# Llama meets EU: Investigating the European Political Spectrum through the Lens of LLMs

arXiv: https://arxiv.org/abs/2403.13592 · HTML: https://mlsanity.com/paper/2403.13592/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 18 references that are in the map.
- First posted on arXiv 20 Mar 2024.

## Builds on (most cited first, 9 of 9)

- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [Learning to summarize from human feedback](https://mlsanity.com/paper/2009.01325/) (2020)
- [Whose Opinions Do Language Models Reflect?](https://mlsanity.com/paper/2303.17548/) (2023)
- [Scalable agent alignment via reward modeling: a research direction](https://mlsanity.com/paper/1811.07871/) (2018)
- [BOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generation](https://mlsanity.com/paper/2101.11718/) (2021)
- [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)
- [Adapting Large Language Models to Domains via Reading Comprehension](https://mlsanity.com/paper/2309.09530/) (2023)
- [OpinionGPT: Modelling Explicit Biases in Instruction-Tuned LLMs](https://mlsanity.com/paper/2309.03876/) (2023)

## Built on by (most cited first, 14 of 14)

- [The Potential and Challenges of Evaluating Attitudes, Opinions, and Values in Large Language Models](https://mlsanity.com/paper/2406.11096/) (2024)
- [Bias in the Mirror: Are LLMs opinions robust to their own adversarial attacks ?](https://mlsanity.com/paper/2410.13517/) (2024)
- [Investigating LLMs as Voting Assistants via Contextual Augmentation: A Case Study on the European Parliament Elections 2024](https://mlsanity.com/paper/2407.08495/) (2024)
- [Do Political Opinions Transfer Between Western Languages? An Analysis of Unaligned and Aligned Multilingual LLMs](https://mlsanity.com/paper/2508.05553/) (2025)
- [Decoding Alignment: A Critical Survey of LLM Development Initiatives through Value-setting and Data-centric Lens](https://mlsanity.com/paper/2508.16982/) (2025)
- [Better Aligned with Survey Respondents or Training Data? Unveiling Political Leanings of LLMs on U.S. Supreme Court Cases](https://mlsanity.com/paper/2502.18282/) (2025)
- [PoliCon: Evaluating LLMs on Achieving Diverse Political Consensus Objectives](https://mlsanity.com/paper/2505.19558/) (2025)
- [When Do Language Models Endorse Limitations on Human Rights Principles?](https://mlsanity.com/paper/2603.04217/) (2026)
- [Probing the Subtle Ideological Manipulation of Large Language Models](https://mlsanity.com/paper/2504.14287/) (2025)
- [Persona-driven Simulation of Voting Behavior in the European Parliament with Large Language Models](https://mlsanity.com/paper/2506.11798/) (2025)
- [PledgeTracker: A System for Monitoring the Fulfilment of Pledges](https://mlsanity.com/paper/2509.11804/) (2025)
- [A Multi-Dimensional Audit of Politically Aligned Large Language Models](https://mlsanity.com/paper/2604.24429/) (2026)
- [Polistemics: Evaluating LLMs as Information Mediators in Politics & Elections](https://mlsanity.com/paper/2607.25953/) (2026)
- [EviMap: Evidence-Grounded Hierarchical Topic Maps for Exploring Unlabeled Corpora](https://mlsanity.com/paper/2609.06664/) (2026)

## Abstract (arXiv)

Instruction-finetuned Large Language Models inherit clear political leanings that have been shown to influence downstream task performance. We expand this line of research beyond the two-party system in the US and audit Llama Chat in the context of EU politics in various settings to analyze the model's political knowledge and its ability to reason in context. We adapt, i.e., further fine-tune, Llama Chat on speeches of individual euro-parties from debates in the European Parliament to reevaluate its political leaning based on the EUandI questionnaire. Llama Chat shows considerable knowledge of national parties' positions and is capable of reasoning in context. The adapted, party-specific, models are substantially re-aligned towards respective positions which we see as a starting point for using chat-based LLMs as data-driven conversational engines to assist research in political science.