How do multimodal LLMs integrate text, vision, audio, and video understanding?

Multimodal LLMs process multiple data modalities by converting images, audio, and video into shared representation spaces compatible with transformer architectures.

Traditional LLMs process only text tokens. Multimodal systems extend transformer architectures to handle:

The core idea is representation alignment.

Different modalities are transformed into embeddings that share compatible semantic spaces.

Architecture components often include:

CNNs or vision transformers convert images into embeddings.

Speech or acoustic models process sound signals.

Enable interaction between modalities.

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