Obscuring Data Contamination Through Translation: Evidence from Arabic Corpora
This addresses the problem of ensuring fair and transparent LLM evaluation for researchers and practitioners by highlighting a blind spot in multilingual contamination detection, though it is incremental as it builds on existing detection methods.
The study tackled the problem of data contamination in multilingual Large Language Model evaluation by showing that translation into Arabic can obscure contamination indicators, yet models still benefit from exposed data, with rising Min-K% scores and cross-lingual consistency as contamination increases. The result was a proposed Translation-Aware Contamination Detection method that reliably exposes contamination where English-only methods fail.
Data contamination undermines the validity of Large Language Model evaluation by enabling models to rely on memorized benchmark content rather than true generalization. While prior work has proposed contamination detection methods, these approaches are largely limited to English benchmarks, leaving multilingual contamination poorly understood. In this work, we investigate contamination dynamics in multilingual settings by fine-tuning several open-weight LLMs on varying proportions of Arabic datasets and evaluating them on original English benchmarks. To detect memorization, we extend the Tested Slot Guessing method with a choice-reordering strategy and incorporate Min-K% probability analysis, capturing both behavioral and distributional contamination signals. Our results show that translation into Arabic suppresses conventional contamination indicators, yet models still benefit from exposure to contaminated data, particularly those with stronger Arabic capabilities. This effect is consistently reflected in rising Mink% scores and increased cross-lingual answer consistency as contamination levels grow. To address this blind spot, we propose Translation-Aware Contamination Detection, which identifies contamination by comparing signals across multiple translated benchmark variants rather than English alone. The Translation-Aware Contamination Detection reliably exposes contamination even when English-only methods fail. Together, our findings highlight the need for multilingual, translation-aware evaluation pipelines to ensure fair, transparent, and reproducible assessment of LLMs.