MT2ST: Adaptive Multi-Task to Single-Task Learning

• URL: http://arxiv.org/abs/2406.18038v4
• Date: Mon, 10 Feb 2025 15:13:16 GMT
• Title: MT2ST: Adaptive Multi-Task to Single-Task Learning
• Authors: Dong Liu, Yanxuan Yu,
• Abstract summary: Multi-Task to Single-Task (MT2ST) is designed to enhance training efficiency and accuracy in word embedding tasks. Our framework employs two strategies: *Diminish*, which gradually reduces the influence of auxiliary tasks, and *Switch*, which transitions training from MTL to STL at a specific point. Empirical results show that MT2ST reduces training time by 67% compared to STL and by 13% compared to traditional MTL, while maintaining high accuracy.
• Score: 6.185573921868495
• License:
• Abstract: Efficient machine learning (ML) has become increasingly important as models grow larger and data volumes expand. In this work, we address the trade-off between generalization in multi-task learning (MTL) and precision in single-task learning (STL) by introducing the Multi-Task to Single-Task (MT2ST) framework. MT2ST is designed to enhance training efficiency and accuracy in word embedding tasks, showcasing its value as a practical application of efficient ML. Our framework employs two strategies: *Diminish*, which gradually reduces the influence of auxiliary tasks, and *Switch*, which transitions training from MTL to STL at a specific point. Empirical results show that MT2ST reduces training time by 67\% compared to STL and by 13\% compared to traditional MTL, while maintaining high accuracy. These findings highlight MT2ST as an efficient ML solution tailored for optimizing word embedding training. Code is available at https://github.com/NoakLiu/MT2ST.

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