Harnessing Generative Llms to Detect and Explain Suicidal Ideation in Brazilian Portuguese Texts

Suicide remains a critical public health problem, with increasing cases of Suicide Ideation (SI) necessitating improved identification and intervention strategies. Although generative Large Language Models (LLMs) have demonstrated potential in text analysis for mental health applications, their effectiveness in accurately detecting SI and generating reliable explanations remains underexplored, specially in Brazilian Portuguese (PT-BR) language. This study proposes a new architecture for the Boamente, an AI-based system for suicide prevention. The proposal aims to incorporate into Boamente architecture an explanation generation stage through prompt engineering. The aim is to improve text detection through the classifier model and exploit prompt engineering through an explainer model to generate explanations of why a text in PT-BR does or does not contain SI, increasing the model's interpretability and providing more transparent justifications for its predictions. The proposed structure expands the Boamente architecture by exploring generative LLMs as classifier models (i.e., SI identification) and integrating an explainer model, which generates justifications for predictions. A quantitative evaluation was conducted with different LLMs using classification performance metrics, in which Qwen 2.5 (14B) achieved the highest AUC (0.9898), while the 3B and 7B versions of the same model achieved the best Recall (0.9545). In addition, a qualitative evaluation was conducted with the participation of three professionals (a computer scientist, a linguist, and a psychologist) to analyze the explanations generated by LLMs. The participants considered that LLaMA 3.1 (8B) produced the highest quality justifications. The findings highlight the potential of combining classification and explanation LLMs to enhance explainability and trust in an AI-driven system for suicide prevention.