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        <title>IM-BERT: Enhancing Robustness of BERT through the Implicit Euler Method</title>
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        <namePart type="given">MiHyeon</namePart>
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            <namePart type="given">Mohit</namePart>
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    <abstract>Pre-trained Language Models (PLMs) have achieved remarkable performance on diverse NLP tasks through pre-training and fine-tuning. However, fine-tuning the model with a large number of parameters on limited downstream datasets often leads to vulnerability to adversarial attacks, causing overfitting of the model on standard datasets. To address these issues, we propose IM-BERT from the perspective of a dynamic system by conceptualizing a layer of BERT as a solution of Ordinary Differential Equations (ODEs). Under the situation of initial value perturbation, we analyze the numerical stability of two main numerical ODE solvers: *the explicit and implicit Euler approaches.* Based on these analyses, we introduce a numerically robust IM-connection incorporating BERT’s layers. This strategy enhances the robustness of PLMs against adversarial attacks, even in low-resource scenarios, without introducing additional parameters or adversarial training strategies. Experimental results on the adversarial GLUE (AdvGLUE) dataset validate the robustness of IM-BERT under various conditions. Compared to the original BERT, IM-BERT exhibits a performance improvement of approximately 8.3%p on the AdvGLUE dataset. Furthermore, in low-resource scenarios, IM-BERT outperforms BERT by achieving 5.9%p higher accuracy.</abstract>
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%0 Conference Proceedings
%T IM-BERT: Enhancing Robustness of BERT through the Implicit Euler Method
%A Kim, MiHyeon
%A Park, Juhyoung
%A Kim, YoungBin
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F kim-etal-2024-im
%X Pre-trained Language Models (PLMs) have achieved remarkable performance on diverse NLP tasks through pre-training and fine-tuning. However, fine-tuning the model with a large number of parameters on limited downstream datasets often leads to vulnerability to adversarial attacks, causing overfitting of the model on standard datasets. To address these issues, we propose IM-BERT from the perspective of a dynamic system by conceptualizing a layer of BERT as a solution of Ordinary Differential Equations (ODEs). Under the situation of initial value perturbation, we analyze the numerical stability of two main numerical ODE solvers: *the explicit and implicit Euler approaches.* Based on these analyses, we introduce a numerically robust IM-connection incorporating BERT’s layers. This strategy enhances the robustness of PLMs against adversarial attacks, even in low-resource scenarios, without introducing additional parameters or adversarial training strategies. Experimental results on the adversarial GLUE (AdvGLUE) dataset validate the robustness of IM-BERT under various conditions. Compared to the original BERT, IM-BERT exhibits a performance improvement of approximately 8.3%p on the AdvGLUE dataset. Furthermore, in low-resource scenarios, IM-BERT outperforms BERT by achieving 5.9%p higher accuracy.
%U https://aclanthology.org/2024.emnlp-main.907
%P 16217-16229

Markdown (Informal)

[IM-BERT: Enhancing Robustness of BERT through the Implicit Euler Method](https://aclanthology.org/2024.emnlp-main.907) (Kim et al., EMNLP 2024)

• IM-BERT: Enhancing Robustness of BERT through the Implicit Euler Method (Kim et al., EMNLP 2024)

ACL

• MiHyeon Kim, Juhyoung Park, and YoungBin Kim. 2024. IM-BERT: Enhancing Robustness of BERT through the Implicit Euler Method. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 16217–16229, Miami, Florida, USA. Association for Computational Linguistics.