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.

翻译：大型语言模型（LLM）在变革电信行业方面具有巨大潜力。它们可以帮助专业人员理解复杂标准、生成代码并加速开发。然而，传统LLM难以满足电信工作所必需的精确性和来源可验证性。为解决此问题，需要专门针对电信标准定制的基于LLM的解决方案。检索增强生成（RAG）提供了一种生成精确、基于事实答案的途径。本文提出TelecomRAG，这是一个用于构建电信标准助手的框架，旨在提供准确、详尽且可验证的响应。我们的实现基于从3GPP Release 16和Release 18规范文档构建的知识库，展示了该助手如何超越通用LLM，在准确性、技术深度和可验证性方面表现更优，从而为电信领域带来显著价值。