标题： GOFAI 与生成式 AI：通过大型语言模型开发专家系统 显示英文标题

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

摘要： 大规模语言模型（LLMs）的发展成功地改变了基于知识的系统，例如开放领域问答，这些系统可以自动生成大量看似连贯的信息。 然而，这些模型有几个缺点，如幻觉或自信地生成错误或不可验证的事实。 在本文中，我们介绍了一种使用LLMs以受控和透明方式开发专家系统的新方法。 通过限制领域并采用结构良好的基于提示的提取方法，我们生成了Prolog中的知识符号表示，该表示可由人类专家验证和修正。 这种方法还保证了所开发的专家系统的可解释性、可扩展性和可靠性。 通过与Claude Sonnet 3.7和GPT-4.1的定量和定性实验，我们展示了对事实和语义连贯性的强烈遵循。 我们提出了一种透明的混合解决方案，结合了LLMs的召回能力与符号系统的精确性，从而为敏感领域中的可靠AI应用奠定了基础。 显示英文摘要

摘要： The development of large language models (LLMs) has successfully transformed knowledge-based systems such as open domain question nswering, which can automatically produce vast amounts of seemingly coherent information. Yet, those models have several disadvantages like hallucinations or confident generation of incorrect or unverifiable facts. In this paper, we introduce a new approach to the development of expert systems using LLMs in a controlled and transparent way. By limiting the domain and employing a well-structured prompt-based extraction approach, we produce a symbolic representation of knowledge in Prolog, which can be validated and corrected by human experts. This approach also guarantees interpretability, scalability and reliability of the developed expert systems. Via quantitative and qualitative experiments with Claude Sonnet 3.7 and GPT-4.1, we show strong adherence to facts and semantic coherence on our generated knowledge bases. We present a transparent hybrid solution that combines the recall capacity of LLMs with the precision of symbolic systems, thereby laying the foundation for dependable AI applications in sensitive domains.