When Flores Bloomz Wrong: Cross-Direction Contamination in Machine Translation Evaluation (2026.eacl-short)
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| Challenge: | Large language models (LLMs) can be benchmark-contaminated, resulting in inflated scores that mask memorization as generalization. |
| Approach: | They use the FLORES-200 translation benchmark as a diagnostic to investigate cross-direction data contamination. |
| Outcome: | The proposed model can be cross-directional, boosting performance in unseen translation directions due to target-side memorization. |
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