BiasEdit: Debiasing Stereotyped Language Models via Model Editing

• URL: http://arxiv.org/abs/2503.08588v1
• Date: Tue, 11 Mar 2025 16:25:36 GMT
• Title: BiasEdit: Debiasing Stereotyped Language Models via Model Editing
• Authors: Xin Xu, Wei Xu, Ningyu Zhang, Julian McAuley,
• Abstract summary: We propose BiasEdit, an efficient model editing method to remove stereotypical bias from language models.<n>BiasEdit employs a debiasing loss guiding editor networks to conduct local edits on partial parameters of a language model.<n> Experiments on StereoSet and Crows-Pairs demonstrate the effectiveness, efficiency, and robustness of BiasEdit in eliminating bias.
• Score: 40.57172805190225
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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