通过字节级模拟解耦子词分词对语言模型训练的益处

摘要

子词分词是现代大型语言模型（LLMs）的核心组成部分，但其对训练效率和模型性能的具体贡献仍未被充分理解。本研究通过将子词分词的效果解耦并置于可控的字节级预训练流程中，从样本吞吐量、词汇表缩放以及子词边界的语言先验等多个维度提出了假设并进行了验证。通过在字节级环境中模拟这些效应，我们深化了对子词模型为何优于原始字节模型的认识，并为改进未来字节级与子词模型的预训练提供了见解。具体而言，我们的实验凸显了提升训练吞吐量的关键作用，以及将子词边界作为显式先验或归纳偏差加以整合的重要性。

English

Subword tokenization is an essential part of modern large language models (LLMs), yet its specific contributions to training efficiency and model performance remain poorly understood. In this work, we decouple the effects of subword tokenization by isolating them within a controlled byte-level pretraining pipeline. We formulate and test hypotheses across various dimensions, including sample throughput, vocabulary scaling, and the linguistic prior of subword boundaries. By simulating these effects in a byte-level setting, we refine our understanding of why subword models outperform raw byte models and offer insights to improve the pretraining of future byte-level and subword models. Specifically, our experiments highlight the critical role of increased training throughput and the integration of subword boundaries as either explicit priors or inductive biases.