KG-Retriever: Efficient Knowledge Indexing for Retrieval-Augmented Large Language Models

• URL: http://arxiv.org/abs/2412.05547v1
• Date: Sat, 07 Dec 2024 05:49:14 GMT
• Title: KG-Retriever: Efficient Knowledge Indexing for Retrieval-Augmented Large Language Models
• Authors: Weijie Chen, Ting Bai, Jinbo Su, Jian Luan, Wei Liu, Chuan Shi,
• Abstract summary: We introduce a novel Knowledge Graph-based RAG framework with a hierarchical knowledge retriever, termed KG-Retriever. The associative nature of graph structures is fully utilized to strengthen intra-document and inter-document connectivity. 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.
• Score: 38.93603907879804
• License:
• Abstract: 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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