Neuro-Symbolic Query Compiler

• URL: http://arxiv.org/abs/2505.11932v1
• Date: Sat, 17 May 2025 09:36:03 GMT
• Title: Neuro-Symbolic Query Compiler
• Authors: Yuyao Zhang, Zhicheng Dou, Xiaoxi Li, Jiajie Jin, Yongkang Wu, Zhonghua Li, Qi Ye, Ji-Rong Wen,
• Abstract summary: This paper presents QCompiler, a neuro-symbolic framework inspired by linguistic grammar rules and compiler design, to bridge this gap.<n>It theoretically designs a minimal yet sufficient Backus-Naur Form (BNF) grammar $G[q]$ to formalize complex queries.<n>The atomicity of the sub-queries in the leaf ensures more precise document retrieval and response generation, significantly improving the RAG system's ability to address complex queries.
• Score: 57.78201019000895
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 designs 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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