Enhancing Large Language Models through Neuro-Symbolic Integration and Ontological Reasoning
This addresses reliability issues in LLMs for domains requiring factual accuracy, but it is incremental as it builds on existing neuro-symbolic approaches.
The paper tackles the problem of hallucinations and logical inconsistencies in Large Language Models (LLMs) by integrating symbolic ontological reasoning with machine learning to enhance output consistency and reliability, reporting significant improvements in semantic coherence and factual accuracy in a defined domain.
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.