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.

翻译：预训练的多语言大型语言模型通常采用基于温度的启发式采样方法以平衡不同语言之间的表示。然而，先前的工作尚未系统评估不同预训练语言分布在模型规模变化下的有效性。本文提出一种新的采样方法UniMax，通过对每种语言语料库的重复次数进行显式上限约束，在更均匀覆盖头部语言的同时，缓解对尾部语言的过拟合问题。我们在多语言基准测试套件上开展了广泛的消融实验，测试了多种采样策略并变化模型规模。实验结果表明，UniMax优于标准温度采样方法，且其优势随模型规模增大而持续存在。作为贡献的一部分，我们发布了：(i) 改进并更新的mC4多语言语料库，包含107种语言的29万亿字符；(ii) 使用UniMax采样训练的一组预训练umT5模型检查点。