Papers by Yuzhe Zi
ESDM: Early Sensing Depression Model in Social Media Streams (2024.lrec-main)
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| Challenge: | Existing approaches to use social media data for depression detection are based on traditional risk detection (TRD) and early risk detection of depression (ERD). |
| Approach: | They propose a model that uses two modules: classification with partial information module (CPI) and decision for classification moment module (DMC) and an early detection loss function. |
| Outcome: | The proposed model outperforms benchmarks in both accuracy and accuracy with evolving partial data. |
End-to-End Learnable Psychiatric Scale Guided Risky Post Screening for Depression Detection on Social Media (2025.emnlp-main)
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| Challenge: | Existing methods to detect depression from social media posting history are limited by frozen screening models and lack of learning. |
| Approach: | They propose to use a frozen screening model to train a risky post detection model with psychiatric scales to enable a learnable end-to-end learning process. |
| Outcome: | The proposed model outperforms several strong baseline methods and qualitative analysis confirms that it better captures users’ mental states than others. |
Balancing Forget Quality and Model Utility: A Reverse KL-Divergence Knowledge Distillation Approach for Better Unlearning in LLMs (2025.naacl-long)
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| Challenge: | Existing methods for unlearning large language models struggle with forget quality and model utility, leading to over-unlearning or partial unlearning. |
| Approach: | They propose a method that uses reverse KL-divergence based knowledge distillation for unlearning to achieve significant forget quality while maintaining model utility. |
| Outcome: | The proposed method outperforms existing methods in forget quality and model utility with larger unlearning datasets. |