Pretrained Language Model Embryology: The Birth of ALBERT

• URL: http://arxiv.org/abs/2010.02480v2
• Date: Thu, 29 Oct 2020 00:07:43 GMT
• Title: Pretrained Language Model Embryology: The Birth of ALBERT
• Authors: Cheng-Han Chiang, Sung-Feng Huang and Hung-yi Lee
• Abstract summary: We investigate the developmental process from a set of randomly parameters to a totipotent language model. Our results show that ALBERT learns to reconstruct and predict tokens of different parts of speech (POS) in different learning speeds during pretraining. These findings suggest that knowledge of a pretrained model varies during pretraining, and having more pretrain steps does not necessarily provide a model with more comprehensive knowledge.
• Score: 68.5801642674541
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While behaviors of pretrained language models (LMs) have been thoroughly examined, what happened during pretraining is rarely studied. We thus investigate the developmental process from a set of randomly initialized parameters to a totipotent language model, which we refer to as the embryology of a pretrained language model. Our results show that ALBERT learns to reconstruct and predict tokens of different parts of speech (POS) in different learning speeds during pretraining. We also find that linguistic knowledge and world knowledge do not generally improve as pretraining proceeds, nor do downstream tasks' performance. These findings suggest that knowledge of a pretrained model varies during pretraining, and having more pretrain steps does not necessarily provide a model with more comprehensive knowledge. We will provide source codes and pretrained models to reproduce our results at https://github.com/d223302/albert-embryology.

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