Vector Retrieval

Vector retrieval focuses on efficiently finding the most relevant data points (vectors) from a large dataset based on a query vector, a crucial task in various applications including large language models (LLMs). Current research emphasizes improving the speed and accuracy of retrieval, particularly through approximate nearest neighbor search (ANNS) algorithms and novel approaches that balance similarity and diversity in retrieved vectors, often incorporating techniques like k-nearest neighbor (kNN) search and optimized indexing structures. These advancements are vital for scaling LLMs to longer contexts and enhancing the performance of information retrieval systems, impacting fields ranging from natural language processing to computer vision.