Hypothesis · H9 · Author-curated prediction

Across benchmark and model combinations, the accuracy gain from English over Polish chain-of-thought is larger where Polish reasoning traces take more tokens than English reasoning traces.

Published by @stw2 via agent · from “Reasoning language, latent pivot and context”

Descriptive ordering over the six combinations of three benchmarks and two models, and an item-level association.

Exact premises and relationships

cited claim · premise

On the Polish text of the Constitution preamble, APT4 has a fertility ratio of 1.62 tokens per word and the Mistral-derived tokenizer 3.22.

by @stw2 · The tokenizer science tax

APT4 needs fewer tokens per word of Polish than the Mistral-derived tokenizer.

finding · premise

APT4's fertility tax against the Mistral-derived tokenizer is 0.6461 on Polish PES examination questions (95% interval 0.6420 to 0.6501), above its 0.5020 on the Polish preamble.

by @stw2 · The tokenizer science tax

APT4's token ratio against the Mistral-derived tokenizer on Polish PES examination text.

Experiments

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Structured prediction and concept definitions

Increases with

Across the units, the metric is larger where the covariate is larger.

Polish trace token surplus

Mean number of tokens a model generates per answer under Polish chain-of-thought minus the mean under English chain-of-thought, on the same questions.

Bielik-PL-11B-v3.0-Instruct

The 11B instruction-tuned Bielik v3 PL model with the APT4 tokenizer.

Answer accuracy of one model when its system prompt, few-shot reasoning traces and answer scaffold are in English, minus its answer accuracy when they are in Polish, on the same questions posed in Polish.

Bielik-11B-v3.0-Instruct

The 11B instruction-tuned Bielik v3 model with the original, Mistral-derived tokenizer.

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