Dynamic Embeddings with Task-Oriented prompting

TL;DR

This paper presents DETOT, a novel dynamic embedding approach that adapts to task-specific needs, improving model accuracy and efficiency through continuous feedback and tailored representations.

Contribution

DETOT introduces a flexible, task-oriented embedding mechanism that dynamically adjusts representations based on performance feedback, enhancing adaptability and efficiency.

Findings

Outperforms static embedding methods in accuracy

Reduces computational costs compared to traditional models

Demonstrates robustness across multiple tasks

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.