Transfer Learning for Structured Pruning under Limited Task Data

Autor:	Dery, Lucio, Grangier, David, Hannun, Awni
Rok vydání:	2023
Předmět:	Computer Science - Computation and Language Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Large, pre-trained models are problematic to use in resource constrained applications. Fortunately, task-aware structured pruning methods offer a solution. These approaches reduce model size by dropping structural units like layers and attention heads in a manner that takes into account the end-task. However, these pruning algorithms require more task-specific data than is typically available. We propose a framework which combines structured pruning with transfer learning to reduce the need for task-specific data. Our empirical results answer questions such as: How should the two tasks be coupled? What parameters should be transferred? And, when during training should transfer learning be introduced? Leveraging these insights, we demonstrate that our framework results in pruned models with improved generalization over strong baselines. Comment: 8 pages, 7 figures and 3 tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2311.06382 Zobrazit plný text záznamu View this record from Arxiv