TY - GEN
T1 - Are Large Language Models Possible to Conduct Cognitive Behavioral Therapy?
AU - Shen, Hao
AU - Li, Zihan
AU - Yang, Minqiang
AU - Ni, Minghui
AU - Tao, Yongfeng
AU - Yu, Zhengyang
AU - Zheng, Weihao
AU - Xu, Chen
AU - Hu, Bin
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - cognitive behavioral therapy
KW - evaluation metrics
KW - knowledge base
KW - large language models
UR - http://www.scopus.com/inward/record.url?scp=85217280029&partnerID=8YFLogxK
U2 - 10.1109/BIBM62325.2024.10821773
DO - 10.1109/BIBM62325.2024.10821773
M3 - Conference contribution
AN - SCOPUS:85217280029
T3 - Proceedings - 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024
SP - 3695
EP - 3700
BT - Proceedings - 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024
A2 - Cannataro, Mario
A2 - Zheng, Huiru
A2 - Gao, Lin
A2 - Cheng, Jianlin
A2 - de Miranda, Joao Luis
A2 - Zumpano, Ester
A2 - Hu, Xiaohua
A2 - Cho, Young-Rae
A2 - Park, Taesung
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024
Y2 - 3 December 2024 through 6 December 2024
ER -