CloChat: Understanding How People Customize, Interact, and Experience
  Personas in Large Language Models
Juhye Ha Hyeon Jeon DaEun Han Jinwook Seo Changhoon Oh
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.