Date of Publication :2nd September 2024

Abstract:This paper presents a theoretical analysis of a novel method for enhancing contextual understanding in Large Language Models (LLMs) using an algorithm based on Eigenvector Centrality. I provide a mathematical proof demonstrating that the proposed approach can lead to significant improvements in the models' ability to comprehend user intentions and handle lexical ambiguity. By integrating graph theory with deep learning techniques, my method offers a promising direction for advancing natural language processing capabilities.

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