剖析检索增强生成的成败原因，揭示其在现代大模型中的新价值
When Retrieval Succeeds and Fails: Rethinking Retrieval-Augmented Generation for LLMs
- 系统梳理RAG的核心机制与目标，定位其在大模型时代的适用场景
- 指出传统RAG在面对快速演进信息时的瓶颈与失效环节
- 为需要精准知识的应用提供可信赖的RAG增强方案
大语言模型（LLMs）凭借强大的语言理解与生成能力，推动了众多应用的发展。然而，由于训练数据静态，它们难以应对快速变化的信息或特定领域问题。检索增强生成（RAG）通过结合外部检索机制，使模型能访问实时且上下文相关的知识，从而克服这一局限。但随着大模型自身能力不断增长，传统RAG的优势逐渐减弱。本文全面回顾RAG的初衷与核心组件，分析其面临的关键挑战，指出可能限制其效果的薄弱环节。同时，展示在仅靠大模型表现不佳的场景中，结合RAG可显著提升性能。希望本研究促使研究者重新思考RAG的角色，并推动下一代RAG系统的开发。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have enabled a wide range of applications through their powerful capabilities in language understanding and generation. However, as LLMs are trained on static corpora, they face difficulties in addressing rapidly evolving information or domain-specific queries. Retrieval-Augmented Generation (RAG) was developed to overcome this limitation by integrating LLMs with external retrieval mechanisms, allowing them to access up-to-date and contextually relevant knowledge. However, as LLMs themselves continue to advance in scale and capability, the relative advantages of traditional RAG frameworks have become less pronounced and necessary. Here, we present a comprehensive review of RAG, beginning with its overarching objectives and core components. We then analyze the key challenges within RAG, highlighting critical weakness that may limit its effectiveness. Finally, we showcase applications where LLMs alone perform inadequately, but where RAG, when combined with LLMs, can substantially enhance their effectiveness. We hope this work will encourage researchers to reconsider the role of RAG and inspire the development of next-generation RAG systems.
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