mlsanityData to 7 Oct 2026

DREQ: Document Re-Ranking Using Entity-based Query Understanding

arXiv 2401.05939 ↗First posted 11 Jan 2024cs.IR cs.AIcited by 5

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Abstract (arXiv)

While entity-oriented neural IR models have advanced significantly, they often overlook a key nuance: the varying degrees of influence individual entities within a document have on its overall relevance. Addressing this gap, we present DREQ, an entity-oriented dense document re-ranking model. Uniquely, we emphasize the query-relevant entities within a document's representation while simultaneously attenuating the less relevant ones, thus obtaining a query-specific entity-centric document representation. We then combine this entity-centric document representation with the text-centric representation of the document to obtain a "hybrid" representation of the document. We learn a relevance score for the document using this hybrid representation. Using four large-scale benchmarks, we show that DREQ outperforms state-of-the-art neural and non-neural re-ranking methods, highlighting the effectiveness of our entity-oriented representation approach.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 35 of its 74 references that are in the map. First posted on arXiv 11 Jan 2024.

Builds on

35 of 74 references.

References · 35

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23 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 1.

5 papers.

  1. 2025DailyQA: A Benchmark to Evaluate Web Retrieval Augmented LLMs Based on Capturing Real-World Changescited by 3
  2. 2025VeritasFi: An Adaptable, Multi-tiered RAG Framework for Multi-modal Financial Question Answeringcited by 3
  3. 2025QDER: Query-Specific Document and Entity Representations for Multi-Vector Document Re-Rankingcited by 1
  4. 2026Entity Labels Are Not Entity Signals: A Framework for Observable Relevance in Document Re-Rankingcited by 1
  5. 2025Dynamic Injection of Entity Knowledge into Dense Retrieverscited by 0