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.

大语言模型为生成会话代理带来了重大进展，使得在各种话题上实现无缝、情境相关的对话成为可能。然而，现有的以语言模型为驱动的对话代理拥有固定的个性和功能，限制了它们适应个人用户需求的能力。通过创建具有特定专长或特点的个性化代理人物，可以解决这个问题。尽管如此，我们对人们如何定制和与代理人物互动缺乏了解。在这项研究中，我们调查了用户如何定制代理人物以及其对互动质量、多样性和动力的影响。为此，我们开发了 CloChat，这是一个支持在大语言模型中轻松准确地定制代理人物的接口。我们进行了一项研究，比较了参与者如何与 CloChat 和 ChatGPT 互动。结果表明，参与者与定制代理人物建立了情感纽带，进行了更加动态的对话，并表现出对维持互动的兴趣。这些发现为未来使用大语言模型的对话代理系统的设计提供了启示。