mlsanityData to 7 Oct 2026

Investigating LLMs as Voting Assistants via Contextual Augmentation: A Case Study on the European Parliament Elections 2024

arXiv 2407.08495 ↗First posted 11 Jul 2024cs.CLcited by 6

On the map: Retrieval-augmented generation → Legal and financial RAG

Abstract (arXiv)

In light of the recent 2024 European Parliament elections, we are investigating if LLMs can be used as Voting Advice Applications (VAAs). We audit MISTRAL and MIXTRAL models and evaluate their accuracy in predicting the stance of political parties based on the latest "EU and I" voting assistance questionnaire. Furthermore, we explore alternatives to improve models' performance by augmenting the input context via Retrieval-Augmented Generation (RAG) relying on web search, and Self-Reflection using staged conversations that aim to re-collect relevant content from the model's internal memory. We find that MIXTRAL is highly accurate with an 82% accuracy on average with a significant performance disparity across different political groups (50-95%). Augmenting the input context with expert-curated information can lead to a significant boost of approx. 9%, which remains an open challenge for automated RAG approaches, even considering curated content.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Placed in Legal and financial RAG, one of 15 directions in Retrieval-augmented generation. Builds on 8 of its 14 references that are in the map. First posted on arXiv 11 Jul 2024.

Builds on

8 of 14 references.

References · 8

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  3. 2020MPNet: Masked and Permuted Pre-training for Language Understandingcited by 973
  4. 2023Whose Opinions Do Language Models Reflect?cited by 759
  5. 2023From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Modelscited by 257
  6. 2023The political ideology of conversational AI: Converging evidence on ChatGPT's pro-environmental, left-libertarian orientationcited by 183
  7. 2024Political Compass or Spinning Arrow? Towards More Meaningful Evaluations for Values and Opinions in Large Language Modelscited by 162
  8. 2024Llama meets EU: Investigating the European Political Spectrum through the Lens of LLMscited by 14

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 2.

6 papers.

  1. 2024Hidden Persuaders: LLMs' Political Leaning and Their Influence on Voterscited by 71
  2. 2025To Mask or to Mirror: Human-AI Alignment in Collective Reasoningcited by 5
  3. 2025Better Aligned with Survey Respondents or Training Data? Unveiling Political Leanings of LLMs on U.S. Supreme Court Casescited by 2
  4. 2025Persona-driven Simulation of Voting Behavior in the European Parliament with Large Language Modelscited by 0
  5. 2026LegalMALR:Multi-Agent Query Understanding and LLM-Based Reranking for Chinese Statute Retrievalcited by 0
  6. 2026Polistemics: Evaluating LLMs as Information Mediators in Politics & Electionscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Large Language Models (LLMs) as Agents for Augmented Democracy
  2. 2024GermanPartiesQA: Benchmarking Commercial Large Language Models and AI Companions for Political Alignment and Sycophancy
  3. 2026Who Would You Vote For? Auditing Political Alignment in LLMs: An Italian Case-Study
  4. 2026Graph-Augmented LLMs for Swiss MP Ideology Prediction
  5. 2025Human Preferences in Large Language Model Latent Space: A Technical Analysis on the Reliability of Synthetic Data in Voting Outcome Prediction
  6. 2025From Delegates to Trustees: How Optimizing for Long-Term Interests Shapes Bias and Alignment in LLM
  7. 2026Auditing Political Alignment in LLM Assistants: Engagement, Stance, and User Identity
  8. 2024Towards More Accurate US Presidential Election via Multi-step Reasoning with Large Language Models