GLIDE is a Python library that packages multiple PPI estimators and samplers for reliable GenAI evaluation and reports annotation savings in an agentic case study.
AutoEval Done Right: Using Synthetic Data for Model Evaluation
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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. These algorithms increase the effective human-labeled sample size by up to 50% on experiments with GPT-4.
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cs.AI 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Industrializing Prediction-Powered Inference: The GLIDE Library for Reliable GenAI and Agentic Systems Evaluation
GLIDE is a Python library that packages multiple PPI estimators and samplers for reliable GenAI evaluation and reports annotation savings in an agentic case study.