Summary of Transformers Provably Learn Sparse Token Selection While Fully-connected Nets Cannot, by Zixuan Wang et al.
Transformers Provably Learn Sparse Token Selection While Fully-Connected Nets Cannot
by Zixuan Wang, Stanley Wei, Daniel Hsu, Jason D. Lee
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Information Theory (cs.IT); 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 The proposed paper strengthens the lower bound for fully-connected networks (FCNs) in the sparse token selection task, which is a challenging problem that transformers excel at. The authors establish an algorithmic separation between transformers and FCNs, showing that transformers can provably learn this task with one layer and gradient descent, while FCNs fail in the worst case. Additionally, the paper demonstrates strong out-of-distribution length generalization for transformer models. Empirical simulations are provided to support the theoretical findings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to test how well artificial intelligence models can work on certain tasks. The authors found that one type of model, called transformers, is really good at solving this task, while another type of model, called fully-connected networks, does very badly. They showed that transformers can solve the task with just one layer and some special learning rules, but fully-connected networks need many layers and don’t do as well. The authors also tested how well these models work when given data that is a bit different from what they were trained on, and found that transformers are much better at this too. |
Keywords
» Artificial intelligence » Generalization » Gradient descent » Token » Transformer
