Summary of Data Mixing Laws: Optimizing Data Mixtures by Predicting Language Modeling Performance, By Jiasheng Ye et al.
Data Mixing Laws: Optimizing Data Mixtures by Predicting Language Modeling Performance
by Jiasheng Ye, Peiju Liu, Tianxiang Sun, Yunhua Zhou, Jun Zhan, Xipeng Qiu
First submitted to arxiv on: 25 Mar 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 This paper explores the impact of large language model pretraining data composition on their competence, showing that the proportions of different domains (e.g., web texts, academic papers) significantly influence model performance. Researchers develop data mixing laws, which predict model performance based on mixture proportions, enabling optimal selection of training data without extensive experimentation. The proposed method also predicts model performance under various mixtures with only small-scale training, showcasing its effectiveness in optimizing training parameters. Experimental results demonstrate that the approach can reach comparable performance to longer training times or larger models, highlighting its potential for real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are trained on a mix of different types of text data. The way this data is mixed affects how well the model performs. This paper finds patterns in how different mixes of data affect model performance and uses these patterns to predict how well a model will perform before it’s even trained. It also shows that this approach can help models learn better without needing as much training time or large amounts of data. |
Keywords
* Artificial intelligence * Large language model * Pretraining
