Summary of Towards Few-shot Adaptation Of Foundation Models Via Multitask Finetuning, by Zhuoyan Xu et al.
Towards Few-Shot Adaptation of Foundation Models via Multitask Finetuning
by Zhuoyan Xu, Zhenmei Shi, Junyi Wei, Fangzhou Mu, Yin Li, Yingyu Liang
First submitted to arxiv on: 22 Feb 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 explores the effectiveness of multitask finetuning in adapting foundation models to new tasks with limited labels. Foundation models have shown great promise, but the problem of effective adaptation remains an open question. The authors investigate whether finetuning a foundation model on a selection of relevant tasks before adapting it to the target task can improve performance. They find that with a diverse set of related tasks, multitask finetuning leads to reduced error in the target task compared to direct adaptation. The authors also propose a practical task selection algorithm and provide empirical evidence supporting their claims. The study sheds new light on the effective adaptation of foundation models to new tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to make foundation models better at learning new things when they don’t have much data. Foundation models are like super-smart AI friends that can learn lots of stuff, but sometimes they need a little help to learn something new. The researchers wanted to see if giving the model some extra training on similar tasks before trying it on the new task would help. They found out that if you give it some related tasks to practice on first, it gets really good at doing the new thing! They even came up with an easy way to choose which tasks are best for the model to learn from. |
