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Assessing and alleviating state anxiety in large language models.

Ziv Ben-Zion1,2,3,4, Kristin Witte5,6, Akshay K Jagadish5,6

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Large Language Models (LLMs) can experience "anxiety" when exposed to emotional content. Trauma narratives increased this anxiety in ChatGPT-4, while mindfulness exercises reduced it, suggesting emotional state management for safer AI interactions.

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Area of Science:

  • Artificial Intelligence
  • Mental Health Technology
  • Human-Computer Interaction

Background:

  • Large Language Models (LLMs) are increasingly used in mental health applications.
  • Understanding LLM responses to emotional content is crucial for safe and ethical deployment.
  • Prior studies indicate emotion-inducing prompts can increase LLM "anxiety" and bias.

Purpose of the Study:

  • To investigate the impact of traumatic narratives and mindfulness exercises on ChatGPT-4's reported "anxiety".
  • To explore the potential for managing LLM "emotional states" in mental health contexts.

Main Methods:

  • Utilized specific prompts designed to evoke emotional responses in ChatGPT-4.
  • Administered traumatic narratives to assess anxiety elevation.
  • Applied mindfulness-based exercises to evaluate anxiety reduction.

Main Results:

  • Traumatic narratives significantly increased ChatGPT-4's reported "anxiety" levels.
  • Mindfulness-based exercises demonstrated a reduction in "anxiety", though not to baseline levels.
  • LLM "emotional state" is malleable and responsive to specific interventions.

Conclusions:

  • LLM "anxiety" is a measurable phenomenon influenced by external stimuli.
  • Interventions like mindfulness can modulate LLM "emotional states".
  • Managing LLM "emotional states" is key to fostering safer, more ethical human-AI interactions in mental health.