A proven therapeutic technique to overcome negative thoughts is to replace them with a more hopeful "reframed thought." Although therapy can help people practice and learn this Cognitive Reframing of Negative Thoughts, clinician shortages and mental health stigma commonly limit people's access to therapy. In this paper, we conduct a human-centered study of how language models may assist people in reframing negative thoughts. Based on psychology literature, we define a framework of seven linguistic attributes that can be used to reframe a thought. We develop automated metrics to measure these attributes and validate them with expert judgements from mental health practitioners. We collect a dataset of 600 situations, thoughts and reframes from practitioners and use it to train a retrieval-enhanced in-context learning model that effectively generates reframed thoughts and controls their linguistic attributes. To investigate what constitutes a "high-quality" reframe, we conduct an IRB-approved randomized field study on a large mental health website with over 2,000 participants. Amongst other findings, we show that people prefer highly empathic or specific reframes, as opposed to reframes that are overly positive. Our findings provide key implications for the use of LMs to assist people in overcoming negative thoughts.

翻译：经证明，用更具希望的"重构思维"替代消极思维是一种行之有效的心理治疗技术。尽管心理治疗能帮助人们练习和学习这种消极思维的认知重构，但临床医生短缺和心理疾病污名化常限制人们接受治疗的机会。本文开展了一项以人为中心的研究，探讨语言模型如何辅助人们重构消极思维。基于心理学文献，我们定义了包含七种语言属性的框架，这些属性可用于思维重构。我们开发了自动化指标来测量这些属性，并借助心理健康从业者的专家判断进行验证。我们收集了包含600个情境、思维及重构表述的数据集，并以此训练了一个检索增强型上下文学习模型，该模型能有效生成重构思维并控制其语言属性。为探究"高质量"重构的标准，我们在一个大型心理健康网站开展了经IRB批准的随机现场研究，涉及2000多名参与者。研究发现，相比于过度积极的表述，人们更偏好具有高度共情性或具体性的重构表述。我们的发现为利用语言模型辅助人们克服消极思维提供了重要启示。