LLM Unlearning

LLM: Large Language Model

Large language model unlearning aims to remove targeted information, such as private data, hazardous knowledge, or memorized content, without retraining the model from scratch or damaging useful abilities. Common approaches use gradient-based fine-tuning with retention constraints, parameter or representation edits, and inference-time steering; retrieval-augmented systems can also forget by changing their external knowledge stores. Benchmarks such as TOFU, MUSE, and WMDP test forgetting alongside retained utility, while newer evaluations probe recovery through paraphrases, multi-hop reasoning, quantization, and interaction. A central challenge is that suppressing an answer does not necessarily erase the information internally, and stronger forgetting can cause collateral damage or remain reversible.

Latest papers

192 papers32 papers in the last four weeks, up 68% on the four weeks before. 0.2% of all new papers.

Harmonizing Multi-Objective LLM Unlearning via Unified Domain Representation and Bidirectional Logit Distillation
Apr 16, 2026Yisheng Zhong, Sijia Liu, Zhuangdi ZhuPrivacy-Preserving Language Models LLM Unlearning
George Mason University · Michigan State University
Modeling LLM Unlearning as an Asymmetric Two-Task Learning Problem
Apr 16, 2026Zeguan Xiao, Siqing Li, Yong Wang +4Gradient Interference Multi-Task Learning
1Shanghai University of Finance and Economics · 2Southern University of Science and Technology · 3Alibaba Group +2
CURaTE: Continual Unlearning in Real Time with Ensured Preservation of LLM Knowledge
Apr 16, 2026Seyun Bae, Seokhan Lee, Eunho YangPrivacy-Preserving Language Models Factual Knowledge in Language Models
1KT Corporation · 2KAIST
CausalDetox: Causal Head Selection and Intervention for Language Model Detoxification
Apr 16, 2026Yian Wang, Yuen Chen, Agam Goyal +1Causal Reasoning in Language Models LLM Safety
Department of Computer Science University of Illinois, Urbana-Champaign Champaign, IL 61801
From Anchors to Supervision: Memory-Graph Guided Corpus-Free Unlearning for Large Language Models
Apr 15, 2026Wenxuan Li, Zhenfei Zhang, Mi Zhang +4LLM Unlearning Machine Unlearning
College of Computer Science and Artificial Intelligence, Fudan University, Shanghai, China · Shanghai University of Electric Power, Shanghai, China · School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China
WIN-U: Woodbury-Informed Newton-Unlearning as a retain-free Machine Unlearning Framework
Apr 15, 2026Xingjian Zhao, Mohammad Mohammadi Amiri, Malik Magdon-IsmailMachine Unlearning LLM Unlearning
Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
RePAIR: Interactive Machine Unlearning through Prompt-Aware Model Repair
Apr 14, 2026Jagadeesh Rachapudi, Pranav Singh, Ritali Vatsi +2Machine Unlearning LLM Unlearning
Indian Institute of Technology Mandi, Mandi, India
Operationalising the Right to be Forgotten in LLMs: A Lightweight Sequential Unlearning Framework for Privacy-Aligned Deployment in Politically Sensitive Environments
Apr 14, 2026Esen Kurt, Haithem AfliLLM Unlearning Machine Unlearning
Department of Mathematics Munster Technological University · Department of Computer Science Munster Technological University
Mitigating Privacy Risk via Forget Set-Free Unlearning
Apr 12, 2026Aviraj Newatia, Michael Cooper, Viet Nguyen +1Machine Unlearning Privacy-Preserving ML
University of Toronto, Vector Institute, Toronto, ON, Canada
Latent Instruction Representation Alignment: defending against jailbreaks, backdoors and undesired knowledge in LLMs
Apr 12, 2026Eric Easley, Sebastian FarquharBackdoor Defense in LLMs LLM Jailbreak Attacks
ML Alignment & Theory Scholars Berkeley, CA, USA · OATML, Department of Computer Science University of Oxford, Oxford, UK
Exclusive Unlearning
Apr 7, 2026Mutsumi Sasaki, Kouta Nakayama, Yusuke Miyao +2LLM Unlearning LLM Safety
Tohoku University · NII LLMC · The University of Tokyo
Towards Unveiling Vulnerabilities of Large Reasoning Models in Machine Unlearning
Apr 5, 2026Aobo Chen, Chenxu Zhao, Chenglin Miao +1Machine Unlearning LLM Unlearning
Iowa State University, Ames IA 50011, USA
CURE:Circuit-Aware Unlearning for LLM-based Recommendation
Apr 4, 2026Ziheng Chen, Jiali Cheng, Zezhong Fan +4Machine Unlearning LLM Unlearning
Walmart Global Tech, Sunnyvale, CA, USA · University of Massachusetts Lowell, Lowell, MA, USA · Zhejiang University, Hangzhou, China +2
Selective Forgetting for Large Reasoning Models
Apr 4, 2026Tuan Le, Wei Qian, Mengdi HuaiLLM Unlearning Large Reasoning Models
Iowa State University, Ames IA 50011, USA
Secure Forgetting: A Framework for Privacy-Driven Unlearning in Large Language Model (LLM)-Based Agents
Apr 1, 2026Dayong Ye, Tainqing Zhu, Congcong Zhu +5LLM Unlearning Privacy-Preserving Language Models
The Unlearning Mirage: A Dynamic Framework for Evaluating LLM Unlearning
Mar 11, 2026Raj Sanjay Shah, Jing Huang, Keerthiram Murugesan +2LLM Unlearning Machine Unlearning Evaluation
Georgia Institute of Technology · Stanford University · IBM Research
ALTER: Asymmetric LoRA for Token-Entropy-Guided Unlearning of LLMs
Mar 2, 2026Xunlei Chen, Jinyu Guo, Yuang Li +5Machine Unlearning LLM Unlearning
School of Information and Software Engineering, University of Electronic Science and Technology of China · School of Computer Science and Engineering, University of Electronic Science and Technology of China
Attention Smoothing Is All You Need For Unlearning
Mar 1, 2026Saleh Zare Zade, Xiangyu Zhou, Sijia Liu +1Machine Unlearning LLM Unlearning
Department of Computer Science, Wayne State University · Department of Computer Science and Engineering, Michigan State University · Institute for AI and Data Science, Wayne State University
A Comprehensive Evaluation of LLM Unlearning Robustness under Multi-Turn Interaction
Feb 28, 2026Ruihao Pan, Suhang WangLLM Unlearning Machine Unlearning Evaluation
Pennsylvania State University
MPU: Towards Secure and Privacy-Preserving Knowledge Unlearning for Large Language Models
Feb 27, 2026Tiantong Wang, Xinyu Yan, Tiantong Wu +3Machine Unlearning LLM Unlearning
College of Computing and Data Science, Nanyang Technological University · Alibaba-NTU Global e-Sustainability CorpLab (ANGEL) · Tongyi Lab, Alibaba Group
U-CAN: Utility-Aware Contrastive Attenuation for Efficient Unlearning in Generative Recommendation
Feb 26, 2026Zezheng Wu, Rui Wang, Xinghe Cheng +4Machine Unlearning LLM Unlearning
Guilin University of Electronic Technology, Guilin, China · University of Southampton, Southampton, United Kingdom · Jinan University, Guangzhou, China +1
Anatomy of Unlearning: The Dual Impact of Fact Salience and Model Fine-Tuning
Feb 23, 2026Anna Borisiuk, Andrey Savchenko, Alexander Panchenko +1Machine Unlearning LLM Unlearning
AIRI · Skoltech · Sber AI Lab +2
GONE: Structural Knowledge Unlearning via Neighborhood-Expanded Distribution Shaping
Feb 21, 2026Chahana Dahal, Ashutosh Balasubramaniam, Zuobin XiongMachine Unlearning LLM Unlearning
University of Nevada, Las Vegas · Indian Institute of Technology Guwahati
CALIBURN: Self-Calibrated LLM Unlearning Alignment
Feb 2, 2026Zhengbang Yang, Yisheng Zhong, Junyuan Hong +1LLM Unlearning
George Mason University · National University of Singapore
Contrastive Representation Shaping for LLM Unlearning
Jan 29, 2026Haoran Tang, Rajiv KhannaDisentangled Representation Learning Machine Unlearning
Department of Computer Science, Purdue University.
Beyond Forgetting: Representation Misdirection Elicits Controllable Side Behaviors and Capabilities
Jan 29, 2026Tien Dang, The-Hai Nguyen, Dinh Mai Phuong +5Linear Representation Hypothesis Controllable Text Generation
Japan Advanced Institute of Science and Technology · Monash University · Quantum AI and Cyber Security Institute, FPT Corporation +1
BalDRO: A Distributionally Robust Optimization based Framework for Large Language Model Unlearning
Jan 14, 2026Pengyang Shao, Naixin Zhai, Lei Chen +4Distributionally Robust Optimization LLM Unlearning
National University of Singapore Singapore · University of Science and Technology of China Hefei, China · Hefei University of Technology Hefei, China
A Fast and Effective Solution to the Problem of Look-ahead Bias in LLMs
Dec 7, 2025Humzah Merchant, Bradford LevyLook-Ahead Bias in Language Models LLM-Based Time Series Forecasting
University of Chicago Chicago, IL 60637
RapidUn: Influence-Driven Parameter Reweighting for Efficient Large Language Model Unlearning
Dec 4, 2025Guoshenghui Zhao, Huawei Lin, Weijie ZhaoMachine Unlearning LLM Unlearning
Golisano College of Computing and Information Sciences, Rochester Institute of Technology
RippleBench: Capturing Ripple Effects Using Existing Knowledge Repositories
Dec 3, 2025Roy Rinberg, Usha Bhalla, Igor Shilov +2LLM Evaluation LLM Unlearning
Harvard University · MATS · Imperial College London +1

Apr 16, 2026J/K move · Enter open · S save

Harmonizing Multi-Objective LLM Unlearning via Unified Domain Representation and Bidirectional Logit Distillation

Yisheng Zhong, Sijia Liu, Zhuangdi Zhu

George Mason University · Michigan State University

Privacy-Preserving Language Models LLM Unlearning

Open paper AskPDF arXiv

Large Language Models (LLMs) unlearning is crucial for removing hazardous or privacy-leaking information from the model. Practical LLM unlearning demands satisfying multiple challenging objectives simultaneously: removing undesirable knowledge, preserving general utility, avoiding over-refusal of neighboring concepts, and, crucially, ensuring robustness against adversarial probing attacks. However, existing unlearning methods primarily focus on a limited subset of these goals, typically unlearning efficacy and utility preservation while overlooking robustness and boundary behaviors. Naively extending these methods to multi-objective settings may lead to unlearning task interference. We propose a novel multi-objective unlearning framework that harmonizes multiple unlearning objectives through a data and optimization co-design: We standardize training corpora into a unified data representation to reduce the domain gap, and then introduce a bidirectional distillation method that simultaneously elicits desired behavior from a context-instructed teacher while suppressing undesirable behavior in the student model. Theoretical and empirical analyses show that our method aligns domain distributions and converts seemingly irrelevant unlearning tasks into cooperative optimization. Evaluation demonstrates state-of-the-art performance, which enables balanced and reliable unlearning across diverse, challenging requirements.