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    <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.</abstract>
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%0 Conference Proceedings
%T MAQuA: Multi-outcome Adaptive Question-Asking for Mental Health using Item Response Theory
%A Varadarajan, Vasudha
%A Xu, Hui
%A Böhme, Rebecca Astrid
%A Mirström, Mariam Marlen
%A Sikström, Sverker
%A Schwartz, H. Andrew
%Y Demberg, Vera
%Y Inui, Kentaro
%Y Marquez, Lluís
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-380-7
%F varadarajan-etal-2026-maqua
%X 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.
%U https://aclanthology.org/2026.eacl-long.313/
%P 6659-6677

Markdown (Informal)

[MAQuA: Multi-outcome Adaptive Question-Asking for Mental Health using Item Response Theory](https://aclanthology.org/2026.eacl-long.313/) (Varadarajan et al., EACL 2026)

• MAQuA: Multi-outcome Adaptive Question-Asking for Mental Health using Item Response Theory (Varadarajan et al., EACL 2026)

ACL

• Vasudha Varadarajan, Hui Xu, Rebecca Astrid Böhme, Mariam Marlen Mirström, Sverker Sikström, and H. Andrew Schwartz. 2026. MAQuA: Multi-outcome Adaptive Question-Asking for Mental Health using Item Response Theory. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6659–6677, Rabat, Morocco. Association for Computational Linguistics.