TelecomRAG: Taming Telecom Standards with Retrieval Augmented Generation and LLMs

• URL: http://arxiv.org/abs/2406.07053v1
• Date: Tue, 11 Jun 2024 08:35:23 GMT
• Title: TelecomRAG: Taming Telecom Standards with Retrieval Augmented Generation and LLMs
• Authors: Girma M. Yilma, Jose A. Ayala-Romero, Andres Garcia-Saavedra, Xavier Costa-Perez,
• Abstract summary: Large Language Models (LLMs) have immense potential to transform the telecommunications industry. LLMs could help professionals understand complex standards, generate code, and accelerate development. Retrieval-augmented generation (RAG) offers a way to create precise, fact-based answers.
• Score: 7.67846565247214
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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