Reducing Inference Hallucinations in Large Language Models through Contextual Positional Double Encoding

Language models have reached remarkable performance levels in natural language processing tasks, yet they continue to face challenges related to inference hallucination, which compromises the factual accuracy and reliability of generated content. The introduction of contextual positional double encoding represents a novel and significant advancement, providing a dual mechanism that simultaneously captures static positional information and dynamic contextual relationships among tokens. Modifications to the GPT-Neo architecture incorporated this encoding method, resulting in a model that demonstrated enhanced contextual awareness and reduced hallucination frequency. Comprehensive evaluations showed improvements in perplexity, BLEU scores, and qualitative assessments of text coherence and factual accuracy, demonstrating the method's effectiveness. The results indicate that the enhanced GPT-Neo model produces more reliable and contextually accurate outputs, addressing critical challenges in natural language processing and paving the way for more dependable AI-driven text generation systems. The findings highlight the potential of contextual enhancements to substantially improve the robustness and accuracy of language models.