Keywords: Keywords: multilingual, pretraining, language models, language sampling, language distribution, low-resource languages, overfitting
TL;DR: We propose a novel language sampling method that is close to being uniform across languages without introducing harmful repetition and that outperforms the temperature-based sampling.
Abstract: Pretrained multilingual large language models have typically used heuristic temperature-based sampling to balance between different languages. However previous work has not systematically evaluated the efficacy of different pretraining language distributions across model scales. In this paper, we propose a new sampling method, UniMax, that delivers more uniform coverage of head languages while mitigating overfitting on tail languages by explicitly capping the number of repeats over each language's corpus. We perform an extensive series of ablations testing a range of sampling strategies on a suite of multilingual benchmarks, while varying model scale. We find that UniMax outperforms standard temperature-based sampling, and the benefits persist as scale increases. As part of our contribution, we release: (i) an improved and refreshed mC4 multilingual corpus consisting of 29 trillion characters across 107 languages, and (ii) a suite of pretrained umT5 model checkpoints trained with UniMax sampling.
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Please Choose The Closest Area That Your Submission Falls Into: Deep Learning and representational learning