Summary of Regmix: Data Mixture As Regression For Language Model Pre-training, by Qian Liu et al.
RegMix: Data Mixture as Regression for Language Model Pre-training
by Qian Liu, Xiaosen Zheng, Niklas Muennighoff, Guangtao Zeng, Longxu Dou, Tianyu Pang, Jing Jiang, Min Lin
First submitted to arxiv on: 1 Jul 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 The proposed RegMix framework automatically identifies an effective data mixture for large language model pre-training, which is crucial in determining the performance of these models. By formulating it as a regression task, RegMix trains many small models on diverse mixtures and uses regression to predict the performance of unseen mixtures, ultimately applying the best predicted mixture to train a larger-scale model with significant computational resources. The framework’s effectiveness is empirically validated by training multiple models with varying parameters and tokens, demonstrating its ability to consistently outperform human selection while matching or exceeding DoReMi using fewer resources. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RegMix is a new way to choose the right mix of data for large language models. These models are trained on lots of text data to get better at understanding language. But it’s hard to decide which mix of data will work best. RegMix uses many small models to find the mix that works best and then trains a bigger model using those results. This helps make the big model stronger and more efficient. |
Keywords
» Artificial intelligence » Large language model » Regression
