Knowledge Rumination for Pre-trained Language Models

Yunzhi Yao, Peng Wang, Shengyu Mao, Chuanqi Tan, Fei Huang, Huajun Chen, Ningyu Zhang

Submission Type: Regular Long Paper

Submission Track: Commonsense Reasoning

Submission Track 2: Machine Learning for NLP

Keywords: Knowledge rumination, pretrained language model

TL;DR: We propose Knowledge Rumination to help the PLMs utilize that related latent knowledge without retrieving them from the external corpus.

Abstract: 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 \textbf{Knowledge Rumination} to help the pre-trained language model utilize that related latent knowledge without retrieving them from the external corpus. By simply adding a prompt like \emph{``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\footnote{Code is in the supplementary and will be released.}.

Submission Number: 1415