系统梳理大模型查询优化技术，构建全流程框架与分类体系。
A Survey of Query Optimization in Large Language Models
- 提出五阶段查询优化生命周期框架，覆盖从意图识别到生成的全过程。
- 建立查询复杂度分类体系，按证据类型与数量匹配优化策略。
- 剖析四大核心操作方法，为实际应用提供可落地的技术指南。
查询优化（QO）已成为提升大语言模型（LLM）效能的关键，尤其在检索增强生成（RAG）系统中，查询质量直接影响检索与生成效果。本文系统综述了查询优化技术，主要贡献包括：首先，提出查询优化生命周期（QOL）框架，包含意图识别、查询转换、检索执行、证据整合和响应合成五个阶段，为理解优化过程提供统一视角；其次，构建查询复杂度分类体系，从证据类型（显式/隐式）和证据数量（单个/多个）两个维度对查询进行分类，建立查询特征与优化策略间的映射关系；第三，深入分析查询扩展、分解、消歧和抽象四种原子操作，整合顶会代表性方法。此外，还评估现有评测方法，指出基准缺失等关键问题，并探讨过程奖励模型、效率优化及多模态查询处理等开放挑战。本综述为研究者提供结构化基础，也为从业者提供实践指导。
原文摘要 · Abstract (English)
Query Optimization (QO) has become essential for enhancing Large Language Model (LLM) effectiveness, particularly in Retrieval-Augmented Generation (RAG) systems where query quality directly determines retrieval and response performance. This survey provides a systematic and comprehensive analysis of query optimization techniques with three principal contributions. \textit{First}, we introduce the \textbf{Query Optimization Lifecycle (QOL) Framework}, a five-phase pipeline covering Intent Recognition, Query Transformation, Retrieval Execution, Evidence Integration, and Response Synthesis, providing a unified lens for understanding the optimization process. \textit{Second}, we propose a \textbf{Query Complexity Taxonomy} that classifies queries along two dimensions, namely evidence type (explicit vs.\ implicit) and evidence quantity (single vs.\ multiple), establishing principled mappings between query characteristics and optimization strategies. \textit{Third}, we conduct an in-depth analysis of four atomic operations, namely \textbf{Query Expansion}, \textbf{Query Decomposition}, \textbf{Query Disambiguation}, and \textbf{Query Abstraction}, synthesizing a broad spectrum of representative methods from premier venues. We further examine evaluation methodologies, identify critical gaps in existing benchmarks, and discuss open challenges including process reward models, efficiency optimization, and multi-modal query handling. This survey offers both a structured foundation for research and actionable guidance for practitioners.
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