XRAG：深入核心——在高级检索增强生成中评估基础组件
XRAG: eXamining the Core -- Benchmarking Foundational Components in Advanced Retrieval-Augmented Generation
AI总结：
XRAG通过系统分析RAG各核心阶段性能，提供全面基准，揭示系统潜在故障点并提出优化方案。
摘要：
检索增强生成（RAG）结合了相关数据检索与大语言模型（LLMs）的生成能力，确保生成输出在上下文相关性和准确性方面均达标。我们引入XRAG，一个开源模块化代码库，用于对高级RAG模块的基础组件进行详尽评估。这些组件被系统地分为四个核心阶段：预检索、检索、后检索和生成。我们通过重新配置的数据集对它们进行系统分析，提供全面的基准评估。随着RAG系统的复杂性持续增加，我们强调识别RAG系统潜在故障点的必要性。我们制定了一系列实验方法和诊断测试协议，以剖析RAG工程中的固有故障点。随后，我们提出定制解决方案，以提升这些模块的整体性能。我们的工作全面评估了RAG系统中高级核心组件的性能，提供了对常见故障点优化的见解。
英文摘要：
Retrieval-augmented generation (RAG) synergizes the retrieval of pertinent data with the generative capabilities of Large Language Models (LLMs), ensuring that the generated output is not only contextually relevant but also accurate and current. We introduce XRAG, an open-source, modular codebase that facilitates exhaustive evaluation of the performance of foundational components of advanced RAG modules. These components are systematically categorized into four core phases: pre-retrieval, retrieval, post-retrieval, and generation. We systematically analyse them across reconfigured datasets, providing a comprehensive benchmark for their effectiveness. As the complexity of RAG systems continues to escalate, we underscore the critical need to identify potential failure points in RAG systems. We formulate a suite of experimental methodologies and diagnostic testing protocols to dissect the failure points inherent in RAG engineering. Subsequently, we proffer bespoke solutions aimed at bolstering the overall performance of these modules. Our work thoroughly evaluates the performance of advanced core components in RAG systems, providing insights into optimizations for prevalent failure points.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
