BM𝒳: A Freshly Baked Take on BM25

Mixedbread and Hong Kong Polytechnic University researchers introduced BMX, an open-source lexical search algorithm available through the Baguetter library that aims to improve on BM25 while retaining the efficiency and generalization strengths of keyword-based retrieval. BMX adds entropy-weighted similarity to emphasize informative, less common query terms and uses weighted query augmentation to incorporate semantic variations in a single retrieval process without separate reranking. Evaluations on BEIR found BMX outperformed BM25 variants on 11 of 15 datasets, while BMX with weighted query augmentation achieved the strongest average result on the reasoning-intensive BRIGHT benchmark, surpassing the compared lexical, embedding, and proprietary models. Additional tests in Chinese, Japanese, Korean, German, and French showed consistent gains over BM25. The researchers argue that BMX can improve search quality and downstream NLP pipelines without the large training datasets or computational costs commonly associated with embedding-based semantic search.

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