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

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arxiv 2403.16008 v1 pith:XWHRL6CY submitted 2024-03-24 cs.CL

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

classification cs.CL
keywords healthlanguagepsychologicalcbt-llmmodelbehavioralcognitivedataset
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Modeling Multi-Dimensional Cognitive States in Large Language Models under Cognitive Crowding

    cs.CL 2026-04 unverdicted novelty 7.0

    CognitiveBench reveals LLMs suffer representation overlap on joint cognitive tasks due to hierarchical structure; HyCoLLM in hyperbolic space fixes the mismatch and outperforms GPT-4o with far fewer parameters.

  2. Cognivia: A Cognitive Behavioral Therapy Copilot for Evidence-Based Mental Healthcare

    cs.AI 2026-07 reject novelty 5.0

    Fine-tuning Qwen2.5-7B on LLM-generated CBT triplets yields responses that beat GPT-5 Mini on a rubric embedded in the generation prompt, but the clinical value is unvalidated.