6 papers
Harmonizing Multi-Objective LLM Unlearning via Unified Domain Representation and Bidirectional Logit Distillation
Yisheng Zhong, Sijia Liu, Zhuangdi Zhu
Large Language Models (LLMs) unlearning is crucial for removing hazardous or privacy-leaking information from the model. Practical LLM unlearning demands satisfying multiple challe…
DUET: Distilled LLM Unlearning from an Efficiently Contextualized Teacher
Yisheng Zhong, Zhengbang Yang, Zhuangdi Zhu
LLM unlearning is a technique to remove the impacts of undesirable knowledge from the model without retraining from scratch, which is indispensable towards trustworthy AI. Existing…
CALIBURN: Self-Calibrated LLM Unlearning Alignment
Zhengbang Yang, Yisheng Zhong, Junyuan Hong +1
LLM unlearning aims to remove the influence of undesirable knowledge from pretrained language models, which offers a practical mechanism for addressing safety and privacy concerns.…
Hierarchical Federated Unlearning for Large Language Models
Yisheng Zhong, Zhengbang Yang, Zhuangdi Zhu
Large Language Models (LLMs) are increasingly integrated into real-world applications, raising concerns about privacy, security and the need to remove undesirable knowledge. Machin…
Web Intellectual Property at Risk: Preventing Unauthorized Real-Time Retrieval by Large Language Models
Yisheng Zhong, Yizhu Wen, Junfeng Guo +4
The protection of cyber Intellectual Property (IP) such as web content is an increasingly critical concern. The rise of large language models (LLMs) with online retrieval capabilit…
PROFL: A Privacy-Preserving Federated Learning Method with Stringent Defense Against Poisoning Attacks
Yisheng Zhong, Li-Ping Wang
Federated Learning (FL) faces two major issues: privacy leakage and poisoning attacks, which may seriously undermine the reliability and security of the system. Overcoming them sim…