用预训练模型自动标注数据，保证标签误差可控且高效。
Probably Approximately Correct Labels
- 基于预训练模型生成标签，通过概率保证整体误差小。
- 无需强假设，适用于文本、图像、蛋白质折叠等多场景。
- 适合需要高质量低成本数据集的研究者和工业应用。
获取高质量标注数据通常成本高昂，需人工标注或昂贵实验。理论上，强大的预训练AI模型可自动标注数据以降低成本。然而，这些模型无精度保证，难以完全替代人工标注。本文提出一种方法，利用预训练AI模型构建成本低且高质量的数据集。该方法可实现‘可能近似正确’的标签：在高概率下，整体标注误差较小。方法在对数据集或模型的最小假设下非渐近有效，从而实现使用现代AI模型进行严格而高效的数据库构建。我们在大语言模型文本标注、预训练视觉模型图像标注以及AlphaFold蛋白质折叠分析中验证了该方法的优势。
原文摘要 · Abstract (English)
Obtaining high-quality labeled datasets is often costly, requiring either human annotation or expensive experiments. In theory, powerful pre-trained AI models provide an opportunity to automatically label datasets and save costs. Unfortunately, these models come with no guarantees on their accuracy, making wholesale replacement of manual labeling impractical. In this work, we propose a method for leveraging pre-trained AI models to curate cost-effective and high-quality datasets. In particular, our approach results in probably approximately correct labels: with high probability, the overall labeling error is small. Our method is nonasymptotically valid under minimal assumptions on the dataset or the AI model being studied, and thus 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.
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