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<p>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.</p>

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Assessing and alleviating state anxiety in large language models

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月3日

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  • Ziv Ben-Zion,
  • Kristin Witte,
  • Akshay K. Jagadish,
  • Or Duek,
  • Ilan Harpaz-Rotem,
  • Marie-Christine Khorsandian,
  • Achim Burrer,
  • Erich Seifritz,
  • Philipp Homan,
  • Eric Schulz,
  • Tobias R. Spiller

摘要

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.