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K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters
[article]
2020
arXiv
pre-print
We study the problem of injecting knowledge into large pre-trained models like BERT and RoBERTa. Existing methods typically update the original parameters of pre-trained models when injecting knowledge. However, when multiple kinds of knowledge are injected, the historically injected knowledge would be flushed away. To address this, we propose K-Adapter, a framework that retains the original parameters of the pre-trained model fixed and supports the development of versatile knowledge-infused
arXiv:2002.01808v5
fatcat:cqyzyiuljrgitkz27fs4jefcg4