LIT
LIT’s visual interface will help you find examples that the model processes incorrectly, dig into similar examples, see how the model behaves when you change a context, and more language issues related to transformer models.
LIT does not display the activities of the attention heads like BertViz does. However, it’s worth analyzing why things went wrong and trying to find solutions.
You can choose a Uniform Manifold Approximation and Projection (UMAP) visualization or a PCA projector representation. PCA will make more linear projections in specific directions and magnitude. UMAP will break its projections down into mini-clusters. Both approaches make sense depending on how far you want to go when analyzing the output of a model. You can run both and obtain different perspectives of the same model and examples.
This section will use PCA to run LIT. Let’s begin with a brief reminder of how PCA works.
