Synergizing Multiple Expert LLMs for Generalist Framework: Expert Token Routing

Core Concepts

Introducing Expert-Token-Routing for seamless integration of multiple expert LLMs into a generalist framework.

Abstract

The content introduces the concept of Expert-Token-Routing, a framework that integrates multiple expert Large Language Models (LLMs) into a unified system. It outlines the challenges faced by existing methods and presents the benefits of this new approach. The framework allows for dynamic extension of new expert LLMs in a plug-and-play manner, conceals collaboration complexities from users, and outperforms existing paradigms across diverse domains.

Abstract:

• Introduces Expert-Token-Routing for integrating multiple expert LLMs.
• Represents expert LLMs as special tokens within a meta LLM.
• Supports learning implicit expertise and dynamic extension of new expert LLMs.

Introduction:

• Discusses the limitations of current large language models in specialized domains.
• Highlights efforts to develop "expert" LLMs tailored for specific tasks.
• Focuses on synergizing various expert LLMs into a singular generalist framework.

Approach:

• Presents ETR framework to integrate multiple expert LLMs into a generalist system.
• Encodes expert LLMs as special tokens within the meta LLM's vocabulary.
• Describes how ETR facilitates task allocation to expert LLMs seamlessly.

Experiments:

• Conducted experiments across six different domains to compare ETR with existing paradigms.
• Demonstrates superior performance of ETR in building a generalist framework.
• Compares routing accuracy and overall accuracy with other methods.

Related Works:

• Mentions previous methods like Meta-Prompting and Multi-LLM Debate for enhancing reasoning capabilities of language models.

Stats

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 systems via synergizing multiple expert LLMs.

Quotes

"Our framework not only supports learning the implicit expertise of expert LLMs but also allows for dynamic extension of new ones."
"Our method maintains routing accuracy exceeding 65% across all six domains, demonstrating greater robustness."

Key Insights Distilled From

An Expert is Worth One Token

by Ziwei Chai,G... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.16854.pdf

Deeper Inquiries

How can the ETR framework be adapted to handle an increasing number of expert LMMs efficiently?

In order to efficiently handle an increasing number of expert LLMs within the ETR framework, a dynamic extension approach can be implemented. This involves training expert tokens for newly added expert LLMs and seamlessly integrating them into the existing framework as plug-ins. By only training the new expert tokens and appending them to the frozen meta LLM without modifying other parts of the system, providers can easily add their trained expert LLMs without disrupting the overall functionality. This plug-and-play integration allows for scalability and flexibility in incorporating additional experts while maintaining efficiency.

What are potential drawbacks or challenges associated with concealing collaboration complexities from users?

While concealing collaboration complexities from users offers a seamless interaction experience with large language models (LLMs), there are some potential drawbacks and challenges to consider:

Loss of Transparency: Concealing collaboration processes may lead to a lack of transparency regarding how responses are generated, potentially raising concerns about accountability and bias.
Limited User Control: Users may have limited control over which specific expert is utilized for certain queries, impacting customization options based on individual preferences.
Difficulty in Troubleshooting: If issues arise during collaboration between different experts within the framework, troubleshooting becomes more challenging when these interactions are hidden from users.
User Understanding: Users might not fully grasp how expertise is leveraged across multiple domains within the system, affecting their trust in the accuracy and reliability of responses.

How might the concept of plug-and-play integration impact future developments in large language models?

The concept of plug-and-play integration has significant implications for future developments in large language models (LLMs):

Scalability: Plug-and-play integration enables easy incorporation of new features or modules into existing frameworks without extensive modifications, enhancing scalability.
Flexibility: Developers can quickly adapt to changing requirements by adding or removing components as needed, promoting flexibility in model architecture design.
Collaboration Efficiency: With plug-and-play capabilities, collaborating multiple specialized LLMs becomes more efficient as new experts can seamlessly join existing systems without disruption.
Innovation Acceleration: The ability to integrate novel technologies or domain-specific expertise through plug-and-play mechanisms accelerates innovation cycles in developing advanced LLM applications.

By leveraging plug-and-play integration strategies, future advancements in large language models can benefit from enhanced adaptability, streamlined development processes, and improved collaborative capabilities across diverse domains.