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.

本研究探讨大型语言模型在自然语言推理任务上的表现。通过使用少量样本指导大型语言模型进行任务适应，以及可应用于多个任务的可重用的知识模块，结合逻辑形式输入的答案程序，实现了在多项 NLP 基准测试上的最新性能，包括 bAbI，StepGame，CLUTRR 和 gSCAN，并成功解决了机器人规划任务，而大型语言模型单独无法解决。