Summary of Looped Transformers For Length Generalization, by Ying Fan et al.
Looped Transformers for Length Generalization
by Ying Fan, Yilun Du, Kannan Ramchandran, Kangwook Lee
First submitted to arxiv on: 24 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 Recent research has shown that Transformers trained from scratch can excel in arithmetic and algorithmic tasks, such as adding numbers and computing parity. However, these Transformers struggle with length generalization, i.e., handling inputs of unseen lengths. To address this limitation, we propose a novel approach using looped Transformers with an adaptive number of steps, which significantly improves length generalization. We focus on tasks with known iterative solutions involving the RASP-L operation, a length-generalizable operation that can be expressed by a finite-sized Transformer. Our proposed learning algorithm enables looped Transformers to learn highly length-generalizable solutions for various tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Scientists have developed special computers called Transformers that are great at solving math problems and performing tasks like adding numbers. However, these computers struggle with understanding inputs of different lengths. To solve this problem, researchers created a new kind of Transformer that can adapt to different input lengths. They focused on math problems that can be solved in multiple steps, and developed an algorithm for training these Transformers. The result is that these computers can now solve a wide range of math problems and understand inputs of different lengths. |
Keywords
» Artificial intelligence » Generalization » Transformer
