The development of chatbots requires collecting a large number of human-chatbot dialogues to reflect the breadth of users' sociodemographic backgrounds and conversational goals. However, the resource requirements to conduct the respective user studies can be prohibitively high and often only allow for a narrow analysis of specific dialogue goals and participant demographics. In this paper, we propose LLM-Roleplay: a goal-oriented, persona-based method to automatically generate diverse multi-turn dialogues simulating human-chatbot interaction. LLM-Roleplay can be applied to generate dialogues with any type of chatbot and uses large language models (LLMs) to play the role of textually described personas. To validate our method we collect natural human-chatbot dialogues from different sociodemographic groups and conduct a human evaluation to compare real human-chatbot dialogues with our generated dialogues. We compare the abilities of state-of-the-art LLMs in embodying personas and holding a conversation and find that our method can simulate human-chatbot dialogues with a high indistinguishability rate.

本文提出了LLM-Roleplay：一种面向目标的、基于人物角色的自动生成多样化多轮对话的方法，通过大型语言模型扮演描述人物的角色，从而模拟人与聊天机器人的互动对话。通过收集不同社会人口群体的自然人-聊天机器人对话数据，并进行人类评估，发现我们的方法能够以高可分辨率模拟人-聊天机器人对话。

LLM角色扮演：模拟人机交互