Knowledge Density, Not Task Format, Key to Multimodal Scaling

A new study published on arXiv challenges conventional wisdom about scaling multimodal large language models (MLLMs). The research argues that the primary bottleneck in multimodal scaling is not task format but the knowledge density in training data. The study shows that task-specific supervision, such as Visual Question Answering (VQA), contributes little incremental semantic information beyond what is already present in image captions.

The findings suggest that increasing model size and task diversity often yields diminishing returns. This is because the additional tasks do not significantly enhance the semantic richness of the training data. The researchers propose that focusing on increasing the knowledge density of the training data, rather than diversifying task formats, could lead to more effective scaling of MLLMs.

The implications of this research are significant for the development of MLLMs. It shifts the focus from task-specific supervision to the quality and density of the training data. Future work may explore methods to enhance knowledge density in training datasets, potentially leading to more efficient and effective multimodal models. The study also raises questions about the current approaches to scaling MLLMs and the potential benefits of re-evaluating training data strategies.