Reframing a negative into a positive thought is at the crux of several cognitive approaches to mental health and psychotherapy that could be made more accessible by large language model-based solutions. Such reframing is typically non-trivial and requires multiple rationalization steps to uncover the underlying issue of a negative thought and transform it to be more positive. However, this rationalization process is currently neglected by both datasets and models which reframe thoughts in one step. In this work, we address this gap by augmenting open-source datasets for positive text rewriting with synthetically-generated Socratic rationales using a novel framework called \textsc{SocraticReframe}. SocraticReframe uses a sequence of question-answer pairs to rationalize the thought rewriting process. We show that such Socratic rationales significantly improve positive text rewriting for different open-source LLMs according to both automatic and human evaluations guided by criteria from psychotherapy research. We validate our framework and the synthetic rationalizations with expert judgements from domain experts and psychology students in an IRB-approved annotation study. Our findings highlight the potential of utilizing the synergy between LLM reasoning and established psychotherapy techniques to build assistive solutions for reframing negative thoughts.

翻译：将负面想法重构为积极想法是心理健康与心理治疗中多种认知方法的核心，基于大语言模型的解决方案有望提升该过程的可及性。此类重构通常具有非平凡性，需要经过多步理性分析以揭示负面想法的潜在问题并将其转化为更积极的表达。然而，当前的数据集与模型均采用单步重构方式，忽视了这一理性分析过程。本研究通过使用新型框架 \textsc{SocraticReframe} 生成合成式苏格拉底推理依据，对开源积极文本改写数据集进行增强，从而填补该研究空白。SocraticReframe 采用序列化问答对的形式为思维改写过程提供理性依据。根据心理治疗研究标准指导的自动评估与人工评估结果表明，此类苏格拉底推理依据能显著提升不同开源大语言模型的积极文本改写性能。我们通过机构审查委员会批准的标注研究，结合领域专家与心理学专业学生的专家判断，验证了本框架及合成推理依据的有效性。本研究结果凸显了利用大语言模型推理能力与成熟心理治疗技术之间的协同效应，构建用于重构负面想法的辅助解决方案的潜力。