Machine Unlearning

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Large Language Model Unlearning

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Machine Unlearning

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Unlearning Aims

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Several Approximate Unlearning Strategies

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Exact Unlearning

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164 papers

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PreUnlearn: Auditing Collateral Knowledge Damage Before Large Language Model Unlearning
Jun 16, 2026Bo Su, Ankit Shah, Thai LeLarge Language Model Unlearning Unlearning Aims
SAGE: Retain-Aware Post-Hoc Sanitization of Final Unlearning Vector
Jun 16, 2026Jingyuan Zhang, Yucheng Bai, Peixi Wen +6Exact Unlearning Post-Hoc
RepSelect: Robust LLM Unlearning via Representation Selectivity
Jun 15, 2026Filip Sondej, Yushi Yang, Adam MahdiLarge Language Model Unlearning Selectivity
TrustErase: Auditable Instant Machine Unlearning with Passport-Embedded Representations
Jun 15, 2026Rutger Hendrix, Leonardo G. Russo, Concetto Spampinato +2Machine Unlearning Several Approximate Unlearning Strategies
To forget is to preserve: Machine Unlearning for 3D medical image segmentation
Jun 15, 2026Nitesh Kumar Singh, Akhilesh Singh, Arjun AroraMachine Unlearning Several Approximate Unlearning Strategies
Auditing Machine Unlearning: A Systematic Research on Whether Models Truly Forget
Jun 15, 2026Dayong Ye, Tianqing Zhu, Ruiding Huang +5Machine Unlearning Machine Learning
Replay What Matters: Off-Policy Replay for Efficient LLM Reinforcement Unlearning
Jun 13, 2026Zirui Pang, Chenlong Zhang, Haosheng Tan +3Large Language Model Unlearning Reinforcement Learning Post-Training
Behavioral Audit of Machine Unlearning Has a Privacy Cost
Jun 12, 2026Liou Tang, James Joshi, Ashish KunduMachine Unlearning Efficient Privacy Loss
Natively Unlearnable Large Language Models
Jun 11, 2026Gaurav R. Ghosal, Pratyush Maini, Aditi RaghunathanLarge Language Model Unlearning Exact Unlearning
MLUBench: A Benchmark for Lifelong Unlearning Evaluation in MLLMs
Jun 11, 2026He Li, Haoang Chi, Qizhou Wang +6Large Language Model Unlearning Multi-Modal Large Language Models
Null-Space Constrained Low-Rank Adaptation for Response-Specified Large Language Model Unlearning
Jun 9, 2026Bocheng Ju, Jianhua Wang, Chengliang Liu +1Large Language Model Unlearning Several Approximate Unlearning Strategies
Routing-Aware Expert Calibration for Machine Unlearning in Mixture-of-Experts Language Models
Jun 9, 2026Jingyi Xie, Yijun Lin, Yinjiang Xiong +2Mixture-Of-Expert Language Models Large Language Model Unlearning
TRACER: Token ReAssignment for Concept ERasure in Generative Recommendation
Jun 5, 2026Ziheng Chen, Jiali Cheng, Zezhong Fan +4Large-Scale Recommendation Systems Large Language Model Unlearning
Learning What to Forget: Improving LLM Unlearning via Learned Token-Level Importance
Jun 4, 2026Gizem Yüce, Giorgos Nikolaou, Nicolas FlammarionLarge Language Model Unlearning Autoregressive Language Models
Exact Unlearning in Reinforcement Learning
Jun 2, 2026Thanh Nguyen-Tang, Raman AroraSeveral Approximate Unlearning Strategies Exact Unlearning
Backdoor Unlearning Generalization: A Path Toward the Removal of Unknown Triggers in LLMs
Jun 2, 2026Lisa Bouger, Théo Lasnier, Philippe Loubet Moundi +2Backdoor Attacks Large Language Model Unlearning
Don't Forget Your Embeddings: Robust Knowledge Erasure via Precise Editing of Embeddings
Jun 2, 2026Clara Haya Suslik, Or Shafran, Mor GevaConcept Erasure Global Embeddings
Multilingual Unlearning in LLMs: Transfer, Dynamics, and Reversibility
Jun 2, 2026Chaoyi Xiang, Olga Ohrimenko, Benjamin I. P. Rubinstein +1Large Language Model Unlearning
Fast Unlearning at Scale via Margin Self-Correction
Jun 1, 2026Federico Di Gennaro, Alexander Shevchenko, Fanny YangSeveral Approximate Unlearning Strategies Forgetting
How Hard Can It Be? Hardness-Aware Multi-Objective Unlearning
Jun 1, 2026Jiangwei Chen, Xinyuan Niu, Rachael Hwee Ling Sim +3Unlearning Aims Catastrophic Forgetting
SPACE: Source-free Proxy Anchor Concept Erasure for MLLMs
Jun 1, 2026Zhijing Zhang, Jiaqi Ding, Qianshan Wei +5Large Language Model Unlearning Several Approximate Unlearning Strategies
Near-Optimal Machine Unlearning Utility for Smooth Strongly Convex Losses
Jun 1, 2026Matthew Regehr, Gautam Kamath, Andrew LowyMachine Unlearning Several Approximate Unlearning Strategies
Multi-Objective Reference-Aligned Machine Unlearning
May 29, 2026Rasa Khosrowshahli, Stephen Asobiela, Beatrice Ombuki-Berman +1Machine Unlearning Unlearning Aims
SUPREME: A Multi-GPU Framework for Reproducible Image Unlearning Method Evaluation
May 29, 2026Petros Andreou, Jamie Lanyon, Axel Finke +1Machine Unlearning Unlearning Aims
Forgetting Has Neighbors: Localized Collateral Forgetting in Machine Unlearning
May 29, 2026Polina Dolgova, Sebastian U. StichMachine Unlearning Forgetting
Divergence Decoding: Inference-Time Unlearning via Auxiliary Models
May 29, 2026Humzah Merchant, Bradford LevyLarge Language Model Unlearning Unlearning Aims
De-attribute to Forget for LLM Unlearning
May 29, 2026Xinyang Lu, Jiabao Pan, Rachael Hwee Ling Sim +3Large Language Model Unlearning Large Language Model Post-Training
Unlearning in Diffusion Models: A Unified Framework with KL Divergence and Likelihood Constraints
May 29, 2026Shervin Khalafi, Alejandro Ribeiro, Dongsheng DingSeveral Approximate Unlearning Strategies Unlearning Aims
AMNESIA: A Large Scale Medical Unlearning Benchmark Suite with Disease-Informed Analysis
May 28, 2026Saeedeh Davoudi, Reihaneh Iranmanesh, Ophir Frieder +1Unlearning Aims Clinical Notes
RULER: Representation-Level Verification of Machine Unlearning
May 26, 2026Georgina Cosma, Axel FinkeMachine Unlearning Representation-Level Certification
ICCU: In-Context Continual Unlearning via Pattern-Induced Refusal Rules
May 26, 2026Ruihao Pan, Suhang WangLarge Language Model Unlearning In-Context Learning
On the Hidden Costs of Counterfactual Knowledge Training in LLM Unlearning
May 26, 2026Xiaotian Ye, Xiaohan Wang, Mengqi Zhang +1Large Language Model Unlearning Counterfactual Learning
On the Robustness of Machine Unlearning for Vision-Language Models
May 26, 2026Yujie Lin, Kaidi Jia, Jiayao Ma +2Large Language Model Unlearning Machine Unlearning
Model Unlearning Objectives Vary for Distinct Language Functions
May 26, 2026Berk Atil, Vipul Gupta, Rebecca J. PassonneauLarge Language Model Unlearning Large Language Model Safety
Concept Unlearning via Cross-Attention Activation Projection for Diffusion Models
May 25, 2026Saemi Moon, Suhyeon Jun, Seoyeon Lee +1Text-To-Image Diffusion Models Unlearning Aims
Measuring the Depth of LLM Unlearning via Activation Patching
May 23, 2026Jaeung Lee, Dohyun Kim, Jaemin JoLarge Language Model Unlearning Unlearning Aims
PIU: Proximity-guided Identity Unlearning in ID-Conditioned Diffusion Models
May 21, 2026Jose Edgar Hernandez Cancino Estrada, Mauro Díaz Lupone, Žiga Emeršič +3Identity-Conditioned Latent Diffusion Models Several Approximate Unlearning Strategies
Calibration vs Decision Making: Revisiting the Reliability Paradox in Unlearned Language Models
May 20, 2026Divyaksh Shukla, Ashutosh ModiLarge Language Model Unlearning Unlearning Aims
DualOptim+: Bridging Shared and Decoupled Optimizer States for Better Machine Unlearning in Large Language Models
May 20, 2026Xuyang Zhong, Qizhang Li, Yiwen Guo +1Machine Unlearning Memory-Augmented Large Language Models
Approximate Machine Unlearning through Manifold Representation Forgetting Guided by Self Mode Connectivity
May 20, 2026Weiqi Wang, Zhiyi Tian, Chenhan Zhang +2Machine Unlearning Retraining
Causal Unlearning in Collaborative Optimization: Exact and Approximate Influence Reversal under Adversarial Contributions
May 19, 2026Ali Mahdavi, Azadeh Zamanifar, Amirfarhad Farhadi +1Federated Learning Exact Unlearning
CATA: Continual Machine Unlearning via Conflict-Averse Task Arithmetic
May 18, 2026Shen Lin, Junhao Dong, Rongjie Chen +3Large Language Model Unlearning Character Fidelity
Machine Unlearning for Masked Diffusion Language Models
May 18, 2026Georu Lee, Seungwon Jeong, Hoki Kim +2Masked Diffusion Language Models Diffusion Language Models
Form and Function: Machine Unlearning as a Problem of Misaligned States
May 17, 2026Kennon StewartMachine Unlearning Learning-Augmented Algorithms
The Unlearnability Phenomenon in RLVR for Language Models
May 16, 2026Yulin Chen, He He, Chen ZhaoReinforcement Learning With Verifiable Reward Large Language Model Unlearning
Distinguishable Deletion: Unifying Knowledge Erasure and Refusal for Large Language Model Unlearning
May 16, 2026Puning Yang, Junchi Yu, Qizhou Wang +3Large Language Model Unlearning Unlearning Aims
ZeroUnlearn: Few-Shot Knowledge Unlearning in Large Language Models
May 16, 2026Yujie Lin, Chengyi Yang, Zhishang Xiang +2Large Language Model Unlearning Machine Unlearning
Statistical Unlearning of Distributions: A Hypothesis Testing Approach
May 15, 2026Aaradhya Pandey, Sanjeev KulkarniSeveral Approximate Unlearning Strategies Distributional Learning
BARRIER: Bounded Activation Regions for Robust Information Erasure
May 15, 2026Jan Miksa, Patryk Krukowski, Przemysław Spurek +2Exact Unlearning Concept Erasure
ASRU: Activation Steering Meets Reinforcement Unlearning for Multimodal Large Language Models
May 15, 2026Jiahui Guang, Haiyan Wang, Yingjie Zhu +4Large Language Model Unlearning Multi-Modal Large Language Models
Knowledge Beyond Language: Bridging the Gap in Multilingual Machine Unlearning Evaluation
May 14, 2026Kyomin Hwang, Hyeonjin Kim, Sangyeon Cho +1Large Language Model Unlearning Legal Natural Language Processing Benchmarks
ICED: Concept-level Machine Unlearning via Interpretable Concept Decomposition
May 14, 2026Shen Lin, Jing Lin, Junhao Dong +2Several Approximate Unlearning Strategies Cross-Modal Learning
Robust LLM Unlearning Against Relearning Attacks: The Minor Components in Representations Matter
May 12, 2026Zeguan Xiao, Xuanzhe Xu, Yun Chen +4Unlearning Aims Vulnerability Detection & Repair
SoK: Unlearnability and Unlearning for Model Dememorization
May 12, 2026Mengying Zhang, Derui Wang, Ruoxi Sun +3Several Approximate Unlearning Strategies Machine Learning
Unlearning with Asymmetric Sources: Improved Unlearning-Utility Trade-off with Public Data
May 11, 2026Ahmed Mehdi Inane, Vincent Quirion, Gintare Karolina Dziugaite +1Unlearning Aims Differential Privacy
Exact Unlearning from Proxies Induces Closeness Guarantees on Approximate Unlearning
May 11, 2026Virgile Dine, Teddy FuronSeveral Approximate Unlearning Strategies Exact Unlearning
PPU-Bench:Real World Benchmark for Personalized Partial Unlearning in Vision Language Models
May 9, 2026Jiahui Guang, Zexun Zhan, Zhenlin Xu +5Several Approximate Unlearning Strategies Multi-Modal Large Language Models
Unlearners Can Lie: Evaluating and Improving Honesty in LLM Unlearning
May 9, 2026Renjie Gu, Jiazhen Du, Yihua Zhang +1Large Language Model Unlearning Large Language Model Reliability
Position: The Term "Machine Unlearning" Is Overused in LLMs
May 8, 2026Sangyeon Yoon, Yeachan Jun, Albert NoMachine Unlearning Deletion
SHRED: Retain-Set-Free Unlearning via Self-Distillation with Logit Demotion
May 8, 2026Zizhao Hu, Ameya Godbole, Johnny Tian-Zheng Wei +3Large Language Model Unlearning Logit Lens

Jun 16, 2026 · cs.CLJ/K move · Enter open · S save

PreUnlearn: Auditing Collateral Knowledge Damage Before Large Language Model Unlearning

Bo Su, Ankit Shah, Thai Le

Indiana University Bloomington, IN, USA

Machine unlearning for large language models (LLMs) aims to remove specified knowledge while preserving the rest of the model's capabilities. However, the boundary between knowledge to forget and knowledge to retain is often unclear, since related and even distant information may be entangled in the model. In this paper, we study LLM unlearning from a data-centric perspective and measure how unlearning effects propagate from the forget set to same-domain and distant-domain knowledge not after but before unlearning. We find a consistent decay pattern: collateral damage is strongest near the forget set, weakens with semantic distance, but does not disappear at domain boundaries. We further ask whether such damage can be audited before unlearning is executed. We formulate forget-set auditing as a pre-unlearning prediction task and analyze which data features are most predictive of downstream damage. Our results show that interaction features between the forget set and evaluation set provide the strongest signals, suggesting that collateral damage is partly reflected in data geometry before model updates occur. These findings position forget-set auditing as an early warning tool for identifying risky unlearning runs and designing more reliable unlearning procedures. Code and data are available at https://github.com/BartSu/PreUnlearn.

Large Language Model Unlearning Unlearning Aims Algorithm Auditing

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