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We introduce TeleQnA, the first benchmark dataset designed to evaluate theknowledge of Large Language Models (LLMs) in telecommunications. Comprising10,000 questions and answers, this dataset draws from diverse sources,including standards and research articles. This paper outlines the automatedquestion generation framework responsible for creating this dataset, along withhow human input was integrated at various stages to ensure the quality of thequestions. Afterwards, using the provided dataset, an evaluation is conductedto assess the capabilities of LLMs, including GPT-3.5 and GPT-4. The resultshighlight that these models struggle with complex standards related questionsbut exhibit proficiency in addressing general telecom-related inquiries.Additionally, our results showcase how incorporating telecom knowledge contextsignificantly enhances their performance, thus shedding light on the need for aspecialized telecom foundation model. Finally, the dataset is shared withactive telecom professionals, whose performance is subsequently benchmarkedagainst that of the LLMs. The findings illustrate that LLMs can rival theperformance of active professionals in telecom knowledge, thanks to theircapacity to process vast amounts of information, underscoring the potential ofLLMs within this domain. The dataset has been made publicly accessible onGitHub.

TeleQnA: A Benchmark Dataset to Assess Large Language Models Telecommunications Knowledge