CBT-LLM：一种用于基于认知行为疗法的心理健康问答的中文大语言模型
CBT-LLM: A Chinese Large Language Model for Cognitive Behavioral Therapy-based Mental Health Question Answering
AI总结：
本研究针对心理健康问答中的数据稀缺与缺乏专业基础等挑战，基于认知行为疗法原则构建了CBT QA数据集，并微调出专为认知行为疗法设计的中文大语言模型CBT-LLM，能生成专业、结构化且高度相关的心理支持回复。
摘要：
人工智能的最新进展凸显了语言模型在心理健康支持方面的潜力。虽然基于心理健康服务平台数据训练的模型已取得初步成功，但在数据稀缺、数据质量以及确保心理学技术坚实基础等方面仍存在挑战。为应对这些挑战，本研究引入了一种新方法，通过大语言模型提升心理支持的精准度与有效性。具体而言，我们设计了源自认知行为疗法（CBT）原则的特定提示，并生成了CBT QA数据集，该数据集专门用于基于CBT结构化干预策略的中文心理健康问答。与以往方法不同，我们的数据集强调专业且结构化的回复。利用该数据集，我们对大语言模型进行了微调，诞生了CBT-LLM，这是专为认知行为疗法技术设计的大规模语言模型。实证评估表明，CBT-LLM在心理健康支持任务中擅长生成结构化、专业且高度相关的回复，展现了其实用性与质量。该模型可在Hugging Face获取：https://huggingface.co/Hongbin37/CBT-LLM。
英文摘要：
The recent advancements in artificial intelligence highlight the potential of language models in psychological health support. While models trained on data from mental health service platform have achieved preliminary success, challenges persist in areas such as data scarcity, quality, and ensuring a solid foundation in psychological techniques. To address these challenges, this study introduces a novel approach to enhance the precision and efficacy of psychological support through large language models. Specifically, we design a specific prompt derived from principles of Cognitive Behavioral Therapy (CBT) and have generated the CBT QA dataset, specifically for Chinese psychological health Q&A based on CBT structured intervention strategies. Unlike previous methods, our dataset emphasizes professional and structured response. Utilizing this dataset, we fine-tuned the large language model, giving birth to CBT-LLM, the large-scale language model specifically designed for Cognitive Behavioral Therapy techniques. Empirical evaluations demonstrate that CBT-LLM excels in generating structured, professional, and highly relevant responses in psychological health support tasks, showcasing its practicality and quality. The model is available on Hugging Face: https://huggingface.co/Hongbin37/CBT-LLM.
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。
