Summary of Next Token Prediction Towards Multimodal Intelligence: a Comprehensive Survey, by Liang Chen et al.

Next Token Prediction Towards Multimodal Intelligence: A Comprehensive Survey

by Liang Chen, Zekun Wang, Shuhuai Ren, Lei Li, Haozhe Zhao, Yunshui Li, Zefan Cai, Hongcheng Guo, Lei Zhang, Yizhe Xiong, Yichi Zhang, Ruoyu Wu, Qingxiu Dong, Ge Zhang, Jian Yang, Lingwei Meng, Shujie Hu, Yulong Chen, Junyang Lin, Shuai Bai, Andreas Vlachos, Xu Tan, Minjia Zhang, Wen Xiao, Aaron Yee, Tianyu Liu, Baobao Chang

First submitted to arxiv on: 16 Dec 2024

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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 introduces a comprehensive taxonomy for multimodal learning, building on the foundations of Next Token Prediction (NTP) in natural language processing. The NTP framework has achieved success across various modalities, and this survey aims to unify understanding and generation tasks within multimodal intelligence. The proposed taxonomy covers five key aspects: multimodal tokenization, MMNTP model architectures, unified task representation, datasets & evaluation, and open challenges. This research aims to aid researchers in their exploration of multimodal intelligence.
Low	GrooveSquid.com (original content)	Low Difficulty Summary Multimodal learning is a way for machines to understand and work with different types of data, like text, images, and sounds. The paper shows how one type of machine learning, called Next Token Prediction (NTP), can be used for many different tasks. NTP helps machines predict what comes next in a sequence of words, images, or sounds. This research creates a system to help scientists understand and work with multimodal data.