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Surveying the MLLM Landscape: A Meta-Review of Current Surveys

Ming Li, Keyu Chen, Ziqian Bi, Ming Liu, Xinyuan Song, Zekun Jiang, Tianyang Wang, Benji Peng, Qian Niu, Junyu Liu, Jinlang Wang, Sen Zhang, Xuanhe Pan, Jiawei Xu, Pohsun Feng

TL;DR

This survey of surveys synthesizes the rapidly expanding literature on Multimodal Large Language Models (MLLMs) by organizing 58 recent reviews into 11 core themes, spanning architectures, evaluation, security, bias, agents, applications, RAG, graphs, data, continual learning, and efficiency. It provides a taxonomy of methodologies, benchmarks, and trends, identifies gaps such as modality integration beyond text/image, longitudinal performance, and cross-domain transfer, and proposes directions to improve evaluation standards and data-centric development. The work clarifies how contrastive multimodal alignment and model-based fusion underpin cross-modal understanding, and highlights practical implications for safety, fairness, and deployment in real-world tasks. Overall, it serves as a navigational aid for researchers and practitioners to track progress, align evaluation practices, and prioritize data-quality and efficiency in MLLM research.

Abstract

The rise of Multimodal Large Language Models (MLLMs) has become a transformative force in the field of artificial intelligence, enabling machines to process and generate content across multiple modalities, such as text, images, audio, and video. These models represent a significant advancement over traditional unimodal systems, opening new frontiers in diverse applications ranging from autonomous agents to medical diagnostics. By integrating multiple modalities, MLLMs achieve a more holistic understanding of information, closely mimicking human perception. As the capabilities of MLLMs expand, the need for comprehensive and accurate performance evaluation has become increasingly critical. This survey aims to provide a systematic review of benchmark tests and evaluation methods for MLLMs, covering key topics such as foundational concepts, applications, evaluation methodologies, ethical concerns, security, efficiency, and domain-specific applications. Through the classification and analysis of existing literature, we summarize the main contributions and methodologies of various surveys, conduct a detailed comparative analysis, and examine their impact within the academic community. Additionally, we identify emerging trends and underexplored areas in MLLM research, proposing potential directions for future studies. This survey is intended to offer researchers and practitioners a comprehensive understanding of the current state of MLLM evaluation, thereby facilitating further progress in this rapidly evolving field.

Surveying the MLLM Landscape: A Meta-Review of Current Surveys

TL;DR

This survey of surveys synthesizes the rapidly expanding literature on Multimodal Large Language Models (MLLMs) by organizing 58 recent reviews into 11 core themes, spanning architectures, evaluation, security, bias, agents, applications, RAG, graphs, data, continual learning, and efficiency. It provides a taxonomy of methodologies, benchmarks, and trends, identifies gaps such as modality integration beyond text/image, longitudinal performance, and cross-domain transfer, and proposes directions to improve evaluation standards and data-centric development. The work clarifies how contrastive multimodal alignment and model-based fusion underpin cross-modal understanding, and highlights practical implications for safety, fairness, and deployment in real-world tasks. Overall, it serves as a navigational aid for researchers and practitioners to track progress, align evaluation practices, and prioritize data-quality and efficiency in MLLM research.

Abstract

The rise of Multimodal Large Language Models (MLLMs) has become a transformative force in the field of artificial intelligence, enabling machines to process and generate content across multiple modalities, such as text, images, audio, and video. These models represent a significant advancement over traditional unimodal systems, opening new frontiers in diverse applications ranging from autonomous agents to medical diagnostics. By integrating multiple modalities, MLLMs achieve a more holistic understanding of information, closely mimicking human perception. As the capabilities of MLLMs expand, the need for comprehensive and accurate performance evaluation has become increasingly critical. This survey aims to provide a systematic review of benchmark tests and evaluation methods for MLLMs, covering key topics such as foundational concepts, applications, evaluation methodologies, ethical concerns, security, efficiency, and domain-specific applications. Through the classification and analysis of existing literature, we summarize the main contributions and methodologies of various surveys, conduct a detailed comparative analysis, and examine their impact within the academic community. Additionally, we identify emerging trends and underexplored areas in MLLM research, proposing potential directions for future studies. This survey is intended to offer researchers and practitioners a comprehensive understanding of the current state of MLLM evaluation, thereby facilitating further progress in this rapidly evolving field.
Paper Structure (60 sections, 4 equations, 3 figures)

This paper contains 60 sections, 4 equations, 3 figures.

Table of Contents

  1. Introduction
  2. Purpose of the Survey
  3. Classical Natural Language Processing Methods
  4. Large Language Models (LLM) and Multimodal Large Language Models (MLLM)
  5. Transformer, BERT, and GPT
  6. Transformer
  7. Self-Attention Mechanism
  8. Multi-Head Attention Mechanism
  9. Feed-Forward Neural Network
  10. BERT and GPT
  11. Multimodal Large Language Models (MLLM)
  12. Contrastive Learning-based Multimodal Alignment
  13. Model-Based Techniques for Multimodal Alignment
  14. Taxonomy and Categorization
  15. Methodology
  16. ...and 45 more sections

Figures (3)

  • Figure 1: Literature Survey Tree
  • Figure 2: Distribution of Survey Domains in MLLM Studies
  • Figure 3: Word Cloud Of Survey Titles In MLLM Studies