  Recent days have witnessed a diverse set of knowledge injection models for
pre-trained language models (PTMs); however, most previous studies neglect the
PTMs' own ability with quantities of implicit knowledge stored in parameters. A
recent study has observed knowledge neurons in the Feed Forward Network (FFN),
which are responsible for expressing factual knowledge. In this work, we
propose a simple model, Kformer, which takes advantage of the knowledge stored
in PTMs and external knowledge via knowledge injection in Transformer FFN
layers. Empirically results on two knowledge-intensive tasks, commonsense
reasoning (i.e., SocialIQA) and medical question answering (i.e., MedQA-USMLE),
demonstrate that Kformer can yield better performance than other knowledge
injection technologies such as concatenation or attention-based injection. We
think the proposed simple model and empirical findings may be helpful for the
community to develop more powerful knowledge injection methods. Code available
in https://github.com/zjunlp/Kformer.
