Tongxu Luo


2024

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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
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

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.

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Unlocking Continual Learning Abilities in Language Models
Wenyu Du | Shuang Cheng | Tongxu Luo | Zihan Qiu | Zeyu Huang | Ka Chun Cheung | Reynold Cheng | Jie Fu
Findings of the Association for Computational Linguistics: EMNLP 2024