Large Language Models (LLMs) have revolutionized open-domain dialogue agents but encounter challenges in multi-character role-playing (MCRP) scenarios. To address the issue, we present Neeko, an innovative framework designed for efficient multiple characters imitation. Unlike existing methods, Neeko employs a dynamic low-rank adapter (LoRA) strategy, enabling it to adapt seamlessly to diverse characters. Our framework breaks down the role-playing process into agent pre-training, multiple characters playing, and character incremental learning, effectively handling both seen and unseen roles. This dynamic approach, coupled with distinct LoRA blocks for each character, enhances Neeko's adaptability to unique attributes, personalities, and speaking patterns. As a result, Neeko demonstrates superior performance in MCRP over most existing methods, offering more engaging and versatile user interaction experiences. Code and data are available at https://github.com/weiyifan1023/Neeko.

翻译：大型语言模型（LLMs）革新了开放域对话智能体，但在多角色扮演（MCRP）场景中仍面临挑战。为解决此问题，我们提出Neeko——一种面向高效多角色模仿的创新框架。与现有方法不同，Neeko采用动态低秩适配器（LoRA）策略，使其能无缝适配不同角色。该框架将角色扮演过程分解为智能体预训练、多角色扮演和角色增量学习三个阶段，有效处理已知与未知角色。通过为每个角色配置独立LoRA模块的动态机制，Neeko显著增强了对独特属性、个性特征及说话模式的适配能力。实验表明，Neeko在MCRP任务中的表现超越现有大多数方法，提供了更具参与感和多功能的用户交互体验。代码与数据已开源至https://github.com/weiyifan1023/Neeko。