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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.10150 |
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| _version_ | 1866918148469424128 |
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| author | Huang, Haoyu Huang, Yongfeng Yang, Junjie Pan, Zhenyu Chen, Yongqiang Ma, Kaili Chen, Hongzhi Cheng, James |
| author_facet | Huang, Haoyu Huang, Yongfeng Yang, Junjie Pan, Zhenyu Chen, Yongqiang Ma, Kaili Chen, Hongzhi Cheng, James |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_10150 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Retrieval-Augmented Generation with Hierarchical Knowledge Huang, Haoyu Huang, Yongfeng Yang, Junjie Pan, Zhenyu Chen, Yongqiang Ma, Kaili Chen, Hongzhi Cheng, James Computation and Language Artificial Intelligence 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. |
| title | Retrieval-Augmented Generation with Hierarchical Knowledge |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2503.10150 |