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

arXiv:2403.11203v1 Announce Type: new
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. 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 …

abstract acquisition arxiv cs.cl embeddings graphs however knowledge knowledge acquisition knowledge graphs language language models language understanding pre-trained models pre-training robust tasks tokens training type understanding