Cover Image

Fast Exact Unlearning for In-Context Learning Data for LLMs

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Muresanu, Andrei I., Thudi, Anvith, Zhang, Michael R., Papernot, Nicolas
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence Cryptography and Security
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Gradients Look Alike: Sensitivity is Often Overestimated in DP-SGD
by: Thudi, Anvith, et al.
Published: (2023)

Efficient Public Verification of Private ML via Regularization
by: Bell, Zoë Ruha, et al.
Published: (2025)

UnUnlearning: Unlearning is not sufficient for content regulation in advanced generative AI
by: Shumailov, Ilia, et al.
Published: (2024)

Unlearned but Not Forgotten: Data Extraction after Exact Unlearning in LLM
by: Wu, Xiaoyu, et al.
Published: (2025)

ACU: Analytic Continual Unlearning for Efficient and Exact Forgetting with Privacy Preservation
by: Tang, Jianheng, et al.
Published: (2025)

Have it your way: Individualized Privacy Assignment for DP-SGD
by: Boenisch, Franziska, et al.
Published: (2023)

Data-Free Privacy-Preserving for LLMs via Model Inversion and Selective Unlearning
by: Zhou, Xinjie, et al.
Published: (2026)

Backdoor Detection through Replicated Execution of Outsourced Training
by: Jia, Hengrui, et al.
Published: (2025)

In-Context Unlearning: Language Models as Few Shot Unlearners
by: Pawelczyk, Martin, et al.
Published: (2023)

Unlink to Unlearn: Simplifying Edge Unlearning in GNNs
by: Tan, Jiajun, et al.
Published: (2024)

Causal Unlearning in Collaborative Optimization: Exact and Approximate Influence Reversal under Adversarial Contributions
by: Mahdavi, Ali, et al.
Published: (2026)

Unlearning Isn't Deletion: Investigating Reversibility of Machine Unlearning in LLMs
by: Xu, Xiaoyu, et al.
Published: (2025)

Hierarchical Dual-Strategy Unlearning for Biomedical and Healthcare Intelligence Using Imperfect and Privacy-Sensitive Medical Data
by: Zhang, Yi, et al.
Published: (2025)

Machine Unlearning with Minimal Gradient Dependence for High Unlearning Ratios
by: Huang, Tao, et al.
Published: (2024)

Unlearning Inversion Attacks for Graph Neural Networks
by: Zhang, Jiahao, et al.
Published: (2025)

GraphToxin: Reconstructing Full Unlearned Graphs from Graph Unlearning
by: Song, Ying, et al.
Published: (2025)

Oblivionis: A Lightweight Learning and Unlearning Framework for Federated Large Language Models
by: Zhang, Fuyao, et al.
Published: (2025)

Data Unlearning Beyond Uniform Forgetting via Diffusion Time and Frequency Selection
by: Park, Jinseong, et al.
Published: (2025)

Towards Lifecycle Unlearning Commitment Management: Measuring Sample-level Unlearning Completeness
by: Wang, Cheng-Long, et al.
Published: (2025)

Data-adaptive Differentially Private Prompt Synthesis for In-Context Learning
by: Gao, Fengyu, et al.
Published: (2024)

SoK: Unlearnability and Unlearning for Model Dememorization
by: Zhang, Mengying, et al.
Published: (2026)

FRAMU: Attention-based Machine Unlearning using Federated Reinforcement Learning
by: Shaik, Thanveer, et al.
Published: (2023)

Inexact Unlearning Needs More Careful Evaluations to Avoid a False Sense of Privacy
by: Hayes, Jamie, et al.
Published: (2024)

SAEs $\textit{Can}$ Improve Unlearning: Dynamic Sparse Autoencoder Guardrails for Precision Unlearning in LLMs
by: Muhamed, Aashiq, et al.
Published: (2025)

DP-TabICL: In-Context Learning with Differentially Private Tabular Data
by: Carey, Alycia N., et al.
Published: (2024)

Unlearn to Relearn Backdoors: Deferred Backdoor Functionality Attacks on Deep Learning Models
by: Shin, Jeongjin, et al.
Published: (2024)

LAMD: Context-driven Android Malware Detection and Classification with LLMs
by: Qian, Xingzhi, et al.
Published: (2025)

Confidential Guardian: Cryptographically Prohibiting the Abuse of Model Abstention
by: Rabanser, Stephan, et al.
Published: (2025)

TracLLM: A Generic Framework for Attributing Long Context LLMs
by: Wang, Yanting, et al.
Published: (2025)

Breaking the Trilemma of Privacy, Utility, Efficiency via Controllable Machine Unlearning
by: Liu, Zheyuan, et al.
Published: (2023)

PRUNE: A Patching Based Repair Framework for Certifiable Unlearning of Neural Networks
by: Li, Xuran, et al.
Published: (2025)

Scalable Federated Unlearning via Isolated and Coded Sharding
by: Lin, Yijing, et al.
Published: (2024)

WARP: Weight Teleportation for Attack-Resilient Unlearning Protocols
by: Maheri, Mohammad M, et al.
Published: (2025)

Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks
by: Andriushchenko, Maksym, et al.
Published: (2024)

Scaling Trends for Data Poisoning in LLMs
by: Bowen, Dillon, et al.
Published: (2024)

MCP Safety Audit: LLMs with the Model Context Protocol Allow Major Security Exploits
by: Radosevich, Brandon, et al.
Published: (2025)

Recalling The Forgotten Class Memberships: Unlearned Models Can Be Noisy Labelers to Leak Privacy
by: Sui, Zhihao, et al.
Published: (2025)

Concealing Backdoor Model Updates in Federated Learning by Trigger-Optimized Data Poisoning
by: Zhang, Yujie, et al.
Published: (2024)

LMEraser: Large Model Unlearning through Adaptive Prompt Tuning
by: Xu, Jie, et al.
Published: (2024)

Textual Unlearning Gives a False Sense of Unlearning
by: Du, Jiacheng, et al.
Published: (2024)