Dynamic Embeddings with Task-Oriented prompting
<|reference_start|>Dynamic Embeddings with Task-Oriented prompting: 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.<|reference_end|>
@article{balloccu2024dynamic,
    title={Dynamic Embeddings with Task-Oriented prompting},
    author={Allmin Balloccu, Jack Zhang},
    journal={arXiv preprint arXiv:2405.11117},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.11117},
    primaryClass={cs.CL cs.LG}
}