@inproceedings{l-etal-2024-detecting,
    title = "Detecting Suicide Risk Patterns using Hierarchical Attention Networks with Large Language Models",
    author = "L, Koushik  and
      M, Vishruth  and
      M, Anand Kumar",
    editor = "Yates, Andrew  and
      Desmet, Bart  and
      Prud{'}hommeaux, Emily  and
      Zirikly, Ayah  and
      Bedrick, Steven  and
      MacAvaney, Sean  and
      Bar, Kfir  and
      Ireland, Molly  and
      Ophir, Yaakov",
    booktitle = "Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024)",
    month = mar,
    year = "2024",
    address = "St. Julians, Malta",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.clpsych-1.21/",
    pages = "227--231",
    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 Lan- guage 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 rea- son 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 Hierarchi- cal Attention Networks (HAN) and Llama2 for finding supporting evidence about an individ- ual`s suicide risk level."
}