ArticleJournal of psychopathology and clinical science2026
Explaining GPTs' schema of depression: A machine behavior analysis.
Article in Journal of psychopathology and clinical science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Scalable, context-sensitive psychiatric assessment with large language models and brief diaries.Psychological medicine · 2026Article
- Assessing personality using zero-shot generative AI scoring of brief open-ended text.Nature human behaviour · 2026Article
- Systematic Evaluation of Auto-Encoding and Large Language Model Representations for Capturing Author States and Traits.Findings of ACL. ACL · 2025Article
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Authors and funding
12 authors.
Funding
Abstract
The use of large language models (LLMs) such as ChatGPT (GPT-4/GPT-5) for mental health is growing rapidly, including their use to assess and help people with mood disorders, such as depression. However, we have a limited understanding of the schema of mental disorders in these language models, that is, how they internally associate and interpret the symptoms. In this work, we used contemporary measurement theory to decode how GPT-4 and GPT-5 interrelate depressive symptoms, providing an explanation of how large language models behave in clinical applications. First, we evaluated whether these models can reliably detect depressive constructs, finding that GPT-4 (a) demonstrated strong convergent validity with standard instruments and expert judgments (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.