Enhancing Large Language Models (LLMs) for Telecom Using Dynamic Knowledge Graphs and Explainable Retrieval-Augmented Generation
Yuan, Dun Zhou, Hao Liu, Xue Chen, Hao Xin, Yan Zhang, Jianzhong
Yuan, Dun
Zhou, Hao
Liu, Xue
Chen, Hao
Xin, Yan
Zhang, Jianzhong
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Department
Machine Learning
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Journal article
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English
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Abstract
Large language models (LLMs) have shown strong potential across a variety of tasks, but their application in the telecom field remains challenging due to domain complexity, evolving standards, and specialized terminology. Therefore, general-domain LLMs may struggle to provide accurate and reliable outputs in this context, leading to increased hallucinations and reduced utility in telecom operations. To address these limitations, this work introduces KG-RAG—a novel framework that integrates knowledge graphs (KGs) with retrieval-augmented generation (RAG) to enhance LLMs for telecom-specific tasks. In particular, the KG provides a structured representation of domain knowledge derived from telecom standards and technical documents, while RAG enables dynamic retrieval of relevant facts to ground the model’s outputs. Such a combination improves factual accuracy, reduces hallucination, and ensures compliance with telecom specifications. Experimental results across benchmark datasets demonstrate that KG-RAG outperforms both LLM-only and standard RAG baselines, e.g., KG-RAG achieves an average accuracy improvement of 14.3% over RAG and 21.6% over LLM-only models. These results highlight KG-RAG’s effectiveness in producing accurate, reliable, and explainable outputs in complex telecom scenarios.
Citation
D. Yuan, H. Zhou, X. Liu, H. Chen, Y. Xin, J. Zhang, "Enhancing Large Language Models (LLMs) for Telecom Using Dynamic Knowledge Graphs and Explainable Retrieval-Augmented Generation," IEEE Wireless Communications, vol. PP, no. 99, pp. 1-9, 2026, https://doi.org/10.1109/mwc.2026.3667495.
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IEEE Wireless Communications
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46 Information and Computing Sciences, 4602 Artificial Intelligence
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IEEE