Exploring Text-Generating Large Language Models (LLMs) for Emotion Recognition in Affective Intelligent Agents

Abstract

An intelligent agent interacting with a individual will be able to improve its communication with its inter-locutor if the agent adapts its behavior according to the individual’s emotional state. In order to do this, it is necessary for the agent to be able to detect the individual’s emotional state through the content of the conversation the agent has with the individual. This paper investigates the application of text-generating Large Language Models (LLMs) for emotion recognition in dialogue settings with the aim of generating emotional knowledge, in the form of beliefs, that can be used by a BDI emotional agent. We compare the performance of several LLMs in recognizing the emotions that an affective BDI agent can employ in its reasoning. Results demonstrate the promising capabilities of diverse models in a Zero-shot prediction (without training and without examples), showcasing the potential for LLMs in emotion recognition tasks. The study advocates for further refinement of LLMs to balance accuracy and efficiency, paving the way for their integration into diverse intelligent agent applications.