@inproceedings{tran-matsui-2024-team,
title = "Team {ISM} at {CLP}sych 2024: Extracting Evidence of Suicide Risk from {R}eddit Posts with Knowledge Self-Generation and Output Refinement using A Large Language Model",
author = "Tran, Vu and
Matsui, Tomoko",
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-1.16",
pages = "191--196",
abstract = "This paper presents our approach to the CLPsych 2024 shared task: utilizing large language models (LLMs) for finding supporting evidence about an individual{'}s suicide risk level in Reddit posts. Our framework is constructed around an LLM with knowledge self-generation and output refinement. The knowledge self-generation process produces task-related knowledge which is generated by the LLM and leads to accurate risk predictions. The output refinement process, later, with the selected best set of LLM-generated knowledge, refines the outputs by prompting the LLM repeatedly with different knowledge instances interchangeably. We achieved highly competitive results comparing to the top-performance participants with our official recall of 93.5{\%}, recall{--}precision harmonic-mean of 92.3{\%}, and mean consistency of 96.1{\%}.",
}
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<abstract>This paper presents our approach to the CLPsych 2024 shared task: utilizing large language models (LLMs) for finding supporting evidence about an individual’s suicide risk level in Reddit posts. Our framework is constructed around an LLM with knowledge self-generation and output refinement. The knowledge self-generation process produces task-related knowledge which is generated by the LLM and leads to accurate risk predictions. The output refinement process, later, with the selected best set of LLM-generated knowledge, refines the outputs by prompting the LLM repeatedly with different knowledge instances interchangeably. We achieved highly competitive results comparing to the top-performance participants with our official recall of 93.5%, recall–precision harmonic-mean of 92.3%, and mean consistency of 96.1%.</abstract>
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%0 Conference Proceedings
%T Team ISM at CLPsych 2024: Extracting Evidence of Suicide Risk from Reddit Posts with Knowledge Self-Generation and Output Refinement using A Large Language Model
%A Tran, Vu
%A Matsui, Tomoko
%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 tran-matsui-2024-team
%X This paper presents our approach to the CLPsych 2024 shared task: utilizing large language models (LLMs) for finding supporting evidence about an individual’s suicide risk level in Reddit posts. Our framework is constructed around an LLM with knowledge self-generation and output refinement. The knowledge self-generation process produces task-related knowledge which is generated by the LLM and leads to accurate risk predictions. The output refinement process, later, with the selected best set of LLM-generated knowledge, refines the outputs by prompting the LLM repeatedly with different knowledge instances interchangeably. We achieved highly competitive results comparing to the top-performance participants with our official recall of 93.5%, recall–precision harmonic-mean of 92.3%, and mean consistency of 96.1%.
%U https://aclanthology.org/2024.clpsych-1.16
%P 191-196
Markdown (Informal)
[Team ISM at CLPsych 2024: Extracting Evidence of Suicide Risk from Reddit Posts with Knowledge Self-Generation and Output Refinement using A Large Language Model](https://aclanthology.org/2024.clpsych-1.16) (Tran & Matsui, CLPsych-WS 2024)
ACL