Summary of Explicitly Encoding Structural Symmetry Is Key to Length Generalization in Arithmetic Tasks, by Mahdi Sabbaghi et al.
Explicitly Encoding Structural Symmetry is Key to Length Generalization in Arithmetic Tasks
by Mahdi Sabbaghi, George Pappas, Hamed Hassani, Surbhi Goel
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); 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 Despite the success of Transformers in various tasks, they struggle with basic arithmetic operations like addition and multiplication due to the significant structural difference between numbers and text. To overcome this limitation, we propose a novel approach that encodes semantic structure into the model using modified number formatting and custom positional encodings. Our method enables a Transformer trained on short numbers (up to 5 digits) to generalize to longer sequences (up to 50 digits) for addition and multiplication without requiring additional data. We also demonstrate that traditional absolute positional encodings fail to generalize to longer sequences, even when augmented with task symmetries. By explicitly incorporating structure into the model, we prove the necessity of this approach for out-of-distribution generalization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A Transformer is a type of artificial intelligence that’s great at understanding language and doing math problems. However, it struggles to do simple arithmetic operations like addition and multiplication when dealing with really long numbers. The reason is that numbers have a specific structure that text doesn’t. We came up with a new way to teach the Transformer about this structure by changing how it processes numbers. This lets the model do longer math problems without needing more training data. We also showed that other ways of teaching the model don’t work as well. By teaching the model about the right structure, we can help it generalize and make better predictions. |
Keywords
» Artificial intelligence » Generalization » Transformer
