[Microsoft]
简介：
本文提出了LLMA，一个LLM加速器，用于无损地加速有参考文献的大型语言模型（LLM）推理。
LLMA的动机是观察到在许多现实世界的场景中（如检索的文件），LLM的解码结果与参考文献之间存在大量相同的文本跨度。LLMA首先从参考文献中选择一个文本跨度，并将其标记复制到解码器中，然后在一个解码步骤中有效地检查这些标记是否适合作为解码结果。改进的计算并行性使LLMA在许多实际的生成场景中实现了2倍以上的速度，其生成结果与贪婪解码相同（如搜索引擎和多轮对话），在这些场景中，上下文参考和输出之间存在大量重叠。
https://arxiv.org/pdf/2304.04487.pdf
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