The Distributional Hypothesis Does Not Fully Explain the Benefits of Masked Language Model Pretraining
This work addresses a fundamental gap in understanding pretraining mechanisms for researchers in natural language processing, though it is incremental in nature.
The study investigated whether the distributional hypothesis explains the benefits of masked language model pretraining, finding it accounts for better sample efficiency but not generalization capability, based on synthetic and real-world dataset analyses.
We analyze the masked language modeling pretraining objective function from the perspective of the distributional hypothesis. We investigate whether better sample efficiency and the better generalization capability of models pretrained with masked language modeling can be attributed to the semantic similarity encoded in the pretraining data's distributional property. Via a synthetic dataset, our analysis suggests that distributional property indeed leads to the better sample efficiency of pretrained masked language models, but does not fully explain the generalization capability. We also conduct analyses over two real-world datasets and demonstrate that the distributional property does not explain the generalization ability of pretrained natural language models either. Our results illustrate our limited understanding of model pretraining and provide future research directions.