用知识图谱增强大模型，让电信领域问答更准更懂行。
Enhancing Large Language Models (LLMs) for Telecommunications using Knowledge Graphs and Retrieval-Augmented Generation
- 结合知识图谱与检索增强生成，动态获取最新专业信息。
- 在电信数据集上问答准确率达88%，显著优于纯大模型。
- 适合需要高精度、懂标准的电信技术场景使用。
大语言模型在通用自然语言任务中取得显著进展，但在电信等专业领域仍面临挑战，因该领域需专业知识且标准持续更新。本文提出一种融合知识图谱（KG）与检索增强生成（RAG）的新框架，利用KG结构化表示网络协议、标准及电信实体间的复杂关系，并通过RAG实现生成时动态访问最新知识。该混合方法有效衔接了结构化知识与生成能力，显著提升准确性、适应性与领域理解力。实验表明，在常用电信数据集上，该KG-RAG模型在问答任务中达到88%准确率，优于仅用RAG的82%和仅用大模型的48%。
原文摘要 · Abstract (English)
Large language models (LLMs) have made significant progress in general-purpose natural language processing tasks. However, LLMs are still facing challenges when applied to domain-specific areas like telecommunications, which demands specialized expertise and adaptability to evolving standards. This paper presents a novel framework that combines knowledge graph (KG) and retrieval-augmented generation (RAG) techniques to enhance LLM performance in the telecom domain. The framework leverages a KG to capture structured, domain-specific information about network protocols, standards, and other telecom-related entities, comprehensively representing their relationships. By integrating KG with RAG, LLMs can dynamically access and utilize the most relevant and up-to-date knowledge during response generation. This hybrid approach bridges the gap between structured knowledge representation and the generative capabilities of LLMs, significantly enhancing accuracy, adaptability, and domain-specific comprehension. Our results demonstrate the effectiveness of the KG-RAG framework in addressing complex technical queries with precision. The proposed KG-RAG model attained an accuracy of 88% for question answering tasks on a frequently used telecom-specific dataset, compared to 82% for the RAG-only and 48% for the LLM-only approaches.
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