AutoEval Done Right: Using Synthetic Data for Model Evaluation
<|reference_start|>AutoEval Done Right: Using Synthetic Data for Model Evaluation: 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. These algorithms increase the effective human-labeled sample size by up to 50% on experiments with GPT-4.<|reference_end|>
@article{boyeau2024autoeval,
    title={AutoEval Done Right: Using Synthetic Data for Model Evaluation},
    author={Pierre Boyeau, Anastasios N. Angelopoulos, Nir Yosef, Jitendra Malik,
  Michael I. Jordan},
    journal={arXiv preprint arXiv:2403.07008},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.07008},
    primaryClass={cs.LG cs.AI cs.CL stat.ME}
}