  Large pre-trained language models (PLMs) have been shown to retain implicit
knowledge within their parameters. To enhance this implicit knowledge, we
propose Knowledge Injection into Language Models (KILM), a novel approach that
injects entity-related knowledge into encoder-decoder PLMs, via a generative
knowledge infilling objective through continued pre-training. This is done
without architectural modifications to the PLMs or adding additional
parameters. Experimental results over a suite of knowledge-intensive tasks
spanning numerous datasets show that KILM enables models to retain more
knowledge and hallucinate less, while preserving their original performance on
general NLU and NLG tasks. KILM also demonstrates improved zero-shot
performances on tasks such as entity disambiguation, outperforming
state-of-the-art models having 30x more parameters.
