Summary of Model Merging in Llms, Mllms, and Beyond: Methods, Theories, Applications and Opportunities, by Enneng Yang et al.
Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities
by Enneng Yang, Li Shen, Guibing Guo, Xingwei Wang, Xiaochun Cao, Jie Zhang, Dacheng Tao
First submitted to arxiv on: 14 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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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 This paper presents a comprehensive review of model merging techniques, which is an efficient method for empowering machine learning models without requiring raw training data or expensive computation. The authors propose a new taxonomic approach to categorize existing model merging methods and discuss their applications in various domains, including large language models, multimodal language models, and multiple machine learning subfields such as continual learning, multi-task learning, few-shot learning, etc. The paper also highlights the remaining challenges of model merging and suggests future research directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Model merging is a way to make machine learning models better without needing lots of data or powerful computers. This technique is important because it can be used in many different areas, like language processing and image recognition. The authors of this paper group existing methods together and explain how they are used in different situations. They also discuss the challenges that still need to be solved and what should come next. |
Keywords
» Artificial intelligence » Continual learning » Few shot » Machine learning » Multi task
