Summary of Gzip Predicts Data-dependent Scaling Laws, by Rohan Pandey
gzip Predicts Data-dependent Scaling Laws
by Rohan Pandey
First submitted to arxiv on: 26 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 challenges the notion that neural language models’ performance is agnostic to training data complexity. It generates datasets with varying complexities by manipulating a probabilistic context-free grammar and finds that scaling laws are sensitive to differences in data complexity. The study proposes a new data-dependent scaling law that takes into account the compressibility of training data, showing that as data becomes harder to compress, the optimal allocation of compute resources shifts from parameter count to dataset size. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how well language models work based on the type of text they’re trained on. It makes these text datasets more or less complex by changing their structure and finds that the way language models scale depends on the complexity of the data. The researchers propose a new way to predict how well language models will do based on the compressibility of their training data. They show that as the training data gets harder to shrink, it becomes more important to have a lot of data rather than just lots of computer power. |
Keywords
» Artificial intelligence » Scaling laws
