Summary of From Efficient Multimodal Models to World Models: a Survey, by Xinji Mai et al.
From Efficient Multimodal Models to World Models: A Survey
by Xinji Mai, Zeng Tao, Junxiong Lin, Haoran Wang, Yang Chang, Yanlan Kang, Yan Wang, Wenqiang Zhang
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 recent surge in research focuses on Multimodal Large Models (MLMs), which combine large language models with multimodal learning to tackle complex tasks across various data modalities. This paper reviews the latest developments and challenges in MLMs, highlighting their potential for achieving artificial general intelligence and as a pathway to world models. Key techniques include Multimodal Chain of Thought (M-COT), Multimodal Instruction Tuning (M-IT), and Multimodal In-Context Learning (M-ICL). The paper also discusses fundamental and specific technologies of multimodal models, their applications, input/output modalities, and design characteristics. Despite significant progress, developing a unified multimodal model remains elusive. To address this challenge, the authors propose integrating 3D generation and embodied intelligence to enhance world simulation capabilities and incorporating external rule systems for improved reasoning and decision-making. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large models that can understand and work with different types of data are becoming more important in research. These Multimodal Large Models (MLMs) can be used for many tasks, such as artificial general intelligence and creating a kind of “world model”. The paper talks about the latest developments and challenges in MLMs, including techniques like M-COT, M-IT, and M-ICL. It also looks at the different technologies that are being developed to make multimodal models work better. While there has been progress, making a single model that can handle all types of data is still a big challenge. The authors suggest ways to overcome this challenge, such as adding 3D generation and embodied intelligence to make world simulation more realistic. |
Keywords
» Artificial intelligence » Instruction tuning
