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arXiv · 2608.05467

A Mechanistic Analysis of Gender Sensitivity in Dense Retrieval Models

Catherine Chen·Maarten de Rijke·Carsten Eickhoff
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Abstract

While gender bias in dense retrieval models is well documented, with prior work showing that models often score male-gendered documents higher than female or neutral variants, the internal mechanisms producing these disparities are poorly understood. In this paper, we mechanistically analyze bi-encoder models to localize gender sensitivity, finding that the signal originates in input embeddings and propagates through a small set of late-layer attention heads that carry both gender and term-matching signals. Guided by these findings, we test steering interventions at both identified points and find distinct effects: embedding-level steering non-specifically neutralizes score differences, while attention-level steering produces directional shifts. Our findings provide a mechanistic basis for targeted debiasing and highlight the challenge of disentangling gender from relevance signals in shared model components.

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Catherine Chen, Maarten de Rijke, Carsten Eickhoff. 2026-08-05. A Mechanistic Analysis of Gender Sensitivity in Dense Retrieval Models. https://arxiv.org/abs/2608.05467

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