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A Comprehensive Survey and Guide to Multimodal Large Language Models in Vision-Language Tasks

Chia Xin Liang, Pu Tian, Caitlyn Heqi Yin, Yao Yua, Wei An-Hou, Li Ming, Xinyuan Song, Tianyang Wang, Ziqian Bi, Ming Liu

TL;DR

This survey comprehensively maps multimodal large language models (MLLMs), detailing their foundations, architectures, training paradigms, and cross-modal capabilities that fuse language with vision and beyond. It synthesizes core methods such as contrastive learning, masked language modeling, and vision-language pretraining, alongside fine-tuning, instruction tuning, and few/zero-shot strategies, underscoring practical workflows and benchmarks. The paper highlights a broad spectrum of applications—from image captioning, VQA, and cross-modal retrieval to robotics, content creation, and accessibility—paired with prominent case studies and real-world platforms. It also critically examines challenges in scalability, data alignment, robustness, evaluation, and ethics, arguing for responsible development, transparency, and governance to maximize societal benefit while mitigating risks.

Abstract

This survey and application guide to multimodal large language models(MLLMs) explores the rapidly developing field of MLLMs, examining their architectures, applications, and impact on AI and Generative Models. Starting with foundational concepts, we delve into how MLLMs integrate various data types, including text, images, video and audio, to enable complex AI systems for cross-modal understanding and generation. It covers essential topics such as training methods, architectural components, and practical applications in various fields, from visual storytelling to enhanced accessibility. Through detailed case studies and technical analysis, the text examines prominent MLLM implementations while addressing key challenges in scalability, robustness, and cross-modal learning. Concluding with a discussion of ethical considerations, responsible AI development, and future directions, this authoritative resource provides both theoretical frameworks and practical insights. It offers a balanced perspective on the opportunities and challenges in the development and deployment of MLLMs, and is highly valuable for researchers, practitioners, and students interested in the intersection of natural language processing and computer vision.

A Comprehensive Survey and Guide to Multimodal Large Language Models in Vision-Language Tasks

TL;DR

This survey comprehensively maps multimodal large language models (MLLMs), detailing their foundations, architectures, training paradigms, and cross-modal capabilities that fuse language with vision and beyond. It synthesizes core methods such as contrastive learning, masked language modeling, and vision-language pretraining, alongside fine-tuning, instruction tuning, and few/zero-shot strategies, underscoring practical workflows and benchmarks. The paper highlights a broad spectrum of applications—from image captioning, VQA, and cross-modal retrieval to robotics, content creation, and accessibility—paired with prominent case studies and real-world platforms. It also critically examines challenges in scalability, data alignment, robustness, evaluation, and ethics, arguing for responsible development, transparency, and governance to maximize societal benefit while mitigating risks.

Abstract

This survey and application guide to multimodal large language models(MLLMs) explores the rapidly developing field of MLLMs, examining their architectures, applications, and impact on AI and Generative Models. Starting with foundational concepts, we delve into how MLLMs integrate various data types, including text, images, video and audio, to enable complex AI systems for cross-modal understanding and generation. It covers essential topics such as training methods, architectural components, and practical applications in various fields, from visual storytelling to enhanced accessibility. Through detailed case studies and technical analysis, the text examines prominent MLLM implementations while addressing key challenges in scalability, robustness, and cross-modal learning. Concluding with a discussion of ethical considerations, responsible AI development, and future directions, this authoritative resource provides both theoretical frameworks and practical insights. It offers a balanced perspective on the opportunities and challenges in the development and deployment of MLLMs, and is highly valuable for researchers, practitioners, and students interested in the intersection of natural language processing and computer vision.

Paper Structure

This paper contains 190 sections, 5 equations, 9 figures.

Table of Contents

  1. Introduction to Multimodal Large Language Models (MLLMs)
  2. Definition and Importance of MLLMs
  3. The Convergence of Natural Language Processing (NLP) and Computer Vision: The Emergence of MLLMs
  4. Conclusion and Future Prospects
  5. Foundations of Multimodal Large Language Models (MLLMs)
  6. From NLP to LLMs: A Brief Overview
  7. Traditional NLP Methods
  8. Bag-of-Words (BoW)
  9. Term Frequency-Inverse Document Frequency (TF-IDF)
  10. Example: Calculating TF-IDF
  11. Rise of Large Language Models (LLMs)
  12. Neural Network Foundations
  13. Emergence of Transformer Architecture
  14. Pre-training and Transfer Learning
  15. Scaling Up: From BERT to GPT
  16. ...and 175 more sections

Figures (9)

  • Figure 1: Key Component of AI Theory
  • Figure 2: Transformer Backbone
  • Figure 3: Multimodal Embeddings
  • Figure 4: Create Multimodal Embeddings
  • Figure 5: Benefits of Cross-attention Layer
  • ...and 4 more figures