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MentalChat16K: A Benchmark Dataset for Conversational Mental Health Assistance
Jia Xu, Tianyi Wei, Bojian Hou1
1University of Pennsylvania, Philadelphia, Pennsylvania, USA.
KDD : Proceedings. International Conference on Knowledge Discovery & Data Mining
|October 16, 2025
Summary
A new dataset, MentalChat16K, combines synthetic and real-world conversations to train AI for mental health support. This resource aids in developing empathetic AI for conditions like depression and anxiety.
Area of Science:
- Natural Language Processing
- Artificial Intelligence
- Mental Health Technology
Background:
- Conversational AI shows promise for mental health support.
- Existing datasets may lack diversity or real-world applicability.
- Ethical considerations are paramount in mental health data.
Purpose of the Study:
- Introduce MentalChat16K, a novel benchmark dataset.
- Facilitate development and evaluation of AI for mental health.
- Advance research in empathetic and personalized AI solutions.
Main Methods:
- Combined synthetic mental health counseling data.
- Incorporated anonymized transcripts of coach-caregiver interactions.
- Dataset covers diverse conditions including depression, anxiety, and grief.
Main Results:
- Created a high-quality, curated dataset (MentalChat16K).
- Dataset prioritizes patient privacy and ethical data usage.
- Provides a resource for AI model training and evaluation.
Conclusions:
- MentalChat16K enables innovation in AI for mental well-being.
- Aims to improve access to mental health support services.
- Encourages research into responsible AI for mental healthcare.
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