21 citations · 50 across the 11 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…