glide-py 0.3.0

Helping humans ride the GenAI evaluation wave

GLIDE

Generated Label Inference & Debiasing Engine

🧭 What is GLIDE?

GLIDE is a Python library for rigorous evaluation of GenAI systems using hybrid human/proxy annotations.

GLIDE implements methods from the field of semi-supervised inference — the science of system evaluation that combines a small labeled dataset with a large unlabeled (or proxy-labeled) dataset to produce valid, debiased estimates. See the implemented papers below.

🤔 Why GLIDE?

• 🤖 GenAI applications are everywhere — and imperfect. Deployed systems make mistakes, and measuring how often matters.
• ⚖️ LLM-as-judge is biased. Proxy evaluators (models, heuristics) are cheap but systematically over- or under-estimate true performance.
• 🧑 Rigorous evaluation requires a human in the loop. Ground-truth labels from humans are expensive, so only a small subset is feasible.
• 📐 GLIDE bridges the gap. It combines a small set of human annotations with a large set of proxy predictions to produce statistically valid metrics — correcting proxy bias without requiring full human labeling.

⚡ Quick Start

Install the package with your favorite package manager :

uv add glide-py

or

pip install glide-py

And look at our practical quickstart.

📚 Documentation

Explore the full documentation — from practical tutorials and user guides to scientific deep dives into the methods behind GLIDE.

🤝 Contributing

Contributions are welcome! Please read the contributing guide for setup instructions, an architectural overview, and the checklist to follow before opening a pull request. Feel free to open an issue to report a bug or suggest a feature.

🔢 Versioning

This project follows Semantic Versioning (SemVer): MAJOR.MINOR.PATCH.

📦 Dependency Support

This project follows SPEC 0 for dependency support windows.

📄 License & Citation

This project is licensed under the Apache 2.0 License. If you use Glide in your research, please cite:

@software{glide,
  title  = {GLIDE: Generated Label Inference \& Debiasing Engine},
  year   = {2026},
  url    = {https://github.com/EmertonData/glide},
}

📰 Implemented Papers

Year	Title	Venue	Original Implementation	GLIDE class
2023	Prediction-powered inference	Science	Link	estimators.PPIMeanEstimator (with power_tuning=False)
2023	PPI++: Efficient Prediction-Powered Inference	Preprint	Link	estimators.PPIMeanEstimator
2024	Active Statistical Inference	ICML'24	Link	estimators.ASIMeanEstimator
2024	Stratified Prediction-Powered Inference for Hybrid Language Model Evaluation	NeurIPS'24	NA	estimators.StratifiedPPIMeanEstimator
2024	A framework for efficient model evaluation through stratification, sampling, and estimation	ECCV'24	Link	estimators.StratifiedPPIMeanEstimator
2025	Can Unconfident LLM Annotations Be Used for Confident Conclusions?	NAACL'25	Link	estimators.ASIMeanEstimator

🏛️ Affiliation

Developed at Emerton Data.