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

Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction