TelecomRAG: Taming Telecom Standards with Retrieval Augmented Generation and LLMs

TL;DR

TelecomRAG introduces a retrieval-augmented generation framework that enhances large language models with telecom standards knowledge, providing accurate, verifiable, and detailed responses to support telecom professionals.

Contribution

The paper presents TelecomRAG, a novel framework integrating telecom standards knowledge bases with LLMs to improve accuracy and verifiability in telecom-specific tasks.

Findings

Outperforms generic LLMs in accuracy and depth

Uses a knowledge base from 3GPP standards

Provides verifiable, detailed responses

Abstract

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.