Unified Pre Training

Unified pre-training aims to leverage the power of large-scale pre-trained models across diverse downstream tasks by learning a generalizable representation from a variety of data sources. Current research focuses on developing unified frameworks that handle multiple modalities (e.g., image, text, audio, graph data) and adapting pre-trained models to specific tasks using techniques like prompt tuning and task hypergraphs. This approach promises to improve efficiency and generalization in various fields, including computer vision, natural language processing, and recommender systems, by reducing the need for extensive task-specific training data and model architectures.