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Enhancing Large Language Models (LLMs) for Telecommunications using Knowledge Graphs and Retrieval-Augmented Generation

A paper proposing a framework that combines knowledge graphs and retrieval-augmented generation to enhance large language models in the telecommunications

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Enhancing Large Language Models (LLMs) for Telecommunications using Knowledge Graphs and Retrieval-Augmented Generation

By Dun Yuan, Hao Zhou, Di Wu, Xue Liu, Hao Chen, Yan Xin, Jianzhong Charlie Zhang
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The authors present a novel framework combining knowledge graph and retrieval-augmented generation techniques to improve large language model performance in telecommunications.

The framework leverages a knowledge graph to capture structured information about network protocols, standards, and entities. Results demonstrate the effectiveness of the KG-RAG framework in addressing complex technical queries with precision.

Abstract

The authors present a novel framework combining knowledge graph and retrieval-augmented generation techniques to improve large language model performance in telecommunications. The framework leverages a knowledge graph to capture structured information about network protocols, standards, and entities. Results demonstrate the effectiveness of the KG-RAG framework in addressing complex technical queries with precision.

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telecommunicationsknowledge graphretrieval-augmented generationlarge language modelsdomain-specific knowledgeKnowledge GraphsLarge Language ModelsRetrieval & RAGSemantic Interoperability
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Enhancing Large Language Models (LLMs) for Telecommunications using Knowledge Graphs and Retrieval-Augmented Generation | Aramai