mlsanityData to 14 Oct 2026

Observations on LLMs for Telecom Domain: Capabilities and Limitations

arXiv 2305.13102 ↗First posted 22 May 2023cs.HC cs.AI cs.CLcited by 11

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Abstract (arXiv)

The landscape for building conversational interfaces (chatbots) has witnessed a paradigm shift with recent developments in generative Artificial Intelligence (AI) based Large Language Models (LLMs), such as ChatGPT by OpenAI (GPT3.5 and GPT4), Google's Bard, Large Language Model Meta AI (LLaMA), among others. In this paper, we analyze capabilities and limitations of incorporating such models in conversational interfaces for the telecommunication domain, specifically for enterprise wireless products and services. Using Cradlepoint's publicly available data for our experiments, we present a comparative analysis of the responses from such models for multiple use-cases including domain adaptation for terminology and product taxonomy, context continuity, robustness to input perturbations and errors. We believe this evaluation would provide useful insights to data scientists engaged in building customized conversational interfaces for domain-specific requirements.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 12 of its 23 references that are in the map. First posted on arXiv 22 May 2023.

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12 of 23 references.

References · 12

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  3. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
  4. 2022Training Compute-Optimal Large Language Modelscited by 3,019
  5. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,592
  6. 2020Learning to summarize from human feedbackcited by 2,588
  7. 2022LaMDA: Language Models for Dialog Applicationscited by 1,220
  8. 2022Scaling Laws for Reward Model Overoptimizationcited by 1,095
  9. 2023OpenAssistant Conversations -- Democratizing Large Language Model Alignmentcited by 721
  10. 2023Cerebras-GPT: Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Clustercited by 109
  11. 2021Evaluating the Robustness of Neural Language Models to Input Perturbationscited by 87
  12. 2022Large Language Models and the Reverse Turing Testcited by 38

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 4; 2025: 5; 2026: 1.

11 papers.

  1. 2024Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunitiescited by 83
  2. 2023AI-native Interconnect Framework for Integration of Large Language Model Technologies in 6G Systemscited by 19
  3. 2024Using Large Language Models to Understand Telecom Standardscited by 13
  4. 2024Observations on Building RAG Systems for Technical Documentscited by 8
  5. 2025MoE2: Optimizing Collaborative Inference for Edge Large Language Modelscited by 6
  6. 2025Mapping the Landscape of Generative AI in Network Monitoring and Managementcited by 4
  7. 2024TeleOracle: Fine-Tuned Retrieval-Augmented Generation with Long-Context Support for Networkcited by 2
  8. 2025Energy-Aware LLMs: A step towards sustainable AI for downstream applicationscited by 1
  9. 2025Evaluating Open-Source Large Language Models for Technical Telecom Question Answeringcited by 1
  10. 2025Knowledge Distillation of Domain-adapted LLMs for Question-Answering in Telecomcited by 0
  11. 2026Threat Modelling using Domain-Adapted Language Models: Empirical Evaluation and Insightscited by 0