Multimodal LLMs can process and generate content across different data types, combining the strengths of language models with capabilities in vision, audio, and other modalities.
Processing and reasoning about images, diagrams, charts, and videos
Understanding speech, music, sounds, and acoustic environments
Working with tables, databases, code, and other formal representations
Responding to UI interactions, pointing, highlighting, and simulated environments
Specialized Systems (Pre-2020)
Separate models for different modalities with limited integration
Early Multimodal Models (2021-2022)
Models with dual capabilities but limited integration
Unified Architectures (2023+)
End-to-end models with deep integration across modalities
Multi-generation Systems (Emerging)
Models that can both understand and generate across modalities
Processing inputs in one modality and generating in another
Components:
Examples:
Mapping different modalities into a shared embedding space
Technique:
Examples:
Adding specialized modules to base LLMs for new modalities
Architecture:
Examples:
End-to-end models processing all modalities together
Features:
Examples:
Visual Understanding & Analysis
Visual Content Creation
Video Understanding
Applications:
Examples:
Capabilities:
Examples:
Applications:
Examples:
Visual Problem Solving
Document Analysis
Scene Understanding
Specialized Analysis
Multimodal LLMs represent a paradigm shift from specialized AI toward general-purpose systems that can seamlessly work across different forms of information.