The significant advancements in large language models (LLMs) give rise to a promising research direction, i.e., leveraging LLMs as recommenders (LLMRec). The efficacy of LLMRec arises from the open-world knowledge and reasoning capabilities inherent in LLMs. LLMRec acquires the recommendation capabilities through instruction tuning based on user interaction data. However, in order to protect user privacy and optimize utility, it is also crucial for LLMRec to intentionally forget specific user data, which is generally referred to as recommendation unlearning. In the era of LLMs, recommendation unlearning poses new challenges for LLMRec in terms of \textit{inefficiency} and \textit{ineffectiveness}. Existing unlearning methods require updating billions of parameters in LLMRec, which is costly and time-consuming. Besides, they always impact the model utility during the unlearning process. To this end, we propose \textbf{E2URec}, the first \underline{E}fficient and \underline{E}ffective \underline{U}nlearning method for LLM\underline{Rec}. Our proposed E2URec enhances the unlearning efficiency by updating only a few additional LoRA parameters, and improves the unlearning effectiveness by employing a teacher-student framework, where we maintain multiple teacher networks to guide the unlearning process. Extensive experiments show that E2URec outperforms state-of-the-art baselines on two real-world datasets. Specifically, E2URec can efficiently forget specific data without affecting recommendation performance. The source code is at \url{https://github.com/justarter/E2URec}.
CURE:Circuit-Aware Unlearning for LLM-based Recommendation
University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校）；Zhejiang University（浙江大学）；The Chinese University of Hong Kong（香港中文大学）；National University of Singapore（新加坡国立大学）
TRACER: Token ReAssignment for Concept ERasure in Generative Recommendation
Stony Brook University（石英布鲁克大学）；University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校）；Columbia University（哥伦比亚大学）；Institute of Science and Technology Austria（奥地利科学技术研究院）；Sapienza University of Rome（罗马大学 sapienza）；National University of Singapore（新加坡国立大学）
UniErase: Towards Balanced and Precise Unlearning in Language Models
University of Science and Technology of China（中国科学技术大学）；University of the Chinese Academy of Sciences（中国科学院大学）；National University of Singapore（新加坡国立大学）；Nanyang Technological University（南洋理工大学）；A*STAR（科技研究局）；Zhejiang University（浙江大学）
The Frontier of Data Erasure: Machine Unlearning for Large Language Models
Data61, Commonwealth Scientific and Industrial Research Organization（联邦科学与工业研究组织Data61）；School of Systems & Computing, University of New South Wales（新南威尔士大学系统与计算学院）；School of Computer Science, University of Sydney（悉尼大学计算机科学学院）；Faculty of Data Science, City University of Macau（澳门城市大学数据科学学院）；College of Computing & Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
A Survey on Recommendation Unlearning: Fundamentals, Taxonomy, Evaluation, and Open Questions
School of Communication Engineering, Hangzhou Dianzi University（杭州电子科技大学通信工程学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Hong Kong Polytechnic University, Academy for AI（香港理工大学人工智能学院）
A Comprehensive Survey of Machine Unlearning Techniques for Large Language Models
Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)（莫扎德大学人工智能学院）；Fraunhofer Institute for Open Communication Systems (FOKUS)（弗劳恩霍夫开放通信系统研究所）；Technical University of Munich（慕尼黑技术大学）；Technical University of Darmstadt（达姆施塔特技术大学）；University of Toronto（多伦多大学）
Selective Forgetting: Advancing Machine Unlearning Techniques and Evaluation in Language Models
Harbin Institute of Technology（哈尔滨工业大学）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；Unlimidata Ltd（Unlimidata有限公司）；The Chinese University of Hong Kong（香港中文大学）；University of Calabria（卡拉布里亚大学）
From Anchors to Supervision: Memory-Graph Guided Corpus-Free Unlearning for Large Language Models
College of Computer Science and Artificial Intelligence, Fudan University（复旦大学计算机科学与人工智能学院）；Shanghai University of Electric Power（上海电力大学）；School of Information Science and Engineering, East China University of Science and Technology（东华大学信息科学与工程学院）
Soft Token Attacks Cannot Reliably Audit Unlearning in Large Language Models
Ludwig Maximilian University of Munich（慕尼黑路德维希-马克西米利安大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；Aalto University（艾尔沃斯大学）；Intel Labs（英特尔实验室）
PPU-Bench:Real World Benchmark for Personalized Partial Unlearning in Vision Language Models
Harbin Institute of Technology（哈尔滨工业大学）；Pengcheng Laboratory（鹏城实验室）；The Hong Kong Polytechnic University（香港理工大学）；Sichuan University（四川大学）；Zhejiang Normal University（浙江师范大学）
Machine Unlearning of Pre-trained Large Language Models
University of Virginia（弗吉尼亚大学）；Georgia Institute of Technology（佐治亚理工学院）；The Hong Kong Polytechnic University（香港理工大学）；University of Melbourne（墨尔本大学）；Carnegie Mellon University（卡内基梅隆大学）
OFFSIDE: Benchmarking Unlearning Misinformation in Multimodal Large Language Models
Harbin Institute of Technology（哈尔滨工业大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；BIAI, ZJUT & D5Data.ai（BIAI、浙江工业大学及D5Data.ai）；National University of Singapore（新加坡国立大学）
MPU: Towards Secure and Privacy-Preserving Knowledge Unlearning for Large Language Models
College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；Alibaba-NTU Global e-Sustainability CorpLab (ANGEL)（阿里云-南洋理工大学全球可持续发展科技实验室）；Tongyi Lab, Alibaba Group（通义实验室，阿里巴巴集团）
Towards Reasoning-Preserving Unlearning in Multimodal Large Language Models
Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)（Mohamed bin Zayed人工智能大学）；University of Queensland（昆士兰大学）；Provable Responsible AI and Data Analytics (PRADA) Lab（可证明责任人工智能与数据分析实验室）；King Abdullah University of Science and Technology (KAUST)（卡塔尔科学与技术大学）
Single Image Unlearning: Efficient Machine Unlearning in Multimodal Large Language Models
School of Cyber Science and Engineering, Southeast University（东南大学网络空间科学与工程学院）；College of Artificial Intelligence and Automation, Hohai University（河海大学人工智能与自动化学院）；School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）
MLLMEraser: Achieving Test-Time Unlearning in Multimodal Large Language Models through Activation Steering
University of Science and Technology of China（中国科学技术大学）；National University of Singapore（新加坡国立大学）；The Hong Kong Polytechnic University（香港理工大学）；Huawei（华为）
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）
RWKU: Benchmarking Real-World Knowledge Unlearning for Large Language Models
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Approximate Domain Unlearning for Vision-Language Models
Tokyo University of Science（东京科学大学）；National University of Singapore（新加坡国立大学）；National Institute of Advanced Industrial Science and Technology (AIST)（国家先进工业科学与技术研究院）；University of Oxford（牛津大学）
