# Large Language Models Reflect the Ideology of their Creators

**Authors:** Maarten Buyl, Alexander Rogiers, Sander Noels, Iris Dominguez-Catena, Edith Heiter, Raphael Romero, Iman Johary, Alexandru-Cristian Mara, Jefrey Lijffijt, Tijl De Bie  
**arXiv:** [2410.18417](https://arxiv.org/abs/2410.18417)  
**Date:** 2024-10-24  
**Summary page:** https://www.summarizepaper.com/en/paper/2410.18417v1/

## Key points

- Large language models (LLMs) are powerful AI tools trained on vast data for generating natural language
- Behavior of LLMs can vary based on design, training, and use
- Study revealed normative differences in responses across different LLMs and languages
- Disagreements observed between Western and non-Western LLMs in assessments of geopolitical actors
- Concerns raised about bias and potential political manipulation in LLMs
- Need for awareness around ideological positions of LLMs and development of tunable models
- Study provides insights into ideological landscape within LLMs
- Researchers aim to promote transparency in AI technology through their methodology

## Summary

Large language models (LLMs) are powerful AI tools that have been trained on vast amounts of data to generate natural language. These models, such as ChatGPT, have become integral in how humans access information. However, the behavior of LLMs can vary based on their design, training, and use. In a recent study, researchers explored the  exhibited across different LLMs and languages by prompting popular models to describe prominent and controversial figures from recent world history in both English and Chinese. The analysis revealed consistent normative differences between how LLMs respond in different languages and regions. Western and non-Western LLMs also showed disagreements in their assessments of geopolitical actors, reflecting disparities in political goals among these models. The results suggest that the  of an LLM often mirrors the worldview of its creators, raising concerns about  and potential political manipulation. The study highlights the need for  around the ideological positions of LLMs and calls for the development of tunable models that can align with desired ideological positions even after deployment. While the study has limitations such as sample size and language diversity, it provides valuable insights into the ideological landscape embedded within LLMs. The researchers hope that their methodology will promote greater transparency in AI technology and pave the way for future tools that allow individuals to explore the ideological positions of various LLMs. The data and methods from this study are freely available for further research and development efforts.