arXiv — cs.AI preprintsInternational9 October 2026
Obscuring Data Contamination Through Translation: Evidence from Arabic Corpora
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arXiv:2601.14994v2 Announce Type: replace-cross Abstract: Data contamination can invalidate benchmark evaluation when a model benefits from memorized evaluation content rather than genuine generalization. Yet contamination is difficult to audit when the exposed content differs in language from the evaluation benchmark. We study this failure mode by deliberately exposing four open-weight instruction-tuned LLMs to Arabic translations of MMLU and XQuAD evaluation items at increasing exposure levels, then evaluating them on the original English tasks. This controlled setup is a proxy for contamina
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