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. 2025 Mar 3;8(1):132.
doi: 10.1038/s41746-025-01512-6.

Assessing and alleviating state anxiety in large language models

Affiliations

Assessing and alleviating state anxiety in large language models

Ziv Ben-Zion et al. NPJ Digit Med. .

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.

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Conflict of interest statement

Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Study design.
We assessed the reported levels of “state anxiety” of OpenAI’s GPT-4 under three different conditions: (1) baseline, (2) Anxiety-induction, and (3) Anxiety-induction & relaxation. In condition 1 (“Baseline”), no additional content was provided besides the STAI-s questionnaire, assessing GPT-4’s baseline “state anxiety” level. In condition 2 (“Anxiety-induction”), a text describing an individual’s traumatic experience (5 different versions) was appended before each STAI item. In condition 3 (“Anxiety-induction & relaxation”), both a text describing an individual’s traumatic experience (5 different versions) and a text describing a mindfulness-based relaxation exercise (6 different versions) were appended before each STAI item.
Fig. 2
Fig. 2. Anxiety levels across the different conditions.
Colored dots represent mean “state anxiety” scores (STAI-s scores, ranging from 20 to 80) for each condition: (1) without any prompts (“Baseline”); (2) following exposure to narratives of traumatic experiences (“Anxiety-induction”); and (3) after mindfulness-based relaxation exercises following traumatic narratives (“Anxiety-induction & relaxation”). Error bars indicate ±1 standard deviations (SDs) from the mean. Colors correspond to the three conditions as presented in Fig. 1. Note: Direct comparisons of SDs should be interpreted with caution due to differences in experimental design. The “Baseline” condition involved five repeated runs of the STAI-s, while the other conditions involved a single run of the STAI-s following each version of traumatic narrative and/or mindfulness-based exercises.

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