@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Retrieval-Augmented Generation with Hierarchical Knowledge
%A Huang, Haoyu
%A Huang, Yongfeng
%A Junjie, Yang
%A Pan, Zhenyu
%A Chen, Yongqiang
%A Ma, Kaili
%A Chen, Hongzhi
%A Cheng, James
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F huang-etal-2025-retrieval
%X 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.
%R 10.18653/v1/2025.findings-emnlp.321
%U https://aclanthology.org/2025.findings-emnlp.321/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.321
%P 6044-6060
Markdown (Informal)
[Retrieval-Augmented Generation with Hierarchical Knowledge](https://aclanthology.org/2025.findings-emnlp.321/) (Huang et al., Findings 2025)
ACL
- Haoyu Huang, Yongfeng Huang, Yang Junjie, Zhenyu Pan, Yongqiang Chen, Kaili Ma, Hongzhi Chen, and James Cheng. 2025. Retrieval-Augmented Generation with Hierarchical Knowledge. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 6044–6060, Suzhou, China. Association for Computational Linguistics.