# Large Means Left: Political Bias in Large Language Models Increases with Their Number of Parameters

arXiv: https://arxiv.org/abs/2505.04393 · HTML: https://mlsanity.com/paper/2505.04393/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 53 references that are in the map.
- First posted on arXiv 7 May 2025.

## Builds on (most cited first, 13 of 13)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [s1: Simple test-time scaling](https://mlsanity.com/paper/2501.19393/) (2025)
- [A Comprehensive Overview of Large Language Models](https://mlsanity.com/paper/2307.06435/) (2023)
- [Large language models in healthcare and medical domain: A review](https://mlsanity.com/paper/2401.06775/) (2023)
- [Scientific Large Language Models: A Survey on Biological & Chemical Domains](https://mlsanity.com/paper/2401.14656/) (2024)
- [Are large language models superhuman chemists?](https://mlsanity.com/paper/2404.01475/) (2024)
- [Assessing Political Bias in Large Language Models](https://mlsanity.com/paper/2405.13041/) (2024)
- [Political-LLM: Large Language Models in Political Science](https://mlsanity.com/paper/2412.06864/) (2024)
- [ChatGPT "contamination": estimating the prevalence of LLMs in the scholarly literature](https://mlsanity.com/paper/2403.16887/) (2024)

## Built on by (most cited first, 12 of 12)

- [Multilingual Political Views of Large Language Models: Identification and Steering](https://mlsanity.com/paper/2507.22623/) (2025)
- [Analysis of LLM Bias (Chinese Propaganda & Anti-US Sentiment) in DeepSeek-R1 vs. ChatGPT o3-mini-high](https://mlsanity.com/paper/2506.01814/) (2025)
- [How Similar Are Grokipedia and Wikipedia? A Multi-Dimensional Textual and Structural Comparison](https://mlsanity.com/paper/2510.26899/) (2025)
- [Don't Change My View: Ideological Bias Auditing in Large Language Models](https://mlsanity.com/paper/2509.12652/) (2025)
- [Polar: A Benchmark for Evaluating Political Bias in LLMs](https://mlsanity.com/paper/2606.12922/) (2026)
- [Reasoning Boosts Opinion Alignment in LLMs](https://mlsanity.com/paper/2603.01214/) (2026)
- [Beyond Arrow's Impossibility: Fairness as an Emergent Property of Multi-Agent Collaboration](https://mlsanity.com/paper/2604.13705/) (2026)
- [Ideological Bias in LLMs' Economic Causal Reasoning](https://mlsanity.com/paper/2604.21334/) (2026)
- [Progressive in Principle, Centrist in Practice: LLM Political Bias Is Instrument-Dependent](https://mlsanity.com/paper/2606.00048/) (2026)
- [Who Would You Vote For? Auditing Political Alignment in LLMs: An Italian Case-Study](https://mlsanity.com/paper/2608.11649/) (2026)
- [How Identity and Opinion Shape Political Sycophancy in LLMs](https://mlsanity.com/paper/2608.29198/) (2026)
- [Framing the Narrative: Ideological Mimicry in Large Language Models](https://mlsanity.com/paper/2609.38256/) (2026)

## Abstract (arXiv)

With the increasing prevalence of artificial intelligence, careful evaluation of inherent biases needs to be conducted to form the basis for alleviating the effects these predispositions can have on users. Large language models (LLMs) are predominantly used by many as a primary source of information for various topics. LLMs frequently make factual errors, fabricate data (hallucinations), or present biases, exposing users to misinformation and influencing opinions. Educating users on their risks is key to responsible use, as bias, unlike hallucinations, cannot be caught through data verification. We quantify the political bias of popular LLMs in the context of the recent vote of the German Bundestag using the score produced by the Wahl-O-Mat. This metric measures the alignment between an individual's political views and the positions of German political parties. We compare the models' alignment scores to identify factors influencing their political preferences. Doing so, we discover a bias toward left-leaning parties, most dominant in larger LLMs. Also, we find that the language we use to communicate with the models affects their political views. Additionally, we analyze the influence of a model's origin and release date and compare the results to the outcome of the recent vote of the Bundestag. Our results imply that LLMs are prone to exhibiting political bias. Large corporations with the necessary means to develop LLMs, thus, knowingly or unknowingly, have a responsibility to contain these biases, as they can influence each voter's decision-making process and inform public opinion in general and at scale.