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A Comprehensive Review of Multimodal Large Language Models: Performance and Challenges Across Different Tasks

Jiaqi Wang, Hanqi Jiang, Yiheng Liu, Chong Ma, Xu Zhang, Yi Pan, Mengyuan Liu, Peiran Gu, Sichen Xia, Wenjun Li, Yutong Zhang, Zihao Wu, Zhengliang Liu, Tianyang Zhong, Bao Ge, Tuo Zhang, Ning Qiang, Xintao Hu, Xi Jiang, Xin Zhang, Wei Zhang, Dinggang Shen, Tianming Liu, Shu Zhang

TL;DR

<3-5 sentence high-level summary> Addresses the need to unify processing across text, image, video, audio, and sequential data in AI systems. Systematically surveys MLLMs, their architectures, fusion strategies, and task performance. Offers a comparative analysis across image, video, and audio tasks, identifies strengths and limitations, and suggests directions. Proposes future research directions focusing on interpretability, efficiency, specialization vs generality, security, and ethical use.

Abstract

In an era defined by the explosive growth of data and rapid technological advancements, Multimodal Large Language Models (MLLMs) stand at the forefront of artificial intelligence (AI) systems. Designed to seamlessly integrate diverse data types-including text, images, videos, audio, and physiological sequences-MLLMs address the complexities of real-world applications far beyond the capabilities of single-modality systems. In this paper, we systematically sort out the applications of MLLM in multimodal tasks such as natural language, vision, and audio. We also provide a comparative analysis of the focus of different MLLMs in the tasks, and provide insights into the shortcomings of current MLLMs, and suggest potential directions for future research. Through these discussions, this paper hopes to provide valuable insights for the further development and application of MLLM.

A Comprehensive Review of Multimodal Large Language Models: Performance and Challenges Across Different Tasks

TL;DR

<3-5 sentence high-level summary> Addresses the need to unify processing across text, image, video, audio, and sequential data in AI systems. Systematically surveys MLLMs, their architectures, fusion strategies, and task performance. Offers a comparative analysis across image, video, and audio tasks, identifies strengths and limitations, and suggests directions. Proposes future research directions focusing on interpretability, efficiency, specialization vs generality, security, and ethical use.

Abstract

In an era defined by the explosive growth of data and rapid technological advancements, Multimodal Large Language Models (MLLMs) stand at the forefront of artificial intelligence (AI) systems. Designed to seamlessly integrate diverse data types-including text, images, videos, audio, and physiological sequences-MLLMs address the complexities of real-world applications far beyond the capabilities of single-modality systems. In this paper, we systematically sort out the applications of MLLM in multimodal tasks such as natural language, vision, and audio. We also provide a comparative analysis of the focus of different MLLMs in the tasks, and provide insights into the shortcomings of current MLLMs, and suggest potential directions for future research. Through these discussions, this paper hopes to provide valuable insights for the further development and application of MLLM.
Paper Structure (32 sections, 5 figures)

This paper contains 32 sections, 5 figures.

Table of Contents

  1. Introduction
  2. Overview of Multimodal Large Language Models
  3. Definitions and Basic Concepts
  4. Main Components of Multimodal Large Language Models
  5. Mutimodal Input Encoder
  6. Feature Fusion Mechanism
  7. Overview of Multimodal Feature in LLMs
  8. Task Classification of Multimodal Large Language Models
  9. Image Tasks
  10. Image Understanding
  11. Image Understanding Based on Traditional Feature Extraction Methods
  12. Application of Deep Learning Technologies in Image Understanding
  13. Multimodal Image Understanding and Cross-Modal Learning
  14. Application of Reinforcement Learning in Image Understanding
  15. Integration of Image Generation and Understanding
  16. ...and 17 more sections

Figures (5)

  • Figure 1: A timeline of representative MLLMs.
  • Figure 2: Summary of MLLMs on Image Tasks
  • Figure 3: Summary of MLLMs on Video Understanding.
  • Figure 4: Summary of MLLMs on Video Generation.
  • Figure 5: Summary of MLLMs on Audio Tasks.