Attribution-based Task-specific Pruning for Multi-task Language Models

• URL: http://arxiv.org/abs/2205.04157v1
• Date: Mon, 9 May 2022 10:12:08 GMT
• Title: Attribution-based Task-specific Pruning for Multi-task Language Models
• Authors: Nakyeong Yang, Yunah Jang, Hwanhee Lee, Seohyeong Jung, Kyomin Jung
• Abstract summary: Multi-task language models show outstanding performance for various natural language understanding tasks with only a single model. We propose a novel training-free task-specific pruning method for multi-task language models.
• Score: 19.106042468549187
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-task language models show outstanding performance for various natural language understanding tasks with only a single model. However, these language models inevitably utilize unnecessary large-scale model parameters, even when they are used for only a specific task. In this paper, we propose a novel training-free task-specific pruning method for multi-task language models. Specifically, we utilize an attribution method to compute the importance of each neuron for performing a specific task. Then, we prune task-specifically unimportant neurons using this computed importance. Experimental results on the six widely-used datasets show that our proposed pruning method significantly outperforms baseline compression methods. Also, we extend our method to be applicable in a low-resource setting, where the number of labeled datasets is insufficient.

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