用知识图谱提升大模型跨文档检索效率，解决多跳问答信息碎片化问题。
KG-Retriever: Efficient Knowledge Indexing for Retrieval-Augmented Large Language Models
- 构建分层索引图，融合知识图谱与文档协作信息
- 在五个公开数据集上显著提升多跳问答效果
- 适合需要高效跨文档推理的AI应用开发
具有检索增强生成的大语言模型在复杂检索任务（如多跳问答）中面临挑战，需在多个文档间导航并基于零散信息生成完整回答。为此，我们提出一种基于知识图谱的RAG框架——KG-Retriever，其检索索引采用包含知识图谱层和协同文档层的分层索引图。充分利用图结构的关联特性，强化文档内与文档间的连接性，从根本上缓解信息碎片化问题，并提升大模型跨文档检索效率。借助邻近文档的粗粒度协同信息与知识图谱的简洁信息，KG-Retriever在五个公开问答数据集上取得显著改进，验证了所提RAG框架的有效性与高效性。
原文摘要 · Abstract (English)
Large language models with retrieval-augmented generation encounter a pivotal challenge in intricate retrieval tasks, e.g., multi-hop question answering, which requires the model to navigate across multiple documents and generate comprehensive responses based on fragmented information. To tackle this challenge, we introduce a novel Knowledge Graph-based RAG framework with a hierarchical knowledge retriever, termed KG-Retriever. The retrieval indexing in KG-Retriever is constructed on a hierarchical index graph that consists of a knowledge graph layer and a collaborative document layer. The associative nature of graph structures is fully utilized to strengthen intra-document and inter-document connectivity, thereby fundamentally alleviating the information fragmentation problem and meanwhile improving the retrieval efficiency in cross-document retrieval of LLMs. With the coarse-grained collaborative information from neighboring documents and concise information from the knowledge graph, KG-Retriever achieves marked improvements on five public QA datasets, showing the effectiveness and efficiency of our proposed RAG framework.
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