- The paper introduces a novel taxonomy categorizing expert model design, routing mechanisms, and application strategies for enhanced performance.
- It examines diverse methods like AdapterFusion and AdapterSoup, detailing training processes, routing datasets, and expert selection.
- The survey outlines future research directions and practical challenges in deploying decentralized, continuously integrated expert models.
A Survey on Model MoErging: Recycling and Routing Among Specialized Experts for Collaborative Learning
The paper "A Survey on Model MoErging: Recycling and Routing Among Specialized Experts for Collaborative Learning" provides a comprehensive examination of the emerging field of model merging, or "MoErging." It aims to consolidate and organize the rapid advancements in methodologies designed to combine specialized expert models to create aggregate systems boasting superior performance and generalization capabilities.
Abstract and Motivation
The advent of performant pre-trained models has facilitated a proliferation of fine-tuned expert models tailored to specific domains or tasks. MoErging seeks to leverage these expert models by recycling them into a cohesive system that outperforms or generalizes better than individual models. This process involves creating a router that selects the appropriate expert models for a given input. The paper presents a structured survey of existing MoErging methods, including a novel taxonomy to categorize key design choices and elucidate their suitable applications.
Taxonomy Overview
One of the core contributions of the paper is its taxonomy for MoErging methods, which categorizes design choices into three primary components: expert model design, routing mechanisms, and application designs.
- Expert Model Design:
- Expert Training: Methods are divided into standard and custom training processes, the latter requiring specific procedures to align with the MoErging framework.
- Expert Data: It differentiates between methods that assume shared access to the expert training datasets and those that do not.
- Routing Design:
- Routing Dataset: Types of datasets used to train the router, including target-task datasets, expert datasets, general datasets, or none.
- Routing Granularity: This includes granularity at both the input level (task, example, step) and depth level (module, model).
- Expert Selection: Mechanisms of sparse versus dense routing.
- Expert Aggregation: Methods vary in combining expert outputs or parameters.
- Application Design:
- Generalization: Strategies aimed at improving in-distribution versus out-of-distribution task performance.
- User Dataset: Requirements of labeled data from the target domain, ranging from zero-shot to few-shot and full datasets.
Surveyed Methods
The paper meticulously surveys various MoErging methods, providing a standardized "infobox" for each to catalog their design choices according to the proposed taxonomy. Notable methods include:
- AdapterFusion: Combines specialized adapters by learning a fusion module to integrate multiple task-specific adapters, evaluated on 16 natural language understanding tasks.
- Retrieval of Experts (RoE): Proposes selecting the best expert model for target tasks using retrieval mechanisms based on dataset embeddings.
- AdapterSoup: Employs clustering techniques to select and average specialized adapters' parameters for better few-shot domain adaptation.
- PHATGOOSE: Involves a two-stage training process with sigmoid gates to facilitate zero-shot task generalization.
The survey reveals many methods gravitate towards either embedding-based routing or classifier-based routing to select the most pertinent expert models. Despite their different implementations, many methods resort to simple heuristic-based merging or a multi-step procedure where a model learns to combine outputs from various experts dynamically.
Implications and Future Work
MoErging represents a paradigm shift in decentralized model development, with several implications:
- Practical Impact: Existing methods have made strides in enhancing task-specific performance. However, the lack of user-friendly tools and platforms for applying these models at scale limits their practical impact.
- Research Developments: The field could benefit significantly from benchmarks and empirical surveys akin to those in model merging. These would help clarify under which conditions specific MoErging methods excel, thereby driving further innovation.
- Open Questions: Issues such as model redundancy, the continual addition of new models, and security against adversarial contributions remain underexplored. Additionally, the integration of MoErging within platforms facilitating widespread model sharing and continuous development could catalyze broader adoption.
The paper highlights several existing tools and platforms supporting MoErging, such as Hugging Face's model hub and various specialized libraries like Predibase's Lorax and Arcee's MergeKit. These tools aim to democratize the deployment of MoErging methods by simplifying the process of loading, managing, and aggregating expert models. The ongoing development of such infrastructure is crucial for translating MoErging from a theoretical construct to a practical tool used in day-to-day AI development.
Conclusion
"A Survey on Model MoErging" establishes a foundational framework for understanding, comparing, and advancing methods for aggregating specialized expert models into a unified, high-performing system. The paper’s taxonomy and comprehensive survey provide a valuable resource for researchers interested in decentralized model development, illuminating both current practices and potential future directions. As this field evolves, the systematic approach outlined in this paper will likely play a pivotal role in guiding research and application.
