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A Detailed Factor Analysis for the Political Compass Test: Navigating Ideologies of Large Language Models

arXiv — cs.LG•Thursday, November 13, 2025 at 5:00:00 AM
NeutralArtificial Intelligence
The study published on arXiv examines the Political Compass Test (PCT) and its role in assessing political bias in large language models (LLMs). Through rigorous statistical experiments, the researchers found that changes to standard generation parameters had minimal effects on PCT scores. However, they discovered that prompt phrasing and fine-tuning could significantly influence the results. Interestingly, fine-tuning on politically rich versus neutral datasets did not produce different shifts in scores. In contrast, human responses to questions remained stable regardless of prompt variations, raising concerns about the validity of these tests for measuring model bias. This discrepancy between human and model responses suggests a deeper exploration is necessary to understand how political and social views are encoded in LLMs, emphasizing the importance of refining bias measurement methodologies in AI.
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