Training Models to Generate, Recognize, and Reframe Unhelpful Thoughts
Mounica Maddela Megan Ung Jing Xu Andrea Madotto Heather Foran Y-Lan Boureau
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.