Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

Authors: Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela (2020)

arXiv: 2005.11401

TLDR (English)

RAG (Retrieval-Augmented Generation) combines pretrained LMs with information retrieval: for each query, retrieve relevant documents from a knowledge base, then generate answers with the documents in context. This addresses LLM knowledge staleness and hallucination, and is now a core architecture in enterprise AI applications.

TLDR（中文）

RAG（检索增强生成）将预训练语言模型与信息检索系统结合：对于每个查询，先从知识库 检索相关文档，再将文档拼接进上下文后生成答案。这解决了语言模型知识过期和 幻觉问题的一大途径，是今天企业 AI 应用的核心架构之一。