Emotions are experienced and expressed differently across the world. In order to use Large Language Models (LMs) for multilingual tasks that require emotional sensitivity, LMs must reflect this cultural variation in emotion. In this study, we investigate whether the widely-used multilingual LMs in 2023 reflect differences in emotional expressions across cultures and languages. We find that embeddings obtained from LMs (e.g., XLM-RoBERTa) are Anglocentric, and generative LMs (e.g., ChatGPT) reflect Western norms, even when responding to prompts in other languages. Our results show that multilingual LMs do not successfully learn the culturally appropriate nuances of emotion and we highlight possible research directions towards correcting this.

翻译：世界各地对情感的体验和表达方式各不相同。为了使大型语言模型（LM）能够完成需要情感敏感性的多语言任务，模型必须反映这种情感上的文化差异。在本研究中，我们探究了2023年广泛使用的多语言LM是否反映了不同文化和语言中情感表达的差异。我们发现，从LM（例如XLM-RoBERTa）获得的嵌入具有英美中心倾向，而生成式LM（例如ChatGPT）则反映了西方规范，即使在使用其他语言进行提示时也是如此。我们的结果表明，多语言LM未能成功学习情感的文化适当细微差别，我们并指出了纠正这一问题的可能研究方向。