Explainable Suicide Risk Assessment on Social Media with Multi-Task QLoRA
Explainable suicide-risk assessment requires models not only to estimate risk severity, but also to identify supporting language and the risk and protective factors expressed in a post. We present our system for the IEEE BigData 2026 Cup on Explainable Suicide Risk Assessment on Social Media, which addresses three tasks: risk-level classification, evidence phrase extraction, and multi-label factor identification. Our approach adapts Qwen2.5-Instruct models using quantized low-rank adaptation (QLoRA) and an answer-masked causal language-model objective. We jointly train across all three tasks for risk classification, jointly train on Tasks 1a and 1b for evidence extraction, and adapt Task ...
| Authors | Xuan Zhong Feng, Geoffrey Martin, Hexin Dong et al. |
|---|---|
| Primary category | cs.CL |
| Published | 30 September 2026 |
| Last updated at source | 2 October 2026 |
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