arXiv AI September 1, 2026 By Jaeung Lee, Dohyun Kim, Jaemin Jo

Measuring the Depth of LLM Unlearning via Activation Patching

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arXiv Machine Learning

Jun 2

Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs

arXiv:2506. 14003v5 Announce Type: replace Abstract: Machine unlearning (MU) for large language models (LLMs), commonly referred to as LLM unlearning, seeks to remove specific undesirable data or knowledge from a trained model, while maintaining its performance on standard tasks.

By Yiwei Chen, Soumyadeep Pal, Yimeng Zhang, Qing Qu, Sijia Liu

llms safety

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 Computation and Language

Aug 25

Can LLMs Truly Forget? Revealing Unlearning Gaps Through Adversarial Evaluation

arXiv:2608.21606v1 Announce Type: new Abstract: Machine unlearning aims to remove the influence of targeted training data from a model while preserving its remaining capabilities, but evaluating whet...

By Ayush Gupta, Hima Varshini Surisetty, Sreevidya Bollineni, Varad Ingale, Tuhina Tripathi, Abhishek Lalwani, Somya Chatterjee, Sadid Hasan

llms fine-tuning multimodal benchmarks safety

arXiv AI

Oct 15

Open-MMUnlearning: Unifying Methods and Evaluation for MLLM Unlearning

arXiv:2610.10358v2 Announce Type: replace Abstract: As multimodal large language models (MLLMs) become more capable and widely deployed, concerns about privacy and safety have become increasingly pre...

By Junkai Chen, Yuhao He, Qianshan Wei, Junxiang You, Jingwen Shao, Junkai Lin, Zhongkai Yue, Xiaotian Ye, Zhengbo Jiao, Jiali Cheng, Zhijie Deng, Kening Zheng, Ruiqi Liu, Hadi Amiri, Yi Yu, Zhenan Sun, Qi Li, Ka-Ho Chow, Sijia Liu, Liang Wang, Jiaqi Li, Shu Wu

llms efficiency multimodal benchmarks safety

arXiv AI

Jul 3

LACUNA: A Testbed for Evaluating Localization Precision for LLM Unlearning

arXiv:2607. 02513v1 Announce Type: cross Abstract: LLMs memorize sensitive training data, including personally identifiable information (PII), creating a pressing need for reliable post hoc removal methods.

By Matteo Boglioni, Thibault Rousset, Siva Reddy, Marius Mosbach, Verna Dankers

llms benchmarks

arXiv Machine Learning

Jun 16

Auditing Machine Unlearning: A Systematic Research on Whether Models Truly Forget

arXiv:2606. 16110v1 Announce Type: new Abstract: Machine unlearning has been extensively studied in response to growing privacy concerns and regulatory requirements.

By Dayong Ye, Tianqing Zhu, Ruiding Huang, Xinbo Fu, Jiayang Li, Bo Liu, Huan Huo, Wanlei Zhou

llms fine-tuning safety

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Unlearning Isn't Invisible: Detecting Unlearning Traces in LLMs from Model Outputs

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

Can LLMs Truly Forget? Revealing Unlearning Gaps Through Adversarial Evaluation

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Auditing Machine Unlearning: A Systematic Research on Whether Models Truly Forget