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Progressive in Principle, Centrist in Practice: LLM Political Bias Is Instrument-Dependent

arXiv 2606.00048 ↗First posted 3 May 2026cs.CY cs.CLcited by 0

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Abstract (arXiv)

Prior work establishes that instruction-tuned LLMs exhibit left-of-center political bias, but measures it exclusively through abstract questionnaires. We show it does not predict how models vote on concrete policies. We introduce a dual-instrument methodology grounded in Swiss direct democracy. First, we administer the Smartvote questionnaire (75 policy questions) to 66 LLMs and compare their answers to those of 184 elected members of the Swiss National Council. Second, we put 48 real federal referenda (Volksabstimmungen) to 9 flagship LLMs in four national languages and three information conditions, and compare their votes to the actual outcomes and to party recommendations (Parolen). The instruments disagree. (1) The left-to-right agreement gradient that dominates Smartvote replicates prior work (mean rho = -0.77). On referenda it shifts to center-peaked: models align most with centrist Die Mitte and FDP rather than leftist SP and Gr"une (Wilcoxon p = 0.008). (2) For some models the language of a question changes the answer: cross-linguistic consistency ranges from 50% (Mistral) to 98% (GPT-5.4). (3) Two models vote Nein on 83-94% of referenda at similar rates on progressive and conservative proposals (binomial p < 0.0001), change-aversion rather than a left-right bias. What prior work measured as "leftward bias" may not extend beyond abstract instruments: confronted with real decisions, LLMs behave less like coalition partners of the left than like cautious civil servants, centrist and inconsistent across languages.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 39 references that are in the map. First posted on arXiv 3 May 2026.

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19 of 39 references.

References · 19

  1. 2022Training language models to follow instructions with human feedbackcited by 15,971
  2. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 8,065
  3. 2023Whose Opinions Do Language Models Reflect?cited by 773
  4. 2023From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Modelscited by 260
  5. 2023The political ideology of conversational AI: Converging evidence on ChatGPT's pro-environmental, left-libertarian orientationcited by 189
  6. 2024Political Compass or Spinning Arrow? Towards More Meaningful Evaluations for Values and Opinions in Large Language Modelscited by 162
  7. 2024Hidden Persuaders: LLMs' Political Leaning and Their Influence on Voterscited by 71
  8. 2024Large Language Models Reflect the Ideology of their Creatorscited by 57
  9. 2024Beyond prompt brittleness: Evaluating the reliability and consistency of political worldviews in LLMscited by 55
  10. 2025Only a Little to the Left: A Theory-grounded Measure of Political Bias in Large Language Modelscited by 15
  11. 2025Large Means Left: Political Bias in Large Language Models Increases with Their Number of Parameterscited by 12
  12. 2025Measuring Political Preferences in AI Systems: An Integrative Approachcited by 9
7 more
  1. 2025The Political Ideology of Large Language Models: Measurement, Inconsistency, and Persuasive Influencecited by 7
  2. 2025A Cautionary Tale About "Neutrally" Informative AI Tools Ahead of the 2025 Federal Elections in Germanycited by 5
  3. 2025Framing Political Bias in Multilingual LLMs Across Pakistani Languagescited by 5
  4. 2026Political Alignment in Large Language Models: A Multidimensional Audit of Psychometric Identity and Behavioral Biascited by 5
  5. 2026Bias Beyond Borders: Political Ideology Evaluation and Steering in Multilingual LLMscited by 4
  6. 2024Who Would Chatbots Vote For? Political Preferences of ChatGPT and Gemini in the 2024 European Union Electionscited by 1
  7. 2025PReSS: An Automated Black-Box Framework for Evaluating Political Stance Stability in LLMscited by 1

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