文本块大小对检索增强生成性能的影响

中文摘要 AI 辅助

检索增强生成（RAG）系统已成为一种强大的过程，使大语言模型在文本生成时能检索相关信息作为源材料。这些系统中一个关键但未充分探索的组件是源文档被分割成可检索块的粒度。块大小可能显著影响生成质量、上下文正确性、检索精度和计算效率。本文评估块大小以及检索段数量如何影响生成质量和检索效果，通过比较这些配置，试图更好地理解文档分割如何影响检索增强生成系统的性能和效率。

英文摘要

Retrieval-Augmented Generation (RAG) systems have emerged as a powerful process for allowing large language models (LLMs) to retrieve relevant information to use as source material during text generation. A critical yet under-explored component of these systems is the granularity at which source documents are segmented into retrievable chunks. The size of these chunks has the potential to significantly influence generation quality, contextual correctness, retrieval precision, and computational efficiency. Despite its importance, chunk size is often selected without proper evaluation of its impact on generation quality. Smaller chunks, such as individual sentences, may allow for precise retrieval by narrowing the focus of each chunk. However, they contain less information, which may limit the model's ability to generate coherent responses. Larger chunks, such as entire chapters, contain lots of broad information that may improve correctness, but also introduce additional noise and increase computational cost. Because larger chunks contain more information, the number of chunks returned to the model must also be considered. This paper evaluates how chunk size, along with the number of retrieved segments, influences generation quality and retrieval effectiveness. By comparing these configurations, this study seeks to better understand how document segmentation affects the performance and efficiency of Retrieval-Augmented Generation systems. segmentation affects the performance and efficiency of Retrieval-Augmented Generation systems.

发表机构

• Milwaukee School of Engineering（密尔沃基工程学院）

机构由 AI 辅助整理，请以论文原文为准。