Neuro-Symbolic Causal Language Planning with Commonsense Prompting

• URL: http://arxiv.org/abs/2206.02928v1
• Date: Mon, 6 Jun 2022 22:09:52 GMT
• Title: Neuro-Symbolic Causal Language Planning with Commonsense Prompting
• Authors: Yujie Lu, Weixi Feng, Wanrong Zhu, Wenda Xu, Xin Eric Wang, Miguel Eckstein, William Yang Wang
• Abstract summary: Language planning aims to implement complex high-level goals by decomposition into simpler low-level steps. Previous methods require either manual exemplars or annotated programs to acquire such ability from large language models. This paper proposes Neuro-Symbolic Causal Language Planner (CLAP) that elicits procedural knowledge from the LLMs with commonsense-infused prompting.
• Score: 67.06667162430118
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Language planning aims to implement complex high-level goals by decomposition into sequential simpler low-level steps. Such procedural reasoning ability is essential for applications such as household robots and virtual assistants. Although language planning is a basic skill set for humans in daily life, it remains a challenge for large language models (LLMs) that lack deep-level commonsense knowledge in the real world. Previous methods require either manual exemplars or annotated programs to acquire such ability from LLMs. In contrast, this paper proposes Neuro-Symbolic Causal Language Planner (CLAP) that elicits procedural knowledge from the LLMs with commonsense-infused prompting. Pre-trained knowledge in LLMs is essentially an unobserved confounder that causes spurious correlations between tasks and action plans. Through the lens of a Structural Causal Model (SCM), we propose an effective strategy in CLAP to construct prompts as a causal intervention toward our SCM. Using graph sampling techniques and symbolic program executors, our strategy formalizes the structured causal prompts from commonsense knowledge bases. CLAP obtains state-of-the-art performance on WikiHow and RobotHow, achieving a relative improvement of 5.28% in human evaluations under the counterfactual setting. This indicates the superiority of CLAP in causal language planning semantically and sequentially.

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