Understanding Telecom Language Through Large Language Models

Lina Bariah, Hang Zou, Qiyang Zhao, Belkacem Mouhouche, Faouzi Bader, Merouane Debbah

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    5 Scopus citations

    Abstract

    The recent progress of artificial intelligence (AI) opens up new frontiers in the possibility of automating many tasks involved in Telecom networks design, implementation, and deployment. This has been further pushed forward with the evolution of generative artificial intelligence (AI), including the emergence of large language models (LLMs), which is believed to be the cornerstone toward realizing self-governed, interactive AI agents. Motivated by this, in this paper, we aim to adapt the paradigm of LLMs to the Telecom domain. In particular, we fine-tune several LLMs including BERT, distilled BERT, RoBERTa and GPT-2, to the Telecom domain languages, and demonstrate a use case for identifying the 3rd Generation Partnership Project (3GPP) standard working groups. We consider training the selected models on 3GPP technical documents (Tdoc) pertinent to years 2009-2019 and predict the Tdoc categories in years 2020-2023. The results demonstrate that fine-tuning BERT and RoBERTa model achieves 84.6% accuracy, while GPT-2 model achieves 83% in identifying 3GPP working groups. The distilled BERT model with around 50% less parameters achieves similar performance as others. This corroborates that fine-tuning pretrained LLM can effectively identify the categories of Telecom language. The developed framework shows a stepping stone towards realizing intent-driven and self-evolving wireless networks from Telecom languages, and paves the way for the implementation of generative AI in the Telecom domain.

    Original languageBritish English
    Title of host publicationGLOBECOM 2023 - 2023 IEEE Global Communications Conference
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages6542-6547
    Number of pages6
    ISBN (Electronic)9798350310900
    DOIs
    StatePublished - 2023
    Event2023 IEEE Global Communications Conference, GLOBECOM 2023 - Kuala Lumpur, Malaysia
    Duration: 4 Dec 20238 Dec 2023

    Publication series

    NameProceedings - IEEE Global Communications Conference, GLOBECOM
    ISSN (Print)2334-0983
    ISSN (Electronic)2576-6813

    Conference

    Conference2023 IEEE Global Communications Conference, GLOBECOM 2023
    Country/TerritoryMalaysia
    CityKuala Lumpur
    Period4/12/238/12/23

    Keywords

    • 3GPP
    • Generative AI
    • Large Language Models
    • Pre-trained Transformer
    • Telecom Language

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