研究人员引入了一个新的语言模型令牌混合层框架，旨在平衡解码速度和内存效率。该框架将当前输入对输出的影响与信息通过过去输出来传播分离开来。它包含了像注意力（attention）和状态空间模型（state-space models）这样的现有架构，同时也泛化了递归（recurrence），允许状态依赖于多个过去的状态，为运行时与表现力之间的权衡提供了一个有原则的方法。 AI
影响 为设计更高效、更具表现力的语言模型令牌混合器引入了一个统一的工具包。
排序理由 该集群包含一篇学术论文，详细介绍了语言模型令牌混合的新框架。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
arXiv:2605.31367v1 Announce Type: cross Abstract: Token mixing layers play a key role in how language models can learn and generate long-range dependencies. Their efficiency relies on the necessary trade-off between decoding speed and the memory requirements, along with the cache…
Token mixing layers play a key role in how language models can learn and generate long-range dependencies. Their efficiency relies on the necessary trade-off between decoding speed and the memory requirements, along with the cache size. Considering causal generation, this paper e…