Generative Foundation Model

Generative foundation models are large-scale, pre-trained models capable of generating diverse data types, including text, images, audio, and tabular data, aiming to create realistic and controllable synthetic data across various domains. Current research focuses on adapting these models to specific applications, such as financial market simulation, sleep staging, and 3D mesh generation, often employing diffusion models and transformers. These models offer significant potential for accelerating scientific discovery, particularly in areas with limited data, by generating synthetic training data for downstream tasks and enabling efficient exploration of complex systems.