4 papers
Towards Benign Memory Forgetting for Selective Multimodal Large Language Model Unlearning
Zhen Zeng, Leijiang Gu, Zhangling Duan +4
Multimodal large language models (MLLMs) can inadvertently memorize privacy-sensitive information during training. While existing unlearning methods can remove such content, they o…
Towards Localized and Disentangled Knowledge Editing for Multimodal Large Language Models
Leijiang Gu, Zhen Zeng, Feng Li +2
Existing methods in Multimodal Knowledge Editing (MKE) have advanced the ability to correct outdated or inaccurate knowledge in Multimodal Large Language Models (MLLMs). However, t…
CrossCult-KIBench: A Benchmark for Cross-Cultural Knowledge Insertion in MLLMs
Zhen Zeng, Leijiang Gu, Feng Li +2
Multimodal Large Language Models (MLLMs), trained primarily on English-centric data, frequently generate culturally inappropriate or misaligned responses in cross-cultural settings…
Visual-Oriented Fine-Grained Knowledge Editing for MultiModal Large Language Models
Zhen Zeng, Leijiang Gu, Xun Yang +3
Knowledge editing aims to efficiently and cost-effectively correct inaccuracies and update outdated information. Recently, there has been growing interest in extending knowledge ed…