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English(EN) UNIFUSION: Adapting Autoregressive Language Models into Discrete Diffusion under a Unified Reverse-Rate Objective

新的UNIFUSION方法将自回归模型适配到离散扩散模型

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了UNIFUSION，一种将自回归语言模型适配到离散扩散模型的新颖方法。该方法在单一的广义Kullback--Leibler目标下统一了现有的扩散目标，允许在掩码和均匀噪声等不同损坏核之间无缝切换。在GPT2检查点上的评估表明，UNIFUSION改善了生成困惑度与一元熵之间的权衡，在WinoGrande和SIQA等基准测试中优于其他扩散模型。 AI

影响 这项研究通过连接自回归和扩散架构，有望带来更高效、更多功能的文本生成模型。

排序理由 该集群包含一篇详细介绍语言模型适配新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 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UNIFUSION方法将自回归模型适配到离散扩散模型

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该集群包含一篇详细介绍语言模型适配新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Xiaoyi Jiang, Jingyuan Li, Yixuan Jiang, Wei Liu, Yi Zhu, Zuoqiang Shi, Pipi Hu ·

    UNIFUSION：在统一反向速率目标下将自回归语言模型适配为离散扩散模型

    arXiv:2607.24507v1 Announce Type: cross Abstract: Existing methods mainly adapt pretrained autoregressive (AR) language models to masked diffusion, whereas we directly adapt them to uniform-noise diffusion, where every token remains editable during sampling. However, adapting AR …

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