Summary of Critical Data Size Of Language Models From a Grokking Perspective, by Xuekai Zhu et al.
Critical Data Size of Language Models from a Grokking Perspective
by Xuekai Zhu, Yao Fu, Bowen Zhou, Zhouhan Lin
First submitted to arxiv on: 19 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 investigates the minimum amount of data required for language models to transition from rapid memorization to slow generalization. The authors formalize this threshold as the Data Efficiency Hypothesis and identify three regimes: data insufficiency, sufficiency, and surplus, based on training dynamics. They propose a grokking configuration that reproduces stable learning in simplistic language models by adjusting initialization and weight decay. Experimental results show that generalization occurs only when models reach a critical size, which increases as model size grows, indicating the need for more data. The study provides new insights into language model training, highlighting the crucial role of data in the learning mechanism. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how much data is needed to train language models. It shows that there’s a special point where language models switch from quickly memorizing things to slowly understanding them. The researchers created a formula to predict when this happens and found that it depends on the size of the model and the amount of training data. They also discovered that as the model gets bigger, it needs even more data to learn. Overall, the study helps us understand how language models work and why they need specific amounts of data to be effective. |
Keywords
* Artificial intelligence * Generalization * Language model
