Summary of Zyda: a 1.3t Dataset For Open Language Modeling, by Yury Tokpanov et al.
Zyda: A 1.3T Dataset for Open Language Modeling
by Yury Tokpanov, Beren Millidge, Paolo Glorioso, Jonathan Pilault, Adam Ibrahim, James Whittington, Quentin Anthony
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 introduces Zyda, a new open-source dataset for large language model (LLM) pretraining, comprising 1.3 trillion tokens. Zyda is assembled from several respected datasets, with rigorous filtering and deduplication processes to maintain quality. The authors evaluate Zyda’s performance and find it competitive with other open datasets like Dolma, FineWeb, and RefinedWeb. Additionally, they show that Zyda can improve the performance of comparable models from the Pythia suite. This paper addresses the growing need for large-scale datasets in LLM pretraining. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to build a super powerful language model, but it needs lots and lots of data to learn from. Researchers have been working hard to create big datasets, but they’ve gotten so big that it’s hard to find good ones to use. This paper solves this problem by creating a new dataset called Zyda, which has 1.3 trillion words in it! They put together words from many other good datasets and made sure they’re all accurate and correct. The authors tested Zyda and found it works really well with certain language models. This is important because it helps us make better language models that can understand humans better. |
Keywords
» Artificial intelligence » Language model » Large language model » Pretraining
