🤖 AI Summary
The precise mechanisms by which subword tokenization enhances language model performance remain unclear. This work systematically disentangles the independent effects of subword tokenization on training efficiency and linguistic priors within a controlled byte-level pretraining framework, by simulating subword boundaries, vocabulary scaling, and throughput variations. For the first time, subword boundaries are explicitly modeled as an inductive bias, revealing that their combination with increased training throughput constitutes the core advantage of subword-based models. These findings provide both theoretical grounding and practical guidance for hybrid pretraining approaches that integrate byte-level and subword representations.
📝 Abstract
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.