ICML 2025poster17 citations
AutoEval Done Right: Using Synthetic Data for Model Evaluation
Pierre Boyeau, Anastasios Nikolas Angelopoulos, Tianle Li, Nir Yosef, Jitendra Malik, Michael I. Jordan
Abstract
The evaluation of machine learning models using human-labeled validation data can be expensive and time-consuming. AI-labeled synthetic data can be used to decrease the number of human annotations required for this purpose in a process called autoevaluation. We suggest efficient and statistically principled algorithms for this purpose that improve sample efficiency while remaining unbiased.
Prediction-powered inferencemodel evaluationlarge language modelsannotationsynthetic datastatistical inference
BibTeX
@inproceedings{
boyeau2025autoeval,
title={AutoEval Done Right: Using Synthetic Data for Model Evaluation},
author={Pierre Boyeau and Anastasios Nikolas Angelopoulos and Tianle Li and Nir Yosef and Jitendra Malik and Michael I. Jordan},
booktitle={Forty-second International Conference on Machine Learning},
year={2025},
url={https://openreview.net/forum?id=S8kbmk12Oo}
}