LLM Roleplay：模拟人机对话交互
LLM Roleplay: Simulating Human-Chatbot Interaction
AI总结：
提出LLM Roleplay方法，利用大型语言模型扮演文本角色自动生成多轮人机对话，有效模拟不同社会人口背景的交互，并在高不可区分率下验证了其有效性。
摘要：
聊天机器人的开发需要收集大量的人机对话，以反映用户社会人口背景和对话目标的广度。然而，进行相关用户研究所需的资源可能高得令人望而却步，且通常只能对特定对话目标和参与者人口特征进行狭窄分析。在本文中，我们提出LLM Roleplay：一种基于目标、基于角色的方法，用于自动生成模拟人机交互的多样化多轮对话。LLM Roleplay可应用于生成与任何类型聊天机器人的对话，并使用大型语言模型（LLMs）扮演文本描述的角色。为验证我们的方法，我们从不同社会人口群体中收集自然的人机对话，并进行用户研究将其与生成的对话进行比较。我们评估了最先进的LLMs在体现特定角色时维持对话的能力，发现我们的方法能以高不可区分率模拟人机对话。
英文摘要：
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 user study to compare these with our generated dialogues. We evaluate the capabilities of state-of-the-art LLMs in maintaining a conversation during their embodiment of a specific persona and find that our method can simulate human-chatbot dialogues with a high indistinguishability rate.
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。
