Answer Set Programming (ASP) and Large Language Models (LLMs) have emerged as powerful tools in Artificial Intelligence, each offering unique capabilities in knowledge representation and natural language understanding, respectively. In this paper, we combine the strengths of the two paradigms to couple the reasoning capabilities of ASP with the attractive natural language processing tasks of LLMs. We introduce a YAML-based format for specifying prompts, allowing users to encode domain-specific background knowledge. Input prompts are processed by LLMs to generate relational facts, which are then processed by ASP rules for knowledge reasoning, and finally the ASP output is mapped back to natural language by LLMs, so to provide a captivating user experience.
Answer Set Programming and Large Language Models interaction with YAML: Preliminary Report
Alviano M.;
2024-01-01
Abstract
Answer Set Programming (ASP) and Large Language Models (LLMs) have emerged as powerful tools in Artificial Intelligence, each offering unique capabilities in knowledge representation and natural language understanding, respectively. In this paper, we combine the strengths of the two paradigms to couple the reasoning capabilities of ASP with the attractive natural language processing tasks of LLMs. We introduce a YAML-based format for specifying prompts, allowing users to encode domain-specific background knowledge. Input prompts are processed by LLMs to generate relational facts, which are then processed by ASP rules for knowledge reasoning, and finally the ASP output is mapped back to natural language by LLMs, so to provide a captivating user experience.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
