A Survey on Model MoErging: Recycling and Routing Among Specialized Experts for Collaborative Learning
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866908482862579712 |
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| author | Yadav, Prateek Raffel, Colin Muqeeth, Mohammed Caccia, Lucas Liu, Haokun Chen, Tianlong Bansal, Mohit Choshen, Leshem Sordoni, Alessandro |
| author_facet | Yadav, Prateek Raffel, Colin Muqeeth, Mohammed Caccia, Lucas Liu, Haokun Chen, Tianlong Bansal, Mohit Choshen, Leshem Sordoni, Alessandro |
| contents | The availability of performant pre-trained models has led to a proliferation of fine-tuned expert models that are specialized to a particular domain or task. Model MoErging methods aim to recycle expert models to create an aggregate system with improved performance or generalization. A key component of MoErging methods is the creation of a router that decides which expert model(s) to use for a particular input or application. The promise, effectiveness, and large design space of MoErging has spurred the development of many new methods over the past few years. This rapid pace of development has made it challenging to compare different MoErging methods, which are rarely compared to one another and are often validated in different experimental setups. To remedy such gaps, we present a comprehensive survey of MoErging methods that includes a novel taxonomy for cataloging key design choices and clarifying suitable applications for each method. Apart from surveying MoErging research, we inventory software tools and applications that make use of MoErging. We additionally discuss related fields of study such as model merging, multitask learning, and mixture-of-experts models. Taken as a whole, our survey provides a unified overview of existing MoErging methods and creates a solid foundation for future work in this burgeoning field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_07057 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Survey on Model MoErging: Recycling and Routing Among Specialized Experts for Collaborative Learning Yadav, Prateek Raffel, Colin Muqeeth, Mohammed Caccia, Lucas Liu, Haokun Chen, Tianlong Bansal, Mohit Choshen, Leshem Sordoni, Alessandro Machine Learning Artificial Intelligence Computation and Language The availability of performant pre-trained models has led to a proliferation of fine-tuned expert models that are specialized to a particular domain or task. Model MoErging methods aim to recycle expert models to create an aggregate system with improved performance or generalization. A key component of MoErging methods is the creation of a router that decides which expert model(s) to use for a particular input or application. The promise, effectiveness, and large design space of MoErging has spurred the development of many new methods over the past few years. This rapid pace of development has made it challenging to compare different MoErging methods, which are rarely compared to one another and are often validated in different experimental setups. To remedy such gaps, we present a comprehensive survey of MoErging methods that includes a novel taxonomy for cataloging key design choices and clarifying suitable applications for each method. Apart from surveying MoErging research, we inventory software tools and applications that make use of MoErging. We additionally discuss related fields of study such as model merging, multitask learning, and mixture-of-experts models. Taken as a whole, our survey provides a unified overview of existing MoErging methods and creates a solid foundation for future work in this burgeoning field. |
| title | A Survey on Model MoErging: Recycling and Routing Among Specialized Experts for Collaborative Learning |
| topic | Machine Learning Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2408.07057 |