Overview
The REALM model was proposed in REALM: Retrieval-Augmented Language Model Pre-Training by Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat and Ming-Wei Chang. It's a
retrieval-augmented language model that firstly retrieves documents from a textual knowledge corpus and then
utilizes retrieved documents to process question answering tasks.
The abstract from the paper is the following:
Language model pre-training has been shown to capture a surprising amount of world knowledge, crucial for NLP tasks
such as question answering. However, this knowledge is stored implicitly in the parameters of a neural network,
requiring ever-larger networks to cover more facts. To capture knowledge in a more modular and interpretable way, we
augment language model pre-training with a latent knowledge retriever, which allows the model to retrieve and attend
