Detecting Suicide Risk Patterns using Hierarchical Attention Networks with Large Language Models

Abstract

Suicide has become a major public health and social concern in the world . This Paper looks into a method through use of LLMs (Large Language Model) to extract the likely reason for a person to attempt suicide, through analysis of their social media text posts detailing about the event, using this data we can extract the reason for the cause such mental state which can provide support for suicide prevention. This submission presents our approach for CLPsych Shared Task 2024. Our model uses Hierarchical Attention Networks (HAN) and Llama2 for finding supporting evidence about an individual’s suicide risk level. ©2024 Association for Computational Linguistics.