PSY-STEP: Structuring Therapeutic Targets and Action Sequences for Proactive Counseling Dialogue Systems
📰 ArXiv cs.AI
PSY-STEP introduces a dataset and model for proactive counseling dialogue systems using Cognitive Behavioral Therapy principles
Action Steps
- Identify automatic negative thoughts in user input
- Structure therapeutic targets and action sequences using the PSY-STEP dataset
- Train a model like STEPPER to generate proactive counseling responses
- Evaluate and refine the model for real-world applications
Who Needs to Know This
AI engineers and researchers on a team can benefit from this as it provides a new approach to building more effective counseling dialogue systems, while product managers can consider applying this technology to mental health support products
Key Insight
💡 The PSY-STEP dataset and STEPPER model can be used to build more effective counseling dialogue systems that address automatic negative thoughts
Share This
🤖 Introducing PSY-STEP: a new approach to building proactive counseling dialogue systems using CBT principles #AI #MentalHealth
Key Takeaways
PSY-STEP introduces a dataset and model for proactive counseling dialogue systems using Cognitive Behavioral Therapy principles
Full Article
Title: PSY-STEP: Structuring Therapeutic Targets and Action Sequences for Proactive Counseling Dialogue Systems
Abstract:
arXiv:2604.04448v1 Announce Type: new Abstract: Cognitive Behavioral Therapy (CBT) aims to identify and restructure automatic negative thoughts pertaining to involuntary interpretations of events, yet existing counseling agents struggle to identify and address them in dialogue settings. To bridge this gap, we introduce STEP, a dataset that models CBT counseling by explicitly reflecting automatic thoughts alongside dynamic, action-level counseling sequences. Using this dataset, we train STEPPER,
Abstract:
arXiv:2604.04448v1 Announce Type: new Abstract: Cognitive Behavioral Therapy (CBT) aims to identify and restructure automatic negative thoughts pertaining to involuntary interpretations of events, yet existing counseling agents struggle to identify and address them in dialogue settings. To bridge this gap, we introduce STEP, a dataset that models CBT counseling by explicitly reflecting automatic thoughts alongside dynamic, action-level counseling sequences. Using this dataset, we train STEPPER,
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