Summary of Transformers Can Achieve Length Generalization but Not Robustly, by Yongchao Zhou et al.
Transformers Can Achieve Length Generalization But Not Robustly
by Yongchao Zhou, Uri Alon, Xinyun Chen, Xuezhi Wang, Rishabh Agarwal, Denny Zhou
First submitted to arxiv on: 14 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper investigates a crucial challenge for language models called length generalization, where they can accurately extrapolate from shorter training sequences to longer test ones. Specifically, it focuses on the Transformer architecture’s ability to handle this issue using a simple addition task involving two integers. The results show that the success of length generalization depends on the data format and position encoding used. By combining these elements correctly, the paper demonstrates for the first time that standard Transformers can generalize to sequence lengths 2.5 times longer than the input. However, unlike in-distribution generalization, length generalization remains fragile, influenced by factors like random weight initialization and training data order, leading to significant variances across different seeds. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to solve a big problem for computers that understand language called “length generalization”. This means being able to predict longer sentences or sequences just from seeing shorter ones. The researchers tested this using simple addition problems with numbers. They found that the way they set up the data and how they represent positions in the sentence matters a lot. By doing it just right, they were able to make computers that understand language (called Transformers) predict longer sequences than before. But there’s still more work to do because the results are not always the same. |
Keywords
* Artificial intelligence * Generalization * Transformer
