General Purpose Model

General-purpose models (GPMs) are large-scale machine learning models designed to perform well across a wide range of tasks without extensive task-specific retraining. Current research focuses on improving GPM efficiency and adaptability, exploring techniques like knowledge distillation to create specialized, more efficient application models from larger GPMs, and investigating methods for efficient data selection to optimize training. This work is significant because it addresses the limitations of specialized models by leveraging the breadth of knowledge in GPMs while simultaneously improving their efficiency and reducing resource demands for specific applications. The resulting advancements have implications for various fields, including computer vision, natural language processing, and even protein structure prediction.