Instruction-finetuned Large Language Models inherit clear political leanings
that have been shown to influence downstream task performance. We expand this
line of research beyond the two-party system in the US and audit Llama Chat in
the context of EU politics in various settings to analyze the model's political
knowledge and its ability to reason in context. We adapt, i.e., further
fine-tune, Llama Chat on speeches of individual euro-parties from debates in
the European Parliament to reevaluate its political leaning based on the EUandI
questionnaire. Llama Chat shows considerable knowledge of national parties'
positions and is capable of reasoning in context. The adapted, party-specific,
models are substantially re-aligned towards respective positions which we see
as a starting point for using chat-based LLMs as data-driven conversational
engines to assist research in political science.

Instruction-finetuned Large Language Models display political leanings, influencing downstream task performance; auditing Llama Chat in the context of EU politics demonstrates its political knowledge and reasoning abilities, offering potential as data-driven conversational engines for political science research.