Modern Sparse Neural Retrieval: From Theory to Practice

Modern sparse neural retrieval models offer a sophisticated approach to overcoming the limitations of traditional keyword-based retrievers like BM25, which are fast but struggle with vocabulary and semantic mismatches. Sparse neural retrieval, exemplified by models like SPLADE++, combines the strengths of dense vector representations with the explainability and speed of sparse representations, addressing gaps such as synonyms and homonyms by expanding both documents and queries. These models often leverage BERT-based architectures to produce sparse encodings, balancing result quality and resource efficiency. While external document expansion methods like docT5query can be resource-intensive, internal expansion techniques integrated within models like SPLADE++ enhance retrieval efficiency. Despite the improvements, challenges such as generalization across diverse datasets remain, although sparse models can significantly reduce false positives compared to dense retrievers. Sparse neural retrieval is particularly advantageous in domains requiring precise term matching and semantic comprehension, such as medicine and e-commerce, and can be integrated with existing systems to enhance search relevance and scalability.