3 papers
cs.LG2026
Confess What You Know: Forget-Set Misalignment with Model Knowledge in LLM Unlearning
Miso Kim, Georu Lee, Seungwon Jeong +1
Machine unlearning for large language models (LLMs) often assumes that a pre-defined forget set matches what the model has memorized, but this frequently breaks in realistic privac…
cs.CV2026
Co-occurring Associated REtained concepts in Diffusion Unlearning
Miso Kim, Georu Lee, Yunji Kim +3
Unlearning has emerged as a key technique to mitigate harmful content generation in diffusion models. However, existing methods often remove not only the target concept, but also b…
cs.CL2026
Machine Unlearning for Masked Diffusion Language Models
Georu Lee, Seungwon Jeong, Hoki Kim +2
Recent masked diffusion language models (MDLMs), such as LLaDA and Dream, have achieved performance comparable to autoregressive large language models. Unlike autoregressive models…