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English(EN) The COUNT: Counting N-grams Unlocks Scaling on Host Memory and Training-Free Adaptation

新的COUNT方法利用主机内存中的N-gram嵌入来增强LM

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员推出了一种新颖的方法COUNT（Counting Offline Unlocks N-gram Transformers），通过整合存储在主机内存中的N-gram嵌入来增强语言模型。该方法利用预先计算的N-gram计数，避免了额外的学习参数和昂贵的GPU内存。COUNT可以在更多的计算量下，将10亿参数LM的验证损失提高多达57%，并且只需切换N-gram计数表即可在推理时进行任务适应，无需重新训练。 AI

影响 该方法可以显著降低训练和适应大型语言模型的计算成本和内存需求。

排序理由 该集群描述了在arXiv上的一篇学术论文中提出的一种新方法。

在 Hugging Face Daily Papers 阅读 →

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新的COUNT方法利用主机内存中的N-gram嵌入来增强LM

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报道来源 [2]

  1. arXiv cs.CL TIER_1 English(EN) · Ian Magnusson, Noah A. Smith, Pang Wei Koh ·

    The COUNT：计数N-gram解锁主机内存上的扩展和无训练适应

    arXiv:2610.20456v1 Announce Type: new Abstract: The scaling relationship of compute and performance for language models (LMs) has been extensively studied; more recently, models are being designed to scale memory use, as well. In particular, adding embeddings for n-grams scales m…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    COUNT：计数N-gram解锁主机内存上的扩展和无训练适应

    The scaling relationship of compute and performance for language models (LMs) has been extensively studied; more recently, models are being designed to scale memory use, as well. In particular, adding embeddings for n-grams scales memory separately from compute use, because each …

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