dialect-bias

This paper audits and mitigates dialect bias in large language models, showing they systematically prefer Standard American English over African American English. The authors introduce activation steering, a training-free method that reduces bias significantly while preserving fluency, and release the largest real-AAE parallel corpus to date.

This research paper finds that language models exhibit increased dialect bias when comparing Standard American English and African-American Vernacular English side-by-side, even after safety fine-tuning. Counterfactual fairness fine-tuning can reduce some biases in isolation but not consistently in contrastive settings.