# Evaluating Open-Source Large Language Models for Technical Telecom Question Answering Source: [Evaluating Open-Source Large Language Models for Technical Telecom Question Answering]() Collection index: [llms.txt]() Data updated: 2025-10-26T05:45:26.405Z Authors: Arina Caraus, Alessio Buscemi, Sumit Kumar, Ion Turcanu Published: 2025-09-26T06:29:15.000Z ## Abstract Large Language Models (LLMs) have shown remarkable capabilities across various fields. However, their performance in technical domains such as telecommunications remains underexplored. This paper evaluates two open-source LLMs, Gemma 3 27B and DeepSeek R1 32B, on factual and reasoning-based questions derived from advanced wireless communications material. We construct a benchmark of 105 question-answer pairs and assess performance using lexical metrics, semantic similarity, and LLM-as-a-judge scoring. We also analyze consistency, judgment reliability, and hallucination through source attribution and score variance. Results show that Gemma excels in semantic fidelity and LLM-rated correctness, while DeepSeek demonstrates slightly higher lexical consistency. Additional findings highlight current limitations in telecom applications and the need for domain-adapted models to support trustworthy Artificial Intelligence (AI) assistants in engineering. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()