  Understanding the internal mechanisms by which multi-modal large language
models (LLMs) interpret different modalities and integrate cross-modal
representations is becoming increasingly critical for continuous improvements
in both academia and industry. In this paper, we propose a novel method to
identify key neurons for interpretability -- how multi-modal LLMs bridge visual
and textual concepts for captioning. Our method improves conventional works
upon efficiency and applied range by removing needs of costly gradient
computation. Based on those identified neurons, we further design a multi-modal
knowledge editing method, beneficial to mitigate sensitive words or
hallucination. For rationale of our design, we provide theoretical assumption.
For empirical evaluation, we have conducted extensive quantitative and
qualitative experiments. The results not only validate the effectiveness of our
methods, but also offer insightful findings that highlight three key properties
of multi-modal neurons: sensitivity, specificity and causal-effect, to shed
light for future research.
