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.

大型语言模型（LLMs）在不同领域的应用越来越广泛。然而，目前即使是最先进的 LLMs，如 GPT-4，在没有大量预处理的情况下，从真实世界的技术文档中提取信息仍然存在挑战。本文讨论了处理电信行业专家生成的技术信息时，现有自然语言处理（NLP）工具的局限性，并将技术语言处理（TLP）的概念拓展到电信领域。此外，我们探讨了领域特定 LLMs 在规范工程师工作中的影响，并强调采用领域特定 LLMs 可以加快学习不同电信领域的专家的潜在好处。

电信规格的技术语言处理

Technical Language Processing for Telecommunications Specifications

We consider the problem of translating high-level textual descriptions to
formal representations in technical documentation as part of an effort to model
the meaning of such documentation. We focus specifically on the problem of
learning translational correspondences between text descriptions and grounded
representations in the target documentation, such as formal representation of
functions or code templates. Our approach exploits the parallel nature of such
documentation, or the tight coupling between high-level text and the low-level
representations we aim to learn. Data is collected by mining technical
documents for such parallel text-representation pairs, which we use to train a
simple semantic parsing model. We report new baseline results on sixteen novel
datasets, including the standard library documentation for nine popular
programming languages across seven natural languages, and a small collection of
Unix utility manuals.

本文通过挖掘技术文档中的文本 - 表达式对来训练一个基于语义分析的模型，以学习高级文本描述和目标文档中表现函数或代码模板的形式表示之间的翻译对应关系，并在 16 个新颖数据集上报告了新的基线结果，包括 7 种自然语言的九种流行编程语言的标准库文档和一些 UNIX 实用程序手册。