Retrieving Multimodal Information for Augmented Generation: A Survey

• URL: http://arxiv.org/abs/2303.10868v3
• Date: Fri, 1 Dec 2023 02:58:09 GMT
• Title: Retrieving Multimodal Information for Augmented Generation: A Survey
• Authors: Ruochen Zhao, Hailin Chen, Weishi Wang, Fangkai Jiao, Xuan Long Do, Chengwei Qin, Bosheng Ding, Xiaobao Guo, Minzhi Li, Xingxuan Li, Shafiq Joty
• Abstract summary: We review methods that assist and augment generative models by retrieving multimodal knowledge. Such methods offer a promising solution to important concerns such as factuality, reasoning, interpretability, and robustness.
• Score: 35.33076940985081
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As Large Language Models (LLMs) become popular, there emerged an important trend of using multimodality to augment the LLMs' generation ability, which enables LLMs to better interact with the world. However, there lacks a unified perception of at which stage and how to incorporate different modalities. In this survey, we review methods that assist and augment generative models by retrieving multimodal knowledge, whose formats range from images, codes, tables, graphs, to audio. Such methods offer a promising solution to important concerns such as factuality, reasoning, interpretability, and robustness. By providing an in-depth review, this survey is expected to provide scholars with a deeper understanding of the methods' applications and encourage them to adapt existing techniques to the fast-growing field of LLMs.

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