Document Expansion

Document expansion enhances information retrieval by augmenting queries or documents with additional relevant information, aiming to improve the accuracy and recall of search results. Current research focuses on leveraging large language models to generate this supplemental information, employing techniques like contrastive learning and curriculum learning within various retrieval architectures, including dual-encoders. These advancements address limitations of existing methods, particularly in handling imbalanced datasets and improving performance across diverse domains and retrieval models, ultimately leading to more effective and robust information retrieval systems.