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：对大语言模型（LLMs）政治偏见的分析主要将这些系统视为具有固定观点的单一实体。虽然存在多种测量此类偏见的方法，但基于角色的提示对LLMs政治取向的影响仍未得到探索。在本研究中，我们利用PersonaHub（一个合成角色描述的集合），通过政治指南针测试（PCT）来映射基于角色提示的LLMs的政治分布。随后，我们探究这些初始的指南针分布是否能够通过明确的意识形态提示被操纵至截然相反的政治取向：右翼-威权主义和左翼-自由意志主义。我们的实验表明，合成角色主要聚集在左翼-自由意志主义象限，当使用明确的意识形态描述符进行提示时，模型表现出不同程度的响应性。虽然所有模型都显示出向右翼-威权主义立场的显著偏移，但它们向左翼-自由意志主义位置的偏移则更为有限，这表明对意识形态操纵存在不对称响应，这可能反映了模型训练中固有的偏见。