Benchmark Contamination — AI Glossary

Benchmark contamination occurs when an AI model's training data accidentally or deliberately includes questions and answers from the benchmark tests used to evaluate it. Because the model has effectively "seen the exam" during training, its benchmark scores are artificially inflated and no longer reflect genuine capability. This makes it harder to compare models fairly and can mislead users and researchers about a model's true performance on novel tasks.