Cross-Modal Projection in Multimodal LLMs Doesn't Really Project Visual Attributes to Textual Space
<|reference_start|>Cross-Modal Projection in Multimodal LLMs Doesn't Really Project Visual Attributes to Textual Space: 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/<|reference_end|>
@article{verma2024cross-modal,
    title={Cross-Modal Projection in Multimodal LLMs Doesn't Really Project Visual
  Attributes to Textual Space},
    author={Gaurav Verma, Minje Choi, Kartik Sharma, Jamelle Watson-Daniels,
  Sejoon Oh, Srijan Kumar},
    journal={arXiv preprint arXiv:2402.16832},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.16832},
    primaryClass={cs.CL cs.AI cs.CV}
}