  Previous studies have revealed that vanilla pre-trained language models
(PLMs) lack the capacity to handle knowledge-intensive NLP tasks alone; thus,
several works have attempted to integrate external knowledge into PLMs.
However, despite the promising outcome, we empirically observe that PLMs may
have already encoded rich knowledge in their pre-trained parameters but fail to
fully utilize them when applying them to knowledge-intensive tasks. In this
paper, we propose a new paradigm dubbed Knowledge Rumination to help the
pre-trained language model utilize that related latent knowledge without
retrieving it from the external corpus. By simply adding a prompt like "As far
as I know" to the PLMs, we try to review related latent knowledge and inject
them back into the model for knowledge consolidation. We apply the proposed
knowledge rumination to various language models, including RoBERTa, DeBERTa,
and GPT-3. Experimental results on six commonsense reasoning tasks and GLUE
benchmarks demonstrate the effectiveness of our proposed approach, which proves
that the knowledge stored in PLMs can be better exploited to enhance
performance. Code is available in
https://github.com/zjunlp/knowledge-rumination.
