BM42 – a new baseline for hybrid search
Qdrant introduces BM42, combining BM25 with embeddings to enhance text retrieval. Addressing SPLADE's limitations, it leverages transformer models for semantic information extraction, promising improved retrieval quality and adaptability across domains.
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Qdrant introduces BM42 as a new baseline for hybrid search, aiming to enhance text retrieval systems by combining BM25 with embeddings in a hybrid approach. The evolution of search systems, particularly with the introduction of RAG, has challenged the traditional assumptions underlying BM25's effectiveness. While SPLADE emerged as an alternative, it comes with drawbacks such as inappropriate tokenization, domain dependency, and high computational costs. In response, Qdrant proposes a solution that integrates the simplicity of BM25 with the intelligence of transformers, focusing on leveraging IDF calculations and attention matrices from transformer models. By utilizing transformer outputs to extract semantic information and addressing tokenization issues, BM42 aims to improve retrieval quality without the limitations of SPLADE. This approach allows for efficient inference and adaptability to various domains, making it a promising advancement in lexical search systems.
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