Technical Language Processing for Telecommunications Specifications

TL;DR

This paper discusses the challenges of applying general-purpose LLMs to telecommunications technical documentation and advocates for domain-specific models to improve information extraction and expert training.

Contribution

It introduces the concept of Technical Language Processing tailored for telecommunications and highlights the benefits of domain-specific LLMs over generic NLP tools.

Findings

Out-of-the-box NLP tools struggle with telecom specifications.

Domain-specific LLMs can enhance information extraction accuracy.

Using specialized models accelerates training for telecommunications experts.

Abstract

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.