Using Large Language Models to Understand Telecom Standards
arxiv(2024)
摘要
The Third Generation Partnership Project (3GPP) has successfully introduced
standards for global mobility. However, the volume and complexity of these
standards has increased over time, thus complicating access to relevant
information for vendors and service providers. Use of Generative Artificial
Intelligence (AI) and in particular Large Language Models (LLMs), may provide
faster access to relevant information. In this paper, we evaluate the
capability of state-of-art LLMs to be used as Question Answering (QA)
assistants for 3GPP document reference. Our contribution is threefold. First,
we provide a benchmark and measuring methods for evaluating performance of
LLMs. Second, we do data preprocessing and fine-tuning for one of these LLMs
and provide guidelines to increase accuracy of the responses that apply to all
LLMs. Third, we provide a model of our own, TeleRoBERTa, that performs on-par
with foundation LLMs but with an order of magnitude less number of parameters.
Results show that LLMs can be used as a credible reference tool on telecom
technical documents, and thus have potential for a number of different
applications from troubleshooting and maintenance, to network operations and
software product development.
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