@inproceedings{adib-etal-2026-linguiutics,
    title = "{L}ingu{IUT}ics at {P}sy{D}ef{D}etect: Iterative Imbalance-Aware Fine-tuning of Qwen3-8{B} for Psychological Defense Mechanism Classification",
    author = "Adib, Shefayat  and
      Sani, Ahmed  and
      Alif, Md Hasibur  and
      Abrar, Ajwad",
    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-2.18/",
    pages = "123--131",
    ISBN = "979-8-89176-435-4",
    abstract = "Detecting psychological defense mechanisms in conversational text remains a challenging clinical NLP problem. For the PsyDefDetect 2026 shared task (9-class utterance classification evaluated via macro F1), our team LinguIUTics1 achieves a macro F1-score of 0.3917 on the official positive-class leaderboard, ranking 4th out of 21 registered teams and improving over the Ministral-8B task baseline (31.48 macro F1) by +7.7 absolute points (+24.4{\%} relative). BERT-family encoders and zero-shot LLMs proved ineffective on rare classes due to severe class imbalance, leading us to QLoRA fine-tuning of Qwen3-8B. We leverage three key strategies: grouped stratified cross-validation (preventing leakage), minority-class round-robin lexical augmentation, and a post-processing pipeline with logitbias tuning and ensemble blending. Together, these components close much of the validation{--}leaderboard gap and substantially improve minority-class recall, driving the critical ``Unclear'' class (Level 8) from near-zero performance to F1=0.797."
}