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This paper introduces TokEval, a framework for evaluating language model tokenizers using intrinsic metrics that correlate with downstream task performance.
This paper presents QuechuaTok, a benchmark for evaluating tokenization strategies for Southern Quechua, and introduces Morphological Boundary Accuracy (MorphAcc) as a necessary metric. It shows that BPE achieves low fertility but poor morphological accuracy, while a morphology-aware PRPE tokenizer achieves 83% MorphAcc, demonstrating that fertility rate alone is insufficient for agglutinative languages.
This paper investigates the impact of subword tokenization on LLM training efficiency and performance by conducting controlled byte-level pretraining experiments. It reveals key factors such as training throughput and the integration of subword boundaries as linguistic priors.