Summary of Arithmetic in Transformers Explained, by Philip Quirke et al.
Arithmetic in Transformers Explained
by Philip Quirke, Clement Neo, Fazl Barez
First submitted to arxiv on: 4 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 paper explores the arithmetic capabilities of transformers in machine learning. Recent studies have shown that transformers can learn addition, but previous models have limitations such as poor prediction accuracy and being confined to small numbers. To address this, researchers trained 44 autoregressive transformer models on various arithmetic operations, including addition and subtraction. The study finds that addition-only models converge on a common logical algorithm, achieving high prediction accuracy. Subtraction-only models have lower accuracy, while mixed models initialized with parameters from addition-only models show improved performance in both addition and subtraction tasks. The paper provides mechanistic explanations of how the algorithms are implemented within the network architecture. This work has implications for multitask learning dynamics and introduces a reusable library for interpreting algorithmic circuits. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at whether computers can do math problems better. Right now, computers can learn to add numbers, but they’re not very good at it and can only handle small numbers. The researchers trained 44 special computer models on different math tasks, like adding and subtracting. They found that when the models just did addition, they got really good at it! When the models also had to do subtraction, they didn’t do as well. But if the models started with an addition-only model and then learned to do subtraction too, they got even better! This helps us understand how computers can learn new things and might be useful for other tasks. |
Keywords
* Artificial intelligence * Autoregressive * Machine learning * Transformer
