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GOFAI meets Generative AI: Development of Expert Systems by means of Large Language Models

Created by
  • Haebom

Author

Eduardo C. Garrido-Merch an, Cristina Puente

Outline

This paper presents a novel approach to develop expert systems in a controlled and transparent manner using Large Language Models (LLMs). We generate a symbolic representation of knowledge in Prolog using a domain-specific, well-structured, prompt-based extraction method, which can be verified and modified by experts. This results in an expert system that guarantees interpretability, scalability, and reliability. Quantitative and qualitative experiments using Claude Sonnet 3.7 and GPT-4.1 demonstrate the factual accuracy and semantic consistency of the generated knowledge base. We present a transparent hybrid solution that combines the reproducibility of LLMs with the accuracy of symbolic systems, paving the way for reliable AI applications in sensitive domains.

Takeaways, Limitations

Takeaways:
A new method to overcome the limitations of LLM (hallucinations, factual errors) through prompt engineering and symbolic representation (Prolog) is presented.
Improving interpretability, scalability, and reliability of expert system development
Presenting the possibility of developing reliable AI applications in sensitive areas
Demonstrating the utility of a hybrid approach that combines the reproducibility of LLM with the accuracy of a symbolic system
Limitations:
Applicable only within a limited scope to specific areas
The accuracy of the results can vary greatly depending on the quality of the prompt engineering.
Knowledge and verification process required by Prolog experts
May depend on the performance of the LLM used
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