Summary of Carrying Over Algorithm in Transformers, by Jorrit Kruthoff
Carrying over algorithm in transformers
by Jorrit Kruthoff
First submitted to arxiv on: 15 Jan 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 paper explores how transformer models implement the addition algorithm, which is typically performed using the carrying over method. The study focuses on two-layer encoder-only models and shows that this algorithm is implemented modularly, with the first layer primarily responsible for adding digits in the same position. The second layer uses attention to decide which positions need a carried one or not, and then performs the actual carry operation through a multi-layer perceptron (MLP). The researchers provide a method for precisely identifying which neurons are involved in this process. This implementation is found across a range of hyperparameters for both two- and three-layer models. Additionally, the study finds similar results for small decoder-only models and provides evidence that this implementation exists in larger language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how computers do addition using transformers. Transformers are special kinds of neural networks that help with tasks like language translation and text generation. The researchers found out how transformers do addition by looking at simple versions of these networks. They saw that the first part of the network adds numbers together, while the second part decides which numbers need to be “carried” (added) later. This is like when you’re adding numbers in your head and need to “carry” a digit to the next column. The researchers also found this out for bigger networks and smaller ones, and even saw hints of it in really big language models. |
Keywords
* Artificial intelligence * Attention * Decoder * Encoder * Text generation * Transformer * Translation
