TeleQnA: A Benchmark Dataset to Assess Large Language Models
  Telecommunications Knowledge
Ali Maatouk Fadhel Ayed Nicola Piovesan Antonio De Domenico Merouane Debbah Zhi-Quan Luo
We introduce TeleQnA, the first benchmark dataset designed to evaluate the
knowledge of Large Language Models (LLMs) in telecommunications. Comprising
10,000 questions and answers, this dataset draws from diverse sources,
including standards and research articles. This paper outlines the automated
question generation framework responsible for creating this dataset, along with
how human input was integrated at various stages to ensure the quality of the
questions. Afterwards, using the provided dataset, an evaluation is conducted
to assess the capabilities of LLMs, including GPT-3.5 and GPT-4. The results
highlight that these models struggle with complex standards related questions
but exhibit proficiency in addressing general telecom-related inquiries.
Additionally, our results showcase how incorporating telecom knowledge context
significantly enhances their performance, thus shedding light on the need for a
specialized telecom foundation model. Finally, the dataset is shared with
active telecom professionals, whose performance is subsequently benchmarked
against that of the LLMs. The findings illustrate that LLMs can rival the
performance of active professionals in telecom knowledge, thanks to their
capacity to process vast amounts of information, underscoring the potential of
LLMs within this domain. The dataset has been made publicly accessible on
GitHub.