Summary of Cola: Collaborative Adaptation with Gradient Learning, by Enmao Diao et al.
ColA: Collaborative Adaptation with Gradient Learning
by Enmao Diao, Qi Le, Suya Wu, Xinran Wang, Ali Anwar, Jie Ding, Vahid Tarokh
First submitted to arxiv on: 22 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposes Collaborative Adaptation (ColA) with Gradient Learning (GL), a novel fine-tuning approach for large models. Unlike Parameter-Efficient Fine-Tuning (PEFT) methods, ColA decouples the computation of hidden representations and parameters, reducing computational costs. The authors demonstrate that ColA can perform on par or better than existing PEFT methods on various benchmarks, making it a cost-effective solution for Fine-Tuning as a Service (FTaaS) in cloud-based models. By offloading gradient computations to low-cost devices, ColA facilitates more efficient training and deployment of large models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a super powerful computer that can help train other computers to do tasks like image recognition or language translation. This paper introduces a new way to make this process faster and cheaper by breaking it down into smaller pieces that can be done on different devices. The authors test their approach, called Collaborative Adaptation (ColA), and show that it can work just as well as other methods while using fewer resources. This could lead to more efficient training of large models and make it easier for people to use these powerful computers in the future. |
Keywords
» Artificial intelligence » Fine tuning » Parameter efficient » Translation
