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English(EN) The Expressive Power of Low Precision Softmax Transformers with (Summarized) Chain-of-Thought

研究发现低精度Transformer可模拟图灵机

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员分析了标准Transformer解码器的表达能力，重点关注低精度和softmax注意力等实际方面。他们的工作通过证明这些实际配置仍能模拟图灵机，从而弥合了理论模型与现实世界Transformer之间的差距。研究还表明，总结的思维链范式对于这种模拟更有效，其扩展与模型大小相关，属于空间界限而非时间界限。 AI

影响 为实用的Transformer架构提供了理论基础，可能影响未来的模型设计和效率。

排序理由 分析Transformer模型理论特性的学术论文。[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 →

研究发现低精度Transformer可模拟图灵机

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分析Transformer模型理论特性的学术论文。[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) · Moritz Br\"osamle, Stephan Eckstein ·

    低精度Softmax Transformer的表达能力与（已总结的）思维链

    arXiv:2605.18079v2 Announce Type: replace-cross Abstract: Existing expressivity results for transformers typically rely on hardmax attention, high precision, and other architectural modifications that disconnect them from the models used in practice. We bridge this gap by analyzi…

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