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The study investigates how exposing large language models to Arabic translations of English benchmark tasks can hide data contamination from standard English-only detection methods. By testing four instruction-tuned models with increasing Arabic exposure, the authors find that traditional probes like TS-Guessing and Min-K%++ fail to detect contamination, yet English performance still improves. They propose a new Translation-Aware Contamination Detection (TACD) method that uses cross-lingual consistency and choice reordering to reveal contamination effects that are otherwise invisible to English-only diagnostics.
arXiv:2608. 03859v1 Announce Type: cross Abstract: Large language models (LLMs) pose challenges to academic integrity and peer review.
The paper presents a bilingual (English/Arabic) benchmark for evaluating how well open-weight, locally deployable language models can accurately recall specific Hadith texts. Five models—Mistral‑7B, Gemma3‑4B, Jais‑7B, ALLaM‑7B, and Phi4‑mini‑3.8B—were tested on 3,000 Hadiths per language across six canonical Sunni collections, with each model given only a bibliographic reference and asked to reproduce the exact text. None of the 30,000 responses achieved both high lexical and semantic fidelity, and Arabic outputs performed uniformly worse than English, indicating that these models, without retrieval augmentation, cannot reliably provide verifiable religious scripture.
arXiv:2608.28645v1 Announce Type: cross Abstract: Low-resource languages without an adequate training corpus often use a related, higher-resource language as a scaffold for comprehension. Still, ther...
arXiv:2608. 05163v1 Announce Type: cross Abstract: A common assumption holds that switching to a non-English language makes a multilingual RAG system easier to attack for personal information.
A new large-scale Arabic fact‑checking dataset called Arafa has been created using an automated pipeline that generates claims from Arabic Wikipedia, mutates them into counterfactuals, and validates them against supporting or refuting evidence. The dataset contains 181,976 claim‑evidence pairs labeled as supported, refuted, or not enough information, and human evaluation shows high inter‑annotator agreement and strong validation accuracy. Fine‑tuned transformer models on Arafa achieve a Macro F1‑score of 77%, demonstrating its usefulness for Arabic fact‑checking tasks.