mlsanityData to 14 Oct 2026

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

arXiv 2503.24245 ↗First posted 31 Mar 2025cs.CLcited by 1

On the map: Retrieval-augmented generation → Knowledge-graph and structured-knowledge RAG

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Knowledge-graph and structured-knowledge RAG, one of 15 directions in Retrieval-augmented generation. Builds on 14 of its 16 references that are in the map. First posted on arXiv 31 Mar 2025.

Builds on

14 of 16 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarization24 Apr

Other references · 13

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2023Head-to-Tail: How Knowledgeable are Large Language Models (LLMs)? A.K.A. Will LLMs Replace Knowledge Graphs?cited by 168
  3. 2024Medical Graph RAG: Towards Safe Medical Large Language Model via Graph Retrieval-Augmented Generationcited by 90
  4. 2024Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunitiescited by 83
  5. 2024Extract, Define, Canonicalize: An LLM-based Framework for Knowledge Graph Constructioncited by 70
  6. 2023TeleQnA: A Benchmark Dataset to Assess Large Language Models Telecommunications Knowledgecited by 63
  7. 2024Knowledge Graph Large Language Model (KG-LLM) for Link Predictioncited by 36
  8. 2024TSpec-LLM: An Open-source Dataset for LLM Understanding of 3GPP Specificationscited by 27
  9. 2024ORAN-Bench-13K: An Open Source Benchmark for Assessing LLMs in Open Radio Access Networkscited by 24
  10. 2023SPEC5G: A Dataset for 5G Cellular Network Protocol Analysiscited by 20
  11. 2024Evaluation of RAG Metrics for Question Answering in the Telecom Domaincited by 20
  12. 2024An Enhanced Prompt-Based LLM Reasoning Scheme via Knowledge Graph-Integrated Collaborationcited by 16
1 more
  1. 2024Enhancing Startup Success Predictions in Venture Capital: A GraphRAG Augmented Multivariate Time Series Methodcited by 2

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Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Graph-Symbolic Policy Enforcement and Control (G-SPEC): A Neuro-Symbolic Framework for Safe Agentic AI in 5G Autonomous Networkscited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2026Enhancing Large Language Models (LLMs) for Telecom using Dynamic Knowledge Graphs and Explainable Retrieval-Augmented Generation
  2. 2025KG2QA: Knowledge Graph-enhanced Retrieval-augmented Generation for Communication Standards Question Answering
  3. 2024KAG: Boosting LLMs in Professional Domains via Knowledge Augmented Generation
  4. 2025A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models
  5. 2024Domain-Specific Retrieval-Augmented Generation Using Vector Stores, Knowledge Graphs, and Tensor Factorization
  6. 2024TeleOracle: Fine-Tuned Retrieval-Augmented Generation with Long-Context Support for Network
  7. 2026UltRAG: a Universal Simple Scalable Recipe for Knowledge Graph RAG
  8. 2024QMOS: Enhancing LLMs for Telecommunication with Question Masked loss and Option Shuffling