Large Scale Retrieval

Large-scale retrieval focuses on efficiently finding relevant information (e.g., documents, images, items) from massive datasets. Current research emphasizes developing advanced models, such as hierarchical neural networks and graph neural networks, alongside optimized indexing and search techniques like approximate nearest neighbor search and multi-vector search, to improve retrieval accuracy and speed. These advancements are crucial for various applications, including recommendation systems, information retrieval, and even reinforcement learning, where efficient access to relevant contextual information is paramount. The field is also exploring innovative approaches like generative retrieval and integrating retrieval with ranking within a unified model for end-to-end optimization.