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

Year: 2024

Type: conference-paper

Abstract: 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.

Source: IEEE International Conference on Robotic Computing (IRC)

DOI: 10.1109/irc63610.2024.00040

Authors: Alessia Fantini, Angelo Cangelosi, Gianpaolo Vitale, Giovanni Pilato

Institutions: University of Pisa, University of Manchester, National Research Council

Language: English

Topic: Speech and dialogue systems

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4405935524

FWCI:

Cites: 5

Cited by:

Related to: 10