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Tapered Language Models 通过重新分配参数来提高性能

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员推出了一种名为 Tapered Language Models (TLMs) 的架构创新，该创新重新分配了模型层之间的参数。TLMs 不采用均匀分布，而是为早期层分配更多容量，为后期层分配更少容量，这已被证明可以提高困惑度（perplexity）和下游性能。这种方法应用于 Transformers 和 Gated Attention 等各种架构中的 MLPs，在不增加参数数量或计算成本的情况下实现了这些收益。 AI

影响 这一架构原则为优化语言模型提供了一种新方法，有望带来更高效、性能更佳的 AI 系统。

排序理由 该集群描述了一种在学术论文中发布的新型语言模型架构原则。

在 Hugging Face Daily Papers 阅读 →

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Tapered Language Models 通过重新分配参数来提高性能

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报道来源 [2]

  1. arXiv cs.AI TIER_1 Dansk(DA) · Aaron Courville ·

    Tapered Language Models

    Modern language models, including transformer, recurrent, and memory-based variants, share a common chassis: a stack of identical layers in which parameters are allocated uniformly across depth. This is a default inherited from the original transformer and largely unchanged since…

  2. Hugging Face Daily Papers TIER_1 Dansk(DA) ·

    Tapered Language Models

    Tapered language models allocate more parameters to earlier layers and fewer to later layers, improving performance without increasing total parameters or compute costs.

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