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.

通过结构约束和组合推理，我们构建了一种基于提示的方法用于零和少样本的语言结构预测，在两个结构预测任务和五个数据集上的结果表明，强制一致性不仅构建了结构上有效的输出，而且还改善了性能。