医疗对话系统中知识传递与情感安抚的平衡
Balancing Knowledge Delivery and Emotional Comfort in Healthcare Conversational Systems
AI总结：
本研究针对医疗对话中知识传递与情感支持的平衡问题，通过改写真实医疗对话数据集生成带情绪的医患交互数据，用多种微调方法优化大模型，在保留知识回答准确性的同时显著提升了其情感回复能力。
摘要：
随着大语言模型的发展，如今许多对话系统能够针对患者的病情给出合理且信息丰富的回复。然而，患者向医生咨询时，可能会因自身病情的严重性和紧迫性产生负面情绪。如果模型在回答医疗问题的同时，能针对患者的负面情绪给予恰当的安慰与共情，很可能会在问诊过程中提供更让人安心的体验。为解决这一问题，本文探究了医疗对话过程中知识分享与情感支持之间的平衡关系。我们利用大语言模型改写了一个真实世界的交互式医疗对话数据集，生成带有负面情绪的患者提问，以及旨在安抚患者情绪同时解答其疑虑的对应医疗回复。经修改的数据被用于通过多种微调方法优化最新的大语言模型，使其能够针对患者的问题准确生成兼具情感安抚作用与建设性建议的语句。我们的实验结果表明，与原始大语言模型相比，我们的方法在保持模型原有准确提供知识性答案能力的同时，显著提升了其生成共情性回复的能力。
英文摘要：
With the advancement of large language models, many dialogue systems are now capable of providing reasonable and informative responses to patients' medical conditions. However, when patients consult their doctor, they may experience negative emotions due to the severity and urgency of their situation. If the model can provide appropriate comfort and empathy based on the patient's negative emotions while answering medical questions, it will likely offer a more reassuring experience during the medical consultation process. To address this issue, our paper explores the balance between knowledge sharing and emotional support in the healthcare dialogue process. We utilize a large language model to rewrite a real-world interactive medical dialogue dataset, generating patient queries with negative emotions and corresponding medical responses aimed at soothing the patient's emotions while addressing their concerns. The modified data serves to refine the latest large language models with various fine-tuning methods, enabling them to accurately provide sentences with both emotional reassurance and constructive suggestions in response to patients' questions. Compared to the original LLM model, our experimental results demonstrate that our methodology significantly enhances the model's ability to generate emotional responses while maintaining its original capability to provide accurate knowledge-based answers.
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。
