通过神经符号整合和本体推理增强大型语言模型
Enhancing Large Language Models through Neuro-Symbolic Integration and Ontological Reasoning
AI总结：
本文提出一种结合符号本体推理和机器学习的方法，通过迭代反馈提升LLM输出的一致性和事实准确性。
摘要：
大型语言模型（LLMs）在自然语言处理方面表现出色，但存在不准确性和逻辑不一致的问题，即幻觉现象。这影响了其可靠性，特别是在需要事实准确性领域。我们提出了一种神经符号方法，结合符号本体推理和机器学习方法，以提高LLM输出的一致性和可靠性。我们的工作流程利用OWL本体，一个符号推理器（如HermiT）进行一致性检查，以及一个轻量级机器学习模型（逻辑回归）将自然语言陈述映射成与本体兼容的逻辑形式。当检测到LLM输出与本体之间存在不一致时，系统会生成解释性反馈，引导LLM朝着更正的、逻辑上一致的响应方向迭代优化。我们展示了一个工作原型Python程序，演示了这一流程。在定义领域中的实验结果表明，LLM输出的语义一致性和事实准确性显著提高，展示了将LLM流畅性与形式语义的严谨性相结合的潜力。
英文摘要：
Large Language Models (LLMs) demonstrate impressive capabilities in natural language processing but suffer from inaccuracies and logical inconsistencies known as hallucinations. This compromises their reliability, especially in domains requiring factual accuracy. We propose a neuro-symbolic approach integrating symbolic ontological reasoning and machine learning methods to enhance the consistency and reliability of LLM outputs. Our workflow utilizes OWL ontologies, a symbolic reasoner (e.g., HermiT) for consistency checking, and a lightweight machine learning model (logistic regression) for mapping natural language statements into logical forms compatible with the ontology. When inconsistencies between LLM outputs and the ontology are detected, the system generates explanatory feedback to guide the LLM towards a corrected, logically coherent response in an iterative refinement loop. We present a working Python prototype demonstrating this pipeline. Experimental results in a defined domain suggest significant improvements in semantic coherence and factual accuracy of LLM outputs, showcasing the potential of combining LLM fluency with the rigor of formal semantics.
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