Understanding and Mitigating Gender Bias in LLMs via Interpretable Neuron Editing

ACL ARR 2025 February Submission105 Authors

Abstract: Large language models (LLMs) often exhibit gender bias, posing challenges for their safe deployment. Existing methods to mitigate bias lack a comprehensive understanding of its mechanisms or compromise the model’s core capabilities. To address these issues, we propose the CommonWords dataset, to systematically evaluate gender bias in LLMs. Our analysis reveals pervasive bias across models and identifies specific neuron circuits, including "gender neurons" and "general neurons," responsible for this behavior. Notably, editing even a small number of general neurons can disrupt the model’s overall capabilities due to hierarchical neuron interactions. Based on these insights, we propose an interpretable neuron editing method that combines logit-based and causal-based strategies to selectively target biased neurons. Experiments on five LLMs demonstrate that our method effectively reduces gender bias while preserving the model’s original capabilities, outperforming existing fine-tuning and editing approaches. Our findings contribute a novel dataset, a detailed analysis of bias mechanisms, and a practical solution for mitigating gender bias in LLMs.

Paper Type: Long

Research Area: Interpretability and Analysis of Models for NLP

Research Area Keywords: mechanistic interprertability

Contribution Types: Model analysis & interpretability

Languages Studied: English

Submission Number: 105