S3: Sequential Self-feeding Slot Prediction for Explainable Cognitive Reframing in Task-Oriented Dialogue
TL;DR
It is suggested that cognitive-theoretic structure is central to robust therapeutic state tracking, offering a practical path toward lightweight, interpretable, and clinically accountable AI systems for CBT-based counseling.
Abstract
Cognitive behavioral therapy (CBT) is a well-established psychotherapeutic approach, yet its delivery through AI systems remains challenging: small language models (SLMs) struggle with multi-step therapeutic reasoning, while large language models (LLMs) are often impractical for resource-constrained deployment. We address this gap by formulating CBT-based cognitive reframing as a task-oriented dialogue (TOD) problem with dialogue state tracking (DST), which exposes intermediate belief states—such as event, thought, and cognitive distortion—making the reasoning process explainable and auditable for clinical oversight. To support controlled experimentation, we introduce CREPAN, a synthetic dataset of over 6,800 expertguided CBT reframing dialogues for panic disorder, designed as a controlled experimental setting to investigate whether models can learn the structured reasoning prescribed by the CBT ABC model. We further propose Sequential Self-feeding Slot Prediction (S3), a lightweight reasoning scaffold that guides models through interpretable cognitive stages in a theoretically grounded order derived from Ellis’s ABC model. Experiments show that S3-T5 attains a JGA of 0.7781, narrowing the gap with 8B-scale LLMs, with the sequential self-feeding structure enabling interpretable step-by-step reasoning. These results suggest that cognitive-theoretic structure is central to robust therapeutic state tracking, offering a practical path toward lightweight, interpretable, and clinically accountable AI systems for CBT-based counseling1.