  This paper introduces Dynamic Embeddings with Task-Oriented prompting
(DETOT), a novel approach aimed at improving the adaptability and efficiency of
machine learning models by implementing a flexible embedding layer. Unlike
traditional static embeddings [14], DETOT dynamically adjusts embeddings based
on task-specific requirements and performance feedback, optimizing input data
representation for individual tasks [4]. This method enhances both accuracy and
computational performance by tailoring the representation layer to meet the
unique needs of each task. The structure of DETOT is detailed, highlighting its
task-specific adaptation, continuous feedback loop, and mechanisms for
preventing overfitting. Empirical evaluations demonstrate its superiority over
existing methods.
