@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://preview.aclanthology.org/jlcl-multiple-ingestion/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{\%}."
}