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.

翻译：大语言模型（LLMs）已在多种任务中展现出强大潜力，但其在电信领域的应用仍面临挑战，原因在于该领域复杂度高、标准持续演进且专业术语密集。因此，通用领域的大语言模型在此情境下可能难以提供准确可靠的输出，导致幻觉增多，在电信运营中的实用性降低。为应对这些局限，本研究提出了KG-RAG——一种将知识图谱（KGs）与检索增强生成（RAG）相结合的新型框架，旨在增强大语言模型处理电信特定任务的能力。具体而言，知识图谱提供了从电信标准与技术文档中提取的领域知识的结构化表示，而检索增强生成则支持动态检索相关事实以支撑模型的输出。这种结合提升了事实准确性，减少了幻觉，并确保输出符合电信规范。在多个基准数据集上的实验结果表明，KG-RAG在性能上优于仅使用大语言模型及标准检索增强生成的基线方法，例如，KG-RAG相比检索增强生成平均准确率提升14.3%，相比仅使用大语言模型的方案提升21.6%。这些结果凸显了KG-RAG在复杂电信场景中生成准确、可靠且可解释输出方面的有效性。