Large language models (LLMs) possess vast knowledge acquired from extensive training corpora, but they often cannot remove specific pieces of information when needed, which makes it hard to handle privacy, bias mitigation, and knowledge correction. Traditional model unlearning approaches require computationally expensive fine-tuning or direct weight editing, making them impractical for real-world deployment. In this work, we introduce LoRA-based Unlearning with Negative Examples (LUNE), a lightweight framework that performs negative-only unlearning by updating only low-rank adapters while freezing the backbone, thereby localizing edits and avoiding disruptive global changes. Leveraging Low-Rank Adaptation (LoRA), LUNE targets intermediate representations to suppress (or replace) requested knowledge with an order-of-magnitude lower compute and memory than full fine-tuning or direct weight editing. Extensive experiments on multiple factual unlearning tasks show that LUNE: (I) achieves effectiveness comparable to full fine-tuning and memory-editing methods, and (II) reduces computational cost by about an order of magnitude.
Priority-Aware Learning-Unlearning Correction for Dynamic Decentralized LoRA Fine-Tuning
Beijing Key Laboratory of Network System Architecture and Convergence, Beijing University of Posts and Telecommunications（网络系统架构与融合北京市重点实验室，北京邮电大学）；Information Systems Technology and Design Pillar, Singapore University of Technology and Design（新加坡科技设计大学信息系统技术与设计系）
Storage Is Not Strategy: State-Conditioned Support Control for LLM Unlearning
Southeast University（东南大学）；University of Sydney（悉尼大学）；Northwestern University（西北大学）；Together AI；The Chinese University of Hong Kong Shenzhen（香港中文大学（深圳））
Data-Free Privacy-Preserving for LLMs via Model Inversion and Selective Unlearning
School of Software Technology, Zhejiang University（浙江大学软件技术学院）；Zhejiang University（浙江大学）；Hefei University of Technology（合肥工业大学）；Institute of Fundamental and Transdisciplinary Research, Zhejiang University（浙江大学基础与交叉学科研究院）；Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security（杭州高新技术区（滨江区）区块链与数据安全研究院）；The State Key Laboratory of Blockchain and Data Security（区块链与数据安全国家重点实验室）；Zhejiang Key Laboratory of Big Data Intelligent Computing（浙江省大数据智能计算重点实验室）
Orthogonal Subspace Projection for Continual Machine Unlearning via SVD-Based LoRA
Institute for Digital Technologies, Loughborough University, UK（伦敦大学数字技术研究所，英国）；School of Artificial Intelligent Medicine, Guilin Medical University（桂林医学院人工智能医学院）
Indian Institute of Information Technology, Bhopal, India（印度比哈尔理工学院）；University of California, Santa Cruz, USA（加州大学圣克鲁兹分校）；Independent Researcher（独立研究者）；Stanford University, USA（斯坦福大学）；BITS Pilani Goa, India（比斯拉米印度学院）
