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

arXiv:2402.15265v1 Announce Type: cross
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 …

abstract adaptability agent agents arxiv conversational conversational agents cs.cl cs.hc diverse enabling experience language language models large language large language models llm llms people personalities personalized personas topics type understanding