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
this https URL

我们提出了一个使用大型语言模型将单轮心理疗法咨询会话转化为多轮交互的流程，通过信息提取和多轮咨询生成两个主要步骤，显著提高了大型语言模型在心理健康咨询方面生成高质量多轮对话的能力。

增强心理疗法咨询：利用大型语言模型增强咨询对话的数据增强管道

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

The demand for psychological counseling has grown significantly in recent
years, particularly with the global outbreak of COVID-19, which has heightened
the need for timely and professional mental health support. Online
psychological counseling has emerged as the predominant mode of providing
services in response to this demand. In this study, we propose the Psy-LLM
framework, an AI-based system leveraging Large Language Models (LLMs) for
question-answering in online psychological consultation. Our framework combines
pre-trained LLMs with real-world professional Q&A from psychologists and
extensively crawled psychological articles. The Psy-LLM framework serves as a
front-end tool for healthcare professionals, allowing them to provide immediate
responses and mindfulness activities to alleviate patient stress. Additionally,
it functions as a screening tool to identify urgent cases requiring further
assistance. We evaluated the framework using intrinsic metrics, such as
perplexity, and extrinsic evaluation metrics, with human participant
assessments of response helpfulness, fluency, relevance, and logic. The results
demonstrate the effectiveness of the Psy-LLM framework in generating coherent
and relevant answers to psychological questions. This article concludes by
discussing the potential of large language models to enhance mental health
support through AI technologies in online psychological consultation.

该研究提出了 Psy-LLM 框架，这是利用大型语言模型（LLMs）进行在线心理咨询问答的基于人工智能的系统，旨在提供心理健康支持，并通过评估证明了其在生成连贯和相关答案方面的有效性。