The analysis of political biases in large language models (LLMs) has primarily examined these systems as single entities with fixed viewpoints. While various methods exist for measuring such biases, the impact of persona-based prompting on LLMs' political orientation remains unexplored. In this work we leverage PersonaHub, a collection of synthetic persona descriptions, to map the political distribution of persona-based prompted LLMs using the Political Compass Test (PCT). We then examine whether these initial compass distributions can be manipulated through explicit ideological prompting towards diametrically opposed political orientations: right-authoritarian and left-libertarian. Our experiments reveal that synthetic personas predominantly cluster in the left-libertarian quadrant, with models demonstrating varying degrees of responsiveness when prompted with explicit ideological descriptors. While all models demonstrate significant shifts towards right-authoritarian positions, they exhibit more limited shifts towards left-libertarian positions, suggesting an asymmetric response to ideological manipulation that may reflect inherent biases in model training.

翻译：当前针对大语言模型政治偏见的分析，主要将这些系统视为具有固定立场的单一实体。尽管存在多种测量此类偏见的方法，但基于角色提示对大语言模型政治倾向的影响尚未得到探索。本研究利用PersonaHub（一个合成角色描述集合），通过政治罗盘测试对基于角色提示的大语言模型进行政治分布映射。随后，我们探究这些初始罗盘分布能否通过指向两极对立政治取向（右翼-威权主义与左翼-自由意志主义）的显性意识形态提示进行操纵。实验结果表明：合成角色主要聚集在左翼-自由意志主义象限；当使用显性意识形态描述符进行提示时，各模型展现出不同程度的响应性。虽然所有模型均显示出向右翼-威权主义立场的显著偏移，但向左翼-自由意志主义方向的偏移较为有限，这种对意识形态操纵的非对称响应可能反映了模型训练中固有的偏见。