Can We Edit Multimodal Large Language Models?

Siyuan Cheng, Bozhong Tian, Qingbin Liu, Xi Chen, Yongheng Wang, Huajun Chen, Ningyu Zhang

Submission Type: Regular Long Paper

Submission Track: Resources and Evaluation

Submission Track 2: Language Grounding to Vision, Robotics and Beyond

Keywords: Model Editing, Multimodal Language Models, Large Language Models

TL;DR: We construct a new benchmark, dubbed MMEdit, for editing multimodal LLMs.

Abstract: In this paper, we focus on editing multimodal Large Language Models (LLMs). Compared to editing single-modal LLMs, multimodal model editing is more challenging, which demands a higher level of scrutiny and careful consideration in the editing process. To facilitate research in this area, we construct a new benchmark, dubbed MMEdit, for editing multimodal LLMs and establishing a suite of innovative metrics for evaluation. We conduct comprehensive experiments involving various model editing baselines and analyze the impact of editing different components for multimodal LLMs. Empirically, we notice that previous baselines can implement editing multimodal LLMs to some extent, but the effect is still barely satisfactory, indicating the potential difficulty of this task. We hope that our work can provide the NLP community with insights.

Submission Number: 1273