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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.11117 |
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| _version_ | 1866909229227442176 |
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| author | Balloccu, Allmin Zhang, Jack |
| author_facet | Balloccu, Allmin Zhang, Jack |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_11117 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Dynamic Embeddings with Task-Oriented prompting Balloccu, Allmin Zhang, Jack Computation and Language Machine Learning 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. |
| title | Dynamic Embeddings with Task-Oriented prompting |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2405.11117 |