Transformer模型可执行上下文内数据生成，模拟生成式采样器

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-10 04:00

研究人员已证明，大型语言模型（特别是Transformer模型）可以充当数据生成的上下文内采样器。该研究证明，这些模型可以模拟迭代式生成采样器，将上下文内学习的范围从监督学习扩展到数据生成。这是通过识别softmax注意力在计算责任权重和经验平均值中的生成作用，以及前馈层执行欧拉更新来实现的。实证表明，当Transformer模型遇到与特定语义主题相关的提示时，它们可以近似能量模型采样器。 AI

影响展示了大型语言模型在数据生成方面的新能力，可能对生成式AI应用产生影响。

排序理由该集群包含一篇详细介绍Transformer模型理论和实证结果的研究论文。[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 →

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Arman Adibi, Alireza Jafari, Mohammad Ghavamzadeh, Hadi Daneshmand · 2026-09-10 04:00

Transformer作为上下文采样器：从闭式扩散到无估计采样

arXiv:2609.08981v1 Announce Type: cross Abstract: A growing body of work establishes that large language models are not mere statistical memorizers, but are capable of in-context learning: performing inference at test time using only examples provided in the prompt, without any p…

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报道来源 [1]

Transformer作为上下文采样器：从闭式扩散到无估计采样

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