@inproceedings{saha-etal-2026-transformer,
    title = "transformer{\_}1376 at {P}sy{D}ef{D}etect: A {QL}o{RA}-Based Generative Framework for Context-Aware Psychological Defense Mechanism Detection",
    author = "Saha, Pritha  and
      Saha, Shuvodwip  and
      Shanto, Anik Mahmud",
    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-2.15/",
    pages = "99--103",
    ISBN = "979-8-89176-435-4",
    abstract = "Psychological defense mechanisms play a cru-cial role in shaping human responses duringemotionally charged conversations, yet remainunderexplored in natural language processing.In this work, we address the PSYDEFCONVshared task, which involves classifying defensemechanisms in multi-turn dialogues using clin-ically grounded annotations based on the De-fense Mechanism Rating Scales (DMRS). Wepropose a generative supervised fine-tuningframework that reformulates the task as con-ditional text generation. A pre-trained causallanguage model (Gemma-2-2B) is adapted us-ing parameter-efficient fine-tuning (PEFT) with4-bit quantization, enabling efficient trainingunder limited computational resources. To han-dle class imbalance, we apply random oversam-pling, and we design a prompt-based input rep-resentation to incorporate conversational con-text effectively. Experimental results demon-strate that our generative approach is compet-itive with discriminative baselines while of-fering improved flexibility in modeling sub-tle and context-dependent defensive behaviors.The findings highlight the potential of genera-tive large language models for psychologicallygrounded dialogue understanding tasks."
}