Retrieval or Reasoning: The Roles of Graphs and Large Language Models in Efficient Knowledge-Graph-Based Retrieval-Augmented Generation
Keywords: Knowledge Graphs, Large Language Models, Retrieval-Augmented Generation, Retrieval
Abstract: Large Language Models (LLMs) demonstrate strong reasoning abilities but face limitations such as hallucinations and outdated knowledge. Knowledge Graph (KG)-based Retrieval-Augmented Generation (RAG) addresses these issues by grounding LLM outputs in structured external knowledge from KGs. However, current KG-based RAG frameworks still struggle to optimize the trade-off between retrieval accuracy and efficiency in identifying a suitable amount of relevant graph information for the LLM to digest. We introduce SubgraphRAG, extending the KG-based RAG framework that retrieves subgraphs centered on query/topic entities and leverages LLMs for reasoning. Our approach innovatively integrates a lightweight multilayer perceptron (MLP) with a parallel triple-scoring mechanism for efficient subgraph retrieval while encoding directional structural distances to enhance retrieval accuracy. The size of retrieved subgraphs can be flexibly adjusted to match the query's need and the downstream LLM's reasoning capacity. This design strikes a balance between model complexity and reasoning power, enabling scalable and generalizable retrieval processes. Notably, based on our retrieved subgraphs, smaller models like Llama3.1-8B deliver competitive results with explainable reasoning, while larger models like GPT-4o achieve comparable or better state-of-the-art accuracy compared with previous baselines—all without fine-tuning. Extensive evaluations on the WebQSP and CWQ benchmarks highlight SubgraphRAG's strengths in efficiency, accuracy, and reliability by reducing hallucinations and improving response grounding.
Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 12839
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