Newest papers

Training with ML-labeled Data via Stochastic First- and Zeroth-Order Optimization

Maria-Eleni Sfyraki, Jun-Kun Wang · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26790 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

培训机器学习(ML)模型是一个优化过程,通常需要一组实例以实证标签提前。获取这些实证真理通常需要艰苦的人力努力,并可能昂贵。当需要标签的大量数据点时,人类标签的可扩展性成为一个问题。 为了解决这个实际问题,我们考虑了一个优化设置,其中一组样本具有实证标签,而剩余数据点被ML预测器标记,这可能对这些点分配错误标签。 我们建议第一类和零类的数据优化方法,以训练数据集,其中包括真标签和ML标签的样本。 我们进一步开展从训练线性回归模型到精细调整大型语言模型(MLL)的综合经验研究,以展示与提出的ML标签数据的培训算法的有效性。

Machine translation (Meta M2M100); the English original follows.

Abstract

Training a machine learning (ML) model is an optimization process that typically requires a set of examples with ground-truth labels beforehand. Obtaining these ground truths usually requires laborious human effort and can be costly. The scalability of human labeling becomes a concern when there are a large number of data points that need to be labeled. To address this practical issue, we consider an optimization setting where a subset of samples has ground-truth labels, while the remaining data points were labeled by an ML predictor, which could potentially assign incorrect labels to those points. We propose both first-order and zeroth-order stochastic optimization methods for training on datasets consisting of a mix of true-labeled and ML-labeled samples. We further conduct comprehensive empirical studies from training a linear regression model to fine-tuning a large language model (LLM) to showcase the effectiveness of the proposed algorithms for training with ML-labeled data.

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Topics: Computer science, Machine learning, Training set, Training (meteorology), Process (computing), Set (abstract data type)

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