@inproceedings{chu-etal-2026-dal,
    title = "{DAL} Team at {P}sy{D}ef{D}etect: From Supervised Encoders to Hierarchical {LLM}-{RAG} for Psychological Defense Detection",
    author = "Chu, Anh  and
      Tran, Luong  and
      Do, Dat  and
      Mai, Phuong  and
      Le, Quynh  and
      Can, Cat",
    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-2.23/",
    pages = "164--170",
    ISBN = "979-8-89176-435-4",
    abstract = "We propose a hierarchical framework for psychological defense mechanism detection in multi-turn dialogues, integrating large language models, retrieval-augmented generation, and heuristic calibration. Our approach decomposes prediction into coarse-to-fine reasoning stages and incorporates dialogue reconstruction, explanation-enhanced retrieval, and hybrid LLM{--}supervised filtering to address severe label imbalance and implicit, context-dependent labeling. Experiments on the PsyDefDetect dataset show that LLM-based RAG improves performance on minority and ambiguous classes, achieving a Macro F1 of 0.31, while also revealing persistent challenges in fine-grained discrimination of latent psychological constructs."
}