A new arXiv paper introduces MLUBench, a benchmark for lifelong unlearning in multimodal large language models.
Unlearning is difficult because models may need to remove specific knowledge while preserving general ability across text and vision tasks. The challenge becomes harder when updates happen repeatedly over a model’s lifetime.
The benchmark is relevant for privacy, compliance, and model maintenance, where deletion requests and safety updates require more than one-time retraining.