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  • 整体思路以及计算方式
  • 代码
  • 简评
  1. Inference
  2. Extrapolation

Structured Prompting: Scaling In-Context Learning to 1,000 Examples

PreviousParallel Context Windows for Large Language ModelsNextNaive Bayes-based Context Extension

Last updated 2 years ago

论文地址：

整体思路以及计算方式

提供了一个让LM处理更长序列的思路，图示如下：

步骤：

  • 将输入拆成MMM组，组内位置编码右对齐，统一为pn,pn−1,…p_{n}, p_{n-1},\ldotspn​,pn−1​,…，输入的位置编码为pn+1p_{n+1}pn+1​；

  • 组内做attention，得到MMM组向量yZi\mathbf y_{\mathcal Z_i}yZi​​，Zi={1+∑j<iNj,…,∑j≤iNj}\mathcal Z_i =\{1+\sum_{j<i} N_j,\ldots,\sum_{j\le i}N_j \}Zi​={1+∑j<i​Nj​,…,∑j≤i​Nj​}；

  • 将输入x\mathbf xx作为query，x,yZ1,…,yZM\mathbf x, \mathbf y_{\mathcal Z_1}, \ldots, \mathbf y_{\mathcal Z_M}x,yZ1​​,…,yZM​​作为key, value做attention得到最终结果；

    • 注意x\mathbf xx和x\mathbf xx的attention score加强了MMM倍；

代码

简评

还可以的思路，不过细节得看代码。

https://github.com/microsoft/LMOps/tree/main/structured_prompting/fairseq-version
https://arxiv.org/pdf/2212.06713.pdf