# Measuring Political Stance and Consistency in Large Language Models

arXiv: https://arxiv.org/abs/2601.17016 · HTML: https://mlsanity.com/paper/2601.17016/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 21 references that are in the map.
- First posted on arXiv 15 Jan 2026.

## Builds on (most cited first, 16 of 16)

- [The political ideology of conversational AI: Converging evidence on ChatGPT's pro-environmental, left-libertarian orientation](https://mlsanity.com/paper/2301.01768/) (2023)
- [Do LLMs exhibit human-like response biases? A case study in survey design](https://mlsanity.com/paper/2311.04076/) (2023)
- [Political Compass or Spinning Arrow? Towards More Meaningful Evaluations for Values and Opinions in Large Language Models](https://mlsanity.com/paper/2402.16786/) (2024)
- [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)
- [Hidden Persuaders: LLMs' Political Leaning and Their Influence on Voters](https://mlsanity.com/paper/2410.24190/) (2024)
- [Investigating Bias in LLM-Based Bias Detection: Disparities between LLMs and Human Perception](https://mlsanity.com/paper/2403.14896/) (2024)
- [Assessing Political Bias in Large Language Models](https://mlsanity.com/paper/2405.13041/) (2024)
- [A Comprehensive Survey of Bias in LLMs: Current Landscape and Future Directions](https://mlsanity.com/paper/2409.16430/) (2024)
- [PoliTune: Analyzing the Impact of Data Selection and Fine-Tuning on Economic and Political Biases in Large Language Models](https://mlsanity.com/paper/2404.08699/) (2024)
- [Whose Side Are You On? Investigating the Political Stance of Large Language Models](https://mlsanity.com/paper/2403.13840/) (2024)
- [Multi-OphthaLingua: A Multilingual Benchmark for Assessing and Debiasing LLM Ophthalmological QA in LMICs](https://mlsanity.com/paper/2412.14304/) (2024)
- [A Large-Scale Simulation on Large Language Models for Decision-Making in Political Science](https://mlsanity.com/paper/2412.15291/) (2024)
- [In Generative AI we Trust: Can Chatbots Effectively Verify Political Information?](https://mlsanity.com/paper/2312.13096/) (2023)
- [Exploring the Jungle of Bias: Political Bias Attribution in Language Models via Dependency Analysis](https://mlsanity.com/paper/2311.08605/) (2023)
- [Language-Dependent Political Bias in AI: A Study of ChatGPT and Gemini](https://mlsanity.com/paper/2504.06436/) (2025)

## Abstract (arXiv)

With the incredible advancements in Large Language Models (LLMs), many people have started using them to satisfy their information needs. However, utilizing LLMs might be problematic for political issues where disagreement is common and model outputs may reflect training-data biases or deliberate alignment choices. To better characterize such behavior, we assess the stances of nine LLMs on 24 politically sensitive issues using five prompting techniques. We find that models often adopt opposing stances on several issues; some positions are malleable under prompting, while others remain stable. Among the models examined, Grok-3-mini is the most persistent, whereas Mistral-7B is the least. For issues involving countries with different languages, models tend to support the side whose language is used in the prompt. Notably, no prompting technique alters model stances on the Qatar blockade or the oppression of Palestinians. We hope these findings raise user awareness when seeking political guidance from LLMs and encourage developers to address these concerns.