KEPLMs are pre-trained models that utilize external knowledge to enhance
language understanding. Previous language models facilitated knowledge
acquisition by incorporating knowledge-related pre-training tasks learned from
relation triples in knowledge graphs. However, these models do not prioritize
learning embeddings for entity-related tokens. Moreover, updating the entire
set of parameters in KEPLMs is computationally demanding. This paper introduces
TRELM, a Robust and Efficient Pre-training framework for Knowledge-Enhanced
Language Models. We observe that entities in text corpora usually follow the
long-tail distribution, where the representations of some entities are
suboptimally optimized and hinder the pre-training process for KEPLMs. To
tackle this, we employ a robust approach to inject knowledge triples and employ
a knowledge-augmented memory bank to capture valuable information. Furthermore,
updating a small subset of neurons in the feed-forward networks (FFNs) that
store factual knowledge is both sufficient and efficient. Specifically, we
utilize dynamic knowledge routing to identify knowledge paths in FFNs and
selectively update parameters during pre-training. Experimental results show
that TRELM reduces pre-training time by at least 50% and outperforms other
KEPLMs in knowledge probing tasks and multiple knowledge-aware language
understanding tasks.

TRELM 是一种强大高效的预训练框架，用于提升语言模型的知识扩充能力，并在知识探测任务和多种知识感知语言理解任务中表现优于其他基于知识的语言模型。