Dynamic Corrective Self-Distillation for Better Fine-Tuning of Pretrained Models

• URL: http://arxiv.org/abs/2312.07028v1
• Date: Tue, 12 Dec 2023 07:26:36 GMT
• Title: Dynamic Corrective Self-Distillation for Better Fine-Tuning of Pretrained Models
• Authors: Ibtihel Amara, Vinija Jain, and Aman Chadha
• Abstract summary: We tackle the issue of aggressive fine-tuning encountered during the process of transfer learning of pre-trained language models (PLMs) Inspired by the adaptive boosting method in traditional machine learning, we present an effective dynamic corrective self-distillation approach to improve the fine-tuning of the PLMs. Our technique involves performing a self-distillation mechanism where, at each iteration, the student model actively adapts and corrects itself by dynamically adjusting the weights assigned to individual data points.
• Score: 0.9217021281095907
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We tackle the challenging issue of aggressive fine-tuning encountered during the process of transfer learning of pre-trained language models (PLMs) with limited labeled downstream data. This problem primarily results in a decline in performance on the subsequent task. Inspired by the adaptive boosting method in traditional machine learning, we present an effective dynamic corrective self-distillation (DCS) approach to improve the fine-tuning of the PLMs. Our technique involves performing a self-distillation mechanism where, at each iteration, the student model actively adapts and corrects itself by dynamically adjusting the weights assigned to individual data points. This iterative self-correcting process significantly enhances the overall fine-tuning capability of PLMs, leading to improved performance and robustness. We conducted comprehensive evaluations using the GLUE benchmark demonstrating the efficacy of our method in enhancing the fine-tuning process for various PLMs across diverse downstream tasks.

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