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

arXiv: https://arxiv.org/abs/2601.06194 · HTML: https://mlsanity.com/paper/2601.06194/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 38 references that are in the map.
- First posted on arXiv 8 Jan 2026.

## Builds on (most cited first, 9 of 9)

- [Political Compass or Spinning Arrow? Towards More Meaningful Evaluations for Values and Opinions in Large Language Models](https://mlsanity.com/paper/2402.16786/) (2024)
- [HalluLens: LLM Hallucination Benchmark](https://mlsanity.com/paper/2504.17550/) (2025)
- [Measuring Political Bias in Large Language Models: What Is Said and How It Is Said](https://mlsanity.com/paper/2403.18932/) (2024)
- [The Political Preferences of LLMs](https://mlsanity.com/paper/2402.01789/) (2024)
- [Beyond prompt brittleness: Evaluating the reliability and consistency of political worldviews in LLMs](https://mlsanity.com/paper/2402.17649/) (2024)
- [Dbias: Detecting biases and ensuring Fairness in news articles](https://mlsanity.com/paper/2208.05777/) (2022)
- [Media Bias Detector: Designing and Implementing a Tool for Real-Time Selection and Framing Bias Analysis in News Coverage](https://mlsanity.com/paper/2502.06009/) (2025)
- [Profiling News Media for Factuality and Bias Using LLMs and the Fact-Checking Methodology of Human Experts](https://mlsanity.com/paper/2506.12552/) (2025)
- [High Risk of Political Bias in Black Box Emotion Inference Models](https://mlsanity.com/paper/2407.13891/) (2024)

## Built on by (most cited first, 5 of 5)

- [Narrative over Numbers: The Identifiable Victim Effect and its Amplification Under Alignment and Reasoning in Large Language Models](https://mlsanity.com/paper/2604.12076/) (2026)
- [The Shared Latent Structure of LLM Psychometric Responses](https://mlsanity.com/paper/2605.05080/) (2026)
- [Progressive in Principle, Centrist in Practice: LLM Political Bias Is Instrument-Dependent](https://mlsanity.com/paper/2606.00048/) (2026)
- [LLM-Ideoplasticity: Measuring Ideological Plasticity in the Political Behavior of LLMs as a Context-Conditioned Distribution](https://mlsanity.com/paper/2606.28335/) (2026)
- [Auditing Alignment Controllability in LLMs via Political Axes](https://mlsanity.com/paper/2607.23519/) (2026)

## Abstract (arXiv)

As large language models (LLMs) are increasingly deployed, understanding how they express political positioning is important for evaluating alignment and downstream effects. We audit 26 contemporary LLMs using three political psychometric inventories (Political Compass, SapplyValues, 8Values) and a news bias labeling task. To test robustness, inventories are administered across multiple semantic prompt variants and analyzed with a two-way ANOVA separating model and prompt effects. Most models cluster in a similar ideological region, with 96.3% located in the Libertarian-Left quadrant of the Political Compass, and model identity explaining most variance across prompt variants (η2 > 0.90). Cross-instrument comparisons suggest that the Political Compass social axis aligns more strongly with cultural progressivism than authority-related measures (r=-0.64). We observe differences between open-weight and closed-source models and asymmetric performance in detecting extreme political bias in downstream classification. Regression analysis finds that psychometric ideological positioning does not significantly predict classification errors, providing no evidence of a statistically significant relationship between conversational ideological identity and task-level behavior. These findings suggest that single-axis evaluations are insufficient and that multidimensional auditing frameworks are important to characterize alignment behavior in deployed LLMs. Our code and data are publicly available at https://github.com/sakhadib/PolAlignLLM.