用大模型分析西语文本，提前识别抑郁风险并给出可解释理由。
Hacia la interpretabilidad de la detección anticipada de riesgos de depresión utilizando grandes modelos de lenguaje
- 基于专家标准设计推理准则，结合上下文学习提升模型判断力。
- 在西班牙语文本上实现高精度预测，且推理过程可被人理解。
- 适合心理健康监测、临床辅助决策等需要透明性的场景。
网络上的早期风险检测（EDR）旨在尽早识别潜在风险用户。尽管大型语言模型（LLMs）在多种语言任务中表现出高效性，但在特定领域评估其推理能力仍至关重要。本文提出一种基于大模型的西班牙语抑郁风险早期检测方法，确保生成结果具备人类可解释性。我们定义了专家级推理标准，对Gemini模型采用上下文学习策略，并从定量与定性两个层面评估其性能。结果显示，模型能实现准确预测，并附带可解释的推理过程，从而深化对解决方案的理解。该方法为利用大模型应对EDR问题提供了新视角。
原文摘要 · Abstract (English)
Early Detection of Risks (EDR) on the Web involves identifying at-risk users as early as possible. Although Large Language Models (LLMs) have proven to solve various linguistic tasks efficiently, assessing their reasoning ability in specific domains is crucial. In this work, we propose a method for solving depression-related EDR using LLMs on Spanish texts, with responses that can be interpreted by humans. We define a reasoning criterion to analyze users through a specialist, apply in-context learning to the Gemini model, and evaluate its performance both quantitatively and qualitatively. The results show that accurate predictions can be obtained, supported by explanatory reasoning, providing a deeper understanding of the solution. Our approach offers new perspectives for addressing EDR problems by leveraging the power of LLMs.
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