arXiv Machine Learning

Beyond Forgetting: Representation Misdirection Elicits Controllable Side Behaviors and Capabilities

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

arXiv AI

Aug 5

Does Forgetting Transfer Across Modalities? A Real-World Benchmark for Cross-Modal Knowledge Unlearning Evaluation

arXiv:2608. 03791v1 Announce Type: new Abstract: Vision-Language Models (VLMs), like Large Language Models (LLMs), may memorize sensitive, copyrighted, or harmful knowledge from their pretraining corpora.

By Chunlin Liu, Junnian Chen, Haitong Jiang, Jianyu Zhao, Yingsen Pang, Jingchen Li, Jiabiao He, Youming Lu, Jinhe Bi, Yuntao Du

llms multimodal benchmarks

arXiv AI

Jun 29

Towards Benign Memory Forgetting for Selective Multimodal Large Language Model Unlearning

arXiv:2511. 20196v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) can inadvertently memorize privacy-sensitive information during training.

By Zhen Zeng, Leijiang Gu, Zhangling Duan, Feng Li, Cees G. M. Snoek, Meng Wang, Zenglin Shi

llms fine-tuning multimodal benchmarks safety

arXiv Computation and Language

Oct 8

A Deafening Silence: Catastrophic Forgetting Lives in the Output Embeddings of Tokens the Data Never Speaks

arXiv:2610.09835v1 Announce Type: new Abstract: Continual pre-training and fine-tuning in Large Language Models (LLMs) inevitably induce catastrophic forgetting, typically mitigated by replay using o...

By Jonghyun Han, Younghoon Song, Jongyoul Park

llms rag fine-tuning

Hugging Face Trending Papers

Aug 4

Does Forgetting Transfer Across Modalities? A Real-World Benchmark for Cross-Modal Knowledge Unlearning Evaluation

Vision-Language Models (VLMs), like Large Language Models (LLMs), may memorize sensitive, copyrighted, or harmful knowledge from their pretraining corpora. Removing such knowledge is essential for building trustworthy AI systems.

llms multimodal benchmarks

arXiv Machine Learning

Sep 30

Do LLMs Really Forget? Hidden-State Leakage in Model Unlearning and How to Fix it

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

llms benchmarks safety

arXiv Machine Learning

Sep 16

Test-Time Unlearning via Sparse Autoencoder

arXiv:2609.16229v1 Announce Type: new Abstract: Machine unlearning aims to remove specific knowledge from a trained large language model (LLM) without retraining from scratch. Existing methods modify...

By Pingzhi Li, Jinhao Duan, Vaishnav Tadiparthi, Nakul Agarwal, Kwonjoon Lee, Ehsan Moradi Pari, Hossein Nourkhiz Mahjoub, Sijia Liu, Tianlong Chen

llms fine-tuning benchmarks safety

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