Cross-Scale Transfer Learning for Depression Severity Prediction: From PHQ-8 to HAMD-17 Across Languages and Clinical Paradigms
Sequential LoRA transfer from English PHQ-8 interviews to Chinese HAMD-17 consultations improves depression-score prediction with Qwen3.
The paper proposes sequential LoRA transfer for depression-severity prediction when labeled clinical interviews are scarce. A Qwen3 model is first fine-tuned on 189 English DAIC-WOZ sessions scored with PHQ-8, then the adapter initializes training on 100 Chinese PDCH consultations scored with HAMD-17. On the target task, Qwen3-0.6B reached MAE 4.96, RMSE 6.59, and macro-F1 0.36, while Qwen3-1.7B reached 4.38, 5.62, and 0.46, outperforming target-only and non-LLM baselines. The authors describe a single-site exploratory evaluation and do not claim screening or diagnostic utility.
- Sequential LoRA fine-tunes Qwen3 on DAIC-WOZ PHQ-8, then PDCH HAMD-17.
- Qwen3-1.7B reached MAE 4.38, RMSE 5.62, and macro-F1 0.46.
- Native Chinese input outperformed machine-translated English on the target task.
- Authors report a single-site study without claimed diagnostic utility.
- Countries
- China
- AI models
- Qwen3-0.6BQwen3-1.7B
From the original article
1 min read at arxiv.orgThis work addresses continuous depression-severity score prediction from clinical interview transcripts under data scarcity.
By Wenjie Feng · arxiv.org
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