English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Assessing and alleviating state anxiety in large language models

Ben-Zion, Witte, K., Jagadish, A., Duek, O., Harpaz-Rotem, I., Khorsandian, M.-C., et al. (2025). Assessing and alleviating state anxiety in large language models. npj digital medicine, 8(1): 132. doi:10.1038/s41746-025-01512-6.

Item is

Files

show Files

Locators

hide
Description:
-
OA-Status:
Not specified

Creators

hide
 Creators:
Ben-Zion, Author
Witte, K1, Author           
Jagadish, AK1, Author                 
Duek, O, Author
Harpaz-Rotem, I, Author
Khorsandian, M-C, Author
Burrer, A, Author
Seifritz, E, Author
Homann, P, Author
Schulz, E1, Author                 
Spiller, TR, Author
Affiliations:
1Research Group Computational Principles of Intelligence, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_3189356              

Content

hide
Free keywords: -
 Abstract: 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.

Details

hide
Language(s):
 Dates: 2025-03
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1038/s41746-025-01512-6
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

hide
Title: npj digital medicine
  Abbreviation : npj Digit. Med.
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Basingstoke : Springer Nature
Pages: 6 Volume / Issue: 8 (1) Sequence Number: 132 Start / End Page: - Identifier: ISSN: 2398-6352
CoNE: https://pure.mpg.de/cone/journals/resource/23986352