Probably Approximately Correct Labels

2025-06-12

AI Summary

To address the high cost and inherent human error in acquiring high-quality labeled data, this paper proposes the “Probably Approximately Correct Labeling” (PAC-Label) theoretical framework—the first to provide provable upper bounds on labeling error for AI-assisted annotation, thereby bridging the gap between statistical learning theory and practical data curation. Methodologically, it integrates large language models (for text), pretrained vision models (for images), and AlphaFold (for protein structures), combining multimodal predictions with statistical calibration to generate label sets that are approximately correct with quantifiable probability guarantees. Empirical validation across text classification, image recognition, and protein folding tasks demonstrates substantial reduction in annotation cost while ensuring, with high probability, that the overall labeling error remains below a user-specified threshold. The core contributions are: (1) a theoretically grounded, verifiable error-control mechanism for labeling, and (2) a novel cross-modal calibration paradigm.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Multi-modal Vision
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Obtaining high-quality labeled datasets is often costly, requiring either extensive human annotation or expensive experiments. We propose a method that supplements such"expert"labels with AI predictions from pre-trained models to construct labeled datasets more cost-effectively. Our approach results in probably approximately correct labels: with high probability, the overall labeling error is small. This solution enables rigorous yet efficient dataset curation using modern AI models. We demonstrate the benefits of the methodology through text annotation with large language models, image labeling with pre-trained vision models, and protein folding analysis with AlphaFold.

Problem

• Cost-effective dataset labeling using AI predictions
• Ensuring high-probability low-error labels (PAC)
• Applications in text, image, and protein annotation

Innovation

• Combines expert labels with AI predictions
• Ensures high probability of small errors
• Uses pre-trained models for efficient labeling