The demand for psychological counselling 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 counselling 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 assistive tool leveraging Large Language Models (LLMs) for question-answering in psychological consultation settings to ease the demand for mental health professions. 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 discusses the potential and limitations of using large language models to enhance mental health support through AI technologies.

翻译：近年来，心理咨需求显著增长，尤其是新冠疫情全球爆发后，人们对及时、专业的心理健康支持的需求更为迫切。在线心理咨商已成为响应这一需求的主要服务模式。本研究提出Psy-LLM框架，一种基于人工智能的辅助工具，利用大语言模型（LLMs）实现心理咨商场景中的问答功能，以缓解心理健康专业人员的需求压力。该框架将预训练大语言模型与来自心理学家的真实专业问答数据及大规模爬取的心理文章相结合。Psy-LLM框架作为面向医疗专业人员的界面工具，可提供即时响应和正念活动以缓解患者压力；同时，它还可作为筛查工具，识别需要进一步干预的紧急病例。我们采用困惑度等内在指标，以及包含参与者对回答有用性、流畅性、相关性和逻辑性评估的外在评价指标对框架进行验证。结果表明，Psy-LLM框架能有效生成连贯且切题的心理问题回答。本文探讨了通过人工智能技术运用大语言模型增强心理健康支持的潜力与局限性。