Summary of A Comprehensive Review Of Multimodal Large Language Models: Performance and Challenges Across Different Tasks, by Jiaqi Wang et al.
A Comprehensive Review of Multimodal Large Language Models: Performance and Challenges Across Different Tasks
by Jiaqi Wang, Hanqi Jiang, Yiheng Liu, Chong Ma, Xu Zhang, Yi Pan, Mengyuan Liu, Peiran Gu, Sichen Xia, Wenjun Li, Yutong Zhang, Zihao Wu, Zhengliang Liu, Tianyang Zhong, Bao Ge, Tuo Zhang, Ning Qiang, Xintao Hu, Xi Jiang, Xin Zhang, Wei Zhang, Dinggang Shen, Tianming Liu, Shu Zhang
First submitted to arxiv on: 2 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 A novel type of artificial intelligence system, Multimodal Large Language Models (MLLMs), is capable of processing diverse data types such as text, images, videos, audio, and physiological sequences. These models are designed to tackle complex real-world applications that single-modality systems cannot handle. The paper investigates the applications of MLLMs in multimodal tasks like natural language processing, computer vision, and audio processing. A comparative analysis is also conducted to identify the strengths and limitations of different MLLMs and potential research directions for future development. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence (AI) has made huge progress in recent years. One exciting area is called Multimodal Large Language Models (MLLMs). These models can understand and work with many types of data, like words, pictures, sounds, and even physiological signals from our bodies! This paper looks at how MLLMs can be used for tasks that involve multiple senses, such as recognizing natural language, understanding images, and processing audio. The researchers also compare different MLLM models and discuss their limitations to help us understand what’s working well and where we need to improve. |
Keywords
» Artificial intelligence » Natural language processing
