Designing Incremental Knowledge Enrichment in Generative Pre-trained Transformers

Abstract This article presents a novel approach to Incremental Knowledge Enrichment tailored for GPT-Neo, addressing the challenge of keeping Large Language Models (LLMs) updated with the latest information without undergoing comprehensive retraining. We introduce a dynamic linking mechanism that enables real-time integration of diverse data sources, thereby enhancing the model's accuracy, timeliness, and relevance. Through a rigorous evaluation, our method demonstrates significant improvements in model performance across several metrics. The research contributes a scalable and efficient solution to one of the most pressing issues in AI, potentially revolutionizing the maintenance and applicability of LLMs. The findings underscore the feasibility of creating more adaptive, responsive, and sustainable generative models, opening new avenues for future advancements in the field.