Robots with Attitudes: Influence of LLM-Driven Robot Personalities on Motivation and Performance

TL;DR

This study investigates how LLM-driven robot personalities, especially agreeableness, influence likability, motivation, and task performance, demonstrating that agreeable robots are more likable and can improve cooperation outcomes.

Contribution

It introduces a method for modeling robot personalities with LLMs and empirically evaluates the impact of agreeableness on human-robot interaction.

Findings

Agreeableness increases robot likability.

No significant effect on intrinsic motivation.

Agreeableness and openness improve task performance.

Abstract

Large language models enable unscripted conversations while maintaining a consistent personality. One desirable personality trait in cooperative partners, known to improve task performance, is agreeableness. To explore the impact of large language models on personality modeling for robots, as well as the effect of agreeable and non-agreeable personalities in cooperative tasks, we conduct a two-part study. This includes an online pre-study for personality validation and a lab-based main study to evaluate the effects on likability, motivation, and task performance. The results demonstrate that the robot's agreeableness significantly enhances its likability. No significant difference in intrinsic motivation was observed between the two personality types. However, the findings suggest that a robot exhibiting agreeableness and openness to new experiences can enhance task performance. This study highlights the advantages of employing large language models for customized modeling of robot personalities and provides evidence that a carefully chosen agreeable robot personality can positively influence human perceptions and lead to greater success in cooperative scenarios.