Mitigating Structural Hallucination in Large Language Models with Local Diffusion

Abstract Large language models (LLMs) often produce text with inaccuracies, logical inconsistencies, or fabricated information, known as structural hallucinations, which undermine their reliability and trustworthiness. Implementing local diffusion mechanisms within the Mistral LLM architecture has demonstrated significant potential in addressing these issues, enhancing both the accuracy and coherence of the generated text. The modified model exhibited substantial improvements across various performance metrics, including accuracy, precision, recall, and F1 score, validated through rigorous statistical testing. The architectural adjustments, involving the integration of diffusion layers, facilitated better information propagation and reduced the occurrence of structurally flawed outputs. Quantitative analyses highlighted the model's enhanced performance, while qualitative comparisons revealed its improved structural integrity and factual accuracy. Additionally, error analysis revealed a notable reduction in the frequency of factual and logical errors, further affirming the effectiveness of the local diffusion approach. The findings reveal the transformative potential of local diffusion in mitigating structural hallucinations and advancing the field of natural language processing.