赋能心理治疗的大语言模型：通过思想诊断提示进行认知扭曲检测
Empowering Psychotherapy with Large Language Models: Cognitive Distortion Detection through Diagnosis of Thought Prompting
AI总结：
本文提出思想诊断提示方法，通过三阶段诊断检测认知扭曲，显著优于ChatGPT，生成高质量推理以辅助心理治疗专业人员。
摘要：
精神疾病仍然是我们这个时代最关键的公共卫生问题之一，这源于专业人员的严重短缺和可及性限制。心理治疗需要高水平的专业知识，对患者的认知建模进行深入、复杂的推理和分析。在大语言模型时代，我们认为现在是开发用于计算心理治疗的AI辅助工具的恰当时机。我们研究了认知扭曲检测任务，并提出了思想诊断（DoT）提示方法。DoT通过三个阶段对患者的言语进行诊断：主观性评估以区分事实与思想；对比推理以引出支持和反驳思想的推理过程；以及图式分析以总结认知图式。通过这三个阶段生成的诊断推理对于辅助专业人员至关重要。实验表明，与ChatGPT相比，DoT在认知扭曲检测方面取得了显著改进，同时生成了经过人类专家认可的高质量推理。
英文摘要：
Mental illness remains one of the most critical public health issues of our time, due to the severe scarcity and accessibility limit of professionals. Psychotherapy requires high-level expertise to conduct deep, complex reasoning and analysis on the cognition modeling of the patients. In the era of Large Language Models, we believe it is the right time to develop AI assistance for computational psychotherapy. We study the task of cognitive distortion detection and propose the Diagnosis of Thought (DoT) prompting. DoT performs diagnosis on the patient's speech via three stages: subjectivity assessment to separate the facts and the thoughts; contrastive reasoning to elicit the reasoning processes supporting and contradicting the thoughts; and schema analysis to summarize the cognition schemas. The generated diagnosis rationales through the three stages are essential for assisting the professionals. Experiments demonstrate that DoT obtains significant improvements over ChatGPT for cognitive distortion detection, while generating high-quality rationales approved by human experts.
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。
