Rethinking the Construction of Effective Metrics for Understanding the
  Mechanisms of Pretrained Language Models
You Li Jinhui Yin Yuming Lin
Pretrained language models are expected to effectively map input text to a
set of vectors while preserving the inherent relationships within the text.
Consequently, designing a white-box model to compute metrics that reflect the
presence of specific internal relations in these vectors has become a common
approach for post-hoc interpretability analysis of pretrained language models.
However, achieving interpretability in white-box models and ensuring the rigor
of metric computation becomes challenging when the source model lacks inherent
interpretability. Therefore, in this paper, we discuss striking a balance in
this trade-off and propose a novel line to constructing metrics for
understanding the mechanisms of pretrained language models. We have
specifically designed a family of metrics along this line of investigation, and
the model used to compute these metrics is referred to as the tree topological
probe. We conducted measurements on BERT-large by using these metrics. Based on
the experimental results, we propose a speculation regarding the working
mechanism of BERT-like pretrained language models, as well as a strategy for
enhancing fine-tuning performance by leveraging the topological probe to
improve specific submodules.