Hallucination Reduction in Large Language Models with Retrieval-Augmented Generation Using Wikipedia Knowledge
Jason Kirchenbauer, Caleb Barns
Abstract
Natural language understanding and generation have seen great progress, yet the persistent issue of hallucination undermines the reliability of model outputs. Introducing retrieval-augmented generation (RAG) with external knowledge sources, such as Wikipedia, presents a novel and significant approach to enhancing factual accuracy and coherence in generated content. By dynamically integrating relevant information, the Mistral model demonstrates substantial improvements in precision, recall, and overall quality of responses. This research offers a robust framework for mitigating hallucinations, providing valuable insights for deploying reliable AI systems in critical applications. The comprehensive evaluation underscores the potential of RAG to advance the performance and trustworthiness of large language models.