Chapter 11: Multimodal LLMs

Human intelligence is inherently multimodal -- we seamlessly integrate information from sight, sound, and language. Early AI systems were confined to single modalities: vision models processed images, language models processed text, and speech models processed audio. Multimodal LLMs break these barriers by processing and generating across modalities simultaneously.

The breakthrough came from recognizing that different modalities can share a common representation space. CLIP demonstrated that images and text can be embedded into the same vector space, enabling zero-shot image classification by computing similarity between image embeddings and text descriptions. This simple idea unlocked an explosion of capabilities.

Modern multimodal LLMs like GPT-4V, Gemini, and Claude can understand images, charts, diagrams, and documents alongside text. They can answer questions about visual content, extract structured data from images, and reason about spatial relationships. Some models extend to audio, video, and even 3D content.

The architectural innovations enabling this include visual encoders (like ViT) that convert images to token sequences compatible with transformer processing, cross-modal attention that allows text and image tokens to attend to each other, and contrastive pre-training that aligns representations across modalities.

This chapter covers:

• Vision-Language Models: Architectures that process images and text together for understanding and generation
• Audio Models: Speech recognition, text-to-speech, and audio understanding with transformer architectures
• Cross-Modal Attention: Mechanisms that allow information flow between different modalities
• CLIP & Contrastive Learning: Aligning image and text representations in a shared embedding space
• Multimodal Applications: Real-world systems combining multiple modalities for powerful capabilities