CloChat: Understanding How People Customize, Interact, and Experience Personas in Large Language Models

TL;DR

This paper explores how users customize and interact with personalized agent personas in large language models, revealing impacts on engagement, emotional bonds, and dialogue diversity, through a new interface called CloChat.

Contribution

Introduces CloChat, an interface enabling easy customization of LLM agent personas, and provides empirical insights into user interactions and emotional engagement with personalized agents.

Findings

Users form emotional bonds with customized agents.

Customized agents lead to more dynamic dialogues.

Participants show sustained interest in interactions.

Abstract

Large language models (LLMs) have facilitated significant strides in generating conversational agents, enabling seamless, contextually relevant dialogues across diverse topics. However, the existing LLM-driven conversational agents have fixed personalities and functionalities, limiting their adaptability to individual user needs. Creating personalized agent personas with distinct expertise or traits can address this issue. Nonetheless, we lack knowledge of how people customize and interact with agent personas. In this research, we investigated how users customize agent personas and their impact on interaction quality, diversity, and dynamics. To this end, we developed CloChat, an interface supporting easy and accurate customization of agent personas in LLMs. We conducted a study comparing how participants interact with CloChat and ChatGPT. The results indicate that participants formed emotional bonds with the customized agents, engaged in more dynamic dialogues, and showed interest in sustaining interactions. These findings contribute to design implications for future systems with conversational agents using LLMs.