Summary of Faster Convergence For Transformer Fine-tuning with Line Search Methods, by Philip Kenneweg et al.
Faster Convergence for Transformer Fine-tuning with Line Search Methods
by Philip Kenneweg, Leonardo Galli, Tristan Kenneweg, Barbara Hammer
First submitted to arxiv on: 27 Mar 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 The paper extends line search methods to the Transformer architecture in natural language processing, combining Armijo line search with Adam optimizer and subdividing networks into local units. It outperforms traditional Adam optimizer for small datasets or budgets, while performing equally well for other cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this work, researchers take a common technique called line search and apply it to the popular Transformer architecture in natural language processing. They combine this technique with another optimization method called Adam and make some adjustments to work better with this new architecture. The result is an optimizer that performs well even when working with small amounts of data or limited training time. |
Keywords
* Artificial intelligence * Natural language processing * Optimization * Transformer
