Large Language Models Capability in Handling Users' Frustration: A Case Study

In Human-Robot Interaction, the robot's ability to recognize human frustration signals and respond appropriately is crucial. Large Language Models (LLMs) are increasingly used within conversational agents and social robots. In this preliminary study we test four LLMs by simulating three different scenarios: health care, education, and public administration. The goal is twofold: first, we develop a qualitative and quantitative conversation analysis methodology, and second, we test the ability of these LLMs to recognize and respond appropriately to frustration in different contexts.