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English(EN) FlowPipe: LLM-Enhanced Conditional Generative Flow Networks for Data Preparation Pipeline Construction

FlowPipe框架使用LLM自动化数据准备管道

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了FlowPipe，一个用于自动构建数据准备管道的新框架。该系统利用条件生成流网络（C-GFlowNets），并通过特征维度线性调制（FiLM）增强了从LLM派生的逻辑先验。FlowPipe通过改进长时信用分配、更好地注入数据集上下文和提高探索效率，解决了现有方法的局限性。实验表明，FlowPipe在准确性和训练收敛速度方面均优于最先进的基线方法。 AI

影响 自动化复杂的数据准备任务，可能加速机器学习工作流程并提高数据质量。

排序理由 该集群包含一篇详细介绍数据准备管道新方法的论文。

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

FlowPipe框架使用LLM自动化数据准备管道

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报道来源 [2]

  1. arXiv cs.AI TIER_1 English(EN) · Kunyu Ni, Lei Cao, Jie He, Xiaotong Zhang, Jianfeng Jin, Junyu Dong, Yanwei Yu ·

    FlowPipe：增强的LLM条件生成流网络用于数据准备管道构建

    arXiv:2606.24679v1 Announce Type: cross Abstract: Data preparation pipelines improve data quality in machine learning by transforming raw tables into learning-ready data through sequential cleaning and feature transformation operators. However, automatically constructing such pip…

  2. arXiv cs.AI TIER_1 English(EN) · Yanwei Yu ·

    FlowPipe：增强的LLM条件生成流网络用于数据准备管道构建

    Data preparation pipelines improve data quality in machine learning by transforming raw tables into learning-ready data through sequential cleaning and feature transformation operators. However, automatically constructing such pipelines is computationally difficult because operat…

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