令人惊讶的空白。然而，情感与性别在社会话语中紧密相连。例如，女性常被认为更具同理心，而男性的愤怒更易被社会接受。为填补这一空白，我们首次对五种最先进的大语言模型（包括开源和闭源模型）中的性别化情感归因进行了全面研究。我们探讨情感是否具有性别化特征，以及这些差异是否基于社会刻板印象。我们引导模型采用性别化角色，并对“当我与一个亲近的人发生激烈争吵时”这类事件进行情感归因。随后，我们分析模型生成的情感与性别-事件对之间的关系。结果发现，所有模型均一致表现出受性别刻板印象影响的性别化情感。这些发现与心理学和性别研究领域已有成果一致。我们的研究揭示了语言、性别与情感之间复杂的社会互动。LLMs 对情感刻板印象的复现使我们能够利用这些模型深入研究该主题，但也引发了关于将这些 LLMs 用于情感应用预测的疑问。