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arXiv 2406.08718cs.CL
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增强心理治疗咨询:利用大型语言模型处理咨询对话的数据增强流水线

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

Jun-Woo Kim, Ji-Eun Han, Jun-Seok Koh, Hyeon-Tae Seo, Du-Seong Chang

AI总结:

本文提出利用LLM将单轮心理治疗咨询转为多轮对话的两阶段数据增强流水线,并通过零样本与少样本实验验证其可提升心理健康咨询多轮对话生成质量。

摘要:

我们提出了一种利用大型语言模型(LLM)将单轮心理治疗咨询会话转化为多轮交互的流水线。尽管面向精神障碍患者的AI支持在线咨询服务已经存在,但它们通常受到多轮训练数据集有限可用性的制约,并且往往未能充分利用治疗师的专业知识。我们提出的流水线有效解决了这些局限。该流水线包括两个主要步骤:1)信息抽取;2)多轮咨询生成。每个步骤都经过精心设计,以从可用数据集中抽取并生成全面的多轮咨询对话。来自零样本和少样本生成场景的实验结果表明,我们的方法显著增强了LLM在心理健康咨询情境中生成更高质量多轮对话的能力。我们的流水线和数据集已公开发布于 https://github.com/jwkim-chat/A-Data-Augmentation-Pipeline-Leveraging-Large-Language-Models-for-Counseling-Conversations。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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