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在各种自然语言处理应用中已得到广泛研究，但在情感分析领域却存在一个令人惊讶的空白。然而，在社会话语中，情感与性别紧密相连。例如，女性通常被认为更具同理心，而男性的愤怒则更易被社会接受。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。我们探究了情感是否被性别化，以及这些差异是否基于社会刻板印象。我们提示模型采用性别化的人物角色，并对“当我与一位亲密的人发生激烈争吵时”这样的事件进行情感归因。随后，我们分析了模型生成的、与性别-事件配对相关的情感。我们发现，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的既有成果相符。我们的研究揭示了语言、性别与情感之间复杂的社会相互作用。大语言模型对情感刻板印象的复现，使我们能够利用这些模型对该主题进行详细研究，但也引发了关于这些模型在情感应用中进行预测性使用的疑问。