Customizable Combination of Parameter-Efficient Modules for Multi-Task Learning

• URL: http://arxiv.org/abs/2312.03248v1
• Date: Wed, 6 Dec 2023 02:47:56 GMT
• Title: Customizable Combination of Parameter-Efficient Modules for Multi-Task Learning
• Authors: Haowen Wang, Tao Sun, Cong Fan, Jinjie Gu
• Abstract summary: We introduce a novel approach that combines task-common skills and task-specific skills. A skill assignment matrix is jointly learned. Our findings demonstrate that C-Poly outperforms fully-shared, task-specific, and skill-indistinguishable baselines.
• Score: 11.260650180067278
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modular and composable transfer learning is an emerging direction in the field of Parameter Efficient Fine-Tuning, as it enables neural networks to better organize various aspects of knowledge, leading to improved cross-task generalization. In this paper, we introduce a novel approach Customized Polytropon C-Poly that combines task-common skills and task-specific skills, while the skill parameters being highly parameterized using low-rank techniques. Each task is associated with a customizable number of exclusive specialized skills and also benefits from skills shared with peer tasks. A skill assignment matrix is jointly learned. To evaluate our approach, we conducted extensive experiments on the Super-NaturalInstructions and the SuperGLUE benchmarks. Our findings demonstrate that C-Poly outperforms fully-shared, task-specific, and skill-indistinguishable baselines, significantly enhancing the sample efficiency in multi-task learning scenarios.

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