mlsanityData to 14 Oct 2026

MAQuA: Adaptive Question-Asking for Multidimensional Mental Health Screening using Item Response Theory

arXiv 2508.07279 ↗First posted 10 Aug 2025cs.CL cs.AIcited by 0

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Abstract (arXiv)

Recent advances in large language models (LLMs) offer new opportunities for scalable, interactive mental health assessment, but excessive querying by LLMs burdens users and is inefficient for real-world screening across transdiagnostic symptom profiles. We introduce MAQuA, an 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.

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References · 13

  1. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  2. 2019Publicly Available Clinical BERT Embeddingscited by 1,002
  3. 2018Multi-Task Learning as Multi-Objective Optimizationcited by 821
  4. 2024Nomic Embed: Training a Reproducible Long Context Text Embeddercited by 265
  5. 2021MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcarecited by 161
  6. 2024Modeling Future Conversation Turns to Teach LLMs to Ask Clarifying Questionscited by 76
  7. 2022CLAM: Selective Clarification for Ambiguous Questions with Generative Language Modelscited by 69
  8. 2024Do Large Language Models Align with Core Mental Health Counseling Competencies?cited by 19
  9. 2020Towards Preemptive Detection of Depression and Anxiety in Twittercited by 13
  10. 2024LLM Questionnaire Completion for Automatic Psychiatric Assessmentcited by 12
  11. 2023ALBA: Adaptive Language-based Assessments for Mental Healthcited by 6
  12. 2024Comparing the Efficacy of GPT-4 and Chat-GPT in Mental Health Care: A Blind Assessment of Large Language Models for Psychological Supportcited by 5
1 more
  1. 2025Measuring Mental Health Variables in Computational Research: Toward Validated, Dimensional, and Transdiagnostic Approachescited by 1

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