REALM: Retrieval-Augmented Language Model Pre-Training
- Thesis: Pretrain end-to-end retriever and generator and then fine-tune on downstream tasks. The external knowledge would augment the LM and increase its performance. This will lead to back propagate through the retriever.
- Contribution: End-to-end training of both retriever and LM.
- Method(s):
- Pretraining:
- [CLS] question masked some tokens [SEP] document
- The retriever retrieves the document that is most similar to the question. Embedding is obtained from BERT encoder.
- The LM encoder (BERT) predicts the masked token given the question and the answer
- Fine-tune
- [CLS] question [SEP] answer
- LM encoder predicts the start and end of answer
- Pretraining:
#nlp #rag
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