Unverified50% confidenceFactExact time
Machine learning evaluation assumes that evaluation samples are mutually independent and that no information from the evaluation set has flowed into the training process in any form; randomly splitting multiple dimensions of the same document breaks this assumption, causing data leakage and inflating evaluation scores.
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50%
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Long-term
Relevance
10/16/2026
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Related Claims
UnverifiedThe root cause of the issue in this case was data leakage in the data split, where random assignment placed nearly duplicate form templates on both the training set and regression test set sides simultaneously73% similarUnverifiedWhen out-of-distribution (OOD) failure occurs, learned selectors may persistently ignore key evidence, resulting in answers that appear fluent but omit key facts, increasing the difficulty of troubleshooting.73% similarUnverifiedClone copies from the same model share nearly identical weights, training data, and inductive biases, making their errors highly correlated and preventing systematic biases from being eliminated through averaging.73% similarVerified若用含测试集的全量数据计算均值和标准差，会导致预处理泄漏，使评估指标虚高，无法真实反映模型泛化能力72% similar
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