Summary of Transformers Can Do Arithmetic with the Right Embeddings, by Sean Mcleish et al.
Transformers Can Do Arithmetic with the Right Embeddings
by Sean McLeish, Arpit Bansal, Alex Stein, Neel Jain, John Kirchenbauer, Brian R. Bartoldson, Bhavya Kailkhura, Abhinav Bhatele, Jonas Geiping, Avi Schwarzschild, Tom Goldstein
First submitted to arxiv on: 27 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 abstract discusses a limitation of transformers in performing arithmetic tasks, specifically their inability to track digit positions within large numbers. The authors propose a solution by adding position embeddings to each digit, enabling better performance on these tasks. This fix not only improves the model’s original performance but also allows for additional architectural modifications such as input injection and recurrent layers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Transformers struggle with arithmetic tasks due to their difficulty in keeping track of digits within large numbers. To solve this problem, researchers added special “position embeddings” to each digit that tell it where it is in the number. This fix not only helps the model do better on its own but also lets it use new techniques like injecting more information and using layers that remember things. |
