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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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: Guo, Zirui
Xia, Lianghao
Yu, Yanhua
Huang, Chao
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: hybrid
doi.org
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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LightRAG: Simple and Fast Retrieval-Augmented Generation

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