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：不同文化背景下，人们体验和表达情绪的方式存在差异。为使大型语言模型（LMs）能够完成需要情感敏感性的多语言任务，这些模型必须反映情绪上的文化差异。本研究探讨了2023年广泛使用的多语言语言模型是否能够反映不同文化和语言中情绪表达的差异。我们发现，从语言模型（如XLM-RoBERTa）获得的嵌入具有盎格鲁中心倾向，而生成式语言模型（如ChatGPT）则反映西方规范，即便在使用其他语言提示时也是如此。我们的结果表明，多语言语言模型未能成功学习文化上恰当的情绪细微差别，并指出了纠正这一问题的可能研究方向。