Auto-Search and Refinement: An Automated Framework for Gender Bias Mitigation in Large Language Models

Pre-training large language models (LLMs) on vast text corpora enhances natural language processing capabilities but risks encoding social biases, particularly gender bias. While parameter-modification methods like fine-tuning mitigate bias, they are resource-intensive, unsuitable for closed-source models, and lack adaptability to evolving societal norms. Instruction-based approaches offer flexibility but often compromise general performance on normal tasks.

To address these limitations, we propose FaIRMaker, an automated and model-independent framework that employs an auto-search and refinement paradigm to adaptively generate Fairwords, which act as instructions to reduce gender bias and enhance response quality. FaIRMaker enhances the debiasing capacity by enlarging the Fairwords search space while preserving the utility and making it applicable to closed-source models by training a sequence-to-sequence model that adaptively refines Fairwords into effective debiasing instructions when facing gender-related queries and performance-boosting prompts for neutral inputs.

Extensive experiments demonstrate that FaIRMaker effectively mitigates gender bias while preserving task integrity and ensuring compatibility with both open- and closed-source LLMs.