University of Michigan - Ann Arbor

Across comprehensive evaluations on standardized academic and professional exams, pre-training data contamination did not act as a substantive confounding factor for GPT-4's performance. The calculated degradation between non-contaminated and contaminated questions was generally minor and occurred approximately as often with a positive sign as with a negative sign, demonstrating that prior exposure to exam questions in the training corpus did not artificially drive the model's high test scores.

Contamination as a Non-Substantive Confounder in GPT-4 Exam Evaluation

What did comprehensive evaluations of GPT-4 on standardized academic and professional exams reveal regarding the effect of pre-training data contamination?

In the evaluation of GPT-4's exam results, how did the calculated degradation between non-contaminated and contaminated questions behave in terms of both its magnitude and directional sign?

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