Prompt-based methods have been used extensively across NLP to build zero- and few-shot label predictors. Many NLP tasks are naturally structured: that is, their outputs consist of multiple labels which constrain each other. Annotating data for such tasks can be cumbersome. Can the promise of the prompt-based paradigm be extended to such structured outputs? In this paper, we present a framework for constructing zero- and few-shot linguistic structure predictors. Our key insight is that we can use structural constraints -- and combinatorial inference derived from them -- to filter out inconsistent structures predicted by large language models. We instantiated this framework on two structured prediction tasks, and five datasets. Across all cases, our results show that enforcing consistency not only constructs structurally valid outputs, but also improves performance over the unconstrained variants.

翻译：基于提示的方法已广泛应用于自然语言处理领域，用于构建零样本和少样本标签预测器。许多自然语言处理任务天然具有结构性特点：其输出由多个相互约束的标签组成。为这类任务标注数据往往较为繁琐。基于提示的方法能否拓展到此类结构性输出？在本文中，我们提出一个用于构建零样本和少样本语言结构预测器的框架。我们的关键见解在于，可以利用结构约束及其推导出的组合推理，过滤掉大型语言模型预测的不一致结构。我们将该框架应用于两项结构化预测任务和五个数据集。在所有案例中，实验结果均表明：强制一致性不仅能够构建结构上有效的输出，还能提升相较于无约束变体的性能。