TRELM: Towards Robust and Efficient Pre-training for Knowledge-Enhanced Language Models

Abstract

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. Updating all parameters in KEPLM is computationally demanding. This paper introduces TRELM, a Robust and Efficient Pre-training framework for Knowledge-Enhanced Language Models. We observe that text corpora contain entities that follow a long-tail distribution, where some are suboptimally optimized and hinder the pre-training process. To tackle this, we employ a robust approach to inject knowledge triples and employ a knowledge-augmented memory bank to capture valuable information. Moreover, 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 achieves at least a 50% reduction in pre-training time and outperforms other KEPLMs in knowledge probing tasks and multiple knowledge-aware language understanding tasks.