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Cited by 392Topic ModelingInformation Retrieval and Search BehaviorMultimodal Machine Learning Applications

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LightRAG: Simple and Fast Retrieval-Augmented Generation

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Guo, Zirui, Xia, Lianghao, Yu, Yanhua, Ao, Tu, Huang, Chao·2025·Findings of the Association for Computational Linguistics: EMNLP 2025
DOI 10.18653/v1/2025.findings-emnlp.568
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Abstract

Retrieval-Augmented Generation (RAG) systems enhance large language models (LLMs) by integrating external knowledge sources, enabling more accurate and contextually relevant responses tailored to user needs.However, existing RAG systems have significant limitations, including reliance on flat data representations and inadequate contextual awareness, which can lead to fragmented answers that fail to capture complex interdependencies.To address these challenges, we propose Ligh-tRAG, a novel framework that incorporates graph structures into text indexing and retrieval processes.This innovative approach employs a dual-level retrieval system that enhances comprehensive information retrieval from both lowand high-level knowledge discovery.Additionally, the integration of graph structures with vector representations facilitates efficient retrieval of related entities and their relationships, significantly improving response times while maintaining contextual relevance.This capability is further enhanced by an incremental update algorithm that ensures the timely integration of new data, allowing the system to remain effective and responsive in rapidly changing data environments.Extensive experimental validation demonstrates considerable improvements in retrieval accuracy and efficiency compared to existing approaches.

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Research works printed but not linked: 21

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Metadata

Authors
Guo, Z., Xia, L., Yu, Y., Ao, T., & Huang, C.
Affiliations
  • Beijing University of Posts and Telecommunications (CN)
  • University of Hong Kong (HK)
ORCID
Venue
Findings of the Association for Computational Linguistics: EMNLP 2025
Publisher
Association for Computational Linguistics
Published
2025
Pages
10746-10761
DOI
10.18653/v1/2025.findings-emnlp.568
OpenAlex
W4416034797
Cited by
208
References
0
Citations by year
2026: 159 · 2025: 49
FWCI
198.29
Type
conference paper
Language
en
Open access
Licence
cc-by
Locations
  • Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion, cc-by-nc-sa)
  • arXiv (Cornell University)
Funders
National Natural Science Foundation of China (U22B2019)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
Keywords
retrieval-augmented generation, vector representation, large language models, external knowledge integration, context awareness

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