Procedural planning aims to implement complex high-level goals by decomposition into sequential simpler low-level steps. Although procedural planning is a basic skill set for humans in daily life, it remains a challenge for large language models (LLMs) that lack a deep understanding of the cause-effect relations in procedures. Previous methods require manual exemplars to acquire procedural planning knowledge from LLMs in the zero-shot setting. However, such elicited pre-trained knowledge in LLMs induces spurious correlations between goals and steps, which impair the model generalization to unseen tasks. In contrast, this paper proposes a neuro-symbolic procedural PLANner (PLAN) that elicits procedural planning knowledge from the LLMs with commonsense-infused prompting. To mitigate spurious goal-step correlations, we use symbolic program executors on the latent procedural representations to formalize prompts from commonsense knowledge bases as a causal intervention toward the Structural Causal Model. Both automatic and human evaluations on WikiHow and RobotHow show the superiority of PLAN on procedural planning without further training or manual exemplars.

翻译：程序规划旨在通过将复杂的高层目标分解为连续的简单低层步骤来实现。尽管程序规划是人类日常生活中的一项基本技能，但对于缺乏对程序中因果关系深入理解的大型语言模型（LLMs）而言，这仍是一个挑战。以往的方法需要人工示例，以在零样本设置下从LLMs中获取程序规划知识。然而，这种从LLMs中激发出的预训练知识会导致目标与步骤之间的虚假相关性，从而削弱模型对未见任务的泛化能力。相比之下，本文提出了一种神经-符号式程序规划器（PLAN），它通过注入常识的提示从LLMs中激发程序规划知识。为缓解虚假的目标-步骤相关性，我们在潜在程序表示上使用符号程序执行器，将常识知识库中的提示形式化为对结构因果模型的因果干预。在WikiHow和RobotHow上的自动评估与人工评估均表明，PLAN在无需进一步训练或人工示例的情况下，在程序规划任务中具有优越性。