Detection of Hallucinations in Large Language Models using Retrieval-Based Verification

This paper presents a retrieval-based hallucination detection system for Large Language Models (LLMs). The system verifies generated responses using Wikipedia-based knowledge and semantic similarity techniques. Experimental evaluation on a dataset of 100 questions achieved 98% accuracy with high recall and an F1-score of 0.75. The system also demonstrates performance variations in real-world scenarios due to retrieval limitations and semantic ambiguity. The proposed approach highlights the importance of integrating external knowledge verification with generative AI systems to improve reliability and factual correctness.