LEGO-GraphRAG: Modularizing Graph-based Retrieval-Augmented Generation for Design Space Exploration
GraphRAG integrates (knowledge) graphs with large language models (LLMs) to improve reasoning accuracy and contextual relevance. Despite its promising applications and strong relevance to multiple research communities, such as databases and natural language processing, GraphRAG currently lacks modul...
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Zusammenfassung: | GraphRAG integrates (knowledge) graphs with large language models (LLMs) to
improve reasoning accuracy and contextual relevance. Despite its promising
applications and strong relevance to multiple research communities, such as
databases and natural language processing, GraphRAG currently lacks modular
workflow analysis, systematic solution frameworks, and insightful empirical
studies. To bridge these gaps, we propose LEGO-GraphRAG, a modular framework
that enables: 1) fine-grained decomposition of the GraphRAG workflow, 2)
systematic classification of existing techniques and implemented GraphRAG
instances, and 3) creation of new GraphRAG instances. Our framework facilitates
comprehensive empirical studies of GraphRAG on large-scale real-world graphs
and diverse query sets, revealing insights into balancing reasoning quality,
runtime efficiency, and token or GPU cost, that are essential for building
advanced GraphRAG systems. |
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DOI: | 10.48550/arxiv.2411.05844 |