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[2602.17529] Enhancing Large Language Models (LLMs) for Telecom using Dynamic Knowledge Graphs and Explainable Retrieval-Augmented Generation
Summary
This article presents a novel framework, KG-RAG, that enhances large language models (LLMs) for telecom applications by integrating dynamic knowledge graphs with retrieval-augmented generation, improving accuracy and reducing hallucinations.
Why It Matters
The telecom industry faces unique challenges due to its complexity and specialized terminology. This research addresses these challenges by improving LLMs' performance in telecom contexts, which can lead to more reliable applications in customer service, network management, and technical support.
Key Takeaways
- KG-RAG framework combines knowledge graphs with retrieval-augmented generation for telecom.
- Improves factual accuracy by 14.3% over standard RAG and 21.6% over LLM-only models.
- Reduces hallucinations in LLM outputs, enhancing reliability in telecom tasks.
- Provides explainable outputs, crucial for compliance in the telecom sector.
- Demonstrates the potential of LLMs in specialized domains through innovative integration.
Computer Science > Artificial Intelligence arXiv:2602.17529 (cs) [Submitted on 19 Feb 2026] Title:Enhancing Large Language Models (LLMs) for Telecom using Dynamic Knowledge Graphs and Explainable Retrieval-Augmented Generation Authors:Dun Yuan, Hao Zhou, Xue Liu, Hao Chen, Yan Xin, Jianzhong (Charlie)Zhang View a PDF of the paper titled Enhancing Large Language Models (LLMs) for Telecom using Dynamic Knowledge Graphs and Explainable Retrieval-Augmented Generation, by Dun Yuan and 5 other authors View PDF HTML (experimental) 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 this http URL 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 this http URL results across b...
