What Has Been Enhanced in my Knowledge-Enhanced Language Model?
This provides insights for researchers and practitioners in NLP on the limitations of current knowledge-enhanced models, highlighting that incremental improvements may not suffice for better knowledge integration.
The paper tackles the problem of understanding how knowledge integration methods work in language models, revealing that only a small amount of factual knowledge is effectively integrated in models like ERNIE and K-Adapter, with varying extents across relation types.
Pretrained language models (LMs) do not capture factual knowledge very well. This has led to the development of a number of knowledge integration (KI) methods which aim to incorporate external knowledge into pretrained LMs. Even though KI methods show some performance gains over vanilla LMs, the inner-workings of these methods are not well-understood. For instance, it is unclear how and what kind of knowledge is effectively integrated into these models and if such integration may lead to catastrophic forgetting of already learned knowledge. This paper revisits the KI process in these models with an information-theoretic view and shows that KI can be interpreted using a graph convolution operation. We propose a probe model called \textit{Graph Convolution Simulator} (GCS) for interpreting knowledge-enhanced LMs and exposing what kind of knowledge is integrated into these models. We conduct experiments to verify that our GCS can indeed be used to correctly interpret the KI process, and we use it to analyze two well-known knowledge-enhanced LMs: ERNIE and K-Adapter, and find that only a small amount of factual knowledge is integrated in them. We stratify knowledge in terms of various relation types and find that ERNIE and K-Adapter integrate different kinds of knowledge to different extent. Our analysis also shows that simply increasing the size of the KI corpus may not lead to better KI; fundamental advances may be needed.