21 citations · 50 across the 11 of their papers we have counts for
11 papers
CLIPErase: Efficient Unlearning of Visual-Textual Associations in CLIP
Tianyu Yang, Lisen Dai, Xiangqi Wang +3
Machine unlearning (MU) has gained significant attention as a means to remove specific data from trained models without requiring a full retraining process. While progress has been…
Protecting Privacy in Multimodal Large Language Models with MLLMU-Bench
Zheyuan Liu, Guangyao Dou, Mengzhao Jia +4
Generative models such as Large Language Models (LLM) and Multimodal Large Language models (MLLMs) trained on massive web corpora can memorize and disclose individuals' confidentia…
Machine Unlearning in Generative AI: A Survey
Zheyuan Liu, Guangyao Dou, Zhaoxuan Tan +2
Generative AI technologies have been deployed in many places, such as (multimodal) large language models and vision generative models. Their remarkable performance should be attrib…
Avoiding Copyright Infringement via Large Language Model Unlearning
Guangyao Dou, Zheyuan Liu, Qing Lyu +2
Pre-trained Large Language Models (LLMs) have demonstrated remarkable capabilities but also pose risks by learning and generating copyrighted material, leading to significant legal…
Personalized Pieces: Efficient Personalized Large Language Models through Collaborative Efforts
Zhaoxuan Tan, Zheyuan Liu, Meng Jiang
Personalized large language models (LLMs) aim to tailor interactions, content, and recommendations to individual user preferences. While parameter-efficient fine-tuning (PEFT) meth…
Can we Soft Prompt LLMs for Graph Learning Tasks?
Zheyuan Liu, Xiaoxin He, Yijun Tian +1
Graph plays an important role in representing complex relationships in real-world applications such as social networks, biological data and citation networks. In recent years, Larg…
Graph Learning for Parameter Prediction of Quantum Approximate Optimization Algorithm
Zhiding Liang, Gang Liu, Zheyuan Liu +8
In recent years, quantum computing has emerged as a transformative force in the field of combinatorial optimization, offering novel approaches to tackling complex problems that hav…
UGMAE: A Unified Framework for Graph Masked Autoencoders
Yijun Tian, Chuxu Zhang, Ziyi Kou +3
Generative self-supervised learning on graphs, particularly graph masked autoencoders, has emerged as a popular learning paradigm and demonstrated its efficacy in handling non-Eucl…
Towards Safer Large Language Models through Machine Unlearning
Zheyuan Liu, Guangyao Dou, Zhaoxuan Tan +2
The rapid advancement of Large Language Models (LLMs) has demonstrated their vast potential across various domains, attributed to their extensive pretraining knowledge and exceptio…
Democratizing Large Language Models via Personalized Parameter-Efficient Fine-tuning
Zhaoxuan Tan, Qingkai Zeng, Yijun Tian +3
Personalization in large language models (LLMs) is increasingly important, aiming to align the LLMs' interactions, content, and recommendations with individual user preferences. Re…
Breaking the Trilemma of Privacy, Utility, Efficiency via Controllable Machine Unlearning
Zheyuan Liu, Guangyao Dou, Yijun Tian +3
Machine Unlearning (MU) algorithms have become increasingly critical due to the imperative adherence to data privacy regulations. The primary objective of MU is to erase the influe…