@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://preview.aclanthology.org/display_plenaries/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."
}