arXiv Machine Learning June 5, 2026 By Zizhao Hu, Ameya Godbole, Johnny Tian-Zheng Wei, Mohammad Rostami, Jesse Thomason, Robin Jia

SHRED: Retain-Set-Free Unlearning via Self-Distillation with Logit Demotion

Read the original on arXiv Machine Learning →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jul 13

Forget Narrowly, Retain Broadly: Unlearning as an Asymmetric Generalization Problem

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

llms benchmarks safety

arXiv AI

Jun 3

PURGE: Projected Unlearning via Retain-Guided Erasure

arXiv:2606. 03808v1 Announce Type: cross Abstract: We propose PURGE, a machine unlearning algorithm built on a simple but an under-exploited observation: continual learning (CL) and machine unlearning (MU) which are fundamentally dual problems.

By Vedant Jawandhia, Daksh Ahuja, Ghufran Alam Siddiqui, Prashant Trivedi, Yash Sinha, Pratik Narang

safety

arXiv Machine Learning

Jun 3

Fast Unlearning at Scale via Margin Self-Correction

arXiv:2606. 02920v1 Announce Type: new Abstract: Language-model unlearning updates a trained model to behave as if it had not seen selected training examples, while preserving utility and avoiding costly retraining.

By Federico Di Gennaro, Alexander Shevchenko, Fanny Yang

fine-tuning

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

Jul 15

Inference-Time Machine Unlearning via Gated Activation Redirection

arXiv:2605. 12765v3 Announce Type: replace Abstract: Large Language Models memorize vast amounts of training data, raising concerns regarding privacy, copyright infringement, and safety.

By Vin\'icius Conte Turani, Ot\'avio Parraga, Jo\~ao Vitor Boer Abitante, Kristen K. Arguello, Joana Pasquali, Ramiro N. Barros, Flavio du Pin Calmon, Christian Mattjie, Rodrigo C. Barros, Lucas S. Kupssinsk\"u

llms efficiency safety

arXiv Machine Learning

Jun 25

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning

arXiv:2510. 04773v2 Announce Type: replace Abstract: As Large Language Models (LLMs) demonstrate remarkable capabilities learned from vast corpora, concerns regarding data privacy and safety are receiving increasing attention.

By Kai Qin, Jiaqi Wu, Jianxiang He, Haoyuan Sun, Yifei Zhao, Xu Wang, Bin Liang, Yongzhe Chang, Cheng Li, Tiantian Zhang, Houde Liu

llms fine-tuning benchmarks safety

Related stories

Forget Narrowly, Retain Broadly: Unlearning as an Asymmetric Generalization Problem

PURGE: Projected Unlearning via Retain-Guided Erasure

Fast Unlearning at Scale via Margin Self-Correction

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

Inference-Time Machine Unlearning via Gated Activation Redirection

Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning