  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.
