End-to-End Training of Retrieval-Augmented Models.
Given the advantage of knowledge retrieval, a key question
is how to get learning signal to train the retrieval model. For
tasks with annotated retrieval ground-truth, retrieval train-
ing can be conducted via standard contrastive learning [22].
However, most tasks do not provide clear indications of
which knowledge entries are relevant for generating answers.
To this end, a series of studies have investigated retrieval
training using supervision derived from downstream tasks.
REALM [14] trains a single-document retriever by concate-
nating each retrieved result with the query, to calculate the
final loss independently. A similar approach has been used
by EMDR2 [34] for multi-document retrieval training. FID-
KD [17] proposes to use the aggregated attention score cal-
culated by the generator as a distillation signal to train the
retriever. Atlas [18] further introduces a perplexity distilla-
REVEAL-Retrieval-AugmentedVisual-LanguagePre-Trainingwith Multi-SourceMultimodalKnowledgeMemory