Original title: Multimodal Large Language Models: A Survey
Authors: Jiayang Wu, Wensheng Gan, Zefeng Chen, Shicheng Wan, Philip S. Yu
This article dives into multimodal language models, which go beyond text to understand images, audio, and diverse data types. While large language models are great with text, they struggle with other data. Multimodal models solve this by blending different types of information, offering a broader view of various data. The paper begins by explaining multimodal concepts and traces their historical evolution. It then highlights multimodal products by major tech companies and provides a practical guide, delving into the technical side. For researchers, it’s a goldmine, featuring the latest algorithms and popular datasets for experimentation. Additionally, it explores where these models can be used and the challenges in developing them. Ultimately, the goal is to help people grasp multimodal models better and unlock their potential in diverse fields.
Original article: https://arxiv.org/abs/2311.13165