Language-Agnostic Suicidal Risk Detection Using Large Language Models

2025-05-26

AI Summary

Existing approaches to adolescent suicide risk identification suffer from strong language dependency and poor generalizability across languages. To address this, we propose the first language-agnostic cross-lingual risk assessment framework. Our method first standardizes spoken input via automatic speech recognition (ASR) into Chinese text, then leverages large language models to extract bilingual (Chinese–English) risk features; subsequently, language-specific pre-trained models are fine-tuned independently. Crucially, the framework requires no multilingual annotated data—enabling, for the first time, cross-lingual suicide risk detection with joint bilingual feature modeling and knowledge transfer. Experiments demonstrate that our approach matches monolingual baselines in accuracy while substantially improving cross-lingual robustness and generalization. It establishes a scalable, annotation-efficient paradigm for mental health screening in low-resource language settings.

Technology Category

• Natural Language Processing: Machine Translation, Multilinguality, Cross-Lingual NLP
• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Language and Vision

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

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.

Problem

• Detect suicidal risk in adolescents across languages
• Overcome language-specific model limitations with LLMs
• Enable cross-linguistic analysis of suicidal risk features

Innovation

• Language-agnostic framework using LLMs
• ASR-generated Chinese transcripts analysis
• Cross-linguistic feature retention for robustness