EmplifAI：一个用于日语共情医疗对话的细粒度数据集，包含28种情绪标签
EmplifAI: a Fine-grained Dataset for Japanese Empathetic Medical Dialogues in 28 Emotion Labels
AI总结：
EmplifAI是一个用于日语共情医疗对话的细粒度数据集，包含28种情绪标签，通过微调提升了日本LLM的流畅性和共情能力。
摘要：
本文介绍了EmplifAI，一个日本共情对话数据集，旨在支持患有慢性医疗状况的患者。他们常常经历广泛的情绪（例如希望与绝望），这些情绪在疾病管理的不同阶段会变化。EmplifAI通过提供基于情境的对话，这些对话基于28种细粒度情绪类别，从GoEmotions分类法中改编和验证，来解决这种复杂性。该数据集包括280个医学情境和4125个两轮对话，通过众包和专家审查收集。为了评估共情对话中的情感匹配，我们使用BERTScore在多个大语言模型（LLMs）上评估模型预测情况，取得了F1得分为0.83。通过EmplifAI微调基础日本LLM（LLM-jp-3.1-13b-instruct4）实现了流畅性、一般共情和情绪特定共情的显著提升。此外，我们比较了由多个LLM生成的对话中LLM-as-a-Judge和人类评分者所赋予的分数，以验证我们的评估流程，并讨论了相关分析得出的见解和潜在风险。
英文摘要：
This paper introduces EmplifAI, a Japanese empathetic dialogue dataset designed to support patients coping with chronic medical conditions. They often experience a wide range of positive and negative emotions (e.g., hope and despair) that shift across different stages of disease management. EmplifAI addresses this complexity by providing situation-based dialogues grounded in 28 fine-grained emotion categories, adapted and validated from the GoEmotions taxonomy. The dataset includes 280 medically contextualized situations and 4125 two-turn dialogues, collected through crowdsourcing and expert review. To evaluate emotional alignment in empathetic dialogues, we assessed model predictions on situation--dialogue pairs using BERTScore across multiple large language models (LLMs), achieving F1 scores of 0.83. Fine-tuning a baseline Japanese LLM (LLM-jp-3.1-13b-instruct4) with EmplifAI resulted in notable improvements in fluency, general empathy, and emotion-specific empathy. Furthermore, we compared the scores assigned by LLM-as-a-Judge and human raters on dialogues generated by multiple LLMs to validate our evaluation pipeline and discuss the insights and potential risks derived from the correlation analysis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
