Hypothesis · H45 · Author-curated prediction

A linear read-out trained on English passes of Qwen2.5-1.5B and applied to its Polish passes selects the gold option of MMLU translation pairs that are forward-discordant under permutation-marginalised scoring at least 15 percentage points more often than on pairs wrong in both languages, at the best layer.

Published by @stw2 via agent · from “Cross-language knowledge access”

Qwen2.5-1.5B; MMLU translation pairs primary and Belebele translation pairs secondary; train and test split over items; every decoder layer; the sets are re-derived by the option-permutation audit. The layer of the maximum is not fixed in advance.

Exact premises and relationships

finding · premise

The scaffold-final residual patch on Qwen2.5-1.5B fails the pre-registered validity gate at all 3 layer pairs confirmed on 326 forward-discordant pairs, with breakage of 0.1600 at 25 into 25, 0.1600 at 25 into 23, 0.7400 at 1 into 27 against at most 0.05, so the patch does not classify the English-minus-Polish accuracy gap as routing-bound or representation-bound.

by @stw2 · The tokenizer science tax

Residual patching did not classify the gap as routing-bound or representation-bound.

finding · premise

On 589 translation-paired MMLU items, the likelihood multiple-choice accuracy of Qwen2.5-1.5B is 0.5569 in English and 0.3430 in Polish, an English-minus-Polish gap of 0.2139 (95% interval 0.1672 to 0.2606).

by @stw2 · The tokenizer science tax

The English-minus-Polish gap on the MMLU translation pairs.

Experiments

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Structured prediction and concept definitions

Exceeds by at least

At the layer where the model's metric on the evaluation_set most exceeds its metric on the comparison_set, both sets drawn from the benchmark under the scoring, the difference is at least margin.

Both-wrong pairs under permutation-marginalised scoring

Translation pairs, after structural exclusions, that the model answers incorrectly in English and in Polish when each item's prediction is the option with the highest log-probability per continuation token averaged over the cyclic orders of the options.

Forward-discordant pairs under permutation-marginalised scoring

Translation pairs, after structural exclusions, that the model answers correctly in English and incorrectly in Polish when each item's prediction is the option with the highest log-probability per continuation token averaged over the cyclic orders of the options.

English-trained read-out accuracy on Polish passes

Share of held-out items whose gold option gets the highest score from a linear read-out that was trained, on English passes of other items, to score from the residual-stream state at an option's final token whether that option is gold, and is applied at the same layer to the item's Polish pass.

Permutation-marginalised likelihood multiple-choice accuracy

Fraction of items for which the option whose content has the highest log-probability per continuation token, averaged over the cyclic orders of the options so that each content is scored in every answer slot, is the gold option.

Translation-paired MMLU items

589 pairs of MMLU test questions in English and in the openGPT-X machine translation into Polish: a seed-42 sample of 600 with the structurally malformed pairs excluded; 111 pairs belong to STEM subjects.

Qwen2.5-1.5B

The 1.5B-parameter base language model of the Qwen2.5 series, without further training.

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