arXivPaperNeeds Review
TokEval: A Tokenizer Evaluation Suite
TokEval links tokenizer properties to language, math, and code performance, offering cheaper screening signals before committing compute to pretraining sweeps.
arXiv
Source Summary
TokEval measures tokenizer properties beyond fertility and compression, including **UTF-8 boundary integrity**, digit place-value alignment, and line-break handling. In controlled pretraining, information-theoretic metrics predicted language modeling results with **Spearman rho up to 0.80**.
Practical Implication
Model builders should evaluate tokenizers against the structure of their workload, especially digits, code lines, and multilingual text. Intrinsic checks could narrow candidates before spending compute on full training runs.
Agent-Ready Context
TokEval measures tokenizer properties beyond fertility and compression, including **UTF-8 boundary integrity**, digit place-value alignment, and line-break handling. In controlled pretraining, information-theoretic metrics predicted language modeling results with **Spearman rho up to 0.80**. Model builders should evaluate tokenizers against the structure of their workload, especially digits, code lines, and multilingual text. Intrinsic checks could narrow candidates before spending compute on full training runs. The reported relationships differ by capability: no single metric predicts everything. TokEval can replace pretraining comparisons only where intrinsic measures have been shown to agree with downstream results.
Context Map
benchmarkcodingdata#benchmark-integrity#model-selectionUncertainty
The reported relationships differ by capability: no single metric predicts everything. TokEval can replace pretraining comparisons only where intrinsic measures have been shown to agree with downstream results.