As large language models (LLMs) are shaping the way information is shared and accessed online, their opinions have the potential to influence a wide audience. This study examines who the LLMs view as the most prominent figures across various fields, using prompts in ten different languages to explore the influence of linguistic diversity. Our findings reveal low diversity in responses, with a small number of figures dominating recognition across languages (also known as the "superstar effect"). These results highlight the risk of narrowing global knowledge representation when LLMs retrieve subjective information.

翻译：随着大语言模型（LLMs）正在塑造在线信息共享与获取的方式，其观点具有影响广泛受众的潜力。本研究通过使用十种不同语言的提示，探讨了语言多样性的影响，以考察LLMs如何看待各领域中最杰出的人物。我们的研究结果显示，回应中的多样性较低，少数人物在不同语言中占据主导地位（亦称为“超级明星效应”）。这些结果突显了当LLMs检索主观信息时，全球知识表征趋于狭隘的风险。