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Technical Language Processing for Telecommunications Specifications

Felipe A. Rodriguez Y.

TL;DR

This paper addresses the challenge of extracting reliable information from internal telecommunications specifications using generic NLP tools. It advocates Technical Language Processing (TLP) tailored to telecom, emphasizing domain adaptation, human-in-the-loop knowledge sources, and internal data resources to overcome format and access limitations. The authors characterize internal specs, discuss preprocessing and context issues, and propose a workflow involving data separation by release, internal datasets and dictionaries, and proprietary models to improve information extraction and engineer training. They also outline practical steps for preparing internal specifications for GenAI deployment and suggest future work on tokenization and evaluation metrics to measure generation quality in this domain.

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.

Technical Language Processing for Telecommunications Specifications

TL;DR

This paper addresses the challenge of extracting reliable information from internal telecommunications specifications using generic NLP tools. It advocates Technical Language Processing (TLP) tailored to telecom, emphasizing domain adaptation, human-in-the-loop knowledge sources, and internal data resources to overcome format and access limitations. The authors characterize internal specs, discuss preprocessing and context issues, and propose a workflow involving data separation by release, internal datasets and dictionaries, and proprietary models to improve information extraction and engineer training. They also outline practical steps for preparing internal specifications for GenAI deployment and suggest future work on tokenization and evaluation metrics to measure generation quality in this domain.

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.
Paper Structure (16 sections, 3 figures, 1 table)

This paper contains 16 sections, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Internal Specifications
  4. Writing of Internal Specifications
  5. Characteristics of Technical Specifications
  6. Limitations of NLP on Technical Specifications
  7. Preprocessing Techniques
  8. Context Building and Information Abstraction
  9. Need for TLP in Technical Specifications
  10. TLP in Telecommunications Industry
  11. Use Case Definitions
  12. Data representation
  13. Internal Datasets and Dictionaries
  14. Proprietary Models
  15. Preparing Internal Specifications for the Deployment of Generative AI
  16. ...and 1 more sections

Figures (3)

  • Figure 1: An example of internal technical specification requirement.
  • Figure 2: An example of internal technical specification requirement with different behavior for two different releases.
  • Figure 3: Diagram of the conceptual application of TLP to Telecommunications Internal Specifications.