Enhancing Psychotherapy Counseling: A Data Augmentation Pipeline Leveraging Large Language Models for Counseling Conversations

TL;DR

This paper presents a pipeline that uses large language models to convert single-turn psychotherapy sessions into multi-turn dialogues, improving AI counseling capabilities despite limited training data.

Contribution

The authors introduce a novel data augmentation pipeline that extracts and generates multi-turn counseling conversations from limited datasets, enhancing AI mental health support.

Findings

Significantly improved multi-turn dialogue quality in zero-shot and few-shot scenarios.

Effective extraction and generation steps tailored for mental health counseling.

Public availability of the pipeline and dataset for further research.

Abstract

We introduce a pipeline that leverages Large Language Models (LLMs) to transform single-turn psychotherapy counseling sessions into multi-turn interactions. While AI-supported online counseling services for individuals with mental disorders exist, they are often constrained by the limited availability of multi-turn training datasets and frequently fail to fully utilize therapists' expertise. Our proposed pipeline effectively addresses these limitations. The pipeline comprises two main steps: 1) Information Extraction and 2) Multi-turn Counseling Generation. Each step is meticulously designed to extract and generate comprehensive multi-turn counseling conversations from the available datasets. Experimental results from both zero-shot and few-shot generation scenarios demonstrate that our approach significantly enhances the ability of LLMs to produce higher quality multi-turn dialogues in the context of mental health counseling. Our pipeline and dataset are publicly available https://github.com/jwkim-chat/A-Data-Augmentation-Pipeline-Leveraging-Large-Language-Models-for-Counseling-Conversations.