TSR Desk · science · 28 October 2026, 01:00 UTC
What the Query Remembers: Query Abstraction Governs Lexical and Dense Retrieval of Classical
- What
- What the Query Remembers: Query Abstraction Governs Lexical and Dense Retrieval of Classical Arabic Poetry
- Who
- arxiv.org
- When
- 27 October 2026, 04:00 UTC
- Category
- Science
- Primary source
- https://arxiv.org/abs/2610.31821
- What is not known
- This brief does not claim independent replication. Claims that appear only on X and not in the primary source stay unknown.
On tip-of-the-tongue queries BM25 leads every dense encoder, including an 8B state-of-the-art model (0.746 vs. It comes from a paper posted to arXiv on 27 October 2026. How well a poetry archive can be searched depends on what the user still remembers, not only on what the model understands. We show this for Classical Arabic poetry, benchmarking nine dense encoders and BM25 over two corpora, three indexing modes and two evaluation protocols. Which retriever wins is governed by how much of a poem's wording the query still carries. 0.717 Recall@5); on abstract queries it falls to eighth out of ten (0.392 vs. 0.748). Editing the wording of the same queries shows that this ranking largely reflects query composition: adding one remembered half-verse raises BM25 by 38 points, rewriting the quoted words by hand leaves BM25 level with the 8B encoder, and BM25's sensitivity to remembered wording is significantly steeper than that of the strongest dense encoder. The same axis controls explanation augmentation, whose effect swings from +37 to -39 points. The mechanism splits by system type: lexical containment predicts BM25's behaviour, while embedding proximity predicts dense encoders. Finally, the field's two standard protocols, known-item retrieval and thematic relevance, dissociate on the same systems, a result reproduced by two blind human annotators and by two open-weights judges, one of them under a rubric with no literal-match criterion. A benchmark that reports one protocol, or one query register, can give a materially wrong picture. Code and data are available at https://github.com/Ellzo/what-the-query-remembers.
Why it counts
On tip-of-the-tongue queries BM25 leads every dense encoder, including an 8B state-of-the-art model (0.746 vs.
Sources
Primary source: primary source
What is not known
This brief does not claim independent replication. Claims that appear only on X and not in the primary source stay unknown.
No clip. The article still stands.