AI Insight
This study introduces TokEval, a comprehensive evaluation framework for language model tokenizers that measures properties beyond standard metrics like compression rate. Through controlled pretraining experiments where only tokenizer characteristics varied, researchers found that information-theoretic metrics predict language modeling performance (correlation up to 0.80), while structure-sensitive metrics measuring digit and line-break handling correlate with mathematical reasoning and code generation accuracy. The framework aims to enable tokenizer selection through intrinsic measurements rather than costly pretraining experiments.
Why it matters
This work addresses a overlooked component of language model development by providing systematic methods to evaluate tokenizers before expensive model training. Better tokenizer selection could improve model performance across diverse tasks while reducing computational costs associated with trial-and-error pretraining approaches.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
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Abstract: Language model tokenizers are typically selected with minimal evaluation, despite the fact that their design choices directly impact model capabilities. This can be partly attributed to a limited understanding of which tokenizer properties affect which aspects of downstream performance. We introduce TokEval, a framework of tokenizer evaluation metrics that goes beyond standard measures like fertility and compression rate to capture linguistically and structurally meaningful properties, e.g., UTF-8 character boundary integrity and digit place-value boundary alignment for mathematics. To validate whether these metrics are predictive of downstream model performance, we conduct controlled language model pretraining experiments, varying solely the tokenizers’ training data mixture, pretokenization strategy, and training algorithm. We evaluate the resulting models on bits-per-byte (a tokenizer-agnostic version of perplexity) and several benchmarks, spanning linguistic understanding, mathematical reasoning, and code generation. Our experiments suggest that different intrinsic properties have different impacts on model abilities: information-theoretic metrics predict language modeling abilities (Spearman rho up to 0.80), while structure-sensitive metrics, such as those measuring digit and line-break handling, correlate with task accuracy. We hope TokEval enables more principled tokenizer evaluation, replacing pretraining sweeps with intrinsic measurement wherever the two agree.