Researchers from CMU and UC Santa Barbara Propose Innovative AI-Based ‘Diagnosis of Thought’ Prompting for Cognitive Distortion Detection in Psychotherapy

In the entire world, about one in eight persons have mental problems. However, mental health disorders are significantly underserved for various reasons, such as a lack of mental health specialists, subpar treatments, prohibitive costs, and societal stigma. In high-income regions, treatment coverage for mental health services is 33%; in low- and lower-middle-income areas, it is just 8%. According to an APA report published recently, six of ten psychologists “no longer have openings for new patients.” Continuous work has gone into creating automated tools for mental health assistance, such as compassionate chatbots and sentiment analysis, to lessen the impact of such circumstances. 

Existing efforts, however, typically make superficial heuristic attempts, such as emotion analysis and producing consoling reactions. Such systems still have much to learn about how to contribute to professional psychotherapy, which calls for in-depth research into the patient’s thought processes, the creation of cognition models, and techniques for reconstructing cognition models. Commonly used traditional treatment paradigms like cognitive-behavior therapy (CBT) and acceptance and commitment therapy (ACT) are built around these techniques. Building professional support for psychotherapy is made more difficult because most data sources documenting the interactions between patients and licensed professionals are confidential. 

Recent advancements in the Large Language Model (LLM) development reveal this model’s astounding aptitude for different textual reasoning problems in a zero-shot environment. ChatGPT and GPT-4 provide highly promising results in the traditional Sally-Anne exam, which assesses the fundamental theory of the mind’s capacity to ascribe mental states, including beliefs, emotions, wants, etc. Further, using this capacity for intricate cognitive analysis and reasoning is promising. The moment is perfect for building expert, focused, organized AI support for psychotherapy. They take the first step in this work by examining the first essential procedure in cognitive behavior therapy (CBT), the job of cognitive distortion identification. 

Researchers from Carnegie Mellon University and the University of California, Santa Barbara, suggest the Diagnosis of Thought (DoT) prompting, which was inspired by how psychotherapy specialists undertake sophisticated diagnosis over the patient’s speech. In DoT, they use three steps to diagnose the patient’s speech: subjective evaluation, contrastive reasoning, and schema analysis. They separate the patient’s subjective ideas from the objective facts while doing a subjective evaluation. In contrastive reasoning, they extract the justifications for and against the patient’s ideas. Finally, they summarise the underlying thinking schema and connect it to the various forms of cognitive distortions in schema analysis. 

With the most recent top-performing LLMs, they do extensive trials. DoT achieves over 10% and 15% relative gains for distortion evaluation and classification on ChatGPT in zero-shot settings, respectively. The diagnostic procedure is fully interpretable thanks to the generated justifications during the three steps, and human specialists further confirm their quality. They demonstrate the enormous potential of LLM in enhancing professional psychotherapy. This investigation acts as the starting point for a bigger project; they invite the communities of AI and psychotherapy to work together on a joint venture. Their ultimate objective is to provide expert, secure, AI-driven help that can significantly improve mental health support systems.

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