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In natural language processing, data contamination refers to test examples from an evaluation benchmark appear in a model's pretraining data, which can inflate measured performance. This meaning comes from Magar (2022) in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers).[1]
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