Vision-language models and multimodal LLMs
Vision-language models let language systems consume visual information. They can caption images, answer questions about screenshots, read documents, inspect charts, and connect visual evidence to text reasoning.
Architecture pattern
| Part | Role |
|---|---|
| Vision encoder | converts image patches or regions into visual embeddings |
| Projector | maps visual embeddings into the language model's token space |
| Language model | performs instruction following and text generation |
| Grounding layer | links text outputs to image regions or evidence |
Some systems fuse modalities early inside one model; others use separate encoders and connect them through adapters.
Product tasks
- Screenshot understanding and UI assistance.
- Document OCR, table extraction, and layout-aware QA.
- Image captioning and accessibility descriptions.
- Visual search and product matching.
- Chart interpretation and report generation.
- Robot or agent perception in controlled environments.
Evaluation needs
Visual answers should be checked for object recognition, spatial relationships, OCR, grounding, refusal behavior, and hallucinated visual details. A fluent answer can still invent objects that are not in the image.
Pitfall
Multimodal LLMs can sound certain about visual content they misread. For high-stakes work, expose evidence regions or screenshots so users can verify the answer.
Connects to: instruction-tuned models · hallucination detection · hallucinations in UI