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Understanding Telecom Language Through Large Language Models

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

arXiv:2306.07933v1cs.CLcs.AI

TL;DR

Telecom remains a relatively untouched domain for language-model adaptation, despite the need for telecom-specific understanding in future network tasks. The paper fine-tunes pretrained models on 3GPP documents to classify their working groups, and reports accurate classification across models and scenarios.

  • Problem

    Telecom remains almost untouched by domain-specific fine-tuning, while telecom network agents are described as needing comprehensive terminology and relationship understanding for downstream tasks.

    Source: B. Contributions · p. 2

  • Method

    The framework fine-tunes pretrained BERT, DistilBERT, RoBERTa, and GPT-2 on 3GPP technical documents for working-group classification.

    Source: B. Contributions · pp. 2–3

  • Results

    All fine-tuned models showed accurate classification under different scenarios, with RoBERTa strongest and GPT-2 weakest in the reported comparison.

    Source: §III · p. 4, §IV · p. 6

  • Takeaways & Limitations

    Telecom language models are positioned as a building block for self-evolving networks and for telecom-specific tasks by future generative agents.

    Source: B. Contributions · p. 2

Abstract

from arXiv · show

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.

I. INTRODUCTION

The paper frames self-evolving wireless networks as requiring capabilities beyond adaptation and automation, including handling diverse and unfamiliar scenarios. It presents large language models as pretrained models that can be fine-tuned for downstream tasks.

I. INTRODUCTION

  • Self-evolving networks aim to sustain performance and accommodate complex, unfamiliar scenarios beyond adaptivity and automation.The introduction says conventional AI algorithms may fall short of these requirements.Source: §I · p. 1
  • Foundation models are self-supervised neural networks trained on massive unlabeled datasets for a wide range of downstream tasks.The passage notes that this can reduce the time and cost associated with human labeling.Source: §I · p. 1
  • Pretrained LLMs such as BERT and GPT can be fine-tuned for new domains rather than retrained from scratch.LLMs are described as a language-related subfield of foundation models.Source: §I · p. 1

A. Related Work

Prior work applies domain-specific language-model adaptation to classification, sentiment analysis, authorship, multilingual processing, and healthcare. These studies motivate fine-tuning pretrained models across specialized text tasks.

A. Related Work

  • Several domain-specific pretrained language-model variations demonstrate opportunities from fine-tuning and retraining: SCIBERT was fine-tuned to the scientific domain [1], BERT to perform patent classification, and a generative model for zero-shot text classification [3].Source: A. Related Work · p. 1
  • ULMFiT reduced error by 18-24% in text classification with up to six classes on general Wikipedia articles [4].Other work explored cross-domain sentiment analysis, transaction classification [6], and authorship classification.Source: A. Related Work · p. 2
  • Fine-tuned BERT frameworks have also been studied for multilingual text and healthcare disease-name recognition -[11].The cited language studies report robustness across languages, while the healthcare work reports improved performance over prior literature.Source: A. Related Work · p. 2

B. Contributions

The paper addresses limited telecom-specific language-model work by adapting pretrained models to 3GPP technical documents. It contributes an annotated dataset and a working-group classification use case.

B. Contributions

  • The paper identifies telecom as a largely untouched domain for adapting large generative models.It positions telecom language understanding as relevant to models used in wireless-network design, building, and operation.Source: B. Contributions · p. 2
  • A prior telecom BERT-like question-answering resource comprised 347 documents and 2,021 question-answer pairs and was not publicly available.The paper argues for a broader technical-document dataset and develops a framework using 3GPP specifications.Source: B. Contributions · p. 2
  • The selected models are motivated by their contextual representations of words using both preceding and following context.The passage identifies this as suitable for technical text classification.Source: B. Contributions · p. 3
  • The work creates an annotated 3GPP dataset spanning technical topics such as radio protocols, network architecture, security, and routing.The dataset covers specifications from different working groups.Source: B. Contributions · p. 3
  • The framework fine-tunes BERT, DistilBERT, RoBERTa, and GPT-2 to classify 3GPP technical text by cellular architecture category and corresponding working group.The categories are RAN, SA, and CT.Source: B. Contributions · p. 3

A. LLMs for Telecom Language Classification

The method adapts pretrained language models to telecom text classification using a single-task, single-label setup. It compares four model families and specifies the fine-tuning and evaluation choices.

A. LLMs for Telecom Language Classification

  • BERT and its variants use surrounding words on both sides to represent context, supporting text classification.The paper uses BERT, DistilBERT, RoBERTa, and GPT-2 as pretrained language models adaptable through fine-tuning.Source: A. LLMs for Telecom Language… · p. 3
  • A SoftMax linear classification layer is added to the pretrained models to produce working-group predictions.The compared models include BERT-Base, DistilBERT, RoBERTa, and GPT-2; DistilBERT is described as having 40% fewer parameters than BERT-Base.Source: A. LLMs for Telecom Language… · p. 3
  • The models are fine-tuned for single-task, single-label 3GPP document classification using cross-entropy loss and F1 score evaluation.Training uses batch size 32, learning rate 2e-5, and L2 regularization rate 0.01.Source: A. LLMs for Telecom Language… · p. 3

B. 3GPP Technical Document Dataset

The dataset comprises 3GPP technical documents from working groups across RAN, SA, and CT. Documents from 2009–2019 are used for training, and those from 2020–2023 for testing.

B. 3GPP Technical Document Dataset

C. Data Pre-Processing

The preprocessing pipeline cleans extracted Tdoc text and creates segments of varying lengths for evaluating model understanding of technical descriptions.

C. Data Pre-Processing

  • HTML tags and URLs are removed from the extracted text using BeautifulSoup and URL-pattern matching.Source: C. Data Pre-Processing · p. 3
  • Tables are removed, and documents are split into text segments with different word counts to evaluate understanding across description lengths.Source: C. Data Pre-Processing · p. 4
  • Headers, footers, captions, pseudocode, references, change requests, drafts, and templates are excluded, and paragraph length is capped.Source: C. Data Pre-Processing · p. 4

III. EXPERIMENT RESULTS AND DISCUSSIONS

The experiments compare fine-tuned models across training-data amounts, document periods, working-group combinations, and text-segment lengths. Results vary with the model, data selection, and segment length.

III. EXPERIMENT RESULTS AND DISCUSSIONS

  • The test set uses Tdocs from 2020 through April 2023; training and validation use either 2010–2019 or 2015–2019 data in an 80/20 split.Segments contain 200 words unless otherwise specified.Source: §III · p. 4
  • RoBERTa performs strongest and GPT-2 weakest at full file use; the performance gap widens as the number of Tdoc files decreases.Fig. 2 compares classification accuracy across fine-tuned models and portions of 3GPP files.Source: §III · p. 4, Figure 2 · p. 5
  • With BERT, 2015–2019 data yields around 80% accuracy using just 20% of text segments; below 10% of files, 2010–2015 data performs better.The analysis evaluates accuracy and ROC-AUC as the share of segments used for fine-tuning changes.Source: §III · p. 4, Figure 3 · p. 5
  • BERT classifies RAN1, SA1, and CT1 combinations more accurately than RAN1, RAN2, and RAN3; test accuracy is determined mainly by RAN1, RAN2, and RAN3 documents.The paper attributes the lower accuracy for categories within the same TSG to correlated Tdoc topics, while topics across TSGs are less correlated.Source: §III · p. 5, Table III · p. 5
  • Accuracy increases as test segments grow, but the gains taper, making segment size a trade-off between performance and computing complexity.Training and validation segments are fixed at 200 words while test length varies.Source: §III · p. 5, Figure 4 · p. 5

IV. CONCLUSION

The paper proposes fine-tuning pretrained language models on 3GPP Tdocs to identify specification categories and their working groups. Its experiments support the applicability of this approach to Telecom text classification.

IV. CONCLUSION

  • Fine-tuned BERT, DistilBERT, RoBERTa, and GPT-2 models classify 3GPP technical documents into specification categories and corresponding working groups.The models are adapted using Tdocs from the RAN, SA, and CT TSGs.Source: §IV · p. 6

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