Coupling Large Language Models with Logic Programming for Robust and
  General Reasoning from Text
Zhun Yang Adam Ishay Joohyung Lee
While large language models (LLMs), such as GPT-3, appear to be robust and
general, their reasoning ability is not at a level to compete with the best
models trained for specific natural language reasoning problems. In this study,
we observe that a large language model can serve as a highly effective few-shot
semantic parser. It can convert natural language sentences into a logical form
that serves as input for answer set programs, a logic-based declarative
knowledge representation formalism. The combination results in a robust and
general system that can handle multiple question-answering tasks without
requiring retraining for each new task. It only needs a few examples to guide
the LLM's adaptation to a specific task, along with reusable ASP knowledge
modules that can be applied to multiple tasks. We demonstrate that this method
achieves state-of-the-art performance on several NLP benchmarks, including
bAbI, StepGame, CLUTRR, and gSCAN. Additionally, it successfully tackles robot
planning tasks that an LLM alone fails to solve.