Language-Agnostic Suicidal Risk Detection Using Large Language Models

• URL: http://arxiv.org/abs/2505.20109v1
• Date: Mon, 26 May 2025 15:12:10 GMT
• Title: Language-Agnostic Suicidal Risk Detection Using Large Language Models
• Authors: June-Woo Kim, Wonkyo Oh, Haram Yoon, Sung-Hoon Yoon, Dae-Jin Kim, Dong-Ho Lee, Sang-Yeol Lee, Chan-Mo Yang,
• Abstract summary: This study introduces a novel language-agnostic framework for suicidal risk assessment with large language models (LLMs)<n>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.<n> Experimental results show that our method performance comparable to direct fine-tuning with ASR results or to models trained solely on Chinese suicidal risk-related features.
• Score: 9.90722058486037
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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