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English(EN) WPG-MoE: Weak-Prior-Guided Dense Mixture-of-Experts for User-Level Social Media Depression Detection

WPG-MoE框架利用LLM增强社交媒体抑郁检测 · 跟踪2个来源

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了WPG-MoE，一个利用大型语言模型（LLM）骨干来改进社交媒体数据抑郁检测的新型框架。该系统采用弱先验引导的密集专家混合方法，允许用户根据其抑郁风险的独特表达被路由到专业专家。该框架在训练期间使用特权信息来指导路由，而在推理时则依赖简化的健康状况问卷-9（PHQ-9）筛查。在中国和英语数据集上的实验表明，WPG-MoE优于现有方法并展示了可解释的路由。 AI

影响 这项研究通过更好地考虑用户的个体表达模式，可能带来更准确和个性化的人工智能驱动的心理健康筛查工具。

排序理由 该集群包含一篇详细介绍新模型架构的研究论文。

在 arXiv cs.CL 阅读 →

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WPG-MoE框架利用LLM增强社交媒体抑郁检测 · 跟踪2个来源

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报道来源 [2]

  1. arXiv cs.CL TIER_1 English(EN) · Xian Li, Yuanhe Tian, Yang Yang, Guoqing Wang, Yan Song ·

    WPG-MoE：弱先验引导的密集专家混合模型用于用户级别社交媒体抑郁检测

    arXiv:2607.04350v1 Announce Type: new Abstract: Online social media posts provide scalable signals for early depression screening, and recent studies mainly improve pre-classification evidence through risk-post selection, symptom grounding, and clinically informed feature constru…

  2. arXiv cs.CL TIER_1 English(EN) · Yan Song ·

    WPG-MoE：弱先验引导的密集专家混合模型用于用户级别社交媒体抑郁检测

    Online social media posts provide scalable signals for early depression screening, and recent studies mainly improve pre-classification evidence through risk-post selection, symptom grounding, and clinically informed feature construction. However, these screening-stage designs of…

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