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.

翻译：在日常生活中，人类通常通过遵循目标导向的脚本步骤来规划行动。以往研究利用语言模型为刻板活动的抽象目标（例如"做蛋糕"）进行规划，但较少涉及具有多约束条件的更具体目标（例如"为糖尿病患者做蛋糕"）。本文首次定义了受限语言规划任务，提出了一种"过生成-再过滤"方法以提升大语言模型在该任务上的表现，并利用该方法蒸馏出包含55,000个脚本的新型受限语言规划数据集CoScript。实验结果表明，该方法显著提升了大语言模型的受限语言规划能力，尤其在约束符合性方面表现突出。此外，CoScript被证明能有效赋予小语言模型受限语言规划能力。