KMI: A Dataset of Korean Motivational Interviewing Dialogues for Psychotherapy

TL;DR

This paper introduces KMI, a synthetic Korean Motivational Interviewing dialogue dataset created using a novel framework with expert-validated quality, aimed at enhancing AI-driven mental health tools in non-English contexts.

Contribution

It presents the first MI-based synthetic Korean dialogue dataset, developed through a new framework combining expert models and LLMs, with innovative MI-specific evaluation metrics.

Findings

KMI contains 1,000 high-quality dialogues.

Expert evaluation confirms the dataset's quality and practicality.

Dialogue models trained on KMI show promising results.

Abstract

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.