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.

该论文提出了一种叫做 “Graph Convolution Simulator”（GCS）的探针模型，用于解释知识增强语言模型（Knowledge-enhanced LMs）中知识整合的方式，并发现只有少量的事实知识被整合进 ERNIE 和 K-Adapter 中。

我的知识增强语言模型有哪些变化？

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

Non-local self-similarity is well-known to be an effective prior for the
image denoising problem. However, little work has been done to incorporate it
in convolutional neural networks, which surpass non-local model-based methods
despite only exploiting local information. In this paper, we propose a novel
end-to-end trainable neural network architecture employing layers based on
graph convolution operations, thereby creating neurons with non-local receptive
fields. The graph convolution operation generalizes the classic convolution to
arbitrary graphs. In this work, the graph is dynamically computed from
similarities among the hidden features of the network, so that the powerful
representation learning capabilities of the network are exploited to uncover
self-similar patterns. We introduce a lightweight Edge-Conditioned Convolution
which addresses vanishing gradient and over-parameterization issues of this
particular graph convolution. Extensive experiments show state-of-the-art
performance with improved qualitative and quantitative results on both
synthetic Gaussian noise and real noise.

本文提出了一种基于图卷积操作的全新的可端到端训练的神经网络架构，该架构创建具有非局部感受野的神经元，从而利用网络的强大表示学习能力来发现自相似模式，并通过轻量级的基于边缘条件的卷积来解决梯度消失和过参数化问题。实验结果表明了在合成高斯噪声和真实噪声上的最先进性能和改进的定性和定量结果。