How does multi-modal architecture extend ChatGPT beyond text understanding?

Multi-modal architecture integrates text, image, and audio encoders into a unified model representation.

Multi-modal ChatGPT systems extend transformer architecture to handle multiple input types such as text, images, and audio. Each modality is encoded into embeddings and aligned into a shared latent space so the model can reason across formats.

For example, vision encoders process images into feature vectors, which are then projected into the language model embedding space. Cross-attention layers allow the model to integrate information across modalities.

This enables tasks like image description, visual reasoning, and audio transcription within a single system.