A Representation-Level Assessment of Bias Mitigation in Foundation Models

This research investigates how bias mitigation reshapes the embedding space of encoder-only and decoder-only foundation models like BERT and Llama2. Findings show that bias mitigation reduces gender-occupation disparities in the embedding space, leading to more neutral internal representations, confirming embedding analysis as a valuable debiasing validation tool.