mlsanityData to 7 Oct 2026

TelecomRAG: Taming Telecom Standards with Retrieval Augmented Generation and LLMs

arXiv 2406.07053 ↗First posted 11 Jun 2024cs.NI cs.LGcited by 13

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

Large Language Models (LLMs) have immense potential to transform the telecommunications industry. They could help professionals understand complex standards, generate code, and accelerate development. However, traditional LLMs struggle with the precision and source verification essential for telecom work. To address this, specialized LLM-based solutions tailored to telecommunication standards are needed. Retrieval-augmented generation (RAG) offers a way to create precise, fact-based answers. This paper proposes TelecomRAG, a framework for a Telecommunication Standards Assistant that provides accurate, detailed, and verifiable responses. Our implementation, using a knowledge base built from 3GPP Release 16 and Release 18 specification documents, demonstrates how this assistant surpasses generic LLMs, offering superior accuracy, technical depth, and verifiability, and thus significant value to the telecommunications field.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 13 of its 18 references that are in the map. First posted on arXiv 11 Jun 2024.

Builds on

13 of 18 references.

References · 13

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  2. 2020Language Models are Few-Shot Learnerscited by 32,199
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1 more
  1. 2022ChatGPT Makes Medicine Easy to Swallow: An Exploratory Case Study on Simplified Radiology Reportscited by 100

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 6; 2026: 4.

13 papers.

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  1. 2024Telco-DPR: A Hybrid Dataset for Evaluating Retrieval Models of 3GPP Technical Specificationscited by 3
  2. 2025Telco-oRAG: Optimizing Retrieval-augmented Generation for Telecom Queries via Hybrid Retrieval and Neural Routingcited by 3
  3. 2025ORAN-GUIDE: RAG-Driven Prompt Learning for LLM-Augmented Reinforcement Learning in O-RAN Network Slicingcited by 3
  4. 2025AI5GTest: AI-Driven Specification-Aware Automated Testing and Validation of 5G O-RAN Componentscited by 3
  5. 2024TeleOracle: Fine-Tuned Retrieval-Augmented Generation with Long-Context Support for Networkcited by 2
  6. 2024NetOrchLLM: Mastering Wireless Network Orchestration with Large Language Modelscited by 2
  7. 2025Benchmarking Vector, Graph and Hybrid Retrieval Augmented Generation (RAG) Pipelines for Open Radio Access Networks (ORAN)cited by 2
  8. 2025TeleEval-OS: Performance evaluations of large language models for operations schedulingcited by 1
  9. 2025RePCS: Diagnosing Data Memorization in LLM-Powered Retrieval-Augmented Generationcited by 1
  10. 2026TeleDoCTR: Domain-Specific and Contextual Troubleshooting for Telecommunicationscited by 1
  11. 2026TeleEmbedBench: A Multi-Corpus Embedding Benchmark for RAG in Telecommunicationscited by 0
  12. 2026Enwar 3.0: An Agentic Multi-Modal LLM Orchestrator for Situation-Aware Beamforming, Blockage Prediction, and Handover Managementcited by 0
1 more
  1. 2026TelecomGPT-R1: Unified Post-Training for Reasoning Across Heterogeneous Telecom Taskscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Telco-RAG: Navigating the Challenges of Retrieval-Augmented Language Models for Telecommunications
  2. 2026Enhancing Large Language Models (LLMs) for Telecom using Dynamic Knowledge Graphs and Explainable Retrieval-Augmented Generation
  3. 2025Enhancing Large Language Models (LLMs) for Telecommunications using Knowledge Graphs and Retrieval-Augmented Generation
  4. 2024TSpec-LLM: An Open-source Dataset for LLM Understanding of 3GPP Specifications
  5. 2024Telecom Language Models: Must They Be Large?
  6. 2025MM-Telco: Benchmarks and Multimodal Large Language Models for Telecom Applications
  7. 2024Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities
  8. 2024QMOS: Enhancing LLMs for Telecommunication with Question Masked loss and Option Shuffling