RoAST: Robustifying Language Models via Adversarial Perturbation with Selective Training

• URL: http://arxiv.org/abs/2312.04032v1
• Date: Thu, 7 Dec 2023 04:23:36 GMT
• Title: RoAST: Robustifying Language Models via Adversarial Perturbation with Selective Training
• Authors: Jaehyung Kim, Yuning Mao, Rui Hou, Hanchao Yu, Davis Liang, Pascale Fung, Qifan Wang, Fuli Feng, Lifu Huang, Madian Khabsa
• Abstract summary: We propose Robustifying LMs via Adversarial perturbation with Selective Training (RoAST) RoAST incorporates two important sources for the model robustness, robustness on the perturbed inputs and generalizable knowledge in pre-trained LMs. We demonstrate the effectiveness of RoAST compared to state-of-the-art fine-tuning methods on six different types of LMs.
• Score: 105.02614392553198
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Fine-tuning pre-trained language models (LMs) has become the de facto standard in many NLP tasks. Nevertheless, fine-tuned LMs are still prone to robustness issues, such as adversarial robustness and model calibration. Several perspectives of robustness for LMs have been studied independently, but lacking a unified consideration in multiple perspectives. In this paper, we propose Robustifying LMs via Adversarial perturbation with Selective Training (RoAST), a simple yet effective fine-tuning technique to enhance the multi-perspective robustness of LMs in a unified way. RoAST effectively incorporates two important sources for the model robustness, robustness on the perturbed inputs and generalizable knowledge in pre-trained LMs. To be specific, RoAST introduces adversarial perturbation during fine-tuning while the model parameters are selectively updated upon their relative importance to minimize unnecessary deviation. Under a unified evaluation of fine-tuned LMs by incorporating four representative perspectives of model robustness, we demonstrate the effectiveness of RoAST compared to state-of-the-art fine-tuning methods on six different types of LMs, which indicates its usefulness in practice.

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