@inproceedings{yu-etal-2024-neeko,
    title = "Neeko: Leveraging Dynamic {L}o{RA} for Efficient Multi-Character Role-Playing Agent",
    author = "Yu, Xiaoyan  and
      Luo, Tongxu  and
      Wei, Yifan  and
      Lei, Fangyu  and
      Huang, Yiming  and
      Peng, Hao  and
      Zhu, Liehuang",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2024.emnlp-main.697/",
    doi = "10.18653/v1/2024.emnlp-main.697",
    pages = "12540--12557",
    abstract = "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. 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."
}