@inproceedings{ponomareva-etal-2026-self,
    title = "Self-State Identification with Retrieved In-Context Examples and Open-Weight {LLM}s",
    author = "Ponomareva, Alina  and
      Stekacheva Sancho, Nina  and
      Litvinova, Karina",
    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-1.42/",
    pages = "521--530",
    ISBN = "979-8-89176-421-7",
    abstract = "We describe a system for the CLPsych 2026 shared task on post-level identification of adaptive and maladaptive self-states. The system addresses subelement classification (Task 1.1) and presence rating (Task 1.2) with a retrieval-augmented in-context learning ensemble of two open-weight LLMs (Qwen3.5-27B and Mistral-Small-3.2-24B-Instruct) and a three-call prompt decomposition (unified, adaptive-focused, and Affect-focused extraction). Outputs are merged across models via deterministic aggregation with element-selection strategies tuned per subtask. The system placed 2nd of 17 on Task 1.1 (subelement Macro F1 = 0.441) and 5th of 17 on Task 1.2 (Avg RMSE = 0.994)."
}