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

TL;DR

Telco-RAG tackles the challenge of applying retrieval-augmented language models to highly technical telecom standards, focusing on 3GPP documents. It introduces a dual-stage RAG pipeline with glossary-based query augmentation, an NN router to reduce RAM usage, and a structured prompt design to improve MCQ answering. Through systematic hyperparameter tuning, lexicon integration, and prompt optimization, Telco-RAG achieves measurable gains in accuracy and efficiency over baselines and benchmark RAG setups, validating its practical viability for telecom professionals. The work offers generalizable guidelines for deploying RAG in fast-evolving, standards-driven domains and provides open-source access to the framework for broader adoption.

Abstract

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.

Figures (5)

• Figure 1: The proposed Telco-RAG architecture.
• Figure 2: The proposed NN router architecture.
• Figure 3: RAG's accuracy vs context length.
• Figure 4: Comparison the accuracy of Telco-RAG system with a baseline GPT 3.5 with/without the Benchmark RAG on the TeleQnA questions related to 3GPP documents.
• Figure 5: PDF of the RAM usage of Telco-RAG vs Benchmark RAG.