@inproceedings{varadarajan-etal-2026-maqua,
    title = "{MAQ}u{A}: Multi-outcome Adaptive Question-Asking for Mental Health using Item Response Theory",
    author = {Varadarajan, Vasudha  and
      Xu, Hui  and
      B{\"o}hme, Rebecca Astrid  and
      Mirstr{\"o}m, Mariam Marlen  and
      Sikstr{\"o}m, Sverker  and
      Schwartz, H. Andrew},
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.313/",
    pages = "6659--6677",
    ISBN = "979-8-89176-380-7",
    abstract = "Recent advances in LLMs offer new opportunities for scalable, interactive mental health assessment, but excessive querying burdens users and is inefficient for real-world screening across transdiagnostic symptom profiles. We introduce MAQuA, a multi-outcome modeling and adaptive question-asking framework for simultaneous, multidimensional mental health screening. Combining multi-outcome modeling on language responses with item response theory (IRT) and factor analysis, MAQuA selects the questions with most informative responses across multiple dimensions at each turn to optimize diagnostic information, improving accuracy and potentially reducing response burden. Empirical results on a novel dataset reveal that MAQuA reduces the number of assessment questions required for score stabilization by 50{--}87{\%} compared to random ordering (e.g., achieving stable depression scores with 71{\%} fewer questions and eating disorder scores with 85{\%} fewer questions). MAQuA demonstrates robust performance across both internalizing (depression, anxiety) and externalizing (substance use, eating disorder) domains, with early stopping strategies further reducing patient time and burden. These findings position MAQuA as a powerful and efficient tool for scalable, nuanced, and interactive mental health screening, advancing the integration of LLM-based agents into real-world clinical workflows."
}