4 papers
Label Smoothing Improves Gradient Ascent in LLM Unlearning
Zirui Pang, Hao Zheng, Zhijie Deng +3
LLM unlearning has emerged as a promising approach, aiming to enable models to forget hazardous/undesired knowledge at low cost while preserving as much model utility as possible.…
OFFSIDE: Benchmarking Unlearning Misinformation in Multimodal Large Language Models
Hao Zheng, Zirui Pang, Ling li +5
Advances in Multimodal Large Language Models (MLLMs) intensify concerns about data privacy, making Machine Unlearning (MU), the selective removal of learned information, a critical…
When VLMs Meet Image Classification: Test Sets Renovation via Missing Label Identification
Zirui Pang, Haosheng Tan, Yuhan Pu +4
Image classification benchmark datasets such as CIFAR, MNIST, and ImageNet serve as critical tools for model evaluation. However, despite the cleaning efforts, these datasets still…
GUARD: Generation-time LLM Unlearning via Adaptive Restriction and Detection
Zhijie Deng, Chris Yuhao Liu, Zirui Pang +5
Large Language Models (LLMs) have demonstrated strong capabilities in memorizing vast amounts of knowledge across diverse domains. However, the ability to selectively forget specif…