Summary of Scaling Laws For Multilingual Language Models, by Yifei He et al.
Scaling Laws for Multilingual Language Models
by Yifei He, Alon Benhaim, Barun Patra, Praneetha Vaddamanu, Sanchit Ahuja, Parul Chopra, Vishrav Chaudhary, Han Zhao, Xia Song
First submitted to arxiv on: 15 Oct 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 This paper proposes a novel scaling law for decoder-only language models trained on multilingual data, tackling the problem of balancing languages during pretraining. The authors introduce a hypothesis that the test cross-entropy loss for each language family is determined solely by its own sampling ratio, independent of other languages in the mixture. This insight simplifies the complexity of multilingual scaling and enables prediction of performance across various combinations of dataset size, model size, and sampling ratios. The paper validates this hypothesis through a large-scale empirical study, training over 100 models on 23 languages spanning 5 language families. The results show that the optimal sampling ratios derived from small models generalize effectively to larger models, offering a resource-efficient approach for multilingual LM training at scale. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to train language models that can work with many different languages. The authors found a way to simplify the process of balancing languages during pretraining, which makes it easier to train bigger and better models. They did this by looking at groups of languages together instead of individual languages. This allows them to predict how well a model will perform based on its size, the amount of data it has, and how it’s being used. The authors tested their idea with many different language models and showed that it works really well. |
Keywords
» Artificial intelligence » Cross entropy » Decoder » Pretraining
