Neeko: Leveraging Dynamic LoRA for Efficient Multi-Character Role-Playing Agent (2402.13717v3)

Abstract: 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.

• The paper introduces Neeko as a multi-character role-playing agent that employs dynamic LoRA for efficient role-specific dialogue management.
• It details a novel gating network that activates dedicated LoRA blocks to reduce computational overhead while preserving distinct character nuances.
• Empirical results show Neeko’s superior dialogue coherency and flexibility in adding new roles without complete retraining.

Exploring Multi-Character Role-Playing with Neeko: A Dynamic LoRA-Based Approach

Introduction to Neeko and the Challenge of MCRP

The emergence of LLMs has significantly propelled the capabilities of open-domain dialogue agents. However, when navigating the complexities of multi-character role-playing (MCRP), these agents face a distinctive set of challenges. Neeko, an incremental role-playing agent, is introduced to specifically address these challenges by leveraging a dynamic Low-Rank Adapter (LoRA) strategy. This allows Neeko to adeptly handle multiple roles within extended dialogues, covering both familiar and novel characters. The imperative necessity of such a framework arises from the observed limitations of existing methods, which predominantly focus on single-character representations and lack adaptability to new, undefined characters.

Methodological Insights into Neeko

Role-Playing with Dynamic LoRA

Neeko's architecture comprehends three principal phases: pre-training, role-playing, and incremental learning. Through non-overlapping LoRA blocks designated for each character, Neeko undergoes pre-training covering various character dialogues. This structure ensures individuality and distinctiveness in character portrayal, effectively mitigating catastrophic forgetting and enhancing adaptability in role shifts.

Role Selection with Gating Network

The intricacy of activating relevant LoRA blocks based on role prompts is managed through a gating network inspired by the Mix of Experts model. Neeko differentiates itself by using this network to assess role identities, hence dynamically aligning model parameters to the role being enacted. This approach not only substantially reduces computation overhead but also meticulously preserves role-specific nuances.

Lifelong Role-Playing with LoRA Expansion

Addressing the challenge of incorporating new characters into the agent's repertoire, Neeko introduces two strategies: fusion and expansion. These strategies facilitate the model's growth in character coverage without necessitating complete retraining, thereby streamlining the inclusion of unseen roles and significantly lowering computational demands.

Empirical Validation and Implications

The comparative analysis demonstrates Neeko's superiority over contemporary methods in the field of MCRP, particularly highlighting its ability to maintain high-quality character consistency, knowledge fidelity, and dialogue coherency. Notably, Neeko exhibits exceptional proficiency in seamlessly transitioning between roles while preserving the distinct attributes and knowledge bases associated with each character.

Theoretical and Practical Contributions

The formulation of the MCRP task, accompanied by the introduction of Neeko and its innovative use of dynamic LoRA, marks a significant step forward in the research on role-playing agents. This research elucidates a pathway for future investigations into more complex interaction scenarios involving multiple characters, suggesting a potential expansion of dialogue systems' capabilities to provide more engaging and personalized user experiences.

Future Directions in AI Research

The findings and methodologies presented in this paper open up several avenues for further exploration, including the refinement of role embeddings, optimization of gating mechanisms, and expansion of Neeko's application to broader domains beyond role-playing scenarios. Additionally, the fundamental principles underpinning Neeko's design might inspire the development of more sophisticated models capable of navigating the nuanced demands of multi-role interactions in dynamic and unpredictable environments.

In conclusion, Neeko represents a pivotal advancement in addressing the nuanced requirements of multi-character role-playing, significantly broadening the horizons of what is achievable with LLMs and dialogue agents. The insights garnered from this research not only contribute to the academic discourse but also hold considerable promise for enhancing the practical implementations of AI-driven interactive systems.