  Pre-trained language models(PLM) have made impressive results in various NLP
tasks. It has been revealed that one of the key factors to their success is the
parameters of these models implicitly learn all kinds of knowledge during
pre-training. However, encoding knowledge implicitly in the model parameters
has two fundamental drawbacks. First, the knowledge is neither editable nor
scalable once the model is trained, which is especially problematic in that
knowledge is consistently evolving. Second, it lacks interpretability and
prevents humans from understanding which knowledge PLM requires for a certain
problem. In this paper, we introduce PlugLM, a pre-training model with
differentiable plug-in memory(DPM). The key intuition is to decouple the
knowledge storage from model parameters with an editable and scalable key-value
memory and leverage knowledge in an explainable manner by knowledge retrieval
in the DPM. To justify this design choice, we conduct evaluations in three
settings including: (1) domain adaptation. PlugLM obtains 3.95 F1 improvements
across four domains on average without any in-domain pre-training. (2)
knowledge update. PlugLM could absorb new knowledge in a training-free way
after pre-training is done. (3) in-task knowledge learning. PlugLM could be
further improved by incorporating training samples into DPM with knowledge
prompting.
