HALLUCINATION IN LARGE LANGUAGE MODELS: CHARACTERIZATION, DETECTION, AND MITIGATION APPROACHES

A significant barrier to preserving factual accuracy and dependability in AI-generated outputs is hallucination in large language models. Using a benchmark Kaggle dataset, this work provides a comprehensive evaluation of both advanced transformer-based architectures and traditional machine learning classifiers for hallucination identification. They compared refined transformer models, such as DistilBERT, RoBERTa, and DeBERTa, with baseline models, including Random Forest, SVM, and Logistic Regression. The results show that transformer-based models were more robust and better at understanding context; however, more conventional models, such as Random Forest, achieved a high overall accuracy of 94.10%. DistilBERT struck a wonderful balance between precision and readability. The confusion matrix analysis demonstrated that the models helped reduce false alarms for non-hallucination outputs. The ROC-AUC ratings confirmed the transformers’ precision and capability for identifying a slight rate of semantic discrepancies. Other studies provided supporting evidence that deeper context modeling will provide real benefits to the reliability of detection rates, demonstrated by the reduced hallucinations and assessments of the frequency of errors made. In conclusion, this research shows that combining traditional and modern approaches is beneficial and that tuning with transformer models holds promise for reducing hallucinations. This research provides an example of early steps of increasing trustworthiness and human-like models as AI models.