Summary of Towards Empirical Interpretation Of Internal Circuits and Properties in Grokked Transformers on Modular Polynomials, by Hiroki Furuta et al.
Towards Empirical Interpretation of Internal Circuits and Properties in Grokked Transformers on Modular Polynomials
by Hiroki Furuta, Gouki Minegishi, Yusuke Iwasawa, Yutaka Matsuo
First submitted to arxiv on: 26 Feb 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 This paper explores the phenomenon of delayed generalization in machine learning models and focuses on understanding their interpretable representations and algorithms. Researchers have successfully applied grokking to modular addition, revealing Fourier representation and trigonometric identities within Transformer models. The study hypothesizes that similar explanations can be extended to other modular operations beyond addition, leveraging common features transferable among similar operations and mixing datasets with similar tasks. The authors extensively examine this concept by training Transformers on complex modular arithmetic tasks, including polynomials. Their novel progress measures, Fourier Frequency Density and Fourier Coefficient Ratio, characterize distinctive internal representations of grokked models per modular operation. The study also investigates the ablation effects of pre-grokked models, revealing limited transferability among models and highlighting the potential for co-grokking to accelerate generalization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about understanding how machine learning models work and what makes them good at certain tasks. Researchers have been trying to figure out why some models are great at adding numbers together, but not as good at doing more complicated math problems. They think that the key might be in finding common patterns and features among different math operations, like addition and multiplication. To test this idea, they trained a special kind of model called Transformers on lots of math problems, including really hard ones. By looking at how these models work internally, they found some clues about what makes them good or bad at certain tasks. The study also shows that mixing different types of math problems together can actually help the models learn and improve faster. |
Keywords
* Artificial intelligence * Generalization * Machine learning * Transferability * Transformer
