Political Alignment in Large Language Models: A Multidimensional Audit of Psychometric Identity and Behavioral Bias

• URL: http://arxiv.org/abs/2601.06194v1
• Date: Thu, 08 Jan 2026 03:26:15 GMT
• Title: Political Alignment in Large Language Models: A Multidimensional Audit of Psychometric Identity and Behavioral Bias
• Authors: Adib Sakhawat, Tahsin Islam, Takia Farhin, Syed Rifat Raiyan, Hasan Mahmud, Md Kamrul Hasan,
• Abstract summary: This study presents a sociotechnical audit of 26 prominent large language models (LLMs)<n> Alignment signals appear to be consistent architectural traits rather than noise.<n>Models exhibit a systematic "center-shift," frequently categorizing neutral articles as left-leaning.
• Score: 2.464003792743989
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: As large language models (LLMs) are increasingly integrated into social decision-making, understanding their political positioning and alignment behavior is critical for safety and fairness. This study presents a sociotechnical audit of 26 prominent LLMs, triangulating their positions across three psychometric inventories (Political Compass, SapplyValues, 8 Values) and evaluating their performance on a large-scale news labeling task ($N \approx 27{,}000$). Our results reveal a strong clustering of models in the Libertarian-Left region of the ideological space, encompassing 96.3% of the cohort. Alignment signals appear to be consistent architectural traits rather than stochastic noise ($η^2 > 0.90$); however, we identify substantial discrepancies in measurement validity. In particular, the Political Compass exhibits a strong negative correlation with cultural progressivism ($r=-0.64$) when compared against multi-axial instruments, suggesting a conflation of social conservatism with authoritarianism in this context. We further observe a significant divergence between open-weights and closed-source models, with the latter displaying markedly higher cultural progressivism scores ($p<10^{-25}$). In downstream media analysis, models exhibit a systematic "center-shift," frequently categorizing neutral articles as left-leaning, alongside an asymmetric detection capability in which "Far Left" content is identified with greater accuracy (19.2%) than "Far Right" content (2.0%). These findings suggest that single-axis evaluations are insufficient and that multidimensional auditing frameworks are necessary to characterize alignment behavior in deployed LLMs. Our code and data will be made public.

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