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.

翻译：以往研究表明，普通预训练语言模型（PLMs）难以独立处理知识密集型自然语言处理任务，因此多项研究尝试将外部知识融入PLMs。然而，尽管取得了令人鼓舞的成果，我们通过实证观察发现，PLMs可能已在预训练参数中编码了丰富的知识，但在应用于知识密集型任务时未能充分加以利用。本文提出一种名为"知识反刍"的新范式，旨在帮助预训练语言模型利用相关潜在知识，而无需从外部语料中检索。通过在PLMs中简单添加"据我所知"（As far as I know）等提示，我们尝试回顾相关潜在知识并将其重新注入模型进行知识整合。我们将所提出的知识反刍方法应用于多种语言模型（包括RoBERTa、DeBERTa和GPT-3）。在六个常识推理任务和GLUE基准上的实验结果验证了该方法的有效性，表明PLMs中存储的知识可被更充分地开发以提升性能。代码已开源至https://github.com/zjunlp/knowledge-rumination。