Generation and Classification of Motivational-Interviewing-Style
Reflections for Smoking Behaviour Change Using Few-Shot Learning with
Transformers
Abstract
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%.