Polar is a new cross-context benchmark showing LLM political bias measurements are not fixed but vary with country, issue, model, and language.
Findings of the Association for Computational Linguistics: ACL 2025 , pages =
3 Pith papers cite this work. Polarity classification is still indexing.
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LLMs exhibit higher perplexity on far-right and nationalist party texts than social-democratic ones, consistent across models and languages with correlation to translation metrics.
LLMs default to U.S. frameworks for English prompts and China frameworks for Chinese prompts on jurisdiction-underspecified legal-administrative queries, with the pattern holding across all seven tested models.
citing papers explorer
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Polar: A Benchmark for Evaluating Political Bias in LLMs
Polar is a new cross-context benchmark showing LLM political bias measurements are not fixed but vary with country, issue, model, and language.
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Large Language Models are Perplexed by some Political Parties
LLMs exhibit higher perplexity on far-right and nationalist party texts than social-democratic ones, consistent across models and languages with correlation to translation metrics.
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Which Institutional Frameworks Do Chatbots Assume? Auditing Jurisdictional Defaults in Multilingual LLMs
LLMs default to U.S. frameworks for English prompts and China frameworks for Chinese prompts on jurisdiction-underspecified legal-administrative queries, with the pattern holding across all seven tested models.