What Has Been Enhanced in my Knowledge-Enhanced Language Model?

We show that existing model interpretation methods such as linear probes and prompts have some key limitations in answering these questions. We revisit KI from an information-theoretic view and propose a new theoretically sound probe called Graph Convolution Simulator (GCS) for KI interpretation. GCS uses graph attention on the corresponding knowledge graph for interpretation. In our experiments we verify that GCS can provide reasonable interpretation results for two well-known knowledge-enhanced LMs: ERNIE and K-Adapter. We also find that only a marginal amount of knowledge is successfully integrated in these models, and simply increasing the size of the KI corpus may not lead to better knowledge-enhanced LMs.