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While large language models (LLMs), such as GPT-3, appear to be robust andgeneral, their reasoning ability is not at a level to compete with the bestmodels trained for specific natural language reasoning problems. In this study,we observe that a large language model can serve as a highly effective few-shotsemantic parser. It can convert natural language sentences into a logical formthat serves as input for answer set programs, a logic-based declarativeknowledge representation formalism. The combination results in a robust andgeneral system that can handle multiple question-answering tasks withoutrequiring retraining for each new task. It only needs a few examples to guidethe LLM's adaptation to a specific task, along with reusable ASP knowledgemodules that can be applied to multiple tasks. We demonstrate that this methodachieves state-of-the-art performance on several NLP benchmarks, includingbAbI, StepGame, CLUTRR, and gSCAN. Additionally, it successfully tackles robotplanning tasks that an LLM alone fails to solve.

Coupling Large Language Models with Logic Programming for Robust and General Reasoning from Text