ScholarWorks@성균관대학교

초록

Despite improvements in survival rates, childhood cancer survivors in South Korea still face significant challenges in accessing the psychological and informational support they need. To address these challenges, we developed the Korean Childhood Cancer Survivor Question-Answer (KCCSQA) dataset in which contains 3876 questionanswer pairs. The questions were sourced from websites, academic articles, and an online survey, where 119 childhood cancer survivors contributed 1283 questions. We used GPT-4 Turbo to generate the responses, followed by an expert evaluation by 11 specialists to ensure factual accuracy, complementarity, comprehensibility, and empathy. The overall quality of the GPT-generated responses was rated 4.98 out of 6, indicating a high level of quality. To enhance the dataset, we integrated a relational knowledge graph to mitigate hallucinations in the AIgenerated answers, achieving a performance of 0.979 in hallucination detection. Additionally, a pseudo-scoring system was implemented for continuous quality assessment. The dataset's effectiveness was evaluated through a pilot study involving 14 childhood cancer survivors, who interacted with a retrieval-based QA system using a single-turn chatbot format. The mean satisfaction rating was 4.36 on a 6-point Likert scale, and all participants expressed a willingness to use the system again.

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