Sparse vs. Dense Retrieval

Sparse vs. Dense Retrieval refers to two fundamental approaches for information retrieval systems that differ in their document representation and matching strategies. Sparse retrieval uses traditional methods like BM25 and TF-IDF, representing documents as high-dimensional vectors with mostly zero values, focusing on exact keyword matching and statistical term frequency. Dense retrieval employs neural networks to create low-dimensional, semantically-rich vector embeddings where documents are represented as dense vectors in continuous space, enabling semantic similarity matching beyond exact keyword overlap. Sparse methods excel at precise keyword matching and are computationally efficient, while dense methods capture semantic relationships and contextual meaning but require more computational resources. Modern retrieval systems often combine both approaches in hybrid architectures to leverage the precision of sparse methods with the semantic understanding of dense methods, optimizing retrieval performance across diverse query types and information needs.