Recent advances in machine learning, particularly in Natural Language Processing (NLP), have produced powerful models trained on vast datasets. However, these models risk leaking sensitive information, raising privacy concerns. In response, regulatory measures such as the European Union's General Data Protection Regulation (GDPR) have driven increasing interest in Machine Unlearning techniques, which enable models to selectively forget specific data entries. Early unlearning approaches primarily relied on pre-processing methods, while more recent research has shifted towards training-based solutions. Despite their effectiveness, a key limitation persists: most methods require access to original training data, which is often unavailable. Additionally, directly applying unlearning techniques bears the cost of undermining the model's expressive capabilities. To address these challenges, we introduce the Iterative Contrastive Unlearning (ICU) framework, which consists of three core components: A Knowledge Unlearning Induction module designed to target specific knowledge for removal using an unlearning loss; A Contrastive Learning Enhancement module to preserve the model's expressive capabilities against the pure unlearning goal; And an Iterative Unlearning Refinement module that dynamically adjusts the unlearning process through ongoing evaluation and updates. Experimental results demonstrate the efficacy of our ICU method in unlearning sensitive information while maintaining the model's overall performance, offering a promising solution for privacy-conscious machine learning applications.
Textual Unlearning Gives a False Sense of Unlearning
The State Key Laboratory of Blockchain and Data Security, Zhejiang University, P. R. China（区块链与数据安全国家重点实验室，浙江大学，中华人民共和国）；School of Cyber Science and Technology, Zhejiang University, P. R. China（网络安全与技术学院，浙江大学，中华人民共和国）；ETH Zurich, Switzerland（苏黎世联邦理工学院，瑞士）
Towards Robust Knowledge Unlearning: An Adversarial Framework for Assessing and Improving Unlearning Robustness in Large Language Models
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
SOUL: Unlocking the Power of Second-Order Optimization for LLM Unlearning
Michigan State University（密歇根州立大学）；Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）；MIT-IBM Watson AI Lab, IBM Research（麻省理工学院-IBM沃森人工智能实验室，IBM研究院）
Machine Unlearning with Minimal Gradient Dependence for High Unlearning Ratios
School of Information, Renmin University of China（中国人民大学信息学院）；School of Computer Science and Big Data, Minjiang University（闽江学院计算机与大数据学院）；RMIT University（皇家墨尔本理工大学）
Machine unlearning through fine-grained model parameters perturbation
College of Computer Science and Electronic Engineering, Hunan University（湖南大学计算机与电子工程学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；Shenzhen Research Institute, Hunan University（湖南大学深圳研究院）；Faculty of Computing, Engineering & Media, De Montfort University（德蒙福特大学计算、工程与媒体学院）
Agentic Unlearning: When LLM Agent Meets Machine Unlearning
Center for Medical Artificial Intelligence, Shandong University of Traditional Chinese Medicine（山东中医药大学中医人工智能中心）；School of Software, Shandong University（山东大学软件学院）；School of Medical Information Engineering, Shandong University of Traditional Chinese Medicine（山东中医药大学医学信息工程学院）；Shandong Huazhi Talent Technology Co., Ltd.（山东华智人才科技有限公司）
On the importance of multiple training seeds for evaluating machine unlearning
Department of Computer Science（计算机科学系）；School of Mathematics（数学学院）；School of Science（科学学院）；Statistics and Physics（统计学与物理学）；Loughborough University（洛桑大学）；Newcastle University（新castle大学）
Towards Lifecycle Unlearning Commitment Management: Measuring Sample-level Unlearning Completeness
Provable Responsible AI and Data Analytics Lab（可证负责任人工智能与数据 analytics 实验室）；King Abdullah University of Science and Technology（卡塔尔国王 Abdullah 科学与技术大学）；National University of Singapore（新加坡国家大学）；Johns Hopkins University（约翰霍普金斯大学）
本文提出 IAM 框架，通过插值模型在样本上的泛化—拟合差距度量样本级遗忘完整性，可低成本扩展至 LLM，并揭示近似遗忘中过度与不足遗忘风险。
Langevin Unlearning: A New Perspective of Noisy Gradient Descent for Machine Unlearning
Department of Electrical and Computer Engineering（电气与计算机工程系）；Georgia Institute of Technology（佐治亚理工学院）；Department of Mathematics（数学系）；Massachusetts Institute of Technology（麻省理工学院）
Learning to Unlearn: Machine Unlearning via Learning the Unlearning Behaviors
State Key Laboratory for Novel Software Technology Nanjing University（南京大学计算机软件新技术国家重点实验室）；Centre for Cyber Resilience and Trust Deakin University（迪肯大学网络韧性与信任中心）
