KMI: A Dataset of Korean Motivational Interviewing Dialogues for Psychotherapy
This addresses a data scarcity problem for developers of AI-driven mental health chatbots in Korean, though it is incremental as it builds on existing synthetic data methods for a specific domain.
The authors tackled the lack of publicly available datasets for training AI chatbots in psychotherapy, particularly for non-English languages like Korean, by creating KMI, a synthetic dataset of 1,000 Korean Motivational Interviewing dialogues generated using a framework that simulates professional therapist expertise.
The increasing demand for mental health services has led to the rise of AI-driven mental health chatbots, though challenges related to privacy, data collection, and expertise persist. Motivational Interviewing (MI) is gaining attention as a theoretical basis for boosting expertise in the development of these chatbots. However, existing datasets are showing limitations for training chatbots, leading to a substantial demand for publicly available resources in the field of MI and psychotherapy. These challenges are even more pronounced in non-English languages, where they receive less attention. In this paper, we propose a novel framework that simulates MI sessions enriched with the expertise of professional therapists. We train an MI forecaster model that mimics the behavioral choices of professional therapists and employ Large Language Models (LLMs) to generate utterances through prompt engineering. Then, we present KMI, the first synthetic dataset theoretically grounded in MI, containing 1,000 high-quality Korean Motivational Interviewing dialogues. Through an extensive expert evaluation of the generated dataset and the dialogue model trained on it, we demonstrate the quality, expertise, and practicality of KMI. We also introduce novel metrics derived from MI theory in order to evaluate dialogues from the perspective of MI.