Multimodal LLMs

Multimodal LLMs (MLLMs) unify text, image, and audio processing into a single model, enabling human-like, context-aware reasoning across diverse data types.

Multimodal LLMs represent a critical leap beyond text-only AI, integrating diverse data—text, images, audio, and video—into a unified system. These models use specialized encoders to convert each modality into a shared embedding space: a common vector representation. The core transformer architecture then employs cross-attention mechanisms, allowing the model to correlate information across modalities (e.g., aligning a specific image region with a text token). This unified processing enables complex tasks like visual question answering (VQA), real-time conversation (GPT-4o), and video analysis (Gemini), driving significant gains in industries from healthcare diagnostics to autonomous systems.

https://azure.microsoft.com/en-us/resources/cloud-computing-dictionary/what-are-multimodal-llms/