Large language models (LLMs) are increasingly used in everyday tools and applications, raising concerns about their potential influence on political views. While prior research has shown that LLMs often exhibit measurable political biases--frequently skewing toward liberal or progressive positions--key gaps remain. Most existing studies evaluate only a narrow set of models and languages, leaving open questions about the generalizability of political biases across architectures, scales, and multilingual settings. Moreover, few works examine whether these biases can be actively controlled. In this work, we address these gaps through a large-scale study of political orientation in modern open-source instruction-tuned LLMs. We evaluate seven models, including LLaMA-3.1, Qwen-3, and Aya-Expanse, across 14 languages using the Political Compass Test with 11 semantically equivalent paraphrases per statement to ensure robust measurement. Our results reveal that larger models consistently shift toward libertarian-left positions, with significant variations across languages and model families. To test the manipulability of political stances, we utilize a simple center-of-mass activation intervention technique and show that it reliably steers model responses toward alternative ideological positions across multiple languages. Our code is publicly available at https://github.com/d-gurgurov/Political-Ideologies-LLMs.

翻译：大型语言模型（LLMs）正日益融入日常工具与应用，引发了对其可能影响政治观点的担忧。尽管先前研究表明，LLMs常表现出可测量的政治偏见——往往偏向自由主义或进步主义立场——但关键空白依然存在。现有研究大多仅评估少数模型和语言，导致关于政治偏见在不同架构、规模和多语言环境中的普适性问题悬而未决。此外，鲜有研究探讨这些偏见是否可被主动调控。本研究通过大规模分析现代开源指令微调LLMs的政治倾向来填补这些空白。我们使用政治罗盘测试评估了七个模型（包括LLaMA-3.1、Qwen-3和Aya-Expanse），覆盖14种语言，每个测试陈述均采用11种语义等效的复述以确保测量稳健性。结果表明，更大规模的模型持续向自由左翼立场偏移，且不同语言和模型系列间存在显著差异。为检验政治立场的可操控性，我们采用简单的质心激活干预技术，证明该方法能可靠地将模型响应引导至多种语言下的替代意识形态立场。相关代码已公开于https://github.com/d-gurgurov/Political-Ideologies-LLMs。