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Position: AI Competitions Provide the Gold Standard for Empirical Rigor in GenAI Evaluation

D. Sculley, Will Cukierski, Phil Culliton, Sohier Dane, Maggie Demkin, Ryan Holbrook, Addison Howard, Paul Mooney, Walter Reade, Megan Risdal, Nate Keating

TL;DR

This paper argues that GenAI evaluation has entered a crisis where traditional benchmarks fail to capture novelty, leakage risks, and context-dependent outputs. It contends that AI Competitions offer the gold standard for empirical rigor by supplying continuous, novel evaluation tasks and robust leak-proof structures, such as prospective ground truth and post-deadline data collection. The authors synthesize leakage case studies, critique current leakage mitigation approaches, and articulate a concrete shift toward competition-based evaluation and meta-analysis to improve reliability and comparability. The work aims to provide a practical, scalable path for rigorous GenAI evaluation with broad community adoption and cross-domain impact.

Abstract

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 leakage and 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.

Position: AI Competitions Provide the Gold Standard for Empirical Rigor in GenAI Evaluation

TL;DR

This paper argues that GenAI evaluation has entered a crisis where traditional benchmarks fail to capture novelty, leakage risks, and context-dependent outputs. It contends that AI Competitions offer the gold standard for empirical rigor by supplying continuous, novel evaluation tasks and robust leak-proof structures, such as prospective ground truth and post-deadline data collection. The authors synthesize leakage case studies, critique current leakage mitigation approaches, and articulate a concrete shift toward competition-based evaluation and meta-analysis to improve reliability and comparability. The work aims to provide a practical, scalable path for rigorous GenAI evaluation with broad community adoption and cross-domain impact.

Abstract

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 leakage and 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.
Paper Structure (23 sections, 2 figures)

This paper contains 23 sections, 2 figures.

Table of Contents

  1. Introduction
  2. Summarizing Our Position
  3. Structure of This Paper
  4. Background: Revisiting Benchmarking
  5. The Rise of Reproducible Benchmarks
  6. Surprisingly, Overfitting Was Not the Main Issue
  7. Reconsidering Generalization for GenAI
  8. The IID Assumption is Broken
  9. Leakage and Contamination Are the Biggest Pitfalls
  10. Leakage Case Studies
  11. Reproducibility and Robustness in Conflict
  12. Evaluations Aiming to Avoid Leakage
  13. Unreleased Holdout Sets
  14. Dynamic Benchmarks
  15. Community Benchmarks
  16. ...and 8 more sections

Figures (2)

  • Figure 1: IID Evaluations vs. Novelty-Centric Evaluations. In the IID evaluation, left, both training and test data are drawn from the same distribution, resulting in significant overlap in examples in each set. In the novelty-centric version, right, no test example is allowed to be too similar to any given training example. We argue that the latter conceptualization more closely mirrors desired behavior for GenAI evaluations, where generalization is expected to connote the ability to respond well on totally novel inputs.
  • Figure 2: Comparing sequential and parallelized evaluation structures. In the traditional research structure, top, each new idea is evaluated in a linear sequence that typically requires several months for a single pass. The parallelized structure, bottom, allows hundreds or thousands of approaches to be simultaneously.