mlsanityData to 13 Oct 2026

LLM Questionnaire Completion for Automatic Psychiatric Assessment

arXiv 2406.06636 ↗First posted 9 Jun 2024cs.CL cs.LGcited by 12

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Abstract (arXiv)

We employ a Large Language Model (LLM) to convert unstructured psychological interviews into structured questionnaires spanning various psychiatric and personality domains. The LLM is prompted to answer these questionnaires by impersonating the interviewee. The obtained answers are coded as features, which are used to predict standardized psychiatric measures of depression (PHQ-8) and PTSD (PCL-C), using a Random Forest regressor. Our approach is shown to enhance diagnostic accuracy compared to multiple baselines. It thus establishes a novel framework for interpreting unstructured psychological interviews, bridging the gap between narrative-driven and data-driven approaches for mental health assessment.

Key facts. Cited by 12 papers in the map (data through 13 Oct 2026). Builds on 6 of its 29 references that are in the map. First posted on arXiv 9 Jun 2024.

Builds on

6 of 29 references.

References · 6

  1. 2020Language Models are Few-Shot Learnerscited by 32,320
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,322
  3. 2023Towards Expert-Level Medical Question Answering with Large Language Modelscited by 384
  4. 2022Text and Code Embeddings by Contrastive Pre-Trainingcited by 361
  5. 2023MentaLLaMA: Interpretable Mental Health Analysis on Social Media with Large Language Modelscited by 63
  6. 2023The Capability of Large Language Models to Measure Psychiatric Functioningcited by 26

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2024: 1; 2025: 6; 2026: 5.

12 papers.

  1. 2025Are LLMs effective psychological assessors? Leveraging adaptive RAG for interpretable mental health screening through psychometric practicecited by 10
  2. 2025Enhanced Large Language Models for Effective Screening of Depression and Anxietycited by 9
  3. 2025Trustworthy AI Psychotherapy: Multi-Agent LLM Workflow for Counseling and Explainable Mental Disorder Diagnosiscited by 8
  4. 2024Multi-Session Client-Centered Treatment Outcome Evaluation in Psychotherapycited by 5
  5. 2025Noise, Adaptation, and Strategy: Assessing LLM Fidelity in Decision-Makingcited by 4
  6. 2026Responsible Evaluation of AI for Mental Healthcited by 2
  7. 2026Toward Zero-Egress Psychiatric AI: On-Device LLM Deployment for Privacy-Preserving Mental Health Decision Supportcited by 2
  8. 2025LLM Assistance for Pediatric Depressioncited by 1
  9. 2025MAQuA: Adaptive Question-Asking for Multidimensional Mental Health Screening using Item Response Theorycited by 0
  10. 2026EmoTrack: Clinical-Semantic Modeling for Text-Based Depression Severity Estimationcited by 0
  11. 2026HiMA-MDD: A Hierarchical Multi-Agent Harness for Interpretable Multimodal Depression Detection in Clinical Interviewscited by 0
  12. 2026LongCounsel-8: A Benchmark Suite for Longitudinal Depression Tracking from Multi-Session Counseling Dialoguescited by 0