How do multimodal LLMs work?

Multimodal LLMs process multiple data types such as text, images, audio, and video within a unified model architecture.

Multimodal systems extend transformer architectures beyond text by converting different modalities into embeddings.

For example:

These embeddings are projected into shared representation spaces where the model learns cross-modal relationships.

This allows capabilities such as:

A major challenge is aligning modalities consistently so semantic meaning remains stable across data types.