Code and Data Repository for Mitigating Age-Related Bias in Large Language Models: Strategies for Responsible AI Development
FairLLM项目提出了两种创新策略（Self-BMIL和Coop-BMIL）以及共情视角交换方法，通过自我反思、协作辩论和视角转换来减少大语言模型输出中的年龄偏见，提升模型公平性和包容性。
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.