Ask your chatbot to impersonate an expert from Russia and an expert from US and query it on Chinese politics. How might the outputs differ? Or, to prepare ourselves for the worse, how might they converge? Scholars have raised concerns LLM based applications can homogenize cultures and flatten perspectives. But exactly how much does LLM generated outputs converge despite explicit different role assignment? This study provides empirical evidence to the above question. The critique centres on pretrained models regurgitating ossified political jargons used in the Western world when speaking about China, Iran, Russian, and US politics, despite changes in these countries happening daily or hourly. The experiments combine role-prompting and similarity metrics. The results show that AI generated discourses from four models about Iran and China are the most homogeneous and unchanging across all four models, including OpenAI GPT, Google Gemini, Anthropic Claude, and DeepSeek, despite the prompted perspective change and the actual changes in real life. This study does not engage with history, politics, or literature as traditional disciplinary approaches would; instead, it takes cues from international and area studies and offers insight on the future trajectory of shifting political discourse in a digital space increasingly cannibalised by AI.

翻译：要求您的聊天机器人分别模拟俄罗斯专家和美国专家，并就中国政治议题进行提问。其输出结果可能如何不同？或者，为最坏情况做准备，它们又可能如何趋同？学者们担忧基于大语言模型的应用可能导致文化同质化与视角扁平化。但即便在明确分配不同角色的情况下，大语言模型生成内容的趋同程度究竟如何？本研究为上述问题提供了实证证据。批评焦点在于：预训练模型在讨论中国、伊朗、俄罗斯和美国政治时，会机械复述西方世界固化的政治术语，尽管这些国家的现实情况每日甚至每小时都在变化。实验结合角色提示与相似性度量方法。结果显示，尽管设置了视角转换且现实情境持续演变，但OpenAI GPT、Google Gemini、Anthropic Claude和DeepSeek四种模型生成的关于伊朗和中国的论述，在所有模型中呈现出最高程度的同质性与静态特征。本研究未采用历史学、政治学或文学等传统学科方法，而是借鉴国际与区域研究思路，为数字空间政治话语在人工智能日益侵蚀下的未来演变轨迹提供见解。