Industrializing Prediction-Powered Inference: The G... | AI Research

Industrializing Prediction-Powered Inference: The GLIDE Library for Reliable GenAI and Agentic Systems Evaluation
Evaluating modern AI agents is difficult because it forces a choice between two flawed options: expensive, slow human review or fast but biased "LLM-as-judge" proxies. This paper introduces GLIDE (Generated Label Inference and Debiasing Engine), an open-source Python library designed to bridge this gap. By using Prediction-Powered Inference (PPI), GLIDE combines a small amount of human-labeled data with a large amount of cheap proxy data to produce unbiased performance estimates with valid confidence intervals, effectively saving costs while maintaining statistical reliability.

How the Approach Works

GLIDE operates on a three-step framework: sampling, annotation, and estimation. It provides a unified, user-friendly interface that allows practitioners to select from various statistical methods depending on their specific needs. For example, if a user has access to per-sample uncertainty scores from their LLM judge, they can use "Active Sampling" to focus human review on the most difficult cases. If the data is naturally grouped—such as by tool type or query category—the library offers "Stratified" methods to improve precision. The library’s API is designed to be familiar to users of standard scientific Python tools, making it easy to integrate into existing evaluation workflows.

Why Agentic Systems Benefit

Agentic systems are particularly well-suited for this approach because they exhibit four key traits: extreme cost asymmetry (where human review is significantly more expensive than proxy calls), natural stratification (different tools or tasks have varying performance), available proxy uncertainty (modern judges often provide confidence scores), and the need for high-stakes reliability. GLIDE addresses these by providing specific tools for each, such as cost-optimal samplers that minimize the budget required to reach a target level of precision.

Key Results and Validation

The authors validated GLIDE using both synthetic Monte Carlo simulations and a real-world case study on the R-Judge safety benchmark. The results demonstrate that GLIDE’s estimators maintain valid coverage—meaning the confidence intervals accurately capture the true performance—even when the proxy LLM is significantly biased. In the case study, using GLIDE allowed for a 2.2x effective gain in sample size compared to using human labels alone. This means that by supplementing a small set of human annotations with proxy data, users can achieve the same level of statistical confidence as a much larger, more expensive human-only study.

Practical Considerations

GLIDE includes a decision tree to help users choose the right method based on their available data and budget. It also provides a reproducible validation suite, ensuring that users can verify the statistical integrity of their results. While the library is powerful, it is specifically focused on mean estimation. It is designed to be modular, allowing researchers to contribute new samplers or estimators as single-file modules, which helps keep the library updated with the latest advancements in the field.