# Obscuring Data Contamination Through Translation: Evidence from Arabic Corpora

arXiv: https://arxiv.org/abs/2601.14994 · HTML: https://mlsanity.com/paper/2601.14994/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 20 references that are in the map.
- First posted on arXiv 21 Jan 2026.

## Builds on (most cited first, 15 of 15)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Large Language Models](https://mlsanity.com/paper/2307.05782/) (2023)
- [Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMs](https://mlsanity.com/paper/2402.03927/) (2024)
- [Do Membership Inference Attacks Work on Large Language Models?](https://mlsanity.com/paper/2402.07841/) (2024)
- [Investigating Data Contamination in Modern Benchmarks for Large Language Models](https://mlsanity.com/paper/2311.09783/) (2023)
- [Min-K%++: Improved Baseline for Detecting Pre-Training Data from Large Language Models](https://mlsanity.com/paper/2404.02936/) (2024)
- [ConStat: Performance-Based Contamination Detection in Large Language Models](https://mlsanity.com/paper/2405.16281/) (2024)
- [Evaluation data contamination in LLMs: how do we measure it and (when) does it matter?](https://mlsanity.com/paper/2411.03923/) (2024)
- [Data Contamination Can Cross Language Barriers](https://mlsanity.com/paper/2406.13236/) (2024)
- [Data Contamination Calibration for Black-box LLMs](https://mlsanity.com/paper/2405.11930/) (2024)
- [Dynamic Benchmarking of Reasoning Capabilities in Code Large Language Models Under Data Contamination](https://mlsanity.com/paper/2503.04149/) (2025)
- [Towards Data Contamination Detection for Modern Large Language Models: Limitations, Inconsistencies, and Oracle Challenges](https://mlsanity.com/paper/2409.09927/) (2024)
- [OWL: Probing Cross-Lingual Recall of Memorized Texts via World Literature](https://mlsanity.com/paper/2505.22945/) (2025)

## Built on by (most cited first, 1 of 1)

- [Data Contamination in Neural Hieroglyphic Translation: A Reproducibility Study](https://mlsanity.com/paper/2605.07453/) (2026)

## Abstract (arXiv)

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 contamination rather than a reconstruction of real-world pretraining leakage. We first test two English-centric post-hoc probes, TS-Guessing and Min-K%++, and find that their signals largely disappear under translated exposure: TS-Guessing remains weak except for model-specific positional recall on MMLU, while Min-K%++ stays at or below chance. At the same time, English MMLU performance increases with Arabic exposure, showing that the absence of an English contamination signal does not imply the absence of an exposure effect. We then introduce Translation-Aware Contamination Detection (TACD), a training-data-free diagnostic based on cross-lingual prediction consistency and choice reordering. Cross-lingual consistency is substantially higher than an independence baseline and generally increases relative to the clean condition, although its magnitude is model-dependent and not strictly monotonic. These results show that translation can conceal contamination-related effects from English-only probes and motivate multilingual diagnostics that are explicitly framed as evidence of contamination-consistent behavior rather than definitive membership tests.