标题： yProv4ML：机器学习系统中轻松的来源跟踪 显示英文标题

标题： yProv4ML: Effortless Provenance Tracking for Machine Learning Systems

摘要： 大型语言模型（LLMs）兴趣的快速增长反映了它们在灵活性和泛化能力方面的潜力，并吸引了众多研究人员的关注。 然而，这些技术的出现也揭示了在开发过程中缺乏透明度和严谨性。 特别是，在事先无法确定纪元数量和其他超参数，这给识别最佳模型带来了挑战。 为了解决这一挑战，机器学习框架如MLFlow可以自动化收集此类信息。 然而，这些工具使用专有格式来捕获数据，并对血统关注甚少。 本文提出了yProv4ML，一个在PROV-JSON格式中捕获机器学习过程中生成的血统信息的框架，只需进行少量代码修改。 显示英文摘要

摘要： The rapid growth of interest in large language models (LLMs) reflects their potential for flexibility and generalization, and attracted the attention of a diverse range of researchers. However, the advent of these techniques has also brought to light the lack of transparency and rigor with which development is pursued. In particular, the inability to determine the number of epochs and other hyperparameters in advance presents challenges in identifying the best model. To address this challenge, machine learning frameworks such as MLFlow can automate the collection of this type of information. However, these tools capture data using proprietary formats and pose little attention to lineage. This paper proposes yProv4ML, a framework to capture provenance information generated during machine learning processes in PROV-JSON format, with minimal code modifications.