[[{“value”:”Tokenizer design significantly impacts language model performance,
yet evaluating tokenizer quality remains challenging. While text compression has emerged as a common intrinsic metric, recent work questions its reliability as a quality indicator. We investigate whether evaluating tokenizers on smaller models (350M parameters) reliably predicts their impact at larger scales (2.7B parameters).
Through experiments with established tokenizers from widely-adopted language models, we find that tokenizer choice minimally affects English tasks but yields significant, scale-consistent differences in…”}]] [[{“value”:”Tokenizer design significantly impacts language model performance,
yet evaluating tokenizer quality remains challenging. While text compression has emerged as a common intrinsic metric, recent work questions its reliability as a quality indicator. We investigate whether evaluating tokenizers on smaller models (350M parameters) reliably predicts their impact at larger scales (2.7B parameters).
Through experiments with established tokenizers from widely-adopted language models, we find that tokenizer choice minimally affects English tasks but yields significant, scale-consistent differences in…”}]] Read More