2/20/2026 | USA | technology | ✓ Verified - arxiv.org

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

#Large Language Models #Dynamic Knowledge Graphs #Retrieval‑Augmented Generation #Telecom #Hallucination Reduction #Factual Accuracy #Explainability #Benchmark Evaluation #Domain‑Specific LLM

📌 Key Takeaways

General‑domain LLMs struggle in telecom due to domain complexity, evolving standards, and specialized terminology.
KG‑RAG integrates dynamic knowledge graphs drawn from telecom standards with retrieval‑augmented generation.
The knowledge graph provides a structured representation of telecom domain knowledge.
Retrieval‑augmented generation allows real‑time fetching of relevant facts to ground model outputs.
The framework reduces hallucinations and improves factual accuracy for telecom‑specific tasks.
Benchmark results show KG‑RAG outperforms both LLM‑only and standard RAG baselines.
On average, KG‑RAG achieves a 14.3% accuracy improvement over RAG and a 21.6% improvement over LLM‑only models.
The approach provides explainable outputs, enhancing trust and compliance in telecom scenarios.

📖 Full Retelling

WHO: Authors Dun Yuan, Hao Zhou, Xue Liu, Hao Chen, Yan Xin, and Jianzhong Zhang. WHAT: They propose KG‑RAG, a framework that augments large language models for telecom tasks using dynamic knowledge graphs and explainable retrieval‑augmented generation. WHERE: The study is published on arXiv under the Computer Science > Artificial Intelligence category. WHEN: It was submitted on 19 February 2026. WHY: Telecom presents a complex, rapidly evolving domain with specialized terminology, which challenges general‑domain LLMs by increasing hallucinations and reducing reliability; KG‑RAG aims to reduce these issues by grounding outputs in structured domain knowledge and dynamic retrieval.

🏷️ Themes

Artificial Intelligence, Natural Language Processing, Telecom Engineering, Knowledge Graphs, Retrieval‑Augmented Generation, Explainable AI, Domain‑Specific Model Enhancement, Model Evaluation

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Deep Analysis

Why It Matters

The paper addresses the challenge of applying large language models to telecom, a domain with complex standards and terminology. By integrating dynamic knowledge graphs and explainable retrieval-augmented generation, it improves factual accuracy and reduces hallucinations, making LLMs more reliable for telecom operations.

Context & Background

Telecom domain requires adherence to evolving standards
General-domain LLMs often hallucinate in specialized contexts
Knowledge graphs capture structured telecom knowledge
Retrieval-augmented generation grounds responses in up-to-date facts
Combining KG and RAG yields explainable outputs

What Happens Next

Future work may involve deploying KG-RAG in real-time telecom support systems and extending the knowledge graph to cover emerging 5G and 6G protocols. Industry partners could evaluate the framework on live customer service data to assess impact on operational efficiency.

Frequently Asked Questions

What is KG-RAG?

KG-RAG is a framework that merges a domain knowledge graph with retrieval-augmented generation to enhance large language models for telecom tasks.

How does KG-RAG reduce hallucinations?

By retrieving relevant facts from the knowledge graph and conditioning the LLM on them, the model is less likely to generate unsupported statements.

Is the approach limited to telecom?

While the paper focuses on telecom, the methodology can be adapted to other specialized domains with complex standards.

What are the key performance gains?

KG-RAG achieved a 14.3% accuracy improvement over standard RAG and 21.6% over LLM-only baselines on benchmark telecom datasets.

Original Source

              --> Computer Science > Artificial Intelligence arXiv:2602.17529 [Submitted on 19 Feb 2026] Title: Enhancing Large Language Models for Telecom using Dynamic Knowledge Graphs and Explainable Retrieval-Augmented Generation Authors: Dun Yuan , Hao Zhou , Xue Liu , Hao Chen , Yan Xin , Jianzhong Zhang View a PDF of the paper titled Enhancing Large Language Models for Telecom using Dynamic Knowledge Graphs and Explainable Retrieval-Augmented Generation, by Dun Yuan and 5 other authors View PDF HTML Abstract: Large language models 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 this http URL address these limitations, this work introduces KG-RAG-a novel framework that integrates knowledge graphs with retrieval-augmented generation 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 this http URL 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. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2602.17529 [cs.AI] (or arXiv:2602.17529v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2602.17529 Focus to learn more arXiv-issued DOI via DataCite (p...
            

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