Large language models (LLMs) are increasingly used to understand and even simulate human behavior, but how well do they grasp the emotional subtleties of different cultures? A fascinating new study probes this question by exploring how LLMs interpret mixed emotions—instances where positive and negative feelings occur simultaneously. The study draws on existing research comparing how Japanese and American individuals experience mixed emotions in various scenarios, including self-success and self-failure. Researchers administered a mixed-emotion survey to five different LLMs, prompting them in both English and Japanese and analyzing their responses. The results revealed that LLMs struggled to replicate human responses from previous studies, suggesting a limited understanding of cultural nuances surrounding mixed emotions. Interestingly, the language in which the LLMs were prompted had a greater influence on their responses than textual descriptions providing cultural context. Expanding the study to other languages, including Chinese, Korean, Vietnamese, French, German, and Spanish, the researchers found that LLMs exhibited a greater correlation in responses among East Asian languages than Western languages. This might reflect either a more nuanced understanding of Western emotions or the Western bias of training data. This research highlights a critical challenge: as LLMs become more globally accessible, they need to accurately reflect the diverse ways people experience and express emotions. Future research could involve replicating human studies with a wider range of cultural contexts and languages to develop better methods for evaluating cultural sensitivity in LLMs.