Neeko: Leveraging Dynamic LoRA for Efficient Multi-Character Role-Playing Agent
Xiaoyan Yu, Tongxu Luo, Yifan Wei, Fangyu Lei, Yiming Huang, Hao Peng, Liehuang Zhu · 2024
Large Language Models (LLMs) have revolutionized open-domain dialogue agents but encounter challenges in multi-character roleplaying (MCRP) scenarios.To address this issue, this work presents Neeko, an innovative framework designed for efficient multiplecharacter role-playing.The proposed framework breaks down the role-playing agent's training process into agent pre-tuning, multiple character playing, and character incremental learning, effectively handling both seen and unseen roles.Neeko employs a dynamic low-rank adapter (LoRA) strategy by training separate LoRA blocks independently for each character, alongside incorporating a gating network for role selection.This design allows Neeko to seamlessly adjust to a wide range of characters, thereby bolstering its adaptability to distinctive attributes, personalities, and speech patterns.As a result, Neeko demonstrates superior performance in MCRP over most existing methods, offering more engaging and versatile user interaction experiences.