5 citations · 12 across the 13 of their papers we have counts for
6 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…
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…
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…
Chain-of-Layer: Iteratively Prompting Large Language Models for Taxonomy Induction from Limited Examples
Qingkai Zeng, Yuyang Bai, Zhaoxuan Tan +4
Automatic taxonomy induction is crucial for web search, recommendation systems, and question answering. Manual curation of taxonomies is expensive in terms of human effort, making…
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…