MM-LLMs: Recent Advances in MultiModal Large Language Models
Duzhen Zhang, Yahan Yu, Jiahua Dong, Chenxing Li, Dan Su, Chenhui Chu, Dong Yu · 2024
In the past year, MultiModal Large Language Models (MM-LLMs) have undergone substantial advancements, augmenting off-the-shelf LLMs to support MM inputs or outputs via cost-effective training strategies.The resulting models not only preserve the inherent reasoning and decision-making capabilities of LLMs but also empower a diverse range of MM tasks.In this paper, we provide a comprehensive survey aimed at facilitating further research on MM-LLMs.Initially, we outline general design formulations for model architecture and training pipeline.Subsequently, we introduce a taxonomy encompassing 126 MM-LLMs, each characterized by its specific formulations.Furthermore, we review the performance of selected MM-LLMs on mainstream benchmarks and summarize key training recipes to enhance the potency of MM-LLMs.Finally, we explore promising directions for MM-LLMs while concurrently maintaining a real-time tracking website 1 for the latest developments in the field.We hope that this survey contributes to the ongoing advancement of the MM-LLMs domain.