Adversarial Training Improves User Simulation for Mental Health Dialogue Optimization
Published:Dec 25, 2025 05:00
•1 min read
•ArXiv NLP
Analysis
This paper introduces an adversarial training framework to enhance the realism of user simulators for task-oriented dialogue (TOD) systems, specifically in the mental health domain. The core idea is to use a generator-discriminator setup to iteratively improve the simulator's ability to expose failure modes of the chatbot. The results demonstrate significant improvements over baseline models in terms of surfacing system issues, diversity, distributional alignment, and predictive validity. The strong correlation between simulated and real failure rates is a key finding, suggesting the potential for cost-effective system evaluation. The decrease in discriminator accuracy further supports the claim of improved simulator realism. This research offers a promising approach for developing more reliable and efficient mental health support chatbots.
Key Takeaways
- •Adversarial training improves user simulator realism for mental health chatbots.
- •The approach enhances the simulator's ability to expose system failure modes.
- •The resulting simulator correlates well with real-world failure occurrence rates.
Reference
“adversarial training further enhances diversity, distributional alignment, and predictive validity.”