mlsanityData to 7 Oct 2026

Telco-RAG: Navigating the Challenges of Retrieval-Augmented Language Models for Telecommunications

arXiv 2404.15939 ↗First posted 24 Apr 2024cs.IR eess.SPcited by 26

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

Abstract (arXiv)

The application of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems in the telecommunication domain presents unique challenges, primarily due to the complex nature of telecom standard documents and the rapid evolution of the field. The paper introduces Telco-RAG, an open-source RAG framework designed to handle the specific needs of telecommunications standards, particularly 3rd Generation Partnership Project (3GPP) documents. Telco-RAG addresses the critical challenges of implementing a RAG pipeline on highly technical content, paving the way for applying LLMs in telecommunications and offering guidelines for RAG implementation in other technical domains.

Key facts. Cited by 26 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 19 references that are in the map. First posted on arXiv 24 Apr 2024.

Builds on

13 of 19 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024The Chronicles of RAG: The Retriever, the Chunk and the Generator15 Jan

Other references · 12

  1. 2017Attention Is All You Needcited by 59,932
  2. 2023ChatGPT Outperforms Crowd-Workers for Text-Annotation Taskscited by 849
  3. 2022Matryoshka Representation Learningcited by 268
  4. 2020The Computational Limits of Deep Learningcited by 188
  5. 2023Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMscited by 175
  6. 2018Neural Approaches to Conversational AIcited by 164
  7. 2024RQ-RAG: Learning to Refine Queries for Retrieval Augmented Generationcited by 144
  8. 2022Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answeringcited by 126
  9. 2024RAG vs Fine-tuning: Pipelines, Tradeoffs, and a Case Study on Agriculturecited by 80
  10. 2023TeleQnA: A Benchmark Dataset to Assess Large Language Models Telecommunications Knowledgecited by 62
  11. 2023Large Language Models for Telecom: Forthcoming Impact on the Industrycited by 44
  12. 2024Telecom Language Models: Must They Be Large?cited by 10

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 10; 2025: 12; 2026: 4.

26 papers.

Sort:

By area:

  1. 2024WirelessLLM: Empowering Large Language Models Towards Wireless Intelligencecited by 43
  2. 2024TSpec-LLM: An Open-source Dataset for LLM Understanding of 3GPP Specificationscited by 26
  3. 2024Leveraging Fine-Tuned Retrieval-Augmented Generation with Long-Context Support: For 3GPP Standardscited by 8
  4. 2024Hermes: A Large Language Model Framework on the Journey to Autonomous Networkscited by 5
  5. 2025Chat3GPP: An Open-Source Retrieval-Augmented Generation Framework for 3GPP Documentscited by 5
  6. 2024Telco-DPR: A Hybrid Dataset for Evaluating Retrieval Models of 3GPP Technical Specificationscited by 3
  7. 2024FastRAG: Retrieval Augmented Generation for Semi-structured Datacited by 3
  8. 2025RAG Without the Lag: Interactive Debugging for Retrieval-Augmented Generation Pipelinescited by 3
  9. 2025Telco-oRAG: Optimizing Retrieval-augmented Generation for Telecom Queries via Hybrid Retrieval and Neural Routingcited by 3
  10. 2024QMOS: Enhancing LLMs for Telecommunication with Question Masked loss and Option Shufflingcited by 2
  11. 2024TeleOracle: Fine-Tuned Retrieval-Augmented Generation with Long-Context Support for Networkcited by 2
  12. 2025Towards a Foundation Model for Communication Systemscited by 2
14 more
  1. 2025Benchmarking Vector, Graph and Hybrid Retrieval Augmented Generation (RAG) Pipelines for Open Radio Access Networks (ORAN)cited by 2
  2. 2025Evaluating Open-Source Large Language Models for Technical Telecom Question Answeringcited by 1
  3. 2026TeleDoCTR: Domain-Specific and Contextual Troubleshooting for Telecommunicationscited by 1
  4. 2025How to Discover Knowledge for FutureG: Contextual RAG and LLM Prompting for O-RANcited by 1
  5. 2024ColBERT Retrieval and Ensemble Response Scoring for Language Model Question Answeringcited by 0
  6. 2024Exploring the Meaningfulness of Nearest Neighbor Search in High-Dimensional Spacecited by 0
  7. 2025Concept-Level AI for Telecom: Moving Beyond Large Language Modelscited by 0
  8. 2025Bridging Language Models and Formal Methods for Intent-Driven Optical Network Designcited by 0
  9. 2025DeepSpecs: Expert-Level Questions Answering in 5Gcited by 0
  10. 2025MM-Telco: Benchmarks and Multimodal Large Language Models for Telecom Applicationscited by 0
  11. 2025TelcoAI: Advancing 3GPP Technical Specification Search through Agentic Multi-Modal Retrieval-Augmented Generationcited by 0
  12. 2026Communications-Incentivized Collaborative Reasoning in NetGPT through Agentic Reinforcement Learningcited by 0
  13. 2026SpecMind: Enabling Spectrum Intelligence via Multi-Agent Hybrid Retrieval-Augmented Generationcited by 0
  14. 2026TelecomGPT-R1: Unified Post-Training for Reasoning Across Heterogeneous Telecom Taskscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024TelecomRAG: Taming Telecom Standards with Retrieval Augmented Generation and LLMs
  2. 2026Enhancing Large Language Models (LLMs) for Telecom using Dynamic Knowledge Graphs and Explainable Retrieval-Augmented Generation
  3. 2025Engineering RAG Systems for Real-World Applications: Design, Development, and Evaluation
  4. 2023Retrieval-Augmented Generation for Large Language Models: A Survey
  5. 2025Enhancing Large Language Models (LLMs) for Telecommunications using Knowledge Graphs and Retrieval-Augmented Generation
  6. 2025When Retrieval Succeeds and Fails: Rethinking Retrieval-Augmented Generation for LLMs
  7. 2026RAGe: A Retrieval-Augmented Generation Evaluation Framework
  8. 2024RAG Foundry: A Framework for Enhancing LLMs for Retrieval Augmented Generation