# Technical Language Processing for Telecommunications Specifications

arXiv: https://arxiv.org/abs/2406.02325 · HTML: https://mlsanity.com/paper/2406.02325/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 17 references that are in the map.
- First posted on arXiv 4 Jun 2024.

## Builds on (most cited first, 8 of 8)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Fast WordPiece Tokenization](https://mlsanity.com/paper/2012.15524/) (2020)
- [Understanding LLMs: A Comprehensive Overview from Training to Inference](https://mlsanity.com/paper/2401.02038/) (2024)
- [Stopwords in Technical Language Processing](https://mlsanity.com/paper/2006.02633/) (2020)
- [SPEC5G: A Dataset for 5G Cellular Network Protocol Analysis](https://mlsanity.com/paper/2301.09201/) (2023)
- [Using Large Language Models to Understand Telecom Standards](https://mlsanity.com/paper/2404.02929/) (2024)
- [A Glimpse in ChatGPT Capabilities and its impact for AI research](https://mlsanity.com/paper/2305.06087/) (2023)

## Built on by (most cited first, 1 of 1)

- [DeepSpecs: Expert-Level Questions Answering in 5G](https://mlsanity.com/paper/2511.01305/) (2025)

## Abstract (arXiv)

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.