Retrieval Based Model

Retrieval-based models enhance machine learning by augmenting input data with relevant information retrieved from a separate knowledge base, improving efficiency and performance compared to solely scaling model size. Current research focuses on optimizing retrieval strategies, particularly within transformer architectures, and developing efficient methods for handling multi-distribution data and diverse retrieval tasks like question answering and data augmentation. These models demonstrate significant potential for improving various applications, from personalized recommendations and fact verification to data wrangling and natural language processing in low-resource languages, by reducing the need for massive training datasets and improving generalization.