A Multimodal Framework Using Red Teaming Prompt Engineering and Retrieval Augmented Generation to Mitigate Large Language Model Hallucinations

Abstract Large Language Models (LLMs) have significantly advanced various domains such as healthcare, cybersecurity, and natural language processing. However, their practical deployment is often hindered by hallucinations-instances where the models produce inaccurate or misleading outputs. This study presents a robust CNN-based multimodal framework that integrates Red Teaming for adversarial testing, Prompt Engineering for refined input guidance, and Retrieval-Augmented Generation (RAG) to enhance factual accuracy through real-time information retrieval. The framework processes diverse data modalities including text, audio, and images, leveraging convolutional neural networks for effective feature extraction and alignment. Experimental validation on a synthesized multimodal dataset demonstrates a test accuracy of 75% after 50 training epochs, with notable performance metrics such as a ROC-AUC of 0.93 on neuroimaging data, an F1-score of 0.85 for mobile sensing, and a BLEU score of 0.94 for multilingual translation tasks. These results affirm the framework’s capability in reducing hallucinations across varied modalities and noisy, imbalanced datasets. The proposed approach paves the way for more dependable LLM applications in real-world settings, though future work will focus on integrating transformer architectures and expanding domain-specific datasets to further enhance scalability and precision.