标题： KoACD：首个用于认知扭曲分析的韩国青少年数据集 显示英文标题

标题： KoACD: The First Korean Adolescent Dataset for Cognitive Distortion Analysis

摘要： 认知扭曲指的是可能导致青少年出现抑郁和焦虑等心理健康问题的消极思维模式。先前的研究主要使用自然语言处理（NLP）集中在小规模成人数据集上，对青少年的研究有限。本研究介绍了KoACD，这是首个针对韩国青少年的认知扭曲大规模数据集，包含108,717个实例。我们应用了多大语言模型（LLM）协商方法来改进扭曲分类，并使用两种方法生成合成数据：认知澄清以提高文本清晰度和认知平衡以实现不同扭曲表示的多样性。通过LLM和专家评估验证显示，虽然LLM能够用明确的标记分类扭曲，但在依赖上下文的推理方面存在困难，而人类评估者展示了更高的准确性。KoACD旨在增强未来关于认知扭曲检测的研究。 显示英文摘要

摘要： Cognitive distortion refers to negative thinking patterns that can lead to mental health issues like depression and anxiety in adolescents. Previous studies using natural language processing (NLP) have focused mainly on small-scale adult datasets, with limited research on adolescents. This study introduces KoACD, the first large-scale dataset of cognitive distortions in Korean adolescents, containing 108,717 instances. We applied a multi-Large Language Model (LLM) negotiation method to refine distortion classification and generate synthetic data using two approaches: cognitive clarification for textual clarity and cognitive balancing for diverse distortion representation. Validation through LLMs and expert evaluations showed that while LLMs classified distortions with explicit markers, they struggled with context-dependent reasoning, where human evaluators demonstrated higher accuracy. KoACD aims to enhance future research on cognitive distortion detection.