In everyday life, humans often plan their actions by following step-by-step
instructions in the form of goal-oriented scripts. Previous work has exploited
language models (LMs) to plan for abstract goals of stereotypical activities
(e.g., "make a cake"), but leaves more specific goals with multi-facet
constraints understudied (e.g., "make a cake for diabetics"). In this paper, we
define the task of constrained language planning for the first time. We propose
an overgenerate-then-filter approach to improve large language models (LLMs) on
this task, and use it to distill a novel constrained language planning dataset,
CoScript, which consists of 55,000 scripts. Empirical results demonstrate that
our method significantly improves the constrained language planning ability of
LLMs, especially on constraint faithfulness. Furthermore, CoScript is
demonstrated to be quite effective in endowing smaller LMs with constrained
language planning ability.

本文首次定义了有约束语言规划的任务，并提出了一种过度生成和过滤的方法，以提高大语言模型在此任务中的性能，并使用此方法提取了一个由 55,000 个脚本组成的 CoScript 数据集，并在实验结果中证明了我们的方法显着提高了大型语言模型的有约束语言规划能力，特别是在约束忠实度方面。此外，CoScript 证明对于赋予较小语言模型具有受限语言规划能力是相当有效的。