Dense Retrieval

Dense retrieval aims to efficiently find relevant information within massive datasets by representing both queries and documents as dense vectors, enabling faster and more accurate similarity searches compared to traditional methods. Current research focuses on improving model architectures like bi-encoders and late-interaction models, exploring techniques such as knowledge distillation, multimodal retrieval (incorporating speech), and efficient training strategies to handle large-scale data and stale embeddings. These advancements are significantly impacting information retrieval applications, particularly in open-domain question answering, conversational search, and enterprise search, by enhancing both speed and accuracy.