# BMX: Entropy-weighted Similarity and Semantic-enhanced Lexical Search

arXiv: https://arxiv.org/abs/2408.06643 · HTML: https://mlsanity.com/paper/2408.06643/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Aug 2024.

## Built on by (most cited first, 7 of 7)

- [Evaluating Memory in LLM Agents via Incremental Multi-Turn Interactions](https://mlsanity.com/paper/2507.05257/) (2025)
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- [Re-identification of De-identified Documents with Autoregressive Infilling](https://mlsanity.com/paper/2505.12859/) (2025)
- [Advances and Challenges in Semantic Textual Similarity: A Comprehensive Survey](https://mlsanity.com/paper/2601.03270/) (2025)
- [Nyay-Darpan: Enhancing Decision Making Through Summarization and Case Retrieval for Consumer Law in India](https://mlsanity.com/paper/2507.06090/) (2025)
- [Stronger Re-identification Attacks through Reasoning and Aggregation](https://mlsanity.com/paper/2510.09184/) (2025)
- [Self-MedRAG: a Self-Reflective Hybrid Retrieval-Augmented Generation Framework for Reliable Medical Question Answering](https://mlsanity.com/paper/2601.04531/) (2026)

## Abstract (arXiv)

BM25, a widely-used lexical search algorithm, remains crucial in information retrieval despite the rise of pre-trained and large language models (PLMs/LLMs). However, it neglects query-document similarity and lacks semantic understanding, limiting its performance. We revisit BM25 and introduce BMX, a novel extension of BM25 incorporating entropy-weighted similarity and semantic enhancement techniques. Extensive experiments demonstrate that BMX consistently outperforms traditional BM25 and surpasses PLM/LLM-based dense retrieval in long-context and real-world retrieval benchmarks. This study bridges the gap between classical lexical search and modern semantic approaches, offering a promising direction for future information retrieval research. The reference implementation of BMX can be found in Baguetter, which was created in the context of this work. The code can be found here: https://github.com/mixedbread-ai/baguetter.