@inproceedings{tran-matsui-2025-team,
    title = "Team {ISM} at {CLP}sych 2025: Capturing Mental Health Dynamics from Social Media Timelines using A Pretrained Large Language Model with In-Context Learning",
    author = "Tran, Vu  and
      Matsui, Tomoko",
    editor = "Zirikly, Ayah  and
      Yates, Andrew  and
      Desmet, Bart  and
      Ireland, Molly  and
      Bedrick, Steven  and
      MacAvaney, Sean  and
      Bar, Kfir  and
      Ophir, Yaakov",
    booktitle = "Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2025)",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/corrections-2025-06/2025.clpsych-1.25/",
    doi = "10.18653/v1/2025.clpsych-1.25",
    pages = "287--291",
    ISBN = "979-8-89176-226-8",
    abstract = "We tackle the task by using a pretrained large language model (LLM) and in-context learning with template-based instructions to guide the LLM. To improve generation quality, we employ a two-step procedure: sampling and selection. For the sampling step, we randomly sample a subset of the provided training data for the context of LLM prompting. Next, for the selection step, we map the LLM generated outputs into a vector space and employ the Gaussian kernel density estimation to select the most likely output. The results show that the approach can achieve a certain degree of performance and there is still room for improvement."
}