Neuro-Symbolic Framework for Reducing Hallucinations in Large Language Models through Knowledge Graph Reasoning

Large language models (LLMs) represent the next step in the domain of natural language understanding and generating; however, they still frequently elicit verbal hallucinations, which sound coherent but are factually incorrect. This shortcoming makes them less useful in data-intensive sectors such as healthcare, education, and law, as well as scientific communication, since they would require very precise factual information. In dealing with this difficult issue, this paper presents a neuro-symbolic framework, which involves the combination of a neural text generation algorithm with a knowledge graph to post-generationally verify and re-process the generated text. The proposed method follows a closed-loop pipeline: initially, the large language model generates a response; then, the entities and relations are extracted from the generated text; the extracted facts are linked to the local graph database, and then the symbolic reasoning is performed to validate the facts and identify contradictions and unsupported claims. If there are detected inconsistencies in the content of the response, the system re-evaluates with the assistance of knowledge-grounded feedback. In contrast to sole neural models, the proposed approach increases accuracy and validation by supporting interpretable validation through structured graph evidence. The test results show that hallucination rates go in parallel with the preservation of correctness and coherence of the information flow. The research has inferred that the merging of language models based on neural networks with symbolic reasoning provides a viable method for generating AI systems that are both sober and dictionary-type.