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Ben-Zion, Z.* ; Witte, K. ; Jagadish, A.K. ; Duek, O.* ; Harpaz-Rotem, I.* ; Khorsandian, M.C.* ; Burrer, A.* ; Seifritz, E.* ; Homan, P.* ; Schulz, E. ; Spiller, T.R.*

Assessing and alleviating state anxiety in large language models.

NPJ Digit. Med. 8:132 (2025)
Publ. Version/Full Text DOI PMC
Open Access Gold
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The use of Large Language Models (LLMs) in mental health highlights the need to understand their responses to emotional content. Previous research shows that emotion-inducing prompts can elevate "anxiety" in LLMs, affecting behavior and amplifying biases. Here, we found that traumatic narratives increased Chat-GPT-4's reported anxiety while mindfulness-based exercises reduced it, though not to baseline. These findings suggest managing LLMs' "emotional states" can foster safer and more ethical human-AI interactions.
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Publication type Article: Journal article
Document type Scientific Article
Language english
Publication Year 2025
HGF-reported in Year 2025
ISSN (print) / ISBN 2398-6352
e-ISSN 2398-6352
Journal NPJ digital medicine
Quellenangaben Volume: 8, Issue: 1, Pages: , Article Number: 132 Supplement: ,
Publisher Nature Publishing Group
Reviewing status Peer reviewed
Institute(s) Human-Centered AI (HCA)
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-540011-001
DOI 10.1038/s41746-025-01512-6
PubMed ID 40033130
Erfassungsdatum 2025-05-11
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