Injecting external knowledge can improve the performance of pre-trained
language models (PLMs) on various downstream NLP tasks. However, massive
retraining is required to deploy new knowledge injection methods or knowledge
bases for downstream tasks. In this work, we are the first to study how to
improve the flexibility and efficiency of knowledge injection by reusing
existing downstream models. To this end, we explore a new paradigm
plug-and-play knowledge injection, where knowledge bases are injected into
frozen existing downstream models by a knowledge plugin. Correspondingly, we
propose a plug-and-play injection method map-tuning, which trains a mapping of
knowledge embeddings to enrich model inputs with mapped embeddings while
keeping model parameters frozen. Experimental results on three knowledge-driven
NLP tasks show that existing injection methods are not suitable for the new
paradigm, while map-tuning effectively improves the performance of downstream
models. Moreover, we show that a frozen downstream model can be well adapted to
different domains with different mapping networks of domain knowledge. Our code
and models are available at this https URL

本研究提出了一种新的知识注入方法 -- 插拔式知识注入，并探究了一种新的注入方法 ——map-tuning，并通过三种不同的 NLP 任务验证了 map-tuning 方法的有效性，再次表明了插拔式知识注入的优越性。