@inproceedings{lee-etal-2025-telagentbench,
    title = "{T}el{A}gent{B}ench: A Multi-faceted Benchmark for Evaluating {LLM}-based Agents in Telecommunications",
    author = "Lee, Sunwoo  and
      Jang, Daseong  and
      Arya, Dhammiko  and
      Han, Gyoung-eun  and
      Song, Injee  and
      Kim, SaeRom  and
      Kim, Sangjin  and
      Lee, Seojin  and
      Hong, Seokyoung  and
      Sek, Sereimony  and
      Cho, Seung-Mo  and
      Park, Sohee  and
      Yoon, Sungbin  and
      Jang, Wonbeom  and
      Davis, Eric",
    editor = "Potdar, Saloni  and
      Rojas-Barahona, Lina  and
      Montella, Sebastien",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track",
    month = nov,
    year = "2025",
    address = "Suzhou (China)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-industry.83/",
    pages = "1173--1211",
    ISBN = "979-8-89176-333-3",
    abstract = "As Large Language Models (LLMs) evolve into powerful agentic systems, the telecommunications industry{'}s expansion into AI services necessitates industry-grounded benchmarks to evaluate their underexplored domain-specific capabilities. To address the gap left by generic benchmarks that fail to assess realistic, non-English performance, we present TelAgentBench, a Korean benchmark for the telecommunications domain evaluating five core agentic capabilities: Reasoning, Planning, Action (tool-use), Retrieval-Augmented Generation, and Instruction Following. Evaluations reveal significant performance disparities between models that employ explicit reasoning and those that do not, providing actionable insights for deploying agentic LLMs in real-world telecommunications tasks."
}