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Are Large Language Models Possible to Conduct Cognitive Behavioral Therapy?

  • Hao Shen
  • , Zihan Li
  • , Minqiang Yang*
  • , Minghui Ni
  • , Yongfeng Tao
  • , Zhengyang Yu
  • , Weihao Zheng
  • , Chen Xu
  • , Bin Hu*
  • *此作品的通讯作者
  • Lanzhou University
  • Beijing Institute of Technology

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

In contemporary society, the issue of psychological health has become increasingly prominent, characterized by the diversification, complexity, and universality of mental disorders. Cognitive Behavioral Therapy (CBT), currently the most influential and clinically effective psychological treatment method with no side effects, has limited coverage and poor quality in most countries. In recent years, researches on the recognition and intervention of emotional disorders using large language models (LLMs) have been validated, providing new possibilities for psychological assistance therapy. However, are large language models truly possible to conduct cognitive behavioral therapy? Many concerns have been raised by mental health experts regarding the use of LLMs for therapy. Seeking to answer this question, we collected real CBT corpus from online video websites, designed and conducted a targeted automatic evaluation framework involving three aspects, namely the evaluation of emotion tendency of generated text, structured dialogue pattern and proactive inquiry ability. Considering limited CBT-related texts in a general chat LLM's training corpus, we evaluated the CBT ability of the LLM after integrating a CBT knowledge base to explore the influence of introducing additional knowledge. Four LLM variants with exceptional performance are evaluated, and the experimental result shows the great potential of LLMs in psychological counseling realm, especially after combining with other technological means.

源语言英语
主期刊名Proceedings - 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024
编辑Mario Cannataro, Huiru Zheng, Lin Gao, Jianlin Cheng, Joao Luis de Miranda, Ester Zumpano, Xiaohua Hu, Young-Rae Cho, Taesung Park
出版商Institute of Electrical and Electronics Engineers Inc.
3695-3700
页数6
ISBN(电子版)9798350386226
DOI
出版状态已出版 - 2024
已对外发布
活动2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024 - Lisbon, 葡萄牙
期限: 3 12月 20246 12月 2024

出版系列

姓名Proceedings - 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

会议

会议2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024
国家/地区葡萄牙
Lisbon
时期3/12/246/12/24

联合国可持续发展目标

此成果有助于实现下列可持续发展目标:

  1. 可持续发展目标 3 - 良好健康与福祉
    可持续发展目标 3 良好健康与福祉

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