arXiv Machine Learning September 22, 2026 By Tien Dang, The-Hai Nguyen, Dinh Mai Phuong, Nguyen Minh Phuong, Anh Bui, Hoang Thanh-Tung, Le-Minh Nguyen, Naoya Inoue

Beyond Forgetting: Representation Misdirection Elicits Controllable Side Behaviors and Capabilities

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arXiv Machine Learning

Jun 5

Learning What to Forget: Improving LLM Unlearning via Learned Token-Level Importance

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

llms

arXiv Machine Learning

Aug 6

Unforgettable Generalization in Language Models

arXiv:2409. 02228v2 Announce Type: replace Abstract: When language models (LMs) are trained to forget (or "unlearn'') a skill, how precisely does their behavior change?

By Eric Zhang, Leshem Choshen, Jacob Andreas

llms nlp fine-tuning

arXiv Machine Learning

Jun 29

Retaining by Doing: The Role of On-Policy Data in Mitigating Forgetting

arXiv:2510. 18874v3 Announce Type: replace Abstract: Adapting language models (LMs) to new tasks via post-training carries the risk of degrading existing capabilities -- a phenomenon classically known as catastrophic forgetting.

By Howard Chen, Noam Razin, Karthik Narasimhan, Danqi Chen

llms reinforcement-learning fine-tuning

arXiv Machine Learning

Jun 25

Erased, but Not Gone: Output Forgetting Is Not True Forgetting

arXiv:2606. 25001v1 Announce Type: new Abstract: Machine unlearning (MU) is commonly judged by output forgetting, such as low forget-set accuracy or reduced logit-level membership inference.

By Teresa Pui Yee Yong, Win Kent Ong, Chee Seng Chan

arXiv AI

Oct 15

Toward Fine-Grained Forgetting:Attribute Unlearning for Multimodal Large Language Models

arXiv:2608.01008v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) can memorize and disclose sensitive information, motivating machine unlearning to remove designated knowle...

By Junkai Lin, Junkai Chen, Siqi Hou, Yuhao He, Ruiqi Liu, Chenhan Jin, Shengze Xu, Tieyong Zeng, Xiangyuan Lan

llms multimodal benchmarks safety

arXiv AI

Oct 15

Decoupling Knowledge Forgetting and Refusal Learning in MLLM Unlearning

arXiv:2610.14605v1 Announce Type: new Abstract: Current evaluations of multimodal unlearning typically focus on whether a model stops outputting target information. This surface-level criterion, howe...

By Haodong Chen, Shengtao Wen, Dong Liang, Lingbing Guo, Xiang Chen

efficiency multimodal

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Related stories

Learning What to Forget: Improving LLM Unlearning via Learned Token-Level Importance

Unforgettable Generalization in Language Models

Retaining by Doing: The Role of On-Policy Data in Mitigating Forgetting

Erased, but Not Gone: Output Forgetting Is Not True Forgetting

Toward Fine-Grained Forgetting:Attribute Unlearning for Multimodal Large Language Models

Decoupling Knowledge Forgetting and Refusal Learning in MLLM Unlearning