Multimodal knowledge editing represents a critical advancement in enhancing
the capabilities of Multimodal Large Language Models (MLLMs). Despite its
potential, current benchmarks predominantly focus on coarse-grained knowledge,
leaving the intricacies of fine-grained (FG) multimodal entity knowledge
largely unexplored. This gap presents a notable challenge, as FG entity
recognition is pivotal for the practical deployment and effectiveness of MLLMs
in diverse real-world scenarios. To bridge this gap, we introduce MIKE, a
comprehensive benchmark and dataset specifically designed for the FG multimodal
entity knowledge editing. MIKE encompasses a suite of tasks tailored to assess
different perspectives, including Vanilla Name Answering, Entity-Level Caption,
and Complex-Scenario Recognition. In addition, a new form of knowledge editing,
Multi-step Editing, is introduced to evaluate the editing efficiency. Through
our extensive evaluations, we demonstrate that the current state-of-the-art
methods face significant challenges in tackling our proposed benchmark,
underscoring the complexity of FG knowledge editing in MLLMs. Our findings
spotlight the urgent need for novel approaches in this domain, setting a clear
agenda for future research and development efforts within the community.

通过我们的广泛评估，我们展示了目前最先进的方法在解决我们提出的基准测试中面临着重大挑战，凸显了多模态大型语言模型中细粒度知识编辑的复杂性，这进一步凸显了在这个领域中需要创新方法的迫切需求，为未来的研究和开发工作设定了明确的议程。