arXiv:2507. 04219v5 Announce Type: replace-cross Abstract: Current unlearning methods for LLMs optimize on the private information they seek to remove by incorporating it into their fine-tuning data.
By Yan Scholten, Sophie Xhonneux, Leo Schwinn, Stephan G\"unnemann
arXiv:2608.29943v1 Announce Type: new
Abstract: Large language models (LLMs) can memorize sensitive information, raising serious privacy concerns. Machine unlearning offers a potential solution to re...
By Shicheng Hu, Runzhi Tian, Ziqiao Wang, Yongyi Mao
arXiv:2609.36612v1 Announce Type: new
Abstract: Unlearning in large language models (LLMs) is typically evaluated at the output level, where a model appears to suppress sensitive or undesirable conte...
By Hadi Reisizadeh, Jiajun Ruan, Sijia Liu, Mingyi Hong
arXiv:2607. 16227v1 Announce Type: new Abstract: LLMs are increasingly deployed in security-critical systems across healthcare, finance, education, and decision support, yet their inability to forget creates serious cybersecurity, privacy, and safety risks.
By Ruppikha Sree Shankar, Abhishek Bhardwaj, Arnav Doshi, Anusri Nagarajan, Troy Paulus Asia, Saptarshi Sengupta
arXiv:2605.24614v2 Announce Type: replace-cross
Abstract: Large language model (LLM) unlearning has emerged as a crucial post-hoc mechanism for privacy protection and AI safety, yet auditing whether...
By Jaeung Lee, Dohyun Kim, Jaemin Jo
arXiv:2510.17021v2 Announce Type: replace-cross
Abstract: Large language model (LLM) unlearning is a key approach for removing undesired data, knowledge, or behaviors from pretrained models while ret...
By Bingqi Shang, Yiwei Chen, Yihua Zhang, Bingquan Shen, Sijia Liu