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

• URL: http://arxiv.org/abs/2403.11203v1
• Date: Sun, 17 Mar 2024 13:04:35 GMT
• Title: TRELM: Towards Robust and Efficient Pre-training for Knowledge-Enhanced Language Models
• Authors: Junbing Yan, Chengyu Wang, Taolin Zhang, Xiaofeng He, Jun Huang, Longtao Huang, Hui Xue, Wei Zhang,
• Abstract summary: This paper introduces TRELM, a Robust and Efficient Pre-training framework for Knowledge-Enhanced Language Models. We employ a robust approach to inject knowledge triples and employ a knowledge-augmented memory bank to capture valuable information. We 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.
• Score: 31.209774088374374
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 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.

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