Its All About Context

This paper presents an innovative approach to enhancing context understanding in Generative Pretrained Models (GPTs), a critical step towards achieving Artificial General Intelligence (AGI). While GPTs have significantly advanced natural language processing, their understanding of context remains predominantly limited to linguistic structure. To address this limitation, we introduce a novel layer in the transformer model architecture that computes context weights, integrating both immediate and temporally decaying influences of past tokens. This layer is strategically positioned after the self-attention and before the feed-forward layers, enabling a more nuanced interpretation of sequential language data. Our approach involves the formulation of a decaying temporal factor, which allows the model to consider not only the immediate relevance of tokens but also their historical context. This factor is dynamically adjustable, offering a sophisticated method of context weighting that considers both current and extended contexts. The integration of this context weight into the self-attention mechanism enhances the model's capacity for a deeper, more accurate understanding of language, pushing the boundaries of current AI capabilities towards a system that mirrors human-like intelligence. Our experimental results demonstrate the efficacy of this approach, showing its potential to significantly contribute to the development of AGI.