Position: AI Competitions Provide the Gold Standard for Empirical Rigor
  in GenAI Evaluation
Authors: name: D. Sculley, name: Will Cukierski, name: Phil Culliton, name: Sohier Dane, name: Maggie Demkin, name: Ryan Holbrook, name: Addison Howard, name: Paul Mooney, name: Walter Reade, name: Megan Risdal, name: Nate Keating

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.