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

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

URL: http://arxiv.org/abs/2405.16640v2
Date: Thu, 18 Jul 2024 09:01:52 GMT
Title: A Survey of Multimodal Large Language Model from A Data-centric Perspective
Authors: Tianyi Bai, Hao Liang, Binwang Wan, Yanran Xu, Xi Li, Shiyu Li, Ling Yang, Bozhou Li, Yifan Wang, Bin Cui, Ping Huang, Jiulong Shan, Conghui He, Binhang Yuan, Wentao Zhang,
Abstract summary: Multimodal large language models (MLLMs) enhance the capabilities of standard large language models by integrating and processing data from multiple modalities. Data plays a pivotal role in the development and refinement of these models.
Score: 46.57232264950785
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.

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