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New library and framework enhance AI evaluation with prediction-powered inference

By PulseAugur Editorial · [4 sources] ·

Researchers have introduced GLIDE, an open-source Python library designed to standardize and improve the evaluation of AI systems, particularly agentic ones. GLIDE unifies various prediction-powered inference (PPI) methods, offering debiased estimates and valid uncertainty quantification. A related paper proposes a multi-task PPI framework that leverages related tasks to enhance inference power and preserve task-specific results, especially when ground-truth labels are scarce. These advancements aim to reduce annotation costs while maintaining precision in AI evaluation and social science research. AI

IMPACT These advancements offer more efficient and reliable methods for evaluating AI systems, potentially reducing costs and improving the accuracy of assessments.

RANK_REASON The cluster contains two arXiv papers introducing new methods and a library for AI evaluation.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 4 sources. How we write summaries →

New library and framework enhance AI evaluation with prediction-powered inference

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The cluster contains two arXiv papers introducing new methods and a library for AI evaluation.
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COVERAGE [4]

  1. arXiv cs.AI TIER_1 English(EN) · Gr\'egoire Martinon, Ibrahim Merad, Mohammed Raki ·

    Industrializing Prediction-Powered Inference: The GLIDE Library for Reliable GenAI and Agentic Systems Evaluation

    arXiv:2605.31278v1 Announce Type: new Abstract: Reliable evaluation of agentic systems requires unbiased estimates with valid uncertainty, but standard practice navigates between costly human annotation and biased LLM-as-judge proxies. Prediction-powered inference (PPI) combines …

  2. arXiv cs.AI TIER_1 English(EN) · Mohammed Raki ·

    Industrializing Prediction-Powered Inference: The GLIDE Library for Reliable GenAI and Agentic Systems Evaluation

    Reliable evaluation of agentic systems requires unbiased estimates with valid uncertainty, but standard practice navigates between costly human annotation and biased LLM-as-judge proxies. Prediction-powered inference (PPI) combines both into debiased estimates with valid confiden…

  3. arXiv stat.ML TIER_1 English(EN) · Nicolas Emmenegger, Ellery Stahler, Chara Podimata ·

    Prediction-Powered Inference Across Many Tasks for AI Evaluation & Social Science Research

    arXiv:2605.29249v1 Announce Type: new Abstract: Many applications require statistically valid inference across many related tasks, while using only a handful of high-quality labels per hypothesis. In AI evaluation, these tasks may correspond to model behaviors across prompts, sub…

  4. arXiv stat.ML TIER_1 English(EN) · Chara Podimata ·

    Prediction-Powered Inference Across Many Tasks for AI Evaluation & Social Science Research

    Many applications require statistically valid inference across many related tasks, while using only a handful of high-quality labels per hypothesis. In AI evaluation, these tasks may correspond to model behaviors across prompts, subgroups, or hypotheses; in social science surveys…

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