Dynamic Embeddings with Task-Oriented prompting

TL;DR

This paper tackles the rigidity of static word embeddings that impede task-specific representation efficiency. It proposes Dynamic Embeddings with Task-Oriented prompting (DETOT), a modular framework that adjusts embeddings in real time based on task demands and performance feedback via a continuous optimization loop while mitigating overfitting. The key contributions include task-oriented embedding adjustments, a continuous feedback mechanism, and empirical demonstrations showing improved accuracy and computational efficiency over static embeddings, across prompting configurations and tasks. The results suggest DETOT's potential to enable more adaptable NLP systems and motivate applying dynamic embeddings to broader domains such as vision and audio.

Abstract

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.