Summary of Teaching Transformers Modular Arithmetic at Scale, by Eshika Saxena et al.
Teaching Transformers Modular Arithmetic at Scale
by Eshika Saxena, Alberto Alfarano, Emily Wenger, Kristin Lauter
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 research paper proposes a scalable machine learning solution for modular addition, a fundamental operation that has eluded efficient ML-based solutions despite its simplicity. The authors aim to bridge this gap by developing a novel training pipeline for modular addition models, which can handle large inputs (up to 256 elements) and large moduli (up to 3329). To achieve this, they introduce three key innovations: diverse training data, an angular embedding, and a custom loss function. These advancements enable the model to successfully perform modular addition operations that are relevant to cryptographic applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study creates a machine learning system for adding numbers together in a special way called modular arithmetic. Modular addition is important because it helps solve puzzles used to keep information secure online. The researchers wanted to make their system better by giving it more training data, using new ways to represent the numbers, and adjusting how it learns. They were able to make their system work with bigger sets of numbers and bigger “moduli” than before. This could be useful for making encryption stronger. |
Keywords
» Artificial intelligence » Embedding » Loss function » Machine learning
