Multimodal Design Document

Multimodal design documents integrate text, images, and potentially audio to create richer, more comprehensive design representations. Current research focuses on developing automated systems that generate these documents, often employing large language models (LLMs), visual language models (VLMs), and deep learning techniques like submodular optimization and early-fusion architectures to handle the diverse data modalities. These advancements aim to improve efficiency and creativity in design processes across various fields, from architecture and financial document analysis to creating accessible design materials. The development of large, diverse datasets is also crucial for training and evaluating these increasingly sophisticated multimodal models.