Reducing Political Manipulation with Consistency Training
Summary
This paper introduces Political Consistency Training (PCT), a reinforcement learning approach to reduce covert political bias in large language models while maintaining helpfulness, and releases metrics for sentiment and helpfulness consistency.
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Paper page - Reducing Political Manipulation with Consistency Training
Source: https://huggingface.co/papers/2605.22771
Abstract
Large language models demonstrate systematic political bias in handling opposing viewpoints, which can be mitigated through a reinforcement learning approach that maintains helpfulness while reducing bias.
Large language models(LLMs) exhibit systematicpolitical biasacross a variety of sensitive contexts. We find that LLMs handle counterpart topics from opposing political sides asymmetrically. We refer to this phenomenon ascovert political biasand identify 7 categories of techniques through which it operates. We propose two metrics for covert bias:Sentiment Consistencymeasures symmetry in rhetoric and framing across paired political prompts;Helpfulness Consistencymeasures symmetric depth and engagement. To reduce both types of covert bias, we introducePolitical Consistency Training(PCT), an RL training method with two complementary paradigms:Sentiment ConsistencyTraining andHelpfulness ConsistencyTraining. We show that PCT preserves overall helpfulness, substantially reducescovert political bias, and generalizes to held-out benchmarks. We release our work at https://political-manipulation.ai
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