The paper introduces GONE, a benchmark for evaluating knowledge unlearning in large language models using structured knowledge graphs, and presents Neighborhood-Expanded Distribution Shaping (NEDS), a framework that leverages graph connectivity to separate forgotten facts from their semantic neighborhood. GONE disentangles direct fact removal, reasoning-based leakage, and catastrophic forgetting, while NEDS achieves high unlearning efficacy and locality on LLaMA-3-8B and Mistral-7B. The dataset is publicly available on Hugging Face.
By Chahana Dahal, Ashutosh Balasubramaniam, Zuobin Xiong
arXiv:2607. 09236v1 Announce Type: new Abstract: Machine unlearning in LLMs is the targeted removal of specific knowledge while preserving all other capabilities, critical for privacy and safety.
By Amit Peleg, Naman Deep Singh, Naama Pearl, Bibhabasu Mohapatra, Matthias Hein
CALIBURN is a new approach to large language model (LLM) unlearning that measures a model’s confidence in undesirable knowledge and uses this measure to fine‑tune unlearning gradient updates. By doing so, it offers more precise control over what is forgotten while better preserving the model’s overall utility. Experiments on benchmarks such as MUSE and WMDP show that CALIBURN outperforms existing methods in balancing knowledge removal with utility retention.
By Zhengbang Yang, Yisheng Zhong, Junyuan Hong, Zhuangdi Zhu
arXiv:2609.37076v1 Announce Type: new
Abstract: Large language models trained on vast corpora inherently risk memorizing harmful content that may later re-emerge in their outputs. To mitigate this is...
By Puning Yang, Qizhou Wang, Junchi Yu, Bo Han, Xiuying Chen
arXiv:2605. 07482v2 Announce Type: replace Abstract: Machine unlearning for large language models (LLMs) aims to selectively remove memorized content such as private data, copyrighted text, or hazardous knowledge, without costly full retraining.
By Zizhao Hu, Ameya Godbole, Johnny Tian-Zheng Wei, Mohammad Rostami, Jesse Thomason, Robin Jia
The paper introduces Kernel-Anticipated Unlearning (KAU), a method for approximate unlearning of knowledge-graph embeddings that accounts for relearning during continued training. KAU uses the tangent kernel to model how forgotten facts’ scores change, expressing this response in closed form and guiding edits to minimize future leakage. Experiments show KAU reduces leakage by 46‑63% on FB15k-237 and 50‑82% on CoDEx-L compared to the strongest existing unlearner, while preserving higher utility.
By Parnian Fazel, Mohammad M Maheri, Hamed Haddadi
The paper introduces the problem of cross‑lingual loopholes in large language model (LLM) unlearning, where forgetting a fact in one language can leave it accessible in others. It presents a new 174‑language benchmark, the Cross‑Lingual Unlearning Tensor, and proposes COVER, a method that selects a subset of source languages to maximize unlearning coverage under a language budget. Experiments show COVER reduces residual knowledge by 7.8–27.3% compared to uniform selection and works on both synthetic and real low‑resource news data.
By Tyler Skow, Shravan Chaudhari, Rama Chellappa, Abhay Yadav
arXiv:2606. 06320v1 Announce Type: new Abstract: Machine unlearning aims to remove targeted knowledge from a trained model while preserving its general capabilities.
By Gizem Y\"uce, Giorgos Nikolaou, Nicolas Flammarion
arXiv:2608. 04519v1 Announce Type: new Abstract: Benchmarking machine unlearning methods is critical to understand whether sensitive knowledge is removed from large language models (LLMs) or not.
By Haoting Qian, Qingjie Zhang, Zhicong Huang, Cheng Hong, Han Qiu
Benchmarking machine unlearning methods is critical to understand whether sensitive knowledge is removed from large language models (LLMs) or not. Current unlearning benchmarks include mainly single-hop questions and a narrow set of multi-hop questions.
arXiv:2608.21544v1 Announce Type: cross
Abstract: Large language models (LLMs) are increasingly deployed as tool-augmented agents, where responses can depend on tool calls and external observations r...
By Baicheng Chen, Zheyuan Liu, Jingyu Zhang, Kaize Ding, Ningshan Ma, Yue Huang, Meng Jiang
Nullify is a training‑free method for unlearning sensitive information from large language models by steering activations during inference. It uses steering vectors to redirect privacy‑related activations away from memorized answers while enforcing a null‑space constraint that preserves activations for retained queries, thereby maintaining model utility. Experiments on TOFU and MUSE demonstrate that Nullify matches or outperforms existing baselines in forget quality while keeping utility nearly intact, all without any weight updates.
By Wei Zhai, Xiang Liu, Qiang Huang, Rui Qian, Lemao Liu, Ziwei Li, Ziqi Wang, Zhitao Huang, Dejing Dou