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Mixture of Routers

large-language-models › model-architecture

📄 Abstract

Abstract: Supervised fine-tuning (SFT) is a milestone in aligning large language models with human instructions and adapting them to downstream tasks. In particular, Low-Rank Adaptation (LoRA) has gained widespread attention due to its parameter efficiency. However, its impact on improving the performance of large models remains limited. Recent studies suggest that combining LoRA with Mixture-of-Experts (MoE) can significantly enhance fine-tuning performance. MoE adapts to the diversity and complexity of datasets by dynamically selecting the most suitable experts, thereby improving task accuracy and efficiency. Despite impressive results, recent studies reveal issues in the MoE routing mechanism, such as incorrect assignments and imbalanced expert allocation. Inspired by the principles of Redundancy and Fault Tolerance Theory. We innovatively integrate the concept of Mixture of Experts into the routing mechanism and propose an efficient fine-tuning method called Mixture of Routers (MoR). It employs multiple sub-routers for joint selection and uses a learnable main router to determine the weights of the sub-routers. The results show that MoR outperforms baseline models on most tasks, achieving an average performance improvement of 1%. MoR can serve as a plug-and-play, parameter-efficient fine-tuning method suitable for a wide range of applications. Our code is available here: https://anonymous.4open.science/r/MoR-DFC6.

Key Contributions

This paper proposes Mixture of Routers (MoR), an innovative fine-tuning method that integrates Mixture-of-Experts (MoE) principles into the routing mechanism itself, inspired by Redundancy and Fault Tolerance Theory. MoR aims to address issues like incorrect assignments and imbalanced expert allocation in MoE, thereby enhancing the efficiency and performance of parameter-efficient fine-tuning methods like LoRA.

Business Value

Enables more efficient and effective fine-tuning of large models, reducing training costs and time, and potentially leading to better-performing specialized models for various downstream tasks.

Paper Metadata

Innovation Type

Algorithmic innovation

Deployment Feasibility

High, as it focuses on improving fine-tuning, a crucial step before deployment.

Limitations Addressed

Issues in existing MoE routing mechanisms, such as incorrect assignments and imbalanced expert allocation, which limit the effectiveness of MoE for fine-tuning.

Performance Gains

Expected improvements in fine-tuning performance and efficiency compared to standard LoRA and potentially existing MoE approaches.

Technical Tags

Mixture-of-Experts (MoE)Low-Rank Adaptation (LoRA)Supervised Fine-Tuning (SFT)routing mechanismexpert allocationfault toleranceredundancyefficient fine-tuning

Research Topics

Large Language ModelsModel ArchitecturesEfficient TrainingParameter-Efficient Fine-TuningDeep Learning Optimization

Methods & Architectures

Mixture of Routers (MoR)Integration of MoE principles into routingLoRA fine-tuning Mixture-of-Experts (MoE)LoRA-based models

Applications & Tasks

Natural Language Processing Model Fine-Tuning Improving MoE routing efficiencyAddressing imbalanced expert allocationEnhancing LoRA performance Efficient fine-tuning of LLMsTask adaptationImproving model performance on diverse datasets

Related Fields

Deep LearningMachine LearningNatural Language ProcessingModel Optimization

Keywords

Mixture of ExpertsMoELoRAfine-tuningLLMroutingexpert allocationparameter-efficientSFTfault toleranceredundancydeep learning

Academic Context

#Large Language Models#Model Architectures#Efficient Training#Parameter-Efficient Fine-Tuning#Deep Learning Optimization

Technology Stack

Frameworks & Libraries

LoRA

Commercial Potential

Potential Products

More efficient LLM fine-tuning frameworksSpecialized LLMs for specific tasks

Target Industries

TechnologyAI DevelopmentSaaS

Use Case Examples

Fine-tuning a large language model for a specific domain (e.g., legal, medical) with reduced computational resources.Developing highly accurate task-specific models faster.

Competitive Edge

MoR offers a novel approach to enhance MoE routing, potentially overcoming limitations of current MoE implementations and providing superior fine-tuning performance and efficiency compared to standard LoRA or basic MoE.

Market Opportunity

The market for efficient LLM fine-tuning is substantial and growing.

Revenue Models

Licensing of the MoR techniqueintegration into commercial ML platforms.

Resource Requirements

Compute Needs

Moderate to high, depending on the scale of the LLM and dataset.

Data Requirements

Diverse datasets suitable for fine-tuning and downstream tasks.

Deployment Constraints

Complexity of implementing and tuning the MoR routing mechanism.

Scalability

Designed to improve efficiency, suggesting good scalability for fine-tuning large models.

Production Readiness

Maturity Level

Research

Time to Market

1-2 years for integration into existing frameworks

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