Papers › The Effectiveness of Masked Language Modeling and Adapters for Factual Knowledge Injection
The Effectiveness of Masked Language Modeling and Adapters for Factual Knowledge Injection
Sondre Wold
This paper studies the problem of injecting factual knowledge into large pre-trained language models. We train adapter modules on parts of the ConceptNet knowledge graph using the masked language modeling objective and evaluate the success of the method by a series of probing experiments on the LAMA probe. Mean P@K curves for different configurations indicate that the technique is effective, increasing the performance on subsets of the LAMA probe for large values of k by adding as little as 2.1% additional parameters to the original models.
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