Large Language Models (LLMs) are continuously being applied in a more diverse set of contexts. At their current state, however, even state-of-the-art LLMs such as Generative Pre-Trained Transformer 4 (GTP-4) have challenges when extracting information from real-world technical documentation without a heavy preprocessing. One such area with real-world technical documentation is telecommunications engineering, which could greatly benefit from domain-specific LLMs. The unique format and overall structure of telecommunications internal specifications differs greatly from standard English and thus it is evident that the application of out-of-the-box Natural Language Processing (NLP) tools is not a viable option. In this article, we outline the limitations of out-of-the-box NLP tools for processing technical information generated by telecommunications experts, and expand the concept of Technical Language Processing (TLP) to the telecommunication domain. Additionally, we explore the effect of domain-specific LLMs in the work of Specification Engineers, emphasizing the potential benefits of adopting domain-specific LLMs to speed up the training of experts in different telecommunications fields.

翻译：大型语言模型（LLM）正日益广泛地应用于多样化的场景中。然而，在现有发展阶段，即使是诸如生成式预训练Transformer 4（GPT-4）这类前沿LLM，在处理现实世界技术文档时仍需经过大量预处理才能有效提取信息。电信工程领域正是这类现实技术文档的典型应用场景，其必将从领域专用LLM中获益良多。电信内部规范具有独特的格式与整体结构，与标准英语差异显著，因此直接应用开箱即用的自然语言处理（NLP）工具显然不可行。本文系统阐述了现成NLP工具在处理电信专家生成的技术信息时存在的局限性，并将技术语言处理（TLP）概念拓展至电信领域。此外，我们深入探究了领域专用LLM对规范工程师工作的影响，重点论证了采用领域专用LLM在加速不同电信领域专家培训方面所具有的潜在优势。