# Large Language Models Reflect the Ideology of their Creators

arXiv: https://arxiv.org/abs/2410.18417 · HTML: https://mlsanity.com/paper/2410.18417/

## Key facts

- Cited by 57 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 55 references that are in the map.
- First posted on arXiv 24 Oct 2024.

## Builds on (most cited first, 17 of 17)

- [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://mlsanity.com/paper/2109.07958/) (2021)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [On Faithfulness and Factuality in Abstractive Summarization](https://mlsanity.com/paper/2005.00661/) (2020)
- [Whose Opinions Do Language Models Reflect?](https://mlsanity.com/paper/2303.17548/) (2023)
- [Large Language Models Are Not Robust Multiple Choice Selectors](https://mlsanity.com/paper/2309.03882/) (2023)
- [QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering](https://mlsanity.com/paper/2104.06378/) (2021)
- [News Summarization and Evaluation in the Era of GPT-3](https://mlsanity.com/paper/2209.12356/) (2022)
- [Political Compass or Spinning Arrow? Towards More Meaningful Evaluations for Values and Opinions in Large Language Models](https://mlsanity.com/paper/2402.16786/) (2024)
- [Who is GPT-3? An Exploration of Personality, Values and Demographics](https://mlsanity.com/paper/2209.14338/) (2022)
- [The Political Preferences of LLMs](https://mlsanity.com/paper/2402.01789/) (2024)
- [Are Large Language Models Consistent over Value-laden Questions?](https://mlsanity.com/paper/2407.02996/) (2024)
- [What does ChatGPT return about human values? Exploring value bias in ChatGPT using a descriptive value theory](https://mlsanity.com/paper/2304.03612/) (2023)
- [ValueBench: Towards Comprehensively Evaluating Value Orientations and Understanding of Large Language Models](https://mlsanity.com/paper/2406.04214/) (2024)
- [Why the World Reads Wikipedia: Beyond English Speakers](https://mlsanity.com/paper/1812.00474/) (2018)
- [Pantheon 1.0, a manually verified dataset of globally famous biographies](https://mlsanity.com/paper/1502.07310/) (2015)
- [China and the U.S. produce more impactful AI research when collaborating together](https://mlsanity.com/paper/2304.11123/) (2023)

## Built on by (most cited first, 30 of 57)

- [DeepSeek-R1 Thoughtology: Let's think about LLM Reasoning](https://mlsanity.com/paper/2504.07128/) (2025)
- [The Homogenizing Effect of Large Language Models on Human Expression and Thought](https://mlsanity.com/paper/2508.01491/) (2025)
- [Persuasion with Large Language Models: A Survey of Empirical Evidence, Study Methodologies, and Ethical Implications](https://mlsanity.com/paper/2411.06837/) (2024)
- [An Evaluation of Cultural Value Alignment in LLM](https://mlsanity.com/paper/2504.08863/) (2025)
- [IssueBench: Millions of Realistic Prompts for Measuring Issue Bias in LLM Writing Assistance](https://mlsanity.com/paper/2502.08395/) (2025)
- [Normative Evaluation of Large Language Models with Everyday Moral Dilemmas](https://mlsanity.com/paper/2501.18081/) (2025)
- [Conformity and Social Impact on AI Agents](https://mlsanity.com/paper/2601.05384/) (2026)
- [Understanding How Value Neurons Shape the Generation of Specified Values in LLMs](https://mlsanity.com/paper/2505.17712/) (2025)
- [What Large Language Models Do Not Talk About: An Empirical Study of Moderation and Censorship Practices](https://mlsanity.com/paper/2504.03803/) (2025)
- [Information Suppression in Large Language Models: Auditing, Quantifying, and Characterizing Censorship in DeepSeek](https://mlsanity.com/paper/2506.12349/) (2025)
- [Discovering Forbidden Topics in Language Models](https://mlsanity.com/paper/2505.17441/) (2025)
- [Generative Exaggeration in LLM Social Agents: Consistency, Bias, and Toxicity](https://mlsanity.com/paper/2507.00657/) (2025)
- [Mapping Geopolitical Bias in 11 Large Language Models: A Bilingual, Dual-Framing Analysis of U.S.-China Tensions](https://mlsanity.com/paper/2503.23688/) (2025)
- [Analyzing Political Bias in LLMs via Target-Oriented Sentiment Classification](https://mlsanity.com/paper/2505.19776/) (2025)
- [Watch the Weights: Unsupervised monitoring and control of fine-tuned LLMs](https://mlsanity.com/paper/2508.00161/) (2025)
- [SPICA: Retrieving Scenarios for Pluralistic In-Context Alignment](https://mlsanity.com/paper/2411.10912/) (2024)
- [One world, one opinion? The superstar effect in LLM responses](https://mlsanity.com/paper/2412.10281/) (2024)
- [Understanding and Mitigating Political Stance Cross-topic Generalization in Large Language Models](https://mlsanity.com/paper/2508.02360/) (2025)
- [SocialHarmBench: Revealing LLM Vulnerabilities to Socially Harmful Requests](https://mlsanity.com/paper/2510.04891/) (2025)
- [Are Economists Always More Introverted? Analyzing Consistency in Persona-Assigned LLMs](https://mlsanity.com/paper/2506.02659/) (2025)
- [Interaction Protocol Shapes Moral Judgment in Multi-Agent Debate](https://mlsanity.com/paper/2510.10002/) (2025)
- [Emergence of Biased Consensus in Multi-Agent LLM Debates](https://mlsanity.com/paper/2608.02827/) (2026)
- ["Amazing, They All Lean Left" -- Analyzing the Political Temperaments of Current LLMs](https://mlsanity.com/paper/2507.08027/) (2025)
- [Building and Measuring Trust between Large Language Models](https://mlsanity.com/paper/2508.15858/) (2025)
- [A Scalable Entity-Based Framework for Auditing Bias in Large Language Models](https://mlsanity.com/paper/2601.12374/) (2026)
- [Omissive Bias in Religious Representation: Benchmarking LLM Answers to Everyday Ethical Decision-making](https://mlsanity.com/paper/2605.24319/) (2026)
- [Hedging and Non-Affirmation: Quantifying LLM Alignment on Questions of Human Rights](https://mlsanity.com/paper/2502.19463/) (2025)
- [Artificial Intelligence in Government: Why People Feel They Lose Control](https://mlsanity.com/paper/2505.01085/) (2025)
- [Exploring Chain-of-Thought Reasoning for Steerable Pluralistic Alignment](https://mlsanity.com/paper/2510.04045/) (2025)
- [Reward Model Perspectives: Whose Opinions Do Reward Models Reward?](https://mlsanity.com/paper/2510.06391/) (2025)

## Abstract (arXiv)

Large language models (LLMs) are trained on vast amounts of data to generate natural language, enabling them to perform tasks like text summarization and question answering. These models have become popular in artificial intelligence (AI) assistants like ChatGPT and already play an influential role in how humans access information. However, the behavior of LLMs varies depending on their design, training, and use. In this paper, we prompt a diverse panel of popular LLMs to describe a large number of prominent personalities with political relevance, in all six official languages of the United Nations. By identifying and analyzing moral assessments reflected in their responses, we find normative differences between LLMs from different geopolitical regions, as well as between the responses of the same LLM when prompted in different languages. Among only models in the United States, we find that popularly hypothesized disparities in political views are reflected in significant normative differences related to progressive values. Among Chinese models, we characterize a division between internationally- and domestically-focused models. Our results show that the ideological stance of an LLM appears to reflect the worldview of its creators. This poses the risk of political instrumentalization and raises concerns around technological and regulatory efforts with the stated aim of making LLMs ideologically 'unbiased'.