Editing MLLMs poses unique challenges compared to single-modal LLMs due to the complexity of their input and output modalities. In the paper Can We Edit Multimodal Large Language Models? (Cheng et al., 2023), a new benchmark for MLLM editing, ‘MMEdit,’ is introduced. The study provides an analytical framework with novel metrics for evaluating the efficacy of model editing techniques.

• Establishment of ‘MMEdit’ benchmark to evaluate MLLM editing.
• Analysis of editing impact on various MLLM components.
• Performance assessment of different model editing baselines.
• Highlighting the complexities and potential strategies for effective MLLM editing.

Understanding how to edit MLLMs opens up opportunities for more personalized and adaptable AI systems. MLLM editing can lead to AI models that can better interpret and respond to real-time human input, fostering more interactive and human-like experiences.