LARAG：超链接技术文档中基于链接的RAG系统检索策略
LARAG: Link-Aware Retrieval Strategy for RAG Systems in Hyperlinked Technical Documentation
AI总结：
LARAG通过利用HTML文档中已有的超链接结构，改进RAG系统检索效果，提升答案质量并降低资源消耗。
摘要：
检索增强生成（RAG）通过将大型语言模型的输出与外部文档结合，增强其事实准确性。然而，标准基于嵌入的检索器将自然结构化的语料库（如技术手册）视为片段的扁平集合，忽略了用户在导航此类内容时依赖的超链接拓扑。我们引入LARAG（基于链接的RAG）：一种轻量级、基于链接的检索策略，利用HTML文档中已有的超链接结构，在片段表示中编码超链接关系作为元数据，并利用它们执行一种类似图状的局部相关内容检索。在对Rulex平台技术文档的二十个专家设计查询和四种提示策略的基准测试中，LARAG始终提升答案质量，实现最高的BERTScore F1，同时检索的片段更少且生成的token更少，相较于比较用的基线RAG架构。这些结果表明，直接利用技术文档中已有的超链接拓扑（即使没有显式图构建或推理），能够实现一种隐式的图状检索形式，从而提供更准确且高效的RAG流程，以更低的成本实现更好的事实基础。
英文摘要：
Retrieval-Augmented Generation (RAG) enhances the factual grounding of Large Language Models by conditioning their outputs on external documents. However, standard embedding-based retrievers treat naturally structured corpora, such as technical manuals, as flat collections of passages, thereby overlooking the hyperlink topology that users rely on when navigating such content. We introduce LARAG (Link-Aware RAG): a lightweight, link-aware retrieval strategy that leverages the author-defined hyperlink structure already present in HTML documentation, encoding hyperlink relations as metadata in the chunk representations and exploiting them to perform a form of graph-like retrieval of locally relevant content. In a benchmark of twenty expert-designed queries over Rulex Platform technical documentation and four prompting strategies, LARAG consistently improves answer quality, achieving the highest BERTScore F1, while retrieving fewer chunks and generating fewer tokens than a baseline RAG architecture used for comparison. These results show that directly leveraging the existing hyperlink topology of technical documentation, even without explicit graph construction or inference, enables an implicit form of graph-like retrieval that yields a more faithful and efficient RAG pipeline, providing better grounding at lower cost.
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。
