心理健康预训练语言模型:关于阿拉伯语问答分类的实证研究
Hassan Alhuzali1, Ashwag Alasmari2,3
1Department of Computer Science and Artificial Intelligence, Umm Al-Qura University, Makkah 24382, Saudi Arabia.
Healthcare (Basel, Switzerland)
|May 14, 2025
概括
预先训练有素的语言模型 (PLM) 显示了阿拉伯语心理健康护理的巨大潜力. 微调和基于提示的方法,特别是GPT-3.5,显著提高了问题和答案分类的准确性.
科学领域:
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
- 数字心理健康数字心理健康
背景情况:
- 预先训练有素的语言模型 (PLM) 为可访问的心理健康资源提供了希望.
- 有限的研究存在于PLM在阿拉伯精神健康应用中的有效性.
- 这项研究解决了对阿拉伯精神健康任务的PLM评估的差距.
研究的目的:
- 评估各种预训练语言模型 (PLM) 在分类阿拉伯语心理健康问题和答案方面的表现.
- 将传统的机器学习方法与基于PLM的策略进行比较.
- 调查微调和基于提示的技术对模型性能的影响.
主要方法:
- 使用阿拉伯语MentalQA数据集进行问题和答案分类.
- 试验了传统的特征提取,PLM作为特征提取器,PLM微调和基于提示的方法 (GPT-3.5,GPT-4).
- 评估的阿拉伯语特定的PLM:阿拉伯特,卡梅尔伯特和马伯特.
主要成果:
- PLM的表现优于传统方法,有效地捕捉语义细微差别.
- 马尔伯特以0.80 (问题) 和0.86 (答案) 的雅卡德分数取得了最佳表现.
- 微调和基于提示的短时间学习 (GPT-3.5) 显著提高了分类准确性.
结论:
- PLM和基于提示的方法显示出在心理健康方面支持阿拉伯语说话的人的巨大潜力.
- 这项研究强调了PLM在阿拉伯精神健康环境中的可访问性和有效性的提高.
- 这些发现有助于理解PLM在文化敏感数字心理健康解决方案中的应用.
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