Code and Data Repository for Mitigating Age-Related Bias in Large Language Models: Strategies for Responsible AI Development

FairLLM is a project aimed at reducing age-related bias in large language models (LLMs). As LLMs continue to be widely applied across various domains, ensuring their fairness and inclusivity has become crucial. FairLLM introduces two innovative bias mitigation strategies: Self-BMIL (Self-Bias Mitigation in-the-loop) and Coop-BMIL (Cooperative Bias Mitigation in-the-loop), along with an Empathetic Perspective Exchange strategy. These approaches reduce bias in model outputs through self-reflection, collaborative debate, and perspective transformation, thereby enhancing the fairness and inclusivity of the models.