Training Models to Generate, Recognize, and Reframe Unhelpful Thoughts
This work addresses a barrier in self-help mental health tools by providing scalable practice material, though it is incremental as it applies existing models to a new domain.
The paper tackles the lack of widespread adoption of cognitive well-being techniques by addressing the shortage of specific and diverse practice material, showing that current language models can generate virtually unlimited tailored examples of unhelpful thoughts and positive reframes, with a dataset of about 10k thoughts and 27k reframes.
Many cognitive approaches to well-being, such as recognizing and reframing unhelpful thoughts, have received considerable empirical support over the past decades, yet still lack truly widespread adoption in self-help format. A barrier to that adoption is a lack of adequately specific and diverse dedicated practice material. This work examines whether current language models can be leveraged to both produce a virtually unlimited quantity of practice material illustrating standard unhelpful thought patterns matching specific given contexts, and generate suitable positive reframing proposals. We propose PATTERNREFRAME, a novel dataset of about 10k examples of thoughts containing unhelpful thought patterns conditioned on a given persona, accompanied by about 27k positive reframes. By using this dataset to train and/or evaluate current models, we show that existing models can already be powerful tools to help generate an abundance of tailored practice material and hypotheses, with no or minimal additional model training required.