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English(EN) Assessing Suicide Risk in Arabic Crisis Helpline Calls: A Comparison of Arabic and English Large Language Models

大型语言模型评估阿拉伯语危机电话中的自杀风险,表现与英语相当

研究人员开发并评估了用于评估阿拉伯语危机求助电话中自杀风险的大型语言模型,并将其表现与英语翻译进行了比较。该研究使用了黎巴嫩国家情感支持和预防自杀生命线去标识化的通话记录,通过在现场处理音频来确保数据隐私。阿拉伯语和英语模型都经过了微调和测试,表现最佳的阿拉伯语模型在高风险电话的宏观F1值为81.19,ROC-AUC为90.61,而表现最佳的英语模型分别为85.00和92.59。研究结果表明,可以从去标识化的阿拉伯语通话记录中有效分类自杀风险,支持这些模型作为面向接线员的工具的潜力。 AI

影响 展示了大型语言模型在心理健康支持等专业、隐私敏感领域的应用能力,有望提高危机干预效率。

排序理由 学术论文,详细介绍了大型语言模型在特定领域的创新应用及性能指标。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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大型语言模型评估阿拉伯语危机电话中的自杀风险,表现与英语相当

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学术论文,详细介绍了大型语言模型在特定领域的创新应用及性能指标。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Linhai Ma, Rita El Hachem, Mahatab El Hajj, Lilian Ghandour, Samah Fodeh ·

    评估阿拉伯语危机求助热线电话中的自杀风险:阿拉伯语与英语大型语言模型的比较

    arXiv:2609.00191v1 Announce Type: cross Abstract: Crisis helplines assess suicide risk through structured interviews, a process that is slow and dependent on operator training and workload. Natural language processing could support risk assessment and call prioritization, but alm…