Sparse Multimodal Model Alignment and Reasoning Accuracy with Expert Scaling

Abstract

Abstract: The multimedia community has shown a significant interest in perceiving and representing the physical world with multimodal pretrained neural network models, and among them, the visual-language pertaining (VLP) is, currently, the most captivating topic. However, there have been few endeavors dedicated to the exploration of 1) whether essential linguistic knowledge (e.g., semantics and syntax) can be extracted during VLP, and 2) how such linguistic knowledge impact or enhance the multimodal alignment. In response, here we aim to elucidate the impact of comprehensive linguistic knowledge,

Research Question

How does the alignment score (e.g., via RLHF or DPO) of a sparse multimodal model change as the number of experts increases on the VQAv2 benchmark, and does this correlate with improved reasoning accuracy on OK-VQA?

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Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance