Hallucinations continue to significantly restrict large language models (LLMs), particularly in fact-finding and knowledge-intensive tasks. Retrieval-Augmented Generation (RAG), which bases model outputs on external knowledge sources, addresses this problem. This study examines the impact of integrating structured knowledge from Wikidata into an LLM-based question-answering pipeline to reduce hallucinated material. The proposed Wikidata-based RAG technique injects this structured context into the LLM prompt before response synthesis by first extracting entities from the input question and then gathering candidate entities and verified attributes from Wikidata. Both the prompt-only baseline models and the Wikidata-grounded RAG system were used to evaluate 80 multi-hop compositional problems. The suggested RAG model is assessed using four different hallucination evaluation methods. The model is evaluated against a prompt-only baseline using three of them: the AimonLabs hallucination-detection model, the Vectara HHEM, and an LLM-as-a-Judge rubric. Retrieved-Context Faithfulness (RAGTruth-style evaluation) is assessed using the last evaluation method. (Vectara HHEM: 77.5\% vs 38.75\% factual replies; AIMON: 60\% vs 32.5\% factual responses; LLM-as-a-Judge: 75\% vs 38.75\% factual responses) RAG consistently surpasses the baseline across all four evaluators. 54 of 79 assessed examples had a retrieved-context fidelity of 68.35\%. This suggests that the observed frequency of hallucinations is influenced by the evaluation approach used. These results show how fact accuracy may be greatly increased by basing LLMs on structured knowledge from Wikidata. They also show how important it is to have several assessors when studying hallucinations.

Conference Title

2026 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems – ICETSIS

Conference Country

Bahrain

Conference Date

May 6, 2026 - May 7, 2026

Conference Sponsor

IEEE (Institute of Electrical and Electronics Engineers) – Bahrain Section

Additional Info

Conference Website