@inproceedings{huang-etal-2025-retrieval,
    title = "Retrieval-Augmented Generation with Hierarchical Knowledge",
    author = "Huang, Haoyu  and
      Huang, Yongfeng  and
      Junjie, Yang  and
      Pan, Zhenyu  and
      Chen, Yongqiang  and
      Ma, Kaili  and
      Chen, Hongzhi  and
      Cheng, James",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.321/",
    doi = "10.18653/v1/2025.findings-emnlp.321",
    pages = "6044--6060",
    ISBN = "979-8-89176-335-7",
    abstract = "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."
}