Summary of A Survey Of Multimodal Large Language Model From a Data-centric Perspective, by Tianyi Bai et al.
A Survey of Multimodal Large Language Model from A Data-centric Perspective
by 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
First submitted to arxiv on: 26 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a comprehensive review of multimodal large language models (MLLMs) from a data-centric perspective. It explores methods for preparing multimodal data during pretraining and adaptation phases, evaluates datasets and benchmarks, and outlines future research directions. The survey aims to provide researchers with a detailed understanding of the data-driven aspects of MLLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper reviews how big language models can be made better by combining different types of data like text, pictures, sounds, videos, and 3D environments. It looks at how people prepare this mixed data for training and using these models. The paper also checks out the methods used to test these models and sees what they’re good at. It wants to help researchers understand how to use these big language models in new ways. |
Keywords
» Artificial intelligence » Pretraining
