发表机构
Vellore Institute of Technology; Mohamed bin Zayed University of Artificial Intelligence（韦洛尔理工大学; 穆罕默德·本·扎耶德人工智能大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本综述提出四轴分类法（效率、防御、交互、推理），系统梳理RAG领域进展，涵盖检索优化、安全加固、交互工作流及复杂推理，并总结评估实践与未来挑战。
AI 中文摘要
大型语言模型（LLMs）在许多任务上表现出显著的流畅性，但仍受限于其静态的、参数约束的知识，以及容易产生幻觉信息的倾向。检索增强生成（RAG）通过将外部检索纳入生成过程，将模型输出基于可验证且最新的来源，从而解决了这些问题。虽然先前的综述主要关注核心RAG架构和标准流水线，但最近的研究探索了超越这些基础设计的更广泛的挑战和能力。本综述对当代RAG发展进行了整合和结构化的审视，将领域组织为四轴分类法：提高检索效率、增强鲁棒性和安全性、支持用户驱动的交互式工作流，以及实现多步骤或复杂推理。我们形式化了RAG框架的关键组成部分，并回顾了涵盖稠密和稀疏检索、融合策略、嵌入优化以及基于强化学习的检索策略的方法，强调了这些进展如何影响实际部署和系统设计。我们还综合了评估实践、特定领域的应用，以及诸如朴素RAG、高级RAG和模块化RAG等架构变体。最后，我们概述了与检索质量、可靠性、领域适应性、可扩展性和可解释性相关的持续挑战，并确定了构建更可靠、更适应和更透明的RAG系统的机会。
英文摘要
Large Language Models (LLMs) have demonstrated remarkable fluency and versatility across natural language tasks but remain fundamentally limited by their static knowledge and susceptibility to hallucinations, especially in domains requiring up to date or attribute grounded information. Retrieval Augmented Generation (RAG) addresses these challenges by integrating external retrieval mechanisms with generative models, enabling dynamic, context aware generation grounded in verifiable data sources. This survey presents a comprehensive examination of RAG as a modular and evolving paradigm that enhances factual reliability, adaptability, and task alignment in LLM based systems. We formalize the RAG framework through its three foundational components retrieval, generation, and augmentation and survey state of the art methods spanning dense and sparse retrievers, fusion strategies, embedding optimizations, and reinforcement learning based retrieval policies. Anchored around four emerging axes efficiency, security, user centric interactivity, and complex reasoning we categorize recent innovations and highlight their implications for scalability, robustness, and personalization. The paper also reviews advances in evaluation protocols, domain specific applications, and architectural variants such as Naïve RAG, Advanced RAG, and Modular RAG. Finally, we identify persistent challenges and outline future directions aimed at advancing the integration of retrieval with LLMs for more grounded, interpretable, and controllable generation.
