Leveraging Large Language Models to Generate Answer Set Programs

Understand

Large language models (LLMs), such as GPT-3 and GPT-4, have demonstrated exceptional performance in various natural language processing tasks and have shown the ability to solve certain reasoning problems.

• However, their reasoning capabilities are limited and relatively shallow, despite the application of various prompting techniques.
• In contrast, formal logic is adept at handling complex reasoning, but translating natural language descriptions into formal logic is a challenging task that non-experts struggle with.
• This paper proposes a neuro-symbolic method that combines the strengths of large language models and answer set programming.