Summary of A Survey on Model Moerging: Recycling and Routing Among Specialized Experts For Collaborative Learning, by Prateek Yadav et al.
A Survey on Model MoErging: Recycling and Routing Among Specialized Experts for Collaborative Learning
by Prateek Yadav, Colin Raffel, Mohammed Muqeeth, Lucas Caccia, Haokun Liu, Tianlong Chen, Mohit Bansal, Leshem Choshen, Alessandro Sordoni
First submitted to arxiv on: 13 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a comprehensive survey of Model MoErging (MoErging) methods, which aim to recycle expert models to create an aggregate system with improved performance or generalization. The survey includes a novel taxonomy for cataloging key design choices and clarifying suitable applications for each method. It also inventories software tools and applications that make use of MoErging. Additionally, the paper discusses related fields such as model merging, multitask learning, and mixture-of-experts models. The promise and effectiveness of MoErging methods have spurred the development of many new approaches over the past few years, making it challenging to compare different methods. This survey aims to remedy these gaps by providing a unified overview of existing MoErging methods and creating a solid foundation for future work. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about how experts in machine learning are using special models called Model MoErging (MoErging) to make new, better models. These models help computers do tasks like recognizing pictures or understanding speech. There are many different ways to use these models, and this paper tries to figure out what they all have in common and how they work best. |
Keywords
» Artificial intelligence » Generalization » Machine learning » Mixture of experts
