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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2505.20109 |
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| _version_ | 1866910968914640896 |
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| author | Kim, June-Woo Oh, Wonkyo Yoon, Haram Yoon, Sung-Hoon Kim, Dae-Jin Lee, Dong-Ho Lee, Sang-Yeol Yang, Chan-Mo |
| author_facet | Kim, June-Woo Oh, Wonkyo Yoon, Haram Yoon, Sung-Hoon Kim, Dae-Jin Lee, Dong-Ho Lee, Sang-Yeol Yang, Chan-Mo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_20109 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Language-Agnostic Suicidal Risk Detection Using Large Language Models Kim, June-Woo Oh, Wonkyo Yoon, Haram Yoon, Sung-Hoon Kim, Dae-Jin Lee, Dong-Ho Lee, Sang-Yeol Yang, Chan-Mo Computation and Language Artificial Intelligence 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. |
| title | Language-Agnostic Suicidal Risk Detection Using Large Language Models |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2505.20109 |