Retrieval-Augmented Generation (RAG) is a technique that enhances the capabilities of large language models (LLMs) by incorporating external knowledge sources. This method addresses common LLM limitations, including outdated information and the tendency to produce inaccurate "hallucinated" content. However, the evaluation of RAG systems is challenging, as existing benchmarks are limited in scope and diversity. Most of the current benchmarks predominantly assess question-answering applications, overlooking the broader spectrum of situations where RAG could prove advantageous. Moreover, they only evaluate the performance of the LLM component of the RAG pipeline in the experiments, and neglect the influence of the retrieval component and the external knowledge database. To address these issues, this paper constructs a large-scale and more comprehensive benchmark, and evaluates all the components of RAG systems in various RAG application scenarios. Specifically, we have categorized the range of RAG applications into four distinct types-Create, Read, Update, and Delete (CRUD), each representing a unique use case. "Create" refers to scenarios requiring the generation of original, varied content. "Read" involves responding to intricate questions in knowledge-intensive situations. "Update" focuses on revising and rectifying inaccuracies or inconsistencies in pre-existing texts. "Delete" pertains to the task of summarizing extensive texts into more concise forms. For each of these CRUD categories, we have developed comprehensive datasets to evaluate the performance of RAG systems. We also analyze the effects of various components of the RAG system, such as the retriever, the context length, the knowledge base construction, and the LLM. Finally, we provide useful insights for optimizing the RAG technology for different scenarios.

翻译：检索增强生成（RAG）是一种通过整合外部知识源来增强大语言模型（LLM）能力的技术。该方法解决了LLM的常见局限，包括信息过时和易产生不准确的“幻觉”内容。然而，RAG系统的评估具有挑战性，因为现有基准在范围和多样性上均存在不足。当前大多数基准主要评估问答应用，忽视了RAG可能发挥优势的更广泛场景。此外，它们在实验中仅评估RAG流程中LLM组件的性能，而忽略了检索组件和外部知识库的影响。为解决这些问题，本文构建了一个大规模且更全面的基准，并在多种RAG应用场景下评估了RAG系统的所有组件。具体而言，我们将RAG应用范围划分为四种独特类型——创建、读取、更新与删除（CRUD），每种类型代表一种特定的用例。“创建”指需要生成原创性、多样化内容的场景；“读取”涉及在知识密集型情境中回答复杂问题；“更新”侧重于修正既有文本中的错误或不一致之处；“删除”则关乎将冗长文本提炼为更简洁形式的任务。针对每个CRUD类别，我们构建了综合数据集以评估RAG系统的性能。我们还分析了RAG系统各组件（如检索器、上下文长度、知识库构建方式及LLM）的影响。最后，我们为不同场景下优化RAG技术提供了有益的见解。