In-context learning has shown great success in i.i.d semantic parsing splits,
where the training and test sets are drawn from the same distribution. In this
setup, models are typically prompted with demonstrations that are similar to
the input question. However, in the setup of compositional generalization,
where models are tested on outputs with structures that are absent from the
training set, selecting similar demonstrations is insufficient, as often no
example will be similar enough to the input. In this work, we propose a method
to select diverse demonstrations that aims to collectively cover all of the
structures required in the output program, in order to encourage the model to
generalize to new structures from these demonstrations. We empirically show
that combining diverse demonstrations with in-context learning substantially
improves performance across three compositional generalization semantic parsing
datasets in the pure in-context learning setup and when combined with
finetuning.

本文提出一种使用不同的演示程序来鼓舞模型学习新体系结构的方法，以解决复合泛化的问题，并表明在纯实际学习设置和与微调结合使用时，结合多样化的演示可以显著提高三个组合泛化语义分析数据集的性能。