BM25 improves traditional Lexical search results by refining how text relevance is measured based on term frequency, document length, and term importance across the corpus. Traditional keyword matching systems, like plain TF-IDF, simply count how often a word appears and weigh it by how rare that word is in other documents. BM25 builds on this by introducing parameters that account for diminishing returns and normalization. For example, the more times a word appears in a document, the higher its relevance—but with BM25, that benefit plateaus to avoid over-rewarding repetition. This leads to more balanced and realistic relevance scoring.
BM25 also adjusts scores based on document length using its b parameter, which helps prevent longer documents from dominating search results simply because they contain more words. For short texts like titles or FAQs, developers can reduce b to lessen the penalty for brevity, while for longer articles or manuals, increasing b helps normalize scores. The k1 parameter controls how quickly term frequency saturates, typically tuned between 1.2 and 2.0. Together, these adjustments make BM25 adaptable to different datasets and more representative of human notions of relevance.
In hybrid systems where BM25 results feed into a vector database like Milvus for semantic enhancement, well-tuned BM25 rankings improve overall precision. Developers can first use BM25 to select the top N candidates, then let vector embeddings handle semantic refinement. This layered approach improves efficiency and interpretability while ensuring that the first stage (Lexical) remains grounded in strong statistical evidence. By balancing frequency, rarity, and length, BM25 provides a solid, mathematically transparent foundation that makes hybrid retrieval systems both effective and explainable.