TelecomGPT: A Framework to Build Telecom-Specfic Large Language Models
<|reference_start|>TelecomGPT: A Framework to Build Telecom-Specfic Large Language Models: Large Language Models (LLMs) have the potential to revolutionize the Sixth Generation (6G) communication networks. However, current mainstream LLMs generally lack the specialized knowledge in telecom domain. In this paper, for the first time, we propose a pipeline to adapt any general purpose LLMs to a telecom-specific LLMs. We collect and build telecom-specific pre-train dataset, instruction dataset, preference dataset to perform continual pre-training, instruct tuning and alignment tuning respectively. Besides, due to the lack of widely accepted evaluation benchmarks in telecom domain, we extend existing evaluation benchmarks and proposed three new benchmarks, namely, Telecom Math Modeling, Telecom Open QnA and Telecom Code Tasks. These new benchmarks provide a holistic evaluation of the capabilities of LLMs including math modeling, Open-Ended question answering, code generation, infilling, summarization and analysis in telecom domain. Our fine-tuned LLM TelecomGPT outperforms state of the art (SOTA) LLMs including GPT-4, Llama-3 and Mistral in Telecom Math Modeling benchmark significantly and achieve comparable performance in various evaluation benchmarks such as TeleQnA, 3GPP technical documents classification, telecom code summary and generation and infilling.<|reference_end|>
@article{zou2024telecomgpt:,
    title={TelecomGPT: A Framework to Build Telecom-Specfic Large Language Models},
    author={Hang Zou, Qiyang Zhao, Yu Tian, Lina Bariah, Faouzi Bader, Thierry
  Lestable, and Merouane Debbah},
    journal={arXiv preprint arXiv:2407.09424},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.09424},
    primaryClass={eess.SP cs.AI}
}