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，人类常通过遵循目标导向脚本中的逐步指令来规划行动。先前研究利用语言模型为刻板化活动的抽象目标（如“制作蛋糕”）进行规划，但对具有多方面约束的更具体目标（如“为糖尿病患者制作蛋糕”）却关注不足。本文首次定义了受约束语言规划任务。我们提出一种"过度生成再过滤"方法以提升大型语言模型在该任务上的表现，并利用该方法蒸馏出一个包含55，000个脚本的新型受约束语言规划数据集CoScript（受约束脚本集）。实验结果表明，我们的方法显著增强了大型语言模型的受约束语言规划能力，尤其在约束遵循度方面表现突出。此外，CoScript被证明能极其有效地赋予较小语言模型受约束语言规划能力。