缓解大语言模型（LLMs）中的幻觉问题：面向应用的RAG、推理与智能体系统综述
Mitigating Hallucination in Large Language Models: A Capability-Oriented Survey on RAG, Reasoning, and Agentic Systems
AI总结：
该综述从应用导向的能力增强视角出发，提出基于知识与逻辑的幻觉分类体系，系统分析RAG、推理增强及二者在智能体系统中的整合对幻觉的缓解作用，构建结合实际应用与基准的统一框架。
摘要：
幻觉问题仍是大语言模型（LLMs）可靠部署的核心障碍之一，在实际应用场景中尤为突出。在各类缓解策略中，检索增强生成（Retrieval-Augmented Generation，RAG）与推理增强已成为效果最显著、应用最广泛的两类方法，标志着相关研究从单纯抑制幻觉转向平衡创造力与可靠性。但二者在幻觉缓解上的协同潜力与底层机制尚未得到系统性梳理。本综述从能力增强的应用导向视角出发，分析RAG、推理增强以及二者在Agentic Systems（智能体系统）中的整合方式如何缓解幻觉。我们提出区分基于知识的幻觉与基于逻辑的幻觉的分类体系，系统考察RAG与推理分别应对两类幻觉的机制，并结合实际应用、评估方法与基准数据集呈现一个统一框架。
英文摘要：
Hallucination remains one of the key obstacles to the reliable deployment of large language models (LLMs). Although various mitigation approaches have been proposed, existing studies often analyze different technical paradigms independently, lacking a unified perspective to understand the underlying mechanisms of different approaches and their correspondence with different types of hallucinations. This survey adopts a capability enhancement perspective to systematically examine hallucination mitigation approaches, focusing on Retrieval-Augmented Generation (RAG), reasoning enhancement, and their integration within agentic systems. Based on their primary mitigation mechanisms, we categorize hallucinations into knowledge-based hallucinations and logic-based hallucinations, analyze how RAG and reasoning enhancement methods respectively improve knowledge acquisition and reasoning reliability, and further discuss the integration mechanisms of retrieval and reasoning capabilities in Agentic Systems for mitigating composite hallucinations. By considering the applicability, mitigation mechanisms, and limitations of different approaches, this survey establishes a unified analytical framework connecting hallucination types, key capability dimensions, and technical paradigms.
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