Enhancing Psychotherapy Counseling: A Data Augmentation Pipeline Leveraging Large Language Models for Counseling Conversations
<|reference_start|>Enhancing Psychotherapy Counseling: A Data Augmentation Pipeline Leveraging Large Language Models for Counseling Conversations: 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.<|reference_end|>
@article{kim2024enhancing,
    title={Enhancing Psychotherapy Counseling: A Data Augmentation Pipeline
  Leveraging Large Language Models for Counseling Conversations},
    author={Jun-Woo Kim, Ji-Eun Han, Jun-Seok Koh, Hyeon-Tae Seo, Du-Seong Chang},
    journal={arXiv preprint arXiv:2406.08718},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.08718},
    primaryClass={cs.CL}
}