  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.
