Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction
Ashish Sharma, Kevin Rushton, Inna Wanyin Lin, David Wadden, Khendra, G. Lucas, Adam S. Miner, Theresa Nguyen, Tim Althoff

TL;DR
This study explores how language models can assist individuals in cognitively reframing negative thoughts, demonstrating effective generation of empathetic and specific reframes through a validated framework and a large-scale user study.
Contribution
The paper introduces a framework of seven linguistic attributes for reframing thoughts, develops automated metrics, and trains a retrieval-enhanced model to generate high-quality rephrases validated by mental health experts.
Findings
Participants prefer highly empathetic reframes
Specific reframes are more effective than overly positive ones
Language models can generate personalized, high-quality reframes
Abstract
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…
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Taxonomy
TopicsMental Health via Writing · Digital Mental Health Interventions · Mental Health Research Topics
