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.- Anthology ID:
- 2024.clpsych-1.21
- Volume:
- Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024)
- Month:
- March
- Year:
- 2024
- Address:
- St. Julians, Malta
- Editors:
- Andrew Yates, Bart Desmet, Emily Prud’hommeaux, Ayah Zirikly, Steven Bedrick, Sean MacAvaney, Kfir Bar, Molly Ireland, Yaakov Ophir
- Venues:
- CLPsych | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 227–231
- Language:
- URL:
- https://aclanthology.org/2024.clpsych-1.21
- DOI:
- Cite (ACL):
- Koushik L, Vishruth M, and Anand Kumar M. 2024. Detecting Suicide Risk Patterns using Hierarchical Attention Networks with Large Language Models. In Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024), pages 227–231, St. Julians, Malta. Association for Computational Linguistics.
- Cite (Informal):
- Detecting Suicide Risk Patterns using Hierarchical Attention Networks with Large Language Models (L et al., CLPsych-WS 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.clpsych-1.21.pdf