MM-LLMs:
Recent Advances in MultiModal Large Language Models
https://arxiv.org/pdf/2401.13601
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 of 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 website1 for the latest developments in the field. We hope that this survey contributes to the ongoing advancement of the MM-LLMs domain.
在过去一年中，多模态大型语言模型（MM-LLMs）取得了显著进展，通过成本效益高的训练策略，增强了现成的LLMs（大型语言模型），以支持多模态输入或输出。这些模型不仅保留了LLMs固有的推理和决策能力，还增强了多种多模态任务的处理能力。本文旨在通过全面综述，促进MM-LLMs领域的进一步研究。
首先，本文概述了模型架构和训练流程的一般设计公式。
随后，本文引入了一个包含126个MM-LLMs的分类体系，每个模型都有其特定的设计公式。
此外，本文还回顾了部分MM-LLMs在主流基准测试中的表现，并总结了增强MM-LLMs效能的关键训练策略。
最后，本文在探索MM-LLMs未来发展方向的同时，还维护了一个实时跟踪网站1，以关注该领域的最新进展。我们希望本次综述能为MM-LLMs领域的持续发展做出贡献。
