BiasEdit: Debiasing Stereotyped Language Models via Model Editing
Authors: name: Xin Xu, name: Wei Xu, name: Ningyu Zhang, name: Julian McAuley

Previous studies have established that language models manifest stereotyped
biases. Existing debiasing strategies, such as retraining a model with
counterfactual data, representation projection, and prompting often fail to
efficiently eliminate bias or directly alter the models' biased internal
representations. To address these issues, we propose BiasEdit, an efficient
model editing method to remove stereotypical bias from language models through
lightweight networks that act as editors to generate parameter updates.
BiasEdit employs a debiasing loss guiding editor networks to conduct local
edits on partial parameters of a language model for debiasing while preserving
the language modeling abilities during editing through a retention loss.
Experiments on StereoSet and Crows-Pairs demonstrate the effectiveness,
efficiency, and robustness of BiasEdit in eliminating bias compared to
tangental debiasing baselines and little to no impact on the language models'
general capabilities. In addition, we conduct bias tracing to probe bias in
various modules and explore bias editing impacts on different components of
language models.