用分层知识增强检索生成，提升大模型理解与结构捕捉能力。
Retrieval-Augmented Generation with Hierarchical Knowledge
- 构建分层知识图谱，优化索引与检索中的语义理解。
- 在多个领域任务上超越现有最佳基线方法。
- 适合需要深层知识推理的智能问答与专业领域应用。
基于图的检索增强生成（RAG）方法显著提升了大语言模型在特定领域任务中的表现。然而，现有RAG方法未能充分利用人类认知中天然存在的分层知识，限制了RAG系统的潜力。本文提出一种新RAG方法HiRAG，利用分层知识增强索引与检索过程中的语义理解与结构捕捉能力。大量实验表明，HiRAG在多个基准测试中显著优于当前最优基线方法。
原文摘要 · Abstract (English)
Graph-based Retrieval-Augmented Generation (RAG) methods have significantly enhanced the performance of large language models (LLMs) in domain-specific tasks. However, existing RAG methods do not adequately utilize the naturally inherent hierarchical knowledge in human cognition, which limits the capabilities of RAG systems. In this paper, we introduce a new RAG approach, called HiRAG, which utilizes hierarchical knowledge to enhance the semantic understanding and structure capturing capabilities of RAG systems in the indexing and retrieval processes. Our extensive experiments demonstrate that HiRAG achieves significant performance improvements over the state-of-the-art baseline methods.
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