With the growing humanlike nature of dialog agents, people are now engaging
in extended conversations that can stretch from brief moments to substantial
periods of time. Understanding the factors that contribute to sustaining these
interactions is crucial, yet existing studies primarily focusing on short-term
simulations that rarely explore such prolonged and real conversations.
In this paper, we investigate the factors influencing retention rates in real
interactions with roleplaying models. By analyzing a large dataset of
interactions between real users and thousands of characters, we systematically
examine multiple factors and assess their impact on user retention rate.
Surprisingly, we find that the degree to which the bot embodies the roles it
plays has limited influence on retention rates, while the length of each turn
it speaks significantly affects retention rates. This study sheds light on the
critical aspects of user engagement with role-playing models and provides
valuable insights for future improvements in the development of large language
models for role-playing purposes.

在该研究中，通过分析真实用户与数千个角色之间的互动，系统地研究了影响用户留存率的多个因素，发现对话机器人扮演角色的程度对留存率的影响有限，而其每次发言的长度显著影响留存率，从而为角色扮演模型的用户参与关键方面提供了重要的见解。