@inproceedings{guo-etal-2025-empowering,
title = "Empowering {G}raph{RAG} with Knowledge Filtering and Integration",
author = "Guo, Kai and
Shomer, Harry and
Zeng, Shenglai and
Han, Haoyu and
Wang, Yu and
Tang, Jiliang",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.1293/",
doi = "10.18653/v1/2025.emnlp-main.1293",
pages = "25439--25453",
ISBN = "979-8-89176-332-6",
abstract = "In recent years, large language models (LLMs) have revolutionized the field of natural language processing. However, they often suffer from knowledge gaps and hallucinations. Graph retrieval-augmented generation (GraphRAG) enhances LLM reasoning by integrating structured knowledge from external graphs. However, we identify two key challenges that plague GraphRAG: (1) Retrieving noisy and irrelevant information can degrade performance and (2) Excessive reliance on external knowledge suppresses the model{'}s intrinsic reasoning.To address these issues, we propose GraphRAG-FI (Filtering {\&} Integration), consisting of GraphRAG-Filtering and GraphRAG-Integration. GraphRAG-Filtering employs a two-stage filtering mechanism to refine retrieved information. GraphRAG-Integration employs a logits-based selection strategy to balance external knowledge from GraphRAG with the LLM{'}s intrinsic reasoning, reducing over-reliance on retrievals. Experiments on knowledge graph QA tasks demonstrate that GraphRAG-FI significantly improves reasoning performance across multiple backbone models, establishing a more reliable and effective GraphRAG framework."
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<abstract>In recent years, large language models (LLMs) have revolutionized the field of natural language processing. However, they often suffer from knowledge gaps and hallucinations. Graph retrieval-augmented generation (GraphRAG) enhances LLM reasoning by integrating structured knowledge from external graphs. However, we identify two key challenges that plague GraphRAG: (1) Retrieving noisy and irrelevant information can degrade performance and (2) Excessive reliance on external knowledge suppresses the model’s intrinsic reasoning.To address these issues, we propose GraphRAG-FI (Filtering & Integration), consisting of GraphRAG-Filtering and GraphRAG-Integration. GraphRAG-Filtering employs a two-stage filtering mechanism to refine retrieved information. GraphRAG-Integration employs a logits-based selection strategy to balance external knowledge from GraphRAG with the LLM’s intrinsic reasoning, reducing over-reliance on retrievals. Experiments on knowledge graph QA tasks demonstrate that GraphRAG-FI significantly improves reasoning performance across multiple backbone models, establishing a more reliable and effective GraphRAG framework.</abstract>
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%0 Conference Proceedings
%T Empowering GraphRAG with Knowledge Filtering and Integration
%A Guo, Kai
%A Shomer, Harry
%A Zeng, Shenglai
%A Han, Haoyu
%A Wang, Yu
%A Tang, Jiliang
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F guo-etal-2025-empowering
%X In recent years, large language models (LLMs) have revolutionized the field of natural language processing. However, they often suffer from knowledge gaps and hallucinations. Graph retrieval-augmented generation (GraphRAG) enhances LLM reasoning by integrating structured knowledge from external graphs. However, we identify two key challenges that plague GraphRAG: (1) Retrieving noisy and irrelevant information can degrade performance and (2) Excessive reliance on external knowledge suppresses the model’s intrinsic reasoning.To address these issues, we propose GraphRAG-FI (Filtering & Integration), consisting of GraphRAG-Filtering and GraphRAG-Integration. GraphRAG-Filtering employs a two-stage filtering mechanism to refine retrieved information. GraphRAG-Integration employs a logits-based selection strategy to balance external knowledge from GraphRAG with the LLM’s intrinsic reasoning, reducing over-reliance on retrievals. Experiments on knowledge graph QA tasks demonstrate that GraphRAG-FI significantly improves reasoning performance across multiple backbone models, establishing a more reliable and effective GraphRAG framework.
%R 10.18653/v1/2025.emnlp-main.1293
%U https://aclanthology.org/2025.emnlp-main.1293/
%U https://doi.org/10.18653/v1/2025.emnlp-main.1293
%P 25439-25453
Markdown (Informal)
[Empowering GraphRAG with Knowledge Filtering and Integration](https://aclanthology.org/2025.emnlp-main.1293/) (Guo et al., EMNLP 2025)
ACL
- Kai Guo, Harry Shomer, Shenglai Zeng, Haoyu Han, Yu Wang, and Jiliang Tang. 2025. Empowering GraphRAG with Knowledge Filtering and Integration. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 25439–25453, Suzhou, China. Association for Computational Linguistics.