3 papers
cs.CL2026
Measuring the Depth of LLM Unlearning via Activation Patching
Jaeung Lee, Dohyun Kim, Jaemin Jo
Large language model (LLM) unlearning has emerged as a crucial post-hoc mechanism for privacy protection and AI safety, yet auditing whether target knowledge is truly erased remain…
cs.CV2026
Suppression or Deletion: A Restoration-Based Representation-Level Analysis of Machine Unlearning
Yurim Jang, Jaeung Lee, Dohyun Kim +2
As pretrained models are increasingly shared on the web, ensuring that models can forget or delete sensitive, copyrighted, or private information upon request has become crucial. M…
cs.CR2026
Unlearning Comparator: A Visual Analytics System for Comparative Evaluation of Machine Unlearning Methods
Jaeung Lee, Suhyeon Yu, Yurim Jang +2
Machine Unlearning (MU) aims to remove target training data from a trained model so that the removed data no longer influences the model's behavior, fulfilling "right to be forgott…