In this position paper, we observe that empirical evaluation in Generative AI is at a crisis point since traditional ML evaluation and benchmarking strategies are insufficient to meet the needs of evaluating modern GenAI models and systems. There are many reasons for this, including the fact that these models typically have nearly unbounded input and output spaces, typically do not have a well defined ground truth target, and typically exhibit strong feedback loops and prediction dependence based on context of previous model outputs. On top of these critical issues, we argue that the problems of {\em leakage} and {\em contamination} are in fact the most important and difficult issues to address for GenAI evaluations. Interestingly, the field of AI Competitions has developed effective measures and practices to combat leakage for the purpose of counteracting cheating by bad actors within a competition setting. This makes AI Competitions an especially valuable (but underutilized) resource. Now is time for the field to view AI Competitions as the gold standard for empirical rigor in GenAI evaluation, and to harness and harvest their results with according value.
View on arXiv@article{sculley2025_2505.00612,
title={ Position: AI Competitions Provide the Gold Standard for Empirical Rigor in GenAI Evaluation },
author={ D. Sculley and Will Cukierski and Phil Culliton and Sohier Dane and Maggie Demkin and Ryan Holbrook and Addison Howard and Paul Mooney and Walter Reade and Megan Risdal and Nate Keating },
journal={arXiv preprint arXiv:2505.00612},
year={ 2025 }
}