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English(EN) Synthesis Without Training: An Inference-Only Pipeline for Tabular, Temporal, and Relational Synthetic Data

新的GENSCRIPT管道在无需模型训练的情况下生成合成数据

研究人员开发了GENSCRIPT,一种新颖的合成数据生成管道,它绕过了传统的训练阶段。这种仅推理的方法创建了源数据的确定性统计配置文件,然后由语言模型利用该配置文件来推断语义和约束。该系统将这些编译成一个可审计的采样器,支持单表、时间序列和关系型数据,而无需特定任务的模型。GENSCRIPT展示了其效率,在几分钟内生成数据生成器,并快速采样大型数据集,同时保持高保真度和数据完整性,包括复杂的关系。 AI

影响 这种仅推理的方法可以简化合成数据的生成,使其在各种数据模式下更加易于访问和高效。

排序理由 关于合成数据生成新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的GENSCRIPT管道在无需模型训练的情况下生成合成数据

本文如何被排名

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关于合成数据生成新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
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paper, other
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完整方法见我们的编辑标准。

报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Zilong Zhao, Abdul Raheem, Jiayu Li, Sohei Arisaka, Darius Lim Hong Yi, Milad Abdollahzadeh, Uzair Javaid, Biplab Sikdar ·

    无须训练的合成:用于表格、时序和关系型合成数据的仅推理流水线

    arXiv:2609.38414v1 Announce Type: new Abstract: Synthetic data generation is dominated by the fit-then-sample paradigm: a generative model is trained on a private dataset and then sampled from. Despite its widespread adoption, this paradigm faces three challenges: (1) a new train…