  Projecting visual features into word embedding space has become a significant
fusion strategy adopted by Multimodal Large Language Models (MLLMs). However,
its internal mechanisms have yet to be explored. Inspired by multilingual
research, we identify domain-specific neurons in multimodal large language
models. Specifically, we investigate the distribution of domain-specific
neurons and the mechanism of how MLLMs process features from diverse domains.
Furthermore, we propose a three-stage framework for language model modules in
MLLMs when handling projected image features, and verify this hypothesis using
logit lens. Extensive experiments indicate that while current MLLMs exhibit
Visual Question Answering (VQA) capability, they may not fully utilize
domain-specific information. Manipulating domain-specific neurons properly will
result in a 10\% change of accuracy at most, shedding light on the development
of cross-domain, all-encompassing MLLMs in the future. Our code will be
released upon paper notification.
