用大模型实现跨语言自杀风险检测，不依赖特定语言数据。
Language-Agnostic Suicidal Risk Detection Using Large Language Models
- 通过语音转写生成中文文本，再用提示词引导大模型提取风险特征。
- 在中英文双语特征上微调模型，效果接近直接使用中文或原生数据。
- 适合需要多语言适配的青少年心理安全监测系统开发者。
青少年自杀风险检测是一项关键挑战，现有方法依赖语言特定模型，限制了可扩展性和泛化能力。本文提出一种新型无语言限制的自杀风险评估框架，利用语音识别（ASR）将语音转为中文文本，再通过提示工程引导大语言模型（LLM）提取相关风险特征。这些特征同时保留中英文形式，用于独立微调对应的预训练语言模型。实验表明，该方法性能与直接使用ASR结果微调或仅基于中文风险特征训练的模型相当，展现出克服语言限制、提升风险评估鲁棒性的潜力。
原文摘要 · Abstract (English)
Suicidal risk detection in adolescents is a critical challenge, yet existing methods rely on language-specific models, limiting scalability and generalization. This study introduces a novel language-agnostic framework for suicidal risk assessment with large language models (LLMs). We generate Chinese transcripts from speech using an ASR model and then employ LLMs with prompt-based queries to extract suicidal risk-related features from these transcripts. The extracted features are retained in both Chinese and English to enable cross-linguistic analysis and then used to fine-tune corresponding pretrained language models independently. Experimental results show that our method achieves performance comparable to direct fine-tuning with ASR results or to models trained solely on Chinese suicidal risk-related features, demonstrating its potential to overcome language constraints and improve the robustness of suicidal risk assessment.
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