Multi-modal Models

Multi-modal Models

Most early AI models could only handle one type of data. Language models processed text. Image models processed pictures. Audio models processed sound. Multi-modal models break down these walls by understanding multiple types of data at once, often within the same model.

A multi-modal model can accept input in different forms and combine them to produce better results. For example, you could show it a photo of a broken appliance, describe the problem in text, and ask it to diagnose the issue. The model combines what it sees in the image with what it reads in your text to give a more accurate answer than either input alone would allow.

There are two main approaches to building multi-modal models. The first is to train a single model from scratch on multiple data types. Google's Gemini was built this way, with native understanding of text, images, audio, and video. The second approach is to connect existing specialist models together, like using a vision encoder to convert images into text tokens that a language model can understand.

The second approach is more common in practice. Models like LLaVA and GPT-4V use a vision encoder, often based on CLIP or SigLIP, to turn images into token representations. These tokens are then fed into the language model just like text tokens. This lets existing language models gain visual understanding without being retrained from scratch.

Multi-modal capability is becoming standard. Most frontier models now accept images, and support for audio and video is growing quickly. This trend toward multi-modal AI reflects how humans naturally process information. We do not just read or just look, we combine all our senses. Multi-modal models bring AI closer to this natural way of understanding the world.