5 papers
TextCloak: Thwarting Unauthorized LLM Exploitation via RL-Driven Unlearnable Text
Chengshuai Zhao, Pingchuan Ma, Dawei Li +4
The rapid development of Large Language Models (LLMs) has led to significant advances across a wide range of language tasks, while simultaneously raising growing concerns about una…
To See is Not to Learn: Protecting Multimodal Data from Unauthorized Fine-Tuning of Large Vision-Language Model
Chengshuai Zhao, Zhen Tan, Dawei Li +2
The rapid advancement of Large Vision-Language Models (LVLMs) is increasingly accompanied by unauthorized scraping and training on multimodal web data, posing severe copyright and…
Utility-Oriented Visual Evidence Selection for Multimodal Retrieval-Augmented Generation
Weiqing Luo, Zongye Hu, Xiao Wang +3
Visual evidence selection is a critical component of multimodal retrieval-augmented generation (RAG), yet existing methods typically rely on semantic relevance or surface-level sim…
Layer-Targeted Multilingual Knowledge Erasure in Large Language Models
Taoran Li, Varun Chandrasekaran, Zhiyuan Yu
Recent work has demonstrated that machine unlearning in Large Language Models (LLMs) fails to generalize across languages: knowledge erased in one language frequently remains acces…
MaskPro: Linear-Space Probabilistic Learning for Strict (N:M)-Sparsity on LLMs
Yan Sun, Qixin Zhang, Zhiyuan Yu +3
The rapid scaling of large language models~(LLMs) has made inference efficiency a primary bottleneck in the practical deployment. To address this, semi-structured sparsity offers a…