Sudden Drops in the Loss: Syntax Acquisition, Phase Transitions, and Simplicity Bias in MLMs

Most interpretability research in NLP focuses on understanding the behavior and features of a fully trained model. However, certain insights into model behavior may only be accessible by observing the trajectory of the training process. In this paper, we present a case study of syntax acquisition in masked language models (MLMs). Our findings demonstrate how analyzing the evolution of interpretable artifacts throughout training deepens our understanding of emergent behavior. In particular, we study Syntactic Attention Structure (SAS), a naturally emerging property of MLMs wherein specific Transformer heads tend to focus on specific syntactic relations. We identify a brief window in training when models abruptly acquire SAS and find that this window is concurrent with a steep drop in loss. Moreover, SAS precipitates the subsequent acquisition of linguistic capabilities. We then examine the causal role of SAS by introducing a regularizer to manipulate SAS during training, and demonstrate that SAS is necessary for the development of grammatical capabilities. We further find that SAS competes with other beneficial traits and capabilities during training, and that briefly suppressing SAS can improve model quality. These findings reveal a real-world example of the relationship between disadvantageous simplicity bias and interpretable breakthrough training dynamics.