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

Dun Yuan, Hao Zhou, Xue Liu, Hao Chen, Yan Xin, Jianzhong, Zhang

TL;DR

KG-RAG-a novel framework that integrates knowledge graphs (KGs) with retrieval-augmented generation (RAG) to enhance LLMs for telecom-specific tasks and highlights KG-RAG's effectiveness in producing accurate, reliable, and explainable outputs in complex telecom scenarios.

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.

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

TL;DR

KG-RAG-a novel framework that integrates knowledge graphs (KGs) with retrieval-augmented generation (RAG) to enhance LLMs for telecom-specific tasks and highlights KG-RAG's effectiveness in producing accurate, reliable, and explainable outputs in complex telecom scenarios.

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.
Paper Structure (17 sections, 5 figures, 1 table)

This paper contains 17 sections, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Knowledge Graphs for Telecom
  3. Knowledge Graphs in Telecom Domain
  4. Entity Extraction
  5. Link Prediction
  6. Dynamic Knowledge Graph Updates
  7. Retrieval-Augmented Generation-enhanced LLM for Telecom
  8. KG-RAG framework
  9. Construction of the Telecom KG Database
  10. Retrieval with KG-RAG
  11. Explainable KG-RAG-based Generation
  12. Case studies
  13. Experiment settings
  14. KG-RAG Results Comparison and Analysis
  15. Dynamic KG Update
  16. ...and 2 more sections

Figures (5)

  • Figure 1: Overview of the proposed KG-RAG framework: domain-specific telecom documents are processed by an LLM to extract entities and predict relations, which builds a KG. A retriever then surfaces relevant KG context, feeding it into an LLM prompt to generate final answers for user queries.
  • Figure 2: Comparison of different entity extraction approaches: (i) Lexicon-based methods are limited to predefined dictionaries, missing newly introduced terms; (ii) Rule-based methods require explicit rules and deeper semantic understanding, limiting their adaptability; (iii) Machine learning methods effectively recognize complex terms but incur high dataset collection and training costs; (iv) Pre-trained LLMs efficiently handle emerging terms and semantic nuances without additional training.
  • Figure 3: Illustration of a RAG workflow.
  • Figure 4: Illustration of the KG-RAG framework applied to a 5G network use case, where an LLM retrieves from a KG created using domain knowledge from 3GPP standards. Then, it generates an answer for the input query.
  • Figure 5: Evaluation results of LLM-only, RAG, self-RAG, RAPTOR, and KG-RAG across text summarization metrics, QA accuracy, hallucination reduction, and difficulty-based robustness.