Summary of Separations in the Representational Capabilities Of Transformers and Recurrent Architectures, by Satwik Bhattamishra et al.
Separations in the Representational Capabilities of Transformers and Recurrent Architectures
by Satwik Bhattamishra, Michael Hahn, Phil Blunsom, Varun Kanade
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 explores the representational capabilities of Transformer and Recurrent Neural Network (RNN) architectures across various tasks, including index lookup, nearest neighbor, recognizing bounded Dyck languages, and string equality. The authors analyze the size requirements for different models to perform these tasks, showing separations between Transformers and RNNs. For example, a one-layer Transformer can perform index lookup with a logarithmic width, while an RNN requires a hidden state of linear size. The paper also demonstrates that two-layer Transformers can perform decision tasks like string equality or disjointness with logarithmic size, whereas RNNs require linear size. Additionally, the authors show that a log-size two-layer Transformer can implement the nearest neighbor algorithm in its forward pass. The theoretical results are supplemented by experiments highlighting the differences in performance on practical-size sequences. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper compares how good Transformers and old-school RNNs are at doing certain tasks. It shows that some tasks, like finding something in a list or recognizing special patterns in strings, need more powerful Transformers than simple RNNs. On the other hand, some tasks, like checking if two strings are equal or different, can be done with simpler models. The paper also finds ways to make these old models work better for specific jobs. It even shows that one special Transformer can do a tricky task called nearest neighbor search on its own! |
Keywords
* Artificial intelligence * Nearest neighbor * Neural network * Rnn * Transformer
