An Expert is Worth One Token: Synergizing Multiple Expert LLMs as Generalist via Expert Token Routing

Abstract: We present Expert-Token-Routing, a unified generalist framework that facilitates seamless integration of multiple expert LLMs. Our framework represents expert LLMs as special expert tokens within the vocabulary of a meta LLM. The meta LLM can route to an expert LLM like generating new tokens. Expert-Token-Routing not only supports learning the implicit expertise of expert LLMs from existing instruction dataset but also allows for dynamic extension of new expert LLMs in a plug-and-play manner. It also conceals the detailed collaboration process from the user's perspective, facilitating interaction as though it were a singular LLM. Our framework outperforms various existing multi-LLM collaboration paradigms across benchmarks that incorporate six diverse expert domains, demonstrating effectiveness and robustness in building generalist LLM system via synergizing multiple expert LLMs.

• The paper introduces the Expert-Token-Routing (ETR) framework to synergize multiple specialized LLMs using a dynamic expert token routing approach.
• It employs expert tokens to activate domain-specific models automatically, achieving a 5.64% improvement in answer accuracy.
• The modular, plug-and-play design enables seamless integration of new experts for scalable and efficient AI performance.

Overview of "An Expert is Worth One Token: Synergizing Multiple Expert LLMs as Generalist via Expert Token Routing"

The paper "An Expert is Worth One Token: Synergizing Multiple Expert LLMs as Generalist via Expert Token Routing" introduces the Expert-Token-Routing (ETR) framework. This innovative architecture allows the integration of multiple domain-specific LLMs into a unified, generalist system by leveraging a meta LLM to manage expert LLMs using a unique method called expert token routing.

The authors address issues prevalent in current LLM applications, where general models can struggle with domain-specific queries and specialized models require manual selection from users. The framework encapsulates the complexity of expert collaboration within a single LLM, thus presenting a seamless user experience.

Core Proposition

The central idea of the Expert-Token-Routing framework is to use expert tokens within a meta LLM's vocabulary to dynamically select and activate the appropriate expert LLM during query processing. By treating these expert models as tokens, the framework simplifies the routing process and allows for straightforward integration of additional experts. This eliminates the need to retrain the entire meta model or craft complex prompt instructions.

The authors propose that this system can automatically learn when to activate a specific expert model by training these special tokens using datasets of questions where the expert model's performance surpasses that of the generalist LLM. This methodology ensures the efficient utilization of the specialized capabilities of the expert models, thus enhancing the overall system efficacy.

Methodological Strengths

The ETR framework's capability to encapsulate multiple LLMs as a singular entity broadens LLM applicability without complicating the user experience. The meta LLM functions as an overarching manager that judiciously delegates queries to expert LLMs, thus optimizing resource utilization across domains. This dynamic model increases efficiency, demonstrates superior performance in evaluations, and presents a scalable solution for integrating new domain expertise.

Furthermore, the implementation of plug-and-play expansion capabilities is particularly notable. This feature allows for the easy incorporation of new expert models into the system, reflecting a modular design that supports continuous learning and adaptation to emerging fields or knowledge areas.

Experimental Insights

The empirical evaluation across six distinct domains illustrates the ETR framework's superior performance, with an overall improvement of 5.64% in answer accuracy compared to existing methodologies. The use of domain-specific expert LLMs, trained via supervised fine-tuning on synthetically generated datasets, results in significant enhancements over the base model across various specialized subjects. The expert routing accuracy of 82.11% underlines the effectiveness of the expert token strategy in selecting the best-suited expert for specific queries.

Implications and Speculation on Future Developments

This framework has substantial implications for the future of AI and LLM integration. Its design facilitates not only improved performance in current applications but also supports ongoing adaptation and expansion as new domains of expertise emerge. As AI continues to penetrate diverse sectors, the capability to integrate and manage specialist models effectively will become increasingly important.

Future advancements might focus on refining the token embedding learning process for even tighter integration and exploring more rapid training methodologies. Additionally, the streamlined incorporation of new experts will be crucial in rapidly expanding fields, supporting a dynamic and responsive AI ecosystem.

In conclusion, the Expert-Token-Routing framework represents an effective step toward achieving seamless expert collaboration within LLMs. By transforming the approach to LLM integration, this research offers a promising avenue for further exploration and development in the field of AI, providing valuable insights into scalable model management and expertise application.