# Schema-Guided Response Generation using Multi-Frame Dialogue State for Motivational Interviewing Systems

## TL;DR

This paper introduces a schema-guided method for response generation in motivational interviewing dialogue systems, enabling LLMs to produce responses aligned with MI principles and effectively promote client deliberation.

## Contribution

It presents a novel multi-frame dialogue state update and response focus strategy grounded in MI principles for improved dialogue system responses.

## Key findings

- Generated MI-favorable responses successfully.
- Encouraged user deliberation with eliciting questions.
- System outperformed baseline in user study.

## Abstract

The primary goal of Motivational Interviewing (MI) is to help clients build their own motivation for behavioral change. To support this in dialogue systems, it is essential to guide large language models (LLMs) to generate counselor responses aligned with MI principles. By employing a schema-guided approach, this study proposes a method for updating multi-frame dialogue states and a strategy decision mechanism that dynamically determines the response focus in a manner grounded in MI principles. The proposed method was implemented in a dialogue system and evaluated through a user study. Results showed that the proposed system successfully generated MI-favorable responses and effectively encouraged the user's (client's) deliberation by asking eliciting questions.