5 papers
Gaussian Core LoRA: Distribution-Aware Dynamic Adaptation for Broad Concept Erasure
Qinghui Gong, Xunlei Chen, Yu-Xuan Zhang +2
Concept erasure aims to suppress unsafe, privacy-sensitive, or undesirable generations in text-to-image diffusion models while preserving benign semantics, visual quality, and depl…
STU: Stateful Test-Time Unlearning via Restricted Knowledge Boundary Control
Xunlei Chen, Qinghui Gong, Ruini Xue +3
Controlling restricted knowledge in large language models is essential for model alignment and safe deployment. Test-time unlearning avoids costly retraining and parameter updates…
Unlearning Is Not Just Erasing: Temporal Decoupling via Generation Inequality
Xunlei Chen, Qirui Ye, Yuang Li +5
Large language models (LLMs) require effective unlearning to address privacy regulations and safety concerns. However, achieving precise forgetting without compromising general uti…
ALTER: Asymmetric LoRA for Token-Entropy-Guided Unlearning of LLMs
Xunlei Chen, Jinyu Guo, Yuang Li +5
Large language models (LLMs) have advanced to encompass extensive knowledge across diverse domains. Yet controlling what a LLMs should not know is important for ensuring alignment…
HASH-RAG: Bridging Deep Hashing with Retriever for Efficient, Fine Retrieval and Augmented Generation
Jinyu Guo, Xunlei Chen, Qiyang Xia +5
Retrieval-Augmented Generation (RAG) encounters efficiency challenges when scaling to massive knowledge bases while preserving contextual relevance. We propose Hash-RAG, a framewor…