Wonbeom Jang


2024

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TelBench: A Benchmark for Evaluating Telco-Specific Large Language Models
Sunwoo Lee | Dhammiko Arya | Seung-Mo Cho | Gyoung-eun Han | Seokyoung Hong | Wonbeom Jang | Seojin Lee | Sohee Park | Sereimony Sek | Injee Song | Sungbin Yoon | Eric Davis
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track

The telecommunications industry, characterized by its vast customer base and complex service offerings, necessitates a high level of domain expertise and proficiency in customer service center operations. Consequently, there is a growing demand for Large Language Models (LLMs) to augment the capabilities of customer service representatives. This paper introduces a methodology for developing a specialized Telecommunications LLM (Telco LLM) designed to enhance the efficiency of customer service agents and promote consistency in service quality across representatives. We present the construction process of TelBench, a novel dataset created for performance evaluation of customer service expertise in the telecommunications domain. We also evaluate various LLMs and demonstrate the ability to benchmark both proprietary and open-source LLMs on predefined telecommunications-related tasks, thereby establishing metrics that define telcommunications performance.