Summary of Arbitrary-length Generalization For Addition in a Tiny Transformer, by Alexandre Galvao Patriota
Arbitrary-Length Generalization for Addition in a Tiny Transformer
by Alexandre Galvao Patriota
First submitted to arxiv on: 31 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Applications (stat.AP); 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 The proposed methodology enables a Transformer model to generalize adding two-digit numbers to numbers with unseen lengths of digits. The approach employs an autoregressive generation technique, processing from right to left, mimicking manual addition methods for large numbers. This novel training method has not been previously explored in the literature. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to teach a computer model to add big numbers that it’s never seen before. It uses a special technique called autoregressive generation, which is like how humans do math by working from right to left. This approach helps the model understand how to add numbers with more digits than it has seen before. |
Keywords
» Artificial intelligence » Autoregressive » Transformer
