研究人员开发了一个新的多视图学习框架，旨在提高使用文本数据进行的心理健康预测的可信度。该框架整合了来自仅编码器模型（encoder-only models）的语义信息和来自仅解码器模型（decoder-only models）的推理信息，并采用证据学习（evidential learning）方法来显式建模和管理不确定性。该系统旨在提供更可靠的预测和可信的不确定性估计，使其适用于敏感的心理健康应用。 AI
影响 该框架通过提供更好的不确定性估计，可以提高AI在敏感心理健康应用中的可靠性。
排序理由 这是一篇详细介绍心理健康预测新框架的研究论文。
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arXiv:2605.05121v1 Announce Type: new Abstract: Automated mental health prediction using textual data has shown promising results with deep learning and large language models. However, deploying these models in high-stakes real-world settings remains challenging, as existing appr…
Automated mental health prediction using textual data has shown promising results with deep learning and large language models. However, deploying these models in high-stakes real-world settings remains challenging, as existing approaches largely rely on semantic representations …