# "Amazing, They All Lean Left" -- Analyzing the Political Temperaments of Current LLMs

arXiv: https://arxiv.org/abs/2507.08027 · HTML: https://mlsanity.com/paper/2507.08027/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 51 references that are in the map.
- First posted on arXiv 8 Jul 2025.

## Builds on (most cited first, 24 of 24)

- [Constitutional AI: Harmlessness from AI Feedback](https://mlsanity.com/paper/2212.08073/) (2022)
- [Semantics derived automatically from language corpora contain human-like biases](https://mlsanity.com/paper/1608.07187/) (2016)
- [Whose Opinions Do Language Models Reflect?](https://mlsanity.com/paper/2303.17548/) (2023)
- [Out of One, Many: Using Language Models to Simulate Human Samples](https://mlsanity.com/paper/2209.06899/) (2022)
- [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)
- [The political ideology of conversational AI: Converging evidence on ChatGPT's pro-environmental, left-libertarian orientation](https://mlsanity.com/paper/2301.01768/) (2023)
- [Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices](https://mlsanity.com/paper/1906.09208/) (2019)
- [Large Language Models Often Know When They Are Being Evaluated](https://mlsanity.com/paper/2505.23836/) (2025)
- [Deception Abilities Emerged in Large Language Models](https://mlsanity.com/paper/2307.16513/) (2023)
- [The Moral Integrity Corpus: A Benchmark for Ethical Dialogue Systems](https://mlsanity.com/paper/2204.03021/) (2022)
- [The Political Preferences of LLMs](https://mlsanity.com/paper/2402.01789/) (2024)
- [Bias in Large Language Models: Origin, Evaluation, and Mitigation](https://mlsanity.com/paper/2411.10915/) (2024)
- [Is ChatGPT a Good Causal Reasoner? A Comprehensive Evaluation](https://mlsanity.com/paper/2305.07375/) (2023)
- [The Self-Perception and Political Biases of ChatGPT](https://mlsanity.com/paper/2304.07333/) (2023)
- [Large Language Models Reflect the Ideology of their Creators](https://mlsanity.com/paper/2410.18417/) (2024)
- [Ethical Reasoning and Moral Value Alignment of LLMs Depend on the Language we Prompt them in](https://mlsanity.com/paper/2404.18460/) (2024)
- [Investigating Implicit Bias in Large Language Models: A Large-Scale Study of Over 50 LLMs](https://mlsanity.com/paper/2410.12864/) (2024)
- [Whose Emotions and Moral Sentiments Do Language Models Reflect?](https://mlsanity.com/paper/2402.11114/) (2024)
- [Whose Side Are You On? Investigating the Political Stance of Large Language Models](https://mlsanity.com/paper/2403.13840/) (2024)
- [Measuring Political Preferences in AI Systems: An Integrative Approach](https://mlsanity.com/paper/2503.10649/) (2025)
- [Analyzing the Ethical Logic of Eight Large Language Models](https://mlsanity.com/paper/2501.08951/) (2025)
- [Auditing the Ethical Logic of Generative AI Models](https://mlsanity.com/paper/2504.17544/) (2025)
- [I'm Spartacus, No, I'm Spartacus: Measuring and Understanding LLM Identity Confusion](https://mlsanity.com/paper/2411.10683/) (2024)
- [The Convergent Ethics of AI? Analyzing Moral Foundation Priorities in Large Language Models with a Multi-Framework Approach](https://mlsanity.com/paper/2504.19255/) (2025)

## Built on by (most cited first, 2 of 2)

- [FlockVote: LLM-Empowered Agent-Based Modeling for Simulating U.S. Presidential Elections](https://mlsanity.com/paper/2512.05982/) (2025)
- [The Third Ambition: Artificial Intelligence and the Science of Human Behavior](https://mlsanity.com/paper/2603.07329/) (2026)

## Abstract (arXiv)

Recent studies have revealed a consistent liberal orientation in the ethical and political responses generated by most commercial large language models (LLMs), yet the underlying causes and resulting implications remain unclear. This paper systematically investigates the political temperament of seven prominent LLMs - OpenAI's GPT-4o, Anthropic's Claude Sonnet 4, Perplexity (Sonar Large), Google's Gemini 2.5 Flash, Meta AI's Llama 4, Mistral 7b Le Chat and High-Flyer's DeepSeek R1 -- using a multi-pronged approach that includes Moral Foundations Theory, a dozen established political ideology scales and a new index of current political controversies. We find strong and consistent prioritization of liberal-leaning values, particularly care and fairness, across most models. Further analysis attributes this trend to four overlapping factors: Liberal-leaning training corpora, reinforcement learning from human feedback (RLHF), the dominance of liberal frameworks in academic ethical discourse and safety-driven fine-tuning practices. We also distinguish between political "bias" and legitimate epistemic differences, cautioning against conflating the two. A comparison of base and fine-tuned model pairs reveals that fine-tuning generally increases liberal lean, an effect confirmed through both self-report and empirical testing. We argue that this "liberal tilt" is not a programming error or the personal preference of programmers but an emergent property of training on democratic rights-focused discourse. Finally, we propose that LLMs may indirectly echo John Rawls' famous veil-of ignorance philosophical aspiration, reflecting a moral stance unanchored to personal identity or interest. Rather than undermining democratic discourse, this pattern may offer a new lens through which to examine collective reasoning.