Generation and Classification of Motivational-Interviewing-Style Reflections for Smoking Behaviour Change Using Few-Shot Learning with Transformers
If conversational agents can take on a therapeutic role, they may provide a scalable way to help many people suffering from addictions. Motivational Interviewing (MI) is a validated therapy for behaviour change that can be applied to addiction, including smoking cessation. A core technique in MI (and many other kinds of talk therapy) is to pose an open-ended question concerning a negative behaviour, and then to provide a reflection of the response. Reflections can be a simple restatement of the response, or a more complex inference from prior statements or general knowledge, and they help someone contemplate the behaviour more deeply. We describe a method to generate reflections that uses few-shot priming of the GPT-2 and GPT-3 language models. These produce very promising simple and complex reflections, but also some that are off-topic or irrelevant. To filter these, we train a classifier to detect poor reflections, employing samples labeled by an MI expert. Its accuracy is 81%, sensitivity 90% and specificity 71%. We show that GPT-2 can generate acceptable reflections at a 54% success rate, and when combined with the classifier/filter produces acceptable reflections 73% of the time. The GPT-3 model has a native success rate of 89%.
NSERC Discovery Grant RGPIN-2019-04395
Email Address of Submitting AuthorJonathan.Rose@ece.utoronto.ca
ORCID of Submitting Author0000-0002-3551-2175
Submitting Author's InstitutionUniversity of Toronto
Submitting Author's Country