Technical Language Processing for Telecommunications Specifications
This work tackles the problem of processing technical telecommunications documentation for specification engineers, but it is incremental as it extends an existing concept to a new domain without demonstrating concrete improvements.
The paper addresses the challenge of extracting information from telecommunications specifications using large language models (LLMs), finding that out-of-the-box NLP tools are ineffective due to the unique format and structure of this technical documentation, and proposes domain-specific LLMs as a solution to speed up expert training.
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.