Summary of Bimix: a Bivariate Data Mixing Law For Language Model Pretraining, by Ce Ge et al.
BiMix: A Bivariate Data Mixing Law for Language Model Pretraining
by Ce Ge, Zhijian Ma, Daoyuan Chen, Yaliang Li, Bolin Ding
First submitted to arxiv on: 23 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 introduces BiMix, a novel framework for understanding and optimizing the composition of pretraining data in large language models (LLMs). The authors demonstrate that BiMix provides high accuracy in loss extrapolation and generalization to unseen mixtures. Optimization of domain proportions yields superior model performance compared to existing methods. Additionally, the authors establish entropy-based measures as efficient proxies for data mixing, offering a computationally lightweight strategy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models have impressive abilities across various tasks, mostly thanks to using diverse data. But how does the type of pretraining data affect model performance? This paper answers this question by introducing BiMix, a new way to mix different types of data together in LLMs. They show that BiMix is good at predicting loss and works well even with new types of mixed data. By adjusting the proportions of different data sources, they can make models perform better than before. |
Keywords
» Artificial intelligence » Generalization » Optimization » Pretraining
