Knowledge Rumination for Pre-trained Language Models

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 fails to fully utilize them when applying to knowledge-intensive tasks. In this paper, we propose a new paradigm dubbed Knowledge Rumination to help the pre-trained language model utilize those related latent knowledge without retrieving them 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 to the model for knowledge consolidation. We apply the proposed knowledge rumination to various language models, including RoBERTa, DeBERTa, GPT-3 and OPT. Experimental results on six commonsense reasoning tasks and GLUE benchmarks demonstrate the effectiveness of our proposed approach, which further proves that the knowledge stored in PLMs can be better exploited to enhance the downstream performance. Code will be available in https://github.com/zjunlp/knowledge-rumination.