检索增强生成（Retrieval Augmented Generation, RAG）是一种在不修改底层模型参数的前提下，为大语言模型（Large Language Models, LLMs）补充上下文相关、时效性强或领域特定信息的技术。然而，构建能够有效综合大量多样化文档信息的RAG系统仍是一项重大挑战。我们提出了一种面向大语言模型的、以数据为中心的新型RAG工作流，将传统的“检索-阅读”系统改造为更先进的“准备-改写-检索-阅读”框架，以实现对知识库达到领域专家级的深度理解。我们的方法核心在于为每份文档生成元数据与合成问答对（Questions and Answers, QA），同时提出元知识摘要（Meta Knowledge Summary, MK Summary）这一新概念，用于处理基于元数据聚类的文档组。这些创新点可实现个性化的用户查询增强，以及跨知识库的深度信息检索。本研究有两项重要贡献：通过以大语言模型作为评估器并采用新的对比性能指标，我们证明：（1）使用结合合成问题匹配的增强查询，性能显著优于依赖文档分块的传统RAG流水线（p < 0.01）；（2）元知识增强的查询可进一步显著提升检索的精确率和召回率，同时提升最终答案的广度、深度、相关性和特异性。我们的方法具备成本效益，使用Claude 3 Haiku处理每2000篇研究论文的成本低于20美元，且可适配语言模型或嵌入模型的任意微调，以进一步提升端到端RAG流水线的性能。
英文摘要：
Retrieval Augmented Generation (RAG) is a technique used to augment Large Language Models (LLMs) with contextually relevant, time-critical, or domain-specific information without altering the underlying model parameters. However, constructing RAG systems that can effectively synthesize information from large and diverse set of documents remains a significant challenge. We introduce a novel data-centric RAG workflow for LLMs, transforming the traditional retrieve-then-read system into a more advanced prepare-then-rewrite-then-retrieve-then-read framework, to achieve higher domain expert-level understanding of the knowledge base. Our methodology relies on generating metadata and synthetic Questions and Answers (QA) for each document, as well as introducing the new concept of Meta Knowledge Summary (MK Summary) for metadata-based clusters of documents. The proposed innovations enable personalized user-query augmentation and in-depth information retrieval across the knowledge base. Our research makes two significant contributions: using LLMs as evaluators and employing new comparative performance metrics, we demonstrate that (1) using augmented queries with synthetic question matching significantly outperforms traditional RAG pipelines that rely on document chunking (p < 0.01), and (2) meta knowledge-augmented queries additionally significantly improve retrieval precision and recall, as well as the final answers breadth, depth, relevancy, and specificity. Our methodology is cost-effective, costing less than $20 per 2000 research papers using Claude 3 Haiku, and can be adapted with any fine-tuning of either the language or embedding models to further enhance the performance of end-to-end RAG pipelines.
LevelRAG: Enhancing Retrieval-Augmented Generation with Multi-hop Logic Planning over Rewriting Augmented Searchers
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Chinese Academy of Sciences（中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）；Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））
A Large Language Model-based Framework for Semi-Structured Tender Document Retrieval-Augmented Generation
School of Information Management Sun Yat-sen University Guangzhou, China；School of Information Management Sun Yat-sen University Guangzhou, China corresponding author
IRSC: A Zero-shot Evaluation Benchmark for Information Retrieval through Semantic Comprehension in Retrieval-Augmented Generation Scenarios
PengCheng Laboratory（鹏城实验室）；Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Southern University of Science and Technology（南方科技大学）
Retrieval-Augmented Large Language Models for Evidence-Informed Guidance on Cannabidiol Use in Older Adults
Lawrence Bloomberg Faculty of Nursing, University of Toronto（多伦多大学劳伦斯·布隆伯格护理学院）；KITE Research Institute, Toronto Rehabilitation Institute, University Health Network（大学健康网络多伦多康复研究所KITE研究所）；College of Engineering and Technology, American University of the Middle East（中东美国大学工程与技术学院）
CausalRAG: Integrating Causal Graphs into Retrieval-Augmented Generation
Department of Computer and Data Sciences, Case Western Reserve University（计算机与数据科学系，凯斯西储大学）；Department of Design and Innovation, Case Western Reserve University（设计与创新系，凯斯西储大学）
Long Context Modeling with Ranked Memory-Augmented Retrieval
University of New South Wales（新南威尔士大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；University of Southampton（南安普顿大学）；Mohamed Bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
