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English(EN) Mix, Don't Tune: Bilingual Pre-Training Outperforms Hyperparameter Search in Data-Constrained Settings

苹果研究：双语预训练在超参数调优方面提升LLM性能

作者 PulseAugur 编辑部 · [1 个来源] ·

来自Apple Machine Learning Research和丹麦技术大学的研究人员发现，将来自高资源语言（如英语）的数据与低资源目标语言（如阿拉伯语）混合，可以显著提高模型性能。这种双语预训练方法被证明比激进的超参数调优更有效，尤其是在模型规模增加的情况下。这项研究涉及了约1000次不同模型规模的预训练运行，表明数据混合在验证损失方面提供了相当于2-3倍的独特目标数据的好处，在下游任务准确性方面则提供了2-13倍的好处。 AI

影响 这项研究表明，在数据受限的环境中，数据混合比超参数调优是提高语言模型性能更有效的策略，可能导致更高效的模型开发。

排序理由 该集群包含一篇详细介绍预训练语言模型新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 Apple Machine Learning Research 阅读 →

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苹果研究：双语预训练在超参数调优方面提升LLM性能

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该集群包含一篇详细介绍预训练语言模型新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. Apple Machine Learning Research TIER_1 English(EN) ·

    混合而非微调：在数据受限的情况下，双语预训练优于超参数搜索

    For most languages of the world, language model pre-training operates in a data-constrained regime where models must repeat their training data many times, degrading generalization. Two remedies exist: aggressive hyperparameter tuning such as high weight decay, and mixing in data…

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