Summary of Improving Large Models with Small Models: Lower Costs and Better Performance, by Dong Chen et al.
Improving Large Models with Small models: Lower Costs and Better Performance
by Dong Chen, Shuo Zhang, Yueting Zhuang, Siliang Tang, Qidong Liu, Hua Wang, Mingliang Xu
First submitted to arxiv on: 15 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 a new approach for collaborating between small and large pre-trained models (PLMs) like ChatGPT. The authors highlight the limitations of using only large PLMs, which require significant computational resources, making them expensive to use or fine-tune. Instead, they introduce Data Shunt^+ (DS^+), a paradigm that allows small models to handle simple tasks while large models focus on challenging ones, improving overall performance. The authors demonstrate the effectiveness of DS^+ by achieving better results than fine-tuning and reducing costs by up to 68%. They also show that DS^+ can better inject task-specific knowledge into PLMs compared to traditional fine-tuning methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how computers can work together to do tasks. Right now, we have really powerful models like ChatGPT that are great at some things, but they take a lot of computer power and money to use. The authors came up with an idea called Data Shunt^+ (DS^+) where smaller models can help with easy parts of the task while the big models focus on harder parts. This makes it cheaper and more efficient. They tested DS^+ and showed that it works better than using just one big model and reduces costs by a lot. |
Keywords
* Artificial intelligence * Fine tuning
