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English(EN) Gumbel Distillation for Parallel Text Generation

Gumbel蒸馏提升并行文本生成质量

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种名为Gumbel蒸馏的新技术，以提高并行文本模型的生成质量。该方法利用Gumbel-Max技巧在潜在噪声空间和教师模型的输出token之间创建确定性连接。Gumbel蒸馏是模型无关的，可以与各种并行解码架构集成。在LM1B和OpenWebText数据集上的实验表明，与标准的MDLM训练相比，MAUVE分数显著提高了30.0%，生成困惑度降低了10.5%。 AI

影响 这项技术可能带来更快、更高质量的非自回归模型文本生成。

排序理由 该集群包含一篇详细介绍文本生成新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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

Gumbel蒸馏提升并行文本生成质量

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该集群包含一篇详细介绍文本生成新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Chi Zhang, Xixi Hu, Bo Liu, Qiang Liu ·

    用于并行文本生成的Gumbel蒸馏

    arXiv:2603.22216v2 Announce Type: replace Abstract: The slow, sequential nature of autoregressive (AR) language models has driven the adoption of parallel decoding methods. However, these non-AR models often sacrifice generation quality as they struggle to model the complex joint…

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