Wonbeom Jang
2024
TelBench: A Benchmark for Evaluating Telco-Specific Large Language Models
Sunwoo Lee
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Dhammiko Arya
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Seung-Mo Cho
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Gyoung-eun Han
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Seokyoung Hong
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Wonbeom Jang
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Seojin Lee
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Sohee Park
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Sereimony Sek
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Injee Song
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Sungbin Yoon
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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.
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Co-authors
- Sunwoo Lee 1
- Dhammiko Arya 1
- Seung-Mo Cho 1
- Gyoung-eun Han 1
- Seokyoung Hong 1
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