information: 05779 (cs) [Submitted on 8 Oct 2024] Title:LightRAG: Simple and Fast Retrieval-Augmented Generation Authors:Zirui Guo, Lianghao Xia, Yanhua Yu, Tu Ao, Chao Huang View a PDF of the paper titled LightRAG: Simple and Fast Retrieval-Augmented Generation, by Zirui Guo and 4 other authors View PDF HTML (experimental) 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 inter-dependencies
This innovative framework employs a dual-level retrieval system that enhances comprehensive information retrieval from both low-level and high-level knowledge discovery
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
We have made our LightRAG open-source and available at the link: this https URL
IR] for this version) https://doi
05779 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Zirui Guo [view email] [v1] Tue, 8 Oct 2024 08:00:12 UTC (1,262 KB) Full-text links: Access Paper: View a PDF of the paper titled LightRAG: Simple and Fast Retrieval-Augmented Generation, by Zirui Guo and 4 other authorsView PDFHTML (experimental)TeX SourceOther Formats view license Current browse context: cs
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