A Survey of Multimodal Large Language Model from A Data-centric Perspective

Hao Liang

Abstract:

Multimodal large language models (MLLMs) enhance the capabilities of standard large language models by integrating and processing
data from multiple modalities, including text, vision, audio, video, and 3D environments. Data plays a pivotal role in the development
and refinement of these models. In this survey, we comprehensively review the literature on MLLMs from a data-centric perspective.
Specifically, we explore methods for preparing multimodal data during the pretraining and adaptation phases of MLLMs. Additionally,
we analyze the evaluation methods for the datasets and review the benchmarks for evaluating MLLMs. Our survey also outlines
potential future research directions. This work aims to provide researchers with a detailed understanding of the data-driven aspects of
MLLMs, fostering further exploration and innovation in this field