@inproceedings{ravenda-etal-2026-p2p,
    title = "{P}2{P} - from Posts to Patterns: An {LLM} Ensemble Approach to Mental Health Dynamics Detection",
    author = "Ravenda, Federico  and
      Karpenko, Volodymyr  and
      Mira, Antonietta  and
      Raballo, Andrea",
    editor = "Zirikly, Aya  and
      Bar, Kfir  and
      MacAvaney, Sean  and
      Ireland, Molly  and
      Ophir, Yaakov  and
      Atzil-Slonim, Dana  and
      Varadarajan, Vasudha  and
      Bedrick, Steven  and
      Desmet, Bart",
    booktitle = "Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology ({CLP}sych 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.clpsych-1.39/",
    pages = "498--503",
    ISBN = "979-8-89176-421-7",
    abstract = "This paper presents the USAI team{'}s submission to the CLPsych 2026 Shared Task, targeting Tasks{\textasciitilde}1.1, 1.2, 2, and{\textasciitilde}3.1. We propose an ensemble-based approach combining multiple open-source large language models, where the contribution of each model is weighted according to its alignment with clinically grounded human annotations on the training set. Our system achieves competitive results across the evaluated subtasks, with particularly strong performance on Tasks{\textasciitilde}1.2 and{\textasciitilde}2."
}