Reducing Political Manipulation with Consistency Training
Addresses political manipulation in LLMs for developers and users concerned with fairness, with measurable reductions in bias.
LLMs exhibit covert political bias through asymmetric handling of opposing political topics. The proposed Political Consistency Training (PCT) reduces this bias while preserving helpfulness, generalizing to held-out benchmarks.
Large language models (LLMs) exhibit systematic political bias across a variety of sensitive contexts. We find that LLMs handle counterpart topics from opposing political sides asymmetrically. We refer to this phenomenon as covert political bias and identify 7 categories of techniques through which it operates. We propose two metrics for covert bias: Sentiment Consistency measures symmetry in rhetoric and framing across paired political prompts; Helpfulness Consistency measures symmetric depth and engagement. To reduce both types of covert bias, we introduce Political Consistency Training (PCT), an RL training method with two complementary paradigms: Sentiment Consistency Training and Helpfulness Consistency Training. We show that PCT preserves overall helpfulness, substantially reduces covert political bias, and generalizes to held-out benchmarks. We release our work at https://political-manipulation.ai