Reducing Political Manipulation with Consistency Training
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Computer Science > Computation and Language
Title:Reducing Political Manipulation with Consistency Training
Abstract: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 this https URL
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2605.22771 [cs.CL] |
| (or arXiv:2605.22771v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.22771
arXiv-issued DOI via DataCite (pending registration)
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