Before revision:  Over-clustering implies that the model cannot efficiently learn the feature representation from excessive negative sample pairs, which include many outliers and thus enforce the model to over-cluster samples of the same actual categories into different clusters.
After revision:  Over-clustering implies that the model cannot efficiently learn the feature representation from excessive negative sample pairs, which enforces the model to over-cluster samples of the same actual categories into different clusters.