A Study on Knowledge Distillation from Weak Teacher for Scaling Up Pre-trained Language Models

• URL: http://arxiv.org/abs/2305.18239v1
• Date: Fri, 26 May 2023 13:24:49 GMT
• Title: A Study on Knowledge Distillation from Weak Teacher for Scaling Up Pre-trained Language Models
• Authors: Hayeon Lee, Rui Hou, Jongpil Kim, Davis Liang, Sung Ju Hwang, Alexander Min
• Abstract summary: Distillation from Weak Teacher (DWT) is a method of transferring knowledge from a smaller, weaker teacher model to a larger student model to improve its performance. This study examines three key factors to optimize DWT, distinct from those used in the vision domain or traditional knowledge distillation.
• Score: 104.64899255277443
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Distillation from Weak Teacher (DWT) is a method of transferring knowledge from a smaller, weaker teacher model to a larger student model to improve its performance. Previous studies have shown that DWT can be effective in the vision domain and natural language processing (NLP) pre-training stage. Specifically, DWT shows promise in practical scenarios, such as enhancing new generation or larger models using pre-trained yet older or smaller models and lacking a resource budget. However, the optimal conditions for using DWT have yet to be fully investigated in NLP pre-training. Therefore, this study examines three key factors to optimize DWT, distinct from those used in the vision domain or traditional knowledge distillation. These factors are: (i) the impact of teacher model quality on DWT effectiveness, (ii) guidelines for adjusting the weighting value for DWT loss, and (iii) the impact of parameter remapping as a student model initialization technique for DWT.

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