Hybrid AI System Combines LLMs and AMR for ASP Programming

A new hybrid AI system aims to bridge the gap between natural language and Answer Set Programming (ASP) by combining Large Language Models (LLMs) and Abstract Meaning Representation (AMR). Developed by a team of researchers, this innovative system translates unconstrained English into ASP programs, enabling users to solve complex logic problems without programming expertise.

The system leverages LLMs for tasks such as sentence simplification and keyword identification, while AMR graphs are used to systematically generate ASP constraints. This approach mitigates common limitations of LLM-based systems, such as hallucination and lack of explainability, by incorporating the structured reasoning capabilities of AMR.

How the Hybrid AI System Works

The hybrid AI system minimizes the role of LLMs to specific tasks, such as simplifying sentences and generating simple facts. AMR graphs are then parsed from the simplified language to create ASP constraints. This combination allows the system to generate complete, runnable ASP programs that represent and solve the original problem.

By integrating LLMs with AMR, the system achieves a lighter-weight, more explainable solution that is less prone to errors and biases associated with purely LLM-based approaches.

Applications and Demonstrations

The system has been successfully demonstrated on example logic puzzles, showcasing its ability to generate ASP programs that accurately represent and solve complex problems. This hybrid approach represents a significant advancement in making AI tools accessible to users unfamiliar with programming, allowing them to interact with code using natural language.

Implications for AI Development

The hybrid AI system highlights the potential of combining different AI technologies to address the limitations of individual approaches. By leveraging the strengths of LLMs and AMR, the system provides a more robust and reliable solution for natural language processing and declarative programming.

This innovation paves the way for future developments in AI tools that are more explainable, reliable, and user-friendly, ultimately expanding the accessibility of AI technologies to a broader audience.