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.

翻译：随着对话代理类人化程度的提升，人们现在能够进行从短暂片刻到较长时间不等的持续对话。理解促进这些交互持续的因素至关重要，但现有研究主要集中于短期模拟，极少探索此类长期真实的对话。本文探讨了影响角色扮演模型真实交互中留存率的因素。通过分析真实用户与数千个角色之间的大规模交互数据集，我们系统考察了多种因素并评估其对用户留存率的影响。令人惊讶的是，我们发现机器人对其扮演角色的体现程度对留存率影响有限，而每轮对话的长度则显著影响留存率。本研究揭示了角色扮演模型中用户参与度的关键方面，并为未来改进大型语言模型在角色扮演应用中的发展提供了宝贵见解。