CBT-LLM: A Chinese Large Language Model for Cognitive Behavioral Therapy-based Mental Health Question Answering
Hongbin Na

TL;DR
This paper introduces CBT-LLM, a Chinese large language model tailored for cognitive behavioral therapy-based mental health question answering, addressing data scarcity and quality issues with a specialized dataset and structured prompts.
Contribution
The study presents a novel CBT-based dataset and fine-tunes a large language model to improve psychological support responses in Chinese, emphasizing professionalism and structure.
Findings
CBT-LLM generates highly relevant, structured responses.
The model outperforms previous approaches in psychological health support.
Empirical results confirm its practicality and quality.
Abstract
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…
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Taxonomy
TopicsMental Health via Writing
Methodstravel james
