Assessing and alleviating state anxiety in large language models - 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 (npj Digital Medicine)
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.