  Transformers have revolutionized various domains of artificial intelligence
due to their unique ability to model long-range dependencies in data. However,
they lack in nuanced, context-dependent modulation of features and information
flow. This paper introduces two significant enhancements to the transformer
architecture - the Evaluator Adjuster Unit (EAU) and Gated Residual Connections
(GRC) - designed to address these limitations. The EAU dynamically modulates
attention outputs based on the relevance of the input context, allowing for
more adaptive response patterns. Concurrently, the GRC modifies the
transformer's residual connections through a gating mechanism that selectively
controls the information flow, thereby enhancing the network's ability to focus
on contextually important features. We evaluate the performance of these
enhancements across several benchmarks in natural language processing. Our
results demonstrate improved adaptability and efficiency, suggesting that these
modifications could set new standards for designing flexible and context-aware
transformer models.
