  Multimodal large language models (MLLMs) like LLaVA and GPT-4(V) enable
general-purpose conversations about images with the language modality. As
off-the-shelf MLLMs may have limited capabilities on images from domains like
dermatology and agriculture, they must be fine-tuned to unlock domain-specific
applications. The prevalent architecture of current open-source MLLMs comprises
two major modules: an image-language (cross-modal) projection network and a
large language model. It is desirable to understand the roles of these two
modules in modeling domain-specific visual attributes to inform the design of
future models and streamline the interpretability efforts on the current
models. To this end, via experiments on 4 datasets and under 2 fine-tuning
settings, we find that as the MLLM is fine-tuned, it indeed gains
domain-specific visual capabilities, but the updates do not lead to the
projection extracting relevant domain-specific visual attributes. Our results
indicate that the domain-specific visual attributes are modeled by the LLM,
even when only the projection is fine-tuned. Through this study, we offer a
potential reinterpretation of the role of cross-modal projections in MLLM
architectures. Project webpage:
https://claws-lab.github.io/projection-in-MLLMs/
