Boolean-aware Attention for Dense Retrieval
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Abstract (arXiv)
We present Boolean-aware attention, a novel attention mechanism that dynamically adjusts token focus based on Boolean operators (e.g., and, or, not). Our model employs specialized Boolean experts, each tailored to amplify or suppress attention for operator-specific contexts. A predefined gating mechanism activates the corresponding experts based on the detected Boolean type. Experiments on Boolean retrieval datasets demonstrate that integrating BoolAttn with BERT greatly enhances the model's capability to process Boolean queries.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 10 of its 19 references that are in the map. First posted on arXiv 3 Mar 2025.
Builds on
10 of 19 references.
References · 10
- 2017Attention Is All You Needcited by 59,932
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
- 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
- 2017FiLM: Visual Reasoning with a General Conditioning Layercited by 2,286
- 2023QUEST: A Retrieval Dataset of Entity-Seeking Queries with Implicit Set Operationscited by 30
- 2024BoolQuestions: Does Dense Retrieval Understand Boolean Logic in Language?cited by 6
- 2024SetBERT: Enhancing Retrieval Performance for Boolean Logic and Set Operation Queriescited by 5
- 2024NeIn: Telling What You Don't Wantcited by 3
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