A Dual-Prompting for Interpretable Mental Health Language Models

Hyolim Jeon, Dongje Yoo, Daeun Lee, Sejung Son, Seungbae Kim, Jinyoung Han


Abstract
Despite the increasing demand for AI-based mental health monitoring tools, their practical utility for clinicians is limited by the lack of interpretability. The CLPsych 2024 Shared Task (Chim et al., 2024) aims to enhance the interpretability of Large Language Models (LLMs), particularly in mental health analysis, by providing evidence of suicidality through linguistic content. We propose a dual-prompting approach: (i) Knowledge-aware evidence extraction by leveraging the expert identity and a suicide dictionary with a mental health-specific LLM; and (ii) Evidence summarization by employing an LLM-based consistency evaluator. Comprehensive experiments demonstrate the effectiveness of combining domain-specific information, revealing performance improvements and the approach’s potential to aid clinicians in assessing mental state progression.
Anthology ID:
2024.clpsych-1.24
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:
247–255
Language:
URL:
https://aclanthology.org/2024.clpsych-1.24
DOI:
Bibkey:
Cite (ACL):
Hyolim Jeon, Dongje Yoo, Daeun Lee, Sejung Son, Seungbae Kim, and Jinyoung Han. 2024. A Dual-Prompting for Interpretable Mental Health Language Models. In Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024), pages 247–255, St. Julians, Malta. Association for Computational Linguistics.
Cite (Informal):
A Dual-Prompting for Interpretable Mental Health Language Models (Jeon et al., CLPsych-WS 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.clpsych-1.24.pdf
Video:
 https://aclanthology.org/2024.clpsych-1.24.mp4