CloChat：理解人们如何在大语言模型中定制、交互和体验智能体人格
CloChat: Understanding How People Customize, Interact, and Experience Personas in Large Language Models
AI总结：
本文通过开发CloChat界面研究用户如何定制LLM智能体人格，发现定制能增强情感联结、对话动态性和持续交互意愿，为对话智能体设计提供启示。
摘要：
大语言模型（LLMs）在生成对话智能体方面取得了显著进展，能够在多样话题上实现无缝且上下文相关的对话。然而，现有由LLM驱动的对话智能体具有固定的人格和功能，限制了它们对个体用户需求的适应性。创建具有独特专长或特质的个性化智能体人格可以解决这一问题。尽管如此，我们仍缺乏关于人们如何定制和与智能体人格交互的知识。在本研究中，我们调查了用户如何定制智能体人格及其对交互质量、多样性和动态性的影响。为此，我们开发了CloChat，这是一个支持在LLMs中轻松且准确地定制智能体人格的界面。我们开展了一项研究，比较参与者与CloChat和ChatGPT交互的情况。结果表明，参与者与定制智能体形成了情感纽带，进行了更具动态性的对话，并表现出持续交互的兴趣。这些发现为未来使用LLMs的对话智能体系统提供了设计启示。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
