Abstract
Cognitive distortion amplifies negative emotions and contributes to mental health disorders. Cognitive Behavioral Therapy (CBT) is an effective way to address cognitive distortions, but its large-scale application is limited by the shortage of professional therapists. Although large language models (LLMs) have recently been explored for mental health applications, existing methods still suffer from limited domain specificity, overly flattering responses, and the absence of well-defined annotations for cognitive distortions.
This paper proposes Cognivia, an evidence-based artificial intelligence therapist that integrates automatic cognitive distortion identification and rational response generation. Our framework is built on authoritative CBT texts widely regarded as core paradigms and standard references. It is further augmented with mental health question-answer (Q and A) data, and employs multi-stage prompting and structured generation strategies under the supervision of behavioral science experts. Then we fine-tune a lightweight LLM on this augmented CBT dataset to obtain Cognivia.
In addition, we propose the first hierarchical quality evaluation framework for assessing LLM-generated rational responses, developed through collaboration between AI researchers and behavioral science experts. Cognivia is evaluated using lexical metrics, LLM-based judges with two complementary criteria, and human evaluation by 10 behavioral science experts. It consistently outperforms baseline methods in cognitive distortion recognition and rational response generation, demonstrating its effectiveness.
Blogger's Review: Cognivia showcases an innovative application in mental health by integrating authoritative CBT texts with rich Q&A data. Its multi-stage prompting and structured generation strategies offer new insights into the automation of psychological therapy, warranting further research and promotion. The hierarchical quality evaluation framework enhances the reliability of generated content, which is crucial in the exploration of AI and mental health integration.