3 papers
cs.CR2026
From Prompts to Responses: Dual-Sided Data Leakage and Defense in Split Large Language Models
Zixuan Gu, Xiaojun Ye, Yang Liu
Large language models (LLMs) are increasingly deployed in privacy-sensitive domains, where users must balance the risk of data exposure through external APIs against the high compu…
cs.LG2024
Label Smoothing Improves Machine Unlearning
Zonglin Di, Zhaowei Zhu, Jinghan Jia +6
The objective of machine unlearning (MU) is to eliminate previously learned data from a model. However, it is challenging to strike a balance between computation cost and performan…
cs.LG2024
Rethinking Machine Unlearning for Large Language Models
Sijia Liu, Yuanshun Yao, Jinghan Jia +11
We explore machine unlearning (MU) in the domain of large language models (LLMs), referred to as LLM unlearning. This initiative aims to eliminate undesirable data influence (e.g.,…