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

arXiv — cs.CL•Tuesday, November 4, 2025 at 5:00:00 AM

A new automated framework aims to tackle gender bias in large language models (LLMs), which are crucial for natural language processing. While traditional methods like fine-tuning can help reduce bias, they often require significant resources and aren't always adaptable to changing societal norms. This innovative approach promises to enhance the flexibility of bias mitigation without sacrificing performance, making it a significant step forward in creating more equitable AI systems. This matters because addressing bias in AI is essential for ensuring fair and accurate technology that reflects diverse perspectives.

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