Empowering Psychotherapy with Large Language Models: Cognitive
  Distortion Detection through Diagnosis of Thought Prompting
Zhiyu Chen Yujie Lu William Yang Wang
Mental illness remains one of the most critical public health issues of our
time, due to the severe scarcity and accessibility limit of professionals.
Psychotherapy requires high-level expertise to conduct deep, complex reasoning
and analysis on the cognition modeling of the patients. In the era of Large
Language Models, we believe it is the right time to develop AI assistance for
computational psychotherapy. We study the task of cognitive distortion
detection and propose the Diagnosis of Thought (DoT) prompting. DoT performs
diagnosis on the patient's speech via three stages: subjectivity assessment to
separate the facts and the thoughts; contrastive reasoning to elicit the
reasoning processes supporting and contradicting the thoughts; and schema
analysis to summarize the cognition schemas. The generated diagnosis rationales
through the three stages are essential for assisting the professionals.
Experiments demonstrate that DoT obtains significant improvements over ChatGPT
for cognitive distortion detection, while generating high-quality rationales
approved by human experts.