3 citations · 6 across the 5 of their papers we have counts for
6 papers
EmbTracker: Traceable Black-box Watermarking for Federated Language Models
Haodong Zhao, Jinming Hu, Yijie Bai +6
Federated Language Model (FedLM) allows a collaborative learning without sharing raw data, yet it introduces a critical vulnerability, as every untrustworthy client may leak the re…
Depth Gives a False Sense of Privacy: LLM Internal States Inversion
Tian Dong, Yan Meng, Shaofeng Li +3
Large Language Models (LLMs) are increasingly integrated into daily routines, yet they raise significant privacy and safety concerns. Recent research proposes collaborative inferen…
Step-wise Adaptive Integration of Supervised Fine-tuning and Reinforcement Learning for Task-Specific LLMs
Jack Chen, Fazhong Liu, Naruto Liu +7
Large language models (LLMs) excel at mathematical reasoning and logical problem-solving. The current popular training paradigms primarily use supervised fine-tuning (SFT) and rein…
Model Inversion in Split Learning for Personalized LLMs: New Insights from Information Bottleneck Theory
Yunmeng Shu, Shaofeng Li, Tian Dong +2
Personalized Large Language Models (LLMs) have become increasingly prevalent, showcasing the impressive capabilities of models like GPT-4. This trend has also catalyzed extensive r…
Learn What You Want to Unlearn: Unlearning Inversion Attacks against Machine Unlearning
Hongsheng Hu, Shuo Wang, Tian Dong +1
Machine unlearning has become a promising solution for fulfilling the "right to be forgotten", under which individuals can request the deletion of their data from machine learning…
The Philosopher's Stone: Trojaning Plugins of Large Language Models
Tian Dong, Minhui Xue, Guoxing Chen +5
Open-source Large Language Models (LLMs) have recently gained popularity because of their comparable performance to proprietary LLMs. To efficiently fulfill domain-specialized task…