Neuro-Symbolic Query Compiler
Authors: name: Yuyao Zhang, name: Zhicheng Dou, name: Xiaoxi Li, name: Jiajie Jin, name: Yongkang Wu, name: Zhonghua Li, name: Qi Ye, name: Ji-Rong Wen
Organization: Renmin University of China

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.