Summary of Bipeft: Budget-guided Iterative Search For Parameter Efficient Fine-tuning Of Large Pretrained Language Models, by Aofei Chang et al.
BIPEFT: Budget-Guided Iterative Search for Parameter Efficient Fine-Tuning of Large Pretrained Language Models
by Aofei Chang, Jiaqi Wang, Han Liu, Parminder Bhatia, Cao Xiao, Ting Wang, Fenglong Ma
First submitted to arxiv on: 4 Oct 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 Parameter Efficient Fine-Tuning (PEFT) is an efficient approach for fine-tuning large language models for specific tasks. However, existing automatic PEFT strategies often face challenges such as entangled search spaces and inefficiency. To address these issues, we propose Budget-guided Iterative search strategy for automatic PEFT (BIPEFT), which significantly improves search efficiency. BIPEFT employs an iterative search strategy to disentangle search spaces and designs early selection strategies based on parameter budgets to accelerate the learning process. Our experiments demonstrate the superior performance of BIPEFT in achieving efficient and effective PEFT with a low parameter budget, outperforming existing methods on public benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a big computer program that’s already good at doing some tasks, but you want it to be even better at specific jobs. This is called fine-tuning. However, making these programs work well requires a lot of computation and data. We developed a new way to make this process more efficient and effective by using an iterative search strategy. Our method, called BIPEFT, helps the program learn faster and better by focusing on the most important parts first. We tested our approach on public datasets and found that it outperforms other methods in achieving good results with limited computer resources. |
Keywords
» Artificial intelligence » Fine tuning » Parameter efficient
