Summary of Empirical Analysis Of Efficient Fine-tuning Methods For Large Pre-trained Language Models, by Nigel Doering et al.
Empirical Analysis of Efficient Fine-Tuning Methods for Large Pre-Trained Language Models
by Nigel Doering, Cyril Gorlla, Trevor Tuttle, Adhvaith Vijay
First submitted to arxiv on: 8 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 This paper investigates the fine-tuning of large pre-trained language models for downstream tasks, a crucial challenge in natural language processing. Two efficient fine-tuning methods – BitFit and adapter modules – are compared to standard full model fine-tuning. The experiments on GLUE benchmark datasets (MRPC, COLA, STS-B) reveal that the BitFit approach matches full fine-tuning performance across varying amounts of training data and time constraints, even with only 30% of data. It also outperforms full fine-tuning at intermediate data levels. Adapter modules exhibit high variability, but offer inconsistent gains over default models. The findings suggest that BitFit provides an attractive balance between performance and parameter efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make big language models better for specific tasks. It compares two ways to do this – BitFit and adapter modules – with the usual method of fine-tuning the whole model. They tested these methods on some benchmark datasets and found that BitFit does just as well as the full model, even when they only had a small amount of training data. Adapter modules didn’t work consistently, but could sometimes be better than the default models. The results show that BitFit is a good choice because it balances how well it performs with how many extra parameters it needs. |
Keywords
* Artificial intelligence * Fine tuning * Natural language processing
