Large Language Model Bias Mitigation from the Perspective of Knowledge Editing

no code implementations • 15 May 2024 • Ruizhe Chen, Yichen Li, Zikai Xiao, Zuozhu Liu

Existing debiasing methods inevitably make unreasonable or undesired predictions as they are designated and evaluated to achieve parity across different social groups but leave aside individual facts, resulting in modified existing knowledge.

FedLoGe: Joint Local and Generic Federated Learning under Long-tailed Data

1 code implementation • 17 Jan 2024 • Zikai Xiao, Zihan Chen, Liyinglan Liu, Yang Feng, Jian Wu, Wanlu Liu, Joey Tianyi Zhou, Howard Hao Yang, Zuozhu Liu

Federated Long-Tailed Learning (Fed-LT), a paradigm wherein data collected from decentralized local clients manifests a globally prevalent long-tailed distribution, has garnered considerable attention in recent times.

Personalized Federated Learning Representation Learning

Fed-GraB: Federated Long-tailed Learning with Self-Adjusting Gradient Balancer

1 code implementation • NeurIPS 2023 • Zikai Xiao, Zihan Chen, Songshang Liu, Hualiang Wang, Yang Feng, Jin Hao, Joey Tianyi Zhou, Jian Wu, Howard Hao Yang, Zuozhu Liu

Data privacy and long-tailed distribution are the norms rather than the exception in many real-world tasks.