Polistemics: Evaluating LLMs as Information Mediators in Politics & Elections
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Abstract (arXiv)
As LLMs increasingly shape the political information citizens rely on, no standard exists to assess whether they do so responsibly. We introduce Polistemics, a theory-grounded diagnostic benchmark for evaluating LLMs as mediators of political information in elections. Prior work has treated this task as reproduction rather than mediation, leaving its epistemic dimensions and interaction with imperfect information unaddressed. We ground the evaluation in Epistemic Modesty, a normative standard derived from citizens' epistemic agency, and test it across controlled settings that vary the clarity, noise, and consistency of the available evidence. Applying the benchmark to three state-of-the-art LLMs across the 2025 German and Dutch elections, we find that high aggregate scores mask systematic failures. Models mediate reliably under clear evidence but break down when it is absent, vague, or contradictory, while flattening the intensity of political language throughout. These failures point to party priors, shifting with party labels and output language. Reliable mediation appears achievable, but no model delivers it consistently.
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References · 24
- 2017Deep reinforcement learning from human preferencescited by 3,912
- 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,455
- 2023Towards Understanding Sycophancy in Language Modelscited by 1,040
- 2023Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Modelscited by 698
- 2024LLM Evaluators Recognize and Favor Their Own Generationscited by 686
- 2024Replacing Judges with Juries: Evaluating LLM Generations with a Panel of Diverse Modelscited by 326
- 2024Knowledge Conflicts for LLMs: A Surveycited by 283
- 2024Political Compass or Spinning Arrow? Towards More Meaningful Evaluations for Values and Opinions in Large Language Modelscited by 162
- 2023Sociotechnical Safety Evaluation of Generative AI Systemscited by 157
- 2025AbstentionBench: Reasoning LLMs Fail on Unanswerable Questionscited by 145
- 2024ClashEval: Quantifying the tug-of-war between an LLM's internal prior and external evidencecited by 113
- 2024Relying on the Unreliable: The Impact of Language Models' Reluctance to Express Uncertaintycited by 106
12 more
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- 2024CheckEval: A reliable LLM-as-a-Judge framework for evaluating text generation using checklistscited by 94
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- 2024FaithEval: Can Your Language Model Stay Faithful to Context, Even If "The Moon is Made of Marshmallows"cited by 86
- 2024Hidden Persuaders: LLMs' Political Leaning and Their Influence on Voterscited by 71
- 2025The Distracting Effect: Understanding Irrelevant Passages in RAGcited by 33
- 2024Llama meets EU: Investigating the European Political Spectrum through the Lens of LLMscited by 14
- 2024GermanPartiesQA: Benchmarking Commercial Large Language Models and AI Companions for Political Alignment and Sycophancycited by 14
- 2025Conversational AI increases political knowledge as effectively as self-directed internet searchcited by 8
- 2024Investigating LLMs as Voting Assistants via Contextual Augmentation: A Case Study on the European Parliament Elections 2024cited by 7
- 2025The Political Ideology of Large Language Models: Measurement, Inconsistency, and Persuasive Influencecited by 7
- 2025A Cautionary Tale About "Neutrally" Informative AI Tools Ahead of the 2025 Federal Elections in Germanycited by 5
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