# Beyond Partisan Leaning: A Comparative Analysis of Political Bias in Large Language Models

arXiv: https://arxiv.org/abs/2412.16746 · HTML: https://mlsanity.com/paper/2412.16746/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 44 references that are in the map.
- First posted on arXiv 21 Dec 2024.

## Builds on (most cited first, 11 of 11)

- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Towards Understanding Sycophancy in Language Models](https://mlsanity.com/paper/2310.13548/) (2023)
- [Whose Opinions Do Language Models Reflect?](https://mlsanity.com/paper/2303.17548/) (2023)
- [MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation](https://mlsanity.com/paper/2310.03302/) (2023)
- [From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models](https://mlsanity.com/paper/2305.08283/) (2023)
- [Cultural Bias and Cultural Alignment of Large Language Models](https://mlsanity.com/paper/2311.14096/) (2023)
- [Hidden Persuaders: LLMs' Political Leaning and Their Influence on Voters](https://mlsanity.com/paper/2410.24190/) (2024)
- [CommunityLM: Probing Partisan Worldviews from Language Models](https://mlsanity.com/paper/2209.07065/) (2022)
- [Assessing Political Bias in Large Language Models](https://mlsanity.com/paper/2405.13041/) (2024)
- [Mapping and Influencing the Political Ideology of Large Language Models using Synthetic Personas](https://mlsanity.com/paper/2412.14843/) (2024)
- [Measurement in the Age of LLMs: An Application to Ideological Scaling](https://mlsanity.com/paper/2312.09203/) (2023)

## Built on by (most cited first, 13 of 13)

- [Emergence of polarization in networks of large language model agents](https://mlsanity.com/paper/2501.05171/) (2025)
- [Benchmarking Overton Pluralism in LLMs](https://mlsanity.com/paper/2512.01351/) (2025)
- [Tracing Partisan Bias to Its Emotional Fingerprints: A Computational Approach to Mitigation](https://mlsanity.com/paper/2501.01284/) (2025)
- [Recalibrating the Compass: Integrating Large Language Models into Classical Research Methods](https://mlsanity.com/paper/2505.19402/) (2025)
- [Left Leaning Models: How AI Evaluates Economic Policy?](https://mlsanity.com/paper/2507.15771/) (2025)
- [Hidden Topics: Measuring Sensitive AI Beliefs with List Experiments](https://mlsanity.com/paper/2602.21939/) (2026)
- [Multi-Agent Strategic Games with LLMs](https://mlsanity.com/paper/2605.03604/) (2026)
- [NewsLens: A Multi-Agent Framework for Adversarial News Bias Navigation](https://mlsanity.com/paper/2605.17364/) (2026)
- [Grokipedia vs Wikipedia: An LLM-Based Audit of Political Neutrality along Ideologies](https://mlsanity.com/paper/2607.15146/) (2026)
- [Who Verifies the Benchmark? Decentralizing Trust in Large Language Model Evaluation](https://mlsanity.com/paper/2608.07762/) (2026)
- [Who Would You Vote For? Auditing Political Alignment in LLMs: An Italian Case-Study](https://mlsanity.com/paper/2608.11649/) (2026)
- [Position: The Alignment Community is Unintentionally Building a Censor's Toolkit](https://mlsanity.com/paper/2608.12346/) (2026)
- [Framing the Narrative: Ideological Mimicry in Large Language Models](https://mlsanity.com/paper/2609.38256/) (2026)

## Abstract (arXiv)

As large language models (LLMs) become increasingly embedded in civic, educational, and political information environments, concerns about their potential political bias have grown. Prior research often evaluates such bias through simulated personas or predefined ideological typologies, which may introduce artificial framing effects or overlook how models behave in general use scenarios. This study adopts a persona-free, topic-specific approach to evaluate political behavior in LLMs, reflecting how users typically interact with these systems-without ideological role-play or conditioning. We introduce a two-dimensional framework: one axis captures partisan orientation on highly polarized topics (e.g., abortion, immigration), and the other assesses sociopolitical engagement on less polarized issues (e.g., climate change, foreign policy). Using survey-style prompts drawn from the ANES and Pew Research Center, we analyze responses from 43 LLMs developed in the U.S., Europe, China, and the Middle East. We propose an entropy-weighted bias score to quantify both the direction and consistency of partisan alignment, and identify four behavioral clusters through engagement profiles. Findings show most models lean center-left or left ideologically and vary in their nonpartisan engagement patterns. Model scale and openness are not strong predictors of behavior, suggesting that alignment strategy and institutional context play a more decisive role in shaping political expression.