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A Study on the Application and Quality Comparison of LLM-Based Task-Oriented Dialogue Data Generation Methodology in the Korean Cafe Domain

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2025, 30(12), pp.61~72
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : October 17, 2025
  • Accepted : December 8, 2025
  • Published : December 31, 2025

Changgou Kang 1 Namgyu Kim 1

1국민대학교

Accredited

ABSTRACT

HR-MultiWOZ methodology has recently emerged as an efficient approach for generating Task-Oriented Dialogue (TOD) data with Large Language Models (LLMs). It introduced the first synthetic TOD dataset in the Human Resources (HR) domain, highlighting cost-effectiveness through schema-based design and minimal human input. However, the influence of prompt design on data quality and its applicability to non-English languages or domains beyond HR remain underexplored. Accordingly, this study is conducted as a case study to empirically examine the applicability of the HR-MultiWOZ methodology to Korean and café ordering. Results show that simple prompt adjustments can effectively control the characteristics of LLM-generated dialogue (LGD), underscoring the methodology’s scalability and the pivotal role of prompt design in shaping dialogue data quality.

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