@inproceedings{zhu-etal-2024-xinhai,
title = "{X}in{H}ai@{CLP}sych 2024 Shared Task: Prompting Healthcare-oriented {LLM}s for Evidence Highlighting in Posts with Suicide Risk",
author = "Zhu, Jingwei and
Xu, Ancheng and
Tan, Minghuan and
Yang, Min",
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://aclanthology.org/2024.clpsych-1.23",
pages = "238--246",
abstract = "In this article, we introduce a new method for analyzing and summarizing posts from r/SuicideWatch on Reddit, overcoming the limitations of current techniques in processing complex mental health discussions online. Existing methods often struggle to accurately identify and contextualize subtle expressions of mental health problems, leading to inadequate support and intervention strategies. Our approach combines the open-source Large Language Model (LLM), fine-tuned with health-oriented knowledge, to effectively process Reddit posts. We also design prompts that focus on suicide-related statements, extracting key statements, and generating concise summaries that capture the core aspects of the discussions. The preliminary results indicate that our method improves the understanding of online suicide-related posts compared to existing methodologies.",
}
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<abstract>In this article, we introduce a new method for analyzing and summarizing posts from r/SuicideWatch on Reddit, overcoming the limitations of current techniques in processing complex mental health discussions online. Existing methods often struggle to accurately identify and contextualize subtle expressions of mental health problems, leading to inadequate support and intervention strategies. Our approach combines the open-source Large Language Model (LLM), fine-tuned with health-oriented knowledge, to effectively process Reddit posts. We also design prompts that focus on suicide-related statements, extracting key statements, and generating concise summaries that capture the core aspects of the discussions. The preliminary results indicate that our method improves the understanding of online suicide-related posts compared to existing methodologies.</abstract>
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%0 Conference Proceedings
%T XinHai@CLPsych 2024 Shared Task: Prompting Healthcare-oriented LLMs for Evidence Highlighting in Posts with Suicide Risk
%A Zhu, Jingwei
%A Xu, Ancheng
%A Tan, Minghuan
%A Yang, Min
%Y Yates, Andrew
%Y Desmet, Bart
%Y Prud’hommeaux, Emily
%Y Zirikly, Ayah
%Y Bedrick, Steven
%Y MacAvaney, Sean
%Y Bar, Kfir
%Y Ireland, Molly
%Y Ophir, Yaakov
%S Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024)
%D 2024
%8 March
%I Association for Computational Linguistics
%C St. Julians, Malta
%F zhu-etal-2024-xinhai
%X In this article, we introduce a new method for analyzing and summarizing posts from r/SuicideWatch on Reddit, overcoming the limitations of current techniques in processing complex mental health discussions online. Existing methods often struggle to accurately identify and contextualize subtle expressions of mental health problems, leading to inadequate support and intervention strategies. Our approach combines the open-source Large Language Model (LLM), fine-tuned with health-oriented knowledge, to effectively process Reddit posts. We also design prompts that focus on suicide-related statements, extracting key statements, and generating concise summaries that capture the core aspects of the discussions. The preliminary results indicate that our method improves the understanding of online suicide-related posts compared to existing methodologies.
%U https://aclanthology.org/2024.clpsych-1.23
%P 238-246
Markdown (Informal)
[XinHai@CLPsych 2024 Shared Task: Prompting Healthcare-oriented LLMs for Evidence Highlighting in Posts with Suicide Risk](https://aclanthology.org/2024.clpsych-1.23) (Zhu et al., CLPsych-WS 2024)
ACL