用动态知识图谱提升大模型在电信领域的准确性与可解释性。
Enhancing Large Language Models (LLMs) for Telecom using Dynamic Knowledge Graphs and Explainable Retrieval-Augmented Generation
- 结合知识图谱与动态检索生成，增强大模型对电信领域理解。
- 相较纯大模型提升21.6%准确率，较传统RAG提升14.3%。
- 适合需要高可靠性的电信运维、标准合规等场景使用。
大语言模型在多种任务中展现出强大潜力，但在电信领域仍面临领域复杂、标准演进快和专业术语多等挑战。通用大模型在此类场景下易产生幻觉，输出不可靠。为此，本文提出KG-RAG框架，将知识图谱（KG）与检索增强生成（RAG）融合，利用从电信标准和技术文档中提取的结构化知识，动态检索相关事实以约束模型输出。该方法显著提升事实准确性，降低幻觉，确保符合电信规范。在基准数据集上的实验表明，KG-RAG相比纯大模型平均提升21.6%准确率，相比标准RAG提升14.3%。结果验证了其在复杂电信场景中生成精准、可信且可解释输出的有效性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。