2 citations · 4 across the 11 of their papers we have counts for
5 papers · 1 filter
Optimizing Sparse Outcomes Through Dense Behavioral Signals via Value-Guided Preference Distillation
Ziyi Zhu, Daniel R. Cahn, Thomas D. Hull +4
Aligning multi-turn dialogue agents is usually framed as matching turn-level human preferences, yet direct optimization of long-term outcomes is often ineffective and prone to rewa…
Fine-tuning LLMs for Passive Depression Severity Estimation from AI Mental Health Dialogue
Olivier Tieleman, Ziyi Zhu, Ting Su +3
Depression is the leading cause of disability worldwide, and early detection of symptom change is essential for timely intervention. Validated instruments such as the Patient Healt…
Language Markers of Emotion Flexibility Predict Depression and Anxiety Treatment Outcomes
Benjamin Brindle, George A. Bonanno, Thomas Derrick Hull +2
Predicting treatment non-response for anxiety and depression is challenging, in part because of sparse symptom assessments in real-world care. We examined whether passively capture…
DIAL: Direct Iterative Adversarial Learning for Realistic Multi-Turn Dialogue Simulation
Ziyi Zhu, Olivier Tieleman, Caitlin A. Stamatis +5
Realistic user simulation is crucial for training and evaluating multi-turn dialogue systems, yet creating simulators that accurately replicate human behavior remains a significant…
Deep Representations of First-person Pronouns for Prediction of Depression Symptom Severity
Xinyang Ren, Hannah A Burkhardt, Patricia A Areán +2
Prior work has shown that analyzing the use of first-person singular pronouns can provide insight into individuals' mental status, especially depression symptom severity. These fin…