@inproceedings{zhang-etal-2025-neuro,
title = "Neuro-Symbolic Query Compiler",
author = "Zhang, Yuyao and
Dou, Zhicheng and
Li, Xiaoxi and
Jin, Jiajie and
Wu, Yongkang and
Li, Zhonghua and
Qi, Ye and
Wen, Ji-Rong",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.628/",
doi = "10.18653/v1/2025.findings-acl.628",
pages = "12138--12155",
ISBN = "979-8-89176-256-5",
abstract = "Precise recognition of search intent in Retrieval-Augmented Generation (RAG) systems remains a challenging goal, especially under resource constraints and for complex queries with nested structures and dependencies. This paper presents **QCompiler**, a neuro-symbolic framework inspired by linguistic grammar rules and compiler design, to bridge this gap. It theoretically presents a minimal yet sufficient Backus-Naur Form (BNF) grammar $G[q]$ to formalize complex queries. Unlike previous methods, this grammar maintains completeness while minimizing redundancy. Based on this, QCompiler includes a query expression translator, a Lexical syntax parser, and a Recursive Descent Processor to compile queries into Abstract Syntax Trees (ASTs) for execution. The atomicity of the sub-queries in the leaf nodes ensures more precise document retrieval and response generation, significantly improving the RAG system{'}s ability to address complex queries."
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<abstract>Precise recognition of search intent in Retrieval-Augmented Generation (RAG) systems remains a challenging goal, especially under resource constraints and for complex queries with nested structures and dependencies. This paper presents **QCompiler**, a neuro-symbolic framework inspired by linguistic grammar rules and compiler design, to bridge this gap. It theoretically presents a minimal yet sufficient Backus-Naur Form (BNF) grammar G[q] to formalize complex queries. Unlike previous methods, this grammar maintains completeness while minimizing redundancy. Based on this, QCompiler includes a query expression translator, a Lexical syntax parser, and a Recursive Descent Processor to compile queries into Abstract Syntax Trees (ASTs) for execution. The atomicity of the sub-queries in the leaf nodes ensures more precise document retrieval and response generation, significantly improving the RAG system’s ability to address complex queries.</abstract>
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%0 Conference Proceedings
%T Neuro-Symbolic Query Compiler
%A Zhang, Yuyao
%A Dou, Zhicheng
%A Li, Xiaoxi
%A Jin, Jiajie
%A Wu, Yongkang
%A Li, Zhonghua
%A Qi, Ye
%A Wen, Ji-Rong
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F zhang-etal-2025-neuro
%X Precise recognition of search intent in Retrieval-Augmented Generation (RAG) systems remains a challenging goal, especially under resource constraints and for complex queries with nested structures and dependencies. This paper presents **QCompiler**, a neuro-symbolic framework inspired by linguistic grammar rules and compiler design, to bridge this gap. It theoretically presents a minimal yet sufficient Backus-Naur Form (BNF) grammar G[q] to formalize complex queries. Unlike previous methods, this grammar maintains completeness while minimizing redundancy. Based on this, QCompiler includes a query expression translator, a Lexical syntax parser, and a Recursive Descent Processor to compile queries into Abstract Syntax Trees (ASTs) for execution. The atomicity of the sub-queries in the leaf nodes ensures more precise document retrieval and response generation, significantly improving the RAG system’s ability to address complex queries.
%R 10.18653/v1/2025.findings-acl.628
%U https://aclanthology.org/2025.findings-acl.628/
%U https://doi.org/10.18653/v1/2025.findings-acl.628
%P 12138-12155
Markdown (Informal)
[Neuro-Symbolic Query Compiler](https://aclanthology.org/2025.findings-acl.628/) (Zhang et al., Findings 2025)
ACL
- Yuyao Zhang, Zhicheng Dou, Xiaoxi Li, Jiajie Jin, Yongkang Wu, Zhonghua Li, Ye Qi, and Ji-Rong Wen. 2025. Neuro-Symbolic Query Compiler. In Findings of the Association for Computational Linguistics: ACL 2025, pages 12138–12155, Vienna, Austria. Association for Computational Linguistics.