ResearcharXivNEW

TokEval: A Tokenizer Evaluation Suite

Meister 2026-08-18

Clara Meister

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 fertil

Read on arXiv

Data aggregated and editorially reviewed by TrendMing.

Key Contributions

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 fertil

Research Themes

AIResearch

Back to AI Research