  Multimodal large language models (MLLMs) are prone to non-factual or outdated
knowledge issues, which can manifest as misreading and misrecognition errors
due to the complexity of multimodal knowledge. Previous benchmarks have not
systematically analyzed the performance of editing methods in correcting these
two error types. To better represent and correct these errors, we decompose
multimodal knowledge into its visual and textual components. Different error
types correspond to different editing formats, which edits distinct part of the
multimodal knowledge. We present MC-MKE, a fine-grained Multimodal Knowledge
Editing benchmark emphasizing Modality Consistency. Our benchmark facilitates
independent correction of misreading and misrecognition errors by editing the
corresponding knowledge component. We evaluate three multimodal knowledge
editing methods on MC-MKE, revealing their limitations, particularly in terms
of modality consistency. Our work highlights the challenges posed by multimodal
knowledge editing and motivates further research in developing effective
techniques for this task.
