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

• URL: http://arxiv.org/abs/2307.07696v1
• Date: Sat, 15 Jul 2023 03:29:59 GMT
• Title: Coupling Large Language Models with Logic Programming for Robust and General Reasoning from Text
• Authors: Zhun Yang, Adam Ishay, Joohyung Lee
• Abstract summary: We show that a large language model can serve as a highly effective few-shot semantically. It can convert natural language sentences into a logical form that serves as input for answer set programs. We demonstrate that this method achieves state-of-the-art performance on several benchmarks, including bAbI, StepGame, CLUTRR, and gSCAN.
• Score: 5.532477732693001
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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