English(EN) FactorEngram: Factorized N-gram Memory with Basis-Level Gating for Language Models

FactorEngram 通过因子化N-gram记忆增强语言模型

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-28 00:00

研究人员推出 FactorEngram，一种通过结合因子化N-gram记忆和基于基向量的上下文门控来增强语言模型的新方法。该方法通过允许根据上下文更选择性地检索和调制记忆组件，改进了现有的基于查找的记忆系统。通过利用共享的基向量词典，FactorEngram 允许相关模式重用公共元素，并允许模型的隐藏状态单独门控每个记忆组件。在不同大小的 Transformer 模型上进行的实验表明，语言建模和下游任务性能有所提高。 AI

影响引入了一种新的记忆架构，可以提高大型语言模型的效率和性能。

排序理由该集群包含一篇详细介绍语言模型新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 Hugging Face Daily Papers 阅读 →

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报道来源 [1]

Hugging Face Daily Papers TIER_1 English(EN) · 2026-09-28 00:00

FactorEngram: 用于语言模型的基于基的门控的因子化N-gram记忆

Lookup-based memory has been a promising way to scale the parameters of large language models (LLMs). It retrieves learned representations of local token patterns, such as n-grams, instead of reconstructing them through successive layers of computation. However, existing designs …

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报道来源 [1]

FactorEngram: 用于语言模型的基于基的门控的因子化N-gram记忆

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