MC-MKE：一个强调模态一致性的细粒度多模态知识编辑基准
MC-MKE: A Fine-Grained Multimodal Knowledge Editing Benchmark Emphasizing Modality Consistency
AI总结：
针对多模态大语言模型存在的误读、误识别类知识错误及现有基准缺乏细粒度分析的问题，提出强调模态一致性的细粒度多模态知识编辑基准MC-MKE，评估发现现有编辑方法在模态一致性上存在局限，为相关研究指明方向。
摘要：
多模态大语言模型（MLLMs）容易出现非事实性或过时知识的问题，由于多模态知识的复杂性，这些问题可能表现为误读和误识别错误。此前的基准尚未系统分析编辑方法在纠正这两类错误时的性能。为了更好地刻画并纠正这些错误，我们将多模态知识分解为视觉组件和文本组件。不同的错误类型对应不同的编辑格式，分别对多模态知识的不同部分进行编辑。我们提出了MC-MKE，一个强调模态一致性的细粒度多模态知识编辑基准。该基准通过编辑对应的知识组件，实现对误读和误识别错误的独立纠正。我们在MC-MKE上评估了四种多模态知识编辑方法，揭示了它们的局限性，尤其是在模态一致性方面的不足。本研究凸显了多模态知识编辑带来的挑战，也为该任务有效技术的后续研究提供了方向。
英文摘要：
Multimodal large language models (MLLMs) are prone to non-factual or outdated knowledge issues, which can manifest as misreading and misrecognition errors due to the complexity of multimodal knowledge.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 edit distinct parts 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 four 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
