Modern information retrieval must reconcile short, ambiguous queries with increasingly diverse and dynamic corpora. Query expansion (QE) remains a core technique for mitigating vocabulary mismatch, but its design space has been reshaped by pre-trained and large language models (PLMs/LLMs). This survey reviews QE methods in the PLM/LLM era and provides a unified view of the emerging landscape. We first summarize how different model families enable new expansion behaviors, including stronger contextualization, more controllable generation, and instruction-following. We then organize recent techniques along four complementary design dimensions: where expansion is injected in the pipeline, how it is grounded and interacts with corpus evidence, how it is learned or aligned, and how structured knowledge such as knowledge graphs is incorporated. Beyond taxonomy, we synthesize application patterns and deployment considerations across representative retrieval settings, highlighting practical trade-offs among effectiveness, controllability, grounding quality, and operating cost. Finally, we outline open challenges and future directions toward more reliable, safe, efficient, and continually adaptive QE under real-world constraints.

翻译：现代信息检索必须在简短模糊的查询与日益多样化和动态的语料库之间达成平衡。查询扩展（QE）作为缓解词汇匹配问题的核心技术，其设计空间已被预训练模型和大型语言模型（PLM/LLM）重新塑造。本综述系统梳理了PLM/LLM时代的查询扩展方法，并构建了这个新兴领域的统一框架。我们首先总结不同模型架构如何催生新的扩展行为，包括更强的上下文语义建模、更可控的文本生成能力以及指令遵循特性。进而沿四个互补性设计维度组织最新技术：扩展在检索流程中的注入位置、如何基于语料证据进行验证与交互、扩展策略的学习或对齐方式，以及知识图谱等结构化知识的融合机制。在分类体系之外，我们综合分析了典型检索场景中的应用模式与部署考量，揭示了效果、可控性、验证质量与运行成本之间的实际权衡。最后，围绕真实世界约束环境下更可靠、安全、高效且持续自适应的查询扩展，我们提出待解难题与未来研究方向。