@inproceedings{babakova-etal-2026-explainators,
    title = "Explainators at {P}sy{D}ef{D}etect: Hierarchical Prompting and Representation-Based Classification for Psychological Defenses",
    author = "Babakova, Liudmila  and
      Luongo-Vazquez, Christopher  and
      Stepin, Ilia",
    editor = "Gupta, Deepak  and
      Demner-Fushman, Dina",
    booktitle = "Proceedings of the {B}io{NLP} 2026 (Shared Tasks)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.bionlp-2.16/",
    pages = "104--108",
    ISBN = "979-8-89176-435-4",
    abstract = "Psychological defense detection is one of essential present-day challenges in clinical practice. The state-of-the-art natural language processing (NLP) tools aim to automate this task. However, their potential and efficiency remain largely unexplored. This manuscript attempts to address this problem from various perspectives: it first explores the efficiency of direct large language model (LLM)-prompting. Then, it applies NLP techniques for LLM fine-tuning applied to the psychological defense classification task. Finally, it attempts to generate states of mind based on the speaker{'}s psychological state. The results show that the complexity of the task requires further improvement of the software solutions used."
}