An LLM-based Simulation Framework for Embodied Conversational Agents in Psychological Counseling

TL;DR

This paper introduces ECAs, a simulation framework using Large Language Models and psychological principles to generate authentic and diverse counseling dialogue data, addressing privacy and data scarcity issues.

Contribution

The paper presents a novel LLM-based embodied agent simulation framework that incorporates psychological theories to improve dialogue authenticity in mental health counseling data.

Findings

ECAs dataset created and validated with counselors

Outperforms baselines in authenticity and necessity

Higher quality dialogues confirmed by automated evaluation

Abstract

Due to privacy concerns, open dialogue datasets for mental health are primarily generated through human or AI synthesis methods. However, the inherent implicit nature of psychological processes, particularly those of clients, poses challenges to the authenticity and diversity of synthetic data. In this paper, we propose ECAs (short for Embodied Conversational Agents), a framework for embodied agent simulation based on Large Language Models (LLMs) that incorporates multiple psychological theoretical principles.Using simulation, we expand real counseling case data into a nuanced embodied cognitive memory space and generate dialogue data based on high-frequency counseling questions.We validated our framework using the D4 dataset. First, we created a public ECAs dataset through batch simulations based on D4. Licensed counselors evaluated our method, demonstrating that it significantly outperforms baselines in simulation authenticity and necessity. Additionally, two LLM-based automated evaluation methods were employed to confirm the higher quality of the generated dialogues compared to the baselines. The source code and dataset are available at https://github.com/AIR-DISCOVER/ECAs-Dataset.